Joint Public Review:

Summary:

Ledamoisel et al. examined the evolution of visual and chemical signals in closely related Morpho butterfly species to understand their role in species coexistence. Using an integrative, state-of-the-art approach combining spectrophotometry, visual modeling, and behavioral mate choice experiments, they quantified differences in wing iridescence and assessed its influence on mate preference in allopatry and sympatry. They also performed chemical analyses to determine whether sympatric species exhibit divergent chemical cues that may facilitate species recognition and mate discrimination. The authors found iridescent coloration to be similar in sympatric Morpho species. Furthermore, male mate choice experiments revealed that in sympatry, males fail to discriminate conspecific females based on coloration, reinforcing the idea that visual signal convergence is primarily driven by predation pressure. In contrast, the divergence of chemical signals among sympatric species suggests their potential role in facilitating species recognition and mate discrimination. The authors conclude that interactions between ecological pressures and signal evolution may shape species coexistence.

Strengths:

The study is well-designed and integrates multiple methodological approaches to provide a thorough assessment of signal evolution in the studied species. We appreciate the authors' careful consideration of multiple selective pressures and their combined influence on signal divergence and convergence. Additionally, the inclusion of both visual and chemical signals adds an interesting and valuable dimension to the study, enhancing its importance. Beyond butterflies, this research broadens our understanding of multimodal communication and signal evolution in the context of species coexistence.

Reviewing Editor comment:

The authors have improved their submission after revisions and responded to the previous concerns of the reviewers.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors demonstrate for the first time that opioid signaling has opposing effects on the same target neuron depending on the source of the input. Further, the authors provide evidence to support the role of potassium channels in regulating a brake on glutamatergic and cholinergic signaling, with the latter finding being developmentally regulated and responsive to opioid treatment. This evidence solves a conundrum regarding cholinergic signaling in the interpeduncular nucleus that evaded elucidation for many years.

Strengths:

This manuscript provides 3 novel and important findings that significantly advance our understanding of the medial habenula-interpeduncular circuitry:

(1) Mu opioid receptor activation (mOR) reduces postsynaptic glutamatergic currents elicited from substance P neurons while simultaneously enhancing postsynaptic glutamatergic currents from cholinergic neurons, with the latter being developmentally regulated.

(2) Substance P neurons from the Mhb provide functional input to the rostral nucleus of the IPN, in addition to the previously characterized lateral nuclei.

(3) Potassium channels (Kv1.2) provide a break on neurotransmission in the IPN,

The findings here suggest that the authors have identified a novel mechanism for the normal function of neurotransmission in the IPN, so it would be expected to be observable in almost any animal. In the revised manuscript, the authors put forth significant effort to increase the n, thus increasing the confidence in the observations.

There are also significant sex differences in nAChR expression in the IPN that might not be functionally apparent using the low n presented here. In the revised manuscript, the authors increased the n, and provided data to the reviewers that no significant sex differences were apparent, although there was a trend. Future studies should examine sex differences in detail.

There are also some particularly novel observations that are presented but not followed up on, and this creates a somewhat disjointed story. For example, in Figure 2, the authors identify neurons in which no response is elicited by light stimulation of ChAT-neurons, but application of DAMGO (mOR agonist) un-silences these neurons. Are there baseline differences in the electrophysiological or morphological properties of these "silent" neurons compared to the responsive neurons? In the revised manuscript, the authors directly tested this with new experiments in SST+ neurons in the IPN, demonstrating convincingly that mOR activation unsilences these neurons.

With the revisions, the authors have addressed the reviewers' concerns and significantly improved the manuscript. I find no further weaknesses.

Reviewer #1 (Public review):

Summary:

The study by Lin et al. studies the role of EXOC6A in ciliogenesis and its relationship with the interactor myosin-Va using a range of approaches based on the RPE1 cell line model. They establish its spatio-temporal organization at centrioles, the forming ciliary vesicle and ciliary sheath using ExM, various super-resolution techniques, and EM, including correlative light and electron microscopy. They also perform live imaging analyses and functional studies using RNAi and knockout. They establish a role of EXOC6A together with myosin-Va in Golgi-derived, microtubule- and actin-based vesicle trafficking to and from the ciliary vesicle and sheath membranes. Defects in these functions impair robust ciliary shaft and axoneme formation due to defective transition zone assembly.

Strengths:

The study provides very high-quality data that support the conclusions. In particular, the imaging data is compelling. It also integrates all findings in a model that shows how EXOC6A participates in multiple stages of ciliogenesis and how it cooperates with other factors.

Weaknesses:

The precise role of EXOC6A remains somewhat unclear. While it is described as a component of the exocyst, the authors do not address its molecular functions and whether it indeed works as part of the exocyst complex during ciliogenesis.

Reviewer #1 (Public review):

Summary:

The manuscript by Gamen et al. analyzed the functional role of HIF signaling in the epicardium providing evidence that stabilization of the hypoxia signaling pathway might contribute to neonatal heart regeneration. By generating different conditionally mouse mutants and performing pharmacological interventions, the authors demonstrate that stabilizing HIF signaling enhances cardiac regeneration after MI in P7 neonatal hearts.

Strengths:

The study presents convincing genetic and pharmacological approaches on the role of hypoxia signaling enhance the regenerative potential of the epicardium

Weaknesses:

The major weakness remains the lack of convincing evidence demonstrating the role of hypoxia signaling in EMT modulation in the epicardial cells. The authors claimed that EMT assays adopted in this study are based on similar previous studies. Surprisingly, two of the references provided correspond to their own research group (PMID: 17108969, PMID: 19235142), limiting the credit for such claims, and the other two (PMID: 27023710, PMID: 12297106) assessment of cell migration but not EMT is reported. Thus, EMT remains to be convincingly demonstrated.

Joint Public Review:

This manuscript tests the notion that bulky membrane glycoproteins suppress viral infection through non-specific interactions. Using a suite of biochemical, biophysical, and computational methods in multiple contexts (ex vivo, in vitro, and in silico), the authors collect compelling evidence supporting the notion that (1) a wide range of surface glycoproteins erect an energy barrier for the virus to form stable adhesive interface needed for fusion and uptake and (2) the total amount of glycan, independent of their molecular identity, additively enhanced the suppression.

As a functional assay the authors focus on viral infection starting from the assumption that a physical boundary modulated by overexpressing a protein-of-interest could prevent viral entry and subsequent infection. Here they find that glycan content (measured using the PNA lectin) of the overexpressed protein and total molecular weight, that includes amino acid weight and the glycan weight, is negatively correlated with viral infection. They continue to demonstrate that it is in effect the total glycan content, using a variety of lectin labelling, that is responsible for reduced infection in cells. Because the authors do not find a loss in virus binding this allows them to hypothesize that the glycan content presents a barrier for the stable membrane-membrane contact between virus and cell. They subsequently set out to determine the effective radius of the proteins at the membrane and demonstrate that on a supported lipid bilayer the glycosylated proteins do not transition from the mushroom to the brush regime at the densities used. Finally, using Super Resolution microscopy they find that above an effective radius of 5 nm proteins are excluded from the virus-cell interface.

The experimental design does not present major concerns and the results provide insight on a biophysical mechanism according to which, repulsion forces between branched glycan chains of highly glycosylated proteins exert a kinetic energy barrier that limits the formation of a membrane/viral interface required for infection.

In their revised manuscript and rebuttal, the authors address several general and specific concerns that were raised about their first submission. The revised manuscript now makes the strength of the evidence supporting their claims, compelling.

Reviewer #1 (Public review):

Summary:

The manuscript analyses the effects of deleting the TgfbR1 and TgfbR2 receptors from endothelial cells at postnatal stages on vascular development and blood-retina barrier maturation in the retina. The authors find that deletion of these receptors affects vascular development in the retina but importantly it affects the infiltration of immune cells across the vessels in the retina. The findings demonstrate that Tgf-beta signaling through TgfbR1/R2 heterodimers regulates primarily the immune phenotypes of endothelial cells in addition to regulating vascular development, but has minor effects on the BRB maturation. The data provided by the authors provides a solid support for their conclusions.

Strengths:

(1) The manuscript uses a variety of elegant genetic studies in mice to analyze the role of TgfbR1 and TgfbR2 receptors in endothelial cells at postnatal stages of vascular development and blood-retina barrier maturation in the retina.

(2) The authors provide a nice comparison of the vascular phenotypes in endothelial-specific knockout of TgfbR1 and TgfbR2 in the retina (and to a lesser degree in the brain) with those from Npd KO mice (loss of Ndp/Fzd4 signaling) or loss of VEGF-A signaling to dissect the specific roles of Tgf-beta signaling for vascular development in the retina.

(3) The snRNAseq data of vessel segments from the brains of WT versus TgfbR1 -iECKO mice provides a nice analysis of pathways and transcripts that are regulated by Tgf-beta signaling in endothelial cells.

Weaknesses (Original Submission):

(1) The authors claim that choroidal neovascular tuft phenotypes are similar in TgfbrR1 KO and TgfbrR2 KO mice. However, the phenotypes look more severe in the TgfbrR1 KO rather than TgfbrR2 KO mice. Can the authors show a quantitative comparison of the number of choroidal neovascular tufts per whole eye cross-section in both genotypes?

(2) In the analysis of Sulfo-NHS-Biotin leakage in the retina to assess blood-retina barrier maturation, the authors claim that there is increased vascular leakage in the TgfbR1 KO mice. However, there does not seem like Sulfo-NHS-biotin is leaking outside the vessels. Therefore, it cannot be increased vascular permeability. Can the authors provide a detailed quantification of the leakage phenotype?

(3) The immune cell phenotyping by snRNAseq seems premature as the number of cells is very small. The authors should sort for CD45+ cells and perform single cell RNA sequencing.

(4) The analysis of BBB leakage phenotype in TgfbR1 KO mice needs to be more detailed and include some tracers in addition to serum IgG leakage.

(5) A previous study (Zarkada et al., 2021, Developmental Cell) showed that EC-deletion of Alk5 affects the D tip cells. The phenotypes of those mice look very similar to those shown for TgfbrR1 KO mice. Are D tip cells lost in these mutants by snRNAseq?

Comments on revisions:

The authors have addressed the major weaknesses that I raised with the original submission adequately in the revised manuscript.

Reviewer #1 (Public review):

Summary:

Zhang et al. addressed the question of whether advantageous and disadvantageous inequality aversion can be vicariously learned and generalized. Using an adapted version of the ultimatum game (UG), in three phases, participants first gave their own preference (baseline phase), then interacted with a "teacher" to learn their preference (learning phase), and finally were tested again on their own (transfer phase). The key measure is whether participants exhibited similar choice preference (i.e., rejection rate and fairness rating) influenced by the learning phase, by contrasting their transfer phase and baseline phase. Through a series of statistical modeling and computational modeling, the authors reported that both advantageous and disadvantageous inequality aversion can indeed be learned (Study 1), and even be generalised (Study 2).

Strengths:

This study is very interesting, that directly adapted the lab's previous work on the observational learning effect on disadvantageous inequality aversion, to test both advantageous and disadvantageous inequality aversion in the current study. Social transmission of action, emotion, and attitude have started to be looked at recently, hence this research is timely. The use of computational modeling is mostly appropriate and motivated. Study 2 that examined the vicarious inequality aversion on conditions where feedback was never provided is interesting and important to strengthen the reported effects. Both studies have proper justifications to determine the sample size.

Weaknesses:

Despite the strengths, a few conceptual aspects and analytical decisions have to be explained, justified, or clarified.

INTRODUCTION/CONCEPTUALIZATION

(1) Two terms seem to be interchangeable, which should not, in this work: vicarious/observational learning vs preference learning. For vicarious learning, individuals observe others' actions (and optionally also the corresponding consequence resulted directly by their own actions), whereas, for preference learning, individuals predict, or act on behalf of, the others' actions, and then receive feedback if that prediction is correct or not. For the current work, it seems that the experiment is more about preference learning and prediction, and less so about vicarious learning. But the intro and set are heavily around vicarious learning, and late the use of vicarious learning and preference learning is rather mixed in the text. I think either tone down the focus on vicarious learning, or discuss how they are different. Some of the references here may be helpful: Charpentier et al., Neuron, 2020; Olsson et al., Nature Reviews Neuroscience, 2020; Zhang & Glascher, Science Advances, 2020

EXPERIMENTAL DESIGN

(2) For each offer type, the experiment "added a uniformly distributed noise in the range of (-10 ,10)". I wonder how this looks like? With only integers such as 25:75, or even with decimal points? More importantly, is it possible to have either 70:30 or 90:10 option, after adding the noise, to have generated an 80:20 split shown to the participants? If so, for the analyses later, when participants saw the 80:20 split, which condition did this trial belong to? 70:30 or 90:10? And is such noise added only to the learning phase, or also to the baseline/transfer phases? This requires some clarification.

(3) For the offer conditions (90:10, 70:30, 50:50, 30:70, 10:90) - are they randomized? If so, how is it done? Is it randomized within each participants, and/or also across participants (such that each participant experienced different trial sequences)? This is important, as the order especially for the leanring phase can largely impact on the preference learning of the participants.

STATISTICAL ANALYSIS & COMPUTATIONAL MODELING

(4) In Study 1 DI offer types (90:10, 70:30), the rejection rate for DI-AI averse looks consistently higher than that for DI averse (ie, blue line is above the yellow line). Is this significant? If so, how come? Since this is a between-subject design, I would not anticipate such a result (especially for the baseline). Also, for the LME results (eg, Table S3), only interactions were reported but not the main results.

(5) I do not particularly find this analysis appealing: "we examined whether participants' changes in rejection rates between Transfer and Baseline, could be explained by the degree to which they vicariously learned, defined as the change in punishment rates between the first and last 5 trials of the Learning phase." Naturally, participants' behavior in the first 5 trials in the learning phase will be similar to those in the baseline; and their behavior in the last 5 trials in the learning phase would echo those at the transfer phase. I think it would be stronger to link the preference learning results to the chance between baseline and transfer phase, eg, by looking at the difference between alpha (beta) at the end of the learning phase and the initial alpha (beta).

(6) I wonder if data from the baseline and transfer phases can also be modeled, using a simple Fehr-Schimdt model? This way, the change in alpha/beta can also be examined between the baseline and transfer phase.

(7) I quite liked Study 2 that tests the generalization effect, and I expected to see an adapted computational modeling to directly reflect this idea. Indeed, the authors wrote "[...] given that this model [...] assumes the sort of generalization of preferences between offer types [...]". But where exactly did the preference learning model assumed the generalization? In the methods, the modeling seems to be only about Study 1; did the authors advise their model to accommodate Study 2? The authors also ran simulation for the learning phase in Study 2 (Figure 6), and how did the preference updated (if at all) for offers (90:10 and 10:90) where feedback was not given? Extending/Unpacking the computational modeling results for Study2 will be very helpful for the paper.

Comments on revisions:

I kept my original public review, so that future readers can see the progress and development of the manuscript.

The authors have largely addressed my original questions/concerns, and I have two outstanding comments.

(a) Related to my original comment #6, where I suggested to apply the F-S model also to the baseline and transfer phase. The authors were inclined not to do it, but in fact later in comment #7 and in the manuscript they opted to use a more complex F-S-based model to their learning phase. I agree that the rejection rate is indeed a clear indication, but for completeness, it'd be more consistent and compelling if the paper follows a model-free (model-agnostic) and model-based approach in all phases of the experiment.

(b) Related to my original comment #4, I appreciate that the authors have provided more details of their LMM models. But I don't think it is accurate regardless. First, all offer levels (50:50, 30:70, 10:90), should not be coded as pure categorical levels. In fact, they have an ordinal meaning, a single ordinal predictor with three levels should be used. This also avoids the excessive number of interactions the authors have pointed out.

Second, running a model with only interactions without main effects is flawed. All textbooks on stats emphasize that without the presence of the main effects, the interpretation of interaction only is biased.

So these LMMs needs to be revised before the manuscript eventually gets to a version of record.

Reviewer #1 (Public review):

Turner et al. present an original approach to investigate the role of Type-1 nNOS interneurons in driving neuronal network activity and in controlling vascular network dynamics in awake head-fixed mice. Selective activation or suppression of Type-1 nNOS interneurons has previously been achieved using either chemogenetic, optogenetic or local pharmacology. Here, the authors took advantage of the fact that Type-1 nNOS interneurons are the only cortical cells that express the tachykinin receptor 1 to ablate them with a local injection of saporin conjugated to substance P (SP-SAP). SP-SAP causes cell death in 90 % of type1 nNOS interneurons without affecting microglia, astrocytes and neurons. The authors report that the ablation has no major effects on sleep or behavior. Refining the analysis by scoring neural and hemodynamic signals with electrode recordings, calcium signal imaging and wide field optical imaging, they observe that Type-1 nNOS interneuron ablation does not change the various phases of the sleep/wake cycle. However, it does reduce low-frequency neural activity, irrespective of the classification of arousal state. Analyzing neurovascular coupling using multiple approaches, they report small changes in resting-state neural-hemodynamic correlations across arousal states, primarily mediated by changes in neural activity. Finally, they show that nNOS type 1 interneurons play a role in controlling interhemispheric coherence and vasomotion.

In conclusion, these results are interesting, use state-of-the-art methods and are well supported by the data and their analysis. I have only a few comments on the stimulus-evoked haemodynamic responses that can be easily addressed:

Comments on revisions:

As I mentioned in my initial review, this study is important. In my opinion, it could be published as is. Nonetheless, I am still somewhat dissatisfied with the authors' responses to my earlier comments. I understand that the same animals were not used for both stimulation paradigms, which is unfortunate. Nonetheless, I would have appreciated it if the authors had provided a couple of experiments illustrating GCaMP7 signals during brief stimulation in their reply to the reviewers. I am still unconvinced by the authors' suggestion that the GCaMP7 signal would remain stable during removal of the vascular undershoot. Since the absence of the undershoot is notable, I anticipate that a significant part of the initial response to prolonged stimulation is influenced by processes that occur during the 0.1-second stimulation, processes that may involve a change in the bulk neuronal response.

In short, the data could support or refute the following statement: "Loss of type-I nNOS neurons drove minimal changes in the vasodilation elicited by brief stimulation..."

Reviewer #1 (Public review):

Most human traits and common diseases are polygenic, influenced by numerous genetic variants across the genome. These variants are typically non-coding and likely function through gene regulatory mechanisms. To identify their target genes, one strategy is to examine if these variants are also found among genetic variants with detectable effects on gene expression levels, known as eQTLs. Surprisingly, this strategy has had limited success, and most disease variants are not identified as eQTLs, a puzzling observation recently referred to as "missing regulation".

In this work, Jeong and Bulyk aimed to better understand the reasons behind the gap between disease-associated variants and eQTLs. They focused on immune-related diseases and used lymphoblastoid cell lines (LCLs) as a surrogate for the cell types mediating the genetic effects. Their main hypothesis is that some variants without eQTL evidence might be identifiable by studying other molecular intermediates along the path from genotype to phenotype. They specifically focused on variants that affect chromatin accessibility, known as caQTLs, as a potential marker of regulatory activity.

The authors present data analyses supporting this hypothesis: several disease-associated variants are explained by caQTLs but not eQTLs. They further show that although caQTLs and eQTLs likely have largely overlapping underlying genetic variants, some variants are discovered only through one of these mapping strategies. Notably, they demonstrate that eQTL mapping is underpowered for gene-distal variants with small effects on gene expression, whereas caQTL mapping is not dependent on the distance to genes. Additionally, for some disease variants with caQTLs but no corresponding eQTLs in LCLs, they identify eQTLs in other cell types.

Altogether, Jeong and Bulyk convincingly demonstrate that for immune-related diseases, discovering the missing disease-eQTLs requires both larger eQTL studies and a broader range of cell types in expression assays. It remains to be seen what fractions of the missing disease-eQTLs will be discovered with either strategy and whether these results can be extended to other diseases or traits.

It should be noted that the problem of "missing regulation" has been investigated and discussed in several recent papers, notably Umans et al., Trends in Genetics 2021; Connally et al., eLife 2022; Mostafavi et al., Nat. Genet. 2023. The results reported by Jeong and Bulyk are not unexpected in light of this previous work (all of which they cite), but they add valuable empirical evidence that mostly aligns with the model and discussions presented in Mostafavi et al.

Reviewer #1 (Public review):

The study analyzes the gastric fluid DNA content identified as a potential biomarker for human gastric cancer. However, the study lacks overall logicality, and several key issues require improvement and clarification. In the opinion of this reviewer, some major revisions are needed:

(1) This manuscript lacks a comparison of gastric cancer patients' stages with PN and N+PD patients, especially T0-T2 patients.

(2) The comparison between gastric cancer stages seems only to reveal the difference between T3 patients and early-stage gastric cancer patients, which raises doubts about the authenticity of the previous differences between gastric cancer patients and normal patients, whether it is only due to the higher number of T3 patients.

(3) The prognosis evaluation is too simplistic, only considering staging factors, without taking into account other factors such as tumor pathology and the time from onset to tumor detection.

(4) The comparison between gfDNA and conventional pathological examination methods should be mentioned, reflecting advantages such as accuracy and patient comfort.

(5) There are many questions in the figures and tables. Please match the Title, Figure legends, Footnote, Alphabetic order, etc.

(6) The overall logicality of the manuscript is not rigorous enough, with few discussion factors, and cannot represent the conclusions drawn

Reviewer #1 (Public review):

This study examines the spatiotemporal properties of feedback signals in the human brain during an object discrimination task. Using 7T fMRI and MEG, the authors show that task-relevant object category information can be decoded from both deep and superficial layers of V1, originating from occipito-temporal and posterior parietal cortices. In contrast, task-irrelevant category feedback does not appear in V1, even when the same objects are foveally presented. Low-level orientation information, however, is decodable from V1 regardless of task relevance and is supported by recurrence with occipito-temporal regions. These findings suggest that category decoding in V1 depends on task-driven feedback rather than feedforward visual features.

Strengths

This study leverages two advanced neuroimaging modalities attempting to connect object recognition across cortical layer and whole-brain levels. The revised manuscript strengthens the connection between the fMRI and MEG components.<br /> It also demonstrates that a peripheral object discrimination task is effective for isolating feedforward and feedback signals using 7T fMRI.<br /> It is particularly notable that no low-level features were fed back to V1's superficial layers in the peripheral object discrimination task. The authors further show that high- and low-level feedback to the foveal V1 are comparable in strength, supporting the idea that the superficial layer in V1 selectively represents task-relevant content.

Weaknesses

One alternative explanation for the absence of task-irrelevant category decoding in the foveal task could be that feedback enhancement may be required to decode complex features from V1 (compared to a coarse orientation feature). It would be informative to test whether the findings hold if the categorical boundary were defined through a low level feature other than orientation (e.g., frequency) (e.g. Ester, Sprague and Serences, 2020).

I would like to echo the concerns raised by the other reviewer regarding multiple comparisons correction. It is important to apply correction procedures, especially given the number of statistical tests performed across brain regions where strict a priori hypotheses are unlikely. In the case of cluster-based statistics, the manuscript should clearly specify both the cluster-forming threshold and the significance threshold used for comparing true cluster masses to the shuffled distribution.

Conclusion

Overall, the results support the study's conclusions. This work addresses a timely question in object categorization and predictive coding-specifically, how feedback signals vary in content and timing across cortical layers.

Reviewer #1 (Public review):

Wang, Junxiu et al. investigated the underlying molecular mechanisms of the insecticidal activity of betulin against the peach aphid, Myzus persicae. There are two important findings described in this manuscript: (a) betulin inhibits the gene expression of GABA receptor in the aphid, and (b) betulin binds to the GABA receptor protein, acting as an inhibitor. The first finding is supported by RNA-Seq and RNAi, and the second one is convinced with MST and electrophysiological assays. Further investigations on the betulin binding site on the receptor protein provided a fundamental discovery that T228 is the key amino acid residue for its affinity, thereby acting as an inhibitor, backed up by site-directed mutagenesis of the heterologously-expressed receptor in E. coli and by CRISPR-genome editing in Drosophila.

Comments on revisions:

All of my review comments have been addressed, and the manuscript has been revised accordingly.

Reviewer #1 (Public review):

Summary:

The authors study the steady-state solutions of ODE models for molecular signaling involving ligand binding coupled to multi-site phosphorylation at saturating ligand concentrations. Although the results are in principle general, the work highlights the receptor tyrosine kinases (RTK) as model systems. After presenting previous ODE model solutions, the authors present their own "kinetic sorting" model, which is distinguished by ligand-induced phosphorylation-dependent receptor degradation and the property that every phosphorylation state is signaling competent. The authors show that this model recovers the two types of non-monotonicity experimentally reported for RTKs: maximum activity for intermediate ligand affinity and maximum activity for intermediate kinase activity.

The main contribution of the work is in demonstrating that their model can capture both types of non-monotonicity, whereas previous models could at most capture non-monotonicity of ligand binding.

Strengths:

The question of how energy dissipating, and thus non-equilibrium, molecular systems can achieve steady-state solutions not accessible to equilibrium systems is of fundamental importance in biomolecular information processing and self-organization. Although the authors do not address the energy requirements of their non-equilibrium model, their comparative analysis of different alternative non-equilibrium models provides insight into the design choices necessary to achieve non-monotonic control, a property that is inaccessible at equilibrium.

The paper is succinctly written and easy to follow, and the authors achieve their aims by providing convincing numerical solutions demonstrating non-monotonicity over the range of parameter values encompassing the biologically relevant regime.

Weaknesses:

(1) A key motivating framework for this work is the argument that the ability to tune to recognize intermediate ligand affinities provides a control knob for signal selection that is available to non-equilibrium systems. As such, this seems like a compelling type of ligand selectivity, which is a question of broad interest. However, as the authors note in the results, the previously published "limited signaling model" already achieves such non-monotonicity to ligand binding affinity. The introduction and abstract do not clearly delineate the new contributions of the model.

The novel benefit of the model introduced by the authors is that it also achieves non-monotonic response to kinase activity. Because such non-monotonicity is observed for RTK, this would make the authors' model a better fit for capturing RTK behavior. However, the broad significance of achieving non-monotonicity to kinase activity is not motivated or supported by empirical evidence in the paper. As such, the conceptual significance of the modified model presented by the authors is not clear.

UPDATE: The authors have now clarified the significance of the model in elucidating how known motifs (multisite phosphorylation and active receptor degradation) could explain the behavior, including non-monotonicity. The authors have also provided compelling arguments for the biological significance of achieving non-monotonic kinase activity response.

(2) Whereas previous models used in the literature are schematized in Figure 1, the model proposed by the author is missing (See line 97 of page 3). Without the schematic, the text description of the model is incomplete.

UPDATE: this issue has been resolved.

(3) The authors use the activity of the first phosphorylation site as the default measure of activity. This choice needs to be justified. Why not use the sum of the activities at all sites?

UPDATE: This was a non-issue. The potential misunderstanding has been mitigated by clarifications in the text.

Comments on revisions:

All issues previously identified were convincingly addressed. I have no additional suggestions.

Reviewer #1 (Public review):

Summary:

The manuscript by Cupollilo et al describes the development, characterization and application of a novel activity labeling system; fast labelling of engram neurons (FLEN). Several such systems already exist but this study adds additional capability by leveraging an activity marker that is destabilized (and thus temporally active) as well as being driven by the full-length promoter of cFos. The authors demonstrate the activity dependent induction and timecourse of expression, first in cultured neurons and then in vivo in hippocampal CA3 neurons after one trial contextual fear conditioning. In a series of ex vivo experiments the authors perform patch clamp analysis of labeled neurons to determine if these putative engram neurons differ from non-labelled neurons using both the FLEN system as well as the previously characterized RAM system. Interestingly the early labelled neurons at 3 h post CFC (FLEN+) demonstrated no differences in excitability whereas the RAM labeled neurons at 24h after CFC had increased excitability. Examination of synaptic properties demonstrated an increase in sEPCS and mEPSC frequencies as well as those for sIPSCs and mIPSCs which was not due to a change in the mossy fiber input to these neurons.

