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 #2 (Public review):

Summary:

The authors show that in E. coli the initiator protein DnaA oscillates post-translationally: its activity rises and peaks exactly when DNA replication begins, even if dnaA transcription is held constant. To explain this, they propose an "extrusion" mechanism in which nucleoid-associated proteins such as H-NS, whose amount grows with cell volume, dislodge DnaA from chromosomal binding sites; modelling and H-NS perturbations reproduce the observed drop in initiation mass and extra initiations seen after dnaA shut-down. Together, the data and model link biomass growth to replication timing through chromosome-driven, post-translational control of DnaA, filling gaps left by classic titration and ATP/ADP-switch models.

Strengths:

(1) Introduces an "extrusion" model that adds a new post-translational layer to replication control and explains data unexplained by classic titration or ATP/ADP-switch frameworks.

(2) A major asset of the study is that it bridges the longstanding gap between DnaA oscillations and DNA-replication initiation, providing direct single-cell evidence that pulses of DnaA activity peak exactly at the moment of initiation across multiple growth conditions and genetic perturbations.

(3) A tunable dnaA strain and targeted H-NS manipulations shift initiation mass exactly as the model predicts, giving model-driven validation across growth conditions.

(4) A purpose-built Psyn66 reporter combined with mRNA-FISH captures DnaA-activity pulses with cell-cycle resolution, providing direct, compelling data.

Weaknesses:

(1) What happens to the (C+D) period and initiation time as the dnaA mRNA level changes? This is not discussed in the text or figure and should be addressed.

(2) It is unclear what is meant by "relative dnaA mRNA level." Relative to what? Wild-type expression? Maximum expression? This should be explicitly defined.

(3) It would be helpful to provide some intuition for why an increase in dnaA mRNA level leads to a decrease in initiation mass per ori and an increase in oriC copy number.

(4) The titration and switch models do not explicitly include dnaA mRNA in the dynamics of DnaA protein. Yet, in Figure 2G, initiation mass is shown to decrease linearly with dnaA mRNA level in these models. How was dnaA mRNA level represented or approximated in these simulations?

(5) Is Schaechter's law (i.e., exponential scaling of average cell size with growth rate) still valid under the different dnaA mRNA expression conditions tested?

(6) The manuscript should explain more explicitly how the extrusion model implements post-translational control of DnaA and, in particular, how this yields the nonlinear drop in relative initiation mass versus dnaA mRNA seen in Fig. 6E. Please provide the governing equation that links total DnaA, the volume-dependent "extruder" pool, and the threshold of free DnaA at initiation, and show-briefly but quantitatively-how this equation produces the observed concave curve.

(7) Does this Extrusion model give well well-known adder per origin, i.e., initiation to initiation is an adder.

(8) DnaA protein or activity is never measured; mRNA is treated as a linear proxy. Yet the authors' own narrative stresses post-translational (not transcriptional) control of DnaA. Without parallel immunoblots or activity readouts, it is impossible to know whether a six-fold mRNA increase truly yields a proportional rise in active DnaA.

(9) Figure 2 infers both initiation mass and oriC copy number from bulk measurements (OD₆₀₀ per cell and rifampicin-cephalexin run-out) instead of measuring them directly in single cells. Any DnaA-dependent changes in cell size, shape, or antibiotic permeability could skew these bulk proxies, so the plotted relationships may not accurately reflect true initiation events.

Comments on revisions:

The authors have addressed all of my previous concerns, questions, and suggestions sufficiently.

Reviewer #2 (Public review):

Summary:

This paper introduces a new function within the Fam3Pro package that addresses the problem of breaking loops in family structures. When a loop is present, standard genotype peeling algorithms fail, as they cannot update genotypes correctly. The solution is to break these loops, but until now, this could not be done automatically and optimally.

The manuscript provides useful background on constructing graphs and trees from family data, detecting loops, and determining how to break them optimally for the case of no loops with multiple matings. For this situation, the algorithm switches between Prim's algorithm and a simple greedy approach and provides a solution. However, here, an optimal solution is not guaranteed.

The theoretical foundations-such as the representation of families as graphs or trees and the identification of loops-are clearly explained and well-illustrated with example pedigrees. The practical utility of the new function is demonstrated by applying it to a dataset containing families with loops.

This work has the potential for considerable impact, especially for medical researchers and individuals from families with loops. These families could previously not be analysed automatically and optimally. The new function changes that, enabling risk assessments and genetic calculations that were previously infeasible.

Strengths:

(1) The theoretical explanation of graphs, trees, and loop detection is clear and well-structured.

(2) The idea of switching between algorithms is original and appears effective.

(3) The function is well implemented, with minimal additional computational cost.

Weaknesses:

(1) In cases with multiple matings, the notion of a "close-to-optimal" solution is not clearly defined. It would be helpful to explain what this means-whether it refers to empirical performance, theoretical bounds, or something else.

(2) In the example pedigree discussed, multiple options exist for breaking loops, but it is unclear which is optimal.

(3) No example is provided where the optimal solution is demonstrably not reached.

(4) It is also unclear whether the software provides a warning when the solution might not be optimal.

This leads to political division, mistrust, confusion, and harm to the individuals who have fake content being made about them.

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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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:

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 #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 #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 #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 #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 #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 #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 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 #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 #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 #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:

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 #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 #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 #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 #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 #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 #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 #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 #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 #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.

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 #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 #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:

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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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):

Summary:

Kunkel et al aim to answer a fundamental question: Do placebo and nocebo effects differ in magnitude or longevity? To address this question, they used a powerful within-participants design, with a very large sample size (n=104), in which they compared placebo and nocebo effects - within the same individuals - across verbal expectations, conditioning, testing phase, and a 1-week follow-up. With elegant analyses, they establish that different mechanisms underlie the learning of placebo vs nocebo effects, with the latter being acquired faster and extinguished slower. This is an important finding for both the basic understanding of learning mechanisms in humans and for potential clinical applications to improve human health.

Strengths:

Beyond the above - the paper is well-written and very clear. It lays out nicely the need for the current investigation and what implications it holds. The design is elegant, and the analyses are rich, thoughtful, and interesting. The sample size is large which is highly appreciated, considering the longitudinal, in-lab study design. The question is super important and well-investigated, and the entire manuscript is very thoughtful with analyses closely examining the underlying mechanisms of placebo versus nocebo effects.

Comments on revisions:

The authors have addressed all of my concerns and comments - one point for them to verify is that indeed analyses that have not been preregistered will be flagged as such. The provided pre-registration link doesn't specify much about the analysis plans and specific tests used.

Reviewer #2 (Public Review):

Summary:

The authors develop a highly detailed pipeline to analyze hemodynamic signals from in vivo two-photon fluorescence microscopy. This includes motion correction, segmentation of the vascular network, diameter measurements across time, mapping neuronal position relative to the vascular network, and analyzing vascular network properties (interactions between different vascular segments). For the segmentation, the authors use a Convolution Neural Network to identify vessel (or neural) and background pixels and train it using ground truth images based on semi-automated mapping followed by human correction/annotation. Considerable processing was done on the segmented images to improve accuracy, extract vessel center lines, and compute frame-by-frame diameters. The model was tested with artificial diameter increases and Gaussian noise and proved robust to these manipulations.

Network-level properties include Assortativity - a measure of how similar a vessel's response is to nearby vessels - and Efficiency - the ease of flow through the network (essentially, the combined resistance of a path based on diameter and vessel length between two points).

Strengths:

This is a very powerful tool for cerebral vascular biologists as many of these tasks are labor intensive, prone to subjectivity, and often not performed due to the complexity of collecting and managing volumes of vascular signals. Modelling is not my specialty so I cannot speak too specifically, but the model appears to be well-designed and robust to perturbations. It has many clever features for processing the data.

The authors rightly point out that there is a real lack in the field of knowledge of vascular network activity at single-vessel resolution. Network anatomy has been studied, but hemodynamics are typically studied either with coarse resolution or in only one or a few vessels at a time. This pipeline has the potential to change that.

[Editors' note: this work has been through three rounds of revisions, and most recently the authors have added caveats to the discussion. This version of the paper has been assessed by the editors and the weaknesses identified previously remain with earlier versions of the work.]

Reviewer #2 (Public review):

Summary:

Gu and Liang et. al investigated how auditory information is mapped and transformed as it enters and exits a auditory cortex. They use anterograde transsynaptic tracers to label and perform calcium imaging of thalamorecipient neurons in A1 and retrograde tracers to label and perform calcium imaging of corticothalamic output neurons. They demonstrate a degradation of tonotopic organization from the input to output neurons.

Strengths:

The experiments appear well executed, well described, and analyzed.

Weaknesses:

(1) Given that the CT and TR neurons were imaged at different depths, the question as to whether not these differences could otherwise be explained by layer-specific differences is still not 100% resolved. Control measurements would be needed either by recording 1) CT neurons upper layers 2) TR in deeper layers 3) non-CT in deeper layers and/or 4) non-TR in upper layers.

(2) What percent of the neurons at the depths being are CT neurons? Similar questions for TR neurons?

(3) V-shaped, I-shaped, or O-shaped is not an intuitively understood nomenclature, consider changing. Further, the x/y axis for Figure 4a is not labeled, so it's not clear what the heat maps are supposed to represent.

(4). Many references about projection neurons and cortical circuits are based on studies from visual or somatosensory cortex. Auditory cortex organization is not necessarily the same as other sensory areas. Auditory cortex references should be used specifically, and not sources reporting on S1, V1.

Comments on revisions:

The authors have fully addressed my concerns.

Reviewer #2 (Public review):

Summary:

In this paper, Chittajallu and colleagues present compelling evidence that mu opioid receptor (MOR) activation can potentiate synaptic neurotransmission in a medial habenula to interpeduncular nucleus (mHb-IPN) subcircuit. While, projections from mHb tachykinin 1 (Tac1) neurons onto lateral IPN neurons show a canonical opioid-induced synaptic depression in glutamate release, excitatory neurotransmission in mHb choline acetyltransferase (ChAT) projections to the rostral IPN is potentiated by opioids. This function emerges around age P27 in mice, when MOR expression in the IPN peaks.

Strengths:

Carefully executed electrophysiological experiments with appropriate controls. Interesting description of a neurodevelopmental change in the effects of opioids on mHb-IPN signaling.

Weaknesses:

A minor concern is that the genetic strategy used to target the mHb-IPN pathway (constitutive ChR2 expression in all ChAT+ and Tac1+ neurons) might not specifically target this projection. Future studies are needed to examine the precise mechanism whereby MOR signaling can potentiate glutamatergic neurotransmission in ChAT+ MHb-IPN projections."

Reviewer #2 (Public review):

Summary:

The molecular mechanisms underlying ciliogenesis are not well understood. Previously, work from the same group (Wu et al., 2018) identified myosin-Va as an important protein in transporting preciliary vesicles to the mother vesicles, allowing for initiation of ciliogenesis. The exocyst complex has previously been implicated in ciliogenesis and protein trafficking to cilia. Here, Lin et al. investigate the role of exocyst complex protein EXOC6A in cilia formation. The authors find that EXOC6A localizes to preciliary vesicles, ciliary vesicles, and the ciliary sheath. EXOC6A colocalizes with Myo-Va in the ciliary vesicle and the ciliary sheath, and both proteins are removed from fully assembled cilia. EXOC6A is not required for Myo-Va localization, but Myo-VA and EHD1 are required for EXOC6A to localize in ciliary vesicles. The authors propose that EXOC6A vesicles continually remodel the cilium: FRAP analysis demonstrates that EXOC6A is a dynamic protein, and live imaging shows that EXOC6A fuses with and buds off from the ciliary membrane. Loss of EXOC6A reduces, but does not eliminate, the number of cilia formed in cells. Any cilia that are still present are structurally abnormal, with either bent morphologies or the absence of some transition zone proteins. Overall, the analyses and imaging are well done, and the conclusions are well supported by the data. The work will be of interest to cell biologists, especially those interested in centrosomes and cilia.

Strengths:

The TEM micrographs are of excellent quality. The quality of the imaging overall is very good, especially considering that these are dynamic processes occurring in a small region of the cell. The data analysis is well done and the quantifications are very helpful. The manuscript is well-written and the final figure is especially helpful in understanding the model.

Weaknesses:

Additional information about the functional and mechanistic roles of EXOC6A would improve the manuscript greatly.

