Reviewer #1 (Public Review):

Summary:

Argunşah et al. describe and investigate the mechanisms underlying the differential response dynamics of barrel vs septa domains in the whisker-related primary somatosensory cortex (S1). Upon repeated stimulation, the authors report that the response ratio between multi- and single-whisker stimulation increases in layer (L) 4 neurons of the septal domain, while remaining constant in barrel L4 neurons. The authors attribute this divergence to differences in short-term synaptic plasticity, particularly within somatostatin-expressing (SST⁺) interneurons. This interpretation is supported by 1) the increased density of SST+ neurons in L4 of the septa compared to barrel domain, 2) the stronger response of (L2/3) SST+ neurons to repeated multi- vs single-whisker stimulation and 3) the reduced functional difference in single- versus multi-whisker response ratios across barrel and septal domains in Elfn1 KO mice, which lack a synaptic protein that confers characteristic short-term plasticity, notably in SST+ neurons. Consistently, a decoder trained on WT data fails to generalize to Elfn1 KO responses. Finally, the authors report a relative enrichment of S2- and M1-projecting cell densities in L4 of the septal domain compared to the barrel domain, suggesting that septal and barrel circuits may differentially route information about single vs multi-whisker stimulation downstream of S1.

Strengths:

This paper describes and aims to study a circuit underlying differential response between barrel columns and septal domains of the primary somatosensory cortex. This work supports the view these two domains contribute distinctly to the processing single versus multi-whisker inputs and highlight the role of SST+ neuron and their short-term plasticity. Together, this study suggests that the barrel cortex multiplexes whisker-derived sensory information across its domains, enabling parallel processing within S1.

Weaknesses:

Although the divergence in responses to repeated single- versus multi-whisker stimulation between barrel and septal domains is consistent with a role for SST⁺ neuron short-term plasticity, the evidence presented does not conclusively demonstrate that this mechanism is the critical driver of the difference. The lack of targeted recordings and manipulations limits the strength of this conclusion: SST⁺ neuron activity is not measured in L4, nor is it assessed in a domain-specific manner. The Elfn1 knockout manipulation does not appear to selectively affect either stimulus condition, domain or interneuron subtype. Finally, all experiments were performed under anesthesia, which raises concerns about how well the reported dynamics generalize to awake cortical processing.

Reviewer #2 (Public review):

Summary:

Argunsah and colleagues demonstrate that SST expressing interneurons are concentrated in the mouse septa and differentially respond to repetitive multi-whisker inputs. Identifying how a specific neuronal phenotype impacts responses is an advance.

Strengths:

(1) Careful physiological and imaging studies.

(2) Novel result showing the role of SST+ neurons in shaping responses.

(3) Good use of a knockout animal to further the main hypothesis.

(4) Clear analytical techniques.

Comments on revisions:

The authors have effectively responded to my initial critiques - I have no further concerns.

Reviewer #3 (Public review):

Summary:

This study investigates the functional differences between barrel and septal columns in the mouse somatosensory cortex, focusing on how local inhibitory dynamics (particularly involving SST⁺ interneurons) may mediate temporal integration of multi-whisker (MW) stimuli in septa. Using a combination of in vivo multi-unit recordings, calcium imaging, and anatomical tracing, the authors propose a model in which Elfn1-dependent synaptic facilitation onto SST⁺ interneurons contributes to the distinct sensory responses to MW input in barrels and septa, enabling functional segregation between these domains.

Strengths:

The study presents a thought-provoking and useful conceptual model for understanding sensory processing in the somatosensory cortex. While barrel columns have been widely studied, septal regions remain relatively understudied in mice. If septa indeed act as selective integrators of distributed sensory input, this would suggest a novel computational role for cortical microcircuits beyond the classical view focused on barrels. Although still hypothetical, the proposed model in which SST⁺ interneurons contribute to domain-specific sensory responses between barrel and septal domains is intriguing and opens new avenues for investigating inhibitory circuit mechanisms.

Weaknesses:

The primary limitation of this study lies in the spatial and cellular specificity of the recording techniques. The physiological data rely predominantly on unsorted multi-unit activity (MUA) recorded with low-channel-count silicon probes. Because MUA aggregates signals from multiple neurons over a radius of approximately 50-100 µm (often wider than the typical septal width in mice), this approach makes it difficult to confidently isolate activity originating strictly from within septal domains. The manuscript would benefit from additional analyses to validate the spatial specificity of these recordings, such as systematically varying spike detection thresholds to test the robustness of domain attribution, as suggested by the reviewer. Furthermore, although the authors now appropriately frame their findings in the Elfn1 knockout mice as indirect evidence, it is worth emphasizing that the study lacks direct in vivo, cell-type-specific recordings and manipulations to more definitively test the proposed mechanism.

Reviewer #2 (Public review):

Summary:

In this manuscript, Pereira de Castro and coworkers are studying potential competition between a more standard splicing factor SF1 and an alternative splicing factor called QK1. This is interesting because they bind to overlapping sequence motifs and could potentially have opposing effects on promoting the splicing reaction. To test this idea, the authors KD either SF1 or QK1 in mammalian cells and uncover several exons whose splicing regulation follows the predicted pattern of being promoted for splicing by SF1 and repressed by QK1. Importantly, these have introns enriched in SF1 and QK1 motifs. The authors then focus on one exon in particular with two tandem motifs to study the mechanism of this in greater detail and their results confirm the competition model. Mass spec analysis largely agrees with their proposal; however, it is complicated by apparently quick transition of SF1 bound complexes to later splicing intermediates. An inspired experiment in yeast shows how QK1 competition could potentially have a determinental impact on splicing in an orthogonal system. Overall these results show how splicing regulation can be achieved by competition between a "core" and alternative splicing factor and provide additional insight into the complex process of branch site recognition. The manuscript is exceptionally clear and the figures and data very logically presented. The work will be valuable to those in the splicing field who are interested in both mechanism and bioinformatics approaches to deconvolve any apparent "splicing code" being used by cells to regulate gene expression.

Strengths:

(1) The main discovery of the manuscript involving evidence for SF1/QK1 competition is quite interesting and important for this field. This evidence has been missing and may change how people think about branch site recognition.

(2) The experiments and the rationale behind them are clearly and logically presented.

(3) The experiments are carried out to a high standard and well-designed controls are included.

(4) The extrapolation of the result to yeast in order to show the potentially devastating consequences of QK1 competition was creative and informative.

Weaknesses:

Overall the weaknesses are relatively minor and involve cases where conclusions could potentially have been strengthened with additional experimentation. For example, pull-down of the U2 snRNP could be strengthened by detection of the snRNA whereas the proteins may themselves interact with these factors in the absence of the snRNA. In addition the discussion is a bit speculative given the data, but compelling nonetheless.

Reviewer #3 (Public review):

Summary:

In this manuscript the authors were trying to establish whether competition between the RNA binding proteins SF1 and QKI controlled splicing outcomes. These two proteins have similar binding sites and protein sequences, but SF1 lacks a dimerization motif and seems to bind a single version of the binding sequence. Importantly, these binding sequences correspond to branchpoint consensus sequences, with SF1 binding leading to productive splicing, but QKI binding leading instead to association with paraspeckle proteins. They show that in human cells SF1 generally activates exons and QKI represses, and a large group of the jointly regulated exons (43% of joint targets) are reciprocally controlled by SF1 and QKI. They focus on one of these exons RAI14 that shows this reciprocal pattern of regulation, and has 2 repeats of the binding site that make it a candidate for joint regulation, and confirm regulation within a minigene context. The authors used assembly of proteins within nuclear extracts to explain the effect of QKI versus SF1 binding. Finally the authors show that expression of QKI is lethal in yeast, and causes splicing defects.

How this fits in the field. This study is interesting and provides a conceptual advance by providing a general rule how SF1 and QKI interact with relation to binding sites, and the relative molecular fates followed, so is very useful. Most of the analysis seems to focus on one example, but the choice of this example was carefully explained in the text. The molecular analysis and global work significantly adds to the picture from the previously published paper about NUMB joint regulation by QKI and SF (Zong et al, cited in text as reference 50, that looked at SF1 and QKI binding in relation to a duplicated binding site/branchpoint sequence in NUMB).

Strengths:

The data presented are strong and clear. The ideas discussed in this paper are of wide interest, and present a simple model where two binding sites generates a potentially repressive QKI response, whereas exons that have a single upstream sequence are just regulated by SF1. The assembly of splicing complexes on RNAs derived from RAI14 in nuclear extracts, followed by mass spec gave interesting mechanistic insight into what was occurring as a result of QKI versus SF1 binding.

Weaknesses:

The authors have addressed the previous weaknesses of the study, resulting in a much stronger manuscript

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors investigate the nanoscopic distribution of glycine receptor subunits in the hippocampus, dorsal striatum, and ventral striatum of the mouse brain using single-molecule localization microscopy (SMLM). They demonstrate that only a small number of glycine receptors are localized at hippocampal inhibitory synapses. Using dual-color SMLM, they further show that clusters of glycine receptors are predominantly localized within gephyrin-positive synapses. A comparison between the dorsal and ventral striatum reveals that the ventral striatum contains approximately eight times more glycine receptors and this finding is consistent with electrophysiological data on postsynaptic inhibitory currents. Finally, using cultured hippocampal neurons, they examine the differential synaptic localization of glycine receptor subunits (α1, α2, and β). This study is significant as it provides insights into the nanoscopic localization patterns of glycine receptors in brain regions where this protein is expressed at low levels. Additionally, the study demonstrates the different localization patterns of GlyR in distinct striatal regions and its physiological relevance using SMLM and electrophysiological experiments. However, several concerns should be addressed.

Specific comments on the original version:

(1) Colocalization analysis in Figure 1A. The colocalization between Sylite and mEos-GlyRβ appears to be quite low. It is essential to assess whether the observed colocalization is not due to random overlap. The authors should consider quantifying colocalization using statistical methods, such as a pixel shift analysis, to determine whether colocalization frequencies remain similar after artificially displacing one of the channels.

(2) Inconsistency between Figure 3A and 3B. While Figure 3B indicates an ~8-fold difference in the number of mEos4b-GlyRβ detections per synapse between the dorsal and ventral striatum, Figure 3A does not appear to show a pronounced difference in the localization of mEos4b-GlyRβ on Sylite puncta between these two regions. If the images presented in Figure 3A are not representative, the authors should consider replacing them with more representative examples or providing an expanded images with multiple representative examples. Alternatively, if this inconsistency can be explained by differences in spot density within clusters, the authors should explain that.

(3) Quantification in Figure 5. It is recommended that the authors provide quantitative data on cluster formation and colocalization with Sylite puncta in Figure 5 to support their qualitative observations.

(4) Potential for pseudo replication. It's not clear whether they're performing stats tests across biological replica, images, or even synapses. They often quote mean +/- SEM with n = 1000s, and so does that mean they're doing tests on those 1000s? Need to clarify.

(5) Does mEoS effect expression levels or function of the protein? Can't see any experiments done to confirm this. Could suggest WB on homogenate, or mass spec?

(6) Quantification of protein numbers is challenging with SMLM. Issues include i) some of FP not correctly folded/mature, and ii) dependence of localisation rate on instrument, excitation/illumination intensities, and also the thresholds used in analysis. Can the authors compare with another protein that has known expression levels- e.g. PSD95? This is quite an ask, but if they could show copy number of something known to compare with, it would be useful.

(7) Rationale for doing nanobody dSTORM not clear at all. They don't explain the reason for doing the dSTORM experiments. Why not just rely on PALM for coincidence measurements, rather than tagging mEoS with a nanobody, and then doing dSTORM with that? Can they explain? Is it to get extra localisations- i.e. multiple per nanobody? If so, localising same FP multiple times wouldn't improve resolution. Also, no controls for nanobody dSTORM experiments- what about non-spec nb, or use on WT sections?

(8) What resolutions/precisions were obtained in SMLM experiments? Should perform Fourier Ring Correlation (FRC) on SR images to state resolutions obtained (particularly useful for when they're presenting distance histograms, as this will be dependent on resolution). Likewise for precision, what was mean precision? Can they show histograms of localisation precision.

(9) Why were DBSCAN parameters selected? How can they rule out multiple localisations per fluor? If low copy numbers (<10), then why bother with DBSCAN? Could just measure distance to each one.

(10) For microscopy experiment methods, state power densities, not % or "nominal power".

(11) In general, not much data presented. Any SI file with extra images etc.?

(12) Clarification of the discussion on GlyR expression and synaptic localization: The discussion on GlyR expression, complex formation, and synaptic localization is sometimes unclear, and needs terminological distinctions between "expression level", "complex formation" and "synaptic localization". For example, the authors state: "What then is the reason for the low protein expression of GlyRβ? One possibility is that the assembly of mature heteropentameric GlyR complexes depends critically on the expression of endogenous GlyR α subunits." Does this mean that GlyRβ proteins that fail to form complexes with GlyRα subunits are unstable and subject to rapid degradation? If so, the authors should clarify this point. The statement "This raises the interesting possibility that synaptic GlyRs may depend specifically on the concomitant expression of both α1 and β transcripts." suggests a dependency on α1 and β transcripts. However, is the authors' focus on synaptic localization or overall protein expression levels? If this means synaptic localization, it would be beneficial to state this explicitly to avoid confusion. To improve clarity, the authors should carefully distinguish between these different aspects of GlyR biology throughout the discussion. Additionally, a schematic diagram illustrating these processes would be highly beneficial for readers.

(13) Interpretation of GlyR localization in the context of nanodomains. The distribution of GlyR molecules on inhibitory synapses appears to be non-homogeneous, instead forming nanoclusters or nanodomains, similar to many other synaptic proteins. It is important to interpret GlyR localization in the context of nanodomain organization.

Significance:

The paper presents biological and technical advances. The biological insights revolve mostly on the documentation of Glycine receptors in particular synapses in forebrain, where they are typically expressed at very low levels. The authors provide compelling data indicating that the expression is of physiological significance. The authors have done a nice job of combining genetically tagged mice with advanced microscopy methods to tackle the question of distributions of synaptic proteins. Overall, these advances are more incremental than groundbreaking.

Comments on revised version:

The authors have addressed the majority of the significant issues raised in the review and revised the manuscript appropriately. One issue that can be further addressed relates to the issue of pseudo-replication. The authors state in their response that "All experiments were repeated at least twice to ensure reproducibility (N independent experiments). Statistical tests were performed on pooled data across the biological replicates; n denotes the number of data points used for testing (e.g., number of synaptic clusters, detections, cells, as specified in each case).". This suggests that they're not doing their stats on biological replicates, and instead are pseudo replicating. It's not clear how they have ensured reproducibility, when the stats seem to have been done on pooled data across repeats.

Reviewer #2 (Public review):

Summary:

In their manuscript "Single molecule counting detects low-copy glycine receptors in hippocampal and striatal synapses" Camuso and colleagues apply single molecule localization microscopy (SMLM) methods to visualize low copy numbers of GlyRs at inhibitory synapses in the hippocampal formation and the striatum. SMLM analysis revealed higher copy numbers in striatum compared to hippocampal inhibitory synapses. They further provide evidence that these low copy numbers are tightly linked to post-synaptic scaffolding protein gephyrin at inhibitory synapses. Their approach profits from the high detection sensitivity and resolution of SMLM and challenges the controversial view on the presence of GlyRs in these formations although there are reports (electrophysiology) on the presence of GlyRs in these particular brain regions. These new datasets in the current manuscript may certainly assist in understanding the complexity of fundamental building blocks of inhibitory synapses.

Strengths:

The manuscript provides new insights to presence of low-copy numbers by visualizing them via SMLM. This is the first report that visualizes GlyR optically in the brain applying the knock-in model of mEOS4b tagged GlyRß and quantifies their copy number comparing distribution and amount of GlyRs from hippocampus and striatum. Imaging data correspond well to electrophysiological measurements in the manuscript.

Comments on revised version:

My concerns have been successfully addressed by the authors during the revision process.

Reviewer #3 (Public review):

In this study, Camuso et al., make use of a knock-in mouse model expressing endogenously mEos4b-tagged GlyRβ subunits to detect endogenous glycine receptors in mouse brain using single-molecule localization microscopy (SMLM). At synapses in the hippocampus GlyRβ molecules are detected at very low copy numbers. Assuming that each detected GlyRβ molecule is incorporated in a pentameric glycine receptor, it was estimated that while the majority of hippocampal inhibitory synapses do not contain glycine receptors, a small population of inhibitory synapses contain a few (up to 10) glycine receptors. Using dual-color SMLM approaches it is furthermore confirmed that the detected GlyRβ molecules are embedded in the postsynaptic domain marked by gephyrin. In contrast to the hippocampus, at inhibitory synapses in the striatum GlyRβ molecules were detected at considerably higher copy numbers. Interestingly, the observed number of GlyRβ detections was significantly higher in the ventral striatum compared to the dorsal striatum. These findings are corroborated by electrophysiological recordings showing that postsynaptic glycinergic currents can be readily detected in the ventral striatum but are almost absent in the dorsal striatum. Using lentiviral overexpression of recombinant GlyRalpha1, alpha2, and beta subunits in cultured hippocampal neurons, it is shown that GlyR alpha1 subunits are readily detectable at synapses, but overexpressed GlyRalpha2 and beta subunits did not strongly enrich at synapses. This could indicate that GlyRa1 expression is limiting the synaptic expression of GlyRβ-containing glycine receptors in hippocampal neurons.

Comments on revised version:

This revised manuscript is significantly improved. New experimental and quantitative analysis is presented that strengthen the conclusions. Overall, the results presented in this manuscript are based on carefully performed SMLM experiments and are well-presented and described. The knock-in mouse with endogenously tagged GlyRβ molecules is a very strong aspect of this study and provides confidence in the labeling, the combination with SMLM is very strong as it provides high sensitivity and spatial resolution. These results confirm previous studies and will be of interest to a specialised audience interested in glycine receptors, inhibitory synapse biology and super-resolution microscopy.

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 if the target organs provides a solid basis for advancing the understanding 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.

Comments on revisions:

The manuscript has improved after fixing many small issues/errors. The new sections in the discussion are likewise adding to the quality of the manuscript.

Reviewer #2 (Public review):

Summary:

Cheverra et al. present a comprehensive anatomical and functional analysis of the motor neurons innervating the male reproductive tract in Drosophila melanogaster, addressing a gap in our understanding of the peripheral circuits underlying ejaculation and male fertility. They identify two classes of multi-transmitter motor neurons-OGNs (octopamine/glutamate) and SGNs (serotonin/glutamate)-with distinct innervation patterns across reproductive organs. The authors further characterize the differential expression of glutamate, octopamine, and serotonin receptors in both epithelial and muscular tissues of these organs. Behavioral assays reveal that SGNs are essential for male fertility, whereas OGNs and glutamatergic transmission are dispensable. This work provides a high-resolution map linking neuromodulatory identity to organ-specific motor control, offering a valuable framework to explore the neural basis of male reproductive function.

Strengths:

Through the use of an extensive set of GAL4 drivers and antibodies, this work successfully and precisely defines the neurons that innervate the male reproductive tract, identifying the specific organs they target and the nature of the neurotransmitters they release. It also characterizes the expression patterns and localization of the corresponding neurotransmitter receptors across different tissues. The authors describe two distinct groups of dual-identity neurons innervating the male reproductive tract: OGNs, which co-express octopamine and glutamate, and SGNs, which co-express serotonin and glutamate. They further demonstrate that the various organs within the male reproductive system differentially express receptors for these neurotransmitters. Based on these findings, the authors propose that a single neuron capable of co-releasing a fast-acting neurotransmitter along side a slower-acting one may more effectively synchronize and stagger events that require precise timing. This, together with the differential expression of ionotropic glutamate receptors and metabotropic aminergic receptors in postsynaptic muscle tissue, adds an additional layer of complexity to the coordinated regulation of fluid secretion, organ contractility, and directional sperm movement-all contributing to the optimization of male fertility.

Weaknesses:

One potential limitation of the study is the absence of information regarding the number of individuals examined for the various characterizations, which may weaken the strength of the conclusions. Another limitation may be the lack of quantitative analyses in the colocalization and morphological differentiation experiments. Nevertheless, the authors have indicated that such quantifications will be provided in a forthcoming publication; therefore, this should be considered only a partial limitation, as it is expected to be addressed in the near future.

Wider context:

This study delivers the first detailed anatomical map connecting multi-transmitter motor neurons with specific male reproductive structures. It highlights a previously unrecognized functional specialization between serotonergic and octopaminergic pathways and lays the groundwork for exploring fundamental neural mechanisms that regulate ejaculation and fertility in males. The principles uncovered here may help explain how males of Drosophila and other organisms adjust reproductive behaviors in response to environmental changes. Furthermore, by shedding light on how multi-transmitter systems operate in reproductive control, this model could provide insights into therapeutic targets for conditions such as male infertility and prostate cancer-where similar neuronal populations are involved in humans. Ultimately, this genetically accessible system serves as a powerful tool for uncovering how multi-transmitter neurons orchestrate coordinated physiological actions necessary for the functioning of complex organs.

Reviewer #3 (Public review):

Summary:

This work provides an overview of the motor neuron landscape in the male reproductive system. Some work had been done to elucidate the circuits of ejaculation in the spine, as well as, the cord but this work fills a gap of knowledge at the level of the reproductive organs. Using complementary approaches the authors show that there are two types of motor neurons that are mutually exclusive: neurons that co-express octopamine and glutamate and neurons that co-express serotonin and glutamate. They also show evidence that both types of neurons express large dense core vesicles indicating that neuropeptides play a role in male fertility. This paper provides a thorough characterization of expression of the different glutamate, octopamine and serotonin receptors in the different organs and tissues of the male reproductive system. The differential expression in different tissues and organs allows building initial theories on the control of emission and expulsion. Additionally, the authors characterize the expression of synaptic proteins and the neuromuscular junction sites. On a mechanistic level, the authors show that neither octopamine/glutamate neuron transmission nor glutamate transmission in serotonin/glutamate neurons are required for male fertility. This final result is quite surprising and opens up many questions on how ejaculation is coordinated.

Strengths:

This work fills an important gap on characterization of innervation of the male reproductive system by providing an extensive characterization of the motor neurons and the potential receptors of motor neuron release.The authors show convincing evidence of glutamate/monoamine co-release and of mutual exclusivity of serotonin/glutamate and octopamine/glutamate neurons.

Weaknesses:

The experiment looking at peristaltic waves in the male organs is missing labeling of the different regions and quantification of the observed waves.

Reviewer #1 (Public review):

Summary:

In this paper, the authors developed a chemical labeling reagent for P2X7 receptors, called X7-uP. This labeling reagent selectively labels endogenous P2X7 receptors with biotin based on ligand-directed NASA chemistry. After labeling the endogenous P2X7 receptor with biotin, the receptor can be fluorescently labeled with streptavidin-AlexaFluor647. The authors carefully examined the binding properties and labeling selectivity of X7-uP to P2X7, characterized the labeling site of P2X7 receptors, and demonstrated fluorescence imaging of P2X7 receptors. The data obtained by SDS-PAGE, Western blot, and fluorescence microscopy clearly shows that X7-uP labels the P2X7 receptor. Finally, the authors fluorescently labeled the endogenous P2X7 in BV2 cells, which are a murine microglia model, and used dSTORM to reveal a nanoscale P2X7 redistribution mechanism under inflammatory conditions at high resolution.

Strengths:

X7-uP selectively labels endogenous P2X7 receptors with biotin. Streptavidin-AlexaFluor647 binds to the biotin labeled to the P2X7 receptor, allowing visualization of endogenous P2X7 receptors.

Reviewer #2 (Public review):

Summary:

In this manuscript, Arnould et. al. develop an unbiased, affinity-guided reagent to label P2X7 receptor and use super-resolution imaging to monitor P2X7 redistribution in response to inflammatory signaling.

Strengths:

I think the X7-uP probe that they developed is very useful for visualizing localization of P2X7 receptor. They convincingly show that under inflammatory conditions, there is a reorganization of P2X7 localization into receptor clusters. Moreover, I think they have shown a very clever way to specifically label any receptor of interest. This has broad appeal.

I think the authors have done a very nice job addressing my original concerns. Here are those original concerns and my new comments related to how the authors address them.

(1) While the authors state that chemical modification of AZ10606120 to produce the X7-UP reagent has "minimal impact" on the inhibition of P2X7, we can see from Figure 2A and 2B that it does not antagonize P2X7 as effectively as the original antagonist. For the sake of completeness and quantitation, I think it would be great if the authors could determine the IC50 for X7-uP and compare it to the IC50 of AZ10606120.

The authors now show the relative inhibition of X7-uP compared to AZ10606120 at different concentrations. This provides a nice comparison to give the reader an idea of how effectively X7-uP inhibits P2X7 receptor. This is great.

(2) Do the authors know whether modification of the lysines with biotin affects the receptor's affinity for ATP (or ability to be activated by ATP)? What about P2X7 that has been modified with biotin and then labeled with Alexa 647? For the sake of completeness and quantitation, I think it would be great if the authors could determine the EC50 of biotinylated P2X7 for ATP as well as biotinylated and then Alexa 647 labeled P2X7 for ATP and compare these values to the affinity of unmodified WT P2X7 for ATP.

I agree with the authors that assessing the functional integrity of P2X7 following biotinylation and fluorophore labeling is outside the scope of this paper but would be important for studies involving dynamic or post-labeling functional analyses such as live trafficking.

(3) It is a little misleading to color the fluorescence signal from mScarlet green (for example, in Figure 3 and Figure 4). The fluorescence is not at the same wavelength as GFP. In fact, the wavelength (570 nm - 610 nm) for emission is closer to orange/red than to green. I think this color should be changed to differentiate the signal of mScarlet from the GFP signal used for each of the other P2X receptor subtypes.

The authors have now changed the mScarlet color to orange, which solves my concern.

(4) It is my understanding that P2X6 does not form homotrimers. Thus, I was a little surprised to see that the density and distribution of P2X6-GFP in Figure 3 looks very similar to the density and distribution of the other P2X subtypes. Do the authors have an explanation for this? Are they looking at P2X6 protomers inserted into the plasma membrane? Does the cell line have endogenous P2X receptor subtypes? Is Figure 3 showing heterotrimers with P2X6 receptor? A little explanation might be helpful.

The authors address this point very well and include nice data to show that P2X6 does not insert into the plasma membrane as a homotrimer.

(5) It is easy to overlook the fact that the antagonist leaves the binding pocket once the biotin has been attached to the lysines. It might be helpful if the authors made this a little more apparent in Figure 1 or in the text describing the NASA chemistry reaction.

The authors have modified Figure 1 to make it easier to understand the NASA chemistry reaction.

I congratulate the authors on an outstanding paper!

Reviewer #1 (Public review):

Summary:

The co-localization of large conductance calcium- and voltage activated potassium (BK) channels with voltage-gated calcium channels (CaV) at the plasma membrane is important for the functional role of these channels in controlling cell excitability and physiology in a variety of systems. An important question in the field is where and how do BK and CaV channels assemble as 'ensembles' to allow this coordinated regulation - is this through preassembly early in the biosynthetic pathway, during trafficking to the cell surface or once channels are integrated into the plasma membrane. These questions also have broader implications for assembly of other ion channel complexes. Using an imaging based approach, this paper addresses the spatial distribution of BK-CaV ensembles using both overexpression strategies in tsa201 and INS-1 cells and analysis of endogenous channels in INS-1 cells using proximity ligation and superesolution approaches. In addition, the authors analyse the spatial distribution of mRNAs encoding BK and Cav1.3. The key conclusion of the paper that BK and CaV1.3 are co-localised as ensembles intracellularly in the ER and Golgi is well supported by the evidence. The experiments and analysis are carefully performed and the findings are very well presented.

Reviewer #3 (Public review):

Summary:

The authors present a clearly written and beautifully presented piece of work demonstrating clear evidence to support the idea that BK channels and Cav1.3 channels can co-assemble prior to their assertion in the plasma membrane.

Strengths:

The experimental records shown back up their hypotheses and the authors are to be congratulated for the large number of control experiments shown in the ms.

Reviewer #1 (Public review):

Summary:

Adult (4mo) rats were tasked to either press one lever for an immediate reward or another for a delayed reward. The task had an adjusting amount structure in which (1) the number of pellets provided on the immediate reward lever changed as a function of the decisions made, (2) rats were prevented from pressing the same lever three times in a row.

While the authors have been very responsive to the reviews, and I appreciate that, unfortunately, the new analyses reported in this revision actually lead me to deeper concerns about the adequacy of the data to support the conclusions. In this revision, it has become clear that the conclusions are forced and not supported by the data. Alternative theories are not considered or presented. This revision has revealed deep problems with the task, the analyses, and the modeling.

Data Weaknesses

Most importantly, the inclusion of the task behavior data has revealed a deep problem with the entire structure of the data. As is obvious in Figure 1D, there is a slow learning effect that is changing over the sessions as the animals learn to stop taking the delayed outcome. Unfortunately, the 8s delays came *after* the 4s. The first 20 sessions contain 19 4s delays and 1 8s delay, while the last 20 sessions contain 14 8s delays and 6 4s delays. Given the changes across sessions, it is likely that a large part of the difference is due to across-session learning (which is never addressed or considered).

These data are not shown by subject and I suspect that individual subjects did all 4s then all 8s and some subjects switched tasks at different times. If my suspicion is true, then any comparisons between the 4s and 8s conditions (which are a major part of the author's claims) may have nothing to do with the delays, but rather with increased experience on the task.

Furthermore, the four "groups", which are still poorly defined, seem to have been assessed at a session-by-session level. So when did each animal fall into a given group? Why is Figure 1D not showing which session fell into which group and why are we not seeing each animal's progression? They also admit that animals used a mixture of strategies, which implies that the "group" assignment is an invalid analysis, as the groups do not accommodate strategy mixing.

Figure 2 shows that none of the differences of the group behavior against random choice with a basic p(delay) are significant. The use a KS test to measure these differences. KS tests are notoriously sensitive as KS tests simply measure whether there are any statistical differences between two distributions. They do not report the full statistics for Figure 2, but only say that the 4HI group was not significant (KS p-value = 0.72) and the 8LO showed a p-value of 0.1 (which they interpret as significant). p=0.1 is not significant. They don't report the value of the 4LO or 8HI groups (why not?), but say they are in-between these two extremes. That means *none* of the differences are significant.

They then test a model with additional parameters, and say that the model includes more than the minimal p_D parameter, but never report BIC or AIC model comparisons. In order to claim that the model is better than the bare p_D assumption, they should be reporting model-comparison statistics. But given that the p_D parameters are enough (q.v. Figure 2), this entire model seems unnecessary

It took me a while to determine what was being shown in Figure 3, but I was eventually able to determine that 0 was the time after the animal made the choice to wait out the delay side, so the 4s in Figure 3A1 with high power in the low-frequency (<5 Hz) range is the waiting time. They don't show the full 8s time. Nor do they show the spectrograms separated by group (assuming that group is the analytical tool they are using). In B they show only show theta power, but it is unclear how to interpret these changes over time.

In Figure 4, panel A is mostly useless because it is just five sample sessions showing firing rate plotted on the same panels as the immediate reward amount. If they want to claim correlation, they should show and test it. But moreover, this is not how neural data should be presented - we need to know what the cells are doing, population-wise. We need to have an understanding of the neural ensemble. These data are clearly being picked and chosen, which is not OK.

Figure 4, panels B and C show that the activity trivially reflects the reward that has been delivered to the animal, if I am understanding the graphs correctly. (The authors do not interpret it this way, but the data is, to my eyes, clear.) The "immediate" signal shows up immediately at choice and reflects the size of the immediate reward (which is varying). The "delay" signal shows up after the delay and does not, which makes sense as the animals get 6 pellets on the delayed side no matter what. In fact, the max delayed side activity = the max immediate side activity, which is 6 pellets. This is just reward-related firing.

Figure 5 is poorly laid out, switching the order in 5C to be 2 1 3 in E and F. (Why?!) The statistics for Figure 5 on page 17 should be asking whether there are differences between neuron types, not whether there is a choice x time interaction in a given neuron type. When I look at Figure 5F1-3, all three types look effectively similar with different levels of noise. It is unclear why they are doing this complicated PC analysis or what we should be drawing from it.

Figure 6 mis-states pie charts as "total number" rather than proportions.

Interpretation Weaknesses

The separation of cognitive effort into "resource-based" and "resistance-based" seems artificial to me. I still do not understand why the ability to resist a choice does not also depend on resource or why using resources are not a form of resistance. Doesn't every action in the end depend on the resources one has available? And doesn't every use of a resource resist one option by taking another? Even if one buys these two separate cognitive control processes (which at this point in reading the revision, I do not), the paper starts from the assumption that a baseline probability of waiting out the delays is a "resistance-based cognitive control" (why?) and a probability of choice that takes into account the size of the immediate value (confusingly abbreviated as ival) is a "resource-based cognitive control" (again, why?)

Reviewer #2 (Public review):

Summary:

I appreciate the considerable work the authors have done on the revision. The manuscript is markedly improved.

Strengths still include the strong theoretical basis, well-done experiments, and clear links to LFP / spectral analyses that have links to human data. The task is now more clearly explained, and the neural correlates better articulated.

Weaknesses:

I had remaining questions, many related to my previous questions.<br /> (1) The results have some complexity, but I still had questions about which is resource and which is resistance based. The authors say in the last sentence of the discussion: "Prominent pre-choice theta power was associated with a behavioral strategy characterized by a strong bias towards a resistance-based strategy, whereas the neural signature of ival-tracking was associated with a strong bias towards a resource-based strategy.".<br /> I might suggest making this simpler and clear in the abstract and the first paragraph of the discussion. A simple statement like 'pre-choice theta was biased towards resistance whereas single neurons were biased towards resources" might make this idea come across?

(2) I think most readers would like to see raw single trial LFP traces in Figure 3, single unit rasters in Figure 4, and spike-field records in Figure 5.

(3) What limitations are there to this work? I wonder if readers might benefit from some contextualization - the sample size, heterogenous behavior - lack of cell-type specificity - using PC3 to define spectral relationships - I might suggest pointing these out.

(4) I still wasn't sure what 4 Hz vs. theta 6-12 Hz meant - is it all based on PC3's pos/neg correlation? I wonder if showing a scatter plot with the y-axis being PC3 and the x-axis being theta 4 Hz power would help distinguish these? Is this the first time this sort of analysis has been done? If so, it requires clearer definitions.

Reviewer #3 (Public review):

Summary:

The study investigated decision making in rats choosing between small immediate rewards and larger delayed rewards, in a task design where the size of the immediate rewards decreased when this option was chosen and increased when it was not chosen. The authors conceptualise this task as involving two different types of cognitive effort; 'resistance-based' effort putatively needed to resist the smaller immediate reward, and 'resource-based' effort needed to track the changing value of the immediate reward option. They argue based on analyses of the behaviour, and computational modelling, that rats use different strategies in different sessions, with one strategy in which they preferentially choose the delayed reward option irrespective of the current immediate reward size, and another strategy in which they preferentially choose the immediate reward option when the immediate reward size is large, and the delayed reward option when the immediate reward size is small. The authors recorded neural activity in anterior cingulate cortex. They propose that oscillatory activity in the 6-12Hz theta band occurs when subjects use a 'resistance-based' strategy of choosing the delayed option irrespective of the current value of the immediate reward option. They also examine neural representation of the current value of the immediate reward option, and suggest that this value is more strongly represented when subjects are using this value information to guide choice. They further argue that neurons whose activity is modulated by theta oscillations are less involved in tracking the value of the immediate reward option than neurons whose activity is not theta modulated. If solid, these findings will be of interest to researchers working on cognitive control and ACCs involvement in decision making. However, there are some issues with the modelling and analysis which preclude high confidence in the validity of the conclusions.

Strengths:

The behavioural task used is interesting and the recording methods used (64 channel silicon probes) should enable the collection of good quality single unit and LFP electrophysiology data. The authors recorded from a sizable sample of subjects for this type of study. The approach of splitting the data into sessions where subjects used different strategies and then examining the neural correlates of each is in principle interesting, though I have some reservations about the strength of evidence for the existence of multiple strategies.

Limitations:

The dataset is unbalanced in terms of both the number of sessions contributed by each subject, and their distribution across the different putative behavioural strategies (see Table 1), with some subjects contributing 7 sessions to a given strategy and others 0. Further, only 2 of 10 subjects contribute any sessions to one of the behavioural strategies (8LO), and a single subject contributes >50% of the sessions (7 of 13) sessions to another strategy (8HI). Apparent differences in brain activity between the strategies could therefore in fact reflect differences between subjects, which could arise due to e.g. differences in electrode placement. To make firm conclusions that neural activity is different in sessions where different strategies are thought to be employed, it would be necessary to account for potential cross-subject variation in the data. The current statistical methods don't appear to do this as they use within subject measures (e.g. trials or neurons) as the experimental unit and ignore which subject the neuron/trial came from.

The starting point for the analysis was the splitting of sessions into 4 groups based on the duration of the delay (4 vs 8 seconds) and then clustering within each delay category into two sub-groups. It was not clear why 2 clusters per delay category were used, nor whether the data did in fact have a clear split into two distinct clusters or continuous variation across the population of sessions. The simplified RL model used in the revised manuscript (which is an improvement from that used in the previous version) could in principle help to quantify variation across the populations of sessions, by using model fitting and comparison methods to evaluate variation in strategy across subjects. However, as far as I could tell no model-fitting or comparison was performed, and the only attempt to link the model to data was by simulating data using a fixed probability of choosing the delayed lever (i.e. with no learning across trials) and comparing the distribution of total rewards obtained per session with that of the subjects in each group (Figure 2). Total reward per session is a very coarse behavioural metric and using likelihood-based methods to fit model parameters to subjects trial-by-trial choice data would provide a more sensitive way of using the modelling to assess behavioural strategy across sessions.

Conceptually, it is not obvious that choices towards the delayed vs immediate lever reflect use of different strategies employing different types of cognitive effort. Rather these could reflect a single strategy which compares the estimated value of the two levers, with differences in behaviour between sessions accounted for either by differences in the task itself (between the 8s and 4s delay condition) or differences in the parameters of the strategy, such as the strength of temporal discounting.

Even if one accepts the claim that the task recruits two distinct types of cognitive control, the argument that theta oscillations, which occur on delay choice trials in the 4s delay condition, are a correlate of a 'resistance-based' strategy (resisting the immediate reward), is hard to reconcile with the fact that theta oscillations do not occur on delay choice trials in the 8s delay condition (Figure 3). The authors note this discrepancy, but state that 'The reason was because these groups largely avoided the delayed lever (Figure 1) and thereby abandoned the need to implement resistance-based control altogether.' However, the data in Figure 1D show that even in the 8s condition the subjects choose the delayed lever on around 50% of trials. It is not obvious why choosing the delayed lever on 50% of trials in the 8s condition does not require 'resistance-based' cognitive effort, while choosing it in the 4s delay condition does.

The other main claims regarding the neural data are that the neuronal representation of the value of the immediate reward lever (ival) is stronger in sessions where subjects are choosing that lever more often, particularly the 8LO group, and that neurons whose activity tracks ival are a different population from neurons whose activity is theta modulated. However, the analysis methods used to make these claims are rather convoluted and make it hard to assess the strength of the evidence for them.

To evaluate the strength of ival representation in neural activity, the authors first fit a regression model predicting each neuron's activity at different timepoints as a function of behavioural variables including ival, which is a sensible first step. However, they then perform clustering on the regression coefficients and then plot neural activity only for the cluster which they state 'provided the clearest example of value tracking'. It is not clear how the clustering was done, whether there were in fact well defined clusters in the neural activity, how the clusters whose activity is plotted were chosen, nor the proportion of neurons in this cluster for each group of sessions. The analysis therefore provides only limited information about the strength of ival representation in different session groups. It would be useful to quantify the variance explained by ival in neural activity for each group of sessions using a simpler quantification of the regression analysis, such as cross-validated coefficient of partial determination.

The analysis of how theta modulation related to representation of ival across neurons was also complicated and non-standard. To determine whether individual neurons were theta modulated, the authors did PCA on a matrix comprised of spike train autocorrelations for individual neurons, and then grouped neurons according to the projection of their autocorrelation function onto the 3rd Principal Component, on the basis that neurons with negative projection onto this component showed a peak roughly at theta frequency in the power spectrum of their autocorrelation. Even ignoring the fact that the peak in the power spectrum is broad and centred above the standard theta frequency (see figure 5B3), this is an arbitrary and unnecessarily complex way to determine if neurons are theta modulated. It would be much simpler and greatly preferable to either directly assess the modulation depth of individual neurons spike train autocorrelation in the theta band, or to use a metric of spike-LFP coupling in the theta band instead. The authors do include some analysis of spike field coherence in Figure 6 and this is a much more sensible approach. However, it is worth noting that the only session group which shows a difference in coherence at theta frequency relative to the other groups is 8LO, to which only 2 of 8 animals contribute any data and 70% of sessions come from one animal. It is therefore unclear whether differences in this group are due to differences in behavioural strategy, or reflect other sources of cross-animal variation.

Reviewer #1 (Public review):

The paper from Hudait and Voth details a number of coarse-grained simulations as well as some experiments focused on the stability of HIV capsids in the presence of the drug lenacapavir. The authors find that LEN hyperstabilizes the capsid, making it fragile and prone to breaking inside the nuclear pore complex.

I found the paper interesting. I have a few suggestions for clarification and/or improvement.

(1) How directly comparable are the NPC-capsid and capsid-only simulations? A major result rests on the conclusion that the kinetics of rupture are faster inside the NPC, but are the numbers of LENs bound identical? Is the time really comparable, given that the simulations have different starting points? I'm not really doubting the result, but I think it could be made more rigorous/quantitative.

(2) Related to the above, it is stated on page 12 that, based on the estimated free-energy barrier, pentamer dissociation should occur in ~10 us of CG time. But certainly, the simulations cover at least this length of time?

(3) At first, I was surprised that even in a CG simulation, LEN would spontaneously bind to the correct site. But if I read the SI correctly, LEN was parameterized specifically to bind to hexamers and not pentamers. This is fine, but I think it's worth describing in the main text.

Reviewer #2 (Public review):

Here, Hudait et al. use CG modeling to investigate the mechanism by which lenacapavir (LEN) treats HIV capsids that dock to the nuclear pore complex (NPC). However, the manuscript fails to present meaningful findings that were previously unreported in the literature, and is thus of low impact. Many claims made in the manuscript are not substantiated by the presented data. Key mechanistic details that the work purports to reveal are artifacts of the parameterization choices or simulation/analysis design, with the simulations said to reveal details that they were specifically biased to reproduce. This makes the manuscript highly problematic, as its contributions to the literature would represent misconceptions based on oversights in modeling, and thus mislead future readers.

(1) Considering the literature, it is unclear that the manuscript presents new scientific discoveries. The following are results from this paper that have been previously reported:

(a) LEN-bound capsid can dock to the nuclear pore (Figure 2; see e.g. 10.1016/j.cell.2024.12.008 or 10.1128/mbio.03613-24).

(b) NUP98 interacts with the docked capsid (Figure 2; see e.g. 10.1016/j.virol.2013.02.008 or 10.1038/s41586-023-06969-7 or 10.1016/j.cell.2024.12.008).

(c) LEN and NUP98 compete for a binding interface (Figure 2; see e.g. 10.1126/science.abb4808 or 10.1371/journal.ppat.1004459).

(d) LEN creates capsid defects (Figure 3 and 5, see e.g. 10.1073/pnas.2420497122).

(e) RNP can emerge from a damaged capsid (Figure 3 and 5; see e.g. 10.1073/pnas.2117781119 or 10.7554/eLife.64776).

(f) LEN hyperstabilizes/reduces the elasticity of the capsid lattice (Figure 6; see e.g. 10.1371/journal.ppat.1012537).

(2) The mechanistic findings related to how these processes occur are problematic, either based on circular reasoning or unsubstantiated, based on the presented data. In some cases, features of parameterization and simulation/analysis design are erroneously interpreted as predictions by the CG models.

