Reviewer #2 (Public review):

Summary:

This computational work examines whether the inputs that neurons receive through electrical synapses (gap junctions) have different signatures in the extracellular local field potential (LFP) compared to inputs via chemical synapses. The authors present the results of a series of model simulations where either electric or chemical synapses targeting a single hippocampal pyramidal neuron are activated in various spatio-temporal patterns, and the resulting LFP in the vicinity of the cell is calculated and analyzed. The authors find several notable qualitative differences between the LFP patterns evoked by gap junctions vs. chemical synapses. For some of these findings, the authors demonstrate convincingly that the observed differences are explained by the electric vs. chemical nature of the input, and these results likely generalize to other cell types. However, in other cases, it remains plausible (or even likely) that the differences are caused, at least partly, by other factors (such as different intracellular voltage responses due to differences in the amplitudes and time courses of the input currents). Furthermore, it was not immediately clear to me how the results could be applied to analyze more realistic situations where neurons receive partially synchronized excitatory and inhibitory inputs via chemical and electric synapses.

Strengths:

The main strength of the paper is that it draws attention to the fact that inputs to a neuron via gap junctions are expected to give rise to a different extracellular electric field compared to inputs via chemical synapses, even if the intracellular effects of the two types of input are similar. This is because, unlike chemical synaptic inputs, inputs via gap junctions are not directly associated with transmembrane currents. This is a general result that holds independent of many details such as the cell types or neurotransmitters involved.

Another strength of the article is that the authors attempt to provide intuitive, non-technical explanations of most of their findings, which should make the paper readable also for non-expert audiences (including experimentalists).

Weaknesses:

The most problematic aspect of the paper relates to the methodology for comparing the effects of electric vs. chemical synaptic inputs on the LFP. The authors seem to suggest that the primary cause of all the differences seen in the various simulation experiments is the different nature of the input, and particularly the difference between the transmembrane current evoked by chemical synapses and the gap junctional current that does not involve the extracellular space. However, this is clearly an oversimplification: since no real attempt is made to quantitatively match the two conditions that are compared (e.g., regarding the strength and temporal profile of the inputs), the differences seen can be due to factors other than the electric vs. chemical nature of synapses. In fact, if inputs were identical in all parameters other than the transmembrane vs. directly injected nature of the current, the intracellular voltage responses and, consequently, the currents through voltage-gated and leak currents would also be the same, and the LFPs would differ exactly by the contribution of the transmembrane current evoked by the chemical synapse. This is evidently not the case for any of the simulated comparisons presented, and the differences in the membrane potential response are rather striking in several cases (e.g., in the case of random inputs, there is only one action potential with gap junctions, but multiple action potentials with chemical synapses). Consequently, it remains unclear which observed differences are fundamental in the sense that they are directly related to the electric vs. chemical nature of the input, and which differences can be attributed to other factors such as differences in the strength and pattern of the inputs (and the resulting difference in the neuronal electric response).

Some of the explanations offered for the effects of cellular manipulations on the LFP appear to be incomplete. More specifically, the authors observed that blocking leak channels significantly changed the shape of the LFP response to synchronous synaptic inputs - but only when electric inputs were used, and when sodium channels were intact. The authors seemed to attribute this phenomenon to a direct effect of leak currents on the extracellular potential - however, this appears unlikely both because it does not explain why blocking the leak conductance had no effect in the other cases, and because the leak current is several orders of magnitude smaller than the spike-generating currents that make the largest contributions to the LFP. An indirect effect mediated by interactions of the leak current with some voltage-gated currents appears to be the most likely explanation, but identifying the exact mechanism would require further simulation experiments and/or a detailed analysis of intracellular currents and the membrane potential in time and space.

In every simulation experiment in this study, inputs through electric synapses are modeled as intracellular current injections of pre-determined amplitude and time course based on the sampled dendritic voltage of potential synaptic partners. This is a major simplification that may have a significant impact on the results. First, the current through gap junctions depends on the voltage difference between the two connected cellular compartments and is thus sensitive to the membrane potential of the cell that is treated as the neuron "receiving" the input in this study (although, strictly speaking, there is no pre- or postsynaptic neuron in interactions mediated by gap junctions). This dependence on the membrane potential of the target neuron is completely missing here. A related second point is that gap junctions also change the apparent membrane resistance of the neurons they connect, effectively acting as additional shunting (or leak) conductance in the relevant compartments. This effect is completely missed by treating gap junctions as pure current sources.

One prominent claim of the article that is emphasized even in the abstract is that HCN channels mediate an outward current in certain cases. Although this statement is technically correct, there are two reasons why I do not consider this a major finding of the paper. First, as the authors acknowledge, this is a trivial consequence of the relatively slow kinetics of HCN channels: when at least some of the channels are open, any input that is sufficiently fast and strong to take the membrane potential across the reversal potential of the channel will lead to the reversal of the polarity of the current. This effect is quite generic and well-known, and is by no means specific to gap junctional inputs or even HCN channels. Second, and perhaps more importantly, the functional consequence of this reversed current through HCN channels is likely to be negligible. As clearly shown in Supplementary Figure S4, the HCN current becomes outward only for an extremely short time period during the action potential, which is also a period when several other currents are also active and likely dominant due to their much higher conductances. I also note that several of these relevant facts remain hidden in Figure 3, both because of its focus on peak values, and because of the radically different units on the vertical axes of the current plots.

Finally, I missed an appropriate validation of the neuronal model used, and also the characterization of the effects of the in silico manipulations used on the basic behavior of the model. As far as I understand, the model in its current form has not been used in other studies, although it is closely related to models used in earlier modeling work from the same laboratory. If this is the case, it would be important to demonstrate convincingly through (preferably quantitative) comparisons with experimental data using different protocols that the model captures the physiological behavior of at least the relevant compartments (in this case, the dendrites and the soma) of hippocampal pyramidal neurons sufficiently well that the results of the modeling study are relevant to the real biological system. In addition, the correct interpretation of various manipulations of the model would be strongly facilitated by investigating and discussing how the physiological properties of the model neuron are affected by these alterations.

Comments on revised version:

The authors made mainly cosmetic changes in the manuscript (primarily by adding more discussion), and most of these do not affect my earlier assessment. I have updated my Public Review in a few places to reflect those few changes that substantially address my previous concerns.

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.

Reviewers comments to revised manuscript.

The authors successfully established an isogenic, iPSC-derived human liver co-culture model to investigate the role of hepatocyte-macrophage interactions in driving Kupffer cell (KC) identity and hepatocyte maturation. By utilizing a single genetic background, the authors effectively minimized the experimental variability often encountered in non-isogenic systems. A significant highlight of this work is the demonstration that direct co-culture-as opposed to conditioned media alone-is a primary driver for critical KC identity markers such as ID1 and ID3. Furthermore, the model's ability to recapitulate complex clinical IL-6 responses to known hepatotoxicants where standard models have failed underscores its potential utility for early-stage DILI screening. However, there are significant methodological concerns regarding the data analysis. While the study compares four or five distinct experimental groups (e.g., Day 0, Day 7, Day 3 co-culture, and Day 7 co-culture), the authors utilized Student's t-tests for these comparisons. This approach does not account for the multiple comparisons problem and increases the risk of Type I errors. Additionally, while IL-6 secretion is used as a primary functional readout, the individual mechanisms behind these drug responses were not explored experimentally. Finally, Pearson correlation analysis indicates that the iMacs remain poorly correlated with actual in vivo human embryonic liver macrophages, suggesting that the "imprinting" of true KC identity remains incomplete.

Reviewer #2 (Public review):

Summary

In this study, Farnsworth et al. ask whether the previously established expansion of mushroom bodies in the pollen foraging Heliconius genus of Heliconiini butterflies co-evolved with adaptations in the central complex. Heliconius trap line foraging strategies to acquire pollen as a novel resource require advanced spatial memory mediated by larger mushroom bodies but the authors show that related navigation circuits in the central complex are highly conserved across the Heliconiini tribe, with a few interesting exceptions. Using general immunohistochemical stains and 3D reconstruction, the authors compared volumes of central complex regions and unlike the mushroom bodies, there was no evidence of expansion associated with pollen feeding. However, a second dataset of neuromodulator and neuropeptide antibody labeling reveal more subtle differences between pollen and non-pollen foragers and highlight sub-circuits that may mediate species-specific differences in behavior. Specifically, the authors found an expansion of GABAergic ER neurons projecting to the fan shaped body in Heliconius which may enhance their ability to path-integrate. They also found differences in Allatostatin A immunoreactivity, particularly increased expression in the noduli associated with pollen feeding. These differences warrant closer examination in future studies to determine their functional implication on navigation and foraging behaviors.

Strengths

The authors leveraged a large morphological data set from the Heliconiini to achieve excellent phylogenetic coverage across the tribe with 41 species represented. Their high quality histology resolves anatomical details to the level of specific, identifiable tracts and cell body clusters. They revealed differences at a circuit level, which would not be obvious from a volumetric comparison. The discussion of these adaptations in the context of central complex models is useful for generating new hypotheses for future studies on the function of ER-FB neurons and the role of Allatostatin A modulation in navigation.<br /> The conclusions drawn in this paper are measured and supported by rigorous statistics and evidence from micrographs.

Weaknesses

The majority of results in this study do not reveal adaptations in the central complex associated with pollen foraging. However, reporting conserved traits is useful and illustrates where developmental or functional constraints may be acting. The authors have now revised the introduction to set up two alternate hypotheses..

In the main text, the authors describe differences in GABAergic ER neurons between H. melpomene and an outgroup species, with additional images from other species in Figure S4. Quantification of ER cells in these other species would strengthen the claim that these are increased in Heliconius and not just the focal species, but this may hopefully be pursued in future studies.

Comments on revisions:

I am satisfied with the authors' revisions.

Reviewer #2 (Public review):

Summary:

This study introduces a novel knowledge-driven approach, miRTarDS, which enables microRNA-Target Interaction (MTI) prediction by leveraging the disease association degree between a miRNA and its target gene. The core hypothesis is that this single feature is sufficient to distinguish experimentally validated functional MTIs from computationally predicted MTIs in a binary classification setting. To quantify the disease association, the authors fine-tuned a Sentence-BERT (SBERT) model to generate embeddings of disease descriptions and compute their semantic similarity. Using only this disease association feature, miRTarDS achieved an F1 score of 0.88 on the test set.

Strengths:

The primary strength is the innovative use of the disease association degree as an independent feature for MTI classification. In addition, this study successfully adapts and fine-tunes the Sentence-BERT (SBERT) model to quantify the semantic similarity between biomedical texts (disease descriptions). This approach establishes a critical pathway for integrating powerful language models and the vast growth in clinical/disease data into biochemical discovery, like MTI prediction.

Weaknesses:

The main weakness lies in its definition of the ground-truth dataset, which serves as a foundation for methodological evaluation. The study defines the Negative Set as computationally predicted MTIs that lack experimental evidence. However, the absence of experimental validation does not equate to non-functionality. Similarly, the miRAW sets are classified by whether the target and miRNA could form a stable duplex structure according to RNA structure prediction. This definition is biologically irrelevant, as duplex stability does not fully encapsulate the complex in vivo binding of miRNAs within the AGO protein complex.

Reviewer #3 (Public review):

Summary:

In this study, authors studied the effects of traumatic brain injury created by LFPI procedure on the CA1 at network level. The major findings in this study seem to be that the TBI reduces theta and gamma powers in CA1, reduces phase amplitude coupling in between theta and gamma bands as well as disrupts the gamma entrainment of interneurons. I think the authors have made some important discoveries that could help advance the understanding of TBI effects at physiological level, however, more investigations into deciphering the relationship of the behavioral and brain states to the observed effects would help clarify the interpretations for the readers.

Strengths:

The authors in this study were able to combine behavioral verification of the TBI model with the laminar electrophysiological recordings of CA1 region to bring forward network level anomalies such as the temporal coordination of network level oscillations as well as in the firing of the interneurons. Indeed, it seems that the findings may serve future studies to functionally better understand and/or refine the therapies for the TBI.

Weaknesses:

Discoveries made in the paper and their broad interpretations can be helped with further characterization and comparison among the brain and behavioral states both during immobility and movement. The impact of brain injury in several parts of the brain can alter brain wide LFP and/or behavior. The altered behavior and/or LFP patterns might then lead to reduced spiking and unreliable LFP oscillations in the hippocampus. Hence, claims made in abstract such as "These results reveal deficits in information encoding and retrieval schemes essential to cognition that likely underlie TBI-associated learning and memory impairments, and elucidate potential targets for future neuromodulation therapies" does not have enough evidence in testing whether the disruptions were information encoding and retrieval related or due to sensory-motor and/or behavioral deficits that could also occur during TBI.

Movement velocity is already known to be correlated to the entrainment of spikes with the theta rhythm and also in some cases with the gamma oscillations. So, it is of importance to disentangle the differences in behavioral variables and the observed effects. As an example, the author's claims of disrupted temporal coding (as shown in the graphical abstract) might have suffered from these confounds. The observed results of reduced entrainment might on one hand be due to the decreased LFP power (induced by injury in different brain areas) resulting in altered behavior and/or the unreliable oscillations of the LFP bands such as theta and gamma, rather than memory encoding and retrieval related disruption of spikes synchrony to the rhythms, while on the other hand they may simply be due to reduced excitability in the neurons particularly in the behavioral and brain state in which the effects were observed, rather than disrupted temporal code. Hence, further investigations into dissociating these factors could help readers mechanistically understand the interesting results observed by the authors.

Comments on revisions:

The authors have substantially improved the manuscript in response to the previous reviews. In particular, the revisions addressing the issue of behavioral deficits that could be caused due to the TBI, which were surprisingly not present (if anything minimal) in the injured rats, have strengthened the study and improved the support for the main conclusions. Overall, the manuscript is now clearer and more rigorous. Authors have also addressed all the minor points raised in the study. As a result, the study is now solid, with the major findings broadly supported by the data.

Reviewer #2 (Public review):

Strengths:

The article addresses a topic of significant importance, focusing on early life growth faltering in low-income countries-a key marker of undernutrition-and its impact on brain functional connectivity (FC) and cognitive development. The study's strengths include the laborious data collection process, as well as the rigorous data preprocessing methods employed to ensure high data quality. The use of cutting-edge preprocessing techniques further enhances the reliability and validity of the findings, making this a valuable contribution to the field of developmental neuroscience and global health.

Weaknesses:

The study lacks specificity in identifying which specific brain networks are affected by growth faltering, as the current exploratory analyses mainly provide an overall conclusion that infant brain network development is impacted without pinpointing the precise neural mechanisms or networks involved.

Reviewer #2 (Public review):

Summary:

Many fly species exhibit male-specific visual behaviors during courtship while little is known about the circuit underlying the dimorphic visuomotor transformations. Nicholas et al focus on two types of visual descending neurons (DNs) in hoverflies, a species in which only males exhibit high-speed pursuit of conspecifics. They combined electrophysiology and behavior analysis to identify these DNs and characterize their response to a variety of visual stimuli in both male and female flies. The results show that the neurons in both sexes have similar receptive fields but exhibit speed-dependent dimorphic responses to different optic flow stimuli.

Strengths:

Hoverflies, though not a common model system, show very interesting dimorphic behaviors and provide a unique and valuable entry point to explore the brain organization behind sexual dimorphism. The findings here are not only interesting on their own right but will also likely inspire those working in other systems, particularly Drosophila.

The authors employed rigorous morphology, electrophysiology, and behavior methods to deliver comprehensive characterization of the neurons in question. The precision of the measurements allowed for identifying a subtle and nuanced neuronal dimorphism and set a standard for future work in this area.

Weaknesses:

I'd like to thank the authors for the revised manuscript, especially the new analyses and figures. Most of my earlier concerns have been satisfactorily addressed by now. Interested readers are kindly referred to the authors' responses for the discussion of the limitations of this work.

Reviewer #2 (Public review):

Review of the previous version:

The study design involves infecting HaCaT cells (immortalised keratinocytes mimicking basal cells of a target tissue) and observing virus localization with and without actin polymerization inhibition by cytochalasin D (cytoD) to analyze virion transfer from the ECM to the cell via filopodial structures, using cellular proteins as markers.

In the context of the model system, the authors stress in the revised version the importance of using HaCaT cells as a relevant 'polarized' cell model for infection. The term 'polarized' is used in the cell biological literature for epithelial cells to describe a strict apical vs. basolateral demarcation of the plasma membrane with an established diffusion barrier of the tight junction. However, HaCat cells do not form tight junctions. In squamous epithelia, such barriers are only found in granular layers of the epithelium. The published work cited in support of their claims either does not refer to polarity or only in the context of other cells such as CaCo-2 cells.

Overall, the matter of polarity would be important, if indeed the virus could only access cell-associated HSPGs as primary binding receptor, or the elusive secondary receptor via the ECM in the used model system (HaCaT cells), if they would locate exclusively basolaterally. This is at least not the case for binding, as observed in several previous publications (just two examples: Becker et al, 2018, Smith et al., 2008). With only a rather weak attempt at experimental verification of their model system with regards to polarity of binding, the authors then go on to base their conclusions on this unverified assumption.

This is one example of several in the manuscript, where claims for foundational premises, observations, and/or conclusions remain undocumented or not supported by experimental data.

Another such example is the assumption of transfer of the virus from ECM to the tetraspanin CD151. Here, the conclusions are based on the poorly documented inability of the virus to bind to the cell body, which is in stark contrast to several previous publications, and raises questions. Thus, association with CD151 likely occurs both from ECM derived virus AND virus that binds to cells, so that any conclusions on the mode of association is possible only in live cell data (which is not provided). Overall, their proposed model thus remains largely unsubstantiated with regards to receptor switching.

There are a number of important additional issues with the manuscript:

First, none of the inhibitors have been tested in their system for efficacy and specificity, but rely on published work in other cell types. This considerably weakens the confidence on the conclusion drawn by the authors.

Second, the authors aim to study transfer from ECM to the cell body and effects thereof. However, there are still substantial amounts of viruses that bind to the cell body compared to ECM-bound viruses in close vicinity to the cells. This is in part obscured by the small subcellular regions of interest that are imaged by STED microscopy, or by the use of plasma membrane sheets. This remains an issue despite the added Supple. Fig. 1, where also only sub cellular regions are being displayed. As a consequence the obtained data from time point experiments is skewed, and remains for the most part unconvincing, largely because the origin of virions in time and space cannot be taken into account. This is particularly important when interpreting the association with HS, the tetraspanin CD151, and integral alpha 6, as the low degree of association could be originating from cell bound and ECM-transferred virions alike.

Third, the use of fixed images in a time course series also does not allow to understand the issue of a potential contribution of cell membrane retraction upon cytoD treatment due to destabilisation of cortical actin. Or, of cell spreading upon cytoD washout. The microscopic analysis uses an extension of a plasma membrane stain as marker for ECM bound virions, this may introduce a bias and skew the analysis.

Fourth, while the use of randomisation during image analysis is highly recommended to establish significance (flipping), it should be done using only ROIs that have a similar density of objects for which correlations are being established. For instance, if one flips an image with half of the image showing the cell body, and half of the image ECM, it is clear that association with cell membrane structures will only be significant in the original. But given the high density of objects on the plasma membrane, I am not convinced that doing the same by flipping only the plasma membrane will not also obtain similar numbers than the original.

Reviewer #2 (Public review):

Summary:

The authors used single-nuclei sequencing of benign fallopian tubes and ovarian cancer to delineate the plausible cell of origin of high-grade serous ovarian cancer.

Strengths:

These substantial data provide the field with significant research resources to examine additional features in normal fallopian tubes and ovarian cancers. The highly detailed bioinformatic analysis, rooted in a strong biological framework, is convincing. The methodology was appropriate and used validated methodology based on biological relevance (region selection and transcriptomics analysis).

The authors propose a convincing model of epithelial progenitor cells and their localisation in high-grade serous ovarian cancers. These findings are important and useful.

Weaknesses:

Overall, the weaknesses are clearly stated in the discussion. The study provides a novel framework for future study, and proposes a model which will require validation.

Within the ovarian cancer field, the endometrioid and clear cell histotypes are thought to arise from ciliated or secretory cells. Typically these are thought to be from the cervix or uterus. This concept was not mentioned in the work.

Further, in the ovarian cancer field, stemness is judged by some classic assays - aldehyde assays looking at ALDH1A1 and spheroid-producing ability. These were not mentioned - could these be useful in a population of fallopian tube epithelial cells, or would other assays/markers be more appropriate?

The choice of ES2 and OVCAR was not sufficiently justified, as ES2 is widely regarded as a clear cell ovarian cancer cell line in many research circles. Additionally, I did not see confirmation of gene knockdown by Western blot or qPCR.

PGR loss through copy number variant was surprising, as this was a marker. So would the marker be lost through one of these mechanisms randomly or specifically?

Reviewer #2 (Public review):

Summary:

The study by Zhang et al. focuses on how condensation of a chromatin-associated protein MORC2 regulates gene expression. Their study shows that MORC2 forms dynamic nuclear condensates in cells. In vitro, MORC2 phase separation is driven by dimerization and multivalent interactions involving the C-terminal domain but interplay with other parts of MORC2 too. A key finding is that the intrinsically disordered region (IDR) of MORC2 exhibits strong DNA binding. They report that DNA binding enhances MORC2's phase separation and its ATPase activity, offering new insights into how MORC2 contributes to chromatin organization and gene regulation. Authors correlate MORC2's condensate forming ability and material properties with its gene silencing function using a few variants. Moreover, they investigate the effect of disease-linked mutations in the N-terminal domain of MORC2 on its ability to form cellular condensates, ATPase activity and DNA-binding. Their work implies that proper material properties of MORC2 condensates may be important to their biological function.

Strengths:

The authors determined a 3.1 Å resolution crystal structure of the dimeric coiled-coil 3 (CC3) domain of MORC2, revealing a hydrophobic interface that stabilizes dimer formation. They present extensive evidence that MORC2 phase separates across multiple contexts, including in vitro, in cellulo, and in vivo. Through systematic cellular screening, they identified the C-terminal domain of MORC2 as a key driver of condensate formation. Biophysical and biochemical analyses further show that the IDR within the C-terminal domain interacts with the C-terminal end region (IBD) and also exhibit strong DNA-binding capacity (using 601 DNA), both of which promote MORC2 phase separation. Together, this study emphasizes that interactions mediated by multiple domains-CC3, IDR, and IBD- drives MORC2 phase separation. Additionally, the work uses a unique kill-switch peptide fused to the MORC2 sequence to disrupt its material properties -- this permits the authors to examine the link between material properties and transcription function. The study is overall strengthened by (1) the combination of variants tested both in vitro and in cellulo, and (2) the systematic examination of domain contributions that highlight the multivalent interactions at play mediating MORC2 condensation.

Weaknesses:

The employed MORC2 variants have enabled the beginning of an investigation linking condensation and biological function, but more work will be needed to really dissect the contribution of condensation to DNA-binding, ATPase activity, and gene silencing. A systematic investigation of differential material properties on MORC2 condensates will be needed to assess the link to biological function, especially as the authors' work is reminiscent of how the liquidity of Caulobacter crescentus PopZ condensates tunes bacterial fitness.

Reviewer #2 (Public review):

Summary:

In this study, Baas-Thomas et al. aim to study the neural mechanisms underlying ingestive versus rejection responses to taste stimuli by developing an EMG-based approach to identify ingestion-related orofacial movements. Whereas prior work has focused primarily on detecting rejection-related gapes, the authors introduce a machine-learning classifier that uses waveform features extracted from anterior digastric (AD) EMG signals to detect mouth- and tongue-movement (MTM) events associated with ingestion. Clustering analyses further suggest that ingestive behavior consists of multiple MTM subtypes whose relative frequencies vary across trial time and taste conditions. Finally, simultaneous recordings indicate that shifts in MTM expression follow transitions in gustatory cortex (GC) population dynamics into palatability-related firing states, supporting a role for cortical ensemble activity in coordinating ingestive motor responses.

Strengths:

Overall, the scientific question addressed in this study is well motivated. A mechanistic understanding of ingestive decision-making requires a precise characterization of the motor patterns that implement ingestion, and these behaviors have remained insufficiently resolved in prior work. The authors take a reasonable and technically innovative approach by leveraging AD EMG recordings to classify distinct orofacial movement patterns. The extracted waveform features appear effective in separating gapes from ingestion-related mouth-tongue movements, and clustering analyses further suggest the presence of distinguishable MTM subtypes that show meaningful temporal structure and neural correlates. Taken together, the work provides a potentially useful framework for linking gustatory cortical dynamics to the motor expression of taste-guided decisions.

A particularly valuable aspect of this work is the attempt to move beyond a binary characterization of ingestive behavior and instead identify multiple subtypes of ingestion-related movements. This finer behavioral resolution has the potential to provide a more realistic account of how complex consummatory actions are organized. More broadly, the effort to relate structured behavioral motifs to population-level neural dynamics is conceptually interesting and could prove useful for future studies seeking to connect circuit dynamics with the motor implementation of motivated behaviors.

Weaknesses:

(1) I have several concerns regarding the methodological comparisons used to establish the superiority of the proposed XGBoost classifier. In particular, the comparison between the XGBoost classifier and previously used QDA approaches (Figure 3) may not be entirely well-matched. The QDA framework was originally designed primarily to detect gape events and does not explicitly assign labels to MTM movements. As a result, the apparent advantage of XGBoost in identifying MTMs may partly reflect differences in task formulation rather than intrinsic differences in classification performance. From visual inspection, gape detection performance appears broadly comparable across methods.

A more informative benchmark would involve comparing XGBoost to an extended pipeline in which QDA-based gape detection is combined with a secondary movement-detection stage, distinguishing MTMs from periods of no movement. Such a comparison would better isolate the contribution of classifier architecture per se. Without this control analysis, the strength of the claim that XGBoost provides superior performance for behavioral decoding remains somewhat uncertain.

(2) The presentation of the neural ensemble analyses is considerably less comprehensive and intuitive than that of the behavioral analyses. The manuscript would benefit from more direct visualization of inferred neural state transitions. For example, plotting predicted neural states in a manner analogous to the behavioral states illustrated in Figure 6B would improve interpretability and help readers understand how neural dynamics relate temporally to behavioral changes.

In addition, the interpretation that GC ensemble dynamics drive behavioral state transitions may require further clarification. If GC activity plays a causal role in initiating behavioral changes, one might expect a consistent brain-to-behavior lag across changepoints. However, Figure 6 appears to show such lag primarily at the second transition but not at the first. This raises questions about how uniformly the proposed causal interpretation applies across state boundaries, and additional analysis or discussion is needed.

(3) The neural ensemble analyses primarily focus on constructing higher-level behavioral state variables rather than directly testing how individual movement subtypes relate to neural activity. The behavioral interpretation of the inferred state structure, therefore, remains somewhat unclear. While this approach is consistent with previous work from the authors and with broader state-transition frameworks of gustatory processing, it is not immediately obvious that this is the most informative level of analysis for the present dataset.

In particular, it would strengthen the manuscript to examine whether GC neurons or ensembles also encode lower-level motor structure, such as the occurrence of gapes or specific MTM subtypes. Demonstrating selective or mixed encoding across hierarchical levels (movement motifs versus abstract behavioral states) would help clarify the functional interpretation of the reported neural dynamics. At present, the manuscript largely assumes that GC activity reflects higher-order behavioral states without directly testing alternative representational possibilities.

(4) Because direct behavioral ground truth for intra-oral ingestive movements is difficult to obtain, MTM subtypes are inferred primarily through clustering of EMG waveform features. Although the authors demonstrate statistical separability and cross-session stability of these clusters, it remains unclear whether they correspond to discrete motor programs or instead reflect a structured partitioning of a continuous behavioral space shaped by feature selection and preprocessing choices. Perhaps some additional robustness analyses or convergent validation (e.g., alternative clustering methods, feature perturbation tests, or stronger neural and behavioral dissociations) would help clarify the biological significance of the inferred subtype structure.

Reviewer #2 (Public review):

Summary:

There are astonishingly few papers trying to reproduce the process of initiation and spreading that Braaks studies have suggested and postulated. The authors should be applauded for pioneering such a difficult experiment. They overexpressed the TDP-43 protein in the motor neuron pool of the brachioradialis muscle and showed that by this technique, motor neurons in this pool died, and the muscle got denervated. They had evidence of a spreading process from the spinal cord to the cortex, demonstrated by showing widespread deposits of phosphorylated TDP-43 bilaterally in the cervical cord and the motor cortex. By their experiment, they created a dying-backwards model, not a model of corticofugal spread, like that shown by Braak. No muscle weakness was observed, not even in the brachioradialis.