Reviewer #2 (Public review):

Summary:

In this study, Gamen et al. investigated the roles of hypoxia and HIF1a signaling in regulating epicardial function during cardiac development and neonatal heart regeneration. The authors identified hypoxic regions in the epicardium during development and demonstrated that genetic and pharmacological stabilization of HIF1a during neonatal heart injury prolonged epicardial activation, preserved myocardium, enhanced infarct resolution, and maintained cardiac function beyond the normal postnatal regenerative window.

Strengths:

HIF1a signaling was manipulated in an epicardium-specific manner using appropriate genetic tools.

Weaknesses:

Some conclusions still need clarification.

Comments on revisions:

(1) The authors' comment on the partial overlap of HP1 and HIF1a IF signals (HIF1a is highly unstable ... broader regions of hypoxia) is reasonable and would help readers interpret the data if included in the text describing Fig. 1.

(2) The conclusion regarding WT1+ cells in Fig. 2a and b remains unclear. Both panels display larger and smaller magenta cells, and when all are taken into account, the overall number does not appear substantially different. Additional clarification is needed on how the quantification was performed.

(3) Regarding Figure 6-figure supplement 1c, it seems difficult to conclude the endothelial identity of WT1+ cells based on EMCN staining, as the markers do not overlap. The authors note that WT1 is upregulated in endothelial cells, but this has been reported in the context of injury, which differs from the context of the present study involving Molidustat.

Decision simplifies access to legal abortion for women and girls who became pregnant due to rape by removing the need for proof or judicial approval by relying international rights standards it holds the state accountable for ensuring timely and unimpeded access to abortion service.

Reviewer #2 (Public review):

Summary:

The authors meticulously characterized EC-specific Tgfbr1, Tgfbr2, or double knockout in the retina, demonstrating through convincing immunostaining data that loss of TGF-β signaling disrupts retinal angiogenesis and choroidal neovascularization. Compared to other genetic models (Fzd4 KO, Ndp KO, VEGF KO), the Tgfbr1/2 KO retina exhibits the most severe immune cell infiltration. The authors proposed that TGF-β signaling loss triggers vascular inflammation, attracting immune cells - a phenotype specific to CNS vasculature, as non-CNS organs remain unaffected.

Strengths:

The immunostaining results presented are clear and robust. The authors performed well-controlled analyses against relevant mouse models. snRNA-seq corroborates immune cell leakage in the retina and vascular inflammation in the brain.

Comments on revisions:

The authors have revised the manuscript and addressed all my questions.

Reviewer #2 (Public review):

Summary:

This study investigates whether individuals can learn to adopt egalitarian norms that incur a personal monetary cost, such as rejecting offers that benefit them more than the giver (advantageous inequitable offers). While these behaviors are uncommon, two experiments aim to demonstrate that individuals can learn to reject such offers by observing a "teacher" who follows these norms. The authors use computational modelling to argue that learners adopt these norms through a sophisticated process, inferring the latent structure of the teacher's preferences, akin to theory of mind.

Strengths:

This paper is well-written and tackles an important topic relevant to social norms, morality, and justice. The findings are promising (though further control conditions are necessary to support the conclusions). The study is well-situated in the literature, with a clever experimental design and a computational approach that may offer insights into latent cognitive processes. In the revision, the authors clarified some questions related to the initial submission.

Weaknesses:

Despite these strengths, I remain unconvinced that the current evidence supports the paper's central claims. Below, I outline several issues that, in my view, limit the strength of the conclusions.

(1) Experimental Design and Missing Control Condition:

The authors set out to test whether observing a "teacher" who is averse to advantageous inequity (Adv-I) will affect observers' own rejection of Adv-I offers. However, I think the design of the task lacks an important control condition needed to address this question. At present, participants are assigned to one of two teachers: DIS or DIS+ADV. Behavioral differences between these groups can only reveal relative differences in influence; they cannot establish whether (and how) either teacher independently affects participants' own behavior. For example, a significant difference between conditions can emerge even if participants are only affected by the DIS teacher and are not affected at all by the DIS+ADV teacher. What is crucially missing here is a no-teacher control condition, which can then be compared with each teacher condition separately. This control condition would also control for pure temporal effects unrelated to teacher influence (e.g., increasing Adv-I rejections due to guilt build-up).

While this criticism applies to both experiments, it is especially apparent in Experiment 2. As shown in Figure 4, the interaction for 10:90 offers reflects a decrease in rejection rates following the DIS teacher, with no significant change following the DIS+ADV teacher. Ignoring temporal effects, this pattern suggests that participants may be learning NOT to reject from the DIS teacher, rather than learning to reject from the DIS+ADV teacher. On this basis, I do not see convincing evidence that participants' own choices were shaped by observing Adv-I rejections.

In the Discussion, the authors write that "We found that participants' own Adv-I-averse preferences shifted towards the preferences of the Teacher they just observed, and the strength of these contagion effects related to the degree of behavior change participants exhibited on behalf of the Teachers, suggesting that they internalized, at least somewhat, these inequity preferences." However, there is no evidence that directly links the degree of behaviour change (on the teacher's behalf) to contagion effects (own behavioural change). I think there was a relevant analysis in the original version, but it was removed from the current version.

(2) Modelling Efforts: The modelling approach is underdeveloped. The identification of the "best model" lacks transparency, as no model-recovery results are provided. Additionally, behavioural fits for the losing models are not shown, leaving readers in the dark about where these models fail. Readers would benefit from seeing qualitative/behavioural patterns that favour the winning model. Moreover, the reinforcement learning (RL) models used are overly simplistic, treating actions as independent when they are likely inversely related. For example, the feedback that the teacher would have rejected an offer provides evidence that rejection is "correct" but also that acceptance is "an error," and the latter is not incorporated into the modelling. In other words, offers are modelled as two-armed bandits (where separate values are learned for reject and accept actions), but the situation is effectively a one-armed bandit (if one action is correct, the other is mistaken). It is unclear to what extent this limitation affects the current RL formulations. Can the authors justify/explain their reasoning for including these specific variants? The manuscript only states Q-values for reject actions, but what are the Q-values for accept actions? This is unclear.

In Experiment 2, only the preferred model is capable of generalization, so it is perhaps unsurprising that this model "wins." However, this does not strongly support the proposed learning mechanism, lacking a comparison with simpler generalizing mechanisms (see following comments).

(3) Conceptual Leap in Modelling Interpretation: The distinction between simple RL models and preference-inference models seems to hinge on the ability to generalize learning from one offer to another. Whereas in the RL models, learning occurs independently for each offer (hence no cross-offer generalization), preference inference allows for generalization between different offers. However, the paper does not explore "model-free" RL models that allow generalization based on the similarity of features of the offers (e.g., payment for the receiver, payment for the offer-giver, who benefits more). Such models are more parsimonious and could explain the results without invoking a theory of mind or any modelling of the teacher. In such model versions, a learner acquires a functional form that allows prediction of the teacher's feedback based on offer features (e.g., linear or quadratic weighting). Because feedback for an offer modulates the parameters of this function (feature weights), generalization occurs without necessarily evoking any sophisticated model of the other person. This leaves open the possibility that RL models could perform just as well or even outperform the preference learning model, casting doubt on the authors' conclusions.

Of note: even the behaviourists knew that when Little Albert was taught to fear rats, this fear generalized to rabbits. This could occur simply because rabbits are somewhat similar to rats. But this doesn't mean Little Albert had a sophisticated model of animals that he used to infer how they behave.

In their rebuttal letter, the authors acknowledge these possibilities, but the manuscript still does not explore or address alternative mechanisms.

(4) Limitations of the Preference-Inference Model: The preference-inference model struggles to capture key aspects of the data, such as the increase in rejection rates for 70:30 DI offers during the learning phase (e.g., Fig. 3A, AI+DI blue group). This is puzzling. Thinking about this, I realized the model makes quite strong, unintuitive predictions which are not examined. For example, if a subject begins the learning phase rejecting the 70:30 offer more than 50% of the time (meaning the starting guilt parameter is higher than 1.5), then, over learning, the tendency to reject will decrease to below 50% (the guilt parameter will be pulled down below 1.5). This is despite the fact that the teacher rejects 75% of the offers. In other words, as learning continues, learners will diverge from the teacher. On the other hand, if a participant begins learning by tending to accept this offer (guilt < 1.5), then during learning, they can increase their rejection rate but never above 50%. Thus, one can never fully converge on the teacher. I think this relates to the model's failure in accounting for the pattern mentioned above. I wonder if individuals actually abide by these strict predictions. In any case, these issues raise questions about the validity of the model as a representation of how individuals learn to align with a teacher's preferences (given that the model doesn't really allow for such an alignment).

In their rebuttal letter, the authors acknowledged these anomalies and stated that they were able to build a better model (where anomalies are mitigated, though not fully eliminated). But they still report the current model and do not develop/discuss alternatives. A more principled model may be a Bayesian model where participants learn a belief distribution (rather than point estimates) regarding the teacher's parameters.

(5) Statistical Analysis: The authors state in their rebuttal letter that they used the most flexible random effect structure in mixed-effects models. But this seems not to be the case in the model reported in Table SI3 (the very same model was used for other analyses too). Indeed, here it seems only intercepts are random effects. This left me confused about which models were used.

Reviewer #2 (Public review):

Summary:

This important study by Turner et al., examines the functional role of a sparse but unique population of neurons in the cortex that express Nitric oxide synthase (Nos1). To do this, they pharmacolologically ablate these neurons in focal region of whisker related primary somatosensory (S1) cortex using a saponin-Substance P conjugate. Using widefield and 2-photon microscopy, as well as field recordings, they examine the impact of this cell specific lesion on blood flow dynamics and neuronal population activity. Within primary somatosensory cortex after Nos1 ablation, they find changes in neural activity patterns, decreased delta band power, reduced sensory evoked changes in blood flow (specifically eliminates the sustained blood flow change after stimulation) and decreased vasomotion.

Strengths:

This was a technically challenging study and the experiments were executed in an expert manner. The manuscript was well written and I appreciated the cartoon summary diagrams included in each figure. The analysis was rigorous and appropriate. Their discovery that Nos1 neurons can have significant effects on blood flow dynamics and neural activity is quite novel that should seed many follow up, mechanistic experiments to explain this phenomenon. The conclusions were justified by the convincing data presented.

Weaknesses:

I did not find any major flaws with the study. I originally noted some potential issues with the authors' characterization of the lesion and its extent, but that has been resolved in the revised manuscript.

Comments on revisions:

The authors have thoughtfully addressed the relatively minor concerns I had originally raised. Congratulations to the authors for producing this important paper.

Reviewer #2 (Public review):

eQTLs have emerged as a method for interpreting GWAS signals. However, some GWAS signals are difficult to explain with eQTLs. In this paper, the authors demonstrated that caQTLs can explain these signals. This suggests that for GWAS signals to actually lead to disease phenotypes, they must be accessible in the chromatin. This implies that for GWAS signals to translate into disease phenotypes, they need to be accessible within the chromatin.

However, fundamentally, caQTLs, like GWAS, have the limitation of not being able to determine which genes mediate the influence on disease phenotypes. This limitation is consistent with the constraints observed in this study.

(1) Reproducibility / Methods. The concrete numbers provided in the authors' response (e.g., 20 YRI LCL ATAC‑seq samples used only for peak discovery; caQTL mapping restricted to 100 GBR LCLs; 99,320 ATAC peaks tested vs 14,872 genes for eQTL; 373 European RNA‑seq samples, with clarification of overlap) do not appear to be reflected in the Methods. These specifics should be incorporated directly into the Methods sections.

(2) Experimental evidence demonstrating transcription factor binding at representative caQTL peaks would strengthen causal interpretation of these loci.

(3) Tissue/cell‑type specificity of caQTLs: Prior work supports that chromatin‑level effects are broadly shared across cellular states, whereas expression effects are more context‑specific; thus, caQTLs are generally less "state‑specific" than eQTLs. However, this does not imply equivalence across distinct cell types: caQTLs derived from different cell types may yield different results, particularly where accessibility is cell‑type restricted.

