- Last 7 days
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Reviewer #2 (Public review):
Summary:
In the current study, the authors aim to identify the mode of action/molecular mechanism of characterized a fungicide, quinofumelin, and its biological impact on transcriptomics and metabolomics in Fusarium graminearum and other Fusarium species. Two sets of data were generated between quinofumelin and no treatment group, and differentially abundant transcripts and metabolites were identified, suggesting a potential role of pyrimidine biosynthesis. Upon studying the genetic mutants of the uridine/uracil biosynthesis pathway with quinofumelin treatment and metabolite supplementation, combining in vitro biochemical assay of quinofumelin and F.graminearum dihydroorotate dehydrogenase protein, the authors identified that quinofumelin inhibits the dihydroorotate dehydrogenase and blocks downstream metabolite biosynthesis, limiting fungal metabolism and growth.
Strengths:
Omics datasets were leveraged to understand the physiological impact of quinofumelin, showing the intracellular impact of the fungicide. The characterization of FgDHODHII deletion strains with supplemented metabolites clearly showed the impact of the enzyme on fungal growth. Corroborating in vitro and in vivo data revealed the direct interaction of quinofumelin with Fusarium protein target.
Potential Impact:
Understanding this new mechanism could facilitate rational design or screen for molecules targeting the same pathway, or improve binding affinity and inhibitor potency. Confirming the target of quinofumelin may also help understand its resistance mechanism, and further development of other inhibitory molecules against the target.
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Reviewer #3 (Public review):
Summary:
The manuscript shows the mechanism of action of quinofumelin, a novel fungicide, against the fungus Fusarium graminearum. Through omics analysis, phenotypic analysis and in silico approaches, the role of quinofumelin in targeting DHODH is uncovered.
Strengths:
The phenotypic analysis and mutant generation are nice data and add to the role of metabolites in bypassing pyrimidine biosynthesis.
Weaknesses:
The role of DHODH in this class of fungicides has been known and this data does not add any further significance to the field.
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www.medrxiv.org www.medrxiv.org
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Reviewer #2 (Public review):
The manuscript by Ma et al. reports the identification of three unrelated people who are heterozygous for de novo missense variants in PLCG1, which encodes phospholipase C-gamma 1, a key signaling protein. These individuals present with partially overlapping phenotypes, including hearing loss, ocular pathology, cardiac defects, abnormal brain imaging results, and immune defects. None of the patients present with all of the above phenotypes. PLCG1 has also been implicated as a possible driver for cell proliferation in cancer.
The three missense variants found in the patients result in the following amino acid substitutions: His380Arg, Asp1019Gly, and Asp1165Gly. PLCG1 (and the closely related PLCG2) have a single Drosophila ortholog called small wing (sl). sl-null flies are viable but have small wings with ectopic wing veins and supernumerary photoreceptors in the eye. As all three amino acids affected in the patients are conserved in the fly protein, in this work Ma et al. tested whether they are pathogenic by expressing either reference or patient variant fly or human genes in Drosophila and determining the phenotypes produced by doing so.
Expression in Drosophila of the variant forms of PLCG1 found in these three patients is toxic; highly so for Asp1019Gly and Asp1165Gly, much more modestly for His380Arg. Another variant, Asp1165His which was identified in lymphoma samples and shown by others to be hyperactive, was also found to be toxic in the Drosophila assays. However, a final variant, Ser1021Phe, identified by others in an individual with severe immune dysregulation, produced no phenotype upon expression in flies.
Based on these results, the authors conclude that the PLCG1 variants found in patients are pathogenic, producing gain-of-function phenotypes through hyperactivity. In my view, the data supporting this conclusion are robust, despite the lack of a detectable phenotype with Ser1021Phe, and I have no concerns about the core experiments that comprise the paper.
Fig. 6, the last in the paper, provides information about PLCG1 structure and how the different variants would affect it. It shows that His380, Asp1019 and Asp1165 all lie within catalytic domains or intramolecular interfaces, and that variants in the latter two affect residues essential for autoinhibition. It also shows that Ser1021 falls outside the key interface occupied by Asp1019, but more could have been said about the potential effects of Ser1021Phe.
Overall, I believe the authors fully achieved the aims of their study. The work will have a substantial impact because it reports the identification of novel disease-linked genes, and because it further demonstrates the high value of the Drosophila model for finding and understanding gene-disease linkages.
Comments on revisions:
The single recommendation I made on the original version, which was to further examine H380 mutants, has been satisfactorily addressed in the revised version.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The authors use longitudinal in vivo 1-photon calcium recordings in mouse prefrontal cortex throughout the learning of an odor-guided spatial memory task, with the goal of examining the development of task-related prefrontal representations over the course of learning in different task stages and during sleep sessions. They report replication of their previous results, Muysers et al. 2025, that task and representations in prefrontal cortex arise de novo after learning, comprising of goal selective cells that fire selectively for left or right goals during the spatial working memory component of the task, and generalized task phase selective cells that fire equivalently in the same place irrespective of goal, together comprising task-informative cells. The number of task-informative cells increases over learning, and covariance structure changes resulting in increased sequential activation in the learned condition, but with limited functional relevance to task representation. Finally, the authors report that similar to hippocampal trajectory replay, prefrontal sequences are replayed at reward locations.
Strengths:
The major strength of the study is the use of longitudinal recordings, allowing identification of task-related activity in the prefrontal cortex that emerges de novo after learning, and identification of sub-second sequences at reward wells.
Weaknesses:
(1) The study mainly replicates the authors' previously reported results about generalized and trajectory-specific coding of task structure by prefrontal neurons, and stable and changing representations over learning (Muysers et al., 2024, PMID: 38459033; Muysers et al., 2025, PMID: 40057953), although there are useful results about changes in goal-selective and task-phase selective cells over learning. There are basic shortcomings in the scientific premise of two new points in this manuscript, that of the contribution of pre-existing spatial representations, and the role of replay sequences in the prefrontal cortex, both of which cannot be adequately tested in this experimental design.
(2) The study denotes neurons that show precise spatial firing equivalently irrespective of goal, as generalized task representations, and uses this as a means to testing whether pre-existing spatial representations can contribute to task coding and learning. A previous study using this data has already shown that these neurons preferentially emerge during task learning (Muysers et al., 2025, PMID: 40057953). Furthermore, in order to establish generalization for abstract task rules or cognitively flexibility, as motivated in the manuscript, there is a need to show that these neurons "generalize" not just to firing in the same position during learning of a given task, but that they can generalize across similar tasks, e.g., different mazes with similar rules, different rules with similar mazes, new odor-space associations, etc. For an adequate test of pre-existing spatial structure, either a comparison task, as in the examples above, is needed, or at least a control task in which animals can run similar trajectories without the task contingencies. An unambiguous conclusion about pre-existing spatial structure is not possible without these controls.
(3) The scientific premise for the test of replay sequences is motivated using hippocampal activity in internally guided spatial working memory rule tasks (Fernandez-Ruiz et al., 2019, PMID: 31197012; Kay et al., PMID: 32004462; Tang et al., 2021, PMID: 33683201), and applied here to prefrontal activity in a sensory-cue guided spatial memory task (Muysers et al., 2024, PMID: 38459033; Symanski et al., PMID: 36480255; Taxidis et al, 2020, PMID: 32949502). There are several issues with the conclusion in the manuscript that prefrontal replay sequences are involved in evaluating behavioral outcomes rather than planning future outcomes.
(4) First, odor sampling in odor-guided memory tasks is an active sensory processing state that leads to beta and other oscillations in olfactory regions, hippocampus, prefrontal cortex, and many other downstream networks, as documented in a vast literature of studies (Martin et al., 2007, PMID: 17699692; Kay, 2014, PMID: 24767485; Martin et al., 2014; Ramirez-Gordillo, 2022, PMID: 36127136; Symanski et al., 2022, PMID: 36480255). This is an active sensory state, not conducive to internal replay sequences, unlike references used in this manuscript to motivate this analysis, which are hippocampal spatial memory studies with internally guided rather than sensory-cue guided decisions, where internal replay is seen during immobility at reward wells. These two states cannot be compared with the expectation of finding similar replay sequences, so it is trivially expected that internal replay sequences will not be seen during odor sampling.
(5) Second, sequence replay is not the only signature of reactivation. Many studies have quantified prefrontal replay using template matching and reactivation strength metrics that do not involve sequences (Peyrache et al., 2009, PMID: 19483687; Sun et al., 2024, PMID: 38872470). Third, previous studies have explicitly shown that prefrontal activity can be decoded during odor sampling to predict future spatial choices - this uses sensory-driven ensemble activity in prefrontal cortex and not replay, as odor sampling leads to sensory driven processing and recall rather than a reactivation state (Symanski et al., 2022, PMID: 36480255). It is possible that 1-photon recordings do not have the temporal resolution and information about oscillatory activity to enable these kinds of analyses. Therefore, an unambiguous conclusion about the existence and role of prefrontal reactivation is not possible in this experimental and analytical design.
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Reviewer #2 (Public review):
Summary:
The first part of the manuscript quantifies the proportion of goal-arm specific and task-phase specific cells during the learning and learned conditions, and similar to their previously published Muysers et al. (2025) paper, find that the task-phase coding cells (Muysers et al. call them path equivalent cells) increase in the learned condition. However, compared to the Muysers et al. 2025 paper, this work quantifies the proportion of cells that change coding type across learning and learned conditions. The second part of the paper reports firing sequences using a sequence similarity clustering-based method that the group developed previously and applied to hippocampal data in the past.
Strengths:
Identifying sequences by a clustering method in which sequence patterns of individual events are compared is an interesting idea.
Weaknesses:
Further controls are needed to validate the results.
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Reviewer #3 (Public review):
In the study, the authors performed longitudinal 1P calcium imaging of mouse mPFC across 8 weeks during learning of an olfactory-guided task, including habituation, training, and sleep periods. The task had 3 arms. Odor was sampled at the end of the middle arm (named the "Sample" period). The animal then needed to run to one of the two other arms (R or L) based on the odor. The whole period until they reached the end of one of the choice arms was the "Outward" period. The time at the reward end was the "Reward" period. They noted several changes from the learning condition to the learned condition (there are some questions for the authors interspersed):
(1) They classified cells in a few ways. First, each cell was classified as SI (spatially informative) if it had significantly more spatial information than shuffled activity, and ~50% of cells ended up being SI cells. Then, among the SI cells, they classified a cell as a TC (task cell) if it had statistically similar activity maps for R versus L arms, and a GC (goal arm cell) otherwise. Note that there are 4 kinds of these cells: outer arm TCs and GCs, and middle arm TCs and GCs (with middle arm GCs essentially being like "splitter cells" since they are not similarly active in the middle arm for R versus L trials). There was an increase in TCs from the learning to the learned condition sessions.
(2) They analyze activity sequences across cells. They extracted 500 ms duration bursts (defined as periods of activity > 0.5 standard deviations over what I assume is the mean - if so, the authors can add "over the mean" to the burst definition in the methods). They first noted that the resulting "Burst rates were significantly larger during behavioral epochs than during sleep and during periods of habituation to the arena", and "Moreover, burst rates during correct trials were significantly lower than during error trials". For the sequence analysis, they only considered bursts consisting of at least 5 active cells. A cell's activity within the burst was set to the center of mass of calcium activity. Then they took all the sequences from all learned and learning sessions together and hierarchically clustered them based on Spearman's rank correlation between the order of activity in each pair of sequences (among the cells active in both). The iterative hierarchical clustering process produces groups (clusters) of sequences such that there are multiple repeats of sequences within a cluster. Different sequences are expressed across all the longitudinally recorded sessions. They noted "large differences of sequence activation between learning and learned condition, both in the spatial patterns (example animal in Figure 3D) and the distribution of the sequences (Figures 3D, E). Rastermap plots (Figure 3D) also reveal little similarity of sequence expression between task and habituation or sleep condition." They also note that the difference in the sequences between learning and learned conditions was larger than the difference between correct and error trials within each condition. They conclude that during task learning, new representations are established, as measured by the burst sequence content. They do additional analyses of the sequence clusters by assessing the spatial informativeness (SI) of each sequence cluster. Over learning, they find an increase in clusters that are spatially informative (clusters that tend to occur in specific locations). Finally, they analyzed the SI clusters in a similar manner to SI cells and classified them as task phase selective sequences (TSs) and goal arm selective sequences (GSs), and did some further analysis. However, they themselves conclude that the frequency of TSs and GSs is limited (I believe because most sequence clusters were non-SI - the authors can verify this and write it in the text?). In the discussion, they say, "In addition to GSs and TSs, we found that most of the recurring sequences are not related to behavior".
(3) As an alternative to analyzing individual cells and sequences of individual cells, they then look for trajectory replay using Bayesian population decoding of location during bursts. They analyze TS bursts, GS bursts, and non-SI bursts. They say "we found correlations of decoded position with time bin (within a 500 ms burst) strongly exceeding chance level only during outward and reward phase, for both GSs and TSs (Fig 4H)." Figure 4H shows distributions indicating statistically significant bias in the forward direction (using correlations of decoded location versus time bin across 10 bins of 50 ms each within each 500-ms burst). They find that the Outward trajectories appear to reflect the actual trajectory during running itself, so they are likely not replay. But the sequences at the Reward are replay as they do not reflect the current location. Furthermore, replay at the Reward is in the forward direction (unlike the reverse replay at Reward seen in the hippocampus), and this replay is only seen in the learned and not the learning condition. At the same time, they find that replay is not seen during odor Sampling, from which they conclude there is no evidence of replay used for planning. Instead, they say the replay at the Reward could possibly be for evaluation during the Reward phase, though this would only be for the learned condition. They conclude "Together with our finding of strong changes in sequence expression after learning (Figure 3E) these findings suggest that a representation of task develops during learning, however, it does not reflect previous network structure." I am not sure what is meant here by the second part of this sentence (after "however ..."). Is it the idea that the replay represents network structure, and the lack of Reward replay in the learning condition means that the network structure must have been changed to get to the learned condition? Please clarify.
This study provides valuable new information about the evolution of mPFC activity during the learning of an odor-based 2AFC T-maze-like task. They show convincing evidence of changes in single-cell tuning, population sequences, and replay events. They also find novel forward replay at the Reward, and find that this is present only after the animal has learned the task. In the discussion, the authors note "To our knowledge, this study identified for the first time fast recurring neural sequence activity from 1-p calcium data, based on correlation analysis."
(1) There are some statements that are not clear, such as at the end of the introduction, where the authors write, "Both findings suggest that the mPFC task code is locally established during learning." What is the reasoning behind the "locally established" statement? Couldn't the learning be happening in other areas and be inherited by the mPFC? Or are the authors assuming that newly appearing sequences within a 500-ms burst period must be due to local plasticity? I have also pointed out a question about the statement "however, it does not reflect previous network structure" in (3) above.
(2) The threshold for extracting burst events (0.5 standard deviations, presumably above the mean, but the authors should verify this) seems lower than what one usually sees as a threshold for population burst detection. What fraction of all data is covered by 500 ms periods around each such burst? However, it is potentially a strength of this work that their results are found by using this more permissive threshold.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
This manuscript provides an open-source tool including hardware and software, and a dataset to facilitate and standardize behavioral classification in laboratory mice. The hardware for behavioral phenotyping was extensively tested for safety. The software is GUI-based, facilitating the usage of this tool across the community of investigators who do not have a programming background. The behavioral classification tool is highly accurate, and the authors deposited a large dataset of annotations and pose tracking for many strains of mice. This tool has great potential for behavioral scientists who use mice across many fields; however, there are many missing details that currently limit the impact of this tool and publication.
Strengths:
(1) There is software-hardware integration for facilitating cross-lab adaptation of the tool and minimizing the need to annotate new data for behavioral classification.
(2) Data from many strains of mice were included in the classification and genetic analyses in this manuscript.
(3) A large dataset was annotated and deposited for the use of the community.
(4) The GUI-based software tool decreases barriers to usage across users with limited coding experience.
Weaknesses:
(1) The authors only report the quality of the classification considering the number of videos used for training, but not considering the number of mice represented or the mouse strain. Therefore, it is unclear if the classification model works equally well in data from all the mouse strains tested, and how many mice are represented in the classifier dataset and validation.
(2) The GUI requires pose tracking for classification, but the software provided in JABS does not do pose tracking, so users must do pose tracking using a separate tool. Currently, there is no guidance on the pose tracking recommendations and requirements for usage in JABS. The pose tracking quality directly impacts the classification quality, given that it is used for the feature calculation; therefore, this aspect of the data processing should be more carefully considered and described.
(3) Many statistical and methodological details are not described in the manuscript, limiting the interpretability of the data presented in Figures 4,7-8. There is no clear methods section describing many of the methods used and equations for the metrics used. As an example, there are no details of the CNN used to benchmark the JABS classifier in Figure 4, and no details of the methods used for the metrics reported in Figure 8.
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Reviewer #2 (Public review):
Summary:
This manuscript presents the JAX Animal Behavior System (JABS), an integrated mouse phenotyping platform that includes modules for data acquisition, behavior annotation, and behavior classifier training and sharing. The manuscript provides details and validation for each module, demonstrating JABS as a useful open-source behavior analysis tool that removes barriers to adopting these analysis techniques by the community. In particular, with the JABS-AI module, users can download and deploy previously trained classifiers on their own data, or annotate their own data and train their own classifiers. The JABS-AI module also allows users to deploy their classifiers on the JAX strain survey dataset and receive an automated behavior and genetic report.
Strengths:
(1) The JABS platform addresses the critical issue of reproducibility in mouse behavior studies by providing an end-to-end system from rig setup to downstream behavioral and genetic analyses. Each step has clear guidelines, and the GUIs are an excellent way to encourage best practices for data storage, annotation, and model training. Such a platform is especially helpful for labs without prior experience in this type of analysis.
(2) A notable strength of the JABS platform is its reuse of large amounts of previously collected data at JAX Labs, condensing this into pretrained pose estimation models and behavioral classifiers. JABS-AI also provides access to the strain survey dataset through automated classifier analyses, allowing large-scale genetic screening based on simple behavioral classifiers. This has the potential to accelerate research for many labs by identifying particular strains of interest.
(3) The ethograph analysis will be a useful way to compare annotators/classifiers beyond the JABS platform.
Weaknesses:
(1) The manuscript as written lacks much-needed context in multiple areas: what are the commercially available solutions, and how do they compare to JABS (at least in terms of features offered, not necessarily performance)? What are other open-source options? How does the supervised behavioral classification approach relate to the burgeoning field of unsupervised behavioral clustering (e.g., Keypoint-MoSeq, VAME, B-SOiD)? What kind of studies will this combination of open field + pose estimation + supervised classifier be suitable for? What kind of studies is it unsuited for? These are all relevant questions that potential users of this platform will be interested in.
(2) Throughout the manuscript, I often find it unclear what is supported by the software/GUI and what is not. For example, does the GUI support uploading videos and running pose estimation, or does this need to be done separately? How many of the analyses in Figures 4-6 are accessible within the GUI?
(3) While the manuscript does a good job of laying out best practices, there is an opportunity to further improve reproducibility for users of the platform. The software seems likely to perform well with perfect setups that adhere to the JABS criteria, but it is very likely that there will be users with suboptimal setups - poorly constructed rigs, insufficient camera quality, etc. It is important, in these cases, to give users feedback at each stage of the pipeline so they can understand if they have succeeded or not. Quality control (QC) metrics should be computed for raw video data (is the video too dark/bright? are there the expected number of frames? etc.), pose estimation outputs (do the tracked points maintain a reasonable skeleton structure; do they actually move around the arena?), and classifier outputs (what is the incidence rate of 1-3 frame behaviors? a high value could indicate issues). In cases where QC metrics are difficult to define (they are basically always difficult to define), diagnostic figures showing snippets of raw data or simple summary statistics (heatmaps of mouse location in the open field) could be utilized to allow users to catch glaring errors before proceeding to the next stage of the pipeline, or to remove data from their analyses if they observe critical issues.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
The authors note that very premature infants experience the visual world early and, as a consequence, sustain lasting deficits including compromised motion processing. Here they investigate the effects of early eye opening in ferret, choosing a time point after birth when both retinal waves and light traveling through closed lids drive sensory responses. The laboratory has long experience in quantitative studies of visual response properties across development and this study reflects their expertise.
The investigators find little or no difference in mean orientation and direction selectivity, or in spatial frequency tuning, as a result of early eye opening but marked differences in temporal frequency tuning. These changes are especially interesting as they relate to deficits seen in prematurely delivered children. Temporal frequency bandwidth for responses evoked from early-opened contralateral eyes were broader than for controls; this is the case for animals in which either one or both eyes were opened prematurely. Further, when only one eye was opened early, responses to low temporal frequencies were relatively stronger.
The investigators also found changes in firing rate and sign of response to visual stimuli. Premature eye-opening increased spontaneous rates in all test configurations. When only one eye was opened early, firing rates recorded from the ipsilateral cortex were strongly suppressed, with more modest effects in other test cases.
As the authors' discussion notes, these observations are just a starting point for studies underlying mechanism. The experiments are so difficult to perform and so carefully described that the results will be foundational for future studies of how premature birth influences cortical development.
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Reviewer #2 (Public review):
In this paper, Griswold and Van Hooser investigate what happens if animals are exposed to patterned visual experience too early, before its natural onset. To this end, they make use of the benefits of the ferret as a well-established animal model for visual development. Ferrets naturally open their eyes around postnatal day 30; here, Griswold and Van Hooser opened either one or both eyes prematurely. Subsequent recordings in the mature primary visual cortex show that while some tuning properties like orientation and direction selectivity developed normally, the premature visual exposure triggered changes in temporal frequency tuning and overall firing rates. These changes were widespread, in that they occurred even for neurons responding to the eye that was not opened prematurely. These results demonstrate that the nature of the visual input well before eye opening can have profound consequences on the developing visual system.
The conclusions of this paper are well supported by the data, but in the initially submitted version of the paper, there were a few questions regarding the data processing and suggestions for the discussion:
(1) The assessment of the tuning properties is based on fits to the data. Presumably, neurons for which the fits were poor were excluded? It would be useful to know what the criteria were, how many neurons were excluded, and whether there was a significant difference between the groups in the numbers of neurons excluded (which could further point to differences between the groups).
(2) For the temporal frequency data, low- and high-frequency cut-offs are defined, but then only used for the computation of the bandwidth. Given that the responses to low temporal frequencies change profoundly with premature eye opening, it would be useful to directly compare the low- and high-frequency cut-offs between groups, in addition to the index that is currently used.
(3) In addition to the tuning functions and firing rates that have been analyzed so far, are there any differences in the temporal profiles of neural responses between the groups (sustained versus transient responses, rates of adaptation, latency)? If the temporal dynamics of the responses are altered significantly, that could be part of an explanation for the altered temporal tuning.
(4) It would be beneficial for the general interpretation of the results to extend the discussion. First, it would be useful to provide a more detailed discussion of what type of visual information might make it through the closed eyelids (the natural state), in contrast to the structured information available through open eyes. Second, it would be useful to highlight more clearly that these data were collected in peripheral V1 by discussing what might be expected in binocular, more central V1 regions. Third, it would be interesting to discuss the observed changes in firing rates in the context of the development of inhibitory neurons in V1 (which still undergo significant changes through the time period of premature visual experience chosen here).
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www.biorxiv.org www.biorxiv.org
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Joint Public Review:
Summary:
This work aims to improve our understanding of the factors that influence female-on-female aggressive interactions in gorilla social hierarchies, using 25 years of behavioural data from five wild groups of two gorilla species. Researchers analysed aggressive interactions between 31 adult females, using behavioural observations and dominance hierarchies inferred through Elo-rating methods. Aggression intensity (mild, moderate, severe) and direction (measured as the rank difference between aggressor and recipient) were used as key variables. A linear mixed-effects model was applied to evaluate how aggression direction varied with reproductive state (cycling, trimester-specific pregnancy, or lactation) and sex composition of the group. This study highlights the direction of aggressive interactions between females, with most interactions being directed from higher- to lower-ranking adult females close in social rank. However, the results show that 42% of these interactions are directed from lower- to higher-ranking females. Particularly, lactating and pregnant females targeted higher-ranking individuals, which the authors suggest might be due to higher energetic needs, which increase risk-taking in lactating and pregnant females. Sex composition within the group also influenced which individuals were targeted. The authors suggest that male presence buffers female-on-female aggression, allowing females to target higher-ranking females than themselves. In contrast, females targeted lower-ranking females than themselves in groups with a larger ratio of females, which supposes a lower risk for the females since the pool of competitors is larger. The findings provide an important insight into aggression heuristics in primate social systems and the social and individual factors that influence these interactions, providing a deeper understanding of the evolutionary pressures that shape risk-taking, dominance maintenance, and the flexibility of social strategies in group-living species.
The authors achieved their aim by demonstrating that aggression direction in female gorillas is influenced by factors such as reproductive condition and social context, and their results support the broader claim that aggression heuristics are flexible. However, some specific interpretations require further support. Despite this, the study makes a valuable contribution to the field of behavioural ecology by reframing how we think about intra-sexual competition and social rank maintenance in primates.
Strengths:
One of the study's major strengths is the use of an extensive dataset that compiles 25 years of behavioural data and 6871 aggressive interactions between 31 adult females in five social groups, which allows for a robust statistical analysis. This study uses a novel approach to the study of aggression in social groups by including factors such as the direction and intensity of aggressive interactions, which offers a comprehensive understanding of these complex social dynamics. In addition, this study incorporates ecological and physiological factors such as the reproductive state of the females and the sex composition of the group, which allows an integrative perspective on aggression within the broader context of body condition and social environment. The authors successfully integrate their results into broader evolutionary and ecological frameworks, enriching discussions around social hierarchies and risk sensitivity in primates and other animals.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Can a plastic RNN serve as a basis function for learning to estimate value. In previous work this was shown to be the case, with a similar architecture to that proposed here. The learning rule in previous work was back-prop with an objective function that was the TD error function (delta) squared. Such a learning rule is non-local as the changes in weights within the RNN, and from inputs to the RNN depends on the weights from the RNN to the output, which estimates value. This is non-local, and in addition, these weights themselves change over learning. The main idea in this paper is to examine if replacing the values of these non-local changing weights, used for credit assignment, with random fixed weights can still produce similar results to those obtained with complete bp. This random feedback approach is motivated by a similar approach used for deep feed-forward neural networks.
This work shows that this random feedback in credit assignment performs well but is not as well as the precise gradient-based approach. When more constraints due to biological plausibility are imposed performance degrades. These results are consistent with previous results on random feedback.
Strengths:
The authors show that random feedback can approximate well a model trained with detailed credit assignment.
The authors simulate several experiments including some with probabilistic reward schedules and show results similar to those obtained with detailed credit assignments as well as in experiments.
The paper examines the impact of more biologically realistic learning rules and the results are still quite similar to the detailed back-prop model.
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Reviewer #2 (Public review):
Summary:
Tsurumi et al. show that recurrent neural networks can learn state and value representations in simple reinforcement learning tasks when trained with random feedback weights. The traditional method of learning for recurrent network in such tasks (backpropogation through time) requires feedback weights which are a transposed copy of the feed-forward weights, a biologically implausible assumption. This manuscript builds on previous work regarding "random feedback alignment" and "value-RNNs", and extends them to a reinforcement learning context. The authors also demonstrate that certain non-negative constraints can enforce a "loose alignment" of feedback weights. The author's results suggest that random feedback may be a powerful tool of learning in biological networks, even in reinforcement learning tasks.
Strengths:
The authors describe well the issues regarding biologically plausible learning in recurrent networks and in reinforcement learning tasks. They take care to propose networks which might be implemented in biological systems and compare their proposed learning rules to those already existing in literature. Further, they use small networks on relatively simple tasks, which allows for easier intuition into the learning dynamics.
Weaknesses:
The principles discovered by the authors in these smaller networks are not applied to larger networks or more complicated tasks with long temporal delays (>100 timesteps), so it remains unclear to what degree these methods can scale or can be used more generally.
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Reviewer #3 (Public review):
Summary:
The paper studies learning rules in a simple sigmoidal recurrent neural network setting. The recurrent network has a single layer of 10 to 40 units. It is first confirmed that feedback alignment (FA) can learn a value function in this setting. Then so-called bio-plausible constraints are added: (1) when value weights (readout) is non-negative, (2) when the activity is non-negative (normal sigmoid rather than downscaled between -0.5 and 0.5), (3) when the feedback weights are non-negative, (4) when the learning rule is revised to be monotic: the weights are not downregulated. In the simple task considered all four biological features do not appear to impair totally the learning.
Strengths:
(1) The learning rules are implemented in a low-level fashion of the form: (pre-synaptic-activity) x (post-synaptic-activity) x feedback x RPE. Which is therefore interpretable in terms of measurable quantities in the wet-lab.
(2) I find that non-negative FA (FA with non negative c and w) is the most valuable theoretical insight of this paper: I understand why the alignment between w and c is automatically better at initialization.
(3) The task choice is relevant, since it connects with experimental settings of reward conditioning with possible plasticity measurements.
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www.biorxiv.org www.biorxiv.org
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Joint Public Review:
This manuscript investigates a mechanism between the histone reader protein YEATS2 and the metabolic enzyme GCDH, particularly in regulating epithelial-to-mesenchymal transition (EMT) in head and neck cancer (HNC).
The authors addressed most of the concerns of the reviewers. They have:
(1) Increased the patient cohort size from 10 to 23 for evaluating the levels of YEATS2 and H3K27cr.
(2) Checked the expression of major genes involved in the YEATS2-mediated histone crotonylation axis (YEATS2, GCDH, ECHS1, Twist1, along with H3K27cr levels) in head and neck cancer tissues using immunohistochemistry.
(3) Analyzed publicly available head and neck cancer patient datasets, which revealed a significant positive correlation between YEATS2 expression and increasing tumor grade.
(4) Performed GSEA on TCGA HNC patient samples stratified by high versus low YEATS2 expression. This analysis robustly demonstrated a positive enrichment of metastasis-related gene sets in the high YEATS2 expression group, compared to the low YEATS2 group.
(5) Performed extensive experiments to look into the role of p300 in assisting YEATS2 in regulating promoter histone crotonylation. The p300 was knocked down in BICR10 cells, followed by immunoblotting to assess SPARC protein levels.
(6) Performed co-immunoprecipitation assays to check for an interaction between endogenous YEATS2 and p300. The results clearly demonstrate the presence of YEATS2 in the p300-immunoprecipitate sample, indicating that YEATS2 and p300 physically interact and likely function together as a complex to drive the expression of target genes like SPARC.
(7) Performed RNA Polymerase II ChIP-qPCR on the SPARC promoter in YEATS2 knockdown cells.
(8) To confirm p300's specific role in crotonylation at this locus, they performed H3K27cr ChIP-qPCR after p300 knockdown.
(9) Performed SP1 knockdown (which reduces YEATS2 expression) followed by ectopic YEATS2 overexpression, and then assessed p300 occupancy and H3K27cr levels on the SPARC promoter.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public Review):
The authors reported that mutations were identified in the ZC3H11A gene in four adolescents from 1015 high myopia subjects in their myopia cohort. They further generated Zc3h11a knockout mice utilizing the CRISPR/Cas9 technology.
The main claims are only partially supported. The reviewers still have the concerns of 1) the modes of inheritance for the families need to be shown; 2) the phenotype of heterozygous mutant mice is too weak; 3) the authors still have not addressed the biological question of whether there are fewer bipolar cells or decreased expression of the marker protein. This would involve counting cells, which they have not done. The blots they show do not appear to support their quantifications. Considering the sensitivity of quantifying nearly saturated blots, the authors should show blots that are not exposed to that level of saturation.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
This is an interesting follow-up to a paper published in Human Molecular Genetics reporting novel roles in corticogenesis of the Kif7 motor protein that can regulate the activator as well as the repressor functions of the Gli transcription factors in Shh signalling. This new work investigates how a null mutation in the Kif7 gene affects the formation of corticofugal and thalamocortical axon tracts and the migration of cortical interneurons. It demonstrates that Kif7 null mutant embryos present with ventriculomegaly and heterotopias as observed in patients carrying KIF7 mutations. The Kif7 mutation also disrupts the connectivity between cortex and thalamus and leads to an abnormal projection of thalamocortical axons. Moreover, cortical interneurons show migratory defects that are mirrored in cortical slices treated with the Shh inhibitor cyclopamine suggesting that the Kif7 mutation results in a down-regulation of Shh signalling. Interestingly, these defects are much less severe at later stages of corticogenesis.
Strengths/weaknesses:
The findings of this manuscript are clearly presented and are based on detailed analyses. Using a compelling set of experiments, especially the live imaging to monitor interneuron migration, the authors convincingly investigate Kif7's roles and their results support their major claims. The migratory defects in interneurons and the potential role of Shh signalling present novel findings and provide some mechanistic insights but rescue experiments would further support Kif7's role in interneuron migration. Similarly, the mechanism underlying the misprojection which has previously been reported in other cilia mutants remains unexplored. Taken together, this manuscript makes novel contributions to our understanding of the role of primary cilia in forebrain development and to the aetiology of the neural symptons in ciliopathy patients.
Comments on revisions:
The authors addressed most of the points I raised in my original review.
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Reviewer #2 (Public review):
Summary:
This study investigates the role of KIF7, a ciliary kinesin involved in the Sonic Hedgehog (SHH) signaling pathway, in cortical development using Kif7 knockout mice. The researchers examined embryonic cortex development (mainly at E14.5), focusing on structural changes and neuronal migration abnormalities.
Strengths:
(1) The phenotype observed is interesting, and the findings provide neurodevelopmental insight into some of the symptoms and malformations seen in patients with KIF7 mutations.
(2) The authors assess several features of cortical development, including structural changes in layers of the developing cortex, connectivity of the cortex with thalamus, as well as migration of cINs from CGE and MGE to cortex.
Comments on revisions:
The authors have made significant and thoughtful responses as well as experimental additions to the authors comments. Their efforts are appreciated and the manuscript is much improved.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The study by Zhuomin Yin and colleagues focuses on the relationship between cell-free HPV (cfHPV) DNA and metastatic or recurrent cervical cancer patients. It expands the application of cfHPV DNA in tracking disease progression and evaluating treatment response in cervical cancer patients. The study is overall well-designed, including appropriate analyses.
Strengths:
The findings provide valuable reference points for monitoring drug efficacy and guiding treatment strategies in patients with recurrent and metastatic cervical cancer. The concordance between HPV cfDNA fluctuations and changes in disease status suggests that cfDNA could play a crucial role in precision oncology, allowing for more timely interventions. As with similar studies, the authors used Droplet Digital PCR to measure cfDNA copy numbers, a technique that offers ultrasensitive nucleic acid detection and absolute quantification, lending credibility to the conclusions.
Weaknesses:
Despite including 28 clinical cases, only 7 involved recurrent cervical cancer, which may not be sufficient to support some of the authors' conclusions fully. Future studies on larger cohorts could solidify HPV cfDNA's role as a standard in the personalized treatment of recurrent cervical cancer patients.
Comments on revisions:
Thanks for your additional efforts and for addressing my concerns.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Chao et al. produced an updated version of the SpliceAI package using modern deep learning frameworks. This includes data preprocessing, model training, direct prediction, and variant effect prediction scripts. They also added functionality for model fine-tuning and model calibration. They convincingly evaluate their newly trained models against those from the original SpliceAI package and investigate how to extend SpliceAI to make predictions in new species. While their comparisons to the original SpliceAI models are convincing on the grounds of model performance, their evaluation of how well the new models match the original's understanding of non-local mutation effects is incomplete. Further, their evaluation of the new calibration functionality would benefit from a more nuanced discussion of what set of splice sites their calibration is expected to hold for, and tests in a context for which calibration is needed.
Strengths:
(1) They provide convincing evidence that their new implementation of SpliceAI matches the performance of the original model on a similar dataset while benefiting from improved computational efficiencies. This will enable faster prediction and retraining of splicing models for new species as well as easier integration with other modern deep learning tools.
(2) They produce models with strong performance on non-human model species and a simple, well-documented pipeline for producing models tuned for any species of interest. This will be a boon for researchers working on splicing in these species and make it easy for researchers working on new species to generate their own models.
(3) Their documentation is clear and abundant. This will greatly aid the ability of others to work with their code base.
Weaknesses:
(1) The authors' assessment of how much their model retains SpliceAI's understanding of "non-local effects of genomic mutations on splice site location and strength" (Figure 6) is not sufficiently supported. Demonstrating this would require showing that for a large number of (non-local) mutations, their model shows the same change in predictions as SpliceAI or that attribution maps for their model and SpliceAI are concordant even at distances from the splice site. Figure 6A comes close to demonstrating this, but only provides anecdotal evidence as it is limited to 2 loci. This could be overcome by summarizing the concordance between ISM maps for the two models and then comparing across many loci. Figure 6B also comes close, but falls short because instead of comparing splicing prediction differences between the models as a function of variants, it compares the average prediction difference as a function of the distance from the splice site. This limits it to only detecting differences in the model's understanding of the local splice site motif sequences. This could be overcome by looking at comparisons between differences in predictions with mutants directly and considering non-local mutants that cause differences in splicing predictions.
(2) The utility of the calibration method described is unclear. When thinking about a calibrated model for splicing, the expectation would be that the models' predicted splicing probabilities would match the true probabilities that positions with that level of prediction confidence are splice sites. However, the actual calibration that they perform only considers positions as splice sites if they are splice sites in the longest isoform of the gene included in the MANE annotation. In other words, they calibrate the model such that the model's predicted splicing probabilities match the probability that a position with that level of confidence is a splice site in one particular isoform for each gene, not the probability that it is a splice site more broadly. Their level of calibration on this set of splice sites may very well not hold to broader sets of splice sites, such as sites from all annotated isoforms, sites that are commonly used in cryptic splicing, or poised sites that can be activated by a variant. This is a particularly important point as much of the utility of SpliceAI comes from its ability to issue variant effect predictions, and they have not demonstrated that this calibration holds in the context of variants. This section could be improved by expanding and clarifying the discussion of what set of splice sites they have demonstrated calibration on, what it means to calibrate against this set of splice sites, and how this calibration is expected to hold or not for other interesting sets of splice sites. Alternatively, or in addition, they could demonstrate how well their calibration holds on different sets of splice sites or show the effect of calibrating their models against different potentially interesting sets of splice sites and discuss how the results do or do not differ.
(3) It is difficult to assess how well their calibration method works in general because their original models are already well calibrated, so their calibration method finds temperatures very close to 1 and only produces very small and hard to assess changes in calibration metrics. This makes it very hard to distinguish if the calibration method works, as it doesn't really produce any changes. It would be helpful to demonstrate the calibration method on a model that requires calibration or on a dataset for which the current model is not well calibrated, so that the impact of the calibration method could be observed.
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Reviewer #2 (Public review):
Summary:
The paper by Chao et al offers a reimplementation of the SpliceAI algorithm in PyTorch so that the model can more easily/efficiently be retrained. They apply their new implementation of the SpliceAI algorithm, which they call OpenSpliceAI, to several species and compare it against the original model, showing that the results are very similar and that in some small species, pre-training on other species helps improve performance.
Strengths:
On the upside, the code runs fine, and it is well documented.
Weaknesses:
The paper itself does not offer much beyond reimplementing SpliceAI. There is no new algorithm, new analysis, new data, or new insights into RNA splicing. There is no comparison to many of the alternative methods that have since been published to surpass SpliceAI. Given that some of the authors are well-known with a long history of important contributions, our expectations were admittedly different. Still, we hope some readers will find the new implementation useful.
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Reviewer #3 (Public review):
Summary:
The authors present OpenSpliceAI, a PyTorch-based reimplementation of the well-known SpliceAI deep learning model for splicing prediction. The core architecture remains unchanged, but the reimplementation demonstrates convincing improvements in usability, runtime performance, and potential for cross-species application.
Strengths:
The improvements are well-supported by comparative benchmarks, and the work is valuable given its strong potential to broaden the adoption of splicing prediction tools across computational and experimental biology communities.
Major comments:
Can fine-tuning also be used to improve prediction for human splicing? Specifically, are models trained on other species and then fine-tuned with human data able to perform better on human splicing prediction? This would enhance the model's utility for more users, and ideally, such fine-tuned models should be made available.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
This fundamental work employed multidisciplinary approaches and conducted rigorous experiments to study how a specific subset of neurons in the dorsal striatum (i.e., "patchy" striatal neurons) modulates locomotion speed depending on the valence of naturalistic contexts.
Strengths:
The scientific findings are novel and original and significantly advance our understanding of how the striatal circuit regulates spontaneous movement in various contexts.
Weaknesses:
This is extensive research involving various circuit manipulation approaches. Some of these circuit manipulations are not physiological. A balanced discussion of the technical strengths and limitations of the present work would be helpful and beneficial to the field.
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Reviewer #2 (Public review):
Hawes et al. investigated the role of striatal neurons in the patch compartment of the dorsal striatum. Using Sepw1-Cre line, the authors combined a modified version of the light/dark transition box test that allows them to examine locomotor activity in different environmental valence with a variety of approaches, including cell-type-specific ablation, miniscope calcium imaging, fiber photometry, and opto-/chemogenetics. First, they found ablation of patchy striatal neurons resulted in an increase in movement vigor when mice stayed in a safe area or when they moved back from more anxiogenic to safe environments. The following miniscope imaging experiment revealed that a larger fraction of striatal patchy neurons was negatively correlated with movement speed, particularly in an anxiogenic area. Next, the authors investigated differential activity patterns of patchy neurons' axon terminals, focusing on those in GPe, GPi, and SNr, showing that the patchy axons in SNr reflect movement speed/vigor. Chemogenetic and optogenetic activation of these patchy striatal neurons suppressed the locomotor vigor, thus demonstrating their causal role in the modulation of locomotor vigor when exposed to valence differentials. Unlike the activation of striatal patches, such a suppressive effect on locomotion was absent when optogenetically activating matrix neurons by using the Calb1-Cre line, indicating distinctive roles in the control of locomotor vigor by striatal patch and matrix neurons. Together, they have concluded that nigrostriatal neurons within striatal patches negatively regulate movement vigor, dependent on behavioral contexts where motivational valence differs.
The strengths of this work include the use of multiple experimental approaches, including genetic/viral ablation of patch neurons, miniscope single-cell imaging, as well as projection-specific recording of axonal activity by fiber photometry, and causal manipulation of the neurons by chemogenetic and optogenetics. Although similar findings were reported previously, the authors' results will be of value owing to multiple levels of investigation. In my view, this study will add to the important literature by demonstrating how patch (striosomal) neurons in the striatum controls movement vigor.
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Reviewer #3 (Public review):
Hawes et al. combined behavioral, optical imaging, and activity manipulation techniques to investigate the role of striatal patch SPNs in locomotion regulation. Using Sepw1-Cre transgenic mice, they found that patch SPNs encode locomotion deceleration in a light-dark box procedure through optical imaging techniques. Moreover, genetic ablation of patch SPNs increased locomotion speed, while chemogenetic activation of these neurons decreased it. The authors concluded that a subtype of patch striatonigral neurons modulates locomotion speed based on external environmental cues.
In the revision, the authors have largely addressed my concerns with additional explanation and discussion, although some of the key experiments to strengthen the authors' claim by identifying the function of specific cell populations remain to be conducted due to technical challenges. Nevertheless, the current results remain valuable and interesting to a wide audience in the field.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The paper describes the initial characterization of Eml3 knockout mice. Eml3 global inactivation leads to delayed embryonic development, perinatal lethality apparently due to failure to inflate lungs, and a cobblestone brain-like phenotype represented by focal neuronal ectopias in the marginal zone or subarachnoid space of dorsal telencephalon. The neural ectopias are associated with interruptions in the pial basal membrane (PBM), which appear around E11.5. The authors also confirmed previously described protein interactions, using coIP-MS experiments of placenta and embryonic tissues (TUBB3, several 14-3-3 proteins, and DYNLL). The authors generated mice carrying a TQT86AAA homozygous mutation in EML3 (a motif required for EML3-DYNLL interactions) that were normal and showed no focal neuronal ectopias, indicating that this particular protein interaction is dispensable. The authors propose Eml3 knockout mice as a model of cobblestone brain malformation.
Strengths:
The brain phenotype described in this work is relevant for the neural development field and with potential clinical relevance. The initial phenotyping is appropriate but will require additional experiments to establish the cause of the failure to inflate the lungs. The study shows convincing data regarding the main characteristics of the brain phenotype and data supporting the timing when these abnormalities arise during development.
Weaknesses:
The study would benefit from clearer evidence and additional experiments that would help to establish the molecular and cellular mechanisms underlying the brain phenotype, the central topic of the work.
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Reviewer #2 (Public review):
Summary:
In this manuscript, the authors investigate the role of the microtubule-binding protein EML3 during cortical development through the generation and characterization of an Eml3 mouse mutant. The authors focus mainly on the effects of EML3 loss on brain development, although Eml3 mouse mutants also present with developmental delay and growth restriction, and die perinatally due to respiratory distress caused by delayed maturation of the lungs. The main finding in the developing cortex is the presence of focal neuronal ectopias, which contain neurons from all cortical layers, as revealed by immunostaining. The authors use electron microscopy to show that ectopias seem to be caused by disruption to the pial basement membrane at early stages of development, which allows neurons to breach through it. To find a functional link between EML3 and the observed phenotype, studies are conducted that demonstrate expression of EML3 in radial glia cells and mesenchymal cells, both cell types involved in the formation and maintenance of the pial basement membrane. Furthermore, interaction partners for EML3 are identified through coIP-MS analysis, including tubulin beta-3, 14-3-3 proteins, and cytoplasmic dynein light chain. However, mice carrying a mutant EML3 allele engineered to abolish the interaction between EML3 and cytoplasmic dynein light chain do not recapitulate any of the symptoms of complete EML3 loss.
Strengths:
The manuscript offers several important strengths that contribute significantly to the field. This study presents the first characterization of Eml3 knockout animals, providing novel insights into the role of Eml3 in vivo. Information on Eml3 function so far was restricted to cell culture data, so the results in this manuscript start to fill an important gap in our knowledge about this microtubule-binding protein. The experimental approach is carefully designed, with appropriate controls that ensure the reliability of the data. Moreover, the authors have addressed a key challenge in the analysis, namely the developmental delay of the knockout animals. By implementing a strategy to match developmental stages between wild-type and knockout groups, they allow for meaningful and valid comparisons between the two genotypes. Importantly, the authors have successfully generated three different Eml3 mutant mouse lines (knockout, floxed, and with disrupted binding to cytoplasmic dynein light chain), which are very valuable tools for the broader scientific community to further study the roles of this gene in development and disease in the future.
Weaknesses:
While the manuscript presents valuable data, there are also several weaknesses that limit the overall impact of the study. Most notably, there is no clear mechanistic link established between the loss of Eml3 function and the observed phenotype, leaving the biological significance of the findings somewhat speculative, as it is not straightforward how a microtubule-associated protein can have an impact on the stability of the pial basement membrane. In this respect, but also in general for the whole manuscript, there seems to be a considerable amount of experimental work that has been conducted but is not presented, possibly due to the negative nature of the results. At least some of those results could be shown, particularly (but not only) the stainings for the composition of the ECM components. Additionally, the phenotype reported appears to be dependent on the genetic background, as it is absent in the CD1 strain. This observation raises concerns as to how robust the results are and how much they can be generalized to other mouse strains, but, more importantly, to humans. There is no data included in the manuscript about the generation and analysis of the Eml3AAA/AAA mouse line. This is an important omission, especially as no details on the validation or phenotypic characterization of this additional mouse line are provided. Including these elements would greatly strengthen the rigor and interpretability of the work, especially if that mouse line is to be shared with the scientific community.
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Reviewer #3 (Public review):
Summary:
This work aims to understand the role of Echinoderm Microtubule-associated Protein-like 3 (EML3) in embryogenesis and neocortical development. Importantly, this work shows that depletion of EML3 causes focal neuronal ectopias by disrupting the structural integrity of the pial basement membrane, describing a new model of cobblestone brain malformation. Another member of the EML family, EML1, has already been shown to trigger neuronal migration disorders, particularly subcortical band heterotopia, by affecting cell polarity. The results presented here point to a different mechanism of action. The authors show that EML3 is expressed in radial glia cells and mesenchymal cells in the pial region, and upon EML3 depletion (i.e., Eml3 mutant mice), the pial basement membrane is structurally damaged, allowing migrating neuroblasts to ectopically migrate through. Answering, in this case, that the weakening of the pial basement membrane is a prerequisite for focal neuronal ectopias. The authors provide a meticulous characterization of the Eml3 mutant mice, strengthening the conclusions of the results.
Strengths:
The authors provide a very detailed analysis of the defects observed in Eml3 mutant mice, by providing not only results by inferred day of conception but also by classifying embryos by their number of somite pairs.
Weaknesses:
(1) Besides the data provided in the figures, the authors report a significant amount of experiments/results as "Data not shown". Negative data is still important data to report, and the authors may want to choose some crucial "not shown data" to report in the manuscript.
(2) Results in Figure 3A apparently contradict results in 3B. A better explanation of the results should improve understanding of the data. Even though the conclusion that the "onset and progression of neurogenesis is normal in Eml3 null mice" seems logical based on the data, the final numbers are not (Figure 3A) and this should be acknowledged, as well.
(3) The authors should define which cell types are identified by SOX1 and PAX6.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
During early Drosophila pupal development, a subset of larval abdominal muscles (DIOMs) is remodelled using an autophagy dependent mechanism.
To better understand this not very well studied process, the authors have generated a systematic transcriptomics time course using dissected larval abdominal muscles of various stages from wild type and autophagy deficient mutants. The authors have further identified a function for BNIP3 for executing mitophagy during DIOM remodelling.
Strengths:
The paper does provide a detailed mRNA time course resource for the DIOM remodelling.
The paper does find an interesting BNIP3 loss of function phenotype, a block of mitophagy during muscle remodelling and hence identifies a specific linker between mitochondria and the core autophagy
machinery. This adds to the mechanism how mitochondria are degraded.
Sophisticated fly genetics demonstrates that the larval muscle mitochondria are, to a large extend, degraded by autophagy during DIOM remodelling.
Quantitative electron microscopy data show that BNIP3 is required for initiating mito-phagosomes. It needs either its LIR and MER domain for function.
Weakness:
Mitophagy during DIOM remodelling is not novel (earlier papers from Fujita et al.).
Other weaknesses have been eliminated during the revision.
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Reviewer #2 (Public review):
Summary:
Autophagy (macroautophagy) is known to be essential for muscle function in flies and mammals. To date, many mitophagy (selective mitochondrial autophagy) receptors have been identified in mammals and other species. While loss of mitophagy receptors has been shown to impair mitochondrial degradation (e.g., OPTN and NDP52 in Parkin-mediated mitophagy and NIX and BNIP3 in hypoxia-induced mitophagy) at the level of cultured cells, it remains unclear, especially under physiological conditions in vivo. In this study, the authors revealed that one of the receptors BNIP3 plays a critical role in mitochondrial degradation during muscle remodeling in vivo.
