Reviewer #1 (Public review):

This was a clearly written manuscript that did an excellent job summarizing complex data. In this manuscript, Cuevas-Zuviría et al. use protein modeling to generate over 5,000 predicted structures of nitrogenase components, encompassing both extant and ancestral forms across different clades. The study highlights that key insertions define the various Nif groups. The authors also examined the structures of three ancestral nitrogenase variants that had been previously identified and experimentally tested. These ancestral forms were shown in earlier studies to exhibit reduced activity in Azotobacter vinelandii, a model diazotroph.

Reviewer #2 (Public review):

Summary:

This work aims to study the evolution of nitrogenanses, understanding how their structure and function adapted to changes in environment, including oxygen levels and changes in metal availability.

The study predicts > 3000 structures of nitrogenases, corresponding to extant, ancestral and alternative ancestral sequences. It is observed that structural variations in the nitrogenases correlate with phylogenetic relationships. The amount of data generated in this study represents a massive and admirable undertaking. The study also provides strong insight into how structural evolution correlates with environmental and biological phenotypes.

Reviewer #2 (Public review):

Summary:

The authors aimed to understand the biophysical properties of archeal membranes made of bolalipids. Bacterial and eukaryotic membranes are made of lipids that self-assemble into bilayers. Archea, instead, use bolalipids, lipids that have two headgroups and can span the entire bilayer. The authors wanted to determine if the unique characteristics of archaea, which are often extremophiles, are in part due to the fact that their membranes contain bolalipids.

The authors develop a minimal computational model to compare the biophysics of bilayers made of lipids, bolalipids, and mixtures of the two. Their model enables them to determine essential parameters such as bilayer phase diagrams, mechanical moduli, and the bilayer behavior upon cargo inclusion and remodeling.

The author demonstrates that bolalipid bilayers behave as binary mixtures, containing bolalipids organized either in a straight conformation, spanning the entire bilayer, or in a u-shaped one, confined to a single leaflet. This dynamic mixture allows bolalipid bilayers to be very sturdy but also provides remodeling. However, remodeling is energetically more expensive than with standard lipids. The authors speculate that this might be why lipids were more abundant in the evolutionary process.

Strengths:

This is a wonderful paper, a very fine piece of scholarship. It is interesting from the point of view of biology, biophysics, and material science. The authors mastered the modeling and analysis of these complex systems. The evidence for their findings is really strong and complete. The paper is written superbly, the language is precise and the reading experience very pleasant. The plots are very well-thought.

Weaknesses:

None. The authors have addressed all the potential weaknesses that were raised by the reviewers.

Reviewer #3 (Public review):

Summary:

The authors have studied the mechanics of bolalipid and archaeal mixed-lipid membranes via comprehensive molecular dynamics simulations. The Cooke-Deserno 3-bead-per-lipid model is extended to bolalipids with 6 bead. Phase diagrams, bending rigidity, mechanical stability of curved membranes, and cargo uptake are studied. Effects such as formation of U-shaped bolalipids, pore formation in highly curved regions, and changes in membrane rigidity are studied and discussed. The main aim has been to show how the mixture of bolalipids and regular bilayer lipids in archaeal membrane models enhances the fluidity and stability of these membranes.

The authors have presented a wide range of simulation results for different membrane conditions and conformations. Analyses and findings are presented clearly and concisely. Figures, supplementary information and movies are of very high quality and very well present what has been studied. The manuscript is well written and is easy to follow.

The authors have provided detailed response to the points I raised on the first version and have revised their manuscript accordingly. Hence, I only mention what, in my opinion, still deserves to be noted.

Comments:

I previously raised an issue with respect to the resort to the Hamm-Kozlov model for fitting the power spectrum of membrane undulations. The authors provided very nice arguments against my concerns. For the sake of completeness, I include a simple scenario, which will better highlight the issue:

The tilt contribution to the Helfrich Hamiltonian can be written as a quadratic term 1/2 k_t |T|^2, where T is a tilt vector field. This field is written as the difference between the surface normal and the director field aligned with the lipid orientations. In the small deviation Monge description with z=h(x, y) as the height function, the surface normal has the form N=(-dh/dx, -dh/dy, 1). Now assume the director field, n = (b_x, b_y, 1) with small b_x and b_y components. The tilt contribution to the energy thus reads as 1/2 k_t (N - n)^2 ~= 1/2 k_t [|grad h|^2 + 2 b . grad h]. The first term, 1/2 k_t |grad h|^2, is indeed similar to a surface tension term, \sigma |grad h|^2 that you get from the (1 + 1/2 |grad h|^2) approximation to the area element. Therefore, if you only look at height fluctuations, while your membrane actually has some surface tension, it will make distinguishing the tilt contributions to the fluctuations in the linear Monge gauge impossible.

However, considering that the authors have made sure that the membrane is indeed tensionless, this argument is settled.

I had also raised an issue about the correct NpT sampling in the simulations, and I'm glad that the authors also set up more rigorously thermostatted/barostatted simulations to check the validity of their findings.

Also, from the SI, I previously noted that the authors had neglected the longest wavelength mode because it was not equilibrated. This was an important problem and the authors looked into it and ran more simulations that were better equilibrated.

The analysis of energy of U-shaped lipids with the linear model E=c_0 + c_1 * k_bola is indeed very interesting. I am glad that the authors have expanded this analysis and included mean energy measurements.

Reviewer #1 (Public review):

Summary:

The study by Druker et al. shows that siRNA depletion of PHD1, but not PHD2, increases H3T3 phosphorylation in cells arrested in prometaphase. Additionally, the expression of wild-type RepoMan, but not the RepoMan P604A mutant, restored normal H3T3 phosphorylation localization in cells arrested in prometaphase. Furthermore, the study demonstrates that expression of the RepoMan P604A mutant leads to defects in chromosome alignment and segregation, resulting in increased cell death. These data support a role for PHD1-mediated prolyl hydroxylation in controlling progression through mitosis. This occurs, at least in part, by hydroxylating RepoMan at P604, which regulates its interaction with PP2A during chromosome alignment.

Strengths:

The data support most of the conclusions made. However, some issues need to be addressed.

Weaknesses:

(1) Although ectopically expressed PHD1 interacts with ectopically expressed RepoMan, there is no evidence that endogenous PHD1 binds to endogenous RepoMan or that PHD1 directly binds to RepoMan.

(2) There is no genetic evidence indicating that PHD1 controls progression through mitosis by catalyzing the hydroxylation of RepoMan.

(3) Data demonstrating the correlation between dynamic changes in RepoMan hydroxylation and H3T3 phosphorylation throughout the cell cycle are needed.

(4) The authors should provide biochemical evidence of the difference in binding ability between RepoMan WT/PP2A and RepoMan P604A/PP2A.

(5) PHD2 is the primary proline hydroxylase in cells. Why does PHD1, but not PHD2, affect RepoMan hydroxylation and subsequent control of mitotic progression? The authors should discuss this issue further.

Reviewer #2 (Public review):

Summary:

This is a concise and interesting article on the role of PHD1-mediated proline hydroxylation of proline residue 604 on RepoMan and its impact on RepoMan-PP1 interactions with phosphatase PP2A-B56 complex leading to dephosphorylation of H3T3 on chromosomes during mitosis. Through biochemical and imaging tools, the authors delineate a key mechanism in the regulation of the progression of the cell cycle. The experiments performed are conclusive with well-designed controls.

Strengths:

The authors have utilized cutting-edge imaging and colocalization detection technologies to infer the conclusions in the manuscript.

Weaknesses:

Lack of in vitro reconstitution and binding data.

Reviewer #3 (Public review):

Summary:

The manuscript is a comprehensive molecular and cell biological characterisation of the effects of P604 hydroxylation by PHD1 on RepoMan, a regulatory subunit of the PPIgamma complex. The identification and molecular characterisation of the hydroxylation site have been written up and deposited in BioRxiv in a separate manuscript. I reviewed the data and came to the conclusion that the hydroxylation site has been identified and characterised to a very high standard by LC-MS, in cells and in vitro reactions. I conclude that we should have no question about the validity of the PHD1-mediated hydroxylation.

In the context of the presented manuscript, the authors postulate that hydroxylation on P604 by PHD1 leads to the inactivation of the complex, resulting in the retention of pThr3 in H3.

Strengths:

Compelling data, characterisation of how P604 hydroxylation is likely to induce the interaction between RepoMan and a phosphatase complex, resulting in loading of RepoMan on Chromatin. Loss of the regulation of the hydroxylation site by PHD1 results in mitotic defects.

Weaknesses:

Reliance on a Proline-Alanine mutation in RepoMan to mimic an unhydroxylatable protein. The mutation will introduce structural alterations, and inhibition or knockdown of PHD1 would be necessary to strengthen the data on how hydroxylates regulate chromatin loading and interactions with B56/PP2A.

Reviewer #1 (Public review):

The authors conducted a comprehensive investigation into sleep and circadian rhythm disturbances in Fmr1 knockout (KO) mice, a model for Fragile X Syndrome (FXS). They began by monitoring daily home cage behaviors to identify disruptions in sleep and circadian patterns, then assessed the mice's adaptability to altered light conditions through photic suppression and skeleton photoperiod experiments. To uncover potential mechanisms, they examined the connectivity between the retina and the suprachiasmatic nucleus. The study also included an analysis of social behavior deficits in the mutant mice and tested whether scheduled feeding could alleviate these issues. Notably, scheduled feeding not only improved sleep, circadian, and social behaviors but also normalized plasma cytokine levels. The manuscript is strengthened by its focus on a significant and underexplored area-sleep deficits in an FXS model-and by its robust experimental design, which integrates a variety of methodological approaches to provide a thorough understanding of the observed phenomena and potential therapeutic avenues.

Reviewer #2 (Public review):

Summary:

In the present study, the authors, using a mouse model of Fragile X syndrome, explore the intriguing hypothesis that restricting food access over the daily schedule will improve sleep patterns and subsequently enhance behavioral capacities. By restricting food access from 12h to 6h over the nocturnal period (the active period for mice), they show, in these KO mice, an improvement in the sleep pattern accompanied by reduced systemic levels of inflammatory markers and improved behavior. These data, using a classical mouse model of neurodevelopmental disorder (NDD), suggest that modifying eating patterns might improve sleep quality, leading to reduced inflammation and enhanced cognitive/behavioral capacities in children with NDD.

Overall, the paper is well-written and easy to follow. The rationale of the study is generally well introduced. Data are globally sound. The interpretation is overall supported by the provided data.

Reviewer #1 (Public review):

Summary:

The manuscript by Li and colleagues describes the impact of deficiency on the DKGα and ζ on Treg cells and follicular responses. The experimental approach is based on the characterization of double KO mice that show the emergence of autoimmune manifestations that include the production of autoantibodies. Additionally, there is an increase in Tfh cells, but also Tfr cells in these mice deficient in both DKGα and ζ. Although the observations are interesting, the interpretation of the observations is difficult in the absence of data related to single mutations. While a supplementary figure shows that the autoimmune manifestations are more severe in the DKGα and ζ deficient mice, prior observations show that a single DKGα deficiency has an impact on Treg homeostasis. As such, the contribution of the two chains to the overall phenotype is hard to establish.

Strengths:

Well-conducted experiments with informative mouse models with defined genetic defects.

Weaknesses:

The major weakness is the lack of clarity concerning what can be attributed to simultaneous DKGα and ζ deficiency versus deficiency on DKGα or ζ alone. Technical concerns related to a number of figures were raised in the initial report and not adequately addressed by the authors in the revised manuscript.

In conclusion, the claims in the manuscript are not convincingly supported by the data,

Reviewer #2 (Public review):

Summary:

In this manuscript, Li et al investigates the combined role of diacylglycerol (DAG) kinases (DGK) a and z in Foxp3+ Treg cells function that prevent autoimmunity. The authors generated DGK a and z Treg-specific double knock out mice (DKO) by crossing Dgkalpha-/- mice to DgKzf/f and Foxp3YFPCre/+ mice. The resulting "DKO" mice thus lack DGK a in all cells and DGK and z in Foxp3+Treg cells. The authors show that the DKO mice spontaneously develop autoimmunity, characterized by multiorgan inflammatory infiltration and elevated anti double strand DNA (dsDNA), -single strand DNA (ssDNA), and -nuclear autoantibodies. The authors attribute the DKO mice phenotype to Foxp3+Treg dysfunction, including accelerated conversion into "exTreg" cells with pathogenic activity. Interestingly, the combined deficiency of DGK a and z seems to release Treg cell dependence on CD28-mediated costimulatory signals, which the authors show by crossing their DKO mice to CD28-/- mice (TKO mice), which also develop autoimmunity.

Strengths:

The phenotypes of the mutant mice described in the manuscript are striking, and the authors provide a comprehensive analysis of the functional processes alters by the lack of DGKs.

Weaknesses:

One aspect that could be better explored is the direct role of "ex-Tregs" in causing pathogenesis in the models utilized.

But overall, this is an important report that makes a significant addition to the understanding of DAG kinases to Treg cells biology.

Reviewer #1 (Public review):

When you search for something, you need to maintain some representation (a "template") of that target in your mind/brain. Otherwise, how would you know what you were looking for? If your phone is in a shocking pink case, you can guide your attention to pink things based on a target template that includes the attribute 'pink'. That guidance should get you to the phone pretty effectively, if it is in view. Most real-world searches are more complicated. If you are looking for the toaster, you will make use of your knowledge of where toasters can be. Thus, if you are asked to find a toaster, you might first activate a template of a kitchen or a kitchen counter. You might worry about pulling up the toaster template only after you are reasonably sure you have restricted your attention to a sensible part of the scene.

Zhou and Geng are looking for evidence of this early stage of guidance by information about the surrounding scene in a search task. They train Os to associate four faces with four places. Then, with Os in the scanner, they show one face - the target for a subsequent search. After an 8 sec delay, they show a search display where the face is placed on the associated scene 75% of the time. Thus, attending to the associated scene is a good idea. The questions of interest are "When can the experimenters decode which face Os saw from fMRI recording?" "When can the experimenters decode the associated scene?" and "Where in the brain can the experimenters see evidence of this decoding? The answer is that the face but not the scene can be read out during the face's initial presentation. The key finding is that the scene can be read out (imperfectly but above chance) during the subsequent delay when Os are looking at just a fixation point. Apparently, seeing the face conjures up the scene in the mind's eye.

This is a solid and believable result. The only issue, for me, is whether it is telling us anything specifically about search. Suppose you trained Os on the face-scene pairing but never did anything connected to search. If you presented the face, would you not see evidence of recall of the associated scene? Maybe you would see the activation of the scene in different areas and you could identify some areas as search specific. I don't think anything like that was discussed here.

You might also expect this result to be asymmetric. The idea is that the big scene gives the search information about the little face. The face should activate the larger useful scene more than the scene should activate the more incidental face, if the task was reversed. That might be true if finding is related to search where the scene context is presumed to be the useful attention guiding stimulus. You might not expect an asymmetry if Os were just learning an association.

It is clear in this study that the face and the scene have been associated and that this can be seen in the fMRI data. It is also clear that a valid scene background speeds the behavioral response in the search task. The linkage between these two results is not entirely clear but perhaps future research will shed more light.

It is also possible that I missed the clear evidence of the search-specific nature of the activation by the scene during the delay period. If so, I apologize and suggest that the point be underlined for readers like me.

Comments on revised version:

I am satisfied with the revision.

Reviewer #2 (Public review):

Summary:

This work is one of the best instances of a well-controlled experiment and theoretically impactful findings within the literature on templates guiding attentional selection. I am a fan of the work that comes out of this lab and this particular manuscript is an excellent example as to why that is the case. Here, the authors use fMRI (employing MVPA) to test whether during the preparatory search period, a search template is invoked within the corresponding sensory regions, in the absence of physical stimulation. By associating faces with scenes, a strong association was created between two types of stimuli that recruit very specific neural processing regions - FFA for faces and PPA for scenes. The critical results showed that scene information that was associated with a particular cue could be decoded from PPA during the delay period. This result strongly supports invoking of a very specific attentional template.

Strengths:

There is so much to be impressed with in this report. The writing of the manuscript is incredibly clear. The experimental design is clever and innovative. The analysis is sophisticated and also innovative. The results are solid and convincing.

Weaknesses:

I only have a few weaknesses to point out.<br /> This point is not so much of a weakness, but a further test of the hypothesis put forward by the authors. The delay period was long - 8 seconds. It would be interesting to split the delay period into the first 4seconds and the last 4seconds and run the same decoding analyses. The hypothesis here is that semantic associations take time to evolve, and it would be great to show that decoding gets stronger in the second delay period as opposed to the period right after the cue. I think it would be a stronger test of the template hypothesis.

Typo in the abstract "curing" vs "during."

It is hard to know what to do with significant results in ROIs that are not motivated by specific hypotheses. However, for Figure 3, what are explanations for ROIs that show significant differences above and beyond the direct hypotheses set out by the authors?

Following the revision, I have no further comments or concerns.

Reviewer #3 (Public review):

The manuscript contains a carefully designed fMRI study, using MVPA patter analysis to investigate which high-level associate cortices contain target-related information to guide visual search. A special focus is hereby on so-called 'target-associated' information, that has previously been shown to help in guiding attention during visual search. For this purpose the author trained their participants and made them learn specific target-associations, in order to then test which brain regions may contain neural representations of those learnt associations. They found that at least some of the associations tested were encoded in prefrontal cortex during the cue and delay period.

The manuscript is very carefully prepared. As far as I can see, the statistical analyses are all sound and the results integrate well with previous findings.

I have no strong objections against the presented results and their interpretation.

The authors have addressed all my previous comments and questions in their revision of the text.

Reviewer #1 (Public review):

Summary:

In this study by Li et al., the authors re-investigated the role of cDC1 for atherosclerosis progression using the ApoE model. First, the authors confirmed the accumulation of cDC1 in atherosclerotic lesions in mice and humans. Then in order to examine the functional relevance of this cell type, the authors developed a new mouse model to selectively target cDC1. Specifically, they inserted the Cre recombinase directly after the start codon of endogenous XCR1 gene, thereby avoiding off-target activity. Following validation of this model, the authors crossed it with ApoE-deficient mice and found a striking reduction of aortic lesions (numbers and size) following high fat diet. The authors further characterized the impact of cDC1 depletion on lesional T cells and their activation state. Also, they provide in-depth transcriptomic analyses of lesional in comparison to splenic and nodal cDC1. These results imply cellular interactions between lesion T cells and cDC1. Finally, the authors show that the chemokine XCL1, which is produced by activated CD8 T cells (and NK cells) plays a key role for the interaction with XCR1-expressing cDC1 and particularly for the atherosclerotic disease progression.

Strengths:

The surprising results on XCL1 represent a very important gain in knowledge. The role of cDC1 is clarified with a new genetic mouse model.

Comments on revised version:

The authors have addressed my concerns in the revised version of this manuscript.

Reviewer #2 (Public review):

This study investigates the role of cDC1 in atherosclerosis progression using Xcr1Cre-Gfp Rosa26LSL-DTA ApoE-/- mice. The authors demonstrate that selective depletion of cDC1 reduces atherosclerotic lesions in hyperlipidemic mice. While cDC1 depletion did not alter macrophage populations, it suppressed T cell activation (both CD4+ and CD8+ subsets) within aortic plaques. Further, targeting the chemokine Xcl1 (ligand of Xcr1) effectively inhibits atherosclerosis. The manuscript is well-written, and data are clearly presented. The data provided in the article can well support the author's conclusion.

Comments on revised version:

The authors have addressed all previous concerns and made appropriate revisions to the data. I have no further questions.

Reviewer #1 (Public review):

In the revised manuscript, Meng et al. report that SARS-CoV-2 infection suppresses YAP target gene transcription in both patient lung samples and iPSC-derived cardiomyocytes. Among the tested viral proteins, the helicase nonstructural protein 13 (NSP13) was identified as a key factor that impairs YAP/TEAD transcriptional activity. Through mutagenesis and protein-protein interaction studies, the authors propose a mechanism where NSP13 binds YAP/TEAD complex, remodels chromatin structure, and recruits transcriptional repressors to inhibit YAP/TEAD's transcriptional activity.

Overall, this study uncovers a novel regulation of Hippo signaling by SARS-CoV-2 through NSP13, suggesting a potential role of this growth-related pathway in host innate immune response to viral infection. While these findings are intriguing, future studies are needed to validate the involvement of YAP/TEAD in patient tissues and to assess their potential as therapeutic targets against SARS-CoV-2.

Reviewer #2 (Public review):

Summary:

The manuscript by Meng et al. describes a role for the coronavirus helicase NSP13 in the regulation of YAP-TEAD-mediated transcription. The authors present data that NSP13 expression in cells reduces YAP-induced TEAD luciferase reporter activity and that NSP13 transduction in cardiomyocytes blocks hyperactive YAP-mutant phenotypes in vivo. Mechanisms by which viral proteins (particularly those from coronaviruses) intersect with cellular signaling events is an important research topic, and the intersection of NSP13 with YAP-TEAD transcriptional activity (independent of upstream Hippo pathway mediated signals) offers new knowledge that is of interest to a broad range of researchers.

Strengths:

The manuscript presents convincing data mapping the effects of NSP13 on YAP-TEAD reporter activity to the helicase domain. Moreover, the in vivo data demonstrating that NSP13 expression in YAP5SA mouse cardiomyocytes increased survival animal rates, and restored cardiac function is striking and is supportive of the model presented.

Weaknesses:

While there are some hints at the mechanisms by which NSP13 regulates YAP-TEAD activity through the identification of NSP13-associated proteins by mass spec, the relationships and functions of these factors in the context of YAP-TEAD regulation requires further study in the future.

Reviewer #1 (Public review):

Summary:

The authors tackle a long-standing question in developmental theory: given a gene-regulatory network that includes extracellular signalling, which topologies are even capable of transforming an initial spatial profile into a genuinely new pattern? Building on the classical reaction-diffusion framework in one dimension, but imposing biologically motivated constraints, they prove that every one-signal sub-network must be either Hierarchical (H), self-activating (L+), or self-inhibiting (L-). They further demonstrate that only three composite classes of full networks - pure H, a coupled L+ L- "Turing" pair, and an L- module fed by an intracellular positive loop ("noise-amplifying")-can create non-trivial spatial transformations. Analytical criteria and illustrative simulations are provided, together providing a closed taxonomy, which is supposed to be relevant for real systems.

Strengths:

(1) Useful classification framework. Reducing a vast number of possible gene circuits to three canonical pattern-forming motifs is a valuable organising insight for both theorists and experimentalists.

(2) Logical completeness. All required cases are addressed, and the proofs elevate previous computational observations to formal statements.

(3) Practical interpretability. Given a reaction network diagram, one can now decide (assuming the model applies to the real systems) whether spatial patterning is even possible, saving experimental effort on in-silico screens that could never succeed.

Weaknesses:

(1) The Results section is difficult to follow. Key logical steps and network configurations are described shortly in prose, which constantly require the reader to address either SI or other parts of the text (see numerous links on the requirements R1-R5 listed at the beginning of the paper) to gain minimal understanding. As a result, a scientifically literate but non-specialist reader may struggle to grasp the argument with a reasonable time invested.

(2) A central step in the model formulation is the linearisation of the reaction term around a homogeneous steady state; higher-order kinetics, including ubiquitous bimolecular sinks such as A + B → AB, are simply collapsed into the Jacobian without any stated amplitude bound on the perturbations. Because the manuscript never analyses how far this assumption can be relaxed, the robustness of the three-class taxonomy under realistic nonlinear reactions or large spike amplitudes remains uncertain.

(3) All modelling is confined to one spatial dimension, and the very definition of a "non-trivial" transformation is framed in terms of peak positions along a line, which clearly must be reformulated for higher dimensions. It's well-known that diffusions in 1, 2, and 3 dimensions are also dramatically different, so the relevance of the three-class taxonomy to real multicellular tissues remains unclear, or at least should be explained in more detail.

Discussion:

As stated above, there are several uncertainties about the relevance of the presented framework for real systems. However, if the results hold, researchers could look at a gene-network diagram and quickly judge whether it can make spatial patterns and, if so, which of the three known mechanisms it will use. That shortcut would save experimental and computational time. In the case that the results don't hold for the real systems, the authors' proof tools at least give theorists a solid base they can extend to more complex cases.

Reviewer #2 (Public review):

Summary:

This study explores how gene regulatory networks that include intra- and extracellular signaling can give rise to spatial patterns of gene expression in cells. The authors investigate this question in a simplified theoretical framework, where all cells are assumed to respond identically to signals, and spatial details such as cell boundaries and extensions are abstracted away. Within this setting, they identify three distinct signaling topologies, referred to as L and H types, and combine them into three minimal subnetworks capable of generating patterns. The study analyzes possible combinations of these topologies and examines how each subnetwork behaves under three different initial conditions. Combining the analyses with mathematical proofs and heuristic arguments, the authors define necessary conditions under which such networks can produce non-trivial spatial patterns.