Reviewer #2 (Public review):

Summary:

This manuscript reports high-resolution functional MRI data and MEG data revealing additional mechanistic information about an established paradigm studying how foveal regions of primary visual cortex (V1) are involved in processing peripheral visual stimuli. Because of the retinotopic organization of V1, peripheral stimuli should not evoke responses in the regions of V1 that represent stimuli in the center of the visual field (the fovea). However, functional MRI responses in foveal regions do reflect the characteristics of peripheral visual stimuli - this is a surprising finding first reported in 2008. The present study uses fMRI data with sub-millimeter resolution to study the how responses at different depths in the foveal gray matter do or don't reflect peripheral object characteristics during 2 different tasks: one in which observers needed to make detailed judgments about object identity, and one in which observers needed to make more coarse judgments about object orientation. FMRI results reveal interesting and informative patterns in these two conditions. A follow-on MEG study yields information about the timing of these responses. Put together, the findings settle some questions in the field and add new information about the nature of visual feedback to V1.

Strengths:

(1) Rigorous and appropriate use of "laminar fMRI" techniques.

(2) The introduction does an excellent job of contextualizing the work.

(3) Control experiments and analyses are designed and implemented well

Weaknesses:

(1) The use of the term "low order" to describe object orientation is potentially confusing. During review, the authors considered this issue and responded that they would continue with the use of the term low-order to describe object orientation because a low-pass spatial frequency filter would provide object orientation information. This is certainly a reasonable perspective; nonetheless, this reviewer thinks spatial frequencies that low are not readily represented by neurons in early visual cortex and it is common to use "low-order" to refer to features extracted in early visual areas, so I think this causes confusion.

(2) The methods contain a nice description of the methods for "correcting the vascular-related signals". I'm guessing this is the method that removed, e.g., 22% of foveal voxels (previous paragraph), but it's not entirely clear whether the voxel selection methods described in the "correcting the vascular-related signals" are describing the same processing step referred to in the previous paragraph as "a portion of voxels was removed based on large vein distribution".

(3) It is quite difficult to perform laminar analyses across multiple visual areas because distortion compensation is not perfect and registration of functional to anatomical data will always be a bit better in some places and a bit worse in others. An ideal manuscript would include some images showing registration quality in V1, LOC, and IPS regions for a few different participants, or include some kind of quality metric indicating the confidence in depth assignments in different regions.

(4) For the decoding analysis, it would be helpful to have more information about how samples were defined for each condition -- were the beta values for entire blocks used as samples for each condition, or were separate timepoints during a block used in the SVM as repeated samples for each condition?

Reviewer #2 (Public review):

Summary:

This important study shows that betulin from wild peach trees disrupts neural signaling in aphids by targeting a conserved site in the insect GABA receptor. The authors present a nicely integrated set of molecular, physiological, and genetic experiments to establish the compound's species-specific mode of action. While the mechanistic evidence is solid, the manuscript would benefit from a broader discussion of evolutionary conservation and potential off-target ecological effects.

Strengths:

The main strengths of the study lie in its mechanistic clarity and experimental rigor. The identification of a betulin-binding single threonine residue was supported by (1) site-directed mutagenesis and (2) functional assays. These experiments strongly support the specificity of action. Furthermore, the use of comparative analyses between aphids and fruit flies demonstrates an important effort to explore species specificity, and the integration of quantitative data further enhances the robustness of the conclusions.

Comments on revisions:

The revision satisfactorily addresses my concerns on evolutionary context, methodological clarity, and ecological risk.

Reviewer #2 (Public review):

Summary:

In classical models of signaling network, the signaling activity increases monotonically with the ligand affinity. However, certain receptors prefer ligands of intermediate affinity. In the paper, the authors present a new minimal model to derive generic conditions for ligand specificity. In brief, this requires multi-site phosphorylation and that high-affinity complexes be more prone to degrade. This particular type of kinetic discrimination allows to overcome equilibrium constraints.

Strengths:

The model is simple, and it adds only a few parameters to classical generic models. They moreover vary these additional parameters in ranges based on experimental observations. They explain how the introduction of these new parameters is essential to ligand specificity. Their model quantitatively reproduces the ligand specificity of a certain receptor. They finally provide testable prediction.

Weaknesses:

The naming of multiple variables as activity without precise definitions may be confusing to readers.

Comments on revisions:

I thank the authors for addressing my comments. One point remains regarding the naming of multiple variables as activity. Besides using other words, the authors may consider giving precise definitions of terms, e.g. by writing "We define kinase activity as the phosphorylation rate $\omega=k_p\tau$." A connection that appears only at line 204 in the present manuscript.

Reviewer #2 (Public review):

Summary:

Cupollilo et al. investigate the properties of hippocampal CA3 neurons that express the immediate early gene cFos in response to a single foot shock. They compare ex-vivo the electrophysiological properties of these "engram neurons" labeled with two different cFos promoter-driven green markers: Their new virally delivered tool FLEN labels neurons 2-6 h after activity, while RAM contains additional enhancers and peaks considerably later (>24 h). Since the fraction of labeled CA3 cells is comparable with both constructs, it is assumed (but not tested) that they label the same population of activated neurons at different time points. Both FLEN+ and RAM+ neurons in CA3 receive more synaptic inputs compared to non-expressing control neurons, which could be a causal factor for cFos activation, or a very early consequence thereof. Frequency facilitation and E/I ratio of mossy fiber inputs were also tested, but are not different in both cFos+ groups of neurons. One day after foot shock, RAM+ neurons are more excitable than RAM- neurons, suggesting a slow increase in excitability as a major consequence of cFos activation.

Strengths:

The study is conducted to high standards and contributes significantly to our understanding of memory formation and consolidation in the hippocampus. Modifications of intrinsic neuronal properties seem to be more salient than overall changes in the total number of (excitatory and inhibitory) inputs, although a switch in the source of the synaptic inputs would not have been detected by the methods employed in this study

Weaknesses:

The new tool FLEN is not quantitatively compared to e.g. the TetTag reporter mouse. Nevertheless, the fluorescent images of FLEN+ neurons are quite convincing.

Reviewer #2 (Public review):

Summary:

Using the theory of efficient coding, the authors study how neural gains may be adjusted to optimize information transmission by noisy neural populations while minimizing metabolic cost, under the assumption that other aspects of neural activity (i.e. tuning) are determined by the computation performed by the network.

The manuscript first presents mathematical results for the general case where the computational goals of the neural population are not specified (the computation is implicit in the assumed tuning curves). It then develops the theory for a specific probabilistic coding scheme. The general theory provides an explanation for firing rate homeostasis at the level of neural clusters with firing rate heterogeneity within clusters. The specific application further explains stimulus-specific adaptation in visual cortex.

The mathematical derivations, simulations and application to visual cortex data are solid as far as I can tell.

This remains a highly technical manuscript although the authors have improved the clarity of presentation of the general theory (which is the bulk of the work presented) and better motivated/explained modeling assumptions and choices. In the second part, the manuscript focuses on a specific code (homeostatic DDC) showing that this can be implemented by divisive normalization and can explain stimulus-specific adaptation.

Strengths:

The problem of efficient coding is a long-standing and important one. This manuscript contributes to that field by proposing a theory of efficient coding through gain adjustments, independent of the computational goals of the system. The main assumption, and insight, is that computational goals and efficiency can be in some sense factorized: tuning curve shapes are determined by the computational goal, whereas gains can be adjusted to optimize transmission of information.

One key result is a normative explanation for firing rate homeostasis at the level of neural clusters (groups of neurons that perform a similar computation) with firing rate heterogeneity within each cluster. Both phenomena are widely observed, and reconciling them under one theory is important.

The mathematical derivations are thorough. Although the model of neural activity is artificial, the authors make sure to include many aspects of cortical physiology, while also keeping the models quite general.

Section 2.5 derives the conditions in which homeostasis would be near-optimal in cortex, which appear to be consistent with many empirical observations in V1. This indicates that homeostasis in V1 might be indeed a close to optimal solution to code efficiently in the face of noise.

The application to the data of Benucci et al 2013 is the first to offer a normative explanation of stimulus-specific adaptation in V1.

The novelty and significance of the work are presented clearly in the newly extended Introduction and Discussion.

Weaknesses:

The manuscript remains hard to read. The general theory occupies most of the manuscript, as needed to convey it fully. But as a result the second part on homeostatic DDC and adaptation is somewhat underdeveloped and risks having less visibility than it might deserve.

The paper Benucci et al 2013 shows that homeostasis holds for some stimulus distributions, but not others i.e. when the 'adapter' is present too often. This manuscript, like the Benucci paper, discards those datasets. But from a theoretical standpoint, it seems important to consider why that would be the case, and if it can be predicted by the theory proposed here. The authors now acknowledge this limitation in the Discussion.

Reviewer #3 (Public review):

Summary:

The authors convincingly demonstrate that a population of CCK+ spinal neurons in the deep dorsal horn express the G protein coupled estrogen receptor GPR30 to modulate pain sensitivity in the chronic constriction injury (CCI) model of neuropathic pain in mice. Using complementary pharmacological and genetic knockdown experiments they convincingly show that GPR30 inhibition or knockdown reverses mechanical, tactile and thermal hypersensitivity, conditioned place aversion, and c-fos staining in the spinal dorsal horn after CCI. They propose that GPR30 mediates an increase in postsynaptic AMPA receptors after CCI using slice electrophysiology which may underlie the increased behavioral sensitivity. They then use anterograde tracing approaches to show that CCK and GPR30 positive neurons in the deep dorsal horn may receive direct connections from primary somatosensory cortex. Chemogenetic activation of these dorsal horn neurons proposed to be connected to S1 increased nociceptive sensitivity in a GPR30 dependent manner. Overall, the data are very convincing and the experiments are well conducted and adequately controlled. However, the proposed model of descending corticospinal facilitation of nociceptive sensitivity through GPR30 in a population of CCK+ neurons in the dorsal horn is not fully supported.

Strengths:

The experiments are very well executed and adequately controlled throughout the manuscript. The data are nicely presented and supportive of a role for GPR30 signaling in the spinal dorsal horn influencing nociceptive sensitivity following CCI. The authors also did an excellent job of using complementary approaches to rigorously test their hypothesis.

Weaknesses:

The primary weakness in this manuscript involves overextending the interpretations of the data to still propose a role for corticospinal descending facilitation. While the viral tracing demonstrates a potential connection between S1 and CCK+ or GPR30+ spinal neurons, no direct evidence is provided for S1 in facilitating any activity of these neurons in the dorsal horn.

Comments on the latest version:

The authors did an excellent job addressing many of the critiques raised. Despite acknowledging that a direct functional corticospinal projection to CCK/GPR30+neurons is not supported by the data and revising the title, these claims still persist throughout the manuscript. Manipulating gene expression or the activity of postsynaptic neurons through a trans-synaptic labeling strategy does not directly support any claim that those upstream neurons are directly modulating spinal neurons through the proposed pathway. Indeed they might, but that is not demonstrated here.

Reviewer #2 (Public review):

Summary:

This is a very interesting study from Vandendoren and colleagues examining the role of PVN oxytocin neurons during thermoregulatory behaviors, in particular during thermoregulatory huddling. The findings are important and compelling, and have implications for the thermoregulation field as well as the social/naturalistic behavior field.

Strengths:

The study is very creative and tackles a challenging task to examine how natural and social behavior influences neural circuits for a homeostatic system such as thermoregulation. The authors use a combination of state-of-the-art tools (photometry, optogenetics, automated behavior tracking, thermal imaging, and core body temperature measurement), often in combination with each other, to produce a rigorous and high-dimensional dataset. Carrying out tightly temperature-controlled experiments and examining natural behavior, neural activity, and body physiology simultaneously is quite a feat. I applaud the authors for taking this on in a rigorous and detailed manner. This paper will be valuable for both the thermoregulation field as well as for researchers interested in naturalistic social behaviors. The conclusions are supported by the data.

Weaknesses:

I have a number of questions and suggestions for clarification that would help improve the interpretation of the findings.

(1) Figure 1D-F: It would be helpful to include representative images of cFos expression in the PVN, LS, and DMH during both quiescent and solo huddling conditions, to better illustrate the reported differences.

(2) Figure 1C: The data suggest a general suppression of neural activity during sleep-associated quiescent huddling, which somewhat complicates the interpretation of what specifically the active huddling cells are responding to. A more informative control might have been a comparison between huddling and a more generic form of social engagement (e.g., dyadic sniffing) to assess whether huddling-responsive neurons are broadly tuned to social stimuli. While it may not be feasible to add this experimentally at this time, a brief discussion of this limitation in the main text would be valuable.