Strengths:

The strength of this innovative study is the fact that this spreading experiment uses the phylogenetically young connectome of primates (macaques). They also made the thought-provoking observation of spreading from the cord to the motor cortex, not the corticofugal spread model observed by Heiko Braak. This is thought-provoking because this enables the observer to compare their model with the findings in humans.

Weaknesses:

The following aspects are not a weakness but need to be better explained for the interested reader - and potentially improved in future studies for which the authors laid the foundation:

(1) Why do the authors use the brachioradialis motor neuron pool to overexpress TDP-43? More is known about other muscles and how they are embedded in the motor connectome of primates. Why not the biceps brachii or the hand extensors or - even better - the small muscles of the hand? These are known to be strongly monosynaptically connected with the motor cortex. The authors should explain this. I am unclear if there was a specific reason which I did not see or understand. In my view, the brachioradialis is not the best representative of the primate connectome, for example, to examine this model and compare it with the corticofugal spread.

(2) In the Braaks experiment, only (seemingly soluble) non-phoshorylated TDP-43 "crossed" synapses. Phosphorylated TDP-43 did not do this. The authors of this study saw phosphorylated TDP43 in motor neurons and the cortex. Is there any potential explanation for how it crosses synapses? If it really does, there is an obvious difference to the human situation which needs to be emphasized and explained (in the future).

(3) There were significant deposits of phosphorylated TDP-43 in oligodendrocytes in humans. Whilst I understand that one experiment cannot solve every question - I am curious about whether the authors saw anything in oligodendrocytes?

(4) Which was the pattern of damage? Of course, this pattern is not likely to have a monosynaptic pattern - like in humans........but was there a pattern? Did it have a physiologically meaningful basis? Was there any relation to the corticofugal monosynaptic pattern? What are the differences? The authors speak of "multiple waves". Does this mean that if this were a corticofugal model, for example, oculomotor neurons would also degenerate?

Reviewer #2 (Public review):

The authors aim to understand how intensive training with the method of loci changes the brain systems that support memory in both elite "memory athletes" and previously untrained adults. They combine a cross-sectional comparison of athletes and matched controls with a longitudinal training study including mnemonic training, active working-memory training, and passive control groups, and use fMRI pattern-similarity analyses to characterise how brain activity patterns during learning and temporal-order judgments become more distinct or more shared within and across individuals.

The dual design is a major strength. It combines findings from both real-world expertise and experimentally induced training and adds well-matched control groups. The representational similarity analyses are appropriate and reveal a clear, internally consistent picture in which learning with the method of loci leads to more idiosyncratic prefrontal and posterior cortical patterns during encoding, and more shared hippocampal-precuneus patterns during temporal-order retrieval, observed in both athletes and trained novices.

However, the study is complex and the manuscript dense, and some secondary analyses feel less central or are difficult to interpret. More importantly, while the neural evidence for training-related changes in representational format is compelling, the behavioural relevance of these changes is less clearly supported. The key per-group brain-behaviour correlations are weak and inconsistent, and the direct association between neural and behavioural change across all subjects is not clearly presented.

Overall, the work convincingly shows that extensive mnemonic practice reorganises neural representations in specific networks, but the strength and specificity of the claimed link to long-term memory improvements should be viewed as more tentative.

Reviewer #3 (Public review):

This paper investigates the role of Chi3l1 in regulating the fate of liver macrophages in the context of metabolic dysfunction leading to the development of MASLD.

Comments on revisions:

My comments have been addressed.

Reviewer #2 (Public review):

Summary:

This manuscript investigates the causes and consequences of human-specific DNA methylation divergence relative to chimpanzees. The main aim of this study is to disentangle cis- and trans-regulatory contributions to DNA methylation differences, which the authors address using an innovative interspecies hybrid cell system differentiated into multiple cell types. This design allows them to control for trans-acting environments and directly compare allelic regulation.

The authors show that cis-regulatory mechanisms dominate DNA methylation divergence and that methylation-expression coupling is strongest when both are cis-regulated. They further explore potential mechanisms underlying these patterns, including CpG-disrupting mutations and transcription factor-associated trans effects, and identify pathways that may reflect lineage-specific regulatory evolution.

This study provides a valuable dataset and a compelling framework for understanding how local sequence variation contributes to epigenetic and transcriptional divergence, with likely broad impact in comparative and evolutionary genomics.

Strengths:

A major strength of this study is the use of human-chimpanzee hybrid cells, which provides a powerful system to disentangle cis- and trans-regulatory effects in a shared cellular environment. This experimental design allows for a more definitive assessment of regulatory mechanisms than traditional cross-species comparisons.

The study also benefits from the inclusion of multiple differentiated cell types, increasing the robustness and generality of the conclusions. The consistent observation that cis-regulatory mechanisms dominate methylation divergence across these contexts is well supported by both CpG-level and DMR-level analyses.

Another important contribution is the finding that methylation-expression coupling is strongest when both are cis-regulated. This provides a mechanistic explanation for previously observed weak global correlations between methylation and gene expression. Given that the nature of regulatory evolution is likely highly heterogeneous, this study adds valuable insights and guidelines for future investigations. I recommend that the authors provide a list of cis-cis-regulated promoters and their associated genes, which would be a valuable resource for the field.

The application of the two-step sign test identifies biologically relevant pathways, suggesting links between epigenetic divergence and human-specific traits.

The dataset itself, namely, comprehensive DNA methylation and gene expression across multiple cell types in shared cellular contexts, as well as a primary cell type, is a valuable resource for the field. Additionally, the application of the two-step sign test identifies biologically relevant pathways, suggesting links between epigenetic divergence and human-specific traits.

Weaknesses:

Although the authors identify transcription factors associated with differential methylation, it is unclear what proportion of differentially methylated CpGs or DMRs can be attributed to these factors. Providing a quantitative estimate would help assess the relative contribution of trans-acting regulation.

The analysis of CpG-disrupting mutations is interesting but raises two concerns. First, other classes of variants-such as transcription factor binding site-disrupting mutations-could also influence local methylation patterns and are not considered here. Second, the causal direction remains ambiguous: CpG-disrupting mutations may result from methylation-associated mutational processes (e.g., C→T transitions at methylated CpGs) rather than being the primary drivers of methylation divergence. While additional analyses may not be necessary, explicitly acknowledging these alternative explanations would strengthen the interpretation.

Regarding the discussion comparing the distance between CpG-disrupting SNVs and trans-DMRs, without information on the absolute or relative distance distributions, it was difficult to assess the magnitude of the observed differences. Moreover, trans-DMRs, by definition, are not driven by local (cis) variation, and the lack of proximity to CpG-disrupting SNVs is expected. Clarifying what additional insight this analysis provides beyond this expectation may improve this section.

One potential extension would be to examine whether the same cis-acting SNVs are consistently associated with methylation differences across multiple cell types. If these variants are mechanistically causal, one might expect their effects to be reproducible across contexts, or at least more frequent than expected by chance. Such an analysis could further support the proposed link between sequence variation and methylation divergence.

Regarding their two-step sign test analysis, because enrichment-based approaches can sometimes overemphasize statistical significance without reflecting effect size, I wonder if incorporating the magnitude of methylation change would provide additional information or strengthen these findings. While the authors highlight some cases, such as TUBB2 and GRIK, a more general overview and/or integration of effect size into the analysis or discussion would improve interpretability.

Reviewer #2 (Public review):

In this manuscript, the authors describe using "in extracto" cryo-EM to obtain high-resolution structures of mammalian ribosomes from concentrated cell extracts without further purification or reconstitution. This approach aims to solve two related problems. The first is that purified ribosomes often lose cellular cofactors, which are often reconstituted in vitro; this precludes the ability to find novel interactions. The second is that while it is possible to perform cryo-EM on cellular lamella, FIB milling is a slow and laborious process, making it unfeasible to collect datasets sufficiently large to allow for high-resolution structure determination. Extracts should contain all cellular cofactors and allow for grid preparation similar to standard single-particle analysis (SPA) approaches. While cryo-EM of cell extracts is not in itself novel, this manuscript uses 2D template matching (2DTM) for particle picking prior to structure determination using more standard SPA pipelines. This should allow for improved picking over other approaches, in order to obtain in large datasets for high-resolution SPA.

This manuscript has two main results: novel structures of ribosomes in hibernating states; and a proof-of-principle for in extracto cryo-EM using 2DTM. Overall, I think the results presented here are strong and serve as a proof-of-principle for an approach that may be useful to many others.

Comments on revisions:

This current draft addresses my prior comments regarding usability for readers through the addition of text describing how parameters were optimized as well as an additional supplementary figure outlining the processing workflow. With these additions, I have no further comments.

Reviewer #2 (Public review):

Goal summary

The authors sought to (i) demonstrate correlations between the dynamics of the dinoflagellate Alexandrium pacificum and the bacterim Vibrio atlanticus in natural populations, ii) demonstrate the occurrence of predation in laboratory experiments, iii) demonstrate that predation is induced by predator starvation, and iv) test for effects of quorum sensing and iron-uptake genes on the predation process.

Strengths include

- Data indicating correlated dynamics in a natural environment that increase the motivation for study of in vitro interactions<br /> - Experimental design allowing clear inference of predation based on population counts of both prey and predators in addition to microscopy-based evidence<br /> - Supplementation of population-level data with molecular approaches to test hypotheses regarding possible involvement of quorum sensing and iron update in predation

Weaknesses include

- A quantitative analysis of effects of manipulating V. atlanticus density on rates of predation would have been valuable<br /> - Lack of clarity in some of the methodological descriptions

Appraisal

The authors convincingly demonstrate that V. atlanticus can prey on A. pacificum, provide strongly suggestive evidence that such predation is induced by starvation and clearly demonstrate that both iron availability and correspondingly the presence of genes involved in iron uptake strongly influence the efficacy of predation.

Discussion of impact

This paper will interest those interested in the diversity of forms of microbial predation and how microbial predatory behavior responds to environmental fluctuations. It will also interest those investigating bacteria-algae interactions and potential ecological controls of algal blooms. It may also interest researchers of microbial cooperation in light of the suggestion of communication between predator cells.

Reviewer #2 (Public review):

Summary:

Large oocytes show prominent waves of cortical contractions. Previous works combining experiments and computational modeling have shown that the waves are driven by gradients of CDK1 kinase activity that trigger excitable Rho activity patterns on the cortex. This present work combines two previously published mathematical models for CDK1 activation and Rho activation, respectively. They show that the models combined can explain diverse shapes of cortical contractions observed in different species and at various stages of development. This shows how the same molecular machinery can generate diverse patterns dependent on the size of the system and the size and position of the cell nucleus.

Strengths:

(1) Carefully done modeling work providing a simple and elegant explanation for a complex cellular behavior.

(2) Very nicely illustrated, simulations can be directly compared to previous experimental observations.

(3) Explains observations made in different model systems, providing a unifying model.

Weaknesses:

(1) Purely theoretical work, no experimental validation.

(2) Adopts previously published models more or less 'as is', without detailed re-evaluation and re-assessment, or without developing them further.

Overall, I find this work important, as it shows that combining models of the CDK1 gradient and Rho activation modules can explain the surface contraction waves observed in oocytes. Strikingly, it elegantly explains the differences seen between different experimental systems. While previously these were considered a 'controversy', modeling shows that the differences are simply a consequence of the difference in the size of the oocytes. In addition, the model makes several intriguing predictions that can be tested in future experiments.

Reviewer #2 (Public review):

Summary:

The inability of the mammalian retina to regenerate poses a major clinical challenge. Much has been learned about the regenerative potential of the retina from teleost fish, where Müller glia (MG) are able to proliferate and produce new neurons after injury. However, MG do not retain this potential in the mammalian retina. The authors showed previously that forcing MG to re-enter the cell cycle by downregulating p27 and upregulating cyclin D1 could induce MG to dedifferentiate, but the results were transient, and these cells eventually reverted back to MG and did not form neurons. Here, they expand on this to show that in MG, coupling forced cell cycle re-entry with deletion of Rbpj, which inhibits the transcriptional effects of Notch signaling, induces some MG to proliferate and take on features of multiple cell types, including MG precursor cells, amacrine-like cells, and bipolar-like cells. This work lends valuable insight into the regenerative potential of mammalian MG, particularly when Notch signaling is manipulated.

Strengths:

The major claims of the authors are well-supported. They show convincingly - and through multiple methods including immunostaining, single-nucleus RNA sequencing, and in situ hybridization - that coupling notch inhibition with cell cycle reactivation induces the expression of neuronal markers in mammalian MG. The snRNA-seq data are particularly valuable in demonstrating the induction of bipolar-cell subtypes. Edu labeling is effective in demonstrating the induction of proliferation, and the long-term viability of the generated neuron-like cells is intriguing.

Weaknesses:

Whether the newly generated neurons are functionally integrated remains unclear, and the effect of the manipulation on the function of the retina was not tested. Imaging data suggests that many of the newly generated neurons persist for months, but often appear mislocalized. It is also not clear if the manipulation of MG affects long-term MG function. Cell death was not evaluated, and although the authors evaluated the long-term effect on tight junctions, this data was not quantified, and further analysis on morphology or function was not done. Control eyes were untreated, not vehicle-injected.

Reviewer #2 (Public review):

Summary:

The relationships among the phyla making up Spiralia - a major clade of animals including molluscs, annelids, flatworms, nemerteans and brachiopods - have been challenging from a phylogenomic perspective despite decades of molecular phylogenetic effort. Every topology uniting subsets of these phyla has been recovered with apparent support in at least one study, yet no consensus has emerged even from large-scale genomic datasets. Serra Silva and Telford set out to determine whether this instability reflects a genuine biological signal being obscured by analytical limitations, or whether it reflects a rapid, near-simultaneous origin of these phyla that has left behind in modern genomes far too little phylogenetic information to resolve. They focused deliberately on five phyla, reducing the problem to a tractable set of 15 unrooted and 105 rooted topologies, and applied a suite of complementary approaches across two independent datasets and multiple substitution models to test whether any topology is significantly preferred over alternatives.

Strengths:

(1) The conceptual framing of the problem is excellent, and the study makes a convincing case across several lines of evidence. By enumerating all possible topologies and demonstrating empirically that every one of the 15 unrooted arrangements has been recovered as the preferred solution in at least one published study, the authors make a strong argument about the state of the field. The use of two entirely independent datasets as a consistency check is great, and convergence between them, where it occur,s substantially strengthens confidence in the conclusions.

(2) It is my view that the simulation framework is a particular strength. Generating data on a fully unresolved star tree and scoring those data under both correctly-specified and misspecified substitution models provides convincing evidence that the strong preference for rooting Spiralia on the flatworm branch is, at least partly, an analytical artefact driven by the exceptionally long branch in combination with compositional heterogeneity across sites. This is an important methodological demonstration with implications beyond spiralian phylogenetics, as the same issue is likely to affect other deep, long-branched lineages in the animal tree of life.

(3) The randomised taxon-jackknifing approach is a very nice addition here. The demonstration that preferred topologies shift depending on which species happen to be sampled (even within the same phylum) is a convincing indicator of weak signal, and provides a practical caution for future studies that may report strong support for a particular spiralian arrangement based on a fixed taxon sample.

(4) The branch-length analyses, benchmarking internal interphylum branches against the already disputed and extremely short branch uniting deuterostomes (work also by this group), are well-conceived and solid.

(5) I think it is worth highlighting the notable intellectual honesty throughout the paper: the authors do not overstate their results, correctly acknowledging that while the unrooted topology grouping molluscs with brachiopods and flatworms with nemerteans emerges most consistently, this preference is not statistically significant under more adequate substitution models and may itself carry some artefactual component.

Weaknesses:

(1) The restriction to five phyla is the most significant limitation, as the authors acknowledge this and give a clear computational justification, but readers should be aware that the paper's convincing conclusions apply specifically to the five focal phyla and the evidence remains incomplete with respect to spiralian phylogeny as a whole.

(2) The treatment of substitution model adequacy, while commendably thorough for site-heterogeneous models, is necessarily bounded. The authors note that models accounting for non-stationarity, across-lineage compositional heterogeneity, or mixtures of tree histories might yield different results, and that even the most sophisticated currently available approaches have not produced consistent spiralian topologies across studies. This is not a criticism of what has been done here - the analytical scope is reasonable and well-implemented - but it means the paper cannot be read as a definitive demonstration that no model will ever resolve these relationships. The distinction between a true hard polytomy and a radiation that is effectively unresolvable given current data and methods could be drawn more sharply in the discussion.

(3) The reticulation-aware coalescent analyses are presented somewhat briefly relative to the likelihood-based topology scoring. The finding that flatworms are recovered within a paraphyletic jaw-bearing animal clade in both summary trees - interpreted as long-branch attraction - is striking, and its implications for gene-tree-based approaches to spiralian rooting deserve more discussion than they currently receive.

(4) The central conclusions - that interphylum branches in Spiralia are extraordinarily short, that topological preferences are strongly model-dependent and taxon-sampling-sensitive, and that an ancient rapid radiation is the most parsimonious explanation - are convincingly supported by the evidence presented. The identification of flatworm long-branch attraction as an important confounding factor in rooting analyses is itself an important and well-demonstrated result.

Conclusion:

This paper clearly makes an important contribution to the ongoing debate about spiralian relationships and, more broadly, to methodological discussions about how to handle anciently diversified clades where phylogenetic signal is genuinely limited. The exhaustive topology-scoring framework combined with taxon-jackknifing and simulation under unresolved trees is a valuable methodological template that could usefully be applied to other notoriously difficult nodes in the animal tree. I thoroughly enjoyed the discussion of the implications of these findings for interpreting Cambrian fossils and the evolutionary history of shells, segmentation, larval types and other characters - it is both thoughtful and thought-provoking and will be of broad interest well beyond the phylogenomics and zoology communities. From a very practical perspective, the data and scripts provided make the work useful to researchers wishing to apply similar approaches to other groups.

Reviewer #2 (Public review):

The authors show that GM-CSF prevents the loss of ILC3 populations and inhibits pro-inflammatory cytokine production during gut inflammation. They combine a preclinical model of gut inflammation in zebrafish with spatial transcriptomic analysis of samples from Crohn's disease patients. The data show that GM-CSF ameliorates gut inflammation by (1) curtailing the differentiation of disease-associated ILC1 and (2) by "boosting" the tissue repair function of ILC3.

The topic of the manuscript is interesting. However, there are various limitations that are summarized below.

(1) The main finding of the manuscript, that GM-CSF maintains ILC3 populations, is not analyzed in depth. Since the authors' own data and other publications show that the receptors for GM-CSF are expressed in myeloid cells, a better analysis of the transcriptional changes of these populations upon GM-CSF administration is needed.

(2) The authors could compare the transcriptome of macrophages and monocytes from inflamed and uninvolved sections in their Xenium dataset. In addition, investigating how zebrafish macrophages change due to the lack of GM-CSF and comparing them with the human findings would add to the data.

(3) Since the authors developed a novel mutation in zebrafish that is predicted to affect myeloid populations, a detailed characterization of the myeloid immune compartment in these organisms is missing.

(4) Niche analysis in the Xenium slides could provide direct evidence on how macrophages close to ILC3 are different from those closer to other cell types, like ILC1.

Reviewer #2 (Public review):

Summary:

Hong et. al. aimed to elucidate the structural basis of the Egalitarian recognition of the K10 mRNA. Using X-ray crystallography and several biochemical, biophysical, and cellular techniques, they were able to shed light on the formation, stability, and basis of interaction of the complex. The authors successfully accomplished their goal.

Strengths:

The experiments are well-performed and convincing. The manuscript is well-written.

Weaknesses:

(1) Some statistical analysis would improve the manuscript. In particular, the manuscript has several results that are based on comparisons, such as Kd. Adding p-values for significance is recommended, and this would improve the treatment of data.

(2) When showing interactions (dotted lines) in structural figures, adding the distance would be useful and is recommended.

(3) Additional SI Figure. It would enrich the manuscript to have the composite simulated annealing-omit 2|Fo| - |Fc | electron density maps for the structures contoured at a given sigma, superimposed on the final refined model. This would represent how well the data fits into the model.

Reviewer #2 (Public review):

Summary

This valuable study combines atomic force microscopy with genetic manipulations of the lamin meshwork and microinjection of cytoskeletal depolymerizing drugs to probe the mechanical responses of intracellular organelles to combinations of cytoskeletal perturbations. This study demonstrates both local and distal responses of intracellular organelles to mechanical forces, and shows that these responses are affected by disruption of the actin, microtubule, and lamin cytoskeletal systems.

Strengths:

This study uses a sensitive micromanipulation system to apply and visualize the effects of force on intracellular organelles.

Reviewer #2 (Public review):

Summary:

The mechanism(s) by which the therapeutic drug metformin lowers blood glucose in type 2 diabetes and inhibits cell proliferation at higher concentrations remain contentious. Inhibition of complex 1 of the mitochondrial respiratory chain with consequent changes in cellular metabolites which favour allosteric activation of phosphofructokinase-1, allosteric inhibition of fructose bisphosphatase-1 and cAMP signalling and activation of AMPK which phosphorylates transcription factors are candidate mechanisms. The current manuscript proposes the e-subunit of ATP-synthase as a putative binding protein of biguanides and demonstrates that it regulates the expressivity of the Complex 1 protein NDUFB8.

Strengths:

(1) The metformin conjugate and metformin show comparable efficacy on inhibition of cell proliferation in the millimolar range.

(2) Demonstration of compromised expression of the Complex I protein NDUFB8 by the ATP5I knock out and its reversal by ATP5I expression is an important strength of the study. This shows that the decreased "sensitivity" to metformin in the ATP5I knock out cells could be due to various proteins.

(3) Demonstration of converse effects of ATP5I KO and re-expression ATP5I on the NAD/NADH ratio.

Weaknesses:

(1) The interpretation of the cellular co-localization of the biotin-biguanide conjugate with TOMM20 (Figure 1-D) as mitochondrial "accumulation" of the conjugate is overstated because it cannot exclude binding of the conjugate to the mitochondrial membrane. It would have been more convincing if additional incubations with the biotin-biguanide conjugate in combination with metformin had shown that metformin is competitive with the biotin-conjugate.

(2) The manuscript reports the identification of 69 proteins by mass spectrometry of the pull-down assay of which 31 proteins were eluted by metformin. However, no Mass Spectrometry data is presented of the peptides identified. The methodology does not state the minimum number of peptides (1, 2?) that were used for the identification of the 31/69 proteins.

(3) The validation of ATP5I was based on the use of recombinant protein (which was 90% pure) for the SPR and use of a single antibody to ATP5I. The validity of the immunoblotting rests on the assumption that there is no "non-specific" immunoactivity in the relevant mol wt range. Information on the validation of the antibody would be helpful.

(4) Knock-out of ATP5I markedly compromised the NAD/NADH ratio (Fig.3A) and cell proliferation (Fig.3D). These effects may be associated with decreased mitochondrial membrane potential which could explain the low efficacy for metformin (and most of the data in Figs 3-5). This possibility should be discussed. Effects of [metformin] on the NAD/NADH ratio in control cells and ATP5I-KO would have been helpful because the metformin data on cell growth is normalized as fold change relative to control, whereas the NAD/NADH ratio would represent a direct absolute measurement enabling comparison of the absolute effect in control cells with ATP5I KO.

(5) Figure-6 CRISPR/Cas9 KO at 16mM metformin in comparison with 70nM rotenone and 2 micromolar oligomycin (in serum containing medium). The rationale for use of such a high concentration of metformin has not been explained. In liver cells metformin concentrations above 1mM cause severe ATP depletion, whereas therapeutic (micromolar) concentrations have minimal effects on cellular ATP status. The 16mM concentration is ~2 orders of magnitude higher than therapeutic concentrations and likely linked to compromised energy status. The stronger inhibition of cell proliferation by 16mM metformin compared with rotenone or oligomycin raises the issue whether the changes in gene expression may be linked to the greater inhibition of mitochondrial metabolism. Validation of the cellular ATP status and NAD/NADH with metformin as compared with the two inhibitors could help the interpretation of this data.

Comments on revisions:

No further comments.

Reviewer #2 (Public review):

Summary:

This study reexamined the idea that action potential broadening serves as a homeostatic mechanism to compensate for changes in network activity. The key finding was that, while action potential broadening does occur in certain neurons - such as CA3 pyramidal cells-it is far from a universal response. This is important because it helps resolve longstanding discrepancies in the field, thereby contributing to a better understanding of network dynamics. The replication of these findings across multiple laboratories further strengthened the study's rigor.

Strengths:

Mechanisms of network homeostasis are essential to understand network dynamics.

Reviewer #2 (Public review):

Summary:

The authors used whole-network imaging to identify sensory neurons that responded to the repellant 1-octanol. While several olfactory neurons responded to the initial onset of odor pulses, two neurons consistently responded to all the pulses, ASH and AWC. ASH typically activates in response to repellants, and AWC typically activates in response to the removal of attractants. However in this case, AWC activated in response to the removal of 1-octanol, which was unexpected because 1-octanol is a harmful repellant to the worm. The authors further investigated this phenomenon by testing different concentrations of 1-octanol in a chemotaxis assay, and found that at lower (less harmful) concentrations the odor is actually an attractant, but becomes repulsive at higher concentrations. The amplitude of the ASH response appeared to be modulated by concentration, but this was not true for AWC. The authors propose a model where the behavioral response of the worm is the result of integrating these two opposing drives, where repulsion is a result of the increased ASH activity over-riding the positive drive from AWC. The authors further tested this theory by testing mutants that ablated the AWC response (tax-4 or AWC::HisCl) or ASH response (osm-9 or ASH::HisCl). The chemo-silencing (HisCl) and tax-4 experiments were consistent with their hypothesis, while the osm-9 mutation had a limited impact on chemotaxis behavior, highlighting the potential role of osm-9-independent signaling in ASH in response to 1-octanol. While the interneuron(s) that integrate these signals to influence behavior were not identified, the authors did find that increasing concentrations of 1-octanol did increase the likelihood of AVA activity, a neuron which drives reversals (and hence, behavioral repulsion).

Strengths:

This was simple and elegant work that identified specific neurons of interest which generated a hypothesis, which was further tested with mutants that altered neuronal activity. The authors performed both neuronal imaging and behavioral experiments to verify their claims.

Weaknesses:

The authors note that other sensory neurons likely contribute to 1-octanol chemotaxis. Given the NeuroPAL data, it would have been nice to identify these other neurons as well. However, the reviewer is aware that this is tangential to the primary focus of this study.

Reviewer #2 (Public review):

Summary:

Measurements of the reward positivity, an electrophysiological component elicited during reward evaluation, have previously been used to understand how self-benefitting effort expenditure influences processing of rewards. The present study is the first to complement those measurements with electrophysiological reward after-effects of effort expenditure during prosocial acts. The results provide solid evidence that effort adds reward value when the recipient of the reward is the self but discounts reward value when the beneficiary is another individual.

Strengths:

An important strength of the study is that amount of effort, the prospective reward, the recipient of the reward, and whether the reward was actually gained or not were parametrically and orthogonally varied. In addition, the researchers examined whether the pattern of results generalized to decisions about future efforts. The sample size (N=40) and mixed-effects regression models are also appropriate for addressing the key research questions. Those conclusions are plausible and adequately supported by statistical analyses.

Reviewer #2 (Public review):

Summary:

The manuscript by Sajid et al. describes a comprehensive behavioral, imaging, and optogenetic dataset investigating the role of the mPFC in avoidance and escape behaviors. Although many movement- and task-related variables are encoded by mPFC GABAergic neurons, the main conclusion is that they are unlikely to control behavioral output.

Strengths:

The manuscript is generally well executed and plausible in its conclusions. It provides an alternative viewpoint to many articles describing the involvement of mPFC in behavior, based on a complex multi-stage behavioral paradigm acquired and analyzed in an unbiased way.

Weaknesses:

This reviewer sees three main weaknesses.

