Reviewer #2 (Public review):

Summary:

This study aimed to test experimentally a theoretical framework that aims to explain the perception of tinnitus, i.e., the perception of a phantom sound in the absence of external stimuli, through differences in auditory predictive coding patterns. To this aim, the researchers compared the neural activity preceding and following the perception of a sound using MEG in two different studies. The sounds could be highly predictable or random, depending on the experimental condition. They revealed that individuals with tinnitus and controls had different anticipatory predictions. This finding is a major step in characterizing the top-down mechanisms underlying sound perception in individuals with tinnitus.

Strengths:

This article uses an elegant, well-constructed paradigm to assess the neural dynamics underlying auditory prediction. The findings presented in the first experiment were partially replicated in the second experiment, which included 80 participants. This large number of participants for an MEG study ensures very good statistical power and a strong level of evidence. The authors used advanced analysis techniques - Multivariate Pattern Analysis (MVPA) and classifier weights projection - to determine the neural patterns underlying the anticipation and perception of a sound for individuals with or without tinnitus. The authors evidenced different auditory prediction patterns associated with tinnitus. Overall, the conclusions of this paper are well supported, and the limitations of the study are clearly addressed and discussed.

Reviewer #1 (Public review):

Summary of the work:

In this work Fruchard et. al. study the enzyme Tgt and how it modifies guanine in tRNAs to queuosine (Q), essential for Vibrio cholerae's growth under aminoglycoside stress. Q's role in codon decoding efficiency and its proteomic effects during antibiotic exposure is examined, revealing Q modification impacts tyrosine codon decoding and influences RsxA translation, affecting the SoxR oxidative stress response. The research proposes Q modification's regulation under environmental cues reprograms the translation of genes with tyrosine codon bias, including DNA repair factors, crucial for bacterial antibiotic response.

The experiments are well-designed and conducted and the conclusions, for the most part, are well-supported by the data.

Comments on revisions:

The authors have answered my queries

Reviewer #2 (Public review):

Fruchard et al. investigate the role of the queuosine (Q) modification of the tRNA (Q-tRNA) in the human pathogen Vibrio cholerae. First, the authors state that the absence of Q-modified tRNAs (tgt mutant) increases the translation of TAT codons and proteins with a high TAT codon bias. Second, the absence of Q increases rsxA translation, because rsxA gene has a high TAT codon bias. Third, increased RsxA in the absence of Q inhibits SoxR response, reducing resistance towards the antibiotic tobramycin (TOB). Authors also predict in silico which genes harbor a higher TAT bias and found that among them are some involved in DNA repair, experimentally observing that a tgt mutant is more resistant to UV than the wt strain. It is worth noting that authors employ a wide variety of techniques, both experimental and bioinformatics.

The authors have satisfactorily responded to most of the comments that needed clarification. Particularly interesting was the addition of the new results section "Q modification impacts decoding fidelity in V. cholerae", after the suggestion to explore the role of Q in prevention of stop codon readthrough. Although it is not a major problem, since the article is very complete and interesting, the interpretation of the results of RiboSeq data carried out in this work remains controversial. This technique, at least when it has been used in eukaryotes to investigate whether there is a bias in the translation of certain codons affected by Q (Tuorto et al., EMBO J. 2018; doi: 10.15252/embj.201899777), has been interpreted as meaning that ribosomes spend less time in the optimal codons and therefore there is an increase in occupancy at codons where translation slows down. On the other hand, it has been observed that "in ribosome profiling experiments conducted without cycloheximide pretreatment, there is a clear inverse relationship between tRNA abundance and ribosome occupancy, showing that ribosomes spend less time at optimal codons and specifically this has been observed in experiments in which a translation inhibitor such as cycloheximide is not used (see review: Hanson G & Coller J. Nat Rev Mol Cell Biol. doi: 10.1038/nrm.2017.91, and experiments in yeast: Hussmann JA et al. PLoS Genet. doi: 10.1371/journal.pgen.1005732). On the other hand, we believe that the comparison between RiboSeq and proteomic data would be interesting to check whether this interpretation of the RiboSeq data is correct. It should not be a problem that the proteomics data could be incomplete, it would just be a more limited study. If the correct interpretation of the RiboSeq results is as proposed by the authors, a correlation should be observed between the abundance of TAT-enriched RNA fragments and the most abundant proteins. Therefore, it would be interesting to perform this comparison and see if significant results are obtained that help to understand the correct interpretation of the RiboSeq experiments.

Reviewer #3 (Public review):

Summary:

In this manuscript the authors begin with the interesting phenotype of sub inhibitory concentrations of the aminoglycoside tobramycin proving toxic to a knockout of the tRNA-guanine transglycosylase (Tgt) of the important human pathogen, Vibrio cholerae. Tgt is important for incorporating queuosine (Q) in place of guanosine at the wobble position of GUN codons. The authors go on to define a mechanism of action where environmental stressors control expression of tgt to control translational decoding of particularly tyrosine codons, skewing the balance from TAC towards TAT decoding in the absence of the enzyme. The authors use advanced proteomics and ribosome profiling to reveal that the loss of tgt results in increased translation of proteins like RsxA and a cohort of DNA repair factors, whose genes harbor an excess of TAT codons in many cases. These findings are bolstered by a series of molecular reporters, mass spectrometry, and tRNA overexpression strains to provide support for a model where Tgt serves as a molecular pivot point to reprogram translational output in response to stress. The manuscript therefore improves our understanding of the phenotype of focus and will prove useful for the field in our understanding of Modification Tunable Transcripts.

Strengths:

The manuscript has many strengths. The authors use a variety of strains, assays, and advanced techniques to discover a mechanism of action for Tgt in mediating tolerance to sub inhibitory concentrations of tobramycin. They observe a clear phenotype for a tRNA modification in facilitating reprogramming of the translational response, and the manuscript certainly has value in defining how microbes tolerate antibiotics.

Weaknesses:

The conclusions of the manuscript are mostly very well-supported by the data, but a few experimental directions remain inconclusive. The finding linking Tgt and UV damage susceptibility is one example where the phenotype is striking, but the mechanism remains somewhat unclear. Future work in this direction will likely be required to fully understand how Tgt influences the repair of DNA after UV.

Joint Public Review:

Based on bioinformatics and expression analysis using mouse and human samples, the authors claim that the adhesion G-protein coupled receptor ADGRA3 may be a valuable target for increasing thermogenic activity and metabolic health. Genetic approaches to deplete ADGRA3 expression in vitro resulted in reduced expression of thermogenic genes including Ucp1, reduced basal respiration and metabolic activity as reflected by reduced glucose uptake and triglyceride accumulation. In line, nanoparticle delivery of shAdgra3 constructs is associated with increased body weight, reduced thermogenic gene expression in white and brown adipose tissue (WAT, BAT), and impaired glucose and insulin tolerance. On the other hand, ADGRA3 overexpression is associated with an improved metabolic profile in vitro and in vivo, which can be explained by increasing the activity of the well-established Gs-PKA-CREB axis. Notably, a computational screen suggested that ADGRA3 is activated by hesperetin. This metabolite is a derivative of the major citrus flavonoid hesperidin and has been described to promote metabolic health. Using appropriate in vitro and in vivo studies, the authors show that hesperitin supplementation is associated with increased thermogenesis, UCP1 levels in WAT and BAT, and improved glucose tolerance, an effect that was attenuated in the absence of ADGRA3 expression.

The revised manuscript fails to address several reviewer concerns, such as the measurement of whole-body energy expenditure through indirect calorimetry and the assessment of food intake.

The previous reviews are here: https://elifesciences.org/reviewed-preprints/100205v2/reviews#tab-content

Reviewer #1 (Public review):

Summary:

This paper presents a data processing pipeline to discover causal interactions from time-lapse imaging data and convincingly illustrates it on a challenging application for the analysis of tumor-on-chip ecosystem data.

The core of the discovery module is the original tMIIC method of the authors, which is shown in supplementary material to compare favourably to two state-of-the-art methods on synthetic temporal data on a 15 nodes network.

Strengths:

This paper tackles the problem of learning causal interactions from temporal data which is an open problem in presence of latent variables.

The core of the method tMIIC of the authors is nicely presented in connection to Granger-Schreiber causality and to the novel graphical conditions used to infer latent variables and based on a theorem about transfer entropy.

tMIIC compares favourably to PC and PCMCI+ methods using different kernels on synthetic datasets generated from a network of 15 nodes.

A full application to tumor-on-chip cellular ecosystems data including cancer cells, immune cells, cancer-associated fibroblasts, endothelial cells and anti cancer drugs, with convincing inference results with respect to both known and novel effects between those components and their contact.

The code and dataset are available online for the reproducibility of the results.

Weaknesses:

The references to "state-of-the-art methods" concerning the inference of causal networks should be more precise by giving citations in the main text, and better discussed in general terms, both in the first section and in the section of presentation of CausalXtract. It is only in the legend of the figures of the supplementary material that we get information.

Of course, comparison on our own synthetic datasets can always be criticized but this is rather due to the absence of a common benchmark in this domain compared to other domains. I recommend the authors to explicitly propose their datasets made accessible in supplementary material as benchmark for the community.

Comments on revisions:

This is a very nice paper.

Reviewer #2 (Public review):

Summary:

The authors propose a methodology to perform causal (temporal) discovery. The approach appears to be robust and is tested in the different scenarios: one related to live-cell imaging data, and another one using synthetic (mathematically defined) time series data. They compare the performance of their findings against another well-known method by using metrics like F-score, precision and recall,

Strengths:

--Performance, robustness, the text is clear and concise, The authors provide the code to review.

Comments on revisions:

The authors have addressed my concerns properly providing the needed explanations.

Reviewer #1 (Public review):

This study presents a novel application of inverted encoding (i.e., decoding) to detect non-linear correlates of crossmodal integration in human neural activity, using EEG (electroencephalography). The method is successfully applied to data from a group of 41 participants, performing a spatial localization task on auditory, visual, and audio-visual events. The analyses clearly show a behavioural superiority for audio-visual localization. Like previous studies, the results when using traditional univariate ERP analyses were inconclusive, showing once more the need for alternative, more sophisticated approaches. The inverted encoding approach of this study, harnessing on the multivariate nature of the signal, captured clear signs of super-additive responses, considered by many as the hallmark of multisensory integration. Despite the removal of eye-movement artefacts from the signal eliminated the significant decoding effect, the author's control analyses showed that decoding is more effective from parietal, compared to frontal electrodes, thereby ruling out ocular contamination as the sole origin of the relevant signal.

This significant finding can bear important advances in the many fields where multisensory integration has been shown to play an important role, by providing a way to bring much needed coherence across levels of analysis, from behaviour to single-cell electrophysiology. To achieve this, it would be ideal to contrast whether the pattern of super-additive effects in other scenarios where clear behavioural signs of multisensory integration are also observed. One could also try to further support the posterior origin of the super-additive effects by source localization.

Comments on revised version:

All my previous concerns have been addressed. I congratulate the authors on a very nice paper.

Reviewer #2 (Public review):

Summary:

This manuscript seeks to reconcile observations in multisensory perception - from behavior and from neural responses. It is intuitively obvious that perceiving a stimulus via two senses results in better performance than one alone. However, the nature of this interaction is complicated and relating different measures (behavioural, neural) is challenging.

It is not uncommon to observe that for a perceptual task the percentage of correct responses seen with two senses is higher than the sum of the percentage correct obtained with each modality individually. i.e. the gains are "superadditive". The gains of adding a second sense are typically larger when the performance with the first sense is relatively poor - this effect is often called the principle inverse effectiveness. More generally, what this tells us is that performance in a multisensory perceptual task is a non-linear sum of performance for each sensory modality alone. In invasive recordings from single neurons, a wide range of non-linear interactions is observed - some superadditive, and some sub-additive.

Despite this abundance evidence of non-linearity in some measures of multisensory integration, evoked responses (EEG) to such sensory stimuli often show little evidence of it - and this is the problem this manuscript tackles. The key assertion made is that a univariate analysis of the EEG signal is likely to average out non-linear effects of integration. This is a reasonable assertion, and their analysis does indeed provide evidence that a multivariate approach can reveal non-linear interactions in the evoked responses.

Strengths:

It is of great value to understand how the process of multisensory integration occurs, and despite a wealth of observations of the benefits of perceiving the world with multiple senses, we still lack a reasonable understanding of how the brain integrates information. For example - what underlies the large individual differences in the benefits of two senses over one? One way to tackle this is via brain imaging, but this is problematic if important features of the processing - such as non-linear interactions are obscured by the lack of specificity of the measurements. The approach they take to analysis of the EEG data allows the authors to look in more detail at the variation in activity across EEG electrodes, which averaging across electrodes cannot.

This version of the manuscript is well written and for the most part clear and the report of non-linear summation of neural responses is convincing. A particular strength of the paper is their use of a statistical model of multisensory integration as their "null" model of neural responses, and the "inverted-encoder" which infers an internal representation of the stimulus which can explain the EEG responses. This encoder generates a prediction of decoding performance, which can be used to generate predictions of multisensory decoding from unisensory decoding, or from a sum of the unisensory internal representations.

In behavioural performance, it is frequently observed that the performance increase from two senses is close to what is expected from the optimal integration of information across the senses, in a statistical sense. It can be plausibly explained by assuming that people are able to weight sensory inputs according to their reliability - and somewhat optimally. Critically the apparent "superadditive" effect on performance described above does not require any non-linearity in the sum of information across the senses, but can arise from correctly weighting the information according to reliability.

The authors apply a similar model to predict the neural responses expected to audiovisual stimuli from the neural responses to audio and visual stimuli alone, assuming optimal statistical integration of information. The neural responses to audiovisual stimuli exceed the predictions of this model and this is the main evidence supporting their conclusion, and it is convincing.

Weaknesses:

The main weakness of the manuscript is that their behavioural data show no evidence of performance that exceeds the predictions of these statistical models. In fact, the models predict multisensory performance from unisensory performance pretty well. This makes it hard to interpret their results, as surely if these nonlinear neural interactions underlie the behaviour, then we should be able to see evidence of it in the behaviour. I cannot offer an easy explanation for this.

Overall, therefore, I applaud the motivation and the sophistication of the analysis method and think it shows great promise for tackling these problems.

Reviewer #1 (Public review):

Summary:

The authors aim to elucidate the diversity and gene expression patterns of marine plankton using innovative collection and sequencing methodologies. Their work investigates the taxonomic and functional profiles of planktonic communities, providing insights into their ecological roles and responses to environmental changes.

Strengths:

The methodology utilized in this study, particularly the combination of single-cell sequencing and advanced bioinformatics techniques, represents a significant advancement in the field of plankton research. The application of the Smart-seq2 protocol for cDNA synthesis, followed by rigorous quality control measures, ensures high-quality data generation. This comprehensive approach not only enhances the resolution of the obtained genetic information but also allows for a more detailed exploration of the diversity and functional potential of the phytoplankton community.

One of the major strengths of this study is the rigorous methodological approach, including precise sampling techniques and robust data analysis protocols, which enhance the reliability of the results. The use of advanced sequencing technologies allows for a comprehensive assessment of gene expression, significantly contributing to our understanding of plankton diversity and its implications for marine ecosystems.

Weaknesses:

While the evidence presented is solid, there are areas where the analysis could be expanded. The authors could further explore the ecological interactions within plankton communities, which would provide a more holistic view of their functional roles. Additionally, a broader discussion of the implications of their findings for marine conservation efforts could enhance the manuscript's impact.

The choice of both the plankton net and filter pore size during the plankton collection process is critical, as these factors directly impact the types of phytoplankton collected. The use of a 25 μm filter paper, in particular, may result in the omission of many eukaryotic phytoplankton species. This limitation, combined with the characteristics of the plankton net, could affect the comprehensiveness and accuracy of the results, potentially influencing the study's conclusions regarding phytoplankton diversity.

The timing of fixation is crucial, as it directly affects whether the measured transcriptome accurately represents the organisms' actual transcriptional state in their native water environment. If fixation occurred a significant time after sample collection, the transcriptomic data may not reflect their true in situ transcriptional activity, which greatly reduces the relevance of this method.

Reviewer #2 (Public review):

Summary:

The paper introduces Ukiyo-e-Seq, a novel method integrating microscopy with single-cell transcriptomics to study individual, uncultured eukaryotic plankton cells. By combining microscopic imaging with transcriptomic analysis, the approach links plankton morphology to gene expression, enabling taxonomic identification and functional protein exploration. Ukiyo-e-Seq was tested on 66 microbial eukaryotic cells, revealing taxonomic diversity across four superkingdoms and allowing analysis of protein complexes and developmental genes in individual species. According to the authors, this method has the potential to advance single-cell marine biodiversity studies by addressing limitations in traditional taxonomy and metatranscriptomics, especially for rare or uncultured organisms.

However, the study's conclusions are often weakly supported by data, particularly given that this is not the first study to combine microscopy and single-cell transcriptomics of eukaryotic plankton using Smart-seq2.

Strengths:

A notable strength is the authors' generation of several single-cell transcriptomes for the diatom Chaetoceros, which could benefit from greater focus rather than broadly addressing eukaryotic single cells.

Weaknesses:

The study lacks comparison with other single-cell transcriptomics studies and it was presented as the first study that combines imaging and single-cell transcriptomics (smart-seq2) of eukaryotic plankton while in fact it is not. The sampling methodology is not replicable as the authors used a tea strainer instead of standard plankton collection equipment to filter larger cells. Terminology throughout the paper is unconventional, such as "public and private contigs," "single-organism genomics," "highly expressed contigs," and "optical methods." Additionally, the authors did not specify which database was used for taxonomic assignments. These issues may stem from the authors' limited background in microbial ecology. Overall, the study has many drawbacks and it could benefit from complete rewriting and focusing mainly on single-cell transcriptomics of diatoms.

Reviewer #3 (Public review):

Gatt et al. present a novel take on single-cell RNA-sequencing from complex planktonic samples, introducing an approach they aptly named Ukiyo-e-Seq. This work combines environmental sampling with cell picking, microscopic imaging, and Smart-seq2 single-cell RNA sequencing to profile uncultured eukaryotic plankton. Developing single-cell approaches for such ecosystems is critical, given the poor representation of many planktonic species in cultures and reference databases. This work could help bridge existing technological gaps between morphological and molecular studies of aquatic microeukaryotes

The authors argue that microscopy does not provide information on the biochemistry of species under consideration. At best, it provides taxonomic labeling of species within a sample, yet imaging fails to assess their metabolic state or to disentangle cryptic species. In a standard metatranscriptomic setup, the sequence pool is described by aligning assembled contigs with reference databases to obtain functional and taxonomic information. This complex community-level data is impossible to parse at the single-organism level. Moreover, by relying on reference datasets, a lot of potential information can be missed. The aim of the approach is to combine the strengths of both methods, generating single-cell transcriptomic data linked to individual plankton images.

Strengths:

Ukiyo-e-Seq generated a valuable dataset by combining imaging and transcriptomics for individual planktonic organisms from environmental samples. This multimodal approach has the potential to improve taxonomic predictions and functional insights at the single-organism level. This manuscript demonstrates the technical feasibility of such an approach. Data of this type is rare and thus represents a valuable resource to further advance single-cell sequencing of planktonic species from environmental samples.

Weaknesses:

(1) The merge-split strategy, where single-cell reads are pooled prior to assembly, is counterintuitive. Pooling obscures the single-organism resolution that single-cell methods aim to achieve. The approach might be useful for assembling low-coverage contigs, but risks masking unique expression profiles for transcripts unique to a given well. As an alternative, the authors could assemble each well independently to obtain well-specific transcriptomic bins. Assemblies could then be clustered based on sequence similarity, thereby imposing strict clustering parameters to maintain resolution, to create a common reference for downstream analysis if needed. In my opinion, better results would be obtained by implementing a per-well assembly and read mapping.

(2) The focus on the top five most expressed contigs throughout the manuscripts' data analysis is a limiting choice, as it excludes most contigs. In the preprint, we are presented with a very narrow view of the data. Visualising the entire range of assembled contigs would provide a better picture of the transcriptomic composition and diversity per well. It would be interesting to assess if the full information could be used to preliminary bin transcriptomic sequences from individual wells, for example, by gathering all 'private' contigs with high read coverage in a single well. Does such a set represent a single complete eukaryotic transcriptome?

(3) I missed a verification with (broad-scale) taxonomic assessments based on the associated microscopic images. In their goals, the authors state that a joint approach has the potential to discover new taxonomic biodiversity. I agree, and to me, this is what is exciting about the preprint, yet I miss an example or the right bioinformatic implementation to drive home this claim. Are there organisms in wells where poor taxonomic annotations, based on alignment to a reference database or the LCA approach implemented in Kraken2, would usually result in ignoring the species in classic metatranscriptomics? Can you advance the taxonomic annotation by referring back to the organisms' picture? Can manual assessment of taxonomy advance the results from the LCA approach?

(4) The current use of AlphaFold to predict protein structures does not convincingly add to the study's core objectives.

Overall, Ukiyo-e-Seq presents a promising method for studying single-cell diversity in environmental samples, though the bioinformatic pipeline requires refinement to support some of the claims made by the authors. Additionally, the manuscript would benefit from clarity and additional details in its methods and a more consistent approach to presenting results and summary statistics across all assembled contigs and all sampled wells, rather than focusing on selected wells.

Reviewer #1 (Public review):

Summary:

This study by Fuqua et al. studies the emergence of sigma70 promoters in bacterial genomes. While there have been several studies to explore how mutations lead to promoter activity, this is the first to explore this phenomena in a wide variety of backgrounds, which notably contain a diverse assortment of local sigma70 motifs in variable configurations. By exploring how mutations affect promoter activity in such diverse backgrounds, they are able to identify a variety of anecdotal examples of gain/loss of promoter activity and propose several mechanisms for how these mutations are interacting within the local motif landscape. Ultimately, they show how different sequences have different probabilities of gaining/losing promoter activity and may do so through a variety of mechanisms.

Major strengths and weaknesses of the methods and results:

This study uses Sort-Seq to characterize promoter activity, which has been adopted by multiple groups and shown to be robust. Furthermore, they use a slightly altered protocol which allows measurements of bi-directional promoter activity. This combined with their pooling strategy allows them to characterize expression of many different backgrounds in both directions in extremely high-throughput which is impressive! A second key approach this study relies on is the identification of promoter motifs using position weight matrices (PWMs). While these methods are prone to false positives, the authors implement a systematic approach which is standard in the field. However, drawing these types of binary definitions (is this a motif? yes/no) should always come with the caveat that gene expression is quantitative traits that we oversimplify when drawing boundaries.

Their approach to randomly mutagenize promoters allowed them to find many examples of different types of evolutions that may occur to increase or decrease promoter activity. They have supported these with validations in more controlled backgrounds which convincingly support their proposed mechanisms for promoter evolution.

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions:

The authors express a key finding that the specific landscape of promoter motifs in a sequence affect the likelihood that local mutations create or destroy regulatory elements. The authors have described many examples, including several that are non-obvious, and show convincingly that different sequence backgrounds have different probabilities for gaining or losing promoter activity. This overarching conclusion is supported by trend and mechanistic data which show differences in probabilities of evolving promoters, as well as the mechanisms underlying these evolutions. Furthermore, these mutations are well described and presented, showing the strength of emergent promoter motifs and their specific spacings from existing motifs within the sequence.

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

This study enhances our understanding of the diverse mechanisms by which promoters can evolve or devolve, potentially improving models that predict mutational outcomes. While this study reveals complex mutational patterns, modeling them could significantly advance our ability to predict bacterial evolutionary trajectories and interpret genomes, bringing us closer to that goal.

Recent work in the field of bacterial gene regulation has raised interest in bidirectional promoter regions. While the authors do not discuss how mutations that raise expression in one direction may affect another, they have created an expansive dataset which may enable other groups to study this interesting phenomenon. Also, their variation of the Sort-Seq protocol will be a valuable example for other groups who may be interested in studying bidirectional expression. Lastly, this study may be of interests to groups studying eukaryotic regulation as it can inform how the evolution of transcription factor binding sites influences short-range interactions with local regulator elements.

Any additional context to understand the significance of the work:

Predicting whether a sequence drives promoter activity is a challenging task. By learning the types of mutations that create or destroy promoters, this study provides valuable insights for computational models aimed at predicting promoter activity.

Comments on revised version:

I am satisfied with the extensive changes made by the author. This manuscript is excellent.

I very much like the change in figures to incorporate the sequence information. It is great to see clear representations of the emergent sigma70 motifs and their spacing relative to existing motifs. This addition significantly improves the clarity of the findings.

The validation of mutations on a clean background is well-executed, and the results are convincing. I appreciate the effort put into validating their results. The additional analyses that include TGn and UP-element motifs are also well done and highly relevant, as these elements are known to compensate for weaker or absent -35 sequences.

Most or all perceived inconsistencies from the previous version have been resolved. While I don't think the fluorescence threshold of 1.5 a.u. for promoter activity is justified, the authors do acknowledge this shortcoming, and even empirically-derived thresholds are still technically arbitrary.

I particularly enjoyed Figure 1E, thank you for entertaining my analysis request! Also, the H-NS story is a nice addition showing how transcription factors influence this evolution

Overall, this revised manuscript is an excellent contribution to the field, and I have no further recommendations for improvement.

Reviewer #2 (Public review):

Summary:

Fuqua et al investigated the relationship between prokaryotic box motifs and the activation of promoter activity using a mutagenesis sequencing approach. From generating thousands of mutant daughter sequences from both active and non-active promoter sequences they were able to produce a fantastic dataset to investigate potential mechanisms for promoter activation. From these large numbers of mutated sequences, they were able to generate mutual information with gene expression to identify key mutations relating to the activation of promoter island sequences.

Strengths:

The data generated from this paper is an important resource to address this question of promoter activation. Being able to link promoter modulated gene expression to mutational changes in previously nonactive promoter regions is exciting. This approach allows future large-scale studies to investigate evolutionary processes relating to changes in gene regulation in a statistically robust manner. Here there is a focus on the -10 and -35 boxes but other elements and interactions were explored including; H-NS binding, UP-element and TGn. Alongside this, the method of identifying key mutations using mutual information in this paper is well done and should be a standard in future studies for identifying regions of interest.

Weaknesses:

While the generation of the data is superb, as the authors have stated clearly themselves, there is a lot of scope for future studies to understand both causal relationships and utilise the data more effectively. The authors look at changes in regulatory expression based on a few observations that are treated independently but occur concurrently. While this study has backed up findings experimentally this may not always be possible. Previously this reviewer had suggested addressing this using complementary approaches such as analysis focusing on identifying important motifs, using something like a glm lasso regression to identify significant motifs, and then combining with mutational hotspot information would be more robust. The authors tried to implement such an approach in response to the review, but its complexity became beyond the scope. I look forward to the development of such methods that allow more complete exploration of similar datasets.

Comments on revised version:

The authors addressed all my previous comments. I believe the study is much improved and thank them for the time and effort they put into addressing the comments.

Reviewer #3 (Public review):

This work brings a computational approach to the study of promoters and transcription. The paper is improved but there are still factual errors and implausible explanations. I am not convinced by the response from the authors, concerning the promoter -35 element, in their rebuttal.

Comments on author rebuttal:

- We respectfully but strongly disagree that our analysis has misrepresented the true nature of -35 boxes. First, accounting for more A's at position 5 in the PWM is not going to lead to a "critical error." This is because positions 4-6 of the motif barely have any information content (bits) compared to positions 1-3 (see Fig 1A).

The analysis does misrepresent the consensus -35 element, which is, unequivocally, TTGACA. I agree that positions 4-6 of the element are less well-conserved.

- This assertion is not just based on our own PWM, but based on ample precedent in the literature. In PMID 14529615, TTG is present in 38% of all -35 boxes, but ACA only in 8%.

This does not mean that TTGACA is not the consensus, or that "ACA" is not important at promoters where it's present.

- In PMID 29388765, with the -10 instance TATAAT, the -35 instance TTGCAA yields stronger promoters compared to the -35 instance TTGACA (See their Figure 3B).

This is a known phenomenon and results from "perfect" promoters being limited at the point of RNA polymerase promoter escape (because the RNAP struggles to "let go" of perfect promoters). This does not mean the TTGACA is not the consensus. Indeed, and this is a key point, it is evident in the figure the authors refer to that TTGACA stimulates more transcription than alternative -35 sequences when -10 elements are not perfect.

- In PMID 29745856 (Figure 2), the most information content lies in positions 1-3, with the A and C at position 5 both nearly equally represented, as in our PWM.

The motif shown in this paper suffers from exactly the same issue as the paper under review; the variable spacing between the -35 hexamer and -10 element isn't taken into account by MEME.

- In PMID 33958766 (Figure 1) an experimentally-derived -35 box is even reduced to a "partial" -35 box which only includes positions 1 and 2, with consensus: TTnnnn.

This paper does not show an "experimentally-derived -35 box" in Figure 1 (or anywhere else, as far as I can see).

- In addition, we did not derive the PWMs as the reviewer describes. The PWMs we use are based on computational predictions that are in excellent agreement with experimental results. Specifically, the PWMs we use are from PMID 29728462, which acquired 145 -10 and -35 box sequences from the top 3.3% of computationally predicted boxes from Regulon DB.

The paper mentioned states "for the genomic RNAP logo, sequences were taken from computationally predicted RNAP binding sites on RegulonDB" so these are not experimentally defined promoters? It's not obvious from the paper, or regulon DB, which sequences these are or how they were predicted.

- Thank you for pointing out that our original submission was incomplete in this regard. We address these concerns by new analyses, including some new experiments. First, Rho dependent termination is associated with the RUT motif, which is very rich in Cytosines (PMID: 30845912). Given that our sequences confer between 65%-78% of AT-content, canonical rho dependent termination is unlikely. However, we computationally searched for rho-dependent terminators using the available code from PMID: 30845912, but the algorithm did not identify any putative RUTs. Because this analysis was not informative, we did not include it in the paper.

I don't believe it is the case that Rho absolutely requires a RUT sequence. My understanding is that, if an RNA is not translated, Rho will intervene (e.g. see PMID: 18487194).

- We respectfully disagree that the reviewer's point is pertinent because what the reviewer is referring to is the likelihood that the sequence is a promoter, which indeed increases with AT content, but we are focused on the likelihood that a sequence becomes a promoter through DNA mutation

I disagree that this distinction is relevant. An AT-rich sequence will much more closely resemble a promoter by chance than a GC rich sequence. As an extreme example, the sequence TTTTTT can be converted into a reasonable -10 element by one change (to TATTTT) but the sequence GGGGGG can't.

Reviewer #1 (Public review):

Summary:

In this manuscript, Liu et al. present CROWN-seq, a technique that simultaneously identifies transcription-start nucleotides and quantifies N6,2'-O-dimethyladenosine (m6Am) stoichiometry. This method is derived from ReCappable-seq and GLORI, a chemical deamination approach that differentiates A and N6-methylated A. Using ReCappable-seq and CROWN-seq, the authors found that genes frequently utilize multiple transcription start sites, and isoforms beginning with an Am are almost always N6-methylated. These findings are consistently observed across nine cell lines. Unlike prior reports that associated m6Am with mRNA stability and expression, the authors suggest here that m6Am may increase transcription when combined with specific promoter sequences and initiation mechanisms. Additionally, they report intriguing insights on m6Am in snRNA and snoRNA and its regulation by FTO. Overall, the manuscript presents a strong body of work that will significantly advance m6Am research.

Strengths:

The technology development part of the work is exceptionally strong, with thoughtful controls and well-supported conclusions.

Weaknesses:

Given the high stoichiometry of m6Am, further association with upstream and downstream sequences (or promoter sequences) does not appear to yield strong signals. As such, transcription initiation regulation by m6Am, suggested by the current work, warrants further investigation.

Reviewer #2 (Public review):

Summary:

In the manuscript "Decoding m6Am by simultaneous transcription-start mapping and methylation quantification" Liu and co-workers describe the development and application of CROWN-Seq, a new specialized library preparation and sequencing technique designed to detect the presence of cap-adjacent N6,2'-O-dimethyladenosine (m6Am) with single nucleotide resolution. Such a technique was a key need in the field since prior attempts to get accurate positional or quantitative measurements of m6Am positioning yielded starkly different results and failed to generate a consistent set of targets. As noted in the strengths section below the authors have developed a robust assay that moves the field forward.

Furthermore, their results show that most mRNAs whose transcription start nucleotide (TSN) is an 'A' are in fact m6Am (85%+ for most cell lines). They also show that snRNAs and snoRNAs have a substantially lower prevalence of m6Am TSNs.

Strengths:

Critically, the authors spent substantial time and effort to validate and benchmark the new technique with spike-in standards during development, cross-comparison with prior techniques, and validation of the technique's performance using a genetic PCIF1 knockout. Finally, they assayed nine different cell lines to cross-validate their results. The outcome of their work (a reliable and accurate method to catalog cap-adjacent m6Am) is a particularly notable achievement and is a needed advance for the field.

Weaknesses:

No major concerns were identified by this reviewer.

Mid-level Concerns:

(1) In Lines 625 and 626, the authors state that "our data suggest that mRNAs initate (mis-spelled by authors) with either Gm, Cm, Um, or m6Am." This reviewer took those words to mean that for A-initiated mRNAs, m6Am was the 'default' TSN. This contradicts their later premise that promoter sequences play a role in whether m6Am is deposited.

(2) Further, the following paragraph (lines 633-641) uses fairly definitive language that is unsupported by their data. For example in lines 637 and 638 they state "We found that these differences are often due to the specific TSS motif." Simply, using 'due to' implies a causative relationship between the promoter sequences and m6Am has been demonstrated. The authors do not show causation, rather they demonstrate a correlation between the promoter sequences and an m6Am TSN. Finally, despite claiming a causal relationship, the authors do not put forth any conceptual framework or possible mechanism to explain the link between the promoter sequences and transcripts initiating with an m6Am.

(3) The authors need to soften the language concerning these data and their interpretation to reflect the correlative nature of the data presented to link m6Am and transcription initiation.

Reviewer #3 (Public review):

Summary:

m6Am is an abundant mRNA modification present on the TSN. Unlike the structurally similar and abundant internal mRNA modification m6A, m6Am's function has been controversial. One way to resolve controversies surrounding mRNA modification functions has been to develop new ways to better profile said mRNA modification. Here, Liu et al. developed a new method (based on GLORI-seq for m6A-sequencing), for antibody-independent sequencing of m6Am (CROWN-seq). Using appropriate spike-in controls and knockout cell lines, Liu et al. clearly demonstrated CROWN-seq's precision and quantitative accuracy for profiling transcriptome-wide m6Am. Subsequently, the authors used CROWN-seq to greatly expand the number of known m6Am sites in various cell lines and also determine m6Am stoichiometry to generally be high for most genes. CROWN-seq identified gene promoter motifs that correlate best with high stoichiometry m6Am sites, thereby identifying new determinants of m6Am stoichiometry. CROWN-seq also helped reveal that m6Am does not regulate mRNA stability or translation (as opposed to past reported functions). Rather, m6Am stoichiometry correlates well with transcription levels. Finally, Liu et al. reaffirmed that FTO mainly demethylates m6Am, not of mRNA but of snRNAs and snoRNAs.

Strengths:

This is a well-written manuscript that describes and validates a new m6Am-sequencing method: CROWN-seq as the first m6Am-sequencing method that can both quantify m6Am stoichiometry and profile m6Am at single-base resolution. These advantages facilitated Liu et al. to uncover new potential findings related to m6Am regulation and function. I am confident that CROWN-seq will likely be the gold standard for m6Am-sequencing henceforth.

Weaknesses:

Though the authors have uncovered a potentially new function for m6Am, they need to be clear that without identifying a mechanism, their data might only be demonstrating a correlation between the presence of m6Am and transcriptional regulation rather than causality.

Reviewer #1 (Public review):

Summary:

In this work, Qiu and colleagues examined the effects of preovulatory (i.e., proestrous or late follicular phase) levels of circulating estradiol on multiple calcium and potassium channel conductances in arcuate nucleus kisspeptin neurons. Although these cells are strongly linked to a role as the "GnRH pulse generator," the goal here was to examine the physiological properties of these cells in a hormonal milieu mimicking late proestrus, the time of the preovulatory GnRH-LH surge. Computational modeling is used to manipulate multiple conductances simultaneously and support a role for certain calcium channels in facilitating a switch in firing mode from tonic to bursting. CRISPR knockdown of the TRPC5 channel reduced overall excitability, but this was only examined in cells from ovariectomized mice without estradiol treatment.

Comments to address most recent author response:

The concern regarding the CRISPR experiments being confined to OVX mice is that the results can only suggest that CRISPR-mediated knockdown of TRPC5 can, at best, phenocopy the OVX+E condition. A reciprocal experiment in the opposite direction (for example, that returning TRPC5 to OVX levels in OVX+E mice prevents the changes in firing activity and pattern typical of the OVX+E2 condition) would strengthen the indication that E2-sensitive changes in TRPC5 expression and function are critically important to surge function. Acknowledging this as a limitation of the studies would help to better contextualize the value of the CRISPR experiments to an understanding of surge mechanisms when done only in OVX conditions.

The nature of the confusion regarding the consideration of OVX+E2 conditions in the computational model primarily arises from the methods description in the supplemental file: "The effect of E2 on ionic currents is modelled as a change in the maximum conductance parameter. For currents IM,IT, ICa and ITRPC5 this change is inferred from the qPCR data assuming that the conductance is directly proportional to the mRNA expression." If these were instead based on the whole-cell recordings as the authors now indicate in their response, then this description needs to be edited and clarified accordingly. Furthermore, the section states, "For ISK, IBK, Ileak, the OVX and OVX+E2 conductances are obtained from current-voltage relationships recorded from Kiss1ARH neurons in the absence/presence of iberiotoxin (BK blocker) and apamin (SK blocker). All other currents were assumed to be unaffected by E2." This section thus does not directly indicate that the recordings in the stated figures were used in the model, and moreover suggests that currents besides ISK, IBK, and Ileak were not different in OVX+E2 conditions.

The prior evidence stated for correlation of mRNA and channel conductance is not explicitly cited in the manuscript. It is well known that post-translational modifications, physiological modulation of individual channel biophysical properties, and many other factors can influence the end output of a membrane conductance. Therefore, the authors should, at minimum, provide a literature citation supporting the assumption used here.

Reviewer #2 (Public review):

Summary:

Kisspeptin neurons of the arcuate nucleus (ARC) are thought to be responsible for the pulsatile GnRH secretory pattern and to mediate feedback regulation of GnRH secretion by estradiol (E2). Evidence in the literature, including the work of the authors, indicates that ARC kisspeptin coordinate their activity through reciprocal synaptic interactions and the release of glutamate and of neuropeptide neurokinin B (NKB), which they co-express. The authors show here that E2 regulates the expression of genes encoding different voltage-dependent calcium channels, calcium-dependent potassium channels and canonical transient receptor potential (TRPC5) channels and of the corresponding ionic currents in ARC kisspeptin neurons. Using computer simulations of the electrical activity of ARC kisspeptin neurons, the authors also provide evidence of what these changes translate into in terms of these cells' firing patterns. The experiments reveal that E2 upregulates various voltage-gated calcium currents as well as 2 subtypes of calcium-dependent potassium currents, while decreasing TRPC5 expression (an ion channel downstream of NKB receptor activation), the slow excitatory synaptic potentials (slow EPSP) elicited in ARC kisspeptin neurons by NKB release and expression of the G protein-associated inward-rectifying potassium channel (GIRK). Based on these results, and on those of computer simulations, the authors propose that E2 promotes a functional transition of ARC kisspeptin neurons from neuropeptide-mediated sustained firing that supports coordinated activity for pulsatile GnRH secretion to a less intense burst-like firing pattern that could favor glutamate release from ARC kisspeptin. The authors suggest that the latter might be important for the generation of the preovulatory surge in females.

Strengths:

The authors combined multiple approaches in vitro and in silico to gain insights into the impact of E2 on the electrical activity of ARC kisspeptin neurons. These include patch-clamp electrophysiology combined with selective optogenetic stimulation of ARC kisspeptin neurons, reverse transcriptase quantitative PCR, pharmacology and CRISPR-Cas9-mediated knockdown of the Trpc5 gene. The addition of computer simulations for understanding the impact of E2 on the electrical activity of ARC kisspeptin cells is also a strength.

The authors add interesting information on the complement of ionic currents in ARC kisspeptin neurons and on their regulation by E2 to what was already known in the literature. Pharmacological and electrophysiological experiments appear of the highest standards and robust statistical analyses are provided throughout. The impact of E2 replacement on calcium and potassium currents is compelling. Likewise, the results of Trpc5 gene knockdown do provide good evidence that the TRPC5 channel plays a key role in mediating the NKB-mediated slow EPSP. Surprisingly, this also revealed an unsuspected role for this channel in regulating the membrane potential and excitability of ARC kisspeptin neurons.