(3) Figure 2H-J vs. Figure 1: The fiber photometry data suggest increased PVN activity during quiescent huddling vs active huddling, which appears to contrast with the cFos results from Figure 1. It would be helpful for the authors to comment on possible reasons for this discrepancy-e.g., methodological differences, temporal resolution, or cell-type specificity.

(4) Figure 2O: A comparable linear regression for active huddling would be informative to assess whether the observed relationships extend across behavioral states.

(5) Temperature manipulation: The use of floor temperature changes presents a distinct physiological and sensory experience from, for example, manipulation of ambient temperature. A discussion of how this choice may affect neural circuit engagement or interpretation of thermoregulatory responses would be beneficial.

(6) Correlations with behavior: Across the manuscript, it would be informative to see correlations between huddle duration and neural activity (e.g., cFos expression, calcium signal magnitude). Similarly, do longer huddles produce greater thermogenic effects?

(7) Lactating vs. virgin mothers: The inclusion of maternal data is intriguing but feels somewhat disconnected from the central huddling-thermoregulation narrative. If these experiments are to remain, additional explanation of their rationale and how they fit into the broader story would help clarify their relevance.

(8) Optogenetic manipulation: Have the authors tested the effect of PVN OT neuron stimulation or inhibition during huddling? Even a negative result would be of interest to the field. If these data exist (main or supplementary), I apologize for missing them. If not, the authors might consider including them or commenting briefly on any attempts or challenges in carrying out these experiments.

Reviewer #2 (Public review):

Summary:

This study investigates the role of the enzyme Alcohol Dehydrogenase 5 (ADH5) in brown adipose tissue (BAT) during aging. BAT is crucial for thermogenesis and energy balance, but its function and mass diminish with age, contributing to metabolic dysfunction and age-related diseases. ADH5, also known as S-nitrosoglutathione reductase, regulates nitric oxide (NO) signaling by damaging S-nitrosylation modifications from proteins. The authors show that aging in mice leads to increased protein S-nitrosylation but reduced ADH5 expression in BAT, resulting in impaired metabolic and cognitive functions. Deletion of ADH5 in BAT accelerates tissue senescence and systemic metabolic decline.

Mechanisticaremoving lly, aging suppresses ADH5 via downregulation of heat shock factor 1 (HSF1), a master regulator of protein homeostasis. Importantly, pharmacologically boosting HSF1 improves BAT function and mitigates both metabolic and cognitive declines in aged mice. The findings highlight a critical HSF1-ADH5 pathway in BAT that protects against aging-related dysfunction, suggesting that targeting this pathway may offer new therapeutic strategies for improving metabolic health and cognition during aging.

Strengths:

This research provides insight into the interplay between redox biology, proteostasis, and metabolic decline in aging. By identifying a specific enzyme that controls SNO status in BAT and further developing a therapy to target ADH5 in BAT to prevent age-related decline, the authors have identified a putative mechanism to combat age-related decline in BAT function.

Weaknesses:

(1) Sex needs to be considered as a biological variable, at a minimum in the reporting of the phenotypes observed in this manuscript, but also potentially by further experimentation. The only mention of sex I could find is that the authors reported the general protein SNO status in BAT is increased with age in male C57Bl/6J mice. Is this also true in female mice? For all of the ADH5 knockout mouse data, are these also male mice? Do female ADH5 knockout mice have a consistent phenotype, or are the sex differences?

(2) It would be helpful to know the extent of ADH5 loss in the adipose tissue of knockout mice, either by mRNA or by immunoblotting for ADH5. It could also be helpful to know if ADH5 is deleted from the inguinal adipose tissue of these mice, especially since they seem to accumulate fat mass as they age (Figure 2B).

(3) For Figure 4D, the ChiP, it would be better to show the IgG control pulldowns. Also, there's an unexpected thing where all the values for the Adh5 flox mice are exactly the same - how is this possible? Finally, it's not clear how these BAT samples were treated with HSF1A - was this done in vivo or ex vivo?

(4) I didn't understand what was on the y-axis in Figure 5A, nor how it was measured. I assume it's HSF1A, and maybe it's the part in the methods with the Metabolomic Analysis, but this wasn't clear. It would also help if release from the NC-Vehicle formulation could be included as a negative control.

(5) What happens to BAT protein S-nitrosylation in HSF1A-treated mice?

(6) Figure 1B: What is the age of the positive (ADH5BKO) and negative (Adh5 fl) mice?

(7) Figure 1F: Can you clarify what I'm looking at in the P16ink4a panels? The red staining? Is the blue staining DAPI? This is also a problem in Figures 3C, 3D and 5G, and 5I. Figure 4B looks great - maybe this could be used as an example?

(8) Figure 3B looks a bit odd since 7 of the 12 total mice seem to have an IL-beat level of exactly 5. I was a bit unclear about why arbitrary units were used for IL-1β levels since it says an ELISA was used to quantify IL-1β; however, in the methods the authors describe a Bio-Rad Laboratories Bio-plex Pro Mouse Cytokine 23-Plex approach, which I don't think is an ELISA. Can the approach to measuring IL-1β be clarified, and could the authors explain why they can't show units of mass for IL-1β levels?

(9) Figure 2C and 2D: I don't really understand why the Heat or VO2 need to be expressed as fold changes. Can't these just be expressed with absolute units? It's also confusing why the heat fold change is 1.0 in the light and the dark for the floxed animal. I bet this is because the knockout is normalized to the floxed animal for light and then normalized again for the dark period, but since both are on the same graph, readers could be confused into thinking there is no difference in the heat production or VO2 between light and dark, which would be surprising. This could all just be solved if absolute units were used.

Reviewer #2 (Public review):

Summary:

The intracellular pathogen Toxoplasma gondii scavenges metal ions such as iron and zinc to support its replication; however, mechanistic studies of iron and zinc uptake are limited. This study investigates the function of a putative iron and zinc transporter, ZFT. In this paper, the authors provide evidence that ZFT mediates iron and zinc uptake by examining the regulation of ZFT expression by iron and zinc levels, the impact of altered ZFT expression on iron sensitivity, and the effects of ZFT depletion on intracellular iron and zinc levels in the parasite. The effects of ZFT depletion on parasite growth are also investigated, showing the importance of ZFT function for the parasite.

Strengths:

A key strength of the study is the use of multiple complementary approaches to demonstrate that ZFT is involved in iron and zinc uptake. Additionally, the authors build on their finding that loss of ZFT impairs parasite growth by showing that ZFT depletion induces stage conversion and leads to defects in both the apicoplast and mitochondrion.

Weaknesses:

(1) Excess zinc was shown not to alter ZFT expression, but a cation chelator (TPEN) did lead to decreased expression. While TPEN is often used to reduce zinc levels, does it have any effect on iron levels? Could the reduction in ZFT after TPEN treatment be due to a reduction in the level of iron or another cation?

(2) ZFT expression was found to be dynamic depending on the size of the vacuole, based on mean fluorescence intensity measurements. Looking at protein levels by Western blot at different times during infection would strengthen this finding.

(3) ZFT localization remained at the parasite periphery under low iron conditions. However, in the images shown in Figure S1c, larger vacuoles (containing 4-8 parasites) are shown for the untreated conditions, and single parasite-containing vacuoles are shown for the low iron condition. As ZFT localization is predominantly at the basal end of the parasite in larger PV and at the parasite periphery for smaller vacuoles, it would be better to compare vacuoles of similar size between the untreated and low-iron conditions.

Reviewer #2 (Public review):

Summary:

The authors find that the bacterial pathogen Shigella flexneri uses the T3SS effector IpaH1.4 to induce degradation of the IFNg-induced protein RNF213. They show that in the absence of IpaH1.4, cytosolic Shigella is bound by RNF213. Furthermore, RNF213 conjugates linear and lysine-linked ubiquitin to Shigella independently of LUBAC. Intriguingly, they find that Shigella lacking ipaH1.4 or mxiE, which regulates the expression of some T3SS effectors, are not killed even when ubiquitylated by RNF213 and that these mutants are still able to replicate within the cytosol, suggesting that Shigella encodes additional effectors to escape from host defenses mediated by RNF213-driven ubiquitylation.

Strengths:

The authors take a variety of approaches, including host and bacterial genetics, gain-of-function and loss-of-function assays, cell biology, biochemistry, . Overall, the experiments are elegantly designed, rigorous, and convincing.

Reviewer #2 (Public review):

Summary:

The authors present a very interesting collection of methods and results using brain ultrasound localization microscopy (ULM) in awake mice. They emphasize the effect of the level of anesthesia on the quantifiable elements assessable with this technique (i.e. vessel diameter, flow speed, in veins and arteries, area perfused, in capillaries) and demonstrate the possibility of achieving longitudinal cerebrovascular assessment in one animal during several weeks with their protocol.

The authors made a good rewriting the article based on the reviewers' comments. One of the message of the first version of the manuscript was that variability in measurements (vessel diameter, flow velocity, vascularity) were much more pronounced under changes of anesthesia than when considering longitudinal imaging across several weeks. This message is now not quite mitigated, as longitudinal imaging seems to show a certain variability close to the order of magnitude observed under anesthesia. In that sense, the review process was useful in avoiding hasty conclusion and calls for further caution in ULM awake longitudinal imaging, in particular regarding precision of positioning and cancellation of tissue motion.

Strengths:

Even if the methods elements considered separately are not new (brain ULM in rodents, setup for longitudinal awake imaging similar to those used in fUS imaging, quantification of vessel diameters/bubble flow/vessel area), when masterfully combined as it is done in this paper, they answer two questions that have been long-running in the community: what is the impact of anesthesia on the parameters measured by ULM (and indirectly in fUS and other techniques)? Is it possible to achieve ULM in awake rodents for longitudinal imaging? The manuscript is well constructed, well written, and graphics are appealing.

The manuscript has been much strengthened by the round of review, with more animals for the longitudinal imaging study.

Weaknesses:

The manuscript has been only marginally modified since our last round of review, so there is probably not much we reviewers can additionally elaborate to improve it. Therefore my last concerns about the reliability of longitudinal quantifications and on certain discrepancies remains for this paper. As a general piece of advice, I would just say that every claim (' is higher', is lower', is stable') should be supported by evidence and statistical testing if it is not already the case.

Response 06: the authors' response is not satisfactory. Even if the difference in terms of ROI boundaries between fig 4e and fig 4j has been underlined by the authors, they only provide a wordy comment and no additional quantitative analysis that could explain the discrepancy I pointed out. By doing so they take the risk of making misinterpretations. The reader is left with a discrepancy that could be explained by 2 mechanisms: -pial vessel population behave differently from penetrating arterioles and venules OR - the imaging of pial vessels with ULM is not good enough to enable proper quantification because the vessels are not clearly visible (out of plane extent). In any case Figure 4j does not "provides a more comprehensive representation of cortical vasculature" as stated. If the changes in pial vessels cannot be reliably measured, they should be excluded from the ROI.

Line 161: be careful with the use of vessel density, as pointed by reviewer 1.

Line 196: "the decrease in venous vessel area (averaging 55% across mice) was greater than that of arterial (averaging 35%)" no stat test has been performed.

Reviewer #2 (Public review):

Summary:

This paper aims to elucidate the gene regulatory network governing the development of cone photoreceptors, the light-sensing neurons responsible for high acuity and color vision in humans. The authors provide a comprehensive analysis through stage-matched comparisons of gene expression and chromatin accessibility using scRNA-seq and scATAC-seq from the cone-dominant 13-lined ground squirrel (13LGS) retina and the rod-dominant mouse retina. The abundance of cones in the 13LGS retina arises from a dominant trajectory from late retinal progenitor cells (RPCs) to photoreceptor precursors and then to cones, whereas only a small proportion of rods are generated from these precursors.

Strengths:

The paper presents intriguing insights into the gene regulatory network involved in 13LGS cone development. In particular, the authors highlight the expression of cone-promoting transcription factors such as Onecut2, Pou2f1, and Zic3 in late-stage neurogenic progenitors, which may be driven by 13LGS-specific cis-regulatory elements. The authors also characterize candidate cone-promoting genes Zic3 and Mef2C, which have been previously understudied. Overall, I found that the across-species analysis presented by this study is a useful resource for the field.