(a) Claim: LEN-bound capsids remain associated with the NPC after rupture. CG simulations did not reach the timescale needed to demonstrate continued association or failure to translocate, leaving the claim unsubstantiated.

(b) Claim: LEN contributes to loss of capsid elasticity. The authors do not measure elasticity here, only force constants of fluctuations between capsomers in freely diffusing capsids. Elasticity is defined as the ability of a material to undergo reversible deformation when subjected to stress. Other computational works that actually measure elasticity (e.g., 0.1371/journal.ppat.1012537) could represent a point of comparison, but are not cited. The changes in force constants in the presence of LEN are shown in Figure 6C, but the text of the scale bar legend and units of k are not legible, so one cannot discern the magnitude or significance of the change.

(c) Claim: Capsid defects are formed along striated patterns of capsid disorder. Data is not presented that correlates defects/cracks with striations.

(d) Claim: Typically 1-2 LEN, but rarely 3 bind per capsid hexamer. The authors state: "The magnitude of the attractive interactions was adjusted to capture the substoichiometric binding of LEN to CA hexamers (Faysal et al., 2024). ... We simulated LEN binding to the capsid cone (in the absence of NPC), which resulted in a substoichiometric binding (~1.5 LEN per CA hexamer), consistent with experimental data (Singh et al., 2024)." This means LEN was specifically parameterized to reproduce the 1-2 binding ratio per hexamer apparent from experiments, so this was a parameterization choice, not a prediction by CG simulations as the authors erroneously claim: "This indicates that the probability of binding a third LEN molecule to a CA hexamer is impeded, likely due to steric effects that prevent the approach of an incoming molecule to a CA hexamer where 2 LEN molecules are already associated. ... Approximately 20% of CA hexamers remain unoccupied despite the availability of a large excess of unbound LEN molecules. This suggests a heterogeneity in the molecular environment of the capsid lattice for LEN binding." These statements represent gross over-interpretation of a bias deliberately introduced during parameterization, and the "finding" represents circular reasoning. Also, if "steric effects" play any role, the authors could analyze the model to characterize and report them rather than simply speculate.

(e) Claim: Competition between NUP98 and LEN regulates capsid docking. The authors state: "A fraction of LEN molecules bound at the narrow end dissociate to allow NUP98 binding to the capsid ... Therefore, LEN can inhibit the efficient binding of the viral cores to the NPC, resulting in an increased number of cores in the cytoplasm." Capsid docking occurs regardless of the presence of LEN, and appears to occur at the same rate as the LEN-free capsid presented in the authors' previous work (Hudait &Voth, 2024). The presented data simply show that there is a fluctuation of bound LEN, with about 10 fewer (<5%) bound at the end of the simulation than at the beginning, and the curve (Figure 2A) does not clearly correlate with increased NUP98 contact. In that case, no data is shown that connects LEN binding with the regulation of the docking process. Further, the two quoted statements contradict each other. The presented data appear to show that NUP outcompetes LEN binding, rather than LEN inhibiting NUP binding. The "Therefore" statement is an attempt to reconcile with experimental studies, but is not substantiated by the presented data.

(f) Claim: LEN binding leads to spontaneous dissociation of pentamers. The CG simulation trajectories show pentamer dissociation. However, it is quite difficult to believe that a pentamer in the wide end of the capsid would dissociate and diffuse 100 nm away before a hexamer in the narrow end (previously between two pentamers and now only partially coordinated, also in a highly curved environment, and further under the force of the extruding RNA) would dissociate, as in Figure 2B. A more plausible explanation could be force balance between pent-hex versus hex-hex contacts, an aspect of CG parameterization. No further modeling is presented to explain the release of pentamers, and changes in pent-hex stiffness are not apparent in the force constant fluctuation analysis in Figure 6C.

(g) Claim: WTMetaD simulations predict capsid rupture. The authors state: "In WTMetaD simulations, we used the mean coordination number (Figure S6) between CA proteins in pentamers and in hexamers as the reaction coordinate." This means that the coordination number, the number of pent-hex contacts, is the bias used to accelerate simulation sampling. Yet the authors then interpret a change in coordination number leading to capsid rupture as a discovery, representing a fundamental misuse of the WTMetaD method. Changes in coordination number cannot be claimed as an emergent property when they are in fact the applied bias, when the simulation forced them to sample such states. The bias must be orthogonal to the feature of interest for that feature to be discoverable. While the reported free energies are orthogonal to the reaction coordinate, the structural and stepwise-mechanism "findings" here represent circular reasoning.

(3) Another major concern with this work is the excessive self-citation, and the conspicuous lack of engagement with similar computational modeling studies that investigate the HIV capsid and its interactions with LEN, capsid mechanical properties relevant to nuclear entry, and other capsid-NPC simulations (e.g., 10.1016/j.cell.2024.12.008 and 10.1371/journal.ppat.1012537). Other such studies available in the literature include examination of varying aspects of the system at both CG and all-atom levels of resolution, which could be highly complementary to the present work and, in many cases, lend support to the authors' claims rather than detract from them. The choice to omit relevant literature implies either a lack of perspective or a lack of collegiality, which the presentation of the work suffers from. Overall, it is essential to discuss findings in the context of competing studies to give readers an accurate view of the state of the field and how the present work fits into it. It is appropriate in a CG modeling study to discuss the potential weaknesses of the methodology, points of disagreement with alternative modeling studies, and any lack of correlation with a broader range of experimental work. Qualitative agreement with select experiments does not constitute model validation.

(4) Other critiques, questions, concerns:

(a) The first Results sub-heading presents "results", complete with several supplementary figures and a movie that are from a previous publication about the development of the HIV capsid-NPC model in the absence of LEN (Hudait &Voth, 2024). This information should be included as part of the introduction or an abbreviated main-text methods section rather than being included within Results as if it represents a newly reported advancement, as this could be misleading.

(b) The authors say the unbiased simulations of capsid-NPC docking were run as two independent replicates, but results from only one trajectory are ever shown plotted over time. It is not mentioned if the time series data are averaged or smoothed, so what is the shadow in these plots (e.g., Figures 1,2, and Supplementary Figure 5)?

(c) Why do the insets showing LEN binding in Figure 2A look so different from the models they are apparently zoomed in on? Both instances really look like they are taken from different simulation frames, rather than being a zoomed-in view.

(d) What are the sudden jerks apparent in the SI movies? Perhaps this is related to the rate at which trajectory frames are saved, but occasionally, during the relatively smooth motion of the capsid-NPC complex, something dramatic happens all of a sudden in a frame. For example, significant and apparently instantaneous reorientation of the cone far beyond what preceding motions suggest is possible (SI movie 2, at timestamp 0.22), RNP extrusion suddenly in a single frame (SI movie 2, at timestamp 0.27), and simultaneous opening of all pentamers all at once starting in a single frame (SI movie 2, at timestamp 0.33). This almost makes the movie look generated from separate trajectories or discontinuous portions of the same trajectory. If movies have been edited for visual clarity (e.g., to skip over time when "nothing" is happening and focus on the exciting aspects), then the authors should state so in the captions.

(e) Figure 3c presents a time series of the degree of defects at pent-hex and hex-hex interfaces, but I do not understand the normalization. The authors state, "we represented the defects as the number of under-coordinated CA monomers of the hexamers at the pentamer-hexamer-pentamer and hexamer-hexamer interface as N_Pen-Hex and N_Hex-Hex ... Note that in N_Pen-Hex and N_Hex-Hex are calculated by normalizing by the total number of CA pentamer (12) and hexamer rings (209) respectively." Shouldn't the number of uncoordinated monomers be normalized by the number of that type of monomer, rather than the number of capsomers/rings? E.g., 12*5 and 209*6, rather than 12 and 209?

(f) The authors state that "Although high computational cost precluded us from continuing these CG MD simulations, we expect these defects at the hexamer-hexamer interface to propagate towards the high curvature ends of the capsid." The defects being reported are apparently propagating from (not towards) the high curvature ends of the capsid.

(g) The first half of the paper uses the color orange in figures to indicate LEN, but the second half uses orange to indicate defects, and this could be confusing for some readers. Both LEN and "defects" are simply a cluster of spheres, so highlighted defects appear to represent LEN without careful reading of captions.

(h) SI Figure S3 captions says "The CA monomers to which at least one LEN molecule is bound are shown in orange spheres. The CA monomers to which no LEN molecule is bound are shown in white spheres. " While in contradiction, the main-text Fig 2 says "The CA monomers to which at least one LEN molecule is bound are shown in white spheres. The CA monomers to which no LEN molecule is bound are shown in orange spheres. " One of these must be a typo.

(i) The authors state that: "CG MD simulations and live-cell imaging demonstrate that LEN-treated capsids dock at the NPC and rupture at the narrow end when bound to the central channel and then remain associated to the NPC after rupture." However, the live cell imaging data do not show where rupture occurs, such that this statement is at least partially false. It is also unclear that CG simulations show that cores remain bound following rupture, given that simulations were not extended to the timescale needed to observe this, again rendering the statement partially false.

(j) The authors state: "We previously demonstrated that the RNP complex inside the capsid contributes to internal mechanical strain on the lattice driven by CACTD-RNP interactions and condensation state of RNP complex (Hudait &Voth, 2024). " In that case, why do the present CG models detect no difference in results for condensed versus uncondensed RNP?

(k) The authors state: "The distribution demonstrates that the binding of LEN to the distorted lattice sites is energetically favorable. Since LEN localizes at the hydrophobic pocket between two adjoining CA monomers, it is sterically favorable to accommodate the incoming molecule at a distorted lattice site. This can be attributed to the higher available void volume at the distorted lattice relative to an ordered lattice, the latter being tightly packed. This also allows the drug molecule to avoid the multitude of unfavorable CA-LEN interactions and establish the energetically favorable interactions leading to a successful binding event. " What multitude of unfavorable interactions are the authors referring to? Data is not presented to substantiate the claim of increased void volume between hexamers in the distorted lattice. Capsomer distortion is shown as a schematic in Figure 6A rather than in the context of the actual model.

(l) The authors state that "These striated patterns also demonstrate deviations from ideal lattice packing. " What does ideal lattice packing mean in this context, where hexamers are in numerous unique environments in terms of curvature? What is the structural reference point?

(m) If pentamer-hexamer interactions are weakened in the presence of LEN, why are differences at these interfaces not apparent in the Figure 6C data that shows stiffening of the interactions between capsomer subunits?

(n) The authors state: "Lattice defects arising from the loss of pentamers and cracks along the weak points of the hexameric lattice drive the uncoating of the capsid." The word rupture or failure should be used here rather than uncoating; it is unclear that the authors are studying the true process of uncoating and whether the defects induced by LEN binding relate in any way to uncoating.

(o) The authors state: "LEN-treated broken cores are stabilized by the interaction with the disordered FG-NUP98 mesh at the NPC." But no data is presented to demonstrate that capsid stability is increased by NUP98 interaction. In fact, the presented data could suggest the opposite since capsids in contact with NUP98 in the NPC appeared to rupture faster than freely diffusing capsids.

(p) The authors state: "LEN binding stimulates similar changes in free capsids, but they occur with lower frequency on similar time scales, suggesting that the cores docked at the NPC are under increased stress, resulting in more frequent weakening of the hexamer-pentamer and hexamer-hexamer interactions, as well as more nucleation of defects at the hexamer-hexamer<br /> Interface. ... Our results suggest that in the presence of the LEN, capsid docking into the NPC central channel will increase stress, resulting in more frequent breaks in the capsid lattice compared to free capsids." The first is a run-on sentence. The results shown support that LEN stimulates changes in free capsids to happen faster, but not more frequently. The frequency with which an event occurs is separate from the speed with which the event occurs.

(q) The authors state: "A possible mechanistic pathway of capsid disassembly can be that multiple pentamers are dissociated from the capsid sequentially, and the remaining hexameric lattice remains stabilized by bound LEN molecules for a time, before the structural integrity of the remaining lattice is compromised." This statement is inconsistent with experimental studies that say LEN does not lead to capsid disassembly, and may even prevent disassembly as part of its disruption of proper uncoating (e.g., 10.1073/pnas.2420497122 previously published by the authors).

(r) Finally, it remains a concern with the authors' work that the bottom-up solvent-free CG modeling software used in this and supporting works is not open source or even available to other researchers like other commonly used molecular dynamics software packages, raising significant questions about transparency and reproducibility.

Reviewer #1 (Public review):

Summary:

Spinal projection neurons in the anterolateral tract transmit diverse somatosensory signals to the brain, including touch, temperature, itch, and pain. This group of spinal projection neurons is heterogeneous in their molecular identities, projection targets in the brain, and response properties. While most anterolateral tract projection neurons are multimodal (responding to more than one somatosensory modality), it has been shown that cold-selective projection neurons exist in lamina I of the spinal cord dorsal horn. Using a combination of anatomical and physiological approaches, the authors discovered that the cold-selective lamina I projection neurons are heavily innervated by Trpm8+ sensory neuron axons, with calb1+ spinal projection neurons primarily capturing these cold-selective lamina I projection neurons. These neurons project to specific brain targets, including the PBNrel and cPAG. This study adds to the ongoing effort in the field to identify and characterize spinal projection neuron subtypes, their physiology, and functions.

Strengths:

(1) The combination of anatomical and physiological analyses is powerful and offers a comprehensive understanding of the cold-selective lamina I projection neurons in the spinal cord dorsal horn. For example, the authors used detailed anatomical methods, including EM imaging of Trpm8+ axon terminals contacting the Phox2a+ lamina I projection neurons. Additionally, they recorded stimulus-evoked activity in Trpm8-recipient neurons, carefully selected by visual confirmation of tdTomato and GFP juxtaposition, which is technically challenging.

(2) This study identifies, for the first time, a molecular marker (calb1) that labels cold-selective lamina I projection neurons. Although calb1+ projection neurons are not entirely specific to cold-selective neurons, using an intersectional strategy combined with other genes enriched in this ALS group or cold-induced FosTRAP may further enhance specificity in the future.

(3) This study shows that cold-selective lamina I projection neurons specifically innervate certain brain targets of the anterolateral tract, including the NTS, PBNrel, and cPAG. This connectivity provides insights into the role of these neurons in cold sensation, which will be an exciting area for future research.

Weaknesses:

(1) The sample size for the ex vivo electrophysiology is small. Given the difficulty and complexity of the preparation, this is understandable. However, a larger sample size would have strengthened the authors' conclusions.

(2) The authors used tdTomato expression to identify brain targets innervated by these cold-selective lamina I projection neurons. Since tdTomato is a soluble fluorescent protein that fills the entire cell, using synaptophysin reporters (e.g., synaptophysin-GFP) would have been more convincing in revealing the synaptic targets of these projection neurons.

(3) The summary cartoon shown in Figure 7 can be misleading because this study did not determine whether these cold-selective lamina I projection neurons have collateral branches to multiple brain targets or if there are anatomical subtypes that may project exclusively to specific targets. For example, a recent study (Ding et al., Neuron, 2025) demonstrated that there are PBN-projecting spinal neurons that do not project to other rostral brain areas. Furthermore, based on the authors' bulk labeling experiments, the three main brain targets are NTS, PBNrel, and cPAG. The VPL projection is very sparse and almost negligible.

Reviewer #2 (Public review):

Summary:

In this study, the authors took advantage of a semi-intact ex vivo somatosensory preparation that includes hindlimb skin to characterize the response of projection neurons in the dorsal horn of the spinal cord to peripheral stimulation, including cold thermal stimuli. The main aim was to characterize the connectivity between peripheral afferents expressing the cold-sensing receptor TRPM8 and a set of genetically tagged neurons of the anterolateral system (ALS). These ALS neurons expressed high levels of the calcium-binding protein calbindin 1.

In addition, combining different viral tracing methods, the authors could identify the anatomical targets of this specific subset of projection neurons within the brainstem and diencephalon.

Strengths:

The use of a relatively new (seldom used previously) transgenic line to label TRPM8-expressing afferents, combined with the genetic characterization of a previously identified subset of projection neurons, adds a specificity to the characterization. The transgenic line appears to capture well the subpopulation of Trpm8-expressing neurons

In addition, the use of electron microscopy techniques makes the interpretation of the structural contacts more compelling.

The writing is clear, and the presentation of findings follows a logical flow.

Overall, this study provides solid, novel information about the brain circuits involved in cold thermosensation.

Weaknesses:

In the characterization of recorded neurons in close contact or in the absence of this contact with TRPM8 afferents, the number of recorded neurons is relatively low. In addition, the strength of thermal stimuli is not very well controlled, preventing a more precise characterization of the connectivity.

The authors could provide some sense of the effort needed to record from the 6 cold-activated neurons described. How many preparations were needed, etc?

Reviewer #3 (Public review):

Summary:

Razlan and colleagues provide a detailed anatomical characterization of lamina I projection neurons in the mouse spinal cord that are densely innervated by primary afferents activated by cooling of the skin. The authors, building on their previous anatomical work, validate a Trpm8-Flp mouse line, show synaptic contacts between Trpm8⁺ boutons and projection neurons at the ultrastructural level, and demonstrate at the physiological level that these neurons specifically respond to cooling stimuli. Next, by taking advantage of their previous transcriptomic analysis of ALS neurons, they identify calbindin as a marker for cold-activated lamina I projection neurons and map their ascending projections to the rostral lateral parabrachial area, caudal periaqueductal gray, and ventral posterolateral thalamus, well-known thermosensory and thermoregulatory centers. Altogether, these findings provide strong anatomical and functional evidence for a direct line of transmission from Trpm8⁺ sensory afferents through Calb1⁺ lamina I neurons to key supraspinal centers controlling perception of cold and thermoregulatory responses.

Strengths:

The combination of mouse genetics, electron microscopy, ex vivo physiology, and viral tracing provides convincing evidence for a direct cold pathway. The work validates the Trpm8-Flp line by extensive anatomical and molecular characterization. Integration with previous transcriptomic and anatomical data neatly links the cold-selective lamina I neurons to a molecularly defined cluster of ALS neurons, strengthening the bridge between molecular identity, anatomy, and physiological function.

Weaknesses:

While anatomical evidence for direct synaptic connectivity between Trpm8+ afferents and lamina I projection neurons is compelling, a physiological demonstration of strict monosynaptic transmission is not shown. The conclusion that these inputs are exclusively monosynaptic should be toned down. Similarly, the statement that "Lamina I ALS neurons that are surrounded by Trpm8 afferents are cold-selective" should also be toned down as only a few neurons have been tested and it cannot be excluded that other neurons with similar characteristics may be polymodal.

Reviewer #3 (Public review):

Summary:

Razlan and colleagues provide a detailed anatomical characterization of lamina I projection neurons in the mouse spinal cord that are densely innervated by primary afferents activated by cooling of the skin. The authors, building on their previous anatomical work, validate a Trpm8-Flp mouse line, show synaptic contacts between Trpm8⁺ boutons and projection neurons at the ultrastructural level, and demonstrate at the physiological level that these neurons specifically respond to cooling stimuli. Next, by taking advantage of their previous transcriptomic analysis of ALS neurons, they identify calbindin as a marker for cold-activated lamina I projection neurons and map their ascending projections to the rostral lateral parabrachial area, caudal periaqueductal gray, and ventral posterolateral thalamus, well-known thermosensory and thermoregulatory centers. Altogether, these findings provide strong anatomical and functional evidence for a direct line of transmission from Trpm8⁺ sensory afferents through Calb1⁺ lamina I neurons to key supraspinal centers controlling perception of cold and thermoregulatory responses.

Strengths:

The combination of mouse genetics, electron microscopy, ex vivo physiology, and viral tracing provides convincing evidence for a direct cold pathway. The work validates the Trpm8-Flp line by extensive anatomical and molecular characterization. Integration with previous transcriptomic and anatomical data neatly links the cold-selective lamina I neurons to a molecularly defined cluster of ALS neurons, strengthening the bridge between molecular identity, anatomy, and physiological function.

Weaknesses:

While anatomical evidence for direct synaptic connectivity between Trpm8+ afferents and lamina I projection neurons is compelling, a physiological demonstration of strict monosynaptic transmission is not shown. The conclusion that these inputs are exclusively monosynaptic should be toned down. Similarly, the statement that "Lamina I ALS neurons that are surrounded by Trpm8 afferents are cold-selective" should also be toned down as only a few neurons have been tested and it cannot be excluded that other neurons with similar characteristics may be polymodal.

Reviewer #3 (Public review):

Summary:

Razlan and colleagues provide a detailed anatomical characterization of lamina I projection neurons in the mouse spinal cord that are densely innervated by primary afferents activated by cooling of the skin. The authors, building on their previous anatomical work, validate a Trpm8-Flp mouse line, show synaptic contacts between Trpm8⁺ boutons and projection neurons at the ultrastructural level, and demonstrate at the physiological level that these neurons specifically respond to cooling stimuli. Next, by taking advantage of their previous transcriptomic analysis of ALS neurons, they identify calbindin as a marker for cold-activated lamina I projection neurons and map their ascending projections to the rostral lateral parabrachial area, caudal periaqueductal gray, and ventral posterolateral thalamus, well-known thermosensory and thermoregulatory centers. Altogether, these findings provide strong anatomical and functional evidence for a direct line of transmission from Trpm8⁺ sensory afferents through Calb1⁺ lamina I neurons to key supraspinal centers controlling perception of cold and thermoregulatory responses.

Strengths:

The combination of mouse genetics, electron microscopy, ex vivo physiology, and viral tracing provides convincing evidence for a direct cold pathway. The work validates the Trpm8-Flp line by extensive anatomical and molecular characterization. Integration with previous transcriptomic and anatomical data neatly links the cold-selective lamina I neurons to a molecularly defined cluster of ALS neurons, strengthening the bridge between molecular identity, anatomy, and physiological function.

Weaknesses:

While anatomical evidence for direct synaptic connectivity between Trpm8+ afferents and lamina I projection neurons is compelling, a physiological demonstration of strict monosynaptic transmission is not shown. The conclusion that these inputs are exclusively monosynaptic should be toned down. Similarly, the statement that "Lamina I ALS neurons that are surrounded by Trpm8 afferents are cold-selective" should also be toned down as only a few neurons have been tested and it cannot be excluded that other neurons with similar characteristics may be polymodal.

Reviewer #1 (Public review):

This manuscript provides several important findings that advance our current knowledge about the function of the gustatory cortex (GC). The authors used high-density electrophysiology to record neural activity during a sucrose/NaCl mixture discrimination task. They observed population-based activity capable of representing different mixtures in a linear fashion during the initial stimulus sampling period, as well as representing the behavioral decision (i.e., lick left or right) at a later time point. Analyzing this data at the single neuron level, they observed functional subpopulations capable of encoding the specific mixture (e.g., 45/55), tastant (e.g., sucrose), and behavioral choice (e.g., lick left). To test the functional consequences of these subpopulations, they built a recurrent neural network model in order to "silence" specific functional subpopulations of GC neurons. The virtual ablation of these functional subpopulations altered virtual behavioral performance in a manner predicted by the subpopulation's presumed contribution.

Strengths:

Building a recurrent neural network model of the gustatory cortex allows the impact of the temporal sequence of functionally identifiable populations of neurons to be tested in a manner not otherwise possible. Specifically, the author's model links neural activity at the single neuron and population level with perceptual ability. The electrophysiology methods and analyses used to shape the network model are appropriate. Overall, the conclusions of the manuscript are well supported.

Weaknesses:

One potential concern is the apparent mismatch between the neural and behavioral data. Neural analyses indicate a clear separation of the activity associated with each mixture that is independent of the animal's ultimate choice. This would seemingly indicate that the animals are making errors despite correctly encoding the stimulus. Based solely on the neural data, one would expect the psychometric curve to be more "step-like" with a significantly steeper slope. One potential explanation for this observation is the concentration of the stimuli utilized in the mixture discrimination task. The authors utilize equivalent concentrations, rather than intensity-matched concentrations. In this case, a single stimulus can (theoretically) dominate the perception of a mixture, resulting in a biased behavioral response despite accurate concentration coding at the single neuron level. Given the difficulty of isointensity matching concentrations, this concern is not paramount. However, the apparent mismatch between the neural and behavioral data should be acknowledged/addressed in the text.

Reviewer #2 (Public review):

Lang et al. investigate the contribution of individual neuronal encoding of specific task features to population dynamics and behavior. Using a taste-based decision-making behavioral task with electrophysiology from the mouse gustatory cortex and computational modeling, the authors reveal that neurons encoding sensory, perceptual, and decision-related information with linear and categorical patterns are essential for driving neural population dynamics and behavioral performance. Their findings suggest that individual linear and categorical coding units have a significant role in cortical dynamics and perceptual decision-making behavior.

Overall, the experimental and analytical work is of very high quality, and the findings are of great interest to the taste coding field, as well as to the broader systems neuroscience field.

I have a couple of suggestions to further enhance the authors' important conclusions:

My main comment is the distinction between constrained and unconstrained units. The authors train a small percentage of units to match the real neural data (constrained units), and then find some unconstrained units that are similar to the real neural data and some that are not. As far as I could tell, the relative fraction of constrained and unconstrained units in the trained RNN is not reported; I assume the constrained ones are a much smaller population, but this is unclear. The selection of different groups of neurons for the RNN ablation experiments appears to be based on their response profiles only. Therefore, if I understood correctly, both constrained and unconstrained units and ablated together for a given response category (e.g., linear or step-perception). It would be useful, therefore, to separately compare the effects of constrained vs. unconstrained RNN units.

Specifically:

(1) For the analyses in the initial version of the manuscript, the authors should specify how many units in each ablation category are constrained and unconstrained.

(2) The authors should repeat Figure 6, but only for unconstrained units to test how much of the effects in the initial version of Figure 6 are driven by constrained vs. unconstrained RNN units.

(3) The authors should repeat Figure 7, but performing ablations separately on the constrained and unconstrained units to examine how the network behaves in each case and the resulting "behavioral" effect.

Reviewer #3 (Public review):

Primary taste cortex neurons show a variety of dynamic response profiles during taste decision-making tasks, reflecting both sensory and decision variables. In the present study, Lang et al. set out to determine how neurons with distinct response profiles contribute to perceptual decisions about taste stimuli.

The methods, with reference to the behavioral task and electrophysiological recordings/data analysis, are straightforward, solid, and appropriate. The computational model is presented in a clear and conceptually intuitive manner, although the details are outside of my area of expertise.

The experimental design features a simple 2-alternative forced-choice design that yielded clear psychometric curves across a range of stimuli. In vivo recordings were performed using Neuropixels and yielded an appropriate sample of single neuron responses. The strength of the model lies in the fact that it consists of single neurons whose response profiles mimic those recorded in vivo, and allows neuron-selective manipulation.

By virtually lesioning specific subsets of neurons in the network, the authors demonstrate that a relatively small population of neurons with specific tuning profiles was sufficient to produce the observed neural dynamics and behavioral responses. This effect was selective as lesioning other responsive neurons did not affect overall response dynamics or performance.

These findings provide new insight into the relation between the response profiles of single neurons in sensory cortex, their population-level activity dynamics, and the perceptual decisions they inform.

The approach is particularly innovative as it uses computational modeling to target functionally-defined "cell types", which cannot necessarily be targeted by more conventional genetic approaches.

Reviewer #1 (Public review):

Summary:

In this manuscript, Green et al. attempt to use large-scale protein structure analysis to find signals of selection and clustering related to antibiotic resistance. This was applied to the whole proteome of Mycobacterium tuberculosis, with a specific focus on the smaller set of known antibiotic-resistance-related proteins.

Strengths:

The use of geospatial analysis to detect signals of selection and clustering on the structural level is really intriguing. This could have a wider use beyond the AMR-focussed work here and could be applied to a more general evolutionary analysis context. Much of the strength of this work lies in breaking ground into this structural evolution space, something rarely seen in such pathogen data. Additional further research can be done to build on this foundation, and the work presented here will be important for the field.

The size of the dataset and use of protein structure prediction via AlphaFold, giving such a consistent signal within the dataset, is also of great interest and shows the power of these approaches to allow us to integrate protein structure more confidently into evolution and selection analyses.

Weaknesses:

There are several issues with the evolutionary analysis and assumptions made in the paper, which perhaps overstate the findings, or require refining to take into account other factors that may be at play.

(1) The focus on antimicrobial resistance (AMR) throughout the paper contains the findings within that lens. This results in a few different weaknesses:

(a) While the large size of the analysis is highlighted in the abstract and elsewhere, in reality, only a few proteins are studied in depth. These are proteins already associated with AMR by many other studies, somewhat retreading old ground and reducing the novelty.

(b) Beyond the AMR-associated proteins, the proteome work is of great interest, but only casually interrogated and only in the context of AMR. There appears to be an assumption that all signals of positive selection detected are related to AMR, whereas something like cas10 is part of the CRISPR machinery, a set of proteins often under positive selection, and thus unlikely to be AMR-related.

(2) The strength of the signal from the structural information and the novelty of the structural incorporation into prediction are perhaps overstated.

(a) A drop of 13% in F1 for a gain of 2% in PPV is quite the trade-off. This is not as indicative of a strong predictor that could be used as the abstract claims. While the approach is novel and this is a good finding for a first attempt at such complex analysis, this is perhaps not as significant as the authors claim

(b) In relation to this, there is a lack of situating these findings within the wider research landscape. For instance, the use of structure for predicting resistance has been done, for example, in PncA (https://academic.oup.com/jacamr/article/6/2/dlae037/7630603, https://www.sciencedirect.com/science/article/pii/S1476927125003664, https://www.nature.com/articles/s41598-020-58635-x) and in RpoB (https://www.nature.com/articles/s41598-020-74648-y). These, and other such works, should be acknowledged as the novelty of this work is perhaps not as stark as the authors present it to be.

(3) The authors postulate that neutral AA substitutions would be randomly distributed in the protein structure and thus use random mutations as a negative control to simulate this neutral evolution. However, I am unsure if this is a true negative control for neutral evolution. The vast majority of residues would be under purifying selection, not neutral selection, especially in core proteins like rpoB and gyrA. Therefore, most of these residues would never be mutated in a real-world dataset. Therefore, you are not testing positive selection against neutral selection; you are testing positive against purifying, which will have a much stronger signal. This is likely to, in turn, overestimate the signal of positive selection. This would be better accounted for using a model of neutral evolution, although this is complex and perhaps outside the scope. Still, it needs to be made clear that these negative controls are not representative of neutral evolution.

(4) In a similar vein, the use of 15 Å as a cut-off for stating co-localisation feels quite arbitrary. The average radius of a globular protein is about 20 Å, so this could be quite a large patch of a protein. I think it may be good to situate the cut-off for a 'single location' within a size estimator of the entire protein, as 15 Å could be a neighbourhood in a large protein, but be the whole protein for smaller ones.

Reviewer #2 (Public review):

Summary:

This is an important study that, for the first time, systematically places the homoplastic genetic variation observed in the coding regions in a large collection of >31,000 M. tuberculosis samples into the protein structural context. This should be much more informative when, e.g. predicting antimicrobial resistance. The authors imaginatively apply the Getis-Ord score, which originated in geographical spatial analysis but has also been used in human disease to demonstrate that missense mutations in M. tuberculosis known to be associated with antimicrobial resistance are clustered in space. That they are able to consider almost all of the proteome using a large dataset of 31,000 M. tuberculosis complex clinical samples, which makes the evidence convincing.

Strengths:

To my knowledge, this is the first study to place the homoplastic missense mutations from a large clinical dataset into their protein structural context and attempt to look for clustering in space, which could be indicative of a recent evolutionary pressure, such as the use of antibiotics. The field usually only views resistance through the genetic paradigm, so it is delightful to see a structural paradigm being brought to bear, as this should, in theory, be much more informative, as protein structure is much closer to function. In addition, the dataset used is large (>31,000 clinical M. tuberculosis samples), and the authors are able to consider almost all of the ORFs (3,687/3,996) in the M. tuberculosis reference, and hence the analysis is comprehensive.

Weaknesses:

It is not apparent at the time of this review if the study could be reproduced by other researchers as e.g. whilst the authors state that the raw sequencing files (FASTQ) underpinning the dataset of 31,428 M. tuberculosis isolates can be downloaded the table in the Supplement containing the sample and accession identifiers contains rows that do not contain NCBI accessions e.g. '01R0685' or 'IDR 1600023875' or '1479144813357T181715lib5022nextseqn0035151bp' instead of the expected form e.g. 'SAMEA1016138'. I have searched the NCBI SRA using these terms and got no results, so they cannot be used to download any FASTQ files. There is also no information in the preprint on how the reads were processed (which is a complex process) and the dataset of SNPs subsequently built. One can trace back through the references, but I cannot find anywhere where one can download the SNP dataset, which would permit researchers to reproduce at least the latter stages of the work -- one obvious option would be to make the SNP dataset available. Likewise, the authors have constructed a "M. tuberculosis structureome", which would be very useful for the community but does not appear to be publicly available. At the time of the review, not all the GitHub repositories were public, so these points may have been rectified when that was corrected.

The authors correctly point out in the Introduction that supervised methods like GWAS or ML need datasets with matching genetic and phenotypic drug susceptibility data, which are much difficult/expensive to obtain, but don't then close the loop by comparing their results back to such supervised methods. They pick out RnJ as having previously been identified by a GWAS, but it would have provided a useful validation of their method to e.g. demonstrating that X% of the genes they identify were also identified by GWAS/ML studies, and therefore their method can achieve similar results but without having to collect pDST data.

Whilst the authors acknowledge that assuming all sites are equally likely to mutate in their random shuffling procedure is a shortcoming, a bigger weakness is, I suspect, that one should also only consider which amino acids could arise at each codon due to a SNP. Shuffling assumes any amino acid can arise at any codon which is only possible with multiple nucleotide changes, which is possible but highly unlikely.

Finally, the authors implicitly assume that the mutations do not perturb the structure of the proteins, which is likely to be generally true for essential genes but less likely to be true for non-essential genes. This assumption underpins their entire approach and should be borne in mind when evaluating the results.

Reviewer #1 (Public review):

Summary:

This manuscript by Xie and colleagues presents an intriguing behavioral finding for the field of perceptual learning (PL): combining the reactivation-based training paradigm with anodal tDCS induces complete generalization of the learning effect. Notably, this generalization is achieved without compromising the magnitude of learning effects and with an 80% reduction in total training time. The experimental design is well-structured, and the observed complete generalization is robustly replicated across two stimulus dimensions (orientation and motion direction).

However, while the empirical results are methodologically valid and scientifically surprising, the theoretical framework proposed to explain them appears underdeveloped and, in some cases, difficult to reconcile with the existing literature. Several arguments are insufficiently justified. In addition, the introduction of a non-standard metric (NGI: normalized learning gain index) raises concerns about the interpretability and comparability with existing PL literature.

Strengths:

(1) Rigorous experimental design

In this study, Xie and colleagues employed a 2×2 factorial design (Training paradigm: Reactivation vs. Full-Practice × tDCS protocols: Anodal vs. Sham), which allowed clear dissociation of the main and interaction effects.

(2) High statistical credibility

Sample sizes were predetermined using G*Power, non-significant effects were evaluated using the Bayes factor, and the core behavioral findings were replicated in a second stimulus dimension. These strengthen the credibility of the findings.

(3) Strong translational potential

The observed complete generalization could have useful implications for sensory rehabilitation. The large reduction (80%) in total training time is particularly compelling.

Weaknesses:

(1) NGI (Normalized learning gain index) is a non-standard behavioral metric and may distort interpretability.

NGI (pre - post / ((pre + post) / 2)) is rarely used in PL studies to measure learning effects. Almost all PL studies rely on raw thresholds and percent improvements (pre - post / pre), making it difficult to contextualize the current NGI-based results within the broader field. The current manuscript provides no justification for adopting NGI.

A more critical issue is the NGI's nonlinearity: by normalizing to the mean of pre- and post-test thresholds, it disproportionately inflates learning effects for participants with lower post-test thresholds. Notably, the "complete generalization" claims are illustrated mainly with NGI plots. Although the authors also analyze thresholds directly and the results also support the core claim, the interpretation in the text relies heavily on NGI.

The authors may consider rerunning key analyses using the standard percent improvement metric. If retaining NGI, the authors should provide explicit justification for why NGI is superior to standard measures.

(2) The proposed theoretical framework is sometimes unclear and insufficiently supported.

The authors propose the following mechanistic chain:

(a) reactivation-based learning depends on offline consolidation mediated by GABA (page 4 line 73);

(b) online a-tDCS reduces GABA (page 4, line 76), thereby disrupting offline consolidation (page 11, line 225);

(c) disrupted offline consolidation reduces perceptual overfitting (page 4, line 77; page 11, line 225), thereby enabling generalization;

(d) under full-practice training, a-tDCS increases specificity via a different mechanism (page 11 line 235).

While this framework is plausible in broad terms, several components are speculative at best in the absence of neurochemical or neural measurements.

(3) Several reasoning steps require further clarification.

(a) Mechanisms of Reactivation-based Learning.

The manuscript focuses on the neurochemical basis of reactivation-based learning. However, reactivation-induced neurochemical changes differ across brain regions. In the motor cortex, Eisenstein et al. (2023) reported that after reactivation, increased GABA and decreased E/I ratio were associated with offline gains. In contrast, Bang et al. (2018) demonstrated that, in the visual cortex, reactivation decreased GABA and increased E/I ratio. While both studies are consistent with GABA involvement, the direction of GABA modulation differs. The authors should clarify this discrepancy.<br /> More importantly, Bang et al. (2018) demonstrated that reactivation-based (3 blocks) and full-practice (16 blocks) training produced similar time courses of E/I ratio changes in V1: an initial increase followed by a decrease. Given this similarity, the manuscript would benefit from a more thorough discussion of how the two paradigms diverge mechanistically. For example, behaviorally, Song et al. (2021) reported greater generalization with reactivation-based training than with full-practice training, aligning with Kondat et al. (2025). Neurally, Kondat et al. (2024) showed that reactivation-based training increased activity in higher-order brain regions (e.g., IPS), whereas full practice training reduced connectivity between temporal and parietal regions.

(b) tDCS Mechanisms and Protocols.

The effect of a-tDCS on GABA is not consistent across brain regions. While a-tDCS reliably reduces GABA in the motor cortex, recently, a more related work (Abuleli et al., 2025) reports no significant modulation of GABA or Glx in V1, challenging the authors' assumption of tDCS-induced GABA reduction in the visual cortex.

The manuscript proposes that online a-tDCS disrupts offline consolidation is somewhat difficult to interpret conceptually. Online tDCS typically modulates processes occurring during stimulation (e.g., encoding process, attentional state), whereas consolidation occurs afterward. Thus, stating that online tDCS protocols only disrupt offline consolidation without considering the possibility that they first modulate the encoding process is difficult to interpret. Even if tDCS has prolonged effects, the link between online stimulation and disruption of offline consolidation remains unelucidated.

(c) Missing links between GABA modulation and perceptual overfitting.

The proposed chain ("tDCS disrupts consolidation → reduced overfitting → improved generalization") skips a critical step: how GABA modulation translates to changes in neural representational properties (e.g., tuning width, representational overlap between trained/untrained stimuli) that define "perceptual overfitting." The PL literature has not established a link between GABA levels and these representational changes, leaving a key component of the mechanistic explanation underspecified.

(d) Insufficient explanation of the opposite effects.

The manuscript does not fully explain why the same a-tDCS promotes generalization in reactivation-based training but increases specificity in full-practice training. Both paradigms engage offline consolidations, and, as mentioned above, the time courses of E/I ratio changes are similar for 3-block reactivation-based or 16-block training. Thus, if offline consolidation mechanisms (and their associated E/I changes) are comparable across paradigms, it is unclear why identical a-tDCS would produce opposite outcomes in the two paradigms.

Reviewer #2 (Public review):

Xie et al., combined transcranial direct current brain stimulation (tDCS) and a reactivation-based training protocol to investigate the generalization of learning. Using visual perceptual learning as a model, they found that a reactivation-based training protocol, when combined with anodal tDCS over the visual cortex, can induce learning transfer to untrained visual orientations and motion directions. Interestingly, extending reactivation-based training to a full-training protocol with more training trials did not induce generalization of learning. Furthermore, even when paired with tDCS, extending the training protocol did not provide benefits for generalization of learning. This study provides interesting insights into the mechanisms of brain plasticity and how future training protocols could be designed to achieve robust and generalizable learning outcomes.

The authors supported their arguments with a series of well-constructed experiments. The conclusions are largely supported by the data, although some clarifications about their hypotheses and control analyses could strengthen the work:

(1) The authors hypothesize that tDCS can reduce perceptual overfitting through reduced GABA concentrations in the visual cortex, which leads to learning transfer. However, without a clear description of the role of GABA in perceptual learning and perceptual overfitting, it is difficult for the reader to understand why reduced GABA concentrations would contribute to generalization. Do the authors imply that increased GABA can lead to specificity? Are there studies that can support this argument? The authors also did not describe clearly how reactivation-based visual perceptual learning can modify GABA levels in the visual cortex differently (compared to full-practice) during training and during the offline consolidation phase. In order for the reader to better understand their hypotheses and the motivation of the current study, it is beneficial for the authors to provide a concise but clearer description of the roles of GABA in perceptual learning with a focus on the roles of GABA in generalization and during off-line consolidation for different types of training protocols (see for instance Bang et al., 2018; Frangou et al., 2019; Frank et al., 2022; Jia et al., 2024; Shibata et al., 2011; Tamaki et al., 2020; Yamada et al., 2024).

(2) Based on the results, an alternative explanation is that the amount of transfer to the untrained visual feature might be related to the amount of learning for the trained visual feature, which might be different depending on the training protocol and brain stimulation combination. Is it beneficial to compare the amount of learning gains across different training and stimulation protocols to rule out this possibility? Would more learning gains for the trained visual feature predict less transfer for the untrained visual feature? Are there correlations between learning gains and learning transfer?

(3) The authors argued that a reactivation-based training protocol, rather than the amount of training, was critical for the generalization of learning. The control experiment in the study showed that full-practice training combined with tDCS did not lead to transfer, as in reactivation-based training. However, in order to rule out the confounding effects from the amount of training, it is crucial to examine whether a training protocol in which a similar number of trials as in the reactivation-based training but not separated across training sessions would lead to similar generalization of learning.

Reviewer #3 (Public review):

Summary:

This research focuses on a long-lasting and interesting phenomenon in human plasticity. When humans learn basic perceptual skills such as judging the orientation of a simple line, the learned abilities are often limited to the trained condition but not generalizable to untrained conditions. The authors hypothesized that this learning specificity was related to GABA, an inhibitory neurotransmitter in the brain. Using a novel training method that combines reactivation and a brain stimulation method (tDCS) that hypothetically inactivates GABA, the authors hypothesized that learned visual perceptual skills would show greater transfer.

Strengths:

The authors conducted a list of well-conceived behavior studies to demonstrate the effectiveness of their proposed method in enabling learning transfer in two different visual tasks, and carefully conducted comparison studies to elucidate other possible explanations. The sample size was adequate to convey convincing results, and the analyses were thorough.

Weaknesses:

While the authors built their training paradigm on

(1) the hypothetical role GABA plays in inhibiting learning transfer, and

(2) the hypothetical impact tDCS may have on GABA, there was no direct evidence supporting these hypotheses in the current study.

Further, learning specificity takes many formats from features to locations to tasks; it is not yet clear the scope of the observed transfer with the proposed method.

Reviewer #1 (Public review):

Summary:

The authors generated mouse and zebrafish models for DeSanto-Shinawi Syndrome, caused by loss-of-function variants in the WAC gene. Using these vertebrate systems, they demonstrate conserved craniofacial and social-behavioral phenotypes that parallel human clinical features, along with deficits in GABAergic markers. They observe increased seizure susceptibility and male-biased brain volumetric changes in Wac mutant mice. Together, these findings begin to define the biological consequences of Wac haploinsufficiency and provide valuable resources for future mechanistic studies.

Strengths:

WAC is a high-confidence neurodevelopmental disorder gene and one of the genes identified by large-scale exome sequencing efforts, including the Satterstrom et al. (2020) autism spectrum disorder cohort. This study establishes the first vertebrate Wac models, addressing a major gap in the understanding of DeSanto-Shinawi Syndrome, and provides a framework for studying other syndromic forms of autism. The models generated will be impactful and useful to the community to study and understand DeSanto-Shinawi Syndrome.