Weaknesses:

The manuscript also has weaknesses that obscure some of the conclusions drawn by the authors.

One is that the authors compare here two conditions, OVX versus OVX replaced with high E2, that may not reflect the physiological conditions under which the proposed transition between neuropeptide-dependent sustained firing and less intense burst firing might take place (i.e. the diestrous [low E2] and proestrous [high E2] stages of the estrous cycle). This is an important caveat to keep in mind when interpreting the authors' findings. Indeed, that E2 alters certain ionic currents when added back to OVX females, does not mean that the magnitude of all of these ionic currents will vary during the estrous cycle.<br /> In addition, although the computational modeling indicates a role of the various E2-modulated conductances in causing a transition in ARC kisspeptin neuron firing pattern, their role is not directly tested in physiological recordings, weakening the link between these changes and the shift in firing patterns.

Overall, the manuscript provides interesting information about the effects of E2 on specific ionic currents in ARC kisspeptin neurons and some insights into the functional impact of these changes. However, some of the conclusions of the work, with regard, in particular, to the role of these changes in ion channels and to their implications for the LH surge, are not fully supported by the findings.

Reviewer #1 (Public review):

Summary:

This study from Abssy et al. aims to determine if different non-invasive peripheral stimulation techniques - such as magnetic and electrical stimulations - may influence pain intensity, unpleasantness, and secondary hyperalgesia using a 4-arm parallel-group study. They observed no effect on pain intensity and unpleasantness. Also, they reported that only the TENS (electrical stimulation) did not impact secondary hyperalgesia. They hypothesized that the effects were probably due to the sound emitted by RPMS (magnetic stimulation). In a follow-up study, they tried to determine if covering the sound of RPMS would abolish the effect on secondary hyperalgesia using a single-arm design. They observed no effect of RPMS.

Strengths:

(1) The research team recruited a relatively large sample size for this type of study.

(2) The phasic heat pain protocol appears rigorous and well-described.

(3) The Figures are helpful in facilitating the understanding of the study design and results.

(4) The statistical analyses appear sound.

Weaknesses:

(1) The proposed design is not sufficient to answer the research question. The rationale of the study proposed in the introduction is that auditory stimulation may explain the analgesic effects of RPMS. To answer this question, the authors should have used a factorial design using 4 groups (active RPMS + sound; active RPMS + no sound; sham RPMS + sound; sham RPMS + no sound). Using this design, it would have been possible to determine if the sound, the afferent stimulation, or both are necessary to produce analgesia. Rather, they tested two types of RPMS (iTBS, cTBS) without real rationale, one electrical stimulation and a placebo.

(2) There are multiple ways that the current design could have introduced biases. The study was not randomized but pseudo-randomised. What does that mean? Was their allocation concealment? Was the assessor and data analyst blinded to group allocation? Did an intention to treat analyses were performed? Did the participants were adequately blinded (was it measured)?

(3) The TENS parameters used were not optimal and are not those commonly used in clinical practice. This could have explained the lack of TENS effects. The lack of TENS effects has not been discussed and it is concerning. If TENS had been effective (as expected), the story about the auditory effects would not have been presented as the primary mechanisms underlying the current results.

(4) No primary outcome has been identified. It is important to mention that the interpretation of results is based on the presence of only one statistically significant result. Pain intensity and pain unpleasantness are not affected. This was not properly addressed in the Discussion. What does that mean that secondary hyperalgesia is affected but not pain?

(5) The use of secondary hyperalgesia as a variable requires further clarification. How is it possible to measure secondary hyperalgesia if there is no lesioned tissue? If heat creates secondary hyperalgesia without lesion, what does that mean physiologically? Is it a valid and reliable "pain" variable?

(6) The follow-up study has been designed to cover the RPMS sound using pink noise. However, the pink noise was also present during the PHP measurement. How can we determine whether the absence of change is due to the pink noise during the RPMS or the presence of pink noise during PHP? I don't think this is possible to discriminate.

Appraisal:

(7) Despite all these potential issues, authors interpret their data with high confidence and with several overstatements in the Title, Abstract, and Discussion. The results do not support their conclusions. The fact that auditory stimulation may produce an analgesic effect is a hypothesis, but the current study cannot ascertain it.

Reviewer #2 (Public review):

Summary:

In this article, Abssy, Osokin, Osborne, et al. aimed to demonstrate the effect of Peripheral Magnetic Stimulation (PMS) as a pain relief tool, studying its effects in an experimentally induced pain paradigm applied over healthy subjects. This is a relevant objective, as it will give a proxy indication of its utility as a clinical intervention to treat pain. Shockingly, in the first experiment, the authors found that this effect existed, not only in the active PMS groups but also in the sham PMS. With a clever second experiment, the authors used pink noise to mask the clicking sound and the PMS: this modification abolished the hypoalgesic effect of PMS.

Strengths:

This study presents an adequately calculated sample size (n = 100 for study 1 and n = 32 for study 2). This gives trustability to the results and allows for a correct disaggregated analysis to assess gender effects, which correct execution does not often occur. Nuisance variables are adequately addressed, figures and writing are clear, and I especially liked figures 4 and 5 for their easiness of interpretation. They explore two different stimulation protocols for the PMS, extending their results beyond parametrization. Secondary hyperalgesia is a particularly relevant measurement, as it is a common symptom in many relevant painful conditions. Pseudorandomization and counterbalanced design are also appreciated, as well as reinforcement of the results through Bayesian statistical approaches. Regarding the scientific content, the main result (auditory modulation of pain in PMS) is exciting and very interesting by itself and will be relevant for the pain community, granting further research, both from a fundamental and clinical perspective. Personally, I respect that they recognize that results did not match their a priori hypothesis, instead of committing HARKing. And it is a very thrilling mismatch for sure!

It will be especially interesting for those among us dedicated to neural stimulation for pain treatment.

Weaknesses:

Although the study presents solid results, some specific concerns make me reluctant to accept the interpretations that the authors take from said results. I list the most important here.

(1) My biggest concern in this paper is that the stimulation protocols are not applied after pain was induced in the subjects, but before. This is not bad in itself, but as the paper presents the stimulations as potential "treatments" it generates a severe mismatch between the objective, context (introduction), and impact (discussion) presented for the experiments, and how they are actually designed. This adds to the fact that healthy volunteers are used here to generate a study with low translational capability, that aims to be translational and provide an indication for clinics (maybe this is why the reduction in pain intensity caused by PMS when applied in patients, reported in references [29, 35 and 39], is not observed here).

(2) TENS treatment duration is simply too short (90s) to be considered a therapeutic TENS intervention. I get that this duration was chosen to match the one of PMS, but TENS is never applied like this in the clinics, in which the duration varies from 10 minutes to an hour (or more). This specific study comparing different durations recommends 40 minutes for knee osteoarthritis pain relief (PMID: 12691335). Under these conditions, this stimulation is more similar to a sham TENS than to a real TENS treatment: I would suggest interpreting it as such. As the paper is right now, it could give the impression that PMS could produce clinical effects not observed in TENS, but while the PMS application resembles a clinical one, the TENS application does not (due to its extremely short duration). As an example, giving paracetamol at a dose 10 times below its effective dose is a placebo, not a paracetamol treatment.

(3) This study measured pain, not central sensitization. Specifically, the effects refer to the area of secondary hyperalgesia. The IASP definition for central sensitization is "Increased responsiveness of nociceptive neurons in the central nervous system to their normal or subthreshold afferent input." (PMID: 32694387). No neuronal results are reported in this article. Therefore, central sensitization is not measured here, and we do not know if it is reduced by sound. This frontally clashes with the title of the article and with many interpretations of the results. For a deep review on this topic, I recommend PMID: 39278607 and the short article PMID: 30416715.

(4) There is no mention of blinding/masking/concealing in this manuscript. Was the therapist blind to whether they applied one protocol, another, or a placebo? Were the evaluators blind, as this can heavily influence their measurements? And the volunteers? Was allocation concealed? Was this blinding measured afterwards? Blinding is, together with randomization, the most important methodological feature for those interventional studies. For example, not introducing blinding and concealing directly makes a study lose 4 out of 10 points in the PEDro scale, failing to fulfill criteria 3, 5, 6, and 7 (https://pedro.org.au/english/resources/pedro-scale/). Continuing with methodological considerations, the dropout percentage is high (18% for the first and 25% for the second study), both above the 15% cutoff for criterion 8 of the PEDro, losing another point. It is not mentioned whether the statistical analysis was intention-to-treat or per-protocol. Assuming the second, criterion 9 is failed too. Also, although between-group comparisons are done for study 1, they are not for study 2. Criterion 10 depends on this, so I would recommend doing it to avoid failing it. As it is right now, the study will be a 3/10 on the PEDro scale, being therefore considered "low-quality level evidence". As some of these criteria can be fulfilled in this study, I will recommend doing so to increase its quality level to medium (more in "recommendations for authors").

(5) Data reporting and statistical treatment can be improved, as only differences are reported and regression to the mean is not accounted for in this study. Moreover, baseline levels for the dependent variables (control session) are not accessible for evaluation and they are not compared statistically, making it impossible to know if the groups were similar at baseline. This will imply failing criterion 3 of the PEDro, for a total of 2/10 points.

Reviewer #1 (Public Review):

Summary:

Park et al. conducted various analyses attempting to elucidate the biological significance of SARS-CoV-2 mutations. However, the study lacks a clear objective. The specific goals of the analyses in each subsection are unclear, as is how the results from these subsections are interconnected. Compiling results from unrelated analyses into a single paper can be confusing for readers. Clarifying the objective and narrowing down the topics would make the paper's purpose clearer.

The logic of the study is also unclear. For instance, the authors developed an evaluation score, APESS, for analyzing viral sequences. Although they state that the APESS score correlates with viral infectivity, there is no explanation in the results section about why this is the case.

In summary, I recommend reconsidering the structure of the paper.

Reviewer #2 (Public review):

Summary:

The authors have developed a machine learning tool AIVE to predict the infectivity of SARS-CoV-2 variants and also a scoring metric to measure infectivity. A large number of virus sequences were used with very detailed analysis that incorporates hydrophoic, hydrophiclic, acid and alkaline characteristics. The protein structures were also considered to measure infectivity and search for core mutations. The study especially focused on the S protein of SARS-CoV-2. The contents of this study would be of interest to many researchers related to this area and the web-service would be helpful to easily analyze such data without indepth bioinformatics expertise.

Strengths:

- Analysis on large scale data<br /> - Experimental validation on a partial set of searched mutations<br /> - A user-friendly web-based analysis platform that is made public

Weaknesses:

- Complexity of the research

Comments on revisions:

The authors have addressed all my comments and is much more readable.

Reviewer #1 (Public review):

Summary:

This important study by Takano et. al. describes a novel approach for optogenetically evoking seizures in an etiologically relevant mouse model of epilepsy. The authors developed a model that can trigger seizures "on demand" using optogenetic stimulation of CA1 principal cells in mice rendered epileptic by an intra-hippocampal kainate (IHK) injection into CA3. The authors discuss their model in the context of the limitations of current animal models used in epilepsy drug development. In particular, their model addresses concerns regarding existing models where testing typically involves inducing acute seizures in healthy animals or waiting on infrequent, spontaneous seizures in epileptic animals.

Strengths:

A strength of this manuscript is that this approach may facilitate the evaluation of novel therapeutics since these evoked seizures are demonstrated as being sufficiently similar to spontaneous seizures in these same mice which are more laborious to analyze. The data demonstrating the commonality of pharmacology and EEG features between evoked seizures and spontaneous seizures in epileptic mice, while also being different from evoked seizures in naïve mice, are convincing despite concerns regarding the biological significance of the differences in effect sizes of these features. The structural, functional, and behavioral differences between a seizure-naïve and epileptic mouse are complex and important issues. This study positively impacts the wider epilepsy research community by investigating seizure semiology and pharmaceutical responses in these populations.

Weaknesses:

While the data generally supports the authors' conclusions, a weakness of this manuscript lies in their analytical approach where EEG feature-space comparisons used the number of spontaneous or evoked seizures as their replicates as opposed to the number of IHK mice; these large data sets tend to identify relatively small effects of uncertain biological significance as being highly statistically significant. Furthermore, the clinical relevance of similarly small differences in EEG feature space measurements between seizure-naïve and epileptic mice is also uncertain. Finally, the multiple surgeries and long timetable to generate these mice may limit the value compared to existing models in drug-testing paradigms.

Reviewer #2 (Public review):

Summary:

The authors have attempted to modify and adapt the IH-KA model in mice to provide an improved approach to screening for new ASDs by partially mitigating the problem of randomly occurring seizures and relatively low seizure frequency in the IH-KA model. The authors used KA micro-injections to selectively kill the hippocampal CA3 area as a way to induce temporal lobe "epileptogenesis" (TLE), and then used optogenetics to activate CA1 pyramidal cells specifically. This approach allowed the authors to trigger generalized seizures where the tonic-clonic pattern of electrical activity was reminiscent of actual tonic-clonic behavioral convulsions. Administration of levitracetam (LEV) and diazepam (DZP), two widely used ASDs with different mechanisms, reduced the probability of optogenetically activated epileptic seizures in IH-KA mice, thus seeming to provide evidence for a new approach to screen ASDs. A variety of problems and issues with the approach and the results lead to confounds that raise serious concerns about the conclusions.

Major strengths and weaknesses of the Methods and Results:

Strengths:

The authors have designed a method for triggering seizures, and the figures show bona fide electrographic seizures with concomitant convulsive behavioral components. The optogenetically evoked seizures in IH-KA mice had the electrical properties of actual seizures and the tonic-clonic components were readily apparent. These seizures appeared different from seizures evoked in naïve mice, and the authors attribute this difference to the epileptogenic process, but this may not be correct.

The ASDs (i.e., LEV and DZP) reduced the success rate of the optogenetically evoked seizures in IH-KA mice, thus suggesting the potential usefulness of the model for testing ASDs. The paper discusses whether the Epilepsy Therapy Screening Program (ETSP) will be able to use this modification of the IH-KA model in place of (1) ASD screening with acute seizures in naïve animals, where the brain has not undergone "epileptogenesis", (2) testing ASDs on hippocampal paroxysmal discharges (HPDs) in the IH-KA model, which has undergone epileptogenesis, or (3) spontaneous epileptic seizures in animal models of TLE based on systemic treatments that lead to acute convulsive status epilepticus that have later undergone epileptogenesis. This proposed version of the IH-KA model aims to address the former problem (#1, above) by using a mouse model of TLE, and to address the latter problems (#2 and #3, above) of the seemingly random occurrence of epileptic seizures and the low seizure frequency by using optogenetically "triggered" seizures.

Weaknesses

Although the figures provide excellent examples of individual electrographic seizures and compare induced seizures in epileptic and naïve animals, it is unclear which criteria were used to identify an actual seizure induced by the optogenetic stimulus, versus a hippocampal paroxysmal discharge (HPD), an "afterdischarge", an "electrophysiological epileptiform event" (EEE, Ref #36, D'Ambrosio et al., 2010 Epilepsy Currents), or a so-called "spike-wave-discharge" (SWD). Were HPDs or these other non-seizure events ever induced using stimulation in animals with IH-KA? A critical issue is that these other electrical events are not actual seizures, and it is unclear whether they were included in the column showing data on "electrographic afterdischarges" in Figure 5 for the studies on ASDs. This seems to be a problem in other areas of the paper, also.

The differences between the optogenetically evoked seizures in IH-KA vs naïve mice are interpreted to be due to the "epileptogenesis" that had occurred, but the lesion from the KA-induced injury would be expected to cause differences in the electrically and behaviorally recorded seizures - even if epileptogenesis had not occurred. This is not adequately addressed.

The authors did not test whether an apparent "kindling" effect, apparently seen in naïve controls, also occurred in animals micro-injected with kainic acid (KA). This effect could cause model instability that might result in variability in response to ASDs. It is not clear whether the number of optogenetically induced seizures in epileptic animals would affect the response to drugs. It is also unclear how much of an improvement the animal model in the present work is over other similar models of TLE, where electrically triggered seizures could simply be applied to one of them.

The authors offer little mention of other research using animal models of TLE to screen ASDs, of which there are many published studies - many of them with other strengths and/or weaknesses. For example, although Grabenstatter and Dudek (2019, Epilepsia) used a version of the systemic KA model to obtain dose-response data on the effects of carbamazepine on spontaneous seizures, that work required use of KA-treated rats selected to have very high rates of spontaneous seizures, which requires careful and tedious selection of animals. The ETSP has published studies with an intra-amygdala kainic acid (IA-KA) model (West et al., 2022, Exp Neurol), where the authors claim that they can use spontaneous seizures to identify ASDs for DRE; however, their lack of a drug effect of carbamazepine may have been a false negative secondary to low seizure rates. The approach described in this paper may help with confounds caused by low or variable seizure rates. These types of issues should be discussed, along with others.

While the paper may be relevant for the ETSP and contract research organizations (CROs), the paper was not written to attract the interest of biological scientists, even those in this specific area of epilepsy research. It may be of low interest to other neuroscientists.

The outcome measure for testing LEV and DZP on seizures was essentially the fraction of unsuccessful or successful activations of seizures, where high ASD efficacy is based on showing that the optogenetic stimulation causes fewer seizures when the drug is present. The final outcome measure is thus a percentage, which would still lead to a large number of tests to be assured of adequate statistical power. Thus, there is a concern about whether this proposed approach will have high enough resolution to be more useful than conventional screening methods so that one can obtain actual dose-response data on ASDs.

The key issue the authors aim to address is the 30-40% of patients with DRE, but the real problem with DRE patients is not that these people have seizures with no effect of the ASDs; rather, although ASD may reduce seizure burden, these patients continue to have some remaining seizures even after high doses of ASDs, which often leads to adverse effects from the particular ASDs.

In several sections of the paper, the authors argue that two different groups are similar on the basis that no statistical difference was found between the two groups (i.e., p > 0.05); however, the failure to find a statistically significant difference, particularly with relatively small sample sizes, is not rigorous evidence that the two groups are actually similar - they are just "not significantly different."

It remains unclear that the optogenetically induced seizures in this model are better than similarly induced seizures in a naïve animal, and there is no evidence that the model will be useful for finding new ASDs to treat DRE.

Do the results support the conclusions?

Although the Results show examples of clear tonic-clonic seizures, it is not at all clear whether this approach is a significant improvement over previous methods used on animal models of TLE. The presented data from this method shows it provides an ability to detect the effect of widely used ASDs, but not that it will have the resolution to find better ASDs. The outcome measure of successful vs failed seizure inductions does not necessarily translate to a pathway for finding new ASDs for DRE, which often is a function of the side effects of the proposed new ASD. Although the recorded seizures in IH-KA rats differ in waveform from the ones in naïve mice, this could be due to the pattern of damage resulting from the micro-injection of KA or the density of expressed Chr2, which could be affected by sclerosis.

Impact and utility of methods and data.

The authors state that this approach should be used to test for and discover new ASDs for DRE, and also used for various open/closed loop protocols with deep-brain stimulation; however, the paper does not actually discuss rigorously or critically the background literature on other published studies in these areas or how this approach will improve future research for a broader audience than the ETSP and CROs. Thus, it is not clear whether the utility will apply more widely and how extensive a readership will be attracted to this work.

Final Conclusions:

Although this is an Interesting if not elegant new model for testing ASDs, it could be seen as a version of kindling (plus brain damage) in a rodent model, where some of the pathology of TLE is induced through focal injection of KA in the CA3 area of the hippocampus. Unfortunately, no evidence was presented that it will be any better than other models, although it could be faster and maybe easier than models based on spontaneous seizures. Although it has some similarities to the pathology of human TLE, the ablating part of the hippocampus does not account for the more widespread pathology that usually occurs elsewhere in the brain, as studied with imaging and with anatomy in surgical specimens from patients with DRE.

Although this approach with seizure induction via an optogenetic approach adds specificity to the type of cell that is stimulated (i.e., CA1 pyramidal cells), it is not apparent why this provides a better or more effective tool than simple electrical induction of seizures in any TLE model. Most important, it remains unclear how this addresses any aspect of drug resistance. To improve the ASD discovery process, an important new model must make a significant reduction in seizure burden, and would ideally improve the percentage of patients that become seizure-free. It is not clear how this model will do that.

In the end, the authors have created a model with some of the pathology of TLE, where they can then induce actual seizures via specific optogenetic stimulation. So, although it is potentially elegant work, it remains unclear what new information this model will tell us about epilepsy, and most importantly DRE - or how it will improve treatment outcomes.

Reviewer #3 (Public review):

Summary:

Chen et al. develop and characterize a new approach for screening drugs for epilepsy. The idea is to increase the ability to study seizures in animals with epilepsy because most animal models have rare seizures. Thus, the authors use the existing intrahippocampal kainic acid (IHKA) mouse model, which can have very unpredictable seizures with long periods of time between seizures. The authors employ an additional method to trigger seizures in the IHKA model. This method is closed-loop optogenetic stimulation of area CA1. There are several assumptions: area CA1 is the best location, triggered seizures are the same as spontaneous seizures, and this method will be useful despite requiring a great deal of effort. Regarding the latter, using a mouse model with numerous seizures (such as the pilocarpine model) might be more efficient than using a modified IHKA protocol that requires viral injection for optogenetics, fiber insertion requiring additional surgery, and accurate targeting to reliably trigger seizures on-demand. Aside from these caveats, the authors do succeed in studying seizures more readily in a mouse model of rare seizures. However, the seizures are evoked, not spontaneous. As currently presented, it is not clear how the triggered seizures can be used to investigate if antiseizure medication can reduce seizure burden as measured by seizure severity and seizures per day.

The authors modified the IHKA model to inject KA into CA3 instead of CA1 in order to preserve the CA1 pyramidal cells that they will later stimulate. To express the excitatory opsin channelrhodopsin (ChR2) in area CA1, they use a virus that expresses ChR2 in cells that express the Thy-1 promoter. The authors demonstrate that CA3 delivery of KA can induce a very similar chronic epilepsy phenotype to the injection of KA in CA1 and show that optical excitation of CA1 can reliably induce seizures. These are the strengths of the study.

While the authors show that electrophysiological signatures of induced vs spontaneous seizures are similar in many ways, the authors also show several differences and it is not clear if these differences are meaningful. Notably, the induced seizures are robustly inhibited by the antiseizure medication levetiracetam and variably but significantly inhibited by diazepam, similar to many mouse models with chronic recurrent seizure activity. I agree with the authors that this modified IHKA model will be of most value for higher throughput screening of potential antiseizure therapies, but with the caveat that the data may not generalize to other epilepsy models or humans. The authors evaluate the impact of repeated stimulation on the reliability of seizure induction and show that seizures can be reliably induced by CA1 stimulation for as long as 16 days, but the utility of the model would be better demonstrated if seizures could be shown to be inducible over the range of weeks to months.

Strengths:

(1) The authors show that the IHKA model of chronic epilepsy can be modified to preserve CA1 pyramidal cells (but at a cost of CA3 cells), allowing on-demand optogenetic stimulation of CA1 that appears to lower seizure threshold and thus trigger a seizure event.

(2) The authors show that repeated reactivation of CA1 even in untreated mice can promote kindling and induction of seizure activity, indeed generating two mouse models in total.

(3) Many electrophysiological signatures are similar between the induced and spontaneous seizures, and induced seizures reliably respond to treatment with antiseizure medications.

(4) Given that more seizures can be observed per mouse using on-demand optogenetics, this model enhances the utility of each individual mouse.

Weaknesses:

(1) Evaluation of seizure similarity using the SVM modeling and clustering is not sufficiently explained to show if there are meaningful differences between induced and spontaneous seizures. SVM modeling did not include analysis to assess the overfitting of each classifier since mice were modeled individually for classification.

(2) The difference between seizures and epileptiform discharges or trains of spikes (which are not seizures) is not made clear.

(3) The utility of increasing the number of seizures for enhancing statistical power is limited unless the sample size under evaluation is the number of seizures. However, the standard practice is for the sample size to be the number of mice.

(4) Seizure burden is not easily tested.

(5) It is unlikely that long-term adaptation to CA1-stimulated seizure induction is absent in these mice. A duration of evaluation longer than 16 days is warranted in light of the downward slope at days 13-16 for induced seizures in Figure 4C.

(6) Human epilepsy is extensively heterogeneous in both etiology and individual phenotype, and it may be hard to generalize the approach.

(7) No mention or assessment of mouse sex as a biological variable.

Reviewer #2 (Public review):

Summary:

Wilson's disease is a rare genetic disorder caused by mutations in the ATP7B gene. Previous studies have documented that ATP7B mutations can disrupt copper metabolism, affecting brain and liver function. In this paper, the authors performed a retrospective clinical study and found that Wilson's disease has a high incidence of cholecystitis. Single-cell RNA-seq analysis revealed changes in the immune microenvironment, including the activation of immune responses and the exhaustion of natural killer cells.

Strengths:

A key finding of this study is that the predominant ATP7B gene mutation in the Chinese population is the 2333G>T (p. R778L) mutation. The authors reported associations between Wilson's disease and cholecystitis, as well as the exhaustion of natural killer cells.

Weaknesses:

The underlying mechanisms linking ATP7B mutations to cholecystitis and natural killer cell exhaustion remain unclear. Specifically, it is not yet determined whether copper metabolism alterations directly cause cholecystitis and natural killer cell exhaustion, or if these effects are secondary to liver dysfunction.

Comments on revisions:

The authors fully addressed my questions and I don't have further comments.

Reviewer #1 (Public review):

Summary:

The research study under review investigated the relationship between gut and identified potential biomarkers derived from the nasopharyngeal and gut microbiota-based that could aid predicting COVID-19 severity. The study reported significant changes in the richness and Shannon diversity index in nasopharyngeal microbiome associated with severe symptoms.

Strengths:

The study successfully identified differences in the microbiome diversity that could indicate or predict disease severity. Furthermore, the authors demonstrated a link between individual nasopharyngeal organisms and the severity of SARS-CoV-2 infection. The density of the nasopharyngeal organism was shown to be a potential predictors of severity of COVID-19.

Reviewer #3 (Public review):

Summary:

How the microbial composition of the human body is influenced by and influences disease progression is an important topic. For people with COVID-19, symptomatic progression and deterioration can be difficult to predict. This manuscript attempts to associate the nasal and fecal microbiomes of COVID-19 patients with the severity of disease symptoms, with the goal of identifying microbial markers that can predict disease outcomes.

Strengths:

Analysis of microbiomes from two distinct anatomical locations and across three distinct patient groups is a substantial undertaking. How these microbiomes influence and are influenced by COVID-19 disease progression is an important question. In particular, the putative biomarker identified here could be of clinical value with additional research.

Weaknesses:

The primary weaknesses of this analysis is the relatively low sample size for analyzing disease subsets and moderate correlation values observed for putative biomarkers. Regardless, this data can be used to inform future studies aiming to understand the contribution of multifactorial dysbiosis to COVID-19 disease progression.

Reviewer #1 (Public review):

This manuscript from Schwintek and coworkers describes a system in which gas flow across a small channel (10^-4-10^-3 m scale) enables the accumulation of reactants and convective flow. The authors go on to show that this can be used to perform PCR as a model of prebiotic replication.

Strengths:

The manuscript nicely extends the authors' prior work in thermophoresis and convection to gas flows. The demonstration of nucleic acid replication is an exciting one, and an enzyme-catalyzed proof-of-concept is a great first step towards a novel geochemical scenario for prebiotic replication reactions and other prebiotic chemistry.

The manuscript nicely combines theory and experiment, which generally agree well with one another, and it convincingly shows that accumulation can be achieved with gas flows and that it can also be utilized in the same system for what one hopes is a precursor to a model prebiotic reaction. This continues efforts from Braun and Mast over the last 10-15 years extending a phenomenon that was appreciated by physicists and perhaps underappreciated in prebiotic chemistry to increasingly chemically relevant systems and, here, a pilot experiment with a simple biochemical system as a prebiotic model.

I think this is exciting work and will be of broad interest to the prebiotic chemistry community. The techniques described will be useful to the community as well.

Weaknesses:

This work stands well on its own in advancing the field and is well-supported by the evidence presented. The weaknesses below are thus more hopes for future work than limitations of a study that I find to be a complete and well-executed piece of work.

This paper's use of highly evolved protein enzymes is a potential limitation in its direct relevance to prebiotic chemistry. But this is less a limitation of the manuscript than the state of the field after the authors' advances. It will be of interest to see how these systems function in, e.g., RiboPCR (10.1073/pnas.1610103113) and with non enzymatic systems.

Similarly, some of the artifacts in this work (appreciated and noted by the authors) arising from gas bubbles evolving prevent the simulations from fully describing their results. However, gas-liquid interactions were likely important in prebiotic chemistry and the authors note several areas in which these could be important in future systems.

Reviewer #2 (Public review):

Schwintek et al. investigated whether a geological setting of a rock pore with water inflow on one end and gas passing over the opening of the pore on the other end could create a non-equilibrium system that sustains nucleic acid reactions under mild conditions. The evaporation of water as the gas passes over it concentrates the solutes at the boundary of evaporation, while the gas flux induces momentum transfer that creates currents in the water that push the concentrated molecules back into the bulk solution. This leads to the creation of steady state regions of differential salt and macromolecule concentrations that can be used to manipulate nucleic acids. First, the authors showed that fluorescent bead behavior in this system closely matched their fluid dynamic simulations. With that validation in hand, the authors next showed that fluorescently-labeled DNA behaved according to their theory as well. Using these insights, the authors performed a FRET experiment that clearly demonstrated hybridization of two DNA strands as they passed through the high Mg++ concentration zone, and, conversely, the dissociation of the strands as they passed through low Mg++ concentration zone. This isothermal hybridization and dissociation of DNA strands allowed the authors to perform an isothermal DNA amplification using a DNA polymerase enzyme. Crucially, the isothermal DNA amplification required the presence of the gas flux and could not be recapitulated using a system that was at equilibrium. These experiments advance our understanding of the geological settings that could support nucleic acid reactions that were key for the origin of life.

The presented data compellingly supports the conclusions made by the authors. In the revised submission, the authors have made convincing arguments supported by simulations that the present findings obtained with DNA would translate to RNA as well, thus making this work highly relevant for the field of origin of life.

A potential future experiment the authors could consider includes performing a prebiotically relevant reaction, such as non-enzymatic primer extension or ligation, in the described model of the rock pore geological setting.

Reviewer #1 (Public review):

This study provides compelling evidence that RAR, rather than its obligate dimerization partner RXR, is functionally limiting for chromatin binding. This manuscript provides a paradigm for how to dissect the complicated regulatory networks formed by dimerizing transcription factor families.

Dahal and colleagues use advanced SMT techniques to revisit the role of RXR in DNA-binding of the type-2 nuclear receptor (T2NR) RAR. The dominant consensus model for regulated DNA binding of T2NRs poses that they compete for a limited pool of RXR to form an obligate T2NR-RXR dimer. Using advanced SMT and proximity-assisted photoactivation technologies Dahal et al. now test the effect of manipulating the endogenous pool size of RAR and RXR on heterodimerization and DNA-binding in live U2OS cells. Surprisingly, it turns out that RAR, rather than RXR, is functionally limiting for heterodimerization and chromatin binding. By inference, the relative pool size of various T2NRs expressed in a given cell, rather than RXR, is likely determine chromatin binding and transcriptional output.

The conclusions of this study are well supported by the experimental results and provides unexpected novel insights in the functioning of the clinically important class of T2NR TFs. Moreover, the presented results show how the use of novel technologies can put long-standing theories on how transcription factors work upside down. This manuscript provides a paradigm for how to further dissect the complicated regulatory networks formed by T2NRs or other dimerizing TFs. I am convinced by the revised manuscript and have no additional concerns or comments.

Reviewer #2 (Public review):

Summary:

In the manuscript "Surprising Features of Nuclear Receptor Interaction Networks Revealed by Live Cell Single Molecule Imaging", Dahal et al combine fast single molecule tracking (SMT) with proximity-assisted photoactivation (PAPA) to study the interaction between RARa and RXRa. The prevalent model in the nuclear receptor field suggests that type II nuclear receptors compete for a limiting pool of their partner RXRa. Contrary to this, the authors find that over-expression of RARa but not RXRa increases the fraction of RXRa molecules bound to chromatin, which leads them to conclude that the limiting factor is the abundance of RARa and not RXRa. The authors also perform experiments with a known RARa agonist, all trans retinoic acid (atRA) which has little effect on the bound fraction. Using PAPA, they show that chromatin binding increases upon dimerization of RARa and RXRa.

The authors have done well to address my comments and specify limitations where they could not.

Reviewer #3 (Public review):

Summary:

This study aims to investigate the stoichiometric effect between core factors and partners forming the heterodimeric transcription factor network in living cells at endogenous expression levels. Using state-of-the-art single-molecule analysis techniques, the authors tracked individual RARα and RXRα molecules labeled by HALO-tag knock-in. They discovered an asymmetric response to the overexpression of counter-partners. Specifically, the fact that an increase in RARα did not lead to an increase in RXRα chromatin binding is incompatible with the previous competitive core model. Furthermore, by using a technique that visualizes only molecules proximal to partners, they directly linked transcription factor heterodimerization to chromatin binding.

Strengths:

The carefully designed experiments, from knock-in cell constructions to single-molecule imaging analysis, strengthen the evidence of the stoichiometric perturbation response of endogenous proteins. The novel finding that RXR, previously thought to be a target of competition among partners, is in excess provides new insight into key factors in dimerization network regulation. By combining the cutting-edge single-molecule imaging analysis with the technique for detecting interactions developed by the authors' group, they have directly illustrated the relationship between the physical interactions of dimeric transcription factors and chromatin binding. This has enabled interaction analysis in live cells that was challenging in single-molecule imaging, proving it is a powerful tool for studying endogenous proteins.

Weaknesses:

None noted.

Reviewer #1 (Public review):

Summary:

The manuscript by Yao S. and colleagues aims to monitor the potential autosomal regulatory role of the master regulator of X chromosome inactivation, the Xist long non-coding RNA. It has recently become apparent that in the human system, Xist RNA can not only spread in cis on the future inactive X chromosome but also reach some autosomal regions where it recruits transcriptional repression and Polycomb marking. Previous work has also reported that Xist RNA can show a diffused signal in some biological contexts in FISH experiments.

In this study, the authors investigate whether Xist represses autosomal loci in differentiating female mouse embryonic stem cells (ESCs) and somatic mouse embryonic fibroblasts (MEFs). They perform a time course of ESC differentiation followed by Capture Hybridization of Associated RNA Targets (CHART) on both female and male ESCs, as well as pulldowns with sense oligos for Xist. The authors also examine transcriptional activity through RNA-seq and integrate this data with prior ChIP-seq experiments. Additional experiments were conducted in MEFs and Xist-ΔB repeat mutants, the latter fails to recruit Polycomb repressors.

Based on this experimental design, the authors make several bold claims:

(1) Xist binds to about a hundred specific autosomal regions.<br /> (2) This binding is specific to promoter regions rather than broad spreading.<br /> (3) Xist autosomal signal is inversely correlated with PRC1/2 marks but positively correlated with transcription.<br /> (4) Xist targeting results in the attenuation of transcription at autosomal regions.<br /> (5) The B-repeat region is important for autosomal Xist binding and gene repression.<br /> (6) Xist binding to autosomal regions also occurs in somatic cells but does not lead to gene repression.

Together, these claims suggest that Xist might play a role in modulating the expression of autosomal genes in specific developmental and cellular contexts in mice.

Strengths:

This paper deals with an interesting hypothesis that Xist ncRNA can also function at autosomal loci.

Weaknesses:

The revised manuscript now includes many additional bioinformatic analyses to support the premise that Xist RNA targets a specific set of about 100 promoters and attenuates their expression in the early stages of differentiation. I have previously raised significant concerns about the bioinformatic analyses and the robustness of the data, especially those linked to CHART-seq datasets. Despite some improvements, fundamental problems with the analysis remain, precluding a conclusion on whether Xist RNA binds specific autosomal promoters. The main concerns include:

(1) The authors nicely explain the use of biological replicates; however, they still fail to provide the sufficient analysis I requested on d0 and sense probes. While some quantification is presented in Figures 1E and 1F, the peak calling I asked for has still not been performed. In the response document, the authors report that about 600 peaks were identified in d0 female ESCs compared to about 100 in differentiated conditions. They explain this by the well-known phenomenon of having a background of differentiated cells in d0. In my opinion, this reasoning is flawed. With 98% of cells not inducing Xist in the culture, it is unimaginable why 600 peaks would be detected in the peak calling analysis. Rather, this demonstrates a high background in the CHART peak calling. To assess this further, I have reanalyzed d7 CHART datasets and found robust enrichment of the sense probe on promoters of genes, even stronger than the antisense probe. MACS peak calling also identifies a robust number of peaks on the sense probe. Indeed, even though Figure 1F shows low sense probe enrichment, this is because it focuses on the anti-sense peaks only. An opposite effect is observed when focusing on all genes or on sense-peaks. Thefore it is tough to decide which of the signal is truelly due to Xist binding and what is an inherent problem with the CHART signal. These results cast serious doubts on the biological conclusions of this work and point to a very high background level of promoter signal in both sense and antisense samples.

(2) The authors do not address the conundrum of their results: how is it possible to have a genome-wide autosomal accumulation of Xist signal at promoters (see Figures 1A and 1B), while simultaneously specifically affecting only 100 promoters in the genome? The signal is either general (as Figures 1A and 1B suggest) or specific (as implied by the peak calling), but it cannot be both. Current data points to the fact that CHART has a bias for the most open parts of the chromatin.

(3) The text is still very confusing when it comes to Polycomb. Some experiments point to the fact that there are few PRC1/2 marks at putative Xist autosomal binding sites (Figure 3C), while the use of X1 induces the loss of PRC2 marks. I still find this internally contradictory. The authors sadly do not address my concerns with additional analysis. Their current data indicate that upon Xist upregulation, Xist-RNA binds to autosomal regions that are highly expressed and devoid of Polycomb. These loci then become transcriptionally attenuated and gain some (but low) level of PRC2 in a Xist-dependent fashion. If this model is true, then all these regions should not have Xist in d0 of differentiation and should also have slightly lower levels of PRC2. The argument that there is a low level of Xist in 2-5% of cells should not be a problem because most of the signal will come from the 98% of cells not expressing Xist (as seen in Figure 1A). Without timepoint 0, the whole premise of the paper is difficult to interpret. Either the d0 samples are good enough, or the system is so leaky that it is nearly impossible to identify Xist-specific effects. Males are a useful control but are obviously a genetically very different line with distinct epigenetic and signaling statuses. It is crucial to compare the timing of repression/PRC accumulation to conclude if and how Xist is functional on these loci.

(4) The authors did not address my concerns about the transcriptional analysis. I belive that the control genes are not selected properly. This analysis should not have been performed on just 100 randomly selected regions/genes. Instead, bootstrapping of 100 randomly selected regions/genes should be done, e.g., 1000 times. Additionally, one should only sample from expressed genes to have a comparable control gene set. For example, in Figures 4D and 4E, the distribution of control regions is entirely different. To stress again, relying on a set of 100 randomly selected genes/regions is not statistically robust; controls have to be matched, and bootstrapping has to be performed. Finally, each timepoint uses a different set of autosomal targets. There is a need to visualize the same set of genes across all timepoints (including d0). For example, are genes bound by Xist at d7 highly expressed at d0 and then attenuated only at d7? What happens to them at d14 (see points from 3)? The arguments about d0 heterogeneity are again not convincing (nor is Figure 3H, which shows a different set of genes).

(5) Transcriptional analysis is often shown only as tracks however the reads for key example genes have to be quantified properly and not just visualized or amalgamated in a violin plot.

Reviewer #2 (Public review):

Summary:

To follow-up on recent reports of Xist-autosome interaction the authors examine female (and male transgenic) mESCs and MEFs by CHARTseq. Upon finding that only 10% of reads map to X, they sought to identify reproducible alternative sites of Xist-binding, and identify ~100 autosomal Xist-binding sites in active chromatin regions. They demonstrate a transient down-regulation of autosomal expression. They utilize published male transgenic inducible Xist mESC data to support their findings. In their system, inhibition of Xist reduces autosomal impact.

Strengths:

The authors address a topical and interesting question with a series of models including developmental timepoints and utilize unbiased approaches (CHARTseq, RNAseq). For the CHARTseq they have controls of both sense probes and male cells; and indeed do detect considerable background with their controls. The use of 'metagene' plots provides a visual summation of genic impact. They compare with published data.

Weaknesses:

The revised text and rebuttal clarified my confusion of the 'follow-up' analyses (Figure 4) compared to published datasets. Further, the figure legends have been improved.