(1) There are few details on the linear mixed models in the methods. This section could be improved by including a mathematical description. More importantly, the reader never learns how accurately the models capture the data. Given that most conclusions rely on the models, it seems central to address this point carefully. For example, what is the explained variance, marginal, and conditional? Were the nested models compared to non-nested ones (e.g., AIC), what are the specific outputs of the likelihood ratio tests briefly mentioned in the methods?

(2) For several figures, there is a disconnect with the main text, in the sense that it is difficult to understand how statements in the main text connect with specific figure panels or bars in their graphs. This is particularly the case for the most complex figures, e.g., Figures 3, 4, and their supplements. It would be beneficial to introduce subfigure labels (A1, etc) and state explicitly in the main text what figure panel is described (in parentheses). Alternatively breakdown the figures into multiple ones, decreasing ambiguity. This is important because it will help the reader better assess the strength of the results.

(3) It does not appear that the code and data used to produce the figures are made available. That would be very beneficial, given the complexity of the analysis and dataset collection procedures. It would also help readers better understand the results and probe their validity.

Reviewer #2 (Public review):

Summary:

This study investigates how the human brain categorizes visual words from distinct writing systems (alphabetic vs. non-alphabetic) as a neural basis for the social-categorization function of language. Using a repetition suppression paradigm combined with electroencephalography and magnetoencephalography, the authors conducted nine experiments with independent participants to identify the neural network underlying language-based categorization, characterize its temporal dynamics, and test whether this process operates independently of linguistic properties such as semantic meaning and pronunciation.

Strengths:

(1) The study employs a well-validated design with clear control conditions and systematically manipulates key variables, including writing system, language familiarity, and native language background. The use of nine experiments with independent participant samples strengthens the reliability and replicability of the results.

(2) The work combines EEG and MEG, cross-validating findings across imaging modalities to support the reported neural effects. A combination of univariate, multivariate, and connectivity analyses is used to characterize neural responses and network interactions.

(3) Results are consistent across multiple language groups and for both familiar and unfamiliar languages, supporting the generalizability of the identified neural mechanism beyond specific languages or prior experience.

Weaknesses:

The authors provide compelling evidence that the identified neural network supports the categorization of words by language, including computations of intra-language similarity and inter-language difference. However, the conceptual framing of this finding as directly reflecting the social-categorization function of language may be premature. While the task captures spontaneous language categorization, it does not involve social evaluation or intergroup processes. The connection to social categorization is inferred from prior literature rather than demonstrated within the current experimental design. Clarifying this distinction would strengthen the conceptual precision of the manuscript.

Reviewer #2 (Public review):

Summary:

The authors investigate the behavior of oncogenic cells in mammary and bronchial epithelia. They observe that individual oncogenic cells are preferentially excluded from the mammary epithelium, but they remain integrated in the bronchial epithelium. They also observe that clusters of oncogenic cells form a compact cluster in mammary epithelium, but they disperse in the bronchial epithelium. The authors demonstrate experimentally and in the vertex model simulations that the difference in observed behavior is due to the differential tension between the mutant and wild-type cells due to a differential expression of actin and myosin.

Strengths:

* Very detailed analysis of experiments to systematically characterize and quantify differences between mammary and bronchial epithelia

* Detailed comparison between the experiments and vertex model simulations to identify the differential cell line tension between the oncogenic and wild-type cells as one of the key parameters that are responsible for the different behavior of oncogenic cells in mammary and bronchial epithelia

Weaknesses:

* It is unclear what is the mechanistic origin of the shape-tension coupling, which is used in the vertex model, and how important that coupling is for the presented results. Authors claim that the shape-tension coupling is due to the anisotropic distribution of stress fibers when cells are under external stress. It is unclear why the stress fibers should affect an effective line tension on the cell boundaries and why the stress fibers should be sensitive to the magnitude of the internal isotropic cell pressure. In experiments, it makes sense that stress fibers form when cells are stretched. Similar stress fibers form when cytoskeleton or polymer networks are stretched. It is unclear why the stress fibers should be sensitive to the magnitude of internal isotropic cell pressure. If all the surrounding cells have the same internal pressure, then the cell would not be significantly deformed due to that pressure and stress fibers would not form. Authors should better justify the use of the shape-tension coupling in the model, since most of the observed behavior is already captured by the differential tension even if there is no shape-tension coupling.

* The observed difference of shape indices between the interfacial and bulk cells in simulations in the absence of differential line tension is concerning. This suggests that either there are not enough statistics from the simulations or that something is wrong with the simulations. For all presented simulation results, the authors should repeat multiple simulations and then present both averages and standard deviations. This way it would be easier to determine whether the observed differences in simulations are statistically significant.

Reviewer #2 (Public review):

Summary:

This work presents direct magnetic resonance imaging (MRI) of collagen, which is not possible with conventional MRI or other tomographic imaging modalities.

Strengths:

The experimental work is impressive, and the presentation of results is clear and convincing.

Reviewer #2 (Public review):

The study by Angla et al. proposes a model in which NT-3 produced by motor neurons regulates interneuron numbers and distribution in a non-cell autonomous manner. The authors demonstrate that ablation of motor neurons (MNs) and global and conditional deletion of OC transcription factors lead to changes in interneuron distribution. They identify that NT3 is upregulated after MN-specific OC deletion in RNA-seq experiments and show that olig2-cre mediated NT3 deletion leads to increased ventral interneuron numbers, altered distribution, and defects in locomotor behavior. The authors conclude that MN-derived NT3, under OC control, regulates interneuron development. While this is an intriguing hypothesis, additional experiments are needed to support it and strengthen the link between the different experiments described here.

(1) The study primarily quantifies interneuron numbers and distribution at different levels of the spinal cord and under different genetic manipulations. Experimental details are lacking, defining how many sections were analyzed (several are noted in the methods) and how the rostrocaudal levels of the spinal cord were precisely aligned. In different figures, the values and distributions shown for controls vary quite a lot. For example, in Figure 2B vs Figure 4B, the number of FoxP2+ V1 neurons at brachial levels is ~350 vs 125. Similarly, the control distributions in 2I and 4I are quite different. This makes it challenging to determine whether the conclusions regarding the impact of each genetic manipulation on interneuron numbers and distribution are valid.

(2) The relationship between OC and NT3 deletion data is not entirely clear. Both deletions presumably lead to changes in interneuron distribution, but is there any reverse relationship between the two that relates to relative changes in NT3 levels? The authors do not directly compare NT3 and OC KO IN distributions. Similarly, one might expect a decrease in interneuron numbers in OC mutants, which is only reported for V2c neurons. However, the image presented in Figure 2G shows an equal number of V2c INs in control and mutant.

(3) It is not clear that the behavioral phenotypes seen in the olig2-cre mediated deletion of NT3 can be attributed to changes in interneuron development. How about a role of NT3 in oligodendrocytes? There is a big gap between the embryonic changes shown here and behavior, with no in-between circuit-level changes in locomotor circuits shown. A more restricted manipulation would be deleting TrkC from specific interneuron populations. Related to this, although TrkC is shown to be broadly expressed in ventral interneurons, it is not shown specifically to colocalize with any of the interneuron markers. The authors should validate that the receptor is expressed in the subsets that they are investigating.

(4) The rationale for following up on NT3 seems to be the chick electroporation experiments; however, no changes in distribution are shown in those experiments, and only a very minor decrease in Chx10 interneurons. Shouldn't NT3 overexpression lead to substantial decreases in IN numbers according to the authors' model? The "data not shown", which presumably refers to distribution, would be important to show here, to further support this rationale.

(5) The idea that NT3 downregulation causes an increase in IN numbers is not intuitive. Also, considering the DTA experiments in Figure 1, showing that MN ablation leads to a decrease in several IN subtypes and no changes in V2a neurons. It would be helpful for the reader if the authors could synthesize their results in the discussion and reconcile their experimental findings.

Reviewer #2 (Public review):

Summary:

This paper presents data using the Drosophila model to analyze the effects of a rare human mutation in the gene encoding the ryanodine receptor (ryr). The authors present a nice, comprehensive phylogenetic analysis that shows the Drosophila version of Ryr to be most similar to human RYR2 and that the known "hot spots" for mutations in RYR2 coincide with highly conserved regions of the Drosophila Ryr. They characterize the functional effects of ryr knockdown and overexpression on both adult heart function and larval body wall muscle. They identified embryonic ryr expression in association with actin-stained muscle precursor cells and provide beautiful stains, which clearly showed that embryonic muscle cell development was disrupted in ryr mutants. In support of these findings, KD of Calmodulin in larva (an Ryr inhibitor) phenocopied Ryr OE. They recreated a human variant of unknown function (RyR1 p.Met4881Ile ) in the conserved region of the fly gene and tested the effect on larval muscle. Their data suggested that this variant was likely deleterious as it negatively affected most muscle parameters.

Major comments:

(1) Fig, 1 In G there is no data for the RNAi KD situation.

(2) Fig. 2 Authors should include Diastolic Diameters; they mention dilated cardiomyopathy but don't show the dilation. The authors should also show staining in hearts with RYR OE and RNAi. It would be nice to have some kind of quantification of disorganized myofibrils.

(3) To evaluate and reproduce the data on the larva muscle parameters the authors should provide more details on how sarcomere length was quantified in each larva (replicates, ROI size, etc). Similarly, how were # of nuclei quantified / normalized? Importantly for these measurements, did the authors know what the contraction state of the muscles were when fixed?

(4) Fig. 3, Are RNAi and OE in the same background? I only see one control in the graphs for the RNAi line background.

(5) Fig. 3 How VL3 length was determined needs more detail, the Zhang ref is not adequate.

(6) In order to be able to evaluate the data, the statistical tests used should be cited in the figure legends along with what *, ** ,*** stand for (or just provide p values).

Significance:

The authors nicely characterized the role of Ryr in muscle development and function and recreated a human variant of unknown function (RyR1 p.Met4881Ile ) in the conserved region of the fly gene. Their data suggested that this variant was likely deleterious as it negatively affected most muscle parameters. This work supports a role for the fly model in testing potential human disease gene variants.

Comments on Revised Version:

The authors have very adequately addressed the points raised by all reviewers.

Reviewer #2 (Public review):

Summary:

Despite their common co-occurrence, depression and anxiety are known to alter mood fluctuations in opposite ways. Here, the authors aimed at distinguishing depression-specific from anxiety-specific from psychopathology-general effects of reward processing on mood fluctuations, focusing on reward prediction errors (RPEs), which are known to be linked to mood fluctuations. This mechanistic study aims at uncovering the process through which these psychopathologies are associated with mood modulations. The authors were able to appropriately test their hypothesis and obtained results corroborating their conclusions.

This work provides a convincing demonstration of the relevance of computational psychiatry (Huys et al, 2016) and the use of decision neuroscience to shed light on the interplay of anxiety, depression, and mood.

Strengths:

The authors used a tripartite model to distinguish depression vs anxiety, as well as a computational model distinguishing reward expectation (EV in the model) from outcome processing through RPE, which are two sequential cognitive processes.

The manuscript adequately addresses the concerns one would have regarding risk-attitudes and regarding referring to trending statistical results.

Weaknesses:

The sample size of the clinical sample (N=116) may not be sufficient to detect anxiety-specific effects due to the high rate of comorbid anxious depression. It would be beneficial to include the number of MDD vs GAD vs anxious depression diagnoses in the clinical population, as this would likely shine light on the power limitations.

Reviewer #2 (Public review):

Summary:

This paper asks an important question that has not been discussed much in the extensive literature on the High Frequency Oscillations (HFOs) that have been extensively studied in patients with epilepsy and experimental models of epilepsy. The question is whether the Fast Ripples (FRs), the HFOs in the 250-500 Hz frequency band, represent a pathological phenomenon or represent a physiological phenomenon that occurs in the healthy brain but happens to be more frequent in epileptic tissue. It is an important question that has not been systematically addressed until now. The authors conclude, from very extensive simulations, from extensive experimental animal studies (the systemic kianate model of epilepsy in rats), and from a modest amount of human data, that FRs occur in healthy brains as a result of the chance occurrence of bursts of action potentials, and that in epileptic tissue, their frequency of occurrence is approximately 30% higher than what is expected by chance. They conclude that FRs are not a separate phenomenon of epileptic tissue. This finding is reinforced by the recent findings of FRs in experimental models of Alzheimer's disease.

Strengths:

This is a valuable study because it asks an important and original question and because it evaluates it from several angles (simulation, tissue culture, experimental animals, and human patients). The simulations and the analyses of real data are performed very carefully and with original and solidly documented approaches, using extensive simulations and extensive data sets in the cultured cell data and in the in vivo experiments. The paper is clearly written and well-illustrated.

Weaknesses:

I found only one serious weakness in this study, but it is one that is of importance. Although the original work on FRs was done in an experimental model of epilepsy, the field really became prominent when ripples and fast ripples were found first in microelectrode recordings of epileptic patients and then in the intracerebral EEG of such patients. Numerous studies have been performed since then, with a valuable meta-analysis including 700 patients (Wang Z, Guo J, van 't Klooster M, Hoogteijling S, Jacobs J, Zijlmans M. Prognostic Value of Complete Resection of the High-Frequency Oscillation Area in Intracranial EEG: A Systematic Review and Meta-Analysis. Neurology. 2024 May 14;102(9). Although the consensus at this point is that FRs are not the ideal and totally specific marker of epileptic tissue that many thought it could be, FRs are nevertheless much more frequent in epileptic tissue than in non-epileptic tissue and are a solid biomarker. It is also well established that they are much more frequent in NREM sleep than in wakefulness, as reported in the original paper of Staba et al (Staba RJ, Wilson CL, Bragin A, Jhung D, Fried I, Engel J Jr. High-frequency oscillations recorded in human medial temporal lobe during sleep. Ann Neurol. 2004 Jul;56(1):108-15., not mentioned in this paper) and in the study of Bagshaw et al (2009). In this last paper, using SEEG in various brain regions, the average rate of FRs in NREM sleep is about 6 times that in wakefulness. In the paper by Staba, with microelectrodes in mesial temporal structures, it is about twice. As a separate issue, the paper of Fraucher et al (Frauscher B, von Ellenrieder N, Zelmann R, Rogers C, Nguyen DK, Kahane P, Dubeau F, Gotman J. High-Frequency Oscillations in the Normal Human Brain. Ann Neurol. 2018 Sep;84(3):374-385), which is not quoted, found that, in an extensive sample, non-epileptic human tissue sampled with SEEG generated extremely rare FRs (an average rate of 0.04/min/channel, i.e. 1 every 25 min).

The results above are mentioned because they do not fit with the data provided in the present study: FRs are much more frequent in NREM sleep than in wakefulness in human epileptic patients, and they are much more frequent (not 30% more, but many hundreds of percent more) in epileptic tissue than in non-epileptic human tissue. The fundamental phenomenon of interest is, I believe, the FRs in epileptic patients. The animal experiments, tissue studies, and simulations are models to study the human phenomenon. With respect to the modulation by sleep and the differentiation between epileptic and non-epileptic tissue, it seems that the systems studied in this paper are not good models of the human condition. The human results presented in the study only reflect wakefulness recordings, which is not the condition in which most HFO studies have been done and in which most HFOs occur. The authors refer to the study of long-term fluctuations in HFO rates by Gliske et al. (2018) to say that one has to be careful with the results regarding sleep, for example, Bagshaw et al (2009), but the clear predominance in of HFOs in NREM sleep has been observed by many studies. The cautions regarding fluctuations over extended periods also apply to the awake human data analyzed in this study.

The study's conclusions regarding the generation of FRs are therefore questionably applicable to the human condition. I do not dispute their validity for the models and situations in which they were studied.

Reviewer #2 (Public review):

Summary:

Zylberberg et al. reanalyze eye-tracking and behavioral data (mostly from Krajbich et al., 2010) to test two predictions of the attentional Drift Diffusion Model, finding that these predictions are not met. Similarly, predictions of normative models (inspired by rational inattention) are not in line with the data, and the authors propose a post-choice model of attention. This model better accounts for the two effects but also does not account for all patterns, so the authors conclude that eye movements most likely reflect both pre- and post-decisional processes.

Strengths:

A clear strength is the systematic falsification-based approach of the paper, establishing (partially) new predictions and testing to what extent these are met by extant models and by a newly developed theory. The authors do a good job in providing intuitions behind the effects and the reasons why models such as the aDDM predict them. The paper is of substantial relevance for the field, as it shows that effects pertaining to the last fixation(s) should be interpreted with caution. Another strength is the paper's transparency as the authors clearly acknowledge that their new model does not do a perfect job either.

Weaknesses:

The paper focuses on analyzing the Krajbich 2010 data, but shows that the second effect replicates in many other datasets. A more principled approach, in which both effects are analyzed and presented for all datasets, would be more convincing. The results should then be shown together for clarity/readability.

Similarly, it would be nice to show to what extent the models' predictions depend (not depend) on using the best-fitting parameter values (are there any parameter settings under which the two effects are not predicted?)

Reviewer #2 (Public review):

Summary:

Zylberberg et al. reanalyze eye-tracking and behavioral data (mostly from Krajbich et al., 2010) to test two predictions of the attentional Drift Diffusion Model, finding that these predictions are not met. Similarly, predictions of normative models (inspired by rational inattention) are not in line with the data, and the authors propose a post-choice model of attention. This model better accounts for the two effects but also does not account for all patterns, so the authors conclude that eye movements most likely reflect both pre- and post-decisional processes.

Strengths:

A clear strength is the systematic falsification-based approach of the paper, establishing (partially) new predictions and testing to what extent these are met by extant models and by a newly developed theory. The authors do a good job in providing intuitions behind the effects and the reasons why models such as the aDDM predict them. The paper is of substantial relevance for the field, as it shows that effects pertaining to the last fixation(s) should be interpreted with caution. Another strength is the paper's transparency as the authors clearly acknowledge that their new model does not do a perfect job either.

Weaknesses:

The paper focuses on analyzing the Krajbich 2010 data, but shows that the second effect replicates in many other datasets. A more principled approach, in which both effects are analyzed and presented for all datasets, would be more convincing. The results should then be shown together for clarity/readability.

Similarly, it would be nice to show to what extent the models' predictions depend (not depend) on using the best-fitting parameter values (are there any parameter settings under which the two effects are not predicted?)

Reviewer #2 (Public review):

Summary:

Hippocampal remapping - the collective reorganization of neural tuning properties - is thought to be a crucial determinant of memory outcomes. Understanding its mechanistic bases is a fundamental goal of neuroscience and likely to be critical to understanding memory in health and disease. Here, Lykken et al. 2025 leverage a unique empirical manipulation paired with computational modeling to investigate how one mechanism - reorganization of grid cell subfield firing rates - impacts hippocampal remapping. The authors find that repeated chemogenetic excitation of MEC stellate cells induces reliable reorganization of grid cell subfield firing rates, which is in turn coupled with reliable hippocampal remapping. Notably, the authors show that this hippocampal remapping is not random but predictable, with changes in field location that can be predicted based on weak out-of-field firing observed during control sessions. These findings were well-replicated by a simple model of grid-to-place transformation.

Strengths:

This work has many strengths. One key strength of this work is its compelling demonstration that chemogenetic activation of stellate cells induces changes to the grid and place cell representations, which are reliable across repeated activations. This reliability means that the functional changes induced by this manipulation are not merely noise but rather contain a consistent structure that can be investigated to gain insight into the entorhinal-hippocampal transformation. Similarly, the demonstration that hippocampal remapping during this manipulation is not random, but predictable at the single-cell level, is also a strength. This predictability can help us distinguish competing mechanisms of remapping and place field formation more generally. Finally, by reproducing key experimental outcomes with a straightforward grid-to-place computational model, the authors show that this relatively simple model is sufficient to understand their results.

Weaknesses:

This work also has limitations that leave some relevant questions open at this time. One such set of questions which might be addressable with the author's data and modeling concerns population analyses. Do grid fields at similar locations exhibit similar changes in field properties, or do these fields change independently? Are changes in field location consistent or inconsistent among simultaneously recorded place cells? Would we expect or not expect such a structure given the model? These results might help discriminate between different mechanisms possibly at play.

Another limitation of this work is its reliance on a single measure of predictability. While this is a great start, and the various controls and modeling are appreciated, I wonder whether the modeling could be used to generate additional verifiable predictions. For example, perhaps analyzing whether there is or is not structure to unpredictable errors (are these distributed around predictions but further away, or are they random)?

Finally, one limitation comes from the between-group nature of the recordings. Because the MEC and hippocampus are recorded in separate groups of animals, the authors lose the ability to test whether each mouse's particular grid field reorganization predicts its particular pattern of remapping. If the author's model is correct, then one might hope to be able to predict with even higher accuracy the particular patterns of remapping in CA1 given sufficiently well-characterized grid field changes. This ambitious goal would require simultaneous recordings from the hippocampus and entorhinal cortex, which are beyond the scope of the current work, but would ultimately yield even more compelling evidence of the grid-to-place transformation underlying this form of remapping.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors aim to identify active, long-range regulatory interactions in cerebellar granule cell progenitors (GCps). As such, the authors perform promoter capture Hi-C to map long-range interactions for all gene promoters, using cells isolated from P7 mouse brain samples. While the resolution of these maps is limited by the relatively large fragment sizes generated from a 6-bp cutter, the authors combine these interactions with other available published datasets, including from their own previous work, (e.g. ATAC-seq and ChIP-seq) to more precisely map putative enhancers within the long-range interacting regions of captured promoters. The paper further focuses on the importance of transcription factor Atoh1 and chromatin remodeller CHD7 in regulation of these putative enhancers in GCps. The authors suggest a direct interaction between CHD7 and Atoh1 by overexpression and co-immunoprecipitation in human embryonic kidney cells.

As stated by the authors, this study represents a valuable resource for researchers interested in the identification of enhancers in GCps cells, and their linked target genes. While broadly descriptive, the study does highlight some gene loci of interest and of biological relevance. For example, through integration of previously published datasets, the study resolves which putative regulatory elements at the Reln locus may regulate its activity.

This manuscript will be of interest to researchers interested in analysing long-distance targets of as well as researchers trying to understand the precise gene regulation in cerebellar development. It may also be of interest to clinical geneticists to interpret novel putative non-coding disease mutations.

Strengths:

The strengths of this manuscript are the integrated approach to identify cell-type specific enhancers utilizing available epigenomic datasets, and leveraging 3D genome topology to directly link them to their target genes. For example for the Reln gene previously implicated in cerebellar phenotypes for CHD7 mutants. The pcHi-C dataset generated in this study provides a valuable reference for the community of enhancer-promoter pairs for a specific cell-type of interest with human disease relevance.

Weaknesses:

The limitations of the study are partially addressed in the text by the authors, including the resolution from the pcHi-C using a 6-bp cutter, the limitation of sequencing depth (more interactions may have been identified with more depth), and the limited of correlation between replicates (likely due to undersampling the library). Page 9 "some additional interactions with the nearest gene promoters might be identified in our pcHi-C dataset with deeper sequencing".

Reviewer #3 (Public review):

Summary:

The revised manuscript presents a compelling study of radially propagating metachronal waves on the surface of Pseudomonas nitroreducens biofilms, combining experiments with two theoretical descriptions (a local phase-oscillator model and an active solid/active gel model). The central experimental findings-spiral/target/planar wave patterns, their controllability via water/PEG/temperature perturbations, and the correlation between frequency gradients and propagation direction-remain highly interesting and relevant to both bacterial biophysics and active-matter physics. The revised manuscript also adds substantial new material, including additional analyses of defect dynamics and clearer discussion of the relationship between the two models. The study continues to have a strong interdisciplinary appeal and the potential to stimulate further work on collective oscillations in biological active media.

Strengths:

The authors have substantially addressed the major conceptual issue raised in the previous round by clearly distinguishing between nonreciprocity and frequency gradients / global asymmetry. This clarification significantly improves the theoretical interpretation and resolves an important source of confusion in the original version.

The revised manuscript also improves the connection between the phase-oscillator and active-solid descriptions. In particular, the authors now explain more explicitly how the phase variable is defined in the reduced oscillatory dynamics of confined biofilm motion, and they state that they added a schematic illustration and simulation details (including parameter values and the elastic-force definition) to improve reproducibility. This directly addresses one of my previous major concerns.

A notable improvement is the newly added defect-based analysis of waveform transitions (spiral -> target -> planar). The revised text argues that defect motility is a key control parameter, linked experimentally to moisture-dependent elasticity and theoretically to nonreciprocity / defect-pair stability. This provides a more concrete mechanistic bridge between experimental perturbations and the modeling framework than in the previous version.

The manuscript now gives a clearer experimental-theoretical narrative for how environmental manipulations (drying, water addition, PEG, heating) affect wave patterns through changes in effective elasticity and activity, including a useful distinction between short-timescale and long-timescale temperature effects. This added discussion strengthens the biological interpretation and makes the modeling assumptions easier to follow.

Weaknesses:

The main remaining limitation is the level of quantitative correspondence between theory and experiment. The revised manuscript now provides a stronger qualitative/mechanistic link, but the mapping between model parameters (e.g., effective coupling terms / elasto-active parameters) and directly measurable biofilm properties is still limited. The authors acknowledge this point, and I agree that it is technically challenging in the present system. However, this means the theoretical framework is currently most convincing as an effective mechanistic model rather than a quantitatively predictive one.

Relatedly, some conclusions about parameter-level control (especially in connecting moisture/temperature manipulations to specific model parameters) remain qualitative. I do not view this as fatal, but I recommend that the manuscript clearly state this scope and avoid overstating the quantitative predictive power of the theory.

Although the terminology has improved compared with the original version, the revised manuscript still uses "left-right asymmetry" in places where the underlying geometry and symmetry are more general (e.g., radial inward propagation in circular colonies). Since this wording was one of the original points of confusion, I suggest one final pass to ensure the symmetry language is consistently precise throughout the main text and figure captions.

Reviewer #2 (Public review):

Summary:

Sullivan and colleagues studied the fast, involuntary, sensorimotor feedback control in interpersonal coordination. Using a cleverly designed joint-reaching experiment that separately manipulated the accuracy demands for a pair of participants, they demonstrated that the rapid visuomotor feedback response of a human participant to a sudden visual perturbation is modulated by his/her partner's control policy and cost. The behavioral results are well matched with the predictions of the optimal feedback control framework implemented with the dynamic game theory model. Overall, the study provides an important and novel set of results on the fast, involuntary feedback response in human motor control in the context of interpersonal coordination.

Review:

Sullivan and colleagues investigated whether fast, involuntary sensorimotor feedback control is modulated by the partner's state (e.g., cost and control policy) during interpersonal coordination. They asked a pair of participants to make a reaching movement to control a cursor and hit a target, where the cursor's position was a combination of each participant's hand position. To examine fast visuomotor feedback response, the authors applied a sudden shift in either the cursor (experiment 1) or the target (experiment 2) position in the middle of movement. To test the involvement of partner's information in the feedback response, they independently manipulated the accuracy demand for each participant by varying the lateral length of the target (i.e., a wider/narrower target has a lower/higher demand for correction when movement is perturbed). Because participants could also see their partner's target, they could theoretically take this information (e.g., whether their partner would correct, whether their correction would help their partner, etc.) into account when responding to the sudden visual shift. Computationally, the task structure can be handled using dynamic game theory, and the partner's feedback control policy and cost function are integrated into the optimal feedback control framework. As predicted by the model, the authors demonstrated that the rapid visuomotor feedback response to a sudden visual perturbation is modulated by the partner's control policy and cost. When their partner's target was narrow, they made rapid feedback corrections even when their own target was wide (no need for correction), suggesting integration of their partner's cost function. Similarly, they made corrections to a lesser degree when both targets were narrower than when the partner's target was wider, suggesting that the feedback correction takes the partner's correction (i.e., feedback control policy) into account.

The strength of the current paper lies in the combination of clever behavioral experiments that independently manipulate each participant's accuracy demand and a sophisticated computational approach that integrates optimal feedback control and dynamic game theory. Both the experimental design and data analysis sound good and the main claim is well supported by the results.

A future direction would be to investigate how this mechanism is implemented in the CNS and to examine whether the same cooperative mechanism also applies to human-AI interactions.

Reviewer #2 (Public review):

Summary:

The manuscript investigates which social navigation mechanisms, with different cognitive demands, can explain experimental data collected from homing pigeons. Interestingly, the results indicate that the simplest strategy - route averaging - aligns best with the experimental data, while the most demanding strategy - selectively propagating the best route - offers no advantage. Further, the results suggest that a mixed strategy of weighted averaging may provide significant improvements.