Strengths:

The authors break down larger gene regulatory networks into smaller subnetworks, which allows for a more tractable analysis of pattern formation. These minimal subnetworks are examined under different initial conditions, providing a range of examples for how patterns can emerge in simplified settings. The study also proposes necessary conditions for pattern formation, which may be useful for identifying relevant network structures. In addition, the manuscript offers heuristic explanations for the emergence of patterns in each subnetwork, which help to interpret the simulation results and analytical criteria.

Weaknesses:

(1) We have serious concerns regarding the validity of the simulation results presented in the manuscript. Rather than simulating the full nonlinear system described by Equation (1), the authors base their results on a truncated expansion (Equation S.8.2) that captures only the time evolution of small deviations around a spatially homogeneous steady state. However, it remains unclear how this reduced system is derived from the full equations - specifically, which terms are retained or neglected and why - and how the expansion of the nonlinear function can be steady-state independent, as claimed. Additionally, in simulations involving the spike plus homogeneous initial condition, it is not evident - or, where equations are provided, it is not correct - that the assumed global homogeneous background actually corresponds to a steady state of the full dynamics. We elaborate on these concerns in the following:

It is assumed that the homogeneous steady states are given by g_i=0 and g_i=c_i, where 1/c_i = \mu_i or \hat{\mu}_i​, independently of the specific network structure. However, the basis for this assumption is unclear, especially since some of the functions do not satisfy this condition - for example, f5​ as defined below Eq. S8.10.5. Moreover, if g_i=c_i does not correspond to a true steady state, then the time evolution of deviations from this state is not correctly described by Eq. S8.2, as the zeroth-order terms do not vanish in that case.

Additionally, the equations used contain only linear terms and a cubic degradation term for each species g_i, while neglecting all quadratic terms and cubic terms involving cross-species interactions (i≠j). An explanation for this selective truncation is not provided, and without knowledge of the full equation (f), it is impossible to assess whether this expansion is mathematically justified. If, as suggested in the Supplementary Information, the linear and cubic terms are derived from f, then at the very least, the Jacobian matrix should depend on the background steady-state concentration. However, the equations for the small deviation around a steady state (including the Jacobian matrix) used in the simulations appear to be independent of the particular steady state concentration.

This is why we believe that the differences observed between the spike-only initial condition and the spike superimposed on a homogeneous background are not due to the initial conditions themselves, but rather result from a modified reaction scheme introduced through a questionable cutoff.

"In simulations with spike initial patterns, the reference value g≡0 represents an actual concentration of 0 and therefore, we must add to (S8.2) a Heaviside function Φ acting of f (i.e., Φ(f(g))=f(g) if f(g)>0 , Φ(f(g))=0 if f(g){less than or equal to}0 ) to prevent the existence of negative concentrations for any gene product (i.e., g_i<0 for some i )." (SI chapter S8).

This cutoff alters the dynamics (no inhibition) and introduces a different reaction scheme between the two simulations. The need for this correction may itself reflect either a problem in the original equations (which should fulfill the necessary conditions and prevent negative concentrations (R4 in main text)) or the inappropriateness of using an expanded approximation which assumes independence on the steady state concentration. It is already questionable if the linearized equations with a cubic degradation term are valid for the spike initial conditions (with different background concentration values), as the amplitude of this perturbation seems rather large.

Lastly, we note that under the current simulation scheme, it is not possible to meaningfully assess criteria RH2a and RH2b, as they rely on nonlinear interactions that are absent from the implemented dynamics.

(2) Most of the proofs presented in the Supplementary Information rely on linearized versions of the governing equations, and it remains unclear how these results extend to the fully nonlinear system. We are concerned that the generality of the conclusions drawn from the linear analysis may be overstated in the main text. For example, in Section S3, the authors introduce the concept of dynamic equivalence of transitive chains (Proposition S3.1) and intracellular transitive M-branching (Proposition S3.2), which pertains to the system's steady-state behavior. However, the proof is based solely on the linearized equations, without additional justification for why the result should hold in the presence of nonlinearities. Moreover, the linearized system is used to analyze the response to a "spike initial pattern of arbitrary height C" (SI Chapter S5.1), yet it is not clear how conclusions derived from the linear regime can be valid for large perturbations, where nonlinear effects are expected to play a significant role. We encourage the authors to clarify the assumptions under which the linearized analysis remains valid and to discuss the potential limitations of applying these results to the nonlinear regime.

(3) Several statements in the main text are presented without accompanying proof or sufficient explanation, which makes it difficult to assess their validity. In some cases, the lack of justification raises serious doubts about whether the claims are generally true. Examples are:

"For the purpose of clarity we will explain our results as if these cells have a simple arrangement in space (e.g., a 1D line or a 2D square lattice) but, as we will discuss, our results shall apply with the same logic to any distribution of cells in space." (Main text l.145-l.148).

"For any non-trivial pattern transformation (as long as it is symmetric around the initial spike), there exists an H gene network capable of producing it from a spike initial pattern." (Main text l.366f).

"In 2D there are no peaks but concentric rings of high gene product concentration centered around the spike, while in 3D there are concentric spherical shells." (Main text l. 447ff).

(4) The study identifies one-signal networks and examines how combinations of these structures can give rise to minimal pattern-forming subnetworks. However, the analysis of the combinations of these minimal pattern-forming subnetworks remains relatively brief, and the manuscript does not explore how the results might change if the subnetworks were combined in upstream and downstream configurations. In our view, it is not evident that all possible gene regulatory networks can be fully characterized by these categories, nor that the resulting patterns can be reliably predicted. Rather, the approach appears more suited to identifying which known subnetworks are present within a larger network, without necessarily capturing the full dynamics of more complex configurations.

(5) The definition of non-trivial pattern formation is provided only in the Supplementary Information, despite its central importance for interpreting the main results. It would significantly improve clarity if this definition were included and explained in the main text. Additionally, it remains unclear how the definition is consistently applied across the different initial conditions. In particular, the authors should clarify how slope-based measures are determined for both the random noise and sharp peak/step function initial states. Furthermore, the authors do not specify how the sign function is evaluated at zero. If the standard mathematical definition sgn(0)=0 is used, then even a simple widening of a peak could fulfill the criterion for non-trivial pattern transformation.

(6) The manuscript lacks a clear and detailed explanation of the underlying model and its assumptions. In particular, it is not well-defined what constitutes a "cell" in the context of the model, nor is it justified why spatial features of cells - such as their size or boundaries - can be neglected. Furthermore, the concept of the extracellular space in the one-dimensional model remains ambiguous, making it unclear which gene products are assumed to diffuse.

Reviewer #3 (Public review):

Pattern formation is responsible for generating the spatial organization of cells, tissues, and organs during embryogenesis. It operates within a multifactorial system including initial conditions, gene regulatory networks, extracellular signals, mechanical forces, stochastic noise, and environmental inputs. Finally, it ensures the functional anatomy of an organism.

This study focuses on the one central aspect in pattern formation: how spatial heterogeneity arises from an initial condition and evolves into a more complex or distinct spatial pattern (non-trivial pattern formation, as they termed). The authors made efforts to explore and characterize all possible ways to achieve the pattern formation. They do this by discussing how extracellular signals spread, how individual cells respond to those signals, and how those responses, in turn, modulate signal propagation.

Finally, their comprehensive analysis summarizes that there are three classes of interactions between extracellular signals and intracellular responses, corresponding to previously known mechanisms that can generate spatial patterns: difference in morphogen concentrations in space, noise-amplification, and Turing pattern.

Reviewer #1 (Public review):

Summary:

The authors developed a sequence-based method to predict drug-interacting residues in IDP, based on their recent work, to predict the transverse relaxation rates (R2) of IDP trained on 45 IDP sequences and their corresponding R2 values. The discovery is that the IDPs interact with drugs mostly using aromatic residues that are easy to understand, as most drugs contain aromatic rings. They validated the method using several case studies, and the predictions are in accordance with chemical shift perturbations and MD simulations. The location of the predicted residues serves as a starting point for ligand optimization.

Strengths:

This work provides the first sequence-based prediction method to identify potential drug-interacting residues in IDP. The validity of the method is supported by case studies. It is easy to use, and no time-consuming MD simulations and NMR studies are needed.

Weaknesses:

The method does not depend on the information of binding compounds, which may give general features of IDP-drug binding. However, due to the size and chemical structures of the compounds (for example, how many aromatic rings), the number of interacting residues varies, which is not considered in this work. Lacking specific information may restrict its application in compound optimization, aiming to derive specific and potent binding compounds.

Reviewer #2 (Public review):

Summary:

In this work, the authors introduce DIRseq, a fast, sequence-based method that predicts drug-interacting residues (DIRs) in IDPs without requiring structural or drug information. DIRseq builds on the authors' prior work looking at NMR relaxation rates, and presumes that those residues that show enhanced R2 values are the residues that will interact with drugs, allowing these residues to be nominated from the sequence directly. By making small modifications to their prior tool, DIRseq enables the prediction of residues seen to interact with small molecules in vivo.

Strengths:

The preprint is well written and easy to follow

Weaknesses:

(1) The DIRseq method is based on SeqDYN, which itself is a simple (which I do not mean as a negative - simple is good!) statistical predictor for R2 relaxation rates. The challenge here is that R2 rates cover a range of timescales, so the physical intuition as to what exactly elevated R2 values mean is not necessarily consistent with "drug interacting". Presumably, the authors are not using the helix boost component of SeqDYN here (it would be good to explicitly state this). This is not necessarily a weakness, but I think it would behove the authors to compare a few alternative models before settling on the DIRseq method, given the somewhat ad hoc modifications to SeqDYN to get DIRseq.

Specifically, the authors previously showed good correlation between the stickiness parameter of Tesei et al and the inferred "q" parameter for SeqDYN; as such, I am left wondering if comparable accuracy would be obtained simply by taking the stickiness parameters directly and using these to predict "drug interacting residues", at which point I'd argue we're not really predicting "drug interacting residues" as much as we're predicting "sticky" residues, using the stickiness parameters. It would, I think, be worth the authors comparing the predictive power obtained from DIRseq with the predictive power obtained by using the lambda coefficients from Tesei et al in the model, local density of aromatic residues, local hydrophobicity (note that Tesei at al have tabulated a large set of hydrophobicity scores!) and the raw SeqDYN predictions. In the absence of lots of data to compare against, this is another way to convince readers that DIRseq offers reasonable predictive power.

(2) Second, the DIRseq is essentially SeqDYN with some changes to it, but those changes appear somewhat ad hoc. I recognize that there is very limited data, but the tweaking of parameters based on physical intuition feels a bit stochastic in developing a method; presumably (while not explicitly spelt out) those tweaks were chosen to give better agreement with the very limited experimental data (otherwise why make the changes?), which does raise the question of if the DIRseq implementation of SeqDYN is rather over-parameterized to the (very limited) data available now? I want to be clear, the authors should not be critiqued for attempting to develop a model despite a paucity of data, and I'm not necessarily saying this is a problem, but I think it would be really important for the authors to acknowledge to the reader the fact that with such limited data it's possible the model is over-fit to specific sequences studied previously, and generalization will be seen as more data are collected.

(3) Third, perhaps my biggest concern here is that - implicit in the author's assumptions - is that all "drugs" interact with IDPs in the same way and all drugs are "small" (motivating the change in correlation length). Prescribing a specific lengthscale and chemistry to all drugs seems broadly inconsistent with a world in which we presume drugs offer some degree of specificity. While it is perhaps not unexpected that aromatic-rich small molecules tend to interact with aromatic residues, the logical conclusion from this work, if one assumes DIRseq has utility, is that all IDRs bind drugs with similar chemical biases. This, at the very least, deserves some discussion.

(4) Fourth, the authors make some general claims in the introduction regarding the state of the art, which appear to lack sufficient data to be made. I don't necessarily disagree with the author's points, but I'm not sure the claims (as stated) can be made absent strong data to support them. For example, the authors state: "Although an IDP can be locked into a specific conformation by a drug molecule in rare cases, the prevailing scenario is that the protein remains disordered upon drug binding." But is this true? The authors should provide evidence to support this assertion, both examples in which this happens, and evidence to support the idea that it's the "prevailing view" and specific examples where these types of interactions have been biophysically characterized.

Similarly, they go on to say:

"Consequently, the IDP-drug complex typically samples a vast conformational space, and the drug molecule only exhibits preferences, rather than exclusiveness, for interacting with subsets of residues." But again, where is the data to support this assertion? I don't necessarily disagree, but we need specific empirical studies to justify declarative claims like this; otherwise, we propagate lore into the scientific literature. The use of "typically" here is a strong claim, implying most IDP complexes behave in a certain way, yet how can the authors make such a claim?

Finally, they continue to claim:

"Such drug interacting residues (DIRs), akin to binding pockets in structured proteins, are key to optimizing compounds and elucidating the mechanism of action." But again, is this a fact or a hypothesis? If the latter, it must be stated as such; if the former, we need data and evidence to support the claim.

Reviewer #1 (Public review):

The authors responded to multiple criticisms with additional data and more detailed statistics, in some instances improving the quality of the work. However, I had difficulty understanding some of the authors' responses. The logic was not always apparent, the writing was occasionally confusing or would benefit from more careful wording, and some of the provided responses were superficial or raised new concerns. In some cases, the underlying data needed to support their responses were not shown. Thus, the current version of the manuscript does not sufficiently resolve the following critical issues raised by myself and other reviewers.

(1) A clear new insight into a physiological process or cellular behavior remains lacking. The study largely confirms prior observations of MCAK binding to both the microtubule wall and end. However, it is still unclear whether direct binding to the tip-as opposed to accumulation via wall diffusion or interaction with other tip-binding proteins-is a significant mechanism.

(2) The newly revealed adenosine-nucleotide-dependent binding preferences do not help clarify MCAK's catalytic function or its mechanisms of tip recognition. Consequently, the final summary figure remains speculative and is not convincingly supported by the data. It is also unclear what exactly is meant by the "working model" (figure title), or by the claim of "a simple rule of how the end-binding regulators coordinate their activities" (abstract).

(3) As noted in my previous review, the effects of adding different adenosine nucleotides on MCAK binding to microtubules are much more pronounced than the differences in MCAK binding to tubulin with various guanosine-containing nucleotides, or to lattice versus tip (e.g., Fig. 5E). Therefore, the manuscript title-"MCAK recognizes the nucleotide-dependent feature at growing microtubule ends"-does not do justice to the scale of these effects.

(4) The title implies that MCAK selectively recognizes a feature determined by the tubulin-bound guanosine nucleotide. However, the authors frequently claim that MCAK binds to the "entire GTP cap." It appears that they exclude structural protrusions from their definition of the cap, which is debatable. Even using their definition, the conclusion that MCAK recognizes a specific "nucleotide-dependent feature" seems inconsistent with the claim that it binds uniformly across the cap. These distinctions were not made clear.

(5) Some important technical details are still absent. For example, when reading the authors' response to another reviewer's question, I could not find an explanation of how the kon values for end and wall binding were calculated. These calculations clearly require assumptions, e.g. about the number of binding sites, but these details are not described. In addition, the binding data are expressed in units per tubulin dimer, which are non-standard and make comparisons to other published results difficult. There are other instances where more technical detail would be desirable, but they are too numerous to list here.

(6) Several aspects of data presentation as graphs will make it difficult for other researchers to analyze or interpret the findings. Numerical Excel-style data sheets should be provided for all measurements, including raw data-not just the ratios or derived values shown in plots. Other, more significant issues include use of mean values for non-Gaussian distributions (e.g., dwell times); binding affinities inferred from single-concentration measurements, often under varying conditions (e.g., Figs. 3C, 4); and absence of side-by-side plotted controls (e.g., Fig. 6).

(7) While the authors have added some quantitative values and descriptive detail, the manuscript still lacks a critical comparison of their findings with existing literature. This weakens the impact of the study and limits the reader's ability to place the results in a broader context.

Reviewer #4 (Public review):

The revised manuscript from Chen et al. implements many of the changes requested by the 3 reviewers of the initial submission. These changes are well-described in the corresponding Response to Reviews document. Of course, not every request from the reviewers was addressed, and the following major concerns remain:

(1) The authors argue that MCAK binds to the same region as EB proteins, which they refer to as the "EB cap". Reviewers asked for experiments that would increase the size of the EB cap to create "comets" (e.g. by increasing the microtubule growth rate); the prediction is that the MCAK signal should increase in size as well. The authors declined to pursue these experiments. As a result, the EB signals and MCAK signals are diffraction-limited spots, as opposed to the predicted exponential decay signals characteristic of EB comets. The various diffraction-limited spots are then aligned with the diffraction-limited signal of the microtubule end. These alignments and sub-pixel comparisons are technically challenging. The revised manuscript does not go far enough to provide compelling evidence that all technical challenges were overcome. Thus, while the authors can safely conclude that MCAK, EBs, and the microtubule end do occupy the same diffraction-limited spot, more precise conclusions are not supported.

(2) The reviewers criticized the initial manuscript for neglecting key references, particularly Kinoshita et al., Science 2001. Indeed, I cannot fathom writing a manuscript about MCAK and XMAP215 without putting a citation to such a landmark paper front and center. The authors have responded by including more discussion of the relevant literature (and citing Kinoshita et al.). However, the revised manuscript is often still cursory in giving credit where credit is due, contextualizing the new data, and generally engaging with the scholarship on MCAK.

(3) The data presented does not include a simple measurement of the impact of MCAK on the catastrophe frequency of microtubules. The authors explain this absence by pointing out that their movies are short (5 min) and high frame rate (10 fps). While I understand that such imaging parameters are necessary to capture single molecule end-binding events, I do not understand why a separate set of experiments could not be performed. This type of "positive control" is often missing, as pointed out by the 3 reviewers.

(4) Salt conditions, protein concentrations, and other key experimental parameters are not varied, even when varying them would provide excellent tests of the authors' hypotheses.

In summary, the revised manuscript is improved in many ways, but the interested reader should look carefully at the previous reviews and compare the measurements presented here with those of other labs.

Reviewer #2 (Public Review):

Summary:

This paper describes a new approach to detecting directed causal interactions between two genes without directly perturbing either gene. To check whether gene X influences gene Z, a reporter gene (Y) is engineered into the cell in such a way that (1) Y is under the same transcriptional control as X, and (2) Y does not influence Z. Then, under the null hypothesis that X does not affect Z, the authors derive an equation that describes the relationship between the covariance of X and Z and the covariance of Y and Z. Violation of this relationship can then be used to detect causality.

The authors benchmark their approach experimentally in several synthetic circuits. In 4 positive control circuits, X is a TetR-YFP fusion protein that represses Z, which is an RFP reporter. The proposed approach detected the repression interaction in 2 of the 4 positive control circuits. The authors constructed 16 negative control circuit designs in which X was again TetR-YFP, but where Z was either a constitutively expressed reporter, or simply the cellular growth rate. The proposed method detected a causal effect in two of the 16 negative controls, which the authors argue is perhaps not a false positive, but due to an unexpected causal effect. Overall, the data support the potential value of the proposed approach.

Strengths:

The idea of a "no-causality control" in the context of detected directed gene interactions is a valuable conceptual advance that could potentially see play in a variety of settings where perturbation-based causality detection experiments are made difficult by practical considerations.

By proving their mathematical result in the context of a continuous-time Markov chain, the authors use a more realistic model of the cell than, for instance, a set of deterministic ordinary differential equations.

The authors have improved the clarity and completeness of their proof compared to a previous version of the manuscript.

Limitations:

The authors themselves clearly outline the primary limitations of the study: The experimental benchmark is a proof of principle, and limited to synthetic circuits involving a handful of genes expressed on plasmids in E. coli. As acknowledged in the Discussion, negative controls were chosen based on the absence of known interactions, rather than perturbation experiments. Further work is needed to establish that this technique applies to other organisms and to biological networks involving a wider variety of genes and cellular functions. It seems to me that this paper's objective is not to delineate the technique's practical domain of validity, but rather to motivate this future work, and I think it succeeds in that.

Might your new "Proposed additional tests" subsection be better housed under Discussion rather than Results?

I may have missed this, but it doesn't look like you ran simulation benchmarks of your bootstrap-based test for checking whether the normalized covariances are equal. It would be useful to see in simulations how the true and false positive rates of that test vary with the usual suspects like sample size and noise strengths.

It looks like you estimated the uncertainty for eta_xz and eta_yz separately. Can you get the joint distribution? If you can do that, my intuition is you might be able to improve the power of the test (and maybe detect positive control #3?). For instance, if you can get your bootstraps for eta_xz and eta_yz together, could you just use a paired t-test to check for equality of means?

The proof is a lot better, and it's great that you nailed down the requirement on the decay of beta, but the proof is still confusing in some places:

On pg 29, it says "That is, dividing the right equation in Eq. 5.8 with alpha, we write the ..." but the next equation doesn't obviously have anything to do with Eq. 5.8, and instead (I think) it comes from Eq 5.5. This could be clarified.

Later on page 29, you write "We now evoke the requirement that the averages xt and yt are stationary", but then you just repeat Eq. 5.11 and set it to zero. Clearly you needed the limit condition to set Eq. 5.11 to zero, but it's not clear what you're using stationarity for. I mean, if you needed stationarity for 5.11 presumably you would have referenced it at that step.

It could be helpful for readers if you could spell out the practical implications of the theorem's assumptions (other than the no-causality requirement) by discussing examples of setups where it would or wouldn't hold.

Reviewer #1 (Public review):

Summary:

The authors developed SHERLOCK4AAT, a CRISPR-Cas13a-based diagnostic toolbox for detecting multiple trypanosome species responsible for animal African trypanosomiasis. They created species-specific assays targeting six prevalent parasite species and validated the system using dried blood spots from domestic pigs in Guinea and Côte d'Ivoire. Field testing revealed high infection rates (62.7% of pigs infected) and, notably, the presence of human-infective parasites in domestic animals.

Major Strengths:

This study represents a valuable application of CRISPR-based detection technology to veterinary diagnostics, with strong potential for practical implementation. The authors conducted comprehensive validation, including statistical analyses to determine sensitivity and specificity, and demonstrated field utility through large-scale testing of 424 samples from two geographically distinct regions. The detection of human-infective parasites in pigs at both sites provides important One Health insights supporting integrated disease surveillance and has direct implications for public health policy and disease elimination programs. The methodology is robust, incorporating Bayesian statistical modeling and offering clear practical advantages such as dried blood spot compatibility and detection of active infections. The revised manuscript also addresses implementation considerations, including cost, training needs, and field logistics.

Major Weaknesses:

Some technical limitations constrain broader applicability. The assay for one key parasite species (T. vivax) shows suboptimal sensitivity, which may limit its utility in detecting this important pathogen. The current assay design does not distinguish between closely related species within the same subgenus-an important factor for certain epidemiological studies. Additionally, some assays relied on synthetic controls due to unavailable biological material, and the discussion on potential cross-reactivity with related kinetoplastid parasites is limited.<br /> Achievement of Aims: The authors clearly achieved their primary objectives of developing a sensitive, species-specific diagnostic system and demonstrating its applicability in real-world settings. The detection of human-infective trypanosomes in domestic pigs provides valuable epidemiological evidence in support of One Health strategies and targeted disease elimination efforts.

Impact and Utility:

This work responds to a well-documented need in veterinary diagnostics, where current methods often lack sensitivity or species discrimination. The system offers practical benefits for resource-limited settings through a short assay duration and compatibility with dried blood spot samples. While certain performance limitations may restrict broader adoption, the species identification capability represents a substantial advancement over existing approaches. The findings enhance our understanding of parasite diversity in livestock and their potential role as zoonotic reservoirs, with implications extending beyond veterinary medicine to public health surveillance and policy development.

Context:

This study makes a timely and relevant contribution to diagnostic epidemiology and One Health surveillance frameworks. The field-adapted use of advanced molecular detection technologies represents a significant step toward improved disease monitoring in regions where trypanosomiasis poses ongoing threats to animal health, agriculture, and human livelihoods. The cross-disciplinary implications for veterinary medicine, public health, and disease elimination programs underscore the broader significance of this work.

Reviewer #2 (Public review):

Summary:

The manuscript is fundamental due to the significance of its findings. The strength of the evidence is compelling, and the manuscript is publishable since the corrections have been made.

Strengths:

Using a Novel SHERLOCK4AAT toolkit for diagnosis.

Identification of various sub-species of Trypanosomes.

Differentiating the animal sub-species from the human one.

Corrections Made:

Definite articles have been removed from the title.

The words of the title have been reduced to 15.

Typographical errors have been corrected.

Weaknesses:

None

Reviewer #3 (Public review):

Summary:

The study adapts CRISPR-based detection toolkit (SHERLOCK assay) using conserved and species-specific targets for the detection of some members of the Trypanosomatidae family of veterinary importance and species-specific assays to differentiate between the six most common animal trypanosomes species responsible for AAT (SHERLOCK4AAT). The assays were able to discriminate between Trypanozoon (T. b. brucei, T. evansi and T. equiperdum), T. congolense (Savanah, Forest Kilifi and Dzanga sangha), T. vivax, T. theileri, T. simiae and T. suis. The design of both broad and species-specific assays was based primarily on sequences of the 18S rRNA, GAPDH (Glyceraldehyde-3-phosphate dehydrogenase) and invariant flagellum antigen (IFX) genes for species identification. Most importantly the authors showed varying limit of detection for the different SHERLOCK assays which is somewhat comparable to PCR-derived molecular techniques currently used for detecting animal trypanosomes even though some of these methodologies have used other primers that target genes such as ITS1 and 7SL sRNA.