While the controls were a strength, it appears that when focussed on bound regions, the background (from sense probes) is now also substantially higher than global background (compare 1E to 1A/B). Thus, why do these autosomal targets enrich for the sense probes, and how to distinguish from such background for the ∆B experiments? If male and sense are both controls, then why is sense lower for males than females, doesn't this suggest Xist impact? While authors note d0 might detect Tsix, the signal is only slightly reduced by d14 and never equivalent. Indeed, the new PCA (S1C) does show as noted that female Xist interactions are distinct from sense and male, but the male signal is even more distinct from sense probes.

It would have been preferable to see the dispersion of the Xist RNA cloud in these ∆B cells, rather than a reference.

Only 2 replicates were used, but there were multiple time-points: D0, D4, d7, d14; further, the correlation analysis showed good reproducibility, and in response to reviews they note that 2 replicates are standard of practice.

The conclusion that RepB is "required for localization to the ~100 genes" is based on density (panel 2E); however, these autosomal targets retain enrichment at TSSs (panel 2A) and indeed the text suggests they are the same sites, suggesting that in fact the choice of autosomal region binding is not RepB dependent. Thus, this remains unresolved for me.

The introduction is clear, and the senior author is a leader in the field; however, by this reviewer's count 19 of the 52 references include the senior author.

Better descriptors for the supplemental Excel files would be helpful.

Aim achievement: The authors do identify autosomal sites with enrichment of chromatin marks and evidence of silencing. Their revised text clarifies many issues, although this reviewer still remains unconvinced that the autosomal targeting is repB-dependent.

The impact of Xist on autosomes is important for consideration of impact of changes in Xist expression with disease (notably cancers). Knowing the targets (if consistent) would enable assessment of such impact.

Reviewer #3 (Public review):

Summary:

Yao et al use CHART to identify chromatin associated with Xist in female mouse ESCs, and, as control, male ESCs at various timepoints of differentiation. Besides binding of Xist to X chromosome regions they found significant binding to autosomes, concentrating mostly on promoter regions of around 100 autosomal genes, as elucidated by MACS. The authors went on to show that the RepB repeat is mostly responsible for these autosomal interactions using a female ESC line in which RepB is deleted. Evidence is provided that Xist interacts with active autosomal genes containing lower coverage of repressive marks H3K27me3 and H2AK119ub and that RepB dependent Xist binding leads to dampening of expression, but not silencing of autosomal genes. These results were confirmed by overexpression studies using transgenic ESCs with doxycycline-inducible Xist as well as via a small molecule inhibitor of Xist (X1), inducing/inhibiting the dampening of autosomal genes, respectively. Finally, using MEFs and Xist mutants RepB or RepE the authors provide evidence that Xist is bound to autosomal genes in cells after the XCI process but appears not to affect gene expression. The data presented appear generally clear and consistent and indicate some differences between human and mouse autosomal regulation by Xist. Thus, these results are timely and should be published.

Strengths:

Regulation of autosomal gene expression by Xist is a "big deal" as misregulation of this lncRNA causes developmental defects and human disease. Moreover, this finding may explain sex-specific developmental differences between the sexes. The results in this manuscript identify specific mouse autosomal genes bound by Xist and decipher critical Xist regions that mediate this binding and gene dampening. The methods used in this study are appropriate, and the overall data presented appear convincing and are consistent, indicating some differences between human and mouse autosomal regulation by Xist.

Comments on revisions:

In the revised manuscript, the authors have addressed my previous criticisms satisfactorily. Moreover, the manuscript has been much improved with new confirmatory results and additional control experiments. This, combined with more detailed descriptions/explanations facilitates data interpretation, making the paper more transparent and easier to read.

Reviewer #1 (Public review):

The study by Longhurst et al. investigates the mechanisms of chemoresistance and chemosensitivity towards three compounds that inhibit cell cycle progression: camptothecin, colchicine, and palbociclib. Genome-wide genetic screens were conducted using the HAP1 Cas9 cell line, revealing compound-specific and shared pathways of resistance and sensitivity. The researchers then focused on novel mechanisms that confer resistance to palbociclib, identifying PRC2.1. Genetic and pharmacological disruption of PRC2.1 function, but not related PRC2.2, leads to resistance to palbociclib. The researchers then show that disruption of PRC2.1 function (for example, by MTF2 deletion), results in locus-specific changes in H3K27 methylation and increases in D-type cyclin expression. The study shows that increased expression of D-type cyclins results in palbociclib resistance.

Strengths:

The results of this study are interesting, and the study contributes insights into the molecular mechanisms of CDK4/6 inhibitors. Importantly, while CDK4/6 inhibitors are effective in the clinic, tumour recurrence is very high due to acquired resistance.

Weaknesses:

A key resistance mechanism is Rb loss, so it is important to understand if resistance conferred by PRC2.1 loss is mediated by Rb, and whether restoration of PRC2.1 function in Rb-deplete cells results in renewed palbociclib sensitivity. It is also important to understand the clinical implications of the results presented. Inclusion of these data would significantly improve the paper. At present, it is unclear if mutations in PRC2.1 are found in genetic analyses of tumour samples in patients with acquired resistance.

Reviewer #2 (Public review):

Summary:

Longhurst et al. assessed cell cycle regulators using a chemogenetic CRISPR-Cas9 screen in the haploid human cell line HAP1. Besides known cell cycle regulators they identified the PRC2.1 subcomplex to be specifically involved in G1 progression, given that the absence of members of the complex makes the cells resistant to Palbociclib. They further showed that in HAP1 cells the PRC2.1, but not the PRC2.2 complex is important to repress the cyclins CCND1 and CCND2. This can explain the enhanced resistance to Palbociclib, a CDK4/6-Inhibitor, after PRC2.1 deletion.

Strengths:

The initial CRISPR screen is very interesting, because it uses three distinct chemicals that disturb the cell cycle at various stages. This screen mostly identified known cell cycle regulators, which demonstrates the validity of the approach. The results can be used as a resource for future research.

The most interesting outcome of the experiment is the finding that knockouts of the PRC2.1 complex make the cell resistant to Palbociclib. In further experiments, the authors focused on MTF2 and JARID2 as main components of PRC2.1 and PRC2.2, respectively. Via extensive analyses, including genome-wide experiments, they confirmed that MTF2 is particularly important to repress the cyclins CCND1 and CCND2. Absence of MTF2 therefore leads to increased expression of these genes, sufficient to make the cell resistant to Palbociclib. This result will likely be of wide interest to the community.

Weaknesses:

The work is limited to specific biological contexts, and the generality of the conclusions is uncertain.

Comments on revisions:

The revision offers new insights and is overall satisfying. I have no further recommendations that I consider essential.

Reviewer #3 (Public review):

This study begins with a chemogenetic screen to discover previously unrecognized regulators of the cell cycle. Using a CRISPR-Cas9 library in HAP1 cells and an assay that scores cell fitness, the authors identify genes that sensitize or desensitize cells to the presence of palbociclib, colchicine, and camptothecin. The results suggest that these three drugs inhibit proliferation through different mechanisms, and with each treatment, expected and unexpected pathways were found to affect drug sensitivity. The authors focus the rest of the experiments and analysis on the polycomb complex PRC2, as deletion of several of its subunits in the screen conferred palbociclib resistance. The authors find that PRC2, specifically a complex dependent on the MTF2 subunit, methylates histone 3 lysine 27 (H3K27) in promoters of genes associated with various processes including cell-cycle control. Further experiments demonstrate that Cyclin D expression increases upon loss of PRC2 subunits, providing a potential mechanism for palbociclib resistance.

The strengths of the paper are the design and execution of the chemogenetic screen, which provides a wealth of potentially useful information. The data convincingly demonstrate in the HAP1 cell line that the MTF2-PRC2 complex sustains the effects of palbociclib (Fig. 4), methylates H3K27 in CpG-rich promoters (Fig. 5), and represses Cyclin D expression (Fig. 6). The correlation between MTF2-PRC2 inhibition and increased Cyclin D levels is shown in multiple cell lines using both genetic and chemical approaches. These results could be of great interest to those studying cell-cycle control, resistance mechanisms to therapeutic cell-cycle inhibitors, and chromatin regulation and gene expression.

There are a few weaknesses that somewhat temper the overall quality and potential impact of the study. First, the results from the colchicine and camptothecin screens (Fig. 1 and 2) are not experimentally validated, which lessens the rigor of those data and conclusions. Second, some experiments validating and further exploring results from the palbociclib screen (Figs. 4 and 5) are restricted to the Hap1 cell line, so the generality of some conclusions is not established. Third, conclusions drawn from data in Fig. 4D are not fully supported by proper use of biological replicates and analysis of the results.

Comments on revisions:

Proper statistical analysis considering biological replicates is still not applied to determine whether differences in palbociclib IC50 values at different GSK126 concentrations are significant.

Reviewer #1 (Public review):

The authors aim to investigate the relationship between low estrogen levels, postmenopausal hypertension, and the potential role of the molecule L-AABA as a biomarker for hypertension. By employing metabolomic analysis and various statistical methods, the study seeks to understand how estrogen deficiency affects blood pressure and identify key metabolites involved in this process, with a particular focus on L-AABA.

Strengths:

The study addresses a relevant and understudied area: the role of estrogen and metabolites in postmenopausal hypertension. It presents a novel hypothesis that L-AABA may serve as a protective factor against hypertension, which could have significant clinical implications if proven.

Weaknesses:

The evidence linking L-AABA to hypertension is largely correlative, lacking experimental validation or mechanistic proof. Key limitations, such as the inadequacy of the ovariectomy model in replicating human menopause, are acknowledged but not addressed with alternative approaches. In summary, while the study offers an intriguing hypothesis, its conclusions are premature and require further experimental validation and human data to substantiate the claims.

Reviewer #2 (Public review):

Summary:

In this manuscript, Dr. Yao Li et al. documented the metabolomic profile of the aorta from OVX rats and that from OVX plus E2. These conditions mimic post-menopause hypertension and hormonal replacement therapy.

Strengths:

The authors state that this is probably the first study to examine the metabolic changes in the aorta of post-menopause hypertension.

Weaknesses:

There are several weaknesses, and a few of them are quite serious.

(1) The aorta is not a resistant artery and has little to do with hypertension. The authors should have used resistant arteries for this study. The expression of several adrenergic receptors and cholinergic receptors in the aorta and resistant arteries are different. It is unknown whether the aorta metabolomic profile has any relevance to BP and whether they are similar to that of the resistant arteries. I understand the logistics issue of obtaining enough tissues from resistant arteries. At least, once some leads are discovered in the aorta, the authors should validate it in resistant arteries. This should be feasible.

(2) The aorta and all the arteries have three layers. It is critically important to know whether the metabolic changes occur in the intima or in the media, while the adventitia probably has little to do with vasoconstriction and hypertension. If the authors want to use the aorta to conduct the preliminary study, they should completely remove the adventitia and then use samples with and without their endothelium stripped and then assess their metabolomic profiles. After the leads are obtained from this preliminary profiling, they should be validated in endothelium and smooth muscles of the resistant artery. The current experiments are not appropriately designed.

(3) The tail-cuff BP measurement is a technique of the last century. The current gold standard of BP measurement is by telemetry. The tail-cuff method is particularly problematic in this study because the 1-2 h restraining of the rats for more than 10 times BP measurement will cause significant stress in the animal, and their stress hormone secretion might cause biased metabolomic profiles in the OVX versus shames operated mice. The problem can be totally avoided by using telemetry.

(4) Although the L-AABA showed a high p-value (10^-4) of a decrease in the OVX rats, the fold change is small (2-3 folds). Such a small change should be validated using a different method to be convincing.

(5) The authors claim (or hypothesize) that the reduced AABA level in OVX can cause vascular remodeling. This can be easily validated by the histology of the OVX-resistant artery, and they should do that during the revision. The authors should also examine the M1 macrophage function from the OVX mice to validate their claimed link of AABA to M1.

(6) As mentioned above, the authors need to pinpoint the changes of AABA to target cells, i.e., endothelial cells, SMC, or M1, and then use in vitro or in vivo cell biology approaches to assess whether these cells in the OVX rat indeed have an abnormality in function and, indeed, such functional changes are responsible for the BP phenotype.

(7) The results of the current study can be condensed into 1 or 2 figures that can serve as a base or a starting point for a deeper scientific study.

Summary

The experimental design of this manuscript is inappropriate, and the methods are not up to the current standards. The whole study is descriptive and rudimentary. It lacks validation and mechanism. The data from this manuscript might be of some value and can serve as the first step for more investigation of the mechanism of post-menopause hypertension.

Reviewer #3 (Public review):

Summary:

The decrease in estrogen levels is strongly associated with postmenopausal hypertension. Dr. Yao Li and colleagues aimed to investigate the metabolomic mechanisms of underlying postmenopausal hypertension using OVX and OVX+E2 rat models. They successfully established a correlation between reduced estrogen levels and the development of hypertension in rats. They identified L-alpha-aminobutyric acid (AABA) as a potential marker for postmenopausal hypertension. The research explored the metabolic alterations in aortic tissues and proposed several potential mechanisms contributing to postmenopausal hypertension.

Strengths:

The group performed a comprehensive enrichment analysis and various statistical analyses of the metabolomics data.

Weaknesses:

(1) The manuscript is descriptive in nature, although they mentioned their primary objective is to explore the potential mechanisms linking low estrogen levels with postmenopausal hypertension. No mechanism insights have been interrogated in this study, which has been mentioned by the authors in the discussion. The connection between E2, AABA, and macrophage needs to be validated in endothelial cells, vascular smooth muscle cells, and other aortic tissue cells. Without such verification, the manuscript predominantly raises hypotheses only based on metabolomic data.

(2) The serum contains three forms of estrogen: Estradiol, Estrone, and Estriol. The authors used the Rat E2 ELISA kit. Ideally, all three forms of estrogen should be measured.

Reviewer #1 (Public review):

Summary:

Kan et al. report the serendipitous discovery of a Bacillus amyloliquefaciens strain that kills N. gonorrhoeae. They use TnSeq to identify that the anti-gonococcal agent is oxydifficidin and show that it acts at the ribosome and that one of the dedA gene products in N. gonorrhoeae MS11 is important for moving the oxydifficidin across the membrane.

Strengths:

- This is an impressive amount of work, moving from a serendipitous observation through TnSeq to characterize the mechanism by which Oxydifficidin works.

Weaknesses:

- The genetic diversity of dedA and rplL in N. gonorrhoeae is still not clear, as the authors looked at diversity of these genes in only 220 isolates (of unclear relationship to each other).

It's not so much a weakness as a source of confusion: how did the authors choose to screen a tiny transposon library of 50 mutants? Since they were surprised to find 4 transposon insertions (if I'm reading it correctly), what was the motivation for even looking at this small library? And since the mutation that led them to the biosynthetic gene cluster wasn't even a transposon insertion but a frameshift, it seems they had another huge episode of serendipity.

Reviewer #2 (Public review):

Summary:

Kan et al. presents the discovery of oxydifficidin as a potential antimicrobial against N. gonorrhoeae, including multi-drug resistant strains. The authors show the role of DedA flippase assisted uptake and the specificity of RplL in the mechanism of action for oxydifficidin. This mode of action could potentially offer a new therapeutic avenue, providing a critical addition to the limited arsenal of antibiotics effective against gonorrhea.

Strengths:

This study shows the potential of revisiting anti-bacterial agents/products for antibacterial activity against modern-day-concerning pathogens and highlights a new anti-gonoccoal mechanism of action. Indeed there is a recent growing body of research to revisit potential antimicrobial agents and metabolites from cultured bacterial species. The discovery of oxydifficidin interaction with RplL and its DedA-assisted uptake mechanism opens new research directions in understanding and combating antibiotic resistant N. gonorrhoeae. The antimicrobial activity of oxydifficidin is also active against N. meningitidis, a closely related species. Methodologically, the study is rigorous employing various experimental techniques including Tn-mutagenesis (TraDIS, Tn-Seq).

Weaknesses:

While the study demonstrates the in vitro effectiveness of oxydifficidin, there is a lack of in vivo validation (i.e., animal models) for assessing pre-clinical potential of oxydifficidin. However, I acknowledge that this would be a tremendous amount of work and likely outside the scope of this study. Potential SNPs within dedA or RplL raises concerns about how quickly resistance could emerge in clinical settings.

Reviewer #3 (Public review):

Summary:

The authors have shown that oxydifficidin is a potent inhibitor of Neisseria gonorrhoeae. They were able to identify the target of action to rpsL and showed that resistance could occur via mutation in the DedA flippase and RpsL.

Strengths:

This was a very thorough and clearly argued set of experiments that supported their conclusions.

Weaknesses:

There was no obvious weakness in the experimental design. Although it is promising that the DedA mutations resulted in attenuation of fitness, it remains an open question whether secondary rounds of mutation could overcome this selective disadvantage which was untried in this study.

Comments on revisions:

All of my suggestions were considered and the responses to the other reviewer's appears sound and has improved the manuscript.

Reviewer #1 (Public Review):

In this manuscript, Yong and colleagues link perturbations in lysosomal lipid metabolism with the generation of protein aggregates resulting from proteosome inhibition. The main tool used is the ProteoStat stain to assess protein aggregate burden in native cells (i.e. cells under no exogenous or endogenous stress). They initially use CRISPR-based genome-wide screens to identify several genes that affect this aggregate burden. Interestingly, knockdown of genes involved in lysosomal acidification was a major signature which led to identification of other culprit lysosome-associated genes that included ones involved in lipid metabolism. Subsequent CRISPR screen focused on lipidomic analysis led to identification of sphingolipid and cholesterol esters as lipid classes with effects on proteostasis.

Comments on revised version:

They did a decent job addressing most of my comments and the new data (including LysoIP) makes for much more plausible conclusions.

They propose the idea that microautophagy is mediating the delivery of these aggregates to lysosomes.

It appears there are enough experiments and support now for their premise.

The lysosomal lipid metabolism link to proteostasis is still a lingering question in this work but they addressed each of the points I raised regarding it and revised the manuscript accordingly with pertinent discussion.

It is difficult to truly address the lipid link and I think we have to acknowledge that. But overall, looking at the effort and conclusions, this has been improved enough to be a valuable contribution to the field.

Reviewer #2 (Public Review):

In this paper, starting with unbiased CRISPRi screening, the authors found that perturbations in lipid homeostasis lead to proteostasis impairment. The screen and most follow-up experiments used the dye ProteoStat, which detects protein aggregates and the aggresome. Based upon their screen hits and subsequent analyses, the authors determined that increased levels of sphingolipids and cholesterol esters induce proteostasis defects, along with formation of protein aggregates that appear to be localized in the lysosome. The lysosome increases in content, but its function is not detectably perturbed.

Comments on revised version:

I am satisfied with the authors' actions in response to my public and specific suggestions, but not yet with the manuscript itself. I think that the paper would be improved if they showed the evidence arguing against an effect on proteasome activity but I can live with this omission. I think that the readability and ease of grasping the main points are improved by Figure 7. Inclusion of these simple but informative conceptual summaries is a must.

Reviewer #1 (Public review):

Summary:

This manuscript constitutes further analysis of a dataset generated for a previously-published study from the same group. The experiments in the previous work use an RNA-DNA proximity assay to capture RNAs that interact with chromatin, especially beyond their site of transcription, by crosslinking-and proximity ligation. The previous work added one novel feature to this treatment, compared to other studies by the same group, where they treated the nuclei with RNase A prior to crosslinking. The initial study concluded that long-range chromatin interaction via chromatin looping is affected by RNase treatment. In the current manuscript, the group analyze the data from this experiment in more detail. They describe some notable features of RNAs that remain after RNase treatment and where they are associated within the genome. Overall, the further analyses are somewhat useful, with some exceptions for specific analyses that are not clear in the current manuscript. The work is very complementary to the previously published original study, to the point that it is surprising it was not included in that study.

Strengths:

(1) The analyses are a useful complement that fill in gaps from the Calandrelli et al paper. Some of the findings are suggestive of RNA-protein networks that operate at long distances to regulate promoters.

Weaknesses:

(1) The beginning of the Results section, and elsewhere, describes steps that likely were performed in the previous publication from which the data are being further analyzed and possibly partially reanalyzed. The current manuscript should more clearly describe if there are any aspects of the pipeline that have been modified from the Calandrelli study (which does not have much detail regarding iMARGI parameters in the published paper) for the further analysis in this manuscript.

(2) The RNase treatment approach is similar to that addressed in recent papers from the Jenner and Davidovich groups (https://doi.org/10.1016/j.celrep.2024.113856; https://doi.org/10.1016/j.celrep.2024.113858) where these groups found RNase treatment significantly affected solubility of chromatin, causing aggregation. The authors should address this work and place it in light of their current study.

(3) Figure 1f: it is not clear what it means for genes to be "non-differentially expressed" in this context. Isn't this also RNase-insensitive? And how is the "Ctrl specific" RNA set determined? This is confusing, since RNase is assumed to degrade most of the RNA in these samples.

(4) Figure 2a: The results are somewhat surprising, given that protein-coding genes are depleted more in the RNase treatment. Is the Ctrl set the same as in 1f? This emphasizes the importance of defining that population better.

(5) Figure 3a: The text references this figure in ways that do not match the figure, referencing at least nine column clusters when there are only six. Heatmaps of certain TFs and "RAH explained" percentages don't seem to match the Results section description, either. The authors claim EZH2 binding sites are the top TF overlap with RAHs and yet do not include EZH2 in Figure 3a. Suz12 (EZH2 binding partner) and H3K27me3 (EZH2 product) are also referenced in the text for this figure, but not included in the figure itself.

(6) The manuscript uses the term "non-diffusive RNA-chromatin interactome" which is not directly supported by data. The authors use the term initially to describe the RNase-resistant species in their previous work, but through the current study, they support a model where the RNase resistance is simply due to protection by protein binding, not by any constraints on diffusion in particular chromatin environments.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors re-analyze RNase-treated iMARGI data to systematically identify and analyze RNase-resistant RNA-chromatin interactions. In general

Strengths:

Analyses are well-thought-out and generally solid.

Weaknesses:

Conclusions are massively overstated, and though the analytical pipelines used are solid, the conclusions deriving from them lack the backing of solid computational and molecular controls.

Reviewer #3 (Public review):

Summary:

The study investigated stable RNA-chromatin interactions by applying RNase treatment before the iMARGI (in situ mapping of RNA-genome interactome) procedure to remove promiscuous, unprotected RNA transcripts and selectively enrich for RNA-inaccessible, potentially functional RNA-chromatin interactions (RNA-Transcription factor and RNA-histone). The researchers found that short-range interactions (<1kb) are RNase resistant, possibly due to the protection from RNA polymerases. They noticed that long-range RNA-chromatin interactions (>2Mbp or interchromosomal) were also enriched after RNase treatment, hypothesizing that these interactions are stabilized by chromatin-binding proteins. They found that genic caRNAs were sensitive, while repeat-derived caRNAs, such as rRNA and satellite repeats, were resistant to RNase. Long non-coding RNAs (lncRNAs), particularly those associated with diseases, were over-represented among RNase-insensitive transcripts, indicating their potential regulatory significance. Additionally, RNase-insensitive caRNAs exhibited higher evolutionary conservation, implying that they are protected by protein complexes, especially in long-range interactions. RNA Attachment Hot Zones (RAHs) enriched post-RNase treatment were found to localize in functional genomic regions such as promoters, transcription factor binding sites (TFBS), and histone modification sites. Importantly, RNase treatment amplified specific RNA-transcription factor interactions, with caRNA signals being preserved at TFBS for factors with RNA-binding capabilities, suggesting that direct RNA-protein binding helps protect caRNAs from degradation. They also found that different TFs are enriched with specific caRNA species, distinguishing them from their genomic footprints. In addition, transcripts with higher abundance tend to enrich at more TFBS. Overall, the study highlights the role of RNase-inaccessible caRNAs in chromatin regulation and provides insight into their functional significance in genome organization.

Strengths:

This study involves rigorous and comprehensive data analysis involving datasets with very high sequencing depth and appropriate statistical tests (e.g., chi-square tests to validate the association between caRNAs and TFBS statistically). This analysis was further strengthened by comparing their results with orthogonal datasets, such as RedChIP and fRIP-seq, providing robust, cross-validated evidence for the caRNA-TFBS associations. In addition to examining broad interactions, the authors identified specific long-range RNA-chromatin interactions and pinpointed specific transcription factors and histone modification markers that are associated with these interactions. The authors explored the evolutionary implications of RNase-insensitive caRNAs and their potential medical relevance, particularly by identifying caRNAs linked to disease-associated genes and long non-coding RNAs (lncRNAs). This combination of detailed analysis, along with functional relevance, broadens the scope of the research, making it a significant contribution to chromatin biology.

Weaknesses:

However, I have the following concerns regarding the studies:<br /> (1) I don't understand the logic behind calling promoters, enhancers, and similar regions "functionally important regions" when describing the enrichment of RNase-insensitive interactions. Genic regions that are RNase-sensitive are also functionally relevant. So, what makes promoters, enhancers, etc, unique in terms of functionality?<br /> (2) First, while the study offers strong evidence for associations between caRNAs, transcription factors, and chromatin markers, it lacks direct functional validation experiments such as RNA knockdown or CRISPR interference, to confirm the specific roles of these RNAs in gene regulation or chromatin structure modifications.<br /> (3) Another limitation is the incomplete investigation of caRNAs with short-range interactions (<1kb). The authors hypothesized that these are protected by RNA polymerases but did not provide supporting experimental evidence or references to previous studies. Offering either experimental validation or a rationale for excluding these short-range interactions would strengthen this hypothesis. The conclusion that authors drew on that "chromatin-associated RNAs (caRNAs) involved in short- to middle-range interactions are more susceptible to RNase treatment" was unclear for the specific "short-range" distance. The data shown in Supplementary Figure 2a contradicted the conclusion in the discussion that "long-distance RNA-chromatin interactions are preferentially preserved after RNase treatment, while short-range interactions are depleted." as well as the suggestion made linking RNase inaccessibility to evolutionarily conserved in the paper.<br /> (4) The study heavily relies on RNase treatment to isolate stable RNA-chromatin interactions, which might neglect important transient or weak interactions and overlook the functional relevance of RNase-sensitive interactions, hence missing the dynamic nature of RNA-chromatin interactions.<br /> (5) Tthe analysis is limited to human embryonic stem cells (H1 cells), which might restrict the generalizability of the findings. Expanding the study to include a cell type that represents a broader range of cell types or tissues will strengthen the conclusions.<br /> (6) The term "RNase A treatment" in the methods section could be clearer if specified as "RNase-treated iMARGI," which encompasses the standard iMARGI protocol.<br /> (7) There is some ambiguity regarding whether the researchers generated new data or reanalyzed existing datasets. While it is mentioned early on that previously published RNase-treated iMARGI datasets were reanalyzed, the text later states that "three biological replicates were generated for the RNase-treated samples." Clarifying whether the data were newly generated in this study or obtained from public datasets would improve the clarity.<br /> (8) The color scheme should be the same for heatmaps for control, and RNase-treated samples in Figure 4.

Reviewer #1 (Public review):

Summary:

This work examines the binding of several phosphonate compounds to a membrane-bound pyrophosphatase using several different approaches, including crystallography, electron paramagnetic resonance spectroscopy, and functional measurements of ion pumping and pyrophosphatase activity. The work attempts to synthesize these different approaches into a model of inhibition by phosphonates in which the two subunits of the functional dimer interact differently with the phosphonate.

Strengths:

This study integrates a variety of approaches, including structural biology, spectroscopic measurements of protein dynamics, and functional measurements. Overall, data analysis was thoughtful, with careful analysis of the substrate binding sites (for example calculation of POLDOR omit maps).

Weaknesses:

Unfortunately, the protein did not crystallize with the more potent phosphonate inhibitors. Instead, structures were solved with two compounds with weak inhibitory constants >200 micromolar, which limits the molecular insight into compounds that could possibly be developed into small molecule inhibitors. Likewise, the authors choose to focus the spectroscopy experiments on these weaker binders, missing an opportunity to provide insight into the interaction between more potent binders and the protein.

In general, the manuscript falls short of providing any major new insight into membrane-bound pyrophosphatases, which are a very well-studied system. Subtle changes in the structures and ensemble distance distributions suggest that the molecular conformations might change a little bit under different conditions, but this isn't a very surprising outcome. It's not clear whether these changes are functionally important, or just part of the normal experimental/protein ensemble variation.

The ZLD-bound crystal structure doesn't predict the DEER distances, and the conformation of Na+ binding site sidechains in the ZLD structure doesn't predict whether sodium currents occur. This might suggest that the ZLD structure captures a conformation that does not recapitulate what is happening in solution/ a membrane.

Reviewer #2 (Public review):

Summary:

Crystallographic analysis revealed the asymmetric conformation of the dimer in the inhibitor-bound state. Based on this result, which is consistent with previous time-resolved analysis, authors verified the dynamics and distance between spin introduced label by DEER spectroscopy in solution and predicted possible patterns of asymmetric dimer.

Strengths:

Crystal structures with inhibitor bound provide detailed coordination in the binding pocket thus useful information for the PPase field and maybe for drug development.

Weaknesses:

The distance information measured by DEER is advantageous for verifying the dynamics and structure of membrane protein in solution. However, regarding T211 data, which, as the authors themselves stated, lacks measurement precision, it is unclear for readers how confident one can judge the conclusion leading from these data for the cytoplasmic side.

The distance information for the luminal site, which the authors claim is more accurate, does not indicate either the possibility or the basis for why it is the ensemble of two components and not simply a structure with a shorter distance than the crystal structure.

Reviewer #3 (Public review):

Summary:

Membrane-bound pyrophosphatases (mPPases) are homodimeric proteins that hydrolyze pyrophosphate and pump H+/Na+ across membranes. They are attractive drug targets against protist pathogens. Non-hydrolysable PPi analogue bisphosphonates such as risedronate (RSD) and pamidronate (PMD) serve as primary drugs currently used. Bisphosphonates have a P-C-P bond, with its central carbon can accommodate up to two substituents, allowing a large compound variability. Here the authors solved two TmPPase structures in complex with the bisphosphonates etidronate (ETD) and zoledronate (ZLD) and monitored their conformational ensemble using DEER spectroscopy in solution. These results reveal the inhibition mechanism of these compounds, which is crucial for developing future small molecule inhibitors.

Strengths:

The authors show that seven different bisphosphonates can inhibit TmPPase with IC50 values in the micromolar range. Branched aliphatic and aromatic modifications showed weaker inhibition.

High-resolution structures for TmPPase with ETD (3.2 Å) and ZLD (3.3 Å) are determined. These structures reveal the binding mode and shed light on the inhibition mechanism. The nature of modification on the bisphosphonate alters the conformation of the binding pocket.

The conformational heterogeneity is further investigated using DEER spectroscopy under several conditions.

Weaknesses:

The authors observed asymmetry in the TmPPase-ELD structure above the hydrolytic center. The structural asymmetry arises due to differences in the orientation of ETD within each monomer at the active site. As a result, loop5-6 of the two monomers is oriented differently, resulting in the observed asymmetry. The authors attempt to further establish this asymmetry using DEER spectroscopy experiments. However, the (over)interpretation of these data leads to more confusion than any further understanding. DEER data suggest that the asymmetry observed in the TmPPase-ELD structure in this region might be funneled from the broad conformational space under the crystallization conditions.

DEER data for position T211R1 at the enzyme entrance reveal a highly flexible conformation of loop5-6 (and do not provide any direct evidence for asymmetry, Figure EV8). Similarly, data for position S521R1 near the exit channel do not directly support the proposed asymmetry for ETD. Despite the high quality of the data, they reveal a very similar distance distribution. The reported changes in distances are very small (+/- 0.3 nm), which can be accommodated by a change of spin label rotamer distribution alone. Further, these spin labels are located on a flexible loop, thereby making it difficult to directly relate any distance changes to the global conformation.

The interpretations listed below are not supported by the data presented:

(1) 'In the presence of Ca2+, the distance distribution shifts towards shorter distances, suggesting that the two monomers come closer at the periplasmic side, and consistent with the predicted distances derived from the TmPPase:Ca structure.'

Problem: This is a far-stretched interpretation of a tiny change, which is not reliable for the reasons described in the paragraph above.

(2) 'Based on the DEER data on the IDP-bound TmPPase, we observed significant deviations between the experimental and the in silico distances derived from the TmPPase:IDP X-ray structure for both cytoplasmic- (T211R1) and periplasmic-end (S525R1) sites (Figure 4D and Figure EV8D). This deviation could be explained by the dimer adopting an asymmetric conformation under the physiological conditions used for DEER, with one monomer in a closed state and the other in an open state.'

Problem: The authors are trying to establish asymmetry using the DEER data. Unfortunately, no significant difference is observed (between simulation and experiment) for position 525 as the authors claim (Figure 4D bottom panel). The observed difference for position 112 must be accounted for by the flexibility and the data provide no direct evidence for any asymmetry.

(3) 'Our new structures, together with DEER distance measurements that monitor the conformational ensemble equilibrium of TmPPase in solution, provide further solid experimental evidence of asymmetry in gating and transitional changes upon substrate/inhibitor binding.'

Problem: See above. The DEER data do not support any asymmetry.

(4) Based on these observations, and the DEER data for +IDP, which is consistent with an asymmetric conformation of TmPPase being present in solution, we propose five distinct models of TmPPase (Figure 7).

Problem: Again, the DEER data do not support any asymmetry and the authors may revisit the proposed models.

(5) 'In model 2 (Figure 7), one active site is semi-closed, while the other remains open. This is supported by the distance distributions for S525R1 and T211R1 for +Ca/ETD informed by DEER, which agrees with the in silico distance predictions generated by the asymmetric TmPPase:ETD X-ray structure'

Problem: Neither convincing nor supported by the data

Reviewer #1 (Public review):

Summary:

This study used whole genome data to investigate Beefalo ancestry for the first time, filling the gap in the field of Beefalo ancestry. The authors used preserved semen samples to generate genomic data on 47 registered Beefalo and 3 bison hybrids, further questioning the ABA's stated goal of ⅜ bison ancestry. In addition, the authors also show that ancestry profiles of Beefalo and bison hybrid genomes are consistent with repeated backcrossing to either parental species, demonstrating the value of genomic information in examining gene flow between species in the genus Bison. This is an interesting study that still has some major weaknesses that exist, but overall, the work demonstrates the utility of genomic information in validating specific breeding claims for a more complete understanding of gene flow and genetic variation among bovine species.

Strengths:

Numerous genetic analysis methods such as PCA, ADMIXTURE, F4 ratios, and local ancestry inference techniques revealed that no single Beefalo set meets the ancestry requirements set by the American Beefalo Association (ABA) and some beefalo had detectable indicine cattle ancestry.

Weaknesses:

While this study contributes to our knowledge of Beefalo ancestry, there are some key issues that need to be addressed in terms of analysing the specific results as well as writing the article.

Reviewer #2 (Public review):

Summary:

Shapiro et al. set out to verify the American Beefalo Association's claim that Beefalo cattle possess 37.5% bison ancestry. They employ a comprehensive range of well-established population genomics methods to estimate ancestry in these hybrid populations, including PCA, ADMIXTURE, D and F statistics, and local ancestry inference. Their findings conclusively demonstrate that most Beefalo lack the claimed bison ancestry, with only 8 out of 47 samples showing any detectable bison ancestry, ranging from 2 - 18%.

Strengths:

The primary strength of this analysis lies in the comprehensive dataset available to the authors, which includes important foundational Beefalo individuals and various reference populations. The rigorous and multi-faceted methodological approach employs several well-established techniques in population genomics for detecting and measuring admixture. Each method used has a firm basis in the field, providing consistent and robust results. The authors' approach of using PCA to initially assess the data within a global context, followed by more specific analyses using ADMIXTURE and D-statistics, provides a clear and logical progression of evidence. The presentation of these results in figures is particularly effective, clearly illustrating the key findings of the study. Additionally, the examination of both autosomal and sex chromosome ancestry offers a more complete understanding of Beefalo genetic composition and the mechanics of bison-cattle hybridisation.

Weaknesses:

One limitation of this analysis is the relatively low coverage (~2x) of many Beefalo samples. However, the authors have taken steps to mitigate biases that may arise from this. Another weakness is the limited sampling of contemporary Beefalo populations, as the study focuses primarily on historical samples. This may limit our understanding of how Beefalo genetics may have changed over time.

Appraisal:

The authors have clearly achieved their primary aim using a rigorous and comprehensive methodology. Their extensive dataset and multi-faceted analytical approach provide strong support for their conclusions. The study not only addresses its main research question but also reveals unexpected insights into Beefalo genetics, particularly the presence of zebu ancestry.

Discussion:

This study is valuable for several reasons beyond its primary findings. First, it definitively addresses and refutes the claim of 37.5% bison ancestry in Beefalo, providing crucial information for those studying these interspecies hybrids and the viability of their offspring. Second, it reveals the unexpected presence of zebu ancestry in many Beefalo, raising intriguing questions about the breed's development and the potential role of zebu cattle in achieving desired traits. This finding suggests that the distinctive appearance of Beefalo may be due in part to zebu admixture rather than bison ancestry. Third, the study highlights the significant barriers to admixture between bison and cattle, both in controlled breeding programs and potentially in wild populations. This has important implications for conservation genetics and our understanding of gene flow between these species. Lastly, the study demonstrates the power of genomic analysis in verifying breed claims and understanding the complex history of domestic animal breeds. These findings open new avenues for research in bovine genomics, breed development, and the dynamics of interspecies hybridisation.

Reviewer #3 (Public review):

Summary:

I really like this topic and study. But I think much can be more focused and tightened up. All the components are here - just some more refining to really make the storyline clear, the journey of discovery, and the impact of such knowledge.

Strengths:

The authors dive directly into the question of genomic ancestry as compared to the breed club's reported ancestry with heavy, quantitative data and critical analytical methods. The questioning line is direct and does not meander. The reader learns about the challenges of breeding associations, and values of understood ancestry, and presents a clear need of re-evaluating the breed standards and expectations of beefalo (if ancestry is indeed the primary goal instead of a phenotype-driven breed mission).

Weaknesses:

Much of the quantitative results are only referred to in the main text with qualitative language. Please incorporate more written quantitative results to highlight evidence that underlines the study narrative because it is quite an interesting study!

Reviewer #1 (Public review):

In this paper, the authors reveal that the MK2 inhibitor CMPD1 can inhibit the growth, migration, and invasion of breast cancer cells both in vitro and in vivo by inducing microtubule depolymerization, preferentially at the microtubule plus-end, leading to cell division arrest, mitotic defects, and apoptotic cell death. They also showed that CMPD1 treatment upregulates genes associated with cell migration and cell death, and downregulates genes related to mitosis and chromosome segregation in breast cancer cells, suggesting a potential mechanism of CMPD1 inhibition in breast cancer. Besides, they used the combination of an MK2-specific inhibitor, MK2-IN-3, with the microtubule depolymerizer vinblastine to simultaneously disrupt both the MK2 signaling pathway and microtubule dynamics, and they claim that inhibiting the p38-MK2 pathway may help to enhance the efficacy of MTAs in the treatment of breast cancer. However, there are a few concerns, including:

(1) What is the effect of CMPD1 on breast cancer metastasis?

(2) The mechanism is lacking as to how MK2 inhibitors enhance the efficacy of MTAs.

Reviewer #2 (Public review):

Summary:

This study explores the potential of inhibiting the p38-MK2 signaling pathway to enhance the efficacy of microtubule-targeting agents (MTAs) in breast cancer treatment using a dual-target inhibitor.

Strengths:

The study identifies the p38-MK2 pathway as a promising target to enhance the efficacy of microtubule-targeting agents (MTAs), offering a novel therapeutic strategy for breast cancer treatment. In addition, the study employs a wide range of techniques, especially live-cell imaging, to assess the microtubule dynamics in TNBC cells.

Weaknesses:

The study primarily uses RPE1 cells as the control for normal cells, which may not fully capture the response of normal mammary epithelial cells. While CMPD1 is shown to be effective in suppressing tumor growth in MDA-MB-231 xenograft, the study lacks detailed toxicity data to confirm its safety profile in vivo.

Reviewer #3 (Public review):

Summary:

The authors demonstrated MK2i could enhance the therapeutic efficacy of MTAs. With Tumor xenograft and migration assay, the author suggested that the p38-MK2 pathway may serve as a promising therapeutic target in combination with MTAs in cancer treatment.

Strengths:<br /> The authors provided a potential treatment for breast cancer.

Weaknesses:

(1) In Figure 2, the authors used a human retinal pigment epithelial-1 (RPE1) cell line to show that breast cancer cells are more sensitive to CMPD1 treatment. MCF10A cells would be suggested here as a suitable control. Besides, to compare the sensitivity, IC50 indifferent cell lines should be measured.

(2) The data of MDA-MB-231 in Figure 1D is not consistent with CAL-51 and T47D, also not consistent with the data in Figures 2B-C.

(3) To support the authors' conclusion in Figure 5, an additional animal experiment performed by tail vein injection would be helpful.