The manuscript addresses the important problem of identifying possible mechanisms that could explain observed animal behavior by systematically comparing different candidate models. A core aspect of the study is the calculation of collective routes from individual bird routes using different models that were hypothesized to be employed by the animals but which differ in their cognitive demands.

The manuscript is well written, with high-quality figures supporting both the description of the approach taken and the presentation of results. The results should be of interest to a broad community of researchers investigating (collective) animal behavior, ranging from experiment to theory. The general approach and mathematical methods appear reasonable and show no obvious flaws. The statistical methods also appear.

Strengths:

The main strength of the manuscript is the systematic comparison of different meta-mechanisms for social navigation by modeling social trajectories from solitary trajectories and directly comparing them with experimental results on social navigation. The results show that the experimentally observed behavior could, in principle, arise from simple route averaging without the need to identify "knowledgeable" individuals. Another strength of the work is the establishment of a connection between social navigation behavior and the broader literature on the wisdom of crowds through the concept of effective group size.

Comments on revision:

The authors made substantial revisions to the manuscript, addressing my comments. While I do think that regarding my second comment on CCE the authors could be a bit more bold, I am overall satisfied with the revisions made.

Reviewer #2 (Public review):

Original review:

Summary:

The search for new repellent odors for honey bees has significant practical implications. The authors developed an iterative pipeline through machine learning to predict honey bee-repellent odors based on molecular structures. By screening a large number of candidate compounds, they identified a series of novel repellents. Behavioral tests were then conducted to validate the effectiveness of these repellents. Both the discovery and the methodological approach hold value for related fields.

Strengths:

* The study demonstrates that using molecular structures and a relatively small training dataset, the model could predict repellents with a reasonably high success rate. If the iterative approach works as described, it could benefit a wide range of olfaction-related fields.<br /> * The effectiveness of the predicted repellents was validated through both laboratory and field behavioral tests.

Weaknesses:

The small size of the training dataset poses a common challenge for machine learning applications. However, the authors did not clearly explain how their iterative approach addresses this limitation in this study. Quantitative evidence demonstrating improvements achieved in the second round of training would strengthen their claims. For instance, details on whether the success rate of predictions or the identification of higher-affinity components would be helpful. Furthermore, given that only 15 new components were added for the second round of training, it is surprising that such a small dataset could result in significant improvements.

Reviewer #2 (Public review):

Parietal lobe TMS, targeted to the episodic memory network via connections with the structures in the medial temporal lobe, improves episodic memory. This is one of very few robustly reproduced cognitive findings in noninvasive brain stimulation. The comprehensive review and detailed meta-analysis by Goicoechea et al. makes a convincing case for efficacy in healthy people and will be important for neuroscientists and clinical researchers in memory and dementia.

In 2014, Wang et al. showed that noninvasive stimulation of a parietal site, connected functionally to the hippocampus, increased resting state functional connectivity throughout a canonical network associated with episodic memory. It also caused a memory boost which was proportional to the connectivity increase within subjects. Their discovery that an imaging biomarker could (1) be used to target a functional network with critical nodes too deep to reach directly with TMS, (2) enable individualized, functionally confirmed, targeting, and (3) provide a scaling measure of target engagement, is one of the signal advances in noninvasive brain stimulation.

The meta-analytical methodology used by these authors is rigorous, and the central finding, viz. that high-frequency, network-targeted stimulation reproducibly improves event recall, is amply supported. The question of whether to stimulate before or after memory encoding is also answered. While there is a hint that individualized anatomical or functional MRI-based targeting may be superior to atlas or group average-based techniques, the finding did not survive correction for multiple comparisons. Additional studies will be needed to resolve this issue, optimize the stimulation delivery parameters, and further define the behavioral effect.

While the authors appropriately emphasize the associated network rather than the hippocampus itself, naming the target after a single node could suggest a primary role for the hippocampus in the observed outcomes, a conclusion not supported by the data reviewed here. Other nodes in the network are be equally important to aspects of episodic memory and could be useful targets for stimulation.

Despite encouraging results from small clinical samples, the question of efficacy in patients with static lesions and ongoing neurodegeneration remains open. The information gathered here, including the absence of reported adverse events, should spur Phase 2 clinical trials in patients with disorders of memory.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigated the interactions between IRE and unfolded peptides using all-atom molecular dynamics simulations. The interactions between a couple of unfolded peptides and IRE provide mechanistic insight on the activation of the UPR.

Strengths:

- Well-written manuscript accessible for a broad biological audience

- State-of-art structural predictions and all-atom simulations

- Validation with existing experimental data<br /> - Clear schematic diagram summarizing mechanisms learned from simulations

- Error estimate included

- Shared simulation data and code in public repository

Weakness:

No major concerns remain after revision.

Comments on revisions:

The authors have addressed all my questions from the previous assessment. I do not have more suggestions.

Reviewer #2 (Public review):

Summary:

The manuscript reports a cryo-EM structure of TMAO demethylase from Paracoccus sp. This is an important enzyme in the metabolism of trimethylamine oxide (TMAO) and trimethylamine (TMA) in human gut microbiota, so new information about this enzyme would certainly be of interest.

Strengths:

The cryo-EM structure for this enzyme is new and provides new insights into the function of the different protein domains, and a channel for formaldehyde between the two domains.

Weaknesses:

(1) The proposed catalytic mechanism in this manuscript does not make sense. Previous mechanistic studies on the Methylocella silvestris TMAO demethylase (FEBS Journal 2016, 283, 3979-3993, reference 7) reported that, as well as a Zn2+ cofactor, there was a dependence upon non-heme Fe2+, and proposed a catalytic mechanism involving deoxygenation to form TMA and an iron(IV)-oxo species, followed by oxidative demethylation to form DMA and formaldehyde.

In this work, the authors do not mention the previously proposed mechanism, but instead just say that elemental analysis "excluded iron". This is alarming, since the previous work has a key role for non-heme iron in the mechanism. The elemental analysis here gives a Zn content of about 0.5 mol/mol protein (and no Fe), whereas the Methylocella TMAO demethylase was reported to contain 0.97 mol Zn/mol protein, and 0.35-0.38 mol Fe/mol protein. It does, therefore, appear that their enzyme is depleted in Zn, and the absence of Fe impacts on the mechanism, as explained below.

The proposed catalytic mechanism in this manuscript, I am sorry to say, does not make sense, for several reasons:

i) Demethylation to form formaldehyde is not a hydrolytic process; it is an oxidative process (normally accomplished by either cytochrome P450 or non-heme iron-dependent oxygenase). The authors propose that a zinc (II) hydroxide attacks the methyl group, which (a) is unprecedented, (b) even if it were possible, would generate methanol, not formaldehyde.

ii) The amine oxide is proposed to deoxygenate, with hydroxide appearing on the Zn - unfortunately, amine oxide deoxygenation is a reductive process, for which a reducing agent is needed, and Zn2+ is not a redox active metal ion;

iii) The authors say "forming a tetrahedral intermediate, as described for metalloprotease" but zinc metalloproteases attack an amide carbonyl to form an oxyanion intermediate, whereas in this mechanism there is no carbonyl to attack, so this statement is just wrong.

So on several counts the proposed mechanism cannot be correct. Some redox cofactor is needed in order to carry out amine oxide deoxygenation, and Zn2+ cannot fulfil that role. Fe2+ could do, which is why the previously proposed mechanism involving an iron(IV)-oxo intermediate is feasible. But the authors claim that their enzyme has no Fe. If so then there must be some other redox cofactor present. Therefore, the authors need to re-analyse their enzyme carefully and look either for Fe or for some other redox-active metal ion, and then provide convincing experimental evidence for a feasible catalytic mechanism. As it stands the proposed catalytic mechanism is unacceptable.

Revised version. The authors have essentially not changed the proposed mechanism. They have removed the reference to zinc metalloproteases, but still propose a mechanism mediated only by Zn2+. As explained above, attack by zinc (II) hydroxide is unprecedented and would generate methanol, not formaldehyde, and amine deoxygenation is a reductive process that cannot be fulfilled by Zn2+. So the proposed mechanism is still not feasible at all. The authors now say that "oxidative chemistry....remains unresolved", I'm sorry, but that is not acceptable.

I have urged the authors to re-examine the metal content of their enzyme, In the Supporting Information (Figure S5) they give ICPMS data that indicates a Zn stoichiometry of 0.5 mol Zn/mol protein, and Fe is not detected. Have the authors analysed for other redox active metals? The authors say that there is no evidence for any other metal binding site, but there is only 50% occupancy of Zn in their protein, so could there be a different metal ion present in place of Zn in the other 50% of the protein, that accounts for the observed activity?

Since there is clearly a major discrepancy here, the onus is on the authors to explain the discrepancy, rather than just returning with the same data. For example, they could treat the enzyme with EDTA to remove all metals (and check the treated enzyme by ICPMS), and then add different metal ions to test activity with different metals (could even titrate with different molar equivalents of metal ions). They could then test a range of different redox-active metal ions.

(2) Given the metal content reported here, it is important to be able to compare the specific activity of the enzyme reported here with earlier preparations. The authors have now done this in the revised version.

(3) The consumption of formaldehyde to form methylene-THF is potentially interesting, but the authors say "HCHO levels decreased in the presence of THF", which could potentially be due to enzyme inhibition by THF. Is there evidence that this is a time-dependent and protein-dependent reaction? Not yet addressed.

Also in Figure 1C, HCHO reduction (%) is not very helpful, because we don't know what concentration of formaldehyde is formed under these conditions; it would be better to quote in units of concentration, rather than %. This point has been addressed by the authors in the revised version.

(4) Has this particular TMAO demethylase been reported before? It's not clear which Paracoccus strain the enzyme is from; the Experimental Section just says "Paracoccus sp.", which is not very precise. There has been published work on the Paracoccus PS1 enzyme, is that the strain used? Details about the strain are needed, and the accession for the protein sequence. Addressed in the revised version.

Reviewer #3 (Public review):

Summary:

This paper develops a model to account for flexible and context-dependent behaviors, such as where the same input must generate different responses or representations depending on context. The approach is anchored in the hippocampal place cell literature. The model consists of a module X, which represents context, and a module H (hippocampus), which generates "sequences". X is a binary attractor RNN, and H appears to be a discrete binary network, which is called recurrent but seems to operate primarily in a feedforward mode. H has two types of units (those that are directly activated by context, and transition/sequence units). An input from X drives a winner-take-all activation of a single unit H_context unit, which can trigger a sequence in the H_transition units. When a new/unpredicted context arises, a new stable context in X is generated, which in turn can trigger a new sequence in H. The authors use this model to account for some experimental findings, and on a more speculative note, propose to capture key aspects of contextual processing associated with schizophrenia and autism.

Strengths:

Context-dependency is an important problem. And for this reason, there are many papers that address context-dependency - some of this work is cited. To the best of my knowledge, the approach of using an attractor network to represent and detect changes in context is novel and potentially valuable.

Reviewer #2 (Public review):

Summary:

This study addresses an important gap in our understanding of how pain‑related neuroadaptations interact with opioid exposure at the cellular and molecular levels, particularly in terms of cell‑type-specific responses within reward‑related brain regions. By applying single‑nucleus RNA sequencing, the authors generate a comprehensive atlas of transcriptional changes in the rat VTA associated with chronic inflammatory pain and acute morphine administration.

Strengths:

Overall, the study is important, and the experiments are carefully designed and executed. The manuscript is logically structured and well written. The sample size is appropriate: nuclei were collected from 14 male and 14 female Sprague‑Dawley rats, with 6-8 animals per experimental group. The inclusion of both sexes further strengthens the study by enhancing the generalizability of the findings.

To increase translational relevance, the authors also employ a human‑derived astrocyte culture model, which helps bridge findings from rodent tissue to human‑related cellular mechanisms.

Weaknesses:

A limitation is that the study examines only a single time point after morphine administration. However, this is balanced by the use of state‑of‑the‑art , and inherently expensive, molecular tools that allow deep transcriptional profiling.

One area requiring clarification is compliance with methodological standards. The manuscript does not specify whether ARRIVE guidelines were followed, whether a power analysis was performed to justify the number of animals used, or how randomization and blinding procedures were implemented.

Reviewer #2 (Public review):

Summary:

This manuscript describes the fully in silico design of a new variant of Staphylococcus aureus Cas9 (SaCas9) using an improved UniDesign workflow.

The design strategy consists of three sequential steps:

(1) Reducing positional bias at PAM position 3;<br /> (2) Restoring DNA binding through nonspecific interactions;<br /> (3) Combining individually favorable substitutions.

The overall pipeline is conceptually elegant and logically structured, and the genome-editing activity of the designed variants is comprehensively characterized. The resulting KRH variant exhibits relaxed PAM specificity, expanding the targeting range of SaCas9 across diverse cell types. Notably, the KRH variant demonstrates performance comparable to that of the evolution-derived KKH variant, underscoring the effectiveness of the proposed computational design framework.

Reviewer #3 (Public review):

Summary

The authors set out to explore the potential relationship between adult neurogenesis of inhibitory granule cells in the olfactory bulb and cumulative changes over days in odor-evoked spiking activity (representational drift) in the olfactory stream. They developed a richly detailed spiking neuronal network model based on Izhikevich (2003), allowing them to capture the diversity of spiking behaviors of multiple neuron types within the olfactory system. This model recapitulates the circuit organization of both the main olfactory bulb (MOB) and the piriform cortex (PCx), including connections between the two (both feedforward and corticofugal). Adult neurogenesis was captured by shuffling the weights of the model's granule cells, preserving the distribution of synaptic weights. Shuffling of granule cell connectivity resulted in cumulative changes in stimulus-evoked spiking of the model's M/T cells. Individual M/T cell tuning changed with time, and ensemble correlations dropped sharply over the temporal interval examined (long enough that almost all granule cells in the model had shuffled their weights). Interestingly, these changes in responsiveness did not disrupt low-dimensional stability of olfactory representations: when projected into a low-dimensional subspace, population vector correlations in this subspace remained elevated across the temporal interval examined. Importantly, in the model's downstream piriform layer this was not the case. There, shuffled GC connectivity in the bulb resulted in a complete shift in piriform odor coding, including for low-dimensional projections. This is in contrast to what the model exhibited in the M/T input layer. Interestingly, these changes in PCx extended to the geometrical structure of the odor representations themselves. Finally, the authors examined the effect of experience on representational drift. Using an STDP rule, they allowed the inputs to and outputs from adult-born granule cells to change during repeated presentations of the same odor. This stabilized stimulus-evoked activity in the model's piriform layer.

Strengths

This paper suggests a link between adult neurogenesis in the olfactory bulb and representational drift in the piriform cortex. Using an elegant spiking network that faithfully recapitulates the basic physiological properties of the olfactory stream, the authors tackle a question of longstanding interest in a creative and interesting manner. As a purely theoretical study of drift, this paper presents important insights: synaptic turnover of recurrent inhibitory input can destabilize stimulus-evoked activity, but only to a degree, as representations in the bulb (the model's recurrent input layer) retain their basic geometrical form. However, this destabilized input results in profound drift in the model's second (piriform) layer, where both the tuning of individual neurons and the layer's overall functional geometry are restructured. This is a useful and important idea in the drift field and to my knowledge is novel. The bulb is not the only setting where inhibitory synapses exhibit turnover (whether through neurogenesis or synaptic dynamics), and so this exploration of the consequences of such plasticity on drift is valuable. The authors also elegantly explore a potential mechanism to stabilize representations through experience, using an STDP rule specific to the inhibitory neurons in the input layer. This has an interesting parallel with other recent theoretical work on drift in the piriform (Morales et al., 2025 PNAS), in which STDP in the piriform layer was also shown to stabilize stimulus representations there. It is fascinating to see that this same rule also stabilizes piriform representations when implemented in the bulb's granule cells.

The authors also provide a thoughtful discussion regarding differential roles of mitral and tufted cells in drift in piriform and AON and potential roles of neurogenesis in archicortex.

In general, this paper puts an important and much-needed spotlight on the role of neurogenesis and inhibitory plasticity in drift. In this light, it is a valuable and exciting contribution to the drift conversation.

Comments on revisions:

I appreciate the substantial revisions the authors have made to the manuscript. The paper is clearly improved and addresses an important and timely question: the relationship between adult neurogenesis and drift. In particular, the effort to link adult neurogenesis in the olfactory bulb to the long-term stability of odor representations downstream is valuable, and the modeling provides useful mechanistic intuition about how inhibitory circuit remodeling could influence representational drift across layers.

That said, I remain concerned that the manuscript, as currently framed, risks giving readers the incorrect impression that experimental work has established progressive, time-dependent drift in the odor tuning of olfactory bulb neurons. Experimental studies do show that ongoing experience with a set of odors can profoundly alter bulbar responses to those odors, but longitudinal measurements in which the tested odors are not repeatedly presented between sessions have instead emphasized remarkable stability of mitral/tufted tuning over days to months across multiple groups. I also think it would strengthen the manuscript to avoid anchoring the empirical comparison too heavily on a single paradigm (Yamada et al., 2017). The experimental literature spans multiple regimes, including daily odor exposure with ongoing experience and longitudinal measurements in which the tested odors are not repeatedly presented between sessions, and these regimes can yield qualitatively different degrees of reorganization. Situating the model explicitly within this broader landscape, rather than emphasizing one dataset, would make the interpretation clearer and prevent readers from overgeneralizing the Yamada findings to baseline bulbar stability. This distinction is especially important because it contrasts with what has been reported in piriform cortex, where representational drift is observed even in the absence of ongoing experience with a given odor set, and where repeated daily encounters with the same odors can slow or arrest that drift.

Reviewer #3 (Public review):

The author developed a useful methodology for generating all combinations of multiple reagents using standard lab equipment. This methodology has clear uses in for studying of microbial ecology as they demonstrated. The methodology will likely be useful for other types of experiments that required exhaustive testing of all possible combinations of a given set of reagents (e.g., drug-drug antagonism and synergy).

The authors provided a useful R script that generates a detailed experimental protocol for building desired combination from any number of reagents. The produced document is useful and has clear instructions. The output of the computer script will be strengthened if graphical output is also provided (similar to the one provided in Figure 1C).

The authors show that the error rate of the method doesn't go up with the number of combinations using dyes (Figure 2).

The authors demonstrate the value of their methodology for studying interactions within microbial consortia by assembling all possible combinations of eight strains of Pseudomonas aeruginosa. The value of their methodology for this application is well founded. However, it is also unclear why specific experimental choices were made for this application. It is unclear why authors continue to show the absorbance measurements of strain assemblies over the entire wavelength spectrum and not just for ABS 600 nm (figures 3 and 4). It is also unclear why the authors provided information on the "sum of the three spectra" as this reference line is meaningless and not a reasonable null model for estimating how well specific strain combinations will grow together.

Figure 5 illustrates the various analysis types that can be performed on the data collected from growing combinations of eight Pseudomonas aeruginosa strains. It is a very informative figure since it provides a "roadmap" on the various ways in which the dataset produced can be explored. The information in Figure 5 and S6 will likely be very useful for a wide audience.

Comments on revisions:

We thank the author for considering the review and providing additional clarifications. The authors disagree with some of the points we raised and decided to reject some of our recommendations. All the points of disagreement are minor and clearly subjective (e.g., stylistic). Congratulations again for this elegant manuscript.

Reviewer #2 (Public review):

Summary:

The authors show that pre-onset neural encoding is likely not a product of predictive processing. They demonstrate this primarily through two analyses:

(1) They decorrelate the neural responses between pre- and post-word onset and show that this does not eliminate pre-onset neural encoding. This suggests that this pre-onset neural encoding is not a result of pre-activation driven by an underlying predictive process.

(2) They show that the future word improvement to encoding performance shown in Caucheteux et al. is likely a result deriving from the low temporal resolution in fMRI, as it does not reproduce in MEG or ECoG data, modalities that have a higher temporal resolution better suited to this kind of analysis.

Strengths:

Both of the paper's arguments are overall very compelling and point to potential problems in the underlying literature that may require reevaluation. The paper does not make any unreasonable claims. The limitations of the study are appropriately addressed. The paper is well-reasoned and well-written. Overall, I believe the paper is a worthy addition to the literature on this subject.

Weaknesses:

One concern is that I wonder about the degree to which the residualization/decorrelation that the authors employ in Figure 4 is truly forcing the model to unlearn all the interactions between pre- and post-word onset when referencing the neural activity. This point is explicitly noted in Schonmann et al. (which the authors cite): "While residualised word embeddings no longer contain temporal stimulus dependencies, these dependencies are still represented in the neural data, and can hence be 're-learned' when fitting the regression model." I imagine the inverse of this could be true here - the dependencies are still represented in the stimulus and so can be relearned when mapping to the neural data. It is possible that the small positive onset correlation that occurs after decorrelation can be entirely explained by this. This is not a bad thing per se (as it aligns with the overall point of the article), but it is a potential methodological oversight. A clear description of the decorrelation process is necessary in the methods section.

The paper correctly notes that their removal of bigram/n-gram information does not entirely exclude all stimulus dependencies. However, removing this fully would be extremely difficult, and the small reduction in performance of the bigram-ablated model does not point to this being a major issue.

Separately, some of the figures are a little rough. Suggestions have been provided to the authors.

Reviewer #2 (Public review):

Summary:

Wang et al. engineered an ACE2 mutant by introducing two mutations (T92Q and H374N), and fused this ACE2 mutant to human IgG1-Fc (B5-D3). Experimental results suggest that B5-D3 exhibits broad-spectrum neutralization capacity and confers effective protection upon intranasal administration in SARS-CoV-2-infected K18-hACE2 mice. Transcriptomic analysis suggests that B5-D3 induces early immune activation in lung tissues of infected mice. Fluorescence-based bio-distribution assay further indicates rapid accumulation of B5-D3 in the respiratory tract, particularly in airway macrophages. Further investigation shows that B5-D3 promotes viral phagocytic clearance by macrophages via an Fc-mediated effector function, namely antibody-dependent cellular phagocytosis (ADCP), while simultaneously blocking ACE2-mediated viral infection in epithelial cells. These results provide some insights into improving decoy treatments against SARS-CoV-2 and other potential respiratory viruses.

Strengths:

The protective effect of this ACE2-Fc fusion protein against SARS-CoV-2 infection has been evaluated in a reasonable way.

Weaknesses:

(1) Some of the mice experiments suffer from insufficient sample numbers, which affect the statistical power and reliability of the results. The author acknowledged this weakness, noting that the supply of aged mice was limited, while arguing that, although the sample size is small, the data from these mice are consistent.

(2) Compared to 6 hours, intranasal administration of B5-D3 at 24 hours before viral infection results in reduced protective efficacy. However, only survival and body weight data are provided, with no supporting evidence from virological assays such as viral titer measurement. The author acknowledged that such data would be more comprehensive and attributed the limitation to constraints in animal services.

(3) The efficacy of the B5-D3-LALA group was not as good as that of the B5-D3 group. The author suggested that there might be a certain degree of viral variation, and viral infection in the lungs may be uneven in the B5-D3-LALA group.

Reviewer #2 (Public review):

C. difficile infection (CDI) is one of the most common nosocomial intestinal infections with a high rate of disease recurrence. Importantly, antibiotics used to treat CDI are a double-edged sword because disruption of the gut microbiome also increases the susceptibility to CDI. Therefore, there is an unmet need for alternative therapeutic approaches against CDI. CDI pathogenesis is initiated by the cytotoxic toxins TcdA and TcdB that target and induce cell death of intestinal epithelial cells, leading to epithelial barrier breakdown and inflammation. Innate immune cells such as neutrophils and innate lymphoid cells (ILCs) were shown to be crucial to control CDI during the acute phase. Based on previous reports that the pro-resolving mediator Lipoxin A4 (LXA4) inhibits neutrophil infiltration and promotes efferocytosis as well as mucosal repair, the authors reason that LXA4 could be leveraged as a therapy against CDI.

The authors developed and validated a gut-on-chip (GOC) system to mimic the gut environment for C. difficile infection in vitro studies. LXA4 was able to decrease C. difficile-induced inflammation only when used as a prevention but not as a therapy. IEC RNA-seq revealed that LXA4 treatment upregulates a transcriptional program that reinforces barrier function. These data were replicated in an in vivo model of CDI. Overall, the study provides evidence that LXA4 could be repurposed for CDI treatment, but some claims are not fully supported by the data, such as the synergy between LXA4 and vancomycin, which has not been experimentally tested in vivo.

Reviewer #3 (Public review):

Summary:

This reanalysis of a classic study of visual perceptual learning in a texture discrimination task convincingly demonstrates the presence of sequential dependence effects, commonly seen in response time analyses in 2-alternative tasks, on response accuracy in the texture task in visual periphery and in a simultaneous central letter report at fixation. Overall, this paper provides a new and interesting analysis of the effects of sequential dependencies from trial to trial on performance, learning, and generalizability in perceptual learning.

Strengths:

This new analysis of sequential dependency effects (SDEs) extends commonly observed sequential effects in two-choice reaction times to accuracy and relates them to response accuracy during visual learning in a frequently used perceptual learning task. The paper makes a convincing case that different conditions known to impact generalization of learning to a second visual location also expresses quantitatively distinct n-back SDEs.

Weaknesses:

Additional analyses now back up the analysis of effects of SDEs using trials selected to enhance the size of the effects, specifically when the current trial is low visibility and the prior trial is of high visibility. The authors now provide a practical analytic reason for this choice.

Comments on revisions:

The revision has successfully addressed comments in the original reviews.

Reviewer #2 (Public review):

The authors use a library of influenza A viruses from different strains, classified in lab-adapted, human, avian, and swine according to the animal from which they were isolated. They propose that the cow mammary gland serves as a mixing vessel for influenza A viruses. As a first approach, the authors assess susceptibility to infection across different cell types, including continuous and primary cell lines, bovine mammary cells, and mammary explants. All these cells support polymerase activity. Then, they analyzed changes in the bovine virus's viral fitness relative to an avian precursor. The authors use single-gene replacement to study whether and which RNP segments improve viral transcription. As part of this section, they also test IFN-specific antagonism by NS1 to assess the input of segment 8. Quantitative glycomic analysis was performed on the continuous bovine mammary cell line to demonstrate the presence of both a2,3 and a2,6, which is consistent with their observation that these cells can be co-infected with human and avian IAVs simultaneously. The main question, however, is: what is the glycome in the explants, or directly from tissues?

Overall, the manuscript is clearly written and provides new insights into the behaviour of the cattle isolate, now compared with a representative group of model or precursor HAs of different origins.

It would be great if a consistent nomenclature for the IAV strains could be used in the study. There is a mix of origin (Texas), animal from which the virus was isolated (mallard), or abbreviations that do not follow guidelines (IAV07). Are the USSR and Udorn not lab-adapted?

The experimental setup includes bovine mammary primary and continuous cells, as well as mammary explants. Some of the most significant differences, for example, in viral fitness studies and co-infection experiments, are observed in these explants. Perhaps there could be some additional focus on this observation. The implications in comparison to the results obtained in cultured cells could be described. How will the human and other HA subtype viruses fare in the explants?

Reviewer #2 (Public review):

This manuscript explores endodermal lineage specification during metamorphosis in Styela clava. As biphasic lifestyle organisms, the endoderm exists as a rudiment in the non-feeding larvae that differentiates throughout metamorphosis to build the digestive components of the adult body plan. The authors of this manuscript use scRNA sequencing of individuals throughout the metamorphic process, as well as maturing juveniles, to follow the trajectories of the endodermal precursors. They identify two distinct populations that give rise to the stomach and intestinal lineages, and they suggest that there are homologous relationships between tunicate & vertebrate dual-origin endodermal lineages. Additionally, the authors highlight the role of conserved FGF signal-dependent programs in digestive organ patterning and suggest that endodermal fate restriction occurs earlier in Styela in comparison with the mouse gut.

Overall, the paper is the first in-depth look at tunicate endodermal fate from a single-cell sequencing perspective and provides a robust framework for understanding the evolutionary origins of the deuterostome/chordate gut. The data is substantial and of great interest. However, we find their discussion of evolutionary implications to be highly problematic, and there are also numerous major issues regarding the clarity and cogency of their data presentation. Thus, we consider that substantial revision is required to provide a more accurate analysis of this data and its evolutionary implications. This revision would not require further experimentation.