Overall, the manuscript provides solid evidence that BNIP3 is involved in mitophagy during muscle remodeling with in vivo analyses performed. In particular, all experiments in this study are well designed. The text is well written and the figures are very clear.
Strengths:
(1) In each experiment, appropriate positive and negative controls are used to indicate what is responsible for the phenomenon observed by the authors: e.g. FIP200, Atg18, Stx17 siRNAs during DIOM remodeling in Fig2 and Full, del-LIR, del-MER in Fig5.
(2) Although the transcriptional dynamics of DIOM remodeling during metamorphosis is autophagy-independent, the transcriptome data obtained by the authors would be valuable for future studies.
(3) In addition to the simple observation that loss of BNIP3 causes mitochondrial accumulation, the authors further observed that, by combining siRNA against STX17, which is required for fusion of autophagosomes with lysosomes, BNIP3 KO abolishes mitophagosome formation, which will provide solid evidence for BNIP3-mediated mitophagy. Furthermore, using a Gal80 temperature-sensitive approach, the authors showed that mitochondria derived from larval muscle, but not those synthesized during hypertrophy, remain in BNIP3 KO fly muscles.
Weaknesses:
(1) Because BNIP3 KO causes mitochondrial accumulation, it is expected that adult flies will have some physiological defects, but this has not been fully analyzed or sufficiently mentioned in the manuscript.
(2) In Fig 5, the authors showed that BNIP3 binds to Atg18a by co-IP, but no data are provided on whether MER-mut or del-MER attenuates the affinity for Atg18a.
Comments on revisions: The authors answered all the reviewer's concerns.
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Reviewer #3 (Public review):
Summary:
Fujita et al build on their earlier, 2017 eLife paper that showed the role of autophagy in the developmental remodeling of a group of muscles (DIOM) in the abdomen of Drosophila. Most larval muscles undergo histolysis during metamorphosis, while DIOMs are programmed to regrow after initial atrophy to give rise to temporary adult muscles, which survive for only 1 day after eclosion of the adult flies (J Neurosci. 1990;10:403-1. and BMC Dev Biol 16, 12, 2016). The authors carry out transcriptomics profiling of these muscles during metamorphosis, which are in agreement with the atrophy and regrowth phases of these muscles. Expression of the known mitophagy receptor BNIP3/NIX is high during atrophy, so the authors start to delve more into the role of this protein/mitophagy in their model. BNIP3 KO indeed impairs mitophagy and muscle atrophy, which they convincingly demonstrate via nice microscopy images. They also show that the already known Atg8a-binding LIR and Atg18a-binding MER motifs of human NIX are conserved in the Drosophila protein, although the LIR turned out to be less critical for in vivo protein function than the MER motif.
Strengths:
Established methodology, convincing data, in vivo model
Weaknesses:
Significance for Drosophila physiology and for human muscles remains to be established
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
This work uses a novel, ethologically relevant behavioral task to explore decision-making paradigms in C. elegans foraging behavior. By rigorously quantifying multiple features of animal behavior as they navigate in a patch food environment, the authors provide strong evidence that worms exhibit one of three qualitatively distinct behavioral responses upon encountering a patch: (1) "search", in which the encountered patch is below the detection threshold; (2) "sample", in which animals detect a patch encounter and reduce their motor speed, but do not stay to exploit the resource and are therefore considered to have "rejected" it; and (3) "exploit", in which animals "accept" the patch and exploit the resource for tens of minutes. Interestingly, the probability of these outcomes varies with the density of the patch as well as the prior experience of the animal. Together, these experiments provide an interesting new framework for understanding the ability of the C. elegans nervous system to use sensory information and internal state to implement behavioral state decisions.
Strengths:
The work uses a novel, neuroethologically-inspired approach to studying foraging behavior
The studies are carried out with an exceptional level of quantitative rigor and attention to detail
Powerful quantitative modeling approaches including GLMs are used to study the behavioral states that worms enter upon encountering food, and the parameters that govern the decision about which state to enter
The work provides strong evidence that C. elegans can make 'accept-reject' decisions upon encountering a food resource
Accept-reject decisions depend on the quality of the food resource encountered as well as on internally represented features that provide measurements of multiple dimensions of internal state, including feeding status and time.
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Reviewer #2 (Public review):
This study provides an experimental and computational framework to examine and understand how C. elegans make decisions while foraging environments with patches of food. The authors show that C. elegans reject or accept food patches depending on a number of internal and external factors.
The key novelty of this paper is the explicit demonstration of behavior analysis and quantitative modeling to elucidate decision-making processes. In particular, the description of the exploring vs. exploiting phases, and sensing vs. non-sensing categories of foraging behavior based on the clustering of behavioral states defined in a multi-dimensional behavior-metrics space, and the implementation of a generalized linear model (GLM) whose parameters can provide quantitative biological interpretations.
The work builds on the literature of C. elegans foraging by adding the reject/accept framework.
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Reviewer #3 (Public review):
Summary:
In this study by Haley et al, the authors investigated explore-exploit foraging using C. elegans as a model system. Through an elegant set of patchy environment assays, the authors built a GLM based on past experience that predicts whether an animal will decide to stay on a patch to feed and exploit that resource, instead of choosing to leave and explore other patches.
Strengths:
I really enjoyed reading this paper. The experiments are simple and elegant, and address fundamental questions of foraging theory in a well-defined system. The experimental design is thoroughly vetted, and the authors provide a considerable volume of data to prove their points.
Weakness:
History-dependence of the GLM. The logistic GLM seems like a logical way to model a binary choice, and I think the parameters you chose are certainly important. However, the framing of them seem odd to me. I do not doubt the animals are assessing the current state of the patch with an assessment of past experience; that makes perfect logical sense. However, it seems odd to reduce past experience to the categories of recently exploited patch, recently encountered patch, and time since last exploitation. This implies the animals have some way of discriminating these past patch experiences and committing them to memory. Also, it seems logical that the time on these patches, not just their density, should also matter, just as the time without food matters. Time is inherent to memory. This model also imposes a prior categorization in trying to distinguish between sensed vs. not-sensed patches, which I criticized earlier. Only "sensed" patches are used in the model, but it is questionable whether worms genuinely do not "sense" these patches.
It seems more likely the worm simply has some memory of chemosensation and relative satiety, both of which increase on patches, and decrease while off of patches. The magnitudes are likely a function of patch density. That being said, I leave it up to the reader to decide how best to interpret the data.
Impact:
I think this work will have a solid impact on the field, as it provides tangible variables to test how animals assess their environment and decide to exploit resources. I think the strength of this research could be strengthened by a reassessment of their model that would both simplify it and provide testable timescales of satiety/starvation memory.
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www.biorxiv.org www.biorxiv.org
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Joint Public Review:
In this manuscript, Wafer and Tandon et al. present a thoughtful and well-designed genetic screen for regulators of adipose remodeling using zebrafish as a model system. The authors cross-referenced several human adipocyte-related transcriptomic and genetic association datasets to identify candidate genes, which they then tested in zebrafish. Importantly, the authors devised an unbiased microscopy-based screening platform to document quantitative adipose phenotypes with whole animal imaging, while also employing rigorous statistical methods. From their screen, the authors identified 6 genes that resulted in robust adipose phenotypes out of a total of 25 that were tested. Overall, this work will be a useful resource for the field because of both the genes identified and the quantitative, rigorous screening pipeline. However, there are limitations that preclude a definitive distinction between developmental and remodeling effects that should be acknowledged and discussed, or addressed with new experiments.
Strengths:
(1) This work combines multiple omic datasets to identify candidate genes that informed a CRISPR-based screen to identify genes underlying adipose tissue development and adaptation. This approach offers a new avenue to improve our understanding and testing of new genetic mechanisms underlying the development of obesity.
(2) Using a clever screening approach, this study identifies new genes that are associated with adipose tissue lipid droplet size change. Importantly, the study provides further validation using a stable CRISPR line to show the phenotype in basal and high-fat diet conditions.
(3) The experiments are well-designed and rigorous. Sample sizes are large. Statistical analyses are highly rigorous, contributing to a high-quality study.
Weaknesses:
(1) The image quantification established in Figures 3 and 4 and used in CRISPR screening showed the relationship among zebrafish development, adipose tissue size, and lipid droplet size. Although adipose tissue development patterning is linked with adipose tissue adaptation, as shown by the evidence provided in this paper, it will be more powerful if the imaging method and pipeline were established to directly access the adipose tissue plasticity rather than just the developmental patterning. Furthermore, the authors should perform additional analysis of their existing data to more accurately determine lipid droplet size along the AP axis in response to HFD.
(2) In the absence of tissue-specific manipulations, definitively establishing the mechanisms underlying the genetic regulation of adipose tissue physiology presents limitations.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Two important factors in visual performance are the resolving power of the lens and the signal-to-noise ratio of the photoreceptors. These both compete for space: a larger lens has improved resolving power over a smaller one, and longer photoreceptors capture more photons and hence generate responses with lower noise. The current paper explores the tradeoff of these two factors, asking how space should be allocated to maximize eye performance (measured as encoded information).
The revisions, to my read, have greatly improved the paper. Most of this was due to setting clear expectations from the start of the paper. Nice work!
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Reviewer #2 (Public review):
Summary:
In short, the paper presents a theoretical framework that predicts how resources should be optimally distributed between receptors and optics in eyes.
After revision of an already excellent contribution, the manuscript is now even better. The authors have responded carefully to all reviewer comments.
Strengths:
The authors build on the principle of resource allocation within an organism and develop a formal theory for optimal distribution of resources within an eye between the receptor array and the optics. Because the two parts of eyes, receptor arrays and optics, share the same role of providing visual information to the animal it is possible to isolate these from resource allocation in the rest of the animal. This allows for a novel and powerful way of exploring the principles that govern eye design. By clever and thoughtful assumptions/constraints, the authors have built a formal theory of resource allocation between the receptor array and the optics for two major types of compound eye as well as for camera-type eyes. The theory is formalized with variables that are well characterized in a number of different animal eyes, resulting in testable predictions.
The authors use the theory to explain a number of design features that depend on different optimal distribution of resources between the receptor array and the optics in different types of eye. As an example, they successfully explain why eye regions with different spatial resolution should be built in different ways. They also explain differences between different types of eye, such as long photoreceptors in apposition compound eyes and much shorter receptors in camera type eyes. The predictive power in the theory is impressive.
To keep the number of parameters at a minimum, the theory was developed for two types of compound eye (neural superposition, and apposition) and for camera-type eyes. It is possible to extend the theory to other types of eye, although it would likely require more variables and assumptions/constraints to the theory. It is thus good to introduce the conceptual ideas without overdoing the applications of the theory.
The paper extends a previous theory, developed by the senior author, that develops performance surfaces for optimal cost/benefit design of eyes. By combining this with resource allocation between receptors and optics, the theoretical understanding of eye design takes a major leap and provides entirely new sets of predictions and explanations for why eyes are built the way they are.
The paper is well written and even though the theory development in the Results may be difficult to take in for many biologists, the Discussion very nicely lists all the major predictions under separate headings, and here the text is more tuned for readers that are not entirely comfortable with the formalism of the Results section. I must point out though that the Results section is kept exemplary concise. The figures are excellent and help explain concepts that otherwise may go above the head of many biologists.
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Reviewer #3 (Public review):
Summary:
This is a proposal for a new theory for the geometry of insect eyes. The novel cost-benefit function combines the cost of the optical portion with the photoreceptor portion of the eye. These quantities are put on the same footing using a specific (normalized) volume measure, plus an energy factor for the photoreceptor compartment. An optimal information transmission rate then specifies each parameter and resource allocation ratio for a variable total cost. The elegant treatment allows for comparison across a wide range of species and eye types. Simple eyes are found to be several times more efficient across a range of eye parameters than neural superposition eyes. Some trends in eye parameters can be explained by optimal allocation of resources between the optics and photoreceptors compartments of the eye.
Strengths:
Data from a variety of species roughly align with rough trends in the cost analysis, e.g. as a function of expanding the length of the photoreceptor compartment.
New data could be added to the framework once collected, and many species can be compared.
Eyes of different shapes are compared.
Weaknesses:
Detailed quantitative conclusions are not possible given the approximations and simplifying assumptions in the models and weak accounting for trends in the data across eye types.
Comments on revisions:
I have no additional comments for the authors and appreciate the revisions and corrections implemented - I think those changes have improved the clarity of the manuscript and expanded the potential readership for the paper.
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Reviewer #1 (Public review):
The objectives of this research are to understand how key selector transcription factors, Tal1, Gata2, Gata3, determine GABAergic vs glutamatergic neuron fate from the rhombencephalic V2 precursor domain and how their spatiotemporal expression is controlled by upstream regulators. Toward these goals, the authors have generated an impressive array of scRNA, scATAC-seq, and CUT&Tag datasets obtained from dissociated E12.5 ventral R1 dissections. The rV2 was subsetted with well-known markers. The authors use an extensive set of computational approaches to identify temporal patterns of chromatin accessibility, TF motif binding activities (footprints), gene expression and regulatory motifs at the different selector gene loci. These analyses are used to predict upstream regulators, candidate accessible CREs, and DNA binding motifs through which the selectors may be controlled in rV2 by upstream regulators. Further analyses predict auto- and cross-regulatory interactions for maintenance of selector expression and the downstream effectors of alternative transmitter identities controlled by the selectors. The authors have achieved their aim of making predictions about upstream and downstream selector TF regulatory networks; their conclusions and predictions are largely well supported. The work clearly illustrates the daunting gene regulatory complexity likely at play in controlling rV2 transmitter fate.
This is data-rich study and a valuable resource for future hypothesis testing, through perturbation approaches, of the many putative regulators and motifs identified in the study. The strengths of this work are the overall high quality of the datasets and in depth analyses. Through its comprehensive data and predictions, it is likely to have impact in advancing the understanding of GABAergic vs glutamatergic neuron fate decisions. The authors present a "simplified" gene regulatory model. However, the model does not illustrate the complexity of potential stage-specific upstream TF interactions with Tal1 and Vsx2 selector genes uncovered in TF footprinting analyses. While this seems nearly impossible to achieve given the plethora of potential functional TF inputs, the authors should consider assembling a focussed model by selectively illustrating the most robust, evidence-backed upstream TF input predictions, which are considered the strongest candidates for future hypothesis-driven perturbation experiments. It seems Insm1, Sox4, E2f1, Ebf1 and Tead2 TFs might be the strongest upstream candidates for future testing of Tal1 activation given the extensive analyses of their spatiotemporal expression patterns relative to Tal1, presented in Fig 4.
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Reviewer #2 (Public review):
Summary:
In this study, the authors seek to discover putative gene regulatory interactions underlying the lineage bifurcation process of neural progenitor cells in the embryonic mouse anterior brainstem into GABAergic and glutamatergic neuronal subtypes. The authors analyze single-cell RNA-seq and single-cell ATAC-seq datasets derived from the ventral rhombomere 1 of embryonic mouse brainstems to annotate cell types and make predictions or where TFs bind upstream and downstream of the effector TFs using computational methods. They add data on the genomic distributions of some of the key transcription factors, and layer these onto the single cell data to develop a model of the transcription factors interactions that define this fate choice.
Strengths:
The authors use a well-defined fate decision point from brainstem progenitors that can make two very different kinds of neurons. They already know the key TFs for selecting the neuronal type from genetic studies, so they focus their gene regulatory analysis on the mechanisms that are immediately upstream and downstream of these key factors. The authors use a combination of single-cell and bulk sequencing data, prediction and validation, and computation.
Weaknesses:
The study does not go as far as to experimentally test the transcription factor network from their model.
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Reviewer #1 (Public review):
Summary:
In the present manuscript, Mashiko and colleagues describe a novel phenotype associated with deficient SLC35G3, a testis-specific sugar transporter that is important in glycosylation of key proteins in sperm function. The study characterizes a knockout mouse for this gene and the multifaceted male infertility that ensues. The manuscript is well-written and describes novel physiology through a broad set of appropriate assays.
Strengths:
Robust analysis with detailed functional and molecular assays
Weaknesses:
(1) The abstract references reported mutations in human SLC35G3, but this is not discussed or correlated to the murine findings to a sufficient degree in the manuscript. The HEK293T experiments are reasonable and add value, but a more detailed discussion of the clinical phenotype of the known mutations in this gene and whether they are recapitulated in this study (or not) would be beneficial.
(2) Can the authors expand on how this mutation causes such a wide array of phenotypic defects? I am surprised there is a morphological defect, a fertilization defect, and a transit defect. Do the authors believe all of these are present in humans as well?
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Reviewer #2 (Public review):
Summary:
This study characterized the function of SLC35G3, a putative transmembrane UDP-N-acetylglucosamine transporter, in spermatogenesis. They showed that SLC35G3 is testis-specific and expressed in round spermatids. Slc35g3-null males were sterile, but females were fertile. Slc35g3-null males produced a normal sperm count, but sperm showed subtle head morphology. Sperm from Slc35g3-null males have defects in uterotubal junction passage, ZP binding, and oocyte fusion. Loss of SLC35G3 causes abnormal processing and glycosylation of a number of sperm proteins in the testis and sperm. They demonstrated that SLC35G3 functions as a UDP-GlcNAc transporter in cell lines. Two human SLC35G3 variants impaired their transporter activity, implicating these variants in human infertility.
Strengths:
This study is thorough. The mutant phenotype is strong and interesting. The major conclusions are supported by the data. This study demonstrated SLC35G3 as a new and essential factor for male fertility in mice, which is likely conserved in humans.
Weaknesses:
Some data interpretations need to be revised.
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Reviewer #1 (Public review):
Summary:
GPR3 is an orphan receptor that plays a crucial role in central nervous system development and cold-induced thermogenesis, with potential implications for treating neurodegenerative and metabolic diseases. Although previous structural studies of GPR3 have been reported, Qiu et al. presented both active and inactive structures of GPR3 in its dimeric form. Notably, they identified AF64394 as a negative allosteric modulator that binds at the dimerization interface. This interface, primarily formed by transmembrane helices TM5 and TM6, is significantly larger than the dimerization interfaces previously reported for class A GPCRs. The authors further elucidate GPR3's activation mechanism and propose that dimerization may serve as a regulatory feature of GPR3 function. Overall, the study is well-executed, and the conclusions are sound.
Strengths:
Reported a unique dimerization interface of GPR3 and identified AF64394 as a negative allosteric modulator that binds at the dimerization interface.
Weaknesses:
There are some minor issues in the figure presentation.
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Reviewer #2 (Public review):
Qiu et al. present active and inactive state dimeric structures of GPR3 with and without the previously identified inverse agonist AF64394. The manuscript combines cryo-EM processing, mutagenesis studies, and live-cell cAMP measurements to provide insights into the mechanism of action of AF64394 as a negative allosteric modulator of GPR3. All resolved structures show the density of a presumably hydrophobic endogenous, co-purified ligand in the orthosteric receptor binding pocket, supporting previous publications by this and other groups that endogenous lipids are endogenous ligands of GPR3. However, the authors also show that none of the proposed endogenous lipids (e.g., oleoylethanolamide) are able to further increase cAMP in living cells in a GPR3-dependent manner when applied exogenously. These data are in contrast to previous studies, but are of interest to the field as they may suggest that GPR3 expressed in different cell types is already saturated by endogenous lipids.
The overall findings are novel and exciting. GPR3 has not previously been proposed to assemble into a homodimeric complex, and no information has been published on where AF64394 binds to the receptor. Several comparative analyses between GPR3 and its close relatives, GPR6 and GPR12, including live cell experiments with GPR3/6 chimera, provide intriguing mechanistic explanations for the different dimerisation behaviour and activity of AF64394 at this GPCR cluster.
The only weakness of the study is that the population shift towards homodimer induced by AF6439, as suggested by 2D classifications of purified GPR3, is not supported by live cell experiments. The fact that AF64394 reduces GPR3-mediated cAMP production in a concentration-dependent manner may also be due to mechanisms independent of homodimerisation. Therefore, a live cell assay that directly detects dimer formation and/or dissociation upon different stimuli would significantly strengthen the findings of Qiu et al.
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Reviewer #3 (Public review):
Summary:
The manuscript by Qiu and co-workers describes the single-particle cryo-electron microscopy structures of various oligomeric states of the orphan GPCR, GPR3. It describes the monomeric and dimeric structure of a mutant of GPR3 with a modified G-protein complex (miniGs) and then builds on this work to attempt an inactive 'apo' dimer and an allosteric modulator (AF bound dimer structure, by using an ICL3 insertion and stabilizing FAB fragments.
In general, I'm supportive of the work done in this study, and it does indeed provide valuable insight into GPR3 function. It may be that dimerization of certain class A GPCRs may be a means of signalling regulation or perhaps even amplification. However, some of the interpretation of the single particle data needs some extra attention to strengthen the hypothesis presented in the manuscript.
Firstly, I want to thank the authors for providing the unfiltered half-maps and PDB models for careful assessment. During this review, I did my own post-processing of the half-maps and used the resultant maps for careful analysis of models.
So to begin, I understand that the authors didn't model any lipid in the binding orthosteric binding site in any of the maps, but it may be worthwhile to model something in there, as many readers only download coordinates and not the maps.
A more general point about all the maps. In no case were any focussed refinements carried out. As the point of this paper are some of the finer details between active and intermediate states and the effect of an allosteric modulator, masking out hypervariable portions of the structure and doing local Euler searches would most certainly provide richer insights of the details in GPR3 (especially as the BRIL:Fab structures are not of interest). And also, generally, no 3D-variability studies were performed to see if minor differences in, say, TM4/5/6 positions were due to large variation in the single particles or were a stable consensus position.
As for the PFK dimeric structure. It appears to be refined with C2 point group symmetry (which is not mentioned anywhere except in a tiny bit of text in a supplemental figure). Was this also calculated in C1 to assess if there is any difference in either GPR3 protomer? Also, how certain are the authors of the cholesterol positions at the bottom of TM4/5? At lower map thresholds in the PFK dimer structure, one of them appears to be continuous with the orthosteric lipid. It also appears that there are many unmodelled lipids in this structure, and only two were assigned as cholesterol. It appears that many of the unmodelled lipids are forming bridging connections between the GPR3 protomers. Also, it may be worthwhile to provide a table of the key interactions between the protomers (although I note that there was a figure highlighting them).
With the PFK monomer structure, there was weak density for the same cholesterol, which was not modelled in this one; perhaps some commentary on the authors' approach for deciding how to assign density would be helpful. It also appears that the refinement mask was probably a bit tight in this one (something that cryoSPARC is notorious for), and rerefining with a much looser mask around the TM domain may be helpful in resolving the inner lipid leaflet positions.
The Apo structure, I think, I have the most issues with. Firstly, it is not 'apo'. There is definitely unaccounted for density in the orthosteric site. Also, the structure definitely needs a bit more attention. Firstly, masking out the BRIL and FABs would be a good start in helping better resolve the TMD regions, and then even focussing on a single monomer to increase the map interpretability. My major problem here is that, if this is being called 'apo' and inactive, the map doesn't reflect this; also, the TM5/6 does not look to be in a fully inactive position. The map density (at least around one of the protomers) in this region looks to be poorly resolved, most likely due to averaging due to internal motion. I think some 3DVA is certainly warranted here to strengthen the hypothesis that they have solved an 'apo' inactive.
The AF (allosteric modulator) bound structure is of significantly better quality. But again, only AF is modelled, and no lipids are. How are the authors sure? Perhaps some focussed refinements (and changing the Euler Origin to centre it on the AF molecule could be a good start). To this reviewer, at least in one of the protomers, adjacent to the AF position, there is a density that looks very much like the allosteric modulator, so it could even be forming a bridging dimer. Also, some potential assignments of the lipids may enlighten some of the structure-activity relationship of this modulator, as it seems to make as many contacts with surrounding lipids as it does with TM4/5. Also, it may be worthwhile exploring carefully the 3DVA of this data. In our studies (Russel et al.), we noted that the orthosteric lipid appears to ratchet back-and-forth in concert with TM4/5 twisting. Perhaps in the AF bound structure, as it binds at the 'exit' site of the lipid, perhaps it is locking in a specific conformation.
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Reviewer #1 (Public review):
Summary:
D. Fuller et al. set out to study the molecular partners that cooperate with ATG2A, a lipid transfer protein essential for phagophore elongation, during the process of autophagy. Through a series of experiments combining microscopy and biochemistry, the authors identify ARFGAP1 and Rab1A as components of early autophagic membranes, which accumulate at the periphery of aberrant pre-autophagosomal structures induced by loss of ATG2. While ARFGAP1 has no apparent function in autophagy, the authors show that RAB1A is implicated in autophagy, although the mechanisms are not explored in the manuscript.
Strengths:
The work presented by Fuller et al. provides new insights into the composition of early autophagic membranes. The authors provide a series of MS experiments identifying proteins in close proximity to ATG2A, which is a valuable dataset for the field. Furthermore, they show for the first time the interaction between ATG2A and RAB1A, both in fed and starved conditions, which extends the characterisation of the pre-autophagosomal structures observed in ATG2 DKO cells.
Weaknesses:
The authors claim that this study elucidates the role of early secretory membranes in phagophore formation. However, this work is largely observational, which presents compelling evidence on the association between RAB1A GTPase and ATG2A without providing mechanistic insights into the functional relevance of this interaction. It remains unclear whether Rab1A depletion phenocopies ATG2A depletion in terms of autophagy progression or accumulation of pre-autophagosomal structures.
Furthermore, this research is conducted exclusively in HEK293 cells. Including at least one additional cell line would significantly strengthen the main findings (i.e., effects on LC3-II accumulation observed for RAB1A/B knockdown, given the previously published data on this topic).
A notable weakness of this manuscript, in this reviewer's opinion, lies in the discussion of the data in the context of existing literature. The discussion is rather short, mostly focused on the phenotype observed in ATG2 DKO cells. While this phenotype is certainly intriguing, it feels the discussion overlooks some important aspects, as outlined in the comments to the authors.
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Reviewer #2 (Public review):
The mechanisms governing autophagic membrane expansion remain incompletely understood. ATG2 is known to function as a lipid transfer protein critical for this process; however, how ATG2 is coordinated with the broader autophagic machinery and endomembrane systems has remained elusive. In this study, the authors employ an elegant proximity labeling approach and identify two ER-Golgi intermediate compartment (ERGIC)-localized proteins-Rab1 and ARFGAP1-as novel regulators of ATG2 during autophagic membrane expansion.
Their findings support a model in which autophagosome formation occurs within a specialized subdomain of the ER that is enriched in both ER exit sites (ERES) and ERGIC, providing valuable mechanistic insight. The overall study is well-executed and offers an important contribution to our understanding of autophagy.
Specific Comments
(1) Integration with Prior Literature<br /> The data convincingly implicate the ERES-ERGIC interface in autophagosome biogenesis. It would strengthen the manuscript to discuss previous studies reporting ERES and ERGIC remodeling and formation of ERERS-ERGIC contact sites (PMID: 34561617; PMID: 28754694) in the context of the current findings.
(2) Experimental Conditions<br /> In Figures 2A-C and Figure 4, it is unclear how the cells were treated. Were they starved in EBSS? This information should be included in the corresponding figure legends.
(3) LC3 Lipidation vs. Cleavage<br /> In Figure 2A, ARFGAP1 knockdown appears to reduce LC3 lipidation without affecting Halo-LC3 cleavage. Clarifying this observation would help readers better understand the functional specificity of ARFGAP1 in the pathway.
(4) Use of HT-mGFP in Figure 2C<br /> It should be clarified whether the assay in Figure 2C was performed in the presence of HT-mGFP. Explaining the rationale would aid the interpretation of the results.
(5) COPII Inhibition Strategy<br /> The authors used the dominant-active SAR1(H79G) mutant to inhibit COPII function. While this is effective in in vitro budding assays, the GDP-locked mutant SAR1(T39N) has been shown to be more effective in blocking COPII-mediated trafficking in cells. Including SAR1(T39N) in the analysis would provide stronger support for the conclusions.
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Reviewer #3 (Public review):
The manuscript by Fuller et al describes a crosstalk between ARTG2A with components of the early secretory pathway, namely RAB1A and ARFGAP1. They show that ATG2A is recruited to membranes positive for RAB1A, which they also show to interact with ATG2A. In agreement with earlier findings by other groups, silencing RAB1A negatively affects autophagy. While ARFGAP1 was also found on ATG2A-positive membranes, silencing ARFGAP1 had no impact on autophagy. Notably, these ARFGAP1-positive membranes are not Golgi membranes.
The findings are interesting, and in general, the data are of good quality; however, I have outstanding questions. An answer to any of these questions might strengthen the manuscript:
(1) Are the membranes to which ATG2A is recruited a form of ERGIC?
(2) Figure 3A/B: Is it possible to show a better example? The difference is barely detectable by eye. Since immunoblotting is not really a quantitative method, I think that such a weak effect is prone to be wrong. Is there another tool/assay to validate this result?
(3) Is the curvature-sensitive region of ARFGAP1 required for its co-localization with ATG2A?
(4) What does Rab1A do? What is its effector? Or does the GTPase itself remodel the membrane?
(5) What about Arf1? It appears that the role of ARFGAP1 is unrelated to Arf1 and COPI? Thus, one would predict that Arf1 does not localize to these structures and does not affect ATG2A function.
(6) Does ARFGAP1 promote fission of the membrane from its donor compartment?
(7) What are ARFGAP1 and Rab1A recruited to? What is the lipid composition or protein that recruits these two players to regulate autophagy?
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Reviewer #1 (Public review):
Summary:
In this lovely paper, McDermott and colleagues tackle an enduring puzzle in the cognitive neuroscience of perceptual prediction. Though many scientists agree that top-down predictions shape perception, previous studies have yielded incompatible results - with studies showing 'sharpened' representations of expected signals, and others showing a 'dampening' of predictable signals to relatively enhance surprising prediction errors. To deepen the paradox further, it seems like there are good reasons that we would want to see both influences on perception in different contexts.
Here, the authors aim to test one possible resolution to this 'paradox' - the opposing process theory (OPT). This theory makes distinct predictions about how the timecourse of 'sharpening' and 'dampening' effects should unfold. The researchers present a clever twist on a leading-trailing perceptual prediction paradigm, using AI to generate a large dataset of test and training stimuli, so that it is possible to form expectations about certain categories without repeating any particular stimuli. This provides a powerful way of distinguishing expectation effects from repetition effects - a perennial problem in this line of work.
Using EEG decoding, the researchers find evidence to support the OPT. Namely, they find that neural encoding of expected events is superior in earlier time ranges (sharpening-like) followed by a relative advantage for unexpected events in later time ranges (dampening-like). On top of this, the authors also show that these two separate influences may emerge differently in different phases of learning - with superior decoding of surprising prediction errors being found more in early phases of the task, and enhanced decoding of predicted events being found in the later phases of the experiment.
Strengths:
As noted above, a major strength of this work lies in important experimental design choices. Alongside removing any possible influence of repetition suppression mechanisms in this task, the experiment also allows us to see how effects emerge in 'real time' as agents learn to make predictions. This contrasts with many other studies in this area - where researchers 'over-train' expectations into observers to create the strongest possible effects, or rely on prior knowledge that was likely to be crystallised outside the lab.
Weaknesses:
This study reveals a great deal about how certain neural representations are altered by expectation and learning on shorter and longer timescales, so I am loath to describe certain limitations as 'weaknesses'. But one limitation inherent in this experimental design is that, by focusing on implicit, task-irrelevant predictions, there is not much opportunity to connect the predictive influences seen at the neural level to perceptual performance itself (e.g., how participants make perceptual decisions about expected or unexpected events, or how these events are detected or appear).
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Reviewer #2 (Public review):
Summary:
There are two accounts in the literature that propose that expectations suppress activity of neurons that are (a) not tuned to the expected stimulus to increase the signal-to-noise ratio for expected stimuli (sharpening model) or (b) tuned to the expected stimulus to highlight novel information (dampening model). One recent account, the opposing process theory, brings the two models together and suggests that both processes occur, but at different time points: initial sharpening is followed by later dampening of the neural activity of the expected stimulus. In this study, the authors aim to test the opposing process theory in a statistical learning task by applying multivariate EEG analyses and find evidence for the opposing process theory based on the within-trial dynamics.
Strengths:
This study addresses a very timely research question about the underlying mechanisms of expectation suppression. The applied EEG decoding approach offers an elegant way to investigate the temporal characteristics of expectation effects. A strength of the study lies in the experimental design that aims to control for repetition effects, one of the common confounds in prediction suppression studies. The reported results are novel in the field and have the potential to improve our understanding of expectation suppression in visual perception.
Weaknesses:
Although some of the findings are in line with the opposing process theory, especially the EEG results only partly support the hypothesis. While the initial dampening effect occurs in the grand average ERP and in image memory decoding, the expected later sharpening effect is lacking. Moreover, some methodological decisions still remain arbitrary. One of the interesting aspects of the study - prediction decoding - had to be removed due to the fact that it could not be disentangled from category decoding. This weakens the overall scope and impact of the manuscript.
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Reviewer #3 (Public review):
Summary:
In their study McDermott et al. investigate the neurocomputational mechanism underlying sensory prediction errors. They contrast two accounts: representational sharpening and dampening. Representational sharpening suggests that predictions increase the fidelity of the neural representations of expected inputs, while representational dampening suggests the opposite (decreased fidelity for expected stimuli). The authors performed decoding analyses on EEG data, showing that first expected stimuli could be better decoded (sharpening), followed by a reversal during later response windows where unexpected inputs could be better decoded (dampening). These results are interpreted in the context of opposing process theory (OPT), which suggests that such a reversal would support perception to be both veridical (i.e., initial sharpening to increase the accuracy of perception) and informative (i.e., later dampening to highlight surprising, but informative inputs).
Strengths:
The topic of the present study is of significant relevance for the field of predictive processing. The experimental paradigm used by McDermott et al. is well designed, allowing the authors to avoid several common confounds in investigating predictions, such as stimulus familiarity and adaptation. The introduction of the manuscript provides a well written summery of the main arguments for the two accounts of interest (sharpening and dampening), as well as OPT. Overall, the manuscript serves as a good overview of the current state of the field.
Weaknesses:
In my opinion some details of the methods, results and manuscript raise some doubts about the reliability of the reported findings. Key concerns are:
(1) In the previous round of comments, I noted that: "I am not fully convinced that Figures 3A/B and the associated results support the idea that early learning stages result in dampening and later stages in sharpening. The inference made requires, in my opinion, not only a significant effect in one-time bin and the absence of an effect in other bins. Instead to reliably make this inference one would need a contrast showing a difference in decoding accuracy between bins, or ideally an analysis not contingent on seemingly arbitrary binning of data, but a decrease (or increase) in the slope of the decoding accuracy across trials. Moreover, the decoding analyses seem to be at the edge of SNR, hence making any interpretation that depends on the absence of an effect in some bins yet more problematic and implausible". The authors responded: "we fitted a logarithmic model to quantify the change of the decoding benefit over trials, then found the trial index for which the change of the logarithmic fit was < 0.1%. Given the results of this analysis and to ensure a sufficient number of trials, we focused our further analyses on bins 1-2". However, I do not see how this new analysis addresses the concern that the conclusion highlights differences in decoding performance between bins 1 and 2, yet no contrast between these bins are performed. While I appreciate the addition of the new model, in my current understanding it does not solve the problem I raised. I still believe that if the authors wish to conclude that an effect differs between two bins they must contrast these directly and/or use a different appropriate analysis approach.
Relatedly, the logarithmic model fitting and how it justifies the focus on analysis bin 1-2 needs to be explained better, especially the rationale of the analysis, the choice of parameters (e.g., why logarithmic, why change of logarithmic fit < 0.1% as criterion, etc), and why certain inferences follow from this analysis. Also, the reporting of the associated results seems rather sparse in the current iteration of the manuscript.
(2) A critical point the authors raise is that they investigate the buildup of expectations during training. They go on to show that the dampening effect disappears quickly, concluding: "the decoding benefit of invalid predictions [...] disappeared after approximately 15 minutes (or 50 trials per condition)". Maybe the authors can correct me, but my best understanding is as follows: Each bin has 50 trials per condition. The 2:1 condition has 4 leading images, this would mean ~12 trials per leading stimulus, 25% of which are unexpected, so ~9 expected trials per pair. Bin 1 represents the first time the participants see the associations. Therefore, the conclusion is that participants learn the associations so rapidly that ~9 expected trials per pair suffice to not only learn the expectations (in a probabilistic context) but learn them sufficiently well such that they result in a significant decoding difference in that same bin. If so, this would seem surprisingly fast, given that participants learn by means of incidental statistical learning (i.e. they were not informed about the statistical regularities). I acknowledge that we do not know how quickly the dampening/sharpening effects develop, however surprising results should be accompanied with a critical evaluation and exceptionally strong evidence (see point 1). Consider for example the following alternative account to explain these results. Category pairs were fixed across and within participants, i.e. the same leading image categories always predicted the same trailing image categories for all participants. Some category pairings will necessarily result in a larger representational overlap (i.e., visual similarity, etc.) and hence differences in decoding accuracy due to adaptation and related effects. For example, house barn will result in a different decoding performance compared to coffee cup barn, simply due to the larger visual and semantic similarity between house and barn compared to coffee cup and barn. These effects should occur upon first stimulus presentation, independent of statistical learning, and may attenuate over time e.g., due to increasing familiarity with the categories (i.e., an overall attenuation leading to smaller between condition differences) or pairs.
(3) In response to my previous comment, why the authors think their study may have found different results compared to multiple previous studies (e.g. Han et al., 2019; Kumar et al., 2017; Meyer and Olson, 2011), particularly the sharpening to dampening switch, the authors emphasize the use of non-repeated stimuli (no repetition suppression and no familiarity confound) in their design. However, I fail to see how familiarity or RS could account for the absence of sharpening/dampening inversion in previous studies.
First, if the authors argument is about stimulus novelty and familiarity as described by Feuerriegel et al., 2021, I believe this point does not apply to the cited studies. Feuerriegel et al., 2021 note: "Relative stimulus novelty can be an important confound in situations where expected stimulus identities are presented often within an experiment, but neutral or surprising stimuli are presented only rarely", which indeed is a critical confound. However, none of the studies (Han et al., 2019; Richter et al., 2018; Kumar et al., 2017; Meyer and Olson, 2011) contained this confound, because all stimuli served as expected and unexpected stimuli, with the expectation status solely determined by the preceding cue. Thus, participants were equally familiar with the images across expectation conditions.
Second, for a similar reason the authors argument for RS accounting for the different results does not hold either in my opinion. Again, as Feuerriegel et al. 2021 correctly point out: "Adaptation-related effects can mimic ES when the expected stimuli are a repetition of the last-seen stimulus or have been encountered more recently than stimuli in neutral expectation conditions." However, it is critical to consider the precise design of previous studies. Taking again the example of Han et al., 2019; Kumar et al., 2017; Meyer and Olson, 2011. To my knowledge none of these studies contained manipulations that would result in a more frequent or recent repetition of any specific stimulus in the expected compared to unexpected condition. The crucial manipulation in all these previous studies is not that a single stimulus or stimulus feature (which could be subject to familiarity or RS) determines the expectation status, but rather the transitional probability (i.e. cue-stimulus pairing) of a particular stimulus given the cue. Therefore, unless I am missing something critical, simple RS seems unlikely to differ between expectation condition in the previous studies and hence seems implausible to account for differences in results compared to the current study.
Moreover, studies cited by the authors (e.g. Todorovic & de Lange, 2012) showed that RS and ES are separable in time, again making me wonder how avoiding stimulus repetition should account for the difference in the present study compared to previous ones. I am happy to be corrected in my understanding, but with the currently provided arguments by the authors I do not see how RS and familiarity can account for the discrepancy in results.
I agree with the authors that stimulus familiarity is a clear difference compared to previous designs, but without a valid explanation why this should affect results I find this account rather unsatisfying. I see the key difference in that the authors manipulated category predictability, instead of exemplar prediction - i.e. searching for a car instead of your car. However, if results in support of OPT would indeed depend on using novel images (i.e. without stimulus repetition), would this not severely limit the scope of the account and hence also its relevance? Certainly, the account provided by the authors casts the net wider and tries to explain visual prediction. Relatedly, if OPT only applies during training, as the authors seem to argue, would this again not significantly narrow the scope of the theory? Combined these two caveats would seem to demote the account from a general account of prediction and perception to one about perception during very specific circumstances. In my understanding the appeal of OPT is that it accounts for multiple challenges faced by the perceptual system, elegantly integrating them into a cohesive framework. Most of this would be lost by claiming that OPT's primary prediction would only apply to specific circumstances - novel stimuli during learning of predictions. Moreover, in the original formulation of the account, as outlined by Press et al., I do not see any particular reason why it should be limited to these specific circumstances. This does of course not mean that the present results are incorrect, however it does require an adequate discussion and acknowledgement in the manuscript.
Impact:
McDermott et al. present an interesting study with potentially impactful results. However, given my concerns raised in this and the previous round of comments, I am not entirely convinced of the reliability of the results. Moreover, the difficulty of reconciling some of the present results with previous studies highlights the need for more convincing explanations of these discrepancies and a stronger discussion of the present results in the context of the literature.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The issue of how the brain can maintain the serial order of presented items in working memory is a major unsolved question in cognitive neuroscience. It has been proposed that this serial order maintenance could be achieved thanks to periodic reactivations of different presented items at different phases of an oscillation, but the mechanisms by which this could be achieved by brain networks, as well as the mechanisms of read-out, are still unclear. In an influential 2008 paper, the authors have proposed a mechanism by which a recurrent network of neurons could maintain multiple items in working memory, thanks to `population spikes' of populations of neurons encoding for the different items, occurring at alternating times. These population spikes occur in a specific regime of the network and are a result of synaptic facilitation, an experimentally observed type of synaptic short-term dynamics with time scales of order hundreds of ms.
In the present manuscript, the authors extend their model to include another type of experimentally observed short-term synaptic plasticity termed synaptic augmentation, which operates on longer time scales on the order of 10s. They show that while a network without augmentation loses information about serial order, augmentation provides a mechanism by which this order can be maintained in memory thanks to a temporal gradient of synaptic efficacies. The order can then be read out using a read-out network whose synapses are also endowed with synaptic augmentation. Interestingly, the read-out speed can be regulated using background inputs.
Strengths:
This is an elegant solution to the problem of serial order maintenance that only relies on experimentally observed features of synapses. The model is consistent with a number of experimental observations in humans and monkeys. The paper will be of interest to a broad readership, and I believe it will have a strong impact on the field.
Weaknesses:
(1) The network they propose is extremely simple. This simplicity has pros and cons: on the one hand, it is nice to see the basic phenomenon exposed in the simplest possible setting. On the other hand, it would also be reassuring to check that the mechanism is robust when implemented in a more realistic setting, using, for instance, a network of spiking neurons similar to the one they used in the 2008 paper. The more noisy and heterogeneous the setting, the better.
(2) One major issue with the population spike scenario is that (to my knowledge) there is no evidence that these highly synchronized events occur in delay periods of working memory experiments. It seems that highly synchronized population spikes would imply (a) a strong regularity of spike trains of neurons, at odds with what is typically observed in vivo (b) high synchronization of neurons encoding for the same item (and also of different items in situations where multiple items have to be held in working memory), also at odds with in vivo recordings that typically indicate weak synchronization at best. It would be nice if the authors at least mention this issue, and speculate on what could possibly bridge the gap between their highly regular and synchronized network, and brain networks that seem to lie at the opposite extreme (highly irregular and weakly synchronized). Of course, if they can demonstrate using a spiking network simulation that they can bridge the gap, even better.
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Reviewer #2 (Public review):
In this manuscript, the authors present a model to explain how working memory (WM) encodes both existence and timing simultaneously using transient synaptic augmentation. A simple yet intriguing idea.
The model presented here has the potential to explain what previous theories like 'active maintenance via attractors' and 'liquid state machine' do not, and describe how novel sequences are immediately stored in WM. Altogether, the topic is of great interest to those studying higher cognitive processes, and the conclusions the authors draw are certainly thought-provoking from an experimental perspective. However, several questions remain that need to be addressed.
The study relates to the well-known computational theory for working memory, which suggests short-term synaptic facilitation is required to maintain working memory, but doesn't rely on persistent spiking. This previous theory appears similar to the proposed theory, except for the change from facilitation to augmentation. A more detailed explanation of why the authors use augmentation instead of facilitation in this paper is warranted: is the facilitation too short to explain the whole process of WM? Can the theory with synaptic facilitation also explain the immediate storage of novel sequences in WM?
In Figure 1, the authors mention that synaptic augmentation leads to an increased firing rate even after stimulus presentation. It would be good to determine, perhaps, what the lowest threshold is to see the encoding of a WM task, and whether that is biologically plausible.
In the middle panel of Figure 4, after 15-16 sec, when the neuronal population prioritizes with the second retro-cue, although the second retro-cue item's synaptic spike dominates, why is the augmentation for the first retro-cue item higher than the second-cue augmentation until the 20 sec?
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The authors introduce a novel algorithm for the automatic identification of long-range axonal projections. This is an important problem as modern high-throughput imaging techniques can produce large amounts of raw data, but identifying neuronal morphologies and connectivities requires large amounts of manual work. The algorithm works by first identifying points in three-dimensional space corresponding to parts of labelled neural projections, these are then used to identify short sections of axon using an optimisation algorithm and the prior knowledge that axonal diameters are relatively constant. Finally, a statistical model that assumes axons tend to be smooth is used to connect the sections together into complete and distinct neural trees. The authors demonstrate that their algorithm is far superior to existing techniques, especially when a dense labelling of the tissue means that neighbouring neurites interfere with the reconstruction. Despite this improvement, however, the accuracy of reconstruction remains below 90%, so manual proof-reading is still necessary to produce accurate reconstructions of axons.
Strengths:
The new algorithm combines local and global information to make a significant improvement on the state-of -the-art for automatic axonal reconstruction. The method could be applied more broadly and might have applications to reconstructions of electron microscopy data, where similar issues of high-throughput imaging and relatively slow or inaccurate reconstruction remain.
Weaknesses:
There are three weaknesses with the algorithm and manuscript.
(1) The best reconstruction accuracy is below 90%, which does not fully solve the problem of needing manual proof-reading.
(2) The 'minimum information flow tree' model the authors use to construct connected axonal trees has the potential to bias data collection. In particular, the assumption that axons should always be as smooth as possible is not always correct. This is a good rule-of-thumb for reconstructions, but real axons in many systems can take quite sharp turns and this is also seen in the data presented in the paper (Fig 1C). I would like to see explicit acknowledgement of this bias in the current manuscript and ideally a relaxation of this rule in any later versions of the algorithm.
(3) The writing of the manuscript is not always as clear as it could be. The manuscript would benefit from careful copy editing for language, and the Methods section in particular should be expanded to more clearly explain what each algorithm is doing. The pseudo code of the Supplemental Information could be brought into the Methods if possible as these algorithms are so fundamental to the manuscript.
Comments on revisions: I have no further comments or recommendations.
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Reviewer #2 (Public review):
The authors have addressed my comments in this revised version of their manuscript. PointTree is an improved method for the reconstruction of neuronal anatomy that will be useful for neuroscientists.
In this manuscript, Cai et al. introduce PointTree, a new automated method for the reconstruction of complex neuronal projections. This method has the potential to drastically speed up the process of reconstructing complex neurites. The authors use semi-automated manual reconstruction of neurons and neurites to provide a 'ground-truth' for comparison between PointTree and other automated reconstruction methods. The reconstruction performance is evaluated for precision, recall and F1-score and positions. The performance of PointTree compared to other automated reconstruction methods is impressive based on these 3 criteria.
As an experimentalist, I will not comment on the computational aspects of the manuscript. Rather, I am interested in how PointTree's performance decrease in noisy samples. This is because many imaging datasets contain some level of background noise for which the human eye appears essential for accurate reconstruction of neurites. Although the samples presented in Figure 5 represent an inherent challenge for any reconstruction method, the signal to noise ratio is extremely high (also the case in all raw data images in the paper). It would be interesting to see how PointTree's performance change in increasingly noisy samples, and for the author to provide general guidance to the scientific community as to what samples might not be accurately reconstructed with PointTree.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The authors tried to identify the relationships between gut microbiota, lipid metabolites and the host in type 2 diabetes (T2DM) by using spontaneously developed T2DM in macaques, considered among the best human models.
Strengths:
The authors compared comprehensively the gut microbiota, plasma fatty acids between spontaneous T2DM and the control macaques, and tried verified the results with macaques in high-fat diet-fed mice model.
Weaknesses:
The observed multi-omics on macaques can be done on humans, which weakens the conclusion of the manuscript, unless the observation/data on macaques could cover during the onset of T2DM that would be difficult to obtain from humans.<br /> Regarding the metabolomic analysis on fatty acids, the authors did not include the results obtained form the macaque fecal samples which should be important considering the authors claimed the importance of gut microbiota in the pathogenesis of T2DM. Instead, the authors measured palmitic acid in the mouse model and tried to validate their conclusions with that.
In murine experiments, palmitic acid-containing diet were fed to mice to induce diabetic condition, but this does not mimic spontaneous T2DM in macaques, since the authors did not measure in macaque feces (or at least did not show the data from macaque feces of) palmitic acid or other fatty acids; instead, they assumed from blood metabolome data that palmitic acid would be absorbed from the intestine to affect the host metabolism, and added palmitic acid in the diet in mouse experiments. Here involves the probable leap of logic to support their conclusions and title of the study.
In addition, the authors measured omics data after, but not before, the onset of spontaneous T2DM of macaques. This can reveal microbiota dysbiosis driven purely by disease progression, but does not support the causative effect of gut microbiota on T2DM development that the authors claims.
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www.biorxiv.org www.biorxiv.org
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Reviewer #2 (Public review):
In this manuscript, the authors investigated how partial loss of SynGap1 affects inhibitory neurons derived from the MGE in the auditory cortex, focusing on their synaptic inputs and excitability. While haplo-insufficiently of SynGap1 is known to lead to intellectual disabilities, the underlying mechanisms remain unclear.
This is the third revision of the manuscript that has improved further, and the main issues were addressed. Specifically, the Authors addressed the contradiction of mEPSC and sEPSC data of the previous version by new experiments and revision of the manuscript text. While alternative explanations are still possible, the new control experiments provide necessary background for reproducibility and the manuscript text puts the observations in the right context. Furthermore, the manuscript now appropriately emphasizes that anatomical analysis was restricted to somatic excitatory synapses. Thus, the readers will be aware of the potential limitations of these measurements.
Strengths:
The questions are novel and relevant. Most of the issues in the experimental design are solved or answered.
Weaknesses:
Despite the interesting and novel questions, there are potential alternative interpretations of the observations, but these cannot be addressed within the breadth of a single paper.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Cheong et al. use a synapse-resolution wiring map of the fruit fly nerve cord to comprehensively investigate circuitry between descending neurons (DNs) from the brain and motor neurons (MNs) that enact different behaviours. These neurons were painstakingly identified, categorised, and linked to existing genetic driver lines; this allows the investigation of circuitry to be informed by the extensive literature on how flights walk, fly, and escape from looming stimuli. New motifs and hypotheses of circuit function were presented. This work will be a lasting resource for those studying nerve cord function.
