Reviewer #1 (Public review):

Summary:

In Drosophila melanogaster, ITP has functions in feeding, drinking, metabolism, excretion, and circadian rhythm. In the current study, the authors characterized and compared the expression of all three ITP isoforms (ITPa and ITPL1&2) in the CNS and peripheral tissues of Drosophila. An important finding is that they functionally characterized and identified Gyc76C as an ITPa receptor in Drosophila using both in vitro and in vivo approaches. In vitro, the authors nicely confirmed that the inhibitory function of recombinant Drosophila ITPa on MT secretion is Gyc76C-dependent (knockdown of Gyc76C specifically in two types of cells abolished the anti-diuretic action of Drosophila ITPa on renal tubules). They also confirmed that ITPa activates Gyc76C in a heterologous system. The authors used a combination of multiple approaches to investigate the roles of ITPa and Gyc76C on osmotic and metabolic homeostasis modulation in vivo. They revealed that ITPa signaling to renal tubules and fat body modulates osmotic and metabolic homeostasis via Gyc76C.

Furthermore, they tried to identify the upstream and downstream of ITP neurons in the nervous system by using connectomics and single-cell transcriptomic analysis. I found this interesting manuscript to be well-written and described. The findings in this study are valuable to help understand how ITP signals work on systemic homeostasis regulation. Both anatomical and single-cell transcriptome analysis here should be useful to many in the field.

Strengths:

The question (what receptors of ITPa in Drosophila) that this study tries to address is important. The authors ruled out the Bombyx ITPa receptor orthologs as potential candidates. They identified a novel ITP receptor by using phylogenetic, anatomical analysis, and both in vitro and in vivo approaches.

The authors exhibited detailed anatomical data of both ITP isoforms and Gyc76C (in the main and supplementary figures), which helped audiences understand the expression of the neurons studied in the manuscript.

They also performed connectomes and single-cell transcriptomics analyses to study the synaptic and peptidergic connectivity of ITP-expressing neurons. This provided more information for better understanding and further study of systemic homeostasis modulation.

Comments on revisions:

In the revised manuscript, the authors addressed all my concerns.

There is one more suggestion: The scale bar for fly and ovary images should be included in Figures 9, 10, and 12.

Reviewer #2 (Public review):

The physiology and behaviour of animals are regulated by a huge variety of neuropeptide signalling systems. In this paper, the authors focus on the neuropeptide ion transport peptide (ITP), which was first identified and named on account of its effects on the locust hindgut (Audsley et al. 1992). Using Drosophila as an experimental model, the authors have mapped the expression of three different isoforms of ITP, all of which are encoded by the same gene.

The authors then investigated candidate receptors for isoforms of ITP. Firstly, Drosophila orthologs of G-protein coupled receptors (GPCRs) that have been reported to act as receptors for ITPa or ITPL in the insect Bombyx mori were investigated. Importantly, the authors report that ITPa does not act as a ligand for the GPCRs TkR99D and PK2-R1. Therefore, the authors investigated other putative receptors for ITPs. Informed by a previously reported finding that ITP-type peptides cause an increase in cGMP levels in cells/tissues (Dircksen, 2009, Nagai et al., 2014), the authors investigated guanylyl cyclases as candidate receptors for ITPs. In particular, the authors suggest that Gyc76C may act as an ITP receptor in Drosophila. Evidence that Gyc76C may be involved in mediating effects of ITP in Bombyx was first reported by Nagai et al. (2014) and here the authors present further evidence, based on a proposed concordance in the phylogenetic distribution ITP-type neuropeptides and Gyc76C and experimental demonstration that ITPa causes dose-dependent stimulation of cGMP production in HEK cells expressing Gyc76C. Having performed detailed mapping of the expression of Gyc76C in Drosophila, the authors then investigated if Gyc76C knockdown affects the bioactivity of ITPa in Drosophila. The inhibitory effect of ITPa on leucokinin- and diuretic hormone-31-stimulated fluid secretion from Malpighian tubules was found to be abolished when expression of Gyc76C was knocked down in stellate cells and principal cells, respectively.

Having investigated the proposed mechanism of ITPa signalling in Drosophila, the authors then investigate its physiological roles at a systemic level. The authors present evidence that ITPa is released during desiccation and accordingly overexpression of ITPa increases survival when animals are subjected to desiccation. Furthermore, knockdown of Gyc76C in stellate or principal cells of Malphigian tubules decreases survival when animals are subject to desiccation. Furthermore, the relevance of the phenotypes observed to potential in vivo actions of ITPa is also explored and publicly available connectomic data and single-cell transcriptomic data are analysed to identify putative inputs and outputs of ITPa expressing neurons.

Strengths of this paper.

(1) The main strengths of this paper are:

i) the detailed analysis of the expression and actions of ITP and the phenotypic consequences of over-expression of ITPa in Drosophila.

ii). the detailed analysis of the expression of Gyc76C and the phenotypic consequences of knockdown of Gyc76C expression in Drosophila.

iii). the experimental demonstration that ITPa causes dose-dependent stimulation of cGMP production in HEK cells expressing Gyc76C, providing biochemical evidence that the effects of ITPa in Drosophila are, at least in part, mediated by Gyc76C.

(2) Furthermore, the paper is generally well written and the figures are of good quality.

Weaknesses of this paper.

A weakness of this paper is the phylogenetic analysis to investigate if there is correspondence in the phylogenetic distribution of ITP-type and Gyc76C-type genes/proteins. Unfortunately, the evidence presented is rather limited in scope. Essentially, the authors report that they only found ITP-type and Gyc76C-type genes/proteins in protostomes, but not in deuterostomes. What is needed is a more fine-grained analysis at the species level within the protostomes. However, I recognise that such a detailed analysis may extend beyond the scope of this paper, which is already rich in data.

Reviewer #3 (Public review):

Summary:

The goal of this paper is to characterize an anti-diuretic signaling system in insects using Drosophila melanogaster as a model. Specifically, the authors wished to characterize a role for ion transport peptide (ITP) and its isoforms in regulating diverse aspects of physiology and metabolism. The authors combined genetic and comparative genomic approaches with classical physiological techniques and biochemical assays to provide a comprehensive analysis of ITP and its role in regulating fluid balance and metabolic homeostasis in Drosophila. The authors further characterized a previously unrecognized role for Gyc76C as a receptor for ITPa, an amidated isoform of ITP, and in mediating the effects of ITPa on fluid balance and metabolism. The evidence presented in favor of this model is very strong as it combines multiple approaches and employs ideal controls. Taken together, these findings represent an important contribution to the field of insect neuropeptides and neurohormones and has strong relevance for other animals. The authors have addressed all weaknesses raised in my previous review.

Reviewer #1 (Public review):

This work provides a new Python toolkit for combining generative modeling of neural dynamics and inversion methods to infer likely model parameters that explain empirical neuroimaging data. The authors provided tests to show the toolkit's broad applicability, accuracy, and robustness; hence, it will be very useful for people interested in using computational approaches to better understand the brain.

Strengths:

The work's primary strength is the tool's integrative nature, which seamlessly combines forward modelling with backward inference. This is important as available tools in the literature can only do one and not the other, which limits their accessibility to neuroscientists with limited computational expertise. Another strength of the paper is the demonstration of how the tool can be applied to a broad range of computational models popularly used in the field to interrogate diverse neuroimaging data, ensuring that the methodology is not optimal to only one model. Moreover, through extensive in-silico testing, the work provided evidence that the tool can accurately infer ground-truth parameters even in the presence of noise, which is important to ensure results from future hypothesis testing are meaningful.

Weaknesses

The paper still lacks appropriate quantitative benchmarking relative to other inference tools, especially with respect to performance accuracy and computational complexity and efficiency. Without this benchmarking, it is difficult to fully comprehend the power of the software or its ability to be extended to contexts beyond large-scale computational brain modelling.

Reviewer #2 (Public review):

Summary:

Whole-brain network modeling is a common type of dynamical systems-based method to create individualized models of brain activity incorporating subject-specific structural connectome inferred from diffusion imaging data. This type of model has often been used to infer biophysical parameters of the individual brain that cannot be directly measured using neuroimaging but may be relevant to specific cognitive functions or diseases. Here, Ziaeemehr et al introduce a new toolkit, named "Virtual Brain Inference" (VBI), offering a new computational approach for estimating these parameters using Bayesian inference powered by artificial neural networks. The basic idea is to use simulated data, given known parameters, to train artificial neural networks to solve the inverse problem, namely, to infer the posterior distribution over the parameter space given data-derived features. The authors have demonstrated the utility of the toolkit using simulated data from several commonly used whole-brain network models in case studies.

Strength:

Model inversion is an important problem in whole-brain network modeling. The toolkit presents a significant methodological step up from common practices, with the potential to broadly impact how the community infers model parameters.

Notably, the method allows the estimation of the posterior distribution of parameters instead of a point estimation, which provides information about the uncertainty of the estimation, which is generally lacking in existing methods.

The case studies were able to demonstrate the detection of degeneracy in the parameters, which is important. Degeneracy is quite common in this type of models. If not handled mindfully, they may lead to spurious or stable parameter estimation. Thus, the toolkit can potentially be used to improve feature selection or to simply indicate the uncertainty.

In principle, the posterior distribution can be directly computed given new data without doing any additional simulation, which could improve the efficiency of parameter inference on the artificial neural network is well-trained.

Weaknesses:

The z-scores used to measure prediction error are generally between 1-3, which seems quite large to me. It would give readers a better sense of the utility of the method if comparisons to simpler methods, such as k-nearest neighbor methods, are provided in terms of accuracy.

A lot of simulations are required to train the posterior estimator, which is computationally more expensive than existing approaches. Inferring from Figure S1, at the required order of magnitudes of the number of simulations, the simulation time could range from days to years, depending on the hardware. The payoff is that once the estimator is well-trained, the parameter inversion will be very fast given new data. However, it is not clear to me how often such use cases would be encountered. It would be very helpful if the authors could provide a few more concrete examples of using trained models for hypothesis testing, e.g., in various disease conditions.

Reviewer #1 (Public review):

Summary:

The authors aim to predict ecological suitability for transmission of highly pathogenic avian influenza (HPAI) using ecological niche models. This class of models identify correlations between the locations of species or disease detections and the environment. These correlations are then used to predict habitat suitability (in this work, ecological suitability for disease transmission) in locations where surveillance of the species or disease has not been conducted. The authors fit separate models for HPAI detections in wild birds and farmed birds, for two strains of HPAI (H5N1 and H5Nx) and for two time periods, pre- and post-2020. The authors also validate models fitted to disease occurrence data from pre-2020 using post-2020 occurrence data. I thank the authors for taking the time to respond to my initial review and I provide some follow-up below.

Detailed comments:

In my review, I asked the authors to clarify the meaning of "spillover" within the HPAI transmission cycle. This term is still not entirely clear: at lines 409-410, the authors use the term with reference to transmission between wild birds and farmed birds, as distinct to transmission between farmed birds. It is implied but not explicitly stated that "spillover" is relevant to the transmission cycle in farmed birds only. The sentence, "we developed separate ecological niche models for wild and domestic bird HPAI occurrences ..." could have been supported by a clear sentence describing the transmission cycle, to prime the reader for why two separate models were necessary.

I also queried the importance of (dead-end) mammalian infections to a model of the HPAI transmission risk, to which the authors responded: "While spillover events of HPAI into mammals have been documented, these detections are generally considered dead-end infections and do not currently represent sustained transmission chains. As such, they fall outside the scope of our study, which focuses on avian hosts and models ecological suitability for outbreaks in wild and domestic birds." I would argue that any infections, whether they are in dead-end or competent hosts, represent the presence of environmental conditions to support transmission so are certainly relevant to a niche model and therefore within scope. It is certainly understandable if the authors have not been able to access data of mammalian infections, but it is an oversight to dismiss these infections as irrelevant.

Correlative ecological niche models, including BRTs, learn relationships between occurrence data and covariate data to make predictions, irrespective of correlations between covariates. I am not convinced that the authors can make any "interpretation" (line 298) that the covariates that are most informative to their models have any "influence" (line 282) on their response variable. Indeed, the observation that "land-use and climatic predictors do not play an important role in the niche ecological models" (line 286), while "intensive chicken population density emerges as a significant predictor" (line 282) begs the question: from an operational perspective, is the best (e.g., most interpretable and quickest to generate) model of HPAI risk a map of poultry farming intensity?

I have more significant concerns about the authors' treatment of sampling bias: "We agree with the Reviewer's comment that poultry density could have potentially been considered to guide the sampling effort of the pseudo-absences to consider when training domestic bird models. We however prefer to keep using a human population density layer as a proxy for surveillance bias to define the relative probability to sample pseudo-absence points in the different pixels of the background area considered when training our ecological niche models. Indeed, given that poultry density is precisely one of the predictors that we aim to test, considering this environmental layer for defining the relative probability to sample pseudo-absences would introduce a certain level of circularity in our analytical procedure, e.g. by artificially increasing to influence of that particular variable in our models." The authors have elected to ignore a fundamental feature of distribution modelling with occurrence-only data: if we include a source of sampling bias as a covariate and do not include it when we sample background data, then that covariate would appear to be correlated with presence. They acknowledge this later in their response to my review: "...assuming a sampling bias correlated with poultry density would result in reducing its effect as a risk factor." In other words, the apparent predictive capacity of poultry density is a function of how the authors have constructed the sampling bias for their models. A reader of the manuscript can reasonably ask the question: to what degree are is the model a model of HPAI transmission risk, and to what degree is the model a model of the observation process? The sentence at lines 474-477 is a helpful addition, however the preceding sentence, "Another approach to sampling pseudo-absences would have been to distribute them according to the density of domestic poultry," (line 474) is included without acknowledgement of the flow-on consequence to one of the key findings of the manuscript, that "...intensive chicken population density emerges as a significant predictor..." (line 282). The additional context on the EMPRES-i dataset at line 475-476 ("the locations of outbreaks ... are often georeferenced using place name nomenclatures") is in conflict with the description of the dataset at line 407 ("precise location coordinates"). Ultimately, the choices that the authors have made are entirely defensible through a clear, concise description of model features and assumptions, and precise language to guide the reader through interpretation of results. I am not satisfied that this is provided in the revised manuscript.

The authors have slightly misunderstood my comment on "extrapolation": I referred to "environmental extrapolation" in my review without being particularly explicit about my meaning. By "environmental extrapolation", I meant to ask whether the models were predicting to environments that are outside the extent of environments included in the occurrence data used in the manuscript. The authors appear to have understood this to be a comment on geographic extrapolation, or predicting to areas outside the geographic extent included in occurrence data, e.g.: "For H5Nx post-2020, areas of high predicted ecological suitability, such as Brazil, Bolivia, the Caribbean islands, and Jilin province in China, likely result from extrapolations, as these regions reported few or no outbreaks in the training data" (lines 195-197). Is the model extrapolating in environmental space in these regions? This is unclear. I do not suggest that the authors should carry out further analysis, but the multivariate environmental similarly surface (MESS; see Elith et al., 2010: https://doi.org/10.1111/j.2041-210X.2010.00036.x ) is a useful tool to visualise environmental extrapolation and aid model interpretation.

On the subject of "extrapolation", I am also concerned by the additions at lines 362-370: "...our models extrapolate environmental suitability for H5Nx in wild birds in areas where few or no outbreaks have been reported. This discrepancy may be explained by limited surveillance or underreporting in those regions." The "discrepancy" cited here is a feature of the input dataset, a function of the observation distribution that should be captured in pseudo-absence data. The authors state that Kazakhstan and Central Asia are areas of interest, and that the environments in this region are outside the extent of environments captured in the occurrence dataset, although it is unclear whether "extrapolation" is informed by a quantitative tool like a MESS or judged by some other qualitative test. The authors then cite Australia as an example of a region with some predicted suitability but no HPAI outbreaks to date, however this discussion point is not linked to the idea that the presence of environmental conditions to support transmission need not imply the occurrence of transmission (as in the addition, "...spatial isolation may imply a lower risk of actual occurrences..." at line 214). Ultimately, the authors have not added any clear comment on model uncertainty (e.g., variation between replicated BRTs) as I suggested might be helpful to support their description of model predictions.

All of my criticisms are, of course, applied with the understanding that niche modelling is imperfect for a disease like HPAI, and that data may be biased/incomplete, etc.: these caveats are common across the niche modelling literature. However, if language around the transmission cycle, the niche, and the interpretation of any of the models is imprecise, which I find it to be in the revised manuscript, it undermines all of the science that is presented in this work.

Reviewer #2 (Public review):

Summary:

The geographic range of highly pathogenic avian influenza cases changed substantially around the period 2020, and there is much interest in understanding why. Since 2020 the pathogen irrupted in the Americas and the distribution in Asia changed dramatically. This study aimed to determine which spatial factors (environmental, agronomic and socio-economic) explain the change in numbers and locations of cases reported since 2020 (2020--2023). That's a causal question which they address by applying correlative environmental niche modelling (ENM) approach to the avian influenza case data before (2015--2020) and after 2020 (2020--2023) and separately for confirmed cases in wild and domestic birds. To address their questions they compare the outputs of the respective models, and those of the first global model of the HPAI niche published by Dhingra et al 2016.

ENM is a correlative approach useful for extrapolating understandings based on sparse geographically referenced observational data over un- or under-sampled areas with similar environmental characteristics in the form of a continuous map. In this case, because the selected covariates about land cover, use, population and environment are broadly available over the entire world, modelled associations between the response and those covariates can be projected (predicted) back to space in the form of a continuous map of the HPAI niche for the entire world.

Strengths:

The authors are clear about expected bias in the detection of cases, such geographic variation in surveillance effort (testing of symptomatic or dead wildlife, testing domestic flocks) and in general more detections near areas of higher human population density (because if a tree falls in a forest and there is no-one there, etc), and take steps to ameliorate those. The authors use boosted regression trees to implement the ENM, which typically feature among the best performing models for this application (also known as habitat suitability models). They ran replicate sets of the analysis for each of their model targets (wild/domestic x pathogen variant), which can help produce stable predictions. Their code and data is provided, though I did not verify that the work was reproducible.

The paper can be read as a partial update to the first global model of H5Nx transmission by Dhingra and others published in 2016 and explicitly follows many methodological elements. Because they use the same covariate sets as used by Dhingra et al 2016 (including the comparisons of the performance of the sets in spatial cross-validation) and for both time periods of interest in the current work, comparison of model outputs is possible. The authors further facilitate those comparisons with clear graphics and supplementary analyses and presentation. The models can also be explored interactively at a weblink provided in text, though it would be good to see the model training data there too.

The authors' comparison of ENM model outputs generated from the distinct HPAI case datasets is interesting and worthwhile, though for me, only as a response to differently framed research questions.

Weaknesses:

This well-presented and technically well-executed paper has one major weakness to my mind. I don't believe that ENM models were an appropriate tool to address their stated goal, which was to identify the factors that "explain" changing HPAI epidemiology.

Here is how I understand and unpack that weakness:

(1) Because of their fundamentally correlative nature, ENMs are not a strong candidate for exploring or inferring causal relationships.

(2) Generating ENMs for a species whose distribution is undergoing broad scale range change is complicated and requires particular caution and nuance in interpretation (e.g., Elith et al, 2010, an important general assumption of environmental niche models is that the target species is at some kind of distributional equilibrium (at time scales relevant to the model application). In practice that means the species has had an opportunity to reach all suitable habitats and therefore its absence from some can be interpreted as either unfavourable environment or interactions with other species). Here data sets for the response (N5H1 or N5Hx case data in domestic or wild birds ) were divided into two periods; 2015--2020, and 2020--2023 based on the rationale that the geographic locations and host-species profile of cases detected in the latter period was suggestive of changed epidemiology. In comparing outputs from multiple ENMs for the same target from distinct time periods the authors are expertly working in, or even dancing around, what is a known grey area, and they need to make the necessary assumptions and caveats obvious to readers.

(3) To generate global prediction maps via ENM, only variables that exist at appropriate resolution over the desired area can be supplied as covariates. What processes could influence changing epidemiology of a pathogen and are their covariates that represent them? Introduction to a new geographic area (continent) with naive population, immunity in previously exposed populations, control measures to limit spread such as vaccination or destruction of vulnerable populations or flocks? Might those control measures be more or less likely depending on the country as a function of its resources and governance? There aren't globally available datasets that speak to those factors, so the question is not why were they omitted but rather was the authors decision to choose ENMs given their question justified? How valuable are insights based on patterns of correlation change when considering different temporal sets of HPAI cases in relation to a common and somewhat anachronistic set of covariates?

(4) In general the study is somewhat incoherent with respect to time. Though the case data come from different time periods, each response dataset was modelled separately using exactly the same covariate dataset that predated both sets. That decision should be understood as a strong assumption on the part of the authors that conditions the interpretation: the world (as represented by the covariate set) is immutable, so the model has to return different correlative associations between the case data and the covariates to explain the new data. While the world represented by the selected covariates *may* be relatively stable (could be statistically confirmed), what about the world not represented by the covariates (see point 3)?

References:

Dhingra et al, 2016, Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation, eLife 5, https://doi.org/10.7554/eLife.19571

Elith, J., Kearney, M., & Phillips, S. (2010). The art of modelling range‐shifting species. Methods in Ecology and Evolution, 1(4), 330-342.

Reviewer #1 (Public review):

In recent years, our understanding of the nuclear steps of the HIV-1 life cycle has made significant advances. It has emerged that HIV-1 completes reverse transcription in the nucleus and that the host factor CPSF6 forms condensates around the viral capsid. The precise function of these CPSF6 condensates is under investigation, but it is clear that the HIV-1 capsid protein is required for their formation. This study by Tomasini et al. investigates the genesis of the CPSF6 condensates induced by HIV-1 capsid, what other co-factors may be required and their relationship with nuclear speckels (NS). The authors show that disruption of the condensates by the drug PF74, added post-nuclear entry, blocks HIV-1 infection, which supports their functional role. They generated CPSF6 KO THP-1 cell lines, in which they expressed exogenous CPSF6 constructs to map by microscopy and pull down assays the regions critical for the formation of condensates. This approach revealed that the LCR region of CPSF6 is required for capsid binding but not for condensates whereas the FG region is essential for both. Using SON and SRRM2 as markers of NS, the authors show that CPSF6 condensates precede their merging with NS but that depletion of SRRM2, or SRRM2 lacking the IDR domain, delays the genesis of condensates, which are also smaller.

The study is interesting and well conducted and defines some characteristics of the CPSF6-HIV-1 condensates. Their results on the NS are valuable. The data presented are convincing.

I have two main concerns.

Firstly, the functional outcome of the various protein mutants and KOs is not evaluated. Although Figure 1 shows that disruption of the CPSF6 puncta by PF74 impairs HIV-1 infection, it is not clear if HIV-1 infection is at all affected by expression of the mutant CPSF6 forms (and SRRM2 mutants), or KO/KD of the various host factors. The cell lines are available, and so it should be possible to measure HIV-1 infection and reverse transcription. Secondly, the authors have not assessed if the effects observed on the NS impact HIV-1 gene expression, which would be interesting to know given that NS are sites of highly active gene transcription. With the reagents at hand, it should be possible to investigate this too.

Comments on revisions:

The revised version of this paper addresses my concerns.

Reviewer #2 (Public review):

Summary:

HIV-1 infection induces CPSF6 aggregates in the nucleus that contain the viral protein CA. The study of the functions and composition of these nuclear aggregates have raised considerable interest in the field, and they have emerged as sites in which reverse transcription is completed and in the proximity of which viral DNA becomes integrated. In this work, the authors have mutated several regions of the CPSF6 protein to identify the domains important for nuclear aggregation, in addition to the already-known FG-region; they have characterized the kinetics of fusion between CPSF6 aggregates and SC35 nuclear speckles and have determined the role of two nuclear speckle components in this process (SRRM2, SUN2).

Strengths:

The work examines systematically the domains of CPSF6 of importance for nuclear aggregate formation in an elegant manner in which these mutants complement an otherwise CPSF6-KO cell line. In addition, this work evidences a novel role for the protein SRRM2 in HIV-induced aggregate formation, overall advancing our comprehension of the components required for their formation and regulation.

Reviewer #3 (Public review):

In this study, the authors investigate the requirements for the formation of CPSF6 puncta induced by HIV-1 under a high multiplicity of infection conditions. Not surprisingly, they observe that mutation of the Phe-Gly (FG) repeat responsible for CPSF6 binding to the incoming HIV-1 capsid abrogates CPSF6 punctum formation. Perhaps more interestingly, they show that the removal of other domains of CPSF6, including the mixed-charge domain (MCD), does not affect the formation of HIV-1-induced CPSF6 puncta. The authors also present data suggesting that CPSF6 puncta form individual before fusing with nuclear speckles (NSs) and that the fusion of CPSF6 puncta to NSs requires the intrinsically disordered region (IDR) of the NS component SRRM2. While the study presents some interesting findings, there are some technical issues that need to be addressed and the amount of new information is somewhat limited. Also, the authors' finding that deletion of the CPSF6 MCD does not affect the formation of HIV-1-induced CPSF6 puncta contradicts recent findings of Jang et al. (https://doi.org/10.1093/nar/gkae769).

Comments on revisions:

The authors have generally addressed my comments.

Reviewer #1 (Public review):

Zhu and colleagues used high-density Neuropixel probes to perform laminar recordings in V1 while presenting either small stimuli that stimulated the classical receptive field (CRF) or large stimuli whose border straddled the RF to provide nonclassical RF (nCRF) stimulation. Their main question was to understand the relative contribution of feedforward (FF), feedback (FB), and horizontal circuits to border ownership (B_own ), which they addressed by measuring cross-correlation across layers. They found differences in cross-correlation between feedback/horizontal (FH) and input layers during CRF and nCRF stimulation.

Comments on revisions:

In the revision, the authors have added a paragraph in the Discussion to address the question of layers 2/3 neurons leading layer 4 neurons, and have provided answers to the questions in the public review without making substantial changes in the paper. However, there were several other recommendations, which I am not sure why were not considered. I am adding those again below.

* For CRF stimulation, the zero lag between 4C and 4A/B with layer 5/6 (Figure 3D last two columns on the right) was surprising to me. I just felt that this could be because layer 6 may also be getting FF inputs. Perhaps better not to club layer 5 with 6, as mentioned earlier also.

* Interpreting the nCRF delays, with often negative delays, was very challenging for me. For example, 4C -> 5/6 (third column in Figure 3) has a significantly negative peak (although that does not show up in statistical analysis because it seems to be a signed test to just test if the median was greater than zero, not if the median was different from zero; line 285). What is the interpretation here? Are spikes in 5/6 causing spikes in 4C (which, as mentioned earlier, would require anatomical projections from 5/6 to 4C)? On the other hand, if FB inputs arrive in 5/6 but there are no inputs going to 4C, then why should there even be a significant cross-correlation?

The only explanation I could think of is somehow an alignment of inputs in these two layers such that FH inputs come in Layer 5/6 just before FF inputs arrive in 4C, each causing a spike in a neuron in each layer which are otherwise not anatomically interconnected. But this would require both a very precise temporal coupling between FF and FH inputs arriving in these areas AND neurons in layer 5/6 which very strongly respond to FH stimulation (I thought that FH inputs are mainly modulatory and not as strong). Anyway, it would be good to see some cross correlation functions which have a negative lag (all examples in Fig 3B has positive or zero lag).

* I think cross-correlation analysis would have been useful if there was data from a feedback area (say V2). In its absence, perhaps latency analysis (by just comparing the PSTH) could have revealed something interesting, given that the hypothesis is about differences in the timings in FH versus FF inputs. Do PSTHs across layers show the type of differences that are being claimed (e.g. in line 295-297)?

* Line 262-63: "Notably, the rates were nearly identical under the two stimulus conditions" - I would have thought CRF stimulation would produce higher rates. Can the authors explain this?

* Line 174-175: Isn't the proportion of border ownership cells in layer 4C higher than one would expect under the assumption that nCRF effects are mediated by horizontal and feedback connections which layer 4C does not receive? Can authors explain?

* Figure 3D: it would also be good to show the heatmaps stacked up in the increasing order of the interelectrode distance of the pairs so that it will be easy to see how the peak lag changes with distance as well.

* It will be good to show the shift in peak lag and CCG asymmetry between CRF and nCRF conditions for the same pairs, using a violin or bar plot with lines connecting each pair in Figure 3.

* Line 594, 603, 628 and 630: What procedure was used to determine the size, location of the CRF, and optimal orientation manually online?

* Line 733-734: Although a reference is cited, please explicitly mention the rationale for keeping the peak lag cutoff at 10 ms.

* It is unclear why a grating was used for the CRF condition, instead of just having the portion of the stimulus within the RF for the nCRF condition, as the comparisons for FHi with FF are with different FF drives in each case.

* Figure 5 - the scatter is enormous, can you please provide the R2 values?

Reviewer #2 (Public review):

Summary:

The authors present a study of how modulatory activity from outside the classical receptive field (cRF) differs from cRF stimulation. They study neural activity across the different layers of V1 in two anesthetized monkeys using Neuropixels probes. The monkeys are presented with drifting gratings and border-ownership tuning stimuli. They find that border-ownership tuning is organized into columns within V1, which is unexpected and exciting, and that the flow of activity from cell-to-cell (as judged by cross-correlograms between single units) is influenced by the type of visual stimulus: border-ownership tuning stimuli vs. drifting-grating stimuli.

Strengths:

The questions addressed by the study are of high interest, and the use of Neuropixels probes yields extremely high numbers of single-units and cross-correlation histograms (CCHs) which makes the results robust. The study is well-described.

Comments on revisions:

The results are interesting and seem robust. However, several of my main points were not addressed. The authors do not analyze or discuss the problem the border ownership stimuli do uniquely isolate feedback from feedforward influences. Here are my remaining points/recommendations:

(1) In my previous review I indicated that the border-ownership signal also provides a strong feedforward drive, a black-white edge, in addition to the border ownership signal. Calling this a "nCRF stimulus" is a misnomer. Please correct this terminology and replace it by something that is appropriate, e.g. changing it into "grating stimulation" (instead of CRF stimulation) and BO-stimulation (instead of nCRF stimulation).

(2) In my previous review I asked if the initial response for the border ownership stimulus show the feedforward signature. It is unclear to me why this suggestions did not lead to an analysis of the feedforward response. I repeat the text from my previous review: "The authors state that they did not look at cross-correlations during the initial response, but if they do, do they see the feedforward-dominated pattern? The jitter CCH analysis might suffice in correcting for the response transient." Can the authors address this point?

(3) In my previous review I asked the authors show the average time course of the response elicited by preferred and nonpreferred border ownership stimuli across all significant neurons. It remains unclear why this plot was not provided.

Reviewer #3 (Public review):

Summary:

The paper by Zhu et al is on an important topic in visual neuroscience, the emergence in the visual cortex of signals about figure and ground. This topic also goes by the name border ownership. The paper utilizes modern recording techniques very skillfully to extend what is known about border ownership. It offers new evidence about the prevalence of border ownership signals across different cortical layers in V1 cortex. Also, it uses pairwise cross correlation to study signal flow under different conditions of visual stimulation that include the border ownership paradigm.

Strengths: The paper's strengths are results of its use of multi-electrode probes to study border ownership in many neurons simultaneously across the cortical layers in V1. Also it provides new useful data about the dynamics of interaction of signals from the non-classical receptive field (NCRF) and the Classical receptive field (CRF).

Weaknesses:

The paper's weakness is that it does not challenge consensus beliefs about mechanisms. Also, the paper combines data about border ownership with data about the NCRF without making it clear how they are similar or different.

Critique:

The border ownership data on V1 offered in the paper replicate experimental results obtained by Zhou and von der Heydt (2000) and confirm the earlier results. The incremental addition is that the authors found border ownership in all cortical layers of V1, extending Zhou and von der Heydt's results that were only about layer 2/3 in V2 cortex. This is an interesting new result using the same stimuli but new measurement techniques.

The cross-correlation results show that the pattern of the cross correlogram (CCG) is influenced by the visual pattern being presented. However, in the initial submitted ms. the results were not analyzed mechanistically, and the interpretation was unclear. For instance, the authors show in Figure 3 (and in Figure S2) that the peak of the CCG can indicate layer 2/3 excites layer 4C when the visual stimulus is the border ownership test pattern, a large square 8 deg on a side. More than one reviewer asked, " how can layer 2/3 excite layer 4C"? . In the revised ms. the authors added a paragraph to the Discussion to respond to the reviewers about this point. The authors could provide an even better response to the reviewers by emphasizing that, consistently, layer 5/6 neurons lead neurons in layer 4, and for the CRF pattern and even more when the NCRF patterns are used.

The problems in understanding the CCG data are indirectly caused by the lack of a critical analysis of what is happening in the responses that reveal the border ownership signals, as in Fig.2. Let's put it bluntly--are border ownership signals excitatory or inhibitory? As the authors pointed out in their rebuttal, Zhang and von der Heydt (2010, JNS) did experiments to answer this question but I do not agree with the authors rebuttal letter about what Zhang and von der Heydt (2010) reported. If you examine Zhang and von der Heydt's Figure 6, you see that the major effect of stimulating border ownership neurons is suppression from the non-preferred side. That result is consistent with many papers on the NCRF (many cited by the authors) that indicate that it is mostly suppressive. That experimental fact about border ownership should be mentioned in the present paper.

What I should have pointed out in the first round, but didn't understand it then, is that there is a disconnect between the the border ownership laminar analysis (Figure 2) and the laminar correlations with CCGs (Figures 3-5) because the CCGs are not limited to border ownership neurons (or at least we are not told they were limited to them). So the CCG results are not mostly about border ownership--they are about the difference between signal flow in responses to small drifting Gabor patterns vs big flashed squares. Since only 21% of all recorded neurons were border ownership neurons, it is likely that most of the CCG statistics is based on neurons that do not show border ownership. Nevertheless, Figures 3 and 4 are very useful for the study of signal flow in the NCRF. It wasn't clear to me and I think the authors could make it clearer what those figures are about.<br /> And I wonder if it might be possible to make a stronger link with border ownership by restricting the CCG analysis to pairs of neurons in which one neuron is a border ownership neuron. Are there enough data?

My critique of the CCG analysis applies to Figure 5 also. That figure shows a weak correlation of CCG asymmetry with Border Ownership Index. Perhaps a stronger correlation might be present if the population were restricted to the much smaller population of neuron pairs that had at least one border ownership neuron.

Reviewer #1 (Public review):

Using several zebrafish reporter lines, the authors characterized immune cells in the adult zebrafish brain, identifying a population of DC-like cells with distinct regional distribution and transcriptional profiles. These cells were distinct from microglia and other macrophages, closely resembling murine cDC1s. Analysis of different mutants revealed that this DC population depends on Irf8, Batf3 and Csf1rb, but not Csf1ra.

This elegantly designed study provides compelling evidence for additional heterogeneity among brain mononuclear phagocytes in zebrafish, encompassing microglia, macrophages, and DC-like cells. It advances our understanding of the immune landscape in the zebrafish brain and facilitates better distinction of these cell types from microglia.

Reviewer #2 (Public review):

The authors made an atlas of single-cell transcriptome of on a pure population of leukocytes isolated from the brain of adult Tg(cd45:DsRed) transgenic animals by flow cytometry. Seven major leukocyte populations were identified, comprising microglia, macrophages, dendritic-like cells, T cells, natural killer cells, innate lymphoid-like cells and neutrophils. Each cluster was analyzed to characterize subclusters. Among lymphocytes, in addition to 2 subclusters expressing typical T cell markers, a group of il4+ il13+ gata3+ cells was identified as possible ILC2. This hypothesis is supported by the presence of this population in rag2KO fish, in which the frequency of lck and zap70+ cells is strongly reduced. The use of KO lines for such validations is a strength of this work (and the zebrafish model).

The subcluster analysis of mpeg1.1 + myeloid cells identified 4 groups of microglial cells, one novel group of macrophage like cells (expressing s100a10b, sftpbb, icn, fthl27, anxa5b, f13a1b and spi1b), and several groups of DC like cells expressing the markers siglec15l, ccl19a.1, ccr7, id2a, xcr1a.1, batf3, flt3, chl1a and hepacam2.Combining these new markers and transgenic reporter fish lines, the authors then clarified the location of leukocyte subsets within the brain, showing for example that DC-like cells stand as a parenchymal population along with microglia. Reporter lines were also used to perform detailed analysis of cell subsets, and cross with a batf3 mutant demonstrated that DC like cells are batf3 dependent, which was similar to mouse and human cDC1. Finally, analysis of classical mononuclear phagocyte deficient zebrafish lines showed they have reduced numbers of microglia but exhibit distinct DC-like cell phenotypes. A weakness of this study is that it is mainly based on FACS sorting, which might modify the proportion of different subtypes.

This atlas of zebrafish brain leukocytes is an important new resource to scientists using the zebrafish models for neurology, immunology and infectiology, and for those interested in the evolution of brain and immune system.

Reviewer #3 (Public review):

Rovira, et al., aim to characterize immune cells in the brain parenchyma and identify a novel macrophage population referred to as "dendritic-like cells". They use a combination of single-cell transcriptomics, immunohistochemistry, and genetic mutants to conclude the presence of this "dendritic-like cell" population in the brain. The strength of this manuscript is the identification of dendritic cells in the brain, which are typically found in the meningeal layers and choroid plexus. In addition, Rovira, et al., findings are supported by the findings of the Wen lab and a recent Cell Reports paper. Congratulations on the nice work!

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors describe a new computational method (SegPore), which segments the raw signal from nanopore direct RNA-Seq data to improve the identification of RNA modifications. In addition to signal segmentation, SegPore includes a Gaussian Mixture Model approach to differentiate modified and unmodified bases. SegPore uses Nanopolish to define a first segmentation, which is then refined into base and transition blocks. SegPore also includes a modification prediction model that is included in the output. The authors evaluate the segmentation in comparison to Nanopolish and Tombo (RNA002) as well as f5c and Uncalled 4 (RNA004), and they evaluate the impact on m6A RNA modification detection using data with known m6A sites. In comparison to existing methods, SegPore appears to improve the ability to detect m6A, suggesting that this approach could be used to improve the analysis of direct RNA-Seq data.

Strengths:

SegPore address an important problem (signal data segmentation). By refining the signal into transition and base blocks, noise appears to be reduced, leading to improved m6A identification at the site level as well as for single read predictions. The authors provide a fully documented implementation, including a GPU version that reduces run time. The authors provide a detailed methods description, and the approach to refine segments appears to be new.

Weaknesses:

The authors show that SegPore reduces noise compared to other methods, however the improvement in accuracy appears to be relatively small for the task of identifying m6A. To run SegPore, the GPU version is essential, which could limit the application of this method in practice.

Reviewer #2 (Public review):

Summary:

The work seeks to improve detection of RNA m6A modifications using Nanopore sequencing through improvements in raw data analysis. These improvements are said to be in the segmentation of the raw data, although the work appears to position the alignment of raw data to the reference sequence and some further processing as part of the segmentation, and result statistics are mostly shown on the 'data-assigned-to-kmer' level.<br /> As such, the title, abstract and introduction stating the improvement of just the 'segmentation' does not seem to match the work the manuscript actually presents, as the wording seems a bit too limited for the work involved.<br /> The work itself shows minor improvements in m6Anet when replacing Nanopolish' eventalign with this new approach, but clear improvements in the distributions of data assigned per kmer. However, these assignments were improved well enough to enable m6A calling from them directly, both at site-level and at read-level.

A large part of the improvements shown appear to stem from the addition of extra, non-base/kmer specific, states in the segmentation/assignment of the raw data, removing a significant portion of what can be considered technical noise for further analysis. Previous methods enforced assignment of (almost) all raw data, forcing a technically optimal alignment that may lead to suboptimal results in downstream processing as datapoints could be assigned to neighbouring kmers instead, while random noise that is assigned to the correct kmer may also lead to errors in modification detection.

For an optimal alignment between the raw signal and the reference sequence, this approach may yield improvements for downstream processing using other tools.<br /> Additionally, the GMM used for calling the m6A modifications provides a useful, simple and understandable logic to explain the reason a modification was called, as opposed to the black models that are nowadays often employed for these types of tasks.

Weaknesses:

The manuscript suggests the eventalign results are improved compared to Nanopolish. While this is believably shown to be true (Table 1), the effect on the use case presented, downstream differentiation between modified and unmodified status on a base/kmer, is likely limited for during downstream modification calling the noisy distributions are often 'good enough'. E.g. Nanopolish uses the main segmentation+alignment for a first alignment and follows up with a form of targeted local realignment/HMM test for modification calling (and for training too), decreasing the need for the near-perfect segmentation+alignment this work attempts to provide. Any tool applying a similar strategy probably largely negates the problems this manuscript aims to improve upon. Should a use-case come up where this downstream optimisation is not an option, SegPore might provide the necessary improvements in raw data alignment.

Appraisal:

The authors have shown their methods ability to identify noise in the raw signal and remove their values from the segmentation and alignment, reducing its influences for further analyses. Figures directly comparing the values per kmer do show a visibly improved assignment of raw data per kmer. As a replacement for Nanopolish' eventalign it seems to have a rather limited, but improved effect, on m6Anet results. At the single read level modification modification calling this work does appear to improve upon CHEUI.

Impact:

With the current developments for Nanopore based modification calling largely focusing on Artificial Intelligence, Neural Networks and the likes, improvements made in interpretable approaches provide an important alternative that enables deeper understanding of the data rather than providing a tool that plainly answers the question of wether a base is modified or not, without further explanation. The work presented is best viewed in context of a workflow where one aims to get an optimal alignment between raw signal data and the reference base sequence for further processing. For example, as presented, as a possible replacement for Nanopolish' eventalign. Here it might enable data exploration and downstream modification calling without the need for local realignments or other approaches that re-consider the distribution of raw data around the target motif, such as a 'local' Hidden Markov Model or Neural Networks. These possibilities are useful for a deeper understanding of the data and further tool development for modification detection works beyond m6A calling.

Reviewer #3 (Public review):

Summary:

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

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

Strengths:

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

Weaknesses:

However, the manuscript has a significant drawback in its current version. The most recent nanopore RNA base callers can distinguish between different ribonucleotide modifications, however, SegPore has not been benchmarked against these models.

The manuscript would be strengthened by benchmarking against the rna004_130bps_hac@v5.1.0 and rna004_130bps_sup@v5.1.0 dorado models, which are reported to detect m5C, m6A_DRACH, inosine_m6A and PseU.

A clear demonstration that SegPore also outperforms the newer RNA base caller models will confirm the utility of this method.

Reviewer #1 (Public review):

Summary:

The authors attempted to clarify the impact of N protein mutations on ribonucleoprotein (RNP) assembly and stability using analytical ultracentrifugation (AUC) and mass photometry (MP). These complementary approaches provide a more comprehensive understanding of the underlying processes. Both SV-AUC and MP results consistently showed enhanced RNP assembly and stability due to N protein mutations.

The overall research design appears well planned, and the experiments were carefully executed.

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

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

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

Weaknesses:

(1) The arrangement of N dimers around LRS helices is presented in Figure 1C, but the text concedes that "the arrangement sketched in Figure 1C is not unique" (lines 144-146) and that AF3 modeling attempts yielded "only inconsistent results" (line 149).<br /> The authors should therefore present the models more cautiously as hypotheses instead. Additional alternative arrangements should be included in the Supplementary Information, so the readers do not over-interpret a single schematic model.

(2) Negative-stained EM fibrils (Figure 2A) and CD spectra (Figure 2B) are presented to argue that P13L promotes β-sheet self-association. However, the claim could benefit from more orthogonal validation of β-sheet self-association. Additional confirmation via FTIR spectra or ThT fluorescence could be used to further distinguish structured β-sheets from amorphous aggregation.

(3) In the main text, the authors alternate between emphasizing non-covalent effects ("a major effect of the cysteines already arises in reduced conditions without any covalent bonds," line 576) and highlighting "oxidized tetrameric N-proteins of N:G214C and N:G215C can be incorporated into RNPs". Therefore, the biological relevance of disulfide redox chemistry in viral assembly in vivo remains unclear. Discussing cellular redox plausibility and whether the authors' oxidizing conditions are meant as a mechanistic stress test rather than physiological mimicry could improve the interpretation of these results.

The paper could benefit if the authors provide a summary figure or table contrasting reduced vs. oxidized conditions for G214C/G215C mutants (self-association, oligomerization state, RNP stability). Explicitly discuss whether disulfides are likely to form in infected cells.

(4) VLP assays (Figure 7) show little enhancement for P13L or G215C alone, whereas Figure 8 shows that P13L provides clear fitness advantages. This discrepancy is acknowledged but not reconciled with any mechanistic or systematic rationale. The authors should consider emphasizing the limitations of VLP assays and the sources of the discrepancy with respect to Figure 8.