Reviewer #2 (Public review):

Summary:

In this paper, Andriani and colleagues are examining the potential role of Zn flux in sperm and its effect on Slo3 channels. This is an interesting question that is likely critical to how sperm function properly and Slo3 channels are a possible candidate for a downstream molecule that is impacted by Zn. In this paper the authors using Zn imaging, sperm motility assays, and electrophysiology to show that Zn flux has impacts on sperm function. They then go on to look at the impact Zn has on Slo3 current and propose a binding site based on MD simulations. Revisions of the paper added new critical controls and improved description of the methodology.

Strengths:

The question of how Zn flux impacts membrane potential and sperm motility is an important one. Moreover, Slo3 make present an interesting candidate or the target of Zn regulation. The combination of methods used here also has the potential to uncover mechanisms of Zn regulation of Slo3.

Weaknesses:

The responses sufficiently answered my original concerns.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

The production of the lysate requires special instrumentation, limiting accessibility.

Comments on revisions:

Thank you, authors, for addressing the minor concerns outlined in my comments. I have no further recommendations.

Reviewer #2 (Public review):

Summary:

The manuscript by Leshem et al. presents a transcriptomic analysis of the developing human outflow tract (OFT) at embryonic and fetal stages using snRNAseq and spatial transcriptomic. Additionally, the authors analyze transcriptomic data from the adult aortic valve to compare embryonic and adult cell population, aiming to identify persistent embryonic transcriptional signatures in adult cells. A total of 15 clusters were identified from the embryonic and fetal OFT samples, including three mesenchymal and four endothelial clusters. Using SCENIC analysis on the embryonic snRNAseq data, the authors identified GATA6 as a key regulator of valve precursor cells. Spatial transcriptomic analysis of four fetal OFT sections further revealed the spatial distribution of mesenchymal nuclei, smooth muscle cells, and valvular interstitial cells. Trajectory analysis identified two distinct developmental origins of fetal mesenchymal cells: the neural crest and the second heart field. Finally, the authors used snRNAseq data from the adult aortic valve to propose that embryonic transcriptional signatures persist in a subset of adult cells.

Strengths:

(1) The study offers a rich and detailed dataset, combining snRNA-seq and spatial transcriptomics in human embryonic and fetal OFT, which are challenging to obtain.

(2) The use of SCENIC and trajectory analysis adds mechanistic insight into cell lineage and regulatory programs during valve development.

(3) This study confirms GATA6 ss a key regulator of valve precursor cells.

(4) Comparison between embryonic/fetal and adult datasets represents a novel attempt to trace persistence of developmental transcriptional programs.

Weaknesses:

(1) A major limitation is the lack of experimental validation to support key conclusions, particularly the claim of persistent embryonic transcriptional signatures in adult cells.

(2) The manuscript would benefit from a clearer discussion of how these results advance beyond previous studies in human heart and valve development.

(3) The comparison between embryonic and adult data is interesting but would be more convincing with additional evidence supporting the proposed persistence of embryonic transcriptional signatures in adult cells

Comments on revisions:

The final section of the results concludes with the search for a distribution pattern similar to JAG1. The authors end their article by identifying the FOXC1 and OSR1 genes without providing further validation for their discovery, which is regrettable.

Reviewer #2 (Public review):

FOXC1 is a transcription factor essential for the development of neural crest-derived tissues and has been identified as a key biomarker in various cancers. However, the molecular mechanisms underlying its function remain poorly understood. In this study, the authors used RNA-seq, ChIP-seq, and FOXC1-overexpressing cell models to show that FOXC1 directly activates transcription of ARHGAP36 by binding to specific cis-regulatory elements. Elevated expression of FOXC1 or ARHGAP36 was found to enhance Hedgehog (Hh) signaling and suppress PKA activity. Notably, overexpression of either gene also conferred resistance to Smoothened (SMO) inhibitors, indicating ligand-independent activation of Hh signaling. Analysis of public gene expression datasets further revealed that ARHGAP36 expression correlates with improved 5-year overall survival in neuroblastoma patients. Together, these findings uncover a novel FOXC1-ARHGAP36 regulatory axis that modulates Hh and PKA signaling, offering new insights into both normal development and cancer progression.

Main strengths of the study are:

(1) Identification of a novel signaling pathway involving FOXC1 and ARHGAP36, which may play a critical role in both normal development and cancer biology. 2) Mechanistic investigation using RNA-seq, ChIP-seq, and functional assays to elucidate how FOXC1 regulates ARHGAP36 and how this axis modulates Hh signaling. 3) Clinical relevance demonstrated through analysis of neuroblastoma patient datasets, linking ARHGAP36 expression to improved 5-year overall survival.

Comments on revisions:

Consider adding subsection titles to the Results section to better organize the findings and improve readability.

The authors may consider adding a statement in paragraph 4 of the Results section or in the Discussion noting that ARHGAP36 has been reported to inhibit PKAC activity and promote PKAC degradation.

Reviewer #2 (Public review):

Summary:

A particular challenge in treating infections caused by the parasite Toxoplasma gondii is to target (and ultimately clear) the tissue cysts that persist for the lifetime of an infected individual. The study by Maus and colleagues leverages the development of a powerful in vitro culture system for the cyst-forming bradyzoite stage of Toxoplasma parasites to screen a compound library for candidate inhibitors of parasite proliferation and survival. They identify numerous inhibitors capable of inhibiting both the disease-causing tachyzoite and the cyst-forming bradyzoite stages of the parasite. To characterize the potential targets of some of these inhibitors, they undertake metabolomic analyses. The metabolic signatures from these analyses lead them to identify one compound (MMV1028806) that interferes with aspects of parasite mitochondrial metabolism. In the revised version of the manuscript, the authors present convincing evidence that MMV1028806 targets the mitochondrial electron transport (ETC) chain of the parasite (although they don't identify the actual target in the ETC). The revised manuscript also nicely addresses my other criticisms of the original version. Overall, the study presents an exciting approach for identifying and characterizing much-needed inhibitors for targeting tissue cysts in these parasites.

Strengths:

The study presents convincing proof-of-principle evidence that the myotube-based in vitro culture system for T. gondii bradyzoites can be used to screen compound libraries, enabling the identification of compounds that target the proliferation and/or survival of this stage of the parasite. The study also utilizes metabolomic approaches to characterize metabolic 'signatures' that provide clues to the potential targets of candidate inhibitors. In addition to insights into candidate bradyzoite inhibitors, the study also provides new insights into the physiological role of the mitochondrial electron transport chain of bradyzoites, and raises a host of interesting questions around the functional roles of mitochondria in this stage of the parasite.

Weaknesses:

As noted in my previous review, the authors present convincing evidence that one of the compounds they have identified (MMV1028806) is targeting the mitochondrial electron transport chain (ETC). However, in the absence of an assay that directly measures bc1 activity (e.g. an enzymatic assay), they cannot be certain that it targets the bc1 complex in the ETC. I appreciate that the authors have toned down some of the conclusions around this. I do still think there are some places where the text is overstating the finding (noted below).

Line 30. "Stable isotope-resolved metabolic profiling on tachyzoites and bradyzoites identified the mitochondrial bc1-complex as a target of bradyzocidal compounds".

Line 546. "Metabolic profiling and stable isotope tracing in treated tachyzoites suggested the inhibition of the mitochondrial bc1-complex by MMV1028806 and the reference compound BPQ."

Line 622. "In addition to abundance data, the incorporation of ¹³C and ¹⁵N stable isotopes from glucose and glutamine, respectively, into TCA cycle and pyrimidine biosynthesis intermediates suggest the bc1-complex as a target."

Reviewer #2 (Public Review):

Chong Wang et al. investigated the role of H3K4me2 during the reprogramming processes in mouse preimplantation embryos. The authors show that H3K4me2 is erased from GV to MII oocytes and re-established in the late 2-cell stage by performing Cut & Run H3K4me2 and immunofluorescence staining. Erasure and re-establishment of H3K4me2 have not been studied well, and profiling of H3K4me2 in germ cells and preimplantation embryos is valuable to understanding the reprogramming process and epigenetic inheritance.

(1) The authors claim that the Cut & Run worked for MII oocytes, zygotes, and the 2-cell embryos. However, it is unclear if H3K4me2 is erased during the stage or if the Cut & Run did not work for these samples. To support the hypothesis of the erasure of H3K4me2, the authors conducted immunofluorescence staining, and H3k4me2 was undetected in the MII oocyte, PN5, and 2-cell stage. However, the published papers showed strong staining of H3K4me2 at the zygote stage and 2-cell stage ((Ancelin et al., 2016; Shao et al., 2014)). The authors need to cite these papers and discuss the contradictory findings.

The authors used 165 MII oocytes and 190 GV oocytes for the Cut & Run. The amount of DNA in MII oocytes is halved because of the emission of the first polar body. Would it be a reason that H3K4me2 has fewer H3K4me2 peaks in MII oocytes than GV oocytes?

In Figure 3C, 98% (13,183/13,428) of H3K4me2 marked genes in GV oocytes overlap with those in the 4-cell stage. Furthermore, 92% (14,049/15,112) of H3K4me2 marked genes in sperm overlap with those in the 4-cell stage. Therefore, most regions maintain germ line-derived H3K4me2 in the 4-cell stage. The authors need to clarify which regions of germ line-derived H3K4me2 are maintained or erased in preimplantation embryos. Additionally, it would be interesting to investigate which regions show the parental allele-specific H3K4me2 in preimplantation embryos since the authors used hybrid preimplantation embryos (B6 x DBA).

(2) The authors claim that Kdm1a is rarely expressed during mouse embryonic development (Figure 4A). However, the published paper showed that KDM1a is present in the zygote and 2-cell stage using immunostaining and western blotting ((Ancelin et al., 2016)). Additionally, this paper showed that depletion of maternal KDM1A protein results in developmental arrest at the two-cell stage, and therefore, KDM1a is functionally important in early development. The authors should have cited the paper and described the role of KDM1a in early embryos.

(3) The authors used the published RNA data set and interpreted that KDM1B (LSD2) was highly expressed at the MII stage (Figure S3A). However, the heat map shows that KDM1B expression is high in growing oocytes but not at 8w_oocytes and MII oocytes. The authors need to interpret the data accurately.

(4) All embryos in the TCP group were arrested at the four-cell stage. Embryos generated from KDM1b KO females can survive until E10.5 (Ciccone et al., 2009); therefore, TCP-treated embryos show a more severe phenotype than oocyte-derived KDM1b deleted embryos. Depletion of maternal KDM1A protein results in developmental arrest at the two-cell stage ((Ancelin et al., 2016)). The authors need to examine whether TCP treatment affects KDM1a expression. Western blotting would be recommended to quantify the expression of KDM1A and KDM1B in the TCP-treated embryos.

(5) H3K4me2 is increased dramatically in the TCP-treated embryos in Figure 4 (the intensity is 1,000 times more than the control). However, the Cut & Run H3K4me2 shows that the H3K4me2 signal is increased in 251 genes and decreased in 194 genes in the TCP-treated embryos (Fold changes > 2, P < 0.01). The authors need to explain why the gain of H3K4me2 is less evident in the Cut & Run data set than in the immunofluorescence result.

References

Ancelin, K., ne Syx, L., Borensztein, M., mie Ranisavljevic, N., Vassilev, I., Briseñ o-Roa, L., Liu, T., Metzger, E., Servant, N., Barillot, E., Chen, C.-J., Schü le, R., & Heard, E. (2016). Maternal LSD1/KDM1A is an essential regulator of chromatin and transcription landscapes during zygotic genome activation. https://doi.org/10.7554/eLife.08851.001

Ciccone, D. N., Su, H., Hevi, S., Gay, F., Lei, H., Bajko, J., Xu, G., Li, E., & Chen, T. (2009). KDM1B is a histone H3K4 demethylase required to establish maternal genomic imprints. Nature, 461(7262), 415-418. https://doi.org/10.1038/nature08315

Shao, G. B., Chen, J. C., Zhang, L. P., Huang, P., Lu, H. Y., Jin, J., Gong, A. H., & Sang, J. R. (2014). Dynamic patterns of histone H3 lysine 4 methyltransferases and demethylases during mouse preimplantation development. In Vitro Cellular and Developmental Biology - Animal, 50(7), 603-613. https://doi.org/10.1007/s11626-014-9741-6

Reviewer #2 (Public review):

Summary:

The authors are trying to broaden the understanding of SARS-CoV2 Nsp13 activity to show that a single viral protein can accomplish multiple functions. Additionally, they try to show that helicase function is not limited to ATP-driven, unidirectional unwinding.

Strengths:

The consistent application of statistics to triplicate experiments is a strength of the manuscript. The ToPif1 control in Figure S12 is a good control.

Weaknesses:

(1) All the experiments except the one in Figure S2 use N-terminally His-tagged Nsp13. Because the N-terminal tag is known to have large effects on Nsp13 activity, this calls into question virtually all of the results in this manuscript.

(2) The ATP-independent, bidirectional duplex unwinding shown for short duplex substrates is reminiscent of the trapping of thermal fraying intermediates that have been reported for other helicases. Because they are only observed on short duplexes, do not require ATP, and are bidirectional, this does not suggest strand displacement as suggested in the manuscript. Instead, it suggests trapping of partially melted intermediates.

(3) Results that may be artifacts of unusual in vitro conditions are interpreted as if similar results will occur in the cell, where ATP is likely always present. Along those same lines, SARS-CoV-2 replicates in compartments of the endoplasmic reticulum, which would limit the ability of Nsp13 to access DNA substrates.

(4) There is no evidence to support the conclusion that "Duplex DNA supports bidirectional remodeling via both ATP-dependent and ATP-independent mechanisms." 3'-5' duplex melting is limited to short duplexes and is ATP-independent, suggesting it may be due to trapping of thermal fraying intermediates by the ssDNA binding Nsp13. The ATP-dependent and ATP-independent melting on the substrates with the 3'-overhang are the same, suggesting that ATP-dependent melting does not occur on this substrate, which would indicate that bidirectional ATP-dependent translocation does not occur.

(5) The description of ATP-independent unwinding as having "limited processivity," is likely not accurate. These experiments were multiturnover reactions with very high Nsp13 concentrations and no protein trap to ensure single turnover conditions. Because the reactions were multi-turnover, no information about the processivity of Nsp13 can be obtained. On the contrary, it seems likely that the product formed over the 30-minute reaction with a vast excess of Nsp13 is due to binding and dissociation of multiple Nsp13 molecules instead of processive translocation by a single enzyme.

(6) G4s are much more stable at cellular K+ concentrations than they are at 20 mM K+. As such, Nsp13's ability to unfold a G4 in the absence of ATP may be diminished or eliminated at a physiological K+ concentration.

Although the authors show that His-tagged Nsp13 can melt DNA and RNA duplexes and G-quadruplexes in an ATP-dependent and independent manner, in addition to annealing single-stranded nucleic acids into duplexes, the use of His-tagged Nsp13, which is known to cause artifacts, makes their results difficult to draw conclusions from. As such, in the opinion of this reviewer, this manuscript is likely to have little impact on the field.

Reviewer #2 (Public review):

Summary:

The authors perform confirmation studies of Paul Basch's seminal schistosome work from 1981, demonstrating the development of transformed schistosomules into sexually dimorphic adult parasites, albeit without successful egg production. In addition to the findings from Basch's earlier work, the authors add some new molecular data in the form of an analysis of proliferative cells in in-vitro-derived animals.

Strengths:

The authors successfully confirm experimental results from earlier schistosome researchers, providing a potential new tool for studying schistosome biology without the need for vertebrate hosts.

Weaknesses:

The display of data from the authors is sometimes difficult to follow/understand where it comes from. For example:

(1) Line 136: The authors claim that parasites in HS and FBS conditions have substantially different mortality rates (11.3 +/- 2.7 vs 5 +/- 2.3) but a quite high p-value (0.8). Analyzing the raw data myself, I obtained a mean of 8.2 +/- 1.7% vs 4.8% +/- 4.3% with a p-value of 0.15. Either the data are not clearly presented, and I did not follow them, or the data presented in the text do not match the raw data in the supplemental files.

(2) Line 187/Figure 4: Though it is not clearly stated, it appears that the authors treat their EdU counts as an ordinal data set of 61 steps (from 0 to >60) rather than a continuous measure of EdU+ cells per animal. In this author's opinion, the graph strongly suggests a continuous data set, and the fact that this reviewer had to dig through poorly-labeled raw data to discover the nature of the data is problematic. The authors should either switch to a continuous data set or make it explicit that the data shown are ordinal. If counting EdU+ cells is too arduous, the authors could consider comparing the amount of EdU+ area to the amount of DAPI+ area in maximum intensity projections of their confocal images, as this would roughly approximate the amount of proliferative cells in the animals.

There are some minor issues as well:

(1) Line 122: It is perhaps incorrect to refer to humans as "the" definitive host of schistosomes, as S. japonicum is primarily considered a zoonotic infection with water buffalo/cows being the primary definitive host.

(2) Line 185/298: The authors refer to EdU pulse-chase experiments, but the experiments described here are EdU pulse experiments.

Reviewer #2 (Public review):

Summary:

This paper describes a novel tool (CRYO2PHR-MiroTM), which aims to create contact sites between mitochondria. One elegant aspect of the technique is that it is controlled by the exposure of cells to blue-light and reversible when cells are put back in the dark. Through an unknown and unexplored mechanism, the mitochondrial membrane potential is raised at the mitochondrial contact sites. The oligomerization of CRYOPHR-MiroTM is protective against the toxic effect of prolonged blue light exposure in cells and nematodes.

Strengths:

This work might open novel perspectives in the fundamental study of mitochondria.

(1) CRYO2PHR-MiroTM represents an interesting tool to manipulate mitochondria interaction/proximity/distribution without playing with the classical components of the mitochondrial fusion and fission machinery.

(2) This work suggests that, without the need for fusion, the relative proximity of mitochondria might influence their activity, opening novel fields of investigation in mitochondrial biology.

(3) Finally, targeting CRYO2PHR not only to mitochondria but also to their partner organelles (ER, LD, peroxisomes...) could provide a tool to reversibly manipulate the interaction of mitochondria with the rest of the organelle community.

Weaknesses:

As detailed below, the claims made by the author that CRYOPHR induce mitochondrial contact sites are not fully convincing at this stage. The method used to define and analyse contact sites is not clear enough, and the image presented in the present manuscript does not convincingly illustrate contact sites between mitochondria. Finally, the evidence that CRYOPHR does not trigger mitochondrial fusion should be strengthened.

Comments on the results:

(1) The quantification of mitochondrial contacts is a crucial point of this study. At this stage, the data are not sufficient to demonstrate that CRYOPHR-MiroTM oligomerisation tethers mitochondria. CRYOPHR-MiroTM can oligomerise in Trans, leading to mitochondrial tethering, but it can also oligomerise in Cis. In that later case, one could hypothesise that the massive aggregation of CRYOPHR-MiroTM at the mitochondrial outer membrane could locally push lipids away and/or create membrane curvature. The image and quantification provided by the author make it difficult to decide whether CRYOPHR-MiroTM tethers mitochondria or pinches their membranes. Below are detailed comments on these aspects:

a) It is claimed that "the proportion of mitochondria having one or more mito-contacts increased by nearly 50% following optogenetic stimulation". However, it is unclear how the authors have calculated this parameter. In the methods for contact ratio calculation, it is written that "the contacted area of CRY2PHR puncta was calculated", but I do not understand what it means and how it relates to contact ratio calculation. Then the authors have written, "Based on the area or distance (between mitochondria), the mitochondria were classified as either non-contact or contact". It is not clear to which parameter the term " area " refers: the area of mito-contacts based on MitoTracker or the area of CRY2PHR puncta. It is not clear how the authors integrate the two parameters "area" and "distance" to decide whether two mitochondria are in contact or not.

b) The method states that "Contact ratio refers to the number of contact mitochondria by the total number of mitochondria". What does "number of contact mitochondria" mean? The number of contacts between mitochondria? The number of mitochondria in contact? What is the distance range between two mitochondria, taking into account optic resolution, for which the authors consider that two mitochondria are "in contact"?

c) The quantification of the contact ratio made on the TEM picture should be explained.

d) The following data should be added, as contact site formation is a critical point. On cells treated or not with blue light, the author should measure systematically what is the distance of a given mitochondrion to the nearest one. The distribution of these distance values should be shown and analysed to determine whether or not there are more mitochondria at short distances upon blue light induction of CRYOPHR oligomerization. In addition, the author should determine the number of CRYO2PHR puncta that are simply lying on a mitochondrion and the number of CRYO2PHR puncta that are bridging two clear, distinct mitochondria.

e) Based on the images provided in Figure 1, there is no convincing evidence of mitochondrial contacts. In image 1g, the CRYO2PHR puncta seem to be lying on mitochondrial tubules. Sometimes, it looks that CRYO2PHR puncta decorate mitochondrial constriction sites, suggesting that the CRYOPHR might pinch membranes. The authors claim that they "found various types of mitochondrial contacts (Figure 1f, 1g), such as head-to-head, side-by-side, and head-to-side", but it is not clearly visible on the images. One problem is that the authors show the merge of MTDR and CRYOPHR-mCherry staining, in which the mitochondria contact are hidden by very bright CRYOPHR-mCherry aggregates. The authors should provide high magnification images (like in 1g) showing not only the merge of mitochondria and CRYOPHR-mCherry but also the staining of mitochondria by themselves. The authors should mark "head-to-head, side-by-side, and head-to-side contacts" with arrows.

f) Continuing on Figure 1f and 1g, it does not sound optimal to use CRYOPHR-mcCherry in combination with MTDR (MitoTracker Deep Red) to precisely delimitate subtle membrane contact sites between mitochondria because the emission and excitation spectra of these two fluorochromes partially overlap. One better alternative could be to use MTG (MitoTracker Green) as for Figure 1a. However, here we come to the point that MitoTraker stains the mitochondrial matrix that is delimited by the mitochondrial inner membrane, which can be discontinuous in a given mitochondrion. To formally visualise mitochondrial contact sites and demonstrate that CRYOPHR tethers mitochondria, the author should rather mark the mitochondrial outer membrane (with TOM20::GFP and anti-TOM20, for instance).

g) Figure S2 presents snapshots of a movie clearly showing the rapid aggregation of CRYOPHR into distinct puncta upon blue light exposure. The author should perform the same experiment on cells in which mitochondria would be stained with a fluorophore, allowing live imaging (MTG or TOM20::GP, for instance). This would allow for tracking of mitochondria and CRYOPHR puncta at the same time. Hence, high magnification views should allow for capturing events where CRYOPHR puncta formation coincides with mitochondrial tethering if the authors' claims are correct, or with, for instance, membrane pinching if they are wrong.

h) If CRYOPHR-TMMiro bring mitochondrial membrane closer, it would be surprising that it does not increase the probability of Mitofusin-dependent fusion events. The author should conduct analysis of the mitochondrial network in cells exposed to the conditions shown in Figure 1. Rather than relying only on the aspect ratio (as shown in Figure 2 in cells stressed by prolonged blue light exposure), the author should also analyse the mitochondrial total branch length (sum of the length of all branches from a mitochondrion) and the number of branches on each mitochondrion.

i) Ideally, the author should not only rely on the analysis of mitochondrial architecture, which only partially informs on mitochondrial fusion rate. Fragmented mitochondria can indeed fuse efficiently via kiss-and-run events, for instance. To formally demonstrate that there are no permanent nor transcient fusion at the mitochondrial contact sites induced by CRYOPHR, the most powerful method would be to analyse diffusion of matrix fluorescent dyes. This can be conducted using photoconvertible probes (mt-dendra2) (Pham et al., 2012) or a PEG-induced cell fusion assay (Detmer et al., 2007).

(2) Regarding the quantification of local MMP at mitochondrial contact, it would be important to better explain how the authors have set up their microscope to avoid technical issues that could lead to fluorescent artifacts at CRYOPHR puncta. Because the emission of Rhodamine 123 overlaps the excitation of mCherry, it should be explained in the methods how the detection of Rhodamine 123 has been filtered to avoid the detection of the red light coming from the mCherry light coming from CRYOPHR puncta. This is critical as fluorescent protein aggregates can be very bright.

Comments on the introduction and discussion

(1) In the results section, the authors state that they were "Inspired by previous studies indicating that nanoscale proximity of a charged membrane or protein 119 condensate to a membrane amplifies the local membrane potential". It could be useful to the readers to have a bit of background regarding these observations (references 55 and 56) to better understand what supports the rationale of the authors' strategy. Then, the discussion part should address in more detail the possible mechanisms that could explain why bringing the mitochondrial membranes without fusing them influences mitochondrial membrane potential.

(2) I would suggest finding a simple name for the CRYOPHR-MiroTM tool that could evoke more clearly that it is an optogenetic tool designed to tether mitochondria with blue light.

Reviewer #2 (Public review):

Sun et al. have developed a midbrain-like organoid (MLO) model for neuronopathic Gaucher disease (nGD). The MLOs recapitulate several features of nGD molecular pathology, including reduced GCase activity, sphingolipid accumulation, and impaired dopaminergic neuron development. They also characterize the transcriptome in the MLO nGD model. CRISPR correction of one of the GBA1 mutant alleles rescues most of the nGD molecular phenotypes. The MLO model was further deployed in proof-of-principle studies of investigational nGD therapies, including SapC-DOPS nanovesicles, AAV9-mediated GBA1 gene delivery, and substrate-reduction therapy (GZ452). This patient-specific 3D model provides a new platform for studying nGD mechanisms and accelerating therapy development. Overall, only modest weaknesses are noted, and these have been adequately addressed in the revision.

Comments on revisions:

I have no further recommendations. The revised manuscript addresses the few questions and concerns that I had initially shared.

Reviewer #2 (Public review):

This manuscript by Tsay et al. reports an EPR (electron paramagnetic resonance) approach based on double electron electron resonance spectroscopy (DEER) to characterize the supramolecular packing of amyloid fibrils. The authors claim that this approach can "deliver an apparent dimensionality of the supramolecular organization of tau fibrils", "assess the amyloid core location and packing order, and track time-resolved formation of aggregation intermediates".

Specifically, the authors used the electron spin echo (ESE) decay to report the arrangement of spin labels in the amyloid fibrils. When the spin labels are arranged in a straight line, a planar surface, or a 3D space, the dimensionality of the ESE decay would be 1, 2, and 3, respectively. To demonstrate their methods, the authors used a singly spin-labeled tau protein, which is involved in several amyloid diseases, including Alzheimer's and other tauopathies. For the truncated 0N4R tau (residues 244-441, named tau187), four labeling sites were studied (272, 313, 322, and 404). Residues 272, 313, and 322 gave a dimensionality of ~1.5, while residue 404 gave a dimensionality of ~2.0. The authors explained that residues 272, 313, and 322 are expected to be part of the amyloid core, while 404 is part of the so-called fuzzy coat. However, the authors then explained that all three amyloid core sites are misaligned because their dimensionality is ~1.5 instead of 1. Using a short tau fragment of 16 amino acids (residues 295-313), the authors show that this peptide formed fibrils with a dimensionality of 0.8. Using the short tau fragment fibrils as seeds, the authors obtained tau187 fibrils with a dimensionality of 1.3. Furthermore, the α parameter (a fitting parameter used to obtain the dimensionality) was used to interpret the protofilament composition.

While this approach has great potential in providing structural insights into amyloid fibrils, there are several critical flaws in experimental design, data analysis, and interpretation in the current version.

(1) The authors didn't rigorously establish the central premise of the DEER approach to characterize the supramolecular structure of amyloid fibrils. The parallel in-register β-sheet structure of amyloid fibrils is supposed to give a dimensionality of 1 in the ESE decay analysis. For tau187 fibrils, the authors obtained 1.5. For tau16 fibrils, the authors obtained 0.8. Because the theoretical lower limit of dimensionality is 1, tau16 fibrils do not serve as evidence that this approach can identify a perfectly aligned parallel in-register β-sheets. A 20% deviation from the theoretical value suggests the low accuracy of using ESE decay to report amyloid core structures. The high-resolution structures of tau fibrils have been widely reported using cryo-EM methods; it shouldn't be difficult for the authors to identify a good protein candidate to obtain a dimensionality of 1 to establish their methods. With a good protein candidate, rigorous data analysis should be presented to show how reliable a core site can be distinguished from a supposedly disordered site.