The cross-species analysis is important and well executed, and reveals both conserved and divergent phenotypes. The behavioral and anatomical assays are rigorously executed and well-controlled, and the inclusion of RNA-sequencing analyses adds valuable insights into the mechanisms underlying brain function in Wac mutants. Notably, the RNA-seq data reveal upregulation of several clustered protocadherins, genes central to neuronal identity and cell-cell interactions, which are known to be regulated by dynamic developmental regulation of chromatin architecture. This observation provides an intriguing hint that could link Wac function to higher-order chromatin organization and neuronal connectivity.

Weaknesses:

The evidence is solid, but the study remains incomplete in its mechanistic depth and molecular interpretation. The authors compellingly describe behavioral, anatomical, and transcriptomic phenotypes associated with WAC loss, yet do not explore how WAC mechanistically regulates chromatin or transcription. Given prior evidence that WAC interacts with the RNF20/40 ubiquitin ligase complex and promotes histone H2B ubiquitination and transcriptional elongation, the paper would benefit from a discussion of these functions as a potential link between Wac haploinsufficiency and the observed changes in neuronal gene expression. Similarly, the authors mention WAC's WW and coiled-coil domains but do not consider how these domains could mediate nuclear interactions or recruitment of transcriptional cofactors that shape gene regulation and chromatin organization in neurons.

The transcriptomic analysis is rich but largely descriptive. Although the upregulation of clustered protocadherins is particularly intriguing, these findings are not validated or localized to specific neuronal populations. The study would be strengthened by independently validating the most significant RNA-seq changes, such as protocadherin gamma genes, using in situ hybridization methods to confirm the spatial and cellular specificity of expression changes.

Finally, while the behavioral and MRI results add valuable breadth, their interpretation would be improved by clearer reporting of sample sizes, statistical corrections, and effect sizes to support claims of sex-specific and regional brain volume differences.

Reviewer #2 (Public review):

The authors describe the first deep neurological characterization of WAC mutation in two vertebrate species (zebrafish and mouse). They examine these at various levels, guided by the work in humans that has associated a heterozygous WAC mutation with DeSantos Shinawi Syndrome (DESSH). Therefore, they investigate the animals for a variety of phenotypes, following a template for what is seen when characterizing a new mouse/fish model of a developmental disability gene. Investigations include analysis of skull and jaw for abnormalities(both species), MRI of brain structure(in mice), electrophysiology(mice), assessment of signaling pathways (by Western blot, in mice), cell counts (both, more in mice), transcriptomics (mice), and behavior (both).

Generally, this describes an important first characterization of the consequences of the mutation. Most of the studies appear well-conducted and reasonably powered, thus solid or convincing. However, there are a few places where the data presentation could be improved for clarity, and a few concerns about some choices in analytical approach for a couple of the experiments, where improved statistical approaches could improve their sensitivity and/or better rule out false positives, and thus the support of some of these claims is currently incomplete. There is also some lack of clarity about the rationale for some decisions regarding the fish genetics. Nonetheless, this is an important and useful first characterization of many phenotypes of these lines. Such experiments form a baseline for future mechanistic studies in the same lines and a platform to test approaches to reverse phenotypes.

Individual claims and their strength & weaknesses:

(1) The authors developed mouse and zebrafish models of WAC deletion

They used the existing KOMP floxed WAC line to generate a null allele. For the mouse, there is a Western showing that it is indeed null for the protein. The fish data is less robustly validated - they don't confirm the allele in null at the protein or RNA level, and fish have two paralogs (waca and wacb), and this paper only characterizes one of these. So this evidence is less clear. The evaluated mice are heterozygous (Het), similar to patients, while the fish appear to be evaluated as homozygous mutants.

(2) The authors show that both species show altered craniofacial features

These data appear well powered, and the findings are robust.

(3) Each model altered GABAergic neurons

In mice, the authors stained with PV antibodies and saw a decrease in cells positive for this staining. A second marker, Lhx6, does not show a difference, suggesting this might be a change in PV expression rather than cell number. They could maybe look into the literature to see if this loss of just the protein also occurs in other models. Overall, the sample size here is a bit smaller than other parts of the paper (n=3), and the methods on the cell counts were less clear, so it is not as clear that this finding is as robust. The authors counted several other broad classes of cells, and those appear normal. Interestingly, there might also be some TBR1 mislocalization in layer 6 that might be significant with added power.

The fish data is based on an in situ hybridization for GAD. The measure shown is the width of the positive area in the forebrain. This measure is not one I have seen much before, and has potential to be driven by something unrelated to GABA (e.g., if the whole forebrain were simply a bit smaller). So this analysis could use a couple of other approaches (density of signal?) and/or a control probe for some other brain gene showing the measure is normal, and thus it is not just a size issue.

(4) Mice were more susceptible to the seizure-inducing agent PTZ

These data appear well powered, and the findings are robust. The authors also did a fair amount of useful electrophysiology that was all normal, but appeared to be well executed.

(5) Mice had changes in brain volume that interact with sex

The authors conducted an MRI on a good number of mice and reported a slight increase in global volume just in males. Sample size is fair, but the statistical approach here may be better if it puts males and females in the same model (to boost power and explicitly test for sex by genotype interaction that they report), and there is some chance that the brain region level differences that they report could include some false positives. They tested many regions, and it is not clear whether or not they corrected for the number of tests. Often, an FDR correction would be used in such imaging studies. It may be that only the most robust regional findings will survive those corrections. It is interesting data either way, but the analysis could be improved.

(6) Several behaviors are altered in the mice as well

These studies were fairly well-powered (n=15,16), and they found several positive and negative results, including alterations in memory and sociability in both species. There is a minor statistical flaw in the three-chamber analysis (they don't actually compare the Hets directly to the wildtypes in their statistical testing - a common mistake in neuroscience that should be addressed. But the data look like they will probably still be significant when correctly analyzed. In the supplement, the authors could do a bit more with the data they have to look at hyperactivity (i.e., show total motion in open field, not just time in center vs. periphery), and adding sex to their model might improve sensitivity for genotype effects.

(7) Some biochemical signaling pathways are altered in the brain

These are n=4 immunoblots, and show altered phospho ERK, but no changes in other signaling events predicted from prior WAC literature like H2B ubiquitination. They appear well done, and the authors share the full blots in the supplement.

(8) WAC deletion also alters gene expression in the brain

These studies were well-powered for RNAseq, with 10 and 14 samples, using neonates (P2), just the forebrain. The sequencing quality metrics all looked good, and the approach to analysis was okay. It would be stronger to again include sex in the model, rather than separate by sex. There were some typos in this part of the paper that made part of the conclusions unclear, but the RNAseq nicely confirmed the mutation of the mice, and discovered many differentially expressed genes, consistent with the role of this gene as a regulator of transcription. The presentation could be expanded to make more use of the data. Overall, though, this is a useful first characterization of the transcriptome in the line.

Reviewer #1 (Public review):

Summary:

This manuscript investigates whether newborns can use speaker identity to separate verbal memories, aiming to shed light on the earliest mechanisms of language learning and memory formation. The authors employ a well-designed experimental paradigm using functional near-infrared spectroscopy (fNIRS) to measure neural responses in newborns exposed to familiar and novel words, with careful counterbalancing and acoustic controls. Their main finding is that newborns show differential neural activation to novel versus familiar words, particularly when speaker identity changes, suggesting that even at birth, infants can use indexical cues to support memory.

Strengths:

Major strengths of the work include its innovative approach to a longstanding question in developmental science, the use of appropriate and state-of-the-art neuroimaging methods for this age group, and a thoughtful experimental design that attempts to control for order and acoustic confounds. The study addresses a significant gap in our understanding of how infants process and remember speech, and the data are presented transparently, with clear reporting of both significant and non-significant results.

Weaknesses:

However, there are notable weaknesses that limit the strength of the conclusions. The main recognition effect is restricted to a specific subgroup of participants and emerges only during a particular testing window, raising questions about the robustness and generalizability of the findings. The sample size, while typical for infant neuroimaging, is modest, and the statistical power is further reduced by missing data and group-dependent effects. Additionally, the claims regarding episodic memory and evolutionary implications are somewhat overstated, as the paradigm primarily demonstrates memory retention over a few minutes without evidence of the rich, contextually bound recall characteristic of fully developed episodic memory.

Overall, the authors have achieved their primary aim of demonstrating that speaker identity can facilitate memory separation in newborns, providing valuable preliminary evidence for early indexical processing in language learning. The results are intriguing and likely to stimulate further research, but the limitations in effect robustness and theoretical interpretation mean that the findings should be viewed as an important step forward rather than a definitive answer. The methods and data will be of interest to researchers studying infant cognition, memory, and language, and the study highlights both the promise and the challenges of probing complex cognitive processes in the earliest stages of life.

Reviewer #2 (Public review):

Summary:

Previous studies by some of the same authors of the actual manuscript showed that healthy human newborns memorize recently learned nonsense words. They exposed neonates to a familiarization period (several minutes) when multiple repetitions of a bisyllabic word were presented, uttered by the same speaker. Then they exposed neonates to an "interference period" when newborns listened to music or the same speaker uttering a different pseudoword. Finally, neonates were exposed to a test period when infants hear the familiarized word again. Interestingly, when the interference was music, the recognition of the word remained. The word recognition of the word was measured by using the NIRS technique, which estimates the regional brain oxygenation at the scalp level. Specifically, the brain response to the word in the test was reduced, unveiling a familiarity effect, while an increase in regional brain oxygenation corresponds to the detection of a "new word" due to a novelty effect. In previous studies, music does not erase the memory traces for a word (familiarity effect), while a different word uttered by the same speaker does.

The current study aims at exploring whether and how word memory is interfered with by other speech properties, specifically the changes in the speaker, while young children can distinguish speakers by processing the speech. The author's main hypothesis anticipates that new speaker recognition would produce less interference in the familiarized word because somehow neonates "separate" the processing of both words (familiarized uttered by one speaker, and interfering word, uttered by a different speaker), memorizing both words as different auditory events.

From my point of view, this hypothesis is interesting, since the results would contribute to estimating the role of the speaker in word learning and speech processing early in life.

Strengths:

(1) New data from neonates. Exploring neonates' cognitive abilities is a big challenge, and we need more data to enrich the knowledge of the early steps of language acquisition.

(2) The study contributes new data showing the role of speaker (recognition) on word learning (word memory), a quite unexplored factor. The idea that neonates include speakers in speech processing is not new, but its role in word memory has not been evaluated before. The possible interpretation is that neonates integrate the process of the linguistic and communicative aspects of speech at this early age.

(3) The study proposes a quite novel analytic approach. The new mixed models allow exploring the brain response considering an unbalanced design. More than the loss of data, which is frequent in infants' studies, the familiarization, interference and learning processes may take place at different moments of the experiment (e.g. related to changes in behavioural states along the experiment) or expressed in different regions (e.g. related to individual variations in optodes' locations and brain anatomy).

Weaknesses:

I did not find major weaknesses. However, I would like to have more discussion or explanation on the following points.

(1) It would be fine to report the contribution of each infant to the analysis, i.e. how many good blocks, 1 to 5 in sequence 1 and 2, were provided by each infant.

(2) Why did the factor "blocknumber" range from 0 to 4? The authors should explain what block zero means and why not 1 to 5.

(3) I may suggest intending to integrate the changes in brain activity across the 3 phases. That is, whether changes in familiarization relate to changes in the test and interference phases. For instance, in Figure 2, the brain response distinguishes between same and novel words that occurred over IFG and STG in both hemispheres. However, in the right STG there was no initial increase in the brain response, and the response for the same was higher than the one for novels in the 5th block.

(4) Similarly, it is quite amazing that the brain did not increase the activity with respect to the familiarization during the interference phase, mainly over the left hemisphere, even if both the word and speaker changed. Although the discussion considers these findings, an integrated discussion of the detection of novel words and the detection of a novel speaker over time may benefit from a greater integration of the results.

Appraisal:

The authors achieved their aims because the design and analytic approaches showed significant differences. The conclusions are based on these results. Specifically, the hypothesis that neonates would memorize words after interference, when interfered speech is pronounced by a different speaker, was supported by the data in blocks 2 and 5, and the potential mechanisms underlying these findings were discussed, such as separate processing for different speakers, likely related to the recognition of speaker identity.

I think the discussion is well-structured, although I may suggest integrating the changes into the three phases of the study. Maybe comparing with other regions, not related to speech processing.

Evaluating neonates is a challenge. Because physiology is constantly changing. For instance, in 9 minutes, newborns may transit from different behavioral states and experience different physiological needs.

This study offers the opportunity to inspire looking for commonalities and individual differences when investigating early memory capacities of newborns.

Reviewer #1 (Public review):

Working memory affects sensory processing. Observers make faster and more accurate perceptual decisions at remembered locations, and corresponding regions of retinotopic visual cortex display enhanced response gain and modulations in oscillatory activity and spike-phase coupling.

Roshanaei et al investigate the relationship between working memory, oscillatory activity, and response gain by reanalyzing extracellular laminar probe recordings from area MT of rhesus monkeys performing a spatial working memory task. During the memory period, visual probes were flashed in the receptive field of the recorded neurons, allowing a comparison of visual responses when memory overlapped with this receptive field (IN) or a location in the opposite hemifield (OUT). They first replicate a range of findings, including increased power in lower frequency bands (theta and alpha/beta) and increased visually-evoked responses in the IN condition. The authors next deployed a spectral technique (MODWT) to decompose the local field potential on single trials into 6 non-arbitrary component frequency bands. This approach allows the authors to observe shifts in peak spectral frequencies across IN and OUT trials. Finally, these single-trial spectral decompositions allowed the authors to relate frequency band power and response gain. This analysis revealed that response gain tended to increase with power in lower (alpha, beta, and theta) frequency bands, and this effect minimally interacted with the remembered location.

Together, these interesting results provide correlational evidence that the effect of working memory on response gain may be mediated by oscillatory power. As the authors note, these results are also consistent with theories positing that lower frequency oscillatory activity primarily reflects working-memory related feedback signals from prefrontal and parietal cortex.

These findings also suggest opportunities for further exploration. From a methodological perspective, it's not clear if the particular spectral decomposition highlighted here is necessary for obtaining these results, or if applying more standard approaches to single trials (as in Lundqvist et al., 2016) would have provided similar sensitivity. Additionally, although the relationship among working memory, oscillatory power, and response gain explored here is necessarily correlational, it could be of interest to subject these factors to a mediation analysis in this or future studies. Finally, the careful analysis of oscillatory phenomena reported here can ideally be used to inform large-scale circuit models and constrain the underlying mechanism.

Reviewer #2 (Public review):

Summary:

Roshanaei et al investigate how working memory (WM) modulates neural activity in the primate visual system by examining local field potentials (LFPs) and spiking activity recorded in area MT. This work is an extension and the reuse of the dataset of the group's prior manuscript, Bahmani et al, Neuron 2018. The animals perform a spatial working memory task where they need to remember the location of a probe stimulus presented within (IN condition) or outside (OUT condition) the neuron's mapped receptive field (RF).

As the first step, the authors replicate the findings in their Neuron 2018 paper by showing:<br /> (1) Significant modulation of the LFP power in αβ band during the working memory period in IN vs OUT conditions. This effect was absent in the gamma band.<br /> (2) A significant increase in phase-coded mutual information for probe location for the IN condition compared to the OUT condition.

The authors then apply the Maximal Overlap Discrete Wavelet Transform (MODWT) to decompose LFP signals at the single-trial level, an approach that allows them to identify oscillatory components without imposing pre-defined frequency bands. They find that the precise frequencies of low-frequency oscillations (theta, alpha, and beta) correlate with the visually evoked firing rates of MT neurons.

Strengths:

The work addresses an important question: how cognitive states such as working memory modulate sensory processing in the visual cortex. More specifically, as we are expanding our understanding of the role of feedback in the brain, a me role of oscillations.

The application of MODWT to single-trial LFPs represents a methodological advance over traditional bandpass filtering, which typically relies on trial-averaged power and may miss fine-grained frequency variability.

The work aligns with ongoing efforts to understand how feedback and oscillatory dynamics contribute to top-down modulation in the brain.

Weaknesses:

(1) Several early results (e.g., increases in alpha/beta power and phase coding) closely replicate previous work from the same group and may be better placed in the Supplementary Information or omitted entirely. The novelty of the current paper lies mainly in the single-trial decomposition and frequency-rate relationship. However, the manuscript fails to expand the prior findings using the traditional methods, or at least offer a more mechanistic insight into the role of top-down modulation of the MT area during working memory tasks. Single-trial analysis can offer new avenues for mechanistic insight. For example, authors could have investigated the relationship of Cross-frequency coupling (CFC) with trial-by-trial behavior of the animal (Voytek et al., 2010) or transient synchronous oscillations for memory maintenance (Buschman et al, 2012).

(2) The statistical methods require greater transparency. Details such as whether tests were one- or two-sided, how multiple comparisons were controlled, and how correlations among nearby electrodes were handled are not fully reported.

Reviewer #1 (Public review):

Summary:

In this paper, the authors develop a biologically plausible recurrent neural network model to explain how the hippocampus generates and uses barcode-like activity to support episodic memory. They address key questions raised by recent experimental findings: how barcodes are generated, how they interact with memory content (such as place and seed-related activity), and how the hippocampus balances memory specificity with flexible recall. The authors demonstrate that chaotic dynamics in a recurrent neural network can produce barcodes that reduce memory interference, complement place tuning, and enable context-dependent memory retrieval, while aligning their model with observed hippocampal activity during caching and retrieval in chickadees.

Strengths:

(1) The manuscript is well-written and structured.

(2) The paper provides a detailed and biologically plausible mechanism for generating and utilizing barcode activity through chaotic dynamics in a recurrent neural network. This mechanism effectively explains how barcodes reduce memory interference, complement place tuning, and enable flexible, context-dependent recall.

(3) The authors successfully reproduce key experimental findings on hippocampal barcode activity from chickadee studies, including the distinct correlations observed during caching, retrieval, and visits.

(4) Overall, the study addresses a somewhat puzzling question about how memory indices and content signals coexist and interact in the same hippocampal population. By proposing a unified model, it provides significant conceptual clarity.

Weaknesses:

The recurrent neural network model incorporates assumptions and mechanisms, such as the modulation of recurrent input strength, whose biological underpinnings remain unclear. The authors acknowledge some of these limitations thoughtfully, offering plausible mechanisms and discussing their implications in depth. It may be worth exploring the robustness of the results to certain modeling assumptions. For instance, the choice to run the network for a fixed amount of time and then use the activity at the end for plasticity could be relaxed.

Reviewer #2 (Public review):

Summary:

Striking experimental results by Chettih et al 2024 have identified high-dimensional, sparse patterns of activity in the chickadee hippocampus when birds store or retrieve food at a given site. These barcode-like patterns were interpreted as "indexes" allowing the birds to retrieve from memory the locations of stored food.

The present manuscript proposes a recurrent network model that generates such barcode activity and uses it to form attractor-like memories that bind information about location and food. The manuscript then examines the computational role of barcode activity in the model by simulating two behavioral tasks, and by comparing the model with an alternate model in which barcode activity is ablated.

Strengths of the study:

proposes a potential neural implementation for the indexing theory of episodic memory\

Provides a mechanistic model of striking experimental findings: barcode-like, sparse patterns of activity when birds store a grain at a specific location

A particularly interesting aspect of the model is that it proposes a mechanism for binding discrete events to a continuous spatial map, and demonstrates the computational advantages of this mechanism

Weaknesses:

The importance of different modeling ingredients and dynamical mechanisms could be made more clear.

Reviewer #1 (Public review):

The authors did a good job addressing my concerns. This is an important paper on the basis of appetitive associative learning.

Reviewer #2 (Public review):

A long-standing debate in the field of Pavlovian learning relates to the phenomenon of timescale invariance in learning i.e. that the rate at which an animal learns about a Pavlovian CS is driven by the relative rate of reinforcement of the cue (CS) to the background rate of reinforcement. In practice, if a CS is reinforced on every trial, then the rate of acquisition is determined by the relative duration of the CS (T) and the ITI (C = inter-US-interval = duration of CS + ITI), specifically the ratio of C/T. Therefore, the point of acquisition should be the same with a 10s CS and a 90s ITI (T = 10; C = 90 + 10 = 100, C/T = 100/10 = 10) and with a 100s CS and a 900s ITI (T = 100; C = 900 + 100 = 1000, C/T = 1000/100 = 10). That is to say, the rate of acquisition is invariant to the absolute timescale as long as this ratio is the same. This idea has many other consequences, but is also notably different from more popular prediction-error based associative learning models such as the Rescorla-Wagner model. The initial demonstrations that the ratio C/T predicts the point of acquisition across a wide range of parameters (both within and across multiple studies) was conducted in Pigeons using a Pavlovian autoshaping procedure. What has remained under contention is whether or not this relationship holds across species, particularly in the standard appetitive Pavlovian conditioning paradigms used in rodents. The results from rodent studies aimed at testing this have been mixed, and often the debate around the source of these inconsistent results focuses on the different statistical methods used to identify the point of acquisition for the highly variable trial-by-trial responses at the level of individual animals.

The authors successfully replicate the same effect found in pigeon autoshaping paradigms decades ago (with almost identical model parameters) in a standard Pavlovian appetitive paradigm in rats. They achieve this through a clever change the experimental design, using a convincingly wide range of parameters across 14 groups of rats, and by a thorough and meticulous analysis of these data. It is also interesting to note that the two authors have published on opposing sides of this debate for many years, and as a result have developed and refined many of the ideas in this manuscript through this process.

Main findings

(1) The present findings demonstrate that the point of initial acquisition of responding is predicted by the C/T ratio.

(2) The terminal rates of responding to the CS appear to be related to the reinforcement rate of the CS (T; specifically, 1/T) but not its relation to the reinforcement rate of the context (i.e. C or C/T). In the present experiment, all CS trials were reinforced so it is also the case that the terminal rate of responding was related to the duration of the CS.

(3) An unexpected finding was that responding during the ITI was similarly related to the rate of contextual reinforcement (1/C). This novel finding suggests that the terminal rate of responding during the ITI and the CS are related to their corresponding rates of reinforcement. This finding is surprising as it suggests that responding during the ITI is not being driven by the probability of reinforcement during the ITI.

(4) Finally, the authors characterised the nature of increased responding from the point of initial acquisition until responding peaks at a maximum. Their analyses suggest that nature of this increase was best described as linear in the majority of rats, as opposed to the non-linear increase that might be predicted by prediction error learning models (e.g. Rescorla-Wagner). However, more detailed analyses revealed that these changes can be quite variable across rats, and more variable when the CS had lower informativeness (defined as C/T).

Strengths and Weaknesses:

There is an inherent paradox regarding the consistency of the acquisition data from Gibbon & Balsam's (1981) meta-analysis of autoshaping in pigeons, and the present results in magazine response frequency in rats. This consistency is remarkable and impressive, and is suggestive of a relatively conserved or similar underlying learning principle. However, the consistency is also surprising given some significant differences in how these experiments were run. Some of these differences might reasonably be expected to lead to differences in how these different species respond. For example:

The autoshaping procedure commonly used in the pigeons from these data were pretrained to retrieve rewards from a grain hopper with an instrumental contingency between head entry into the hopper and grain availability. During Pavlovian training, pecking the key light also elicited an auditory click feedback stimulus, and when the grain hopper was made available, the hopper was also illuminated.

In the present experimental procedure, the rats were not given contextual exposure to the pellet reinforcers in the magazine (e.g. a magazine training session is typically found in similar rodent procedures). The Pavlovian CS was a cue light within the magazine itself.

These design features in the present rodent experiment are clearly intentional. Pretraining with the reinforcer in the testing chambers would reasonably alter the background rate of reinforcement (parameter), so it make sense not to include this but differs from the paradigm used in pigeons. Having the CS inside the magazine where pellets are delivered provides an effective way to reduce any potential response competition between CS and US directed responding and combines these all into the same physical response. This makes the magazine approach response more like the pecking of the light stimulus in the pigeon autoshaping paradigm. However, the location of the CS and US is separated in pigeon autoshaping, raising questions about why the findings across species are consistent despite these differences.

Intriguingly, when the insertion of a lever is used as a Pavlovian cue in rodent studies, CS directed responding (sign-tracking) often develops over training such that eventually all animals bias their responding towards the lever than towards the US (goal-tracking at the magazine). However, the nature of this shift highlights the important point that these CS and US directed responses can be quite distinct physically as well as psychologically. Therefore, by conflating the development of these different forms of responding, it is not clear whether the relationship between C/T and the acquisition of responding describes the sum of all Pavlovian responding or predominantly CS or US directed responding.

Another interesting aspect of these findings is that there is a large amount of variability that scales inversely with C/T. A potential account of the source of this variability is related to the absence of preexposure to the reward pellets. This is normally done within the animals' homecage as a form of preexposure to reduce neophobia. If some rats take longer to notice and then approach and finally consume the reward pellets in the magazine, the impact of this would systematically differ depending on the length of the ITI. For animals presented with relatively short CSs and ITIs, they may essentially miss the first couple of trials and/or attribute uneaten pellets accumulating in the magazine to the background/contextual rate of reinforcement. What is not currently clear is whether this was accounted for in some way by confirming when the rats first started retrieving and consuming the rewards from the magazine.

While the generality of these findings across species is impressive, the very specific set of parameters employed to generate these data raise questions about the generality of these findings across other standard Pavlovian conditioning parameters. While this is obviously beyond the scope of the present experiment, it is important to consider that the present study explored a situation with 100% reinforcement on every trial, with a variable duration CS (drawn form a uniform distribution), with a single relatively brief CS (maximum of 122s) CS and a single US. Again, the choice of these parameters in the present experiment is appropriate and very deliberately based on refinements from many previous studies from the authors. This includes a number of criteria used to define magazine response frequency which includes discarding specific responses (discussed and reasonably justified clearly in the methods section). Similarly, the finding that terminal rates of responding are reliably related to 1/T is surprising, and it is not clear whether this might be a property specific to this form of variable duration CS, the use of a uniform sampling distribution, or the use of only a single CS. However, it is important to keeps these limitations in mind when considering some of the claims made in the discussion section of this manuscript that go beyond what these data can support.

Reviewer #1 (Public review):

Summary:

The authors presented a simplified E. coli cell-free protein synthesis (eCFPS) system that reduces core reaction components from 35 to 7, improving protein expression levels. They also presented a "fast lysate" protocol that simplifies extract preparation, enhancing accessibility and robustness for diverse applications.

Strengths:

The authors present a valuable new protocol for eCFPS, which simplifies its application.

Weaknesses:

The authors only provided the data for optimization, leaving the underlying mechanism that explains the phenomena unexplained.

Reviewer #2 (Public review):

Summary:

The authors have made a convincing argument that the current system of in vitro translation using E. coli extracts can be significantly optimized to work with much lesser components, while maintaining activity. They have showcased their improved activity using not only physical but also functional readouts.

Strengths:

The experiments are designed in a very logical and easy-to-understand manner, which makes it easier not only to follow the paper but also to reproduce the results. Functional assays with the synthesized proteins are a good way to demonstrate functionality and applicability of the system.

Weaknesses:

The production of the lysate requires special instrumentation, limiting accessibility. While the strengths of the study are well-emphasized, the limitations are not mentioned. Representation of some experiments could be done in a more complete manner.

Reviewer #3 (Public review):

Summary:

The authors aimed to overcome the challenges associated with complex, conventional prokaryotic cell-free protein synthesis (CFPS) systems, which require up to thirty-five components, by developing a streamlined and efficient E. coli CFPS platform to encourage broader adoption. The main objective was to reduce the number of reaction components from thirty-five to seven, while also developing an accessible 'fast lysate' preparation protocol that eliminates time-consuming runoff and dialysis steps. The authors also sought to demonstrate the robustness and translational quality of this streamlined system by efficiently synthesising challenging functional proteins, including the cytotoxic restriction endonuclease BsaI and the self-assembling intermediate filament protein vimentin.

Strengths:

This study presents several key strengths of the optimised E. coli cell-free protein synthesis system in terms of its design, performance and accessibility.

(1) The reaction mixture has been dramatically simplified, with the number of essential core components successfully reduced from up to thirty-five in conventional systems to just seven.

(2) The "fast lysate" protocol is a significant advance in terms of procedure.

(3) The system's ability to synthesise challenging, functional proteins is evidence of its robustness.

Weaknesses:

(1) Title: "A simplified and highly efficient cell-free protein synthesis system for prokaryotes".

(a) This title is misleading since one would expect a simplified and highly efficient cell-free protein synthesis system to yield similar protein levels compared to current cell-free protein synthesis systems. What this study shows is that the composition of cell-free protein synthesis systems can be simplified while maintaining a certain level of protein synthesis. Here, optimisation does not involve maintaining protein synthesis yield while simplifying the cell-free protein synthesis system; rather, it involves developing a simplified cell-free protein synthesis system. As mentioned in my comments below, this study lacks a comparison of protein levels with a typical cell-free protein synthesis system.

(b) What do the authors mean by "highly efficient"? Highly efficient compared to what experimental conditions? If one is interested in the yield of protein synthesis, is this simplified system highly efficient compared to current systems?

(2) Figures 1, 3-5 :

(a) What do relative luciferase units represent? How are these units calculated?

(b) In this system, the level of expression depends mainly on the level of NLuc transcripts and the efficiency of NLuc translation. How did the authors ensure that the chemical composition of the different eCFPS buffers only affected protein translation and not transcript levels? In other words, are luciferase units solely an indicator of protein synthesis efficiency, or do they also depend on transcription efficiency, which could vary depending on the experimental conditions?

(c) How long were the eCFPS reactions allowed to proceed before performing the luciferase activity measurement? Depending on the reaction time, the absence or presence of certain compounds may or may not impact NLuc expression. For example, it can be assumed that tRNA does not significantly affect NLuc levels over a short period of time, and that endogenous tRNA in the lysate is present at sufficient concentrations. However, over a longer period of time, the addition of tRNA could be essential to achieve optimal NLuc levels.

(d) The authors show that tRNA and amino acids are not strictly essential for the expression of NLuc, likely due to residual amounts within the cell lysate. However, are the protein levels achieved without added amino acids and tRNA sufficient for biochemical assays that require a certain amount of protein? It is important to note that the focus here is on optimising the simplicity of the buffer rather than the level of protein expression. In fact, the simplicity of the buffer is prioritised over the amount of protein produced. This should be made clear.

(e) How would the NLuc level compare if all the components were optimised individually and present in an optimised buffer, compared to a buffer optimised for simplicity as described by the authors?

(3) Line 71, Streamlining eCFPS: removal of dispensable components. This title is misleading because it creates the false impression that proteins can be produced in vitro without the addition of certain compounds. While this is true, the level of protein produced may not be sufficient for subsequent biochemical analyses. This should be made clear.

(4) Figure 2: In the legend, "(A) Protein expression levels of the eCFPS system measured at varying concentrations of KGlu and MgGlu2" would be more accurate if changed to "(A) Protein expression levels of the eCFPS system using an Nanoluciferase (NLuc) reporter DNA measured at varying concentrations of KGlu and MgGlu2".

(5) Lanes 302-303: "The thorough optimization of the seven core components was a critical step in achieving high protein expression levels". What are "high expression levels"? Compared to what?

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

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

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

(4) 2P imaging relates the cellular dysfunction in PV neurons to epilepsy.

Reviewer #2 (Public review):

Summary:

Wengert et al. generated and comprehensively characterized the Kcnc1 A421V/+ knock-in mouse, which models developmental epileptic encephalopathy. The Kcnc1 gene encodes the Kv3.1 channel subunit, which, similar to the role of BK-channels in some excitatory neurons, facilitates high-frequency firing in inhibitory neurons by accelerating the downward hyperpolarization of individual action potentials. Although various Kcnc1 mutations are linked to developmental epileptic encephalopathies, the functional impact of the A421V mutation remained controversial. To elucidate its effect on the neuronal excitability and neurological functions, the authors generated cre-dependent KI mice and thoroughly characterized them using neonatal neurological assessments, high-quality in vitro electrophysiology, and in vivo imaging/electrophysiology analyses. These studies revealed impaired excitability in the PV+ inhibitory interneurons, correlating with the emergence of epilepsy and premature death. Overall, this study provides strong support for the role of the A421V mutation in disrupting inhibitory function.

Overall, the study is well-designed and conducted at a high quality. The use of a Cre-dependent KI system is effective for maintaining the mutant line despite the premature death phenotype, and may also minimize the phenotype drift that can arise when breeding from mice using milder phenotype manifestation (as ones with severe phenotype often fail to reproduce). The neonatal behavior analysis is thoroughly conducted, and the in vitro electrophysiology studies are of high quality, providing robust insights into the functional impact of the mutation.

One limitation of this study is the demonstration of the trafficking defect of mutant Kv3.1, which relies solely on the fluorescence density, and such analysis often lacks a rigorous quantitative measurement. A biochemical analysis (surface biotinylation or immunoblot using membrane fractionation) will make the conclusion more convincing, although this poses a technical challenge as the Kv3.1 is expressed primarily expressed only in a subset of PV+ cells.

While the study focused on the superficial layer because Kv3.1 is the major channel subunit, some of the neurons co-express Kv3.2, and Kv3.1 and Kv3.2 can form heteromeric channels. It would be interesting to explore whether the mutant Kv3.1 subunits exert a dominant-negative effect on Kv3.2 in these populations.

Reviewer #3 (Public review):

Summary:

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

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

Strengths:

This manuscript establishes a new rodent model of KCNC1-developmental and epileptic encephalopathy. The manuscript provides strong evidence that parvabumin interneurons are impaired by the Kcnc1-A421V mutation and that cortical excitatory neurons are not impaired. Together, these findings support the conclusion that seizure phenotypes associated with Kcnc1-A421V are caused by impaired cortical inhibition.

Weaknesses:

The manuscript identifies a partial mechanism of disease that leaves several aspects unresolved including the possible role of subcortical regions in the seizure mechanism. Similarly, while the authors identify a reduction in potassium currents and a reduction in PV cell surface expression of Kv3.1 why the A421V missense mutation leads to a more severe phenotype than previously reported loss-of-function mutations in Kv3.1is not clear.

Reviewer #1 (Public review):

Sandkuhler et al. re-evaluated the biological functions of TANGO2 homologs in C. elegans, yeast, and zebrafish. Compared to the previously reported role of TANGO2 homologs in transporting heme, Sandkuhler et al. expressed a different opinion on the biological functions of TANGO2 homologs. With the support of some results from their tests, they conclude that 'there is insufficient evidence to support heme transport as the primary function of TANGO2', in addition to the evidence that C. elegans TANGO2 helps counteract oxidative stress.. While the differences are reported in this study, more work is needed to elucidate the intuitive biological function of TANGO2.

Strengths:

(1) This work revisits a set of key experiments, including the toxic heme analog GaPP survival assay, the fluorescent ZnMP accumulation assay, and the multi-organismal investigations documented by Sun et al. in Nature (2022), which are critical for comparing the two works. Meanwhile, the authors also highlight the differences in reagents and methods between the two studies, demonstrating significant academic merit.

(2) This work reported additional phenotypes for the C. elegans mutant of the TANGO2 homologs, including lawn avoidance, reduced pharyngeal pumping, smaller brood size, faster exhaustion under swimming test, and a shorter lifespan. These phenotypes are important for understanding the biological function of TANGO2 homologs, while they were missing from the report by Sun et al.

(3) Investigating the 'reduced GaPP consumption' as a cause of increased resistance against the toxic GaPP for the TANGO2 homologs, hrg-9 hrg-10 double null mutant provides a valuable perspective for studying the biological function of TANGO2 homologs.

(4) The induction of hrg-9 gene expression by paraquat indicates a strong link between TANGO2 and mitochondrial function.

(5) This work thoroughly evaluated the role of TANGO2 homologs in supporting yeast growth using multiple yeast strains and also pointed out the mitochondrial genome instability feature of the yeast strain used by Sun et al.

Weakness:

It is always a challenge to replicate someone else's work, but it is worthwhile to take on the challenge, provide evidence, and raise concerns about it. These authors attempted to replicate the experiment using the same biological material as that used by Sun et al. in Nature (2022), despite some experimental differences between the two studies. This study does not have many technical weaknesses, but it can become a much better project by focusing on the new phenotypes discovered here.

Reviewer #2 (Public review):

This work offers a valuable re-evaluation of earlier claims from other groups about TANGO2 functions and proposes that energy-related and stress-related pathways may be more important to the disorder than previously thought. A key strength of this work is the use of multiple model systems. The authors provide solid data that show how TANGO2 is probably only indirectly involved in heme transport and provide support for alternative mechanisms where TANGO2 is actually directly control. These findings provide valuable information for researchers seeking more accurate therapeutic targets.

Strengths:

The study refutes earlier claims about TANGO2's involvement in heme transport and extends previous findings by implicating TANGO2 in metabolism and oxidative stress, thereby highlighting new aspects of its role in cell physiology. The use of different model systems (Saccharomyces cerevisiae, Caenorhabditis elegans, Danio rerio) to address the main research questions is useful and demonstrates evolutionary conservation of the studied processes. Finally, the results suggest a broader impact than previously described, somewhat supporting the novelty of the study.

Weaknesses:

Although the phenotypic analyses are broad and generally well executed, a key limitation is that the main conclusions mainly rely on these readouts. While informative, sole phenotypic analyses cannot directly demonstrate the underlying molecular mechanisms proposed by the authors. The study includes limited functional or biochemical assays connecting TANGO2 orthologs to the proposed energy and stress pathways. Some observations would benefit from additional orthogonal validation to strengthen the overall interpretation. As a result, the evidence supporting the central mechanistic interpretation remains indirect, although compelling.

Overall, the authors have achieved their stated aims, and their results mainly support their main conclusion (i.e., TANGO2 is unlikely to function in heme transport and is probably linked to energy and stress pathways). However, much of the evidence comes from phenotypic analyses, which limits the strength of the mechanistic claims, leaving the proposed pathways somewhat indirect.

This work is likely to have a valuable impact on the subfield by clarifying that TANGO2 is not involved (at least directly) in heme transport and clarifying its actual role in energy and stress-related processes. By rigorously reassessing and confuting earlier claims from other studies across multiple model systems, the current work will help to guide the future research and therapeutic exploration in the context of TANGO2 deficiencies. This study will provide a solid foundation for more mechanistic insights into TANGO2 function.

Reviewer #3 (Public review):

In this paper, Sandkuhler et al. reassessed the role of TANGO2 as a heme chaperone proposed by Sun et al in a recently published paper (https://doi.org/10.1038/s41586-022-05347-z). Overall, Sandkuhler et al. conclude that the heme-related roles of TANGO2 had been overemphasized by Sun et al. especially because the hrg9 gene does not exclusively respond to different regimens of heme synthesis/uptake but is susceptible to a greater extent to, for example, oxidative stress. Impaired heme trafficking is then interpreted as due to general mitochondrial dysfunction. In recent years, the discussion around the heme-related roles of TANGO2 has been tantalizing but is still far from a definitive consensus. Discrepancies between results and their interpretation are testament to how ambitious the understanding of TANGO2 and the phenotypes associated with TANGO2 defects are.

The work presented by Sandkuhler et al. is methodologically sound, and the authors have appropriately addressed my concerns in the first round of review. Overall, this paper challenges the recent developments in the field in relation to heme trafficking and provides a wider perspective on the biological roles of TANGO2.

Reviewer #1 (Public review):

Summary:

Carloni et al. comprehensively analyze which proteins bind repetitive genomic elements in Trypanosoma brucei. For this, they perform mass spectrometry on custom-designed, tagged programmable DNA-binding proteins. After extensively verifying their programmable DNA-binding proteins (using bioinformatic analysis to infer target sites, microscopy to measure localization, ChIP-seq to identify binding sites), they present, among others, two major findings: 1) 14 of the 25 known T. brucei kinetochore proteins are enriched at 177bp repeats. As T. brucei's 177bp repeat-containing intermediate-sized and mini-chromosomes lack centromere repeats but are stable over mitosis, Carloni et al. use their data to hypothesize that a 'rudimentary' kinetochore assembles at the 177bp repeats of these chromosomes to segregate them. 2) 70bp repeats are enriched with the Replication Protein A complex, which, notably, is required for homologous recombination. Homologous recombination is the pathway used for recombination-based antigenic variation of the 70bp-repeat-adjacent variant surface glycoproteins.

Strengths and Weaknesses:

The manuscript was previously reviewed through Review Commons. As noted there, the experiments are well controlled, the claims are well supported, and the methods are clearly described. The conclusions are convincing. All concerns I raised have been addressed except one (minor point #8):

"The way the authors mapped the ChIP-seq data is potentially problematic when analyzing the same repeat type in different genomic regions. Reads with multiple equally good mapping positions were assigned randomly. This is fine when analyzing repeats by type, independent of genomic position, which is what the authors do to reach their main conclusions. However, several figures (Fig. 3B, Fig. 4B, Fig. 5B, Fig. 7) show the same repeat type at specific genomic locations." Due to the random assignment, all of these regions merely show the average signal for the given repeat. I find it misleading that this average is plotted out at "specific" genomic regions.<br /> Initially, I suggested a workaround, but the authors clarified why the workaround was not feasible, and their explanation is reasonable to me. That said, the figures still show a signal at positions where they can't be sure it actually exists. If this cannot be corrected analytically, it should at least be noted in the figure legends, Results, or Discussion.

Importantly, the authors' conclusions do not hinge on this point; they are appropriately cautious, and their interpretations remain valid regardless.

Significance:

This work is of high significance for chromosome/centromere biology, parasitology, and the study of antigenic variation. For chromosome/centromere biology, the conceptual advancement of different types of kinetochores for different chromosomes is a novelty, as far as I know. It would certainly be interesting to apply this study as a technical blueprint for other organisms with mini-chromosomes or chromosomes without known centromeric repeats. I can imagine a broad range of labs studying other organisms with comparable chromosomes to take note of and build on this study. For parasitology and the study of antigenic variation, it is crucial to know how intermediate- and mini-chromosomes are stable through cell division, as these chromosomes harbor a large portion of the antigenic repertoire. Moreover, this study also found a novel link between the homologous repair pathway and variant surface glycoproteins, via the 70bp repeats. How and at which stages during the process, 70bp repeats are involved in antigenic variation is an unresolved, and very actively studied, question in the field. Of course, apart from the basic biological research audience, insights into antigenic variation always have the potential for clinical implications, as T. brucei causes sleeping sickness in humans and nagana in cattle. Due to antigenic variation, T. brucei infections can be chronic.

Comments on revised version:

All my recommendations have been addressed.

Reviewer #2 (Public review):

The Trypanosoma brucei genome, like that of other eukaryotes, contains diverse repetitive elements. Yet, the chromatin-associated proteome of these regions remains largely unexplored. This study represents a very important conceptual and technical advancement by employing synthetic TALE DNA-binding proteins fused to YFP to selectively capture proteins associated with specific repetitive sequences in T. brucei chromatin. The data presented here are convincing, supported by appropriate controls and a well-validated methodology, aligned with current state-of-the-art approaches.

The authors used synthetic TALE DNA binding proteins, tagged with YFP, which were designed to target five specific repeat elements in T. brucei genome, including centromere and telomeres-associated repeats and those of a transposon element. This is in order to identify specific proteins that bind to these repetitive sequences in T. brucei chromatin. Validation of the approach was done using a TALE protein designed to target the telomere repeat (TelR-TALE) that detected many of the proteins that were previously implicated with telomeric functions. A TALE protein designed to target the 70 bp repeats that reside adjacent to the VSG genes (70R-TALE) detected proteins that function in DNA repair and a protein designed to target the 177 bp repeat arrays (177R-TALE) identified kinetochore proteins associated T. brucei mega base chromosomes, as well as in intermediate and mini-chromosomes, which imply that kinetochore assembly and segregation mechanisms are similar in all T. brucei chromosomes.