Weaknesses:

The functional analysis on Zic3 and Mef2C in mice does not convincingly establish that these factors are sufficient or necessary to promote cone photoreceptor specification. Several analyses lack clarity or consistency, and figure labeling and interpretation need improvement.

Reviewer #2 (Public review):

Summary:

The paper introduced the pipeline to analyze brain imaging of freely moving animals: registering deforming tissues and maintaining consistent cell identities over time. The pipeline consists of three neural networks that are built upon existing models: BrainAlignNet for non-rigid registration, AutoCellLabeler for supervised annotation of over 100 neuronal types, and CellDiscoveryNet for unsupervised discovery of cell identities. The ambition of the work is to enable high-throughput and largely automated pipelines for neuron tracking and labeling in deforming nervous systems.

Strengths:

(1) The paper tackles a timely and difficult problem, offering an end-to-end system rather than isolated modules.

(2) The authors report high performance within their dataset, including single-pixel registration accuracy, nearly complete neuron linking over time, and annotation accuracy that exceeds individual human labelers.

(3) Demonstrations across two organisms suggest the methods could be transferable, and the integration of supervised and unsupervised modules is of practical utility.

Weaknesses:

(1) Lack of solid evaluation. Despite strong results on their own data, the work is not benchmarked against existing methods on community datasets, making it hard to evaluate relative performance or generality.

(2) Lack of novelty. All three models do not incorporate state-of-the-art advances from the respective fields. BrainAlignNet does not learn from the latest optical flow literature, relying instead on relatively conventional architectures. AutoCellLabeler does not utilize the advanced medNeXt3D architectures for supervised semantic segmentation. CellDiscoveryNet is presented as unsupervised discovery but relies on standard clustering approaches, with limited evaluation on only a small test set.

(3) Lack of robustness. BrainAlignNet requires dataset-specific training and pre-alignment strategies, limiting its plug-and-play use. AutoCellLabeler depends heavily on raw intensity patterns of neurons, making it brittle to pose changes. By contrast, current state-of-the-art methods incorporate spatial deformation atlases or relative spatial relationships, which provide robustness across poses and imaging conditions. More broadly, the ANTSUN 2.0 system depends on numerous manually tuned weights and thresholds, which reduces reproducibility and generalizability beyond curated conditions.

Evaluation:

To make the evaluation more solid, it would be great for the authors to (1) apply the new method on existing datasets and (2) apply baseline methods on their own datasets. Otherwise, without comparison, it is unclear if the proposed method is better or not. The following papers have public challenging tracking data: https://elifesciences.org/articles/66410, https://elifesciences.org/articles/59187, https://www.nature.com/articles/s41592-023-02096-3.

Methodology:

(1) The model innovations appear incrementally novel relative to existing work. The authors should articulate what is fundamentally different (architectural choices, training objectives, inductive biases) and why those differences matter empirically. Ablations isolating each design choice would help.

(2) The pipeline currently depends on numerous manually set hyperparameters and dataset-specific preprocessing. Please provide principled guidelines (e.g., ranges, default settings, heuristics) and a robustness analysis (sweeps, sensitivity curves) to show how performance varies with these choices across datasets; wherever possible, learn weights from data or replace fixed thresholds with data-driven criteria.

Appraisal:

The authors partially achieve their aims. Within the scope of their dataset, the pipeline demonstrates impressive performance and clear practical value. However, the absence of comparisons with state-of-the-art algorithms such as ZephIR, fDNC, or WormID, combined with small-scale evaluation (e.g., ten test volumes), makes the strength of evidence incomplete. The results support the conclusion that the approach is useful for their lab's workflow, but they do not establish broader robustness or superiority over existing methods.

Impact:

Even though the authors have released code, the pipeline requires heavy pre- and post-processing with numerous manually tuned hyperparameters, which limits its practical applicability to new datasets. Indeed, even within the paper, BrainAlignNet had to be adapted with additional preprocessing to handle the jellyfish data. The broader impact of the work will depend on systematic benchmarking against community datasets and comparison with established methods. As such, readers should view the results as a promising proof of concept rather than a definitive standard for imaging in deformable nervous systems.

Reviewer #2 (Public review):

Summary:

Pablo Ruiz Cuenca et al. conducted a GPS logger study with 124 adult participants across four different slum areas in Salvador, Brazil, recording GPS locations every 35 seconds for 48 hours. The aim of their study was to investigate step-selection models, a technique widely used in movement ecology to quantify contact with environmental risk factors for exposure to leptospires (open sewers, community streams, and rubbish piles). The authors built two different types of models based on distance and based on buffer areas to model human environmental exposure to risk factors. They show differences in movement/contact with these risk factors based on gender and seropositivity status. This study shows the existence of modest differences in contact with environmental risk factors for leptospirosis at small spatial scales based on socio-demographics and infection status.

Strengths:

The authors assembled a rich dataset by collecting human GPS logger data, combined with field-recorded locations of open sewers, community streams, and rubbish piles, and testing individuals for leptospirosis via serology. This study was able to capture fine-scale exposure dynamics within an urban environment and shows differences by gender and seropositive status, using a method novel to epidemiology (step selection).

[Editors' note: I have reviewed the authors' revised submission and confirm that they have adequately addressed the reviewers' comments for this manuscript.]

Reviewer #2 (Public review):

This work presents theoretical results concerning the effect of punctuated mutation on multistep adaptation and empirical evidence for that effect in cancer. The empirical results seem to agree with the theoretical predictions. However, it is not clear how strong the effect should be on theoretical grounds, and there are other plausible explanations for the empirical observations.

For various reasons, the effect of punctuated mutation may be weaker than suggested by the theoretical and empirical analyses:

(1) The effect of punctuated mutation is much stronger when the first mutation of a two-step adaptation is deleterious (Figure 2). For double inactivation of a TSG, the first mutation--inactivation of one copy--would be expected to be neutral or slightly advantageous. The simulations depicted in Figure 4, which are supposed to demonstrate the expected effect for TSGs, assume that the first mutation is quite deleterious. This assumption seems inappropriate for TSGs, and perhaps the other synergistic pairs considered, and exaggerates the expected effects.

(2) More generally, parameter values affect the magnitude of the effect. The authors note, for example, that the relative effect decreases with mutation rate. They suggest that the absolute effect, which increases, is more important, but the relative effect seems more relevant and is what is assessed empirically.

(3) Routes to inactivation of both copies of a TSG that are not accelerated by punctuation will dilute any effects of punctuation. An example is a single somatic mutation followed by loss of heterozygosity. Such mechanisms are not included in the theoretical analysis nor assessed empirically. If, for example, 90% of double inactivations were the result of such mechanisms with a constant mutation rate, a factor of two effect of punctuated mutagenesis would increase the overall rate by only 10%. Consideration of the rate of apparent inactivation of just one TSG copy and of deletion of both copies would shed some light on the importance of this consideration.

Several factors besides the effects of punctuated mutation might explain or contribute to the empirical observations:

(1) High APOBEC3 activity can select for inactivation of TSGs (references in Butler and Banday 2023, PMID 36978147). This selective force is another plausible explanation for the empirical observations.

(2) Without punctuation, the rate of multistep adaptation is expected to rise more than linearly with mutation rate. Thus, if APOBEC signatures are correlated with a high mutation rate due to the action of APOBEC, this alone could explain the correlation with TSG inactivation.

(3) The nature of mutations caused by APOBEC might explain the results. Notably, one of the two APOBEC mutation signatures, SBS13, is particularly likely to produce nonsense mutations. The authors count both nonsense and missense mutations, but nonsense mutations are more likely to inactivate the gene, and hence to be selected.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

The main weakness of the manuscript is the lack of detail in the presentation of the results. Specifically, all microscopy image figures are missing information about the number of samples (N), and in the case of colocalization experiments, quantitative analyses are not provided. Additionally, in the first behavioral section, it would be beneficial to complement the data table with figures similar to those presented later in the manuscript for consistency and clarity.

Wider context:

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

Reviewer #2 (Public review):

Summary:

In this EEG study, Huang et al. investigated the relative contribution of two accounts to the process of conflict control, namely the stimulus-control association (SC), which refers to the phenomenon that the ratio of congruent vs. incongruent trials affects the overall control demands, and the stimulus-response association (SR), stating that the frequency of stimulus-response pairings can also impact the level of control. The authors extended the Stroop task with novel manipulation of item congruencies across blocks in order to test whether both types of information are encoded and related to behaviour. Using decoding and RSA, they showed that the SC and SR representations were concurrently present in voltage signals, and they also positively co-varied. In addition, the variability in both of their strengths was predictive of reaction time. In general, the experiment has a solid design, but there are some confounding factors in the analyses that should be addressed to provide strong support for the conclusions.

Strengths:

(1) The authors used an interesting task design that extended the classic Stroop paradigm and is potentially effective in teasing apart the relative contribution of the two different accounts regarding item-specific proportion congruency effect, provided that some confounds are addressed.

(2) Linking the strength of RSA scores with behavioural measures is critical to demonstrating the functional significance of the task representations in question.

Weakness:

(1) While the use of RSA to model the decoding strength vector is a fitting choice, looking at the RDMs in Figure 7, it seems that SC, SR, ISPC, and Identity matrices are all somewhat correlated. I wouldn't be surprised if some correlations would be quite high if they were reported. Total orthogonality is, of course, impossible depending on the hypothesis, but from experience, having highly covaried predictors in a regression can lead to unexpected results, such as artificially boosting the significance of one predictor in one direction, and the other one to the opposite direction. Perhaps some efforts to address how stable the timed-resolved RSA correlations for SC and SR are with and without the other highly correlated predictors will be valuable to raising confidence in the findings.

(2) In "task overview", SR is defined as the word-response pair; however, in the Methods, lines 495-496, the definition changed to "the pairing between word and ISPC" which is in accordance with the values in the RDMs (e.g., mccbb and mcirb have similarity of 1, but they are linked to different responses, so should they not be considered different in terms of SR?). This needs clarification as they have very different implications for the task design and interpretation of results, e.g., how correlated the SC and SR manipulations were.

Reviewer #2 (Public review):

Summary:<br /> This article reports a cost-effectiveness comparison of intramural and extramural that NIH funded between 2009 and 2019. Using data obtained from NIH RePORTER, they linked total project costs to publication output, using robust validated metrics including Relative Citation Ratio (RCR), Approximate Potential to Translate (APT), and clinical citations. They find that after adjusting for confounders in regression and propensity-score analyses, extramural projects were generally more cost-effective, though intramural projects were more cost effective for generating clinical citations. They also describe differences in the topics of intramural- and extramural-funded publications, with intramural projects more likely to generate papers on viral infections and immunity or cancer metastases and survival, but less likely to generate papers on pregnancy and maternal health, brain connectivity and tasks, and adolescent experiences and depression. The authors aptly describe the different natures of the intramural and extramural funding models, including that extramural researchers spend much time writing grant applications and that the work described in extramural publications often receives funding from sources other than NIH grants.

Strengths:<br /> The authors leveraged publicly available data (including RePORTER and the iCite repository) and used robust validated metrics (RCR, APT, clinical citations). They carefully considered a large number of confounders, including those related to the PI, and performed several well-described regression analyses.

Weaknesses:<br /> Figure 3A shows intramural projects producing about 2.75 papers per year in 2009, whereas extramural projects are producing just over 1 paper per year. Extramural projects appear to catch up over the next five years. While the authors attempt to explain the difference in their figure legend, another explanation is that the intramural projects started well before 2009 but, as the authors state, intramural data only became available in 2009.

As the authors note, funding information is often complex and difficult to characterize for an analysis like this. How did the authors handle: i) publications linked to multiple extramural grants; ii) publications linked to intramural and extramural grants; iii) publications linked NIH grants and non-NIH grants?<br /> I would think it necessary to somehow apportion credit, as otherwise it would appear that extramural projects are more productive than they truly are.

Also, it is not clear if the authors took account of the indirect costs paid by the NIH to universities that have received extramural grants.