(2) Regarding the claim of probing protofilament composition using the α parameter, the authors should prepare fibrils with defined protofilament composition. A number of amyloid fibril structures have been solved to show different numbers of protofilaments.

(3) Regarding the claim of tracking "time-resolved formation of aggregation intermediates", the authors need to show more than a couple of data points, and the real-time aggregation needs to be accompanied by characterizations with complementary methods such as TEM.

(4) The authors largely ignored progress that has been made on the previous spin labeling studies of amyloid fibrils. A lot of the claims, such as identifying amyloid core, real-time aggregation, and the effects of seeding on structures, have been characterized extensively using continuous-wave EPR. It would be to the benefit of the readers to show what additional values this approach provides over existing methods.

Reviewer #2 (Public review):

Summary:

The authors have provided valuable and solid evidence for the hypothesis, of which Choder is an early advocate, that transcription facilitates the assembly of an mRNA-protein complex that can affect the expression of mRNA (e.g., translation or degradation) in the cytoplasm.

Strengths:

In this work the authors have used two orthogonal approaches: an IP of a Flag labeled Pol II and RNAse digestion to release nascent chain associated proteins followed by mass spectrometry to identify cotranscriptional-associated proteins and then verifying this association with the transcriptional apparatus by proximity labeling technology using biotinylation of a specific sequence (Avi-tag) by the bacterial enzyme, BirA fused to a subunit of Pol II. Many of the proteins identified are thought to be exclusively cytoplasmic, for instance, those important for translation, such as the components of initiation factor EF3. The work represents a significant advance in support of the model where specific mRNAs can assemble proteins needed for their function in the cytoplasm during their transcription.

They also discover that a mutant Pol II subunit, Rbp4, which does not bind certain Avi-tagged proteins, does not facilitate their biotinylation. These results lend credible support to the hypothesis.

Weaknesses:

While the proximity labeling provides strong evidence that is consistent with the hypothesis, a proof is still lacking because it is inferred that the enzymatic labeling occurs at the site of transcription (a reasonable assumption). More definitive evidence could be provided by imaging the presence of the cytoplasmic proteins at the transcription site, although this may not be within the expertise of the investigator, so it would require a collaboration.

While not necessarily a significant weakness, it is worth considering that a remote possibility is that the cytoplasmic proteins discovered in this way were not tagged with biotin in the nucleus, but rather in the cytoplasm, where the Pol II-complex, either Flag or BirA tagged, may come in contact with the substrate before it is imported to the nucleus. The authors presumably rule out that the tagging could occur during translation of the Avi-tag on polysomes by inhibiting translation and showing that the tagging of the target protein is not inhibited (the data here is not totally convincing). Whether the Pol II-(BirA or Flag) could react with Avi-tagged proteins, even while briefly in the cytoplasm before nuclear import, is not completely resolved by these experiments since the Avi-tagged proteins could reside in the cytoplasm, not associated with polysomes, but complexed with Pol II subunits. The mutant Rpb does not rule out this possibility since it would not bind its substrate in the cytoplasm. In order to get into the nucleus in the first place, the cytoplasmic proteins would need to be transported there by a complex, possibly involving Pol II subunits, Rpbs. Perhaps the authors could address this possibility in the text.

One confusing issue in the protocol is the efficacy of the biotin-depleted media in which the cells are grown. Biotin is an essential cofactor for many reactions, so there are still endogenous biotin and biotin ligase needed that may add a background level of promiscuous biotinylation of some cytoplasmic proteins, for instance, those containing a universal biotin binding site.

Reviewer #2 (Public review):

Summary:

This paper utilizes long-read transcriptomics across 12 representative spider species to propose a new evolutionary framework for spider silk proteins (spidroins). By identifying ancestral templates in the most basal spider lineages, the authors trace how simple genetic materials diversified into the high-performance fibers used by modern spiders.

Strengths:

(1) The authors utilized PacBio ISO-Seq (long-read transcriptomics), which is essential for resolving the massive, highly repetitive sequences of spidroin genes that often cause gaps in traditional short-read assemblies.

(2) The researchers sampled 12 species representing the major nodes of spider evolution, including the basal Mesothelae, the Mygalomorphae (tarantulas), and the highly diverse Araneomorphae.

(3) The study successfully identified two distinct primordial spidroins in basal spiders: the AS-type (alanine-serine-rich) and the GS-type (glycine-serine-rich) proteins.

Weaknesses:

(1) The GS-Type "Base Gene" Paradox

The paper proposes that the GS-type gene (Liphistius sp._5400) in Liphistius (the most ancient spider lineage) is the prototype for all modern dragline silk. However, the data presented significantly undermines this conclusion.

Every functional spider silk protein requires N-terminal and C-terminal domains to control fiber assembly. The authors admit that neither the N- nor the C-terminal of this GS-type protein shows homology to any known spidroins. Because it lacks these domains, the authors explicitly state that it "may not assemble into typical silk fibers". The authors are identifying this as a "base gene" solely because it contains poly-GS motifs. Their logic is that because GS motifs are found in modern silk and other silk-producing insects, this must be the ancestor.

In the same spider, the AS-type gene (Liphistius sp._6705) does have recognizable C-terminal sequences and motifs similar to modern eggcase silk. This proves that "real" spidroins existed in Liphistius, making the claim that the non-homologous GS-type is a "spidroin ancestor" look like a misidentification of a general repetitive protein.

(2) Overstated Classification of FLAG in RTA Spiders

The authors identified a transcript in the RTA spider Heteropoda davidbowie (H.dav_6495) and labeled it a "Flag-like spidroin". This label is based on the repetitive internal motifs, which contain "GPGGX" and "GPG"-the classic building blocks of flagelliform capture silk. However, both the N- and C-termini of this gene are highly homologous to ampullate spidroins (MaSp), not typical Flag proteins. By calling it a "Flag-like spidroin" rather than a "MaSp with GPG motifs," the authors are forcing an evolutionary narrative. It is equally possible that this is simply a divergent Major Ampullate spidroin that evolved capture-like motifs, rather than a capture silk gene that "moved" into the ampullate gland.

The authors explicitly state, "Its origin could not be traced through sequence analysis". This admission directly contradicts the confidence with which they propose a "revised evolutionary trajectory".

Appraisal and Impact

This study provides a high-resolution map of spider silk evolution by utilizing long-read transcriptomics to bridge the gap between basal and derived lineages. By identifying the earliest known genetic templates for silk, the paper offers a significant leap forward in understanding how complex biological materials originate, though it raises critical questions about the functional definition of a "spidroin".

Reviewer #2 (Public review):

The authors investigate the role of the long non-coding RNA Dreg1 for the development, differentiation or maintenance of group 2 ILC (ILC2). Dreg1 is encoded close to the Gata3 locus, a transcription factor implicated in the differentiation of T cells and ILC, and in particular of type 2 immune cells (i.e., Th2 cells and ILC2). The center of the paper is the generation of a Dreg1-deficient mouse. The role of Dreg1 in ILC2 was documented by mixed bone marrow experiments. While Dreg1-/- mice did not show any profound ab T or gd T cell, ILC1, ILC3 and NK cell phenotypes, ILC2 frequencies were reduced in various organs tested (small intestine, lung, visceral adipose tissue). In the bone marrow, immature ILC2 or ILC2 progenitors were reduced whereas a common ILC progenitor was overrepresented suggesting a differentiation block. Using ATAC-seq, the authors find the promoter of Dreg1 is open in early lymphoid progenitors and the acquisition of chromatin accessibility downstream correlates with increased Dreg1 expression in ILC2 progenitors. Examining publicly available Tcf1 CUT&Run data, they find that Tcf1 was specifically bound to the accessible sites of the Dreg1 locus in early innate lymphoid progenitors. Finally, the syntenic region in the human genome contains two non-coding RNA genes with an expression pattern resembling mouse Dreg1.

The topic of the manuscript is interesting. The article is focused on the first description of the Dreg1 knockout mouse and the specific effect of Dreg1 deficiency on ILC2 development.

(1) The data of how Dreg1 contributes to the differentiation and or maintenance of ILC2 is not addressed at a very definitive level. Does Dreg1 affect Gata3 expression, mRNA stability or turnover in ILC2? Previous work of the authors indicated that knock-down of Dreg1 does not affect Gata3 expression (PMID: 32970351). The current data (Figure 2H) showed small differences in Gata3 expression in CHILP which were, however, not statistically significant. No differences were found in ILCP and ILC2P.

(2) How Dreg1 exactly affects ILC2 differentiation remains unclear.

Reviewer #2 (Public review):

Summary:

This study explores the mechanisms by which binding of the nuclear receptor RXRg regulates its heterodimeric partner Nur77. Previously, this group made the interesting discovery that ligand-dependent activation of RXRg bound to a related partner, Nurr1, does not occur through a classical pharmacological mechanism but through agonist-dependent dissociation of the complex through disruption of their ligand binding domain (LBD) interactions. Here, they revisit this paradigm with Nur77. In contrast to Nurr1, the authors do not have the reagents to clearly support a role for LBD dissociation. Following from the model of partial ligand-dependent dissociation of the LBD heterodimer, the experimental data (NMR, ITC, SEC) are interesting and quite complex.

Strengths:

The authors do a rigorous job of describing the data and providing possible interpretations and caveats. Revisiting the analysis of Nurr1, they identify the crucial role that selective Nurr1-RXRg agonists played in supporting the LBD dissociation model; without analogous compounds for the Nur77-RXRg complex, it is difficult to invoke this mechanism. Interestingly, treatment with the Nurr1-RXRg selective agonist HX600 suggests it can induce some LBD dissociation. Therefore, there may be some similarities between regulation of Nurr1 and Nur77 by RXRg.

Weaknesses:

Despite evidence supporting a partial role for RXRg LBD dissociation as a mechanism to activate Nur77, other data demonstrate that a fundamentally different regulatory mechanism likely exists in the Nur77-RXRg complex that involves the RXRg disordered NTD. The decision to describe further study of this as outside the scope of this work is unfortunate, as it closed off an avenue that could have provided fruitful data informing the apparently distinct regulatory mechanisms of the Nur77-RXRg complex. Given the uncertainty in the importance of the partial roles of the pharmacological mechanism, LBD dissociation, and the RXRg NTD, this study may have limited impact on the field.

Comments on revisions:

I'm satisfied with the revision.

Reviewer #2 (Public review):

Summary:

In this work the authors investigated the extent of shared variability in cortical population activity in the visual cortex in mice and macaques under conditions of spontaneous activity and visual stimulation. They argue that by studying the average response to repeated presentations of sensory stimuli, investigators are discounting the contribution of variable population responses that can have significant impact at the single trial level. They hypothesized that, because these fluctuations are to some degree shared across cortical populations depending on the sources of these fluctuations and the relative connectivity between cortical populations within a network, one should be able to predict the response in one cortical population given the response of another cortical population on a single trial, and the degree of predictability should vary with factors such as retinotopic overlap, visual stimulation, and the directionality of canonical cortical circuits.

To test this, the authors analyzed previously collected and publicly available datasets and data recorded themselves. These include calcium imaging of the primary visual cortex in mice and electrophysiology recordings in V1 and V4 of macaques under different conditions of visual stimulation. The strength of this data is that it includes simultaneous recordings of hundreds of neurons across cortical layers or areas and under different conditions of sensory stimulation and behavioral state. However, the weaknesses of calcium dynamics (which has lower temporal resolution and misses some non-linear dynamics in cortical activity) and multi-unit envelope activity and LFPs (which reflects fluctuations in population activity rather than the variance in individual unit spike trains), underestimates the variability of individual neurons which may vary widely in their participation in shared sources of variance.

From their analysis, they found that there was significant predictability of activity between layer II/III and layer IV responses in mice and V1 and V4 activity in macaques, although the specific degree of predictability varied somewhat with the condition of the comparison and with differences in the quality of recordings between the datasets. The authors deployed a variety of analytic controls and explored a variety of comparisons that are both appropriate and convincing that there is a significant degree of predictability in population responses at the single trial level consistent with their hypothesis. This demonstrates that a significant fraction of cortical responses to stimuli are not due solely to the feedforward response to sensory input, and if we are to understand the computations that take place in cortex, we must also understand how sensory responses interact with other sources of activity in cortical networks. Overall, this work highlights that, beyond the traditionally studied average evoked responses considered in systems neuroscience, there is a significant contribution of shared variability in cortical populations that may contextualize sensory representations depending on a host of factors that may be independent from the sensory signals being studied.

Strengths:

This work considers a variety of conditions that may influence the relative predictability between cortical populations, including receptive field overlap, latency that may reflect feed-forward or feedback delays, and stimulus type and sensory condition. Their analytic approach is well designed and statistically rigorous. They acknowledge the limitations of the data and do not over-interpret their findings.

Weaknesses:

The different recording modalities between species and scales (within vs. across cortical areas) limit the interpretability of the inter-species comparisons, and while this is not the stated goal of the authors, the juxtaposition of these two datasets invites comparison.

Reviewer #2 (Public review):

Summary:

The authors aim to map neurons encoding negative valence at the whole-brain scale in larval zebrafish. Using two-photon light-sheet imaging combined with various aversive stimuli, they visualize and quantify stimulus-evoked neural responses, identify the anatomical locations of responsive neurons, and explore the possibility of genetically accessing Rl neurons that respond preferentially to strongly noxious stimuli.

Strengths:

The major strength of this study lies in its use of two-photon light-sheet imaging, which provides a system-level characterization of neuronal response to aversive stimuli. The authors systematically compare multiple classes of aversive stimuli (heat, electric shock, looming, etc.), showing that strongly threatening stimuli converge on a compact neuronal population in the Rl, supporting the robustness of the finding. Finally, the identification of Tiam2a expression in these neurons provides a potential genetic handle for future functional studies.

Weaknesses:

The main weakness of the study is the lack of causal evidence supporting the functional role of the identified neurons. Without optogenetic, chemogenetic, or ablation experiments, it is difficult to determine whether these neurons are required for or sufficient to encode negative valence. In addition, the study does not include positive-valence or neutral stimuli controls, making it difficult to distinguish whether the observed neural responses reflect valence per se or more general downstream response such as motor output. Finally, the lack of behavioral readouts limits the ability to directly link the identified neural populations to defensive behaviors.

Reviewer #2 (Public review):

This study provides neuroimaging evidence supporting the integration-segregation theory of inhibition of return (IOR), a widely studied attentional phenomenon. It also explores the neural interactions between IOR and cognitive conflict, demonstrating that conflict processing is potentially modulated by attentional orienting.

The integration-segregation theory was investigated using a sophisticated, well-executed experimental task that accounted for cognitive conflict processing, which is phenomenologically related to IOR but is non-spatial. The behavioral and neuroimaging data were carefully analyzed.

The authors have thoughtfully addressed all my previous concerns. By demonstrating how attentional orienting can modulate neural processing of cognitive conflict, this study helps to advance a more unified and mechanistic understanding of the cognitive and neural processes that govern our visual perception and response selection.

Reviewer #2 (Public review):

Summary:

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

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

Strengths:

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

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

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

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

Weaknesses:

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

The authors acknowledge that, technically, this is a very difficult preparation with very low yield as far as obtaining successful recordings. Moreover, the tissue needs to be maintained at room temperature which is obviously not ideal when characterizing cold thermoreceptors due to the unavoidable effects of low temperature on cold-activated receptors.

Reviewer #2 (Public review):

This is an interesting paper with important results. The authors, working in V1, have previously, in a 2022 paper, defined sensitizing and depressing excitatory (E) cells as those whose response increases or decreases, respectively, across the 10 seconds of showing a drifting grating stimulus. They showed that sensitizing E cells are dominantly inhibited by SST inhibitory cells, which are dominantly depressing, and that depressing E cells are dominantly inhibited by PV inhibitory cells, which are very largely sensitizing. It's been well established that locomotion greatly increases E-cell firing rates in V1 compared to rest, but much remains to be worked out as to the mechanism. Here, they find that locomotion increases the responses of the sensitizing E cells much more than depressing cells. They develop a model of changes in synaptic weights between rest and locomotion to account for the changes. One reason that sensitizers are increased more by locomotion than depressors is that PV cells, which more strongly inhibit depressors, have increased firing for locomotion, whereas SST cells, which more strongly inhibit sensitizers, don't change their firing rates with locomotion. However, in the mode,l a complex array of postulated changes in connection strengths is also involved.

I have, though, a number of concerns: with the model, with the lack of proper discussion of connection to some previous works, and with an overall unclear and confusing presentation and certain controls that should be done.

In the model, they postulate that synapses within the 6-cell-type network - sensitizing, intermediate, and depressing E cells, and PV, SST, and VIP I cells - and from three sources of external input to each of the six types all change between rest and locomotion (except that connections between the E cells don't depend on their types). There are a lot of degrees of freedom, and this makes interpretation of the results difficult. I would have liked to have seen more efforts to constrain the degrees of freedom. For example, there seems to be very little difference between the three E cell types in any of the three types of external input received. Why not constrain them all to get the same external input and see if it significantly affects model fit? Or what if synapses from the three types of external input are left unchanged, and only change their strengths between rest and locomotion? How well could this do? During optimization, why not constrain the changes between rest and locomotion, for example, by putting an L1 penalty on the changes or the relative changes, trying to force them to be sparse, and see whether there are roughly equally good fits? And then, if the main changes are in a small set of synapses, can the authors isolate changes to that small set and do roughly equally well? What about looking at the principal components of the weight changes across models, to isolate patterns of change that are most important?

In terms of comparing to previous works, when optogenetic manipulations of SST and PV are done to test various hypotheses, I would like to see some discussion of what is already known from the authors' 2022 paper and what they are adding or testing that wasn't known or tested from that paper. And Dipoppa et al (2018) also found weight changes to account for the difference between rest and locomotion. They were looking at a fixed point of responses of neurons across retinotopic space to stimuli of various sizes with only one E-cell type, whereas they are accounting for trajectories across time considering 3 E-cell subtypes but without variation in stimuli or retinotopic position of neurons, so the efforts are somewhat different, but still, it would be good to see a bit more discussion of what is in agreement or in contradiction in the conclusions.

In terms of presentation and controls, I have many concerns, which include:

(1) The main result is that sensitizers increase their responses with locomotion ~2X (for dF/F) or about 3.5X (for spikes) more than depressors. But there are other differences between sensitizers and depressors, for example sensitizers have smaller initial stimulus responses at rest, and depressors have larger. What if cells were divided into tertiles by initial stimulus response at rest? Would the authors see the same differences in the effects of locomotion? If so, can they establish whether the difference is really attached to the adaptation properties rather than to, for example, the initial responses, for example, by comparing the regression of response increase against AI vs the regression of response increase against initial resting response? And there might be other controls to be done for other features in which sensitizers and depressors differ.

(2) Lines 103 and following: the authors refer to a "second notable change" which is the narrower distribution of adaptive effects, but I think this is trivial. The adaptive index is AI=(R1-R2)/(R1+R2), where R1 is response 0.5-2.5s after stimulus onset and R2 over 8-10s. But if the change is additive, as suggested by the dF/F figures (and I believe the distributions of AI here are based on dF/F measurements) -- adding the same constant to R1 and R2 will shrink |AI| without changing the sign of AI. So this would seem to just be a signature of a change that is primarily additive rather than multiplicative.

Also, if the authors do decide that they are going to focus on spikes after showing the raw dF/F, then this analysis should be repeated for spikes.

(3) Figure 2, F is supposed to be D minus E, but it doesn't look like it. For example, the initial response under locomotion is very similar in sensitizers and depressors, so the initial difference in F should be small, but it's not; and at rest, depressors initially have larger responses than sensitizers, whereas later depressors have smaller responses than sensitizers, yet the difference at rest is positive at all times. Something seems wrong here.

Reviewer #2 (Public review):

Summary:

In this study from Stahl et al., the authors demonstrate that medaka pluripotent embryonic cells can self-organise into eye organoids containing both retina and lens tissues. While these organoids can self-organize into an eye structure that resembles the vertebrate eye, they are built from a fundamentally different morphogenetic process - an "inside-out" mechanism where the lens forms centrally and moves outward, rather than the normal "outside-in" embryonic process. This is a very interesting discovery, both for our understanding of developmental biology and the potential for tissue engineering applications. The study would benefit from some additional experiments and a few clarifications. The authors suggest that the lens cells are the ones that move from the central to a more superficial position. Is this an active movement of lens cells or just the passive consequence of the retina cells acquiring a cup shape? Are the retina cells migrating behind the lens or the lens cells pushing outwards? High-resolution imaging of organoid cup formation, tracking retina cells in combination with membrane labeling of all cells would help elucidate the morphogenetic processes occurring in the organoids. Membrane labeling would also be useful as Prox1 positive lens cells appear elongated in embryos while in the organoids, cell shapes seem less organised, less compact and not elongated (for example as shown in Fig 3f,g).

The organoids could be a useful tool to address how cell fate is linked to cell shape acquisition. In the forming organoids, retinal tissue initially forms on the outside, while non-retinal tissue is located in the centre; this central tissue later expresses lens markers. Do the authors have any insights into why fate acquisition occurs in this pattern? Is there a difference in proliferation rates between the centrally located cells and the external ones? Could it be that highly proliferative cells give rise to neural retina (NR), while lower proliferating cells become lens?

What happens in organoids that do not form lenses? Do these organoids still generate foxe3 positive cells that fail to develop into a proper lens structure? And in the absence of lens formation, does the retina still acquire a cup shape?

The author suggest that lens formation occurs even in the absence of Matrigel. Is the process slower in these conditions? Are the resulting organoids smaller? While there are indeed some LFC expressing cells by day2, these cells are not very well organised and the pattern of expression seems dotty. Moreover, LFC staining seems to localise posterior to the LFC negative, lens-like structure (e.g. Fig.S1 3o'clock).

How do these organoids develop beyond day 4? Do they maintain their structural integrity at later stages?

The role of HEPES in promoting organoid formation is intriguing. Do the authors have any insights into why it is important in this context? Have the authors tried other culture conditions and does culture condition influence the morphogenetic pathways occurring within the organoids?

Significance:

This is a very interesting paper, and it will be important to determine whether this alternative morphogenetic process is specific to medaka or if similar developmental routes can be recapitulated in organoid cultures from other vertebrate species.

Comments on revised version:

The revised manuscript is much improved and addresses all of the points raised by the reviewers.

Reviewer #2 (Public review):

The revised manuscript offers little new information and fails to address the critical weaknesses identified in the original submission.

While we can agree that phosphorylation of Thr495 would likely affect Hsp70 function-given the known biochemistry of Hsp70s and the author's previous work on LegK4-the significance of this finding hinges on whether it is a regulated process. If a meaningful fraction of Hsp70 were phosphorylated in a regulated manner triggered by DNA damage or cell cycle progression, it would constitute an important discovery, regardless of its specific impact on fitness in a given context.

However, beyond highlighting the temporal profile of Hsp70 phosphorylation in MMS-treated cells (Figure 4e), the paper fails to rule out the possibility that this correlation is merely an irrelevant side reaction. This "bystander" phosphorylation could simply be caused by the activation of kinases during the experimental MMS treatment and subsequent washout. The authors' claim-that the fraction of phosphorylated Hsp70 increases in a "regulated, cell-cycle dependent manner"-does not sufficiently counter the possibility of it being a non-functional side effect.

This concern could be resolved if the authors had identified the specific kinase, demonstrated its specificity, and manipulated it either genetically or pharmacologically. While I acknowledge this is a "tall order," the lack of such data limits the paper's significance. Furthermore, the current data fails to meet a much lower bar: confirming that a substantial fraction of Hsp70 is actually phosphorylated under the tested conditions. Such a finding would at least suggest the event is capable of impacting the overall Hsp70 pool.

It is surprising that the authors have not provided a ratiometric assay to settle this, such as an immunoblot of total Hsp70 separated on a Phos-tag or IEF gel. Instead, they rely on indirect evidence and data subject to alternative interpretations. Specifically, they argue that the fitness cost of the Thr495Ala mutation (or the phosphomimetic mutation) is due to the loss of regulatory phosphorylation (or deregulated phosphorylation); however, it is equally plausible that the mutations create Hsp70 hypomorphs whose defects are only exposed under stressful experimental conditions.

Reviewer #2 (Public review):

Summary:

The author's central hypothesis was that the strength of cortico-respiratory coupling in infants is negatively associated with apnoea rate. To prove this, they first investigated the existence of cortico-respiratory coupling in premature and term-born infants, the spatial localisation of the cortical activity and its relationship with the phase of the respiratory cycle, and the directionality of coupling.

Strengths:

The researchers used synchronised EEG and impedance pneumography to detect the phase amplitude coupling.

They have studied a wide range of gestations, from 28 weeks to 42 weeks, including males and females. Their exclusion criteria ensured that healthy babies were studied and potential confounders of impaired respiratory activity were avoided. Their sequential approach in addressing the objectives was appropriate.

Weaknesses:

As a neonatal clinician and neuroscientist, I have commented based on my expertise. I have not commented on signal processing.

There are no major weaknesses to the study. Some minor weaknesses include:

(1) Data relating to the cortical oscillations and the respiratory phase is given. However, whether this would lead to their hypothesis that the strength of cortico-respiratory coupling is negatively associated with apnoea rate is unclear. What preceding data enabled the authors to link the strength of coupling to the rate of apnoea?

(2) If we did not know of data showing the existence of cortico-respiratory coupling in newborn infants, then should it not be the first research question to examine?

(3) What are the characteristics of the infants who contributed data to establish the cortico-respiratory coupling (Figures 2 and 3)?

(4) Although it is the most plausible direction of the relationship, with neural activation driving respiratory muscle contraction, how can the authors prove this with their data? Given that they show coherence between signals, how do we know that the cortical signal precedes the respiratory muscle contraction?

(5) Apgar score is an ordinal variable. The authors should summarise this as median (range).

Comments on revisions:

All the weaknesses are adequately addressed. No more comments

Reviewer #2 (Public review):

Summary:

A fundamental problem in developmental biology is how a group of apparently identical cells breaks symmetry and differentiates into, for instance, type A and type B cells in the absence of any external influence such as a gradient of something causing cells at the left side of the group to become type A cells. The authors use the model system Dictyostelium to explore the interplay between a known cell-cycle-dependent musical chairs mechanism (cells are at random phases of the cell cycle, and a signal that hits all the cells causes cells that happen to be in one set of cell cycle phases to become the A cells, and cells that happen to be in other phases become the B cells), and stochastic gene expression. They identified genes whose expression is stochastic (unusually high cell-cell variation). Using a very clever and elegant genetic screen, they then show that these genes often are associated with cell fate choice. The authors then show that the stochastic genes have reduced levels of histone (H3K4) Me3 methylation, and that a histone methylase called Set1 is important for this process. They then bring the work together to show that the cell-cycle-dependent mechanism and stochastic gene expression work in combination to generate the observed differentiation of Dictyostelium cells.

Strengths:

Combination of theory, clever genetic screens, single-cell RNA-seq, and molecular and cell biology to dive into the fundamental problem of cell fate choice.

Results support the conclusions.

Very significant contribution to developmental biology.

Weaknesses:

Because the paper is co-written by people doing theoretical work and people doing experimental work, the theory sections will be difficult for an experimentalist and vice versa, but it is very much worth the effort to read this paper, there is a lot in here. There are no weaknesses of the methods and results.

Reviewer #2 (Public review):

Summary:

The authors quantify human fMRI BOLD responses in pulvinar and mediodorsal thalamic nuclei during a fear conditioning and extinction task across two days, in a large sample size (hundreds of participants). They show that the BOLD responses in these areas differentiate the conditioned (CS+) and safety (CS-) stimulus. Additionally this changes with repeated trials which could be a neural correlate of fear learning. They show that the anterior pulvinar is most correlated with the MD, and that this is not due to anatomical proximity. They perform graph analysis on the pulvinar sub nuclei which suggests that the medial pulvinar is a hub between the sensory (lateral/inferior) and associative (anterior) pulvinar. They show different patterns of thalamic activity across conditioning, extinction, recall, and renewal.