Strengths:

Overall the data is of high quality and the study introduces a new tool while also reassessing some principles of circuit plasticity in the CA3 that have been the focus of prior studies.

Weaknesses:

No major weaknesses were noted

Reviewer #1 (Public review):

This work derives a general theory of optimal gain modulation in neural populations. It demonstrates that population homeostasis is a consequence of optimal modulation for information maximization with noisy neurons. The developed theory is then applied to the distributed distributional code (DDC) model of the primary visual cortex to demonstrate that homeostatic DDCs can account for stimulus specific adaptation.

Strengths:

The theory of gain modulation proposed in the paper is rigorous and the analysis is thorough. It does address the issue in an interesting, general setting. The proposed approach separates the question of which bits of sensory information are transmitted (as defined by a specific computation and tuning curve shapes) and how well are they transmitted (as defined by the tuning curve gain optimized to combat noise). This separation permits the application of the developed theory to different neural systems.

Weaknesses:

The manuscript effectively consits of two parts: a general theory of optimal gain modulation and a DDC model of the visual cortex. From my perspective it is not entirely clear which components of the developed theory and the model it is applied to are essential to explain the experimental phenomena in the visual cortex (Fig. 12). This "separation" into two parts makes this work, in my view, somewhat diffused.

Overall, I think this is an interesting contribution and I assess it positively. It has the potential of deepening our understanding of efficient neural representations beyond sensory periphery.

Reviewer #1 (Public review):

In this manuscript, Chen et al. investigate the role of the membrane estrogen receptor GPR30 in spinal mechanisms of neuropathic pain. Using a wide variety of techniques, they first provide convincing evidence that GPR30 expression is restricted to neurons within the spinal cord, and that GPR30 neurons are well-positioned to receive descending input from the primary sensory cortex (S1). In addition, the authors put their findings in the context the previous knowledge in the field, presenting evidence demonstrating that GRP30 is expressed in the majority of CCK-expressing spinal neurons. Overall, this manuscript furthers our understanding of neural circuity that underlies neuropathic pain and will be of broad interest to neuroscientists, especially those interested in somatosensation. Nevertheless, the manuscript would be strengthened by additional analyses and clarification of data that is currently presented.

Strengths:

The authors present convincing evidence for expression of GPR30 in the spinal cord that is specific to spinal neurons. Similarly, complementary approaches including pharmacological inhibition and knockdown of GPR30 are used to demonstrate a role for the receptor in driving nerve injury-induced pain in rodent models.

Weaknesses:

Although steps were taken to put their data into the broader context of what is already known about the spinal circuitry of pain, more considerations and analyses would help the authors better achieve their goal. For instance, to determine whether GPR30 is expressed in excitatory or inhibitory neurons, more selective markers for these subtypes should be used over CamK2. Moreover, quantitative analysis of the extent of overlap between GPR30+ and CCK+ spinal neurons is needed to understand the potential heterogeneity of the GPR30 spinal neuron population, and to interpret experiments characterizing descending SI inputs onto GPR30 and CCK spinal neurons. Filling these gaps in knowledge would make their findings more solid.

Revised Manuscript Update:

In their revised manuscript, Chen et al. have added additional data that establishes GPR30 spinal neurons as a population of excitatory neurons, half of which express CCK. These data help to position GPR30 neurons in the existing framework of spinal neuron populations that contribute to neuropathic pain, strengthening the author's findings.

Reviewer #1 (Public review):

Summary:

The authors identify and investigate a specific population of PVNOT neurons (oxytocin neurons of the paraventricular hypothalamus) that seem to be involved in both behavioral and autonomic thermoregulation. These cells are activated by social thermoregulatory behaviors, but can influence thermoregulation in both social and nonsocial contexts, specifically during transitions and when mice are at low core body temperature (Tb).

Strengths:

The manuscript has many strengths.

This is a novel study, with a clear question that is addressed using an array of well-designed experiments employing integrative methods. Most of the figures are well-developed, and the analysis is generally rigorous and well-detailed. The authors are clearly very experienced in this field, and indeed, their scholarly introduction and discussion sections are to their credit.

The link between thermoregulation and the oxytocin system is well established, as is the link between social behavior and the same broad system. However, the link between these three things is novel, if it can be well substantiated. I am not persuaded that was achieved here, but I do think this manuscript has many novel and useful offerings.

The authors use a cooling floor, and only go down to 10 degrees Celsius. This is fine, but I would like to see the effects using ambient temperature also. This is not a crucial issue, as it is not necessary for the authors' interpretations, but it could improve measurement sensitivity.

Through an elegant behavioral experiment in Figure 1, the authors identify c-Fos patterns in the PVN that are activated by active social huddling, and they show that at the RNA level these cells overlap with oxytocin, indicating that they are oxytocin-producing cells. But this is not well discussed or indeed quantified.

The authors engage in a deep analysis of fiber photometry experiments, first by observing PVNOT neuron overall activity during a variety of different behaviors in the context of three different temperatures. Activity was associated with nesting, quiescence, and both types of huddling (when social opportunities exist). Social situations did not strongly affect this, nor did temperature conditions. These analyses indicate that the PVNOT neurons are involved in mediating specific behavioral outputs.

With more detailed analysis, the authors investigated how PVNOT neuronal activity relates to behavioral state transition. They found that the probability of peak PVNOT neural activity strongly predicts the offset of quiescence or quiescent huddling, and therefore can be argued to signal an increase in physical activity, and as such, increased metabolism. However, the opposite pattern was observed for huddling and nesting (onset being associated with PVNOT activity), again arguing for increased thermogenesis as a function.

What is particularly compelling is that these peaks of activity tend to occur during low Tb, again arguing for the function in increasing body warmth.

The authors then employ an impressive setup where they image brown adipose tissue (BAT) in tandem with DeepLabCut (DLC) based animal tracking. Crucially, BAT activity and surface temperature correlated with the calcium peak of PVNOT neurons.

Lastly, optogenetic activation of PVNOT neurons increased Tb when it was in the lower range, but not when in the higher range. It also affected BAT and rump temperature, again at low Tb. However, there is no real effect on behavior, except a trend in activity.

The authors do some interesting tracing work at the end, though this is not functionally explored. That is not a criticism, as it does seem like this would be a whole follow-up study.

Weaknesses:

While novel and valuable, the manuscript feels incomplete in its current form.

The main evidence lacking is a loss of function of the experiment. Ideally, the authors would chronically and/or acutely inhibit PVNOT neurons to establish their necessity. I know this seems obvious, but I think it is important.

The relative lack of behavioral analysis following optogenetic activation of PVNOT neurons is puzzling. The authors must surely want to study what this intervention does to behavioral state transitions. I feel that the current level of analysis limits the overall conclusions of this study to a large extent.

A broader criticism is that the social dimension of this manuscript seems overplayed. Naturally, oxytocin signalling can be implicated in social behavior based on a large literature. However, the focus on social thermogenesis seems like a crude integration of social behavior and thermogenesis. Given that the authors see their effects in both social and nonsocial cases of thermoregulation, I am not sure the attempts at integrating social functions and thermogenic functions of PVNOT neurons are warranted. That is, unless the authors have further experiments or analysis that can convincingly justify this link.

In addition, the analysis of virgin females and lactating mothers seems out of place in Figure 4.

The c-Fos/oxytocin overlap needs to be quantified.

The methods section could be improved by explaining how the authors exclude animals that exhibit both types of huddling, if they occur within a 90-minute time window. This seems like it could cause significant confounds.

The computer vision model is not well-explained. The authors need to be far more explicit here about how it was validated.

The authors should cite and consider this preprint: https://www.biorxiv.org/content/10.1101/2024.09.17.613378v1

Reviewer #1 (Public review):

Sebag et al. addressed the role of ADH5 in BAT in the development of aging and metabolic disarrangements associated with it. This is a follow-up study after the authors' demonstration of the role of BAT ADH5 in glucose homeostasis, obesity, and cold tolerance. By ablating ADH5 specifically in brown adipocytes or pharmacologically modulating ADH5 through activation of its transcription factor, the authors conclude that preservation of BAT function is crucial for healthy aging and ADH5 is causally involved in this process. The topic is appealing given the rise in the aging population and the unclear role of BAT function in this process. Overall, the study uses several techniques, is easy to follow, and addresses several physiological and molecular manifestations of aging. However, the study lacks an appropriate statistical analysis, which severely affects the conclusions of the work. Therefore, interpretation of the findings is limited and must be done with caution.

Joint Public Review:

Summary:

This study uses data from a recent RVFV serosurvey among transhumant cattle in The Gambia to inform the development of an RVFV transmission model. The model incorporates several hypotheses that capture the seasonal nature of both vector-borne RVFV transmission and cattle migration. These natural phenomena are driven by contrasting wet and dry seasons in The Gambia's two main ecoregions and are purported to drive cyclical source-sink transmission dynamics. Although the Sahel is hypothesized to be unsuitable for year-long RVFV transmission, findings suggest that cattle returning from the Gambia River to the Sahel at the beginning of the wet season could drive repeated RVFV introductions and ensuing seasonal outbreaks. The model is also used to evaluate the potential impacts of cattle movement bans on transmission dynamics, although there is doubt about the certainty of these latter findings in light of various simplifying assumptions.

Strengths:

Like most infectious diseases in animal systems in low- and middle-income countries, the transmission dynamics of RVFV in cattle in The Gambia are poorly understood. This study harnesses important data on RVFV seroepidemiology to develop and parameterize a novel transmission model, providing plausible estimates of several epidemiological parameters and transmission dynamic patterns.

This study is well written and easy to follow.

The authors consider both deterministic and stochastic formulations of their model, demonstrating potential impacts of random events (e.g., extinctions) and providing confidence regarding model robustness.

The authors use well-established Bayesian estimation techniques for model fitting and confront their transmission model with a seroepidemiological model to assess model fit.

Elasticity analyses help to understand the relative importance of competing demographic and epidemiological drivers of transmission in this system.

Weaknesses:

The model predicts relatively stable annual dynamics reminiscent of a seasonal endemic pathogen, but RVF in sub-Saharan Africa is often characterized as causing periodic epizootics with sustained lulls in between outbreaks. Do the authors believe this conventional wisdom regarding RVF epidemiology is wrong, and that their results better support that transmission patterns are seasonal but truly relatively stable year-over-year, at least in the Gambia? The authors should discuss whether these predicted dynamics could be an artefact of the model's structure, and what ramifications this could have for their conclusions.

It is unclear how the network analysis is used to inform the model. The network (Figure S2) suggests a highly fragmented population, which could better support, for example, a herd metapopulation approach. The first results section highlights that transhumant movements cover large distances (perhaps to justify the assumption of homogenous mixing within each ecoregion?), but the median (13.5km) is quite short.

The model does not include an impact of infection on cattle birth rates, but the authors highlight the well-known impacts of RVF epizootics on cattle abortion and neonatal death.

ODEs for M herds in the dry season are missing from the appendix. Even in the absence of transmission among this subpopulation in this season, demographic turnover should influence its SIR population dynamics. Were these not included in the model or simply omitted from the text?

The importance of the LVFV positivity decay rate is highlighted, but the loss of immunity is not considered in the SIR model. The authors do discuss uncertainty regarding model structure, but could better justify their choice. Is there evidence of reduced infection risk among previously infected seronegatives, and why was an SIRS model not considered? How might findings be expected to differ under an SIRS model?

Shouldn't disease-induced host death be included in the serocatalytic model? A high RVF mortality rate has been estimated, and FOI is relatively high, suggesting a non-negligible impact of RVF death on seroprevalence dynamics, and indeed possibly a greater impact than seroreversion.

It is helpful that the authors have described findings from the previously conducted household survey, which is a key foundation for the model, but it needs to be made clearer what work was already conducted as part of the previous study, in particular the Methods sections RVFV seroprevalence & household survey data and Epidemiological setting & cattle population structure. Same for the sections Study Area and Data Collection in the appendix.

The study limitations paragraph is vague. What modelling assumptions have introduced the greatest uncertainty, and what implications could this have for study conclusions?

Two main issues with the simulations of a ban on transhuman movement:

The introduction rightly highlights the importance of pastoral lifestyles for subsistence farmers in the Gambia. It therefore seems likely that transhumant movement bans would have great socioeconomic and ethical challenges in addition to obvious practical challenges. Is such an intervention even a remote possibility?

The model's structure, including homogenous mixing within each ecoregion and step-change seasonality, allows for estimation of generalized transmission rates at a macro scale. However, it greatly simplifies the movement process itself and assumes that transhumant cattle movement is the only mechanism for RVF reintroduction into the Sahel region. The model is therefore likely to misrepresent the potential impacts of movement bans on transmission. As studies, for example, in healthcare settings have shown, where fine-scaled contact data are available, incorporating the specific and complex nature of inter-individual contact can change not only the magnitude but the direction of intervention impacts relative to predictions from a model with homogenous mixing assumptions. Conclusions from this work regarding the impacts of movement bans, therefore, seem poorly supported.

This model seems perhaps better suited to exploring, for example, cattle vaccination, and potential differential efficiency when targeting T herds relative to M or L.

Reviewer #1 (Public review):

In this manuscript, Aghabi et al. present a comprehensive characterization of ZFT, a metal transporter located at the plasma membrane of the eukaryotic parasite Toxoplasma gondii. The authors provide convincing evidence that ZFT plays a crucial role in parasite fitness, as demonstrated by the generation of a conditional knockdown mutant cell line, which exhibits a marked impact on mitochondrial respiration, a process dependent on several iron-containing proteins. Consistent with previous reports, the authors also show that disruption of mitochondrial metabolism leads to conversion into the persistent bradyzoite stage. The study then employed advanced techniques, such as inductively coupled plasma-mass spectrometry (ICP-MS) and X-ray fluorescence microscopy (XFM), to demonstrate that ZFT depletion results in reduced parasite-associated metals, particularly iron and zinc. Additionally, the authors show that ZFT expression is modulated by the availability of these metals, although defects in the transporter could not be compensated for by exogenous addition of iron or zinc.

While the manuscript does not directly investigate the transport function of ZFT through biochemical assays, the authors indirectly support the notion that ZFT can transport zinc by demonstrating its ability to compensate for a lack of zinc transport in a yeast heterologous system. Furthermore, phenotypic analyses suggest defects in iron availability, particularly with regard to Fe-S mitochondrial proteins and mitochondrial function. Overall, the manuscript provides a solid, well-rounded argument for ZFT's role in metal transport, using a combination of complementary approaches. Although direct biochemical evidence for the transporter's substrate specificity and transport activity is lacking, the converging evidence, including changes in metal concentrations upon ZFT depletion, yeast complementation data, and phenotypic changes linked to iron deficiency, presents a convincing case. Some aspects of the results may appear somewhat unbalanced, particularly since iron transport could not be confirmed through heterologous complementation, while zinc-related phenotypes in the parasites have not been thoroughly explored (which is challenging given the limited number of zinc-dependent proteins characterized in Toxoplasma). Nevertheless, given that metal acquisition remains largely uncharacterized in Toxoplasma, this manuscript provides an important first step in identifying a metal transporter in these parasites, and the data presented are generally convincing and insightful.

Reviewer #1 (Public review):

Shigella flexneri is a bacterial pathogen that is an important globally significant cause of diarrhea. Shigella pathogenesis remains poorly understood. In their manuscript, Saavedra-Sanchez et al report their discovery that a secreted E3 ligase effector of Shigella, called IpaH1.4, mediates the degradation of a host E3 ligase called RNF213. RNF213 was previously described to mediate ubiquitylation of intracellular bacteria, an initial step in their targeting to xenophagosomes. Thus, Shigella IpaH1.4 appears to be an important factor to permit evasion of RNF213-mediated host defense. Strengths: The work is focused, convincing, well-performed and important, and the manuscript is well-written. The revised version addressed all the concerns raised during the initial review.

Reviewer #1 (Public review):

Summary:

Wang and Colleagues present a study aimed at demonstrating the feasibility of repeated ultrasound localization microscopy (ULM) recording sessions on mice chronically implanted with a cranial window transparent to US. They provided quantitative information on their protocol, such as the required number of Contrast enhancing microbubbles (MBs) to get a clear image of the vasculature of a brain coronal section. Also, they quantified the co-registration quality over time-distant sessions and the vasodilator effect of isoflurane.

Strengths:

Strengths:

The study showed a remarkable performance in recording precisely the same brain coronal section over repeated imaging sessions. In addition, it sheds light on the vasodilator effect of isoflurane (an anesthetic whose effects are not fully understood) on the different brain vasculature compartments, although, as the Authors stated, some insights in this aspect have already been published with other imaging techniques. The experimental setting and protocol are very well described.

In this newly revised version, the Authors made evident efforts to strengthen the messages of their study. All the limitations of their research have been clearly acknowledged.

A central issue remains. To answer my concerns about the need for multivariate analyses, the Author stated that: "Due to the limited number of animals used, the analyses presented in this work should be interpreted as example case studies." Although this sentence does not convince me, if the purpose of this study was to showcase the potentialities of ULM for future longitudinal awake studies, why don't they avoid any statistics? The trend for decreased vein size and increased arterial blood flow during wakefulness is evident from the plot and physiologically plausible. Why impose wrong statistics instead of dropping them altogether? I do not see the lack of statistics as detrimental to this study, based on the feedback received from the Authors.

Reviewer #1 (Public review):

Summary:

In this manuscript, Weir et al. investigate why the 13-lined ground squirrel (13LGS) retina is unusually rich in cone photoreceptors, the cells responsible for color and daylight vision. Most mammals, including humans, have rod-dominant retinas, making the 13LGS retina both an intriguing evolutionary divergence and a valuable model for uncovering novel mechanisms of cone generation. The developmental programs underlying this adaptation were previously unknown.

Using an integrated approach that combines single-cell RNA sequencing (scRNAseq), scATACseq, and histology, the authors generate a comprehensive atlas of retinal neurogenesis in 13LGS. Notably, comparative analyses with mouse datasets reveal that in 13LGS, cones can arise from late-stage neurogenic progenitors, a striking contrast to mouse and primate retinas, where late progenitors typically generate rods and other late-born cell types but not cones. They further identify a shift in the timing (heterochrony) of expression of several transcription factors. Further, the authors show that these factors act through species-specific regulatory elements. And overall, functional experiments support a role for several of these candidates in cone production.

Strengths:

This study stands out for its rigorous and multi-layered methodology. The combination of transcriptomic, epigenomic, and histological data yields a detailed and coherent view of cone development in 13LGS. Cross-species comparisons are thoughtfully executed, lending strong evolutionary context to the findings. The conclusions are, in general, well supported by the evidence, and the datasets generated represent a substantial resource for the field. The work will be of high value to both evolutionary neurobiology and regenerative medicine, particularly in the design of strategies to replace lost cone photoreceptors in human disease.

Weaknesses:

(1) Overall, the conclusions are strongly supported by the data, but the paper would benefit from additional clarifications. In particular, some of the conclusions could be toned down slightly to reflect that the observed changes in candidate gene function, such as those for Zic3 by itself, are modest and may represent part of a more complex regulatory network.

(2) Additional explanations about the cell composition of the 13LGS retina are needed. The ratios between cone and rod are clearly detailed, but do those lead to changes in other cell types?

(3) Could the lack of a clear trajectory for rod differentiation be just an effect of low cell numbers for this population?

(4) The immunohistochemistry and RNA hybridization experiments shown in Figure S2 would benefit from supporting controls to strengthen their interpretability. While it has to be recognized that performing immunostainings on non-conventional species is not a simple task, negative controls are necessary to establish the baseline background levels, especially in cases where there seems to be labeling around the cells. The text indicates that these experiments are both immunostainings and ISH, but the figure legend only says "immunohistochemistry". Clarifying these points would improve readers' confidence in the data.

(5) Figure S3: The text claims that overexpression of Zic3 alone is sufficient to induce the cone-like photoreceptor precursor cells as well as horizontal cell-like precursors, but this is not clear in Figure S3A nor in any other figure. Similarly, the effects of Pou2f1 overexpression are different in Figure S3A and Figure S3B. In Figure S3B, the effects described (increased presence of cone-like and horizontal-like precursors) are very clear, whereas it is not in Figure S3A. How are these experiments different?

(6) The analyses of Zic3 conditional mutants (Figure S4) reveal an increase in many cone, rod, and pan-photoreceptor genes with only a reduction in some cone genes. Thus, the overall conclusion that Zic3 is essential for cones while repressing rod genes doesn't seem to match this particular dataset.

(7) Throughout the text, the authors used the term "evolved". To substantiate this claim, it would be important to include sequence analyses or to rephrase to a more neutral term that does not imply evolutionary inference.

Reviewer #1 (Public review):

Li et al. investigate Ca2+ signaling in T. gondii and argue that Ca2+ tunnels through the ER to other organelles to fuel multiple aspects of T. gondii biology. They focus in particular on TgSERCA as the presumed primary mechanism for ER Ca2+ filling. Although, when TgSERCA was knocked out there was still a Ca2+ release in response to TG present. Overall the data supports a model where the Ca2+ filling state of the ER modulates Ca2+ dynamics in other organelles.

Comments on revisions:

I thank the authors for their careful revisions and response to my comments, which have been addressed.

Regarding the most critical point of the paper that is Ca2+ transfer from the ER to other organelles, the authors in their rebuttal and in the revised manuscript argue that ER Ca2+ is critical to redistribute and replenish Ca2+ in other organelles in the cell. I agree this conclusion and think it is best stated in the authors' response to point #7: "We propose that this leaked calcium is subsequently taken up by other intracellular compartments. This effect is observed immediately upon TG addition. However, pre-incubation with TG or knockdown of SERCA reduces calcium storage in the ER, thereby diminishing the transfer of calcium to other stores."

In their rebuttal the authors particularly highlight experiments in Figures 1H-K, 4G-H, and 5H-K in support of this conclusion. The data in Fig 1H-K show that with TG there is increased Ca2+ release from acidic stores. In all cases TG results in a rise in cytoplasmic Ca2+ that could load the acidic stores. So under those conditions the increased acidic organelle Ca2+ is likely due to a preceding high cytosolic Ca2+ transient due to TG. The experiments in 4G-H and 5H-K are more convincing and supportive of an important role of ER Ca2+ to maintain Ca2+ levels in other organelles. Overall, and to avoid a detailed, lengthy discussion of every point, the data support a model where in the absence of SERCA activity ER Ca2+ is reduced as well as Ca2+ in other organelles. I think it would be helpful to present and discuss this finding throughout the manuscript as under physiological conditions ER Ca2+ is regularly mobilized for signaling and homeostasis and this maintains Ca2+ levels in other organelles. This is supported by the new experiment in Supp Fig. 2A.

Reviewer #1 (Public review):

In this important study, the authors develop a suite of machine vision tools to identify and align fluorescent neuronal recording images in space and time according to neuron identity and position. The authors provide compelling evidence for the speed and utility of these tools. While such tools have been developed in the past (including by the authors), the key advancement here is the speed and broad utility of these new tools. While prior approaches based on steepest descent worked, they required hundreds of hours of computational time, while the new approaches outlined here are >600-fold faster. The machine vision tools here should be immediately useful to readers specifically interested in whole-brain C. elegans data, but also for more general readers who may be interested in using BrainAlignNet for tracking fluorescent neuronal recordings from other systems.

I really enjoyed reading this paper. The authors had several ground truth examples to quantify the accuracy of their algorithms and identified several small caveats users should consider when using these tools. These tools were primarily developed for C. elegans, an animal with stereotyped development, but whose neurons can be variably located due to internal motion of the body. The authors provide several examples of how BrainAlignNet reliably tracked these neurons over space and time. Neuron identity is also important to track, and the authors showed how AutoCellLoader can reliably identify neurons based on their fluorescence in the NeuroPAL background. A challenge with NeuroPAL though, is the high expression of several fluorophores, which compromises behavioral fidelity. The authors provide some possible avenues where this problem can be addressed by expressing fewer fluorophores. While using all four channels provided the best performance, only using the tagRFP and CyOFP channels was sufficient for performance that was close to full performance using all 4 NeuroPAL channels. This result indicates that the development of future lines with less fluorophore expression could be sufficient for reliable neuronal identification, which would decrease the genetic load on the animal, but also open other fluorescent channels that could be used for tracking other fluorescent tools/markers. Even though these tools were developed for C. elegans specifically, they showed BrainAlignNet can be applied to other organisms as well (in their case, the cnidarian C. hemisphaerica), which broadens the utility of their tools.

Strengths:

(1) The authors have a wealth of ground-truth training data to compare their algorithms against, and provide a variety of metrics to assess how well their new tools perform against hand annotation and/or prior algorithms.

(2) For BrainAlignNet, the authors show how this tool can be applied to other organisms besides C. elegans.

(3) The tools are publicly available on GitHub, which includes useful README files and installation guidance.

Weaknesses:

(1) Most of the utility of these algorithms is for C. elegans specifically. Testing their algorithms (specifically BrainAlignNet) on more challenging problems, such as whole-brain zebrafish, would have been interesting. This is a very, very minor weakness, though.

(2) The tools are benchmarked against their own prior pipeline, but not against other algorithms written for the same purpose.

(3) Considerable pre-processing was done before implementation. Expanding upon this would improve accessibility of these tools to a wider audience.

Reviewer #1 (Public review):

Summary:

The study investigated how individuals living in urban slums in Salvador, Brazil, interact with environmental risk factors, particularly focusing on domestic rubbish piles, open sewers, and a central stream. The study makes use of the step selection functions using telemetry data, which is a method to estimate how likely individuals move towards these environmental features, differentiating among groups by gender, age, and leptospirosis serostatus. The results indicated that women tended to stay closer to the central stream while avoiding open sewers more than men. Furthermore, individuals who tested positive for leptospirosis tended to avoid open sewers, suggesting that behavioral patterns might influence exposure to risk factors for leptospirosis, hence ensuring more targeted interventions.

Strengths:

(1) The use of step selection functions to analyze human movement represents an innovative adaptation of a method typically used in animal ecology. This provides a robust quantitative framework for evaluating how people interact with environmental risk factors linked to infectious diseases (in this case, leptospirosis).

(2) Detailed differentiation by gender and serological status allows for nuanced insights, which can help tailor targeted interventions and potentially improve public health measures in urban slum settings.

(3) The integration of real-world telemetry data with epidemiological risk factors supports the development of predictive models that can be applied in future infectious disease research, helping to bridge the gap between environmental exposure and health outcomes.

Reviewer #1 (Public review):

Summary:

Grasper et al. present a combined analysis of the role of temporal mutagenesis in cancer, which includes both theoretical investigation and empirical analysis of point mutations in TCGA cancer patient cohorts. They find that temporally elevated mutation rates contribute to cancer fitness by allowing fast adaptation when the fitness drops (due to previous deleterious mutations). This may be relevant in the case of tumor suppressor genes (TSG), which follow the 2-hit hypothesis (i.e., biallelic 2 mutations are necessary to deactivate TS), and in cases where temporal mutagenesis occurs (e.g., high APOBEC, ROS). They provide evidence that this scenario is likely to occur in patients with some cancer types. This is an interesting and potentially important result that merits the attention of the target audience. Nonetheless, I have some questions (detailed below) regarding the design of the study, the tools and parametrization of the theoretical analysis, and the empirical analysis, which I think, if addressed, would make the paper more solid and the conclusion more substantiated.

Strengths:

Combined theoretical investigation with empirical analysis of cancer patients.

Weaknesses:

Parametrization and systematic investigation of theoretical tools and their relevance to tumor evolution.

Reviewer #1 (Public review):

Summary:

This very thorough anatomical study addresses the innervation of the Drosophila male reproductive tract. Two distinct glutamatergic neuron types were classified: serotonergic (SGNs) and octopaminergic (OGNs). By expansion microscopy, it was established that glutamate and serotonin /octopamine are co-released. The expression of different receptors for 5-HT and OA in muscles and epithelial cells of the innervation target organs was characterized. The pattern of neurotransmitter receptor expression in the target organs suggests that seminal fluid and sperm transport and emission are subjected to complex regulation. While silencing of abdominal SGNs leads to male infertility and prevents sperm from entering the ejaculatory duct, silencing of OGNs does not render males infertile.