The data presented in the study are particularly useful and of significant interest for diagnosis of AAT in affected areas.

Strengths:

The assays convincingly allow for the analysis and detection of most trypanosomes in AAT

Weaknesses:

Inability for the assay to distinguish T. b. brucei, T. evansi and T. equiperdum using the 18S rRNA gene as well as the IFX gene not achieving the sensitivity requirements for detection of T. vivax. Both T. brucei brucei and T. vivax are the most predominant infective species in animals (in addition to T. congolense), therefore a reliable assay should be able to convincingly detect these to allow for proper use of diagnostic assay.

Reviewer #2 (Public review):

This is a revised version of a paper I reviewed previously.

Again, the purpose of the paper is to suggest that common metrics, such as friction or any given physical property of the surface, are probably inadequate to predict the perception of the surface or its discriminability. Instead, the authors propose a very interesting and original idea that, instead, frictional instabilities are related to fine touch perception (title).

Overall, the authors have put much effort into improving the manuscript, enhancing clarity, and avoiding overstatements. And I feel the narrative is indeed much improved and less ambiguous.

However, the authors have systematically avoided addressing the main comment of all reviewers: the link made between the mock finger passive experiment and the active human psychophysics is incorrect and should not be done, because its interpretation could be flawed.<br /> - First, this link is very weak (the correlation of 6 datapoints is barely significant).<br /> - Second, the real and mock fingers have very different properties (think about moisture, compliance, roughness,...).<br /> - Third, the comparison is made between a passive and well-controlled experiment and an active exploration. Yet, the comparison metrics (number of events) are clearly dependent on exploration procedures.

In your response to my comments:<br /> "We have made changes throughout the manuscript to acknowledge that our findings are correlative, clarifying this throughout, and incorporating into the discussion how our work may enable biomechanical measurements and tactile decision making models"

The authors admit that the analysis is flawed, yet they did not remove it. If they cannot demonstrate that the mock finger and the human finger behave the same way during the perceptual experiment, then they should remove Fig2 that combines apples and oranges. OR, they should look at the active exploration data and compute the same metrics on that data.

"This "weird choice" is the central innovation of this paper. This choice was necessary because we demonstrated that the common usage of friction coefficient is fundamentally flawed: we see that friction coefficient suggests that surface which are more different would feel more similar - indeed the most distinctive surfaces would be two surfaces that are identical, which is clearly spurious. "

They did not "demonstrate" such a flaw. Again, the difference in friction is between the mock finger trials. At the very least, the authors should verify that it is true of the active human experiment.

"To fully implement this, a decision-making model is necessary because, as a counter example, a participant could have generated 10 swipes of SFW and 1 swipe of a Sp, but the Sp may have been the most important event for making a tactile decision. This type of scenario is not compatible with the analysis suggested - and similar counterpoints can be made for other types of seemingly straightforward analysis."

The suggested analyses are straightforward and would be much more valuable than the data from the mock finger, even with the potential variability stated above.

"We recognize that, with all factors being equal, this sample size is on the smaller end"

Yet, the authors did not collect additional data to confirm their findings.

Reviewer #3 (Public review):

Strengths:

The paper describes a new perspective on friction perception, with the hypothesis that humans are sensitive to the instabilities of the surface rather than the coefficient of friction. The paper is very well written and with a comprehensive literature survey.

One of the central tools used by the author to characterize the frictional behavior is the frictional instabilities maps. With these maps, it becomes clear that two different surfaces can have both similar and different behavior depending on the normal force and the speed of exploration. It puts forward that friction is a complicated phenomenon, especially for soft

The psychophysics study is centered around an odd-one-out protocol, which has the advantage of avoiding any external reference to what would mean friction or texture for example. The comparisons are made only based on the texture being similar or not.

The results show a significant relationship between the distance between frictional maps and the success rate in discriminating two kinds of surface.

Weaknesses:

The main weakness of the paper comes from the fact that the frictional maps and the extensive psychophysics study are not made at the same time, nor with the same finger. The frictional maps are produced with an artificial finger made out of PDMS which is a poor substitute for the complex tribological properties of skin.

The evidence would have been much stronger if the measurement of the interaction was done during the psychophysical experiment. In addition, because of the protocol, the correlation is based on aggregates rather than on individual interactions. However the current data already bring new light on the nature of frictional oscillation and their link to perception.

The authors compensate with a third experiment where they used a 2AFC protocol and an online force measurement. But the results of this third study fail to solidify the relation.

No map of the real finger interaction is shown, bringing doubt to the validity of the frictional map for something as variable as human fingers.

Reviewer #4 (Public review):

Summary:

In this paper, Derkaloustian et al. look at the important topic of what affects fine touch perception. The observations that there may be some level of correlation with instabilities are intriguing. They attempted to characterize different materials by counting the frequency (occurrence #, not of vibration) of instabilities at various speeds and forces of a PDMS slab pulled lengthwise over the material. They then had humans perform the same vertical motion to discriminate between these samples. They correlated the % correct in discrimination with differences in frequency of steady sliding over the design space as well as other traditional parameters such as friction coefficient and roughness.

The authors pose an interesting hypothesis and make an interesting observation about the occurrences of instability regimes in different materials while in contact with PDMS, which is interesting for the community to see in publication. It should be noted however that the finger is complex, and there are many factors that may be over simplified, and perhaps even incorrect, with the use of the PDMS finger. There are trends, such as the trend of surfaces that are more similar in PDMS friction coefficient being easier to discriminate than those with more different PDMS friction coefficient, that contradict multiple other papers in the literature (Fehlberg et al., 2024; Smith and Scott, 1996). This may be due to the PDMS finger not being representative of the real finger conditions. A measurement of friction and the instabilities with a human finger, or demonstration that the PDMS finger is producing the same results (friction coefficient, instabilities, etc.) as a human finger, is needed.

Strengths:

The strength of this paper is in its intriguing hypothesis and important observation that instabilities may contribute to what humans are detecting as differences in these apparently similar samples.

Weaknesses:

There is are significant weaknesses in the representativeness of the PDMS finger, the vertical motion, and the speed of sliding to real human exploration. The real finger has multiple layers with different moduli. In fact, the stratum corneum cells, which are the outer layer at the interface and determine the friction, have much higher modulus than PDMS. In addition, the flat contact area can cause shifting of contact points. Both can contribute to making the PDMS finger have much more stick slip than a real finger. In fact, if you look at the regime maps, there is very little space that has steady sliding. This does not represent well human exploration of surfaces. We do not tend to use force and velocity that will cause extensive stick slip (frequent regions of 100% stick slip) and, in fact, the speeds used in the study are on the slow side, which also contributes to more stick slip. At higher speeds and lower forces, all of the materials had steady sliding regions. Further, on these very smooth surfaces, the friction and stiction are more complex and cannot dismiss considerations such as finger material property change with sweat pore occlusion and sweat capillary forces. Also, the vertical motion of both the PDMS finger and the instructed human subjects is not the motion that humans typically use to discriminate between surfaces.

This all leads to the critical question, why is the friction, normal force, and velocity not measured during the measured human exploration using the real human finger? An alternative would be showing that the PDMS finger reproduces the results of the human finger. I have checked the author's previous papers with this setup and did not find one that showed that the PDMS finger produced the same results as a human finger (Carpenter et al., 2018; Dhong et al., 2018; Nolin et al., 2022, 2021). The reviewer is not asking to do a more detailed psychophysical study with a decision-making model. All that is being asked is to use a human finger for the friction coefficient and instability measurements at typical human forces and speeds, or at least doing these measurements with both for one or two samples to show that the PDMS finger produces the same results as a human finger. The authors posed an extremely interesting hypothesis that humans may alter their speed to feel the instability transition regions. This is something that could be measured with a real finger but is not likely to be correlated accurately enough to match regime boundaries determined with such a simplified artificial finger.

References

Carpenter CW, Dhong C, Root NB, Rodriquez D, Abdo EE, Skelil K, Alkhadra MA, Ramírez J, Ramachandran VS, Lipomi DJ. 2018. Human ability to discriminate surface chemistry by touch. Mater Horiz 5:70-77. doi:10.1039/C7MH00800G<br /> Dhong C, Kayser LV, Arroyo R, Shin A, Finn M, Kleinschmidt AT, Lipomi DJ. 2018. Role of fingerprint-inspired relief structures in elastomeric slabs for detecting frictional differences arising from surface monolayers. Soft Matter 14:7483-7491. doi:10.1039/C8SM01233D<br /> Fehlberg M, Monfort E, Saikumar S, Drewing K, Bennewitz R. 2024. Perceptual Constancy in the Speed Dependence of Friction During Active Tactile Exploration. IEEE Transactions on Haptics 17:957-963. doi:10.1109/TOH.2024.3493421<br /> Nolin A, Licht A, Pierson K, Lo C-Y, Kayser LV, Dhong C. 2021. Predicting human touch sensitivity to single atom substitutions in surface monolayers for molecular control in tactile interfaces. Soft Matter 17:5050-5060. doi:10.1039/D1SM00451D<br /> Nolin A, Pierson K, Hlibok R, Lo C-Y, Kayser LV, Dhong C. 2022. Controlling fine touch sensations with polymer tacticity and crystallinity. Soft Matter 18:3928-3940. doi:10.1039/D2SM00264G<br /> Smith AM, Scott SH. 1996. Subjective scaling of smooth surface friction. Journal of Neurophysiology 75:1957-1962. doi:10.1152/jn.1996.75.5.1957

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors used a coarse-grained DNA model (cgNA+) to explore how DNA sequences and CpG methylation/hydroxymethylation influence nucleosome wrapping energy and the probability density of optimal nucleosomal configuration. Their findings indicate that both methylated and hydroxymethylated cytosines lead to increased nucleosome wrapping energy. Additionally, the study demonstrates that methylation of CpG islands increases the probability of nucleosome formation.

Strengths:

The major strength of this method is the model explicitly includes phosphate group as DNA-histone binding site constraints, enhancing CG model accuracy and computational efficiency and allowing comprehensive calculations of DNA mechanical properties and deformation energies.

Weaknesses:

A significant limitation of this study is that the parameter sets for the methylated and hydroxymethylated CpG steps in the cgNA+ model are derived from all-atom molecular dynamics (MD) simulations that use previously established force field parameters for modified cytosines (Pérez A, et al. Biophys J. 2012; Battistini, et al. PLOS Comput Biol. 2021). These parameters suggest that both methylated and hydroxymethylated cytosines increase DNA stiffness and nucleosome wrapping energy, which could predispose the coarse-grained model to replicate these findings. Notably, conflicting results from other all-atom MD simulations, such as those by Ngo T in Nat. Commun. 2016, shows that hydroxymethylated cytosines increase DNA flexibility, contrary to methylated cytosines. If the cgNA+ model were trained on these later parameters or other all-atom MD force fields, different conclusions might be obtained regarding the effects of methylated and hydroxymethylation on nucleosome formation.

Despite the training parameters of the cgNA+ model, the results presented in the manuscript indicate that methylated cytosines increase both DNA stiffness and nucleosome wrapping energy. However, when comparing nucleosome occupancy scores with predicted nucleosome wrapping energies and optimal configurations, the authors find that methylated CGIs exhibit higher nucleosome occupancies than unmethylated ones, which seems to contradict the expected relationship where increased stiffness should reduce nucleosome formation affinity. In the manuscript, the authors also admit that these conclusions "apparently runs counter to the (perhaps naive) intuition that high nucleosome forming affinity should arise for fragments with low wrapping energy". Previous all-atom MD simulations (Pérez A, et al. Biophys J. 2012; Battistini, et al. PLOS Comput Biol. 202; Ngo T, et al. Nat. Commun. 20161) show that the stiffer DNA upon CpG methylation reduces the affinity of DNA to assemble into nucleosomes or destabilizes nucleosomes. Given these findings, the authors need to address and reconcile these seemingly contradictory results, as the influence of epigenetic modifications on DNA mechanical properties and nucleosome formation are critical aspects of their study.

Understanding the influence of sequence-dependent and epigenetic modifications of DNA on mechanical properties and nucleosome formation is crucial for comprehending various cellular processes. The authors' study, focusing on these aspects, definitely will garner interest from the DNA methylation research community.

Comments on revised version:

The authors have addressed most of my comments and concerns regarding this manuscript.

Reviewer #2 (Public review):

Summary:

This study uses a coarse-grained model for double stranded DNA, cgNA+, to assess nucleosome sequence affinity. cgNA+ coarse-grains DNA on the level of bases and accounts also explicitly for the positions of the backbone phosphates. It has been proven to reproduce all-atom MD data very accurately. It is also ideally suited to be incorporated into a nucleosome model because it is known that DNA is bound to the protein core of the nucleosome via the phosphates.

It is still unclear whether this harmonic model parametrized for unbound DNA is accurate enough to describe DNA inside the nucleosome. Previous models by other authors, using more coarse-grained models of DNA, have been rather successful in predicting base pair sequence dependent nucleosome behavior. This is at least the case as long as DNA shape is concerned whereas assessing the role of DNA bendability (something this paper focuses on) has been consistently challenging in all nucleosome models to my knowledge.

It is thus of major interest whether this more sophisticated model is also more successful in handling this issue. As far as I can tell the work is technically sound and properly accounts for not only the energy required in wrapping DNA but also entropic effects, namely the change in entropy that DNA experiences when going from the free state to the bound state. The authors make an approximation here which seems to me to be a reasonable first step.

Of interest is also that the authors have the parameters at hand to study the effect of methylation of CpG-steps. This is especially interesting as this allows to study a scenario where changes in the physical properties of base pair steps via methylation might influence nucleosome positioning and stability in a cell-type specific way.

Overall, this is an important contribution to the questions of how sequence affects nucleosome positioning and affinity. The findings suggest that cgNA+ has something new to offer. But the problem is complex, also on the experimental side, so many questions remain open. Despite of this, I highly recommend publication of this manuscript.

Strengths:

The authors use their state-of-the-art coarse grained DNA model which seems ideally suited to be applied to nucleosomes as it accounts explicitly for the backbone phosphates.

Weaknesses:

The authors introduce penalty coefficients c_i to avoid steric clashes between the two DNA turns in the nucleosome. This requires c_i-values that are so high that standard deviations in the fluctuations of the simulation are smaller than in the experiments.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #1 (Public review):

Summary:

In this manuscript, Cho et al. present a comprehensive and multidimensional analysis of glutamine metabolism in the regulation of B cell differentiation and function during immune responses. They further demonstrate how glutamine metabolism interacts with glucose uptake and utilization to modulate key intracellular processes. The manuscript is clearly written, and the experimental approaches are informative and well-executed. The authors provide a detailed mechanistic understanding through the use of both in vivo and in vitro models. The conclusions are well supported by the data, and the findings are novel and impactful. I have only a few, mostly minor, concerns related to data presentation and the rationale for certain experimental choices.

Detailed Comments:

(1) In Figure 1b, it is unclear whether total B cells or follicular B cells were used in the assay. Additionally, the in vitro class-switch recombination and plasma cell differentiation experiments were conducted without BCR stimulation, which makes the system appear overly artificial and limits physiological relevance. Although the effects of glutamine concentration on the measured parameters are evident, the results cannot be confidently interpreted as true plasma cell generation or IgG1 class switching under these conditions. The authors should moderate these claims or provide stronger justification for the chosen differentiation strategy. Incorporating a parallel assay with anti-BCR stimulation would improve the rigor and interpretability of these findings.

(2) In Figure 1c, the DMK alone condition is not presented. This hinders readers' ability to properly asses the glutaminolysis dependency of the cells for the measured readouts. Also, CD138+ in developing PCs goes hand in hand with decreased B220 expression. A representative FACS plot showing the gating strategy for the in vitro PCs should be added as a supplementary figure. Similarly, division number (going all the way to #7) may be tricky to gate and interpret. A representative FACS plot showing the separation of B cells according to their division numbers and a subsequent gating of CD138 or IgG1 in these gates would be ideal for demonstrating the authors' ability to distinguish these populations effectively.

(3) A brief explanation should be provided for the exclusive use of IgG1 as the readout in class-switching assays, given that naïve B cells are capable of switching to multiple isotypes. Clarifying why IgG1 was preferentially selected would aid in the interpretation of the results.

(4) The immunization experiments presented in Figures 1 and 2 are well designed, and the data are comprehensively presented. However, to prevent potential misinterpretation, it should be clarified that the observed differences between NP and OVA immunizations cannot be attributed solely to the chemical nature of the antigens - hapten versus protein. A more significant distinction lies in the route of administration (intraperitoneal vs. intranasal) and the resulting anatomical compartment of the immune response (systemic vs. lung-restricted). This context should be explicitly stated to avoid overinterpretation of the comparative findings.

(5) NP immunization is known to be an inducer of an IgG1-dominant Th2-type immune response in mice. IgG2c is not a major player unless a nanoparticle delivery system is used. However, the authors arbitrarily included IgG2c in their assays in Figures 2 and 3. This may be confusing for the readers. The authors should either justify the IgG2c-mediated analyses or remove them from the main figures. (It can be added as supplemental information with proper justification).

(6) Similarly, in affinity maturation analyses, including IgM is somewhat uncommon. I do not see any point in showing high affinity (NP2/NP20) IgMs (Figure 3d), since that data probably does not mean much.

(7) Following on my comment for the PC generation in Figure 1 (see above), in Figure 4, a strategy that relies solely on CD40L stimulation is performed. This is highly artificial for the PC generation and needs to be justified, or more physiologically relevant PC generation strategies involving anti-BCR, CD40L, and various cytokines should be shown.

(8) The effects of CB839 and UK5099 on cell viability are not shown. Including viability data under these treatment conditions would be a valuable addition to the supplementary materials, as it would help readers more accurately interpret the functional outcomes observed in the study.

(9) It is not clear how the RNA seq analysis in Figure 4h was generated. The experimental strategy and the setup need to be better explained.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigate the functional requirements for glutamine and glutaminolysis in antibody responses. The authors first demonstrate that the concentrations of glutamine in lymph nodes are substantially lower than in plasma, and that at these levels, glutamine is limiting for plasma cell differentiation in vitro. The authors go on to use genetic mouse models in which B cells are deficient in glutaminase 1 (Gls), the glucose transporter Slc2a1, and/or mitochondrial pyruvate carrier 2 (Mpc2) to test the importance of these pathways in vivo.

Interestingly, deficiency of Gls alone showed clear antibody defects when ovalbumin was used as the immunogen, but not the hapten NP. For the latter response, defects in antibody titers and affinity were observed only when both Gls and either Mpc2 or Slc2a1 were deleted. These latter findings form the basis of the synthetic auxotrophy conclusion. The authors go on to test these conclusions further using in vitro differentiations, Seahorse assays, pharmacological inhibitors, and targeted quantification of specific metabolites and amino acids. Finally, the authors document reduced STAT3 and STAT1 phosphorylation in response to IL-21 and interferon (both type 1 and 2), respectively, when both glutaminolysis and mitochondrial pyruvate metabolism are prevented.

Strengths:

(1) The main strength of the manuscript is the overall breadth of experiments performed. Orthogonal experiments are performed using genetic models, pharmacological inhibitors, in vitro assays, and in vivo experiments to support the claims. Multiple antigens are used as test immunogens--this is particularly important given the differing results.

(2) B cell metabolism is an area of interest but understudied relative to other cell types in the immune system.

(3) The importance of metabolic flexibility and caution when interpreting negative results is made clear from this study.

Weaknesses:

(1) All of the in vivo studies were done in the context of boosters at 3 weeks and recall responses 1 week later. This makes specific results difficult to interpret. Primary responses, including germinal centers, are still ongoing at 3 weeks after the initial immunization. Thus, untangling what proportion of the defects are due to problems in the primary vs. memory response is difficult.

(2) Along these lines, the defects shown in Figure 3h-i may not be due to the authors' interpretation that Gls and Mpc2 are required for efficient plasma cell differentiation from memory B cells. This interpretation would only be correct if the absence of Gls/Mpc2 leads to preferential recruitment of low-affinity memory B cells into secondary plasma cells. The more likely interpretation is that ongoing primary germinal centers are negatively impacted by Gls and Mpc2 deficiency, and this, in turn, leads to reduced affinities of serum antibodies.

(3) The gating strategies for germinal centers and memory B cells in Supplemental Figure 2 are problematic, especially given that these data are used to claim only modest and/or statistically insignificant differences in these populations when Gls and Mpc2 are ablated. Neither strategy shows distinct flow cytometric populations, and it does not seem that the quantification focuses on antigen-specific cells.

(4) Along these lines, the conclusions in Figure 6a-d may need to be tempered if the analysis was done on polyclonal, rather than antigen-specific cells. Alum induces a heavily type 2-biased response and is not known to induce much of an interferon signature. The authors' observations might be explained by the inclusion of other ongoing GCs unrelated to the immunization.

Reviewer #3 (Public review):

Summary:

In their manuscript, the authors investigate how glutaminolysis (GLS) and mitochondrial pyruvate import (MPC2) jointly shape B cell fate and the humoral immune response. Using inducible knockout systems and metabolic inhibitors, they uncover a "synthetic auxotrophy": When GLS activity/glutaminolysis is lost together with either GLUT1-mediated glucose uptake or MPC2, B cells fail to upregulate mitochondrial respiration, IL 21/STAT3 and IFN/STAT1 signaling is impaired, and the plasma cell output and antigen-specific antibody titers drop significantly. This work thus demonstrates the promotion of plasma cell differentiation and cytokine signaling through parallel activation of two metabolic pathways. The dataset is technically comprehensive and conceptually novel, but some aspects leave the in vivo and translational significance uncertain.

Strengths:

(1) Conceptual novelty: the study goes beyond single-enzyme deletions to reveal conditional metabolic vulnerabilities and fate-deciding mechanisms in B cells.

(2) Mechanistic depth: the study uncovers a novel "metabolic bottleneck" that impairs mitochondrial respiration and elevates ROS, and directly ties these changes to cytokine-receptor signaling. This is both mechanistically compelling and potentially clinically relevant.

(3) Breadth of models and methods: inducible genetics, pharmacology, metabolomics, seahorse assay, ELISpot/ELISA, RNA-seq, two immunization models.

(4) Potential clinical angle: the synergy of CB839 with UK5099 and/or hydroxychloroquine hints at a druggable pathway targeting autoantibody-driven diseases.

Weaknesses:

(1) Physiological relevance of "synthetic auxotrophy"

The manuscript demonstrates that GLS loss is only crippling when glucose influx or mitochondrial pyruvate import is concurrently reduced, which the authors name "synthetic auxotrophy". I think it would help readers to clarify the terminology more and add a concise definition of "synthetic auxotrophy" versus "synthetic lethality" early in the manuscript and justify its relevance for B cells.

While the overall findings, especially the subset specificity and the clinical implications, are generally interesting, the "synthetic auxotrophy" condition feels a little engineered. Therefore, the findings strongly raise the question of the likelihood of such a "double hit" in vivo and whether there are conditions, disease states, or drug regimens that would realistically generate such a "bottleneck". Hence, the authors should document or at least discuss whether GC or inflamed niches naturally show simultaneous downregulation/lack of glutamine and/or pyruvate. The authors should also aim to provide evidence that infections (e.g., influenza), hypoxia, treatments (e.g., rapamycin), or inflammatory diseases like lupus co-limit these pathways.

It would hence also be beneficial to test the CB839 + UK5099/HCQ combinations in a short, proof-of-concept treatment in vivo, e.g., shortly before and after the booster immunization or in an autoimmune model. Likewise, it may also be insightful to discuss potential effects of existing treatments (especially CB839, HCQ) on human memory B cell or PC pools.

(2) Cell survival versus differentiation phenotype

Claims that the phenotypes (e.g., reduced PC numbers) are "independent of death" and are not merely the result of artificial cell stress would benefit from Annexin-V/active-caspase 3 analyses of GC B cells and plasmablasts. Please also show viability curves for inhibitor-treated cells.

(3) Subset specificity of the metabolic phenotype

Could the metabolic differences, mitochondrial ROS, and membrane-potential changes shown for activated pan-B cells (Figure 5) also be demonstrated ex vivo for KO mouse-derived GC B cells and plasma cells? This would also be insightful to investigate following NP-immunization (e.g., NP+ GC B cells 10 days after NP-OVA immunization).

(4) Memory B cell gating strategy

I am not fully convinced that the memory-B-cell gate in Supplementary Figure 2d is appropriate. The legend implies the population is defined simply as CD19+GL7-CD38+ (or CD19+CD38++?), with no further restriction to NP-binding cells. Such a gate could also capture naïve or recently activated B cells. From the descriptions in the figure and the figure legend, it is hard to verify that the events plotted truly represent memory B cells. Please clarify the full gating hierarchy and, ideally, restrict the MBC gate to NP+CD19+GL7-CD38+ B cells (or add additional markers such as CD80 and CD273). Generally, the manuscript would benefit from a more transparent presentation of gating strategies.