Strengths:
The authors present an impressive amount of work in reconstructing and categorising the neurons in the DN to MN pathways. There is always a strong link between the circuitry identified and what is known in the literature, making this an excellent resource for those interested in connectomics analysis or experimental circuits neuroscience. Because of this, there are many testable hypotheses presented with clear predictions, which I expect will result in many follow-up publications. Most MNs were mapped to the individual muscles that they innervate by linking this connectome to pre-existing light microscopy datasets. When combined with past fly brain connectome datasets (Hemibrain, FAFB) or future ones, there is now a tantalising possibility of following neural pathways from sensory inputs to motor neurons and muscle.
Weaknesses:
As with all connectome datasets, the sample size is low, limiting statistical analyses. Readers should keep this in mind, but note that this is the current state-of-the-art. Some figures are weakened by relying too much on depictions of wiring diagrams without additional quantification of connectivity. Readers may find the length of this work challenging, particularly the initial anatomical descriptions of the dataset, which span many figures and may not be of interest to those outside of the subfield.
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Reviewer #2 (Public review):
Summary:
In Cheong et al., the authors analyze a new motor system (ventral nerve cord) connectome of Drosophila. Through proofreading, cross-referencing with another female VNC connectome, they define key features of VNC circuits with a focus on descending neurons (DNs), motor neurons (MNs), and local interneuron circuits. They define DN tracts, MNs for limb and wing control and their nerves (although their sample suffers for a subset of MNs). They establish connectivity between DNs and MNs (minimal). They perform topological analysis of all VNC neurons including interneurons. They focus specifically on identifying core features of flight circuits (control of wings and halteres), leg control circuits with a focus on walking rather than other limbed behaviors (grooming, reaching, etc.), intermediate circuits like those for escape (GF). They put these features in the context of what is known or has been posited about these various circuits.
Strengths
Some strengths of the manuscript include the matching of new DN and MN types to light microscopy, including serial homology of leg motor neurons. This is a valuable contribution that will certainly open up future lines of experimental work. As well, the analysis of conserved connectivity patterns within each leg neuromere and interconnecting connectivity patterns between neuromeres will be incredibly valuable. The standard leg connectome is very nice. Finally, the finding of different connectivity statistics (degrees of feedback) in different neuropils is quite interesting and will stimulate future work aimed at determining its functional significance.
Weaknesses
The degradation of many motor neurons is unfortunate. Figure 5 supplement 1 shows that roughly 50% of the leg motor neurons have significantly compromised connectivity data, whereas for non-leg motor neurons, few seem to be compromised. As well, the infomap communities don't seem to be so well controlled/justified. Community detection can be run on any graph - why should I believe that the VNC graph is actually composed of discrete communities? Perhaps this comes from a lack of familiarity with the infomap algorithm, but I imagine most readers will be similarly unfamiliar with it, so more work should be done to demonstrate the degree to which these communities are really communities that connect more within than across communities.
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www.biorxiv.org www.biorxiv.org
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Reviewer #2 (Public review):
This manuscript describes the role of the production of c-di-AMP on the chlamydial developmental cycle. The main findings remain the same. The authors show that overexpression of the dacA-ybbR operon results in increased production of c-di-AMP and early expression of transitionary and late genes. The authors also knocked down the expression of the dacA-ybbR operon and reported a modest reduction in the expression of both hctA and omcB. The authors conclude with a model suggesting the amount of c-di-AMP determines the fate of the RB, continued replication, or EB conversion.
Overall, this is a very intriguing study with important implications however, the data is very preliminary, and the model is very rudimentary. The data support the observation that dramatically increased c-di-AMP has an impact on transitionary gene expression and late gene expression suggesting dysregulation of the developmental cycle. This effect goes away with modest changes in c-di-AMP (detaTM-DacA vs detaTM-DacA (D164N)). However, the model predicts that low levels of c-di-AMP delays EB production is not not well supported by the data. If this prediction were true then the growth rate would increase with c-di-AMP reduction and the data does not show this. The levels of c-di-AMP at the lower levels need to be better validated as it seems like only very high levels make a difference for dysregulated late gene expression. However, on the low end it's not clear what levels are needed to have an effect as only DacAopMut and DacAopKD show any effects on the cycle and the c-di-AMP levels are only different at 24 hours.
The authors responded to reviewers' critiques by adding the overexpression of DacA without the transmembrane region. This addition does not really help their case. They show that detaTM-DacA and detaTM-DacA (D164N) had the same effects on c-di-AMP levels but the figure shows no effects on the developmental cycle.
Describing the significance of the findings:
The findings are important and point to very exciting new avenues to explore the important questions in chlamydial cell form development. The authors present a model that is not quantified and does not match the data well.
Describing the strength of evidence:
The evidence presented is incomplete. The authors do a nice job of showing that overexpression of the dacA-ybbR operon increases c-di-AMP and that knockdown or overexpression of the catalytically dead DacA protein decreases the c-di-AMP levels. However, the effects on the developmental cycle and how they fit the proposed model are less well supported.
Overall this is a very intriguing finding that will require more gene expression data, phenotypic characterization of cell forms, and better quantitative models to fully interpret these findings.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Advances in machine vision and computer learning have meant that there are now state-of-the-art and open-source toolboxes that allow for animal pose estimation and action recognition. These technologies have the potential to revolutionize behavioral observations of wild primates but are often held back by labor intensive model training and the need for some programming knowledge to effectively leverage such tools. The study presented here by Fuchs et al unveils a new framework (ASBAR) that aims to automate behavioral recognition in wild apes from video data. This framework combines robustly trained and well tested pose estimate and behavioral action recognition models. The framework performs admirably at the task of automatically identifying simple behaviors of wild apes from camera trap videos of variable quality and contexts. These results indicate that skeletal-based action recognition offers a reliable and lightweight methodology for studying ape behavior in the wild and the presented framework and GUI offer an accessible route for other researchers to utilize such tools.
Given that automated behavior recognition in wild primates will likely be a major future direction within many subfields of primatology, open-source frameworks, like the one presented here, will present a significant impact on the field and will provide a strong foundation for others to build future research upon.
Strengths:
Clearly articulated the argument as to why the framework was needed and what advantages it could convey to the wider field.
For a very technical paper it was very well written. Every aspect of the framework the authors clearly explained why it was chosen and how it was trained and tested. This information was broken down in a clear and easily digestible way that will be appreciated by technical and non-technical audiences alike.
The study demonstrates which pose estimation architectures produce the most robust models for both within context and out of context pose estimates. This is invaluable knowledge for those wanting to produce their own robust models.
The comparison of skeletal-based action recognition with other methodologies for action recognition are helpful in contextualizing the results.
Weaknesses:
While I note that this is a paper most likely aimed at the more technical reader, it will also be of interest to a wider primatological readership, including those who work extensively in the field. When outlining the need for future work I felt the paper offered almost exclusively very technical directions. This may have been a missed opportunity to engage the wider readership and suggest some practical ways those in the field could collect more ASBAR friendly video data to further improve accuracy.
Comments on latest version:
I think the new version is an improvement and applaud the authors on a well-written article that conveys some very technical details excellently. The authors have addressed my initial comments about reaching out to a wider, sometimes less technical, primatological audience by encouraging researchers to create large annotated datasets and make these publicly accessible. I also agree that fostering interdisciplinary collaboration is the best way to progress this field of research. These additions have certainly strengthened the paper but I still think some more practical advice for the actual collection of high-quality training data used to improve the pose estimates and behavioral classification in tough out-of-context environments could have been added. This doesn't detract from the quality of the paper though.
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Reviewer #2 (Public review):
Fuchs et al. propose a framework for action recognition based on pose estimation. They integrate functions from DeepLabCut and MMAction2, two popular machine learning frameworks for behavioral analysis, in a new package called ASBAR.
They test their framework by:
Running pose estimation experiments on the OpenMonkeyChallenge (OMC) dataset (the public train + val parts) with DeepLabCut
Also annotating around 320 images pose data in the PanAf dataset (which contains behavioral annotations). They show that the ResNet-152 model generalizes best from the OMC data to this out-of-domain dataset.
They then train a skeleton-based action recognition model on PanAf and show that the top-1/3 accuracy is slightly higher than video-based methods
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
This work by Ding et al uses agent-based simulations to explore the role of the structure of molecular motor myosin filaments in force generation in cytoskeletal structures. The focus of the study is on disordered actin bundles which can occur in the cell cytoskeleton and can be investigated with in vitro purified protein experiments. A key finding is that the force generation depends on the number of myosin motor heads and the spatial distribution of the myosin thick filaments in relation to passive crosslinkers.
Strengths:
The work develops a model where the detailed structure of the myosin motor filaments with multiple heads is represented. This allows the authors to test the dependence of myosin-generated forces on the number of myosin heads and their spatial distribution.
The work highlights that forces from multiple myosin motors within a disordered actin bundle may not simply add up, but depend on their spatial distribution in relation to passive crosslinkers.
This may explain prior experimental observations in in vitro reconstituted actomyosin bundles that the tension developed in the bundle was proportional to the number of myosin motor heads per filament rather than the number of myosin filaments. More generally, this type of modeling can guide fundamental understanding of the relationship between structure and mechanical force production.
Weaknesses:
The work focuses on the structure of myosin filaments but ignores other processes that may determine contractility of actomyosin structures such as the dynamics of crosslinker binding/unbinding and actin polymerization/depolymerization.
The authors did not vary the relative concentration of myosin motors and passive crosslinkers. This would have revealed interesting competing effects between motor and crosslink density and distribution, that their model and other studies suggest are important.
Given the above factors and the lack of direct quantitative comparisons with the experiment, the physiological significance of the work remains hard to ascertain.
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Reviewer #2 (Public review):
Summary:
In this study, the authors use a mechanical model to investigate how the geometry and deformations of myosin II filaments influence their force generation. They introduce a force generation efficiency that is defined as the ratio of the total generated force and the maximal force that the motors can generate. By changing the architecture of the myosin II filaments, they study the force generation efficiency in different systems: two filaments, a disorganized bundle, and a 2D network. In the simple two-filament systems, they found that in the presence of actin cross-linking proteins motors cannot add up their force because of steric hindrances. In the disorganized bundle, the authors identified a critical overlap of motors for cooperative force generation. This overlap is also influenced by the arrangement of the motor on the filaments and influenced by the length of the bare zone between the motor heads.
Strengths:
The strength of the study is the identification of organizational principles in myosin II filaments that influence force generation. It provides a complementary mechanistic perspective on the operation of these motor filaments. The force generation efficiency and the cooperative overlap number are quantitative ways to characterize the force generation of molecular motors in clusters and between filaments. These quantities and their conceptual implications are most likely also applicable in other systems.
Weaknesses:
The detailed model that the authors present relies on over 20 numerical parameters that are listed in the supplement. Because of this vast number of parameters, it is not clear how general the findings are. On the other hand, it was not obvious how specific the model is to myosin II, meaning how well it can describe experimental findings or make measurable predictions. Although the authors partially addressed this point in the revisions, I still think it is not easy to see what are the fundamental principles that govern the behavior and how they could be different for different motor proteins.
The model seems to be quantitative, but the interpretation and connection to real experiments is rather qualitative in my point of view.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The authors aim to explore how interdisciplinarity and internationalization-two increasingly prominent characteristics of scientific publishing-have evolved over the past century. By constructing entropy-based indices from a large-scale bibliometric dataset (OpenAlex), they examine both long-term trends and recent dynamics in these two dimensions across a selection of leading disciplinary and multidisciplinary journals. Their goal is to identify field-specific patterns and structural shifts that can inform our understanding of how science has become more globally collaborative and intellectually integrated.
Strengths and Weaknesses:
The paper's primary strength lies in its comprehensive temporal scope and use of a rich, openly available dataset covering over 56 million articles. The interdisciplinary and internationalization indices are well-founded and allow meaningful comparisons across fields and time. Moreover, the distinction between disciplinary and multidisciplinary journals adds valuable nuance. However, some methodological choices, such as the use of a 5-year sliding window to compute trend values, are insufficiently justified and under-explained. The paper also does not fully address disparities in data coverage across disciplines and time, which may affect the reliability of historical comparisons. Finally, minor issues in grammar and clarity reduce the overall polish of the manuscript.
Evaluation of Findings:
Overall, the authors have largely succeeded in achieving their stated aims. The findings-such as the sharp rise in internationalization in fields like Physics, and the divergence in interdisciplinarity trends across disciplines-are clearly presented and generally well-supported by the data. The authors effectively demonstrate that scientific journals have not followed a uniform trajectory in terms of structural evolution. However, greater clarity in trend estimation methods and better acknowledgment of dataset limitations would help to further substantiate the conclusions and enhance their generalizability.
Impact and Relevance:
This study makes a timely and meaningful contribution to the fields of scientometrics, sociology of science, and science policy. Its combination of scale, historical depth, and field-level comparison offers a useful framework for understanding changes in scientific publishing practices. The entropy-based indicators are simple yet flexible, and the use of open bibliometric data enhances reproducibility and accessibility for future research. Policymakers, journal editors, and researchers interested in publication dynamics will likely find this work informative, and its methods could be applied or extended to other structural dimensions of scholarly communication.
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Reviewer #2 (Public review):
Summary:
This paper uses large-scale publication data to examine the dynamics of interdisciplinarity and international collaborations in research journals. The main finding is that interdisciplinarity and internationalism have been increasing over the past decades, especially in prestigious general science journals.
Strengths:
The paper uses a state-of-the-art large-scale publication database to examine the dynamics of interdisciplinarity and internationalism. The analyses span over a century and in major scientific fields in natural sciences, engineering, and social sciences. The study is well designed and has provided a range of robustness tests to enhance the main findings. The writing is clear and well organized.
Weaknesses:
While the research provides interesting perspectives for the reader to learn about the trends of journal preferences, I have a few points for the authors to consider that might help strengthen their work.
The first thing that comes to mind is the epistemic mechanism of the study. Why should there be a joint discussion combining internationalism and interdisciplinarity? While internationalism is the tendency to form multinational research teams to work on research projects, interdisciplinarity refers to the scope and focus of papers that draw inspiration from multiple fields. These concepts may both fall into the realm of diversity, but it remains unclear if there is any conceptual interplay that underlies the dynamics of their increase in research journals.
It is also unclear why internationalization is increasing. Although the authors have provided a few prominent examples in physics, such as CERN and LAGO, which are complex and expensive experimental facilities that demand collective efforts and investments from the global scientific community, whether some similar concerns or factors drive the growth of internationalism in other fields remains unknown. I can imagine that these concerns do not always apply in many fields, and the authors need to come up with some case studies in diverse fields with some sociological theory to support their empirical findings.
The authors use Shannon entropy as a measure of diversity for both internationalism and interdisciplinarity. However, entropy may fail to account for the uneven correlations between fields, and the range of value chances when the number of categories changes. The science of science and scientometrics community has proposed a range of diversity indicators, such as the Rao-Stirling index and its derivatives. One obvious advantage of the RS index is that it explicitly accounts for the heterogeneous connections between fields, and the value ranges from 0 to 1. Using more state-of-the-art metrics to quantify interdisciplinarity may help strengthen the data analytics.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
This study uses optogenetics to activate CA3, while recording from CA1 neurons and characterizing the excitation/inhibition (E/I) balance. They observe use-dependent alterations in the E/I balance as a result of STP, and they develop a model to describe these observations. This is a very ambitious paper that deals with many issues using both experimental and modeling approaches.
Strengths:
This paper examines important principles regarding the manner in which synaptic circuitry and use-dependent synaptic plasticity can transform inputs and perform computations.
Weaknesses:
The use of selective ChR2 expression in CA3 cells is a good approach, but there are numerous issues that cause concern regarding the applicability of their slice recordings to physiological conditions and that make some aspects of their results difficult to interpret. Experiments are not performed under physiological conditions (high external calcium and low temperature), which makes the interpretation of their findings difficult. In addition, the reliability of stimulating action potentials in CA3 pyramidal cells needs to be determined, particularly during high-frequency trains. If it is unreliable, there are alternative approaches that might prove to be superior, such as the use of somatically targeted ChR2. In addition, a clearer, more detailed discussion of their model that distinguishes it from previous modeling studies would be helpful (and would make it seem less incremental).
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Reviewer #2 (Public review):
Summary:
The authors investigate EI balance in the CA3-CA1 projections, emphasizing synaptic depletion and the implied rebalancing of excitatory and inhibitory projections onto a single CA1 Pyramidal cell. They present physiological results with optical stimulation in CA3 and measuring various response features in CA1, showing signatures consistent with the adjustment of EI balance. In particular, the authors emphasize a transient effect where the neuron escapes from EI balance, which can be used for mismatch detection. They partially replicate these results in a computational model that looks at detailed properties of synaptic plasticity in CA1.
Strengths:
The authors provide compelling evidence that non-specific modulation of synaptic plasticity, combined with their differential effects on excitatory and inhibitory neurons, can be used by CA1 excitatory neurons to detect changes in the population activity of CA3 neurons. Indeed, they provide insight into the potential computational role of transient EI imbalance.
Weaknesses:
The authors observe that "little is known about how EI balance itself evolves dynamically due to activity-driven plasticity in sparsely active networks." This is an overstatement, or better an understatement, given the extensive literature on EI balance (e.g. Wen W, Turrigiano GG. Keeping Your Brain in Balance: Homeostatic Regulation of Network Function. Ann Rev Neurosci. 2024. https://doi.org/10.1146/annurev-neuro-092523-110001 PMID:38382543). This way of framing the question does a disservice to the field and fails to contextualize the current research properly.
The evidence is incomplete because the authors do not show a specific relationship between synaptic change in CA1 and EI balance adjustment, i.e., the alternative could be that this is an unspecific effect unrelated to the specific regulation of EI balance and its functional role in the hippocampus and the cortex. Indeed, the paper drifts from addressing EI balance to elucidating the mismatch detection. The second shortcoming is that they do not show that the stimulation of the CA3 neurons occurs in a physiologically realistic regime, nor do they analyze what the impact will be of the excitatory transient in "mismatch detection", and CA1, when this would occur at the level of the whole population, i.e., the physiological impossibility of triggering uncontrolled chaotic excitatory responses. In particular, when we consider CA3 as an attractor memory system, the range of deviations (mismatches) that a CA1 neuron can be exposed to and detect, given the model presented in this paper, might be below those generated due to CA3 pattern-completion dynamics. In addition, the match between the model and the physiological results is not fully quantified, leaving it to the reader to make a leap of faith.
In addition, the manuscript suffers from poor analysis and presentation. The work could be improved by putting more effort into translating results into insightful metrics.
Overall, the authors have not achieved their original aim to show that the observed phenomenon is relevant to computation in CA1 or the brain outside of a highly controlled in vitro setup and reductionist single cell model.
The authors combine several techniques for in vitro whole-cell patch-clamp recordings with patterned optical stimulation of the CA3 network in the mouse hippocampus, which is consistent with the state-of-the-art.
They introduce a metric of similarity between expected and observed response patterns, called gamma. The name is confusing given the wide use of the label gamma for oscillation frequencies above 20 Hz. Gamma is calculated as (E*O)/(E-O). This means that gamma approximates infinity as the difference goes to 0, to mention one of the problems. This metric is not interpretable, and it is not clear why the authors did not follow a standard approach, e.g., likelihood, correlation, or percent error.
The authors aim to replicate the physiological results with an "abstract model of the hippocampal FFEI network. In practice, this is a conductance-based model of a single CA1 neuron, including chemical kinetics-based multi-step neurotransmitter vesicle release. This is an abstraction from the FFEI network that the paper starts with. It raises the question whether this is the right level at which to model the computational impacts of EI imbalance on CA1 neurons. Given the highly reduced model they have elaborated, the generalization to the complete CA3-CA1 network that the authors suggest can be achieved in the discussion is overoptimistic. Network models of CA3 and C1 must be considered, together with afferents from the entorhinal cortex to accomplish this generalization.
The authors reveal a potentially interesting physiological feature of CA1 excitatory neurons under very specific stimulus conditions. It could warrant follow-up studies to place EI imbalance in a physiologically realistic context.
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Reviewer #3 (Public review):
Summary:
This work shows experimentally and computationally that single CA1 neurons can perform mismatch detection on patterned CA3 inputs and that STP and EI balance underlie this detection.
Strengths:
It has been known that STP can enhance the EPSP when the corresponding presynaptic input exhibits abrupt changes in firing rate. This work provides experimental evidence and further computational support for the hypothesis that the basic computation through STP is useful for detecting abrupt changes in the spatial pattern of synaptic inputs at the Schaffer collaterals. Further, their results indicate the novel view that mismatch detection is most efficient when gamma-frequency bursting inputs exhibit mismatches between theta cycles.
Weaknesses:
Their model assumes that patterned activities in CA3 do not have overlaps. However, overlaps between memory engrams have been shown. Therefore, this assumption may not hold, and whether the proposed mechanism is valid for overlapping CA3 inputs needs further clarification.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Zare‑Eelanjegh et al. investigate how the endoplasmic reticulum, the nucleus, and the cell periphery are mechanically linked by indenting intact cells with specially shaped atomic‑force probes that double as drug injection devices. Fluorescence‑lifetime imaging of the membrane tension reporter Flipper‑TR reveals that these three compartments are mechanically linked and that the actin cytoskeleton, microtubules, and lamins modulate this coupling in complex ways.
Strengths:
(1) The study makes an important advance by applying FluidFM to probe organelle mechanics in living cells, a technically demanding but powerful approach.
(2) Experimental design is quantitative, the data are clearly presented, and the conclusions are broadly consistent with the measurements.
Weaknesses:
(1) Calcium‑dependent effects: Indentation can evoke cytoplasmic Ca²⁺ elevations that drive myosin contraction and reshape the internal membrane network (e.g., vesiculation: PMID : 9200614, 32179693) possibly confounding the Flipper-TR responses; without simultaneous/matching Ca²⁺ imaging, cell viability assays (e.g., Sytox), and intracellular Ca²⁺ sequestration or myosin inhibition experiments, a more complex mechanochemical coupling cannot be excluded, weakening conclusions.
(2) Baseline measurements: Flipper‑TR lifetime images acquired without indentation do not exclude potential light‑induced or time‑dependent changes, which weaken the conclusions.
(3) Indentation depth versus nuclear stiffness/tension: Because lamin‑A/C depletion softens nuclei, a given force may produce a deeper pit and thus greater membrane stretch. It is unclear how the cytoskeletal perturbations affect indentation depth, which weakens the conclusions.
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Reviewer #2 (Public review):
Summary:
This useful study combines atomic force microscopy with genetic manipulations of the lamin meshwork and microinjection of cytoskeletal depolymerizing drugs to probe the mechanical responses of intracellular organelles to combinations of cytoskeletal perturbations. This study demonstrates both local and distal responses of intracellular organelles to mechanical forces and shows that these responses are affected by disruption of the actin, microtubule, and lamin cytoskeletal systems. Interpretation of these effects is limited by the absence of key data determining whether acute microinjection of cytoskeleton-depolymerizing drugs has complete or partial effects on the targeted cytoskeletal networks.
Strengths:
This study uses a sensitive micromanipulation system to apply and visualize the effects of force on intracellular organelles.
Weaknesses:
The choice to deliver cytoskeleton-depolymerizing drugs by local microinjection is unusual, and it is unclear to what extent actin and microtubule filaments are actually depolymerized immediately after microinjection and on the minutes-length timescale being evaluated in this study. This omission limits the interpretation of these data.
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Reviewer #3 (Public review):
Summary:
Using an approach developed by the authors (FluidFM) combined with FLIM, they discover that a mechanical force applied over the cell nucleus triggers mechanical responses dependent on the Lamina composition.
Strengths:
The authors present a new approach to study mechano-transduction in living cells, with which they uncover lamin-dependent properties of the nucleus.
Weaknesses:
(1) The transfer of the mechanical response from the Lamina to the ER is not fully covered.
(2) In Figure 4D, WT dots are the same for each compartment. Why do the authors not make one graph for each compartment with WT, A-KO, B-KD, and A-KO/B-KD together?
(2) In Figure 1E, the authors showed well how the probe deforms the nucleus. It is not indicated in the material and methods section or in the figure legend, where, in Z, the acquisition of FLIM images was made or if it is a maximum projection. I assume it was made at a plane in the middle of the nucleus to see the nuclear envelope border and the ER at the same time. Did the authors look at the nuclear membrane facing upward, where most of the deformation should occur? Are there more lifetime changes? In Figure D, before injection of CytoD, we can clearly see a difference at the pyramidal indentation site with two different lifetime colors.
(3) A great result of this article regards the importance of Lamins, A and B, in triggering the response to a mechanical force applied to the nucleus. Could 3D imaging for LaminA and LaminB be performed at the different time points of indentation to see how the lamins meshworks are deformed and how they return to basal state? This could be correlated with the FLIM results described in the article.
(4) Lamins form a meshwork underneath the nuclear membrane. They are connected to the cytoskeletons mainly by the LINC complex. Results presented here show that the cytoskeletons are implicated in transferring the stimulus from the nuclear envelope to the ER. Could the author perform the same experiments using Nesprin-2 or/and Nesprin-1 or/and SUN1/2 knockdowns to determine if this transmission is occurring through the LINC complex or rather in a passive way by modifying the nuclear close surroundings?
(5) The authors used cytoskeleton drugs, CytoD and Nocodazole, with their FluidFM probe, but did not show if the drugs actually worked and to what extent by performing actin or microtubule stainings. In the original paper describing FluidFM, 15s were enough to obtain a full FITC-positive cell after injection. Here, the experiments are around 5 minutes long. I therefore interrogate the rationale behind the injection of the drugs compared to direct incubation, besides affecting only the cell currently under indentation.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The authors show that corticotropin-releasing factor (CRF) neurons in the central amygdala (CeA) and bed nucleus of the stria terminalis (BNST) monosynaptically target cholinergic interneurons (CINs) in the dorsal striatum of rodents. Functionally, activation of CRFR1 receptors increases CIN firing rate, and this modulation was reduced by pre-exposure to ethanol. This is an interesting finding, with potential significance for alcohol use disorders, but some conclusions could use additional support.
Strengths:
Well-conceived circuit mapping experiments identify a novel pathway by which the CeA and BNST can modulate dorsal striatal function by controlling cholinergic tone. Important insight into how CRF, a neuropeptide that is important in mediating aspects of stress, affective/motivational processes, and drug-seeking, modulates dorsal striatal function.
Weaknesses:
(1) Tracing and expression experiments were performed both in mice and rats (in a mostly non-overlapping way). While these species are similar in many ways, some conclusions are based on assumptions of similarities that the presented data do not directly show. In most cases, this should be addressed in the text (but see point number 2).
(2) Experiments in rats show that CRFR1 expression is largely confined to a subpopulation of striatal CINs. Is this true in mice, too? Since most electrophysiological experiments are done in various synaptic antagonists and/or TTX, it does not affect the interpretation of those data, but non-CIN expression of CRFR1 could potentially have a large impact on bath CRF-induced acetylcholine release.
(3) Experiments in rats show that about 30% of CINs express CRFR1 in rats. Did only a similar percentage of CINs in mice respond to bath application of CRF? The effect sizes and error bars in Figure 5 imply that the majority of recorded CINs likely responded. Were exclusion criteria used in these experiments?
(4) The conclusion that prior acute alcohol exposure reduces the ability of subsequent alcohol exposure to suppress CIN activity in the presence of CRF may be a bit overstated. In Figure 6D (no ethanol pre-exposure), ethanol does not fully suppress CIN firing rate to baseline after CRF exposure. The attenuated effect of CRF on CIN firing rate after ethanol pre-treatment (6E) may just reduce the maximum potential effect that ethanol can have on firing rate after CRF, due to a lowered starting point. It is possible that the lack of significant effect of ethanol after CRF in pre-treated mice is an issue of experimental sensitivity. Related to this point, does pre-treatment with ethanol reduce the later CIN response to acute ethanol application (in the absence of CRF)?
(5) More details about the area of the dorsal striatum being examined would be helpful (i.e., a-p axis).
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Reviewer #2 (Public review):
Summary:
Essoh and colleagues present a thorough and elegant study identifying the central amygdala and BNST as key sources of CRF input to the dorsal striatum. Using monosynaptic rabies tracing and electrophysiology, they show direct connections to cholinergic interneurons. The study builds on previous findings that CRF increases CIN firing, extending them by measuring acetylcholine levels in slices and applying optogenetic stimulation of CRF+ fibers. It also uncovers a novel interaction between alcohol and CRF signaling in the striatum, likely to spark significant interest and future research.
Strengths:
A key strength is the integration of anatomical and functional approaches to demonstrate these projections and assess their impact on target cells, striatal cholinergic interneurons.
Weaknesses:
The nature of the interaction between alcohol and CRF actions on cholinergic neurons remains unclear. Also, further clarification of the ACh sensor used and others is required
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Reviewer #3 (Public review):
Summary:
The authors demonstrate that CRF neurons in the extended amygdala form GABAergic synapses onto cholinergic interneurons and that CRF can excite these neurons. The evidence is strong, however, the authors fail to make a compelling connection showing CRF released from these extended amygdala neurons is mediating any of these effects. Further, they show that acute alcohol appears to modulate this action, although the effect size is not particularly robust.
Strengths:
This is an exciting connection from the extended amygdala to the striatum that provides a new direction for how these regions can modulate behavior. The work is rigorous and well done.
Weaknesses:
While the authors show that opto stim of these neurons can increase firing, this is not shown to be CRFR1 dependent. In addition, the effects of acute ethanol are not particularly robust or rigorously evaluated. Further, the opto stim experiments are conducted in an Ai32 mouse, so it is impossible to determine if that is from CEA and BNST, vs. another population of CRF-containing neurons. This is an important caveat.
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Reviewer #4 (Public review):
Summary:
This manuscript presents a compelling and methodologically rigorous investigation into how corticotropin-releasing factor (CRF) modulates cholinergic interneurons (CINs) in the dorsal striatum - a brain region central to cognitive flexibility and action selection-and how this circuit is disrupted by alcohol exposure. Through an integrated series of anatomical, optogenetic, electrophysiological, and imaging experiments, the authors uncover a previously uncharacterized CRF⁺ projection from the central amygdala (CeA) and bed nucleus of the stria terminalis (BNST) to dorsal striatal CINs.
Strengths:
Key strengths of the study include the use of state-of-the-art monosynaptic rabies tracing, CRF-Cre transgenic models, CRFR1 reporter lines, and functional validation of synaptic connectivity and neurotransmitter release. The finding that CRF enhances CIN excitability and acetylcholine (ACh) release via CRFR1, and that this effect is attenuated by acute alcohol exposure and withdrawal, provides important mechanistic insight into how stress and alcohol interact to impair striatal function. These results position CRF signaling in CINs as a novel contributor to alcohol use disorder (AUD) pathophysiology, with implications for relapse vulnerability and cognitive inflexibility associated with chronic alcohol intake.
The study is well-structured, with a clear rationale, thorough methodology, and logical progression of results. The discussion effectively contextualizes the findings within broader addiction neuroscience literature and suggests meaningful future directions, including therapeutic targeting of CRFR1 signaling in the dorsal striatum.
Weaknesses:
Minor areas for improvement include occasional redundancy in phrasing, slightly overlong descriptions in the abstract and significance sections, and a need for more concise language in some places. Nevertheless, these do not detract from the manuscript's overall quality or impact.
Overall, this is a highly valuable contribution to the fields of addiction neuroscience and striatal circuit function, offering novel insights into stress-alcohol interactions at the cellular and circuit level, which requires minor editorial revisions.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
In this meta-analysis, Ng and colleagues review the association between slow-oscillation spindle coupling during sleep and overnight memory consolidation. The coupling of these oscillations (and also hippocampal sharp-wave ripples) have been central to theories and mechanistic models of active systems consolidation, that posit that the coupling between ripples, spindles, and slow oscillations (SOs) coordinate and drive the coordinated reactivation of memories in hippocampus and cortex, facilitating cross-regional information and ultimately memory strengthening and stabilisation.
Given the importance that these coupling mechanisms have been given in theory, this is a timely and important contribution to the literature in terms of determining whether these theoretical assumptions hold true in human data. The results show that the timing of sleep spindles relative to the SO phase, and the consistency of that timing, predicted overnight memory consolidation in meta-analytic models. The overall amount of coupling events did not show as strong a relationship. Coupling phase in particular was moderated by a number of variables including spindle type (fast, slow), channel location (frontal, central, posterior), age, and memory type. The main takeaway is that fast spindles that consistently couple close to the peak of the SO in frontal channel locations are optimal for memory consolidation, in line with theoretical predictions. These findings will be very useful for future researchers in terms of determining necessary sample sizes to observe coupling - memory relationships, and in the selection and reporting of relevant coupling metrics.
Although the meta-analysis covers the three main coupling metrics that are typically assessed (occurrence, timing, and consistency), the meta-analysis also includes spindle amplitude. This may be confusing to readers, as this is not a measurement of SO-spindle coupling but instead a measurement of spindles in general (which may or may not be coupled).
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Reviewer #2 (Public review):
This article reviews the studies on the relationship between slow oscillation (SO)-spindle (SP) coupling and memory consolidation. It innovatively employs non-normal circular linear correlations through a Bayesian meta-analysis. A systematic analysis of the retrieved studies highlighted that co-coupling of SO and the fast SP's phase and amplitude at the frontal part better predicts memory consolidation performance.
Regarding the moderator of age, this study not only provided evidence of the effect across all age groups but also the effect in a younger age group (without the small sample of elders that has a large gap from the younger age groups). The ageing effects become less pronounced, but the model still shows a moderate effect.
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Reviewer #3 (Public review):
This manuscript presents a meta-analysis of 23 studies, which report 297 effect sizes, on the effect of SO-spindle coupling on memory performance. The analysis has been done with great care, and the results are described in great detail. In particular, there are separate analyses for coupling phase, spindle amplitude, coupling strength (e.g., measured by vector length or modulation index), and coupling percentage (i.e., the percentage of SPs coupled with SOs). The authors conclude that the precision and strength of coupling showed significant correlations with memory retention.
There are two main points where I do not agree with the authors.
First, the authors conclude that "SO-SP coupling should be considered as a general physiological mechanism for memory consolidation". However, the reported effect sizes are smaller than what is typically considered a "small effect" (0.10<br /> Second, the study implements state-of-the-art Bayesian statistics. While some might see this as a strength, I would argue that it is not. A classical meta-analysis is relatively easy to understand, even for readers with only a limited background in statistics. A Bayesian analysis, on the other hand, introduces a number of subjective choices that render it much less transparent. This becomes obvious in the forest plots. It is not immediately apparent to the reader how the distributions for each study represent the reported effect sizes (gray dots), which makes the analyses unnecessarily opaque. It is commendable that the authors now provide classical forest plots as Figs. S10.1-4.
However, analyses that require a "Markov chain Monte Carlo (MCMC) method, [..] with the no-U-turn Hamiltonian Monte Carlo (HMC) samplers, [..] with each chain undergoing 12,000 iterations (including 2,000 warm-ups)" for calculating accurate Bayes Factors (BF), and checking its convergence "through graphical posterior predictive checks, [..] trace plots, and [..] Gelman and Rubin Diagnostic", which should then result in something resembling "a uniformly undulating wave with high overlap between chains" still seems overly complex. It follows a recent trend in using more and more opaque methods. Where we had to trust published results a decade ago because the data were not openly available, today we must trust the results because methods (including open source software toolboxes) can no longer be checked with reasonable effort.
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www.biorxiv.org www.biorxiv.org
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Reviewer #2 (Public review):
The revised manuscript by Altan et al. includes some real improvements to the visualizations and explanations of the authors' thesis statement with respect to fMRI measurements of pRF sizes. In particular, the deposition of the paper's data has allowed me to probe and refine several of my previous concerns. While I still have major concerns about how the data are presented in the current draft of the manuscript, my skepticism about data quality overall has been much alleviated. Note that this review focuses almost exclusively on the fMRI data as I was satisfied with the quality of the psychophysical data and analyses in my previous review.
Major Concerns
(I) Statistical Analysis
In my previous review, I raised the concern that the small sample size combined with the noisiness of the fMRI data, a lack of clarity about some of the statistics, and a lack of code/data likely combine to make this paper difficult or impossible to reproduce as it stands. The authors have since addressed several aspects of this concern, most importantly by depositing their data. However their response leaves some major questions, which I detail below.
First of all, the authors claim in their response to the previous review that the small sample size is not an issue because large samples are not necessary to obtain "conclusive" results. They are, of course, technically correct that a small sample size can yield significant results, but the response misses the point entirely. In fact, small samples are more likely than large samples to erroneously yield a significant result (Button et al., 2013, DOI:10.1038/nrn3475), especially when noise is high. The response by the authors cites Schwarzkopf & Huang (2024) to support their methods on this front. After reading the paper, I fail to see how it is at all relevant to the manuscript at hand or the criticism raised in the previous review. Schwarzkopf & Huang propose a statistical framework that is narrowly tailored to situations where one is already certain that some phenomenon (like the adaptation of pRF size to spatial frequency) either always occurs or never occurs. Such a framework is invalid if one cannot be certain that, for example, pRF size adapts in 98% of people but not the remaining 2%. Even if the paper were relevant to the current study, the authors don't cite this paper, use its framework, or admit the assumptions it requires in the current manuscript. The observation that a small dataset can theoretically lead to significance under a set of assumptions not appropriate for the current manuscript is not a serious response to the concern that this manuscript may not be reproducible.
To overcome this concern, the authors should provide clear descriptions of their statistical analyses and explanations of why these analyses are appropriate for the data. Ideally, source code should be published that demonstrates how the statistical tests were run on the published data. (I was unable to find any such source code in the OSF repository.) If the effects in the paper were much stronger, this level of rigor might not be strictly necessary, but the data currently give the impression of being right near the boundary of significance, and the manuscript's analyses needs to reflect that. The descriptions in the text were helpful, but I was only able to approximately reproduce the authors analyses based on these descriptions alone. Specifically, I attempted to reproduce the Mood's median tests described in the second paragraph of section 3.2 after filtering the data based on the criteria described in the final paragraph of section 3.1. I found that 7/8 (V1), 7/8 (V2), 5/8 (V3), 5/8 (V4), and 4/8 (V3A) subjects passed the median test when accounting for the (40) multiple comparisons. These results are reasonably close to those reported in the manuscript and might just differ based on the multiple comparisons strategy used (which I did not find documented in the manuscript). However, Mood's median test does not test the direction of the difference-just whether the medians are different-so I additionally required that the median sigma of the high-adapted pRFs be greater than that of the low-adapted pRFs. Surprisingly, in V1 and V3, one subject each (not the same subject) failed this part of the test, meaning that they had significant differences between conditions but in the wrong direction. This leaves 6/8 (V1), 7/8 (V2), 4/8 (V3), 5/8 (V4), and 4/8 (V3A) subjects that appear to support the authors' conclusions. As the authors mention, however, this set of analyses runs the risk of comparing different parts of cortex, so I also performed Wilcox signed-rank tests on the (paired) vertex data for which both the high-adapted and low-adapted conditions passed all the authors' stated thresholds. These results largely agreed with the median test (only 5/8 subjects significant in V1 but 6/8 in in V3A, other areas the same, though the two tests did not always agree which subjects had significant differences). These analyses were of course performed by a reviewer with a reviewer's time commitment to the project and shouldn't be considered a replacement for the authors' expertise with their own data. If the authors think that I have made a mistake in these calculations, then the best way to refute them would be to publish the source code they used to threshold the data and to perform the same tests.
Setting aside the precise values of the relevant tests, we should also consider whether 5 of 8 subjects showing a significant effect (as they report for V3, for example) should count as significant evidence of the effect? If one assumes, as a null hypothesis, that there is no difference between the two conditions in V3 and that all differences are purely noise, then a binomial test across subjects would be appropriate. Even if 6 of 8 subjects show the effect, however (and ignoring multiple comparisons), the p-value of a one-sided binomial test is not significant at the 0.05 level (7 of 8 subjects is barely significant). Of course, a more rigorous way to approach this question could be something like an ANOVA, and the authors use an ANOVA analysis of the medians in the paragraph following their use of Mood's median test. However, ANOVA assumes normality, and the authors state in the previous paragraph that they employed Mood's median test because "the distribution of the pRF sizes is zero-bounded and highly skewed" so this choice does not make sense. The Central Limits Theorem might be applied to the medians in theory, but with only 8 subjects and with an underlying distribution of pRF sizes that is non-negative, the relevant data will almost certainly not be normally distributed. These tests should probably be something like a Kruskal-Wallis ANOVA on ranks.
All of the above said, my intuition about the data is currently that there are significant changes to the adapted pRF size in V2. I am not currently convinced that the effects in other visual areas are significant, and I suspect that the paper would be improved if authors abandoned their claims that areas other than V2 show a substantial effect. Importantly, I don't think this causes the paper to lose any impact-in fact, if the authors agree with my assessments, then the paper might be improved by focusing on V2. Specifically, the authors' already discuss psychophysical work related to the perception of texture on pages 18 and 19 and link it to their results. V2 is also implicated in the perception of texture (see, for example, Freeman et al., 2013; DOI:10.1038/nn.3402; Ziemba et al., 2016, DOI:10.1073/pnas.1510847113; Ziemba et al., 2019; DOI:10.1523/JNEUROSCI.1743-19.2019) and so would naturally be the part of the visual cortex where one might predict that spatial frequency adaptation would have a strong effect on pRF size. This neatly connects the psychophysical and imaging sides of this project and could make a very nice story out of the present work.
(II) Visualizations
The manuscript's visual evidence regarding the pRF data also remains fairly weak (but I found the pRF size comparisons in the OSF repository and Figure S1 to be better evidence-more in the next paragraph). The first line of the Results section still states, "A visual inspection on the pRF size maps in Figure 4c clearly shows a difference between the two conditions, which is evident in all regions." As I mentioned in my previous review, I don't agree with this claim (specifically, that it is clear). My impression when I look at these plots is of similarity between the maps, and, where there is dissimilarity, of likely artifacts. For example, the splotch of cortex near the upper vertical meridian (ventral boundary) of V1 that shows up in yellow in the upper plot but not the lower plot also has a weirdly high eccentricity and a polar angle near the opposite vertical meridian: almost certainly not the actual tuning of that patch of cortex. If this is the clearest example subject in the dataset, then the effect looks to me to be very small and inconsistently distributed across the visual areas. That said, I'm not convinced that the problem here is the data-rather, I think it's just very hard to communicate a small difference in parameter tuning across a visual area using this kind of side-by-side figure. I think that Figure S2, though noisy (as pRF maps typically are), is more convincing than Figure 4c, personally. For what it's worth, when looking at the data myself, I found that plotting log(𝜎(H) / 𝜎(L)), which will be unstable when noise causes 𝜎(H) or 𝜎(L) to approach zero, was less useful than plotting plotting (𝜎(H) - 𝜎(L)) / (𝜎(H) + 𝜎(L)). This latter quantity will be constrained between -1 and 1 and shows something like a proportional change in the pRF size (and thus should be more comparable across eccentricity).
In my opinion, the inclusion of the pRF size comparison plots in the OSF repository and Figure S1 made a stronger case than any of the plots of the cortical surface. I would suggest putting these on log-log plots since the distribution of pRF size (like eccentricity) is approximately exponential on the cortical surface. As-is, it's clear in many plots that there is a big splotch of data in the compressed lower left corner, but it's hard to get a sense for how these should be compared to the upper right expanse of the plots. It is frequently hard to tell whether there is a greater concentration of points above or below the line of equality in the lower left corner as well, and this is fairly central to the paper's claims. My intuition is that the upper right is showing relatively little data (maybe 10%?), but these data are very emphasized by the current plots. The authors might even want to consider putting a collection of these scatter-plots (or maybe just subject 007, or possible all subjects' pRFs on a single scatter-plot) in the main paper and using these visualizations to provide intuitive supporting for the main conclusions about the fMRI data (where the manuscript currently use Figure 4c for visual intuition).
Minor Comments
(1) Although eLife does not strictly require it, I would like to see more of the authors' code deposited along with the data (especially the code for calculating the statistics that were mentioned above). I do appreciate the simulation code that the authors added in the latest submission (largely added in response to my criticism in the previous reviews), and I'll admit that it helped me understand where the authors were coming from, but it also contains a bug and thus makes a good example of why I'd like to see more of the authors' code. If we set aside the scientific question of whether the simulation is representative of an fMRI voxel (more in Minor Comment 5, below), Figures 1A and the "AdaptaionEffectSimulated.png" file from the repository (https://osf.io/d5agf) imply that only small RFs were excluded in the high-adapted condition and only large RFs were excluded in the low-adapted condition. However, the script provided (SimlatePrfAdaptation.m: https://osf.io/u4d2h) does not do this. Lines 7 and 8 of the script set the small and large cutoffs at the 30th and 70th percentiles, respectively, then exclude everything greater than the 30th percentile in the "Large RFs adapted out" condition (lines 19-21) and exclude anything less than the 70th percentile in the "Small RFs adapted out" condition (lines 27-29). So the figures imply that they are representing 70% of the data but they are in fact representing only the most extreme 30% of the data. (Moreover, I was unable to run the script because it contains hard-coded paths to code in someone's home directory.) Just to be clear, these kinds of bugs are quite common in scientific code, and this bug was almost certainly an honest mistake.
(2) I also noticed that the individual subject scatter-plots of high versus low adapted pRF sizes on the OSF seem to occasionally have a large concentration of values on the x=0 and y=0 axes. This isn't really a big deal in the plots, but the manuscript states that "we denoised the pRF data to remove artifactual vertices where at least one of the following criteria was met: (1) sigma values were equal to or less than zero ..." so I would encourage the authors to double-check that the rest of their analysis code was run with the stated filtering.
(3) The manuscript also says that the median test was performed "on the raw pRF size values". I'm not really sure what the "raw" means here. Does this refer to pRF sizes without thresholding applied?
(4) The eccentricity data are much clearer now with the additional comments from the authors and the full set of maps; my concerns about this point have been met.
(5) Regarding the simulation of RFs in a voxel (setting aside the bug), I will admit both to hoping for a more biologically-grounded situation and to nonetheless understanding where the authors are coming from based on the provided example. What I mean by biologically-grounded: something like, assume a 2.5-mm isotropic voxel aligned to the surface of V1 at 4{degree sign} of eccentricity; the voxel would span X to Y degrees of eccentricity, and we predict Z neurons with RFs in this voxel with a distribution of RF sizes at that eccentricity from [reference], etc. eventually demonstrating a plausible pRF size change commensurate to the paper's measurements. I do think that a simulation like this would make the paper more compelling, but I'll acknowledge that it probably isn't necessary and might be beyond the scope here.
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Reviewer #3 (Public review):
This is a well-designed study examining an important, surprisingly understudied question: how does adaptation affect spatial frequency processing in human visual cortex? Using a combination of psychophysics and neuroimaging, the authors test the hypothesis that spatial frequency tuning is shifted to higher or lower frequencies, depending on preadapted state (low or high s.f. adaptation). They do so by first validating the phenomenon psychophysically, showing that adapting to 0.5 cpd stimuli causes an increase perceived s.f., and 3.5 cpd causes a relative decrease in perceived s.f. Using the same stimuli, they then port these stimuli to a neuroimaging study, in which population receptive fields are measured under high and low spatial frequency adaptation states. They find that adaptation changes pRF size, depending on adaptation state: adapting to high s.f. led to broader overall pRF sizes across early visual cortex, whereas adapting to low s.f. led to smaller overall pRF sizes. Finally the authors carry out a control experiment to psychophysically rule out the possibility that the perceived contrast change w/ adaptation may have given rise to these imaging results (doesn't appear to be the case). All in all, I found this to be a good manuscript: the writing is taut, and the study is well designed.
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Review #1 (Public Review):
Summary:
The authors employ a combination of repetitive transcranial magnetic stimulation (intermittent theta burst-iTBS) and transcranial alternating current stimulation (gamma tACS) as an approach aimed to improve memory in a face/name/profession task.
Strengths:
The paper has many strengths. The approach of stimulating the human brain non-invasively is potentially impactful because it could lead to a host of interesting applications. The current study aims to evaluate one such exciting application. The paper contains an unusual combination of noninvasive stimulation and brain imaging data, and includes independent replication samples.
Weaknesses:
(1) It remains unclear how this stimulation protocol is proposed to enhance memory. Memories are believed to be stored by precise inputs to specific neurons and highly tuned changes in synaptic strengths. It remains unclear whether proposed neural activity generated by the stimulation reflects the activation of specific memories or generally increased activity across all classes of neurons.
(2) The claim that effects directly involve the precuneus lacks strong support. The measurements shown in Figure 3 appear to be weak (i.e., Figure 3A top and bottom look similar, and Figure 3C left and right look similar). The figure appears to show a more global brain pattern rather than effects that are limited to the precuneus. Related to this, it would perhaps be useful to show the different positions of the stimulation apparatus. This could perhaps show that the position of the stimulation matters and could perhaps illustrate a range of distances over which position of the stimulation matters.
(3) Behavioral results showing an effect on memory would substantiate claims that the stimulation approach produces significant changes in brain activity. However, placebo effects can be extremely powerful and useful, and this should probably be mentioned. Also, in the behavioral results that are currently presented, there are several concerns:
a) There does not appear to be a significant effect on the STMB task.
b) The FNAT task is minimally described in the supplementary material. Experimental details that would help the reader understand what was done are not described. Experimental details are missing for: the size of the images, the duration of the image presentation, the degree of image repetition, how long the participants studied the images, whether the names and occupations were different, genders of the faces, and whether the same participant saw different faces across the different stimulation conditions. Regarding the latter point, if the same participant saw the same faces across the different stimulation conditions, then there could be memory effects across different conditions that would need to be included in the statistical analyses. If participants saw different faces across the different stimulus conditions, then it would be useful to show that the difficulty was the same across the different stimuli.
c) Also, if I understand FNAT correctly, the task is based on just 12 presentations, and each point in Figure 2A represents a different participant. How the performance of individual participants changed across the conditions is unclear with the information provided. Lines joining performance measurements across conditions for each participant would be useful in this regard. Because there are only 12 faces, the results are quantized in multiples of 100/12 % in Figure 3A. While I do not doubt that the authors did their homework in terms of the statistical analyses, it seems as though these 12 measurements do not correspond to a large effect size. For example, in Figure 3A for the immediate condition (total), it seems that, on average, the participants may remember one more face/name/occupation.
d) Block effects. If I understand correctly, the experiments were conducted in blocks. This is potentially problematic. An example study that articulates potential problems associated with block designs is described in Li et al (TPAMI 2021, https://ieeexplore.ieee.org/document/9264220). It is unclear if potential problems associated with block designs were taken into consideration.
e) In the FNAT portion of the paper, some results are statistically significant, while others are not. The interpretation of this is unclear. In Figure 3A, it seems as though the authors claim that iTBS+gtACS > iTBS+sham-tACS, but iTBS+gtACS ~ sham+sham. The interpretation of such a result is unclear. Results are also unclear when separated by name and occupation. There is only one condition that is statistically significant in Figure 3A in the name condition, and no significant results in the occupation condition. In short, the statistical analyses, and accompanying results that support the authors’ claims, should be explained more clearly.