This study represents a significant conceptual and technical advancement. To the best of our knowledge, it is the first report of employing TALE-YFP for affinity-based detection of protein complexes bound to repetitive genomic sequences in T. brucei. This approach enhances our understanding the organization in these important regions of the trypanosomal chromatin and provides the foundation for investigating the functional roles of associated proteins in parasite biology. These findings will be of particular interest to researchers studying the molecular biology of kinetoplastid parasites and other unicellular organisms, as well as to scientists investigating the roles of repetitive genomic elements in chromatin structure and their functional role in higher eukaryotes.

Importantly, any essential or unique interacting partners identified using the approach employed here, could serve as a potential target for therapeutic intervention in severe tropical diseases cause by kinetoplastids.

Reviewer #1 (Public review):

Summary:

The authors set out on the ambitious task of establishing the reproducibility of claims from the Drosophila immunity literature. Starting out from a corpus of 400 articles from 1959 and 2011, the authors sought to determine whether their claims were confirmed or contradicted by previous or subsequent publications. Additionally, they actively sought to replicate a subset of the claims for which no previous replications were available (although this set was not representative of the whole sample, as the authors focused on suspicious and/or easily testable claims). The focus of the article is on inferential reproducibility; thus, methods don't necessarily map exactly to the original ones.

The authors present a large-scale analysis of the individual replication findings, which are presented in a companion article (Westlake et al., 2025. DOI 10.1101/2025.07.07.663442). In their retrospective analysis of reproducibility, the authors find that 61% of the original claims were verified by the literature, 7.5% were partialy verified, and only 6.8% were challenged, with 23.8% having no replication available. This is in stark contrast with the result of their prospective replications, in which only 16% of claims were successfully reproduced.

The authors proceed to investigate correlates of replicability, with the most consistent finding being that findings stemming from higher-ranked universities (and possibly from very high impact journals) were more likely to be challenged.

Strengths:

(1) The work presents a large-scale, in-depth analysis of a particular field of science that includes authors with deep domain expertise of the field. This is a rare endeavour to establish the reproducibility of a particular subfield of science, and I'd argue that we need many more of these in different areas.

(2) The project was built on a collaborative basis (https://ReproSci.epfl.ch/), using an online database (https://ReproSci.epfl.ch/), which was used to organize the annotations and comments of the community about the claims. The website remains online and can be a valuable resource to the Drosophila immunity community.

(3) Data and code are shared in the authors' GitHub repository, with a Jupyter notebook available to reproduce the results.

Main concerns:

(1) Although the authors claim that "Drosophila immunity claims are mostly replicable", this conclusion is strictly based on the retrospective analysis - in which around 84% of the claims for which a published verification attempt was found. This is in very stark contrast with the findings that the authors replicate prospectively, of which only 16% are verified.

Although this large discrepancy may be explained by the fact that the authors focused on unchallenged and suspicious claims (which seems to be their preferred explanation), an alternative hypothesis is that there is a large amount of confirmation bias in the Drosophila immunity literature, either because attempts to replicate previous findings tend to reach similar results due to researcher bias, or because results that validate previous findings are more likely to be published.

Both explanations are plausible (and, not being an expert in the field, I'd have a hard time estimating their relative probability), and in the absence of prospective replication of a systematic sample of claims - which could determine whether the replication rate for a random sample of claims is as high as that observed in the literature -, both should be considered in the manuscript.

(2) The fact that the analysis of factors correlating with reproducibility includes both prospective and retrospective replications also leads to the possibility of confusion bias in this analysis. If most of the challenged claims come from the authors' prospective replications, while most of the verified ones come from those that were replicated by the literature, it becomes unclear whether the identified factors are correlated with actual reproducibility of the claims or with the likelihood that a given claim will be tested by other authors and that this replication will be published.

(3) The methods are very brief for a project of this size, and many of the aspects in determining whether claims were conceptually replicated and how replications were set up are missing.

Some of these - such as the PubMed search string for the publications and a better description of the annotation process - are described in the companion article, but this could be more explicitly stated. Others, however, remain obscure. Statements such as "Claims were cross-checked with evidence from previous, contemporary and subsequent publications and assigned a verification category" summarize a very complex process for which more detail should be given - in particular because what constitutes inferential reproducibility is not a self-evident concept. And although I appreciate that what constitutes a replication is ultimately a case-by-case decision, a general description of the guidelines used by the authors to determine this should be provided. As these processes were done by one author and reviewed by another, it would also be useful to know the agreement rates between them to have a general sense of how reproducible the annotation process might be.

The same gap in methods descriptions holds for the prospective replications. How were labs selected, how were experimental protocols developed, and how was the validity of the experiments as a conceptual replication assessed? I understand that providing the methods for each individual replication is beyond the scope of the article, but a general description of how they were developed would be important.

(4) As far as I could tell, the large-scale analysis of the replication results was not preregistered, and many decisions seem somewhat ad hoc. In particular, the categorization of journals (e.g. low impact, high impact, "trophy") and universities (e.g. top 50, 51-100, 101+) relies on arbitrary thresholds, and it is unclear how much the results are dependent on these decisions, as no sensitivity analyses are provided.

Particularly, for analyses that correlate reproducibility with continuous variable (such as year of publication, impact factor or university ranking, I'd strongly favor using these variables as continuous variables in the analysis (e.g. using logistic regression) rather than performing pairwise comparisons between categories determined by arbitrary cutoffs. This would not only reduce the impact of arbitrary thresholds in the analysis, but would also increase statistical power in the univariate analyses (as the whole sample can be used in at once) and reduce the number of parameters in the multivariate model (as they will be included as a single variable rather than multiple dummy variables when there are more than two categories).

(5) The multivariate model used to investigate predictors of replicability includes unchallenged claims along with verified ones in the outcome, which seems like an odd decision. If the intention is to analyze which factors are correlated with reproducibility, it would make more sense to remove the unchallenged findings, as these are likely uninformative in this sense. In fact, based on the authors' own replications of unchallenged findings, they may be more likely to belong the "challenged" category than to the "unchallenged" one if they were to be verified.

Reviewer #2 (Public review):

Summary:

Lemaitre et al. conducted an analysis of 400 publications in the Drosophila immunity field (1959-2011), performing both univariable and multivariable analyses to identify factors that correlate with or influence the irreproducibility of scientific claims. Some of the findings are unexpected, for instance, neither the career stage of the PI nor that of the first author appears to matter that much, while others, such as the influence of institutional prestige or publication in "trophy journals," are more predictable. The results provide valuable insight into patterns of irreproducibility in academia and may help inform policies to improve research reproducibility in the field.

Strengths:

This study is based on a large, manually curated dataset, complemented by a companion paper (Westlake et al., 2025. DOI 10.1101/2025.07.07.663442) that provides additional details on experimentally documented cases. The statistical methods are appropriate, and the findings are both important and informative. The results are clearly presented and supported by accessible documentation through the ReproSci project.

Weaknesses:

The analysis is limited to a specific field (immunity) and model system (Drosophila). Since biological context may influence reproducibility -- for example, depending on whether mechanisms are more hardwired or variable -- and the model system itself may contribute to these effects (as the authors note), it remains unclear to what extent these findings generalize to other fields or organisms. The authors could expand the discussion to address the potential scope and limitations of the study's generalizability.

Reviewer #3 (Public review):

Summary:

The authors of this paper were trying to identify how reproducible, or not, their subfield (Drosophilia immunity) was since its inception over 50 years ago. This required identifying not only the papers, but the specific claims made in the paper, assessing if these claims were followed up in the literature, and if so whether the subsequent papers supported or refuted the original claim. In addition to this large manually curated effort, the authors further investigated some claims that were left unchallenged in the literature by conducting replications themselves. This provided a rich corpus of the subfield that could be investigated into what characteristics influence reproducibility.

Strengths:

A major strength of this study is the focus on a subfield, the detailing of identifying the main, major, and minor claims - which is a very challenging manual task - and then cataloging not only their assessment of if these claims were followed up in the literature, but also what characteristics might be contributing to reproducibility, which also included more manual effort to supplement the data that they were able to extract from the published papers. While this provides a rich dataset for analysis, there is a major weakness with this approach, which is not unique to this study.

Weaknesses:

The main weakness is relying heavily on the published literature as the source for if a claim was determined to be verified or not. There are many documented issues with this stemming from every field of research - such as publication bias, selective reporting, all the way to fraud. It's understandable why the authors took this approach - it is the only way to get at a breadth of the literature - however the flaw with this approach is it takes the literature as a solid ground truth, which it is not. At the same time, it is not reasonable to expect the authors to have conducted independent replications for all of the 400 papers they identified. However, there is a big difference trying to assess the reproducibility of the literature by using the literature as the 'ground truth' vs doing this independently like other large-scale replication projects have attempted to do. This means the interpretation of the data is a bit challenging.

Below are suggestions for the authors and readers to consider:

(1) I understand why the authors prefer to mention claims as their primary means of reporting what they found, but it is nested within paper, and that makes it very hard to understand how to interpret these results at times. I also cannot understand at the high-level the relationship between claims and papers. The methods suggest there are 3-4 major claims per paper, but at 400 papers and 1,006 claims, this averages to ~2.5 claims per paper. Can the authors consider describing this relationship better (e.g., distribution of claims and papers) and/or considering presenting the data two ways (primary figures as claims and complimentary supplementary figures with papers as the unit). This will help the reader interpret the data both ways without confusion. I am also curious how the results look when presented both ways (e.g., does shifting to the paper as the unit of analysis shift the figures and interpretation?). This is especially true since the first and last author analysis shows there is varying distribution of papers and claims by authors (and thus the relationship between these is important for the reader).

(2) As mentioned above, I think the biggest weakness is that the authors are taking the literature at face value when assigning if a claim was validated or challenged vs gathering new independent evidence. This means the paper leans more on papers, making it more like a citation analysis vs an independent effort like other large-scale replication projects. I highly recommend the authors state this in their limitations section.

On top of that, I have questions that I could not figure out (though I acknowledge I did not dig super deep into the data to try). The main comment I have is How was verified (and challenged) determined? It seems from the methods it was determined by "Claims were cross-checked with evidence from previous, contemporary and subsequent publications and assigned a verification category". If this is true, and all claims were done this way - are verified claims double counted then? (e.g., an original claim is found by a future claim to be verified - and thus that future claim is also considered to be verified because of the original claim).

Related, did the authors look at the strength of validation or challenged claims? That is, if there is a relationship mapping the authors did for original claims and follow-up claims, I would imagine some claims have deeper (i.e., more) claims that followed up on them vs others. This might be interested to look at as well.

(3) I recommend the authors add sample sizes when not present (e.g., Fig 4C). I also find that the sample sizes are a bit confusing, and I recommend the authors check them and add more explanation when not complete, like they did for Fig 4A. For example, Fig 7B equals to 178 labs (how did more than 156 labs get determined here?), and yet the total number of claims is 996 (opposed to 1,006). Another example, is why does Fig 8B not have all 156 labs accounted for? (related to Fig 8B, I caution on reporting a p value and drawing strong conclusions from this very small sample size - 22 authors). As a last example, Fig 8C has al 156 labs and 1,006 claims - is that expected? I guess it means authors who published before 1995 (as shown in Figure 8A continued to publish after 1995?) in that case, it's all authors? But the text says when they 'set up their lab' after 1995, but how can that be?

(4) Finally, I think it would help if the authors expanded on the limitations generally and potential alternative explanations and/or driving factors. For example, the line "though likely underestimated' is indicated in the discussion about the low rate of challenged claims, it might be useful to call out how publication bias is likely the driver here and thus it needs to be carefully considered in the interpretation of this. Related, I caution the authors on overinterpreting their suggestive evidence. The abstract for example, states claims of what was found in their analysis, when these are suggestive at best, which the authors acknowledge in the paper. But since most people start with the abstract, I worry this is indicating stronger evidence than what the authors actually have.

The authors should be applauded for the monumental effort they put into this project, which does a wonderful job of having experts within a subfield engage their community to understand the connectiveness of the literature and attempt to understand how reliable specific results are and what factors might contribute to them. This project provides a nice blueprint for others to build from as well as leverage the data generated from this subfield, and thus should have an impact in the broader discussion on reproducibility and reliability of research evidence.

Reviewer #1 (Public review):

One of the roadblocks in PfEMP1 research has been the challenges in manipulating var genes to incorporate markers to allow the transport of this protein to be tracked and to investigate the interactions taking place within the infected erythrocyte. In addition, the ability of Plasmodium falciparum to switch to different PfEMP1 variants during in vitro culture has complicated studies due to parasite populations drifting from the original (manipulated) var gene expression. Cronshagen et al have provided a useful system with which they demonstrate the ability to integrate a selectable drug marker into several different var genes that allows the PfEMP1 variant expression to be 'fixed'. This on its own represents a useful addition to the molecular toolbox and the range of var genes that have been modified suggests that the system will have broad application. As well as incorporating a selectable marker, the authors have also used selective linked integration (SLI) to introduce markers to track the transport of PfEMP1, investigate the route of transport and probe interactions with PfEMP1 proteins in the infected host cell.

One of the major strengths of this paper is that the authors have not only put together a robust system for further functional studies, but they have used it to produce a range of interesting findings including:

Co-activation of rif and var genes when in a head-to-head orientation.

The reduced control of expression of var genes in the 3D7-MEED parasite line.

More support for the PTEX transport route for PfEMP1.<br /> Identification of new proteins involved in PfEMP1 interactions in the infected erythrocyte, including some required for cytoadherence.

In most cases the experimental evidence is straightforward, and the data support the conclusions strongly. The authors have been very careful in the depth of their investigation, and where unexpected results have been obtained, they have looked carefully at why these have occurred.

A weakness of the paper is, as mentioned above, that the results are sometimes not as clear as might have been expected, for example, in the requirement for panning modified parasites to produce binding to EPCR. Where this has happened, the authors take a robust and thoughtful approach, and acknowledge that (as in most research) there are more questions to address. Being able to select specific var gene switches using drug markers will provide some useful starting points to understand how switching happens in P. falciparum. However, our trypanosome colleagues might remind us that forcing switches may show us some mechanisms, but perhaps not all.

Despite these sometimes complicated findings, the authors have achieved their aim as stated in the title of the paper, and in doing so have provided an excellent resource to themselves and other researchers in the field to answer some important questions.

Overall, the authors have produced a useful and robust system to support functional studies on PfEMP1, which provides a platform for future studies manipulating the domain content in var genes. They have used this system to produce a range of interesting findings and to support its use by the research community.

Comments on revisions:

I have no further recommendations for changes by the authors. They have addressed my concerns, and the paper reads very well.

Reviewer #2 (Public review):

Summary

Croshagen et al develop a range of tools based on selection-linked integration (SLI) to study PfEMP1 function in P. falciparum. PfEMP1 is encoded by a family of ~60 var genes subject to mutually exclusive expression. Switching expression between different family members can modify the binding properties of the infected erythrocyte while avoiding the adaptive immune response. Although critical to parasite survival and Malaria disease pathology, PfEMP1 proteins are difficult to study owing to their large size and variable expression between parasites within the same population. The SLI approach previously developed by this group for genetic modification of P. falciparum is employed here to selectively and stably activate expression of target var genes at the population level. Using this strategy, the binding properties of specific PfEMP1 variants were measured for several distinct var genes with a novel semi-automated pipeline to increase throughput and reduce bias. Activation of similar var genes in both the common lab strain 3D7 and the cytoadhesion competent FCR3/IT4 strain revealed higher binding for several PfEMP1 IT4 variants with distinct receptors, indicating this strain provides a superior background for studying PfEMP1 binding. SLI also enables modifications to target var gene products to study PfEMP1 trafficking and identify interacting partners by proximity-labeling proteomics, revealing two novel exported proteins required for cytoadherence. Overall, the data demonstrate a range of SLI-based approaches for studying PfEMP1 that will be broadly useful for understanding the basis for cytoadhesion and parasite virulence.

Comments:

While the capability of SLI to active selected var gene expression was initially reported by Omelianczyk et al., the present study greatly expands the utility of this approach. Several distinct var genes are activated in two different P. falciparum strains and shown to modify the binding properties of infected RBCs to distinct endothelial receptors; development of SLI2 enables multiple SLI modifications in the same parasite line; SLI is used to modify target var genes to study PfEMP1 trafficking and determine PfEMP1 interactomes with BioID. Along the way, the authors also demonstrate a new selection marker for P. falciparum transfection (a mutant FNT lactate transporter that provides resistance to the compound BH267.meta). Curiously, Omelianczyk et al activated a single var (Pf3D7_0421300) and observed elevated expression of an adjacent var arranged in a head to tail manner, possibly resulting from local chromatin modifications enabling expression of the neighboring gene. In contrast, the present study observed activation of neighboring genes with head to head but not head to tail arrangement, which may be the result of shared promoter regions. The reason for these differing results is unclear although it should be noted that the two studies examined different var loci.

The IT4var19 panned line that became binding-competent showed increased expression of both paralogs of ptp3 (as well as a phista and gbp), suggesting that overexpression of PTP3 may improve PfEMP1 display and binding. Interestingly, IT4 appears to be the only known P. falciparum strain (only available in PlasmoDB) that encodes more than one ptp3 gene (PfIT_140083100 and PfIT_140084700). PfIT_140084700 is almost identical to the 3D7 PTP3 (except for a ~120 residue insertion in 3D7 beginning at residue 400). In contrast, while the C-terminal region of PfIT_140083100 shows near perfect conservation with 3D7 PTP3 beginning at residue 450, the N-terminal regions between the PEXEL and residue 450 are quite different. This may indicate the generally stronger receptor binding observed in IT4 relative to 3D7 results from increased PTP3 activity due to multiple isoforms or that specialized trafficking machinery exists for some PfEMP1 proteins.

Revisions:

The authors thoughtfully addressed all the reviewer comments.

Reviewer #3 (Public review):

Summary:

The submission from Cronshagen and colleagues describes the application of a previously described method (selection linked integration) to the systematic study of PfEMP1 trafficking in the human malaria parasite Plasmodium falciparum. PfEMP1 is the primary virulence factor and surface antigen of infected red blood cells and is therefore a major focus of research into malaria pathogenesis. Since the discovery of the var gene family that encodes PfEMP1 in the late 1990s, there have been multiple hypotheses for how the protein is trafficked to the infected cell surface, crossing multiple membranes along the way. One difficulty in studying this process is the large size of the var gene family and the propensity of the parasites to switch which var gene is expressed, thus preventing straightforward gene modification-based strategies for tagging the expressed PfEMP1. Here the authors solve this problem by forcing expression of a targeted var gene by fusing the PfEMP1 coding region with a drug selectable marker separated by a skip peptide. This enabled them to generate relatively homogenous populations of parasites all expressing tagged (or otherwise modified) forms of PfEMP1 suitable for study. They then applied this method to study various aspects of PfEMP1 trafficking.

Strengths:

The study is very thorough, and the data are well presented. The authors used SLI to target multiple var genes, thus demonstrating the robustness of their strategy. They then perform experiments to investigate possible trafficking through PTEX, they knockout proteins thought to be involved in PfEMP1 trafficking and observe defects in cytoadherence, and they perform proximity labeling to further identify proteins potentially involved in PfEMP1 export. These are independent and complimentary approaches that together tell a very compelling story.

Weaknesses:

(1) When the authors targeted IT4var19, they were successful in transcriptionally activating the gene, however they did not initially obtain cytoadherent parasites. To observe binding to ICAM-1 and EPCR, they had to perform selection using panning. This is an interesting observation and potentially provides insights into PfEMP1 surface display, folding, etc. However, it also raises questions about other instances in which cytoadherence was not observed. Would panning of these other lines have successfully selected for cytoadherent infected cells? Did the authors attempt panning of their 3D7 lines? Given that these parasites do export PfEMP1 to the infected cell surface (Figure 1D), it is possible that panning would similarly rescue binding. Likewise, the authors knocked out PTP1, TryThrA and EMPIC3 and detected a loss of cytoadhesion, but they did not attempt panning to see if this could rescue binding. The strong selection that panning exerts on parasite populations could result in selection of compensatory changes that enable cytoadherence, which could be very informative, although the analysis could potentially be quite complicated and beyond the scope of the current paper. Nonetheless, these are important concepts to consider when assessing these phenotypes.

(2) The authors perform a series of trafficking experiments to help discern whether PfEMP1 is trafficked through PTEX. While the results were not entirely definitive, they make a strong case for PTEX in PfEMP1 export. The authors then used BioID to obtain a proxiome for PfEMP1 and identified proteins they suggest are involved in PfEMP1 trafficking. However, it seemed that components of PTEX were missing from the list of interacting proteins. Is this surprising and does this observation shed any additional light on the possibility of PfEMP1 trafficking through PTEX? This warrants a comment or discussion.

Comments on revisions:

The authors have responded thoroughly and constructively to suggestions and comments in the initial review. I have no additional comments. This is a great contribution to the literature.

Reviewer #1 (Public review):

Summary:

This study resolves a cryo-EM structure of the GPCR, GPR30, in the presence of bicarbonate, which the author's lab recently identified as the physiological ligand. Understanding the ligand and the mechanism of activation is of fundamental importance to the field of receptor signaling. This solid study provides important insight into the overall structure and suggests a possible bicarbonate binding site.

Strengths:

The overall structure, and proposed mechanism of G-protein coupling are solid. Based on the structure, the authors identify a binding pocket that might accommodate bicarbonate. Although assignment of the binding pocket is speculative, extensive mutagenesis of residues in this pocket identifies several that are important to G-protein signaling. The structure shows some conformational differences with a previous structure of this protein determined in the absence of bicarbonate (PMC11217264). To my knowledge, bicarbonate is the only physiological ligand that has been identified for GPR30, making this study an important contribution to the field. However, the current study provides novel and important circumstantial evidence for the bicarbonate binding site based on mutagenesis and functional assays.

Weaknesses:

Bicarbonate is a challenging ligand for structural and biochemical studies, and because of experimental limitations, this study does not elucidate the exact binding site. Higher resolution structures would be required for structural identification of bicarbonate. The functional assay monitors activation of GPR30, and thus reports on not only bicarbonate binding, but also the integrity of the allosteric network that transduces the binding signal across the membrane. However, biochemical binding assays are challenging because the binding constant is weak, in the mM range.

The authors appropriately acknowledge the limitations of these experimental approaches, and they build a solid circumstantial case for the bicarbonate binding pocket based on extensive mutagenesis and functional analysis. However, the study does fall short of establishing the bicarbonate binding site.

Reviewer #2 (Public review):

Summary:

In this manuscript, "Cryo-EM structure of the bicarbonate receptor GPR30," the authors aimed to enrich our understanding of the role of GPR30 in pH homeostasis by combining structural analysis with a receptor function assay. This work is a natural development and extension of their previous work on Nature Communications (PMID: 38413581). In the current body of work, they solved the cryo-EM structure of the human GPR30-G-protein (mini-Gsqi) complex in the presence of bicarbonate ions at 3.15 Å resolution. From the atomic model built based on this map, they observed the overall canonical architecture of class A GPCR and also identified 3 extracellular pockets created by ECLs (Pockets A-C). Based on the polarity, location, size, and charge of each pocket, the authors hypothesized that pocket A is a good candidate for the bicarbonate binding site. To identify the bicarbonate binding site, the authors performed an exhaustive mutant analysis of the hydrophilic residues in Pocket A and analyzed receptor reactivity via calcium assay. In addition, the human GPR30-G-protein complex model also enabled the authors to elucidate the G-protein coupling mechanism of this special class A GPCR, which plays a crucial role in pH homeostasis.

Strengths:

As a continuation of their recent Nature Communications publication, the authors used cryo-EM coupled with mutagenesis and functional studies to elucidate bicarbonate-GPR30 interaction. This work provided atomic-resolution structural observations for the receptor in complex with G-protein, allowing us to explore its mechanism of action, and will further facilitate drug development targeting GPR30. There were 3 extracellular pockets created by ECLs (Pockets A-C). The authors were able to filter out 2 of them and hypothesized that pocket A was a good candidate for the bicarbonate binding site based on the polarity, location, and charge of each pocket. From there, the authors identified the key residues on GPR30 for its interaction with the substrate, bicarbonate. Together with their previous work, they mapped out amino acids that are critical for receptor reactivity.

Weaknesses:

When we see a reduction of a GPCR-mediated downstream signaling, several factors could potentially contribute to this observation: 1) a reduced total expression of this receptor due to the mutation (transcription and translation issue); 2) a reduced surface expression of this receptor due to the mutation (trafficking issue); and 3) a dysfunctional receptor that doesn't signal due to the mutation. In the current revision, based on the gating strategy, the surface expression of the HA-positive WT GPR30-expressing cells is only 10.6% of the total population, while the surface expression levels of the mutants range from 1.89% (P71A) to 64.4% (D111A). Combining this information with the functional readout in Figure 3F and G, as well as their previous work, the authors concluded that mutations at P71, E115, D125, Q138, C207, D210, and H307 would decrease bicarbonate responses. Among those sites,

E115, Q138, and H307 were from their previous Nature Comm paper.

Authors claim P71 and C207 make a structural-stability contribution, as their mutations result in a significant reduction in surface expression: P71A (1.89%) and C207A (2.71%). However, compared to 10.6% of the total population in the WT, (P71A is 17.8% of the WT, and C207A is 25.6% of the WT), this doesn't rule out the possibility that the mutated receptor is also dysfunctional: at 10 mM NaHCO3, RFU of WT is ~500, RFU of P71 and C207 are ~0.

The authors also interpret "The D125ECL1A mutant has lost its activity but is located on the surface" and only mention "D125 is unlikely to be a bicarbonate binding site, and the mutational effect could be explained due to the decreased surface expression". Again, compared to 10.6% of the total population in the WT, D125A (3.94%) is 37.2% of the WT. At 10 mM NaHCO3, the RFU of the WT is ~500, the RFU of D125 is ~0. This doesn't rule out the possibility that the mutated receptor is also dysfunctional. It is not clear why D125A didn't make it to the surface.

Other mutants that the authors didn't mention much in their text: D111A (64.4%, 607.5% of WT surface expression), E121A (50.4%, 475.5% of WT surface expression), R122 (41.0%, 386.8% of WT surface expression), N276A (38.9%, 367.0% of WT surface expression) and E218A (24.6%, 232.1% of WT surface expression) all have similar RFU as WT, although the surface expression is about 2-6 times more. On the other hand, Q215A (3.18%, 30% of WT surface expression) has similar RFU as WT, with only a third of the receptor on the surface.

Altogether, the wide range of surface expression across the different cell lines, combined with the different receptor function readouts, makes the cell functional data only partially support their structural observations.

Reviewer #3 (Public review):

Summary

GPR30 responds to bicarbonate and plays a role in regulating cellular pH and ion homeostasis. However, the molecular basis of bicarbonate recognition by GPR30 remains unresolved. This study reports the cryo-EM structure of GPR30 bound to a chimeric mini-Gq in the presence of bicarbonate, revealing mechanistic insights into its G-protein coupling. Nonetheless, the study does not identify the bicarbonate-binding site within GPR30.

Strengths

The work provides strong structural evidence clarifying how GPR30 engages and couples with Gq.

Weaknesses

Several GPR30 mutants exhibited diminished responses to bicarbonate, but their expression levels were also reduced. As a result, the mechanism by which GPR30 recognizes bicarbonate remains uncertain, leaving this aspect of the study incomplete.

Reviewer #1 (Public review):

Summary:

This study presents a new Bayesian approach to estimate importation probabilities of malaria combining epidemiological data, travel history, and genetic data through pairwise IBD estimates. Importation is an important factor challenging malaria elimination, especially in low transmission settings. This paper focus on Magude and Matutuine, two districts in south Mozambique with very low malaria transmission. The results show isolation-by-distance in Mozambique, with genetic relatedness decreasing with distances larger than 100 km, and no spatial correlation for distances between 10 and 100 km. But again strong spatial correlation in distances smaller than 10 km. They report high genetic relatedness between Matutuine and Inhambane, higher than between Matutuine and Magude. Inhambane is the main source of importation in Matutuine, accounting for 63.5% of imported cases. Magude, on the other hand, shows smaller importation and travel rates than Matutuine, as it is a rural area with less mobility. Additionally, they report higher levels of importation and travel in the dry season, when transmission is lower. Also, no association with importation was found for occupation, sex and other factors. These data have practical implications for public health strategies aiming malaria elimination, for example, testing and treating travelers from Matutuine in the dry season.

Strengths:

The strength of this study relies in the combination of different sources of data - epidemiological, travel and genetic data - to estimate importation probabilities, the statistical analyses.

Weaknesses:

The authors recognize the limitations related to sample size and the biases of travel reports.

Reviewer #2 (Public review):

Summary:

Based on a detailed dataset, the authors present a novel Bayesian approach to classify malaria cases as either imported or locally acquired.

Strengths:

The proposed Bayesian approach for case classification is simple, well justified, and allows the integration of parasite genomics, travel history, and epidemiological data.

Weakness:

While the authors aim to classify cases as imported or locally acquired, the work lacks a quantification of the contribution of each case type to overall transmission.

Comments on revisions:

All my questions and concerns were satisfactorily addressed.

Reviewer #3 (Public review):

This work provides a novel statistical model to identify imported malaria cases, which are an important challenge for elimination, particularly in low-transmission areas. This tool was applied in Plasmodium falciparum populations in Mozambique and determined differences in importation rates in 2 low-transmission districts in the South.

Strengths:

The study has several strengths, mainly the development of a novel Bayesian model that integrates genomic, epidemiological, and travel data to estimate importation probabilities. The results showed insights into malaria transmission dynamics, particularly identifying importation sources and differences in importation rates in Mozambique. Finally, the relevance of the findings is to suggest interventions focusing on the traveler population to support efforts for malaria elimination.

Weaknesses:

The study also has some limitations, although the authors have plans to address them. The sample collection was not representative of some provinces, and not all samples had sufficient metadata for the risk factor analysis. Additionally, the authors used a proxy for transmission intensity and assumed some other conditions to calculate the importation probability for specific scenarios. They plan to conduct a new sample collection and include monthly malaria incidence estimates in the future.

Comments on revisions:

- Delete "We added this text to the discussion" in line 302 (Discussion)<br /> - I recommend adding the plans to address limitations indicated in the Response to Reviewers document in the Discussion. This would really strengthen the limitation section.

Reviewer #1 (Public review):

Summary:

Colorectal cancer (CRC) is the third most common cancer globally and the second leading cause of cancer-related deaths. Colonoscopy and fecal immunohistochemical testing are among the early diagnostic tools that have significantly enhanced patient survival rates in CRC. Methylation dysregulation has been identified in the earliest stages of CRC, offering a promising avenue for screening, prediction, and diagnosis. The manuscript entitled "Early Diagnosis and Prognostic Prediction of Colorectal Cancer through Plasma Methylation Regions" by Zhu et al. presents that a panel of genes with methylation pattern derived from cfDNA (27 DMRs), serving as a noninvasive detection method for CRC early diagnosis and prognosis.

Strengths:

The authors provided evidence that the 27 DMRs pattern worked well in predicting CRC distant metastasis, and the methylation score remarkably increased in stages III-IV. Additionally, compared with the traditional tumor marker CEA, 27 DMRs prediction showed a superior sensitivity, highlighting the potential clinical application.

Weaknesses:

The major concerns are the design of DMRs screening, the relatively low sensitivity of this DMRs' pattern in detecting early-stage of CRC, the limited size of the cohorts, and the lack of comparison with the traditional diagnosis test.

Comments on revisions:

All my concerns have been cleared, and I have no further questions.

Reviewer #2 (Public review):

In this study, the authors aimed to develop cfDNA markers for comprehensive diagnosis, metastatic assessment, and prognostic prediction of colorectal cancer (CRC). Through integrative analysis of public 450K DNA methylation datasets and in-house targeted bisulfite sequencing (BS-seq) data from CRC and paired normal tissues, as well as plasma samples, they identified a signature comprising 27 differentially methylated regions (DMRs). This signature was subsequently validated for three clinical applications: cancer detection, metastasis prediction, and prognosis assessment.

Strengths:

The 27-DMR signature demonstrates value for both diagnosis and prognosis of CRC. Additionally, the datasets generated in this study serve as a valuable resource for the research community.

Weaknesses:

The validation cohorts for cancer detection and metastasis prediction were relatively small, which may limit the generalizability of the findings. The cancer detection model's performance does not surpass some published methods or commercial products.

Reviewer #1 (Public review):

Summary:

In this study the authors use a Drosophila model to demonstrate that Tachykinin (Tk) expression is regulated by the microbiota. In Drosophila conventionally reared (CR) flies are typically shorter lived than those raised without a microbiota (axenic). Here, knockdown of Tk expression is found to prevent lifespan shortening by the microbiota and the reduction of lipid stores typically seen in CR flies when compared to axenic counterparts. It does so without reducing food intake or fecundity which are often seen as necessary trade-offs for lifespan extension. Further, the strength of the interaction between Tk and the microbiota is found to be bacteria specific and is stronger in Acetobacter pomorum (Ap) mono-associated flies compared to Levilactobacillus brevis (Lb) mono-association. The impact on lipid storage was also only apparent in Ap-flies.

Building on these findings the authors show that gut specific knockdown is largely sufficient to explain these phenotypes. Knockdown of the Tk receptor, TkR99D, in neurons recapitulates the lifespan phenotype of intestinal Tk knockdown supporting a model whereby Tk from the gut signals to TkR99D expressing neurons to regulate lifespan. In addition, the authors show that FOXO may have a role in lifespan regulation by the Tk-microbiota interaction. However, they rule out a role for insulin producing cells or Akh-producing cells suggesting the microbiota-Tk interaction regulates lifespan through other, yet unidentified, mechanisms.

Major comments:

Overall, I find the key conclusions of the paper convincing. The authors present an extensive amount of experimental work, and their conclusions are well founded in the data. In particular, the impact of TkRNAi on lifespan and lipid levels, the central finding in this study, has been demonstrated multiple times in different experiments and using different genetic tools. As a result, I don't feel that additional experimental work is necessary to support the current conclusions.

However, I find it hard to assess the robustness of the lifespan data from the other manipulations used (TkR99DRNAi, TkRNAi in dFoxo mutants etc.) because information on the population size and whether these experiments have been replicated is lacking. Can the authors state in the figure legends the numbers of flies used for each lifespan and whether replicates have been done? For all other data it is clear how many replicates have been done, and the methods give enough detail for all experiments to be reproduced.

Significance:

Overall, I find the key conclusions of the paper convincing. The authors present an extensive amount of experimental work, and their conclusions are well founded in the data. We have known that the microbiota influence lifespan for some time but the mechanisms by which they do so have remained elusive. This study identifies one such mechanism and as a result opens several avenues for further research. The Tk-microbiota interaction is shown to be important for both lifespan and lipid homeostasis, although it's clear these are independent phenotypes. The fact that the outcome of the Tk-microbiota interaction depends on the bacterial species is of particular interest because it supports the idea that manipulation of the microbiota, or specific aspects of the host-microbiota interaction, may have therapeutic potential.

These findings will be of interest to a broad readership spanning host-microbiota interactions and their influence on host health. They move forward the study of microbial regulation of host longevity and have relevance to our understanding of microbial regulation of host lipid homeostasis. They will also be of significant interest to those studying the mechanisms of action and physiological roles of Tachykinins.

Field of expertise: Drosophila, gut, ageing, microbiota, innate immunity

Reviewer #2 (Public review):

Summary:

The main finding of this work is that microbiota impacts lifespan though regulating the expression of a gut hormone (Tk) which in turn acts on its receptor expressed on neurons. This conclusion is robust and based on a number of experimental observations, carefully using techniques in fly genetics and physiology: 1) microbiota regulates Tk expression, 2) lifespan reduction by microbiota is absent when Tk is knocked down in gut (specifically in the EEs), 3) Tk knockdown extends lifespan and this is recapitulated by knockdown of a Tk receptor in neurons. These key conclusions are very convincing. Additional data are presented detailing the relationship between Tk and insulin/IGF signalling and Akh in this context. These are two other important endocrine signalling pathways in flies. The presentation and analysis of the data are excellent.

There are only a few experiments or edits that I would suggest as important to confirm or refine the conclusions of this manuscript. These are:

(1) When comparing the effects of microbiota (or single bacterial species) in different genetic backgrounds or experimental conditions, I think it would be good to show that the bacterial levels are not impacted by the other intervention(s). For example, the lifespan results observed in Figure 2A are consistent with Tk acting downstream of the microbes but also with Tk RNAi having an impact on the microbiota itself. I think this simple, additional control could be done for a few key experiments. Similarly, the authors could compare the two bacterial species to see if the differences in their effects come from different ability to colonise the flies.

(2) The effect of Tk RNAi on TAG is opposite in CR and Ax or CR and Ap flies, and the knockdown shows an effect in either case (Figure 2E, Figure 3D). Why is this? Better clarification is required.

(3) With respect to insulin signalling, all the experiments bar one indicate that insulin is mediating the effects of Tk. The one experiment that does not is using dilpGS to knock down TkR99D. Is it possible that this driver is simply not resulting in an efficient KD of the receptor? I would be inclined to check this, but as a minimum I would be a bit more cautious with the interpretation of these data.

(4) Is it possible to perform at least one lifespan repeat with the other Tk RNAi line mentioned? This would further clarify that there are no off-target effects that can account for the phenotypes.

There are a few other experiments that I could suggest as I think they could enrich the current manuscript, but I do not believe they are essential for publication:

(5) The manuscript could be extended with a little more biochemical/cell biology analysis. For example, is it possible to look at Tk protein levels, Tk levels in circulation, or even TkR receptor activation or activation of its downstream signalling pathways? Comparing Ax and CR or Ap and CR one would expect to find differences consistent with the model proposed. This would add depth to the genetic analysis already conducted. Similarly, for insulin signalling - would it be possible to use some readout of the pathway activity and compare between Ax and CR or Ap and CR?

(6) The authors use a pan-acetyl-K antibody but are specifically interested in acetylated histones. Would it be possible to use antibodies for acetylated histones? This would have the added benefit that one can confirm the changes are not in the levels of histones themselves.

(7) I think the presentation of the results could be tightened a bit, with fewer sections and one figure per section.

Significance:

The main contribution of this manuscript is the identification of a mechanism that links the microbiota to lifespan. This is very exciting and topical for several reasons:

(1) The microbiota is very important for overall health but it is still unclear how. Studying the interaction between microbiota and health is an emerging, growing field, and one that has attracted a lot of interest, but one that is often lacking in mechanistic insight. Identifying mechanisms provides opportunities for therapies. The main impact of this study comes from using the fruit fly to identify a mechanism.

(2) It is very interesting that the authors focus on an endocrine mechanism, especially with the clear clinical relevance of gut hormones to human health recently demonstrated with new, effective therapies (e.g. Wegovy).

(3) Tk is emerging as an important fly hormone and this study adds a new and interesting dimension by placing TK between microbiota and lifespan.

I think the manuscript will be of great interest to researchers in ageing, human and animal physiology and in gut endocrinology and gut function.

Reviewer #3 (Public review):

Summary:

Marcu et al. demonstrate a gut-neuron axis that is required for the lifespan-shortening effects mediated by gut bacteria. They show that the presence of commensal bacteria-particularly Acetobacter pomorum-promotes Tk expression in the gut, which then binds to neuronal tachykinin receptors to shorten lifespan. Tk has also recently been reported to extend lifespan via adipokinetic hormone (Akh) signaling (Ahrentløv et al., Nat Metab 7, 2025), but the mechanism here appears distinct. The lifespan shortening by Ap via Tk seems to be partially dependent on foxo and independent of both insulin signaling and Akh-mediated lipid mobilization.

Although the detailed mechanistic link to lifespan is not fully resolved, the experiment and its results clearly show the involvement of the molecules tested. This work adds a valuable dimension to our growing understanding of how gut bacteria influence host longevity. However, there are some points that should be addressed.

(1) Tk+ EEC activity should be assessed directly, rather than relying solely on transcript levels. Approaches such as CaLexA or GCaMP could be used.

(2) In Line243, the manuscript states that the reporter activity was not increased in the posterior midgut. However, based on the presented results in Fig4E, there is seemingly not apparent regional specificity. A more detailed explanation is necessary.

(3) If feasible, assessing foxo activation would add mechanistic depth. This could be done by monitoring foxo nuclear localization or measuring the expression levels of downstream target genes.

(4) Fig1C uses Adh for normalization. Given the high variability of the result, the authors should (1) check whether Adh expression levels changed via bacterial association and/or (2) compare the results using different genes as internal standard.

(5) While the difficulty of maintaining lifelong axenic conditions is understandable, it may still be feasible to assess the induction of Tk (i.e.. Tk transcription or EE activity upregulation) by the microbiome on males.

(6) We also had some concerns regarding the wording of the title.<br /> Fig6B and C suggests that TkR86C, in addition to TkR99D, may be involved in the A. pomorum-lifespan interaction. Consider revising the title to refer more generally to the "tachykinin receptor" rather than only TkR99D.<br /> The difference between "aging" and "lifespan" should also be addressed. While the study shows a role for Tk in lifespan, assessment of aging phenotypes (e.g. Climbing assay, ISC proliferation) beyond the smurf assay is required to make conclusions about aging.

(7) The statement in Line 82 that EEs express 14 peptide hormones should be supported with an appropriate reference, if available.

Significance:

General assessment: The main strength of this study is the careful and extensive lifespan analyses, which convincingly demonstrate the role of gut microbiota in regulating longevity. The authors clarify an important aspect of how microbial factors contribute to lifespan control. The main limitation is that the study primarily confirms the involvement of previously reported signaling pathways, without identifying novel molecular players or previously unrecognized mechanisms of lifespan regulation.

Advance: The lifespan-shortening effect of Acetobacter pomorum (Ap) has been reported previously, as has the lifespan-shortening effect of Tachykinin (Tk). However, this study is the first to link these two factors mechanistically, which represents a significant and original contribution to the field. The advance is primarily mechanistic, providing new insight into how microbial cues converge on host signaling pathways to influence ageing.

Audience: This work will be of particular interest to a specialized audience of basic researchers in ageing biology. It will also attract interest from microbiome researchers who are investigating host-microbe interactions and their physiological consequences. The findings will be useful as a conceptual framework for future mechanistic studies in this area.

Field of expertise: Drosophila ageing, lifespan, microbiome, metabolism

Reviewer #1 (Public review):

Summary:

Taylar Hammond and colleagues identified new regulators of the G1/S transition of the cell cycle. They did so by screening publicly available data from the Cancer Dependency Map and identified FAM53C as a positive regulator of the G1/S transition. Using biochemical assays they then show that FAM53 interacts with the DYRK1A kinase to inhibit its function. They show in RPE1 cells that loss of FAMC53 leads to a DYRK1A + P53-dependent cell cycle arrest. Combined inactivation of FAM53C and DYRK1A in a TP53-null background caused S-phase entry with subsequent apoptosis. Finally the authors assess the effect of FAM53C deletion in a cortical organoid model, and in Fam53c knockout mice. Whereas proliferation of the organoids is indeed inhibited, mice show virtually no phenotype.

The authors have revised the manuscript, and I respond here point-by-point to indicate which parts of the revision I found compelling, and which parts were less convincing. So the numbering is consistent with the numbering in my first review report.

(1) The p21 knockdowns are a valuable addition, and the claim that other p53 targets than p21 are involved in the FAMC53 RNAi-mediated arrest is now much more solid. Minor detail: if S4D is a quantification of S4C, it is hard to believe that the quantification was done properly (at least the DYRK1Ai conditions). Perhaps S4C is not the best representative example, or some error was made?