(5) Deletion efficiency

mRNA data show residual GLS/MPC2 transcripts (Supplementary Figure 8). Please quantify deletion efficiency in GC B cells and plasmablasts.

Reviewer #1 (Public Review):

The study by Sianga-Mete et al revisits the effects of substitution model selection on phylogenetics by comparing reversible and non-reversible DNA substitution models. This topic is not new, previous works already showed that non-reversible, and also covarion, substitution models can fit the real data better than the reversible substitution models commonly used in phylogenetics. In this regard, the results of the present study are not surprising.

Reviewer #2 (Public Review):

The authors evaluate whether non time reversible models fit better data presenting strand-specific substitution biases than time reversible models. Specifically, the authors consider what they call NREV6 and NREV12 as candidate non time-reversible models. On the one hand, they show that AIC tends to select NREV12 more often than GTR on real virus data sets. On the other hand, they show using simulated data that NREV12 leads to inferred trees that are closer to the true generating tree when the data incorporates a certain degree of non time-reversibility. Based on these two experimental results, the authors conclude that "We show that non-reversible models such as NREV12 should be evaluated during the model selection phase of phylogenetic analyses involving viral genomic sequences". This is a valuable finding, and I agree that this is potentially good practice. However, I miss an experiment that links the two findings to support the conclusion: in particular, an experiment that solves the following question: does the best-fit model also lead to better tree topologies?

[Editors' note: the reviewers were sent the revised submission and rebuttal and based on their response, an amended eLife Assessment has been formulated.]

Reviewer #1 (Public review):

Yang et al. describes CCDC32 as a new clathrin mediated endocytosis (CME) accessory protein. The authors show that CCDC32 binds directly to AP2 via a small alpha helical region and cells depleted for this protein show defective CME. Finally, the authors show that the CCDC32 nonsense mutations found in patients with cardio-facial-neuro-developmental syndrome (CFNDS) disrupt the interaction of this protein to the AP2 complex. The results presented suggest that CCDC32 may act as both a chaperone (as recently published) and a structural component of the AP2 complex.

Reviewer #2 (Public review):

Summary:<br /> The authors responded to my previous concerns with additional arguments and discussion. While I do not object to the publication of this work, two critical experiments are still missing.

Weaknesses:<br /> First, biochemical assays using recombinant proteins should be conducted to determine whether CCDC32 binds to the full AP2 adaptor or to specific AP2 intermediates, such as hemicomplexes. The current co-IP data from mammalian cell lysates are too complex to interpret conclusively. Second, cell fractionation should be performed to assess whether, and how, CCDC32 associates with membrane-bound AP2.

Reviewer #3 (Public review):

In this manuscript, Yang et al. characterize the endocytic accessory protein CCDC32, which has implications in cardio-facio-neuro-developmental syndrome (CFNDS). The authors clearly demonstrate that the protein CCDC32 has a role in the early stages of endocytosis, mainly through the interaction with the major endocytic adaptor protein AP2, and they identify regions taking part in this recognition. Through live cell fluorescence imaging and electron microscopy of endocytic pits, the authors characterize the lifetimes of endocytic sites, the formation rate of endocytic sites and pits and the invagination depth, in addition to transferrin receptor (TfnR) uptake experiments. Binding between CCDC32 and CCDC32 mutants to the AP2 alpha appendage domain is assessed by pull down experiments.

Together, these experiments allow deriving a phenotype of CCDC32 knock-down and CCDC32 mutants within endocytosis, which is a very robust system, in which defects are not so easily detected. A mutation of CCDC32, mimicking CFNDS mutations, is also addressed in this study and shown to have endocytic defects.

An experimental proof for the resistance of the different CCDC32 mutants to siRNA treatment would have helped to strengthen the conclusions.

In summary, the authors present a strong combination of techniques, assessing the impact of CCDC32 in clathrin mediated endocytosis and its binding to AP2.

Reviewer #1 (Public review):

Ono et al. compared the activity of prime editor Nickase PE2 and prime editor nuclease PEn in introducing SNPs and short exogenous DNA sequences into the zebrafish genome to model human disease variants. They find the nickase PE2 prime editor had a higher rate of precise integration for introducing single-nucleotide substitutions, whereas the nuclease PEn prime editor showed improved precision of integration of short DNA sequences. In somatic tissue, the percentage of SNP variant precision edits improved when using PE2 RNP injection instead of mRNA injection, but increased precision editing correlated with elevated indel formation. While PEn overall had higher rates of precision edits, the indel rate was also elevated. Similar rates were observed when introducing a 3 bp stop codon into the ror gene using a standard pegRNA with a 13-nucleotide homology arm, or a springRNA lacking the homology arm that drives integration via NHEJ. Inclusion of an abasic sequence in the springRNA prevented imprecise edits caused by scaffold incorporation, but did not improve the overall percentage of precise edits in somatic tissue. Recovery of a germline ror-TGA integration allele using PEn with RNP was robust, resulting in 5 out of 10 founders transmitting a precise allele. Lastly, the authors demonstrate that PEn was effective at the integration of a 30 bp nuclear localization signal into the 5' end of GFP in an existing muscle-specific reporter line. However, the undefined number of cassettes in this multicopy transgene complicates accurate measurements of editing frequency. Integration of the NLS or other longer sequences at an endogenous locus would demonstrate the broad utility of this approach. From the work presented, it is unclear how prime editing could be used to transiently model human pathogenic variants, given the low frequency of precision edits in somatic tissue, or to isolate stable germline alleles of variants that are potentially dominant negative or gain-of-function in nature. Without a direct comparison with CRISPR/Cas9 nuclease HDR-based methods that use oligonucleotide templates to introduce edits, the advantage of prime editing is unclear. A cost comparison between prime editing and HDR methods would also be of interest, particularly for integration of longer DNA sequences.

The conclusions of the paper are mostly well supported, but some changes to the text and additional analyses would strengthen the conclusion that PE2 vs. PEn is preferred for introducing variants, short or long DNA sequences.

(1) In Figure 3, the data indicate a significant increase in precise edits of the 3 bp TGA using PE2 RNP (11.5%) vs. PE2 mRNA (1.3%). At the adgrf3b locus, only PEn mRNA was tested for introducing the 3 bp and 12 bp insertions. The previous study testing PE2 for 3 and 12 bp insertions was mentioned, but the frequency was not listed, and the study wasn't cited (lines 204 - 207). A comparison of germline transmission rates using PE2 vs. PEn would support the conclusion that PEn allows precise integration of longer templates and recovery of germline integration alleles.

(2) Figure 4 shows the results of introducing a TGA stop codon that is predicted to result in nonsense-mediated decay. Testing the ability to also isolate different substitution mutations in the germline would be useful information for identifying the most effective approach for generating human disease variant models.

(3) A comparison with the prime editing variant knock-in frequencies reported in the recent publication by Vanhooydonck et al., 2025, Lab Animal should be included in the Discussion.

Reviewer #2 (Public review):

Summary:

The manuscript provides a comparison of nickase-based (PE2) and nuclease-based (PEn) Prime Editors in zebrafish, evaluating their efficiencies for substitutions, short insertions (3-30 bp), and germline transmission.

Strengths:

The manuscript has demonstrated for the first time that nuclease-based PEn more efficiently inserts nucleotide sequences up to 30 bp (nuclear localization sequence) than PE2, providing an improvement for the application of gene editing in functional genetics research. Additionally, the demonstration of stable zebrafish lines with edited ror2 and smyhc1:gfp loci is well-supported by sequencing and phenotypic data, confirming functional consequences of edits.

Weaknesses:

The study lacks conceptual innovation, as the central methodology-RNP-based Prime Editor delivery in zebrafish-was previously established by Petri et al. (2022). The present study extends this by testing longer insertions (30 bp) with nuclease-based PEn, but this incremental advance does not substantially shift the field's understanding or capabilities. The manuscript does not sufficiently differentiate its contributions from these precedents.

The comparative analysis between PE2 and PEn systems suffers from limited evidentiary support. The comparison relies on single loci for substitutions (crbn) and insertions (ror2), raising concerns about generalizability. Additional validation across multiple loci is necessary to support broad conclusions about PE2/PEn performance.

Reviewer #3 (Public review):

The manuscript by Ono et al describes the application of prime editors to introduce precise genetic changes in the zebrafish model system. Probably the most important observation is that, compared to the "standard" PE2, the prime editor with full nuclease activity appears to be more efficient at introducing insertions into the genome. Although many laboratories around the world have successfully used oligonucleotide-mediated HDR to insert short exogenous sequences such as epitope tags or loxP sites into the zebrafish genome, the method suffers from a high frequency of indels at the edit site. Thus, additional tools are badly needed, making this manuscript very important. Length of the longer reported insertion (+30) is quite close to the range of V5 (14 amino acids) and ALFA (12 amino acids without "spacer" prolines) epitope tags, as well as loxP site (34 nucleotides). Conclusions drawn in the paper are supported by compelling evidence. I only have a few minor comments:

(1) The logic for introducing two nucleotide changes (at +3 and +10) to change a single amino acid (I378) should be explicitly explained in the main body of the manuscript. It is indeed self-explanatory when looking at Supplementary Figure 1. One way of doing it could be to include Supplementary Figure 1a in Figure 1.

(2) It is not clear why a 3-nucleotide insertion was used to generate W722X. The human W720X is a single-nucleotide polymorphism, and it should be possible to make a corresponding zebrafish mutant by introducing two nucleotide changes.

(3) Lines 137-138: T7 Endonuclease assay used in Figure 2d detects all polymorphisms, both precise changes and indels. Thus, if this assay were performed on embryos shown in Figure 1c-d, the overall percentage of modified alleles would be similarly higher for PEn over PE2 (add up precise prime edits and indels). The conclusion in the last sentence of the paragraph is, therefore, incorrect, I believe.

(4) Use of terminology. "Germline transmission" is typically used to refer to the fraction of F0s transmitting desired changes (or transgenes) to their progeny, while "germline mosaicism" refers to the fraction of F1s with the desired change in the progeny of a given F0. "Germline transmission" in line 217 should be replaced with "germline mosaicism".

(5) Lines 253-255: The fraction of injected embryos that had mosaic nuclear expression of GFP, indicative of NLS insertion, should be clarified. It should also be clarified whether embryos positive for nuclear GFP were preselected for amplicon sequencing and germline transmission analyses. This is extremely important for extrapolation to scenarios like epitope tagging, where preselection is not possible.

(6) Statistical analyses. It would be helpful to clarify why different statistical tests are sometimes used to assess seemingly very similar datasets (Figures 1c, 1d, 2b, 2c, 2f).

(7) Discussion. Since authors suggest that PEn might be especially beneficial for insertion of additional sequences, it is important to stress locus-to-locus variability of success. While the precise +3 insertion was indeed tremendously efficient at both tested loci (ror2 and adgrf3b), +12 addition into adgrf3b was over 10 times less efficient (lines 193-194). In contrast, +30 into smyhc:GFP using the shorter pegRNA was highly efficient again with an average of 8.5% of sequence reads indicating precise integration (line 257, Figure 5c). Longer pegRNA did not work nearly as well (Figure 5c), but was still much better than +12 into adgrf3b. As dangerous as it is to extrapolate from small datasets, perhaps these observations indicate that optimization of RT template and PBS may be needed for each new locus in order to significantly outperform oligonucleotide-mediated HDR? If so, would the cost of ordering several pegRNAs and the effort needed to compare them factor in when deciding which method to use? Reported germline transmission rates for both ror2 W722X (+3, Figure 4a) and smyhc:NLS-GFP (+30, Figure 5f) are tantalizingly high.

Reviewer #1 (Public review):

Summary:

In this work the authors provide evidence that impairment of cell envelope protein homeostasis through blocking the machinery for disulfide bond formation restores efficacy of antibiotics including beta-lactam drugs and colistin against AMR in Gram-negative bacteria.

Strengths:

The authors employ a thorough approach to showcase the restoration of antibiotic sensitivity through inhibition of the DSB machinery, including the evaluation of various antibiotics on both normal and Dsb-deficient pathogenic bacteria (i.e. Pseudomonas and Stenotrophomonas). The authors corroborate these findings by employing Dsb inhibitors in addition to delta dsbA strains. The methodology is appropriate and includes measuring MICs as well as validating their observations in vivo using the Galleria model.

Reviewer #2 (Public review):

Summary:

This work by Kadeřábková and Furniss et al. demonstrates the importance of a specific protein folding system to effectively folding β-lactamase proteins, which are responsible for resistance to β-lactam antibiotics, and shows that inhibition of this system sensitize multidrug-resistant pathogens to β-lactam treatment. In addition, the authors extend these observations to a two-species co-culture model where β-lactamases provided by one pathogen can protect another, sensitive pathogen from β-lactam treatment. In this model, disrupting the protein folding system also disrupted protection of the sensitive pathogen from antibiotic killing. Overall, the data presented provide a convincing foundation for subsequent investigations and development of inhibitors for β-lactamases and other resistance determinants. This and similar strategies may have application to polymicrobial contexts when molecular interactions are suspected to confer resistance to natively antibiotic-sensitive pathogens.

Strengths:

The authors use clear and reliable molecular biology strategies to show that β-lactamase proteins from P. aeruginosa and Burkholderia species, expressed in E. coli in the absence of the dsbA protein folding system, are variably less capable of resisting the effects of different β-lactam antibiotics compared to the dsbA-competent parent strain (Figure 1). The appropriate control is included in the supplemental materials to demonstrate that this effect is specifically dependent on dsbA, since complementing the mutant with an intact dsbA gene restores antibiotic resistance (Figure S1). The authors subsequently show that this lack of activity can be explained by significantly reduced protein levels and loss-of-function protein misfolding in the dsbA mutant background (Figure 2). These data support the importance of this protein folding mechanism in the activity of multiple clinically relevant β-lactamases.

Native bacterial species are used for subsequent experiments, and the authors provide important context for their antibiotic choices and concentrations by referencing the breakpoints that guide clinical practice. In Figure 4, the authors show that loss of the DsbA system in P. aeruginosa significantly sensitizes clinical isolates expressing different classes of β-lactamases to clinically relevant antibiotics. The appropriate control showing that the dsbA1 mutation does not result in sensitivity to a non-β-lactam antibiotic is included in Figure S2. The authors further show, using an in vivo model for antibiotic treatment, that treatment of a dsbA1 mutant results in moderate and near-complete survival of the infected organisms. The importance of this system in S. maltophilia is then investigated similarly (Figure 5), showing that a dsbA dsbL mutant is also sensitive to β-lactams and colistin, another antibiotic whose resistance mechanism is dependent on the DsbA protein folding system. Importantly, the authors show that a small-molecule inhibitor that disrupts the DsbA system, rather than genetic mutations, is also capable of sensitizing S. maltophilia to these antibiotics. It should be noted that while the sensitization is less pronounced, this molecule has not been optimized for S. maltophilia and would be expected to increase in efficacy following optimization. Together, the data support that interference with the DsbA system in native hosts can sensitize otherwise resistant pathogens to clinically relevant antibiotic therapy.

Finally, the authors investigate the effects of co-culturing S. maltophilia and P. aeruginosa (Figure 5E). These assays are performed in synthetic cystic fibrosis sputum medium (SCFM), which provides a nutritional context similar to that in CF but without the presence of more complex components such as mucin. The authors show that while P. aeruginosa alone is sensitive to the antibiotic, it can survive moderate concentrations in the presence of S. maltophilia and even grow in higher concentrations where S. maltophilia appears to overproduce its β-lactamases. However, this protection is lost in S. maltophilia without the DsbA protein folding system, showing that the protective effect depends on functional production of β-lactamase in the presence of viable S. maltophilia. The authors further achieved the difficult task of labeling these multi-drug resistant pathogens with selection markers to determine co-infection CFUs in the supplemental materials. Overall, the data support a protective role for DsbA-dependent β-lactamase under these co-culture conditions.

Weaknesses:

No significant weaknesses are noted beyond the limitations identified and discussed by the authors.

Reviewer #3 (Public review):

Summary:

In the face of emerging antibiotic resistance and slow pace of drug discovery, strategies that can enhance the efficacy of existing clinically used antibiotics are highly sought after. In this manuscript, through genetic manipulation of a model bacterium (Escherichia coli) and clinically isolated and antibiotic resistant strains of concern (Pseudomonas, Burkholderia, Stenotrophomonas), an additional drug target to combat resistance and potentiate existing drugs is put forward. These observations were validated in both pure cultures, mixed bacterial cultures and in worm models. The drug target investigated in this study appears to be broadly relevant to the challenge posed by lactamases enzyme that render lactam antibiotics ineffective in the clinic. The compounds that target this enzyme are being developed already, some of which were tested in this study displaying promising results and potential for further optimization by medicinal chemists.

Strengths:

The work is well designed and well executed and targets an urgent area of research with the unprecedented increase in antibiotic resistance.

Weaknesses:

The impact of the work can be strengthened by demonstrating increased efficacy of antibiotics in mice models or wound models for Pseudomonas infections. Worm models are relevant, but still distant from investigations in animal models.

Reviewer #1 (Public review):

Dixit, Noe, and Weikl apply coarse-grained and all-atom molecular dynamics to determine the response of the mechanosensitive proteins Piezo 1 and Piezo 2 proteins to tension. Cryo-EM structures in micelles show a high curvature of the protein whereas structures in lipid bilayers show lower curvature. Is the zero-stress state of the protein closer to the micelle structure or the bilayer structure? Moreover, while the tension sensitivity of channel function can be inferred from experiment, molecular details are not clearly available. How much does the protein's height and effective area change in response to tension? With these in hand, a quantitative model of its function follows that can be related to the properties of the membrane and the effect of external forces.

Simulations indicate that in a bilayer the protein relaxes from the highly curved cryo-EM dome (Figure 1).

Under applied tension the dome flattens (Figure 2) including the underlying lipid bilayer. The shape of the system is a combination of the membrane mechanical and protein conformational energies (Eq. 1). The membrane mechanical energy is well-characterized. It requires only the curvature and bending modulus as inputs. They determine membrane curvature and the local area metric (Eq. 4) by averaging the height on a grid and computing second derivatives (Eqs. 7, 8) consistent with known differential geometric formulas.

While I am still critical generally of a precise estimate of the energy from simulated membrane shapes (after all it is not trivial to precisely determine even the bending modulus from a simulation), I believe with their revision the authors have convinced me that their estimate is a high quality one, without obvious issues. Although there appears to have been a miscommunication about increasing the density of grain or lowering the density of grain, the authors have tried two grains and determined a similar deformation energy, which addresses my concern. Furthermore, they have computed a dramatically reduced simplification of the curve and determined a similar value.

In summary, this paper uses molecular dynamics simulations to quantify the force of the Piezo 1 and Piezo 2 proteins on a lipid bilayer using simulations under controlled tension, observing the membrane deformation, and using that data to infer protein mechanics. While much of the physical mechanism was previously known, the study itself is a valuable quantification.

Reviewer #2 (Public review):

Summary:

In this study the authors suggest that the structure of Piezo2 in a tensionless simulation is flatter compared to the electron microscopy structure. This is an interesting observation and highlights the fact that the membrane environment is important for Piezo2 curvature. Additionally, the authors calculate the excess area of Piezo2 and Piezo1, suggesting that it is significantly smaller compared the area calculated using the EM structure or simulations with restrained Piezo2. Finally, the authors propose an elastic model for Piezo proteins. Those are very important findings, which would be of interest to the mechanobiology field.

Whilst I like the suggestion that the membrane environment will change Piezo2 flatness, could this be happening because of the lower resolution of the MARTINI simulations? In other words, would it be possible that MARTINI is not able to model such curvature due to its lower resolution?

Related to my comment above, the authors say that they only restrained the secondary structure using an elastic network model. Whilst I understand why they did this, Piezo proteins are relatively large. How can the authors know that this type of elastic network model restrains, combined with the fact that MARTINI simulations are perhaps not very accurate in predicting protein conformations, can accurately represent the changes that happen within Piezo channel during membrane tension?

Modelling or Piezo1, seems to be based on homology to Piezo2. However, the authors need to further evaluate their model, e.g. how it compares with an Alphafold model.

To calculate the tension induce flattening of Piezo channel, the authors "divide all simulation trajectories into 5 equal intervals and determine the nanodome shape in each interval by averaging over the conformations of all independent simulation runs in this interval.". However, probably the change in the flattening of Piezo channel happens very quickly during the simulations, possibly within the same interval. Is this the case? and if yes does this affects their calculations?

Finally, the authors use a specific lipid composition, which is asymmetric. Is it possible that the asymmetry of the membrane causes some of the changes in the curvature that they observe? Perhaps more controls, e.g. with a symmetric POPC bilayer is needed to identify whether membrane asymmetry plays a role in the membrane curvature they observe.

Reviewer #3 (Public review):

Strengths:

This work focuses on a problem of deep significance: quantifying the structure-tension relationship and underlying mechanism for the mechanosensitive Piezo 1 and 2 channels. Such an objective is challenging for molecular dynamics simulations, due to the relatively large size of each membrane-protein system. Nonetheless, the approach chosen here is based on methodology that is, in principle, established and widely accessible. Therefore, another group of practitioners would likely be able to reproduce these findings with reasonable effort.

More specifically, while acknowledging the limitations of the MARTINI force field, this work makes a significant improvement compared to previous simulations of Piezo proteins by adopting a range of membrane tensions that includes physiologically relevant values (below 10 mN/m).

Weaknesses:

The two main results of this paper are (1) that both channels exhibit a flatter structure compared to cryo-EM measurements, and (2) their estimated force vs. displacement relationship. Although the former correlates at least quantitatively with prior experimental work, the latter relies exclusively on simulation results and model parameters.

My remaining technical concerns in the revised manuscript are as follows:

(1) At each membrane tension, all concurrent atomistic simulations were initialized from the same snapshot of a previous CG simulation: in my opinion, it is inaccurate to refer to those atomistic simulations as "independent" from each other (as is done twice in the caption of Figure 3, as well as in the text).

(2) Continuum mechanics calculations were employed to model the membrane's curvature energetics. The bending modulus, k, was not determined for the specific lipid composition used in this study, but was instead taken from previous MARTINI simulations involving the same primary lipid, POPC. Given that these calculations are intended to describe MARTINI simulations specifically, this approximation may be acceptable. However, it does not account for the increased stiffness observed in POPC/cholesterol mixtures-an effect measured experimentally but not reproduced by the MARTINI model-nor does it reflect the asymmetric conditions, as all referenced simulations involve symmetric bilayers. As a result, the bending energies and forces shown in Figure 5(c,d) are internally consistent within the model, but they probably correspond to real values up to an unknown multiplicative factor.

Reviewer #1 (Public review):

The authors report on a thorough investigation of the interaction of megakaryocytes (MK) with their associated ECM during maturation. They report convincing evidence to support the existence of a dense cage-like pericellular structure containing laminin γ1 and α4 and collagen IV, which interacts with integrins β1 and β3 on MK and serve to fix the perisinusoidal localization of MK and prevent their premature intravasation. As with everything in nature, the authors support a Goldilocks range of MK-ECM interactions - inability to digest the ECM via inhibition of MMPs leads to insufficient MK maturation and development of smaller MK. This important work sheds light into the role of cell-matrix interactions in MK maturation, and suggests that higher-dimensional analyses are necessary to capture the full scope of cellular biology in the context of their microenvironment. The authors have responded appropriately to the majority of my previous comments.

Some remaining points:

In a previous critique, I had suggested that "it is unclear how activation of integrins allows the MK to become "architects for their ECM microenvironment" as the authors posit. A transcriptomic analysis of control and DKO MKs may help elucidate these effects". The authors pointed out the technical difficulty of obtained sufficient numbers of MK for such analysis, which I accept, and instead analyzed mature platelets, finding no difference between control and DKO platelets. This is not necessarily surprising, since mature circulating platelets have no need to engage an ECM microenvironment, and for the same reason I would suggest that mature platelet analyses are not representative of MK behavior as regards ECM interactions.

Reviewer #2 (Public review):

Summary:

This study makes a significant contribution to understanding the microenvironment of megakaryocytes (MKs) in the bone marrow, identifying an extracellular matrix (ECM) cage structure that influences MK localization and maturation. The authors provide compelling evidence for the presence of this ECM cage and its role in MK homeostasis, employing an array of sophisticated imaging techniques and molecular analyses.

The authors have addressed most of the concerns raised in the previous review, providing clarifications and additional data that strengthen their conclusions

More broadly, this work adds to a growing recognition of the ECM as an active participant in haematopoietic cell regulation in the bone marrow microenvironment. This work could pave the way to future studies investigating how the megakaryocytes' ECM cage affects their function as part of the haematopoietic stem cell niche, and by extension, influences global haematopoiesis.

Reviewer #1 (Public review):

Summary:

The mechanism by which WNT signals are received and transduced into the cell has been the topic of extensive research. Cell surface levels of the WNT receptors of the FZD family are subject to tight control and it's well established that the transmembrane ubiquitin ligases ZNRF3 and RNF43 target FZDs for degradation and that proteins of the R-spondin family block this effect. This manuscript explores the role that WNT proteins play in receptor internalization, recycling and degradation, and the authors provide evidence that WNTs promote interactions of FZD with the ubiquitin ligases. Using cells mutant in all 3 DVL genes, the authors demonstrate that this effect of WNT on FZD is DVL-independent.

