Reviewer #3 (Public review):

Summary:

This study aims to develop and characterize phenylhydrazone-based small molecules that selectively activate the ATF6 arm of the unfolded protein response by covalently modifying a subset of ER-resident PDIs. The authors identify AA263 as a lead scaffold and optimize its structure to generate analogs with improved potency and ATF6 selectivity, notably AA263-20. These compounds are shown to restore proteostasis and functional expression of disease-associated misfolded proteins in cellular models involving both secretory (AAT-Z) and membrane (GABAA receptor) proteins. The findings provide valuable chemical tools for modulating ER proteostasis and may serve as promising leads for therapeutic development targeting protein misfolding diseases.

Strengths:

The study presents a well-defined chemical biology framework integrating proteomics, transcriptomics, and disease-relevant functional assays.

Identification and optimization of a new electrophilic scaffold (AA263) that selectively activates ATF6 represents a valuable advance in UPR-targeted pharmacology.

SAR studies are comprehensive and logically drive the development of more potent and selective analogs such as AA263-20.

Functional rescue is demonstrated in two mechanistically distinct disease models of protein misfolding-one involving a secretory protein and the other a membrane protein-underscoring the translational relevance of the approach.

Weaknesses:

ATF6 activation is primarily inferred from reporter assays and transcriptional profiling; direct biochemical evidence of ATF6 cleavage or nuclear translocation remains missing. However, the authors have added supporting data showing that co-treatment with the ATF6 inhibitor CP7 suppresses target gene induction, which partially strengthens the evidence for ATF6-dependent activity.

Although the proposed mechanism involving PDI modification and ATF6 activation is plausible, it is still not experimentally demonstrated and remains incompletely characterized.

In vivo validation is absent, and thus the pharmacological feasibility, selectivity, and bioavailability of these compounds in physiological systems remain untested.

Comments on revisions:

The authors have generally addressed my comments.

Reviewer #3 (Public review):

Summary:

This is an important and well-conceived study that identifies the Bearded-type small protein E(spl)m4 as a physical and genetic interactor of TRAF4 in Drosophila. By combining classical genetics, yeast two-hybrid assays, and AlphaFold in silico modeling, the authors convincingly demonstrate that E(spl)m4 acts as an inhibitor of TRAF4-mediated induction of JNK-driven apoptosis in developing larval imaginal wing discs, while not affecting TRAF4's role in adherence junction remodeling.

Based primarily on modeling, the authors propose that the specificity of E(spl)m4 towards TRAF4-mediated signaling arises from its interference with TRAF4 trimerization, which is likely required for the activation of the JNK signaling arm but not for the maintenance of adherence junctions and stability of E-cadherin/β-catenin complex.

Overall, this study is of broad interest to cell and developmental biologists. It also holds potential biomedical relevance, particularly for strategies aimed at modulating TRAF protein activities to dissect and modulate canonical versus non-canonical signaling functions.

Strengths:

(1) The work identifies the Bearded-type small protein E(spl)m4 as a physical and genetic interactor of TRAF4 in Drosophila, extending the understanding of E(spl)m4 beyond its established functions in Notch signaling.

(2) The study is experimentally solid, well-executed, and written, combining classical genetics with protein-protein interaction assays and modeling to reveal E(spl)m4 as a new regulator of TRAF4 signaling.

(3) The genetic and biochemical data convincingly show the ability of E(spl)m4 overexpression to inhibit TRAF4-induced JNK-dependent apoptosis, while leaving the TRAF4 role in adherens junction remodeling unaffected.

(4) The findings have important implications for the regulation of cell signaling and apoptosis and may guide pharmacological targeting of TRAF proteins.

Weaknesses:

The study is overall strong; however, several aspects could be clarified or expanded to strengthen the proposed mechanism and data presentation:

(1) The proposed mechanism that E(spl)m4 inhibits TRAF4 activation of JNK signaling by affecting TRAF4 trimerization relies mainly on modeling. Experimental evidence would strengthen this claim. For example, a native or non-denaturing SDS-PAGE could be used to assess TRAF4 oligomerization states in the absence or presence of E(spl)m4 overexpression, testing whether E(spl)m4 interferes with high-molecular-weight TRAF4 assemblies.

(2) The study depends largely on E(spl)m4 overexpression, which may not reflect physiological conditions. It would be valuable to test, or at least discuss, whether loss-of-function or knockdown of E(spl)m4 modulates the strength or duration of JNK-mediated signaling, potentially accelerating apoptosis. Such data would reinforce the model that E(spl)m4 acts as a physiological modulator of TRAF4-JNK signaling in vivo.

(3) The authors initially identify both E(spl)m4 and E(spl)m2 as TRAF4 interactions, but subsequently focus on E(spl)m4. It would be helpful to clarify or discuss the rationale for prioritizing E(spl)m4 for detailed functional analysis.

(4) E(spl)m4 overexpression appears to protect RpS3 loser clones (Figure 6H-K), yet caspase-3-positive cells are still visible in mosaic wing discs. Please comment on the nature of these Caspase 3-positive cells, whether they are cell-autonomous to the clone or non-autonomous (Figure 6K)?

(5) This is a clear, well-executed, and conceptually strong study that significantly advances understanding of TRAF4 signaling specificity and its modulation by the Bearded-type protein E(spl)m4.

Reviewer #3 (Public review):

Summary and strengths:

In this manuscript, Grimes presents an extension of the Ellipse of Insignificant (EOI) and Region of Attainable Redaction (ROAR) metrics to the meta-analysis setting as metrics for fragility and robustness evaluation of meta-analysis. The author applies these metrics to three meta-analyses of Vitamin D and cancer mortality, finding substantial fragility in their conclusions. Overall, I think extension/adaptation is a conceptually valuable addition to meta-analysis evaluation, and the manuscript is generally well-written.

Specific comments:

(1) The manuscript would benefit from a clearer explanation of in what sense EOIMETA is generalizable. The author mentions this several times, but without a clear explanation of what they mean here.

(2) The authors mentioned the proposed tools assume low between-study heterogeneity. Could the author illustrate mathematically in the paper how the between-study heterogeneity would influence the proposed measures? Moreover, the between-study heterogeneity is high in Zhang et al's 2022 study. It would be a good place to comment on the influence of such high heterogeneity on the results, and specifying a practical heterogeneity cutoff would better guide future users.

(3) I think clarifying the concepts of "small effect", "fragile result", and "unreliable result" would be helpful for preventing misinterpretation by future users. I am concerned that the audience may be confusing these concepts. A small effect may be related to a fragile meta-analysis result. A fragile meta-analysis doesn't necessarily mean wrong/untrustworthy results. A fragile but precise estimate can still reflect a true effect, but whether that size of true effect is clinically meaningful is another question. Clarifying the effect magnitude, fragility, and reliability in the discussion would be helpful.

Reviewer #3 (Public review):

Summary:

In their manuscript entitled "Ubiquitous predictive processing in the spectral domain of sensory cortex", Sennesh and colleagues perform spectral analysis across multiple layers and areas in the visual system of mice. Their results are timely and interesting as they provide a complement to a study from the same lab focussed on firing rates, instead of oscillations. Together, the present study argues for a hypothesis called predictive routing, which argues that non-predictable stimuli are gated by Gamma oscillations, while alpha/beta oscillations are related to predictions.

Strengths:

(1) The study contains a clear introduction, which provides a clear contrast between a number of relevant theories in the field, including their hypotheses in relation to the present data set.

(2) The study provides a systematic analysis across multiple areas and layers of the visual cortex.

Weaknesses:

(1) It is claimed in the abstract that the present study supports predictive routing over predictive coding; however, this claim is nowhere in the manuscript directly substantiated. Not even the differences are clearly laid out, much less tested explicitly. While this might be obvious to the authors, it remains completely opaque to the reader, e.g., as it is also not part of the different hypotheses addressed. I guess this result is meant in contrast to reference 17, by some of the same authors, which argues against predictive coding, while the present work finds differences in the results, which they relate to spectral vs firing rate analysis (although without direct comparison).

(2) Most of the claims about a direction of propagation of certain frequency-related activities (made in the context of Figures 2-4) are - to the eyes of the reviewer - not supported by actual analysis but glimpsed from the pictures, sometimes, with very little evidence/very small time differences to go on. To keep these claims, proper statistical testing should be performed.

(3) Results from different areas are barely presented. While I can see that presenting them in the same format as Figures 2-4 would be quite lengthy, it might be a good idea to contrast the right columns (difference plots) across areas, rather than just the overall averages.

(4) Statistical testing is treated very generally, which can help to improve the readability of the text; however, in the present case, this is a bit extreme, with even obvious tests not reported or not even performed (in particular in Figure 5).

(5) The description of the analysis in the methods is rather short and, to my eye, was missing one of the key descriptions, i.e., how the CSD plots were baselined (which was hinted at in the results, but, as far as I know, not clearly described in the analysis methods). Maybe the authors could section the methods more to point out where this is discussed.

(6) While I appreciate the efforts of the authors to formulate their hypotheses and test them clearly, the text is quite dense at times. Partly this is due to the compared conditions in this paradigm; however, it would help a lot to show a visualization of what is being compared in Figures 2-4, rather than just showing the results.

Reviewer #3 (Public review):

Summary:

The article explores the brain's ability to generalize information, with a specific focus on the entorhinal cortex (EC) and its role in learning and representing structural regularities that define relationships between entities in networks. The research provides empirical support for the longstanding theoretical and computational neuroscience hypothesis that the EC is crucial for structure generalization. It demonstrates that EC codes can generalize across non-spatial tasks that share common structural regularities, regardless of the similarity of sensory stimuli and network size.

Strengths:

At first glance, a potential limitation of this study appears to be its application of analytical methods originally developed for high-resolution animal electrophysiology (Samborska et al., 2022) to the relatively coarse and noisy signals of human fMRI. Rather than sidestepping this issue, however, the authors embrace it as a methodological challenge. They provide compelling empirical evidence and biologically grounded simulations to show that key generalization properties of entorhinal cortex representations can still be robustly detected. This not only validates their approach but also demonstrates how far non-invasive human neuroimaging can be pushed. The use of multiple independent datasets and carefully controlled permutation tests further underscores the reliability of their findings, making a strong case that structural generalization across diverse task environments can be meaningfully studied even in abstract, non-spatial domains that are otherwise difficult to investigate in animal models.

Weaknesses:

While this study provides compelling evidence for structural generalization in the entorhinal cortex (EC), several limitations remain that pave the way for promising future research. One issue is that the generalization effect was statistically robust in only one task condition, with weaker effects observed in the "community" condition. This raises the question of whether the null result genuinely reflects a lack of EC involvement, or whether it might be attributable to other factors such as task complexity, training order, or insufficient exposure possibilities that the authors acknowledge as open questions. Moreover, although the study leverages fMRI to examine EC representations in humans, it does not clarify which specific components of EC coding-such as grid cells versus other spatially tuned but non-grid codes-underlie the observed generalization. While electrophysiological data in animals have begun to address this, the human experiments do not disentangle the contributions of these different coding types. This leaves unresolved the important question of what makes EC representations uniquely suited for generalization, particularly given that similar effects were not observed in other regions known to contain grid cells, such as the medial prefrontal cortex (mPFC) or posterior cingulate cortex (PCC). These limitations point to important future directions for better characterizing the computational role of the EC and its distinctiveness within the broader network supporting learning and decision making based on cognitive maps.

Reviewer #3 (Public review):

The authors use high throughput neutralisation data to explore how different summary statistics for population immune responses relate to strain success, as measured by growth rate during the 2023 season. The question of how serological measurements relate to epidemic growth is an important one, and I thought the authors present a thoughtful analysis tackling this question, with some clear figures. In particular, they found that stratifying the population based on the magnitude of their antibody titres correlates more with strain growth than using measurements derived from pooled serum data. The updated manuscript has a stronger motivation, and there is substantial potential to build on this work in future research.

Comments on revisions:

I have no additional recommendations. There are several areas where the work could be further developed, which were not addressed in detail in the responses, but given this is a strong manuscript as it stands, it is fine that these aspects are for consideration only at this point.

Reviewer #3 (Public review):

Summary:

The stable production of learned vocalizations like human language and birdsong requires auditory feedback. What happens in the brain areas that generate stable vocalizations as performance deteriorates is not well understood. Using a species of songbird, the current study investigates individual cells within the evolutionarily-conserved brain regions that generate learned vocalizations to describe that the complement of neuropeptide (short proteins) signals may be a key feature of behavioral change. Because neuropeptides are important across species, these findings may help explain diminishing stability in learned behaviors even in humans.

Strengths:

The experiments are solid and follow a strong progression from description through manipulation. The songbird model is appropriate and powerful to inform on generalizable biological mechanisms of precisely learned behaviors, including human speech.

Weaknesses:

While it is always possible to perform more experiments, most of the weaknesses are in the presentation of the project, not in the evidence or analysis, which are leading-edge and appropriate. Generally, the ability to follow the findings and to independently assess rigor would be enhanced with increased explicit mention of the statistical thresholds and subjective descriptions. In addition, two prior pieces of relevant work seem to be omitted, including one performing deafening, gene expression measures, and behavioral assessment in zebra finches, and another describing neuropeptide complements in zebra finch singing nuclei based largely on mass spectrometry. The former in particular should be related to the current findings.

Reviewer #3 (Public review):

This manuscript provides novel insights into altered glucose metabolism and KC status during early MASLD. The authors propose that hyperactivated glycolysis drives a spatially patterned KC depletion that is more pronounced than the loss of hepatocytes or hepatic stellate cells. This concept significantly enhances our understanding of early MASLD progression and KC metabolic phenotype.

Through a combination of TUNEL staining and MS-based metabolomic analyses of KCs from HFHC-fed mice, the authors show increased KC apoptosis alongside dysregulation of glycolysis and the pentose phosphate pathway. Using in vitro culture systems and KC-specific ablation of Chil1, a regulator of glycolytic flux, they further show that elevated glycolysis can promote KC apoptosis.

However, it remains unclear whether the observed metabolic dysregulation directly causes KC death or whether secondary factors, such as low-grade inflammation or macrophage activation, also contribute significantly. Nonetheless, the results, particularly those derived from the Chil1-ablated model, point to a new potential target for the early prevention of KC death during MASLD progression.

The manuscript is clearly written and thoughtfully addresses key limitations in the field, especially the focus on glycolytic intermediates rather than fatty acid oxidation. The authors acknowledge the missing mechanistic link between increased glycolysis and KC death. Still, several interpretations require moderation to avoid overstatement, and certain experimental details, particularly those concerning flow cytometry and population gating, need further clarification.

Strengths:

(1) The study presents the novel observation of profound metabolic dysregulation in KCs during early MASLD and identifies these cells as undergoing apoptosis. The finding that Chil1 ablation aggravates this phenotype opens new avenues for exploring therapeutic strategies to mitigate or reverse MASLD progression.

(2) The authors provide a comprehensive metabolic profile of KCs following HFHC diet exposure, including quantification of individual metabolites. They further delineate alterations in glycolysis and the pentose phosphate pathway in Chil1-deficient cells, substantiating enhanced glycolytic flux through 13C-glucose tracing experiments.

(3) The data underscore the critical importance of maintaining balanced glucose metabolism in both in vitro and in vivo contexts to prevent KC apoptosis, emphasizing the high metabolic specialization of these cells.

(4) The observed increase in KC death in Chil1-deficient KCs demonstrates their dependence on tightly regulated glycolysis, particularly under pathological conditions such as early MASLD.

Weaknesses:

(1) The novelty is questionable. The presented work has considerable overlap with a study by the same lab, which is currently under review (citation 17), and it should be considered whether the data should not be presented in one paper.

(2) The authors report that 60% of KCs are TUNEL-positive after 16 weeks of HFHC diet and confirm this by cleaved caspase-3 staining. Given that such marker positivity typically indicates imminent cell death within hours, it is unexpected that more extensive KC depletion or monocyte infiltration is not observed. Since Timd4 expression on monocyte-derived macrophages takes roughly one month to establish, the authors should consider whether these TUNEL-positive KCs persist in a pre-apoptotic state longer than anticipated. Alternatively, fate-mapping experiments could clarify the dynamics of KC death and replacement.

(3) The mechanistic link between elevated glycolytic flux and KC death remains unclear.

(4) The study does not address the polarization or ontogeny of KCs during early MASLD. Given that pro-inflammatory macrophages preferentially utilize glycolysis, such data could provide valuable insight into the reason for increased KC death beyond the presented hyperreliance on glycolysis.

(5) The gating strategy for monocyte-derived macrophages (moMFs) appears suboptimal and may include monocytes. A more rigorous characterization of myeloid populations by including additional markers would strengthen the study's conclusions.

(6) While BMDMs from Chil1 knockout mice are used to demonstrate enhanced glycolytic flux, it remains unclear whether Chil1 deficiency affects macrophage differentiation itself.

(7) The authors use the PDK activator PS48 and the ATP synthase inhibitor oligomycin to argue that increased glycolytic flux at the expense of OXPHOS promotes KC death. However, given the high energy demands of KCs and the fact that OXPHOS yields 15-16 times more ATP per glucose molecule than glycolysis, the increased apoptosis observed in Figure 4C-F could primarily reflect energy deprivation rather than a glycolysis-specific mechanism.

(8) In Figure 1C, KC numbers are significantly reduced after 4 and 16 weeks of HFHC diet in WT male mice, yet no comparable reduction is seen in Clec4Cre control mice, which should theoretically exhibit similar behavior under identical conditions.

Reviewer #3 (Public review):

Summary:

The authors report converging evidence from behavioral studies as well as several brain-imaging techniques that geometric figures, notably quadrilaterals, are processed differently in visual (lower activation) and spatial (greater) areas of the human brain than representative figures. Comparison of mathematical models to fit activity for geometric figures shows the best fit for abstract geometric features like parallelism and symmetry. The brain areas active for geometric figures are also active in processing mathematical concepts even in blind mathematicians, linking geometric shapes to abstract math concepts. The effects are stronger in adults than in 6-year-old Western children. Similar phenomena do not appear in great apes, suggesting that this is uniquely human and developmental.

Strengths:

Multiple converging techniques of brain imaging and testing of mathematical models showing special status of perception of abstract forms. Careful reasoning at every step of research and presentation of research, anticipating and addressing possible reservations. Connecting these findings to other findings, brain, behavior, and historical/anthropological to suggest broad and important fundamental connections between abstract visual-spatial forms and mathematical reasoning.

Weaknesses:

I have reservations of the authors' use of "symbolic." They seem to interpret "symbolic" as relying on "discrete, exact, rule-based features." Words are generally considered to symbolic (that is their major function), yet words do not meet those criteria. Depictions of objects can be regarded as symbolic because they represent real objects, they are not the same as the object (as Magritte observed). If so then perhaps depictions of quadrilaterals are also symbolic but then they do not differ from depictions of objects on that quality. Relatedly, calling abstract or generalized representations of forms a distinct "language of thought" doesn't seem supportable by the current findings. Minimally, a language has elements that are combined more or less according to rules. The authors present evidence for geometric forms as elements but nowhere is there evidence for combining them into meaningful strings.

Further thoughts

Incidentally, there have been many attempts at constructing visual languages from visual elements combined by rules, that is, mapping meaning to depictions. Many written languages like Egyptian hieroglyphics or Mayan or Chinese, began that way; there are current attempts using emoji. Apparently, mapping sound to discrete letters, alphabets, is more efficient and was invented once but spread. That said, for restricted domains like maps, circuit diagrams, networks, chemical interactions, mathematics, and more, visual "languages" work quite well.

The findings are striking and as such invite speculation about their meaning and limitations. The images of real objects seem to be interpreted as representations of 3D objects as they activate the same visual areas as real objects. By contrast, the images of 2D geometric forms are not interpreted as representations of real objects but rather seemingly as 2D abstractions. It would be instructive to investigate stimuli that are on a continuum from representational to geometric, e. g., real objects that have simple geometric forms like table tops or boxes under various projections or balls or buildings that are rectangular or triangular. Objects differ from geometric forms in many ways: 3D rather than 2D, more complicated shapes; internal features as well as outlines. The geometric figures used are flat, 2-D, but much geometry is 3-D (e. g. cubes) with similar abstract features. The feature space of geometry is more than parallelism and symmetry; angles are important for example. Listing and testing features would be fascinating.

Can we say that mathematical thinking began with the regularities of shapes or with counting, or both? External representations of counting go far back into prehistory; tallies are frequent and wide-spread. Infants are sensitive to number across domains as are other primates (and perhaps other species). Finding overlapping brain areas for geometric forms and number is intriguing but doesn't show how they are related.

Categories are established in part by contrast categories; are quadrilaterals and triangles and circles different categories? As for quadrilaterals, the authors say some are "completely irregular." Not really; they are still quadrilaterals, if atypical. See Eleanor Rosch's insightful work on (visual) categories. One wonders about distinguishing squashed quadrilaterals from squashed triangles.

What in human experience but not the experience of close primates would drive the abstraction of these geometric properties? It's easy to make a case for elaborate brain processes for recognizing and distinguishing things in the world, shared by many species, but the case for brain areas sensitive to abstracting geometric figures is harder. The fact that these areas are active in blind mathematicians and that they are parietal areas suggest that what is important is spatial far more than visual. Could these geometric figures and their abstract properties be connected in some way to behavior, perhaps with fabrication, construction or use of objects? Or with other interactions with complex objects and environments where symmetry and parallelism (and angles and curvature--and weight and size) would be important? Manual dexterity and fabrication also distinguish humans from great apes (quantitatively not qualitatively) and action drives both visual and spatial representations of objects and spaces in the brain. I certainly wouldn't expect the authors to add research to this already packed paper, but raising some of the conceptual issues would contribute to the significance of the paper.

Reviewer #3 (Public review):

Summary:

This research shows that a-mangostin, a proposed nutraceutical, with cardiovascular protective properties, could act through the activation of large conductance potassium permeable channels (BK). The authors provide convincing electrophysiological evidence that the compound binds to BK channels and induces a potent activation, increasing the magnitude of potassium currents. Since these channels are important modulators of the membrane potential of smooth muscle in vascular tissue, this activation leads to muscle relaxation, possibly explaining cardiovascular protective effects.

Strengths:

The authors present evidence based on several lines of experiments that a-mangostin is a potent activator of BK channels. The quality of the experiments and the analysis is high and represents an appropriate level of analysis. This research is timely and provides a basis to understand the physiological effects of natural compounds with proposed cardio-protective effects.

Weaknesses:

The identification of the binding site is not the strongest point of the manuscript. The authors show that the binding site is probably located in the hydrophobic cavity of the pore and show that point mutations reduce the magnitude of the negative voltage shift of activation produced by a-mangostin. However, these experiments do not demonstrate binding to these sites, and could be explained by allosteric effects on gating induced by the mutations themselves.

Reviewer #3 (Public review):

Summary:

In this study, Freier et al. demonstrate that 3 distinct metabolic pathways are critical for the synthesis of 1C-THF, a metabolite that is crucial for the growth and virulence of Listeria monocytogenes. Using an elegant suppressor screen, they also demonstrate the hierarchical importance of these metabolic pathways with respect to the biosynthesis of 1C-THF.

Strengths:

This study uses elegant bacterial genetics to confirm that 3 distinct metabolic pathways are critical for 1C-THF synthesis in L. monocytogenes, and the lack of either one of these pathways compromises bacterial growth and virulence. The study uses a combination of in vitro growth assays, macrophage-CFU assays, and murine infection models to demonstrate this.

Weaknesses:

(1) The primary finding of the study is that the perturbation of any of the 3 metabolic pathways important for the synthesis of 1C-THF results in reduced growth and virulence of L. monocytogenes. However, there is no evidence demonstrating the levels of 1C-THF in the various knockouts and suppressor mutants used in this study. It is important to measure the levels of this metabolite (ideally using mass spectrometry) in the various knockouts and suppressor mutants, to provide strong causality.

(2) The story becomes a little hard to follow since macrophage-CFU assays and murine infection model data precede the in vitro growth assays. The manuscript would benefit from a reorganization of Figures 2,3, and 4 for better readability and flow of data.

Reviewer #3 (Public review):

Summary:

This is an extremely important manuscript in the evolution of cerebral perfusion imaging using Arterial Spin Labelling (ASL). The number of subjects that were scanned has provided the authors with a unique opportunity to explore many potential associations between regional cerebral blood flow (CBF) and clinical and demographic variables.

Strengths:

The major strength of the manuscript is the access to an unprecedentedly large cohort of subjects. It demonstrates the sensitivity of regional tissue blood flow in the brain as an important marker of resting brain function. In addition, the authors have demonstrated a thorough analysis methodology and good statistical rigour.

Weaknesses:

This reviewer did not identify any major weaknesses in this work.

Reviewer #3 (Public review):

Summary:

Ceravolo et al. employed functional magnetic resonance imaging (fMRI) to examine how the temporal voice areas (TVA) in the human brain respond to vocalizations from different nonhuman primate species. Their findings reveal that the human TVA is not only responsible for human vocalizations but also exhibits sensitivity to the vocalizations of other primates, particularly chimpanzee vocalizations sharing acoustic similarities with human voices, which offers compelling evidence for cross-species vocal processing in the human auditory system. Overall, the study presents intellectually stimulating hypotheses and demonstrates methodological originality. However, the current findings are not yet solid enough to fully support the proposed claims, and the presentation could be enhanced for clarity and impact.

Strengths:

The study presents intellectually stimulating hypotheses and demonstrates methodological originality.

Weaknesses:

(1) The analysis of the fMRI data does not account for the participants' behavioral performance, specifically their reaction times (RTs) during the species categorization task.

(2) The figure organization/presentation requires significant revision to avoid confusion and redundancy.

Reviewer #3 (Public review):

Summary:

In this study, authors utilize biophysical modeling to investigate differences in free energies and nucleosomal configuration probability density of CpG islands and nonmethylated regions in the genome. Toward this goal, they develop and apply the cgNA+ coarse-grained model, an extension of their prior molecular modeling framework.

Strengths:

The study utilizes biophysical modeling to gain mechanistic insight into nucleosomal occupancy differences in CpG and nonmethylated regions in the genome.

Weaknesses:

Although the overall study is interesting, the manuscripts need more clarity in places. Moreover, the rationale and conclusion for some of the analyses are not well described.

Comments on revised version:

The authors have addressed my concerns.

Reviewer #3 (Public review):

Summary:

This study examined how young children with minimal reading instruction process letters, focusing on their familiarity with letter shapes, knowledge of letter names, and visual discrimination of upright versus inverted letters. Across four experiments, kindergarten and Grade 1 children could identify the correct orientation of letters even without knowing their names.

Strengths:

This study addresses an important research gap by examining whether children develop letter familiarity prior to formal literacy instruction and how this skill relates to reading-related cognitive abilities. By emphasizing letter familiarity alongside letter recognition, the study highlights a potentially overlooked yet important component of emergent literacy development.

Weaknesses:

The study's methods and results do not effectively test its stated research goals. Reading ability was not directly measured; instead, the authors inferred its relationship with reading from correlations between letter familiarity and reading-related cognitive measures, which limits the validity of their conclusions. Furthermore, the analytical approach was rather limited, relying primarily on simple and partial correlations without employing more advanced statistical methods that could better capture the underlying relationships.

Major Comments:

(1) Limited Novelty and Unclear Theoretical Contribution:

The authors aim to challenge the view that children acquire letter shape knowledge only through formal literacy instruction, but similar questions regarding letter familiarity have already been explored in previous research. The manuscript does not clearly articulate how the present study advances beyond existing findings or why examining letter familiarity specifically before formal instruction provides new theoretical insight. Moreover, if letter familiarity and letter recognition are treated as distinct constructs, the authors should better justify their differentiation and clarify the theoretical significance of focusing on familiarity as an independent component of emergent literacy.

(2) Overgeneralization to Reading Ability:

Although the study measured several literacy-related cognitive skills and examined correlations with letter familiarity, it did not directly assess children's reading ability, as participants had not yet received formal literacy instruction. Therefore, the conclusion that letter familiarity influences reading skills (e.g., Line 519: "Our results are broadly consistent with previous work that has highlighted print letter knowledge as a strong predictor of future reading skills") is not fully supported and should be clarified or revised. To draw conclusions about the impact on reading ability, a longitudinal study would be more appropriate, assessing the relationship between letter familiarity and reading skills after children have received formal literacy instruction. If a longitudinal study is not feasible, measuring familial risk for dyslexia could provide an alternative approach to infer the potential influence of letter familiarity on later reading development.