(5) Figures 5 and 6 are dense, and the several overlays make it hard to read. The authors should consider picking the most extreme results to make a point in the main Figure 5 and move the other overlays to the Supplementary. Additionally, annotating MP peaks directly with "2×, 4×, 6× subunits" can help non-experts.

(6) The paper has several names and shorthand notations for the mutants, making it hard to keep up. The authors could include a table that contains mutation keys, with each shorthand (Ancestral, Nο/No, Nλ, etc.) mapped onto exact N mutations (P13L, Δ31-33, R203K/G204R, G214C/G215C, etc.). They could then use the same glyphs (Latin vs Greek) consistently in text and figure labels.

(7) The EM fibrils (Figure 2A) and CD spectra (Figure 2B) were collected at mM peptide concentrations. These are far above physiological levels and may encourage non-specific aggregation. Similarly, the authors mention" ultra-weak binding energies that require mM concentrations to significantly populate oligomers". On the other hand, the experiments with full-length protein were performed at concentrations closer to biologically relevant concentrations in the micromolar range. While I appreciate the need to work at high concentrations to detect weak interactions, this raises questions about physiological relevance. Specifically:

a) Could some of the fibril/β-sheet features attributed to P13L (Figure 2A-C) reflect non-specific aggregation at high concentrations rather than bona fide self-association motifs that could play out in biologically relevant scenarios?

b) How do the authors justify extrapolating from the mM-range peptide behaviors to the crowded but far lower effective concentrations in cells?

The authors should consider adding a dedicated section (either in Methods or Discussion) justifying the use of high concentrations, with estimation of local concentrations in RNPs and how they compare to the in vitro ranges used here. For concentration-dependent phenomena discussed here, it is vital to ensure that the findings are not artefacts of non-physiological peptide aggregation..

Reviewer #1 (Public review):

As presented in this short report, the focus is to only establish that acetohydroxyacid synthase II can have underground activity to generate 2-ketobutyrate (from glyoxylate and pyruvate). Additionally, the gene that encodes this protein has an inactivating point mutation in the lab strain of E. coli. In strains lacking the conventional Ile biosynthesis pathway, this enzyme gets reactivated (after short-term laboratory evolution) and putatively can contribute to producing sufficient 2-ketobutyrate, which can feed into Ile production. This is clearly a very interesting observation and finding, and the paper focuses on this single point.

However, the manuscript as it currently stands is 'minimal', and just barely shows that this reaction/pathway is feasible. There is no characterization of the restored enzyme's activity, rate, or specificity. Additionally, there is no data presented on how much isoleucine can be produced, even at saturating concentrations of glyoxylate or pyruvate. This would greatly benefit from more rigorous characterization of this enzyme's activity and function, as well as better demonstration of how effective this pathway is in generating 2-ketobutyrate (and then its subsequent condensation with pyruvate).

Reviewer #2 (Public review):

Summary:

The manuscript by Rainaldi et al. reports a new sub-pathway for isoleucine biosynthesis by demonstrating the promiscuous activity of the native enzyme acetohydroxyacid synthase II (AHAS II). AHAS-II is primarily known to catalyze the condensation of 2-ketobutyrate (2KB) with pyruvate to form a further downstream intermediate, AHB, in the isoleucine biosynthesis pathway. However, the catalysis of pyruvate and glyoxylate condensation to produce 2KB via the ilvG encoded AHAS II is reported in this manuscript for the first time.

Using an isoleucine/2KB auxotrophic E. coli strain, the authors report (i) repair of the inactivating frameshift mutation in the ilvG gene, which encodes AHAS-II, supports growth in glyoxylate-supplemented media, (ii) the promiscuity of AHAS-II in glyoxylate and pyruvate condensation, resulting in the formation of isoleucin precursors (2-KB), aiding the biosynthesis of isoleucine, and (iii) comparable efficiency of the recursive AHAS-II route to the canonical routes of isoleucin biosynthesis via computational Flux-based analysis.

Strengths:

The authors have used laboratory evolution to uncover a non-canonical metabolic route. The metabolomics and FBA have been used to strengthen the claim.

Weaknesses:

While the manuscript proposes an interesting metabolic route for the isoleucine biosynthesis, the data lack key controls, biological replicates, and consistency. The figures and methods are presented inadequately. In the current state, the data fails to support the claims made in the manuscript.

Reviewer #1 (Public review):

Summary:

The study explored the biomechanics of kangaroo hopping across both speed and animal size to try and explain the unique and remarkable energetics of kangaroo locomotion.

Strengths:

Brings kangaroo locomotion biomechanics into the 21st century. Remarkably difficult project to accomplish. Excellent attention to detail. Clear writing and figures.

General Comments

This is a very impressive tour de force by an all-star collaborative team of researchers. The study represents a tremendous leap forward (pun intended) in terms of our understanding of kangaroo locomotion. Some might wonder why such an unusual species is of much interest. But, in my opinion, the classic study by Dawson and Taylor in 1973 of kangaroos launched the modern era of running biomechanics/energetics and applies to varying degrees to all animals that use bouncing gaits (running, trotting, galloping and of course hopping). The puzzling metabolic energetics findings of Dawson & Taylor (little if any increase in metabolic power despite increasing forward speed) remain a giant unsolved problem in comparative locomotor biomechanics and energetics. It is our "dark matter problem".

This study is certainly a hop towards solving the problem. The study clearly shows that the ankle and to a lesser extent the mtp joint are where the action is. They show in great detail by how much and by what means the ankle joint tendons experience increased stress at faster forward speeds. Since these were zoo animals, direct measures were not feasible, but the conclusion that the tendons are storing and returning more elastic energy per hop at faster speeds is solid. The conclusion that net muscle work per hop changes little from slow to fast forward speeds is also solid. Doing less muscle work can only be good if one is trying to minimize metabolic energy consumption. However, to achieve the greater tendon stresses, there must be greater muscle forces. Unless one is willing to reject the premise of the cost of generating force hypothesis, that is an important issue to confront. Further, the present data support the Kram & Dawson finding of decreased contact times at faster forward speeds. Kram & Taylor and subsequent applications of (and challenges to) their approach support the idea that shorter contact times (tc) require recruiting more expensive muscle fibers and hence greater metabolic costs. The present authors have clarified that this study has still not tied up the metabolic energetics across speed problem and they now point out how the group is now uniquely and enviably poised to explore the problem more using a dynamic SIMM model that incorporates muscle energetics.

Reviewer #1 (Public review):

This is a contribution to the field of developmental bioelectricity. How do changes of resting potential at the cell membrane affect downstream processes? Zhou et al. reported in 2015 that phosphatidylserine and K-Ras cluster upon plasma membrane depolarization and that voltage-dependent ERK activation occurs when constitutively active K-RasG12V mutants are overexpressed. In this paper, the authors advance the knowledge of this phenomenon by showing that membrane depolarization up-regulates mitosis and that this process is dependent on voltage-dependent activation of ERK. ERK activity's voltage-dependence is derived from changes in the dynamics of phosphatidylserine in the plasma membrane and not by extracellular calcium dynamics. This paper reports an interesting and important finding. It is somewhat derivative of Zhou et al., 2015 (https://www.science.org/doi/full/10.1126/science.aaa5619). The main novelty seems to be that they find quantitatively different conclusions upon conducting similar experiments, albeit with a different cell line (U2OS) than those used by Zhou et al. Sasaki et al. do show that increased K+ levels increase proliferation, which Zhou et al. did not look at. The data presented in this paper are a useful contribution to a field often lacking such data.

Reviewer #2 (Public review):

Sasaki et al. use a combination of live-cell biosensors and patch-clamp electrophysiology to investigate the effect of membrane potential on the ERK MAPK signaling pathway, and probe associated effects on proliferation. This is an effect that has long been proposed, but a convincing demonstration has remained elusive, because it is difficult to perturb membrane potential without disturbing other aspects of cell physiology in complex ways. The time-resolved measurements here are a nice contribution to this question, and the perforated patch clamp experiments with an ERK biosensor are fantastic - they come closer to addressing the above difficulty of perturbing voltage than any prior work. It would have been difficult to obtain these observations with any other combination of tools.

Comments on previous revisions:

The authors have done a good job addressing the comments on the previous submission.

Reviewer #3 (Public review):

Summary:

This paper demonstrates that membrane depolarization induces a small increase in cell entry into mitosis. Based on previous work from another lab, the authors propose that ERK activation might be involved. They show convincingly using a combination of assays that ERK is activated by membrane depolarization. They show this is Ca2+ independent and is a result of activation of the whole K-Ras/ERK cascade which results from changed dynamics of phosphatidylserine in the plasma membrane that activates K-Ras. Although the activation of the Ras/ERK pathway by membrane depolarization is not new, linking it to an increase in cell proliferation is novel.

Strengths:

A major strength of the study is the use of different techniques - live imaging with ERK reporters, as well as Western blotting to demonstrate ERK activation as well as different methods for inducing membrane depolarization. They also use a number of different cell lines. Via Western blotting the authors are also able to show that the whole MAPK cascade is activated.

Weaknesses:

In the previous round of revisions, the authors addressed the issues with Figure 1, and the data presented are much clearer. The authors did also attempt to pinpoint when in the cell cycle ERK is having its activity, but unfortunately, this was not conclusive.

Reviewer #1 (Public review):

Summary:

This paper presents results from four independent experiments, each of them testing for rhythmicity in auditory perception. The authors report rhythmic fluctuations in discrimination performance at frequencies between 2 and 6 Hz. The exact frequency depends on the ear and experimental paradigm, although some frequencies seem to be more common than others.

Strengths:

The first sentence in the abstract describes the state of the art perfectly: "Numerous studies advocate for a rhythmic mode of perception; however, the evidence in the context of auditory perception remains inconsistent". This is precisely why the data from the present study is so valuable. This is probably the study with the highest sample size (total of > 100 in 4 experiments) in the field. The analysis is very thorough and transparent, due to the comparison of several statistical approaches and simulations of their sensitivity. Each of the experiments differs from the others in a clearly defined experimental parameter, and the authors test how this impacts auditory rhythmicity, measured in pitch discrimination performance (accuracy, sensitivity, bias) of a target presented at various delays after noise onset.

Weaknesses:

The authors find that the frequency in auditory perception changes between experiments. Possible reasons for such differences are described, but they remain difficult to interpret, as it is unclear whether they merely reflect some natural variability (independent of experimental parameters) or are indeed driven by the specific experimental paradigm (and therefore replicable).

Therefore, it remains to be shown whether there is any systematic pattern in the results that allows conclusions about the roles of different frequencies.

Reviewer #2 (Public review):

Summary:

The current study aims to shed light on why previous work on perceptual rhythmicity has led to inconsistent results. They propose that the differences may stem from conceptual and methodological issues. In a series of experiments, the current study reports perceptual rhythmicity in different frequency bands that differ between different ear stimulations and behavioral measures. The study suggests challenges regarding the idea of universal perceptual rhythmicity in hearing.

Strengths:

The study aims to address differences observed in previous studies about perceptual rhythmicity. This is important and timely because the existing literature provides quite inconsistent findings. Several experiments were conducted to assess perceptual rhythmicity in hearing from different angles. The authors use sophisticated approaches to address the research questions. The manuscript has greatly improved after the revision.

Weaknesses:

Additional variance: In several experiments, a fixation cross preceded - at a variable interval - the onset of the background noise that aimed to reset the phase of an ongoing oscillation. There is the chance that the fixation cross also resets the phase, potentially adding variance to the data. In addition, the authors used an adaptive procedure during the experimental blocks such that the stimulus intensity was adjusted throughout. There is good reason for doing so, but it means that correctly identified/discriminated targets will on average have a greater stimulus intensity. This may add variance to the data. These two aspects may potentially contribute to the observation of weak perceptual rhythmicity.

Figures: The text in Figures 4 and 6 is small. I think readers would benefit from a larger font size. Moreover, Figure 1A is not very intuitive. Perhaps it could be made clearer. The new Figure 5 was not discussed in the text. I wonder whether analyses with traditional t-tests could be placed in the supplements.

50% significant samples: The authors consider 50% of significant bootstrapped samples robust. For example: "This revealed that the above‐mentioned effects prevail for at least 50% of the simulated experiments, corroborating their robustness within the participant sample". Many of the effects have even lower than 50% of significant samples. It is a matter of opinion of what is robust or not, but I think combined with the overall variable nature of the effects in different frequency bands and ears etc. leaves more the impression that the effects are not very robust. I think the authors state it correctly in the last sentence of the first paragraph of the discussion: "At the same time the prevalence of significant effects in random samples of participants were mostly below 50%, raising questions as to the ubiquity of such effects." I think the authors should update the abstract in this regard to avoid that readers who only read the abstract get the wrong impression about the robustness of the effects. It is not clear to me if the same study (using the same conditions) was done in a different lab that the results would come out similarly to the results reported here.

Reviewer #3 (Public review):

Summary:

The finding of rhythmic activity in the brain has for a long time engendered the theory of rhythmic modes of perception, that humans might oscillate between improved and worse perception depending on states of our internal systems. However, experiments looking for such modes have resulted in conflicting findings, particularly in those where the stimulus itself is not rhythmic. This paper seeks to take a comprehensive look at the effect and various experimental parameters which might generate these competing findings: in particular, the presentation of the stimulus to one ear or the other, the relevance of motor involvement, attentional demands, and memory: each of which are revealed to effect the consistency of this rhythmicity.

The need the paper attempts to resolve is a critical one for the field. However, as presented, I remain unconvinced that the data would not be better interpreted as showing no consistent rhythmic mode effect.

Strengths:

The paper is strong in its experimental protocol and its comprehensive analysis which seeks to compare effects across several analysis types and slight experiment changes to investigate which parameters could effect the presence or absence of an effect of rhythmicity. The prescribed nature of its hypotheses and its manner to set out to test them is very clear which allows for a straightforward assessment of its results

Weaknesses:

The papers cited to justify a rhythmic mode are largely based on the processing of rhythmic stimuli. The authors assume the rhythmic mode to be the general default but its not so clear to me why this would be so. The task design seems better suited to a continuous vigilance mode task.

Secondly, the analysis to detect a "rhythmic mode", assumes a total phase rest at noise onset which is highly implausible given standard nonlinear dynamical analysis of oscillator performance. It's not clear that a rhythmic mode (should it be applied in this task) would indeed generate a consistent phase as the analysis searches for.

Thirdly, the number of statistical tests used here make trusting any single effect quite difficult and very few of the effects replicate more than once. I think the better would be interpreted as not confirming evidence for rhythmic mode processing in the ears.

Comments on revised version:

No further comments. The paper has much of the same issues that I expressed in the initial review but I don't think they can be addressed without a replication study which I appreciate is not always plausible.

Reviewer #1 (Public review):

Summary:

The authors investigate the role of H3K115ac in mouse embryonic stem cells. They report that H3K115ac localizes to regions enriched for fragile nucleosomes, CpG islands, and enhancers, and that it correlates with transcriptional activity. These findings suggest a potential role for this globular domain modification in nucleosome dynamics and gene regulation. If robust, these observations would expand our understanding of how non-tail histone modifications contribute to chromatin accessibility and transcriptional control.

Strengths:

(1) The study addresses a histone PTM in the globular domain, which is relatively unexplored compared to tail modifications.

(2) The implication of a histone PTM in fragile nucleosome localization is novel and, if substantiated, could represent a significant advance for the field.

Weaknesses:

(1) The absence of replicate paired-end datasets limits confidence in peak localization.

(2) The analyses are primarily correlative, making it difficult to fully assess robustness or to support strong mechanistic conclusions.

(3) Some claims (e.g., specificity for CpG islands, "dynamic" regulation during differentiation) are not fully supported by the analyses as presented.

(4) Overall, the study introduces an intriguing new angle on globular PTMs, but additional rigor and mechanistic evidence are needed to substantiate the conclusions.

Reviewer #2 (Public review):

Summary:

Kumar et al. aimed to assess the role of the understudied H3K115 acetylation mark, which is located in the nucleosomal core. To this end, the authors performed ChIP-seq experiments of H3K115ac in mouse embryonic stem cells as well as during differentiation into neuronal progenitor cells. Subsequent bioinformatic analyses revealed an association of H3K115ac with fragile nucleosomes at CpG island promoters, as well as with enhancers and CTCF binding sites. This is an interesting study, which provides important novel insights into the potential function of H3K115ac. However, the study is mainly descriptive, and functional experiments are missing.

Strengths:

(1) The authors present the first genome-wide profiling of H3K115ac and link this poorly characterized modification to fragile nucleosomes, CpG island promoters, enhancers, and CTCF binding sites.

(2) The study provides a valuable descriptive resource and raises intriguing hypotheses about the role of H3K115ac in chromatin regulation.

(3) The breadth of the bioinformatic analyses adds to the value of the dataset

Weaknesses:

(1) I am not fully convinced about the specificity of the antibody. Although the experiment in Figure S1A shows a specific binding to H3K115ac-modified peptides compared to unmodified peptides, the authors do not show any experiment that shows that the antibody does not bind to unrelated proteins. Thus, a Western of a nuclear extract or the chromatin fraction would be critical to show. Also, peptide competition using the H3K115ac peptide to block the antibody may be good to further support the specificity of the antibody. Also, I don't understand the experiment in Figure S1B. What does it tell us when the H3K115ac histone mark itself is missing? The KLF4 promoter does not appear to be a suitable positive control, given that hundreds of proteins/histone modifications are likely present at this region.

It is important to clearly demonstrate that the antibody exclusively recognizes H3K115ac, given that the conclusion of the manuscript strongly depends on the reliability of the obtained ChIP-Seq data.

(2) The association of H3K115ac with fragile nucleosomes based on MNase-Sensitivity and fragment length, which are indirect methods and can have technical bias. Experiments that support that the H3K115ac modified nucleosomes are indeed more fragile are missing.

(3) The comparison of H3K115ac with H3K122ac and H3K64ac relies on publicly available datasets. Since the authors argue that these marks are distinct, data generated under identical experimental conditions would be more convincing. At a minimum, the limitations of using external datasets should be discussed.

(4) The enrichment of H3K115ac at enhancers and CTCF binding sites is notable but remains descriptive. It would be interesting to clarify whether H3K115ac actively influences transcription factor/CTCF binding or is a downstream correlate.

(5) No information is provided about how H3K115ac may be deposited/removed. Without this information, it is difficult to place this modification into established chromatin regulatory pathways.

At the very least, the authors should acknowledge these limitations and provide additional validation of antibody specificity.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #1 (Public review):

Summary:

This is an interesting study characterizing and engineering so-called bathy phytochromes, i.e. those that respond to near infrared (NIR) light in the ground state, for optogenetic control of bacterial gene expression. Previously, the authors have developed a structure-guided approach to functionally link several light responsive protein domains to the signaling domain of the histidine kinase FixL, which ultimately controls gene expression. Here, the authors use the same strategy to link bathy phytochrome light responsive domains to FixL, resulting in sensors of NIR light. Interestingly, they also link these bathy phytochrome light sensing domains to signaling domains from the tetrathionate-sensing SHK TtrS and the toluene-sensing SHK TodS, demonstrating generality of their protein engineering approach more broadly across bacterial two-component systems.

This is an exciting result that should inspire future bacterial sensor design. The authors go on to leverage this result to develop what is, to my knowledge, the first system for orthogonally controlling the expression of two separate genes in the same cell with NIR and Red light, a valuable contribution to the field.

Finally, the authors reveal new details of the pH-dependent photocycle of bathy phytochromes and demonstrate their sensors work in the gut- and plant-relevant strains E. coli Nissle 1917 and A. tumefaciens.

Strengths:

The experiments are well founded, well executed, and rigorous.

The manuscript is clearly written.

The sensors developed exhibit large responses to light, making them valuable tools for ontogenetic applications.

This study is a valuable contribution to photobiology and optogenetics.

Weaknesses:

As the authors note, the sensors are relatively insensitive to NIR light due to the rapid dark reversion process in bathy phytochromes. Though NIR light is generally non-phototoxic, one would expect this characteristic to be a limitation in some downstream applications where light intensities are not high (e.g. in vivo).

Though they can be multiplexed with Red light sensors, these bathy phytochrome NIR sensors are more difficult to multiplex with other commonly used light sensors (e.g. blue) due to the broad light responsivity of the Pfr state. This challenge may be overcome by careful dosing of blue light, as the authors discuss, but other bacterial NIR sensing systems with less cross-talk may be preferred in some applications.

Comments on revisions:

My concerns have been addressed.

Reviewer #2 (Public review):

In this manuscript, Meier et al. engineer a new class of light-regulated two-component systems. These systems are built using bathy-bacteriophytochromes that respond to near-infrared (NIR) light. Through a combination of genetic engineering and systematic linker optimization, the authors generate bacterial strains capable of selective and tunable gene expression in response to NIR stimulation. Overall, these results are an interesting expansion of the optogenetic toolkit into the NIR range. The cross-species functionality of the system, modularity, and orthogonality have the potential to make these tools useful for a range of applications.

Strengths:

(1) The authors introduce a novel class of near-infrared light-responsive two-component systems in bacteria, expanding the optogenetic toolbox into this spectral range.

(2) Through engineering and linker optimization, the authors achieve specific and tunable gene expression, with minimal cross-activation from red light in some cases.

(3) The authors show that the engineered systems function robustly in multiple bacterial strains, including laboratory E. coli, the probiotic E. coli Nissle 1917, and Agrobacterium tumefaciens.

(4) The combination of orthogonal two-component systems can allow for simultaneous and independent control of multiple gene expression pathways using different wavelengths of light.

(5) The authors explore the photophysical properties of the photosensors, investigating how environmental factors such as pH influence light sensitivity.

Comments on revisions:

The authors have addressed all my prior concerns.

Reviewer #3 (Public review):

Summary:

This paper by Meier et al introduces a new optogenetic module for regulation of bacterial gene expression based on "bathy-BphP" proteins. Their paper begins with a careful characterization of kinetics and pH dependence of a few family members, followed by extensive engineering to produce infrared-regulated transcriptional systems based on the authors' previous design of the pDusk and pDERusk systems, and closing with characterization of the systems in bacterial species relevant for biotechnology.

Strengths:

The paper is important from the perspective of fundamental protein characterization, since bathy-BphPs are relatively poorly characterized compared to their phytochrome and cyanobacteriochrome cousins. It is also important from a technology development perspective: the optogenetic toolbox currently lacks infrared-stimulated transcriptional systems. Infrared light offers two major advantages: it can be multiplexed with additional tools, and it can penetrate into deep tissues with ease relative to the more widely used blue light activated systems. The experiments are performed carefully and the manuscript is well written.

Weaknesses:

Some of the light-inducible responses described in this compelling paper are complex and difficult to rationalize, such as the dependence of light responses on linker length and differences in responses observed from the bathy-BphPs in isolation versus strains in which they are multiplexed. Nevertheless, the authors should be commended for carrying out rigorous experiments and reporting these results accurately. These are minor weaknesses in an overall very strong paper.

Joint Public Review:

Summary:

The Major Histocompatibility Complex (MHC) region is a collection of numerous genes involved in both innate and adaptive immunity. MHC genes are famed for their role in rapid evolution and extensive polymorphism in a variety of vertebrates. This paper presents a summary of gene-level gain and loss of orthologs and paralogs within MHC across the diversity of primates, using publicly available data.

Strengths:

This paper provides a strong case that MHC genes are rapidly gained (by paralog duplication) and lost over millions of years of macroevolution. The authors are able to identify MHC loci by homology across species, and from this infer gene duplications and losses using phylogenetic analyses. There is a remarkable amount of genic turnover, summarized in Figure 6 and Figure 7, either of which might be a future textbook figure of immune gene family evolution. The authors draw on state-of-the-art phylogenetic methods, and their inferences are robust.

Editorial note:

The authors have responded to the previous reviews and the Assessment was updated without involving the reviewers again.

Reviewer #1 (Public review):

This is an interesting study on the nature of representations across the visual field. The question of how peripheral vision differs from foveal vision is a fascinating and important one. The majority of our visual field is extra-foveal yet our sensory and perceptual capabilities decline in pronounced and well-documented ways away from the fovea. Part of the decline is thought to be due to spatial averaging ('pooling') of features. Here, the authors contrast two models of such feature pooling with human judgments of image content. They use much larger visual stimuli than in most previous studies, and some sophisticated image synthesis methods to tease apart the prediction of the distinct models.

More importantly, in so doing, the researchers thoroughly explore the general approach of probing visual representations through metamers-stimuli that are physically distinct but perceptually indistinguishable. The work is embedded within a rigorous and general mathematical framework for expressing equivalence classes of images and how visual representations influence these. They describe how image-computable models can be used to make predictions about metamers, which can then be compared to make inferences about the underlying sensory representations. The main merit of the work lies in providing a formal framework for reasoning about metamers and their implications, for comparing models of sensory processing in terms of the metamers that they predict, and for mapping such models onto physiology. Importantly, they also consider the limits of what can be inferred about sensory processing from metamers derived from different models.

Overall, the work is of a very high standard and represents a significant advance over our current understanding of perceptual representations of image structure at different locations across the visual field. The authors do a good job of capturing the limits of their approach I particularly appreciated the detailed and thoughtful Discussion section and the suggestion to extend the metamer-based approach described in the MS with observer models. The work will have an impact on researchers studying many different aspects of visual function including texture perception, crowding, natural image statistics and the physiology of low- and mid-level vision.

The main weaknesses of the original submission relate to the writing. A clearer motivation could have been provided for the specific models that they consider, and the text could have been written in a more didactic and easy to follow manner. The authors could also have been more explicit about the assumptions that they make.

Comments following re-submission:

Overall, I think the authors have done a satisfactory job of addressing most of the points I raised.

There's one final issue which I think still needs better discussion.

I think reviewer 2 articulated better than I have the point I was concerned about: the relationship between JNDs and metamers as depicted in the schematics and indeed in the whole conceptualization.

I think the issue here is that there seems to be a conflating of two concepts- 'subthreshold' and 'metamer'-and I'm not convinced it is entirely unproblematic. It's true that two stimuli that cannot be discriminated from one another due to the physical differences being too small to detect reliably by the visual system are a form of metamer in the strict definition 'physically different, but perceptually the same'.<br /> However, I don't think this is the scientifically substantial notion of metamer that enabled insights into trichromacy. That form of metamerism is due to the principle of univariance in feature encoding, and involves conditions in which physically very different stimuli are mapped to one and the same point in sensory encoding space whether or not there is any noise in the system. When I say 'physically very different' I mean different by a large enough amount that they would be far above threshold, potentially orders of magnitude larger than a JND if the system's noise properties were identical but the system used a different sensory basis set to measure them. This seems to be a very different kind of 'physically different, but perceptually the same'.

I do think the notion of metamerism can obviously be very usefully extended beyond photoreceptors and photon absorptions. In the interesting case of texture metamers, what I think is meant is that stimuli would be discriminable if scrutinised in the fovea, but because they have the same statistics they are treated as equivalent. I think the discussion of this could still be clearly articulated in the manuscript. It would benefit from a more thorough discussion of the difference between metamerism and subthreshold, especially in the context of the Voronoi diagrams at the beginning.

It needs to be made clear to the reader why it is that two stimuli that are physically similar (e.g., just spanning one of the edges in the diagram) can be discriminable, while at the same time, two stimuli that are very different (e.g., at opposite ends of a cell) can't.

Do the cells include BOTH those sets of stimuli that cannot be discriminated just because of internal noise AND those that can't be discriminated because they are projected to literally the same point in the sensory encoding space? What are the strengths and limits of models that involve the strict binarization of sensory representations, and how can they be integrated with models dealing with continuous differences? These seem like important background concepts that ought to be included in either the introduction of discussion sections. In this context it might also be helpful to refer to the notion of 'visual equivalence' as described by:

Ramanarayanan, G., Ferwerda, J., Walter, B., & Bala, K. (2007). Visual equivalence: towards a new standard for image fidelity. ACM Transactions on Graphics (TOG), 26(3), 76-es.

Other than that, I congratulate the authors on a very interesting study, and look forward to reading the final version.

Reviewer #2 (Public review):

Summary:

The authors have improved clarity overall and have spoken to most of the issues raised by the reviewers. There are still two outstanding problems however, where issues raised during the review were inappropriately dismissed in the manuscript. These should be explicitly addressed as limitations to the results presented (no eye tracking), and early pilot experiments that informed the experiments as presented (pink noise) rather than brushed off as 'unnecessary' and 'would be uninformative'.

Eye tracking:

It is generally accepted that experiments testing stimuli presented at specific locations in peripheral vision require eye tracking to ensure that the stimulus is presented as expected, in particular, in the correct location. As I stated in the previous round of review, while a stimulus presentation time of 200ms does help eliminate some saccades, it does not eliminate the possibility that subjects were not fixating well during stimulus onset. I am also unclear what the authors mean by 'trained observer' in this context, though the authors state that an author subject in a different portion of the paper is an 'expert observer'. Does this mean the 'trained observers' are non-expert recruited subjects? Given the conditions tested differ from previous work (Freeman & Simoncelli, 2011) *these differences are a main contribution of the paper!* which DID include eye tracking in a subset of subjects, it is entirely possible to get similar results to this work in the context of non eye-tracking controlled stimulus presentation. The reasons now in the manuscript are not reasons that make eye tracking 'considered unnecessary'.

I appreciate that the authors now state the lack of eye tracking explicitly, but believe the paper needs to at least state that this is a limitation of the results reported, and eyetracking being 'considered unnecessary' is unreasonable, nor a norm in this subfield.

N=1: The authors now state clearly the limitations of a single subject in the manuscript, and state the expertise level of this subject.

Large number of trials: The authors now address this and include an enumeration of the large number of trials.

Simple Models / Physiology comparison: I support the choice to reduce claims regarding tight connections to physiology, and appreciate the explanation of the luminance model.

Previous Work: I appreciate the author's changes to the introduction, both in discussing previous work and citation fixes.

Blurred White, Pink Noise: While the authors now address pink noise, the explanation for such stimuli being expected to be uninformative is confusing to me. The manuscript now first states that pink noise is a natural choice, then claims it would be uninformative, while also stating in the rebuttal (not the manuscript) that they tried it and it indeed reduced the artifacts they note. The logic of the experiments indeed relies on finding the smallest critical scaling value, which is measured by subjects determining if a synthesis is similar or different to a target or second synth. A synthesis free from artifacts would surely affect the subjects responses and the smallest critical scaling measured.

The statement that the authors experimented with pink noise early on and found this able to address the artifacts should be stated in the manuscript itself, not just in the rebuttal, and the blanket statement that this experiment would be 'uninformative' is incorrect. Surely this early pilot the authors mention in the rebuttal was informative to designing the experiments that appear in the final paper, and would be an informative experiment to include.

Reviewer #1 (Public review):

Summary:

Johnston and Smith used linear electrode arrays to record from small populations of neurons in the superior colliculus (SC) of monkeys performing a memory-guided saccade (MGS) task. Dimensionality reduction (PCA) was used to reveal low-dimensional subspaces of population activity reflecting the slow drift of neuronal signals during the delay period across a recording session (similar to what they reported for parts of cortex: Cowley et al., 2020). This SC drift was correlated with a similar slow-drift subspace recorded from the prefrontal cortex, and both slow-drift subspaces tended to be associated with changes in arousal (pupil size). These relationships were driven primarily by neurons in superficial layers of the SC, where saccade sensitivity/selectivity is typically reduced. Accordingly, delay-period modulations of both spiking activity and pupil size were independent of saccade-related activity, which was most prevalent in deeper layers of the SC. The authors suggest that these findings provide evidence of a separation of arousal- and motor-related signals. The analysis techniques expand upon the group's previous work and provides useful insight into the power of large-scale neural recordings paired with dimensionality reduction. This is particularly important with the advent of recording technologies which allow for the measurement of spiking activity across hundreds of neurons simultaneously. Together, these results provide a useful framework for comparing how different populations encode signals related to cognition, arousal, and motor output in potentially different subspaces.

Comments on revised manuscript:

The authors have done a very good job of responding to all of the reviewers' concerns.

Reviewer #2 (Public review):

Summary:

Neurons in motor-related areas have increasingly shown to carry also other, non-motoric signals. This creates a problem of avoidance of interference between the motor and non-motor-related signals. This is a significant problem that likely affects many brain areas. The specific example studied here is interference between saccade-related activity and slow-changing arousal signals in the superior colliculus. The authors identify neuronal activity related to saccades and arousal. Identifying saccade-related activity is straightforward, but arousal-related activity is harder to identify. The authors first identify a potential neuronal correlate of arousal using PCA to identifying a component in the population activity corresponding to slow drift over the recording session. Next, they link this component to arousal by showing that the component is present across different brain areas (SC and PFC), and that it is correlated with pupil size, an external marker of arousal. Having identified an arousal-related component in SC, the authors show next that SC neurons with strong motor-related activity are less strongly affected by this arousal component (both SC and PFC). Lastly, they show that SC population activity pattern related to saccades and pupil size form orthogonal subspaces in the SC population.

Strengths:

A great strength of this research is the clear description of the problem, its relationship with the performed analysis and the interpretation of the results. the paper is very well written and easy to follow. An additional strength is the use of fairly sophisticated analysis using population activity.

Weaknesses:

(1) The greatest weakness in the present research is the fact that arousal is a functionally less important non-motoric variable. The authors themself introduce the problem with a discussion of attention, which is without any doubt the most important cognitive process that needs to be functionally isolated from oculomotor processes. Given this introduction, one cannot help but wonder, why the authors did not design an experiment, in which spatial attention and oculomotor control are differentiated. Absent such an experiment, the authors should spend more time on explaining the importance of arousal and how it could interfere with oculomotor behavior.

(2) In this context, it is particularly puzzling that one actually would expect effects of arousal on oculomotor behavior. Specifically, saccade reaction time, accuracy, and speed could be influenced by arousal. The authors should include an analysis of such effects. They should also discuss the absence or presence of such effects and how they affect their other results.

(3) The authors use the analysis shown in Figure 6D to argue that across recording sessions the activity components capturing variance in pupil size and saccade tuning are uncorrelated. however, the distribution (green) seems to be non-uniform with a peak at very low and very high correlation specifically. The authors should test if such an interpretation is correct. If yes, where are the low and high correlations respectively? Are there potentially two functional areas in SC?

Comments on revised manuscript:

I remain somewhat concerned that the authors jump immediately into an analysis of the 'arousal-related' effects on SC activity. Before that, I would like to see a more detailed discussion justifying the use pupil size alone (i.e., w/o other indicators such as RT) as indicative of fluctuations in general arousal that are causal to concomitant changes in SC activity. Instead, in its current form, the authors find changes in SC activity and describe them immediately as 'arousal-related'.

Other than this conceptual issue, I do not have major problems with the analysis per se.

Reviewer #3 (Public review):

Summary:

This study looked at slow changes in neuronal activity (on the order of minutes to hours) in the superior colliculus (SC) and prefrontal cortex (PFC) of two monkeys. They found that SC activity shows slow drift in neuronal activity like in the cortex. They then computed a motor index in SC neurons. By definition, this index is low if the neuron has stronger visual responses than motor response, and it is low if the neuron has weaker visual responses and stronger motor responses. The authors found that the slow drift in neuronal activity was more prevalent in the low motor index SC neurons and less prevalent in the high motor index neurons. In addition, the authors measured pupil diameter and found it to correlate with slow drifts in neuronal activity, but only in the neurons with lower motor index of the SC. They concluded that arousal signals affecting slow drifts in neuronal modulations are brain-wide. They also concluded that these signals are not present in the deepest SC layers, and they interpreted this to mean that this minimizes the impact of arousal on unwanted eye movements.

Strengths:

The paper is clear and well-written.

Showing slow drifts in the SC activity is important to demonstrate that cortical slow drifts could be brain-wide.

Weaknesses:

The authors find that the SC cells with the low motor index are modulated by pupil diameter. However, this could be completely independent of an "arousal signal". These cells have substantial visual sensitivity. If the pupil diameter changes, then their activity should be influenced since the monkey is watching a luminous display. So, in this regard, the fact that they do not see "an arousal signal" in the most motor neurons (through the pupil diameter analyses) is not evidence that the arousal signal is filtered out from the motor neurons. It could simply be that these neurons simply do not get affected by the pupil diameter because they do not have visual sensitivity. So, even with the pupil data, it is still a bit tricky for me to interpret that arousal signals are excluded from the "output layers" of the SC.

Of course, the general conclusion is that the motor neurons will not have the arousal signal. It's just the interpretation that is different in the sense that the lack of the arousal signal is due to a lack of visual sensitivity in the motor neurons.

I think that it is important to consider the alternative caveat of different amounts of light entering the system. Changes in light level caused by pupil diameter variations can be quite large. Please also note that I do not mean the luminance transient associated with the target onset. I mean the luminance of the gray display. it is a source of light. if the pupil diameter changes, then the amount of light entering to the visually sensitive neurons also changes.

Comments on revised manuscript:

The authors have addressed my first primary comment. For the light comment, I'm still not sure they addressed it. At the very least, they should explicitly state the possibility that the amount of light entering from the gray background can matter greatly, and it is not resolved by simply changing the analysis interval to the baseline pre-stimulus epoch. I provide more clear details below:

In line 194 of the redlined version of the article (in the Introduction), the citation to Baumann et al., PNAS, 2023 is missing near the citation of Jagadisan and Gandhi, 2022. Besides replicating Jagadisan and Gandhi, 2022, this other study actually showed that the subspaces for the visual and motor epochs are orthogonal to each other

Line 683 (and around) of the redlined version of the article (in the Results): I'm very confused here. When I mentioned visual modulation by changed pupil diameter, I did not mean the transient changes associated with the brief onset of the cue in the memory-guided saccade task. I meant the gray background of the display itself. This is a strong source of light. If the pupil diameter changes across trials, then the amount of light entering the eye also changes from the gray background. Thus, visually-responsive neurons will have different amount of light driving them. This will also happen in the baseline interval containing only a fixation spot. The arguments made by the authors here do not address this point at all. So, please modify the text to explicitly state the possibility that the global luminance of the display (as filtered by the pupil diameter) alters the amount of light driving the visually-responsive neurons and could contribute to the higher effects seen in the more visual neurons.

The figures (everywhere, including the responses to reviewers) are very low resolution and all equations in methods are missing.

I'm very confused by Fig. 2 - supplement 2. Panel B shows a firing rate burst aligned to *microsaccade* onset. Does that mean you were in the foveal SC? i.e. how can neurons have a motor burst to the target of the memory-guided saccade and also for microsaccades? And which microsaccade directions caused such a burst? And what does it mean to compute the motor index and spike count for microsaccades in panel C? if you were in the proper SC location for the saccade target, then shouldn't you *not* get any microsaccade-related burst at all? This is very confusing to me and needs to be clarified

Reviewer #1 (Public review):

Summary:

This study provides the first evidence that glucose availability, previously shown to support cell survival in other models, is also a key determinant for post-implantation MSC survival in the specific context of pulmonary fibrosis. To address glucose depletion in this context, the authors propose an original, elegant, and rational strategy: enhancing intracellular glycogen stores to provide transplanted MSCs with an internal energy reserve. This approach aims to prolong their viability and therapeutic functionality after implantation.

Strengths:

The efficacy of this metabolic engineering strategy is robustly demonstrated both in vitro and in an orthotopic mouse model of pulmonary fibrosis.

Reviewer #2 (Public review):

Summary:

In this article, the authors investigate enhancing the therapeutic and regenerative properties of mesenchymal stem cells (MSCs) through genetic modification, specifically by overexpressing genes involved in the glycogen synthesis pathway. By creating a non-phosphorylatable mutant form of glycogen synthase (GYSmut), the authors successfully increased glycogen accumulation in MSCs, leading to significantly improved cell survival under starvation conditions. The study highlights the potential of glycogen engineering to improve MSC function, especially in inflammatory or energy-deficient environments. However, critical gaps in the study's design, including the lack of validation of key findings, limited differentiation assessments, and missing data on MSC-GYSmut resistance to reactive oxygen species (ROS), necessitate further exploration.

Strengths:

(1) Novel Approach: The study introduces an innovative method of enhancing MSC function by manipulating glycogen metabolism.

(2) Increased Glycogen Storage: The genetic modification of GYS1, resulting in GYSmut, significantly increased glycogen accumulation, leading to improved MSC survival under starvation, which has strong implications for enhancing MSC therapeutic properties in energy-deficient environments.

(3) Potential Therapeutic Impact: The findings suggest significant therapeutic potential for MSCs in conditions that require improved survival, persistence, and immunomodulation, especially in inflammatory or energy-limited settings.

(4) In Vivo Validation: The in vivo murine model of pulmonary fibrosis demonstrated the improved survival and persistence of MSC-GYSmut, supporting the translational potential of the approach.

Weaknesses:

(1) Lack of Differentiation Assessments: The study did not evaluate key MSC differentiation pathways, including chondrogenic and osteogenic differentiation. The absence of analysis of classical MSC surface markers and multipotency limits the understanding of the full potential of MSC-GYSmut.

(2) Missing Validation of RNA Sequencing Data: Although RNA sequencing data revealed promising transcriptomic changes in chondrogenesis and metabolic pathways, these findings were not experimentally validated, limiting confidence.

(3) Lack of ROS Resistance Analysis: Resistance to reactive oxygen species (ROS), an important feature for MSCs under regenerative conditions, was not assessed, leaving out a critical aspect of MSC function.

(4) Limited Exploration of Immunosuppressive Properties: The study did not address the immunosuppressive functions of MSC-GYSmut, which are critical for MSC-based therapies in clinical settings.

Conclusion:

The study presents an exciting new direction for enhancing MSC function through glycogen metabolism engineering. While the results show promise, key experiments and validations are missing, and several areas, such as differentiation capacity, ROS resistance, and immunosuppressive properties, require further investigation. Addressing these gaps would solidify the conclusions and strengthen the potential clinical applications of MSC-GYSmut in regenerative medicine.

Reviewer #1 (Public review):

Summary:

Overall, this is a well-designed and carefully executed study that delivers clear and actionable guidance on the sample size and representative demographic requirements for robust normative modelling in neuroimaging. The central claims are convincingly supported.

Strengths:

The study has multiple strengths. First, it offers a comprehensive and methodologically rigorous analysis of sample size and age distribution, supported by multiple complementary fit indices. Second, the learning-curve results are compelling and reproducible and will be of immediate utility to researchers planning normative modelling projects. Third, the study includes both replication in an independent dataset and an adaptive transfer analysis from UK Biobank, highlighting both the robustness of the results and the practical advantages of transfer learning for smaller clinical cohorts. Finally, the clinical validation ties the methodological work back to clinical application.

Weaknesses:

There are two minor points for consideration:

(1) Calibration of percentile estimates could be shown for the main evaluation (similar to that done in Figure 4E). Because the clinical utility of normative models often hinges on identifying individuals outside the 5th or 95th percentiles, readers would benefit from visual overlays of model-derived percentile curves on the curves from the full training data and simple reporting of the proportion of healthy controls falling outside these bounds for the main analyses (i.e., 2.1. Model fit evaluation).

(2) The larger negative effect of left-skewed sampling likely reflects a mismatch between the younger training set and the older test set; accounting explicitly for this mismatch would make the conclusions more generalisable.

Reviewer #2 (Public review):

Summary:

The authors test how sample size and demographic balance of reference cohorts affect the reliability of normative models in ageing and Alzheimer's disease. Using OASIS-3 and replicating in AIBL, they change age and sex distributions and number of samples and show that age alignment is more important than overall sample size. They also demonstrate that models adapted from a large dataset (UK Biobank) can achieve stable performance with fewer samples. The results suggest that moderately sized but demographically well-balanced cohorts can provide robust performance.

Strengths:

The study is thorough and systematic, varying sample size, age, and sex distributions in a controlled way. Results are replicated in two independent datasets with relatively large sample sizes, thereby strengthening confidence in the findings. The analyses are clearly presented and use widely applied evaluation metrics. Clinical validation (outlier detection, classification) adds relevance beyond technical benchmarks. The comparison between within-cohort training and adaptation from a large dataset is valuable for real-world applications.

The work convincingly shows that age alignment is crucial and that adapted models can reach good performance with fewer samples. However, some dataset-specific patterns (noted above) should be acknowledged more directly, and the practical guidance could be sharper.

Weaknesses:

The paper uses a simple regression framework, which is understandable for scalability, but limits generalization to multi-site settings where a hierarchical approach could better account for site differences. This limitation is acknowledged; a brief sensitivity analysis (or a clearer discussion) would help readers weigh trade-offs. Other than that, there are some points that are not fully explained in the paper:

(1) The replication in AIBL does not fully match the OASIS results. In AIBL, left-skewed age sampling converges with other strategies as sample size grows, unlike in OASIS. This suggests that skew effects depend on where variability lies across the age span.

(2) Sex imbalance effects are difficult to interpret, since sex is included only as a fixed effect, and residual age differences may drive some errors.

(3) In Figure 3, performance drops around n≈300 across conditions. This consistent pattern raises the question of sensitivity to individual samples or sub-sampling strategy.