Strengths:

Overall, the data are of good quality and support the authors' hypothesis. Strengths of this study is the use of CRISPR-mutated cell lines to establish genetic requirements for the various components. The finding that FZD internalization and degradation is WNT dependent and does not involve DVL is novel.

Weaknesses:

A weakness of the work includes a heavy reliance on overexpression of FZD proteins. To detect endogenous FZDs, the authors have inserted a V5 tag into the endogenous gene, which may affect their activity(ies).

Reviewer #2 (Public review):

In this manuscript Luo et al uncover that the ZNRF3/RNF43 E3 ubiquitin ligases participate in the selective endocytosis and degradation of FZD5/8 receptors in response to Wnt stimulation. In my opinion there are three significant findings of this study: 1) Wnt proteins are required for ZNRF3/RNF43 mediated endocytosis and degradation of FZD receptors and this constitutes an important negative regulatory loop. 2) Wnt can induce FZD endocytosis in the absence of ZNRF3/RNF43 but this does not influence total or cell surface levels. 3) The ZNRF3/RNF43 substrate selectivity for FZD5/8 over the other 8 Frizzleds. Of course, many questions remain, and new ones emerge as it is often the case, but these findings challenge our dogmatic view on how the ZNRF3/RNF43 regulate Wnt signaling and emphasize their role in Wnt-dependent Frizzled endocytosis/degradation and beta-catenin signaling.

This is an elegant study employing several CRISPR-edited cell lines to tag endogenous Frizzled receptors and to knockout ZNRF3/RNF43 and all three Dishevelled proteins. One major strength of the study is therefore the careful assessment of the roles of RNF43 and ZNFR3 in endogenous expression contexts. This is especially relevant since overexpression of membrane E3 ligases have been shown to ectopically degrade membrane proteins and could have blurred previous interpretations. A second strength is clarifying the role of Dishevelled proteins in FZD endocytosis. Indeed, although previous studies suggested that the Wnt-promoted interaction between FZD and RNF43/ZNFR3 was mediated through Dvl, the authors clearly show that this is not the case (using Dvl knockout cells and functional assays). Dvl proteins, on the other han,d are still required for ligand-independent FZD-endocytosis.

The only weakness pertains to the difference in signaling outcome, comparing elevated signaling seen when FZD levels are upregulated following ZNFR3/RNF43 KO vs ectopic overexpression. Indeed, the authors suggest that in the absence of RNF43/ZNFR3 the receptors could be recycled back to the PM and thereby contribute to increased signaling seen in the mutant cells. This has not been directly demonstrated.

Reviewer #1 (Public review):

Summary:

In this manuscript, Gerken et al examined how neurons in the human medial temporal lobe respond to and potentially code dynamic movie content. They had 29 patients watch a long-form movie while neurons within their MTL were monitored using depth electrodes. They found that neurons throughout the region were responsive to the content of the movie. In particular, neurons showed significant responses to people, places, and to a lesser extent, movie cuts. Modeling with a neural network suggests that neural activity within the recorded regions was better at predicting the content of the movies as a population, as opposed to individual neural representations. Surprisingly, a subpopulation of unresponsive neurons performed better than the responsive neurons at decoding the movie content, further suggesting that while classically nonresponsive, these neurons nonetheless provided critical information about the content of the visual world. The authors conclude from these results that low-level visual features, such as scene cuts, may be coded at the neuronal level, but that semantic features rely on distributed population-level codes.

Strengths:

Overall, the manuscript presents an interesting and reasonable argument for their findings and conclusions. Additionally, the large number of patients and neurons that were recorded and analyzed makes this data set unique and potentially very powerful. On the whole, the manuscript was very well written, and as it is, presents an interesting and useful set of data about the intricacies of how dynamic naturalistic semantic information may be processed within the medial temporal lobe.

Weaknesses:

There are a number of concerns I have based on some of the experimental and statistical methods employed that I feel would help to improve our understanding of the current data.

In particular, the authors do not address the issue of superposed visual features very well throughout the manuscript. Previous research using naturalistic movies has shown that low-level visual features, particularly motion, are capable of driving much of the visual system (e.g, Bartels et al 2005; Bartels et al 2007; Huth et al 2012; Çukur et al 2013; Russ et al 2015; Nentwich et al 2023). In some of these papers, low-level features were regressed out to look at the influence of semantics, in others, the influence of low-level features was explicitly modeled. The current manuscript, for the most part, appears to ignore these features with the exception of scene cuts. Based on the previous evidence that low-level features continue to drive later cortical regions, it seems like including these as regressors of no interest or, more ideally, as additional variables, would help to determine how well MTL codes for semantic features over top of these lower-order variables.

Following on this, much of the current analyses rely on the training of deep neural networks to decode particular features. The results of these analyses are illuminating, however, throughout the manuscript, I was increasingly wondering how the various variables interact with each other. For example, separate analyses were done for the patients, regions, and visual features. However, the logistic regression analysis that was employed could have all of these variables input together, obtaining beta weights for each one in an overall model. This would potentially provide information about how much each variable contributes to the overall decoding in relation to the others.

A few more minor points that would help to clarify the current results involve the selection of data for particular analyses. For some analyses, the authors chose to appropriately downsample their data sets to compare across variables. However, there are a few places where similar downsampling would be informative, but was not completed. In particular, the analyses for patients and regions may have a more informative comparison if the full population were downsampled to match the size of the population for each patient or region of interest. This could be done with the Monte Carlo sampling that is used in other analyses, thus providing a control for population size while still sampling the full population.

Reviewer #2 (Public review):

Summary:

This study introduces an exciting dataset of single-unit responses in humans during a naturalistic and dynamic movie stimulus, with recordings from multiple regions within the medial temporal lobe. The authors use both a traditional firing-rate analysis as well as a sophisticated decoding analysis to connect these neural responses to the visual content of the movie, such as which character is currently on screen.

Strengths:

The results reveal some surprising similarities and differences between these two kinds of analyses. For visual transitions (such as camera angle cuts), the neurons identified in the traditional response analysis (looking for changes in firing rate of an individual neuron at a transition) were the most useful for doing population-level decoding of these cuts. Interestingly, this wasn't true for character decoding; excluding these "responsive" neurons largely did not impact population-level decoding, suggesting that the population representation is distributed and not well-captured by individual-neuron analyses.

The methods and results are well-described both in the text and in the figures. This work could be an excellent starting point for further research on this topic to understand the complex representational dynamics of single neurons during naturalistic perception.

Weaknesses:

(1) I am unsure what the central scientific questions of this work are, and how the findings should impact our understanding of neural representations. Among the questions listed in the introduction is "Which brain regions are informative for specific stimulus categories?". This is a broad research area that has been addressed in many neuroimaging studies for decades, and it's not clear that the results tell us new information about region selectivity. "Is the relevant information distributed across the neuronal population?" is also a question with a long history of work in neuroscience about localist vs distributed representations, so I did not understand what specific claim was being made and tested here. Responses in individual neurons were found for all features across many regions (e.g., Table S1), but decodable information was also spread across the population.

(2) The character and indoor/outdoor labels seem fundamentally different from the scene/camera cut labels, and I was confused by the way that the cuts were put into the decoding framework. The decoding analyses took a 1600ms window around a frame of the video (despite labeling these as frame "onsets" like the feature onsets in the responsive-neuron analysis, I believe this is for any frame regardless of whether it is the onset of a feature), with the goal of predicting a binary label for that frame. Although this makes sense for the character and indoor/outdoor labels, which are a property of a specific frame, it is confusing for the cut labels since these are inherently about a change across frames. The way the authors handle this is by labeling frames as cuts if they are in the 520ms following a cut (there is no justification given for this specific value). Since the input to a decoder is 1600ms, this seems like a challenging decoding setup; the model must respond that an input is a "cut" if there is a cut-specific pattern present approximately in the middle of the window, but not if the pattern appears near the sides of the window. A more straightforward approach would be, for example, to try to discriminate between windows just after a cut versus windows during other parts of the video. It is also unclear how neurons "responsive" to cuts were defined, since the authors state that this was determined by looking for times when a feature was absent for 1000ms to continuously present for 1000ms, which would never happen for cuts (unless this definition was different for cuts?).

(3) The architecture of the decoding model is interesting but needs more explanation. The data is preprocessed with "a linear layer of same size as the input" (is this a layer added to the LSTM that is also trained for classification, or a separate step?), and the number of linear layers after the LSTM is "adapted" for each label type (how many were used for each label?). The LSTM also gets to see data from 800 ms before and after the labeled frame, but usually LSTMs have internal parameters that are the same for all timesteps; can the model know when the "critical" central frame is being input versus the context, i.e., are the inputs temporally tagged in some way? This may not be a big issue for the character or location labels, which appear to be contiguous over long durations and therefore the same label would usually be present for all 1600ms, but this seems like a major issue for the cut labels since the window will include a mix of frames with opposite labels.

(4) Because this is a naturalistic stimulus, some labels are very imbalanced ("Persons" appears in almost every frame), and the labels are correlated. The authors attempt to address the imbalance issue by oversampling the minority class during training, though it's not clear this is the right approach since the test data does not appear to be oversampled; for example, training the Persons decoder to label 50% of training frames as having people seems like it could lead to poor performance on a test set with nearly 100% Persons frames, versus a model trained to be biased toward the most common class. There is no attempt to deal with correlated features, which is especially problematic for features like "Summer Faces" and "Summer Presence", which I would expect to be highly overlapping, making it more difficult to interpret decoding performance for specific features.

(5) Are "responsive" neurons defined as only those showing firing increases at a feature onset, or would decreased activity also count as responsive? If only positive changes are labeled responsive, this would help explain how non-responsive neurons could be useful in a decoding analysis.

(6) Line 516 states that the scene cuts here are analogous to the hard boundaries in Zheng et al. (2022), but the hard boundaries are transitions between completely unrelated movies rather than scenes within the same movie. Previous work has found that within-movie and across-movie transitions may rely on different mechanisms, e.g., see Lee & Chen, 2022 (10.7554/eLife.73693).

Reviewer #3 (Public review):

This is an excellent, very interesting paper. There is a groundbreaking analysis of the data, going from typical picture presentation paradigms to more realistic conditions. I would like to ask the authors to consider a few points in the comments below.

(1) From Figure 2, I understand that there are 7 neurons responding to the character Summer, but then in line 157, we learn that there are 46. Are the other 39 from other areas (not parahippocampal)? If this is the case, it would be important to see examples of these responses, as one of the main claims is that it is possible to decode as good or better with non-responsive compared to single responsive neurons, which is, in principle, surprising.

(2) Also in Figure 2, there seem to be relatively very few neurons responding to Summer (1.88%) and to outdoor scenes (1.07%). Is this significant? Isn't it also a bit surprising, particularly for outdoor scenes, considering a previous paper of Mormann showing many outdoor scene responses in this area? It would be nice if the authors could comment on this.

(3) I was also surprised to see that there are many fewer responses to scene cuts (6.7%) compared to camera cuts (51%) because every scene cut involves a camera cut. Could this have been a result of the much larger number of camera cuts? (A way to test this would be to subsample the camera cuts.)

(4) Line 201. The analysis of decoding on a per-patient basis is important, but it should be done on a per-session basis - i.e., considering only simultaneously recorded neurons, without any pooling. This is because pooling can overestimate decoding performances (see e.g. Quian Quiroga and Panzeri NRN 2009). If there was only one session per patient, then this should be called 'per-session' rather than 'per-patient' to make it clear that there was no pooling.

(5) In general, the decoding results are quite interesting, and I was wondering if the authors could give a bit more insight by showing confusion matrices, with the predictions of the appearance of each of the characters, etc. Some of the characters may appear together, so this could be another entry of the decoder (say, predicting person A, B, C, A&B, A&C, B&C, A&B&C). I guess this could also show the power of analyzing the population activity.

(6) Lines 406-407. The claim that stimulus-selective responses to characters did not account for the decoding of the same character is very surprising. If I understood it correctly, the response criterion the authors used gives 'responsiveness' but not 'selectivity'. So, were people's responses selective (e.g., firing only to Summer) or non-selective (firing to a few characters)? This could explain why they didn't get good decoding results with responsive neurons. Again, it would be nice to see confusion matrices with the decoding of the characters. Another reason for this is that what are labelled as responsive neurons have relatively weak and variable responses.

(7) Line 455. The claim that 500 neurons drive decoding performance is very subjective. 500 neurons gives a performance of 0.38, and 50 neurons gives 0.33.

(8) Lines 492-494. I disagree with the claim that "character decoding does not rely on individual cells, as removing neurons that responded strongly to character onset had little impact on performance". I have not seen strong responses to characters in the paper. In particular, the response to Summer in Figure 2 looks very variable and relatively weak. If there are stronger responses to characters, please show them to make a convincing argument. It is fine to argue that you can get information from the population, but in my view, there are no good single-cell responses (perhaps because the actors and the movie were unknown to the subjects) to make this claim. Also, an older paper (Quian Quiroga et al J. Neurophysiol. 2007) showed that the decoding of individual stimuli in a picture presentation paradigm was determined by the responsive neurons and that the non-responsive neurons did not add any information. The results here could be different due to the use of movies instead of picture presentations, but most likely due to the fact that, in the picture presentation paradigm, the pictures were of famous people for which there were strong single neuron responses, unlike with the relatively unknown persons in this paper.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors explore a novel concept: GPCR-mediated regulation of miRNA release via extracellular vesicles (EVs). They perform an EV miRNA cargo profiling approach to investigate how specific GPCR activations influence the selective secretion of particular miRNAs. Given that GPCRs are highly diverse and orchestrate multiple cellular pathways - either independently or collectively - to regulate gene expression and cellular functions under various conditions, it is logical to expect alterations in gene and miRNA expression within target cells.

Strengths:

The novel idea of GPCRs-mediated control of EV loading of miRNAs.

Weaknesses:

Incomplete findings failed to connect and show evidence of any physiological parameters that are directly related to the observed changes. The mechanical detail is lacking.

The manuscript falls short of providing a comprehensive understanding. Identifying changes in cellular and EV-associated miRNAs without elucidating their physiological significance or underlying regulatory mechanisms limits the study's impact. Without demonstrating whether these miRNA alterations have functional consequences, the findings alone are insufficient. The findings may be suitable for more specialized journals.

Furthermore, a critical analysis of the relationship between cellular miRNA levels and EV miRNA cargo is essential. Specifically, comparing the intracellular and EV-associated miRNA pools could reveal whether specific miRNAs are preferentially exported, a behavior that should be inversely related to their cellular abundance if export serves a beneficial function by reducing intracellular levels. This comparison is vital to strengthen the biological relevance of the findings and support the proposed regulatory mechanisms by GPCRs.

Reviewer #2 (Public review):

Summary:

This study examines how activating specific G protein-coupled receptors (GPCRs) affects the microRNA (miRNA) profiles within extracellular vesicles (EVs). The authors seek to identify whether different GPCRs produce unique EV miRNA signatures and what these signatures could indicate about downstream cellular processes and pathological processes.

Methods:

(1) Used U2OS human osteosarcoma cells, which naturally express multiple GPCR types.

(2) Stimulated four distinct GPCRs (ADORA1, HRH1, FZD4, ACKR3) using selective agonists.

(3) Isolated EVs from culture media and characterized them via size exclusion chromatography, immunoblotting, and microscopy.

(4) Employed qPCR-based miRNA profiling and bioinformatics analyses (e.g., KEGG, PPI networks) to interpret expression changes.

Key Findings:

(1) No significant change in EV quantity or size following GPCR activation.

(2) Each GPCR triggered a distinct EV miRNA expression profile.

(3) miRNAs differentially expressed post-stimulation were linked to pathways involved in cancer, insulin resistance, neurodegenerative diseases, and other physiological/pathological processes.

(4) miRNAs such as miR-550a-5p, miR-502-3p, miR-137, and miR-422a emerged as major regulators following specific receptor activation.

Conclusions:

The study offers evidence that GPCR activation can regulate intercellular communication through miRNAs encapsulated within extracellular vesicles (EVs). This finding paves the way for innovative drug-targeting strategies and enhances understanding of drug side effects that are mediated via GPCR-related EV signaling.

Strengths:

(1) Innovative concept: The idea of linking GPCR signaling to EV miRNA content is novel and mechanistically important.

(2) Robust methodology: The use of multiple validation methods (biochemical, biophysical, and statistical) lends credibility to the findings.

(3) Relevance: GPCRs are major drug targets, and understanding off-target or systemic effects via EVs is highly valuable for pharmacology and medicine.

Weaknesses:

(1) Sample Size & Scope: The analysis included only four GPCRs. Expanding to more receptor types or additional cell lines would enhance the study's applicability.

(2) Exploratory Nature: This study is primarily descriptive and computational. It lacks functional validation, such as assessing phenotypic effects in recipient cells, which is acknowledged as a future step.

(3) EV heterogeneity: The authors recognize that they did not distinguish EV subpopulations, potentially confounding the origin and function of miRNAs.

Reviewer #1 (Public review):

Summary:

As TDP-43 mislocalization is a hallmark of multiple neurodegenerative diseases, the authors seek to identify pathways that modulate TDP-43 levels. To do this, they use a FACS based genome wide CRISPR KD screen in a Halo tagged TDP-43 KI iPSC line. Their screen identifies a number of genetic modulators of TDP-43 expression including BORC which plays a role in lysosome transport.

Strengths:

Genome wide CRISPR based screen identifies a number of modulators of TDP-43 expression to generate hypotheses regarding RNA BP regulation and perhaps insights into disease

Reviewer #2 (Public review):

Summary:

The authors employ a novel CRISPRi FACS screen and uncover the lysosomal transport complex BORC as a regulator of TDP-43 protein levels in iNeurons. They also find that BORC subunit knockouts impair lysosomal function, leading to slower protein turnover and implicating lysosomal activity in the regulation of TDP-43 levels. This is highly significant for the field given that a) other proteins could also be regulated in this way, b) understanding mechanisms that influence TDP-43 levels are significant given that its dysregulation is considered a major driver of several neurodegenerative diseases and c) the novelty of the proposed mechanism.

Strengths:

The novelty and information provided by the CRISPRi screen. The authors provide evidence indicating that BORC subunit knockouts impair lysosomal function, leading to slower protein turnover and implicating lysosomal activity in the regulation of TDP-43 levels and show a mechanistic link between lysosome mislocalization and TDP-43 dysregulation. The study highlights the importance of localized lysosome activity in axons and suggests that lysosomal dysfunction could drive TDP-43 pathologies associated with neurodegenerative diseases like FTD/ALS. Further, the methods and concepts will have an impact to the larger community as well. The work also sets up for further work to understand the somewhat paradoxical findings that even though the tagged TDP-43 protein is reduced in the screen, it does not alter cryptic exon splicing and there is a longer TDP-43 half-life with BORC KD.

Reviewer #3 (Public review):

Summary:

In this work, Ryan et al. have performed a state-of-the-art full genome CRISP-based screen of iNEurons expressing a teggd version of TDP-43 in order to determine expression modifiers of this protein. Unexpectedly, using this approach the authors have uncovered a previously undescribed role of the BORC complex in affecting the levels of TDP-43 protein, but not mRNA expression. Taken together, these findings represent a very solid piece of work that will certainly be important for the field.

Strengths:

BORC is a novel TDP-43 expression modifier that has never been described before and it seemingly acts on regulating protein half life rather than transcriptome level. It has been long known that different labs have reported different half-lives for TDP-43 depending on the experimental system but no work has ever explained these discrepancies. Now, the work of Ryan et al. has for the time identified one of these factors which could account for these differences and play an important role in disease (although this is left to be determined in future studies).

The genome wide CRISPR screening has demonstrated to yield novel results with high reproducibility and could eventually be used to search for expression modifiers of many other proteins involved in neurodegeneration or other diseases

Reviewer #1 (Public review):

Summary:

Compared to placental mammals, marsupials have a short gestation period and give birth to altricial young. To assist with the detection and response to cues that direct the neonate joeys to the mother's pouch, as well as latching onto the teat, marsupial craniofacial development at this stage is rapid and heterochronous relative to placentals. Cook et al. have presented an important study on the transcriptomic and epigenomic signature underlying this heterochronous development of craniofacial features across mammals, using the fat-tailed dunnart as a marsupial model.

Given the lack of a dunnart genome, the authors prepared long and short read sequence datasets to assemble and annotate a novel genome to allow for mapping of RNAseq and ChIPseq data against H3K4me3 and H3K27ac, which allowed for identification of putative promoter and enhancer sites in dunnart. They found that genes proximal to these regulatory loci were enriched for functions related to bone, skin, muscle and embryonic development, verifying the precocious state of newborn dunnart facial tissue. When compared with mouse, the authors found a much higher proportion of promoter regions aligned between species than for enhancer regions, and subsequent profiling identified regulatory elements conserved across species and are important for mammalian craniofacial development. In contrast, identification of dunnart-specific enhancers and patterns of RNA expression further confirm the precocious state of muscle development, as well as for sensory system development, in dunnart, suggesting that early formation of these features are critical for neonate marsupials.

Marsupials are emerging as an important model for studying mammalian development and evolution, and the authors have performed a novel and thorough analysis that helps to elucidate the regulatory profile underlying craniofacial heterochrony. Impressively, this study also includes the assembly of a new marsupial reference genome that will benefit many future studies of mammalian developmental biology.

Strengths:

The genome assembly method was thorough, using two different long-read methods (Pacbio and ONT) to generate the long reads for contig and scaffold construction, increasing the quality of the final assembled genome, which was effectively annotated and used for functional analysis of orthologous regulatory elements.

The birth of altricial young in marsupials is an important feature of their development that is distinct from placental mammals which are separated by about 160 million years of evolution. Very little is known, however, about the regulatory profile that contributes to the advanced craniofacial development required for joey survival. This is one of the few epigenomic studies performed in marsupials (of any organ) and the first performed in fat-tailed dunnart (also of any organ), which begins to address this lack of knowledge.

The study also provides evidence supporting the important role enhancer elements play in mammalian phenotypic evolution, relative to promoters.

Weaknesses:

Biological replicates of facial tissue were collected at a single developmental time point of the fat-tailed dunnart within the first postnatal day (P0), and analysed this in the context of similar mouse facial samples from the ENCODE consortium at six developmental time points, where previous work from the authors have shown that the younger mouse samples (E11.5-12.5) approximately corresponds to the dunnart developmental stage (Cook et al. 2021). However, it would be useful to have samples from at least one older dunnart time point, for example, at a developmental stage equivalent to mouse E15.5. This would provide additional insight into the extent of accelerated face development in dunnart relative to mouse, i.e. how long do the regulatory elements that are activated early in dunnart remain active for and does their function later influence other aspects of craniofacial development?

Reviewer #2 (Public review):

Summary:

This study aims to investigate how social observation influences risky decision-making. Using a gambling task, the study explored how participants adjusted their risk-taking behavior when they believed their decisions were being observed by either a risk-averse or risk-seeking partner. The authors hypothesized that individuals would simulate the choices of their observers based on learned preferences and integrate these simulated choices into their own decision-making. In addition to behavioral experiments, the study employed computational modeling to formalize decision processes and fMRI to identify the neural underpinnings of risky decision-making under social observation.

Strengths:

The study provides a fresh perspective on social influence in decision-making, moving beyond the simple notion that social observation leads to uniformly riskier behavior. Instead, it shows that individuals adjust their choices depending on their beliefs about the observer's risk preferences, offering a more nuanced understanding of how social contexts shape decision-making. The authors provide evidence using comprehensive approaches, including behavioral data based on a well-designed task, computational modeling, and neuroimaging. The three models are well selected to compare at which level (e.g., computing utility, risk preference shift, and choice probability) the social influence alters one's risky decision-making. This approach allows for a more precise understanding of the cognitive processes underlying decision-making under social observation.

Weaknesses:

While the neuroimaging results are generally consistent with the behavioral and computational findings, the strength of the neural evidence could be improved. The authors' claims about the involvement of the TPJ and mPFC in integrating social information are plausible, but further analysis, such as model comparisons at the neuroimaging level, is needed to decisively rule out alternative interpretations that other computational models suggest.

My concern raised above in the previous round has been addressed with the newly added results. I now find the manuscript substantially improved.

I have only a minor suggestion: when discussing the conflict-related signals observed in the dACC and dlPFC, I encourage the authors to include alternative interpretations beyond conflict monitoring per se. For example, these signals may also reflect processes related to information updating during social learning or inference. While the study does not aim to dissociate these possibilities, acknowledging them would enrich the discussion and provide a broader perspective for readers.

Comments on revised version:

Thank you for the substantial revision. I believe the additional analyses have meaningfully strengthened the manuscript, particularly by improving the connection between the behavioral modeling and neuroimaging results. The findings are consistent with prior work while also providing novel insights.

When discussing the conflict-related signals observed in the dACC/dlPFC, I encourage the authors to include alternative interpretations in addition to conflict monitoring per se. For example, these signals may also reflect processes related to information updating during social learning or inference. While the study does not aim to dissociate these possibilities, acknowledging them would enrich the discussion and offer a broader perspective for readers.

