Reviewer #1 (Public Review):

This paper by Zhuang and colleagues seeks to answer an important clinical question by trying to come up with novel predictive biomarkers to predict high-risk T1 colorectal cancers that are at risk for nodal involvement. The current clinical features may both miss patients who underwent local therapy and who should have gone on to have surgery and patients for whom surgery was done based on risk features but perhaps unnecessarily. Using a training and validation set, they developed a protein-based classifier with an AUC of 0.825 based on mass spec analyses and proteomic analyses of patients with and without LN importantly linking biological rationale to the proteomic discoveries.

In the training cohort, they took 105 candidate proteins reduced to 55, and did a validation in the training cohort first and then in two validation cohorts (one of which was prospective). They also looked at a 9 protein classifier which also performed well and furthermore looked at IHC for clinical ease.

Reviewer #2 (Public Review):

De Filippo et al. investigated the spatiotemporal dynamics of the ripples propagation in the hippocampus of head-fixed mice. By leveraging the LFP and the isolated units of an open dataset of 49 animals with ~6 Neuropixels probes in the longitudinal axis of the hippocampus, they found: first, that stronger ripples (>ninth decile of power) originated in the most septal pole of the hippocampus (medially, anatomically) tend to travel more (M to L) than more lateral ripples (closer to the temporal pole). Second, while strong ripples were mainly local, the authors found that they are most likely to be generated in the temporal pole of the hippocampus, from where they can travel with relatively small attenuation. Finally, they found that strong/septal ripples elicit high spiking activity along the entire mediolateral axis of the hippocampus. Longer/stronger ripples have been proposed to be important in situations with high memory load, and these analyses increase our understanding of their physiology and mechanisms of generation.

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

1) High amplitude ripples preferentially occur in distal CA1, and ripples can propagate at a higher degree on the proximo-distal than in the septo-temporal axis of the hippocampus (Kumar and Deshmuckh, 2020). Therefore, a proximo-distal bias in the Neuropixel positioning could explain part of the variance the authors report. Authors should consider (or control for) the proximodistal positioning of the electrodes.

2) In my opinion, the dynamics of the ripple-induced spiking activity for the events generated in the medial or lateral section of the hippocampus are very striking, more even considering that only a minority of the detected ripples are strong/long events (less than 5% in a familiar environment, Fernandez-Ruiz et al, 2019), while, according to the authors, majority of the ripples (grouped as 'common' by the authors) travel on the opposite direction (from the lateral section towards the septal pole, figure 2). Moreover, in the 50-120ms window, the most lateral positions (>3500um) seem to be more influenced by the medial ripples than relatively more central electrodes (~3000um). How can the authors explain this? To understand a little bit more how ripple features relate to the spiking dynamics, authors could try to generate heatmaps of the differential spiking between medial and lateral ripples (as they did in Fig. 4D-E) for 'strong' and 'common' ripples, or for local and propagating ripples.

Reviewer #3 (Public Review):

Using a large Neuropixels dataset provided by the Allen Institute (https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html), Filippo & Schmitz examined propagation profiles of the hippocampal ripples along the longitudinal axis. In addition to the previously described correlation between the ripple strength and distance (Patel et al., 2013; Kumar et al., 2019), the authors revealed heterogeneous propagation patterns depending on the strength and the origin. Within the septal half of the hippocampus, 'strong' ripples (top 10% strength in a session) is more likely to propagate from the medial to the lateral while the other ripples move in the other direction. Interestingly, these strong ripples are unique in that they are generated locally and more in the medial part of the septal hippocampus. Finally, the authors found that more neurons, with higher firing rates, are engaged in the strong ripples generated in the medial part of the septal hippocampus.

The major strength of the present study is their finding of the unique propagation of the strong ripples across the longitudinal axis. Past studies examining ripple propagations did not have a particular focus on the strength of ripples and thus have not described this feature. On the other hand, however, I believe the manuscript would represent a higher significance if the authors provided more thoughts on physiological impacts and or particular roles of this unique propagation pattern. The authors propose 1) the integration of the different kinds of information and 2) the contribution of the septal hippocampus to higher memory demand (Lines 275-296). Although these views are interesting, the former only explains the longer propagation of the ripples but not the direction (i.e., the ripples could propagate from the lateral to the medial), and the latter idea is less convincing because the Neuropixels data is collected from the mice only passively receiving visual stimuli.

The propagation of the locally generated ripples across the septotemporal axis has been well described in past studies (Patel et al., 2013; Kumar & Deshmukh, 2019). The authors' findings about different directionalities of ripple propagation depending on the origin would provide a valuable view for the expert in the field of the hippocampal physiology.

Reviewer #1 (Public Review):

This paper presents a systematic and novel examination of how pupil size relates to BOLD fMRI signal in a set of subcortical nuclei. It provides some important novel findings that should help advance understanding of how pupil size relates to activity in subcortical nuclei as well as providing important advances in how to measure these relationships.

The authors first tried replicating prior findings of a relationship between pupil size and BOLD signal using the prior methods. They could not (despite replicating pupil-cortical region relationships), and so tested whether the delay in the hemodynamic response function might differ in subcortical and cortical regions. They found that BOLD signal in the subcortical nuclei showed associations with pupil size at short delays. This is a critical finding as typical fMRI analyses assume a longer delay and so likely obscure the ability to see effects in these subcortical regions. The authors provide a number of helpful 'control' analyses that help strengthen confidence in their findings. For instance, it buttresses their findings that the pons control region did not show any significant effect to time-to-peak on correlations with pupil size or derivative measures. It also is helpful to know that pupil size fluctuations were associated with cortical activity in the regions expected from prior studies. The rigor of the study is also supported by the fact that there was a preregistration and that data are publicly shared.

Reviewer #2 (Public Review):

Lloyd et al examine the relationship between pupil size and fMRI signals in six brain nuclei responsible for providing the four major neuromodulators in the brain: norepinephrine from the locus coeruleus (LC), dopamine from ventral tegmental area (VTA) and substantia nigra, serotonin from the dorsal and median raphe nuclei, and acetylcholine from the cholinergic basal forebrain. Importantly, the authors focus on the relationship between these nuclei in the ascending arousal system (AAS) and the pupil at rest, outside of the context of any task, to determine the extent that small changes in pupil size are predictive of AAS activity.

Very few previous studies have examined this relationship at rest, perhaps in part because of the increased sensitivity required in the absence of event-based averaging. These nuclei are small (especially the LC), and thus are difficult to measure with standard fMRI.

The authors use a number of data collection and processing techniques to increase the sensitivity and precision of their recordings targeted to small ROIs. They find robust correlations between multiple AAS nuclei and pupil size with a time course that is not well captured by a standard hemodynamic response function (HRF).

The latter methodological finding is likely to be useful to the field for future studies focused on extracting useful signals from these nuclei, and the observed relationship between multiple AAS nuclei and the pupil support an emerging consensus from animal research that pupil fluctuations are correlated with neuromodulators besides norepinephrine.

Reviewer #3 (Public Review):

The authors took a comprehensive set of analyses to examine the relationship between pupil diameter / derivative and BOLD-signal during rest in the ascending arousal system nuclei in 72 young participants. Focus is on the locus coeruleus, ventral tegmental area, substantia nigra, dorsal and median raphe nuclei and the basal forebrain. Analyses were performed using various processing pipelines: canonical versus custom hemodynamic response functions, with/without smoothing, time to peak analyses and cross spectral power density analyses to define the time lag between both measurements. The authors could not replicate previous correlations between locus coeruleus BOLD and pupil measurements using standard analytic approaches, and also found no relationship between locus coeruleus BOLD and pupil measurements when using custom hemodynamic response functions. When using time to peak and cross-correlation analyses, the authors found that coupling between pupil size and AAS BOLD patterns increases with decreasing time to peak, when the two signals were close in time. The authors conclude that these findings suggest that pupil size could be used as a noninvasive readout of AAS activity under passive conditions.

These authors did a thorough assessment, and described the methods and results well and in a balanced manner.<br /> Outstanding questions:<br /> - the reliability of these observations? would we see the same findings in a different cohort or using a different sequence/field strength?<br /> - What is the independent association of each assessed nucleus with pupil dilation? That could be informative to understand their shared or unique role.

Reviewer #1 (Public Review):

The manuscript by Mansur et al examines the roles of KLHL40, mutations which lead to the development of skeletal muscle disease (nemaline myopathy, NM). The authors use CRISPR-based gene editing in a model organism (zebrafish) to disrupt the two fish isoforms of KLHL40 (a and b) and examine the resulting phenotypes. The authors find that disease-like phenotypes develop in adulthood selectively with the deletion of the KLHL40a isoform. Phenotypes include reduced body size, reduced endurance, and reduced life span with cellular effects that include perturbations to sarcomere organization, perturbed morphology of secretory organelles and mitochondria, and defects in collagen secretion and ECM deposition. The system provided the advantage of following both development and pre-disease state (onset) allowing the authors to look at changes in translation but mainly in the proteome with a focus on ubiquitylation (followed by mass spectrometry). Selective changes to the proteomthe e in KLHL40a deletion mutant are evident in the pre-symptomatic stage. Pathway analysis suggests that mutant cells show selective increases in glycolytic and biosynthetic enzymes/pathways, perhaps, akin to a Warburg effect. Monitoring the correlation between loss of KLHL40a-dependent ubiquitylation and increased protein levels defined the small GTPase Sar1a as a direct target for KLHL40a-directed degradation. Sar1a interacts with KLHL40a and is ubiquitylated by Cul3-KLHL40 in cell-free and over-expression assays in mammalian cells. Overexpression of Sar1a in muscle leads to endoplasmic reticulum (ER) membrane tubulation and thickening of the Z-lines similar to ones showing in KHLH40a deletion and NM patients. Markedly elevated levels of Sar1a and defects in collagen secretion are also recorded in patients with KLHL40 mutations. These observations suggest that selective control of COPII coat protein Sar1a levels (and thus the activity of the COPII coat, which mediates biosynthetic secretion from the ER), perturbs collagen secretion and ECM deposition. Overall this comprehensive work delineates the roles of Cul3-KLHL40a in the development of NM and specifically in regulating secretion by controlling the levels of one component of the COPII coat. The work is very interesting yet requires additional experimental clarifications and analysis.

Strength

This is a very interesting study showing global developmental and disease onset-related changes to the proteome focusing on changes derived from KLHL40a deletion. The work demonstrates a key role of ubiquitylation and selective protein degradation in the development and muscle disease onset. The global proteome view identified changes to energy production modes and defined direct regulation of Sar1a levels by Cul3-KHLH40a ubiquitylation which regulates ECM secretion, providing a mechanistic explanation for the development of NM in patients with KLHL40 mutations. Furthermore, the study highlights an interesting mechanism in which the levels of an individual component of the COPII coat are controlled by degradation to regulate biosynthetic secretion from the ER.

Weaknesses

There are weaknesses in the analysis that would markedly benefit from added clarifications. The differential outcome with the deletion of klhl40 a and b requires explanation. Morphological observations, which are key to understanding the overall phenotypes of KLHL40a deletion should be developed to provide a better definition of effects on organelle morphology and in particular ones involved in secretion. Some of the transcriptome-proteome data are left unexplored, in particular a view of the unfolded protein response (UPR) within the data, which will complement the documented defects in protein secretion and provide intrinsic controls to the work. The findings on Sar1a and the role of controlled degradation in regulating COPII activities are highly interesting yet a more complete analysis of COPII components is missing. Information on Sar1b, previously implicated in selective effects on secretion, Sec23-Sec24 and ratio (where levels are regulated by ubiquitylation and de ubiquitylation), and outer layer COPII proteins Sec13 and in particular Sec31, which is by itself a target for Cul3-KLHL12 regulation during development and modifies selective biosynthetic secretion, is lacking. Added analysis can provide new perspectives on the potential broader implications and significance of this study.

Reviewer #2 (Public Review):

Mansur et al highlight interesting aspects of KLHL40-mediated proteostatic mechanisms in secretion and skeletal muscle development in zebrafish. They propose that KLHL40-mediated ubiquitylation of functional modules in the muscle proteome, particularly membrane traffic components, regulates protein abundance to control development. The authors present solid evidence for the role of KLHL40-mediated ubiquitylation and degradation of the cellular proteome but would benefit from further supporting evidence for their direct consequences on protein secretion.

Reviewer #3 (Public Review):

The manuscript is addressing the hypothesis that KLHL40, of which mutations lead to a nemaline myopathy, leads to aberrant processing/turnover via the UPS of specific proteins. The aberrant turnover of these specific proteins then leads to the disease phenotype.

The manuscript creates two fish models knocking out orthologs of KHL40 in fish and finds that KHLH40a is necessary for maintaining fish size.

A multi-omic approach identifies potential candidates that are KHLH40 targets, specifically, Sar1a. Overexpression of Sar1a leads to some phenotypic changes ultrastructurally that resemble khl40a knockout. In vitro studies suggest some co-regulation of KHL40a with sar1a but lack the methodologic rigor at this point to be convincing. In addition, whether Sar1a dysregulation leads to more global issues seen in patients and fish remains to be established.

Reviewer #1 (Public Review):

The authors aimed to study the contribution of bacterial factors to poor treatment outcomes in drug-susceptible TB, an important issue that has not been well studied. The authors performed GWAS on a very large population-based (3 sites in China) dataset of 3416 Mtb WGS data of pre-treatment isolates linked with clinical data to predict treatment outcomes. Logistic regression was used to assess the association between predictors and outcomes and ROC curves were generated to assess the value of the genomic signatures to predict poor TB treatment outcomes. The authors were successful in identifying 14 Mtb variants in 13 genes and reactive oxygen species that were more likely to occur in patients with poor treatment outcomes.

The investigators were very thorough, in investigating both fixed and unfixed mutations, and analyzing the changes in gene expression under stress (exposure to first-line drugs and hypoxic conditions) for the 13 genes identified, which further strengthened the evidence generated by GWAS. The authors attempted to perform an external validation of their findings but could not identify a suitable existing dataset.

These data can be used by others to guide their analyses, and confirm if these 13 genes are also found in other settings. If confirmed, then the results could open the possibility for individualised tailoring of treatment of drug-susceptible TB, especially to prevent the risk of relapse.

Reviewer #2 (Public Review):

The availability of large collections of Mycobacterium tuberculosis (Mtb) isolates has enabled many important studies looking to identify mycobacterial genetic polymorphisms associated with anti-tuberculosis (TB) drug resistance, including both classical "resistance-conferring" mutations and novel "resistance-enabling" mutations. Importantly, these studies have expanded our understanding of mycobacterial genetic adaptations undermining chemotherapy, in many cases allowing for improved diagnostic tests and predictions of treatment failure. In this submission, Gao and colleagues adopt a different approach to the problem: although also applying a GWAS-type analysis, they instead attempt to elucidate polymorphisms implicated in poor outcomes of TB patients undergoing treatment for the drug-susceptible disease. Starting with a large dataset comprising 3496 samples with corresponding clinical (host) metadata, the authors generate Mtb whole-genome sequence data for 91 samples obtained from patients with "poor" outcomes and 3105 patients with "good" outcomes. These are used to identify 14 fixed and >230 unfixed mutations that might be associated with "poor" treatment outcomes, a conclusion which they argue is plausible given transcriptional evidence implicating many of the identified genes in the mycobacterial response in vitro to first-line drug exposure and/or hypoxia, both of which are considered relevant to clinical disease. Notably, they also identify a tendency for a greater proportion of "ROS mutational signatures" in unfixed mutations from "poor" outcome samples. Finally, incorporating these observations in a prediction model, the authors observe that the mycobacterial factors aren't adequate on their own but, when combined with key host factors - including patient age, sex, and duration of diagnostic delay (which have stronger predictive value) - they enhance predictive capacity. In summary, this paper reports a novel approach yielding observations that offer tantalizing insight into the mycobacterial factors which might influence TB treatment outcomes independent of drug resistance, however, the following must be considered:

(i) The manuscript provides little to no detail about how the samples were obtained, other than the fact that they comprise "pre-treatment" samples: are they all sputum samples? Were they induced? Similarly, no information is provided about sample propagation: were the samples cultured to achieve sufficient biomass for whole-genome sequencing? If so, in what growth media, for how long, and how many passages? Were all samples treated identically? And were they plated to single colonies - or are the "isolates" referred to throughout the manuscript actually heterogenous populations of potentially different Mtb clones obtained - and propagated - as a mixed sample? This information is critical given the potential that the identified polymorphisms - both fixed and (perhaps even more so) unfixed - might have arisen as a consequence of in vitro (laboratory) manipulation under standard aerobic conditions.

(ii) A key question that arises from this study (and others like it) is whether causation has been adequately established. Ideally, the Mtb genotypes contained within samples obtained pre-treatment should be compared with samples obtained from the same patients following treatment - that is, when the "poor" outcome was manifest. The expectation is that the polymorphisms identified prior to initiation of therapy - especially the 14 fixed mutations - should be evident (even dominant) at the later stage when therapy failed (or at the subsequent presentation in cases of relapse). Recognizing that this is not easily accomplished, though, it seems fair to suggest that the perceived relevance of the identified mutations would be strengthened if the authors were able to provide any other evidence - perhaps from studies of drug-resistant Mtb isolates - supporting their inferred role in undermining frontline treatment.

(iii) Related to the above, the authors make the valid point that their intention here was different from other studies which have deliberately utilized drug-resistant Mtb isolates to identify resistance-conferring and resistance-enabling mutations (such as in the study they cite by Hicks et al). It would be interesting to know, however, if any of the mutations identified in those other studies were also picked up in this work - and, if not, why that might be the case.

(iv) Finally, the analyses presented in this study are heavily dependent on the use of appropriate statistical methods to identify potentially rare genetic polymorphisms. However, as noted for sample processing (see my earlier comment above), there is very little detail provided about the methodology applied. This omission detracts from the interpretation, especially given that the predominance of lineage 2 (which contributes >75% of the isolates, with sublineage 2.3 constituting >50%) risks a lineage-specific association, rather than a more generalizable pathogenicity phenotype. Similarly, the heavy skew in the numbers of "good" (3105 samples) versus "poor" (91 samples) collections (approximately 34x difference in sample size) raises the possibility that mutations identified in the "poor" category might be artificially over-represented. More clarity in detailing the statistical methods is required to allay any concerns about the identification of candidate polymorphisms.

Reviewer #1 (Public Review):

This study sought to establish a model of targeted lung endothelial ablation and subsequently study the regeneration process post-ablation using single-cell RNA-sequencing in order to identify key subpopulations and underlying mechanisms of regeneration.

Strengths of the study include:

1. The elegance of the DT endothelial ablation model which leverages local lung instillation of DT to locally ablate the endothelium and cause significant lung vascular leakiness while keeping the endothelium of other organs intact, as is convincingly demonstrated in Fig 1 and Fig 2.

2. The temporal analyses using scRNA-seq demonstrate key shifts in endothelial and non-endothelial cell populations following endothelial injury. These experiments identify a highly proliferative subpopulation of endothelial cells that expresses the transcription factor FoxM1 during the regeneration phase.

3. The authors discover that the traditionally designated "gCap" lung endothelial population contains additional subpopulations that have regenerative potential and that there is a transient expression of apelin in the regenerative population. Pharmacological inhibition of the apelin receptor increase mortality.

Potential weaknesses include:

1. The description of the "stem-like" nature of endothelial cells is not experimentally proven. "Stem-like" is a vague term and the usage of this term is primarily based on the expression of Procr. However, that itself does not justify the usage of "stem-like" unless there is more clear evidence of what "stem-like" properties these cells have, such as multipotency.

2. The intriguing finding of the proliferative EC population raises the question as to how these cells emerge. Do they have a specific subpopulation/cluster origin in the baseline lung endothelium, and was Apelin expression both necessary as well as sufficient to induce the switch to the proliferative state? Such mechanistic analyses would be very helpful in understanding the coordination of the lung endothelial regeneration program.

3. The authors mention that endothelial ablation also induces shifts in the numbers of other cell types such as epithelial cells, alveolar macrophages, and immune cells but there is no analysis beyond the quantification of the cells. Are these cells involved in the regeneration of the endothelium by providing ligands such as growth factors?

Reviewer #2 (Public Review):

Acute lung injury (ALI) and ARDS are major causes of morbidity and mortality in critically ill patients and patients infected with Sars-Cov-2. There are no effective therapies for ALI/ARDS, and the 28-day mortality rate is ~40%. One of the main pathological features of ALI/ARDS is a vascular injury characterized by endothelial dysfunction, inflammation, and in situ thrombosis. Using a murine model of ALI/ARDS triggered by diphtheria toxin (DT) mediated endothelial specific ablation, the authors apply sc-RNA-seq analysis to study how lung cell populations respond to injury and identify two main endothelial subpopulations responsible for regenerating lung vasculature over seven days. The study's implications are exciting as they provide evidence of intrinsic repair mechanisms that could be targeted for vascular regeneration and recovery of lung function in the context of ALI/ARDS. In particular, the apelin pathway rises as a prime therapeutic candidate given its role in coordinating the behavior of general and aerocyte capillary cells in lung vascular repair.

While the results of this study are exciting and novel, it must be recognized that several limitations need to be properly addressed to facilitate the translation of the findings toward medical care. For instance, the animal model used in this study (DT mediated EC ablation) does not fully recapitulate all the pathological hallmarks of ALI/ARDS, the most important of which is that repair proceeds at a very slow pace as a result of multiple factors that are not recapitulated in this made. Since the authors use only one model of ALI/ARDS, it is not entirely clear whether the current findings can be generalized to other models. Since no one model truly recapitulates the complexity of human ALI/ARDS, it is important to use at least two or more models that can narrow genetic and molecular mechanisms fundamental to lung injury and recovery. Another important aspect is the lack of validation in human samples and cells, which could strengthen the conclusions raised by the authors in the discussion. Finally, the authors appropriately emphasize how this study could help efforts to understand Sars-Cov2 mediated ALI/ARDS. Still, no studies explore any overlap with currently available Omics data from COVID lungs.

Despite these weaknesses, this study is the first to apply rigorous scRNA-seq analysis to this unique model of ALI/ARDS. It also provides data to support the importance of the two newly discovered endothelial cell subpopulations (gCap and aCap) in lung repair and regeneration, which hold the potential to offer unique mechanistic insights into the genetic and molecular mechanisms responsible for vascular repair and offers the opportunity to consider apelin based therapeutic approaches to treat ALI/ARDS. In conclusion, this study is expected to contribute to our lung biology understanding greatly. It provides the research community with novel resources and tools that greatly aid efforts to understand ALI/ARDS and identify therapeutics to treat this devastating disease.

Reviewer #3 (Public Review):

This highly innovative study makes elegant use of single-cell RNA sequencing in a transgenic murine model of selective lung endothelial depletion to study endothelial repair and regeneration. Within 3 days after ablation of 70% of lung endothelial cells, a new stem-like endothelial population expressing markers of general capillary endothelial cells (gCap), yet also apelin, Procr, Angpt2, and CD93, yet not the gCap-typical apelin receptor emerged. This was followed at day 5 by a population of highly proliferative gCap-like endothelial cells expressing the apelin receptor along with FoxM1, which replenished all depleted endothelial populations and allowed for rapid resolution of microvascular injury. These newly identified cell states are highly reminiscent of tip and stalk cells in sprouting angiogenesis and may guide the development of new regenerative strategies.

Strengths:<br /> The present work provides important novel insights into the mechanisms of endothelial repair and reconstitution. Importantly, the authors identify a subset of gCap cells that upon endothelial depletion develops into a stem cell-like population expressing (among others) apelin, which signals via the apelin receptor to another, progenitor-like cell population that arises subsequently from the former stem cell-like population. These findings shed new light on the process of microvascular "healing" in acute lung injury and ARDS, and open up intriguing parallels to processes well known from angiogenic sprouting that may be exploited for therapeutic purposes.

Weaknesses:<br /> As with every innovative study, the emerging answers give rise to a series of new questions. Notable among those is the identity of the signal that initially drives the transition of the stem cell-like gCap population from their basal state - the recognition of such a signal may allow replicating the proposed cycle in vitro, with the opportunity to harvest cells at specific time points for both research and therapeutic purposes. Similarly, one may wonder how a lung may survive with 70% of its endothelial cells gone - do the respective vascular segments simply get excluded from perfusion (and, possibly, ventilation, as AT-II cells also decline in parallel, resulting in an emphysematous phenotype) or does fluid simply leak into the interstitium (which seems hard to reconcile with survival)? From a methodological point of view, RNA velocity analyses may be considered in follow-up studies to further substantiate the notion of a gradual transition of a subset of gCap cells from a basal to a stem cell-like to a progenitor-like and back to a basal state.

Reviewer #2 (Public Review):

The authors utilized a label-free LC-MS/MS analysis in formalin-fixed paraffin-embedded (FFPE) tumors from 143 LNM-negative and 78 LNM-positive patients with T1 CRC to identify protein biomarkers to determine LNM in T1 CRC.

The authors used a fair number of clinical samples for the proteomics investigation. The experimental design is reasonable, and the statistical methods used in this manuscript are solid.

The authors largely achieved their aims and the results supported their conclusion. The method used in this proteomic study can also be used for the proteomics analysis of other cancer types to identify diagnostic and prognostic biomarkers. In addition, the 9 marker panel has a potential clinical diagnosis practice in determining LNM in T1 CRC.

Nevertheless, the authors need to justify their standards in selecting the biomarkers. For example, a p-value cut-off of 0.1 is not a usual criterion in similar proteomic studies. In addition, an identification frequency of 30% in patients seems not preferable for biomarker identification. The authors also need to justify the definition of fold change in the three subtypes with Kruskal-Walli's test. The authors need to describe more details on how they identified the 13 proteins from a 55-protein database. In addition, what is the connection between the final 9 proteins and the 19 proteins? What is the criterion to select 5 proteins for IHC validation from the 9 proteins?

Reviewer #3 (Public Review):

This work provides a proteomic analysis of 132 early-stage (pT1) colorectal cancers (CRC) to attempt to identify proteins (or a signature pattern thereof) that might be used to predict the patient risk of lymph node metastases (LNM) and potentially stratify patients for further treatment or surveillance. The generated dataset is extensive and the methods appear solid. The work identifies a 55-protein signature that is strongly predictive of LNM in the training cohort and two validation cohorts and then generates two simplified classifiers: a 9-protein proteomic and a 5-protein immunohistochemical classifier. These also perform very well in predicting LNM. Loss of the small GTPase RHOT2 is identified as a poor prognostic factor and validated in a migration assay. The findings could allow better prognostication in CRC and, if confirmed and better validated and contextualized, might impact patient care.

Strengths:<br /> A large training cohort of resected early-stage (pT1M0) CRCs was analyzed by rigorous methods including careful quantitative analysis. The data generated are unbiased and potentially useful. A number of proteins are found to be different between CRCs with and without lymph node metastases, which are used to train a machine learning model that performs flawlessly in predicting LNM in the training cohort and very well in predicting LNM in two validation cohorts. The authors then develop two simplified classifiers that might be more readily extended into clinical care: a 9-protein proteomic assay and a 5-protein immunohistochemical assay; both of these also perform well in predicting LNM. Because LNM is a key prognostic factor, and colectomy (which includes removal of lymph nodes needed to assess LNM) carries significant risk and morbidity, particularly in rectal cancer, classifiers like these are potentially interesting. Finally, the authors identify the loss of expression of RHOT2 as a novel prognostic factor.

Weaknesses:<br /> Major points:<br /> The data are limited by a number of assumptions about metastasis, minimal contextualization of the results, and claims that are too strong given the data. Critically, the authors use the presence or absence of LNM as the study's only outcome; while LNM is a key predictor in CRC, it is uncommon in T1 CRC (generally 3-10%, 12% in this study), stochastic, inefficient, and incompletely identified by histologic evaluation. Larger resection (here, colectomy) removes both identified and occult LNM, which is probably best studied in randomized trials of lymphadenectomy in Japanese gastric cancer cohorts and should be better discussed. Critically, patient survival or disease-free survival would be more relevant outcomes. Further, absent longer-term data, many patients without identified LNM might nonetheless be high-risk and skew the cohorts. It is also not clear whether these findings would be generalizable to other early-stage colon cancers.

The data are also not correlated with the genetics of the cases, which were not discussed. The results would benefit from the inclusion of standard-of-care MSI status. The classifiers would also be much more impactful if they were generalizable beyond T1 CRCs; this could be readily tested in public datasets.

The authors explain the data as mechanistic, but, aside from one experiment modulating RHOT2 levels, they are fundamentally correlative and should be described as such.

Although they focused on areas containing >80% tumor as judged by the reading pathologist, it is unclear whether the identified proteomic changes originate from the tumor or the microenvironment.

The authors fail to properly contextualize the results or overstate the novelty of their study. A number of examples - the study is claimed as "the first proteomic study of T1 CRC" and "the first comprehensive proteomics study to focus on LNM in patients with submucosal T1 CRCs"; neither of these appears to be true, for example, Steffen et al. (Journal of Proteome Research, 2021, reference 18) may satisfy both of these, although the numbers are smaller. Many other results are reported without context, for example, proteomic characterization of mucinous carcinomas has been performed previously, a modest correlation in mucinous carcinoma is ascribed a large mechanistic role, and PDPN is discussed but is not contextualized as a protein that has been well-studied in the context of metastasis.

The data on RHOT2 are promising but very preliminary. RHOT2 is described as ubiquitous in colorectal cancer cell lines; a brief search in Human Protein Atlas shows RHOT2 RNA and proteins are ubiquitously expressed throughout the body. While its loss appears potentially prognostic, it is unclear whether this is simply a surrogate for other features, such as loss of differentiation state, and whether this is unique to CRC; multivariate analysis would be important.

Reviewer #1 (Public Review):

Lammer et al. examined the effects of social loneliness, and longitudinal change in social loneliness, on cognitive and brain aging. In a large sample longitudinal dataset, the authors found that both baseline loneliness and an increase in loneliness at follow-up were significantly associated with smaller hippocampal volume, reduced cortical thickness, and worse cognition in healthy older adults. In addition, those older adults with high loneliness at baseline showed even smaller hippocampal volume at follow-up. These results are interesting in identifying the importance of social support to cognitive and brain health in old age. With a longitudinal design, they were able to show that increased loneliness was related to reduced brain structural measures. Such results could help guide clinicians and policymakers in designing social support systems that would benefit the growing aging population.

The strength of the current study lies in the large sample size and longitudinal follow-up design. The multilevel models used to separate within and between subject effects are well constructed. Combining neuroimaging data with behavioral changes provided further evidence that social loneliness may be related to accelerated brain aging. Stringent FDR correction, Bayes factor comparison, and the additional analyses for sensitivity showed the robustness and credibility of the results.

Weaknesses of the study were related to the interpretation and discussion of their findings.

Social loneliness is a relatively little-studied factor in cognitive ageing, and the authors should consider expanding the discussion, with some additional analyses, as to how their results could be used by clinicians and older adults to monitor social behaviors.

The authors examined the interaction between baseline and age change to see if higher baseline loneliness was associated with accelerated decline. The interaction was significant, but the authors did not further explore the interaction effect, which may have clinical significance. The authors should consider identifying a cut-off point in LSNS that suggests persons scoring less than this score on the LSNS may be at greater risk of accelerated brain decline than others. Such a cut-off point is important for clinicians, as well as for future researchers to compare their results.

Although it was not directly tested in the paper, LSNS scores did not seem to change with increasing age (Table 1). This general stability of LSNS scores in older adults should be discussed further. The authors should consider how their relatively healthy and high SES sample may be less vulnerable to loss of family or friends in old age, making this sample sub-optimal for the question they have. The significance of the subject effect suggests that some individuals still experience a loss of social connectedness. The authors may want to elaborate on this and give some explanations of such subject differences in the ageing effect on social loneliness. Although stress was not a significant mediating factor, is it related to baseline loneliness or changes in loneliness in the current sample?

The presentation of longitudinal data (Figure 1) lacks dimensionality. The scatter plots presented here are more suitable for cross-sectional studies and could cause confusion regarding the interpretation of the results. The authors should consider individual growth curves or spaghetti plots in visualizing change within subjects.

Reviewer #2 (Public Review):

The paper by Laurenz Lammer and colleagues used cohort data to investigate the cross-sectional and longitudinal association between loneliness and brain structure and cognitive function. The main finding was that baseline social isolation and change in social isolation were associated with smaller hippocampus volumes, reduced cortical thickness, and poorer cognitive function. Given that more and more people feel lonely nowadays (e.g., due to the pandemic), the study by Lammer and colleagues addresses a highly relevant health concern of our time.

Significant strengths of the study:

- large cohort;<br /> - the cross-sectional and longitudinal analyses confirmed the findings;<br /> - the study was preregistered;<br /> - the study included men and women;<br /> - analyses were sound and controlled for essential confounders.

The major weaknesses of the study:

- it is unclear whether loneliness causally contributes to brain structure and cognitive function;<br /> - the factors that may cause loneliness are unclear.

Reviewer #1 (Public Review):

Muscle is a major insulin-responsive tissue for the disposal of glucose, a process dependent on the translocation of GLUT4 glucose transporter from intracellular compartments to the plasma membrane. Knudsen and co-workers provide an analysis of the impact of microtubule-based movement on GLUT4 biology in muscle cell lines, and rodent and human muscle fibers ex vivo. A role for microtubules in the control of GLUT4 vesicle dynamics in both unstimulated and insulin-stimulated adipocytes (cultured and primary) has been previously reported by a number of groups. Less is known about the requirement for microtubules for GLUT4 translocation in muscle. A strength of this study is that key aspects of the work were performed in muscle fibers rather than muscle cell lines.

Conclusions that are strongly supported by the data presented include:

1. Demonstration of constitutive GLUT4 movement along microtubule tracks in both unstimulated and insulin-stimulated muscle fibers. GLUT4 dynamics in unstimulated fibers were captured by fluorescence recover after photobleaching (FRAP) and by quantifying vesicle movements by live cell microscopy, whereas in insulin-stimulated cells GLUT4 dynamics were captured by following the movements of GLUT4-containing vesicles. These data support a model in which intracellular GLUT4 is dynamic in both unstimulated and insulin-stimulated muscle fibers rather than being static in unstimulated conditions and only mobilized upon insulin-stimulation.

2. Similar microscopy analyses of GLUT4-containing vesicles demonstrate that depolymerization of microtubules reduced GLUT4 vesicle movement and impacted insulin-stimulated glucose uptake. Short term depolymerization of microtubules (5 min) did not affect insulin-stimulated glucose uptake, whereas insulin-stimulated glucose uptake was blocked after prolonged depolymerization (2 hrs). The use of a muscle on a chip method to monitor glucose uptake in real time was critical for these experiments.

The changes in glucose uptake were accompanied by changes in the morphologies of intracellular GLUT4-containing structures. The differences between short and long term depolymerization of microtubules support a model in which GLUT4 can be translocated to the plasma membrane by insulin stimulation in the absence of microtubules but an intact microtubule cytoskeleton is required to maintain GLUT4 in a "compartment" that can be recruited by insulin. Stated another way, the microtubule-dependent dynamics of GLUT4-containing vesicles in unstimulated cells is permissive for insulin-stimulated GLUT4 translocation.

3. Knockdown of the microtubule motor protein, Kif5b, blunts insulin-stimulated translocation of GLUT4 to the plasma membrane of cultured muscle cells. These findings agree with previously demonstrated role for Kif5b in adipocytes.

4. In an in vitro model of insulin resistance (incubation of muscle fibers with short chain C2 ceramide) unstimulated and insulin-stimulated GLUT4-containing vesicle movement was blunted and unstimulated and insulin-stimulated microtubule polymerization was reduced.

Weakness of the study include:

1. There are no data supporting a role for insulin regulation of microtubule-dependent GLUT4-containg vesicle movement. The data in Fig.2B do not support a differences in the number of "moving" GLUT4 vesicles between basal and insulin-stimulated fibers. The statement on line 103 that they "observed a ~16% but insignificant increase" to be confusing. These data do not support an effect of insulin on the number of moving GLUT4 vesicles that can be detected in an individual experiment. There is also effect of insulin on GLUT4 vesicles in the data reported in Fig.S2D, Fig.S5B, and Fig.S5F. However, the data in Fig. 2C suggest there was a consistent increase in "moving" vesicles in insulin-stimulated conditions in 4 independent experiments (how are these data normalized?). Because the basis of insulin-regulation of glucose uptake is the control of GLUT4 translocation to the plasma membrane, the authors need to clarify their thinking on why they do not detect insulin robust effects on GLUT4 dynamics in the individual experiments. Is it that they are not measuring the correct parameter? That the assay is not sensitive to the changes?

The small (or no effect) of insulin distracts a bit from the findings that there is microtubule-dependent GLUT4 movement in basal and stimulated muscle fibers, and that disruption of this movement by depolymerization of microtubules or Kif5b knockdown blunts GLUT4 translocation. As noted above, the data strongly support microtubule-dependent GLUT4 dynamics as permissive for insulin-stimulated GLUT4 translocation even if this dynamics might not be a target of insulin action.

2. The analyses of GLUT4-containing structures are not particularly informative. Co-localization with other markers (beyond syntaxin6) are needed to understand these structures. Defining structures as small, medium or large is incomplete. In particular, it is important to probe the microtubule nucleation site clusters for other membrane markers. Transferrin receptor? IRAP?

3. The Kinesore data do not support the authors hypothesis. The data show that Kinesore increases the amount of GLUT4 in the plasma membrane of basal cells and that insulin further increases plasma membrane GLUT4 to the same extent as it does in control cells. How does that provide insight into the role microtubules (or kif5b) in GLUT4 biology? Why does Kinesore increase plasma membrane GLUT4? Is it an effect of Kinesin 1 on GLUT4 vesicles? Kinesore is reported to remodel the microtubule cytoskeleton by a mechanism dependent on Kinesin 1. Is that the reason for the change in GLUT4?

4. The analysis of Kif5b is a bit cursory. Depolymerization of microtubules in muscle fibers essentially blocks all GLUT4 movement (only the insulin condition is shown in Fig.2B but I assume basal would be equally inhibited), and fully inhibits insulin-stimulated glucose uptake in muscle fibers. What are the effects of nocodazole in L6 cells (cell used for kif5b studies) and is it similar in magnitude to kif5b knockdown? Those data would identify there are non-Kif5b microtubule-dependent effects.

5. The authors need to show that the fibers isolated from the HFD mice remain insulin-resistant ex vivo by measuring glucose uptake. It is possible that once removed from the mice they "revert" to normal insulin-sensitivity, which might contribute to the differences reported in Fig5.

6. Although it is interesting that the authors have included the insulin-resistance models/experiments, they are not well developed and therefore the conclusions are not particularly strong.

7. The data do not support the title.

Reviewer #2 (Public Review):

Overall, this manuscript provides a thorough characterization of the role of microtubules in the movement of GLUT4 in muscle fibers, and demonstrates the need for an intact microtubule network for GLUT4 responsiveness but only after the initial round of response.<br /> The study poses a very interesting question, rooted in studies in the literature studying the effects of Nocodazole (Noco) and C2-ceramide on GLUT4 traffic in cell systems. It is important to validate or refute predictions from those studies and, largely through this group's work, the quest to examine these questions in isolated muscle fibers and intact muscles as feasible is commendable. The authors develop very interesting imaging approaches to this end, and quantify the results in a convincing and elegant fashion. The system to measure 2-DG uptake and glucose uptake by electrochemical sensing in isolated fibers using the microfluidic pump is very ingenious.<br /> The main conclusion that microtubules are important for GLUT4 proper localization is important and adds mechanistic insight beyond that obtained from work in myoblasts and pre/adipocytes. The observation that microtubules are not engaged in GLUT4 traffic in the first round of insulin action but it is thereafter is also very revealing and should lead to more insights into the first and subsequent rounds of GLUT4 translocation.

Reviewer #1 (Public Review):

The authors study the control of the timing of Q neuroblast migration, through the precisely timed expression of the Wnt receptor MIG-1/Frizzled, which halts migration of the QR.pa cell at its intended position. Understanding the underlying mechanism is important, as similar mechanisms might play a role in controlling the timing of biological processes in development much more broadly. The authors use precise measurements of mig-1 mRNA molecules, fitted to mathematical models of different mechanisms to control the timing of mig-1 expression, and couple this with experimental perturbations of mig-1 expression. In this way, the authors convincingly show that mig-1 dynamics is best explained by a model where mig-1 expression is controlled by the accumulation of an activator, rather than the degradation of a repressor, which is an important result. In addition, they show that the asymmetric division of QR.p into the larger QR.pa and smaller QR.pp cells is important for proper mig-1 expression in Qr.pa, likely by asymmetric inheritance of the activator. In the process, the authors identify novel conserved binding motifs that are responsible for different aspects of mig-1 dynamics, which will potentially allow identifying the putative activator in the future.

In its current form, I find the manuscript has two main weak points: First, the connection between the experiments and models is relatively weak. Now, the model is mostly used to aid the interpretation of experiments, by predicting rough trends. However, even though the model is in principle fitted to the experimental data in some cases, a detailed comparison between experimental results and the model is often lacking. For example, there are multiple occasions where the data appears to not fit the model in some aspects, but the potential origin of these mismatches is typically not discussed. Second, the authors present experimental evidence of an earlier model prediction, that positive feedback loops in mig-1 expression reduce variability in timing. Here, the authors speculate that this feedback loop might be due to the activation of mig-1 expression by mig-1-induced Wnt signaling, which in itself is an interesting idea. However, the genetic perturbation used here - manipulation of the Wnt pathway, rather than perturbing specifically the induction of mig-1 expression by Wnt signaling - likely changes the expression of many genes in the cell, making it difficult to establish whether the increased variability in Qr.pa position is indeed due breaking the proposed feedback loop.

Reviewer #2 (Public Review):

Schild et al. investigate the regulation of temporal control during neuroblast migration in the roundworm C. elegans. The authors find that expression of the Wnt pathway receptor Mig1 is regulated early through a specific noncoding conserved intronic element and later through two specific upstream conserved DNA elements. The expression levels of Mig1 in QR.pa cells are further regulated through Ced-3 and pig-1. The variability in the timing of later expression of Mig1 in QR.pa cells through bar-1 or a terminally truncated version of Bar1 was modulated but the mean expression did not change.

The single molecule RNA-FISH data is strong, and this method is sensitive enough to detect differences between different single cells and mutants. The mutants are very precise and straightforward to interpret. An additional strength is that many cells and replicas have been measured. The data analysis is simple.