(4) The total outlier count (tOC) analysis is interesting but hard to generalize. For example, in AIBL, left-skew sometimes performs slightly better despite a weaker model fit. Clearer guidance on how to weigh model fit versus outlier detection would strengthen the practical message.

(5) The suggested plateau at n≈200 seems context-dependent. It may be better to frame sample size targets in relation to coverage across age bins rather than as an absolute number.

Reviewer #2 (Public review):

Summary:

Sereesongsaeng et al. aimed to develop degraders for LMO2, an intrinsically disordered transcription factor activated by chromosomal translocation in T-ALL. The authors first focused on developing biodegraders, which are fusions of an anti-LMO2 intracellular domain antibody (iDAb) with cereblon. Following demonstrations of degradation and collateral degradation of associated proteins with biodegraders, the authors proceeded to develop PROTACs using antibody paratopes (Abd) that recruit VHL (Abd-VHL) or cereblon (Abd-CRBN). The authors show dose-dependent degradation of LMO2 in LMO2+ T-ALL cell lines, as well as concomitant dose-dependent degradation of associated bHLH proteins in the DNA-binding complex. LMO2 degradation via Abd-VHL was also determined to inhibit proliferation and induce apoptosis in LMO2+ T-ALL cell lines.

Strengths:

The topic of degrader development for intrinsically disordered proteins is of high interest and the authors aimed to tackle a difficult drug target. The authors evaluated methods including the development of biodegraders, as well as PROTACs that recruit two different E3 ligases. The study includes important chemical control experiments, as well as proteomic profiling to evaluate selectivity.

Weaknesses:

Several weaknesses remain in this study:

(1) The overall degradation achieved is not highly potent (although important proof-of-concept);

(2) The mechanism of collateral degradation is not completely addressed. The authors acknowledge possible explanations, which would require mutagenesis and structural studies to further dissect;

(3) The proteomics experiments do not detect LMO2, which the authors attribute to its size, making it difficult to interpret.

Reviewer #1 (Public review):

Summary:

The manuscript addresses the discordant reports of the Murphy (Moore et al., 2019; Kaletsky et al., 2020; Sengupta et al., 2024) and Hunter (Gainey et al., 2025) groups on the existence (or robustness) of transgenerational epigenetic inheritance (TEI) controlling learned avoidance of C. elegans to Pseudomonas aeruginosa. Several papers from Colleen Murphy's group describe and characterize C. elegans transgenerational inheritance of avoidance behaviour. In the hands of the Murphy group, the learned avoidance is maintained for up to four generations, however, Gainey et al. (2025) reported an inability to observe inheritance of learned avoidance beyond the F1 generation. Of note, Gainey et al used a modified assay to measure avoidance, rather than the standard assay used by the Murphy lab. A response from the Murphy group suggested that procedural differences explained the inability of Gainey et al.(2025) to observe TEI. They found two sources of variability that could explain the discrepancy between studies: the modified avoidance assay and bacterial growth conditions (Kaletsky et al., 2025). The standard avoidance assay uses azide as a paralytic to capture worms in their initial decision, while the assay used by the Hunter group does not capture the worm's initial decision but rather uses cold to capture the location of the population at one point in time.

In this short report, Akinosho, Alexander, and colleagues provide independent validation of transgenerational epigenetic inheritance (TEI) of learned avoidance to P. aeruginosa as described by the Murphy group by demonstrating learned avoidance in the F2 generation. These experiments used the protocol described by the Murphy group, demonstrating reproducibility and robustness.

Strengths:

Despite the extensive analyses carried out by the Murphy lab, doubt may remain for those who have not read the publications or for those who are unfamiliar with the data, which is why this report from the Vidal-Gadea group is so important. The observation that learned avoidance was maintained in the F2 generation provides independent confirmation of transgenerational inheritance that is consistent with reports from the Murphy group. It is of note that Akinosho, Alexander et al. used the standard avoidance assay that incorporates azide, and followed the protocol described by the Murphy lab, demonstrating that the data from the Moore and Kaletsky publications are reproducible, in contrast to what has been asserted by the Hunter group.

Comments on revised version:

I am happy with the responses to reviews.

Reviewer #2 (Public review):

Summary:

The manuscript "Independent validation of transgenerational inheritance of learned pathogen avoidance in C. elegans" by Akinosho and Vidal-Gadea offers evidence that learned avoidance of the pathogen PA14 can be inherited for at least two generations. In spite of initial preference for the pathogen when exposed in a 'training session', 24 hours of feeding on this pathogen evoked avoidance. The data are robust, replicated in 4 trials, and the authors note that diminished avoidance is inherited in generations F1 and F2.

Strengths:

These results contrast with those reported by Gainey et al, who only observed intergenerational inheritance for a single generation. Although the authors' study does not explain why Gainey et el fail to reproduce the Murphy lab results, one possibility is that a difference in a media ingredient could be responsible.

Comments on revised version:

The responses to the reviewer comments appear reasonable for the most part.

Reviewer #3 (Public review):

Summary:

This short paper aims to provide an independent validation of the transgenerational inheritance of learned behaviour (avoidance) that has been published by the Murphy lab. The robustness of the phenotype has been questioned by the Hunter lab. In this paper, the authors present one figure showing that transgenerational inheritance can be replicated in their hands. Overall, it helps to shed some light on a controversial topic.

Strengths:

The authors clearly outline their methods, particularly regarding the choice of assay, so that attempting to reproduce the results should be straightforward. It is nice to see these results repeated in an independent laboratory.

Comments on revised version:

I'm happy with the response to reviewers.

Reviewer #1 (Public review):

Summary:

The authors build a network model of the olfactory bulb and the piriform cortex and use it to run simulations and test their hypotheses. Given the model's settings, the authors observe drift across days in the responses to the same odors of both the mitral/tufted cells, as well as of piriform cortex neurons. When representing the M/T and PCx responses within a lower-dimensional space, the apparent drift is more prominent in the PCx, while the M/T responses appear in comparison more stable. The authors further note that introducing spike-time dependent plasticity (STDP) at bulb synapses involving abGCs slows down the drift in the PCx representations, and further link this to the observation that repeated exposure to the same odorant slows down drift in the piriform cortex.

The model is clearly explained and relies on several assumptions and observations:

(1) Random projections of MTC from the olfactory bulb to the piriform cortex, random intra-piriform connectivity, and random piriform to bulb connectivity.

(2) Higher dimensionality of piriform cortex representations compared to M/T responses, which enables superior decoding of odor identity in the piriform cortex.

(3) Spike time-dependent plasticity (STDP) at synapses involving the abGCs.

The authors address an open topical problem, and the model is elegant in its simplicity. I have however, several major concerns with the hypotheses underlying the model and with its biological plausibility.

Concerns:

(1) In their model, the authors propose that MTC remain stable at the population level, despite changes in individual MTC responses.

The authors cite several experimental studies to support their claims that individual MTC responses to the same odors change (some increase, some decrease) across days. Interpreting the results of these studies must, however, take into account the variability of M/T responses across odor presentation repeats within the same session vs. across sessions. In the Shani-Narkiss et al., Frontiers in Neural Circuits, 2023 study referenced, a large fraction of the variability across days in M/T responses is also observed across repeats to the same odorant in the same session (Shani-Narkiss et al., Figure 4), while the authors have M/T responses in the same session that are highly reproducible. This is an important point to consider and address, since it constrains how much of the variability in M/T responses can be attributed to adult neurogenesis in the olfactory bulb versus to other networks' inhibitory mechanisms, which do not rely on neurogenesis. In the authors' model, the variability in M/T responses observed across days emerges as a result of adult-born neurogenesis, which does not need to be the main source of variability observed in imaging experiments (Shani-Narkiss et al., Figure 4).

Another study (Kato et al., Neuron, 2012, Figure 4) reported that mitral cell responses to odors experienced repeatedly across 7 days tend to sparsen and decrease in amplitude systematically, while mitral cell responses to the same odor on day 1 vs. day 7 when the odor is not presented repeatedly in between seem less affected (although the authors also reported a decrease in the CI for this condition). As such, Kato et al. mostly report decreases in mitral cell odor responses with repeated odor exposure at both the individual and population level, and not so much increases and decreases in the individual mitral cell responses, and stability at the population level.

(2) In Figure 1, a set of GCs is killed off, and new GCs are integrated in the network as abGC. Following the elimination of 10% of GCs in the network, new cells are added and randomly assigned synaptic weights between these abGCs and MTC, GCs, SACs, and top-down projections from PCx. This is done for 11 days, during which time all GCs have gone through adult neurogenesis.

Is the authors' assumption here that across the 11 days, all GCs are being replaced? This seems to depart from the known biology of the olfactory bulb granule cells, i.e., GCs survive for a large fraction of the animal's life.

(3) The authors' model relies on several key assumptions: random projections of MTC from the olfactory bulb to the piriform cortex, random intra-piriform connectivity, and random piriform to bulb connectivity. These assumptions are not necessarily accurate, as recent work revealed structure in the projections from the olfactory bulb to the piriform cortex and structure within the piriform cortex connectivity itself (Fink et al., bioRxiv, 2025; Chae et al., Cell, 2022; Zeppilli et al., eLife, 2021).

How do the results of the model relating adult neurogenesis in the bulb to drift in the piriform cortex representations change when considering an alternative scenario in which the olfactory bulb to piriform and intra-piriform connectivity is not fully distributed and indistinguishable from random, but rather is structured?

(4) I didn't understand the logic of the low-dimensional space analysis for M/T cells and piriform cortex neurons (Figures 2 & 3). In the authors' model, the full-ensemble M/T responses are reorganized over time, presumably due to the adult-born neurogenesis. Analyzing a lower-dimensional projection of the ensemble trajectories reveals a lower degree of re-organization. This is the same for the piriform cortex, but relatively, the piriform ensembles displayed in a low-dimensional embedding appear to drift more compared to the M/T ensembles.

This analysis triggers a few questions: which representation is relevant for the brain function - the high or the low-dimensional projection? What fraction of response variance is included in the low-dimensional space analysis? How did the authors decide the low-dimensional cut-off? Why does STDP cause more drift in piriform cortex ensembles vs. M/T ensembles? Is this because of the assumed higher dimensionality of the piriform cortex representations compared to the mitral cells?

(5) Could the authors comment whether STDP at abGC synapses and its impact on decreasing drift represent a new insight, and also put it into context? Several studies (e.g., Lledo, Murthy, Komiyama groups) reported that abGC integrates in the network in an activity-dependent manner, and not randomly, and as such stabilizes the active neuronal responses, which is consistent with the authors' report.

Related, I couldn't find through the manuscript which synapses involving abGCs they focus on, or what is the relative contribution of the various plastic synapses shown in the cartoon from Figure 4 A1 (circles and triangles).

6) The study would be strengthened, in my opinion, by including specific testable predictions that the authors' models make, which can be further food for thought for experimentalists.<br /> How does suppression of adult-born neurogenesis in the OB impact the stability of mitral cell odor responses? How about piriform cortex ensembles?

Reviewer #2 (Public review):

Summary:

The authors address a critical problem in olfactory coding. It has long been known that adult neurogenesis, specifically in the form of adult-born granule cells that embed into the existing inhibitory networks on the olfactory bulb, can potentially alter the responses of Mitral/Tufted neurons that project activity to the Piriform Cortex and to other areas of the brain. Fundamentally, it would seem that these granule cells could alter the stability of neural codes in the OB over time. The authors develop a spiking network model to explore how stability can be achieved both in the OB over time and in the PC, which receives inputs. The model recapitulates published activity recordings of M/T cells and shows how activity in different M/T cells from the same glomerulus shifts over time in ways that, in spite of the shift, preserve population/glomerular level codes. However, these different M/T cells fan out onto different pyramidal cells of the PC, which gives rise to instability at that level. STDP then, is necessary to maintain stability at the PC level as long as odor environments remain constant. These results may also apply to a similar neurogenesis-based change in the Dentate Gyrus, which generates instability in CA1/3 regions of the hippocampus

Strengths:

A robust network model that untangles important, seemingly contradictory mechanisms that underlie olfactory coding.

Weaknesses:

The work is a significant contribution to understanding olfactory coding. But the manuscript would benefit from a brief discussion of why neurogenesis occurs in the first place - e.g., injury, ongoing needs for plasticity, and adapting to turnover of ORNs. There is literature on this topic. It seems counterintuitive to have a process in the MOB (and for that matter in the DG) that potentially disrupts the ability to generate stable codes both in the MOB and PC, and in particular a disruption that requires two different mechanisms - multiple M/T cells per glomerulus in the MOB and STDP in the PC - to counteract.

Given that neurogenesis has an important function, and a mechanism is in place to compensate for it in the MOB, why would it then be disrupted in fan-out projections to the PC? The answer may lie in the need for fan-out projections so that pyramidal neurons in the PC can combinatorially represent many different inputs from the MOB. So something like STDP would be needed to maintain stability in the face of the need for this coding strategy.

This kind of discussion, or something like it, would help readers understand why these mechanisms occur in the first place. It is interesting that PC stability requires that odor environments be stable, and that this stability drives PC representational stability. This result suggests experimental work to test this hypothesis. As such, it is a novel outcome of the research.

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, it 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 the differential roles of mitral and tufted cells in drift in piriform and AON and the 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.

Weaknesses

I have one major, general concern that I think must be addressed to permit proper interpretation of the results.

I worry that the authors' model may confuse thinking on drift in the olfactory system, because of differences in the behavior of their model from known features of the olfactory bulb. In their model, the tuning of individual bulbar neurons drifts over time. This is inconsistent with the experimental literature on the stability of odor-evoked activity in the olfactory bulb.

In a foundational paper, Bhalla & Bower (1997) recorded from mitral and tufted cells in the olfactory bulb of freely moving rats and measured the odor tuning of well-isolated single units across a five-day interval. They found that the tuning of a single cell was quite variable within a day, across trials, but that this variability did not increase with time. Indeed, their measure of response similarity was equivalent within and across days. In what now reads as a prescient anticipation of the drift phenomenon, Bhalla and Bower concluded: "it is clear, at least over five days, that the cell is bounded in how it can respond. If this were not the case, we would expect a continual increase in relative response variability over multiple days (the equivalent of response drift). Instead, the degree of variability in the responses of single cells is stable over the length of time we have recorded." Thus, even at the level of single cells, this early paper argues that the bulb is stable.

This basic result has since been replicated by several groups. Kato et al. (2012) used chronic two-photon calcium imaging of mitral cells in awake, head-fixed mice and likewise found that, while odor responses could be modulated by recent experience (odor exposure leading to transient adaptation), the underlying tuning of individual cells remained stable. While experience altered mitral cell odor responses, those responses recovered to their original form at the level of the single neuron, maintaining tuning over extended periods (two months). More recently, the Mizrahi lab (Shani-Narkiss et al., 2023) extended chronic imaging to six months, reporting that single-cell odor tuning curves remained highly similar over this period. These studies reinforce Bhalla and Bower's original conclusion: despite trial-to-trial variability, olfactory bulb neurons maintain stable odor tuning across extended timescales, with plasticity emerging primarily in response to experience. (The Yamada et al., 2017 paper, which the authors here cite, is not an appropriate comparison. In Yamada, mice were exposed daily to odor. Therefore, the changes observed in Yamada are a function of odor experience, not of time alone. Yamada does not include data in which the tuning of bulb neurons is measured in the absence of intervening experience.)

Therefore, a model that relies on instability in the tuning of bulbar neurons risks giving the incorrect impression that the bulb drifts over time. This difference should be explicitly addressed by the authors to avoid any potential confusion. Perhaps the best course of action would be to fit their model to Mizrahi's data, should this data be available, and see if, when constrained by empirical observation, the model still produces drift in piriform. If so, this would dramatically strengthen the paper. If this is not feasible, then I suggest being very explicit about this difference between the behavior of the model and what has been shown empirically. I appreciate that in the data there is modest drift (e.g., Shani-Narkiss' Figure 8C), but the changes reported there really are modest compared to what is exhibited by the model. A compromise would be to simply apply these metrics to the model and match the model's similarity to the Shani-Narkiss data. Then the authors could ask what effect this has on drift in piriform.

The risk here is that people will conclude from this paper that drift in piriform may simply be inherited from instability in the bulb. This view is inconsistent with what has been documented empirically, and so great care is warranted to avoid conveying that impression to the community.

Major comments (all related to the above point)

(1) Lines 146-168: The authors find in their model that "individual M/T cells changed their responses to the same odor across days due to adult-neurogenesis, with some cells decreasing the firing rate responses (Fig.2A1 top) while other cells increased the magnitude of their responses (Fig. 2A2 bottom, Fig. S2)" they also report a significant decrease in the "full ensemble correlation" in their model over time. They claim that these changes in individual cell tuning are "similar to what has been observed by others using calcium imaging of M/T cell activity (Kato et al., 2012 and Yamada et al., 2017)" and that the decrease in full ensemble correlation is "consistent with experimental observations (Yamada et al., 2017)." However, the conditions of the Kato and Yamada experiments that demonstrate response change are not comparable here, as odors were presented daily to the animals in these experiments. Therefore, the changes in odor tuning found in the Kato and Yamada papers (Kato Figure 4D; Yamada Figure 3E) are a function of accumulated experience with odor. This distinction is crucial because experience-induced changes reflect an underlying learning process, whereas changes that simply accumulate over time are more consistent with drift. The conditions of their model are more similar to those employed in other experiments described in Kato et al. 2012 (Figure 6C) as well as Shani-Narkiss et al. (2023), in which bulb tuning is measured not as a function of intervening experience, but rather as a function of time (Kato's "recovery" experiment). What is found in Kato is that even across two months, the tuning of individual mitral cells is stable. What alters tuning is experience with odor, the core finding of both the Kato et al., 2012 paper and also Yamada et al., 2017. It is crucial that this is clarified in the text.

(2) The authors show that in a reduced-space correlation metric, the correlation of low-dimensional trajectories "remained high across all days"..."consistent with a recent experimental study" (Shani-Narkiss et al., 2023). It is true that in the Shani-Narkiss paper, a consistent low-dimensional response is found across days (t-SNE analysis in Shani-Narkiss Figure 7B). However, the key difference between the Shani-Narkiss data and the results reported here is that Shani-Narkiss also observed relative stability in the native space (Shani-Narkiss Figure 8). They conclude that they "find a relatively stable response of single neurons to odors in either awake or anesthetized states and a relatively stable representation of odors by the MC population as a whole (Figures 6-8; Bhalla and Bower, 1997)." This should be better clarified in the text.

(3) In the discussion, the authors state that "In the MOB, individual M/T cells exhibited variable odor responses akin to gain control, altering their firing rate magnitudes over time. This is consistent with earlier experimental studies using calcium-imaging." (L314-6). Again, I disagree that these data are consistent with what has been published thus far. Changes in gain would have resulted in increased variability across days in the Bhalla data. Moreover, changes in gain would be captured by Kato's change index ("To quantify the changes in mitral cell responses, we calculated the change index (CI) for each responsive mitral cell-odor pair on each trial (trial X) of a given day as (response on trial X - the initial response on day 1)/(response on trial X + the initial response on day 1). Thus, CI ranges from −1 to 1, where a value of −1 represents a complete loss of response, 1 represents the emergence of a new response, and 0 represents no change." Kato et al.). This index will capture changes in gain. However, as shown in Figure 4D (red traces), Figure 6C (Recovery and Odor set B during odor set A experience and vice versa), the change index is either zero or near zero. If the authors wish to claim that their model is consistent with these data, they should also compute Kato's change index for M/T odor-cell pairs in their model and show that it also remains at 0 over time, absent experience.

Reviewer #1 (Public review):

Summary:

This manuscript addresses an important question: whether cortical population codes for cochlear-implant (CI) stimulation resemble those for natural acoustic input or constitute a qualitatively different representation. The authors record intracranial EEG (µECoG) responses to pure tones in normal-hearing rats and to single-channel CI pulses in bilaterally deafened, acutely implanted rats, analysing the data with ERP/high-gamma measures, tensor component analysis (TCA), and information-theoretic decoding. Across several readouts, the acoustic condition supports better single-trial stimulus classification than the CI condition. However, stronger decoding does not, on its own, establish that the acoustic responses instantiate a "richer" cortical code, and the evidence for orderly spatial organisation is not compelling for CI, and is also less evident than expected for normal-hearing, given prior knowledge. The overall narrative is interesting, but at present, the conclusions outpace the data because of statistical, methodological, and presentation issues.

Strengths:

The study poses a timely, clinically relevant question with clear implications for CI strategy. The analytical toolkit is appropriate: µECoG captures mesoscale patterns; TCA offers a transparent separation of spatial and temporal structure; and mutual-information decoding provides an interpretable measure of single-trial discriminability. Within-subject recordings in a subset of animals, in principle, help isolate modality effects from inter-animal variability. Where analyses are most direct, the acoustic condition yields higher single-trial decoding accuracy, which is a meaningful and clearly presented result.

Weaknesses:

Several limitations constrain how far the conclusions can be taken. Parts of the statistical treatment do not match the data structure: some comparisons mix paired and unpaired animals but are analysed as fully paired, raising concerns about misestimated uncertainty. Methodological reporting is incomplete in places; essential parameters for both acoustic and electrical stimulation, as well as objective verification of implantation and deafening, are not described with sufficient detail to support confident interpretation or replication. Figure-level clarity also undermines the message. In Figure 2, non-significant slopes for CI, repeated identification of a single "best channel," mismatched axes, and unclear distinctions between example and averaged panels make the assertion of spatial organisation unconvincing; importantly, the normal-hearing panels also do not display tonotopy as clearly as expected, which weakens the key contrast the paper seeks to establish. Finally, the decoding claims would be strengthened by simple internal controls, such as within-modality train/test splits and decoding on raw ERP/high-gamma features to demonstrate that poor cross-modal transfer reflects genuine differences in the underlying responses rather than limitations of the modelling pipeline.

Reviewer #2 (Public review):

Summary:

This article reports measurements of iEEG signals on the rat auditory cortex during cochlear implant or sound stimulation in separate groups of rats. The observations indicate some spatial organization of cochlear implant stimuli, but that is very different from cochlear implants.

Strengths:

The study includes interesting analyses of the sound and cochlear implant representation structure based on decoders.

Weaknesses:

The observation that responses to cochlear implant stimulation (stimulation) are spatially organized is not new (e.g., Adenis et al. 2024).

The claim that spatial and temporal dimensions contribute information about the sound is also not new; there is a large literature on this topic. Moreover, the results shown here are extremely weak. They show similar levels of information in the spatial and temporal dimensions, and no synergy between the two dimensions. This is however, likely the consequence of high measurement noise leading to poor accuracy in the information estimates, as the authors state.

The main claim of the study - the mismatch between cochlear implant and sound representation - is not supported. The responses to each modality are measured in different animals. The authors do not show that they actually can compare representations across animals (e.g., for the same sounds). Without this positive control, there is no reason to think that it is possible to decode from one animal with a decoder trained on another, and the negative result shown by the authors is therefore not surprising.

Reviewer #3 (Public review):

Summary:

Through micro-electroencephalography, Hight and colleagues studied how the auditory cortex in its ensemble responds to cochlear implant stimulation compared to the classic pure tones. Taking advantage of a double-implanted rat model (Micro-ECoG and Cochlear Implant), they tracked and analyzed changes happening in the temporal and spatial aspects of the cortical evoked responses in both normal hearing and cochlear-implanted animals. After establishing that single-trial responses were sufficient to encode the stimuli's properties, the authors then explored several decoder architectures to study the cortex's ability to encode each stimulus modality in a similar or different manner. They conclude that a) intracranial EEG evoked responses can be accurately recorded and did not differed between normal hearing and cochlear-implanted rats; b) Although coarsely spatially organized, CI-evoked responses had higher trial-by-trial variability than pure tones; c) Stimulus identity is independently represented by temporal and spatial aspect of cortical representations and can be accurately decoded by various means from single trials; d) and that Pure tones trained decoder can't decode CI-stimulus identity accurately.

Strength:

The model combining micro-eCoG and cochlear implantation and the methodology to extract both the Event Related Potentials (ERPs) and High-Gammas (HGs) is very well designed and appropriately analyzed. Likewise, the PCA-LDA and TCA-LDA are powerful tools that take full advantage of the information provided by the cortical ensembles.

The overall structure of the paper, with a paced and exhaustive progress through each step and evolution of the decoder, is very appreciable and easy to follow. The exploration of single-trial encoding and stimulus identity through temporal and spatial domains is providing new avenues to characterize the cortical responses to CI stimulations and their central representation. The fact that single trials suffice to decode the stimulus identity regardless of their modality is of great interest and noteworthy. Although the authors confirm that iEEG remains difficult to transpose in the clinic, the insights provided by the study confirm the potential benefit of using central decoders to help in clinic settings.

Weaknesses:

The conclusion of the paper, especially the concept of distinct cortical encoding for each modality, is unfortunately partially supported by the results, as the authors did not adequately consider fundamental limitations of CI-related stimulation.

First, the reviewer assumed that the authors stimulated in a Monopolar mode, which, albeit being clinically relevant, notoriously generates a high current spread in rodent models. Second, comparing the averaged BF maps for iEEG (Figure 2A, C), BFs ranged from 4 to 16kHz with a predominance of 4kHz BFs. The lack of BFs at higher frequencies hints at a potential location mismatch between the frequency range sampled at the level of the cortex (low to medium frequencies) and the frequency range covered by the CI inserted mostly in the first turn-and-a-half of the cochlea (high to medium frequencies). Looking at Figure 2F (and to some extent 2A), most of the CI electrodes elicited responses around the 4kHz regions, and averaged maps show a predominance of CI-3-4 across the cortex (Figure 2C, H) from areas with 4kHz BF to areas with 16kHz BF. It is doubtful that CI-3-4 are located near the 4kHz region based on Müller's work (1991) on the frequency representation in the rat cochlea.

Taken together with the Pearsons correlations being flat, the decoder examples showing a strong ability to identify CI-4 and 3 and the Fig-8D, E presenting a strong prediction of 4kHz and 8kHz for all the CI electrodes when using a pure tone trained decoder, it is possible that current spread ended stimulating indistinctly higher turns of the cochlea or even the modiolus in a non-specific manner, greatly reducing (or smearing) the place-coding/frequency resolution of each electrode, which in turn could explain the coarse topographic (or coarsely tonotopic according to the manuscript) organization of the cortical responses. Thus, the conclusion that there are distinct encodings for each modality is biased, as it might not account for monopolar smearing. To that end, and since it is the study's main message and title, it would have benefited from having a subgroup of animals using bipolar stimulations (or any focused strategy since they provide reduced current spread) to compare the spatial organization of iEEG responses and the performances of the different decoders to dismiss current spread and strengthen their conclusion.

Nevertheless, the reviewer wants to reiterate that the study proposed by Hight et al. is well constructed, relevant to the field, and that the overall proposal of improving patient performances and helping their adaptation in the first months of CI use by studying central responses should be pursued as it might help establish new guidelines or create new clinical tools.

Reviewer #1 (Public review):

In this manuscript, Clausner and colleagues use simultaneous EEG and fMRI recordings to clarify how visual brain rhythms emerge across layers of early visual cortex. They report that gamma activity correlates positively with feature-specific fMRI signals in superficial and deep layers. By contrast, alpha activity generally correlated negatively with fMRI signals, with two higher frequencies within the alpha reflecting feature-specific fMRI signals. This feature-specific alpha code indicates an active role of alpha oscillations in visual feature coding, providing compelling evidence that the functions of alpha oscillations go beyond cortical idling or feature-unspecific suppression.

The study is very interesting and timely. Methodologically, it is state-of-the-art. The findings on a more active role of alpha activity that goes beyond the classical idling or suppression accounts are in line with recent findings and theories. In sum, this paper makes a very nice contribution. I still have a few comments that I outline below, regarding the data visualization, some methodological aspects, and a couple of theoretical points.

(1) The authors put a lot of effort into the figure design. For instance, I really like Figure 1, which conveys a lot of information in a nice way. Figures 3 and 4, however, seem overengineered, and it takes a lot of time to distill the contents from them. The fact that they have a supplementary figure explaining the composition of these figures already indicates that the authors realized this is not particularly intuitive. First of all, the ordering of the conditions is not really intuitive. Second, the indication of significance through saturation does not really work; I have a hard time discerning the more and less saturated colors. And finally, the white dots do not really help either. I don't fully understand why they are placed where they are placed (e.g., in Figure 3). My suggestion would be to get rid of one of the factors (I think the voxel selection threshold could go: the authors could run with one of the stricter ones, and the rest could go into the supplement?) and then turn this into a few line plots. That would be so much easier to digest.

(2) The division between high- and low-frequency alpha in the feature-specific signal correspondence is very interesting. I am wondering whether there is an opposite effect in the feature-unspecific signal correspondence. Would the high-frequency alpha show less of a feature-unspecific correlation with the BOLD?

(3) In the discussion (line 330 onwards), the authors mention that low-frequency alpha is predominantly related to superficial layers, referencing Figure 4A. I have a hard time appreciating this pattern there. Can the authors provide some more information on where to look?

(4) How did the authors deal with the signal-to-noise ratio (SNR) across layers, where the presence of larger drain veins typically increases BOLD (and thereby SNR) in superficial layers? This may explain the pattern of feature-unspecific effects in the alpha (Figure 3). Can the authors perform some type of SNR estimate (e.g., split-half reliability of voxel activations or similar) across layers to check whether SNR plays a role in this general pattern?

(5) The GLM used for modelling the fMRI data included lots of regressors, and the scanning was intermittent. How much data was available in the end for sensibly estimating the baseline? This was not really clear to me from the methods (or I might have missed it). This seems relevant here, as the sign of the beta estimates plays a major role in interpreting the results here.

(6) Some recent research suggests that gamma activity, much in contrast to the prevailing view of the mechanism for feedforward information propagation, relates to the feedback process (e.g., Vinck et al., 2025, TiCS). This view kind of fits with the localization of gamma to the deep layer here?

(7) Another recent review (Stecher et al., 2025, TiNS) discusses feature-specific codes in visual alpha rhythms quite a bit, and it might be worth discussing how your results align with the results reported there.

Reviewer #2 (Public review):

The authors address a long-standing controversy regarding the functional role of neural oscillations in cortical computations and layer-specific signalling. Several studies have implicated gamma oscillations in bottom-up processing, while lower-frequency oscillations have been associated with top-down signalling. Therefore, the question the authors investigate is both timely and theoretically relevant, contributing to our understanding of feedforward and feedback communication in the brain. This paper presents a novel and complicated data acquisition technique, the application of simultaneous EEG and fMRI, to benefit from both temporal and spatial resolution. A sophisticated data analysis method was executed in order to understand the underlying neural activity during a visual oddball task. Figures are well-designed and appropriately represent the results, which seem to support the overall conclusions. However, some of the claims (particularly those regarding the contribution of gamma oscillations) feel somewhat overstated, as the results offer indeed some significant evidence, but most seem more like a suggestive trend. Nonetheless, the paper is well-written, addresses a relevant and timely research question, introduces a novel and elegant analysis approach, and presents interesting findings. Further investigation will be important to strengthen and expand upon these insights.

One of the main strengths of the paper lies in the use of a well-established and straightforward experimental paradigm (the visual oddball task). As a result, the behavioural effects reported were largely expected and reassuring to see replicated. The acquisition technique used is very novel, and while this may introduce challenges for data analysis, the authors appear to have addressed these appropriately.

Later findings are very interesting, and mainly in line with our current understanding of feedback and feedforward signalling. However, the layer weight calculation is lacking in the manuscript. While it is discussed in the methods, it would help to briefly explain in the results how these weights are calculated, so that the reader can better follow what is being interpreted.

Line 104 states there is one virtual channel per hemisphere for low and high frequencies. It may be helpful to include the number of channels (n=4) in the results section, as specified in the methods. Also, this raises the question of whether a single virtual channel (i.e., voxel) provides sufficient information for reproducibility.

One area that would benefit from further clarification is the interpretation of gamma oscillations. The evidence for gamma involvement in the observed effects appears somewhat limited. For example, no significant gamma-related clusters were found for the feature-unspecific BOLD signal (Figure 2). Significant effects emerged only when the analysis was restricted to positively responding voxels, and even then, only for the contrast between EEG-coherent and EEG-incoherent conditions in the feature-specific BOLD response. It remains unclear how to interpret this selective emergence of gamma-related effects. Given previous literature linking gamma to feedforward processing, one might expect more robust involvement in broader, feature-unspecific contrasts. The current discussion presents the gamma-related findings with some confidence, and the manuscript would benefit from a more nuanced reflection on why these effects may not have appeared more broadly. The explanation provided in line 230, that restricting the analysis to positively responding voxels may have increased the SNR, is reasonable, but it may not fully account for the absence of gamma effects in V1's feature-unspecific response. Including the actual beta values from Figure 4 in the legend or main text would also help readers better assess the strength and specificity of the reported effects.

Relating to behavioural findings for underlying neural activity, could the authors test on a trial-by-trial basis how behavioural performance relates to the BOLD signal / oscillatory activity change? Line 305 states that "Since behavioural performance in the present study was consistently high at 94% on average and participants were instructed to respond quickly to potential oddball stimuli, a higher alpha frequency might reflect a more successful stimulus encoding and hence faster and more accurate behavioural performance." Also, this might help to relate the findings to the lower vs upper alpha functionality difference.

In Figure 4, the EEG alpha specificity plot shows relatively large error bars, and there is visible overlap between the lower and upper alpha in both congruent and incongruent conditions. While upper alpha shows a positive slope across conditions and lower alpha remains flat, the interaction appears to be driven by the change from congruent to incongruent in upper alpha. It is worth clarifying whether the simple effects (e.g., lower vs upper within each condition) were tested, given the visual similarity at the incongruent condition. Overall, the significant interaction (p < 0.001, FDR-corrected) is consistent with diverging trends, but a breakdown of simple effects would help interpret the result more clearly. Was there a significant difference between lower and upper alpha in congruent or incongruent conditions?

Overall, this study provides a valuable contribution to the literature on oscillatory dynamics and laminar fMRI, though some interpretations would benefit from further clarification or qualification.

Reviewer #3 (Public review):

Summary:

Clausner et al. investigate the relationship between cortical oscillations in the alpha and gamma bands and the feature-specific and feature-unspecific BOLD signals across cortical layers. Using a well-designed stimulus and GLM, they show a method by which different BOLD signals can be differentiated and investigated alongside multiple cortical oscillatory frequencies. In addition to the previously reported positive relationship between gamma and BOLD signals in superficial layers, they show a relationship between gamma and feature-specific BOLD in the deeper layers. Alpha-band power is shown to have a negative relationship with the negative BOLD response for both feature-specific and feature-unspecific contrasts. When separated into lower (8-10Hz) and upper (11-13Hz) alpha oscillations, they show that higher frequency alpha showed a significantly stronger negative relationship with congruency, and can therefore be interpreted as more feature-specific than lower frequency alpha.

Strengths:

The use of interleaved EEG-fMRI has provided a rich dataset that can be used to evaluate the relationship of cortical layer BOLD signals with multiple EEG frequencies. The EEG data were of sufficient quality to see the modulation of both alpha-band and gamma-band oscillations in the group mean VE-channel TFS. The good EEG data quality is backed up with a highly technical analysis pipeline that ultimately enables the interpretation of the cortical layer relationship of the BOLD signal with a range of frequencies in the alpha and gamma bands. The stimulus design allowed for the generation of multiple contrasts for the BOLD signal and the alpha/gamma oscillations in the GLM analysis. Feature-specific and unspecific BOLD contrasts are used with congruently or incongruently selected EEG power regressors to delineate between local and global alpha modulations. A transparent approach is used for the selection of voxels contributing to the final layer profiles, for which statistical analysis is comprehensive but uses an alternative statistical test, which I have not seen in previous layer-fMRI literature.

A significant negative relationship between alpha-band power and the BOLD signal was seen in congruently (EEGco) selected voxels (predominantly in superficial layers) and in feature-contrast (EEGco-inco) selected (superficial and deep layers). When separated into lower (8-10Hz) and upper (11-13Hz) alpha oscillations, they show that higher frequency alpha showed a significantly stronger negative relationship with congruency than lower frequency alpha. This is interpreted as a frequency dissociation in the alpha-BOLD relationship, with upper frequency alpha being feature-specific and lower frequency alpha corresponding to general modulation. These results are a valuable addition to the current literature and improve our current understanding of the role of cortical alpha oscillations.

There is not much work in the literature on the relationship between alpha power and the negative BOLD response (NBR), so the data provided here are particularly valuable. The negative relationship between the NBR and alpha power shown here suggests that there is a reduction in alpha power, linked to locally reduced BOLD activity, which is in line with the previously hypothesized inhibitory nature of alpha.

Weaknesses:

It is not entirely clear how the draining vein effect seen in GE-BOLD layer-fMRI data has been accounted for in the analysis. For the contrast of congruent-incongruent, it is assumed that the underlying draining effect will be the same for both conditions, and so should be cancelled out. However, for the other contrasts, it is unclear how the final layer profiles aren't confounded by the bias in BOLD signal towards the superficial layers. Many of the profiles in Figure 3 and Figure 4A show an increased negative correlation between alpha power and the BOLD signal towards the superficial layers.

When investigating if high alpha (8-10 Hz) and low alpha (11-13 Hz) are two different sources of alpha, it would be beneficial to show if this effect is only seen at the group level or can be seen in any single subjects. Inter-subject variability in peak alpha power could result in some subjects having a single low alpha peak and some a single high alpha peak rather than two peaks from different sources.

The figure layout used to present the main findings throughout is an innovative way to present so much information, but it is difficult to decipher the main findings described in the text. The readability would be improved if the example (Appendix 0 - Figure 1) in the supplementary material is included as a second panel inside Figure 3, or, if this is not possible, the example (Appendix 0 - Figure 1) should be clearly referred to in the figure caption.

Reviewer #1 (Public review):

Summary:

The current study sought to understand which reference frames humans use when doing visual search in naturalistic conditions. To this end, they had participants do a visual search task in a VR environment while manipulating factors such as object orientation, body orientation, gravitational cues, and visual context (where the ground is). They generally found that all cues contributed to participants' performance, but visual context and gravitational cues impacted performance the most, suggesting that participants represent space in an allocentric reference frame during visual search.

Strengths:

The study is valuable in that it sheds light on which cues participants use during visual search. Moreover, I appreciate the use of VR and precise psychophysical predictions (e.g., slope vs. intercept) to dissociate between possible reference frames.

Weaknesses:

It's not clear what the implications of the study are beyond visual search. Moreover, I have some concerns about the interpretation of Experiment 1, which relies on an incorrect interpretation of mental rotation. Thus, most of the conclusions rely on Experiment 2, which has a small sample size (n = 10). Finally, the statistical analyses could be strengthened with measures of effect size and non-parametric statistics.

Reviewer #2 (Public review):

Summary:

This paper addresses an interesting issue: how is the search for a visual target affected by its orientation (and the viewer's) relative to other items in the scene and gravity? The paper describes a series of visual search tasks, using recognizable targets (e.g., a cat) positioned within a natural scene. Reaction times and accuracy at determining whether the target was present or absent, trial-to-trial, were measured as the target's orientation, that of the context, and of the viewer themselves (via rotation in a flight simulator) were manipulated. The paper concludes that search is substantially affected by these manipulations, primarily by the reference frame of gravity, then visual context, followed by the egocentric reference frame.

Strengths:

This work is on an interesting topic, and benefits from using natural stimuli in VR / flight simulator to change participants' POV and body position.

Weaknesses:

There are several areas of weakness that I feel should be addressed.

(1) The literature review/introduction seems to be lacking in some areas. The authors, when contemplating the behavioral consequences of searching for a 'rotated' target, immediately frame the problem as one of rotation, per se (i.e., contrasting only rotation-based explanations; "what rotates and in which 'reference frame[s]' in order to allow for successful search?"). For a reader not already committed to this framing, many natural questions arise that are worth addressing.

1a) Why do we need to appeal to rotation at all as opposed to, say, familiarity? A rotated cat is less familiar than a typically oriented one. This is a long-standing literature (e.g., Wang, Cavanagh, and Green (1994)), of course, with a lot to unpack.

1b) What are the triggers for the 'corrective' rotation that presumably brings reference frames back into alignment? What if the rotation had not been so obvious (i.e. for a target that may not have a typical orientation, like a hand, or a ball, or a learned, nonsense object?) or the background had not had such clear orientation (like a cluttered non-naturalistic background of or a naturalistic backdrop, but viewed from an unfamiliar POV (e.g., from above) or a naturalistic background, but not all of the elements were rotated)? What, ultimately, is rotated? The entire visual field? Does that mean that searching for multiple targets at different angles of rotation would interfere with one another?

1c) Relatedly, what is the process by which the visual system comes to know the 'correct' rotation? (Or, alternatively, is 'triggered to realize' that there is a rotation in play?) Is this something that needs to be learned? Is it only learned developmentally, through exposure to gravity? Could it be learned in the context of an experiment that starts with unfamiliar stimuli?

1d) Why the appeal to natural images? I appreciate any time a study can be moved from potentially too stripped-down laboratory conditions to more naturalistic ones, but is this necessary in the present case? Would the pattern of results have been different if these were typical laboratory 'visual search' displays of disconnected object arrays?

1e) How should we reconcile rotation-based theories of 'rotated-object' search with visual search results from zero gravity environments (e.g., for a review, see Leone (1998))?

1f) How should we reconcile the current manipulations with other viewpoint-perspective manipulations (e.g., Zhang & Pan (2022))?

(2) The presentation/interpretation of results would benefit from more elaboration and justification.

2a) All of the current interpretations rely on just the RT data. First, the RT results should also be presented in natural units (i.e., seconds/ms), not normalized. As well, results should be shown as violin plots or something similar that captures distribution - a lot of important information is lost when just presenting one 'average' dot across participants. More fundamentally, I think we need to have a better accounting for performance (percent correct or d') to help contextualize the RT results. We should at least be offered some visualization (Heitz, 2014) of the speed accuracy trade-off for each of the conditions. Following this, the authors should more critically evaluate how any substantial SAT trends could affect the interpretation of results.

2b) Unless I am missing something, the interpretation of the pattern of results (both qualitatively and quantitatively in their 'relative weight' analysis) relies on how they draw their contrasts. For instance, the authors contrast the two 'gravitational' conditions (target 0 deg versus target 90 deg) as if this were a change in a single variable/factor. But there are other ways to understand these manipulations that would affect contrasts. For instance, if one considers whether the target was 'consistent' (i.e., typically oriented) with respect to the context, egocentric, and gravitational frames, then the 'gravitational 0 deg' condition is consistent with context, egocentric view, but inconsistent with gravity. And, the 'gravitational 90 deg' condition, then, is inconsistent with context, egocentric view, but consistent with gravity. Seen this way, this is not a change in one variable, but three. The same is true of the baseline 0 deg versus baseline 90 deg condition, where again we have a change in all three target-consistency variables. The 'one variable' manipulations then would be: 1) baseline 0 versus visual context 0 (i.e., a change only in the context variable); 2) baseline 0 versus egocentric 0 (a change only in the egocentric variable); and 3) baseline 0 versus gravitational 0 (a change only in the gravitational variable). Other contrasts (e.g., gravitational 90 versus context 90) would showcase a change in two variables (in this case, a change in both context and gravity). My larger point is, again, unless I am really missing something, that the choice of how to contrast the manipulations will affect the 'pattern' of results and thereby the interpretation. If the authors agree, this needs to be acknowledged, plausible alternative schemes discussed, and the ultimate choice of scheme defended as the most valid.

2c) Even with this 'relative weight' interpretation, there are still some patterns of results that seem hard to account for. Primarily, the egocentric condition seems hard to account for under any scheme, and the authors need to spend more time discussing/reconciling those results.

2d) Some results are just deeply counterintuitive, and so the reader will crave further discussion. Most saliently for me, based on the results of Experiment 2 (specifically, the fact that gravitational 90 had better performance than gravitational 0), designers of cockpits should have all gauges/displays rotate counter to the airplane so that they are always consistent with gravity, not the pilot. Is this indeed a fair implication of the results?

2e) I really craved some 'control conditions' here to help frame the current results. In keeping with the rhetorical questions posed above in 1a/b/c/d, if/when the authors engage with revisions to this paper, I would encourage the inclusion of at least some new empirical results. For me the most critical would be to repeat some core conditions, but with a symmetric target (e.g. a ball) since that would seem to be the only way (given the current design) to tease out nuisance confounding factors such as, say, the general effect of performing search while sideways (put another way, the authors would have to assume here that search (non-normalized RT's and search performance) for a ball-target in the baseline condition would be identical to that in the gravitational condition.)

Reviewer #3 (Public review):

The study tested how people search for objects in natural scenes using virtual reality. Participants had to find targets among other objects, shown upright or tilted. The main results showed that upright objects were found faster and more accurately. When the scene or body was rotated, performance changed, showing that people use cues from the environment and gravity to guide search.