I have updated my evaluation of the strength of evidence from Solid to Convincing.

Reviewer #3 (Public review):

Summary:

This is an important paper using a novel paradigm to examine how observation affects social contagion of risk preferences. There is a lot of interest in the field on the mechanisms of social influence, and adding in the factor of whether observation also influences these contagion effects is intriguing.

Strengths:

There is an impressive combination of a multi-stage behavioural task as well as computational modelling and neuroimaging. The analyses are well conducted and the sample size is reasonable.

Comments on revised version:

Thank you for your helpful responses to my concerns. The manuscript is much improved and will make an important contribution to the literature. I have one remaining clarification. My request was for the authors to speculate in the discussion about lifespan differences in susceptibility to social influence, because the paper talks about how observing others' choices makes people riskier. I think it is important to explicitly acknowledge in the discussion that the sample tested was young adults, and it may be that the effects they observe are not the same in adolescents or older adults, as suggested in recent work (e.g. Reiter et al., 2019 Nat Comms, Su et al., 2024, Comms Psych). This is important to qualify general statements about how humans behave when observing others' risky decisions.

Reviewer #1 (Public review):

Summary:

Felipe and colleagues try to answer an important question in Sarbecovirus Orf9b-mediated interferon signaling suppression, given that this small viral protein adopts two distinct conformations, a dimeric β-sheet-rich fold and a helix-rich monomeric fold when bound by Tom70 protein. Two Orf9b structures determined by X-ray crystallography and Cryo-EM suggest an equilibrium between the two Orf9b conformations, and it is important to understand how this equilibrium relates to its functions. To answer these questions, the authors developed a series of ordinary differential equations (ODE) describing the Orf9b conformation equilibrium between homodimers and monomers binding to Tom70. They used SPR and a fluorescent polarization (FP) peptide displacement assay to identify parameters for the equilibrium and create a theoretical model. They then used the model to characterize the effect of lipid-binding and the effects of Orf9b mutations in homodimer stability, lipid binding, and dimer-monomer equilibrium. They used their model to further analyze dimerization, lipid binding, and Orf9b-Tom70 interactions for truncated Orf9b, Orf9b fusion mutant S53E (blocking Tom70 binding), and Orf9b from a set of Sars-CoV-2 VOCs. They evaluated the ability of different Orf9b variants for binding Tom70 using Co-IP experiments and assessed their activity in suppressing IFN signaling in cells.

Overall, this work is well designed, the results are of high quality and well-presented; the results support their conclusions.

Strengths:

(1) They developed a working biophysical model for analyzing Orf9b monomer-dimer equilibrium and Tom70 binding based on SPR and FP experiments; this is an important tool for future investigation.

(2) They prepared lipid-free Orf9b homodimer and determined its crystal structure.

(3) They designed and purified obligate Orf9b monomer, fused-dimer, etc., a very important Orf9b variant for further investigations.

(4) They identified the lipid bound by Orf9b homodimer using mass spectra data.

(5) They proposed a working model of Orf9b-Tom70 equilibrium.

Weaknesses:

(1) It is difficult to understand why the obligate Orf9b dimer has similar IFN inhibition activity as the WT protein and obligate Orf9b monomer truncations.

(2) The role of Orf9b homodimer and the role of Orf9b-bound lipid in virus infection, remains unknown.

Comments on revisions:

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

Reviewer #2 (Public review):

Summary:

This study focuses on Orf9b, a SARS-COV1/2 protein that regulates innate signaling through interaction with Tom70. San Felipe et al use a combination of biophysical methods to characterize the coupling between lipid-binding, dimerization, conformational change, and protein-protein-interaction equilibria for the Orf9b-Tom70 system. Their analysis provides a detailed explanation for previous observations of Orf9b function. In a cellular context, they find other factors may also be important for the biological functioning of Orf9b.

Strengths:

San Felipe et al elegantly combine structural biology, biophysics, kinetic modelling, and cellular assays, allowing detailed analysis of the Orf9b-Tom70 system. Such complex systems involving coupled equilibria are prevalent in various aspects of biology, and a quantitative description of them, while challenging, provides a detailed understanding and prediction of biological outcomes. Using SPR to guide initial estimates of the rate constants for solution measurements is an interesting approach.

Weaknesses:

This study would benefit from a more quantitative description of uncertainties in the numerous rate constants of the models, either through a detailed presentation of the sensitivity analysis or another approach such as MCMC. Quantitative uncertainty analysis, such as MCMC is not trivial for ODEs, particularly when they involve many parameters and are to be fitted to numerous data points, as is the case for this study. The authors use sensitivity analysis as an alternative, however, the results of the sensitivity analysis are not presented in detail, and I believe the authors should consider whether there is a way to present this analysis more quantitatively. For example, could the residuals for each +/-10% parameter change for the peptide model be presented as a supplementary figure, and similarly for the more complex models? Further details of the range of rate constants tested would be useful, particularly for the ka and kB parameters.

The authors build a model that incorporates an α-helix-β-sheet conformational change, but the rate constant for the conversion to the α-helix conformation is required to be second order. Although the authors provide some rationale, I do not find this satisfactorily convincing given the large number of adjustable parameters in the model and the use of manual model fitting. The authors should discuss whether there is any precedence for second-order rate constants for conformational changes in the literature. On page 14, the authors state this rate constant "had to be non-linear in the monomer β-sheet concentration" - how many other models did the authors explore? For example, would αT↔α↔αα↔ββ (i.e., conformational change before dimer dissociation) or α↔βαT↔ββ (i.e., Tom70 binding driving dimer dissociation) be other plausible models for the conformational change that do not require assumptions of second-order rate constants for the conformational change?

Overall, this study progresses the analysis of coupled equilibria and provides insights into Orf9b function.

Comments on revisions:

The authors have done a satisfactory job addressing my concerns.

Regarding my recommendations to the authors - point 7: "Orf9b-FITC:Tom70" and "PT", representing the same species, are still both used in the equations on page 14, which is confusing for anyone who may wish to re-use the model. I appreciate this is quite a subtle point but given the importance of the model for the manuscript I feel the authors should do their due diligence to ensure it is presented as clearly as possible.

Reviewer #1 (Public review):

Summary:

The authors stated aim is to introduce so-called Minkowski tensors to characterize and quantify the shape of cells in tissues. The authors introduce Minkowski tensors and then define the p-atic order q_p as a cell shape measure, where p is an integer. They also introduce a previously defined measure of p-atic order in the form of the parameter \gamma_p. The authors compute q_p for data obtained by simulating an active vertex model and a multiphase field model, where they focus on p=2 and p=6 - so-called nematic and hexatic order - as the two values of highest biological relevance. Based on their analysis, the authors state that q_2 and q_6 are independent, that there is no crossover for the coarse-grained quantities, that a comparison of q_p for different values of p is not meaningful, and determine the dependence of the mean value of q_2 and q_6 on cell activity and deformability. Subsequently, they apply their method to data from MDCK monolayers and argue that the full range of q_p values needs to be considered to characterize shape and positional order in epithelia..

Strength:

The work presents a set of parameters that are useful for analyzing cell shape.

Weaknesses:

The introduction of the Minkowski tensors is hardly accessible for typical biologists. Eventually, most quantification is done using q_p, which can be defined without recursion to Minkowski functionals. The relation to Minkowski functionals makes the important properties of robustness and stability evident. However, for an audience of biologists, the derivation of this property could be relegated to an Appendix. Instead, the text could directly go to the results of the analysis of experimental and modeling data.

Important details about how the cell shapes are extracted from the experimental data are missing. The two data sets the authors consider are not analyzed in the same way.

Reviewer #3 (Public review):

Hapel et al. present an article entitled Quantifying the shape of cells - from Minkowski tensors to p-atic order. The paper reports the p-atic quantitative method - established in physics - to extract cell full shapes in biological experiments using their images of epithelial MDCK cells (phase contrast) and also images reported in another paper as well as their own simulations based on active vertex model and multiphase phase fields approaches. Authors present the rationale of this new strategy for quantification. They adapt the method of Minkowski tensors and they extract distributions of cell shapes readouts with plots of their distributions. An emphasis is given to changes in cell shapes captured by this method. Higher rank tensors are considered as well as representations with intuitive meanings and q_i orders and their potential correlations or absence of correlations - for example q_2 and q_6, leading to statements about nematic and hexatic orders.

This analysis and its strength are contrasted with Armengol-Collade et al. (2023) quoted in the paper, who consider polygonal shapes for cells and their shape function 𝛾_p. Authors support the notion of a key improvement thanks to Minkowski tensors approach and doing so, they challenge the former crossovers correlations statements reported in Armengol-Collade et al. (2023). In this context, they defend that nematic liquid crystals approach is not sufficient to capture cell dynamics in tissues. Also they propose that q_2 and q_6 could serve as readout for activity and deformability of cells among other statements related to their approach.

A variety of analytical methods have been realised to track cells in monolayers in vitro and in vivo during morphogenesis - for example, shear decomposition (from MPI-PKS Dresden) or links joining centroids and their neighbours approach (MSC/Curie Paris) to name few examples. It will be interesting in the future that systematic comparisons between these analytical methods are performed with highlights on their respective advantages and drawbacks. This will allow experimentalists to identify the best relevant methods to address their morphogenetic questions.

Reviewer #1 (Public review):

Summary:

The authors aimed to develop a fully scalable, feeder-free protocol for deriving dorsal forebrain neural rosette stem cells (NRSCs) from human pluripotent stem cells, eliminating the need for manual rosette isolation. Using dynamic suspension culture combined with single-SMAD inhibition (RepSox), they sought to generate FOXG1⁺/OTX2⁺ NRSCs within ten days and expand them through at least twelve passages while retaining regional identity. They also aimed to demonstrate the cells' capacity to differentiate into functional neurons, astrocytes, and oligodendrocytes under defined conditions.

Strengths:

A key strength is the elimination of labour-intensive manual rosette picking, which significantly reduces operator variability and enhances throughput. The authors provide diverse validation in the form of flow cytometry showing >95% OTX2⁺ over passages 2-12, immunocytochemistry, single-cell RNA-seq, and functional MEA recordings, confirming both regional fidelity and neuronal activity. They also demonstrate glial differentiation and reproducibility across two hESC lines.

The results convincingly demonstrate that the RepSox/suspension approach yields high-purity dorsal forebrain neural progenitor cells (NRSCs) that maintain marker expression and multipotency through passage 12 and differentiate into electrophysiologically active neurons and mature glia. Thus, the authors have achieved their primary objectives.

This protocol addresses a significant bottleneck in neural stem cell production by providing a reproducible, high-throughput alternative that is well-suited to drug screening, disease modelling, and potential cell therapy manufacturing. Standardised, scalable NRSC banks will accelerate neurodevelopmental and neurodegenerative disorder studies, enable automated bioreactor workflows, and encourage the sharing of resources across academia and industry.

Weaknesses:

Weaknesses include a lack of direct comparison to conventional manual-selection protocols, and the need to improve the statistical rigor of all quantitative assays by applying appropriate hypothesis tests (e.g., t-tests or ANOVA with multiple-comparison correction) rather than reporting mean {plus minus} SD alone.

Additional Context:

Beyond the core technical advance, it's important to situate this work within the broader landscape of neural stem cell research and its downstream applications. Traditionally, dorsal forebrain NSCs have been generated via manual rosette picking after dual-SMAD inhibition (Chambers et al., 2009), a process that is labor-intensive, low-throughput, and prone to operator-dependent variability. By eliminating that step, this protocol directly addresses a key barrier to standardizing NSC production under GMP-compatible conditions - critical for both large-scale drug screening and eventual clinical use. Stable, regionally specified forebrain NSCs are especially valuable for modeling early neurodevelopmental disorders (e.g., autism spectrum disorders, microcephaly) and late-onset pathologies (e.g., Alzheimer's disease) in vitro, where precise cortical patterning is essential to recapitulate disease phenotypes. Moreover, establishing long-term epigenetic fidelity (e.g., via future ATAC-seq or histone-mark profiling) will further reassure users that transcriptional consistency reflects preserved regulatory networks, not just transient marker expression. Finally, demonstrating robust cryopreservation viability (>80%) makes these cells a readily shareable resource for the community, accelerating cross-lab reproducibility and comparative studies of patient-derived iPSC lines. This context underscores how scalable, high-purity forebrain NSCs can transform both basic neuroscience research and translational pipelines.

Reviewer #2 (Public review):

In the present manuscript, Dannulat Frazier et al. provide a novel and advanced protocol for obtaining almost pure populations of neural rosette stem cells (NRSCs) expressing the general markers NES and SOX2. These NSCs are expandable and exhibit dorsal forebrain properties and markers that are maintained throughout passages in culture (at least until passage 12). The authors also demonstrate the multipotency of these NSCs by their ability to differentiate into functional neurons, and precursors of astrocytes and oligodendrocytes.

This method does not require the usual step of manual rosette selection and allows a greater homogeneity of the NSCs obtained and the standardization of the protocol, which will allow greater advances in the applications of these NSCs in research and as models of disease or compound testing. The manuscript is of great interest for the research area, since it describes a new methodology that can facilitate the research and therapeutic application of NSCs.

The manuscript is well-written; the results are clear, robust, and well-explained. The conclusions reached in this paper are well-supported by the data, but some aspects could be better clarified.

(1) The results presented in the present manuscript of the NSCS are performed up to passage 12; it would be interesting to know up to which passages these cells can be expanded, maintaining their initial properties. Have the authors analyzed passages beyond 12?

(2) In Figure 2A, where different markers are shown in NSCs at different passages, it seems that at passage 12, there is a decrease in TJP1+ zones in relation to earlier passages, which could indicate a reduction in the potential to generate rosettes. Have the authors done any quantification along these lines? Could this be the case, or is it just an effect of the image chosen?

(3) In Figure 3A, it is very striking and intriguing that the decrease in the expression of the PAX6 gene in passage 8 in relation to passage 2, which does not correspond to what is observed at the protein level. Have the authors verified this result using another technique, such as for example RT-q-PCR?

(4) In Figure 5B, the labeling for GFAP, appears rather nuclear, despite being a cytoskeleton protein. How can the authors explain this?

Reviewer #1 (Public review):

In this manuscript, Gruber et al perform serial EM sections of the antennal lobe and reconstruct the neurites innervating two types of glomeruli - one that is narrowly tuned to geosmin and one that is broadly tuned to other odours. They quantify and describe various aspects of the innervations of olfactory sensory neurons (OSNs), uniglomerlular projection neurons (uPNs), and the multiglomerular Local interneurons (LNs) and PNs (mPNs). They find that narrowly tuned glomeruli had stronger connectivity from OSNs to PNs and LNs, and considerably more connections between sister OSNs and sister PNs than the broadly tuned glomeruli. They also had less connectivity with the contralateral glomerluli. These observations are suggestive of strong feed-forward information flow with minimal presynaptic inhibition in narrowly tuned gomeruli, which might be ecologically relevant, for example, while making quick decisions such as avoiding a geosmin-laden landing site. In contrast, information flow in more broadly tuned glomeruli show much more lateralisation of connectivity to the contralateral glomerulus, as well as to other ipsilateral glomeruli.

The data are well presented, the manuscript clearly written, and the results will be useful to the olfaction community. I had earlier suggested comparisons with other EM datasets that exist to investigate stereotypy, and am convinced by their efforts and reasons for which these were either not possible to do or not possible within the timeframe of a revision.

Comments on revisions:

Thank you for the careful responses to my suggestions. I hope that such approaches will be possible by others going forward.

Reviewer #2 (Public review):

The chemoreceptor proteins expressed by olfactory sensory neuron differ in their selectivity such that glomeruli vary in the breadth of volatile chemicals to which they respond. Prior work assessing the relationship between tuning breadth and the demographics of principal neuron types that innervate a glomerulus demonstrated that narrowly tuned glomeruli are innervated more projection neurons (output neurons) and fewer local interneurons relative to more broadly tuned glomeruli. The present study used high resolution electron microscopy to determine which synaptic relationships between principal cell types also vary with glomerulus tuning breadth using a narrowly tuned glomerulus (DA2) and a broadly tuned glomerulus (DL5). The strength of this study lies in the comprehensive, synapse-level resolution of the approach. Furthermore, the authors implement a very elegant approach of using a 2-photon microscope to score the upper and lower bounds of each glomerulus thus defining the bounds of their restricted regions of interest. Using the approach, the authors identify several architectural motifs that differ between glomeruli with different tuning properties

In the revised version of this study the authors discuss several important limitations. There was a technical need to group all local interneurons, centrifugal neurons and multiglomerular projection neurons into one category ("multiglomerular neurons") which complicates interpretations as even multiglomerular projection neurons are very diverse. With only 2 narrowly tuned glomeruli and 1 broadly tuned glomerulus, architecture differences may reflect more than just differences in tuning breadth. Finally, the degree to which inter-animal variability may contribute to differences between glomeruli is discussed. If these caveats are kept in mind, this work reveals some very interesting potential differences in circuit architecture associated with glomerular tuning breadth.

This work establishes specific hypotheses about network function within the olfactory system that can be pursued using targeted physiological approaches. It also identifies key traits that can be explored using other high resolution EM datasets and other glomeruli that vary in their tuning selectivity. Finally, the laser "branding" technique used in this study establishes a reduced cost procedure for obtaining smaller EM datasets from targeted volumes of interest by leveraging the ability to transgenically label brain regions in Drosophila.

Comments on revisions:

I appreciate the thoughtful responses that the authors made regarding the initial assessment of their study. The authors discuss these limitations in their manuscript which should not be viewed as criticisms, but rather caveats to be considered for this study specifically and in some instances, for all connectomics studies.

I still believe there is a lost opportunity to make use of the FlyWire dataset to make specific strategic comparisons. I do not propose attempting to replicate the comprehensive nature of the main study, but querying cell type based on glomerular innervation would allow the authors to address consistency of observed differences between glomeruli as ORNs and uPNs have been thoroughly annotated and analysis can be limited by neuropil. I agree that it is unclear how many individuals would need to be examined to achieve sufficient statistical power, but some of the circuit motifs revealed in this study can be readily tested in the FlyWire dataset. For instance, the observation from this study that narrowly tuned ORNs receive less synaptic input from LNs is supported in FlyWire, with DL5 ORNs getting far more synaptic input from LNs relative to DA2 and VA1v. I'm not proposing repeating all of the analyses from this study, and there is no doubt that inter-animal variability and technical differences can explain different observations across datasets, but I believe these are considerations of which the readers (who can query these synaptic relationships in FlyWire) should be made aware.

Reviewer #1 (Public review):

Summary:

The authors performed genome assemblies for two Fagaceae species and collected transcriptome data from four natural tree species every month over two years. They identified seasonal gene expression patterns and further analyzed species-specific differences.

Strengths:

The study of gene expression patterns in natural environments, as opposed to controlled chambers, is gaining increasing attention. The authors collected RNA-seq data monthly for two years from four tree species and analyzed seasonal expression patterns. The data are novel. The authors could revise the manuscript to emphasize seasonal expression patterns in three species (with one additional species having more limited data). Furthermore, the chromosome-scale genome assemblies for the two Fagaceae species represent valuable resources, although the authors did not cite existing assemblies from closely related species.

Weaknesses:

The study design has a fundamental flaw regarding the evaluation of genetic or evolutionary effects. As a basic principle in biology, phenotypes, including gene expression levels, are influenced by genetics, environmental factors, and their interaction. This principle is well-established in quantitative genetics.

In this study, the four species were sampled from three different sites (see Materials and Methods, lines 543-546), and additionally, two species were sampled from 2019-2021, while the other two were sampled from 2021-2023 (see Figure S2). This critical detail should be clearly described in the Results and Materials and Methods. Due to these variations in sampling sites and periods, environmental conditions are not uniform across species.

Even in studies conducted in natural environments, there are ways to design experiments that allow genetic effects to be evaluated. For example, by studying co-occurring species, or through transplant experiments, or in common gardens. To illustrate the issue, imagine an experiment where clones of a single species were sampled from three sites and two time periods, similar to the current design. RNA-seq analysis would likely detect differences that could qualitatively resemble those reported in this manuscript.

One example is in line 197, where genus-specific expression patterns are mentioned. While it may be true that the authors' conclusions (e.g., winter synchronization, phylogenetic constraints) reflect real biological trends, these conclusions are also predictable even without empirical data, and the current dataset does not provide quantitative support.

If the authors can present a valid method to disentangle genetic and environmental effects from their dataset, that would significantly strengthen the manuscript. However, I do not believe the current study design is suitable for this purpose.

Unless these issues are addressed, the use of the term "evolution" is inappropriate in this context. The title should be revised, and the result sections starting from "Peak months distribution..." should be either removed or fundamentally revised. The entire Discussion section, which is based on evolutionary interpretation, should be deleted in its current form.

If the authors still wish to explore genetic or evolutionary analyses, the pair of L. edulis and L. glaber, which were sampled at the same site and over the same period, might be used to analyze "seasonal gene expression divergence in relation to sequence divergence." Nevertheless, the manuscript would benefit from focusing on seasonal expression patterns without framing the study in evolutionary terms.

To better support the seasonal expression analysis, the early RNA-seq analysis sections should be strengthened. There is little discussion of biological replicate variation or variation among branches of the same individual. These could be important factors to analyze. In line 137, the mapping rate for two species is mentioned, but the rates for each species should be clearly reported. One RNA-seq dataset is based on a species different from the reference genome, so a lower mapping rate is expected. While this likely does not hinder downstream analysis, quantification is important.

In Figures 2A and 2B, clustering is used to support several points discussed in the Results section (e.g., lines 175-177). However, clustering is primarily a visualization method or a hypothesis-generating tool; it cannot serve as a statistical test. Stronger conclusions would require further statistical testing.

The quality of the genome assemblies appears adequate, but related assemblies should be cited and discussed. Several assemblies of Fagaceae species already exist, including Quercus mongolica (Ai et al., Mol Ecol Res, 2022), Q. gilva (Front Plant Sci, 2022), and Fagus sylvatica (GigaScience, 2018), among others. Is there any novelty here? Can you compare your results with these existing assemblies?

Most importantly, Figure 1B-D shows synteny between the two genera but also indicates homology between different chromosomes. Does this suggest paleopolyploidy or another novel feature? These chromosome connections should be interpreted in the main text-even if they could be methodological artifacts.

In both the Results and Materials and Methods sections, descriptions of genome and RNA-seq data are unclear. In line 128, a paragraph on genome assembly suddenly introduces expression levels. RNA-seq data should be described before this. Similarly, in line 238, the sentence "we assembled high-quality reference genomes" seems disconnected from the surrounding discussion of expression studies. In line 632, Illumina short-read DNA sequencing is mentioned, but it's unclear how these data were used.

Reviewer #2 (Public review):

Summary:

This study explores how gene expression evolves in response to seasonal environments, using four evergreen Fagaceae species growing in similar habitats in Japan. By combining chromosome-scale genome assemblies with a two-year RNA-seq time series in leaves and buds, the authors identify seasonal rhythms in gene expression and examine both conserved and divergent patterns. A central result is that winter bud expression is highly conserved across species, likely due to shared physiological demands under cold conditions. One of the intriguing implications of this study is that seasonal cycles might play a role similar to ontogenetic stages in animals. The authors touch on this by comparing their findings to the developmental hourglass model, and indeed, the recurrence of phenological states such as winter dormancy may act as a cyclic form of developmental canalization, shaping expression evolution in a way analogous to embryogenesis in animals.

Strengths:

(1) The evolutionary effects of seasonal environments on gene expression are rarely studied at this scale. This paper fills that gap.

(2) The dataset is extensive, covering two years, two tissues, and four tree species, and is well suited to the questions being asked.

(3) Transcriptome clustering across species (Figure 2) shows strong grouping by season and tissue rather than species, suggesting that the authors effectively controlled for technical confounders such as batch effects and mapping bias.

(4) The idea that winter imposes a shared constraint on gene expression, especially in buds, is well argued and supported by the data.

(5) The discussion links the findings to known concepts like phenological synchrony and the developmental hourglass model, which helps frame the results.

Weaknesses:

(1) While the hierarchical clustering shown in Figure 2A largely supports separation by tissue type and season, one issue worth noting is that some leaf samples appear to cluster closely with bud samples. The authors do not comment on this pattern, which raises questions about possible biological overlap between tissues during certain seasonal transitions or technical artifacts such as sample contamination. Clarifying this point would improve confidence in the interpretation of tissue-specific seasonal expression patterns.

(2) While the study provides compelling evidence of conserved and divergent seasonal gene expression, it does not directly examine the role of cis-regulatory elements or chromatin-level regulatory architecture. Including regulatory genomic or epigenomic data would considerably strengthen the mechanistic understanding of expression divergence.

(3) The manuscript includes a thoughtful analysis of flowering-related genes and seasonal GO enrichment (e.g., Figure 3C-D), providing an initial link between gene expression timing and phenological functions. However, the analysis remains largely gene-centric, and the study does not incorporate direct measurements of phenological traits (e.g., flowering or bud break dates). As a result, the connection between molecular divergence and phenotypic variation, while suggestive, remains indirect.