(2a) I appreciate the decision to remove the cyclin D1 phosphorylation data. A more nuanced model now emerges. It is not clear to me however why the Protein Simple immunoassay was used for experiments with RPE cells, and not the cortical organoids. Even though no direct claims are made based on the phospho-cyclin D data in Figure 5E+G, showing these data suggests that FAM53C deletion increases DYRK1A-mediated cyclin D1 phosphorylation. I find it tricky to show these data, while knowing now that this effect could not be shown in the RPE1 cells.<br /> (2b) The quantifications of the immunoassays are not convincing. In multiple experiments, the HSP90 levels vary wildly, which indicates big differences in protein loading if HSP90 is a proper loading control. This is for example problematic for the interpretation of figure 3F and S3I. The cyclin D1 "bands" look extremely similar between siCtrl and siFAM53C (Fig S3I), in fact the two series of 6 samples with different dosages of DYRK1Ai look seem an identical repetition of each other. I did not have to option to overlay them, but it would be important to check if a mistake was made here. The cyclin D1 signals aside, the change in cycD1/HSP90 ratios seems to be entirely caused by differences in HSP90 levels. Careful re-analysis of the raw data and more equal loading seem necessary. The same goes (to a lesser extent) for S3J+K.<br /> (2c) the new model in Fig S4L: what do the arrows at the right FAM53C and p53 that merge a point straight towards S-phase mean? They suggest that p53 (and FAM53C) directly promote S-phase progression, but most likely this is not what the authors intended with it.

(3) Clear; nicely addressed.

(4) Thank you for correcting.

(5) I appreciate that the authors are now more careful to call the IMPC analysis data preliminary. This is acceptable to me, but nevertheless, I suggest the authors to seriously consider taking this part entirely out. The risk of chance finding and the extremely skewed group sizes (as reviewer #2 had pointed out) hamper the credibility of this statistical analysis.

Reviewer #2 (Public review):

The authors sought to identify new regulators of the G1/S transition by mining the Cancer Dependency Map (DepMap) co-dependency dataset. This analysis successfully identified FAM53C, a poorly characterized protein, as a candidate. The strength of the paper lies in this initial discovery and the subsequent biochemical work convincingly showing that FAM53C can directly interact with the kinase DYRK1A, a known cell cycle regulator.

The authors then present evidence, primarily from acute siRNA knockdown in RPE-1 cells, that loss of FAM53C induces a strong G1 cell cycle arrest. Their follow-up investigation proposes a model where FAM53C normally inhibits DYRK1A, thereby protecting Cyclin D from degradation and preventing p53 activation, to allow for G1/S progression. The authors have commendably addressed some concerns from the initial review: they have now demonstrated the G1 arrest using two independent siRNAs (an improvement over the initial pool), shown the effect in several additional cancer cell lines (U2OS, A549, HCT-116), and developed a more nuanced model that incorporates p53 activation, which helps to explain some of the complex data.

However, a central and critical weakness persists. The entire functional model is built upon the very strong G1 arrest phenotype observed in vitro following acute knockdown. This finding is in stark contrast to data from other contexts. As the authors note, the knockout of Fam53c in mice results in minimal phenotypes, and the DepMap data itself suggests the gene is largely non-essential in most cancer cell lines.

This major discrepancy creates two competing interpretations:

As the authors suggest, FAM53C has a critical role in the cell cycle, but its loss is rapidly masked by compensatory mechanisms in long-term knockout models (like iPSCs and mice) or in established cancer cell lines.

The strong acute G1 arrest is an experimental artifact of the siRNA-mediated knockdown, and not a true reflection of FAM53C's primary function.

The authors' new controls (using two individual siRNAs and showing the arrest is RB-dependent) make an off-target effect less likely, but they do not definitively rule it out. The gold-standard experiment to distinguish between these two possibilities-a rescue of the phenotype using an siRNA-resistant cDNA-has not been performed.

Because this key control is missing, the foundation of the paper's functional claims is not as solid as it needs to be. While the study provides an interesting and valuable new candidate for the cell cycle field to investigate, readers should be cautious in accepting the strength of FAM53C's role in the G1/S transition until this central discrepancy is definitively resolved.

Reviewer #3 (Public review):

Summary:

In this study Hammond et al. investigated the role of Dual-specificity Tyrosine Phosphorylation regulated Kinase 1A (DYRK1) in G1/S transition. By exploiting Dependency Map portal, they identified a previously unexplored protein FAM53C as potential regulator of G1/S transition. Using RNAi, they confirmed that depletion of FAM53C suppressed proliferation of human RPE1 cells and that this phenotype was dependent on the presence protein RB. In addition, they noted increased level of CDKN1A transcript and p21 protein that could explain G1 arrest of FAM53C-depleted cells but surprisingly, they did not observe activation of other p53 target genes. Proteomic analysis identified DYRK1 as one of the main interactors of FAM53C and the interaction was confirmed in vitro. Further, they showed that purified FAM53C blocked the ability of DYRK1 to phosphorylate cyclin D in vitro although the activity of DYRK1 was likely not inhibited (judging from the modification of FAM53C itself). Instead, it seems more likely that FAM53C competes with cyclin D in this assay. Authors claim that the G1 arrest caused by depletion of FAM53C was rescued by inhibition of DYRK1 but this was true only in cells lacking functional p53. This is quite confusing as DYRK1 inhibition reduced the fraction of G1 cells in p53 wild type cells as well as in p53 knock-outs, suggesting that FAM53C may not be required for regulation of DYRK1 function. Instead of focusing on the impact of FAM53C on cell cycle progression, authors moved towards investigating its potential (and perhaps more complex) roles in differentiation of IPSCs into cortical organoids and in mice. They observed a lower level of proliferating cells in the organoids but if that reflects an increased activity of DYRK1 or if it is just an off-target effect of the genetic manipulation remains unclear. Even less clear is the phenotype in FAM53C knock-out mice. Authors did not observe any significant changes in survival nor in organ development but they noted some behavioral differences. Weather and how these are connected to the rate of cellular proliferation was not explored. In the summary, the study identified previously unknown role of FAM53C in proliferation but failed to explain the mechanism and its physiological relevance at the level of tissues and organism. Although some of the data might be of interest, in current form the data is too preliminary to justify publication.

Major comments:

(1) Whole study is based on one siRNA to Fam53C and its specificity was not validated. Level of the knock down was shown only in the first figure and not in the other experiments. The observed phenotypes in the cell cycle progression may be affected by variable knock-down efficiency and/or potential off target effects.

(2) Experiments focusing on the cell cycle progression were done in a single cell line RPE1 that showed a strong sensitivity to FAM53C depletion. In contrast, phenotypes in IPSCs and in mice were only mild suggesting that there might be large differences across various cell types in the expression and function of FAM53C. Therefore, it is important to reproduce the observations in other cell types.

(3) Authors state that FAM53C is a direct inhibitor of DYRK1A kinase activity (Line 203), however this model is not supported by the data in Fig 4A. FAM53C seems to be a good substrate of DYRK1 even at high concentrations when phosphorylations of cyclin D is reduced. It rather suggests that DYRK1 is not inhibited by FAM53C but perhaps FAM53C competes with cyclin D. Further, authors should address if the phosphorylation of cyclin D is responsible for the observed cell cycle phenotype. Is this Cyclin D-Thr286 phosphorylation, or are there other sites involved?

(4) At many places, information on statistical tests is missing and SDs are not shown in the plots. For instance, what statistics was used in Fig 4C? Impact of FAM53C on cyclin D phosphorylation does not seem to be significant. IN the same experiment, does DYRK1 inhibitor prevent modification of cyclin D?

(5) Validation of SM13797 compound in terms of specificity to DYRK1 was not performed.

(6) A fraction of cells in G1 is a very easy readout but it does not measure progression through the G1 phase. Extension of the S phase or G2 delay would indirectly also result in reduction of the G1 fraction. Instead, authors could measure the dynamics of entry to S phase in cells released from a G1 block or from mitotic shake off.

Comments to the revised manuscript:

In the revised version of the manuscript, authors addressed most of the critical points. They now include new data with depletion of FAM53C using single siRNAs that show small but significant enrichment of population of the G1 cells. This G1 arrest is likely caused by a combined effects on induction of p21 expression and decreased levels of cyclin D1. Authors observed that inhibition of DYRK1 rescued cyclin D1 levels in FAM53 depleted cells suggesting that FAM53C may inhibit DYRK1. This possibility is also supported by in vitro experiments. On the other hand, inhibition of DYRK1 did not rescue the G1 arrest upon depletion of FAM53C, suggesting that FAM53C may have also DYRK1-independent role in G1. Functional rescue experiments with cyclin D1 mutants and detection of DYRK1 activity in cells would be necessary to conclusively explain the function of FAM53C in progression through G1 phase but unfortunately these experiments were technically not possible. Knock out of FAM53C in iPSCs and in mice suggest that FAM53C may have additional functions besides the cell cycle control and/or that adaptation may have occurred in these model systems. Overall, the study implicated FAM53C in fine tuning DYRK1 activity in cells that may to some extent influence the progression through G1 phase. In addition, FAM53C may also have DYRK1 and cell cycle independent functions that remain to be addressed by future studies.

Reviewer #1 (Public review):

Summary:

Cotton et al. investigated the role of tusB in antibiotic tolerance in Yersinia pseudotuberculosis. They used the IP2226 strain and introduced appropriate mutations and complementation constructs. Assays were performed to measure growth rates, antibiotic tolerance, tRNA modification, gene expression and proteomic profiles. In addition, experiments to measure ribosome pausing and bioinformatic analysis of codon usage in ribosomal proteins provided in-depth mechanistic support for the conclusions.

Strengths:

The findings are consistent with the authors having uncovered new mechanistic insights into bacterial antibiotic tolerance mediated by reducing ribosomal protein abundance.

Weaknesses:

Since the WT strain grows faster than the tusB mutant, there is a question of how growth rate, per se, impacts some of the analysis done. The authors should address this issue. In addition, it may not be essential, but would analysis of another slow-growing mutant (in some other antibiotic tolerance pathway if available) serve as a good control in this context?

Reviewer #2 (Public review):

Summary:

This study addresses a critical clinical challenge-bacterial antibiotic tolerance (a key driver of treatment failure distinct from genetic resistance)-by uncovering a novel regulatory role of the conserved s2U tRNA modification in Yersinia pseudotuberculosis. Its strengths are notable and lay a solid foundation for understanding phenotypic drug tolerance. The study is the first to link s2U tRNA modification loss to antibiotic tolerance, specifically targeting translation/transcription-inhibiting antibiotics (doxycycline, gentamicin, rifampicin). By establishing a causal chain - s2U deficiency → codon-specific ribosome pausing (at AAA/CAA/GAA) → reduced ribosomal protein translation → global translational suppression → tolerance - it expands the functional landscape of tRNA modifications beyond canonical translation fidelity, filling a gap in how RNA epigenetics shapes bacterial stress adaptation.

Strengths:

This study makes a valuable contribution to understanding tRNA modification-mediated antibiotic tolerance.

Weaknesses:

There are several limitations that weaken the robustness of the study's mechanistic conclusions. Addressing these gaps would significantly enhance its impact and translational potential.

Reviewer #3 (Public review):

Summary:

In the manuscript of Cotten et al., the authors study the 2-thiolation of tRNA in bacterial antibiotic resistance. The wildtype organism, Yersinia pseudotuberculosis, downregulates 2-thiolation as a response to antibiotics targeting the ribosome. In this manuscript, the authors show that a knockout of tusB causes slower translation. They provide evidence on the mechanisms of the slowing by determining transcription and translation, ribosome profiling and performing codon-usage analysis. They successfully determined that 2 codons are drivers of the translation slowdown, and the data is highly conclusive. Technically, I have nothing to criticize.

Strengths:

All in all, the study is very well made, and the writing is clear and concise. It covers a wide array of state-of-the-art analyses to unravel the interplay of tRNA modifications in translation.

Weaknesses:

The only question that remains to be asked is why the slowed translation leads to a better survival of the bacteria under antibiotic stress. In my opinion, the mechanism itself remains unclear. Thus, the statement that "We expect that this reduction in ribosomal proteins is globally reducing the translational capacity of the cell and is responsible for inducing tolerance to ribosome and RNA polymerase-targeting antibiotics" does not truly emphasize the remaining open question of why slowed translation favors survival. Therefore, I would recommend a minor text revision.

Reviewer #1 (Public review):

Summary:

This unique study reports original and extensive behavioral data collected by the authors on 21 living mammal taxa in zoo conditions (primates, tree shrew, rodents, carnivorans, and marsupials) on how descent along a vertical substrate can be done effectively and securely using gait variables. Ten morphological variables reflecting head size and limb proportions are examined in relationship to vertical descent strategies and then applied to reconstruct modes of vertical descent in fossil mammals.

Strengths:

This is a broad and data-rich comparative study, which requires a good understanding of the mammal groups being compared and how they are interrelated, the kinematic variables that underlie the locomotion used by the animals during vertical descent, and the morphological variables that are associated with vertical descent styles. Thankfully, the study presents data in a cogent way with clear hypotheses at the beginning, followed by results and a discussion that addresses each of those hypotheses using the relevant behavioral and morphological variables, always keeping in mind the relationships of the mammal groups under investigation. As pointed out in the study, there is a clear phylogenetic signal associated with vertical descent style. Strepsirrhine primates much prefer descending tail first, platyrrhine primates descend sideways when given a choice, whereas all other mammals (with the exception of the raccoon) descend head first. Not surprisingly, all mammals descending a vertical substrate do so in a more deliberate way, by reducing speed, and by keeping the limbs in contact for a longer period (i.e., higher duty factors).

Reviewer #2 (Public review):

Summary:

This paper contains kinematic analyses of a large comparative sample of small to medium-sized arboreal mammals (n = 21 species) traveling on near-vertical arboreal supports of varying diameter. This data is paired with morphological measures from the extant sample to reconstruct potential behaviors in a selection of fossil euarchontaglires. This research is valuable to anyone working in mammal locomotion and primate evolution.

Strengths:

The experimental data collection methods align with best research practices in this field and are presented with enough detail to allow for reproducibility of the study as well as comparison with similar datasets. The four predictions in the introduction are well aligned with the design of the study to allow for hypothesis testing. Behaviors are well described and documented, and Figure 1 does an excellent job in conveying the variety of locomotor behaviors observed in this sample. I think the authors took an interesting and unique angle by considering the influence of encephalization quotient on descent and the experience of forward pitch in animals with very large heads.

Comment from the Reviewing Editor on the revised version:

The authors responded to many comments of the reviewers, and I would be happy to see the authors make this version the Version of Record.

Reviewer #2 (Public review):

The Revision title and abstract are not updated enough to distinguish the special niche piRNA clusters from the more prominent major dual strand piRNA clusters that are widely known in the field for Drosophila, like 42AB and 38C. This revision mainly adds the term "piRNA source loci (piSL)" that is too vague and not a well-accepted name that would distinguish just these particularly niche piRNA clusters from major dual strand piRNA clusters like 42AB and 38C. This piSL term is problematic because it seems to imply these piSL's are connected to or would eventually become major dual strand piRNA clusters, but there is zero evidence in this study for any genetic or evolutionary connection between these two distinct types of piRNA sources. This revision still lacks the necessary changes needed to point out like in the abstract that major dual strand piRNA clusters like 42AB, 38C, 80F, and 102F in Drosophila that make up the bulk of piRNAs cannot be shown to be impacted by changes aimed at depleting ADMA-histones from these loci, and the authors' current evidence is still only limited to showing in these few 'niche' piRNA clusters that ADMA-histones may exhibit a direct interaction with Rhino as supported only by the knockdown of Drosophila Art4.

The author's rebuttal letter argues that 42AB and 38C are just conserved piRNA clusters that may no longer be regulated by ADMA. This is still a weak claim for dismissing the potential genetic redundancy problem when this study can only report strong knockdown of Art4. First, the dual strand 42AB piRNA cluster's conservation as a Drosophilid piRNA cluster is actually still a relatively recent evolutionary innovation in just D.simulans and D.melanogaster that are less than 3MYA diverged. This 42AB cluster is no longer conserved in D.sechelia and is also younger than the uni-strand Flamenco piRNA cluster that is conserve to 7MYA. The evolutionary arguments by the authors are not well-grounded. Second, the 42AB and 38C are the largest major dual strand piRNA clusters with very significant localization of Rhino and impact from Rhino loss of function, and if this paper's central thesis is that ADMA-histones directed by Art1 or Art4 is critical for the expression of dual-strand piRNA cluster loci by impacting Rhino, the current data still remain weak with no new experiments to help bolster their claims.

The author's rebuttal letter argues that the challenges they faced in trying to knock down Art1 in the fly was thwarted by reagent issues, and the explanations are unsatisfactory. They claim they only tested two RNAi cross lines to try to knock down Art1: the strain BDSC #36891, y[1] sc[*] v[1] sev[21]; P{y[+t7.7], v[+t1.8]=TRiP.GL01072}attP2/TM3, Sb[1] that they said they could not obtain this strain to be alive from the stock center? And then testing an alternative line VDRC #v110391P{KK101196}VIE-260B that displayed mediocre knockdown, the authors seemed to suggest they have given up trying to make this very important experiment work? They should have tried to figure out with the BDSC, a venerable stock center for Drosophila genetic tools, why they could not receive that fly strain alive (shipping flies at the economy rate internationally may be cheaper but often is too strenuous for flies to survive), and the authors have not acknowledged testing two other available knockdown lines for Art1: BDSC #31348, y[1] v[1]; P{y[+t7.7] v[+t1.8]=TRiP.JF01306}attP2 dsRNA and VDRC #w1118 P{GD11959}v40388. Trying to get good knockdown of Art1 would be a critical must-have experiment to address whether this arginine methyltransferase has an in vivo impact on ADMA-histones in the Drosophila ovary and showing an impact on 42AB and 38C. The revision does not address this major deficiency in impact on these two major dual strand piRNA clusters, only the very few niche piRNA clusters that are responsive to Art4 knockdown.

The rebuttal letter argues that "Therefore, conserved clusters such as 42AB and 38C may no longer be regulated by ADMA." but then the revision discussion is still speculating much too wildly that the piRNA source loci are then precursors for the eventual large piRNA clusters of 42AB and 38C. This renaming of the term piRNA source loci and the model in Fig. 7C is still misleading because 42AB and 38C are the main largest dual-strand piRNA clusters, and the pictures depict the ADMA-histones as recruiting Rhino and then Kipferl at a piRNA cluster. The term "piRNA source loci" does not sound distinct enough to separate it from the main piRNA clusters of 42AB and 38C, and I had suggested calling them 'niche piRNA clusters' to denote they are very special and distinct to only be responsive to Drosophila Art4 knockdown.

In regards to the revision's changing of gene names, the convention for gene names is to use the previous name designation. Rather than calling the gene DART1, the conventional name of this gene in Flybase is Art1 (CG6554). There is the same problem with using the new name DART4 when in Flybase the gene is called Art4 (CG5358). Alternatively, the authors should clarify the re-naming up front and make it consistent with Drosophila genetics nomenclature, perhaps dArt1 or dArt4 would be more appropriate.

Reviewer #3 (Public review):

Summary:

This study investigates how Rhino, a chromatin-associated HP1-family protein essential for germline piRNA biogenesis in Drosophila, is initially recruited to specific genomic loci. Although canonical dual-strand piRNA clusters such as 42AB, 38C, 80F, and 102F produce the majority of germline piRNAs, the mechanisms guiding Rhino to these regions remain poorly understood. To explore the earliest steps of Rhino loading, the authors use a doxycycline-inducible Rhino transgene in OSC cells, a system that expresses only the primary Piwi pathway and therefore provides an experimentally accessible, epigenetically naïve context distinct from the endogenous germline environment. Through a combination of inducible Rhino expression, knockdown of selected Drosophila PRMTs (DARTs), ChIP-seq, small RNA sequencing, and imaging, the authors propose that asymmetric arginine-methylated histones, particularly those deposited by DART4, contribute to defining initial sites of Rhino association. They identify a subset of Rhino-bound loci, termed DART4-dependent piRNA source loci (piSL), which lose Rhino, Kipferl, and piRNA production upon DART4 depletion and may represent nascent or transitional piRNA clusters. Overall, the study provides intriguing evidence for a link between ADMA histone marks and de novo Rhino recruitment, particularly in the simplified OSC context, and offers new candidate loci for further exploration of early piRNA-cluster chromatin dynamics.

Strengths:

This study offers important insights into how asymmetric dimethylarginine (ADMA) histone marks contribute to the initial recruitment of Rhino, a Drosophila HP1-family protein essential for dual-strand piRNA cluster specification. Using an integrative approach that includes ectopic expression of a Rhino transgene in OSC cells, germline knockdown of DART4 in Drosophila ovaries, ChIP-seq, small RNA-seq, and imaging, the authors show that ADMA marks particularly H3R17me2a and H4R3me2acorrelate with Rhino binding at the boundaries of canonical piRNA clusters and at DART4-dependent piRNA source loci (piSL). These piSL may represent nascent or transitional piRNA-generating regions. Overall, the dataset presented here provides a valuable resource for understanding the chromatin features associated with the emergence and maturation of piRNA clusters.

Weaknesses:

Despite the strengths of the study, several important limitations remain. Although Rhino binding correlates with ADMA-enriched boundaries, the data do not directly demonstrate that these histone marks are required for Rhino spreading, leaving the mechanistic relationship correlative rather than causal. The DART4-dependent piRNA source loci identified here produce only low levels of piRNAs, and their functional contribution remains uncertain. In addition, redundancy among DART family methyltransferases remains unresolved: only DART4 was tested in the germline, and effective knockdown of DART1 or other DARTs could not be achieved, limiting the ability to evaluate whether ADMA-histones more broadly regulate Rhino recruitment at canonical clusters. Consequently, the current dataset primarily supports DART4-dependent effects at a small subset of evolutionarily young loci, and both the model and the title may overstate the generality of this mechanism across the full repertoire of dual-strand piRNA clusters.

In conclusion, this study is carefully executed and puts forward compelling hypotheses regarding the early chromatin environment that may underlie piRNA cluster formation. The findings will be relevant to researchers interested in genome regulation, small RNA biology, and chromatin-mediated transposon control.

Reviewer #1 (Public review):

Summary:

Even though mutations in LRRK2 and GBA1 (which encodes the protein GCase) increase the risk of developing Parkinson's disease (PD), the specific mechanisms driving neurodegeneration remain unclear. Given their known roles in lysosomal function, the authors investigate how LRRK2 and GCase activity influence the exocytosis of the lysosomal lipid BMP via extracellular vesicles (EVs). They use fibroblasts carrying the PD-associated LRRK2-R1441G mutation and pharmacologically modulate LRRK2 and GCase activity.

Strengths:

The authors examine both proteins at endogenous levels, using MEFs instead of cancer cells. The study's scope is potentially interesting and could yield relevant insights into PD disease mechanisms.

Weaknesses:

Many of the authors' conclusions are overstated and not sufficiently supported by the data. Several statistical errors undermine their claims. Pharmacological treatment is very long, leading to potential off target effects. Additionally, the authors should be more rigorous when using EV markers.

Comments on revisions:

The authors have not addressed most of my concerns. For example, instead of trying with a 1-2 hour MLi2 treatment, they cited all the papers that use extremely long time points for LRRK2 inhibition; the fact that other groups do it does not mean it is biologically correct. They also refused to quantify their western blots in a proper manner, without the "hyper-normalization" claiming that it is an accepted way to quantify western blots. Again, it is statistically incorrect and biologically impossible. They also do not have a satisfactory explanation as to why the R1441G cells (which increase LRRK2 kinase activity) have no effect on EV release, but they still claim it is LRRK2 kinase activity dependent.

Overall, I am very confused by the model proposed by the authors. They only see increased EV release in the G2019S expressing cells, but not the R1441G cells, yet they claim that the increase of EV release is LRRK2 kinase activity dependent. Then, they claim that the presence of BMP (unchanged in R1441G vs CTL) in EVs is also LRRK2 kinase activity dependent. Finally, they perform TIRF with pHluorin-CD63 construct and observed an increase in G2019S cells vs CTL "further confirming that BMP release is associated with EV secretion". First, I could not see the increase in BMP release in G2019S cells (if I missed it, I apologize). And second, why didn't they do this experiment in R1441G cells? As, the R1441G cells have not displayed an increase in EV release compared to CTL cells, it could also be possible that the BMP release might be more abundant through lysosomal exocytosis (which could explain the pHluorin results) than EVs. Overall, the authors nicely demonstrate that the R1441G cells have more BMP species, likely due to increase CLN5 expression, but the release of the BMP is still not clear to this reviewer.

Reviewer #2 (Public review):

Summary:

In this paper, authors used MEFs expressing the R1441G mutant of leucine-rich repeat kinase 2 (LRRK2), a mutant associated with the early onset of Parkinson's disease. They report that in these cells LAMP2 fluorescence is higher but BMP fluorescence is lower, MVE size is reduced and that MVEs contain less ILVs. They also report that LAMP2-positive EVs are increased in mutant cells in a process sensitive to LRRK2 kinase inhibition but are further increased by glucocerebrosidase (GCase) inhibition, and that total di-22:6-BMP and total di-18:1-BMP are increased in mutant LRRK2 MEFs compared to WT cells by mass spectrometry. They also report that LRRK2 kinase inhibition partially restores cellular BMP levels, and that GCase inhibition further increased BMP levels, and that in EVs from the LRRK2 mutant, LRRK2 inhibition decreases BMP while GCase inhibition has the opposite effect. Moreover, they report that BMP increase is not due to increased BMP synthesis, although authors observe that CLN5 is increased in LRRK2 mutant cells. Finally, they report that GW4869 decreases EV release and exosomal BMP, while bafilomycin A1 increases EV release. They conclude that LRRK2 regulates BMP levels (in cells) and release (via EVs). They also conclude that the process is modulated by GCase in LRRK2 mutant cells, and that these studies may contribute to the use of BMP-positive EVs as a biomarker for Parkinson's disease and associated treatments.

Strengths:

This is a potentially interesting paper,. However, I had comments that authors needed to address to clarify some aspects of their study.

Weaknesses:

(1) The authors seem to have missed the point in their reply to my first comment. They mention the paper by Stuffers et al., who reports that endosome biogenesis continues without ESCRT. This is a nice paper, but it is irrelevant to the subject at hand. In my initial comment, I drew the author's attention to an apparent contradiction: higher LAMP2 staining in R1441G LRRK2 knock-in MEFs and yet smaller MVEs with a reduced surface area. LAMP2 being one of the major glycoproteins of MVE's limiting membrane, one would have expected lower LAMP2 staining if cells contain fewer and smaller MVEs. Authors now state that elevated LAMP2 expression in cells expressing R1441G reflects a cell type-specific effect (differential penetrance of LRRK2 signaling on lysosomal biogenesis), because amounts of LAMP1 and CD63 are similar in cells from LRRK2 G2019S PD patients and control cells (new Fig 7A-F). However, authors still conclude that LRRK2 modulates the lysosomal network, including LAMP2 and CLN5. Does it?

Similarly, the mass spec analysis of BMP (Fig S1H) does not support the data in Fig 1. Does this Table include all major isoforms found in these cells? If so, the dominant isoform is by far the di-18:1 isoform in wt and R1441G cells (at least 10X more abundant than other isoforms). Now, di-18:1-BMP is roughly 4X more abundant in R1441G cells when compared to wt cells, while BMP is reduced by half in R1441G cells (light microscopy in Fig 1). Authors argue that light microscopy may only detects a so-called antibody accessible pool. What is this? And why would this pool decrease in R1441G cells when LAMP2 is higher? Alternatively, they argue that the anti-BMP antibody may be less specific and detect other analytes. As I had already mentioned, this makes no sense, since the observed signal is lower and not higher. If authors do not trust their light microscopy analysis, why show the data?

(2) Cells contain 3 LAMP2 isoforms. Which one is upregulated and/or secreted in exosomes?

(3) The new Fig S4A is far from convincing. How were cells fractionated and what are the gradients (not described in Methods)? CD63 (presumably endolysosomes) is spread over fractions 8 - 13. LRRK2 (fractions 8-9) does not copurify with CD63. The bulk of LRRK2 is at the bottom (presumably cytosol if this is a floatation gradient), and a minor fraction moves into the gradient. CLN5 is even less clear since the bulk is also at the bottom with a tiny fraction only between LRRK2 and CD63. Also, why do authors conclude that a considerable pool of newly synthesized CLN5 did not reach its final destination at the endolysosome and may instead be retained in the ER? Where is the ER on the gradient?

(4) Fig S4B shows blots of whole cell lysates from CTRL and LRRK2 mutant-derived fibroblasts: 6 lanes are shown but without captions, containing varying amounts of calnexin and CD63. In addition, the blots look very dirty. Where is CD63? Is it the minor band at ≈37 kD (as in Fig S4A)? Or the major band below the 50kD marker? What are the other bands on these blots? As a result, the quantification shown in the bar graph does not mean much.

(5) The cell content of 18.1-BMP is increased approx. 5X by BafA1 (Fig 6C) but amounts of 18.1-BMP secreted in EVs hardly changes (Fig 6E). Since BMP is mostly present as 18.1 isoform (22:6-BMP being only a minor species, Fig S1H), does it mean that BafA1 does not increase BMP secretion and/or only a minor fraction of total cellular BMP is secreted in exosomes?

Comments on revisions:

How come 0.2 mmol/L of 22:6 and 18:1 fatty acid both correspond to 65 µg/mL (Fig 4A)?

It is stated in the Legend of Fig4 that long (B-C) and short (D) chase time points are shown as fold change. There is no panel D in the figure.

Reviewer #1 (Public review):

Summary:

Marchand et al. seek to understand how basement membrane (BM) is initially assembled around developing vasculature (and by extension basement membrane assembly generally progresses). To do this, they use an established cell culture system that is amenable to advanced microscopy techniques, including high-resolution fluorescence imaging and atomic force microscopy. This allows them to produce very high-quality imaging data that includes both protein localization and matrix topography in 3D. They show that fibronectin (FN) is remodeled as collagen IV (Col IV) assembles. Lysyl oxidase-like-2 (LOXL2) is needed for this process, and without it, BM does not form correctly, cells cannot adhere to BM, and cells also don't correctly form junctions with other cells.

Detailed Review:

The authors provide quantitative measures of BM fibril assembly at the earliest timepoints. They show two phases of growth - initial deposition, elongation, and interconnection of short fibers; the second is a significant thickening. As the BM forms, FN is immediately associated with filaments, but laminin and Col IV are not associated with fibers as detected by AFM. LOXL2 is associated with fibers, similar to FN. At a later time point, Col IV becomes associated with fibers, but laminin never does. Likely FN templates LOXL2, which crosslink Col IV into fibrils over time. Could the authors comment on how this data fits with in vivo data from model organisms? Also, I would like to know if they can uncouple LOXL2 from the FN matrix? Could you express a mutated form of LOXL2 that cannot interact with FN but still is able to crosslink Col IV?)

Depletion of LOXL2 supports this mechanism. Without it, Col IV and FN are uncoupled and accumulate as large aggregates rather than a complex fibrous network. Further, long-term thickening/growth of the fibronectin network is inhibited, indicating LOXL2 and/or the Col IV network positively reinforces fibronectin assembly. (Does LOXL2 directly act on FN, or is this effect dependent on Col IV? The nature of the molecular interactions between COL IV, LOXL2, and FN will be an important future research area.)

Next, Marchand et al. ask if failure to produce mature BM (induced by LOXL2 depletion) has consequences for underlying cells. They demonstrate a clear shift in the orientation of actin towards a linear alignment, and similarly, cells change shape from round to very elongated. Cell junctions also shifted to a linear arrangement in LOXL2 depletion. This fits with the known balance between cell-ECM and cell-cell adhesion. The changes in actin network and cell shape/adhesion correlate with a change in B1 integrin localization upon LOXL2 depletion. B1 integrin colocalized with sparse early FN fibers, but was absent from large FN aggregates that occur if LOXL2 is depleted. Similar reorganization of integrin adhesion components (FAK, Vinc, Pax). Clearly, there is feedback between BM assembly and cell junction organization. But I think the authors might emphasize to the reader that this normally reinforces the epithelial fate of these cells. It's less a balance and more like a tipping point. (Related to this section, I could not read Figure 4C graphs unless I enlarged them to 300%.)

Finally, they culture cells on micro groove plates, with or without LOXL2. The grooved substrate can orient the cells, and they show this is superseded by BM once it assembles. Without LOXL2 cells on micro-grooved substrates become disorganized, similar to their observation on flat surfaces (elongated cells, linear actin, etc.). This demonstrates a switch from external topographical cues to self-generated BM. This is consistent with the idea of reorganizing junctions to produce a stable epithelial tube. I was very interested in their 3D culture. The effect of BM assembly on tube diameter makes sense. But how does BM assembly support complex capillary functions like branching? (Can they force branching with targeted mutations that decouple integrin from the BM?) Is this a question of change to cell fate? (Are other remodeling enzymes activated after initial BM assembly that could support growth and/or branching?)

Reviewer #2 (Public review):

Summary:

The manuscript entitled "Adaptation of endothelial cells to microenvironment 1 topographical cues through lysyl oxidase like-2-mediated basement membrane scaffolding" by Marchand et al., aims to determine the impact of LOXL2 on the dynamic formation of vascular basement membranes (BMs).

Strengths:

This manuscript includes a nice combination of different methods and presents the results in an appropriate manner.

Furthermore, the results clearly demonstrate an impact of LOXL2 on collagen IV-fibronectin organization and topography. Finally, the proper arrangement of collagen IV-fibronectin impacts cell alignment.

Weaknesses:

An open question for this reviewer is what the real take-home message of the present study is? Can the authors deliver novel insight into BM formation transferable to the in vivo situation? Why do the authors not see a "real" BM? Could it be that in vivo endothelial cells do not build the vascular BM alone? Thus, are additional cell types needed? And what will happen then if LOXL2 expression is altered?

Major comments:

(1) Can the authors show that LOXL2 cross-links fibronectin and collagen IV?

(2) The authors stated that LOXL2 depletion affects cytoskeleton arrangements and cell shape. Could it be that this is simply a secondary effect mediated primarily through the altered cross-linking of fibronectin and collagen IV?

(3) Can the authors perform cell adhesion studies on CDMs derived from wild-type versus LOXL2-deficient cells?

(4) Line 226-230: Can the authors provide the proliferation data of wildtype and LOXL2-depleted cells supporting their Src and Akt activity findings?

(5) Line 298-299: The authors made a statement about laminin. Can the authors think of a co-culture of wild-type versus LOXL2-depleted endothelial cells in combination with pericytes or fibroblasts, as these cells contribute to the efficient assembly of a functional vascular basement membrane (10.1182/blood-2009-05-222364). Here, you can determine the impact of altered fibronectin-collagen IV cross-linking on laminin network formation. This will affect their conclusion in lines 299-304, as these facts are solely based on endothelial cells.

(6) Suggestion: can the authors supplement recombinant LOXL2 protein in its active version to the LOXL2-depleted endothelial cells to rescue the observed changes? And further compare LOXL2 enzymatic function with LOXL2 protein harbouring Zn instead of Cu, making it enzymatic inactive. Here, the authors might be able to strengthen their hypothesis that LOXL2 might bridge fibronectin and collagen IV or link both proteins.

(7) There are grammatical errors in the manuscript that the authors should work on.

Reviewer #3 (Public review):

This important study shows that basement membrane (BM) generation is a key event mediating cell 3D organization in response to microenvironmental cues. Such a mechanism participates in the endothelial cell capacity to organize into a capillary vessel segment through the shift of interactions with the interstitial ECM to interactions with vascular BM. This is particularly important for the developing, sprouting vasculature. The authors conclusively show, using TIRF and atomic force microscopy substantiated by 3D sprouting assays, that the lysyl oxidase Loxl2 plays a key role herein. With respect to translation into clinical practice, the dysregulation of adherens junctions and barrier properties associated with Loxl2 dysfunction mediated defects in BM supports its involvement in the progression of long-term microvascular diseases.

An outstanding question not answered in the current MS is how Loxl2 integrates into the Dll4-Notch mediated control of tip-stalk-phalanx cell differentiation in the developing (embryonic) vasculature. The authors focused a lot on Loxl2 loss of function; however, in a (patho)physiological context, Loxl2 gain of function would be relevant. Loxl2 is a hypoxia target and Loxl2 accumulates in the ECM upon hypoxic stress (as occurs during ischemic CVD, stroke/heart infarct). It would be interesting to know how Loxl2 gain-of-function impacts BM assembly, endothelial behavior, mechanosensing, and vessel angiogenic remodeling.

Reviewer #1 (Public review):

Summary:

The authors utilize genetic code expansion to tag TDP-43 and G3BP1, and evaluate this protein tagging system (ANAP) compared to antibodies, and evaluate protein trafficking and stress granule formation in response to stress with sodium arsenite treatment. They find similar staining to antibodies in HeLa cells, mouse embryonic stem cells, and primary mouse cortical neurons. This is a useful study that demonstrates the utility of ANAP tagging to evaluate ALS proteins.

Strengths:

Rescue of cell survival by ANAP-tagged TDP-43 is compelling

Weaknesses:

While the ANAP-tagged proteins had similar distributions to antibody staining, there were some discrepancies that may be more explained by the technique than by novel findings, as the authors suggested. The inclusion of additional controls to evaluate this would be helpful.

Reviewer #2 (Public review):

Summary:

In this manuscript, Chen and colleagues describe a novel means of labeling two RNA-binding proteins, G3BP1 and TDP-43, using genetic code expansion. Overexpressed constructs that incorporate the intrinsically-fluorescent non-canonical amino acid Anap redistribute to cytoplasmic granules upon application of external stressors such as sodium arsenite. Similar labeling and redistribution of overexpressed G3BP1 and TDP-43 were observed in cultures of mouse primary neurons.

Strengths:

Genetic code expansion and non-canonical amino acid labeling have quite a few advantages over traditional fusion proteins for tracking protein redistribution in living cells. The authors show that they are able to label exogenous G3BP1 and TDP-43 with the non-canonical amino acid Anap and follow labeled proteins in living cells with and without stress.

Weaknesses:

The authors do not convincingly leverage the advantages of genetic code expansion in the current study. There is no specific question posed by the authors that can be or is answered using this approach, and several of the experiments lack critical controls. This is also not the first example of TDP-43 labeling by genetic code expansion (see PMID: 38290242). As a result, the study as a whole adds little to our understanding of protein trafficking and behavior under stress.

Reviewer #2 (Public review):

This paper proposes two changes to classic RSA, a popular method to probe neural representation in neuroimaging experiments: computing RSA at row/column level of RDM, and using linear mixed modeling to compute second level statistics, using the individual row/columns to estimate a random effect of stimulus. The benefit of the new method is demonstrated using simulations and a re-analysis of a prior fMRI dataset on object perception and memory encoding.

The author's claim that tRSA is a promising approach to perform more complete modeling of cogneuro data, and to conceptualize representation at the single trial/event level (cf Discussion section on P42), is appealing.

In their revised manuscript, the authors have addressed some previous concerns, now referencing more literature aiming to improve RSA and its associated statistical inferences, and providing more guidance on methodological considerations in the Discussion. However, I wish the authors had more extensively edited the Introduction to better contextualize the work and clarify the specific settings in which they see the method as being beneficial over classic RSA. For example, some of the limitations of cRSA mentioned on page 6, e.g. related to presenting the same stimuli to multiple subjects, seem to be quite specific to settings where the researcher expects differential responses across subjects to fundamentally alter the interpretation, rather than something that will just average out by repeatedly offering the same stimulus, or combining data across subjects. It's not clear to me how the switch from 'matrix-level' to 'row-level' analysis in tRSA necessarily addresses this problem. I would be very helpful if the authors would more explicitly outline what problem the row-level aspect of tRSA is solving; what problem statistical inference via LMM is solving; and walk the reader through a very specific use case (perhaps a toy version of the real-data experiment which is now at the end of the paper). Explaining the utility of tRSA for experimental settings in which assessing representational strength for a single-events is crucial would clarify the contribution of this new method better.

A few weaknesses mentioned in my previous review were not adequately addressed. To demonstrate the utility of the method on real neural recordings, only a single dataset is used with a quite complicated experimental design; it's not clear if there is any benefit of using tRSA on a simpler real dataset. Moreover, the cells of an RDM/RSM reflect pairwise comparisons between response patterns. Because the response patterns are repeatedly compared, the cells of this matrix are not independent of one another. While the authors show examples that failure to meet independence assumptions do not affect results in their specific dataset, it does not get acknowledged as a problem at a more fundamental level. Finally, while the paper now states that 'simulations and example tRSA code' are publicly available, the link points to the lab's general github page containing many lab repositories, in which I could not identify a specific repository related to this paper. This is disappointing given that the main goal of this manuscript is to provide a new method that they encourage others to use; a clear pointer to available code is only a minimal requirement to achieve that goal. A dedicated repository, including documentation, READMEs and tutorials/demo's to run simulations, compare methods, etc. would greatly enhance the paper's contribution.

Reviewer #2 (Public review):

Summary:

Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

Significance:

In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

Reviewer #3 (Public review):

Summary:

The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing.

Significance:

The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.

Editors' note: The authors have written an effective rebuttal to the previous round of reviews.

Reviewer #1 (Public review):

Summary:

The authors attempted to clarify the impact of N protein mutations on ribonucleoprotein (RNP) assembly and stability using analytical ultracentrifugation (AUC) and mass photometry (MP). These complementary approaches provide a more comprehensive understanding of the underlying processes. Both SV-AUC and MP results consistently showed enhanced RNP assembly and stability due to N protein mutations.<br /> The overall research design appears well planned, and the experiments were carefully executed.

Strengths:

SV-AUC, performed at higher concentrations (3 µM), captured the hydrodynamic properties of bulk assembled complexes, while MP provided crucial information on dissociation rates and complex lifetimes at nanomolar concentrations. Together, the methods offered detailed insights into association states and dissociation kinetics across a broad concentration range. This represents a thorough application of solution physicochemistry.

Weaknesses:

Unlike AUC, MP observes only a part of solution. In MP, bound molecules are accumulated on the glass surface (not dissociated) thus concentration in solution should change as time develops. How does such concentration change impact the result shown here?

Comments on revisions:

The response from the authors is appropriate and reasonable.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors apply a variety of biophysical and computational techniques to characterize the effects of mutations in the SARS-CoV-2 N protein on the formation of ribonucleoprotein particles (RNPs). They find convergent evolution in multiple repeated independent mutations strengthening binding interfaces, compensating for other mutations that reduce RNP stability but which enhance viral replication.

Strengths:

The authors assay the effects of a variety of mutations found in SARS-CoV-2 variants of concern using a variety of approaches, including biophysical characterization of assembly properties of RNPs, combined with computational prediction of the effects of mutations on molecular structures and interactions. The findings of the paper contribute to our increasing understanding of the principles driving viral self-assembly, and increases the foundation for potential future design of therapeutics such as assembly inhibitors.

Weaknesses:

For the most part, the paper is well-written, the data presented support the claims made, and the arguments made easy to follow. However, I believe that parts of the presentation could be substantially improved. I found portions of the text to be overly long and verbose and likely could be substantially edited; the use of acronyms and initialisms is pervasive, making parts of the exposition laborious to follow; and portions of the figures are too small and difficult to read/understand.

Comments on revisions:

The authors have adequately addressed all of my concerns.

Reviewer #3 (Public review):

Summary:

This manuscript investigates how mutations in the SARS-CoV-2 nucleocapsid protein (N) alter ribonucleoprotein (RNP) assembly, stability, and viral fitness. The authors focus on mutations such as P13L, G214C, G215C combining biophysical assays (SV-AUC, mass photometry, CD spectroscopy, EM), VLP formation, and reverse genetics. They propose that SARS-CoV-2 exploits "fuzzy complex" principles, where distributed weak interfaces in disordered regions allow both stability and plasticity, with measurable consequences for viral replication.

Strengths:

* The paper demonstrates a comprehensive integration of structural biophysics, peptide/protein assays, VLP systems, and reverse genetics.

* Identification of both de novo (P13L) and stabilizing (G214C/G215C) interfaces provides a mechanistic insight into RNP formation.

* Strong application of the "fuzzy complex" framework to viral assembly, showing how weak/disordered interactions support evolvability, is a significant conceptual advance in viral capsid assembly.

* Overall, the study provides a mechanistic context for mutations that have arisen in major SARS-CoV-2 variants (Omicron, Delta, Lambda) and a mechanistic basis for how mutations influence phenotype via altered biomolecular interactions.

Weaknesses:

The weaknesses are shared via detailed comments to follow.

Comments on revisions:

The authors have addressed the criticisms of the original manuscript satisfactorily.