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Reviewer #2 (Public review):
Summary:
The manuscript "Dual transcranial electromagnetic stimulation of the precuneus-hippocampus network boosts human long-term memory" by Borghi and colleagues provides evidence that the combination of intermittent theta burst TMS stimulation and gamma transcranial alternating current stimulation (γtACS) targeting the precuneus increases long-term associative memory in healthy subjects compared to iTBS alone and sham conditions. Using a rich dataset of TMS-EEG and resting-state functional connectivity (rs-FC) maps and structural MRI data, the authors also provide evidence that dual stimulation increased gamma oscillations and functional connectivity between the precuneus and hippocampus. Enhanced memory performance was linked to increased gamma oscillatory activity and connectivity through white matter tracts.
Strengths:
The combination of personalized repetitive TMS (iTBS) and gamma tACS is a novel approach to targeting the precuneus, and thereby, connected memory-related regions to enhance long-term associative memory. The authors leverage an existing neural mechanism engaged in memory binding, theta-gamma coupling, by applying TMS at theta burst patterns and tACS at gamma frequencies to enhance gamma oscillations. The authors conducted a thorough study that suggests that simultaneous iTBS and gamma tACS could be a powerful approach for enhancing long-term associative memory. The paper was well-written, clear, and concise.
Weaknesses:
(1) The study did not include a condition where γtACS was applied alone. This was likely because a previous work indicated that a single 3-minute γtACS did not produce significant effects, but this limits the ability to isolate the specific contribution of γtACS in the context of this target and memory function
(2) The authors applied stimulation for 3 minutes, which seems to be based on prior tACS protocols. It would be helpful to present some rationale for both the duration and timing relative to the learning phase of the memory task. Would you expect additional stimulation prior to recall to benefit long-term associative memory?
(3) How was the burst frequency of theta iTBS and gamma frequency of tACS chosen? Were these also personalized to subjects' endogenous theta and gamma oscillations? If not, were increases in gamma oscillations specific to patients' endogenous gamma oscillation frequencies or the tACS frequency?
(4) The authors do a thorough job of analyzing the increase in gamma oscillations in the precuneus through TMS-EEG; however, the authors may also analyze whether theta oscillations were also enhanced through this protocol due to the iTBS potentially targeting theta oscillations. This may also be more robust than gamma oscillations increases since gamma oscillations detected on the scalp are very low amplitude and susceptible to noise and may reflect activity from multiple overlapping sources, making precise localization difficult without advanced techniques.
(5) Figure 4: Why are connectivity values pre-stimulation for the iTBS and sham tACS stimulation condition so much higher than the dual stimulation? We would expect baseline values to be more similar.
(6) Figure 2: How are total association scores significantly different between stimulation conditions, but individual name and occupation associations are not? Further clarification of how the total FNAT score is calculated would be helpful.
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Reviewer #3 (Public review):
Summary:
Borghi and colleagues present results from 4 experiments aimed at investigating the effects of dual γtACS and iTBS stimulation of the precuneus on behavioral and neural markers of memory formation. In their first experiment (n = 20), they found that a 3-minute offline (i.e., prior to task completion) stimulation that combines both techniques leads to superior memory recall performance in an associative memory task immediately after learning associations between pictures of faces, names, and occupation, as well as after a 15-minute delay, compared to iTBS alone (+ tACS sham) or no stimulation (sham for both iTBS and tACS). Performance in a second task probing short-term memory was unaffected by the stimulation condition. In a second experiment (n = 10), they show that these effects persist over 24 hours and up to a full week after initial stimulation. A third (n = 14) and fourth (n = 16) experiment were conducted to investigate the neural effects of the stimulation protocol. The authors report that, once again, only combined iTBS and γtACS increase gamma oscillatory activity and neural excitability (as measured by concurrent TMS-EEG) specific to the stimulated area at the precuneus compared to a control region, as well as precuneus-hippocampus functional connectivity (measured by resting-state MRI), which seemed to be associated with structural white matter integrity of the bilateral middle longitudinal fasciculus (measured by DTI).
Strengths:
Combining non-invasive brain stimulation techniques is a novel, potentially very powerful method to maximize the effects of these kinds of interventions that are usually well-tolerated and thus accepted by patients and healthy participants. It is also very impressive that the stimulation-induced improvements in memory performance resulted from a short (3 min) intervention protocol. If the effects reported here turn out to be as clinically meaningful and generalizable across populations as implied, this approach could represent a promising avenue for the treatment of impaired memory functions in many conditions.
Methodologically, this study is expertly done! I don't see any serious issues with the technical setup in any of the experiments (with the only caveat that I am not an expert in fMRI functional connectivity measures and DTI). It is also very commendable that the authors conceptually replicated the behavioral effects of experiment 1 in experiment 2 and then conducted two additional experiments to probe the neural mechanisms associated with these effects. This certainly increases the value of the study and the confidence in the results considerably.
The authors used a within-subject approach in their experiments, which increases statistical power and allows for stronger inferences about the tested effects. They are also used to individualize stimulation locations and intensities, which should further optimize the signal-to-noise ratio.
Weaknesses:
I want to state clearly that I think the strengths of this study far outweigh the concerns I have. I still list some points that I think should be clarified by the authors or taken into account by readers when interpreting the presented findings.
I think one of the major weaknesses of this study is the overall low sample size in all of the experiments (between n = 10 and n = 20). This is, as I mentioned when discussing the strengths of the study, partly mitigated by the within-subject design and individualized stimulation parameters. The authors mention that they performed a power analysis but this analysis seemed to be based on electrophysiological readouts similar to those obtained in experiment 3. It is thus unclear whether the other experiments were sufficiently powered to reliably detect the behavioral effects of interest. That being said, the authors do report significant effects, so they were per definition powered to find those. However, the effect sizes reported for their main findings are all relatively large and it is known that significant findings from small samples may represent inflated effect sizes, which may hamper the generalizability of the current results. Ideally, the authors would replicate their main findings in a larger sample. Alternatively, I think running a sensitivity analysis to estimate the smallest effect the authors could have detected with a power of 80% could be very informative for readers to contextualize the findings. At the very least, however, I think it would be necessary to address this point as a potential limitation in the discussion of the paper.
It seems that the statistical analysis approach differed slightly between studies. In experiment 1, the authors followed up significant effects of their ANOVAs by Bonferroni-adjusted post-hoc tests whereas it seems that in experiment 2, those post-hoc tests where "exploratory", which may suggest those were uncorrected. In experiment 3, the authors use one-tailed t-tests to follow up their ANOVAs. Given some of the reported p-values, these choices suggest that some of the comparisons might have failed to reach significance if properly corrected. This is not a critical issue per se, as the important test in all these cases is the initial ANOVA but non-significant (corrected) post-hoc tests might be another indicator of an underpowered experiment. My assumptions here might be wrong, but even then, I would ask the authors to be more transparent about the reasons for their choices or provide additional justification. Finally, the authors sometimes report exact p-values whereas other times they simply say p < .05. I would ask them to be consistent and recommend using exact p-values for every result where p >= .001.
While the authors went to great lengths trying to probe the neural changes likely associated with the memory improvement after stimulation, it is impossible from their data to causally relate the findings from experiments 3 and 4 to the behavioral effects in experiments 1 and 2. This is acknowledged by the authors and there are good methodological reasons for why TMS-EEG and fMRI had to be collected in sperate experiments, but it is still worth pointing out to readers that this limits inferences about how exactly dual iTBS and γtACS of the precuneus modulate learning and memory.
There were no stimulation-related performance differences in the short-term memory task used in experiments 1 and 2. The authors argue that this demonstrates that the intervention specifically targeted long-term associative memory formation. While this is certainly possible, the STM task was a spatial memory task, whereas the LTM task relied (primarily) on verbal material. It is thus also possible that the stimulation effects were specific to a stimulus domain instead of memory type. In other words, could it be possible that the stimulation might have affected STM performance if the task taxed verbal STM instead? This is of course impossible to know without an additional experiment, but the authors could mention this possibility when discussing their findings regarding the lack of change in the STM task.
While the authors discuss the potential neural mechanisms by which the combined stimulation conditions might have helped memory formation, the psychological processes are somewhat neglected. For example, do the authors think the stimulation primarily improves the encoding of new information or does it also improve consolidation processes? Interestingly, the beneficial effect of dual iTBS and γtACS on recall performance was very stable across all time points tested in experiments 1 and 2, as was the performance in the other conditions. Do the authors have any explanation as to why there seems to be no further forgetting of information over time in either condition when even at immediate recall, accuracy is below 50%? Further, participants started learning the associations of the FNAT immediately after the stimulation protocol was administered. What would happen if learning started with a delay? In other words, do the authors think there is an ideal time window post-stimulation in which memory formation is enhanced? If so, this might limit the usability of this procedure in real-life applications.
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Reviewer #1 (Public review):
Summary:
In this study, Liu et al use optogenetics and genetically encoded neuromodulator sensors to test the extent to which dopamine neuron stimulation produces striatal serotonin release, and vice versa. The study is timely given growing interest in dopamine/serotonin interactions and in the context of recent work showing bidirectional and dynamic regulation of striatal dopamine by another neuromodulator, acetylcholine. The authors find that striatal dopamine and serotonin afferents function largely independently, with dopamine neuron stimulation producing no striatal serotonin release and serotonin neuron stimulation producing minimal striatal dopamine release. This work will inform future work seeking to dissect the contributions of striatal dopamine, serotonin, and their interactions to various motivated behaviors. While the paper's main conclusions are adequately supported (see Strengths), additional controls and experiments would significantly broaden the paper's impact (see Weaknesses). Finally, this draft of the work is poorly presented with numerous errors, omissions, and inconsistencies evident throughout the text and the figures that should be addressed.
Strengths:
The study employs optogenetic stimulation simultaneously with fiber photometry recording of dopamine or serotonin release measured with genetically encoded sensors. These methods are state-of-the-art, offering tighter temporal control compared to pharmacological methods for manipulating dopamine and serotonin and improved selectivity over techniques like electrochemistry and microdialysis used to record neuromodulator release in previous studies on the subject. As a result, the paper's main conclusions are well supported.
Weaknesses:
(1) The electrophysiology experiments in Figure 3 are only tangentially related to the focus of the study, and their findings are almost entirely irrelevant to the paper's main conclusions. The results of these experiments are also not novel. Glutamate corelease from 5HT neurons has been previously shown, including in the OFC and VTA (Ren et al, 2018, Cell, McDevitt et al, 2014, Cell Rep, Liu et al 2014, Neuron; and others). The authors should explain more clearly what they think these data add to the manuscript and/or consider removing them altogether.
(2) Related to the point above, as far as I can tell, the only value the electrophysiology data add is to suggest that perhaps activation of serotonin neurons may drive minimal striatal dopamine release via glutamate corelease in the VTA. The evidence provided in this version of the manuscript is insufficient to support that claim, but the manuscript would be significantly strengthened if the authors tested this hypothesis more directly. One way to do that could be to stimulate serotonin axons in the striatum (as opposed to the serotonin cell bodies) and record striatal dopamine release. A complementary anatomical approach would be to use retrograde tracing to test whether the DR 5HT neurons projecting to the striatum are the same or different from the VTA projecting population.
(3) The findings would be strengthened by the addition of a fluorophore-only control group lacking opsin expression in all experiments in Figures 1 and 2.
(4) The experiment of stimulating serotonin neurons and recording serotonin release in the NAc was not performed. It would be useful to be able to compare the magnitudes of evoked serotonin release in these two striatal regions, though it is not central to the main claims of the paper.
(5) The interpretation of the results from Figure 2 is described inconsistently throughout the manuscript. The title implies there is significant crosstalk between the dopamine and serotonin systems. The abstract calls the crosstalk "transient", which is a description of its temporal dynamics, not its magnitude. Then the introduction figures and discussion all suggest the crosstalk is minimal. I suggest the authors describe the main findings - minimal crosstalk between the dopamine and serotonin systems - clearly and consistently in the title, abstract, and main text.
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Reviewer #2 (Public review):
Summary:
This brief communication aims to clarify interactions between the dopamine (DA) and serotonin (5HT) systems of mice. The authors use optogenetic stimulation of DA neurons in the VTA or of 5HT neurons in the DRN, while monitoring the fluorescence of DA and 5HT sensors in the nucleus accumbens (NAc) and dorsal striatum (DS) using fiber photometry. The authors report on a small release of DA in the NAc following DRN stimulation, which they attribute to glutamate co-release onto VTA DA neurons using slice electrophysiology. The authors also report on cocaine-induced 5-HT release in the striatum.
Strengths:
This is a topic well worth studying.
Weaknesses:
In its current form, this is an incomplete and underpowered study that does little to clarify the complicated relationship that exists between DA and 5HT in the mammalian brain under physiological conditions or during cocaine use.
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Reviewer #3 (Public review):
The authors suggest that the small release of DA may be due to a release of glutamate from DRN 5-HT neurons to the VTA that stimulates weakly and in a transient fashion the VTA DA neurons, which in the end, produce a transient and small release of DA in the NAc.
Their findings give more information on the previously reported complex and partial known crosstalk between 5-HT and DA in the NAc.
I only have some minor concerns about the manuscript:
(1) In Figure 2F, there is a missing curve for 5-HT in NAc. Besides, the legend shows n=2, making it difficult to perform statistical analysis with that data.
(2) In Figure 3, the use of NBQX/AP5 is shown, but it is not mentioned either in the methodology or in the discussion. What is the meaning of those results?
(3) Line 98 compares results from two different places of stimulation. The results are related to stimulation in the VTA, but the comparison indicates that the stimulation was made in the DRN.
(4) If the release of 5-HT in Nac does not occur, it needs to be precise in the abstract that 5-HT is released in the dorsal striatum (DS) but not in the NAc (line 19).
(5) Be consistent with the way you mention the 5-HT neurons. For example, in lines from 106 to 119, SERT neurons are used. Previously, 5-HT neurons were used.
(6) There are several points of confusion when referring to the figures, making the text difficult to follow because the text explains something that is not shown in the figure cited.
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Reviewer #1 (Public review):
Summary:
In this manuscript, the authors aim to address significant limitations of existing experimental paradigms used to study dyadic social interactions by introducing a novel experimental setup - the Dyadic Interaction Platform (DIP). The DIP uniquely allows participants to interact dynamically, face-to-face, with simultaneous access to both social cues and task-related stimuli. The authors demonstrate the versatility and utility of this platform across several exemplary scenarios, notably highlighting cases of significant behavioral differences in conditions involving direct visibility of a partner.
Major strengths include comprehensive descriptions of previous paradigms, detailed explanations of the DIP's technical features, and clear illustrations of multimodal data integration. These elements greatly enhance the reproducibility of the methods and clarify the potential applications across various research domains and species. Particularly compelling is the authors' demonstration of behavioral impacts related to transparency in interactions, as evidenced by the macaque-human experiments using the Bach-or-Stravinsky game scenario.
Strengths:
The DIP represents a methodological advance in the study of social cognition. Its transparent, touch-sensitive display elegantly solves the problem of enabling participants to attend to both their social partner and task stimuli simultaneously without requiring attention switching. This paper marks a notable step forward toward more options for naturalistic yet still lab-based studies of social decision-making, an area where the field is actively moving, especially given recent research highlighting significant differences in neural activity depending upon the context in which an action is performed. The DIP offers researchers a valuable tool to bridge the gap between tightly controlled laboratory paradigms and the dynamic, bidirectional nature of real-world social interactions.
The authors do well to provide comprehensive documentation of the technical specifications for the four different implementations of the platform, allowing other researchers to adapt and build upon their work. The detailed information about hardware configurations demonstrates careful attention to practical implementation details. They also highlight numerous options for integration with other tools and software, further demonstrating the versatility of this apparatus and the variety of research questions to which it could be applied.
The historical review of dyadic experimental paradigms is thorough and effectively positions the DIP as addressing a critical gap in existing methodologies. The authors convincingly argue that studying continuous, dynamic social interactions is essential for understanding real-world social cognition, and that existing paradigms often force unnatural attention-splitting or turn-taking behaviors that don't reflect naturalistic interaction patterns.
The four example applications showcase the DIP's versatility across diverse research questions. The Bach-or-Stravinsky economic game example is particularly compelling, demonstrating how continuous access to partners' actions substantially changes coordination strategies in non-human primates. This highlights a key strength of the DIP, which is that it removes a level of abstraction that can make tasks more difficult for non-human primates to learn. By being able to see their partner and actions directly, rather than having to understand that a cursor on a screen represents a partner, the platform makes the task more accessible to non-human primates and possibly children as well. This opens up important avenues for enhanced cross-species investigations of cognition, allowing researchers to study social dynamics in a setting that remains naturalistic yet controlled across different populations.
Weaknesses:
Some of the experimental applications would benefit from stronger evidence demonstrating the unique advantages of the transparent setup. For instance, in the dyadic foraging example, it's not entirely clear how participants' behavior differs from what might be observed when simply tracking each other's cursor movements in a non-transparent setup. More evidence showing how direct visibility of the partner, beyond simply being able to track the position of the partner's cursor, influences behavior would strengthen this example. Similarly, in the continuous perceptual report (CPR) task, the subjects could perform this task and see feedback from their partners' actions without having to see their partner through the transparent screen. Evidence showing that 1) subjects do indeed look at their partner during the task and 2) viewing their partner influences their performance on the task would significantly strengthen the claim that the ability to view the partner brings in a new dimension to this task. These additions would better demonstrate the specific value added by the transparent nature of the DIP beyond what could be achieved with standard cursor-tracking paradigms.
A significant limitation that is inadequately addressed relates to neural investigations. While the authors position the platform's ability to merge attention to social stimuli and task stimuli as a key advantage, they don't sufficiently acknowledge the challenges this creates for dissociating neural signals attributed to social cues versus task-based stimuli. More traditional lab-based experiments intentionally separate components like task-stimulus perception, social perception, and decision-making periods so that researchers can isolate the neural signals associated with each process. This deliberate separation, which the authors frame as a weakness, actually serves an important functional purpose in neural investigations. The paper would be strengthened by explicitly discussing this limitation and offering potential approaches to address it in experimental design or data analysis. For instance, the authors could suggest methodological innovations or analytical techniques that might help disentangle the overlapping neural signals that would inevitably arise from the integrated presentation of social and task stimuli in the DIP setup.
Furthermore, the authors' suggestion to arrange task stimuli around the periphery of the screen to maintain a clear middle area for viewing the partner appears to contradict their own critique of traditional paradigms. This recommended arrangement would seemingly reintroduce the very problem of attentional switching between task stimuli and social partners that the authors identified as a limitation of previous approaches. The paper would be strengthened by discussing the potential trade-offs associated with their suggested stimulus arrangement. Additionally, offering potential approaches to address these limitations in experimental design or data analysis would enhance the paper's contribution to the field.
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Reviewer #2 (Public review):
Summary:
This work proposes a new platform to study social cognition in a more naturalistic setting. The authors give an overview of previous work that extends from static unidirectional paradigms (i.e., subject is presented with social stimuli such as still images or faces), to more dynamic unidirectional paradigms (i.e., the subject is presented with movies, or another individual's behavior) to dyadic interactions in a laboratory setting or in real life (i.e., interacting with a real person). Overall, this literature demonstrates that findings from realistic social situations can differ dramatically from unidirectional laboratory settings. Moreover, current and previous work are put in the perspective of an experimental framework that has tightly controlled experimental set-ups and low ecological validity on one end, and high ecological validity, naturalistic, without any experimental constraints on the other end, and all that is in between. The authors frame previous work along a spectrum, ranging from highly controlled, low-ecological-validity experiments to naturalistic, unconstrained approaches with high ecological validity, situating their current work within this continuum. They focus on a specific sub-domain of social interactions, i.e., goal-directed contexts in which interactions are purposeful for solving joint tasks or obtaining rewards. This new dyadic interaction platform claims to embed tight experimental control in a naturalistic face-to-face social interaction with the goal of investigating social information processing in bidirectional, dynamic social interactions.
Strengths:
The proposed dyadic interaction platform (DIP) is highly flexible, accommodating diverse visual displays, interactive components, and recording devices, making it suitable for various experiments.
The manuscript does a good job of highlighting the strengths and weaknesses of the various display options. This clarity allows readers to easily assess which display best suits their specific experimental setup and objectives.
One of the platform's key strengths is its versatility, allowing the same experimental setup to be used across multiple species and developmental stages, and enabling NHPs and humans to be studied as subjects within the same paradigm. Highlighting this capability could further underscore the platform's broad applicability.
Weaknesses:
The manuscript emphasizes the importance of ecological validity alongside tight experimental control, a significant challenge in naturalistic neuroscience. While the platform achieves tight control, the ecological validity of such a set-up remains questionable and warrants further testing and validation. For example, while the platform is designed to be more naturalistic in principle, its application to NHPs is still complex and may be comparably constrained as traditional NHP research. To realize its full potential for animal studies, the platform should be combined with complementary methodologies - such as wireless electrophysiology and freely moving paradigms - to truly achieve a balance between ecological validity and experimental control. Further validation in this direction could significantly enhance its utility.
The manuscript is somewhat lengthy and occasionally reads more like a review paper, which slightly shifts the focus away from the primary emphasis on the innovative technological advancement and the considerable effort invested in optimizing this new platform. Streamlining the presentation to more directly highlight these key contributions could enhance clarity and impact.
Overall, there is compelling evidence supporting the feasibility and value of DIP for investigating specific types of social interactions, particularly in contexts where individuals share a workspace and have full transparency regarding their opponent's actions. While I believe that DIP has the potential to significantly impact the field, which is supported by preliminary data, its broader applicability remains an open question. This platform aligns well with recent initiatives aimed at enhancing ecological validity in neuroscience research across both human and animal models. To maximize its impact, it would be beneficial to more explicitly situate this work within that broader movement, emphasizing its relevance and potential to advance ecologically valid approaches in the field.
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Reviewer #1 (Public review):
Summary:
In this study titled 'The Lipocone Superfamily: A Unifying Theme In Metabolism Of Lipids, Peptidoglycan, And Exopolysaccharides, Inter-Organismal Conflicts And Immunity' from L. Aravind's group, the authors report the identification of a novel domain superfamily termed "Lipocone" superfamily. This superfamily unifies Wnt protein with a spectrum of domains from about 30 families, including those from phosphatidylserine synthases (PTDSS1/2), TelC toxin, VanZ proteins, and the animal Serum Amyloid A (SAA). The authors provide evidence that this superfamily originated as membrane proteins, with few (including Wnt and SAA) evolving into soluble domains. The authors also provide contextual evidence for the Lipocone members recruited as effectors in biological conflicts in both prokaryotes and eukaryotes. Importantly, to my knowledge, this study is the first to decipher the origins of Wnt signaling (emerging from a membrane protein context) and provide novel insights into immunity.
- The study is well-executed and provides many interesting leads for further experimental studies, which makes it very important. One of the significant hypotheses in this context is metazoan Wnt Lipocone domain interactions with lipids, which remain to be explored.
- The manuscript is generally navigable for interesting reading despite being content-rich.
- Overall, the figures are easy to follow.
Significance:
This study not only provides a plausible solution to the origins of metazoan Wnt signaling but also hypothesizes, based on retained ancestral substrate binding pocket, potential lipid interactions for lipocone wnt domains. The study also predicts novel enzymatic roles for many poorly characterized proteins that are involved in immunity across lineages/superkingdoms. This work is likely to inspire numerous experimental studies attempting to verify the hypotheses described in the study.
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Reviewer #2 (Public review):
Summary:
This is a remarkable study, one of a kind. The authors trace the entire huge superfamily containing Wnt proteins which origins remained obscure before this work. Even more amazingly, they show that Wnts originated from transmembrane enzymes. The work is masterfully executed and presented. The conclusions are strongly supported by multiple lines of evidence. Illustrations are beautifully crafted. This is an exemplary work of how modern sequence and structure analysis methods should be used to gain unprecedented insights into protein evolution and origins.
Significance:
Wnts are essential in animal development and their studies attracted significant attention. Therefore, this work is of high importance. Moreover, the authors delineated the entire superfamily consisting of many families with unique functional roles throughout all domains of live. The broad reach of this work further elevates its significance.
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Reviewer #3 (Public review):
Summary:
The manuscript by Burroughs et al. uses informatic sequence analysis and structural modeling to define a very large, new superfamily which they dub the Lipocone superfamily, based on its function on lipid components and cone-shaped structure. The family includes known enzymatic domains as well as previously uncharacterized proteins (30 families in total). Support for the superfamily designation includes conserved residues located on the homologous helical structures within the fold. The findings include analyses that shed light on important evolutionary relationships including a model in which the superfamily originated as membrane proteins where one branch evolved into a soluble version. Their mechanistic proposals suggest possible functions for enzymes currently unassigned. There is also support for the evolutionary connection of this family with the human immune system. The work will be of interest to those in the broad areas of bioinformatics, enzyme mechanisms, and evolution. The work is technically well performed and presented.
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arxiv.org arxiv.org
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Reviewer #1 (Public review):
This paper concerns mechanisms of foraging behavior in C. elegans. Upon removal from food, C. elegans first executes a stereotypical local search behavior in which it explores a small area by executing many random, undirected reversals and turns called "reorientations." If the worm fails to find food, it transitions to a global search in which it explores larger areas by suppressing reorientations and executing long forward runs (Hills et al., 2004). At the population level, reorientation rate declines gradually. Nevertheless, about 50% of individual worms appear to exhibit an abrupt transition between local and global search, which is evident as a discrete transition from high to low reorientation rate (Lopez-Cruz et al., 2019). This observation has given rise to the hypothesis that local and global search correspond to separate internal states with the possibility of sudden transitions between them (Calhoun et al., 2014). The objective of the paper is to demonstrate that is not necessary to posit distinct internal states to account for discrete transitions from high to low reorientation rate. On the contrary, discrete transitions can occur simply because of the stochastic nature of the reorientation behavior itself.
Major strengths and weaknesses of the methods and results
The model was not explicitly designed to match the sudden, stable changes in reorientation rates observed in the experimental data from individual worms. Kinetic parameters were simply chosen to match the average population behavior. Nevertheless, many sudden stable changes in reorientation rates occurred. This is a strong argument that apparent state changes can arise as an epiphenomenon of stochastic processes.
The new stochastic model is more parsimonious than reorientation-state change model because it posits one state rather than two.
A prominent feature of the empirical data is that 50% of the worms exhibit a single (apparent) state change and the rest show either no state changes or multiple state changes. Does the model reproduce these proportions? This obvious question was not addressed.
There is no obvious candidate for the neuronal basis of the decaying factor M. The authors speculate that decreasing sensory neuron activity might be the correlate of M but then provide contradictory evidence that seems to undermine that hypothesis. The absence of a plausible neuronal correlate of M weakens the case for the model.
Appraisal of whether the authors achieved their aims, and whether the results support their conclusions
The authors have made a convincing case that is not necessary to posit distinct internal states to account for discrete transitions from high to low reorientation rate. On the contrary, discrete transitions can occur simply because of the stochastic nature of the reorientation behavior itself.
Impact of the work on the field, and the utility of the methods and data to the community
Posting hidden internal states to explain behavioral sequences is gaining acceptance in behavioral neuroscience. The likely impact of the paper is to establish a compelling example of how statistical reasoning can reduce the number of hidden states to achieve models that are more parsimonious.
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Reviewer #2 (Public review):
Summary:
In this study, the authors build a statistical model that stochastically samples from a time-interval distribution of reorientation rates. The form of the distribution is extracted from a large array of behavioral data, is then used to describe not only the dynamics of individual worms (including the inter-individual variability in behavior), but also the aggregate population behavior. The authors note that the model does not require any assumptions about behavioral state transitions, or evidence accumulation, as has been done previously, but rather that the stochastic nature of behavior is "simply the product of stochastic sampling from an exponential function".
Strengths:
This model provides a strong juxtaposition to other foraging models in the worm. Rather than evoking a behavioral transition function (that might arise from a change in internal state or the activity of a cell type in the network), or evidence accumulation (which again maps onto a cell type, or the activity of a network) - this model explains behavior via the stochastic sampling of a function of an exponential decay. The underlying model and the dynamics being simulated, as well as the process of stochastic sampling are well described, and the model fits the exponential function (equation 1) to data on a large array of worms exhibiting diverse behaviors (1600+ worms from Lopez-Cruz et al). The work of this study can explain or describe the inter-individual diversity of worm behavior across a large population. The model is also able to capture two aspects of the reorientations, including the dynamics (to switch or not to switch) and the kinetics (slow vs fast reorientations). The authors also work to compare their model to a few others including the Levy walk (whose construction arises from a Markov process) to a simple exponential distribution, all of which have been used to study foraging and search behaviors.
Weaknesses:
The weaknesses are one of framework, which may nonetheless stir discussion and motivate new ideas based on these results.
First, the examples the authors cite where a Gillespie algorithm is used to sample from a distribution, be it the kinetics associated with chemical dynamics, or a Lotka-Volterra Competition Model, there are underlying processes that govern the evolution of the dynamics, and thus the sampling from distributions. In one of their references for instance, the stochasticity arises from the birth and death rates, thereby influencing the genetic drift in the model. In these examples, the process governing the dynamics (and thus generating the distributions from which one samples) are distinct from the behavior being studied. In this manuscript, the distribution being sampled from is the exponential decay function of the reorientation rate. That the model performs well, and matches the data is commendable, but it is unclear how that could not be the case if the underlying function generating the distribution was fit to the data.
The second weakness is related to the first, in that absent an underlying mechanism or framework, one is left wondering what insight the model provides. Stochastic sampling a function generated by fitting the data to produce stochastic behavior is where one ends up in this framework. But if that is the case, what do we learn about how the foraging is happening. The authors suggest that the decay parameter M can be considered a memory timescale, which offers some suggestion, but then go on to say that the "physical basis of M can come from multiple sources". Here is where one is left for want: Molecular dynamics models that generate distributions can point to certain properties of the model, such as the binding kinetics (on and off rates, etc.) as explanations for the mechanisms generating the distributions, and therefore point to how a change in the biology affects the stochasticity of the process. It is unclear how this model provides such a connection.
The authors provide possible roadmaps, but where they lead and how to relate that back to testable mechanistic studies remains unclear. Weighing the significance of the finding relative to the weaknesses appears to depend on how one feels about the possible mechanisms the authors identify in their responses.
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Reviewer #1 (Public review):
Dovek and colleagues aimed at investigating the cellular and circuitry mechanisms underlying the recruitment of dentate gyrus neurons (including two morpho-physiologically-distinct subpopulations of excitatory cells called granular cells or GCs, and semilunar cells or SGCs) into memory representations, also known as engrams. To this end, the authors used TRAP2 mice to investigate the dentate gyrus "engram" neurons that were activated or not (i.e., labeled or not) in a non-fear-based context (mostly enriched environment or EE, but also Barnes Maze or BM).
A significant proportion of dentate gyrus neurons are labeled after EE exposure (35%) or after BM acquisition (15%). SGCs, distinguished from GCs using morphology-based classification, showed disproportionately context-dependent recruitment. Consistent with previous observations (Erwin et al., 2022), SGCs account for a third of behaviorally recruited "engram" neurons, although they represent less than 5% of excitatory neurons in the dentate gyrus.
Then, the authors compared the intrinsic physiological properties of GCs and SGCs that are recruited or not during EE. Consistent with previous observations (Williams et al., 2007, Afrasiabi et al., 2022), SGCs and GCs exhibited numerous differences (e.g., Rin, firing frequency) regardless of whether they were behaviorally activated or not. Differences in physiology between excitatory neuron subtypes might explain the preferential recruitment of SGCs. Interestingly, "engram" SGCs displayed lower values of adaptation in firing rate than non-recruited SGCs.
To examine how GCs and SGCs activated during EE are integrated into the local dentate gyrus microcircuits, the authors next performed a dual patch-clamp recording combined with wide-field optogenetics. Despite the presence of spontaneous EPSCs, no direct functional glutamatergic interconnection was observed between pairs of "engram" GCs and SGCs. In addition, although optogenetic stimulation of a large, random, population of neurons evokes IPSCs (indicating efficient lateral inhibition as in Stefanelli et al., 2016), the specific stimulation of behaviorally recruited GCs or SGCs rarely elicits IPSCs onto surrounding non-engram excitatory neurons.
To assess whether neurons recruited or not during EE receive differential glutamatergic drive, the authors recorded spontaneous excitatory inputs received by labeled and unlabeled GCs and SGCs. They observed that sEPSCs in labeled GCs and SGCs are more frequent and larger than in unlabeled GCs and SGCs, respectively.
Last, the authors investigated whether neurons (without discriminating GCs and SGCs) recruited in the same context were characterized by a higher propensity to receive temporally correlated inputs. To this end, they performed dual patch-clamp and analyzed the temporal correlation of spontaneous EPSCs received by pairs of neurons (either two dentate gyrus "engram" neurons, or one "engram" neuron and one "non-engram" neuron in an EE context). They observed that the temporal correlation of excitatory events received by pairs of engram neurons was greater than that of pairs of neurons that do not belong to the same ensemble, and that expected by chance.
Altogether, the data suggest that the context-dependent recruitment of dentate gyrus excitatory neurons, particularly SGCs is correlated to distinctive intrinsic properties and (correlated) excitatory afferent. Contrary to a leading hypothesis, the authors found no evidence that recruited neurons drive robust feedforward excitation of other engram neurons or feedback inhibition of non-engram neurons.
Strengths:
This article provides some information about the mechanisms that may be involved in the recruitment of neural ensembles that form non-fear-based memory engrams in the dentate gyrus. I find it interesting that the authors considered not only granular cells, the main population of excitatory neurons in the dentate gyrus, but also a sparse subpopulation of semilunar cells, a relatively understudied type of dentate excitatory neuron.
Weakness:
Most of the data presented are descriptive and based on correlation rather than causation.
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Reviewer #2 (Public review):
Summary:
The authors use the TRAP2 mouse line to label dentate gyrus cells active during and enriched environment paradigm and cut brain slices from these animals one week later to determine whether granule cells (GC) and semilunar granule cells (SGC) labelled during the exposure share common features. They particularly focus on the role of SGCs and potential circuit mechanisms by which they could be selectively embedded in the labelled assembly. The authors claim that SGCs are disproportionately recruited into IEG expressing assemblies due to intrinsic firing characteristics but cannot identify any contributing circuit connectivity motives in the slice preparation, although they claim that an increased correlation between spontaneous synaptic currents in the slice could signify common synaptic inputs as the source of assembly formation.
Strengths:
The authors chose a timely and relevant question, namely, how memory-bearing neuronal assemblies, or 'engrams', are established and maintained in the dentate gyrus. After the initial discovery of such memory-specific ensembles of immediate-early gene expressing engrams in 2012 (Ramirez et al.) this issue has been explored by several high-profile studies that have considerably expanded our understanding of the underlying molecular and cellular mechanisms, but still leave a lot of unanswered questions.
Weaknesses:
(1) The authors claim that recurrent excitation from SGCs onto GCs or other SGCs is irrelevant because they did not find any connections in 32 simultaneous recordings (plus 63 in the next experiment). Without a demonstration that other connections from SGCs (e.g. onto mossy cells or interneurons) are preserved in their preparation and if so at what rates, it is unclear whether this experiment is indicative of the underlying biology or the quality of the preparation. The argument that spontaneous EPSCs are observed is not very convincing as these could equally well arise from severed axons (in fact we would expect that the vast majority of inputs are not from local excitatory cells). The argument on line 418 that SGCs have compact axons isn't particularly convincing either given that the morphologies from which they were derived were also obtained in slice preparations and would be subject to the same likelihood of severing the axon. Finally, even in paired slice recordings from CA3 pyramidal cells the experimentally detected connectivity rates are only around 1% (Guzman et al., 2016). The authors would need to record from a lot more than 32 pairs (and show convincing positive controls regarding other connections) to make the claim that connectivity is too low to be relevant.
The authors now provide evidence that at least some synaptic connections are preserved by recruiting GC assemblies with channelrhodopsin, resulting in feedback inhibition which supports their argument.
(2) Another concern is that optogenetic GC stimulation rarely ever evokes feedback inhibition onto other cells which contrasts with both other in vitro (e.g. Braganza et al., 2020) and in vivo studies (Stefanelli et al., 2016) studies. Without a convincing demonstration that monosynaptic connections between SGCs/GCs and interneurons in both directions is preserved at least at the rates previously described in other slice studies (e.g. Geiger et al., 1997, Neuron, Hainmueller et al., 2014, PNAS, Savanthrapadian et al., 2014, J. Neurosci). The authors now provide evidence that at least some synaptic connections are preserved by stimulating a random subset of granule cells optogenetically, although it still remains unclear how the rate of connectivity compares to other studies or a live organism.
(3) Probably the most convincing finding in this study is the higher zero-time lag correlation of spontaneous EPSCs in labelled vs. unlabeled pairs. Unfortunately, the authors use spontaneous EPSCs to begin with, which likely represent a mixture of spontaneous release from severed axons, minis, and coordinated discharge from intact axon segments or entire neurons, make it very hard to determine the meaning and relevance of this finding. The authors now show the baseline EPSC rates and conventional Cross correlograms (CCG; see e.g. English et al., 2017, Neuron; Senzai and Buzsaki, 2017, Neuron) lending more support to this conclusion.
(4) Finally, one of the biggest caveats of the study is that the ensemble is labelled a full week before the slice experiment and thereby represents a latent state of a memory rather than encoding, consolidation, or recall processes. The authors acknowledge that in the discussion but they should also be mindful of this when discussing other (especially in vivo) studies and comparing their results to these. For instance, Pignatelli et al 2018 show drastic changes in GC engram activity and features driven by behavioral memory recall, so the results of the current study may be very different if slices were cut immediately after memory acquisition (if that was possible with a different labelling strategy), or if animals were re-exposed to the enriched environment right before sacrificing the animal. The authors discuss this limitation appropriately.
There are also a few minor issues limiting the extent of interpretations of the data:
(1) Only about 7% of the 'engram' cells are re-activated one week after exposure (line 147), it is unclear how meaningful this assembly is given the high number of cells that may either be labelled unrelated to the EE or no longer be part of the memory-related ensemble.
(2) Line 215: The wording '32 pairwise connections examined' suggests that there actually were synaptic connections; would recommend altering the wording to 'simultaneously recorded cells examined' to avoid confusion.
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Reviewer #3 (Public review):
Summary:
The study explores the cellular and circuit features that distinguish dentate gyrus semilunar granule cells and granule cells activated during contextual memory formation. The authors tag memory and enriched environment-activated dentate granule cells and semilunar granule cells and show their reactivation in an appropriate context a week later. They perform patch clamp recordings from activated and surrounding neurons to understand the cellular driving of the selective activation of semilunar granule cells and granule cells. Authors perform dual patch clamp recordings from various pairs of labeled semilunar granule cells, labeled granule cells, unlabeled granule cells, and unlabeled semilunar granule cells. The sustained firing of semilunar granule cells explained their preferential activation. In addition, activated neurons received correlated inputs.
Strengths:
The authors confirmed the engram cell properties of activated semilunar granule cells and granule cells in two different paradigms, validating these findings using an enriched environment paradigm.
The authors carefully separate semilunar granule cells from granule cells, using electrophysiology and morphology. Cell filling to confirm morphology further strengthens confidence.
The dual patch recordings, which are technically challenging, are carefully performed, and the presence of synaptic activity is confirmed.
The authors report that sEPSCs recorded from labelled sGCS are more frequent, higher in amplitude, and temporally correlated than their counterparts.
The authors provide evidence that lateral inhibition is not playing a role in the selective activation of sGCs during contextual learning.
Exclusive use of slice physiology limits some of these conclusions due to the shearing of connections during the slicing process.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Frelih et al. investigated both periodic and aperiodic activity in EEG during working memory tasks. In terms of periodic activity, they found post-stimulus decreases in alpha and beta activity, while in terms of aperiodic activity, they found a bi-phasic post-stimulus steepening of the power spectrum, which was weakly predictive of performance. They conclude that it is crucial to properly distinguish between aperiodic and periodic activity in event-related designs as the former could confound the latter. They also add to the growing body of research highlighting the functional relevance of aperiodic activity in the brain.
Strengths:
This is a well-written, timely paper that could be of interest to the field of cognitive neuroscience, especially to researchers investigating the functional role of aperiodic activity. The authors describe a well-designed study that looked at both the oscillatory and non-oscillatory aspects of brain activity during a working memory task. The analytic approach is appropriate, as a state-of-the-art toolbox is used to separate these two types of activity. The results support the basic claim of the paper that it is crucial to properly distinguish between aperiodic and periodic activity in event-related designs as the former could confound the latter. They also add to the growing body of research highlighting the functional relevance of aperiodic activity in the brain. Commendably, the authors include replications of their key findings on multiple independent data sets.
Weaknesses:
The authors also claim that their results speak to the interplay between oscillatory and non-oscillatory activity, and crucially, that task-related changes in the theta frequency band - often attributed to neural oscillations in the field - are in fact only a by-product of non-oscillatory changes. I believe these claims are too bold and are not supported by compelling evidence in the paper. Some control analyses - e.g., contrasting the scalp topographies of purportedly theta and non-oscillatory effects - could help strengthen the latter argument, but it may be safest to simply soften these two claims.
In terms of the methodology used, I suggest the authors make it clearer to readers that the primary results were obtained on a sample of middle-aged-to-older-adults, some with subjective cognitive complaints, and note that while stimulus-locked event-related potentials (ERPs) were removed from the data prior to analyses, response-locked ERPs were not. This could potentially confound aperiodic findings. Contrasting the scalp topographies of response-related ERPs and the identified aperiodic components, especially the later one, could bring some clarity here too.
I have also found certain parts of the introduction to be somewhat confusing.
Comments on the latest version:
The authors have addressed several of the weaknesses I noted in my original review, specifically, they softened their claims regarding the theta findings, while simultaneously strengthening these findings with additional analyses (using simulations as well as a new measure of rhythmicity, the phase autocorrelation function, pACF). Most of the other suggested control analyses were also implemented. While I believe the fact that the participants in the main sample were not young adults could be made even more explicit, and the potential interaction between age and aperiodic changes could be unpacked a little in the discussion, the age of the sample is definitely addressed upfront.
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Reviewer #2 (Public review):
Summary:
In this manuscript, Frelih et al, investigate the relationship between aperiodic neural activity, as measured by EEG, and working memory performance, and compares this to the more commonly analyzed periodic, and in particular theta, measures that are often associated with such tasks. To do so, they analyze a primary dataset of 57 participants engaging in an n-back task, as well as a replication dataset, and use spectral parameterization to measure periodic and aperiodic features of the data, across time. In the revision, the authors have clarified some key points, and added a series of additional analyses and controls, including the use of an additional method, that helps to complement the original analyses and further corroborates their claims. In doing so, they find both periodic and aperiodic features that relate to the task dynamics, but importantly, the aperiodic component appears to explain away what otherwise looks like theta activity in a more traditional analysis. This study therefore helps to establish that aperiodic activity is a task-relevant dynamic feature in working memory tasks and may be the underlying change in many other studies that reported 'theta' changes, but did not use methods that could differentiate periodic and aperiodic features.
Strengths:
Key strengths of this paper include that it addresses an important question - that of properly adjudicating which features of EEG recordings relate to working memory tasks - and in doing so provides a compelling answer, with important implications for considering prior work and contributing to understanding the neural underpinnings of working memory. The revision is improved by showing this using an additional analysis method. I do not find any significant faults or error with the design, analysis, and main interpretations as presented by this paper, and as such, find the approach taken to be a valid and well-enacted. The use of multiple variants of the working memory task, as well as a replication dataset significantly strengthens this manuscript, by demonstrating a degree of replicability and generalizability. This manuscript is also an important contribution to motivating best practices for analyzing neuro-electrophysiological data, including in relation to using baselining procedures. I think the updates in the revision have helped to clarify the findings and impact of this study.
Weaknesses:
Overall, I do not find any obvious weaknesses with this manuscript and it's analyses that challenge the key results and conclusions. Updates through the revision have addressed my previous points about adding some additional notes on the methods and conclusions.
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Reviewer #3 (Public review):
Summary:
Using a specparam (1/f) analysis of task-evoked activity, the authors propose that "substantial changes traditionally attributed to theta oscillations in working memory tasks are, in fact, due to shifts in the spectral slope of aperiodic activity." This is a very bold and ambitious statement, and the field of event-related EEG would benefit from more critical assessments of the role of aperiodic changes during task events. Unfortunately, the data shown here does not support the main conclusion advanced by the authors.
Strengths:
The field of event-related EEG would benefit from more critical assessments of the role of aperiodic changes during task events. The authors perform a number of additional control analyses, including different types of baseline correction, ERP subtraction, as well as replication of the experiment with two additional datasets.
Weaknesses:
The authors did not first show that their first task successfully evoked theta power, nor that specparam is capable of quantifying the background around a short theta burst, nor that theta effects are different between baseline corrected vs. spectral parameterized quantification.
Comments on revisions:
The authors have completed a substantial revision based on the comments from all of the reviewers. Overall, the major claims of the initial report have been profoundly tempered, but more of the conclusions are supported by the data.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Most studies in sensory neuroscience investigate how individual sensory stimuli are represented in the brain (e.g., the motion or color of a single object). This study starts tackling the more difficult question of how the brain represents multiple stimuli simultaneously and how these representations help to segregate objects from cluttered scenes with overlapping objects.
Strengths
The authors first document the ability of humans to segregate two motion patterns based on differences in speed. Then they show that a monkey's performance is largely similar; thus establishing the monkey as a good model to study the underlying neural representations.
Careful quantification of the neural responses in the middle temporal area during the simultaneous presentation of fast and slow speeds leads to the surprising finding that, at low average speeds, many neurons respond as if the slowest speed is not present, while they show averaged responses at high speeds. This unexpected complexity of the integration of multiple stimuli is key to the model developed in this paper.
One experiment in which attention is drawn away from the receptive field supports the claim that this is not due to the involuntary capture of attention by fast speeds.
A classifier using the neuronal response and trained to distinguish single speed from bi-speed stimuli shows a similar overall performance and dependence on the mean speed as the monkey. This supports the claim that these neurons may indeed underlie the animal's decision process.
The authors expand the well-established divisive normalization model to capture the responses to bi-speed stimuli. The incremental modeling (eq 9 and 10) clarifies which aspects of the tuning curves are captured by the parameters.
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Reviewer #3 (Public review):
Summary:
This study concerns how macaque visual cortical area MT represents stimuli composed of more than one speed of motion.
Strengths:
The study is valuable because little is known about how the visual pathway segments and preserves information about multiple stimuli. The study presents compelling evidence that (on average) MT neurons shift from faster-speed-takes-all at low speeds to representing the average of the two speeds at higher speeds. An additional strength of the study is the inclusion of perceptual reports from both humans and one monkey participant performing a task in which they judged whether the stimuli involved one vs two different speeds. Ultimately, this study raises intriguing questions about how exactly the response patterns in visual cortical area MT might preserve information about each speed, since such information is potentially lost in an average response as described here.
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www.biorxiv.org www.biorxiv.org
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Reviewer #2 (Public review):
In this valuable manuscript, Lin et al attempt to examine the role of long non coding RNAs (lncRNAs) in human evolution, through a set of population genetics and functional genomics analyses that leverage existing datasets and tools. Although the methods are incomplete and at times inadequate, the results nonetheless point towards a possible contribution of long non coding RNAs to shaping humans, and suggest clear directions for future, more rigorous study.
Comments on revisions:
I thank the authors for their revision and changes in response to previous rounds of comments. As it had been nearly two years since I last saw the manuscript, I reread the full text to familiarise myself again with the findings presented. While I appreciate the changes made and think they have strengthened the manuscript, I still find parts of it a bit too speculative or hyperbolic. In particular, I think claims of evolutionary acceleration and adaptation require more careful integration with existing human/chimpanzee genetics and functional genomics literature. For example:
Line 155: "About 5% of genes have significant sequence differences in humans and chimpanzees," This statement needs a citation, and a definition of what is meant by 'significant', especially as multiple lines below instead mention how it's not clear how many differences matter, or which of them, etc.
line 187: "Notably, 97.81% of the 105141 strong DBSs have counterparts in chimpanzees, suggesting that these DBSs are similar to HARs in evolution and have undergone human-specific evolution." I do not see any support for the inference here. Identifying HARs and acceleration relies on a far more thorough methodology than what's being presented here. Even generously, pairwise comparison between two taxa only cannot polarise the direction of differences; inferring human-specific change requires outgroups beyond chimpanzee.
line 210: "Based on a recent study that identified 5,984 genes differentially expressed between human-only and chimpanzee-only iPSC lines (Song et al., 2021), we estimated that the top 20% (4248) genes in chimpanzees may well characterize the human-chimpanzee differences" I do not agree with the rationale for this claim, and do not agree that it supports the cutoff of 0.034 used below. I also find that my previous concerns with the very disparate numbers of results across the three archaics have not been suitably addressed.
I also think that there is still too much of a tendency to assume that adaptive evolutionary change is the only driving force behind the observed results in the results. As I've stated before, I do not doubt that lncRNAs contribute in some way to evolutionary divergence between these species, as do other gene regulatory mechanisms; the manuscript leans down on it being the sole, or primary force, however, and that requires much stronger supporting evidence. Examples include, but are not limited to:
line 230: "These results reveal when and how HS lncRNA-mediated epigenetic regulation influences human evolution." This statement is too speculative.
Line 268: "yet the overall results agree well with features of human evolution." What does this mean? This section is too short and unclear.
Line 325: "and form 198876 HS lncRNA-DBS pairs with target transcripts in all tissues." This has not been shown in this paper - sequence based analyses simply identify the *potential* to form pairs.
Line 423: "Our analyses of these lncRNAs, DBSs, and target genes, including their evolution and interaction, indicate that HS lncRNAs have greatly promoted human evolution by distinctly rewiring gene expression." I do not agree that this conclusion is supported by the findings presented - this would require significant additional evidence in the form of orthogonal datasets.
I also return briefly to some of my comments before, in particular on the confounding effects of gene length and transcript/isoform number. In their rebuttal the authors argued that there was no need to control for this, but this does in fact matter. A gene with 10 transcripts that differ in the 5' end has 10 times as many chances of having a DBS than a gene with only 1 transcript, or a gene with 10 transcripts but a single annotated TSS. When the analyses are then performed at the gene level, without taking into account the number of transcripts, this could introduce a bias towards genes with more annotated isoforms. Similarly, line 246 focuses on genes with "SNP numbers in CEU, CHB, YRI are 5 times larger than the average." Is this controlled for length of the DBS? All else being equal a longer DBS will have more SNPs than a shorter one. It is therefore not surprising that the same genes that were highlighted above as having 'strong' DBS, where strength is impacted by length, show up here too.