Reviewer #2 (Public review):

Summary:

In this paper, the authors used a multi-level modelling approach to reanalyse trial data from Takeda's Phase III randomised control trial investigating the efficacy of the TAK-003 vaccine against dengue. The aim of the paper is to refine uncertainty by incorporating all the available data into the model and pooling across stratifications that are correlated. A major challenge in constructing a likelihood that allows for data available at differing levels of aggregation by group and outcome, and at different time intervals. This is done by first supposing that the data is available without aggregation for all groups, outcomes and time points, and then marginalising over the aggregated levels. The model is validated using simulations and then applied to trial data from Takeda. Results appear to corroborate those of Takeda with reduced uncertainty in the estimates.

Strengths:

The main strength of the paper is the multi-level modelling approach. It is a particularly natural one for this setting. One reason for this, as discussed in the paper, is that correlations across stratifications can arise when there are similarities in their underlying causal structure. It is more realistic to model this nested data structure hierarchically. Another reason, also well discussed in the paper, is the reduction in uncertainty you get when you pool estimates across related groups. Multi-level modelling is also beneficial when group sizes are different. For example, there were too few cases of DENV-4 from seronegatives, which resulted in hospitalised disease for the original analysis to produce estimates, but by using multi-level modelling, this paper can produce estimates. The modelling framework developed in this paper will be simple to extend to further trial data collected in the future.

Another strength is that it is reanalysing existing trial data, which is both cost-effective and beneficial for scientific reproducibility. This approach also helps to assess the robustness of conclusions about the efficacy of the TAK-003 vaccine to use of different analytical methods.

The paper is well-written. The tables and figures presented in this paper are particularly informative. Protection conferred by the vaccine varies depending upon which variant a person is exposed to, their serostatus, and time since vaccination. The analysis presented supports the discussed conclusions. Comparisons between the results of this paper and the results of the original trial analysis are also shown and demonstrate a reduction in the uncertainty of parameter estimates, as desired.

Weaknesses:

The weakness of the paper is that it reports per-exposure protection instead of vaccine efficacy. This is methodologically sound, but it does limit the comparability of this study with the original trial analyses, which reported vaccine efficacy. It is therefore unclear whether the reduction in uncertainty observed is due solely to the multi-level modelling approach or whether it may be due in part to the parameters of interest being slightly different.

Reviewer #2 (Public review):

Summary:

Across two experiments, this work presents a novel spatial predictive inference paradigm that facilitates the investigation of meta-learning across multiple environments with distinct statistics, as well as more local learning from sequences of observations within an environment. The authors present behavioral data indicating that people can indeed learn to distinguish between noise levels and calibrate their learning rates accordingly across environments, even on initial trials when revisiting an environment. They complement their behavioral results with computational modeling, further bolstering claims of both local and global adaptation. Additional fMRI results support the role of OFC in this meta-learning process, with central OFC activity reflecting similarity between environments. This similarity emerges over time with task experience. Holistically, this paradigm and these data add to our understanding of how humans dynamically adapt their behavior on different timescales.

Strengths:

The novel paradigm represents a clever and creative expansion of spatial predictive inference tasks. The cover story was well chosen to facilitate an intuitive understanding of both the differences between environments and the estimation of the mean within environments.

Additionally, the authors present complementary results from two experiments, which strengthen the behavioral findings. This is especially effective as the initial experiment's results were a bit noisy, and the modifications within the second experiment increased both power and the specificity/accuracy of participant predictions. Taken together, the behavioral results provide convincing evidence that participants did distinguish environments based on their underlying statistics and adapted their initial behavior accordingly.

Beyond this, the combination of behavioral results, computational modeling, and neuroimaging enhances the impact of the work. It paints a fuller picture of whether and how humans meta-learn the global statistics of environments, and this is an important direction for the field of adaptive learning.

Weaknesses:

(1) The authors make the distinction between meta-learned "global" learning rates and within-environment learning rate adaptation in response to "local" fluctuations/observations. Though the experimental paradigm is novel, there are certainly links to prior work - for instance, though change point structures don't entail revisiting unique environments, they do require meta-learning from environmental statistics that is distinct from transient local adaptation to prediction errors. This tendency to increase one's learning rate after large prediction errors is appropriate in change point environments, though, as is true in this study, the amount of increase should be dependent on. This represents a similar kind of slower-timescale learning or reuse of more "global" parameters, and can be seen to different extents in prior work. It might benefit readers if the authors were to link the current work to previous research more explicitly to draw clearer connections between the approaches and findings.

(2) Throughout much of the paper, the authors refer to the distinctions between environments primarily as differences in "initial learning rates" or "environment-specific learning rates." This is particularly prominent when discussing fMRI results. Though the optimal initial learning rate did differ across environments, this was the result of differences in underlying task statistics. It will be important to clarify this throughout the text, because of the confounds between task statistics and initial learning rate (and to some extent, the position on the screen), it is not possible to separate the impact of these specific variables. This is also relevant to understanding the justification for using methods like RSA to test whether brain regions represent task states similarly. If the main hypothesis is that neural activity reflects the (initial) learning rate itself, then a univariate analysis approach would seem more natural.

(3) For the neuroimaging results in particular, the specificity of some of the results (e.g. ventral striatum showing an effect of prediction error only in the low noise condition in the second half of task experience, only on the first trial) is a bit surprising. Additional justification of or context for these results would be useful to help readers gauge how expected or surprising these findings are.

(4) There are some methodological details that are unclear (e.g., how were the positions of the crabs selected relative to the location they emerged from? Looking at Figure 1C, it looks like the crabs spread out unevenly, and that the single position they emerge from is not necessarily at the center of the crab locations.) Additional detail and clarity would help address some unanswered questions (more details below).

Reviewer #2 (Public review):

Summary:

The authors aim to develop Squidly, a sequence-only catalytic residue prediction method. By combining protein language model (ESM2) embedding with a biologically inspired contrastive learning pairing strategy, they achieve efficient and scalable predictions without relying on three-dimensional structure. Overall, the authors largely achieved their stated objectives, and the results generally support their conclusions. This research has the potential to advance the fields of enzyme functional annotation and protein design, particularly in the context of screening large-scale sequence databases and unstructured data. However, the data and methods are still limited by the biases of current public databases, so the interpretation of predictions requires specific biological context and experimental validation.

Strengths:

The strengths of this work include the innovative methodological incorporation of EC classification information for "reaction-informed" sample pairing, thereby enhancing the discriminative power of contrastive learning. Results demonstrate that Squidly outperforms existing machine learning methods on multiple benchmarks and is significantly faster than structure prediction tools, demonstrating its practicality.

Weaknesses:

Disadvantages include the lack of a systematic evaluation of the impact of each strategy on model performance. Furthermore, some analyses, such as PCA visualization, exhibit low explained variance, which undermines the strength of the conclusions.

Reviewer #2 (Public review):

Summary:

The authors derive an integrate-and-fire model to describe the dynamics of a more complex Wang-Buzsaki model and compare the two models. A detailed discussion of bifurcation schemes in both models is convincing and allows us to evaluate the simpler model.

Strengths:

The idea is interesting, and the mathematical approach appears to be convincing. In addition, differences between the simple and original models are also discussed.

Weaknesses:

A comparison to experimental data is necessary to support the theoretical work.

Reviewer #3 (Public review):

Summary:

In this contribution, the authors report atomistic, coarse-grained and lattice simulations to analyze the mechanism of supercomplex (SC) formation in mitochondria. The results highlight the importance of membrane deformation as one of the major driving forces for the SC formation, which is not entirely surprising given prior work on membrane protein assembly, but certainly of major mechanistic significance for the specific systems of interest.

Strengths:

The combination of complementary approaches, including an interesting (re)analysis of cryo-EM data, is particularly powerful, and might be applicable to the analysis of related systems. The calculations also revealed that SC formation has interesting impacts on the structural and dynamical (motional correlation) properties of the individual protein components, suggesting further functional relevance of SC formation. In the revision, the authors further clarified and quantified their analysis of membrane responses, leading to further insights into membrane contributions. They have also toned down the decomposition of membrane contributions into enthalpic and entropic contributions, which is difficult to do. Overall, the study is rather thorough, highly creative and the impact on the field is expected to be significant.

Weaknesses:

Upon revision, I believe the weakness identified in previous work has been largely alleviated.

Reviewer #3 (Public review):

Summary:

Lmx1a is an orthologue of apterous in flies, which is important for dorsal-ventral border formation in the wing disc. Previously, this research group has described the importance of the chicken Lmx1b in establishing the boundary between sensory and non-sensory domains in the chicken inner ear. Here, the authors described a series of cellular changes during border formation in the chicken inner ear, including alignment of cells at the apical border and concomitant constriction basally. The authors extended these observations to the mouse inner ear and showed that these morphological changes occurred at the border of Lmx1a positive and negative regions, and these changes failed to develop in Lmx1a mutants. Furthermore, the authors demonstrated that the ROCK-dependent actomyosin contractility is important for this border formation and blocking ROCK function affected epithelial basal constriction and border formation in both in vitro and in vivo systems.

Strengths:

The morphological changes described during border formation in the developing inner ear are interesting. Linking these changes to the function of Lmx1a and ROCK dependent actomyosin contractile function are provocative.

Weaknesses:

There are several outstanding issues that need to be clarified before one can pin the morphological changes observed being causal to border formation and that Lmx1a and ROCK are involved.

Comments on the latest version:

The revised manuscript has provided clarity of their results on some levels, but unfortunately, the basal restriction during border formation remains unclear and the study did not advance the understanding of role of Lmx1a in boundary formation. Overall comments are indicated below:

(1) The authors states in the rebuttal, "we do not think that ROCK activity is required for the formation or maintenance of the basal constriction at the interface of Lmx1a-expressing and non-expressing cells"<br /> If the above is the sentiment of the authors, then the manuscript is not written to support this sentiment clearly, starting with this misleading sentence in the Abstract, "The boundary domain is absent in Lmx1a-deficient mice, which exhibit defects in sensory organ segregation, and is disrupted by the inhibition of ROCK-dependent actomyosin contractility."

(2) As acknowledged by the authors, the data as they currently stand could be explained by Lmx1a functioning in specifying the non-sensory fate and may not function directly in boundary formation. With this caveat in mind, the role of Lmx1a in boundary formation remains unclear.

(3) I feel like the word "orchestrate" in the title is an overstatement.

Reviewer #2 (Public review):

This study presents a useful inventory of polyadenylated RNAs cleaved by the double-stranded RNA endonuclease Rnt1 in yeast. The data were obtained with solid methodology based on high-throughput sequencing, and the evidence that Rnt1 contributes to cellular homeostasis through controlling the turnover of selected mRNAs is convincing.

Comments on revisions:

I appreciate the authors' thorough and thoughtful response, and I find that the manuscript has been substantially strengthened by the additional data, analyses, and textual clarifications.

Reviewer #2 (Public review):

Summary:

Wethington et al. investigated the mechanistic principles underlying antigen-specific proliferation and memory formation in mouse natural killer (NK) cells following exposure to mouse cytomegalovirus (MCMV), a phenomenon predominantly associated with CD8+ T cells. Using a stochastic modeling approach, the authors aimed to develop a quantitative model of NK cell clonal dynamics during MCMV infection. Starting from a single immature Ly49+CD27+ NK cell, a two-state linear model (with a death variant) explained the negative correlation between clone size at 8 dpi and the CD27+ fraction, but failed to reproduce the first and second moments of CD27+ and CD27− NK cell populations at 8 dpi. To address this limitation, the authors added an intermediate maturation state, yielding a three-stage model (CD27+Ly6C− → CD27−Ly6C− → CD27−Ly6C+) that fits the first and second moments under two constraints: CD27+ NK cells proliferate faster than CD27− NK cells, and clone size is negatively correlated with the CD27+ fraction (upper bound of −0.2). The model predicts high proliferation in the intermediate state and high death in mature CD27−Ly6C+ cells, and it was validated using Adams et al. (2021) NK reporter mice tracking CD27+/− populations after tamoxifen, allowing discrimination between bone marrow-derived and pre-existing peripheral NK cells. To test the prediction that mature CD27− NK cells have a higher death rate, the authors measured Ly49H+ NK cell viability in the mouse spleen at different time points post-MCMV infection. Data confirmed lower viability of mature (CD27−) than immature (CD27+) cells during days 4-8 post-infection, and a model variant supported that higher CD27− death increases their proportion in the dead cell compartment. Altogether, the authors propose a three-stage quantitative model of antigen-specific expansion and maturation of naïve Ly49H+ NK cells with the trajectory CD27+Ly6C− (immature) → CD27−Ly6C− (mature I) → CD27−Ly6C+ (mature II), highlighting high proliferation in the mature I state and increased death in the mature II state.