The proposed model is simple with few intuitive parameters. This makes parameter identification straightforward. The qualitative predictions do make sense and are consistent with most experimental observations.

Overall the manuscript addresses the important question of timing regulation in transcription.

Reviewer #1 (Public Review)

This paper utilizes two well-established mathematical models of colorectal cancer (CRC) screening to estimate the impact of disruptions in screening caused by the COVID-19 pandemic on long-term outcomes related to CRC. For screening, the authors use two recommendations from the US Preventive Services Task Force (USPSTF) (which were informed by the results of these models): screening colonoscopy every 10 years at ages 50, 60, and 70, and annual fecal immunochemical tests (FIT) from ages 50-75. Separate model runs were performed for 8 different cohorts at the time of the pandemic based on age, screening history, and adherence to screening. For each cohort, microsimulations were performed for 3 different scenarios--no disruption, delays in screening, or discontinuation from screening. The primary outcome was life-years gained (LYG) from screening.

In general, severe prolonged disruptions in any screening led to the largest loss of benefit from screening - for example, unscreened 50-year-olds forced to wait until age 65 (Medicare eligibility) had the largest absolute and relative loss in screening-associated LYG compared to shorter delays of 18 months or less. Losses were also higher in those who were semi-adherent to screening recommendations. The prolonged disruption had a consistently much greater impact than short-term reductions, changes in regimen, or assumptions about test sensitivity. The results are consistent between the two models. The authors point out that, since pandemic-induced disruptions in insurance coverage had a greater impact on minority populations already at risk for reduced access to screening and other preventive services, the pandemic may lead to further exacerbations in existing disparities in CRC incidence and mortality.

The strengths of this paper include the use of well-validated models, the consistent results between the models, the relatively intuitive nature of the findings, and the use of LYG, a commonly used metric for screening recommendations. As the authors point out, estimates of the population impact of the pandemic given the current age structure of the US would be helpful, these would be inherently speculative given the lack of empirical data on pandemic effects on screening. Although prioritizing screening individuals with long pandemic-induced delays is clearly the optimal policy approach, how this might be achieved is unclear.

Reviewer #1 (Public Review):

In this study, the authors use open-access datasets of Neuropixel recordings to explore the relationship between ripple strength and propagation in the septal/dorsal hippocampal pole. They found that the ripple strength correlates with the direction of propagation and that the duration of the events is dependent on the site of initiation. Medial pole ripples are longer and engage significantly more neurons than lateral ripples. These findings may have theoretical and practical implications for the study of sharp-wave ripples, a main oscillatory event underlying memory consolidation. While the approach is not entirely novel (e.g. Patel et al., JN 2013; Kumar and Deshmukh 2020), the study provides some additional insights. The strength of evidence of propagation dynamics is solid and claims are broadly supported. Some points however may require revision. In particular, issues regarding the definition of the longitudinal and transversal axes, as well as additional analysis on microcircuit interactions and neuronal dynamics per cell types and hippocampal sectors should be more thoroughly addressed in support of mechanisms.

Reviewer #1 (Public Review):

Nikolaos Koutras et al shed light on potential distinct functions of the Src family kinases (SFKs) Lck and Lyn in lymphoid signal transduction. The authors therefore overexpress Lyn and ectopic Lck in the B lymphoid cell line BJAB in an elegant Dox-inducible manner and compare the SFK's ability to trigger and shape B-lymphoid signal transduction. The findings indicate that ectopic expression of Lck is sufficient to phosphorylate the B cell receptor (BCR) ITAMs in BJAB cells. In these cells, constitutive ITAM and ITIM phosphorylation by both overexpressed Lck and Lyn induces BCR signaling, as demonstrated by phosphorylation of Syk and Akt, as well as CD22 inhibitory signaling, as shown by SHP-1 phosphorylation. In direct comparison, the influence of Lyn on said phosphorylation is stronger when it is (over-)expressed in the same amounts as Lck. This outcome was somewhat expected, since ITIM/ITAM phosphorylation is considered to be the principal function of Lyn in B cells.

The study finds Lyn to be degraded more efficiently via the proteasome and to be more tightly controlled by phosphatases when compared to Lck. However, rather than interpreting the findings as distinct kinase-intrinsic properties, one could attribute the slower degradation and stricter PTP control of Lyn to the fact that Lyn is the principal and predominant SFK in B cells and thus a "standard target" of the B-lymphoid molecular machinery, to which it is better adapted to.

Next, the authors present a RNAseq transcriptome analysis of Lck- and Lyn-expressing B cells and validate selected findings via qPCR. The data show Lyn and Lck to regulate pathways and biological functions of critical importance to B lymphocytes. Generally, most of the Lck/Lyn-regulated biological functions and pathways shown here (antigen presentation, cytokine production, migration, apoptosis, autophagy, etc.) are well known to be controlled by BCR signaling, which the overexpression of SFKs are constitutively activating, as shown earlier. While the authors draw a Venn diagram depicting differentially regulated transcripts between Lck- and Lyn-expressing cells, it does not seem like Lck is able to regulate pathways which are not "canonically" regulated by Lyn. There is also the persisting problem of Lck being expressed to a much higher extent and the effect of the endogenously expressed Lyn, since the model systems are not based on a Lyn-deficient cell line.

Lastly, the authors follow up their finding of deregulated transcripts belonging to the ER/UPR ontology cluster. Flow cytometric analysis indeed shows an influence of Lck and Lyn expression on ER homeostasis, which can be reverted with SFK inhibitors. Alas, additional follow-up experiments to functionally investigate the deregulated pathways suggested by the RNAseq analysis are not included in this study.

While there definitely are implications for the role of ectopic expression of Lck in CLL cells, this work however presents no direct comparison of expression strength or signaling outcomes between the study's BJAB (Burkitt lymphoma) cell line-based model and a model of CLL - be it a mouse model, human patient samples or a CLL cell line. Since the B-lymphoid cell line used, the Burkitt lymphoma line BJAB, is not CLL-derived, the conclusions that can be drawn for the pathophysiology of CLL is limited.

In principal, the authors show that the Src kinase Lck - when ectopically expressed - largely fills out the role of the predominant B-lymphoid Src kinase Lyn, namely phosphorylation of the CD79-ITAMs and induction of constitutive antigen receptor signaling. Given that the established role of Lck is the phosphorylation of ITAMs and activation of the T cell receptor in T cells, where it is predominantly expressed, these findings provide limited advancement of our current understanding of antigen receptor signal transduction. As a distinct functional difference between Lck and Lyn is not established in this work, said SFKs' largely exclusive expression in T and B cells remains enigmatic.

Reviewer #2 (Public Review):

The normally T cell restricted Src family tyrosine kinase Lck is ectopically expressed in most B cell Chronic Lymphocytic Leukemias. This, along with the fact that ectopic expression of other SFKs, such are Fyn and Fgr, are not seen, suggests that Lck may have some unique function, distinct from the endogenous Lyn SFK, that promotes malignant transformation. Using inducible expression in a human B cell lymphoma, the study explores this possibility. Studies reveal no qualitative functional differences in Lck and Lyn that are likely to explain its unique ectopic expression of Lck in CLL.

The strengths of this study include the use of Lentiviral transfer of genes encoding SFKs in conjunction with Doxycycline inducible expression. This allows comparative analysis of acute Lyn and Lck overexpression effects, free of cell resetting artifacts consequent to long term expression of the SFK. Strength is also seen in the authors fluorescent tagging of the SFK so analysis could be gated on ectopic expression level. Strength exists in the authors dissection of SFK effects on early events in the BCR signaling pathway, which reveal the ability of both overexpressed SFKs to drive receptor ITAM tyrosine phosphorylation and initiating BCR signaling. These studies reveal little difference in the function of the SFKs, though it appears that Lck may be less sensitive to phosphatase regulation.

It is unclear from the material and methods whether the overexpressed Lyn is LynA or Lyn B. It appears in the text (lines 130-133) that they overexpress LynB specifically. A recent paper from Tania Freedman (Sci Adv 2022 PMID:35452291) suggests that LynA is more activating whereas LynB is more balanced with an inhibitory bias. The point is that it is important to discuss this because they may not be making a relevant comparison.

If Lck promotes pathophysiology by transduction of a qualitatively unique signal, one would expect that transcriptome analysis should reveal this difference. The authors look for this signal using transcriptome analysis of bulk populations expressing similar levels of SFK. Although differences were seen in the transcriptome, finding were not consistent with a qualitatively unique function. However, bulk transcriptomic analysis may miss important differences. Single cell RNAseq, e.g., by 10x, may have been more incisive because gene expression could have been normalized to SFK expression in individual cells.

Finally, while some interesting differences are seen in the biology of Lyn and Lck, weakness exists in the failure to explore the causality of these differences in driving CLL phenotype. A final thought relevant to this comment. It is a truism that "absence of proof is not proof of absence".

Reviewer #1 (Public Review):

Members of the SLC11/NRAMP family of transporters permit the movement of transition metals across cell membranes in all kingdoms of life. The current study builds off previous structural and mechanistic work on the SLC11/NRAMP family of transporters by Manatschal and colleagues reported in eLife; the current study presents a cryo-EM structure of a plant aluminum (Al3+) transporter that combats aluminum toxicity in soil. The structure was not determined in the presence of added metal ions, so the paper also employs a variety of established functional assays to test the effects of mutating suggested binding site residues. One notable result is the identification of a mutation (S68A) that maintains divalent transport but disrupts trivalent binding/transport. Strengths of the manuscript include the extensive legwork required to identify a combination of plant homologue, cameloid nanobody, and amphipol that is required to provide homogenous protein and interpretable cryo-EM data. The cryo-EM maps are reliable with low orientation bias and clear features. In addition, the authors perform a number of biochemical and transport assays with divalent metals to bolster their structural model.

Reviewer #2 (Public Review):

In this work, the authors aimed to understand the ion selectivity mechanism of a plant NRAMP-related aluminum transporter by structural and biochemical characterizations.

The authors successfully identified SiNRAT as a promising candidate for structural and biochemical analyses, showed that SiNRAT transport various divalent cations as well as binding to trivalent cations, determined the cryo-EM structure of SiNRAT, and performed structure-based mutational analysis to identify a potential binding site for metal ions. Unfortunately, the authors failed to show direct evidence of Al3- transport, due to technical problems. Furthermore, the structure of SiNRAT in complex with Al3+ was also not shown.

Despite such weakness, the structural comparison with other NRAMP members with different ion selectivity properties together with the extensive biochemical analyses would support the statement by the authors on a mechanism of ion selectivity for Al3+.

In the discussion section, the authors posed an important question. Considering the weak ion selectivity of SiNRAT over divalent cations, it is still unclear how NRAT proteins can function as an Al3+ transporter in a physiological condition where other divalent cations are also abundant. This would be an important question to be addressed in the related research field in the future.

The methods section is well written and the atomic coordinates and EM map file will be available to the community.

Reviewer #3 (Public Review):<br /> <br /> This paper addresses the structure and mechanism of a presumed Al3+ transporter from the NRAMP superfamily from the plant Setaria italica. This protein belongs to a small clade of NRAMPs, termed NRATs that are postulated to protect plants from Al3+ which is both toxic and prevalent in soil. The NRAT clade is characterized by the substitution of key amino acids at the substrate binding site which has been shown to coordinate either Mg2+ in NRMTs or Mn2+ in classical NRAMP transporters. Evidence for Al3+ transport comes from a previous study utilizing heterologous expression in yeast; this study concluded that NRAT1 from rice (Oryza sativa) is highly specific for Al3+ over Mn2+, Fe2+, Cd2+, Mg2+ which have been shown to be transported by homologs in other clades of the NRAMP family. The current study screened the expression of five homologues of NRAT1, choosing SiNRAT for structural and functional analysis. Unlike previous work on NRAT1, SiNRAT readily transported Mn2+, and experiments with Ca2+ and Mg2+ indicate that these ions are likely also transported. Unlike classical NRAMPs, Mn2+ transport appears to be passive and not coupled to proton transport. Although technical limitations precluded direct measurement of Al3+ transport, ITC measurements provided qualitative evidence for binding in the uM range. A cryo-EM structure is presented, showing an occluded conformation similar to the recent high-resolution X-ray structure of a classical NRAMP bound to Mn2+. The structure of SiNRAT does not show bound ions, but allows comparison of the substrate binding pocket and shows the disposition of key amino acids that distinguish the NRAT clade. Finally, mutagenesis was used to evaluate the role of four of these residues, thus concluding that Ser68 plays a role in coordinating Al3+ as well as its analog Ga3+. Thus, although the transport data with Mn2+ are rigorous, interactions of the putative substrate, Al3+, are only addressed in a qualitative way. The cryo-EM structure is similarly rigorous but provides only modest insight into substrate specificity. Furthermore, the discussion of proton coupling - or the lack thereof - is very speculative. Thus, although new information on this novel clade of NRAMP transporters will be welcomed by specialists in this field, the paper is likely to have only a modest impact beyond this cohort.

Reviewer #1 (Public Review):

In this paper the authors are estimating the amount of transmission (via the force of infection) of EV-D^8 in England. The strengths of the study are the use of serological data for understanding underlying transmission, and the assessment of the sensitivity of the conclusions to the seropositivity cut off and the model form used. The weaknesses are the data not being annually and the lack of link to HFMD cases,, but these do not detract from the conclusions that can be drawn from the paper. The results do support the conclusions.

Reviewer #2 (Public Review):

The authors use data from 3 cross-sectional age-stratified serosurveys on Enterovirus D68 from England between 2006 and 2017 to examine the transmission dynamics of this pathogen in this setting. A key public health challenge on EV-D68 has been its implication in outbreaks of acute flaccid myelitis over the past decade, and past circulation patterns and population immunity to this pathogen are not yet well-understood. Towards this end, the authors develop and compare a suite of catalytic models as fitted to this dataset and incorporate different assumptions on how the force of infection varies over time and age. They find high overall EV-D68 seroprevalence as measured by neutralizing antibodies, and detect increased transmission during this time period as measured by the annual probability of infection and basic reproduction number. Interestingly, their data indicate very high seroprevalence in the youngest children (1 year-olds), and to accommodate this observation, the authors separate the force of infection in this age class from the other groups. They then reconstruct the historical patterns of EV-D68 circulation using their models and conclude that, while the serologic data suggest that transmissibility has increased between serosurvey rounds, additional factors not accounted for here (e.g., changes in pathogenicity) are likely necessary to explain the recent emergence of AFM outbreaks, particularly given the broader age-profile of reported AFM cases. The Discussion mentions important current unknowns on the biological interpretation of EV-D68 neutralizing antibody titers for protection against infection and disease. The analysis is rigorous and the conclusions are well-supported, but a few aspects of the work need to be clarified and extended, detailed below:

1) Due to the lack of a clear single cut-point for seropositivity on this assay, the authors sensibly present results for two cut-points in the main text (1:16 and 1:64). While some differences that stem from using different cut-points are fully expected (i.e., seroprevalence being higher using the less stringent cut-point), differences that are less expected should be further discussed. For instance, it was not clear in Figure 2 why the annual probability of infection decreased after 2010 using the 1:64 cut-point, while it continued to increase using the 1:16 cut-point. It would also be helpful to explain why overall seroprevalence and R0 continue to increase over this time period using the 1:64 cut-point. Lastly, it would be useful to see the x-axis in Figure 4 extended to the start of the time period that FOI is estimated, with accompanying credible intervals.

2) Additional context of EV-D68 in the study setting of England would be useful. While the Introduction does mention AFM cases "in the UK and elsewhere in Europe" (line 53), a summary of reported data on EV-D68/AFM in England prior to this study would provide important context. The Methods refers to "whether transmission had increased over time (before the first reported big outbreak of EV-D68 in the US in 2014)" (lines 133-134), rather than in this setting. It would be useful to summarize the viral genomic data from the region for additional context - particularly since the emergence of a viral clade is highlighted as a co-occurrence with the increased transmissibility detected in this analysis.

Reviewer #3 (Public Review):

In the proposed manuscript, the authors use cross-sectional seroprevalence data from blood samples that were tested for evidence of antibodies against D68 for the UK. Samples were collected at 3 time points from individuals of all ages. The authors then fit a suite of serocatalytic models to explain the changing level of seropositivity by age. From each model they estimate the force of infection and assess whether there have been changes in transmissibility over the study period. D68 is an important pathogen, especially due to its links with acute flaccid myelitis, and its transmission intensity remains poorly understood. Serocatalytic models appear to be appropriate here. I have a few comments.

The biggest challenge to this project is the difficulty in assigning individuals as seronegative or seropositive. There is no clear bimodal distribution in titers that would allow obvious discrimination and apparently no good validation data with controls with known serostatus. The authors tackle this problem by presenting results to four different cut-points (1:16 to 1:128) - resulting in seropositivity ranging from around 50% to around 80%. They then run the serocatalytic models with two of these (1:16 and 1:64) - leading to a range of FoI values of 0.25-0.90 for the 1 year olds and 0.05-0.25 for older age groups (depending on model and cutpoint). This represents a substantial amount of variability. While I certainly see the benefit of attacking this uncertainty head on, it does ultimately limit the inferences that can be made about the underlying risk of infection in UK communities, except that it's very uncertain and possibly quite high.

I find the force of infection in 1 year olds very high (with a suggestion that up to 75% get infected within a year) and difficult to believe, especially as the force of infection is assumed much lower for all other ages.

The authors exclude all <1s due to maternal antibodies, which seems sensible, however, does this mean that it is impossible for <1s to become infected in the model? We know for other pathogens (e.g., dengue virus) with protection from maternal antibodies that the protection from infection is gone after a few months. Maybe allowing for infections in the first year of life too would reduce the very large, and difficult to believe, difference in risk between 1 year olds and older age groups. I suspect you wouldn't need to rely on <1 serodata - just allow for infections in this time period.

Relatedly, would it be possible to break the age data into months rather than years in these infants to help tease apart what happens in the critical early stages of life.

One of the major findings of the paper is that there is a steadily increasing R0. This again is difficult to understand. It would suggest there are either year on year increases in inherent transmissibility of the virus through fitness changes, or year on year increases in the mixing of the population. It would be useful for the authors to discuss potential explanations for an inferred gradual increase in R0.

On a similar note, I struggle to reconcile evidence of a stable or even small drop in FoI in the 1:64 models 4 and 5 from 2010/11 (Figure 3) with steadily increasing R0 in this period (Figure 4). Is this due to changes in the susceptibility proportion. It would be good to understand if there are important assumptions in the Farrington approach that may also contribute to this discrepancy.

The R0 estimates (Figure 4) should also be presented with uncertainty.

Finally, given the substantial uncertainty in the assay, it seems optimistic to attempt to fit annual force of infections in the 30 year period prior to the start of the sampling periods. I would be tempted to include a constant lambda prior to the dates of the first study across the models considered.

Reviewer #1 (Public Review):

In this paper, the authors developed a method that allows one to test a large number of drug combinations in a single cell culture sample. In principle, the experiments rely on the randomness of drug uptake in individual cells as a tool to create and encode drug treatments. They used a single sample containing thousands of cells treated with a combination of fluorescent barcoded drugs, and created transient drug gradients. They also developed segmentation- free image analysis capable of handling optical fields with a substantial number of cells. The major strength of this work is the demonstration of the feasibility of testing drug combinations in a relatively straightforward manner that could be used by many laboratories. As such this paper could have a significant impact on the early drug discovery of combinatorial therapy. One of the weaknesses in this manuscript is the absence of studies beyond just HeLa cells. In addition, the phenotype tested is cell death, which might limit the application to other drug interactions that might look at other phenotypes; e.g inhibition of cell proliferation or changes in differentiation phenotypes. Finally, there is a basic assumption that drug leakage does not occur or is minimal, but secondary uptake of the drug is likely and may not be homogeneous. Notwithstanding, the approach is feasible and likely will be applied in several laboratories.

Reviewer #2 (Public Review):

This manuscript explores a novel technique to use dyes co-injected with various pharmaceutical reagents, like chemotherapic agents, to assess cellular effects in a cell culture model.

The major premise is that dye diffusion can be detected through fluorescent microscopy and be used as a measure of co-injected drug concentration. In chemotherapy commonly multiple drugs are given simultaneously, however, understanding how to tailor the concentrations of a multi-drug cocktail to each individual is largely trial and error. The authors surmise that perhaps using a cell culture model whereby cancer cells are cultured and then exposed to dye-tracked molecules an optimal multi-drug combination and concentrations can be determined. In other words, the intermixing of various connected drugs can then be fluorescently monitored to elucidate optimal concentrations of multi-drug combinations.

The concept overall is interesting but is relatively preliminary in its proof of concept. The authors note that varying free-diffusion of drugs out of the cell could complicate interpretation and that most of the analysis was done on a relatively short time basis and not longer evaluation periods that were more typical of chemotherapy.

Reviewer #3 (Public Review):

The ability to rapidly test a large combination of drug cocktails on patient cells in culture would enhance personalized therapeutic regimens. Currently, testing 10 concentrations of 3 drugs in combination is intractable. Elgart & Loscalzo propose to take advantage of diverse drug responses within a single dish to streamline the exploration of multi-drug combinations. By sampling the population variation in uptake of multiple dyes within individual cells and delivering the dyes by a variety of modes (i.e. point injection, sequential homogenous mixing), a pipeline is developed for estimating a "response space" that arises from the complex intersections of multiple drug/dye concentration gradients.

The paper is in places very rigorous in establishing bounds in which this pipeline may have utility by defining the linearity of two-drug co-delivery, explicitly illustrating the pre-processing/binning performed on the data, reporting distributions of uptake under different environmental dye gradients, and finding a tight correlation between dye and drug response to justify the surrogate use of dye characterization for the end-goal of drug cocktail formulation. I am particularly impressed with the results depicted in Figure 6 and the associated supplemental figures as a demonstration of an application of this approach for nanocarrier-based combinatorial siRNA delivery. However, there are major weaknesses in interpretability and underlying assumptions.

A large body of work in the literature has established that the diversity in cells of identical genetic background occurs due to two components: 1) intrinsic noise - such as stochastic fluctuations in gene expression - as well as 2) extrinsic noise - variability that arises from sources that are external to the biochemical process of gene expression, such as abundances of ribosomes or stage in the cell cycle. Note that this widely-accepted definition does not separate intrinsic and extrinsic from intracellular and extracellular. The authors cite a few of these seminal papers (which focus on noise introduced to gene expression) but then define their interpretation of intrinsic noise much more broadly "... intrinsic noise as phenotype(s) fluctuations across isogenic cell populations cultured under the same conditions. Measurement noise in some cases can also be thought of as intrinsic noise. Fluctuations in cellular phenotype(s) driven by the global environment will be referred to as extrinsic noise." This misuse of widely accepted terminology creates significant confusion in the interpretation of the results.

A point of contention with redefining noise as the authors have done is that they are lumping all processes unique to the cell as intrinsic and all environmental factors as extrinsic. Thus, when statements are made such as "external factors that contribute to noise are principally manifest through convection" (line 40-41, page 2) the veracity of these assumptions must be established. For example, when a ligand binds and unbinds from a receptor due to thermal energy, that "noise" in cellular stimulation is not convection-based, yet an example of how extrinsic noise can influence cellular responses. The definition is important because the underlying premise for the pipeline presented is that "While intrinsic cell variability can be significant, we believe that it is the extrinsic factor(s) that drive sample variability in most experimental cellular systems" (lines 42-43, page 4).

Throughout, figures lack labels and sufficient explanation for interpretation, as well as the number of experiments used to generate the data that is processed through the pipeline for each condition. For a study designed to eliminate replicate culture conditions, the onus is on the authors to show that replicates are in fact fully recapitulated in the population variance after statistical binning/processing.

Ultimately, when the paper presents results such as Figure 9 as the culmination of the pipeline as applied to cell viability studies, it is unclear how useful insight is extracted from this methodology. Four drugs are applied in combination to adherent HeLa cells and time-dependent local cell density is provided as a proxy for cell viability. While it is stated that "The absolute drug concentration can be determined using the homogeneous delivery method discussed above" (line 421-422, page 19), this analysis is not performed, and I am left unsure of whether extrinsic factors are truly driving sample variability under this context. It is unclear to the reader how the point injections were administered, and no discussion of how the confounding factors of synergy or antagonism will be addressed through this methodology.

Reviewer #1 (Public Review):

In this manuscript, Nocka and colleagues reveal a novel layer of regulation of the Btk tyrosine kinase, a key signaling protein in B lymphocyte signaling and an important drug target with 3 recently FDA-approved drugs, by the SH3-SH2 domain-containing adaptor protein Grb2. The authors nicely demonstrate a critical role of the interaction of the Grb2 SH3 domains with the Pro-rich linker C-terminal to the Btk PH-TH domains on membranes for full kinase activation of Btk. Hence this interaction recruits Btk to scaffold-mediated signaling clusters.

This is a technically sound paper with high-quality experiments. The manuscript is easy to follow and excellently written. The findings are novel and of high relevance towards a complete understanding of Btk regulation and signaling in cancer and normal cells.

Reviewer #2 (Public Review):

The authors unexpectedly found that the protein Grb2, an adaptor protein that mediates the recruitment of the Ras guanine-nucleotide exchange factor, SOS, to the EGF receptor, can be recruited to membranes by the immune cell tyrosine kinase Btk. The authors show, using total internal reflection fluorescence (TIRF) microscopy that the interaction with Grb2 is reversible, dependent on the proline-rich region of Btk, and independent of PIP3. These experiments are well performed and unambiguous.

The authors next asked whether Grb2 binding to Btk influences its kinase activity, by evaluating (i) Btk autophosphorylation and (ii) the phosphorylation of a peptide from the endogenous substrate PLC1. The readout relies on non-specific antibody-mediated detection of phosphotyrosine but nevertheless reveals a concentration-dependent increase in both Btk autophosphorylation and PLCy1 phosphorylation. The experiments, however, have only been performed in duplicate and, particularly in the case of PLCy1 phosphorylation, exhibit enormous variability which is not reflected in the example blot the authors have chosen to display in Figure 3C. Comparison of the same, duplicate experiment presented in Figure 3 Supplement 2 paints a very different picture.

The authors next sought to determine which domains of Grb2 are required for activation of Btk. Again, these experiments were only performed in duplicates, and the authors' claims that Grb2 can moderately stimulate the SH3-SH2-kinase module of Grb2 are not well supported by their data (Figure 4C-D).

The authors next asked whether Grb2 stimulates Btk by promoting its dimerization and trans-autophosphorylation. The authors measured the diffusion coefficient of Btk on PIP3-containing supported lipid bilayers in the presence and absence of Grb2. They noted that the diffusion coefficient of individual Btk particles decreases with increasing unlabeled Btk, which they interpret as Btk dimerization. Grb2 does not appear to influence the diffusion of Btk on the membrane (Figure 5A). Presumably, the diffusion coefficient reported here is the average of a number of single-molecule tracks, which should result in error bars. It is unclear why these have not been reported. Next, the authors assessed the ability of Grb2 to stimulate a mutant of Btk that is impaired in its ability to dimerize on PIP3-containing membranes. In contrast to wild-type Btk, autophosphorylation of dimerization-deficient Btk is not enhanced by Grb2. Whilst the data are consistent with this conclusion, again, the experiment has only been repeated once and the western blot presented in Figure 5 Supplement 2 is unreadable. It is also puzzling why Grb2 gets phosphorylated in this experiment, but not in the same experiment reported in Figure 3 Supplement 2.

Finally, the authors argue that Grb2 facilitates the recruitment of Btk to molecular condensates of adaptor and scaffold proteins immobilized on a supported lipid bilayer (SLB) (Figure 6). This is a highly complex series of experiments in which various components are added to supported lipid bilayers and the diffusion of labelled Btk is measured. When Btk is added to SLBs containing the LAT adaptor protein (phosphorylated in situ by Hck immobilized on the membrane via its His tag), it exhibits similar mobility to LAT alone, and its mobility is decreased by the addition of Grb2. The addition of the proline-rich region (PRR) of SOS further decreases this mobility. In this final condition, the authors incubate the reactions for 1 h until LAT undergoes a phase transition, forming gel-like, protein-rich domains on the membrane, shown in Figure 6B. The authors' conclusion that Btk is recruited into these phase-separated domains based on a slow-down in its diffusion is not well supported by the data, which rather indicates that Btk is excluded from these domains (Figure 6B - Btk punctae (green) are almost exclusively found in between the LAT condensates (red)). As such, the restricted mobility of Btk that the authors report may simply reflect the influence of barriers to diffusion on the membrane that result from LAT condensation into phase-separated domains. The authors also present data in Figure 6 Supplement 1 indicating that Grb2 recruitment to Btk is out-competed by SOS-PRR and that Btk does not support the co-recruitment of Grb2 and SOS-PRR to the membrane. These data would appear to suggest that the authors' interpretation of the decreased mobility of Btk on the membrane may not be correct.

Reviewer #3 (Public Review):

The study of Nocka and colleagues examines the role of membrane scaffolding in Btk kinase activation by the Grb2 adaptor protein. The studies appear to make a case for a reinterpretation of the "Saraste dimer" of Btk as a signaling entity and assigns roles to the component domains in the Src module in Btk activation. The point of distinction from earlier studies is that this work ascribes a function to an adaptor protein as promoting the kinase activation, rather than vice versa, and also illustrates why Btk can be activated via modes distinct from its close relative, such as Itk. Importantly, these studies address these key questions through membrane tethering of Btk, which is a successful, reductionist way to mimic cellular scenarios. The writing could be improved and can absolutely be more economical in word choice and use; currently, there is a good deal of background to each section that is not always comprehensive or crucial to contextualise the findings, while key information is often omitted. The results are currently not described in a detailed manner so there is an imbalance between the findings, which should be the focus, relative to background and interpretations or models.

Reviewer #1 (Public Review):

The study by Oikawa and colleagues demonstrates for the first time that a descending inhibitory pathway for nociception exists in non-mammalian organisms, such as Drosophila. This descending inhibitory pathway is mediated by a Drosophila neuropeptide called Drosulfakinin (DSK), which is homologous to mammalian cholecystokinin (CCK). The study creates and uses several Drosophila mutants to convincingly show that DSK negatively regulates nociception. They then use several sophisticated transgenic manipulations to demonstrate that a descending inhibitory pathway for nociception exists in Drosophila.

Strengths:

This study creates the possibility of using Drosophila to study descending nociceptive systems.

CRISPR/Cas9 is used to generate mutants of dsk, CCKLR-17D1, and CCKLR-17D3. The authors then use these mutants to clearly show that DSK negatively regulates nociception.

Several GAL4s are used to clearly show that these effects are likely mediated by two sets of neurons in the brain, MP1 and Sv.

RNAi and rescue experiments further show that CCKLR-17D1, a DSK receptor, functions in Goro neurons to negatively regulate nociception.

Thermogenetic experiments nicely show that activation of DSK neurons attenuates the nociceptive response.

Weaknesses:

A minor weakness in the study is that it is unclear how DSK negatively regulates nociception. An earlier study at the Drosophila nmj shows that loss of DSK signaling impairs neurotransmission and synaptic growth. In the current study, loss of CCKLR-17D1 in Goro neurons seems to increase intracellular calcium levels in the presence of noxious heat. An interesting future study would be the examination of the underlying mechanisms for this increase in intracellular calcium.

Reviewer #2 (Public Review):

This is an exceptional study that provides conclusive evidence for the existence of a descending pathway from the brain that inhibits nociceptive behavioral outputs in larvae of Drosophila melanogaster. The authors identify molecular both molecular and neuronal/cellular components of this pathway. Converging lines of evidence and conclusive genetic experiments indicate that the neuropeptide, drosulfakinin (DSK), and its receptors (CCK1 and CCK2) function to inhibit nociception behaviors. Interestingly, the authors show that the relevant DSK neurons have cell bodies that are in the larval brain and that these neurons send projections into the thoracic ganglion and ventral nerve cord. Several lines of evidence support the hypothesis that fourth-order nociceptive neurons called Goro, are one relevant target for these outputs. RNAi knockdown of the CCK1 receptor in these cells sensitizes behavioral and physiological responses to noxious heat. Second, the axons of DSK neurons form physical contact with processes of Goro neurons as revealed by GRASP analysis. However, the authors' careful experiments indicate that the contacts between axons and Goro neurites might not be indicative of direct synapses and instead might operate through the bulk transmission of the peptidergic signals. The study raises many interesting questions for future study such as what behavioral contexts might depend on this pathway. Using the CAMPARI approach, the authors do not find that the DSK neurons are activated in response to nociceptive input but instead suggest that these cells may be tonically active in gating nociception. Future studies may find contexts in which the output of the DSK neurons is inhibited to facilitate nociception, or contexts in which the cells are more active to inhibit nociception.

Reviewer #3 (Public Review):

This study describes a descending circuit that can modulate pain perception in the drosophila larvae. While descending inhibition is a major component of mammalian pain perception, it is not known if a similar circuit design exists in fruit flies. Overall the authors use clean logic to establish a role for DSK and its receptor in regulating nociception. I have made a few suggestions that I believe would strengthen the manuscript as this is an important discovery.

Major comments:

1) It's not completely clear why the authors are staining animals with an FLRFa antibody. Can the authors stain WT and DSK KO animals with a DSK antibody? Also, can the authors show in supplemental what antigen the FLRFa antibody was raised against, and what part of that peptide sequence is retained in the DSK sequence? This overall seems like a weakness in the study that could be improved on in some way by using DSK-specific tools.

2) What is the phenotype of DSK-Gal4 x UAS-TET animals? They should be hyper-reactive. If it's lethal maybe try an inducible approach.

3) Figure 9. This was not totally clear, but I think the authors were evaluating spontaneous (i.e. TRPA1-driven) rolling at 35C. The critical question is "does activating DSK-expressing neurons suppress acute heat nociception" and this hasn't really been addressed. The inclusion of PPK Gal4 + DSK Gal4 in the same animal kind of clouds the overall conclusions the reader can draw. The essential experiment is to express UAS-dTRPA1 in DSK-Gal4 or GORO-Gal4 cells, heat the animals to ~29C, and then test latency to a thermal heat probe (over a range of sub and noxious temperatures). Basically prove the model in Figure 10 showing ectopic activation or inhibition for each major step, then test heat probe responses.

4) It would also then be interesting to see how strong the descending inhibition circuit is in the context of UV burn. If this is a real descending circuit, it should presumably be able to override sensitization after injury.

Reviewer #1 (Public Review):

Chan et al. tried identifying the binding sites or pockets for the KCNQ1-KCNE1 activator mefenamic acid. Because the KCNQ1-KCNE1 channel is responsible for cardiac repolarization, genetic impairment of either the KCNQ1 or KCNE1 gene can cause cardiac arrhythmia. Therefore, the development of activators without side effects is highly demanded. Because the binding of mefenamic acid requires both KCNQ1 and KCNE1 subunits, the authors performed drug docking simulation by using KCNQ1-KCNE3 structural model (because this is the only available KCNQ1-KCNE structure) with substitution of the extracellular five amino acids (R53-Y58) into D39-A44 of KCNE1. That could be a limitation of the work because the binding mode of KCNE1 might differ from that of KCNE3. Still, they successfully identified some critical amino acid residues, including W323 of KCNQ1 and K41 and A44 of KCNE1. They subsequently tested these identified amino acid residues by analyzing the point mutants and confirmed that they attenuated the effects of the activator. They also examined another activator, yet structurally different DIDS, and reported that DIDS and mefenamic acid share the binding pocket, and they concluded that the extracellular region composed of S1, S6, and KCNE1 is a generic binding pocket for the IKS activators.

The data are solid and well support their conclusions, although there are a few concerns regarding the choice of mutants for analysis and data presentation.

Other comments:

1. One of the limitations of this work is that they used psKCNE1 (mostly KCNE3), not real KCNE1, as written above. It is also noted that KCNQ1-KCNE3 is in the open state. Unbinding may be facilitated in the closed state, although evaluating that in the current work is difficult.<br /> 2. According to Figure 2-figure supplement 2, some amino acid residues (S298 and A300) of the turret might be involved in the binding of mefenamic acid. On the other hand, Q147 showing a comparable delta G value to S298 and A300 was picked for mutant analysis. What are the criteria for the following electrophysiological study?<br /> 3. It is an interesting speculation that K41C and W323A stabilize the extracellular region of KCNE1 and might increase the binding efficacy of mefenamic acid. Is it also the case for DIDS? K41 may not be critical for DIDS, however.<br /> 4. Same to #2, why was the pore turret (S298-A300) not examined in Figure 7?

Reviewer #2 (Public Review):

The voltage-gated potassium channel KCNQ1/KCNE1 (IKs) plays important physiological functions, for instance in the repolarization phase of the cardiac action potential. Loss-of-function of KCNQ1/KCNE1 is linked to disease. Hence, KCNQ1/KCNE1 is a highlighted pharmacological target and mechanistic insights into how channel modulators enhance the function of the channel is of great interest. The authors have through several previous studies provided mechanistic insights into how small-molecule activators like ML277 act on KCNQ1. However, less is known about the binding site and mechanism of action of other type of channel activators, which require KCNE1 for their effect. In this study, Chan and co-workers use molecular dynamics approaches, mutagenesis and electrophysiology to propose an overall similar binding site for the KCNQ1/KCNE1 activators mefenamic acid and DIDS, located at the extracellular interface of KCNQ1 and KCNE1. The authors propose an induced-fit model for the binding site, which critically engages residues in the N-terminus of KCNE1. Moreover, the authors discuss possible mechanisms of action of how drug binding to this site may enhance channel function.

The authors address an important question, of broad relevance to researchers in the field. The manuscript is generally well written and the text easy to follow. A strength of the work is the parallel use of experimental and simulation approaches, which enables both functional testing and mechanistic predictions and interpretations. For instance, the authors have experimentally assessed the putative relevance of a large set of residues based on simulation predictions. A limitation is that several methods need to be described in more detail to allow for evaluation of the presented data. Also, a more extensive presentation of representative data would be useful, along with discussions on the putative impact on drug effects of the diverse intrinsic properties of tested mutants.

Reviewer #3 (Public Review):

The author is trying to identify the mefenamic (Mef) binding site and DIDS binding site on the KCNQ1 KCNE1 complex. The authors also try to identify the mechanism of interactions using electrophysiological recording, calculating V1/2 of different mutants, and looking at the instantaneous current and the tail current. The contribution of each residue within the binding pocket was analysed using GBSA and PBSA and traditional molecular dynamics simulation. The author is trying to argue that they share the same binding pocket and their mechanism of activation.

Strengths:

1. The effect of the WT channel in the presence of 100 uM Mef is very clear, and such an effect is clearly decreased with the E1-K41C and W323A mutation. The milder effect was observed with Q147C and Y148C mutants.

2. The effect of the WT channel in the presence of 100 uM DIDS is, again, very clear, and such an effect is clearly decreased with the E1-Y46C.

3. The author has indeed achieved their aim in addressing that the binding site for both DIDS and Mef are adjacent to each other and may indeed share a pocket in the S1-E1-pore pocket. This may help the field with drug development, targeting that region in the future.

Weaknesses:

1. The computational aspect of the work is rather under-sampled - Figure 2 and Figure 4. The lack of quantitative analysis on the molecular dynamic simulation studies is striking, as only a video of a single representative replica is being shown per mutant/drug. Given that the simulations shown in the video are extremely short; some video only lasts up to 80 ns. Could the author provide longer simulations in each simulation condition (at least to 500 ns or until a stable binding pose is obtained in case the ligand does not leave the binding site), at least with three replicates per each condition? If not able to extend the length of the simulations due to resources issue, then further quantitative analysis should be conducted to prove that all simulations are converged and are sufficient. Please see the rest of the quantitative analysis in other comments.

2. Given that the protein is a tetramer, at least 12 datasets could have been curated to improve the statistic. It was also unclear how frequently the frames from the simulations were taken in order to calculate the PBSA/GBSA.

3. The lack of labels on several structures is rather unhelpful (Figure 2B, 2C, 4B). The lack of clarity of the interaction map in Figures 2D and 6A.

4. The RMSF analysis is rather unclear and unlabelled thoroughly. In fact, I still don't quite understand why n = 3, given that the protein is a tetramer. If only one out of four were docked and studied, this rationale needs to be explained and accounted for in the manuscript.

5. For the condition that the ligands suppose to leave the site (K42C for Mef and Y46A for DIDS), can you please provide simulations at a sufficient length of time to show that ligand left the site over three replicates? Given that the protein is a tetramer, I would be expecting three replicates of data to have four data points from each subunit. I would be expecting distance calculation or RMSD of the ligand position in the binding site to be calculated either as a time series or as a distribution plot to show the difference between each mutant in the ligand stability within the binding pocket. I would expect all the videos to be translatable to certain quantitative measures.

6. Given that K41 (Mef) and Y46 are very important in the coordination, could you calculate the frequency at which such residues form hydrogen bonds with the drug in the binding site? Can you also calculate the occupancy or the frequency of contact that the residues are making to the ligand (close 4-angstrom proximity etc.) and show whether those agree with the ligand interaction map obtained from ICM pro in Figure 2D?

7. Given that the author claims that both molecules share the same binding site and the mode of ligand binding seems to be very dynamic, I would expect the authors to show the distribution of the position of ligand, or space, or volume occupied by the ligand throughout multiple repeats of simulations, over sufficient sampling time that both ligand samples the same conformational space in the binding pocket. This will prove the point in the discussion - Line 463-464. "We can imagine a dynamic complex... bind/unbind from Its at a high frequency".

8. I would expect the authors to explain the significance and the importance of the PBSA/GBSA analysis as they are not reporting the same energy in several cases, especially K41 in Figure 2 - figure supplement 2. It was also questionable that Y46, which seems to have high binding energy, show no difference in the EPhys works in figure 3. These need to be commented on.

9. Can the author prove that the PBSA/GBSA analysis yielded the same average free energy throughout the MD simulation? This should be the case when the simulations are converged. The author may takes the snapshots from the first ten ns, conduct the analysis and take the average, then 50, then 100, then 250 and 500 ns. The author then hopefully expects that as the simulations get longer, the system has reached equilibrium, and the free energy obtained per residue corresponds to the ensemble average.

10. The phrase "Lowest interaction free energy fort residues in ps-KCNE1 and selected KCNQ1 domains are shown as enlarged panels (n=3 for each point)" needs further explanation. Is this from different frames? I would rather see this PBSA and GBSA calculated on every frame of the simulations, maybe at the one ns increment across 500 ns simulations, in 4 binding sites, in 3 replicas, and these are being plotted as the distribution instead of plotting the smallest number. Can you show each data point corresponding to n = 3?