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Reviewer #1 (Public review):
Summary:
This study used explicit-solvent simulations and coarse-grained models to identify the mechanistic features that allow for the unidirectional motion of SMC on DNA. Shorter explicit-solvent models describe relevant hydrogen bond energetics, which were then encoded in a coarse-grained structure-based model. In the structure-based model, the authors mimic chemical reactions as signaling changes in the energy landscape of the assembly. By cycling through the chemical cycle repeatedly, the authors show how these time-dependent energetic shifts naturally lead SMC to undergo translocation steps along DNA that are on a length scale that has been identified.
Strengths:
Simulating large-scale conformational changes in complex assemblies is extremely challenging. This study utilizes highly-detailed models to parameterize a coarse-grained model, thereby allowing the simulations to connect the dynamics of precise atomistic-level interactions with a large-scale conformational rearrangement. This study serves as an excellent example for this overall methodology, where future studies may further extend this approach to investigated any number of complex molecular assemblies.
Weaknesses:
The only relative weakness is that the text does not always clearly communicate which aspects of the dynamics are expected to be robust. That is, which aspects of the dynamics/energetics are less precisely described by this model? Where are the limits of the models, and why should the results be considered within the range of applicability of the models?
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Reviewer #2 (Public review):
Summary:
The authors perform coarse grained and all atom simulations to provide a mechanism for loop extrusion that is involved in genome compaction.
Strengths:
The simulations are very thoughtful. They provide insights into the translocation process, which is only one of the mechanisms. Much of the analyses is very good. Over all the study advances the use of simulations in this complicated systems.
Weaknesses:
Even the authors point out several limitations, which cannot be easily overcome in the paper because of the paucity of experimental data. Nevertheless, the authors could have done so to illustrate the main assertion that loop extrusion occurs by the motor translocating on DNA. They should mention more clearly that there are alternative theories that have accounted for a number of experimental data,
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Reviewer #3 (Public review):
Summary:
In this manuscript, Yamauchi and colleagues combine all-atom and coarse-grained MD simulations to investigate the mechanism of DNA translocation by prokaryotic SMC complexes. Their multiscale approach is well-justified and supports a segment-capture model in which ATP-dependent conformational changes lead to the unidirectional translocation of DNA. A key insight from the study is that asymmetry in the kleisin path enforces directionality. The work introduces an innovative computational framework that captures key features of SMC motor action, including DNA binding, conformational switching, and translocation.
This work is well executed and timely, and the methodology offers a promising route for probing other large molecular machines where ATP activity is essential.
Strengths:
This manuscript introduces an innovative yet simple method that merges all-atom and coarse-grained, purely equilibrium, MD simulations to investigate DNA translocation by SMC complexes, which is triggered by activated ATP processes. Investigating the impact of ATP on large molecular motors like SMC complexes is extremely challenging, as ATP catalyses a series of chemical reactions that take and keep the system out of equilibrium. The authors simulate the ATP cycle by cycling through distinct equilibrium simulations where the force field changes according to whether the system is assumed to be in the disengaged, engaged, and V-shaped states; this is very clever as it avoids attempting to model the non-equilibrium process of ATP hydrolysis explicitly. This equilibrium switching approach is shown to be an effective way to probe the mechanistic consequences of ATP binding and hydrolysis in the SMC complex system.
The simulations reveal several important features of the translocation mechanism. These include identifying that a DNA segment of ~200 bp is captured in the engaged state and pumped forward via coordinated conformational transitions, yielding a translocation step size in good agreement with experimental estimates. Hydrogen bonding between DNA and the top of the ATPase heads is shown to be critical for segment capturtrans, as without it, translocation is shown to fail. Finally, asymmetry in the kleisin subunit path is shown to be responsible for unidirectionally.
This work highlights how molecular simulations are an excellent complement to experiments, as they can exploit experimental findings to provide high-resolution mechanistic views currently inaccessible to experiments. The findings of these simulations are plausible and expand our understanding of how ATP hydrolysis induces directional motion of the SMC complex.
Weaknesses:
There are aspects of the methodology and modelling assumptions that are not clear and could be better justified. The major ones are listed below:
(1) The all-atom MD simulations involve a 47-bp DNA duplex interacting with the ATPase heads, from which key residues involved in hydrogen bonding are identified. However, DNA mechanics-including flexibility and hydrogen bond formation-are known to be sequence-dependent. The manuscript uses a single arbitrary sequence but does not discuss potential biases. Could the authors comment on how sequence variability might affect binding geometry or the number of hydrogen bonds observed?
(2) A key feature of the coarse-grained model is the inclusion of a specific hydrogen-bonding potential between DNA and residues on the ATPase heads. The authors select the top 15 hydrogen-bond-forming residues from the all-atom simulations (with contact probability > 0.05), but the rationale for this cutoff is not explained. Also, the strength of hydrogen bonds in coarse-grained models can be sensitive to context. How did the authors calibrate the strength of this interaction relative to electrostatics, and did they test its robustness (e.g., by varying epsilon or residue set)? Could this interaction be too strong or too weak under certain ionic conditions? What happens when salt is changed?
(3) To enhance sampling, the translocation simulations are run at 300 mM monovalent salt. While this is argued to be physiological for Pyrococcus yayanosii, such a concentration also significantly screens electrostatics, possibly altering the interaction landscape between DNA and protein or among protein domains. This may significantly impact the results of the simulations. Why did the authors not use enhanced sampling methods to sample rare events instead of relying on a high-salt regime to accelerate dynamics?
(4) Only a small fraction of the simulated trajectories complete successful translocation (e.g., 45 of 770 in one set), and this is attributed to insufficient simulation time. While the authors are transparent about this, it raises questions about the reliability of inferred success rates and about possible artefacts (e.g., DNA trapping in coiled-coil arms). Could the authors explore or at least discuss whether alternative sampling strategies (e.g., Markov State Models, transition path sampling) might address this limitation more systematically?
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Reviewer #2 (Public review):
Based on the controversy of whether the Desmodium intercrop emits bioactive volatiles that repel the fall armyworm, the authors conducted this study to assess the effects of the volatiles from Desmodium plants in the push-pull system on behavior of FAW oviposition. This topic is interesting and the results are valuable for understanding the push-pull system for the management of FAW, the serious pest. The methodology used in this study is valid, leading to reliable results and conclusions. I just have a few concerns and suggestions for the improvement of this paper:
(1) The volatiles emitted from D. incanum were analyzed and their effects on the oviposition behavior of FAW moth were confirmed. However, it would be better and useful to identify the specific compounds that are crucial for the success of the push-pull system.
(2) That would be good to add "symbols" of significance in Figure 4 (D).
(3) Figure A is difficult for readers to understand.
(4) It will be good to deeply discuss the functions of important volatile compounds identified here with comparison with results in previous studies in the discussion better.
Comments on revisions:
The authors addressed all my concerns, and I believe that the current version is appropriate for publication.
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Reviewer #1 (Public review):
Summary:
This paper addresses an important and topical issue: how temporal context, at various time scales, affects various psychophysical measures, including reaction times, accuracy, and localization. It offers interesting insights, with separate mechanisms for different phenomena, which are well discussed.
Strengths:
The paradigm used is original and effective. The analyses are rigorous.
Weaknesses:
Here I make some suggestions for the authors to consider. Most are stylistic, but the issue of precision may be important.
(1) The manuscript is quite dense, with some concepts that may prove difficult for the non-specialist. I recommend spending a few more words (and maybe some pictures) describing the difference between task-relevant and task-irrelevant planes. Nice technique, but not instantly obvious. Then we are hit with "stimulus-related", which definitely needs some words (also because it is orthogonal to neither of the above).
(2) While I understand that the authors want the three classical separations, I actually found it misleading. Firstly, for a perceptual scientist to call intervals in the order of seconds (rather than milliseconds), "micro" is technically coming from the raw prawn. Secondly, the divisions are not actually time, but events: micro means one-back paradigm, one event previously, rather than defined by duration. Thirdly, meso isn't really a category, just a few micros stacked up (and there's not much data on this). And macro is basically patterns, or statistical regularities, rather than being a fixed time. I think it would be better either to talk about short-term and long-term, which do not have the connotations I mentioned. Or simply talk about "serial dependence" and "statistical regularities". Or both.
(3) More serious is the issue of precision. Again, this is partially a language problem. When people use the engineering terms "precision" and "accuracy" together, they usually use the same units, such as degrees. Accuracy refers to the distance from the real position (so average accuracy gives bias), and precision is the clustering around the average bias, usually measured as standard deviation. Yet here accuracy is percent correct: also a convention in psychology, but not when contrasting accuracy with precision, in the engineering sense. I suggest you change "accuracy" to "percent correct". On the other hand, I have no idea how precision was defined. All I could find was: "mixture modelling was used to estimate the precision and guess rate of reproduction responses, based on the concentration (k) and height of von Mises and uniform distributions, respectively". I do not know what that means.
(4) Previous studies show serial dependence can increase bias but decrease scatter (inverse precision) around the biased estimate. The current study claims to be at odds with that. But are the two measures of precision relatable? Was the real (random) position of the target subtracted from each response, leaving residuals from which the inverse precision was calculated? (If so, the authors should say so..) But if serial dependence biases responses in essentially random directions (depending on the previous position), it will increase the average scatter, decreasing the apparent precision.
(5) I suspect they are not actually measuring precision, but location accuracy. So the authors could use "percent correct" and "localization accuracy". Or be very clear what they are actually doing.
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Reviewer #2 (Public review):
Summary:
This study investigates the influence of prior stimuli over multiple time scales in a position discrimination task, using pupillometry data and a reanalysis of EEG data from an existing dataset. The authors report consistent history-dependent effects across task-related, task-unrelated, and stimulus-related dimensions, observed across different time scales. These effects are interpreted as reflecting a unified mechanism operating at multiple temporal levels, framed within predictive coding theory.
Strengths:
The goal of assessing history biases over multiple time scales is interesting and resonates with both classic (Treisman & Williams, 1984) and recent work (Fritsche et al., 2020; Gekas et al., 2019). The manipulations used to distinguish task-related, unrelated, and stimulus-related reference frames are original and promising.
Weaknesses:
I have several concerns regarding the text, interpretation, and consistency of the results, outlined below:
(1) The abstract should more explicitly mention that conclusions about feedforward mechanisms were derived from a reanalysis of an existing EEG dataset. As it is, it seems to present behavioral data only.
(2) The EEG task seems quite different from the others, with location and color changes, if I understand correctly, on streaks of consecutive stimuli shown every 100 ms, with the task involving counting the number of target events. There might be different mechanisms and functions involved, compared to the behavioral experiments reported.
(3) How is the arbitrary choice of restricting EEG decoding to a small subset of parieto-occipital electrodes justified? Blinks and other artifacts could have been corrected with proper algorithms (e.g., ICA) (Zhang & Luck, 2025) or even left in, as decoders are not necessarily affected by noise. Moreover, trials with blinks occurring at the stimulus time should be better removed, and the arbitrary selection of a subset of electrodes, while reducing the information in input to the decoder, does not account for trials in which a stimulus was missed (e.g., due to blinks).
(4) The artifact that appears in many of the decoding results is puzzling, and I'm not fully convinced by the speculative explanation involving slow fluctuations. I wonder if a different high-pass filter (e.g., 1 Hz) might have helped. In general, the nature of this artifact requires better clarification and disambiguation.
(5) Given the relatively early decoding results and surprisingly early differences in decoding peaks, it would be useful to visualize ERPs across conditions to better understand the latencies and ERP components involved in the task.
(6) It is unclear why the precision derived from IEM results is considered reliable while the accuracy is dismissed due to the artifact, given that both seem to be computed from the same set of decoding error angles (equations 8-9).
(7) What is the rationale for selecting five past events as the meso-scale? Prior history effects have been shown to extend much further back in time (Fritsche et al., 2020).
(8) The decoding bias results, particularly the sequence of attraction and repulsion, appear to run counter to the temporal dynamics reported in recent studies (Fischer et al., 2024; Luo et al., 2025; Sheehan & Serences, 2022).
(9) The repulsive component in the decoding results (e.g., Figure 3h) seems implausibly large, with orientation differences exceeding what is typically observed in behavior.
(10) The pattern of accuracy, response times, and precision reported in Figure 3 (also line 188) resembles results reported in earlier work (Stewart, 2007) and in recent studies suggesting that integration may lead to interference at intermediate stimulus differences rather than improvement for similar stimuli (Ozkirli et al., 2025).
(11) Some figures show larger group-level variability in specific conditions but not others (e.g., Figures 2b-c and 5b-c). I suggest reporting effect sizes for all statistical tests to provide a clearer sense of the strength of the observed effects.
(12) The statement that "serial dependence is associated with sensory stimuli being perceived as more similar" appears inconsistent with much of the literature suggesting that these effects occur at post-perceptual stages (Barbosa et al., 2020; Bliss et al., 2017; Ceylan et al., 2021; Fischer et al., 2024; Fritsche et al., 2017; Sheehan & Serences, 2022).
(13) If I understand correctly, the reproduction bias (i.e., serial dependence) is estimated on a small subset of the data (10%). Were the data analyzed by pooling across subjects?
(14) I'm also not convinced that biases observed in forced-choice and reproduction tasks should be interpreted as arising from the same process or mechanism. Some of the effects described here could instead be consistent with classic priming.
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Reviewer #1 (Public review):
Summary:
Liu et al. investigated the mechanisms by which apolipoprotein L1 (APOL1) G1 and G2 variants cause inflammation and lipid accumulation in macrophages by bone-marrow-derived macrophages from transgenic mice and human iPS cells. Although these findings are not novel, this work provides solid evidence to prove enhanced inflammation and lipid accumulation in macrophages by APOL1 G1 and G2 variants by a variety of in vitro assays and metabolomics measurements. Further, metabolomics measurements identified that the spermidine synthesis pathway was altered by APOL1 G1 and G2 variants, and the polyamine inhibitor reversed the variants-induced phenotypes.
Strengths:
Their hypothesis and choice of experiments in each section were clear and mostly solid. Mitochondrial morphological quantification by transmission electron microscopy images was convincing. The authors confirmed APOL1 localization inside macrophages and built stories based on their findings. Showing relevant positive and negative findings in line with current knowledge of APOL1-variants-driven pathologies, such as cation flux, cGAS-STING pathways, indicates a good rigor.
Weaknesses:
Although most methods in this work were solid, the choice of α-difluoromethylornithine (DFMO) as an inhibitor of spermidine synthesis was not direct. It was still unclear if DFMO was reversing the phenotypes by lowering spermidine levels. Seahorse assay results would have avoided potential variabilities in cell densities by normalization. Heatmaps showing RNA-seq results would be appreciated better with a clear description of how the color is defined and calculated.
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Reviewer #2 (Public review):
Summary:
The G1 and G2 variants of the Apolipoprotein L1 (APOL1) gene are well-established risk factors for chronic kidney disease. While macrophages have been implicated in the pathogenesis of APOL1-mediated kidney diseases (AMKD), the precise impact of the G1 and G2 APOL1 variants on macrophage function and the underlying molecular mechanisms remains insufficiently characterized. In this manuscript, the authors investigate pathological phenotypes in macrophages carrying the G1 and G2 APOL1 variants. They report an accumulation of neutral lipids and activation of pro-inflammatory pathways, which appear to be at least partly driven by an accumulation of the polyamine spermidine and upregulation of the spermidine synthesis pathway. These findings reveal a pro-inflammatory role for G1 and G2 APOL1 in macrophages and identify the spermidine synthesis pathway as a potential therapeutic target.
Strengths:
The authors employ a comprehensive set of approaches to characterize macrophage phenotypes, including assessments of lipid accumulation, pro-inflammatory cytokine release, responses to M2-polarizing cytokines, autophagy, mitochondrial function, and metabolic profiling. The reversal of pathological phenotypes in G1 and G2 APOL1 macrophages by the polyamine synthesis inhibitor α-difluoromethylornithine provides compelling evidence supporting a causal role for spermidine in mediating APOL1 variant-associated dysfunction. Furthermore, the inclusion of both mouse and human models strengthens the translational relevance of the findings.
Weaknesses:
The manuscript would benefit from a clearer articulation of the specific role macrophages play in the pathogenesis of APOL1-associated kidney diseases to better emphasize the significance of the study. Additionally, the experimental design lacks a clear, logical progression, and the rationale behind some experiments is insufficiently justified, making certain conclusions difficult to fully support based on the presented data. Given the availability of established animal models of APOL1-associated kidney diseases, it is unclear why the authors chose to derive macrophages from the bone marrow of G1 and G2 APOL1 mice for in vitro assays rather than isolating and testing macrophages in vivo within these models. In vitro assays may exaggerate macrophage responses compared to physiological conditions, which could affect the interpretation of the data. Addressing this point would strengthen the manuscript.
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Reviewer #3 (Public review):
Summary:
Liu et al investigate the impact of G1 and G2 variants of the gene encoding Apolipoprotein L1 (APOL1) on macrophage inflammation. The authors have used bone marrow-derived macrophages and human induced pluripotent stem cell-derived macrophages as their model to identify altered immune signaling caused by G1 and G2 APOL1. The unbiased metabolite analysis indicates the possible involvement of altered polyamine metabolism in the regulation of inflammatory response in G1 and G2 macrophages. This study shows that targeting polyamine metabolism can limit macrophage inflammation and lipid accumulation in vitro conditions.
Strengths:
This study shows the importance of polyamine metabolism in the regulation of macrophage inflammatory response. The authors showed that spermidine synthesis is closely associated with altered macrophage functions with two risk-variant forms of APOL1 (G1 and G2). The altered macrophage lipid metabolism is known to be associated with macrophage dysfunction in G1 and G2 APOL1. However, the involvement of polyamine in the regulation of lipid accumulation and inflammation in macrophages in G1 and G2 variants is interesting and could be explored as a novel therapeutic approach for chronic inflammation.
Weaknesses:
The novelty of this study lies in the association of polyamine metabolism with lipid metabolism dysregulation in macrophages. The weakness of the manuscript is that insufficient experiments to support the claim of involvement of polyamine metabolism in the regulation of macrophage inflammation, which undermines the novelty of this study. The authors performed in vitro experiments targeting spermidine synthesis to show reduced inflammation and lipid accumulation, but have not performed any in vivo analysis of chronic kidney inflammation progression in G1 and G2 mice, which they have used to generate bone-marrow-derived macrophages. They have not shown any data that supports the specificity of DFMO in targeting spermidine synthesis.
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Reviewer #1 (Public review):
This study is focused on identifying unique, innovative surface markers for mature Achilles tendons by combining the latest multi-omics approaches and in vitro evaluation, which would address the knowledge gap of controversial identity of TPSCs with unspecific surface markers. The use of multi-omics technologies, in vivo characterization, in vitro standard assays of stem cells, and in vitro tissue formation is a strength of this work and could be applied for other stem cell quantification in the musculoskeletal research. The evaluation and identification of Cd55 and Cd248 in TPSCs have not been conducted in tendon, which is considered as innovative. Additionally, the study provided solid sequencing data to confirm co-expressions of Cd55 and Cd248 with other well-described surface markers such as Ly6a, Tpp3, Pdgfra, and Cd34. Generally, the data shown in the manuscript support the claims that the identified surface antigens mark TPSCs in juvenile tendons.
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Reviewer #2 (Public review):
Summary:
The molecular signature of tendon stem cells is not fully identified. The endogenous location of tendon stem cells within native tendon is also not fully elucidated. Several molecular markers have been identified to isolate tendon stem cells but they lack tendon specificity. Using the declining tendon repair capacity of mature mice, the authors compared the transcriptome landscape and activity of juvenile (2 weeks) and mature (6 weeks) tendon cells of mouse Achilles tendons and identified CD55 and CD248 as novel surface markers for tendon stem cells. CD55+ CD248+ FACS-sorted cells display a preferential tendency to differentiate into tendon cells compared to CD55neg CD248neg cells.
Strengths:
The authors generated a lot of data of juvenile and mature Achilles tendons, using scRNAseq, snRNAseq, ATACseq strategies. This constitutes a resource datasets.
Weaknesses:
The analyses and validation of identified genes are not complete and could be pushed further. The endogenous expression of newly-identified genes in native tendons would be informative. The comparison of scRNAseq and snRNAseq datasets for tendon cell populations would strengthen the identification of tendon cell populations.
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www.biorxiv.org www.biorxiv.org
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Reviewer #2 (Public review):
Summary:
The manuscript, by Xu and Peng, et al. investigates whether co-expression of 2 T cell receptor (TCR) clonotypes can be detected in FoxP3+ regulatory CD4+ T cells (Tregs) and if it is associated with identifiable phenotypic effects. This paper presents data reanalyzing publicly available single-cell TCR sequencing and transcriptional analysis, convincingly demonstrating that dual TCR co-expression can be detected in Tregs, both in peripheral circulation as well as among Tregs in tissues. They then compare metrics of TCR diversity between single-TCR and dual TCR Tregs, as well as between Tregs in different anatomic compartments, finding the TCR repertoires to be generally similar though with dual TCR Tregs exhibiting a less diverse repertoire and some moderate differences in clonal expansion in different anatomic compartments. Finally, they examine the transcriptional profile of dual TCR Tregs in these datasets, finding some potential differences in expression of key Treg genes such as Foxp3, CTLA4, Foxo3, Foxo1, CD27, IL2RA, and Ikzf2 associated with dual TCR-expressing Tregs, which the authors postulate implies a potential functional benefit for dual TCR expression in Tregs.
Strengths:
This report examines an interesting and potentially biologically significant question, given recent demonstrations that dual TCR co-expression is a much more common phenomenon than previously appreciated (approximately 15-20% of T cells) and that dual TCR co-expression has been associated with significant effects on the thymic development and antigenic reactivity of T cells. This investigation leverages large existing datasets of single-cell TCRseq/RNAseq to address dual TCR expression in Tregs. The identification and characterization of dual TCR Tregs is rigorously demonstrated and presented, providing convincing new evidence of their existence.
Weaknesses:
The existence of dual TCR expression by Tregs has previously been demonstrated in mice and humans, limiting the novelty of the reported findings. The presented results should be considered in the context of these prior important findings. The focus on self-citation of their previous work, using the same approach to measure dual TCR expression in other datasets. limits the discussion of other more relevant and impactful published research in this area. Also, Reference #7 continues to list incorrect authors. The authors do not present a balanced or representative description of the available knowledge about either dual TCR expression by T cells or TCR repertoires of Tregs.
The approach used follows a template used previously by this group for re-analysis of existing datasets generated by other research groups. The descriptions and interpretations of the data as presented are still shallow, lacking innovative or thoughtful approaches that would potentially be innovation or provide new insight.
This demonstration of dual TCR Tregs is notable, though the authors do not compare the frequency of dual TCR co-expression by Tregs with non-Tregs. This limits interpreting the findings in the context of what is known about dual TCR co-expression in T cells. The response to this criticism in a previous review is considered non-responsive and does not improve the data or findings.
Comparison of gene expression by single- and dual TCR Tregs is of interest, but as presented is difficult to interpret. The interpretations of the gene expression analyses are somewhat simplistic, focusing on single-gene expression of some genes known to have function in Tregs. However, the investigators continue to miss an opportunity to examine larger patterns of coordinated gene expression associated with developmental pathways and differential function in Tregs (Yang. 2015. Science. 348:589; Li. 2016. Nat Rev Immunol. Wyss. 2016. 16:220; Nat Immunol. 17:1093; Zenmour. 2018. Nat Immunol. 19:291). No attempt to define clusters is made. No comparison is made of the proportions of dual TCR cells in transcriptionally-defined clusters. The broad assessment of key genes by single- and dual TCR cells is conceptually interesting, but likely to be confounded by the heterogeneity of the Treg populations. This would need to be addressed and considered to make any analyses meaningful.
The study design, re-analysis of existing datasets generated by other scientific groups, precludes confirmation of any findings by orthogonal analyses.
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Reviewer #3 (Public review):
Summary:
This study addressed the TCR pairing types and CDR3 characteristics of Treg cells. By analyzing scRNA and TCR-seq data, it claims that 10-20% of dual TCR Treg cells exist in mouse lymphoid and non-lymphoid tissues and suggests that dual TCR Treg cells in different tissues may play complex biological functions.
Strengths:
The study addresses an interesting question of how dual-TCR-expressing Treg cells play roles in tissues.
Weaknesses:
This study is inadequate, particularly regarding data interpretation, statistical rigor, and the discussion of the functional significance of Dual TCR Tregs.
Comments on revisions:
Although the authors have provided brief explanations in response to the reviewers' comments, they do not present any additional analyses that would address the fundamental concerns in a convincing manner.<br /> Moreover, the in silico analyses presented in the manuscript alone are insufficient to support the conclusions, and the functional experiments requested by the reviewers have not been conducted.
In the current rebuttal, while some textual additions have been made to the manuscript, the only substantial revision to the figures appears to be the inclusion of statistical significance annotations (e.g., Fig. 1G, Fig. 3G). These changes do not adequately strengthen the overall data or address the core issues raised.
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Reviewer #1 (Public review):
Summary:
This manuscript describes a series of lab and field experiments to understand the role of tadpole transport in shaping the microbiome of poison frogs in early life. The authors conducted a cross-foster experiment in which R. variabilis tadpoles were carried by adults of their own species, carried by adults of another frog species, or not carried at all. After being carried for 6 hours, tadpole microbiomes resembled those of their caregiving species. Next, the authors reported higher microbiome diversity in tadpoles of two species that engage in transport-based parental care compared to one species that does not. Finally, they collected tadpoles either from the backs of an adult (i.e., they had recently been transported) or from eggs (i.e., not transported) but did not find significant overlap in microbiome composition between transported tadpoles and their parents.
Strengths:
The cross-foster experiment and the field experiment that reared transported and non-transported tadpoles are creative ways to address an important question in animal microbiome research. Together, they imply a small role for parental care in the development of the tadpole microbiome. The manuscript is generally well-written and easy to understand. The authors make an effort (improved since the first version of the manuscript) to acknowledge the limitations of their experimental design.
Weaknesses:
This manuscript has improved since the initial version and now more clearly discusses the limitations of its experimental design. I have no further revisions to request.
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Reviewer #2 (Public review):
Summary:
Here, the Fischer et al. attempt to understand the role of parental care, specifically the transport of offspring, in the development of the amphibian microbiome. The amphibian microbiome is an important study system due to its association with host health and disease outcomes. This study provides vertical transfer of bacteria through parental transport of tadpoles as one mechanism, among others, influencing tadpole microbiome composition. This paper gives insight into the relative roles of the environment, species, and parental care in amphibian microbiome assembly.
The authors determine the time of bacterial colonization during tadpole development using PCR, observing that tadpoles were not colonized by bacteria prior to hatching from the vitelline membrane. This is an important finding for amphibian microbiome research and I would be curious to see if this is seen broadly across amphibian species. By doing this, the impact of transport can be more accurately assessed in their laboratory experiments. The authors found that caregiver species influenced community composition, with transported tadpoles sharing a greater proportion of their skin communities with the transporting species.
In a comparison of three sympatric amphibian species that vary in their reproductive strategies, the authors found that tadpole community diversity was not reflective of habitat diversity, but may be associated with the different reproductive strategies of each species. Parental care explained some of the variance of tadpole microbiomes between species, however, transportation by conspecific adults did not lead to more similar microbiomes between tadpoles and adults compared to species that do not exhibit parental transport. This finding is in agreement with the understanding that the amphibian microbiome is distinct between developmental stages (eggs/tadpoles/adults) and also that amphibian microbiome composition is generally species specific.
When investigating contributions of caretakers to transported offspring, the authors found that tadpole-adult pairs with a history of direct contact were not more similar than tadpole-adult pairs lacking that history. This conclusion was surprising when considering the direct contact between the adults and tadpoles, however if only certain taxa from the adults are capable of colonizing tadpoles, then one could expect that similar ASVs might be donated between tadpole-adult pairs.
I did not find any major weaknesses in my review of this paper. I think that the data and conclusions here are of value to other researchers looking into the assembly of the amphibian microbiome. This paper offers insight into how tadpole-transport could influence the microbiome and adds to our overall understanding of amphibian microbiome assembly across the varied life histories of frogs.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary
This manuscript from Azeroglu et al. presents the application of END-Seq to examine the sequence composition of chromosome termini, i.e., telomeres. END-seq is a powerful genome sequencing strategy developed in Andre Nussesweig's lab to examine the sequences at DNA break sites. Here, END-Seq is applied to explore the nucleotide sequences at telomeres and to ascertain (i) whether the terminal end sequence is conserved in cells that activate ALT telomere elongation mechanism and (ii) whether the processes responsible for telomere end sequence regulation are conserved. With these aims clearly articulated, the authors convincingly show the power of this technique to examine telomere end-processing.
Strengths
(1) The authors effectively demonstrate the application of END-seq for these purposes. They verify prior data that 5'terminal sequences of telomeres in Hela and RPE cells end in a canonical ATC sequence motif. They verify that the same sequence is present at the 5' ends of telomeres by performing END-seq across a panel of ALT cancer cells. As in non-ALT cells, the established role of POT1, a ssDNA telomere binding protein, in coordinating the mechanism that maintains the canonical ATC motif is likewise verified. However, by performing END-Seq in mouse cells lacking POT1 isoforms, POT1a and POT1b, the authors uncover that POT1b is dispensable for this process. This reveals a novel, important insight relating to the evolution of POT1 as a telomere regulatory factor.
(2) The authors then demonstrate the utility of S1-END-seq, a variation of END-Seq, to explore the purported abundance of single-stranded DNA at telomeres within telomeres of ALT cancer cells. Here, they demonstrate that ssDNA abundance is an intrinsic aspect of ALT telomeres and is dependent on the activity of BLM, a crucial mediator of ALT.
Overall, the authors have effectively shown that END-seq can be applied to examine processes maintaining telomeres in normal and cancerous cells across multiple species. Using END-Seq, the authors confirm prior cell biological and sequencing data and the role of POT1 and BLM in regulating telomere termini sequences and ssDNA abundance. The study is nice and well-written, with the experimental rationale and outcomes clearly explained.
Weaknesses
This reviewer finds little to argue with in this study. It is timely and highly valuable for the telomere field. One minor question would be whether the authors could expand more on the application of END-Seq to examine the processive steps of the ALT mechanism? Can they speculate if the ssDNA detected in ALT cells might be an intermediate generated during BIR (i.e., is the ssDNA displaced strand during BIR) or a lesion? Furthermore, have the authors assessed whether ssDNA lesions are due to the loss of ATRX or DAXX, either of which can be mutated in the ALT setting?
Comments on revisions:
The authors addressed the comments. Thank you.
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Reviewer #2 (Public review):
This is a short yet very clear manuscript demonstrating that two methods (END-seq and S1-END-seq), previously developed in the Nussenzweig laboratory to study DSBs in the genome, can also be applied to the 5' ends of mammalian telomeres and the accumulation of telomeric single-stranded DNA.
The authors first validate the applicability of END-seq using different approaches and confirm that mammalian telomeres preferentially end with an ATC 5' end through a mechanism that requires intact POT1 (POT1a in mice). They then extend their analysis to cells that maintain telomeres through the ALT mechanism and demonstrate that, in these cells as well, telomeres frequently end in an ATC 5' sequence via a POT1-dependent mechanism. Using S1-END-seq, the authors further show that ALT telomeres contain single-stranded DNA and estimate that each telomere in ALT cells harbors at least five regions of ssDNA.
I find this work very interesting and incisive. It clearly demonstrates that END-seq can be applied with unprecedented depth and precision to the study of telomeric features such as the 5' end and ssDNA. The data are very clear and thoroughly interpreted, and the manuscript is well written. The results are carefully analyzed and effectively presented. Overall, I find this manuscript worthy of publication, as the optimized END-seq methods described here will likely be widely utilized in the telomere field.
Also, the authors have satisfactorily addressed my previous comments.
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Reviewer #3 (Public review):
Summary:
A subset of cancer cells attain replicative immortality by activating the ALT mechanism of telomere maintenance, which is currently the subject of intense research due to its potential for novel targeted therapies. Key questions remain in the field, such as whether ALT telomeres adhere to the same end-protection rules as telomeres in telomerase-expressing cells, or if ALT telomeres possess unique properties that could be targeted with new, less toxic cancer therapies. Both questions, along with the approaches developed by the authors to address them, are highly relevant.
Strengths:
Since chromosome ends resemble one-ended DSBs, the authors hypothesized that the previously described END-SEQ protocol could be used to accurately sequence the 5' end of telomeres on the C-rich strand. As expected, most reads corresponded to the C-rich strand and, confirming previous observation by the de Lange's group, most chromosomes end with the ATC-5' sequence, a feature that was found to be dependent on POT1 and to be conserved in both human ALT cells and mouse cells. Through a complementary method, S1-END-SEQ, the authors further explored ssDNA regions at telomeres, providing new insights into the characteristics of ALT telomeres. The study is original, the experiments were well-controlled and excellently executed.
Weaknesses:
A few additional experiments would have strengthened the results such as combining error-free long-read sequencing with END-SEQ to compare the abundance of VTRs within telomeres versus at their distal ends.<br /> Along this line, are VTRs increased at ssDNA regions of ALT telomeres? What is the frequency of VTRs in the END-SEQ analysis of TRF1-FokI-expressing ALT cells? Is it also increased? Has TRF1-FokI been applied to telomerase-expressing cells to compare VTR frequencies at internal sites between ALT and telomerase-expressing cells?<br /> To what extent do ECTRs contribute to telomeric ssDNA?<br /> Future experiments may help shed light on this
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Genome-wide association studies have been an important approach to identifying the genetic basis of human traits and diseases. Despite their successes, for many traits, a substantial amount of variation cannot be explained by genetic factors, indicating that environmental variation and individual 'noise' (stochastic differences as well as unaccounted for environmental variation) also play important roles. The authors' goal was to address how gene expression variation in genetically identical individuals, driven by historical environmental differences and 'noise', could be used to predict reproductive trait differences.
Strengths:
To address this question, the authors took advantage of genetically identical C. elegans individuals to transcriptionally profile 180 adult hermaphrodite individuals that were also measured for two reproductive traits. A major strength of the paper is in its experimental design. While experimenters aim to control the environment that each worm experiences, it is known that there are small differences even when worms are grown together on the same agar plate - e.g., the age of their mother, their temperature, the amount of food they eat, and the oxygen and carbon dioxide levels depending on where they roam on the plate. Instead of neglecting this unknown variation, the authors design the experiment up front to create two differences in the historical environment experienced by each worm: 1) the age of its mother and 2) 8 8-hour temperature difference, either 20 or 25 C. This helped the authors interpret the gene expression differences and trait expression differences that they observed.
Using two statistical models, the authors measured the association of gene expression for 8824 genes with the two reproductive traits, considering both the level of expression and the historical environment experienced by each worm. Their data supports several conclusions. They convincingly show that gene expression differences are useful for predicting reproductive trait differences, predicting ~25-50% of the trait differences depending on the trait. Using RNAi, they also show that the genes they identify play a causal role in trait differences. Finally, they demonstrate an association with trait variation and the H3K27 trimethylation mark, suggesting that chromatin structure can be an important causal determinant of gene expression and trait variation.
Overall, this work supports the use of gene expression data as an important intermediate for understanding complex traits. This approach is also useful as a starting point for other labs in studying their trait of interest.
Weaknesses:
There are no major weaknesses that I have noted. Some important limitations of their work are worth highlighting, though (and I believe the authors would agree with these points):
(1) A large remaining question in the field of complex traits remains in splitting the role of non-genetic factors between environmental variation and stochastic noise. It is still an open question which role each of these factors plays in controlling the gene expression differences they measured between the individual worms.
(2) The ability of the authors to use gene expression to predict trait variation was strikingly different between the two traits they measured. For the early brood trait, 448 genes were statistically linked to the trait difference, while for egg-laying onset, only 11 genes were found. Similarly, the total R2 in the test set was ~50% vs. 25%. It is unclear why the differences occur, but this somewhat limits the generalizability of this approach to other traits.
(3) For technical reasons, this approach was limited to whole worm transcription. The role of tissue and cell-type expression differences is important to the field, so this limitation is relevant.
Comments on revisions: The authors have addressed my previous comments to my satisfaction.
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Reviewer #2 (Public review):
This paper measures associations between RNA transcript levels and important reproductive traits in the model organism C. elegans. The authors go beyond determining which gene expression differences underlie reproductive traits, but also (1) build a model that predicts these traits based on gene expression and (2) perform experiments to confirm that some transcript levels indeed affect reproductive traits. The clever study design allows the authors to determine which transcript levels impact reproductive traits, and also which transcriptional differences are driven by stochastic vs environmental differences. In sum, this is a comprehensive study that highlights the power of gene expression as a driver of phenotype, and also teases apart the various factors that affect the expression levels of important genes.
Overall, this study has many strengths, is very clearly communicated, and has no substantial weaknesses that I can point to.
One question that emerges for me is whether these findings apply broadly. In other words, I wonder whether gene expression levels are predictive of other phenotypes in other organisms. I think this question has largely been explored in microbes, where some studies (PMID: 17959824) but not others (PMID: 38895328) found that differences in gene expression were predictive of phenotypes like growth rate. Microbes are not the focus here, and instead, the discussion is mainly focused on using gene expression to predict health and disease phenotypes in humans. This feels a little complicated since humans have so many different tissues. Perhaps an area where this approach might be useful is in examining infectious single-cell populations (bacteria, tumors, fungi). But I suppose this idea might still work in humans, assuming the authors are thinking about targeting specific tissues for RNAseq.
In sum, this is a great paper that really got me thinking about the predictive power of gene expression and where/when it could inform about (health-related) phenotypes.
Comments on revisions: No additional comments
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Reviewer #3 (Public review):
Summary:
Webster et al. sought to understand if phenotypic variation in the absence of genetic variation can be predicted by variation in gene expression. To this end they quantified two reproductive traits, the onset of egg laying and early brood size in cohorts of genetically identical nematodes exposed to alternative ancestral (two maternal ages) and same generation life histories (either constant 20 ºC temperature or 8-hour temperature shift to 25 ºC upon hatching) in a two-factor design; then, they profiled genome-wide gene expression in each individual.
Using multiple statistical and machine learning approaches, they showed that, at least for early brood size, phenotypic variation can be quite well predicted by molecular variation, beyond what can be predicted by life history alone.<br /> Moreover, they provide some evidence that expression variation in some genes might be causally linked to phenotypic variation.
Strengths:
Cleverly designed and carefully performed experiments that provide high-quality datasets useful for the community.
Good evidence that phenotypic variation can be predicted by molecular variation.
Weaknesses:
What drives the molecular variation that impacts phenotypic variation remains unknown. While the authors show that variation in expression of some genes might indeed be causal, it is still not clear how much of the molecular variation is a cause rather than a consequence of phenotypic variation.
Comments on revisions: I have no more comments for the authors
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The authors address a fundamental question for cell and tissue biology using the skin epidermis as a paradigm and ask how stratifying self-renewing epithelia induce differentiation and upwards migration in basal dividing progenitor cells to generate suprabasal barrier-forming cells that are essential for a functional barrier formed by such an epithelium. The authors show for the first time that an increase in intracellular actomyosin contractility, a hallmark of barrier-forming keratinocytes, is sufficient to trigger terminal differentiation. Hence the data provide in vivo evidence of the more general interdependency of cell mechanics and differentiation. The data appear to be of high quality and the evidences are strengthened through a combination of different genetic mouse models, RNA sequencing and immunofluorescence analysis.
To generate and maintain the multilayered, barrier-forming epidermis, keratinocytes of the basal stem cell layer differentiate and move suprabasally accompanied by stepwise changes not only in gene expression but also in cell morphology, mechanics and cell position. If any of these changes are instructive for differentiation itself, and whether consecutive changes in differentiation are required, remains unclear. Also, there are few comprehensive data sets on the exact changes in gene expression between different states of keratinocyte differentiation. In this study, through genetic fluorescence labeling of cell states at different developmental timepoints the authors were able to analyze gene expression of basal stem cells and suprabasal differentiated cells at two different stages of maturation: E14 (embryonic day 14) when the epidermis comprises mostly two functional compartments (basal stem cells and suprabasal so called intermediate cells) and E16 when the epidermis comprise three (living) compartments where the spinous layer separates basal stem cells from the barrier forming granular layer, as is the case in adult epidermis. Using RNA bulk sequencing, the authors developed useful new markers for suprabasal stages of differentiation like MafB and Cox1. The transcription factor MafB was then shown to inhibit suprabasal proliferation in a MafB transgenic model.
The data indicate that early in development at E14 the suprabasal intermediate cells resemble in terms of RNA expression, the barrier-forming granular layer at E16, suggesting that keratinocyte can undergo either stepwise (E16) or more direct (E14) terminal differentiation.
Previous studies by several groups found an increased actomyosin contractility in the barrier forming granular layer and showed that this increase in tension is important for epidermal barrier formation and function. However, it was not clear whether contractility itself serves as an instructive signal for differentiation. To address this question, the authors use a previously published model to induce premature hypercontractility in the spinous layer by using spastin overexpression (K10-Spastin) to disrupt microtubules (MT) thereby indirectly inducing actomyosin contractility. A second model activates myosin contractility more directly through overexpression of a constitutively active RhoA GEF (K10-Arhgef11CA). Both models induce late differentiation of suprabasal keratinocytes regardless of the suprabasal position in either spinous or granular layer indicating that increased contractility is key to induce late differentiation of granular cells. A potential weakness is the use of the K10-spastin model that disrupts MT and likely has additional roles in altering differentiation next to the induction of hypercontractility. Their previous publications provided some evidence that the effect on differentiation is driven by the increase in contractility (Ning et al. cell stem cell 2021). Moreover, their data are now further supported by a second model activating myosin through RhoA. This manuscript extends their previous findings that indicated a role for contractility in early differentiation, now focussing on the regulation of late differentiation in barrier forming cells. This data set thus help to unravel the interdependencies of cell position, mechanical state and differentiation in the epidermis, and suggest that an increase in cellular contractility within the epidermis can induce terminal differentiation. Importantly the authors show that despite contractility induced nuclear localization of the mechanoresponsive transcription factor YAP in the barrier forming granular layer, YAP nuclear localization is not sufficient to drive premature differentiation when forced to the nucleus in the spinous layer.
Overall, this is a well written manuscript and comprehensive dataset.
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Reviewer #2 (Public review):
Summary:
The manuscript from Prado-Mantilla and co-workers addresses mechanisms of embryonic epidermis development, focusing on the intermediate layer cells, a transient population of suprabasal cells that contributes to the expansion of the epidermis through proliferation. Using bulk-RNA they show that these cells are transcriptionally distinct from the suprabasal spinous cells and identify specific marker genes for these populations. They then use transgenesis to demonstrate that one of these selected spinous layer-specific markers, the transcription factor MafB is capable of suppressing proliferation in the intermediate layers, providing a potential explanation for the shift of suprabasal cells into a non-proliferative state during development. Further, lineage tracing experiments show that the intermediate cells become granular cells without a spinous layer intermediate. Finally, the authors show that the intermediate layer cells express high levels of contractility-related genes than spinous layers and overexpression of cytoskeletal regulators accelerates differentiation of spinous layer cells into granular cells.
Overall, the manuscript presents a number of interesting observations on the developmental stage-specific identities of suprabasal cells and their differentiation trajectories, and points to a potential role of contractility in promoting differentiation of suprabasal cells into granular cells. The precise mechanisms by which MafB suppresses proliferation, how the intermediate cells bypass the spinous layer stage to differentiate into granular cells and how contractility feeds into these mechanisms remain open. Interestingly, while the mechanosensitive transcription factor YAP appears differentially active in the two states, it is shown to be downstream rather than upstream of the observed differences in mechanics.
Strengths:
The authors use a nice combination of RNA sequencing, imaging, lineage tracing and transgenesis to address the suprabasal to granular layer transition. The imaging is convincing and the biological effects appear robust. The manuscript is clearly written and logical to follow.
Weaknesses:
While the data overall supports the authors claims, there are a few minor weaknesses that pertain to the aspect of the role of contractility, The choice of spastin overexpression to modulate contractility is not ideal as spastin has multiple roles in regulating microtubule dynamics and membrane transport which could also be potential mechanisms explaining some of the phenotypes. Use of Arghap11 overexpression mitigates this effect to some extent but overall it would have been more convincing to manipulate myosin activity directly. It would also be important to show that these manipulations increase the levels of F-actin and myosin II as shown for the intermediate layer. It would also be logical to address if further increasing contractility in the intermediate layer would enhance the differentiation of these cells.
Despite these minor weaknesses, the manuscript is overall of high quality, sheds new light on the fundamental mechanisms of epidermal stratification during embryogenesis and will likely be of interest to the skin research community.
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Reviewer #3 (Public review):
Summary:
This is an interesting paper by Lechler and colleagues describing the transcriptomic signature and fate of intermediate cells (ICs), a transient and poorly defined embryonic cell type in the skin. ICs are the first suprabasal cells in the stratifying skin and unlike later-developing suprabasal cells, ICs continue to divide. Using bulk RNA seq to compare ICs to spinous and granular transcriptomes, the authors find that IC-specific gene signatures include hallmarks of granular cells, such as genes involved in lipid metabolism and skin barrier function that are not expressed in spinous cells. ICs were assumed to differentiate into spinous cells, but lineage tracing convincingly shows ICs differentiate directly into granular cells without passing through a spinous intermediate. Rather, basal cells give rise to the first spinous cells. They further show that transcripts associated with contractility are also shared signatures of ICs and granular cells, and overexpression of two contractility inducers (Spastin and ArhGEF-CA) can induce granular and repress spinous gene expression. This contractility-induced granular gene expression does not appear to be mediated by the mechanosensitive transcription factor, Yap. The paper also identifies new markers that distinguish IC and spinous layers, and shows the spinous signature gene, MafB, is sufficient to repress proliferation when prematurely expressed in ICs.
Strengths:
Overall this is a well-executed study, and the data are clearly presented and the findings convincing. It provides an important contribution to the skin field by characterizing the features and fate of ICs, a much understudied cell type, at a high levels of spatial and transcriptomic detail. The conclusions challenge the assumption that ICs are spinous precursors through compelling lineage tracing data. The demonstration that differentiation can be induced by cell contractility is an intriguing finding, and adds a growing list of examples where cell mechanics influence gene expression and differentiation.
Weaknesses:
A weakness of the study is an over-reliance on overexpression and sufficiency experiments to test the contributions of MafB, Yap, and contractility in differentiation. The inclusion of loss-of-function approaches would enable one to determine if, for example, contractility is required for the transition of ICs to granular fate, and whether MafB is required for spinous fate. Second, whether the induction of contractility-associated genes is accompanied by measurable changes in the physical properties or mechanics of the IC and granular layers is not directly shown. Inclusion of physical measurements would bolster the conclusion that mechanics lies upstream of differentiation.
Finally, the role of ICs in epidermal development remains unclear. Although not essential to support the conclusions of this study, insights into the function of this transient cell layer would strengthen the overall impact.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public Review):
Summary:
The study "Impact of Maximal Overexpression of a Non-toxic Protein on Yeast Cell Physiology" by Fujita et al. aims to elucidate the physiological impacts of overexpressing non-toxic proteins in yeast cells. By identifying model proteins with minimal cytotoxicity, the authors claim to provide insights into cellular stress responses and metabolic shifts induced by protein overexpression.
Strengths:
The study introduces a neutrality index to quantify cytotoxicity and investigates the effects of protein burden on yeast cell physiology. The study identifies mox-YG (a non-fluorescent fluorescent protein) and Gpm1-CCmut (an inactive glycolytic enzyme) as proteins with the lowest cytotoxicity, capable of being overexpressed to more than 40% of total cellular protein while maintaining yeast growth. Overexpression of mox-YG leads to a state resembling nitrogen starvation probably due to TORC1 inactivation, increased mitochondrial function, and decreased ribosomal abundance, indicating a metabolic shift towards more energy-efficient respiration and defects in nucleolar formation.
Weaknesses:
While the introduction of the neutrality index seems useful to differentiate between cytotoxicity and protein burden, the biological relevance of the effects of overexpression of the model proteins is unclear.
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Reviewer #2 (Public Review):
Summary:
In this manuscript, Fujita et al. characterized the neutrality indexes of several protein mutants in S. cerevisiae and uncovered that mox-YG and Gpm1-CCmut can be expressed as abundant as 40% of total proteins without causing severe growth defects. The authors then looked at the transcriptome and proteome of cells expressing excess mox-YG to investigate how protein burden affects yeast cells. Based on RNA-seq and mass-spectrometry results, the authors uncover that cells with excess mox-YG exhibit nitrogen starvation, respiration increase, inactivated TORC1 response, and decreased ribosomal abundance. The authors further showed that the decreased ribosomal amount is likely due to nucleoli defects, which can be partially rescued by nuclear exosome mutations.
Strengths:
Overall, this is a well-written manuscript that provides many valuable resources for the field, including the neutrality analysis on various fluorescent proteins and glycolytic enzymes, as well as the RNA-seq and proteomics results of cells overexpressing mox-YG. Their model on how mox-YG overexpression impairs the nucleolus and thus leads to ribosomal abundance decline will also raise many interesting questions for the field.
Weaknesses:
The authors concluded from their RNA-seq and proteomics results that cells with excess mox-YG expression showed increased respiration and TORC1 inactivation. I think it will be more convincing if the authors can show some characterization of mitochondrial respiration/membrane potential and the TOR responses to further verify their -omic results.
In addition, the authors only investigated how overexpression of mox-YG affects cells. It would be interesting to see whether overexpressing other non-toxic proteins causes similar effects, or if there are protein-specific effects. It would be good if the authors could at least discuss this point considering the workload of doing another RNA-seq or mass-spectrum analysis might be too heavy.
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Reviewer #3 (Public Review):
Summary:
Protein overexpression is widely used in experimental systems to study the function of the protein, assess its (beneficial or detrimental) effects in disease models, or challenge cellular systems involved in synthesis, folding, transport, or degradation of proteins in general. Especially at very high expression levels, protein-specific effects and general effects of a high protein load can be hard to distinguish. To overcome this issue, Fujita et al. use the previously established genetic tug-of-war system to identify proteins that can be expressed at extremely high levels in yeast cells with minimal protein-specific cytotoxicity (high 'neutrality'). They focus on two versions of the protein mox-GFP, the fluorescent version and a point mutation that is non-fluorescent (mox-YG) and is the most 'neutral' protein on their screen. They find that massive protein expression (up to 40% of the total proteome) results in a nitrogen starvation phenotype, likely inactivation of the TORC1 pathway, and defects in ribosome biogenesis in the nucleolus.
Strengths:
This work uses an elegant approach and succeeds in identifying proteins that can be expressed at surprisingly high levels with little cytotoxicity. Many of the changes they see have been observed before under protein burden conditions, but some are new and interesting. This work solidifies previous hypotheses about the general effects of protein overexpression and provides a set of interesting observations about the toxicity of fluorescent proteins (that is alleviated by mutations that render them non-fluorescent) and metabolic enzymes (that are less toxic when mutated into inactive versions).
Weaknesses:
The data are generally convincing, however in order to back up the major claim of this work - that the observed changes are due to general protein burden and not to the specific protein or condition - a broader analysis of different conditions would be highly beneficial.