(4) Page 14, to evaluate the combination result of MK2i and vinblastine, an in vivo animal assay must be performed.

(5) The authors used RNA-seq to show some pathways affected by CMPD1. What are the key/top genes that were affected? How about the mechanism?

(6) Line 127, more experiments should be involved to support the conclusion.

Reviewer #1 (Public review):

Summary:

By using an established NAFLD model, choline-deficient high-fat diet, Barros et al show that LPS challenge causes excessive IFN-γ production by hepatic NK cells which further induces recruitment and polarization of a PD-L1 positive neutrophil subset leading to massive TNFα production and increased host mortality. Genetic inhibition of IFN-γ or pharmacological blockade of PD-L1 decreases recruitment of these neutrophils and TNFα release, consequently preventing liver damage and decreasing host death.

Since NAFLD is often accompanied by chronic, low-grade inflammation, it can lead to an overactive but dysfunctional immune response and increase the body's overall susceptibility to infections, therefore this is a very important research question.

Weaknesses:

I have quite a lot of concerns with this manuscript. One of those is that the authors did not indeed show that the seen effect is really due to NAFLD itself. The role of choline is already known in the context of sepsis since its deficiency (which can be observed in about two weeks through deterioration of liver structure and function) leads to body organ dysfunction both in humans and animals. Nolan and Vilayat in 1968 showed that the hepatic injury and mortality due to endotoxinaemic shock induced by intraperitoneal injection of LPS was significantly increased in adult female Holtzman rats fed on a choline-deficient diet. Therefore, in order to really show that the effect is mediated due to NAFLD some other diet model must be used (e.g. high-fat, high-fructose, and high-cholesterol diet).

Reviewer #2 (Public review):

Summary:

This is an extremely interesting mouse study, trying to understand how sepsis is tolerated during obesity/NAFLD. The researchers combine a well-established model of NASH (Choline-deficiency with High Fat Diet) with a sepsis model (IP injection of 10mg/kg LPS), leading to dramatic mortality in mice. Using this model, they characterize the complex contributions of immune cells. Specifically, they find that NK-cells and Neutrophils contribute the most to mortality in this model due to IFNG and PD-L1+ Neutrophils.

Strengths:

The biggest strength of the manuscript is how clear the primary phenotypes/endpoints of their model are. Within 6 hours of LPS injection, there is a stark elevation of liver inflammation and damage, which is exacerbated by a High Fat/CholineDeficient diet (HFCD). And after 1 day, almost all of the mice die. Using these endpoints, the authors were able to identify which cells were critical for mortality in the model and the specific mediators involved.

Weaknesses:

A few key details regarding the experimental design and interpretation are missing.

Most important is the choice of a high-fat diet with choline deficiency. I believe this model was chosen because the experiments are shorter and typically result in a liver inflammatory phenotype with not as clear of an adipose/obesity phenotype. I actually think it is typically considered a NASH (Non-alcoholic Steatohepatitis) model. I don't think the manuscript includes any data regarding the physiology of these mice that you would expect in an obesity model: body weight, liver weight, blood glucose, etc.

You should include a description in the methods for how the survival studies were conducted. Were the mice just checked on once a day for death, or were there other endpoints for euthanasia, like severe weight loss?

The measurement of IFNG and TNF in tissue throughout the manuscript seemed inconsistent. For example, IFNG in Figure 3A is 0.05pg/g for Chow+LPS, and 0.15pg.g for HFCD+LPS. But in Figure 4H, Chow+LPS is 0.18pg/g and HFCD+LPS is 0.18pg/g, so there is no effect of HFCD in the IgG controls. Also, in Figure 4I and 4J, the TNF values are dramatically different for the controls (0.1 vs 1pg/g).

You can't conclude that CD4+ and CD8+ T cells or monocytes don't play a role in liver damage from your data, because you did not measure liver damage, only mortality. I understand using mortality as an endpoint, but without ALT/AST measurements or histology, it's hard to say what exactly happened in the livers.

I'm not sure the authors can conclude that neutrophils expressing PD-L1 live longer in the hepatic environment from an in vitro experiment. I think this is an interesting result in terms of crosstalk between these two cell types, but I'm not sure that in vivo the neutrophils would live longer.

Reviewer #3 (Public review):

Summary:

The authors investigated how non-alcoholic fatty liver disease (NAFLD) influences liver damage during endotoxemia (a condition characterized by elevated endotoxins, like lipopolysaccharide or LPS, in the bloodstream) using a mouse model. Mice with NAFLD were given a moderate dose of LPS, which intensified liver inflammation and mortality compared to controls. The study concludes that targeting neutrophil activity and TNF-α signaling could be a promising approach to reducing excessive inflammation and liver injury in NAFLD patients experiencing endotoxemia. This can have important implications for the treatment but I think the manuscript requires revisions.

Strengths:

(1) The study presents both in vivo and ex vivo assay and results to support their hypothesis.

(2) Several cell types and their interaction with each other have been analyzed.

(3) The authors made use of the publicly available databases.

Weaknesses:

(1) Some figures contradict each other.

(2) Some of the cause-and-effect presentations need additional experiments and different approaches to be proven correct.

(3) Candidate/mechanism selection strategies are not very clear.

Reviewer #1 (Public review):

Summary:

The authors are trying to determine if SFN treatment results in dephosphorylation of TFEB, subsequent activation of autophagy-related genes, exocytosis of lysosomes, and reduction in lysosomal cholesterol levels in models of NPC disease.

Strengths:

(1) Clear evidence that SFN results in translocation of TFEB to the nucleus.

(2) In vivo data demonstrating that SFN can rescue Purkinje neuron number and weight in NPC1-/- animals.

Weaknesses:

(1) Lack of molecular details regarding how SFN results in dephosphorylation of TFEB leading to activation of the aforementioned pathways. Currently, datasets represent correlations.

(2) Based on the manuscript narrative, discussion, and data it is unclear exactly how steady-state cholesterol would change in models of NPC disease following SFN treatment. Yes, there is good evidence that lysosomal flux to (and presumably across) the plasma membrane increases with SFN. However, lysosomal biogenesis genes also seem to be increasing. Given that NPC inhibition, NPC1 knockout, or NPC1 disease mutations are constitutively present and the cell models of NPC disease contain lysosomes (even with SFN) how could a simple increase in lysosomal flux decrease cholesterol levels? It would seem important to quantify the number of lysosomes per cell in each condition to begin to disentangle differences in steady state number of lysosomes, number of new lysosomes, and number of lysosomes being exocytosed.

(3) Lack of evidence supporting the authors' premise that "SFN could be a good therapeutic candidate for neuropathology in NPC disease".

Reviewer #2 (Public review):

Summary:

This study presents a valuable finding that the activation of TFEB by sulforaphane (SFN) could promote lysosomal exocytosis and biogenesis in NPC, suggesting a potential mechanism by SFN for the removal of cholesterol accumulation, which may contribute to the development of new therapeutic approaches for NPC treatment.

Strengths:

The cell-based assays are convincing, utilizing appropriate and validated methodologies to support the conclusion that SFN facilitates the removal of lysosomal cholesterol via TFEB activation.

Weaknesses:

(1) The in vivo experiments demonstrate the therapeutic potential of SFN for NPC. A clear dose-response analysis would further strengthen the proposed therapeutic mechanism of SFN. Additional data supporting the activation of TFEB by SFN for cholesterol clearance in vivo would strengthen the overall impact of the study

(2) In Figure 4, the authors demonstrate increased lysosomal exocytosis and biogenesis by SFN in NPC cells. Including a TFEB-KO/KD in this assay would provide additional validation of whether these effects are TFEB-dependent.

(3) For lysosomal pH measurement, the combination of pHrodo-dex and CF-dex enables ratiometric pH measurement. However, the pKa of pHrodo red-dex (according to Invitrogen) is ~6.8, while lysosomal pH is typically around 4.7. This discrepancy may account for the lack of observed lysosomal pH changes between WT and U18666A-treated cells. Notably, previous studies (PMID: 28742019) have reported an increase in lysosomal pH in U18666A-treated cells.

(4) The authors are also encouraged to perform colocalization studies between CF-dex and a lysosomal marker, as some researchers may be concerned that NPC1 deficiency could reduce or block the trafficking of dextran along endocytosis.

(5) In vivo data supporting the activation of TFEB by SFN for cholesterol clearance would significantly enhance the impact of the study. For example, measuring whole-animal or brain cholesterol levels would provide stronger evidence of SFN's therapeutic potential.

Reviewer #3 (Public review):

Summary:

The authors demonstrate that activation of TFEB facilitates cholesterol clearance in cell models of Niemann-Pick type C (NPC). This is done through a variety of approaches including activation of TFEB by sulforaphane (SFN), a naturally occurring small-molecule TFEB agonist. SFN induces TFEB nuclear translocation and promotes lysosomal exocytosis. In an NPC mouse model, SFN dephosphorylates/activates TFEB in the brain and rescues the loss of Purkinje cells.

Strengths:

NPC is a severe disease and there is little in the way of treatment. The manuscript points towards some treatment options. However, the title, the title "Small-molecule activation of TFEB Alleviates Niemann-Pick Disease..." is far too strong and should be changed.

Weaknesses:

(1) The manuscript is extremely hard to read due to the writing; it needs careful editing for grammar and English.

(2) There are a number of important technical issues that need to be addressed.

(3) The TFEB influence on filipin staining in Figure 1A is somewhat subtle. In the mCherry alone panels there is a transfected cell with no filipin staining and the mCherry-TFEBS211A cells still show some filipin staining.

(4) Figure 1C is impressive for the upregulation of filipin with U18666A treatment. However, SFN is used at 15 microM. This must be hitting multiple pathways. Vauzour et al (PMID: 20166144) use SFN at 10 nM to 1microM. Other manuscripts use it in the low microM range. The authors should repeat at least some key experiments using SFN at a range of concentrations from perhaps 100 nM to 5 microM. The use of 15 microM throughout is an overall concern.

Reviewer #1 (Public Review):

Summary:

The work from Petazzi et al. aimed at identifying novel factors supporting the differentiation of human hematopoietic progenitors from induced-pluripotent stem cells (iPSCs). The authors developed an inducible CRISPR-mediated activation strategy (iCRISPRa) to test the impact of newly identified candidate factors on the generation of hematopoietic progenitors in vitro. They first compared previously published transcriptomic data of iPSC-derived hemato-endothelial populations with cells isolated ex vivo from the aorta-gonad-mesonephros (AGM) region of the human embryo and they identified 9 transcription factors expressed in the aortic hemogenic endothelium that were poorly expressed in the in vitro differentiated cells. They then tested the activation of these candidate factors in an iPSC-based culture system supporting the differentiation of hematopoietic progenitors in vitro. They found that the IGF binding protein 2 (IGFBP2) was the most upregulated gene in arterial endothelium after activation and they demonstrated that IGFBP2 promotes the generation of functional hematopoietic progenitors in vitro.

Strengths:

The authors developed a very useful doxycycline-inducible system to activate the expression of specific candidate genes in human iPSC. This approach allows us to simultaneously test the impact of 9 different transcription factors on in vitro differentiation of hematopoietic cells, and the system appears to be very versatile and applicable to a broad variety of studies. Using this approach, the authors exhaustively demonstrated the role of IGFBP2 in promoting the generation of functional hematopoietic progenitors in vitro from iPSCs.

Weaknesses:

The authors performed a very thorough characterization of the system in proof-of-principle experiments activating a single transcription factor. However, when 9 independent factors were used, it is not always clear whether the observed results were the consequence of the simultaneous activation of all 9 TF or just a subset of them.

Reviewer #3 (Public review):

Summary:

Machhua et al. in their work focused on unravelling the molecular mechanism of daptomycin binding and interaction with bacterial cell membranes. Daptomycin (Dap) is an acidic, cyclic lipopeptide composed of 13 amino acids, known for preferential binding to anionic lipids, particularly phosphatidylglycerol (PG), which are prevalent components in the membranes of Gram-positive bacteria. The process of binding and antimicrobial efficacy of Dap are significantly influenced by the ionic composition of the surrounding environment, especially the presence of Ca2+ ions. The authors underscore the presence of significant knowledge gaps in our understanding of daptomycin's mode of action. Several critical questions remain unanswered, including the basis for selective recognition and accumulation in membranes of Gram-positive strains, the specific role of Ca2+ ions in this process, and the mechanisms by which daptomycin binds to and inserts into the cell membrane.

Dap is intrinsically fluorescent due to its kynurenine residue (Kyn-13) and this property allows direct imaging of Dap binding to model cell membranes without the need of additional labeling. Taking advantage of this Dap autofluorescence, authors monitored the emission intensity of micelles, composed of varying DMPG content upon their exposure to Dap and compared it with the kinetics of fluorescence observed for zwitterionic DMPC and other negatively charged lipids such as cardiolipin (CA), POPA and POPS. The authors noted that the linear relationship between DMPG content and Dap fluorescence is strongly lipid-specific, as it was not observed for other anionic lipids. The manuscript sheds light on the specificity of Dap's interaction with CA and DMPG lipids. Through Ca2+ sequestration with EGTA, the authors demonstrated that the binding of Dap with CA is reversible, while its interaction with DMPG results in the irreversible insertion of Dap into the lipid membrane structure, caused by the significant conformational change of this lipopeptide. The formation of a stable DMPG-Dap complex was also verified in bacterial cells isolated from Gram-positive bacteria B. subtilis, where Dap exhibited a permanent binding to PG lipids.

Altogether, the authors endeavored to illuminate novel insights into the molecular basis of Dap binding, interaction, and the mechanism of insertion into bacterial cell membranes. Such understanding holds promise for the development of innovative strategies in combating drug resistance and the emerging of the so-called superbugs.

Strengths:

- The manuscript by Machhua et al. provides a comprehensive analysis of the Dap mechanism of binding and interaction with the membrane. It discusses various aspects of this, only apparently trivial interaction such as the importance of PG presence in the membrane, the impact of Ca2+ ions, and different mechanisms of Dap binding with other negatively charged lipids.<br /> - The authors focused not only on model membranes (micelles) but also extended their research to bacterial cell membranes obtained from B. subtilis<br /> - The research is not only a report of the experimental findings but tries to give potential hypotheses explaining the molecular mechanisms behind the observed results

Weaknesses:

- The authors overestimate their findings, stating that they propose a novel mechanism of Dap interaction with bacterial cell membranes. This research is the extension of the hypotheses that have already been reported.<br /> - The literature study and overall discussion about the mechanism of action of Ca2+ ions or conformational changes of daptomycin could be improved.

Reviewer #1 (Public review):

Summary:

This work used a comprehensive dataset to compare the effects of species diversity and genetic diversity within each trophic level and across three trophic levels. The results stated that species diversity had negative effects on ecosystem functions, while genetic diversity had positive effects. Additionally, these effects were observed only within each trophic level and not across the three trophic levels studied. Although the effects of biodiversity, especially genetic diversity across multi-trophic levels, have been shown to be important, there are still very few empirical studies on this topic due to the complex relationships and difficulty in obtaining data. This study collected an excellent dataset to address this question, enhancing our understanding of genetic diversity effects in aquatic ecosystems.

Strengths:

The study collected an extensive dataset that includes species diversity of primary producers (riparian trees), primary consumers (macroinvertebrate shredders), and secondary consumers (fish). It also includes genetic diversity of the dominant species in each trophic level, biomass production, decomposition rates, and environmental data. The writing is logical and easy to follow.

Weaknesses:

The two main conclusions-(1) species diversity had negative effects on ecosystem functions, while genetic diversity had positive effects, and (2) these effects were observed only within each trophic level, not across the three levels-are overly generalized. Analysis of the raw data shows that species and genetic diversity have different effects depending on the ecosystem function. For example, neither affected invertebrate biomass, but species diversity positively influenced fish biomass, while genetic diversity had no effect. Furthermore, Table S2 reveals that only four effect sizes were significant (P < 0.05): one positive genetic effect, one negative genetic effect, and two negative species effects, with two effects within a trophic level and two across trophic levels. Additionally, using a P < 0.2 threshold to omit lines in the SEMs is uncommon and was not adequately justified. A more cautious interpretation of the results, with acknowledgment of the variability observed in the raw data, would strengthen the manuscript.

Reviewer #1 (Public review):

Summary:

This manuscript reports the substrate-bound structure of SiaQM from F. nucleatum, which is the membrane component of a Neu5Ac-specific Tripartite ATP-dependent Periplasmic (TRAP) transporter. Until recently, there was no experimentally derived structural information regarding the membrane components of TRAP transporter, limiting our understanding of the transport mechanism. Since 2022, there have been 3 different studies reporting the structures of the membrane components of Neu5Ac-specific TRAP transporters. While it was possible to narrow down the binding site location by comparing the structures to proteins of the same fold, a structure with substrate bound has been missing. In this work, the authors report the Na+-bound state and the Na+ plus Neu5Ac state of FnSiaQM, revealing information regarding substrate coordination. In previous studies, 2 Na+ ion sites were identified. Here, the authors also tentatively assign a 3rd Na+ site. The authors reconstitute the transporter to assess the effects of mutating the binding site residues they identified in their structures. Of the 2 positions tested, only one of them appears to be critical to substrate binding.

Strengths:

The main strength of this work is the capture of the substrate bound state of SiaQM, which provides insight into an important part of the transport cycle.

Weaknesses:

The main weakness is the lack of experimental validation of the structural findings. The authors identified the Neu5Ac binding site, but only test 2 residues for their involvement in substrate interactions, which is quite limited. However, comparison with previous mutagenesis studies on homologues supports the location of the Neu5Ac binding site. The authors tentatively identified a 3rd Na+ binding site, which if true would be an impactful finding, but this site was not sufficiently experimentally tested for its contribution to Na+ dependent transport. This lack of experimental validation prevents the authors from unequivocally assigning this site as a Na+ binding site. However, the reporting of these new data is important as it will facilitate follow up studies by the authors or other researchers.

Comments on revisions:

Overall, the authors have done a good job of addressing the reviewers' comments. It's good to know that the authors are working on the characterisation of the potential metal binding site mutants - characterising just a few of these will provide much needed experimental support for this potential Na+ site.<br /> The new MD simulations provide some additional support for the new Na+ site and could be included. However, as the authors know, direct experimental characterisation of mutants is the ideal evidence of the Na+ site.

Aside from the characterisation of mutants, which seems to be held up by technical issues, the only remaining issue is the comparison of the Na+- and Na+/Neu5Ac-bound states with ASCT2.<br /> It still does not make sense to me why the authors are not directly comparing their Na+ only and Na+/Neu5Ac states with the structures of VcINDY in the Na+-only and Na+/succinate bound states. These VcINDY structures also revealed no conformational changes in the HP loops upon binding succinate, as the authors see for SiaQM. Therefore, this comparison is very supportive. It is understood that the similarity to the DASS structure is mentioned on p.17, but it is also interesting and useful to note that TRAP and DASS transporters also share a lack of substrate-induced local conformational changes, to the extent these things have been measured.

Reviewer #3 (Public review):

The manuscript by Goyal et al report substrate-bound and substrate-free structures of a tripartite ATP independent periplasmic (TRAP) transporter from a previously uncharacterized homolog, F. nucleatum. This is one of most mechanistically fascinating transporter families, by means of its QM domain (the domain reported in his manuscript) operating as a monomeric 'elevator', and its P domain functioning as a substrate-binding 'operator' that is required to deliver the substrate to the QM domain; together, this is termed an 'elevator with an operator' mechanism. Remarkably, previous structures had not demonstrated the substrate Neu5Ac bound. In addition, they confirm the previously reported Na+ binding sites, and report a new metal binding site in the transporter, which seems to be mechanistically relevant. Finally, they mutate the substrate binding site and use proteoliposomal uptake assays to show the mechanistic relevance of the proposed substrate binding residues.

Strengths:

The structures are of good quality, the presentation of the structural data has improved, the functional data is robust, the text is well-written, and the authors are appropriately careful with their interpretations. Determination of a substrate bound structure is an important achievement and fills an important gap in the 'elevator with an operator' mechanism.

Weaknesses:

Although the possibility of the third metal site is compelling, I do not feel it is appropriate to model in a publicly deposited PDB structure without directly confirming experimentally. The authors do not extensively test the binding sites due to technical limitations of producing relevant mutants; however, their model is consistent with genetic assays of previously characterized orthologs, which will be of benefit to the field. Finally, some clarifications of EM processing would be useful to readers, and it would be nice to have a figure visualizing the unmodeled lipid densities - this would be important to contextualize to their proposed mechanism.

Reviewer #1 (Public review):

Summary:

The authors analyzed the bacterial colonization of human sperm using 16S rRNA profiling. Patterns of microbiota colonization were subsequently correlated with clinical data, such as spermiogram analysis, presence of reactive oxygen species (ROS), and DNA fragmentation. The authors identified three main clusters dominated by Streptococcus, Prevotella, and Lactobacillus & Gardnerella, respectively, which aligns with previous observations. Specific associations were observed for certain bacterial genera, such as Flavobacterium and semen quality. Overall, it is a well-conducted study that further supports the importance of the seminal microbiota.

Strengths:

- The authors performed the analysis on 223 samples, which is the largest dataset in semen microbiota analysis so far<br /> - Inclusion of negative controls to control contaminations.<br /> - Inclusion of a positive control group consisting of men with proven fertility.

Weaknesses:

- The manuscript needs comprehensive proofreading for language and formatting. In many instances spaces are missing or not required.<br /> - Could the authors explore correlation network analyses to get additional insights in the structure of different clusters?<br /> - The github link is not correct.<br /> - It is not possible to access the dataset on ENA.<br /> - Add the graphs obtained with decontam analysis as a supplementary figure.<br /> - There is nothing about the RPL group in the results section, while the authors discuss this issue in the introduction. What about the controls with proven fertility?<br /> - While correctly stated in the title, the term microbiota should be used throughout the manuscript instead of "microbiome"

Comments on revised version:

Discussion: Could the authors discuss more the findings about Flavobacterium? Has it ever been associated with the urogenital tract? What is the relative abundance in the present study: this type of bacterium has been previously associated with contaminations (PMID: 25387460, 30497919).

Figure 1: Increase the size of panel A.

Figure 3: Can the authors indicate the relative abundance of each genus/species by the size of the node?

Supplementary data: I don't see anywhere the decontam plots.

Reviewer #1 (Public review):

Summary:

In the manuscript entitled "Magnesium modulates phospholipid metabolism to promote bacterial phenotypic resistance to antibiotics", Li et al demonstrated the role of magnesium in promoting phenotypic resistance in V. alginolyticus. Using standard microbiological and metabolomic techniques, the authors have shown the significance of fatty acid biosynthesis pathway behind the resistance mechanism. This study is significant as it sheds light on the role of an exogenous factor in altering membrane composition, polarization and fluidity which ultimately leads to antimicrobial resistance.

Strengths:

Authors have used different approaches to demonstrate the effect of Mg+2 on drug resistance in Vibrio alginolyticus. The revised version of the manuscript is much improved, with a very informative introduction and a variety of methodologies with clear explanation of the experiments performed. Also, additional experiments were performed as suggested by the reviewers which certainly enhanced the quality of the paper. I believe the findings of this study will be of high impact in the bacterial community.

Weaknesses:

There are a few grammatical mistakes.

Comments on revisions:

The authors have done a comprehensive job of addressing all my concerns in their revised version.

Reviewer #2 (Public review):

Summary:

In this study, the authors aimed to identify if and how magnesium affects the ability of two particular bacteria species to resist the action of antibiotics. In my view, the authors succeeded in their goals and present a compelling study that will have important implications for the antibiotic resistance research community. Since metals like magnesium are present in all lab media compositions and are present in the host, the data presented in this study certainly will inspire additional research by the community. These could include research into whether other types of metals also induce multi-drug resistance, whether this phenomenon can be observed in other bacterial species, especially pathogenic species that cause clinical disease, and whether the underlying molecular determinants (i.e. enzymes) of metal-induced phenotypic resistance could be new antimicrobial drug targets themselves.

Strengths:

This study's strengths include that the authors used a variety of methodologies, all of which point to a clear effect of exogenous Mg2+ on drug resistance in the targeted species. I also comment the authors for carrying out a comprehensive study, spanning evaluation of whole cell phenotypes, metabolic pathways, genetic manipulation, to enzyme activity level evaluation. The fact that the authors uncovered a molecular mechanism underlying Mg2+-induced phenotypic resistance is particularly important as the key proteins should be studied further.

Weaknesses:

I thank the authors for improving their manuscript based on my previous suggestions. I still believe the Results section is long and bogs down at times.

In general, the conclusions drawn by the authors are justified by the data, except for the interpretation of some experiments. Importantly, this paper has discovered new antimicrobial resistance mechanisms and has also pointed to potential new targets for antimicrobials.

Comments on revisions:

I just wanted to thank the authors for addressing most of my previous comments.

Reviewer #1 (Public review):

Summary:

In this paper Homan et al used mouse models of Metabolic Dysfunction-Associated Steatotic Liver Disease and different specific target deletions in cells to rule out the role of Complement 3a Receptor 1 in the pathogenesis of disease. They provided limited evidence and only descriptive results that despite C3aR being relevant in different contexts of inflammation, however, these tenets did not hold true.

Comments on revisions:

The revised version fulfilled my queries.

Reviewer #2 (Public review):

Summary:

Homan et al. examined the effect of macrophage- or Kupffer cell-specific C3aR1 KO on MASLD/MASH-related metabolic or liver phenotypes.

Strengths:

Established macrophage- or Kupffer cell-specific C3aR1 KO mice, and showing comparable liver metabolic phenotypes between WT and macrophage-specific C3aR1KO mice in response to normal chow diet or MASH diet feeding.

Weaknesses:

Insufficient data showing the effects of C3aR1KO on liver macrophage phenotypes, such as hepatic macrophage profiles, macrophage activation status, etc, which are important for the development of liver steatosis and fibrosis.

Reviewer #2 (Public review):

Summary:

The manuscript by Bock and colleagues describes generation of an AAV-delivered adenine base editing strategy to knockdown PTBP1 and the behavioral and neurorestorative effects of specifically knocking down striatal or nigral PTBP1 in astrocytes or neurons in a mouse model of Parkinson disease. The authors found that knocking down PTBP1 in neurons, but not astrocytes, and in striatum, but not nigra, results in the phenotypic reorganization of neurons to TH+ cells sufficient to rescue motor phenotypes, though insufficient to normalize responses to dopaminomimetic drugs.

Strengths:

The manuscript is well-written and adds to the growing literature challenging previous findings by Qian et al., 2020 and Zhou et al., 2020 indicating that astrocytic downregulation of PTBP1 can induce conversion to dopaminergic neurons in the midbrain and improve parkinsonian symptoms. The base editing approach is interesting and potentially more therapeutically relevant than previous approaches.

Weaknesses:

The animal model utilized, the 6-OHDA model, though useful to examine dopaminergic cell loss, exhibits accelerated neurodegeneration and none of the typical pathological hallmarks (synucleinopathy, Lewy bodies, etc.) compared to the typical etiology of Parkinson disease, limiting its translational interpretation. The identity of the converted neurons is unclear. Though the immunohistochemical methodology indicates they may be MSNs and/or interneurons, a more comprehensive identity is still lacking. There remains no real evidence that these cells actually release dopamine. Since striatal dopamine was assessed by whole-tissue analysis, which is not necessarily reflective of synaptic dopamine availability, it is difficult to assess whether the ~10% increase in TH+ cells in the striatum was sufficient to improve dopamine function. However, the improvement in motor activity suggests that it was.

Reviewer #3 (Public review):

This study explores the use of an adenine base editing strategy to knock down PTBP1 in astrocytes and neurons of a Parkinson's disease mouse model, as a potential AAV-BE therapy. The results indicate that editing Ptbp1 in neurons, but not astrocytes, leads to the formation of tyrosine hydroxylase (TH)+ cells, rescuing some motor symptoms.

Several aspects of the manuscript stand out positively. Firstly, the clarity of the presentation. The authors communicate their ideas and findings in a clear and understandable manner, making it easier for readers to follow.

The Materials and Methods section is well-elaborated, providing sufficient detail for reproducibility.

The logical flow of the manuscript makes sense, with each section building upon the previous one coherently.

The ABE strategy employed by the authors appears sound, and the manuscript presents a coherent and well-supported argument.

Positively, some of the data in this study effectively counteracts previous work in line with more recent publications, demonstrating the authors' ability to contribute to the ongoing conversation in the field.

Comments on revisions:

The authors have adequately addressed all the previous questions and suggestions by providing new data and/or adding necessary clarifications and deeper discussions. The newly presented data convincingly fills the gaps identified during the initial review process. The additional discussions and clarifications enhance both the clarity and transparency of the manuscript.

Reviewer #2 (Public review):

Summary:

The authors aimed to investigate the functionality of the GnRH (gonadotropin-releasing hormone) pulse generator in different mouse models to understand its role in reproductive physiology and its implications for conditions like polycystic ovary syndrome (PCOS). They compared the GnRH pulse generator activity in control mice, peripubertal androgen (PPA) treated mice, and prenatal androgen (PNA) exposed mice. The study sought to elucidate how androgen exposure affects the GnRH pulse generator and subsequent LH (luteinizing hormone) secretion, contributing to the pathophysiology of PCOS.

Strengths:

(1) Comprehensive Model Selection: The use of both PPA and PNA mouse models allows for a comparative analysis that can distinguish the effects of different timings of androgen exposure.

(2) Detailed Methodology: The methods employed, such as photometry recordings and serial blood sampling, are robust and allow for precise measurement of GnRH pulse generator activity and LH secretion.

(3) Clear Results Presentation: The experimental results are well-documented with appropriate statistical analyses, ensuring the findings are reliable and reproducible.

(4) Relevance to PCOS: The study addresses a significant gap in understanding the neuroendocrine mechanisms underlying PCOS, making the findings relevant to both basic science and potentially clinical research.

Weaknesses

(1) Model Limitations: While the PNA mouse model is suggested as the most appropriate for studying PCOS, the authors acknowledge that it does not completely replicate the human condition, particularly the elevated LH response seen in women with PCOS.

(2) Complex Data Interpretation: The reduced progesterone feedback and its effects on the GnRH pulse generator in PNA mice add complexity to data interpretation, making it challenging to draw straightforward conclusions.

(3) Machine Learning (ML) Selection and Validation: While k-means clustering is a useful tool for pattern recognition, the manuscript lacks detailed justification for choosing this specific algorithm over other potential methods. The robustness of clustering results has not been validated.

(4) Biological Interpretability: Although the machine learning approach identified cyclical patterns, the biological interpretation of these clusters in the context of PCOS is not thoroughly discussed. A deeper exploration of how these clusters correlate with physiological and pathological states could enhance the study's impact.

(5) Sample Size: The study uses a relatively small number of animals (n=4-7 per group), which may limit the generalisability of the findings. Larger sample sizes could provide more robust and statistically significant results.

(6) Scope of Application: The findings, while interesting, are primarily applicable to mouse models. The translation to human physiology requires cautious interpretation and further validation.

Comments on revised version:

I did not find the response to my main concerns regarding justification for the choice of the number of clusters (k) and providing evidence of cluster robustness satisfactory at all. It sounds contradictory to me to state that the authors have used unsupervised ML approach when at the same time had clear understanding of the data and the features they wanted to capture. Unsupervised approaches are meant to reveal features that are not apparent by eye... however in their response the authors state, "...our aim was to develop an unsupervised approach that would automatically detect the onset and existence of the key features of pulse generator cyclicity that were apparent by eye...". This sounds like a rather supervised ML approach to me.<br /> Furthermore, I am still unsure why did the authors choose k=5, i.e. assumed there are 5 clusters in the data, and did they explore other possible values for k?<br /> - If not why not? How does this fit with the claims that their ML approach is unsupervised, in other words purely data-driven without making any assumptions?<br /> - If yes did they compare the robustness of their clustering results obtained for different values of k?

Reviewer #3 (Public review):

Summary:

Zhou and colleagues elegantly used pre-clinical mouse models to understand the nature of abnormally high GnRH/LH pulse secretion in polycystic ovary syndrome (PCOS), a major endocrine disorder affecting female fertility worldwide. This work brings a fundamental question of how altered gonadotropin secretion takes place upstream within the GnRH pulse generator core, which is defined by arcuate nucleus kisspeptin neurons.

Strengths:

Authors use state-of-the-art in vivo calcium imaging with fiber photometry and important physiological manipulations and measurements to dissect the possible neuronal mechanisms underlying such neuroendocrine derangements in PCOS. The additional use of unsupervised k-means clustering analysis for the evaluation of calcium synchronous events greatly enhances the quality of their evidence. The authors nicely propose that neuroendocrine dysfunction in PCOS might involve different setpoints through the hypothalamic-pituitary-gonadal (HPG) axis, and beyond kisspeptin neurons, which importantly pushes our field forward toward future investigations.

Weaknesses:

The reviewer agrees that the authors provide important evidence and have improved the quality of the manuscript following first-round revisions. However, they seem resistant to show frequency and amplitude averages in Figure 1 or as supplemental data. Whether the amplitude is dependent on fiber position and its influences on the analysis should be a point of discussion and not data omission. A more detailed analysis of frequency data would enhance the quality of their manuscript.

Comments on revised version:

This comment is related to Reviewer 3's comment # 2 (major) response:

The response does not justify why authors could simply show frequency and amplitude averages in Figure 1 or as supplemental data. Whether the amplitude is dependent on fiber position and its influences on the analysis should be a point of discussion and not data omission.

Reviewer #1 (Public Review):

Summary:

This paper presents an innovative decoding approach for brain-computer interfaces (BCIs), introducing a new method named MINT. The authors develop a trajectory-centric approach to decode behaviors across several different datasets, including eight empirical datasets from the Neural Latents Benchmark. Overall, the paper is well written and their method shows impressive performance compared to more traditional decoding approaches that use a simpler approach. While there are some concerns (see below), the paper's strengths, particularly its emphasis on a trajectory-centric approach and the simplicity of MINT, provide a compelling contribution to the field.

Strengths:

The adoption of a trajectory-centric approach that utilizes statistical constraints presents a substantial shift in methodology, potentially revolutionizing the way BCIs interpret and predict neural behaviour. This is one of the strongest aspects of the paper.

The thorough evaluation of the method across various datasets serves as an assurance that the superior performance of MINT is not a result of overfitting. The comparative simplicity of the method in contrast to many neural network approaches is refreshing and should facilitate broader applicability.

Weaknesses:

Scope: Despite the impressive performance of MINT across multiple datasets, it seems predominantly applicable to M1/S1 data. Only one of the eight empirical datasets comes from an area outside the motor/somatosensory cortex. It would be beneficial if the authors could expand further on how the method might perform with other brain regions that do not exhibit low tangling or do not have a clear trial structure (e.g. decoding of position or head direction from hippocampus)

When comparing methods, the neural trajectories of MINT are based on averaged trials, while the comparison methods are trained on single trials. An additional analysis might help in disentangling the effect of the trial averaging. For this, the authors could average the input across trials for all decoders, establishing a baseline for averaged trials. Note that inference should still be done on single trials. Performance can then be visualized across different values of N, which denotes the number of averaged trials used for training.

Comments on revisions:

I have looked at the responses and they are thorough and answer all of my questions.

Reviewer #2 (Public Review):

Summary:

The goal of this paper is to present a new method, termed MINT, for decoding behavioral states from neural spiking data. MINT is a statistical method which, in addition to outputting a decoded behavioral state, also provides soft information regarding the likelihood of that behavioral state based on the neural data. The innovation in this approach is neural states are assumed to come from sparsely distributed neural trajectories with low tangling, meaning that neural trajectories (time sequences of neural states) are sparse in the high-dimensional space of neural spiking activity and that two dissimilar neural trajectories tend to correspond to dissimilar behavioral trajectories. The authors support these assumptions through analysis of previously collected data, and then validate the performance of their method by comparing it to a suite of alternative approaches. The authors attribute the typically improved decoding performance by MINT to its assumptions being more faithfully aligned to the properties of neural spiking data relative to assumptions made by the alternatives.

Strengths:

The paper did an excellent job critically evaluating common assumptions made by neural analytical methods, such as neural state being low-dimensional relative to the number of recorded neurons. The authors made strong arguments, supported by evidence and literature, for potentially high-dimensional neural states and thus the need for approaches that do not rely on an assumption of low dimensionality.

The paper was thorough in considering multiple datasets across a variety of behaviors, as well as existing decoding methods, to benchmark the MINT approach. This provided a valuable comparison to validate the method. The authors also provided nice intuition regarding why MINT may offer performance improvement in some cases and in which instances MINT may not perform as well.

In addition to providing a philosophical discussion as to the advantages of MINT and benchmarking against alternatives, the authors also provided a detailed description of practical considerations. This included training time, amount of training data, robustness to data loss or changes in the data, and interpretability. These considerations not only provided objective evaluation of practical aspects but also provided insights to the flexibility and robustness of the method as they relate back to the underlying assumptions and construction of the approach.

Impact:

This work is motivated by brain-computer interfaces applications, which it will surely impact in terms of neural decoder design. However, this work is also broadly impactful for neuroscientific analysis to relate neural spiking activity to observable behavioral features. Thus, MINT will likely impact neuroscience research generally. The methods are made publicly available, and the datasets used are all in public repositories, which facilitates adoption and validation of this method within the greater scientific community.

Reviewer #2 (Public review):

Summary:

This work proposes a synaptic plasticity rule which explains the generation of learned stochastic dynamics during spontaneous activity. The proposed plasticity rule assumes that excitatory synapses seek to minimize the difference between the internal predicted activity and stimulus-evoked activity, and inhibitory synapses tries to maintain the E-I balance by matching the excitatory activity. By implementing this plasticity rule in a spiking recurrent neural network, the authors show that the state-transition statistics of spontaneous excitatory activity agrees with that of the learned stimulus patterns, which is reflected in the learned excitatory synaptic weights. The authors further demonstrate that inhibitory connections contribute to well-defined state-transitions matching the transition patterns evoked by the stimulus. Finally, they show that this mechanism can be expanded to more complex state-transition structures including songbird neural data.

Strengths:

This study makes an important contribution to computational neuroscience, by proposing a possible synaptic plasticity mechanism underlying spontaneous generations of learned stochastic state-switching dynamics that are experimentally observed in the visual cortex and hippocampus. This work is also very clearly presented and well-written, and the authors conducted comprehensive simulations testing multiple hypotheses. Overall, I believe this is a well-conducted study providing interesting and novel aspects on the capacity of recurrent spiking neural networks with local synaptic plasticity.

Weaknesses:

This study is very well-thought out and theoretically valuable to the neuroscience community, and I think the main weaknesses are in regard to how much biological realism is taken into account. For example, the proposed model assumes that only synapses targeting excitatory neurons are plastic, and uses an equal number of excitatory and inhibitory neurons.<br /> The model also assumes Markovian state dynamics while biological systems can depend more on history. This limitation, however, is acknowledged in the Discussion.<br /> Finally, to simulate spontaneous activity, the authors use a constant input of 0.3 throughout the study. Different amplitudes of constant input may correspond to different internal states, so it will be more convincing if the authors test the model with varying amplitudes of constant inputs.

Comments on revisions:

The authors have addressed all of the previously raised concerns satisfactorily, by running extra simulations with a biologically plausible composition of excitatory and inhibitory neurons, plasticity assumed for all synapses, and varied amounts of constant inputs representing internal states or background activities. While in some of these cases the stochastic dynamics during spontaneous activity change or do not replicate those of the learned stimulus patterns as well as before, these extended studies provide thorough evaluations of the strengths and limitations of the proposed plasticity rule as the underlying mechanism of stochastic dynamics during spontaneous activity. Overall, the revision has strengthened the paper significantly.

Reviewer #3 (Public review):

Summary:

Asabuki and Clopath study stochastic sequence learning in recurrent networks of Poisson spiking neurons that obey Dale's law. Inspired by previous modeling studies, they introduce two distinct learning rules, to adapt excitatory-to-excitatory and inhibitory-to-excitatory synaptic connections. Through a series of computer experiments, the authors demonstrate that their networks can learn to generate stochastic sequential patterns, where states correspond to non-overlapping sets of neurons (cell assemblies) and the state-transition conditional probabilities are first-order Markov, i.e., the transition to a given next state only depends on the current state. Finally, the authors use their model to reproduce certain experimental songbird data involving highly-predictable and highly-uncertain transitions between song syllables. While the findings are only moderately surprising, this is a well-written and welcome detailed study that may be of interest to experts of plasticity and learning in recurrent neural networks that respect Dale's law.

Strengths:

This is an easy-to-follow, well-written paper, whose results are likely easy to reproduce. The experiments are clear and well-explained. In particular, the study of the interplay between excitation and inhibition (and their different plasticity rules) is a highlight of the study. The study of songbird experimental data is another good feature of this paper; finches are classical model animals for understanding sequence learning in the brain. I also liked the study of rapid task-switching, it's a good-to-know type of result that is not very common in sequence learning papers.