11. I cannot wrap my head around what you are trying to show in Figure 2B. This could be genuinely improved with better labelling. Can you explain whether this predicted binding pose for Mef in the figure is taken from the docking or from the last frame of the simulation? Given that the binding mode seems to be quite dynamic, a single snapshot might not be very helpful. I suggest a figure describing different modes of binding. Figure 2B should be combined with figure 2C as both are not very informative.

12. Similar to the comment above, but for figure 4B. I do not understand the argument. If the author is trying to say that the pocket is closed after Mef is removed - then can you show, using MD simulation, that the pocket is openable in an apo to the state where Mef can bind? I am aware that the open pocket is generated through batches of structures through conformational sampling - but as the region is supposed to be disordered, can you show that there is a possibility of the allosteric or cryptic pocket being opened in the simulations? If not, can you show that the structure with the open pocket, when the ligand is removed, is capable of collapsing down to the structure similar to the cryo-EM structure? If none of the above work, the author might consider using PocketMiner tools to find an allosteric pocket (https://doi.org/10.1038/s41467-023-36699-3) and see a possibility that the pocket exists.

13. Figure 4C - again, can you show the RMSF analysis of all four subunits leading to 12 data points? If it is too messy to plot, can you plot a mean with a standard deviation? I would say that a 1-1.5 angstroms increase in the RMSF is not a "markedly increased", as stated on line 280. I would also encourage the authors to label whether the RMSF is calculated from the backbone, side-chain or C-alpha atoms and, ideally, compare them to see where the dynamical properties are coming from.

14. In the discussion - Lines 464-467. "Slowed deactivation of the S1/KCNE1/Pore domain/drug complex ....... By stabilising the activated complex. MD simulation suggests the latter is most likely the case." Can you point out explicitly where this has been proven? If the drug really stabilised the activated complex, can you show which intermolecular interaction within E1/S1/Pore has the drug broken and re-form to strengthen the complex formation? The authors have not disproven the point on steric hindrance either. Can this be disproved by further quantitative analysis of existing unbiased equilibrium simulations?

15. Figure 4D - Can you show this RMSF analysis for all mutants you conducted in this study, such as Y46C? Can you explain the difference in F dynamics in the KCNE3 for both Figure 4C and 4D?

16. Line 477: the author suggested that K41 and Mef may stabilise the protein-protein interface at the external region of the channel complex. Can you prove that through the change in protein-protein interaction, contact is made over time on the existing MD trajectories, whether they are broken or formed? The interface from which residues help to form and stabilise the contact? If this is just a hypothesis for future study, then this has to be stated clearly.

17. The author stated on lines 305-307 that "DIDS is stabilised by its hydrophobic and vdW contacts with KCNQ1 and KCNE1 subunits as well as by two hydrogen bonds formed between the drug and ps-KCNE1 residue L42 and KCNQ1 residue Q147" Can you show, using H-bond analysis that these two hydrogen bonds really exist stably in the simulations? Can you show, using minimum distance analysis, that L42 are in the vdW radii stably and are making close contact throughout the simulations?

18. Discussion - In line 417, the author stated that the "S1 appears to pull away from the pore" and supplemented the claim with the movie. This is insufficient. The author should demonstrate distance calculation between the S1 helix and the pore, in WT and mutants, with and without the drug. This could be shown as a time series or distribution of centre-of-mass distance over time.

19. Given that all the work were done in the open state channel with PIP2 bound (PDB entry: 6v01), could the author demonstrate, either using docking, or simulations, or alignment, or space-filling models - that the ligand, both DIDS and Mef, would not be able to fit in the binding site of a closed state channel (PDB entry: 6v00). This would help illustrate the point denoted Lines 464-467. "Slowed deactivation of the S1/KCNE1/Pore domain/drug complex... By stabilising the activated complex. MD simulation suggests the latter is most likely the case."

20. I struggle with the term "normalised response" on Line 208. What is it being normalised to? Can this be put more explicitly in the text? If normalised to WT, why is WT EQ response only 0.8?

21. The author stated that the binding pose changed in one run (lines 317 to 318). Can you comment on those changes? If the pose has changed - what has it changed to? Can you run longer simulations to see if it can reverse back to the initial confirmation? Or will it leave the site completely?

22. Binding free energy of -32 kcal/mol = -134 kJ/mol. If you try to do dG = -RTlnKd, your lnKd is -52. Your Kd is e^-52, which means it will never unbind if it exists. I am aware that this is the caveat with the methodologies. But maybe these should be highlighted throughout the manuscript.

Reviewer #1 (Public Review):

This is a valuable study demonstrating convincingly that PI3K signaling lies downstream of Pdgfra signaling in zebrafish cardiomyocyte progenitors as they undergo latero-medial migration and midline fusion, essential for heart tube formation, likely via chemotaxis. Whereas the authors used both multiple inhibitory drugs and dominant negative transgene expression to interrupt PI3K expression, with findings strongly aligning, the manuscript would have been stronger if genetic approaches were used to complement the above approaches. Nonetheless, the impact of dnPI3K inhibition allowed the authors to suggest that the effects were cell autonomous to migrating cardiomyocytes. The authors used contemporary live imaging techniques allowing quantification of key cell behaviors, and this is a strength of the paper. There are some issues about the inter-study alignment of trajectory data that need to be addressed. Perhaps the most conspicuous weakness is that the authors have not advanced the model for cardiomyocyte migration beyond adding the involvement of PI3K downstream of Pdgfra, which is to a significant degree expected. The recording of cardiomyocyte protrusions biased in their orientation towards the direction of migration, which is lost in the mutants, is an interesting advance, although it was not shown whether protrusions are causally related to migration.

Reviewer #2 (Public Review):

The authors provide comprehensive results showing that pharmacological inhibition of PI3K negatively affects heart tube formation via misoriented and slower cardiac movements. They used several cellular and molecular assays to demonstrate the potential mechanisms involved in PI3K-dependent cardiac fusion defects. Moreover, they use several imaging techniques and quantitative assessments to support their findings. Although the manuscript is well-written and most of their results support their conclusions, the manuscript and its findings heavily rely on high concentrations of PI3K small-molecule inhibitors, which will have off-target effects. The off-targets of PI3K pharmacological inhibition should be interpreted with caution and further evaluated. The authors suggest PI3K inhibition mediates heart tube formation throughout PI3K-mediated migration defects rather than PI3K-mediated proliferative defects. However, the authors did not further evaluate this later point; it should be considered carefully.

Reviewer #3 (Public Review):

This manuscript provides new insights into an important process during cardiac development that is not well understood. The authors combined chemical inhibition experiments for PI3K as well as a genetic tool to overexpress a dominant negative PI3K specifically in cardiac progenitor cells and found that PI3K is important during cardiac fusion. By incubating embryos with the chemical inhibitor at different stages they concluded that PI3K is required between 12-20 somite stages, which corresponds to the time points that cardiac fusion occurs. They performed live imaging on cardiac progenitors during cardiac fusion and observed that inhibiting Pi3K reduces the velocity at which the cells move and affects their direction. The latter seems consistent with the observation that PI3K is not required for protrusion formation but affects the location of these protrusions. Finally, using a low dose of the PI3K inhibitor together with the previously identified Pdgf mutant suggests that both act in the same pathway to regulate the direction of migration of cardiac progenitor cels towards the midline. Overall, the manuscript is well written and experiments are well controlled providing sufficient evidence to substantiate most of their conclusions. Some open questions remain unanswered such as the mode of migration (individual or collective) that drives cardiac fusion.

Reviewer #1 (Public Review):

This manuscript addresses the important and understudied issue of circuit-level mechanisms supporting habituation, particularly in pursuit of the possible role of increases in the activity of inhibitory neurons in suppressing behavioral output during long-term habituation. The authors make use of many of the striking advantages of the larval zebrafish to perform whole brain, single neuronal calcium imaging during repeated sensory exposure, and high throughput screening of pharmacological agents in freely moving, habituating larvae. Notably, several blockers/antagonists of GABAA(C) receptors completely suppress habituation of the O-bend escape response to dark flashes, suggesting a key role for GABAergic transmission in this form of habituation. Other substances are identified that strikingly enhance habituation, including melatonin, although here the suggested mechanistic insight is less specific. To add to these findings, a number of functional clusters of neurons are identified in the larval brain that has divergent activity through habituation, with many clusters exhibiting suppression of different degrees, in line with adaptive filtration during habituation, and a single cluster that potentiates during habituation. Further assessment reveals that all of these clusters include GABAergic inhibitory neurons and excitatory neurons, so we cannot take away the simple interpretation that the potentiating cluster of neurons is inhibitory and therefore exerts an influence on the other adapting (depressing) clusters to produce habituation. Rather, a variety of interpretations remain in play.

Overall, there is great potential in the approach that has been used here to gain insight into circuit-level mechanisms of habituation. There are many experiments performed by the authors that cannot be achieved currently in other vertebrate systems, so the manuscript serves as a potential methodological platform that can be used to support a rich array of future work. While there are several key observations that one can take away from this manuscript, a clear interpretation of the role of GABAergic inhibitory neurons in habituation has not been established. This potential feature of habituation is emphasized throughout, particularly in the introduction and discussion sections, meaning that one is obliged as a reader to interrogate whether the results as they currently stand really do demonstrate a role for GABAergic inhibition in habituation. Currently, the key piece of evidence that may support this conclusion is that picrotoxin, which acts to block some classes of GABA receptors, prevents habituation. However, there are interpretations of this finding that do not specifically require a role for modified GABAergic inhibition. For instance, by lowering GABAergic inhibition, an overall increase in neural activity will occur within the brain, in this case below a level that could cause a seizure. That increase in activity may simply prevent learning by massively increasing neural noise and therefore either preventing synaptic plasticity or, more likely, causing indiscriminate synaptic strengthening and weakening that occludes information storage. Sensory processing itself could also be disrupted, for instance by altering the selectivity of receptive fields. Alternatively, it could be that the increase in neural activity produced by the blockade of inhibition simply drives more behavioral output, meaning that more excitatory synaptic adaptation is required to suppress that output. The authors propose two specific working models of the ways in which GABAergic inhibition could be implemented in habituation. An alternative model, in which GABAergic neurons are not themselves modified but act as a key intermediary between Hebbian assemblies of excitatory neurons that are modified to support memory and output neurons, is not explored. As yet, these or other models in which inhibition is not required for habituation, have not been fully tested.

This manuscript describes a really substantial body of work that provides evidence of functional clusters of neurons with divergent responses to repeated sensory input and an array of pharmacological agents that can influence the rate of a fundamentally important form of learning.

Reviewer #2 (Public Review):

In this study, Lamire et al. use a calcium imaging approach, behavioural tests, and pharmacological manipulations to identify the molecular mechanisms behind visual habituation. Overall, the manuscript is well-written but difficult to follow at times. They show a valuable new drug screen paradigm to assess the impact of pharmacological compounds on the behaviour of larval zebrafish, the results are convincing, but the description of the work is sometimes confusing and lacking details.

The volumetric calcium imaging of habituation to dark flashes is valuable, but the mix of responses to visual cues that are not relevant to the dark flash escape, such as the slow increase back to baseline luminosity, lowers the clarity of the results. The link between the calcium imaging results and free-swimming behaviour is not especially convincing, however, that is a common issue of head-restrained imaging with larval zebrafish.

The strong focus on GABA seems unwarranted based on the pharmacological results, as only Picrotoxinin gives clear results, but the other antagonists do not give a consistent results. On the other hand, the melatonin receptor agonists, and oestrogen receptor agonists give more consistent results, including more convincing dose effects.

The pharmacological manipulation of the habituation circuits mapped in the first part does not arrive at any satisfying conclusion, which is acknowledged by the authors. These results do reinforce the disconnect between the calcium imaging and the behavioural experiments and undercut somewhat the proposed circuit-level model.

Overall, the authors did identify interesting new molecular pathways that may be involved in habituation to dark flashes. Their screening approach, while not novel, will be a powerful way to interrogate other behavioural profiles. The authors identified circuit loci apparently involved in habituation to dark flashes, and the potentiation and no adaptation clusters have not been previously observed as far as I know.

The data will be useful to guide follow-up experiments by the community on the new pathway candidates that this screen has uncovered, including behaviours beyond dark flash habituation.

Reviewer #3 (Public Review):

To analyze the circuit mechanisms leading to the habituation of the O-bed responses upon repeated dark flashes (DFs), the authors performed 2-photon Ca2+ imaging in larvae expressing nuclear-targeted GCaMP7f pan-neuronally panning the majority of the midbrain, hindbrain, pretectum, and thalamus. They found that while the majority of neurons across the brain depress their responsiveness during habituation, a smaller population of neurons in the dorsal regions of the brain, including the torus longitudinalis, cerebellum, and dorsal hindbrain, showed the opposite pattern, suggesting that motor-related brain regions contain non-depressed signals, and therefore likely contribute to habituation plasticity.

Further analysis using affinity propagation clustering identified 12 clusters that differed both in their adaptation to repeated DFs, as well as the shape of their response to the DF.

Next by the pharmacological screening of 1953 small molecule compounds with known targets in conjunction with the high-throughput assay, they found that 176 compounds significantly altered some aspects of measured behavior. Among them, they sought to identify the compounds that 1) have minimal effects on the naive response to DFs, but strong effects during the training and/or memory retention periods, 2) have minimal effects on other aspects of behaviors, 3) show similar behavioral effects to other compounds tested in the same molecular pathway, and identified the GABAA/C Receptor antagonists Bicuculline, Amoxapine, and Picrotoxinin (PTX). As partial antagonism of GABAAR and/or GABACR is sufficient to strongly suppress habituation but not generalized behavioral excitability, they concluded that GABA plays a very prominent role in habituation. They also identified multiple agonists of both Melatonin and Estrogen receptors, indicating that hormonal signaling may also play a prominent role in habituation response.

To integrate the results of the Ca2+ imaging experiments with the pharmacological screening results, the authors compared the Ca2+ activity patterns after treatment with vehicle, PTX, or Melatonin in the tethered larvae. The behavioral effects of PTX and Melatonin were much smaller compared with the very strong behavioral effects in freely-swimming animals, but the authors assumed that the difference was significant enough to continue further experiments. Based on the hypothesis that Melatonin and GABA cooperate during habituation, they expected PTX and Melatonin to have opposite effects. This was not the case in their results: for example, the size of the 12(Pot, M) neuron population was increased by both PTX and Melatonin, suggesting that pharmacological manipulations that affect habituation behavior manifest in complex functional alterations in the circuit, making capturing these effects by a simple difficult.

Since the 12(𝑃𝑜𝑡, 𝑀) neurons potentiate their responses and thus could act to progressively depress the responses of other neuronal classes, they examined the identity of these neurons with GABA neurons. However, GABAergic neurons in the habituating circuit are not characterized by their Adaptation Profile, suggesting that global manipulations of GABAergic signaling through PTX have complex manifestations in the functional properties of neurons.

Overall, the authors have performed an admirably large amount of work both in whole-brain neural activity imaging and pharmacological screening. However, they are not successful in integrating the results of both experiments into an acceptably consistent interpretation due to the incongruency of the results of different experiments. Although the authors present some models for interpretation, it is not easy for me to believe that this model would help the readers of this journal to deepen the understanding of the mechanisms for habituation in DF responses at the neural circuit level.

This reviewer would rather recommend the authors divide this manuscript into two and publish two papers by adding some more strengthening data for each part such as cellular manipulations, e.g. ablation to prove the critical involvement of 12(Pot, M) neurons in habituation.

Reviewer #1 (Public Review):

Rosas et al studied the mechanism/s that enabled carbapenems resistance of a Klebsiella isolate, FK688, which was isolated from an infected patient. To identify and characterize this mechanism, they used a combination of multiple methods. They started by sequencing the genome of this strain by a combination of short and long read sequencing. They show that Klebsiella FK688 does not encode a carbapenemase, and thus looked for other mechanisms that can explain this resistance. They discover that both DHA-1 (located on the mega-plasmid) and an inactivation of the porin OmpK36, are required for carbapenem resistance in this strain. By using experimental evolution, it was shown that resistance is lost rapidly in the absence of antibiotics selection, by a deletion in pNAR1 that removed blaDHA-1. Moreover, their results suggested that it is likely that exposure to other antibiotics selected for the acquisition of the mega-plasmid that carries DHA-1, which then enabled this strain to gain resistance to carbapenemase by a single deletion.

The major strength of this study is the use of various approaches, to tackle an important and interesting problem.

The conclusions of this paper are mostly well supported by data, but one aspect is not clear enough. The description of the evolutionary experiment is not clear. I could not find a clear description of the names of the evolved populations. However, the authors describe strains B3 and A2, but their source is not clear. The legends of the relevant figure (Figure 5) are confusing. For example, the text describing panel B is not related to the image shown in this panel. Moreover, it is shown in panel C (and written in the main text) that the OmpK36+ evolved populations had only translucent colonies, so what is the source of B3(o)?

Reviewer #2 (Public Review):

The authors sequenced a clinical pathogen, Klebsiella FK688, and definitively establish the genetic basis of the carbapenem-resistance phenotype of this strain. They also show that the causal mutations confer reduced fitness under laboratory conditions, and that carbapenem sensitivity readily re-evolves in the lab due to the fitness costs associated with the resistance mutations in the clinical isolate. They also establish that subinhibitory concentrations of ceftazidime select for the otherwise deleterious blaDHA-1 gene. Based on this finding the authors speculate that prior beta-lactam selection faced by the ancestors of Klebsiella FK688 potentiated the evolution of the carbapenem-resistance phenotype of this strain. If this hypothesis is true, then prior history of beta-lactam exposure may generally potentiate the evolution of carbapenem resistance.

Strengths:

From a technical perspective, the findings in this paper are solid. In addition, the authors establish a simple genetic basis for carbapenem resistance in a clinical strain, which is a valuable and non-trivial finding (i.e. they show that the CRE phenotype in this strain is not an omnigenic trait distributed over hundreds of loci).

Weaknesses:

The main weakness of this paper is that the authors draw overly broad conclusions of a conceptual nature from narrow experimental findings. This could be addressed by drawing more modest and narrow implications from the findings.

1) The title of this paper is "Treatment history shapes the evolution of complex carbapenem-resistant phenotypes in Klebsiella spp." But they provide no data on the treatment history of the patient from whom this strain was isolated from. Therefore, the authors have no evidence to support their central claim. Indeed, it is completely possible that this strain never faced beta-lactam selection in the past, or that the patient's hypothetical history of betalactamase was irrelevant for the evolution of FK688. First, it is completely possible that this is a hospital-acquired infection, such that the history of this strain is due to selection in other contexts in the hospital that have little to do with the patient's treatment history. Second, it is completely possible that this strain (the chromosome anyway) has no prior history of beta-lactamase selection, and that it acquired the megaplasmid containing blaDHA-1 via conjugation from some other strain. In this second hypothetical scenario, it is possible that the fitness cost of the blaDHA-1 gene is not particularly high in a different source strain, but that it has some cost in the FK688 strain that it was isolated from. And of course, fitness costs in the human host could be very different than fitness costs in the laboratory, where strains are evolving under strong selection for fast growth. And given the benefit of resistance, it's clear that this strain clearly has a strong fitness advantage over faster-growing sensitive strains in the context of the source patient under antibiotic treatment.

My general point here is that the broad claims made about patient history or prior history shaping the evolution of this strain are largely indefensible because there is no data here to make solid inferences about *how* prior history shaped the evolution of this strain.

2) Historical contingency. The authors claim that their work shows how historical contingency shapes the evolution of resistance. One problem with this claim is that it is trivial- this is only a significant claim if the reader believes that prior history is not important in the evolution of antibiotic resistance, which is a straw-man null hypothesis, to mix a couple metaphors. To be more concrete, clearly strain background (prior history) matters-eliminating the plasmid with the resistance gene eliminates resistance. But that is not particularly surprising, given the past 50 years of evolutionary microbiology literature on plasmids and resistance. By contrast to this work, the major contribution of papers that examine the role of historical contingency in evolution (i.e. various Lenski papers) is that those works *quantitatively* measure the role of history in comparison to other factors (chance, adaptation). Since this work is a deep dive into a single clinical isolate, the data presented here do not and cannot shed light on the role of historical contingency in the emergence of this strain. The authors' claims about the prior history that led to the CRE phenotype are reasonable- but are fundamentally speculative. I have nothing against speculation, as long as it is clear what claims are speculative, and what are concrete implications. But the authors frame these speculative claims as concrete implications of their findings.

3) The authors claim that "[This work] suggests that the strategic combinations of antibiotics could direct the evolution of low-fitness, drug-resistant genotypes". I suppose this is true, but I also think this is a stretch of an implication given these findings. To be blunt, while I suppose it's better to have costly resistance variants that re-evolve sensitivity than to have low-cost high-resistance strains circulating, I think the patient's family would probably disagree that the evolution of a low-fitness drug-resistant genotype was good or strategic in the clinical context, even if better from a public health perspective. Low-fitness drug-resistant strains are just as lethal under clinical antibiotic concentrations!

The authors do show the plausibility of their hypothesis/model that prior beta-lactam selection is sufficient to potentiate the evolution of carbapenem-resistance (by the additional ompK loss-of-function mutation). I think those findings are very nice. But the authors undermine their results by extrapolating too far from their data. Hence, I think narrowing the scope of the implications would improve this paper.

In addition to narrowing the scope of the implications as written, I also would like to add that there may be other ways of framing this paper (other than historical contingency) that may make the significance of this work more apparent to a broader audience. This may be worth considering during the revision process.

Reviewer #1 (Public Review):

The glideosome-associated connector is an essential piece of the machinery used by the apicomplexa parasites as they invade host cells. This GAC makes important interactions with the membrane and with actin during this process. Here, Kumar et al present the first structure of the GAC from T. gondii, showing a complex fold in a closed form. This structure was determined at pH 5, and they show that at more physiological pH values the structure is far more open. However, this is not in the context of actin, membrane, or other binding partners, and so the question remains about how open the structure is in its physiological context. The authors next use molecular dynamics, NMR, and mutagenesis to identify the residues involved in membrane binding and also assess actin binding through modelling which is not validated by experiment. This paper presents an important contribution to our understanding of the molecular machinery involved in host cell invasion but leaves many questions remaining about how this protein links to the cytoskeleton and functions during the invasion process.

• The structure of TgGAC provides the first such structure of this complex and is an important contribution to our understanding. The structure presented in Figure 1A is a composite, containing the crystal structure of the majority of the protein, determined at pH 5, to which has been docked the PH domain structure, determined by NMR. It would be good to see more clarity in the figure about what is experimentally determined and what is modelled.<br /> • SAXS data shows that, at pH 8, a substantial fraction of the protein is in a very extended conformation, which differs significantly from the compact structure seen in crystals at pH 5. I would prefer to see the models in Figure 2d represented as spheres or surfaces, to prevent over-interpretation associated with showing models with low-resolution data. However, the SAXS findings are robust and this is clearly a dynamic molecule in solution. It will be interesting to see what the situation is in the context of binding partners.<br /> • Molecular dynamic simulations next indicate the region which binds to a lipid bilayer, with contact residues forming a consistent interaction surface in three independent simulations. This identified the PH domain and neighbouring residues as the membrane interaction surface.<br /> • Switching to Plasmodium falciparum protein, the authors next use NMR to investigate the binding of the PH domain to membrane nanodiscs, and show that the same protein region identified in the MD simulations was found to bind in the NMR experiments.<br /> • These membrane binding assays were then followed up through liposome pelleting assays, using TgGAP, which showed that the protein only pellets in the presence of PA lipid and that mutation of residues identified through NMR abolished liposome binding. The mutations didn't have the same effect on full-length and PH domains (noting KER for example) suggesting that lipid binding is not entirely mediated by the PH domain in the full-length protein.<br /> • The authors next put the mutants into toxoplasma and assay the effect on apical localisation and on invasion percentage. Interestingly the mutants had little effect, perhaps due to the role of other regions of the GAC on lipid binding, suggesting that abolishing PH domain lipid binding is not sufficient. Unfortunately, as the mutations only partly reduced lipid binding in the context of full-length GAC, as shown in liposome experiments, it is hard to come to a firm conclusion about the importance of lipid binding from this data as the protein used in this experiment will still have partial lipid binding properties.<br /> • The authors next investigate actin binding by TgGAC and show that most of the N-terminal half of the protein is required for this function. The authors propose, using AlphaFold2 and similarities to catenins, how GAC might bind to actin. In the absence of any validation from experimental data, caution is needed here, and I would personally not rely on the accuracy of these models.

Reviewer #2 (Public Review):

Toxoplasma gondii (Tg) and Plasmodium falciparum (Pf) are two protozoan parasites that both present threats to global human health as the causal agents of toxoplasmosis and malaria, respectively. In absence of effective vaccines, disease control relies heavily on the use of drugs aimed at treating infected patients to inhibit parasitic growth and eventually kill parasites to interrupt the parasitic lifecycle. These obligate intracellular vacuole-dwelling parasites quickly attach to their host cells before actively pinching through their plasma membranes and completing their complex respective lifecycles.

Kumar et al. seek to understand the complex process of host cell recognition, attachment, and invasion in order to devise possible strategies to possibly interfere and/or block to prevent invasion of the host cell or compromise egress from the infected cell. Characterizing the 3D structure at atomic resolution and dynamics of the glideosome molecular machinery involved in parasite attachment and invasion/egress provides grounds for the future rational design of novel anti-parasitic therapies targeting novel molecular targets and phylum-specific biological processes. Toxoplasma belongs to the same large family of obligate intracellular parasites such as the malaria parasite Plasmodium. These protozoa actively attach and glide at the surface of their target host cell before invading it. Such motility and propulsion at the surface of the host cell are powered by a large protein complex, the glideosome.

The article elegantly combines structural, biophysical, biochemical, computational, and cell biology approaches to dissect the structure and mechanism of action of TgGAC (and PfGAC).

The crystal structure of TgGAC was solved at an apparent 2.7A resolution by se-mad and although it is overall well described it requires further polishing in terms of model quality and accuracy. This is a very large protein, so it represents a considerable amount of work to build and refine. We note deficiencies in the way refinement (atomic displacement parameters and model building in general) and phasing statistics description were carried out or presented. This warrants further inspection and requires significant improvement and corrections to meet the usual standards expected from this field of research.

Solution scattering data while supporting the model of a conformational change between a compact (closed) conformation observed in the crystal obtained at pH 5 and an extended monomeric conformation observed at pH 8 more amenable to interactions with other cellular partners in the context of a functional glideosome needs some clarification. Because of the way proteins seem to be prepared for the SAXS analysis, I have some objections to the interpretation of some of the data.

The biochemical analysis of lipid binding specificity of the small c-terminal pleckstrin-like domain of TgGAC and PfGAC (full-length or c-terminal domain) using liposome binding assays, elegant NMR relaxation methods but also molecular dynamics on full-length GAC models are extremely convincing and support all authors claim.

The fact that however the CTD lipid binding activity is not required in vivo is a bit surprising although CTD seems required to stabilize the protein in vitro.

The section describing the hydrogen-deuterium exchange analysis of TgGAC conformation is confusing as it stands and requires clarification. It fails to be compelling in my personal opinion.

Reviewer #3 (Public Review):

The authors present a multi-disciplinary structural analysis of the glideosome-associated connector (GAC), which is important for the motility of parasites within the Apicomplexa phylum. Strengths of the study include the first crystal of the GAC, revealing an elaborate pyramid structure with a protruding arch bearing a PH domain. The lipid binding analyses, featuring NMR experiments and simulations to identify key residues, provide a nice complement to the crystal structure. There are interesting differences between the structure obtained and the small-angle X-ray scattering data, which are plausibly (but not conclusively) explained by a model in which GAC uses multiple conformations. It is also puzzling that the lipid binding residues in the PH domain do not seem vital for parasite invasion, although this may be explained by the second lipid binding site in the GAC arch. The AlphaFold prediction of the interface between the GAC and a peptide from MIC2 is interesting, in that it is reminiscent of the B-catenin/E-cadherin interaction, but requires validation. The study will be useful for researchers investigating the structural mechanism of parasite motility.

Reviewer #1 (Public Review):

During the height of the Covid19-pandemic, there was great and widely spread concern about the lowered protection the screening programs within the cancer area could offer. Not only were programs halted for some periods because of a lack of staff or concern about the spreading of SARS CoV2. When screening activities were upheld, participation decreased, and follow-up of positive test results was delayed. Mariam El-Zein and coworkers have addressed this concern in the context of cervical screening in Canada, one of the rather few countries in the world with well organized, population-based, although regionalized, cervical screening program.

Despite the existence of screening registries, they choose to do this in form of a survey on the internet, to different professional groups within the chain of care in cervical screening and colposcopy. The reason for taking this "soft data" approach is somewhat diffuse. The authors claim they want to "capture modifications". However, the suggestions that come from this study are limited and are submitted for publication 2 years after the survey when the height of the pandemic has passed long since, and its burden on the screening program has largely disappeared. The value of the study had been larger if either the conclusions had been communicated almost directly, or if the survey had been done later, to sum up the total effect of the pandemic on the Canadian cervical screening program.

Another major problem with this study is the coverage. The results of persistent activities to get a large uptake is somewhat depressing although this is not expressed by the authors. 510 professionals filled out the survey partially or in total. 10 professions were targeted. The authors make no attempt to assess the coverage or the validity of the sample. They state the method used does not make that possible. But the number of family practicians, colposcopists, cytotechnicians, etc. involved in the program should roughly be known and the proportion of those who answered the survey could have been calculated. My guess is that it is far below 10%. Also, the national distribution seems shewed despite the authors boosting its pan-Canadian character. I am just faintly familiar with the Canadian regions, but, as an example, only 2 replies from Quebec must question the national validity of this survey.

The result section is dominated by quantitative data from the responses to the 61 questions. All questions and their answers are tabulated. As there is no way to assess the selection bias of the answers these quantitative results have no real value from an epidemiological standpoint. The replies to the open-ended questions are summarized in a table and in the text. The main conclusion of the content analysis of the answers to the direct questions, and one of the main conclusions of the study, is that the majority favors HPV self-sampling in light of the pandemic. However, this not-surprising view is taken by only 80 responders while almost as many (n=60) had no knowledge about HPV self-sampling.

The authors conclude that their study identified the need for recommendations and strategies and building resilience in the screening system. No one would dispute the need, but the additional weight this study adds, unfortunately, is low, from a scientific standpoint.

The conclusion I draw from this study is that the authors have done a good job in identifying some possible areas within the Canadian screening programs where the SARS-Cov2 pandemic had negative effects and received some support for that in a survey. Furthermore, they listed a few actions that could be taken to alleviate the vulnerability of the program in a future similar situation, and received limited support for that. No more, no less.

Reviewer #2 (Public Review):

The study aimed to provide information on the extent to which the COVID-19 pandemic impacted cervical cancer (CC) screening and treatment in 3 Canadian provinces. The survey methodology is appropriate, and the results provide detailed descriptive statistics by province and type of practice. The results support the authors' conclusions. This evidence together with data gathered from other national surveys may provide baseline data on the impact of the pandemic on CC outcomes such as late-stage diagnoses and CC treatment outcomes due to these delays.

Reviewer #1 (Public Review):

OTOP ion channels are proton-activated, proton-permeable proteins that participate in sour tasting but for which other physiological roles are just beginning to be elucidated. The authors of this manuscript noticed that the isoform OTOP3 shows activation by protons that are potentiated in the presence of Zn2+ and other divalent ions, while other isoforms are not weakly or not at all potentiated. This allowed them to apply a chimeric approach to define which regions of the protein are responsible for the Zn2+ effect. The authors found that a single extracellular loop and a single histidine residue located in it are sufficient to explain the potentiation and propose that this histidine is part of a binding site that allosterically couples to yet undefined proton binding sites(s) responsible for proton gating.

The authors have performed very high-quality experiments and carried out a careful analysis of the data. This characterization of gating behavior of OTOP channels should be a step in elucidating physiological roles and in understanding the dynamics of these proteins. For these reasons, it should be of interest to researchers working in molecular biophysics and the physiological roles of ion channels.

Reviewer #2 (Public Review):

OTOP channels are relatively newly discovered and their physiology is poorly understood. Zn activation appears to be a differentiating feature of OTOP function and Zn is a pharmacological tool for research. The Zn potentiation of OTOP3 is a curious phenomenon that is studied very carefully here. The language in this manuscript is appropriately nuanced in the interpretation of results and is delightfully agnostic with regards to function vs binding. The major strengths of this work are the very thorough characterization of the zinc effect and the identification of the 11-12 loop as necessary and sufficient for the zinc effect.

Reviewer #3 (Public Review):

The authors characterized the effect of Zn2+ in potentiating OTOP1 and OTOP3 proton-activated H+ currents. They took advantage of a set of chimeras with swapped extracellular loops between OTOP3 (Zn2+-dependent potentiation) and OTOP2 (no potentiation) by neatly identifying an extracellular loop that is sufficient to confer Zn2+ potentiation. The results support the idea that within this loop resides at least part of the Zn2+ binding site, a hypothesis also confirmed by the role of a histidine residue. The authors suggested that Zn2+ potentiation of OTOP3 involves different structural elements than those required for inhibition, the conclusion that is supported by the data on the OTOP3-OTOP2 chimeras. These results shed light on a new aspect of the gating mechanism of these channels, adding an important piece to the puzzle to decipher their role in cells. This manuscript provides an important result for scientists whose research is focused on proton channels, and ion channel gating mechanisms.

Weaknesses: Although the identification of the extracellular loop represents an important result to define the structural element that confers Zn2+ potentiation to OTOP3, there are several aspects of the gating mechanism that would require a deeper analysis. The mutagenesis of the OTOP3 tm11-12 linker is very limited and does not include mutagenesis experiments in OTOP2 and OTOP1 that would further support the conclusion proposed by the authors and extend the importance of the tm11-12 linker to all the three OTOP channels (as stated in the manuscript title).<br /> Moreover, only one residue has been identified as important for Zn2+ binding. Given the three-dimensional structures of OTOP channels available to this date, particularly the chicken OTOP3 structure (PDB:6NF6), a structural analysis would certainly provide a set of putative partners for the histidine identified as the key residue for Zn2+ potentiation. Even if it is hard to understand what conformational state is represented in the structure, this analysis will provide a valid starting point to investigate the functional relevance of these residues.

Reviewer #1 (Public Review):

In this study, the authors set out to determine the degree to which early language experience affects neural representations of concepts. To do so, they use fMRI to measure responses to 90 words in adults who are deaf. One group of deaf adults (n=16) were native signers (and thus had early language exposure); a second group (n=21) was exposed to sign language later on. The groups were relatively well-matched in other respects. The primary finding was that the high dimensional representations of concepts in the left lateral anterior temporal lobe (ATL) differed between native and delayed signers, suggesting a role for early language experience in concept representation.

The analyses are carefully conducted and reflect a number of thoughtful choices. These include the "inverted MDS" method for constructing semantic RDMs, a normal hearing comparison group for both behavioral and fMRI data, and care taken to avoid bias in defining functional ROIs. And, comparing early and delayed signing groups is a clever way to study the role of early language experience on adult language representations.

One interesting result that I struggled to put in a broader context relates to the disconnect between behavioral and neural results. Specifically, the behavioral semantic RDMs (Figure 1a) did not differ between any of the groups of participants. This suggests that the representations of the 90 concepts are represented similarly in all of the participants. However, the similarity of the neural RDMs in left lateral ATL differs between the native and delayed signing groups (but not in other regions). Given the similarity of the behavioral semantic RDMs, it is unclear how to interpret the difference in left lateral ATL representations. In other words, the neural differences in left ATL do not affect behavior (semantic representation). The importance of the differences in neural RDMs is therefore questionable.

An important point is that, if I understand correctly, the semantic space is defined by the 90 experimental items. That is, behavioral RDMs were created by having normal hearing participants arrange 90 items spatially, and neural RDMs were created by comparing patterns of responses to these 90 experimental items. This 90-dimensional space is thus both (a) lower dimensional than many semantic space models that include hundreds of directions and (b) constrained by the specific 90 experimental items chosen. On the one hand, this seems to limit the generalizability of the findings for semantic representations more broadly.

The logic behind using a categorical semantic RDM (e.g., Figure 2a) was not clear. The behavioral semantic RDMs (Figure 1a) clearly show gradations in dissimilarity, particularly for the abstract categories. It would seem that using the behavioral semantic RDM would capture a more accurate representation of the semantic space than the categorical one.

Reviewer #2 (Public Review):

The authors investigated patterns of fMRI activation for familiar words in two groups of deaf people. One "language rich" group received exposure to sign from birth, whereas the "language poor" group included kids born to hearing parents who had limited exposure to language during the first few years of life. The primary findings involved group differences in BOLD activation patterns across different areas of interest within the semantic network when participants made intermittent 1-back category judgments for words appearing in succession.

There was much to be liked about this study, including the rigor of the methods and the novel contrasts of two deaf samples. These strengths were balanced by a number of questions about the assumptions and theoretical interpretations underlying the data. I will elaborate on the major points in the paragraphs to follow, but briefly, the ways in which the authors are framing critical period constraints in language fundamentally differ from the standard nativist perspectives (e.g., Chomsky, Lenneberg). The assumptions of what constitutes a deprivation model require further justification and perhaps recasting to avoid unnecessary stigma (i.e., this reviewer was uncomfortable with the assertion that being born deaf to hearing parents by default constitutes deprivation). The introduction lacked principled hypotheses that motivated the choice of comparing abstract and concrete words, and potential accounts of group differences were underdeveloped (e.g., how do parents in China typically react to having a deaf child, and what supports are in place for preventing language deprivation? Are newborn infants universally screened for hearing loss in China? The answers to these questions might help the readers to understand why/how deaf children in this circumstance might experience deprivation).

References to critical periods require a bit more elaboration with respect to lexical-semantic vs. semantic acquisition. The nature of the critical period in language acquisition remains controversial with respect to its constraints. Lenneberg and Chomsky speculated that the limit of the critical period for language acquisition was about puberty (13ish years of age). This is much older than the deaf sample tested here so arguments about aging out of the critical period at least for language acquisition need more nuance. Another issue relates to learning semantic mappings vs. learning language as falling under the same critical period umbrella. This seems highly unlikely as semantic acquisition in early childhood is aided by linguistic labeling but would likely occur in parallel even in the context of language deprivation. Much of the prior literature on critical periods and nativist approaches to language development has focused on syntactic acquisition and elements such as recursion rather than a mapping of symbols to conceptual referents. This makes the critical period group comparison somewhat tenuous because what you are really interested in is a critical period for word meaning acquisition not the more general case of syntactic competency.

The point above is highlighted in the following statement underlying one of the primary assumptions of the study:<br /> Pg. 3, "Here, we take advantage of a special early-life language-deprivation human model: individuals who were born profoundly deaf in hearing families and thus had very limited natural language exposure (speech or sign) during the critical period of language acquisition in early childhood"

"hypofunction of the language system as a result of missing the critical period of language acquisition" (pg 3), same critique as previous - the critical period window is thought to be 13ish years old.

There are a couple of problems with this assertion/assumption. Although it is true that most children who are born deaf have hearing parents, it is not justifiable to label this condition an early-life deprivation model. Hearing parents who are extremely motivated to learn sign language and pursue related language enrichment strategies can successfully offset many of these effects. Similarly, it is not inconceivable that a deaf child born to a deaf parent might be neglected or abandoned without the benefit of early sign exposure. My argument here is that classifying deaf children born to hearing parents as automatically 'language deprived' is potentially both stigmatizing and scientifically unjustified.

Pg. 6 "It should be noted that the neural semantic abstractness effect does not equate with language-derived semantic knowledge, as it might arise from some nonverbal cognitive processes that are more engaged in abstract word processing (Binder et al., 2016)." - I had great difficulty understanding what this meant.

Reviewer #3 (Public Review):

This work extends earlier findings from this group which showed in congenitally blind individuals preserved, presumably language-derived, representations of colour knowledge are present only in dATL. While the present study confirms the importance of language in representations in dATL, the specificity of dATL hinges on descriptive rather than inferential statistics, and future studies may be needed to demonstrate the primacy of dATL in language-based representation as well as the generalisability of effects across different flavours of conceptual knowledge.

Reviewer #1 (Public Review):

In this paper, the authors present a method for discovering response properties of neurons, which often have complex relationships with other experimentally measured variables, like stimuli and animal behaviors. To find these relationships, the authors fit neural data with artificial neural networks, which are chosen to have an architecture that is tractable and interpretable. To interpret the results, they examine the first- and second-order approximations of the fitted artificial neural network models. They apply their method profitably to two datasets.

The strength of this paper is in the problem it is attempting to solve: it is important for the field to develop more useful ways to analyze and understand the massive neural datasets collected with modern imaging techniques.

The weaknesses of this paper lie in its claims (1) to be model free and (2) to distinguish the method from prior methods for systems identification, including spike triggered averaging and covariance (or rather their continuous response equivalents). On the first claim, the systems identification methods are arguably substantially more model free approach. On the second claim, this reviewer would require more evidence that the presented approach is substantially different from or an improvement on systems identification methods in common use applied directly to the data.

Reviewer #2 (Public Review):

This paper describes a relatively unbiased and sensitive method for identifying the contributions of different behavioral parameters to neural activity. Their approach addresses, in an elegant way, several difficulties that arise in modeling of neuronal responses in population imaging data, namely variations in temporal filtering and latency, the effects of calcium indicator kinetics, interactions between different variables, and non-linear computations. Typical approaches to solving these problems require the introduction of prior knowledge or assumptions that bias the output, or involve a trade-off between model complexity and interpretability. The authors fit individual neuron's responses using neural network models that allow for complex non-linear relationships between behavioral variables and outputs, but combine this with analysis, based on Taylor series approximations of the network function, that gives insight into how different variables are contributing to the model.

The authors have thoroughly validated their method using simulated data as well as showing its applicability to example state of the art data sets from mouse and zebrafish. They provide evidence that it can outperform current approaches based on linear regression for the identification of neurons carrying behaviorally relevant signals. They also demonstrate use cases showing how their approach can be used to classify neurons based on computational features. They have provided Python code for the implementation and have explained the methods well, so it will be easy for other groups to replicate their work. The method could be applied productively to many types of experiments in behavioral and systems neuroscience across different model systems. Overall, the paper is clearly written and the experiments are well designed and analysed, and represent a useful contribution to the neuroscience field.

Reviewer #3 (Public Review):<br /> <br /> In the current study, the authors present a novel and original approach (termed MINE) to analyze neuronal recordings in terms of task features. The method proposed combines the interpretability of regressor-based methods with the flexibility of convolutional neural networks and the aim is to provide an unbiased, "model-free" approach to this very important problem.