Strengths:

Models explaining correlations and first and second moments, supported by analytical investigations, stochastic simulations, and model selection, identify key processes in antigen-specific NK expansion and maturation. The work distinguishes expansion, contraction, and memory in NK cells from CD8+ T cells and informs NK therapy development.

Weaknesses (relating to initial submission):

The conclusions of this paper are largely supported by the available data. However, a comparative analysis with more recent works in the field would be desirable. Clarifications:

(1) Initial Conditions and Grassmann Data: The Grassmann data is used solely as a constraint, while the simulated values of CD27+/CD27− cells could have been directly fitted to the Grassmann data, which assumes a 1:1 ratio of CD27+/CD27− at t = 0. This would allow an alternative initial condition rather than starting from a single CD27+ cell.

(2) Correlation Coefficients in the Three-State Model: Although the parameter scan of the three-stage model (Figure 2) demonstrates the potential for negative correlations between colony size and the fraction of CD27+ cells, the calculated correlation coefficients using the fitted parameter values are not shown. Including these would validate that the fitted parameters lie in the negative-correlation regime.

(3) Viability Dynamics and Adaptive Response: The authors measured the time evolution of CD27+/− dynamics and viability over 30 days post-infection (Figure 4). It would be valuable to test whether the three-state model can reproduce the adaptive response of CD27− cells to MCMV infection, particularly the observed drop in CD27− viability at 5 dpi and its rebound at 8 dpi. Demonstrating this would test whether the model can simultaneously explain viability dynamics and moment dynamics, and would enable sensitivity analysis of CD27− viability with respect to model parameters.

Reviewer #2 (Public review):

Summary:

Meiotic recombination initiates with the formation of DNA double-strand break (DSB) formation, catalyzed by the conserved topoisomerase-like enzyme Spo11. Spo11 requires accessory factors that are poorly conserved across eukaryotes. Previous genetic studies have identified several proteins required for DSB formation in C. elegans to varying degrees; however, how these proteins interact with each other to recruit the DSB-forming machinery to chromosome axes remains unclear.

In this study, Raices et al. characterized the biochemical and genetic interactions among proteins that are known to promote DSB formation during C. elegans meiosis. The authors examined pairwise interactions using yeast two-hybrid (Y2H) and co-immunoprecipitation and revealed an interaction between a chromatin-associated protein HIM-17 and a transcription factor XND-1. They further confirmed the previously known interaction between DSB-1 and SPO-11 and showed that DSB-1 also interacts with a nematode-specific HIM-5, which is essential for DSB formation on the X chromosome. They also assessed genetic interactions among these proteins, categorizing them into four epistasis groups by comparing phenotypes in double vs. single mutants. Combining these results, the authors proposed a model of how these proteins interact with chromatin loops and are recruited to chromosome axes, offering insights into the process in C. elegans compared to other organisms.

Weaknesses:

This work relies heavily on Y2H, which is notorious for having high rates of false positives and false negatives. Although the interactions between HIM-17 and XND-1 and between DSB-1 and HIM-5 were validated by co-IP, the significance of these interactions was not tested in vivo. Cataloging Y2H and genetic interactions does not yield much more insight. The model proposed in Figure 4 is also highly speculative.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public review):

Summary:

Toxoplasma gondii is an obligate intracellular parasite and the causative agent of Toxoplasmosis. Parasite invasion into host cells, intracellular replication, and then egress, which results in the destruction of the infected cell, is central to pathogenicity. This manuscript focuses on understanding how maternal resources (in this case, cellular organelles) are shared between daughter parasites during cell division. Many organelles are single copy, meaning that division and inheritance by the daughters is crucial for successful replication. The major strength of this study was the use of a Halo-based pulse chase assay to characterize patterns of organelle inheritance. The results show that both microneme and rhoptries (secretory vesicles) previously thought to be synthesized de novo are inherited by daughter parasites. Thus, this paper adds new insight to our understanding of cell division in this important parasite.

Strengths:

This study demonstrated that pulse labeling of proteins can be used to monitor protein synthesis, turnover, and movement. This approach will be of great interest to the field. Using this method, the authors demonstrate three main modes of organelle inheritance.

(1) Organelles, where there are multiple copies (such as secretory vesicles, micronemes, and rhoptries), are divided between the daughter parasites, with additional contribution of newly formed vesicles. New and old material remain as separate entities in the cell.

(2) Single-copy organelles, which are expanded to include newly synthesized material prior to division, such as the Golgi and apicoplast.

(3) Cytoskeletal structures that are synthesized anew during each round of division. These studies provide more refined insight into patterns or organelle inheritance and demonstrate that secretory organelles are not made de novo during each round of division as previously thought. The paper has a logical flow, and overall, the data is presented in a clear and organized fashion.

Weaknesses:

(1) Descriptions of methodology and statistical analysis were incomplete.

(2) There are inconsistencies between the data in Figures 1 and 5. In Figure 1, a small amount of maternal IMC is visible in stage 2 parasites. Although this is a ~90% reduction, these parasites should be quantified as parasites with material IMC. However, the graph in Figure 5C indicates that no material parasites have GAPM1a, given that graph 5C is a binary measure (present vs. absent), one would expect a non-zero percent of parasites to have maternal material.

(3) The conclusion from Figure 6 was not justified based on the data. I agree with the author's conclusion that the accumulation of micronemes and rhoptries in the residual body was time-dependent. In Figure 6A, the signal observed in the residual body at times 6:30, 13, and 14 hours is not observed in subsequent time points. However, the fate of these micronemes and rhoptries is unclear. It cannot be concluded that these vesicles are recycled back to the mother. They could also have been degraded. In fact, the graphs of microneme inheritance in Figure 2B show a decrease in maternal signal from 100% to 80% between stages 1 and 2, indicating that some microneme degradation is taking place.

(4) To convincingly demonstrate that the redistribution of micronemes and rhoptries was due to recovery of MyoF protein levels after auxin washout, a Western blot should be performed to show MyoF protein levels over time. In addition, the decrease in mMIC2 protein levels in the residual body in Figure 8F should be measured and normalized for photobleaching. Both apical and basal signals appear to be reduced over the time course of imaging.

Reviewer #2 (Public review):

Summary:

Fan and colleagues measure proteomics and transcriptomics in 3 organs (liver, skeletal muscle, cerebral cortex) from male C57BL/6 mice to investigate whether intermittent fasting (IF; 16h daily fasting over 4 months) produces systemic and organ-specific adaptations.

They find shared signaling pathways, certain metabolic changes, and organ-specific responses that suggest IF might affect energy utilization, metabolic flexibility, while promoting resilience at the cellular level.

Strengths:

The fact that there are 3 organs and 2 -omics approaches is a strength of this study.

Weaknesses:

The analytical approach of the data generated by the present study is not well posed, because it doesn't help to answer key questions implicit in the experimental design. Consequently, the paper, as it is for now, reads as a mere description of results and not a response to specific questions.

The presentation of the figures, the knowledge of the literature, and the inclusion of only one sex (male) are all weaknesses.

Reviewer #2 (Public review):

Summary:

The authors analyzed PopART data to better characterize the age and sex-specific heterosexual HIV transmission dynamics in Zambia, with the goal of allocating resources.

Strengths:

Important analysis to hone in on the key driver of HIV transmission in Zambia, which hopefully can be used to tune prevention efforts to maximize effect while limiting required resources. Two analytic approaches were used, and while the phylogenetic data were markedly more limited, they mirrored the simulated epidemic. The authors did a nice job reviewing the limitations of the data and the analyses. The authors did a nice job of providing analyses to support their goals and hypothesis, and this work may have more impact now that resources in SSA for HIV prevention and treatment may become more scarce

Weaknesses:

To increase the impact and utility of this work, it would be helpful to parse the analysis just a bit further to estimate the roles of undiagnosed vs diagnosed and untreated subpopulations on this transmission. PopART is a multifaceted intervention, but the cost, effort, and approach to reengagement in care vs testing/treatment can be quite different.

Reviewer #2 (Public review):

Summary:

In this study, the authors identified a previously unrecognized organ interaction where limb immobilization induces thermogenesis in BAT. They showed that limb immobilization by cast fixation enhances the expression of UCP1 as well as amino acid transporters in BAT, and amino acids are supplied from skeletal muscle to BAT during this process, likely contributing to increased thermogenesis in BAT. Furthermore, the experiments with IL-6 knockout mice and IL-6 administration to these mice suggest that this cytokine is likely involved in the supply of amino acids from skeletal muscle to BAT during limb immobilization.

Strengths:

The function of BAT plays a crucial role in the regulation of an individual's energy and body weight. Therefore, identifying new interventions that can control BAT function is not only scientifically significant but also holds substantial promise for medical applications. The authors have thoroughly and comprehensively examined the changes in skeletal muscle and BAT under these conditions, convincingly demonstrating the significance of this organ interaction.

Weaknesses:

Through considerable effort, the authors have demonstrated that limb-immobilized mice exhibit changes in thermogenesis and energy metabolism dynamics at their steady state. However, The impact of immobilization on the function of skeletal muscle and BAT during cold exposure has not been thoroughly analyzed.

Comments on revisions:

The authors appropriately responded to the reviewers' recommendations made during the previous round of peer review.

Reviewer #2 (Public review):

Summary:

Immunogenic Plasmodium falciparum proteins that could be targeted to prevent parasite development in the liver are of significant interest for novel anti-malarial vaccine development. In this study, McConville et al evaluate the trafficking and functional importance of LSA3, a protein expressed in the blood and liver stages and previously shown to provide protection in immunized chimpanzees. LSA3 contains a PEXEL motif, but the authors have previously shown that this protein does not appear to be exported beyond the PVM in the liver stage (McConville et al, PNAS 2024). However, LSA3 trafficking and functional importance have not been comprehensively evaluated across stages. In the present study, the authors find that blood-stage LSA3 undergoes PEXEL processing, and a portion of the protein is exported into the erythrocyte, where it localizes to punctate structures distinct from Maurer's clefts. Using a knockout mutant, LSA3 is shown to be dispensable for blood and mosquito stages but important to liver-stage development. Collectively, these results validate LSA3 as a liver-stage target and place it among several other PEXEL proteins that display differential trafficking beyond the PVM in the erythrocyte but not the hepatocyte.

Strengths:

(1) The authors present a thorough analysis of LSA3 trafficking in the blood stage. PEXEL processing by Plasmepsin 5 is clearly demonstrated through a combination of mini LSA3-GFP reporters and Plasmepsin 5 inhibitors. Importantly, an LSA3 knockout mutant is used to show that the LSA3-C anti-sera also react with additional, unidentified parasite proteins in the blood stage. Nonetheless, comparison between the WT and KO parasites clearly indicates that a portion of LSA3 is exported into the erythrocyte, which is further supported by protease-protection assays with fractionated iRBCs. This contrasts with the liver stage, where LSA3 does not appear to traffic beyond the PVM, similar to what has been observed for other PEXEL proteins in the rodent malaria model.

(2)This study provides the first direct analysis of LSA3 function by reverse genetics, showing this protein is important for liver stage development in chimeric human liver mice. Several PEXEL proteins in P. berghei have been shown to be exported into the host cell in the blood stage, but do not appear to cross the PVM in the liver stage. These observations reinforce that even without detectable export into the hepatocyte, PEXEL proteins play critical roles during liver stage development.

Weaknesses:

(1) A previous study reported that anti-LSA3 antibodies inhibit blood-stage growth, suggesting a role for LSA3 during erythrocyte infection. While the authors carefully evaluate the LSA3 mutant in mosquito and liver stages, the impact on blood stage fitness is not tested. While the knockout shows LSA3 is not essential in the blood stage, its importance during erythrocyte infection remains unclear.

(2) The authors previously reported that anti-LSA3-C signal in the liver stage localizes within the parasite and at the parasite periphery but is not exported into the hepatocyte. In the present study, it is shown that anti-LSA3-C reacts with other parasite proteins beyond LSA3 in the blood stage, and this may also occur in the liver stage. However, since liver-stage IFAs were only performed on samples co-infected with both WT and ∆LSA3 parasites, non-specific anti-LSA3-C reactivity at this stage could not be determined, and the localization of LSA3 in the liver stage remains somewhat unclear.