The manuscript is clearly written and well designed, but there are some aspects related to methods and analyses that would benefit from stronger support.

First, the sample size is not justified with a power analysis, nor is it explained how it was determined. This is an important point to ensure robustness and replicability.

Second, the reaction time data were processed using different procedures, such as the use of the median to exclude outliers and an ad hoc cut-off of 50 ms. These choices are not sufficiently supported by a theoretical rationale, and could appear as post-hoc decisions.

Third, the mixed-model analyses are overall well-conducted; however, the specification of the random structure deserves further consideration. The authors included random intercepts for participants and object categories, which is appropriate. However, they did not include random slopes (e.g., for orientation or set size), meaning that variability in these effects across participants was not modelled. This simplification can make the models more stable, but it departs from the maximal random structure recommended by Barr et al. (2013). The authors do not explicitly justify this choice, and a reviewer may question why participant-specific variability in orientation effects, for example, was not allowed. Given the modest sample sizes (20 in Experiment 1 and 10 in Experiment 2), convergence problems with more complex models are likely. Nonetheless, ignoring random slopes can, in principle, inflate Type I error rates, so this issue should at least be acknowledged and discussed.

Reviewer #1 (Public review):

The authors conducted a series of experiments using two established decision-making tasks to clarify the relationship between internalizing psychopathology (anxiety and depression) and adaptive learning in uncertain and volatile environments. While prior literature has reported links between internalizing symptoms - particularly trait anxiety - and maladaptive increases in learning rates or impaired adjustment of learning rates, findings have been inconsistent. To address this, the authors designed a comprehensive set of eight experiments that systematically varied task conditions. They also employed a bifactor analysis approach to more precisely capture the variance associated with internalizing symptoms across anxiety and depression. Across these experiments, they found no consistent relationship between internalizing symptoms and learning rates or task performance, concluding that this purported hallmark feature may be more subtle than previously assumed.

Strengths:

(1) A major strength of the paper lies in its impressive collection of eight experiments, which systematically manipulated task conditions such as outcome type, variability, volatility, and training. These were conducted both online and in laboratory settings. Given that trial conditions can drive or obscure observed effects, this careful, systematic approach enables a robust assessment of behavior. The consistency of findings across online and lab samples further strengthens the conclusions.

(2) The analyses are impressively thorough, combining model-agnostic measures, extensive computational modeling (e.g., Bayesian, Rescorla-Wagner, Volatile Kalman Filter), and assessments of reliability. This rigor contributes meaningfully to broader methodological discussions in computational psychiatry, particularly concerning measurement reliability.

(3) The study also employed two well-established, validated computational tasks: a game-based predictive inference task and a binary probabilistic reversal learning task. This choice ensures comparability with prior work and provides a valuable cross-paradigm perspective for examining learning processes.

(4) I also appreciate the open availability of the analysis code that will contribute substantially to the field using similar tasks.

Weakness:

(1) While the overall sample size (N = 820 across eight experiments) is commendable, the number of participants per experiment is relatively modest, especially in light of the inherent variability in online testing and the typically small effect sizes in correlations with mental health traits (e.g., r = 0.1-0.2). The authors briefly acknowledge that any true effects are likely small; however, the rationale behind the sample sizes selected for each experiment is unclear. This is especially important given that previous studies using the predictive inference task (e.g., Seow & Gillan, 2020, N > 400; Loosen et al., 2024, N > 200) have reported non-significant associations between trait anxiety symptoms and learning rates.

(2) The motivation for focusing on the predictive inference task is also somewhat puzzling, given that no cited study has reported associations between trait anxiety and parameters of this task. While this is mitigated by the inclusion of a probabilistic reversal learning task, which has a stronger track record in detecting such effects, the study misses an opportunity to examine whether individual differences in learning-related measures correlate across the two tasks, which could clarify whether they tap into shared constructs.

(3) The parameterization of the tasks, particularly the use of high standard deviations (SDs) of 20 and 30 for outcome distributions and hazard rates of 0.1 and 0.16, warrants further justification. Are these hazard rates sufficiently distinct? Might the wide SDs reduce sensitivity to volatility changes? Prior studies of the circle version of this predictive inference task (e.g., Vaghi et al., 2019; Seow & Gillan, 2020; Marzuki et al., 2022; Loosen et al., 2024; Hoven et al., 2024) typically used SDs around 12. Indeed, the Supplementary Materials suggest that variability manipulations did not seem to substantially affect learning rates (Figure S5)-calling into question whether the task manipulations achieved their intended cognitive effects.

(4) Relatedly, while the predictive inference task showed good reliability, the reversal learning task exhibited only "poor-to-moderate" reliability in its learning-rate estimates. Given that previous findings linking anxiety to learning rates have often relied on this task, these reliability issues raise concerns about the robustness and generalizability of conclusions drawn from it.

(5) As the authors note, the study relies on a subclinical sample. This limits the generalizability of the findings to individuals with diagnosed disorders. A growing body of research suggests that relationships between cognition and symptomatology can differ meaningfully between general population samples and clinical groups. For example, Hoven et al. (2024) found differing results in the predictive inference task when comparing OCD patients, healthy controls, and high- vs. low-symptom subgroups.

(6) Finally, the operationalization of internalizing symptoms in this study appears to focus on anxiety and depression. However, obsessive-compulsive disorder is also generally considered an internalizing disorder, which presents a gap in the current cited literature of the paper, particularly when there have been numerous studies with the predictive inference task and OCD/compulsivity (e.g., Vaghi et al., 2019; Seow & Gillan, 2020; Marzuki et al., 2022; Loosen et al., 2024; Hoven et al., 2024), rather than trait anxiety per se.

Overall:

Despite the named limitations, the authors have done very impressive work in rigorously examining the relationship between anxiety/internalizing symptoms and learning rates in commonly used decision-making tasks under uncertainty. Their conclusion is well supported by the consistency of their null findings across diverse task conditions, though its generalizability may be limited by some features of the task design and its sample. This study provides strong evidence that will guide future research, whether by shifting the focus of examining dysfunctions of larger effect sizes or by extending investigations to clinical populations.

Reviewer #2 (Public review):

Summary:

In this work, the authors recruited a large sample of participants to complete two well-established paradigms: the predictive inference task and the volatile reversal learning task. With this dataset, they not only replicated several classical findings on uncertainty-based learning from previous research but also demonstrated that individual differences in learning behavior are not systematically associated with internalizing psychopathology. These results provide valuable large-scale evidence for this line of research.

Strengths:

(1) Use of two different tasks.

(2) Recruitment of a large sample of participants.

(3) Inclusion of multiple experiments with different conditions, demonstrating strong scientific rigor.

Weaknesses:

Below are questions rather than 'weaknesses':

(1) This study uses a large human sample, which is a clear strength. However, was the study preregistered? It would also be useful to report a power analysis to justify the sample size.

(2) Previous studies have tested two core hypotheses: (a) that internalizing psychopathology is associated with overall higher learning rates, and (b) that it is associated with learning rate adaptation. In the first experiment, the findings seem to disconfirm only the first hypothesis. I found it unclear how, in the predator task, participants were expected to adjust their learning rate to adapt to volatility. Could the authors clarify this point?

(3) According to the Supplementary Information, Model 13 showed the best fit, yet the authors selected Model 12 due to the larger parameter variance in Model 13. What would the results of Model 13 look like? Furthermore, do Models 12 and 13 correspond to the optimal models identified by Gagne et al. (2020)? Please clarify.

(4) In the Discussion, the authors addressed both task reliability and parameter reliability. However, the term reliability seems to be used differently in these two contexts. For example, good parameter recovery indicates strong reliability in one sense, but can we then directly equate this with parameter reliability? It would be helpful to define more precisely what is meant by reliability in each case.

(5) The Discussion also raises the possibility that limited reliability may represent a broader challenge facing the interdisciplinary field of computational psychiatry. What, in the authors' view, are the key future directions for the field to mitigate this issue?

Reviewer #1 (Public review):

Summary:

The authors present MerQuaCo, a computational tool that fills a critical gap in the field of spatial transcriptomics: the absence of standardized quality control (QC) tools for image-based datasets. Spatial transcriptomics is an emerging field where datasets are often imperfect, and current practices lack systematic methods to quantify and address these imperfections. MerQuaCo offers an objective and reproducible framework to evaluate issues like data loss, transcript detection variability, and efficiency differences across imaging planes.

Strengths:

(1) The study draws on an impressive dataset comprising 641 mouse brain sections collected on the Vizgen MERSCOPE platform over two years. This scale ensures that the documented imperfections are not isolated or anecdotal but represent systemic challenges in spatial transcriptomics. The variability observed across this large dataset underscores the importance of using sufficiently large sample sizes when benchmarking different image-based spatial technologies. Smaller datasets risk producing misleading results by over-representing unusually successful or unsuccessful experiments. This comprehensive dataset not only highlights systemic challenges in spatial transcriptomics but also provides a robust foundation for evaluating MerQuaCo's metrics. The study sets a valuable precedent for future quality assessment and benchmarking efforts as the field continues to evolve.

(2) MerQuaCo introduces thoughtful metrics and filters that address a wide range of quality control needs. These include pixel classification, transcript density, and detection efficiency across both x-y axes (periodicity) and z-planes (p6/p0 ratio). The tool also effectively quantifies data loss due to dropped images, providing tangible metrics for researchers to evaluate and standardize their data. Additionally, the authors' decision to include examples of imperfections detectable by visual inspection but not flagged by MerQuaCo reflects a transparent and balanced assessment of the tool's current capabilities.

Weaknesses:

(1) The study focuses on cell-type label changes as the main downstream impact of imperfections. Broadening the scope to explore expression response changes of downstream analyses would offer a more complete picture of the biological consequences of these imperfections and enhance the utility of the tool.

(2) While the manuscript identifies and quantifies imperfections effectively, it does not propose post-imaging data processing solutions to correct these issues, aside from the exclusion of problematic sections or transcript species. While this is understandable given the study is aimed at the highest quality atlas effort, many researchers don't need that level of quality to compare groups. It would be important to include discussion points as to how those cut-offs should be decided for a specific study.

(3) Although the authors demonstrate the applicability of MerQuaCo on a large MERFISH dataset, and the limited number of sections from other platforms, it would be helpful to describe its limitations in its generalizability.

Reviewer #2 (Public review):

Summary:

The authors present MerQuaCo, a computational tool for quality control in image-based spatial transcriptomic, especially MERSCOPE. They assessed MerQuaCo on 641 slides that are produced in their institute in terms of the ratio of imperfection, transcript density, and variations of quality by different planes (x-axis).

Strengths:

This looks to be a valuable work that can be a good guideline of quality control in future spatial transcriptomics. A well-controlled spatial transcriptomics dataset is also important for the downstream analysis.

Weaknesses:

The results section needs to be more structured.

Reviewer #3 (Public review):

Summary:

MerQuaCo is an open-source computational tool developed for quality control in image-based spatial transcriptomics data, with a primary focus on data generated by the Vizgen MERSCOPE platform. The authors analyzed a substantial dataset of 641 fresh-frozen adult mouse brain sections to identify and quantify common imperfections, aiming to replace manual quality assessment with an automated, objective approach, providing standardized data integrity measures for spatial transcriptomics experiments.

Strengths:

The manuscript's strengths lie in its timely utility, rigorous empirical validation, and practical contributions to methodology and biological discovery in spatial transcriptomics.

Weaknesses:

While MerQuaCo demonstrates utility in large datasets and cross-platform potential, its generalizability and validation require expansion, particularly for non-MERSCOPE platforms and real-world biological impact.

Reviewer #1 (Public review):

The authors present MerQuaCo, a computational tool that fills a critical gap in the field of spatial transcriptomics: the absence of standardized quality control (QC) tools for image-based datasets. Spatial transcriptomics is an emerging field where datasets are often imperfect, and current practices lack systematic methods to quantify and address these imperfections. MerQuaCo offers an objective and reproducible framework to evaluate issues like data loss, transcript detection variability, and efficiency differences across imaging planes.

Strengths

(1) The study draws on an impressive dataset comprising 641 mouse brain sections collected on the Vizgen MERSCOPE platform over two years. This scale ensures that the documented imperfections are not isolated or anecdotal but represent systemic challenges in spatial transcriptomics. The variability observed across this large dataset underscores the importance of using sufficiently large sample sizes when benchmarking different image-based spatial technologies. Smaller datasets risk producing misleading results by over-representing unusually successful or unsuccessful experiments. This comprehensive dataset not only highlights systemic challenges in spatial transcriptomics but also provides a robust foundation for evaluating MerQuaCo's metrics. The study sets a valuable precedent for future quality assessment and benchmarking efforts as the field continues to evolve.

(2) MerQuaCo introduces thoughtful metrics and filters that address a wide range of quality control needs. These include pixel classification, transcript density, and detection efficiency across both x-y axes (periodicity) and z-planes (p6/p0 ratio). The tool also effectively quantifies data loss due to dropped images, providing tangible metrics for researchers to evaluate and standardize their data. Additionally, the authors' decision to include examples of imperfections detectable by visual inspection but not flagged by MerQuaCo reflects a transparent and balanced assessment of the tool's current capabilities.

Comments on revisions:

All previous concerns have been fully addressed. The revised manuscript presents a robust, well-documented, and user-friendly tool for quality control in image-based spatial transcriptomics, a rapidly advancing area where objective assessment tools are urgently needed.

Reviewer #3 (Public review):

Summary:

MerQuaCo is an open-source computational tool developed for quality control in image-based spatial transcriptomics data, with a primary focus on data generated by the Vizgen MERSCOPE platform. The authors analyzed a substantial dataset of 641 fresh-frozen adult mouse brain sections to identify and quantify common imperfections, aiming to replace manual quality assessment with an automated, objective approach, providing standardized data integrity measures for spatial transcriptomics experiments.

Strengths:

The manuscript's strengths lie in its timely utility, rigorous empirical validation, and practical contributions to methodology and biological discovery in spatial transcriptomics.

Weaknesses:

While MerQuaCo demonstrates utility in large datasets and cross-platform potential, its generalizability and validation are currently limited by the availability of sufficient datasets from non-MERSCOPE platforms and non-brain tissues. The evaluation of data imperfections' impact on downstream analyses beyond cell typing (e.g., differential expression, spatial statistics, and cell-cell interactions) is also constrained by space and scope. However, these represent valuable directions for future work as more datasets become available.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors develop a novel method to infer ecologically-informative parameters across healthy and diseased states of the gut microbiota, although the method is generalizable to other datasets for species abundances. The authors leverage techniques from theoretical physics of disordered systems to infer different parameters-mean and standard deviation for the strength of bacterial interspecies interactions, a bacterial immigration rate, and the strength of demographic noise-that describe the statistics of microbiota samples from two groups-one for healthy subjects and another one for subjects with chronic inflammation syndromes. To do this, the authors simulate communities with a modified version of the Generalized Lotka-Volterra model and randomly-generated interactions, and then use a moment-matching algorithm to find sets of parameters that better reproduce the data for species abundances. They find that these parameters are different for the healthy and diseased microbiota groups. The results suggest, for example, that bacterial interaction strengths, relative to noise and immigration, are more dominant of microbiota dynamics in diseased states than in healthy states.

We think that this manuscript brings an important contribution that will be of interest in the areas of statistical physics, (microbiota) ecology and (biological) data science. The evidence of their results is solid and the work improves the state-of-the-art in terms of methods.

Strengths:

• Using a fairly generic ecological model, the method can identify the change in the relative importance of different ecological forces (distribution of interspecies interactions, demographic noise and immigration) in different sample groups. The authors focus on the case of the human gut microbiota, showing that the data is consistent with a higher influence of species interactions (relative to demographic noise and immigration) in a disease microbiota state than in healthy ones.

• The method is novel, original and it improves the state-of-the-art methodology for the inference of ecologically-relevant parameters. The analysis provides solid evidence on the conclusions.

Weaknesses:

• As a proof of concept for a new inference method, this text maintains a technical focus, which may require some familiarity with statistical physics. Nevertheless, the authors' clear introduction of key mathematical terms and their interpretations, along with a clear discussion of the ecological implications, make the results accessible and easy to follow.

Reviewer #2 (Public review):

Summary:

This valuable work aims to infer, from microbiome data, microbial species interaction patterns associated with healthy and unhealthy human gut microbiomes. Using solid techniques from statistical physics, the authors propose that healthy and unhealthy microbiome interaction patterns substantially differ. Unhealthy microbiomes are closer to instability and single-strain dominance; whereas healthy microbiomes showcase near-neutral dynamics, mostly driven by demographic noise and immigration.

Strengths:

This is a well-written article, relatively easy to follow and transparent despite the high degree of technicality of the underlying theory. The authors provide a powerful inferring procedure, which bypasses the issue of having only compositional data. This work shows that embracing the complexity of microbial systems can be used to our advantage, instead of being an insurmountable obstacle. This is a powerful counterpoint to the classic reductionist view that pushes researchers to study much simpler systems, and only hope to one day scale up their findings.

Weaknesses:

As acknowledged by the authors themselves, this is only a proof of concept. Further research is to better understand the dynamical nature of gut-microbiomes. The authors do however point at ways in which species abundance distributions could be better reproduced by dynamical models. They also suggest that they work could explain prior empirical findings invoking the "Anna Karenina principle", where healthy microbiomes resemble one another, but disease states tend to all differ.

Reviewer #3 (Public review):

Summary:

I found the manuscript to be well-written. I have a few questions regarding the model, though the bulk of my comments are requests to provide definitions and additional clarity. There are concepts and approaches used in this manuscript that are clear boons for understanding the ecology of microbiomes but are rarely considered by researchers approaching the manuscript from a traditional biology background. The authors have clearly considered this in their writing of S1 and S2, so addressing these comments should be straightforward. The methods section is particularly informative and well-written, with sufficient explanations of each step of the derivation that should be informative to researchers in the microbial life sciences that are not well-versed with physics-inspired approaches to ecology dynamics.

Strengths:

The modeling efforts of this study primarily rely on a disordered for of the generalized Lotka-Volterra (gLV) model. This model can be appropriate for investigating certain systems and the authors are clear about when and how more mechanistic models (i.e., consumer-resource) can lead to gLV. Phenomenological models such as this have been found to be highly useful for investigating the ecology of microbiomes, so this modeling choice seems justified, and the limitations are laid out.

Weaknesses:

The authors use metagenomic data of diseased and healthy patients that was first processed in Pasqualini et al. (2024). The use of metagenomic data leads me into a question regarding the role of sampling effort (i.e., read counts) in shaping model parameters such as $h$. This parameter is equal to the average of 1/# species across samples because the data are compositional in nature. My understanding is that $h$ was calculated using total abundances (i.e., read counts). The number of observed species is strongly influenced by sampling effort and the authors addressed this point in their revised manuscript.

However, the role of sampling effort can depend on the type of data and my instinct about the role that sampling effort plays in species detection is primarily based on 16S data. The dependency between these two variables may be less severe for the authors' metagenomic pipeline. This potential discrepancy raises a broader issue regarding the investigation of microbial macroecological patterns and the inference of ecological parameters. Often microbial macroecology researchers rely on 16S rRNA amplicon data because that type of data is abundant and comparatively low-cost. Some in microbiology and bioinformatics are increasingly pushing researchers to choose metagenomics over 16S. Sometimes this choice is valid (discovery of new MAGs, investigate allele frequency changes within species, etc.), sometimes it is driven by the false equivalence "more data = better". The outcome though is that we have a body of more-or-less established microbial macroecological patterns which rest on 16S data and are now slowly incorporating results from metagenomics. To my knowledge there has not been a systematic evaluation of the macroecological patterns that do and do not vary by one's choice in 16S vs. metagenomics. Several of the authors in this manuscript have previously compared the MAD shape for 16S and metagenomic datasets in Pasqualini et al., but moving forward a more comprehensive study seems necessary (2024). These points were addressed by the authors in their revised manuscript.

Final review: The authors addressed all comments and I have no additional comments.

References

Pasqualini, Jacopo, et al. "Emergent ecological patterns and modelling of gut microbiomes in health and in disease." PLOS Computational Biology 20.9 (2024): e1012482.

Reviewer #1 (Public review):

Summary:

The study characterises an RNA polymerase (Pol) I mutant (RPA135-F301S) named SuperPol. This mutant was previously shown to increase yeast ribosomal RNA (rRNA) production by Transcription Run-On (TRO). In this work, the authors confirm this mutation increases rRNA transcription using a slight variation of the TRO method, Transcriptional Monitoring Assay (TMA), which also allows the analysis of partially degraded RNA molecules. The authors show a reduction of abortive rRNA transcription in cells expressing the SuperPol mutant and a modest occupancy decrease at the 5' region of the rRNA genes compared to WT Pol I. These results suggest that the SuperPol mutant displays a lower frequency of premature termination. Using in vitro assays, the authors found that the mutation induces an enhanced elongation speed and a lower cleavage activity on mismatched nucleotides at the 3' end of the RNA. Finally, SuperPol mutant was found to be less sensitive to BMH-21, a DNA intercalating agent that blocks Pol I transcription and triggers the degradation of the Pol I subunit, Rpa190. Compared to WT Pol I, short BMH-21 treatment has little effect on SuperPol transcription activity, and consequently, SuperPol mutation decreases cell sensitivity to BMH-21.

Significance:

The work further characterises a single amino acid mutation of one of the largest yeast Pol I subunits (RPA135-F301S). While this mutation was previously shown to increase rRNA synthesis, the current work expands the SuperPol mutant characterisation, providing details of how RPA135-F301S modifies the enzymatic properties of yeast Pol I. In addition, their findings suggest that yeast Pol I transcription can be subjected to premature termination in vivo. The molecular basis and potential regulatory functions of this phenomenon could be explored in additional studies.

Our understanding of rRNA transcription is limited, and the findings of this work may be interesting to the transcription community. Moreover, targeting Pol I activity is an open strategy for cancer treatment. Thus, the resistance of SuperPol mutant to BMH-21 might also be of interest to a broader community, although these findings are yet to be confirmed in human Pol I and with more specific Pol I inhibitors in future.

Comments on revision:

The authors' response addressed all the points I raised adequately.

Reviewer #2 (Public review):

Summary:

This article presents a study on a mutant form of RNA polymerase I (RNAPI) in yeast, referred to as SuperPol, which demonstrates increased rRNA production compared to the wild-type enzyme. While rRNA production levels are elevated in the mutant, RNAPI occupancy as detected by CRAC is reduced at the 5' end of rDNA transcription units. The authors interpret these findings by proposing that the wild-type RNAPI pauses in the external transcribed spacer (ETS), leading to premature transcription termination (PTT) and degradation of truncated rRNAs by the RNA exosome (Rrp6). They further show that SuperPol's enhanced activity is linked to a lower frequency of PTT events, likely due to altered elongation dynamics and reduced RNA cleavage activity, as supported by both in vivo and in vitro data.

The study also examines the impact of BMH-21, a drug known to inhibit Pol I elongation, and shows that SuperPol is less sensitive to this drug, as demonstrated through genetic, biochemical, and in vivo approaches. The authors show that BMH-21 treatment induces premature termination in wild-type Pol I, but only to a lesser extent in SuperPol. They suggest that BMH-21 promotes termination by targeting paused Pol I complexes and propose that PTT is an important regulatory mechanism for rRNA production in yeast.

The data presented are of high quality and support the notion that 1) premature transcription termination occurs at the 5' end of rDNA transcription units; 2) SuperPol has an increased elongation rate with reduced premature termination; and 3) BMH-21 promotes both pausing and termination. The authors employ several complementary methods, including in vitro transcription assays. These results are significant and of interest for a broad audience.

Adding experiments in different growth conditions to support the claim of regulation by PTT (as the authors propose) will also be an important addition. The revisions further support the claim, with in particular the notion that increased elongation rate of superpol occurs at the expense of fidelity.

Significance:

These results are significant and of interest for a basic research audience.

Reviewer #3 (Public review):

In the manuscript "Ribosomal RNA synthesis by RNA polymerase I is regulated by premature termination of transcription", Azouzi and co-authors investigate the regulatory mechanisms of ribosomal RNA (rRNA) transcription by RNA Polymerase I (RNAPI) in the budding yeast S. cerevisiae. They follow up on exploring the molecular basis of a mutant allele of the second-largest subunit of RNAPI, RPA135-F301S, also dubbed SuperPol, that they had previously reported (Darrière et al, 2019), and which was shown to rescue Rpa49-linked growth defects, possibly by increasing rRNA production.

Through a combination of genomic and in vitro approaches, the authors test the hypothesis that RNAPI activity could be subjected to a premature transcription termination (PTT) mechanism, akin to what is observed for RNA Polymerase II (RNAPII). The authors demonstrate that SuperPol increased processivity "desensitizes" RNAPI to abortive transcription cycles at the expense of decreased fidelity. In agreement, SuperPol is shown to be resistant to BMH-21, a drug previously shown to impair RNAPI elongation.

Overall, this work expands the mechanistic understanding of the early dynamics of RNAPI transcription. The presented results are of interest for researchers studying transcription regulation, particularly those interested in RNAPI's transcription mechanisms and fidelity.

Strengths:

Overall, the experiments are performed with rigor and include the appropriate controls and statistical analyses. Conclusions are drawn from appropriate experiments. Both the figures and the text present the data clearly. The Materials and Methods section is detailed enough.

Weaknesses:

The biological significance of this phenomenon remains unaddressed and thus unclear. The lack of experiments to test a specific regulatory function (such as UTP-A loading checkpoint or other mechanisms) limit these termination events to possibly abortive actions of unclear significance.

Comments on revised version:

I appreciated the additional experiments and the other changes made by the authors in the revised version.

Reviewer #1 (Public review):

Liu et al., present glmSMA, a network-regularized linear model that integrates single-cell RNA-seq data with spatial transcriptomics, enabling high-resolution mapping of cellular locations across diverse datasets. Its dual regularization framework (L1 for sparsity and generalized L2 via a graph Laplacian for spatial smoothness) demonstrates robust performance of their model. It offers novel tools for spatial biology, despite some gaps in fully addressing spatial communication.

The study presents a clear methodological framework that balances sparsity and smoothness, with parameter guidelines for different tissue contexts. It is commendable for its application to multiple spatial omics platforms, including both sequencing-based and imaging-based data, with results that can be generalized across both structured and less-structured tissues. After revision, there is a more transparent discussion of assumptions, including the correlation between expression and physical distance, and how performance may vary by tissue heterogeneity.

Limitations are modest - the spatial communication application is mentioned but not fully developed, and resolution reporting is primarily qualitative, which may limit direct comparability between datasets. The imaging-based validation is currently limited to simulated or lower-plex data, and expansion to high-plex datasets would further support platform versatility, although this is not essential to the core claims.

Overall, the manuscript delivers on its main objective, which is to present and validate a practical, flexible, and accurate framework for spatial mapping. The methods are clearly described, and the resource will be useful for researchers seeking to integrate single-cell and spatial datasets in diverse biological contexts.

Reviewer #2 (Public review):

Summary:

The author proposes a novel method for mapping single-cell data to specific locations with higher resolution than several existing tools.

Strengths:

The spatial mapping tests were conducted on various tissues, including the mouse cortex, human PDAC, and intestinal villus.

Comments on revised version:

The authors have sufficiently addressed all of my comments.

Reviewer #3 (Public review):

Summary:

The authors have provided a thorough and constructive response to the comments. They effectively addressed concerns regarding the dependence on marker gene selection by detailing the incorporation of multiple feature selection strategies, such as highly variable genes and spatially informative markers (e.g., via Moran's I), which enhance glmSMA's robustness even when using gene-limited reference atlases.

Furthermore, the authors thoughtfully acknowledged the assumption underlying glmSMA-that transcriptionally similar cells are spatially proximal-and discussed both its limitations and empirical robustness in heterogeneous tissues such as human PDAC. Their use of real-world, heterogeneous datasets to validate this assumption demonstrates the method's practical utility and adaptability.

Overall, the response appropriately contextualizes the limitations while reinforcing the generalizability and performance of glmSMA. The authors' clarifications and experimental justifications strengthen the manuscript and address the reviewer's concerns in a scientifically sound and transparent manner.

Reviewer #1 (Public review):

Summary:

This study provides a comprehensive single-cell and multiomic characterization of trabecular meshwork (TM) cells in the mouse eye, a structure critical to intraocular pressure (IOP) regulation and glaucoma pathogenesis. Using scRNA-seq, snATAC-seq, immunofluorescence, and in situ hybridization, the authors identify three transcriptionally and spatially distinct TM cell subtypes. The study further demonstrates that mitochondrial dysfunction specifically in one subtype (TM3) contributes to elevated IOP in a genetic mouse model of glaucoma carrying a mutation in the transcription factor Lmx1b. Importantly, treatment with nicotinamide (vitamin B3), known to support mitochondrial health, prevents IOP elevation in this model. The authors also link their findings to human datasets, suggesting the existence of analogous TM3-like cells with potential relevance to human glaucoma.

Strengths:

The study is methodologically rigorous, integrating single-cell transcriptomic and chromatin accessibility profiling with spatial validation and in vivo functional testing. The identification of TM subtypes is consistent across mouse strains and institutions, providing robust evidence of conserved TM cell heterogeneity. The use of a glaucoma model to show subtype-specific vulnerability-combined with a therapeutic intervention-gives the study strong mechanistic and translational significance. The inclusion of chromatin accessibility data adds further depth by implicating active transcription factors such as LMX1B, a gene known to be associated with glaucoma risk. The integration with human single-cell datasets enhances the potential relevance of the findings to human disease.

Weaknesses:

Although the LMX1B transcription factor is implicated as a key regulator in TM3 cells, its role in directly controlling mitochondrial gene expression is not fully explored. Additional analysis of motif accessibility or binding enrichment near relevant target genes could substantiate this mechanistic link. The therapeutic effect of vitamin B3 is clearly demonstrated phenotypically, but the underlying cellular and molecular mechanisms remain somewhat underdeveloped-for instance, changes in mitochondrial function, oxidative stress markers, or NAD+ levels are not directly measured. While the human relevance of TM3 cells is suggested through marker overlap, more quantitative approaches, such as cell identity mapping or gene signature scoring in human datasets, would strengthen the translational connection.

Overall, this is a compelling and carefully executed study that offers significant advances in our understanding of TM cell biology and its role in glaucoma. The integration of multimodal data, disease modeling, and therapeutic testing represents a valuable contribution to the field. With additional mechanistic depth, the study has the potential to become a foundational resource for future research into IOP regulation and glaucoma treatment.

Reviewer #3 (Public review):

Summary:

In this study, the authors perform multimodal single-cell transcriptomic and epigenomic profiling of 9,394 mouse TM cells, identifying three transcriptionally distinct TM subtypes with validated molecular signatures. TM1 cells are enriched for extracellular matrix genes, TM2 for secreted ligands supporting Schlemm's canal, and TM3 for contractile and mitochondrial/metabolic functions. The transcription factor LMX1B, previously linked to glaucoma, shows the highest expression in TM3 cells and appears to regulate mitochondrial pathways. In Lmx1bV265D mutant mice, TM3 cells exhibit transcriptional signs of mitochondrial dysfunction associated with elevated IOP. Notably, vitamin B3 treatment significantly mitigates IOP elevation, suggesting a potential therapeutic avenue.<br /> This is an excellent and collaborative study involving investigators from two institutions, offering the most detailed single-cell transcriptomic and epigenetic profiling of the mouse limbal tissues-including both TM and Schlemm's canal (SC), from wild-type and Lmx1bV265D mutant mice. The study defines three TM subtypes and characterizes their distinct molecular signatures, associated pathways, and transcriptional regulators. The authors also compare their dataset with previously published murine and human studies, including those by Van Zyl et al., providing valuable cross-species insights.

Strengths:

(1) Comprehensive dataset with high single-cell resolution

(2) Use of multiple bioinformatic and cross-comparative approaches

(3) Integration of 3D imaging of TM and SC for anatomical context

(4) Convincing identification and validation of three TM subtypes using molecular markers.

Weaknesses:

(1) Insufficient evidence linking mitochondrial dysfunction to TM3 cells in Lmx1bV265D mice: While the identification of TM3 cells as metabolically specialized and Lmx1b-enriched is compelling, the proposed link between Lmx1b mutation and mitochondrial dysfunction remains underdeveloped. It is unclear whether mitochondrial defects are a primary consequence of Lmx1b-mediated transcriptional dysregulation or a secondary response to elevated IOP. Although authors have responded to this, the manuscript is not sufficiently altered to address these points. I would like to suggest that authors tone down mitochondrial connection with Lmx1b from the title and abstract, and clearly discuss that these events are associated, and future work is needed to dissect the role of mitochondria in this pathway.<br /> Furthermore, the protective effects of nicotinamide (NAM) are interpreted as evidence of mitochondrial involvement, but no direct mitochondrial measurements (e.g., immunostaining, electron microscopy, OCR assays) are provided. It is essential to validate mitochondrial dysfunction in TM3 cells using in vivo functional assays to support the central conclusion of the paper. Without this, the claim that mitochondrial dysfunction drives IOP elevation in Lmx1bV265D mice remains speculative. Alternatively, authors should consider revising their claims that mitochondrial dysfunction in these mice is a central driver of TM dysfunction.

(2) Mechanism of NAM-mediated protection is unclear: The manuscript states that NAM treatment prevents IOP elevation in Lmx1bV265D mice via metabolic support, yet no data are shown to confirm that NAM specifically rescues mitochondrial function. Do NAM-treated TM3 cells show improved mitochondrial integrity? Are reactive oxygen species (ROS) reduced? Does NAM also protect RGCs from glaucomatous damage? Addressing these points would clarify whether the therapeutic effects of NAM are indeed mitochondrial.

Reviewer #1 (Public review):

Summary:

This manuscript by the Yin group presents interesting findings that organelle-tethered intrinsically disordered "MEMCA" scaffolds, as exemplified by ZDHHC18 at the Golgi and MARCH8 at endosomes, enhance the engagement of cGAS with organelle-proximal condensates, thereby sequestering cGAS from cytosolic DNA sensing and negatively regulating innate immunity.

Strengths:

These findings suggest a previously unrecognized mechanism by which Golgi/endosomal IDR scaffolds modulate cGAS activity, with implications for antiviral defense and tumor immunology. The study is conceptually intriguing and potentially impactful.

Weaknesses:

While the manuscript addresses a novel aspect of cGAS regulation, additional mechanistic insights and targeted validations are needed to ensure robustness:

(1) How do ZDHHC18/MARCH8 enhance cGAS engagement? Do they act as bridges to form a ternary, membrane-tethered cGAS-DNA-MEMCA complex, or alter cGAS condensate properties allosterically?

(2) Is organelle cGAS capture selective? For instance, can other palmitoyltransferases/E3 ligases be substituted for ZDHHC18/MARCH8?

(3) Why does membrane association suppress cGAS enzymic activity, as dsDNA still resides in cGAS condensation?

Reviewer #2 (Public review):

Summary:

The authors found that cGAS, a DNA sensor, relocalizes to organelle membranes (ER, Golgi, endosomes) upon DNA stimulation, revealing spatial regulation of its activity. ZDHHC18 and MARCH8 recruit cGAS to Golgi/endosomes via intrinsically disordered regions (IDRs), driving phase-separated condensates. This sequestration of cGAS-dsDNA complexes suppresses innate immune signaling, uncovering a novel regulatory mechanism.

Strengths:

The work overall is very interesting. The authors provided molecular and biochemical evidence.

Weaknesses:

Overall, the work is very interesting. However, the quality of some of the data does need to be improved, and more experiments need to be performed.

The following points need to be addressed:

(1) In Figure S7, no direct binding between cGAS and MARCH8 or ZD18 IDR is observed, and the interaction only occurs after DNA stimulation. However, Figure 5 shows cGAS recruitment to ZD18 or MARCH8 IDR droplets, suggesting direct interactions. This apparent discrepancy should be clarified.

(2) The authors propose that recruiting cGAS to organelle membranes reduces its activity, as demonstrated by the FKBP experiment. However, ZD18 and MARCH8 also post-translationally modify cGAS. Do both mechanisms contribute to this effect, and can the authors test this?

(3) To demonstrate the functional importance of MEMCA, the authors should test IFN production or STING activation in cells.

(4) Does the IDR of MARCH8 or ZD18 influence the interaction between cGAS and DNA?

(5) Which region of cGAS does the IDR of MARCH8 or ZD18 interact with: the cGAS-CD or the cGAS-N-terminus?

(6) The in vitro LLPS experiments with cGAS, DNA, and ZD18/MARCH8 should be conducted under physiological conditions.

Reviewer #3 (Public review):

Summary:

In this study by Shi et al., the authors evaluate if cGAS is recruited to the membranes of intracellular organelles. Using a combination of biochemical fractionation and imaging techniques, the authors propose that upon recognition of DNA, cGAS translocates to various subcellular locations, including the golgi, endoplasmic reticulum, and endosomes. Mechanistically, the authors propose that upon localizing to the Golgi or endosome, cGAS binding to MARCH8 and ZDHHC18 prevents cGAS activity by incorporating cGAS and dsDNA into biomolecular condensates. However, in its current form, the study does not directly address this question.

Strengths:

The question of evaluating cGAS sub-cellular localization as a mechanism for controlling activity is interesting, and there is some evidence that cGAS is localized to sub-cellular organelle membranes.

Weaknesses:

(1) The well-established nuclear localization of cGAS is not adequately addressed in the cell lines used and is inconsistent with the findings.

(2) Previous studies have shown that ZDHHC18 and MARCH8 control cGAS activity, which detracts somewhat from the novelty.

(3) A lot of inconsistency in the cell lines and artificial expression systems used across the study.

(4) A key element missing is showing that in the absence of ZDHHC18 or MARCH8, the loss of endogenous cGAS localization to the various sub-cellular organelles increases cGAMP synthesis and downstream STING activation in primary cells. There is an over-reliance on artificial expression systems. An important experiment to validate the hypothesis would be to evaluate endogenous cGAS localization in MARCH8- and ZDHHC18-deficient primary cells. Further, there should be evaluation of endogenous STING responses in MARCH8- and ZDHHC18-deficient primary cells in tandem with the localization studies.

(5) There are a large number of grammatical errors throughout the manuscript which should be addressed.

Reviewer #1 (Public review):

Summary:

Activation of thermogenesis by cold exposure and dietary protein restriction are two lifestyle changes that impact health in humans and lead to weight loss in model organisms - here, in mice. How these affect liver and adipose tissues has not been thoroughly investigated side by side. In mice, the authors show that the responses to methionine restriction and cold exposure are tissue-specific, while the effects on beige adipose are somewhat similar.

Strengths:

The strength of the work is the comparative approach, using transcriptomics and bioinformatic analyses to investigate the tissue-specific impact. The work was performed in mouse models and is state-of-the-art. This represents an important resource for researchers in the field of protein restriction and thermogenesis.

Weaknesses:

The findings are descriptive, and the conclusions remain associative. The work is limited to mouse physiology, and the human implications have not been investigated yet.

Reviewer #2 (Public review):

Summary:

This study provides a library of RNA sequencing analysis from brown fat, liver, and white fat of mice treated with two stressors - cold challenge and methionine restriction - alone and in combination (interaction between diet and temperature). They characterize the physiologic response of the mice to the stressors, including effects on weight, food intake, and metabolism. This paper provides evidence that while both stressors increase energy expenditure, there are complex tissue-specific responses in gene expression, with additive, synergistic, and antagonistic responses seen in different tissues.

Strengths:

The study design and implementation are solid and well-controlled. Their writing is clear and concise. The authors do an admirable job of distilling the complex transcriptome data into digestible information for presentation in the paper. Most importantly, they do not overreach in their interpretation of their genomic data, keeping their conclusions appropriately tied to the data presented. The discussion is well thought out and addresses some interesting points raised by their results.

Weaknesses:

The major weakness of the paper is the almost complete reliance on RNA sequencing data, but it is presented as a transcriptomic resource.

Reviewer #3 (Public review):

Summary:

Ruppert et al. present a well-designed 2×2 factorial study directly comparing methionine restriction (MetR) and cold exposure (CE) across liver, iBAT, iWAT, and eWAT, integrating physiology with tissue-resolved RNA-seq. This approach allows a rigorous assessment of where dietary and environmental stimuli act additively, synergistically, or antagonistically. Physiologically, MetR progressively increases energy expenditure (EE) at 22{degree sign}C and lowers RER, indicating a lipid utilization bias. By contrast, a 24-hour 4 {degree sign}C challenge elevates EE across all groups and eliminates MetR-Ctrl differences. Notably, changes in food intake and activity do not explain the MetR effect at room temperature.

Strengths:

The data convincingly support the central claim: MetR enhances EE and shifts fuel preference to lipids at thermoneutrality, while CE drives robust EE increases regardless of diet and attenuates MetR-driven differences. Transcriptomic analysis reveals tissue-specific responses, with additive signatures in iWAT and CE-dominant effects in iBAT. The inclusion of explicit diet×temperature interaction modeling and GSEA provides a valuable transcriptomic resource for the field.

Weaknesses:

Limitations include the short intervention windows (7 d MetR, 24 h CE), use of male-only cohorts, and reliance on transcriptomics without complementary proteomic, metabolomic, or functional validation. Greater mechanistic depth, especially at the level of WAT thermogenic function, would strengthen the conclusions.

Reviewer #1 (Public review):

Summary:

Redchuk et al. explore the dynamic properties of chromatin upon serum starvation using machine learning approaches. They use CRISPR-tagging to visualize a region on chromosome 1 in human cells and show that in their system, chromosome 1, but not the previously reported chromosomes 10, 13, and X, undergo a change in radial position upon serum starvation. Live cell imaging showed a position change towards the periphery after serum starvation. They then apply a machine learning algorithm for the analysis of the imaging data, which reveals changes in nuclear area during serum starvation and longer displacements of the chromosome 1 locus near the nuclear periphery. Differential behavior of homologues is also reported.

Strengths:

(1) The study of chromatin dynamics is an interesting and important area of research.

(2) The use of machine learning approaches to analyze live cell imaging data is timely.

(3) With serum starvation, the authors use a simple, well-controllable model system.

Weaknesses:

(1) This study only provides limited new insight into chromatin dynamics.

(2) It was not immediately evident what the use of machine learning approaches added to this study. It appears that the main conclusions could have been reached by conventional analysis.

(3) There are several specific technical points:

a) It was not clear what the CRISRP-Sirius probes actually labelled. The chromosome 1 sgRNA sequence is provided, but I could not find information as to which region(s) of the chromosome are actually labelled (size, location, etc.).

b) The authors visualize a relatively small region of chromosome 1 but make conclusions regarding the entire chromosome. Additional probes on the same chromosome should be used.

Related to this point, the discussion of why the authors are unable to reproduce the prior findings of relocation of chromosomes 10, 13, and X is not satisfying. It would be worth comparing the FISH-based painting of entire chromosomes, which generated the results suggesting relocation of these chromosomes, with the point-labelling method used here.

c) The study lacks controls. Since in their hands chromosomes 10, 13, and X do not change position, they should be used as a negative control in all experiments demonstrating a shift in the location of chromosome 1.

d) I did not find information about the spatial or temporal resolution of the imaging modality. This is important to assess whether the observed changes in position, relative to time, are meaningful.

e) The authors analyze surprisingly early timepoints (up to 40 minutes) of serum starvation. Would these results look different if longer serum starvation timepoints of several hours were analyzed?

f) The authors can do a better job of explaining what the biological meaning of the various parameters (DistR, TDist, etc.) they measure is.

g) I did not understand the reasoning for the authors' conclusion of differential behavior of homologues. Please explain this better, or idealy use more direct labeling methods that identify the individual homologues.

h) In many figures, statistical analysis of the data is missing, including, but not limited to, Figures 1B, C, G, Figures 4, 5, 6.

i) No information is provided throughout the manuscript as to how many cells were analyzed in each experiment. This should be indicated in every figure legend.

Reviewer #2 (Public review):

Summary:

The study demonstrates that CRISPR-Sirius provides a powerful approach to investigating chromosome dynamics in living cells during environmental stress. By focusing on serum starvation, the authors show that this process induces global nuclear changes, including a reduction in nuclear area and increased morphological dynamism, while at the same time driving specific reorganization of chromosome 1. Chromosome 1 relocates toward the nuclear periphery and displays distinctive patterns of motion, maintaining overall motility but punctuated by occasional long-distance displacements, particularly near the nuclear envelope. Importantly, the analysis reveals that homologous copies of chromosome 1 do not behave uniformly: peripheral loci become more mobile and responsive to starvation, whereas central homologs remain comparatively stable, often associated with nucleolar subcompartments. By integrating live imaging with machine learning and explainable AI analysis, the study highlights the complexity of nuclear organization and provides valuable insights into how chromosome-specific and locus-specific responses to stress are orchestrated within the three-dimensional nuclear landscape.