Weaknesses:

One weakness I see in this paper is the derivation of the learning rules, which is semi-heuristic. The paper studies Poisson spiking neurons, for which learning rules can be derived from a statistical objective, typically maximum likelihood, as previously done in the cited literature. The authors provide a brief section connecting the learning rules to gradient descent on objective functions, but the link is only heuristic or at least not entirely presented. The reason is that the neural network state is not fully determined by (or "clamped to") the target during learning (for instance, inhibitory neurons do not even have a target assigned). So, the (total) gradient should take into account the recurrent contributions from other neurons, and equation 13 does not appear to be complete/correct to me. Moreover, the target firing rate is a mixture of external currents with currents arising from other neurons in the recurrent network. The authors ideally should start from an actual distribution matching objective (e.g., KL divergence, and not such a squared error), so that their main claims immediately follow from the mathematical derivations. Along the same line, it would be excellent to get some additional insights on the interaction of the two distinct plasticity rules, one of the highlights of the study. This could be naturally achieved by relating their distinct rules to a common principled objective.

The other major weakness (albeit one that is clearly discussed by the authors) is that the study assumes that every excitatory neuron is directly given its target state when learning. In machine learning language, there are no 'hidden' excitatory neurons. While this assumption greatly simplifies the derivation of efficient and biologically-plausible learning rules that can be mapped to synaptic plasticity, it also limits considerably the distributions that can be learned by the network, more precisely to those that satisfy the Markov property.

Reviewer #1 (Public review):

Summary:

The authors aimed to quantify feral pig interactions in eastern Australia to inform disease transmission networks. They used GPS tracking data from 146 feral pigs across multiple locations to construct proximity-based social networks and analyze contact rates within and between pig social units.

Strengths:

(1) Addresses a critical knowledge gap in feral pig social dynamics in Australia.

(2) Uses robust methodology combining GPS tracking and network analysis.

(3) Provides valuable insights into sex-based and seasonal variations in contact rates.

(4) Effectively contextualizes findings for disease transmission modeling and management.

(5) Includes comprehensive ethical approval for animal research.

(6) Utilizes data from multiple locations across eastern Australia, enhancing generalizability.

Weaknesses:

(1) Limited discussion of potential biases from varying sample sizes across populations

(2) Some key figures are in supplementary materials rather than the main text.

(3) Economic impact figures are from the US rather than Australia-specific data.

(4) Rationale for spatial and temporal thresholds for defining contacts could be clearer.

(5) Limited discussion of ethical considerations beyond basic animal ethics approval.

The authors largely achieved their aims, with the results supporting their conclusions about the importance of sex and seasonality in feral pig contact networks. This work is likely to have a significant impact on feral pig management and disease control strategies in Australia, providing crucial data for refining disease transmission models.

Reviewer #2 (Public review):

Summary:

The paper attempts to elucidate how feral (wild) pigs cause distortion of the environment in over 54 countries of the world, particularly Australia.

The paper displays proof that over $120 billion worth of facilities were destroyed annually in the United States of America.

The authors have tried to infer that the findings of their work were important and possess a convincing strength of evidence.

Strengths:

(1) Clearly stating feral (wild) pigs as a problem in the environment.

(2) Stating how 54 countries were affected by the feral pigs.

(3) Mentioning how $120 billion was lost in the US, annually, as a result of the activities of the feral pigs.

(4) Amplifying the fact that 14 species of animals were being driven into extinction by the feral pigs.

(5) Feral pigs possessing zoonotic abilities.

(6) Feral pigs acting as reservoirs for endemic diseases like brucellosis and leptospirosis.

(7) Understanding disease patterns by the social dynamics of feral pig interactions.

(8) The use of 146 GPS-monitored feral pigs to establish their social interaction among themselves.

Weaknesses:

(1) Unclear explanation of the association of either the female or male feral pigs with each other, seasonally.

(2) The "abstract paragraph" was not justified.

(3) Typographical errors in the abstract.

Reviewer #3 (Public review):

Summary:

The authors sought to understand social interactions both within and between groups of feral pigs, with the intent of applying their findings to models of disease transmission. The authors analyzed GPS tracking data from across various populations to determine patterns of contact that could support the transmission of a range of zoonotic and livestock diseases. The analysis then focused on the effects of sex, group dynamics, and seasonal changes on contact rates that could be used to base targeted disease control strategies that would prioritize the removal of adult males for reducing intergroup disease transmission.

Strengths:

It utilized GPS tracking data from 146 feral pigs over several years, effectively capturing seasonal and spatial variation in the social behaviors of interest. Using proximity-based social network analysis, this work provides a highly resolved snapshot of contact rates and interactions both within and between groups, substantially improving research in wildlife disease transmission. Results were highly useful and provided practical guidance for disease management, showing that control targeted at adult males could reduce intergroup disease transmission, hence providing an approach for the control of zoonotic and livestock diseases.

Weaknesses:

Despite their reliability, populations can be skewed by small sample sizes and limited generalizability due to specific environmental and demographic characteristics. Further validation is needed to account for additional environmental factors influencing social dynamics and contact rates

Reviewer #1 (Public review):

The paper explored cross-species variance in albumin glycation and blood glucose levels in the function of various life-history traits. Their results show that<br /> (1) blood glucose levels predict albumin gylcation rates<br /> (2) larger species have lower blood glucose levels<br /> (3) lifespan positively correlates with blood glucose levels and<br /> (4) diet predicts albumin glycation rates.

The data presented is interesting, especially due to the relevance of glycation to the ageing process and the interesting life-history and physiological traits of birds. Most importantly, the results suggest that some mechanisms might exist that limit the level of glycation in species with the highest blood glucose levels.

While the questions raised are interesting and the amount of data the authors collected is impressive, I have some major concerns about this study:

(1) The authors combine many databases and samples of various sources. This is understandable when access to data is limited, but I expected more caution when combining these. E.g. glucose is measured in all samples without any description of how handling stress was controlled for. E.g glucose levels can easily double in a few minutes in birds, potentially introducing variation in the data generated. The authors report no caution of this effect, or any statistical approaches aiming to check whether handling stress had an effect here, either on glucose or on glycation levels.

(2) The database with the predictors is similarly problematic. There is information pulled from captivity and wild (e.g. on lifespan) without any confirmation that the different databases are comparable or not (and here I'm not just referring to the correlation between the databases, but also to a potential systematic bias (e.g. captivate-based sources likely consistently report longer lifespans). This is even more surprising, given that the authors raise the possibility of captivity effects in the discussion, and exploring this question would be extremely easy in their statistical models (a simple covariate in the MCMCglmms).

(3) The authors state that the measurement of one of the primary response variables (glycation) was measured without any replicability test or reference to the replicability of the measurement technique.

(4) The methods and results are very poorly presented. For instance, new model types and variables are popping up throughout the manuscript, already reporting results, before explaining what these are e.g. results are presented on "species average models" and "model with individuals", but it's not described what these are and why we need to see both. Variables, like "centered log body mass", or "mass-adjusted lifespan" are not explained. The results section is extremely long, describing general patterns that have little relevance to the questions raised in the introduction and would be much more efficiently communicated visually or in a table.

Reviewer #2 (Public review):

Summary

In this extensive comparative study, Moreno-Borrallo and colleagues examine the relationships between plasma glucose levels, albumin glycation levels, diet, and life-history traits across birds. Their results confirmed the expected positive relationship between plasma blood glucose level and albumin glycation rate but also provided findings that are somewhat surprising or contradicting findings of some previous studies (relationships with lifespan, clutch mass, or diet). This is the first extensive comparative analysis of glycation rates and their relationships to plasma glucose levels and life history traits in birds that are based on data collected in a single study and measured using unified analytical methods.

Strengths

This is an emerging topic gaining momentum in evolutionary physiology, which makes this study a timely, novel, and very important contribution. The study is based on a novel data set collected by the authors from 88 bird species (67 in captivity, 21 in the wild) of 22 orders, which itself greatly contributes to the pool of available data on avian glycemia, as previous comparative studies either extracted data from various studies or a database of veterinary records of zoo animals (therefore potentially containing much more noise due to different methodologies or other unstandardised factors), or only collected data from a single order, namely Passeriformes. The data further represents the first comparative avian data set on albumin glycation obtained using a unified methodology. The authors used LC-MS to determine glycation levels, which does not have problems with specificity and sensitivity that may occur with assays used in previous studies. The data analysis is thorough, and the conclusions are mostly well-supported (but see my comments below). Overall, this is a very important study representing a substantial contribution to the emerging field of evolutionary physiology focused on the ecology and evolution of blood/plasma glucose levels and resistance to glycation.

Weaknesses

My main concern is about the interpretation of the coefficient of the relationship between glycation rate and plasma glucose, which reads as follows: "Given that plasma glucose is logarithm transformed and the estimated slope of their relationship is lower than one, this implies that birds with higher glucose levels have relatively lower albumin glycation rates for their glucose, fact that we would be referring as higher glycation resistance" (lines 318-321) and "the logarithmic nature of the relationship, suggests that species with higher plasma glucose levels exhibit relatively greater resistance to glycation" (lines 386-388). First, only plasma glucose (predictor) but not glycation level (response) is logarithm transformed, and this semi-logarithmic relationship assumed by the model means that an increase in glycation always slows down when blood glucose goes up, irrespective of the coefficient. The coefficient thus does not carry information that could be interpreted as higher (when <1) or lower (when >1) resistance to glycation (this only can be done in a log-log model, see below) because the semi-log relationship means that glycation increases by a constant amount (expressed by the coefficient of plasma glucose) for every tenfold increase in plasma glucose (for example, with glucose values 10 and 100, the model would predict glycation values 2 and 4 if the coefficient is 2, or 0.5 and 1 if the coefficient is 0.5). Second, the semi-logarithmic relationship could indeed be interpreted such that glycation rates are relatively lower in species with high plasma glucose levels. However, the semi-log relationship is assumed here a priori and forced to the model by log-transforming only glucose level, while not being tested against alternative models, such as: (i) a model with a simple linear relationship (glycation ~ glucose); or (ii) a log-log model (log(glycation) ~ log(glucose)) assuming power function relationship (glycation = a * glucose^b). The latter model would allow for the interpretation of the coefficient (b) as higher (when <1) or lower (when >1) resistance in glycation in species with high glucose levels as suggested by the authors.

Besides, a clear explanation of why glucose is log-transformed when included as a predictor, but not when included as a response variable, is missing.

The models in the study do not control for the sampling time (i.e., time latency between capture and blood sampling), which may be an important source of noise because blood glucose increases because of stress following the capture. Although the authors claim that "this change in glucose levels with stress is mostly driven by an increase in variation instead of an increase in average values" (ESM6, line 46), their analysis of Tomasek et al.'s (2022) data set in ESM1 using Kruskal-Wallis rank sum test shows that, compared to baseline glucose levels, stress-induced glucose levels have higher median values, not only higher variation.

Although the authors calculated the variance inflation factor (VIF) for each model, it is not clear how these were interpreted and considered. In some models, GVIF^(1/(2*Df)) is higher than 1.6, which indicates potentially important collinearity; see for example https://www.bookdown.org/rwnahhas/RMPH/mlr-collinearity.html). This is often the case for body mass or clutch mass (e.g. models of glucose or glycation based on individual measurements).

It seems that the differences between diet groups other than omnivores (the reference category in the models) were not tested and only inferred using the credible intervals from the models. However, these credible intervals relate to the comparison of each group with the reference group (Omnivore) and cannot be used for pairwise comparisons between other groups. Statistics for these contrasts should be provided instead. Based on the plot in Figure 4B, it seems possible that terrestrial carnivores differed in glycation level not only from omnivores but also from herbivores and frugivores/nectarivores.

Given that blood glucose is related to maximum lifespan, it would be interesting to also see the results of the model from Table 2 while excluding blood glucose from the predictors. This would allow for assessing if the maximum lifespan is completely independent of glycation levels. Alternatively, there might be a positive correlation mediated by blood glucose levels (based on its positive correlations with both lifespan and glycation), which would be a very interesting finding suggesting that high glycation levels do not preclude the evolution of long lifespans.

Reviewer #1 (Public review):

Summary:

This manuscript explores the RNA binding activities of the fission yeast Swi6 (HP1) protein and proposes a new role for Swi6 in RNAi-mediated heterochromatin establishment. The authors claim that Swi6 has a specific and high affinity for short interfering RNAs (siRNAs) and recruits the Clr4 (Suv39h) H3K9 methyltransferases to siRNA-DNA hybrids to initiate heterochromatin formation. These claims are not in any way supported by the incomplete and preliminary RNA binding or the in vivo experiments that the authors present. The proposed model also lacks any mechanistic basis as it remains unclear (and unexplored) how Swi6 might bind to specific small RNA sequences or RNA-DNA hybrids. Work by several other groups in the field has led to a model in which siRNAs produced by the RNAi pathway load onto the Ago1-containing RITS complex, which then binds to nascent transcripts at pericentromeric DNA repeats and recruits Clr4 to initiate heterochromatin formation. Swi6 facilitates this process by promoting the recruitment of the RNA-dependent RNA polymerase leading to siRNA amplification.

Weaknesses:

(1) The claims that Swi6 binds to specific small RNAs or to RNA-DNA hybrids are not supported by the evidence that the authors present. Their experiments do not rule out non-specific charged-based interactions. Claims about different affinities of Swi6 for RNAs of different sizes are based on a comparison of KD values derived by the authors for a handful of S. pombe siRNAs with previous studies from the Buhler lab on Swi6 RNA binding. The authors need to compare binding affinities under identical conditions in their assays. The regions of Swi6 that bind to siRNAs need to be identified and evidence must be provided that Swi6 binds to RNAs of a specific length, 20-22 mers, to support the claim that Swi6 binds to siRNAs. This is critical for all the subsequent experiments and claims in the study.

(2) The in vivo results do not validate Swi6 binding to specific RNAs, as stated by the authors. Swi6 pulldowns have been shown to be enriched for all heterochromatic proteins including the RITS complex. The sRNA binding observed by the authors is therefore likely to be mediated by Ago1/RITS.

Most of the binding in Figure S8C seems to be non-specific.

In Figure S8D, the authors' data shows that Swi6 deletion does not derepress the rev dh transcript while dcr1 delete cells do, which is consistent with previous reports but does not relate to the authors' conclusions.

Previous results have shown that swi6 delete cells have 20-fold fewer dg and dh siRNAs than swi6+ cells due to decreased RNA-dependent RNA polymerase complex recruitment and reduced siRNA amplification.

(3) The RIP-seq data are difficult to interpret as presented. The size distribution of bound small RNAs, and where they map along the genome should be shown as for example presented in previous Ago1 sRNA-seq experiments.

It is also unclear whether the defects in sRNA binding observed by the authors represent direct sRNA binding to Swi6 or co-precipitation of Ago1-bound sRNAs.

The authors should also sequence total sRNAs to test whether Swi6-3A affects sRNA synthesis, as is the case in swi6 delete cells.

(4) The authors examine the effects of Swi6-3A mutant by overexpression from the strong nmt1 promoter. Heterochromatin formation is sensitive to the dosage of Swi6. These experiments should be performed by introducing the 3A mutations at the endogenous Swi6 locus and effects on Swi6 protein levels should be tested.

(5) The authors' data indicate an impairment of silencing in Swi6-3A mutant cells but whether this is due to a general lower affinity for nucleosomes, DNA, RNA, or as claimed by the authors, siRNAs is unclear. These experiments are consistent with previous findings suggesting an important role for basic residues in the HP1 hinge region in gene silencing but do not reveal how the hinge region enhances silencing.

(6) RNase H1 overexpression may affect Swi6 localization and silencing indirectly as it would lead to a general reduction in R loops and RNA-DNA hybrids across the genome. RNaseH1 OE may also release chromatin-bound RNAs that act as scaffolds for siRNA-Ag1/RITS complexes that recruit Clr4 and ultimately Swi6.

(7) Examples of inaccurate presentation of the literature.<br /> a. The authors state that "RNA binding by the murine HP1 through its hinge domains is required for heterochromatin assembly (Muchardt et al, 2002). The cited reference provides no evidence that HP1 RNA binding is required for heterochromatin assembly. Only the hinge region of bacterially produced HP1 contributes to its localization to DAPI-stained heterochromatic regions in fixed NIH 3T3 cells.<br /> b. "... This scenario is consistent with the loss of heterochromatin recruitment of Swi6 as well as siRNA generation in rnai mutants (Volpe et al, 2002)." Volpe et al. did not examine changes in siRNA levels in swi6 mutant cells. In fact, no siRNA analysis of any kind was reported in Volpe et al., 2002.

Reviewer #2 (Public review):

The aim of this study is to investigate the role of Swi6 binding to RNA in heterochromatin assembly in fission yeast. Using in vitro protein-RNA binding assays (EMSA) they showed that Swi6/HP1 binds centromere-derived siRNA (identified by Reinhardt and Bartel in 2002) via the chromodomain and hinge domains. They demonstrate that this binding is regulated by a lysine triplet in the conserved region of the Swi6 hinge domain and that wild-type Swi6 favours binding to DNA-RNA hybrids and siRNA, which then facilitates, rather than competes with, binding to H3K9me2 and to a lesser extent H3K9me3.

However, the majority of the experiments are carried out in swi6 null cells overexpressing wild-type Swi6 or Swi63K-3A mutant from a very strong promoter (nmt1). Both swi6 null cells and overexpression of Swi6 are well known to exhibit phenotypes, some of which interfere with heterochromatin assembly. This is not made clear in the text. Whilst the RNA binding experiments show that Swi6 can indeed bind RNA and that binding is decreased by Swi63K-3A mutation in vitro (confusingly, they only much later in the text explained that these 3 bands represent differential binding and that II is likely an isotherm). The gels showing these data are of poor quality and it is unclear which bands are used to calculate the Kd. RNA-seq data shows that overall fewer siRNAs are produced from regions of heterochromatin in the Swi63K-3A mutant so it is unsurprising that analysis of siRNA-associated motifs also shows lower enrichment (or indeed that they share some similarities, given that they originate from repeat regions).

The experiments are seemingly linked yet fail to substantiate their overall conclusions. For instance, the authors show that the Swi63K-3A mutant displays reduced siRNA binding in vitro (Figure 1D) and that H3K9me2 levels at heterochromatin loci are reduced in vivo (Figure 3C-D). They conclude that Swi6 siRNA binding is important for Swi6 heterochromatin localization, whilst it remains entirely possible that heterochromatin integrity is impaired by the Swi63K-3A mutation and hence fewer siRNAs are produced and available to bind. Their interpretation of the data is really confusing.

The authors go on to show that Swi63K-3A cells have impaired silencing at all regions tested and the mutant protein itself has less association with regions of heterochromatin. They perform DNA-RNA hybrid IPs and show that Swi63K-3A cells which also overexpress RNAseH/rnh1 have reduced levels of dh DNA-RNA hybrids than wild-type Swi6 cells. They interpret this to mean that Swi6 binds and protects DNA-RNA hybrids, presumably to facilitate binding to H3K9me2. The final piece of data is an EMSA assay showing that "high-affinity binding of Swi6 to a dg-dh specific RNA/DNA hybrid facilitates the binding to Me2-K9-H3 rather than competing against it." This EMSA gel shown is of very poor quality, and this casts doubt on their overall conclusion.

Unfortunately, the manuscript is generally poorly written and difficult to comprehend. The experimental setups and interpretations of the data are not fully explained, or, are explained in the wrong order leading to a lack of clarity. An example of this is the reasoning behind the use of the cid14 mutant which is not explained until the discussion of Figure 5C, but it is utilised at the outset in Figure 5A.

Another example of this lack of clarity/confusion is that the abstract states "Here we provide evidence in support of RNAi-independent recruitment of Swi6". Yet it then states "We show that...Swi6/HP1 displays a hierarchy of increasing binding affinity through its chromodomain to the siRNAs corresponding to specific dg-dh repeats, and even stronger binding to the cognate siRNA-DNA hybrids than to the siRNA precursors or general RNAs." RNAi is required to produce siRNAs, so their message is very unclear. Moreover, an entire section is titled "Heterochromatin recruitment of Swi6-HP1 depends on siRNA generation" so what is the author's message?

The data presented, whilst sound in some parts is generally overinterpreted and does not fully support the author's confusing conclusions. The authors essentially characterise an overexpressed Swi6 mutant protein with a few other experiments on the side, that do not entirely support their conclusions. They make the point several times that the KD for their binding experiments is far higher than that previously reported (Keller et al Mol Cell 2012) but unfortunately the data provided here are of an inferior quality and thus their conclusions are neither fully supported nor convincing.

Reviewer #1 (Public review):

Summary:

Brdar, Osterburg, Munick, et al. present an interesting cellular and biochemical investigation of different p53 isoforms. The authors investigate the impact of different isoforms on the in-vivo transcriptional activity, protein stability, induction of the stress response, and hetero-oligomerization with WT p53. The results are logically presented and clearly explained. Indeed, the large volume of data on different p53 isoforms will provide a rich resource for researchers in the field to begin to understand the biochemical effects of different truncations or sequence alterations.

Strengths:

The authors achieved their aims to better understand the impact/activity of different p53 is-forms, and their data will support their statements. Indeed, the major strengths of the paper lie in its comprehensive characterization of different p53 isoforms and the different assays that are measured. Notably, this includes p53 transcriptional activity, protein degradation, induction of the chaperone machinery, and hetero-oligomerization with wtp53. This will provide a valuable dataset where p53 researchers can evaluate the biological impact of different isoforms in different cell lines. The authors went to great lengths to control and test for the effect of (1) p53 expression level, (2) promotor type, and (3) cell type. I applaud their careful experiments in this regard.

Weaknesses:

One thing that I would have liked to see more of is the quantification of the various pull-down/gel assays - to better quantify the effect of, e.g., hetero-oligomerization among the various isoforms. In addition, a discussion about the role of isoforms that contain truncations in the IDRs is not available. It is well known that these regions function in an auto-inhibitory manner (e.g. work by Wright/Dyson) and also mediate many PPIs, which likely have functional roles in vivo (e.g. recruiting p53 to various complexes). The discussion could be strengthened by focusing on some of these aspects of p53 as well.

Reviewer #2 (Public review):

Summary:

In this manuscript entitled "p53 isoforms have a high aggregation propensity, interact with chaperones and lack 1 binding to p53 interaction partners", the authors suggest that the p53 isoforms have high aggregation propensity and that they can co-aggregate with canonical p53 (FLp53), p63 and p73 thus exerting a dominant-negative effect.

Strengths:

Overall, the paper is interesting as it provides some characterization of most p53 isoforms DNA binding (when expressed alone), folding structure, and interaction with chaperones. The data presented support their conclusion and bring interesting mechanistic insight into how p53 isoforms may exert some of their activity or how they may be regulated when they are expressed in excess.

Weaknesses:

The main limitation of this manuscript is that the isoforms are highly over-expressed throughout the manuscript, although the authors acknowledge that the level of expression is a major factor in the aggregation phenomenon and "that aggregation will only become a problem if the expression level surpasses a certain threshold level" (lines 273-274 and results shown in Figures S3D, 6E). The p53 isoforms are physiologically expressed in most normal human cell types at relatively low levels which makes me wonder about the physiological relevance of this phenomenon.

Furthermore, it was previously reported that some isoforms clearly induce transcription of target genes which are not observed here. For example, p53β induces p21 expression (Fujita K. et al. p53 isoforms Delta133p53 and p53beta are endogenous regulators of replicative cellular senescence. Nat Cell Biol. 2009 Sep;11(9):1135-42), and Δ133p53α induces RAD51, RAD52, LIG4, SENS1 and SOD1 expression (Gong, L. et al. p53 isoform D113p53/D133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell Res. 2015, 25, 351-369. / Gong, L. et al. p53 isoform D133p53 promotes the efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Sci. Rep. 2016, 6, 37281. / Horikawa, I. et al. D133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell Death Differ. 2017, 24, 1017-1028. / Gong, L. p53 coordinates with D133p53 isoform to promote cell survival under low-level oxidative stress. J. Mol. Cell Biol. 2016, 8, 88-90. / Joruiz et al. Distinct functions of wild-type and R273H mutant Δ133p53α differentially regulate glioblastoma aggressiveness and therapy-induced senescence. Cell Death Dis. 2024 Jun 27;15(6):454.) which demonstrates that some isoforms can induce target genes transcription and have defined normal functions (e.g. Cellular senescence or DNA repair).

However, in this manuscript, the authors conclude that isoforms are "largely unfolded and not capable of fulfilling a normal cellular function" (line 438), that they do not have "well defined physiological roles" (line 456), and that they only "have the potential to inactivate members of the p53 protein family by forming inactive hetero complexes with wtp53" (line 457-458).

Therefore, I think it is essential that the authors better discuss this major discrepancy between their study and previously published research.

Reviewer #1 (Public review):

Summary:

Wang et al. investigate sexual dimorphic changes in the transcriptome of aged humans. This study relies upon analysis of the Genotype-Tissue Expression dataset that includes 54 tissues from human donors. The authors investigate 17,000 transcriptomes from 35 tissues to investigate the effect of age and sex on transcriptomic variation, including the analysis of alternative splicing. Alternative splicing is becoming more appreciated as an influence in the aging process, but how it is affected by sexual dimorphism is still largely unclear. The authors investigated multiple tissues but ended up distilling brain tissue down to four separate regions: decision, hormone, memory, and movement. Building upon prior work, the authors used an analysis method called principal component-based signal-to-variation ratio (pcSVR) to quantify differences between sex or age by considering data dispersion. This method also considers differentially expressed genes and alternative splicing events.

Strengths:

(1) The authors investigate sexual dimorphism on gene expression and alternative splicing events with age in multiple tissues from a large publicly available data set that allows for reanalysis.

(2) Furthermore, the authors take into account the ethnic background of donors. Identification of aging-modulating genes could be useful for the reanalysis of prior data sets.

Weaknesses:

The models built off of the GTEx dataset should be tested in another data set (ex. Alzheimer's disease) where there are functional changes that can be correlated. Gene-length-dependent transcription decline, which occurs with age and disease, should also be investigated in this data set for potential sexual dimorphism.

Reviewer #2 (Public review):

Summary:

In this manuscript, Wang et al analyze ~17,000 transcriptomes from 35 human tissues from the GTEx database and address transcriptomic variations due to age and sex. They identified both gene expression changes as well as alternative splicing events that differ among sexes. Using breakpoint analysis, the authors find sex dimorphic shifts begin with declining sex hormone levels with males being affected more than females. This is an important pan-tissue transcriptomic study exploring age and sex-dependent changes although not the first one.

Strengths:

(1) The authors use sophisticated modeling and statistics for differential, correlational, and predictive analysis.

(2) The authors consider important variables such as genetic background, ethnicity, sampling bias, sample sizes, detected genes, etc.

(3) This is likely the first study to evaluate alternative splicing changes with age and sex at a pan-tissue scale.

(4) Sex dimorphism with age is an important topic and is thoroughly analyzed in this study.

Weaknesses:

(1) The findings have not been independently validated in a separate cohort or through experiments. Only selective splicing factor regulation has been verified in other studies.

(2) It seems the authors have not considered PMI or manner of death as a variable in their analysis.

(3) The manuscript is very dense and sometimes difficult to follow due to many different types of analyses and correlations.

(4) Short-read data can detect and quantify alternative splicing events with only moderate confidence and therefore the generalizability of these findings remains to be experimentally validated.

Reviewer #3 (Public review):

Summary:

In this study, Wang et al utilized the available GTEx data to compile a comprehensive analysis that attempt to reveal aging-related sex-dimorphic gene expression as well as alternative splicing changes in humans.

The key conclusions based on their analysis are that

(1) extensive sex-dimorphisms during aging with distinct patterns of change in gene expression and alternative splicing (AS), and

(2) the male-biased age-associated AS events have a stronger association with Alzheimer's disease, and

(3) the female-biased events are often regulated by several sex-biased splicing factors that may be controlled by estrogen receptors. They further performed break-point analysis and revealed that in males there are two main breakpoints around ages 35 and 50, while in females, there is only one breakpoint at 45.

Strengths:

This study sets an ambitious goal, leveraging the extensive GTEx dataset to investigate aging-related, sex-dimorphic gene expression and alternative splicing changes in humans. The research addresses a significant question, as our understanding of sex-dimorphic gene expression in the context of human aging is still in its early stages. Advancing our knowledge of these molecular changes is vital for identifying therapeutic targets for age-related diseases and extending the human health span. The study is highly comprehensive, and the authors are commendable for their attempted thorough analysis of both gene expression and alternative splicing - an area often overlooked in similar studies.

Weaknesses:

Due to the inherent noise within the GTEx dataset - which includes numerous variables beyond aging and sex - there are significant technical concerns surrounding this study. Additionally, the lack of cross-validation with independent, existing data raises questions about whether the observed gene expression changes genuinely reflect those associated with human aging. For instance, the break-point analysis in this study identifies two major breakpoints in males around ages 35 and 50, and one breakpoint in females at age 45; however, these findings contradict a recent multi-omics longitudinal study involving 108 participants aged 25 to 75 years, where breakpoint at 44 and 60 years was observed in both male and females (Shen et al, 2024). These issues cast doubt on the robustness of the study's conclusions. Specific concerns are outlined below:

(1) The primary method used in this study is linear regression, incorporating age, sex, and age-by-sex interactions as covariates, alongside other confounding factors (such as ethnicity) as unknown variables. However, the analysis overlooks two critical known variables in the GTEx dataset: time of death (TOD) and postmortem interval (PMI). Both TOD and PMI are recorded for each sample and account for substantial variance in gene expression profiles. A recent study by Wucher et al.(Wucher et al, 2023) demonstrated the powerful impact of TOD on gene expression by using it to reconstruct human circadian and even circannual datasets. Similarly, Ferreira et al. (Ferreira et al, 2018) highlighted PMI's influence on gene expression patterns. Without properly adjusting for these two variables, confidence in the study's conclusions remains limited at best.

(2) To demonstrate that their analysis is robust and that the covariates TOD and PMI are otherwise negligible - the authors should cross-validate their findings with independent datasets to confirm that the identified gene expression changes are reproducible for some tissues. For instance, the recent study by Shen et al. (Shen et al., 2024) in Nature Aging offers an excellent dataset for cross-validation, particularly for blood samples. Comparing the GTEx-derived results with this longitudinal transcriptome dataset would enable verification of gene expression changes at both the individual gene and pathway levels. Without such validation, confidence in the study's conclusions remains limited.

(3) As a demonstration of the lack of such validation, in the Shen et al. study (Shen et al., 2024), breakpoints at 44 and 60 years were observed in both males and females, while this study identifies two major breakpoints in males around ages 35 and 50, and one breakpoint in females at age 45. What caused this discrepancy?

(4) Although the alternative splicing analysis is intriguing, the authors did not differentiate between splicing events that alter the protein-coding sequence and those that do not. Many splicing changes occurring in the 5' UTR and 3' UTR regions do not impact protein coding, so it is essential to filter these out and focus specifically on alternative splicing events that can modify protein-coding sequences.

(5) One of the study's main conclusions - that "male-biased age-associated AS events have a stronger association with Alzheimer's disease" - is not supported by the data presented in Figure 4A, which shows an association with "regulation of amyloid precursor formation" only in female, not male, alternative splicing genes. Additionally, the gene ontology term "Alzheimer's disease" is absent from the unbiased GO analysis in Figure S6. These discrepancies suggest that the focus on Alzheimer's disease may reflect selective data interpretation rather than results driven by an unbiased analysis.

(6) The experimental data presented in Figures 5E - I merely demonstrate that estrogen receptor regulates the expression of two splicing factors, SRSF1 and SRSF7, in an estradiol-dependent manner. However, this finding does not support the notion that this regulation actually contributes to sex-dimorphic alternative splicing changes during human aging. Notably, the authors do not provide evidence that SRSF1 and SRSF7 expression changes actually occur in a sex-dependent manner with human aging (in a manner similar to TIA1). As such, this experimental dataset is disconnected from the main focus of the study and does not substantiate the conclusions on sex-dimorphic splicing during human aging. The authors performed RNA-seq in wild-type and ER mutant cells, and they should perform a comprehensive analysis of ER-dependent alternative splicing and compare the results with the GTEx data. It should be straightforward.

References:

Ferreira PG, Muñoz-Aguirre M, Reverter F, Sá Godinho CP, Sousa A, Amadoz A, Sodaei R, Hidalgo MR, Pervouchine D, Carbonell-Caballero J et al (2018) The effects of death and post-mortem cold ischemia on human tissue transcriptomes. Nature Communications 9: 490.

Shen X, Wang C, Zhou X, Zhou W, Hornburg D, Wu S, Snyder MP (2024) Nonlinear dynamics of multi-omics profiles during human aging. Nature Aging.

Wucher V, Sodaei R, Amador R, Irimia M, Guigó R (2023) Day-night and seasonal variation of human gene expression across tissues. PLOS Biology 21: e3001986.

Reviewer #1 (Public review):

Summary:

Hurtado et al. show that Sox9 is essential for retinal integrity, and its null mutation causes the loss of the outer nuclear layer (ONL). The authors then show that this absence of the ONL is due to apoptosis of photoreceptors and a reduction in the numbers of other retinal cell types such as ganglion cells, amacrine cells, and horizontal cells. They also describe that Müller Glia undergoes reactive gliosis by upregulating the Glial Fibrillary Acidic Protein. The authors then show that Sox9+ progenitors proliferate and differentiate to generate the corneal cells through Sox9 lineage-tracing experiments. They validate Sox9 expression and characterize its dynamics in limbal stem cells using an existing single-cell RNA sequencing dataset. Finally, the authors argue that Sox9 deletion causes progenitor cells to lose their clonogenic capacity by comparing the sizes of control and Sox9-null clones. Overall, Hurtado et al. underline the importance of Sox9 function in retinal and corneal cells.

Strengths:

The authors have characterized a myriad of striking phenotypes due to Sox9 deletion in the retina and limbal stem cells which will serve as a basis for future studies.

Weaknesses:

Hurtado et al. investigate the importance of Sox9 in the retina and limbal stem cells. However, the overall experimental narrative appears dispersed.

The authors begin by characterizing the phenotype of Sox9 deletion in the retina and show that the absence of the ON layer is due to photoreceptor apoptosis and a reduction in other retinal cell types. The authors also note that Müller glia undergoes gliosis in the Sox9 deletion condition. These striking observations are never investigated further, and instead, the authors switch to lineage-tracing experiments in the limbus that seem disconnected from the first three figures of the paper. Another example of this disconnect is the comparison of Sox9 high and Sox9 low populations using an existing scRNA-seq dataset and the subsequent GO term analysis, which does not directly tie in with the lineage-tracing data of the succeeding Sox9∆/∆ experiments.

A major concern is that a single Sox9∆/∆ limbal clone has a sufficiently large size, comparable to wild-type clones, as seen in Figure 6D. This singular result is contrary to their conclusion, which states that Sox9-deficient stem cells minimally contribute to the maintenance of the cornea.

Reviewer #2 (Public review):

Summary:

Sox9 is a transcription factor crucial for development and tissue homeostasis, and its expression continues in various adult eye cell types, including retinal pigmented epithelium cells, Müller glial cells, and limbal and corneal basal epithelia. To investigate its functional roles in the adult eye, this study employed inducible mouse mutagenesis. Adult-specific Sox9 depletion led to severe retinal degeneration, including the loss of Müller glial cells and photoreceptors. Further, lineage tracing revealed that Sox9 is expressed in a basal limbal stem cell population that supports stem cell maintenance and homeostasis. Mosaic analysis confirmed that Sox9 is essential for the differentiation of limbal stem cells. Overall, the study highlights that Sox9 is critical for both retinal integrity and the differentiation of limbal stem cells in the adult mouse eye.

Strengths:

In general, inducible genetic approaches in the adult mouse nervous system are rare and difficult to carry out. Here, the authors employ tamoxifen-inducible mouse mutagenesis to uncover the functional roles of Sox9 in the adult mouse eye.

Careful analysis suggests that two degeneration phenotypes (mild and severe) are detected in the adult mouse eye upon tamoxifen-dependent Sox9 depletion. Phenotype severity nicely correlates with the efficiency of Cre-mediated Sox9 depletion.

Molecular marker analysis provides strong evidence of Mueller cell loss and photoreceptor degeneration.

A clever genetic tracing strategy uncovers a critical role for Sox9 in limbal stem cell differentiation.

Weaknesses:

The Introduction can be improved by explaining clearly what was previously known about Sox9 in the eye. A lot of this info is mentioned in a single, 3-page long paragraph in the Discussion. However, the current study's significance and novelty would become clearer if the authors articulated in more detail in the Introduction what was already known about Sox9 in retina cell types (in vitro and in vivo).

Because a ubiquitous tamoxifen-inducible CreER line is employed, non-cell autonomous mechanisms possibly contribute to the observed retina degeneration. There is precedence for this in the literature. For example, RPE-specific ablation of Otx2 results in photoreceptor degeneration (PMID: 23761884). Have the authors considered the possibility of non-cell autonomous effects upon ubiquitous Sox9 deletion?

Given the similar phenotypes between animals lacking Otx2 and Sox9 in specific cell types of the eye, the authors are encouraged to evaluate Otx2 expression in the tamoxifen-induced Sox9 adult retina.

The most parsimonious explanation for the dual role of Sox9 in retinal cell types and limbal stem cells is that the cell context is different. For example, Sox9 may cooperate with TF1 in photoreceptors, TF2, in Mueller cells, and TF3 in limbal stem cells, and such cell type-specific cooperation may result in different outcomes (retinal integrity, stem cell differentiation). The authors are encouraged to add a paragraph to the discussion and share their thoughts on the dual role of Sox9.

One more molecular marker for Mueller glial cells would strengthen the conclusion that these cells are lost upon Sox9 deletion.

Using opsins as markers, the authors conclude that the photoreceptors are lost upon Sox9 deletion. However, an alternate possibility is that the photoreceptors are still present and that Sox9 is required for the transcription of opsin genes. In that case, Sox9 (like Otx2) may act as a terminal selector in photoreceptor cells. This point is particularly important because vertebrate terminal selectors (e.g., Nurr1, Otx2, Brn3a) initially affect neuron type identity and eventually lead to cell loss.

Quantification is needed for the TUNEL and GFAP analysis in Figure 3.

Line 269-320: The authors examined available scRNA-Seq data on adult retina. This data provides evidence for Sox9 expression in distinct cell types. However, the dataset does not inform about the functional role of Sox9 because Sox9 mutant cells were not analyzed with RNA-Seq. Hence, all the data that claim that this experiment provides insights into possible Sox9 functional roles must be removed. This includes panels F, G, and H in Figure 5. In general, this section of the paper (Lines 269-320) needs a major revision. Similarly, lines 442-446 in the Discussion should be removed.

Reviewer #1 (Public review):

Summary:

This work investigates numerically the propagation of subthreshold waves in a model neural network that is derived from the C. elegans connectome. Using a scattering formalism and tight-binding description of the network -- approximations which are commonplace in condensed matter physics -- this work attempts at showing the relevance of interference phenomena, such as wavenumber-dependent propagation, for the dynamics of subthreshold waves propagating in a network of electrical synapses.

Strengths:

The primary strength of the work is in trying to use theoretical tools from a far-away corner of fundamental physics to shed light on the properties of a real neural system.

Weaknesses:

The authors provide a good introduction and motivation for studying the propagation of subthreshold oscillations in the inferior olive nuclei. However, they chose to use the C elegans connectome for their study, and the implications of this work for C elegans neuroscience remain unclear by the end of the preprint. The authors should also give more evidence for the claim that their study may give a mechanism for synchronized rhythmic activity in the mammalian inferior olive nucleus, or refrain from making this conclusion. In the same vein, since the work emphasizes the dependence on the wavenumber for the propagation of subthreshold oscillations, they should make an attempt at estimating the wavenumber of subthreshold oscillations in C elegans if they were to exist and be observed. Next, the presence of two "mobility edges" in the transmission coefficient calculated in this work is unmistakably due to the discrete nature of the system, coming from the tight-binding approximation, and it is unclear to me if this approximation is justified in the current system. Similarly, it is possible that the wavenumber-dependent transmission observed depends strongly on the addition of a large number of virtual nodes (VNs) in the network, which the authors give little to no motivation for. As these nodes are not present in the C elegans connectome, the authors should explain the motivation for their inclusion in the model and should discuss their consequences on the transmission properties of the network. As it stands, I think the work would only have a very limited impact on the understanding of subthreshold oscillations in the rat or in C elegans. Indeed, the preprint falls short of relating its numerical results to any phenomena which could be observed in the lab.