Reviewer #2 (Public review):

The manuscript by Yang et al. investigates how a prior experience (notably by the activation of sensory/reinforcing dopaminergic neurons) alters olfactory response and memory expression in Drosophila. They refer to a priming effect with the definition: "Priming is a process by which exposure to a stimulus affects the response to a subsequent stimulus in Humans". The authors observed that exposing flies to a series of shocks (or the optogenetic activation of aversively reinforcing dopaminergic neurons) decreases ensuing odour avoidance. Conversely, optogenetic activation of sweet-sensing neurons increases following odour avoidance. They proposed that the reduced odour avoidance was due to the involvement of reward dopaminergic neurons involved during shock (or the optogenetic activation of aversively reinforcing dopaminergic neurons). They indeed show the involvement of reward dopaminergic neurons innervating the mushroom body (the fly learning and memory centre) during shock preexposure. Recording (calcium activity) from reward dopaminergic neurons before and after shock preexposure shows that only a small subset of dopaminergic neurons innervating the mushroom body γ4 compartment increases their response to odour after shock. They then showed the requirement of the γ4 reward dopaminergic neurons during shock preexposure on ensuing odour avoidance. They also tested the role of the dopamine receptor in the mushroom body. They finally recorded from different mushroom body output neurons, including the one (MBON-γ4γ5) likely affected by the increased activity of the corresponding γ4 reward dopaminergic neurons after shock preexposure. They recorded odour-evoked responses from these neurons before and after shock preexposure, but did not find any plasticity, while they found a logical effect during spaced cycles of aversive training.

Overall, the study is very interesting with a substantial amount of behavioural analysis and in vivo 2-photon calcium imaging data, but some major (and some minor) issues have to be resolved to strengthen their conclusions.

(1) According to neuropsychological work (Henson, Encyclopedia of Neuroscience (2009), vol. 7, pp. 1055-1063), « Priming refers to a change in behavioral response to a stimulus, following prior exposure to the same, or a related, stimulus. Examples include faster reaction times to make a decision about the stimulus, a bias to produce that stimulus when generating responses, or the more accurate identification of a degraded version of the stimulus". Or "Repetition priming refers to a change in behavioural response to a stimulus following re-exposure" (PMID: 18328508). I therefore do not think that the effects observed by the authors are really the investigation of the neural mechanisms of priming. To me, the effect they observed seems more related to sensitisation, especially for the activation of sweet-sensing neurons. For the shock effect, it could be a safety phenomenon, as in Jacob and Waddell, 2020, involving (as for sugar reward) different subsets for short-term and long-term safety.

(2) The author missed the paper from Thomas Preat, The Journal of Neuroscience, October 15, 1998, 18(20):8534-8538 (Decreased Odor Avoidance after Electric Shock in Drosophila Mutants Biases Learning and Memory Tests). In this paper, one of the effects observed by the authors has already been described, and the molecular requirement of memory-related genes is investigated. This paper should be mentioned and discussed.

(3) Overall, the bidirectional effect they observed is interesting; however, their results are not always clear, and the use of a delta PI is sometimes misleading. The authors have mentioned that shocks induced attraction to the novel odour, while they should stick to the increase or decrease in preference/avoidance. As not all experiments are done in parallel logic, it is not always easy to understand which protocol the authors are using. For example, only optogenetics is used in the appetitive preexposure. Does exposing flies to sugar or activating reward dopaminergic neurons also increase odour avoidance? The observed increased odour avoidance after optogenetic activation of sweet-sensing neurons involve reward (e.g., decreased response) and/or punishment (e.g., increased response) to increase odour avoidance? The author should always statistically test the fly behavioural performances against 0 to have an idea of random choice or a clear preference toward an odour. On the appetitive side, the internal hunger state would play an important role. The author should test it or at least discuss it.

(4) The authors found a discrepancy between genetic backgrounds; sometimes the same odour can be attractive or aversive. Different effects between the T-maze and the olfactory arena are found. The authors proposed that: "Punishment priming effect was still not detected, probably due to the insensitivity of the optogenetic arena". This is unclear to me, considering all prior work using this arena. The author should discuss it more clearly. They mentioned that flies could not be conditioned with air and electric shock. However, flies could be conditioned with the context + shock, which is changing in the T-maze and not in the optogenetic area.

Reviewer #2 (Public review):

Summary:

In this single center, single arm, open label non-randomised study the authors tested the use of paclitaxel at 180-220 mg/m2 and cisplatin at 60mg/m2 in patients with squamous NSCLC and pemetrexed at 500mg/m2 and cisplatin at 60mg/m2 in adenocarcinoma of lung origin in the neoadjuvant setting. The chemotherapy appears to have been given at a relatively standard dose; though the platin dose at 60mg/m2 is somewhat lower than has been used in the checkmate 816 trial (75mg/m2/dose), this is a well-established dose for NSCLC.

Key differences to currently approved neoadjuvant chemo-ICI treatment is that anti-PD1 antibody sintilimab (at 200mg/dose) was given on day 5 and that only 2 cycles of chemotherapy were given pre surgery, but then repeated on two occasions post surgery. Between May/2020 and Nov/2023 50 patients were screened, 38 went on to have this schedule of tx, 31 (~82%) went on to have surgery and 27 had the adjuvant treatment. The rate of surgery is entirely consistent with the checkmate 816 data.

Question to the authors:

It would be very helpful to understand why 7 (~18% of the population) patients did not make it to surgery and whether this is related to disease progression, toxicity or other reasons for withdrawal.

The key clinical endpoints were pCR and mPR rates. 2/38 patients are reported to have achieved a radiological pCR but only 31 patients underwent surgery with histological verification. Supp table2 suggests that 10/31 patients achieved a pCR, 6/31 additional patients achieved a major pathological response and that 13/31 did not achieve a major pathological response

It would be really helpful for understanding the clinical outcome to present the histopathological findings in the text in a bit more detail and to refer the outcome to the radiological findings. I note that the reference for pathological responses incorrectly is 38 patients as only 31 patients underwent surgery and were evaluated histologically.

The treatment was very well tolerated with only 1 grade 3 AE reported. The longer term outcome will need to be assessed over time as the cohort is very 'young'. It is not clear what the adjuvant chemo-ICI treatment would add and how this extra treatment would be evaluated for benefit - if all the benefit is in the neoadjuvant treatment then the extra post-operative tx would only add toxicity

Please consider what the two post-operative chemo-ICI cycles might add to the outcome and how the value of these cycles would be assessed. Would there be a case for a randomised assessment in the patients who have NOT achieved a mPR histologically?

While the clinical dataset identifies that the proposed reduced chemo-ICI therapy has clinical merit and should be assessed in a randomized study, the translational work is less informative.

The authors suggest that the treatment has a positive impact on T lymphocytes. Blood sampling was done at day 0 and day 5 of each of the four cycle of chemotherapy with an additional sample post cycle 4. The authors state that data were analysed at each stage.

The data in Figure 3B are reported for three sets of pairs: baseline to pre day 5 in cycle 1, day 5 to day 21 in cycle 1, baseline of cycle to to day 5. It remains unclear whether the datasets contain the same top 20 clones and it would be very helpful to show kinetic change for the individual 'top 20 clones' throughout the events in individual patients; as it stands the 'top20 clones' may vary widely from timepoint to timepoint. Of note, the figures do not demonstrate that the top 20 TCR clones were 'continuously increased'.

Instead, the data suggest that there are fluctuations in the relative distributions over time but that may simply be a reflection of shifts in T cell populations following chemotherapy rather than of immunological effects in the cancer tissue.<br /> Consistent with this the authors conclude (line 304/5): "No significant difference was observed in the diversity, evenness, and clonality of TCR clones across the whole treatment procedure" and this seems to be a more persuasive conclusion than the statement 'that a positive effect on T lymphocytes was observed' - where it is also not clear what 'positive' means.

The text needs a more balanced representation of the data: only a small subset of four patients appear to have been evaluated to generate the data for figure 3B and only three patients (P5, P6, P7) can have contributed to figure 3C if the sample collection is represented accurately in Figure 3A.

The text refers to flow cytometric results in SF3. However, no information is given on the flow cytometry in M&M, markers or gating strategy.

Please consider changing the terminology of the 'phases' into something that is easier to understand. One option would be to use a reference to a more standard unit (cycle 1-4 of chemotherapy and then d0/d5/d21).

Please make it explicit in the text that molecular analyses were undertaken for some patients only, and how many patients contribute to the data in figures 3B-F. Figure 3A suggests paired mRNA data were obtained in 2 patients (P2 and P5) but I cannot find the results on these analyses; four individual blood samples to assess TCR changes int PH1/PH2/PH3and PH4 were only available in four patients (P4,P5,P7,P9). Only three patients seem to have the right samples collected to allow the analysis for 'C3' in figure 3C.

Please display for each of the 'top 20 clones' at any one timepoint how these clones evolve throughout the study; I expect that a clone that is 'top 20' at a given timepoint may not be among the 'top twenty' at all timepoints.

Please also assess if the expanded clonotypes are present (and expanded) in the cancer tissue at resection, to link the effect in blood to the tumour. Given that tissue was collected for 31 patients, mRNA sequencing to generate TCR data should be possible to add to the blood analyses in the 12 patients in Figure 3A. Without this data no clear link can be made to events in the cancer.

Please provide in M&M the missing information on the flow cytometry methodology (instrument, antibody clones, gating strategy) and what markers were used to define T cell subsets (naïve, memory, central memory, effector memory).

The authors also describe that ctDNA reduces after chemo-ICI treatment. This is well documented in their data but ultimately irrelevant: if the cancer volume is reduced to the degree of a radiological or pathological response /complete response then the quantity of circulating DNA from the cancer cells must reduce. More interesting would be the question whether early changes predict clinical outcome and whether recurrent ct DNA elevations herald recurrence.

Please probe whether the molecular data identify good radiological or pathological outcomes before cycle 2 is started and whether the ctDNA levels identify patients who will have a poor response and/or who relapse early.

Reviewer #2 (Public review):

This study provides some interesting observations on how different flavour e-cigarettes can affect lung immunology; however, there are numerous flaws, including a low replicate number and a lack of effective validation methods, meaning findings may not be repeated. This is a revised article but several weaknesses remain related to the analysis and interpretation of the data.

Strengths:

The strength of the study is the successful scRNA-seq experiment which gives some preliminary data that can be used to create new hypotheses in this area.

Weaknesses:

Although some text weaknesses have been addressed since resubmission, other specific weaknesses remain: The major weakness is the n-number and analysis methods. Two biological n per group is not acceptable to base any solid conclusions. Any validatory data was too little (only cell % data) and not always supporting the findings (e.g. figure 3D does not match 3B/4A). Other examples include:

(1) There aren't enough cells to justify analysis - only 300-1500 myeloid cells per group with not many of these being neutrophils or the apparent 'Ly6G- neutrophils'

(2) The dynamic range of RNA measurement using scRNAseq is known to be limited - how do we know whether genes are not expressed or just didn't hit detection? This links into the Ly6G negative neutrophil comments, but in general the lack of gene expression in this kind of data should be viewed with caution, especially with a low n number and few cells. The data in the entire paper is not strong enough to base any solid conclusion - it is not just the RNA-sequencing data.

(3) There is no data supporting the presence of Ly6G negative neutrophils. In the flow cytometry only Ly6G+ cells are shown with no evidence of Ly6G negative neutrophils (assuming equal CD11b expression). There is no new data to support this claim since resubmission and the New figures 4C and D actually show there are no Ly6G negative cells - the cells that the authors deem Ly6G negative are actually positive - but the red overlay of S100A8 is so strong it blocks out the green signal - looking to the Ly6G single stains (green only) you can see that the reported S100A8+Ly6G- cells all have Ly6G (with different staining intensities).

(4) Eosinophils are heavily involved in lung macrophage biology, but are missing from the analysis - it is highly likely the RNA-sequence picked out eosinophils as Ly6G- neutrophils rather than 'digestion issues' the authors claim

(5) After author comments, it appears the schematic in Figure 1A is misleading and there are not n=2/group/sex but actually only n=1/group/sex (as shown in Figure 6A). Meaning the n number is even lower than the previous assumption.