In my opinion, the authors succeed in most of these aspects. They use three datasets: an artificially-generated one that provides a ground-truth, a published dataset from wide-scale cortical mouse recordings and a novel one that studies thermosensation in larval zebrafish. MINE compares favorably in all three cases.

I believe that the paper would mostly benefit from an increased effort in clear exposition of the Taylor expansion approach, which is at the core of the method. The methods section describes the mathematics, but I wonder whether it would be possible to illustrate or schematize this in a main Figure, e.g. as an addition to Figure 1 or as a new figure. Around line 185, the manuscript reads: "We therefore perform local Taylor expansions of the network at different experimental timepoints. In other words, we differentiate the network's learned transfer function that transforms predictors into neural activity."

It would help to explicitly state with respect to what the derivative is being computed (i.e. time) and maybe a diagram (which I had to draw to understand the paper) in which a neuronal activity trace is shown and from time t onwards a prediction is computed using terms in the Taylor expansion would be very instructive (showing on an actual trace how disregarding certain terms changes the prediction and hence the conclusions about the actual dependence of the trace on the behavioral features). The formulation in terms of Jacobians and Hessians can then be restricted to the Methods section and the paper will be easier to read for a wider audience. The method is presented as a "model-free" approach (title and introduction). I think it would help to discuss this with some precision. The Taylor expansion approach does imply certain beliefs on the structure of the data (which are well founded in most cases). Do the authors agree that MINE would encapsulate any regression model where both linear and interaction terms are allowed to include an arbitrary non-linearity (in the case of the interaction terms, different non-linearities for both variables)? If this is the case, maybe an explicit statement would allow the reader to quickly identify the versatility of MINE.

I find the section relating to non-linearities interesting, but was slightly disappointed to find that the authors do not propose a single method. In Figure 3E, the authors show that a logistic regression model that combines the curvature and NLC apporaches outperforms either, but the model is not described in any sort of detail. I appreciate the attempt made by the authors to apply this to the zebrafish imaging dataset in Figure 7, but it was still unclear to me how non-linearities and complexity are related.

Reviewer #1 (Public Review):

Li et al investigated the behavioral response and fMRI activations associated with deep brain stimulation (DBS) of the lateral habenula (LHb) in 2 distinct rodent models of depression. They found that a) LHb DBS reduces depressive and anxiety behaviors using multiple behavioral tests: sucrose preference, forced swim, and open field. These results held across multiple models of depression and multiple tests, and generally restored results of these behavioral tests to parity with controls. Furthermore, fMRI activations of brain regions with known connectivity to LHb strongly correlated with behavioral responses to LHb DBS, particularly in limbic regions. These behavioral responses clearly depended on electrode location, with more medial placements within the LHb producing a more robust behavioral effect.

The conclusions of this paper are generally well supported by the data, with the primary weaknesses of the study being 1) limited novelty due to LHb already being a well-established target for DBS in depression, and 2) the questionable validity of rodent models of depression in general. The authors deal with the first point (novelty) by extending their study to electrode localization and fMRI correlates with the behavioral response, leading to insight into surgical targeting as well as mechanism of effect, respectively. They also partially mitigate fundamental problems with rodent models of depression by using 2 different models and showing consistent responses to LHb DBS across both. The methods used in this study were sound, with high-quality techniques used for electrode implantation, confirmation of electrode placement, fMRI acquisition, anesthesia and physiological monitoring, as well as an appropriate statistical analytic approach.

Reviewer #2 (Public Review):

This important paper is a real tour de force and combines functional MRI, behaviour, and brain stimulation to characterise the effect of stimulation of the lateral habenula in a rodent model for depression. The results are stunning and the data presented seems compelling.

My only comment is I would like more discussion on the relevance of these results for the treatment of depression in humans, both in terms of the rodent model and in terms of the results shown in this study.

Reviewer #1 (Public Review):

Chromosomal aneuploidy in humans causes diseases such as Down syndrome associated with changes in cognitive and metabolic activities, but how extra copies of chromosomes cause the changes remains largely unknown. In this important paper, the authors characterized the metabolisms and physiology of the transgenic mouse with most of human chromosome 21 thoroughly and nicely showed the overexpression of sarcolipin which uncouples Ca2+ import with ATP hydrolysis of sarcoplasmic reticulum Ca2+ ATPase (SERCA), which results in heat production and hyperactive mitochondria activity.

Reviewer #2 (Public Review):

This manuscript is clear in that it shows no/minimal weight gain in a mouse model of trisomy 21 compared to the control mouse, even under a high-calorie diet. The difference is the clear demonstration of the increased expression of sarcolipin. It is important that the expression of SERCA was also shown not different between the genotypes. Additionally, an important result is that manipulating the skeletal muscle was sufficient to promote weight loss without the need for hypermetabolism in other tissues such as adipose tissue.

- A clear explanation of why the expression of sarcolipin/hypermetabolism is different between mouse and human under the same condition would be useful.

- p.12-13 and15. The language around 'futile' cycling is not correct because Ca movement through the sarcoplasmic reticulum of the resting fiber is essential to the function of the muscle. Firstly, the cycle of Ca through the SR is through the ryanodine receptor (RyR) as well as due to slippage through the SERCA (PMID: 11306667, PMID: 35311921). This is not made clear anywhere in the manuscript. Ca leak out of the SR through RyR is an essential component to the control/setting of the resting cytoplasmic [Ca2+] via the activation of store-operated Ca2+ entry, which is in a balance with the activation of the PMCA on the t-system membrane (PMID: 35218018). The SERCA resequesters the leaked Ca2+ from the SR. It is not possible that the resting [Ca2+] is set by the reduced efficiency of the SERCA, as indicated in the ms (PMID: 20709761). It is expected that the mito [Ca2+] steady state is set by the raised resting cyto [Ca2+] (PMID: 20709761). Ca2+ transients during EC coupling will promote transient increases in mito Ca2+ (PMID: 21795684, PMID: 36121378), but not steady-state increases. Some of these problems are highlighted by the errors in the diagram Fig 5D: please change/correct (i) the invagination of the sarcolemma is called the t-system; (ii) the cycle of Ca leak through the SR starts with RyR Ca leak, where the Ca is resequestered by the SERCA, in addition to Ca slippage through the pump. Draw a RyR opposite the t-system on the SR terminal cisternae. The heat generated by SERCA is absorbed in the cytoplasm, metabolites enter the mito and the OxPhos generates heat (PMID: 31346851). (iii) Ca does not enter mito because it cannot get into the SR (the resting cyto Ca is controlled by the t-system/plasma membrane, PMID: 20709761, PMID: 35218018). Please redraw.

- The changing of the properties of the muscle towards oxidative properties is consistent with the expression of sarcolipin in mouse muscle (all of it is in type II fibers). It is important to show whether the muscles have fiber-type shifts. Please report the fiber types of the muscles that have been surveyed in this project.

- Non-shivering thermogenesis (NST) is mentioned in this manuscript as the means of hypermetabolism, as has the lengthened duration of the cyto Ca transients during EC coupling. It is not clear at all what the contribution of NST compared to the increased work of the SERCA to clear released Ca from the cyto to the hypermetabolism. What are the relative proportions? If sarcolipin is largely for NST, then hypermetabolism is about the resting muscle.

- The link that SLN is causing more ATP use at the pump but the heat generated by OxPhos in mito is important and should be made, see Barclays' work (eg. PMID: 31346851). A direct link between the SERCA function and mito function is occurring but I currently don't see one being made in the ms. This could be made clear in Fig 5D diagram.

- p.22. "The reprogramming of glycolytic...elevated Ca transients...". The language is wrong here. Oxidative fibers do not have elevated Ca transients compared to glycolytic. The amplitude of Ca release is greater in glycolytic and the duration of the transient is longer in the oxidative (eg. PMID: 12813151).

- p.22. "as less calcium is being transported into the SR due to uncoupling of the SERCA pumps". The same amount of Ca is being transported, just at the expense of more ATP than would be the case in the absence of SLN. Otherwise, the SR Ca2+ content would not be at a steady state while the SR continuously leaks Ca2+.

- p.23. Tavi & Westerblad (PMID: 21911615) show how Ca transient amplitude and frequency signal in slow and fast twitch fibres. Here, we are not concerned with what is happening in myotubes, where the SR is less developed than in adult fibres.

Reviewer #3 (Public Review):

Sarver et al., propose that TcMAC21 mice are hypermetabolic and that this is the cause of their reduced weight. Unfortunately, the developmental defects of TcMAC21 mice make this a challenging question to definitively answer. The authors claim that TcMAC21 mice are hypermetabolic due to a futile calcium cycling in skeletal muscle, which is caused by up-regulation of SLN. However, all of the data that would go into the energy balance equation (food intake, energy absorption, and energy expenditure) have been improperly analyzed. TcMAC21 pups are 8.5 g lighter than euploid littermates. The body weight data and images in Fig. 3A indicate that TcMAC21 mice runted. This difference is primarily a result of lower lean mass (FIG. 2B). This is important as it sets up many concerns that need to be addressed. Specific comments are noted below.

Specific comments:

1) It is incorrect to normalize EE to lean mass if this parameter is different between groups. Normalizing the EE data to lean mass makes it appear as though TcMAC21 mice exhibited increased EE when in fact this is a mathematical artefact. EE data should simply be plotted as ml/h (or kcal/h) per mouse. Alternatively, ANCOVA can be applied using lean mass as a covariate. Excellent reviews on this topic have been written (PMID: 20103710; PMID: 22205519).

2) It makes no sense to normalize food intake to weight, as it makes no sense to divide metabolic rate by weight as well (see above). If food intake is not normalized, this will clearly show that TcMAC21 mice eat much less than controls, and if plotted as cumulative food intake will show that TcMAC21 are smaller and gain less weight on a high-fat diet because they simply eat less. This further indicates that the major tenet of this paper is not correct.

3) The authors have tried to address the smaller weight of TcMAC21 mice by including weight-matched wild-type mice. However, they only focus on analyzing surface temperature, which is not an indicator of thermogenesis. Moreover, there is no information on whether these weight-matched wild-type mice are similar in age or body composition to the TcMAC21 mice. Nevertheless, the increased surface temperature can also indicate increased heat conservation, which is opposite to thermogenesis. It would make sense that TcMAC21 mice with massive reductions in lean mass would activate compensatory mechanisms of heat conservation to offset increased heat dissipation to the environment. This does seem to be the case, based on the data shown in Fig. 6D (see below).

4) A more optimal method of testing whether increased heat dissipation plays a role in the EE of TcMAC21 mice, is to measure EE at thermoneutrality, where energy dissipation to the environment will be minimized. Here the authors have attempted this in Fig. 6D. Unfortunately, the authors normalized EE to lean mass, artefactually elevating TcMAC21 EE. Despite this mistake, it now looks as though the large differences in EE that were seen at room temp have been attenuated, and only significantly limited to the dark phase. This indicates that in addition to the normalization artefact, higher heat dissipation from smaller TcMAC21 mice may also contribute to the elevated EE at 22C.

5) In Fig. 6D, why is the hourly plot not shown here (like 2D and 4C)? The data clearly are not as striking as the EE data at 22C?

6) GTT was similar between TcMAC21 and controls (Fig. 3I). However, the smaller insulin response could be due to the fact that glucose was normalized to body weight. It would be better to normalize to lean mass, since that is different as well, or simply give all mice the same amount of glucose that the control group receives since this is how it is done in humans.

7) The fecal energy in Fig. 4B only measures the concentration of energy per gram of feces. However, this analysis has failed to take into account total fecal excretion, which should be used to multiply the energy density of the feces. Thus, these data are incomplete and not sufficient to exclude absorption differences between the groups. And it is now curious why if all other metabolic measurements (even though wrong), such as food intake and EE are normalized to body weight, why have the authors not normalized to body weight for the feces data? Is this because if this was done this would show massive elevating in fecal energy in TcMAC21 mice and thus falsify their hypothesis?

8) I cannot find any indication of sample size in any of the EE experiments, aside from the bar graph in Fig. 6D. In any case, this experiment only an n=4 to 5 per group. This is an extremely small number for these types of experiments, so how can the authors be sure of reproducibility with such a low sample size? Are all of the other EE experiments also of similarly small sample sizes?

Reviewer #1 (Public Review):

This is a very interesting and timely paper and one of very few that crosses species. Linear multielectrode array recordings are rapidly becoming state-of-the-art. This means that there is a greater need for finding motifs and/or reliable markers that characterize activity in different cortical layers.

Reviewer #2 (Public Review):

The authors present a new method of determining the boundaries of superficial, input, and deep cortical layers from laminar multielectrode recordings in non-human primates.<br /> It is based on using the generalized phase (GP) of the LFP (filtered between 5-50Hz) in conjunction with phase coupling (to the GP) of spiking activity (from single or multi-units). They report that phase coupling differs between layers. Critically the preferred LFP phase differs between the deep layers and layers above (input/superficial layers), and this measure can be reliably used to infer input/deep layer boundaries.

Spiking on a given channel (for all channels) tended to occur at +/- pi relative to LFPs recorded at superficial/input layers, but at 0pi relative to deep-layer LFPs. This relationship can be used to estimate the input/deep layer boundary. Generally, the estimate obtained was well correlated with measures derived from traditional CSD analysis. Where discrepancies occurred between CSD and phase coupling-based depth estimates, phase coupling-based depth estimates correlated better with additional measures such as firing rates, and low/high-frequency spectral power cross-over, that have been previously reported to align with cortical depth.

These results were present in areas MT (marmoset), V4 (macaque), and PFC (marmoset), and can be performed on short sequences of data under multiple experimental conditions.

This is a novel, easier, and potentially more precise way to assign cortical depth in non-human primates, which may prove useful to the wider research community.

Reviewer #1 (Public Review):

This important study by Di et al., focuses on the mechanism by which potassium channels are activated prior to NLRP3 inflammasome activation. Using confocal- and electron-microscopy studies the authors demonstrate that the potassium channel, TWIK2, located in the endosomal compartment during basal conditions, is translocated onto the plasmalemma upon ATP stimulation. The authors suggest that this translocation triggers potassium efflux and subsequent NLRP3 inflammasome activation. Using Rab11a-deficient cells, the authors also show an essential role for Rab11a in this process.

This is a well written mechanistic study that has novel findings that are of interest to the inflammasome field. It addresses a long-standing question in the field, the exact mechanism by which potassium channel is activated upon treatment with NLRP3 stimuli. However, to make the conclusions more convincing, the authors should include additional stimuli such as pore-forming toxins, LPS transfection, and/or infections with bacterial pathogens to show that the Rab11a-dependent TWIK2 translocation is a universal requirement for initiation of potassium efflux by multiple stimuli and not specific to ATP. Similarly, the authors should include important controls in their inhibitor/siRNA experiments to show that the cells are still functional and the defects they observe are specific to NLRP3 inflammasome.

Reviewer #2 (Public Review):

Previous work by the same group has shown that the potassium channel TWIK2 contributes to the activation of the NLRP3 inflammasome in macrophages. In this manuscript, the authors provide new insights into the biology of TWIK2 and show that TWIK2 translocated to the plasma membrane of macrophages following stimulation with ATP. They show that ATP stimulation induced exocytosis, via a process dependent on the purinergic receptor P2X7, the presence of calcium and vesicle fusion. Genetic deletion of P2X7, depletion of calcium, and pharmacological inhibition of vesicle fusion collectively contributed to the inhibition of current changes and NLRP3 inflammasome activation. The authors also show that the endosomal protein Rab11a translocated to the plasma membrane following ATP stimulation and that Rab11a contributed to NLRP3 inflammasome activation. Depletion of Rab11a in macrophages prevented lung injuries and NLRP3 inflammasome activation in mice treated with LPS.

The major strength of the work is the use of a combination of cell culture work and a mouse model to address the cell biology of inflammasome activation.<br /> The weakness is that the current set of data is not able to fully support the conclusion that Rab11a, P2X7 and calcium influx mediate the translocation of TWIK2 to the plasma membrane. The characterisation of inflammasome activation is also partial. If these weaknesses can be addressed, the authors would have achieved their aims and increased the impact of their work in the field of inflammasome biology.

Reviewer #3 (Public Review):

Here, the authors aim to uncover the mechanism by which the K+ efflux channel TWIK2 contributes to activation of the canonical NLRP3 inflammasome, as a follow on from their 2018 publication identifying TWIK2 as an essential factor in ATP-induced inflammasome activation. They firstly use immunofluorescence to identify TWIK2 trafficking to the membrane following ATP challenge, and is found to colocalise with early and recycling endosomes during homeostasis. The strengths of the paper are the finding that TWIK2 localisation in cells may be altered by ATP. Biophysical investigation of membrane potential identifies extracellular Ca2+ as essential for NLRP3 activation, and the calcium-dependent small GTPase Rab11a was found to colocalise with the plasma membrane upon ATP treatment. Finally, mice harbouring Rab11a siRNA-treated macrophages were found to exhibit reduced inflammation in response to induction of sepsis, further reinforcing the potential of Rab11a targeting for novel therapeutics. However, mechanistic exploration do not provide direct evidence on TWIK2 trafficking or the involvement of Rab11a specifically with NLRP3 inflammasomes, and results with non-specific inhibitors needs to be supported by further experiments.

Peer review report

Title: If it’s real, could it be an eel?

version: 2

Referee: Dr Don Jellyman

Institution: National Institute of Water and Atmosphere (New Zealand)

email: don.jellyman@niwa.co.nz

ORCID iD: 0000-0002-6941-2703

General assessment

An interesting assessment that verifies the obvious – that any monster of ~ 6 m cannot be an eel (Anguilla anguilla), although there is a reasonable likelihood that eels of ~ 1 m could account for some of the “sightings” of elongate animals in the loch. However, even though the outcome is unsurprising, the author approaches the subject in a rigorous and systematic way. As such, the manuscript is of value in eliminating eels as possible candidate species for the mythical monster.

The manuscript is well written and referenced.

Essential revisions that are required to verify the manuscript

Nil

Other suggestions to improve the manuscript

Nil

Decision

Verified: The content is academically sound, only minor amendments (if any) are suggested.

Reviewer #1 (Public Review):

In this paper, Liu et al. analyze a dataset of primate retinal ganglion cell responses to visual stimuli in order to find maximally informative dimensions in the inputs. They use models based on these analyses to examine features of early visual processing that influence predictive coding of visual motion in the early retina. This is an important set of questions because it remains unclear what principles drive sensory encoding and how those principles relate to circuit mechanisms found in sensory systems.

The strength in this paper lies in its rigorous analysis of the maximally informative dimensions (MIDs) of primate retinal ganglion cell signals, and the connections it makes between those dimensions and circuit models for retinal function.

The weakness of this paper lies in drawing strong connections between those analyses and predictive coding by these cells. These analyses of predictive coding are interesting but not tightly related to the MID analysis. This paper also does little to address how the structure of the stimuli affect the conclusions they draw about what circuit features contribute to predictive coding of motion.

Reviewer #2 (Public Review):

Overall, I thoroughly enjoyed reading and reviewing this manuscript. I think that it contributes importantly to the literature and illustrates an appealing way to connect neural data to normative ideas, phenomenological models, and mechanic explanations. In particular, the suggestion that the retina is specifically tailored to support predictive information encoding is normatively appealing, because animals obtain ecological advantages by anticipating their environment. It would be very exciting to figure out how the retina accomplishes this task. The authors begin their analysis of this question by using spatiotemporal receptive fields to phenomenologically describe how retinal ganglion cells nonlinearly integrate visual signals presented in different regions of the visual field. This allows them to identify several spatiotemporal components of the receptive field, termed kernels, that contribute differentially to predictive information encoding. The authors then use neural circuit modeling to reproduce these receptive field properties using biologically plausible bipolar cell inputs to the retinal ganglion cells. This allows them to hypothesize how specific circuit properties may contribute to predictive information encoding. For example, the authors' current models allow them to address the roles of bipolar cell nonlinearities, spatially local coupling between bipolar cells, patchy bipolar cell to retinal ganglion cell connectivity, and activity-dependent neuronal adaptation.

By connecting predictive information encoding to receptive field properties and candidate circuit mechanisms, the authors hope to identify biological fingerprints of predictive information encoding that could carry over to other neural circuits in the brain. I did not find this component of the argument to be convincing. My main concern is that stimulus statistics and neuronal activity statistics dually contribute to the meaning of predictive information, but this study did not dissect the role of stimulus statistics at all. As a result, I think the paper places too much emphasis on mechanism, and not enough emphasis on natural sensory statistics. The authors do devote a figure to illustrating that their receptive field estimation procedure is insensitive to the stimulus ensemble used for fitting (Fig. 4). Indeed, perhaps the receptive field kernels would stay similar if they were fit to natural stimuli. However, it would still be the case that the pattern of predictive information encoding captured by these kernels would strongly vary as a function of stimulus ensemble. For example, here the authors use random synthetic stimuli with relatively short correlation times, which means that the temporal horizon for predictive information encoding is limited (see Liu et al., Nat Neuro, 2021). The pattern of predictive information encoding for natural stimuli may be very different, and it may be that different receptive field components and neural circuit mechanisms contribute to predictive information encoding in that context. Similarly, other sensory systems are adapted to process stimuli with other sensory statistics, and I do not think it's clear that the receptive field components and neural circuit mechanisms identified here will be universally relevant.

The manuscript uses information theoretic methods to infer multiple kernels that describe linear stimulus features that modulate spiking activity of retinal ganglion cells. A potentially interesting limitation of the study is that it assumes that "outputs of these kernels are summed prior to passing through a common nonlinearity." However, many other papers have found that neuronal activity is sometimes governed by multiple linear features that cannot be summed prior to their nonlinear action. It would be interesting to know whether these kinds of features contribute to predictive information encoding in the retina.

A major problem with the manuscript is that its methods are inadequately described. I think that a major revision will be required before readers will be able reproduce the manuscript's results. These missing methodological details also make it difficult for readers to fully assess the manuscript's conclusions, strengths, and limitations.

Reviewer #3 (Public Review):

This is a very interesting and sound work. It has been postulated that sensory neurons could optimize their information about future stimuli, but we still don't know how they can do that. This paper tackled this issue in depth with both phenomenological and mechanistic models, to understand which mechanisms could help optimize this predictive information, and show convincingly that several mechanisms can help for this.

The main limitation is that this is tested for motion at constant speed, and it would be interesting to know what happens in other cases. Also, the part about phenomenological modeling might need clarifications to understand better what really increases predictive information: it is clear the real system does it better than alternative, less realistic models, but in some cases it is not clear what is the key feature of the model.

Reviewer #1 (Public Review):

In this study, Hara and Kuraku identified the genes lost multiple times across the mammalian phylogenetic tree and termed them "elusive genes." They then investigated the features of these elusive genes in the species where they are well preserved. The authors identified several genomic features that drive gene fates toward loss, in addition to the long-presumed functional dispensability. This analysis explains why some genes are more likely to lose during evolution than others.

This study extends the selection-mutation balance theory from nucleotide substitutions to gene losses. In the context of gene losses, functional dispensability determines the selective coefficient, and the genomic features determine the rate of gene loss mutations. While the selective force has been long presumed to be important, the heterogenous genomic features that led to the mutability of gene losses were not carefully investigated in previous studies. This study fills this gap and shows that some genes are intrinsically prone to be lost (and why).

Strengths:<br /> Identification of gene losses across the phylogenetic tree is not trivial, especially when considering the incompleteness of genomes. The authors conducted their bioinformatic analyses carefully and required two independent gene loss events, each supported by multiple species in a monophyletic group. The accuracy in the identification of elusive genes provides a solid basis for the following analyses.

The authors identified genomic features associated with the gene losses in the species where the gene is preserved. This is an important strategy to avoid identifying genomic features that are formed during the gene losses but to identify the genomic features that likely formed before the gene loss. Using this strategy, the authors were able to recognize the intrinsic properties of elusive genes.

Weaknesses:

Gene expression level as a confounding factor was not well controlled throughout the study. Higher gene expression often makes genes less dispensable after gene duplication. Gene expression level is also a major determining factor of evolutionary rates (reviewed in http://www.ncbi.nlm.nih.gov/pubmed/26055156). Some proposed theories explain why gene expression level can serve as a proxy for gene importance (http://www.ncbi.nlm.nih.gov/pubmed/20884723, http://www.ncbi.nlm.nih.gov/pubmed/20485561). In that sense, many genomic/epigenomic features (such as replication timing and repressed transcriptional regulation) that were assumed "neutral" or intrinsic by the authors (or more accurately, independent of gene dispensability) cannot be easily distinguishable from the effect of gene dispersibility.

Ks was used by the authors to indicate mutation rates. However, synonymous mutations substantially affect gene expression levels (https://pubmed.ncbi.nlm.nih.gov/25768907/, https://pubmed.ncbi.nlm.nih.gov/35676473/). Thus, synonymous mutations cannot be simply assumed as neutral ones and may not be suitable for estimating local mutation rates. If introns can be aligned, they are better sequences for estimating the mutability of a genomic region.

The term "elusive gene" is not necessarily intuitive to readers.

Reviewer #2 (Public Review):

By analyzing hundreds of genomes, authors studied the so-called elusive genes, i.e., genes present in human genome but their orthologs deleted in some other mammals. Authors showed their bioinformatic pipeline of identifying these genes (Fig. 1), the genomic or evolutionary features of these genes (e.g. high GC content, Fig. 2), conservation of these features in other vertebrates including remotely related gar or shark (Fig. 3) together with polymorphism level, transcriptional features, epigenetic features of these genes (Fig. 4-6). Finally, in the Discussion section, the authors showed the chromosomal contributions of elusive genes and argued that these genes could be derived from ancient microchromosomes (Fig. 7).

Reviewer #3 (Public Review):

The manuscript by Hara and Kuraku addresses the question of whether some genes have a diverging gene fate (gene loss) due to underlying sequence or genomic properties. To approach this task, the authors introduce a gene loss detection pipeline that takes some previously raised technical concerns of overestimating gene loss (e.g. variations in assembly quality) into account. When applying their pipeline to >100 species, the authors report ~1,000 human genes whose orthologues were lost in multiple mammalian lineages (which they refer to as elusive genes). The study then focuses on integrating all functional evidence that can be obtained from large-scale databases for these elusive genes and test whether their genomic and evolutionary properties in the genomes of human and various other vertebrates (chimpanzee, mouse, chicken, turkey, green anole, central bearded dragon, western clawed frog, coelacanth, spotted gar, bamboo shark, whale shark) differs from the properties of the ~8,000 non-elusive genes (genes stably conserved across the compared species). In addition, the authors further analyse the human genome for the population-level variations, expression profiles and epigenetic features of elusive genes.

Overall, the study is descriptive and adds incremental evidence to an existing body of extensive gene loss literature. The topic is specialised and will be of interest to a niche audience. The text is highly redundant, repeating the same false positive issue in the introduction, methods, and discussion sections, while no clear conclusion or interpretation of their main findings are presented.

Major comments

- While some of the false discovery rate issues of gene loss detection were addressed in the presented pipeline, the authors fail to test one of the most severe cases of mis-annotating gene loss events: frameshift mutations which cause gene annotation pipelines to fail reporting these genes in the first place. Running a blastx or diamond blastx search of their elusive and non-elusive gene sets against all other genomes, should further enlighten the robustness of their gene loss detection approach

- Along this line, we noticed that when annotation files were pooled together via CD-Hit clustering, a 100% identity threshold was chosen (Methods). Since some of the pooled annotations were drawn from less high quality assemblies which yield higher likelihoods of mismatches between annotations, enforcing a 100% identity threshold will artificially remove genes due to this strict constraint. It will be paramount for this study to test the robustness of their findings when 90% and 95% identity thresholds were selected.

- While some statistical tests were applied (although we do recommend consulting a professional statistician, since some identical distributions tend to show significantly low p-values), the authors fail to discuss the fact that their elusive gene set comprises of ~5% of all human genes (assuming 21,000 genes), while their non-elusive set represents ~40% of all genes. In other words, the authors compare their sequence and genomic features against the genomic background rather than a biological signal (non-elusiveness). An analysis whereby 1,081 genes (same number as elusive set) are randomly sampled from the 21,000 gene pool is compared against the elusive and non-elusive distributions for all presented results will reveal whether the non-elusive set follows a background distribution (noise) or not.

- We also wondered whether the authors considered testing the links between recombination rate / LD and the genomic locations of their elusive genes (again compared against randomly sampled genes)?

- Given the evidence presented in Figure 6b, we do not agree with the statement (l.334-336): "These observations suggest that the elusive genes are unlikely to be regulated by distant regulatory elements". Here, a data population of ~1k genes is compared against a data population of ~8k genes and the presented difference between distributions could be a sample size artefact. We strongly recommend retesting this result with the ~1k randomly sampled genes from the total ~21,000 gene pool and then compare the distributions.

- Analogous random sampling analysis should be performed for Fig 6a,d

- We didn't see a clear pattern in Figure 7. Please quantify enrichments with statistical tests. Even if there are enriched regions, why did the authors choose a Shannon entropy cutoff configuration of <1 (low) and >1 (high)? What was the overall entropy value range? If the maximum entropy value was 10 or 100 or even more, then denoting <1 as low and >1 as high seems rather biased.

Reviewer #1 (Public Review):

The first synapses of the pain pathway are concentrated in the superficial spinal cord dorsal horn. Here peripheral inputs are processed by local interneuron circuitry before ascending to the brain. The spinal dorsal horn is organized into lamina with the resident interneurons differentiated by their anatomy, physiological and molecular properties. Over the past decade, the restricted expression of select genes has been used to assign potential function to dorsal horn neuron "cell types". This type of work has relied on the genesis of Cre-reporter mouse strains and intersectional tools to generate mice where select sets of neurons can be activated, inhibited, or ablated. The picture that has emerged from these types of experiments is murky but favors the model where there exist genetically defined cell-types play distinct roles in the detection of painful, itchy, thermal, and mechanical stimuli under normal and pathological situations. The current work by Boyle and colleagues concerns itself with the dorsal horn neurons expressing the neuropeptide NPY. This study is directly related to previously published work that demonstrated that ablating spinal cord neurons that express Npy, including those who express this gene transiently during development, resulted in mice that had heightened touch-evoked itch that seemed different from the canonical chemical itch pathways previously identified. A major conclusion from this previous work was that other modalities were unaffected. Subsequent work built on these findings to identify the potential touch inputs and spinal neuron expressing the Npy receptor as part of a mechanical itch circuit.

This current work by Boyle and colleagues challenge challenges this view by providing evidence that in adult mice, the dorsal horn neurons expressing Npy function to broadly inhibit pain and itch. The authors use direct injection of viral vectors, chemogenetics and synaptic silencing to probe the behavioral effects of stimulating or silencing Npy-expressing dorsal horn neurons in a variety of assays under normal and pathological conditions known to produce allodynia and hyperalgesia. Overall, this is a rather carefully conducted study with the appropriate controls. The data are clear, the effect sizes robust and the presentation easy to follow. These findings challenge the conclusion that these neurons are involved selectively in mechanical itch and instead reveal a potentially clinical important group of neurons for pain.

Reviewer #2 (Public Review):

Whether and how molecularly defined neuronal groups in the spinal cord process distinct modalities are of great interest. In this study, Boyle et al. characterized roles of inhibitory neurons expressing NPY in adult mice. By using chemogenetic, electrophysiological tools and behavioral measurements, the authors discovered that activating NPY+ interneurons strongly reduced pruritogen-evoked itch and reflexive behaviors (acute nociception or under inflammation / neuropathic pain states). Silencing NPY+ spinal interneurons enhanced spontaneous and chemical itch in a GRPR+ neurons dependent manner. The authors concluded that, unlike previous findings suggesting that these neurons are selective for mechanical itch, adult NPY+ interneurons play dual roles in gating various types of itch and pain.

Strengths:

The authors performed careful characterization and comparisons between development lineage and adult spinal neurons expressing NPY. This lays the foundation of the current study. The behavioral measurements were also well designed with proper controls.

Weaknesses:

There is inadequate discussion about previous studies of NPY interneurons. Specifically, the authors should address why a more restricted subset of these neurons (this study) have broader effects than seen previously.

I cannot see the reason for including results from manipulation of Dyn+ interneurons in this paper. First, the title does not reflect roles of spinal Dyn+ population. In addition, without further experiments characterizing relationships between NPY and Dyn interneurons in modulating itch and/or nociception, Dyn datasets seem to deviate from the main theme.

While the authors provided convincing evidence that GRPR+ neurons serve as a downstream effector of NPY+ neuron evoked itch, the relationship between GRPR and NPY neurons in modulating pain is not examined. Therefore, Fig. 7B is pure speculation and should be removed.

Reviewer #3 (Public Review):

In the present study by Boyle et al., the function of NPY expressing spinal neurons in pain and itch perception is studied. While the function of these neurons has been addressed previously, the difference to previous studies is the combinatorial use of AAV encoded effectors and cre transgenic mice whereas previous studies relied on cre transgenic mice and reporter mice encoding the effector or only viruses. Boyle at al. demonstrate that their strategy enabled them to restrict the analysis to only those neurons expressing NPY in the adult mouse compared to a more heterogenous population that had been studied before. By using a combination of morphology, electrophysiology and behavioral paradigms they convincingly show that NPY neurons impact pruritoception via inhibiting GRPR neurons. Furthermore, they indicate a role of NPY neurons also in nociception as activation attenuates not only responses to acute nociceptive stimuli but also blocks inflammation or nerve injury induced mechanical and heat hypersensitivity. Selectively activating NPY neurons in vivo may therefore be a promising strategy to treat neuropathic pain.

The result of this study extends and partially contrasts previous studies. The authors argue that contrasting results may be due to the different experimental strategies (e.g. only neurons expressing NPY adult in the present study versus a more heterogeneous population before).

Overall, the experiments are convincing, and the quality of the data/figures is exceptionally high.

Reviewer #1 (Public Review):

Huang C-K. and colleagues in this work address the understudied role of environmental conditions and external forces in cell extrusion as a fundamental part of epithelial homeostasis. They suggest that hydrostatic stress plays a significant role in counteracting cell extrusion forces through the indirect regulation of the focal adhesion kinase (FAK) - protein kinase B (AKT) survival pathway. The team nicely exploits their expertise in fabricating cell culture substrates to control hydrostatic stress on a common epithelial cell model from the kidney (i.e., MDCK). This was done by creating waving surfaces with different lengths from 50µm to 200 µm, thus creating a heterogenous distribution of monolayer forces towards the substrate. Finally, using a specific inhibitor for FAK, they suggest that the survivor pathway FAK-AKT is involved in the observed phenomenon.

In conclusion, the presented data underline the importance of considering external forces and tissue geometry in regulating epithelial homeostasis and the selective transport of water and solutes. These results may have a significant impact on understanding the basic mechanisms of epithelial physiology and pathology, such as in the kidney, intestine, or retina.

Reviewer #2 (Public Review):

The paper by Huan, Yong, et al. studies epithelial cell extrusion in MDCK monolayers grown on sinusoidally wavy surfaces in varying media osmolarities, finding that both curvature and osmolarity-mediated basal hydraulic stress spatially regulate extrusion events. The authors fabricated wavy substrates of varying periods and amplitude out of PDMS (and PA hydrogels) and monitored monolayer evolution and cell extrusion over time, by combining live-cell imaging with a convolutional network-based algorithm for automatic detection of extrusions.

In general, the study has been elegantly designed, starting with convincing evidence for enhanced extrusion rates in concave valleys with respect to convex hills. Next, the authors showed that hyper-osmotic medium reduced cell extrusion rate, which was demonstrated in a variety of different media compositions (e.g. with sucrose, DMSO, or NaCl), while hypo-osmotic medium increased cell extrusion rate. Additionally, the authors applied reflection interference contrast microscopy to reveal fluid spaces between the substrate and the basal side of the monolayer, which were found to grow when media composition was altered from hyper-osmotic to normal osmotic conditions. Using a 3D traction force microscopy approach, the authors demonstrated that cells on convex regions apply a downward pointing force on the substrate, opposite to cells on the concave regions. This was linked to a larger basal separation on the concave valleys as opposed to the convex hills. Finally, the authors focussed on the FAK-Akt pathway to explore the hypothesis that basal hydraulic stress interferes with focal adhesions, leading to differences in cell extrusion rates in media of different osmolarity and on convex or concave surfaces.

Despite the host of relevant experiments and the interesting data acquired with a variety of techniques, some aspects of the manuscript would need to be strengthened or explained in more detail to better support the claims and to provide more convincing evidence.

1) The sinusoidal wavy substrate that the authors use in their investigation is interesting and relevant, but it is important to realise that this is a single-curved surface (also known as a developable surface). This means that the Gaussian curvature is zero and that monolayers need to undergo (almost) no stretching to conform to the curvature. The authors should at least discuss other curved surfaces as an option for future research, and highlight how the observations might change. Convex and concave hemispherical surfaces, for example, might induce stronger differences than observed on the sinusoidal substrates, due to potentially higher vertical resultant forces that the monolayer would experience. The authors could discuss this geometry aspect more in their manuscript and potentially link it to some other papers exploring cell-curvature interactions in more complex environments (e.g. non-zero Gaussian curvature).

2) The discussion of the experiments on PAM gels is rather limited. The authors describe that cells on the PAM gels experience fewer extrusions than on the PDMS substrates, but this is not discussed in sufficient detail (e.g. why is this the case). Additionally, the description of the 3D traction force microscopy and its validation is quite limited and should be extended to provide more convincing evidence that the measured force differences are not an artefact of the undulations of the surface.

3) The authors show nuclear deformation on the hills and use this as evidence for a resultant downward-pointing force vector. This has, indeed, also been observed in other works referenced by the authors (e.g. Werner et al.), and could be interesting evidence to support the current observations, provided the authors also show a nuclear shape on the concave and flat regions. The authors could potentially also characterise this shape change better using higher-resolution data.

4) The U-net for extrusion detection is a central tool used within this study, though the explanation and particularly validation of the tool are somewhat lacking. More clarity in the explanation and more examples of good (or bad) detections would help establish this tool as a more robust component of the data collection (on all geometries).

5) The authors study the involvement of FAK in the observed curvature-dependent and hydraulic stress-dependent spatial regulation of cell extrusion. In one of the experiments, the authors supplement the cell medium with FAK inhibitors, though only in a hyper-osmotic medium. They show that FAK inhibition counteracts the extrusion-suppressing effect of a hyper-osmotic medium. However, no data is shown on the effect of FAK inhibitors within the control medium. Would the extrusion rates be even higher then?

Reviewer #3 (Public Review):

The authors study monolayers of MDCK cells on curved surfaces. These surfaces consist of hemicylindrical valleys and hills obtained through microfabrication involving glass rods and repeated molding steps. They find higher apoptotic extrusion rates in valleys compared to hills for patterns with 25 and 50 µm curvature radii, but not in valleys of 100 µm curvature radius. By using osmotic shocks and reflection interference contrast microscopy, they identify hydraulic stress to drive cell extrusion. 3D force microscopy reveals that cytoskeletal forces point towards the substrate on hills and away from the substrate in valleys. From these observations, the authors conclude that hydraulic stress-induced cell extrusion is assisted by cytoskeletal forces in the valleys and opposed on the hills. Finally, they link the hydraulic stress to the activity of focal adhesion kinase, which in turn affects cell survival through Akt signaling.

Strengths:

This work combines a new microfabrication method with state of the art 3d force microscopy that allows the authors to study curvature-dependent cell extrusion. The application of various osmotic shocks to the system clearly identifies the role of hydraulic stress in cell extrusion. The decoupling of the main driver of cell extrusion (hydraulic stress) from its curvature-dependent modulation through cytoskeletal forces, together with the mechanical activation of apoptosis is an important new finding that significantly advances our understanding of epithelial cell extrusion and could be important during developmental processes and for maintaining intact epithelia in adult organisms.

Weaknesses:

The main weakness of this work is a lack of quantification of the hydraulic stress. Furthermore, the authors do not present data on other cell types such that the phenomenon studied in this work might be specific to MDCK cells. Finally, The authors do not modify cytoskeleton contractility to check how this parameter affects the threshold curvature below which cell extrusion is no longer curvature dependent.

Reviewer #1 (Public Review): 

How morphogens spread within tissues remains an important question in developmental biology. Here the authors revisit the role of glypicans in the formation of the Dpp gradient in wing imaginal discs of Drosophila. They first use sophisticated genome engineering to demonstrate that the two glypicans of Drosophila are not equivalent despite being redundant for viability. They show that Dally is the relevant glypican for Dpp gradient formation. They then provide genetic evidence that, surprisingly, the core domain of Dally suffices to trap Dpp at the cell surface (suggesting a minor role for GAGs). They conclude with a model that Dally modulates the range of Dpp signaling by interfering with Dpp's degradation by Tkv. These are important conclusions, but more independent (biochemical/cell biological) evidence is needed.

As indicated above, the genetic evidence for the predominant role of Dally in Dpp protein/signalling gradient formation is strong. In passing, the authors could discuss why overexpressed Dlp has a negative effect on signaling, especially in the anterior compartment. The authors then move on to determine the role of GAG (=HS) chains of Dally. They find that in an overexpression assay, Dally lacking GAGs traps Dpp at the cell surface and, counterintuitively, suppresses signaling (fig 4 C, F). Both findings are unexpected and therefore require further validation and clarification, as outlined in a and b below. 

a) In loss of function experiments (dallyDeltaHS replacing endogenous dally), Dpp protein is markedly reduced (fig 4R), as much as in the KO (panel Q), suggesting that GAG chains do contribute to trapping Dpp at the cell surface. This is all the more significant that, according to the overexpression essays, DallyDeltaHS seems more stable than WT Dally (by the way, this difference should also be assessed in the knock-ins, which is possible since they are YFP-tagged). The authors acknowledge that HS chains of Dally are critical for Dpp distribution (and signaling) under physiological conditions. If this is true, one can wonder why overexpressed dally core 'binds' Dpp and whether this is a physiologically relevant activity. 

b) Although the authors' inference that dallycore (at least if overexpressed) can bind Dpp. This assertion needs independent validation by a biochemical assay, ideally with surface plasmon resonance or similar so that an affinity can be estimated. I understand that this will require a method that is outside the authors' core expertise but there is no reason why they could not approach a collaborator for such a common technique. In vitro binding data is, in my view, essential. 