(4) Although species were sampled from similar habitats, one species (Q. acuta) was collected at a higher elevation, and factors such as microclimate or local photoperiod conditions could influence expression patterns. These potential confounding variables are not fully accounted for, and their effects should be more thoroughly discussed or controlled in future analyses.

(5) Statistical and Interpretive Concerns Regarding Δφ and dN/dS Correlation (Figures 5E and 5F):

(a) Statistical Inappropriateness: Δφ is a discrete ordinal variable (likely 1-11), making it unsuitable for Pearson correlation, which assumes continuous, normally distributed variables. This undermines the statistical validity of the analysis.

(b) Biological Interpretability: Even with the substantial statistical power afforded by genome-wide analysis, the observed correlations are extremely weak. This suggests that the relationship, if any, between temporal divergence in expression and protein-coding evolution is negligible.

Taken together, these issues weaken the case for any biologically meaningful association between Δφ and dN/dS. I recommend either omitting these panels or clearly reframing them as exploratory and statistically limited observations.

Reviewer #1 (Public review):

Summary:

This is a strong paper that presents a clear advance in multi-animal tracking. The authors introduce an updated version of idtracker.ai that reframes identity assignment as a contrastive learning problem rather than a classification task requiring global fragments. This change leads to gains in speed and accuracy. The method eliminates a known bottleneck in the original system, and the benchmarking across species is comprehensive and well executed. I think the results are convincing and the work is significant.

Strengths:

The main strengths are the conceptual shift from classification to representation learning, the clear performance gains, and the fact that the new version is more robust. Removing the need for global fragments makes the software more flexible in practice, and the accuracy and speed improvements are well demonstrated. The software appears thoughtfully implemented, with GUI updates and integration with pose estimators.

Weaknesses:

I don't have any major criticisms, but I have identified a few points that should be addressed to improve the clarity and accuracy of the claims made in the paper.

(1) The title begins with "New idtracker.ai," which may not age well and sounds more promotional than scientific. The strength of the work is the conceptual shift to contrastive representation learning, and it might be more helpful to emphasize that in the title rather than branding it as "new."

(2) Several technical points regarding the comparison between TRex (a system evaluated in the paper) and idtracker.ai should be addressed to ensure the evaluation is fair and readers are fully informed.

(2.1) Lines 158-160: The description of TRex as based on "Protocol 2 of idtracker.ai" overlooks several key additions in TRex, such as posture image normalization, tracklet subsampling, and the use of uniqueness feedback during training. These features are not acknowledged, and it's unclear whether TRex was properly configured - particularly regarding posture estimation, which appears to have been omitted but isn't discussed. Without knowing the actual parameters used to make comparisons, it's difficult to assess how the method was evaluated.

(2.2) Lines 162-163: The paper implies that TRex gains speed by avoiding Protocol 3, but in practice, idtracker.ai also typically avoids using Protocol 3 due to its extremely long runtime. This part of the framing feels more like a rhetorical contrast than an informative one.

(2.3) Lines 277-280: The contrastive loss function is written using the label l, but since it refers to a pair of images, it would be clearer and more precise to write it as l_{I,J}. This would help readers unfamiliar with contrastive learning understand the formulation more easily.

(2.4) Lines 333-334: The manuscript states that TRex can fail to track certain videos, but this may be inaccurate depending on how the authors classify failures. TRex may return low uniqueness scores if training does not converge well, but this isn't equivalent to tracking failure. Moreover, the metric reported by TRex is uniqueness, not accuracy. Equating the two could mislead readers. If the authors did compare outputs to human-validated data, that should be stated more explicitly.

(2.5) Lines 339-341: The evaluation approach defines a "successful run" and then sums the runtime across all attempts up to that point. If success is defined as simply producing any output, this may not reflect how experienced users actually interact with the software, where parameters are iteratively refined to improve quality.

(2.6) Lines 344-346: The simulation process involves sampling tracking parameters 10,000 times and selecting the first "successful" run. If parameter tuning is randomized rather than informed by expert knowledge, this could skew the results in favor of tools that require fewer or simpler adjustments. TRex relies on more tunable behavior, such as longer fragments improving training time, which this approach may not capture.

(2.7) Line 354 onward: TRex was evaluated using two varying parameters (threshold and track_max_speed), while idtracker.ai used only one (intensity_threshold). With a fixed number of samples, this asymmetry could bias results against TRex. In addition, users typically set these parameters based on domain knowledge rather than random exploration.

(2.8) Figure 2-figure supplement 3: The memory usage comparison lacks detail. It's unclear whether RAM or VRAM was measured, whether shared or compressed memory was included, or how memory was sampled. Since both tools dynamically adjust to system resources, the relevance of this comparison is questionable without more technical detail.

(3) While the authors cite several key papers on contrastive learning, they do not use the introduction or discussion to effectively situate their approach within related fields where similar strategies have been widely adopted. For example, contrastive embedding methods form the backbone of modern facial recognition and other image similarity systems, where the goal is to map images into a latent space that separates identities or classes through clustering. This connection would help emphasize the conceptual strength of the approach and align the work with well-established applications. Similarly, there is a growing literature on animal re-identification (ReID), which often involves learning identity-preserving representations across time or appearance changes. Referencing these bodies of work would help readers connect the proposed method with adjacent areas using similar ideas, and show that the authors are aware of and building on this wider context.

(4) Some sections of the Results text (e.g., lines 48-74) read more like extended figure captions than part of the main narrative. They include detailed explanations of figure elements, sorting procedures, and video naming conventions that may be better placed in the actual figure captions or moved to supplementary notes. Streamlining this section in the main text would improve readability and help the central ideas stand out more clearly.

Overall, though, this is a high-quality paper. The improvements to idtracker.ai are well justified and practically significant. Addressing the above comments will strengthen the work, particularly by clarifying the evaluation and comparisons.

Reviewer #2 (Public review):

This work introduces a new version of the state-of-the-art idtracker.ai software for tracking multiple unmarked animals. The authors aimed to solve a critical limitation of their previous software, which relied on the existence of "global fragments" (video segments where all animals are simultaneously visible) to train an identification classifier network, in addition to addressing concerns with runtime speed. To do this, the authors have both re-implemented the backend of their software in PyTorch (in addition to numerous other performance optimizations) as well as moving from a supervised classification framework to a self-supervised, contrastive representation learning approach that no longer requires global fragments to function. By defining positive training pairs as different images from the same fragment and negative pairs as images from any two co-existing fragments, the system cleverly takes advantage of partial (but high-confidence) tracklets to learn a powerful representation of animal identity without direct human supervision. Their formulation of contrastive learning is carefully thought out and comprises a series of empirically validated design choices that are both creative and technically sound. This methodological advance is significant and directly leads to the software's major strengths, including exceptional performance improvements in speed and accuracy and a newfound robustness to occlusion (even in severe cases where no global fragments can be detected). Benchmark comparisons show the new software is, on average, 44 times faster (up to 440 times faster on difficult videos) while also achieving higher accuracy across a range of species and group sizes. This new version of idtracker.ai is shown to consistently outperform the closely related TRex software (Walter & Couzin, 2021\), which, together with the engineering innovations and usability enhancements (e.g., outputs convenient for downstream pose estimation), positions this tool as an advancement on the state-of-the-art for multi-animal tracking, especially for collective behavior studies.

Despite these advances, we note a number of weaknesses and limitations that are not well addressed in the present version of this paper:

(1) The contrastive representation learning formulation

Contrastive representation learning using deep neural networks has long been used for problems in the multi-object tracking domain, popularized through ReID approaches like DML (Yi et al., 2014\) and DeepReID (Li et al., 2014). More recently, contrastive learning has become more popular as an approach for scalable self-supervised representation learning for open-ended vision tasks, as exemplified by approaches like SimCLR (Chen et al., 2020), SimSiam (Chen et al., 2020\), and MAE (He et al., 2021\) and instantiated in foundation models for image embedding like DINOv2 (Oquab et al., 2023). Given their prevalence, it is useful to contrast the formulation of contrastive learning described here relative to these widely adopted approaches (and why this reviewer feels it is appropriate):

(1.1) No rotations or other image augmentations are performed to generate positive examples. These are not necessary with this approach since the pairs are sampled from heuristically tracked fragments (which produces sufficient training data, though see weaknesses discussed below) and the crops are pre-aligned egocentrically (mitigating the need for rotational invariance).

(1.2) There is no projection head in the architecture, like in SimCLR. Since classification/clustering is the only task that the system is intended to solve, the more general "nuisance" image features that this architectural detail normally affords are not necessary here.

(1.3) There is no stop gradient operator like in BYOL (Grill et al., 2020\) or SimSiam. Since the heuristic tracking implicitly produces plenty of negative pairs from the fragments, there is no need to prevent representational collapse due to class asymmetry. Some care is still needed, but the authors address this well through a pair sampling strategy (discussed below).

(1.4) Euclidean distance is used as the distance metric in the loss rather than cosine similarity as in most contrastive learning works. While cosine similarity coupled with L2-normalized unit hypersphere embeddings has proven to be a successful recipe to deal with the curse of dimensionality (with the added benefit of bounded distance limits), the authors address this through a cleverly constructed loss function that essentially allows direct control over the intra- and inter-cluster distance (D\_pos and D\_neg). This is a clever formulation that aligns well with the use of K-means for the downstream assignment step.

No concerns here, just clarifications for readers who dig into the review. Referencing the above literature would enhance the presentation of the paper to align with the broader computer vision literature.

(2) Network architecture for image feature extraction backbone

As most of the computations that drive up processing time happen in the network backbone, the authors explored a variety of architectures to assess speed, accuracy, and memory requirements. They land on ResNet18 due to its empirically determined performance. While the experiments that support this choice are solid, the rationale behind the architecture selection is somewhat weak. The authors state that:

"\[W\]e tested 23 networks from 8 different families of state-of-the-art convolutional neural network architectures, selected for their compatibility with consumer-grade GPUs and ability to handle small input images (20 × 20 to 100 × 100 pixels) typical in collective animal behavior videos."

(2.1) Most modern architectures have variants that are compatible with consumer-grade GPUs. This is true of, for example, HRNet (Wang et al., 2019), ViT (Dosovitskiy et al., 2020), SwinT (Liu et al., 2021), or ConvNeXt (Liu et al., 2022), all of which report single GPU training and fast runtime speeds through lightweight configuration or subsequent variants, e.g., MobileViT (Mehta et al., 2021). The authors may consider revising that statement or providing additional support for that claim (e.g., empirical experiments) given that these have been reported to outperform ResNet18 across tasks.

(2.2) The compatibility of different architectures with small image sizes is configurable. Most convolutional architectures can be readily adapted to work with smaller image sizes, including 20x20 crops. With their default configuration, they lose feature map resolution through repeated pooling and downsampling steps, but this can be readily mitigated by swapping out standard convolutions with dilated convolutions and/or by setting the stride of pooling layers to 1, preserving feature map resolution across blocks. While these are fairly straightforward modifications (and are even compatible with using pretrained weights), an even more trivial approach is to pad and/or resize the crops to the default image size, which is likely to improve accuracy at a possibly minimal memory and runtime cost. These techniques may even improve the performance with the architectures that the authors did test out.

(2.3) The authors do not report whether the architecture experiments were done with pretrained or randomly initialized weights.

(2.4) The authors do not report some details about their ResNet18 design, specifically whether a global pooling layer is used and whether the output fully connected layer has any activation function. Additionally, they do not report the version of ResNet18 employed here, namely, whether the BatchNorm and ReLU are applied after (v1) or before (v2) the conv layers in the residual path.

(3) Pair sampling strategy

The authors devised a clever approach for sampling positive and negative pairs that is tailored to the nature of the formulation. First, since the positive and negative labels are derived from the co-existence of pretracked fragments, selection has to be done at the level of fragments rather than individual images. This would not be the case if one of the newer approaches for contrastive learning were employed, but it serves as a strength here (assuming that fragment generation/first pass heuristic tracking is achievable and reliable in the dataset). Second, a clever weighted sampling scheme assigns sampling weights to the fragments that are designed to balance "exploration and exploitation". They weigh samples both by fragment length and by the loss associated with that fragment to bias towards different and more difficult examples.

(3.1) The formulation described here resembles and uses elements of online hard example mining (Shrivastava et al., 2016), hard negative sampling (Robinson et al., 2020\), and curriculum learning more broadly. The authors may consider referencing this literature (particularly Robinson et al., 2020\) for inspiration and to inform the interpretation of the current empirical results on positive/negative balancing.

(4) Speed and accuracy improvements

The authors report considerable improvements in speed and accuracy of the new idTracker (v6) over the original idTracker (v4?) and TRex. It's a bit unclear, however, which of these are attributable to the engineering optimizations (v5?) versus the representation learning formulation.

(4.1) Why is there an improvement in accuracy in idTracker v5 (L77-81)? This is described as a port to PyTorch and improvements largely related to the memory and data loading efficiency. This is particularly notable given that the progression went from 97.52% (v4; original) to 99.58% (v5; engineering enhancements) to 99.92% (v6; representation learning), i.e., most of the new improvement in accuracy owes to the "optimizations" which are not the central emphasis of the systematic evaluations reported in this paper.

(4.2) What about the speed improvements? Relative to the original (v4), the authors report average speed-ups of 13.6x in v5 and 44x in v6. Presumably, the drastic speed-up in v6 comes from a lower Protocol 2 failure rate, but v6 is not evaluated in Figure 2 - figure supplement 2.

(5) Robustness to occlusion

A major innovation enabled by the contrastive representation learning approach is the ability to tolerate the absence of a global fragment (contiguous frames where all animals are visible) by requiring only co-existing pairs of fragments owing to the paired sampling formulation. While this removes a major limitation of the previous versions of idtracker.ai, its evaluation could be strengthened. The authors describe an ablation experiment where an arc of the arena is masked out to assess the accuracy under artificially difficult conditions. They find that the v6 works robustly up to significant proportions of occlusions, even when doing so eliminates global fragments.

(5.1) The experiment setup needs to be more carefully described.<br /> What does the masking procedure entail? Are the pixels masked out in the original video or are detections removed after segmentation and first pass tracking is done?<br /> What happens at the boundary of the mask? (Partial segmentation masks would throw off the centroids, and doing it after original segmentation does not realistically model the conditions of entering an occlusion area.)<br /> Are fragments still linked for animals that enter and then exit the mask area?<br /> How is the evaluation done? Is it computed with or without the masked region detections?

(5.2) The circular masking is perhaps not the most appropriate for the mouse data, which is collected in a rectangular arena.

(5.3) The number of co-existing fragments, which seems to be the main determinant of performance that the authors derive from this experiment, should be reported for these experiments. In particular, a "number of co-existing fragments" vs accuracy plot would support the use of the 0.25(N-1) heuristic and would be especially informative for users seeking to optimize experimental and cage design. Additionally, the number of co-existing fragments can be artificially reduced in other ways other than a fixed occlusion, including random dropout, which would disambiguate it from potential allocentric positional confounds (particularly relevant in arenas where egocentric pose is correlated with allocentric position).

(6) Robustness to imaging conditions

The authors state that "the new idtracker.ai can work well with lower resolutions, blur and video compression, and with inhomogeneous light (Figure 2 - figure supplement 4)." (L156).

Despite this claim, there are no speed or accuracy results reported for the artificially corrupted data, only examples of these image manipulations in the supplementary figure.

(7) Robustness across longitudinal or multi-session experiments

The authors reference idmatcher.ai as a compatible tool for this use case (matching identities across sessions or long-term monitoring across chunked videos), however, no performance data is presented to support its usage.

This is relevant as the innovations described here may interact with this setting. While deep metric learning and contrastive learning for ReID were originally motivated by these types of problems (especially individuals leaving and entering the FOV), it is not clear that the current formulation is ideally suited for this use case. Namely, the design decisions described in point 1 of this review are at times at odds with the idea of learning generalizable representations owing to the feature extractor backbone (less scalable), low-dimensional embedding size (less representational capacity), and Euclidean distance metric without hypersphere embedding (possible sensitivity to drift).

It's possible that data to support point 6 can mitigate these concerns through empirical results on variations in illumination, but a stronger experiment would be to artificially split up a longer video into shorter segments and evaluate how generalizable and stable the representations learned in one segment are across contiguous ("longitudinal") or discontiguous ("multi-session") segments.

Reviewer #3 (Public review):

Summary:

The authors propose a new version of idTracker.ai for animal tracking. Specifically, they apply contrastive learning to embed cropped images of animals into a feature space where clusters correspond to individual animal identities.

Strengths:

By doing this, the new software alleviates the requirement for so-called global fragments - segments of the video, in which all entities are visible/detected at the same time - which was necessary in the previous version of the method. In general, the new method reduces the tracking time compared to the previous versions, while also increasing the average accuracy of assigning the identity labels.

Weaknesses:

The general impression of the paper is that, in its current form, it is difficult to disentangle the old from the new method and understand the method in detail. The manuscript would benefit from a major reorganization and rewriting of its parts. There are also certain concerns about the accuracy metric and reducing the computational time.

Reviewer #1 (Public review):

The authors' goal was to arrest PsV capsids on the extracellular matrix using cytochalasin D. The cohort was then released, and interaction with the cell surface, specifically with CD151, was assessed.

The model that fragmented HS associated with released virions mediates the dominant mechanism of infectious entry has only been suggested by research from a single laboratory and has not been verified in the 10+ years since publication. The authors are basing this study on the assumption that this model is correct, and these data are referred to repeatedly as the accepted model despite much evidence to the contrary. The discussion in lines 65-71 concerning virion and HSPG affinity changes is greatly simplified. The structural changes in the capsid induced by HS interaction and the role of this priming for KLK8 and furin cleavage have been well researched. Multiple laboratories have independently documented this. If this study aims to verify the shedding model, additional data need to be provided. The model should be fitted into established entry events, or at minimum, these conflicting data, a subset of which is noted below, need to be acknowledged.

(1) The Sapp lab (Richards et al., 2013) found that HSPG-mediated conformational changes in L1 and L2 allowed the release of the virus from primary binding and allowing secondary receptor engagements in the absence of HS shedding.

(2) Becker et al. found that furin-precleaved capsids could infect cells independently of HSPG interaction, but this infection was still inhibited with cytochalasin D.

(3) Other work from the Schelhaas lab showed that cytochalasin D inhibition of infection resulted in the accumulation of capsids in deep invaginations from the cell surface, not on the ECM.

(4) Selinka et al., 2007, showed that preventing HSPG-induced conformational changes in the capsid surface resulted in noninfectious uptake that was not prevented with cytochalasin D.

(5) The well-described capsid processing events by KLK8 and furin need to be mechanistically linked to the proposed model. Does inhibition of either of these cleavages prevent engagement with CD151?

The authors need to consider an explanation for these discrepancies.

Other issues:

(1) Line 110-111. The statement about PsVs in the ECM being too far away from the cell surface to make physical contact with the cell surface entry receptors is confusing. ECM binding has not been shown to be an obligatory step for in vitro infection. This idea is referred to again on lines 158-159 and 199. The claim (line 158) that PsV does not interact with the cell within an hour needs to be demonstrated experimentally and seems at odds with multiple laboratories' data. PsV has been shown to directly interact with HSPG on the cell surface in addition to the ECM. Why are these PsVs not detected?

(2) The experiments shown in Figure 5 need to be better controlled. Why is there no HS staining of the cell surface at the early timepoints? This antibody has been shown to recognize N-sulfated glucosamine residues on HS and, therefore, detects HSPG on the ECM and cell surface. Therefore, the conclusion that this confirms HS coating of PsV during release from the ECM (line 430-431) is unfounded. How do the authors distinguish between "HS-coated virions" and HSPG-associated virions?

It is difficult to comprehend how the addition of 50 vge/cell of PsV could cause such a global change in HS levels. The claim that the HS levels are decreased in the non-cytochalasin-treated cells due to PsV-induced shedding needs to be demonstrated. If HS is actually shed, staining of the cell periphery could increase with the antibody 3G10, which detects the HS neoepitope created following heparinase cleavage.

Reviewer #2 (Public review):

Summary:

Massenberg and colleagues aimed to understand how Human papillomavirus particles that bind to the extracellular matrix (ECM) transfer to the cell body for later uptake, entry, and infection. The binding to ECM is key for getting close to the virus's host cell (basal keratinocytes) after a wounding scenario for later infection in a mouse vaginal challenge model, indicating that this is an important question in the field.

Strengths:

The authors take on a conceptually interesting and potentially very important question to understand how initial infection occurs in vivo. The authors confirm previous work that actin-based processes contribute to virus transport to the cell body. The superresolution microscopy methods and data collection are state-of-the art and provide an interesting new way of analysing the interaction with host cell proteins on the cell surface in certain infection scenarios. The proposed hypothesis is interesting and, if substantiated, could significantly advance the field.

Weaknesses:

As a study design, the authors use infection of HaCaT keratinocytes, and follow virus localisation with and without inhibition of actin polymerisation by cytochalasin D (cytoD) to analyse transfer of virions from the ECM to the cell by filopodial structures using important cellular proteins for cell entry as markers.

First, the data is mostly descriptive besides the use of cytoD, and does not test the main claim of their model, in which virions that are still bound to heparan sulfate proteoglycans are transferred by binding to tetraspanins along filopodia to the cell body.

Second, using cytoD is a rather broad treatment that not only affects actin retrograde flow, but also virus endocytosis and further vesicular transport in cells, including exocytosis. Inhibition of myosin II, e.g., by blebbistatin, would have been a better choice as it, for instance, does not interfere with endocytosis of the virus.

Third, the authors aim to study transfer from ECM to the cell body and the effects thereof. However, there are substantial, if not the majority of, viruses that bind to the cell body compared to ECM-bound viruses in close vicinity to the cells. This is in part obscured by the small subcellular regions of interest that are imaged by STED microscopy, or by the use of plasma membrane sheets. As a consequence, the obtained data from time point experiments is skewed, and remains for the most part unconvincing due to the fact that the origin of virions in time and space cannot be taken into account. This is particularly important when interpreting association with HS, the tetraspanin CD151, and integral alpha 6, as the low degree of association could originate from cell-bound and ECM-transferred virions alike.

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

Fifth, while the use of randomisation during image analysis is highly recommended to establish significance (flipping), it should be done using only ROIs that have a similar density of objects for which correlations are being established. For instance, if one flips an image with half of the image showing the cell body, and half of the image ECM, it is clear that association with cell membrane structures will only be significant in the original. I am rather convinced that using randomisation only on the plasma membrane ROIs will not establish any clear significance of the correlating signals. Also, there should be a higher n for the measurements.

Reviewer #1 (Public Review):

In this study, Deng et al. investigate the antibody response against HA antigen following repeated vaccination with the H1N1 2009 pandemic influenza vaccine strain, using in silico modeling. The proposed model provides valuable mechanistic insights into how the broadening of the antibody response takes place upon repeated vaccination.

Overall, the authors' model effectively explains the mechanistic principles underlying antibody responses against the viral antigens harboring epitope immunodominancy.

Reviewer #2 (Public Review):

The authors have been studying the mechanism of breadth expansion in antibody responses with repeated vaccinations using their own mathematical model. In this study, they applied this mathematical model to a cohort data analyzing anti-HA antibody responses after multiple influenza virus vaccination and investigated the mechanism of antibody breadth expansion to diversified target viral strains.<br /> The manuscript is well written, and the mathematical model is well built that incorporates various parameters related to B cell activation in GC and EGC based on experimental data.

Strengths:

By carefully reanalyzing the published cohort data (Nunez IA et al 2017 PLoS One), they have clearly demonstrated that the repeated influenza virus vaccinations result in an expansion of the breadth to unmatched viral strains.

Using their mathematical model, they have determined the major factors for the breadth expansion following multiple immunizations.

Weaknesses:

The overall concept of their model has already been published (Yang L et al 2023 Cell Reports) with a SRAS-CoV-2 vaccine model, and they have applied it to influenza virus vaccine in this study, with the conclusions being largely the same.

It is unclear how the re-evaluation of public data in the first half part is related to the validation of their model in the later part.

Other points:

In the original data by Nurez LA et al., HAI (the inhibitory effect of anti-HA antibodies on the binding of HA to sialic acid on erythrocytes) was used as the lead-out. The authors conclude that the breadth expansion with repeated vaccinations is primarily due to the activation of B cells with BCRs that recognize minor common epitopes, induced by covering up of strain specific major epitopes by pre-existing antibodies. However, as they themselves show in Fig 1, once the sialic acid-binding region is covered, it seems difficult for another BCR to bind to this region. When the target epitope is limited like this, the effect of increasing antigen supply to DCs by pre-existing antibodies and the effect of increasing the presentation of minor epitopes appears to compete with each other. Could the author please explain this point? In relation to this point, please explain the meaning of analysis of the entire ectodomain when the original data's lead-out is HAI.

Minor point:

The description "The purpose of this model is ...." starting at line 171 and the description of "we obtain results in harmony with the clinical findings ...." starting at line 478 sound to be contradictory. As the authors themselves state at line 171, if the purpose of this model is not to fit the data but to demonstrate the principle, then the prudent sampling and reanalyzing data itself seems to have less meaning.