(3) Confusing and Limited Analytical Approach with Potential for More Sophisticated Modeling:

The study employs a confusing analytical approach, alternating between simple correlational analyses and group-based comparisons, which may introduce circularity - for example, defining high vs. low familiarity groups partly based on performance differences in upright versus inverted letters and then observing a visual search advantage for upright letters within these groups. Moreover, the analyses are relatively simple: although multiple linear regression is mentioned, the results are not fully reported. These approaches may not fully capture the complex relationships among letter familiarity, recognition, visual search performance, RAN, and other covariates. More sophisticated modeling, such as mixed-effects models to account for repeated measures, structural equation modeling to examine latent constructs, or multivariate approaches jointly modeling familiarity and recognition effects, could provide a clearer understanding of the unique contribution of letter shape familiarity to early literacy outcomes. In addition, a large number of correlations were conducted without correction for multiple comparisons, which may increase the risk of false positives and raise concerns about the reliability of some significant findings.

Reviewer #3 (Public review):

Despite the abundance of RNA velocity tools, there are still major limitations, and there is strong skepticism about the results these methods lead to. In this paper, the authors try to address some limitations of current RNA velocity approaches by proposing a unified framework to jointly infer transcriptional and splicing dynamics. The method is then benchmarked on 6 real datasets against the most popular RNA velocity tools.

While the approach has the potential to be of interest for the field, and may present improvements compared to existing approaches, there are some major limitations that should be addressed, particularly concerning the benchmark (see major comment 1).

Major comments:

(1) My main criticism concerns the benchmarking: real data lack a ground truth, and are absolutely not ideal for comparing methods, because one can only speculate what results appear to be more plausible.<br /> A solid and extensive simulation study, which covers various scenarios and possibly distinct data-generating models, is needed for comparing approaches. The authors should check, for example, the simulation studies in the BayVel approach (Section 4, BayVel: A Bayesian Framework for RNA Velocity Estimation in Single-Cell Transcriptomics). Clearly, all methods should be included in the simulation.

(2) Related to the above: since a ground truth is missing, the real data analyses need to be interpreted with caution. I recommend avoiding strong statements, such as "successfully captures the correct gene dynamics", or "accurately infer", in favour of milder statements supported by the data, such as "... aligns with the biological processes described" (as in page 12), or "results are compatible with current biological knowledge", etc...

(3) Many methods perform RNA velocity analyses. While there is a brief description, I think it'd be useful to have a schematic summary (e.g., via a Table) of the main conceptual, mathematical, and computational characteristics of each approach.

(4) Related to the above: I struggled to identify the main conceptual novelty of TSvelo, compared to existing approaches. I recommend explaining this aspect more extensively.

(5) A computational benchmark is missing; I'd appreciate seeing the runtime and memory cost of all methods in a couple of datasets.

(6) I think BayVel (mentioned above) should be added to the list of competing methods (both in the text and in the benchmarks). The package can be found here: https://github.com/elenasabbioni/BayVel_pkgJulia .

Reviewer #3 (Public review):

Summary:

The authors use fluorescent microscopy and fluorescent markers to investigate the requirement of P-bodies during growth on methanol, a common substrate available on plant leaves, by using a yeast edc3 mutant defective in P-body formation. Growth on methanol upregulates the transcription of methanol metabolic genes, which accumulate in granular structures, as observed by microscopy. Co-localization of P-bodies and granules was quantified and described as dynamically enhanced during oxidative stress. Ultimately, the authors suggest a model where methanol induces the accumulation of methanol-induced mRNAs in cytosolic granules, which dynamically interact with P-bodies, especially during oxidative stress, to protect the mRNAs from degradation. However, this model is not strongly supported by the provided data, as the quantification of the co-localization between different markers (of organelles and between P-body and granules) is not well presented or described in the text.

Considering that there is only a small EDC3-dependent overlap between P-bodies and mimRNA granules, the claim that P-bodies regulate mimRNAs is not fully justified. Rather, EDC3 could also be involved in mimRNA granule formation, independent of P-bodies.

Strengths:

(1) The authors could show convincingly that P-bodies (using a P-body-deficient edc3-KO strain) are important for colonizing the plant phyllosphere and for the regulation of methanol-induced mRNAs (mimRNA).

(2) The visualization of mimRNA granules and P-bodies using fluorescent markers is interesting and was validated by alternative methods, such as FISH staining.

(3) The dynamic formation of mimRNA granules and P-bodies was demonstrated during growth on leaves and in artificial medium during oxidative stress. The mimRNA granules showed a similar dynamic as the abundances of several mimRNAs and their corresponding proteins.

(4) A role of EDC3 in the formation of mimRNA granules was demonstrated. However, the link between P-bodies and mimRNA granules was not clearly shown.

Weaknesses:

(1) The study largely relies on fluorescent microscopy and co-localization measurements. However, the subcellular resolution is not very high; it is unclear how dot-like structures were measured and, importantly, how co-localization was quantified.

(2) The text does not clarify to what degree P-bodies and mimRNA granules are different structures. Based on the images, the size of P-bodies and granules seems to be vastly different, making it unclear whether these structures are fused or separate, even if their markers are reported to overlap.

(3) The evidence that mimRNA granules contain ribosome-free and ribosome-associated RNA is only based on inhibitors and microscopy, without providing further evidence measuring granule content by isolation and sequencing approaches.

(4) Similarly, the co-localization with other organelle markers is not supported by quantitative data.

Reviewer #3 (Public review):

Summary:

The authors propose three types of Gaussian process kernels that extend and generalize standard kernels used for sequence-function prediction tasks, giving rise to the connectedness, Jenga, and general product models. The associated hyperparameters are interpretable and represent epistatic effects of varying complexity. The proposed models significantly outperform the simpler baselines, including the additive model, pairwise interaction model, and Gaussian process with a geometric kernel, in terms of R^2.

Strengths:

(1) The demonstrated performance boost and improved scaling with increasing training data are compelling.

(2) The hyperparameter selection step using the marginal likelihood, as implemented by the authors, seems to yield a reasonable hyperparameter combination that lends itself to biologically plausible interpretations.

(3) The proposed kernels generalize existing kernels in domain-interpretable ways, and can correspond to cases that would not be "physical" in the original models (e.g., $\mu_p>1$ in the original connectedness model that allows modeling of anticorrelated phenotypes).

Weaknesses:

(1) While enabling uncertainty quantification is a key advantage of Gaussian processes, the authors do not present metrics specific to the predicted uncertainties; all metrics seem to concern the mean predictions only. It would be helpful to evaluate coverage metrics and maybe include an application of the uncertainties, such as in active learning or Bayesian optimization.

(2) The more complex models, like the general product model, place a heavier burden on the hyperparameter selection step. Explicitly discussing the optimization routine used here would be helpful to potential users of the method and code.

Reviewer #3 (Public review):

Summary:

This study presents a powerful and rigorous approach for characterizing stimulus discriminability throughout a sensory manifold, and is applied to the specific context of predicting color discrimination thresholds across the chromatic plane.

Strengths:

Color discrimination has played a fundamental role in studies of human color vision and for color applications, but as the authors note, it remains poorly characterized. The study leverages the assumption that thresholds should vary smoothly and systematically within the space, and validates this with their own tests and comparisons with previous studies.

Weaknesses:

The paper assumes that threshold variations are due to changes in the level of intrinsic noise at different stimulus levels. However, it's not clear to me why they could not also be explained by nonlinearities in the responses, with fixed noise. Indeed, most accounts of contrast coding (which the study is at least in part measuring because the presentation kept the adapt point close to the gray background chromaticity, and thus measured increment thresholds), assume a nonlinear contrast response function, which can at least as easily explain why the thresholds were higher for colors farther from the gray point. It would be very helpful if a section could be added that explains why noise differences rather than signal differences are assumed and how these could be distinguished. If they cannot, then it would be better to allow for both and refer to the variation in terms of S/N rather than N alone.

Related to this point, the authors note that the thresholds should depend on a number of additional factors, including the spatial and temporal properties and the state of adaptation. However, many of these again seem to be more likely to affect the signal than the noise.

An advantage of the approach is that it makes no assumptions about the underlying mechanisms. However, the choice to sample only within the equiluminant plane is itself a mechanistic assumption, and these could potentially be leveraged for deciding how to sample to improve the characterization and efficiency. For example, given what we know about early color coding, would it be more (or less) efficient to select samples based on a DKL space, etc?

Reviewer #3 (Public review):

Summary:

Solyga, Zelechowski, and Keller present a concise report of an innovative study demonstrating clear visuomotor mismatch responses in ambulating humans, using a mobile EEG setup and virtual reality. Human subjects walked around a virtual corridor while EEGs were recorded. Occasionally, motion and visual flow were uncoupled, and this evoked a mismatch response that was strongest in occipitally placed electrodes and had a considerable signal-to-noise ratio. It was robust across participants and could not be explained by the visual stimulus alone.

Strengths:

This is an important extension of their prior work in mice, and represents an elegant translation of those previous findings to humans, where future work can inform theories of e.g., psychiatric diseases that are believed to involve disordered predictive processing. For the most part, the authors are appropriately circumspect in their interpretations and discussions of the implications. I found the discussion of the polarity differences they found in light of separate positive and negative prediction errors, intriguing.

Weaknesses:

The primary weaknesses rest in how the results are sold and interpreted.

Most notably, the interpretation of the results of the comparison of visuomotor mismatches to the passive auditory oddball induced mismatch responses is inappropriate, as suboptimal electrode choices, unclear matching of trial numbers, and other factors. To clarify, regarding the auditory oddball portion in Figure 5, the data quality is a concern for the auditory ERPs, and the choice of Occipital electrodes is a likely culprit. Typically, auditory evoked responses are maximal at Cz or FCz, although these contacts don't seem to be available with this setup. In general, caution is warranted in comparing ERP peaks between two different sensory modalities - especially if attention is directed elsewhere (to a silent movie) during one recording and not during the other. The authors discuss this as a purely "qualitative" comparison in the text, which is appreciated, and do acknowledge the limitations within the results section, but the figure title and, importantly, the abstract set a different tone. At least, for comparisons between auditory mismatch and visuomotor mismatch, trial numbers need to be equated, as ERP magnitude can be augmented by noise (which reduces with increased numbers of trials in the average). And more generally, the size of the mismatch event at the scalp does not scale one-to-one with the size at the level of the neural tissue. One can imagine a number of variables that impact scalp level magnitudes, which are orthogonal to actual cortex-level activation - the size, spread, and polarity variance of the activated source (which all would diminish amplitude at the scalp due to polyphasic summation/cancelation). The variance of phase to a stimulus across trials (cross trial phase locking) vs magnitude of underlying power - the former, in theory, relates to bottom-up activity and the latter can reflect feedback (which has more variability in time across trials; the distance of the scalp electrode from the activated tissue (which, for the auditory system, would be larger (FCz to superior temporal gyrus) than for the visual system (O1 to V1/2)). None of this precludes the inclusion of the auditory mismatch, which is a strength of the study, but interpretations about this supporting a supremacy of sensory-motor mismatch - regardless of validity - are not warranted. I would recommend changing the way this is presented in the abstract.

Otherwise, the data are of adequate quality to derive most of their conclusions.

The authors claim that the mismatch responses emanate from within the occipital cortex, but I would require denser scalp coverage or a demonstration of consistent impedances across electrodes and across subjects to make conclusions about the underlying cortical sources (especially given the latencies of their peaks). In EEG, the distribution of voltage on the scalp is, of course, related to but not directly reflective of the distribution of the underlying sources. The authors are mostly careful in their discussion of this, but I would strongly recommend changing the work choice of "in occipital cortex" to "over occipital cortex" or even "posteriorly distributed". Even with very dense electrode coverage and co-registration to MRIs for the generation of forward models that constrain solutions, source localization of EEG signals is very challenging and not a simple problem. Given the convoluted and interior nature of human V1, the ability to reliably detect early evoked responses (which show the mismatch in mouse models) at the scalp in ERP peaks is challenging - especially if one is collapsing ERPs across subjects. And - given the latency of the mismatch responses, I'd imagine that many distributed cortical regions contribute to the responses seen at the scalp.

I think that Figure 3C, but as a difference of visual mismatch vs halting flow alone (in the open loop) might be additionally informative, as it clarifies exactly where the pure "mismatch" or prediction error is represented.

As a suggestion, the authors are encouraged to analyse time-frequency power and phase locking for these mismatch responses, as is common in much of the literature (see Roach et al 2008, Schizophrenia Bulletin). This is not to say that doing so will yield insights into oscillations per se, but converting the data to the time-frequency domain provides another perspective that has some advantages. It fosters translations to rodent models, as ERP peaks do not map well between species, but e.g., delta-theta power does (see Lee et al 2018, Neuropsychopharmacology; Javitt et al 2018, Schizophrenia research; Gallimore et al 2023, Cereb Ctx). Further, ERP peaks can be influenced by the actual neuroanatomy of an individual (especially for quantifying V1 responses). Time frequency analyses may aid in interpreting the "early negative deflection with a peak latency of 48 ms " finding as well.

Finally, the sentence in the abstract that this paradigm " can trigger strong prediction error responses and consequently requires shorter recording 20 times would simplify experiments in a clinical setting" is a nice setup to the paper, but the very fact that one third of recordings had to be removed due to movement artifact, and that hairstyle modulates the recording SnR, is reason that this paradigm, using the reported equipment, may have limited clinical utility in its current form. Further, auditory oddball paradigms are of great clinical utility because they do not require explicit attention and can be recorded very quickly with no behavioral involvement of a hospitalized patient. This should be discussed, although it does not detract from the overall scientific importance of the study. The authors should reconsider putting this statement in the abstract.

Reviewer #3 (Public review):

Summary:

The manuscript by Goicoechea et al. assesses the influence of hippocampal-network targeted TMS to parietal cortex on episodic memory using a meta-analytic approach. This is an important contribution to the literature, as the number of studies using this approach to modulate memory/hippocampal function has clearly increased since the initial publication by Wang et al. 2014. This manuscript makes an important contribution to the literature. In general, the analysis is straightforward and the conclusions are well-supported by the results; I have mostly minor comments/concerns.

Strengths:

(1) A meta-analysis across published work is used to evaluate the influence of hippocampal-network-targeted TMS in parietal cortex on episodic memory. By pooling results across studies, the meta-analytic effects demonstrate an influence of TMS on memory across the diversity of many details in study design (specific tasks, stimuli, TMS protocols, study populations).

(2) Selectivity with regard to episodic memory vs. non-episodic memory tasks is evaluated directly in the meta-analysis.

(3) The investigation into supplemental factors as predictors of TMS's influence on memory was tested. This is helpful given the diversity of study designs in the literature. This analysis helps to shed light on which study designs, e.g., TMS protocols, etc., are most effective in memory modulation.

Weaknesses:

(1) My only significant concern is how studies are categorized in the 'Timing' factor (when stimulation is applied). Currently, protocols in which TMS is administered across days are categorized as 'pre-encoding' in the Timing factor. This has the potential to be misleading and may lead to inaccurate conclusions. When TMS is administered across multiple days, followed by memory encoding and retrieval (often on a subsequent day), it is not possible to attribute the influence of TMS to a specific memory phase (i.e., encoding or retrieval) per se. Thus, labeling multi-day TMS studies as 'pre-encoding' may be misleading to readers, as it may imply that the influence of TMS is due to modulation of encoding mechanisms per se, which cannot be concluded. For example, multi-day TMS protocols could be labeled as 'pre-retrieval' and be similarly accurate. This approach also pools results from TMS protocols with temporal specificity (i.e., those applied immediately during encoding and not on board during memory testing) and without temporal specificity (i.e., the case of multi-day TMS) regarding TMS timing. Given the variety of paradigms employed in the literature, and to maximize the utility/accuracy of this analysis, one suggestion is to modify the categories within the Timing factor, e.g., using labels like 'Temporally-Specific' and 'Temporally Non-specific'. The 'Temporally-Specific' category could be subdivided based on the specific memory process affected: 'encoding', 'retrieval', or 'consolidation' (if possible). I think this would improve the accuracy of the approach and help to reach more meaningful conclusions, given the variety of protocols employed in the literature.

(2) As the scope of the meta-analysis is limited to TMS applied to parietal or superior occipital cortex, it is important to highlight this in the Introduction/Abstract. The 'HITS' terminology suggests a general approach that would not necessarily be restricted to parietal/nearby cortical sites.

Minor:

(1) To reduce the number of study factors tested, data reduction was performed via Lasso regression to remove factors that were not unique predictors of the influence of TMS on memory. This approach is reasonable; however, one limitation is that factors strongly correlated with others (and predict less unique variance) will be dropped. This may result in a misrepresentation, i.e., if readers interpret factors left out of this analysis as not being strongly related to the influence of TMS on memory. I do see and appreciate the paragraph in the Discussion which appropriately addresses this issue. However, it may be worth also considering an alternative analysis approach, if the authors have not already done so, which explicitly captures the correlation structure in the data (i.e., shown in Figure S2) using a tool like PCA or an appropriate factor analysis. Then, this shared covariance amongst factors can be tested as predictors of the influence of TMS - e.g., by testing whether component scores for dominant PCs are indeed predictive of the influence of TMS. This complementary approach would capture rather than obfuscate the extent to which different factors are correlated and assess their joint (rather than independent) influence on memory, potentially resulting in more descriptive conclusions. For example, TMS intensity and protocol may jointly influence memory.

(2) Given the specific focus on TMS applied to parietal cortex to modulate hippocampal and related network function, it would be fruitful if the authors could consider adding discussion/speculation regarding whether this approach may be effectively broadened using other stimulation methods (e.g., tACS, tDCS), how it may compare to other non-invasive brain stimulation methods with depth penetration to target hippocampal function directly (transcranial temporal interference, or transcranial focused ultrasound), and/or how or whether other stimulation sites may or may not be effective.

(3) Studies were only included in the meta-analysis if they contained objective episodic memory tests. How were studies handled that included both objective and subjective memory, or other non-episodic memory measures? For example, Yazar et al. 2014 showed no influence of TMS on objective recall, but an impairment in subjective confidence. I assume confidence was not included in the meta-analysis. Similarly, Webler et al. 2024 report results from both the mnemonic similarity task (presumably included) and a fear conditioning paradigm (presumably excluded). Please clarify in the methods how these distinctions were handled.

(4) The analysis comparing memory to non-memory measures is important, showing the specificity of stimulation. Did the authors consider further categorizing the non-memory tasks into distinct domains (i.e., language, working memory, etc.)? If possible, this could provide a finer detail regarding the selectivity of influences on memory vs. other aspects of cognition. It is likely that other aspects of cognition dependent on hippocampal function may be modulated as well, i.e., tasks with high relational/associative processing demands.

(5) In the analysis of the Intensity factor, how were studies using Active (rather than resting) MT categorized? Only resting MT is mentioned in Table S1. This is important as the original theta-burst TMS protocol from Huang et al. 2005 determines intensity based on Active Motor Threshold.

(6) Is there a reason why the study by Koen et al. 2018 (Cognitive Neuroscience) was not included? TMS was performed during encoding to the left AG, and objective memory was assessed, so it would seemingly meet the inclusion criterion.

(7) It would be helpful to briefly differentiate the current meta-analysis from that performed by Yeh & Rose (How can transcranial magnetic stimulation be used to modulate episodic memory?: A systematic review and meta-analysis, 2019, Frontiers in Psychology) (other than being more current).

(8) For transparency and to facilitate further understanding of the literature and potential data re-use, it would be great if the authors consider sharing a supplementary table or file that describes how individual studies/memory measures were categorized under the factors listed in Table S1.

Reviewer #3 (Public review):

Summary:

This article presented a novel computer model to address an important question in the field of brain stimulation, using the magnetic stimulation iTBS protocol as an example, how stimulation parameters, frequency in particular, interfere with the intrinsic brain oscillations via plastic mechanisms. Brain oscillation is a critical feature of functional brains and its alteration signals the onset of many neuropsychiatric diseases or certain brain states. The authors suggested with their model that harmonic and subharmonic stimulations close to the individual alpha frequency achieved strong broadband power suppression.

Strengths:

The authors focused on the cortico-thalamic circuitry and managed to generate alpha oscillations in their four-population model. By adding the non-monotonic calcium-based BCM rule, they have also achieved both homeostasis and plasticity in response to magnetic stimulation. This work combined computer simulations and statistical analysis to demonstrate the changes in network architecture and network dynamics triggered by varied magnetic stimulation parameters. By delivering the iTBS protocol to the cortical excitatory population, the key findings are that harmonic and subharmonic stimulations close to the individual alpha frequency (IAF) achieved strong broadband power suppression. This resulted from increased synaptic weights of the corticothalamic feed-forward inhibitory projections, which were mediated by the calcium dynamics perturbed by iTBS magnetic stimulation. This finding endorsed the importance of applying customized stimulation to patients based on their IAFs and suggested the underlying mechanism at the circuitry level.

Weaknesses:

The drawbacks of this work are also obvious. Model validation and biological feasibility justification should be better addressed. The primary outcome of their model is the broadband power suppression and the optimal effects of (sub)harmonic stimulation frequency, but it lacks immediate empirical support in the literature. To the best of my knowledge, many alpha frequency tACS studies reported to increase but not suppress the power of certain brain oscillations. A review by Wang et al., 2024 (Frontiers in System Neuroscience) suggested hybrid changes to different brain oscillations by magnetic stimulation. Developing a model to fully capture such changes might be out of the scope of the present study and challenging in the entire field, but it undermines the quality of the present work if not extensively discussed and justified. Clarity and reproducibility of the work can be improved. Although it is intriguing to see how the calcium-dependent BCM plasticity mediates such changes, the writing of the methods part is not hard to follow. It was also not clear why only two populations were considered in the thalamus, how the entire network was connected, or how the LTP/LTD threshold alters with calcium dynamics. The figures were unfortunately prepared in a nested manner. The crowded layout and the tiny font sizes reduce the clarity. The third point comes to contextualization and comparison to existing models. It will strengthen the work if the authors could have compared their work to other TMS modeling work with plasticity rules, e.g, Anil et al., 2024. Besides, magnetic stimulation is unique in being supra-threshold and having focality compared to other brain stimulation modalities, e.g., tDCS and tACS, but they may share certain basic neural mechanisms if accounting for certain parameters, e.g., frequency. A solid literature review and discussion on this part may help the field better perceive the value and potential limitations of this work.

Reviewer #3 (Public Review):

In this manuscript, Verma et al. set out to visualize cytoplasmic dynein in living cells and describe their behaviour. They first generated heterozygous CRISPR-Cas9 knock-ins of DHC1 and p50 subunit of dynactin and used spinning disk confocal microscopy and TIRF microscopy to visualize these EGFP-tagged molecules. They describe robust localization and movement of DHC and p50 at the plus tips of MTs, which was abrogated using SiR tubulin to visualize the pool of DHC and p50 on the MTs. These DHC and p50 punctae on the MTs showed similar, highly processive movement on MTs. Based on comparison to inducible EGFP-tagged kinesin-1 intensity in Drosophila S2 cells, the authors concluded that the DHC and p50 punctae visualized represented 1 DHC-EGFP dimer+1 untagged DHC dimer and 1 p50-EGFP+3 untagged p50 molecules.

Reviewer #3 (Public review):

Summary:

Deletion of the TMA-sensor TAAR5 results in circadian alterations in the gene expression, particularly in the olfactory bulb; plasma hormones; and neurobehaviors.

Strengths:

Genetic background was rigorously controlled.

Comprehensive characterization.

Impact:

These data add to the growing literature pointing to a role for the TMA/TMAO pathway in olfaction and neurobehavior.

Reviewer #3 (Public review):

In this paper, the authors investigate how the RNA-binding protein Ssd1 and calorie restriction (CR) influence yeast replicative lifespan, with a particular focus on age-dependent iron uptake and activation of the iron regulon. For this, they use microfluidics-based single-cell imaging to monitor replicative lifespan, protein localization, and intracellular iron levels across aging cells. They show that both Ssd1 overexpression and CR act through a shared pathway to prevent the nuclear translocation of the iron-regulon regulator Aft1 and the subsequent induction of high-affinity iron transporters. As a result, these interventions block the age-related accumulation of intracellular free iron, which otherwise shortens lifespan. Genetic and chemical epistasis experiments further demonstrate that suppression of iron regulon activation is the key mechanism by which Ssd1 and CR promote replicative longevity.

Overall, the paper is technically rigorous, and the main conclusions are supported by a substantial body of experimental data. The microfluidics-based assays in particular provide compelling single-cell evidence for the dynamics of Ssd1 condensates and iron homeostasis.

My main concern, however, is that the central reasoning of the paper-that Ssd1 overexpression and CR prevent the activation of the iron regulon-appears to be contradicted by previous findings, and the authors may actually be misrepresenting these studies, unless I am mistaken. In the manuscript, the authors state on two occasions:

"Intriguingly, transcripts that had altered abundance in CR vs control media and in SSD1 vs ssd1∆ yeast included the FIT1, FIT2, FIT3, and ARN1 genes of the iron regulon (8)"

"Ssd1 and CR both reduce the levels of mRNAs of genes within the iron regulon: FIT1, FIT2, FIT3 and ARN1 (8)"

However, reference (8) by Kaeberlein et al. actually says the opposite:

"Using RNA derived from three independent experiments, a total of 97 genes were observed to undergo a change in expression >1.5-fold in SSD1-V cells relative to ssd1-d cells (supplemental Table 1 at http://www.genetics.org/supplemental/). Of these 97 genes, only 6 underwent similar transcriptional changes in calorically restricted cells (Table 2). This is only slightly greater than the number of genes expected to overlap between the SSD1-V and CR datasets by chance and is in contrast to the highly significant overlap in transcriptional changes observed between CR and HAP4 overexpression (Lin et al. 2002) or between CR and high external osmolarity (Kaeberlein et al. 2002). Intriguingly, of the 6 genes that show similar transcriptional changes in calorically restricted cells and SSD1-V cells, 4 are involved in iron-siderochrome transport: FIT1, FIT2, FIT3, and ARN1 (supplemental Table 1 at http://www.genetics.org/supplemental/)."

Although the phrasing might be ambiguous at first reading, this interpretation is confirmed upon reviewing Matt Kaeberlein's PhD thesis: https://dspace.mit.edu/handle/1721.1/8318

(page 264 and so on)

Moreover, consistent with this, activation of the iron regulon during calorie restriction (or the diauxic shift) has also been observed in two other articles:

https://doi.org/10.1016/S1016-8478(23)13999-9

https://doi.org/10.1074/jbc.M307447200

Taken together, these contradictory data might blur the proposed model and make it unclear how to reconcile the results.

Comments on revisions:

The authors successfully addressed my requests and concerns

Reviewer #3 (Public review):

Summary:

This manuscript presents an ambitious integration of multiple artificial intelligence technologies to examine social learning in naturalistic mother-infant interactions. The authors aimed to quantify how information flows between mothers and infants across different communicative modalities and timescales, using speech analysis (Whisper), pose detection (MMPose), facial expression recognition, and semantic modeling (GPT-2) in a unified analytical framework. Their goal was to provide unprecedented quantitative precision in measuring behavioral coordination and information transfer patterns during social learning, moving beyond traditional observational coding approaches to examine cross-modal coordination patterns and semantic contingencies in real-time across multiple temporal scales.

Strengths:

The integration of multiple AI tools into a coherent analytical framework represents a genuine methodological breakthrough that advances our capabilities for studying complex social phenomena. The authors successfully analyzed naturalistic interactions at a scale and level of detail that was not previously possible, examining 33 5-month-old and 34 15-month-old dyads across multiple modalities simultaneously. This sophisticated analytical pipeline, combining speech analysis, semantic modeling, pose detection, and facial expression recognition, provides new capabilities for studying social interactions that extend far beyond what traditional observational coding could achieve.

The specific findings about hierarchical information flow patterns across different timescales are particularly valuable and would not have been possible without this sophisticated analytical approach. The discovery that mothers reduce low-level sensory input when infants focus on objects, while increases in object naming and information rate associate with sustained attention, provides new empirical insights into how social learning unfolds in naturalistic settings. The temporal dynamics analyses reveal interesting patterns of behavioral coordination that extend our understanding of how caregivers adaptively modify their responses to support infant attention across multiple communicative channels simultaneously.

The scale of data collection and the comprehensive multi-modal approach are impressive, opening up new possibilities for understanding social learning processes. The methodological innovations demonstrate how modern computational tools can be systematically integrated to reveal new quantitative aspects of well-established developmental phenomena. The computational features developed for this study represent innovative applications of information theory and computer vision to developmental research.

Weaknesses:

Several major limitations affect the reliability and interpretability of the findings. The sample sizes of 33-34 dyads per age group are relatively modest for the complexity of analyses performed, which include eight different features examined across various time lags with extensive statistical comparisons. The study lacks adequate power analysis to demonstrate whether these sample sizes are sufficient to detect meaningful effect sizes, which is particularly concerning given the multiple comparison burden inherent in this type of multi-modal, multi-timescale analysis.