In a subsequent set of experiments, the authors assess the activity of a form of Dpp that is expected not to bind GAGs (DppDeltaN). Overexpression assays show that this protein is trapped by DallyWT but not dallyDeltaHS. This is a good first step validation of the deltaN mutation, although, as before, an invitro binding assay would be preferable. Nevertheless, the authors show that DppDeltaN is surprisingly active in a knock-in strain. At face value (assuming that DeltaN fully abrogates binding to GAGs), this suggests that interaction of Dpp with the GAG chains of Dally is not required for signaling activity. This leads to authors to suggest (as shown in their final model) that GAG chains could be involved in mediating the interactions of Dally with Tkv (and not with Dpp. This is an interesting idea, which would need to be reconciled with the observation that the distribution of Dpp is affected in dallyDeltaHS knock-ins (item a above). It would also be strengthened by biochemical data (although more technically challenging than the experiments suggested above). 

In an attempt to determine the role of Dally (GAGs in particular) in the signaling gradient, the paper next addresses its relation to Tkv. They first show that reducing Tkv leads to Dpp accumulation at the cell surface, a clear indication that Tkv normally contributes to the degradation of Dpp. From this they suggest that Tkv could be required for Dpp internalisation although this is not shown directly. The authors then show that a Dpp gradient still forms upon double knockdown (Dally and Tkv). This intriguing observation shows that Dally is not strictly required for the spread of Dpp, an important conclusion that is compatible with early work by Lander suggesting that Dpp spreads by free diffusion. These result show that Dally is required for gradient formation only when Tkv is present. They suggest therefore that Dally prevents Tkv-mediated internalisation of Dpp. Although this is a reasonable inference, internalisation assays (e.g. with anti-Ollas or anti-HA Ab) would strengthen the authors' conclusions especially because they contradict a recent paper from the Gonzalez-Gaitan lab. 

The paper ends with a model suggesting that HS chains have a dual function of suppressing Tkv internalisation and stimulating signaling. This constitutes a novel view of a glypican's mode of action and possibly an important contribution of this paper. As indicated above, further experiments could considerably strengthen the conclusion. Speculation on how the authors imagine that GAG chains have these activities would also be warranted.

Reviewer #2 (Public Review): 

The authors are trying to distinguish between four models of the role of glypicans (HSPGs) on the Dpp/BMP gradient in the Drosophila wing, schematized in Fig. 1: (1) "Restricted diffusion" (HSPGs transport Dpp via repetitive interaction of HS chains with Dpp); (2) "Hindered diffusion" (HSPGs hinder Dpp spreading via reversible interaction of HS chains with Dpp); (3) "Stabilization" (HSPGs stabilize Dpp on the cell surface via reversible interaction of HS chains with Dpp that antagonizes Tkv-mediated Dpp internalization); and (4) "Recycling" (HSPGs internalize and recycle Dpp). 

To distinguish between these models, the authors generate new alleles for the glypicans Dally and Dally-like protein (Dlp) and for Dpp: a Dally knock-out allele, a Dally YFP-tagged allele, a Dally knock-out allele with 3HA-Dlp, a Dlp knock-out allele, a Dlp allele containing 3-HA tags, and a Dpp lacking the HS-interacting domain. Additionally, they use an OLLAS-tag Dpp (OLLAS being an epitope tag against which extremely high affinity antibodies exist). They examine OLLAS-Dpp or HA-Dpp distribution, phospho-Mad staining, adult wing size. 

They find that over-expressed Dally - but not Dlp - expands Dpp distribution in the larval wing disc. They find that the Dally[KO] allele behaves like a Dally strong hypomorph Dally[MH32]. The Dally[KO] - but not the Dlp[KO] - caused reduced pMad in both anterior and posterior domains and reduced adult wing size (particularly in the Anterior-Posterior axis). These defects can be substantially corrected by supplying an endogenously tagged YFP-tagged Dally. By contrast, they were not rescued when a 3xHA Dlp was inserted in the Dally locus. These results support their conclusion that Dpp interacts with Dally but not Dlp. 

They next wanted to determine the relative contributions of the Dally core or the HS chains to the Dpp distribution. To test this, they over-expressed UAS-Dally or UAS-Dally[deltaHS] (lacking the HS chains) in the dorsal wing. Dally[deltaHS] over-expression increased the distribution of OLLAS-Dpp but caused a reduction in pMad. Then they write that after they normalize for expression levels, they find that Dally[deltaHS] only mildly reduces pMad and this result indicates a major contribution of the Dally core protein to Dpp stability. The "normalization" is a key part of this model and is not mentioned how the normalization was done. When they do the critical experiment, making the Dally[deltaHS] allele, they find that loss of the HS chains is nearly as severe as total loss of Dally (i.e., Dally[KO]). Additionally, experimental approaches are needed here to prove the role of the Dally core.

Prior work has shown that a stretch of 7 amino acids in the Dpp N-terminal domain is required to interact with heparin but not with Dpp receptors (Akiyama, 2008). The authors generated an HA-tagged Dpp allele lacking these residues (HA-dpp[deltaN]). It is an embryonic lethal allele, but they can get some animals to survive to larval stages if they also supply a transgene called “JAX” containing dpp regulatory sequences. In the JAX; HA-dpp[deltaN] mutant background, they find that the distribution and signaling of this Dpp molecule is largely normal. While over-expressed Dally can increase the distribution of HA-dpp[deltaN], over-expression of Dally[deltaHS] cannot. These latter results support the model that the HS chains in Dally are required for Dpp function but not because of a direct interaction with Dpp. 

In the last part of the results, they attempt to determine if the Dpp receptor Thickveins (Tkv) is required for Dally-HS chains interaction. The 2008 (Akiyama) model posits that Tkv activates pMad downstream of Dpp and also internalizes and degrades Dpp. A 2022 (Romanova-Michaelides) model proposes that Dally (not Tkv) internalizes Dpp.  

To distinguish between these models, the authors deplete Tkv from the dorsal compartment of the wing disc and found that extracellular Dpp increased and expanded in that domain. These results support the model that Tkv is required to internalize Dpp. They then tested the model that Dally antagonizes Tkv-mediated Dpp internalization by determining whether the defective extracellular Dpp distribution in Dally[KO] mutants could be rescued by depleting Tkv. Extracellular Dpp did increase in the D vs V compartment, potentially providing some support for their model. However, there are no statistics performed, which is needed for full confidence in the results. The lack of statistics is particularly problematic (1) when they state that extracellular Dpp does not rise in ap>tkv RNAi vs ap>tkv RNAi, dally[KO] wing discs (Fig. 6E) or (2) when they state that extracellular Dpp gradient expanded in the dorsal compartment when tkv was dorsally depleted in dally[deltaHS] mutants (Fig. 6I). These last two experiments are important for their model but the differences are assessed only visually. In fact, extracellular Dpp in ap>tkv RNAi, dally[KO] (Fig. 6B) appears to be lower than extracellular Dpp in ap>tkv RNAi (Fig. 6A) and the histogram of Dpp in ap>tkv RNAi, dally[KO] is actually a bit lower than Dpp in ap>tkv RNAi, But the author claim that there is no difference between the two. Their conclusion would be strengthened by statistical analyses of the two lines. 

Strengths: 

1. New genomically-engineered alleles

A considerable strength of the study is the generation and characterization of new Dally, Dlp and Dpp alleles. These reagents will be of great use to the field.

2. Surveying multiple phenotypes

The authors survey numerous parameters (Dpp distribution, Dpp signaling (pMad) and adult wing phenotypes) which provides many points of analysis.

Weaknesses: 

1. Confusing discussion regarding the Dally core vs HS in Dpp stability. They don't provide any measurements or information on how they "normalize" for the level of Dally vs Dally[deltaHS]? This is important part of their model that currently is not supported by any measurements.

2. Lacking quantifications and statistical analyses: 

a. Why are statistical significance for histograms (pMad and Dpp distribution) not supplied? These histograms provide the key results supporting the authors' conclusions but no statistical tests/results are presented. This is a pervasive shortcoming in the current study. 

b. dpp[deltaN] with JAX transgene - it would strengthen the study to supply quantitative data on the percent survival/lethal stage of dpp[deltaN] mutants with or without the JAK transgene <br /> c. The graphs on wing size etc should start at zero. <br /> d. The sizes of histograms and graphs in each figure should be increased so that the reader can properly assess them. Currently, they are very small. 

The authors' model is that Dally (not Dlp) is required for Dpp distribution and signaling but that this is not due to a direct interaction with Dpp. Rather, they posit that Dally-HS antagonize Tkv-mediated Dpp internalization. Currently the results of the experiments could be considered consistent with their model, but as noted above, the lack of statistical analyses of some parameters is a weakness. One problematic part of their result for me is the role of the Dally core protein (Fig. 7B). There is a mis-match between the over-expression results and Dally allele lacking HS (but containing the core). Finally, their results support the idea that one or more as-yet unidentified proteins interact with Dally-HS chains to control Dpp distribution and signaling in the wing disc. 

There is much debate and controversy in the Dpp morphogen field. The generation of new, high quality alleles in this study will be useful to Drosophila community, and the results of this study support the concept that Tkv but not Dally regulate Dpp internalization. Thus the work could be impactful and fuel new debates among morphogen researchers. <br />

The manuscript is currently written in a manner that really is only accessible to researchers who work on the Dpp gradient. It would be very helpful for the authors to re-write the manuscript and carefully explain in each section of the results (1) the exact question that will be asked, (2) the prior work on the topic, (3) the precise experiment that will be done, and (4) the predicted results. This would make the study more accessible to developmental biologists outside of the morphogen gradient and Drosophila communities.

Reviewer #1 (Public Review):

This study by Cao et al. demonstrates role of Neutrophil in clearing apoptotic hepatocytes by directly burrowing into the apoptotic hepatocytes and ingesting the effete cells from inside without causing inflammation. The authors applied intravital microscopy, Immunostaining and electron microscopy to visualize perforocytosis of neutrophil in hepatocytes. They also found that neutrophil depletion impairs the clearance of apoptotic hepatocytes causing impaired liver function and generation of autoantibodies, implying a role of defective neutrophil- mediated clearance of apoptotic cells in Autoimmune Liver disease. The experiments were well designed and conducted, the results were reasonably interpreted, and the manuscript was clearly written with logical inputs.

One weak point is that the signals/mechanisms that determine why neutrophil specifically target apoptotic hepatocytes in liver and no other organs or cells is not clearly understood.

Reviewer #2 (Public Review):

Neutrophils are the most abundant circulating leukocytes in human. They play important roles in innate immune responses to infections and tissue injuries. Although they are dept in phagocytosis of microbes, neutrophils are not known to normally conduct efferocytosis or phagocytose host cells including apoptotic cells and play a significant role in apoptotic cell removal. In this report the authors provide evidence to suggest that neutrophils are involved in removal of apoptotic hepatocytes with certain specificity (i.e., they do not remove HEK293 or HUVEC endothelial cells). Moreover, the authors also show that neutrophils can burrow into the target cells and possibly ingest the target cells from the inside. The authors thus term this neutrophil-mediated efferocytosis process as "perforocytosis". Furthermore, evidence is provided to suggest that this neutrophil-mediated efferocytosis process keeps the number of apoptotic cells low in the livers and that defects in the processes may associate with autoimmune liver (AIL) disease phenotypes. Therefore, many of these findings are novel and the study is of important implications in our understanding of the role of neutrophils in autoimmune disease.

By examination of HE-stained, noncancerous liver tissue sections from patients with hepatocellular carcinoma and hepatic hemangioma, the authors observed that cells with neutrophil nuclear morphology were inside apoptotic hepatocytes. The authors also further characterized this observation by staining the sections with neutrophil and apoptosis markers. In addition, the authors observed the same phenomena in mouse livers using intravital microscopy, which also recorded the time course of the disappearance of a neutrophil-associated apoptotic cell. The author went on further characterization of neutrophil-mediated efferocytosis of cultured hepatic cells in vitro and demonstrated the process was specific for apoptotic hepatic cells, but not HEK293 or endothelial cells. The in vitro system was then used to characterize the molecular bases for neutrophil-mediated efferocytosis of apoptotic hepatic cells. The evidence was provided to suggest that IL1b and IL-8 released from and selectins upregulated in apoptotic hepatic cells were important. Importantly, the authors used two methods to deplete the neutrophils and showed that the neutrophil depletion increased apoptotic cells in livers. Finally, the authors showed that neutrophil depletion caused defects in liver function parameters. At the end, the authors presented evidence to suggest that AIL disease may be due to defective neutrophils that fail to perform "perforocytosis."

Although the evidence in its totality indicates that neutrophils burrow into apoptotic hepatocytes, the significance of this "perforocytosis" phenomenon and the circumstances under which it may occur remain to be better defined. In both neutrophil depletion models, the TNUEL-positive cells were not definitively identified rather than assuming they were hepatocytes. In addition, there are discrepancies in the number of neutrophils and apoptotic cells in mouse liver studies; Figure 2a WT (many neutrophils; locations unclear) vs Figure 5A Ctr (a few neutrophils that appear in or near a vessel), and Figure 2a DTR (a few apoptotic cells) vs Figure 5A Depletion (many apoptotic cells). Importantly, Figure 5a Ctrl, which is presumably a section from a mouse without any surgical treatment or without inflammation, the sole TUNNEL signal does not appear to be associated with neutrophils. Does this mean that "perforocytosis" primarily occurs in inflamed livers (Of note, human liver samples in Figure 1 are from patient with tumors. There should be inflammation in the livers of these patients). The data on human AIL patient neutrophils raises more questions: how many AIL patients have been examined? Do these AIL neutrophils lack IL1, IL8 receptors, and/or selectin ligands? Are there increases in apoptotic hepatocytes in AIL patients? Additionally, the overall numbers of apoptotic cells even in the absence of neutrophils are rare; thus, it is questionable that such rarity of apoptotic cells can cause significant AIL phenotypes.

Reviewer #1 (Public Review):

In this manuscript, Mastrototaro et al. perform a series of experiments in transgenic murine models assessing the function of Palladin (PALLD) in the heart. Global PALLD KOs are embryonic lethal, precluding the assessment of the roles of this protein in adulthood. To circumvent this limitation, the authors generated a floxed Palld allele and ablated it with two cardiomyocyte-specific Cres: the constitutively active Myh6-Cre and the tamoxifen-inducible aMHC-MerCreMer. Interestingly, ablation with the constitutive Cre (cKO) did not produce any overt phenotype, but ablation in adulthood (cKOi) resulted in compromised cardiac function. These observations suggest a compensation mechanism that takes place when cardiomyocytes develop in the complete absence of this protein but not when cardiomyocytes develop in a wild-type background and are deprived of this protein after achieving full maturation. These experiments were complemented with yeast two-hybrid techniques to identify novel partners that bind to a region of PALLD for each no interactants had been previously identified. Experiments in human samples revealed an upregulation of PALLD transcripts in the hearts of patients.

This manuscript adds important information to our understanding of sarcomeric proteins. Data are generally of good quality and well presented in figures. The numbers of animals in echocardiographic studies are also adequate for proper conclusions. Authors achieve most of their goals, including the identification of novel partners of PALLD and the identification of a requirement for PALLD in cardiomyocytes for normal heart function. However, given that all experiments performed in this study were focused on the loss-of-function of PALLD, it is not clear what is the relevance of the PALLD upregulation observed in human patients. Authors should clearly state this limitation in their results.

Considering that authors have observed evidence for nuclear PALLD, which could hint at potential major gene expression changes when this protein is ablated, it would be interesting to perform an unbiased assessment of transcriptional alterations (RNA-seq) in cardiomyocytes isolated from control and cKOi hearts. In addition, to test if the compensation observed in the embryonic cKO involves mechanisms of transcriptional adaptation, it would be interesting to compare RNA-seq results from cKOi and cKO (genes encoding proteins similar to PALLD that are upregulated in cKO but not cKOi cardiomyocytes would be very strong candidates). However, these transcriptomic data are not essential to support current findings and can be performed in follow-up studies.

Reviewer #2 (Public Review):

The role of the actin-binding protein palladin (PALLD) in cardiomyocyte development, growth, and function has not been defined. In order to address this question, the authors first identified that CARP and FHOD1 interact with PALLD in cardiomyocytes. They then performed cardiomyocyte selective deletion of PALLD in embryonic and adult mice and discovered that deletion of PALLD in adult mice leads to dilated cardiomyopathy (DCM) and intercalated disc ultrastructural changes. In contrast, embryonic deletion of cardiomyocyte PALLD did not cause a cardiomyopathy phenotype in neonatal or adult animals.

1. The divergent cardiac phenotypes of the embryonic deletion of cardiomyocyte PALLD (no cardiomyopathy) versus the adult deletion of cardiomyocyte PALLD (dilated cardiomyopathy(DCM)) is an interesting result. The authors speculate that embryonic deletion of PALLD induces compensatory pathways that prevent the development of adult cardiomyopathy in these mice. However, these compensatory pathways remain unexplored.<br /> 2. The authors discovered that mice with adult cardiomyocyte deletion of PALLD had significant changes in the cardiomyocyte intercalated disc (ICD) ultrastructure. They suggest these changes in ICD ultrastructure contribute to DCM formation in the adult PALLD deletion mice (line 270). However, it remains unclear if these changes in ICD ultrastructure are specific to mice with adult deletion of PALLD.<br /> 3. The different transgenic Cre mouse lines may be an alternative explanation for the divergent cardiac phenotypes in the embryonic versus adult deletion of cardiomyocyte PALLD. The tamoxifen dose administered for the inducible Myh6:MerCreMer mice was 30mg/kg/day x 5 which has been reported to lead to the induction of cardiomyocyte DNA damage response pathways (Dis Model Mech. 2013 Nov; 6(6): 1459-1469, J Cardiovasc Aging 2022;2:8). The electron micrograph experiments in Figure 5 did not include a group of Myh6:MerCreMer mice administered tamoxifen. The authors only compared PALLD fl/fl and Myh6:MerCreMer/PALLD fl/fl mice.<br /> 4. The apoptosis assessment was performed 24 weeks after administration of tamoxifen to the Myh6:MerCreMer/PALLD fl/fl mice. However, cardiomyocyte apoptosis may have occurred much earlier if it was secondary to Myh6:MerCreMer tamoxifen-induced cardiotoxicity (or related to PALLD deletion).<br /> 5. The animal studies in Fig 3D show a DCM phenotype in mice with adult deletion of cardiomyocyte 200kDa PALLD which suggests a potential loss of function mechanism for DCM formation. However, the authors then report in Fig 6 that human DCM heart tissue samples have a ~2.5fold increase in mRNA expression of the 200kDa PALLD transcript which would suggest a possible gain of function mechanism for DCM formation. How do the authors reconcile these divergent results with regard to palladin's role in cardiomyocyte homeostasis and cardiomyopathy formation?

Reviewer #3 (Public Review):

This study shows for the first time changes in palladin expression under disease conditions and mRNA alterations in human samples. The authors have identified novel binding partners for the protein as a first step toward determining how palladin mediates its effects in the heart. Finally, through the use of mouse models to decrease palladin expression they identify a crucial role for palladin in the cardiac response to pathological stress, with some interesting findings that show the effects of palladin depend on when the protein is altered.

The novel findings of the study are supported by the data presented, but there are several instances where clarification is needed of the conclusions drawn from the data reach beyond what is presented in the Results section.

The focus on only male mice is a significant limitation of the paper, as it is well known that there are profound sex differences in the response to pathological stressors. While the ability to obtain sufficient heart samples from male and female patients may be a reasonable justification for focusing on males, the preclinical mouse model should have been examined in both sexes and the limitation of this choice should be clearly noted in the paper.

The changes in myopalladin expression were not measured in the disease model (TAC), which limits the ability to determine if myopalladin was altered in the disease state. This addition would strengthen the study.

Finally, the myofilament data are presented as evidence that changes in the contractile apparatus are contributors to the observed contractile dysfunction at the organ level. But these studies were conducted using levels of calcium that far exceed what is seen in vivo and, therefore, do not support the conclusion drawn.

Peer review report

Title: An Improved Peer-Review System to Compensate for Scientific Misconduct in Health-Sensitive Topics

version: 7

Referee: Cristina Candal-Pedreira

Institution: University of Santiago de Compostela

email: cristina.candal.pedreira@rai.usc.es

ORCID iD: 0000-0002-1703- 3592

General assessment

In this letter, the authors propose a series of actions that the main actors responsible for scientific integrity (researchers, scientific journals, academic institutions, and funding entities) could (and should) implement to prevent and/or detect cases of scientific misconduct. Although the authors refer primarily to high-sensitive publications, in my opinion, many (if not all) of the provided proposals can be applied to any type of publication. Examples of scientific misconduct are commonplace, and the policies implemented so far do not seem to be strong enough to prevent the publication of fraudulent articles. I believe that this article is necessary and very relevant.

Essential revisions that are required to verify the manuscript

In my opinion, this letter presents a very comprehensive list of potential strategies that different stakeholders can undertake to reduce the burden of research misconduct. Of course, there are many other actions that could be implemented, such as promoting post- publication review, imposing sanctions, or auditing research funding from an ethical point of view, among others. However, I consider all the strategies developed by the authors to be important and necessary, so I have no essential revisions to this manuscript.

Other suggestions to improve the manuscript

The text is well written, very clear and easy to follow.

Some comments/suggestions/reflections:

I would suggest introducing the definition of “high sensitivity topic” in the first part of the manuscript. Also, in the title another term is used (health-sensitivity topic), I would homogenize terms.

J2. In addition to solving the problem of coercive citations, open peer review can make the peer review process more transparent by making public how many rounds of review have been done and how the conclusion was reached to publish or reject the article. In addition, reviewers, because they are not anonymous, can take the review more seriously.

R1-R2-R3. Regarding regulatory agencies, funders and institutions, it could also be helpful for them to perform audits of the projects, not only justification of where the money was spent, but also whether the research is being done ethically, including during the phase of dissemination of results.

Decision

Verified: The content is academically sound, only minor amendments (if any) are suggested.

Reviewer #1 (Public Review):

The authors set out to analyse the pattern of movement of T cells in different tissues- lymph nodes, villi, and inflamed/infected lungs. The authors are comparing data sets from multiple sites in different studies but acquired using similar instruments, preparations, and imaging conditions.

The more confined movement pattern in the lung that has a turning angle distribution with more incidence of angles near 180 degrees is striking.

T cells in the infected inflamed lung search a smaller volume over time but will explore it more extensively.

The measurements of T cell movement are context-free such that obstacles and tissue boundaries that could account for some of the confined behaviours in the lung parenchyma are not discussed.

Nonetheless, the work will motivate further study of the biological significance of the different T cell movement patterns in the lung, which may also be considered in the context of recent data on changes in B cell motility- a potential interacting cell.

Reviewer #2 (Public Review):

This paper addresses the topic of how T cells migrate in different tissues. The authors provide experimental evidence that T cell migration in the lung is more confined than in lymph nodes and gut villi. While prior studies have started to define the way T cells migrate during normal and pathological conditions, there is still a lot to learn about the factors that control this process. Thus, the topic is significant and timely. The authors use previously acquired data with two-photon microscopy from murine tissues. They compare multiple motility parameters of T cells in lymph nodes, gut villi, and inflamed lungs. Experiments demonstrate that T cells in the lung have a particular mode of migration characterized by low speeds, back-and-forth motions, and confinement.

Strengths:<br /> Overall, this is a very well-performed study. The data presented is of excellent quality and, for the most part, supports the authors' conclusions. The imaging techniques used to track T cells in various organs and the mouse models implemented are very relevant and robust. The functional analysis of the different migration features of T cells is compelling and should be of use to the community. The conclusion that T cells use different migration modes depending on the organ appears novel. This is considered of major significance.

Weaknesses:<br /> The main weakness of the manuscript is that the study remains descriptive and comparative. It is important to analyze and describe different migration modes depending on the organ. Still, it would have been desirable for the authors to provide information on the reason for such differences. One of the striking observations is the back-and-forth motion of T cells in the lung. Searching for mechanisms underlying this unique mode of displacement would strengthen the quality of the study.

Reviewer #3 (Public Review):

The ability of T cells to move through a variety of complex and disparate tissue environments is fundamental to their success in surveying and responding to infectious challenges. A better understanding of the molecular cues that regulate T cell motility in tissues is needed in order to inform therapeutic targeting of T cell migration. Contributions that are intrinsic and extrinsic to the T cells themselves have been shown to shape the pattern of T cell movement. This study uses advanced quantitative image analysis tools to dissect differences in T cell motility in different tissue locations, to better define how the tissue environment shapes the pattern of motility and scope of tissue explored. The combination of different quantitative measures of motion enables the extensive characterization of CD8 T cell motility in the lymph node, lung, and villi of the small intestine. However, there are too many variables with respect to the CD8 T cell populations used for analysis to be able to gain new insight into the impact of the tissue microenvironment itself.

The use of these advanced quantitative imaging analysis tools has the potential to significantly expand our analysis capabilities of T cell movement within and across tissues. The strength of the paper is the comprehensive analysis of multiple motility parameters designed with T cell function in mind. Specifically, with respect to the need for T cells to search a tissue area to identify antigen-bearing cells for T cell activation and identify cellular targets for the delivery of anti-microbial effector functions. The inclusion of an analysis of the "patrolled volume per time" is seen as a particularly useful advance to compare T cell behaviors across tissues.

However, with the current data sets, it is difficult to draw definitive conclusions on the impact of the tissue environment on how T cell move, given the considerable variability in the CD8 T cells themselves. Extended experimentation would be needed to fully support their key claims. In particular:

1) The authors have separated out naïve and activated CD8 T cells for their analysis, but this is a marked over-simplification. There are too many variables within these groups to be able to distinguish between differences in the T cell populations versus differences in the tissue environment. Variables include:<br /> a) T cells pre-activated in vitro before in vivo transfer (LPS-lung) versus transfer of naïve T cells for activation in vivo (Flu-lung, LCMV-villi)<br /> b) Polyclonal CD8 T cells (naïve, LPS-lung, Flu-lung) versus monoclonal (P14) CD8 T cells (LCMV-villi)<br /> c) Presence of cognate-antigen (Flu-lung, LCMV-villi) versus absence of antigen (LPS-lung)<br /> d) Cell numbers, 104 polyclonal naïve for Flu-lung versus 5 x 104 monoclonal (P14 T cells) for LCMV-villi)<br /> e) Intravital imaging (LCMV-villi) versus tissue explants (Flu-lung)

The authors do present data that suggest similarities of motility patterns within the same tissue occur despite variabilities in the CD8 T cell source, for example, the MSD is not significantly different in the two lung groups despite differences in the way the CD8 T cells were activated. However, these similarities are lost when other parameters are analyzed suggesting additional variability independent of the tissue itself.

2) Controlled experiments are needed, where the input CD8 T cell population is kept constant and the target tissue differs, to substantiate any of the current conclusions. This could be done by using a single source and/or specificity of CD8 T cells (e.g., P14 or OT-I TCR transgenics, or polyclonal in vitro activated CD8 T cells) transferred into mice where the tissue providing the antigen or inflammation source is varied (lung with pOVA-flu versus small intestine with pOVA-LCMV for example).

Alternatively, activated polyclonal CD8 T cells could be analyzed in the LPS-lung draining LN as well as in the LPS-lung to make a direct comparison between the tissues (LN versus lung) using CD8 T cells of the same activation status.

3) Differences in the micro-anatomical regions of the tissues studied may also contribute to tissue differences in movement patterns between the lung and the small intestine. The region of the small intestine imaged was specifically focused on the villi, close to the gut epithelium. Details of the location within the lung where images were taken are missing, therefore the motility differences between the lung and small intestine could reflect differences in the micro-anatomical position of the CD8 T cells within the tissue (proximal to epithelium versus parenchymal), rather than differences between the tissues themselves.

Overall, the authors have developed a quantitative multi-parameter approach to the study of T-cell motility in different tissues. Application of these analytical tools to the study of T-cell behavior in different tissue locations has the potential to reveal tissue and/or T-cell-specific patterns of movement that may help to identify molecular requirements for context-specific dynamic T-cell behavior. Their quantitative approach reveals small but statistically significant differences in particular motility parameters, the functional significance of which will require further study. The careful design of experiments to reduce as many variables as possible will be needed to increase the impact of the work and ensure new insights into this important aspect of T-cell function.

Reviewer #1 (Public Review):

This study presents a resource aiming to unify language and rules used in the literature to describe, curate and assess biology experiments, published or not. Focusing on host-pathogen interactions, the work presents a new ontology and controlled vocabulary, as well as rules to describe 'metagenotypes', a term coined for the joint description of interacting host-pathogen genotypes. 'PHI-Canto' extends a previous resource by also enabling using UniProtKB IDs to curate proteins. Among other important by-products, PHI-Canto could contribute to damping proliferating names and acronyms for genes, processes, and interactions; a chronic annoyance in the biosciences.

The tool does give the impression that, with sufficient time and usage, it could become a rich and robust resource. Just addressing the Uniprot IDs issue is a nice move.

Reviewer #2 (Public Review):

In this paper, the authors propose a system for annotating and curating scientific publications in the context of interspecies host-pathogen interactions. This system, called PHI-Canto (the Pathogen-Host Interaction Community Annotation Tool), is an extension of an existing tool (called Canto). In addition, they present the development of new concepts, controlled vocabularies, and an ontology for annotating relevant aspects in this domain, called PHIPO (Pathogen-Host Interaction Phenotype Ontology).

The approach has been empirically validated by annotating ten publications. The application's source code is available, as well as the associated ontologies and vocabularies and an example of the data resulting from the annotation process.

Reviewer #3 (Public Review):

In this work, the authors have built a framework for the annotation of interactions between species. The framework includes ontologies, methodologies, and an annotation tool called PHI-Canto. The framework makes use of multiple existing ontologies that are in wide use in the biocuration community. In addition, the authors have built their own project-specific controlled vocabularies and ontologies for the capture of pathogen-host interaction phenotypes (PHIPO), diseases (PHIDO), and environmental conditions (PHI-ECO). Their work builds on and extends methods that have been developed within the Gene Ontology Consortium and model organism databases. The tool PHI-Canto is an extension of the tool Canto developed by PomBase for curation. The authors used this framework to annotate pathogen-host interactions within the Pathogen-Host Interactions Database.

Strengths: The manuscript is well-written and includes significant detail regarding curation policies/methods and the use of the actual PHI-Canto tool. The appendices are very detailed and provide useful illustrations of the annotation practices and tool interface. The work has built upon and extended well-established standards and methods that have proven their utility over many years of use in the biocuration community. The authors have rigorously tested their framework with the curation of a variety of publications providing a diverse assortment of annotation challenges. The concept of a "metagenotype" is important and providing such a structured system for the capture of this information is useful. All of the materials produced by the work are completely freely available for use by the wider community.

Weaknesses: There are some areas of the manuscript and appendices which are a bit confusing and could be improved. The authors have developed their own set of disease terms (PHIDO) but do not comment on why existing disease terminologies (such as Mondo or DO) were not used or if the PHIDO terms relate to those other vocabularies. There is no discussion of the possible use of a graph representation for the capture of this complex information (which is being done in many settings including the Gene Ontology with GO Causal Activity Models (GO-CAMs)) or why such a structure was not used. Although the abstract talks about the use of the framework within the PHI database as a test case for broader use regarding interspecies interactions, there is no mention of extending the use of the tool to other species interaction communities beyond pathogen-host interactions.

Reviewer #1 (Public Review):

Motivated by the premise that Alzheimer's disease (ADD) and major depressive disorder (MDD) have shared underlying environmental and genetic risk factors, Petrican and Fornito combine non-imaging risk factors and executive task-based functional network change indices into latent variables of resilience to AD and MDD. The authors find two latent variables (LVs): LV1 represents change in network membership over time of distributed nodes during task, which is associated with greater genetic MDD risk, less psychopathology, and more advanced puberty, all while adjusting for age and indices of environmental stressors. LV2 represents occipital lobe nodal flexibility across task and time, decreased AD genetic risk, increased MDD genetic risk and less psychopathology, again adjusted for age and environmental stressors. The authors validate the latent network variables by assessing their overlap with genes for which SNPs have been associated with both depression risk and change in gene expression. Finally, the authors create simple path models in order to break down the relationships between genetic risk, latent variables, and what the authors term "resilience", finding distinct path for MDD and (non-APOE) AD genetic risk. All of these analyses are then re-run using a different brain parcellation. LV2 replicates, while a new LV1 emerges with similar non-imaging variables now being correlated with a different set of distributed network nodes.

The authors conclude from this work that they have identified imaging indices of resilience manifest during adolescent brain development, and that they have found further evidence linking MDD to AD. However, the analyses do not fully support the conclusions. The premise of this work - to examine links between MDD and AD and to try to define indices of resilience during development - is fascinating and will hopefully motivate future work in this direction. However, the impact of this work as currently presented may be limited.

*STUDY STRENGTHS*

There are two premises motivating this study that deserve praise for their innovation and creativity. First, in the introduction the authors present several fairly new papers showing shared environmental and risk factors between AD and MDD. This is a very interesting line of study that certainly deserves more attention. Second, the authors are interested in finding aspects of adolescent brain development that may be helpful to understanding resilience to genetic or environmental risk later in life. The AD resilience community is very interested in contributions of early life experiences and development, but there is still very little research in this domain. I hope the authors continue to conduct research in the direction of these pursuits.

The authors demonstrate great methodological and statistical rigor in some aspects of data preprocessing and analysis. This is particularly salient in null modeling and permutation, graph-based analysis, treatment of motion for functional imaging, using eQTLs to inform disease-relevant genes, statistical considerations in PLS and path modeling, processing of Allen Brain Atlas gene expression data, and validating certain study variables. The methodology of these steps displays great attention to detail and a mastery of certain data types.

The authors reproduce all analyses using a second parcellation and carefully report the results. This type of painstaking analysis is nonetheless important in the context of network-based graph analysis that is reliant on nodal information.

*STUDY LIMITATIONS*

1) The overarching limitation of this study is that the study variables, both independent and dependent, are abstracted to the point where interpretations are challenging. The authors' own interpretations are not sufficiently justified and are often taken at face value rather than supported by analysis. These are further combined into latent variables with weak conceptual foundation, which are then abstracted even further to other analyses with cortical molecular data maps. It is not clear that the conclusions drawn are convincingly supported by this highly abstracted analysis.

2) The other major limitation of this study is that several PLS models are run but, while appropriate null modeling is used to identify "significant" LVs, none of the LVs are cross-validated. Null modeling can help to protect against overfitting to noise in data, but it does not necessarily provide a good index of generalizability nor reliability. Without cross-validation, I question the reliability of the LVs irrespective of how they are interpreted. This is once again partially driven by the fact that changing the atlas resulted in a different imaging LV.

3) The study notes that participants were selected based on "having contributed high-quality data on all measures of interest". This is of course meritorious from a methodological perspective, but the authors should be aware that this may create an important selection bias (10.1007/s11682-022-00665-2, 10.1016/j.ynirp.2022.100085, 10.1016/j.neuroimage.2022.119296)

4) The premise of this paper was interesting, as described in the Strengths section above. However, what was missing was a clear theory or hypothesis as to how resilience to AD and MDD are related, and how the analyses in this study were conducted in order to support that hypothesis. The relevance of the results to AD was not clear; a clear biological model would help put the pieces together.

5) The selection of relevant features involved in LVs was inconsistent. At several points, the authors use an arbitrary threshold of bootstrap ratio (BSR) > 4, which they equated to a p-value. A p-value doesn't make sense in this context, since bootstrap samples are not independent samples. Instead, features should be selected based on 95% CIs that don't cross 0, which the authors do in some places but not in others.

Reviewer #2 (Public Review):

The authors' manuscript has several strengths. First, the authors consider multiple relevant levels of biology including genomics, transcriptomics, structural and functional neuroimaging, cognitive neuroscience, and psychological/environmental factors. Such an approach is often necessary to deconvolute the complexities of psychiatric phenotypes. The authors have taken careful steps to think about potential confounds (e.g., ancestry for PRS) and to try to define their phenotypes (e.g., psychological resilience and biological aging) as best as they can, given the data they have access to from the ABCD study. The manuscript is well written overall.

My main concerns relate to core assumptions and techniques that underlie the premise of the study. First, while there is comorbidity between AD and MDD, a causal relationship between the two (in either direction) is not established. Though MDD often predates AD, this is to be expected given MDD's high lifetime prevalence (15-20% of the general population) and typical age of onset before age 65. Because AD typically presents late in life (>65 years of age), MDD will, by definition, usually predate AD. While new onset, late life MDD is often the first presenting symptom of AD/Parkinson's disease and other neurodegenerative conditions, it is also not clear that this is the same disorder as idiopathic MDD.

To this point, two genetic tools can help us determine the biological relationship between MDD/AD, genetic correlation and Mendelian Randomization. Using the data from the MDD PRS used in this analysis, the Supplementary Table 3 from the Howard et al. 2019 paper (https://doi.org/10.1038/s41593-018-0326-7) reveals a genetic correlation of -0.041 between the two. This indicates essentially no strong relationship between the MDD/AD (perhaps even a slightly inverse relationship). Mendelian Randomization studies in addition to the Howard et al paper (https://doi.org/10.1212/WNL.0000000000010463) find no causal role for MDD towards AD and vice versa. Thus, their comorbidity is likely mediated by additional factors. Additionally, while stress contributes to AD pathophysiology, AD is strongly genetic and, given its late onset, it is unclear how genetic risk for AD would meaningfully impact the psychological resilience of a 9 to 10-year-old.

My second concern is regarding the statement "adolescents at genetic risk for AD/MDD" when describing the sample. Per Howard et al 2019 out-of-sample prediction testing, the MDD PRS used by the authors explains between 1.5-3.2% of the phenotypic variance in MDD when used on a sample such as ABCD. MDD PRS is in its infancy and cannot reliably be used to identify individuals at high risk of MDD given that even individuals in the top 10th percentile of MDD PRS have an odds ratio for depression of only ~2.4. We would expect 90 or so individuals in this cohort to fall into this group leaving significant concerns about statistical power and the potential for false positive discoveries. While the AD PRS is significantly further along compared to MDD because of AD's simpler genetic architecture, the same concerns apply as, outside of APOE, the AD PRS does not capture the majority of phenotypic variance in AD.

The authors state that they wish to examine the effects of perinatal adversity directly/indirectly on biological aging and then assess the potential effects of biological aging on resilience. The authors use of pubertal age as a measure of accelerated aging is understandable given the data available, though not ideal. There are well validated measures of biological age such as Horvath's epigenetic clock. While advanced pubertal age is technically a form of accelerated aging, the majority of pubertal age as a phenotype is not likely to be explained by perinatal adversity. Rather, a combination of unmeasured variables including genetic variation, dietary factors, environmental exposures (endocrine disrupting chemicals), and obesity that play a substantial role in determining pubertal age. Childhood stress has been shown to have relatively small effects on pubertal age (d = -0.1) (10.1037/bul0000270).

Lastly, the authors employ the use of an as of yet unpublished technique to map neurotransmitters density to structural data from neuroimaging studies. While this technique is certainly interesting, its face validity is not clear given that many of the receptor-disease associations reported in the original preprint do not line up with what we know about the biology of these disorders from strong human genetics data or current FDA approved treatments. Moreover, the authors mention "Excitation/Inhibition" imbalance but the technique used appears to only include glutamate data from one receptor type, mGluR5. This may not be an adequate measure of E/I imbalance, despite there being a statistically significant finding.

Measuring both transcriptional output from GWAS loci and gene expression correlates from MRI data is a noisy and challenging prospect. Indeed, recent research has shown poor correlation between gene expression and neurotransmitter receptor density.(https://doi.org/10.1016/j.neuroimage.2022.119671).

Thus, fundamental aspects of this manuscript including the use of MDD PRS to identify "at risk" individuals, the unclear link between AD and adolescent psychological resilience, the use of prepubertal age as a measure of biological age, and the limited conclusions that can be drawn from the gene expression and receptor density technique limits confidence in the results as presented.

Reviewer #1 (Public Review):

The present study combines quantitative histomorphometry, live cell imaging and tracking, functional analyses, and computational modeling to define potentially pathologic interactions between lung CD8 T cells and fibrocytes in human COPD. The authors use multiple technical approaches to establish the close proximity of CD8 T cells with fibrocytes in peri-bronchial tissue in COPD subjects that notably correlate with functional disease parameters (FEV1/FEV). Their follow-on studies identify specific chemokine pathways and inflammatory consequences of these interactions. Collectively, these seminal data acquired in a unified experimental context, provide support for pathogenic interactions between lung CD8 T cells and fibrocytes and now offer the consideration of mediators and pathways that may be amenable to therapeutic targeting. The strength of the study is the integration of the multi-modality approach, the quality of the quantitative data, and the creation of a tenable model for the interaction role in COPD of CD8 T cells and fibrocytes. While both have been previously implicated in COPD, this new study is more definitive by using this integrated approach.

Reviewer #2 (Public Review):

The authors use a series of elegant methods to describe the nature of the interrelationship among CD8+ T cells and fibrocytes in the airways of COPD patients. They find an increased presence of these interactions in COPD and show that CXCL8-CXCR2 interactions are crucial for this interaction, leading to increased CD8+ T cell proliferation.

Major strengths of the work include the detailed functional experiments used to describe the nature of the CD8+ T cell - fibrocyte interaction. Another key strength is the translational approach of the work, building on clinical data and connecting back to these same clinical data. The conclusions of the authors are supported by the data. The impact of the work is significant and key to our understanding of the interrelationship between inflammation and tissue remodeling in COPD. Understanding this relationship holds strong potential for the identification of new drug targets and for the identification of patients at risk.

The derivation of the CXCL8/CXCR2 dependency is based on a limited number of COPD patients, which could be strengthened. Also, the impact of the interrelationship between CD8 cells and the fibrocytes is not fully described.

Reviewer #3 (Public Review):

Eyraud and colleagues examine how fibrocytes and CD8 cells can interact with each other to promote COPD. The key findings include that CD8 cells and fibrocytes are found to exist in close proximity to each other in COPD lungs using histopathological analysis of patient samples. The authors leverage pre-existing transcriptomic data on CD8 cells to focus on chemokine release by CD8 cells as a potential pathogenic mechanism by which they could affect fibrocyte migration. In vitro studies using peripheral blood-derived CD8 cells and fibrocytes confirm increased fibrocyte migration in the presence of CD8 cells. as drivers of COPD progression. Conversely, in vitro studies show that fibrocytes exert a pro-proliferative effect on CD8 cells. The authors also use a computational model to assess how these interactions could promote the development of fibrocyte-CD8 clusters as COPD progresses over the course of 20 years.