Reviewer #2 (Public review):

This manuscript addresses an interesting and important question: the basic mechanisms underlying subthreshold intrinsic oscillations in the inferior olive. Instead of a direct investigation of the questions, the authors decide to study subthreshold oscillations in the C-elegance, where the connectivity pattern is known but does not exhibit sub-threshold oscillations. Furthermore, instead of the common description of gap-junction coupling by resistors, the authors decide to represent the system as a tight-binding Anderson Hamiltonian.

Weaknesses:

The authors study an architecture of the C-elegance instead of that of the inferior olive of mammals because the architecture of C-elegance is known.

No subthreshold oscillations were identified in the C-elegance.<br /> Instead of representing electrical coupling via resistors that connect neurons, the authors use a quantum formalism and introduce the tight-binding Anderson Hamiltonian. Why?

Equally spaced two virtual nodes were added between cells connected by a gap junction. Why?

Comments on revised version:

Last time, I recommended that the authors should represent electrical coupling via resistors that connect neurons instead of via the quantum formalism. The authors have not tested this direction.

Reviewer #1 (Public review):

What neurophysiological changes support the learning of new sensorimotor transformations is a key question in neuroscience. Many studies have attempted to answer this question at the neuronal population level - with varying degrees of success - but few, if any, have studied the change in activity of the apical dendrites of layer 5 cortical neurons. Neurons in the layer 5 of the sensory cortex appear to play a key role in sensorimotor transformations, showing important decision and reward-related signals, and being the main source of cortical and subcortical projections from the cortex. In particular, pyramidal track (PT) neurons project directly to subcortical regions related to motor activity, such as the striatum and brainstem, and could initiate rapid motor action in response to given sensory inputs. Additionally, layer 5 cortical neurons have large apical dendrites that extend to layer 1 where different neuromodulatory and long-range inputs converge, providing motor and contextual information that could be used to modulate layer 5 neurons output and/or to establish the synaptic plasticity required for learning a new association.

In this study, the authors aimed to test whether the learning of a new sensorimotor transformation could be supported by a change in the evoked response of the apical dendrites of layer 5 neurons in the mouse whisker primary somatosensory cortex. To do this, they performed longitudinal functional calcium imaging of the apical dendrites of layer 5 neurons while mice learned to discriminate between two multiwhiskers stimuli. The authors used a simple conditioning task in which one whisker stimulus (upward or backward air puff, CS+) is associated with reward after a short delay, while the other whisker stimulus (CS-) is not. They found that task learning (measured by the probability of anticipatory licking just after the CS+) was not associated with a significant change of the average population response evoked by the CS+ or the CS-, nor change in the average population selectivity. However, when considering individual dendritic tufts, they found interesting changes in selectivity, with approximately equal numbers of dendrites becoming more selective for CS+ and dendrites becoming more selective for CS-.

One of the major challenges when assessing changes in neural representation during the learning of such Go/NoGo tasks is that the movements and rewards themselves may elicit strong neural responses that may be a confounding factor, that is, inexperienced mice do not lick in response to the CS+, while trained mice do. In this study, the authors addressed this issue in three ways: first, they carefully monitor the orofacial movements of mice and show that task learning is not associated with changes in evoked whisker movements. Second, they show that whisking or licking evokes very little activity in the dendritic tufts compared to whisker stimuli (CS+ and CS-). Finally, the authors introduced into the design of their task a post-conditioning session after the last conditioning session during which the CS+ and the CS- are presented but no reward is delivered. During this post-session, the mice gradually stopped licking in response to the CS+. A better design might have been to perform the pre-conditioning and post-conditioning sessions in non-water-restricted, unmotivated mice to completely exclude any lick response, but the fact that the change in selectivity persists after the mice stopped licking in the last blocks of the post-conditioning session (in mice relying only on their whiskers to perform the task) is convincing.

The clever task design and careful data analysis provide compelling evidence that learning this whisker discrimination task does not result in a massive change in sensory representation in the apical dendritic tufts of layer 5 neurons in the primary somatosensory cortex on average. Nevertheless, individual dendritic tufts do increase their selectivity for one or the other sensory stimulus, likely enhancing the ability of S1 neurons to accurately discriminate the two stimuli and trigger the appropriate motor response (to lick or not to lick).

One limitation of the present study is the lack of evidence for the necessity of the primary somatosensory cortex in the learning and execution of the task. As the authors have strongly emphasized in their previous publications, the primary somatosensory cortex may not be necessary for the learning and execution of simple whisker detection tasks, especially when the stimulus is very salient. Although this new task requires the discrimination between two whisker stimuli, the simplicity and salience of the whisker stimuli used could make this task cortex independent. Especially when considering that some mice seem to not rely entirely on their whiskers to execute the task.

Nevertheless, this is an important result that shows for the first-time changes in the selectivity to sensory stimuli at the level of individual apical dendritic tufts in correlation with the learning of a discrimination task. This study sheds new light on the cortical cellular substrates of reward-based learning, and opens interesting perspectives for future research in this area. In future studies, it will be important to determine whether the change in selectivity of dendritic calcium spikes is causally involved in the learning the task or whether it simply correlates with learning, as a consequence of changes in synaptic inputs caused by reward. The dendritic calcium spikes may be involved in the establishment of synaptic plasticity required for learning and impact the output of layer 5 pyramidal neurons to trigger the appropriate motor response. It would be important also to study the changes in selectivity in the apical dendrite of the identified projection neurons.

Comments on revisions:

The authors have addressed all my questions. I have no further recommendations.

Reviewer #2 (Public review):

Summary:

The authors did not find an increased representation of CS+ throughout reinforcement learning in the tuft dendrites of Rbp4-positive neurons from layer 5B of the barrel cortex, as previously reported for soma from layer 2/3 of the visual cortex.<br /> Alternatively, the authors observed an increased selectivity to both stimuli (CS+ and CS-) during reinforcement learning. This feature 1) was not present in repeated exposures (without reinforcement), 2) was not explained by animal's behaviour (choice, licking and whisking) and 3) was long-lasting, being present even when the mice disengaged from the task.<br /> Importantly, increased selectivity was correlated with learning (% correct choices), and neural discriminability between stimuli increased with learning.

In conclusion, the authors show that tuft dendrites from layer 5B of the barrel cortex increase the representation of conditioned (CS+) and unconditioned stimuli (CS-) applied to the whiskers, during reinforcement learning.

Strengths:<br /> The results presented are very consistent throughout the entire study, and therefore very convincing:

(1) The results observed are very similar using two different imaging techniques (using 2-photon -planar imaging- and SCAPE - volumetric imaging). Fig. 3 and Fig.4 respectively.<br /> (2) The results are similar using "different groups" of tuft dendrites for the analysis (e.g. initially unresponsive and responsive pre- and post-learning). Fig. 5.<br /> (3) The results are similar from a specific set of trials (with the same sensory input, but different choices). Fig.7.<br /> (4) Additionally, the selectivity of tuft dendrites from layer 5B of the barrel cortex was higher in the mice that exclusively used the whisker to respond to the stimuli (CS+ and CS-).

The results presented are controlled against a group of mice that received the same stimuli presentation, except the reinforcement (reward).

Additionally, the behaviour outputs, such as choice, whisking and licking could not account for the results observed.

Although there are no causal experiments, the correlation between selectivity and learning (% of correct choices), as well as the increased neural discriminability with learning, but not in repeated exposure, are very convincing.

Weaknesses:

The biggest weakness is the absence of causality experiments. Although inhibiting specifically tuft dendritic activity in layer 1 from layer 5 pyramidal neurons is very challenging, tuft dendritic activity in layer 1 could be silenced through optogenetic experiments as in Abs et al. 2018. By manipulating NDNF-positive neurons the authors could specifically modify tuft dendritic activity in the barrel cortex during CS presentations, and test if silencing tuft dendritic activity in layer 1 would lead to the lack of selectivity and an impairment of reinforcement learning. Additionally, this experiment will test if the selectivity observed during reinforcement learning is due to changes in the local network, namely changes in local synaptic connectivity, or solely due to changes in the long-range inputs.

Reviewer #2 (Public review):

Summary:

The manuscript proposes a new technology to survey insects. They deployed optical sensors in agricultural landscapes and contrast their results to those in classical malaise and sweep nets survey methodologies. They found the results of optical sensors to be comparable with classical survey methodologies. The authors discuss pros and cons of their near-infrared sensor.

Strengths:

Contrasting the results with optical sensors with those in classical malaise and sweep nets was a clever idea.

Weaknesses:

The submitted materials on Revision 1 (in particular the response to reviewers) are difficult to follow. I encourage the authors to provide a point-by-point response to the first set of comments, as well as to this second review.

A new version of the manuscript needs to make sure that variability in the system (different crops) is taken into consideration. Also, stronger analysis including our current understanding of biodiversity metrics (including measures of sample coverage, sample completeness, Hill numbers, among others) will be important to make sure your new methodology is properly capable to be used as a new standard methodology.

While this new version is stronger and much clearer, I also agree with Reviewer 1 that the usage of terminology is weak. The paper and the new methodology is sound. It is is the application to real ecosystems/questions and datasets that is not properly addressed in the manuscript.

Reviewer #1 (Public review):

Summary:

This paper explores how diverse forms of inhibition impact firing rates in models for cortical circuits. In particular, the paper studies how the network operating point affects the balance of direct inhibition from SOM inhibitory neurons to pyramidal cells, and disinhibition from SOM inhibitory input to PV inhibitory neurons. This is an important issue as these two inhibitory pathways have largely been studies in isolation. Support for the main conclusions is generally solid, but could be strengthened by additional analyses.

Strengths

The paper has improved in revision, and the new intuitive summary statements added to the end of each results section are quite helpful.

Weaknesses

The concern about whether the results hold outside of the range in which neural responses are linear remains. This is particularly true given the discontinuity observed in the stability measure. I appreciate the concern (provided in the response to the first round of reviews) that studying nonlinear networks requires a lot of work. A more limited undertaking would be to test the behavior of a spiking network at a few key points identified by your linearization approach. Such tests could use relatively simple (and perhaps imperfect) measures of gain and stability. This could substantially enhance the paper, regardless of the outcome.

Reviewer #2 (Public review):

Summary:

Bos and colleagues address the important question of how two major inhibitory interneuron classes in the neocortex differentially affect cortical dynamics. They address this question by studying Wilson-Cowan-type mathematical models. Using a linearized fixed point approach, they provide convincing evidence that the existence of multiple interneuron classes can explain the counterintuitive finding that inhibitory modulation can increase the gain of the excitatory cell population while also increasing the stability of the circuit's state to minor perturbations. This effect depends on the connection strengths within their circuit model, providing valuable guidance as to when and why it arises.

Overall, I find this study to have substantial merit. I have some suggestions on how to improve the clarity and completeness of the paper.

Strengths:

(1) The thorough investigation of how changes in the connectivity structure affect the gain-stability relationship is a major strength of this work. It provides an opportunity to understand when and why gain and stability will or will not both increase together. It also provides a nice bridge to the experimental literature, where different gain-stability relationships are reported from different studies.

(2) The simplified and abstracted mathematical model has the benefit of facilitating our understanding of this puzzling phenomenon. (I have some suggestions for how the authors could push this understanding further.) It is not easy to find the right balance between biologically-detailed models vs simple but mathematically tractable ones, and I think the authors struck an excellent balance in this study.

Weaknesses:

(1) The fixed-point analysis has potentially substantial limitations for understanding cortical computations away from the steady-state. I think the authors should have emphasized this limitation more strongly and possibly included some additional analyses to show that their conclusions extend to the chaotic dynamical regimes in which cortical circuits often live.

(2) The authors could have discussed -- even somewhat speculatively -- how VIP interneurons fit into this picture. Their absence from this modelling framework stands out as a missed opportunity.

(3) The analysis is limited to paths within this simple E, PV, SOM circuit. This misses more extended paths (like thalamocortical loops) that involve interactions between multiple brain areas. Including those paths in the expansion in Eqs. 11-14 (Fig. 1C) may be an important consideration.

Comments on revisions:

I think the authors have done a reasonable job of responding to my critiques, and the paper is in pretty good shape. (Also, thanks for correctly inferring that I meant VIP interneurons when I had written SST in my review! I have updated the public review accordingly.)

I still think this line of research would benefit substantially from considering dynamic regimes including chaotic ones. I strongly encourage the authors to consider such an extension in future work.

Reviewer #3 (Public review):

Summary:

Bos et al study a computational model of cortical circuits with excitatory (E) and two subtypes of inhibition - parvalbumin (PV) and somatostatin (SOM) expressing interneurons. They perform stability and gain analysis of simplified models with nonlinear transfer functions when SOM neurons are perturbed. Their analysis suggests that in a specific setup of connectivity, instability and gain can be untangled, such that SOM modulation leads to both increases in stability and gain, in contrast to the typical direction in neuronal networks where increased gain results in decreased stability.

Strengths:

- Analysis of the canonical circuit in response to SOM perturbations. Through numerical simulations and mathematical analysis, the authors have provided a rather comprehensive picture of how SOM modulation may affect response changes.<br /> - Shedding light on two opposing circuit motifs involved in the canonical E-PV-SOM circuitry - namely, direct inhibition (SOM -> E) vs disinhibition (SOM -> PV -> E). These two pathways can lead to opposing effects, and it is often difficult to predict which one results from modulating SOM neurons. In simplified circuits, the authors show how these two motifs can emerge and depend on parameters like connection weights.<br /> - Suggesting potentially interesting consequences for cortical computation. The authors suggest that certain regimes of connectivity may lead to untangling of stability and gain, such that increases in network gain are not compromised by decreasing stability. They also link SOM modulation in different connectivity regimes to versatile computations in visual processing in simple models.

Weaknesses

Computationally, the analysis is solid, but it's very similar to previous studies (del Molino et al, 2017). Many studies in the past few years have done the perturbation analysis of a similar circuitry with or without nonlinear transfer functions (some of them listed in the references). This study applies the same framework to SOM perturbations, which is a useful computational analysis, in view of the complexity of the high-dimensional parameter space.

Link to biology: the most interesting result of the paper with regard to biology is the suggestion of a regime in which gain and stability can be modulated in an unconventional way - however, it is difficult to link the results to biological networks:<br /> - A general weakness of the paper is a lack of direct comparison to biological parameters or experiments. How different experiments can be reconciled by the results obtained here, and what new circuit mechanisms can be revealed? In its current form, the paper reads as a general suggestion that different combinations of gain modulation and stability can be achieved in a circuit model equipped with many parameters (12 parameters). This is potentially interesting but not surprising, given the high dimensional space of possible dynamical properties. A more interesting result would have been to relate this to biology, by providing reasoning why it might be relevant to certain circuits (and not others), or to provide some predictions or postdictions, which are currently missing in the manuscript.<br /> - For instance, a nice motivation for the paper at the beginning of the Results section is the different results of SOM modulation in different experiments - especially between L23 (inhibition) and L4 (disinhibition). But no further explanation is provided for why such a difference should exist, in view of their results and the insights obtained from their suggested circuit mechanisms. How the parameters identified for the two regimes correspond to different properties of different layers?<br /> - One of the key assumptions of the model is nonlinear transfer functions for all neuron types. In terms of modelling and computational analysis, a thorough analysis of how and when this is necessary is missing (an analysis similar to what has been attempted in Figure 6 for synaptic weights, but for cellular gains). A discussion of this, along with the former analysis to know which nonlinearities would be necessary for the results, is needed, but currently missing from the study. The nonlinearity is assumed for all subtypes because it seems to be needed to obtain the results, but it's not clear how the model would behave in the presence or absence of them, and whether they are relevant to biological networks with inhibitory transfer functions.<br /> - Tuning curves are simulated for an individual orientation (same for all), not considering the heterogeneity of neuronal networks with multiple orientation selectivity (and other visual features) - making the model too simplistic.

Reviewer #1 (Public review):

Summary:

In this preprint, Madrigal et al present "Tom1p ubiquitin ligase structure, interaction with Spt6p, and function in maintaining normal transcript levels and the stability of chromatin in promoters" which describes the identification of Tom1p, a conserved ubiquitin ligase, as a potential binding partner for the transcription elongation/histone chaperone Spt6p, and reveal the Tom1p structure as determined by CryoEM. Tom1p is a homolog of human HUWE1, which has been implicated in decay for a variety of basic protein substrates such as ribosomal proteins and histones. Structure-function analyses identify regions required for Spt6p interaction, suggesting that the interaction with Spt6p is phosphorylation dependent, and for interactions with histones, the latter of which confers phenotypes in vivo when mutated, suggesting that the Tom1p acidic region is important for its function. What is less clear is the function or interaction with Spt6p. The manuscript speculates that Spt6p-Tom1p interactions may tune Tom1p localization, and it is shown that Tom1p is recruited to transcribed genes by chromatin IP. In addition, the Tom1p structure will be valuable to those trying to understand the mechanisms of this very large ubiquitin ligase. Here, structures of homologs from other organisms have already been described elsewhere, however, the authors here indicate some details potentially not previously visualized in other structures.

Strengths:

It has not previously been known that the Spt6p tSH2 had any additional targets. Interaction with a ubiquitin ligase already implicated in histone turnover given Spt6p's role as histone chaperone is interesting. A structure of Tom1p also provides insight into this very large, conserved protein and structure-function analysis in a model system is a good start towards mechanistic dissection.

Weaknesses:

Some aspects of the manuscript seem less cohesive in that there are two halves of the manuscript and both don't quite solidify insights into the Spt6p relationship to Tom1p or deepen our understanding of Tom1p mechanism extensively, though results are a great start on both sides of the paper. There are several points that are less clear in that it is not known if Spt6p interacts with Tom1p and in what context. The interaction surface of Spt6p able to interact with Tom1p is the identical tSH2 that would be predicted to be occupied by phosphorylated RNAPII when Spt6p is incorporated into the RNAPII elongation complex. This means how and when Spt6p might be available to interact with Tom1p is not clear. Previous work from the Hill and Formosa groups on the tSH2 domain and its RNAPII linker target have suggested that phenotypes of mutants in the two are similar, suggesting that their main function is to interact with each other. A simple test of examining Tom1p interaction with genes in the tSH2 mutant was not done. Additionally, the Spt6p interacting surface on Tom1p is not narrowed to a specific putatively phosphorylated residue that it might target. It remains possible that mutations in other regions of Tom1p affect potential phosphorylation of this target, and therefore it is possible that some mutations that alter Spt6p interaction could do so indirectly. Finally, the authors might consider additional models for their discussion where Spt6p potentially could function to deliver histones to Tom1p.

Reviewer #2 (Public review):

Summary:

Madrigal et al identified Tom1, a E3 ubiquitin ligase previously known to be involved in ribosome biogenesis, as a protein that binds to terminal tandem Src-homology 2 (tSH2) domain of Spt6. They mapped this interaction to the acid region of Tom1, which is also known to interact with histones. Cells with tom1 mutants that cannot bind Spt6 did not show temperature sensitive phenotypes displaced for tom1 null mutant. Using ChIP assays, they showed that Tom1 is enriched at gene bodies of highly transcribed genes, and a loss of tom1 leads to reduced nucleosomal changes at gene promoters. Finally, they also solved the structures of Tom1 lacking the acidic region and found that Tom1p can adopt a compact a-solenoidal "basket" similar to the previously described structure of HUWE1. Overall, this is an interesting study and I have the following suggestions to improve the manuscript.

Major concerns:

(1) Promoter regions are in general nucleosome free. How does Tom1 mutant affect nucleosome-sized fragments at the promoter regions?

(2) While Tom1 antibodies may not specific, could the author perform Tom1 ChIP-seq in wild type and tom 1 null cells? This dataset may be more informative than tagged Tom1 that may not be functional.

Reviewer #3 (Public review):

Summary:

The authors report a novel, direct interaction of Spt6p tSH2 domain to Tom1p. This extends the function of Spt6p from communication with factors associated with RNAPII transcription to processes of ubiquitination. Tom1p is known to ubiquitinate a large variety of substrates, but it is unknown how substrate recognition is done in a specific manner. The team identified a conserved central acidic region of Tom1p which is essential for in vivo functions and binds to histones and nucleosomes, as well as Spt6p. They further describe the Tom1p occupancy pattern on chromatin, assigning it a stabilizing effect on nucleosomes near promotors and a destabilizing effect on nucleosomes within the gene bodies. The authors were able to resolve two different conformational states of Tom1p which are likely connected to its activity, and possibly substrate selectivity.

Overall, the authors show that an intrinsically disordered region in Tom1p is important for substrate interaction and function of Tom1p. The protein is further involved in chromatin architecture and structural transitions control its activity.

Strengths:

By revealing the interaction of Spt6p and Tom1p, the authors discover a novel connection between transcriptional elongation and processes of ubiquitination.<br /> In recent years, disordered regions of MDa protein complexes have become a focus of research projects. The effects of disordered regions on protein localization and specificity of binding interactions have been discussed in great extent, including proteins that are involved in chromatin remodeling and transcription. Adding to these current efforts, the authors assign a function to a highly conserved disordered region of Tom1p in technically clean experiments. Furthermore, with their data, they pin down a specific functional region in Tom1p which is relevant for the previously observed temperature sensitivity caused by Tom1p deletion in yeast.<br /> The team performs a thorough and complete analysis of the cryo-EM structure and they nicely model the hinge motion and details of an open and closed conformation.

Weaknesses:

Despite the high number of interesting findings, there is little connection between the individual sections of the manuscript. For example, many experiments are not related to Spt6p binding although this protein is presented as a major actor in this manuscript during the introduction. Furthermore, the structural analysis is well done, but it is also not quite clear how structural rearrangements are connected to Spt6 binding or chromatin remodeling. Some experimental results lack novelty, as similar data has previously been presented for the human homolog.

To confirm the novel, direct binding interaction of Spt6p and Tom1p, no orthogonal binding assays (SPR, MST, ITC) have been performed to confirm the interaction. To me, this is insufficient, especially since the team has purified both proteins to high quality levels, or could use peptides to test the function of the relevant regions.<br /> Additionally, interaction of Tom1p with Spt6p in the context of transcription elongation is proposed. Yet it is not clear on the mechanistic level how this is regulated if Tom1p and Rpb1p bind in a competitive manner. How is Tom1p tethered to the elongation complex if not through Spt6p? In addition to WT vs. knockout, the authors should further perform the genetic analyses with the intΔ11 mutant. This way they might be able pin down which interactions on chromatin are mediated by Spt6 vs. by other factors and could strengthen the overall model involving Spt6P.

Although the authors try to describe a final model in the discussion, this section is not easy to follow and needs more explanation, ideally drawn as a Figure of the proposed mechanism.

Reviewer #1 (Public review):

Summary:

The aim of the experiment reported in this paper is to examine the nature of the representation of a template of an upcoming target. To this end, participants were presented with compound gratings (consisting of tilted to the right and tilted to the left lines) and were cued to a particular orientation - red left tilt or blue right tilt (counterbalanced across participants). There are two directly compared conditions: (i) no ping: where there was a cue, that was followed by a 5.5-7.5s delay, then followed by a target grating in which the cued orientation deviated from the standard 45 degrees; and (ii) ping condition in which all aspects were the same with the only difference that a ping (visual impulse presented for 100ms) was presented after the 2.5 seconds following the cue. There was also a perception task in which only the 45 degrees to the right or to the left lines were presented. It was observed that during the delay, only in the ping condition, were the authors able to decode the orientation of the to-be-reported target using the cross-task generalization. Attention decoding, on the other hand, was decoded in both ping and non-ping conditions. It is concluded that the visual system has two different functional states associated with a template during preparation: a predominantly non-sensory representation for guidance and a latent sensory-like for prospective stimulus processing.

Strengths:

There is so much to be impressed with in this report. The writing of the manuscript is incredibly clear. The experimental design is clever and innovative. The analysis is sophisticated and also innovative - the cross-task decoding, the use of Mahalanobis distance as a function of representational similarity, the fact that the question is theoretically interesting, and the excellent figures.

Weaknesses:

While I think that this is an interesting study that addresses an important theoretical question, I have several concerns about the experimental paradigm and the subsequent conclusions that can be drawn.

(1) Why was V1 separated from the rest of the visual cortex, and why the rest of the areas were simply lumped into an EVC ROI? It would be helpful to understand the separation into ROIs.

(2) It would have been helpful to have a behavioral measure of the "attended" orientation to show that participants in fact attended to a particular orientation and were faster in the cued condition. The cue here was 100% valid, so no such behavioral measure of attention is available here.

(3) As I was reading the manuscript I kept thinking that the word attention in this manuscript can be easily replaced with visual working memory. Have the authors considered what it is about their task or cognitive demand that makes this investigation about attention or working memory?

(4) If I understand correctly, the only ROI that showed a significant difference for the cross-task generalization is V1. Was it predicted that only V1 would have two functional states? It should also be made clear that the only difference where the two states differ is V1.

(5) My primary concern about the interpretation of the finding is that the result, differences in cross-task decoding within V1 between the ping and no-ping condition might simply be explained by the fact that the ping condition refocuses attention during the long delay thus "resharpening" the template. In the no-ping condition during the 5.5 to 7.5 seconds long delay, attention for orientation might start getting less "crisp." In the ping condition, however, the ping itself might simply serve to refocus attention. So, the result is not showing the difference between the latent and non-latent stages, rather it is the difference between a decaying template representation and a representation during the refocused attentional state. It is important to address this point. Would a simple tone during the delay do the same? If so, the interpretation of the results will be different.

(6) The neural pattern distances measured using Mahalanobis values are really great! Have the authors tried to use all of the data, rather than the high AMI and low AMI to possibly show a linear relationship between response times and AMI?

(7) After reading the whole manuscript I still don't understand what the authors think the ping is actually doing, mechanistically. I would have liked a more thorough discussion, rather than referencing previous papers (all by the co-author).

Reviewer #2 (Public review):

Summary:

In the present study, the authors investigated the nature of attentional templates during the preparatory period of goal-directed attention. By combining the use of 'pinging' the neural activity with a visual impulse and fMRI-based multivariate decoding, the authors found that the nature of the neural representations of the prospective feature target during the preparatory period was contingent on the presence of the 'pinging' impulse. While the preparatory representations contained highly similar information content as the perceptual representations when the pinging impulse was introduced, they fundamentally differed from perceptual representations in the absence of the pinging impulse. Based on these findings, the authors proposed a dual-format mechanism in which both a "non-sensory" template and a latent "sensory" template coexisted during attentional preparation. The former actively guides activity in the preparatory state, and the latter is utilized for future stimulus processing.

Strengths:

Overall, I think this is an interesting study that introduced a novel perspective concerning the nature of neural representations during attentional processing. Methodologically, the present study combines an innovative utilization of the pinging technique in working memory studies and fMRI-based multivariate pattern analysis. The method is sound and the results are convincing. While I appreciate the conceptual elegance of the dual-format idea proposed by the authors, there are several questions that need to be addressed more thoroughly to clarify some of the potential ambiguities of the results and to increase the plausibility of the author's theory.

Weaknesses:

(1) The origin of the latent sensory-like representation. By 'pinging' the neural activity with a high-contrast, task-irrelevant visual stimulus during the preparation period, the authors identified the representation of the attentional feature target that contains the same information as perceptual representations. The authors interpreted this finding as a 'sensory-like' template is inherently hosted in a latent form in the visual system, which is revealed by the pinging impulse. However, I am not sure whether such a sensory-like template is essentially created, rather than revealed, by the pinging impulses. First, unlike the classical employment of the pinging technique in working memory studies, the (latent) representation of the memoranda during the maintenance period is undisputed because participants could not have performed well in the subsequent memory test otherwise. However, this appears not to be the case in the present study. As shown in Figure 1C, there was no significant difference in behavioral performance between the ping and the no-ping sessions (see also lines 110-125, pg. 5-6). In other words, it seems to me that the subsequent attentional task performance does not necessarily rely on the generation of such sensory-like representations in the preparatory period and that the emergence of such sensory-like representations does not facilitate subsequent attentional performance either. In such a case, one might wonder whether such sensory-like templates are really created, hosted, and eventually utilized during the attentional process. Second, because the reference orientations (i.e. 45 degrees and 135 degrees) have remained unchanged throughout the experiment, it is highly possible that participants implicitly memorized these two orientations as they completed more and more trials. In such a case, one might wonder whether the 'sensory-like' templates are essentially latent working memory representations activated by the pinging as was reported in Wolff et al. (2017), rather than a functional signature of the attentional process.

(2) The coexistence of the two types of attentional templates. The authors interpreted their findings as the outcome of a dual-format mechanism in which 'a non-sensory template' and a latent 'sensory-like' template coexist (e.g. lines 103-106, pg. 5). While I find this interpretation interesting and conceptually elegant, I am not sure whether it is appropriate to term it 'coexistence'. First, it is theoretically possible that there is only one representation in either session (i.e. a non-sensory template in the no-ping session and a sensory-like template in the ping session) in any of the brain regions considered. Second, it seems that there is no direct evidence concerning the temporal relationship between these two types of templates, provided that they commonly emerge in both sessions. Besides, due to the sluggish nature of fMRI data, it is difficult to tell whether the two types of templates temporally overlap.

(3) The representational distance. The authors used Mahalanobis distance to quantify the similarity of neural representation between different conditions. According to the authors' hypothesis, one would expect greater pattern similarity between 'attend leftward' and 'perceived leftward' in the ping session in comparison to the no-ping session. However, this appears not to be the case. As shown in Figures 3B and C, there was no major difference in Mahalanobis distance between the two sessions in either ROI and the authors did not report a significant main effect of the session in any of the ANOVAs. Besides, in all the ANOVAs, the authors reported only the statistic term corresponding to the interaction effect without showing the descriptive statistics related to the interaction effect. It is strongly advised that these descriptive statistics related to the interaction effect should be included to facilitate a more effective and intuitive understanding of their data.

Reviewer #3 (Public review):

This paper discusses how non-sensory and latent, sensory-like attentional templates are represented during attentional preparation. Using multivariate pattern analysis, they found that visual impulses can enhance the decoding generalization from perception to attention tasks in the preparatory stage in the visual cortex. Furthermore, the emergence of the sensory-like template coincided with enhanced information connectivity between V1 and frontoparietal areas and was associated with improved behavioral performance. It is an interesting paper with supporting evidence for the latent, sensory-like attentional template, but several problems still need to be solved.

(1) The title is "Dual-format Attentional Template," yet the supporting evidence for the non-sensory format and its guiding function is quite weak. The author could consider conducting further generalization analysis from stimulus selection to preparation stages to explore whether additional information emerges.

(2) In Figure 2, the author did not find any decodable sensory-like coding in IPS and PFC, even during the impulse-driven session, indicating that these regions do not represent sensory-like information. However, in the final section, the author claimed that the impulse-driven sensory-like template strengthens informational connectivity between sensory and frontoparietal areas. This raises a question: how can we reconcile the lack of decodable coding in these frontoparietal regions with the reported enhancement in network communication? It would be helpful if the author provided a clearer explanation or additional evidence to bridge this gap.

(3) Given that the impulse-driven sensory-like template facilitated behavior, the author proposed that it might also enhance network communication. Indeed, they observed changes in informational connectivity. However, it remains unclear whether these changes in network communication have a direct and robust relationship with behavioral improvements.

(4) I'm uncertain about the definition of the sensory-like template in this paper. Is it referring to the Ping impulse-driven condition or the decodable performance in the early visual cortex? If it is the former, even in working memory, whether pinging identifies an activity-silent mechanism is currently debated. If it's the latter, the authors should consider whether a causal relationship - such as "activating the sensory-like template strengthens the informational connectivity between sensory and frontoparietal areas" - is reasonable.

Joint Public Review:

Summary:

The authors aimed to identify the neural sources of behavioral variation in fruit flies deciding between odor and air, or between two odors.

Strengths:

- The question is of fundamental importance.<br /> - The behavioral studies are automated, and high-throughput.<br /> - The data analyses are sophisticated and appropriate.<br /> - The paper is clear and well-written aside from some initially strong wording.<br /> - The figures beautifully illustrate their results.<br /> - The modeling efforts mechanistically ground observed data correlations.

Weaknesses:

-The correlations between behavioral variations and neural activity/synapse morphology are relatively weak, and sometimes overstated in the wording that describes them.

Reviewer #1 (Public review):

Summary:

Using gene deletion analysis, the authors confirm the molecular function of putative components of an N-glycan-dependent endoplasmic reticulum protein quality control (ERQC) system (UGG1, MNS1, MNS101, MNL1, and MNL2), in the basidiomycetous fungal pathogen Cryptococcus neoformans. Specifically, they confirm the essential role of these components in the ERQC system and their role in ER stress which contributes to cellular fitness and pathogenicity.

The second part of the study links the components to secretion, mainly EV biogenesis and composition. However, this part of the study is less convincing.

Strengths:

Although it is unclear why ER stress in the mutants would not manifest into a classical secretion defect, this is a rigorous, well-controlled study, with the use of complemented strains that demonstrate phenotypic restoration. The diagram in Figure 1 is very useful in orientating the reader to a complex subject matter, although the legend could be more descriptive.

Weaknesses:

A major weakness is the sheer volume of data presented (in the main text and supplement), which makes the results difficult to follow and retain: the work could essentially be two separate studies.<br /> Another major weakness is the lack of mechanistic insight into the role of the ERQC system in EV secretion and its disconnection to "classical" secretion, which is difficult to reconcile. Some insight into why EV secretion is decreased, and classical secretion is unaffected, would strengthen the significance of the findings. No mechanism is provided to explain why the ERQC mutants (Ugg1 mutant in particular) would have reduced and heterogeneously sized EVs. Furthermore, it is not convincing that the EV content changes would greatly impact fitness and virulence. The proteomics data showing reduced cargo in the Ugg1 mutant is not convincing and difficult to follow.

Reviewer #2 (Public review):

Summary:

This study investigates the molecular function of the N-glycan-dependent endoplasmic reticulum protein quality control system (ERQC) in Cryptococcus neoformans and correlates this pathway with key features of C. neoformans virulence, especially those mediated by extracellular vesicle transport. The findings provide valuable insights into the connection between this pathway and the biogenesis of C. neoformans extracellular vesicles.

Strengths:

The strength of this study lies primarily in the careful selection of appropriate and current methodologies, which provide a solid foundation for the authors' results and conclusions across all presented data. All experiments are supported by well-designed and established controls in the study of C. neoformans, further strengthening the validity of the results and conclusions drawn from them. The study presents novel data on this important pathway in C. neoformans, establishing its connection with C. neoformans virulence. Interestingly, the findings led the authors to understand the relationship between this pathway and the transport of key fungal virulence factors via extracellular vesicles. This was demonstrated in the study, paving the way for a deeper understanding of extracellular vesicle biogenesis-a field still filled with gaps but one to which this study contributes solid data, helping to clarify aspects of this process.

Weaknesses:

I do not see significant weaknesses in this study. The experiments are well-grounded, and the results are clearly presented. I believe the only weakness is that the paper could be condensed. Sections like the discussion, for instance, are extremely lengthy, which may make reading and, consequently, understanding more challenging for many readers. Regarding the presentation of the results, while clear, the figures contain a lot of information, and I believe that some of this content could be moved to supplementary figures.

Reviewer #3 (Public review):

Summary:

Cryptococcus neoformans is a global critical threat pathogen and the manuscript by Mota et al demonstrates that the pathogen's N-glycan-dependent protein quality control system regulates the capacity of the fungus to cause disease. The system makes sure that glycoproteins are folded correctly. The system is involved in the fitness and virulence of the fungus by regulating aspects of cellular robustness and the trafficking of virulence-associated compounds outside of the cell via transport in extracellular vesicles.

Strengths:

The investigators use multiple modalities to demonstrate that the system is involved in cryptococcal pathogenesis. The investigators generated mutant C. neoformans to explore the role of genes involved in the protein folding system. Basic microbiology, genetic analyses, proteomics, fluorescence and transmission microscopy, nanotracking analyses, and murine studies were performed. The validity of the findings are thus very high. Hypotheses are robustly demonstrated.

Weaknesses:

Aspects of the results should be better explained. Some results are extrapolated in their meaning beyond the extent of the data.

Reviewer #1 (Public review):

Summary:

The authors have created a new model of KCNC1-related DEE in which a pathogenic patient variant (A421V) is knocked into a mouse in order to better understand the mechanisms through which KCNC1 variants lead to DEE.

Strengths:

(1) The creation of a new DEE model of KCNC1 dysfunction.

(2) InVivo phenotyping demonstrates key features of the model such as early lethality and several types of electrographic seizures.

(3) The ex vivo cellular electrophysiology is very strong and comprehensive including isolated patches to accurately measure K+ currents, paired recording to measure evoked synaptic transmission, and the measurement of membrane excitability at different time points and in two cell types.

Weaknesses:

(1) The assertion that membrane trafficking is impaired by this variant could be bolstered by additional data.

(2) In some experiments details such as the age of the mice or cortical layer are emphasized, but in others, these details are omitted.

(3) The impairments in PV neuron AP firing are quite large. This could be expected to lead to changes in PV neuron activity outside of the hypersynchronous discharges that could be detected in the 2-photon imaging experiments, however, a lack of an effect on PV neuron activity is only loosely alluded to in the text. A more formal analysis is lacking. An important question in trying to understand mechanisms underlying channelopathies like KCNC1 is how changes in membrane excitability recorded at the whole cell level manifest during ongoing activity in vivo. Thus, the significance of this work would be greatly improved if it could address this question.

(4) Myoclonic jerks and other types of more subtle epileptiform activity have been observed in control mice, but there is no mention of littermate control analyzed by EEG.

Reviewer #2 (Public review):

Summary:

Wengert et al. generated and thoroughly characterized the developmental epileptic encephalopathy phenotype of Kcnc1A421V/+ knock-in mice. The Kcnc1 gene encodes the Kv3.1 channel subunit. Analogous to the role of BK channels in excitatory neurons, Kv3 channels are important for the recurrent high-frequency discharge in interneurons by accelerating the downward hyperpolarization of the individual action potential. Various Kcnc1 mutations are associated with developmental epileptic encephalopathy, but the effect of a recurrent A421V mutation was somewhat controversial and its influence on neuronal excitability has not been fully established. In order to determine the neurological deficits and underlying disease mechanisms, the authors generated cre-dependent KI mice and characterized them using neonatal neurological examination, high-quality in vitro electrophysiology, and in vivo imaging/electrophysiology analyses. These analyses revealed excitability defects in the PV+ inhibitory neurons associated with the emergence of epilepsy and premature death. Overall, the experimental data convincingly support the conclusion.

Strengths:

The study is well-designed and conducted at high quality. The use of the Cre-dependent KI mouse is effective for maintaining the mutant mouse line with premature death phenotype, and may also minimize the drift of phenotypes which can occur due to the use of mutant mice with minor phenotype for breeding. The neonatal behavior analysis is thoroughly conducted, and the in vitro electrophysiology studies are of high quality.

Weaknesses:

While not critically influencing the conclusion of the study, there are several concerns.

In some experiments, the age of the animal in each experiment is not clearly stated. For example, the experiments in Figure 2 demonstrate impaired K+ conductance and membrane localization, but it is not clear whether they correlated with the excitability and synaptic defects shown in subsequent figures. Similarly, it is unclear how old mice the authors conducted EEG recordings, and whether non-epileptic mice are younger than those with seizures.

The trafficking defect of mutant Kv3.1 proposed in this study is based only on the fluorescence density analysis which showed a minor change in membrane/cytosol ratio. It is not very clear how the membrane component was determined (any control staining?). In addition to fluorescence imaging, an addition of biochemical analysis will make the conclusion more convincing (while it might be challenging if the Kv3.1 is expressed only in PV+ cells).

While the study focused on the superficial layer because Kv3.1 is the major channel subunit, the PV+ cells in the deeper cortical layer also express Kv3.1 (Chow et al., 1999) and they may also contribute to the hyperexcitable phenotype via negative effect on Kv3.2; the mutant Kv3.1 may also block membrane trafficking of Kv3.1/Kv3.2 heteromers in the deeper layer PV cells and reduce their excitability. Such an additional effect on Kv3.2, if present, may explain why the heterozygous A421V KI mouse shows a more severe phenotype than the Kv3.1 KO mouse (and why they are more similar to Kv3.2 KO). Analyzing the membrane excitability differences in the deep-layer PV cells may address this possibility.

In Table 1, the A421V PV+ cells show a depolarized resting membrane potential than WT by ~5 mV which seems a robust change and would influence the circuit excitability. The authors measured firing frequency after adjusting the membrane voltage to -65mV, but are the excitability differences less significant if the resting potential is not adjusted? It is also interesting that such a membrane potential difference is not detected in young adult mice (Table 2). This loss of potential compensation may be important for developmental changes in the circuit excitability. These issues can be more explicitly discussed.

Reviewer #3 (Public review):

Summary:

Here Wengert et al., establish a rodent model of KCNC1 (Kv3.1) epilepsy by introducing the A421V mutation. The authors perform video-EEG, slice electrophysiology, and in vivo 2P imaging of calcium activity to establish disease mechanisms involving impairment in the excitability of fast-spiking parvalbumin (PV) interneurons in the cortex and thalamic PV cells.