The statistical framework presents several concerns that limit confidence in the findings. Inter-rater reliability for gaze coding shows substantial but not excellent agreement (κ = 0.628), with only 22% of the data undergoing double coding. Given that gaze coding forms the foundation for all subsequent analyses of joint attention and information flow, this reliability level may systematically influence findings. The multiple comparison correction strategies vary inconsistently across different analyses, with some using FDR correction and others treating lower-level and higher-level features separately. Additionally, object naming analyses employed one-sided tests (p<0.05) while others used two-sided tests (p<0.025) without clear theoretical or methodological justification for these differences.

The validation of AI tools in the specific context of mother-infant interactions is insufficient and represents a critical limitation. The performance characteristics of Whisper with infant-directed speech, the precision of MMPose for detecting facial landmarks in young children, and the accuracy of facial expression recognition tools in infant contexts are not adequately validated for this population. These sophisticated tools may not perform optimally in the specific context of mother-infant interactions, where speech patterns, facial expressions, and body movements may differ substantially from their training data.

The theoretical positioning requires substantial refinement to better acknowledge the extensive existing literature. The authors are working within a well-established theoretical framework that has long recognized social learning as an active, bidirectional process. The joint attention literature, beginning with foundational work by Bruner (1983) and continuing through contemporary theories of social cognition by researchers like Tomasello (1995), has emphasized the communicative and adaptive nature of attentional processes. The scaffolding literature, including seminal work by Wood, Bruner, and Ross (1976), has demonstrated how parents adjust their support based on children's developing competencies. Moreover, there is a substantial body of micro-analytic research that has employed sophisticated quantitative methods to study social interactions, including work by Stern (1985) on microsecond-level interactions and research using time-series methods to examine dyadic coordination patterns.

The cross-correlation analyses have inherent limitations for causal inference that are not adequately acknowledged. The interpretation of temporal correlation patterns in terms of directional influence requires more cautious consideration, as observational data have fundamental constraints for establishing causality. The ecological validity is also questionable due to the laboratory tabletop interaction paradigm and the sample's demographic homogeneity, consisting primarily of white, highly educated, high-income mothers.

Reviewer #3 (Public review):

The paper by Maggi et al. builds on earlier work by the team (Paatero et al., 2018) on oriented junction-based lamellipodia (JBL). They validate the role of JBLs in guiding endothelial cell rearrangements and utilise high-resolution time-lapse imaging of novel transgenic strains to visualise the formation of distal junctions and their subsequent fusion with proximal junctions. Through functional analyses of Arp2/3 and actomyosin contractility, the study identifies JBLs as localized mechanical hubs, where protrusive forces drive distal junction formation, and actomyosin contractility brings together the distal and proximal junctions. This forward movement provides a unique directionality which would contribute to proper lumen formation, EC orientation, and vessel stability during these early stages of vessel development.

Time-lapse live imaging of VEC, ZO-1, and actin reveals that VEC and ZO-1 are initially deposited at the distal junction, while actin primarily localizes to the region between the proximal and distal sites. Using a photoconvertible Cdh5-mClav2 transgenic line, the origin of the VEC aggregates was examined. This convincingly shows that VE-cadherin was derived from pools outside the proximal junctions. However, in addition to de novo VEC derived from within the photoconverted cell, could some VEC also be contributed by the neighbouring endothelial cell to which the JBL is connected?

As seen for JAILs in cultured ECs, the study reveals that Arp2/3 is enhanced when JBLs form by live imaging of Arpc1b-Venus in conjunction with ZO-1 and actin. Therefore Arp2/3 likely contributes to the initial formation of the distal junction in the lamellopodium.

Inhibiting Arp2/3 with CK666 prevents JBL formation, and filopodia form instead of lamellopodia. This loss of JBLs leads to impaired EC rearrangements.

Is the effect of CK666 treatment reversible? Since only a short (30 min) treatment is used, the overall effect on the embryo would be minimal, and thus washing out CK666 might lead to JBL formation and normalized rearrangements, which would further support the role of Arp2/3.

From the images in Figure 4d it appears that ZO-1 levels are increased in the ring after CK666 treatment. Has this been investigated, and could this overall stabilization of adhesion proteins further prevent elongation of the ring?

To explore how the distal and proximal junctions merge, imaging of spatiotemporal imaging of Myl9 and VEC is conducted. It indicates that Myl9 is localized at the interjunctional fusion site prior to fusion. This suggests pulling forces are at play to merge the junctions, and indeed Y 27632 treatment reduces or blocks the merging of these junctions.

For this experiment, a truncated version of VEC was use,d which lacks the cytoplasmic domain. Why have the authors chosen to image this line, since lacking the cytoplasmic domain could also impair the efficiency of tension on VEC at both junction sites? This is as described in the discussion (lines 328-332).

Since the time-lapse movies involve high-speed imaging of rather small structures, it is understandable that these are difficult to interpret. Adding labels to indicate certain structures or proteins at essential timepoints in the movies would help the readers understand these.

Reviewer #3 (Public review):

The article's main question is how humans handle spurious transitions between object features when learning a predictive model for decision-making. The authors conjecture that humans use semantic knowledge about plausible causal relations as an inductive bias to distinguish true from spurious links.

The authors simulate a successor feature (SF) model, demonstrating its susceptibility to suboptimal learning in the presence of spurious transitions caused by co-occurring but independent causal factors. This effect worsens with an increasing number of planning steps and higher co-occurrence rates. In a preregistered study (N=100), they show that humans are also affected by spurious transitions, but perform somewhat better when true transitions occur between features within the same semantic category. However, no evidence for the benefits of semantic congruency was found in test trials involving novel configurations, and attempts to model these biases within an SF framework remained inconclusive.

Strengths:

(1) The authors tackle an important question.

(2) Their simulations employ a simple yet powerful SF modeling framework, offering computational insights into the problem.

(3) The empirical study is preregistered, and the authors transparently report both positive and null findings.

(4) The behavioral benefit during learning in the congruent vs incongruent condition is interesting

Weaknesses:

(1) A major issue is that approximately one quarter of participants failed to learn, while another quarter appeared to use conjunctive or configural learning strategies. This raises questions about the appropriateness of the proposed feature-based learning framework for this task. Extensive prior research suggests that learning about multi-attribute objects is unlikely to involve independent feature learners (see, e.g., the classic discussion of configural vs. elemental learning in conditioning: Bush & Mosteller, 1951; Estes, 1950).

(2) A second concern is the lack of explicit acknowledgment and specification of the essential role of the co-occurrence of causal factors. With sufficient training, SF models can develop much stronger representations of reliable vs. spurious transitions, and simple mechanisms like forgetting or decay of weaker transitions would amplify this effect. This should be clarified from the outset, and the occurrence rates used in all tasks and simulations need to be clearly stated.

(3) Another problem is that the modeling approach did not adequately capture participant behavior. While the authors demonstrate that the b parameter influences model behavior in anticipated ways, it remains unclear how a model could account for the observed congruency advantage during learning but not at test.

(4) Finally, the conceptualization of semantic biases is somewhat unclear. As I understand it, participants could rely on knowledge such as "the shape of a building robot's head determines the kind of head it will build," while the type of robot arm would not affect the head shape. However, this assumption seems counterintuitive - isn't it plausible that a versatile arm is needed to build certain types of robot heads?

Reviewer #3 (Public review):

This is an intriguing paper that reports a potentially novel mechanism of reversible phosphorylation of AGC kinase activation segments by changes in sodium and potassium ion concentrations. The authors show for a variety of AGC kinases that incubating diverse eukaryotic cell types in 450 and 600 mM NaCl results in dephosphorylation of the activation segment. In contrast, phosphorylation of the activation segment for p38 kinases increases. No dephosphorylation of AGC kinases activation segment occurs with sorbitol, thus dephosphorylation is independent of osmotic pressure. This effect is rapidly reversed when cells are returned to normal media and the AGC kinase is re-phosphorylated. This phenomenon is also observed for eukaryotic cell-free extracts, and is induced by other alkali metal ions but not lithium. Importantly, no dephosphorylation is observed in the E. coli cell extract.

The authors also make the following observations:

(1) Dephosphorylation is dependent on PP2A.

(2) Re-phosphorylation is not dependent on PDK1, ATP, and Mg2+.

(3) The K/Na-dependent dephosphorylation/phosphorylation is observed even for relatively short protein segments that incorporate the activation segment.

(4) The phosphorylation observed occurs in cis, i.e., only the activation segment of the protein that is dephosphorylated becomes phosphorylated on reduced KCl. An activation segment from a different length protein is not phosphorylated.

(5) No evidence for auto(de)phosphorylation.

(6) The authors propose three models to explain the dephosphorylation/phosphorylation mechanism. Their experimental data suggest that an acceptor molecule is responsible for accepting the phosphate group and then transferring it back to the activation segment.

Comments on results and experiments:

(1) Are these results an artefact of their assay? The authors mainly use immunoblotting to assess the phosphorylation status of AGC kinase. However, an assay artefact would not show a difference between control and okadaic-acid-treated cells (Figure 3A). Moreover, the authors show dephosphorylation/phosphorylation using radiolabelling (Figure 6C).

(2) Preferably, the authors would have a control to test dephosphorylation/phosphorylation does not occur in the absence of cell extract. The E. coli extract shows that dephosphorylation/phosphorylation is specific to eukaryotic cell extracts.

(3) The authors should show that dephosphorylation/phosphorylation occurs on the same residue of the activation segment (by mass spec).

(4) Since phosphorylation levels are assessed using immunoblots, the levels of dephosphorylation/phosphorylation are not quantified. What proportion of AGC kinase is phosphorylated initially (before Na/K-induced dephosphorylation)?

(5) The experiment to test autophosphorylation (Figure 4, Figure supplement 1B) is not completely convincing because the authors use a cell line with a PKN1 mutant knock-in. Possibly PKN2 or another AGC kinase could phosphorylate the proteins expressed from the transfection vector - although the authors do test with AGC kinase inhibitors.

(6) What are the two bands in Figure 6C (lanes 'Con' and 'diluted)? Only one band disappears with KCl. There is one band in Figure 6 Supplement 2.

In summary, the results presented in this paper are highly unusual. Generally, the manuscript is well written and the figures are clear. The authors have performed numerous experiments to understand this process. These appear robust, and most of their data lend credence to their model in Figure 6Aiii. The idea that a phosphate group can be transferred by an enzyme onto/between molecule(s) is not unprecedented, i.e., phosphoglycerate mutase catalyses 3-phosphoglycerate isomerisation through a phosphorylenzyme intermediate. It will be important to identify this transfer enzyme. One observation that does not fit easily with their model is the role of PP2A. Since protein dephosphorylation by PP2A does not involve a phosphorylenzyme intermediate, if the initial dephosphorylation reaction is catalysed by PP2A, it is very difficult to envision how the free phosphate is then used to phosphorylate the activation segment.

Reviewer #3 (Public review):

Ji et al. report a novel and interesting light-induced transcriptional response pathway in the eyeless roundworm Caenorhabditis elegans that involves a cytochrome P450 family protein (CYP-14A5) and functions independently from previously established photosensory mechanisms. Although the exact mechanisms underlying photoactivation of this pathway remain unclear, light-dependent induction of CYP-14A5 requires bZIP transcription factors ZIP-2 and CEBP-2 that have been previously implicated in worm responses to pathogens. The authors then suggest that light-induced CYP-14A5 activity in the C. elegans hypoderm can unexpectedly and cell-non-autonomously contribute to retention of an olfactory memory. Finally, the authors demonstrate the potential for this pathway to enable robust light-induced control of gene expression and behavior, albeit with some restrictions. Overall, the evidence supporting the claims of the authors is convincing, and the authors' work suggests numerous interesting lines of future inquiry.

(1) The authors determine that light, but not several other stressors tested (temperature, hypoxia, and food deprivation), can induce transcription of cyp-15A5. The authors use these experiments to suggest the potential specificity of the induction of CYP-14A5 by light. Given the established relationship between light and oxidative stress and the authors' later identification of ZIP-2, testing the effect of an oxidative stressor or pathogen exposure on transcription of cyp-14A5 would further strengthen the validity of this statement and potentially shed some insight into the underlying mechanisms.

(2) The authors suggest that short-wavelength light more robustly increases transcription of cyp-14A5 compared to equally intense longer wavelengths (Figure 2F and 2G). Here, however, the authors report intensities in lux of wavelengths tested. Measurements of and reporting the specific spectra of the incident lights and their corresponding irradiances (ideally, in some form of mW/mm2 - see Ward et al., 2008, Edwards et al., 2008, Bhatla and Horvitz, 2015, De Magalhaes Filho et al., 2018, Ghosh et al., 2021, among others, for examples) is critical for appropriate comparisons across wavelengths and facilitates cross-checking with previous studies of C. elegans light responses. On a related and more minor note, the authors place an ultraviolet shield in front of a visible light LED to test potential effects of ultraviolet light on transcription of cyp-14A5. A measurement of the spectrum of the visible light LED would help confirm if such an experiment was required. Regardless, the principal conclusions the authors made from these experiments will likely remain unchanged.

(3) The authors report an interesting observation that animals exposed to ambient light (~600 lux) exhibit significantly increased memory retention compared to those maintained in darkness (Figure 4). Furthermore, light deprivation within the first 2-4 hours after learning appears to eliminate the effect of light on memory retention. These processes depend on CYP-14A5, loss of which can be rescued by re-expression of cyp-14A5 in mutant animals using a hypoderm-specific- and non-light-inducible- promoter. Taken together, the authors argue convincingly that hypodermal expression of cyp-14A5 can contribute to the retention of the olfactory memory. More broadly, these experiments suggest that cell-non-autonomous signaling can enhance retention of olfactory memory. How retention of the olfactory memory is enhanced by light generally remains unclear. In addition, the authors' experiments in Figure 1B demonstrate - at least by use of the transcriptional reporter - that light-dependent induction of cyp-14A5 transcription at 500 - 1000 lux is minimal and especially so at short duration exposures. Additional experiments, including verification of light-dependent changes in CYP-14A5 levels in the olfactory memory behavioral setup, would help further interpret these otherwise interesting results.

(4) The experiments in Figure 4 nicely validate the usage of the cyp-14A5 promoter as a potential tool for light-dependent induction of gene expression. Despite the limitations of this tool, including those presented by the authors, it could prove useful for the community.

Reviewer #3 (Public review):

Summary:

The article develops a CNN-based metastasis scoring system to distinguish cell subsets with high brain metastatic potential and validates its performance using patient platelet data. The robustness of this approach is further demonstrated across diverse single-cell and spatial datasets from multiple cancers, supported by transcription factor and gene set analyses, as well as novel drug identification pipelines. Together, these findings provide strong evidence that reinforces the central theme of the study.

Strengths:

Development of a CNN-based scoring system to reveal the potential of brain metastasis that is robust across multiple cancer cell types, validated by multiple datasets. Other approaches, including transcription factor analyses, cell-cell communication analysis, and spatial transcriptomic, etc., were included to strengthen the work.

Weaknesses:

The author could identify/validate more signaling pathways beyond the VEGF pathway since it's well known in metastasis.

Reviewer #3 (Public review):

Summary:

This manuscript examines how locus coeruleus (LC) activity relates to hippocampal ripple events across behavioral states in freely moving rats. Using multi-site electrophysiological recordings, the authors report that LC activity is suppressed prior to ripple events, with the magnitude of suppression depending on the ripple subtype. Suppression is stronger during wakefulness than during NREM sleep and is least pronounced for ripples coupled to spindles.

Strengths:

The study is technically competent and addresses an important question regarding how LC activity interacts with hippocampal and thalamocortical network events across vigilance states.

Weaknesses:

The results are interesting, but entirely observational. Also, the study in its current form would benefit from optimization of figure labeling and presentation, and more detailed result descriptions to make the findings fully interpretable. Also, it would be beneficial if the authors could formulate the narrative and central hypothesis more clearly to ease the line of reasoning across sections.

Comments:

(1) Stronger evidence that recorded units represent noradrenergic LC neurons would reinforce the conclusions. While direct validation may not be possible, showing absolute firing rates (Hz) across quiet wake, active wake, NREM, and REM, and comparing them to published LC values, would help.

(2) The analyses rely almost exclusively on z-scored LC firing and short baselines (~4-6 s), which limits biological interpretation. The authors should include absolute firing rates alongside normalized values for peri-ripple and peri-spindle analyses and extend pre-event windows to at least 20-30 s to assess tonic firing evolution. This would clarify whether differences across ripple subtypes arise from ceiling or floor effects in LC activity; if ripples require LC silence, the relative drop will appear larger during high-firing wake states. This limitation should be discussed and, if possible, results should be shown based on unnormalized firing rates.

(3) Because spindles often occur in clusters, the timing of ripple occurrence within these clusters could influence LC suppression. Indicate whether this structure was considered or discuss how it might affect interpretation (e.g., first vs. subsequent ripples within a spindle cluster).

(4) While the observational approach is appropriate here, causal tests (e.g., optogenetic or chemogenetic manipulation of LC around ripple events and in memory tasks) would considerably strengthen the mechanistic conclusions. At a minimum, a discussion of how such approaches could address current open questions would improve the manuscript.

(5) Please show how "Synchronization Index" (SI) differs quantitatively across behavioral states (wake, NREM, REM) and discuss whether it could serve as a state classifier. This would strengthen interpretations of the correlations between SI, ripple occurrence, and LC activity.

(6) The current use of SI to denote a delta/gamma power ratio is unconventional, as "SI" typically refers to phase-locking metrics. Consider adopting a more standard term, such as delta/gamma power ratio. Similarly, it would be easier to follow if you use common terminology (AUC) to describe the drop in LC-MUA rather than using "MI" and "sub-MI".

(7) The logic in Figure 3 is difficult to follow. The brain state (delta/gamma ratio) appears unchanged relative to surrogate events (3C), while LC activity that is supposedly negatively correlated to delta/gamma changes markedly (3D-E). Could this discrepancy reflect the low temporal resolution (4-s windows) used to calculate delta/gamma when the changes occur on a shorter time scale?

(8) There are apparent inconsistencies between Figures 4B and 4C-D. In B, it seems that the difference between the 10th and 90th percentile is mostly in higher frequencies, but in C and D, the only significant difference is in the delta band.

(9) Because standard sleep scoring is based on EEG and EMG signals, please include an example of sleep scoring alongside the data used for state classification. It would also be relevant to include the delta/gamma power ratio in such an example plot.

(10) Can variability in modulation index (subMI) across ripple subsets reflect differences in recording quality? Please report and compare mean LC firing rates across subsets to confirm this is not a confounding factor.

(11) Figure 6B: If the brown trace represents LC-MUA activity around random time points, why would there be a coinciding negative peak as relative to real sleep spindles? Or is it the subtracted trace?

(12) On page 8, lines 207-209, the authors write "Importantly, neither the LC-MUA rate nor SIs differed during a 2-sec time window preceding either group of spindles". It is unclear which data they refer to, but the statement seems to contradict Figure 6E as well as the following sentence: "Across sessions, MI values exceeded 95% CI in 17/20 datasets for isoSpindles and only 3/20 for ripSpindles". This should be clarified.

(13) The results in Figures 5C and 6F do not align. It seems surprising that ripple-coupled spindles show a considerably higher LC modulation than spindle-coupled ripples, as these events should overlap. Could the discrepancy be due to Z-score normalization as mentioned above? Please include a discussion of this to help the interpretation of the results.

(14) The text implies that 8 recordings came from one rat and two each from six others. This should be confirmed, and it should be explained how the recordings were balanced and analyzed across animals.

Reviewer #3 (Public review):

Summary:

The authors set out to determine how GABAergic inhibitory premotor circuits contribute to the rhythmic alternation of leg flexion and extension during Drosophila grooming. To do this, they first mapped the ~120 13A and 13B hemilineage inhibitory neurons in the prothoracic segment of the VNC and clustered them by morphology and synaptic partners. They then tested the contribution of these cells to flexion and extension using optogenetic activation and inhibition and kinematic analyses of limb joints. Finally, they produced a computational model representing an abstract version of the circuit to determine how the connectivity identified in EM might relate to functional output. The study makes important contributions to the literature.

The authors have identified an interesting question and use a strong set of complementary tools to address it:

They analysed serial‐section TEM data to obtain reconstructions of every 13A and 13B neuron in the prothoracic segment. They manually proofread over 60 13A neurons and 64 13B neurons, then used automated synapse detection to build detailed connectivity maps and cluster neurons into functional motifs.

They used optogenetic tools with a range of genetic driver lines in freely behaving flies to test the contribution of subsets of 13A and 13B neurons.

They used a connectome-constrained computational model to determine how the mapped connectivity relates to the rhythmic output of the behavior.

Reviewer #3 (Public review):

Summary:

In this manuscript, Lamothe et al. sought to identify the neural substrates of voice identity in the human brain by correlating fMRI recordings with the latent space of a variational autoencoder (VAE) trained on voice spectrograms. They used encoding and decoding models, and showed that the "voice" latent space (VLS) of the VAE performs, in general, (slightly) better than a linear autoencoder's latent space. Additionally, they showed dissociations in the encoding of voice identity across the temporal voice areas.

Strengths:

The geometry of the neural representations of voice identity has not been studied so far. Previous studies on the content of speech and faces in vision suggest that such geometry could exist. This study demonstrates this point systematically, leveraging a specifically trained variational autoencoder.

The size of the voice dataset and the length of the fMRI recordings ensure that the findings are robust.

Comments on revisions:

The authors addressed my previous recommendations.

Reviewer #5 (Public review):

Summary:

In the research article, "Functional genomics reveals strain-specific genetic requirements conferring hypoxic growth in Mycobacterium intracellulare" Tateshi et al focussed their research on pulmonary disease caused by Mycobacterium avium-intracellulare complex which has recently become a major health concern. The authors were interested in identifying the genetic requirements necessary for growth/survival within host and used hypoxia and biofilm conditions that partly replicate some of the stress conditions experienced by bacteria in vivo. An important finding of this analysis was the observation that genes involved in gluconeogenesis, type VII secretion system and cysteine desulphurase were crucial for the clinical isolates during standard culture while the same were necessary during hypoxia in the ATCC type strain.

Strength of the study:

Transposon mutagenesis has been a powerful genetic tool to identify essential genes/pathways necessary for bacteria under various in vitro stress conditions and for in vivo survival. The authors extended the TnSeq methodology not only to the ATCC strain but also to the recently clinical isolates to identify the differences between the two categories of bacterial strains. Using this approach they dissected the similarities and differences in the genetic requirement for bacterial survival between ATCC type strains and clinical isolates. They observed that the clinical strains performed much better in terms of growth during hypoxia than the type strain. These in vitro findings were further extended to mouse infection models and similar outcomes were observed in vivo further emphasising the relevance of hypoxic adaptation crucial for the clinical strains which could be explored as potential drug targets.

Weakness:

The authors have performed extensive TnSeq analysis but fail to present the data coherently. The data could have been well presented both in Figures and text. In my view this is one of the major weakness of the study.

Comments on revisions:

There is quite a lot of data and this could have been a really impactful study if the the authors had channelized the Tn mutagenesis by focussing on one pathway or network. It looks scattered. However, from the previous version, the authors have made significant improvements to the manuscript and have provided comments that fairly address my questions.

Reviewer #3 (Public review):

Summary:

In this manuscript, an inducible degron approach is taken to investigate the function of the CHD4 chromatin remodelling complex. The cell lines and approaches used are well thought out, and the data appear to be of high quality. They show that loss of CHD4 results in rapid changes to chromatin accessibility at thousands of sites. Of these locations at which chromatin accessibility is decreased are strongly bound by CHD4 prior to activation of the degron, and so likely represent primary sites of action. Somewhat surprisingly, while chromatin accessibility is reduced at these sites, transcription factor occupancy is little changed. Following CHD4 degradation, occupancy of the key pluripotency transcription factors NANOG and SOX2 increases at many locations genome-wide wide and at many of these sites, chromatin accessibility increases. These represent important new insights into the function of CHD4 complexes.

Strengths:

The experimental approach is well-suited to providing insight into a complex regulator such as CHD4. The data generated to characterise how cells respond to the loss of CHD4 is of high quality. The study reveals major changes in transcription factor occupancy following CHD4 depletion.

Weaknesses:

The main weakness can be summarised as relating to the fact that authors interpret all rapid changes following CHD4 degradation as being a direct effect of the loss of CHD4 activity. The possibility that rapid indirect effects arise does not appear to have been given sufficient consideration. This is especially pertinent where effects are reported at sites where CHD4 occupancy is initially low.

Reviewer #3 (Public review):

Summary:

The authors aimed at a comprehensive phenotypic characterization of the roles of all Rab proteins expressed in PN neurons in the developing Drosophila olfactory system. Important data are shown for a number of these Rabs with small/no phenotypes (in the Supplements) as well as the main endosomal Rabs, Rab5, 7, and 11 in the main figures.

Strengths:

The mosaic analysis is a great strength, allowing visualization of small clones or single neuron morphologies. This also allows some assessment of the cell autonomy of the observed phenotypes. The impact of the work lies in the comprehensiveness of the experiments. The rescue experiments are a strength.

Weaknesses:

The main weakness is that the experiments do not address the mechanisms that are affected by the loss of these Rab proteins, especially in terms of the most significant cargos. The insights thus do not extend far beyond what is already known from other work in many systems.

Reviewer #3 (Public review):

Summary:

The study investigates the control of the subspaces in which sequences propagate, through static external and dynamic self-generated inhibition. For this, it first uses a 1D ring model with an asymmetry in the weights to evoke a drift of its bump. This model is studied in detail, showing and explaining that the trajectories take place in different subspaces due to the inhibition of different sets of contributing neurons. Sequence propagation is preserved, even if large numbers of neurons are silenced. In this regime, trajectories are restricted to near-orthogonal subspaces of neuronal activity space. The last part of the results shows that similar phenomena can be observed in a 2D spiking neural network model.

Strengths:

The results are important and convincing, and the analyses give a good further insight into the phenomena. The interpretation of inhibited networks as near-circulant is very elucidating. The sparsening by dynamically maintained winner-takes-all inhibition and the transfer to a 2D spiking model are particularly nice results.

Weaknesses:

I see no major weaknesses, except that some crucial literature has not yet been mentioned and discussed. Further, Figure 2c might raise doubts whether the sequences are indeed reliable for the largest amount of sparsening inhibition considered, and it is not yet clear whether the dynamical regime of the 2D model is biologically plausible.

Reviewer #3 (Public review):

Strengths:

The core strength of this study lies in its innovative demonstration that an engineered sACE2-Fc fusion redirects virus-decoy complexes to Fc-mediated phagocytosis and lysosomal clearance in macrophages, revealing a distinct antiviral mechanism beyond traditional neutralization. Its complete prophylactic protection in animal models and precise targeting of airway phagocytes establish a novel therapeutic paradigm against SARS-CoV-2 variants and future respiratory viruses.

Weaknesses:

The study attributes the complete antiviral protection to Fc-mediated phagocytic clearance, a central claim that requires more rigorous experimental validation. The observation that abrogating Fc functions compromises protection could be confounded by potential alterations in the protein's stability, half-life, or overall structure. To firmly establish this mechanism, it is crucial to include a control molecule with a mutated Fc region that lacks FcγR binding while preserving the Fc structure itself. Without this critical control, the conclusion that phagocytic clearance is the primary mechanism remains inadequately supported. The strategy of deliberately targeting virus-decoy complexes to phagocytes via Fc receptors inherently raises the question of Antibody-Dependent Enhancement (ADE) of disease. While the authors demonstrate a lack of productive infection in macrophages, this only addresses one facet of ADE. The risk of Fc-mediated exacerbation of inflammation (ADE) remains a critical concern. The manuscript would be significantly strengthened by a direct discussion of this risk and by including data, such as cytokine profiling from treated macrophages, to more comprehensively address the safety profile of this approach. The exclusive use of the K18-hACE2 mouse model, which exhibits severe disease, limits the generalizability of the findings. The "complete protection" observed may not translate to models with more robust and naturalistic immune responses or to human physiology. Furthermore, the lack of data on circulating SARS-CoV-2 variants is a concern. The concept of sACE2-Fc fusion proteins as decoy receptors is not novel, and numerous similar constructs have been previously reported. The manuscript would benefit from a clearer demonstration of how the optimized B5-D3 mutant represents a significant advance over existing sACE2-Fc designs. A direct comparative analysis with previously published benchmarks, particularly in terms of neutralizing potency, Fc effector function strength, and in vivo efficacy, is necessary to establish the incremental value and novelty of this specific agent.