The strengths of the study include:

1) The multi-faceted research approach that integrates histopathology from clinical COPD lung sections, in vitro co-culture studies, and computational modeling.

2) Applying computational modeling to determine how cell-cell interactions of migration and proliferation can result in distribution patterns within the lung that approximate what is found in actual clinical samples

3) Propose a feedback loop of CD8 cells and fibrocytes that could become a potential therapeutic target to interrupt a vicious cycle that promotes COPD.

However, there are also some weaknesses:

1) Specificity of the role of CD8 cells: While much of the focus is on the proximity of and interactions between CD8 cells and fibrocytes, it is not clear whether other cells similarly interact with fibrocytes. For example, CD4 cells, dendritic cells, or interstitial macrophages may similarly interact with fibrocytes as several of these also release chemokines. In the absence of a more comprehensive assessment, it becomes difficult to parse out how specific and relevant the fibrocyte-CD8 cell interactions are for COPD progression when compared to other putative interactions.

2) The transcriptomic analysis which in many ways sets the stage for the chemokine studies uses a pre-existing dataset of COPD and non-COPD samples with only n=2. The robustness of such a sample size is limited and the narrow focus on chemokines or adhesion receptors of CD8 cells in this limited sample size does not provide a more comprehensive analysis that would require larger samples sizes, studying the transcriptomes of other cell types and a broader analysis of which pathways are the most likely to be dysregulated in the cells that surround fibrocytes.

3) Specificity of the findings for COPD: The in vitro studies use circulating cells which are different from lung cells and this is appropriately acknowledged by the authors. However, it appears from the description that the cells are all from COPD patients. It is therefore not clear whether these interactions between fibrocytes and CD8 cells are unique to COPD, whether they also occur between control CD8 and fibrocytes, or only in cells obtained from patients with inflammatory/pulmonary diseases.

Reviewer #1 (Public Review):

While the mechanism about arm-races between plant and specialist herbivores has been studied, such as detoxification of specific secondary metabolites, the mechanism of the wider diet breadth, so-called generalist herbivores have been less studied. Since the heterogeneity of host plant species, the experimental validation of phylogenetic generalism of herbivores seemed as hard to be conducted. The authors declared the two major hypotheses about the large diet breadth ("metabolic generalism" and "multi-host metabolic specialism"), and carefully designed the experiment using Drosophila suzukii as a model herbivore species.

By an untargeted metabolomics approach using UHPLC-MS, authors attempted to falsify the hypotheses both in qualitative- and quantitative metabolomic profiles. Intersections of four fruit (puree) samples and each diet-based fly individual samples from the qualitative data revealed that there were few ions that occur as the specific metabolite in each diet-based fly group, which could reject the "multi-host metabolic specialism" hypothesis. Quantitative data also showed results that could support the "metabolic generalism" hypothesis. Therefore, the wide diet breadth of D. suzukii seemed to be derived from the general metabolism rather than the adaptive traits of the diverse host plant species. On the other hand, the reduction of the metabolites (ions) set using GLM seemed logical and 2-D clustering from the reduced ions set showed that quantitative aspects of diet-associated ions could classify "what the flies ate". These interesting results could enhance the understanding of the diet breadth (niche) of herbivorous insects.

The authors' approach seemed clear to falsify the hypotheses based on the appropriate data processing. The intersection of shared ions from the qualitative dataset could distinguish the diet-specific metabolites in flies and commonly occurring metabolites among flies and/or fruits. Also, filtering on the diet-specific ions seemed to be a logical and appropriate way. Meanwhile, the discussion about the results seemed to be focused on different points regarding the research hypotheses which were raised in the introduction part. Discussion about the results mainly focused on the metabolism of D. suzukii itself, rather than the research hypotheses and questions that were raised from the evolution of the wide diet breadth of generalist herbivores. In particular, the conclusion seems to be far from the main context of the authors' research; e.g. frugivory. It makes the implication of the study weaker.

Reviewer #2 (Public Review):

The manuscript: "Metabolic consequences of various fruit-based diets in a generalist insect species" by Olazcuaga et al., addresses an interesting question. Using an untargeted metabolomics approach, the authors study how diet generalism may have evolved versus diet specialization which is generally more commonly observed, at least in drosophila species. Using the phytophagous species Drosophila suzukii, and by directly comparing the metabolomes of fruit purees and the flies that fed on them, the authors found evidence for "metabolic generalism". Metabolic generalism means that individuals of a generalist species process all types of diet in a similar way, which is in contrast to "multi-host metabolic specialism" which entails the use of specific pathways to metabolize unique compounds of different diets. The authors find strong evidence for the first hypothesis, as they could easily detect the signature of each fruit diet in the flies. The authors then go on to speculate on the evolutionary ramifications of this for how potentially diet specializations may have evolved from diet generalism. Overall, the paper is well written, the experiments well documented, and the conclusions convincing.

Reviewer #3 (Public Review):

Laure Olazcuaga et al. investigated the metabolomes of four fruit-based diets and corresponding individuals of Drosophila suzukii that reared on them using comparative metabolomics analysis. They observed that the four fruit-based diets are metabolically dissimilar. On the contrary, flies that fed on them are mostly similar in their metabolic response. From a quantitative point of view, they find that part of the fly metabolomes correlates well with that of the corresponding diet metabolomes, which is indicative of insect ingestive history. By further focusing on 71 metabolites derived from diet-specific fly ions and highly abundant fruit ions, the authors show that D. suzukii differentially accumulates diet metabolism in a compound-specific manner. The authors claim that the data support the metabolic generalism hypothesis while rejecting the multi-host metabolic specialism hypothesis. This study provides a valuable global chemical comparison of how diverse diet metabolites are processed by a generalist insect species.

Strengths:<br /> The rapid advances in high-resolution mass spectrometry have recently accelerated the discovery of many novel post-ingestive compounds through comparative metabolomics analysis of insect/frass and plant samples. Untargeted metabolomics is thus a very powerful approach for the systematic comparison of global chemical shifts when diverse plant-derived specialized metabolites are further modified or quantitatively metabolized after ingestion by insects. The technique can be readily extended to a larger micro- or macro-evolutionary context for both generalist and specialist insects to systematically investigate how plant chemical diversity contributes to dietary generalism and specialism.

Weaknesses:<br /> The authors claim that their data support the hypothesis of metabolic generalism, however, a total analysis of insect metabolism may not generate a clean dataset for direct comparison of fruit-derived metabolites with those metabolized by D. suzukii, given that much of these metabolites would be "diluted" proportionally by insect-derived metabolites. If the insect-derived metabolites predominate, then, as the authors observed, a tight clustering of D. suzukii metabolomes in the PCA plot would be expected. It is therefore very difficult to interpret these patterns.

The authors generated a qualitative dataset using the peak list produced by XCMS which contains quantitative peak areas, it is unclear how the threshold was selected to determine if a peak is present or absent in a given sample. The qualitative dataset would influence the output of their data analysis.

The authors reply on in-source fragmentation for peak annotation when authentic standards are not available. The accuracy of the annotation thus requires further validation.

Reviewer #1 (Public Review):

Much experimental work on understanding how the visual system processes optic flow during navigation has involved the use of artificial visual stimuli that do not recapitulate the complexity of optic flow patterns generated by actual walking through a natural environment. The paper by Muller and colleagues aims to carefully document "retinal" optic flow patterns generated by human participants walking a straight path in real terrains that differ in "smoothness". By doing so, they gain unique insights into an aspect of natural behavior that should move the field forward and allow for the development of new, more principled, computational models that may better explain the visual processing taking place during walking in humans.

Strengths:<br /> Appropriate, state-of-the-art technology was used to obtain a simultaneous assessment of eye movements, head movements, and gait, together with an analysis of the scene, so as to estimate retinal motion maps across the central 90 deg of the visual field. This allowed the team to show that walkers stabilize gaze, causing low velocities to be concentrated around the fovea and faster velocities at the visual periphery (albeit more the periphery of the camera used than the actual visual field). The study concluded that the pattern of optic flow observed around the visual field was most likely related to the translation of the eye and body in space, and the rotations and counter-rotations this entailed to maintain stability. The authors were able to specify what aspects of the retinal motion flow pattern were impacted by terrain roughness, and why (concentration of gaze closer to the body, to control foot placement), and to differentiate this from the impact of lateral eye movements. They were also able to identify generalizable aspects of the pattern of retinal flow across terrains by subsampling identical behaviors in different conditions.

Weaknesses:<br /> While the study has much to commend, it could benefit from additional methodological information about the computations performed to generate the data shown. In addition, an estimation of inter-individual variability, and the role of sex, age, and optical correction would increase our understanding of factors that could impact these results, thus providing a clearer estimate of how generalizable they are outside the confines of the present experiments.

Reviewer #2 (Public Review):

The goal of this study was to provide in situ measurements of how combined eye and body movements interact with real 3D environments to shape the statistics of retinal motion signals. To achieve this, they had human walkers navigate different natural terrains while they measured information about eyes, body, and the 3D environment. They found average flow fields that resemble the Gibsonian view of optic flow, an asymmetry between upper and lower visual fields, low velocities at the fovea, a compression of directions near the horizontal meridian, and a preponderance of vertical directions modulated by lateral gaze positions.

Strengths of the work include the methodological rigor with which the measurements were obtained. The 3D capture and motion capture systems, which have been tested and published before, are state-of-the-art. In addition, the authors used computer vision to reconstruct the 3D terrain structure from the recorded video. Together this setup makes for an exciting rig that should enable state-of-the-art measurements of eye and body movements during locomotion. The results are presented clearly and convincingly and reveal a number of interesting statistical properties (summarized above) that are a direct result of human walking behavior.

A weakness of the article concerns tying the behavioral results and statistical descriptions to insights about neural organization. Although the authors relate their findings about the statistics of retinal motion to previous literature, the implications of their findings for neural organization remain somewhat speculative and inconclusive. An efficient coding theory of visual motion would indeed suggest that some of the statistics of retinal motion patterns should be reflected in the tuning of neural populations in the visual cortex, but as is the present findings could not be convincingly tied to known findings about the neural code of vision. Thus, the behavioral results remain strong, but the link to neural organization principles appears somewhat weak.

Reviewer #3 (Public Review):

Gaze-stabilizing motor coordination and the resulting patterns of retinal image flow are computed from empirically recorded eye movement and motion capture data. These patterns are assessed in terms of the information that would be potentially useful for guiding locomotion that the retinal signals actually yield. (As opposed to the "ecological" information in the optic array, defined as independent of a particular sensor and sampling strategy).

While the question posed is fundamental, and the concept of the methodology shows promise, there are some methodological details to resolve. Also, some terminological ambiguities remain, which are the legacy of the field not having settled on a standardized meaning for several technical terms that would be consistent across laboratory setups and field experiments.

Technical limits and potential error sources should be discussed more. Additional ideas about how to extend/scale up the approach to tasks with more complex scenes, higher speed, or other additional task demands and what that might reveal beyond the present results could be discussed.

Reviewer #1 (Public Review):

Habituation to noxious insults is a conserved mechanism that may act through varying pain-sensitivity thresholds based on previous sensory experience. Impaired regulation of nociceptive habituation may lead to a chronic pain condition. In the current manuscript, the authors identified additional structural elements of the CaM kinase-1 that regulate the protein shuttling between the cytosol and nucleus during nociceptive habituation. Based on the presented findings, we get a more complex regulatory model and a better understanding of the CMK-1 protein redistribution during stimulation-dependent nociceptive plasticity.

The data is carefully planned and results conclusively support the claims of the authors. The performed experiments are easy to follow and the results obtained are robust and statistically well-powered. The complex regulatory model presented in the manuscript is well supported by the reported data. Finally, the presented data presents a complex and dynamic mechanism of nuclear import and export rates of the CMK-1 protein to control nociceptive plasticity.

Reviewer #2 (Public Review):

In this study, Ippolito and colleagues elucidated the molecular mechanism of CMK-1 shuttling between the nucleus and cytoplasm and its function in the context of regulated thermosensation in C. elegans. This study is built on their previous work that identified a specific Nuclear Export Sequence (NES) required for CMK-1 cytoplasmic localization at 20{degree sign}C, and a specific Nuclear Localization Signal (NLS) to promote prolonged heat (28{degree sign}C)-induced CMK-1 nuclear entry. Here they show additional functional NES and NLS which counteract previously identified elements: the NLS297-307-dependent nuclear entry pathway and the S325-dependent cytoplasmic accumulation. Combined with their previous study, their work suggests a model: upon prolonged FLP neuron stimulation by noxious heat, CaM binding to CMK-1 causes CKK-1-dependent phosphorylation of T179, which in turn has a context-dependent dual effect: it is sufficient for nuclear translocation at 20{degree sign}C in an NLS71-78-dependent manner, and it promotes NES288-294-dependent nuclear export at 28{degree sign}C.

The authors thereby established a direct link between the state of a signal transduction pathway and FLP neuronal activity in response to heat stimulation. They used multiple approaches, including transgenics and reporter quantification analysis to characterize CMK-1 nucleo-cytoplasmic dynamic equilibrium. The experiments are well-designed with appropriate controls and appropriate sample sizes. The data analysis is comprehensive and revealing. The findings expand the functionally relevant intrinsic CMK-1 subcellular localization determinants. The new understanding generated in this study will appeal to readers in the fields of cell biology, signal transduction, and physiology.

Reviewer #1 (Public Review):

In this work, the authors propose a phenomenological grounded theoretical framework to explain why microbial taxonomic richness can show positive, unimodal, as well as negative diversity-temperature gradients. They thus propose to introduce a temperature dependence in the form of the Boltzmann-Arrhenius equation in both species' competitive interaction and growth rates. By means of a mean-field-like approximation, they estimate the probability of having N feasible coexisting species as a function of the normalized growth rate, and average competition strength, which in turn depends on temperature. They find that the shape of the microbial community temperature-richness relationship depends on how rapidly the strength of competition between species pairs increases with temperature relative to an increase in the variance of their growth rates. Furthermore, the mean-field result predicts that the position of richness peak depends on the sign of the covariance between the two main parameters of the Boltzmann-Arrhenius law. Finally, they show that the real-world community-level temperature-richness responses observed are qualitatively reproduced by their model.

I found the work interesting and stimulating, surely tackling a relevant research question such as the effect of thermal physiology on biodiversity patterns through a simple, but quantitative model. Overall, I like the proposed approach.

At the same time, the central mathematical results are not clear in my view, some strong approximations are not discussed, but they hold only in very specific conditions. A lot of important details are missing or scattered here and there, the notation is a little sloppy, and in general, it has been difficult for me to reproduce their finding.

The overall structure and flow of the manuscript can be remarkably improved.

Reviewer #2 (Public Review):

In their paper Variation in thermal physiology can drive the temperature dependence of microbial community richness, Clegg and Parwar present a relatively simple phenomenological model for explaining the wide variety of empirically observed relationships between temperature and diversity in the microbial world. Previous theories such as the Metabolic theory of biodiversity (MTB) and the metabolic niche hypothesis have emphasized the role of energy through either more efficient cellular kinetics or temperature-dependent niches. This paper builds on these works by showing that if one accounts for the variation of temperature sensitivity across species, one can get a much richer set of behaviors consistent with empirical observations.

Overall, I find the manuscript quite compelling and the model presented as a very nice summary of how variability in temperature dependence, simple Arrhenius scaling, and arguments based on modern coexistence theory can be combined to explain empirical observations of species abundance distributions and temperature.

Reviewer #3 (Public Review):

In empirical data, the dependence of microbial diversity on environmental temperature can take multiple different functional forms, while the previous theory has not established a clear understanding of when the temperature-dependence of diversity should take a particular form, and why. The authors seek to understand what forms are possible, and when they will occur, via analysis of the feasibility (i.e. positivity) of Lotka-Volterra equation solutions. This is combined with an assumption for the way that species' growth rates depend on temperature, along with an assumption for the way species interaction rates depend on temperature. Together, this completely specifies the form of the Lotka-Volterra equations, and whether all species in the model can coexist indefinitely at a given temperature, or whether only a lower-diversity subset can persist.

The overall goal is valuable, and the overall approach of using this classic model of species interactions is justifiable. My main question marks relate to the way the conditions on feasibility (i.e. when all species will have positive equilibria), whether and when we need to consider the stability of these feasible solutions, and finally how general the way in which model parameters are specified to depend on temperature. I will expand on these three issues below. A more minor issue is that the authors set up this problem with extensive reference to the interaction of consumers and resources, referencing previous approaches that explicitly model these. Since resources are not explicitly present in the Lotka-Volterra formalism, it would be helpful to have a clearer justification for the authors' rationale in choosing this kind of model.

(1) Conditions on growth and interaction rates for feasibility and stability. The authors approach this using a mean field approximation, and it is important to note that there is no particular temperature dependence assumed here: as far as it goes, this analysis is completely general for arbitrary Lotka-Volterra interactions.

However, the starting point for the authors' mean field analysis is the statement that "it is not possible to meaningfully link the structure of species interactions to the exact closed-form analytical solution for [equilibria] 𝑥^*_𝑖 in the Lotka-Volterra model.

I may be misunderstanding, but I don't agree with this statement. The time-independent equilibrium solution with all species present (i.e. at non-zero abundances) takes the form

x^* = A^{-1}r

where A is the inverse of the community matrix, and r is the vector of growth rates. The exceptions to this would be when one or more species has abundance = 0, or A is not invertible. I don't think the authors intended to tackle either of these cases, but maybe I am misunderstanding that.

So to me, the difficulty here is not in writing a closed-form solution for the equilibrium x^*, it is in writing the inverse matrix as a nice function of the entries of the matrix A itself, which is where the authors want to get to. In this light, it looks to me like the condition for feasibility (i.e. that all x^* are positive, which is necessary for an ecologically-interpretable solution) is maybe an approximation for the inverse of A---perhaps valid when off-diagonal entries are small. A weakness then for me was in understanding the range of validity of this approximation, and whether it still holds when off-diagonal entries of A (i.e. inter-specific interactions) are arbitrarily large. I could not tell from the simulation runs whether this full range of off-diagonal values was tested.

As a secondary issue here, it would have been helpful to understand whether the authors' feasible solutions are always stable to small perturbations. In general, I would expect this to be an additional criterion needed to understand diversity, though as the authors point out there are certain broad classes of solutions where feasibility implies stability.

(2) I did not follow the precise rationale for selecting the temperature dependence of growth rate and interaction rates, or how the latter could be tested with empirical data, though I do think that in principle this could be a valuable way to understand the role of temperature dependence in the Lotka-Volterra equations.

First, as the authors note, "the temperature dependence of resource supply will undoubtedly be an important factor in microbial communities"

Even though resources aren't explicitly modeled here, this suggests to me that at some temperatures, resource supply will be sufficiently low for some species that their growth rates will become negative. For example, if temperature dependence is such that the limiting resource for a given species becomes too low to balance its maintenance costs (and hence mortality rate), it seems that the net growth rate will be negative. The alternative would be that temperature affects resource availability, but never such that a limiting resource leads to a negative growth rate when a taxon is rare.

On the other hand, the functional form for the distribution of growth rates (eq 3) seems to imply that growth rates are always positive. I could imagine that this is a good description of microbial populations in a setting where the resource supply rate is controlled independently of temperature, but it wasn't clear how generally this would hold.

Secondly, while I understand that the growth rate in the exponential phase for a single population can be measured to high precision in the lab as a function of temperature, the assumption for the form of the interaction rates' dependence on temperature seems very hard to test using empirical data. In the section starting L193, the authors seem to fit the model parameters using growth rate dependence on temperature, but then assume that it is reasonable to "use the same thermal response for growth rates and interactions". I did not follow this, and I think a weakness here is in not providing clear evidence that the functional form assumed in Equation (4) actually holds.

Reviewer #2 (Public Review):

Despite high bone mineral density, increased fracture risk has been associated with T2D in humans. In this study, the authors established a model that could mimic some aspects of T2D in mice and then study bone turnover and metabolism in detail.

Strengths<br /> This is an exciting study, the methods are detailed and well done, and the results are presented coherently and support the conclusions.<br /> Previous work from Dr. Long's group over this last decade has established a requirement for glycolysis in osteoblast differentiation. They showed the requirement for glycolysis not only for the anabolic action of PTH but also as an effector downstream of Wnt signaling. Using the T2D mouse model they have generated, they test if manipulating glycolysis and oxidative phosphorylation can rescue some of the detrimental effects on bone in this model.<br /> They use several novel approaches, they use glucose-labeling studies that are relatively underutilized, and it provides some insights into defective TCA cycle. They also utilize BMSCs that have been sorted for performing single-cell sequencing studies to identify specific populations modified with T2D. Unfortunately, the results are modest and need some clarification on what these populations add to the story.<br /> The authors use two approaches: a drug (Metformin) and a number of mouse genetic models to over-express genes involved in the glycolytic pathway using Dox inducible models. The results with overexpressing HIF1 and PFKFB3 show a potential rescue of bone defects with T2D, and Glut1 overexpression does not rescue T2D-induced bone loss.

Concerns<br /> The authors have generated several overexpression models to manipulate the glycolytic pathway to recuse T2D-induced bone loss. The use of DOX in drinking water has been shown to affect mitochondrial metabolism. Did the authors control for these effects? Since both the groups of mice got the DOX in drinking water, there is internal control.<br /> Only one of the rescue experiments had control with the Chow diet. There are some studies that have shown a high-fat diet to be protective of bone loss in TID models.<br /> The use of metformin to correct metabolic dysfunction and, thereby, bone mass is an exciting result. Did the authors test to see if they had in any way rescued this phenotype because of reducing ROS levels? The decrease in OxsPhos seen with the seahorse experiments suggests there could be mitochondrial dysfunction often associated with ROS generation.<br /> All of the experiments used male mice (because STZ use and ease of T2D establishment in males). It would be better if this were made clear in the title.<br /> Is the T2D model presented really represent what is observed in humans? Some experiments to test the other factors implicated in T2D and whether those are modulated in the rescue experiments might help address this.

Reviewer #3 (Public Review):

The manuscript entitled "Osteoblast-intrinsic defect in glucose metabolism impairs bone formation in type II diabetic mice" by Song et al. showed that osteoblast activity was compromised due to impaired glucose metabolism using a youth-onset T2D mouse model. The investigators induced youth-onset T2D in 22-week-old C57BL/6J male mice by a high-fat diet (HFD) starting at 6 weeks of age and injection of low-dose streptozotocin three times at 12-week-old. Then they demonstrated that metformin promoted glycolysis and osteoblast differentiation in vitro and increased bone mass in the diabetic mice. It was also demonstrated that targeted overexpression of Hif1a or Pfkfb3, but not Glut1, in osteoblasts reduced bone loss in T2D mice. Overall, the investigators made a great effort to characterize the changes in metabolism in the bone of the B6/C57 mice by HFD and metformin with microCT, dynamic histomorphometry, C13 isotype labeling in vivo, scRNA-seq and metabolic assays with bone marrow mesenchymal cells in vitro.

Reviewer #1 (Public Review):

The most common genetic cause of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) is a G4C2 repeat expansion within the first intron of the C9ORF72 gene. However, how this repeat contributes to disease pathology is still an active area of research. This study takes a targeted approach to analyzing specifically how the C9ORF72 antisense transcript (C4G2) may be contributing to FTD/ALS.

Using an artificial (C4G2)75 antisense cassette, the authors show in both HEK293T cells and cultured neurons that the C4G2 antisense transcript leads to elevated levels of activated PKR and increased phosphorylated eIF2alpha. This then leads to a decreased level of translation, the formation of stress granules, and decreased survival, phenotypes that can be suppressed through the knockdown of PKR. The authors nicely demonstrate that PKR activation upon transfection with their antisense cassette is independent of toxic dipeptide repeat proteins by using reporter constructs that do not create these dipeptides but are still able to activate PKR. Furthermore, using a construct that expresses both sense and antisense transcripts, the authors show that knockdown of the antisense, but not the sense transcript, abrogates the PKR response (demonstrating the specificity of this stress pathway for the antisense RNA). The authors additionally show the relevance of PKR activation in FTD/ALS through the presence of activated PKR and elevated eIF2alpha in ALS postmortem brain tissue.

This paper shows that, at least in model systems, the C4G2 transcript can have cytotoxic effects through the stimulation of PKR. The experiments are well-controlled and fairly comprehensive. The claim that PKR activation occurs via the antisense RNA, and not the sense, is well supported by the data. However, some limitations exist, some of which the authors explicitly recognize. They are as follows:<br /> 1. It is not clear how the results from these reporter constructs inform on the repeat expansion RNAs produced in disease, which can be significantly longer, and might be expressed at different levels. Perhaps if the C4G2 repeat used in this work were expressed at levels comparable to what the antisense transcript is expressed in an actual disease, or in a similar RNA context, PKR would not be activated. This is important to keep in mind.<br /> 2. It is still unclear how PKR is being activated in the presence of C4G2 (it could be direct or indirect). The authors list a variety of explanations in the discussion. A prior study has shown that a similar repeat expansion leads to the accumulation of cytoplasmic dsRNA inclusions marked by TDP-43 (Rodriguez et al., 2021). It would be interesting to see if these inclusions are present upon expression of the antisense construct.<br /> 3. In the context of C9ORF72 FTD/ALS disease, it is still difficult to say how much of the disease pathology is on account of antisense triggered stress responses as opposed to dipeptide repeat, RBP titration, etc. This study nevertheless provides a new perspective to consider for how the C9ORF72 repeat expansion contributes to the diseased state.

Reviewer #2 (Public Review):

The underlying toxic species in C9ORF72 FTD/ALS is debated, with evidence for the contribution of both loss of function and gain of function of sense G4C2 repeat-expanded mRNAs and DRPS has been shown. The authors ask what the role - if any - of the antisense C4G2 repeat expanded mRNAs, which are equally abundant in patient brains, in producing toxicity. They convincingly show a role for these, independent of DRP expression, and distinct from sense G4C2repeat-expanded in toxicity in cell lines, neurons, and zebrafish, mediated via PKR activation. The latter is shown through increased p-eIF2alpha and reduced protein synthesis rates, associated with toxic phenotypes, rescued by PKR knockdown. The authors have achieved their aims, where the excellent data strongly support their conclusions.

The mechanism for PKR activation by antisense but sense repeat-expanded mRNAs is not examined, but the authors reasonably propose secondary structure differences in PKR activation. This could be tested in future work.

The work adds to our understanding of mechanisms of toxicity in repeat disorders, and this particular mechanism has implications for therapy via ISR modulation to reverse the effects of PKR activation.

The human data adds to the spectrum of protein-misfolding neurodegenerative diseases that show UPR/ISR activation, again with implications for therapy via ISR modulation.

Interestingly, PKR knockdown only partially rescues cell toxicity in neuronal cells, possibly reflecting other toxic mechanisms at play.

Reviewer #1 (Public Review):

In the manuscript "Staphylococcus aureus FtsZ and PBP4 bind to the conformationally dynamic N-terminal domain of GpsB", Sacco et. al. solved the crystal structure of S. aureus GpsB, an essential cell growth and division protein. The authors also identified its interactions with the master regulator of cell division FtsZ and a penicillin-binding protein PBP4 that is implicated in B-lactam insensitivity. Although GpsB is essential for growth in S. aureus the reason for its essentiality is poorly understood. The authors used biochemical, biophysical, and crystallographic methods to determine the structure of GpsB and characterized its binding with FtsZ and PBP4. The authors also solved the co-crystal structure of GpsB with the C-terminal peptide of PBP4. These results are significant because it details the interactions of an essential growth protein in S. aureus with known cell division proteins. However, the impact of the work could be further enhanced if the authors had more functional studies to demonstrate the importance of the new hinge motif, the binding with FtsZ C-terminal tail, and PBP4.

Reviewer #2 (Public Review):

This work continues the exploration of the GspB protein as a cytosolic hub for different cell wall enzymes. In particular, this manuscript presents evidence for the direct interaction of GspB with both FtsZ and PBP4 in Staphylococcus aureus. Structural determination is provided for the N-term region of GspB alone and in complex with the small cytosolic region of PBP4 recognized by GspB.

After previously published works from the same group identifying the connection between GspB and FtsZ, and from another group providing the structural basis for the interaction between GspB and PBPs in different bacterial species; the present work provides incremental information for the S. aureus case. The work is sound, and the experimental evidence supports the presented conclusions.

The main strength of the manuscript is providing pieces of evidence of the protein-protein interaction between GspB and FtsZ and between GspB and PBP4.

However, no structural information is provided for the GspB:FtsZ complex, and the 3D structure of the N-term domain of GspB is very similar to previous ones solved for other bacteria, but with the presence of a three-residues insertion that provides flexibility to the domain, a fact that seems to be important in vivo.<br /> The complex of N-term GspB with the cytosolic micro-domain of PBP4, reveals the interactions involved in the recognition; an interaction network that is similar to the previously reported for GspB and PBPs in bacillus subtilis and in Streptococcus pneumonia.

Reviewer #1 (Public Review):

The authors present a study to test the relationships between a measured dopamine marker in the brain - so-called, dopamine synthesis capacity - and various other measures purported to index dopamine function. These measures include questionnaire answers about behaviour, and measured behaviour. Various studies have used these other measures as indices or proxies of dopamine function with some evidence to support this. However, some of the evidence is in small groups or indirect.

The major strength of this study is the size of the sample (n=66-94) compared to other studies and the three different analytical strategies employed - frequentist, Bayesian, and predictive modelling.

Areas, where the study is more limited, are the use of only one marker of dopamine neurochemistry ([18F]FDOPA) and this does not discount relationships with other markers such as pre-synaptic receptors, post-synaptic receptors, and dynamic release. The authors acknowledge that this study does not speak to the general principle of dopamine relationships with other measures. While the numbers are impressive for this type of study the use of correlation means their power is for correlations of 0.32-0.37 and higher (G*power). It is possible genuine relationships between markers do exist but all studies to date, including this one, are underpowered. The Bayesian analysis conducted speaks to this and is a welcome addition. It is also possible that the conclusions are restricted by the participants recruited as they are limited to the ages of 18-43 and it is not clear how representative they are of the general community from the information provided.

The dopamine system is not one entity in terms of system components (pre-synaptic, post-synaptic, etc), but also in terms of subcortical area with a gradient of input from the brainstem and a distinct connectionist anatomy between the striatum and the cortex (via other structures). Here the authors use a segmentation of the striatum to test the relationships. While this is embedded in the methods and results the introduction's treatment of the subcortical dopamine system is as a single entity. This could be improved.

The results of this work have an important impact in that they strongly suggest one cannot use proxies to estimate endogenous neurochemistry (at least in the dopamine system). However, this implies that any other proxy for any other system needs to be (re-)assessed using similar methods. This is not to say that the proxies are not sensitive to dopamine manipulations, but that they cannot by themselves be used instead of direct measurement. Given the number of studies which suggest that a measure of baseline state may predict the effects of dopaminergic drugs, one must question what the baseline state is being measured.

Despite these limitations, the authors have provided the largest assessment of the relationships between [18F] FDOPA-assessed dopamine synthesis capacity and various markers previously linked to dopamine function. In this respect, it is an important negative. This does mean that the assessments used cannot be used to assess 'baseline' states in relation to dopaminergic drug effects, but the mechanism through which this baseline dependency operates is not well understood.

Reviewer #2 (Public Review):

This study examined the relationship between dopamine synthesis capacity, working memory, impulsivity, and spontaneous eye blink rate. The rationale for the study is sound and well-articulated given the results of prior studies suggesting relations between dopaminergic measures and these behavioral measures. Understanding these relationships is important both for understanding the neural and neurochemical correlates of behavioral traits, but also because it has been proposed that these measures might be used as a proxy for dopamine synthesis capacity, which is extremely expensive to collect and requires exposure to radiation. The study used appropriate methods and a major strength is that it was performed in a larger sample than is typical for PET studies, which are typically underpowered due to the expense of using radioligands. Critically, the study did not find evidence for associations. Although the results can be seen as disappointing in that they failed to confirm hypotheses, the findings nevertheless have substantial implications for the field. Specifically, the results argue against the use of these behavioral constructs as a proxy for dopamine synthesis activity. As such, the findings provide a critical corrective for prior conclusions that were derived from past smaller studies.

Reviewer #1 (Public Review):

The manuscript by Lujan and colleagues describes a series of cellular phenotypes associated with the depletion of TANGO2, a poorly characterized gene product but relevant to neurological and muscular disorders. The authors report that TANGO2 associates with membrane-bound organelles, mainly mitochondria, impacting in lipid metabolism and the accumulation of reactive-oxygen species. Based on these observations the authors speculate that TANGO2 function in Acyl-CoA metabolism.

The observations are generally convincing and most of the conclusions appear logical. While the function of TANGO2 remains unclear, the finding that it interferes with lipid metabolism is novel and important. This observation was not developed to a great extent and based on the data presented, the link between TANGO2 and acyl-CoA, as proposed by the authors, appears rather speculative.

1. The data with overexpressed TANGO2 looks convincing but I wonder if the authors analyzed the localization of endogenous TANGO2 by immunofluorescence using the antibody described in Figure S2. The idea that TANGO2 localizes to membrane contact sites between mitochondria and the ER and LDs would also be strengthened by experiments including multiple organelle markers.

2. The changes in LD size in TANGO2-depleted cells are very interesting and consistent with the role of TANGO2 in lipid metabolism. From the lipidomics analysis, it seems that the relative levels of the main neutral lipids in TANGO2-depleted cells remain unaltered (TAG) or even decrease (CE). Therefore, it would be interesting to explore further the increase in LD size for example analyze/display the absolute levels of neutral lipids in the various conditions.

3. Most of the lipidomics changes in TANGO2-depleted cells are observed in lipid species present in very low amounts while the relative abundance of major phospholipids (PC, PE PI) remains mostly unchanged. It would be good to also display the absolute levels of the various lipids analyzed. This is an important point to clarify as it would be unlikely that these major phospholipids are unaffected by an overall defect in Acyl-CoA metabolism, as proposed by the authors.

Reviewer #2 (Public Review):

This is an interesting study that seeks to deorphanize Tango2, a protein linked to muscle dysfunction but with no known function. It reveals that Tango2 primarily co-localizes with mitochondria, and its loss impacts mitochondrial homeostasis. Tango2-depleted cells also accumulate LDs. Lipidomic analysis indicated a partial depletion of diacyl lipids including PA in Tango2-depleted cells, and an accumulation of lyso-lipids such as LPA. The proposed model suggests that Tango2 plays a role in lipid metabolism, potentially in acyl-CoA trafficking and or delivery to lyso-lipids to generate diacyl-lipids for mitochondrial homeostasis, which is defective in tango2-deficient diseases like rhabdomyslosis. In general, this is a well-conducted and potentially important study. The first section which deals with Tango2 localization and profiling of cellular changes in Tango2-depleted cells is well conducted. However, the latter half which seeks to understand how Tango2 loss impacts lipid homeostasis is more preliminary. Lyso-lipids like LPA are definitely altered with tango2 loss, but additional work is necessary to understand whether this is due to increased lyso-lipid synthesis, a block in their acylation, or some combination of factors. Delineating these possibilities will significantly enhance this study.

Reviewer #1 (Public Review):

The manuscript provides a comprehensive analysis of the consequences of a mutation in WDR62 in human pluripotent stem cell-derived progenitor cells and neurons. The experiments are logical and presented well. The data support the conclusion that WDR62 dysfunction causes impaired cell cycle progression and defective neuronal differentiation. The data corroborate previous findings in mouse and human cells and cell lines and extend knowledge to cells that are relevant to the microcephaly characteristic of individuals with WDR62 mutations. The major shortcoming of the data is that it relies on cells from a single donor and so requires additional validation to support the generalization of the conclusions. In addition, limited mechanistic insight is provided.

Reviewer #2 (Public Review):

Dell'Amico and colleagues examine a C-terminal truncating mutation of WDR62, a gene identified as the 2nd most frequent cause of primary microcephaly. The authors generate neural progenitor cells and neurons from patient-derived IPSCs to examine the cell biological phenotypes of the truncation. This reveals the localization of WDR62 in the Golgi apparatus during interphase and suggests that shuttling from the GA to the spindle poles could be a potential mechanism underlying the effects of WDR62 truncation on cortical development.

Whereas these model systems are useful to study certain cell biological aspects of mutated cells, they do not fully recapitulate all features of the cortical development that the authors study. This model system lacks polarity of the tissue, which is important for a correct cell division of radial glia, which in turn is the key process impaired in microcephaly. Together with the inherent heterogeneity of the differentiation protocols, this poses a major weakness to the authors' approaches. On the other hand, the authors' system is well-suited for the analysis of co-localization and they show compelling evidence of the localization of WDR62 to the GA in interphase, which is the main strength of the study. These data are corroborated by immunostainings in fetal human tissue. Minor experiments are still needed to show a direct interaction of WDR62 with GA proteins and to further assess by immunofluorescence the GA-WDR62 co-localization in the radial glia of fetal human samples. Further, the author's interpretation that premature neurogenesis is not occurring in their system should be better supported by additional immunostaining. Finally, the manuscript is well written and the methods are adequately explained.

Reviewer #3 (Public Review):

WDR62 is a spindle pole-associated scaffold protein. Recessive mutations in WDR62 account for the second most common cause of autosomal recessive primary microcephaly (MCPH). This paper investigates how a C-terminal truncating mutation D955AfsX112 in WDR62 causes MCPH using iPSCs from a patient. The authors generated neuroepithelial (NES) cells, cortical progenitors, and neurons from the patient-derived and isogenic retro-mutated iPSC lines. They found that: (1) the mutant WDR62 fails to localize to the spindle poles during mitosis; (2) patient-derived iPS-NES cells exhibit shorter primary cilia and significantly smaller spindle angles; (3) the mutation leads to differentiation defects in iPSC-derived cortical neurons; (4) during the interphase-to-mitosis transition, WDR62 translocates from the Golgi apparatus to the spindle poles in a microtubule-dependent manner; and (5) the mutation prevents WDR62 shuttling from the Golgi to the spindle poles. Using the isogenic retro-mutated iPSC lines as the control increased the rigor of the current study. In general, this is a very carefully designed study, the data support the authors' conclusions, and confirm previous findings of WDR62 functions.

Reviewer #1 (Public Review):

The authors attempted to delete a rhodopsin allele with single-nucleotide mutation seen in a Chinese subpopulation of autosomal dominant retinitis pigmentosa patients, (Rho-T17M). This was done in vitro and in vivo, while keeping the Rho wild type allele intact in vitro and in vivo using CRISPR-SaCAS9 guide RNA-specific approach, a previously established technique. In this study, solid in vitro data was presented showing that one of the tested guide RNAs was effective to specifically delete targeted the Rho-T17M sequence of synthetic DNA as well as in iPSCs from RP patients. However, the in vivo part of this study is incomplete. The issues are: 1. confusing choice of disease animal model (Rho-5m mice that carry 4 additional rhodopsin mutations other than the targeted T17M); 2. no proof of gene editing efficiency at the cellular level of the targeted cell type (i.e. what percentage of rod photoreceptors lose the T17M disease mutation?); and 3) lack of evidence of therapeutic potential (i.e. is there any rescue of vision in the mouse disease model or any toxicity due to the vector itself?).

Reviewer #2 (Public Review):

The authors attempt to develop an allele-specific editing approach targeting RHO-T17M mutation for potential therapeutic use to treat the mutation associated with autosomal dominant retinitis pigmentosa.

1) The authors reported three sgRNAs for the RHO T17M allele for verification. It would be helpful to describe details of the discovery phase of these sgRNAs, including design, in silico predictions, inclusion criteria, off-target analysis, etc.

2) The authors claim that the targeted gene-editing efficiencies are dose-dependent. However, data were presented from only one mouse for the 5ｘ108 dose group (line 231-237), which might need more explanation.

3) With respect to Fig. 4C, the flat-mount retina is not representative. A better image of flat-mount of retina is preferred.

4) With respect to Fig. 6B & 6C, it seems that T17M protein and RHO-5m protein are likely detected in both cytoplasm and plasma membrane rather than being limited to the cytoplasm alone.

5) The therapeutic efficacy benefit should be supported by data of photoreceptor function and cell preservation after treatment. It is be better to include two more control groups, namely wild-type mice and untreated mutant mice, which may help evaluate improved response after treatment.

6) The mouse lines are confusing. Did the authors generate three lines of mice, including RHOwt/hum, Mut-RHOwt/hum, RHOhum/m-hum mice? Did the authors use the Rhohum/m-hum mice for verification of cutting efficiencies, whereas they use the other two lines of mice for rescue experiments? The authors should clarify.

7) Mut-RHOwt/hum mice have previously been reported to have fundus pigment abnormalities, so the fundus should be examined after rescue. The expression of Rho-5M mRNA was reduced in vitro. Was the expression of RHO mRNA also down regulated after rescue as well as in vitro? Did the subretinal injection of GFP spread to the whole retina? This can be determined with retinal flat mount or panretinal staining using GFP labeling. The authors showed that the cell numbers in the ONL were increased in the treatment group compared with the control group at 9 mpi. Were the other nuclear layers or plexiform layer also affected? Did the other retinal cells develop normally? Figure 8 showed retinal functions with AAV-based SaCas9/17-Sg2 in Mut-Rhowt/hum mice. ERG of Mut-Rhowt/hum mice without treatment are also needed.

The efficiency and safety of RHO T17M allele-specific editing in this paper are well supported by in vitro and in vivo experiments.

The fundamental basis of the study design should be clearly stated, ie which truncation variants in RHO cause disease or not. It is reported that truncation variants occurring before K296 are likely benign, which should be mentioned. This is the key starting point for this kind of study and is not limited to RHO. but as an allele-specific gene editing approach as a potential therapy for dominant mutations in any gene for which heterozygous loss-of-function is tolerated in the whole gene or in part of the gene (mostly at N-terminals). Apart from RHO, in fact, N-terminal truncating variants in several other IRD associated genes have been reported to be benign in heterozygotes, including CRX, TOPORS, RP1, etc. This study verified the efficiency and safety of this approach based on both patient derived iPSC and humanized animal models which are unique compared with other studies on RHO.

Reviewer #1 (Public Review):

According to current knowledge, zebrafish neurons maintain the capacity of regenerating with the exception of adult cerebellar Purkinje cells (PC), which are thought to have lost this property. Regeneration instead occurs at larval stages but whether newly generated PC form fully functional circuits is still unclear. This elegant and well-performed study takes advantage of a transgenic zebrafish line that enables inducing apoptosis under a tamoxifen-inducible system and at the same time visualizes PCs morphology through a membrane tagged RFP. Using this line (and other lines that tag radial glial and ventricular progenitors) in combination with morphological and functional analysis, the authors show that ventricular progenitors retain the lifelong ability to regenerate PCs. At larval stages, the newly regenerated PCs form fully functional circuits that lead to normal behavior. In adults, PC regeneration is less efficient (and PCs are also less prone to undergo apoptosis) but sufficient to support exploratory behavior. This study resolves the controversial issue of whether adult PC regeneration is possible and demonstrates that newly formed PCs at larval and adult stages can form functional circuits that support normal behavior.