Outside-out nucleated patch recordings were used to evaluate the biophysical consequence of the A421V mutation on potassium currents and showed a clear reduction in potassium currents. Similarly, action potential generation in cortical PV interneurons was severely reduced. Given that both potassium currents and action potential generation were found to be unaffected in excitatory pyramidal cells in the cortex the authors propose that loss of inhibition leads to hyperexcitability and seizure susceptibility in a mechanism similar to that of Dravet Syndrome.

Strengths:

This manuscript establishes a new rodent model of KCNC1-developmental and epileptic encephalopathy. The manuscript provides strong evidence that parvabumin-type interneurons are impaired by the A421V Kv3.1 mutation and that cortical excitatory neurons are not impaired. Together these findings support the conclusion that seizure phenotypes are caused by reduced cortical inhibition.

Weaknesses:

The manuscript identifies a partial mechanism of disease that leaves several aspects unresolved including the possible role of the observed impairments in thalamic neurons in the seizure mechanism. Similarly, while the authors identify a reduction in potassium currents and a reduction in PV cell surface expression of Kv3.1 it is not clear why these impairments would lead to a more severe disease phenotype than other loss-of-function mutations which have been characterized previously. Lastly, additional analysis of video-EEG data would be helpful for interpreting the extent of the seizure burden and the nature of the seizure types caused by the mutation.

Reviewer #1 (Public review):

Summary:

In this manuscript, Javid and colleagues worked to understand the molecular mechanisms involved in mistranslation in mycobacteria. They had previously discovered that mistranslation is an important mechanism underlying antibiotic tolerance in mycobacteria. Using a clever genetic screen they identify that deletion of gidB, a 16S ribosomal RNA methyltransferase, leads to lowered mistranslation (i.e. higher translational fidelity), but only in genetic backgrounds or environmental conditions that increase mistranslation rates.

Strengths:

The strengths of this manuscript are the clever genetic screen, the powerful mistranslation assays, and the clear writing and figures explaining a complex biological problem. Their identification of gidB as a factor important for mistranslation deepens our knowledge about this interesting phenomenon.

Weaknesses:

The structural work at the end feels like both an afterthought in terms of the science and the writing. I would suggest re-writing that section to be clearer about what the figure says and does not say. For example, the caption of Figure 6 appears to be more informative than the text and refers to concepts not present in the main text. In general, I found this section to be the most difficult to understand.

Reviewer #2 (Public review):

Summary:

Protein synthesis - translation - involves repeated recognition and incorporation of amino-acyl-tRNAs by the ribosome. This process is a trade-off between the rate and accuracy of selection (for review see (Johansson et al, 2008; Wohlgemuth et al, 2011)). The ribosome does not just maximise the rate or the accuracy, it balances the two. Therefore, it is possible to select mutants that translate faster than the wt (but are sloppy) or that are very accurate (more than the wt) but translate slower. Slow translation is detrimental as it limits the rate of protein synthesis (and, therefore, growth) and hyper-accurate mutants accumulate mis-translated proteins, which is detrimental for the cell.

Bi and colleagues employ genetics, MIC measurements, reporter assays, and structural biology to characterise the role of GidB rRNA methylase in translational accuracy in Mycobacterium smegmatis.

Strengths:

The genetics and phenotypic assays are convincing and establish the biological role of the methylase. The authors use a powerful set of complementary assays that convincingly demonstrate that the loss of GidB results in mistranslation.

Weaknesses:

(1) It would be essential to provide information regarding the growth rate and, ideally, translation rates in the gidB KO and the isogenic WT. As translation balances accuracy and speed, only characterising the speed is not sufficient to understand the phenomenon.

(2) Cryo-EM analysis of vacant 70S ribosomes is not sufficient for understanding the mechanisms underlying the accuracy defects in the gidB KO. One should assemble and solve structurally near-cognate and non-cognate complexes. I believe the authors are over-interpreting the scant structural data they have. Furthermore, current representation makes it impossible to assess the resolution of the structure, especially in the areas of interest.

References:

Johansson M, Lovmar M, Ehrenberg M (2008) Rate and accuracy of bacterial protein synthesis revisited. Curr Opin Microbiol 11: 141-147<br /> Wohlgemuth I, Pohl C, Mittelstaet J, Konevega AL, Rodnina MV (2011) Evolutionary optimization of speed and accuracy of decoding on the ribosome. Philos Trans R Soc Lond B Biol Sci 366: 2979-2986.

Reviewer #1 (Public review):

Summary:

In this manuscript the authors follow up on their published observation that providing a lower glucose parental nutrition (PN) reduces sepsis from a common pathogen [Staphylococcus epidermitis (SE)] in preterm piglets. Here they found that a slightly higher dose of glucose could thread the needle and get the protective effects of low glucose without incurring significant hypoglycemia. They then investigate whether change in low glucose PN impacts metabolism to confer this benefit. The finding that lower glucose reduces sepsis is important as sepsis is a major cause of morbidity and mortality in preterm infants, and adjusting PN composition is a feasible intervention.

Strengths:

(1) They address a highly significant problem of neonatal sepsis in preterm infants using a preterm piglet model.<br /> (2) They have compelling data in this paper (and in a previous publication, ref 27) that low glucose PN confers a survival advantage. A downside of the low glucose PN is hypoglycemia which they mitigate in this paper by using a slightly high amount of glucose in the PN.<br /> (3) The experiment where they change PN from high to low glucose after infection is very important to determine if this approach might be used clinically. Unfortunately, this did not show an ability to reduce sepsis risk with this approach.<br /> (4) They produce an impressive multiomics data set from this model of preterm piglet sepsis which is likely to provide additional insights into the pathogenesis of preterm neonatal sepsis.

Weaknesses:

(1) Piglets on the low glucose PN had consistently lower density of SE (~1 log) across all timepoints. This may be due to changes in immune response leading to better clearance or it could be due to slower growth in lower glucose environment. These possibilities are not fully disentangled in this study.

(2) Many differences in the different omics (transcriptomics, metabolomics, proteomics) were identified in the SE-LOW vs SE-HIGH comparison. Since the bacterial load is very different between these conditions, could the changes be due to bacterial load rather than metabolic reprograming from the low glucose PN? The authors argue in supplementary figure 1F that density of SE in blood does not correlate with sepsis implying that bacterial load is not the driver of outcome. The authors recently published some additional analysis that may be helpful to reference in this manuscript.

(3) Further, expanding upon a model to better understand the complex relationship between differences in supplemental glucose infusion, blood glucose levels, bacterial load, host responses and how they impact the development of sepsis would be helpful. These complex relationships are difficult to fully disentangle, but one could consider infusing the same quantity of heat-killed bacteria under different glucose conditions to see if the glucose levels drive outcomes independently of bacterial burden.

Reviewer #2 (Public review):

The authors demonstrate that a low parenteral glucose regimen can lead to improved bacterial clearance and survival from Staph epi sepsis in newborn pigs without inducing hypoglycemia, as compared to a high glucose regimen. Using RNA-seq, metabolomic, and proteomic data, the authors conclude that this is primarily mediated by altered hepatic metabolism.

The authors have addressed the concerns raised by the reviewers in their revised manuscript and have added additional information in the results and discussion part.<br /> Please address in Fig. 3- the genes PGM2 and GCK, which the authors mention, are downregulated in SE-Low compared to SE-high, but these are actually less downregulated in the SE group compared to Control group, where the the Con-low shows even more decrease in these genes compared to Con-high. So if anything, these genes are getting upregulated by infection.

Reviewer #1 (Public review):

Summary:

In this work, Harpring et al. investigated divisome assembly in Chlamydia trachomatis serovar L2 (Ct), an obligate intracellular bacterium that lacks FtsZ, the canonical master regulator of bacterial cell division. They find that divisome assembly is initiated by the protein FtsK in Ct by showing that it forms discrete foci at the septum and future division sites. Additionally, knocking down ftsK prevents divisome assembly and inhibits cell division, further supporting their hypothesis that FtsK regulates divisome assembly. Finally, they show that MreB is one of the last chlamydial divisome proteins to arrive at the site of division and is necessary for the formation of septal peptidoglycan rings but does not act as a scaffold for division assembly as previously proposed.

Strengths:

The authors use microscopy to clearly show that FtsK forms foci both at the septum as well as at the base of the progenitor cell where the next septum will form. They also show that the Ct proteins PBP2, PBP3, MreC, and MreB localize to these same sites suggesting they are involved in the divisome complex.

Using CRISPRi the authors knock down ftsK and find that most cells are no longer able to divide and that PBP2 and PBP3 no longer localized to sites of division suggesting that FtsK is responsible for initiating divisome assembly. They also performed a knockdown of pbp2 using the same approach and found that this also mostly inhibited cell division. Additionally, FtsK was still able to localize in this strain, however PBP3 did not, suggesting that FtsK acts upstream of PBP2 in the divisome assembly process while PBP2 is responsible for the localization of PBP3.

The authors also find that performing a knockdown of ftsK also prevents new PG synthesis further supporting the idea that FtsK regulates divisome assembly. They also find that inhibiting MreB filament formation using A22 results in diffuse PG, suggesting that MreB filament formation is necessary for proper PG synthesis to drive cell division.

Overall the authors propose a new hypothesis for divisome assembly in an organism that lacks FtsZ and use a combination of microscopy and genetics to support their model that is rigorous and convincing. The finding that FtsK, rather than a cytoskeletal or "scaffolding" protein is the first division protein to localize to the incipient division site is unexpected and opens up a host of questions about its regulation. The findings will progress our understanding of how cell division is accomplished in bacteria with non-canonical cell wall structure and/or that lack FtsZ.

Weaknesses:

No major weaknesses were noted in the data supporting the main conclusions. However, there was a claim of novelty in showing that multiple divisome complexes can drive cell wall synthesis simultaneously that was not well-supported (i.e. this has been shown previously in other organisms). In addition, there were minor weaknesses in data presentation that do not substantially impact interpretation (e.g. presenting the number of cells rather than the percentage of the population when quantifying phenotypes and showing partial western blots instead of total western blots).

Reviewer #2 (Public review):

Summary:

Chlamydial cell division is a peculiar event, whose mechanism was mysterious for many years. C. trachomatis division was shown to be polar and involve a minimal divisome machinery composed of both homologues of divisome and elongasome components, in the absence of an homologue of the classical division organizer FtsZ. In this paper, Harpring et al., show that FtsK is required at an early stage of the chlamydial divisome formation.

Strengths:

The manuscript is well-written and the results are convincing. Quantification of divisome component localization is well performed, number of replicas and number of cells assessed are sufficient to get convincing data. The use of a CRISPRi approach to knock down some divisome components is an asset and allows a mechanistic understanding of the hierarchy of divisome components.

Weaknesses:

The authors did not analyse the role of all potential chlamydial divisome components and did not show how FtsK may initiate the positioning of the divisome. Their conclusion that FtsK initiates the assembly of the divisome is an overinterpretation and is not backed by the data. However, data show convincingly that FtsK, if perhaps not the initiator of chlamydial division, is definitely an early and essential component of the chlamydial divisome.

Reviewer #3 (Public review):

Summary:

The obligate intracellular bacterium Chlamydia trachomatis (Ct) divides by binary fission. It lacks FtsZ, but still has many other proteins that regulate the synthesis of septal peptidoglycan, including FtsW and FtsI (PBP3) as well as divisome proteins that recruit and activate them, such as FtsK and FtsQLB. Interestingly, MreB is also required for the division of Ct cells, perhaps by polymerizing to form an FtsZ-like scaffold. Here, Harpring et al. show that MreB does not act early in division and instead is recruited to a protein complex that includes FtsK and PBP2/PBP3. This indicates that Ct cell division is organized by a chimera between conserved divisome and elongasome proteins. Their work also shows convincingly that FtsK is the earliest known step of divisome activity, potentially nucleating the divisome as a single protein complex at the future division site. This is reminiscent of the activity of FtsZ, yet fundamentally different.

Strengths:

The study is very well written and presented, and the data are convincing and rigorous. The data underlying the proposed localization dependency order of the various proteins for cell division is well justified by several different approaches using small molecule inhibitors, knockdowns, and fluorescent protein fusions. The proposed dependency pathway of divisome assembly is consistent with the data and with a novel mechanism for MreB in septum synthesis in Ct.

Weaknesses:

The paper could be improved by including more information about FtsK, the "focus" of this study. For example, if FtsK really is the FtsZ-like nucleator of the Ct divisome, how is the Ct FtsK different sequence-wise or structurally from FtsK of, e.g. E. coli? Is the N-terminal part of FtsK sufficient for cell division in Ct like it is in E. coli, or is the DNA translocase also involved in focus formation or localization? Addressing those questions would put the proposed initiator role of FtsK in Ct in a better context and make the conclusions more attractive to a wider readership.

Another weakness is that the title of the paper implies that FtsK alone initiates divisome assembly. However, the data indicate only that FtsK is important at an early stage of divisome assembly, not that it is THE initiator. I suggest modifying the title to account for this--perhaps "FtsK is required to initiate....".

Reviewer #1 (Public review):

Summary:

This manuscript uses molecular dynamics simulations to understand how forces felt by the intracellular domain are coupled to the opening of the mechanosensitive ion channel NOMPC. The concept is interesting - as the only clearly defined example of an ion channel that opens due to forces on a tethered domain, the mechanism by which this occurs is yet to be fully elucidated. The main finding is that twisting of the transmembrane portion of the protein - specifically via the TRP domain that is conserved within the broad family of channels- is required to open the pore. That this could be a common mechanism utilised by a wide range of channels in the family, not just mechanically gated ones, makes the result significant. It is intriguing to consider how different activating stimuli can produce a similar activating motion within this family. However, the support for the finding can be strengthened as the authors cannot yet exclude that other forces could open the channel if given longer or at different magnitudes. In addition, they do not see the full opening of the channel, only an initial dilation. Even if we accept that twist is essential for this, it may be that it is not sufficient for full opening, and other stimuli are required.

Strengths:

Demonstrating that rotation of the TRP domain is the essential requirement for channel opening would have significant implications for other members of this channel family.

Weaknesses:

The manuscript centres around 3 main computational experiments. In the first, a compression force is applied on a truncated intracellular domain and it is shown that this creates both a membrane normal (compression) and membrane parallel (twisting) force on the TRP domain. This is a point that was demonstrated in the authors' prior eLife paper - so the point here is to quantify these forces for the second experiment.

The second experiment is the most important in the manuscript. In this, forces are applied directly to two residues on the TRP domain with either a membrane normal (compression) or membrane parallel (twisting) direction, with the magnitude and directions chosen to match that found in the first experiment. Only the twisting force is seen to widen the pore in the triplicate simulations, suggesting that twisting, but not compression can open the pore. This result is intriguing and there appears to be a significant difference between the dilation of pore with the two force directions. However, there are two caveats to this conclusion. Firstly, is the magnitude of the forces - the twist force is larger than the applied normal force to match the result of experiment 1. However, it is possible that compression could also open the pore at the same magnitude or if given longer. It may be that twist acts faster or more easily, but I feel it is not yet possible to say it is the key and exclude the possibility that compression could do something similar. I also note that when force was applied to the AR domain in experiment 1, the pore widened more quickly than with the twisting force alone, suggesting that compression is doing something to assist with opening. Given that the forces are likely to be smaller in physiological conditions it could still be critical to have both twist and compression present. As this is the central aspect of the study, I believe that examining how the channel responds to different force magnitudes could strengthen the conclusions and recommend additional simulations be done to examine this.

The second important consideration is that the study never sees a full pore opening, but rather a widening that is less than that seen in open state structures of other TRP channels and insufficient for rapid ion currents. This is something the authors acknowledge in their prior manuscript in eLife 2021. While this may simply be due to the limited timescale of the simulations, it needs to be clearly stated as a caveat to the conclusions. Twist may be the key to getting this dilation, but we don't know if it is the key to full pore opening. To demonstrate that the observed dilation is a first step in pore opening, then a structural comparison to open-state TRP channels would be beneficial to provide evidence that this motion is along the expected pathway of channel gating.

Experiment three considers the intracellular domain and determines the link between compression and twisting of the intracellular AR domain. In this case, the end of the domain is twisted and it is shown that the domain compresses, the converse to the similar study previously done by the authors in which compression of the domain was shown to generate torque. While some additional analysis is provided on the inter-residue links that help generate this, this is less significant than the critical second experiment.

Reviewer #2 (Public review):

This study uses all-atom MD simulation to explore the mechanics of channel opening for the NOMPC mechanosensitive channel. Previously the authors used MD to show that external forces directed along the long axis of the protein (normal to the membrane) result in AR domain compression and channel opening. This force causes two changes to the key TRP domains adjacent to the channel gate: 1) a compressive force pushes the TRP domain along the membrane normal, while 2) a twisting torque induces a clock-wise rotation on the TRP domain helix when viewing the bottom of the channel from the cytoplasm. Here, the authors wanted to understand which of those two changes is responsible for increasing the inner pore radius, and they show that it is the torque. The simulations in Figure 2 probe this question with different forces, and we can see the pore open with parallel forces in the membrane, but not with the membrane-normal forces. I believe this result as it is reproducible, the timescales are reaching 1 microsecond, and the gate is clearly increasing diameter to about 4 Å. This seems to be the most important finding in the paper, but the impact is limited since the authors already show how forces lead to channel opening, and this is further teasing apart the forces and motions that are actually the ones that cause the opening.

Reviewer #3 (Public review):

Summary:

This manuscript by Duan and Song interrogates the gating mechanisms and specifically force transmission in mechanosensitive NOMPC channels using steered molecular dynamics simulations. They propose that the ankyrin spring can transmit force to the gate through torsional forces adding molecular detail to the force transduction pathways in this channel.

3. Constant velocity or constant force<br /> For the SMD the authors write "and a constant velocity or constant force". It's unclear from this reviewer's perspective what is used to generate the simulation data.

Strengths:

Detailed, rigorous simulations coupled with a novel model for force transduction.

Weaknesses:

Experimental validation of reduced mechanosensitivity through mutagenesis of proposed ankyrin/TRP domain coupling interactions would greatly enhance the manuscript. I have some additional questions documented below:

(1) The membrane-parallel torsion force can open NOMPC<br /> How does the TRP domain interact with the S4-S5 linker? In the original structural studies, the coordination of lipids in this region seems important for gating. In this manner does the TRP domain and S4-S5 linker combined act like an amphipathic helix as suggested first for MscL (Bavi et al., 2016 Nature Communications) and later identified in many MS channels (Kefauver et al., 2020 Nature).

(2) Torsional forces on shorter ankyrin repeats of mammalian TRP channels<br /> Is it possible torsional forces applied to the shorter ankyrin repeats of mammalian TRPs may also convey force in a similar manner?

(3) Constant velocity or constant force<br /> For the SMD the authors write "and a constant velocity or constant force". It's unclear from this reviewer's perspective which is used to generate the simulation data.

Reviewer #1 (Public review):

Summary:

In "Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds," Maloney et al. (2024) investigate changes in individual responses over time, referred to as behavioral drift within the lifespan of an animal. Drift, as defined in the paper, complements stable behavioral variation (animal individuality/personality within a lifetime) over shorter timeframes, which the authors associate with an underlying bet-hedging strategy. The third timeframe of behavioral variability that the authors discuss occurs within seasons (across several generations of some insects), termed "adaptive tracking." This division of "adaptive" behavioral variability over different timeframes is intuitively logical and adds valuable depth to the theoretical framework concerning the ecological role of individual behavioral differences in animals.

Strengths:

While the theoretical foundations of the study are strong, the connection between the experimental data (Figure 1) and the modeling work (Figure 2-4) is less convincing.

Weaknesses:

In the experimental data (Figure 1), the authors describe the changes in behavioral preferences over time. While generally plausible, I identify three significant issues with the experiments:

(1) All of the subsequent theoretical/simulation data is based on changing environments, yet all the experiments are conducted in unchanging environments. While this may suffice to demonstrate the phenomenon of behavioral instability (drift) over time, it does not properly link to the theory-driven work in changing environments. An experiment conducted in a changing environment and its effects on behavioral drift would improve the manuscript's internal consistency and clarify some points related to (3) below.

(2) The temporal aspect of behavioral instability. While the analysis demonstrates behavioral instability, the temporal dynamics remain unclear. It would be helpful for the authors to clarify (based on graphs and text) whether the behavioral changes occur randomly over time or follow a pattern (e.g., initially more right turns, then more left turns). A proper temporal analysis and clearer explanations are currently missing from the manuscript.

(3) The temporal dimension leads directly into the third issue: distinguishing between drift and learning (e.g., line 56). In the neutral stimuli used in the experimental data, changes should either occur randomly (drift) or purposefully, as in a neutral environment, previous strategies do not yield a favorable outcome. For instance, the animal might initially employ strategy A, but if no improvement in the food situation occurs, it later adopts strategy B (learning). In changing environments, this distinction between drift and learning should be even more pronounced (e.g., if bananas are available, I prefer bananas; once they are gone, I either change my preference or face negative consequences). Alternatively, is my random choice of grapes the substrate for the learning process towards grapes in a changing environment? Further clarification is needed to resolve these potential conflicts.

Reviewer #2 (Public review):

Summary:

This is an inspired study that merges the concept of individuality with evolutionary processes to uncover a new strategy that diversifies individual behavior that is also potentially evolutionarily adaptive.

The authors use a time-resolved measurement of spontaneous, innate behavior, namely handedness or turn bias in individual, isogenic flies, across several genetic backgrounds.

They find that an individual's behavior changes over time, or drifts. This has been observed before, but what is interesting here is that by looking at multiple genotypes, the authors find the amount of drift is consistent within genotype i.e., genetically regulated, and thus not entirely stochastic. This is not in line with what is known about innate, spontaneous behaviors. Normally, fluctuations in behavior would be ascribed to a response to environmental noise. However, here, the authors go on to find what is the pattern or rule that determines the rate of change of the behavior over time within individuals. Using modeling of behavior and environment in the context of evolutionarily important timeframes such as lifespan or reproductive age, they could show when drift is favored over bet-hedging and that there is an evolutionary purpose to behavioral drift. Namely, drift diversifies behaviors across individuals of the same genotype within the timescale of lifespan, so that the genotype's chance for expressing beneficial behavior is optimally matched with potential variation of environment experienced prior to reproduction. This ultimately increases the fitness of the genotype. Because they find that behavioral drift is genetically variable, they argue it can also evolve.

Strengths:

Unlike most studies of individuality, in this study, the authors consider the impact of individuality on evolution. This is enabled by the use of multiple natural genetic backgrounds and an appropriately large number of individuals to come to the conclusions presented in the study. I thought it was really creative to study how individual behavior evolves over multiple timescales. And indeed this approach yielded interesting and important insight into individuality. Unlike most studies so far, this one highlights that behavioral individuality is not a static property of an individual, but it dynamically changes. Also, placing these findings in the evolutionary context was beneficial. The conclusion that individual drift and bet-hedging are differently favored over different timescales is, I think, a significant and exciting finding.

Overall, I think this study highlights how little we know about the fundamental, general concepts behind individuality and why behavioral individuality is an important trait. They also show that with simple but elegant behavioral experiments and appropriate modeling, we could uncover fundamental rules underlying the emergence of individual behavior. These rules may not at all be apparent using classical approaches to studying individuality, using individual variation within a single genotype or within a single timeframe.

Weaknesses:

I am unconvinced by the claim that serotonin neuron circuits regulate behavioral drift, especially because of its bidirectional effect and lack of relative results for other neuromodulators. Without testing other neuromodulators, it will remain unclear if serotonin intervention increases behavioral noise within individuals, or if any other pharmacological or genetic intervention would do the same. Another issue is that the amount of drugs that the individuals ingested was not tracked. Variable amounts can result in variable changes in behavior that are more consistent with the interpretation of environmental plasticity, rather than behavioral drift. With the current evidence presented, individual behavior may change upon serotonin perturbation, but this does not necessarily mean that it changes or regulates drift.

However, I think for the scope of this study, finding out whether serotonin regulates drift or not is less important. I understand that today there is a strong push to find molecular and circuit mechanisms of any behavior, and other peers may have asked for such experiments, perhaps even simply out of habit. Fortunately, the main conclusions derived from behavioral data across multiple genetic backgrounds and the modeling are anyway novel, interesting, and in fact more fundamental than showing if it is serotonin that does it or not.

To this point, one thing that was unclear from the methods section is whether genotypes that were tested were raised in replicate vials and how was replication accounted for in the analyses. This is a crucial point - the conclusion that genotypes have different amounts of behavioral drift cannot be drawn without showing that the difference in behavioral drift does not stem from differences in developmental environment.

Reviewer #3 (Public review):

Summary:

The paper begins by analyzing the drift in individual behavior over time. Specifically, it quantifies the circling direction of freely walking flies in an arena. The main takeaway from this dataset is that while flies exhibit an individual turning bias (when averaged over time), their preferences fluctuate over slow timescales.

To understand whether genetic or neuromodulatory mechanisms influence the drift in individual preference, the authors test different fly strains concluding that both genetic background and the neuromodulator serotonin contribute to the degree of drift.

Finally, the authors use theoretical approaches to identify the range of environmental conditions under which drift in individual bias supports population growth.

Strengths:

The model provides a clear prediction of the environmental fluctuations under which a drift in bias should be beneficial for population growth.

The approach attempts to identify genetic and neurophysiological mechanisms underlying drift in bias.

Weaknesses:

Different behavioral assays are used and are differently analysed, with little discussion on how these behaviors and analyses compare to each other.

Some of the model assumptions should be made more explicit to better understand which aspects of the behaviors are covered.

Reviewer #1 (Public review):

Zhu and colleagues used high-density Neuropixel probes to perform laminar recordings in V1 while presenting either small stimuli that stimulated the classical receptive field (CRF) or large stimuli whose border straddled the RF to provide nonclassical RF (nCRF) stimulation. Their main question was to understand the relative contribution of feedforward (FF), feedback (FB), and horizontal circuits to border ownership (Bown), which they addressed by measuring cross-correlation across layers. They found differences in cross-correlation between feedback/horizontal (FH) and input layers during CRF and nCRF stimulation.

Although the data looks high quality and analyses look mostly fine, I had a lot of difficulty understanding the logic in many places. Examples of my concerns are written below.

(1) What is the main question? The authors refer to nCRF stimulation emerging from either feedback from higher areas or horizontal connections from within the same area (e.g. lines 136 to 138 and again lines 223-232). I initially thought that the study would aim to distinguish between the two. However, the way the authors have clubbed the layers in 3D, the main question seems to be whether Bown is FF or FH (i.e., feedback and horizontal are clubbed). Is this correct? If so, I don't see the logic, since I can't imagine Bown to be purely FF. Thus, just showing differences between CRF stimulation (which is mainly expected to be FF) and nCRF stimulation is not surprising to me.

(2) Choice of layers for cross-correlation analysis: In the Introduction, and also in Figure 3C, it is mentioned that FF inputs arrive in 4C and 6, while FB/Horizontal inputs arrive at "superficial" and "deep", which I take as layer 2/3 and 5. So it is not clear to me why (i) layer 4A/B is chosen for analysis for Figure 3D (I would have thought layer 6 should have been chosen instead) and (ii) why Layers 5 and 6 are clubbed.

(3) Addressing the main question using cross-correlation analysis: I think the nice peaks observed in Figure 3B for some pairs show how spiking in one neuron affects the spiking in another one, with the delay in cross-correlation function arising from the conduction delay. This is shown nicely during CRF stimulation in Figure 3D between 4C -> 2/3, for example. However, the delay (positive or negative) is constrained by anatomical connectivity. For example, unless there are projections from 2/3 back to 4C which causes firing in a 2/3 layer neuron to cause a spike in a layer 4 neuron, we cannot expect to get a negative delay no matter what kind of stimulation (CRF versus nCRF) is used.

Reviewer #2 (Public review):

Summary:

The authors present a study of how modulatory activity from outside the classical receptive field (cRF) differs from cRF stimulation. They study neural activity across the different layers of V1 in two anesthetized monkeys using Neuropixels probes. The monkeys are presented with drifting gratings and border-ownership tuning stimuli. They find that border-ownership tuning is organized into columns within V1, which is unexpected and exciting, and that the flow of activity from cell-to-cell (as judged by cross-correlograms between single units) is influenced by the type of visual stimulus: border-ownership tuning stimuli vs. drifting-grating stimuli.

Strengths:

The questions addressed by the study are of high interest, and the use of Neuropixels probes yields extremely high numbers of single-units and cross-correlation histograms (CCHs) which makes the results robust. The study is well-described.

Weaknesses:

The weaknesses of the study are (a) the use of anesthetized animals, which raises questions about the nature of the modulatory signal being measured and the underlying logic of why a change in visual stimulus would produce a reversal in information flow through the cortical microcircuit and (b) the choice of visual stimuli, which do not uniquely isolate feedforward from feedback influences.

(1) The modulation latency seems quite short in Figure 2C. Have the authors measured the latency of the effect in the manuscript and how it compares to the onset of the visually driven response? It would be surprising if the latency was much shorter than 70ms given previous measurements of BO and figure-ground modulation latency in V2 and V1. On the same note, it might be revealing to make laminar profiles of the modulation (i.e. preferred - non-preferred border orientation) as it develops over time. Does the modulation start in feedback recipient layers?

(2) Can the authors show the average time course of the response elicited by preferred and non-preferred border ownership stimuli across all significant neurons?

(3) The logic of assuming that cRF stimulation should produce the opposite signal flow to border-ownership tuning stimuli is worth discussing. I suspect the key difference between stimuli is that they used drifting gratings as the cRF stimulus, the movement of the stimulus continually refreshes the retinal image, leading to continuous feedforward dominance of the signals in V1. Had they used a static grating, the spiking during the sustained portion of the response might also show more influence of feedback/horizontal connections. Do the initial spikes fired in response to the border-ownership tuning stimuli show the feedforward pattern of responses? The authors state that they did not look at cross-correlations during the initial response, but if they do, do they see the feedforward-dominated pattern? The jitter CCH analysis might suffice in correcting for the response transient.

(4) The term "nCRF stimulation" is not appropriate because the CRF is stimulated by the light/dark edge.

Reviewer #3 (Public review):

Summary:

The paper by Zhu et al is on an important topic in visual neuroscience, the emergence in the visual cortex of signals about figures and ground. This topic also goes by the name border ownership. The paper utilizes modern recording techniques very skillfully to extend what is known about border ownership. It offers new evidence about the prevalence of border ownership signals across different cortical layers in V1 cortex. Also, it uses pairwise cross-correlation to study signal flow under different conditions of visual stimulation that include the border ownership paradigm.

Strengths:

The paper's strengths are its use of multi-electrode probes to study border ownership in many neurons simultaneously across the cortical layers in V1, and its innovation of using cross-correlation between cortical neurons -- when they are viewing border-ownership patterns or instead are viewing grating patterns restricted to the classical receptive field (CRF).

Weaknesses:

The paper's weaknesses are its largely incremental approach to the study of border ownership and the lack of a critical analysis of the cross-correlation data. The paper as it is now does not advance our understanding of border ownership; it mainly confirms prior work, and it does not challenge or revise consensus beliefs about mechanisms. However, it is possible that, in the rich dataset the authors have obtained, they do possess data that could be added to the paper to make it much stronger.

Critique:

The border ownership data on V1 offered in the paper replicates experimental results obtained by Zhou and von der Heydt (2000) and confirms the earlier results using the same analysis methods as Zhou. The incremental addition is that the authors found border ownership in all cortical layers extending Zhou's results that were only about layer 2/3.

The cross-correlation results show that the pattern of the cross-correlogram (CCG) is influenced by the visual pattern being presented. However, the results are not analyzed mechanistically, and the interpretation is unclear. For instance, the authors show in Figure 3 (and in Figure S2) that the peak of the CCG can indicate layer 2/3 excites layer 4C when the visual stimulus is the border ownership test pattern, a large square 8 deg on a side. But how can layer 2/3 excite layer 4C? The authors do not raise or offer an answer to this question. Similar questions arise when considering the CCG of layer 4A/B with layer 2/3. What is the proposed pathway for layer 2/3 to excite 4A/B? Other similar questions arise for all the interlaminar CCG data that are presented. What known functional connections would account for the measured CCGs?

The problems in understanding the CCG data are indirectly caused by the lack of a critical analysis of what is happening in the responses that reveal the border ownership signals, as in Figure 2. Let's put it bluntly - are border ownership signals excitatory or inhibitory? The reason I raise this question is that the present authors insightfully place border ownership as examples of the action of the non-classical receptive field (nCRF) of cortical cells. Most previous work on the nCRF (many papers cited by the authors) reveal the nCRF to be inhibitory or suppressive. In order to know whether nCRF signals are excitatory or inhibitory, one needs a baseline response from the CRF, so that when you introduce nCRF signals you can tell whether the change with respect to the CRF is up or down. As far as I know, prior work on border ownership has not addressed this question, and the present paper doesn't either. This is where the rich dataset that the present authors possess might be used to establish a fundamental property of border ownership.

Then we must go back to consider what the consequences of knowing the sign of the border ownership signal would mean for interpreting the CCG data. If the border ownership signals from extrastriate feedback or, alternatively, from horizontal intrinsic connections, are excitatory, they might provide a shared excitatory input to pairs of cells that would show up in the CCG as a peak at 0 delay. However, if the border ownership manuscript signals are inhibitory, they might work by exciting only inhibitory neurons in V1. This could have complicated consequences for the CCG. The interpretation of the CCG data in the present version of the m is unclear (see above). Perhaps a clearer interpretation could be developed once the authors know better what the border ownership signals are.

My critique of the CCG analysis applies to Figure 5 also. I cannot comprehend the point of showing a very weak correlation of CCG asymmetry with Border Ownership Index, especially when what CCG asymmetry means is unclear mechanistically. Figure 5 does not make the paper stronger in my opinion.

In Figure 3, the authors show two CCGs that involve 4C--4C pairs. It would be nice to know more about such pairs. If there are any 6--6 pairs, what they look like also would be interesting. The authors also in Figure 3 show CCG's of two 4C--4A/B pairs and it would be quite interesting to know how such CCGs behave when CRF and nCRF stimuli are compared. In other words, the authors have shown us they have many data but have chosen not to analyze them further or to explain why they chose not to analyze them. It might help the paper if the authors would present all the CCG types they have. This suggestion would be helpful when the authors know more about the sign of border ownership signals, as discussed at length above.

Reviewer #1 (Public review):

The paper by Fournier et al. investigates the sensitivity of neural circuits to changes in intrinsic and synaptic conductances. The authors use models of the stomatogastric ganglion (STG) to compare how perturbations to intrinsic and synaptic parameters impact network robustness. Their main finding is that changes to intrinsic conductances tend to have a larger impact on network function than changes to synaptic conductances, suggesting that intrinsic parameters are more critical for maintaining circuit function.

The paper is well-written and the results are compelling, but I have several concerns that need to be addressed to strengthen the manuscript. Specifically, I have two main concerns:<br /> (1) It is not clear from the paper what the mechanism is that leads to the importance of intrinsic parameters over synaptic parameters.<br /> (2) It is not clear how general the result is, both within the framework of the STG network and its function, and across other functions and networks. This is crucial, as the title of the paper appears very general.

I believe these two elements are missing in the current manuscript, and addressing them would significantly strengthen the conclusions. Without a clear understanding of the mechanism, it is difficult to determine whether the results are merely anecdotal or if they depend on specific details such as how the network is trained, the particular function being studied, or the circuit itself. Additionally, understanding how general the findings are is vital, especially since the authors claim in the title that "Circuit function is more robust to changes in synaptic than intrinsic conductances," which suggests a broad applicability.

I do not wish to discourage the authors from their interesting result, but the more we understand the mechanism and the generality of the findings, the more insightful the result will be for the neuroscience community.

Major comments

(1) Mechanism<br /> While the authors did a nice job of describing their results, they did not provide any mechanism for why synaptic parameters are more resilient to changes than intrinsic parameters. For example, from Figure 5, it seems that there is mainly a shift in the sensitivity curves. What is the source of this shift? Can something be changed in the network, the training, or the function to control it? This is just one possible way to investigate the mechanism, which is lacking in the paper.

(2) Generality of the results within the framework of the STG circuit<br /> (a) The authors did show that their results extend to multiple networks with different parameters (the 100 networks). However, I am still concerned about the generality of the results with respect to the way the models were trained. Could it be that something in the training procedure makes the synaptic parameters more robust than intrinsic parameters? For example, the fact that duty cycle error is weighted as it is in the cost function (large beta) could potentially affect the parameters that are more important for yielding low error on the duty cycle.<br /> (b) Related to (a), I can think of a training scheme that could potentially improve the resilience of the network to perturbations in the intrinsic parameters rather than the synaptic parameters. For example, in machine learning, methods like dropout can be used to make the network find solutions that are robust to changes in parameters. Thus, in principle, the results could change if the training procedure for fitting the models were different, or by using a different optimization algorithm. It would be helpful to at least mention this limitation in the discussion.

(3) Generality of the function<br /> The authors test their hypothesis based on the specific function of the STG. It would be valuable to see if their results generalize to other functions as well. For example, the authors could generate non-oscillatory activity in the STG circuit, or choose a different, artificial function, maybe with different duty cycles or network cycles. It could be that this is beyond the scope of this paper, but it would be very interesting to characterize which functions are more resilient to changes in synapses, rather than intrinsic parameters. In other words, the authors might consider testing their hypothesis on at least another 'function' and also discussing the generality of their results to other functions in the discussion.

(4) Generality of the circuit<br /> The authors have studied the STG for many years and are pioneers in their approach, demonstrating that there is redundancy even in this simple circuit. This approach is insightful, but it is important to show that similar conclusions also hold for more general network architectures, and if not, why. In other words, it is not clear if their claim generalizes to other network architectures, particularly larger networks. For example, one might expect that the number of parameters (synaptic vs intrinsic) might play a role in how resilient the function is with respect to changes in the two sets of parameters. In larger models, the number of synaptic parameters grows as the square of the number of neurons, while the number of intrinsic parameters increases only linearly with the number of neurons. Could that affect the authors' conclusions when we examine larger models?

In addition, how do the authors' conclusions depend on the "complexity" of the non-linear equations governing the intrinsic parameters? Would the same conclusions hold if the intrinsic parameters only consisted of fewer intrinsic parameters or simplified ion channels? All of these are interesting questions that the authors should at least address in the discussion.

Reviewer #2 (Public review):

Summary:

This manuscript presents an important exploration of how intrinsic and synaptic conductances affect the robustness of neural circuits. This is a well-deserved question, and overall, the manuscript is written well and has a logical progression.

The focus on intrinsic plasticity as a potentially overlooked factor in network dynamics is valuable. However, while the stomatogastric ganglion (STG) serves as a well-characterized and valuable model for studying network dynamics, its simplified structure and specific dynamics limit the generalizability of these findings to more complex systems, such as mammalian cortical microcircuits.

Strengths:<br /> Clean and simple model. Simulations are carefully carried out and parameter space is searched exhaustively.

Weaknesses:

(1) Scope and Generalizability:<br /> The study's emphasis on intrinsic conductance is timely, but with its minimalistic and unique dynamics, the STG model poses challenges when attempting to generalize findings to other neural systems. This raises questions regarding the applicability of the results to more complex circuits, especially those found in mammalian brains and those where the dynamics are not necessarily oscillating. This is even more so (as the authors mention) because synaptic conductances in this study are inhibitory, and changes to their synaptic conductances are limited (as the driving force for the current is relatively low).

(2) Challenges in Comparison:<br /> A significant challenge in the study is the comparison method used to evaluate the robustness of intrinsic versus synaptic perturbations. Perturbations to intrinsic conductances often drastically affect individual neurons' dynamics, as seen in Figure 1, where such changes result in single spikes or even the absence of spikes instead of the expected bursting behavior. This affects the input to downstream neurons, leading to circuit breakdowns. For a fair comparison, it would be essential to constrain the intrinsic perturbations so that each neuron remains within a particular functional range (e.g., maintaining a set number of spikes). This could be done by setting minimal behavioral criteria for neurons and testing how different perturbation limits impact circuit function.

(3) Comparative Metrics for Perturbation:<br /> Another notable issue lies in the evaluation metrics for intrinsic and synaptic perturbations. Synaptic perturbations are straightforward to quantify in terms of conductance, but intrinsic perturbations involve more complexity, as changes in maximal conductance result in variable, nonlinear effects depending on the gating states of ion channels. Furthermore, synaptic perturbations focus on individual conductances, while intrinsic perturbations involve multiple conductance changes simultaneously. To improve fairness in comparison, the authors could, for example, adjust the x-axis to reflect actual changes in conductance or scale the data post hoc based on the real impact of each perturbation on conductance. For example, in Figure 6, the scale of the panels of the intrinsic (e.g., g_na-bar) is x500 larger than the synaptic conductance (a row below), but the maximal conductance for sodium hits maybe for a brief moment during every spike and than most of the time it is close to null. Moreover, changing the sodium conductance over the range of 0-250 for such a nonlinear current is, in many ways, unthinkable, did you ever measure two neurons with such a difference in the sodium conductance? So, how can we tell that the ranges of the perturbations make a meaningful comparison?