Reviewer #3 (Public review):

Summary:

Zhu et al. set out to elucidate how the moral emotions of guilt and shame emerge from specific cognitive antecedents - harm and responsibility - and how these emotions subsequently drive compensatory behavior. Consistent with their prediction derived from functionalist theories of emotion, their behavioral findings indicate that guilt is more influenced by harm, whereas shame is more influenced by responsibility. In line with previous research, their results also demonstrate that guilt has a stronger facilitating effect on compensatory behavior than shame. Furthermore, computational modeling and neuroimaging results suggest that individuals integrate harm and responsibility information into a composite representation of the individual's share of the harm caused. Brain areas such as the striatum, insula, temporoparietal junction, lateral prefrontal cortex, and cingulate cortex were implicated in distinct stages of the processing of guilt and/or shame. In general, this work makes an important contribution to the field of moral emotions. Its impact could be further enhanced by clarifying methodological details, offering a more nuanced interpretation of the findings, and discussing their potential practical implications in greater depth.

Strengths:

First, this work conceptualizes guilt and shame as processes unfolding across distinct stages (cognitive appraisal, emotional experience, and behavioral response) and investigates the psychological and neural characteristics associated with their transitions from one stage to the next.

Second, the well-designed experiment effectively manipulates harm and responsibility - two critical antecedents of guilt and shame.

Third, the findings deepen our understanding of the mechanisms underlying guilt and shame beyond what has been established in previous research.

Comments on revisions:

The authors have addressed the issues I raised in the previous review. I have no more comments on the manuscript.

Reviewer #3 (Public review):

Summary & Strengths:

This review by Yu-Tung Li sheds new light on the processes involved in leukocyte extravasation, with a focus on the inter between leukocytes and the extracellular matrix. In doing so, it presents a fresh perspective on the topic of leukocyte extravasation, which has been extensively covered in numerous excellent reviews. Notably, the role of the extracellular matrix in leukocyte extravasation has received relatively little attention until recently. This review synthesizes the substantial knowledge accumulated over the past two decades in a novel and compelling manner.

The author discusses the relevant barriers leukocytes face during extravasation, addresses interactions with and transmigrate through endothelial junctions, mechanisms supporting extravasation, and how minimal plasma leakage is achieved during this process. The question whether extravasation affects leukocyte differentiation and properties is original and thought-provoking and has received limited consideration thus far. The consequences leukocytes extracellular matrix interaction, non-linear responses to substrate stiffness and effects on macrophage polarization, efferocytosis and the outcome of inflammation are relevant topics raised. Finally, a unifying descriptive framework MIKA is introduced, which provides a tool for classifying macrophages based on their expression patterns and could inform the development of targeted therapies aimed at modulating macrophage identity and improving outcomes in inflammatory scenarios.

In summary, this review provides a stimulating perspective on leukocyte extravasation in the context of extracellular matrix biology.

Weaknesses:

One potential drawback of this review is that the attempt to integrate a vast amount of information has resulted in complex figures, which may lead to important details being overlooked by readers.

Reviewer #3 (Public review):

Summary:

In this article, Barnett examines a pressing question regarding citing behavior of authors during the peer review process. In particular, the author studies the interaction between reviewers and authors, focusing on the odds of acceptance, and how this may be affected by whether or not the authors cited the reviewers' prior work, whether the reviewer requested such citations be added, and whether the authors complied/how that affected the reviewer decision-making.

Strengths:

The author uses a clever analytical design, examining four journals that use the same open peer review system, in which the identities of the authors and reviewers are both available and linkable to structured data. Categorical information about the approval is also available as structured data. This design allows a large scale investigation of this question.

Weaknesses:

My original concerns have been largely addressed. Much more detail is provided about the number of documents under consideration for each analysis, which clarifies a great deal.

Much of the observed reviewer behavior disappears or has much lower effect sizes depending on whether "Accept with Reservations" is considered an Accept or a Reject. This is acknowledged in the results text. Language has been toned down in the revised version.

The conditional analysis on the 441 reviews (lines 224-228) does support the revised interpretation as presented.

No additional concerns are noted.

Reviewer #3 (Public review):

This manuscript addresses an important biological question regarding the mechanisms of muscle cell fusion during regeneration. The primary strength of this work lies in the clean and convincing experiments, with the major conclusions being well-supported by the data provided.

The authors have satisfactorily addressed my inquiries.

Reviewer #3 (Public review):

Summary:

In this manuscript by Qiao et al., the authors seek to uncover force and contractility dynamics that drive tissue morphogenesis, using the Ciona atrial siphon primordium as a model. Specifically, the authors perform a detailed examination of epithelial folding dynamics. Generally, the authors' claims were supported by their data, and the conceptual advances may have broader implications for other epithelial morphogenesis processes in other systems.

Strengths:

The strengths of this manuscript include the variety of experimental and theoretical methods, including generally rigorous imaging and quantitative analyses of actomyosin dynamics during this epithelial folding process, and the derivation of a mathematical model based on their empirical data, which they perturb in order to gain novel insights into the process of epithelial morphogenesis.

Weaknesses:

There are concerns related to wording and interpretations of results, as well as some missing descriptions and details regarding experimental methods.

Reviewer #3 (Public review):

Summary:

Overall, the work is fine; however, I find it very preliminary. To the best of my understanding, to make any claims for altered Notch signaling from this study that is physiologically relevant remains to be discerned.

Strengths:

This manuscript systematically analyzes cancer-associated mutations in the Negative Regulatory Region (NRR) of Drosophila Notch to reveal diverse regulatory mechanisms with implications for cancer modelling and therapy development. The study introduces cancer-associated mutations equivalent to human NOTCH1 mutations, covering a broad spectrum across the LNR and HD domains. The authors use rigorous phenotypic assays to classify their functional outcomes. By leveraging the S2 cell-based assay platform, the work identifies mechanistic differences between mutations that disrupt the LNR-HD interface, core HD, and LNR surface domains, enhancing understanding of Notch regulation. The discovery that certain HD and LNR-HD interface mutations (e.g., R1626Q and E1705P) in Drosophila mirror the constitutive activation and synergy with PEST deletion seen in mammalian T-ALL is nice and provides a platform for future cancer modelling. Surface-exposed LNR-C mutations were shown to increase Notch protein stability and decrease turnover, suggesting a previously unappreciated regulatory layer distinct from canonical cleavage-exposure mechanisms. By linking mutant-specific mechanistic diversity to differential signaling properties, the work directly informs targeted approaches for modulating Notch activity in cancer cells.

Weaknesses:

While this is indeed an exciting set of observations, the work is entirely cell-line-based, and is the primary reason why this approach dampens the enthusiasm for the study. The analysis is confined to Drosophila S2 cells, which may not fully recapitulate tissue or organism-level regulatory complexity observed in vivo. Some Drosophila HD domain mutants accumulate in the secretory pathway and do not phenocopy human T-ALL mutations. Possibly due to limitations on physiological inputs that S2 cells cannot account for, or species-specific differences such as the absence of S1 cleavage.

Thus, the findings may not translate directly to understanding Notch 1 function in mammalian cancer models. While the manuscript highlights mechanistic variety, the functional significance of these mutations for hematopoietic malignancies or developmental contexts in live animals remains untested. Overall, the work does not yet provide evidence for altered Notch signaling that is physiologically relevant.

Reviewer #3 (Public review):

Summary:

The authors present a variant of a previously described fluorescence lifetime sensor for calcium. Much of the manuscript describes the process of developing appropriate assays for screening sensor variants, and thorough characterization of those variants (inherent fluorescence characteristics, response to calcium and pH, comparisons to other calcium sensors). The final two figures show how the sensor performs in cultured cells and in vivo drosophila brains.

Strengths:

The work is presented clearly and the conclusion (this is a new calcium sensor that could be useful in some circumstances) is supported by the data.

Weaknesses:

There are probably few circumstances where this sensor would facilitate experiments (calcium measurements) that other sensors would prove insufficient.

Comment on revised version:

I think the manuscript has been significantly improved and I concur with the eLife Assessment statement.

[Editors' note: There are no further requests by the reviewers. All of them expressed their approval of the new version of the manuscript.]

Reviewer #3 (Public review):

Summary:

Here, Bykov et al move the bi-genomic split-GFP system they previously established to the genome-wide level in order to obtain a more comprehensive list of mitochondrial matrix and inner membrane proteins. In this very elegant split-GFP system, the longer GFP fragment, GFP1-10, is encoded in the mitochondrial genome and the shorter one, GFP11, is C-terminally attached to every protein encoded in the genome of yeast Saccharomyces cerevisiae. GFP fluorescence can therefore only be reconstituted if the C-terminus of the protein is present in the mitochondrial matrix, either as part of a soluble protein, a peripheral membrane protein or an integral inner membrane protein. The system, combined with high-throughput fluorescence microscopy of yeast cells grown under six different conditions, enabled the authors to visualize ca. 400 mitochondrial proteins, 50 of which were not visualised before and 8 of which were not shown to be mitochondrial before. The system appears to be particularly well suited for analysis of dually localized proteins and could potentially be used to study sorting pathways of mitochondrial inner membrane proteins.

Strengths:

Many fluorescence-based genome-wide screen were previously performed in yeast and were central to revealing the subcellular location of a large fraction of yeast proteome. Nonetheless, these screens also showed that tagging with full-length fluorescent proteins (FP) can affect both the function and targeting of proteins. The strength of the system used in the current manuscript is that the shorter tag is beneficial for detection of a number of proteins whose targeting and/or function is affected by tagging with full length FPs.

Furthermore, the system used here can nicely detect mitochondrial pools of dually localized proteins. It is especially useful when these pools are minor and their signals are therefore easily masked by the strong signals coming from the major, nonmitochondrial pools of the proteins.

Weaknesses:

My only concern is that the biological significance of the screen performed appears limited. The dataset obtained is largely in agreement with several previous proteomic screens but it is, unfortunately, not more comprehensive than them, rather the opposite. For proteins that were identified inside mitochondria for the first time here or were identified in an unexpected location within the organelle, it remains unclear whether these localizations represent some minor, missorted pools of proteins or are indeed functionally important fractions and/or productive translocation intermediates. The authors also allude to several potential applications of the system but do little to explore any of these directions.

Comments on revisions:

The revised version of the manuscript submitted by Bykov et al addresses the comments and concerns raised by the Reviewers. It is a pity that the verification of the newly obtained data and its further biological exploration is apparently more challenging than perhaps anticipated.

Reviewer #3 (Public review):

In this manuscript, Wang et al employ a chemical biology approach to investigate the differences between the enzymatic and scaffolding roles of tankyrase during Wnt β-catenin signalling. It was previously established that, in addition to its enzymatic activity, tankyrase 1/2 also plays a scaffolding function within the destruction complex, a property conferred by SAM-domain-dependent polymerization (PMID: 27494558). It is also known that TNKS1/2 is an autoregulated protein and that its enzymatic inhibition leads to accumulation of total TNKS proteins and stabilization of Axin punctae (through the scaffolding function of TNKS1/2), leading to rigidification of the DC and decreased β-catenin turnover. The authors surmised that this could, in part, explain the limited efficacy of TNKS1/2 catalytic inhibition for the treatment of colorectal cancers. To test this hypothesis, they evaluated a series of PROTAC molecules promoting the degradation of TNKS1/2 to block both the catalytic and scaffolding activities. They show that IWR1-POMA (their most active molecule) promotes more efficient suppression of beta-catenin-mediated transcription and is more active in inhibiting colorectal cancer cell and CRC patient-derived organoids growth. Mechanistically, the authors used FRAP to demonstrate that catalytic inhibitors of TNKS led to a reduced dynamic assembly of the DC (rigidification), whereas IWR1-POMA did not affect the dynamics.

Overall, this is an interesting study describing the design and development of a PROTAC for TNKS1/2 that could have increased efficacy where catalytic inhibitors have displayed limited activity. Knowing the importance of the scaffolding role of TNKS1/2 within the destruction complex, targeting both the catalytic and scaffolding roles certainly makes sense. The manuscript contains convincing evidence of the different mechanisms of the PROTAC vs catalytic inhibitors. Some additional efforts to quantify several of the experiments and to indicate the reproducibility and statistical analysis would strengthen the manuscript. Ultimately, it would have been great to evaluate the in vivo efficacy of IWR1-POMA in an in vivo CRC assay (APCmin mice or using PDX models); however, I realize that this is likely beyond the scope of this manuscript.

I have some recommendations listed below for consideration by the authors to strengthen their study:

(1) The title is slightly misleading, as it is already known that the scaffolding function of TNKS is important within the DC. The authors should consider incorporating the PROTAC targeting aspect in the title (e.g., PROTAC-mediated targeting of tankyrase leads to increased inhibition of betacat signaling and CRC growth inhibition).

(2) The authors should comment in the manuscript on the bell-shaped curve obtained with treatment of cells with the PROTACs (Figure S2C). This likely indicates tittering of the targets within a bifunctional molecule with increasing concentration (and likely reveals the auto-inhibition conferred by the catalytic inhibition alone).

(3) The authors comment that using G007-LK as warehead was unsuccessful, but they do not show data. Do the authors know why this was the case?

(4) Throughout the manuscript, the authors need to do a better job at quantifying their results (i.e., the western blots and the IF). For example, the degradation of TNKS1/2 in Figure 1D is not overly convincing. Similarly, the IF data in Figure 3 needs to be quantified in some ways. Along the same lines, the effect of IWR1-POMA treatments on the proliferation of cells and organoids should be quantified using viability assays... There is also no indication of how many times these experiments were performed and whether the blots shown are representative experiments. The quantification should include all experiments.

Reviewer #3 (Public review):

Summary:

The manuscript by Shukla and colleagues presents a comprehensive study that addresses a central question in kinesin-1 regulation - how cargo binding to the kinesin light chain (KLC) tetratricopeptide repeat (TPR) domains triggers activation of full-length kinesin-1 (KHC). The authors combine AlphaFold3 modeling, biophysical analysis (fluorescence polarization, hydrogen-deuterium exchange), and electron microscopy to derive a mechanistic model in which the KLC-TPR domains dock onto coiled-coil 1 (CC1) of the KHC to form the "TPR shoulder," stabilizing the autoinhibited (λ-particle) conformation. Binding of a W/Y-acidic cargo motif (KinTag) or deletion of the CC1 docking site (TDS) dislocates this shoulder, liberating the motor domains and enhancing accessibility to cofactors such as MAP7. The results link cargo recognition to allosteric structural transitions and present a unified model of kinesin-1 activation.

Strengths:

(1) The study addresses a fundamental and long-standing question in kinesin-1 regulation using a multidisciplinary approach that combines structural modeling, quantitative biophysics, and electron microscopy.

(2) The mechanistic model linking cargo-induced dislocation of the TPR shoulder to activation of the motor complex is well supported by both structural and biochemical evidence.

(3) The authors employ elegant protein-engineering strategies (e.g., ElbowLock and ΔTDS constructs) that enable direct testing of model predictions, providing clear mechanistic insight rather than purely correlative data.

(4) The data are internally consistent and align well with previous studies on kinesin-1 regulation and MAP7-mediated activation, strengthening the overall conclusion.

Weaknesses:

(1) While the EM and HDX-MS analyses are informative, the conformational heterogeneity of the complex limits structural resolution, making some aspects of the model (e.g., stoichiometry or symmetry of TPR docking) indirect rather than directly visualized.

(2) The dynamics of KLC-TPR docking and undocking remain incompletely defined; it is unclear whether both TPR domains engage CC1 simultaneously or in an alternating fashion.

(3) The interplay between cargo adaptors and MAP7 is discussed but not experimentally explored, leaving open questions about the sequence and exclusivity of their interactions with CC1.

Reviewer #3 (Public review):

Summary:

The study is grounded in the observations that mitochondrial DNA (mtDNA) exhibits a degree of resistance to mutagenesis under genotoxic stress. The manuscript focuses on the effects of UVC-induced DNA damage on TFAM-DNA binding in vitro and in cells. The authors demonstrate increased TFAM-DNA compaction following UVC irradiation in vitro based on high-throughput protein-DNA binding and atomic force microscopy (AFM) experiments. They did not observe a similar trend in fluorescence polarization assays. In cells, the authors found that UVC exposure upregulated TFAM, POLG, and POLRMT mRNA levels without affecting the mitochondrial membrane potential. Overexpressing TFAM in cells or varying TFAM concentration in reconstituted nucleoids did not alter the accumulation or disappearance of mtDNA damage. Based on their data, the authors proposed a plausible model that, following UVC-induced DNA damage, TFAM facilitates nucleoid compaction, which may serve to signal damage in the mitochondrial genome.

Strengths:

The presented data are solid, technically rigorous, and consistent with established literature findings. The experiments are well-executed, providing reliable evidence on the change of TFAM-DNA interactions following UVC irradiation. The proposed model may inspire future follow-up studies to further study the role of TFAM in sensing UVC-induced damage.

Weaknesses:

The manuscript could be further improved by refining specific interpretations and ensuring terminology aligns precisely with the data presented.

(1) In line 322, the claim of increased "nucleoid compaction" in cells should be removed, as there is a lack of direct cellular evidence. Given that non-DNA-bound TFAM is subject to protease digestion, it is uncertain to what extent the overexpressed TFAM actually integrates into and compacts mitochondrial nucleoids in the absence of supporting immunofluorescence data.

(2) In lines 405 and 406, the authors should avoid equating TFAM overexpression with compaction in the cellular context unless the compaction is directly visualized or measured.

(3) In lines 304 and 305 (and several other places throughout the manuscript), the authors use the term "removal rates". A "removal rate" requires a direct comparison of accumulated lesion levels over a time course under different conditions. Given the complexity of UV-induced DNA damage-which involves both damage formation and potential removal via multiple pathways-a more accurate term that reflects the net result of these opposing processes is "accumulated DNA damage levels." This terminology better reflects the final state measured and avoids implying a single, active 'removal' pathway without sufficient kinetic data.

(4) In line 357, the authors refer to the decrease in the total DNA damage level as "The removal of damaged mtDNA". The decrease may be simply due to the turnover and resynthesis of non-damaged mtDNA molecules. The term "removal" may mislead the casual reader into interpreting the effect as an active repair/removal process.

Reviewer #3 (Public review):

Summary:

Overall, this is a well-done study, and the conclusions are largely supported by the data, which will be of interest to the field.

Strengths:

Strengths of this study include experiments with solution NMR that can resolve high-resolution interactions of the highly flexible C-terminal tail of arr2 with clathrin and AP2. Although mainly confirmatory in defining the arr2 CBL 376LIELD380 as the clathrin binding site, the use of the NMR is of high interest (Fig. 1). The 15N-labeled CLTC-NTD experiment with arr2 titrations reveals a span from 39-108 that mediates an arr2 interaction, which corroborates previous crystal data, but does not reveal a second area in CLTC-NTD that in previous crystal structures was observed to interact with arr2.

SEC and NMR data suggest that full-length arr2 (1-418) binding with 2-adaptin subunit of AP2 is enhanced in the presence of CCR5 phospho-peptides (Fig. 3). The pp6 peptide shows the highest degree of arr2 activation, and 2-adaptin binding, compared to less phosphorylated peptide or not phosphorylated at all. It is interesting that the arr2 interaction with CLTC NTD and pp6 cannot be detected using the SEC approach, further suggesting that clathrin binding is not dependent on arrestin activation. Overall, the data suggest that receptor activation promotes arrestin binding to AP2, not clathrin, suggesting the AP2 interaction is necessary for CCR5 endocytosis.

To validate the solid biophysical data, the authors pursue validation experiments in a HeLa cell model by confocal microscopy. This requires transient transfection of tagged receptor (CCR5-Flag) and arr2 (arr2-YFP). CCR5 displays a "class B"-like behavior in that arr2 is rapidly recruited to the receptor at the plasma membrane upon agonist activation, which forms a stable complex that internalizes onto endosomes (Fig. 4). The data suggest that complex internalization is dependent on AP2 binding not clathrin (Fig. 5).

The addition of the antagonist experiment/data adds rigor to the study.

Overall, this is a solid study that will be of interest to the field.

Reviewer #3 (Public review):

Shimogawa et al. describe the generation of acetylated aSyn variants by genetic code expansion to elucidate effects on vesicle binding, aggregation, and seeding effects. The authors compared a semi-synthetic approach to obtain acetylated aSyn variants with genetic code expansion and concluded that the latter was more efficient in generating all 12 variants studied here, despite the low yields for some of them. Selected acetylated variants were used in advanced NMR, FCS, and cryo-EM experiments to elucidate structural and functional changes caused by acetylation of aSyn. Finally, site-specific differences in deacetylation by HDAC 8 were identified.

The study is of high scientific quality, andthe results are convincingly supported by the experimental data provided. The challenges the authors report regarding semi-synthetic access to aSyn are somewhat surprising, as this protein has been made by a variety of different semi-synthesis strategies in satisfactory yields and without similar problems being reported.

The role of PTMs such as acetylation in neurodegenerative diseases is of high relevance for the field, and a particular strength of this study is the use of authentic acetylated aSyn instead of acetylation-mimicking mutations. The finding that certain lysine acetylations can slow down aggregation even when present only at 10-25% of total aSyn is exciting and bears some potential for diagnostics and therapeutic intervention.

Reviewer #3 (Public review):

In this manuscript, Davis and colleagues aimed to identify the molecular sensors and signaling cascade that enable collecting lymphatic vessels to increase their spontaneous contraction frequency in response to intraluminal pressure (pressure-induced chronotropy). They tested whether the process is similar to blood vessel myogenic constriction by relying on cation channels (TRPC6, TRPM4, PKD2, PIEZO1, etc.) or instead require the activation of G-protein-coupled receptors (presumably mechanosensitive GNAQ/GNA11-coupled receptors), using ex vivo pressure myography of mouse popliteal lymphatics, smooth muscle-specific conditional knockouts, quantitative PCR validation, and single-cell RNA sequencing for target prioritization. The authors convincingly demonstrate that pressure-induced chronotropy does not require the cation channels implicated in arterial myogenic tone but is blunted by deletion of GNAQ/GNA11 or IP3 receptor 1, supporting a model of GPCR > IP3 > Ca2+ release > Cl⁻ channel activation > depolarization. The core conclusion is robust. The work redefines lymphatic pacemaking as G-protein-coupled receptor-dependent mechanotransduction, distinct from arterial mechanisms, and provides a genetically validated toolkit that is useful for studying lymphatic function and dysfunction.

Strengths:

(1) The data are of high quality and highly sensitive functional readouts

(2) The systematic genetic targeting is a major strength that overcomes pharmacological artifacts

(3) Careful quantitative analyses of frequency-pressure slopes

Weaknesses:

(1) The use of inguinal-axillary vessels for single-cell RNA sequencing rather than the popliteal segment studied functionally.

(2) No direct testing of the specific G-protein-coupled receptor involved.

Reviewer #3 (Public review):

Summary

Following recent findings that exposure to natural sounds and anthropogenic noise before hatching affects development and fitness in an altricial songbird, this study attempts to estimate the hearing capacities of zebra finch nestlings and the perception of high frequencies in that species. It also tries to estimate whether airborne sound can make zebra finch eggs vibrate, although this is not relevant to the question.

Strength

That prenatal sounds can affect the development of altricial birds clearly challenges the long-held assumption that altricial avian embryos cannot hear. However, there is currently no data to support that expectation. Investigating the development of hearing in songbirds is therefore important, even though technically challenging. More broadly, there is accumulating evidence that some bird species use sounds beyond their known hearing range (especially towards high frequencies), which also calls for a reassessment of avian auditory perception.

Weaknesses

Rather than following validated protocols, the study presents many experimental flaws and two major methodological mistakes (see below), which invalidate all results on responses to frequency-specific tones in nestlings and those on vibration transmission to eggs, as well as largely underestimating hearing sensitivity. Accordingly, the study fails to detect a response in the majority of individuals tested with tones, including adults, and the results are overall inconsistent with previous studies in songbirds. The text throughout the preprint is also highly inaccurate, often presenting only part of the evidence or misrepresenting previous findings (both qualitatively and quantitatively; some examples are given below), which alters the conclusions.

Conclusion and impact

The conclusion from this study is not supported by the evidence. Even if the experiment had been performed correctly, there are well-recognised limitations and challenges of the method that likely explain the lack of response. The preprint fails to acknowledge that the method is well-known for largely underestimating hearing threshold (by 20-40dB in animals) and that it may not be suitable for a 1-gram hatchling. Unlike what is claimed throughout, including in the title, the failure to detect hearing sensitivity in this study does not invalidate all previous findings documenting the impacts of prenatal sound and noise on songbird development. The limitations of the approach and of this study are a much more parsimonious explanation. The incorrect results and interpretations, and the flawed representation of current knowledge, mean that this preprint regrettably creates more confusion than it advances the field.

Detailed assessment

For brevity, only some references are included below as examples, using, when possible, those cited in the preprint (DOI is provided otherwise). A full review of all the studies supporting the points below is beyond the scope of this assessment.

(A) Hearing experiment

The study uses the Auditory Brainstem Response (ABR), which measures minute electrical signals transmitted to the surface of the skull from the auditory nerve and nuclei in the brainstem. ABR is widely used, especially in humans, because it is non-invasive. However, ABR is also a lot less sensitive than other methods, and requires very specific experimental precautions to reliably detect a response, especially in extremely small animals and with high-frequency sounds, as here.

(1) Results on nestling frequency sensitivity are invalid, for failing to follow correct protocols:

The results on frequency testing in nestlings are invalid, since what might serve as a positive control did not work: in adults, no response was detected in a majority of individuals, at the core of their hearing range, with loud 95dB sounds (Figure S1), when testing frequency sensitivity with "tone burst".

This is mostly because the study used a stimulation duration 5 times larger than the norm. It used 25ms tone bursts, when all published avian studies (in altricial or precocial birds) used stimulation of 5ms or less (when using subdermal electrodes as here; e.g., cited: Brittan-Powell et al 2004; not cited: Brittan-Powell et al 2002 (doi: 10.1121/1.1494807), Henry & Lucas 2008 (doi: 10.1016/j.anbehav.2008.08.003)). Long stimulations do not make sense and are indeed known to interfere with the detection of an ABR response, especially at high frequencies, as, for example, explicitly tested and stated in Lauridsen et al 2021 (cited).

Adult response was then re-tested with a correct 5ms tone duration ("tone-pip"), which showed that, for the few individuals that responded to 25ms tones, thresholds were abnormally high (c.a. by 30dB; Figure 2C).<br /> Yet, no nestlings were retested with a correct protocol. There is therefore no valid data to support any conclusion on nestling frequency hearing. Under these circumstances, the fact that some nestlings showed a response to 25ms tones from day 8 would argue against them having very low sensitivity to sound.

(2) Responses to clicks underestimate hearing onset by several days:

Without any valid nestling responses to tones (see # 1), establishing the onset of hearing is not possible based on responses to clicks only, since responses to clicks occur at least 4 days after responses to tones during development (Saunders et al, 1973). Here, 60% of 4-day-old individuals responding to clicks means most would have responded to tones at and before 2 days post-hatch, had the experiment been done correctly.<br /> Responses to tones are indeed observed in other songbirds at 1day post-hatch (see #6).

In budgerigars, hearing onset occurs before 5 days post hatch, since responses to both clicks and tones were detectable at the first age tested at 5dph (Brittan-Powell et al, 2004).

(3) Experimental parameters chosen lower ABR detectability, specifically in younger birds:

Very fast stimulus repetition rate inhibits the ABR response, especially in young:

(a) The stimulus presentation rate (25 stim/ sec) is 6 times faster than zebra finch heat-calls, and 5 to 25 times faster than most previous studies in young birds (e.g., cited: Saunders et al 1973, 1974: 1 stim/sec or less; Katayama 1985: 3.3 clicks/sec; Brittan-Powell et al 2004: 4 stim/sec). Faster rates saturate the neurons and accordingly are known to decrease ABR amplitude and increase ABR latency, especially in younger animals with an immature nervous system. In birds, this occurs especially in the range from 5 to 30 stim/sec (e.g., cited: Saunder et al 1973, Brittan-Powell et al 2004). Values here with 25 rather than 1-4 stim/min are therefore underestimating true sensitivity.

(b) Averaging over only 400 measures is insufficient to reliably detect weak ABR signals:

The study uses 2 to 3 times fewer measures per stimulation type than the recommended value of 1,000 (e.g., Brittan-Powell et al 2002, 2024; Henry & Lucas 2008). This specifically affects the detection of weak signals, as in small hatchlings with tiny brains (adult zebra finches are 12-14g).