Strengths:

The data has a large sample size (n=293 in some measures, n=412 in others). This is a validated human fear conditioning/extinction task that Dr Milad's group has been working with for several years. Few labs have investigated the thalamus activity during fear conditioning and extinction, particularly with a large sample size. There is an independent replication of the pulvinar network structure (Fig. 3), which suggests that the processing in the more sensory-related inferior and lateral pulvinar is relayed to the anterior pulvinar (and possibly thereby to more action-related prefrontal areas) via an intermediate step in the medial pulvinar - potentially a novel discovery but that needs more validation.

Weaknesses:

(1) The authors cannot make causal claims about their results based on correlational neuroimaging evidence. Causal claims should be pared back. E.g. Sentence 1 in results "The anterior pulvinar and MD contribute to early associative threat learning, as evidenced by increased functional activation in response to CS+ compared to CS- at the block level (Fig. 1b-c)." needs to be reworded to something like 'the anterior pulvinar and MD have increased functional activation... This suggests that these areas may contribute to early associate threat learning"

(2) Fig .1 The fact that the difference in BOLD activity between CS+ and CS- goes away on the third trial is not addressed. This is a very large effect in the data.

(3) Fig. 3 Could the observed network structure be due to anatomical proximity? Perhaps the authors should do an analogous analysis to what they did in Fig. 2 for this intra-pulvinar analysis. This analysis doesn't take into account the indirect connections through corticothalamic and thalamocortical connections with visual cortex and the pulvinar. There is an implicit assumption that there are interconnections between the pulvinar sub nuclei, but there are few strong excitatory projections between these sub nuclei to my knowledge. If visual areas are included in the graph, it would make things more complex, but would probably dramatically change the story. In this way, the message is somewhat constructed or arbitrary.

(4) In the results section describing Fig. 4-7, there are no statistics supporting the claims made.<br /> There needs to be a set of graphs comparing the results across the study sessions and days, with statistical comparisons between the different experiments to confirm differences.

(5) FIg. 7 does not include the major corticothalamic and thalamocortical projections from early, mid-level, and higher visual cortex to the different pulvinar nuclei. I doubt that there are strong direct projections between the pulvinar nuclei, rather the functional connections are probably mediated through interconnections with cortical visual areas.

(6) Stylistic: There are a lot of hypotheses and interpretations presented in this primary literature paper which may be better suited for a review or perspective piece.

(7) In the discussion there is an assumption that the fMRI BOLD responses to CS+ and CS- need to be different to indicate that an area is processing these distinctly, but the BOLD signal can only detect large scale changes in overall activity. It's easy to imagine that an area could be involved in processing these two stimuli distinctly without showing an overall difference in the gross amount of activity.

(8) There is strong evidence that the BOLD responses to the threat-related and safety-related stimuli are different, modest evidence for their claims of learning/plasticity in these pathways, and circumstantial evidence supporting their hypothesized graph network models. Overall most of the claims made in the discussion are better considered possible interpretations rather than proven findings - this is not a criticism, as these experiments and subject matter are extremely complex.

(9) This study continues to validate the power and utility of this in human fear conditioning/extinction paradigm, and extends this paradigm to investigating fear learning beyond the traditional limbic system pathways. It's possible that their models for the pulvinar nuclei interconnections could guide future neuromodulation or DBS studies that could provide more causal evidence for their hypotheses.

Comments on revisions:

The reviewers addressed my major concerns appropriately in the modified manuscript. As long as the MRI analysis concerns of Reviewer 3 are satisfied (MRI analysis is not my expertise), I am satisfied with the modified manuscript.

Reviewer #2 (Public review):

Summary:

In this paper, Xu and co-workers unveil two distinct modes of neutralisation by gp41-targeted broadly neutralizing antibodies on HIV-1 Env. So far, it was unclear as to how the mechanism of neutralisation occurred for this subset of neutralising antibodies (that can target the fusion peptide or the membrane proximal external region of the gp41 subunit). Thanks to single-molecule FRET, the authors show that the majority of broadly neutralizing antibodies stabilize the closed Env conformation (named State 1 since the original work by Munro and colleagues PMID: 25298114). Interestingly, the bivalent 10E8.4/iMab stabilized in turn a CD4-bound open state of Env. The two modes of neutralization described for these antibodies show previously unknown allosteric mechanisms that stabilize closed and open Env conformation, stressing the importance of Env conformational dynamics and its efficiency during the process of fusion.

Strengths:

The article is well-written, and the figures fully depict the data in a convincing way. The authors have used smFRET, which is now established in the field as a good tool to assess Env dynamics.

Weaknesses:

(1) The limited controls on how click chemistry affects Env (as labelled Env HIV virions were not evaluated).

(2) Photobleaching of donor and acceptor molecules occurs right after 10sec exposure.

(3) Other limitations are well described in the corresponding section.

Reviewer #2 (Public review):

Summary:

Mubeen and colleagues studied the cellular basis of tooth regeneration in cichlid fish. Using an elegant tooth plunking strategy followed by single-nucleus RNA-sequencing, the authors were hoping to achieve an atlas of cellular and transcriptional changes that occur within and between cells during whole tooth replacement.

Strengths:

The major strengths of the methods and results are high novelty in the approach in a vertebrate with continuous tooth replacement, the temporal analysis of analyzing at plucking and three later time points, the thorough and sophisticated analysis of the snRNA-seq data, including the inference of trajectories and signaling events, and the robust signal of transcriptional differences induced by tooth plucking.

Weaknesses:

The major weaknesses of the methods and results are no validation of any of the inferred cell types, no functional tests of whether any of the changes in signaling pathways affect the plucking-induced tooth replacement process, and perhaps no clear takeaway message for biologists not necessarily interested in tooth replacement.

Conclusion:

The authors achieved their aims of identifying the changes in gene expression and cellular composition that occur during whole tooth replacement accelerated by plucking. Overall, the results support their conclusions, although some slight semantic qualifiers should probably be added (e.g., referring to "cell types" as "putative cell types").

The work should have a high impact in the field of tooth and organ regeneration, and the novel methodological paradigm established here of accelerating tooth replacement three-fold by plucking has great promise for future follow-up studies to further study this process. The work could also have a strong impact through the computational methods used here to infer trajectories and signaling interactions. Specific pathways, genes, and cell types could be tested in other fish, such as zebrafish, to test function during tooth replacement.

The work is unique and interdisciplinary, and also has significance by establishing that robust phenotypically plastic accelerations in regeneration rates occur upon tooth removal. There are very few studies like this one that combine genetic and environmental studies of regeneration. The result that three different species of cichlid fish that normally have very different tooth patterns all accelerate tooth replacement threefold upon tooth plucking also has significance in revealing a highly conserved plucking response.

Reviewer #2 (Public review):

Summary:

The paper by Gao et al. describes that capsaicin (CAP) might act as a novel NRF2 agonist capable of suppressing ethanol (EtOH)-induced oxidative damage in the gastric mucosa by disrupting the KEAP1-NRF2 interaction. Initially the authors established and validated a cell model for EtOH-induced oxidative stress which they used to experiment with different CAP concentrations and to determine changes in a variety of parameters such as cell morphology, ROS production, status of redox balance to mitochondrial function, amongst others.

The proposed mechanism by which CAP activates NRF2 and mitigates oxidative stress is thought to be via non-covalent binding to the Kelch-domain of KEAP1. A variety of assays such as BLI, CETSA, Pull-down, Co-IP, and HDX-MS were employed to investigate the KEAP1 binding behavior of CAP both in vitro and in GES1 cells. Consequently, the authors developed in vivo nanoparticles harboring CAP and tested those in a rat model. They found that pretreatment with the CAP-nanoparticles led to significant upregulation of NRF2 and subsequent effects on pro- (suppression of IL-1β, TNF-α, IL-6 and CXCL1) and anti-inflammatory (activation of IL-10) cyotkines pointing to a resolved state of inflammation and oxidative stress.

Strengths:

The work comprises a comprehensive approach with a variety of in vitro assays as well as cell culture experiments to investigate CAP binding behaviour to KEAP1. In addition, the authors employ in vivo validation by establishing an ethanol-induced acute gastric mucosal damage rat model and providing evidence of the potential therapeutic effect of CAP.

The study further provides novel insights into the mode of CAP action by elucidating the mechanism by which CAP promotes NRF2 expression and downstream antioxidant target gene activation.

The design of IR-Dye800 modified albumin-coated CAP nanoparticles for enhanced drug solubility and delivery efficiency demonstrates a valuable practical application of the research findings.

In summary the study's findings suggest that CAP could be a safe and novel NRF2 agonist with implications for the development of lead drugs for oxidative stress-related diseases. Collectively, the data support the significance and potential impact of CAP as a therapeutic agent for oxidative stress-related conditions.

Weaknesses:

While the study provides valuable insights into the molecular mechanisms and in vivo effects of CAP, further clinical studies are needed to validate its efficacy and safety in human subjects. The study primarily focuses on the acute effects of CAP on ethanol-induced gastric mucosa damage. Long-term studies are necessary to assess the sustained therapeutic effects and potential side effects of CAP treatment.

While the design of CAP nanoparticles is innovative, further research is needed to optimize the nanoparticle formulation for enhanced efficacy and targeted delivery to specific tissues.

Addressing these weaknesses through additional research and clinical trials can strengthen the validity and applicability of CAP as a therapeutic agent for oxidative stress-related conditions.

Reviewer #3 (Public review):

In this manuscript, authors use the Drosophila wing as model system and combine state-of-the-art genetic engineering to identify and validate the molecular players mediating the activity of one of the cis-regulatory enhancers of the apterous gene involved in the regulation of its expression domain in the dorsal compartment of the wing primordium during larval development. The paper is subdivided into the following chapters/figures:

(1) In the first couple of figures, authors describe the methodology to genetically manipulate the apE enhancer (a cartoon summarizing all the previous work with this enhancer might help) and identify two well-conserved domains in the OR463 enhancer required for wing development (the m3 region whose deletion phenocopies OR463 deletion: loss of wing, and the m1 region, whose deletion gives rise to AP identify changes in the P compartment).

(2) In the following three figures, authors characterize the m1 regulatory region, identify HOX and ETS binding sites, functionally validate their role in wing development and the activity of the genes/proteins regulating their activity (eg-. Hth and Pointed) by their ability to phenocopy (when depleted) the m1 loss of function wing phenotype. Authors conclude that Hth and Pointed regulate apterous expression through the m1 region.

(3) In the last few figures, the authors perform similar experiments with the m3 regulatory region to conclude that the Grn and Antennapedia regulate apterous expression through the m3 enhancer.

Comments on revised version:

The authors have adequately addressed my major concerns.

Reviewer #2 (Public review):

Summary:

The authors propose that leftover heparin plasma can serve as a source for cfDNA extraction, which could then be used for downstream genomic analyses such as methylation profiling, CNV detection, metagenomics, and fragmentomics. While the study is potentially of interest, several major limitations reduce its impact; for example, the study does not adequately address key methodological concerns, particularly cfDNA degradation, sequencing depth limitations, statistical rigor, and the breadth of relevant applications.

Strengths:

The paper provides a cheap method to extract cfDNA, which has broad application if the method is solid.

Reviewer #2 (Public review):

Summary:

Feddersen & Bramkamp determined important characteristics of how MinD protein binds/dissociates to/from the membrane, and dimerizes in relation to its ATPase activity. The presented data clearly shows the differences in function of MinD homologs from B. subtilis and E. coli.

Strengths:

The work presents well-executed experiments that lead to interesting conclusions and a new model of how Min system works during B. subtilis mid-cell division. Importantly, this model is supported by in vitro characterization of well-chosen mutants in the functional domains of MinD. Outstandingly, most of the in vitro data are confirmed by single-molecule localization microscopy.

Weaknesses:

The authors immobilized liposomes, for which they used E. coli total lipids, to measure ATPase activity and liposome association and dissociation of B. subtilis MinD. For these experiments would be more suitable to use B. subtilis total lipids as more biologically relevant data could be gained.

Although the work is in detail and nicely compares the function of B. subtilis Min system with E. coli Min system, it lacks the comparison of the Min system function in other rod-shaped Gram-positive bacteria. I would suggest including in the Discussion the complexity of other Min systems. Especially, this complexity is seen in other rod-shaped and spore formers such as Clostridial species in which one of these Min systems or both are present, an oscillating E. coli Min system type and more static as in B. subtilis.

Comments on revisions:

I'm satisfied with the authors response to my private recommendation points. However, I thought that they would also respond to my points mentioned in Public Review part, weaknesses as shown above and update the revised version accordingly.

Reviewer #2 (Public review):

Using chronic intravital two-photon imaging of calcium dynamics in meningeal macrophages in Pf4Cre:TIGRE2.0-GCaMP6 mice, the study identified heterogeneous features of perivascular and non-perivascular meningeal macrophages at steady state and in response to cortical spreading depolarization (CSD). Analyses of calcium dynamics and blood vessels revealed a subpopulation of perivascular meningeal macrophages whose activity is coupled to behaviorally driven diameter fluctuations of their associated vessels. The analyses also investigated synchrony between different macrophage populations and revealed a role for CGRP/RAMP1 signaling in the CSD-induced increase, but not the decrease, in calcium transients.

This is a timely study at both the technical and conceptual levels, examining calcium dynamics of meningeal macrophages in vivo. The conclusions are well supported by the findings and will provide an important foundation for future research on immune cell dynamics within meninges in vivo. The paper is well written and clearly presented.

Reviewer #2 (Public review):

Summary:

Meier et al. explore the variability of locomotion-related modulations in mouse area V1. They present 4 major findings: V1 L2/3 neurons beneath M2- interpatches are more strongly locomotion-modulated than those beneath M2+ patches, while V1 L2/3 neurons are more strongly orientation tuned. They then use viral tracing to examine the relationship of M2- interpatches and M2+ patches with inputs from and outputs to HVOs, MO, RSP, and LP, and find evidence for different closed-loop subnetworks within L1; these relationships, however, are more complicated for cell bodies in L2/3. Finally, they also describe an overlap between M2- interpatches and SOM+ dendrites/axons.

Strengths:

The strength of the manuscript is the detailed anatomical quantification of closed-loop connectivity, and the description of the organizing principles of M2- interpatches and M2+ patches.

Weaknesses:

The major weakness of the manuscript is the lack of a direct connection between the functional and the anatomical data, and the somewhat puzzling effects observed in the analysis of noise correlations. The former issue might be alleviated by modelling, where the authors could explore the space of possibilities that could explain the functional data based on the anatomical connectivity. Some control analyses could be done, for the comparison of noise correlations.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors focus on the identification of the mechanisms involved in the acquired resistance to Sotorasib in non-small lung KRASG12C mutant cells. To perform this study, the authors generate different clones of cell lines, cell-derived xenografts, patient-derived xenograft organoids and patient-derived xenografts. In all these models, the authors generate resistant forms (i.e., resistant cell lines PDXs and organoids) and the genetic and molecular changes were characterised using whole-exome sequencing, proteomics and phospho-proteomics. This analysis led to the identification of an important role of the PI3K/AKT/mTORC1/2 signalling network in the acquisition of resistance in several of the models tested. Molecular characterisation identified changes in the expression of some of the proteins in this network as key changes for the acquisition of resistance, and in particular, the authors show that changes in 4E-BP1 are common to some of the cells downstream of PI3K. Using pharmacological testing, they show that different drugs targeting PI3K, AKT and MTORC1/2 sensitise some of the resistant models to Sotorasib. The analyses showed that the PI3K inhibitor copanlisib has an effect in NSCLC cells that, in some cases, seems to be synergistic with Sotorasib. Based on the work performed, the authors conclude that the PI3K/mTORC1/2 mediated 4E-BP1 phosphorylation is one of the mechanisms associated with the acquisition of resistance to Sotorasib and that targeting this signalling module could result in effective treatments for NSCLC patients.

The work as presented in the reviewed manuscript is still very interesting, provides cell models that benefit the community, and can be used to expand our knowledge of the mechanism of resistance to KRAS targeting therapies. Some changes suggested by reviewer 1 and this reviewer have been made to the text, including changes to text and figures, including quantification of some blots. But for most of it, this version is very similar to the first submission and many of the weaknesses and suggestions I made remain the same.

Strengths:

- One of the stronger contributions of this article is the different models used to study the acquisition of resistance to Sotorasib. The resistant cell lines, PDXs and PDXOs and the fact that the authors have different clones for each, made this collection especially relevant as they seem to show different mechanisms that the cells used to become resistant to Sotorasib. Although logically, the authors focus on one of these mechanisms, the differential responses of the different clones and models to the treatments used in this work show that some of the clones used additional mechanisms of resistance that can be explored in other studies. Importantly, as they use in vitro and in vivo models, the results also consider the tumour microenvironment and other factors in the response to the treatments.

- Another strength is the molecular characterisation of the different Sotorasib-resistant tumour cells by WES, which shows that these cells do not seem to acquire secondary mutations.

- The use of MS-based proteomics also identifies proteome signatures that are associated with the acquisition of resistance, including PI3K/mTORC1/2. The combination of proteomics and phospho-proteomics results should allow the identification of several mechanisms that are deregulated in Sotorasib-resistant cells

- The results show a strong response of the NSCLC cells and PDXs to copanlisib, a drug for which there is limited information in this cancer type.

- The way they develop the PDX-resistant and the PDXO seems to be appropriate.

- The revised manuscript includes the information for the whole exosome sequence, making the finding clearer for the reader.

Weaknesses:

In general, the data is of good quality, but due to the sheer amount of data included and the way it is presented and discussed, several of the claims or conclusions are not clear.

- The abstract is mainly the same, and the authors only indicate that they will update it.

- The tables with the proteomics data are still not included, and again, there is only a comment from the authors that it will be made available. Thus, the way the data is presented in Figure 3 still does not allow the reader to get an idea of many of the findings from this experiment.

- In Figure 3, the authors indicate that the raw data will be included in the revised version, which should improve the understanding of the reader, but this is not included yet. As in the previous version, the MS-based Phosphoproteome is still not really presented in the current manuscript.

- The authors still do not specify where the proteomics data will be deposited, and whether it will be made public to comply with FAIR principles. They indicate that they will comply with the journal requests, but it is still not clear what will be deposited.

- The experiments in Figure 4 are very confusing, and some controls are missing. There is no blot where they show the effect of Sotorasib treatment in H23 and

- The authors do not address the important point made in the previous review about the effect of copanlisib in parental cells. I might not have been clear, so the data in Figure 4D-F seem to support that PI3K treatment of parental cells is as effective as in the resistant cells. Therefore, it is not clear whether the effect shown in the resistant cells is related to the acquisition of resistance to sotorasib or if these cells are simply sensitive to the drug because the parental cells were already sensitive.

Reviewer #2 (Public review):

This study examines how curl in the retinal flow field can be used as a control variable for estimating and controlling the heading of a moving observer. The basic idea (which is not entirely new, see Matthis et al. 2022) is that translation along a path with eccentric gaze (meaning that the subject is not heading toward the point they are looking at) produces a pattern of optic flow on the retina with a rotational component around the point of fixation (which can be captured by the mathematical "curl" operator). The sign and magnitude of retinal curl vary with heading relative to the point of fixation, such that curl can be used as a control variable to steer rightward or leftward to move toward the fixated target. The authors perform behavioral experiments and show that there are biases in perceived heading that seem to be largely governed by retinal curl. They also show that a simple controller model can use curl to steer toward a target, and they provide a neural network model that provides a biologically plausible implementation of the controller (although there are some questions about that).

There is a core of interesting work here that I think can be important to the field. However, there is a lack of clarity on several important fronts, including design of the behavioral experiments, presentation of the behavioral data, conceptual framing of what curl can and cannot do, etc. Equally importantly, the manuscript is not written in a manner that will make it accessible to most vision scientists. I consider myself to be pretty knowledgeable about optic flow, and I had to read most of the manuscript 3 or 4 times to be able to understand the bulk of it. And my experience is that most vision scientists do not understand optic flow well, so I fear that most of the readers that the authors should want to reach would struggle to understand the work. As written, this is mainly going to make an impact on a handful of optic flow gurus. Thus, I consider that this manuscript will need a major overhaul to clarify important issues and make it more accessible.

Major issues:

(1) The manuscript contains inconsistent, if not misleading, messaging about what information retinal curl does, and does not, provide regarding heading estimation. In the Abstract, the authors state: "We propose an alternative: the visual system utilizes retinal curl directly to estimate heading, rendering the explicit recovery of the FOE unnecessary." Based on my understanding of the rest of the manuscript, I find this statement to be a misrepresentation for two main reasons:

a) To "directly estimate heading" relative to what? When not qualified, most people interpret "heading" to mean an observer's heading relative to the world (or some allocentric reference frame). But retinal curl only gives information about an observer's heading relative to the point on which their eyes are fixated. Moreover, that point of fixation will change every few hundred milliseconds in natural viewing, so the retinal curl will change with each new fixation even as heading relative to the world remains unchanged. So I think most readers would grossly misinterpret the claim that retinal curl can be used "directly to estimate heading". Indeed, in the authors' controller model, the initial heading needs to be given, and then the controller can work. But from where does the visual system get the initial heading, since it does not come from curl? These issues are left hanging. Thus, while curl can provide a very useful input for steering toward a fixated target, other signals are needed to estimate heading relative to the world. This has to be made much clearer early on, and a conceptual schematic diagram might help. Also, the authors generally do not specify the reference frame of the variables they are talking about, leaving lots of room for misinterpretations. It should be clear each time they are talking about a variable, such as heading, whether it is relative to the fixation target, body, world, etc.

b) It seems to me that retinal curl will depend on other variables, in addition to heading relative to the fixation target. For example, it seems to me that the magnitude of retinal curl will depend on self-motion speed, the depth structure of the scene, the angle of elevation of the fixated target, and perhaps others. This is not discussed at all, and many readers would get the misguided impression that there is a 1:1 mapping from curl to heading (relative to fixation). If I am right that this is not correct, it means that retinal curl can tell the observer whether to steer right or left to move toward the fixated target, but it cannot tell them how much to steer. Indeed, in the authors' controller model, there is a free parameter that calibrates curl to angle. It makes sense that this works to fit trajectory data that are given from a fixed environment, but it is unclear how the brain would use retinal curl to control steering when these other variables are uncertain or changing unpredictably. Moreover, how does the system change the mapping from curl to steering command as the location of fixation changes relative to the current heading? These are issues that need to be brought up in framing the problem and discussed at some length. If the authors can show mathematically that retinal curl is only dependent on heading (relative to fixation) and not any of these other variables, it would be very valuable to show the equations for this relationship.

(2) The description of the behavioral experiment and presentation of behavioral data leaves a lot to be desired.

a) First, it is stated (line 158) that "Participants continuously reported their perceived direction of self-motion while maintaining fixation on the yellow dot." Again, the reference frame is completely unspecified. Participants were reporting their perceived heading relative to what? The fixation target? The world? What exactly were the instructions given to the subjects to perform the task? Based on the description of how perceived paths are computed (line 166-), it seems to be presumed that subjects are reporting their heading relative to the world because those angles are then converted into x and z coordinates in what I presume is a world-centered reference frame. But how do we know that subjects are accurately reporting their heading relative to the world? What if they are biased in their reports by the location of the fixation target relative to the scene, or by some other reference signal? Is it possible for the authors to rule out the possibility that perceptual biases seen in the unaltered curl condition result from observers not fully adopting the assumed reference frame of the task? If this cannot be firmly excluded, it seems to create problems for the rest of the study.

b) I also feel that there is a mismatch between what the behavioral task requires and what the controller model does. Subjects are apparently asked to report their heading relative to the world, but the controller model only controls their heading relative to the point that they are fixating. I understand how this is resolved in the model, but I think this type of distinction is buried and will not be apparent to most readers. Again, the reference frames of what is being measured and controlled need to be specified explicitly in all parts of the paper, and the authors need to explain how the system would combine curl-based control with some other measures of (at least initial) heading for world-centered heading to be computed. All of the assumptions need to be clearly specified.

c) In addition, I found it frustrating that the authors never present raw perceptual data from the observers. Rather, in Figure 2, we see reconstructed trajectories that are perfectly smooth with no indications of noise whatsoever. Since these paths are computed from the perceptual reports, there must be some noise inherent in them. The figures should represent this uncertainty somehow, and it should be explained how these perfectly smooth trajectories are obtained.

(3) "...the magnitude of retinal curl in the fovea can specify the body trajectory relative to gaze (Matthis et al., 2022)." The main idea put forward by the authors here seems to overlap heavily with this statement that they attribute to Matthis et al. 2022. While I think this paper still adds importantly to the topic, the authors do not discuss how their findings are different from those of Matthis et al. 2022, why they are an important extension, etc. Readers should not have to go read this other paper to have any idea how the present findings are placed in importance relative to the literature.

(4) The analysis and treatment of eye movements is extremely weak. The authors discarded trials for which gaze deviated from the fixation point by more than 3 degrees (which is a LOT given that the eye speeds are generally in the neighborhood of 0.5 deg/sec), and they provide basic stats on the distribution of positions. But this largely misses the point: it is not small position errors that are likely to matter, but rather velocity errors. Even a small amount of retinal slip of the target while it is being pursued will cause image motion that is going to alter the optic flow field around the fixation target. So, for example, the retinal curl field may no longer be centered on the fixation target. How do we know that some of the perceptual biases are not influenced by image motion resulting from imperfect tracking of the fixation target? This needs to be analyzed and discussed.

(5) I found the sections of text comparing the separate and joined fits (starting line 287) to be a bit too rosy. The authors show the separate fits in the main text, and it is not very surprising that these fits are good, given that the model has 30 parameters, and these data are pretty low-dimensional. The authors only show the joined fits in the supplement, and they say that they are almost as good as the separate fits (indeed, they are better in a model comparison sense, but this is 30 parameters vs. 2 parameters). However, when I look at the fits of the joined model in the supplement, I don't find them to be very impressive. In particular, the model grossly misses the data for the straight paths for several subjects (e.g., id5, id6, id8, id10). And fitting the straight paths would presumably be easiest. This implies that the joined model is really missing something and that fitting the curved paths interacts strongly with fitting the data for different fixation target locations on the straight path. I think that the authors should discuss the results a bit more soberly and tone down their conclusions here.

(6) The section of the paper on neural simulations (starting line 387) has a few weaknesses. First, why are only straight paths simulated here? This does not seem to provide a very rigorous test of the model. Second, it is awkward that the simulation results are presented in units of pixels, rather than degrees. Third, the authors seem to downplay the fact that the neural estimates of heading seem to oscillate rather wildly (over a range of hundreds of pixels, whatever that means, see especially Figure S16). It was far from clear to me how an estimate of heading with these large oscillations is useful. It would seem to require that heading estimates are integrated over substantial lengths of time to be reliable. It was therefore unclear how the model produces such smooth paths from these oscillating estimates.

Reviewer #2 (Public review):

Summary:

This paper shows that imiquimod, a compound often used to induce a psoriasis-like skin inflammation in mice, has a TLR7-independent effects that induce the unfolded protein response and amplify cytokine expression in dendritic cells. Although these effects of imiquimod have been described in the literature before, this study provides more detailed evidence and different contexts to this observation. These findings add to existing literature that imiquimod has a pleotropic mechanism of action involving changes in mitochondrial functions and cellular stress responses. Specifically, the authors show that imiquimod can induce calcium signaling in immune cells and potentiate two branches of the unfolded protein response in a TLR7-independent and MyD88-independent manner. They also show that some of these effects might be partially mediated by direct binding of imiquimod to Gelsolin. These findings expand our understanding of imiquimod-mediated inflammation and are useful for the field of experimental skin immunology and mouse models of psoriasis. However, the molecular and cellular mechanisms connecting Gelsolin to the unfolded protein response and skin inflammation presented in this paper require further investigation in the context of TLR-mediated inflammation.

Strengths:

(1) TLR7-independent effects of imiquimod on the expression of genes and proteins involved in the unfolded protein response are well demonstrated.

(2) Gelsolin is identified as a new imiquimod-binding protein in mouse cells.

Weaknesses:

(1) Effects of imiquimod on mitochondrial Ca signaling are not clear from the presented data.

(2) The mechanism of action connecting imiquimod to Gelsolin on the unfolded protein response and cytokine production remains not fully explained.

(3) It remains unclear if Gelsolin contributes to regulating TLR7 (or other types of TLR-mediated) inflammation in vivo.