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.

Reviewer #2 (Public Review):

This work explores the connection between glioblastoma, mito-RQC, and msiCAT-tailing. They build upon previous work concluding that ATP5alpha is CAT-tailed and explore how CAT-tailing may affect cell physiology and sensitivity to chemotherapy. The authors conclude that when ATP5alpha is CAT-tailed, it either incorporates into the proton pump or aggregates and that these events dysregulate MPTP opening and mitochondrial membrane potential and that this regulates drug sensitivity. This work includes several intriguing and novel observations connecting cell physiology, RQC, and drug sensitivity. This is also the first time this reviewer has seen an investigation of how a CAT tail may specifically affect the function of a protein.

Comment from the Reviewing Editor:

The revisions made the work more valuable and convincing. The authors adequately made point-by-point response to the reviewers comments by providing new data. Image acquisition and data analysis were further clarified. NEMF knockdown experiments and additional control data for ATP5α featuring a poly-glycine-serine (GS) tail support their conclusion.

Reviewer #1 (Public review):

This study uses a new 'hidden multivariate pattern method' to parse in time and space the neural events intervening between stimulus and response in an immediately-reported perceptual decision, and use the resultant neural event timing information to show quite convincingly that Pieron's and Fechner's laws can apply in concert at distinct processing levels.

They designed a clever contrast comparison paradigm in which the contrast difference is kept constant while widely manipulating mean contrast, so that sensory encoding of the overall stimulus would be boosted with increasing mean contrast, whereas decision difficulty and hence duration would increase. With this, they found that the time intervening between early sensory-evoked components, up to an 'N200'-type component associated with launching the decision process, varies inversely with contrast according to Pieron's law. Meanwhile, the time intervals running up to neural events peaking near the time of response, consistent with decision termination, increases with contrast, fitting Fechner's law. Further, a diffusion model whose drift rates are scaled by Fechner's law, fit to RT, predicts the observed proportion of correct responses very well.

In the process of review and revision it was highlighted that presumably the full sequence of neural events intervening between stimulus and response is massively task dependent, but;

(1) The method is intended to capture all key components that specifically covary with RT, as opposed to each and every component in general, and

(2) The main conclusions of the study mentioned above do not change whether the method is set up to track three neural events, or five, as was done in the final analysis.

The propensity for topographic parsing algorithms to potentially lump-together distinct processes that partially co-evolve was acknowledged, but a key clarification in review was that even though the method entails a specification of neural event duration - which was changed from 50 to 25 ms - the success of the method is not strongly contingent on the actual underlying neural events in question having that very duration - indeed, the components extracted using that short template duration can be observed to evolve over a longer time frame associated with the Fechner diffusion process.

Notably, standard average event-related potential analysis was able to show expected amplitude effects - where sensory signals increased with contrast but decision signals decreased - but assessment of the by-trial distribution of their timings was grealy aided by the HMP method.

One of the stages of processing implicated in the parsing analysis was linked to attention orientation, and the authors speculate on whether this might reflect a spatially-selective deployment of attention or a resource allocation, but sensibly refrain from speculating too far since the focus here was on the sensory and decision process durations and their respective adherence to Pieron and Fechner's laws.

Reviewer #2 (Public review):

Summary:

The authors decomposed response times into component processes and manipulated the duration of these processes in opposing directions by varying contrast, and overall by manipulating speed-accuracy tradeoffs. They identify different processes and their durations by identifying neural states in time and validate their functional significance by showing that their properties vary selectively as expected with predicted effects of the contrast manipulation. They identify 4 processes: stimulus encoding, attention orienting, decision and motor execution. These map onto 5 classical event related potentials. The decision-making component matched the CPP and its properties varied with contrast and predicted decision-accuracy.

Strengths:

The design of the experiment is remarkable and offers crucial insights. The analyses techniques are beyond-state-of-the art and the analyses are well motivated and offer clear insights.

Weaknesses:

The number of identified events depends on the parameter setting of the analysis. While the authors discuss weaknesses of the approach this needs to be made explicit as well. It is also unclear to what extent topographies map onto processes since e.g., different combinations of sources can lead to the same scalp topography.

Reviewer #3 (Public review):

Summary:

In this manuscript the authors examine the processing stages involved in perceptual decision-making using a new approach to analysing EEG data, combined with a critical stimulus manipulation. This new EEG analysis method enables single-trial estimates of the timing and amplitude of transient changes in EEG time-series recurrent across trials in a behavioural task. The authors find evidence for five events between stimulus onset and the response in a two-spatial-interval visual discrimination task. By analysing the timing and amplitude of these events in relation to behaviour and the stimulus manipulation, the authors interpret these events as related to separable processing stages for stimulus encoding (first two events), attention orientation (second event), motor planning (fourth event) and decision (deliberation, final event). This is largely consistent with previous findings from both event-related potentials (across trials) and single-trial estimates using decoding techniques and neural network approaches. However, by taking a data-driven approach (as opposed to theory-driven decoding analyses) a more nuanced picture emerges: there are several stimulus encoding steps which may contribute differently to behaviour, and decision processes extend beyond the planning of the motor response.

Strengths:

This work is not only important for the conceptual advance, but also in promoting this new analysis technique, which will likely prove useful in future research. For the broader picture, this work is an excellent example of the utility of neural measures for mental chronometry.

Weaknesses:

Though beyond the scope of this manuscript, these results should be considered within the broader decision-making literature, where task or domain-specific processes may not generalise (for example, in value-based decision-making).

Reviewer #1 (Public review):

Summary:

The authors report the results of a tDCS brain stimulation study (verum vs sham stimulation of left DLPFC; between-subjects) in 46 participants, using an intense stimulation protocol over 2 weeks, combined with an experience-sampling approach, plus follow-up measures after 6 months.

Strengths:

The authors are studying a relevant and interesting research question using an intriguing design, following participants quite intensely over time and even at a follow-up time point. The use of an experience-sampling approach is another strength of the work.

Weaknesses:

There are quite a few weaknesses, some related to the actual study and some more strongly related to the reporting about the study in the manuscript. The concerns are listed roughly in the order in which they appear in the manuscript.

(1) In the introduction, the authors present procrastination nearly as if it were the most relevant and problematic issue there is in psychology. Surely, procrastination is a relevant and study-worthy topic, but that is also true if it is presented in more modest (and appropriate) terms. The manuscript mentions that procrastination is a main cause of psychopathology and bodily disease. These claims could possibly be described as 'sensationalized'. Also, the studies to support these claims seem to report associations, not causal mechanisms, as is implied in the manuscript.

(2) It is laudable that the study was pre-registered; however, the cited OSF repository cannot be accessed and therefore, the OSF materials cannot be used to (a) check the preregistration or to (b) fill in the gaps and uncertainties about the exact analyses the authors conducted (this is important because the description of the analyses is insufficiently detailed and it is often unclear how they analyzed the data).

(3) Related to the previous point: I find it impossible to check the analyses with respect to their appropriateness because too little detail and/or explanation is given. Therefore, I find it impossible to evaluate whether the conclusions are valid and warranted.

(4) Why is a medium effect size chosen for the a priori power analysis? Is it reasonable to assume a medium effect size? This should be discussed/motivated. Related: 18 participants for a medium effect size in a between-subjects design strikes me as implausibly low; even for a within-subjects design, it would appear low (but perhaps I am just not fully understanding the details of the power analysis).

(5) It remains somewhat ambiguous whether the sham group had the same number of stimulation sessions as the verum stimulation group; please clarify: Did both groups come in the same number of times into the lab? I.e., were all procedures identical except whether the stimulation was verum or sham?

(6) The TDM analysis and hyperbolic discounting approach were unclear to me; this needs to be described in more detail, otherwise it cannot be evaluated.

(7) Coming back to the point about the statistical analyses not being described in enough detail: One important example of this is the inclusion of random slopes in their mixed-effects model which is unclear. This is highly relevant as omission of random slopes has been repeatedly shown that it can lead to extremely inflated Type 1 errors (e.g., inflating Type 1 errors by a factor of then, e.g., a significant p value of .05 might be obtained when the true p value is .5). Thus, if indeed random slopes have been omitted, then it is possible that significant effects are significant only due to inflated Type 1 error. Without more information about the models, this cannot be ruled out.

(8) Related to the previous point: The authors report, for example, on the first results page, line 420, an F-test as F(1, 269). This means the test has 269 residual degrees of freedom despite a sample size of about 50 participants. This likely suggests that relevant random slopes for this test were omitted, meaning that this statistical test likely suffers from inflated Type 1 error, and the reported p-value < .001 might be severely inflated. If that is the case, each observation was treated as independent instead of accounting for the nestedness of data within participants. The authors should check this carefully for this and all other statistical tests using mixed-effects models.

(9) Many of the statistical procedures seem quite complex and hard to follow. If the results are indeed so robust as they are presented to be, would it make sense to use simpler analysis approaches (perhaps in addition to the complex ones) that are easier for the average reader to understand and comprehend?

(10) As was noted by an earlier reviewer, the paper reports nearly exclusively about the role of the left DLPFC, while there is also work that demonstrates the role of the right DLPFC in self-control. A more balanced presentation of the relevant scientific literature would be desirable.

(11) Active stimulation reduced procrastination, reduced task aversiveness, and increased the outcome value. If I am not mistaken, the authors claim based on these results that the brain stimulation effect operates via self-control, but - unless I missed it - the authors do not have any direct evidence (such as measures or specific task measures) that actually capture self-control. Thus, that self-control is involved seems speculation, but there is no empirical evidence for this; or am I mistaken about this? If that is indeed correct, I think it needs to be made explicit that it is an untested assumption (which might be very plausible, but it is still in the current study not empirically tested) that self-control plays any role in the reported results.

(12) Figures 3F and 3H show that procrastination rates in the active modulation group go to 0 in all participants by sessions 6 and 7. This seems surprising and, to be honest, rather unlikely that there is absolutely no individual variation in this group anymore. In any case, this is quite extraordinary and should be explicitly discussed, if this is indeed correct: What might be the reasons that this is such an extreme pattern? Just a random fluctuation? Are the results robust if these extreme cells are ignored? The authors remove other cells in their design due to unusual patterns, so perhaps the same should be done here, at least as a robustness check.

(13) The supplemental materials, unfortunately, do not give more information, which would be needed to understand the analyses the authors actually conducted. I had hoped I would find the missing information there, but it's not there.

In sum, the reported/cited/discussed literature gives the impression of being incomplete/selectively reported; the analyses are not reported sufficiently transparently/fully to evaluate whether they are appropriate and thus whether the results are trustworthy or not. At least some of the patterns in the results seem highly unlikely (0 procrastination in the verum group in the last 2 observation periods), and the sample size seems very small for a between-subjects design.

Reviewer #2 (Public review):

Summary:

Chen and colleagues conducted a cross-sectional longitudinal study, administering high-definition transcranial direct stimulation targeting the left DLPFC to examine the effect of HD-tDCS on real-world procrastination behavior. They find that seven sessions of active neuromodulation to the left DLPFC elicited greater modulation of procrastination measures (e.g., task-execution willingness, procrastination rates, task aversiveness, outcome value) relative to sham. They report that tDCS effects on task-execution willingness and procrastination are mediated by task outcome value and claim that this neuromodulatory intervention reduces procrastination rates quantified by their task. Although the study addresses an interesting question regarding the role of DLPFC on procrastination, concerns about the validity of the procrastination moderate enthusiasm for the study and limit the interpretability of the mechanism underlying the reported findings.

Strengths:

(1) This is a well-designed protocol with rigorous administration of high-definition transcranial direct current stimulation across multiple sessions. The approach is solid and aims to address an important question regarding the putative role of DLPFC in modulating chronic procrastination behavior.

(2) The quantification of task aversiveness through AUC metrics is a clever approach to account for the temporal dynamics of task aversiveness, which is notoriously difficult to quantify.

Weaknesses:

(1) The lack of specificity surrounding the "real-world measures" of procrastination is problematic and undermines the strength of the evidence surrounding the DLPFC effects on procrastination behavior. It would be helpful to detail what "real-world tasks" individuals reported, which would inform the efficacy of the intervention on procrastination performance across the diversity of tasks. It is also unclear when and how tasks were reported using the ESM procedure. Providing greater detail of these measures overall would enhance the paper's impact.

(2) Additionally, it is unclear whether the reported effects could be due to differential reporting of tasks (e.g., it could be that participants learned across sessions to report more achievable or less aversive task goals, rather than stimulation of DLPFC reducing procrastination per se). It would be helpful to demonstrate whether these self-reported tasks are consistent across sessions and similar in difficulty within each participant, which would strengthen the claims regarding the intervention.

(3) It would be helpful to show evidence that the procrastination measures are valid and consistent, and detail how each of these measures was quantified and differed across sessions and by intervention. For instance, while the AUC metric is an innovative way to quantify the temporal dynamics of task-aversiveness, it was unclear how the timepoints were collected relative to the task deadline. It would be helpful to include greater detail on how these self-reported tasks and deadlines were determined and collected, which would clarify how these procrastination measures were quantified and varied across time.

(4) There are strong claims about the multi-session neuromodulation alleviating chronic procrastination, which should be moderated, given the concerns regarding how procrastination was quantified. It would also be helpful to clarify whether DLPFC stimulation modulates subjective measures of procrastination, or alternatively, whether these effects could be driven by improved working memory or attention to the reported tasks. In general, more work is needed to clarify whether the targeted mechanisms are specific to procrastination and/or to rule out alternative explanations.

Reviewer #3 (Public review):

This manuscript explores whether high-definition transcranial direct current stimulation (HD-tDCS) of the left DLPFC can reduce real-world procrastination, as predicted by the Temporal Decision Model (TDM). The research question is interesting, and the topic - neuromodulation of self-regulatory behavior - is timely.

However, the study also suffers from a limited sample size, and sometimes it was difficult to follow the statistics.

The preregistration and ecological design (ESM) are commendable, but I was not able the find the preregistration, as reported in the paper.

Overall, the paper requires substantial clarification and tightening.

Reviewer #1 (Public review):

Summary:

This paper is a comprehensive review of perturbation studies and the state-dependence of the brain's response to perturbation at the circuit, mesoscale, and macroscale levels.

Strengths:

The strengths of the paper are the thorough description of many perturbation studies at different levels of organization, and the integration of both experimental and modeling studies. The review clearly communicates the need to consider (1) brain or local-population state, and (2) multiple levels of organization, in order to understand perturbation responses. Another major strength is the ability for the reader to reproduce figures using the EBRAINS platform.

Weaknesses:

Two major points of improvement should be resolved with the review, in order to make it useful for a broad audience.

The first is that the review does not include a significant integration across scales, and as a result, reads like three separate (though comprehensive) reviews. Currently, the only integration across the scales is in the brief conclusion paragraph. I would recommend adding an additional section, in which the overarching picture is discussed. (i.e. a unifying view of state dependence, and what is learned by considering across scales). This need not be too long, but it should be longer than a single conclusion paragraph.

The second major weakness is that there is a lack of clarity on many points throughout, which is needed for the reader to fully understand the results described.

Reviewer #2 (Public review):

Summary:

In this review article, the authors discuss the whole-brain activity changes induced by brain stimulation. They review the literature on how these activity changes depend on the cognitive state of the brain and divide the results by the scale of the change being induced, from microscale changes across small groups of neurons, up to macroscale changes across the entire brain. Finally, they describe attempts to model these changes using computational models.

Strengths:

The review provides an overview of the results within this subfield of neuroscience, and the authors are able to discuss a lot of prior results. The framing of the changes in neuronal activity in terms of computational changes is also a helpful approach.

Weaknesses:

However, the authors are not able to contextualize these results within a single framework, i.e. explaining from first principles how different aspects of stimulus-induced changes interact to generate functional changes in the brain, and how different changes - at distinct spatiotemporal scales - combine to form larger effects. This is a significant weakness in generating a review of the literature, since the authors do not provide a cohesive conceptual framework on which to frame the results. Similarly, the authors do not explain how their different computational models fit together, and how one can get a singular computational understanding of the distinct mechanisms of brain activity changes due to stimulation under different brain states, by combining the results derived from each separate model.

Major Comments:

(1) The authors have written this review as if it were intended for an audience who is already familiar with the topics. For example, they introduce concepts like complexity, spiral vs planar waves, without much explanation.

(2) Regarding complexity, the authors present a quantification termed PCI. However, in the associated box, they state that PCI could be implemented in a number of different ways, using analogous metrics (which are, nonetheless, not identical). Yet the authors simply claim that all these metrics are sufficiently similar to be grouped together as "PCI". The authors do not provide much intuition about this, and they also don't present any other potential quantifications. This makes any interpretation of their results strongly dependent on your understanding of the concept of PCI. It would be helpful to present some other, analogous metric to demonstrate that the results that the authors are focusing on are not somehow tied to the specific computational structure of the PCI metric.

(3) The authors divide the review into sections organized by the spatial extent of the effects that they are exploring (e.g. from microscale to macroscale). However, they don't bring together these insights into a cohesive structure - for example, by providing potential explanations of the macroscale effects by using the microscale changes.

(4) The authors completely ignore any aspect of cell-type specificity in their review, despite the known importance of specific cell types at the microcircuit scale. This makes it difficult to map their results onto the true biological system.

(5) The authors introduce several different computational models, such as the Hopf model, the AdEx model, and the MPR model. However, they do not provide the reader with a conceptual understanding of the structure of each of these models (except through potentially more complex terminology, e.g. the Hopf model is a "phenomenological Stuart-Landau nonlinear oscillator"). Additionally, though they present the results of each simulation, they don't provide the reader with intuition about how these models compare against each other, and how best to interpret results derived from each model.

(6) In several cases, the authors make statements that they appear to believe to be completely straightforward (and require no justification), but that do not appear so to the reader. For example, they mention: "In wakefulness and REM sleep, ..., the membrane potential is depolarized and close to the spike threshold, which explains why neurons respond more reliably and with less response variability compared with slow-wave sleep". However, this statement is not obvious to the reader and requires explanation (for example, in a system that is close to balance, bringing cells closer to the firing threshold can result in increased response jitter).

Reviewer #1 (Public review):

Summary:

In the paper, the authors review literature on synchronous activity, its relationship to brain state, and the multi-scale mechanisms underlying it.

Strengths:

The overall strength of the paper is the wide range of information reviewed, and the diversity of perspectives/approaches it brings together.

Weaknesses:

However, this strength is also the source of its major weaknesses - namely, that the overall structure lacks clarity, and there are inconsistencies throughout. Overall, in the opinion of this reviewer, the manuscript reads as disorganized and incomplete. Major and minor points are delineated below.

Major points:

(1) Most of the text in many figures was too small to read.

(2) Terminology is inconsistent throughout the manuscript. What is the difference between slow oscillations and delta waves? Sometimes the term slow waves is used instead. For sleep state, sometimes the term SWS is used, sometimes non-REM. Similarly, "spindle activity" is not defined, but simply stated as if the reader knows. This brings up two issues: (a) the manuscript should be clearer and more consistent about its terminology, and (b) it's unclear who is the intended readership of the review - is it a pedagogical review for people outside the field of sleep and slow oscillations, or is it meant to be a consensus statement for readers who are already in the field in which a pressing concern has been addressed? It seems part way between these two, and as a result, is ineffective at either goal.

(3) I suggest the authors look again at the overall structure and flow of the review... many sections feel redundant, and it's unclear how they fit together into a single review.

(4) There are many speculative statements in the review that are not justified or explained sufficiently for the reader. For example: "While highly regular slow waves in vivo suggest a single mechanism of generation, namely local cortical circuits, irregular cycles are compatible with a larger role of subcortical nuclei, ..."; "The involvement of different cortical areas and subcortical nuclei can form the basis of these different roles in memory.". For these statements, I assume the relationship between slow wave statistics, subcortical nuclei, and memory either has been written about before, and then should be cited and summarized, or is a novel claim of the authors, which then should be explained and defended rather than stated. There are other similar examples, and I suggest the authors go through the manuscript and make sure that it's clear what is a novel claim of the authors vs a cited claim, and make sure that both are sufficiently justified for the reader.

(5) An especially notable example can be found in the section on the role of the thalamus, where the authors state that they "hold that slow oscillations are fundamentally cortical". However, this section is far too short, and very little evidence is provided to back up this claim. Please review the ways in which the thalamus modulates, and, e.g., ways in which up-down is similar/different without the thalamus.

Reviewer #2 (Public review):

Summary:

In this review article, the authors discuss the correlated dynamical states associated with distinct cognitive states, including those associated with anesthesia and sleep. They present evidence that these states are primarily cortically generated, and demonstrate the properties of these dynamical states at different levels, from the microscale dynamics in individual neurons to the macroscale dynamics across the brain.

Strengths:

Multiple groups have been adding to this field over the past decades, and therefore, a review of this literature is very helpful. This review collates a large amount of the literature within this field into a single document, which should make it a valuable resource within this area of neuroscience.

Weaknesses:

Unfortunately, this review does not seem to be a balanced viewpoint of the field in question. Although there are a lot of authors in the review, it feels as if they are from a common school of thought. The authors provide only a single perspective on these dynamical states, focusing on the perspective of wave-like electrical dynamics across the cortex. Their perspective is embedded in methods such as EEG and LFP recordings. This makes the work hard to interpret outside of the field in which the authors reside. Indeed, the review seems intended for a more specialized audience.

In addition, the article reads more like a catalog of prior studies as opposed to a true synthesis across the large volume of data in this field that highlights links across multiple sources. Hence, it does not seem to provide a novel way of understanding the dynamics involved in cognitive state transitions.

We have included more details on these general comments below:

Major Comments:

(1) The authors have written this review as if it were intended for an audience who is already familiar with these topics. They do not define many of the terms that they introduce within the review, including concepts like complexity, metastability, and oscillations that are fundamental to the concepts that the authors are introducing. Though these may seem like first principles concepts to the authors, they often introduce assumptions that may be unfamiliar to the general reader. For example, are slow wave oscillations periodic? A naïve reader may assume that oscillations - characterized by their frequency - should be somewhat periodic, but that is often not the case. For a journal with a general biological science readership, it would be particularly helpful for each of these terms to be formally defined and characterized.

(2) It would be helpful for the authors to reframe their work in different perspectives and to incorporate all the literature on the dynamics of cortical brain states, and not simply the work that is most familiar to them. As one example, the authors do not discuss cell-type-specific changes in brain state during anesthesia and in altered states of consciousness (including dissociative states and hallucinatory states). There is recent work in this vein (Suzuki and Larkum, 2020; Vesuna et al, 2020; Bharioke, Munz et al, 2023), and yet the authors do not discuss these papers.

(3) Given the authors' clear, extensive knowledge of their field, it would also be extremely helpful for the authors to reframe fundamental concepts in terms of neuronal population activity, trajectory analyses, etc. This would enable a more general audience to better understand their work.

(4) The authors have one section focused on thalamic contributions to cortical wave-like activity. This is a cursory treatment of a subject that is quite controversial in the field. It would be helpful if the authors could provide a more balanced consideration of all the evidence regarding potential thalamocortical interactions and their role in wave-like activity.

(5) The authors present many computational models and describe the results of simulations with these different models. However, this doesn't provide the reader with intuition about what each model adds or removes from the true biological picture. It would be helpful for the authors to provide some intuition about the assumptions and constraints that underlie each model.

(6) The authors state that "The main mechanism [of slow oscillatory dynamics] consists of a combination of two ingredients: the recurrent connectivity, which maintains the excitability in the network, and adaptation, an activity-dependent fatigue variable that provides inhibitory feedback". They make this statement as a fact, yet they don't provide much justification for it. Additionally, it's not clear that any other possible combination of ingredients would be able to produce slow oscillatory dynamics.

(7) The authors often define one concept in terms of other equally complex concepts. For example: "EIA (excitatory-inhibitory with adaptation) cortical circuits then display the typical slow-fast dynamics of relaxation oscillators". The reader would need an explanation of slow-fast dynamics and relaxation oscillators to understand this line, neither of which is provided in the text.

(8) When discussing sleep, the authors do not discuss REM sleep, focusing on slow-wave non-REM sleep. It would be helpful if the authors could at least frame the full sleep cycle and discuss why they are focusing on one part of it.

(9) The authors introduce the concept of sleep spindles without any explanation.

Reviewer #1 (Public review):

Summary:

This report demonstrates that the gene expression output of the Wnt pathway, when controlled precisely by a synthetic light-based input, depends substantially on the frequency of stimulation. The particular frequency-dependent trend that is observed - anti-resonance, a suppression of target gene expression at intermediate frequencies given a constant duty cycle - is a novel aspect that has not been clearly shown before for this or other signaling pathways. The paper provides both clear experimental evidence of the phenomenon with engineered cellular systems and a model-based analysis of how the pairing of rate constants in pathway activation/deactivation could result in such a trend.

Strengths:

This report couples in vitro experimental data with an abstracted mathematical model. Both of these approaches appear to be technically sound and to provide consistent and strong support for the main conclusion. The experimental data are particularly clear, and the demonstration that Brachyury expression is subject to anti-resonance in ESCs is particularly compelling. The modeling approach is reasonably scaled for the system at the level of detail that is needed in this case, and the hidden variable analysis provides some insight into how the anti-resonance works.

In this revised manuscript, the authors have addressed issues in presentation and in discussing the broader relevance of their study to other pathways. Other limitations of the paper, including the fact that the anti-resonance phenomenon has not yet been demonstrated using physiological Wnt ligands and that the model has not been validated using experimental manipulations to establish that the mechanisms of the cell system and the model are the same, were deemed out of the scope of this initial demonstration by both the reviewers and authors. These questions will provide an interesting basis for further studies.

Reviewer #2 (Public review):

Summary:

By combining optogenetics with theoretical modelling the authors identify an anti-resonance behavior in the WnT signaling pathway. This behavior is manifested as a minimal response at a certain stimulation frequency. Using an abstracted hidden variable model, the authors explain their findings by a competition of timescales. Furthermore, they experimentally show that this anti-resonance influences the cell fate decision involved in human gastrulation.

Strengths:

- This interdisciplinary study combines precise optogenetic manipulation with advanced modelling.<br /> - The results are directly tested in two different systems: HEK293T cells and H9 human embryonic stem cells.<br /> - The model is implemented based on previous literature and has two levels of detail: i) a detailed biochemical model and ii) an abstract model with a hidden parameter

Weaknesses:

- While the experiments provide both single-cell data and population data, the model only considers population data.<br /> - Although the model captures the experimental data for TopFlash very well, the beta-Cat curves (Fig 2B) are only described qualitatively. This discrepancy is not discussed.

Overall Assessment:

The authors convincingly identified an anti-resonance behavior in a signaling pathway that is involved in cell fate decisions. The focus on a dynamic signal and the identification of such a behavior is important. I believe that the model approach of abstracting a complicated pathway with a hidden variable is an important tool to obtain an intuitive understanding of complicated dependencies in biology. Such a combination of precise ontogenetical manipulation with effective models will provide a new perspective on causal dependencies in signaling pathways and should not be limited only to the system that the authors study.

Comments on revisions:

I don't have any more comments for the authors and would like to congratulate them for the nice piece of work!

Reviewer #1 (Public review):

Summary:

This study presents a valuable contribution of NO signaling in zebrafish retinal regeneration in larval animals. The data on NO signaling are solid. There are multiple limitations to the study, but these are largely acknowledged by the authors in the revised text.

Strengths:

New data on NO signaling is valuable to the field but may be limited to larval "regeneration".

Weaknesses:

A weakness of the approach is testing cone ablation and regeneration in early larval animals. A near identical study was already done by Hoang et al 2020 in the adult zebrafish, a more relevant biological timepoint.

Reviewer #2 (Public review):

Summary:

In this manuscript Ye at al. examine the sequence of events that occur in the damaged zebrafish Muller glia (MG) in states between quiescence and the onset of proliferation. Using an inducible metronidazole (MTZ) and nitroreductase system to ablate red/green cones in larval zebrafish, they identify a novel transitional MG state that is characterized by the expression of cxcl18b. Using trajectory analysis from single-cell RNA-seq datasets, they find that cxcl18b is expressed before MG expression PCNA and become proliferative. They find that cxcl18b expression peaks in MG at approximately 24 hours post injury (hpi) and rapidly declines as MG proliferate following injury. In a most interesting finding, the authors find a link between nos2b-dependent nitric oxide signaling and cxcl18b-mediated proliferation. Mutagenesis of nos2b decreases MG proliferation. The mechanism linking NO signaling to proliferation was suggested to function via notch signaling as pharmacological inhibition of nitric oxide signaling resulted in elevated Notch activity, thus preventing MG proliferation. The authors suggest a model whereby cxcl18b induces autocrine NO signaling in MG to reduce activity of Notch3, thereby promoting MG proliferation.

Strengths:

The authors utilize a number of sophisticated transgenic approaches and generate novel lines that will have value to the field. The identification of a novel cxcl18b transition state is exciting and the putative link between NO signaling and Notch activity would provide new insight into the drivers of Muller glia proliferation.

Weaknesses:

While the overall model is appealing and may serve as a foundation for future studies, some information gaps remain and certain conclusions rely on correlational data. The cellular expression of nos2b remains unclear as the single-cell RNA-seq data cannot provide expression data that matches RT-PCR results. The temporal sequence of events are based on transgene expression in the Tg(cxcl18b:GFP) lines, where persistence of the GFP fluorescence may not reflect endogenous cxcl18b. The identity of putative cxcl18b receptors on MG to support an autocrine signaling pathway remains unclear. Nevertheless, this is an interesting study that should open new avenues of exploration.

Reviewer #1 (Public review):

Summary:

Lai and Doe address the integration of spatial information with temporal patterning and genes that specify cell fate. They identify the Forkhead transcription factor Fd4 as a lineage-restricted cell fate regulator that bridges transient spatial transcription factors to terminal selector genes in the developing Drosophila ventral nerve cord. The experimental evidence convincingly demonstrates that Fd4 is both necessary for late-born NB7-1 neurons, but also sufficient to transform other neural stem cell lineages toward the NB7-1 identity. This work addresses an important question that will be of interest to developmental neurobiologists: How can cell identities defined by initial transient developmental cues be maintained in the progeny cells, even if the molecular mechanism remains to be investigated? In addition, the study proposes a broader concept of lineage identity genes that could be utilized in other lineages and regions in the Drosophila nervous system and in other species.

Strengths:

While the spatial factors patterning the neuroepithelium to define the neuroblast lineages in the Drosophila ventral nerve cord are known, these factors are sometimes absent or not required during neurogenesis. In the current work, Lai and Doe identified Fd4 in the NB7-1 lineage that bridges this gap and explains how NB7-1 neurons are specified after Engrailed (En) and Vnd cease their expression. They show that Fd4 is transiently co-expressed with En and Vnd and is present in all nascent NB7-1 progenies. They further demonstrate that Fd4 is required for later-born NB7-1 progenies and sufficient for the induction of NB7-1 markers (Eve and Dbx) while repressing markers of other lineages when force-expressed in neural progenitors, e.g., in the NB5-6 lineage and in the NB7-3 lineage. They also demonstrate that, when Fd4 is ectopically expressed in NB7-3 and NB5-6 lineages, this leads to the ectopic generation of dorsal muscle-innervating neurons. The inclusion of functional validation using axon projections demonstrates that the transformed neurons acquire appropriate NB7-1 characteristics beyond just molecular markers. Quantitative analyses are thorough and well-presented for all experiments.

Weaknesses:

(1) While Fd4 is required and sufficient for several later-born NB7-1 progeny features, a comparison between early-born (Hb/Eve) and later-born (Run/Eve) appears missing for pan-progenitor gain of Fd4 (with sca-Gal4; Figure 4) and for the NB7-3 lineage (Figure 6). Having a quantification for both could make it clearer whether Fd4 preferentially induces later-born neurons or is sufficient for NB7-1 features without temporal restriction.

(2) Fd4 and Fd5 are shown to be partially redundant, as Fd4 loss of function alone does not alter the number of Eve+ and Dbx+ neurons. This information is critical and should be included in Figure 3.

(3) Several observations suggest that lineage identity maintenance involves both Fd4-dependent and Fd4-independent mechanisms. In particular, the fact that fd4-Gal4 reporter remains active in fd4/fd5 mutants even after Vnd and En disappear indicates that Fd4's own expression, a key feature of NB7-1 identity, is maintained independently of Fd4 protein. This raises questions about what proportion of lineage identity features require Fd4 versus other maintenance mechanisms, which deserves discussion.

(4) Similarly, while gain of Fd4 induces NB7-1 lineage markers and dorsal muscle innervation in NB5-6 and NB7-3 lineages, drivers for the two lineages remain active despite the loss of molecular markers, indicating some regulatory elements retain activity consistent with their original lineage identity. It is therefore important to understand the degree of functional conversion in the gain-of-function experiments. Sparse labeling of Fd4 overexpressing NB5-6 and NB7-3 progenies, as was done in Seroka and Doe (2019), would be an option.

(5) The less-penetrant induction of Dbx+ neurons in NB5-6 with Fd4-overexpression is interesting. It might be worth the authors discussing whether it is an Fd4 feature or an NB5-6 feature by examining Dbx+ neuron number in NB7-3 with Fd4-overexpression.

(6) It is logical to hypothesize that spatial factors specify early-born neurons directly, so only late-born neurons require Fd4, but it was not tested. The model would be strengthened by examining whether Fd4-Gal4-driven Vnd rescues the generation of later-born neurons in fd4/fd5 mutants.

(7) It is mentioned that Fd5 is not sufficient for the NB7-1 lineage identity. The observation is intriguing in how similar regulators serve distinct roles, but the data are not shown. The analysis in Figure 4 should be performed for Fd5 as supplemental information.

Reviewer #2 (Public review):

Summary:

Via a detailed expression analysis, they find that Fd4 is selectively expressed in embryonic NB7-1 and newly born neurons within this lineage. They also undertake a comprehensive genetic analysis to provide evidence that fd4 is necessary and sufficient for the identity of NB7-1 progeny.

Strengths:

The analysis is both careful and rigorous, and the findings are of interest to developmental neurobiologists interested in molecular mechanisms underlying the generation of neuronal diversity. Great care was taken to make the figures clear and accessible. This work takes great advantage of years of painstaking descriptive work that has mapped embryonic neuroblast lineages in Drosophila.

Weaknesses:

The argument that Fd4 is necessary for NB7-1 lineage identity is based on a Fd4/Fd5 double mutant. Loss of fd4 alone did not alter the number of NB7-1-derived Eve+ or Dbx+ neurons. The authors clearly demonstrate redundancy between fd4 and fd5, and the fact that the LOF analysis is based on a double mutant should be better woven through the text. The authors generated an Fd5 mutant. I assume that Fd5 single mutants do not display NB7-1 lineage defects, but this is not stated. The focus on Fd4 over Fd5 is based on its highly specific expression profile and the dramatic misexpression phenotypes. But the LOF analysis demonstrates redundancy, and the conclusions in the abstract and through the results should reflect the existence of Fd5 in the conclusions of this manuscript.

It is notable that Fd4 overexpression can rewire motor circuits. This analysis adds another dimension to the changes in transcription factor expression and, importantly, demonstrates functional consequences. Could the authors test whether U4 and U5 motor axon targeting changes in the fd4/fd5 double mutant? To strengthen claims regarding the importance of fd4/fd5 for lineage identity, it would help to address terminal features of U motorneuron identity in the LOF condition.

Reviewer #3 (Public review):

The goal of the work is to establish the linkage between the spatial transcription factors (STFs) that function transiently to establish the identities of the individual NBs and the terminal selector genes (typically homeodomain genes) that appear in the newborn post-mitotic neurons. How is the identity of the NB maintained and carried forward after the spatial genes have faded away? Focusing on a single neuroblast (NB 7-1), the authors present evidence that the fork-head transcription factor, fd4, provides a bridge linking the transient spatial cues that initially specified neuroblast identity with the terminal selector genes that establish and maintain the identity of the stem cell's progeny.

The study is systematic, concise, and takes full advantage of 40+ years of work on the molecular players that establish neuronal identities in the Drosophila CNS. In the embryonic VNC, fd4 is expressed only in the NB 7-1 and its lineage. They show that Fd4 appears in the NB while the latter is still expressing the Spatial Transcription Factors and continues after the expression of the latter fades out. Fd4 is maintained through the early life of the neuronal progeny but then declines as the neurons turn on their terminal selector genes. Hence, fd4 expression is compatible with it being a bridging factor between the two sets of genes.

Experimental support for the "bridging" role of Fd4 comes from a set of loss-of-function and gain-of-function manipulations. The loss of function of Fd4, and the partially redundant gene Fd5, from lineage 7-1 does not affect the size of the lineage, but terminal markers of late-born neuronal phenotypes, like Eve and Dbx, are reduced or missing. By contrast, ectopic expression of fd4, but not fd5, results in ectopic expression of the terminal markers eve and Dbx throughout diverse VNC lineages.

A detailed test of fd4's expression was then carried out using lineages 7-3 and 5-6, two well-characterized lineages in Drosophila. Lineage 7-3 is much smaller than 7-1 and continues to be so when subjected to fd4 misexpression. However, under the influence of ectopic Fd4 expression, the lineage 7-3 neurons lost their expected serotonin and corazonin expression and showed Eve expression as well as motoneuron phenotypes that partially mimic the U motoneurons of lineage 7-1.

Ectopic expression of Fd4 also produced changes in the 5-6 lineage. Expression of apterous, a feature of lineage 5-6, was suppressed, and expression of the 7-1 marker, Eve, was evident. Dbx expression was also evident in the transformed 5-6 lineages, but extremely restricted as compared to a normal 7-1 lineage. Considering the partial redundancy of fd4 and fd5, it would have been interesting to express both genes in the 5-6 lineage. The anatomical changes that are exhibited by motoneurons in response to Fd4 expression confirm that these cells do, indeed, show a shift in their cellular identity.

Reviewer #1 (Public review):

The study introduces an open-source, cost-effective method for automating the quantification of male social behaviors in Drosophila melanogaster. It combines machine-learning based behavioral classifiers developed using JAABA (Janelia Automatic Animal Behavior Annotator) with inexpensive hardware constructed from off-the-shelf components. This approach addresses the limitations of existing methods, which often require expensive hardware and specialized setups. The authors demonstrate that their new "DANCE" classifiers accurately identify aggression (lunges) and courtship behaviors (wing extension, following, circling, attempted copulation, and copulation), closely matching manually annotated ground-truth data. Furthermore, DANCE classifiers outperform existing rule-based methods in accuracy. Finally, the study shows that DANCE classifiers perform as well when used with low-cost experimental hardware as with standard experimental setups across multiple paradigms, including RNAi knockdown of the neuropeptide Dsk and optogenetic silencing of dopaminergic neurons.

The authors make creative use of existing resources and technology to develop an inexpensive, flexible, and robust experimental tool for the quantitative analysis of Drosophila behavior. A key strength of this work is the thorough benchmarking of both the behavioral classifiers and the experimental hardware against existing methods. In particular, the direct comparison of their low-cost experimental system with established systems across different experimental paradigms is compelling. A weakness of the study is that the use of JAABA-based classifiers to analyze aggression and courtship is not novel (Tao et al., J. Neurosci., 2024; Sten et al., Cell, 2023; Chiu et al., Cell, 2021; Isshi et al., eLife, 2020; Duistermars et al., Neuron, 2018). However, the demonstration the JAABA classifiers they developed work as well without expensive experimental hardware opens the door to more low-cost systems for quantitative behavior analysis.

In summary, this work provides a practical and accessible approach to quantifying Drosophila behavior, reducing the economic barriers to the study of the neural and molecular mechanisms underlying social behavior.

Reviewer #2 (Public review):

Summary:

This manuscript addresses the development of a low-cost behavioural setup and standardised open-source high performing classifiers for aggression and courtship behaviour. It does so by using readily available laboratory equipment and previously developed software packages. By comparing the performance of the setup and the classifiers to previously developed ones, this study shows the classifier's overperformance and the reliability of the low-cost setup in recapitulating previously described effects of different manipulations on aggression and courtship.

Strengths:

The newly developed classifiers for lunges, wing extension, attempted copulation, copulation, following, circling, perform better than previously available developed ones. The behavioural setup developed is low cost and reliably allows analysis of both aggression and courtship behaviour, validated through social experience manipulation (social isolation), gene knock (Dsk in Dilp2 neurons) and neuronal inactivation (dopaminergic neurons) know to affect courtship and aggression.

Weaknesses:

This framework only encompasses analysis of lunges, while aggression encompasses multiple behaviours. Even though DANCE can serve as a template allowing future development of additional classifiers, the current study compares performance to CADABRA which analyses further aggression behaviours, making the comparisons incomplete.

Reviewer #3 (Public review):

The study by Yadav et al. describes a new setup to quantify a number of aggression and mating behaviors in Drosophila melanogaster. The investigation of these behaviors requires the analysis of large number of videos to identify each kind of behavior displayed by a fly. Several approaches to automatize this process have been published before, but each of them has their limitations. The authors set out to develop a new setup that includes a very low-cost, easy to acquire hardware and open-source machine-learning classifiers to identify and quantify the behavior.

Strengths:

(1) The study demonstrates that their cheap, simple, and easy to obtain hardware works just as well as custom-made, specialized hardware for analyzing aggression and mating behavior. This enables the setup to be used in a wide range of settings, from research with limited resources to classroom teaching.

(2) The authors used previously published software to train new classifiers for detecting a range of behaviors related to aggression and mating and make them freely available. The classifiers are very positively benchmarked against a manually acquired ground-truth as well as existing algorithms.

(3) The study demonstrates the applicability of the setup (hardware and classifiers) to common methods in the field by confirming a number of expected phenotypes with their setup.

Taken together, this work can greatly facilitate research of aggression and mating in Drosophila. The combination of low-cost, off-the-shelf hardware and open-source, robust software enables researchers with very little funding or technical expertise to contribute to the scientific process, and also allows large-scale experiments, for example, in classroom teaching with many students, or for systematic screenings.

Reviewer #1 (Public review):

Summary:

Biomolecular condensates are essential part of cellular homeostatic regulation. In this manuscript, authors develop a theoretical framework for phase separation of membrane bound proteins. They show the effect of non-dilute surface binding and phase separation on tight junction protein organization.

Strengths:

It is an important study considering the phase separation of membrane bound molecules are taking the center stage of signaling, spanning from immune signaling to cell-cell adhesion. A theoretical framework will help biologists to quantitatively interpret their findings.

Weaknesses:

Understandably, authors used one system to test their theory (ZO-1). However, to establish a theoretical framework, this is sufficient.

Comments on revisions:

I do not recommend new experiments. The manuscript is clear and establishes a new step in understanding the physical chemistry of biomolecular condensates.

Reviewer #1 (Public review):

Summary:

This useful study provides incomplete evidence of an association between atovaquone-proguanil use (as well as toxoplasmosis seropositivity) and reduced Alzheimer's dementia risk. The study reinforces findings that VZ vaccine lowers AD risk and suggests that this vaccine may be an effect modifier of A-P's protective effect. Strengths of the study include two extremely large cohorts, including a massive validation cohort in the US. Statistical analyses are sound, and the effect sizes are significant and meaningful. The CI curves are certainly impressive.

Weaknesses include the inability to control for potentially important confounding variables. In my view, the findings are intriguing but remain correlative / hypothesis generating rather than causative. Significant mechanistic work needs to be done to link interventions which limit the impact of Toxoplasmosis and VZV reactivation on AD.

Weaknesses:

Major:

(1) Most of the individuals in the study received A-P for malaria prophylaxis as it is not first line for Toxo treatment. Many (probably most) of these individuals were likely to be Toxo negative (~15% seropositive in the US), thereby eliminating a potential benefit of the drug in most people in the cohort. Finally, A-P is not a first line treatment for Toxo because of lower efficacy.

(2) A-P exposure may be a marker of subtle demographic features not captured in the dataset such as wealth allowing for global travel and/or genetic predisposition to AD. This raises my suspicion of correlative rather than casual relationships between A-P exposure and AD reduction. The size of the cohort does not eliminate this issue, but rather narrows confidence intervals around potentially misleading odds ratios which have not been adjusted for the multitude of other variables driving incident AD.