(c) Body temperature is not specified and strongly affects the ABR:

Controlling the body temperature of hatchlings of 1-4 grams (with a temperature probe under a 5mm-wide wing) would be very challenging. Low body temperature entirely eliminates the ABR, and even slight deviance from optimal temperature strongly increases wave latency and decreases wave amplitude (e.g., cited: Katayama 1985).

(d) Other essential information is missing on parameters known to affect the ABR:

This includes i) the weight of the animals, ii) whether and how the response signal was amplified and filtered, iii) how the automatised S/N>2 criteria compared to visual assessment for wave detection, and iv) what measures were taken to allow the correct placement of electrodes on hatchlings less than 5 grams.

(4) Results in adults largely underestimate sensitivity at high frequencies, and are not the correct reference point:

(a) Thresholds measured here at high frequencies for adults (using the correct stimulus duration, only done on adults) are 10-30dB higher than in all 3 other published ABR studies in adult zebra finches (cited: Zevin et al 2004; Amin et al 2007; not cited: Noirot et al 2011 (10.1121/1.3578452)), for both 4 and 6 kHz tone pips.

(b) The underlying assumption used throughout the preprint that hearing must be adult-like to be functional in nestlings does not make sense. Slower and smaller neural responses are characteristic of immature systems, but it does not mean signals are not being perceived.

(5) Failure to account for ABR underestimation leads to false conclusions:

(a) Whether the ABR method is suitable to assess hearing in very small hatchlings is unknown. No previous avian study has used ABR before 5 days post-hatch, and all have used larger bird species than the zebra finch.

(b) Even when performed correctly on large enough animals, the ABR systematically underestimates actual auditory sensitivity by 20-40 dB, especially at high frequencies, compared to behavioural responses (e.g., none cited: Brittan-Powell et al 2002, Henry & Lucas 2008, Noirot et al 2011). Against common practice, the preprint fails to account for this, leading to wrong interpretations. For example, in Figure 1G (comparing to heat call levels), actual hearing thresholds would be 30-40dB below those displayed. In addition, the "heat whistle" level displayed here (from the same authors) is 15dB lower than their second measure that they do not mention, and than measures obtained by others (unpublished data). When these two corrections are made - or even just the first one - the conclusion that heat-call sound levels are below the zebra finch hearing threshold does not hold.

(c) Rather than making appropriate corrections, the preprint uses a reference in humans (L180), where ABR is measured using a much more powerful method (multi-array EEG) than in animals, and from a larger brain. The shift of "10-20dB" obtained in humans is not applicable to animals.

(6) Results are inconsistent with previous findings in developing songbirds:

As expected from all of the above, results and conclusions in the preprint are inconsistent with findings in other songbirds, which, using other methods, show for example, auditory sensitivity in:

(a) zebra finch embryos, in response to song vs silence (not cited: Rivera et al 2018, doi: 10.1097/WNR.0000000000001187)

(b) flycatcher hatchlings at 2-3d post hatch (first age tested), across a wide range of frequencies (0.3 to 5kHz), at low to moderate sound levels (45-65dB) (cited: Aleksandrov and Dmitrieva 1992, not cited: Korneeva et al 2006 (10.1134/S0022093006060056)).

(c) songbird nestlings at 2-6d post hatch, which discriminate and behaviourally respond to relevant parental calls or even complex songs. This level of discrimination requires good hearing across frequencies (e.g., not cited: Korneeva et al 2006; Schroeder & Podos 2023 (doi: 10.1016/j.anbehav.2023.06.015)).

(d) zebra finch nestlings at 13d post-hatch, which show adult-like processing of songs in the auditory cortex (CNM) (Schroeder & Remage‐Healey 2021, doi: 10.1002/dneu.22802).

(e) zebra finch juveniles, which are able to perceive and learn song syllables at 5-7kHz (fundamental frequency) with very similar acoustic properties to heat calls, and also produced during inspiration (Goller & Daley 2001, doi: 10.1098/rspb.2001.1805).

NONE of these results - which contradict results and claims in the preprint - are mentioned. Instead, the preprint focuses on very slow-developing species (parrots and owls), which take 2-4 times longer than songbirds to fledge (cited: Brittan-Powell et al 2004; Köppl & Nickel 2007; Kraemer et al 2017).

(7) Results in figures are misreported in the text, and conclusions in the abstract and headers are not supported by the data:

For example:

(a) The data on Figure 1E shows that at 4 days old, 8 out of 13 nestlings (60%) responded to clicks, but the text says only 5/13 responded (L89). When 60% (4dph) and 90% (6dph) of individuals responded, the correct term would be that "most animals", rather than "some animals" responded (L89). Saying that ABR to loud sound appeared "in the majority only after one week" (L93) is also incorrect, given the data. It follows that the title of the paragraph is also erroneous.

(b) The hearing threshold is underestimated by 40dB at 6 and 8Kz on Fig 2C, not by "10-20dB" as reported in the text (L178).

(B) Egg vibration experiment

(8) Using airborne sound to vibrate eggs is biologically irrelevant:

The measurement of airborne sound levels to vibrate eggs misunderstands bone conduction hearing and is not biologically meaningful: zebra finch parents are in direct contact with the eggs when producing heat calls during incubation, not hovering in front of the nest. This misunderstanding affects all extrapolations from this study to findings in studies on prenatal communication.

(C) Misrepresentation of current knowledge

(9) Values from published papers are misreported, which reverses the conclusions:

Most critical examples:

(a) Preprint: "Zebra finch most sensitive hearing range of 1-to-4 kHz (Amin et al., 2007; Okanoya and Dooling, 1987; Yeh et al., 2023)" (L173).<br /> Actual values in the studies cited are:

1-to-7kHz, in Amin et al 2007 (threshold [=50dB with ABR] is the same at 7kHz and 1KHz).

1-to-6 kHz, in Okanoya and Dooling (the threshold [=30dB with behaviour] is actually lower at 6kHz than at 1KHz).

1-to-7kHz, in Yeh et al (threshold [=35-38dB with behaviour] is the same at 7kHz and 1KHz).

Note that zebra finch nestlings' begging calls peaking at 6kHz (Elie & Theunissen 2015, doi: 10.1007/s10071-015-0933-6), would fall 2kHz above the parents' best hearing range if it were only up to 4kHz.

(b) The preprint incorrectly states throughout (e.g., L139, L163, L248) that heat-calls are 7-10kHz, when the actual value is 6-10kHz in the paper cited (Katsis et al, 2018).

(c) Using the correct values from these studies, and heat-calls at 45 dB SLP (as measured by others (unpublished data), or as measured by the authors themselves, but which is not reported here (Anttonen et a,l 2025), the correct conclusion is that heat calls fall within the known zebra finch hearing range.

(10) Published evidence towards high-frequency hearing, including in early development, is systematically omitted:

(a) Other studies showing birds use high frequencies above the known avian hearing range are ignored. This includes oilbirds (7-23kHz; Brinklov et al 2017; by 1 of the preprint authors, doi: 10.1098/rsos.170255) and hummingbirds (10-20kHz; Duque et al 2020, doi: 10.1126/sciadv.abb9393), and in a lesser extreme, zebra finches' inspiratory song syllables at 5-7kHz (Goller & Dalley, 2001).

(b) The discussion of anatomical development (L228-241) completely omits the well-known fact that the avian basilar papilla develops from high to low frequencies (i.e., base to apex), which - as many have pointed out - is opposite to the low-to-high development of sensitivity (e.g., cited: Cohen & Fermin 1978; Caus Capdevila et al 2021).

(c) High frequency hearing in songbirds at hatching is several orders of magnitude better than in chickens and ducks at the same age, even though songbirds are altricial (e.g., at 4kHz, flycatcher: 47dB, chicken-duck: 90dB; at 5kHz, flycatcher: 65dB, chicken-duck: 115dB; Korneeva et al 2006, Saunders et al 1974). That is because Galliformes are low-frequency specialists, according to both anatomical and ecological evidence, with calls peaking at 0.8 to 1.2kHz rather than 2-6kHz in songbirds. It is incorrect to conclude that altricial embryos cannot perceive high frequencies because low-frequency specialist precocial birds do not (L250;261).

The references used to support the statement on a very high threshold for precocial birds above 6kHz are also wrong (L250). Katayama 1985 did not test embryos, nor frequency tones. Neither of these two references tested ducks.

(11) Incorrect statements do not reflect findings from the references cited

For example:

(a) "in altricial bird species hearing typically starts after hatching" (L12, in abstract), "with little to no functional hearing during embryonic stages (Woolley, 2017)." (L33).

There is no evidence, in any species, to support these statements. This is only a - commonly repeated - assumption, not actually based on any data. On the contrary, the extremely limited evidence to date shows the opposite, with zebra finch embryos showing ZENK activation in the auditory cortex in response to song playback (Rivera et al, 2018, not cited).

The book chapter cited (Woolley 2017) acknowledges this lack of evidence, and, in the context of song learning, provides as only references (prior to 2018), 2 studies showing that songbirds do not develop a normal song if the song tutor is removed before 10d post-hatch. That nestlings cannot memorise (to later reproduce) complex signals heard before d10 does not mean that they are deaf to any sound before day 10.

Studies showing hearing in young songbird nestlings (see point 6 above) also contradict these statements.

(b) "Zebra finch embryos supposedly are epigenetically guided to adapt to high temperatures by their parents high-frequency "heat calls" " (L36 and L135).

This is an extremely vague and meaningless description of these results, which cannot be assessed by readers, even though these results are presented as a major justification for the present study. Rather than giving an interpretation of what "supposedly" may occur, it would be appropriate to simply synthesize the empirical evidence provided in these papers. They showed that embryonic exposure to heat-calls, as opposed to control contact calls, alters a suite of physiological and behavioural traits in nestlings, including how growth and cellular physiology respond to high temperatures. This also leads to carry-over effects on song learning and reproductive fitness in adulthood.

(c) "The acoustic communication in precocial mallard ducks depends specifically on the low-frequency auditory sensitivity of the embryo (Gottlieb, 1975)" (L253)

The study cited (Gottlieb, 1975) demonstrates exactly the opposite of this statement: it shows that duckling embryos, not only perceive high frequency sounds (relative to the species frequency range), but also NEED this exposure to display normal audition and behaviour post-hatch. Specifically, it shows that duckling embryos deprived of exposure to their own high-frequency calls (at 2 kHz), failed to identify maternal calls post-hatch because of their abnormal insensitivity to higher frequencies, which was later confirmed by directly testing their auditory perception of tones (Dimitrieva & Gottlieb, 1994).

(12) Considering all of the mistakes and distortions highlighted above, it would be very premature to conclude, based on these results and statements, that altricial avian embryos are not sensitive to sound. This study provides no actual scientific ground to support this conclusion.

Reviewer #3 (Public Review):

The article presents a comprehensive study on the stratification of viral shedding patterns in saliva among COVID-19 patients. The authors analyze longitudinal viral load data from 144 mildly symptomatic patients using a mathematical model, identifying three distinct groups based on the duration of viral shedding. Despite analyzing a wide range of clinical data and micro-RNA expression levels, the study could not find significant predictors for the stratified shedding patterns, highlighting the complexity of SARS-CoV-2 dynamics in saliva. The research underscores the need for identifying biomarkers to improve public health interventions and acknowledges several limitations, including the lack of consideration of recent variants, the sparsity of information before symptom onset, and the focus on symptomatic infections.

The manuscript is well-written, with the potential for enhanced clarity in explaining statistical methodologies. This work could inform public health strategies and diagnostic testing approaches.

Comments on the revised version from the editor:

The authors comprehensively addressed the concerns of all 3 reviewers. We are thankful for their considerable efforts to do so. Certain limitations remain unavoidable such as the lack of immunologic diversity among included study participants and lack of contemporaneous variants of concern.

One remaining issue is the continued use of the target cell limited model which is sufficient in most cases, but misses key datapoints in certain participants. In particular, viral rebound is poorly described by this model. Even if viral rebound does not place these cases in a unique cluster, it is well understood that viral rebound is of clinical significance.

In addition, the use of microRNAs as a potential biomarker is still not fully justified. In other words, are there specific microRNAs that have a pre-existing mechanistic basis for relating to higher or lower viral loads? As written it still feels like microRNA was included in the analysis simply because the data existed.

Reviewer #3 (Public review):

This manuscript provides evidence that mice have a fusome, a conserved structure most well studied in Drosophila that is important for oocyte specification. Overall, a myriad of evidence is presented demonstrating the existence of a mouse fusome that the authors term visham. This work is important as it addresses a long-standing question in the field of whether mice have fusomes and sheds light on how oocytes are specified in mammals. Concerns that need to be addressed revolve around several conclusions that are overstated or unclear and are listed below.

(1) Line 86 - the heading for this section is "PGCs contain a Golgi-rich structure known as the EMA granule" but there is nothing in this section that shows it is Golgi-rich. It does show that the structure is asymmetric and has branches.

(2) Line 105-106, how do we know if what's seen by EM corresponds to the EMA1 granule?

(3) Line 106-107-states "Visham co-stained with the Golgi protein Gm130 and the recycling endosomal protein Rab11a1". This is not convincing as there is only one example of each image, and both appear to be distorted.

(4) Line 132-133---while visham formation is disrupted when microtubules are disrupted, I am not convinced that visham moves on microtubules as stated in the heading of this section.

(5) Line 156 - the heading for this section states that Visham associates with polarity and microtubule genes, including pard3, but only evidence for pard3 is presented.

(6) Lines 196-210 - it's strange to say that UPR genes depend on DAZ, as they are upregulated in the mutants. I think there are important observations here, but it's unclear what is being concluded.

(7) Line 257-259---wave 1 and 2 follicles need to be explained in the introduction, and how this fits with the observations here clarified.

Reviewer #3 (Public review):

Summary:

The authors dilute fluorescent HCMV stocks in small steps (df ≈ 1.3-1.5) across 23 points, quantify infections by flow cytometry at 3 dpi, and fit a power-law model to estimate a cooperativity parameter n (n > 1 indicates apparent cooperativity). They compare fibroblasts vs epithelial cells and multiple strains/reporters, and explore alternative mechanisms (clumping, accrued damage, viral compensation) via analytical modeling and stochastic simulations. They discuss implications for titer/MOI estimation and suggest a method for detecting "apparent cooperativity," noting that for viruses showing this behavior, MOI estimation may be biased.

Strengths:

(1) High-resolution titration & rigor: The small-step dilution design (23 serial dilutions; tailored df) improves dose-response resolution beyond conventional 10× series.

(2) Clear quantitative signal: Multiple strain-cell pairs show n > 1, with appropriate model fitting and visualization of the linear regime on log-log axes.

(3) Mechanistic exploration: Side-by-side modeling of clumping vs accrued damage vs compensation frames testable hypotheses for cooperativity.

Weaknesses:

(1) Secondary infection control: The authors argue that 3 dpi largely avoids progeny-mediated secondary infection; this claim should be strengthened (e.g., entry inhibitors/control infections) or add sensitivity checks showing results are robust to a small secondary-infection contribution.

(2) Discriminating mechanisms: At present, simulations cannot distinguish between accrued damage and viral compensation. The authors should propose or add a decisive experiment (e.g., dual-color coinfection to quantify true coinfection rates versus "priming" without coinfection; timed sequential inocula) and outline expected signatures for each mechanism.

(3) Decline at high genomes/cell: Several datasets show a downturn at high input. Hypotheses should be provided (cytotoxicity, receptor depletion, and measurement ceiling) and any supportive controls.

(4) Include experimental data: In Figure 6, please include the experimentally measured titers (IU/mL), if available.

(5) MOI guidance: The practical guidance is important; please add a short "best-practice box" (how to determine titer at multiple genomes/cell and cell densities; when single-hit assumptions fail) for end-users.

Reviewer #3 (Public review):

Summary:

This study from Jia et al carried out a variety of analyses of terminating ribosomes, including the development of eRF1-seq to map termination sites, identification of a GA-rich motif that promotes ribosome pausing, characterization of tissue-specific termination dynamics, and elucidation of the regulatory roles of 18S rRNA and RPS26. Overall, the study is thoughtfully designed, and its biological conclusions are well supported by complementary experiments. The tools and datasets generated provide valuable resources for researchers investigating the mechanisms of RNA translation.

Strengths:

(1) The study introduces eRF1-seq, a novel approach for mapping translation termination sites, providing a methodological advance for studying ribosome termination.

(2) Through integrative bioinformatic analyses and complementary MPRA experiments, the authors demonstrate that GA-rich motifs promote ribosome pausing at termination sites and reveal possible regulatory roles of 18S rRNA in this process.

(3) The study characterizes tissue-specific ribosome termination dynamics, showing that the testis exhibits stronger ribosome pausing at stop codons compared to other tissues. Follow-up experiments suggest that RPS26 may contribute to this tissue specificity.

Weaknesses:

The biological significance of ribosome pausing regulation at translation termination sites or of translational readthrough, for example, across different tissue types, remains unclear. Nevertheless, this question lies beyond the primary scope of the current study.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors define a new paradigm for the attachment and endocytosis of SARS-CoV-2 in which cell surface heparan sulfate (HS) is the primary receptor, with ACE2 having a downstream role within endocytic vesicles. This has implications for the importance of targeting virion-HS interactions as a therapeutic strategy.

Strengths:

The authors show that viruses are internalized via dynamin-dependent endocytosis and that endocytic internalization is the major pathway for pseudotyped SARS-CoV-2 genome expression. They show that HS-mediated viral attachment is a critical step preceding viral endocytosis and also subsequent genome expression. Further, they show that hACE2 acts downstream of endocytosis to promote viral infection, and may be co-internalised with virions after HS attachment. Pseudotyped virus and authentic SARS-CoV-2 provide similar results. In addition, the authors demonstrate that remarkable clusters of multiple HS chains exist on the cell surface, visualised by a number of elegant microscopy methods, and that these represent the docking sites for virions. These visualisations are an important general contribution in themselves to understanding the nanoscale interactions of HS at the cell surface.

The use of a complementary range of methods, virus constructs, and cell models is a strength, and the results clearly support the conclusions.

Overall, the results convincingly demonstrate a different model to the currently accepted mechanism in which the ACE2 protein is regarded as the cell surface receptor for SARS-CoV-2. Here, the authors provide compelling evidence that cell surface clusters of HS are the primary docking site, with ACE2 interactions occurring later, after endocytosis (whilst still being essential for viral genome expression). This is an exciting and important landmark evidence which supports the view that HS-virion interactions should be viewed as a key site for anti-viral drug targeting, likely in strategies that also target the downstream ACE2-based mechanism of viral entry within endosomes.

Weaknesses:

This reviewer identified only minor points regarding citing and discussing other studies and typos, which can be corrected.

Reviewer #3 (Public review):

In this paper, authors aimed to investigate carbamylation effects on the function of Cx43-based hemichannels. Such effects have previously been characterized for other connexins, e.g. for Cx26, which display increased hemichannel (HC) opening and closure of gap junction channels upon exposure to increased CO2 partial pressure (accompanied by increased bicarbonate to keep pH constant). The authors used HeLa cells transiently transfected with Cx43 to investigate CO2-dependent carbamylation effects on Cx43 HC function. In contrast to Cx43-based gap junction channels that are here reported to be insensitive to PCO2 alterations, they provide evidence that Cx43 HC opening is highly dependent on the PCO2 pressure in the bath solution, over a range of 20 up to 70 mmHg encompassing the physiologically normal resting level of around 40 mmHg. They furthermore identified several Cx43 residues involved in Cx43 HC sensitivity to PCO2: K105, K109, K144 & K234; mutation of 2 or more of these AAs is necessary to abolish CO2 sensitivity. The subject is interesting and the results indicate that a fraction of HCs is open at a physiological 40 mmHg PCO2, which differs from the situation under HEPES buffered solutions where HCs are mostly closed under resting conditions. The mechanism of HC opening with CO2 gassing is linked to carbamylation and authors pinpointed several Lys residues involved in this process. Overall, the work is interesting as it shows that Cx43 HCs have a significant open probability under resting conditions of physiological levels of CO2 gassing, probably applicable to/relevant for brain, heart and other Cx43 expressing organs. The paper gives a detailed account on various experiments performed (dye uptake, electrophysiology, ATP release to assess HC function) and results concluded from those. They further consider many candidate carbamylation sites by mutating them to negatively charged Glu residues. The paper finalizes with hippocampal slice work showing evidence for connexin-dependent increases of the EPSP amplitude that could be inhibited by HC inhibition with Gap26 (Fig. 10). Another line of evidence comes from the Cx43-linked ODDD genetic disease whereby L90V as well as the A44V mutations of Cx43 prevented the CO2 induced hemichannel opening response (Fig. 11). Although the paper is interesting, in its present state it suffers from (i) a problematic Fig. 3, precluding interpretation of the data shown, and (ii) the poor use of hemichannel inhibitors that are necessary to strengthen the evidence in the crucial experiment of Fig. 2 and others.

Comments on revisions:

The traces in Fig.2B show that the HC current is inward at 20 mmHg PCO2, while it switches to an outward current at 55mmHg PCO2. HCs are non-selective channels, so their current should switch direction around 0 mV but not around -50 mV. As such, the -50 mV switching point indicates involvement of another channel distinct from non-selective Cx43 hemichannels. In the revised version, this problem has not been solved nor addressed. Additionally, I identified another problem in that the experimental traces shown lack a trace at the baseline condition of PCO2 35mmHg, while the summary graph depicts a data point. Not showing a trace at baseline PCO2 35mmHg renders data interpretation in the summary graph questionable.

Reviewer #3 (Public review):

Summary:

In this manuscript Pinon et al. describe the development of a 3D model of human vasculature within a microchip to study Neisseria meningitidis (Nm)- host interactions and validate it through its comparison to the current gold-standard model consisting of human skin engrafted onto a mouse. There is a pressing need for robust biomimetic models with which to study Nm-host interactions because Nm is a human-specific pathogen for which research has been primarily limited to simple 2D human cell culture assays. Their investigation relies primarily on data derived from microscopy and its quantitative analysis, which support the authors' goal of validating their Vessel-on-Chip (VOC) as a useful tool for studying vascular infections by Nm, and by extension, other pathogens associated with blood vessels.

Strengths:

• Introduces a novel human in vitro system that promotes control of experimental variables and permits greater quantitative analysis than previous models<br /> • The VOC model is validated by direct comparison to the state-of-the-art human skin graft on mouse model<br /> • The authors make significant efforts to quantify, model, and statistically analyze their data<br /> • The laser ablation approach permits defining custom vascular architecture<br /> • The VOC model permits the addition and/or alteration of cell types and microbes added to the model<br /> • The VOC model permits the establishment of an endothelium developed by shear stress and active infusion of reagents into the system

Weaknesses:

• The VOC model contains one cell type, human umbilical cord vascular endothelial cells (HUVECs), while true vasculature contains a number of other cell types that associate with and affect the endothelium, such as smooth muscle cells, pericytes, and components of the immune system. However, adding such complexity may be a future goal of this VOC model.

Impact:

The VOC model presented by Pinon et al. is an exciting advancement in the set of tools available to study human pathogens interacting with the vasculature. This manuscript focuses on validating the model, and as such sets the foundation for impactful research in the future. Of particular value is the photoablation technique that permits the custom design of vascular architecture without the use of artificial scaffolding structures described in previously published works.

Comments on revised version:

The authors have nicely addressed my (and other reviewers') comments.

Reviewer #3 (Public review):

Summary:

Fengwen Huang et al. used multiple neuroscience techniques (transgenetic mouse, immunochemistry, bulk calcium recording, neural sensor, hippocampal-dependent task, optogenetics, chemogenetics, and interfer RNA technique) to elucidate the role of the excitatory cholecystokinin-positive pyramidal neurons in the hippocampus in regulating the hippocampal functions, including navigation and neuroplasticity.

Strengths:

(1) The authors provided the distribution profiles of excitatory cholecystokinin in the dorsal hippocampus via the transgenetic mice (Ai14::CCK Cre mice), immunochemistry, and retrograde AAV.

(2) The authors used the neural sensor and light stimulation to monitor the CCK release from the CA3 area, indicating that CCK can be secreted by activation of the excitatory CCK neurons.

(3) The authors showed that the activity of the excitatory CCK neurons in CA3 is necessary for navigation learning.

(4) The authors demonstrated that inhibition of the excitatory CCK neurons and knockdown of the CCK gene expression in CA3 impaired the navigation learning and the neuroplasticity of CA3-CA1 projections.

Weaknesses:

(1) The causal relationship between navigation learning and CCK secretion?

(2) The effect of overexpression of the CCK gene on hippocampal functions?

(3) What are the functional differences between the excitatory and inhibitory CCK neurons in the hippocampus?

(4) Do CCK sources come from the local CA3 or entorhinal cortex (EC) during the high-frequency electrical stimulation?

Reviewer #3 (Public review):

Summary:

This is a clearly written paper that describes the reanalysis of data from a BXD study of the locomotor response to morphine and naloxone. The authors detect significant loci and an epistatic interaction between two of those loci. Single-cell data from outbred rats is used to investigate the interaction. The authors also use network methods and incorporate human data into their analysis.

Strengths:

One major strength of this work is the use of granular time-series data, enabling the identification of time-point-specific QTL. This allowed for the identification of an additional, distinct QTL (the Fgf12 locus) in this work compared to previously published analysis of these data, as well as the identification of an epistatic effect between Oprm1 (driving early stages of locomotor activation) and Fgf12 (driving later stages).

Weaknesses:

(1) What criteria were used to determine whether the epistatic interaction was significant? How many possible interactions were explored?

(2) Results are presented for males and females separately, but the decision to examine the two sexes separately was never explained or justified. Since it is not standard to perform GWAS broken down by sex, some initial explanation of this decision is needed. Perhaps the discussion could also discuss what (if anything) was learned as a result of the sex-specific analysis. In the end, was it useful?

(3) The confidence intervals for the results were not well described, although I do see them in one of the tables. The authors used a 1.5 support interval, but didn't offer any justification for this decision. Is that a 95% confidence interval? If not, should more consideration have been given to genes outside that interval? For some of the QTLs that are not the focus of this paper, the confidence intervals were very large (>10 Mb). Is that typical for BXDs?

Reviewer #3 (Public review):

Summary:

In the submitted article by Lewis et al., the authors investigate how mechanical stimulation influences organ regeneration using the well-characterized zebrafish caudal fin regeneration model. Using a swim flume and a 30min/day exercise regime, the authors found that exercise during the establishment of the blastema reduced regeneration and led to skeletal deformations. Transcriptional profiling of regenerated caudal fin tissue revealed reduced expression of extracellular matrix-associated genes, which were found to be expressed by blastemal fibroblast and osteoblast lineage cells.

Downregulated genes included hyaluronic acid synthases 1 and 2; accordingly, hyaluronic acid levels were found to be reduced in regenerating fins exposed to exercise. The link between regeneration and HA was further confirmed through HA depletion and HA overexpression experiments, which showed a reduction in blastema size and partial rescue of blastema formation, respectively. The authors further show that HA levels, as well as the extent of mechanical loading correlate with nuclear localization of the mechanotransducer Yap and conclude that biomechanical forces play a significant role during regeneration through regulation of HA levels in the ECM and therewith regulation of YAP downstream signaling.

This work expands our understanding of the biochemical signaling connecting biomechanical forces with tissue regeneration. The conclusions are well supported by the data.

Strengths:

(1) Analysis is performed in multiple replicate experimental groups and shows the robust response to the experimental conditions.

(2) The link of HA levels to blastema formation was confirmed through HA overexpression and two different HA depletion experiments.

(3) The use of a previously established fin regeneration single cell dataset does elegantly show the correlation of changes in gene expression levels and specific tissue types, which was further confirmed by in vivo imaging of cell type-specific transgenic lines.

Weaknesses:

Tissue sections stained with hematoxylin and eosin would be helpful to show the changes in tissue architecture more clearly.

Reviewer #3 (Public review):

Summary:

This study identifies a novel energy-sensing circuit in Drosophila and mice that directly regulates sweet taste perception. In flies, hugin+ neurons function as a glucose sensor, activated through Glut1 transport and ATP-sensitive potassium channels. Once activated, hugin neurons release hugin peptide, which stimulates downstream Allatostatin A (AstA)+ neurons via PK2-R1 receptors. AstA+ neurons then inhibit sweet-sensing Gr5a+ gustatory neurons through AstA peptide and its receptor AstA-R1, reducing sweet sensitivity after feeding. Disrupting this pathway enhances sweet taste and increases food intake, while activating the pathway suppresses feeding.

The mammalian homolog of neuromedin U (NMU) was shown to play an analogous role in mice. NMU knockout mice displayed heightened sweet preference, while NMU administration suppressed it. In addition, VMH NMU+ neurons directly sense glucose and project to rNST Calb2+ neurons, dampening sweet taste responses. The authors suggested a conserved hugin/NMU-AstA pathway that couples energy state to taste perception.