Strengths:

The study uses live-cell imaging to investigate the dynamics of loci during starvation. Live-cell tracking and data interpretation are carried out using machine learning and AI models, which is a major strength.

Weaknesses:

The manuscript is at times difficult to follow, partly because the methodological descriptions are highly specialized, especially for non-expert biologists. In addition, the observations are not tested for a mechanistic basis. Experiments that could provide deeper insights are missing, for example, why chromosome 1 moves, why the peripheral homologue dislocates, or why a "long jump" is observed at the periphery even though the speed of the loci does not change. It is also unclear whether a displacement of 0.5 μm is functionally meaningful.

Reviewer #1 (Public review):

Summary:

Hosack and Arce-McShane investigate how the 3D movement direction of the tongue is represented in the orofacial part of the sensory-motor cortex and how this representation changes with the loss of oral sensation. They examine the firing patterns of neurons in the orofacial parts of the primary motor cortex (MIo) and somatosensory cortex (SIo) in non-human primates (NHPs) during drinking and feeding tasks. While recording neural activity, they also tracked the kinematics of tongue movement using biplanar video-radiography of markers implanted in the tongue. Their findings indicate that many units in both MIo and SIo are directionally tuned during the drinking task. However, during the feeding task, directional turning was more frequent in MIo units and less prominent in SIo units. Additionally, in some recording sessions, they blocked sensory feedback using bilateral nerve block injections, which seemed to result in fewer directionally tuned units and changes in the overall distribution of the preferred direction of the units.

Strengths:

The most significant strength of this paper lies in its unique combination of experimental tools. The author utilized a video-radiography method to capture 3D kinematics of the tongue movement during two behavioral tasks while simultaneously recording activity from two brain areas. This specific dataset and experimental setup hold great potential for future research on the understudied orofacial segment of the sensory-motor area.

Weaknesses:

A substantial portion of the paper is dedicated to establishing directional tuning in individual neurons, followed by an analysis of how this tuning changes when sensory feedback is blocked. While such characterizations are valuable, particularly in less-studied motor cortical areas and behaviors, the discrepancies in tuning changes across the two NHPs, coupled with the overall exploratory nature of the study, render the interpretation of these subtle differences somewhat speculative. At the population level, both decoding analyses and state space trajectories from factor analysis indicate that movement direction (or spout location) is robustly represented. However, as with the single-cell findings, the nuanced differences in neural trajectories across reach directions and between baseline and sensory-block conditions remain largely descriptive. To move beyond this, model-based or hypothesis-driven approaches are needed to uncover mechanistic links between neural state space dynamics and behavior.

Reviewer #2 (Public review):

Summary:

This manuscript by Hosack and Arce-McShane examines the directional tuning of neurons in macaque primary motor (MIo) and somatosensory (SIo) cortex. The neural basis of tongue control is far less studied than, for example, forelimb movements, partly because the tongue's kinematics and kinetics are difficult to measure. A major technical advantage of this study is using biplanar video-radiography, processed with modern motion tracking analysis software, to track the movement of the tongue inside the oral cavity. Compared to prior work, the behaviors are more naturalistic behaviors (feeding and licking water from one of three spouts), although the animals were still head-fixed.

The study's main findings are that:

• A majority of neurons in MIo and a (somewhat smaller) percentage of SIo modulated their firing rates during tongue movements, with different modulation depending on the direction of movement (i.e., exhibited directional tuning). Examining the statistics of tuning across neurons, there was anisotropy (e.g., more neurons preferring anterior movement) and a lateral bias in which tongue direction neurons preferred that was consistent with the innervation patterns of tongue control muscles (although with some inconsistency between monkeys).<br /> • Consistent with this encoding, tongue position could be decoded with moderate accuracy even from small ensembles of ~28 neurons.<br /> • There were differences observed in the proportion and extent of directional tuning between the feeding and licking behaviors, with stronger tuning overall during feeding. This potentially suggests behavioral context-dependent encoding.<br /> • The authors then went one step further and used a bilateral nerve block to the sensory inputs (trigeminal nerve) from the tongue. This impaired the precision of tongue movements and resulted in an apparent reduction and change in neural tuning in Mio and SIo.

Strengths:

The data are difficult to obtain and appear to have been rigorously measured, and provide a valuable contribution to this under-explored subfield of sensorimotor neuroscience. The analyses adopt well-established methods especially from the arm motor control literature, and represent a natural starting point for characterizing tongue 3D direction tuning.

Weaknesses:

There are alternative explanations from some of the interpretations, but those interpretations are described in a way that clearly distinguishes results from interpretations, and readers can make their own assessments. Some of these limitations are described in more detail below.

One weakness of the current study is that there is substantial variability in some of the results between monkeys, including the tuning characteristics of primary somatosensory cortex neurons during drinking, and the effect of nerve block on tongue movements and the associated changes in single neuron tuning.

This study focuses on describing directional tuning using the preferred direction (PD) / cosine tuning model popularized by Georgopoulous and colleagues for understanding neural control of arm reaching in the 1980s. This is a reasonable starting point and a decent first order description of neural tuning. However, the arm motor control field has moved far past that viewpoint, and in some ways an over-fixation on static representational encoding models and PDs held that field back for many years. The manuscript benefit from drawing the readers' attention (perhaps in their Discussion) that PDs are a very simple starting point for characterizing how cortical activity relates to kinematics, but that there is likely much richer population-level dynamical structure and that a more mechanistic, control-focused analytical framework may be fruitful. A good review of this evolution in the arm field can be found in Vyas S, Golub MD, Sussillo D, Shenoy K. 2020. Computation Through Neural Population Dynamics. Annual Review of Neuroscience. 43(1):249-75. A revised version of the manuscript incorporates more population-level analyses, but with inconsistent use of quantifications/statistics and without sufficient contextualization of what the reader is to make of these results.

The described changes in tuning after nerve block could also be explained by changes in kinematics between these conditions, which temper the interpretation of these interesting results.

I am not convinced of the claim that tongue directional encoding fundamentally changes between drinking and feeding given the dramatically different kinematics and the involvement of other body parts like the jaw (e.g., the reference to Laurence-Chasen et al. 2023 just shows that there is tongue information independent of jaw kinematics, not that jaw movements don't affect these neurons' activities). I also find the nerve block results inconsistent (more tuning in one monkey, less in the other?) and difficult to really learn something fundamental from, besides that neural activity and behavior both change - in various ways - after nerve block (not at all surprising but still good to see measurements of).

The manuscript states that "Our results suggest that the somatosensory cortex may be less involved than the motor areas during feeding, possibly because it is a more ingrained and stereotyped behavior as opposed to tongue protrusion or drinking tasks". An alternative explanation be more statistical/technical in nature: that during feeding, there will be more variability in exactly what somatosensation afferent signals are being received from trial to trial (because slight differences in kinematics can have large differences in exactly where the tongue is and the where/when/how of what parts of it are touching other parts of the oral cavity)? This variability could "smear out" the apparent tuning using these types of trial-averaged analyses. Given how important proprioception and somatosensation are for not biting the tongue or choking, the speculation that somatosensory cortical activity is suppressed during feedback is very counter-intuitive to this reviewer. In the revised manuscript the authors note these potential confounds and other limitations in the Discussion.

Reviewer #3 (Public review):

Summary

In this study, the authors aim to uncover how 3D tongue direction is represented in the Motor (M1o) and Somatosensory (S1o) cortex. In non-human primates implanted with chronic electrode arrays, they use X-ray based imaging to track the kinematics of the tongue and jaw as the animal is either chewing food or licking from a spout. They then correlate the tongue kinematics with the recorded neural activity. They perform both single-unit and population level analyses during feeding and licking. Then, they recharacterize the tuning properties after bilateral lidocaine injections in the two sensory branches of the trigeminal nerve. They report that their nerve block causes a reorganization of the tuning properties and population trajectories. Overall, this paper concludes that M1o and S1o both contain representations of the tongue direction, but their numbers, their tuning properties and susceptibility to perturbed sensory input are different.

Strengths

The major strengths of this paper are in the state-of-the-art experimental methods employed to collect the electrophysiological and kinematic data. In the revision, the single-unit analyses of tuning direction are robustly characterized. The differences in neural correlations across behaviors, regions and perturbations are robust. In addition to the substantial amount of largely descriptive analyses, this paper makes two convincing arguments 1) The single-neuron correlates for feeding and licking in OSMCx are different - and can't be simply explained by different kinematics and 2) Blocking sensory input alters the neural processing during orofacial behaviors. The evidence for these claims is solid.

Weaknesses

The main weakness of this paper is in providing an account for these differences to get some insight into neural mechanisms. For example, while the authors show changes in neural tuning and different 'neural trajectory' shapes during feeding and drinking - their analyses of these differences are descriptive and provide limited insight for the underlying neural computations.

Reviewer #1 (Public review):

Summary:

This work addresses a key question in cell signalling: how does the membrane composition affect the behaviour of a membrane signalling protein? Understanding this is important, not just to understand basic biological function but because membrane composition is highly altered in diseases such as cancer and neurodegenerative disease. Although parts of this question have been addressed on fragments of the target membrane protein, EGFR, used here, Srinivasan et al. harness a unique tool, membrane nanodisks, which allow them to probe full-length EGFR in vitro in great detail with cutting-edge fluorescent tools. They find interesting impacts on EGFR conformation in differently charged and fluid membranes, explaining previously identified signalling phenotypes.

Strengths:

The nanodisk system enables full-length EGFR to be studied in vitro and in a membrane with varying lipid and cholesterol concentrations. The authors combine this with single-molecule FRET utilising multiple pairs of fluorophores at different places on the protein to probe different conformational changes in response to EGF binding under different anionic lipid and cholesterol concentrations. They further support their findings using molecular dynamics simulations, which help uncover the full atomistic detail of the conformations they observe.

Weaknesses:

Much of the interpretation of the results comes down to a bimodal model of an 'open' and 'closed' state between the intracellular tail of the protein and the membrane. Some of the data looks like a bimodal model is appropriate, but its use is not sufficiently justified (statistically or otherwise) in this work in its current form. The experiments with varying cholesterol in particular appear to suggest an alternate model with longer fluorescent lifetimes. More justification of these interpretations of the central experiment of this work would strengthen the paper.

Reviewer #2 (Public review):

Summary:

Nanodiscs and synthesized EGFR are co-assembled directly in cell-free reactions. Nanodiscs containing membranes with different lipid compositions are obtained by providing liposomes with corresponding lipid mixtures in the reaction. The authors focus on the effects of lipid charge and fluidity on EGFR activity.

Strengths:

The authors implement a variety of complementary techniques to analyze data and to verify results. They further provide a new pipeline to study lipid effects on membrane protein function.

Weaknesses:

Due to the relative novelty of the approach, a number of concerns remain.

(1) I am a little skeptical about the good correlation of the nanodisc compositions with the liposome compositions. I would rather have expected a kind of clustering of individual lipid types in the liposome membrane, in particular of cholesterol. This should then result in an uneven distribution upon nanodisc assembly, i.e., in a notable variation of lipid composition in the individual nanodiscs. Could this be ruled out by the implemented assays, or can just the overall lipid composition of the complete nanodisc fraction be analyzed?

(2) Both templates have been added simultaneously, with a 100-fold excess of the EGFR template. Was this the result of optimization? How is the kinetics of protein production? As EGFR is in far excess, a significant precipitation, at least in the early period of the reaction, due to limiting nanodiscs, should be expected. How is the oligomeric form of the inserted EGFR? Have multiple insertions into one nanodisc been observed?

(3) The IMAC purification does not discriminate between EGFR-filled and empty nanodiscs. Does the TEM study give any information about the composition of the particles (empty, EGFR monomers, or EGFR oligomers)? Normalizing the measured fluorescence, i.e., the total amount of solubilized receptor, with the total protein concentration of the samples could give some data on the stoichiometry of EGFR and nanodiscs.

(4) The authors generally assume a 100% functional folding of EGFR in all analyzed environments. While this could be the case, with some other membrane proteins, it was shown that only a fraction of the nanodisc solubilized particles are in functional conformation. Furthermore, the percentage of solubilized and folded membrane protein may change with the membrane composition of the supplied nanodiscs, while non-charged lipids mostly gave rather poor sample quality. The authors normalize the ATP binding to the total amount of detectable EGFR, and variations are interpreted as suppression of activity. Would the presence of unfolded EGFR fractions in some samples with no access to ATP binding be an alternative interpretation?

Reviewer #1 (Public review):

Summary:

The authors show that genetic deletion of the orphan tumor necrosis factor receptor DR6 in mice does not protect peripheral axons against degeneration after axotomy. Similarly, Schwann cells in DR6 mutant mice react to axotomy similarly to wild-type controls. These negative results are important because previous work has indicated that loss or inhibition of DR6 is protective in disease models and also against Wallerian degeneration of axons following injury. This carefully executed counterexample is important for the field to consider.

Strengths:

A strength of the paper is the use of two independent mouse strains that knock out DR6 in slightly different ways. The authors confirm that DR6 mRNA is absent in these models (western blots for DR6 protein are less convincingly null, but given the absence of mRNA, this is likely an issue of antibody specificity). One of the DR6 knockout strains used is the same strain used in a previous paper examining the effects of DR6 on Wallerian degeneration.

The authors use a series of established assays to evaluate axon degeneration, including light and electron microscopy on nerve histological samples and cultured dorsal root ganglion neurons in which axons are mechanically severed and degeneration is scored in time-lapse microscopy. These assays consistently show a lack of effect of loss of DR6 on Wallerian degeneration in both mouse strains examined.

Therefore, in the specific context of these experiments, the author's data support their conclusion that loss of DR6 does not protect against Wallerian degeneration.

Weaknesses:

The major weaknesses of this paper include the tone of correcting previously erroneous results and the lack of reporting on important details around animal experiments that would help determine whether the results here really are discordant with previous studies, and if so, why.

The authors do not report the genetic strain background of the mice used, the sex distributions of their experimental cohorts, or the age of the mice at the time the experiments were performed. All of these are important variables.

The DR6 knockout strain reported in Gamage et al. (2017) was on a C57BL/6.129S segregating background. Gamage et al. reported that loss of DR6 protected axons from Wallerian degeneration for up to 4 weeks, but importantly, only in 38.5% (5 out of 13) mice they examined. In the present paper, the authors speculate on possible causes for differences between the lack of effect seen here and the effects reported in Gamage et al., including possible spontaneous background mutations, epigenetic changes, genetic modifiers, neuroinflammation, and environmental differences. A likely explanation of the incomplete penetrance reported by Gamage et al. is the segregating genetic background and the presence of modifier loci between C57BL/6 and 129S. The authors do not report the genetic background of the mice used in this study, other than to note that the knockout strain was provided by the group in Gamage et al. However, if, for example, that mutation has been made congenic on C57BL/6 in the intervening years, this would be important to know. One could also argue that the results presented here are consistent with 8 out of 13 mice presented in Gamage et al.

Age is also an important variable. The protective effects of the spontaneous WldS mutation decrease with age, for example. It is unclear whether the possible protective effects of DR6 also change with age; perhaps this could explain the variable response seen in Gamage et al. and the lack of response seen here.

It is unclear if sex is a factor, but this is part of why it should be reported.

The authors also state that they do not see differences in the Schwann cell response to injury in the absence of DR6 that were reported in Gamage et al., but this is not an accurate comparison. In Gamage et al., they examined Schwann cells around axons that were protected from degeneration 2 and 4 weeks post-injury. Those axons had much thinner myelin, in contrast to axons protected by WldS or loss of Sarm1, where the myelin thickness remained relatively normal. Thus, Gamage et al. concluded that the protection of axons from degeneration and the preservation of Schwann cell myelin thickness are separate processes. Here, since no axon protection was seen, the same analysis cannot be done, and we can only say that when axons degenerate, the Schwann cells respond the same whether DR6 is expressed or not.

The authors also take issue with Colombo et al. (2018), where it was reported that there is an increase in axon diameter and a change in the g-ratio (axon diameter to fiber diameter - the axon + myelin) in peripheral nerves in DR6 knockout mice. This change resulted in a small population of abnormally large axons that had thinner myelin than one would expect for their size. The change in g-ratio was specific to these axons and driven by the increased axon diameter, not decreased myelin thickness, although those two factors are normally loosely correlated. Here, the authors report no changes in axon size or g-ratio, but this could also be due to how the distribution of axon sizes was binned for analysis, and looking at individual data points in supplemental figure 3A, there are axons in the DR6 knockout mice that are larger than any axons in wild type. Thus, this discrepancy may be down to specifics and how statistics were performed or how histograms were binned, but it is unclear if the results presented here are dramatically at odds with the results in Colombo et al. (2018).

Finally, it is important to note that previously reported effects of DR6 inhibition, such as protection of cultured cortical neurons from beta-amyloid toxicity, are not necessarily the same as Wallerian degeneration of axons distal to an injury studied here. The negative results presented here, showing that loss of DR6 is not protective against Wallerian degeneration induced by injury, are important given the interest in DR6 as a therapeutic target, but they are specific to these mice and this mechanism of induced axon degeneration. The extent to which these findings contradict previous work is difficult to assess due to the lack of detail in describing the mouse experiments, and care should be taken in attempting to extrapolate these results to other disease contexts, such as ALS or Alzheimer's disease.

Reviewer #2 (Public review):

Summary:

This manuscript by Beirowski, Huang, and Babetto revisits the proposed role of Death Receptor 6 (DR6/Tnfrsf21) in Wallerian degeneration (WD). A prior study (Gamage et al., 2017) suggested that DR6 deletion delays axon degeneration and alters Schwann cell responses following peripheral nerve injury. Here, the authors comprehensively test this claim using two DR6 knockout mouse models (the line used in the earlier report plus a CMV-Cre derived floxed ko line) and multiple WD assays in vivo and in vitro, aligned with three positive controls, Sarm1 WldS and Phr1/Mycbp2 mutants. Contrary to the prior findings, they find no evidence that DR6 deletion affects axon degeneration kinetics or Schwann cell dynamics (assessed by cJun expression or [intact+degenerating] myelin abundance after injury) during WD. Importantly, in DRG explant assays, neurites from DR6-deficient mice degenerated at rates indistinguishable from controls. The authors conclude that DR6 is dispensable for WD, and that previously reported protective effects may have been due to confounding factors such as genetic background or spontaneous mutations.

Strengths:

The authors employ two independently generated DR6 knockout models, one overlapping with the previously published study, and confirm loss of DR6 expression by qPCR and Western blotting.<br /> Multiple complementary readouts of WD are applied (structural, ultrastructural, molecular, and functional), providing a robust test of the hypothesis.

Comparisons are drawn with established positive controls (WldS, SARM1, Phr1/Mycbp2 mutants), reinforcing the validity of the assays.

By directly addressing an influential but inconsistent prior report, the manuscript clarifies the role of DR6 and prevents potential misdirection of therapeutic strategies aimed at modulating WD in the PNS. The discussion thoughtfully considers possible explanations for the earlier results, including colony-specific second-site mutations that could explain the incomplete penetrance of the earlier reported phenotype of only 36%.

Weaknesses:

(1) The study focuses on peripheral nerves. The manuscript frequently refers to CNS studies to argue for consistency with their findings. It would be more accurate to frame PNS/CNS similarities as reminiscences rather than as consistencies (e.g., line 205ff in the Discussion).

(2) The DRG explant assays are convincing, though the slight acceleration of degeneration in the DR6 floxed/Cre condition is intriguing (Figure 4E). Could the authors clarify whether this is statistically robust or biologically meaningful?

(3) In the summary (line 43), the authors refer to Hu et al. (2013) (reference 5) as the study that previously reported AxD delay and SC response alteration after injury. However, this study did not investigate the PNS, and I believe the authors intended to reference Gamage et al. (2017) (reference 10) at this point.

(4) In line 74ff of the results section, the authors claim that developmental myelination is not altered in DR6 mutants at postnatal day 1. However, the variability in Figure S2 appears substantial, and the group size seems underpowered to support this claim. Colombo et al. (2018) (reference 11) reported accelerated myelination at P1, but this study likewise appears underpowered. Possible reasons for these discrepancies and the large variability could be that only a defined cross-sectional area was quantified, rather than the entire nerve cross-section.

(5) The authors stress the data of Gamage et al. (2017) on altered SC responses in DR6 mutants after injury. They employed cJun quantification to show that SC reprogramming after injury is not altered in DR6 mutants. This approach is valid and the conclusion trustworthy. Here, the addition of data showing the combined abundance of intact and degenerated myelin does not add much insight. However, Gamage et al. (2017) reported altered myelin thickness in a subset of axons at 14 days after injury, which is considerably later than the time points analyzed in the present study. While, in the Reviewer's view, the thin myelin observed by Gamage et al. in fact resembles remyelination, the authors may wish to highlight the difference in the time points analyzed.

Reviewer #3 (Public review):

Summary:

The authors revisit the role of DR6 in axon degeneration following physical injury (Wallerian degeneration), examining both its effects on axons and its role in regulating the Schwann cell response to injury. Surprisingly, and in contrast to previous studies, they find that DR6 deletion does not delay the rate of axon degeneration after injury, suggesting that DR6 is not a mediator of this process.

Overall, this is a valuable study. As the authors note, the current literature on DR6 is inconsistent, and these results provide useful new data and clarification. This work will help other researchers interpret their own data and re-evaluate studies related to DR6 and axon degeneration.

Strengths:

(1) The use of two independent DR6 knockout mouse models strengthens the conclusions, particularly when reporting the absence of a phenotype.

(2) The focus on early time points after injury addresses a key limitation of previous studies. This approach reduces the risk of missing subtle protective phenotypes and avoids confounding results with regenerating axons at later time points after axotomy.

Weaknesses:

(1) The study would benefit from including an additional experimental paradigm in which DR6 deficiency is expected to have a protective effect, to increase confidence in the experimental models, and to better contextualize the findings within different pathways of axon degeneration. For example, DR6 deletion has been shown in more than one study to be partially axon protective in the NGF deprivation model in DRGs in vitro. Incorporating such an experiment could be straightforward and would strengthen the paper, especially if some of the neuroprotective effects previously reported are confirmed.

(2) The quality of some figures could be improved, particularly the EM images in Figure 2. As presented, they make it difficult to discern subtle differences.

Reviewer #1 (Public review):

Disclaimer:

This reviewer is not an expert on MD simulations but has a basic understanding of the findings reported and is well-versed with mycobacterial lipids.

Summary:

In this manuscript titled "Dynamic Architecture of Mycobacterial Outer Membranes Revealed by All-Atom 1 Simulations", Brown et al describe outcomes of all-atom simulation of a model outer membrane of mycobacteria. This compelling study provided three key insights:<br /> (1) The likely conformation of the unusually long chain alpha-branched beta-methoxy fatty acids, mycolic acids in the mycomembrane, to be the extended U or Z type rather than the compacted W-type. (2) Outer leaflet lipids such as PDIM and PAT provide regional vertical heterogeneity and disorder in the mycomembrane that is otherwise prevented in a mycolic acid-only bilayer.<br /> (3) Removal of specific lipid classes from the symmetric membrane systems leads to significant changes in membrane thickness and resilience to high temperatures.

Strengths:

The authors take a step-wise approach in building the complexity of the membrane and highlight the limitations of each of the approaches. A case in point is the use of supraphysiological temperature of 333 K or even higher temperatures for some of the simulations. Overall, this is a very important piece of work for the mycobacterial field, and will help in the development of membrane-disrupting small molecules and provide important insights for lipid-lipid interactions in the mycomembrane.

Weaknesses:

(1) The authors used alpha-mycolic acids only for their models. The ratios of alpha, keto, and methoxy-mycolic acids are known in the literature, and it may be worth including these in their model. Future studies can be aimed at addressing changes in the dynamic behavior of the MOM by altering this ratio, but the inclusion of all three forms in the current model will be important and may alter the other major findings of the current study.

(2) The findings from the 14 different symmetric membrane systems developed with the removal of one complex lipid at a time are very interesting but have not been analysed/discussed at length in the current manuscript. I find many interesting insights from Figures S3 and S5, which I find missing in the manuscript. These are as follows:

a) Loss of PDIM resulted in reduced membrane thickness. This is a very important finding given that loss of PDIM can be a spontaneous phenomenon in Mtb cultures in vitro and that this is driven by increased nutrient uptake by PDIM-deficient bacilli (Domenech and Reed, 2009 Microbiology). While the latter is explained by the enhanced solute uptake by several PE/PPE transporter systems in the absence of PDIM (Wang et al, Science 2020), the findings presented by Brown et al could be very important in this context. A discussion on these aspects would be beneficial for the mycobacterial community.

b) I find it interesting that loss of PAT or DAT does not change membrane thickness (Figure S3). While both PAT and PDIM can migrate to the interleaflet space, loss of PDIM and PAT has a different impact on membrane thickness. It is worth explaining what the likely interactions are that shape membrane thickness in the case of the modelled MOM.

c) Figure S5: Is the presence of SGL driving PDIM and PAT to migrate to the inter-leaflet space? Again, a discussion on major lipid-lipid interactions driving these lipid migrations across the membrane thickness would be useful.

Reviewer #2 (Public review):

Summary:

The manuscript reports all-atom molecular dynamics simulations on the outer membrane of Mycobacterium tuberculosis. This is the first all-atom MD simulation of the MTb outer membrane and complements the earlier studies, which used coarse-grained simulation.

Strengths:

The simulation of the outer membrane consisting of heterogeneous lipids is a challenging task, and the current work is technically very sound.

The observation about membrane heterogeneity and ordered inner leaflets vs disordered outer leaflets is a novel result from the study. This work will also facilitate other groups to work on all-atom models of mycobacterial outer membrane for drug transport, etc.

Weaknesses:

Beyond a challenging simulation study, the current manuscript only provides qualitative explanations on the unusual membrane structure of MTb and does not demonstrate any practical utility of the all-atom membrane simulation. It will be difficult for the general biology community to appreciate the significance of the work, based on the manuscript in its current form, because of the high content of technical details and limited evidence on the utility of the work.

Major Points:

(1) The simulation by Basu et al (Phys Chem Chem Phys 2024) has studied drug transports through mycolic acid monolayers. Since the authors of the current study have all atom models of MTb outer membrane, they should carry out drug transport simulations and compare them to the outer membranes of other bacteria through which drugs can permeate. In the current manuscript, it is only discussed in lines 388-392. Can the disruption of MA cyclopropanation be simulated to show its effect on membrane structure ?

(2) In line 277, the authors mention about 6 simulations which mimic lipid knockout strains. The results of these simulations, specifically the outcomes of in silico knockout of lipids, are not described in detail.

(3) Figure 5 shows PDIM and PAT-driven lipid redistribution, which is a significant novel observation from the study. However, comparison of 3B and 3D shows that at 313K, the movement of the PDIM head group is much less. Since MD simulations are sensitive to random initial seeds, repeated simulations with different random seeds and initial structures may be necessary.

(4) As per Figure 1, in the initial structure, the head group of PAT should be on the membrane surface, similar to TDM and TMM, while PDIM is placed towardsthe interior of the outer membrane. However, Figure 5 shows that at t=0, PAT has the same Z position as PDIM. It will be necessary to provide Z-position Figures for TMM and TDM to understand the difference. Is it really dependent on the chemical structure of the lipid moiety or the initial position of the lipid in the bilayer at the beginning of the simulation?

Minor Point:

In view of the complexity of the system undertaken for the study, the manuscript in its current form may not be informative for readers who are not experts in molecular simulations.

Reviewer #1 (Public review):

Summary:

Overexpression of the mRNA-binding protein Ssd1 was shown before to expand the replicative lifespan of yeast cells, whereas ssd1 deletion had the opposite effect. Here, the authors provide evidence that Ssd1 acts via sequestration of mRNAs of the Aft1/2-dependent iron regulon. This restricts activation of the regulon and limits accumulation of Fe2+ inside cells, thereby likely lowering oxidative damage. The effects of Ssd1 overexpression and calorie restriction on lifespan are epistatic, suggesting that they might act through the same pathway.

Strengths:

The study is well-designed and involves analysis of single yeast cells during replicative aging. The findings are well displayed and largely support the derived model, which also has implications for the lifespan of other organisms, including humans.

Weaknesses:

The model is largely supported by the findings, however, they remain largely correlative at the same time. Whether the knockout of ssd1 shortens lifespan by increased intracellular Fe2+ levels has not been tested. The finding that increased Ssd1 levels form condensates in a cell-cycle-dependent manner is interesting, yet the role of the condensates in lifespan expansion remains untested and unlinked.

Reviewer #2 (Public review):

This manuscript describes the use of a powerful technique called microfluidics to elucidate the mechanisms explaining how overexpression (OE) of Ssd1 and caloric restriction (CR) in yeast extend replicative lifespan (RLS). Microfluidics measures RLS by trapping cells in chambers mounted to a slide. The chambers hold the mother cell but allow daughters to escape. The slide, with many chambers, is recorded during the entire process, roughly 72 hours, with the video monitored afterwards to count how many daughters each of the trapped mothers produces. The power of the method is what can be done with it. For example, the entire process can be viewed by fluorescence so that GFP and mCherry-tagged proteins can be followed as cells age. The budding yeast is the only model where bona fide replicative aging can be measured, and microfluidics is the only system that allows protein localization and levels to be measured in a single cell while aging. The authors do a wonderful job of showing what this combination of tools can do.

The authors had previously shown that Ssd1, an mRNA-binding protein, extends RLS when overexpressed. This was attributed to Ssd1 sequestering away specific mRNAs under stress, likely leading to reduced ribosomal function. It remained completely unknown how Ssd1 OE extended RLS. The authors observed that overexpressed, but not normally expressed, Ssd1 formed cytoplasmic condensates during mitosis that are resolved by cytokinesis. When the condensates fail to be resolved at the end of mitosis, this signals death.

It has become clear in the literature that iron accumulation increases with age within the cell. The transcriptional programs that activate the iron regulon also become elevated in aging cells. This is thought to be due to impaired mitochondrial function in aging cells, with increased iron accumulation as an attempt at restoring mitochondrial activity. The authors show that Ssd1 OE and CR both reduce the expression of the iron regulon. The data presented indicate that iron accumulation shortens RLS: deletion of iron regulon components extends RLS, and adding iron to WT cells decreases RLS, but not when Ssd1 is overexpressed or when cells are calorically restricted. Interestingly, iron chelation using BPS has no impact on WT RLS, but decreases the elevated RLS in CR cells and cells overexpressing Ssd1. It was not initially clear why iron chelation would inhibit the extended lifespan seen with CR and Ssd1 OE. This was addressed by an experiment where it was shown that the iron regulon is induced (FIT2 induction) when iron is chelated. Thus, the detrimental effects of induction of the iron regulon by BPS and iron accumulation on RLS cannot be tempered by Ssd1 OE and CR once turned on.

I did not find any weaknesses to be addressed in this paper. The draft was well-written, and the extensive experimentation was well-designed, performed, and controlled. However, I did make minor comments that I recommend the authors address:

(1) Why would BPS not reduce RLS in WT cells? The authors could test whether OE of FIT2 reduces RLS in WT cells.

(2) The authors should add a brief explanation for why the GDP1 promoter was chosen for Ssd1 OE.

(3) On page 12, growth to saturation was described as glucose starvation. This is more accurately described as nutrient deprivation. Referring to it as glucose starvation is akin to CR, which growing to saturation is not. Ssd1 OE formed condensates upon saturation but not in CR. Why do the authors think Ssd1 OE did not form condensates upon CR? Too mild a stress?

(4) The authors conclude that the main mechanism for RLS extension in CR and Ssd1 OE is the inhibition of the iron regulon in aging cells. The data certainly supports this. However, this may be an overstatement as other mutations block CR, such as mutations that impair respiration. The authors do note that induction of the iron regulon in aging cells could be a response to impaired mitochondrial function. Thus, it seems that the main goal of CR and Ssd1 OE may be to restore mitochondrial function in aging cells, one way being inactivation of the iron regulon. A discussion of how other mutations impact CR would be of benefit.

(5) The cell cycle regulation of Ssd1 OE condensates is very interesting. There does not appear to be literature linking Ssd1 with proteasome-dependent protein turnover. Many proteins involved in cell cycle regulation and genome stability are regulated through ubiquitination. It is not necessary to do anything here about it, but it would be interesting to address how Ssd1 condensates may be regulated with such precision.

(6) While reading the draft, I kept asking myself what the relevance to human biology was. I was very impressed with the extensive literature review at the end of the discussion, going over how well conserved this strategy is in yeast with humans. I suggest referring to this earlier, perhaps even in the abstract. This would nail down how relevant this model is for understanding human longevity regulation.

In conclusion, I enjoyed reading this manuscript, describing how Ssd1 OE and CR lead to RLS increases, using different mechanisms. However, since the 2 strategies appear to be using redundant mechanisms, I was surprised that synergism was not observed.

Reviewer #3 (Public review):

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

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

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

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

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

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

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

Although the phrasing might be ambiguous at first reading, this interpretation is confirmed upon reviewing Matt Kaeberlein's PhD thesis: https://dspace.mit.edu/handle/1721.1/8318 (page 264 and so on).

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

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

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

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

Reviewer #1 (Public review):

Liver cancer shows a high incidence in males than females with incompletely understood causes. This study utilized a mouse model that lacks the bile acid feedback mechanisms (FXR/SHP DKO mice) to study how dysregulation of bile acid homeostasis and a high circulating bile acid may underlie the gender-dependent prevalence and prognosis of HCC. By transcriptomics analysis comparing male and female mice, unique sets of gene signatures were identified and correlated with HCC outcomes in human patients. The study showed that ovariectomy procedure increased HCC incidence in female FXR/SHP DKO mice that were otherwise resistant to age-dependent HCC development, and that removing bile acids by blocking intestine bile acid absorption reduced HCC progression in FXR/SHP DKO mice. Based on these findings, the authors suggest that gender-dependent bile acid metabolism may play a role in the male-dominant HCC incidence, and that reducing bile acid level and signaling may be beneficial in HCC treatment. This study include many strengths: 1. Chronic liver diseases often proceed the development of liver and bile duct cancer. Advanced chronic liver diseases are often associated with dysregulation of bile acid homeostasis and cholestasis. This study takes advantage of a unique FXR/SHP DKO model that develop high organ bile acid exposure and spontaneous age-dependent HCC development in males but not females to identify unique HCC-associated gene signatures. The study showed that the unique gene signature in female DKO mice that had lower HCC incidence also correlated with lower grade HCC and better survival in human HCC patients. 2. The study also suggests that differentially regulated bile acid signaling or gender-dependent response to altered bile acids may contribute to gender-dependent susceptibility to HCC development and/or progression. 3. The sex-dependent differences in bile acid-mediated pathology clearly exist but are still not fully understood at the mechanistic level. Female mice have been shown to be more sensitive to bile acid toxicity in a few cholestasis models, while this study showed a male dominance of bile acid promotion of HCC. This study used ovariectomy to demonstrate that female hormones are possible underlying factors. Future studies are needed to understand the interaction of sex hormones, bile acids, and chronic liver diseases and cancer.

Reviewer #1 (Public review):

Sinzato et. al. investigated how shear flow in a rheological chamber affects the assembly and fragmentation of cyanobacterial aggregates, with the goal of understanding how such aggregates might form naturally, and/or be destroyed industrially. The authors used a combination of experiments and models to show that cyanobacterial colonies can be difficult to fragment with fluid flows. Additionally, they provide biophysical support for the idea that such aggregates likely form primarily when cells stay together after cell division, rather than coming together from disparate paths.

This work has significant relevance to the field, both practically and naturally. Combatting or preventing toxic cyanobacterial blooms is an active area of environmental research that offers a practical backbone for this manuscript's ideas. Additionally, the formation and behavior of cellular aggregates in general is of widespread interest in many fields, including marine and freshwater ecology, healthcare and antibiotic resistance research, biophysics, and microbial evolution. In this field, there are still outstanding questions regarding how microbial aggregates form into communities, including if and how they come together from separate places. Therefore, I believe that researchers from many distinct fields would find interest in the topic of this paper, and particularly Figure 5, in which a phase space that is meant to represent the different modes of aggregate formation and destruction is suggested, dependent on properties of the fluid flow and particle concentration.

Altogether, the authors were successful in their investigation, and I find their claims to be justified. In particular, the authors achieve strong results from their experiments. Below, I outline key claims of the paper and indicate the level to which they were supported by their data.

• Their first major claim is that fluid flows alone must be quite strong in order to fragment the cyanobacterial aggregates they have studied. With their rheological chamber, they explicitly show that energy dissipation rates must exceed "natural" conditions by multiple orders of magnitude in order to fragment lab strain colonies, and even higher to disrupt natural strains sampled from a nearby freshwater lake. This claim is well-supported by their experiments and data.

• The authors then claim that the fragmentation of aggregates due to fluid flows occurs primarily through erosion of small pieces from larger aggregates. Because their experimental setup does not allow them to directly observe this process (for example, by watching one aggregate break into pieces), they rely on indirect methods to support the claim. Overall, the experimental evidence is generally supportive, but the models leave some gaps. I describe this conclusion in more detail below.

• The strongest evidence for the erosion-dominated process comes from the authors' measurements of transfer of biomass between large and small size classes, as in Figure 2E and Figure 2D. The authors claim that only the erosion model can reproduce this kind of biomass transfer. However, it also seems that the idealized erosion model alone is not fully sufficient to capture the observed behavior. In Figure 2D, there remains a gap between their experiment and the prediction of the erosion model, which grows larger over time (Supplemental Figure S9). While the authors suggest that the erosion model is better than the equal-fragmentation model, it is also true that tracking the mean size (Figure 2B) or small size distribution (Figure S6) cannot distinguish between these models.

• Taken altogether, the experimental evidence favors an erosion-dominated process. However, a few minor questions remain regarding the models. Why does the equal-fragmentation model predict no biomass transfer between size classes? To what extent, quantitatively, does the erosion model outperform the equal fragments model at capturing the biomass size distributions? Finally, why does the idealized erosion fail to capture the size distribution at late stages in Supplemental Figure S9 - would this discrepancy be resolved if the authors considered individual colony variances in cell adhesion (for instance, as hypothesized by the authors in lines 133-137)? I do not believe these questions curb the other results of the paper.

• Their third major claim is that fluid flows only weakly cause cells to collide and adhere in a "coming together" process of aggregate formation. They test this claim in Figure 3, where they suspend single cells in their test chamber and stir them at moderate intensity, monitoring their size histogram. They show that the size histogram changes only slightly, indicating that aggregation is, by-and-large, not occurring at a high rate. Therefore, they lend support to the idea that cell aggregation likely does not initiate group formation in toxic cyanobacterial blooms. Additionally, they show that the median size of large colonies also does not change at moderate turbulent intensities. These results agree with previous studies (their own citation 25) indicating that aggregates in toxic blooms are clonal in nature. This is an important result, and well-supported by their data, but only for this specific particle concentration and stirring intensity. Later, in Figure 5 they show a much broader range of particle concentrations and energy dissipation rates that they leave untested. However, they refer to other literature that does test these regions of the phase map.

• The fourth major result of the manuscript is displayed in Equation 8 and Figure 5, where the authors derive an expression for the ratio between the rate of increase of a colony due to aggregation vs. the rate due to cell division. They then plot this line on a phase map, altering two physical parameters (concentration and fluid turbulence) to show under what conditions aggregation vs. cell division are more important for group formation. Because these results are derived from relatively simple biophysical considerations, they have the potential to be quite powerful and useful, and represent a significant conceptual advance. By combining their experiments with discussions of other experimental investigations of scum formation in cyanobacterial blooms, the authors have investigated the two most relevant zones of this map for the present study (Zones II and III), and have made a strong contribution to the literature in regards to artificial mixing to disrupt cyanobacterial blooms.

Other notes:

The authors rely heavily on size distributions to make the claims of their paper. I was pleased to find the calibration histograms in Supplemental Figure S8, which provide information as to how and why they made corrections to the histograms they observed. From these calibration histograms, it seems that larger colonies are more accurately measured in the cone-and-plate shear setup, while smaller colonies can be missed, presumably due to resolution issues.

Reviewer #2 (Public review):

Summary:

In this work, the authors investigate the role of fluid flow in shaping the colony size of a freshwater cyanobacterium Microcystis. To do so, they have created a novel assay by combining a rheometer with a bright field microscope. This allows them to exert precise shear forces on cyanobacterial cultures and field samples, and then quantify the effect of these shear forces on the colony size distribution. Shear force can affect the colony size in two ways: reducing size by fragmentation and increasing size by aggregation. They find limited aggregation at low shear rates, but high shear forces can create erosion-type fragmentation: colonies do not break in large pieces, but many small colonies are sheared off the large colonies. Overall, bacterial colonies from field samples seem to be more inert to shear than laboratory cultures, which the authors explain in terms of enhanced intercellular adhesion mediated by secreted polysaccharides.

Strengths:

• This study is timely, as cyanobacterial blooms are an increasing problem in freshwater lakes. They are expected to increase in frequency and severeness because of rising temperatures, and it is worthwhile learning how these blooms are formed. More generally, how physical aspects such as flow and shear influence colony formation is often overlooked, at least in part because of experimental challenges. Therefore, the method developed by the authors is useful and innovative, and I expect applications beyond the presented system here.

• A strong feature of this paper is the highly quantitative approach, combining theory with experiments, and the combination of laboratory experiments and field samples.

Weaknesses:

• Especially the introduction seems to imply that shear force is a very important parameter controlling colony formation. However, if one looks at the results this effect is overall rather modest, especially considering the shear forces that these bacterial colonies may experience in lakes. The main conclusion seems that not shear but bacterial adhesion is the most important factor in determining colony size. The writing could have done more justice to the fact that the importance of adhesion had been described elsewhere. This being said, the same method can be used to investigate systems where shear forces are biologically more relevant.

Joint Public Review:

The study assesses how the rise of the Qinghai-Tibet Plateau affected patterns of bird migration between their breeding and wintering sites.

This is an interesting topic and a novel theme. The visualisations and presentation are to a very high standard. The Introduction is very well-written and introduces the main concepts well, with a clear logical structure and good use of the literature. The Methods are detailed and well-described, and written in such a fashion that they are transparent and repeatable.

Editorial note: These latest revisions are minor in the sense that they expand on the dataset but do not change the primary results.

for - from - search - Google - how new words divide the world in new ways - https://hyp.is/55MHUKUxEfC-TAfy9q1VjA/www.google.com/search?q=how+new+words+divide+the+world+in+new+ways&oq=how+new+words+divide+the+world+in+new+ways&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRigATIHCAIQIRigATIHCAMQIRigAdIBCDgwODFqMGo0qAIAsAIB&sourceid=chrome&ie=UTF-8

• book review - The Language Animal
  • self awareness emerges out of intersubjectivity
  • like Melanie Klein
  • relationship is necessary to form self identity
  • culture and language are intertwingled
  • “The basic thesis of this book is that language can only be understood if we understand its constitutive role in human life.”

for - book review - The Language Animal

Reviewer #1 (Public review):

Summary:

In this article, Mirza et al developed a continuum active gel model of actomyosin cytoskeleton that account for nematic order and density variations in actomyosin. Using this model, they identify the requirements for the formation of dense nematic structures. In particular, they show that self-organization into nematic bundles requires both flow-induced alignment and active tension anisotropy in the system. By varying model parameters that control active tension and nematic alignment, the authors show that their model reproduces a rich variety of actomyosin structures, including tactoids, fibres, asters as well as crystalline networks. Additionally, discrete simulations are employed to calculate the activity parameters in the continuum model, providing a microscopic perspective on the conditions driving the formation of fibrillar patterns.

Strengths:

The strength of the work lies in its delineation of the parameter ranges that generate distinct types of nematic organization within actomyosin networks. The authors pinpoint the physical mechanisms behind the formation of fibrillar patterns, which may offer valuable insights into stress fiber assembly. Another strength of the work is connecting activity parameters in the continuum theory with microscopic simulations.

Weaknesses:

This paper is a very difficult read for nonspecialists, especially if you are not well-versed in continuum hydrodynamic theories. Efforts should be made to connect various elements of theory with biological mechanisms, which is mostly lacking in this paper. The comparison with experiments is predominantly qualitative. It is unclear if the theory is suited for in vitro or in vivo actomyosin systems. The justification for various model assumptions, especially concerning their applicability to actomyosin networks, requires a more thorough examination. The classification of different structures demands further justification. For example, the rationale behind categorizing structures as sarcomeric remains unclear when nematic order is perpendicular to the axis of the bands. Sarcomeres traditionally exhibit a specific ordering of actin filaments with alternating polarity patterns. Similarly, the criteria for distinguishing between contractile and extensile structures need clarification, as one would expect extensile structures to be under tension contrary to the authors' claim. Additionally, it's unclear if the model's predictions for fiber dynamics align with observations in cells, as stress fibers exhibit a high degree of dynamism and tend to coalesce with neighboring fibers during their assembly phase. Finally, it seems that the microscopic model is unable to recapitulate the density patterns predicted by the continuum theory, raising questions about the suitability of the simulation model.