Strengths:

The studied neurons were analysed with different transgenes and methods, as well as antibodies against neurotransmitter synthesis enzymes, building a consistent picture of their neurotransmitter identity. The careful anatomical description of innervation patterns together with receptor expression patterns of the target organs provides a solid basis for advancing the understanding of how seminal fluid and sperm transport and emission are subjected to complex regulation. The functional data showing that SGNs are required for male fertility and for the release of sperm from the seminal vesicle into the ejaculatory duct is convincing.

Weaknesses:

The functional analysis of the characterized neurons is not as comprehensive as the anatomical description, and phenotypic characterization was limited to simple fertility assays. It is understandable that a full functional dissection is beyond the scope of the present work. The paper contains experiments showing neuron-independent peristaltic waves in the reproductive tract muscles, which are thematically not very well integrated into the paper. Although very interesting, one wonders if these experiments would not fit better into a future work that also explores these peristaltic waves and their interrelation with neuromodulation mechanistically.

Reviewer #1 (Public review):

Summary:

This study focuses on characterizing the EEG correlates of item-specific proportion congruency effects. In particular, two types of learned associations are characterized. One being associations between stimulus features and control states (SC), and the other being stimulus features and responses (SR). Decoding methods are used to identify SC and SR correlates and to determine whether they have similar topographies and dynamics.

The results suggest SC and SR associations are simultaneously coactivated and have shared topographies, with the inference being that these associations may share a common generator.

Strengths:

Fearless, creative use of EEG decoding to test tricky hypotheses regarding latent associations.

Nice idea to orthogonalize the ISPC condition (MC/MI) from stimulus features.

Weaknesses:

(1) I'm relatively concerned that these results may be spurious. I hope to be proven wrong, but I would suggest taking another look at a few things.

While a nice idea in principle, the ISPC manipulation seems to be quite confounded with the trial number. E.g., color-red is MI only during phase 2, and is MC primarily only during Phase 3 (since phase 1 is so sparsely represented). In my experience, EEG noise is highly structured across a session and easily exploited by decoders. Plus, behavior seems quite different between Phase 2 and Phase 3. So, it seems likely that the classes you are asking the decoder to separate are highly confounded with temporally structured noise.

I suggest thinking of how to handle this concern in a rigorous way. A compelling way to address this would be to perform "cross-phase" decoding, however I am not sure if that is possible given the design.

The time courses also seem concerning. What are we to make of the SR and SC timecourses, which have aggregate decoding dynamics that look to be <1Hz?

Some sanity checks would be one place to start. Time courses were baselined, but this is often not necessary with decoding; it can cause bias (10.1016/j.jneumeth.2021.109080), and can mask deeper issues. What do things look like when not baselined? Can variables be decoded when they should not be decoded? What does cross-temporal decoding look like - everything stable across all times, etc.?

(2) The nature of the shared features between SR and SC subspaces is unclear.

The simulation is framed in terms of the amount of overlap, revealing the number of shared dimensions between subspaces. In reality, it seems like it's closer to 'proportion of volume shared', i.e., a small number of dominant dimensions could drive a large degree of alignment between subspaces.

What features drive the similarity? What features drive the distinctions between SR and SC? Aside from the temporal confounds I mentioned above, is it possible that some low-dimensional feature, like EEG congruency effect (e.g., low-D ERPs associated with conflict), or RT dynamics, drives discriminability among these classes? It seems plausible to me - all one would need is non-homogeneity in the size of the congruency effect across different items (subject-level idiosyncracies could contribute: 10.1016/j.neuroimage.2013.03.039).

(3) The time-resolved within-trial correlation of RSA betas is a cool idea, but I am concerned it is biased. Estimating correlations among different coefficients from the same GLM design matrix is, in general, biased, i.e., when the regressors are non-orthogonal. This bias comes from the expected covariance of the betas and is discussed in detail here (10.1371/journal.pcbi.1006299). In short, correlations could be inflated due to a combination of the design matrix and the structure of the noise. The most established solution, to cross-validate across different GLM estimations, is unfortunately not available here. I would suggest that the authors think of ways to handle this issue.

(4) Are results robust to running response-locked analyses? Especially the EEG-behavior correlation. Could this be driven by different RTs across trials & trial-types? I.e., at 400 ms post-stim onset, some trials would be near or at RT/action execution, while others may not be nearly as close, and so EEG features would differ & "predict" RT.

(5) I suggest providing more explanation about the logic of the subspace decoding method - what trialtypes exactly constitute the different classes, why we would expect this method to capture something useful regarding ISPC, & what this something might be. I felt that the first paragraph of the results breezes by a lot of important logic.

In general, this paper does not seem to be written for readers who are unfamiliar with this particular topic area. If authors think this is undesirable, I would suggest altering the text.

Reviewer #1 (Public review):

Summary:<br /> This article carefully compares intramural vs. extramural National Institutes of Health funded research during 2009-2019, according to a variety of bibliometric indices. They find that extramural awards more cost-effectively fund outputs commonly used for academic review such as number of publications and citations per dollar, while intramural awards are more cost-effective at generating work that influences future clinical work, more closely in line with agency health goals.

Strengths:<br /> Great care was taken in selecting and cleaning the data, and in making sure that intramural vs. extramural projects were compared appropriately. The data has statistical validation. The trends are clear and convincing.

Weaknesses:<br /> The Discussion is too short and descriptive, and needs more perspective - why are the findings important and what do they mean? Without recommending policy, at least these should discuss possible implications for policy.

The biggest problem I have with this submission is Figure 3, which shows a big decrease in clinical-related parameters between 2014 and 2019 in both intramural and extramural research (panels C, D and E). There is no obvious explanation for this and I did not see any discussion of this trend, but it cries out for investigation. This might, for example, reflect global changes in funding policies which might also influence the observed closing gaps between intramural and extramural research.

Reviewer #1 (Public review):

Summary:

The authors reduce uncertainties in TAK-003 vaccine efficacy estimates by applying a multi-level model to all published Phase III clinical trial case data and sharing parameters across strata consistent with the data generation process. In line with our current understanding of the vaccine, they show that its efficacy depends on the serostatus and infecting serotype.

Strengths:

The methodology is well-described and technically sound, with clear explanations of how the authors reduce uncertainty through the model structure. The comparison of model estimates with and without independence parameter assumptions is particularly valuable. The data come from the Phase III RCT conducted over 4.5 years in 8 countries, and the study is the first to model efficacy using available country-specific data. The analysis is timely and addresses important public health questions regarding TAK-003 efficacy.

Weaknesses:

It is unclear whether the simulation study used to validate the model sampled from the priors (as stated in the methods) or the posterior distributions. Supplementary figures 19-28 show that sampled parameters often derive from narrower distributions than the priors, with sampled areas varying by subgroup. Sampling from posterior distributions makes the validation somewhat circular. As many parameters are estimated stratified by multiple subgroups, identifiability issues may arise. Model variations with fewer parameter dependencies could impact the resulting estimates.

Assessment of aims and conclusions:

The authors achieve their aims of reducing uncertainty in efficacy estimates and show that efficacy varies by serostatus and serotype. The conclusions are well-justified, although they could be strengthened by clarifying the model validation, as discussed above.

Impact and utility:

This work contributes valuable evidence demonstrating TAK-003's serostatus and serotype-specific efficacy and highlights remaining uncertainties in the protection or risk against DENV3/4 in seronegative individuals. The methods are well-described and would be useful to other modellers, and could be applied to additional dengue vaccines like the Butantan-DV vaccine currently under development.

Additional context:

Several factors may influence the estimates but cannot be addressed using public data, including the role of subclinical infections, flavivirus cross-immunity, and the imperfect use of hospitalisation as a proxy for severe disease.

Reviewer #1 (Public review):

Summary:

Simoens and colleagues use a continuous estimation task to disentangle learning rate adjustments on shorter and longer timescales. They show that participants rapidly decrease learning rates within a block of trials in a given "location", but that they also adjust learning rates for the very first trial based on information accrued gradually about the statistics of each location, which can be viewed as a form of metalearning. The authors show that the metalearned learning rates are represented in patterns of neural activity in the orbitofrontal cortex, and that prediction errors are represented in a constellation of brain regions, including the ventral striatum, where they are modulated by expectations about error magnitude to some degree. Overall, the work is interesting, timely, and well communicated. My primary concern with the work was that the link between the brain signals and their role in the behavior of interest was not well explored, raising some questions about the degree to which signals are really involved in the learning process, versus playing some downstream role.

Strengths:

The authors build on an interesting task design, allowing them to distinguish moment-to-moment adjustments in learning rate from slower adjustments in learning rate corresponding to slowly-gained knowledge about the statistics of specific "locations". Behavior and computational modeling clearly demonstrate that individuals adjust to environmental statistics in a sort of metalearning. fMRI data reveal representations of interest, including those related to adjusted learning rates and their impact on the degree of prediction error encoding in the striatum.

Weaknesses:

It was nice to see that the authors could distinguish differences between the OFC signals that they observed and those in the visual regions based on changes through the session. However, the linkage between these brain activations and a functional role in generating behavior was left unexplored. Without further exploration, it is hard to tell exactly what role the signals might be playing, if any, in the behavior of interest.

Reviewer #1 (Public review):

In this well-written and timely manuscript, Rieger et al. introduce Squidly, a new deep learning framework for catalytic residue prediction. The novelty of the work lies in the aspect of integrating per-residue embeddings from large protein language models (ESM2) with a biology-informed contrastive learning scheme that leverages enzyme class information to rationally mine hard positive/negative pairs. Importantly, the method avoids reliance on the use of predicted 3D structures, enabling scalability, speed, and broad applicability. The authors show that Squidly outperforms existing ML-based tools and even BLAST in certain settings, while an ensemble with BLAST achieves state-of-the-art performance across multiple benchmarks. Additionally, the introduction of the CataloDB benchmark, designed to test generalization at low sequence and structural identity, represents another important contribution of this work.

I have only some minor comments:

(1) The manuscript acknowledges biases in EC class representation, particularly the enrichment for hydrolases. While CataloDB addresses some of these issues, the strong imbalance across enzyme classes may still limit conclusions about generalization. Could the authors provide per-class performance metrics, especially for underrepresented EC classes?

(2) An ablation analysis would be valuable to demonstrate how specific design choices in the algorithm contribute to capturing catalytic residue patterns in enzymes.

(3) The statement that users can optionally use uncertainty to filter predictions is promising but underdeveloped. How should predictive entropy values be interpreted in practice? Is there an empirical threshold that separates high- from low-confidence predictions? A demonstration of how uncertainty filtering shifts the trade-off between false positives and false negatives would clarify the practical utility of this feature.

(4) The excerpt highlights computational efficiency, reporting substantial runtime improvements (e.g., 108 s vs. 5757 s). However, the comparison lacks details on dataset size, hardware/software environment, and reproducibility conditions. Without these details, the speedup claim is difficult to evaluate. Furthermore, it remains unclear whether the reported efficiency gains come at the expense of predictive performance.

(5) Given the well-known biases in public enzyme databases, the dataset is likely enriched for model organisms (e.g., E. coli, yeast, human enzymes) and underrepresents enzymes from archaea, extremophiles, and diverse microbial taxa. Would this limit conclusions about Squidly's generalisability to less-studied lineages?

Reviewer #1 (Public review):

Summary:

From a big picture viewpoint, this work aims to provide a method to fit parameters of reduced models for neural dynamics so that the resulting tuned model has a bifurcation diagram that matches that of a more complex, computationally expensive model. The matching of bifurcation diagrams ensures that the model dynamics agree on a region of parameter space, rather than just at specially tuned values, and that the models share properties such as qualitative features of their phase response curves, as the authors demonstrate. A notable point is the inclusion of extracellular potassium concentration dynamics into the reduced model - here, the quadratic integrate-and-fire model; this is straightforward but nonetheless useful for studying certain phenomena.

Strengths:

The paper demonstrates the method specifically on the fitting of the quadratic integrate-and-fire model, with potassium concentration dynamics included, to the Wang-Buzsaki model extended to include the potassium component. The method works very well overall in this instance. The resulting model is thoroughly compared with the original, in terms of bifurcation diagrams, production of various activity patterns, phase response curves, and associated phase-locking and synchronization properties.

Weaknesses:

It is important to note that the proposed method requires that a target bifurcation diagram be known. In practical terms, this means that the method may be well suited to fitting a reduced model to another, more complicated model, but is not likely to be useful for fitting the model to data. Certainly, the authors did not illustrate any such application. Secondly, the authors do not provide any sort of general algorithm but rather give a demonstration of a single example of fitting one specific reduced model to one specific conductance-based model. Finally, the main idea of the paper seems to me to be a natural descendant of the chain of reasoning, starting from Rinzel - continuing through Bertram; Golubitsky/Kaper/Josic; Izhikevich; and others - that a fundamental way to think about neuronal models, especially those involving bursting dynamics, is in terms of their bifurcation structure. According to this line of reasoning, two models are "the same" if they have the same bifurcation structure. Thus, it becomes natural to fit a reduced model to a more complicated model based on the bifurcation structure. The authors deserve credit for recognizing and implementing this step, and their work may be a useful example to the community. But the manuscript should have described and cited this chain of works to put the current study in the correct context.

Reviewer #1 (Public review):

This paper by Poverlein et al reports the substantial membrane deformation around the oxidative phosphorylation super complex, proposing that this deformation is a key part of super complex formation. I found the paper interesting and well-written.

* Analysis of the bilayer curvature is challenging on the fine lengthscales they have used and produces unexpectedly large energies (Table 1). Additionally, the authors use the mean curvature (Eq. S5) as input to the (uncited, but it seems clear that this is Helfrich) Helfrich Hamiltonian (Eq. S7). If an errant factor of one half has been included with curvature, this would quarter the curvature energy compared to the real energy, due to the squared curvature. The bending modulus used (ca. 5 kcal/mol) is small on the scale of typically observed biological bending moduli. This suggests the curvature energies are indeed much higher even than the high values reported. Some of this may be due to the spontaneous curvature of the lipids and perhaps the effect of the protein modifying the nearby lipids properties.

* It is unclear how CDL is supporting SC formation if its effect stabilizing the membrane deformation is strong or if it is acting as an electrostatic glue. While this is a weakenss for a definite quantification of the effect of CDL on SC formation, the study presents an interesting observation of CDL redistribution and could be an interesting topic for future work.

In summary, the qualitative data presented are interesting (especially the combination of molecular modeling with simpler Monte Carlo modeling aiding broader interpretation of the results). The energies of the membrane deformations are quite large. This might reflect the roles of specific lipids stabilizing those deformations, or the inherent difficulty in characterizing nanometer-scale curvature.

Reviewer #1 (Public review):

Summary:

This manuscript investigated the mechanism underlying boundary formation necessary for proper separation of vestibular sensory end organs. In both chick and mouse embryos, it was shown that a population of cells abutting the sensory (marked by high Sox2 expression) /nonsensory cell populations (marked by Lmx1a expression) undergo apical expansion, elongation, alignment and basal constriction to separate the lateral crista (LC) from the utricle. Using Lmx1a mouse mutant, organ cultures, pharmacological and viral-mediated Rock inhibition, it was demonstrated that the Lmx1a transcription factor and Rock-mediated actomyosin contractility is required for boundary formation and LC-utricle separation.

Strengths:

Overall, the morphometric analyses were done rigorously and revealed novel boundary cell behaviors. The requirement of Lmx1a and Rock activity in boundary formation was convincingly demonstrated.

Weaknesses:

However, the precise roles of Lmx1a and Rock in regulating cell behaviors during boundary formation were not clearly fleshed out. For example, phenotypic analysis of Lmx1a was rather cursory; it is unclear how Lmx1a, expressed in half of the boundary domain, control boundary cell behaviors and prevent cell mixing between Lmx1a+ and Lmx1a- compartments? Well-established mechanisms and molecules for boundary formation were not investigated (e.g. differential adhesion via cadherins, cell repulsion via ephrin-Eph signaling). Moreover, within the boundary domain, it is unclear whether apical multicellular rosettes and basal constrictions are drivers of boundary formation, as boundary can still form when these cell behaviors were inhibited. Involvement of other cell behaviors, such as directional cell intercalation and oriented cell division also warrant consideration. With these lingering questions, the mechanistic advance of the present study is modest.

Revision: The clarity of the text was improved. The open questions regarding the mechanisms were not experimentally addressed but discussed.

Reviewer #1 (Public review):

Sarpaning et al. provide a thorough characterization of putative Rnt1 cleavage of mRNA in S. cerevisiae. Previous studies have discovered Rnt1 mRNA substrates anecdotally, and this global characterization expands the known collection of putative Rnt1 cleavage sites. The study is comprehensive, with several types of controls to show that Rnt1 is required for several of these cleavages.

Comments on revisions:

The authors have responded appropriately to the review.

Reviewer #1 (Public review):

Summary:

The objective of this study was to infer the population dynamics (rates of differentiation, division and loss) and lineage relationships of NK cell subsets during an acute immune response and under homeostatic conditions.

Strengths:

A rich dataset and a detailed analysis of a particular class of stochastic models.

Weaknesses: (relating to initial submission)

The stochastic models used are quite simple; each population is considered homogeneous with first-order rates of division, death, and differentiation. In Markov process models such as these there is no dependence of cellular behavior on its history of divisions. In recent years models of clonal expansion and diversification, in the settings of T and B cells, have progressed beyond this picture. So I was a little surprised that there was no mention of the literature exploring the role of replicative history in differentiation (e.g. Bresser Nat Imm 2022), nor of the notion of family 'division destinies' (either in division number, or the time spent proliferating, as described by the Cyton and Cyton2 models developed by Hodgkin and collaborators; e.g. Heinzel Nat Imm 2017). The emerging view is that variability in clone (family) size arises may arise predominantly from the signals delivered at activation, which dictate each precursor's subsequent degree of expansion, rather than from the fluctuations deriving from division and death modeled as Poisson processes.

As you pointed out, the Gerlach and Buchholz Science papers showed evidence for highly skewed distributions of family sizes, and correlations between family size and phenotypic composition. Is it possible that your observed correlations could arise if the propensity for immature CD27+ cells to differentiate into mature CD27- cells increases with division number? The relative frequency of the two populations would then also be impacted by differences in the division rates of each subset - one would need to explore this. But depending on the dependence of the differentiation rate on division number, there may be parameter regimes (and timepoints) at which the more differentiated cells can predominate within large clones even if they divide more slowly than their immature precursors. One might not then be able to rule out the two-state model. I would like to see a discussion or rebuttal of these issues.

Comments on revisions:

The authors have put in a lot of effort to address the reviews and have explored alternative models carefully.

In the sections relating to homeostasis and the endogenous response, as far as I can tell you are estimating net growth rates (the k parameters) throughout - this is to be expected if you're working with just cell numbers and no information relating to proliferation. In these sections there are many places where you refer to proliferation rates and death rates when I think you just mean net positive or net negative growth rates. It's important to be precise about this even if the language can get a bit repetitive. (These net rates of growth or loss relate to clonal rather than cellular dynamics, which may be worth explaining). Later, you do use data relating to dead cells, which in principle can be used to get independent measures of death rates, but these data were not used in the fitting.

There is so much evidence that T and B cell differentiation are often contingent on division that it would be very reasonable to consider it as a possibility for NK cells too. (Differentiation could be asymmetric, as you explored, or simply symmetric with some probability per division). These processes can be cast into simple ODE models but no longer allow you to aggregate division and death rates - so for parameter estimation you need to add measures of proliferation (Ki67 or similar) or death. This may be worth some discussion?

Reviewer #1 (Public review):

Summary:

The manuscript by Raices et al., provides some novel insights into the role and interactions between SPO-11 accessory proteins in C. elegans. The authors propose a model of meiotic DSBs regulation, critical to our understanding of DSB formation and ultimately crossover regulation and accurate chromosome segregation. The work also emphasizes the commonalities and species-specific aspects of DSB regulation.

Strengths:

This study capitalizes on the strengths of the C. elegans system to uncover genetic interactions between a lSPO-11 accessory proteins. In combination with physical interactions, the authors synthesize their findings into a model, which will serve as the basis for future work, to determine mechanisms of DSB regulation.

Weaknesses:

The methodology, although standard, still lacks some rigor, especially with the IPs.

Reviewer #1 (Public review):

Summary:

Badarnee and colleagues analyse fMRI data collected during an associative threat-learning task. They find evidence for parallel processes mediated by the mediodorsal, LGn, and pulvinar nuclei of the thalamus. The evidence for these conclusions is promising, but limited by a lack of clarity regarding the preprocessing and statistical methods.

Strengths:

The approach is inventive and novel, providing information about thalamocortical interactions that are scant in the current literature.

Weaknesses:

(1) There are not sufficient details present to allow for the direct interrogation of the methods used in the study.

(2) The figures do not contain sufficiently granular details, making it challenging to determine whether the observed effects were robust to individual differences.

Joint Public Review:

This manuscript puts forward the provocative idea that a posttranslational feedback loop regulates daily and ultradian rhythms in neuronal excitability. The authors used in vivo long-term tip recordings of the long trichoid sensilla of male hawkmoths to analyze spontaneous spiking activity indicative of the ORNs' endogenous membrane potential oscillations. This firing pattern was disrupted by pharmacological blockade of the Orco receptor. They then use these recordings together with computational modeling to predict that Orco receptor neuron (ORN) activity is required for circadian, not ultradian, firing patterns. Orco did not show a circadian expression pattern in a qPCR experiment, and its conductance was proposed to be regulated by cyclic nucleotide levels. This evidence led the authors to conclude that a post-translational feedback loop (PTFL) clockwork, associated with the ORN plasma membrane, allows for temporal control of pheromone detection via the generation of multi-scale endogenous membrane potential oscillations. The findings will interest researchers in neurophysiology, circadian rhythms, and sensory biology. However, the manuscript has limited experimental evidence to support its central hypothesis and is undermined by several questionable assumptions that underlie their data analysis and model builds, as well as insufficient biological data, including critical controls to validate and/or fully justify the model the authors are proposing.

Strengths:

The study is notable for its combination of long-term in vivo tip recordings with computational modeling, which is technically challenging and adds weight to the authors' claims. The link between Orco, cyclic nucleotides, and circadian regulation is potentially important for sensory neuroscience, and the modeling framework itself - a stochastic Hodgkin-Huxley formulation that explicitly incorporates channel noise - is a solid and forward-looking contribution. Together, these elements make the study conceptually bold and of clear interest to circadian and olfactory biologists.

Major weaknesses:

At the same time, several limitations temper the conclusions. The pharmacological evidence relies on a single antagonist and concentration, without key controls. The circadian analysis is based on relatively small numbers of neurons, with rhythms detected only in subsets, and the alignment procedure used in constant darkness raises concerns of bias. The molecular evidence is sparse, with only three qPCR timepoints, and the model, while creative, rests on assumptions that are not yet fully supported by in vivo data.

Detailed comments are provided below:

(1) The role for Orco proposed in the authors' model largely stems from the effects seen following the administration of (a single dose) of the Orco antagonist, OLC15. However, this hypothesis is undercut by the lack of adequate pharmacological controls, including a basic multipoint OLC15 dose-response series in addition to the administration of blockers for the other channels that are embedded in their model, but which were ruled out as being involved in the modulation of biological rhythms. In addition, these studies would (ideally) also benefit from the inclusion of the same concentration (series) of an inactive OLC15 analog to better control for off-target effects.

(2) The expression pattern of Orco was assessed using qPCR at only three timepoints. Rhythmic transcripts can easily be missed with such sparse sampling (Hughes et al., 2017). A minimum of six evenly spaced timepoints across a 24-hour cycle would be required to confidently rule out circadian transcriptional regulation. In addition, the use of the timeless mRNA control from another study is not acceptable. Furthermore, qPCR analysis measures transcript abundance, not transcription, as the authors repeatedly state. Transcriptional studies would require nuclear run-off or, more recently, can be done with snRNAseq analysis. Taken together, these concerns undermine the authors' desire to rule out TTFL-based control that directly led them to implicate a PTTF-based model.

(3) The modelling presented is based on Orco as a ZT-dependent conductance tied to the cAMP oscillations that were reported by this group in the cockroach and from the presence and functionality in Manduca of homomeric Orco complexes that are devoid of tuning ORs. While these complexes have been generated in cell culture and other heterologous expression systems, as well as presumably exist in vivo in the Drosophila empty neuron and other tuning OR mutants, there is no evidence that these complexes exist in wild-type Manduca ORNs. While this doesn't necessarily undermine every aspect of their models, the authors should note the presence of Orco/OR complexes rather than Orco homomeric complexes.

(4) Some aspects of the authors' models, most notably the decision to phase align/optimize their DD and OLC15 recordings, are likely to bias their interpretations.

(5) The tip recordings from long trichoid sensilla are critical aspects of this study. These recordings were carried out on upper sensillar tips located on the distal-most second annulus. Since there are approximately 80 annuli on the Manduca antennae, it is unclear whether the recordings are representative of the antennal response.

(6) The authors do not provide any data in support of their cAMP/cGMP-based Orco gating, and the PTTF model proposed is somewhat disappointing. The model seems to be influenced by their long-held proposal that insect olfactory signaling has a critical metabotropic component involving cyclic nucleotides, PKC, etc, a view that may be influenced by the use of Orco homomeric complexes generated in HEK cells. Nevertheless, structural studies on Orco do not support a cyclic nucleotide binding site, although PKC-based phosphorylation has been implicated in the fine-tuning/adaptation of olfactory signaling.

(7) Because only 5/11 LD and 7/10 DD animals showed daily rhythms, with averages lacking clear daily modulation, the methods are not sufficiently reliable enough to reveal novel underlying mechanisms of circadian rhythm generation. The reported results are therefore not yet reliable or quantifiable. To quantify their results, the authors should apply tests for circadian rhythmicity using methods such as RAIN, JTK CYCLE, MetaCycle, or Echo. The use of FFT and Wavelet is applauded, but these methods do not have tests of significance for rhythms and can be biased when analyzing data in which there could only be 1-3 circadian cycles. Because the conclusions appear to be based on 11-12 neurons that were recorded for 2-4 days, the reader is concerned that the methods are not yet perfected to provide strong evidence for circadian regulation of spontaneous firing of ORNs. The average data (e.g., Figure 3Bii and 3Cii) highlight the apparent lack of daily rhythms. In summary, the results would be more compelling if more than 50% of the recordings had significant circadian amplitudes and with similar periods and phases.

(8) The statement that circadian patterns of ORN firing are lost with the Orco antagonist (OLC15) is not strongly supported. The manuscript should be revised to quantify how Orco changed circadian amplitude in the 12 recorded neurons. Measures of circadian amplitude can avoid confusing/vague statements like Line 394 "low and high frequency bands appeared to merge during the activity phase around ZT 0 in the animals that showed clear circadian rhythms (N = 5 of 11 in LD)". The conclusion that Orco blocks circadian firing appears to be contradicted by Figure 6, which indicates that ~6 of these neurons had circadian periods detected by wavelet. The manuscript would be strengthened with details about the specificity and reproducibility of the Orco antagonist. The authors quantify the gradual decrease in firing with the slope of a linear fit to estimate how the "effectiveness [of OLC15] increased over time." They conclude that the drug "obliterated circadian rhythms and attenuated the spontaneous activity in several, but not all experiments (N = 8 of 12)." The report would be greatly strengthened with corroborating data from additional Orco antagonists and additional doses of OLC15 (the authors use only 50 uM OLC15).

(9) The manuscript includes several statements that are more speculation than conclusion. For example, there is no evidence for tuning or plasticity in this report. Statements like the following should be removed or addressed with experiments that show changes in odor response specificity or sensitivity: "ORN signalosomes are highly plastic endogenous PTFL clocks comprising receptors for circadian and ultradian Zeitgebers that allow to tune into internal physiological and external environmental rhythms as basis for active sensing." (Discussion Line 622). The paper concludes that (line 380) "mean frequency of spontaneous spiking and the frequency of bursting expressed daily modulation, and are both most likely controlled via a circadian clock that targets the leak channel Orco." This is too bold given the available results.