Strengths:

Interesting findings that extend from insects to mammals. Very comprehensive.

Weaknesses:

Coupling energy status to taste sensitivity is not a new story. Many pathways appear to be involved, and therefore, it raises a question as to how this hugin-AstA pathway is unique.

Reviewer #3 (Public review):

Summary:

The authors set out to extend their previous mapping of Drosophila head mechanosensory neurons (Eichler et al., 2024) by reconstructing their full second-order connectome. Their aim is to reveal how bristle mechanosensory neurons (BMNs) interface with excitatory and inhibitory partners to generate location-specific grooming movements, and to identify the circuit motifs and developmental lineages that support this transformation.

Strengths:

The strengths of this work are clear. The authors present a comprehensive synaptic-resolution connectome for BMNs, identifying nearly all of their pre- and postsynaptic partners. This dataset reveals important circuit motifs:

(1) BMNs provide feedforward excitation to descending neurons, feedforward inhibition to interneurons, and are themselves strongly regulated by GABAergic presynaptic inhibition.

(2) These motifs together support the idea that BMN activity is locally gated and hierarchically suppressed, fitting well with known behavioural sequences of grooming.

(3) The study also shows that connectivity preserves somatotopy, such that BMNs from neighbouring bristle populations converge onto shared partners, while distant BMNs remain segregated.

(4) A developmental analysis reveals both primary and secondary partners, suggesting a layered scaffold plus adult-specific elaborations.

(5) Finally, the identification of hemilineage 23b (LB23) as a core postsynaptic pathway - incorporating previously described antennal grooming neurons (aBN2) - provides a striking link between developmental lineage, anatomical connectivity, and behavioral output.

(6) Together, the dataset represents a valuable resource for the neuroscience community and a foundation for future functional studies.

Weaknesses:

There are also some weaknesses that mostly only limit clarity.

(1) The writing is dense, with results often presented in a cryptic fashion and the functional implications deferred to the discussion. As a result, the significance of circuit motifs such as BMN→motor or reciprocal inhibitory loops is sometimes buried, rather than highlighted when first described.

(2) Some assumptions require more explanation for non-specialist readers - for example, how bristle identity is inferred in EM in the absence of cuticular structures, or what is meant by "ascending" and "descending" in a dataset that does not include the ventral nerve cord. While some of this comes from the earlier paper, it would help readers of this one to explain this.

(3) Visualization choices also sometimes obscure key conclusions: network graphs can be visually appealing but do not clearly convey somatotopy or BMN-type differences; heatmaps or region-level matrices would make the parallel, block-like organization of the circuit more evident.

(4) The data might also speak to roles beyond grooming (e.g., mechanosensory modulation of posture or feeding), and a brief acknowledgement of this would broaden the impact.

(5) The restriction to one hemisphere should be explicitly acknowledged as a limitation when framing this as a 'comprehensive' connectome.

Overall, the authors achieve their main goal: they convincingly show that BMNs connect into parallel, somatotopically organized pathways, with LB23 providing a key lineage-based link from sensory input to grooming output. The dataset is carefully analyzed, and while the presentation could be streamlined, the connectome will be a valuable resource for researchers studying sensory processing, motor control, and the logic of circuit organization.

Reviewer #3 (Public review):

Summary:

In this study, the authors establish a human in vitro liver model by co-culturing induced hepatocyte-like cells (iHEPs) with induced macrophages (iMACs). Through flow cytometry-based sorting of cell populations at days 3 and 7 of co-culture, followed by bulk RNA sequencing, they demonstrate that bidirectional interactions between these two cell types drive functional maturation. Specifically, the presence of iMACs accelerates the hepatic maturation program of iHEPs, while contact-dependent cues from iHEPs enhance the acquisition of Kupffer cell identity in iMACs, indicating that direct cell-cell interactions are critical for establishing tissue-resident macrophage characteristics.

Functionally, the authors show that iMAC-derived Kupffer-like cells respond to pathological stimuli by producing interleukin-6 (IL-6), a hallmark cytokine of hepatic immune activation. When exposed to a panel of clinically relevant hepatotoxic drugs, the co-culture system exhibited concentration-dependent modulation of IL-6 secretion consistent with reported drug-induced liver injury (DILI) phenotypes. Notably, this response was absent when hepatocytes were co-cultured with monocyte-derived macrophages from peripheral blood, underscoring the liver-specific phenotype and functional relevance of the iMAC-derived Kupffer-like cells. Collectively, the study proposes this co-culture platform as a more physiologically relevant model for interrogating macrophage-hepatocyte crosstalk and assessing immune-mediated hepatotoxicity in vitro.

Strengths:

A major strength of this study lies in its systematic dissection of cell-cell interactions within the co-culture system. By isolating each cell type following co-culture and performing comprehensive transcriptomic analyses, the authors provide direct evidence of bidirectional crosstalk between iMACs and iHEPs. The comparison with single-culture controls is particularly valuable, as it clearly demonstrates how co-culture enhances functional maturation and lineage-specific gene expression in both cell types. This approach allows for a more mechanistic understanding of how hepatocyte-macrophage interactions contribute to the acquisition of tissue-specific phenotypes.

Weaknesses:

(1) Overreliance on bulk RNA-seq data:

The primary evidence supporting cell maturation is derived from bulk RNA sequencing, which has inherent limitations in resolving heterogeneous cellular states and functional maturation. The conclusions regarding hepatocyte maturation are based largely on increased expression of a subset of CYP genes and decreased AFP levels - markers that, while suggestive, are insufficient on their own to substantiate functional maturation. Additional phenotypic or functional assays (e.g., metabolic activity, protein-level validation) would significantly strengthen these claims.

(2) Insufficient characterization of input cell populations:

The manuscript lacks adequate validation of the cellular identities prior to co-culture. Although the authors reference previously published protocols for generating iHEPs and iMACs, it remains unclear whether the cells used in this study faithfully retain expected lineage characteristics. For example, hepatocyte preparations should be characterized by flow cytometry for ALB and AFP expression, while iMACs should be assessed for canonical macrophage markers such as CD45, CD11b, and CD14 before co-culture. Without these baseline data, it is difficult to interpret the magnitude or significance of any co-culture-induced changes.

(3) Quantitative assessment of IL-6 production is insufficient:

The analysis of drug-induced IL-6 responses is based primarily on relative changes compared to control conditions. However, percentage changes alone are inadequate to capture the biological relevance of these responses. Absolute cytokine production levels - particularly in response to LPS stimulation - should be reported and directly compared to PBMC-derived macrophages to determine whether iMAC-derived Kupffer-like cells exhibit enhanced cytokine output. Moreover, the Methods section should clearly describe how ELISA results were normalized or corrected to account for potential differences in cell number, viability, or culture conditions.

(4) Unclear mechanistic interpretation of IL-6 modulation:

The observed changes in IL-6 production upon drug treatment cannot be interpreted solely as evidence of Kupffer cell-specific functionality. For instance, IL-6 suppression by NSAIDs such as diclofenac is well known to result from altered prostaglandin synthesis due to COX inhibition, while leflunomide's effects are linked to metabolite-induced modulation of immune cell proliferation and broader cytokine networks. These mechanisms are distinct from Kupffer cell identity and may not directly reflect liver-specific macrophage function. Consequently, changes in IL-6 secretion alone - particularly without additional mechanistic evidence or analysis of other cytokines - are insufficient to conclude that co-culture with hepatocytes drives the acquisition of bona fide Kupffer cell maturity.

Reviewer #3 (Public review):

Summary:

This manuscript reports the discovery of new compounds that selectively inhibit SMARCA4/SMARCA2 ATPase activity and have pronounced effects on uveal melanoma cell proliferation. They induce apoptosis and suppress tumor growth, with no toxicity in vivo. The report provides biological significance by demonstrating that the drugs alter chromatin accessibility at lineage specific gene enhancer regions and decrease expression of lineage specific genes, including SOX10 and SOX10 target genes.

Strengths:

The study provides compelling evidence for the therapeutic use of these compounds and does a thorough job at elucidating the mechanisms by which the drugs work. The study will likely have a high impact on the chromatin remodeling and cancer fields. The datasets will be highly useful to these communities.

[Editors' note: The authors have addressed all of the outstanding issues.]

Reviewer #3 (Public review):

Summary:

Several recent findings indicate that forces perpendicular to the microtubule accelerate kinesin unbinding, where perpendicular and axial forces were analyzed using the geometry in a single-bead optical trapping assay (Khataee and Howard, 2019), comparison between single-bead and dumbbell assay measurements (Pyrpassopoulos et al., 2020), and comparison of single-bead optical trap measurements with and without a DNA tether (Hensley and Yildiz, 2025).

Here, the authors devise an assay to exert forces along the microtubule axis by tethering kinesin to the microtubule via a dsDNA tether. They compared the behavior of kinesin-1, -2, and -3 when pulling against the DNA tether. In line with previous optical trapping measurements, kinesin unbinding is less sensitive to forces when the forces are aligned with the microtubule axis. Surprisingly, the authors find that both kinesin-1 and -2 detach from the microtubule more slowly when stalled against the DNA tether than in unloaded conditions, indicating that these motors act as catch bonds in response to axial loads. Axial loads accelerate kinesin-3 detachment. However, kinesin-3 reattaches quickly to maintain forces. For all three kinesins, the authors observe weakly attached states where the motor briefly slips along the microtubule before continuing a processive run.

Strengths:

These observations suggest that the conventional view that kinesins act as slip bonds under load, as concluded from single-bead optical trapping measurements where perpendicular loads are present due to the force being exerted on the centroid of a large (relative to the kinesin) bead, needs to be reconsidered. Understanding the effect of force on the association kinetics of kinesin has important implications for intracellular transport, where the force-dependent detachment governs how kinesins interact with other kinesins and opposing dynein motors (Muller et al., 2008; Kunwar et al., 2011; Ohashi et al., 2018; Gicking et al., 2022) on vesicular cargoes.

Weaknesses:

The authors attribute the differences in the behaviour of kinesins when pulling against a DNA tether compared to an optical trap to the differences in the perpendicular forces. However, the compliance is also much different in these two experiments. The optical trap acts like a ~ linear spring with stiffness ~ 0.05 pN/nm. The dsDNA tether is an entropic spring, with negligible stiffness at low extensions and very high compliance once the tether is extended to its contour length (Fig. 1B). The effect of the compliance on the results should be addressed in the manuscript.

Compared to an optical trapping assay, the motors are also tethered closer to the microtubule in this geometry. In an optical trap assay, the bead could rotate when the kinesin is not bound. The authors should discuss how this tethering is expected to affect the kinesin reattachment and slipping. While likely outside the scope of this study, it would be interesting to compare the static tether used here with a dynamic tether like MAP7 or the CAP-GLY domain of p150glued.

In the single-molecule extension traces (Figure 1F-H; S3), the kinesin-2 traces often show jumps in position at the beginning of runs (e.g., the four runs from ~4-13 s in Fig. 1G). These jumps are not apparent in the kinesin-1 and -3 traces. What is the explanation? Is kinesin-2 binding accelerated by resisting loads more strongly than kinesin-1 and -3?

When comparing the durations of unloaded and stall events (Fig. 2), there is a potential for bias in the measurement, where very long unloaded runs cannot be observed due to the limited length of the microtubule (Thompson, Hoeprich, and Berger, 2013), while the duration of tethered runs is only limited by photobleaching. Was the possible censoring of the results addressed in the analysis?

The mathematical model is helpful in interpreting the data. To assess how the "slip" state contributes to the association kinetics, it would be helpful to compare the proposed model with a similar model with no slip state. Could the slips be explained by fast reattachments from the detached state?

Reviewer #3 (Public review):

The introduction does a very good job of discussing the issue around whether there is ongoing replication in people with HIV on antiretroviral therapy. Sporadic, non-sustained replication likely occurs in many PWH on ART related to adherence, drug-drug interactions and possibly penetration of antivirals into sanctuary areas of replication and as the authors point out proving it does not occur is likely not possible and proving it does occur is likely very dependent on the population studied and the design of the intervention. Whether the consequences of this replication in the absence of evolution toward resistance have clinical significance challenging question to address.

It is important to note that INSTI-based therapy may have a different impact on HIV replication events that results in differences in virus release for specific cell type (those responsible for "second phase" decay) by blocking integration in cells that have completed reverse transcription prior to ART initiation but have yet to be fully activated. In a PI or NNRTI-based regimen, those cells will release virus, whereas with an INSTI-based regimen, they will not.

Given the very small sample size, there is a substantial risk of imbalance between the groups in important baseline measures.

Comments on the revised version from the editor:

I appreciate that the authors thoroughly address the reviewer's concerns in the response letter. Most importantly, they acknowledge that "The absence of a pre-specified statistical endpoint or sample size calculation reflects the exploratory nature of the trial." This is vital because the transient impact on total HIV DNA in the intensified versus standard dose arm raises questions about any sustained or meaningful anti-reservoir effect and was also not hypothesized a priori. The authors explanation that HIV DNA may have rebounded due to clonal expansion is interesting but not assessed directly in the trial.

The greater decrease in intact HIV DNA between days 0 and 84 in the intensified arm are notable but are somewhat limited by small sample size, small effect size and lack of data between these two timepoints.

Unfortunately, the hypothesis generating nature of the conclusions which is outlined nicely in the author's response letter is only acknowledged in the discussion of the revised paper. The abstract and results are only marginally different than the original version and still read as definitive when the evidence is only hypothesis generating. For these reasons, the level of evidence remains incomplete as before.

Reviewer #3 (Public review):

Summary:

The manuscript demonstrates that starvation induces persister formation in M. abscesses. They also utilized Tn-Seq for the identification of genes involved in persistence. They identified the role of catalase-peroxidase KatG in preventing death from translation inhibitors Tigecycline and Linezolid. They further demonstrated that a combination of these translation inhibitors leads to the generation of ROS in PBS-starved cells.

Strengths:

The authors used high-throughput genomics-based methods for identification of genes playing a role in persistence.

Weaknesses:

The findings could not be validated in clinical strains.

Comments on revisions: No more comments for the authors.

Reviewer #3 (Public review):

Summary

The regulation of NF-κB signaling is complex and central to the differentiation and homeostasis of αβT cells, essential to adaptive immunity. γδ T cells are a distinct population that responds to stress/injury-induced cues by producing inflammatory cytokines, representing an important bridge between innate and adaptive immunity. This study from Islam et al. demonstrates that the IKK complex, a central regulator of NF-κB signaling, plays distinct and essential roles in the differentiation and maintenance of γδ T cells. The authors use elegant murine genetic models to generate clear data that disentangle these requirements in vivo.

Although NF-κB activity was found to be dispensable for specification of γδ T cell progenitors and the generation of adaptive γδ T cells, it was required for both the ontogeny of type 1 γδ T cells and the survival of mature adaptive γδ T cells. Subunit-specific analyses revealed parallels with αβ T cells: RELA was necessary for type 1 γδ T cell development, while maintenance of adaptive γδ T cells relied upon redundancy between REL subunits, with cREL and p50 compensating in the absence of RELA but not vice versa. These findings reflect distinct biological requirements for ontogeny versus maintenance, likely driven by differences in receptor signaling, such as TCR and TNFRSF family members. Moreover, IKK also maintained γδ T cell survival through repression of RIPK1-mediated cell death, echoing its dual role in αβ T cells, where it both prevents TNF-induced apoptosis and provides NF-κB-dependent survival signals.

Strengths:

The multiple, unique murine genetic models employed for detailed analysis of in vivo γδ T cell differentiation and homeostasis are a major strength of this paper. NF-κB signaling processes are devilishly complex. The conditional mutants generated for this study disentangle the requirements for the various IKK-regulated pathways in γδ T cell differentiation, cell survival, and homeostasis. Data are clearly presented and suitably interpreted, with a helpful synthesis provided in the Discussion. These data will provide a definitive account of the requirements for NF-κB signaling in γδ T cells and provide new genetic models for the community to further study the upstream signals.

Weaknesses:

The paper would benefit greatly from a graphical abstract that could summarize the key findings, making the key findings accessible to the general immunology or biochemistry reader. Ideally, this graphic would distinguish the requirements for NF-κB signals sustaining thymic γδ T cell differentiation from peripheral maintenance, taking into account the various subsets and signaling pathways required. In addition, the authors should consider adding further literature comparing the requirements for NF-κB /necroptosis pathways in regulating other non-conventional T cell populations, such as iNKT, MAIT, or FOXP3+ Treg cells. These data might help position the requirements described here for γδ T cells compared to other subsets, with respect to homeostatic cues and transcriptional states.

Last and least, there are multiple grammatical errors throughout the manuscript, and it would benefit from further editing. Likewise, there are some minor errors in figures (e.g., Figure 3A, add percentage for plot from IKKDT.RIPK1D138N mouse; Figure 7, "Adative").

Reviewer #3 (Public review):

Summary:

The manuscript by Kim et al. describes a MuSC subpopulation that loses VCam expression in geriatric muscle and shows reduced ability to contribute to muscle regeneration. They propose that this population underlies the reported decline of MuSCs in aged mice, suggesting that these cells remain present in geriatric muscle but are overlooked due to low or absent VCam expression. The identification of a subpopulation that changes with aging would be compelling and of interest to the field.

Strengths:

The authors employ a wide range of assays, from in vitro to in vivo systems, to characterize Vcam-low/negative cells from geriatric muscle. The loss of Vcam appears strong in geriatric mice. They further identify CD63 and CD200 as potential surface markers that remain stable with age, thereby enabling the isolation of MuSCs across different age groups.

Weaknesses:

Some issues remain before establishing whether this population represents a true functional subset or explains the reported decline in MuSC numbers in aged mice. A stronger fate assessment of Vcam-low/negative cells is needed to assess their propensity for cell death in vitro and in vivo (e.g., engraftment efficiency), and if this plays a role in their conclusions. Comparisons include young, middle-aged, and geriatric mice, but not aged (~24 months) mice, which are needed for direct assessment of previous reports of age-related MuSC decline. The suggestion that the Vcam-low/negative population reflects senescence appears premature, with few consistent markers for this fate, as well as the cells not exhibiting irreversible cell-cycle exit. Finally, validation of CD63 and CD200 as reliable age-independent MuSC markers requires further testing, specifically using the Pax7-YFP tracing model and co-labeling in geriatric mice.

Reviewer #3 (Public Review):

The manuscript by Carayon and Strutt addresses the role of cell-scale signaling during the establishment of planar cell polarity (PCP) in the Drosophila pupal wing. The authors induce locally the expression of a tagged core PCP protein, Frizzled, and observe and analyze the de novo establishment of planar cell polarity. Using this system, the authors show that PCP can be established within several hours, that PCP is robust towards variation in core PCP protein levels, that PCP proteins do not orient microtubules, and that PCP is robust towards 'extrinsic' re-polarization. The authors conclude that the polarization at the cell-scale is strongly intrinsic and only weakly affected by the polarity of neighboring cells.

Major comments (first round of review):

The data are clearly presented and the manuscript is well written. The conclusions are well supported by the data. 

(1) The authors use a system to de novo establish PCP, which has the advantage of excluding global cues orienting PCP and thus to focus on the cell-intrinsic mechanisms. At the same time, the system has the limitation that it is unclear to what extent de novo PCP establishment reflects 'normal' cell scale PCP establishment, in particular because the Gal4/UAS expression system that is used to induce Fz expression will likely result in much higher Fz levels compared with the endogenous levels. The authors should briefly discuss this limitation.

(2) Fig. 3. The authors use heterozygous mutant backgrounds to test the robustness of de novo PCP establishment towards (partial) depletion in core PCP proteins. The authors conclude that de novo polarization is 'extremely robust to variation in protein level'. Since the authors (presumably) lowered protein levels by 50%, this conclusion appears to be somewhat overstated. The authors should tune down their conclusion.

Significance: 

The manuscript contributes to our understanding of how planar cell polarity is established. It extends previous work by the authors (Strutt and Strutt, 2002,2007) that already showed that induction of core PCP pathway activity by itself is sufficient to induce de novo PCP. This manuscript further explores the underlying mechanisms. The authors test whether de novo PCP establishment depends on an 'inhibitory signal', as previously postulated (Meinhardt, 2007), but do not find evidence. They also test whether core PCP proteins help to orient microtubules (which could enhance cell intrinsic polarization of core PCP proteins), but, again, do not find evidence, corroborating previous work (Harumoto et al, 2010). The most significant finding of this manuscript, perhaps, is the observation that local de novo PCP establishment does not propagate far through the tissue. A limitation of the study is that the mechanisms establishing intrinsic cell scale polarity remain unknown. The work will likely be of interest to specialists in the field of PCP.

Summary of comments from the Reviewing Editor on the revised version:

In the introduction, when you refer to Figure 1, the definition of Molecular, cellular, tissue scale is indeed not too clear to outside readers. For example, when you first refer to 'cell scale' you define it 'non-local', but probably it is not clear to many readers 'non-local' means 'the mechanism that cannot be explained by 'molecular scale'. (because 'molecular scale = local' is only inferred).

The 'conclusion paragraph' at the end of the Introduction does not have conclusion (only explained 'which question was tested by which method').

Minor comments that can easily be addressed by textual edits:

– they do not explain why gene dosage affects constitutive but not de novo polarization. It seems to me that one would expect de novo to be at least as sensitive if not more.

– Unconventional nomenclature for tissue axes - mediolateral, horizontal - are frequently used. These are sometimes difficult to parse. Please stick with universally accepted anterior, posterior, proximal and distal.

Reviewer #3 (Public review):

Summary:

Kumar et al. examine the H3K115 epigenetic mark located on the lateral surface of the histone core domain and present evidence that it may serve as a marker enriched at transcription start sites (TSSs) of active CpG island promoters and at polycomb-repressed promoters. They also note enrichment of the H3K115ac mark is found on fragile nucleosomes within nucleosome-depleted regions, on active enhancers, and CTCF-bound sites. They propose that these observations suggest that H3K115ac contributes to nucleosome destabilization and so may serve as a marker of functionally important regulatory elements in mammalian genomes.

Strengths:

The authors present novel observations suggesting that acetylation of a histone residue in a core (versus on a histone tail) domain may serve a functional role in promoting transcription, in CPG islands and polycomb-repressed promoters. They present a solid amount of confirmatory in silico data using appropriate methodology that supports the idea that the H3K115ac mark may function to destabilize nucleosomes and contribute to regulating ESC differentiation.

Weaknesses:

Additional experiments to confirm antibody specificity are needed. The authors use synthetic peptides for other markers (e.g., H3K122) to support the claim that the antibody is specific, but ChIP-ChIP assays are performed under cross-linked, non-denatured conditions, which preserve structure and epitope accessibility differently than synthetic peptides used for dot blots. Does the antibody give a single band in western blots of histones, and can the H3K115ac peptide block western and immunofluorescence signals of the antibody? Given that the antibody is a rabbit polyclonal, specificity is not a trivial consideration.

Reviewer #3 (Public review):

This study investigates the connection between glycolysis and the biosynthesis of sulfur-containing amino acids in controlling fungal morphogenesis, using Saccharomyces cerevisiae and C. albicans as model organisms. The authors identify a conserved metabolic axis that integrates glycolysis with cysteine/methionine biosynthetic pathways to influence morphological transitions. This work broadens the current understanding of fungal morphogenesis, which has largely focused on gene regulatory networks and cAMP-dependent signaling pathways, by emphasizing the contribution of metabolic control mechanisms. However, despite the novel conceptual framework, the study provides limited mechanistic characterization of how the sulfur metabolism and glycolysis blockade directly drive morphological outcomes. In particular, the rationale for selecting specific gene deletions, such as Met32 (and not Met4), or the Met30 deletion used to probe this pathway, is not clearly explained, making it difficult to assess whether these targets comprehensively represent the metabolic nodes proposed to be critical. Further supportive data and experimental validation would strengthen the claims on connections between glycolysis, sulfur amino acid metabolism, and virulence.

Strengths:

(1) The delineation of how glycolytic flux regulates fungal morphogenesis through a cAMP-independent mechanism is a significant advancement. The coupling of glycolysis with the de novo biosynthesis of sulfur-containing amino acids, a requirement for morphogenesis, introduces a novel and unexpected layer of regulation.

(2) Demonstrating this mechanism in both S. cerevisiae and C. albicans strengthens the argument for its evolutionary conservation and biological importance.

(3) The ability to rescue the morphogenesis defect through exogenous supplementation of sulfur-containing amino acids provides functional validation.

(4) The findings from the murine Pfk1-deficient model underscore the clinical significance of metabolic pathways in fungal infections.

Weaknesses:

(1) While the link between glycolysis and sulfur amino acid biosynthesis is established via transcriptomic and proteomic analysis, the specific regulation connecting these pathways via Met30 remains to be elucidated. For example, what are the expression and protein levels of Met30 in the initial analysis from Figure 2? How specific is this effect on Met30 in anaerobic versus aerobic glycolysis, especially when the pentose phosphate pathway is involved in the growth of the cells when glycolysis is perturbed?

(2) Including detailed metabolite profiling could have strengthened the metabolic connection and provided additional insights into intermediate flux changes, i.e., measuring levels of metabolites to check if cysteine or methionine levels are influenced intracellularly. Also, it is expected to see how Met30 deletion could affect cell growth. Data on Met30 deletion and its effect on growth are not included, especially given that a viable heterozygous Met30 strain has been established. Measuring the cysteine or methionine levels using metabolomic analysis would further strengthen the claims in every section.

(3) In comparison with the previous bioRxiv (doi: https://doi.org/10.1101/2025.05.14.654021) of this article in May 2025 to the recent bioRxiv of this article (doi: https://doi.org/10.1101/2025.05.14.654021), there have been some changes, and Met30 deletion has been recently included, and the chemical perturbation of glycolysis has been added as new data. Although the changes incorporated in the recent version of the article improved the illustration of the hypothesis in Figure 6, which connects glycolysis to Sulfur metabolism, the gene expression and protein levels of all genes involved in the illustrated hypothesis are not consistently shown. For example, in some cases, the Met4 expression is not shown (Figure 4), and the Met30 expression is not shown during profiling (gene expression or protein levels) throughout the manuscript. Lack of consistency in profiling the same set of key genes makes understanding more complicated.

(4) The demonstrated link between glycolysis and sulfur amino acid biosynthesis, along with its implications for virulence in C. albicans, is important for understanding fungal adaptation, as mentioned in the article; however, the Met4 activation was not fully characterized, nor were the data presented when virulence was assessed in Figure 4. Why is Met4 not included in Figure 4D and I? Especially, according to Figure 6, Met4 activation is crucial and guides the differences between glycolysis-active and inactive conditions.

(5) Similarly, the rationale behind selecting Met32 for characterizing sulfur metabolism is unclear. Deletion of Met32 resulted in a significant reduction in pseudohyphal differentiation; why is this attributed only to Met32? What happens if Met4 is deleted? It is not justified why Met32, rather than Met4, was chosen. Figure 6 clearly hypothesizes that Met4 activation is the key to the mechanism.

(6) The comparative RT-qPCR in Figure 5 did not account for sulfur metabolism genes, whereas it was focused only on virulence and hyphal differentiation. Is there data to support the levels of sulfur metabolism genes?

(7) To validate the proposed interlink between sulfur metabolism and virulence, it is recommended that the gene sets (illustrated in Figure 6) be consistently included across all comparative data included throughout the comparisons. Excluding sulfur metabolism genes in Figure 5 prevents the experiment from demonstrating the coordinated role of glycolysis perturbation → sulfur metabolism → virulence. The same is true for other comparisons, where the lack of data on Met30, Met4, etc., makes it hard to connect the hypothesis. It is also recommended to check the gene expression of other genes related to the cAMP pathway and report them to confirm the cAMP-independent mechanism. For example, gap2 deletion was used to confirm the effects of cAMP supplementation, but the expression of this gene was not assessed in the RNA-seq analysis in Figure 2. It would be beneficial to show the expression of cAMP-related genes to completely confirm that they do not play a role in the claims in Figure 2.

(8) Although the NAC supplementation study is included in the new version of the article compared to the previous version in BioRxiv (May 2025), the link to sulfur metabolism is not well characterized in Figure 5 and their related datasets. The main focus of the manuscript is to delineate the role of sulfur metabolism; hence, it is anticipated that Figure 5 will include sulfur-related metabolic genes and their links to pfk1 deletion, using RT-PCR measurements as shown for the virulence genes.