Reviewer #2 (Public review):

Summary:

The article by Waleed et al discusses the self-organization of actin cytoskeleton using the theory of active nematics. Linear stability analysis of the governing equations and computer simulations show that the system is unstable to density fluctuations and self-organized structures can emerge.

Strengths:

(i) Analytical calculations complemented with simulations (ii) Theory for cytoskeletal network

Weaknesses:

Not placed in the context or literature on active nematics.

Comments on revised version:

The authors have satisfactorily responded to the comments

Reviewer #3 (Public review):

The manuscript "Theory of active self-organization of dense nematic structures in the actin cytoskeleton" analysis self-organized pattern formation within a two-dimensional nematic liquid crystal theory and uses microscopic simulations to test the plausibility of some of the conclusions drawn from that analysis. After performing an analytic linear stability analysis that indicates the possibility of patterning instabilities, the authors perform fully non-linear numerical simulations and identify the emergence of stripe-like patterning when anisotropic active stresses are present. Following a range of qualitative numerical observations on how parameter changes affect these patterns, the authors identify, besides isotropic and nematic stress, also active self-alignment as an important ingredient to form the observed patterns. Finally, microscopic simulations are used to test the plausibility of some of the most crucial assumptions underlying continuum simulations.

The paper is well written, figures are mostly clear, and the theoretical analysis presented in both, main text and supplement, is rigorous. Mechano-chemical coupling has emerged in recent years as a crucial element of cell cortex and tissue organization and it is plausible to think that both, isotropic and anisotropic active stresses, are present within such effectively compressible structures. Even though not explicitly stated this way by the authors, I would argue that combining these two is one of the key ingredients that distinguishes this theoretical paper from similar ones.

The diversity of patterning processes experimentally observed and theoretically described is nicely elaborated on in the introduction of the paper. The theory development and discussion of the continuum model itself is also well-embedded in a review of the relevant broad literature on active liquid crystals and active nematics, which includes plenty of previous results by the authors themselves. Interestingly, several of the patterns identified in the present work, such as 2D hexagonal and pulsatory patterns (Kumar et al, PRL, 2014), as well as contractile patches (Mietke et al, PRL 2019) have been observed previously in different, but related, active isotropic fluid models. In light of this crowded literature, the authors do good job in delineating key results obtained in the present manuscript from existing work.

The results of numerical simulations are well-presented. The discussion of numerical observations is comprehensive, but also at many times qualitative. Some of the observations resonate with recent discussions in the field, for example the observation of effectively extensile dynamics in a contractile system, which is interesting and reminiscent of ambiguities about extensile/contractile properties discussed in recent preprints (Nejad et al, Nat Comm 2024). It is convincingly concluded that, besides nematic stress on top of isotropic one, active self-alignment is a key ingredient to produce the observed patterns.

The authors must be complimented for trying to gain further mechanistic insights into their conclusions using microscopic filament simulations that were diligently performed. It is rightfully stated that these simulations only provide plausibility tests about key assumptions underlying the hydrodynamic theory. Within this scope, I would say the authors are successful. At the same time, it leaves open questions that could have been discussed more carefully. For example, I wonder what can be said about the regime \kappa>0 microscopically, in which the continuum theory does also predict the formation of stripe patterns? How does the spatial inhomogeneous organization the continuum theory predicts fit in the presented, microscopic picture and vice versa? The authors clearly explain the scope and limitations of the microscopic model, which suggests that questions like these will be interesting directions of future investigations.

Overall, the paper represents a valuable contribution to the field of active matter that should provide a fruitful basis to develop new hypothesis about the dynamic self-organisation and mechanics of dense filamentous bundles in biological systems.

Reviewer #1 (Public review):

Summary:

I read with much attention the manuscript titled "Generation of knock-in Cre and FlpO mouse lines for precise targeting of striatal projection neurons and dopaminergic neurons" in which the authors reveal five transgenic lines to target diverse neuronal populations of the basal ganglia. In addition, the authors also provide some assessments of the functionality of the lines.

Strengths:

Knockin lines made readily available through Jackson. Lines show specific expression.

Weaknesses:

Although I have no doubt these knocking lines will be broadly used by researchers in the field, I find the scientific advances of the study and the breadth of the resource provided quite limited. This is partly because 4 of these lines have been generated by other laboratories. For instance, there are already two other Dat-FlpO lines generated (JAX#: 033673 and 035436), with one of them already characterized (PMID: 33979604). Similarly, Drd1-Cre and Adora2a-Cre have been used abundantly since they were generated over a decade ago, and a novel Drd1-FlpO line has been characterized thoroughly recently (PMID: 38965445). Indeed, some of these lines were BAC transgenic, and I agree with the authors that there is a sound rationale for generating knock-in mice; however, the authors should then demonstrate if/how their new drivers are superior. Overall, the valuable resource generated by the authors would benefit from additional quantification and validation.

Reviewer #2 (Public review):

Summary:

The authors report the generation and validation of new knock-in mouse lines enabling precise targeting of basal ganglia projection neurons and midbrain dopamine neurons. By inserting recombinase sequences at endogenous loci, they provide tools that improve on older BAC-based models, with the additional benefit that all lines are openly available through Jackson Laboratories. This work is timely, fills a longstanding gap for the community, and will support both basic circuit mapping and disease-related research.

Strengths:

The major strength of this study is the provision of new genetic resources that will be widely used by the basal ganglia and dopamine research communities. Anatomical and electrophysiological data indicate appropriate expression and preserved intrinsic properties. The Flp lines, in particular, show labeling largely confined to basal ganglia circuits, making them especially attractive for circuit-based studies. A further strength is the use of a T2A-recombinase insertion at the native gene stop codon, which preserves endogenous regulation and maintains near-physiological expression of Adora2a, Drd1a, and DAT. The availability of both Cre and Flp versions enables powerful intersectional strategies, and open distribution through Jackson Laboratories ensures broad accessibility and long-term value.

Weaknesses:

The major limitation is the discrepancy between Cre and Flp lines, with Cre generally driving broader expression than Flp. This raises concerns about anatomical fidelity that require validation at the cellular level. For the DAT-FlpO line, efficiency remains insufficiently quantified, and higher-resolution co-labeling with TH immunostaining is needed. Electrophysiological comparisons between Cre and Flp versions are also incomplete; current data suggest potential physiological differences, which warrant additional statistical testing and, at a minimum, explicit discussion in the manuscript.

Reviewer #3 (Public review):

Summary:

Using latest knock-in technology, the authors generated a set of five mouse lines with expression of recombinases in striatal projection neurons and dopaminergic neurons for public use. They rigorously characterize the expression of the recombinases by intersectional crossing with reporter lines to demonstrate that these lines are faithful, and they perform electrophysiological experiments in slices to provide evidence that the respective neurons show the expected features in these assays.

Strengths:

The characterization of the new mouse lines is exceptional, and these will be widely used by the community. The mouse lines are openly available for the community to use.

Weaknesses:

No weaknesses were identified by this Reviewer.

Reviewer #1 (Public review):

Summary:

The authors report the structure of the human CTF18-RFC complex bound to PCNA. Similar structures (and more) have been reported by the O'Donnell and Li labs. This study should add to our understanding of CTF18-RFC in DNA replication and clamp loaders in general. However, there are numerous major issues that I recommend the authors fix.

Strengths:

The structures reported are strong and useful for comparison with other clamp loader structures that have been reported lately.

Comments on revisions:

The revised manuscript is greatly improved. The comparison with hRFC and the addition of direct PCNA loading data from the Hedglin group are particular highlights. I think this is a strong addition to the literature.

I only have minor comments on the revised manuscript.

(1) The clamp loading kinetic data in Figure 6 would be more easily interpreted if the three graphs all had the same x axes, and if addition of RFC was t=0 rather than t=60 sec.

(2) The author's statement that "CTF18-RFC displayed a slightly faster rate than RFC" seems to me a bit misleading, even though this is technically correct. The two loaders have indistinguishable rate constants for the fast phase, and RFC is a bit slower than CTF18-RFC in the slow phase. However, the data also show that RFC is overall more efficient than CTF18-RFC at loading PCNA because much more flux through the fast phase (rel amplitudes 0.73 vs 0.36). Because the slow phase represents such a reduced fraction of loading events, the slight reduction in rate constant for the slow phase doesn't impact RFC's overall loading. And because the majority of loading events are in the fast phase, RFC has a faster halftime than CTF18-RFC. (Is it known what the different phases correspond to? If it is known, it might be interesting to discuss.)

(3) AAA+ is an acronym for "ATPases Associated with diverse cellular Activities" rather than "Adenosine Triphosphatase Associated".

Reviewer #2 (Public review):

Summary

Briola and co-authors have performed a structural analysis of the human CTF18 clamp loader bound to PCNA. The authors purified the complexes and formed a complex in solution. They used cryo-EM to determine the structure to high resolution. The complex assumed an auto-inhibited conformation, where DNA binding is blocked, which is of regulatory importance and suggests that additional factors could be required to support PCNA loading on DNA. The authors carefully analysed the structure and compared it to RFC and related structures.

Strength & Weakness

Their overall analysis is of high quality, and they identified, among other things, a human-specific beta-hairpin in Ctf18 that flexible tethers Ctf18 to Rfc2-5. Indeed, deletion of the beta-hairpin resulted in reduced complex stability and a reduction in a primer extension assay with Pol ε. Moreover, the authors identify that the Ctf18 ATP-binding domain assumes a more flexible organisation.

The data are discussed accurately and relevantly, which provides an important framework for rationalising the results.

All in all, this is a high-quality manuscript that identifies a key intermediate in CTF18-dependent clamp loading.

Comments on revisions:

The authors have done a nice job with the revision.

Reviewer #3 (Public review):

Summary:

CTF18-RFC is an alternative eukaryotic PCNA sliding clamp loader which is thought to specialize in loading PCNA on the leading strand. Eukaryotic clamp loaders (RFC complexes) have an interchangeable large subunit which is responsible for their specialized functions. The authors show that the CTF18 large subunit has several features responsible for its weaker PCNA loading activity, and that the resulting weakened stability of the complex is compensated by a novel beta hairpin backside hook. The authors show this hook is required for the optimal stability and activity of the complex.

Relevance:

The structural findings are important for understanding RFC enzymology and novel ways that the widespread class of AAA ATPases can be adapted to specialized functions. A better understanding of CTF18-RFC function will also provide clarity into aspects of DNA replication, cohesion establishment and the DNA damage response.

Strengths:

The cryo-EM structures are of high quality enabling accurate modelling of the complex and providing a strong basis for analyzing differences and similarities with other RFC complexes.

Weaknesses:

The manuscript would have benefited from a more detailed biochemical analysis using mutagenesis and assays to tease apart the differences with the canonical RFC complex. Analysis of the FRET assay could be improved.

Overall appraisal:

Overall, the work presented here is solid and important. The data is mostly sufficient to support the stated conclusions.

Comments on revisions:

While the authors addressed my previous specific concerns, they have now added a new experiment which raises new concerns.

The FRET clamp loading experiments (Fig. 6) appear to be overfitted so that the fitted values are unlikely to be robust and it is difficult to know what they mean, and this is not explained in this manuscript. Specifically, the contribution of two exponentials is floated in each experiment. By eye, CTF18-RFC looks much slower than RFC1-RFC (as also shown previously in the literature) but the kinetic constants and text suggest it is faster. This is because the contribution of the fast exponential is substantially decreased, and the rate constants then compensate for this. There is a similar change in contribution of the slow and fast rates between WT CTF18 and the variant (where the data curves look the same) and this has been balanced out by a change in the rate constants, which is then interpreted as a defect. I doubt the data are strong enough to confidently fit all these co-dependent parameters, especially for CTF18, where a fast initial phase is not visible. I would recommend either removing this figure or doing a more careful and thorough analysis.

Reviewer #1 (Public review):

Summary:

mRNA decapping and decay factors play critical roles in post-transcriptionally regulating gene expression. Here, Kumar and colleagues investigate how deleting two yeast decapping enhancer proteins (Edc3 and Scd6), either alone or in tandem, affects the transcriptome. Using RNA-Seq, CAGE-Seq and ribosome profiling, they conclude that these factors generally act in a redundant fashion, with a mutant lacking both proteins showing an increased abundance of select mRNAs. As these upregulated transcripts are also upregulated in mutants lacking the decapping enzyme, Dcp2, and show no increases in transcription of their cognate genes, the authors conclude that this is at the level of mRNA decapping and decay. This was further supported by CAGE-Seq analyses carried out in WT cells and the scd∆6edc3∆ double mutant. Their ribosome profiling data also lead them to conclude that Scd6 and Edc3 display functional redundancy and cooperativity with Dhh1/Pat1 in repressing the translation of specific transcripts. Finally, as their data suggest that Scd6 and Edc3 repress mRNAs coding for proteins involved in cellular respiration, as well as proteins involved in the catabolism of alternative carbon sources, they go on to show that these decapping activators play a role in repressing oxidative phosphorylation.

Strengths:

Overall, this manuscript is well-written and contains a large amount of compelling high-quality data and analyses. At its core, it helps to shed light on the overlapping roles Edc3 and Scd6 have in sculpting the yeast transcriptome.

Weaknesses:

While not essential, it would be interesting if the authors carried out add-back experiments to determine which domain within Scd6/Edce3 plays a critical role for enforcing the regulation that they see? Their double mutant now puts them in a perfect position to carry out such experiments.

Reviewer #2 (Public review):

Summary:

This manuscript by Kumar and Zhang presents compelling evidence that Edc3 and Scd6 decapping activators, present a high degree of redundancy that can only be overcome by double mutants of both. In addition, the authors provide strong evidence for their role in regulating starvation-induced pathways as evidenced by measurements of mitochondrial membrane potential, metabolomics and analysis of the flux of Krebs cycle intermediates.

Strengths:

Kumar, Zhang et al provide multiple source of evidence of the direct mechanism of Edc3 and Scd6, by using and comparing different approaches such as mRNA-seq, ribosome occupancies and translational efficiencies. By extensive analysis the authors show that this complex can also regulate genes outside the Environmental Stress Response (non-iESR) that are significantly up-regulated in all three mutants. Remarkably, the gene ontology analysis of these non-iESR genes identify enrichment for mitochondrial proteins that are implicated in the Krebs cycle. Overall, this study adds novel mechanistic insight into how nutrients control gene expression by modulating decapping and translational repression.

Weaknesses:

The authors show very nicely that growth phenotypes from scd6Δedc3∆ can be rescued by transformation of EDC3 (pLfz614-7) or SCD6 (pLfz615-5). Future work could make use of these rescue strategies, for example as a platform to further characterise protein-protein interactions between Edc3, Scd6 and Dhh1.

Reviewer #3 (Public review):

Summary:

In this paper, Kumar et al investigated the role of two decapping activators, Edc3 and Scd6, in regulating mRNA decay and translation in yeast. Using a variety of approaches including RNA-seq, ribosome profiling, proteomics, polysome analysis, and metabolomics the authors demonstrate that whereas single deletions of Edc3 or Scd6 have modest effects, the double mutant leads to increased abundance of mRNAs, many of which overlap with those targeted by the decapping activators Dhh1 and Pat1. The data suggest that Edc3 and Scd6 function redundantly to recruit Dhh1 to the Dcp2 decapping complex, thereby promoting mRNA turnover and translational repression. The authors show that these factors cooperate with Dhh1/Pat1 to repress transcripts involved in respiration, mitochondrial function, and alternative carbon source utilization, linking post-transcriptional regulation to nutrient responses. The study establishes Edc3 and Scd6 as important, but redundant regulators that fine-tune gene expression and metabolic adaptation in response to nutrient availability.

Strengths:

The paper has several strengths, including the comprehensive approach taken by the authors using multiple experimental techniques (RNA-seq, ribosome profiling, Western blotting, TMT-MS, polysome profiling, and metabolomics) to provide multiple lines of evidence to support their conclusions. The authors demonstrate clear redundancy of the factors by using single and double mutants for Edc3 and Scd6 and their global approach enables an understanding of these factors' roles across the yeast transcriptome. The work connects post-transcriptional processes to nutrient-dependent gene regulation, providing insights into how cells adapt to changes in their environment. The authors demonstrate the redundant roles of Edc3 and Scd6 in mRNA decapping and translation repression. Their RNA-seq and ribosome profiling results convincingly show that many mRNAs are derepressed only in the double mutants, confirming their hypothesis of redundancy. Furthermore, the functional cooperation between Edc3/Scd6 and Dhh1/Pat1 in regulating specific metabolic pathways, including mitochondrial function and carbon source utilization, is supported by the metabolomic data.

Weaknesses:

The study uses indirect evidence to support claims about the effect on mRNA stability rather than directly measuring mRNA stability. However, the combination of Pol II occupancy and RNA abundance measurements is consistent with the claims regarding mRNA stability. The addition of new experiments in the revision co-IPing Dhh1 and Dcp2 strengthens the argument that Edc3 and Scd6 recruit these factors.

Reviewer #2 (Public review):

Summary:

The paper describes the high-resolution structure of KdpFABC, a bacterial pump regulating intracellular potassium concentrations. The pump consists of a subunit with an overall structure similar to that of a canonical potassium channel and a subunit with a structure similar to a canonical ATP-driven ion pump. The ions enter through the channel subunit and then traverse the subunit interface via a long channel that lies parallel to the membrane to enter the pump, followed by their release into the cytoplasm.

The work builds on the previous structural and mechanistic studies from the authors' and other labs. While the overall architecture and mechanism have already been established, a detailed understanding was lacking. The study provides a 2.1 Å resolution structure of the E1-P state of the transport cycle, which precedes the transition to the E2 state, assumed to be the rate-limiting step. It clearly shows a single K+ ion in the selectivity filter of the channel and in the canonical ion binding site in the pump, resolving how ions bind to these key regions of the transporter. It also resolves the details of water molecules filling the tunnel that connects the subunits, suggesting that K+ ions move through the tunnel transiently without occupying well-defined binding sites. The authors further propose how the ions are released into the cytoplasm in the E2 state. The authors support the structural findings through mutagenesis and measurements of ATPase activity and ion transport by surface-supported membrane (SSM) electrophysiology.

Reviewer #3 (Public review):

Summary:

By expressing protein in a strain that is unable to phosphorylate KdpFABC, the authors achieve structures of the active wildtype protein, capturing a new intermediate state, in which the terminal phosphoryl group of ATP has been transferred to a nearby Asp, and ADP remains covalently bound. The manuscript examines the coupling of potassium transport and ATP hydrolysis by a comprehensive set of mutants. The most interesting proposal revolves around the proposed binding site for K+ as it exits the channel near T75. Nearby mutations to charged residues cause interesting phenotypes, such as constitutive uncoupled ATPase activity, leading to a model in which lysine residues can occupy/compete with K+ for binding sites along the transport pathway.

Strengths:

The high resolution (2.1 Å) of the current structure is impressive, and allows many new densities in the potassium transport pathway to be resolved. The authors are judicious about assigning these as potassium ions or water molecules, and explain their structural interpretations clearly. In addition to the nice structural work, the mechanistic work is thorough. A series of thoughtful experiments involving ATP hydrolysis/transport coupling under various pH and potassium concentrations bolsters the structural interpretations and lends convincing support to the mechanistic proposal. The SSME experiments are generally rigorous.

Weaknesses:

The present SSME experiments do not support quantitative comparisons of different mutants, as in Figures 4D and 5E. Only qualitative inferences can be drawn among different mutant constructs.

Reviewer #1 (Public review):

Summary:

The authors tested two competing mechanisms of expectation (1) a sharpening model that suppresses unexpected information via center-surround inhibition; (2) a cancellation model that predicts a monotonic gradient response profile. Using two psychophysical experiments manipulating feature space distance between expected and unexpected stimuli, the results consistently supported the sharpening model. Computational modeling further showed that expectation effects were explained by either sharpened tuning curves or tuning shifts. Finally, convolutional neural network simulations revealed that feedback connections critically mediate the observed center-surround inhibition.

Strengths:

The manuscript provides compelling and convergent evidence from both psychophysical experiments and computational modeling to robustly support the sharpening model of expectation, demonstrating clear center-surround inhibition of unexpected information.

Comments on revisions:

I appreciate the authors' thoughtful revisions. I have no further comments.

Reviewer #2 (Public review):

Summary:

This is a compelling and methodologically rich manuscript. The authors used a variety of methods, including psychophysics, computational modeling, and artificial neural networks, to reveal a non-monotonic, center-surround "Mexican-hat" profile of expectation in orientation space. Their data convincingly extend analogous findings in attention and working memory, and the modeling nicely teases apart sharpening vs. shift mechanisms.

Strengths:

The findings are novel and important in elucidating the potential neural mechanisms by which expectation shapes perception. The authors conducted a series of well-designed psychophysical experiments to careful examination of the profile of expectation's modulation. Computational modeling also provides further insights, linking the neural mechanisms of expectation to behavioral results.

Comments on revisions:

I think the authors did a great job in addressing my previous comments. I have no further comments.

Reviewer #1 (Public review):

Summary:

The authors performed a multi-funder study to determine if the Matthew effect and early-career setback effect were reproducible across funding programs and processes. The authors extended the analysis of these effects to all applicants and compared the results to the prior studies that only looked at near-hit/near-miss applicants to determine if the effects were generalizable to the whole applicant pool. Further, the authors included new models that also account for researcher behavior and their overall likelihood to reapply for later funding and how this behavior may resolve what appears to be a paradox between the Matthew effect and the early-career setback effect.

Strengths:

Figure 4 shows that the "Post (late) MFCR" is the same for the funded and unfunded groups, indicating that the impact of early career funding (at least, in terms of citation metrics) is transient in researcher's overall careers. This finding should encourage researchers to persevere when needed and that long-term success is attainable.

The inclusion of the collider bias in the models to account for researcher behavioral responses is a key strength of the paper and enhance the analysis and nuanced discussion of the results.

Weaknesses:

The discussion of limitations is thorough and point to the need for additional studies. One limitation that is acknowledged is that the authors only looked at applicants who reapplied for funding at the same funder. Given that the authors had the names and affiliations of the applicants from all of the funders, it would be helpful to understand why they were not able to look at applicants across their full data set. Was the limitation technical or a result of the study design? What would have to change to enable this broader analysis?

In Section 4.1, the authors make a statement that the "between MFCR" difference was seen at 5 years, but not at 10 years, and so the authors chose to use the 5-year period for the presentation of their results. It would be helpful to also see the 10-year analysis and have further justification from the authors on why they selected to look at the 5-year period and how their conclusions might or might not change if they consider the longer time period.

The discussion could also include that many funders require novel research directions as a condition of receiving an early-career award. For those who receive these awards, they must establish the new research program, begin publishing, and they may initially see a lower citation rate until the impact of the research is more broadly recognized. Are there ways to explore how these time lags impact the "Between MFCR" on those who were funded more so than those who were not funded?

Reviewer #2 (Public review):

Summary:

The manuscript evaluates the generalizability of two phenomena of great interest to early-career scientists and scientific policymakers. These phenomena describe how early funding success can promote future funding success (the Matthew Effect) and how initially unsuccessful applicants may later succeed (the early-career setback effect). Given the often-normative aspirations of science-of-science studies, the manuscript represents a much-needed and highly significant effort, as it allows a broader audience to assess whether they should reconsider their behavior or policies.

Strengths:

The evidence provided by the authors for the generalizability of the Matthew Effect is very strong and convincing. The manuscripts addresses an important topic of practical concern to early-career scientists and scientific policymakers.

Weaknesses: If I am correctly interpreting S11 and S12, the statements on the early-career setback effect could be stronger and more direct. The argument in the main text relies on assumptions and simulations to suggest that observations of the early-career setback effect may depend on reapplications. In contrast, S11 and S12 appear to provide more direct evidence against its generalizability, showing that the effect seems to exist in, and be driven by, only one of the six funding agencies considered (FWF). This narrow replication may not be obvious to readers ("the early-career setback effect also replicates, but is not robust across funders").

I would also suggest that the authors provide a more nuanced discussion of the limitations of their Bayesian model. While the model seems appropriate for accounting for major factors, it appears to exclude others, such as the emergence of new scientific fields or the strategic reorientation of funders toward such fields.

Reviewer #3 (Public review):

Summary:

This paper investigates the Matthew effect, where early success in funding peer review can translate into potentially unwarranted later success. It also investigated the previously found "setback" effect for those who narrowly miss out on funding.

Strengths:

The study used data from six funding agencies, which increases the generalisability, and was able to link bibliographic data for around 95% of applicants. The authors nicely illustrate how the previously found "setback" effect for near-miss applicants could be a collider bias due to those who chose to apply sometime later. This is a good explanation for the counter-intuitive effect and is nicely shown in Figure 5.

Weaknesses:

Most of the methods were clearly presented, but I have a few questions and comments, as outlined below.

In Figure 4(a) why are the "post" means much lower than the "pre"? This contradicts the expected research trajectory of researchers. Or is this simply due to less follow-up time? But doesn't the field citation ratio control for follow-up time?

The choice of the log-normal distribution for latent quality was not entirely clear to me. This would create some skew, rather than a symmetric distribution, which may be reasonable but log-normal distributions can have a very long tail which might not mimic reality, as I would not expect a small number of researchers to be extremely above the crowd. However, then the skew was potentially dampened by using percentile scores. Some further reasoning and plots of the priors would help.

Can the authors confirm the results of Figure S9 which show no visible effect of altering the standard deviation for the review parameter or the mean citations? Is this just because the prior for quality is dominated by the data? Could it be that the width of the distribution for quality does not matter, as it's the relative difference/ranking that counts? So the beta in equation 6 changes to adjust to the different quality scale?

The contrary result for the FWF is not explained (Table S3). Does this funder have different rules around re-applicants or many other competing funders?

The outlined qualitative research sounds worthwhile. Another potential mechanism (based on anecdote) is that some researchers react irrationally to rejection or acceptance, tending to think that the whole agency likes or hates their work based on one experience. Many researchers do not appreciate that it was a somewhat random selection of reviewers who viewed their work, and it will unlikely be the same reviewers next time.

"A key implication is the importance of encouraging promising, but initially unsuccessful applicants to reapply." Yes, A policy implication is to give people multiple chances to be lucky, perhaps by giving fewer grants to more people, which could be achieved by shortening the funding period (e.g., 4 year fellowships instead of 5 years). Although this will have some costs as applicants would need to spend more time on applications and suffer increased stress of shorter-term contracts. The bridge grants is potentially an ideal half-way house between many short-term and few long-term awards. Giving more grants to fewer people is supported by this analysis showing a diminishing returns in research outputs with more funding, DOI: 10.1371/journal.pone.0065263.

Making more room for re-applicants also made me wonder if there should be an upper cap on funding, potentially for people who have been incredibly successful. Of course, funders generally want to award successful researchers, but people who've won over some limit, for example $50 million, could likely be expected to win funding from other sources such as philanthropy and business. Graded caps could occur by career stage.

Reviewer #1 (Public review):

Summary:

This work investigated how the sense of control influences perceptions of stress. In a novel "Wheel Stopping" task, the authors used task variations in difficulty and controllability to measure and manipulate perceived control in two large cohorts of online participants. The authors first demonstrate that their behavioral task exhibits good internal consistency and external validity, indicating that perceived control during the task is linked to relevant measures of anxiety, depression, and locus of control. Most importantly, manipulating controllability in the task resulted in reduced subjective stress, demonstrating a direct impact of control on stress perception. However, this work has some minor limitations to this work due to the design of the stressor manipulations/measurements and the necessary logistics associated with online versus in-person stress studies.<br /> Nevertheless, this research adds to our understanding of when and how control can influence the effects of stress and has particular relevance for mental health interventions.

Strengths:

The primary strength of this research is the development of a unique and clever task design that can reliably and validly elicit variations in beliefs about control. Impressively, higher subjective control in the task was associated with decreased psychopathology measures such as anxiety and depression in a non-clinical sample of participants. In addition, the authors found that lower control and higher task difficulty led to higher perceived stress, suggesting that the task can reliably manipulate perceptions of stress. Prior tasks have not included both controllability and difficulty in this manner and have not directly tested the direct influence of these factors on incidental stress, making this work both novel and important for the field.

Weaknesses:

One minor weakness of this research is the validity of the online stress measurements and manipulations. In this study, the authors measure subjective stress via self-report both during the task and after either a Trier Social Stress Test (high-stress condition) or a memory test (low-stress condition). One concern is that these stress manipulations were really "threats" of stress, where participants never had to complete the stress tasks (i.e., recording a speech for judgment). While this is not unusual for an in-lab study and can reliably elicit substantial stress/anxiety, in an online study, there is a possibility for communication between participants (via online forums dedicated to such communication), which could weaken the stress effects. That said, the authors did find sensible increases and decreases in perceived stress between relevant time points; however, future work could improve upon this design by including more comprehensive stress manipulations and by measuring implicit physiological signs of stress.

Comments on revisions:

I appreciate the authors' responses to my comments and concerns. I have decided not to make changes to my public review, as I believe it remains relevant and fair after revisions.

Reviewer #2 (Public review):

Summary:

The authors have developed a behavioral paradigm to experimentally manipulate the sense of control experienced by participants by varying the level of difficulty in a wheel-stopping task. In the first study, this manipulation is tested by administering the task in a factorial design with two levels of controllability and two levels of stressor intensity to a large number of participants online, while simultaneously recording subjective ratings of perceived control, anxiety, and stress. In a second study, the authors employed the wheel stopping task to induce a high sense of controllability and investigate whether this manipulation buffers the response to a subsequent stress induction when compared to a neutral task, such as watching pleasant videos.

Strengths:

(1) The authors validate a method to manipulate stress.

(2) The authors use an experimental manipulation to induce an enhanced sense of controllability to test its impact on the response to stress induction.\

(3) The studies involved big sample sizes.

Weaknesses:

(1) The study was not preregistered.

(2) The control manipulation is conflated with task difficulty and, therefore, the reward rate. In the revised version of the manuscript, the authors perform statistical analysis to demonstrate that the relationship between perceived level of control and subjective stress remains robust after the inclusion of win rate in the model. This analysis strengthens the authors's claims, but the evidence would more substantial if the design did not conflate reward rate and control. The authors properly discuss this issue in the revised manuscript.

This study will be of interest to psychologists and cognitive scientists who are interested in understanding how controllability and its subjective perception influence how people respond to stress exposure. The demonstration that an increased sense of control buffers/protects against subsequent stress is important and may trigger further studies to characterize this phenomenon better. However, beyond the highlighted weaknesses, the current study only studied the effect of stress induction consequent to the performance of the WS task on the same day, and its generalizability is not warranted.

Reviewer #3 (Public review):

Summary:

This is an interesting investigation on the benefits of perceiving control and its impact on the subjective experience of stress. To assess the subjective sense of control, the authors introduce a novel wheel stopping (WS) task where control is manipulated via size and speed to induce conditions of low and high control. The authors demonstrate that the subjective sense of control is associated with experienced subjective stress and individual differences related to mental health measures. In a second experiment, they further demonstrate that an increased sense of control buffers subjective stress induced by a trier social stress manipulation, more so than a typical stress-buffering mechanism of watching neutral/calming videos.

Strengths:

Several strengths of the manuscript can be highlighted. For instance, the paper introduces a new paradigm and a clever manipulation to test a significant and important question. Additionally, it is a well-powered investigation that allows for confidence in replicability and demonstrate both high internal consistency and high external validity, along with an interesting set of individual difference analyses. Finally, the results are quite interesting and support prior literature, while also making a significant contribution to the field in understanding the benefits of perceiving control.

Weaknesses:

The authors have addressed all my queries, and I believe the revised paper has been improved and will make an important contribution to the literature.

Reviewer #1 (Public review):

Summary:

The taxonomic analysis of IRG1 evolution is compelling and fills an important gap in the literature. However, the experimental evidence for IRG1 localization requires greater detail and confirmation.

Strengths:

The phylogenetic analysis of IRG1 evolution fills an important gap in the literature. The identification of independent acquisition of metazoan and fungal IRG1 from prokaryotic sources is novel, and the observation that human IRG1 lost mitochondrial matrix localization is particularly interesting, with potentially significant implications for the study of itaconate biology.

Weaknesses:

The protease protection assay was conducted with MTS-IRG1 but not with wild-type IRG1, which should also be tested. Moreover, no complementary methods, such as microscopy, were employed to validate localization. Beyond humans, the structure and localization of mouse IRG1, highly relevant given the widespread use of the mouse as a model for IRG1 functional studies, are not addressed. Finally, if itaconate is indeed synthesized outside the mitochondrial matrix to safeguard metabolic activity, it is not discussed how this reconciles with its reported inhibitory effect on SDH.

Reviewer #2 (Public review):

Summary:

The authors are trying to explain how the metabolite itaconate evolved, since although it's involved in host defense, it can also limit mitochondrial function. They are trying to probe the trade-off between these two functions.

Strengths:

The evolutionary aspect is novel; this is the first time to my knowledge that the evolution of IRG1 has been analysed, and there are interesting findings here. The key finding appears to be that subcellular localisation is an important aspect, allowing host defense in some organisms without compromising bioenergetics. This is an interesting finding in the context of immunomebolism, although it needs extra analysis.

Weaknesses:

The work concerning sub-mitochondrial localisation is confusing and needs better analysis.

Reviewer #3 (Public review):

Summary:

IRG1 is highly expressed in activated human and mouse myeloid cells. It encodes the mitochondrial enzyme cis-aconitate decarboxylase 1 (ACOD1) that generates itaconate. Itaconate has anti-microbial activity and acts immunoregulatory by interfering with cellular metabolism, signaling to cytokine production, and multiple other processes.

The authors perform a phylogenetic analysis of IRG1 to obtain insight into the evolution of itaconate biosynthesis. Combining BLAST with human IRG1 and a MmgE/Ptrp domain search, they find CAD in all domains of life, but the presence of IRG1 homologs is patchy in eukaryotes, indicating that itaconate biosynthesis is not essential. The phylogenetic analysis showed a more distant relationship of fungal and metazoan CAD/IRG1 to many prokaryotic sequences, suggesting independent acquisition of these metazoan and fungal CAD genes. In metazoans, three subbranches of paleo-IRG1 (in mollusks/early chordates) and two paralogous vertebrate forms (IRG1 and IRG1-like) were identified, with the latter derived from paleo-IRG1, and by genome duplication. While most jawed vertebrates have both IRG1 and IRG1L, metatherian and eutherian mammals have lost IRG1L and contain only IRG1.

Interestingly, sequence analysis of both paralogues showed that many IRG1L genes contain an N-terminal mitochondrial targeting sequence (MTS) that is absent from most IRG1 sequences. Limited proteolysis of submitochondrial localization confirmed that zebrafish IRG1L is only sensitive to proteases in the presence of high Triton X-100, indicative of association with mitochondrial matrix. In contrast, a recent paper from the Galan lab (Lian 2003 Nature Microbiology) reported that human IRG1 is not localized to the mitochondrial matrix, although enriched in mitochondria. Here, the authors generated a matrix-targeted human IRG1 by adding the N-terminal MTS and found that it localizes to the matrix based on a limited proteolysis assay. The loss of MTS-containing IRG1L from most mammals appears, therefore, to indicate that itaconate generation is directed to the cytoplasm, potentially reducing inhibition of TCA cycle activity in the mitochondria.

Next, the authors confirmed that the recombinant IRG1L protein has CAD activity in vitro. The last part of the manuscript addresses the expression of paleo-IRG1 in oysters and amphioxus, where they found high mRNA levels in oyster hemocytes which was further increased by poly(I:C), which was also the case in amphioxus tissues after feeding of LPS or poly(I:C), indicating a role for paleo-IRG1/itaconate in early metazoan innate immunity.

Strengths

(1) Phylogenetic perspective largely lacking so far in the IRG1/itaconate field.

(2) Manuscript clearly written and understandable across disciplines.

(3) Phylogenetic analyses complemented by biochemical and gene expression analyses to link to function.

(4) Lack of MTS in IRG1 and change in localization from mitochondria, highly relevant antimicrobial and cellular effects of itaconate.

Weaknesses:

(1) Biochemical and functional analysis of different CAD mRNA and proteins lacks depth.

(2) The submitochondrial localization assay lacks a native human IRG1 control.

(3) CAD activity shown for IRG1L but not paleo-IRG1.

(4) Itaconate production by early metazoans after PAMP stimulation?

(5) No measurement of energy metabolism (trade-offs?).

I acknowledge that some of these limitations are inevitable because the range of detailed experimental analysis is necessarily limited. However, some of these data would be important to support central claims of the manuscript (further discussed below).

Reviewer #1 (Public review):

Summary:

This paper investigates whether transformer-based models can represent sentence-level semantics in a human-like way. The authors designed a set of 108 sentences specifically to dissociate lexical semantics from sentence-level information and collected 7T fMRI data from 30 participants reading these sentences. They conducted representational similarity analysis (RSA) comparing brain data and model representations, as well as the human behavioral ratings. It is found that transformer-based models match brain representation better than a static word embedding baseline, which ignores word order, but fall short of models that encode the structural relations between words. The main contributions of this paper are:

(1) The construction of a sentence set that disentangles sentence structure from word meaning.

(2) A comprehensive comparison of neural sentence representations (via fMRI), human behavior, and multiple computational models at the sentence level.

Strengths:

(1) The paper evaluates a wide variety of models, including layer-wise analysis for transformers and region-wise analysis in the human brain.

(2) The stimulus design allows precise dissociation between lexical and sentence-level semantics. The RSA-based approach is empirically sound and intuitive.

(3) The constructed sentences, along with the fMRI and behavioral data, represent a valuable resource for studying sentence representation.

Weaknesses:

(1) The rationale behind averaging sentence embeddings across multiple transformer models (with different architectures and training objectives) is unclear. These transformer-based models have different training paradigms and model architectures, which may result in misaligned semantic spaces. The averaging operation may dilute the distinct sentence representations learned by each model, potentially weakening the overall semantic encoding for sentences. Please clarify this choice or cite supporting methodology.

(2) All structure-sensitive models discussed incorporate semantics to some extent. Including a purely syntactic baseline, such as a model based on context-free grammar, would help confirm the importance of syntactic structures.

(3) In Figure 2, human behavioral judgments show weak correlations with neural data, and even fall below those of computational models, suggesting the behavioral judgments may not reflect the sentence structures in a brain-like way. This discrepancy between behavioral and neural data should be clarified, as it affects the interpretation of the results.

(4) To better contextualize model and neural performance, sentence similarity should be anchored to a notion of semantic "ground truth", such as the matrix shown in Figure 1a. Comparing this reference with human judgments, brain responses, and model similarities would help establish an upper bound.

(5) The structure of this paper is confusing. For instance, Figure 5 is cited early but appears much later. Reordering sections and figures would enhance readability.

(6) While the analysis is broad and comprehensive, it lacks depth in some respects. For instance, it remains unclear what specific insights are gained from comparing across brain regions (e.g., whole brain, language network, and other subregions). Similarly, the results of simple-average and group-average RSA appear quite similar and may not advance the interpretation.

(7) While explaining the grid-like pattern due to sentence length is important, this part feels somewhat disconnected from the central question of this paper (word order). It might be better placed in supplementary material.

Reviewer #2 (Public review):

Summary:

The paper used fMRI data while reading a set of sentences. The sentences are designed to disentangle syntax from meaning. RSA was performed using voxel activations and a variety of language models. The results show that transformers are inferior to models with explicit syntactic representation in terms of matching brain representations.

Strengths:

(1) The study controls for some variables that allow for an investigation of sentence structure in the brain. This controlled setting has an advantage over naturalistic stimuli in targeting more specific linguistic phenomena.

(2) The study combines fMRI data with behavioral similarity ratings and a variety of language models (static, transformers, graph-based models).

Weaknesses:

(1) The stimuli are not fully controlled for lexical content across conditions. Residual lexical differences between sentences could still influence both brain and model similarity patterns. To more cleanly isolate syntactic effects, it would be useful to systematically vary only a single structural element while keeping all other lexical content constant (e.g., the boy kicked the ball / the ball kicked the boy). It would be better to engage more with the minimal pair paradigm, which is widely used in large language model probing research.

(2) The comparisons are done across fundamentally different model types, including static embeddings, graph-based parsers, and transformers. The inherent differences in dimensionality and training objectives might make the conclusion drawn from RSA inconclusive. Transformer embeddings typically occupy much higher-dimensional, anisotropic representational spaces, and their similarity structure may reflect richer, more heterogeneous information than models explicitly encoding semantic roles. A lower RSA correlation in this study does not necessarily imply that transformers fail to encode syntactic information; rather, they may represent additional aspects of meaning or context that diverge from the narrow structural contrasts probed here.

(3) The interpretation of the RSA correlation largely depends on the understanding of models. The authors suggest that because hybrid models correlate better than transformers, this implies that transformers are inferior at representing syntax. However, this is not a direct test of syntactic ability. Transformers may encode syntactic information, but it may not be expressed in a way that aligns with the RSA paradigm or the chosen stimuli. RSA does not reveal what the model encodes, and the models might achieve a good correlation for non-syntactic reasons (e.g., length of sentence, orthographic similarity, lexical features).

Reviewer #3 (Public review):

Summary:

Large Language Models have revolutionized Artificial Intelligence and can now match or surpass human language abilities on many tasks. This has fueled interest in cognitive neuroscience in exposing representational similarities between Language Models and brain recordings of language comprehension. The current study breaks from this mold by: (1) Systematically identifying sentence structures for which brain and Large Language Model representations diverge. (2) Demonstrating that brain representations for these sentences can be better accounted for by a model structured by the semantic roles of words in the sentence. As such, the study may now fuel interest in characterizing how Large Language Models and brain representations differ, which may prompt new, more brain-like language models.

Strengths:

(1) This study presents a bold and solid challenge to a literature trend that has touted similarities between Transformer models and human cognition based on representational correlations with brain activity. This challenge is substantiated by identifying sentences for which brain and model representations of sentences diverge and explaining those divergences using models structured by semantic roles/syntax.

(2) This study conducts a rigorous pre-registered analysis of a comprehensive selection of the state-of-the-art Large Language Models, on a controlled sentence comprehension fMRI dataset. The analysis is conducted within a Representation Similarity framework to support similarity comparisons between graph structures and brain activity without needing to vectorize graphs. Transformer models are predicted and shown to diverge from brain representations on subsets of sentences with similar word-level content but different sentence structures.

(3) The study introduces a 7T fMRI sentence comprehension dataset and accompanying human sentence similarity ratings, which may be a fruitful resource for developing more human-like language models. Unlike other model-based sentence datasets, the relation between grammatical structure and word-level content is controlled, and subsets of sentences for which models and brains diverge are identified.

Weaknesses:

(1) The interpretation of findings is nuanced. Although Transformers underperform as brain models on the critical subsets of controlled sentences, a Transformer outperforms all other models when evaluated on the union of all sentences when both word-level content and structure vary. Transformers also yield equivalent or better models of human behavioral data. Thus, although Transformers have demonstrable flaws as human models, which are pinpointed here, in the general case, (some) Transformers are more human-like than the other models considered.

(2) There may be confounds between the critical sentence structure manipulations and visual representations of sentence stimuli. This is inconvenient because activation in brain regions that process semantics tends to partially correlate with visual cortex representations, and computational models tend to reflect the number of words/tokens/elements in sentences. Although the study commendably controls for confounds associated with sentence length, there could still be residual effects that remain. For instance, the Graph model correlates most strongly with the visual cortex despite these sentence length controls.

(3) Sentence similarity computations are emphasized as the basis for unifying comparative analyses of graph structures and vector data. A strength of this approach is that correlation is not always the ideal similarity metric. However, a weakness is that similarity computations are not unified across models. This has practical consequences here because different similarity metrics applied to the same model produce positive or negative correlations with brain data.

Reviewer #1 (Public review):

The authors describe a new computational pipeline designed to identify smFISH probes with improved RNA detection compared to preexisting approaches. smFISH is a powerful and relatively straightforward technique to detect single RNAs in cells at subcellular resolution, which is critical for understanding gene expression regulation at the RNA level. However, existing methods for designing smFISH oligos suffer from several limitations, including off-target binding that produces high background signals, as well as a restricted number of probes that are sufficiently specific to target shorter-than-average mRNAs. To address these challenges, the authors developed TrueProbes, a computational method that aims to minimize off-target-mediated background fluorescence.

Overall, the study addresses a technically relevant problem. If improved, this would allow researchers to study gene expression regulation more effectively using single-molecule FISH. However, based on the current presentation of data, it is not yet clear that TrueProbes offers significant advantages over preexisting pipelines. In the following section, I describe some concerns, which should be adequately addressed.

Major Comments:

(1) The manuscript currently presents only one example in which different pipelines were tested to generate probes (targeting ARF4). While the images suggest that both TrueProbes and Stellaris outperform the other pipelines, the comparison is potentially misleading because the number of probes used differs substantially. I recommend that the authors include at least three independent examples in which an equal number of probes are designed across pipelines, so that signal-to-noise can be assessed in a controlled and comparable way. This would allow the probe number to be held constant while directly evaluating performance.