(10) Because Orco conductance is modulated by cyclic nucleotides, it remains highly plausible that circadian regulation occurs upstream at the level of signaling pathways (e.g., calcium, calcium-binding proteins, GPCRs, cyclases, phosphodiesterases). The possibility that circadian oscillations of cyclic nucleotides are generated by the canonical TTFL mechanism has not been excluded. In fact, extensive work in Drosophila has demonstrated that the TTFL-based molecular clock proteins are required for circadian rhythms in olfaction.

(11) A defining feature of circadian oscillators is the feedback mechanism that generates a time delay (e.g., PERIOD/TIMELESS repressing their own transcription). While the authors describe how cyclic nucleotides can regulate Orco conductance, they do not provide a convincing explanation of how Orco activity could, in turn, feed back into the proposed PTFL to sustain oscillations. For these reasons, the authors should consider:

(a) Providing a broader discussion of non-TTFL models of circadian rhythms (e.g., redox cycles, post-translational modifications).

(b) Reassessing Orco expression using a higher-resolution temporal sampling ({greater than or equal to}6 timepoints per 24 h).

(c) Clarifying or revising the PTFL model to explicitly address how feedback would be achieved. Alternatively, the data may be more consistent with Orco conductance rhythms being regulated by post-translational mechanisms downstream of the canonical TTFL oscillator, as suggested by the Drosophila olfactory system literature.

Minor weaknesses:

(1) The authors should compare the firing patterns of ORN neurons to the bursts, clusters, and packets of retinal efferent spikes reported in Liu JS and Passaglia CL (2011; JBR). By comparing measures in moths to measures in Limulus, the authors might be able to address the question: Is the daily firing pattern of ORN neurons likely a conserved feature of circadian control of sensory sensitivity?

(2) The methods need further details. For example, it is unclear if or how single neuron activity was discriminated and whether the results were compromised by the relatively large environmental fluctuations in temperature (21-27oC), humidity (35-60%), or other cues known to modulate spontaneous firing.

Reviewer #1 (Public review):

Summary:

This work asks the question of how different organelles and structures in the apicomplexan parasite Toxoplasma gondii are recycled and/or segregated to the daughter cells during cell replication. In particular, they consider an unusual cell structure called the residual body that links replicating cells during the intracellular infection stage of this parasite. The residual body has historically been considered a 'dumping ground' for unnecessary relics of the mother cell during division, but this notion is increasingly being revised. Indeed, cell replication in Toxoplasma is often misinterpreted as cell division (cytokinesis), but in fact, the cell replicates its organelles and structures to multiple 10s of copies in seemingly distinctly formed daughter cells, but cytokinesis is delayed for many such cycles and typically only occurs simultaneously with parasite egress from its host cell. The residual body is, in fact, the connection between these pre-cytokinetic replicated daughters, and effectively, this is still a single cell at this stage. The authors have previously shown that an actin network extends through the residual body between these daughter cells, and ER and mitochondria common to all cells are also linked through this structure. This study examining the fates of organelles during cell replication is timely for continuing our understanding of how this fascinating component of the cell participates in these processes. The authors use Halo-tags as their principal tool to track discrete populations of proteins, labelling their organelle locations, and this provides beautiful insight into these processes.

Strengths:

Using dyes conjugated to Halo tags, this work elegantly tracks the fates of proteins synthesised by an original 'mother' cell over several replication cycles of pre-cytokinetic 'daughters'. Using this tool, they show that some organelles are made intact just once and that some of these can be subsequently sorted to the daughters (micronemes and rhoptries) while others are dismantled (IMC) and the daughters must make their own. A third set of organelles (largely synthesis, sorting, and metabolic compartments) is divided and inherited, and new daughter-synthesised proteins are added to the preexisting maternal proteins in these structures. A role for actin and myosin is clearly demonstrated for micronemes and rhoptries, and this correlates with their relatively late inheritance into the developing daughters. Overall, this work gives clarity to the behaviours of several cell structures during replication and paves the way to a better understanding of the mechanisms that drive the differences between structures and the universality of these processes in other apicomplexan parasites.

Weaknesses:

In addressing the question of residual body participation in sorting of organelles, it would be useful to clearly define this structure and when and where it is delineated from the posterior of a mother cell during the formation of daughter structures. This might seem like a moot point, but it would give clarity to notions of recycling and 'reservoirs'. Mother cells retain their active invasion apparatus until very late in daughter formation, and the need for micronemes and rhoptries to be released from this service late in the process might explain why they are only then trafficked to the cell posterior and then into the daughters. So, is this a distinct 'residual body' body function/reservoir or just a spatial constraint of this sequence of daughter formation? In subsequent cell replications (4, 8, 16... stages), is there a separation between the residual body that links them all and the posterior of each new 'mother cell', and if so, when is this distinction lost? This is important because without a definition, we might be confusing different processes. Are rhoptries/micronemes that originate in one 'mother' able to be sorted to the 'daughters' from a distinct mother in this syncytium? If so, this would make it a sorting centre, but otherwise we could be just capturing the activities at the posterior of any given cell during replication. The authors' further thoughts on this would be very interesting.

The Group 2 structures are described as those that are divided between daughters and receive newly synthesised proteins that add to the maternal protein of these compartments. While this is a logical conclusion for several that are mentioned, where the maternal protein signal is seen to be depleted with replication (including for the apicoplast, ER, glideosome, and Golgi). Data for the addition of new proteins to these existing structures is actually only presented in direct support of this for the Golgi.

Reviewer #1 (Public review):

Summary:

In this study, the authors employed comprehensive proteomics and transcriptomics analysis to investigate the systemic and organ-specific adaptations to IF in males. They found that shared biological signaling processes were identified across tissues, suggesting unifying mechanisms linking metabolic changes to cellular communication, which revealed both conserved and tissue-specific responses by which IF may optimize energy utilization, enhance metabolic flexibility, and promote cellular resilience.

Strengths:

This study detected multiple organs, including the liver, brain, and muscle, and revealed both conserved and tissue-specific responses to IF.

Weaknesses:

(1) Why did the authors choose the liver, brain, and muscle, but not other organs such as the heart and kidney? The latter are proven to be the largest consumers of ketones, which is also changed in the IF treatment of this study.

(2) The proteomics and transcriptomics analyses were only performed at 4 months. However, a strong correlation between IF and the molecular adaptations should be time point-dependent.

(3) The context lacks a "discussion" section, which would detail the significance and weaknesses of the study.

(4) There is no confirmation for the proteomic and transcriptomic profiling. For example, the important changes in proteomics could be further identified by a Western blot.

Reviewer #1 (Public review):

Summary:

This manuscript describes the results of phylogenetic and epidemiological modeling of the PopART community cohorts in Zambia. The current manuscript draft is methodologically strong, but needs revision to strengthen the take-home messages. As written, there are many possible take-away conclusions. For example, the agreement between IBM and phylogenetic analysis is noteworthy and provides a methodological focus. The revealed age patterns of transmission could be a focus. The effects of the PopART intervention and the consequences of a 1-year disruption could be a focus. It is important, though, that any main messages summarized by the authors are substantiated by the evidence provided and do not extrapolate beyond the data that have been generated. I recommend that the authors think deeply about what the most important, well-supported messages are and reframe the discussion and abstract accordingly.

Strengths/weaknesses by section:

(1) ABSTRACT

The Abstract summarizes qualitative findings nicely, but the authors should incorporate quantitative results for all of the qualitative findings statements.

The ending claim is not substantiated by the modeling scenarios that have been run: "targeted interventions for demographic groups such as under-35 men may be the key to finally ending HIV." It is straightforward to run this specific scenario in the model to determine whether or not this is true.

The authors should add confidence intervals to the quantitative metrics, such as the 93.8% and 62.1% incidence reduction.

(2) RESULTS

The authors should check the Results section for any qualitative claims not substantiated by the analyses performed, and ensure the corresponding analyses are presented to support the claims.

The Results and Methods describe the model's implementation of the PopART intervention differently. The Methods describes it as including VMMC, TB, and STI services, while the Results only mentions intensified HIV testing and linkage.

A limitation of the model is that HIV disease progression is based on the ATHENA cohort in the Netherlands, which is a different HIV subtype (B) than the one in the research setting (C). The model should be configured using subtype C progression data, which have been published, or at least a sensitivity analysis should be conducted with respect to disease progression assumptions.

In Table 2, the authors should consider adding a p-value to establish whether or not IBM and phylogenetics estimates are different.

(3) DISCUSSION

The literature review and comparison of study results to previously published phylogenetic studies is very nice. The authors could strengthen this by providing quantitative estimates with CIs for a more scientific comparison of the study results vs. prior studies, perhaps as a table or figure.

The authors state that due to "the narrow geographical catchment area... The results should not be automatically extrapolated to apply to other SSA settings." The authors should exercise this caution when comparing the results to studies in South Africa and elsewhere.

There are many other limitations to the analysis, including some mentioned above, that are not acknowledged. The authors should think carefully about what the most important limitations are and acknowledge them honestly at the end of the Discussion section.

Reviewer #1 (Public review):

Summary:

Heat production mechanisms are flexible, depending on a wide variety of genetic, dietary and environmental factors. The physiology associated with each mechanism is important to understand, since loss of flexibility associates with metabolic decline and disease.

The phenomenon of compensatory heat production has been described in some detail in publications and reviews, notably by modifying BAT-dependent thermogenesis (for example by deleting UCP1 or impairing lipolysis, cited in this paper).

These authors chose to eliminate exercise as an alternative means for maintaining body temperature. To do this, they cast either one or both mouse hindlimbs.

This paper is set up as an evaluation of a loss of function of muscle on the functionality of BAT. However, the authors show that cast immobilization (CI) does not work as a (passive) loss of function, instead this procedure produces a dramatic gain of function.

It does not test the hypothesis as stated, instead it adds an extraneous variable, which is that the animal is put under enormous stress, inducing b-adrenergic effectors, increased oxygen consumption, and IL6 expression in a variety of tissues, together with commensurate cachectic effects on muscle and fat. The BAT is stressed by this procedure, becoming super-induced but relatively poor functioning. This is an inaccurate experimental construct, and the paper is therefore full of wrong conclusions.

Within hours and days of CI, there is massive muscle loss (leading to high circulating BCAAs), and loss of lipid reserves in adipose and liver. The lipid cycle that maintains BAT thermogenesis is depleted and the mouse is unable to maintain body temperature.

I cannot agree with these statements in the Discussion -

"We have here shown that cast immobilization suppressed skeletal muscle thermogenesis, resulting in failure to maintain core body temperature in a cold environment."

• This result could also be attributed to high stress and decreased calorie reserves. Note also: CI suppresses 50% locomoter activity, but the actual work done by the mouse carrying bilateral casts is not taken into account (how heavy are they?). Presumably other muscles in the mouse body are compensating to allow the mouse to drag itself to the food source, to maintain food consumption, which remarkably, is unchanged. Is the demand for heat even the same when the mouse is wrapped in gypsum?

I cannot be convinced that this approach (CI) can be interpreted at all in terms of organ communication during thermogenic challenge. This paper describes instead the resilience and adaptation of mouse physiology in the face of dragging around hind limb casts.

From Rebuttal:

"On the other hand, the experiment shown in Fig.1C involved acute cold exposure of mice 2 h after cast immobilization. This result suggests that, even before the depletion of energy stores by immobilization of skeletal muscle, cast immobilization may cause cold intolerance in mice."

Since the mice are in acute recovery from the anesthetic, there can be no conclusions drawn about thermogenesis. Isoflurane is a great way to depress body temperature (http://www.ncbi.nlm.nih.gov/pubmed/12552204), and the recovery time is not known.

"In addition, as the reviewer suggests, cast immobilization may result in BAT thermogenesis and cachectic effects on muscle and fat. However, circulating corticosterone concentrations and hypothalamic CRH gene expression are not significantly altered after cast immobilization (Figure 2_figure supplement 2D-F)."

The absence of positive results from your stress assays does not exclude stress as the primary source of the results. These mice are not proceeding as normal with their lives - they are learning whole new behaviors in order to stay fed and watered.

Reviewer #1 (Public review):

Summary:

The extent to which P. falciparum liver stage parasites export proteins into the host cell is unclear. Most blood-stage exported proteins tested in liver stages were not exported. An exception is LISP2, which is exported in P. berghei but not P. falciparum liver stages. While the machinery for export is present in liver stages, efforts to demonstrate export have so far been mostly unsuccessful. Parasite proteins exported during the liver stage could be presented by MHC and thereby become the target of immune control, an incentive to study liver stage export and identify proteins exported during this stage. However, particularly for P. falciparum, it is very difficult to study liver stages.

This work studies LSA3 in P. falciparum blood and liver stages. The authors show that this protein is exported into the host cell in blood stages, but in liver stages, no or only very little export was detected. A disruption of LSA3 reduced liver stage load in a humanized mouse model, indicating this protein contributes to efficient development of the parasites in the liver.

The paper also studies the localization of LSA3 in blood stages and uses a known inhibitor to show that it is processed by plasmepsin 5, a protease important for protein trafficking. The work also shows that LSA3 is not needed for passage through the mosquito.

Strengths:

The main strength of this work is the use of the humanized mouse model to study liver stages of P. falciparum, which is technically challenging and requires specialized facilities. The biochemical analysis of LSA3 localization and processing by plasmepsin 5 is thorough and mostly overcame adverse issues such as a cross-reactive antibody and the negative influence of the GFP-tag on LSA3 trafficking. The mosquito stage analysis is also notable, as these kinds of studies are difficult with P. falciparum. However, there was no evidence for a function of LSA3 in mosquito stages.

Weaknesses:

The cross-reactivity of the antibody, together with the co-infection strategy, prevents reliable assessment of LSA3 localization in liver stages. Despite this, it seems LSA3 is not exported in liver stages, and the paper does not bring us closer to the original goal of finding an exported liver stage protein.

While the localization analysis in blood stages is well done and thorough, the advance is somewhat limited. LSA3 may be in structures like J dots, but this hypothesis was not tested. Although parasites with a disrupted LSA3 were generated, the function of this protein was not explored. Given that a previous publication found some inhibitory effect of LSA3 antibodies on blood stage growth, a comparison of the growth of the LSA3 disruption clones with the parent would have been very welcome and easy to do. At this point, LSA3 is one more of many proteins exported in blood stages for which the function remains unclear.

It might be possible to refine some of the conclusions. The impact on liver stage development is interesting, but which phase of the liver stage is affected, and the phenotype remains largely unknown. The co-infection (WT together with LSA3 mutant) has the advantage of a direct comparison of the mutant with the control in the same liver, but complicates phenotypic analysis if the LSA3 antibody is also cross-reactive in liver stages. This issue adds a question mark to the shown localization and precludes phenotypic comparisons. The authors write that they do not know if the cross-reactive protein is expressed at that stage. But this should be immediately evident from the mixed WT/mutant infection. If all cells are positive for LSA3, there is a cross-reaction. If about half of the cells are negative, there isn't. In the latter case, the localization shown in the paper is indeed LSA3, and morphological differences between WT and LSA3 disruption could be assessed without additional experiments.

Significance:

The conclusion from the paper that "our study presents just the second PEXEL protein so far identified as important for normal P. falciparum liver-stage development and confirms the hypothesized potential of exported proteins as malaria vaccine candidates" is partially misleading. Neither LISP2 nor LSA3 seems to be exported in P. falciparum liver stages, and we can't confirm the potential of vaccines with proteins exported in this stage. LSA3 is still important and may still be the target of the immune response, but based on this work, probably not due to export in liver stages.

Reviewer #1 (Public review):

Summary:

The authors present an investigation of associative learning in Drosophila in which a previous exposure to an aversive stimulus leads to an increase in approach behaviors to a novel odor relative to a previously paired odor or no odor (air). Moreover, this relative increase is larger compared to that of a control group - i.e., presented with a (different) odor only. Evidence for the opposite effect with an appetitive stimulus, delivered indirectly by optogenetically activating sugar sensory neurons, which leads to a reduction in approach behavior to a novel odor, was also presented. The olfactory memory circuits underpinning these responses, which the authors refer to as 'priming', are revealed and include a feedback loop mediated by dopaminergic neurons to the mushroom body.

Strengths:

(1) The study includes a solid demonstration of the effect of the valence of a previous stimulus on sensory preferences, with an increase or decrease in preference to novel over no odor following an aversive or appetitive stimulus, respectively.

(2) The demonstration of bidirectional effects on odor preferences following aversive or rewarding stimuli is compelling.

(3) The evidence for distinct neural circuits underpinning the odor preferences in each context appears to be robust.

Weaknesses:

(1) The conclusions regarding the links between neural and behavioral mechanisms are mostly well supported by the data. However, what is less convincing is the authors' argument that their study offers evidence of 'priming'. An important hallmark of priming, at least as is commonly understood by cognitive scientists, is that it is stimulus specific: i.e., a repeated stimulus facilitates response times (repetition priming), or a repeated but previously ignored stimulus increases response times (negative priming). That is, it is an effect on a subsequent repeated stimulus, not ANY subsequent stimulus. Because (prime or target) stimuli are not repeated in the current experiments, the conditions necessary for demonstrating priming effects are not present. Instead, a different phenomenon seems to be demonstrated here, and one that might be more akin to approach/avoidance behavior to a novel or salient stimulus following an appetitive/aversive stimulus, respectively.

(2) On a similar note, the authors' claim that 'priming' per se has not been well studied in non-human animals is not quite correct and would need to be revised. Priming effects have been demonstrated in several animal types, although perhaps not always described as such. For example, the neural underpinnings of priming effects on behavior have been very well characterized in human and non-human primates, in studies more commonly described as investigations of 'response suppression'.

(3) The outcome measure - i.e., difference scores between the two odors or odor and non-odor (i.e., the number of flies choosing to approach the novel odor versus the number approaching the non-odor (air)) - appears to be reasonable to account for a natural preference for odors in the mock-trained group. However, it does not provide sufficient clarification of the results. The findings would be more convincing if these relative scores were unpacked - that is, instead of analyzing difference scores, the results of the interaction between group and odor preference (e.g., novel or air) (or even within the pre- and post-training conditions with the same animals) would provide greater clarity. This more detailed account may also better support the argument that the results are not due to conditioning of the US with pure air.

Reviewer #1 (Public review):

Liang et al. have conducted a small-scale pilot study focusing on the feasibility and tolerability of Low-dose chemotherapy combined with delayed immunotherapy in the neoadjuvant treatment of non-small cell lung cancer. The design of delayed immunotherapy after chemotherapy is relatively novel, while the reduced chemotherapy, although somewhat lacking in innovation, still serves as an early clue for exploring future feasible strategies. Also, the dynamic ctDNA and TCR profiles could give some important hints of intrinsic tumor reaction.

However, as the author mentioned in the limitation part, due to the small sample size and lack of a control group, we cannot fully understand the advantages and disadvantages of this approach compared to standard treatment. Compared to standard immunotherapy, the treatment group in this study has three differences: (1) reduced chemotherapy, (2) the use of cisplatin instead of the commonly used carboplatin in neoadjuvant therapy trials, and (3) delayed immunotherapy. Generally, in the exploration of updated treatment strategies, the design should follow the principle of "controlling variables." If there are too many differences at once, it becomes difficult to determine which variable is responsible for the effects, leading to confusion in the interpretation of the results. Moreover, the therapeutic strategy may lack practical clinical operability due to the long treatment duration.

Furthermore, in the exploration of biomarkers, the authors emphasized the procedure of whole RNA sequencing in tumor tissues in the method section, and this was also noted in the flowchart in Figure 1. However, I didn't find any mention of RNA-related analyses in the Results section, which raises some concerns about the quality of this paper for me. If the authors have inadvertently omitted some results, they should supplement the RNA-related analyses so that I can re-evaluate the paper.

To sum up, this article exhibited a certain degree of innovation to some extent, However, due to its intrinsic design defects and data omissions, the quality of the research warranted further improvement.

Reviewer #1 (Public review):

Summary:

Cai et al have investigated the role of msiCAT-tailed mitochondrial proteins that frequently exist in glioblastoma stem cells. Overexpression of msiCAT-tailed mitochondrial ATP synthase F1 subunit alpha (ATP5) protein increases the mitochondrial membrane potential and blocks mitochondrial permeability transition pore formation/opening. These changes in mitochondrial properties provide resistance to staurosporine (STS)-induced apoptosis in GBM cells. Therefore, msiCAT-tailing can promote cell survival and migration, while genetic and pharmacological inhibition of msiCAT-tailing can prevent the overgrowth of GBM cells.

Strengths:

The CATailing concept has not been explored in cancer settings. Therefore, the present provides new insights for widening the therapeutic avenue.

Weaknesses:

Although the paper does have strengths in principle, the weaknesses of the paper are that these strengths are not directly demonstrated.

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

Reviewer #1 (Public review):

Summary:

The authors assess the impact of E-cigarette smoke exposure on mouse lungs using single cell RNA sequencing. Air was used as control and several flavors (fruit, menthol, tobacco) were tested. Differentially expressed genes (DEGs) were identified for each group and compared against the air control. Changes in gene expression in either myeloid or lymphoid cells were identified for each flavor and the results varied by sex. The scRNAseq dataset will be of interest to the lung immunity and e-cig research communities and some of the observed effects could be important. Unfortunately, the revision did not address the reviewers' main concerns about low replicate numbers and lack of validations. The study remains preliminary and no solid conclusions could be drawn about the effects of E-cig exposure as a whole or any flavor-specific phenotypes.

Strengths:

The study is the first to use scRNAseq to systematically analyze the impact of e-cigarettes on the lung. The dataset will be of broad interest.

Weaknesses:

scRNAseq studies may have low replicate numbers due to the high cost of studies but at least 2 or 3 biological replicates for each experimental group is required to ensure rigor of the interpretation. This study had only N=1 per sex per group and some sex-dependent effects were observed. This could have been remedied by validating key observations from the study using traditional methods such as flow cytometry and qPCR, but the limited number of validation experiments did not support the conclusions of the scRNAseq analysis. An important control group (PG:VG) had extremely low cell numbers and was basically not useful. Statistical analysis is lacking in almost all figures. Overall, this is a preliminary study with some potentially interesting observations but no solid conclusions can be made from the data presented.

(1) The only new validation experiment is the immunofluorescent staining of neutrophils in Figure 4. The images are very low resolution and low quality and it is not clear which cells are neutrophils. S100A8 (calprotectin) is highly abundant in neutrophils but not strictly neutrophil-specific. It's hard to distinguish positive cells from autofluorescence in both Ly6g and S100a8 channels. No statistical analysis in the quantification.

(2) It is unclear what the meaning of Fig. 3A and B is, since these numbers only reflect the number of cells captured in the scRNAseq experiment and are not biologically meaningful. Flow cytometry quantification is presented as cell counts, but the percentage of cells from the CD45+ gate should be shown. No statistical analysis is shown, and flow cytometry results do not support the conclusions of scRNAseq data.

Reviewer #1 (Public review):

Summary:

The authors revealed the cellular heterogeneity of companion cells (CCs) and demonstrated that the florigen gene FT is highly expressed in a specific subpopulation of these CCs in Arabidopsis. Through a thorough characterization of this subpopulation, they further identified NITRATE-INDUCIBLE GARP-TYPE TRANSCRIPTIONAL REPRESSOR 1 (NIGT1)-like transcription factors as potential new regulators of FT. Overall, these findings are intriguing and valuable, contributing significantly to our understanding of florigen and the photoperiodic flowering pathway. However, there is still room for improvement in the quality of the data and the depth of the analysis. I have several comments that may be beneficial for the authors.

Strengths:

The usage of snRNA-seq to characterize the FT-expressing companion cells (CCs) is very interesting and important. Two findings are novel: 1) Expression of FT in CCs is not uniform. Only a subcluster of CCs exhibits high expression level of FT. 2) Based on consensus binding motifs enriched in this subcluster, they further identify NITRATE-INDUCIBLE GARP-TYPE TRANSCRIPTIONAL REPRESSOR 1 (NIGT1)-like transcription factors as potential new regulators of FT.

Weaknesses:

(1) Title: "A florigen-expressing subpopulation of companion cells". It is a bit misleading. The conclusion here is that only a subset of companion cells exhibit high expression of FT, but this does not imply that other companion cells do not express it at all.

(2) Data quality: Authors opted for fluorescence-activated nuclei sorting (FANS) instead of traditional cell sorting method. What is the rationale behind this decision? Readers may wonder, especially given that RNA abundance in single nuclei is generally lower than that in single cells. This concern also applies to snRNA-seq data. Specifically, the number of genes captured was quite low, with a median of only 149 genes per nucleus. Additionally, the total number of nuclei analyzed was limited (1,173 for the pFT:NTF and 3,650 for the pSUC2:NTF). These factors suggest that the quality of the snRNA-seq data presented in this study is quite low. In this context, it becomes challenging for the reviewer to accurately assess whether this will impact the subsequent conclusions of the paper. Would it be possible to repeat this experiment and get more nuclei?

(3) Another disappointment is that the authors did not utilize reporter genes to identify the specific locations of the FT-high expressing cells (cluster 7 cells) within the CC population in vivo. Are there any discernible patterns that can be observed?

(4) The final disappointment is that the authors only compared FT expression between the nigtQ mutants and the wild type. Does this imply that the mutant does not have a flowering time defect particularly under high nitrogen conditions?

Comments on revisions:

I think the authors took my comments seriously and addressed most of my concerns. Overall, I find this to be a very interesting paper.

Joint Public Review:

Summary:

Klug et al. use monosynaptic rabies tracing of inputs to D1- vs D2-SPNs in the striatum to study how separate populations of cortical neurons project to D1- and D2-SPNs. They use rabies to express ChR2, then patch D1-or D2-SPNs to measure synaptic input. They report that cortical neurons labeled as D1-SPN-projecting preferentially project to D1-SPNs over D2-SPNs. In contrast, cortical neurons labeled as D2-SPN-projecting project equally to D1- and D2-SPNs. They go on to conduct pathway-specific behavioral stimulation experiments. They compare direct optogenetic stimulation of D1- or D2-SPNs to stimulation of MCC inputs to DMS and M1 inputs to DLS. In three different behavioral assays (open field, intra-cranial self-stimulation, and a fixed ratio 8 task), they show that stimulating MCC or M1 cortical inputs to D1-SPNs is similar to D1-SPN stimulation, but that stimulating MCC or M1 cortical inputs to D2-SPNs does not recapitulate the effects of D2-SPN stimulation (presumably because both D1- and D2-SPNs are being activated by these cortical inputs).

Strengths:

Showing these same effects in three distinct behaviors is strong. Overall, the functional verification of the consequences of the anatomy is very nice to see. It is a good choice to patch only from mCherry-negative non-starter cells in the striatum. This study adds to our understanding of the logic of corticostriatal connections, suggesting a previously unappreciated structure.

Editors' note:

The concerns raised by Reviewers #1, and #2, have been addressed during the first round of revision. The specific concern raised by Reviewer #3 is about the Rabis virus-based circuit tracing itself. This version of the work has been assessed by the editors without going back to the reviewers.

Reviewer #2 (Public review):

Summary:

In this work, the authors present a new Python software package, Avian Vocalization Network (AVN) aimed at facilitating the analysis of birdsong, especially the song of the zebra finch, the most common songbird model in neuroscience. The package handles some of the most common (and some more advanced) song analyses, including segmentation, syllable classification, featurization of song, calculation of tutor-pupil similarity, and age prediction, with a view toward making the entire process friendlier to experimentalists with limited coding experience working in the field.

For many years, Sound Analysis Pro has served as a standard in the songbird field, the first package to extensively automate songbird analysis and facilitate the computation of acoustic features that have helped define the field. More recently, the increasing popularity of Python as a language, along with the emergence of new machine learning methods, has resulted in a number of new software tools, including the vocalpy ecosystem for audio processing, TweetyNet (for segmentation), t-SNE and UMAP (for visualization), and autoencoder-based approaches for embedding.

As with any software package, this one necessarily makes a number of design choices, which may or may not fit the needs of all users. Those who prefer a more automated pipeline with fewer knobs to turn may appreciate AVN in cases where the existing recipes fit their needs, while those who require more customization and flexibility may require a more bespoke (and thus code-intensive) approach.