This is a well-performed and carefully executed and quantified study. There is however a point that needs clarification:

The authors state that acute regeneration occurs between 5-10dpt. However, the graphs in Fig 1D, F, and 2F indicate that most PC generation occurs from 20-30 days. What happens in this period? Does proliferation increase? Can the authors perform BrdU incorporation between 6 days and 1 month? Related to this, as the authors indicate in lines 129-131, the regeneration of new PCs overlaps with normal development. Are other neuronal cell types generated in appropriate numbers?

Reviewer #2 (Public Review):

In this paper, Pose-Méndez and colleagues have investigated the lifelong ability of zebrafish for functional Purkinje cell regeneration after selective ablation. Previous studies have determined that the adult zebrafish cerebellum lacks the capacity to regenerate Purkinje cells after traumatic injury. The authors use an elegant approach to determine whether selective ablation of Purkinje cells, a scenario closer to neurodegenerative disease, would allow for regeneration. The overall message is, that Purkinje cell regeneration is accomplished at every age after targeted ablation. The authors find in a series of well-executed functional and behavioral experiments that selective loss of Purkinje cells leads to a change in neuronal circuit activity and behaviors. During the regeneration process and interestingly before the full recovery of Purkinje cell numbers compared to controls neuronal activity as well as behaviors are recovered.

Reviewer #3 (Public Review):

In "Lifelong regeneration of cerebellar Purkinje neurons after induced cell ablation in zebrafish" by Pose-Mendez and colleagues, the authors followed the regenerative properties that Purkinje cells have in larvae and adult Zebrafish. These properties common in teleostean and other animals are rare in mammals and, therefore, their study is of great interest to the neurodevelopmental community.

In this work, the authors use an already established animal model (PC-ATTACTM) to selectively ablate Purkinje cells in the larvae and adult Zebrafish, in a temporal control manner, that is by administering 4-OHT at defined stages. In doing so, the authors show that a full recovery of an ablated Purkinje cell population can be achieved when the ablation is induced in the larval stage, but this recovery is more modest when the ablation is induced in the adult stage, albeit very significant. The authors also show that regenerated Purkinje cells quickly elaborate their native electrical properties and integrate into functional circuits, which allow for the recuperation of motor behaviors produced by the loss of ablated Purkinje cells.

Overall, the work by Pose-Mendez and colleagues contributes to our understanding of neuronal regeneration in non-mammals. Technically, this study is well conducted and the provided data support most of the conclusions made by the authors.

Reviewer #1 (Public Review):

In this manuscript, Gonzalez et al investigated the dynamics of dopamine signals, measured with optophysiological methods in the lateral shell of the nucleus accumbens (LNAc), in response to different types of visual stimuli. Contrary to most current theories of dopamine signaling, the authors found that LNAcc dopamine transients tracked sensory transitions in visual stimulation rather than any immediately apparent motivational variable. This unorthodox finding is of potential interest to the field, as it suggests that dopamine in this particular area of the striatum supports a very different, albeit unclear behavioral function than what has been previously attributed to this neuromodulator. Many of the approaches used by the authors were very elegant, like the careful selection of visual stimuli parameters and the use of Gnat1/2 KO mice to demonstrate that the dopamine responses were directly dependent on the visual stimulation of rods and cones. That said, the authors did not discuss how their findings relate to much previously published work, many of which offer potential alternative explanations for their results. It is also not clear from the manuscript text which mice were used for which experiments, and how testing history might affect the results.

Reviewer #2 (Public Review):

In this elegant work,  the authors investigated dopamine release (measured by dLight sensor fiber photometry) in the nucleus accumbens shell, in response to salient luminance change. They show that abrupt visual stimuli - including stimuli not detectable by the human eye - can evoke robust dopamine release in the accumbens shell.

The fact that dopamine signals can be evoked by salient sensory stimuli is not itself novel, but the paper manages to make several important and new findings:

1. The authors show that the dopamine signal is not related to the level of threat evoked by the visual stimuli. <br /> 2. They provide important detail about the stimuli parameters relevant to dopamine release. For instance, they show that the rate of luminance change (or abruptness) is a key factor in evoking dopamine responses.<br /> 3. They show that robust dopamine responses can be evoked by visual stimuli of low intensity,  including stimuli not perceptible by the human eye.<br /> 4. They show that these dopamine responses can be evoked by all wavelengths in the visible spectrum (with some higher sensitivity at certain wavelengths).<br /> 5. Finally, by recording dopamine responses in two knockout mice strains, the authors show that the light-evoked dopamine release critically relies on rod and cone photoreceptors, but not melanopsin phototransduction. 

These results add to a series of recent findings showing that dopamine signals are not restricted to the encoding of reward prediction error, but instead contribute to signaling environmental changes more broadly. The study has been skillfully executed, the results are clear and appropriately analyzed, and the manuscript is very well written. Although the work did not include control mice lacking the dLight sensor, the fact that light-evoked dopamine responses were not observed in mice lacking cone + rod phototransduction is strong evidence that the fiberphotometry signals were not due to direct light artifacts.

Comment/concerns are minor:

1. The authors show that the dopamine response evoked by a brief visual stimulus is drastically reduced when the visual stimulus is repeated in rapid succession (stimulus train). The authors interpret this as evidence for the HABITUATION of this light-evoked dopamine release. An alternative explanation is that it is the prediction of the stimulus that is responsible for canceling the dopamine response (i.e. sensory prediction error). The authors should discuss this alternative explanation for this finding.

2. Although the study largely focuses on dopamine responses to visual stimuli, the results are largely consistent with previous studies showing dopamine signals encoding value-neutral changes in sensory inputs (i.e. sensory prediction errors) in different modalities (taste or odors; cf. Takahashi et al., 2017, Neuron; Howard & Kahnt, 2018, Nat. Comm.). The authors might want to cite those papers (note that I am not affiliated with those papers).

Reviewer #3 (Public Review):<br /> <br /> Gonzalez and colleagues investigate dopamine signals in response to visual stimuli. This work builds on the longstanding notion that dopamine neurons respond to unexpected sensory stimuli, including visual cues. Using fiber photometry measurements of a fluorescent dopamine sensor, they find that in the lateral ventral striatum, dopamine signals reliably report salient transitions in illuminance. Dopamine signals scale with light intensity and the speed of illuminance changes. They further find that the frequency of illuminance transitions, rather than the number, dictates the extent that dopamine signals habituate. In a number of studies, they characterize dopamine signals to light of different wavelengths, durations, and intensities. These results shed new "light" on the role of dopamine in signaling salience, independent of reward or threat learning. This work is elegantly done and compelling. While the results are potentially specific to this region of the striatum, rather than a broad dopaminergic profile of visual stimulus encoding, this work offers valuable insight into dopamine function, as well as a practical guide and considerations for the implementation of visual stimuli in behavioral tasks that assay dopamine systems.

Reviewer #1 (Public Review):

The endothelin ETB receptor is a G-protein coupled receptor activated by vasoactive peptide endothelins, causing vaslorelaxtion in smooth muscle. By determining the Cryo EM structure of human ETB in complex with the vasoconstricting peptide ET-1 and the inhibitory G-protein (Gi), the study represents a convincing insight into agonist-induced receptor activation and transducer-coupling. The complex structure is solid and will appeal to the GPCR and pharmacology communities.

Strengthens: The authors have managed to obtain the first G-protein complex structure of an ETB receptor by working with a receptor that still retains G-protein coupling (i.e. not a thermostabilized mutant) and by developing new methodologies into how the G-protein is remotely tethered to the GPCR. The Cryo EM structural details highlight clear differences into how the G-protein binds that also includes the more downward movement of TM7.

Weaknesses: While it is technically challenging to obtain an endothelin-1-ETB-Gi complex, the fusion approach means that there is equilibrium is already pushed towards a complex that may otherwise require lipids, such as PIP2. Whilst I don't know what may alter how alpha 5 interacts with ETB, this cannot be ruled out either.

Reviewer #2 (Public Review):

This study adds value in the relatively new field, specifically in the topic of ET-B receptor. In this study the authors provide a new structure in ET-B receptor that might be beneficial to the development of ET-B agonist. However, from the clinical and physiological point of view, the manuscript did not provide sufficient evidence in its current form.

Reviewer #3 (Public Review):

This manuscript by Sano et al., presents cryo-EM structure of endothelin-1-bound endothelin B receptor (ETbR) in complex with heterotrimeric G-proteins. The structural snapshot provides important information about agonist-induced receptor activation and transducer-coupling. This manuscript also designs and present a successful case example for a variation of previously used NanoBiT-fusion-based strategy to stabilize GPCR-G-protein complexes. This strategy may be broadly applicable to other GPCR-G-protein complexes as well, and therefore, also provides an important methodological advance. Overall, the experimental design and interpretation of the structure are excellent, and the manuscript present an easy-to-follow coherent story. Considering the importance of ETbR signaling in multiple physiological and disease conditions, this structural snapshot, taken together with earlier structural studies by the same laboratory, advances the ETbR biology significantly with potential for novel ligand discovery. This manuscript is also available as a preprint in bioRxiv as well as another manuscript from Xu and Jiang group. Considering the structural information presented in these manuscripts, I would strongly suggest that even if the other manuscript is published somewhere before this one, it should not be viewed as a compromise on novelty, and rather considered as complementary information from independent studies that further strengthen the impact.

Reviewer #1 (Public Review):

This manuscript describes efforts to understand how independence from ribonucleotide reduction might evolve in obligate intracellular bacterial pathogens using E. coli as a model for this process. The authors successfully deleted the three ribonucleotide reductase (RNR) operons present in E. coli and showed that growth of this knockout strain can be achieved with deoxyribonucleotide supplementation. They also performed evolutionary experiments and analysis of cell growth and morphology under conditions of low nucleotide availability. In this work, they established that certain genes are consistently mutated to compensate for the loss of RNR activity and the low availability of deoxynucleotides. Comparison to genomes of intracellular pathogens that lack RNR genes shows that these patterns are largely conserved.

While the experimental results support the conclusions of the study, the authors do report changes in cell morphology upon the growth of the RNR knockout strains with low concentrations of nucleotides. It would be ideal to note this complication earlier in the manuscript. And to clarify how the possibility of cell elongation might affect the OD measurements in Figure 3 describing the experiments to establish that dC is necessary for growth in the knockout strain. It would also be ideal to provide a more detailed explanation for that observation in the discussion.

Reviewer #2 (Public Review):

Ribonucleotide reductase (RNR) is crucial for de novo synthesis of the dNTP building blocks needed for DNA synthesis and is essential in nearly all organisms. In the current study, all three E. coli RNRs have been removed and the essential function of the enzyme is bypassed by the introduction of an exogenous deoxyribonucleoside kinase that enables dNTP production via salvage synthesis. This leads to a complete dependency on exogenously supplied deoxyribonucleosides (dNs), loss of control of dNTP regulation, and a highly increased mutation rate. The bacteria could also grow with only supplied deoxycytidine (and no other dNs), indicating that all dNTPs could be synthesized from deoxycytidine. An evolutionary analysis of the recombinant E. coli strain grown in multiple generations showed that mutations accumulated in genes involved in the catabolism of deoxycytidine and deoxyribose-1-P, supporting a model that all the other deoxyribonucleosides can be produced by a phosphorylase using nucleobases and deoxyribose-1-P as substrates and that the deoxycytidine (besides being a precursor of dCTP) could be a substrate to produce the deoxyribose-1-P needed by the phosphorylase working in the opposite direction.

The story is very interesting with novel findings, and the experiments are well performed. There are a few missing pieces of information, but on the other hand, it is many steps to cover if everything is going to be shown in a single paper and I came to the conclusion that the data is enough at this stage. One of the missing points for future research is to check what happens with the dNTP pools. RNR is a very important enzyme to control the dNTP levels and it is likely that it is unbalanced dNTP pools that lead to the increased mutation rates. However, it would be interesting to really measure the dNTP pools and connect them to the mutations reported. Another missing piece is to identify which nucleoside phosphorylase is involved and investigate its substrate specificity to better understand why the cells can live on deoxycytidine but not other dNs.

Reviewer #3 (Public Review):

The study focuses on a compelling question focusing on a largely indispensable mechanism, ribonucleotide reduction. The authors generate a unique specific bacterial strain where the ribonucleotide reducatase operon, entirely, is deleted. They grow the mutant strain in environments that have various amounts of the necessary deoxyribonucleoside levels, further, they perform evolution experiments to see whether and how the evolved lines would be able to adapt to the limited deoxyribonucleosides. Finally, researchers identify key mutations and generate key isogenic genetic constructs where target mutants are deleted. A summary postulation based on the evolutionary trajectory of ribonucleotide reduction by bacteria is presented. Overall, the study is well presented, well-justified, and builds on fairly classic genetic and evolution experiments. The select question and hypotheses and the overall framing of the story are fairly novel for the respective communities. The results should be interesting to evolutionary biology researchers, especially those interested in RNA>DNA directional evolution, as well as molecular microbiologists interested in the ribonucleotide reception dependence and selection by the environment. A discussion on the limitations of the laboratory study for the broader understanding of the host dependence during endosymbiosis and parasitism would be a good addition given the emphasis on this phenomenon as a part of the broader impacts of the study.

Reviewer #1 (Public Review):

This work presents a unification model (of sorts) for explaining how the flow of evidence through networks can be controlled during decision-making. The authors combine two general frameworks previously used as neural models of cortical decision-making, dynamic normalization (that implement value encoding via firing activity) and recurrent network models (which capture winner-take-all selection processes) into a unified model called the local disinhibition-based decision model (LDDM). The simple motif of the LDDM allows for the disinhibition of excitatory cells that represent the engagement of individual actions that happens through a recurrent inhibitory loop (i.e., a leaky competing accumulator). The authors show how the LDDM works effectively well at explaining both decision dynamics and the properties of cortical cells during perceptual decision-making tasks.

All in all, I thought this was an interesting study with an ambitious goal. But like any good study, there are some open issues worth noting and correcting.

MAJOR CONCERNS

1. Big picture

This was a comprehensive and extremely well-vetted set of theoretical experiments. However, the scope and complexity also made the take-home message hard to discern. The abstract and most of the introduction focus on the framing of LDDM as a hybrid of dynamic normalization models (DNM) and recurrent network models (RNMs). This is sold as a unification of value normalization and selection into a novel unified framework. Then the focus shifts to the role of disinhibition in decision-making. Then in the Discussion, the goal is stated as to determine whether the LDDM generates persistent activity and does this activity differ from RNMs. As a reader, it seems like the paper jumps between two high-level goals: 1) the unification of DNM and RNM architectures, and 2) the role of disinhibition. This constant changing makes it hard to focus as the reader goes on. So what is the big picture goal specifically?

Also, the framing of value normalization and WTA as a novel computational goal is a bit odd as this is a major focus of the field of reinforcement learning (both abstractly at the computational level and more concretely in models of the circuits that regulate it). I know that the authors do not think they are the first to unify value judgements with selection criteria. The writing just comes across that way and should be clarified.

2. Link to other models

The LDDM is described as a novel unification of value normalization and winner-take-all (WTA) selection, combining value processing and selection. While the authors do an excellent job of referencing a significant chunk of the decision neuroscience literature (160 references!) the motif they end up designing has a highly similar structure to a well-known neural circuit linked to decision-making: the cortico-basal ganglia pathways. Extensive work over the past 20+ years has highlighted how cortical-basal ganglia loops work via disinhibition of cortical decision units in a similar way as the LDDM (see the work by Michael Frank, Wei Wei, Jonathan Rubin, Fred Hamker, Rafal Bogacz, and many others). It was surprising to not see this link brought up in the paper as most of the framing was on the possibility of the LDDM representing cortical motifs, yet as far as I know, there does not exist evidence for such architectures in the cortex, but there is in these cortical-basal ganglia systems.

3. Model evaluations

The authors do a great job of extensively probing the LDDM under different conditions and against some empirical data. However, most of the time there is no "control" model or current state-of-the-art model that the LDDM is being compared against. In a few of the simulation experiments, the LDDM is compared against the DNM and RNM alone, so as to show how the two components of the LDDM motif compare against the holistic model itself. But this component model comparison is inconsistently used across simulation experiments.

Also, it is worth asking whether the DNM and RNM are appropriate comparison models to vet the LDDM against for two reasons. First, these are the components of the full LDDM. So these tests show us how the two underlying architectural systems that go into LDDM perform independently, but not necessarily how the LDDM compares against other architectures without these features. Second, as pointed out in my previous comment, the LDDM is a more complex model, with more parameters, than either the DNM or RNM. The field of decision neuroscience is awash in competing decision models (including probabilistic attractor models, non-recurrent integrators, etc.). If we really want to understand the utility of the LDDM, it would be good to know how it performs against similarly complex models, as opposed to its two underlying component models.

4. Comparison to physiological data

I quite enjoyed the comparisons of the excitatory cell activity to empirical data from the Shadlen lab experiments. However, these were largely qualitative in nature. In conjunction with my prior point on the models that the LDDM is being compared against, it would be ideal to have a direct measure of model fits that can be used to compare the performance of different competing "control" models. These measures would have to account for differences in model complexity (e.g., AIC or BIC), but such an analysis would help the reader understand the utility of the LDDM in connecting with empirical data much better.

Reviewer #2 (Public Review):

The aim of this article was to create a biologically plausible model of decision-making that can both represent a choice's value and reproduce winner-take-all ramping behavior that determines the choice, two fundamental components of value-based decision-making. Both of these aspects have been studied and modeled independently but empirical studies have found that single neurons can switch between both of the aspects (i.e., from representing value to winner-take-all ramping behavior) in ways that are not well described by current biological plausible models of decision making.

The current article provides a thorough investigation of a new model (the local disinhibition decision model; LDDM) that has the goal of combining value representations and winner-takes-all ramping dynamics related to choice. Their model uses biologically plausible disinhibition to control the levels of inhibition in a local network of simulated neurons. Through a careful series of simulation experiments, they demonstrate that their network can first represent the value of different options, then switch to winner-takes-all ramping dynamics when a choice needs to be made. They further demonstrate that their single model reproduces key components of value-based and winner-takes-all dynamics found in both neural and behavioral data. They additionally conduct simulation studies to demonstrate that recurrent excitatory properties in their network produce value-persistence behavior that could be related to memory. They end by conducting a careful simulation study of the influence of GABA agonists that provide clear and testable predictions of their proposed role of inhibition in the neural processes that underlie decision-making. This last piece is especially important as it provides a clear set of predictions and experiments to help support or falsify their model.

There are overall many strengths to this paper. As the authors note, current network models do not explain both value-based and ramping-like decision-making properties. Their thorough simulation studies and their validation against empirical neural and behavioral data will be of strong interest to neuroscientists and psychologists interested in value-based decision-making. The simulations related to persistence and the GABA-agonist experiments they propose also provide very clear guidelines for future research that would help advance the field of decision-making research.

Although the methods and model were generally clear, there was a fair amount of emphasis on the role of recurrence in the LDDM, but very little evidence that recurrence was important or necessary for any of the empirical data examined. The authors do demonstrate the importance of recurrence in some of their simulation studies (particularly in their studies of persistence), but these would need to be compared against empirical data to be validated. Nevertheless, the model and thorough simulation investigations will likely help develop more precise theories of value-based decision-making.

Reviewer #3 (Public Review):

Shen et al. attempt to reconcile two distinct features of neural responses in frontoparietal areas during perceptual and value-guided decision-making into a single biologically realistic circuit model. First, previous work has demonstrated that value coding in the parietal cortex is relative (dependent on the value of all available choice options) and that this feature can be explained by divisive normalization, implemented using adaptive gain control in a recurrently connected circuit model (Louie et al, 2011). Second, a wealth of previous studies on perceptual decision-making (Gold & Shadlen 2007) have provided strong evidence that competitive winner-take-all dynamics implemented through recurrent dynamics characterized by mutual inhibition (Wang 2008) can account for categorical choice coding. The authors propose a circuit model whose key feature is the flexible gating of 'disinhibition', which captures both types of computation - divisive normalization and winner-take-all competition. The model is qualitatively able to explain the 'early' transients in parietal neural responses, which show signatures of divisive normalization indicating a relative value code, persistent activity during delay periods, and 'late' accumulation-to-bound type categorical responses prior to the report of choice/action onset.

The attempt to integrate these two sets of findings by a unified circuit model is certainly interesting and would be useful to those who seek a tighter link between biologically realistic recurrent neural network models and neural recordings. I also appreciate the effort undertaken by the authors in using analytical tools to gain an understanding of the underlying dynamical mechanism of the proposed model. However, I have two major concerns. First, the manuscript in its current form lacks sufficient clarity, specifically in how some of the key parameters of the model are supposed to be interpreted (see point 1 below). Second, the authors overlook important previous work that is closely related to the ideas that are being presented in this paper (see point 2 below).

1) The behavior of the proposed model is critically dependent on a single parameter 'beta' whose value, the authors claim, controls the switch from value-coding to choice-coding. However, the precise definition/interpretation of 'beta' seems inconsistent in different parts of the text. I elaborate on this issue in sub-points (1a-b) below:

1a). For instance, in the equations of the main text (Equations 1-3), 'beta' is used to denote the coupling from the excitatory units (R) to the disinhibitory units (D) in Equations 1-3. However, in the main figures (Fig 2) and in the methods (Equation 5-8), 'beta' is instead used to refer to the coupling between the disinhibitory (D) and the inhibitory gain control units (G). Based on my reading of the text (and the predominant definition used by the authors themselves in the main figures and the methods), it seems that 'beta' should be the coupling between the D and G units.

1b). A more general and critical issue is the failure to clearly specify whether this coupling of D-G units (parameterized by 'beta') should be interpreted as a 'functional' one, or an 'anatomical' one. A straightforward interpretation of the model equations (Equations 5-8) suggests that 'beta' is the synaptic weight (anatomical coupling) between the D and G units/populations. However, significant portions of the text seem to indicate otherwise (i.e a 'functional' coupling). I elaborate on this in subpoints (i-iii) below:

(1b-i). One of the main claims of the paper is that the value of 'beta' is under 'external' top-down control (Figure 2 caption, lines 124-126). When 'beta' equals zero, the model is consistent with the previous DNM model (dynamic normalization, Louie et al 2011), but for moderate/large non-zero values of 'beta', the network exhibits WTA dynamics. If 'beta' is indeed the anatomical coupling between D and G (as suggested by the equations of the model), then, are we to interpret that the synaptic weight between D-G is changed by the top-down control signal within a trial? My understanding of the text suggests that this is not in fact the case. Instead, the authors seem to want to convey that top-down input "functionally" gates the activity of D units. When the top-down control signal is "off", the disinhibitory units (D) are "effectively absent" (i.e their activity is clamped at zero as in the schematic in Fig 2B), and therefore do not drive the G units. This would in-turn be equivalent to there being no "anatomical coupling" between D and G. However when the top-down signal is "on", D units have non-zero activity (schematic in Fig 2B), and therefore drive the G units, ultimately resulting in WTA-like dynamics.

(1b-ii). Therefore, it seems like when the authors say that beta equals zero during the value coding phase they are almost certainly referring to a functional coupling from D to G, or else it would be inconsistent with their other claim that the proposed model flexibly reconfigures dynamics only through a single top-down input but without a change to the circuit architecture (reiterated in lines 398-399, 442-444, 544-546, 557-558, 579-590). However, such a 'functional' definition of 'beta' would seem inconsistent with how it should actually be interpreted based on the model equations, and also somewhat misleading considering the claim that the proposed network is a biologically realistic circuit model.

(1b-iii). The only way to reconcile the results with an 'anatomical' interpretation of 'beta' is if there is a way to clamp the values of the 'D' units to zero when the top-down control signal is 'off'. Considering that the D units also integrate feed-forward inputs from the excitatory R units (Fig 2, Equations 1-3 or 5-8), this can be achieved either via a non-linearity, or if the top-down control input multiplicatively gates the synapse (consistent with the argument made in lines 115-116 and 585-586 that this top-down control signal is 'neuromodulatory' in nature). Neither of these two scenarios seems to be consistent with the basic definition of the model (Equations 1-3), which therefore confirms my suspicion that the interpretation of 'beta' being used in the text is more consistent with a 'functional' coupling from D to G.

2) The main contribution of the manuscript is to integrate the characteristics of the dynamic normalization model (Louie et al, 2011) and the winner-take-all behavior of recurrent circuit models that employ mutual inhibition (Wang, 2008), into a circuit motif that can flexibly switch between these two computations. The main ingredient for achieving this seems to be the dynamical 'gating' of the disinhibition, which produces a switch in the dynamics, from point-attractor-like 'stable' dynamics during value coding to saddle-point-like 'unstable' dynamics during categorical choice coding. While the specific use of disinhibition to switch between these two computations is new, the authors fail to cite previous work that has explored similar ideas that are closely related to the results being presented in their study. It would be very useful if the authors can elaborate on the relationship between their work and some of these previous studies. I elaborate on this point in (a-b) below:

2a) While the authors may be correct in claiming that RNM models based on mutual inhibition are incapable of relative value coding, it has already been shown previously that RNM models characterized by mutual inhibition can be flexibly reconfigured to produce dynamical regimes other than those that just support WTA competition (Machens, Romo & Brody, 2005). Similar to the behavior of the proposed model (Fig 9), the model by Machens and colleagues can flexibly switch between point-attractor dynamics (during stimulus encoding), line-attractor dynamics (during working memory), and saddle-point dynamics (during categorical choice) depending on the task epoch. It achieves this via a flexible reconfiguration of the external inputs to the RNM. Therefore, the authors should acknowledge that the mechanism they propose may just be one of many potential ways in which a single circuit motif is reconfigured to produce different task dynamics. This also brings into question their claim that the type of persistent activity produced by the model is "novel", which I don't believe it is (see Machens et al 2005 for the same line-attractor-based mechanism for working memory)

2b) The authors also fail to cite or describe their work in relation to previous work that has used disinhibition-based circuit motifs to achieve all 3 proposed functions of their model - (i) divisive normalization (Litwin-Kumar et al, 2016), (ii) flexible gating/decision making (Yang et al, 2016), and working memory maintenance (Kim & Sejnowski,2021)

Reviewer #1 (Public Review):<br /> <br /> The pH-dependent conformational change of the envelope protein in flaviviruses is required for the infection process, thus it represents an attractive target for drug development. In this study, the authors conducted extensive atomistic simulations for models for the envelope in six flaviviruses. Using a benzene-mapping approach, they were able to identify several cryptic binding sites that can be targeted for drug development. One of the cryptic binding site was observed in a previous study to be occupied by a detergent molecule, while the other cryptic binding site is located at domain interface. The second binding site involves a cluster of ionizable residues. Using constant pH simulations, the authors suggested that the cluster of ionizable residues contribute to the pH dependent conformational rearrangements. This cluster model helps to explain the inconsistencies reported in the literature regarding the role of several key histidine residues as pH sensors. Overall, the study has provided new mechanistic insights that can be taken advantage of in future drug developments that target flaviviruses. The work also highlights the importance of constant pH simulations to the analysis of pH sensitive biological processes.

Reviewer #2 (Public Review):

The authors made an applaudable attempt to identify druggable cryptic pockets and address a controversy regarding a pH switch of a very large system of significant biological and Pharmaceutical interest. Due to the size of the system and uncertainty in the membrane interactions/curvature the draft produces etc, it is a nontrivial task. By using a previously validated mixed solvent (i.e., benzene mapping) protocol, the authors were able to analyze the potential pockets in the entire system. This is big technical advance and the protocol can be used by other works in the field for studying cryptic pockets.

Reviewer #3 (Public Review):

This work dives into the inner molecular workings of viruses such as yellow fever, Zika, and tick borne encephalitis. Due to their pathogenic nature, these are active targets for drug development, and motivated by this, the authors set out to search for so-called "cryptic" binding pockets, concealed from the protein surface and therefore often missed. Using atomistic computer simulations of viral rafts embedded in lipid membranes, the authors present new methodology to detect and characterise structural and electrostatic features of viral envelope proteins. By mixing in a small organic co-solvent (benzene) that acts as a drug proxy, structural fluctuations are enhanced, which reveal hitherto hidden binding pockets. The authors convincingly show that this perturbation has only a minute effect on protein secondary structure. The technique revealed a new cryptic binding pocket that is well conserved across multiple flaviviruses.

The cryptic site involves four potentially charged residues and to understand their interplay, constant pH molecular dynamics simulations are combined with a detailed structural and electrostatic analysis of the binding pocket.<br /> Due it's multi-dimensional nature, the response to a possible pH change is a complex process and the authors present a compelling analysis involving charge states, inter-residue distances (reduced using PCA), and structural features of the pocket. An important conclusion is that the role of histidine is less important than previously thought: the pH dependent behaviour is a collective property of the pocket.

This study is an important contribution to computer aided drug-design. In particular, using co-solutes to induce structural fluctuations seems very helpful for uncovering new binding sites. Of equal importance are methodology to analyse complex trajectories. This work is a good example of how multiple dimensions can be reduced and rationalised using e.g. solvent accessibly surface area (SASA), radius of gyration, net-charge, and principal component analysis. There are likely several other properties that could aid in this rationalising and the present work is a solid platform for exploring these.

Reviewer #1 (Public Review):

This work introduces a new computational model of healthy blood cell formation and chronic myeloid leukemia (CML). By combining data from the literature, animal experiments and patients the authors aim to develop a detailed description of the regulatory mechanisms governing healthy blood cell formation and CML therapy response. The model is used to derive hypotheses explaining why some patients respond to tyrosine kinase inhibitors (TKI) better than others. Based on the model simulations the authors seek predictors of TKI efficacy and for concepts to improve CML therapy.

Strengths:

(1) The authors start from all possible ordinary differential equation models which describe positive and negative regulations of proliferation rates and self-renewal/differentiation probabilities. The models account for hematopoietic stem cells, multipotent progenitors, terminally differentiated myeloid cells, and terminally differentiated lymphoid cells. Using an automated approach referred to as design space analysis (DSA) the authors exclude models with unfeasible qualitative dynamics. Using data from mouse experiments the authors exclude all regulatory configurations except one. This systematic approach combining model analysis and data from various sources is clearly a strength of the work.

(2) The authors consider a large number of parameter sets that are in line with physiological steady-state cell counts and realistic responses to system perturbations. Thus the authors can potentially account for inter-patient differences.

(3) The model predictions are compared to experimental and published data. The proposed predictors of TKI efficacy are tested on retrospective patient data.

Weaknesses:

(1) In my opinion the sub-model of leukemic cells requires a more solid justification. The authors assume that the configuration of regulatory loops and most key parameters are identical for normal and leukemic cells. The only difference the proposed model accounts for is that leukemic cells exhibit a weaker response to the feedback signal acting on stem cell self-renewal. The weaker response of leukemic stem cells is justified by data from the literature supporting differential responses to CCL3. However, the authors propose no justification for the assumption that all other parameters, such as proliferation rates or maximal self-renewal probabilities, are identical or have minor impacts.

(2) The authors come to the conclusion that "a key predictor of refractory response to TKI treatment is an increased probability of self-renewal of normal hematopoietic stem cells" (Abstract). This conclusion is, in my opinion, not fully supported by the model as it is. In the model, it is assumed that normal and leukemic stem cells have the same maximal self-renewal probability. Only the regulation of self-renewal by feedback signals is different. The parameter which is a predictor in the presented analysis (p_{0,max}) is the maximal self-renewal probability of both normal AND leukemic stem cells. Therefore, the conclusion that normal stem cell self-renewal is a predictor of TKI response is, in my opinion, questionable. If I understand the analysis correctly, the authors show the following: Under the assumptions that the maximal self-renewal probability of normal and leukemic stem cells is identical and that the feedback inhibition of self-renewal is weaker in leukemic stem cells compared to normal stem cells, the maximal self-renewal probability of the two stem cell populations is a predictor of TKI response. Notably, if the value of maximal self-renewal probability is increased, the self-renewal probability of leukemic and normal stem cells increases simultaneously at all time points. Therefore, I find it difficult to argue that normal stem cell self-renewal [as opposed to leukemic stem cell self-renewal] is the relevant quantity.

(3) The simulation of differentiation therapy is interesting, however, due to a lack of knowledge in the field, the specific impacts of such therapy on normal versus leukemic cell differentiation have to be rather hypothetical.

(4) The used patient cohort is very small (n = 21).

The proposed model of the regulations governing blood cell formation is a valuable contribution to the fields of computational modeling and experimental hematology. The derived predictors of TKI efficacy are potentially useful.

Reviewer #2 (Public Review):

The authors want to capture the dynamics of CML therapy with TKI and understand why some patients fail to respond to therapy (primary resistance). They develop a mathematical model of hematopoiesis that includes stem cells, progenitor cells, and mature cells linked through feedback mechanisms. They explore parameter space using sophisticated algorithms to reduce this parameter space and the potential models to one final model and then apply it to chronic myeloid leukemia in the chronic phase under therapy with a tyrosine kinase inhibitor. The novelty in the model is the feedback mechanism introduced and the concomitant animal model data to understand the parameters.

The model is tractable and yet captures important physiologic aspects of hematopoiesis that have not been explored previously in CML. The animal data to validate it is also quite important. Finally, the application of the model to clinical data illustrates its applicability to real clinical scenarios and provides interesting insights.

One concern is whether the short-term transplantation experiments truly reflect the steady state of hematopoiesis and how CML develops in humans.

It is possible that the model can be applied to other hematologic conditions such as myeloproliferative disorders since one would expect the dynamics and interactions to be similar.

Reviewer #3 (Public Review):

Rodriguez et al. develop a nonlinear ordinary differential equation model of hematopoiesis under normal and chronic myeloid leukaemia (CML) conditions, incorporating feedback control, lineage branching, and signaling between normal and CML cells. Design space analysis is used to identify viable models of cell-cell signalling interaction. Data from mouse models are used to refine the set of cell-cell interactions considered viable, resulting in a novel feedback-feedforward model. Through this framework, the response to tyrosine kinase inhibitor (TKI) therapy is analysed. Model behaviour is qualitatively consistent with experimental data from mouse models, and clinical data. In particular, the model demonstrates varying responses to tyrosine kinase inhibitor therapy across a range of parameter sets consistent with "normal" hematopoietic cell counts; and predicts that a relatively high proportion of leukemic hematopoietic stem cells is a contributor to (though does not guarantee) primary tyrosine kinase inhibitor resistance, consistent with experimental and clinical data.

Strengths:<br /> Mathematical modelling in the work is validated using both experimental and clinical data.

The approach to model selection and identification of reasonable parameter regions is interesting and appealing, particularly in the context of modelling processes such as CML which can exhibit significant heterogeneity between patients.

I expect that this work will be useful to the community, as the approach employed in this work could be readily adapted to study other similar problems (for example, different conditions or treatments), provided that suitable experimental and/or clinical data are collected or available.

The work is supported by extensive supplementary material, clearly documenting in detail the techniques involved and assumptions made.

Weaknesses:<br /> Clinical data from CML patients treated with TKI therapy is limited (n=21).

As acknowledged by the authors, there are some physiological aspects that may be important that are not modelled; including stem cell-niche interactions in the bone marrow microenvironment, and interactions with immune cells.

Reviewer #1 (Public Review):

This is a fascinating effort from the Ryan laboratory, revisiting fundamental issues of calcium-dependent release probability at cultured synapses. The authors point out that our basic understanding of mammalian synapses rests on a foundation of older research that was not acquired at physiological temperature, and represented a statistical interpretation of data acquired electrophysiologically without direct knowledge of release at individual active zones. The authors employ techniques of calcium imaging and glutamate sensing and argue that single synapses can be 'silenced' by a moderate drop in extracellular calcium, a drop that is within the range of calcium channel inhibition following activation of GABAergic signaling. While fascinating, the conclusions are most powerful when the data can be distilled to direct observation of single release sites and this is not uniformly the case.

Reviewer #2 (Public Review):

Throughout the manuscript, the authors aim to distinguish signal from the lack of it. All conclusions depend on the success of this process. In such an endeavor, the sensitivity of the applied methods is critical. Thus, the authors must use the most sensitive tools to draw meaningful conclusions. The latest iGluSnFR has amazing sensitivity allowing the detection of single AP-evoked responses. This is not the case for vGpH, which requires hundred APs to get a meaningful signal. Similar, synthetic Ca2+ dyes have much better dynamic range, linearity and sensitivity compared to GCaMP6f.

The rate of silent boutons at 2 mM [Ca2+]e is lower for a single AP compared to 20 or 200 APs. The overall failure rate cannot be increased with increasing the number of APs. This clearly indicates a technical issue (e.g. insufficient sensitivity of vGpH and GCaMP6f).

The authors used three different measuring tools and used three different stimulation protocols, making the interpretation of the data challenging. It is impossible to tell how the failure rate changes from 1 to 20 APs without knowing the release probability, the pool size, depletion, recovery of SVs, and facilitation. These are all unknown.

The last experiment with the GABAB agonist has little novelty in its present form. The authors demonstrate that GABAB agonism increases the rate of silent terminals. The interesting issue would be to reveal how the effect of GABAB activation depends on the [Ca2+]e. This information is essential to see whether there is indeed a shoulder in its effectiveness curve.

The authors refer to a theoretical set-point in [Ca2+]e below which the function of the terminals is fundamentally different. From the presented experiments, the reviewer does not see any data that is inconsistent with a continuum. 'Thus, as with Ca2+ influx, SV recycling is modulated in an all-or-none manner by modest changes in [Ca2+]e around the physiological set point.' This statement is not supported by the data. The reviewer cannot see a set point.

Reviewer #3 (Public Review):

In this study Cook and Ryan examine, at physiological temperatures, the sensitivity of neurotransmitter release to external calcium concentrations close to physiological ones. Using hippocampal neurons in culture, field potential-based stimulation, a spatially confined genetically encoded calcium indicator (GCaMP6f) as well as fluorescent reporters of exocytosis and extracellular glutamate, the authors show that as extracellular calcium concentrations are reduced from 2.0, to 1.2 and finally to 0.8 mM, a disproportional fraction of presynaptic terminals cease to respond, as evidenced by no elevations in intracellular calcium concentrations, no detectable exocytosis or changes in extracellular glutamate. The phenomenon is quantitively modulated by blocking particular types of calcium channels, but is qualitatively conserved across all tested conditions. Finally, the authors show that effects of lower extracellular calcium concentrations can be mimicked by applying Baclofen, an agonist of type B GABA receptors. The authors reveal the sensitivity of all-or none calcium influx and exocytosis near extracellular calcium physiological set points and highlight the potential importance of this sensitivity as an effective control point for neural circuit modulation.

The findings described in the manuscript are potentially important as they seem to uncover a new, yet undescribed, all-or none (binary) phenomenon in the field of synaptic neuroscience, that is, of individual presynaptic terminals moving between two 'states' - 'active' and 'silenced'- which are set somehow by levels of extracellular calcium concentrations. Moreover, this dependency is observed at extracellular calcium concentrations that are quite close to the physiological concentration set point. The use of multiple reporters (intracellular calcium concentrations, synaptic vesicle fusion and extracellular glutamate) strengthens the validity of the observations.

On the other hand, there are two major points that need to be addressed.

The first is that alternative explanations should be ruled out more convincingly, first and foremost the matter of membrane excitability. Two observations are relevant here: The qualitative preservation of the phenomenon when two types of voltage gated calcium channels are blocked separately, and the large heterogeneity of the % of silenced boutons among neurons at a given extracellular calcium concentrations, which is at least as great as the range of modulation of the % of silenced synapses by extracellular calcium concentrations at single neurons. One then wonders if the findings might be attributed to a) the fidelity of the field potential-based stimulation system, that is, the degree to which neurons track the stimuli trains; b) the heterogeneity of neurons in this regard, c) this fidelity at different extracellular calcium concentrations for different neurons, and d) the identity of presynaptic sites analyzed in one run (are they all part of the same axon?). Along these lines, there is an assumption that the field potential-based stimulation system is the sole driver of excitation in these networks, which is reasonable given that excitatory synaptic transmission is mostly blocked pharmacologically (by CNQX and APV). Inhibitory transmission, however, was not blocked and thus, there is no guarantee that the inhibitory input neurons receive and its modulation by extracellular calcium does affect the degree to which neurons fire precisely and reliably at 20 Hz at all conditions. If it could be shown, at least for a substantial subset of the data, that all terminals analyzed for a particular neuron are part of an unambiguously identified axon stretch, with no branches (potential conduction failure points) and still demonstrate the claimed heterogeneity, this potential confound would be less of an issue.

The second issue relates to the ties made to neuromodulation. In spite of the title, introduction and discussion, not a single neuromodulator (such as dopamine, acetylcholine, noradrenaline, serotonin) was tested, only baclofen, which as a derivative of GABA, activates GABAB receptors, not receptors of canonical neuromodulators. The title of this manuscript is therefore not appropriate.

Reviewer #1 (Public Review):

In this manuscript the authors describe the development and application of hierarchical machine learning model to identify the likely source of S. Enteritidis using whole genome sequence data. The application makes use of a collection of 2,313 genomes from 4 continents, 11 sub-regions and 38 countries. The approach is, to the best of my knowledge, novel and represents a substantial advance over previous approaches. The model is demonstrated to have good performance at the continental level and - where sufficient training data were available - also at the country level.

Strengths of the work include the clear exposition of the methods, application to a large and detailed genomic database of clinical S. Enteritidis isolates, and the use of five independent validation data-sets.

Limitations include lack of validation using post-pandemic data (as the authors state, the model may need retraining in light of changes to the global food network). Also, claimed novelties of the work include greater geographic granularity and faster turnaround time compared to alternative methods, but no explicit comparison to other methods is made.

Overall, the authors achieve their aims in describing a hierarchical machine learning model for source attribution using pathogen whole genome sequences. The approach is likely to be of broad relevance and considerable public health utility.

Reviewer #2 (Public Review):

In this study, Bayliss et al. built a machine learning algorithm that predicts which country an isolate of Salmonella Enteritidis has come from based on its genome sequence. The study used S. Enteritidis isolates taken from clinical infections in the UK with recently reported travel, with the recent travel location being assumed as the source of infection.