(2) It is also unclear how many biological replicates were performed for the ARF4 experiments. If only a single replicate was included, it is difficult to conclude that TrueProbes consistently outperforms other pipelines in a robust and reproducible manner. I suggest the authors include data from at least three biological replicates with appropriate statistical analysis, and ideally extend this to additional smFISH targets as outlined in Comment 1.

(3) No controls are presented to demonstrate that the TrueProbes-designed smFISH spots are specifically detecting ARF4. The current experiment primarily measures signal-to-noise, but it remains possible that some detected spots do not correspond to ARF4 mRNAs. Since one of the major criteria used by TrueProbes is to limit cross-hybridization, the authors should perform ARF4 knockdown experiments and demonstrate that nearly all ARF4 smFISH signal is lost. A similar approach should be applied to the additional examples recommended in Comment 1.

(4) In the limitations of the study, the authors note that "RNA secondary and tertiary structures are not included, which may lead to inaccuracies if binding sites are structurally occluded." However, I am not convinced that this is a true limitation, since formamide in the smFISH protocol should denature secondary structures and allow oligo access to the RNA. I recommend that the authors comment on this point and clarify whether secondary structure poses a practical limitation in smFISH probe design.

(5) The authors also correctly acknowledge in their limitations that "RNA-protein interactions, which can modulate accessibility of the transcript, are not modeled." I suggest referencing relevant studies on this issue, particularly Buxbaum et al. (2014, Science), which would provide important context.

Reviewer #2 (Public review):

Summary:

Hughes et al present a new single-molecule RNA fluorescence in situ hybridization (smFISH) probe design software, termed "TrueProbes" in this manuscript. They claim that all existing smFISH (and variants) probe design software packages have limitations that ultimately impact experimental performance. The author's claim to address the majority of these limitations in TrueProbes by introducing multiple computational steps to ensure high-quality probe design. The manuscript's goal is clear, and the authors provide some evidence by designing and targeting one gene. Overall, the manuscript lacks rigorous evidence to support the claims, does not demonstrate its suitability for a variety of smFISH-type experiments, and some of the provided quantification data are unclear. While TrueProbes clearly has potential, more data is required, or the authors should tone down the claims.

Strengths:

(1) The problem is well-articulated in the abstract and the introduction.

(2) Figures 3 and 4 follow a consistent color scheme where each probe design method has its own color, which helps the reader visually compare methods.

(3) The authors compared multiple probe design software packages both computationally and experimentally.

(4) TrueProbes does produce visually and quantitatively better results when compared to 2 of the 4 existing smFISH probe design packages (Paintshop and MERFISH panel designer).

(5) The authors introduce a comprehensive steady-state thermodynamic model to help optimally guide probe design.

Weaknesses:

(1) The abstract describes the problem well and introduces the solution (the TrueProbes software), but fails to provide specific ways in which the TrueProbes software performs better. The authors state that "...[TrueProbes] consistently outperformed alternatives across multiple computational metrics and experimental validation assays", but specific, quantitative evidence of improved performance would strengthen the statement.

(2) The text claims that TrueProbes outperforms all other probe design software, but Figure 3 indicates that TrueProbes has neither the greatest number of on-target binding nor the lowest number of off-target binding. The data in Figure 3 does not support the claims made in the text. Specifically, the authors claim that "RNA FISH Experimental Results Demonstrate that Off Target and Binding Affinity Inclusive Probe Design Improve RNA FISH Signal Discrimination" (lines 217-218). However, despite their claim that Stellaris and Oligostan-HT produce more off-target probes when evaluated with the TrueProbes framework, the experiment results are nearly identical. The authors should consider modifying their claims or performing new experiments that more clearly demonstrate their claims.

(3) The bar graphs in Figure 3 do not seem to agree with the probability graphs in Figure 4. For example, Figure 3 indicates that Stellaris probes have higher off-target binding than TrueProbes; however, in Figure 4, their probability graphs lie almost on top of each other.

(4) The authors performed validation for only one gene (ARF4), because "...it had the highest gene expression (in TPM units) and the fewest isoforms among all candidate genes for the Jurkat cell line" (lines 176-177). While the results do look good, this is a minimal use case and does not really showcase the power of their method. One experiment that could be helpful would be two-color (or more) smFISH in tissue, where the chances for off-target binding contributing to higher errors are much greater than in an adherent cell line.

(5) A common strategy for both smFISH and highly multiplexed methods is to use secondary DNA oligos with dye molecules instead of direct conjugation. Given that this is a primary design goal of PaintSHOP and the Zhuang lab's MERFISH probe design code, it would be helpful to demonstrate that TrueProbes can design a two-layer probe strategy for high-quality RNA-FISH labeling.

(6) The authors claim, "For every probe set, TrueProbes can simulate expected smRNA FISH outcomes including optimal probe, RNA, and salt concentrations and optionally account for probe secondary structure, hybridization temperature, multiple targets, fluorophore choice, DNA, nascent RNA, and photon count statistics (Figures S2A, S2B). The model can be used to generate predictions for temperature and cell line sensitivity, multi-target discrimination, multiple fluorophore colocalization; when provided transcript expression levels and probe/background intensity, it can start to generate predictions for spot intensity, background, signal to noise ratio, and false negative rates (Figure S2C)." (lines 156-163). Figure S2 is a flow chart and does not provide evidence for any of these items. The authors should provide evidence for these claims, either as a figure or an example script in their software repository. If that is not possible, then it should be removed.

(7) All thermodynamic equations are performed at steady state. The authors do not justify this assumption, and there is no discussion of the potential impacts of either low molecule numbers or violations of the well-mixed assumption. Can the authors please include a discussion on the potential impacts non non-steady state dynamics?

Reviewer #3 (Public review):

Summary:

This manuscript introduces a new platform termed "TrueProbes" for designing mRNA FISH probes. In comparison to existing design strategies, the authors incorporate a comprehensive thermodynamic and kinetic model to account for probe states that may contribute to nonspecific background. The authors validate their design pipeline using Jurkat cells and provide evidence of improved probe performance.

Strengths:

A notable strength of TrueProbes is the consideration of genome-wide binding affinities, which aims to minimize off-target signals. The work will be of interest to researchers employing mRNA FISH in certain human cell lines.

Weaknesses:

However, in my view, the experimental validation is not sufficient to justify the broad claims of the platform. Given the number of assumptions in the model, additional experimental comparisons across probe design methods, ideally targeting transcripts with different expression levels, would be necessary to establish the general superiority of this approach.

Reviewer #1 (Public review):

Summary:

The manuscript reports a series of experiments designed to test whether optogenetic activation of infralimbic (IL) neurons facilitates extinction retrieval and whether this depends on animals' prior experience. In Experiment 1, rats underwent fear conditioning followed by either one or two extinction sessions, with IL stimulation given during the second extinction; stimulation facilitated extinction retrieval only in rats with prior extinction experience. Experiments 2 and 3 examined whether backward conditioning (CS presented after the US) could establish inhibitory properties that allowed IL stimulation to enhance extinction, and whether this effect was specific to the same stimulus or generalized to different stimuli. Experiments 5 - 7 extended this approach to appetitive learning: rats received backward or forward appetitive conditioning followed by extinction, and then fear conditioning, to determine whether IL stimulation could enhance extinction in contexts beyond aversive learning and across conditioning sequences. Across studies, the key claim is that IL activation facilitates extinction retrieval only when animals possess a prior inhibitory memory, and that this effect generalizes across aversive and appetitive paradigms.

Strengths:

(1) The design attempts to dissect the role of IL activity as a function of prior learning, which is conceptually valuable.

(2) The experimental design of probing different inhibitory learning approaches to probe how IL activation facilitates extinction learning was creative and innovative.

Weaknesses:

(1) Non-specific manipulation.

ChR2 was expressed in IL without distinction between glutamatergic and GABAergic populations. Without knowing the relative contribution of these cell types or the percentage of neurons affected, the circuit-level interpretation of the results is unclear.

(2) Extinction retrieval test conflates processes

The retrieval test included 8 tones. Averaging across this many tone presentations conflate extinction retrieval/expression (early tones) with further extinction learning (later tones). A more appropriate analysis would focus on the first 2-4 tones to capture retrieval only. As currently presented, the data do not isolate extinction retrieval.

(3) Under-sampling and poor group matching.

Sample sizes appear small, which may explain why groups are not well matched in several figures (e.g., 2b, 3b, 6b, 6c) and why there are several instances of unexpected interactions (protocol, virus, and period). This baseline mismatch raises concerns about the reliability of group differences.

(4) Incomplete presentation of conditioning data.

Figure 3 only shows a single conditioning session despite five days of training. Without the full dataset, it is difficult to evaluate learning dynamics or whether groups were equivalent before testing.

(5) Interpretation stronger than evidence.

The authors conclude that IL activation facilitates extinction retrieval only when an inhibitory memory has been formed. However, given the caveats above, the data are insufficient to support such a strong mechanistic claim. The results could reflect non-specific facilitation or disruption of behavior by broad prefrontal activation. Moreover, there is compelling evidence that optogenetic activation of IL during fear extinction does facilitate subsequent extinction retrieval without prior extinction training (Do-Monte et al 2015, Chen et al 2021), which the authors do not directly test in this study.

Impact:

The role of IL in extinction retrieval remains a central question in the fear learning literature. However, because the test used conflates extinction retrieval with new learning and the manipulations lack cell-type specificity, the evidence presented here does not convincingly support the main claims. The study highlights the need for more precise manipulations and more rigorous behavioral testing to resolve this issue.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors examine the mechanisms by which stimulation of the infralimbic cortex (IL) facilitates the retention and retrieval of inhibitory memories. Previous work has shown that optogenetic stimulation of the IL suppresses freezing during extinction but does not improve extinction recall when extinction memory is probed one day later. When stimulation occurs during a second extinction session (following a prior stimulation-free extinction session), freezing is suppressed during the second extinction as well as during the tone test the following day. The current study was designed to further explore the facilitatory role of the IL in inhibitory learning and memory recall. The authors conducted a series of experiments to determine whether recruitment of IL extends to other forms of inhibitory learning (e.g., backward conditioning) and to inhibitory learning involving appetitive conditioning. Further, they assessed whether their effects could be explained by stimulus familiarity. The results of their experiments show that backward conditioning, another form of inhibitory learning, also enabled IL stimulation to enhance fear extinction. This phenomenon was not specific to aversive learning, as backward appetitive conditioning similarly allowed IL stimulation to facilitate extinction of aversive memories. Finally, the authors ruled out the possibility that IL facilitated extinction merely because of prior experience with the stimulus (e.g., reducing the novelty of the stimulus). These findings significantly advance our understanding of the contribution of IL to inhibitory learning. Namely, they show that the IL is recruited during various forms of inhibitory learning, and its involvement is independent of the motivational value associated with the unconditioned stimulus.

Strengths:

(1) Transparency about the inclusion of both sexes and the representation of data from both sexes in figures.

(2) Very clear representation of groups and experimental design for each figure.

(3) The authors were very rigorous in determining the neurobehavioral basis for the effects of IL stimulation on extinction. They considered multiple interpretations and designed experiments to address these possible accounts of their data.

(4) The rationale for and the design of the experiments in this manuscript are clearly based on a wealth of knowledge about learning theory. The authors leveraged this expertise to narrow down how the IL encodes and retrieves inhibitory memories.

Weaknesses:

(1) In Experiment 1, although not statistically significant, it does appear as though the stimulation groups (OFF and ON) differ during Extinction 1. It seems like this may be due to a difference between these groups after the first forward conditioning. Could the authors have prevented this potential group difference in Extinction 1 by re-balancing group assignment after the first forward conditioning session to minimize the differences in fear acquisition (the authors do report a marginally significant effect between the groups that would undergo one vs. two extinction sessions in their freezing during the first conditioning session)?

(2) Across all experiments (except for Experiment 1), the authors state that freezing during the initial conditioning increased across "days". The figures that correspond to this text, however, show that freezing changes across trials. In the methods, the authors report that backward conditioning occurred over 5 days. It would be helpful to understand how these data were analyzed and collated to create the final figures. Was the freezing averaged across the five days for each trial for analyses and figures?

(3) In Experiment 3, the authors report a significant Protocol X Virus interaction. It would be useful if the authors could conduct post-hoc analyses to determine the source of this interaction. Inspection of Figure 4B suggests that freezing during the two different variants of backward conditioning differs between the virus groups. Did the authors expect to see a difference in backward conditioning depending on the stimulus used in the conditioning procedure (light vs. tone)? The authors don't really address this confounding interaction, but I do think a discussion is warranted.

(4) In this same experiment, the authors state that freezing decreased during extinction; however, freezing in the Diff-EYFP group at the start of extinction (first bin of trials) doesn't look appreciably different than their freezing at the end of the session. Did this group actually extinguish their fear? Freezing on the tone test day also does not look too different from freezing during the last block of extinction trials.

(5) The Discussion explored the outcomes of the experiments in detail, but it would be useful for the authors to discuss the implications of their findings for our understanding of circuits in which the IL is embedded that are involved in inhibitory learning and memory. It would also be useful for the authors to acknowledge in the Discussion that although they did not have the statistical power to detect sex differences, future work is needed to explore whether IL functions similarly in both sexes.

Reviewer #3 (Public review):

Summary:

This is a really nice manuscript with different lines of evidence to show that the IL encodes inhibitory memories that can then be manipulated by optogenetic stimulation of these neurons during extinction. The behavioral designs are excellent, with converging evidence using extinction/re-extinction, backwards/forwards aversive conditioning, and backwards appetitive/forwards aversive conditioning. Additional factors, such as nonassociative effects of the CS or US, are also considered, and the authors evaluate the inhibitory properties of the CS with tests of conditioned inhibition.

Strengths:

The experimental designs are very rigorous with an unusual level of behavioral sophistication.

Weaknesses:

(1) More justification for parametric choices (number of days of backwards vs forwards conditioning) could be provided.

(2) The current discussion could be condensed and could focus on broader implications for the literature.

Reviewer #1 (Public review):

The goal of this study was to generate a library of new enhancer-driven AAVs in order to selectively and efficiently target astrocytes and oligodendrocytes in rodents. The implied criteria are that such viral vectors should have high specificity for the intended cell type and effectively express in all astrocytes/oligos in the brain or, alternatively, be specific for defined brain regions, layers, or subtypes of astrocytes/oligos. In addition, they should be compatible with intravenous retro-orbital delivery to facilitate experimentation and brain-wide targeting (i.e., show organ specificity and high efficiency in the brain). Ideally, these new AAVs would also maintain their characteristics across disease contexts and show applicability in non-human primates. Tools with such characteristics are generally lacking in studying glial cells and would be extremely useful to scale up and accelerate glial research, allowing targeting of astrocytes/oligos with distinct molecular identity and intersectional strategies.

At present, however, none of the enhancer-AAVs presented in the study seems to meet this combination of criteria, at least not with the level of stringency typically expected in the field. The main reason is that, in its current form, the study does not present one candidate AAV iteratively improved to meet all these criteria; instead, it presents a catalogue of new AAVs with various degrees of specificity, completeness, and mixed characteristics. Therefore, their utility should be interpreted cautiously. Moreover, the way specificity and completeness are intermixed in the analysis makes it difficult to evaluate the actual utility of any given AAV. The study might have been strengthened by focusing on a small set of the most promising candidates (i.e., AiE0890m_3x2C) and validating them thoroughly for expression specificity, completeness, effective cargo expression, ability to allow specific pan-astrocyte or astrocyte-subtype targeting in vivo, and preserved properties in NHPs and in disease, as this would encourage their adoption by the community. Currently, too many AAVs are assessed inconsistently against the desired criteria, with none being evaluated through and through.

The impact of the catalogue is also greatly diminished by the fact that a suite of AAVs with outstanding specificity and efficiency is already available for the study of astrocytes (e.g., 4x6T AAVs) and was not utilized as a standard to benchmark the new library, making it difficult to appreciate the relative benefits of the new AAVs. The inclusion of expression data in NHPs is very significant, but benchmarking against established AAVs would also be needed to fully appreciate their value.

Importantly, readers should also be aware that the study seems noticeably limited in its literacy with glial biology. The introduction and discussion frame the field in a way that seems outdated, creating the impression that the diverse roles of glia in health and disease have not yet been studied, which may inadvertently be perceived as dismissive and stigmatizing.

In summary, the paper introduces potentially useful viral tools and lays the foundations for future multiplexed targeting of distinct glial cell subpopulations in rodents and in non-human primates, which are extremely important directions. Some of the regionally restricted or even sparsely expressed AAVs may prove valuable in enabling subpopulation-specific targeting or molecular profiling strategies, but currently lack full benchmarking. At present, the promises over the utility of the new tools seem overstated, and the library may not yet represent an actionable resource for targeting astrocytes and oligodendrocytes.

Reviewer #2 (Public review):

Enhancer elements are regulatory DNA sequences that are capable of driving specific expression patterns. As these elements are generally short and context-independent, enhancers can be used in expression vectors (e.g., packaged in an adeno-associated virus, AAV) to limit expression to target cell populations. This approach was identified as a major strategy for cell-type-specific manipulation in the brain and has been pursued by both standard research studies as well as large-scale efforts led by the BRAIN Initiative. This manuscript describes a major effort to generate enhancer-AAVs targeting astrocytes and oligodendrocytes orchestrated by a large research team led by the Allen Institute for Brain Science. This manuscript parallels other recent publications describing sets of enhancer-AAVs, following rigorous, similar methods with relatively broad testing and application.

To identify and screen candidate enhancers, the scientists prioritized candidates via analysis of single-nucleus accessibility and methylation datasets (i.e., snATAC-seq) and tested them in mice. The scientists prioritized candidate enhancers that exhibited specificity of accessibility in the target cell type. Following selection, the scientists cloned the candidate sequences into AAV vectors with a minimal promoter and reporter gene, packaged the virus, delivered it to the mouse brain, and screened for activity based on reporter expression. Candidates that passed initial screening were further characterized via imaging and sorting, followed by single-cell RNA-seq. This process had around a 50% success rate and yielded 25 astrocyte and 21 oligodendrocyte enhancer-AAVs with the targeted cell-type-specific expression patterns.

The scientists went on to test for subtype-specific activity patterns, finding wide diversity in astrocyte activities across sub-populations and conversely, homogenous oligodendrocyte activation. They optimized a few of these via concatenating the enhancer core sequence to increase expression levels of the reporter gene and showed strong specificity and completeness of cell targeting for a set of these enhancer-AAVs. Following characterization and validation, they then deployed these enhancer-AAVs in a number of demonstration applications to show the utility for basic and translational science. All the constructs developed here are available for public use via Addgene, ensuring that these new tools can be used by other researchers.

There really are no obvious weaknesses in the work presented here, from the generation of the enhancer-AAVs to use in sophisticated validation studies. The enhancer-AAV testing is rigorous and provides critical information necessary for other scientists to select and use these constructs. The applications demonstrate the power of enhancer-AAV approaches. The toolbox presented here may not enable specific targeting of all relevant cellular subtypes or activity states for astrocytes and oligodendrocytes, and future work will be needed to fully understand the activity of the enhancers, identity of the target cell types, and context-dependent utility of these constructs. However, the set of enhancer-AAVs developed here should be transformative for researchers working on accessing and manipulating these cell types and have a major impact on the field.

Reviewer #1 (Public review):

Summary:

In this paper, the authors analyze connectome data from Drosophila and compare the physical wiring with functional connectivity estimated from calcium imaging data. They quantify structure-function relationships as a correlation of the two connectivity modalities. They report correlations roughly comparable to what has been described in the literature on sc/fc relationships in mammalian connectome data at the meso-scale. They then repeat their analysis, focusing on segregated versus unsegregated synapses. They derive separate connectomes using one or the other class of synapse. They show differential contributions to the sc/fc relationships by segregated versus unsegregated synapses.

Strengths:

There is nice synthesis of multimodal imaging data (Ca and EM data from flies and meso-scale data from human and marmoset).

Weaknesses:

(1) The paper is written in an unusual way. The introduction intermingles results with background, making it hard to figure out what precisely is being tested.

(2) There are also major methodological gaps. Though the mammalian connectomes are used as a point of reference, no descriptions of their origins or processing are included.

(3) A major weakness stems from the actual calculation of the sc/fc correlation. In general, SC is sparse. In the case of the EM connectomes, it is *exceptionally* sparse (most neural elements are not connected to one another). The authors calculated sc/fc coupling by correlating the off-diagonal elements of sc (the logarithm of its edge weights) and fc matrices with one another. The logarithmic transformation yields a value of infinity for all zero entries. The authors simply impute these elements with 0. This makes no sense and, depending on whether these zero elements are distributed systematically versus uniformly random, could either inflate or deflate the sc/fc correlations. Care must be taken here.

(4) Further, in constructing the segregated versus unsegregated connectomes, they use absolute thresholds for collecting synapses. It is unclear, however, whether similar numbers of synapses were included in both matrices. If the number is different, that might explain the differential relationship with fc; one matrix has more non-zero entries (and as noted earlier, those zero entries are problematic).

(5) There was also considerable text (in the results) describing the processing of the Ca data. In this section, the authors frequently refer to some pipelines as "better" or "worse" (more or less effective). But it is not clear what measures they adopted to assess the effectiveness of a pipeline.

Reviewer #2 (Public review):

Summary:

Okuno et al. investigate the structure-function relationship in the fruit fly Drosophila melanogaster. To do so, they combine published data from two recent synapse-level connectomes ("hemibrain" and "FlyWire") with a dataset comprising functional whole-brain calcium imaging and behavioural data. First, they investigate the applicability of fMRI pre-processing techniques on data from calcium imaging. They then cross-correlate this pre-processed functional data with structural data extracted from the connectomes, including a comparison to humans. The authors proceed to compare the two connectomes and find significant differences, which they attribute to differences in the accuracy of the synapse detections. Next, they present a novel algorithm to quantify whether neurons are segregated (pre- and postsynapses are spatially separate) or unsegregated (pre- and postsynapses are mixed). Using this approach, they find that unsegregated neurons may contribute more to function than segregated neurons. Applying a general linear model to the functional dataset suggests that activity in two brain areas (Wedge and AVLP) is suppressed during walking. The authors identify a GABAergic neuron in the connectome that could be responsible for this effect and suggest it may provide feedback to the fly's "compass" in the central complex.

Strengths:

The study tackles a relevant question in connectomics by exploring the relationship between structural and functional connectivity in the Drosophila brain. The authors apply a range of established and adapted analytical methods, including fMRI-style preprocessing and a novel synaptic segregation index. The effort to integrate multiple datasets and to compare across species reflects a broad and methodical approach.

Weaknesses:

The manuscript would benefit from a clearer overarching narrative to unify the various analyses, which currently appear somewhat disjointed. While the technical methods are extensive, the writing is often convoluted and lacks crucial details, making it difficult to follow the logic and interpret key findings. Additionally, the conclusions are relatively incremental and lack a compelling conceptual advance, limiting the overall impact of the work.

(1) The introduction currently contains a number of findings and conclusions that would be better placed in the results and discussion to clearly delineate past findings from new results and speculations.

(2) The narrative would benefit greatly from some clear statements along the lines of "we wanted to find out X, therefore we did Y".

(3) More concise terminology would be helpful. For example, the connectomes are currently referred to as either "hemibrain", "FlyEM", "whole-brain", or "FlyWire".

(4) The abstract claims "a new, more robust method to quantify the degree of pre- and post-synaptic segregation". However, the study fails to provide evidence that this method is indeed more robust than existing methods.

(5) The authors define unsegregated neurons as having mixed pre- and postsynapses in the same space. However, this ignores the neurons' topology: a neuron can exhibit a clearly defined dendrite with (mostly) postsynapses and a clearly defined axon with (mostly) presynapses, which then occupy the same space. This is different from genuinely unsegregated neurons with no distinct dendritic and axonal compartments, such as CT1.

(6) It is not entirely clear where the marmoset dataset originates from. Was it generated for this study? If not, why is there a note in the Ethics Declaration?

(7) On the differences between hemibrain and FlyWire: What is the "18.8 million post-synapses" for FlyWire referring to? The (thresholded) FlyWire synapse table has 130M connections (=postsynapses). Subsetting that synapse cloud to the hemibrain volume still gives ~47M synapses. Further subsetting to only connections between proofread neurons inside the hemibrain volume gives 19.4M - perhaps the authors did something like that? Similarly, the hemibrain synapse table contains 64M postsynapses. Do the 21M "FlyEM" post-synapses refer to proofread neurons only? If the authors indeed used only (post-)synapses from proofread neurons, they need to make that explicit in results and methods, and account for differences in reconstruction status when making any comparisons. For example, the mushroom body in the hemibrain got a lot more attention than in FlyWire, which would explain the differences reported here. For that reason, connection weights are often expressed as, e.g., a fraction of the target's inputs instead of the total number of synapses when comparing connectivity across connectomic datasets. Furthermore, in Figure 3b, it looks like the FlyWire synapse cloud was not trimmed to the exact hemibrain boundaries: for example, the trimmed FlyWire synapse cloud seems to extend further into the optic lobes than the hemibrain volume does.

Reviewer #3 (Public review):

Summary:

In this manuscript, Okuno et al. re-analyze whole-brain imaging data collected in another paper (Brezovec et al., 2024) in the context of the two currently available Drosophila connectome datasets: the partial "FlyEM" (hemibrain) dataset (Scheffer et al., 2020) and the whole-brain "FlyWire" dataset (Dorkenwald et al., 2024). They apply existing fMRI signal processing algorithms to the fly imaging data and compute function-structure correlations across a variety of post-processing parameters (noise reduction methods, ROI size), demonstrating an inverse relationship between ROI size and FC-SC correlation. The authors go on to look at structural connectivity amongst more polarized or less polarized neurons, and suggest that stronger FC-SC correlations are driven by more polarized neurons.

Strengths:

(1) The result that larger mesoscale ROIs have a higher correlation with structural data is interesting. This has been previously discussed in Drosophila in Turner et al., 2021, but here it is quantified more extensively.

(2) The quantification of neuron polarization (PPSSI) as applied to these structural data is a promising approach for quantifying differences in spatial synapse distribution.

Weaknesses:

One should not score noise/nuisance removal methods solely by their impact on FC-SC correlation values, because we do not know a priori that direct structural connections correspond with strong functional correlations. In fact, work in C. elegans, where we have access to both a connectome and neuron-resolution functional data, suggests that this relationship is weak (Yemini et al., 2021; Randi et al., 2023). Similarly, I don't think it's appropriate to tune the confidence scores on the EM datasets using FC-SC correlations as an output metric.

Any discussion of FC-SC comparisons should include an analysis of excitatory/inhibitory neurotransmitters, which are available in the fly connectome dataset. However, here the authors do not perform any analyses with neurotransmitter information.<br /> Comparisons between fly and human MRI data are also premature here. Firstly, the fly connectomes, which are derived from neuron-scale EM reconstructions, are a qualitatively different kind of data from human connectomes, which are derived from DSI imaging of large-scale tracts. Likewise, calcium data and fMRI data are very different functional data acquisition methods-the fact that similar processing steps can be used on time-series data does not make them surprisingly similar, and does not in my view, constitute evidence of "similar design concepts."

The comparison of FlyEM/FlyWire connectomes concludes that differences are more likely a result of data processing than of inter-individual variability. If this is the case, the title should not claim that the manuscript covers individual variability.<br /> The analysis of the wedge-AVLP neuron strikes me as highly speculative, given that the alignment precision between the connectome and the functional data is around 5 microns (Brezovec* et al, PNAS 2024).

Reviewer #1 (Public review):

Summary:

In this study, Ana Lapao et al. investigated the roles of Rab27 effector SYTL5 in cellular membrane trafficking pathways. The authors found that SYTL5 localizes to mitochondria in a Rab27A-dependent manner. They demonstrated that SYTL5-Rab27A positive vesicles containing mitochondrial material are formed under hypoxic conditions, thus they speculate that SYTL5 and Rab27A play roles in mitophagy. They also found that both SYTL5 and Rab27A are important for normal mitochondrial respiration. Cells lacking SYTL5 undergo a shift from mitochondrial oxygen consumption to glycolysis which is a common process known as the Warburg effect in cancer cells. Based on cancer patient database, the author noticed that low SYTL5 expression is related to reduced survival for adrenocortical carcinoma patients, indicating SYTL5 could be a negative regulator of the Warburg effect and potentially tumorigenesis.

Strengths:

The authors take advantages of multiple techniques and novel methods to perform the experiments.

(1) Live-cell imaging revealed that stably inducible expression of SYTL5 co-localized with filamentous structures positive for mitochondria. This result was further confirmed by using correlative light and EM (CLEM) analysis and western blotting from purified mitochondrial fraction.

(2) In order to investigate whether SYTL5 and RAB27A are required for mitophagy in hypoxic conditions, two established mitophagy reporter U2OS cell lines were used to analyze the autophagic flux.

Weaknesses:

This study revealed a potential function of SYTL5 in mitophagy and mitochondrial metabolism. However, the mechanistic evidence that establishes the relationship between SYTL5/Rab27A and mitophagy is insufficient. The involvement of SYTL5 in ACC needs more investigation. Furthermore, images and results supporting the major conclusions need to be improved.

Comments on revisions: The authors did not revise the paper as suggested.

Reviewer #2 (Public review):

Summary:

The authors provide convincing evidence that Rab27 and STYL5 work together to regulate mitochondrial activity and homeostasis.

Strengths:

The development of models which allow the function to be dissected, and the rigous approach and testing of mitochondrial activity.

This work is carefully done, and supports the importance of the roles of Rab27A and STYL5.

Reviewer #3 (Public review):

In the manuscript by Lapao et al., the authors uncover a role for the RAB27A effector protein SYTL5 in regulating mitochondrial function and apparent selective turnover of mitochondrial components. The authors find that SYTL5 localizes to mitochondria in a RAB27A dependent way and that loss of SYTL5 (or RAB27A) impairs lysosomal turnover of MTCO1 (but not a matrix-based reporter/other mitochondrial proteins). The authors go on to show that loss of SYTL5 impacts mitochondrial respiration and ECAR and as such may influence the Warburg effect and tumorigenesis. Of relevance here, the authors go on to show that SYTL5 expression is reduced in adrenocortical carcinomas and this correlates with reduced survival rates.

As previously reviewed, this is a very intriguing body of work and reveals a new role for SYTL5/RAB27A at the mitochondria. Unfortunately, it appears that SYTL5 is challenging protein to detect endogenously and the authors' cell lines "comprise a heterogenous pool with high variability", which means that a lot of my original concerns remain. It is still also not clear if the conventional autophagy machinery is required for this pathway, especially if SYTL5/RAB27A mitochondrial recruitment is upstream of this. Hopefully, in future work, the authors (and/or others) will be able to address this and build on the mechanisms of this interesting and potentially important pathway.

Reviewer #1 (Public review):

Summary:

The innate immune system serves as the first line of defense against invading pathogens. Four major immune-specific modules-the Toll pathway, the Imd pathway, melanization, and phagocytosis-play critical roles in orchestrating the immune response. Traditionally, most studies have focused on the function of individual modules in isolation. However, in recent years, it has become increasingly evident that effective immune defense requires intricate interactions among these pathways.

Despite this growing recognition, the precise roles, timing, and interconnections of these immune modules remain poorly understood. Moreover, addressing these questions represents a major scientific undertaking.

Strengths:

In this manuscript, Ryckebusch et al. systematically evaluate both the individual and combined contributions of these four immune modules to host defense against a range of pathogens. Their findings significantly enhance our understanding of the layered architecture of innate immunity.

Reviewer #2 (Public review):

Summary:

In this work, the authors take a holistic view at the Drosophila immunity by selecting four major components of fly immunity often studied separately (Toll signaling, Imd signaling, phagocytosis and melanization), and studying their combinatory effects on the efficiency of the immune response. They achieve this by using fly lines mutant for one of these components, or modules, as well as for a combination of them, and testing the survival of these flies upon infection with a plethora of pathogens (bacterial, viral and fungal).

Strengths:

It is clear that this manuscript has required a large amount of hands-on work, considering the number of pathogens, mutations and timepoints tested. In my opinion, this work is a very welcome addition to the literature on fly immune responses, which obviously do not occur one type of a response at a time, but in parallel, subsequently and/or are interconnected. I find that the major strength of this work is the overall concept, which is made possible by the mutations designed to target the specific immune function of each module, without effects on other functions. I believe that the combinatory mutants will be of use for the fly community and enable further studies of interplay of these components of immune response in various settings.

To control for the effects arising from the genetic variation other than the intended mutations, the mutants have been backcrossed into a widely used, isogenized Drosophila strain called w1118. Therefore, the differences accounted for by the genotype are controlled.

I also appreciate that the authors have investigated the two possible ways of dealing with an infection: tolerance and resistance, and how the modules play into those.

Weaknesses:

While controlling for the background effects is vital, the w1118 background is problematic (an issue not limited to this manuscript) because of the wide effects of the white mutation on several phenotypes (also other than eye color/eyesight). It is a possibility that the mutation influences the functionality of the immune response components. I acknowledge that it is not reasonable to ask for data in different backgrounds better representing a "wild type" fly, but I think this matter should be brought up and discussed.

The whole study has been conducted on male flies. Immune responses show quite extensive sex-specific variation across a variety of species studied, also in the fly. But the reasons for this variation are not fully understood. Therefore, I suggest that the authors would conduct a subset of experiments on female flies to see if the findings apply to both sexes, especially the infection-specificity of the module combinations.

Comments on the revised manuscript:

I appreciate the author's responses to the points I raised and the additional work they have conducted. The authors have now discussed the possible background effect and added an experiment on female flies showing that the module function is applicable to both sexes.

Reviewer #1 (Public Review):

(1) Significance of the findings:

Cell-to-cell communication is essential for higher functions in bacterial biofilms. Electrical signals have proven effective in transmitting signals across biofilms. These signals are then used to coordinate cellular metabolisms or to increase antibiotic tolerance. Here, the authors have reported for the first time coordinated oscillation of membrane potential in E. coli biofilms that may have a functional role in photoprotection.

(2) Strengths of the manuscript:

- The authors report original data.<br /> - For the first time, they showed that coordinated oscillations in membrane potential occur in E. Coli biofilms.<br /> - The authors revealed a complex two-phase dynamic involving distinct molecular response mechanisms.<br /> - The authors developed two rigorous models inspired by 1) Hodgkin-Huxley model for the temporal dynamics of membrane potential and 2) Fire-Diffuse-Fire model for the propagation of the electric signal.<br /> - Since its discovery by comparative genomics, the Kch ion channel has not been associated with any specific phenotype in E. coli. Here, the authors proposed a functional role for the putative gated-voltage-gated K+ ion channel (Kch channel) : enhancing survival under photo-toxic conditions.

(3) Weakness:

- Contrarily to what is stated in the abstract, the group of B. Maier has already reported collective electrical oscillations in the Gram-negative bacterium Neisseria gonorrhoeae (Hennes et al., PLoS Biol, 2023).<br /> - The data presented in the manuscript are not sufficient to conclude on the photo-protective role of the Kch channel. The authors should perform the appropriate control experiments related to Fig4D,E, i.e. reproduce these experiments without ThT to rule out possible photo-conversion effects on ThT that would modify its toxicity. In addition, it looks like the data reported on Fig 4E are extracted from Fig 4D. If this is indeed the case, it would be more conclusive to report the percentage of PI-positive cells in the population for each condition. This percentage should be calculated independently for each replicate. The authors should then report the average value and standard deviation of the percentage of dead cells for each condition.<br /> - Although Fig 4A clearly shows that light stimulation has an influence on the dynamics of ThT signal in the biofilm, it is important to rule out possible contributions of other environmental variations that occur when the flow is stopped at the onset of light stimulation. I understand that for technical reasons, the flow of fresh medium must be stopped for the sake of imaging. Therefore, I suggest to perform control experiments consisting in stopping the flow at different time intervals before image acquisition (30min or 1h before). If there is no significant contribution from environmental variations due to medium perfusion arrest, the dynamics of ThT signal must be unchanged regardless of the delay between flow stop and the start of light stimulation.<br /> - To precise the role of K+ in the habituation response, I suggest using the ionophore valinomycin at sub-inhibitory concentrations (5 or 10µM). It should abolish the habituation response. In addition, the Kch complementation experiment exhibits a sharp drop after the first peak but on a single point. It would be more convincing to increase the temporal resolution (1min->10s) to show that there are indeed a first and a second peak. Finally, the high concentration (100µM) of CCCP used in this study completely inhibits cell activity. Therefore, it is not surprising that no ThT dynamics was observed upon light stimulation at such concentration of CCCP.<br /> - Since TMRM signal exhibits a linear increase after the first response peak (Supp Fig1D), I recommend to mitigate the statement at line 78.<br /> - Electrical signal propagation is an important aspect of the manuscript. However, a detailed quantitative analysis of the spatial dynamics within the biofilm is lacking. At minima, I recommend to plot the spatio-temporal diagram of ThT intensity profile averaged along the azimuthal direction in the biofilm. In addition, it is unclear if the electrical signal propagates within the biofilm during the second peak regime, which is mediated by the Kch channel: I have plotted the spatio-temporal diagram for Video S3 and no electrical propagation is evident at the second peak. In addition, the authors should provide technical details of how R^2(t) is measured in the first regime (Fig 7E).<br /> - In the series of images presented in supplementary Figure 4A, no wavefront is apparent. Although the microscopy technics used in this figure differs from other images (like in Fig2), the wavefront should be still present. In addition, there is no second peak in confocal images as well (Supp Fig4B) .<br /> - Many important technical details are missing (e.g. biofilm size, R^2, curvature and 445nm irradiance measurements). The description of how these quantitates are measured should be detailed in the Material & Methods section.<br /> - Fig 5C: The curve in Fig 5D seems to correspond to the biofilm case. Since the model is made for single cells, the curve obtained by the model should be compared with the average curve presented in Fig 1B (i.e. single cell experiments).<br /> - For clarity, I suggest to indicate on the panels if the experiments concern single cell or biofilm experiments. Finally, please provide bright-field images associated to ThT images to locate bacteria.<br /> - In Fig 7B, the plateau is higher in the simulations than in the biofilm experiments. The authors should add a comment in the paper to explain this discrepancy.

Reviewer #2 (Public Review):

The authors use ThT dye as a Nernstian potential dye in E. coli. Quantitative measurements of membrane potential using any cationic indicator dye are based on the equilibration of the dye across the membrane according to Boltzmann's law.

Ideally, the dye should have high membrane permeability to ensure rapid equilibration. Others have demonstrated that E.coli cells in the presence of ThT do not load unless there is blue light present, that the loading profile does not look like it is expected for a cationic Nernstian dye. They also show that the loading profile of the dye is different for E.coli cells deleted for the TolC pump. I, therefore, objected to interpreting the signal from the ThT as a Vm signal when used in E.coli. Nothing the authors have said has suggested that I should be changing this assessment.

Specifically, the authors responded to my concerns as follows:

(1) 'We are aware of this study, but believe it to be scientifically flawed. We do not cite the article because we do not think it is a particularly useful contribution to the literature.' This seems to go against ethical practices when it comes to scientific literature citations. If the authors identified work that handles the same topic they do, which they believe is scientifically flawed, the discussion to reflect that should be included.

(2)'The Pilizota group invokes some elaborate artefacts to explain the lack of agreement with a simple Nernstian battery model. The model is incorrect not the fluorophore.'<br /> It seems the authors object to the basic principle behind the usage of Nernstian dyes. If the authors wish to use ThT according to some other model, and not as a Nernstian indicator, they need to explain and develop that model. Instead, they state 'ThT is a Nernstian voltage indicator' in their manuscript and expect the dye to behave like a passive voltage indicator throughout it.

(3)'We think the proton effect is a million times weaker than that due to potassium i.e. 0.2 M K+<br /> versus 10-7 M H+. We can comfortably neglect the influx of H+ in our experiments.'<br /> I agree with this statement by the authors. At near-neutral extracellular pH, E.coli keeps near-neutral intracellular pH, and the contribution from the chemical concentration gradient to the electrochemical potential of protons is negligible. The main contribution is from the membrane potential. However, this has nothing to do with the criticism to which this is the response of the authors. The criticism is that ThT has been observed not to permeate the cell without blue light. The blue light has been observed to influence the electrochemical potential of protons (and given that at near-neutral intracellular and extracellular pH this is mostly the membrane potential, as authors note themselves, we are talking about Vm effectively). Thus, two things are happening when one is loading the ThT, not just expected equilibration but also lowering of membrane potential. The electrochemical potential of protons is coupled via the membrane potential to all the other electrochemical potentials of ions, including the mentioned K+.

(4) 'The vast majority of cells continue to be viable. We do not think membrane damage is dominating.' In response to the question on how the authors demonstrated TMRM loading and in which conditions (and while reminding them that TMRM loading profile in E.coli has been demonstrated in Potassium Phosphate buffer). The request was to demonstrate TMRM loading profile in their condition as well as to show that it does not depend on light. Cells could still be viable, as membrane permeabilisation with light is gradual, but the loading of ThT dye is no longer based on simple electrochemical potential (of the dye) equilibration.

(5) On the comment on the action of CCCP with references included, authors include a comment that consists of phrases like 'our understanding of the literature' with no citations of such literature. Difficult to comment further without references.

(6) 'Shielding would provide the reverse effect, since hyperpolarization begins in the dense centres of the biofilms. For the initial 2 hours the cells receive negligible blue light. Neither of the referee's comments thus seem tenable.'<br /> The authors have misunderstood my comment. I am not advocating shielding (I agree that this is not it) but stating that this is not the only other explanation for what they see (apart from electrical signaling). The other I proposed is that the membrane has changed in composition and/or the effective light power the cells can tolerate. The authors comment only on the light power (not convincingly though, giving the number for that power would be more appropriate), not on the possible changes in the membrane permeability.

(7) 'The work that TolC provides a possible passive pathway for ThT to leave cells seems slightly niche. It just demonstrates another mechanism for the cells to equilibrate the concentrations of ThT in a Nernstian manner i.e. driven by the membrane voltage.' I am not sure what the authors mean by another mechanism. The mechanism of action of a Nernstian dye is passive equilibration according to the electrochemical potential (i.e. until the electrochemical potential of the dye is 0).

(8) 'In the 70 years since Hodgkin and Huxley first presented their model, a huge number of similar models have been proposed to describe cellular electrophysiology. We are not being hyperbolic when we state that the HH models for excitable cells are like the Schrödinger<br /> equation for molecules. We carefully adapted our HH model to reflect the currently understood electrophysiology of E. coli.'

I gave a very concrete comment on the fact that in the HH model conductivity and leakage are as they are because this was explicitly measured. The authors state that they have carefully adopted their model based on what is currently understood for E.coli electrophysiology. It is not clear how. HH uses gKn^4 based on Figure2 here https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1392413/pdf/jphysiol01442-0106.pdf, i.e. measured rise and fall of potassium conductance on msec time scales. I looked at the citation the authors have given and found a resistance of an entire biofilm of a given strain at 3 applied voltages. So why n^4 based on that? Why does unknown current have gqz^4 form? Sodium conductance in HH is described by m^3hgNa (again based on detailed conductance measurements), so why unknown current in E.coli by gQz^4? Why leakage is in the form that it is, based on what measurement?

Throughout their responses, the authors seem to think that collapsing the electrochemical gradient of protons is all about protons, and this is not the case. At near neutral inside and outside pH, the electrochemical potential of protons is simply membrane voltage. And membrane voltage acts on all ions in the cell.

Authors have started their response to concrete comments on the usage of ThT dye with comments on papers from my group that are not all directly relevant to this publication. I understand that their intention is to discredit a reviewer but given that my role here is to review this manuscript, I will only address their comments to the publications/part of publications that are relevant to this manuscript and mention what is not relevant.

Publications in the order these were commented on.

(1) In a comment on the paper that describes the usage of ThT dye as a Nernstian dye authors seem to talk about a model of an entire active cell.<br /> 'Huge oscillations occur in the membrane potentials of E. coli that cannot be described by the SNB model.' The two have nothing to do with each other. Nernstian dye equilibrates according to its electrochemical potential. Once that happens it can measure the potential (under the assumption that not too much dye has entered and thus lowered too much the membrane potential under measurement). The time scale of that is important, and the dye can only measure processes that are slower than that equilibration. If one wants to use a dye that acts under a different model, first that needs to be developed, and then coupled to any other active cell model.

(2) The part of this paper that is relevant is simply the usage of TMRM dye. It is used as Nernstian dye, so all the above said applies. The rest is a study of flagellar motor.

(3) The authors seem to not understand that the electrochemical potential of protons is coupled to the electrochemical potentials of all other ions, via the membrane potential. In the manuscript authors talk about, PMF~Vm, as DeltapH~0. Other than that this publication is not relevant to their current manuscript.