Strengths:

The AVN package overlaps several of these earlier efforts, albeit with a focus on more traditional featurization that many experimentalists may find more interpretable than deep learning-based approaches. Among the strengths of the paper are its clarity in explaining the several analyses it facilitates, along with high-quality experiments across multiple public datasets collected from different research groups. As a software package, it is open source, installable via the pip Python package manager, and features high-quality documentation, as well as tutorials. For experimentalists who wish to replicate any of the analyses from the paper, the package is likely to be a useful time saver.

Weaknesses:

I think the potential limitations of the work are predominantly on the software end, with one or two quibbles about the methods.

First, the software: It's important to note that the package is trying to do many things, of which it is likely to do several well and a few comprehensively. Rather than a package that presents a number of new analyses or a new analysis framework, it is more a codification of recipes, some of which are reimplementations of existing work (SAP features), some of which are essentially wrappers around other work (interfacing with WhisperSeg segmentations), and some of which are new (similarity scoring). All of this has value, but in my estimation, it has less value as part of a standalone package and potentially much more as part of an ecosystem like vocalpy that is undergoing continuous development and has long-term support. While the code is well-documented, including web-based documentation for both the core package and the GUI, the latter is available only on Windows, which might limit the scope of adoption.

That is to say, whether AVN is adopted by the field in the medium term will have much more to do with the quality of its maintenance and responsiveness to users than any particular feature, but I believe that many of the analysis recipes that the authors have carefully worked out may find their way into other code and workflows.

In the revised version of the paper, the authors have expanded their case for the design choices made in AVN and remain committed to maintaining the tool. Given the low cost for users in trying new methods and the work the authors have put into further reducing this overhead via documentation, those curious about the package are likely best served by simply downloading it and giving it a try on their own data.

Second, two notes about new analysis approaches:

(1) The authors propose a new means of measuring tutor-pupil similarity based on first learning a latent space of syllables via a self-supervised learning (SSL) scheme and then using the earth mover's distance (EMD) to calculate transport costs between the distributions of tutors' and pupils' syllables. While, to my knowledge, this exact method has not previously been proposed in birdsong, I suspect it is unlikely to differ substantially from the approach of autoencoding followed by MMD used in the Goffinet et al. paper. That is, SSL, like the autoencoder, is a latent space learning approach, and EMD, like MMD, is an integral probability metric that measures discrepancies between two distributions. (Indeed, the two are very closely related: https://stats.stackexchange.com/questions/400180/earth-movers-distance-and-maximum-mean-discrepency.) Without further experiments, it is hard to tell whether these two approaches differ meaningfully. Likewise, while the authors have trained on a large corpus of syllables to define their latent space in a way that generalizes to new birds, it is unclear why such an approach would not work with other latent space learning methods.

Update: The authors now provide an extensive comparison with the Goffinet et al. paper and also consider differences between MMD and EMD. This comparison both adds value to the original paper and provides useful benchmarking for others looking to develop latent space comparison methods.

(2) The authors propose a new method for maturity scoring by training a model (a generalized additive model) to predict the age of the bird based on a selected subset of acoustic features. This is distinct from the "predicted age" approach of Brudner, Pearson, and Mooney, which predicts based on a latent representation rather than specific features, and the GAM nicely segregates the contribution of each. As such, this approach may be preferred by many users who appreciate its interpretability.

In summary, my view is that this is a nice paper detailing a well-executed piece of software whose future impact will be determined by the degree of support and maintenance it receives from others over the near and medium term.

Reviewer #1 (Public review):

Summary:

This paper investigates the control signals that drive event model updating during continuous experience. The authors apply predictions from previously published computational models to fMRI data acquired while participants watched naturalistic video stimuli. They first examine the time course of BOLD pattern changes around human-annotated event boundaries, revealing pattern changes preceding the boundary in anterior temporal and then parietal regions, followed by pattern stabilization across many regions. The authors then analyze time courses around boundaries generated by a model that updates event models based on prediction error and another that uses prediction uncertainty. These analyses reveal overlapping but partially distinct dynamics for each boundary type, suggesting that both signals may contribute to event segmentation processes in the brain.

Strengths:

(1) The question addressed by this paper is of high interest to researchers working on event cognition, perception, and memory. There has been considerable debate about what kinds of signals drive event boundaries, and this paper directly engages with that debate by comparing prediction error and prediction uncertainty as candidate control signals.

(2) The authors use computational models that explain significant variance in human boundary judgments, and they report the variance explained clearly in the paper.

(3) The authors' method of using computational models to generate predictions about when event model updating should occur is a valuable mechanistic alternative to methods like HMM or GSBS, which are data-driven.

(4) The paper utilizes an analysis framework that characterizes how multivariate BOLD pattern dissimilarity evolves before and after boundaries. This approach offers an advance over previous work focused on just the boundary or post-boundary points.

Weaknesses:

(1) While the paper raises the possibility that both prediction error and uncertainty could serve as control signals, it does not offer a strong theoretical rationale for why the brain would benefit from multiple (empirically correlated) signals. What distinct advantages do these signals provide? This may be discussed in the authors' prior modeling work, but is left too implicit in this paper.

(2) Boundaries derived from prediction error and uncertainty are correlated for the naturalistic stimuli. This raises some concerns about how well their distinct contributions to brain activity can be separated. The authors should consider whether they can leverage timepoints where the models make different predictions to make a stronger case for brain regions that are responsive to one vs the other.

(3) The authors refer to a baseline measure of pattern dissimilarity, which their dissimilarity measure of interest is relative to, but it's not clear how this baseline is computed. Since the interpretation of increases or decreases in dissimilarity depends on this reference point, more clarity is needed.

(4) The authors report an average event length of ~20 seconds, and they also look at +20 and -20 seconds around each event boundary. Thus, it's unclear how often pre- and post-boundary timepoints are part of adjacent events. This complicates the interpretations of the reported time courses.

(5) The authors describe a sequence of neural pattern shifts during each type of boundary, but offer little setup of what pattern shifts we might expect or why. They also offer little discussion of what cognitive processes these shifts might reflect. The paper would benefit from a more thorough setup for the neural results and a discussion that comments on how the results inform our understanding of what these brain regions contribute to event models.

Reviewer #1 (Public review):

Summary:

Weiss et. al. seek to delineate the mechanisms by which antigen-specific CD8+ T cells outcompete bystanders in the epidermis when active TGF-b is limiting, resulting in selective retention of these cells and more complete differentiation into the TRM phenotype.

Strengths:

They begin by demonstrating that at tissue sites where cognate antigen was expressed, CD8+ T cells adopt a more mature TRM transcriptome than cells at tissue sites where cognate antigen was never expressed. By integrating their scRNA-Seq data on TRM with the much more comprehensive ImmGenT atlas, the authors provide a very useful resource for future studies in the field. Furthermore, they conclusively show that these "local antigen-experienced" TRM have increased proliferative capacity and that TCR avidity during TRM formation positively correlates with their future fitness. Finally, using an elegant experimental strategy, they establish that TCR signaling in CD8+ T cells in the epidermis induces TGFBRIII expression, which likely contributes to endowing them with a competitive advantage over antigen-inexperienced TRM.

Weaknesses:

The main weakness in this paper lies in the authors' reliance on a single experimental model to derive conclusions on the role of local-antigen during the acute phase of the response by comparing T cells in model antigen-vaccinia virus (VV-OVA) exposed skin to T cells in contralateral skin exposed to DNFB 5 days after the VV-OVA exposure. In this setting, antigen-independent factors may contribute to the difference in CD8+ T cell number and phenotype at the two sites. For example, it was recently shown that very early memory precursors (formed 2 days after exposure) are more efficient at seeding the epithelial TRM compartment than those recruited to skin at later times (Silva et al, Sci Immunol, 2023). DNFB-treated skin may therefore recruit precursors with reduced TRM potential. In addition, TRM-skewed circulating memory precursors have been identified (Kok et al, JEM, 2020), and perhaps VV-OVA exposed skin more readily recruits this subset compared to DNFB-exposed skin. Therefore, when the DNFB challenge is performed 5 days after vaccinia virus, the DNFB site may already be at a disadvantage in the recruitment of CD8+ T cells that can efficiently form TRM. In addition, CD8+ T cell-extrinsic mechanisms may be at play, such as differences in myeloid cell recruitment and differentiation or local cytokine and chemokine levels in VV-infected and DNFB-treated skin that could account for differences seen in TRM phenotype and function between these two sites. Although the authors do show that providing exogenous peptide antigen at the DNFB-site rescues their phenotype in relation to the VV-OVA site, the potential antigen-independent factors distinguishing these two sites remain unaddressed. In addition, there is a possibility that peptide treatment of DNFB-treated skin initiates a second phase of priming of new circulatory effectors in the local-draining lymph nodes that are then recruited to form TRM at the DFNB-site, and that the effect does not solely rely on TRM precursors at the DNFB-treated skin site at the time of peptide treatment. These concerns are somewhat alleviated by the fact that in a prior publication (PMID: 33212014), the group has already established a role for local antigen encounter in skin in a setting where they compared contralateral ears infected with VV-OVA and VV expressing an irrelevant antigen.

Secondly, although the authors conclusively demonstrate that TGFBRIII is induced by TCR signals and required for conferring increased fitness to local-antigen experienced CD8+ TRM compared to local antigen-inexperienced cells, this is done in only one experiment, albeit repeated 3 times. The data suggest that antigen encounter during TRM formation induces sustained TGFBRIII expression that persists during the antigen-independent memory phase. It remains however, unclear why only antigen encounter in skin, but not already in the draining lymph nodes, induces sustained TGFBRIII expression. Further characterizing the dynamics of TGFBRIII expression on CD8+ T cells during priming in draining lymph nodes and over the course of TRM formation and persistence may shed more light on this question. Probing the role of this mechanism at other sites of TRM formation would also further strengthen their conclusions and enhance the significance of this finding.

A minor caveat of the study pertains to the use of FTY720 to block T cell egress from lymphoid tissues and thereby prevent a contribution of circulating memory OT-I T cells to the local recall response in skin. Since the half-life of FTY720 is less than a day in mice, its effects wear off rapidly. In their experiments, the authors discontinued treatment at the time of re-challenge, which may have allowed circulating T cells to contribute to the local recall response in skin, limiting the interpretability of the results somewhat. This concern is alleviated by the use of a second method (anti-Thy1.1-depleting antibodies) to eliminate circulating memory cells. For the benefit of readers intending to use this experimental strategy, it should however, be noted that FTY720 needs to be dosed continually (e.g. 3x/week at an appropriate dose) in order to sustain its effect.

Reviewer #1 (Public review):

Summary:

This study investigates how collective navigation improvements arise in homing pigeons. Building on the Sasaki & Biro (2017) experiment on homing pigeons, the authors use simulations to test seven candidate social learning strategies of varying cognitive complexity, ranging from simple route averaging to potentially cognitively demanding selective propagation of superior routes. They show that only the simplest strategy-equal route averaging-quantitatively matches the experimental data in both route efficiency and social weighting. More complex strategies, while potentially more effective, fail to align with the observed data. The authors also introduce the concept of "effective group size," showing that the chaining design leads to a strong dilution of earlier individuals' contributions. Overall, they conclude that cognitive simplicity rather than cumulative cultural evolution explains collective route improvements in pigeons.

Strengths:

The manuscript addresses an important question and provides a compelling argument that a simpler hypothesis is necessary and sufficient to explain findings of a recent influential study on pigeon route improvements, via a rigorous systematic comparison of seven alternative hypotheses. The authors should be commended for their willingness to critically re-examine established interpretations. The introduction and discussion are broad and link pigeon navigation to general debates on social learning, wisdom of crowds, and CCE.

Weaknesses:

The lack of availability of codes and data for this manuscript, especially given that it critically examines and proposes alternative hypotheses for an important published work.

Reviewer #1 (Public review):

Summary:

Zinn and colleagues investigated the role of proteases 2A and 3C of enterovirus D68 (EVD68), an emerging pathogen associated with outbreaks of acute flaccid myelitis (AFM), a polio-like disease, on the nucleocytoplasmic trafficking in different systems, including human neurons derived from pluripotent cells. They found that 2A specifically cleaved Nup98 and POM121. Using reporter proteins and RNA synthesis and trafficking assays in cells expressing viral proteases, they showed that 2A induces broad loss of the nuclear pore barrier function, but, surprisingly, the RNA export appears to be minimally affected. Since nucleocytoplasmic trafficking defects are known to be associated with neuropatologies, they propose a hypothesis that 2A-dependent cleavage of nucleoporins in motoneurons underlies the development of EVD68-induced AFM. They further show that a 2A-specific inhibitor increases the survival of human neurons differentiated from stem cells upon EVD68 infection.

Strengths:

Use of multiple methods to investigate the effect of 2A and 3C expression on nucleoporin cleavage and nucleocytoplasmic trafficking.

Weaknesses:

Overall, the paper follows multiple others that extensively investigated the cleavage of nucleoporins by enterovirus 2As, so the results are of limited novelty. The hypothesis that infection of motoneurons is the cause of EVD68-induced neurological complications so far is supported by only one autopsy report. Other data suggest that infection of other cell types, such as astrocytes, and/or inflammatory cell infiltration in the CNS, are likely to be responsible for the symptoms. In any case, the claim that EVD68 is specifically neurotoxic because of the 2A-dependent cleavage of nucleoporins in neurons is unfounded, as the virus will be just as "toxic" for other infected cell types.

The paper also requires a more convincing presentation of the data.

Reviewer #1 (Public review):

Summary:

This work demonstrates that MORC2 undergoes phase separation (PS) in cells to form nuclear condensates, and the authors demonstrate convincingly the interactions responsible for this phase separation. Specifically, the authors make good use of crystallography and NMR to identify multiple protein:protein interactions and use EMSA to confirm protein:DNA interactions. These interactions work together to promote in vitro and in cell phase separation and boost ATPase activity by the catalytic domain of MORC2.

However, the authors have very weak evidence supporting their potentially valuable claim that MORC2 PS is important for the appropriate gene regulatory role of MORC2 in cells. Exploring causal links between PS and function is an important need in the phase separation field, particularly as regards the role of condensates in gene regulation, and is a non-trivial matter. Any study with convincing data on this matter will be very important. For this reason, it is crucial to properly explore the alternative possibility that soluble complexes, existing in the same conditions as phase-separated condensates, are the functional species. It is also critical to keep in mind that, while a specific protein domain may be essential for PS, this does not mean its only important function pertains to PS.

In this study, the authors do not sufficiently explore the role that soluble MORC2 complexes may play alongside MORC2 condensates. Neither do they include enough data to solidly show that domain deletion leads to phenotypes via a loss of phase separation per se, rather than the loss of phase separation being a microscopically visible result, not cause, of an underlying shift in protein function. For these reasons, the authors' conclusions regarding the functional role of MORC2 condensates are based on incomplete data. This also dampens the utility of this work as a whole, since the very nice work detailing the mechanism of MORC2 PS is not paired with strong data showing the importance of this observation.

Strengths:

Static light scattering and crystallography are nicely used to demonstrate the dimerization of MORC2FL and to discover the structure of the CC3 domain dimer, presumably responsible for the dimerization of MORC2FL (Figure 1).

Extensive use of deletion mutants in multiple cell lines is used to identify regions of MORC2 that are important for forming condensates in the nucleus: the IBD, IDR, and CC3 domains are found to be essential for condensate formation, while the CW domain plays an unknown role in condensate morphology (Figure 3). The authors use NMR to further identify that the IBD domain seems to interact with the first third of the centrally located IDR, termed IDRa, but not with the latter two-thirds of the IDR domain (Figure 4). This leads them to propose that phase separation is the product of IDB:IDRa interaction, CC3 dimerization, and an unknown but important role for the CW domain.

Based on the observation that removal of the NLS resulted in diffuse cytoplasmic localization, they hypothesized that DNA may play an important role in MORC2 PS. EMSA was used to demonstrate interaction between DNA and several MORC2 domains: CC1, CC2, IDR, and TCD-CC3-IBD. Further in vitro microscopy with purified MORC2 showed that DNA addition significantly reduces MORC2 saturation concentration (Figure 5).

These assays convincingly demonstrate that MORC2 phase separates in cells, and identify the protein domains and interactions responsible for this phenomenon, with the notable caveat that the role of the CW domain here is left unexplored.

Weaknesses:

Although the authors demonstrated phase separation of MORC2FL, their evidence that this plays a functional role in the cell is incomplete.

Firstly, looking at differentially upregulated genes under MORC2FL overexpression, the authors acknowledge that only 10% are shared with differentially regulated genes identified in other MORC2FL overexpression studies (Figure 6c,d). No explanation is given for why this overlap is so low, making it difficult to trust conclusions from this data set.

Secondly, of the 21 genes shared in this study and in earlier studies, the authors note that the differential regulation is less pronounced when a phase-separation-deficient MORC2 mutant is overexpressed, rather than MORC2FL (Figure 6e). This is taken as evidence that phase separation is important for the proper function of MORC2. However, no consideration is made for the alternative possibility that the mutant, lacking the CC3 dimerization domain, may result in non-functional complexes involving MORC2, eliminating the need for a PS-centric conclusion. To take the overexpression data as solid evidence for a functional role of MORC2 PS, the authors would need to test the alternative, soluble complex hypothesis. Furthermore, there seems to be low replicate consistency for the MORC2 mutant condition (Figure S6a), with replicate 3 being markedly upregulated when compared to replicates 1 and 2.

Thirdly, the authors close by examining the in-cell PS capabilities and ATPase activity of several disease-associated mutants of MORC2 ( Figure 7). However, the relevance of these mutants to the past 6 figures is unclear. None of these mutations is in regions identified as important for PS. Two of the mutations result in a higher percentage of the cell population being condensate-positive, but this is not seemingly connected to ATPase activity, as only one of these two mutants has increased ATPase activity. Figure 7 does not add any support to the main hypotheses in the paper, and nowhere in the paper do the authors investigate the protein regions where the mutations in Figure 7 are found.

Reviewer #1 (Public review):

Summary:

The authors focused on medaka retinal organoids to investigate the mechanism underlying the eye cup morphogenesis. The authors succeeded to induce lens formation in fish retinal organoids using 3D suspension culture with minimal growth factor-containing media containing the Hepes. At day 1, Rx3:H2B-GFP＋ cells appear in the surface region of organoids. At day 1.5, Prox1+cells appear in the interface area between the organoid surface and the core of central cell mass, which develops a spherical-shaped lens later. So, Prox1+ cells covers the surface of the internal lens cell core. At day 2, foxe3:GFP+ cells appear in the Prox1+ area, where early lens fiber marker, LFC, starts to be expressed. In addition, foxe3:GFP+ cells show EdU+ incorporation, indicating that foxe3:GFP+ cells have lens epithelial cell-characters. At day 4, cry:EGFP+ cells differentiate inside the spherical lens core, whose surface area consists of LFC+ and Prox1+ cells. Furthermore, at day 4, the lens core moves towards the surface of retinal organoids to form an eyecup like structure, although this morphogenesis "inside out" mechanism is different from in vivo cellular "outside -in" mechanism of eye cup formation. From these data, the authors conclude that optic cup formation, especially the positioning of the lens, is established in retinal organoids though the different mechanism of in vivo morphogenesis.

Overall, manuscript presentation is nice. However, there are still obscure points to understand background mechanism. My comments are shown below.

Major comments:

(1) At the initial stage of retinal organoid morphogenesis, a spherical lens is centrally positioned inside the retinal organoids, by covering a central lens core by the outer cell sheet of retinal precursor cells. I wonder if the formation of this structure may be understood by differential cell adhesive activity or mechanical tension between lens core cells and retinal cell sheet, just like the previous study done by Heisenberg lab on the spatial patterning of endoderm, mesoderm and ectoderm (Nat. Cell Biol. 10, 429 - 436 (2008)). Lens core cells may be integrated inside retinal cell mass by cell sorting through the direct interaction between retinal cells and lens cells, or between lens cells and the culture media. After day 1, it is also possible to understand that lens core moves towards the surface of retinal organoids, if adhesive/tensile force states of lens core cells may be change by secretion of extracellular matrix. I wonder if the authors measure physical property, adhesive activity and solidness, of retinal precursor cells and lens core cells. If retinal organoids at day 1 are dissociated and cultured again, do they show the same patterning of internal lens core covering by the outer retinal cell sheet?

(2) Optic cup is evaginated from the lateral wall of neuroepithelium of the diencephalon. In zebrafish, cell movement occurs from the pigment epithelium to the neural retina during eye morphogenesis in an FGF-dependent manner. How the medaka optic cup morphogenesis is coordinated? I also wonder if the authors conduct the tracking of cell migration during optic cup morphogenesis to reveal how cell migration and cell division are regulated in lens of the Medaka retinal organoids. It is also interesting to examine how retinal cell movement is coordinated during Medaka retinal organoids.

(3) The authors showed that blockade of FGF signaling affects lens fiber differentiation in day 1-2, whereas lens formation seems to be intact in the presence of FGF receptor inhibitor in day 0-1. I suggest the authors to examine which tissue is a target of FGF signaling in retinal organoids, using markers such as pea3, which is a downstream target of ERK branch of FGF signaling. Since FGF signaling promotes cell proliferation, is the lens core size normal in SU5402-treated organoids from day 0 to day 1?

(4) Fig. 3f and 3g indicate that there is some cell population located between foxe3:GFP+ cells and rx2:H2B-RFP+ cells. What kind of cell-type is occupied in the interface area between foxe3:GFP+ cells and rx2:H2B-RFP+ cells?

(5) Fig. 5e indicates the depth of Rx3 expression at day 1. Is the depth the thickness of Rx3 expressing cell sheet, which covers the central lens core in the organoids? If so, I wonder if total cell number of Rx3 expressing cell sheet may be different in each seeded-cell number, because thickness is the same across each seeded-cell number, but the surface area size may be different depending on underneath the lens core size. Please clarify this point.

(6) Noggin application inhibits lens formation at day 0-1. BMP signaling regulates formation of lens placode and olfactory placode at the early stage of development. It is interesting to examine whether Noggin-treated organoid expands olfactory placode area. Please check forebrain territory markers.

Significance:

Strength: This study is unique. The authors examined eye cup morphogenesis using fish retinal organoids. Eye cup normally consists of the lens, the neural retina, pigment epithelium and optic stalk. However, retinal organoids seem to be simple and consists of two cell types, lens and retina. Interestingly, a similar optic cup-like structure is achieved in both cases; however, underlying mechanism is different. It is interesting to investigate how eye morphogenesis is regulated in retinal organoids, under the unconstrained embryo-free environment.

Limitation: Description is OK, but analysis is not much profound. It is necessary to apply a bit more molecular and cellular level analysis, such as tracking of cell movement and visualization of FGF signaling in organoid tissues.

Advancement: The current study is descriptive. Need some conceptual advance, which impact cell biology field or medical science.

Audience: The target audience of current study are still within ophthalmology and neuroscience community people, maybe translational/clinical rather than basic biology. To beyond specific fields, need to formulate a general principle for cell and developmental biology.

Reviewer #1 (Public review):

Summary:

In this manuscript by Lopez-Blanch and colleagues, 21 microexons are selected for a deep analysis of their impacts on behavior, development, and gene expression. The authors begin with a systematic analysis of microexon inclusion and conservation in zebrafish and use these data to select 21 microexons for further study. The behavioral, transcriptomic, and morphological data presented are largely convincing and discussion of the potential explanations for the subtle impacts of individual microexon deletions versus loss-of-function in srrm3 and/or srrm4 is quite comprehensive and thoughtful.

Strengths:

The study uses a wide variety of techniques to assess the impacts of microexon deletion, ranging from assays of protein function through regulation of behavior and development.

The authors provide comprehensive analyses of the molecular impact of their microexon deletions, including examining how host-gene and paralog expression is affected.

Reviewer #1 (Public review):

Summary:

This is an interesting and useful review highlighting the complex pathways through which pulmonary colonisation or infection with Mycobacterium tuberculosis (Mtb) may progress to develop symptomatic disease and transmit the pathogen. I found the section on immune correlates associated with individuals who have clearly been exposed to and reacted to Mtb but did not develop latent infections particularly valuable. However, several aspects would benefit from clarification.

Strengths:

The main strengths lie in the arguments presented for a multiplicity of immune pathways to TB disease.

Weaknesses:

The main weaknesses lie in clarity, particularly in the precise meanings of the three figures.

I accept that there is a 'goldilocks zone' that underpins the majority of TB cases we see and predominantly reflects different patterns of immune response, but the analogies used need to be more clearly thought through.

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.

Reviewer #1 (Public review):

Summary:

The aim of this paper is to develop a simple method to quantify fluctuations in the partitioning of cellular elements. In particular, they propose a flow-cytometry based method coupled with a simple mathematical theory as an alternative to conventional imaging-based approaches.

Strengths:

The approach they develop is simple to understand and its use with flow-cytometry measurements is clearly explained. Understanding how the fluctuations in the cytoplasm partition varies for different kinds of cells is particularly interesting.

Weaknesses:

The theory only considers fluctuations due to cellular division events. Fluctuations in cellular components are largely affected by various intrinsic and extrinsic sources of noise and only under particular conditions does partitioning noise become the dominant source of noise. In the revised version of the manuscript, they argue that in their setup, noise due to production and degradation processes are negligible but noise due to extrinsic sources such as those stemming from cell-cycle length variability may still be important. To investigate the robustness of their modelling approach to such noise, they simulated cells following a sizer-like division strategy, a scenario that maximizes the coupling between fluctuations in cell-division time and partitioning noise. They find that estimates remain within the pre-established experimental error margin.

Reviewer #1 (Public review):

Summary:

Planar cell polarity core proteins Frizzled (Fz)/Dishevelled (Dvl) and Van Gogh-like (Vangl)/Prickle (Pk) are localized on opposite sides of the cell and engage in reciprocal repression to modulate cellular polarity within the plane of static epithelium. In this interesting manuscript, the authors explore how the anterior core proteins (Vangl/Pk) inhibit the posterior core protein (Dvl). The authors propose that Pk assists Vangl2 in sequestering both Dvl2 and Ror2, while Ror2 is essential for Dvl to transition from Vangl to Fz in response to non-canonical Wnt signaling. Nevertheless, there are several major and minor points that affect the strength of the author's proposed model (and are listed below).

Strengths:

The strengths of the manuscript are found in the very interesting and new concept along with supportive data for a model of how non-canonical Wnt induces Dvl to transition from Vangl to Fz with an opposing role for PK and Vangl2 to suppress Dvl during convergent extension movements. Ror is key player required for the transition and antagonizes Vangl.

Weaknesses:

The weaknesses are in the clarity and resolution of the data that forms the basis of the model. In addition to general whole embryo morphology that is used as evidence for CE defects, two forms of data are presented, co-expression and IP, as well as a strong reliance on IF of exogenously expressed proteins. Thus, it is critical that both forms of evidence be very strong and clear, and this is where there are deficiencies; 1) For vast majority of experiments general morphology and LWR was used as evidence of effects on convergent extension movements rather than keller explants or actual cell movements in the embryo. 2) the microscopy would benefit from super resolution microscopy since in many cases the differences in protein localization are not very pronounced. 3) the IP and Western analysis data often shows very subtle differences, and some cases not apparent.

Major points.

(1) Assessment of CE movement

The authors conducted an analysis of the subcellular localization of PCP core proteins, including Vangl2, Pk, Fz, and Dvl, within animal cap explants (ectodermal explants). The authors primarily used the length-to-width ratio (LWR) to evaluate CE movement as a basis for their model. However, LWR can be influenced by multiple factors and is not sufficient to directly and clearly represent CE defects. While the author showed that Prickle knockdown suppresses animal cap elongation mediated by Activin treatment, they did not test their model using standard assays such as animal cap elongation or dorsal marginal zone (DMZ) Keller explants. Furthermore, although various imaging analyses were performed in Wnt11-overexpressing animal caps and DMZ explants, the Wnt11-overexpressing animal caps did not undergo CE movement. Given that this study focuses on the molecular mechanisms of Vangl2 and Ror2 regulation of Dvl2 during CE, the model should be validated in more appropriate tissues, such as DMZ explants.