(3) The relationship between herpes virus reactivation and Toxo reactivation seems speculative.

(4) A direct effect on A-P on AD lesions independent on infection is not considered as a hypothesis. Given the limitations above and effects on metabolic pathways, it probably should be. The Toxo hypothesis would be more convincing if the authors could demonstrate an enhanced effect of the drug in Toxo positive individuals without no effect in Toxo negative individuals.

Minor:

(5) "Clinically meaningful" should be eliminated from the discussion given that this is correlative evidence.

Reviewer #2 (Public review):

Summary:

This manuscript examines the association between atovaquone/proguanil use, zoster vaccination, toxoplasmosis serostatus and Alzheimer's Disease, using 2 databases of claims data. The manuscript is well written and concise. The major concerns about the manuscript center around the indications of atovaquone/proguanil use, which would not typically be active against toxoplasmosis at doses given, and the lack of control for potential confounders in the analysis.

Strengths:

(1) Use of 2 databases of claims data.

(2) Unbiased review of medications associated with AD, which identified zoster vaccination associated with decreased risk of AD, replicating findings from other studies.

Weaknesses:

(1) Given that atovaquone/proguanil is likely to be given to a healthy population who is able to travel, concern that there are unmeasured confounders driving the association.

(2) The dose of atovaquone in atovaquone/proguanil is unlikely to be adequate suppression of toxo (much less for treatment/elimination of toxo), raising questions about the mechanism.

(3) Unmeasured bias in the small number of people who had toxoplasma serology in the TriNetX cohort.

Reviewer #1 (Public review):

Disclaimer: While I am familiar with the CFS method and the CFS literature, I am not familiar with primate research or two-photon calcium imaging. Additionally, I may be biased regarding unconscious processing under CFS, as I have extensively investigated this area but have found no compelling evidence in favor of unconscious processing under CFS.

This manuscript reports the results of a nonhuman-primate study (N=2 behaving macaque monkeys) investigating V1 responses under continuous flash suppression (CFS). The results show that CFS substantially suppressed V1 orientation responses, albeit slightly differently in the two monkeys. The authors conclude that CFS-suppressed orientation information "may not suffice for high-level visual and cognitive processing" (abstract).

The manuscript is clearly written and well-organized. The conclusions are supported by the data and analyses presented (but see disclaimer). However, I believe that the manuscript would benefit from a more detailed discussion of the different results observed for monkeys A and B (i.e., inter-individual differences), and how exactly the observed results are related to findings of higher-order cognitive processing under CFS, on the one hand, and the "dorsal-ventral CFS hypothesis", on the other hand.

Major Comments:

(1) Some references are imprecise. For example, l.53: "Nevertheless, two fMRI studies reported that V1 activity is either unaffected or only weakly affected (Watanabe et al., 2011; Yuval-Greenberg & Heeger, 2013)". "To the best of my understanding, the second study reaches a conclusion that is entirely opposite to that of the first, specifically that for low-contrast, invisible stimuli, stimulus-evoked fMRI BOLD activity in the early visual cortex (V1-V3) is statistically indistinguishable from activity observed during stimulus-absent (mask-only) trials. Therefore, high-level unconscious processing under CFS should not be possible if Yuval-Greenberg & Heeger are correct. The two studies contradict each other; they do not imply the same thing.

(2) Line 354: "The flashing masker was a circular white noise pattern with a diameter of 1.89{degree sign}{degree sign}, a contrast of 0.5, and a flickering rate of 10 Hz. The white noise consisted of randomly generated black and white blocks (0.07 × 0.07 each)." Why did the authors choose a white noise stimulus as the CFS mask? It has previously been shown that the depth of suppression engendered by CFS depends jointly on the spatiotemporal composition of the CFS and the stimulus it is competing with (Yang & Blake, 2012). For example, Hesselmann et al. (2016) compared Mondrian versus random dot masks using the probe detection technique (see Supplementary Figure S4 in the reference below) and found only a poor masking performance of the random dot masks.

Yang, E., & Blake, R. (2012). Deconstructing continuous flash suppression. Journal of Vision, 12(3), 8. https://doi.org/10.1167/12.3.8

Hesselmann, G., Darcy, N., Ludwig, K., & Sterzer, P. (2016). Priming in a shape task but not in a category task under continuous flash suppression. Journal of Vision, 16, 1-17.

(3) Related to my previous point: I guess we do not know whether the monkeys saw the CF-suppressed grating stimuli or not? Therefore, could it be that the differences between monkey A and B are due to a different individual visibility of the suppressed stimuli? Interocular suppression has been shown to be extremely variable between participants (see reference below). This inter-individual variability may, in fact, be one of the reasons why the CFS literature is so heterogeneous in terms of unconscious cognitive processing: due to the variability in interocular suppression, a significant amount of data is often excluded prior to analysis, leading to statistical inconsistencies. Moreover, the authors' main conclusion (lines 305-307) builds on the assumption that the stimuli were rendered invisible, but isn't this speculation without a measure of awareness?

Yamashiro, H., Yamamoto, H., Mano, H., Umeda, M., Higuchi, T., & Saiki, J. (2014). Activity in early visual areas predicts interindividual differences in binocular rivalry dynamics. Journal of Neurophysiology, 111(6), 1190-1202. https://doi.org/10.1152/jn.00509.2013

(4) The authors refer to the "tool priming" CFS studies by Almeida et al. (l.33, l.280, and elsewhere) and Sakuraba et al. (l.284). A thorough critique of this line of research can be found here:

Hesselmann, G., Darcy, N., Rothkirch, M., & Sterzer, P. (2018). Investigating Masked Priming Along the "Vision-for-Perception" and "Vision-for-Action" Dimensions of Unconscious Processing. Journal of Experimental Psychology. General. https://doi.org/10.1037/xge0000420

This line of research ("dorsal-ventral CFS hypothesis") has inspired a significant body of behavioral and fMRI/EEG studies (see reference for a review below). The manuscript would benefit from a brief paragraph in the discussion section that addresses how the observed results contribute to this area of research.

Ludwig, K., & Hesselmann, G. (2015). Weighing the evidence for a dorsal processing bias under continuous flash suppression. Consciousness and Cognition, 35, 251-259. https://doi.org/10.1016/j.concog.2014.12.010

Reviewer #2 (Public review):

Summary:

The goal of this study was to investigate the degree to which low-level stimulus features (i.e., grating orientation) are processed in V1 when stimuli are not consciously perceived under conditions of continuous flash suppression (CFS). The authors measured the activity of a population of V1 neurons at single neuron resolution in awake fixating monkeys while they viewed dichoptic stimuli that consisted of an oriented grating presented to one eye and a noise stimulus to the other eye. Under such conditions, the mask stimulus can prevent conscious perception of the grating stimulus. By measuring the activity of neurons (with Ca2+ imaging) that preferred one or the other eye, the authors tested the degree of orientation processing that occurs during CFS.

Strengths:

The greatest strength of this study is the spatial resolution of the measurement and the ability to quantify stimulus representations during CSF in populations of neurons, preferring the eye stimulated by either the grating or the mask. There have been a number of prominent fMRI studies of CFS, but all of them have had the limitation of pooling responses across neurons preferring either eye, effectively measuring the summed response across ocular dominance columns. The ability to isolate separate populations offers an exciting opportunity to study the precise neural mechanisms that give rise to CFS, and potentially provide insights into nonconscious stimulus processing.

Weaknesses:

While this is an impressive experimental setup, the major weakness of this study is that the experiments don't advance any theoretical account of why CFS occurs or what CFS implies for conscious visual perception. There are two broad camps of thinking with regard to CFS. On the one hand, Watanabe et al. (2011) reported that V1 activity remained intact during CFS, implying that CFS interrupts stimulus processing downstream of V1. On the other hand, Yuval-Greenberg and Heeger (2013) showed that V1 activity is, in fact, reduced during CFS. By using a parametric experimental design, they measured the impact of the mask on the stimulus response as a function of contrast and concluded that the mask reduces the gain of neural responses to the grating stimulus. They presented a theoretical model in which the mask effectively reduced the SNR of the grating, making it invisible in the same way that reducing contrast makes a stimulus invisible.

An important discussion point of Yuval-Greenberg and Heeger is that null results (such as those presented by Watanabe et al.) are difficult to interpret, as the lack of an effect may be simply due to insufficient data. I am afraid that this critique also applies to the present study. Here, the authors report that CFS effectively 'abolishes' tuning for stimuli in neurons preferring the eye with the grating stimulus. The authors would have been in a much stronger position to make this claim if they had varied the contrast of the stimulus to show that the loss of tuning was not simply due to masking. So, while this is an incredibly impressive set of measurements that in many ways raises the bar for in vivo Ca2+ imaging in behaving macaques, there isn't anything in the results that constitutes a real theoretical advance.

Reviewer #3 (Public review):

Summary:

In this study, Tang, Yu & colleagues investigate the impact of continuous flash suppression (CFS) on the responses of V1 neurons using 2-photon calcium imaging. The report that CFS substantially suppressed V1 orientation responses. This suppression happens in a graded fashion depending on the binocular preference of the neuron: neurons preferring the eye that was presented with the marker stimuli were most suppressed, while the neurons preferring the eye to which the grating stimuli were presented were least suppressed. The binocular neuron exhibited an intermediate level of suppression.

Strengths:

The imaging techniques are cutting-edge, and the imaging results are convincing and consistent across animals.

Weaknesses:

I am not totally convinced by the conclusions that the authors draw based on their machine learning models.

Reviewer #1 (Public review):

Summary:

In this study, participants completed two different tasks. A perceptual choice task in which they compared the sizes of pairs of items and a value-different task in which they identified the higher value option among pairs of items with the two tasks involving the same stimuli. Based on previous fMRI research, the authors sought to determine whether the superior frontal sulcus (SFS) is involved in both perceptual and value-based decisions or just one or the other. Initial fMRI analyses were devised to isolate brain regions that were activated for both types of choices and also regions that were unique to each. Transcranial magnetic stimulation was applied to the SFS in between fMRI sessions and it was found to lead to a significant decrease in accuracy and RT on the perceptual choice task but only a decrease in RT on the value-different task. Hierarchical drift diffusion modelling of the data indicated that the TMS had led to a lowering of decision boundaries in the perceptual task and a lowering of non-decision times on the value-based task. Additional analyses show that SFS covaries with model derived estimates of cumulative evidence, that this relationship is weakened by TMS.

The paper has many strengths including the rigorous multi-pronged approach of causal manipulation, fMRI and computational modelling which offers a fresh perspective on the neural drivers of decision making. Some additional strengths include the careful paradigm design which ensured that the two types of tasks were matched for their perceptual content while orthogonalizing trial-to-trial variations in choice difficulty. The paper also lays out a number of specific hypotheses at the outset regarding the behavioural outcomes that are tied to decision model parameters and well justified.

Reviewer #2 (Public review):

Summary:

The authors set out to test whether a TMS-induced reduction in excitability of the left Superior Frontal Sulcus influenced evidence integration in perceptual and value-based decisions. They directly compared behaviour-including fits to a computational decision process model---and fMRI pre and post TMS in one of each type of decision-making task. Their goal was to test domain-specific theories of the prefrontal cortex by examining whether the proposed role of the SFS in evidence integration was selective for perceptual but not value-based evidence.

Strengths:

The paper presents multiple credible sources of evidence for the role of the left SFS in perceptual decision making, finding similar mechanisms to prior literature and a nuanced discussion of where they diverge from prior findings. The value-based and perceptual decision making tasks were carefully matched in terms of stimulus display and motor response, making their comparison credible.

Reviewer #1 (Public review):

Summary:

Age-related synaptic dysfunction can have detrimental effects on cognitive and locomotor function. Additionally, aging makes the nervous system vulnerable to late-onset neurodegenerative diseases. This manuscript by Marques et al. seeks to profile the cell surface proteomes of glia to uncover signaling pathways that are implicated in age-related neurodegeneration. They compared the glial cell-surface proteomes in the central brain of young (day 5) and old (day 50) flies, and identified the most up- and down-regulated proteins during the aging process. 48 genes were selected for analysis in a lifespan screen, and interestingly, most sex-specific phenotypes. Among these, adult-specific pan-glial DIP-β overexpression (OE) significantly increased the lifespan of both males and females and improved their motor control ability. To investigate the effect of DIP-β in the aging brain, Marques et al. performed snRNA-seq on 50-day-old Drosophila brains with or without DIP-β OE in glia. Cortex and ensheathing glia showed the most differentially expressed genes. Computational analysis revealed that glial DIP-β OE increased cell-cell communication, particularly with neurons and fat cells.

Strengths:

(1) State-of-the-art methodology to reveal the cell surface proteomes of glia in young and old flies.

(2) Rigorous analyses to identify differentially expressed proteins.

(3) Examination of up- and down-regulated candidates and identification of glial-expressed mediators that impact fly lifespan.

(4) Intriguing sex-specific glial genes that regulate life span.

(5) Follow-up RNA-seq analysis to examine cellular transcriptomes upon overexpression of an identified candidate (DIP-β).

(6) A compelling dataset for the community that should generate extensive interest and spawn many projects.

Weaknesses:

(1) DIP-β OE using flySAM:

a) These flies showed a larger increase in lifespan compared to using UAS-DIP-β (Figure 2 C, D). Do the authors think that flySAM is a more efficient way of OE than UAS? Also, the UAS construct would be specific to one DIP-β isoform, while flySAM would likely express all isoforms. Could this also contribute to the phenotypes observed?

b) The Glial-GS>DIP-β flySAM flies without RU-486 have significantly shorter lifespans (Figure 2C) than their UAS-DIP-β counterparts. flySAM is lethal when expressed under the control of tubulin-GAL4 (Jia et al. 2018), likely due tothe toxicity of such high levels of overexpression. Is it possible that a larger increase in lifespan is due to the already reduced viability of these flies?

c) Statistics: It is stated in the Methods that "statistical methods used are described in the figure legend of each relevant panel." However, there is no description of the statistics or sample sizes used in Figure 2.

(2) Figure 3: The authors use a glial GeneSwitch (GS) to knock down and overexpress candidate genes. In Figure 3A, they look at glial-GS>UAS-GFP with and without RU. Without RU, there is no GFP expression, as expected. With RU, there is GFP expression. It is expected that all cell body GFP signal should colocalize with a glial nuclear marker (Repo). However, there is some signal that does not appear to be glia. Also, many glia do not express GFP, suggesting the glial GS driver does not label all glia. This could impact which glia are being targeted in several experiments.

(3) It is interesting that sex-specific lifespan effects were observed in the candidate screen.

a) The authors should provide a discussion about these sex-specific differences and their thoughts about why these were observed.

b) The authors should also provide information regarding the sex of the flies used in the glial cell surface proteome study.

c) Also, beyond the scope of this study, examining sex-specific glial proteomes could reveal additional insights into age-related pathways affecting males and females differentially.

(4) The behavioral assay used in this study (climbing) tests locomotion driven by motor neurons. The proteomic analysis was performed with the central adult brain, which does not include the nerve cord, where motor neurons reside. While likely beyond the scope of this study, it would be informative to test other behaviors, including learning, circadian rhythms, etc.

(5) It is surprising that overexpressing a CAM in glia has such a broad impact on the transcriptomes of so many different cell types. Could this be due to DIP-β OE maintaining the brain in a "younger" state and indirectly influencing the transcriptomes? Instead of DIP-β OE in glia directly influencing cell-cell interactions? Can the authors comment on this?

Reviewer #2 (Public review):

This manuscript presents an ambitious and technically innovative study that combines in situ cell-surface proteomics, functional genetic screening, and single-nucleus RNA sequencing to uncover glial factors that influence aging in Drosophila. The authors identify DIP-β as a glial protein whose overexpression extends lifespan and report intriguing sex-specific differences in lifespan outcomes. Overall, the study is conceptually compelling and offers a valuable dataset that will be of considerable interest to researchers studying glia-neuron communication, aging biology, and proteomic profiling in vivo.

The in-situ proteomic labeling approach represents a notable methodological advance. If validated more extensively, it has the potential to become a widely used resource for probing glial aging mechanisms. The use of an inducible glial GeneSwitch driver is another strength, enabling the authors to carefully separate aging-relevant effects from developmental confounds. These technical choices meaningfully elevate the rigor of the study and support its central conclusions. The discovery of new candidate genes from the proteomics pipeline, including DIP-β, is intriguing and opens new avenues for understanding glial contributions to organismal lifespan. The observation of sex-specific lifespan effects is particularly interesting and warrants further exploration; the study sets the stage for future work in this direction.

At the same time, several areas would benefit from clarification or additional analysis to fully support the manuscript's claims:

(1) The manuscript frequently refers to "improved" or "increased" cell-cell communication following DIP-β overexpression, but the meaning of this term remains somewhat vague. Because the current analysis relies largely on transcriptomic predictions, it would be helpful to define precisely what metric is being used, e.g., increased numbers of predicted ligand-receptor interactions, enrichment of specific signaling pathways, or altered expression of communication-related components. Strengthening the mechanistic link between DIP-β, cell-cell communication, and lifespan extension, potentially through targeted validation of specific glial interactions, would substantially reinforce the interpretation.

(2) The lifespan screen is central to the paper, and clearer visualization and contextualization of these results would significantly improve the manuscript's impact. For example, Figure 3D is challenging to interpret in its current form. More explicit presentation of which manipulations extend lifespan in each sex, along with effect sizes and significance values, would provide clarity. Including positive controls for lifespan extension would also help contextualize the magnitude of the observed effects. The reported effects of DIP-β, while promising, are modest relative to baseline effects of RU feeding, and a discussion of this would help appropriately calibrate the conclusions.

(3) Several figures would benefit from improved labeling or more detailed legends. For instance, the meaning of "N" and "C" in Figure 1D is unclear; Figure 3A should clarify that Repo is a glial marker; and Figure 5C appears to have truncated labels. Reordering certain panels (e.g., moving control data in Figure 4A-B) may also improve narrative flow. These refinements would greatly aid reader comprehension.

(4) A few claims would be strengthened by more specific references or acknowledgment of alternative interpretations. Examples include the phenoxy-radical labeling radius, the impact of H₂O₂ exposure, and the specificity of neutravidin. Additionally, downregulation of synapse-related GO terms may reflect age-related transcriptional changes rather than impaired glia-neuron communication per se, and this possibility should be recognized. The term "unbiased" to describe the screen may also be reconsidered, given the preselection of candidate genes.

(5) Clarifying the rationale for focusing on central brain glia over optic-lobe glia would be useful.

Reviewer #2 (Public review):

Summary:

The current article adapts standard rhythmic measures to describe the temporal organisation of whale song units.

Strengths:

The detailed description of the internal temporal structure of whale songs is something that has thus far been lacking.

Weaknesses:

Conceptual and terminological bases of the paper are problematical and hamper comparison with other taxa, including humans. According to signal theory, codas are indexical rather than symbolic. They signal an individual's group identity. Borrowing from humans and linguistics, coda inter-group variation represents a case of accents -- phonologically different varieties of the same call -- not dialects, confirming they are an index. Moreover, symbolism is not a feature detectable or confirmed through rhythmic analyses or temporal characterisation. This raises serious doubt whether alleged "dialects," "symbolism" and similarity between whales and humans is factual. The comparative scope and relevance of this paper for the broader field is limited and evolutionary claims are potentially misleading and perilous.

Reviewer #2 (Public review):

Summary:

In the manuscript entitled "Ω-Loop mutations control dynamics 2 of the active site by modulating the 3 hydrogen-bonding network in PDC-3 4 β-lactamase", Chen and coworkers provide a computational investigation of the dynamics of the enzyme Pseudomonas-derived chephalosporinase 3 (PDC3) and some mutants associated with increased antibiotic resistance. After an initial analysis of the enzyme dynamics provided by RMSD/RMSF, the author conclude that the mutations alter the local dynamics within the omega loop and the R2 loop. The authors show that the network of hydrogen bonds in disrupted in the mutants. Constant pH calculations showed that the mutations also change the pKa of the catalytic lysine 67 and pocket volume calculations showed that the mutations expand the catalytic pocket. Finally, time-independent componente analysis (tiCA) showed different profiles for the mutant enzyme as compared to the wild type.

Strengths:

The scope of the manuscript is definitely relevant. Antibiotic resistance is an important problem and, in particular, Pseudomonas aeruginosa resistance is associated with an increasing number of deaths. The choice of the computational methods is also something to highlight here. Although I am not familiar with Adaptive Bandit Molecular Dynamics (ABMD), the description provided in the manuscript that this simulation strategy is well suited for the problem under evaluation.

Weaknesses:

In the revised version, the authors addressed my concerns regarding their use of the MSM, and in my view, their conclusions are now much more robust and well-supported by the data. While it would be very interesting to see a quantitative correlation between the effects of the mutations observed in the MD data and relevant experimental findings, I understand that this may be beyond the scope of the manuscript.

Reviewer #3 (Public review):

Summary:

This manuscript aims to explore how mutations in the PDC-3 3 β-lactamase alter its ability to bind and catalyse reactions of antibiotic compounds. The topic is interesting and the study uses MD simulations and to provide hypotheses about how the size of the binding site is altered by mutations that change the conformation and flexibility of two loops that line the binding pocket. Some greater consideration of the uncertainties and how the method choice affect the ability to compare equilibrium properties would strengthen the quantitative conclusions. While many results appear significant by eye, quantifying this and ensuring convergence would strengthen the conclusions.

Strengths:

The significance of the problem is clearly described the relationship to prior literature is discussed extensively.

Comments on revised version:

I am concerned that the authors state in the response to reviews that it is not possible to get error bars on values due to the use of the AB-MD protocol that guides the simulations to unexplored basins. Yet the authors want to compare these values between the WT and mutants. This relates to RMSD, RMSF, % H-bond and volume calculations. I don't accept that you cannot calculate an uncertainty on a time averaged property calculated across the entire simulation. In these cases you can either run repeat simulations to get multiple values on which to do statistical analysis, or you can break the simulation into blocks and check both convergence and calculate uncertainties.

I note that the authors do provide error bars on the volumes, but the statistics given for these need closer scrutiny (I cant test this without the raw data). For example the authors have p<0.0001 for the following pair of volumes 1072 {plus minus} 158 and 1115 {plus minus} 242, or for SASA p<0.0001 is given for 2 identical numbers 155+/- 3.

I also remain concerned about comparisons between simulations run with the AB-MD scheme. While each simulation is an equilibrium simulation run without biasing forces, new simulations are seeded to expand the conformational sampling of the system. This means that by definition the ensemble of simulations does not represent and equilibrium ensemble. For example, the frequency at which conformations are sampled would not be the same as in a single much longer equilibrium simulation. While you may be able to see trends in the differences between conditions run in this way, I still don't understand how you can compare quantitative information without some method of reweighing the ensemble. It is not clear that such a rewieghting exists for this methods, in which case I advise some more caution in the wording of the comparisons made from this data.

At this stage I don't feel the revision has directly addressed the main comments I raised in the earlier review, although there is a stronger response to the comments of Reviewer #2.

Reviewer #1 (Public review):

Summary:

In this descriptive study, Tateishi et al. report a Tn-seq based analysis of genetic requirements for growth and fitness in 8 clinical strains of Mycobacterium intracellulare Mi), and compare the findings with a type strain ATCC13950. The study finds a core set of 131 genes that are essential in all nine strains, and therefore are reasonably argued as potential drug targets. Multiple other genes required for fitness in clinical isolates have been found to be important for hypoxic growth in the type strain.

Strengths:

The study has generated a large volume of Tn-seq datasets of multiple clinical strains of Mi from multiple growth conditions, including from mouse lungs. The dataset can serve as an important resource for future studies on Mi, which despite being clinically significant, remains a relatively understudied species of mycobacteria.

Weaknesses:

The primary claim of the study that the clinical strains are better adapted for hypoxic growth is yet to be comprehensively investigated. However, this reviewer thinks such an investigation would require a complex experimental design and perhaps form an independent study.

Comments on revisions:

The revised paper has satisfactorily addressed my previous concerns, and I have no further issues with this paper.

Reviewer #1 (Public review):

Summary:

This manuscript addresses the important problem of the uncoupling of oxidative phosphorylation due to hypoxia-ischemia injury in the neonatal brain and provides insight into the neuroprotective mechanisms of hypothermia treatment.

Strengths:

The authors used a combination of in vivo imaging of awake P10 mice and experiments on isolated mitochondria to assess various key parameters of brain metabolism during hypoxia-ischemia with and without hypothermia treatment. This unique approach resulted in a comprehensive data set that provides solid evidence to support the derived conclusions.

Weaknesses:

Several potential weaknesses were identified in the original submission, which the authors subsequently addressed in the revised manuscript. Here is the brief list of the questions:

(1) Is it possible that the observed relatively low baseline OEF and trends of increased OEF and CBF over several hours after the imaging start were partially due to slow recovery from anesthesia?

(2) What was the pain management, and is there a possibility that some of the observations were influenced by the pain-reducing drugs or their absence?

(3) Were P10 mice significantly stressed during imaging in the awake state because they didn't have head-restraint habituation training?

(4) Considering high metabolism and blood flow in the cortex, it could be potentially challenging to predict cortical temperature based on the skull temperature, particularly in the deeper part of the cortex.

(5) The map of estimated CMRO2 looks quite heterogeneous across the brain surface. Could this be partially resulting from the measurement artefact?

(6) It would be beneficial to provide more detailed justification for using P10 mice in the experiments.

Reviewer #3 (Public review):

Sun et al. present a comprehensive study using a novel photoacoustic microscopy setup and mitochondrial analysis to investigate the impact of hypoxia-ischemia (HI) on brain metabolism and the protective role of therapeutic hypothermia. The authors elegantly demonstrate three connected findings: (1) HI initially suppresses brain metabolism, (2) subsequently triggers a metabolic surge linked to oxidative phosphorylation uncoupling and brain damage, and (3) therapeutic hypothermia mitigates HI-induced damage by blocking this surge and reducing mitochondrial stress.

The study's design and execution are great, with a clear presentation of results and methods. Data is nicely presented, and methodological details are thorough.

However, a minor concern is the extensive use of abbreviations, which can hinder readability. As all the abbreviations are introduced in the text, their overuse may render the text hard to read to non-specialist audiences. Additionally, sharing the custom Matlab and other software scripts online, particularly those used for blood vessel segmentation, would be a valuable resource for the scientific community. In addition, while the study focuses on the short-term effects of HI, exploring the long-term consequences and definitively elucidating HI's impact on mitochondria would further strengthen the manuscript's impact.

Despite these minor points, this manuscript is very interesting.

Comments on revisions:

All addressed.

Reviewer #2 (Public Review):

There is increasing evidence that viruses manipulate vectors and hosts to facilitate transmission. For arthropods, saliva plays an essential role for successful feeding on a host and consequently for arthropod-borne viruses that are transmitted during arthropod feeding on new hosts. This is so because saliva constitutes the interaction interface between arthropod and host and contains many enzymes and effectors that allow feeding on a compatible host by neutralizing host defenses. Therefore, it is not surprising that viruses change saliva composition or use saliva proteins to provoke altered vector-host interactions that are favorable for virus transmission. However, detailed mechanistic analyses are scarce. Here, Zhao and coworkers study transmission of rice stripe virus (RSV) by the planthopper Laodelphax striatellus. RSV infects plants as well as the vector, accumulates in salivary glands and is injected together with saliva into a new host during vector feeding.

The authors present evidence that a saliva-contained enzyme - carbonic anhydrase (CA) - might facilitate virus infection of rice by interfering with callose deposition, a plant defense response. In vitro pull-down experiments, yeast two hybrid assay and binding affinity assays show convincingly interaction between CA and a plant thaumatin-like protein (TLP) that degrades callose. Similar experiments show that CA and TLP interact with the RSV nuclear capsid protein NT to form a complex. Formation of the CA-TLP complex increases TLP activity by roughly 30% and integration of NT increases TLP activity further. This correlates with lower callose content in RSV-infected plants and higher virus titer. Further, silencing CA in vectors decreases virus titers in infected plants. Interestingly, aphid CA was found to play a role in plant infection with two non-persistent non-circulative viruses, turnip mosaic virus and cucumber mosaic virus (Guo et al. 2023 doi.org/10.1073/pnas.2222040120), but the proposed mode of action is entirely different.

Editors' note: this version was assessed by the editors, without further input from the reviewers.

Reviewer #1 (Public review):

The medicinal leech preparation is an amenable system in which to understand how the underlying cellular networks for locomotion function. A previously identified non-spiking neuron (NS) was studied and found to alter the mean firing frequency of a crawl-related motoneuron (DE-3), which fires during the contraction phase of crawling. The data are solid. Identifying upstream neurons responsible for crawl motor patterning is essential for understanding how rhythmic behavior is controlled.

Reviewer #2 (Public review):

This study by Radice et al., takes advantage of the very well-established leach preparation to investigate questions related to motor control, more precisely the question of how the activity of motoneurons taking part in leach crawling behavior are finely tuned.

The paper is overall well written. The findings are clearly presented, and the data seems solid overall.

Reviewer #1 (Public review):

Summary:

This study advances the lab's growing body of evidence exploring higher-order learning and its neural mechanisms. They recently found that NMDA receptor activity in the perirhinal cortex was necessary for integrating stimulus-stimulus associations with stimulus-shock associations (mediated learning) to produce preconditioned fear, but it was not necessary for forming stimulus-shock associations. On the other hand, basolateral amygdala NMDA receptor activity is required for forming stimulus-shock memories. Based on these facts, the authors assessed: 1. why the perirhinal cortex is necessary for mediated learning but not direct fear learning and 2. the determinants of perirhinal cortex versus basolateral amygdala necessity for forming direct versus indirect fear memories. The authors used standard sensory preconditioning and variants designed to manipulate the novelty and temporal relationship between stimuli and shock and, therefore, the attentional state under which associative information might be processed. Under experimental conditions where information would presumably be processed primarily in the periphery of attention (temporal distance between stimulus/shock or stimulus pre-exposure), perirhinal cortex NMDA receptor activation was required for learning indirect associations. On the other hand, when information would likely be processed in focal attention (novel stimulus contiguous with shock), basolateral amygdala NMDA activity was required for learning direct associations. Together, the findings indicate that the perirhinal cortex and basolateral amygdala subserve peripheral and focal attention, respectively. The authors provide support for their conclusions using careful, hypothesis-driven experimental design, rigorous methods, and integrating their findings with the relevant literature on learning theory, information processing, and neurobiology. Therefore, this work will be highly interesting to several fields.

Strengths:

(1) The experiments were carefully constructed and designed to test hypotheses that were rooted in the lab's previous work, in addition to established learning theory and information processing background literature.

(2) There are clear predictions and alternative outcomes. The provided table does an excellent job of condensing and enhancing the readability of a large amount of data.

(3) In a broad sense, attention states are a component of nearly every behavioral experiment. Therefore, identifying their engagement by dissociable brain areas and under different learning conditions is an important area of research.

(4) The authors clearly note where they replicated their own findings, report full statistical measures, effect sizes, and confidence intervals, indicating the level of scientific rigor.

(5) The findings raise questions for future experiments that will further test the authors' hypotheses; this is well discussed.

Reviewer #2 (Public review):

This paper continues the authors' research on the roles of the basolateral amygdala (BLA) and the perirhinal cortex (PRh) in sensory preconditioning (SPC) and second order conditioning (SOC). In this manuscript, the authors explore how prior exposure to stimuli may influence which regions are necessary for conditioning to the second-order cue (S2). The authors perform a series of experiments which first confirm prior results shown by the author - that NMDA receptors in the PRh are necessary in SPC during conditioning of the first-order cue (S1) with shock to allow for freezing to S2 at test; and that NMDA receptors in the BLA are necessary for S1 conditioning during the S1-shock pairings. The authors then set out to test the hypothesis that the PRh encodes associations in a peripheral state of attention whereas the BLA encodes associations in a focal state of attention, similar to the A1 and A2 states in Wagner's theory of SOP. To do this, they show that BLA is necessary for conditioning to S2 when the S2 is first exposed during a serial compound procedure - S2-S1-shock. To determine whether pre-exposure of S2 will shift S2 to a peripheral focal state, the authors run a design in which S2-S1 presentations are given prior to the serial compound phase. The authors show that this restores NMDA receptor activity within the PRh as necessary for fear response to S2 at test. They then test whether the presence of S1 during the serial compound conditioning allows the PRh to support the fear responses to S2 by introducing a delay conditioning paradigm in which S1 is no longer present. The authors find that PRh is no longer required and suggest that this is due to S2 remaining in the primary focal state.

Strengths:

As with their earlier work, the authors have performed a rigorous series of experiments to better understand the roles of the BLA and PRh in the learning of first- and second-order stimuli. The experiments are well-designed and clearly presented, and the results show definitive differences in functionality between the PRh and BLA. The first experiment confirms earlier findings from the lab (and others), and the authors then build on their previous work to more deeply reveal how these regions differ in how they encode associations between stimuli. The authors have done a commendable job on pursuing these questions.

Table 1 is an excellent way to highlight the results and provide the reader with a quick look-up table of the findings.

Reviewer #3 (Public review):

Summary:

This manuscript presents a series of experiments that further investigate the roles of the BLA and PRH in sensory preconditioning, with a particular focus on understanding their differential involvement in the association of S1 and S2 with shock.

Strengths:

The motivation for the study is clearly articulated, and the experimental designs are thoughtfully constructed. I especially appreciate the inclusion of Table 1, which makes the designs easy to follow. The results are clearly presented, and the statistical analyses are rigorous.

During the revision, the authors have adequately addressed my minor suggestions from the original version.

Reviewer #1 (Public review):

I have to preface my evaluation with a disclosure that I lack the mathematical expertise to fully assess what seems to be the authors' main theoretical contribution. I am providing this assessment to the best of my ability, but I cannot substitute for a reviewer with more advanced mathematical/physical training.

Summary:

This paper describes a new theoretical framework for measuring parsimony preferences in human judgments. The authors derive four metrics that they associate with parsimony (dimensionality, boundary, volume, and robustness) and measure whether human adults are sensitive to these metrics. In two tasks, adults had to choose one of two flower beds which a statistical sample was generated from, with or without explicit instruction to choose the flower bed perceptually closest to the sample. The authors conduct extensive statistical analyses showing that humans are sensitive to most of the derived quantities, even when the instructions encouraged participants to choose only based on perceptual distance. The authors complement their study with a computational neural network model that learns to make judgments about the same stimuli with feedback. They show that the computational model is sensitive to the tasks communicated by feedback and only uses the parsimony-associated metrics when feedback trains it to do so.

Strengths:

(1) The paper derives and applies new mathematical quantities associated with parsimony. The mathematical rigor is very impressive and is much more extensive than in most other work in the field, where studies often adopt only one metric (such as the number of causes or parameters). These formal metrics can be very useful for the field.

(2) The studies are preregistered, and the statistical analyses are strong.

(3) The computational model complements the behavioral findings, showing that the derived quantities are not simply equivalent to maximum-likelihood inference in the task.

(4) The speculations in the discussion section (e.g., the idea that human sensitivity is driven by the computational demands each metric requires) are intriguing and could usefully guide future work.

Weaknesses:

(1) The paper is very hard to understand. Many of the key details of the derived metrics are in the appendix, with very little accessible explanation in the main text. The figures helped me understand the metrics somewhat, although I am still not sure how some of them (such as boundary or robustness as measured here) are linked to parsimony. I understand that this is addressed by the derivations in the appendix, but as a computational cognitive scientist, I would have benefited from more accessible explanations. Important aspects of the human studies are also missing from the main text, such as the sample size for Experiment 2.

(2) It is not fully clear whether the sensitivity of human participants to some of the quantities convincingly reported here actually means that participants preferred shapes according to the corresponding aspect of parsimony. The title and framing suggest that parsimony "guides" human decision-making, which may lead readers to conclude that humans prefer more parsimonious shapes. I am not sure the sensitivity findings alone support this framing, but it might just be my misunderstanding of the analyses.

(3) The stimulus set included only four combinations of shapes, each designed to diagnostically target one of the theoretical quantities. It is unclear whether the results are robust or specific to these particular 4 stimuli.

(4) The study is framed as measuring "decision-making," but the task resembles statistical inference (e.g., which shape generated the data) or perceptual judgment. This is a minor point since "decision-making" is not well defined in the literature, yet the current framing in the title gave me the initial impression that humans would be making preference choices and learning about them over time with feedback.

Reviewer #2 (Public review):

This manuscript presents a sophisticated investigation into the computational mechanisms underlying human decision-making, and it presents evidence for a preference for simpler explanations (Occam's razor). The authors dissect the simplicity bias into four different components, and they design experiments to target each of them by presenting choices whose underlying models differ only in one of these components. In the learning tasks, participants must infer a "law" (a logical rule) from observed data in a way that operationalizes the process of scientific reasoning in a controlled laboratory setting. The tasks are complex enough to be engaging but simple enough to allow for precise computational modeling.

As a further novel feature, authors derive a further term in the expansion of the log-evidence, which arises from boundary terms. This is combined with a choice model, which is the one that is tested in experiments. Experiments are run, but with humans and with artificial intelligence agents, showing that humans have an enhanced preference for simplicity as compared to artificial neural networks.

Overall, the work is well written, interesting, and timely, bridging concepts in statistical inference and human decision making. Although technical details are rather elaborate, my understanding is that they represent the state of the art.

I have only one main comment that I think deserves more comments. Computing the complexity penalty of models may be hard. It is unlikely that humans can perform such a calculation on the fly. As authors discuss in the final section, while the dimensionality term may be easier to compute, others (e.g., the volume term, which requires an integral) may be considerably harder to compute (it is true that they should be computed once and for all for each task, but still...). I wonder whether the sensitivity of human decision making with reference to the different terms is so different, and in particular whether it aligns with computational simplicity, or with the possibility of approximating each term by simple heuristics. Indeed, the sensitivity to the volume term is significantly and systematically lower than that of other terms. I wonder whether this relation could be made more quantitative using neural networks, using as a proxy of computational hardness the number of samples needed to reach a given error level in learning each of these terms.

Reviewer #3 (Public review):

Summary:

This is a very interesting paper that documents how humans use a variety of factors that penalize model complexity and integrate over a possible set of parameters within each model. By comparison, trained neural networks also use these biases, but only on tasks where model selection was part of the reward structure. In the situation where training emphasizes maximum-likelihood decisions, only neural networks, but not humans, were able to adapt their decision-making. Humans continue to use model integration simplicity biases.

Strengths:

This study used a pre-registered plan for analyzing human data, which exceeds the standards compared to other current studies.

The results are technically correct.

Weaknesses:

The presentation of the results could be improved.

Reviewer #1 (Public review):

Summary:

The study by Klotzsche et al. examines whether emotional facial expressions can be decoded from EEG while participants view 3D faces in immersive VR and whether stereoscopic depth cues affect these neural representations. Participants viewed computer-generated faces (three identities, four emotions) rendered either stereoscopically or monoscopically, while performing an emotion recognition task. Time-resolved multivariate decoding revealed above-chance decodability of facial expressions from EEG. Importantly, decoding accuracy did not differ between monoscopic and stereoscopic viewing. This indicates that the neural representation of expressions is robust against stereoscopic disparity for the relevant features. However, a separate classifier could distinguish the depth condition (mono vs. stereo) from EEG, i.e., the pattern of neuronal activity differs between conditions, but not in ways relevant for the decoding of emotions. It had an early peak and a temporal profile similar to identity decoding, suggesting that early, task-irrelevant visual differences are captured neurally. Cross-decoding further demonstrated that expression decoders trained in one depth condition could generalize to the other, supporting the idea of representational invariance. Eye-tracking analyses showed that expressions and identities could be decoded from gaze patterns, but not the depth condition, and EEG- and gaze-based decoding performances were not correlated across participants. Overall, this work shows that EEG decoding in VR is feasible and sensitive, and suggests that stereoscopic cues are represented in the brain but do not influence the neural processing of facial expressions. This study addresses a relevant question with state-of-the-art experimental and data analysis techniques.

Strengths:

(1) It combines EEG, virtual reality stereoscoptic and monoscopic presentation of visual stimuli, and advanced data analysis methods to address a timely question.

(2) The figures are of very high quality.

(3) The reference list is appropriate and up to date.

Weaknesses:

(1) The introduction-results-discussion-methods order makes it hard to follow the Results without repeatedly consulting the Methods. Please introduce minimal, critical methodological context at the start of each Results subsection; reserve technical details for Methods/Supplement.

(2) Many Results subsections begin with a crisp question and present rich analyses, but end without a short synthesis. Please add 1-2 sentences that explicitly answer the opening question and state what the analyses demonstrate.

(3) The Results compellingly show that (a) expressions are decodable from EEG and (b) mono vs stereo trials are decodable from EEG; yet expression decoding is comparable across mono and stereo. It would help if you articulate why depth is neurally distinguishable while leaving expression representations unchanged. Maybe improve the discussion of the results of source localization and give a more detailed connection to what we already know about the processing of disparity.

Reviewer #2 (Public review):

Summary:

The authors' main aim was to determine the extent to which the emotional expression of face images could be inferred from electrophysiological data under the viewing conditions imposed by immersive virtual reality displays. Further, given that stereoscopic depth cues can be easily manipulated in such displays, the authors wished to investigate whether successful emotion decoding was affected by the presence or absence of these depth cues, and also if the presence/absence of depth cues was itself a property of the viewing experience that could be decoded from neural data.

Overall, the authors use fairly standard approaches to decoding neural data to demonstrate that above-chance results (slightly above the 0.5 chance threshold for their measure of choice) are in general achievable for emotion decoding, decoding the identity of faces from neural data, and decoding the presence/absence of depth cues in an immersive virtual reality display. They further examine the contribution of specific components of the response to visual stimuli with similar outcomes.

Strengths:

The main contribution of the manuscript is methodological. Rather than shedding particular light on the neural mechanisms supporting depth processing or face perception, what is on offer is primarily a straightforward examination of an applied question. With regard to the goal of answering that applied question, I think the paper succeeds. The overall experimental design is not novel, but in this case, that is a good thing. The authors have used relatively unadorned tasks and previous approaches to applying decoding tools to EEG data to see what they can get out of the neural data collected under these viewing conditions. While I would say that there is not a great deal that is especially surprising about these results, the authors do meet the goal they set for themselves.

Weaknesses:

Some of the key weaknesses I see are points that the authors raise themselves in their discussion, particularly with regard to the generalizability of their results. In particular, the 3D faces they have employed here perhaps exhibit a somewhat limited repertoire of emotional expression and do not necessarily cover a representative gamut of emotional face appearances, such as one would encounter in naturalistic settings. Then again, part of the goal of the paper was to examine the decodability of emotional expression in a specific, non-natural viewing environment - a viewing environment in which one could reasonably expect to encounter artificial faces like these. Still, the limitations of the stimuli potentially limit the scope of the conclusions one should draw from the data. I also think that there is a great deal of room for low-level image properties to drive the decoding results for faces, which could have been addressed in a number of ways (matching power spectra, for example, or using an inverted-image control condition). The absence of such control comparisons means that it is difficult to know if this is really a result that reflects face processing or much lower-level image differences that are diagnostic of emotion or identity in this subset of images. Again, to some extent, this is potentially acceptable - if one is mostly interested in whether this result is achievable at all (by hook or by crook), then it is not so important how the goal is met. Then again, one would perhaps like to know if what has been measured here is more a reflection of spatial vision vs. face processing mechanisms.

Reviewer #3 (Public review):

Summary:

This study investigates two main questions:

(1) whether brain activity recorded during immersive virtual reality can differentiate facial expressions and stereoscopic depth, and

(2) whether depth cues modulate facial information processing.

The results show that both expression and depth information can be decoded from multivariate EEG recorded in a head-mounted VR setup. However, the results show that the decoding performance of facial expressions does not benefit from depth information.

Strengths:

The study is technically strong and well executed. EEG data are of high quality despite the challenges of recording inside a head-mounted VR system. The work effectively combines stereoscopic stimulus presentation, eye-tracking to monitor gaze behavior, and time-resolved multivariate decoding techniques. Together, these elements provide an exemplary demonstration of how to collect and analyze high-quality EEG data in immersive VR environments.