Reviewer #1 (Public review):

Summary:

The paper proposes that the placement of criteria for determining whether a stimulus is 'seen' or 'unseen' can significantly impact the validity of neural measures of consciousness. The authors found that conservative criteria, which require stronger evidence to classify a stimulus as 'seen,' tend to inflate effect sizes in neural measures, making conscious processing appear more pronounced than it is. Conversely, liberal criteria, which require less evidence, reduce these effect sizes, potentially underestimating conscious processing. This variability in effect sizes due to criterion placement can lead to misleading conclusions about the nature of conscious and unconscious processing.

Furthermore, the study highlights that the Perceptual Awareness Scale (PAS), a commonly used tool in consciousness research, does not effectively mitigate these criterion-related confounds. This means that even with PAS, the validity of neural measures can still be compromised by how criteria are set. The authors emphasize the need for careful consideration and standardization of criterion placement in experimental designs to ensure that neural measures accurately reflect the underlying cognitive processes. By addressing this issue, the paper aims to improve the reliability and validity of findings in the field of consciousness research.

Strengths:

(1) This research provides a fresh perspective on how criterion placement can significantly impact the validity of neural measures in consciousness research.

(2) The study employs robust simulations and EEG experiments to demonstrate the effects of criterion placement, ensuring that the findings are well-supported by empirical evidence.

(3) By highlighting the limitations of the PAS and the impact of criterion placement, the study offers practical recommendations for improving experimental designs in consciousness research.

Weaknesses:

The primary focused criterion of PAS is a commonly used tool, but there are other measures of consciousness that were not evaluated, which might also be subject to similar or different criterion limitations. A simulation could applied to these metrics to show how generalizable the conclusion of the study is.

Reviewer #2 (Public review):

Summary:

The study investigates the potential influence of the response criterion on neural decoding accuracy in consciousness and unconsciousness, utilizing either simulated data or reanalyzing experimental data with post-hoc sorting data.

Strengths:

When comparing the neural decoding performance of Target versus NonTarget with or without post-hoc sorting based on subject reports, it is evident that response criterion can influence the results. This was observed in simulated data as well as in two experiments that manipulated the subject response criterion to be either more liberal or more conservative. One experiment involved a two-level response (seen vs unseen), while the other included a more detailed four-level response (ranging from 0 for no experience to 3 for a clear experience). The findings consistently indicated that adopting a more conservative response criterion could enhance neural decoding performance, whether in conscious or unconscious states, depending on the sensitivity or overall response threshold.

Weaknesses:

(1) The response criterion plays a crucial role in influencing neural decoding because a subject's report may not always align with the actual stimulus presented. This discrepancy can occur in cases of false alarms, where a subject reports seeing a target that was not actually there, or in cases where a target is present but not reported. Some may argue that only using data from consistent trials (those with correct responses) would not be affected by the response criterion. However, the authors' analysis suggests that a conservative response criterion not only reduces false alarms but also impacts hit rates. It is important for the authors to further investigate how the response criterion affects neural decoding even when considering only correct trials.

(2) The author has utilized decoding target vs. nontarget as the neural measures of unconscious and/or conscious processing. However, it is important to note that this is just one of the many neural measures used in the field. There are an increasing number of studies that focus on decoding the conscious content, such as target location or target category. If the author were to include results on decoding target orientation and how it may be influenced by response criterion, the field would greatly benefit from this paper.

Reviewer #3 (Public review):

Summary:

Fahrenfort et al. investigate how liberal or conservative criterion placement in a detection task affects the construct validity of neural measures of unconscious cognition and conscious processing. Participants identified instances of "seen" or "unseen" in a detection task, a method known as post hoc sorting. Simulation data convincingly demonstrate that, counterintuitively, a conservative criterion inflates effect sizes of neural measures compared to a liberal criterion. While the impact of criterion shifts on effect size is suggested by signal detection theory, this study is the first to address this explicitly within the consciousness literature. Decoding analysis of data from two EEG experiments further shows that different criteria lead to differential effects on classifier performance in post hoc sorting. The findings underscore the pervasive influence of experimental design and participants report on neural measures of consciousness, revealing that criterion placement poses a critical challenge for researchers.

Strengths and Weaknesses:

One of the strengths of this study is the inclusion of the Perceptual Awareness Scale (PAS), which allows participants to provide more nuanced responses regarding their perceptual experiences. This approach ensures that responses at the lowest awareness level (selection 0) are made only when trials are genuinely unseen. This methodological choice is important as it helps prevent the overestimation of unconscious processing, enhancing the validity of the findings.

A potential area for improvement in this study is the use of single time-points from peak decoding accuracy to generate current source density topography maps. While we recognize that the decoding analysis employed here differs from traditional ERP approaches, the robustness of the findings could be enhanced by exploring current source density over relevant time windows. Event-related peaks, both in terms of timing and amplitude, can sometimes be influenced by noise or variability in trial-averaged EEG data, and a time-window analysis might provide a more comprehensive and stable representation of the underlying neural dynamics.

It is helpful that the authors show the standard error of the mean for the classifier performance over time. A similar indication of a measure of variance in other figures could improve clarity and transparency.<br /> That said, the paper appears solid regarding technical issues overall. The authors also do a commendable job in the discussion by addressing alternative paradigms, such as wagering paradigms, as a possible remedy to the criterion problem (Peters & Lau, 2015; Dienes & Seth, 2010). Their consideration of these alternatives provides a balanced view and strengthens the overall discussion.

Impact of the Work:

This study effectively demonstrates a phenomenon that has been largely unexplored within the consciousness literature. Subjective measures may not reliably capture the construct they aim to measure due to criterion confounds. Future research on neural measures of consciousness should account for this issue, and no-report measures may be necessary until the criterion problem is resolved.

Reviewer #1 (Public review):

Summary:

Urination requires precise coordination between the bladder and external urethral sphincter (EUS), while the neural substrates controlling this coordination remain poorly understood. In this study, Li et al. identify estrogen receptor 1-expressing neurons (ESR1+) in Barrington's nucleus as key regulators that faithfully initiate or suspend urination. Results from peripheral nerve lesions suggest that BarEsr1 neurons play independent roles in controlling bladder contraction and relaxation of the EUS. Finally, the authors performed region-specific retrograde tracing, claiming that distinct populations of BarEsr1 neurons target specific spinal nuclei involved in regulating the bladder and EUS, respectively.

Strength:

Overall, the work is of high quality. The authors integrate several cutting-edge technologies and sophisticated, thorough analyses, including opto-tagged single unit recordings, combined optogenetics, and urodynamics, particularly those following distinct peripheral nerve lesions.

Weakness:

(1) My major concern is the novelty of this study. Keller et al. 2018 have shown that BarEsr1 neurons are active during urination and play an essential role in relaxing the external urethral sphincter (EUS). Minimally, substantial content that merely confirms previous findings (e.g. Figures 1A-E; Figures 3A-E) should be move to the supplementary datasets.

(2) I also have concerns regarding the results showing that the inactivation of BarEsr1 neurons led to the cessation of EUS muscle firing (Figures 2G and S5C). As shown in the cartoon illustration of Figure 8, spinal projections of BarEsr1 neurons contact interneurons (presumably inhibitory) that innervate motor neurons, which in turn excite the EUS. I would therefore expect that the inactivation of BarEsr1 should shift the EUS firing pattern from phasic (as relaxation) to tonic (removal of relaxation), rather than stopping their firing entirely. Could the authors comment on this and provide potential reasons or mechanisms for this finding?

(3) Current evidence is insufficient to support the claim that the majority of BarEsr1 neurons innervate the SPN but not DGC. The current spinal images are uninformative, as the fluorescence reflects the distribution of Esr1- or Crh-expressing neurons in the spinal cord, along with descending BarEsr1 or BarCrh axons. Given the close anatomical proximity of these two nuclei, a more thorough histological analysis is required to demonstrate that the spinal injections were accurately confined to either the SPN or the DGC.

Reviewer #2 (Public review):

Summary:

The authors have performed a rigorous study to assess the role of ESR1+ neurons in the PMC to control the coordination of bladder and sphincter muscles during urination. This is an important extension of previous work defining the role of these brainstem neurons, and convincingly adds to the understanding of their role as master regulators of urination. This is a thorough, well-done study that clarifies how the Pontine micturition center coordinates different muscle groups for efficient urination, but there are some questions and considerations that remain.

Strengths:

These data are thorough and convincing in showing that ESR1+ PMC neurons exert coordinated control over both the bladder and sphincter activity, which is essential for efficient urination. The anatomical distinctions in pelvic versus pudendal control are clear, and it's an advance to understand how this coordination occurs. This work offers a clearer picture of how micturition is driven.

Weaknesses:

The dynamics of how this population of ESR1+ neurons is engaged in natural urination events remains unclear. Not all ESR1+neurons are always engaged, and it is not measured whether this is simply variation in population activity, or if more neurons are engaged during more intense starting bladder pressures, for instance. In particular, the response dynamics of single and doubly-projecting neurons are not defined. Additionally, the model for how these neurons coordinate with CRH+ neuron activity in the PMC is not addressed, although these cell types seem to be engaged at the same time. Lastly, it would be interesting to know how sensory input can likely modulate the activity of these neurons, but this is perhaps a future direction.

Reviewer #3 (Public review):

Summary:

The paper by Li et al explored the role of Estrogen receptor 1 (Esr1) expressing neurons in the pontine micturition center (PMC), a brainstem region also known as Barrington's nucleus (Hou et al 2016, Keller et al 2018). First, the author conducted bulk Ca2+ imaging/unit recording from PMCESR1 to investigate the correlations of PMCESR1 neural activity to voiding behavior in conscious mice and bladder pressure/external urethral muscle activity in urethane anesthetized mice. Next, the authors conducted optogenetics inactivation/activation of PMCESR1 to confirm the contribution to the voiding behavior also conducted peripheral nerve transection together with optogenetics activation to confirm the independent control of bladder pressure and urethral sphincter muscle.

Weaknesses:

(1) The study demonstrates that pelvic nerve transection reduces urinary volume triggered by PMCESR1+ cell photoactivation in freely moving mice. Could the role of pudendal nerve transection also be examined in awake mice to provide a more comprehensive understanding of neural involvement?

(2) While the paper primarily focuses on PMCESR1+ cells in bladder-sphincter coordination, the analysis of PMCESR1+-DGC/SPN neural circuits - given their distinct anatomical projections in the sacral spinal cord - feels underexplored. How do these circuits influence bladder and sphincter function when activated or inhibited? Also, do you have any tracing data to confirm whether bladder-sphincter innervation comes from distinct spinal nuclei?

(3) Although the paper successfully identifies the physiological role of PMCESR1+ cells in bladder-sphincter coordination, the study falls short in examining the electrophysiological properties of PMCESR1+-DGC/SPN cells. A deeper investigation here would strengthen the findings.

(4) The parameters for photoactivation (blue light pulses delivered at 25 Hz for 15 ms, every 30 s) and photoinhibition (pulses at 50 Hz for 20 ms) vary. What drove the selection of these specific parameters? Moreover, for photoactivation experiments, the change in pressure (ΔP = P5 sec - P0 sec) is calculated differently from photoinhibition (Δpressure = Ppeak - Pmin). Can you clarify the reasoning behind these differing approaches?

(5) The discussion could further emphasize how PMCESR1+ cells coordinate bladder contraction and sphincter relaxation to control urination, highlighting their central role in the initiation and suspension of this process.

(6) In Figure 8, The authors analyze the temporal sequence of bladder pressure and EUS bursting during natural voiding and PMC activation-induced voiding. It would be acceptable to consider the existence of a lower spinal reflex circuit, however, the interpretation of the data contains speculation. Bladder pressure measurement is hard to say reflecting efferent pelvic nerve activity in real time. (As a biological system, bladder contraction is mediated by smooth muscle, and does not reflect real-time efferent pelvic nerve activity. As an experimental set-up, bladder pressure measurement has some delays to reflect bladder pressure because of tubing, but EUS bursting has no delay.) Especially for the inactivation experiment, these factors would contribute to the interpretation of data. This reviewer recommends a rewrite of the section considering these limitations. Most of the section is suitable for the results.

Reviewer #1 (Public Review):

Summary:

The study aimed to better understand the role of the H3 protein of the Monkeypox virus (MPXV) in host cell adhesion, identifying a crucial α-helical domain for interaction with heparan sulfate (HS). Using a combination of advanced computational simulations and experimental validations, the authors discovered that this domain is essential for viral adhesion and potentially a new target for developing antiviral therapies.

Strengths:

The study's main strengths include the use of cutting-edge computational tools such as AlphaFold2 and molecular dynamics simulations, combined with robust experimental techniques like single-molecule force spectroscopy and flow cytometry. These methods provided a detailed and reliable view of the interactions between the H3 protein and HS. The study also highlighted the importance of the α-helical domain's electric charge and the influence of the Mg(II) ion in stabilizing this interaction. The work's impact on the field is significant, offering new perspectives for developing antiviral treatments for MPXV and potentially other viruses with similar adhesion mechanisms. The provided methods and data are highly useful for researchers working with viral proteins and protein-polysaccharide interactions, offering a solid foundation for future investigations and therapeutic innovations.

Comments on revised version:

The authors have successfully addressed the questions raised in my review.

Reviewer #2 (Public Review):

Summary:

The manuscript presenting the discovery of a heparan-sulfate (HS) binding domain in monkeypox virus (MPXV) H3 protein as a new anti-poxviral drug target, presented by Bin Zhen and co-workers, is of interest, given that it offers a potentially broad antiviral substance to be used against poxviruses. Using new computational biology techniques, the authors identified a new alpha-helical domain in the H3 protein, which interacts with cell surface HS, and this domain seems to be crucial for H3-HS interaction. Given that this domain is conserved across orthopoxviruses, authors designed protein inhibitors. One of these inhibitors, AI-PoxBlock723, effectively disrupted the H3-HS interaction and inhibited infection with Monkeypox virus and Vaccinia virus. The presented data should be of interest to a diverse audience, given the possibility of an effective anti-poxviral drug.

Strengths:

In my opinion, the experiments done in this work were well-planned and executed. The authors put together several computational methods, to design poxvirus inhibitor molecules, and then they test these molecules for infection inhibition.

Comments on revised version:

The authors have addressed the comments I made in my review.

Reviewer #3 (Public Review):

Summary:

The article is an interesting approach to determining the MPOX receptor using "in silico" tools. The results show the presence of two regions of the H3 protein with a high probability of being involved in the interaction with the HS cell receptor. However, the α-helical region seems to be the most probable, since modifications in this region affect the virus binding to the HS receptor.

Strengths:

In my opinion, it is an informative article with interesting results, generated by a combination of "in silico" and wet science to test the theoretical results. This is a strong point of the article.

Comments on revised version:

After a review of the changes to the manuscript and the author's responses, no further changes are needed.

Reviewer #1 (Public review):

This study of mixed glutamate/GABA transmission from axons of the supramammillary nucleus to dentate gyrus seeks to sort out whether the two transmitters are released from the same or different synaptic vesicles. This conundrum has been examined in other dual-transmission cases and even in this particular pathway there are different views. The authors use a variety of electrophysiological and immunohistochemical methods to reach the surprising (to me) conclusion that glutamate and GABA filled vesicles are distinct yet released from the same nerve terminals. While the strength of the conclusion rests on the abundance of data (approaches) rather than the decisiveness of any one approach, I came away believing that the boutons may indeed produce and release distinct types of vesicles. Accepting the conclusion, one is now left with another conundrum: how can a single bouton sort out VGLUTs and VIAATs to different vesicles, position them in distinct locations with nm precision and recycle them without mixing? And why do it this way instead of with single vesicles having mixed chemical content? For example, could a quantitative argument be made that separate vesicles allow for higher transmitter concentrations? Hopefully, future studies will probe these issues.

Reviewer #2 (Public review):

Summary:

In this study, the authors investigated the release properties of glutamate/GABA co-transmission at the supramammillary nucleus (SuM)-dentate granule cell (DGC) synapses using state -of-the-arts in vitro electrophysiology and anatomical approaches at the light and electron microscopy level. They found that SuM to dentate granule cell synapses, which co-release glutamate and GABA, exhibit distinct differences in paired-pulse ratio, Ca2+ sensitivity, presynaptic receptor modulation, and Ca2+ channel-vesicle coupling configuration for each neurotransmitter. The study shows that glutamate/GABA co-release produces independent glutamatergic and GABAergic synaptic responses, with postsynaptic targets segregated. They show that most SuM boutons form distinct glutamatergic and GABAergic synapses at proximity, characterized by GluN1 and GABAAα1 receptor labeling respectively. Furthermore, they demonstrate that glutamate/GABA co-transmission exhibits distinct short-term plasticity, with glutamate showing frequency-dependent depression and GABA showing frequency-independent stable depression. The authors provide compelling evidence at the anatomical and physiological levels that glutamate and GABA are co-release by different synaptic vesicles within the same synaptic terminal at the SuM-DGC synapses and that the distinct transmission modes of the glutamate and GABA release serve as a frequency-dependent filters of SuM inputs on GC outputs.<br /> This is a fundamental work, that significantly advances our understanding of the mechanism by which the two fast-acting and functionally opposing neurotransmitters glutamate and GABA are co-transmitted at the SuM-DGC synapses and the functional role of this type of Glutamate/GABA co-transmission.

Strengths:

The conclusions of this paper are provided by a large number of compelling data

Reviewer #3 (Public review):

Summary:

In this manuscript, Hirai et al investigated the release properties of glutamate/GABA co-transmission at SuM-GC synapses and reported that glutamate/GABA co-transmission exhibits distinct short-term plasticity with segregated postsynaptic targets. Using optogenetics, whole-cell patch-clamp recordings, and immunohistochemistry, the authors reveal distinct transmission modes of glutamate/GABA co-release as frequency-dependent filters of incoming SuM inputs.

Strengths:

Overall, this study is well-designed and executed; conclusions are supported by the results. This study addressed a long-standing question of whether GABA and glutamate are packaged in the same vesicles and co-released in response to the same stimuli in the SuM-GC synapses (Pedersen et al., 2017; Hashimotodani et al., 2018; Billwiller et al., 2020; Chen et al., 2020; Li et al., 2020; Ajibola et al., 2021). Knowledge gained from this study advances our understanding of neurotransmitter co-release mechanisms and their functional roles in the hippocampal circuits.

Comments on revisions:

The authors have addressed my comments, and now the manuscript is in a good form as it currently stands.

Reviewer #1 (Public review):

This paper reports fossil soft-tissue structures (tail vanes) of pterosaurs, and attempts to relate this to flight performance and other proposed functions for the tail

The paper presents new evidence for soft-tissue strengthening of vanes using exciting new methods.

There is now some discussion of bias in the sample selection method as well as some theory to show how the lattice could have functioned, other than a narrative description.

Reviewer #1 (Public review):

Summary:

Lodhiya et al. demonstrate that antibiotics with distinct mechanisms of action, norfloxacin and streptomycin, cause similar metabolic dysfunction in the model organism Mycobacterium smegmatis. This includes enhanced flux through the TCA cycle and respiration as well as a build-up of reactive oxygen species (ROS) and ATP. Genetic and/or pharmacologic depression of ROS or ATP levels protect M. smegmatis from norfloxacin and streptomycin killing. Because ATP depression is protective, but in some cases does not depress ROS, the authors surmise that excessive ATP is the primary mechanism by which norfloxacin and streptomycin kill M. smegmatis. In general, the experiments are carefully executed; alternative hypotheses are discussed and considered; the data are contextualized within the existing literature.

Strengths:

The authors tackle a problem that is both biologically interesting and medically impactful, namely, the mechanism of antibiotic-induced cell death.

Experiments are carefully executed, for example, numerous dose- and time-dependency studies; multiple, orthogonal readouts for ROS; and several methods for pharmacological and genetic depletion of ATP.

There has been a lot of excitement and controversy in the field, and the authors do a nice job of situating their work in this larger context.

Inherent limitations to some of their approaches are acknowledged and discussed e.g., normalizing ATP levels to viable counts of bacteria.

Weaknesses:

All of the experiments performed here were in the model organism M. smegmatis. As the authors point out, the extent to which these findings apply to other organisms (most notably, slow-growing pathogens like M. tuberculosis) is to be determined. To avoid the perception of overreach, I would recommend substituting "M. smegmatis" for Mycobacteria (especially in the title and abstract).

At first glance, a few of the results in the manuscript seem to conflict with what has been previously reported in the (referenced) literature. In their response to reviewers, the authors addressed my concerns. It would also be ideal to include a few lines in the manuscript briefly addressing these points. (Other readers may have similar concerns)

In the first round of review, I suggested that the authors consider removing Figs. 9 and 10A-B as I believe they distract from the main point of the paper and appear to be the beginning of a new story rather than the end of the current one. I still hold this opinion. However, one of the strengths of the eLife model is that we can agree to disagree.

Reviewer #2 (Public review):

Summary:

The authors are trying to test the hypothesis that ATP bursts are the predominant driver of antibiotic lethality of Mycobacteria

Strengths:

No significant strengths in the current state as it is written.

Weaknesses:

A major weakness is that M. smegmatis has a doubling time of three hours and the authors are trying to conclude that their data would reflect the physiology of M. tuberculossi that has a doubling time of 24 hours. Moreover, the authors try to compare OD measurements with CFU counts and thus observe great variabilities.

Comments on revisions:

I am surprised that the authors simply did not repeat the study in figure one with CFU counts and repeated in triplicate. Since this is M. smegmatis, it would take no longer than two weeks to repeat this experiment and replace the figure. I understand that obtaining CFU counts is much more laborious than OD measurements but it is necessary. Your graph still says that there is 0 bacteria at time 0, yet in your legend it says you started with 600,000 CFU/ml. I don't understand why this experiment was not repeated with CFU counts measured throughout. This is not a big ask since this is M. smegmatis but it appears that the authors do not want to repeat this experiment. Minimally, fix the graph to represent the CFU.

Reviewer #1 (Public review):

Summary:

This is a very nice paper in which the authors addressed the potential for NK cell cellular therapy to treat and potentially eliminate previously established metastases after surgical resections, which are a major cause of death in human cancer patients. To do so they developed a model using the EO771 breast cancer cell line, in which they establish and then resect tumors and the draining lymph node, after which the majority of mice eventually succumb to metastatic disease. They found that when the initiating tumors were resected when still relatively small, adoptive transfers of IL-15/12-conditioned NK cells substantially enhanced the survival of tumor-bearing animals. They then delved into the cellular mechanisms involved. Interestingly and somewhat unexpectedly, the therapeutic effect of the transferred NK cells was dependent on the host's CD8+ T cells. Accordingly, the NK cell therapy contributed to the formation of tumor-specific CD8+ T cells, which protected the recipient animals against tumor re-challenge and were effective in protecting mice from tumor formation when transferred to naive mice. Mechanistically, they used Ifng knockout NK cells to provide evidence that IFNgamma produced by the transferred NK cells was crucial for the accumulation and activation of DCs in the metastatic lung, including expression of CD86, CD40 and MHC genes. In turn, IFNgamma production by NK cells was essential for the induced accumulation of activated CD8 effector T cells and stem cell-like CD8 T cells in the metastatic lung. The authors then expanded their findings from the mouse model to a small clinical trial. They found that inoculations of IL-15/12-conditioned autologous NK cells in patients with various malignancies after resection was safe and showed signs of efficacy.

Strengths:

- Monitoring of long-term metastatic disease and survival after resection used in this paper is a physiological model that closely resembles clinical scenarios more than the animal models usually used, a great strength of the approach.<br /> - Previous literature focused on the notion that NK cells clear metastatic lesions directly, within a short period. The authors' use of a more relevant model and time frame revealed the previously unexplored T cell-dependent mechanism of action of infused NK cells for long-term control of metastatic diseases.<br /> - Also important, the paper provides solid evidence for the contribution of IFNgamma produced by NK cells for activation of dendritic cells and T cells. This is an interesting finding that provokes additional questions concerning the action of the interferon gamma in this context.<br /> - The results from the clinical trial in cancer patients based on the same type of IL-15/12-conditioned NK cell infusions, was encouraging with respect to safety and showed signals of efficacy, which support the translatability of the author's findings.

Future studies in this model could shed even more light on the mechanisms. The authors do not address whether the IL-12 in their cocktail is essential for the effects they see. Relatedly, it was of interest that despite the effectiveness of the transferred IL-15/IL-12 cultured NK cells, the cells failed to persist very long after transfer. Published studies have reported that so-called memory-like NK cells, which are pre-activated with a cocktail of IL-12, IL-18 and IL-15, persist much longer in lympho-depleted mice and patients than IL-2 cultured NK cells. It would be illuminating to compare these two types of NK cell products in the author's model system, and with, or without, lymphodepletion, to identify the critical parameters. If greater persistence occurred with the memory-like NK cell product, it is possible that the NK cells might provide greater benefit, including by directly targeting the tumor.

Reviewer #2 (Public review):

Summary:

The authors show convincing data that increasing NK cell function/frequency can reduce development and progression of metastatic disease after primary tumor resection.

Strengths:

The inclusion of a first-in-human trial highlighting some partial responses of metastatic patients treated with in vitro expanded NK cells is tantalising. It is difficult to perform trials in preventing further metastasis since the timelines are very protracted but more data like these highlighting a role for NK cells in improving local cDC1/T cells anti-tumor immunity will encourage deeper thinking around therapeutic approaches to target endogenous NK cells to achieve the same.

Weaknesses:

As always, more patient data would help increase confidence around the human relevance of the approach.

Comments on revisions:

The authors have addressed all my queries

Reviewer #1 (Public review):

Summary:

Previous work has shown that the evolutionarily-conserved division-orienting protein LGN/ Pins/ GPR1/2 (vertebrates/flies/nematodes) participates in division orientation across a variety of cell types, perhaps most importantly those that undergo asymmetric divisions (ACDs). Micromere formation in echinoids relies on asymmetric cell division at the 16-cell stage, and these authors previously demonstrated a role for the LGN/Pins homolog AGS (Activator of G-protein signaling) in that ACD process. Here they extend that work by investigating and exploiting the question of why echinoids but not other echinoderms form micromeres. Using an impressive combination of phylogenetics and molecular experiments, they determine that much of the difference in ACD and micromere formation in echinoids can be attributed to differences in the AGS C-terminus, in particular a GoLoco domain (GL1) that is missing in most other echinoderms. This work helps explain how AGS works and thereby enhances our understanding of a conserved player in division orientation.

Reviewer #2 (Public review):

This study from Dr. Emura and colleagues addresses the relevance of AGS3 mutations in the execution of asymmetric cell divisions promoting the formation of the micromere during sea-searching development. To this aim, the authors use quantitative imaging approaches to evaluate the localisation of AGS3 mutants truncated at the N-terminal region or at the C-terminal region, and correlate these distributions with the formation of micromere and correct development of embryos to the pluteus stage. The authors also analyse the capacity of these mutated proteins to rescue developmental defects observed upon AGS3 depletion by morpholino antisense nucleotides (MO). Collectively these experiments revealed that the C-terminus of AGS3, coding for four GoLoco motifs binding to cortical Gaphai proteins, is the molecular determinant for cortical localisation of AGS3 at the micromeres and correct pluteus development. Further genetic dissections and expression of chimeric AGS3 mutants carrying shuffled copies of the GoLoco motifs or four copies of the same motifs revealed that the position of GoLoco1 is essential for AGS3 functioning. To understand whether the AGS3-GoLoco1 evolved specifically to promote asymmetric cell divisions, the author analyse chimeric AGS3 variants in which they replaced the sea urchin GoLoco region with orthologs from other echinoids that do not form micromeres, or from Drosophila Pins or human LGN. These analyses corroborate the notion that the GoLoco1 position is crucial for asymmetric AGS3 functions. In the last part of the manuscript, the authors explore whether SpAGS3 interacts with the molecular machinery described to promote asymmetric cell division in eukaryotes, including Insc, NuMA, Par3 and Galphai, and show that all these proteins colocalize at the nascent micromere, together with the fate determinant Vasa. Collectively this evidence highlighted how evolutionarily selected AGS3 modifications are essential to sustain asymmetric divisions and specific developmental programs associated with them.

The manuscript addresses an interesting question and uses elegant genetic approaches associated with imaging analyses to elucidate the molecular mechanisms whereby AGS3 and spindle orientation proteins promote asymmetric divisions and specific developmental programs. This considered, it might be worth clarifying a few aspects of the reported findings.

(1) In some experimental settings, the presence of AGS3 mutants exacerbates the AGS3 deletion by MO (Fig. 4F). Can the author speculate on what can be the molecular explanation?

(2) Imaging analyses of Figure 4B-C suggest that the mutant AGS1111 does not localise at the vegetal cortex while AGS2222 does (Fig. 4C). However these mutants induce similar developmental defects (Fig. 4F) . What could be the reason?

(3) Figure 7 shows the crosstalk between AGS3 and other asymmetry players including NuMA. Vertebrate and Drosophila NuMA are ubiquitously present in tissues and localises to the spindle poles in mitosi. However in Figure 7A and 7E NuMA seems expressed only in a subset of sea urchin embryonic cells. Is this the case?

Reviewer #1 (Public review):

Summary:

The authors aimed to classify hepatocellular carcinoma (HCC) patients into distinct subtypes using a comprehensive multi-omics approach. They employed an innovative consensus clustering method that integrates multiple omics data types, including mRNA, lncRNA, miRNA, DNA methylation, and somatic mutations. The study further sought to validate these subtypes by developing prognostic models using machine learning algorithms and extending the findings through single-cell RNA sequencing (scRNA-seq) to explore the cellular mechanisms driving subtype-specific prognostic differences.

Strengths:

(1) Comprehensive Data Integration: The study's integration of various omics data provides a well-rounded view of the molecular characteristics underlying HCC. This multi-omics approach is a significant strength, as it allows for more accurate and detailed classification of cancer subtypes.

(2) Innovative Methodology: The use of a consensus clustering approach that combines results from 10 different clustering algorithms is a notable methodological advancement. This approach reduces the bias that can result from relying on a single clustering method, enhancing the robustness of the findings.

(3) Machine Learning-Based Prognostic Modeling: The authors rigorously apply a wide array of machine learning algorithms to develop and validate prognostic models, testing 101 different algorithm combinations. This comprehensive approach underscores the study's commitment to identifying the most predictive models, which is a considerable strength.

(4) Validation Across Multiple Cohorts: The external validation of findings in independent cohorts is a critical strength, as it increases the generalizability and reliability of the results. This step is essential for demonstrating the clinical relevance of the proposed subtypes and prognostic models.

Weaknesses:

(1) Inconsistent Storyline:<br /> Despite the extensive data mining and rigorous methodologies, the manuscript suffers from a lack of a coherent and consistent narrative. The transition between different sections, particularly from multi-omics data integration to single-cell validation, feels disjointed. A clearer articulation of how each analysis ties into the overall research question would improve the manuscript.

(2) Questionable Relevance of Immune Cell Activity Analysis:<br /> The evaluation of immune cell activities within the cancer cell model raises concerns about its meaningfulness. The methods used to assess immune function in the tumor microenvironment may not be fully appropriate, potentially limiting the insights gained from this part of the study.

(3) Incomplete Single-Cell RNA-Seq Validation:<br /> The validation of the findings using single-cell RNA-seq data appears insufficient to fully support the study's claims. While the authors make an effort to extend their findings to the single-cell level, the analysis lacks depth. A more comprehensive validation is necessary to substantiate the robustness of the identified subtypes.

(4) Figures and Visualizations:<br /> Several figures in the manuscript are missing necessary information, which affects the clarity of the results. For instance, the pathways in Figure 3A could be clustered to enhance interpretability, the blue bar in Figure 4A is unexplained, and Figure 4B is not discussed in the text. Additionally, the figure legend in Figure 7C lacks detail, and many figure descriptions merely repeat the captions without providing deeper insights.

(5) Appraisal of the Study's Aims and Results:<br /> The authors have set out to achieve an ambitious goal of classifying HCC patients into distinct prognostic subtypes and validating these findings through both bulk and single-cell analyses. While the methodologies employed are innovative and the data integration comprehensive, the study falls short of fully achieving its aims due to inconsistencies in the narrative and incomplete validation. The results partially support the conclusions, but the lack of coherence and depth in certain areas limits the overall impact of the study.

(6) Impact on the Field:<br /> If the identified weaknesses are addressed, this study has the potential to significantly impact the field of HCC research. The multi-omics approach combined with machine learning is a powerful framework that could set a new standard for cancer subtype classification. However, the current state of the manuscript leaves some uncertainty regarding the practical applicability of the findings, particularly in clinical settings.

(6) Additional Context<br /> For readers and researchers, this study offers a valuable look into the potential of integrating multi-omics data with machine learning to improve cancer classification and prognostication. However, readers should be aware of the noted weaknesses, particularly the need for more consistent narrative development and comprehensive validation of the methods. Addressing these issues could greatly enhance the study's utility and relevance to the community.

Reviewer #2 (Public review):

Summary:

Overall, this is a well-executed and insightful study. With some refinement to the presentation and a deeper exploration of the implications, the manuscript will make a significant contribution to the field of cancer genomics and personalized medicine.

Strengths:

The manuscript integrates multi-omics data with machine learning to address the significant heterogeneity of hepatocellular carcinoma (HCC). The use of multiple clustering algorithms and a consensus method strengthens the robustness of the findings. The study successfully develops a prognostic model with excellent predictive accuracy, validated across independent datasets. This adds considerable value to the field, particularly in providing individualized treatment strategies. The identification of two distinct liver cancer subtypes with different biological and metabolic characteristics is well-supported by the data, offering a promising direction for personalized medicine.

Weaknesses:

(1) Consider streamlining the presentation of methods, especially regarding the clustering algorithms and machine learning models. Readers may find it difficult to follow the exact process unless more clearly outlined.

(2) Some figures, such as the signaling pathways and heatmaps, are critical to understanding the study's findings. Ensure that all figures are high quality, easy to interpret, and adequately labeled. You may also want to highlight the key findings within the figure captions more explicitly.

(3) While the manuscript does compare its prognostic model to those previously published, the novelty of the findings could be emphasized more clearly. Discussing the potential limitations of the study (e.g., the reliance on computational models and small sample sizes for scRNA-seq) could strengthen the manuscript.

(4) The manuscript mentions that the data was split into training and validation datasets in a 1:1 ratio. How was the performance verified? Is there an independent test set?

(5) The role of the MIF signaling pathway in subtype differentiation is intriguing, but further mechanistic insights into how this pathway drives the differences between CS1 and CS2 could be discussed in more detail. If experimental evidence for this pathway exists in the literature, it should be mentioned.

(6) Some sentences are quite long and complex, which can affect readability. Breaking them down into shorter, clearer sentences would improve the flow.

Reviewer #1 (Public review):

Summary:

Debeuf et al. introduce a new, fast method for the selection of suitable T cell clones to generate TCR transgenic mice, a method claimed to outperform traditional hybridoma-based approaches. Clone selection is based on the assessment of the expansion and phenotype of cells specific for a known epitope following immune stimulation. The analysis is facilitated by a new software tool for TCR repertoire and function analysis termed DALI. This work also introduces a potentially invaluable TCR transgenic mouse line specific for SARS-CoV-2.

Strengths:

The newly introduced method proved successful in the quick generation of a TCR transgenic mouse line. Clone selection is based on more comprehensive phenotypical information than traditional methods, providing the opportunity for a more rational T-cell clone selection.

The study provides a software tool for TCR repertoire analysis and its linkage with function.

The findings entail general practical implications in the preclinical study of a potentially very broad range of infectious diseases or vaccination.

A novel SARS-CoV-2 spike-specific TCR transgenic mouse line was generated.

Weaknesses:

The authors present a novel method to develop TCR transgenic mice and overcome the limitations of the more traditional method based on hybridomas.

The authors indicate that they did not intend to directly compare their new method with the traditional hybridoma-based approach. However, such comparison becomes inevitable when the classical method is presented as suboptimal and an alternative approach is introduced to address its limitations. Nevertheless, the explanations provided in their rebuttal have helped clarify their position. The intention behind supplementary figure 1 is to illustrate that a clone that appears suitable using traditional assays may fail to produce a successful TCR transgenic line. This is a valid point that I think should be emphasized more clearly in the manuscript, as it highlights the limitations of the traditional method.

However, the main question that remains is whether the proposed new method will reliably resolve this issue. As previously noted, only one mouse line was generated (successfully) from a single candidate, and the method presented to generate their new TCR transgenic line starts from a more advanced point (a well characterized epitope is already known, and tetramers are available to preselect specific clones). Although this approach likely increases the chances of success, it also limits applicability.

The authors suggest that tetramers are not absolutely necessary to select a clone of interest. Testing this hypothesis would have added value to this manuscript, demonstrating the ability to rapidly generate new TCR transgenic lines in response to emerging pathogens, as outlined in the introduction. They propose that, in such cases, mice could be immunised and expanded clones retested for reactivity. However, it is unclear how this strategy differs from the classic method in increasing the chances of selecting an optimal clone.

Regarding the practical value and cost-effectiveness of extensive expression profiling for T cell clone selection, it remains unclear how well a clone chosen for specific traits will retain these features when developed into a TCR transgenic line, or what traits are ideal for different applications. T cell fate is plastic, and various parameters could influence marker expression.

Issues remain concerning the statistical analysis. Data are said to have been analyzed using both parametric and non-parametric tests. The described approach of performing a normality test followed by either parametric or non-parametric tests is not a correct method for statistical data analysis.

Reviewer #2 (Public review):

Summary:

The authors seek to use single-cell sequencing approaches to identify TCRs specific for the SARS CoV2 spike protein, select a candidate TCR for cloning and use it to construct a TCR transgenic mouse. The argument is that this process is less cumbersome than the classical approach, which involves the identification of antigen-reactive T cells in vitro and the construction of T cell hybridomas prior to TCR cloning. TCRs identified by single-cell sequencing that is already paired to transcriptomic data would more rapidly identify TCRs that are likely to contribute to a functional response. The authors successfully identify TCRs that have expanded in response to SARS CoV2 spike protein immunization, bind to MHC tetramers and express genes associated with functional response. They then select a TCR for cloning and construction of a transgenic mouse in order to test the response of resulting T cells in vivo following immunization with spike protein of coronavirus infection.

Strengths:

(1) The study provides proof of principle for the identification and characterization of TCRs based on single-cell sequencing data.

(2) The authors employ a recently developed software tool (DALI) that assists in linking transcriptomic data to individual clones.

(3) The authors successfully generate a TCR transgenic animal derived from the most promising T cell clone (CORSET8) using the TCR sequencing approach.

(4) The authors provide initial evidence that CORSET8 T cells undergo activation and proliferation in vivo in response to immunization or infection.

(5) Procedures are well-described and readily reproducible.

Weaknesses:

(1) The purpose of presenting a failed attempt to generate TCR transgenic mice using a traditional TCR hybridoma method is unclear. The reasons for the failure are uncertain, and the inclusion of this data does not really provide information on the likely success rate of the hybridoma vs single cell approach for TCR identification, as only a single example is provided for either.

(2) There is little information provided regarding the functional differentiation of the CORSET8 T cells following challenge in vivo, including expression of molecules associated with effector function, cytokine production, killing activity and formation of memory. The study would be strengthened by some evidence that CORSET8 T cells are successfully recapitulating the functional features of the endogenous immune response (beyond simply proliferating and expressing CD44). This information is important to evaluate whether the presented sequencing-based identification and selection of TCRs is likely to result in T-cell responses that replicate the criteria for selecting the TCR in the first place.

(3) While I find the argument reasonable that the approach presented here has a lot of likely advantages over traditional approaches for generating TCR transgenic animals, the use of TCR sequencing data to identify TCRs for study in variety of areas, including cancer immunotherapy and autoimmunity, is in broad use. While much of this work opts for alternative methods of TCR expression in primary T cells (i.e. CRISPR or retroviral approaches), the process of generating a TCR transgenic mouse from a cloned TCR is not in itself novel. It would be helpful if the authors could provide a more extensive discussion explaining the novelty of their approach for TCR identification in comparison to other more modern approaches, rather than only hybridoma generation.

Comments on revisions:

The authors have provided additional clarification on the comparisons between the presented method for TCR transgenic generation and the hybridoma method that is more commonly used and added additional verification of the functional response in vivo of T cells expressing the selected TCR. Overall, these additions enhance the evidence that the proposed methods are likely to identify TCRs with a strong immune activation profile and are a reasonable response to the first round of review.