(2) Overexpression conditions

Another concern is that most analyses were performed with overexpression conditions. PCP core proteins (Vangl2, Pk, Dvl, and Fz receptors) are known to display polarized subcellular localization in both the neural epithelium and DMZ explants (Ref: PCP and Septins govern the polarized organization of the actin cytoskeleton during convergent extension, Current Biology, 2024). However, in this study, overexpressed PCP core proteins failed to show polarized localization. Previous studies, such as those from the Wallingford lab, typically used 10-30 pg of RNA for PCP core proteins, whereas this study injected 100-500 pg, which is likely excessive and may have created artificial conditions that confound the imaging results.

(3) Subtle and insufficient effects

Several of the reported results show quite modest changes in imaging and immunoprecipitation analyses, which are not sufficient to strongly support the proposed molecular model. For example, most Dvl2 remained localized with Fz7 even under Vangl2 and Pk overexpression (Fig. 4). Similarly, Wnt11 overexpression only slightly reduced the association between Vangl2 and Dvl2 (Sup. Fig. 8), and the Ror2-related experiments also produced only subtle effects (Fig. 8, Sup. Fig. 15).

Reviewer #1 (Public review):

Summary:

This study investigates plasticity effects in brain function and structure from training in navigation and verbal memory.

The authors used a longitudinal design with a total of 75 participants across two sites. Participants were randomised to one of three conditions: verbal memory training, navigation training, or a video control condition. The results show behavioural effects in relevant tasks following the training interventions. The central claim of the paper is that network-based measures of task-based activation are affected by the training interventions, but structural brain metrics (T2w-derived volume and diffusion-weighted imaging microstructure) are not impacted by any of the training protocols tested.

Strengths:

(1) This is a well-designed study which uses two training conditions, an active control, and randomisation, as appropriate. It is also notable that the authors combined data acquisition across two sites to reach the needed sample size and accounted for it in their statistical analyses quite thoroughly. In addition, I commend the authors on using pre-registration of the analysis to enhance the reproducibility of their work.

(2) Some analyses in the paper are exhaustive and compelling in showcasing the presence of longitudinal behavioural effects, functional activation changes, and lack of hippocampal volume changes. The breadth of analysis on hippocampal volume (including hippocampal subfields) is convincing in supporting the claim regarding a lack of volumetric effect in the hippocampus.

Comments on revisions:

All my comments have been addressed. The evidence regarding lack of a volumetric effect at the whole-brain level now seems more robust. Many details are now clearer, particularly regarding the the volumetric analyses methods and the rationale and timeline of preregistration.

Minor comment:

I appreciate that there are limited possibilities with the available Diffusion-Weighted Imaging data. However, I would recommend the authors remove mentions of "white matter connectivity" in the Abstract and elsewhere, which are misleading if no tractography or voxel-wise analyses are performed.

Reviewer #1 (Public Review):

Summary:

Previous research from the Margoliash laboratory has demonstrated that the intrinsic electrophysiological properties of one class of projection neurons in the song nucleus HVC, HVCX neurons, are similar within birds and differ between birds in a manner that relates to the bird's song. The current study builds on this research by addressing how intrinsic properties may relate to the temporal structure of the bird's song and by developing a computational model for how this can influence sequence propagation of activity within HVC during singing.

First, the authors identify that the duration of the song motif is correlated with the duration of song syllables and particularly the length of harmonic stacks within the song. They next found positive correlations between some of the intrinsic properties, including firing frequency, sag ratio, and rebound excitation area with the duration of the birds' longest harmonic syllable and some other measure of motif duration. These results were extended by examining measures of firing frequency and sag ratio between two groups of birds that were experimentally raised to learn songs that only differed by the addition of a long terminal harmonic stack in one of the groups. Lastly, the authors present an HH-based model elucidating how the timing and magnitude of rebound excitation of HVCX neurons can function to support previously reported physiological network properties of these neurons during singing.

Strengths:

By trying to describe how intrinsic properties (IPs) may relate to the structure of learned behavior and providing a potentially plausible model (see below for more on this) for how differences in IPs can relate to sequence propagation in this neural network, this research is addressing an important and challenging issue. An understanding of how cell types develop IPs and how those IPs relate to the function and output of a network is a fundamental issue. Tackling this in the zebra finch HVC is an elegant approach because it provides a quantifiable and reliable behavior that is explicitly tied to the neurons that the authors are studying. Nonetheless, this is a difficult problem, and kudos to the authors for trying to unravel this.

Correlations between harmonic stack durations and song durations are well-supported and interesting. This provides a new insight that can and will likely be used by other research groups in correlating neuronal activity patterns to song behavior and motif duration. Additionally, correlations between IPs associated with rebound excitation are also well supported in this study.

The HH-model presented is important because it meaningfully relates how high or low rebound excitation can set the integration time window for HVCX neurons. Further, the synaptic connectivity of this model provides at least one plausible way in how this functions to permit the bursting activity of HVCX neurons during singing (and potentially during song playback experiments in sleeping birds). Thus, this model will be useful to the field for understanding how this network activity intersects with 'learned' IPs in an important class of neurons in this circuit.

Comments on revised version:

The authors have adequately addressed my previous concerns.

Reviewer #1 (Public review):

Summary:

This paper proposes a new model of perceptual habituation and tests it over two experiments with both infants and adults. The model combines a neural network for visual processing with a Bayesian rational model for attention (i.e., looking time) allocation. This Bayesian framework allows the authors to measure elegantly diverse factors that might drive attention, such as expected information gain, current information gain, and surprise. The model is then fitted to infant and adult participants' data over two experiments, which systematically vary the amount of habituation trials (Experiment 1) and the type of dishabituation stimulus (familiarity, pose, number, identity and animacy). Results show that a model based on (expected) information gain performs better than a model based on surprise. Additionally, while novelty preference is observed when exposure to familiar stimuli is elevated, no familiarity preference is observed when exposure to familiar stimuli is low or intermediate, which is in contrast with past work.

Strengths:

There are three key strengths of this work:

(1) It integrates a neural network model with a Bayesian rational learner, thus bridging the gap between two fields that have often been disconnected. This is rarely seen in the cognitive science field, but the advantages are very clear from this paper: It is possible to have computational models that not only process visual information, but also actively explore the environment based on overarching attentional processes.

(2) By varying parametrically the amount of stimulus exposure and by testing the effects of multiple novel stimulus types, this work allowed the authors to put classical theories of habituation to the test on much finer scales than previous research has done.

(3) The Bayesian model allows the authors to test what specific aspects are different in infants and adults, showing that infants display greater values for the noise parameter.

Weaknesses:

This model pertains visual habituation. What drives infants' (dis)engagement of attention more broadly, for example, when learning the probabilistic structures of the environment around them (e.g., language, action prediction) may follow different principles and dynamics.

Reviewer #1 (Public review):

Summary:

This is a manuscript describing outbreaks of Pseudomonas aeruginosa ST 621 in a facility in the US using genomic data. The authors identified and analysed 254 P. aeruginosa ST 621 isolates collected from a facility from 2011 to 2020. The authors described the relatedness of the isolates across different locations, specimen types (sources), and sampling years. Two concurrently emerged subclones were identified from the 254 isolates. The authors predicted that the most recent common ancestor for the isolates can be dated back to approximately 1999 after the opening of the main building of the facility in 1996. Then the authors grouped the 254 isolates into two categories: 1) patient-to-patient; or 2) environment-to-patient using SNP thresholds and known epidemiological links. Finally, the authors described the changes of resistance gene profiles, virulence genes, cell wall biogenesis and signaling pathway genes of the isolates over the sampling years.

Strengths:

The major strength of this study is the utilisation of genomic data to comprehensively describe the characteristics of a long-term Pseudomonas aeruginosa ST 621 outbreak in a facility. This fills the data gap of a clone that could be clinically important but easily missed from microbiology data alone.

Weaknesses:

As the authors highlighted in the Discussion section, a limitation of this study is that there is potential sampling bias due to partial sampling of clinical P. aeruginosa isolates. However, the work is still important to showcase the potential benefits of applying genomic sequencing techniques to support infection prevention controls in hospital settings. The limitation on potential sampling bias could inspire further work to explore an optimal clinical isolate sampling framework for genomic analyses to support outbreak investigation. The other limitation that the authors have highlighted in the Discussion session is the lack of epidemiology data to support the interpretation of the inferred patient-to-patient and environment-to-patient transmissions, which emphasised the importance of metadata to complement genomic data analysis in outbreak investigation for future studies.

Impact of the work:

First, the work adds to the growing evidence implicating sinks as long-term reservoirs for important MDR pathogens, with direct infection control implications. Moreover, the work could potentially motivate investments in generating and integrating genomic data into routine surveillance. The comprehensive descriptions of the Pseudomonas aeruginosa ST 621 clones outbreak is a great example to demonstrate how genomic data can provide additional information about long-term outbreaks that otherwise could not be detected using microbiology data alone. Moreover, identifying the changes in resistance genes and virulence genes over time would not be possible without genomic data. Finally, this work provided additional evidence for the existence of long-term persistence of Pseudomonas aeruginosa ST 621 clones, which likely occur in other similar settings.

Comments on revisions:

The paper would be further strengthened from an additional timeline indicating when routine surveillance was introduced and examples of actions or changes guided by the surveillance data that resulted in decrease in ST 621 transmission. This additional information would be useful to support the final statement in the Abstract suggesting "Since initial identification, extensive infection control efforts guided by routine, near real- time surveillance have proved successful at slowing transmission."

Reviewer #1 (Public review):

This report addresses a compelling topic. The authors demonstrate that tetanic stimulation of the auditory thalamus induces cortical long-term potentiation (LTP), which can be elicited by either electrical or optical stimulation of the thalamus or by noise bursts. They further show that thalamocortical LTP is abolished when thalamic CCK is knocked down or when cortical CCK receptors are blocked. Notably, in 18-month-old mice, thalamocortical LTP was largely absent but could be restored by cortical application of CCK. The authors conclude that CCK is a critical contributor to thalamocortical plasticity and may enhance this form of plasticity in aged subjects.

The findings presented in this report are valuable and advance our understanding of thalamocortical plasticity.

Reviewer #1 (Public review):

Summary:

This study utilized publicly available Hi-C data to ensemble a comprehensive set of breast cancer cell lines (luminal, Her2+, TNBC) with varying metastatic features to answer whether breast cancer cells would acquire organ-specific feature at the 3D genome level to metastasize to that specific organ. The authors focused on lung metastasis and included several controls as the comparison including normal mammary lines, normal lung epithelial lines, and lung cancer cell lines. Due to the lower resolution at 250KB binning size, the authors only addressed the compartments (A for active compartment and B for inactive compartment) not the other 3D organization of the genome. They started by performing clustering and PCA analysis for the compartment identity and discovered that this panel of cell lines could be well separated based on Her2 and epithelial-mesenchymal features according to the compartment identity. While correlating with the transcriptomic changes, the authors noticed the existence of concordance and divergence between the compartment changes and transcriptomic changes. The authors then switched gear to tackle the core question in metastatic organotropism to the lung. They discovered a set of "lung permissive compartment changes" and concluded that "lung metastatic breast cancer cell lines acquire lung-like genome architecture" and "organotropic 3D genome changes match target organ more than an unrelated organ". To prove the latter point, the authors enlisted additional non-breast cancer cell line (prostate cancer) in the setting of brain metastasis. This is a piece of pure dry computational work without wet bench experiments.

Strengths:

The authors embarked on an ambitious journey to seek for the answer regarding 3D genome changes predisposing metastatic organotropism. The authors succeeded in the assembly of a comprehensive panel of breast cancer cell lines and the aggregation of the 3D genome structure data to conduct a hypothesis driven computation analysis. The authors also achieved in including proper controls representing normal non-cancerous epithelium and the end organ of interest. The authors did well in the citation of relevant references in 3D genome organization and EMT.

Reviewer #1 (Public review):

Summary

The manuscript by K.H. Lee et al. presents Spyglass, a new open-source framework for building reproducible pipelines in systems neuroscience. The framework integrates the NWB (Neurodata Without Borders) data standard with the DataJoint relational database system to organize and manage analysis workflows. It enables the construction of complete pipelines, from raw data acquisition to final figures. The authors demonstrate their capabilities through examples, including spike sorting, LFP filtering, and sharp-wave ripple (SWR) detection. Additionally, the framework supports interactive visualizations via integration with Figurl, a platform for sharing neuroscience figures online.

Strengths:

Reproducibility in data analysis remains a significant challenge within the neuroscience community, posing a barrier to scientific progress. While many journals now require authors to share their data and code upon publication, this alone does not ensure that the code will execute properly or reproduce the original results. Recognizing this gap, the authors aim to address the community's need for a robust tool to build reproducible pipelines in systems neuroscience.

Weaknesses:

The issues identified here may serve as a foundation for future development efforts.

(1) User-friendliness:

The primary concern is usability. The manuscript does not clearly define the intended user base within a modern systems neuroscience lab. Improving user experience and lowering the barrier to entry would significantly enhance the framework's potential for broad adoption. The authors provide an online example notebook and a local setup notebook. However, the local setup process is overly complex, with many restrictive steps that could discourage new users. A more streamlined and clearly documented onboarding process is essential. Additionally, the lack of Windows support represents a practical limitation, particularly if the goal is widespread adoption across diverse research environments.

(2) Dependency management and long-term sustainability:

The framework depends on numerous external libraries and tools for data processing. This raises concerns about long-term maintainability, especially given the short lifespan of many academic software projects and the instability often associated with Python's backward compatibility. It would be helpful for the authors to clarify how flexible and modular the pipeline is, and whether it can remain functional if upstream dependencies become deprecated or change substantially.

(3) Extensibility for custom pipelines:

A further limitation is the insufficient documentation regarding the creation of custom pipelines. It is unclear how a user could adapt Spyglass to implement their own analysis workflows, especially if these differ from the provided examples (e.g., spike sorting, LFP analysis that are very specific to the hippocampal field). A clearer explanation or example of how to extend the framework for unrelated or novel analyses would greatly improve its utility and encourage community contributions.

(4) Flexibility vs. Standardization:

The authors may benefit from more explicitly defining the intended role of the framework: is Spyglass designed as a flexible, general-purpose tool for developing custom data analysis pipelines, or is its primary goal to provide a standardized framework for freezing and preserving pipelines post-publication to ensure reproducibility? While both goals are valuable, attempting to fully support both may introduce unnecessary complexity and result in a tool that is not well-suited for either purpose. The manuscript briefly touches on this tradeoff in the introduction, and the latter-pipeline preservation-may be the more natural fit for the package. If so, this intended use should be clearly communicated in the documentation to help users understand its scope and strengths.

Impact:

This work represents a significant milestone in advancing reproducible data analysis pipelines in neuroscience. Beyond reproducibility, the integration of cloud-based execution and shareable, interactive figures has the potential to transform how scientific collaboration and data dissemination are conducted. The authors are at the forefront of this shift, contributing valuable tools that push the field toward more transparent and accessible research practices.

Reviewer #1 (Public review):

Summary:

This paper aims to characterise the physiological and computational underpinnings of the accumulation of intermittent glimpses of sensory evidence.

Strengths:

(1) Elegant combination of electroencephalography and computational modelling.

(2) The authors describe results of two separate experiments, with very similar results, in effect providing an internal replication.

(3) Innovative task design, including different gap durations.

Weaknesses:

(1) The authors introduce the CPP as tracking an intermediary (motor-independent) evidence integration process, and the MBL as motor preparation that maintains a sustained representation of the decision variable. It would help if the authors could more directly and quantitatively assess whether their current data are in line with this. That is, do these signals exhibit key features of evidence accumulation (slope proportional to evidence strength, terminating at a common amplitude that reflects the bound)? Additionally, plotting these signals report locked (to the button press) would help here. What do the results mean for the narrative of this paper?

(2) The novelty of this work lies partly in the aim to characterize how the CPP and MBL interact (page 5, line 3-5). However, this analysis seems to be missing. E.g., at the single-trial level, do relatively strong CPP pulses predict faster/larger MBL? The simulations in Figure 5 are interesting, but more could be done with the measured physiology.

(3) The focus on CPP and MBL is hypothesis-driven but also narrow. Since we know only a little about the physiology during this "gaps" task, have the authors considered computing TFRs from different sensor groupings (perhaps in a supplementary figure?).

(4) The idea of a potential bound crossing during P1 is elegant, albeit a little simplistic. I wonder if the authors could more directly show a physiological signature of this. For example, by focusing on the MBL or occipital alpha split by the LL, LH, HL and HH conditions, and showing this pulse- as well as report-locked. Related, a primacy effect can also be achieved by modelling (i) self-excitation of the current one-dimensional accumulator, or (ii) two competing accumulators that produce winner-take-all dynamics. Is it possible to distinguish between these models, either with formal model comparison or with diagnostic physiological signatures?

(5) The way the authors specify the random effects of the structure of their mixed linear models should be specified in more detail. Now, they write: "Where possible, we included all main effects of interest as random effects to control for interindividual variability." This sounds as if they started with a model with a full random effect structure and dropped random components when the model would not converge. This might not be sufficiently principled, as random components could be dropped in many different orders and would affect the results. Do all main results hold when using classical random effects statistics on subject-wise regression coefficients?

Reviewer #1 (Public review):

Summary:

This study investigates whether prediction error extends beyond lower-order sensory-motor processes to include higher-order cognitive functions. Evidence is drawn from both task-based and resting-state fMRI, with the addition of resting-state EEG-fMRI to examine power spectral correlates. The results partially support the existence of dissociable connectivity patterns: stronger ventral-dorsal connectivity is associated with high prediction error, while posterior-anterior connectivity is linked to low prediction error. Furthermore, spontaneous switching between these connectivity patterns was observed at rest and correlated with subtle intersubject behavioral variability.

Strengths:

Studying prediction error from the lens of network connectivity provides new insights into predictive coding frameworks. The combination of various independent datasets to tackle the question adds strength, including two well-powered fMRI task datasets, resting-state fMRI interpreted in relation to behavioral measures, as well as EEG-fMRI.

Weaknesses:

Major:

(1) Lack of multiple comparisons correction for edge-wise contrast:

The analysis of connectivity differences across three levels of prediction error was conducted separately for approximately 22,000 edges (derived from 210 regions), yet no correction for multiple comparisons appears to have been applied. Then, modularity was applied to the top 5% of these edges. I do not believe that this approach is viable without correction. It does not help that a completely separate approach using SVMs was FDR-corrected for 210 regions.

(2) Lack of spatial information in EEG:

The EEG data were not source-localized, and no connectivity analysis was performed. Instead, power fluctuations were averaged across a predefined set of electrodes based on a single prior study (reference 27), as well as across a broader set of electrodes. While the study correlates these EEG power fluctuations with fMRI network connectivity over time, such temporal correlations do not establish that the EEG oscillations originate from the corresponding network regions. For instance, the observed fronto-central theta power increases could plausibly originate from the dorsal anterior cingulate cortex (dACC), as consistently reported in the literature, rather than from a distributed network. The spatially agnostic nature of the EEG-fMRI correlation approach used here does not support interpretations tied to specific dorsal-ventral or anterior-posterior networks. Nonetheless, such interpretations are made throughout the manuscript, which overextends the conclusions that can be drawn from the data.

Reviewer #1 (Public review):

Summary:

Overall, this is an interesting paper. The authors have found multiple experimental knobs to perturb a mechanical wave behavior driven by pilli feedback. The authors framed this as nonreciprocal interactions - while I can see how nonreciprocity could play a role - what about mechanical feedback? Phenomenological models are fine, but a lack of mechanistic understanding is a weakness. I think it will be more interesting to frame the model based on potential mechanochemical feedback to understand microscopic mechanisms. Regardless, more can be done to better constrain the model through finding knobs to explain experimental observations (in Figures 3, 4, 5, and 7).

Strengths:

The report of mechanical waves in bacterial collectives. The mechanism has potential application in a multicellular context, such as morphogenesis.

Weaknesses:

My most serious concern is about left-right symmetry breaking. I fail to see how the data in Figure 6 shows LR symmetry breaking. All they show is in-out directionality, which is a boundary condition. LR SM means breaking of mirror symmetry - the pattern cannot be superimposed on its mirror image using only rigid body transformations (translation and rotation) - as far as I am aware, this condition is not satisfied in this pattern-forming system.

Reviewer #1 (Public review):

Summary:

This paper describes a behavioral platform "BuzzWatch" and its application in long-term behavioral monitoring. The study tested the system with different mosquito species and Aedes aegypti colonies and monitored behavioral response to blood feeding, change in photoperiod, and host-cue application at different times of the day.

Strengths:

BuzzWatch is a novel, custom-built behavioral system that can be used to monitor time-of-day-specific and long-term mosquito behaviors. The authors provide detailed documentation of the construction of the assay and custom flight tracking algorithm on a dedicated website, making them accessible to other researchers in the field. The authors performed a wide range of experiments using the BuzzWatch system and discovered differences in midday activity level among Aedes aegypti colonies, and reversible change in the daily activity profile post-blood-feeding.

Weaknesses:

The authors report the population metric "fraction flying" as their main readout of the daily activity profile. It is worth explaining why conventional metrics like travel distance/activity level are not reported. Alternatively, these metrics could be shown, considering the development and implementation of a flight trajectory tracking pipeline in this paper.

The authors defined the sugar-feeding index using occupancy on the sugar feeder. However, the correlation between landing on the sugar feeder and active sugar feeding is not mentioned or tested in this paper. Is sugar feeding always observed when mosquitoes land on the sugar feeder? Do they leave the sugar feeding surface once sugar feeding is complete? One can imagine that texture preference and prolonged occupancy may lead to inaccurate reporting of sugar feeding. While occupancy on the sugar feeder is an informative behavioral readout, its link with sugar feeding activity (consumption) needs to be evaluated. Otherwise, the authors should discuss the caveats that this method presents explicitly to avoid overinterpretation of their results.

Throughout the manuscript, the authors mentioned existing mosquito activity monitoring systems and their drawbacks. However, many of these statements are misleading and sometimes incorrect. The authors claim that beam-break monitors are "limited to counting active versus inactive states". Though these systems provide indirect readouts that may underreport activity, the number of beam-breaks in a time interval is correlated with activity level, as is commonly used and reported in Drosophila and mosquitoes and a number of reports in mosquitoes an updated LAM system with larger behavioral arenas and multiple infrared beams. The authors also mentioned the newer, camera-based alternatives to beam-break monitors, but again referred to these systems as "only detecting activity when a moving insect blocks a light beam"; however, these systems actually use video tracking (e.g., Araujo et al. 2020).

The fold change in behavior presented in Figure 4D is rather confusing. Under the two different photoperiods, it is not clear how an hourly comparison is justified (i.e., comparing the light-on activity in the 20L4D condition with scotophase activity in the 12L12D condition). The same point applies to Figure 4H.

The behavioral changes after changing photoperiod (Figure 4) require a control group (12L12D throughout) to account for age-related effects. This is controlled for the experiment in Figure 3 but not for Figure 4.

Reviewer #1 (Public review):

Summary:

The temporal regulation of neuronal specification and its molecular mechanisms are important problems in developmental neurobiology. This study focuses on Kenyon cells (KCs), which form the mushroom body in Drosophila melanogaster, in order to address this issue. Building on previous findings, the authors examine the role of the transcription factor Eip93F in the development of late-born KCs. The authors revealed that Eip93F controls the activity of flies at night through the expression of the calcium channel Ca-α1T. Thus, the study clarifies the molecular machinery that controls temporal neuronal specification and animal behavior.

Strengths:

The convincing results are based on state-of-the-art molecular genetics, imaging, and behavioral analysis.

Weaknesses:

Temporal mechanisms of neuronal specification are found in many nervous systems. However, the relationship between the temporal mechanisms identified in this study and those in other systems remains unclear.

Reviewer #1 (Public review):

This study provides a thorough analysis of Nup107's role in Drosophila metamorphosis, demonstrating that its depletion leads to developmental arrest at the third larval instar stage due to disruptions in ecdysone biosynthesis and EcR signaling. Importantly, the authors establish a novel connection between Nup107 and Torso receptor expression, linking it to the hormonal cascade regulating pupariation.

The authors have addressed most of the concerns raised in my initial review, particularly those outlined in the public comments. However, I note that they have not directly responded to several specific points raised in the "Author Recommendations" section. That said, a key mechanistic question remains unresolved and deserves further experimental or at least conceptual clarification.

It has been previously shown that Nup107 regulates the nuclear translocation of dpERK (Kim et al., 2010). This observation may provide a mechanistic explanation for the developmental arrest observed upon Nup107 depletion in the prothoracic gland (PG). Given that PG growth and ecdysone biosynthesis are driven by several receptor tyrosine kinases, it is plausible that loss of Nup107 impairs dpERK nuclear translocation, thereby functionally shutting down RTK-dependent transcriptional responses, including those activating Halloween gene expression. This model is supported by the finding that activated Ras (rasV12) can rescue the arrest, likely by generating sufficiently high levels of dpERK such that some fraction enters the nucleus despite impaired translocation. This hypothesis may explain the discrepancy between the complete developmental arrest observed upon Nup107 depletion and the developmental delay seen in Torso mutants.

Similarly, the rescue by Torso, but not EGFR, may reflect differences in receptor activation thresholds. It has been proposed that Torso overexpression might leads to ligand-independent dimerization and constitutive activity, whereas EGFR overexpression may remain ligand-dependent and thus insufficient under compromised dpERK transport conditions. A critical experiment to validate this model would be to examine dpERK localization in PG cells upon Nup107 depletion. This would help establish whether defective nuclear import of dpERK underlies the observed developmental arrest. Even if technically challenging, the authors should at least discuss this hypothesis explicitly in the revised manuscript.

In addition, it has been shown that TGFβ/Activin signaling regulates Torso expression in the prothoracic gland (PG). Therefore, it is plausible that this pathway may also be affected by impaired nuclear translocation of downstream effectors due to Nup107 depletion. This raises the possibility that Nup107 plays a broad regulatory role, impacting multiple signaling cascades-such as RTK and TGFβ/Activin pathways-by controlling the nuclear import of their key effectors.

Reviewer #1 (Public review):

This well-conceived manuscript investigates the mechanisms that shape the chromatin landscape following fertilization, using the Drosophila embryo as a model system. Importantly, the authors revisit conflicting data using new approaches and analysis to show that the silent H3K27me3 mark deposited by PRC2 is established de novo in the embryo in coordination with the slowing of the nuclear division cycle and activation of zygotic transcription. Unexpectedly, they demonstrate that the transcription factor GAF is not required for the deposition of this mark, but that the well-studied pioneer factor Zelda, which is required for widespread gene expression, is required for H3K27me3 deposition at a subset of regions. The experiments are rigorously performed, and interpretations are clear. Strengths of this manuscript include the rigor of the experimental design, careful analysis, and well-supported conclusions. Some additional citations, analysis, and broadening of the Discussion section to include additional models and data would further strengthen this manuscript.

Reviewer #2 (Public review):

In their study, Avraham-Davidi et al. combined scRNA-seq and spatial mapping studies to profile two preclinical mouse models of colorectal cancer: Apcfl/fl VilincreERT2 (AV) and Apcfl/fl LSL-KrasG12D Trp53fl/fl Rosa26LSL-tdTomato/+ VillinCreERT2 (AKPV). In the first part of the manuscript, the authors describe the analysis of the normal colon and dysplastic lesions induced in these models following tamoxifen injection. They highlight broad variations in immune and stromal cell composition within dysplastic lesions, emphasizing the infiltration of monocytes and granulocytes, the accumulation of IL-17+gdT cells and the presence of a distinct group of endothelial cells. A major focus the study is the remodeling of the epithelial compartment, where most significant changes are observed. Using no-negative matrix factorization, the authors identify molecular programs of epithelial cell functions, emphasizing stemness, Wnt signaling, angiogenesis and inflammation as majors features associated with dysplastic cells. They conclude that findings from scRNA-seq analyses in mouse models are transposable to human CRC. In the second part of the manuscript, the authors aim to provide the spatial contexture for their scRNA-seq findings using Slide-seq and TACCO. They demonstrate that dysplastic lesions are disorganized and contain tumor-specific regions, which contextualize the spatial proximity between specific cell states and gene programs. Finally, they claim that these spatial organizations are conserved in human tumors and associate region-based gene signatures with patient outcome in public datasets. Overall, the data were collected and analyzed using solid and validated methodology to offer a useful resource to the community.