(4) The manuscript in fact states precisely that PMF cannot be generated by protons only and some other ions need to be moved out for the purpose. In near neutral environment it stated that these need to be cations (K+ e.g.). The model used in this manuscript is a pump-leak model. Neither is relevant for the usage of ThT dye.

Further comments include, along the lines of:

'The editors stress the main issue raised was a single referee questioning the use of ThT as an indicator of membrane potential. We are well aware of the articles by the Pilizota group and we believe them to be scientifically flawed. The authors assume there are no voltage-gated ion channels in E. coli and then attempt to explain motility data based on a simple Nernstian battery model (they assume E. coli are unexcitable<br /> matter). This in turn leads them to conclude the membrane dye ThT is faulty, when in fact it is a problem with their simple battery model.'

The only assumption made when using a cationic Nernstian dye is that it equilibrates passively across the membrane according to its electrochemical potential. As it does that, it does lower the membrane potential, which is why as little as possible is added so that this is negligible. The equilibration should be as fast as possible, but at the very least it should be known, as no change in membrane potential can be measured that is faster than that.

This behaviour should be orthogonal to what the cell is doing, it is a probe after all. If the cell is excitable, a Nernstian dye can be used, as long as it's still passively equilibrating and doing so faster than any changes in membrane potential due to excitations of the cells. There are absolutely no assumptions made on the active system that is about to be measured by this expected behaviour of a Nernstian dye. And there shouldn't be, it is a probe. If one wants to use a dye that is not purely Nernstian that behaviour needs to be described and a model proposed. As far as I can find, authors do no such thing.

There is a comment on the use of a flagellar motor as a readout of PMF, stating that the motor can be stopped by YcgR citing the work from 2023. Indeed, there is a range of references such as https://doi.org/10.1016/j.molcel.2010.03.001 that demonstrate this (from around 2000-2010 as far as I am aware). The timescale of such slowdown is hours (see here Figure 5 https://www.cell.com/cell/pdf/S0092-8674(10)00019-X.pdf). Needless to say, the flagellar motor when used as a probe, needs to stay that in the conditions used. Thus one should always be on the lookout at any other such proteins that could slow it down and we are not aware of yet or make the speed no longer proportional to the PMF. In the papers my group uses the motor the changes are fast, often reversible, and in the observation window of 30min. They are also the same with DeltaYcgR strain, which we have not included as it seemed given the time scales it's obvious, but certainly can in the future (as well as stay vigilant on any conditions that would render the motor a no longer suitable probe for PMF).

Reviewer #3 (Public Review):

This manuscript by Akabuogu et al. investigates membrane potential dynamics in E. coli. Membrane potential fluctuations have been observed in bacteria by several research groups in recent years, including in the context of bacterial biofilms where they have been proposed to play a role in cellular communication. Here, these authors investigate membrane potential in E. coli, in both single cells and biofilms. I have reviewed the revised manuscript provided by the authors, as well as their responses to the initial reviews; my opinion about the manuscript is largely unchanged. I have focused my public review on those issues that I believe to be most pressing, with additional comments included in the review to authors. Although these authors are working in an exciting research area, the evidence they provide for their claims is inadequate, and several key control experiments are still missing. In some cases, the authors allude to potentially relevant data in their responses to the initial reviews, but unfortunately these data are not shown. Furthermore, I cannot identify any traveling wavefronts in the data included in this manuscript. In addition to the challenges associated with the use of Thioflavin-T (ThT) raised by the second reviewer, these caveats make the work presented in this manuscript difficult to interpret.

First, some of the key experiments presented in the paper lack required controls:

(1) This paper asserts that the observed ThT fluorescence dynamics are induced by blue light. This is a fundamental claim in the paper, since the authors go on to argue that these dynamics are part of a blue light response. This claim must be supported by the appropriate negative control experiment measuring ThT fluorescence dynamics in the absence of blue light- if this idea is correct, these dynamics should not be observed in the absence of blue light exposure. If this experiment cannot be performed with ThT since blue light is used for its excitation, TMRM can be used instead.

In response to this, the authors wrote that "the fluorescent baseline is too weak to measure cleanly in this experiment." If they observe no ThT signal above noise in their time lapse data in the absence of blue light, this should be reported in the manuscript- this would be a satisfactory negative control. They then wrote that "It appears the collective response of all the bacteria hyperpolarization at the same time appears to dominate the signal." I am not sure what they mean by this- perhaps that ThT fluorescence changes strongly only in response to blue light? This is a fundamental control for this experiment that ought to be presented to the reader.

(2) The authors claim that a ∆kch mutant is more susceptible to blue light stress, as evidenced by PI staining. The premise that the cells are mounting a protective response to blue light via these channels rests on this claim. However, they do not perform the negative control experiment, conducting PI staining for WT the ∆kch mutant in the absence of blue light. In the absence of this control it is not possible to rule out effects of the ∆kch mutation on overall viability and/or PI uptake. The authors do include a growth curve for comparison, but planktonic growth is a very different context than surface-attached biofilm growth. Additionally, the ∆kch mutation may have impacts on PI permeability specifically that are not addressed by a growth curve. The negative control experiment is of key importance here.

Second, the ideas presented in this manuscript rely entirely on analysis of ThT fluorescence data, specifically a time course of cellular fluorescence following blue light treatment. However, alternate explanations for and potential confounders of the observed dynamics are not sufficiently addressed:

(1) Bacterial cells are autofluorescent, and this fluorescence can change significantly in response to stress (e.g. blue light exposure). To characterize and/or rule out autofluorescence contributions to the measurement, the authors should present time lapse fluorescence traces of unstained cells for comparison, acquired under the same imaging conditions in both wild type and ∆kch mutant cells. In their response to reviewers the authors suggested that they have conducted this experiment and found that the autofluorescence contribution is negligible, which is good, but these data should be included in the manuscript along with a description of how these controls were conducted.

(2) Similarly, in my initial review I raised a concern about the possible contributions of photobleaching to the observed fluorescence dynamics. This is particularly relevant for the interpretation of the experiment in which catalase appears to attenuate the decay of the ThT signal; this attenuation could alternatively be due to catalase decreasing ThT photobleaching. In their response, the authors indicated that photobleaching is negligible, which would be good, but they do not share any evidence to support this claim. Photobleaching can be assessed in this experiment by varying the light dosage (illumination power, frequency, and/or duration) and confirming that the observed fluorescence dynamics are unaffected.

Third, the paper claims in two instances that there are propagating waves of ThT fluorescence that move through biofilms, but I do not observe these waves in any case:

(1) The first wavefront claim relates to small cell clusters, in Fig. 2A and Video S2 and S3 (with Fig. 2A and Video S2 showing the same biofilm.) I simply do not see any evidence of propagation in either case- rather, all cells get brighter and dimmer in tandem. I downloaded and analyzed Video S3 in several ways (plotting intensity profiles for different regions at different distances from the cluster center, drawing a kymograph across the cluster, etc.) and in no case did I see any evidence of a propagating wavefront. (I attempted this same analysis on the biofilm shown in Fig. 2A and Video S2 with similar results, but the images shown in the figure panels and especially the video are still both so saturated that the quantification is difficult to interpret.) If there is evidence for wavefronts, it should be demonstrated explicitly by analysis of several clusters. For example, a figure of time-to-peak vs. position in the cluster demonstrating a propagating wave would satisfy this. Currently, I do not see any wavefronts in this data.

(2) The other wavefront claim relates to biofilms, and the relevant data is presented in Fig. S4 (and I believe also in what is now Video S8, but no supplemental video legends are provided, and this video is not cited in text.) As before, I cannot discern any wavefronts in the image and video provided; Reviewer 1 was also not able to detect wave propagation in this video by kymograph. Some mean squared displacements are shown in Fig. 7. As before, the methods for how these were obtained are not clearly documented either in this manuscript or in the BioRXiv preprint linked in the initial response to reviewers, and since wavefronts are not evident in the video it is hard to understand what is being measured here- radial distance from where? (The methods section mentions radial distance from the substrate, this should mean Z position above the imaging surface, and no wavefronts are evident in Z in the figure panels or movie.) Thus, clear demonstration of these wavefronts is still missing here as well.

Fourth, I have some specific questions about the study of blue light stress and the use of PI as a cell viability indicator:

(1) The logic of this paper includes the premise that blue light exposure is a stressor under the experimental conditions employed in the paper. Although it is of course generally true that blue light can be damaging to bacteria, this is dependent on light power and dosage. The control I recommended above, staining cells with PI in the presence and absence of blue light, will also allow the authors to confirm that this blue light treatment is indeed a stressor- the PI staining would be expected to increase in the presence of blue light if this is so.

(2) The presence of ThT may complicate the study of the blue light stress response, since ThT enhances the photodynamic effects of blue light in E. coli (Bondia et al. 2021 Chemical Communications). The authors could investigate ThT toxicity under these conditions by staining cells with PI after exposing them to blue light with or without ThT staining.

(3) In my initial review, I wrote the following: "In Figures 4D - E, the interpretation of this experiment can be confounded by the fact that PI uptake can sometimes be seen in bacterial cells with high membrane potential (Kirchhoff & Cypionka 2017 J Microbial Methods); the interpretation is that high membrane potential can lead to increased PI permeability. Because the membrane potential is largely higher throughout blue light treatment in the ∆kch mutant (Fig. 3[BC]), this complicates the interpretation of this experiment." In their response, the authors suggested that these results are not relevant in this case because "In our experiment methodology, cell death was not forced on the cells by introducing an extra burden or via anoxia." However, the logic of the paper is that the cells are in fact dying due to an imposed external stressor, which presumably also confers an increased burden as the cells try to deal with the stress. Instead, the authors should simply use a parallel method to confirm the results of PI staining. For example, the experiment could be repeated with other stains, or the viability of blue light-treated cells could be addressed more directly by outgrowth or colony-forming unit assays.

The CFU assay suggested above has the additional advantage that it can also be performed on planktonic cells in liquid culture that are exposed to blue light. If, as the paper suggests, a protective response to blue light is being coordinated at the biofilm level by these membrane potential fluctuations, the WT strain might be expected to lose its survival advantage vs. the ∆kch mutant in the absence of a biofilm.

Fifth, in several cases the data are presented in a way that are difficult to interpret, or the paper makes claims that are different to observe in the data:

(1) The authors suggest that the ThT and TMRM traces presented in Fig. S1D have similar shapes, but this is not obvious to me- the TMRM curve has very little decrease after the initial peak and only a modest, gradual rise thereafter. The authors suggest that this is due to increased TMRM photobleaching, but I would expect that photobleaching should exacerbate the signal decrease after the initial peak. Since this figure is used to support the use of ThT as a membrane potential indicator, and since this is the only alternative measurement of membrane potential presented in text, the authors should discuss this discrepancy in more detail.

(2) The comparison of single cells to microcolonies presented in figures 1B and D still needs revision:

First, both reviewer 1 and I commented in our initial reviews that the ThT traces, here and elsewhere, should not be normalized- this will help with the interpretation of some of the claims throughout the manuscript.

Second, the way these figures are shown with all traces overlaid at full opacity makes it very difficult to see what is being compared. Since the point of the comparison is the time to first peak (and the standard deviation thereof), histograms of the distributions of time to first peak in both cases should be plotted as a separate figure panel.<br /> Third, statistical significance tests ought to be used to evaluate the statistical strength of the comparisons between these curves. The authors compare both means and standard deviations of the time to first peak, and there are appropriate statistical tests for both types of comparisons.

(3) The authors claim that the curve shown in Fig. S4B is similar to the simulation result shown in Fig. 7B. I remain unconvinced that this is so, particularly with respect to the kinetics of the second peak- at least it seems to me that the differences should be acknowledged and discussed. In any case, the best thing to do would be to move Fig. S4B to the main text alongside Fig. 7B so that the readers can make the comparison more easily.

(4) As I wrote in my first review, in the discussion of voltage-gated calcium channels, the authors refer to "spiking events", but these are not obvious in Figure S3E. Although the fluorescence intensity changes over time, these fluctuations cannot be distinguished from measurement noise. A no-light control could help clarify this.

(5) In the lower irradiance conditions in Fig. 4A, the ThT dynamics are slower overall, and it looks like the ThT intensity is beginning to rise at the end of the measurement. The authors write that no second peak is observed below an irradiance threshold of 15.99 µW/mm2. However, could a more prominent second peak be observed in these cases if the measurement time was extended? Additionally, the end of these curves looks similar to the curve in Fig. S4B, in which the authors write that the slow rise is evidence of the presence of a second peak, in contrast to their interpretation here.

Additional considerations:

(1) The analysis and interpretation of the first peak, and particularly of the time-to-fire data is challenging throughout the manuscript the time resolution of the data set is quite limited. It seems that a large proportion of cells have already fired after a single acquisition frame. It would be ideal to increase the time resolution on this measurement to improve precision. This could be done by imaging more quickly, but that would perhaps necessitate more blue light exposure; an alternative is to do this experiment under lower blue light irradiance where the first spike time is increased (Figure 4A).

(2) The authors suggest in the manuscript that "E. coli biofilms use electrical signalling to coordinate long-range responses to light stress." In addition to the technical caveats discussed above, I am missing a discussion about what these responses might be. What constitutes a long-range response to light stress, and are there known examples of such responses in bacteria?

(3) The presence of long-range blue light responses can also be interrogated experimentally, for example, by repeating the Live/Dead experiment in planktonic culture or the single-cell condition. If the protection from blue light specifically emerges due to coordinated activity of the biofilm, the ∆kch mutant would not be expected to show a change in Live/Dead staining in non-biofilm conditions. The CFU experiment I mentioned above could also implicate coordinated long-range responses specifically, if biofilms and liquid culture experiments can be compared (although I know that recovering cells from biofilms is challenging.)

4. At the end of the results section, the authors suggest a critical biofilm size of only 4 μm for wavefront propagation (not much larger than a single cell!) The authors show responses for various biofilm sizes in Fig. 2C, but these are all substantially larger (and this figure also does not contain wavefront information.) Are there data for cell clusters above and below this size that could support this claim more directly?

(5) In Fig. 4C, the overall trajectories of extracellular potassium are indeed similar, but the kinetics of the second peak of potassium are different than those observed by ThT (it rises minutes earlier)- is this consistent with the idea that Kch is responsible for that peak? Additionally, the potassium dynamics also include the first ThT peak- is this surprising given that the Kch channel has no effect on this peak according to the model?

Detailed comments:

Why are Fig. 2A and Video S2 called a microcluster, whereas Video S3, which is smaller, is called a biofilm?

"We observed a spontaneous rapid rise in spikes within cells in the center of the biofilm" (Line 140): What does "spontaneous" mean here?

"This demonstrates that the ion-channel mediated membrane potential dynamics is a light stress relief process.", "E. coli cells employ ion-channel mediated dynamics to manage ROS-induced stress linked to light irradiation." (Line 268 and the second sentence of the Fig. 4F legend): This claim is not well-supported. There are several possible interpretations of the catalase experiment (which should be discussed); this experiment perhaps suggests that ROS impacts membrane potential but does not indicate that these membrane potential fluctuations help the cells respond to blue light stress. The loss of viability in the ∆kch mutant might indicate a link between these membrane potential experiments and viability, but it is hard to interpret without the no light controls I mention above.

"The model also predicts... the external light stress" (Lines 338-341): Please clarify this section. Where does this prediction arise from in the modeling work? Second, I am not sure what is meant by "modulates the light stress" or "keeps the cell dynamics robust to the intensity of external light stress" (especially since the dynamics clearly vary with irradiance, as seen in Figure 4A).

"We hypothesized that E. coli not only modulates the light-induced stress but also handles the increase of the ROS by adjusting the profile of the membrane potential dynamics" (Line 347): I am not sure what "handles the ROS by adjusting the profile of the membrane potential dynamics" means. What is meant by "handling" ROS? Is the hypothesis that membrane potential dynamics themselves are protective against ROS, or that they induce a ROS-protective response downstream, or something else? Later the authors write that changes in the response to ROS in the model agree with the hypothesis, but just showing that ROS impacts the membrane potential does not seem to demonstrate that this has a protective effect against ROS.

"Mechanosensitive ion channels (MS) are vital for the first hyperpolarization event in E. coli." (Line 391): This is misleading- mechanosensitive ion channels totally ablate membrane potential dynamics, they don't have a specific effect on the first hyperpolarization event. The claim that mechanonsensitive ion channels are specifically involved in the first event also appears in the abstract.

Also, the apparent membrane potential is much lower even at the start of the experiment in these mutants (Fig. 6C-D)- is this expected? This seems to imply that these ion channels also have a blue light-independent effect.

Throughout the paper, there are claims that the initial ThT spike is involved in "registering the presence of the light stress" and similar. What is the evidence for this claim?

"We have presented much better quantitative agreement of our model with the propagating wavefronts in E. coli biofilms..." (Line 619): It is not evident to me that the agreement between model and prediction is "much better" in this work than in the cited work (reference 57, Hennes et al. 2023). The model in Figure 4 of ref. 57 seems to capture the key features of their data.

In methods, "Only cells that are hyperpolarized were counted in the experiment as live" (Line 745): what percentage of cells did not hyperpolarize in these experiments?

Some indication of standard deviation (error bars or shading) should be added to all figures where mean traces are plotted.

Video S8 is very confusing- why does the video play first forwards and then backwards? It is easy to misinterpret this as a rise in the intensity at the end of the experiment.

Reviewer #1 (Public review):

Summary:

This study provides a comprehensive single-cell and multiomic characterization of trabecular meshwork (TM) cells in the mouse eye, a structure critical to intraocular pressure (IOP) regulation and glaucoma pathogenesis. Using scRNA-seq, snATAC-seq, immunofluorescence, and in situ hybridization, the authors identify three transcriptionally and spatially distinct TM cell subtypes. The study further demonstrates that mitochondrial dysfunction, specifically in one subtype (TM3), contributes to elevated IOP in a genetic mouse model of glaucoma carrying a mutation in the transcription factor Lmx1b. Importantly, treatment with nicotinamide (vitamin B3), known to support mitochondrial health, prevents IOP elevation in this model. The authors also link their findings to human datasets, suggesting the existence of analogous TM3-like cells with potential relevance to human glaucoma.

Strengths:

The study is methodologically rigorous, integrating single-cell transcriptomic and chromatin accessibility profiling with spatial validation and in vivo functional testing. The identification of TM subtypes is consistent across mouse strains and institutions, providing robust evidence of conserved TM cell heterogeneity. The use of a glaucoma model to show subtype-specific vulnerability, combined with a therapeutic intervention-gives the study strong mechanistic and translational significance. The inclusion of chromatin accessibility data adds further depth by implicating active transcription factors such as LMX1B, a gene known to be associated with glaucoma risk. The integration with human single-cell datasets enhances the potential relevance of the findings to human disease.

Weaknesses:

Although the LMX1B transcription factor is implicated as a key regulator in TM3 cells, its role in directly controlling mitochondrial gene expression is not fully explored. Additional analysis of motif accessibility or binding enrichment near relevant target genes could substantiate this mechanistic link. The therapeutic effect of vitamin B3 is clearly demonstrated phenotypically, but the underlying cellular and molecular mechanisms remain somewhat underdeveloped - for instance, changes in mitochondrial function, oxidative stress markers, or NAD+ levels are not directly measured. While the human relevance of TM3 cells is suggested through marker overlap, more quantitative approaches, such as cell identity mapping or gene signature scoring in human datasets, would strengthen the translational connection.

Overall, this is a compelling and carefully executed study that offers significant advances in our understanding of TM cell biology and its role in glaucoma. The integration of multimodal data, disease modeling, and therapeutic testing represents a valuable contribution to the field. With additional mechanistic depth, the study has the potential to become a foundational resource for future research into IOP regulation and glaucoma treatment.

Reviewer #2 (Public review):

Summary:

This elegant study by Tolman and colleagues provides fundamental findings that substantially advance our knowledge of the major cell types within the limbus of the mouse eye, focusing on the aqueous humor outflow pathway. The authors used single-cell and single-nuclei RNAseq to very clearly identify 3 subtypes of the trabecular meshwork (TM) cells in the mouse eye, with each subtype having unique markers and proposed functions. The U. Columbia results are strengthened by an independent replication in a different mouse strain at a separate laboratory (Duke). Bioinformatics analyses of these expression data were used to identify cellular compartments, molecular functions, and biological processes. Although there were some common pathways among the 3 subtypes of TM cells (e.g., ECM metabolism), there also were distinct functions. For example:

• TM1 cell expression supports heavy engagement in ECM metabolism and structure, as well as TGFβ2 signaling.

• TM2 cells were enriched in laminin and pathways involved in phagocytosis, lysosomal function, and antigen expression, as well as End3/VEGF/angiopoietin signaling.

• TM3 cells were enriched in actin binding and mitochondrial metabolism.

They used high-resolution immunostaining and in situ hybridization to show that these 3 TM subtypes express distinct markers and occupy distinct locations within the TM tissue. The authors compared their expression data with other published scRNAseq studies of the mouse as well as the human aqueous outflow pathway. They used ATAC-seq to map open chromatin regions in order to predict transcription factor binding sites. Their results were also evaluated in the context of human IOP and glaucoma risk alleles from published GWAS data, with interesting and meaningful correlations. Although not discussed in their manuscript, their expression data support other signaling pathways/ proteins/ genes that have been implicated in glaucoma, including: TGFβ2, BMP signaling (including involvement of ID proteins), MYOC, actin cytoskeleton (CLANs), WNT signaling, etc.

In addition to these very impressive data, the authors used scRNAseq to examine changes in TM cell gene expression in the mouse glaucoma model of mutant Lmxb1-induced ocular hypertension. In man, LMX1B is associated with Nail-Patella syndrome, which can include the development of glaucoma, demonstrating the clinical relevance of this mouse model. Among the gene expression changes detected, TM3 cells had altered expression of genes associated with mitochondrial metabolism. The authors used their previous experience using nicotinamide to metabolically protect DBA2/J mice from glaucomatous damage, and they hypothesized that nicotinamide supplementation of mutant Lmx1b mice would help restore normal mitochondrial metabolism in the TM and prevent Lmx1b-mediated ocular hypertension. Adding nicotinamide to the drinking water significantly prevented Lmxb1 mutant mice from developing high intraocular pressure. This is a laudable example of dissecting the molecular pathogenic mechanisms responsible for a disease (glaucoma) and then discovering and testing a potential therapy that directly intervenes in the disease process and thereby protects from the disease.

Strengths:<br /> There are numerous strengths in this comprehensive study including:<br /> • Deep scRNA sequencing that was confirmed by an independent dataset in another mouse strain at another university.<br /> • Identification and validation of molecular markers for each mouse TM cell subset along with localization of these subsets within the mouse aqueous outflow pathway.<br /> • Rigorous bioinformatics analysis of these data as well as comparison of the current data with previously published mouse and human scRNAseq data.<br /> • Correlating their current data with GWAS glaucoma and IOP "hits".<br /> • Discovering gene expression changes in the 3 TM subgroups in the mouse mutant Lmx1b model of glaucoma.<br /> • Further pursuing the indication of dysfunctional mitochondrial metabolism in TM3 cells from Lmx1b mutant mice to test the efficacy of dietary supplementation with nicotinamide. The authors nicely demonstrate the disease modifying efficacy of nicotinamide in preventing IOP elevation in these Lmx1b mutant mice, preventing the development of glaucoma. These results have clinical implications for new glaucoma therapies.

Weaknesses:<br /> • Occasional over-interpretation of data. The authors have used changes in gene expression (RNAseq) to implicate functions and signaling pathways. For example: they have not directly measured "changes in metabolism", "mitochondrial dysfunction" or "activity of Lmx1b".<br /> • In their very thorough data set, there is enrichment of or changes in gene expression that support other pathways that have been previously reported to be associated with glaucoma (such as TGFβ2, BMP signaling, actin cytoskeletal organization (CLANs), WNT signaling, ossification, etc. that appears to be a lost opportunity to further enhance the significance of this work.

Reviewer #3 (Public review):

Summary:In this study, the authors perform multimodal single-cell transcriptomic and epigenomic profiling of 9,394 mouse TM cells, identifying three transcriptionally distinct TM subtypes with validated molecular signatures. TM1 cells are enriched for extracellular matrix genes, TM2 for secreted ligands supporting Schlemm's canal, and TM3 for contractile and mitochondrial/metabolic functions. The transcription factor LMX1B, previously linked to glaucoma, shows the highest expression in TM3 cells and appears to regulate mitochondrial pathways. In Lmx1bV265D mutant mice, TM3 cells exhibit transcriptional signs of mitochondrial dysfunction associated with elevated IOP. Notably, vitamin B3 treatment significantly mitigates IOP elevation, suggesting a potential therapeutic avenue.

This is an excellent and collaborative study involving investigators from two institutions, offering the most detailed single-cell transcriptomic and epigenetic profiling of the mouse limbal tissues-including both TM and Schlemm's canal (SC), from wild-type and Lmx1bV265D mutant mice. The study defines three TM subtypes and characterizes their distinct molecular signatures, associated pathways, and transcriptional regulators. The authors also compare their dataset with previously published murine and human studies, including those by Van Zyl et al., providing valuable cross-species insights.

Strengths:

(1) Comprehensive dataset with high single-cell resolution<br /> (2) Use of multiple bioinformatic and cross-comparative approaches<br /> (3) Integration of 3D imaging of TM and SC for anatomical context<br /> (4) Convincing identification and validation of three TM subtypes using molecular markers.

Weaknesses:

(1) Insufficient evidence linking mitochondrial dysfunction to TM3 cells in Lmx1bV265D mice: While the identification of TM3 cells as metabolically specialized and Lmx1b-enriched is compelling, the proposed link between Lmx1b mutation and mitochondrial dysfunction remains underdeveloped. It is unclear whether mitochondrial defects are a primary consequence of Lmx1b-mediated transcriptional dysregulation or a secondary response to elevated IOP. Additional evidence is needed to clarify whether Lmx1b directly regulates mitochondrial genes (e.g., via ChIP-seq, motif analysis, or ATAC-seq), or whether mitochondrial changes are downstream effects.<br /> Furthermore, the protective effects of nicotinamide (NAM) are interpreted as evidence of mitochondrial involvement, but no direct mitochondrial measurements (e.g., immunostaining, electron microscopy, OCR assays) are provided. It is essential to validate mitochondrial dysfunction in TM3 cells using in vivo functional assays to support the central conclusion of the paper. Without this, the claim that mitochondrial dysfunction drives IOP elevation in Lmx1bV265D mice remains speculative. Alternatively, authors should consider revising their claims that mitochondrial dysfunction in these mice is a central driver of TM dysfunction.

(2) Mechanism of NAM-mediated protection is unclear: The manuscript states that NAM treatment prevents IOP elevation in Lmx1bV265D mice via metabolic support, yet no data are shown to confirm that NAM specifically rescues mitochondrial function. Do NAM-treated TM3 cells show improved mitochondrial integrity? Are reactive oxygen species (ROS) reduced? Does NAM also protect RGCs from glaucomatous damage? Addressing these points would clarify whether the therapeutic effects of NAM are indeed mitochondrial.

(3) Lack of direct evidence that LMX1B regulates mitochondrial genes: While transcriptomic and motif accessibility analyses suggest that LMX1B is enriched in TM3 cells and may influence mitochondrial function, no mechanistic data are provided to demonstrate direct regulation of mitochondrial genes. Including ChIP-seq data, motif enrichment at mitochondrial gene loci, or perturbation studies (e.g., Lmx1b knockout or overexpression in TM3 cells) would greatly strengthen this central claim.

(4)Focus on LMX1B in Fig. 5F lacks broader context: Figure 5F shows that several transcription factors (TFs)-including Tcf21, Foxs1, Arid3b, Myc, Gli2, Patz1, Plag1, Npas2, Nr1h4, and Nfatc2-exhibit stronger positive correlations or motif accessibility changes than LMX1B. Yet the manuscript focuses almost exclusively on LMX1B. The rationale for this focus should be clarified, especially given LMX1B's relatively lower ranking in the correlation analysis. Were the functions of these other highly ranked TFs examined or considered in the context of TM biology or glaucoma? Discussing their potential roles would enhance the interpretation of the transcriptional regulatory landscape and demonstrate the broader relevance of the findings.

Other weaknesses:

(1) In abstract, they say a number of 9,394 wild-type TM cell transcriptomes. The number of Lmx1bV265D/+ TM cell transcriptomes analyzed is not provided. This information is essential for evaluating the comparative analysis and should be clearly stated in the Abstract and again in the main text (e.g., lines 121-123). Including both wild-type and mutant cell counts will help readers assess the balance and robustness of the dataset.

(2) Did the authors monitor mouse weight or other health parameters to assess potential systemic effects of treatment? It is known that the taste of compounds in drinking water can alter fluid or food intake, which may influence general health. Also, does Lmx1bV265D/+ have mice exhibit non-ocular phenotypes, and if so, does nicotinamide confer protection in those tissues as well? Additionally, starting the dose of the nicotinamide at postnatal day 2, how long the mice were treated with water containing nicotinamide, and after how many days or weeks IOP was reduced, and how long the decrease in the IOP was sustained.<br /> (3) While the IOP reduction observed in NAM-treated Lmx1bV265D/+ mice appears statistically significant, it is unclear whether this reflects meaningful biological protection. Several untreated mice exhibit very high IOP values, which may skew the analysis. The authors should report the mean values for IOP in both untreated and NAM-treated groups to clarify the magnitude and variability of the response.<br /> (4) Additionally, since NAM has been shown to protect RGCs in other glaucoma models directly, the authors should assess whether RGCs are preserved in NAM-treated Lmx1b V265D/+ mice. Demonstrating RGC protection would support a synergistic effect of NAM through both IOP reduction and direct neuroprotection, strengthening the translational relevance of the treatment.<br /> (5) Can the authors add any other functional validation studies to explore to understand the pathways enriched in all the subtypes of TM1, TM2, and TM3 cells, in addition to the ICH/IF/RNAscope validation?<br /> (6) The authors should include a representative image of the limbal dissection. While Figure S1 provides a schematic, mouse eyes are very small, and dissecting unfixed limbal tissue is technically challenging. It is also difficult to reconcile the claim that the majority of cells in the limbal region are TM and endothelium. As shown in Figure S6, DAPI staining suggests a much higher abundance of scleral cells compared to TM cells within the limbal strip. Additional clarification or visual evidence would help validate the dissection strategy and cellular composition of the captured region.

Reviewer #1 (Public review):

Tamao et al. aimed to quantify the diversity and mutation rate of the influenza (PR8 strain) in order to establish a high-resolution method for studying intra-host viral evolution. To achieve this, the authors combined RNA sequencing with single-molecule unique molecular identifiers (UMIs) to minimize errors introduced during technical processing. They proposed an in vitro infection model with a single viral particle to represent biological genetic diversity, alongside a control model using in vitro transcribed RNA for two viral genes, PB2 and HA.

Through this approach, the authors demonstrated that UMIs reduced technical errors by approximately tenfold. By analyzing four viral populations and comparing them to in vitro transcribed RNA controls, they estimated that ~98.1% of observed mutations originated from viral replication rather than technical artifacts. Their results further showed that most mutations were synonymous and introduced randomly. However, the distribution of mutations suggested selective pressures that favored certain variants. Additionally, comparison with a closely related influenza strain (A/Alaska/1935) revealed two positively selected mutations, though these were absent in the strain responsible for the most recent pandemic (CA01).

Overall, the study is well-designed, and the interpretations are strongly supported by the data. However, the following clarifications are recommended:

(1) The methods section is overly brief. Even if techniques are cited, more experimental details should be included. For example, since the study focuses heavily on methodology, details such as the number of PCR cycles in RT-PCR or the rationale for choosing HA and PB2 as representative in vitro transcripts should be provided.

(2) Information on library preparation and sequencing metrics should be included. For example, the total number of reads, any filtering steps, and quality score distributions/cutoff for the analyzed reads.

(3) In the Results section (line 115, "Quantification of error rate caused by RT"), the mutation rate attributed to viral replication is calculated. However, in line 138, it is unclear whether the reported value reflects PB2, HA, or both, and whether the comparison is based on the error rate of the same viral RNA or the mean of multiple values (as shown in Figure 3A). Please clarify whether this number applies universally to all influenza RNAs or provide the observed range.

(4) Since the T7 polymerase introduced errors are only applied to the in vitro transcription control, how were these accounted for when comparing mutation rates between transcribed RNA and cell-culture-derived virus?

(5) Figure 2 shows that a UMI group size of 4 has an error rate of zero, but this group size is not mentioned in the text. Please clarify.

Reviewer #2 (Public review):

Summary:

This manuscript presents a technically oriented application of UMI-based long-read sequencing to study intra-host diversity in influenza virus populations. The authors aim to minimize sequencing artifacts and improve the detection of rare variants, proposing that this approach may inform predictive models of viral evolution. While the methodology appears robust and successfully reduces sequencing error rates, key experimental and analytical details are missing, and the biological insight is modest. The study includes only four samples, with no independent biological replicates or controls, which limits the generalizability of the findings. Claims related to rare variant detection and evolutionary selection are not fully supported by the data presented.

Strengths:

The study addresses an important technical challenge in viral genomics by implementing a UMI-based long-read sequencing approach to reduce amplification and sequencing errors. The methodological focus is well presented, and the work contributes to improving the resolution of low-frequency variant detection in complex viral populations.

Weaknesses:

The application of UMI-based error correction to viral population sequencing has been established in previous studies (e.g., in HIV), and this manuscript does not introduce a substantial methodological or conceptual advance beyond its use in the context of influenza.

The study lacks independent biological replicates or additional viral systems that would strengthen the generalizability of the conclusions. Potential sources of technical error are not explored or explicitly controlled. Key methodological details are missing, including the number of PCR cycles, the input number of molecules, and UMI family size distributions. These are essential to support the claimed sensitivity of the method.

The assertion that variants at {greater than or equal to}0.1% frequency can be reliably detected is based on total read count rather than the number of unique input molecules. Without information on UMI diversity and family sizes, the detection limit cannot be reliably assessed.

Although genetic variation is described, the functional relevance of observed mutations in HA and NA is not addressed or discussed in the context of known antigenic or evolutionary features of influenza. The manuscript is largely focused on technical performance, with limited exploration of the biological implications or mechanistic insights into influenza virus evolution.

The experimental scale is small, with only four viral populations derived from single particles analyzed. This limited sample size restricts the ability to draw broader conclusions about quasispecies dynamics or evolutionary pressures.

Reviewer #2 (Public Review):

The goal of the present study is to better understand the 'control objectives' that subjects adopt in a video-game-like virtual-balancing task. In this task, the hand must move in the opposite direction from a cursor. For example, if the cursor is 2 cm to the right, the subject must move their hand 2 cm to the left to 'balance' the cursor. Any imperfection in that opposition causes the cursor to move. E.g., if the subject were to move only 1.8 cm, that would be insufficient, and the cursor would continue to move to the right. If they were to move 2.2 cm, the cursor would move back toward the center of the screen. This return to center might actually be 'good' from the subject's perspective, depending on whether their objective is to keep the cursor still or keep it near the screen's center. Both are reasonable 'objectives' because the trial fails if the cursor moves too far from the screen's center during each six-second trial.

This task was recently developed for use in monkeys (Quick et al., 2018), with the intention of being used for the study of the cortical control of movement, and also as a task that might be used to evaluate BMI control algorithms. The purpose of the present study is to better characterize how this task is performed. What sort of control policies are used. Perhaps more deeply, what kind of errors are those policies trying to minimize? To address these questions, the authors simulate control-theory style models and compare with behavior. They do in both in monkeys and in humans.

These goals make sense as a precursor to future recording or BMI experiments. The primate motor-control field has long been dominated by variants of reaching tasks, so introducing this new task will likely be beneficial. This is not the first non-reaching task, but it is an interesting one and it makes sense to expand the presently limited repertoire of tasks. The present task is very different from any prior task I know of. Thus, it makes sense to quantify behavior as thoroughly as possible in advance of recordings. Understanding how behavior is controlled is, as the authors note, likely to be critical to interpreting neural data.

From this perspective - providing a basis for interpreting future neural results - the present study is fairly successful. Monkeys seem to understand the task properly, and to use control policies that are not dissimilar from humans. Also reassuring is the fact that behavior remains sensible even when task-difficulty become high. By 'sensible' I simply mean that behavior can be understood as seeking to minimize error: position, velocity, or (possibly) both, and that this remains true across a broad range of task difficulties. The authors document why minimizing position and minimizing velocity are both reasonable objectives. Minimizing velocity is reasonable, because a near-stationary cursor can't move far in six seconds. Minimizing position error is reasonable, because the trial won't fail if the cursor doesn't stray far from the center. This is formally demonstrated by simulating control policies: both objectives lead to control policies that can perform the task and produce realistic single-trial behavior. The authors also demonstrate that, via verbal instruction, they can induce human subjects to favor one objective over the other. These all seem like things that are on the 'need to know' list, and it is commendable that this amount of care is being taken before recordings begin, as it will surely aid interpretation.

Yet as a stand-alone study, the contribution to our understanding of motor control is more limited. The task allows two different objectives (minimize velocity, minimize position) to be equally compatible with the overall goal (don't fail the trial). Or more precisely, there exists a range of objectives with those two at the extreme. So it makes sense that different subjects might choose to favor different objectives, and also that they can do so when instructed. But has this taught us something about motor control, or simply that there is a natural ambiguity built into the task? If I ask you to play a game, but don't fully specify the rules, should I be surprised that different people think the rules are slightly different?

The most interesting scientific claim of this study is not the subject-to-subject variability; the task design makes that quite likely and natural. Rather, the central scientific result is the claim that individual subjects are constantly switching objectives (and thus control policies), such that the policy guiding behavior differs dramatically even on a single-trial basis. This scientific claim is supported by a technical claim: that the authors' methods can distinguish which objective is in use, even on single trials. I am uncertain of both claims.

Consider Figure 8B, which reprises a point made in Figure 1&3 and gives the best evidence for trial-to-trial variability in objective/policy. For every subject, there are two example trials. The top row of trials shows oscillations around the center, which could be consistent with position-error minimization. The bottom row shows tolerance of position errors so long as drift is slow, which could be consistent with velocity-error minimization. But is this really evidence that subjects were switching objectives (and thus control policies) from trial to trial? A simpler alternative would be a single control policy that does not switch, but still generates this range of behaviors. The authors don't really consider this possibility, and I'm not sure why. One can think of a variety of ways in which a unified policy could produce this variation, given noise and the natural instability of the system.

Indeed, I found that it was remarkably easy to produce a range of reasonably realistic behaviors, including the patterns that the authors interpret as evidence for switching objectives, based on a simple fixed controller. To run the simulations, I made the simple assumption that subjects simply attempt to match their hand position to oppose the cursor position. Because subjects cannot see their hand, I assumed modest variability in the gain, with a range from -1 to -1.05. I assumed a small amount of motor noise in the outgoing motor command. The resulting (very simple) controller naturally displayed the basic range of behaviors observed across trials (see Image 1)

Peer review image 1.

Some trials had oscillations around the screen center (zero), which is the pattern the authors suggest reflects position control. In other trials the cursor was allowed to drift slowly away from the center, which is the pattern the authors suggest reflects velocity control. This is true even though the controller was the same on every trial. Trial-to-trial differences were driven both by motor noise and by the modest variability in gain. In an unstable system, small differences can lead to (seemingly) qualitatively different behavior on different trials.

This simple controller is also compatible with the ability of subjects to adapt their strategy when instructed. Anyone experienced with this task likely understands (or has learned) that moving the hand slightly more than 'one should' will tend to shepherd the cursor back to center, at the cost of briefly high velocity. Using this strategy more sparingly will tend to minimize velocity even if position errors persist. Thus, any subject using this control policy would be able to adapt their strategy via a modest change in gain (the gain linking visible cursor position to intended hand position).

This model is simple, and there may be reasons to dislike it. But it is presumably a reasonable model. The nature of the task is that you should move your hand opposite where the cursor is. Because you can't see your hand, you will make small mistakes. Due to the instability of the system, those small mistakes have large and variable effects. This feature is likely common to other controllers as well; many may explicitly or implicitly blend position and velocity control, with different trials appearing more dominated by one versus the other. Given this, I think the study presents only weak evidence that individual subjects are switching their objective on individual trials. Indeed, the more parsimonious explanation may be that they aren't. While the study certainly does demonstrate that the control policy can be influenced by verbal instructions, this might be a small adjustment as noted above.

I thus don't feel convinced that the authors can conclusively tell us the true control policy being used by human and monkey subjects, nor whether that policy is mostly fixed or constantly switching. The data are potentially compatible with any of these interpretations, depending on which control-style model one prefers.

I see a few paths that the authors might take if they chose.<br /> --First, my reasoning above might be faulty, or there might be additional analyses that could rule out the possibility of a unified policy underlying variable behavior. If so, the authors may be able to reject the above concerns and retain the present conclusions. The main scientifically novel conclusion of the present study is that subjects are using a highly variable control policy, and switching on individual trials. If this is indeed the case, there may be additional analyses that could reveal that.<br /> --Second, additional trial types (e.g., with various perturbations) might be used as a probe of the control policy. As noted below, there is a long history of doing this in the pursuit system. That additional data might better disambiguate control policies both in general, and across trials.<br /> --Third, the authors might find that a unified controller is actually a good (and more parsimonious) explanation. Which might actually be a good thing from the standpoint of future experiments. Interpretation of neural data is likely to be much easier if the control policy being instantiated isn't in constant flux.

In any case, I would recommend altering the strength of some conclusions, particularly the conclusion that the presented methods can reliably discriminate amongst objectives/policies on individual trials. This is mentioned as a major motivation on multiple occasions, but in most of these instances, the subsequent analysis infers the objective only across trial (e.g., one must observe a scatterplot of many trials). By Figure 7, they do introduce a method for inferring the control policy on individual trials, and while this seems to work considerably better than chance, it hardly appears reliable.

In this same vein I would suggest toning down aspects of the Introduction and Discussion. The Introduction in particular is overly long, and tries to position the present study as unique in ways that seem strained. Other studies have built links between human behavior, monkey behavior, and monkey neural data (for just one example, consider the corpus of work from the Scott lab that includes Pruszynski et al. 2008 and 2011). Other studies have used highly quantitative methods to infer the objective function used by subjects (e.g. Kording and Wolpert 2004). The very issue that is of interest in the present study - velocity-error-minimization versus position-error-minimization - has been extensively addressed in the smooth pursuit system. That field has long combined quantitative analyses of behavior in humans and monkeys, along with neural recordings. Many pursuit experiments used strategies that could be fruitfully employed to address the central questions of the present study. For example, error stabilization was important for dissecting the control policy used by the pursuit system. By artificially stabilizing the error (position or velocity) at zero, or at some other value, one can determine the system's response. The classic Rashbass step (1961) put position and velocity errors in opposition, to see which dominates the response. Step and sinusoidal perturbations were useful in distinguishing between models, as was the imposition of artificially imposed delays. The authors note the 'richness' of the behavior in the present task, and while one could say the same of pursuit, it was still the case that specific and well-thought through experimental manipulations were pretty critical. It would be better if the Introduction considered at least some of the above-mentioned work (or other work in a similar vein). While most would agree with the motivations outlined by the authors - they are logical and make sense - the present Introduction runs the risk of overselling the present conclusions while underselling prior work.

Reviewer #1 (Public review):

Summary:

The manuscript discusses the role of phosphorylated ubiquitin (pUb) by PINK1 kinase in neurodegenerative diseases. It reveals that elevated levels of pUb are observed in aged human brains and those affected by Parkinson's disease (PD), as well as in Alzheimer's disease (AD), aging, and ischemic injury. The study shows that increased pUb impairs proteasomal degradation, leading to protein aggregation and neurodegeneration. The authors also demonstrate that PINK1 knockout can mitigate protein aggregation in aging and ischemic mouse brains, as well as in cells treated with a proteasome inhibitor. While this study provided some interesting data, several important points should be addressed before being further consideration.

Strengths:

(1) Reveals a novel pathological mechanism of neurodegeneration mediated by pUb, providing a new perspective on understanding neurodegenerative diseases.

(2) The study covers not only a single disease model but also various neurodegenerative diseases such as Alzheimer's disease, aging, and ischemic injury, enhancing the breadth and applicability of the research findings.

Comments on revisions:

This study, through a systematic experimental design, reveals the crucial role of pUb in forming a positive feedback loop by inhibiting proteasome activity in neurodegenerative diseases. The data are comprehensive and highly innovative. However, some of the results are not entirely convincing, particularly the staining results in Figure 1.

In Figure 1A, the density of DAPI staining differs significantly between the control patient and the AD patient, making it difficult to conclusively demonstrate a clear increase in PINK1 in AD patients. Quantitative analysis is needed. In Fig 1C, the PINK1 staining in the mouse brain appears to resemble non-specific staining.