The reason for developing this type of algorithm is to use it for source attribution in the case of gastroenteritis cases caused by imported food or cases of gastroenteritis picked up during travel overseas. S. Enteritidis is a major cause of gastroenteritis worldwide. Its transmission is tied in with the food chain, and understanding where it travels and how is key to reducing the burden of these infections. While a country's efforts to reduce the burden of these bacteria within its own borders can have tremendous benefits, imported food can still introduce contaminated meat and produce, and these have indeed become larger proportional risks following control efforts in the UK.

S. Enteritidis shows strong geographical substructuring across its phylogenetic tree. Traditional phylogenetic analysis is time-consuming (particularly to perform repeatedly on a routine basis) and required highly skilled staff to perform. Machine learning should be able to identify genetic markers linked to clades typically found in a single location, without the need to build and interpret a phylogenetic tree.

There is some nice methods development work in this paper, with the employment of a hierarchical structure to the ML modelling pipeline and the use of an array of classifier, resampler, feature selection and parameter optimisation techniques to increase accuracy.

However, the main strength of this paper is how well tailored the model is to a real world use case. Many groups are applying machine learning to genomic data, but often not with a clearly defined use case or realistic training and testing conditions. The results begin by giving the reader an understanding of the current state of this work in a UK context, where all clinically reported cases of Salmonella are sequenced and when appropriate, travel history is recorded. The algorithm is designed to fit into this existing practise and thought has been put into how this would be operationalised. For example, the authors have shown that this work can truly be done in real-time, by developing an algorithm that works directly on raw reads and takes <4 mins to run. A great touch in this work was determining the time horizon over which the model should be retrained to keep up with contemporary geographic distributions of this pathogen. The time horizon itself may not be highly generalizable in genomic epidemiology, but the methods provided make it easier for others to make the same assessment for their pathogen and use case.

A weakness of the work is the areas where predictions are not as accurate, but this relates to the extent of pathogen sequencing today rather than the method itself. Countries with less accurate predictions are ones which few people return from with an infection and if they do, it tends to be a different strain each time, making building an accurate algorithm for these cases impossible without denser sampling outside of clinical infections or more sequencing of infections occurring in other countries. Without proofs of concept like this, there is less of a strong economic argument to justify these investments. Therefore this work represents an important step in demonstrating the feasibility of the method itself and the value in gathering more data. In contrast, a major strength of this work is that it uses data collected routinely from existing practice in the UK, rather than a bespoke sampling strategy that may not be realistic for routine public health. A comparison of the collection to NCBI also found this sampling to be less biased by specific outbreaks of interest, which is encouraging.

The training dataset appears to be only based on infections acquired overseas, while I suspect the model would be more useful in investigating infections due to imported contaminated food. An unresolved question from this work is therefore whether the source of travel-acquired infections and infections caused by food imported from the same places is the same, or whether exported vs domestically consumed food around the world is treated differently in important ways that would affect the relative prevalence and success of strains in causing infections. Looking at clinical infections also may bias Salmonella to those that cause more severe forms of infection, as many people don't report to a doctor when they have food poisoning. The large egg-related outbreak that did not feature much at all in the UKHSA dataset is potentially a nice example of this.

The low accuracy on countries with low infection numbers and high genetic diversity indicates that these algorithms would likely become less accurate over time if food safety is improved, and that individual countries could avoid being confidently attributed as a source of infection by eliminating or controlling major circulating foodborne clones. More clearly communicating when a prediction is uncertain could be helpful in dealing with isolates from countries where it is hard to make a determination.

One final limitation I see is the exclusion of UK Salmonella isolates - in cases where it is uncertain whether a Salmonella infection is due to import or not, it does not seem possible to make this assessment using the ML tool. This also limits the utility of the tool for other countries that might also benefit.

The authors have done an excellent job of demonstrating the feasibility of this approach and honing their machine learning workflow to the specific demands of the task. The work presents a clear and well thought out use case with the overall performance of the algorithm broken down into test cases where the algorithm is successful and unsuccessful which provide useful insight into what we can expect from the performance of these approaches.

Finding a way to better communicate when the source of an outbreak is unclear due to poor representation of a clade or a clade that is found in many countries would be a valuable extension of this work in the future, but as it is the results represent a promising starting point for initiating investigations into the source of Salmonella infections.

Diarrheal disease is a huge health burden worldwide. Previous work to lower the burden of these infections has shown that targeted interventions can make a substantial difference to the burden of disease and success of clonal outbreaks. The availability of a tool that can be used routinely to assess the most likely overseas origin of an infection could potentially highlight previously unrecognised outbreaks or areas of suddenly increased importation rate. In turn, this could lead to better investigations and targeted improvement of food security.

This paper provides an excellent case for the value of collecting recent travel history and including it in metadata for pathogen genomic data. If this were done in more countries with different patterns of travel and the data could be shared, this would provide a valuable global resource and start to capture the flow of strains internationally.

I am curious about the implications of being better able to attribute clinical gastroenteritis cases in the UK (and elsewhere) to food imported or travel to specific countries with respect to trade and regulation. This is well outside the scope of the paper, however the ability to capture isolates commonly picked up from food around the world without the cooperation of these countries raises interesting issues, particularly when factoring in the authors' scenarios of the true country of origin being obscured by uneven travel patterns and complex food supply networks.

Reviewer #3 (Public Review):

The authors describe a machine learning method for classifying the geographic origin of a Salmonella enterica isolate based on its whole-genome sequencing data. This is done at a continent, region, and country level, and the method is shown to be robust to phylogenetic diversity, temporal trends, and possibly some amount of mislabelling (but please see the first concern below). The authors demonstrate that their pipeline produces results in 5 minutes or less, which makes it applicable to many public health microbiology settings.

Some clear strengths of the paper include:<br /> - the use of a hierarchical classification method, which ensures that only those samples that can be unambiguously classified as belonging to a specific region can get assigned to a sub-region within that region (e.g. continent to country)<br /> - leveraging the UKHSA dataset going back nearly a decade, and containing a comprehensive record of all clinically detected Salmonella enterica infections, which mitigates potential biases and ensures a maximal geographic coverage<br /> - making all the data (microreact) and the source code (GitHub) public, which facilitates replication as well as enables other researchers and public health microbiologists to use the trained models directly on their own data<br /> - the use of unitigs as the basis for prediction, which are more informative than K-mers yet more straightforward to identify than SNPs or gene alleles.

There are several methodological concerns that should ideally be addressed:<br /> - in addition to the more complex situation of a tourist visiting country A and consuming food from country B, it would be good to rule out a simpler one of the tourist visiting both countries on the same trip (including via a stopover at an airport); the authors should elaborate on the plausibility of missing data on such multi-country trips and their frequency based on the available travel data<br /> - similarly, there appears to be an underlying assumption that the UK is never at the origin of a Salmonella enterica infection in the dataset selected; the authors should explain why that is a reasonable assumption for this dataset<br /> - the increase of infection incidence during the summer months might be at least partly attributable to a greater number of trips abroad during that period - if the authors have corrected their data for this, they should explicitly say so<br /> - lastly, in discussing the outbreak due to Polish eggs, it should be possible to check explicitly what fraction of the training data may have originated from this outbreak to see if this is sufficient to explain the observed poor prediction

Overall, this is a paper representing a substantial body of work and combining algorithmic advances with practical utility given the rapid turnaround time. It is likely to be generalisable to other pathogens of public health importance and to become integrated into standard protocols for outbreak origin tracing.

Reviewer #1 (Public Review):

The author constructed a novel rat model with a clinically relevant PLS3 hemizygous E10-16del mutation (PLS3E10-16del/0), which presents a classic form of early-onset osteoporosis, which recapitulate the osteoporotic phenotypes. Treatment with alendronate and teriparatide significantly improved bone mass and bone microarchitecture. Their results showed alendronate and teriparatide treatment could be a potential treatment for early-onset osteoporosis induced by PLS3 mutation.

This experiment is very interesting and has clinical relevance. The authors used common clinical drugs to treat osteoporosis caused by PLS3 mutation and achieved certain results. This result will give a way to the treatment of osteoporosis induced by PLS3 mutation.

Reviewer #2 (Public Review):

The mechanism for early-onset osteoporosis (EOOP) is not well understood. The authors performed PLS3 knockout and characterized its bone phenotype in a rat model. This provides a very useful tool for studying EOOP and the potential treatment for EOOP. The authors did a very nice job of characterizing the phenotype including the assessments of bone turnover markers, bone histomorphometric analyses, and bone biomechanical tests. The results from these assessments led to the conclusion that this PLS3 knockout rat model mimics the human EOOP. In addition, treatment with currently available drugs for osteoporosis is effective in this EOOP model. These results support further clinical investigation of anti-osteoporosis drugs for EOOP management.

Reviewer #1 (Public Review):

The authors have achieved a demonstration of different stellate ganglion nerve cell functions and transmitter subtypes, of potential cardiac importance. They employ viral tracing techniques. These convincingly make this demonstration. The work will be key to our understanding of sympathetic function at the transmitter and physiological levels.

Reviewer #2 (Public Review):

The manuscript at hand by Sharma et al. presents new data on neurons of the stellate ganglia that are relevant for autonomic control of the heart. The authors identify stellate ganglionic neurons (SGN) that innervate the heart by retrograde tracing techniques and differentiate them from SGN neurons innervating other organs and tissues (mostly paw is used as a control). They subsequently employ single-cell RNAseq and morphological and functional (electrophysiological) studies. Their main finding is the identification of 3 SGN subtypes that they were further able to stratify into high and low neuropeptide Y cells. These subpopulations differ with regard to gene expression and action potential generation indicating different electrophysiological properties and different roles in the sympathoexcitation of the heart. They validate these findings by in vivo experiments where electrical stimulation of stellate ganglia after NPY-expressing neurons was depleted and find that heart rate change was lower under stimulation with high frequencies for NPY-depleted mice. The research question is very relevant and might have important therapeutic consequences for patients with cardiac diseases. The paper is written clearly. The methods applied are elegant and appropriate and the data support the conclusion.

The authors do report on some experiments in which stellate ganglion was used. Viral administration and physiological studies were performed on the right, while RNA sequencing was done from the right and left stellate ganglion. As there are physiological lateral differences between the effects of the left and right stellate ganglion, it would be useful to thoroughly report which side was used for which experiment throughout the manuscript and to discuss whether any lateral differences are relevant for the obtained results and conclusions.

Reviewer #3 (Public Review):

Using viral tracing and single-cell transcriptome profiling the authors investigated the electrophysiologic, morphologic, and physiologic roles for subsets of cardiac-specific neurons and found evidence that three adrenergic stellate ganglionic neuron subtypes innervate the heart.

The presented findings provide relevant insights into the properties of neurons modulating cardiac sympathetic control. The findings might open up new avenues to targeted modulation of cardiac sympathetic control. Additional insights from various models addressing for example ischemic and non-ischemic cardiomyopathy might allow to development of targeted therapies for various patient populations in the future.

Reviewer #1 (Public Review):

Pedigo et al, apply statistical modeling to a complete brain nanoscale network - a synaptic connectome of an insect brain: the Drosophila larva. They use a series of approaches to explore the symmetry of the right and left hemispheres. First, they compare network densities and find significant differences between the two hemispheres, with the right hemisphere having a higher density. They further grouped neurons by cell type to determine whether the differences were distributed across the entire brain or to specific connections and find the differences involving neurons in the learning and memory center, the mushroom body. Finally, they explored different definitions of an edge by using different thresholds either based on synaptic counts or proportions of synaptic inputs to a downstream neuron and found that when using the proportion of synaptic inputs, removing fewer edges (compared to when using synaptic count) was necessary to achieve left and right symmetry. The presentation of the methodology and writing is very clear and effective and is accessible to scientists from various backgrounds: both biologists and theoreticians. The methodology and approach used in this paper on the assessment of the degree of bilateral symmetry will serve as a basis for comparing networks and connectomes in general by providing a clear framework for statistical network modeling. This work is particularly timely as an increasing number of synaptic connectomes is being generated giving opportunities for various connectome comparisons. It will be of interest to neuroscientists in order to address various biological questions: to evaluate the degree of inter-individual variability/stereotypy of connectivity in the brain and how it relates to behavioral variability/stereotypy, to characterize changes in network connectivity due to different diseases, etc.

Reviewer #2 (Public Review):

The authors develop statistical tests for assessing whether two hemispheres of the Drosophila larval brain are bilaterally symmetric, and more generally to develop a framework for comparisons of connectomes. The study is organized in order of increasing complexity of the statistical test, beginning with a simple test of whether or not the two sides of the brain have equal connection density. A more sophisticated approach is applied to a model in which neurons are partitioned into groups defined by preexisting known cell types on the left and right hemispheres and densities are allowed to vary between groups (a stochastic block model). A correction is included for an overall difference in density between hemispheres. Finally, analyses are applied to assess which cell types contribute to differences in the larval connectome. This identifies Kenyon cells as particularly distinct - a density-corrected stochastic block model with Kenyon cells removed results in no significant bilateral asymmetry. Results are also compared across different choices for thresholding of connection weights.

This manuscript tackles an interesting and timely problem. The analyses are largely straightforward applications of standard hypothesis tests for binomially distributed random variables. However, the observation that a density correction is needed to account for the two hemispheres' connection probabilities, and that a stochastic block model is sufficient to describe these probabilities, with the exception of the Kenyon cells, is interesting and makes more precise the notion of bilateral symmetry, at least at the level of connection probabilities, than previous approaches.

There are still several questions that remain about the generality of the results. The first concerns assumptions about the generative model for the graph. As the authors acknowledge, an Erdos-Renyi random network is a strong simplifying assumption. In particular, independent edge weights may be a restrictive model of connectome data given the broad degree distribution, spatial dependencies, and other features that characterize biological connectivity. A second question concerns the issue of statistical power. After partitioning neurons into groups, the most significant difference in connection probabilities comes from Kenyon cells, with the smallest p-value in the density-corrected comparison coming from KC-to-KC connections (Fig. 4B). However, KCs represent a large group of neurons, and the KC-to-KC connection probability is among the highest in the larval brain (Fig. 3B), raising the question of whether the observation of a significant difference specifically for these neurons is simply due to increased power. Third, connection density is only one of the many graph features that may be relevant for evaluating connectome similarity.

In total, although the analyses are straightforward, the study represents a first step toward the evaluation of connectome similarity and should spur further work in this important direction.

Reviewer #1 (Public Review):

Tornini et al. investigate the function of long non-coding RNAs in vivo. In the manuscript, the authors show that two of these molecules linc-mipep and linc-wrb encode for a micropeptide that regulates zebrafish behavior. In the absence of this peptide, zebrafish larvae show dysregulation of NMDA receptor and glucocorticoid receptor-mediated signaling and immediate early gene induction. Given the homology of linc-mipep and linc-wrb encoded peptides with homology to chromosome binding and chromatin unwinding domain of HMGN1 the authors explore the altered chromatin accessibility in the mutant animals. This analysis revealed a broad dysregulation of 3D chromatin structure with some enrichment at loci regulating the expression of immediate early response genes. Finally, single cell analysis revealed that oligodendrocyte progenitor cells and cerebellar granule cells are more affected in the mutants.

This work represents a technical tour-de-force with extensive genomics data to characterize the molecular phenotype of linc-mipep and linc-wrb loss of function. This data show interesting findings in part consistent with the behavioral phenotype observed.

The manuscript provides compelling evidence that micropeptides encoded by what were previously identified as long non-coding RNAs have a precise biological function.

Reviewer #2 (Public Review):

The two new micropeptides are well characterized in the manuscript and appear to be functionally important with some chromatin-level consequences of their loss (which can be either direct or indirect), but the finding that lincRNA sequences encode micropeptides is not novel, and the two described in the paper appear to be zebrafish-specific and their function was tested only in zebrafish, which limits the interest in these genes. The use of ribosome profile data along behavioral screening to identify micropeptides is interesting and important, but the scope of the screen, the candidates selected for testing, etc. are not clear enough as presented. The ChIP-seq analysis of the new proteins is very interesting but is not described in any detail. Overall, the experimental part is well designed and the phenotypes reported by the authors appear to be strong and convincing, but the mechanistic understanding of what the two new proteins do and how, and the general interest in the results given the current scope of understanding of micropeptide is limited.

Reviewer #3 (Public Review):

The study aimed at the identification of functional micro-peptides encoded by transcripts previously annotated as long noncoding RNAs (lncRNAs). The authors pre-selected 10 candidates out of the ~500 zebrafish lncRNA data set based on their engagement with the ribosome (by ribosome profiling data) and their expression in the embryonic brain. By performing an F0 CRISPR/Cas9 screen coupled with embryonic behavioral assays, two transcripts encoding sequence-related micro-peptides were identified. Using a set of stable mutant alleles, the authors showed that mutations specifically affecting the open reading frame (ORF) of the putative micro-peptides cause changes in embryonic behavior when compared to wild-type embryos or embryos with mutations in the non-coding regions of the tested transcripts. The locomotor hyperactivity phenotype was even stronger in double homozygous mutants suggesting a redundant function of both micro-peptides. The authors demonstrated that the behavioral phenotype of one of the mutants was rescued by the transgene expression of the coding sequence (CDS). Sequence analyses of both peptides revealed their conservation and homology to the human non-histone chromosomal proteins (HMGN1 proteins). The authors demonstrated that the micro-peptide mutants exhibit changes in chromatin accessibility for transcription factors modifying neural activation, dysregulation of gene expression programs, and changes in oligodendrocyte and cerebellar cell states during development.

The study presents an important discovery of two sequence-related micro-peptides with important and potentially conserved functions during development. While it is still unclear how the micro-peptides act in the cell, it is evident that they are key regulators of cellular states. Whereas the study is well done, the data presentation should be improved as several important details were omitted.

Reviewer #4 (Public Review):

In this manuscript, Tornini and colleagues identify two previously un-characterized micropeptides encoded by linc-mipep and linc-wrb as important modulators of day-time activity in zebrafish larvae. The authors demonstrate that each single mutant shows an increase in day-time activity and that double mutants show a more pronounced effect. Of interest, ubiquitous overexpression of the ORF encoding the linc-mipep-derived peptide can rescue the day-time over-activity phenotype of linc-mipep mutant larvae, establishing that linc-mipep acts indeed as a protein and not at the level of RNA. Using a series of experimental approaches, including ATAC-Seq from double mutant brains and scRNA-Seq and scATAC-seq analyses from linc-mipep mutants as well as linc-mipep and linc-wrb CHIP analyses, the authors furthermore identify differences in chromatin accessibility and gene expression in specific cell types of the larval brain in the absence of linc-mipep (and in case of globale ATAC-Seq, in the absence of both peptides). They conclude that the micropeptides regulate behavior and neuronal states by modulating chromatin accessibility, revealing functional similarities to their known vertebrate homolog HMGN1.

Overall, the key finding of this paper, namely the identification of two functional microproteins that had previously been misannotated as lincRNAs but have homology to HMGN1 both based on their sequence and function is an exciting discovery since relatively few newly predicted micropeptides have been functionally characterized to date, and because it advances our understanding of the molecular mechanisms underlying vertebrate-specific neuronal function and diversity. The F0 screen leading to the identification of 2 functional micropeptides provides a major advance to the field since so far screens in the F0 generation have not been typically done (rather germline-transmission). Thus, this work provides a major step forward in this regard. In addition, it includes a series of scRNA- and scATAC analyses that are technologically at the forefront and not easy to conduct and analyse.

The weakest part of the paper in its current form is on the one hand missing the link between the behavioral phenotype in mutants and the molecular phenotypes in the larval brain. It remains unclear how one can reconcile the broad neuronal expression (in the case of linc-mipep preferentially in Purkinje cells) and linc-wrb with the cell-specific effects. Moreover, it is not clear whether both peptides act redundantly or in parallel but distinct pathways since the rescue is only shown for the single linc-mipep mutant by linc-mipep overexpression (and no rescue is shown for linc-wrb or the double mutant). While the authors suggest throughout the manuscript that both peptides have similar functions (act redundantly), no clear data is provided for this, and the use of either single linc-mipep mutants (all single-cell analyses in the last Figure) or double linc-mipep/linc-wrb mutants (global brain ATAC-Seq analyses) for different brain analyses makes the molecular analyses inconsistent and not easy to interpret. While the overall finding(s) of the paper is really interesting, to make this paper really solid, additional controls and analyses will be needed.

Reviewer #1 (Public Review):

This study presents a useful study, proposing the modelling of Buruli ulcer occurrence in humans based on detection of M. ulcerans in Australia. The data were collected and analyzed using solid and validated methodology and can be used as a starting point for the elucidation of M. ulcerans transmission in Australia.

Reviewer #2 (Public Review):

In this work, the authors have carried out an extensive and highly granular survey of Mycobacterium ulcerans carriage by possums who are living on the outskirts of Melbourne Australia, in areas that are known hot spots for cases of Buruli ulcer (BU). The work is the culmination of many years of endeavour by this team, who first identified that the faeces of possums can be highly positive for M. ulcerans DNA, genetically linked to the strains found in BU patients who live in, or have visited, the area.

Surveys across two seasons were performed. Based on qPCR data to identify M. ulcerans carriage, spatial mapping of this, and BU case data, a statistical model was generated using data from the Mornington Peninsula that was better predicted than a null model. This statistical model was then validated using a second independent site at Geelong. As a result of this data, there can now be little doubt that possums play a vital role in the transmission cycles of BU in the region, and will allow mitigation strategies to be designed and tested. As BU is a necrotising skin disease that can cause disability and permanent disfiguration even in a high-resource setting such as Australia, such approaches are urgently needed.

Strengths:

The scale (both in terms of geographic reach/granularity and time) of the surveillance effort to understand the distribution of M. ulcerans DNA in the local possum population is unprecedented.

Since BU is a notifiable disease in Australia, the researchers have access to comprehensive clinical information across the study period.

The statistical model developed had a strongly positive influence over the ability to predict where BU cases will arise, over areas with a small radius (several km) which is the first time this has been achieved. The process by which this model was developed and validated seems robust.

Weaknesses:

In their model, the authors have used an assumed "exposure window" for when patients were infected with M. ulcerans in the Mornington Peninsula. Correctly defining, and assigning, this is absolutely critical to the accuracy of the statistical model, as is "blinding" of researchers assigning mesh boxes to patients to the results of surveillance data (and vice versa). These aspects are not fully clear in the current version. Furthermore, the effects on the model of changing these assumptions are not discussed.

The presence of M. ulcerans DNA in possum excreta and in patient samples is defined by qPCR for IS2404, a multicopy insertion sequence. Greater justification for using this as the sole marker is required, as this insertion sequence is also present in other mycolactone-producing mycobacteria. Moreover, some samples were claimed to be 'positive' with Ct values of 40 without justification for using this value (such as standard curves).

Comparing the summer and winter surveys at the Mornington Peninsula, the distribution of M. ulcerans positive excreta appears to have changed quite substantially, especially given that the possums are reported to be highly territorial with a range of only 100m. This version of the manuscript does not formally compare these spatial distributions, only the averages. Such an analysis would help understand if it is the possums that are moving, whether the possums undergo 'waves' of carriage (or indeed any other explanation), or if these apparent differences are down to chance.

Reviewer #3 (Public Review):

K. Vandelannoote and collaborators report on using spatially-localized possum feces investigated for Mycobacterium ulcerans, as a proxy for cases of Buruli ulcer, South Australia. The report is a contributive, enforcing survey of animal excreta and is based on strong pieces of evidence.

Reviewer #1 (Public Review):

Francou et al. examine the dynamics of cell ingression at the primitive streak during mouse gastrulation and correlate this with the localization of elements of the apical Crumbs complex and the actomyosin cytoskeleton. Using time-lapse live imaging, they show that cells at the primitive streak ingress in a stochastic manner, by constricting their apical surface through a ratcheting shrinkage of individual junctions. Meticulous evaluation of immunofluorescent staining for many elements of the actomyosin contractile process as well as junctional and apical domain elements reveals anisotropic localization of Crumbs2, ZO1, and ppMLC. In addition, the localization of two groups of proteins showed a close correlation - actomyosin regulators and apical and junctional components - but there was a lack of correlation of localization of these two groups of proteins to each other. The localization of actomyosin and its activity, was altered and more homogeneous in Crumbs2-/- embryos, and there was a significant decrease in aPKC and Rock1. The authors conclude from these observations that Crumbs2 regulates anisotropic actomyosin contractility to promote apical constriction and cell ingression.

The strengths of this manuscript are the very detailed observations on the process of apical constriction and the meticulous evaluation of the localization of the many proteins likely to be involved in the process. While many of the general observations are not new, Francou et al. provide a much richer understanding of this process, as well as a paradigm with which to evaluate the effects of mutations on the gastrulation process. The figures are beautiful, clear, and informative, and support the conclusions made by the authors. The data provide a very compelling picture of both the dynamics of cell behavior and the anisotropies in protein localization associated with it.

However, much of the Crumbs2 mutant phenotype is not sufficiently explained by the authors' data or conclusions. First, the loss of Crumbs2 does not prevent ingression, as there are mesoderm cells evident between the epiblast and endoderm (Ramkumar et al., 2016, Xiao et al., 2011). There are certainly fewer, and the biggest effect appears to be during the elongation of the axis from E7.75 onward and not during the earlier migratory period (E6.5-E7.75) according to data from both previously published work (Xiao et al., 2011; Ramkumar et al., 2015, 2016) and the data presented here. Nor does the loss of Crumbs2 prevent apical constriction. Ramkumar et al. in their 2016 paper show by live imaging that the major effect of the Crumbs2 mutation is to prevent the cells from detaching from the epithelium, but that the apical domain does undergo constriction, leading to many elongated flask-shaped cells still attached at the apical end. These observations do not fit well with the model proposed by the authors of Crumbs2 regulating anisotropic actomyosin contractility to promote apical constriction and suggest a more complicated story. However, the complications of the Crumbs2 mutant do not detract from the value of the basic observations presented in this manuscript, which are solid and well-documented, and will be a valuable resource for the field.

Reviewer #2 (Public Review):

In their manuscript, Francou and colleagues study the delamination of epiblast cells into the mesodermal layers using live imaging of mouse embryos cultured ex vivo. By segmenting the apical area of delaminating cells, they quantify extensively the dynamic behavior of delaminating cells. Using immunostaining and crumbs2 mutants, they propose that apical constriction of cells results from pulsed contractions, which could be guided by crumbs2 signals.

The manuscript is interesting and provides extremely valuable data for our understanding of mouse gastrulation. Occasionally, the manuscript can be a bit confusing and contains a few inaccuracies. However, the main issues I have are with some of the interpretations from the authors, which may be incorrect due to limited time resolution (with a 5 min time resolution that was used, it might be difficult to distinguish pulses from measurement noise) and the analysis of immunostaining data, which would require more rigorous quantification.

Reviewer #3 (Public Review):<br /> <br /> The manuscript by Francou et al investigated cellular mechanisms of epiblast ingression during mouse gastrulation. The authors wanted to know whether/how epiblast cell-cell junctional dynamics correlate with apical constriction and subsequent ingression. Because mouse gastrula adopts an inverted-cup morphology (as a result of differential invasive behavior of polar and mural trophoblast cells), epiblast cells are located in the innermost position and are difficult to image. This is more so when one wants to perform live imaging of epiblast cells' apical surface. The authors tackled such problems/limitations by using a combination of ZO-1 GFP line, confocal time-lapse microscopy, fixed embryo immunostaining, and Crumbs2 mutant embryos. The authors observed that apical constriction was associated with cell ingression, that this constriction occurred in a pulsed fashion (i.e., 2-4 cycles with phases of contraction and expansion, eventually leading to reduction of apical surface and ingression), that this constriction took place asynchronously (i.e., neighboring epiblast cells did not exhibit coordinated behavior) and that junctional shrinkage during apical constriction also occurred in a pulsed and asynchronous manner. The authors also investigated localization/co-localization of several apical proteins (Crumbs2, Myosin2B, pMLC, ppMLC, Rock1, F-actin, PatJ, and aPKC) in fixed samples, uncovering somewhat reciprocal distribution of two groups of proteins (represented by Myosin2B in one group, and Crumbs2 in the other). Finally, the authors showed that Crumbs2 -/- embryos had disturbed actomyosin distribution/levels without affecting junctional integrity (partially explaining the ingression defect reported in Crumbs2 -/- mutant embryos). Overall, this manuscript offers high-quality live imaging data on the dynamic remodeling of epiblast apical junctions during mouse gastrulation. It would be interesting to see whether phenomena reported in this manuscript can be extended to the entire primitive streak (or are they specific only to a subset of mesoderm precursors) and to the entire period of mesendoderm formation. More importantly, it would be interesting to see whether the ingression behavior seen here is representative of all eutherian mammals regardless of their gastrular topography.

Reviewer #1 (Public Review):

In this study, Sapiro et al sought to develop technology for a transcriptomic analysis of B. burgdorferi directly from infected ticks. The methodology has exciting implications to better understand pathogen RNA profiles during specific infection timepoints, even beyond the Lyme spirochete. The authors demonstrate successful sequencing of the B. burgdorferi transcriptome from ticks and perform mass spectrometry to identify possible tick proteins that interact with B. burgdorferi. This technology and first dataset will be useful for the field. The study is limited in that no transcripts/proteins are followed-up by additional experiments and no biological interactions/infectious-processes are investigated.

Critiques and Questions:

This study largely develops a method and is a resource article. This should be more directly stated in the abstract/introduction.

Details of the infection experiment are currently unclear and more information in the results section is warranted. State the species of tick and life-stage (larval vs nymphal ticks) used for experiments. For RNA-seq, are mice are infected and ticks are naïve or are ticks infected and transmitting Borrelia to uninfected mice?

What is the limit of detection for this protocol? Experimental data should be provided about the number of B. burgdorferi required to perform this approach.

More information regarding RNA-seq coverage is required. Line 147-148 "read coverage was sufficient"; what defines sufficient? Browser images of RNA-seq data across different genes would be useful to visualize the read coverage per gene. What is the distribution of reads among tRNAs, mRNAs, UTRs, and sRNAs?

My lab group was excited about the data generated from this paper. Therefore, we downloaded the raw RNA-seq data from GEO and ran it through our RNA-seq computational pipeline. Our QC analysis revealed that day 4 samples have a different GC% pattern and that a high percentage of E. coli sequences were detected. This should be further investigated and addressed in the paper: Are other bacteria being enriched by this method? Why would this be unique to day 4 samples? Does this affect data interpretation?

Comprehensive data comparisons of this study and others are warranted. While the authors note examples of known differentially expressed genes (like lines 235-241), how does this global study compare to other global approaches? Are new expression patterns emerging with this RNA-seq approach compared to other methods? What differences emerged from day 1 to day 4 ticks compared to differences observed in unfed to fed ticks or fed ticks to DMC experiments? Directly compare to the following studies (PMID: 11830671; PMID: 25425211; PMID: 36649080).

Details about the categorization of gene functions should be further described. The authors use functional analysis from Drechtrah et al., 2015, but that study also lacks details of how that annotation file was generated. Here, the authors have seemed to supplement the Drechtrah et al., 2015 list with bacteriophage and lipoprotein predictions - which are the same categories they focus their findings. Have they introduced a bias to these functional groups? While it can be noted that many lipoproteins are upregulated (or comment on specific genes classes), there are even more "unknown" proteins upregulated. I argue that not much can be inferred from functional analysis given the current annotation of the B. burgdorferi genome.

Reviewer #2 (Public Review):

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

The manuscript is overall well written and easy to follow. The data are compelling and support the conclusions.

Reviewer #1 (Public Review):

In this manuscript, the authors used an unbiased method to identify proteins from porcine oocyte extracts associated with permeabilised boar spermatozoa in vitro. The identification of the proteins is done by mass spectrometry. A previous publication of this lab validated the cell-free extract purification methods as recapitulating early events after sperm entry in the oocyte. This novel method with mammalian gametes has the advantage that it can be done with many spermatozoa at the time and allows the identification of proteins associated with many permeabilised boar spermatozoa at the time. This allowed the authors to establish a list of proteins either enriched or depleted after incubation with the oocytes extract or even only associated with spermatozoa after incubation for 4h or 24h. The total number of proteins identified in their test is around 2 hundred and with very few present in the sample only when spermatozoa were incubated with the extracts.

The list of proteins identified using this approach and these criteria provide a list of proteins likely associated with spermatozoa remnants after their entry and either removed or recruited for the transformation of spermatozoa-derived structures.

Using WB and histochemistry labelling of spermatozoa and early embryos using specific antibodies the authors confirmed the association/dissociation of 6 proteins suspected to be involved in autophagy.

While this unique approach provides a list of potential proteins involved in sperm mitochondria clearance it's (only) a starting point for many future studies and does not provide the demonstration that any of these proteins has indeed a role in the processes leading to sperm mitochondria clearance since the protein identified may also be involved in other processes going-on in the oocyte at this time of early development.

Concerning the localisation of the 6 proteins further analysed, the authors must add how much the presented picture represents the observed patterns. They must include the details on the fraction of spermatozoa and embryos displaying the presented pattern.

Reviewer #2 (Public Review):

Mitochondria are essential cellular organelles that generate ATPs as the energy source for maintaining regular cellular functions. However, the degradation of sperm-borne mitochondria after fertilization is a conserved event known as mitophagy to ensure the exclusively maternal inheritance of the mitochondrial DNA genome. Defects on post-fertilization sperm mitophagy will lead to fatal consequences in patients. Therefore, understanding the cellular and molecular regulation of the post-fertilization sperm mitophagy process is critically important. In this study, Zuidema et. al applied mass spectrometry in conjunction with a porcine cell-free system to identify potential autophagic cofactors involved in post-fertilization sperm mitophagy. They identified a list of 185 proteins that might be candidates for mitophagy determinants (or their co-factors). Despite the fact that 6 (out of 185) proteins were further studied, based on their known functions, using a porcine cell-free system in conjunction with immunocytochemistry and Western blotting, to characterize the localization and modification changes these proteins, no further functional validation experiments were performed. Nevertheless, the data presented in the current study is of great interest and could be important for future studies in this field.

Reviewer #3 (Public Review):

In this manuscript, a cytosolic extract of porcine oocytes is prepared. To this end, the authors have aspirated follicles from ovaries obtained from by first maturing oocytes to meiose 2 metaphase stage (one polar body) from the slaughterhouse. Cumulus cells (hyaluronidase treatment) and the zona pellucida (pronase treatment) were removed and the resulting naked mature oocytes (1000 per portion) were extracted in a buffer containing divalent cation chelator, beta-mercaptoethanol, protease inhibitors, and a creatine kinase phosphocreatine cocktail for energy regeneration which was subsequently triple frozen/thawed in liquid nitrogen and crushed by 16 kG centrifugation. The supernatant (1.5 mL) was harvested and 10 microliters of it (used for interaction with 10,000 permeabilized boar sperm per 10 microliter extract (which thus represents the cytosol fraction of 6.67 oocytes).

The sperm were in this assay treated with DTT and lysoPC to prime the sperm's mitochondrial sheath.

After incubation and washing these preps were used for Western blot (see point 2) for Fluorescence microscopy and for proteomic identification of proteins.

Points for consideration:

1) The treatment of sperm cells with DTT and lysoPC will permeabilize sperm cells but will also cause the liberation of soluble proteins as well as proteins that may interact with sperm structures via oxidized cysteine groups (disulfide bridges between proteins that will be reduced by DTT).

2) Figure 3: Did the authors really make Western blots with the amount of sperm cells and oocyte extracts as the description in the figures is not clear? This point relates to point 1. The proteins should also be detected in the following preparations (1) for the oocyte extract only (done) (2) for unextracted nude oocytes to see what is lost by the extraction procedure in proteins that may be relevant (not done) (3) for the permeabilized (LPC and DTT treated and washed) sperm only (not done) (4) For sperm that were intact (done) (5) After the assay was 10,000 permeabilized sperm and the equivalent of 6.67 oocyte extracts were incubated and were washed 3 times (or higher amounts after this incubation; not done). Note that the amount of sperm from one assay (10,000) likely will give insufficient protein for proper Western blotting and or Coomassie staining. In the materials and methods, I cannot find how after incubation material was subjected to western blotting the permeabilized sperm. I only see how 50 oocyte extracts and 100 million sperm were processed separately for Western blot.

3) Figures 4, 5, 6, 7, and 8 see point 2. I do miss beyond these conditions also condition 1 despite the fact that the imaged ooplasm does show positive staining.

4) These points 1-3 are all required for understanding what is lost in the sperm and oocyte treatments prior to the incubation step as well as the putative origin of proteins that were shown to interact with the mitochondrial sheath of the oocyte extract incubated permeabilized sperm cells after triple washing. Is the origin from sperm only (Figs 5-8) or also from the oocyte? Is the sperm treatment prior to incubation losing factors of interest (denaturation by DTT or dissolving of interacting proteins pre-incubation Figs 3-8)?

5) Mass spectrometry of the permeabilized sperm incubated with oocyte extracts and subsequent washing has been chosen to identify proteins involved in the autophagy (or cofactors thereof). The interaction of a number of such factors with the mitochondrial sheath of sperm has been shown in some cases from sperm and others for an oocyte origin. Therefore, it is surprising that the authors have not sub-fractionated the sperm after this incubation to work with a mitochondrial-enriched subfraction.

I am very positive about the porcine cell-free assay approach and the results presented here. However, I feel that the shortcomings of the assay are not well discussed (see points 1-5) and some of these points could easily be experimentally implemented in a revised version of this manuscript while others should at least be discussed.

Reviewer #1 (Public Review):

Mice and humans have two Cylicin genes (X-linked Cylicin 1 and the autosomal Cylicin 2) that encode cytoskeletal proteins. Cylicins are localized in the acrosomal region of round spermatids, yet they resemble a calyx component within the perinuclear theca of mature sperm nuclei. The function of Cylicins during this developmental stage of spermiogenesis (tail formation and head elongation/shaping) was not known. In this study, using CRISPR/Cas genome editing, the authors generated Cylc1-and Cylc2-knockout mouse lines to study the loss-of-function of each Cylicin or all together.

The major strengths of the study are the rigorous and comparative phenotypic analyses of all the combinatorial genotypes from the cross between the two mouse lines (Cylc1-/y, Cylc2-/-, Cylc1-/y Cylc2+/- and Cylc1-/y Cylc2-/-) at the levels of male fertility, cellular, and subcellular levels to support the conclusion of the study. While spermatogenesis appeared undisturbed, with germ cells of all types detected in the testis, low sperm counts in epididymis were observed. Mice were subfertile or infertile in a dose-dependent manner where fewer functional alleles had more severe phenotypes; the loss of Cylc2 was less tolerated than the loss of Cylc1. Thus, loss of Cylc1, and to an even greater extent, loss of Cylc2, leads to sperm structure anomalies and decrease sperm motility. Particularly, the sperm head and sperm head-neck region are affected, with calyx not forming in the absence of Cylicins, the acrosomal region being attached more loosely, and the sperm head itself appearing structurally rounder and shorter. Furthermore, manchette, which disassembles during spermiogenesis, persists in mature sperm of mice missing Cylc2. It is interesting that the study identifies a human male that has mutations in both CYLC1 and CYLC2 genes, and suffers from infertility, with similar motility and sperm structure defects compared to the mouse models. CYLC1 in the sperm from the infertile patient sperm is absent, providing evidence that in both rodents and primates, Cylicins are essential for male fertility.

The major weakness of the study is the less robust or absent of statistical analyses determining the statistical significance of some of the morphological phenotypes observed (e.g., the roundness/shortening of sperm head). Evolutionary analysis of two genes-while interesting- is less congruent with the other parts of the study and disrupts the overall flow of the functional studies. The authors show that the reason for the loss of Cylc2 being more severe is due to the higher conservation of Cylc2 compared to Cylc1 in rodents and primates, however, the conservation of these genes in other species is not discussed.

Overall, the work highlights the relevance and importance of Cylicins in male infertility and advances our understanding of perinuclear theca formation during spermiogenesis.

Reviewer #2 (Public Review):

The work presented in this manuscript focuses on the role of Cylicins in spermiogenesis and the consequences of their absence on infertility. The manuscript is presented in two parts: the first part studies the absence of Cylicins from KO mouse models and shows in mice that both isoforms of Cylicins are necessary for normal spermiogenesis. The evaluation of double heterozygotes is particularly useful for the second part which looks at the presence of mutations in these genes in a cohort of infertile men. A patient with two hemizygous/heterozygous mutations in the CYLC1 and 2 genes, respectively, was identified for the first time and the results obtained with the KO models support the hypothesis of the pathogenicity of the mutations.

In general, the experiments are perfectly performed and the results are clear. Numerous techniques in the state of the art in male reproduction are used to obtain high-quality phenotyping of the mouse models.

The discovery of two concomitant mutations in an infertile patient is very interesting and the work carried out on mice allows supporting that an absence of CYLC1 and a heterozygous mutation of CYLC2 could lead to a phenotype of complete infertility. However, as the mutation on CYLC2 is not identified as pathogenic, the pathogenicity of this mutation remains in question (the authors note this point in the discussion). It would be interesting to see if the mutated amino acid is conserved between different species. In mice, the authors have shown the importance of these proteins on the morphology of the acrosome. What about in humans?

Reviewer #3 (Public Review):

The authors tried to study the role of the cylicin gene in sperm formation and male fertility. They used the Crispr/cas 9 to knockout two mouse cylicin genes, cylicin 1 and cylicin 2. They used comprehensive methods to phenotype the mouse models and discovered that the two genes, particularly cylicin 2 are essential for sperm calyx formation. They further compared the evolution of the two genes. Finally, they identified mutations of the genes in a patient. The major strengths are the high quality of data presented, and the conclusion is supported by their findings from the animal models and patients. The major weakness is that the study is descriptive: no molecular mechanism studies were conducted or proposed, limiting its impact on the field.

Reviewer #3 (Public Review):

In this manuscript, the authors characterize antigen binding sites, mechanism of action, and in vivo efficacy of neutralizing monoclonal antibodies (mAbs) previously isolated from New World hantavirus survivors. Both hantavirus species-specific mAbs and broadly neutralizing hantavirus mAbs are analyzed.

The strengths of the manuscript are the presentation of both in vitro and in vivo data for mAbs that have different antigen binding sites and mechanisms of neutralization. Weaknesses include a lack of authentic virus experiments for the in vitro data.

The impact of the work on the field is the identification of different neutralizing sites on hantavirus glycoproteins in species-specific and broadly reactive mAbs. There are also interesting data on loss of broadly neutralizing activity of mAbs after reversion to the germline sequence.