Major points:
(1) The authors identify several proteins with high neutrality scores but only analyze the effects of mox/mox-YG overexpression in depth. Hence, it remains unclear which molecular phenotypes they observe are general effects of protein burden or more specific effects of these specific proteins. To address this point, a proteome (and/or transcriptome) of at least a Gpm1-CCmut expressing strain should be obtained and compared to the mox-YG proteome. Ideally, this analysis should be done simultaneously on all strains to achieve a good comparability of samples, e.g. using TMT multiplexing (for a proteome) or multiplexed sequencing (for a transcriptome). If feasible, the more strains that can be included in this comparison, the more powerful this analysis will be and can be prioritized over depth of sequencing/proteome coverage.
(2) The genetic tug-of-war system is elegant but comes at the cost of requiring specific media conditions (synthetic minimal media lacking uracil and leucine), which could be a potential confound, given that metabolic rewiring, and especially nitrogen starvation are among the observed phenotypes. I wonder if some of the changes might be specific to these conditions. The authors should corroborate their findings under different conditions. Ideally, this would be done using an orthogonal expression system that does not rely on auxotrophy (e.g. using antibiotic resistance instead) and can be used in rich, complex mediums like YPD. Minimally, using different conditions (media with excess or more limited nitrogen source, amino acids, different carbon source, etc.) would be useful to test the robustness of the findings towards changes in media composition.
(3) The authors suggest that the TORC1 pathway is involved in regulating some of the changes they observed. This is likely true, but it would be great if the hypothesis could be directly tested using an established TORC1 assay.
(4) The finding that the nucleolus appears to be virtually missing in mox-YG-expressing cells (Figure 6B) is surprising and interesting. The authors suggest possible mechanisms to explain this and partially rescue the phenotype by a reduction-of-function mutation in an exosome subunit. I wonder if this is specific to the mox-YG protein or a general protein burden effect, which the experiments suggested in point 1 should address. Additionally, could a mox-YG variant with a nuclear export signal be expressed that stays exclusively in the cytosol to rule out that mox-YG itself interferes with phase separation in the nucleus?
Minor points:
(5) It would be great if the authors could directly compare the changes they observed at the transcriptome and proteome levels. This can help distinguish between changes that are transcriptionally regulated versus more downstream processes (like protein degradation, as proposed for ribosome components).
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www.medrxiv.org www.medrxiv.org
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Reviewer #1 (Public review):
Summary:
The study aimed to develop a liquid biopsy EV miRNA signature associated with radiomics features for early diagnosis of pancreatic cancer. Flawed study design and inadequate description of clinical characteristics of the enrolled samples makes the findings unconvincing.
Strengths:
The concept of developing EV miRNA signature associated with disease relevant radiomics features is a strength.
Weaknesses:
There are many weaknesses in this manuscript, which include drawing association of data derived from unmatched sample sets, selection of low abundance miRNAs for developing the signature with inadequate rationale, incomplete description of experimental methods and confusing statements in the text.
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Reviewer #2 (Public review):
Summary:
This study investigates a low abundance microRNA signature in extracellular vesicles to subtype pancreatic cancer and for early diagnosis. In this revision, there remain several major and minor issues.
Strengths:
The authors did a comprehensive job with numerous analyses of moderately sized cohorts to describe the clinical and translational significance of their miRNA signature.
Weaknesses:
The weaknesses of the study largely revolve around a lack of clarity about the methodology used and the validation of their findings.
(1) The WGCNA analysis was critical to identify the EV miRNAs associated with imaging features, but the "cut-off criteria" for MM and GS have no clear justification. How were these cut-offs determined? How sensitive were the results to these cut-offs?
(2) The authors now clarify that patients for the sub-study on differentiating early stage from benign pancreatic lesions were matched by age and that the benign pancreatic lesions were predominantly IPMNs. This scientific design is flawed. The CT features extracted likely differentiate solid from cystic pancreatic lesions, and the miRNA signature is doing the same. The authors need to incorporate the following benign controls into their imaging analysis and their EV miRNA analysis: pancreatitis and normal pancreata.
(3) For the radiomics features, the authors should include an additional external validation set to better support the ability to use these features reproducibly, especially given that the segmentation was manual and reliant on specific people.
(4) The DF selection process still lacks cited references as originally requested in the first review.
(5) In Figure 2, more quantitative details are needed in the manuscript. The reviewers failed to incorporate this and only responded in their rebuttal. Add details to the manuscript as originally requested.
(6) It is still not clear what Figure 4A is illustrating as regards to model performance. The authors need to state in the manuscript very clearly what they are showing in the figure and what the modules represent.
(7) Figure 5 and the descriptions for the public serum miRNA datasets need more details. Were these pancreatic cancers all adenocarcinoma, what stage, age range, sex distribution, comorbid conditions were the cases? Were the controls all IPMNs or were there other conditions in the controls?
(8) The subtype results in figures 6 and 7 are not convincing. An association on univariate analysis is not sufficient. The explanation that clinical data is not available to do a multivariable analysis indicates that the authors do not have the ability to claim that they have identified unique subtypes that have clinical relevance. A thorough evaluation of the prognostic significance and the associated molecular features of these tumors is needed.
Summary:
There remain key details and validation experiments to better support the conclusions of the study.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Zhu et al., investigate the cellular defects in glia as a result of loss in DEGS1/ifc encoding the dihydroceramide desaturase. Using the strength of Drosophila and its vast genetic toolkit, they find that DEGS1/ifc is mainly expressed in glia and it's loss leads to profound neurodegeneration. This supports a role for DEGS1 in the developing larval brain as it safeguards proper CNS development. Loss of DEGS1/ifc leads to dihydroceramide accumulation in the CNS and induces alteration in the morphology of glial subtypes and a reduction in glial number. Cortex and ensheathing glia appeared swollen and accumulated internal membranes. Astrocyte-glia on the other hand displayed small cell bodies, reduced membrane extension and disrupted organization in the dorsal ventral nerve cord. They also found that DEGS1/ifc localizes primarily to the ER. Interestingly, the authors observed that loss of DEGS1/ifc drives ER expansion and reduced TGs and lipid droplet numbers. No effect on PC and PE and a slight increase in PS.
The conclusions of this paper are well supported by the data.
Strengths:
This is an interesting study that provides new insight into the role of ceramide metabolism in neurodegeneration.
The strength of the paper is the generation of LOF lines, the insertion of transgenes and the use of the UAS-GAL4/GAL80 system to assess the cell-autonomous effect of DEGS1/ifc loss in neurons and different glial subtypes during CNS development.
The imaging, immunofluorescence staining and EM of the larval brain and the use of the optical lobe and the nerve cord as a readout are very robust and nicely done.
Drosophila is a difficult model to perform core biochemistry and lipidomics, but the authors used the whole larvae and CNS to uncover global changes in mRNA levels related to lipogenesis and the unfolded protein responses, as well as specific lipid alterations upon DEGS1/ifc loss.
Weaknesses:
No major weaknesses identified.
Minor point: The authors performed lipidomics and RTqPCR on whole larvae and larval CNS which does not inform of any cell type-specific effects. Performing single-cell RNAseq on larval brains to tease apart the cell-type specific effect of DEGS1/ifc loss would be interesting to explore the future, but beyond the scope of the current study.
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Reviewer #2 (Public review):
Summary:
The manuscript by Zhu et al. describes phenotypes associated with the loss of the gene ifc using a Drosophila model. The authors suggest their findings are relevant to understanding the molecular underpinnings of a neurodegenerative disorder, HLD-18, which is caused by mutations in the human ortholog of ifc, DEGS1.
The work begins with the authors describing the role for ifc during fly larval brain development, demonstrating its function in regulating developmental timing, brain size, and ventral nerve cord elongation. Further mechanistic examination revealed that loss of ifc leads to depleted cellular ceramide levels as well as dihydroceramide accumulation, eventually causing defects in ER morphology and function. Importantly, the authors showed that ifc is predominantly expressed in glia and is critical for maintaining appropriate glial cell numbers and morphology. Many of the key phenotypes caused by the loss of fly ifc can be rescued by overexpression of human DEGS1 in glia, demonstrating the conserved nature of these proteins as well as the pathways they regulate. Interestingly, the authors discovered that the loss of lipid droplet formation in ifc mutant larvae within the cortex glia, presumably driving the deficits in glial wrapping around axons and subsequent neurodegeneration, potentially shedding light on mechanisms of HLD-18 and related disorders.
Strengths:
Overall, the manuscript is thorough in its analysis of ifc function and mechanism. The data images are high quality, the experiments are well controlled, and the writing is clear. There are, however, some concerns that need to be addressed prior to publication.
Weaknesses:
The authors adequately addressed the previously indicated weaknesses, and no new weaknesses have been identified.
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Reviewer #3 (Public review):
Summary:
In this manuscript, the authors report three novel ifc alleles: ifc[js1], ifc[js2], and ifc[js3]. ifc[js1] and ifc[js2] encode missense mutations, V276D and G257S, respectively. ifc[js3] encodes a nonsense mutation, W162*. These alleles exhibit multiple phenotypes, including delayed progression to the late-third larval instar stage, reduced brain size, elongation of the ventral nerve cord, axonal swelling, and lethality during late larval or early pupal stages.
Further characterization of these alleles the authors reveals that ifc is predominantly expressed in glia and localizes to the endoplasmic reticulum (ER). The expression of ifc gene governs glial morphology and survival. Expression of fly ifc cDNA or human DEGS1 cDNA specifically in glia, but not neurons, rescues the CNS phenotypes of ifc mutants, indicating a crucial role for ifc in glial cells and its evolutionary conservation. Loss of ifc results in ER expansion and loss of lipid droplets in cortex glia. Additionally, loss of ifc leads to ceramide depletion and accumulation of dihydroceramide. Moreover, it increases the saturation levels of triacylglycerols and membrane phospholipids. Finally, the reduction of dihydroceramide synthesis suppresses the CNS phenotypes associated with ifc mutations, indicating the key role of dihydroceramide in causing ifc LOF defects.
Strengths:
This manuscript unveils several intriguing and novel phenotypes of ifc loss-of-function in glia. The experiments are meticulously planned and executed, with the data strongly supporting their conclusions.
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www.biorxiv.org www.biorxiv.org
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Reviewer #2 (Public review):
Summary:
The authors aimed to investigate how IL-4 modulates the reactive state of microglia in the context of neuropathic pain. Specifically, they sought to determine whether IL-4 drives an increase in CD11c+ microglial cells, a population associated with anti-inflammatory responses, and whether this change is linked to the suppression of neuropathic pain. The study employs a combination of behavioral assays, pharmacogenetic manipulation of microglial populations, and characterization of microglial markers to address these questions.
Strengths:
Strengths: The methodological approach in this study is robust, providing convincing evidence for the proposed mechanism of IL-4-mediated microglial regulation in neuropathic pain. The experimental design is well thought out, utilizing two distinct neuropathic pain models (SpNT and SNI), each yielding different outcomes. The SpNT model demonstrates spontaneous pain remission and an increase in the CD11c+ microglial population, which correlates with pain suppression. In contrast, the SNI model, which does not show spontaneous pain remission, lacks a significant increase in CD11c+ microglia, underscoring the specificity of the observed phenomenon. This design effectively highlights the role of the CD11c+ microglial population in pain modulation. The use of behavioral tests provides a clear functional assessment of IL-4 manipulation, and pharmacogenetic tools allow for precise control of microglial populations, minimizing off-target effects. Notably, the manipulation targets the CD11c promoter, which presumably reduces the risk of non-specific ablation of other microglial populations, strengthening the experimental precision. Moreover, the thorough characterization of microglial markers adds depth to the analysis, ensuring that the changes in microglial populations are accurately linked to the behavioral outcomes.
Weaknesses:
One potential limitation of the study is that the mechanistic details of how IL-4 induces the observed shift in microglial populations are not fully explored. While the study demonstrates a correlation between IL-4 and CD11c+ microglial cells, a deeper investigation into the specific signaling pathways and molecular processes driving this population shift would greatly strengthen the conclusions. Additionally, the paper does not clearly integrate the findings into the broader context of microglial reactive state regulation in neuropathic pain.
Comments on revisions:
In the revised manuscript, the authors have successfully addressed my previous concerns as well as the other reviewers. I do not have further concerns about this study.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
The study by Lotonin et al. investigates correlates of protection against African swine fever virus (ASFV) infection. The study is based on a comprehensive work, including the measurement of immune parameters using complementary methodologies. An important aspect of the work is the temporal analysis of the immune events, allowing for the capture of the dynamics of the immune responses induced after infection. Also, the work compares responses induced in farm and SPF pigs, showing the latter an enhanced capacity to induce a protective immunity. Overall, the results obtained are interesting and relevant for the field. The findings described in the study further validate work from previous studies (critical role of virus-specific T cell responses) and provide new evidence on the importance of a balanced innate immune response during the immunization process. This information increases our knowledge on basic ASF immunology, one of the important gaps in ASF research that needs to be addressed for a more rational design of effective vaccines. Further studies will be required to corroborate that the results obtained based on the immunization of pigs by a not completely attenuated virus strain are also valid in other models, such as immunization using live attenuated vaccines.
While overall the conclusions of the work are well supported by the results, I consider that the following issues should be addressed to improve the interpretation of the results:
(1) An important issue in the study is the characterization of the infection outcome observed upon Estonia 2014 inoculation. Infected pigs show a long period of viremia, which is not linked to clinical signs. Indeed, animals are recovered by 20 days post-infection (dpi), but virus levels in blood remain high until 141 dpi. This is uncommon for ASF acute infections and rather indicates a potential induction of a chronic infection. Have the authors analysed this possibility deeply? Are there lesions indicative of chronic ASF in infected pigs at 17 dpi (when they have sacrificed some animals) or, more importantly, at later time points? Does the virus persist in some tissues at late time points, once clinical signs are not observed? Has all this been tested in previous studies?
(2) Virus loads post-Estonia infection significantly differ from whole blood and serum (Figure 1C), while they are very similar in the same samples post-challenge. Have the authors validated these results using methods to quantify infectious particles, such as Hemadsorption or Immunoperoxidase assays? This is important, since it would determine the duration of virus replication post-Estonia inoculation, which is a very relevant parameter of the model.
(3) Related to the previous points, do the authors consider it expected that the induction of immunosuppressive mechanisms during such a prolonged virus persistence, as described in humans and mouse models? Have the authors analysed the presence of immunosuppressive mechanisms during the virus persistence phase (IL10, myeloid-derived suppressor cells)? Have the authors used T cell exhausting markers to immunophenotype ASFV Estonia-induced T cells?
(4) A broader analysis of inflammatory mediators during the persistence phase would also be very informative. Is the presence of high VLs at late time points linked to a systemic inflammatory response? For instance, levels of IFN are still higher at 11 dpi than at baseline, but they are not analysed at later time points.
(5) The authors observed a correlation between IL1b in serum before challenge and protection. The authors also nicely discuss the potential role of this cytokine in promoting memory CD4 T cell functionality, as demonstrated in mice previously. However, the cells producing IL1b before ASFV challenge are not identified. Might it be linked to virus persistence in some organs? This important issue should be discussed in the manuscript.
(6) The lack of non-immunized controls during the challenge makes the interpretation of the results difficult. Has this challenge dose been previously tested in pigs of the age to demonstrate its 100% lethality? Can the low percentage of protected farm pigs be due to a modulation of memory T and B cell development by the persistence of the virus, or might it be related to the duration of the immunity, which in this model is tested at a very late time point? Related to this, how has the challenge day been selected? Have the authors analysed ASFV Estonia-induced immune responses over time to select it?
(7) Also, non-immunized controls at 0 dpc would help in the interpretation of the results from Figure 2C. Do the authors consider that the pig's age might influence the immune status (cytokine levels) at the time of challenge and thus the infection outcome?
(8) Besides anti-CD2v antibodies, anti-C-type lectin antibodies can also inhibit hemadsorption (DOI: 10.1099/jgv.0.000024). Please correct the corresponding text in the results and discussion sections related to humoral responses as correlates of protection. Also, a more extended discussion on the controversial role of neutralizing antibodies (which have not been analysed in this study), or other functional mechanisms such as ADCC against ASFV would improve the discussion.
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Reviewer #2 (Public review):
Summary:
In the current study, the authors attempt to identify correlates of protection for improved outcomes following re-challenge with ASFV. An advantage is the study design, which compares the responses to a vaccine-like mild challenge and during a virulent challenge months later. It is a fairly thorough description of the immune status of animals in terms of T cell responses, antibody responses, cytokines, and transcriptional responses, and the methods appear largely standard. The comparison between SPF and farm animals is interesting and probably useful for the field in that it suggests that SPF conditions might not fully recapitulate immune protection in the real world. I thought some of the conclusions were over-stated, and there are several locations where the data could be presented more clearly.
Strengths:
The study is fairly comprehensive in the depth of immune read-outs interrogated. The potential pathways are systematically explored. Comparison of farm animals and SPF animals gives insights into how baseline immune function can differ based on hygiene, which would also likely inform interpretation of vaccination studies going forward.
Weaknesses:
Some of the conclusions are over-interpreted and should be more robustly shown or toned down. There are also some issues with data presentation that need to be resolved and data that aren't provided that should be, like flow cytometry plots.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Li et al describe a novel form of melanosome based iridescence in the crest of an Early Cretaceous enantiornithine avialan bird from the Jehol Group.
This is an interesting manuscript that describes never before seen melanosome structures and explores fossilised feathers through new methods. This paper creates an opening for new work to explore coloration in extinct birds.
Strengths:
A novel set of methods applied to the study of fossil melanosomes.
Comments on revised version:
The authors provided a response to the previous 9 issues, for which additional response is provided here:
(1) I respectfully disagree with the authors justification regarding the crest. They show one specimen of Confuciusornis with short feathers (which appears to be a unique feature of this species, possibly related to the fact it is beaked) but what about the more primitive Eoconfuciusornis, a referred specimen of which superficially has an enormous "crest" (Zheng et al 2017), as does Changchengornis (Ji et al 1999). Regardless, it would make more sense compare this new specimen to other enantiornithines. Although limited by the preservation of body feathers, which is not all that common, the following published enantiornithines also exhibit a "crest": bohaiornithid indet. (Peteya et al 2017); Brevirostruavis (Li et al 2021); Dapingfangornis (Li et al 2006); Eoenantiornis (Zhou et al 2005); Grabauornis (Dalsatt etal 2014); Junornis (Liu et al 2017); Longirostravis (Hou etal 2004); Monoenantiornis (Hu & O'Connor 2016); Neobohaiornis (Shen etal 2024); Orienantiornis (Liu etal 2019); Parabohaironis (Wang 2023); Parapengornis (Hu etal 2015); Paraprotopteryx (Zheng et al 2007); and every specimen of Protopteryx. In fact, every single published enantiornithine that preserves any feathering on the head has the feathers preserved perpendicular to the bone (in fact, the body feathers on all parts of the bed are splayed at a right angle to the bone due to compression), as shown in the confuciuornis specimen image provided by the authors. Since it is highly improbable they all had crests, the authors have no justification for the interpretation that this new specimen was crested. This does not mean that the feathers were not iridescent or take away from the novel methods these authors have used to explore preserved feathers.
(2) Yes, this is possible, but see above for the very strong argument against interpretation of these feathers as forming a crest.
(3) This just further makes the point that the isolated feather is not likely from the head. Since the neck feathers are missing, it is more likely that it is these feathers that have been disarticulated (and sampled) from the neck region rather than from the very complete looking head feathers; this has significant implications with regards to the birds colour pattern.
(4) Thank you for acknowledging taphonomy.
(5) An interesting hypothesis and one I look forward to seeing explored in the future.
(6) Since the compression is in a single direction, in fact it is not reasonable to assume that distortion would be random. One might predict similar distortion, as with the feathers (spread out from the bone at a 90˚ angle) and bone (crushed), which are all compressed in a single direction. However, I agree that such a consistent discovery suggests it is not an artifact of preservation, and only further studies will elucidate this
(7) I still fail to detect this hexagonal pattern - could machine learning be used to quantify this pattern? The random arrangement of white arrows does little to clarify the authors interpretations.
(8) Great to see additional sampling
(9) Thank you for the explanation.
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Reviewer #3 (Public review):
Summary:
The paper presents an in-depth analysis of the original colour of a fossil feather from the crest of a 125-million-year-old enantiornithine bird. From its shape and location, it would be predicted that such a feather might well have shown some striking colour and pattern. The authors apply sophisticated microscopic and numerical methods to determine that the feather was iridescent and brightly coloured, and possibly indicates this was a male bird that used its crest in sexual displays.
Strengths:
The 3D micro-thin-sectioning techniques and the numerical analyses of light transmission are novel and state of the art. The example chosen is a good one, as a crest feather likely to have carried complex and vivid colours as a warning or for use in sexual display. The authors correctly warn that without such 3D study feather colours might be given simply as black from regular 2D analysis, and the alignment evidence for iridescence could be missed.
Weaknesses: Trivial
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Palardy and colleagues examine how transcription factors of the SoxB1 family alter patterning within the zebrafish posterior lateral line primordium and subsequent formation of neuromast organs along the body of the developing fish. They describe how expression of soxb genes changes when Wnt and Fgf signaling pathways are altered, and in addition, how outputs of these signalling pathways change when soxb gene expression is disrupted. Together, experiments suggest a model where the expression of SoxB genes counteracts Wnt signaling. Support comes from the combined inhibition of both pathways, partially restoring the pattern of neuromast deposition. Together, the work reveals an additional layer of control over Wnt and Fgf signals that together ensure proper posterior lateral line development.
Strengths:
The authors provide a clear analysis of changes in RNA expression after systematic manipulation of gene expression and signaling pathways to construct a plausible model of how Sox factors regulate primordium patterning.
Weaknesses:
There is little attempt to capture the variation of expression patterns with each manipulation. Photomicrographs are examples, with little quantification.
While the combined loss of soxb functions shows more severe phenotypes, it is not exactly clear what underlies the apparent redundancy. It would be helpful if the soxb gene family member expression was reported after loss of each. Expression of sox1a is shown in sox2 mutants in Figure 4, but other combinations are not reported. This additional analysis would clarify whether there are alterations in expression that influence apparent redundancy.
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Reviewer #2 (Public review):
Summary:
This manuscript seeks to determine the molecular basis of tissue patterning in the collectively migrating cells of the zebrafish posterior lateral line primordium. In particular, the authors examine the cross-regulation of canonical Wnt signaling, Fgf signaling, and the SoxB1 family members Sox1a, Sox2, and Sox3 in the migrating primordium. Using a combination of mutant lines, morphino (MO) knock down, pharmacological inhibition, and dominant-negative inhibition, the authors propose a model in which Sox2 and Sox3 in the trailing region of the primordium restricts Wnt signaling to the leading region, facilitating the formation of rosettes and the deposition of the first formed neuromast downstream of Fgf pathway activity. In contrast, sox1a is expressed in the leading region of the primordium, and the sox1ay590 -/- mutant shows little phenotype on its own. Together, the authors propose a multistep signaling loop that regulates tissue patterning during lateral line collective cell migration.
Strengths:
The zebrafish posterior lateral line primordium is a well-established model for the study of collective cell migration that is useful for genetic manipulation and live imaging. The manuscript seeks to understand the complex reciprocal regulation of signaling pathways that regulate tissue patterning of collectively migrating cells.
Weaknesses:
(1) The primary tools used in this study are inadequate to support the author's conclusions.
A. The authors state that the phenotype of the sox2y589 homozygous mutant line described in this manuscript changed across generations, but do not specify which generation is used for any given experiment. The sox2y589 mutant line is not properly verified in this manuscript, which could be done by examining ant-Sox2 antibody labeling, Western blot analysis, or complementation to the existing sox2x50 line described in Gou et al., 2018a and Gou et al., 2018b. There are also published sox1a mutant lines Lekk, et al., 2019.
B. The authors acknowledge that the sox2 MO1 used in this manuscript also alters sox3 function, but do not redo the experiments with a specific sox2 MO. In addition, the authors show that the anti-Sox2 and anti-Sox3 antibody labeling is reduced but not absent in sox2 MO1 and sox3 MO-injected embryos, but do not show antibody labeling of the sox2 MO and sox3 MO-double injected embryos to determine if there is an additional knockdown.
C. The authors examine RNA in situ hybridization patterns of sox2 and sox3 following various manipulations, but do not use anti-Sox2 and anti-Sox3 antibody labeling, which would provide more quantifiable information about changes in patterning.
(2) The manuscript lacks important experimental details and appropriate quantification of results.
A. It is unclear for most of the experiments described in this manuscript how many individual embryos were examined for each experiment and how robust the results are for each condition. Only Figure 3 includes information about the numbers for each experiment, and in all cases, the experimental manipulations are not fully penetrant, and there is no statistical analysis.
B. It is not clear at what stage most of the RNA in situ hybridizations were performed.
C. The manuscript lacks quantification of many of the experiments, making it difficult to conclude their significance.
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Reviewer #3 (Public review):
Summary:
This study aims to understand the molecular underpinnings of the complex process of periodic deposition of the neuromast organs of the embryonic posterior lateral line (PLL) sensory system in zebrafish. It was previously established that Fgf signaling in the trailing zone of the migrating PLL primordium is key to protoneuromast establishment, while Wnt signaling in the leading zone must be downregulated to allow new Fgf signaling-dependent protoneuromasts to form. Here, the authors evaluate the role of three SoxB transcription factors (Sox1a, Sox2, and Sox3) in this complex process, generating two novel CRISPR mutants as part of their study. They interrogate the interplay of the SoxB genes with the Fgf and Wnt signaling pathways during PLL primordium migration, using a combination of genetics, knockdown, and imaging approaches, including live time-lapse studies. They report a key role for the SoxB genes in regulating the pace of protoneuromast maturation as the primordium migrates, thus ensuring appropriate deposition and spacing of the neuromast organs.
Strengths:
Strengths of the study are the careful quantitative analysis. based on imaging approaches, of the impact of mutation or knockdown of SoxB genes, coupled with the use of heat shock inducible dominant negative strategies to address how SoxB genes interact with Wnt and Fgf signaling. Functional analyses convincingly uncover a SoxB regulatory network that serves to limit Wnt activity, as directly read out with a live Wnt reporter. The finding that Wnt inhibition (achieved using pharmacological reagents) rescues the SoxB deficiency phenotype provides compelling evidence of the centrality of the Wnt pathway in mediating SoxB function. Use of atoh1 markers to track the stages of development of the neuromasts provides an effective approach to following their maturation, and allows the authors to explore how SoxB/Wnt interplay ultimately translates into the establishment of functional neuromasts. Finally, loss of Sox2 function, together with loss of either Sox1a or Sox3, blocks maturation of the neuromasts, clearly establishing redundancy between these SoxB family genes.
The concepts introduced and explored in this study - of complex gene networks that work within a dynamic cellular environment to enable self-organization and ultimately stabilization of cell fate choices-provide a useful conceptual framework for future studies. This study is therefore of relevance to understanding the morphogenesis of self-organizing tissues more broadly.
Weaknesses:
A minor weakness is the use of SoxB morpholino (MO) knockdown reagents, which are interspersed with mutant analyses. Although the stable mutants are available, they would be challenging to couple with the reporter transgenes used for some of the experiments, providing a reasonable rationale for the use of MO reagents (although the authors don't overtly provide this rationale). Moreover, reduced penetrance of the Sox2 mutants over multiple generations is noted, but no detailed explanation for this finding is offered.
Given that the expression patterns of Sox1a and Sox3 are not merely different but are largely reciprocal, the mechanistic basis of their very similar double mutant phenotypes with Sox2 remains opaque. Related to this, the authors discuss that Sox1a/Sox2 double knockdown produces a more severe phenotype than Sox2/Sox3 double knockdown, yet this difference is not obviously reflected in the data, some of which is not shown.
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- Jul 2025
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
This paper introduces a new class of machine learning models for capturing how likely a specific nucleotide in a rearranged IG gene is to undergo somatic hypermutation. These models modestly outperform existing state-of-the-art efforts, despite having fewer free parameters. A surprising finding is that models trained on all mutations from non-functional rearrangements give divergent results from those trained on only silent mutations from functional rearrangements.
Strengths:
* The new model structure is quite clever and will provide a powerful way to explore larger models.<br /> * Careful attention is paid to curating and processing large existing data sets.<br /> * The authors are to be commended for their efforts to communicate with the developers of previous models and use the strongest possible versions of those in their current evaluation.
Weaknesses:
* No significant weaknesses noted
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Reviewer #2 (Public review):
This work offers an insightful contribution for researchers in computational biology, immunology, and machine learning. By employing a 3-mer embedding and CNN architecture, the authors demonstrate that it is possible to extend sequence context without exponentially increasing the model's complexity. Key findings include:
• Efficiency and Performance: Thrifty CNNs outperform traditional 5-mer models and match the performance of significantly larger models like DeepSHM.<br /> • Neutral Mutation Data: A distinction is made between using synonymous mutations and out-of-frame sequences for model training, with evidence suggesting these methods capture different aspects of SHM, or different biases in the type of data.<br /> • Open Source Contributions: The release of a Python package and pretrained models adds practical value for the community.
However, readers should be aware of the limitations. The improvements over existing models are modest, and the work is constrained by the availability of high-quality out-of-frame sequence data. The study also highlights that more complex modeling techniques, like transformers, did not enhance predictive performance, which underscores the role of data availability in such studies.
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Reviewer #3 (Public review):
Summary:
Modeling and estimating sequence context biases during B cell somatic hypermutation is important for accurately modeling B cell evolution to better understand responses to infection and vaccination. Sung et al. introduce new statistical models that capture a wider sequence context of somatic hypermutation with a comparatively small number of additional parameters. They demonstrate their model's performance with rigorous testing across multiple subjects and datasets. Prior work has captured the mutation biases of fixed 3-, 5-, and 7-mers, but each of these expansions has significantly more parameters. The authors developed a machine-learning-based approach to learn these biases using wider contexts with comparatively few parameters.
Strengths:
Well motivated and defined problem. Clever solution to expand nucleotide context. Complete separation of training and test data by using different subjects for training vs testing. Release of open-source tools and scripts for reproducibility.
The authors have addressed my prior comments.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The authors revisit the specific domains/signals required for redirection of an inner nuclear membrane protein, emerin, to the secretory pathway. They find that epitope tagging influences protein fate, serving as a cautionary tale for how different visualisation methods are used. Multiple tags and lines of evidence are used, providing solid evidence for the altered fate of different constructs.
Strengths:
This is a thorough dissection of domains and properties that confer INM retention vs secretion to the PM/lysosome, and will serve the community well as a caution regarding placement of tags and how this influences protein fate.
Weaknesses:
The specific biogenesis pathway for C-terminally tagged emerin might confound some interpretations. Appending the large GFP to the C-terminus may direct the fusion protein to a different ER insertion pathway than that used by the endogenous protein. How this might influence the fate of the tagged protein remains to be determined. In some ways this is beyond the scope of the current study, but should serve as a warning to epitope-tagging approaches.
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Reviewer #2 (Public review):
In this manuscript, Mella et al. investigate the effect of GFP tagging on the localization and stability of the nuclear-localized tail-anchored (TA) protein Emerin. A previous study from this group demonstrated that C-terminally GFP-tagged Emerin traffics to the plasma membrane and is eventually targeted to lysosomes for degradation. It has been suggested that the C-terminal tagging of TA proteins may shift their insertion from the post-translational TRC/GET pathway to the co-translational SRP-mediated pathway. Consistent with this, the authors confirm that C-terminal GFP tagging causes Emerin to mislocalize to the plasma membrane and subsequently to lysosomes.
In this study, they investigate the mechanism underlying this misrouting. By manipulating the cytosolic domain and the hydrophobicity of the transmembrane domain (TMD), the authors show that an ER retention sequence and increased TMD hydrophobicity contribute to Emerin's trafficking through the secretory pathway.
This reviewer had previously raised the concern that the potential role of the GFP tag within the ER lumen in promoting secretory trafficking was not addressed. In the revised manuscript, the authors respond to this concern by examining the co-localization of Emerin-GFP with the ER exit site marker Sec31A. Their data show that the presence of the C-terminal GFP tag increases Emerin's propensity to engage ER exit sites, supporting the conclusion that GFP tagging promotes its entry into the secretory pathway.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The authors report four cryoEM structures (2.99 to 3.65 Å resolution) of the 180 kDa, full-length, glycosylated, soluble Angiotensin-I converting enzyme (sACE) dimer, with two homologous catalytic domains at the N- and C-terminal ends (ACE-N and ACE-C). ACE is a protease capable of effectively degrading Aβ. The four structures are C2 pseudo-symmetric homodimers and provide insight into sACE dimerization. These structures were obtained using discrete classification in cryoSPARC and show different combinations of open, intermediate, and closed states of the catalytic domains, resulting in varying degrees of solvent accessibility to the active sites.
To deepen the understanding of the gradient of heterogeneity (from closed to open states) observed with discrete classification, the authors performed all-atom MD simulations and continuous conformational analysis of cryo-EM data using cryoSPARC 3DVA, cryoDRGN, and RECOVAR. cryoDRGN and cryoSPARC 3DVA revealed coordinated open-closed transitions across four catalytic domains, whereas RECOVAR revealed independent motion of two ACE-N domains, also observed with cryoSPARC focused classification. The authors suggest that the discrepancy in the results of the different methods for continuous conformational analysis in cryo-EM could results from different approaches used for dimensionality reduction and trajectory generation in these methods.
Strengths:
This is an important study that shows, for the first time, the structure and the snapshots of the dynamics of the full-length sACE dimer. Moreover, the study highlights the importance of combining insights from different cryo-EM methods that address questions difficult or impossible to tackle experimentally, while lacking ground truth for validation.
Weaknesses:
The open, closed, and intermediate states of ACE-N and ACE-C in the four cryo-EM structures from discrete classification were designated quantitatively (based on measured atomic distances on the models fitted into cryo-EM maps). Unfortunately, atomic models were not fitted into cryo-EM maps obtained with cryoSPARC 3DVA, cryoDRGN, and RECOVAR, and the open/closed states in these cases were designated based on a qualitative analysis.
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Reviewer #2 (Public review):
Summary:
The manuscript presents a valuable contribution to the field of ACE structural biology and dynamics by providing the first complete full-length dimeric ACE structure in four distinct states. The study integrates cryo-EM and molecular dynamics simulations to offer important insights into ACE dynamics. The depth of analysis is commendable, and the combination of structural and computational approaches enhances our understanding of the protein's conformational landscape.
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Reviewer #3 (Public review):
Summary:
Mancl et al. report four Cryo-EM structures of glycosylated and soluble Angiotensin-I converting enzyme (sACE) dimer. This moves forward the structural understanding of ACE, as previous analysis yielded partially denatured or individual ACE domains. By performing a heterogeneity analysis, the authors identify three structural conformations (open, intermediate open, and closed) that define the openness of the catalytic chamber and structural features governing the dimerization interface. They show that the dimer interface of soluble ACE consists of an N-terminal glycan and protein-protein interaction regions, as well as C-terminal protein-protein interactions. Further heterogeneity mining and all-atom molecular dynamic simulations show structural rearrangements that lead to the opening and closing of the catalytic pocket, which could explain how ACE binds its substrate. These studies could contribute to future drug design targeting the active site or dimerization interface of ACE.
Strengths:
The authors make significant efforts to address ACE denaturation on cryo-EM grids, testing various buffers and grid preparation techniques. These strategies successfully reduce denaturation and greatly enhance the quality of the structural analysis. The integration of cryoDRGN, 3DVA, RECOVAR, and all-atom simulations for heterogeneity analysis proves to be a powerful approach, further strengthening the overall experimental methodology.
Weaknesses:
No weaknesses noted. The revised manuscript adequately addresses the points I suggested in the review of the first submission.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
This study utilizes polarized second-harmonic generation (pSHG) microscopy to investigate myosin conformation in the relaxed state, distinguishing between the disordered, actin-accessible ON state and the ordered, energy-conserving OFF state. By pharmacologically modulating the ON/OFF equilibrium with a myosin activator (2-deoxyATP) and inhibitor (Mavacamten), the authors demonstrate that pSHG can sensitively quantify the ON/OFF ratio in both skeletal and cardiac muscle. Validation with X-ray diffraction supports the accuracy of the method. Applying this approach to a hypertrophic cardiomyopathy model, the study shows that R403Q/MYH7-mutated minipigs exhibit an increased ON state fraction relative to controls. This difference is eliminated under saturating concentrations of myosin modulators, indicating that the ON/OFF balance can be pharmacologically shifted to its extremes. Additionally, ATPase assays reveal elevated resting ATPase activity in R403Q samples, which persists even when the ON state is saturated, suggesting that increased energy consumption in this mutation is driven by both a shift toward the ON state and inherently higher myosin ATPase activity.
Strengths:
This is a well-written and well-conducted study that clearly reveals the power of SHG microscopy. The study clearly establishes the great utility of SHG to study thick filament regulation.
Weaknesses:
(1) Several studies have shown that the ON state of the thick filament is sensitive to both temperature and filament lattice spacing, with a common recommendation to conduct skinned fiber experiments at temperatures above 27{degree sign}C and in the presence of dextran to better preserve physiological conditions. The authors should clarify the experimental temperature used in their skinned fiber studies, indicate whether dextran was included, and discuss whether adherence to these recommended conditions would have impacted their results.
(2) On page 13, the authors report the proportion of disordered heads as approximately 30% in wild-type and 65% in R403Q fibers. They should clarify whether these values represent the percentage of total myosin heads, or rather the percentage of heads that are responsive to Mavacamten and dATP.
(3) In Figure 5, regarding ATPase measurements, the content of contractile material per unit volume of muscle preparation will influence the results. Did the authors account for this variable, and if not, how might it have affected the conclusions?
(4) For readers primarily interested in assessing the ON/OFF state of thick filaments, could the authors list the specific advantages of polarized second harmonic generation (pSHG) microscopy compared to X-ray diffraction?
(5) Given that many data points were derived from the same fiber or myocyte, how did the authors address the risk of type I errors due to non-independence of measurements? Was a nested or hierarchical statistical approach used?
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Reviewer #2 (Public review):
Summary:
In striated muscle, myosin motors can dynamically switch between an energy-conserving OFF state and an activated ON state. This switching is important for meeting the body's needs under different physiological conditions, and previous studies have shown that disease-causing mutations associated with cardiomyopathies can affect the population of these states, leading to aberrant contractility. Studying these structural states in muscle has previously only been possible via X-ray diffraction, which requires access to a beam line. Here, Arecchi et al. demonstrate that polarized second-harmonic generation microscopy (pSGH), a technique that is more accessible, can be used to probe the ON/OFF states of myosin in both permeabilized and intact muscle.
Strengths:
(1) There is an outstanding need in the field to better understand the regulation of the ON/OFF states of myosin. Currently, this is studied using X-ray diffraction, meaning that it is accessible to only a few labs. The authors demonstrate that pSGH can be used to probe the ON/OFF states of myosin both in intact and permeabilized muscle. This is a significant advance, since it makes it possible to study these states in a standard research laboratory.
(2) The authors demonstrate that this approach can be employed in both skeletal and cardiac muscle. Importantly, it works with both porcine and mouse cardiac muscle, which are two of the most important animal models for preclinical studies.
(3) The authors manipulate the ON/OFF equilibrium using both drugs and a genetic model of hypertrophic cardiomyopathy that has been shown to modulate the ON/OFF equilibrium. Their results generally agree with previous studies conducted using X-ray diffraction as well as biochemical measurements of myosin autoinhibition.
Weaknesses:
(1) While the application of pSGH to the ON/OFF equilibrium is an important advance, there are limited new biological insights since the perturbations used here have been extensively characterized in previous studies.
(2) SGH has previously been applied to study the nucleotide-dependent orientation of myosin motors in the sarcomere (PMID: 20385845). The authors have previously interpreted the value of gamma as being a readout of lever arm position, but here, it is interpreted as a measure of ON/OFF equilibrium. When this technique is applied to intact muscle, it is not clear how to deconvolve the contributions of lever arm angle from the ON/OFF population (especially where there is a mix of states that give rise to the gamma value). This is an important limitation that is not discussed in the manuscript.
(3) The R403Q mutation has previously been shown to cause an increase in ATP usage. Here, the authors measure an elevated basal ATPase rate under relaxing conditions, and they interpret this as showing increased myosin ATPase activity intrinsic to the motors; however, care should be used in interpreting these results. Work from the Spudich lab has shown that the R403Q mutation can appear as increasing motor function in some assays but depressing motor function in others (see PMID: 32284968, 26601291). Moreover, the actin-activated ATPase rate is an order of magnitude higher than the basal ATPase rate, and thus, small changes in the basal ATPase rate are unlikely to be important for physiology.
(4) The authors interpret some of their data based on the assumption that the high concentrations of drugs cause the myosin to either adopt 100% OFF or ON states. This assumption is not validated, limiting the ability to interpret the fraction of myosins in the ON/OFF states.
(5) The ATPase measurements are innovative but hard to interpret. dATP and ATP do not have identical ATPase kinetics, meaning that it is hard to deconvolve whether the elevated ATPase rate with dATP is due to changes in the ON/OFF population and/or intrinsic ATPase activity. Similarly, mavacamten reduces the rate of phosphate release from myosin, and this effect is not strictly coupled to the formation of the OFF state (e.g., see PMID: 40118457). As such, it is difficult to deconvolve drug-based changes in the inherent ATPase kinetics of the myosin from changes in the OFF-state population.
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Reviewer #3 (Public review):
Summary:
This is a very interesting paper extending the use of SHG to the study of relaxed muscle and its use to assess the order-disorder (and on /off) states of myosin heads in the thick filament. The work convincingly shows that SHG and the parameter gamma provide a reliable measure of the state of the myosin heads in a range of different relaxed muscle fibres, both intact and skinned, and in myofibrils. In mini pig cardiac fibres, the use of dATP and mavacamten increased or decreased the number of heads in the disordered state, respectively. On the assumption that these treatments push myosins fully into the disordered or ordered state, then this allows the fraction of ordered heads to be assessed under a wide variety of conditions. It is unfortunate that dATP treatment was not used (as mavacmten was) on rabbit psoas and mouse samples to further test this hypothesis.
The results with the myosin mutant R403Q support the idea that this mutation reduces the fraction of myosin heads in the ordered state and that mavacamten can recover the WT situation.
The results from SHG were compared with parallel studies using X-rays to validate the conclusions. Independent fibre ATPase data further support the conclusions.
The work is solid and provides a novel approach to assessing the activity state of muscle thick filaments. The authors point out some of the potential uses of this approach in the future, including time-resolved SHG measurements. Indeed, jumps in mavacamten or dATP concentration with time-resolved SHG could measure the rates of entry and exit from the ordered, off state of the filament. A measurement is urgently needed in the field.
Strengths:
(1) The SHG signal is convincingly shown to assess the fraction of ordered/disordered myosin heads in the thick filament of a variety of muscle fibres.
(2) The results are similar for rabbit psoas, mouse, and minipig cardiac fibres. Skinning the fibres and production of myofibrils do not change the SHG signal.
(3) Use of myosin R403Q mutant in mini pig confirms a loss of ordered myosin heads, and the ordered heads can be recovered by mavacamten.
(4) Parallel X-ray scattering and ATPase data support the conclusions.
(5) Assuming that dATP and mavacamten generate 100% disordered vs ordered myosin heads respectively, then the percentage of ordered heads can be calculated for a variety of conditions.
Weaknesses:
(1) Issues like the effect of fibre disarray and lattice spacing on the SHG signal are not well defined.
(2) The, now well-defined heterogeneity of thick filament structure is not acknowledged.
(3) dATP was only used on minipig cardiac fibres. The effect of dATP on rabbit psoas and mouse cardiac fibres would be a useful comparison and would help validate the calculation of % ordered heads.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
This manuscript presents findings on the adaptation mechanisms of Saccharomyces cerevisiae under extreme stress conditions. The authors try to generalize this to adaptation to stress tolerance. A major finding is that S. cerevisiae evolves a quiescence-like state with high trehalose to adapt to freeze-thaw tolerance independent of their genetic background. The manuscript is comprehensive, and each of the conclusions is well supported by careful experiments.
Strengths:
This is excellent interdisciplinary work.
Weaknesses: .
I have questions regarding the overall novelty of the proposal, which I would like the authors to explain.
(1) Earlier papers have shown that loss of ribosomal proteins, that slow growth, leads to better stress tolerance in S. cerevisiae. Given this, isn't it expected that any adaptation that slows down growth would, overall, increase stress tolerance? Even for other systems, it has been shown that slowing down growth (by spore formation in yeast or bacteria/or dauer formation in C. elegans) is an effective strategy to combat stress and hence is a likely route to adaptation. The authors stress this as one of the primary findings. I would like the authors to explain their position, detailing how their findings are unexpected in the context of the literature.
(2) Convergent evolution of traits: I find the results unsurprising. When selecting for a trait, if there is a major mode to adapt to that stress, most of the strains would adapt to that mode, independent of the route. According to me, finding out this major route was the objective of many of the previous reports on adaptive evolution. The surprising part in the previous papers (on adaptive evolution of bacteria or yeast) was the resampling of genes that acquired mutations in multiple replicates of an evolution experiments, providing a handle to understand the major genetic route or the molecular mechanism that guides the adaptation (for example in this case it would be - what guides the over-accumulation of trehalose). I fail to understand why the authors find the results surprising, and I would be happy to understand that from the authors. I may have missed something important.
(3) Adaptive evolution would work on phenotype, as all of selective evolution is supposed to. So, given that one of the phenotypes well-known in literature to allow free-tolerance is trehalose accumulation, I think it is not surprising that this trait is selected. For me, this is not a case of "non-genetic" adaptation as the authors point out: it is likely because perturbation of many genes can individually result in the same outcome - upregulation of trehalose accumulation. Thereby, although the adaptation is genetic, it is not homogeneous across the evolving lines - the end result is. Do the authors check that the trait is actually a non-genetic adaptation, i.e., if they regrow the cells for a few generations without the stress, the cells fall back to being similarly only partially fit to freeze-thaw cycles? Additionally, the inability to identify a network that is conserved in the sequencing does not mean that there is no regulatory pathway. A large number of cryptic pathways may exist to alter cellular metabolic states.<br /> This is a point in continuation of point #2, and I would like to understand what I have missed.
(4) To propose the convergent nature, it would be important to check for independently evolved lines and most probably more than 2 lines. It is not clear from their results section if they have multiple lines that have evolved independently.
(5) For the genomic studies, it is not clear if the authors sequenced a pool or a single colony from the evolved strains. This is an important point, since an average sequence will miss out on many mutations and only focus on the mutations inherited from a common ancestral cell. It is also not clear from the section.
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Reviewer #2 (Public review):
Summary:
The authors used experimental evolution, repeatedly subjecting Saccharomyces cerevisiae populations to rapid liquid-nitrogen freeze-thaw cycles while tracking survival, cellular biophysics, metabolite levels, and whole-genome sequence changes. Within 25 cycles, viability rose from ~2 % to ~70 % in all independent lines, demonstrating rapid and highly convergent adaptation despite distinct starting genotypes. Evolved cells accumulated about threefold more intracellular trehalose, adopted a quiescence-like phenotype (smaller, denser, non-budding cells), showed cytoplasmic stiffening and reduced membrane damage, and re-entered growth with shorter lag traits that together protected them from ice-induced injury. Whole-genome sequencing indicated that multiple genetic routes can yield the same mechano-chemical survival strategy. A population model in which trehalose controls quiescence entry, growth rate, lag, and freeze-thaw survival reproduced the empirical dynamics, implicating physiological state transitions rather than specific mutations as the primary adaptive driver. The study therefore concludes that extreme-stress tolerance can evolve quickly through a convergent, trehalose-rich quiescence-like state that reinforces membrane integrity and cytoplasmic structure.
Strengths:
The strengths of the paper are the experimental design, data presentation and interpretation, and that it is well-written.
Weaknesses:
(1) While the phenotyping is thorough, a few more growth curves would be quite revealing to determine the extent of cross-stress protection. For example, comparing growth rates under YPD vs. YPEG (EtOH/glycerol), and measuring growth at 37ºC or in the presence of 0.8 M KCl.
(2) Is GEMS integrated prior to evolution? Are the evolved cells transformable?
(3) From the table, it looks like strains either have mutations in Ras1/2 or Vac8. Given the known requirements of Ras/PKA signaling for the G1/S checkpoint (to make sure there are enough nutrients for S phase), this seems like a pathway worth mentioning and referencing. Regarding Vac8, its emerging roles in NVJ and autophagy suggest another nutrient checkpoint, perhaps through TORC1. The common theme is rewired metabolism, which is probably influencing the carbon shuttling to trehalose synthesis.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
This study builds off prior work that focused on the molecule AA147 and its role as an activator of the ATF6 arm of the unfolded protein response. In prior manuscripts, AA147 was shown to enter the ER, covalently modify a subset of protein disulfide isomerases (PDIs), and improve ER quality control for the disease-associated mutants of AAT and GABAA. Unsuccessful attempts to improve the potency of AA147 have led the authors to characterize a second hit from the screen in this study: the phenylhydrazone compound AA263. The focus of this study on enhancing the biological activity of the AA147 molecule is compelling, and overcomes a hurdle of the prior AA147 drug that proved difficult to modify. The study successfully identifies PDIs as a shared cellular target of AA263 and its analogs. The authors infer, based on the similar target hits previously characterized for AA147, that PDI modification accounts for a mechanism of action for AA263.
Strengths:
The authors are able to establish that, like AA147, AA263 covalently targets ER PDIs. The work establishes the ability to modify the AA263 molecule to create analogs with more potency and efficacy for ATF6 activation. The "next generation" analogs are able to enhance the levels of functional AAT and GABAA receptors in cellular models expressing the Z-variant of AAT or an epilepsy-associated variant of the GABAA receptor, outlining the therapeutic potential for this molecule and laying the foundation for future organism-based studies.
Weaknesses:
Arguably, the work does not fully support the statement provided in the abstract that the study "reveals a molecular mechanism for the activation of ATF6". The identification of targets of AA263 and its analogs is clear. However, it is a presumption that the overlap in PDIs as targets of both AA263 and AA147 means that AA263 works through the PDIs. While a likely mechanism, this conclusion would be bolstered by establishing that knockdown of the PDIs lessens drug impact with respect to ATF6 activation. Alternatively, it has previously been suggested that the cell-type dependent activity of AA263 may be traced to the presence of cell-type specific P450s that allow for the metabolic activation of AA263 or cell-type specific PDIs (Plate et al 2016; Paxman et al 2018). If the PDI target profile is distinct in different cell types, and these target difference correlates with ATF6-induced activity by AA263, that would also bolster the authors' conclusion.