Reviewer #2 (Public review):

Summary:

In this study, across two experiments, the authors wrestle with the question: What is the profile of confidence judgments in presence/absence decisions for audiovisual stimuli? After thresholding observers to 50% target detection rates in each modality, the authors conducted one experiment that included 75% target presence (spread equally across bimodal, auditory, and visual targets) and one experiment with 50% overall target presence. Results showed that, overall, detection performance was higher for audiovisual stimuli compared to unimodal ones, and that a recent model for stimulus detection could be extended to this multisensory scenario. By incorporating a disjunctive rule for absence judgments and a conjunctive rule for presence judgments, the model was able to qualitatively reproduce some of the trends observed in the human data regarding confidence.

Strengths:

(1) The paper makes novel contributions to the study of multisensory confidence judgments for yes/no target detection.

(2) The paper further extends the use of a leading model of stimulus detection (from Mazor et al., 2025).

(3) Pre-registration of the study was implemented, and the code is publicly available (although the GitLab link requires registration to access the materials).

(4) One of the empirical results (higher confidence for absence compared to presence judgments) is especially interesting, contributing another empirical finding to a very mixed literature on this topic (as the authors note).

Weaknesses:

(1) Page 5 - I have concerns about the use of the equal-variance model from Signal Detection Theory to analyze the data. For example, the authors should read the recent paper by Miyoshi, Rahnev, and Lau in iScience, found at this link: https://www.cell.com/iscience/fulltext/S2589-0042(26)00373-1. In this paper, the authors note how the equal variance model should be used with caution in yes/no detection tasks, since the variances of the "stimulus present" and "stimulus absent" distributions are often different from one another. In a revision, I highly recommend that the authors explicitly discuss this paper and review whether the assumptions for the equal-variance model have been met (e.g., since they have confidence data, one way to do this would be to evaluate if the slope of the line in zROC space differs from 1). The authors may also want to incorporate methods from this iScience paper into the current manuscript, or potentially move to using an unequal variance SDT model and compute d'a and c'a.

(2) Related to the computation/measurement of the response criterion, the authors note on page 18 in the Methods that for Experiment 1, signals are actually present on 75% of trials, since a bimodal stimulus is present on 25% of trials, the visual circle only occurs on 25% of trials, the sinusoidal tone occurs on 25% of trials, and then only noise is present on 25% of trials. Did the authors have any a priori hypotheses about the response criteria that participants would exhibit in Experiment 1, considering the unbalanced target presentation rate in this task? Also, in Experiment 2, what did it mean to equate target present and target absent trials? Is it that they broke 50% target present trials down into 16.67% bimodal targets, 16.67% visual targets, and 16.67% auditory targets? A few more details would be good to explicitly note for those trying to replicate the task.

(3) It is important to plot the individual data for Figure 2. If the authors didn't match detection performance for the visual and auditory modalities, it would be good to see the individual data to know why. Is it that the thresholding procedure didn't work for some of the participants in the visual modality, and that's why the "yes" response rate is (on average) ~60% or higher across the two experiments? Similarly, in the auditory domain, do the authors have participants that are at floor? Or is it simply that the staircases failed to successfully target 50% detection on average?

(4) The authors mentioned that data were collected on the Prolific platform. What checks did they conduct to ensure that this data wasn't produced by bots? There are recent high-profile publications in PNAS and Behavioral Research Methods that indicate how online data collection is problematic (e.g., https://www.pnas.org/doi/10.1073/pnas.2535585123 and https://link.springer.com/article/10.3758/s13428-025-02852-7). What analyses or quality checks are there to ensure that humans were the ones completing the task?

(5) Page 7 - Since confidence was collected on a continuous scale, the authors should say a bit more about how they were able to compute measures of metacognitive efficiency. My understanding is that to compute meta-d', the data has to be binned. How was the binning implemented? With whatever bin size the authors chose, would it make any difference to the results if they changed the number of the bins in the analysis?

(6) Page 8 - Is there a prior precedent for using slope of the Bayesian logistic regression predicting accuracy from confidence as a measure of metacognitive sensitivity? If so, can the authors cite those papers as a reference? If not, can they place this analysis within the context of other measures of metacognitive sensitivity that exist? (meta-d', AUROC (Type 2), etc.)

(7) Page 8 - Another one of the results on page 8 is worth reflecting further upon: the authors note how in Experiment 1, no credible difference was found between unimodal and bimodal trials (DeltaM = -0.25 [-0.59, 0.10]), but in Experiment 2, "we observed higher metacognitive efficiency in unimodal compared to bimodal trials (DeltaM = -0.28 [-0.54, -0.02]. Those DeltaM values are nearly identical, so without a power analysis motivating the number of participants the authors collected, how certain are they that the results from these two experiments are really that distinct? It reminds me a bit of the Andrew Gelman blog post, "The difference between significance and non-significance is not significant".

(8) Is there any way to look at whether the presence of multisensory hallucinations (or perhaps that word is too strong, and we should simply consider them miscategorizations) increased as the task progressed? That is, the authors have repeated presentations of audiovisual stimuli for at least some percentage of the trials. Since the percentages for auditory stimuli being correctly categorized as auditory are at 85% in Experiment 1 and 79% in Experiment 2, were the trials where they miscategorized these stimuli equally spread throughout the task? Or did they come later in the experiment, after being repeatedly exposed to multisensory trials?

(9) Would the authors obtain the same results if they got rid of the amodal confidence judgment in their task, and simply had participants report the bimodal confidence following the presence/absence judgment? Part of the reason for asking this is that, according to page 11, the model is only fitted to amodal detection accuracy and response time data. This surprised me. I would have expected that the bimodal confidence would provide more useful information for the model fit. The authors should further explain this rationale in the paper. It seems odd to me to have the multisensory confidence ratings and not have them play a central role in the modeling work.

(10) In Figure 6, it appears the model is a bit off in its estimate of auditory responses (panel B, E) in the AV condition. Do the authors have any intuitions about why this might be happening?

(11) The authors talk about how the model is reproducing effects in the human data, but there's no systematic comparison, quantitatively, of how the two things relate. The authors should include some quantitative measure that reflects this.

(12) Related to this, I am not sure I agree with the characterization in Figure 7 that "when confidence followed a disjunctive rule, the model failed to capture important aspects of the data. On the other hand, when confidence followed a conjunctive rule, it reproduced confidence in presence judgments but failed to capture variability in confidence ratings for absence judgments." What, quantitatively, is the basis of this claim? This applies to Figure 8, too. I am not clear how, specifically, and quantitatively, the authors are justifying their claims about model fits. I don't think the confidence asymmetry index in Figure 8 is enough to quantify the quality of the model fitting procedure.

(13) Is there any chance the higher metacognitive efficiency for auditory trials is simply driven by differences in the d' values across the modalities? It might be good to probe this effect further.

(14) Lastly, I think it would be interesting to look at how instructions about modality-specific attention could modulate these findings, in terms of how unimodal (unimodal visual, unimodal auditory) or bimodal attention might modulate these effects. This is an idea for future work.

Reviewer #2 (Public review):

Summary:

Bot & Davila-Velderrain present a new method to understand expression specificity, based on an analysis of the relation between expression level and breadth for each gene. They show that the method captures biological differences across organs, diverse cell types, and specific cell subtypes, for different biological processes and across species.

Strengths:

This manuscript addresses an important question in an original manner, and was a pleasure to read. The authors frame the question very clearly: gene expression is a complex trait, which can be summarized in an informative manner by its specificity. The method the authors propose (which I'll call "LB" in this review) has several attractive features, summarising different specificity profiles in a more nuanced manner than the widely used tau. They show convincingly that their method captures relevant biology at different scales. I especially appreciated the comparative analyses of specificity within broad cell types and within neuronal subtypes.

Weaknesses:

Surprisingly, while the method works well, the authors never compare it to the state-of-the-art. Thus, comments 1 and 2 are my only "major" comments.

(1) In the Introduction, the authors should explain which shortcomings of existing methods motivate the development of a new one.

(2) In the Results section, the authors should compare the results of LB with other methods, at least tau and Gini (which is conceptually quite similar to LB).

(3) It would be good to show the sensitivity of LB to different numbers of bins.

(4) The conservation of specificity across primates was already reported in Kryuchkova-Mostacci 2016 (https://doi.org/10.1371/journal.pcbi.1005274). But also see Dunn et al 2018 (https://doi.org/10.1073/pnas.1707515115) for criticism of this type of naive pairwise comparisons.

Reviewer #2 (Public review):

Summary:

This manuscript describes the results of an interesting study examining the rate of degradation of extracellular DNA in soil ecosystems using a clever experimental approach. 16S ribosomal RNA genes were amplified from soil samples, and then purified PCR amplicons, containing a 5' linker sequence on the forward primer, were introduced to soils and monitored over time using real-time quantitative PCR and NGS amplicon sequencing. The study was able to measure rates of overall extracellular DNA degradation, but also sequence-specific degradation rates. I like the idea and execution of the study, and the results are interesting. The manuscript needs some help to improve the overall readability. Please see general and editorial comments below.

Strengths:

Innovative experimental design that is well deployed across a large number of soil types, revealing interesting variability in extracellular DNA degradation.

Weaknesses:

(1) The manuscript needs another review to improve the readability of the document.

(2) The authors have used 16S genes to look at sequence-specific degradation. But 16S rRNA genes are actually pretty well conserved, and there isn't as much genetic variation across this gene among organisms as there is for other genes. It might be more relevant to look at metagenomic DNA degradation from high AT, high GC organisms, etc. This would be more generalizable than 16S genes.

(3) Consideration of differential cell lysis during soil DNA extraction needs to be considered as well.

(4) It is not clear why the authors didn't put GAPDH linkers on the reverse primer as well. This would have given an easier amplicon to amplify (no degeneracies at all).

Reviewer #2 (Public review):

Sugarman et al show a major advance in the volumetric imaging of the cephalopod body and nervous system, using wide field high resolution micro-CT imaging. The new detection optics are striking in their performance, and the conclusions made from the images seem well-founded. The technical advance and the conclusions both justify the reader's attention, but the authors should make the figures and the text teach the reader so that the findings are more accessible and convincing.

The paper is now written in a style that will impress those ready to be impressed and fail to impress many of the readers, although it should.

(1) The authors must improve the text so that it cleanly states what was known previously, and how the current results extend this. For example. putting a section in the middle of the results section (page 3) that states: "Long-range connections between sucker and brain were demonstrated by fine chemical and tactile sensing by suckers in behavioral experiments with live O. bimaculoides (Buresch et al., 2022, 2024; Sepela et al., 2025; van Giesen et al., 2020; Wells, 1978a; Wells & Young, 1969) and by loss of chemotactile learning and memory observed after ablation of the "inferior frontal system" (i.e., inferior frontal/subfrontal/buccal lobe complex) (Wells, 1978a)..." seems a bit confusing to me. Similarly, putting in a reference to optical imaging approaches for combining data sets (Preibisch et al., 2009) as only the citation does little to make the work accessible. Please expand the text so that it teaches what the authors are thinking.

(2) The authors must improve the figures so the work is more digestible. The data is a pyramid, and the "google earth" range of magnifications and details is not clear in the figures. In short, the figure will impress those who know to be impressed and fail to impress the majority.

(3) The videos are far more useful in this contribution that in almost any other paper. Use them more so the reader realizes how key they are. Revising them to demonstrate the amazing range of scales in the data would be wise.

(4) The demonstration of the data visualization tool is excellent as far as it goes. Expanding the treatment of the multi-scale rendering would be wise.

With proper expansion of the text and the figures, it will become far more obvious that this is landmark work.

Reviewer #2 (Public review):

Summary:

The goal of this study was to develop a model for CDHR1-based Con-rod dystrophy and study the role of this cadherin in cone photoreceptors. Using genetic manipulation, a cell binging assay, and high- resolution microscopy the authors find that like rods, cones localize CDHR1 to the lateral edge of outer segment (OS) discs and closely opposes PCDH15b which is known to localize to calyceal processes (CPs). Ectopic expression of CDHR1 and PCDH15b in K652 cells indicate these cadherins promote cell aggregation as heterophilic interactants, but not through homophilic binding. This data suggests a model where CDHR1 and PCDH15b link OS and CPs and potential stabilize cone photoreceptor structure. Mutation analysis of each cadherin results in cone structural defects at late larval stages. While pcdh15b homozygous mutants are lethal, cdhr1 mutants are viable and subsequently show photoreceptor degeneration by 3-6 months.

Strengths:

A major strength of this research is the development of an animal model to study the cone specific phenotypes associate with CDHR1-based CRD. The data supporting CDHR1 (OS) and PCDH15 (CP) binding is also a strength, although this interaction could be better characterized in future studies. The quality of the high-resolution imaging (at the light and EM levels) is outstanding. In general, results support the conclusions of the authors.

Weaknesses:

While the cellular phenotyping is strong, the functional consequences of CDHR1 disruption is not addressed. While this is not the focus of the investigation, such analysis would raise the impact of the study overall. This is particularly important given some of the small changes observed in OS and CP structure. While statistically significant, are the subtle changes biologically significant? Examples include cone OS length (Fig 4F, 6E) as well as other morphometric data (Fig 7I in particular). Related, for quantitative data and analysis throughout the manuscript, more information regarding the number of fish/eyes analyzed as well as cells per sample would provide confidence in the rigor. The authors should also not whether analysis was done in an automated and/or masked manner.

Comments on revisions:

Most of my concerns were addressed in this revised version.

Reviewer #2 (Public review):

Summary:

This is a bold and important study and addresses an important question in the field: how species-specific variation in brain oxytocin receptor expression relates to differences in social behavior.

Tsukamoto et al. generated eight independent transgenic mouse lines (Koi lines) carrying a bacterial artificial chromosome (BAC) encompassing the prairie vole Oxtr locus along with flanking intergenic regions, with the goal of probing the behavioral consequences of species-specific variation in brain Oxtr expression. Across these "volized" lines, the authors claim conserved Oxtr expression in the mammary gland but strikingly divergent patterns of brain expression, none of which fully recapitulate endogenous prairie vole Oxtr distribution, and instead exhibit expression patterns that diverge from both mouse and prairie vole brain Oxtr distribution. Nevertheless, some lines exhibit partial overlap with vole Oxtr expression pattern reported in the literature within specific brain regions, and one line displays partner preference behavior reminiscent of prairie voles. The authors further report line-dependent differences in maternal pup retrieval and crouching behaviors, which they interpret as evidence that variation in brain Oxtr expression can drive variation in social behaviors. Together with analyses of topologically associating domain (TAD) architecture, the authors conclude that brain, but not peripheral- Oxtr expression, is shaped by distal regulatory elements beyond the BAC insert, and propose that such regulatory flexibility underlies evolutionary diversification of social behavior.

Strengths:

A particular strength of the study is the generation of multiple independent transgenic lines, which provides a valuable resource for probing regulatory influences on Oxtr expression.

Weaknesses:

While the study addresses an important question, I have several methodological and conceptual concerns regarding the study in its current form. Some aspects of the study fall outside my primary area of expertise, and I am therefore not in a position to fully evaluate the technical difficulty or rigor of those components, or to judge whether my suggestions would be feasible to implement. I defer to reviewers with relevant expertise for a more detailed assessment of these aspects.

(1) Each independent Koi line exhibits a distinct brain expression pattern that differs from both wild-type mouse and prairie vole Oxtr expression, complicating the interpretation of the results. The manuscript does not include a direct comparison of brain Oxtr expression patterns in these transgenic lines with those of prairie voles. Instead, expression similarity is inferred primarily from regional localization and compared indirectly with prior literature (Figures 2-5). For those lines that show partial resemblance to prairie vole Oxtr expression patterns, the authors do not assess whether Oxtr-expressing neurons share comparable anatomical projections or transcriptomic identity with prairie vole Oxtr-expressing neurons. Quantification of expression remains largely descriptive, illustrating expression patterns (Figure 2), OXTR protein distribution (Figure 3; images are difficult to evaluate due to low contrast), or Oxtr mRNA levels across selected brain regions in Koi lines, wild-type mice, and mOxtr-/- mice (Figures 4-5), without directly testing similarity to prairie vole expression. In addition, whole-brain expression data are lacking, with analyses restricted to selected sections. While such analyses may be beyond the scope of the present study, these limitations nonetheless complicate interpretation of the central question - namely, whether the observed behavioral phenotypes arise from vole-like Oxtr circuits rather than from distinct, line-specific expression configurations.

(2) The authors state that Oxtr expression in the mammary gland is similar across all Koi lines and the mOxtr-IRES-Cre knock-in line. However, the images presented in Figure 2 appear to show differences in anatomical detail across lines, and no quantitative analysis is provided to support the claim of equivalence.

(3) The conclusion that integration site rather than copy number determines the observed BAC transgene expression patterns (Lines 202-203) is not fully supported by the data. First, the authors did not compare multiple copy numbers at the same genomic insertion site, making it impossible to disentangle copy-number effects from position effects. Second, BAC copy number does not necessarily scale linearly with expression; higher copy numbers can have a repressive effect on gene expression (Garrick et al, Nat Genet, 1998).

(4) While I am not an expert in TAD analysis, the observed differences in 3D architecture around Oxtr are consistent with a role for long-range regulatory interactions. However, these analyses appear largely descriptive and correlative, and establishing a causal contribution of 3D chromatin organization to Oxtr regulation by distal elements would likely require direct perturbation of TAD boundaries or looping interactions. I recognize that such experiments may be beyond the scope of the present study, but clarifying this limitation in the interpretation would be helpful.

Reviewer #2 (Public review):

Pescher and colleagues present a revised manuscript detailing the multi-omic characterisation of Leishmania donovani amastigote to promastigote differentiation and integration of this data. The molecular pathways that regulate Leishmania life-stage transitions are still poorly understood, with many approaches exploring single proteins/RNAs etc in a reductionist manner. This paper takes a systems-scale approach and does a good job of integrating the disparate -omics datasets to generate hypotheses about the intersections of regulatory proteins that are associated with life-cycle progression. The differentiation step studied is from amastigote to promastigote using hamster-derived amastigotes which is a major strength. The use of hamsters permits the extraction of parasites that are host adapted and represent "normal", host-adapted Leishmania ploidy; the promastigote experiments are performed at a low passage number. Therefore, this is a strength or the work as it reduces the interference from the biological plasticity of Leishmania when it is cultured outside the host for prolonged periods. The multi-omics datasets presented are robust in their acquisition and analysis and will form an excellent resource for researchers studying the molecular events (particularly proteasomal protein degradation, and phosphorylation) during life-stage progression.

General comments on the revisions:

My view is that the authors have made significant, satisfactory changes that address the comments and queries I made on the original manuscript (Review Commons).

There are two areas where the authors had to make major changes/justifications where further comment is merited, these were:

RNA-seq.<br /> The most significant issue was the originally underpowered RNA-seq which had only two replicates. This has been repeated with four replicates now. This has not led to changes in the interpretation of the data between the original study and this one. One comment that the authors make in the response to this was : "Given the robustness of the stage-specific transcriptome, and the legal constrains associated with the use of animals, we chose to limit the number of replicates to the necessary". Ensuring that animal experiments are properly powered and that maximum robustness of the data from the minimum sample size is an important part of experimental design for ethical use of animal models. Essentially the replication here could have been avoided if the original study had used 1 more animal. However, the new version of RNA-seq brings appropriate confidence to the interpretation of the data.

Phosphoproteomics.<br /> The authors provide a robust justification of their strategy for the phosphoproteomics and highlight the inclusion criteria for phosphosites: "Phosphosites were only considered if detected with high confidence (identification FDR<1%) and high localisation confidence (localisation probability >0.75) in at least one replicate". The way missing values were dealt with is explained "For statistical analyses, missing values within a given condition were imputed with a well-established algorithm (MLE) only when at least one observed value was present in that condition." This fills in some of the gaps I was missing from the original manuscript, and I am satisfied that the data analysis is entirely appropriate for a discovery/system -based approach such as this one. The authors also edit the manuscript to reflect that "occupancy" or "stoichiometry" might not be the best description of what they were presenting and switched to the terminology of "normalised phosphorylation level" - I think this is an appropriate response.

Overall, in the absence of follow up experiments on specific individual examples, some of the claims in the original submission were toned down and reflect a more neutral description of the data now. Significantly, the data still underpin a key role for regulation of the ribosome between the amastigote and promastigote stages (and during the differentiation process). The recursive and reciprocal links between the phosphorylation and ubiquitination systems are interesting and present many opportunities for future investigation.

Reviewer #2 (Public review):

This is an extremely important work that shows how one can use generative models to construct specificity-switching mutational paths in complex fitness landscapes. The experimental evidence is very clear, and the theoretical tools are innovative.

The work will likely have a deep impact on future research aimed at understanding how evolution navigates fitness landscapes as well as reconstructing ancestral sequences.

The manuscript is extremely clear and well written, the experimental evidence is strong, and the methods are clearly described, so I do not have major issues to raise. A few minor issues are listed below.

(1) I consider the WW domain as an 'easy' case from the point of view of generative modelling. The domain is rather short, epistatic effects are not very strong (e.g. Boltzmann learning usually converges very quickly to a very paramagnetic state), and the resulting models are well interpretable (e.g. the hidden units of the RBM correlate well with subclasses).

This is not always (not often?) the case, however. In more complex proteins, the learning procedures can be slower and the resulting models less interpretable. Just for completeness, perhaps the authors could comment on the generality of the results and what they would expect for other systems based on their experience.

(2) In Section 3.3, the authors say that direct paths connecting Class I and Class IV behave similarly to indirect paths, despite having lower scores according to the RBM. How generic is this? Does it also happen for other classes? This might be an important point to address, as direct paths are easier to sample.

(3) The path shown in Figure 4 goes through a region of non-functionality around sequences 18-19. It seems that the sample path is basically exploring the functional regions for Class I and Class II/III separately, trying to approach the other class, but then it can't really make the switch.

By contrast, the path going from Class I to Class IV seems able to perform the functional switch in a single step (20-21) without losing too much of the function.

Perhaps the authors could better comment on this? Is this a limitation of the sampling method, or a fundamental biological fact?

(4) On page 12, it is stated that the temperature was chosen to 1/3 to maximize the score. This is important and should be mentioned earlier (I didn't notice it until that point).

(5) On page 13, it is stated that: "However, the scores of the ancestral sequences along the phylogenetic pathways assigned by the RBM are significantly lower than the ones of the RBM-designed sequences. This result is expected as ASR reconstruction does not take into account epistasis, differently from RBM, and we expect ASR sequences to generally be of lesser quality."

I was very surprised by this result. My own experience with ASR shows that, on the contrary, sequences found by ASR (via maximum likelihood) tend to have high scores in the (R)BM, and tend to be more stable than extant sequences. I attribute this to the fact that ASR typically finds a "consensus" sequence that maximizes the contribution to the score coming from the fields (the profile), which is typically dominant over the epistatic signal, resulting in a bigger score. Maybe the authors did not use maximum likelihood in the ASR? Some clarification might be useful here.

Reviewer #2 (Public review):

Summary:

This study identifies the outer‑mitochondrial GTPase MIRO1 as a central regulator of vascular smooth muscle cell (VSMC) proliferation and neointima formation after carotid injury in vivo and PDGF-stimulation ex vivo. Using smooth muscle-specific knockout male mice, complementary in vitro murine and human VSMC cell models, and analyses of mitochondrial positioning, cristae architecture and respirometry, the authors provide solid evidence that MIRO1 couples mitochondrial motility with ATP production to meet the energetic demands of the G1/S cell cycle transition. However, a component of the metabolic analyses are suboptimal and would benefit from more robust methodologies. The work is valuable because it links mitochondrial dynamics to vascular remodelling and suggests MIRO1 as a therapeutic target for vasoproliferative diseases, although whether pharmacological targeting of MIRO1 in vivo can effectively reduce neointima after carotid injury has not been explored. This paper will be of interest to those working on VSMCs and mitochondrial biology.

Strengths:

The strength of the study lies in its comprehensive approach assessing the role of MIRO1 in VSMC proliferation in vivo, ex vivo and importantly in human cells. The subject provides mechanistic links between MIRO1-mediated regulation of mitochondrial mobility and optimal respiratory chain function to cell cycle progression and proliferation. Finally, the findings are potentially clinically relevant given the presence of MIRO1 in human atherosclerotic plaques and the available small molecule MIRO1.

Weaknesses:

(1) High-resolution respirometry (Oroboros) to determine mitochondrial ETC activity in permeabilized VSMCs would be informative.

(2) Therapeutic targeting of MIRO1 failed to prevent neointima formation, however, the technical difficulties of such an experiment is appreciated.

Comments on revisions:

The authors have addressed the concerns I previously raised.

Reviewer #2 (Public review):

Summary:

The author investigates how the HIV-1 Env glycoprotein modulates the nanoscale organisation and dynamics of the CXCR4 co-receptor on CD4⁺ T cells. The author demonstrates that HIV-1 Env induces CXCR4 clustering distinct from that triggered by its natural ligand (CXCL12), implicating spatial receptor organization as a determinant of infection. This study investigates how HIV-1 Env (specifically X4-tropic gp120) alters the membrane organization and dynamics of the chemokine receptor CXCR4 and its WHIM-associated mutant, CXCR4R334X, in a CD4-dependent manner. Using single-particle tracking total internal reflection fluorescence microscopy (SPT-TIRF-M), the authors demonstrate that both soluble gp120 and virus-like particles (VLPs) displaying gp120 induce CXCR4 nanoclustering, reduce receptor diffusivity, and promote immobile nanoclusters of CXCR4 at the membrane of Jurkat T cells and primary CD4⁺ T cell blasts. The work offers new insights into the spatial organisation of receptors during HIV-1 entry and infection. The manuscript is well-written, and the findings are significant.

Significance:

Nature and significance of the advance:<br /> This work marks a conceptual and mechanistic breakthrough in understanding HIV-1 entry. It goes beyond the static view of Env-co-receptor interaction to show that nanoscale reorganization of CXCR4, distinct from chemokine-induced clustering, occurs during HIV-1 Env engagement and may be essential for infection.

Context within existing literature. Previous studies established Env-induced CD4 clustering (Yin et al., 2020) and chemokine-induced CXCR4 nanocluster formation (Martínez-Muñoz et al., 2018), but the exact nanoscale rearrangement of CXCR4 in the context of HIV-1 Env and physiological Env densities remains unquantified. This study addresses this gap using SPT-TIRF, STED microscopy, and functional assays.

Audience and influence: The findings will be of interest to researchers in HIV virology, membrane receptor biology, viral entry mechanisms, and therapeutic target development. The receptor-clustering aspect could also influence broader fields of study, such as GPCR organization and immune receptor signalling.

Reviewer expertise: I can evaluate HIV-1 entry mechanisms, viral glycoprotein-host-host-host receptor interactions, single-molecule fluorescence microscopy, and membrane protein dynamics. I am less equipped to evaluate the deep structural modelling aspects, though the in silico AlphaFold results are straightforward to interpret in context.

Reviewer #2 (Public review):

Summary:

This manuscript aims to identify the central nervous system circuitry, specifically within the mushroom body (MB), that mediates nociception-induced escape behavior in adult Drosophila. The authors provide a detailed map of the neural pathways underlying defensive actions in flies. Overall, the study is technically solid, clearly written, and conceptually<br /> interesting.

Strengths:

The authors present compelling evidence by integrating multiple complementary approaches. The ALTOMS laser system enables precise, automated measurement of escape latency, allowing for high-throughput and objective behavioral quantification. Neuronal silencing experiments assess functional necessity and demonstrate that specific dopaminergic neurons (DANs) and mushroom body output neurons (MBONs) are critical for escape behavior. Trans-Tango anatomical mapping further supports the proposed circuit by identifying putative synaptic connections consistent with the authors' model.

Weaknesses:

A central limitation of the study is its heavy reliance on chronic Kir2.1-mediated neuronal silencing as the primary functional manipulation. This approach raises concerns about potential developmental compensation and indirect network effects. The authors could strengthen their conclusions by incorporating more temporally precise, reversible silencing strategies, such as recently developed optogenetic- or chemogenetic-based methods.

In addition, the study relies on the trans-Tango system to identify downstream synaptic partners, which has several inherent limitations. Trans-Tango detects only chemical synapses and cannot reveal electrical coupling. The system may also yield false negatives due to reporter sensitivity, and anatomical labeling alone does not establish functional connectivity in the context of the specific behavior examined.