Weaknesses:

The major limitation concerns the theoretical question about how stereoscopic depth modulates facial expression processing. While previous work has suggested that stereoscopic depth cues can shape natural face perception and emphasize the importance of binocular information in recognizing facial expressions (lines 95-97), the present study reports a null effect of depth. However, the stimulus configuration they used likely constrained the ability to detect any depth-related effects. All facial stimuli were static, frontal, and presented at a fixed distance. This design leads to near-ceiling behavioral performance and no behavioral effect of depth on expression recognition. It makes the null modulation of depth on expression processing unsurprising and limits the theoretical reach of the study. Adding more subtle or naturalistic features (such as various viewing angles and dynamic expressions) to the stimulus set if the authors aim to advance a strong theoretical claim about the role of binocular disparity. Or reframing the work as a technical validation of EEG decoding in this context.

Another issue relates to the claim that eye movements cannot explain the EEG decoding results. It is a real challenge to remove eye-movement-related artifacts and confounds, as the VR setup tends to encourage viewers to explore the environment freely. However, nearly half of the eye-tracking datasets were lost (usable in only 17 of 33 participants), which substantially weakens the evidence for EEG-gaze dissociation. Moreover, it would be almost impossible to decode facial information from only two-dimensional gaze direction, given that with 60 EEG channels, the decoding accuracy was modest (AUC ≈ 0.60). These two factors together limited the strength of the reported null correlation between neural and eye-data decoding.

The decoding analysis appears to use all 60 EEG channels as input features. I wonder why the authors did not examine using more spatially specific channel subsets. Facial expression and depth cues are known to preferentially engage occipito-temporal regions (e.g., N170-related sites), yet the current approach treats all sensors equally. Including all the channels may add noise and irrelevant signals to facial information decoding. Besides, using a subset of spatial-specific channels would align more directly with the subsequent source reconstruction.

Reviewer #1 (Public review):

Summary:

In the present manuscript, de Bos and Kutay investigate the functional implications of persistent microtubule-ER contacts as cells go through mitosis. To do so, they resorted to investigating phosphorylation mutants of the ER-Microtubule crosslinker Climp63. They found that phosphodeficient Climp63 mutants induce a severe SAC-dependent mitotic delay after normal chromosome alignment, with an impressive mitotic index of approximately 75%. Strikingly, this was often associated with massive nuclear fragmentation into up to 30 micronuclei that are able to recruit both core and non-core nuclear envelope components. One particular residue (S17) that is phosphorylated by Cdk1 seems to account for most, if not all, these phenotypes. Furthermore, the authors use the impact on mitosis as an indirect way to map the microtubule binding domain of Climp63, which has remained controversial, and found that it is mostly restricted to the N-terminal 28 residues of Climp63. Of note, despite the strong impact on mitosis, persistent microtubule-ER contacts did not affect the distribution of other organelles during mitosis, such as mitochondria or lysosomes.

Strengths:

Overall, this work provides important mechanistic insight into the functional implications of ER-microtubule network remodelling during mitosis and should be of great interest to a vast readership of cell biologists.

Weaknesses:

Some of the key findings appear somewhat preliminary and would be worth exploring further to substantiate some of the claims and clarify the respective impact on mitosis and nuclear envelope reassembly on the resulting micronuclei.

The following suggestions would significantly clarify some key points:

(1) The striking increase in mitotic index in cells expressing the Climp63 phosphodefective mutant, together with their live cell imaging data indicating extensive mitotic delays that can be relieved by SAC inhibition, suggests that SAC silencing is significantly delayed or even impossible to achieve. The fact that most chromosomes align in 12 min, irrespective of the expression of the Climp63 phosphodefective mutant, suggests that initial microtubule-kinetochore interactions are not compromised, but maybe cannot be stably maintained. Alternatively, the stripping of SAC proteins from kinetochores by dynein along attached microtubules might be compromised, despite normal microtubule-kinetochore attachments. The authors allude to both these possibilities, but unfortunately, they never really test them. This could easily be done by immunofluorescence with a Mad1 or c-Mad2 antibody to inspect which fraction of kinetochores (co-stained with a constitutive kinetochore marker, such as CENP-A or CENP-C) are positive for these SAC proteins. If just a small fraction, then the stability of some attachments is likely the cause. If most/all kinetochores retain Mad1/c-Mad2, then it is probably an issue of silencing the SAC.

(2) The authors use the increase in mitotic index (H3 S10 phosphorylation levels) as a readout for the MT binding efficiency of Climp63 and respective mutants. Although suggestive, this is fairly indirect and requires additional confirmation. For example, the authors could perform basic immunofluorescence in fixed cells to inspect co-localization of Climp63 (and its mutants) with microtubules.

(3) The authors refer in the discussion that the striking nuclear fragmentation seen upon mitotic exit of cells expressing Climp63 phosphodefective mutant has not been reported before, and yet it is strikingly similar to what has been previously observed in cells treated with taxol (they cite Samwer et al. 2017, but they might elect to cite also Mitchison et al., Open Biol, 2017 and most relevantly Jordan et al., Cancer Res, 1996). This striking similarity and given the extensive mitotic delay observed in the Climp63 phosphodefective mutant, it is tempting to speculate that these cells are undergoing mitotic slippage (i.e., cells exit mitosis without ever satisfying the SAC) because they are unable to silence/satisfy the SAC. Indeed, the scattered micronuclei morphology has also been observed in cells undergoing mitotic slippage (e.g., Brito and Rieder, Curr Biol., 2006). The experiment suggested in point #1 should also shed light on this problem. The authors might want to consider discussing this possible explanation to interpret the observed phenotypes.

(4) One of the most significant implications of the findings reported in this paper is that microtubule proximity does not seem to impact the assembly of either core or non-core nuclear envelope proteins on micronuclei (that possibly form due to mitotic slippage, rather than normal anaphase). These results challenge some models explaining nuclear envelope defects in micronuclei derived from lagging chromosomes due to the proximity of microtubules, and, as the authors point out at the very end, other reasons might underlie these defects. Along this line, the authors might elect to cite Afonso et al. Science, 2014, and Orr et al., Cell Reports, 2022, who provide evidence that a spindle midzone-based Aurora B gradient, rather than microtubules per se, underlie the nuclear envelope defects commonly seen in micronuclei derived from lagging chromosomes during anaphase.

Reviewer #2 (Public review):

Mitotic phosphorylation of the ER-microtubule linker CLIMP63 was discovered decades ago and was shown to release CLIMP63 from microtubules. Here, the authors describe for the first time the significance of CLIMP63 phosphorylation for mitotic division in cells. Expression of non-phosphorylatable CLIMP63 led to a massive re-localization of ER into the area of the mitotic spindle. This was not unexpected, as another ER-microtubule linker, STIM1, is phosphorylated during mitosis to release it from microtubules, and unphosphorylatable STIM1 also leads to an invasion of the ER into the spindle. The authors map CLIMP63's microtubule-binding domain and define S17 as the critical residue that needs to be phosphorylated for release from microtubules and as a target of Cdk1, albeit with an indirect assay that is based on the ability of overexpressed mutants to disrupt mitosis. The authors further demonstrate that aberrant, microtubule-tethered membranes in the spindle disrupt spindle function. This is in line with the group's prior findings that chromosome-tethered membranes lead to severe chromosome segregation defects. Cells overexpressing phospho-deficient CLIMP63 arrested in prometaphase with an active checkpoint. When these cells were forced to exit mitosis, a large number of micronuclei formed. Interestingly, these micronuclei had different compositions and properties from previously described ones, suggesting that there are diverse paths for a cell to become multinucleated. Lastly, the authors asked whether mitochondria and lysosomes depend on ER for their distribution in mitotic cells. However, the position of these other organelles was unchanged in cells in which ER was re-localized due to the overexpression of phospho-deficient CLIMP63. This is an interesting observation in the context of how the interior organisation of mitotic cells is achieved.

Suggestions:

(1) The authors should confirm the mapping of the microtubule-binding domain by more direct assays, such as microtubule co-pelleting or proximity ligation assays.

(2) The authors should clarify why they performed phenotypic studies and live microscopy experiments (Figures 4 and 5) using the CLIMP63(3A) mutant, despite knowing that the relevant phosphorylation site was S17. Were the phenotypes different for S17A versus the triple mutant?

Reviewer #1 (Public review):

Summary:

The goal of this paper was to determine whether the T cell receptor (TCR) repertoire differs between a male and a female human. To address this, this group sequenced TCRs from double-positive and single-positive thymocytes in male and female humans of various ages. Such an analysis on sorted thymocyte subsets has not been performed in the past. The only comparable dataset is a pediatric thymocyte dataset where total thymocytes were sorted.

They report on participant ages and sexes, but not on ethnicity, race, nor provide information about HLA typing of individuals. Though the experiments themselves are heroic, they do represent a relatively small sampling of diverse humans. They observed no differences in TCRbeta or TCRalpha usage, combinational diversity, or differences in the length of the CDR3 region, or amino acid usage in the CD3aa region between males or females. Though they observed some TCRbeta CD3aa sequence motifs that differed between males and females, these findings could not be replicated using an external dataset and therefore were not generalizable to the human population.

They also compared TCRbeta sequences against those identified in the past using computational approaches to recognize cancer-, bacterial-, viral-, or autoimmune-antigens. They found very little overlap of their sequences with these annotated sequences (depending on the individual, ranging from 0.82-3.58% of sequences). Within the sequences that were in overlap, they found that certain sequences against autoimmune or bacterial antigens were significantly over-represented in female versus male CD8 SP cells. Since no other comparable dataset is available, they could not conclude whether this is a finding that is generalizable to the human population.

Strengths:

This is a novel dataset. Overall, the methodologies appear to be sound. There was an attempt to replicate their findings in cases where an appropriate dataset was available. I agree that there are no gross differences in TCR diversity between males and females.

Weaknesses:

Overall, the sample size is small given that it is an outbred population. The cleaner experiment would have been to study the impact of sex in a number of inbred MHC I/II identical mouse strains or in humans with HLA-identical backgrounds.

It is unclear whether there was consensus between the three databases they used regarding the antigens recognized by the TCR sequences. Given the very low overlap between the TCR sequences identified in these databases and their dataset, and the lack of replication, they should tone down their excitement about the CD8 T cell sequences recognizing autoimmune and bacterial antigens being over-represented in females.

The dataset could be valuable to the community.

Reviewer #2 (Public review):

Summary:

This study addresses the hypothesis that the strikingly higher prevalence of autoimmune diseases in women could be the result of biased thymic generation or selection of TCR repertoires. The biological question is important, and the hypothesis is valuable. Although the topic is conceptually interesting and the dataset is rich, the study has a number of major issues that require substantial improvement. In several instances, the authors conclude that there are no sex-associated differences for specific parameters, yet inspection of the data suggests visible trends that are not properly quantified. The authors should either apply more appropriate statistical approaches to test these trends or provide stronger evidence that the observed differences are not significant. In other analyses, the authors report the differences between sexes based on a pulled analysis of TCR sequences from all the donors, which could result in differences driven by one or two single donors (e.g., having particular HLA variants) rather than reflect sex-related differences.

Strengths:

The key strength of this work is the newly generated dataset of TCR repertoires from sorted thymocyte subsets (DP and SP populations). This approach enables the authors to distinguish between biases in TCR generation (DP) and thymic selection (SP). Bulk TCR sequencing allows deeper repertoire coverage than single-cell approaches, which is valuable here, although the absence of TRA-TRB pairing and HLA context limits the interpretability of antigen specificity analyses. Importantly, this dataset represents a valuable community resource and should be openly deposited rather than being "available upon request."

Weaknesses:

Major:

(1) The authors state that there is "no clear separation in PCA for both TRA and TRB across all subsets." However, Figure 2 shows a visible separation for DP thymocytes (especially TRA, and to a lesser degree TRB) and also for TRA of Tregs. This apparent structure should be acknowledged and discussed rather than dismissed.

(2) Supplementary Figures 2-5 involve many comparisons, yet no correction for multiple testing appears to be applied. After appropriate correction, all the reported differences would likely lose significance. These analyses must be re-evaluated with proper multiple-testing correction, and apparent differences should be tested for reproducibility in an external dataset (for example, the pediatric thymus and peripheral blood repertoires later used for motif validation).

(3) Supplementary Figure 6 suggests that women consistently show higher Rényi entropies across all subsets. Although individual p-values are borderline, the consistent direction of change is notable. The authors should apply an integrated statistical test across subsets (for example, a mixed-effects model) to determine whether there is an overall significant trend toward higher diversity in females.

(4) Figures 4B and S8 clearly indicate enrichment of hydrophobic residues in female CDR3s for both TRA and TRB (excluding alanine, which is not strongly hydrophobic). Because CDR3 hydrophobicity has been linked to increased cross-reactivity and self-reactivity (see, e.g., Stadinski et al., Nat Immunol 2016), this observation is biologically meaningful and consistent with higher autoimmune susceptibility in females.

(5) The majority of "hundreds of sex-specific motifs" are probably donor-specific motifs confounded by HLA restriction. This interpretation is supported by the failure to validate motifs in external datasets (pediatric thymus, peripheral blood). The authors should restrict analysis to public motifs (shared across multiple donors) and report the number of donors contributing to each motif.

(6) When comparing TCRs to VDJdb or other databases, it is critical to consider HLA restriction. Only database matches corresponding to epitopes that can be presented by the donor's HLA should be counted. The authors must either perform HLA typing or explicitly discuss this limitation and how it affects their conclusions.

(7) Although the age distributions of male and female donors are similar, the key question is whether HLA alleles are similarly distributed. If women in the cohort happen to carry autoimmune-associated alleles more often, this alone could explain observed repertoire differences. HLA typing and HLA comparison between sexes are therefore essential.

(8) In some analyses (e.g., Figures 8C-D) data are shown per donor, while others (e.g., Fig. 8A-B) pool all sequences. This inconsistency is concerning. The apparent enrichment of autoimmune or bacterial specificities in females could be driven by one or two donors with particular HLAs. All analyses should display donor-level values, not pooled data.

(9) The reported enrichment of matches to certain specificities relative to the database composition is conceptually problematic. Because the reference database has an arbitrary distribution of epitopes, enrichment relative to it lacks biological meaning. HLA distribution in the studied patients and HLA restrictions of antigens in the database could be completely different, which could alone explain enrichment and depletions for particular specificities. Moreover, differences in Pgen distributions across epitopes can produce apparent enrichment artifacts. Exact matches typically correspond to high-Pgen "public" sequences; thus, the enrichment analysis may simply reflect variation in Pgen of specific TCRs (i.e., fraction of high-Pgen TCRs) across epitopes rather than true selection. Consequently, statements such as "We observed a significant enrichment of unique TRB CDR3aa sequences specific to self-antigens" should be removed.

(10) The overrepresentation of self-specific TCRs in females is the manuscript's most interesting finding, yet it is not described in detail. The authors should list the corresponding self-antigens, indicate which autoimmune diseases they relate to, and show per-donor distributions of these matches.

(11) The concept of polyspecificity is controversial. The authors should clearly explain how polyspecific TCRs were defined in this study and highlight that the experimental evidence supporting true polyspecificity is very limited (e.g., just a single TCR from Figure 5 from Quiniou et al.).

Minor:

(1) Clarify why the Pgen model was used only for DP and CD8 subsets and not for others.

(2) The Methods section should define what a "high sequence reliability score" is and describe precisely how the "harmonized" database was constructed.

(3) The statement "we generated 20,000 permuted mixed-sex groups" is unclear. It is not evident how this permutation corrects for individual variation or sex bias. A more appropriate approach would be to train the Pgen model separately for each individual's nonproductive sequences (if the number of sequences is large enough).

Reviewer #1 (Public review):

Summary:

This is a careful, well-powered treatment of age effects in resting-state MEG. Rather than extracting (say) complex connectivity measures, the authors look at the 'simplest possible thing': changes in the overall power spectrum across age.

Strengths:

They find significant age-related changes at different frequency bands: broadly, attenuation at low-frequency (alpha) and increased beta. These patterns are identified in a large dataset (CamCAN) and then verified in other public data.

Weaknesses:

Some secondary interpretations (what is "unique" to age vs global anatomy) may go beyond what the statistics strictly warrant in the current form, but these can be tightened with (I think, fairly quick) additions already foreshadowed by the authors' own analyses.

Aims:

The authors set out to replace piecemeal, band-by-band ageing claims with t-maps, and Cohen's f2 over sensors×frequency ("GLM-Spectrum").

On CamCAN, six spatio-spectral peaks survive relatively strict statistical controls. The larger effects are in low-frequency and upper-alpha/beta ranges (f2 approx 0.2-0.3), while lower-alpha and gamma reach significance but with small practical impact (f2 < 0.075). A nice finding is that the same qualitative profile appears in three additional independent datasets.

Two analyses are especially interesting. First, the authors show a difference between absolute and relative spectral magnitude (basically, within-subject normalization). Relative scaling sharpens the spectral specificity of the spatial maps, while absolute magnitude is dominated by a broad spatial mode that correlates positively across frequencies, likely reflecting head-position/field-spread factors. The replication of the main age profile is robust to preprocessing decisions (e.g., SSS movement compensation choices) - the bigger determinant of the effect is whether they apply sensor normalization (relative vs absolute).

Second, lots of brain-related things might be related to age, and the authors spend some time trying to back out confounds/covariates. This section is handled transparently (in general, I found the writing style very clear throughout) - they examine single covariates (sex, BP, GGMV, etc.) and compare simple vs partial age effects. For example, aging is correlated with reductions in global grey-matter volume (GGMV), but it would be nice to find a measure that is independent of this: controlling for GGMV (via a linear model) reduces age-related effect sizes heterogeneously across space/frequency but does not eliminate them, a nuance the authors treat carefully.

This is a nice paper, and I have only a few concrete suggestions:

(1) High-gamma:

There can be a lot of EMG / eye movement contamination (I know these were RS eyes closed data, but still..) above 30-40 Hz, and these effects are the weakest anyway. Could you add an analysis (e.g., ICA/label-based muscle component removal) and show the gamma band's sensitivity to that step? Or just note this point more clearly?

(2) GGMV confound control:

Controlling for GGMV reduces, but does not eliminate, age effects. I have a few questions about this: a) Could we see the residuals as a function of age? I wonder if there are non-linear effects or something else that the regression is not accounting for. Also, b) GGMV and age are highly colinear - is this an issue? Can regression really split them apart robustly? I think by some cunning orthogonalisation, you can compute the effect of age independent of GGVM. I don't think this is the same as the effect 'adjusted' for GGMV (which is what is shown here if I'm reading it correctly). Finally, of course, GGMV might actually be the thing you want to look at (because it might more accurately reflect clinical issues) - so strong correlations are not really a problem: I think really the focus might even be on using MEG to predict GGMV and controlling for age.

Reviewer #2 (Public review):

This paper describes the application of the "GLM-Spectrum" mass univariate approach to examine the effects of age on M/EEG power spectra. Its strengths include promotion of the unbiased approach, suitable for future meta/mega-analyses, and the provision of effect sizes for powering future studies. These are useful contributions to the literature. What is perhaps lacking is a discussion of the limitations of this approach, in comparison to other methods.

An analogy is the mass univariate approach to spatial localisation of effects in fMRI/PET images. This approach is unbiased by prior assumptions about the organisation of the brain, but potentially also less sensitive, by ignoring that prior knowledge. For example, a voxelwise univariate approach is less sensitive to detecting effects in functionally homogeneous brain regions, where SNR can be increased by averaging over voxels. In the context of power spectra, the authors' approach deliberately ignores knowledge about the dominant frequency bands/oscillations in human power spectra. This is in contrast to approaches like FOOOF and IRASA, which explicitly parametrise frequency components. I am not saying these methods are better; I just think that the authors should acknowledge that these approaches have advantages over their mass univariate approach (in sensitivity and interpretation; see below). I guess it is a type of bias-sensitivity trade-off: the authors want to avoid bias, but they should acknowledge the corresponding loss of sensitivity, as well as loss of interpretation compared to model-based approaches (i.e, models that parameterise frequency; I don't mean the statistical models for each frequency separately).

An example of the interpretational loss can be seen in the authors' observation of opposite-signed effects of age around the alpha peak. While the authors acknowledge that this pattern can arise from a reduction in alpha frequency with age, this is an indirect inference, and a direct (and likely much more sensitive) approach would be to parametrise and estimate the peak alpha frequency directly for each participant, as done with FOOOF for example (possibly with group priors, as in Medrano et al, 2025, EJN). The authors emphasise the nonlinear effects of age in Figure 2A, but their approach cannot test this directly (e.g., in terms of plotting effects of age on frequency, magnitude, and width for each participant), so for me, this figure illustrates a weakness of their approach, not a strength.

Then I think the section "Two dissociable and opposite effects in the alpha range" in the Discussion section is confusing, because if there is a single reduction in alpha peak frequency and magnitude with age, then there is only one "effect", not "two dissociable" ones. If the authors do want to claim that there are two dissociable age effects within the alpha range, then they need to do a statistical test, e.g., that the topographies of low and high alpha are significantly different. This then reveals another limitation of the mass univariate approach - that space (channel) is not parametrised either - so one cannot test for significant channel x effect interactions within this framework, as necessary to really claim a dissociation (e.g., in underlying neural generators).

While the authors show that normalisation of each person's power spectra by the sum across frequencies helps improve some statistics, they might want to say more about disadvantages of this approach, e.g., loss of sensitivity to any effects (eg of age) that are broadly distributed across majority of frequencies, loss of real SI units (absolute effect sizes) (as well as problems if normalisation were used for techniques like FOOOF, where the 1/f exponent would be affected).

The authors should give more information on how artifactual ICs were defined. This may be important for cardiac artefacts, since Schmidt et al (2004, eLife) have pointed out how "standard" ICA thresholds can fail to remove all cardiac effects. This is very important for the effects of age, given that age affects cardiac dynamics (even though the focus of Schmidt et al is the 1/f exponent, could residual cardiac effects cause artifactual age effects in current results, even above ~1Hz?).

The authors should clarify the precise maxfilter arguments, and explain what "reference" was used for the "trans" option - e.g., did the authors consider transforming the data to match a sphere at the centre of the helmet, which might not only remove some of the global power differences due to different head positions, but also be best for generalisation of the effect sizes they report to future studies (assuming the centre of the helmet is the most likely location on average)? And on that matter, did head positions actually differ by age at all?

Reviewer #1 (Public review):

Summary:

The manuscript investigates how exogenous attention modulates spatial frequency sensitivity within the foveola. Using high-precision eye-tracking and gaze-contingent stimulus control, the authors show that exogenous attention selectively improves contrast sensitivity for low- to mid-range spatial frequencies (4-8 cycles/degree), but not for higher frequencies (12-20 CPD). In contrast, improvements in asymptotic performance at the highest contrast levels occur across all spatial frequencies. These results suggest that, even within the foveola, exogenous attention operates through a mechanism similar to that observed in peripheral vision, preferentially enhancing lower spatial frequencies.

Strengths:

The study shows strong methodological rigor. Eye position was carefully controlled, and the stimulus generation and calibration were highly precise. The authors also situate their work well within the existing literature, providing a clear rationale for examining the fine-grained effects of exogenous attention within the foveola. The combination of high spatial precision, gaze-contingent presentation, and detailed modeling makes this a valuable technical contribution.

Weaknesses:

The manipulation of attention raises some interpretive concerns. Clarifying this issue, together with additional detail about statistics, participant profiles, other methodological elements, and further discussion in relation to oculomotor control in general, could broaden the impact of the findings.

Reviewer #2 (Public review):

Summary:

This study aims to test whether foveal and non-foveal vision share the same mechanisms for endogenous attention. Specifically, they aim to test whether they can replicate at the foveola previous results regarding the effects of exogenous attention for different spatial frequencies.

Strengths:

Monitoring the exact place where the gaze is located at this scale requires very precise eye-tracking methods and accurate and stable calibration. This study uses state-of-the-art methods to achieve this goal. The study builds on many other studies that show similarities between foveal vision and non-foveal vision, adding more data supporting this parallel.

Weaknesses:

The study lacks a discussion of the strength of the effect and how it relates to previous studies done away from the fovea. It would be valuable to know if not just the range of frequencies, but the size of the effect is also comparable.

Reviewer #3 (Public review):

Summary:

This paper explores how spatial attention affects foveal information processing across different spatial frequencies. The results indicate that exogenously directed attention enhances contrast sensitivity for low- to mid-range spatial frequencies (4-8 CPD), with no significant benefits for higher spatial frequencies (12-20 CPD). However, asymptotic performance increased as a result of spatial attention independently of spatial frequency.

Strengths:

The strengths of this article lie in its methodological approach, which combines a psychophysical experiment with precise control over the information presented in the foveola.

Weaknesses:

The authors acknowledge that they used the standard approach of analyzing observer-averaged data, but recognize that this method has limitations: it ignores the uncertainty associated with parameter estimates and the relationships between different parameters of the psychometric model. This may affect the interpretation of attentional effects. In the future, mixed-effects models at the trial level could overcome these limitations.

Reviewer #1 (Public review):

Summary:

The study from Wu and Turrigiano investigates how disruption of taste coding in a mouse model of autism spectrum disorders (ASDs) affects aversive learning in the context of a conditioned taste aversion (CTA) paradigm. The experiments combine 2-photon calcium imaging of neurons in the gustatory portion of the anterior insular cortex (i.e., gustatory cortex) with behavioral training and testing. The authors rely on Shank3 knockout mice as a model for ASDs. The authors found that Shank3 mice learn CTA more slowly and extinguish the memory more rapidly than control subjects. Calcium imaging identified impairments in taste-evoked activity associated with memory encoding and extinction. During memory encoding, the authors found less suppressed neuronal activity and increased correlated variability in Shank3 mice compared to controls. During extinction, they observed a faster loss of taste selectivity and degradation of taste discriminability in mutants compared to controls.

Strengths:

This is a well-written manuscript that presents interesting findings. The results on the learning and extinction deficits in Shank3 mice are of particular interest. Analyses of neural activity are well conducted and provide important information on the type of impaired cortical activity that may correlate with behavioral deficits.

Weaknesses:

(1) The experiments rely on three groups: CS-only WT, CTA WT, and CTA KO. Can the authors provide a rationale for not having a CS-only KO group?

(2) The authors design an effective behavioral paradigm comparing consumption of water and saccharin and tracking extinction (Figure 3). This paradigm shows differences in licking across distinct behavioral conditions. For instance, during T1, licking to water strongly differs from licking to saccharin for both WT and KO. During T2, licking to water strongly differs from licking to saccharin only for WT (much less for KO), and licking to saccharin in WT differs from that in KO. These differences in taste sampling across conditions could contribute to some of the effects on neural activity and discriminability reported in Figures 5 and 6. That is sucrose and water trials may be highly discriminable because in one case the mouse licks and in the other it does not (or licks much less). The author may want to address this issue.

(3) Are there any omission trials following CTA? If so, they should be quantified and reported. How are the omission trials treated with regard to the analyses?

(4) The authors describe the extinction paradigm as "alternative choice". In decision-making, alternative choice paradigms typically require 2 lateral spouts to report decisions following the sampling from a central spout. To avoid confusion, the authors may want to define their paradigm as alternative sampling.

(5) Figure 4 reports that CTA increases the proportion of neurons that consistently respond to saccharin and water across days. While the saccharin result could be an effect of aversive learning, it is less clear why the phenomenon would generalize to water as well. Can the authors provide an explanation?

(6) The recordings are performed in the part of the anterior insular cortex that is typically defined as "gustatory cortex" (GC). Given the functional heterogeneity of the anterior insular cortex (AIC) and given that the authors do not sample all of the anteroposterior extent of AIC, I would suggest being more explicit about their positioning in GC. Also, some citations (e.g., Gogolla et al, 2014) refer to the posterior insular cortex, which is considered more inherently multimodal than GC. GC multimodality is typically associative in nature, as only a few neurons respond to sound and light in naïve animals.

(7) It would be useful to add summary figures showing the extent of viral spread as well as GRIN lens placement.

(8) I encourage the authors to add Ns every time percentages are reported. How many neurons have been recorded in each condition? Can the authors provide the average number of neurons recorded per session and per animal?

(9) It looks like some animals learned more than others (Figure 1E or Figure 3C). Is it possible to compare neural activity across animals that showed different degrees of learning?

Reviewer #2 (Public review):

Wu and Turrigiano investigated how cortical taste coding during conditioned taste aversion (CTA) learning is affected in Shank3 knockout (KO) mice, a model of monogenic ASD. Using longitudinal two-photon calcium imaging of AIC neurons, the authors show that Shank3 KO mice exhibit reduced suppression of activity in a subset of neurons and a higher correlated variability in neural activity. This is accompanied by slower learning and faster extinction of aversive taste memories. These results suggest that Shank3 loss compromises the flexibility and stability of cortical representations underlying adaptive behaviour.

Major strengths:

(1) Conceptual significance: The study connects a molecular ASD risk gene (Shank3) to flexible sensory encoding, bridging genetics, systems neuroscience, and behaviour.

(2) Technical rigour: Longitudinal calcium imaging with cell-registration across learning and extinction sessions is technically demanding and well-executed.

(3) Behavioural paradigm: The use of both acquisition and extinction paradigms provides a more nuanced picture of learning dynamics.

(4) Analyses: Correlated variability, discriminability indices, and population decoding analyses are robust and appropriate for addressing behavioural and network-level coding changes.

Major weaknesses:

(1) Causality: The paper infers that increased correlated variability causes learning deficits, but no causal tests (e.g., optogenetic modulation of inhibition or interneuron rescue) are presented to confirm this.

(2) Behavioural scope: The study focuses exclusively on taste aversion; generalisation to other flexible learning paradigms (e.g., reversal or probabilistic tasks) is not addressed.

(3) Mechanistic insights: While providing interesting findings of altered sensory perception and extinction of learning-related signals in AIC, it offered nearly no mechanistic insights. This makes the interpretation, especially on how generalisable these findings are, difficult. Also, different reported findings are "potentially" connected, but the exact relation between increased correlated variability and faster loss of taste selectivity cannot be assessed.

Reviewer #3 (Public review):

In this study, Wu & Turrigiano investigate an ethologically relevant form of associative learning (conditioned taste aversion - CTA) and its extinction in the Shank3 KO mouse model of ASD. They also examine the underlying circuits in the anterior insular cortex (AIC) simultaneously, using two-photon calcium imaging through a GRIN lens. They report that Shank3 KO mice learn CTA slower and suggest that this is mediated by a reduction in tastant-stimulus activity suppression of AIC neurons and a reduced signal-to-noise ratio due to increased noise correlations in AIC neurons. Interestingly, once Shank3 KO mice acquire CTA, they extinguish the aversive memory more rapidly than wild-type mice. This accelerated extinction is accompanied by a faster loss of neuronal and population-level taste selectivity and coding in the AIC compared to WT mice.

This is an important study that uses in vivo methods to assess circuit dysfunction in a mouse model of ASD, related to sensory perception valence (in this case, taste). The study is well executed, the data are of high quality, and the analytical procedures are detailed. Furthermore, the behavioural paradigm is well thought out, particularly the approach for assessing extinction through repeated retrieval sessions (T1-T5), which effectively tests discrimination between saccharin and water rather than relying solely on lick counts or total consumption as a measure of extinction. Finally, the statistical tests used are appropriate and justified.

There is, however, a missing link between the behavioural findings and the underlying mechanisms. More specifically:

(1) The authors don't make a causal link between the behaviour and AIC neurophysiology, both the percentage of suppressed cells and the coactivity measurements. For the % of suppressed cells, it seems that both WT and KO cells are suppressed in the transition between CST1 and CST2 (Figure 1L), yet only the WT mice exhibit CTA (at least by CST2). For the taste-elicited coactivity measure, it seems that there is an increase in coactivity from CST1 to CST2 in WT (Figure 2C - blue, although not statistically tested?), but persistently higher coactivity in KO. Is this change of coactivity in WT important for the expression of CTA? Plotting behavioral performance (from Figure 1G) against coactivity (from Figure 2C) for each animal would be informative.

(2) Shank3 KO cells already show an increase in baseline coactivity (Figure 2- figure supplement 1), and the authors never examine CS-only responses in the KO group, therefore making it difficult to determine whether elevated coactivity and noise correlations reflect a generalized AIC abnormality in Shank3 KOs (perhaps through impaired PV-mediated inhibition in insular cortex - Gogolla et al, 2014) that is not directly responsible/related to CTA?

(3) How do the authors interpret the large range of lick ratios (Figure 1G) for WT (almost bi-modal distribution)? Is there a within-subject correlation with any of the neurophysiological measurements to suggest a relationship between AIC neurophysiology and behavioural expression of CTA?

(4) Indeed, CTA appears to be successfully achieved for Shank3 KO mice delayed by 1 day, as the level of saccharin aversion during the first retrieval session (T1) is comparable between Shank3 KO and WTs. In this context, not extending the first part of the paradigm to include CST3 seems to be a missed opportunity. Doing so would have allowed for within-cell and within-subject comparison of taste-elicited pairwise correlation across the learning and to investigate the neural mechanism of delayed extinction in KOs more effectively.

(5) How to interpret Figure 5F: Absolute discriminability is lower for T5 for CTA WT and CTA KO compared to CS-only? Why would AIC neurons have less information on taste identity by the end of extinction than during the unconditioned (CS-only) condition? And if that is the case, how is decoding accuracy in Figure 6C higher in T5 for CTA WT vs CS-only?

Reviewer #1 (Public review):

Summary:

In the ecological interactions between wild plants and specialized herbivorous insects, structural innovation-based diversification of secondary metabolites often occurs. In this study, Agrawal et al. utilized two milkweed species (Asclepias curassavica and Asclepias incarnata) and the specialist Monarch butterfly (Danaus plexippus) as a model system to investigate the effects of two N,S-cardenolides - formed through structural diversification and innovation in A. curassavica-on the growth, feeding, and chemical sequestration of D. plexippus, compared to other conventional cardenolides. Additionally, the study examined how cardenolide diversification resulting from the formation of N,S-cardenolides influences the growth and sequestration of D. plexippus. On this basis, the research elucidates the ecophysiological impact of toxin diversity in wild plants on the detoxification and transport mechanisms of highly adapted herbivores.

Strengths:

The study is characterized by the use of milkweed plants and the specialist Monarch butterfly, which represent a well-established model in chemical ecology research. On one hand, these two organisms have undergone extensive co-evolutionary interactions; on the other hand, the butterfly has developed a remarkable capacity for toxin sequestration. The authors, building upon their substantial prior research in this field and earlier observations of structural evolutionary innovation in cardenolides in A. curassavica, proposed two novel ecological hypotheses. While experimentally validating these hypotheses, they introduced the intriguing concept of a "non-additive diversity effect" of trace plant secondary metabolites when mixed, contrasting with traditional synergistic perspectives, in their impact on herbivores.

Weaknesses:

The manuscript has two main weaknesses. First, as a study reliant on the control of compound concentrations, the authors did not provide sufficient or persuasive justification for their selection of the natural proportions (and concentrations) of cardenolides. The ratios of these compounds likely vary significantly across different environmental conditions, developmental stages, pre- and post-herbivory, and different plant tissues. The ecological relevance of the "natural proportions" emphasized by the authors remains questionable. Furthermore, the same compound may even exert different effects on herbivorous insects at different concentrations. The authors should address this issue in detail within the Introduction, Methods, or Discussion sections.

Second, the study was conducted using leaf discs in an in vitro setting, which may not accurately reflect the responses of Monarch butterflies on living plants. This limitation undermines the foundation for the novel ecological theory proposed by the authors. If the observed phenomena could be validated using specifically engineered plant lines-such as those created through gene editing, knockdown, or overexpression of key enzymes involved in the synthesis of specific N,S-cardenolides - the findings would be substantially more compelling.

Reviewer #2 (Public review):

This study examined the effects of several cardenolides, including N,S-ring containing variants, on sequestration and performance metrics in monarch larvae. The authors confirm that some cardenolides, which are toxic to non-adapted herbivores, are sequestered by monarchs and enhance performance. Interestingly, N,S-ring-containing cardenolides did not have the same effects and were poorly sequestered, with minimal recovery in frass, suggesting an alternate detoxification or metabolic strategy. These N,S-containing compounds are also known to be less potent defences against non-adapted herbivores. The authors further report that mixtures of cardenolides reduce herbivore performance and sequestration compared to single compounds, highlighting the important role of phytochemical diversity in shaping plant-herbivore interactions.

Overall, this study is clearly written, well-conducted and has the potential to make a valuable contribution to the field. However, I have one major concern regarding the interpretations of the mixture results. From what I understand of the methods, all tested mixtures contain all five compounds. As such, it is not possible to determine whether reduced performance and sequestration result from the complete mixture or from the presence of a single compound, such as voruscharin for performance and uscharin for sequestration. For instance, if all compounds except voruscharin (or uscharin) were combined, would the same pattern emerge? I suspect not, since the effects of the individual N,S-containing compounds alone are generally similar to those of the full mixture (Figure S3). By taking the average of all single compounds, the individual effects of the N,S-containing ones are being inflated by the non-N,S-containing ones (in the main text, Figure 4). In the mix, of course, they are not being 'diluted', as they are always present. This interpretation is further supported by the fact that in the equimolar mix, the relative proportion of voruscharin decreases (from 50% in the 'real mix'), and the target measurements of performance and sequestration tend to increase in the equimolar mix compared to the real mix.

Despite this issue, the discussion of mixtures in the context of plant defence against both adapted and non-adapted herbivores is fascinating and convincing. The rationale that mixtures may serve as a chemical tool-kit that targets different sets of herbivores is compelling. The non-N,S cardenolides are effective against non-adapted herbivores and the N,S-containing cardenolides are effective against adapted herbivores. However, the current experiments focus exclusively on an adapted species. It would be especially interesting to test whether such mixtures reduce overall herbivory when both adapted and non-adapted species are present.

It remains possible that mixtures, even in the absence of voruscharin or uscharin, genuinely reduce sequestration or performance; however, this would need to be tested directly to address the abovementioned concern.

Reviewer #1 (Public review):

Bajohr and colleagues propose a transcription factor-driven approach to generating bonafide oligodendrocyte lineage cells (OLCs) from primary mouse astrocytes. Ectopic expression of Olig2, Sox10, or Nkx6.2 in isolated astrocytes produced a range of OLC-like cell states, with Sox10 emerging from lineage tracing and single cell RNA sequencing experiments as the most successful transcription factor in driving direct lineage reprogramming. The authors strengthened their claims with an unbiased, deep learning perturbation model to predict genetic drivers of the astrocyte cluster to OLC cluster transition observed in their scRNA seq dataset. Here, Sox10 surfaced in the top ten correlated genes, and the top transcription factor, mediating this fate shift. Altogether, this paper presents an interesting approach to generate OLCs, a cell type historically difficult to procure, from primary mouse astrocytes to study this lineage in development and disease and perhaps repopulate it in dysmyelinating conditions. While this certainly addresses a technical gap in the field, authors defined iOLCs as ones with lineage-specific gene expression and morphological characteristics, lacking any functional analysis to assess the reprogrammed cells' capacity to myelinate. This comment and other critiques are discussed below.

While Sox10 and Mbp expression in iOLCs, as confirmed by IHC, is a promising result suggesting that ectopic Sox10 instructs transduced cells to develop into cells of myelinating potential, functional confirmation is essential. As mentioned in the discussion, the absence of a substrate for myelination may have also contributed to the low DLR efficiency. Co-culturing Sox10 iOLCs with primary neurons and examining the cells' potential to engage and enwrap axons would greatly strengthen the authors' claim that this could be an effective therapeutic approach to myelin regeneration in vivo, or even a technical approach to studying myelin dynamics in vitro.

In Figure 1B, it appears that Mbp expression in tdTomato+ cells decreases in Sox10 transduced iOLs during the observed time period. Can the authors elaborate on this result, given that MBP expression is crucial for myelination and should, if anything, increase with time?

The authors acknowledge that there is a conversion of tdTomato- zsGreen+ cells with an astrocyte-like morphology to OLC cells expressing Mbp following Sox10 induction (Supplementary figure 5C,D). While they note the diversity of the astrocyte lineage in the discussion, further analysis should be applied to this subset of cells to confirm the subset of astrocyte or progenitor-like cell type that gives rise to their cell endpoint of interest (Sox10-driven Mbp+ iOLs).

Finally, ectopic expression of Olig2 and Sox10 in primary astrocytes resulted in very different OLC subtypes, as evidenced by OLC marker expression seen in IHC and the subclustering of these cell types in scRNA seq. Although this diversity in OLC type and generation efficiency follows with previous reports showing that these two transcription factors vary in effect, might the authors further discuss this discrepancy given that the two transcription factors regulate one another (as mentioned in the introduction) and should theoretically give rise to more similar cells? Perhaps due to the lower specificity of Olig2 in marking a pure OLC population relative to Sox10?

Reviewer #1 (Public review):

This study explores the connectivity patterns that could lead to fast and slow undulating swim patterns in larval zebrafish using a simplified theoretical framework. The authors show that a pattern of connectivity based only on inhibition is sufficient to produce realistic patterns with a single frequency. Two such networks couple with inhibition but with distinct time constants can produce a range of frequencies. Adding excitatory connections further increases the range of obtainable frequencies, albeit at the expense of sudden transitions in mid-frequency range.

Strengths:

(1) This is an eloquent approach to answering the question of how spinal locomotor circuits generate coordinated activity using a theoretical approach based on moving bump models of brain activity.

(2) The models make specific predictions on patterns of connectivity while discounting the role of connectivity strength or neuronal intrinsic properties in shaping the pattern.

(3) The models also propose that there is an important association between cell-type-specific intersegmental patterns and the recruitment of speed-selective subpopulations of interneurons.

(4) Having a hierarchy of models creates a compelling argument for explaining rhythmicity at the network level. Each model builds on the last and reveals a new perspective on how network dynamics can control rhythmicity. I liked that each model can be used to probe questions in the next/previous model.

Comments on revisions:

I am very happy to see the simplified biophysical model supporting the original findings. The authors have done an excellent job addressing my comments.

Just a small note, please change C. Elegans to C. elegans.

Reviewer #2 (Public review):

Summary:

The authors aimed to show that connectivity patterns within spinal circuits composed of specific excitatory and inhibitory connectivity and with varying degrees of modularity could achieve tail beats at various frequencies as well as proper left-right coordination and rostrocaudal propagation speeds.

Strengths:

The model is simple and the connectivity patterns explored are well supported by the literature

The conclusions are intuitive and support many experimental studies on zebrafish spinal circuits for swimming. The simulations provide strong support for the sufficiency of connectivity patterns to produce and control many hallmark features of swimming in zebrafish

Weaknesses:

The authors have addressed my previous concerns well. I have no further concerns.

Reviewer #3 (Public review):

Summary:

Central pattern generator (CPG) circuits underly rhythmic motor behaviors. Till date, it is thought that these CPG networks are rather local and multiple CPG circuits are serially connected to allow locomotion across the entire body. Distributed CPG networks that incorporate long-range connections have not been proposed although such connectivity has been experimentally shown for several different spinal populations. In this manuscript, the authors use this existing literature on long-range spinal interneuron connectivity to build a new computational model that reproduces basic features of locomotion like left-right alternation, rostrocaudal propagation and independent control of frequency and amplitude. Interestingly, the authors show that a model solely based on inhibitory neurons can recapitulate these basic locomotor features. Excitatory sources were then added that increased the dynamic range of frequencies generated. Finally, the authors were also able to reproduce experimentally observed consequences of cell-type-specific ablations showing that local and long range, cell-type-specific connectivity could be sufficient for generating locomotion.

Strengths:

This work is novel, providing an interesting alternative of distributed CPGs to the local networks traditionally predicted. It shows cell type-specific network connectivity is as important if not more than intrinsic cell properties for rhythmogenesis and that inhibition plays a crucial role in shaping locomotor features. Given the importance of local CPGs in understanding motor control, this alternative concept will be of broad interest to the larger motor control field including invertebrate and vertebrate species.

Weaknesses:

The main weaknesses were addressed in the revision.