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Reviewer #2 (Public review):
Modulating the UPR by pharmacological targeting of its sensors (or regulators) provides mostly uncharted opportunities in diseases associated with protein misfolding in the secretory pathway. Spearheaded by the Kelly and Wiseman labs, ATF6 modulators were developed in previous years that act on ER PDIs as regulators of ATF6. However, hurdles in their medicinal chemistry have hampered further development. In this study, the authors provide evidence that the small molecule AA263 also targets and covalently modifies ER PDIs, with the effect of activating ATF6. Importantly, AA263 turned out to be amenable to chemical optimization while maintaining its desired activity. Building on this, the authors show that AA263 derivatives can improve the aggregation, trafficking, and function of two disease-associated mutants of secretory pathway proteins. Together, this study provides compelling evidence for AA263 (and its derivatives) being interesting modulators of ER proteostasis. Mechanistic details of its mode of action will need more attention in future studies that can now build on this.
In detail, the authors provide strong evidence that AA263 covalently binds to ER PDIs, which will inhibit the protein disulfide isomerase activity. ER PDIs regulate ATF6, and thus their finding provides a mechanistic interpretation of AA263 activating the UPR. It should be noted, however, that AA263 shows broad protein labeling (Figure 1G), which may suggest additional targets, beyond the ones defined as MS hits in this study. Also, a further direct analysis of the IRE1 and PERK pathways (activated or not by AA263) would have been a benefit, as e.g., PDIA1, a target of AA263, directly regulates IRE1 (Yu et al., EMBOJ, 2020), and other PDIs also act on PERK and IRE1. The authors interpret modest activation of IRE1/PERK target genes (Figure 2C) as an effect on target gene overlap, indeed the most likely explanation based on their selective analyses on IRE1 (ERdj4) and PERK (CHOP) downstream genes, but direct activation due to the targeting of their PDI regulators is also a possible explanation. Further key findings of this paper are the observed improvement of AAT behavior and GABAA trafficking and function. Further strength to the mechanistic conclusion that ATF6 activation causes this could be obtained by using ATF6 inhibitors/knockouts in the presence of AA263 (as the target PDIs may directly modulate the behavior of AAT and/or GABAA). Along the same line, it also warrants further investigation why the different compounds, even if all were used at concentrations above their EC50, had different rescuing capacities on the clients.
Together, the study now provides a strong basis for such in-depth mechanistic analyses.
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Reviewer #3 (Public review):
Summary:
This study aims to develop and characterize phenylhydrazone-based small molecules that selectively activate the ATF6 arm of the unfolded protein response by covalently modifying a subset of ER-resident PDIs. The authors identify AA263 as a lead scaffold and optimize its structure to generate analogs with improved potency and ATF6 selectivity, notably AA263-20. These compounds are shown to restore proteostasis and functional expression of disease-associated misfolded proteins in cellular models involving both secretory (AAT-Z) and membrane (GABAA receptor) proteins. The findings provide valuable chemical tools for modulating ER proteostasis and may serve as promising leads for therapeutic development targeting protein misfolding diseases.
Strengths:
(1) The study presents a well-defined chemical biology framework integrating proteomics, transcriptomics, and disease-relevant functional assays.
(2) Identification and optimization of a new electrophilic scaffold (AA263) that selectively activates ATF6 represents a valuable advance in UPR-targeted pharmacology.
(3) SAR studies are comprehensive and logically drive the development of more potent and selective analogs such as AA263-20.
(4) Functional rescue is demonstrated in two mechanistically distinct disease models of protein misfolding-one involving a secretory protein and the other a membrane protein-underscoring the translational relevance of the approach.
Weaknesses:
(1) ATF6 activation is primarily inferred from reporter assays and transcriptional profiling; however, direct evidence of ATF6 cleavage is lacking.
(2) While the mechanism involving PDI modification and ATF6 activation is plausible, it remains incompletely characterized.
(3) No in vivo data are provided, leaving the pharmacological feasibility and bioavailability of these compounds in physiological systems unaddressed.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
This study provided key experimental evidence for the "Solstice-as-Phenology-Switch Hypothesis" through two temperature manipulation experiments.
Strengths:
The research is data-rich, particularly in exploring the effects of pre- and post-solstice cooling, as well as daytime versus nighttime cooling, on bud set timing, showcasing significant innovation. The article is well-written, logically clear, and is likely to attract a wide readership.
Weaknesses:
However, there are several issues that need to be addressed.
(1) In Experiment 1, significant differences were observed in the impact of cooling in July versus August. July cooling induced a delay in bud set dates that was 3.5 times greater in late-leafing trees compared to early-leafing ones, while August cooling induced comparable advances in bud set timing in both early- and late-leafing trees. The study did not explain why the timing (July vs. August) resulted in different mechanisms. Can a link be established between phenology and photosynthetic product accumulation? Additionally, can the study differentiate between the direct warming effect and the developmental effect, and quantify their relative contributions?
(2) The two experimental setups differed in photoperiod: one used a 13-hour photoperiod at approximately 4,300 lux, while the other used an ambient day length of 16 hours with a light intensity of around 6,900 lux. What criteria were used to select these conditions, and do they accurately represent real-world scenarios? Furthermore, as shown in Figure S1, significant differences in soil moisture content existed between treatments - could this have influenced the conclusions?
(3) The authors investigated how changes in air temperature around the summer solstice affected primary growth cessation, but the summer solstice also marks an important transition in photoperiod. How can the influence of photoperiod be distinguished from the temperature effect in this context?
(4) The study utilized potted trees in a controlled environment, which limits the generalization of the results to natural forests. Wild trees are subject to additional variables, such as competition and precipitation. Moreover, climate differences between years (2022 vs. 2023) were not controlled. As such, the conclusions may be overgeneralized to "all temperate tree species", as the experiment only involved potted European beech seedlings. The discussion would benefit from addressing species-specific differences.
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Reviewer #2 (Public review):
In 'Developmental constraints mediate the summer solstice reversal of climate effects on European beech bud set', Rebindaine and co-authors report on two experiments on Fagus sylvatica where they manipulated temperatures of saplings between day and night and at different times of year. I enjoyed reading this paper and found it well written. I think the experiments are interesting, but I found the exact methods somewhat extreme compared to how the authors present them. Further, given that much of the experiment happened outside, I am not sure how much we can generalize from one year for each experiment, especially when conducted on one population of one species. I next expand briefly on these concerns and a few others.
Concerns:
(1) As I read the Results, I was surprised the authors did not give more information on the methods here. For example, they refer to the 'effect of July cooling' but never say what the cooling was. Once I read the methods, I feared they were burying this as the methods feel quite extreme given the framing of the paper. The paper is framed as explaining observational results of natural systems, but the treatments are not natural for any system in Europe that I have worked in. For example, a low of 2 {degree sign}C at night and 7 {degree sign}C during the day through the end of May and then 7/13 {degree sign}C in July is extreme. I think these methods need to be clearly laid out for the reader so they can judge what to make of the experiment before they see the results.
(2) I also think the control is confounded with the growth chamber experience in Experiment 1. That is, the control plants never experience any time in a chamber, but all the treatments include significant time in a chamber. The authors mention how detrimental chamber time can be to saplings (indeed, they mention an aphid problem in experiment 2), so I think they need to be more upfront about this. The study is still very valuable, but again, we may need to be more cautious in how much we infer from the results.
(3) I suggest the authors add a figure to explain their experiments, as they are very hard to follow. Perhaps this could be added to Figure 1?
(4) Given how much the authors extrapolate to carbon and forests, I would have liked to see some metrics related to carbon assimilation, versus just information on timing.
(5) Fagus sylvatica is an extremely important tree to European forests, but it also has outlier responses to photoperiod and other cues (and leafs out very late), so using just this species to then state 'our results likely are generalisable across temperate tree species' seems questionable at best.
(6) Another concern relates to measuring the end of season (EOS). It is well known that different parts of plants shut down at different times, and each metric of end of season - budset, end of radial expansion, leaf coloring, etc - relates to different things. Thus, I was surprised that the authors ignore all this complexity and seem to equate leaf coloring with budset (which can happen MONTHS before leaf coloring often) and with other metrics. The paper needs a much better connection to the physiology of end of season and a better explanation for the focus on budset. Relatedly, I was surprised that the authors cite almost none of the literature on budset, which generally suggests it is heavily controlled by photoperiod and population-level differences in photoperiod cues, meaning results may be different with a different population of plants.
(7) I didn't fully see how the authors' results support the Solstice as Switch hypothesis, since what timing mattered seemed to depend on the timing of treatment and was not clearly related to the solstice. Could it be that these results suggest the Solstice as Switch hypothesis is actually not well supported (e.g., line 135) and instead suggest that the pattern of climate in the summer months affects end-of-season timing?
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The paper describes the cryoEM structure of RAD51 filament on the recombination intermediate. In the RAD51 filament, the insertion of a DNA-binding loop called the L2 loop stabilizes the separation of the complementary strand for the base-pairing with an incoming ssDNA and the non-complementary strand, which is captured by the second DNA-binding channel called the site II. The molecular structure of the RAD51 filament with a recombination intermediate provides a new insight into the mechanism of homology search and strand exchange between ssDNA and dsDNA.
Strong points:
This is the first human RAD51 filament structure with a recombination intermediate called the D-loop. The work has been done with great care, and the results shown in the paper are compelling based on cryo-EM and biochemical analyses. The paper is really nice and important for researchers in the field of homologous recombination, which gives a new view on the molecular mechanism of RAD51-mediated homology search and strand exchange.
Comments on revisions:
The authors nicely address most of the previous points.
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Reviewer #2 (Public review):
Homologous recombination is essential for DNA double-strand break repair, with RAD51-catalyzed strand exchange at its core. This study presents a 2.64 Å resolution cryogenic electron microscopy structure of the RAD51 D-loop complex, achieved through reconstitution of a RAD51 mini-filament. The structure uncovers how specific RAD51 residues drive strand exchange, offering atomic-level insight into the mechanics of eukaryotic HR and DNA repair.
Comments on revisions:
Authors acknowledged:
"We acknowledge that there exists an extensive body of literature that has investigated the polarity of strand exchange by RecA and RAD51 under a variety of experimental conditions, and we have added a brief comment to the text to reflect this, as well as some of the key citations. Undoubtedly, and as we also mention in our reply to the public reviews, further experimental work will be needed for a full reconciliation of the available evidence."
In the revised manuscript, this is reflected in the statement:
"Our mechanistic interpretation of static D-loop structures awaits full reconciliation with earlier efforts to determine strand-exchange polarity for RecA and RAD51 measured under a variety of experimental conditions."
Among the four cited studies, my understanding (as a person who has never studied this subject of polarity) is as follows:<br /> •References 50 (EMBO J. 1997), 51 (Cell. 1995), and 52 (Nature. 2008) suggest that the strand exchange by human RAD51 occurs with a polarity opposite to that of RecA-that is, in the 5′→3′ direction relative to the complementary strand, or 3′→5′ relative to the initiating single-stranded DNA (isDNA).<br /> • In contrast, reference 49 (PNAS 1998) proposed that 5′→3′ polarity (relative to isDNA) is conserved across RecA, human RAD51, and yeast RAD51.
Given the substantial structural analysis provided in the current manuscript, it would strengthen the work to include a concise description of these earlier biochemical findings, rather than citing them without context. This would benefit readers who are not familiar with the longstanding studies in the field and allow for a more informed interpretation of how the structural observations may reconcile or contrast with previous work.
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Reviewer #3 (Public review):
Summary:
Built on their previous pioneer expertise in studying RAD51 biology, in this paper, the authors aim to capture and investigate the structural mechanism of human RAD51 filament bound with a displacement loop (D-loop), which occurs during the dynamic synaptic state of the homologous recombination (HR) strand-exchange step. As the structures of both pre- and post-synaptic RAD51 filaments were previously determined, a complex structure of RAD51 filament during strand exchange is one of the key missing pieces of information for a complete understanding of how RAD51 functions in HR pathway. This paper aims to determine the high-resolution cryo-EM structure of RAD51 filament bound with D-loop. Combined with mutagenesis analysis and biophysical assays, the authors aim to investigate the D-loop DNA structure, RAD51 mediated strand separation and polarity, and a working model of RAD51 during HR strand invasion in comparison with RecA.
Strengths:
(1) The structural work and associated biophysical assays in this paper are solid, elegantly designed and interpreted. These results provide novel insights into RAD51's function in HR.
(2) The DNA substrate used was well designed, taking into consideration of the nucleotide number requirement of RAD51 for stable capture of donor DNA. This DNA substrate choice lays the foundation for successfully determining the structure of the RAD51 filament on D-loop DNA using single-partial cryo-EM.
(3) The authors utilised their previous expertise in capping DNA ends using monometric streptavidin and combined their careful data collection and processing to determine the cryo-EM structure of full-length human RAD51 bound at D-loop in high resolution. This interesting structure forms the core part of this work and allows detailed mapping of DNA-DNA and DNA-protein interaction among RAD51, invading strands, and donor DNA arms (Figures 1, 2, 3, 4). The geometric analysis of D-loop DNA bound with RAD51 and EM density for homologous DNA pairing are also impressive (Figure S5). The previously disordered RAD51's L2-loop is now ordered and traceable in the density map and functions as a physical spacer when bound with D-loop DNA. Interestingly, the authors identified that the side chain position of F279 in the L2_loop of RAD51_H differs from other F279 residues in L2-loops of E, F and G protomers. This asymmetric binding of L2 loops and RAD51_NTD binding with donor DNA arms forms the basis of the proposed working model about the polarity on csDNA during RAD51-mediated strand exchange.
(4) This work also includes mutagenesis analysis and biophysical experiments, especially EMSA, single-molecule fluorescence imaging using an optical tweezer, and DNA strand exchange assay, which are all suitable methods to study the key residues of RAD51 for strand exchange and D-loop formation (Figure 5).
Weaknesses:
(1) The proposed model for the 3'-5' polarity of RAD51-mediated strand invasion is based on the structural observations in the cryo-EM structure. This study lacks follow-up biochemical/biophysical experiments to validate the proposed model compared to RecA or developing methods to capture structures of any intermediate states with different polarity models.
(2) The functional impact of key mutants designed based on structure has not been tested in cells to evaluate how these mutants impact the HR pathway.
The significance of the work for the DNA repair field and beyond:
Homologous recombination (HR) is a key pathway for repairing DNA double-strand breaks and involves multiple steps. RAD51 forms nucleoprotein filaments first with 3' overhang single-strand DNA (ssDNA), followed by a search and exchange with a homology strand. This function serves as the basis of an accurate template-based DNA repair during HR. This research addressed a long-standing challenge of capturing RAD51 bound with the dynamic synaptic DNA and provided the first structural insight into how RAD51 performs this function. The significance of this work extends beyond the discovery biology for the DNA repair field, into its medical relevance. RAD51 is a potential drug target for inhibiting DNA repair in cancer cells to overcome drug resistance. This work offers a structural understanding of RAD51's function with D-loop and provides new strategies for targeting RAD51 to improve cancer therapies.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The authors extended a previous study of selective response to herbivory in Arabidopsis, in order to look specifically for selection on induced epigenetic variation ("Lamarckian evolution"). They found no evidence. In addition, the re-examined result from a previously published study arguing that environmentally induced epigenetic variation was common, and found that these findings were almost certainly artifactual.
Strengths:
The paper is very clearly written, there is no hype, and the methods used are state-of-the-art.
Weaknesses:
The result is negative, so the best you can do is put an upper bound on any effects.
Significance:
Claims about epigenetic inheritance and Lamarckian evolution continue to be made based on very shaky evidence. Convincing negative results are therefore important. In addition, the study presents results that, to this reviewer, suggest that the 2024 paper by Lin et al. [26] should probably be retracted.
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Reviewer #2 (Public review):
In this paper, the authors examine the extent to which epigenetic variation acquired during a selection treatment (as opposed to standing epigenetic variation) can contribute to adaptation in Arabidopsis. They find weak evidence for such adaptation and few differences in DNA methylation between experimental groups, which contrasts with another recent study (reference 26) that reported extensive heritable variation in response to the environment. The authors convincingly demonstrate that the conclusions of the previous study were caused by experimental error, so that standing genetic variation was mistaken for acquired (epigenetic) variation. Given the controversy surrounding the possible role of epigenetic variation in mediating phenotypic variation and adaptation, this is an important, clarifying contribution.
[Editors' note: We thank the authors for responding to the reviewers' comments.]
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www.biorxiv.org www.biorxiv.org
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Reviewer #2 (Public review):
Summary:
Transcriptomics technologies play crucial roles in biological research. Technologies based on second-generation sequencing, such as Illumina RNA-seq, encounter significant challenges due to the short reads, particularly in isoform analysis. In contrast, third-generation sequencing technologies overcome the limitation by providing long reads, but they are much more expensive. The authors present a useful real-time strategy to minimize the cost of RNA sequencing with Oxford Nanopore Technologies (ONT). The revised manuscript demonstrates the utilities with four sets of experiments with convincing evidence: (1) comparation between two cell lines; (2) comparison of RNA preparation procedures; (3) comparation between heat-shock and control conditions; (4) comparison of genetic modified yeast strains. The strategy will probably guide biologists to conduct transcriptomics studies with ONT in a fast and cost-effective way, benefiting both fundamental research and clinical applications.
Strengths:
The authors have recently developed a computational tool called NanopoReaTA to perform real-time analysis when cDNA/RNA samples are sequencing with ONT (Wierczeiko et al., 2023). The advantage of real-time analysis is that sequencing can be terminated once sufficient data has been collected to save cost. In this study, the authors demonstrate how to perform comprehensive quality control during sequencing. Their results indicate that the real-time strategy is effective across different species and RNA preparation methods. The revised manuscript addresses most of the major and minor limitations identified in the previous version, including: (1) explicitly detailing the methodology for isoform analysis and presenting the corresponding results; (2) increasing sample sizes and providing a clear explanation of related considerations; (3) clarifying the issue of sequential analysis; and (4) incorporating a new heat-shock experiment that better reflects real-world biological research.
Weaknesses:
A key advantage of RNA sequencing using ONT is its ability to facilitate isoform analysis. The primary strength of real-time analysis lies in its potential to reduce costs for researchers while enabling significant biological discoveries related to isoforms. Although the authors explicitly describe their approach to isoform analysis and introduce a new experiment in the revised manuscript, the study still lacks a concrete example that clearly demonstrates the substantial impact of their tool and strategy. While such an example may be beyond the intended scope of the current work, its absence limits a better assessment of the significance of the findings. Because the evaluation of a methodological approach ultimately depends on the additional scientific value it provides in research. It is possible that the full potential of this tool will be demonstrated in future studies by the authors or other researchers.
Furthermore, while the tool integrates a set of state-of-the-art methods, it does not introduce any novel methods. Consequently, the strength of evidence can be raised to "convincing".
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The authors quantified information in gesture and speech, and investigated the neural processing of speech and gestures in pMTG and LIFG, depending on their informational content, in 8 different time-windows, and using three different methods (EEG, HD-tDCS and TMS). They found that there is a time-sensitive and staged progression of neural engagement that is correlated with the informational content of the signal (speech/gesture).
Strengths:
A strength of the paper is that the authors attempted to combine three different methods to investigate speech-gesture processing.
Comments on revisions:
I thank the authors for their careful responses to my comments. However, I remain not convinced by their argumentation regarding the specificity of their spatial targeting and the time-windows that they used.
I do not believe the authors have adequately demonstrated the spatial and temporal specificity required to disentangle the contributions of the IFG and pMTG during the gesture-speech integration process. While the authors have made a sincere effort to address the concerns raised by the reviewers, and have done so with a lot of new analyses, I remain doubtful that the current methodological approach is sufficient to draw conclusions about the causal roles of the IFG and pMTG in gesture-speech integration.
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Reviewer #2 (Public review):
Summary
The study is an innovative and fundamental study that clarified important aspects of brain processes for integration of information from speech and iconic gesture (i.e., gesture that depicts action, movement, and shape), based on tDCS, TMS and EEG experiments. They evaluated their speech and gesture stimuli in information-theoretic ways and calculated how informative speech is (i.e., entropy), how informative gesture is, and how much shared information speech and gesture encode. The tDCS and TMS studies found that the left IFG and pMTG, the two areas that were activated in fMRI studies on speech-gesture integration in the previous literature, are causally implicated in speech-gesture integration. The size of tDC and TMS effects are correlated with entropy of the stimuli or mutual information, which indicates that the effects stems from the modulation of information decoding/integration processes. The EEG study showed that various ERP (event-related potential, e.g., N1-P2, N400, LPC) effects that have been observed in speech-gesture integration experiments in the previous literature are modulated by the entropy of speech/gesture and mutual information. This makes it clear that these effects are related to information decoding processes. The authors propose a model of how speech-gesture integration process unfolds in time, and how IFG and pMTG interact with each other in that process.
Strengths:
The key strength of this study is that the authors used information-theoretic measures of their stimuli (i.e., entropy and mutual information between speech and gesture) in all of their analyses. This made it clear that the neuro-modulation (tDCS, TMS) affected information decoding/integration and ERP effects reflect information decoding/integration. This study used tDCS and TMS methods to demonstrate that left IFG and pMTG are causally involved in speech-gesture integration. The size of tDCS and TMS effects are correlated with information-theoretic measures of the stimuli, which indicate that the effects indeed stem from disruption/facilitation of information decoding/integration process (rather than generic excitation/inhibition). The authors' results also showed correlation between information-theoretic measures of stimuli with various ERP effects. This indicates that these ERP effects reflect the information decoding/integration process.
Weaknesses:
The "mutual information" cannot capture all types of interplay of the meaning of speech and gesture. The mutual information is calculated based on what information can be decoded from speech alone and what information can be decoded from gesture alone. However, when speech and gesture are combined, a novel meaning can emerge, which cannot be decoded from a single modality alone. When example, a person produce a gesture of writing something with a pen, while saying "He paid". The speech-gesture combination can be interpreted as "paying by signing a cheque". It is highly unlikely that this meaning is decoded when people hear speech only or see gestures only. The current study cannot address how such speech-gesture integration occur in the brain, and what ERP effects may reflect such a process. The future studies can classify different types of speech-gesture integration and investigate neural processes that underlie each type. Another important topic for future studies is to investigate how the neural processes of speech-gesture integration change when the relative timing between the speech stimulus and the gesture stimulus changes.
Comments on the previous round of revisions: The authors addressed my concerns well.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
Kang et al. provide the first experimental insights from holographic stimulation of auditory cortex. Using stimulation of functionally-defined ensembles, they test whether overactivation of a specific subpopulation biases simultaneous and subsequent sensory-evoked network activations.
Strengths:
The investigators use a novel technique to investigate the sensory response properties in functionally defined cell assemblies in auditory cortex. These data provide the first evidence of how acutely perturbing specific frequency-tuned neurons impacts the tuning across a broader population. Their revised manuscript appropriately tempers any claims about specific plasticity mechanisms involved.
Weaknesses:
Although the single cell analyses in this manuscript are comprehensive, questions about how holographic stimulation impacts population coding are left to future manuscripts, or perhaps re-analyses of this unique dataset.
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Reviewer #2 (Public review):
The goal of HiJee Kang et al. in this study is to explore the interaction between assemblies of neurons with similar pure-tone selectivity in mouse auditory cortex. Using holographic optogenetic stimulation in a small subset of target cells selective for a given pure tone (PTsel), while optically monitoring calcium activity in surrounding non-target cells, they discovered a subtle rebalancing process: co-tuned neurons that are not optogenetically stimulated tend to reduce their activity. The cortical network reacts as if an increased response to PTsel in some tuned assemblies is immediately offset by a reduction in activity in the rest of the PTsel-tuned assemblies, leaving the overall response to PTsel unchanged. The authors show that this rebalancing process affects only the responses of neurons to PTsel, not to other pure tones. They also show that assemblies of neurons that are not selective for PTsel don't participate in the rebalancing process. They conclude that assemblies of neurons with similar pure-tone selectivity must interact in some way to organize this rebalancing process, and they suggest that mechanisms based on homeostatic signaling may play a role.
The authors have successfully controlled for potential artefacts resulting from their optogenetic stimulation. This study is therefore pioneering in the field of the auditory cortex (AC), as it is the first to use single-cell optogenetic stimulation to explore the functional organization of AC circuits in vivo. The conclusions of this paper are very interesting. They raise new questions about the mechanisms that could underlie such a rebalancing process.
(1) This study uses an all-optical approach to excite a restricted group of neurons chosen for their functional characteristics (their frequency tuning), and simultaneously record from the entire network observable in the FOV. As stated by the authors, this approach is applied for the first time to the auditory cortex, which is a tour de force. However, such approach is complex and requires precise controls to be convincing. The authors provide important controls to demonstrate the precise ability of their optogenetic methods. In particular, holographic patterns used to excite 5 cells simultaneously may be associated with out-of-focus laser hot spots. Cells located outside of the FOV could be activated, therefore engaging other cells than the targeted ones in the stimulation. This would be problematic in this study as their tuning may be unrelated to the tuning of the targeted cells. To control for such effect, the authors have decoupled the imaging and the excitation planes, and checked for the absence of out-of-focus unwanted excitation (Suppl Fig1).
(2) In the auditory cortex, assemblies of cells with similar pure-tone selectivity are linked together not only by their ability to respond to the same sound, but also by other factors. This study clearly shows that such assemblies are structured in a way that maintains a stable global response through a rebalancing process. If a group of cells within an assembly increases its response, the rest of the assembly must be inhibited to maintain the total response.<br /> One surprising result is the clear boundary between assemblies: a rebalancing process occurring in one assembly does not affect the response in another assembly comprising cells tuned to a different frequency. However, this is slightly challenged by the data shown in Figure 3.
Figure 3B-left, for example, shows that, compared to controls, non-target 16 kHz-preferring neurons only decrease their response to a 16 kHz pure tone when the cells targeted by the opto stimulation also prefer 16 kHz, but not when the targeted cells prefer 54 kHz. However, the inverse is not entirely true. Again compared to controls, Figure 3B (right) shows that non-target 54 kHz-preferring neurons decrease their response to a 54 kHz pure tone when the targeted cells also prefer 54 kHz; however, they also tend to be inhibited when the targeted cells prefer 16 kHz.
The authors suggest this may be due to the partial activation of 54 kHz-preferring cells by 16 kHz tones and propose examining the response of highly selective neurons. The results are shown in Figure 3F. It would have been more logical to show the same results as in Figure 3B, but with the left part restricted to highly 16 kHz-selective cells and the right part to highly 54 kHz-selective cells. However, the authors chose to pool all responses to 16 kHz and 54 kHz tones in every triplet of conditions (control, opto stimulation on 16 kHz-preferring cells and opto stimulation on 54 kHz-preferring cells), which blurs the result of the analysis.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The authors have developed self-amplifying RNAs (saRNAs) encoding additional genes to suppress dsRNA-related inflammatory responses and cytokine release. Their results demonstrate that saRNA constructs encoding anti-inflammatory genes effectively reduce cytotoxicity and cytokine production, enhancing the potential of saRNAs. This work is significant for advancing saRNA therapeutics by mitigating unintended immune activation.
Strengths:
This study successfully demonstrates the concept of enhancing saRNA applications by encoding immune-suppressive genes. A key challenge for saRNA-based therapeutics, particularly for non-vaccine applications, is the innate immune response triggered by dsRNA recognition. By leveraging viral protein properties to suppress immunity, the authors provide a novel strategy to overcome this limitation. The study presents a well-designed approach with potential implications for improving saRNA stability and minimizing inflammatory side effects.
Comments on revisions:
All comments have been thoroughly addressed, and the manuscript has been significantly improved.
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Reviewer #3 (Public review):
Summary:
Context - this is the 2nd review, of a manuscript that has already undergone some revisions.<br /> The manuscript explores ways to make self-amplifying RNA (saRNA) more silent through the inclusion of genes to inhibit the innate immune response. The readouts are predominantly expression and cell viability. They take a layered approach, adding multiple genes, as well as altering the capping of the anti-immune genes.
Strengths:
As described by the other reviewers, the authors take a stepwise approach to demonstrate that they can lead to sustained expression of the transgene.
Weaknesses:
The following weaknesses need some consideration
(1) The data show sustained expression, but do not directly show amplification. The amount of RFP is constantly decreasing over the time course. There is some evidence for the srIκBα-Smad7-SOCS1 construct. But measuring the RNA itself would be beneficial<br /> (2) The end construct is very large - it has 12 genes, this may have manufacturing considerations, affecting the translatability.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
The study by Wu et al. uses endogenous bruchpilot expression in a cell-type-specific manner to assess synaptic heterogeneity in adult Drosophila melanogaster mushroom body output neurons. The authors performed genomic on locus tagging of the presynaptic scaffold protein bruchpilot (BRP) with one part of splitGFP (GFP11) using the CRISPR/Cas9 methodology and co-expressed the other part of splitGFP (GFP1-10) using the GAL4/UAS system. Upon expression of both parts of splitGFP, fluorescent GFP is assembled at the N-terminus of BRP, exactly where BRP is endogenously expressed in active zones. For manageable analysis, a high-throughput pipeline was developed. This analysis evaluated parameters like location of BRP clusters, volume of clusters, and cluster intensity as a direct measure of the relative amount of BRP expression levels on site, using publicly available 3D analysis tools that are integrated in Fiji. Analysis was conducted for different mushroom body cell types in different mushroom body lobes using various specific GAL4 drivers. To test this new method of synapse assessment, Wu et al. performed an associative learning experiment in which an odor was paired with an aversive stimulus and found that, in a specific time frame after conditioning, the new analysis solidly revealed changes in BRP levels at specific synapses that are associated with aversive learning.
Strengths:
Expression of splitGFP bound to BRP enables intensity analysis of BRP expression levels as exactly one GFP molecule is expressed per BRP. This is a great tool for synapse assessment. This tool can be widely used for any synapse as long as driver lines are available to co-express the other part of splitGFP in a cell-type-specific manner. As neuropils and thus the BRP label can be extremely dense, the analysis pipeline developed here is very useful and important. The authors have chosen an exceptionally dense neuropil - the mushroom bodies - for their analysis and convincingly show that BRP assessment can be achieved with such densely packed active zones. The result that BRP levels change upon associative learning in an experiment with odor presentation paired with punishment is likewise convincing, and strongly suggests that the tool and pipeline developed here can be used in an in vivo context.
Weaknesses:
Although BRP is an important scaffold protein and its expression levels were associated with function and plasticity, I am still somewhat reluctant to accept that synapse structure profiling can be inferred from only assessing BRP expression levels and BRP cluster volume. Also, is it guaranteed that synaptic plasticity is not impaired by the large GFP fluorophore? Could the GFP10 construct that is tagged to BRP in all BRP-expressing cells, independent of GAL4, possibly hamper neuronal function? Is it certain that only active zones are labeled? I do see that plastic changes are made visible in this study after an associative learning experiment with BRP intensity and cluster volume as read-out, but I would be reassured by direct measurement of synaptic plasticity with splitGFP directly connected to BRP, maybe at a different synapse that is more accessible.
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Reviewer #2 (Public review):
Summary:
The authors developed a cell-type specific fluorescence-tagging approach using a CRISPR/Cas9 induced spilt-GFP reconstitution system to visualize endogenous Bruchpilot (BRP) clusters as presynaptic active zones (AZ) in specific cell types of the mushroom body (MB) in the adult Drosophila brain. This AZ profiling approach was implemented in a high-throughput quantification process, allowing for the comparison of synapse profiles within single cells, cell types, MB compartments, and between different individuals. The aim is to analyse in more detail neuronal connectivity and circuits in this centre of associative learning. These are notoriously difficult to investigate due to the density of cells and structures within a cell. The authors detect and characterize cell-type-specific differences in BRP-dependent profiling of presynapses in different compartments of the MB, while intracellular AZ distribution was found to be stereotyped. Next to the descriptive part characterizing various AZ profiles in the MB, the authors apply an associative learning assay and detect consequent AZ re-organisation.
Strengths:
The strength of this study lies in the outstanding resolution of synapse profiling in the extremely dense compartments of the MB. This detailed analysis will be the entry point for many future analyses of synapse diversity in connection with functional specificity to uncover the molecular mechanisms underlying learning and memory formation and neuronal network logics. Therefore, this approach is of high importance for the scientific community and a valuable tool to investigate and correlate AZ architecture and synapse function in the CNS.
Weaknesses:
The results and conclusions presented in this study are, in many aspects, well-supported by the data presented. To further support the key findings of the manuscript, additional controls, comments, and possibly broader functional analysis would be helpful. In particular:
(1) All experiments in the study are based on spilt-GFP lines (BRP:GFP11 and UAS-GFP1-10). The Materials and Methods section does not contain any cloning strategy (gRNA, primer, PCR/sequencing validation, exact position of tag insertion, etc.) and only refers to a bioRxiv publication. It might be helpful to add a Materials and Methods section (at least for the BRP:GFP11 line). Additionally, as this is an on locus insertion the in BRP-ORF, it needs a general validation of this line, including controls (Western Blot and correlative antibody staining against BRP) showing that overall BRP expression is not compromised due to the GFP insertion and localizes as BRP in wild type flies, that flies are viable, have no defects in locomotion and learning and memory formation and MB morphology is not affected compared to wild type animals.
(2) Several aspects of image acquisition and high-throughput quantification data analysis would benefit from a more detailed clarification.
a) For BRP cluster segmentation it is stated in the Materials and Methods state, that intensity threshold and noise tolerance were "set" - this setting has a large effect on the quantification, and it should be specified and setting criteria named and justified (if set manually (how and why) or automatically (to what)). Additionally, if Pyhton was used for "Nearest Neigbor" analysis, the code should be made available within this manuscript; otherwise, it is difficult to judge the quality of this quantification step.
b) To better evaluate the quality of both the imaging analysis and image presentation, it would be important to state, if presented and analysed images are deconvolved and if so, at least one proof of principle example of a comparison of original and deconvoluted file should be shown and quantified to show the impact of deconvolution on the output quality as this is central to this study.
(3) The major part of this study focuses on the description and comparison of the divergent synapse parameters across cell-types in MB compartments, which is highly relevant and interesting. Yet it would be very interesting to connect this new method with functional aspects of the heterogeneous synapses. This is done in Figure 7 with an associative learning approach, which is, in part, not trivial to follow for the reader and would profit from a more comprehensive analysis.
a) It would be important for the understanding and validation of the learning induced changes, if not (only) a ratio (of AZ density/local intensity) would be presented, but both values on their own, especially to allow a comparison to the quoted, previous AZ remodelling analysis quantifying BRP intensities (ref. 17, 18). It should be elucidated in more detail why only the ratio was presented here.
b) The reason why a single instead of a dual odour conditioning was performed could be clarified and discussed (would that have the same effects?).
c) Additionally, "controls" for the unpaired values - that is, in flies receiving neither shock nor odour - it would help to evaluate the unpaired control values in the different MB compartments.
d) The temporal resolution of the effect is very interesting (Figure 7D), and at more time points, especially between 90 and 270 min, this might raise interesting results.
e) Additionally, it would be very interesting and rewarding to have at least one additional assay, relating structure and function, e.g. on a molecular level by a correlative analysis of BRP and synaptic vesicles (by staining or co-expression of SV-protein markers) or calcium activity imaging or on a functional level by additional learning assays
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Reviewer #3 (Public review):
Summary:
The authors develop a tool for marking presynaptic active zones in Drosophila brains, dependent on the GAL4 construct used to express a fragment of GFP, which will incorporate with a genome-engineered partial GFP attached to the active zone protein bruchpilot - signal will be specific to the GAL4-expressing neuronal compartment. They then use various GAL4s to examine innervation onto the mushroom bodies to dissect compartment-specific differences in the size and intensity of active zones. After a description of these differences, they induce learning in flies with classic odour/electric shock pairing and observe changes after conditioning that are specific to the paired conditioning/learning paradigm.
Strengths:
The imaging and analysis appear strong. The tool is novel and exciting.
Weaknesses:
I feel that the tool could do with a little more characterisation. It is assumed that the puncta observed are AZs with no further definition or characterisation.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
In this study, the authors aim to uncover how the Parkinson's disease-linked LRRK2 G2019S mutation affects synaptic integrity through astrocyte-intrinsic mechanisms. Specifically, they investigate whether LRRK2-driven ERM hyperphosphorylation disrupts astrocyte morphology and excitatory synapse maintenance, with a focus on regional specificity within the cortex.
Strengths:
(1) Novelty and significance: The work provides important insights into non-neuronal contributions to Parkinson's disease (PD) pathology by highlighting a previously underappreciated role of astrocytic ERM signaling in synapse maintenance. This astrocyte-specific mechanism might help explain early cognitive dysfunctions in PD.
(2) Mechanistic depth: The authors present a detailed molecular pathway where the LRRK2 G2019S mutation increases ERM phosphorylation, disrupting Ezrin-Atg7 interactions critical for astrocyte morphology.
(3) Robust methodology: The study uses a powerful combination of tools, including AAV-mediated gene delivery, BioID-based interactome mapping, PALE labeling, and patch-clamp electrophysiology to link molecular, morphological, and functional changes.
(4) Physiological relevance: Parallel findings in both mouse models and human post-mortem brains suggest conservation of the observed phenotypes and strengthen the relevance to PD pathogenesis.
Weaknesses:
(1) Causal directionality: While ERM hyperphosphorylation is clearly shown to correlate with morphological and synaptic changes, the specific causal hierarchy-especially between Ezrin-Atg7 interaction loss and synapse alteration, is inferred but not definitively proven. For example, a rescue experiment directly restoring Atg7 function alongside Ezrin manipulation could strengthen this point.
(2) Brain region specificity: Although regional differences between ACC and MOp are well documented, the underlying cause of this differential vulnerability remains speculative. Examining astrocyte heterogeneity within cortical layers or via transcriptomic/proteomic profiling could clarify these regional effects.
(3) Autophagy function: While Atg7 knockdown leads to clear morphological changes, autophagic flux (e.g., LC3-II turnover or p62 accumulation) is not directly assessed. This would strengthen the mechanistic link to autophagy disruption.
(4) GFAP-based astrogliosis interpretation: The conclusion that no astrogliosis occurs in LRRK2 G2019S mice is based solely on GFAP staining. However, GFAP-negative reactive states have been reported. Including additional markers would help validate this interpretation.
(5) Impact on neuronal populations: The authors conclude that changes in inhibitory synapse density in the MOp are not rescued by astrocytic Ezrin manipulation and suggest developmental effects on interneurons. However, this is speculative without neuronal cell-type-specific data. Including interneuron density or synaptic connectivity analysis would make this claim more robust.
(6) Despite these limitations, the authors substantially achieve their stated aims. Their results provide strong support for a model in which astrocytic ERM signaling downstream of LRRK2 contributes to region-specific synaptic changes, particularly in the anterior cingulate cortex. While certain mechanistic links-such as the role of Ezrin-Atg7 interaction in synaptic maintenance-would benefit from further functional validation, the study offers a well-supported framework for understanding astrocyte-intrinsic contributions to synaptic dysfunction in Parkinson's disease.
This work is likely to contribute meaningfully to ongoing research in neurodegeneration, glial biology, and synaptic regulation. The methodological approaches - especially the combination of in vivo models with proteomics and electrophysiology - will be of interest to others studying astrocyte function and neuron-glia interactions. More broadly, the study highlights the importance of astrocyte heterogeneity and regional specialization in shaping neural circuit vulnerability, providing a valuable foundation for future investigations.
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Reviewer #2 (Public review):
Summary:
This is an important study that examines the relationship between a Parkinson's 's-associated mutation in LRRK2 kinase and increased ERM phosphorylation in astrocytes, altered excitatory and inhibitory synapse density and function, and a reduction in astrocyte size. The scope is impressively large and includes human and mouse samples, and employs immunolabeling, whole cell patch clamp recording techniques, molecular manipulation in vivo, and BioID. Experiments have appropriate controls, and the outcomes are mostly convincing. The chief weakness is that the study emphasizes scope over depth, such that it falls short of a unifying model of LRRK2-ERM interactions and leave many outcomes difficult to interpret.
The main idea is that the G2019S Parkinson's mutation in LRRK2 increases its kinase activity and that this either directly or indirectly increases ERM phosphorylation. This excessive ERM phosphorylation is expected to occur within perisynaptic astrocytic processes, reduce astrocyte complexity, and reduce excitatory synapse density and function in ACC. Overexpression of a dominant negative ezrin (phospho-dead) in astrocytes restores their morphology and excitatory synapse density in ACC. This pathway is well supported if taken on its own. But several datapoints presented do not fit this model. The reasoning driving selectivity to ACC and not M1 is not discussed or pursued (is it relevant that pERM levels appear lower in M1 at P21? Do astrocytes in S1 from G2019S mice also show reduced territories?); the differential effects on excitatory versus inhibitory synapses does not fit the model (or is this effect also expected to lie downstream of astrocytes?). Importantly, the effects of ezrin manipulation in wildtype samples (see below) are not integrated into the model, perhaps because the data run counter to expectation.
Specific Concerns and Questions:
(1) Effects in wildtype mice are not fully incorporated into the model. Overexpressing (OE) WT ezrin appears to reduce pERM levels by about half (Figure 1i vs 4B). OE-phospho-dead ezrin also appears to reduce pERM integrated density compared to control levels (same figures). This is not discussed (see also item 2). OE phospho-dead ezrin decreases synapse density and maybe function compared to OE WT ezrin in wildtype mice (4C, 4F), but it is not clear whether or not these data differ from unmanipulated wildtype sections/slices (Figures 2 and 3) because the data are normalized. These synaptic findings in wildtype should also be joined to the morphology findings in wildtype astrocytes, where OE-phospho-dead ezrin reduces astrocyte territory similar to LRRK2-G2019S. The shared morphological outcome is discussed as a potential defect in ERM phospho/dephospho balance, but it was hard to see if this could be similarly related to changes in synapse density.
(2) Labeling for pERMs shown in wildtype mouse and control human is not convincing, but is convincing in the G2019S samples (e.g., Figure 1/S1, Figure 2) (although concentration in perisynaptic astrocytes is not clear). The data presented seem to better support the idea that the mutation confers a pathological gain of ERM phosphorylation (rather than hyperphosphorylation). If the faint labeling in wildtype and control samples is genuine, one would anticipate that pERM labeling would be different in shControl vs. shLrrk2 astrocytes.
(3) Given the data presented, it would seem that overexpressing the BirA2 ezrin construct, like wildtype ezrin, could impact astrocyte biology. If overexpressing a wildtype ezrin reduces pERM levels, then perhaps the BirA2 construct expression already favors a closed conformation. This is not so much a critique of the approach as a request for clarification and to include, if possible, whether there are reasons to believe or data to support that the BirA2 construct adopts both open and closed conformations.
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Reviewer #3 (Public review):
Summary:
Wang et al. reported a new role of LRRK2-GS mutant in astrocyte morphology and synapse maintenance and a potential mechanism that acts through phosphorylation of ERM, which binds to ATG7. In both human LRRK2-GS patients and LRRK2-GS KI mouse brain cortex, they found increased ERM phosphorylation levels. LRRK2-GS alters excitatory and inhibitory synapse densities and functions in the cortex, which can be restored by p-ERM-dead mutant. They further demonstrated that LRRK2 regulates astrocyte morphological complexity in vivo through ERM phosphorylation. Proteomic and biochemistry approaches found that ATG7 interacts with Ezrin, which is inhibited by Ezrin phosphorylation. This provides a potential mechanism by which LRRK2-GS impairs the astrocyte morphology.
Strengths:
(1) Data in human PD patients (Figure 1B, C) is impressive, showing a clear increase of p-ERM in LRRK2-GS samples.
(2) Both LRRK2-GS and siLRRK2 show similar phenotypes, supporting both GOF and LOF decrease astrocyte complexity and size.
(3) Using p-ERM-dead and mimic mutants is elegant. The data is striking that the p-ERM-dead mutant can restore LRRK2-GS-induced excitatory synapse density in the ACC and astrocyte territory volume and complexity, while the p-ERM-mimic mutant can restore the siLRRK2 phenotype.
(4) ATG7 binding to Ezrin provides a potential mechanism. It is compelling that siATG7 shows a similar decrease in astrocyte territory volume and complexity, and siATG7 in LRRK2-GS does not enhance the astrocyte phenotype.
Weaknesses:
(1) The authors claim that p-ERM colocalizes with astrocyte marker ALDH1L1, e.g., Figure 1E, F, G, H, J, K. It is hard to tell from the representative images. Given that this is critical for this paper, it would be appreciated if the authors could improve the images and show clear colocalization. The same concern for Figures S1, 2, 3. Validation of the p-ERM antibody is critical. Figure S4, using λ-PPase to eliminate the phosphorylation signal in general, is very helpful. Additional validation of the p-ERM antibody specific to ERM would be appreciated.
(2) Does the total ERM level change /increase in LRRK2-GS samples? The increased p-ERM levels could be because the total ERM level increases. Then, the follow-up question is whether the total ERM level matters to the astrocyte phenotypes seen in the paper.
(3) WT mice carry WT-LRRK2, which also has kinase activity to phosphorylate ERM. So, what are the effects of overexpression of the p-ERM mutants (dead or mimic) on the excitatory and inhibitory synapse densities and functions in WT mouse samples? In Figure 4, statistics should be done comparing WT+Ezrin O/E vs WT+phosphor-dead Ezrin O/E. From what is shown in the graphs, it looks like phosphor-dead Ezrin worsens the phenotype in WT mice, which is opposite to the GS mice. How to explain? The same question for the graphs in Figure 5.
(4) Rab10 is not a robust substrate for the LRRK2-G2019S mutant, and p-Rab10 is very difficult to detect in mouse brains. The specificity of the pRab10 immunostaining signal in Fig. S8 is not certain.
(5) Would ATG7, Ezrin, and LRRK2 form a complex?
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Reviewer #1 (Public review):
Summary:
The authors have investigated the role of GAT3 in the visual system. First, they have developed a CRISPR/Cas9-based approach to locally knock out this transporter in the visual cortex. They then demonstrated electrophysiologically that this manipulation increases inhibitory synaptic input into layer 2/3 pyramidal cells. They further examined the functional consequences by imaging neuronal activity in the visual cortex in vivo. They found that the absence of GAT3 leads to reduced spontaneous neuronal activity and attenuated neuronal responses and reliability to visual stimuli, but without an effect on orientation selectivity. Further analysis of this data suggests that Gat3 removal leads to less coordinated activity between individual neurons and in population activity patterns, thereby impairing information encoding. Overall, this is an elegant and technically advanced study that demonstrates a new and important role of GAT3 in controlling the processing of visual information.
Strengths:
(1) Development of a new approach for a local knockout (GAT3).
(2) Important and novel insights into visual system function and its dependence on GAT3.
(3) Plausible cellular mechanism.
Weaknesses:
No major weaknesses were identified by this reviewer.
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Reviewer #2 (Public review):
Summary:
Park et al. have made a tool for spatiotemporally restricted knockout of the astrocytic GABA transporter GAT3, leveraging CRISPR/Cas9 and viral transduction in adult mice, and evaluated the effects of GAT3 on neural encoding of visual stimulation.
Strengths:
This concise manuscript leverages state-of-the-art gene CRISPR/Cas9 technology for knocking out astrocytic genes. This has only to a small degree been performed previously in astrocytes, and it represents an important development in the field. Moreover, the authors utilize in vivo two-photon imaging of neural responses to visual stimuli as a readout of neural activity, in addition to validating their data with ex vivo electrophysiology. Lastly, they use advanced statistical modeling to analyze the impact of GAT3 knockout. Overall, the study comes across as rigorous and convincing.
Weaknesses:
Adding the following experiments would potentially have strengthened the conclusions and helped with interpreting the findings:
(1) Neural activity is quite profoundly influenced by GAT3 knockout. Corroborating these relatively large changes to neural activity with in vivo electrophysiology of some sort as an additional readout would have strengthened the conclusions.
(2) Given the quite large effects on neural coding in visual cortex assessed på jRGECO imaging, it would have been interesting if the mouse groups could have been subjected to behavioral testing, assessing the visual system.
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www.biorxiv.org www.biorxiv.org
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Reviewer #1 (Public review):
Summary:
This study aims to investigate the development of infants' responses to music by examining neural activity via EEG and spontaneous body kinematics using video-based analysis. The authors also explore the role of musical pitch in eliciting neural and motor responses, comparing infants at 3, 6, and 12 months of age.
Strengths:
A key strength of the study lies in its analysis of body kinematics and modeling of stimulus-motor coupling, demonstrating how the amplitude envelope of music predicts infant movement, and how higher musical pitch may enhance auditory-motor synchronization.
Weaknesses:
The neural data analysis is currently limited to auditory evoked potentials aligned with beat timing. A more comprehensive approach is needed to robustly support the proposed developmental trajectory of neural responses to music.
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Reviewer #2 (Public review):
Summary:
Infants' auditory brain responses reveal processing of music (clearly different from shuffled music patterns) from the age of 3 months; however, they do not show a related increase in spontaneous movement activity to music until the age of 12 months.
Strengths:
This is a nice paper, well designed, with sophisticated analyses and presenting clear results that make a lot of sense to this reviewer. The additions of EEG recordings in response to music presentations at 3 different infant ages are interesting, and the manipulation of the music stimuli into shuffled, high, and low pitch to capture differences in brain response and spontaneous movements is good. I really enjoyed reading this work and the well-written manuscript.
Weaknesses:
I only have two comments. The first is a change to the title. Maybe the title should refer to the first "postnatal" year, rather than the first year of life. There are controversies about when life really starts; it could be in the womb, so using postnatal to refer to the period after birth resolves that debate.
The other comment relates to the 10 Principal Movements (PMs) identified. I was wondering about the rationale for identifying these different PMs and to what extent many PMs entered in the analyses may hinder more general pattern differences. Infants' spontaneous movements are very variable and poorly differentiated in early development. Maybe, instead of starting with 10 distinct PMs, a first analysis could be run using the combined Quantity of Movements (QoM) without PM distinctions to capture an overall motor response to music. Maybe only 2 PMs could be entered in the analysis, for the arms and for the legs, regardless of the patterns generated. Maybe the authors have done such an analysis already, but describing an overall motor response, before going into specific patterns of motor activation, could be useful to describe the level of motor response. Again, infants provide extremely variable patterns of response, and such variability may potentially hinder an overall effect if the QoM were treated as a cumulated measure rather than one with differentiated patterns.
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Reviewer #3 (Public review):
Summary:
This study provides a detailed investigation of neural auditory responses and spontaneous movements in infants listening to music. Analyses of EEG data (event-related potentials and steady-state responses) first highlighted that infants at 3, 6, and 12 months of age and adults showed enhanced auditory responses to music than shuffled music. 6-month-olds also exhibited enhanced P1 response to high-pitch vs low-pitch stimuli, but not the other groups. Besides, whole body spontaneous movements of infants were decomposed into 10 principal components. Kinematic analyses revealed that the quantity of movement was higher in response to music than shuffled music only at 12 months of age. Although Granger causality analysis suggested that infants' movement was related to the music intensity changes, particularly in the high-pitch condition, infants did not exhibit phase-locked movement responses to musical events, and the low movement periodicity was not coordinated with music.
Strengths:
This study investigates an important topic on the development of music perception and translation to action and dance. It targets a crucial developmental period that is difficult to explore. It evaluates two modalities by measuring neural auditory responses and kinematics, while cross-modal development is rarely evaluated. Overall, the study fills a clear gap in the literature.