Main comments:

(1) Clarity. The manuscript would benefit from a substantial reorganization to improve clarity and accessibility for a broad readership. The text could be shortened and the number of figure panels reduced to emphasize the novel contributions of this work while minimizing extensive discussions on general and expected findings, such as tissue disorganization in dysplastic lesions. Additionally, figure panels are not consistently introduced in the correct order, and some are not discussed at all (e.g., Fig. S1D; Fig. 3C is introduced before Fig. 3A; several panels in Fig. 4 are not discussed). The annotation of scRNA-seq cell states is insufficiently explained, with no corresponding information about associated genes provided in the figures or tables. Multiple annotations are used to describe cell groups (e.g., TKN01 = γδ T and CD8 T, TKN05 = γδT_IL17+), but these are not jointly accessible in the figures, making the manuscript challenging to follow. It is also not clear what is the respective value of the two mouse models and timepoints of tissue collection in the analysis.

(2) Novelty. While the study is of interest, it does not present major findings that significantly advance the field or motivate new directions and hypotheses. Many conclusions related to tissue composition and patient outcomes, such as the epithelial programs of Wnt signaling, angiogenesis, and stem cells, are well-established and not particularly novel. Greater exploration of the scRNA-seq data beyond cell type composition could enhance the novelty of the findings. For instance, several tumor microenvironment clusters uniquely detected in dysplastic lesions (e.g., Mono2, Mono3, Gran01, Gran02) are identified, but no further investigation is conducted to understand their biological programs, such as applying nNMF as was done for epithelial cells. Additional efforts to explore precise tissue localization and cellular interactions within tissue niches would provide deeper insights and go beyond the limited analyses currently displayed in the manuscript.

(3) Validation. Several statements made by the authors are insufficiently supported by the data presented in the manuscript and should be nuanced in the absence of proper validation. For example: 1.) RNA velocity analyses: The conclusions drawn from these analyses are speculative and need further support. 2.) Annotations of epithelial clusters as dysplastic: These annotations could have been validated through morphological analyses and staining on FFPE slides. 3.) Conservation of mouse epithelial programs in human tumors: The data in Figure S5B does not convincingly demonstrate enrichment of stem cell program 16 in human samples. This should be more explicitly stated in the text, given the emphasis placed on this program by the authors. 4.) Figure S6E: Cluster Epi06 is significantly overrepresented in spatial data compared to scRNA-seq, yet the authors claim that cell type composition is largely recapitulated without further discussion, which reduces confidence in other conclusions drawn.<br /> Furthermore, stronger validation of key dysplastic regions (regions 6, 8, and 11) in mouse and human tissues using antibody-based imaging with markers identified in the analyses would have considerably strengthened the study. Such validation would better contextualize the distribution, composition, and relative abundance of these regions within human tumors, increasing the significance of the findings and aiding the generation of new pathophysiological hypotheses.

Comments on revisions:

The authors have improved the clarity of the manuscript and responded adequately to all my initial comments.<br /> I don't have any other comments. Congratulations to the authors on this work.

Reviewer #1 (Public review):

Summary:

Taber et al report the biochemical characterization of 7 mutations in PHD2 that induce erythrocytosis. Their goal is to provide a mechanism for how these mutations cause the disease. PHD2 hydroxylates HIF1a in the presence of oxygen at two distinct proline residues (P564 and P402) in the "oxygen degradation domain" (ODD). This leads to the ubiquitylation of HIF1a by the VHL E3 ligase and its subsequent degradation. Multiple mutations have been reported in the EGLN1 gene (coding for PHD2), which are associated with pseudohypoxic diseases that include erythrocytosis. Furthermore, 3 mutations in PHD2 also cause pheochromocytoma and paraganglioma (PPGL), a neuroendocrine tumour. These mutations likely cause elevated levels of HIF1a, but their mechanisms are unclear. Here, the authors analyze mutations from 152 case reports and map them on the crystal structure. They then focus on 7 mutations, which they clone in a plasmid and transfect into PHD2-KO to monitor HIF1a transcriptional activity via a luciferase assay. All mutants show impaired activation. Some mutants also impaired stability in pulse chase turnover assays (except A228S, P317R, and F366L). In vitro purified PHD2 mutants display a minor loss in thermal stability and some propensity to aggregate. Using MST technology, they show that P317R is strongly impaired in binding to HIF1a and HIF2a, whereas other mutants are only slightly affected. Using NMR, they show that the PHD2 P317R mutation greatly reduces hydroxylation of P402 (HIF1a NODD), as well as P562 (HIF1a CODD), but to a lesser extent. Finally, BLI shows that the P317R mutation reduces affinity for CODD by 3-fold, but not NODD.

Strengths:

(1) Simple, easy-to-follow manuscript. Generally well-written.

(2) Disease-relevant mutations are studied in PHD2 that provide insights into its mechanism of action.

(3) Good, well-researched background section.

Weaknesses:

(1) Poor use of existing structural data on the complexes of PHD2 with HIF1a peptides and various metals and substrates. A quick survey of the impact of these mutations (as well as analysis by Chowdhury et al, 2016) on the structure and interactions between PHD2 peptides of HIF1a shows that the P317R mutation interferes with peptide binding. By contrast, F366L will affect the hydrophobic core, and A228S is on the surface, and it's not obvious how it would interfere with the stability of the protein.

(2) To determine aggregation and monodispersity of the PHD2 mutants using size-exclusion chromatography (SEC), equal quantities of the protein must be loaded on the column. This is not what was done. As an aside, the colors used for the SEC are very similar and nearly indistinguishable.

(3) The interpretation of some mutants remains incomplete. For A228S, what is the explanation for its reduced activity? It is not substantially less stable than WT and does not seem to affect peptide hydroxylation.

(4) The interpretation of the NMR prolyl hydroxylation is tainted by the high concentrations used here. First of all, there is a likely a typo in the method section; the final concentration of ODD is likely 0.18 mM, and not 0.18 uM (PNAS paper by the same group in 2024 reports using a final concentration of 230 uM). Here, I will assume the concentration is 180 uM. Flashman et al (JBC 2008) showed that the affinity of the NODD site (P402; around 10 uM) for PHD2 is 10-fold weaker than CODD (P564, around 1 uM). This likely explains the much faster kinetics of hydroxylation towards the latter. Now, using the MST data, let's say the P317R mutation reduces the affinity by 40-fold; the affinity becomes 400 uM for NODD (above the protein concentration) and 40 uM for CODD (below the protein concentration). Thus, CODD would still be hydroxylated by the P317R mutant, but not NODD.

(5) The discrepancy between the MST and BLI results does not make sense, especially regarding the P317R mutant. Based on the crystal structures of PHD2 in complex with the ODD peptides, the P317R mutation should have a major impact on the affinity, which is what is reported by MST. This suggests that the MST is more likely to be valid than BLI, and the latter is subject to some kind of artefact. Furthermore, the BLI results are inconsistent with previous results showing that PHD2 has a 10-fold lower affinity for NODD compared to CODD.

(6) Overall, the study provides some insights into mutants inducing erythrocytosis, but the impact is limited. Most insights are provided on the P317R mutant, but this mutant had already been characterized by Chowdhury et al (2016). Some mutants affect the stability of the protein in cells, but then no mechanism is provided for A228S or F366L, which have stabilities similar to WT, yet have impaired HIF1a activation.

Comments on revision:

While the authors have addressed my concerns regarding the SEC experiments and the structural interpretation of most mutants, I remain unconvinced by their interpretation of the P317R mutant and affinity measurements. The BLI and MST data remain inconsistent for P317R binding to CODD, and the authors' response is essentially that the fluorescent labeling of P317R (but not other mutants) uniquely interferes with binding to the NODD/CODD peptides, which does not make a lot of sense. The fluorescent labeling target lysine residues; while there are lysine in PHD2 in proximity to the peptide binding site, labeling these sites would affect binding to all mutants, not only P317R (which does not introduce any new labeling site). Furthermore, the authors did not really address the discrepancy with the observations by Flashman et al (2008) that NODD binds more weakly than CODD, which is inconsistent with their BLI results. Another point that makes me doubt the validity of the BLI results is the poor fit of the sensorgrams and the slow dissociation kinetics, which is inconsistent with the relatively low affinity in the 2-6 uM range.

Reviewer #2 (Public review):

Summary:

The authors show that a combination of arginine methyltransferase inhibitors synergize with PARP inhibitors to kill ovarian and triple negative cancer cell lines in vitro and in vivo using preclinical mouse models.

Strengths and weaknesses

The experiments are well-performed, convincing and have the appropriate controls (using inhibitors and genetic deletions) and use statistics.

They identify the DNA damage protein ERCC1 to be reduced in expression with PRMT inhibitors. As ERCC1 is known to be synthetic lethal with PARPi, this provides a mechanism for the synergy. They use cell lines only for their study in 2D as well as xenograph models.

Comments on revisions:

The authors have addressed by final concerns.

Reviewer #1 (Public review):

Summary:

In this manuscript entitled "Molecular dynamics of the matrisome across sea anemone life history", Bergheim and colleagues report the prediction, using an established sequence analysis pipeline, of the "matrisome" - that is, the compendium of genes encoding constituents of the extracellular matrix - of the starlet sea anemone Nematostella vectensis. Re-analysis of an existing scRNA-Seq dataset allowed the authors to identify the cell types expressing matrisome components and different developmental stages. Last, the authors apply time-resolved proteomics to provide experimental evidence of the presence of the extracellular matrix proteins at three different stages of the life cycle of the sea anemone (larva, primary polyp, adult) and show that different subsets of matrisome components are present in the ECM at different life stages with, for example, basement membrane components accompanying the transition from larva to primary polyp and elastic fiber components and matricellular proteins accompanying the transition from primary polyp to the adult stage.

Strengths:

The ECM is a structure that has evolved to support the emergence of multicellularity and different transitions that have accompanied the complexification of multicellular organisms. Understanding the molecular makeup of structures that are conserved throughout evolution is thus of paramount importance.

The in-silico predicted matrisome of the sea anemone has the potential to become an essential resource for the scientific community to support big data annotation efforts and better understand the evolution of the matrisome and of ECM proteins, an important endeavor to better understand structure/function relationships. Toward this goal, the authors provide a comprehensive list with extensive annotations and cross-referencing of the 551 genes encoding matrisome proteins in the sea anemone genome.

This study is also an excellent example of how integrating datasets generated using different -omic modalities can shed light on various aspects of ECM metabolism, from identifying the cell types of origins of matrisome components using scRNA-Seq to studying ECM dynamics using proteomics.

Weakness:

- Prior proteomic studies on the ECM of vertebrate organisms have shown the importance of allowing certain post-translational modifications during database search to ensure maximizing peptide-to-spectrum matching and accurately evaluating protein quantification. Such PTMs include the hydroxylation of lysines and prolines that are collagen-specific PTMs. Multiple reports have shown that omitting these PTMs while analyzing LC-MS/MS data would lead to underestimating the abundance of collagens and the misidentification of certain collagens. While the authors in their response state that the inclusion of these PTMs only led to a modest increase in protein identification, they do not comment on the impact of including these PTMs on PSMs or protein abundance (precursor ion intensity).

Reviewer #1 (Public review):

Summary:

The study examines how pyruvate, a key product of glycolysis that influences TCA metabolism and gluconeogenesis, impacts cellular metabolism and cell size. It primarily utilizes the Drosophila liver-like fat body, which is composed of large post-mitotic cells that are metabolically very active. The study focuses on the key observations that over-expression of the pyruvate importer MPC complex (which imports pyruvate from the cytoplasm into mitochondria) can reduce cell size in a cell-autonomous manner. They find this is by metabolic rewiring that shunts pyruvate away from TCA metabolism and into gluconeogenesis. Surprisingly, mTORC and Myc pathways are also hyper-active in this background, despite the decreased cell size, suggesting a non-canonical cell size regulation signaling pathway. They also show a similar cell size reduction in HepG2 organoids. Metabolic analysis reveals that enhanced gluconeogenesis suppresses protein synthesis. Their working model is that elevated pyruvate mitochondrial import drives oxaloacetate production and fuels gluconeogenesis during late larval development, thus reducing amino acid production and thus reducing protein synthesis.

Strengths:

The study is significant because stem cells and many cancers exhibit metabolic rewiring of pyruvate metabolism. It provides new insights into how the fate of pyruvate can be tuned to influence Drosophila biomass accrual, and how pyruvate pools can influence the balance between carbohydrate and protein biosynthesis. Strengths include its rigorous dissection of metabolic rewiring and use of Drosophila and mammalian cell systems to dissect carbohydrate:protein crosstalk.

Comments on revised version:

The study remains an important dissection of how metabolic compartmentalization can influence cell size. It nicely uses Drosophila and a variety of metabolic approaches. The various pathway analyses and rigorous quantitation are strengths.

Reviewer #1 (Public review):

Summary:

This manuscript describes a chemical screen for activators of the eIF2 kinase GCN2 (EIF2AK4) in the integrated stress response (ISR). Recently, reported inhibitors of GCN2 and other protein kinases have been shown at certain concentrations to paradoxically activate GCN2. The study uses CHO cells and ISR reporter screens to identify a number of GCN2 activator compounds, including a potent "compound 20." These activators have implications for the development of new therapies for ISR-related diseases. For example, although not directly pursued in this study, these GCN2 activators could be helpful for the treatment of PVOD, which is reported for patients with certain GCN2 loss-of-function mutations. The identified activators are also suggested to engage with the GCN2 directly and can function while devoid of GCN1, a co-activator of GCN2.

Strengths:

The manuscript appears to be a largely rigorous study that flows in a logical manner. The topic is interesting and significant.

Weaknesses:

Portions of the manuscript are not fully clear. There are some experimental presentation and design concerns that should be addressed to support the stated conclusions.

Reviewer #1 (Public review):

Summary:

The study by Raiola et al. conducted a quantitative analysis of tissue deformation during the formation of the primitive heart tube from the cardiac crescent in mouse embryos. Using the tools developed to analyze growth, anisotropy, strain, and cell fate from time-lapse imaging data of mouse embryos, the authors elucidated the compartmentalization of tissue deformation during heart tube formation and ventricular expansion. This paper describes how each region of the cardiac tissue changes to form the heart tube and ventricular chamber, contributing to our understanding of the earliest stages of cardiac development.

Strengths:

In order to understand tissue deformation in cardiac formation, it is commendable that the authors effectively utilized time-lapse imaging data, a data pipeline, and in silico fate mapping.

The study clarifies the compartmentalization of tissue deformation by integrating growth, anisotropy, and strain patterns in each region of the heart.

Weaknesses:

The significance of the compartmentalization of tissue deformation for the heart tube formation remains unclear.

Reviewer #1 (Public review):

Summary:

The authors demonstrate the stereoselective role of D-serine in 1C metabolism, showing that D-serine competes with L-serine and inhibits mitochondrial L-serine transport. They observe expression of 1C metabolites in their metabolomics approach in primary cortical neurons treated with L-serine, D-serine, and a mixture of both. Their conclusions are based on the reduction in levels of glycine, polyamines, and their intermediates and formate. Single-cell RNA sequencing of N2a cells showed that cells treated with D-serine enhanced expression of genes associated with mitochondrial functions, such as respiratory chain complex assembly, and mitochondrial functions, with downregulation of genes related to amino acid transport, cellular growth, and neuron projection extension. Their work demonstrates that D-serine inhibits tumor cell proliferation and induces apoptosis in neural progenitor cells, highlighting the importance of D-serine in neurodevelopment.

Strengths:

D-amino acids are a marvel of nature. It is fascinating that nature decided to make two versions of the same molecule, in this case, an amino acid. While the L-stereoisomer plays well-known roles in biology, the D-stereoisomer seems to function in obscurity. Research into these novel signaling molecules is gathering momentum, with newer stereoisomers being discovered. D-serine has been the most well-studied among the different stereoisomers, and we still continue to learn about this novel neurotransmitter. The roles of these molecules in the context of metabolism is not well studied. The authors aim to elucidate the metabolic role of D-serine in the context of neuronal maturation with implications for 1C metabolism and in cell proliferation. The metabolic role of these molecules is just beginning to be uncovered, especially in the context of mammalian biology. This is the strength of the manuscript. The authors have done important work in prior publications elucidating the role of D-amino acids. The advancement of the field of D-amino acids in mammalian biology is significant, as not much is known. The presentation of RNA seq data is a valuable resource to the community, however, with caveats as mentioned below.

Weaknesses:

The following are some of the issues that come out in a critical reading of the manuscript. Addressing these would only strengthen and clarify the work.

(1) Kinetic assessment of D-serine versus L-serine: While the authors mention that D-serine is not a good substrate for SHMT2 compared to L-serine, the kinetic data are presented for only D-serine. In a substrate comparison with an enzyme, data must be presented for L-serine as well to make the conclusion about substrate specificity and affinity. Since the authors talk about one versus another substrate, there needs to be a kinetic comparison of both with Km (affinity). (Ref Figure 2 panel).

(2) Molecular Dynamics simulations, while a good first step in modeling interactions at the active site, rely on force fields. These force fields are approximations and do not represent all interactions occurring in the natural world. Setting up the initial conditions in the simulations can impact the final results in non-equilibrium scenarios. The basic question here is this: Is the simulated trajectory long enough so that the system reaches thermodynamic equilibrium and the measured properties converge? Prior studies have shown mixed results with the conclusion that properties of biological systems tend to converge in multi-second trajectories (not nanosecond scales as reported by the authors) and transition rates to low probability conformations require more time. (Ref Figure 2C).

(3) The authors use N2a cell line to demonstrate D-serine burden on primary cortical neurons. N2a is an immortalized cell line, and its properties are very different from primary neurons. The authors need to mention a rationale for the use of an immortalized cell line versus primary neurons. The transcriptomic profile of an immortalized cell line is different compared to a primary cell. Hence, the response to D-serine may vary between the two different cell types.

(4) In Figure 4D, the authors mention that D-serine activates the cleavage of caspase 3. Figure 4D shows only cleaved caspase 3 as a single band. They need to show the full blot that contains the cleaved fragments along with the major caspase 3 band.

(5) In Figure panel 4, the authors use neural progenitor cells (NPCs). They need to demonstrate that the population they are working with is NPCs and not primary neurons. There must be a figure panel staining for NPC markers like SOX2 and PAX6. Also, Figure S5 needs to be properly labeled. It is confusing from the legend what panels B-E refer to? Also, scale bars are not indicated.

(6) In Supplementary Figure panel 7F, the authors mention phosphatidyl L-serine and phosphatidyl D-serine. A chromatogram of the two species would clarify their presence as they used 2D-HPLC. On an MS platform, these 2 species are not distinguishable. Including a chromatogram of the 2 species would be helpful to the readers.

(7) The authors mention about enantiomeric shift of serine metabolism during neural development, which appears to be a discussion of prior published data from Hubbard et al, 2013, Burk et al, 2020, and Bella et a,l 2021 in Supplementary Figure panels 8 A-E. This should not be presented as a figure panel, as it gives the false impression that the authors have performed the experiment, which is clearly not the case. However, its discussion can well serve as part of the manuscript in the discussion section.

(8) The entire presentation of the section on enantiomeric shift of serine metabolism during neural development (lines 274-312) is a discussion and should be part of the discussion section and not in the results section. This is misleading.

(9) The discussion section is not well written. There is no mention of recent work related to D-serine that has a direct bearing on its metabolic properties. In the discussion section, paragraph 1, the authors mention that their work demonstrates the selective synthesis of D-serine in mature neurons as opposed to neural progenitor cells. This concept has been referred to in prior publications:

(a) Spatiotemporal relationships among D-serine, serine racemase, and D-amino acid oxidase during mouse postnatal development. PMID:14531937.

(b) D-cysteine is an endogenous regulator of neural progenitor cell dynamics in the mammalian brain. PMID:34556581.

(10) In the abstract, in lines 101 and 102, the authors mention "how D-serine contributes to cellular metabolism beyond neurotransmission remains largely unknown". In 2023, a paper in Stem Cell Reports by Roychaudhuri et al (PMID:37352848) showed that D and L-serine availability impacts lipid metabolism in the subventricular zone in mice, affecting proliferative properties of stem-cell derived neurons using a comprehensive lipidomics approach. There is no mention of this work even in the discussion section, as it bears directly on L and D-serine availability in neurons, which the authors are investigating. In the discussion section in lines 410-411, the authors mention the role of D-serine in neurogenesis, but surprisingly don't refer to the above reference. The role of D-serine in neurogenesis has been demonstrated in the Sultan et al (lines 855-857) and Roychaudhuri et al references.

(11) Both D-serine and the structurally similar stereoisomer D-cysteine (sulfur versus oxygen atom) have a bearing on 1C metabolism and the folate cycle. With reference to the folate cycle, Roychaudhuri et al in 2024 (PMID:39368613) have shown in rescue experiments in mice that supplementing a higher methionine diet provides folate cycle precursors to rescue the high insulin phenotype in SR-deficient mice. Since 1C metabolism is being discussed in this manuscript, the authors seem to overlook prior work in the field and not include it in their discussion, even when it is the same enzyme (SR) that synthesizes both serine and cysteine. Since the field of D-amino acid research is in its infancy, the authors must make it a point to include prior work related to D-serine at least, and not claim that it is not known. The known D-stereoisomers are not many, hence any progress in the area must include at least a discussion of the other structurally related stereoisomers.

(12) Racemases (serine and aspartate) in general are promiscuous enzymes and known to synthesize other stereoisomers in addition to D-serine, D-cysteine, and D-aspartate. A few controls, like D-aspartate, D-cysteine, or even D-alanine must be included in their study to demonstrate the specific actions of D-serine, especially in the N2a cell treatment experiments. Cysteine and Serine are almost identical in structure (sulfur versus oxygen atom), and both are synthesized by serine racemase (published). Cysteine has also been very recently shown to inhibit tumor growth and neural progenitor cell proliferation. (PMIDs: 40797101 and 34556581). How the authors' work relates to the existing findings must be discussed, and this would put things in perspective for the reader.

Reviewer #1 (Public review):

Summary:

Plasmodesmata are channels that allow cell-cell communication in plants; based on the functional similarities between facilitated transport within plasmodesmata and into the nucleus, the authors speculate that nuclear pore complex proteins might be involved in plasmodesmata function. In this manuscript, they localize nuclear pore complex proteins to plasmodesmata using proteomics and heterologous overexpression. They also document a possible plasmodesmata transport defect in a mutant affecting one nuclear pore complex protein.

Strengths:

The main strength of this manuscript is the interesting and novel hypothesis. This work could open exciting new directions in our understanding of plasmodesmata function and cell-cell communication in plants. They also localized many NUPs (12/35 Arabidopsis NUPs).

Weaknesses:

The main weakness of this manuscript is that the data are incomplete. While the authors appropriately and frequently acknowledge caveats to their data, two controls are essential to interpret the results that fluorescently-tagged NUPs localize to the plasmodesmata: (1) assessment of the expression level of these fluorescently-tagged NUPs to determine whether the plasmodesmata localization might be an overexpression artefact; (2) assessment of the function of the fluorescently-tagged NUPs, either by molecular complementation of a knockout mutant phenotype or by biochemical methods to test whether the fluorescently-tagged NUP incorporates into nuclear pore complexes. Conducting these experiments for even one fluorescently-tagged NUP would substantially strengthen this manuscript.

Reviewer #1 (Public review):

Summary:

The manuscript presents a comprehensive study on the developing synthetic gene circuits targeting mutant RAS expressing cells. The aim of this study is to use these RAS targeting circuits as cancer cell classifiers and enable the selective expression of an output protein in correlation with RAS activity. The system is based on the bacterial two-component system NarX/NarL. A RAS-binding domain is fused to a NarX mutant either defective in the ATP binding (N509A) or the phosphorylation site (H399Q). Nanocluster formation of RAS-GTP reconstitutes an active histidine kinase sensor dimer that phosphorylates the response regulator NarL thus leading to the expression of an output protein. The integration of RAS-dependent MAPK responsive elements to express the RAS sensor components generates RAS circuits with an extended dynamic range between mutant and wild-type RAS. The selectivity of the RAS circuits is confirmed in a set of cancer cell lines expressing endogenous levels of mutant or wild-type RAS or oncogenes affecting RAS signaling upstream or downstream. Expression of the suicide gene HSV thymidine kinase as an outcome protein kills RAS-driven cancer cells demonstrating the functionality of the system.

Strengths:

This proof-of-concept study convincingly demonstrates the potential of synthetic gene circuits to target oncogenic RAS in tumor cell lines, act as RAS mutant cell classifier, and induce the killing of RAS-driven cells.

Weaknesses:

A therapeutic strategy based on of this four-plasmid system may be difficult to implement in RAS-driven solid cancers. However, potential solutions are discussed.

Reviewer #1 (Public review):

Summary:

The presented study by Centore and colleagues investigates the inhibition of BAF chromatin remodeling complexes. The study is well written and includes comprehensive datasets, including compound screens, gene expression analysis, epigenetics, as well as animal studies. This is an important piece of work for the uveal melanoma research field, and sheds light on a new inhibitor class, as well as a mechanism that might be exploited to target this deadly cancer for which no good treatment options exist.

Strengths:

This is a comprehensive and well-written study.

Weaknesses:

There are minimal weaknesses.

Reviewer #1 (Public review):

The manuscript "Heterozygote advantage cannot explain MHC diversity, but MHC diversity can explain heterozygote advantage" explores two topics. First, it is claimed that the recently published conclusion by Mattias Siljestam and Claus Rueffler (in the following referred to as [SR] for brevity) that heterozygote advantage explains MHC diversity does not withstand even a very slight change in ecological parameters. Second, a modified model that allows an expansion of the MHC gene family shows that homozygotes outperform heterozygotes. This is an important topic and could be of potential interest to readers if the conclusions are valid and non-trivial.

Let me first comment on the second part of the manuscript that describes the fitness advantage of the 'gene family expansion'. I think this, by itself, is a totally predictable result. It appears obvious that with no or a little fitness penalty, it becomes beneficial to have MHC-coding genes specific to each pathogen. A more thorough study that takes into account a realistic (most probably non-linear in gene number) fitness penalty, various numbers of pathogens that could grossly exceed the self-consistent fitness limit on the number of MHC genes, etc, could be more informative. Yet, as I understood the narrative of the manuscript, the expansion of the gene family serves as a mere counter-example to the disputed finding of [SR], rather than a systematic study of the eco-evolutionary consequences of this process.

Now to the first part of the manuscript, which claims that the point made in [RS] is not robust and breaks down under a small change in the parameters. An addition or removal of one of the pathogens is reported to affect "the maximum condition", a key ecological characteristic of the model, by an enormous factor 10^43, naturally breaking down all the estimates and conclusions made in [RS]. This observation is not substantiated by any formulas, recipes for how to compute this number numerically, or other details, and is presented just as a self-standing number in the text. The only piece of information given in the manuscript is that, unlike in [SR], the adjustable parameter c_{max} is kept constant when the number of pathogens is changed.

In my opinion, the information provided in the manuscript does not allow one to conclude anything about the relevance and the validity of its main claim. At the same time, the simulations done in [SR] are described with a fair amount of detail. Which allows me to assume that the conclusions made in [SR] are fairly robust and, in particular, have been demonstrated not to be too sensitive to changes in the main "suspect', c_{max}. Let me briefly justify my point.