(9) The manuscript would benefit from more information added to the introduction section and literature supports for some of the findings reported earlier, including the role of (i) cAMP-PKA and MAPK pathways, (ii) what is known in the literature that reports about the treatment with 2DG (role of Snf1, HXT1, and HXT3), as well as how gpa2 is involved. Some sentences in the manuscripts are repetitive; it would be beneficial to add more relevant sections to the introduction and discussion to clarify the rationale for gene choices.

Reviewer #3 (Public review):

Summary:

Nucleotide modifications are important regulators of biological function, however, until recently, their study has been limited by the availability of appropriate analytical methods. Oxford Nanopore direct RNA sequencing preserves nucleotide modifications, permitting their study, however many different nucleotide modifications lack an available base-caller to accurately identify them. Furthermore, existing tools are computationally intensive, and their results can be difficult to interpret.

Cheng et al. present SegPore, a method designed to improve the segmentation of direct RNA sequencing data and boost the accuracy of modified base detection.

Strengths:

This method is well described and has been benchmarked against a range of publicly available base callers that have been designed to detect modified nucleotides.

Comment from the Reviewing Editor:

The authors have provided responses to the weaknesses highlighted previously and the reviewers were not asked to comment. The authors have now requested a Version of Record.

Reviewer #3 (Public review):

Summary:

Pinho et al., investigated the role of the dorsal VS ventral hippocampus and gender differences in mediated learning. While previous studies already established the engagement of the hippocampus in sensory preconditioning, the authors here took advantages of freely-moving fiber photometry recording and chemogenetics to observe and manipulate sub-regions of the hippocampus (drosal VS ventral) in a cell-specific manner. Importantly, the authors validated the sensory preconditioning procedure in male mice. The authors found no evidence of sensory preconditioning in female mice, but rather a generalization effect, stressing the importance of gender differences in fear learning. After validation of a sensory preconditioning procedure in male mice using light and tone neutral stimuli and a mild foot shock as the unconditioned stimulus, the authors used fiber photometry to record from all neurons VS parvalbumin_positive_only neurons in the dorsal hippocampus or ventral hippocampus of male mice during both preconditioning and conditioning phases. They found an increased activity of all neurons, PV+_only neurons, and CAMKII+ neurons in both sub-regions of the hippocampus during both preconditioning and conditioning phases. Finally, the authors found that chemogenetic inhibition of CaMKII+ neurons (but not PV+_only neurons) in the dorsal (but not ventral) hippocampus specifically prevented the formation of an association between the two neutral stimuli (i.e., light and tone cues). This manipulation had no effect on the direct association between the light cue and the mild foot shock. This set of data (1) validates sensory preconditioning in male mice, and stresses the importance of taking gender effect into account; (2) validates the recruitment of dorsal and ventral hippocampi during preconditioning and conditioning phases; (3) and further establishes the specific role of CaMKII+ neurons in the dorsal hippocampus, but not ventral hippocampus, in the formation of an association between two neutral stimuli, but not between a neutral-stimulus and a mild foot shock.

Strengths:

The authors developed a sensory preconditioning procedure in male mice to investigate mediated learning using light and tone cues as neutral stimuli, and a mild foot shock as the unconditioned stimulus. They provide evidence of a gender effect in the formation of light-cue association. The authors took advantage of fiber-photometry and chemogenetics to target sub-regions of the hippocampus, in a cell-specific manner and investigate their role during different phases of a sensory conditioning procedure, and developed a DeepLabCut-based strategy to assess freezing fear responses.

Weaknesses:

The authors went further than previous studies by investigating the role of sub-regions the hippocampus in mediated learning, however, there are a few weaknesses that should be addressed in future studies:

(1) This study found a generalization effect in female mice only. While the authors attempted to neutralize this effect, the mechanism underlying this gender effect and whether female mice can display evidence for mediated learning has yet to be determined.

(2) One of the main effects from which derives the conclusion of this study (i.e., deficit of mediated learning in male mice when CAMKII+ neurons are inhibited in the dorsal HPC during the preconditioning phase) lies in the absence of a significant difference of the freezing response before and during the tone cue presentation when CAMKII+ are chemogenetically inhibited during the Probe Test Tone phase (cf. Fig. 4 Panel B, DPCd group). The fear response before the tone cue presentation in this group (DPCd) seems higher than in Controls_d and DPTd groups and could have masked a mediated learning effect.

Reviewer #3 (Public review):

This study by the Boddy and Otomo laboratories further characterizes the roles of SMC5/6 loader proteins and related factors in SMC5/6-mediated repression of extrachromosomal circular DNA. The work shows that mutations engineered at an AlphaFold-predicted protein-protein interface formed between the loader SLF2/SIMC1 and SMC6 (similar to the interface in the yeast counterparts observed by cryo-EM) prevent co-IP of the respective proteins. The mutations in SLF2 also hinder plasmid DNA silencing when expressed in SLF2-/- cell lines, suggesting that this interface is needed for silencing. SIMC1 is dispensable for recruitment of SMC5/6 to sites of DNA damage, while SLF1 is required, thus separating the functions of the two loader complexes. Preventing SUMOylation (with a chemical inhibitor) increases transcription from plasmids but does not in SLF2-deleted cell lines, indicating the SMC5/6 silences plasmids in a SUMOylation dependent manner. Expression of LT is sufficient for increased expression, and again, not additive or synergistic with SIMC1 or SLF2 deletion, indicating that LT prevents silencing by directly inhibiting 5/6. In contrast, PML bodies appear dispensable for plasmid silencing.

Reviewer #3 (Public review):

Summary:

In their manuscript, Armand and colleagues investigate the potential of continuing CDK4/6 inhibitors or combining them with CDK2 inhibitors in the treatment of breast cancer that has developed resistance to initial therapy. Utilizing cellular and animal models, the research examines whether maintaining CDK4/6 inhibition or adding CDK2 inhibitors can effectively control tumor growth after resistance has set in. The key findings from the study indicate that the sustained use of CDK4/6 inhibitors can slow down the proliferation of cancer cells that have become resistant, and the combination of CDK2 inhibitors with CDK4/6 inhibitors can further enhance the suppression of tumor growth. Additionally, the study identifies that high levels of Cyclin E play a significant role in resistance to the combined therapy. These results suggest that continuing CDK4/6 inhibitors along with the strategic use of CDK2 inhibitors could be an effective strategy to overcome treatment resistance in hormone receptor-positive breast cancer. However, several issues need to be addressed before considering its publication.

Strengths:

(1) Continuous CDK4/6 Inhibitor Treatment Significantly Suppresses the Growth of Drug-Resistant HR+ Breast Cancer: The study demonstrates that the continued use of CDK4/6 inhibitors, even after disease progression, can significantly inhibit the growth of drug-resistant breast cancer.

(2) Potential of Combined Use of CDK2 Inhibitors with CDK4/6 Inhibitors: The research highlights the potential of combining CDK2 inhibitors with CDK4/6 inhibitors to effectively suppress CDK2 activity and overcome drug resistance.

(3) Discovery of Cyclin E Overexpression as a Key Driver: The study identifies overexpression of cyclin E as a key driver of resistance to the combination of CDK4/6 and CDK2 inhibitors, providing insights for future cancer treatments.

(4) Consistency of In Vitro and In Vivo Experimental Results: The study obtained supportive results from both in vitro cell experiments and in vivo tumor models, enhancing the reliability of the research.

(5) Validation with Multiple Cell Lines: The research utilized multiple HR+/HER2- breast cancer cell lines (such as MCF-7, T47D, CAMA-1) and triple-negative breast cancer cell lines (such as MDA-MB-231), validating the broad applicability of the results.

Comments on revisions:

The authors made a significant effort to improve the manuscript. My comments were sufficiently addressed.

Reviewer #3 (Public review):

Summary:

The authors of this manuscript have addressed a key concept in T cell development: how early thymus gd T cell subsets are specified and the elements that govern gd T17 versus other gd T cell subsets or ab T cell subsets are specified. They show that the transcriptional regulator HEB/Tcf12 plays a critical role in specifying the gd T17 lineage and, intriguingly, that it upregulates the inhibitor Id3, which is later required for further gd T17 maturation.

Strengths:

The conclusions drawn by the authors are amply supported by a detailed analysis of various stages of T cell maturation in WT and KO mouse strains at the single cell level, both phenotypically, by flow cytometry for various diagnostic surface markers, and transcriptionally, by single cell sequencing. Their conclusions are balanced and well supported by the data and citations of previous literature.

Weaknesses:

I actually found this work to be quite comprehensive. I have a few suggestions for additional analyses the authors could explore that are unrelated to the predominant conclusions of the manuscript, but I failed to find major flaws in the current work.

I note that HEB is expressed in many hematopoietic lineages from the earliest progenitors and throughout T cell development. It is also noteworthy that abortive gamma and delta TCR rearrangements have been observed in early NK cells and ILCs, suggesting that, particularly in early thymic development, specification of these lineages may have lower fidelity. It might prove interesting to see whether their single-cell sequencing or flow data reveal changes in the frequency of these other T-cell-related lineages. Is it possible that HEB is playing a role not only in the fidelity of gdT17 cell specification, but also perhaps in the separation of T cells from NK cells and ILCs or the frequency of DN1, DN2, and DN3 cells? Perhaps their single-cell sequencing data or flow analyses could examine the frequency of these cells? That minor caveat aside, I find this to be an extremely exciting body of work.

Reviewer #3 (Public review):

Summary:

The manuscript by Chaya and Syed focuses on understanding the link between cell cycle and temporal patterning in central brain type II neural stem cells (NSCs). To investigate this, the authors perturb the progression of the cell cycle by delaying the entry into M phase and preventing cytokinesis. Their results convincingly show that temporal factor expression requires progression of the cell cycle in both Type 1 and Type 2 NSCs in the Drosophila central brain. Overall, this study establishes an important link between the two timing mechanisms of neurogenesis.

Strengths:

The authors provide solid experimental evidence for the coupling of cell cycle and temporal factor progression in Type 2 NSCs. The quantified phenotype shows an all-or-none effect of cell cycle block on the emergence of subsequent temporal factors in the NSCs, strongly suggesting that both nuclear division and cytokinesis are required for temporal progression. The authors also extend this phenotype to Type 1 NSCs in the central brain, providing a generalizable characterization of the relationship between cell cycle and temporal patterning.

Weaknesses:

One major weakness of the study is that the authors do not explore the mechanistic relationship between the cell cycle and temporal factor expression. Although their results are quite convincing, they do not provide an explanation as to why Cdk1 depletion affects Syp and EcR expression but not the onset of svp. This result suggests that at least a part of the temporal cascade in NSCs is cell-cycle independent, which isn't addressed or sufficiently discussed.

Reviewer #3 (Public review):

Summary:

Hensley et al. present an important study into the force-detachment behaviour of kinesin-1, the most well-characterised motor protein. One of the key techniques used to characterise kinesins is in vitro optical trapping of purified proteins, which has provided remarkable insights into the biochemical and mechanical mechanisms of motor proteins under single- and multi-motor conditions. This study presents an adapted (from Urbanska et al.) methodological approach of DNA-tethering kinesin-1 to a bead, both under single- and multi-motor conditions, which is then trapped to characterise the run length, processivity, and stall behaviour under unloaded and loaded (both assisting and hindering) conditions. The new approach reduces the vertical or z-force and thus provides insights into the role of horizontal or x-forces acting on the motor. Based on their method of imposing dominant horizontal forces on the motor and their data, they conclude that kinesin-1 exhibits a higher asymmetry in its force-detachment kinetics, is less slippery, and exhibits slip-bond behaviour, particularly under hindering loads. Under assisting loads, similar slip-bond kinetics ensue, but detachment from the microtubule is far more sensitive. To demonstrate the implications of their method and data, they conduct a multi-motor assay and show that multiple kinesin-1 motors can generate significantly higher forces, almost proportional to motor number. Overall, this is important work, and the data are compelling.

Strengths:

The method of DNA-tethered motor trapping is effective in reducing vertical forces and can be easily optimised for other motors and protein characterisation. The major strength of the paper is characterising kinesin-1 under low z-forces, which is likely to reflect the physiological scenario. They report that kinesin-1 is more robust and less prone to premature detachment. The motors exhibit higher stall rates and times. Under hindering and assisting loads, kinesin-1 detachment is more asymmetric and sensitive, and with low z-force shows that slip-behaviour kinetics prevail. Another achievement of this paper is the demonstration of the multi-motor kinesin-1 assay using their low-z force method, showing that multiple kinesin-1 motors are capable of generating higher forces (up to 15 pN, and nearly proportional to motor number), thus opening an avenue to study multiple motor coordination.

Weaknesses:

The method of DNA-tethered motor trapping to enable low z-force is not entirely novel, but adapted from Urbanska (2021) for use in conventional optical trapping laboratories without reliance on microfluidics. However, I appreciate that they have fully established it here to share with the community. The authors could strengthen their methods section by being transparent about protein weight, protein labelling, and DNA ladders shown in the supplementary information. What organism is the protein from? Presumably human, but this should be specified in the methods. While the figures show beautiful data and exemplary traces, the total number of molecules analysed or events is not consistently reported. Overall, certain methodological details should be made sufficient for reproducibility.

The major limitation the study presents is overarching generalisability, starting with the title. I recommend that the title be specific to kinesin-1. The study uses two constructs: a truncated K560 for conventional high-force assays, and full-length Kif5b for the low z-force method. However, for the multi-motor assay, the authors use K560 with the rationale of preventing autoinhibition due to binding with DNA, but that would also have limited characterisation in the single-molecule assay. Overall, the data generated are clear, high-quality, and exciting in the low z-force conditions. But why have they not compared or validated their findings with the truncated construct K560? This is especially important in the force-feedback experiments and in comparison with Andreasson et al. and Carter et al., who use Drosophila kinesin-1. Could kinesin-1 across organisms exhibit different force-detachment kinetics? It is quite possible. Similarly, the authors test backward slipping of Kif5b and K560 and measure dwell times in multi-motor assays. Why not detail the backward slippage kinetics of Kif5b and any step-size impact under low z-forces? For instance, with the traces they already have, the authors could determine slip times, distances, and frequency in horizontal force experiments. Overall, the manuscript could be strengthened by analysing both constructs more fully.

Appraisal and impact:

This study contributes to important and debated evidence on kinesin-1 force-detachment kinetics. The authors conclude that kinesin-1 exhibits a slip-bond interaction with the microtubule under increasing forces, while other recent studies (Noell et al. and Kuo et al.), which also use low z-force setups, conclude catch-bond behaviour under hindering loads. I find the results not fully aligned with their interpretation. The first comparison of low z-forces in their setup with Noell et al. (2024), based on stall times, does not hold, because it is an apples-to-oranges comparison. Their data show a stall time constant of 2.52 s, which is comparable to the 3 s reported by Noell et al., but the comparison is made with a weighted average of 1.49 s. The authors do report that detachment rates are lower in low z-force conditions under unloaded scenarios. So, to completely rule out catch-bond-like behaviour is unfair. That said, their data quality is good and does show that higher hindering forces lead to higher detachment rates. However, on closer inspection, the range of 0-5 pN shows either a decrease or no change in detachment rate, which suggests that under a hindering force threshold, catch-bond-like or ideal-bond-like behaviour is possible, followed by slip-bond behaviour, which is amazing resolution. Under assisting loads, the slip-bond character is consistent, as expected. Overall, the study contributes to an important discussion in the biophysical community and is needed, but requires cautious framing, particularly without evidence of motor trapping in a high microtubule-affinity state rather than genuine bond strengthening.

Reviewer #3 (Public review):

This study makes excellent use of a uniquely large dataset of reaching movements collected over several decades to evaluate the origins of systematic motor biases. The analyses convincingly demonstrate that these biases are not explained by errors in sensed hand position or by biomechanical constraints, but instead arise from a misalignment between eye-centric and body-centric representations of position. By testing multiple computational models across diverse contexts-including different effectors, visible versus occluded start positions-the authors provide strong evidence for their transformation model. My earlier concerns have been addressed, and I find the work to be a significant and timely contribution that will be of broad interest to researchers studying visuomotor control, perception, and sensorimotor integration.

Reviewer #3 (Public review):

Liff et al. have made considerable effort to improve their manuscript. In their revised manuscript, the authors have substantiated their claims of intergenerationally inherited changes in the olfactory system in response to odor-dependent fear conditioning. Several new experiments and analyses now strengthen this study.

I still find that the statement that the study provides "insight into the heritability of acquired phenotypes" is somewhat misleading. In their response to this initially raised point the authors correctly point out that their "results provide basic knowledge that will accelerate our ability to uncover the mechanisms driving heritable changes." That said, current "insights" are not mechanistic in nature.

Reviewer #3 (Public review):

In the study the authors performed longitudinal 1P calcium imaging of mouse mPFC across 8 weeks during learning of an olfactory-guided task, including habituation, training, and sleep periods. The authors' goal was to determine how the mPFC representation of the task changed and what aspects of activity emerged between the learning and the learned conditions of the task. The task had 3 arms. Odor was sampled at the end of the middle arm (named the "Sample" period). The animal then needed to run to one of the two other arms (R or L) based on the odor. The whole period until they reached the end of one of the choice arms was the "Outward" period. The time at the reward end was the "Reward" period. They noted several changes from the learning condition to the learned condition:

(1) They classified cells in a few ways. First each cell was classified as SI (spatially informative) if it had significantly more spatial information than shuffled activity, and ~50% of cells ended up being SI cells. Then among the SI cells they classified a cell as a TC (task cell) if it had statistically similar activity maps for R versus L arms, and a GC (goal arm cell) otherwise. Note that there are 4 kinds of these cells: outer arm TCs and GCs and middle arm TCs and GCs (with middle arm GCs essentially being like "splitter cells" since they are not similarly active in the middle arm for R versus L trials). There was an increase in TCs from the learning to the learned condition sessions. They also note the sources of these TCs (some came from GCs, others from non-SI cells).

(2) They analyze activity sequences across cells. They extracted 500 ms duration bursts (defined as periods of activity > 0.5 standard deviations over what I assume is the mean, which is a permissive threshold encompassing a significant fraction of the activity in non-sleep, non-habituation periods). They first noted that the resulting "Burst rates were significantly larger during behavioral epochs than during sleep and during periods of habituation to the arena" and "Moreover, burst rates during correct trials were significantly lower than during error trials". For the sequence analysis they only considered bursts consisting of at least 5 active cells. A cell's activity within the burst was set to the center of mass calcium activity. Then they took all the sequences from all learned and learning sessions together and hierarchically clustered them based on the Spearman's rank correlation between the order of activity in each pair of sequences (among the cells active in both). The iterative hierarchical clustering process produces groups (clusters) of sequences such that there are multiple repeats of sequences within a cluster. Different sequences are expressed across all the longitudinally recorded sessions. They noted "large differences of sequence activation between learning and learned condition, both in the spatial patterns (example animal in Fig. 4D) and the distribution of the sequences (Fig. 4D,E). Rastermap plots (Fig. 4D) also reveal little similarity of sequence expression between task and habituation or sleep condition." They also note the difference in the sequences between learning and learned condition was larger than the different between correct and error trials within each condition. They conclude that during task learning new representations are established, as measured by the burst sequence content. They do additional analyses of the sequence clusters by assessing the spatial informativeness (SI) of each sequence cluster. Over learning they find an increase in clusters that are spatially informative (clusters that tend to occur in specific locations). Finally, they analyzed the SI clusters in a similar manner as SI cells and classified them as task phase selective sequences (TSs) and goal arm selective sequences (GSs) and did some further analysis. However, they themselves conclude that the frequency of TSs and GSs is limited because most sequence clusters were non-SI. In the discussion they say "In addition to GSs and TSs, we found that most of the recurring sequences are not related to behavior (not SI)".

(3) As an alternative to analyzing individual cells and sequences of individual cells, they then look for trajectory replay using Bayesian population decoding of location during bursts. They analyze TS bursts, GS bursts, and non-SI bursts. They say "we found correlations of decoded position with time bin (within a 500 ms burst) strongly exceeding chance level only during outward and reward phase, for both GSs and TSs (Fig 5H)." Fig5H shows distributions indicating statistically significant bias in the forward direction (using correlations of decoded location versus time bin across 10 bins of 50 ms each within each 500ms burst). They find that the Outward trajectories appear to reflect the actual trajectory during running itself, so are likely not replay. But the sequences at the Reward are replay as they do not reflect the current location. Furthermore, replay at the Reward is in the forward direction (unlike the reverse replay at Reward seen in the hippocampus) and this replay is only seen in the learned and not the learning condition. At the same time, they find that replay is not seen during odor Sampling, from which they conclude there is no evidence of replay used for planning. Instead they say the replay at the Reward could possibly be for evaluation during the Reward phase, though this would only be for the learned condition. They conclude "Together with our finding of strong changes in sequence expression after learning (Fig 4E) these findings suggest that a representation of task develops during learning".

This study provides valuable new information about the evolution of mPFC activity during the learning of a odor-based 2AFC T-maze-like task. They show convincing evidence of changes in single cell tuning, population sequences, and replay events. They also find novel forward replay at the Reward, and find that this is present only after the animal learned the task. In the discussion the authors note "the present study, to our knowledge, identified for the first time fast recurring neural sequence activity from 1-p calcium data, based on correlation analysis". Overall, they find a substantial amount of change among the features they analyzed and according to their methods, though they note a small amount of activity was preserved through learning.

One comment is that the threshold for extracting burst events (0.5 standard deviations, presumably above the mean) seems lower than what one usually sees as a threshold for population burst detection, and the authors show (in Supplementary Fig 1) that this means bursts cover ~20-40% of the data. However, it is potentially a strength of this work that their results are found by using this more permissive threshold.

Reviewer #3 (Public review):

Summary:

The authors are reanalyzing previously published data to test the hypothesis that serotonin neurons encode state value. Here, the authors focus on analyzing the firing rate of serotonin neurons during the inter-trial interval, in which no cues or outcomes are delivered. The goal is to quantify and find neurons whose activity is explained by value encoding, and for those that have this property, determine what the timescale of reward integration is (e.g., a few trials, tens of trials, or the entire session) in individual neurons.

Strengths:

The major strengths are the use of a Bayesian modelling approach to extract value and thirst coding features from individual neurons, and comparison of the time course of adaptation of serotonin neurons with a behavioral output, licking in this case. I also appreciate the use of a separate dataset to establish prior distributions for baseline firing rate to be used in the modelling done here, which is an attempt to deal with the main weakness of this study:

Weaknesses:

The weakness of this study is the small number of neurons available for analysis, resulting in a small number of neurons that unequivocally are modulated by value.

The authors did achieve their aims, but the results show that it is hard to unequivocally separate value-coding neurons with long timescales from thirst-coding neurons, which is acknowledged by the authors.

While the experimental results do not allow for a strong conclusion regarding the distinction of value versus thirst coding in serotonin neurons, the methods employed and the rationale for using them are of great utility to the community and for considerations of behavioral task design and data analysis in future studies. This is a point that the authors could discuss/develop more.

Additional significance of the work:

The comparison between time courses for behavior (anticipatory licking) and serotonin activity (as well as the reference to dopamine activity's time course from a previous study) is of great significance for any researcher studying behavioral control. Mounting evidence suggests that multiple brain circuits contribute to any given action selection. Therefore, expecting a perfect alignment between the time course of neuromodulator activity and behavioral output might be unreasonable. For future studies, modelling behavioral output as a combination of policies determined by multiple brain circuits or neuromodulators might be a promising approach.

Reviewer #3 (Public review):

Summary:

Fritz et al. investigate the changes in synaptic connectivity between two different life stages of the Drosophila larva, L1 and L3. They focus on 3 types of nociceptive mechanosensory neurons and their connecting 6 downstream interneurons. Connectomic analysis reveals that connectivity and dendritic density are stable across development; however, axonal density, axodendritic overlap, and the number of synapses increase. Finally, using a modeling approach, they demonstrate that this conservation of most features enables stable output across life stages.

Strengths:

The authors analyse two different connectomes from fly larvae in two different life stages. By now, there are only very few such samples available; thus, this is a novel approach and will be helpful to guide further comparative connectomic studies in the future.

Weaknesses:

The authors analyze only a minimal circuit with 9 different cell types on each hemisphere; thus, their findings might be specialised for this specific nociceptive sensory to interneuron peripheral circuit. Also, more animals might need to be analyzed in different life stages to generalize these findings.

Reviewer #3 (Public review):

Summary:

The study uses the food choice task, a well-established method in eating disorder research, particularly in anorexia nervosa. However, it introduces a novel analytical approach-the diffusion decision model-to deconstruct food choices and assess the influence of negative affect on how and when tastiness and healthiness are considered in decision-making among individuals with bulimia nervosa and healthy controls.

Strengths:

The introduction provides a comprehensive review of the literature, and the study design appears robust. It incorporates separate sessions for neutral and negative affect conditions and counterbalances tastiness and healthiness ratings. The statistical methods are rigorous, employing multiple testing corrections.

A key finding-that negative affect induction biases individuals with bulimia nervosa toward prioritizing tastiness over healthiness-offers an intriguing perspective on how negative affect may drive binge eating behaviors.

Weaknesses:

A notable limitation is the absence of a sample size calculation, which, combined with the relatively small sample, may have contributed to null findings. Additionally, while the affect induction method is validated, it is less effective than alternatives such as image or film-based stimuli (Dana et al., 2020), potentially influencing the results.

Reviewer #3 (Public review):

Summary

This paper investigates how disinformation affects reward learning processes in the context of a two-armed bandit task, where feedback is provided by agents with varying reliability (with lying probability explicitly instructed). They find that people learn more from credible sources, but also deviate systematically from optimal Bayesian learning: They learned from uninformative random feedback and updated too quickly from fully credible feedback (especially following low-credibility feedback). People also appeared to learn more from positive feedback and there is tentative evidence that this bias is exacerbated for less credible feedback.

Overall, this study highlights how misinformation could distort basic reward learning processes, without appeal to higher order social constructs like identity.

Strengths - The experimental design is simple and well-controlled; in particular, it isolates basic learning processes by abstracting away from social context - Modeling and statistics meet or exceed standards of rigor - Limitations are acknowledged where appropriate, especially those regarding external validity and challenges in dissociating positivity bias from perseveration - The comparison model, Bayes with biased credibility estimates, is strong; deviations are much more compelling than e.g. a purely optimal model - The conclusions are of substantial interest from both a theoretical and applied perspective

Weaknesses

The authors have done a great job addressing my concerns with the two previous submission. The one issue that they were not able to truly address is the challenge of dissociating positivity bias from perseveration; this challenge weakens evidence for the conclusion that less credible feedback yields a stronger positivity bias. However, the authors have clearly acknowledged this limitation and tempered their conclusions accordingly. Furthermore, the supplementary analyses on this point are suggestive (if not fully conclusive) and do a better job of at least trying to address the confound than most work on positivity/confirmation bias.

I include my previous review describing the challenge in more detail for reference. I encourage interested readers to see the author response as well. It has convinced me that this weakness is not a reflection of the work, but is instead a fundamental challenge for research on positivity bias.

Absolute or relative positivity bias?

The conclusion of greater positivity bias for lower credible feedback (Fig 5) hinges on the specific way in which positivity bias is defined. Specifically, we only see the effect when normalizing the difference in sensitivity to positive vs. negative feedback by the sum. I appreciate that the authors present both and add the caveat whenever they mention the conclusion. However, without an argument that the relative definition is more appropriate, the fact of the matter is that the evidence is equivocal.

There is also a good reason to think that the absolute definition is more appropriate. As expected, participants learn more from credible feedback. Thus, normalizing by average learning (as in the relative definition) amounts to dividing the absolute difference by increasingly large numbers for more credible feedback. If there is a fixed absolute positivity bias (or something that looks like it), the relative bias will necessarily be lower for more credible feedback. In fact, the authors own results demonstrate this phenomenon (see below). A reduction in relative bias thus provides weak evidence for the claim.

It is interesting that the discovery study shows evidence of a drop in absolute bias. However, for me, this just raises questions. Why is there a difference? Was one just a fluke? If so, which one?

Positivity bias or perseveration?

Positivity bias and perseveration will both predict a stronger relationship between positive (vs. negative) feedback and future choice. They can thus be confused for each other when inferred from choice data. This potentially calls into question all the results on positivity bias.

The authors clearly identify this concern in the text and go to considerable lengths to rule it out. However, the new results (in revision 1) show that a perseveration-only model can in fact account for the qualitative pattern in the human data (the CA parameters). This contradicts the current conclusion:

Critically, however, these analyses also confirmed that perseveration cannot account for our main finding of increased positivity bias, relative to the overall extent of CA, for low-credibility feedback.

Figure 24c shows that the credibility-CA model does in fact show stronger positivity bias for less credible feedback. The model distribution for credibility 1 is visibly lower than for credibilities 0.5 and 0.75.