Besides, the study uses state-of-the-art analyses. All steps are clearly detailed. The manuscript is very clear, well-written, and pleasant to read. Figures are well-designed and informative.
Weaknesses:
(1) Differences in neural responses to high-pitch vs low-pitch stimuli between 6-month-olds and other infants are difficult to interpret.
(2) Making some links between the neural and movement responses that are described in this manuscript could be expected, given the study goal. Although kinematic analyses suggested that movement responses are not phase-locked to the music stimuli, analyses of Granger causality between motion velocity and neural responses could be relevant.
(3) The study considers groups of infants at different ages, but infants within each group might be at different stages of motor development. Was this assessed behaviorally? Would it be possible to explore or take into account this possible inter-individual variability?
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Reviewer #1 (Public review):
Summary:
This article investigates the origin of movement slowdown in weightlessness by testing two possible hypotheses: the first is based on a strategic and conservative slowdown, presented as a scaling of the motion kinematics without altering its profile, while the second is based on the hypothesis of a misestimation of effective mass by the brain due to an alteration of gravity-dependent sensory inputs, which alters the kinematics following a controller parameterization error.
Strengths:
The article convincingly demonstrates that trajectories are affected in 0g conditions, as in previous work. It is interesting, and the results appear robust. However, I have two major reservations about the current version of the manuscript that prevent me from endorsing the conclusion in its current form.
Weaknesses:
(1) First, the hypothesis of a strategic and conservative slowdown implicitly assumes a similar cost function, which cannot be guaranteed, tested, or verified. For example, previous work has suggested that changing the ratio between the state and control weight matrices produced an alteration in movement kinematics similar to that presented here, without changing the estimated mass parameter (Crevecoeur et al., 2010, J Neurophysiol, 104 (3), 1301-1313). Thus, the hypothesis of conservative slowing cannot be rejected. Such a strategy could vary with effective mass (thus showing a statistical effect), but the possibility that the data reflect a combination of both mechanisms (strategic slowing and mass misestimation) remains open.
(2) The main strength of the article is the presence of directional effects expected under the hypothesis of mass estimation error. However, the article lacks a clear demonstration of such an effect: indeed, although there appears to be a significant effect of direction, I was not sure that this effect matched the model's predictions. A directional effect is not sufficient because the model makes clear quantitative predictions about how this effect should vary across directions. In the absence of a quantitative match between the model and the data, the authors' claims regarding the role of misestimating the effective mass remain unsupported.
In general, both the hypotheses of slowing motion (out of caution) and misestimating mass have been put forward in the past, and the added value of this article lies in demonstrating that the effect depended on direction. However, (1) a conservative strategy with a different cost function can also explain the data, and (2) the quantitative match between the directional effect and the model's predictions has not been established.
Specific points:
(1) I noted a lack of presentation of raw kinematic traces, which would be necessary to convince me that the directional effect was related to effective mass as stated.
(2) The presentation and justification of the model require substantial improvement; the reason for their presence in the supplementary material is unclear, as there is space to present the modelling work in detail in the main text. Regarding the model, some choices require justification: for example, why did the authors ignore the nonlinear Coriolis and centripetal terms?
(3) The increase in the proportion of trials with subcomponents is interesting, but the explanatory power of this observation is limited, as the initial percentage was already quite high (from 60-70% during the initial study to 70-85% in flight). This suggests that the potential effect of effective mass only explains a small increase in a trend already present in the initial study. A more critical assessment of this result is warranted.
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Reviewer #2 (Public review):
This study explores the underlying causes of the generalized movement slowness observed in astronauts in weightlessness compared to their performance on Earth. The authors argue that this movement slowness stems from an underestimation of mass rather than a deliberate reduction in speed for enhanced stability and safety.
Overall, this is a fascinating and well-written work. The kinematic analysis is thorough and comprehensive. The design of the study is solid, the collected dataset is rare, and the model tends to add confidence to the proposed conclusions. That being said, I have several comments that could be addressed to consolidate interpretations and improve clarity.
Main comments:
(1) Mass underestimation
a) While this interpretation is supported by data and analyses, it is not clear whether this gives a complete picture of the underlying phenomena. The two hypotheses (i.e., mass underestimation vs deliberate speed reduction) can only be distinguished in terms of velocity/acceleration patterns, which should display specific changes during the flight with a mass underestimation. The experimental data generally shows the expected changes but for the 45{degree sign} condition, no changes are observed during flight compared to the pre- and post-phases (Figure 4). In Figure 5E, only a change in the primary submovement peak velocity is observed for 45{degree sign}, but this finding relies on a more involved decomposition procedure. It suggests that there is something specific about 45{degree sign} (beyond its low effective mass). In such planar movements, 45{degree sign} often corresponds to a movement which is close to single-joint, whereas 90{degree sign} and 135{degree sign} involve multi-joint movements. If so, the increased proportion of submovements in 90{degree sign} and 135{degree sign} could indicate that participants had more difficulties in coordinating multi-joint movements during flight. Besides inertia, Coriolis and centripetal effects may be non-negligible in such fast planar reaching (Hollerbach & Flash, Biol Cyber, 1982) and, interestingly, they would also be affected by a mass underestimation (thus, this is not necessarily incompatible with the author's view; yet predicting the effects of a mass underestimation on Coriolis/centripetal torques would require a two-link arm model). Overall, I found the discrepancy between the 45{degree sign} direction and the other directions under-exploited in the current version of the article. In sum, could the corrective submovements be due to a misestimation of Coriolis/centripetal torques in the multi-joint dynamics (caused specifically -or not- by a mass underestimation)?
b) Additionally, since the taikonauts are tested after 2 or 3 weeks in flight, one could also assume that neuromuscular deconditioning explains (at least in part) the general decrease in movement speed. Can the authors explain how to rule out this alternative interpretation? For instance, weaker muscles could account for slower movements within a classical time-effort trade-off (as more neural effort would be needed to generate a similar amount of muscle force, thereby suggesting a purposive slowing down of movement). Therefore, could the observed results (slowing down + more submovements) be explained by some neuromuscular deconditioning combined with a difficulty in coordinating multi-joint movements in weightlessness (due to a misestimation or Coriolis/centripetal torques) provide an alternative explanation for the results?
(2) Modelling
a) The model description should be improved as it is currently a mix of discrete time and continuous time formulations. Moreover, an infinite-horizon cost function is used, but I thought the authors used a finite-horizon formulation with the prefixed duration provided by the movement utility maximization framework of Shadmehr et al. (Curr Biol, 2016). Furthermore, was the mass underestimation reflected both in the utility model and the optimal control model? If so, did the authors really compute the feedback control gain with the underestimated mass but simulate the system with the real mass? This is important because the mass appears both in the utility framework and in the LQ framework. Given the current interpretations, the feedforward command is assumed to be erroneous, and the feedback command would allow for motor corrections. Therefore, it could be clarified whether the feedback command also misestimates the mass or not, which may affect its efficiency. For instance, if both feedforward and feedback motor commands are based on wrong internal models (e.g., due to the mass underestimation), one may wonder how the astronauts would execute accurate goal-directed movements.
b) The model seems to be deterministic in its current form (no motor and sensory noise). Since the framework developed by Todorov (2005) is used, sensorimotor noise could have been readily considered. One could also assume that motor and sensory noise increase in microgravity, and the model could inform on how microgravity affects the number of submovements or endpoint variance due to sensorimotor noise changes, for instance.
c) Finally, how does the model distinguish the feedforward and feedback components of the motor command that are discussed in the paper, given that the model only yields a feedback control law? Does 'feedforward' refer to the motor plan here (i.e., the prefixed duration and arguably the precomputed feedback gain)?
(3) Brevity of movements and speed-accuracy trade-off
The tested movements are much faster (average duration approx. 350 ms) than similar self-paced movements that have been studied in other works (e.g., Wang et al., J Neurophysiology, 2016; Berret et al., PLOS Comp Biol, 2021, where movements can last about 900-1000 ms). This is consistent with the instructions to reach quickly and accurately, in line with a speed-accuracy trade-off. Was this instruction given to highlight the inertial effects related to the arm's anisotropy? One may however, wonder if the same results would hold for slower self-paced movements (are they also with reduced speed compared to Earth performance?). Moreover, a few other important questions might need to be addressed for completeness: how to ensure that astronauts did remember this instruction during the flight? (could the control group move faster because they better remembered the instruction?). Did the taikonauts perform the experiment on their own during the flight, or did one taikonaut assume the role of the experimenter?
(4) No learning effect
This is a surprising effect, as mentioned by the authors. Other studies conducted in microgravity have indeed revealed an optimal adaptation of motor patterns in a few dozen trials (e.g., Gaveau et al., eLife, 2016). Perhaps the difference is again related to single-joint versus multi-joint movements. This should be better discussed given the impact of this claim. Typically, why would a "sensory bias of bodily property" persist in microgravity and be a "fundamental constraint of the sensorimotor system"?
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Reviewer #3 (Public review):
Summary:
The authors describe an interesting study of arm movements carried out in weightlessness after a prolonged exposure to the so-called microgravity conditions of orbital spaceflight. Subjects performed radial point-to-point motions of the fingertip on a touch pad. The authors note a reduction in movement speed in weightlessness, which they hypothesize could be due to either an overall strategy of lowering movement speed to better accommodate the instability of the body in weightlessness or an underestimation of body mass. They conclude for the latter, mainly based on two effects. One, slowing in weightlessness is greater for movement directions with higher effective mass at the end effector of the arm. Two, they present evidence for an increased number of corrective submovements in weightlessness. They contend that this provides conclusive evidence to accept the hypothesis of an underestimation of body mass.
Strengths:
In my opinion, the study provides a valuable contribution, the theoretical aspects are well presented through simulations, the statistical analyses are meticulous, the applicable literature is comprehensively considered and cited, and the manuscript is well written.
Weaknesses:
Nevertheless, I am of the opinion that the interpretation of the observations leaves room for other possible explanations of the observed phenomenon, thus weakening the strength of the arguments.
First, I would like to point out an apparent (at least to me) divergence between the predictions and the observed data. Figures 1 and S1 show that the difference between predicted values for the 3 movement directions is almost linear, with predictions for 90º midway between predictions for 45º and 135º. The effective mass at 90º appears to be much closer to that of 45º than to that of 135º (Figure S1A). But the data shown in Figure 2 and Figure 3 indicate that movements at 90º and 135º are grouped together in terms of reaction time, movement duration, and peak acceleration, while both differ significantly from those values for movements at 45º.
Furthermore, in Figure 4, the change in peak acceleration time and relative time to peak acceleration between 1g and 0g appears to be greater for 90º than for 135º, which appears to me to be at least superficially in contradiction with the predictions from Figure S1. If the effective mass is the key parameter, wouldn't one expect as much difference between 90º and 135º as between 90º and 45º? It is true that peak speed (Figure 3B) and peak speed time (Figure 4B) appear to follow the ordering according to effective mass, but is there a mathematical explanation as to why the ordering is respected for velocity but not acceleration? These inconsistencies weaken the author's conclusions and should be addressed.
Then, to strengthen the conclusions, I feel that the following points would need to be addressed:
(1) The authors model the movement control through equations that derive the input control variable in terms of the force acting on the hand and treat the arm as a second-order low-pass filter (Equation 13). Underestimation of the mass in the computation of a feedforward command would lead to a lower-than-expected displacement to that command. But it is not clear if and how the authors account for a potential modification of the time constants of the 2nd order system. The CNS does not effectuate movements with pure torque generators. Muscles have elastic properties that depend on their tonic excitation level, reflex feedback, and other parameters. Indeed, Fisk et al.* showed variations of movement characteristics consistent with lower muscle tone, lower bandwidth, and lower damping ratio in 0g compared to 1g. Could the variations in the response to the initial feedforward command be explained by a misrepresentation of the limbs' damping and natural frequency, leading to greater uncertainty about the consequences of the initial command? This would still be an argument for unadapted feedforward control of the movement, leading to the need for more corrective movements. But it would not necessarily reflect an underestimation of body mass.
*Fisk, J. O. H. N., Lackner, J. R., & DiZio, P. A. U. L. (1993). Gravitoinertial force level influences arm movement control. Journal of neurophysiology, 69(2), 504-511.
(2) The movements were measured by having the subjects slide their finger on the surface of a touch screen. In weightlessness, the implications of this contact are expected to be quite different than those on the ground. In weightlessness, the taikonauts would need to actively press downward to maintain contact with the screen, while on Earth, gravity will do the work. The tangential forces that resist movement due to friction might therefore be different in 0g. This could be particularly relevant given that the effect of friction would interact with the limb in a direction-dependent fashion, given the anisotropy of the equivalent mass at the fingertip evoked by the authors. Is there some way to discount or control for these potential effects?
(3) The carefully crafted modelling of the limb neglects, nevertheless, the potential instability of the base of the arm. While the taikonauts were able to use their left arm to stabilize their bodies, it is not clear to what extent active stabilization with the contralateral limb can reproduce the stability of the human body seated in a chair in Earth gravity. Unintended motion of the shoulder could account for a smaller-than-expected displacement of the hand in response to the initial feedforward command and/or greater propensity for errors (with a greater need for corrective submovements) in 0g. The direction of movement with respect to the anchoring point could lead to the dependence of the observed effects on movement direction. Could this be tested in some way, e.g., by testing subjects on the ground while standing on an unstable base of support or sitting on a swing, with the same requirement to stabilize the torso using the contralateral arm?
The arguments for an underestimation of body mass would be strengthened if the authors could address these points in some way.
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Reviewer #1 (Public review):
Summary:
This manuscript focuses on single-cell RNA sequencing (scRNA-seq) analysis following chronic methamphetamine (METH) treatment in mice. The authors propose two hypotheses:
(1) METH induces neuroinflammation involving T and NKT cells, and (2) METH alters neuronal stem cell differentiation.
Strengths:
The authors provide a substantial dataset with numerous replicates, offering valuable resources to the research community.
Weaknesses:
Concerns persist regarding the interpretation of data and the validation of experiments. First, the presence of T cells, NKT cells, and neutrophils in both the control and METH-treated hippocampi suggests that blood contamination rather than immune cell infiltration is the cause. Since the authors claim that METH disrupts the blood-brain barrier, increasing the infiltration of these immune cells, identifying the source of these immune cells is critical.
Secondly, the pseudotime analysis, which suggests altered neural stem cell (NSC) differentiation, is not conclusively supported by the current data and requires further validation.
Overall, the authors provided comprehensive in vivo data on the impact of methamphetamine on the hippocampus; however, further in vivo and in vitro experimental validation of the key findings is needed.
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Reviewer #2 (Public review):
Summary:
Chronic methamphetamine (METH) abuse leads to significant structural and functional deficits in the cortical and hippocampal regions in humans. However, the specific mechanisms underlying chronic METH-induced neurotoxicity in the hippocampus and its contribution to cognitive deficits remain poorly understood. The authors aim to address this knowledge gap using a single-cell transcriptomic atlas of the hippocampus under chronic METH exposure in mice. They present analyses of differential gene expression, cell-cell communication, pseudotemporal trajectories, and transcription factor regulation to characterize the cellular-level impact of METH abuse. However, the overall quality of the manuscript is currently very poor due to a lack of basic quality control, overly descriptive content, and unclear conclusions.
Strengths:
The major strength of this study is that it may represent the first report on the impact of METH on the hippocampus in mice. However, the authors should clarify whether similar studies have been previously conducted, as this point remains uncertain.
Weaknesses:
Despite this potential novelty, the study has numerous weaknesses. Notably, single-cell RNA sequencing was unable to capture an adequate number of neuronal populations. Neurons accounted for only approximately 0.6% of the total nuclei, representing a significant underrepresentation compared to their actual physiological proportion. Given that the behavioral effects of METH are likely mediated by neuronal dysfunction, readers would reasonably expect to see transcriptional changes in neurons. The authors should explain why they were unable to capture a sufficient number of neurons and justify how this incomplete dataset can still provide meaningful scientific insights for researchers studying METH-induced hippocampal damage and behavioral alterations.
Another significant weakness of this study is the lack of a cohesive hypothesis or overarching conclusion regarding how METH impacts neural populations. The authors provide a largely descriptive account of transcriptional alterations across various cell types, but the manuscript lacks clear, biologically meaningful conclusions. This descriptive approach makes it difficult for readers to identify the key findings or take-home messages. To improve clarity and impact, the authors should focus on developing and presenting a few plausible hypotheses or mechanistic scenarios regarding METH-induced neurotoxicity, grounded in their scRNA-seq data. Including schematic figures to illustrate these hypotheses would also help readers better understand and interpret the study.
The final major weakness of this study is its poor readability. It appears that the authors did not adequately proofread the manuscript, as there are numerous typographical errors (e.g., line 333: trisulting; line 756: essencial), unsupported scientific claims lacking citations (e.g., lines 485, 503, 749-753), and grammatically incorrect sentences (e.g., lines 470-472, 540-543, 749-753). In addition, many paragraphs are unorganized and overly descriptive, which further hinders clarity. Some figures are also problematic - too small in size and overcrowded with text in fonts that are difficult to read. It is recommended that the authors carry out quality control. There are too many typographical and grammatical errors to list individually; the authors should carefully review and revise the entire manuscript to address all of these issues.
Overall, this study could have offered some incremental new insights into neurotoxicity following chronic METH exposure, despite the poor capture of neuronal populations. However, the current manuscript feels more like a data dump than a thoughtfully constructed scientific narrative. I encourage the authors to extract and highlight meaningful biological insights from their dataset and clearly articulate these in the conclusion, ideally supported by an additional schematic figure. Furthermore, I strongly urge the authors to substantially improve the basic quality of the manuscript through careful proofreading and by seeking feedback from colleagues or other readers.
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Reviewer #3 (Public review):
Summary:
This study aimed to elucidate the intricate mechanisms underlying cognitive decline induced by chronic METH abuse, focusing on the hippocampus at a single-cell resolution. The authors established a robust mouse model of chronic METH exposure. They observed significant impairments in working memory, spatial cognition, learning, and cognitive memory through Y-maze and novel object recognition tests. To gain deeper insights into the cellular and molecular changes, they utilized single-cell RNA sequencing to profile hippocampal cells. They performed extensive bioinformatics analyses, including cell clustering, differential gene expression, cellular communication, pseudotemporal trajectory, and transcription factor regulation.
Strengths:
(1) The authors performed a comprehensive suite of bioinformatics analyses, including differential gene expression, cellular cross-talk, pseudotime trajectory, and SCENIC analysis, which enable a multifaceted exploration of METH-induced changes at both the cellular and molecular levels.
(2) The study demonstrates an awareness of the potential influence of circadian rhythms, dedicating a specific section in the discussion to the disruption of circadian rhythms, which has rarely been mentioned in previous studies on METH. They highlight the frequent occurrence of circadian regulation in their analysis across several cell types.
(3) The pseudotime analysis provides valuable insights into hindered neurogenesis, showing a shift in NSC differentiation toward astrocytes rather than neuroblasts in METH-treated mice. The detailed analysis of BBB components (endothelial cells, mural cells, SMCs) and their heterogeneous responses to METH is also a significant contribution.
Weaknesses:
(1) While the bioinformatics analyses are extensive, the study is primarily descriptive at the molecular level. The absence of experimental validation, such as targeted mRNA/protein quantification and gene knockdown/overexpression to confirm the causal relationship between these identified genes and METH-induced cognitive deficits, is a notable limitation.
(2) While the discussion extensively covers the functional implications of specific molecular pathways and cell types, it would greatly benefit from a comparison of these findings with existing RNA sequencing data from other METH models in hippocampal tissue.
(3) The conclusion that "prolonged METH use may progressively impair cognitive function" may not be uniformly supported by the behavioral data: Figures 1C and F (discrimination and preference indexes) exhibited that the 4-week test further declined in the METH group compared to the 2-week. In contrast, Figure 1E and H present a contradictory pattern.
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Reviewer #1 (Public review):
Summary:
This work by Hall et al provides a novel and important new finding about communication between the anterior cingulate cortex (ACC) and the CA1 region of the dorsal hippocampus: there is a clear ability of ACC to predict CA1 activity, and that is modulated by learning/experience. Furthermore, they have some evidence that the modulation differs by whether the CA1 neurons were in the deep versus superficial sub-layer of CA1. The evidence is suggestive of new and exciting findings, but some gaps and weaknesses remain to be addressed before I believe all of the authors' claims can be supported. The figures also need to be slightly better organized, and the discussion is missing a major dimension in my opinion. Overall, this is a strong submission, but with some gaps to fill.
Strengths:
(1) This is a well-written manuscript - the introduction was especially clear, well-cited, and motivating.
(2) The sub-layer specific communication between ACC and CA1 represents the discovery of a novel and functionally impactful piece of neurobiology.
(3) Optogenetics was an important verification of ACC-CA1 communication, as was the analysis of neurons by waveform type.
Weaknesses:
(1) Figure 2: Why are the data separated into two groups from the outset? If all data are combined, is there a general drop in prediction gain from pre to post?
(2) 2b and 2c are important since they are complementary means to show the same thing, and it is important that they cross-validate each other, especially since the non-significant task active neuron difference in 2b appears to be nearly as strong as the significant difference to its left. A more holistic analysis can be done to compare these dimensions.
(3) Sup vs deep neuron definition: Did the authors have any means to validate this anatomical separation using histology or otherwise? I don't believe they described anything like that, and instead use physiology to infer anatomical location. I understand anatomy-based methods may be practically impossible with tetrodes, but this limitation should at least be mentioned, and it should be explained that without something like silicon probes or histological validation, anatomy had to be inferred from physiology.
(4) Superficial vs deep differences in firing rate ratio based on PG: there are many fewer CAdeep neurons, but in 4c, the trends appear to be the same pre-training, top PG lower than others. It seems the lack of difference in CA1deep in 4c may be due to the much lower power/n. This should be discussed or addressed.
(5) In Figure 5, the term "firing rate ratio" is used, and it sounds the same as in previous figures, but this is a different ratio (based on modulation by opto stim, not task).
(6) I would like to learn more about these v-type neurons. I understand we do not yet know about their molecular or morphologic correlate, but more analysis can be done with the current data.
(7) I would like more discussion of ACC-CA1 connectivity.
(8) Some elements may be missing from the discussion, relating baseline functioning versus post-learning function.
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Reviewer #2 (Public review):
Summary:
This study uncovers an inhibitory pathway from the anterior cingulate cortex (ACC) to pyramidal cells in the superficial sublayer of hippocampal area CA1 (CA1sup). As ACC neuron spiking tends to precede hippocampal ripples, this presents the intriguing possibility that ACC inputs are selectively inhibiting particular CA1sup neurons, which could play a role in the reactivation of task-related ensembles known to take place during hippocampal ripples. Indeed, through a generalized linear model (GLM) analysis, the authors demonstrate that the ACC activity within the 200ms immediately preceding the ripple is predictive of the ripple content.
Strengths:
The biggest strength of the work is the optogenetic manipulation experiments, which convincingly demonstrate that stimulation of ACC pyramidal neurons activates an interneuron population with symmetric spike waveforms, and inhibits parvalbumin interneurons and pyramidal cells in CA1sup but not CA1deep sublayer.
An additional strength in the GLM analysis which consistently shows that ACC activity preceding the ripple is predictive of hippocampal activity during the ripple considerably more than in shuffled data for all cells and periods tested.
Weaknesses:
The major weakness of this work is that the link with learning and memory is not very well supported.
The only evidence of rebalancing and reorganization appears to be a single statistical test (the test in Figure 1f, p=0.013) demonstrating a decrease of the GLM prediction gain from pre-task sleep to post-task sleep; the same test is repeated for subsets of the data in the rest of the figures. As the idea of rebalancing and reorganization is central to the paper as currently written, exploring it through another measure, independent of the GLM prediction gain, should be expected. The notion that this pathway is suppressed in sleep following learning can be supported by demonstrating a decrease in any of the following measures: ACC spike-triggered average CA1sup responses, cross-covariances (Wierzynski et al 2009) between ACC and CA1sup cells in post-task sleep, or ripple-triggered cross-correlations (Sirota et al. 2009).
The differences between task-active and task-inactive neurons are not convincing. The separation between task-active and task-inactive neurons is to divide a distribution that is far from bimodal into what appears to be two arbitrary groups. Similarly, the authors divide cells relative to their prediction gain ("Top PG" and "Bottom PG" in Figure 2c), which fails to select for the population of significantly predicted cells (relative to the shuffle). Within CA1sup cells, after learning, there is a significant decrease in the prediction gain for "task-inactive" cells but not "task-active" cells, but it is important to keep in mind that the "task-active" group contains only 24 neurons, and there was no difference between the two groups of cells ("task-active" vs "task-inactive") when directly compared.
Finally, it is not clear whether the identity of the pathway-responsive CA1sup neurons is fixed or whether it may change with learning. A deeper analysis into the cell pair cross-correlations or the weights of the GLM analysis may reveal whether there is a reorganization of CA1sup responses (some cells that were inhibited are no longer inhibited, and vice versa) or a dampening (the same CA1sup cells are inhibited in both cases, but the inhibition is less-pronounced in post-task sleep). The possibility of a rigid circuit dampened immediately following fear conditioning, is not discussed by the authors.
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Reviewer #3 (Public review):
Summary:
In this study, Hall and colleagues investigate how the coupling of activity from ACC to CA1is altered by fear learning, showing that during sleep immediately before learning, there is evidence for increased coupling of ACC activity with neurons that will subsequently be inhibited during the learning process. They go on to show that this effect seems to be mediated most by a subpopulation of neurons in the superficial layer of CA1. This fits with previous reports suggesting that these superficial neurons are key for the flexible updating of memory. The authors then go on to show that artificial activation of ACC using optogenetics results in varied effects in CA1, including a subtle decrease in activity of superficial neurons that lasts longer than the stimulus itself. Finally, the authors present some preliminary data suggesting that different interneurons may be recruited by this optogenetic stimulation in different ways and at different times.
Overall, this is an interesting paper, but much of the analysis is very preliminary, and much of the crucial data about the learning effects and alterations to cell firing are not presented clearly and fully. This is further confounded by a rather opaque description of the results and analysis in the text. Overall, there is something very interesting here, but there needs to be a substantial series of extra analyses to clearly say what this is. In many cases, more robust analysis may render the results underpowered, which could dramatically change the conclusions of the paper.
Strengths:
The authors performed difficult, dual-location recordings across a multi-day learning paradigm, which seems like it could be a really nice dataset. They delve into the circuit basis of an interesting finding regarding ACC to CA1 connectivity and how this changes before and after fear conditioning. They provide data to suggest this connectivity may be through specific and distinct subcircuits in CA1.
Weaknesses:
(1) There is essentially no information in the text or figures about what the actual learning was, how it was done, how individual animals performed, and how any of these metrics related to learning. Looking at the methods, the authors did a number of things never mentioned anywhere in the text or figures, including novel arena exposure, contextual reexposure in extinction after learning, etc. It seems that this is a very rich dataset that has not been presented at all. I would recommend at the very least:<br /> a) Plot all of the behavioural training data, and how each mouse relates to one another - did the mice learn? At this stage, we don't know!<br /> b) Explain in the text in detail exactly what was done and why, and what this tells us about the neuronal activity.<br /> c) If there is variance in learning and or conditioning, does this relate to features in the analysis, such as the GLM result.
(2) Along similar lines, a key metric for most of the paper is that neurons most coupled with ACC are more likely to be inhibited during training. However, there is nothing anywhere in the paper showing these data. How do neurons in general respond to contextual shocks? The methods describe this as the average firing rate during training, normalised to pre-sleep activity. This metric seems a bit coarse and may obscure really important task-relevant dynamics. Are the neurons active at specific times, are they tuned to relevant parts of the task, and do any of these features of the cell activity also relate to the coupling with ACC? Similarly, how did the authors mitigate the influence of electrical artefacts caused by the foot shock in their recordings? Again, there is a huge amount of data here that is not being described, and likely holds very valuable information about what is actually happening. The paper would really benefit from the inclusion of these data in an accessible form, such as heatmaps of spiking, how these patterns change over time, and around e.g., foot shock, etc. Also key is how these features are altered by the variability of learning across subjects.
(3) A number of the effects are presented by comparing a statistically significant effect to a non-statistically significant effect (e.g. in Figure 2b, Figure 2d, Figure 4 b,c, and others). This isn't really valid - the key test that the two groups are different is either with a direct test of the difference or an interaction term in an e.g., ANOVA test. In some places, I am not sure the same conclusions will be drawn from the data with these tests.
(4) To what extent is defining superficial and deep CA1 neurons solely by ripple waveform an accepted method? Of the two papers referenced for this approach, one is a 2-photon calcium imaging paper that does not do electrical recordings (as far as I am aware), and the second uses this as a descriptor after defining the positions of units on an array. It would be good to clarify how accepted this is, and also how robust this is. At the very least, some kind of metric or walkthrough in the supplement as to how this was done, and how well each cell was classified and with what confidence, or some metric of how distinct and separate the two populations were (or was it just a smudge).
(5) In the optogenetic experiment in Figure 5, the effect on the CA1 sup neurons seems to be driven by changes in a small subpopulation of this group, with no change in the others. Related to point 2, is there anything else in the data that can pull out what these cells are? More detailed analysis of the firing of these neurons might pull out something really interesting.
(6) Related to this - a number of comparisons simply pool neurons across mice and analyse them as if independent. This is done a lot in the past, but it would be better if an approach that included the interdependence of neurons recorded from the same mouse at the same time were used (such as a hierarchical model). While this is complex, a simpler approach would just be to plot the summary data also per mouse. For example, in Figure 5, how do the neurons inhibited by ACC activation spread across the different mice? Is the level of inhibition related to how well the mice learned the CS-US association?
(7) Figure 6 is interesting, but very preliminary. None of the effects are quantified, and one of the cell types is not identified. I think some proper analysis needs to be done, again across mice, to be able to draw conclusions from these data.
(8) Finally, in general, I felt that the way the paper was written was very hard to follow, often relying on very processed levels of analysis that were hard to relate back to the raw traces and their biological meaning. In general taking more words to really simply and fully explain each analysis, and taking the words and figures to walk through how each analysis was done and what it tells us about the neuronal data/biology would be really beneficial, especially to someone who is not an extracellular electrophysiologist or immersed in the immediate field.
In summary, while this manuscript explores an intriguing hypothesis about pre-learning circuit dynamics, it is currently held back by insufficient clarity in behavioural analysis, data presentation, and statistical quantification. Addressing these core issues would greatly improve interpretability and confidence in the findings.
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Reviewer #1 (Public review):
Summary:
Zhang et al. addressed the question of whether hyperaltruistic preference is modulated by decision context and tested how oxytocin (OXT) may modulate this process. Using an adapted version of a previously well-established moral decision-making task, healthy human participants in this study undergo decisions that gain more (or lose less, termed as context) meanwhile inducing more painful shocks to either themselves or another person (recipient). The alternative choice is always less gain (or more loss) meanwhile less pain. Through a series of regression analyses, the authors reported that hyperaltruistic preference can only be found in the gain context but not in the loss context, however, OXT reestablished the hyperaltruistic preference in the loss context similar to that in the gain context.
Strengths:
This is a solid study that directly adapted a previously well-established task and the analytical pipeline to assess hyperaltruistic preference in separate decision contexts. Context-dependent decisions have gained more and more attention in literature in recent years, hence this study is timely. It also links individual traits (via questionnaires) with task performance, to test potential individual differences. The OXT study is done with great methodological rigor, including pre-registration. Both studies have proper power analysis to determine the sample size.
Weaknesses:
Despite the strengths, multiple analytical decisions have to be explained, justified, or clarified. Also, there is scope to enhance the clarity and coherence of the writing - as it stands, readers will have to go back and forth to search for information. Last, it would be helpful to add line numbers in the manuscript during the revision, as this will help all reviewers to locate the parts we are talking about.
Introduction:<br /> (1) The introduction is somewhat unmotivated, with key terms/concepts left unexplained until relatively late in the manuscript. One of the main focuses in this work is "hyperaltruistic", but how is this defined? It seems that the authors take the meaning of "willing to pay more to reduce other's pain than their own pain", but is this what the task is measuring? Did participants ever need to PAY something to reduce the other's pain? Note that some previous studies indeed allow participants to pay something to reduce other's pain. And what makes it "HYPER-altruistic" rather than simply "altruistic"? Plus, in the intro, the authors mentioned that the "boundary conditions" remain unexplored, but this idea is never touched again. What do boundary conditions mean here in this task? How do the results/data help with finding out the boundary conditions? Can this be discussed within wider literature in the Discussion section? Last, what motivated the authors to examine decision context? It comes somewhat out of the blue that the opening paragraph states that "We set out to [...] decision context", but why? Are there other important factors? Why decision context is more important than studying those others?
Experimental design:<br /> (2) The experiment per se is largely solid, as it followed a previously well-established protocol. But I am curious about how the participants got instructed? Did the experimenter ever mention the word "help" or "harm" to the participants? It would be helpful to include the exact instructions in the SI.
(3) Relatedly, the experimental details were not quite comprehensive in the main text. Indeed, Methods come after the main text, but to be able to guide readers to understand what was going on, it would be very helpful if the authors could include some necessary experimental details at the beginning of the Results section.
Statistical analysis<br /> (3) One of the main analyses uses the harm aversion model (Eq1) and the results section keeps referring to one of the key parameters of it (ie, k). However, it is difficult to understand the text without going to the Methods section below. Hence it would be very helpful to repeat the equation also in the main text. A similar idea goes to the delta_m and delta_s terms - it will be very helpful to give a clear meaning of them, as nearly all analyses rely on knowing what they mean.
(4) There is one additional parameter gamma (choice consistency) in the model. Did the authors also examine the task-related difference of gamma? This might be important as some studies have shown that the other-oriented choice consistency may differ in different prosocial contexts.
(5) I am not fully convinced that the authors included two types of models: the harm aversion model and logistic regression models. Indeed, the models look similar, and the authors have acknowledged that. But I wonder if there is a way to combine them? For example:<br /> Choice ~ delta_V * context * recipient (*Oxt_v._placebo)<br /> The calculation of delta_V follows Equation 1.<br /> Or the conceptual question is, if the authors were interested in the specific and independent contribution of dalta_m and dalta_s to behavior, as their logistic model did, why the authors examine the harm aversion first, where a parameter k is controlling for the trade-off? One way to find it out is to properly run different models and run model comparison. In the end, it would be beneficial to only focus on the "winning" model to draw inferences.
(6) The interpretation of the main OXT results needs to be more cautious. According to the operationalization, "hyperaltruistic" is the reduction of pain of others (higher % of choosing the less painful option) relative to the self. But relative to the placebo (as baseline), OXT did not increase the % of choosing the less painful option for others, rather, it decreased the % of choosing the less painful option for themselves. In other words, the degree of reducing other's pain is the same under OXT and placebo, but the degree of benefiting self-interest is reduced under OXT. I think this needs to be unpacked, and some of the wording needs to be changed. I am not very familiar with the OXT literature, but I believe it is very important to differentiate whether OXT is doing something on self-oriented actions vs other-oriented actions. Relatedly, for results such as that in Fig5A, it would be helpful to not only look at the difference, but also the actual magnitude of the sensitivity to the shocks, for self and others, under OXT and placebo.
Comments on revisions:
I did not change my original public review, as I think it can still be helpful for the field to see the reasoning and argument.
For the revision, the authors have done a thorough job of addressing my previous comments and questions.
The only aspect I would like to ask is that, it would still be great to have a clear definition of hyperaltruism. As it stands, hyperaltruism refers to "people's willingness to pay more to reduce other's pain than<br /> their own pain", ie, this means the "hyper" bit is considered with respect to "self". But shouldn't hyperaltruism be classified contrasting "normal" altruism?
It is fine that it follows a previously published work (Crockett et al., 2014), but it would still be necessary to explain/define the construct being tested in a standalone fashion rather than letting readers to go back to the original work.
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Reviewer #2 (Public review):
Summary:
In this manuscript, the authors reported two studies where they investigated the context effect of hyperaltruistic tendency in moral decision-making. They replicated the hyperaltruistic moral preference in the gain domain, where participants inflicted electric shocks to themselves or another person in exchange for monetary profits for themselves. In the loss domain, such hyperaltruistic tendency abolished. Interestingly, oxytocin administration reinstated the hyperaltruistic tendency in the loss domain. The authors also examined the correlation between individual differences in utilitarian psychology and the context effect of hyperaltruistic tendency.
Strengths:
(1) The research question - the boundary condition of hyperaltruistic tendency in moral decision-making and its neural basis - is theoretically important.<br /> (2) Manipulating the brain via pharmacological means offers causal understanding of the neurobiological basis of the psychological phenomenon in question.<br /> (3) Individual difference analysis reveals interesting moderators of the behavioral tendency.
Weaknesses:
(1) The theoretical hypothesis needs to be better justified. There are studies addressing the neurobiological mechanism of hyperaltruistic tendency, which the authors unfortunately skipped entirely.<br /> (2) There are some important inconsistencies between the preregistration and the actual data collection/analysis, which the authors did not justify.<br /> (3) Some of the exploratory analysis seems underpowered (e.g., large multiple regression models with only about 40 participants).<br /> (4) Inaccurate conceptualization of utilitarian psychology and the questionnaire used to measure it.
Comments on revisions:
The authors have addressed the weakness in the second round of revision
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Reviewer #3 (Public review):
Summary:
In this study, the authors aimed to index individual variation in decision-making when decisions pit the interests of the self (gains in money, potential for electric shock) against the interests of an unknown stranger in another room (potential for unknown shock). In addition, the authors conducted an additional study in which male participants were either administered intranasal oxytocin or placebo before completing the task to identify the role of oxytocin in moderating task responses. Participants' choice data was analyzed using a harm aversion model in which choices were driven by the subjective value difference between the less and more painful options.
Strengths:
Overall, I think this is a well-conducted, interesting, and novel set of research studies exploring decision-making that balances outcomes for the self versus a stranger, and the potential role of the hormone oxytocin (OT) in shaping these decisions. The pain component of the paradigm is well designed, as is the decision-making task, and overall the analyses were well suited to evaluating and interpreting the data. Advantages of the task design include the absence of deception, e.g., the use of a real study partner and real stakes, as a trial from the task was selected at random after the study and the choice the participant made were actually executed.
Weaknesses:
The primary weakness of the paper concerns its framing. Although it purports to be measuring "hyper-altruism," which is the same term used in prior similar (although not identical) designs, I do not believe the task constitutes altruism, but rather the decision to engage, or not engage, in instrumental aggression.
I continue to believe that when in the "other" trials the only outcome possible for the study partner is pain, and the only outcome possible for the participant is monetary gain, these trials measure decisions about instrumental aggression. That is the exact definition of instrumental aggression is: causing others harm for personal gain. Altruism is not equivalent to refraining from engaging in instrumental aggression, although some similar mechanisms may support both. True altruism would be to accept shocks to the self for the other's benefit (e.g., money). The interpretation of this task as assessing instrumental aggression is supported by the fact that only the Instrumental Harm subscale of the OUS was associated with outcomes in the task, but not the Impartial Benevolence subscale. By contrast, the IB subscale is the one more consistently associated with altruism (e.g,. Kahane et al 2018; Amormino at al, 2022) I believe it is important for scientific accuracy for the paper, including the title, to be rewritten to reflect what it is testing.
Although I recognize similar tasks have been previously characterized as "hyper-altruism" I do not believe that is sufficient justification for continuing to promulgate this descriptor without any caveats. I hope the authors will engage more seriously with the idea that this is what the task is measuring.
Relatedly, in the introduction, I believe it would be important to discuss the non-symmetry of moral obligations related to help/harm--we have obligations not to harm strangers but no obligation to help strangers. This is another reason I do not think the term "hyper altruism" is a good description for this task--given it is typically viewed as morally obligatory not to harm strangers, choosing not to harm them is not "hyper" altruistic (and again, I do not view it as obviously altruism at all).
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Reviewer #1 (Public review):
Summary:
Praegel et al. explore the differences in learning an auditory discrimination task between adolescent and adult mice. Using freely-moving (Educage) and head-fixed paradigms, they compare behavioral performance and neuronal responses over the course of learning. The mice were initially trained for seven days on an easy pure frequency tone Go/No-go task (frequency difference of one octave), followed by seven days of a harder version (frequency difference of 0.25 octave). While adolescents and adults showed similar performance on the easy task, adults performed significantly better on the harder task. Quantifying the lick bias of both groups, the authors then argue that the difference in performance is not due to a difference in perception, but rather to a difference in cognitive control. The authors then used neuropixel recordings across 4 auditory cortical regions to quantify the neuronal activity related to the behavior. At the single cell level, the data shows earlier stimulus-related discrimination for adults compared to adolescents in both the easy and hard tasks. At the neuronal population level, adults displayed a higher decoding accuracy and lower onset latency in the hard task as compared to adolescents. Such differences were not only due to learning, but also to age as concluded from recordings in novice mice. After learning, neuronal tuning properties had changed in adults but not in adolescent. Overall, the differences between adolescent and adult neuronal data correlates with the behavior results in showing that learning a difficult task is more challenging for younger mice.
Strengths:
- The behavioral task is well designed, with the comparison of easy and difficult tasks allowing for a refined conclusion regarding learning across age. The experiments with optogenetics and novice mice are completing the research question in a convincing way.<br /> - The analysis, including the systematic comparison of task performance across the two age groups, is most interesting, and reveals differences in learning (or learning strategies?) that are compelling.<br /> - Neuronal recording during both behavioral training and passive sound exposure is particularly powerful, and allows interesting conclusions.
Weaknesses:<br /> - The presentation of the paper must be strengthened. Inconsistencies, missing information or confusing descriptions should be fixed.<br /> - The recording electrodes cover regions in the primary and secondary cortices. It is well known that these two regions process sounds quite differently (for example, one has tonotopy, the other not), and separating recordings from both regions is important to conclude anything about sound representations. The authors show that the conclusions are the same across regions for Figure 4, but is it also the case for the subsequent analysis? Comparing to the original manuscript, the authors have now done the analysis for AuDp and AUDv separately, and say that the differences are similar in both regions. The data however shows that this is not the case (Fig S7). And even if it were the case, how would it compatible with the published literature?
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Reviewer #2 (Public review):
Summary:
The authors aimed to find out how and how well adult and adolescent mice discriminate tones of different frequencies and whether there are differences in processing at the level of the auditory cortex that might explain differences in behavior between the two groups. Adolescent mice were found to be worse at sound frequency discrimination than adult mice. The performance difference between the groups was most pronounced when the sounds are close in frequency and thus difficult to distinguish and could, at least in part, be attributed to the younger mice' inability to withhold licking in no-go trials. By recording the activity of individual neurons in the auditory cortex when mice performed the task or were passively listening as well as in untrained mice the authors identified differences in the way that the adult and adolescent brains encode sounds and the animals' choice that could potentially contribute to the differences in behavior.
Strengths:
The study combines behavioural testing in freely-moving and head-fixed mice, optogenetic manipulation and high density electrophysiological recordings in behaving mice to address important open questions about age differences in sound-guided behavior and sound representation in the auditory cortex.
Weaknesses:
For some of the analyses that the authors conducted it is unclear what the rationale behind them is and, consequently, what conclusion we can draw from them.
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Reviewer #1 (Public review):
Summary:
Both flies and mammals have D1-like and D2-like dopamine receptors, yet the role of D2-like receptors in Drosophila learning and memory remains underexplored. This paper investigates the role of the D2-like dopamine receptor D2R in single pairs of dopaminergic neurons (DANs) during single-odor aversive learning in the Drosophila larva. First, confocal imaging is used to screen GAL4 driver strains that drive GFP expression in just single pairs of dopaminergic neurons. Next, thermogenetic manipulations of one pair of DANs (DAN-c1) suggest that DAN-c1 activity during larval aversive learning is important. Confocal imaging is then used to reveal expression of D2R in the DANs and mushroom body of the larval brain. Finally, optogenetic activation during training phenocopies D2R knockdown in these neurons: aversive learning is impaired when DAN-c1 is targeted, while appetitive and aversive learning are impaired when the mushroom body is manipulated. Finally, a model is proposed in which D2R limits excessive dopamine release to facilitate successful olfactory learning.
Strengths:
The paper convincingly reproduces prior findings that demonstrated D2R knockdown in DL1 DANs or the mushroom body impairs aversive olfactory learning in Drosophila larvae (Qi and Lee, 2014; doi:10.3390/biology3040831). These previous findings were built upon and extended with a comprehensive confocal imaging screen of 57 GAL4 drivers that identified tools driving GFP expression in individual DANs. One of the drivers, R76F02-AD; R55C10-DBD, was consistently shown to label DAN-c1 neurons and no other DANs in the larval brain. Confocal imaging is also used to demonstrate that GFP-tagged D2R is expressed in most DANs and the mushroom body. Behavioral experiments demonstrate that driving D2R knockdown in DAN-c1 neurons impairs aversive learning, as do other loss-of-function manipulations of DAN-c1 neurons.
Limitations:
(1) The single-odor paradigm used to train larvae does not have the advantages of a more conventional balanced or reciprocal training paradigm. The paper describes how the single-odor experimental design could be controlled for non-associative effects, but does not provide an independent validation of the control experiments performed by a different research group using different odors and genotypes 15 to 20 years ago (see Honjo and Furukubo-Tokunaga, 2005; doi:10.1523/jneurosci.2135-05.2005 and Honjo and Furukubo-Tokunaga, 2009; doi:10.1523/jneurosci.1315-08.2009). Whether the involvement of DAN-c1 for aversive learning generalizes to standard paradigms remains unclear (see Eschbach et al., 2020; doi:10.1038/s41593-020-0607-9 and Weber et al., 2023; doi:10.7554/elife.91387.1).
(2) In 11 of 22 larval brains examined in the paper, R76F02-AD; R55C10-DBD appears to drive GFP expression in 1 to 8 additional non-dopaminergic neurons (Figure S1P and Table S3). Of the remaining 11 brains, 4 of their corresponding ventral nerve cords also have expression in 2 to 4 neurons (Table S3). Therefore, experiments involving with the R76F02-AD; R55C10-DBD driver could be manipulating the activity of additional neurons in around 60% of larvae. The conclusions of the paper would be strengthened if key experiments were repeated with other GAL4 drivers that may label DAN-c1 with even greater specificity, such as SS03066 (Truman et al., 2023; doi:10.7554/elife.80594) or MB320C (Hige et al., 2015; doi:10.1016/j.neuron.2015.11.003).
(3) Successful immunostaining with an anti-D2R antibody (Draper et al., 2007; doi:10.1002/dneu.20355 and Love et al., 2023; doi:10.1111/gbb.12836) could validate GFP-tagged D2R expression (Figure 3) in the same way that TH immunostaining was used throughout the paper to determine whether neurons were dopaminergic.
(4) The paper proposes a model in which DAN-c1 activity conveys an aversive teaching signal (Figure 2f) but excessive artificial DAN-c1 activation causes excessive dopamine release that impairs aversive learning (Figures 2i and 5b). According to this model, thermogenetic DAN-c1 activation during training with water or sucrose conveys an aversive teaching signal that reduces performance (Figure 2i) whereas optogenetic DAN-c1 activation does not due to excessive dopamine release (Figures 5c and 5d). The model suggests that optogenetic DAN-c1 activation is strong enough to cause excessive dopamine release by itself whereas thermogenetic DAN-c1 activation can only achieve the same outcome when it occurs in conjunction with natural DAN-c1 activation evoked by quinine. Therefore, an experiment with weaker optogenetic DAN-c1 activation (with lower intensity light or pulsed at a lower frequency) during water or sucrose training would be expected to convey an aversive teaching signal rather than excessive dopamine release, reducing performance. Such an experiment could reconcile the differing thermogenetic and optogenetic results of the paper.
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Reviewer #2 (Public review):
Summary:
The study wanted to functionally identify individual DANs that mediate larval olfactory learning. Then search for DAN-specific driver strains that mark single dopaminergic neurons, which subsequently can be used to target genetic manipulations of those neurons. 56 GAL4 drivers identifying dopaminergic neurons were found (Table 1) and three of them drive the expression of GFP to a single dopaminergic neuron in the third-instar larval brain hemisphere. The DAN driver R76F02-AD;R55C10-DBD appears to drive the expression to a dopaminergic neuron innervating the lower peduncle (LP), which would be DAN-c1.
Split-GFP reconstitution across synaptic partners (GRASP) technique was used to investigate the "direct" synaptic connections from DANs to the mushroom body. Potential synaptic contact between DAN-c1 and MB neurons (at the lower peduncle) were detected.
Then single odor associative learning was performed and thermogenetic tools were used (Shi-ts1 and TrpA1). When trained at 34{degree sign}C, the complete inactivation of dopamine release from DAN-c1 with Shibirets1 impaired aversive learning (Figure 2h), while Shibirets1 did not affect learning when trained at room temperature (22 {degree sign}C). When paired with a gustatory stimulus (QUI or SUC), activation of DAN-c1 during training impairs both aversive and appetitive learning (Figure 2k).<br /> Then examined the expression pattern of D2R in fly brains and were found in dopaminergic neurons and the mushroom body (Figure 3). To inspect whether the pattern of GFP signals indeed reflected the expression of D2R, three D2R enhancer driver strains (R72C04, R72C08, and R72D03-GAL4) were crossed with the GFP-tagged D2R strain.
D2R knockdown (UAS-RNAi) in dopaminergic neurons driven by TH-GAL4 impaired larval aversive learning. Using a microRNA strain (UAS-D2R-miR), a similar deficit was observed. Crossing the GFP-tagged D2R strain with a DAN-c1-mCherry strain demonstrated the expression of D2R in DAN-c1 (Figure 4a). Knockdown of D2R in DAN-c1 impaired aversive learning with the odorant pentyl acetate, while appetitive learning was unaffected (Figure 4e). Sensory and motor functions appear not affected by D2R suppression.
To exclude possible chronic effects of D2R knockdown during development, optogenetics was applied at distinct stages of the learning protocol. ChR2 was expressed in DAN-c1, and blue light was applied at distinct stages of the learning protocol. Optogenetic activation of DAN-c1 during training impaired aversive learning, not appetitive learning (Figure 5b-d).
Knockdown of D2Rs in MB neurons by D2R-miR impaired both appetitive and aversive learning (Figure 6a). Activation of MBNs during training impairs both larval aversive and appetitive learning.
Finally, based on the data the authors propose a model where the effective learning requires a balanced level of activity between D1R and D2R (Figure 7).
Strengths:
The work is well written, clear, and concise. They use well documented strategies to examine GAL4 drivers with expression in a single DAN, behavioral performance in larvae with distinct genetic tools including those to do thermo and optogenetics in behaving flies. Altogether, the study was able to expand our understanding of the role of D2R in DAN-c1 and MB neurons in the larva brain.
The study successfully examined the role of D2R in DAN-c1 and MB neurons in olfactory conditioning. The conclusions are well supported by the data and the model of adequate levels of cAMP (Figure 7b) appears to be able to explain a poor memory after insufficient or excessive cAMP signaling. The study provides insight into the role of D2R in associative learning expanding our understanding and might be a reference similarly to previous key findings (Qi and Lee, 2014, https://doi.org/10.3390/biology3040831).
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