First, it follows from Eqs (4,5) in the main text and (A12-A13) in the Appendix that c_{max} and K do not independently affect the dynamics of the model, but it's rather their ratio K/c_max that matters. It can be seen by dividing the numerator and denominator of (5) by c_max. Figure 3 shows the persistent branching for 4 values of K that cover 4 decades. As it appears from the schemes in the top row of Figure 3, those simulations are done for the same positions and widths/virulences of pathogens. So the position of x* should be the same in all 4 cases, presumably being at the center of pathogens, (x*,x*) = (0,0). According to the definition of x* given in the Appendix after Eqs (A12-A13), this means that c_max remains the same in all 4 cases. So one can interpret the 4 scenarios shown in Figure 3 as corresponding not to various K, but to various c_max that varied inversely to K. That is, the results would have been identical to those shown in Figure 3 if K were kept constant and c_max were multiplied by 0.1, 1, 10, and 100, or scaled as 1/K. This begs the conclusion that the branching remains robust to changes in c_max that span 4 decades as well.

Naturally, most, if not all, the dynamics will break down if one of the ecological characteristics changes by a factor of 10^43, as it is reported in the submitted manuscript. As I wrote above, there is no explanation behind this number, so I can only guess that such a number is created by the removal or addition of a pathogen that is very far away from the other pathogens. Very far in this context means being separated in the x-space by a much greater distance than 1/\nu, the width of the pathogens' gaussians. Once again, I am not totally sure if this was the case, but if it were, some basic notions of how models are set up were broken. It appears very strange that nothing is said in the manuscript about the spatial distribution of the pathogens, which is crucial to their effects on the condition c. In [SP], it is clearly shown where the pathogens are.

Another argument that makes me suspicious in the utility of the conclusions made in the manuscript and plays for the validity of [SP] is the adaptive dynamics derivation of the branching conditions. It is confirmed by numerics with sufficient accuracy, and as it stands in its simple form of the inequality between two widths, the branching condition appears to be pretty robust with respect to reasonable changes in parameters.

Overall, I strongly suspect that an unfortunately poor setup of the model reported in the manuscript has led to the conclusions that dispute the much better-substantiated claims made in [SD].

Reviewer #1 (Public review):

Summary:

Laura Morano and colleagues have performed a screen to identify compounds that interfere with the formation of TopBP1 condensates. TopBP1 plays a crucial role in the DNA damage response, and specifically the activation of ATR. They found that the GSK-3b inhibitor AZD2858 reduced the formation of TopBP1 condensates and activation of ATR and its downstream target CHK1 in colorectal cancer cell lines treated with the clinically relevant irinotecan active metabolite SN-38. This inhibition of TopBP1 condensates by AZD2858 was independent from its effect on GSK-3b enzymatic activity. Mechanistically, they show that AZD2858 thus can interfere with intra-S-phase checkpoint signaling, resulting in enhanced cytostatic and cytotoxic effects of SN-38 (or SN-38+Fluoracil aka FOLFIRI) in vitro in colorectal carcinoma cell lines.

Comments on latest version:

The requested plots are in figure S7 of the latest manuscript version, and look convincing. My last point is now adequately addressed.

Reviewer #1 (Public review):

Summary:

The authors use Dyngo-4a, a known Dynami inhibitor to test its influence on caveolar assembly and surface mobility. They investigate whether it incorporates into membranes with Quartz-Crystal Microbalance, they investigate how it is organized in membranes using simulations. Finally, they use lipid-packing sensitive dyes to investigate lipid packing in the presence of Dyngo-4a, membrane stiffness using AFM and membrane undulation using fluorescence microscopy. They also use a measure they call "caveola duration time" to claim that something happens to caveolae after Dyngo-4a addition and using this parameter, they do indeed see an increase in it in response to Dyngo-4a, which is reduced back to the baseline after addition of cholesterol.

Overall, the authors claim: 1) Dyngo-4a inserts into the membrane and this 2) results in "a dramatic dynamin-independent inhibition of caveola scission". 3) Dyngo-4a was inserted and positioned at the level of cholesterol in the bilayer and 4) Dyngo-4a-treatment resulted in decreased lipid packing in the outer leaflet of the plasma membrane 5) but Dyngo-4a did not affect caveola morphology, caveolae-associated proteins, or the overall membrane stiffness 6) acute addition of cholesterol counteracts the block in caveola scission caused by Dyngo-4a.

Overall, in this reviewers opinion, claims 1, 3, 4, 5 are well-supported by the presented data from electron and live cell microscopy, QCM-D and AFM.

However, there is no convincing assay for caveolar endocytosis presented besides the "caveola duration" which although unclearly described seems to be the time it takes in imaging until a caveolae is not picked up by the tracking software anymore in TIRF microscopy.

Since the main claim of the paper is a mechanism of caveolar endocytosis being blocked by Dyngo-4a, a true caveolar internalization assay is required to make this claim. This means either the intracellular detection of not surface connected caveolar cargo or the quantification of caveolar movement from TIRF into epifluorescence detection in the fluorescence microscope. Otherwise, the authors could remove the claim and just claim that caveolar mobility is influenced.

Significance:

A number of small molecule inhibitors for the GTPase dynamics exist, that are commonly used tools in the investigation of endocytosis. This goes as far that the use of some of these inhibitors alone is considered in some publications as sufficient to declare a process to be dynamin-dependent. However, this is not correct, as there are considerable off-target effects, including the inhibition of caveolar internalization by a dynamin-independent mechanism. This is important, as for example the influence of dynamin small molecule inhibitors on chemotherapy resistance is currently investigated (see for example Tremblay et al., Nature Communications, 2020).

The investigation of the true effect of small molecules discovered as and used as specific inhibitors and their offside effects is extremely important and this reviewer applauds the effort. It is important that inhibitors are not used alone, but other means of targeting a mechanism are exploited as well in functional studies. The audience here thus is besides membrane biophysicists interested in the immediate effect of the small molecule Dyngo-4a also cell biologists and everyone using dynamic inhibitors to investigate cellular function.

Comments on revised version:

Please include the promised data on caveolar internalization and remove the above mentioned claim on membrane undulations from the text.

Reviewer #1 (Public review):

Summary:

This study uses a novel DNA origami nanospring to measure the stall force and other mechanical parameters of the kinesin-3 family member, KIF1A, using light microscopy. The key is to use SNAP tags to tether a defined nanospring between a motor-dead mutant of KIF5B and the KIF1A to be integrated. The mutant KIF5B binds tightly to a subunit of the microtubule without stepping, thus creating resistance to the processive advancement of the active KIF1A. The nanospring is conjugated with 124 Cy3 dyes, which allows it to be imaged by fluorescence microscopy. Acoustic force spectroscopy was used to measure the relationship between the extension of the NS and force as a calibration. Two different fitting methods are described to measure the length of the extension of the NS from its initial diffraction-limited spot. By measuring the extension of the NS during an experiment, the authors can determine the stall force. The attachment duration of the active motor is measured from the suppression of lateral movement that occurs when the KIF1A is attached and moving. There are numerous advantages of this technology for the study of single molecules of kinesin over previous studies using optical tweezers. First, it can be done using simple fluorescence microscopy and does not require the level of sophistication and expense needed to construct an optical tweezer apparatus. Second, the force that is experienced by the moving KIF1A is parallel to the plane of the microtubule. This regime can be achieved using a dual beam optical tweezer set-up, but in the more commonly used single-beam set-up, much of the force experienced by the kinesin is perpendicular to the microtubule. Recent studies have shown markedly different mechanical behaviors of kinesin when interrogated by the two different optical tweezer configurations. The data in the current manuscript are consistent with those obtained using the dual-beam optical tweezer set-up. In addition, the authors study the mechanical behavior of several mutants of KIF1A that are associated with KIF1A-associated neurological disorder (KAND).

Strengths:

The technique should be cheaper and less technically challenging than optical tweezer microscopy to measure the mechanical parameters of molecular motors. The method is described in sufficient detail to allow its use in other labs. It should have a higher throughput than other methods.

Weaknesses:

The experimenter does not get a "real-time" view of the data as it is collected, which you get from the screen of an optical tweezer set-up. Rather, you have to put the data through the fitting routines to determine the length of the nanospring in order to generate the graphs of extension (force) vs time. No attempts were made to analyze the periods where the motor is actually moving to determine step-size or force-velocity relationships.

Reviewer #1 (Public review):

Summary:

It is well known that autophagosomes/autolysosomes move along microtubules. However, as these previous studies did not distinguish between autophagosomes and autolysosomes, it remains unknown whether autophagosomes begin to move after fusion with lysosomes or even before fusion. In this manuscript, the authors show using fusion-deficient vps16a RNAi cells that both pre-fusion autophagosomes and lysosomes can move along the microtubules towards the minus end. This was confirmed in snap29 RNAi cells. By screening motor proteins and Rabs, the authors found that autophagosomal traffic is primarily regulated by the dynein-dynactin system and can be counter-regulated by kinesins. They also show that Rab7-Epg5 and Rab39-ema interactions are important for autophagosome trafficking.

Strengths:

This study uses reliable Drosophila genetics and high-quality fluorescence microscopy. The data are properly quantified and statistically analyzed. It is a reasonable hypothesis that gathering pre-fusion autophagosomes and lysosomes in close proximity improves fusion efficiency.

Weaknesses:

(1) This study investigates the behavior of pre-fusion autophagosomes and lysosomes using fusion-incompetent cells (e.g., vps16a RNAi cells). However, the claim that these cells are truly fusion-incompetent relies on citations from previous studies. Since this is a foundational premise of the research, it should be rigorously evaluated before interpreting the data. It's particularly awkward that the crucial data for vps16a RNAi is only presented at the very end of Figure 10-S1; this should be among the first data shown (the same for SNAP29). It would be important to determine the extent to which autophagosomes and lysosomes are fusing (or tethered in close proximity), within each of these cell lines.

(2) In the new Figures 8 and 9, the authors analyze autolysosomes without knocking down Vps16A (i.e., without inhibiting fusion). However, as this reviewer pointed out in the previous round, it is highly likely that both autophagosomes and autolysosomes are present in these cells. This is particularly relevant given that the knockdown of dynein-dynactin, Rab7, and Epg5 only partially inhibits the fusion of autophagosomes and lysosomes (Figure 10H). If the goal is to investigate the effects of fusion, it would be more appropriate to analyze autolysosomes and autophagosomes separately. The authors mention that they can differentiate these two structures based on the size of mCherry-Atg8a structures. If this is the case, they should perform separate analyses for both autophagosomes and autolysosomes.

(3) This is also a continued Issue from the previous review. The authors suggest that autophagosome movement is crucial for fusion, based on the observed decrease in fusion rates in Rab7 and Epg5 knockdown cells (Fig. 10). However, this conclusion is not well supported. It is known that Rab7 and Epg5 are directly involved in the fusion process itself. Therefore, the possibility that the observed decrease is simply due to a direct defect in fusion, rather than an impairment of movement, has not been ruled out.

(4) The term "autolysosome maturation" appears multiple times, yet its meaning remains unclear. Does it refer to autolysosome formation (autophagosome-lysosome fusion), or does it imply a further maturation process occurring after autolysosome formation? This is not a commonly used term in the field, so it requires a clear definition.

(5) In Figure 1-S1D, the authors state that the disappearance of the mCherry-Atg8a signal after atg8a RNAi indicates that the observed structures are not non-autophagic vacuoles. This reasoning is inappropriate. Naturally, knocking down Atg8 will abolish its signal, regardless of the nature of the vacuoles. This does not definitively distinguish autophagic from non-autophagic structures.

Reviewer #1 (Public review):

In this manuscript, the authors employ a combined proteomic and genetic approach to identify the glycoprotein QC factor malectin as an important protein involved in promoting coronavirus infection. Using proteomic approaches, they show that the non-structural protein NSP2 and malectin interact in the absence of viral infection, but not in the presence of viral infection. However, both NSP2 and malectin engage the OST complex during viral infection, with malectin also showing reduced interactions with other glycoprotein QC proteins. Malectin KD reduce replication of coronaviruses, including SARS-COV2. Collectively, these results identify Malectin as a glycoprotein QC protein involved in regulating coronavirus replication that could potentially be targeted to mitigate coronavirus replication.

In the revised manuscript, the authors have addressed many of my comments from the previous submission. Notably, they've provided some additional mechanistic data, focused primarily on the activation of different stress signaling pathways, to help define malectin impacts viral replication, although this is mostly suggests that activation of these pathways may not be the main mechanism of malectin-dependent reductions in viral replication. Regardless, I'm sure this mechanism will be the focus of continued efforts on this project. They have also addressed other concerns related to interactions between OST and malectin, as well as the curious interactions between non-structural proteins with both ER and mitochondrial proteins. Overall, the authors have been responsive to my comments and comments from other reviewers, and the manuscript has been improved. It will be a good addition to eLife.

Reviewer #1 (Public review):

Summary:

The authors were attempting to identify the molecular and cellular basis for why modulators of the HR pathway, specifically PARPi, are not effective in CDK12 deleted or mutant prostate cancers and they seek to identify new therapeutic agents to treat this subset of metastatic prostate cancer patients. Overall, this is an outstanding manuscript with a number of strengths and in my opinion represents a significant advance in the field of prostate cancer biology and experimental therapeutics.

Strengths:

The patient data cohort size and clinical annotation from Figure 1 are compelling and comprehensive in scope. The associations between tandem duplications and amplifications of oncogenes that have been well-credentialed to be drivers of cancer development and progression are fascinating and the authors identify that in those that have AR amplification for example, there is evidence for AR pathway activation. The association between CDK12 inactivation and various specific gene/pathway perturbations is fascinating and is consistent with previously published studies - it would be interesting to correlate these changes with cell line-based studies in which CDK12 is specifically deleted or inhibited with small molecules to see how many pathways/gene perturbations are shared between the clinical samples and cell and mouse models with CDK12 perturbation. The short-term inhibitor studies related to changes in HRD genes and protein expression with CDK12/13 inhibition are fascinating and suggest differential pathway effects between short inhibition of CDK12/13 and long-term loss of CDK12. The in vivo studies with the inhibitor of CDK12/13 are intriguing but not definitive

Weaknesses:

Given that there are different mutations identified at different CDK12 sites as illustrated in Figure 1B it would be nice to know which ones have been functionally classified as pathogenic and for which ones that the pathogenicity has not been determined. This would be especially interesting to perform in light of the differences in the LOH scores and WES data presented - specifically, are the pathogenic mutations vs the mutations for which true pathogenicity is unknown more likely to display LOH or TD? For the cell inhibition studies with the CDK12/13 inhibitor, more details characterizing the specificity of this molecule to these targets would be useful. Additionally, could the authors perform short-term depletion studies with a PROTAC to the target or short shRNA or non-selected pool CRISPR deletion studies of CDK12 in these same cell lines to complement their pharmacological studies with genetic depletion studies? Also perhaps performing these same inhibitor studies in CDK12/13 deleted cells to test the specificity of the molecule would be useful. Additionally, expanding these studies to additional prostate cancer cell lines or organdies models would strengthen the conclusions being made. More information should be provided about the dose and schedule chosen and the rationale for choosing those doses and schedules for the in vivo studies proposed should be presented and discussed. Was there evidence for maximal evidence of inhibition of the target CDK12/13 at the dose tested given the very modest tumor growth inhibition noted in these studies?

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

Hurtado et al. highlight the importance of Sox9 in the retina and limbal stem cells by describing several affects of Sox9 depletion in the adult eye. However, it is unclear how or where Sox9 precisely acts as a mechanistic investigation of the transcription factor's role in this tissue is lacking.

Reviewer #1 (Public review):

Summary:

This work computationally characterized the threat-reward learning behavior of mice in a recent study (Akiti et al.), which had prominent individual differences. The authors constructed a Bayes-adaptive Markov decision process model, and fitted the behavioral data by the model. The model assumed (i) hazard function staring from a prior (with free mean and SD parameters) and updated in a Bayesian manner through experience (actually no real threat or reward was given in the experiment), (ii) risk-sensitive evaluation of future outcomes (calculating lower 𝛼 quantile of outcomes with free 𝛼 parameter), and (iii) heuristic exploration bonus. The authors found that (i) brave animals had more widespread hazard priors than timid animals and thereby quickly learned that there was in fact little real threat, (ii) brave animals may also be less risk-aversive than timid animals in future outcome evaluation, and (iii) the exploration bonus could explain the observed behavioral features, including the transition of behavior from the peak to steady-state frequency of bout. Overall, this work is a novel interesting analysis of threat-reward learning, and provides useful insights for future experimental and theoretical work. However, there are several issues that I think need to be addressed.

Strengths:

- This work provides a normative Bayesian account for individual differences in braveness/timidity in reward-threat learning behavior, which complements the analysis by Akiti et al. based on model-free threat reinforcement learning.

- Specifically, the individual differences were characterized by (i) the difference in the variance of hazard prior and potentially also (ii) the difference in the risk-sensitivity in evaluation of future returns.

Weakness:

- Theoretically the effect of prior is diluted over experience whereas the effect of biased (risk-aversive) evaluation persists, but these two effects could not be teased apart in the fitting analysis of the current data.

- It is currently unclear how (whether) the proposed model corresponds to neurobiological (rather than behavioral) findings, different from the analysis by Akiti et al.

Comments on revisions:

The authors have adequately replied to all the concerns that I raised in my review of the original manuscript. I do not have any remaining concern, and I am now more convinced that this work provides novel important insights and stimulates future experimental and theoretical examinations.

Editorial note: To ensure a thorough evaluation of the revised manuscript, we invited a third reviewer to assess whether the authors had sufficiently addressed the concerns raised in the initial round of peer review. This additional reviewer confirmed that the authors responded partially to the original reviewers requests. While he/she also provided a set of new comments, these do not alter the original assessment or editorial decision regarding the manuscript. For transparency and completeness, the additional comments are included below.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Li and coworkers present experiments generated with human induced pluripotent stem cells (iPSCs) differentiated to astrocytes through a three-step protocol consisting of neural induction/midbrain patterning, switch to expansion of astrocytic progenitors, and terminal differentiation to astroglial cells. They used lineage tracing with a LMX1A-Cre/AAVS1-BFP iPSCs line, where the initial expression of LMX1A and Cre allows the long-lasting expression of BFP, yielding BFP+ and BFP- populations, that were sorted when in the astrocytic progenitor expansion. BFP+ showed significantly higher number of cells positive to NFIA and SOX9 than BFP- cells, at 45 and 98 DIV. However, no significant differences in other markers such as AQP4, EAAT2, GFAP (which show a proportion of less than 10% in all cases) and S100B were found between BFP-positive or -negative, at these differentiation times. Intriguingly, non-patterned astrocytes produced higher proportions of GFAP positive cells than the midbrain-induced and then sorted populations. BFP+ cells have enhanced calcium responses after ATP addition, compared to BFP- cells. Single-cell RNA-seq of early and late cells from BFP- and BFP+ populations were compared to non-patterned astrocytes and neurons differentiated from iPSCs. Bioinformatic analyses of the transcriptomes resulted in 9 astrocyte clusters, 2 precursor clusters and one neuronal cluster. DEG analysis between BFP+ and BFP- populations showed some genes enriched in each population, which were subject to GO analysis, resulting in biological processes that are different for BFP+ or BFP- cells.

Strengths:

The manuscript tries to tackle an important aspect in Neuroscience, namely the importance of patterning in astrocytes. Regionalization is crucial for neuronal differentiation and the presented experiments constitute a trackable system to analyze both transcriptional identities and functionality on astrocytes.

Weaknesses:

The presented results have several fundamental issues, to be resolved, as listed in the following major points:

(1) It is very intriguing that GFAP is not expressed in late BFP- nor in BFP+ cultures, when authors designated them as mature astrocytes.<br /> (2) In Fig. 2D, authors need to change the designation "% of positive nuclei".<br /> (3) In Fig. 2E, the text describes a decrease caused by 2APB on the rise elicited by ATP, but the graph shows an increase with ATP+2APB. However, in Fig. 2F, the peak amplitude for BFP+ cells is higher in ATP than in ATP+2APD, which is mentioned in the text, but this is inconsistent with the graph in 2E.<br /> (4) The description of Results in the single-cell section is confusing, particularly in the sorted CD49 and unsorted cultures. Where do these cells come from? Are they BFP-, BFP+, unsorted for BFP, or non-patterned? Which are the "all three astrocyte populations"? A more complete description of the "iPSC-derived neurons" is required in this section to allow the reader to understand the type and maturation stage of neurons, and if they are patterned or not.<br /> (5) A puzzling fact is that both BFP- and BFP- cells have similar levels of LMX1A, as shown in Fig. S6F. How do authors explain this observation?<br /> (6) In Fig. 3B, the non-patterned cells cluster away from the BFP+ and BFP-; on the other hand, early and late BFP- are close and the same is true for early and late BFP+. A possible interpretation of these results is that patterned astrocytes have different paths for differentiation, compared to non-patterned cells. If that can be implied from these data, authors should discuss the alternative ways for astrocytes to differentiate.<br /> (7) Fig. 3D shows that cluster 9 is the only one with detectable and coincident expression of both S100B and GFAP expression. Please discuss why these widely-accepted astrocyte transcripts are not found in the other astrocytes clusters. Also, Sox9 is expressed in neurons, astrocyte precursors and astrocytes. Why is that?<br /> (8) Line 337, Why authors selected a log2 change of 0.25? Typically, 1 or a higher number is used to ensure at least a 2-fold increase, or a 50% decrease. A volcano plot generated by the comparison of BFP+ with BFP- cells would be appropriate. The validation of differences by immunocytochemistry, between BFP+ and BFP-, is inconclusive. The staining is blur in the images presented in Fig. S8C. Quantification of the positive cells, without significant background signal, in both populations is required.<br /> (9) Lines 349-351: BFP+ cells did not show higher levels of transcripts for LMX1A nor FOXA2. This fact jeopardizes the claim that these cells are still patterned. In the same line, there are not significant differences with cortical astrocytes, indicating a wider repertoire of the initially patterned cells, that seems to lose the midbrain phenotype. Furthermore, common DGE shared by BFP- and BFP+ cells when compared to non-patterned cells indicate that after culture, the pre-pattern in BFP+ cells is somehow lost, and coincides with the progression of BFP- cells.<br /> (10) For the GO analyses, How did authors select 1153 genes? The previous section mentioned 287 genes unique for BFP+ cells. The Results section should include a rationale for performing a wider search for the enriched processes.<br /> (11) For Fig. 4C and 4D, both p values and the number of genes should be indicated in the graph. I would advise to select the 10 or 15 most significant categories, these panels are very difficult to read. Whereas the listed processes for BFP+ have a relation to Parkinson disease, the ones detected for BFP- cells are related to extracellular matrix and tissue development. Does it mean that BFP+ cells have impaired formation of this matrix, or defective tissue development? This is in contradiction of enhanced calcium responses of BFP+ cells compared to BFP- cells.<br /> (12) Both the comparison between midbrain and cortical astrocytes in Fig. S8A, and the volcano plot in S8B do not show consistent changes. For example, RCAN2 in Fig. S8A has the same intensity for cortical and midbrain cells, but is marked as an enriched gene in midbrain in the p vs log2FC graph in Fig. S8B.

Reviewer #1 (Public review):

This study elucidates the molecular linkage between the mobilization of damaged rDNA from the nucleolus to its periphery and the subsequent repair process by HDR. The authors demonstrate that the nucleolar adaptor protein Treacle mediates rDNA mobilization, and the MDC1-RNF8-RNF168 pathway coordinates the recruitment of the BRCA1-PALB2-BRCA2 complex and RAD51 loading. This stepwise regulation appears to prevent aberrant recombination events between rDNA repeats. This work provides compelling evidence for the recruitment of the Treacle-TOPBP1-NBS1 complex to rDNA DSBs and demonstrates the critical role of MDC1 in the rDNA damage response. There are some issues with the over-interpretation of results as described subsequently. Some aspects could be strengthened, for example, a potential role of the RAP80-Abraxas axis, the origin of the repair synthesis (HDR vs. NHEJ)

Reviewer #1 (Public review):

The authors present an approach that uses the transformer architecture to model epistasis in deep mutational scanning datasets. This is an original and very interesting idea. Applying the approach to 10 datasets, they quantify the contribution of higher-order epistasis, showing that it varies quite extensively.

Suggestions:

(1) The approach taken is very interesting, but it is not particularly well placed in the context of recent related work. MAVE-NN, LANTERN, and MoCHI are all approaches that different labs have developed for inferring and fitting global epistasis functions to DMS datasets. MoCHI can also be used to infer multi-dimensional global epistasis (for example, folding and binding energies) and also pairwise (and higher order) specific interaction terms (see 10.1186/s13059-024-03444-y and 10.1371/journal.pcbi.1012132). It doesn't distract from the current work to better introduce these recent approaches in the introduction. A comparison of the different capabilities of the methods may also be helpful. It may also be interesting to compare the contributions to variance of 1st, 2nd, and higher-order interaction terms estimated by the Epistatic transformer and MoCHI.

(2) https://doi.org/10.1371/journal.pcbi.1004771 is another useful reference that relates different metrics of epistasis, including the useful distinction between biochemical/background-relative and background-averaged epistasis.

(3) Which higher-order interactions are more important? Are there any mechanistic/structural insights?

Reviewer #1 (Public review):

Summary:

This manuscript uses adaptive sampling simulations to understand the impact of mutations on the specificity of the enzyme PDC-3 β-lactamase. The authors argue that mutations in the Ω-loop can expand the active site to accommodate larger substrates.

Strengths:

The authors simulate an array of variants and perform numerous analyses to support their conclusions.

The use of constant pH simulations to connect structural differences with likely functional outcomes is a strength.

Weaknesses:

I would like to have seen more error bars on quantities reported (e.g., % populations reported in the text and Table 1).

Reviewer #1 (Public review):

Summary:

The study is methodologically solid and introduces a compelling regulatory model. However, several mechanistic aspects and interpretations require clarification or additional experimental support to strengthen the conclusions.

Strengths:

(1) The manuscript presents a compelling structural and biochemical analysis of human glutamine synthetase, offering novel insights into product-induced filamentation.

(2) The combination of cryo-EM, mutational analysis, and molecular dynamics provides a multifaceted view of filament assembly and enzyme regulation.

(3) The contrast between human and E. coli GS filamentation mechanisms highlights a potentially unique mode of metabolic feedback in higher organisms.

Weaknesses:

(1) The mechanism underlying spontaneous di-decamer formation in the absence of glutamine is insufficiently explored and lacks quantitative biophysical validation.

(2) Claims of decamer-only behavior in mutants rely solely on negative-stain EM and are not supported by orthogonal solution-based methods.

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.

Comments on the previous 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.

Reviewer #1 (Public review):

The authors tried to quantify the difference between human complex traits by calculating genetic overlap scores between a pair of traits. Sherlock-II was devised to integrate GWAS with eQTL signals. The authors claim that Sherlock-II is superior to the previous version (robustness, accuracy, etc). It appears that their framework provides a reasonable solution to this important question, although the study needs further clarification and improvements.

(1) Sherlock-II incorporates GWAS and eQTL signals to better quantify genetic signals for a given complex trait. However, this approach is based on the hypothesis that "all GWAS signals confer association to complex trait via eQTL", which is not true (PMID: 37857933). This should be acknowledged (through mentioning in the text) and incorporated into the current setup (through differential analysis - for example, with or without eQTL signals, or with strong colocalization only).

(2) When incorporating eQTL, why did the authors use the top p-value tissues for eQTL? This approach seems simpler and probably more robust. But many eQTLs are tissue-specific. Therefore, it would also be important to know if eQTLS from appropriate tissues were incorporated instead.

(3) One of the main examples is the novel association between Alzheimer's disease and breast cancer. Although the authors provided a molecular clue underlying the association, it is still hard to comprehend the association easily, as the two diseases are generally known to be exclusive to each other. This is probably because breast cancer GWAS is performed for germline variants and does not consider the contribution of somatic variants.

(4) It would help readers understand the story better if a summary figure of the entire process were provided. The current Figure 1 does not fulfil that role.

(5) Figure 2 is not very informative. The readers would want to know more quantitative information rather than a heatmap-style display. Is there directionality to the relationship, or is it always unidirectional?

(6) In Figure 3, readers may want to know more specific information. For example, what gene signals are really driving the hypoxia signal in Alzheimer's disease vs breast cancer? And what SNP signals are driving these gene-level signals?