The authors need to be clear that it is the magnitude of the effect that the perseveration-only model cannot account for. Furthermore, they should additionally clarify that this is true only for models fit to data; it is possible that the credibility-CA model could capture the full size of the effect with different parameters (which could fit best if the model was implemented slightly differently).

The authors could make the new analyses somewhat stronger by using parameters optimized to capture just the pattern in CA parameters (for example by MSE). This would show that the models are in principle incapable of capturing the effect. However, this would be a marginal improvement because the conclusion would still rest on a quantitative difference that depends on specific modeling assumptions.

New simulations clearly demonstrate the confound in relative bias

Figure 24 also speaks to the relative vs. absolute question. The model without positivity bias shows a slightly stronger absolute "positivity bias" for the most credible feedback, but a weaker relative bias. This is exactly in line with the logic laid out above. In standard bandit tasks, perseveration can be quite well-captured by a fixed absolute positivity bias, which is roughly what we see in the simulations (I'm not sure what to make of the slight increase; perhaps a useful lead for the authors). However, when we divide by average credit assignment, we now see a reduction. This clearly demonstrates that a reduction in relative bias can emerge without any true differences in positivity bias.

Given everything above, I think it is unlikely that the present data can provide even "solid" evidence for the claim that positivity bias is greater with less credible feedback. This confound could be quickly ruled out, however, by a study in which feedback is sometimes provided in the absence of a choice. This would empirically isolate positivity bias from choice-related effects, including perseveration.

Comments on revisions:

Great work on this. The new paper is very interesting as well. I'm delighted to see that the excessive amount of time I spent on this review has had a concrete impact.

Reviewer #3 (Public review):

Summary:

Rong et al., compare EEG image responses from a large-scale dataset to state-of-the-art vision and language models, as well as their fusion. They find that the fusion of models provides the best predictivity, with early contribution from vision models and later predictivity from language models. The paper has several strengths: high temporal resolution data (though at the expense of spatial resolution), detailed comparison of alignment (and differences) between vision and language model embeddings, and comparison of "fusion" of different DNN models.

Despite the paper's strengths, it is not clear what is at stake with these findings or how they advance our knowledge beyond other recent studies showing vision versus language model predictions of visual cortex responses with fMRI.

Strengths:

The authors use a large-scale EEG dataset and a comprehensive modeling approach. The methods are sound and involve multiple model comparisons. In particular, the disentangling of vision and language model features is something that has been largely ignored in prior related studies.

Weaknesses:

(1) The authors state their main hypothesis (lines 48-51) that human neural responses to visual stimulation are better modelled by combining representations from a vision DNN and an LLM than by the representations from either of the two components alone, and that the vision DNN and LLM components would uniquely predict earlier and later stages of visual processing, respectively.

While they confirm this hypothesis in largely compelling ways, it is not clear whether these results tell us something about the brain beyond how to build the most predictive model.

In particular, why do language models offer advantages over vision models, and what does this tell us about human visual processing? In several places, the discussion of advantages for the language model felt somewhat trivial and did not seem to advance our understanding of human vision, e.g., "responses for visual stimulation encode detailed information about objects and their properties" (lines 266-270) and "LLM representations capture detailed visuo-semantic information about the stimulus images" (line 293).

(2) It is not clear what the high temporal resolution EEG data tell us that the whole-brain fMRI data do not. The latency results seem to be largely in line with fMRI findings, where the early visual cortex is better predicted by vision models, and the language model is better in later/more anterior regions. In addition, it would help to discuss whether the EEG signals are likely to be restricted to the visual cortex, or could the LLM predictivity explain downstream processing captured by whole-brain EEG signals?

Relatedly, it would help the authors to expand on the implications of the frequency analysis.

(3) While the authors test many combinations of vision and language models and show their "fusion" advantages are largely robust to these changes, it is still hard to ignore the vast differences between vision and language models, in terms of architecture and how they are trained. Two studies (Wang et al., 2023, and Conwell et al., 2024) have now shown that when properly controlling for architecture and dataset, there is little to no advantage of language alignment in predicting visual cortex responses. It would help for the authors to both discuss this aspect of the prior literature and to try to address the implications for their own findings (related to pt 1 about what, if anything, is "special" about language models).

(4) Model features - it would help to state the dimensionality of the input embeddings for each model and how much variance is explained and preserved after the PCA step? I wonder how sensitive the findings are to this choice of dimensionality reduction, and whether an approach that finds the optimal model layer (in a cross-validated way) would show less of a difference between vision/language models (I realize this is not feasible with models like GPT-3).

(5) To better understand the fusion advantage, it would help to look at the results, look for a pair of vision models and a pair of language models. Can a similar advantage be found by combining models from the same modality?

Reviewer #4 (Public review):

Summary:

In this manuscript, authors used a learning paradigm in C. elegans; when worms were fed in a saltless plate, its chemotaxis to salt is greatly reduced. To identify learning-related proteins, authors employed nervous system-specific transcriptome analysis to compare whole proteins in neurons between high-salt-fed animals and saltless-fed animals. Authors identified "learning-specific proteins" which are observed only after saltless feeding. They categorized these proteins by GO analyses, pathway analyses and expression site analyses, and further stepped forward to test mutants in selected genes identified by the proteome analysis. They find several mutants that are defective or hyper-proficient for learning, including acc-1/3 and lgc-46 acetylcholine receptors, F46H5.3 putative arginine kinase, and kin-2, a cAMP pathway gene. These mutants were not previously reported to have abnormality in the learning paradigm.

Concerns:

Upon revision, authors addressed all concerns of this reviewer, and the results are now presented in a way that facilitates objective evaluation. Authors' conclusions are supported by the results presented, and the strength of the proteomics approach is persuasively demonstrated.

Significance:

(1) Total neural proteome analysis has not been conducted before for learning-induced changes, though transcriptome analysis has been performed for odor learning (Lakhina et al., http://dx.doi.org/10.1016/j.neuron.2014.12.029). This warrants the novelty of this manuscript, because for some genes, protein levels may change even though mRNA levels remain the same. Although in a few reports TurboID has been used in C. elegans, this is the first report of a systematic analysis of tissue-specific differential proteomics.

(2) Authors found five mutants that have abnormality in the salt learning. These genes have not been described to have the abnormality, providing novel knowledge to the readers, especially those who work on C. elegans behavioural plasticity. Especially, involvement of acetylcholine neurotransmission has not been addressed before. Although transgenic rescue experiments have not been performed except kin-2, and the site of action (neurons involved) has not been tested in this manuscript, it will open the venue to further determine the way in which acetylcholine receptors, cAMP pathway etc. influences the learning process.

Reviewer #3 (Public review):

Summary:

The authors demonstrate that CRF neurons in the extended amygdala form GABAergic synapses onto cholinergic interneurons and that CRF can excite these neurons. The evidence is strong, however, the authors fail to make a compelling connection showing CRF released from these extended amygdala neurons is mediating any of these effects. Further, they show that acute alcohol appears to modulate this action, although the effect size is not particularly robust.

Strengths:

This is an exciting connection from the extended amygdala to the striatum that provides a new direction for how these regions can modulate behavior. The work is rigorous and well done.

Weaknesses:

While the authors show that opto stim of these neurons can increase firing, this is not shown to be CRFR1 dependent. In addition, the effects of acute ethanol are not particularly robust or rigorously evaluated. Further, the opto stim experiments are conducted in an Ai32 mouse, so it is impossible to determine if that is from CEA and BNST, vs. another population of CRF-containing neurons. This is an important caveat.

Reviewer #3 (Public review):

Summary:

In this study, Philipp et al. investigate how a monkey learns to compensate for a large, chronic biomechanical perturbation - a tendon transfer surgery, swapping the actions of two muscles that flex and extend the fingers. After performing the surgery and confirming that the muscle actions are swapped, the authors follow the monkeys' performance on grasping tasks over several months. There are several main findings:

(1) There is an initial stage of learning (around 60 days), where monkeys simply swap the activation timing of their flexors and extensors during the grasp task to compensate for the two swapped muscles.

(2) This is (seemingly paradoxically) followed by a stage where muscle activation timing returns almost to what it was pre-surgery, suggesting that monkeys suddenly swap to a new strategy that is better than the simple swap.

(3) Muscle synergies seem remarkably stable through the entire learning course, indicating that monkeys do not fractionate their muscle control to swap the activations of only the two transferred muscles.

(4) Muscle synergy activation shows a similar learning course, where the flexion synergy and extension synergy activations are temporarily swapped in the first learning stage and then revert to pre-surgery timing in the second learning stage.

(5) The second phase of learning seems to arise from making new, compensatory movements (supported by other muscle synergies) that get around the problem of swapped tendons.

Strengths:

This study is quite remarkable in scope, studying two monkeys over a period of months after a difficult tendon-transfer surgery. As the authors point out, this kind of perturbation is an excellent testbed for the kind of long-term learning that one might observe in a patient after stroke or injury, and provides unique benefits over more temporary perturbations like visuomotor transformations and studying learning through development. Moreover, while the two-stage learning course makes sense, I found the details to be genuinely surprising--specifically the fact that: (1) muscle synergies continue to be stable for months after the surgery, despite now being maladaptive; and (2) muscle activation timing reverts to pre-surgery levels by the end of the learning course. These two facts together initially make it seem like the monkey simply ignores the new biomechanics by the end of the learning course, but the authors do well to explain that this is mainly because the monkeys develop a new kind of movement to circumvent the surgical manipulation.

I found these results fascinating, especially in comparison to some recent work in motor cortex, showing that a monkey may be able to break correlations between the activities of motor cortical neurons, but only after several sessions of coaching and training (Oby et al. PNAS 2019). Even then, it seemed like the monkey was not fully breaking correlations but rather pushing existing correlations harder to succeed at the virtual task (a brain-computer interface with perturbed control).

Weaknesses:

I found the analysis to be reasonably well considered and relatively thorough. However, I do have a few suggestions that I think may elevate the work, should the authors choose to pursue them.

First, I find myself wondering about the physical healing process from the tendon transfer surgery and how it might contribute to the learning. Specifically, how long does it take for the tendons to heal and bear forces? If this itself takes a few months, it would be nice to see some discussion of this.

Second, I see that there are some changes in the muscle loadings for each synergy over the days, though they are relatively small. The authors mention that the cosine distances are very small for the conserved synergies compared to distances across synergies, but it would be good to get a sense for how variable this measure is within synergy. For example, what is the cosine similarity for a conserved synergy across different pre-surgery days? This might help inform whether the changes post-surgery are within a normal variation or whether they reflect important changes in how the muscles are being used over time.

Last, and maybe most difficult (and possibly out of scope for this work): I would have ideally liked to see some theoretical modeling of the biomechanics so I could more easily understand what the tendon transfer did or how specific synergies affect hand kinematics before and after the surgery. Especially given that the synergies remained consistent, such an analysis could be highly instructive for a reader or to suggest future perturbations to further probe the effects of tendon transfer on long-term learning.

Reviewer #4 (Public review):

Thank you to the authors for their detailed responses and changes in relation to my questions. They have addressed all my concerns around methodological and inference clarity. I would still recommend against the use of feature/pathway selection techniques where there is no way of applying formal error control. I am pleased to read, however, that the authors are planning to develop this in future work. My edited review reflects these changes:

The authors apply what I gather is a novel methodology titled "Multi-gradient Permutation Survival Analysis" to identify genes that are robustly associated with prognosis ("GEARs") using tumour expression data from 15 cancer types available in the TCGA. The resulting lists of GEARs are then interrogated for biological insights using a range of techniques including connectivity and gene enrichment analysis.

I reviewed this paper primarily from a statistical perspective. Evidently an impressive amount of work has been conducted, concisely summarised, and great effort has been undertaken to add layers of insight to the findings. I am no stranger to what an undertaking this would have been. My primary concern, however, is that the novel statistical procedure proposed, and applied to identify the gene lists, as far as I can tell offers no statistical error control nor quantification. Consequently we have no sense what proportion of the highlighted GEAR genes and networks are likely to just be noise.

Major comments:

The main methodology used to identify the GEAR genes, "Multi-gradient Permutation Survival Analysis" does not formally account for multiple testing and offers no formal error control. Meaning we are left without knowing what the family wise (aka type 1) error rate is among the GEAR lists, nor the false discovery rate. I appreciate the emphasis on reproducibility, but I would generally recommend against the use of any feature selection methodology which does not provide error quantification because otherwise we do not know if we are encouraging our colleagues and/or readers to put resource into lists of genes that contain more noise than not. I am glad though and appreciative that the authors intend to develop this in future work.

The authors make a good point that, despite lack of validation in an external independent dataset, it is still compelling work given the functional characterisation and literature validation. I am pleased though that the authors agree validation in an independent dataset is an important next step, and plan to do so in future work.

Reviewer #3 (Public review):

Summary:

In the manuscript " Dynamics of mesoscale brain network during decision-making learning revealed by chronic, large-scale single-unit recording", Wang et al investigated mesoscale network reorganization during visual stimulus discrimination learning in mice using chronic, large-scale single-unit recordings across 10 cortical/subcortical regions. During learning, mice improved task performance mainly by suppressing licking on no-go trials. The authors found that learning induced restructuring of functional connectivity, with visual (V1, V2M) and frontal (OFC, M2) regions forming a task-relevant subnetwork during the acquisition of correct No-Go (CR) trials.

Learning also compressed sequential neural activation and broadened stimulus encoding across regions. In addition, a region's network connectivity rank correlated with its timing of peak visual stimulus encoding.

Optogenetic inhibition of orbitofrontal cortex (OFC) and high order visual cortex (V2M) impaired learning, validating its role in learning. The work highlights how mesoscale networks underwent dynamic structuring during learning.

Strengths:

The use of ultra-flexible microelectrode arrays (uFINE-M) for chronic, large-scale recordings across 10 cortical/subcortical regions in behaving mice represents a significant methodological advancement. The ability to track individual units over weeks across multiple brain areas will provide a rare opportunity to study mesoscale network plasticity.

While limited in scope, optogenetic inhibition of OFC and V2M directly ties connectivity rank changes to behavioral performance, adding causal depth to correlational observations.

Weaknesses:

The weakness is also related to the strength provided by the method. It is demonstrated in the original method that this approach in principle can track individual units for four months (Luan et al, 2017). The authors have not showed chronically tracked neurons across learning. Without demonstrating that and taking advantage of analyzing chronically tracked neurons, this approach is not different from acute recording across multiple days during learning. Many studies have achieved acute recording across learning using similar tasks. These studies have recorded units from a few brain areas or even across brain-wide areas.

Another weakness is that major results are based on analyses of functional connectivity that is calculated using the cross-correlation score of spiking activity (TSPE algorithm). Functional connection strengthen across areas is then ranked 1-10 based on relative strength. Without ground truth data, it is hard to judge the underlying caveats. I'd strongly advise the authors to use complementary methods to verify the functional connectivity and to evaluate the mesoscale change in subnetworks. Perhaps the authors can use one key information of anatomy, i.e. the cortex projects to the striatum, while the striatum does not directly affect other brain structures recorded in this manuscript.

Reviewer #3 (Public review):

Summary:

The authors investigate how sleep loss and circadian disruption affect whole-organism metabolism in Drosophila melanogaster. They used chamber-based flow-through respirometry to measure oxygen consumption and carbon dioxide production in wild-type flies and in mutants with impaired sleep or circadian function. These measurements were then integrated with a previously published metabolomics dataset to explore how respiratory dynamics align with metabolic pathways. The central claim is that wild-type flies display anticipatory coordination of metabolic processes with circadian time, while mutants exhibit reactive shifts in substrate use, redox imbalance, and signs of mitochondrial stress.

Strengths:

The study has several strengths. Continuous high-resolution respirometry in flies is challenging, and its application across multiple genotypes provides good comparative insight. The conceptual framework distinguishing anticipatory from reactive metabolic regulation is interesting. The translational framing helps place the work in a broader context of sleep, circadian biology, and metabolic health.

Weaknesses:

At the same time, the evidence supporting the conclusions is somewhat limited. The metabolomics data were not newly generated but repurposed from prior work, reducing novelty. The biological replication in the respirometry assays is low, with only a small number of chambers per genotype. Importantly, respiratory parameters in flies are strongly influenced by locomotor activity, yet no direct measurements of activity were included, making it difficult to separate intrinsic metabolic changes from behavioral differences in mutants. In addition, repeated claims of "mitochondrial stress" are not directly substantiated by assays of mitochondrial function. The study also excluded female flies entirely, despite well-documented sex differences in metabolism, which narrows the generality of the findings.

Reviewer #3 (Public review):

Summary:

The focus of the research is to understand how transcription factors with high expression in neural crest cell-derived cancers (e.g., neuroblastoma) and roles in neural crest cell development function to promote malignancy. The focus is on the transcription factor FOXC1 and using murine cell culture, gain- and loss-of-function approaches, and ChIP profiling, among other techniques, to place PKAC inhibitor ARHGAP36 mechanistically between FOXC1 and another pathway associated with malignancy, Sonic Hedgehog (SHH).

Strengths:

Major strengths are the mechanistic approaches to identify FOXC1 direct targets, definitively showing that FOXC1 transcriptional regulation of ARHGAP36 leads to dysregulation of SHH signaling downstream of ARHGAP36 inhibition of PKC. Starting from a screen of Foxc1 OE to get to ARHGAP36 and then using genetic and pharmacological manipulation to work through the mechanism is very well done. There is data that will be of use to others studying FOXC1 in mesenchymal cell types, in particular, the FOXC1 ChIP-seq.

Weaknesses:

Work is almost all performed in NIH3T3 or similar cells (mouse cells, not patient or mouse-derived cancer cells), so the link to neuroblastoma that forms the major motivation of the work is not clear. The authors look at ARHGAP36 levels in association with the neuroblastoma patient survival; however, the finding, though interesting and quite compelling, is misaligned with what the literature shows about FOXC1 and SHH, their high expression is associated with increased malignancy (also maybe worse outcomes?). Therefore, ARHGAP36 expression may be more complicated in a tumor cell or may be unrelated to FOXC1 or SHH, leaving one to wonder what the work in NIH3T3 cells, though well done, is telling us about the mechanisms of FOXC1 as an oncogene in neuroblastoma cells or in any type of cancer cell. Does it really function as an SHH activator to drive tumor growth? The 'oncogenic relevance' and 'contribution to malignancy' claimed in the last paragraph of the introduction are currently weakly supported by the data as presented. This could be improved by studying some of these mechanisms in patient-derived neuroblastoma cells with high FOXC1 expression. Does inhibiting FOXC1 change SHH and ARHGAP36 and have any effect on cell proliferation or migration? Alternatively, does OE of FOXC1 in NIH3T3 cells increase their migration or stimulate proliferation in some way, and is this dependent on ARHGAP36 or SHH? Application of their mechanistic approaches in cancer cells or looking for hallmarks of cancer phenotypes with FOXC1 OE (and dependent on SHH or ARHGAP36) could help to make a link with cellular phenotypes of malignant cells.

Reviewer #3 (Public review):

Summary:

The authors present an in vitro evaluation of drug-drug interactions between artemisinins and quinoline antimalarials, as an important aspect for screening the current artemisinin-based combination therapies for Plasmodium falciparum. Using a revised pulsing assay, they report antagonism between dihydroartemisinin (DHA) and several quinolines, including chloroquine, piperaquine (PPQ), and amodiaquine. This antagonism is increased in CQ-resistant strains in isobologram analyses. Moreover, CQ co-treatment was found to induce artemisinin resistance even in parasites lacking K13 mutations during the ring-stage survival assay. This implies that drug-drug interactions, not just genetic mutations, can influence resistance phenotypes. By using a chemical probe for reactive heme, the authors demonstrate that quinolines inhibit artemisinin activation by rendering cytosolic heme chemically inert, thereby impairing the cytotoxic effects of DHA. The study also observed negative interactions in triple-drug regimens (e.g., DHA-PPQ-Mefloquine) and in combinations involving OZ439, a next-generation peroxide antimalarial. Taken together, these findings raise significant concerns regarding the compatibility of artemisinin and quinoline combinations, which may promote resistance or reduce efficacy.

Throughout the manuscript, no combinations were synergistic, which necessitates comparing the claims to a synergistic combination as a control. The lack of this positive control makes it difficult to contextualize the observed antagonism. Including a known synergistic pair (e.g., artemisinin + lumefantrine) throughout the study would have provided a useful benchmark to assess the relative impact of the drug interactions described.

Strengths:

This study demonstrates the following strengths:

(1) The use of a pulsed in vitro assay that is more physiologically relevant than the traditional 48h or 72h assays.

(2) Small molecule probes, H-FluNox, and Ac-H-FluNox to detect reactive cytosolic heme, demonstrating that quinolines render heme inert and thereby block DHA activation.

(3) Evaluates not only traditional combinations but also triple-drug combinations and next-generation artemisinins like OZ439. This broad scope increases the study's relevance to current treatment strategies and future drug development.

(4) By using the K13 wild-type parasites, the study suggests that resistance phenotypes can emerge from drug-drug interactions alone, without requiring genetic resistance markers.

Weaknesses:

(1) No combinations are shown as synergistic: it could be valuable to have a combination that shows synergy as a positive control (e.g, artemisinin + lumefantrine) throughout the manuscript. The absence of a synergistic control combination in the experimental design makes it more challenging to evaluate the relative impact of the described drug interactions.

(2) Evaluation of the choice of drug-drug interactions: How generalizable are the findings across a broad range of combinations, especially those with varied modes of action?

(3) The study would also benefit from a characterization of the molecular basis for the observed heme inactivation by quinolines to support this hypothesis - while the probe experiments are valuable, they do not fully elucidate how quinolines specifically alter heme chemistry at the molecular level.

(4) Suggestion of alternative combinations that show synergy could have improved the significance of the work.

(5) All data are derived from in vitro experiments, without accompanying an in vivo validation. While the pulsing assay improves physiological relevance, it still cannot fully capture the complexity of drug pharmacokinetics, host-parasite interactions, or immune responses present in living organisms.

(6) The absence of pharmacokinetic/pharmacodynamic modeling leaves questions about how the observed antagonism would manifest under real-world dosing conditions.

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 #3 (Public review):

Summary

Kong and coauthors describe and implement a method to correct local deformations due to beam-induced motion in cryo-EM movie frames. This is done by fitting a 3D spline model to a stack of micrograph frames using cross-correlation-based local patch alignment to describe the deformations across the micrograph in each frame, and then computing the value of the deformed micrograph at each pixel by interpolating the undeformed micrograph at the displacement positions given by the spline model. A graphical interface in cisTEM allows the user to visualise the deformations in the sample, and the method has been proven to be successful by showing improvements in 2D template matching (2DTM) results on the corrected micrographs using five in situ samples.

Impact

This method has great potential to further streamline the cryo-EM single particle analysis pipeline by shortening the required processing time as a result of obtaining higher quality particles early in the pipeline, and is applicable to both old and new datasets, therefore being relevant to all cryo-EM users.

Strengths

(1) One key idea of the paper is that local beam induced motion affects frames continuously in space (in the image plane) as well as in time (along the frame stack), so one can obtain improvements in the image quality by correcting such deformations in a continuous way (deformations vary continuously from pixel to pixel and from frame to frame) rather than based on local discrete patches only. 3D splines are used to model the deformations: they are initialised using local patch alignments and further refined using cross-correlation between individual patch frames and the average of the other frames in the same patch stack.

(2) Another strength of the paper is using 2DTM to show that correcting such deformations continuously using the proposed method does indeed lead to improvements. This is shown using five in situ datasets, where local motion is quantified using statistics based on the estimated motions of ribosomes.

Weaknesses

(1) While very interesting, it is not clear how the proposed method using 3D splines for estimating local deformations compares with other existing methods that also aim to correct local beam-induced motion by approximating the deformations throughout the frames using other types of approximation, such as polynomials, as done, for example MotionCor2.

(2) The use of 2DTM is appropriate, and the results of the analysis are enlightening, but one shortcoming is that some relevant technical details are missing. For example, the 2DTM SNR is not defined in the article, and it is not clear how the authors ensured that no false positives were included in the particles counted before and after deformation correction. The Jupyter notebooks where this analysis was performed have not been made publicly available.

(3) It is also not clear how the proposed deformation correction method is affected by CTF defocus in the different samples (are the defocus values used in the different datasets similar or significantly different?) or if there is any effect at all.

Reviewer #3 (Public review):

Summary:

In the reviewed manuscript, researchers aimed to overcome the obstacles of high-resolution imaging of intact liver tissue. They report successful modification of the existing CUBIC protocol into Liver-CUBIC, a high-resolution multiplex 3D imaging method that integrates multicolor metallic compound nanoparticle (MCNP) perfusion with optimized liver tissue clearing, significantly reducing clearing time and enabling simultaneous 3D visualization of the portal vein, hepatic artery, bile ducts, and central vein spatial networks in the mouse liver. Using this novel platform, the researchers describe a previously unrecognized perivascular structure they termed Periportal Lamellar Complex (PLC), regularly distributed along the portal vein axis. The PLC originates from the portal vein and is characterized by a unique population of CD34⁺Sca-1⁺ dual-positive endothelial cells. Using available scRNAseq data, the authors assessed the CD34⁺Sca-1⁺ cells' expression profile, highlighting the mRNA presence of genes linked to neurodevelopment, biliary function, and hematopoietic niche potential. Different aspects of this analysis were then addressed by protein staining of selected marker proteins in the mouse liver tissue. Next, the authors addressed how the PLC and biliary system react to CCL4-induced liver fibrosis, implying PLC dynamically extends, acting as a scaffold that guides the migration and expansion of terminal bile ducts and sympathetic nerve fibers into the hepatic parenchyma upon injury.

The work clearly demonstrates the usefulness of the Liver-CUBIC technique and the improvement of both resolution and complexity of the information, gained by simultaneous visualization of multiple vascular and biliary systems of the liver at the same time. The identification of PLC and the interpretation of its function represent an intriguing set of observations that will surely attract the attention of liver biologists as well as hepatologists; however, some claims need more thorough assessment by functional experimental approaches to decipher the functional molecules and the sequence of events before establishing the PLC as the key hub governing the activity of biliary, arterial, and neuronal liver systems. Similarly, the level of detail of the methods section does not appear to be sufficient to exactly recapitulate the performed experiments, which is of concern, given that the new technique is a cornerstone of the manuscript.

Nevertheless, the work does bring a clear new insight into the liver structure and functional units and greatly improves the methodological toolbox to study it even further, and thus fully deserves the attention of readers.

Strengths:

The authors clearly demonstrate an improved technique tailored to the visualization of the liver vasulo-biliary architecture in unprecedented resolution.

This work proposes a new biological framework between the portal vein, hepatic arteries, biliary tree, and intrahepatic innervation, centered at previously underappreciated protrusions of the portal veins - the Periportal Lamellar Complexes (PLCs).

Weaknesses:

Possible overinterpretation of the CD34+Sca1+ findings was built on re-analysis of one scRNAseq dataset.

Lack of detail in the materials and methods section greatly limits the usefulness of the new technique to other researchers.

Reviewer #3 (Public review):

Summary:

Recent studies have established that trypanocidal drugs, including pentamidine and melarsoprol, enter the trypanosomes via the glyceroaquaporin AQP2 (TbAQP2). Interestingly, drug resistance in trypanosomes is, at least in part, caused by recombination with the neighbouring gene, AQP3, which is unable to permeate pentamidine or melarsoprol. The effect of the drugs on cells expressing chimeric proteins is significantly reduced. In addition, controversy exists regarding whether TbAQP2 permeates the drugs like an ion channel, or whether it serves as a receptor that triggers downstream processes upon drug binding. In this study the authors set out to achieve these objectives: 1) to understand the molecular interactions between TbAQP2 and glycerol, pentamidine, and melarsoprol, and 2) to determine the mechanism by which mutations that arise from recombination with TbAQP3 result in reduced drug permeation.

The cryo-EM structures provide details of glycerol and drug binding, and show that glycerol and the drugs occupy the same space within the pore. Finally, MD simulations and lysis assays are employed to determine how mutations in TbAQP2 result in reduced permeation of drugs by making entry and exit of the drug relatively more energy-expensive. Overall, the strength of evidence used to support the author's claims is solid.

Strengths:

The cryo-EM portion of the study is strong, and while the overall resolution of the structures is in the 3.5Å range, the local resolution within the core of the protein and the drug binding sites is considerably higher (~2.5Å).<br /> I also appreciated the MD simulations on the TbAQP2 mutants and the mechanistic insights that resulted from this data.

Weaknesses:

(1) The authors do not provide any experimental validation the drug binding sites in TbAQP2 due to lacking resources. However, the claims have been softened in the revised paper.