Reviewer #1 (Public review):

Summary:

The study by Wu et al presents interesting data on bacterial cell organization, a field that is progressing now, mainly due to the advances in microscopy. Based mainly on fluorescence microscopy images, the authors aim to demonstrate that the two structures that account for bacterial motility, the chemotaxis complex and the flagella, colocalize to the same pole in Pseudomonas aeruginosa cells and to expose the regulation underlying their spatial organization and functioning.

Comments on revisions:

The authors have addressed all major and minor points that I raised in a satisfying way during the revision process. The work can now be regarded as complete: , the assumptions were clarified, the results are convincing, the conclusions are justified, and the novelty has been made clear. This manuscript will be of interest to cell biologists, mainly those studying bacteria, but not only

Reviewer #2 (Public review):

Summary:

Here, the authors studied the molecular mechanisms by which the chemoreceptor cluster and flagella motor of Pseudomonas aeruginosa (PA) are spatially organized in the cell. They argue that FlhF is involved in localizing the receptors and motor to the cell pole, but a separate mechanism colocalizes them. Finally, the authors argue that the functional reason for this colocalization is to insulate chemotactic signaling from other signaling pathways, such as cyclic-di-GMP signaling.

Strengths:

The experiments and data are high quality. It is clear that the motor and receptors co-localize, and that elevated CheY levels lead to elevated c-di-GMP. The signaling crosstalk argument is plausible.

Reviewer #3 (Public review):

Summary:

The authors investigated the assembly and polar localization of the chemosensory cluster in P. aeruginosa. They discovered that a certain protein (FlhF) is required for the polar localization of the chemosensory cluster while core motor structures are necessary for the assembly of the cluster. They found that flagella and chemosensory clusters always co-localize in the cell; either at the cell pole in wild type cells or randomly-located in the cell in FlhF mutant cells. They hypothesize that this co-localization is required to keep the level of another protein (CheY-P), which controls motor switching, at low levels as the presence of high-levels of this protein (if the flagella and chemosensory clusters were not co-localized) is associated with high-levels of c-di-GMP and cell aggregations.

Strengths:

The manuscript is clearly-written and straightforward. The authors applied multiple techniques to study the bacterial motility system including fluorescence light microscopy and gene editing. In general, the work enhances our understanding of the subtlety of interaction between the chemosensory cluster and the flagellar motor to regulate cell motility. This work will be of interest to bacteriologists and cell biologists in general.

Reviewer #1 (Public review):

This revision of the computational study by Mondal et al addresses several issues that I raised in the previous round of reviews and, as such, is greatly improved. The manuscript is more readable, its findings are more clearly described, and both the introduction and the discussion section are tighter and more to the point. And thank you for addressing the three timescales of half activation/inactivation parameters. It makes the mechanism clearer.

Some issues remain that I bring up below.

Comment:

I still have a bone to pick with the claim that "activity-dependent changes in channel voltage-dependence alone are insufficient to attain bursting". As I mentioned in my previous comment, this is also the case for the gmax values (channel density). If you choose the gmax's to be in a reasonable range, then the statement above is simply cannot be true. And if, in contrast, you choose the activation/inactivation parameters to be unreasonable, then no set of gmax's can produce proper activity. So I remain baffled what exactly is the point that the authors are trying to make.

Reviewer #2 (Public review):

Summary:

In this study, Mondal and co-authors present the development of a computational model of homeostatic plasticity incorporating activity-dependent regulation of gating properties (activation, inactivation) of ion channels. The authors show that, similar to what has been observed for activity-dependent regulation of ion channel conductances, implementing activity-dependent regulation of voltage sensitivity participates in the achievement of a target phenotype (bursting or spiking). The results however suggest that activity-dependent regulation of voltage sensitivity is not sufficient to allow this and needs to be associated with the regulation of ion channel conductances in order to reliably reach target phenotype. Although the implementation of this biologically relevant phenomenon is undeniably relevant, a few important questions are left unanswered.

Strengths:

(1) Implementing activity-dependent regulation of gating properties of ion channels is biologically relevant.

(2) The modeling work appears to be well performed and provides results that are consistent with previous work performed by the same group.

Weaknesses:

(1) The main question not addressed in the paper is the relative efficiency and/or participation of voltage-dependence regulation compared to channel conductance in achieving the expected pattern of activity. Is voltage-dependence participating to 50% or 10%. Although this is a difficult question to answer (and it might even be difficult to provide a number), it is important to determine whether channel conductance regulation remains the main parameter allowing the achievement of a precise pattern of activity (or its recovery after perturbation).

(2) Another related question is whether the speed of recovery is significantly modified by implemeting voltage-dependence regulation (it seems to be the case looking at Figure 3). More generally, I believe it would be important to give insights into the overall benefit of implementing voltage-dependence regulation, beyond its rather obvious biological relevance.

(3) Along the same line, the conclusion about how voltage-dependence regulation and channel conductance regulation interact to provide the neuron with the expected activity pattern (summarized and illustrated in Figure 6) is rather qualitative. Consistent with my previous comments, one would expect some quantitative answers to this question, rather than an illustration that approximately places a solution in parameter space.

Reviewer #3 (Public review):

Mondal et al. use computational modeling to investigate how activity-dependent shifts in voltage-dependent (in)activation curves can complement changes in ion channel conductance to support homeostatic plasticity. While it is well established that the voltage-dependent properties of ion channels influence neuronal excitability, their potential role in homeostatic regulation, alongside conductance changes, has remained largely unexplored. The results presented here demonstrate that activity-dependent regulation of voltage dependence can interact with conductance plasticity to enable neurons to attain and maintain target activity patterns, in this case, intrinsic bursting. Notably, the timescale of these voltage-dependent shifts influences the final steady-state configuration of the model, shaping both channel parameters and activity features such as burst period and duration. A major conclusion of the study is that altering this timescale can seamlessly modulate a neuron's intrinsic properties, which the authors suggest may be a mechanism for adaptation to perturbations.

While this conclusion is largely well-supported, additional analyses could help clarify its scope. For instance, the effects of timescale alterations are clearly demonstrated when the model transitions from an initial state that does not meet the target activity pattern to a new stable state. However, Fig. 6 and the accompanying discussion appear to suggest that changing the timescale alone is sufficient to shift neuronal activity more generally. It would be helpful to clarify that this effect primarily applies during periods of adaptation, such as neurodevelopment or in response to perturbations, and not necessarily once the system has reached a stable, steady state. As currently presented, the simulations do not test whether modifying the timescale can influence activity after the model has stabilized. In such conditions, changes in timescale are unlikely to affect network dynamics unless they somehow alter the stability of the solution, which is not shown here. That said, it seems plausible that real neurons experience ongoing small perturbations which, in conjunction with changes in timescale, could allow gradual shifts toward new solutions. This possibility is not discussed but could be a fruitful direction for future work.

Reviewer #1 (Public review):

Summary:

The study by Klug et al. investigated the pathway specificity of corticostriatal projections, focusing on two cortical regions. Using a G-deleted rabies system in D1-Cre and A2a-Cre mice to retrogradely deliver channelrhodopsin to cortical inputs, the authors found that M1 and MCC inputs to direct and indirect pathway spiny projection neurons (SPNs) are both partially segregated and asymmetrically overlapping. In general, corticostriatal inputs that target indirect pathway SPNs are likely to also target direct pathway SPNs, while inputs targeting direct pathway SPNs are less likely to also target indirect pathway SPNs. Such asymmetric overlap of corticostriatal inputs has important implications for how the cortex itself may determine striatal output. Indeed, the authors provide behavioral evidence that optogenetic activation of M1 or MCC cortical neurons that send axons to either direct or indirect pathway SPNs can have opposite effects on locomotion and different effects on action sequence execution. The conclusions of this study add to our understanding of how cortical activity may influence striatal output and offer important new clues about basal ganglia function.

The conceptual conclusions of the manuscript are supported by the data, but the details of the magnitude of afferent overlap and causal role of asymmetric corticostriatal inputs on some behavioral outcomes may be a bit overstated given technical limitations of the experiments.

For example, after virally labeling either direct pathway (D1) or indirect pathway (D2) SPNs to optogenetically tag pathway-specific cortical inputs, the authors report that a much larger number of "non-starter" D2-SPNs from D2-SPN labeled mice responded to optogenetic stimulation in slices than "non-starter" D1 SPNs from D1-SPN labeled mice did. Without knowing the relative number of D1 or D2 SPN starters used to label cortical inputs, it is difficult to interpret the exact meaning of the lower number of responsive D2-SPNs in D1 labeled mice (where only ~63% of D1-SPNs themselves respond) compared to the relatively higher number of responsive D1-SPNs (and D2-SPNs) in D2 labeled mice. While relative differences in connectivity certainly suggest that some amount of asymmetric overlap of inputs exists, differences in infection efficiency and ensuing differences in detection sensitivity in slice experiments make determining the degree of asymmetry problematic.

It is also unclear if retrograde labeling of D1-SPN- vs D2-SPN- targeting afferents labels the same densities of cortical neurons. This gets to the point of specificity in some of the behavioral experiments. If the target-based labeling strategies used to introduce channelrhodopsin into specific SPN afferents label significantly different numbers of cortical neurons, might the difference in the relative numbers of optogenetically activated cortical neurons itself lead to behavioral differences?

Reviewer #2 (Public review):

Summary:

Klug et al. use monosynaptic rabies tracing of inputs to D1- vs D2-SPNs in the striatum to study how separate populations of cortical neurons project to D1- and D2-SPNs. They use rabies to express ChR2, then patch D1-or D2-SPNs to measure synaptic input. They report that cortical neurons labeled as D1-SPN-projecting preferentially project to D1-SPNs over D2-SPNs. In contrast, cortical neurons labeled as D2-SPN-projecting project equally to D1- and D2-SPNs. They go on to conduct pathway-specific behavioral stimulation experiments. They compare direct optogenetic stimulation of D1- or D2-SPNs to stimulation of MCC inputs to DMS and M1 inputs to DLS. In three different behavioral assays (open field, intra-cranial self-stimulation, and a fixed ratio 8 task), they show that stimulating MCC or M1 cortical inputs to D1-SPNs is similar to D1-SPN stimulation, but that stimulating MCC or M1 cortical inputs to D2-SPNs does not recapitulate the effects of D2-SPN stimulation (presumably because both D1- and D2-SPNs are being activated by these cortical inputs).

Strengths:

Showing these same effects in three distinct behaviors is strong. Overall, the functional verification of the consequences of the anatomy is very nice to see. It is a good choice to patch only from mCherry-negative non-starter cells in the striatum. This study adds to our understanding of the logic of corticostriatal connections, suggesting a previously unappreciated structure.

Weaknesses:

One limitation is that all inputs to SPNs are expressing ChR2, so they cannot distinguish between different cortical subregions during patching experiments. Their results could arise because the same innervation patterns are repeated in many cortical subregions or because some subregions have preferential D1-SPN input while others do not. There are also some caveats with respect to the efficacy of rabies tracing. Although they only patch non-starter cells in the striatum, only 63% of D1-SPNs receive input from D1-SPN-projecting cortical neurons. It's hard to say whether this is "high" or "low," but one question is how far from the starter cell region they are patching. Without this spatial indication of where the cells that are being patched are relative to the starter population, it is difficult to interpret if the cells being patched are receiving cortical inputs from the same neurons that are projecting to the starter population. The authors indicate they are patching from mCherry-negative neurons within the region of the mCherry-positive neurons, but since the mCherry population will include both true starter cells and monosynaptically connected cells, this is not perfectly precise. Convergence of cortical inputs onto SPNs may vary with distance from the starter cell region quite dramatically, as other mapping studies of corticostriatal inputs have shown specialized local input regions can be defined based on cortical input patterns (Hintiryan et al., Nat Neurosci, 2016, Hunnicutt et al., eLife 2016, Peters et al., Nature, 2021). A caveat for the optogenetic behavioral experiments is that these optogenetic experiments did not include fluorophore-only controls, although a different control (with light delivered in M1) is provided in Supplementary Figure 3. Another point of confusion is that other studies (Cui et al, J Neurosci, 2021) have reported that stimulation of D1-SPNs in DLS inhibits rather than promotes movement. This study may have given different results due to subtly different experimental parameters, including fiber optic placement and NA.

Reviewer #3 (Public review):

Review of resubmission: The authors provided a response to the reviews from myself and other reviewers. While some points were made satisfactorily, particularly in clarification of the innervation of cortex to striatum and the effects of input stimulation, many of my points remain unaddressed. In several cases, the authors chose to explain their rationale rather than address the issues at hand. A number of these issues (in fact, the majority) could be addressed simply by toning done the confidence in conclusions, so it was disappointing to see that the authors by and large did not do this. I repeat my concerns below and note whether I find them to have been satisfactorily addressed or not.

In the manuscript by Klug and colleagues, the investigators use a rabies virus-based methodology to explore potential differences in connectivity from cortical inputs to the dorsal striatum. They report that the connectivity from cortical inputs onto D1 and D2 MSNs differs in terms of their projections onto the opposing cell type, and use these data to infer that there are differences in cross-talk between cortical cells that project to D1 vs. D2 MSNs. Overall, this manuscript adds to the overall body of work indicating that there are differential functions of different striatal pathways which likely arise at least in part by differences in connectivity that have been difficult to resolve due to difficulty in isolating pathways within striatal connectivity, and several interesting and provocative observations were reported. Several different methodologies are used, with partially convergent results, to support their main points.

However, I have significant technical concerns about the manuscript as presented that make it difficult for me to interpret the results of the experiments. My comments are below.

Major:<br /> There is generally a large caveat to the rabies studies performed here, which is that both TVA and the ChR2-expressing rabies virus have the same fluorophore. It is thus essentially impossible to determine how many starter cells there are, what the efficiency of tracing is, and which part of the striatum is being sampled in any given experiment. This is a major caveat given the spatial topography of the cortico-striatal projections. Furthermore, the authors make a point in the introduction about previous studies not having explored absolute numbers of inputs, yet this is not at all controlled in this study. It could be that their rabies virus simply replicates better in D1-MSNs than D2-MSNs. No quantifications are done, and these possibilities do not appear to have been considered. Without a greater standardization of the rabies experiments across conditions, it is difficult to interpret the results.

This is still an issue. The authors point out why they chose various vectors. I can understand why the authors chose the fluorophores etc. that they did, yet the issues I raised previously are still valid. The discussion should mention that this is a potential issue. It does not necessarily invalidate results, but it is an issue. Furthermore, it is possible (in all systems) that rabies replicates better/more efficiently in some cells than others. This is one possible interpretation that has not really been explored in any study. I don't suggest the authors attempt to do that, but it should be raised as a potential interpretation. If the rabies results could mean several different things, the authors owe it to the readership to state all possible interpretations of data.

The authors claim using a few current clamp optical stimulation experiments that the cortical cells are healthy, but this result was far from comprehensive. For example, membrane resistance, capacitance, general excitability curves, etc are not reported. In Figure S2, some of the conditions look quite different (e.g., S2B, input D2-record D2, the method used yields quite different results that the authors write off as not different). Furthermore, these experiments do not consider the likely sickness and death that occurs in starter cells, as has been reported elsewhere. Health of cells in the circuit is overall a substantial concern that alone could invalidate a large portion, if not all, of the behavioral results. This is a major confound given those neurons are thought to play critical roles in the behaviors being studied. This is a major reason why first-generation rabies viruses have not been used in combination with behavior, but this significant caveat does not appear to have been considered, and controls e.g., uninfected animals, infected with AAV helpers, etc, were not included.

This issue remains unaddressed. I did not request clarity about experimental design, but rather, raised issues about the potential effects of toxicity. I believe this to be a valid concern that needs to be discussed in the manuscript, especially given what look visually like potential differences in S2.

The overall purity (e.g., EnvA pseudotyping efficiency) of the RABV prep is not shown. If there was a virus that was not well EnvA-pseudotyped and thus could directly infect cortical (or other) inputs, it would degrade specificity.

This issue has not been addressed. Viral strain is irrelevant. The quality of the specific preparations used is what matters.

While most of the study focuses on the cortical inputs, in slice recordings, inputs from the thalamus are not considered, yet likely contribute to the observed results. Related to this, in in vivo optogenetic experiments, technically, if the thalamic or other inputs to the dorsal striatum project to the cortex, their method will not only target cortical neurons but also terminals of other excitatory inputs. If this cannot be ruled it, stating that the authors are able to selectively activate the cortical inputs to one or the other population should be toned down.

The authors added text to the discussion to address this point. While it largely does what is intended, based on the one study cited, I disagree with the authors' conclusions that it is "clear" that potential contamination from other sites does not play a role. The simplest interpretation is the one the authors state, and there is some supporting evidence to back up that assertion, but to me that falls short of making the point "clear" that there are no other interpretations.

The statements about specificity of connectivity are not well founded. It may be that in the specific case where they are assessing outside of the area of injections, their conclusions may hold (e.g., excitatory inputs onto D2s have more inputs onto D1s than vice versa). However, how this relates to the actual site of injection is not clear. At face value, if such a connectivity exists, it would suggest that D1-MSNs receive substantially more overall excitatory inputs than D2s. It is thus possible that this observation would not hold over other spatial intervals. This was not explored and thus the conclusions are over-generalized. e.g., the distance from the area of red cells in the striatum to recordings was not quantified, what constituted a high level of cortical labeling was not quantified, etc. Without more rigorous quantification of what was being done, it is difficult to interpret the results.

Again, the goal here would be to make a statement about this in the discussion to clarify limitations of the study. I don't expect the authors to re-do all of these experiments, but since they are discussing the corticostriatal circuits, which have multiple subdomains, this remains a relevant point. It has not been addressed.

The results in Figure 3 are not well controlled. The authors show contrasting effects of optogenetic stimulation of D1-MSNs and D2-MSNs in the DMS and DLS, results which are largely consistent with the canon of basal ganglia function. However, when stimulating cortical inputs, stimulating the inputs from D1-MSNs gives the expected results (increased locomotion) while stimulating putative inputs to D2-MSNs had no effect. This is not the same as showing a decrease in locomotion - showing no effect here is not possible to interpret.

I think that the caveat of showing no clear effects of inputs to D2 stimulation should be pointed out. Yes, I understand that the viruses appeared to express etc., but again it remains possible that the results are driven by a lack of e.g., sufficient ChR2 expression. Aside from a full quantification of the number of cells expressing ChR2, overlap in fiber placement and ChR2 expression (which I don't suggest), this remains a possibility and should be pointed out, as it remains a possibility.

In the light of their circuit model, the result showing that inputs to D2-MSNs drive ICSS is confusing. How can the authors account for the fact that these cells are not locomotor-activating, stimulation of their putative downstream cells (D2-MSNs) does not drive ICSS, yet the cortical inputs drive ICSS? Is the idea that these inputs somehow also drive D1s? If this is the case, how do D2s get activated, if all of the cortical inputs tested net activate D1s and not D2s? Same with the results in Figure 4 - the inputs and putative downstream cells do not have the same effects. Given potential caveats of differences in viral efficiency, spatial location of injections, and cellular toxicity, I cannot interpret these experiments.

The explanation the authors provide in their rebuttal makes sense, however this should be included in the discussion of the manuscript, as it is interesting and relevant.

Reviewer #1 (Public review):

Summary:

This work starts with the observation that embryo polarization is asynchronous starting at the early 8-cell stage, with early polarizing cells being biased towards producing the trophectoderm (TE) lineage. They further found that reduced CARM1 activity and upregulation of its substrate BAF155 promote early polarization and TE specification, this piece of evidence connects the previous finding that at Carm1 heterogeneity 4-cell stage guide later cell lineages - the higher Carm1-expressing blastomeres are biased towards ICM lineage. Thus, this work provides a link between asymmetries at the 4-cell stage and polarization at the 8-cell stage, providing a cohesive explanation regarding the first lineage allocation in mouse embryos.

Strengths:

In addition to what has been put in the summary, the advanced 3D image-based analysis has found that early polarization is associated with a change in cell geometry in blastomeres, regarding the ratio of the long axis to the short axis. This is considered a new observation that has not been identified.

Weaknesses:

For the microinjection-based method to overexpression/deletion of proteins, although it has been shown to be effective in the early embryo settings and has been widely used, it may not fully represent the in vivo situation in some cases, compared to other strategies such as the use of knock-in mice.

This is a minor weakness and has been discussed by the author in the revised manuscript.

Reviewer #1 (Public review):

Summary:

This paper is using state-of-the-art techniques to define the cellular composition and its complexity in two rodent species (mice and rats). The study is built on available datasets but extends those in a way that future research will be facilitated. The study will be of high impact for the study of metabolic control.

Strengths:

After revision, the paper is much improved. I have no further comments.

Reviewer #2 (Public review):

In this manuscript, Hes et al. present a comprehensive multi-species atlas of the dorsal vagal complex (DVC) using single-nucleus RNA sequencing, identifying over 180,000 cells and 123 cell types across five levels of granularity in mice and rats. Intriguingly, the analysis uncovered previously uncharacterized cell populations, including Kcnj3-expressing astrocytes, neurons co-expressing Th and Cck, and a population of leptin receptor-expressing neurons in the rat area postrema, which also express the progenitor marker Pdgfra. These findings suggest species-specific differences in appetite regulation. This study provides a valuable resource for investigating the intricate cellular landscape of the DVC and its role in metabolic control, with potential implications for refining obesity treatments targeting this hindbrain region.

In line with previous work published by the PI, the topic is of clear scientific relevance, and the data presented in this manuscript are both novel and compelling. Additionally, the manuscript is well-structured, and the conclusions are robust and supported by the data. Overall, this study significantly enhances our understanding of the DVC and sheds light on key differences between rats and mice.

I have reviewed the revised manuscript and am pleased to confirm that the authors have addressed my previous comments and concerns.

Reviewer #1 (Public review):

In the Late Triassic and Early Jurassic (around 230 to 180 Ma ago), southern Wales and adjacent parts of England were a karst landscape. The caves and crevices accumulated remains of small vertebrates. These fossil-rich fissure fills are being exposed in limestone quarrying. In 2022 (reference 13 of the article), a partial articulated skeleton and numerous isolated bones from one fissure fill of end-Triassic age (just over 200 Ma) were named Cryptovaranoides microlanius and described as the oldest known squamate - the oldest known animal, by some 20 to 30 Ma, that is more closely related to snakes and some extant lizards than to other extant lizards. This would have considerable consequences for our understanding of the evolution of squamates and their closest relatives, especially for their speed and absolute timing, and was supported in the same paper by phylogenetic analyses based on different datasets.

In 2023, the present authors published a rebuttal (reference 18) to the 2022 paper, challenging anatomical interpretations and the irreproducible referral of some of the isolated bones to Cryptovaranoides. Modifying the datasets accordingly, they found Cryptovaranoides outside Squamata and presented evidence that it is far outside. In 2024 (reference 19), the original authors defended most of their original interpretation and presented some new data, some of it from newly referred isolated bones. The present article discusses anatomical features and the referral of isolated bones in more detail, documents some clear misinterpretations, argues against the widespread but not justifiable practice of referring isolated bones to the same species as long as there is merely no known evidence to the contrary, further argues against comparing newly recognized fossils to lists of diagnostic characters from the literature as opposed to performing phylogenetic analyses and interpreting the results, and finds Cryptovaranoides outside Squamata again.

Although a few of the character discussions and the discussion of at least one of the isolated bones can probably still be improved (and two characters are addressed twice), I see no sign that the discussion is going in circles or otherwise becoming unproductive. I can even imagine that the present contribution will end it.

Reviewer #2 (Public review):

Congratulations on this thorough manuscript on the phylogenetic affinities of Cryptovaranoides. Recent interpretations of this taxon, and perhaps some others, have greatly changed the field's understanding of reptile origins- for better and (likely) for worse.

This manuscript offers a careful review of the features used to place Cryptovaranoides within Squamata and adequately demonstrates that this interpretation is misguided, and therefore reconciles morphological and molecular data, which is an important contribution to the field of paleontology. The presence of any crown squamate in the Permian or Triassic should be met with skepticism, the same sort of skepticism provided in this manuscript.

I have outlined some comments addressing some weaknesses that I believe will further elevate the scientific quality of the work. A brief, fresh read‑through to refine a few phrases, particularly where the discussion references Whiteside et al. could also give the paper an even more collegial tone.

This manuscript can be largely improved by additional discussion and figures, where applicable. When I first read this manuscript, I was a bit surprised at how little discussion there was concerning both non-lepidosauromorph lepidosaurs as well as stem-reptiles more broadly. This paper makes it extremely clear that Cryptovaranoides is not a squamate, but would greatly benefit in explaining why many of the characters either suggested by former studies to be squamate in nature or were optimized as such in phylogenetic analyses are rather widespread plesiomorphies present in crownward sauropsids such as millerettids, younginids, or tangasaurids. I suggest citing this work where applicable and building some of the discussion for a greatly improved manuscript. In sum:

(1) The discussion of stem-reptiles should be improved. Nearly all of the supposed squamate features in Cryptovaranoides are present in various stem-reptile groups. I've noted a few, but this would be a fairly quick addition to this work. If this manuscript incorporates this advice, I believe arguments regarding the affinities of Cryptovaranoides (at least within Squamata) will be finished, and this manuscript will be better off for it.

(2) I was also surprised at how little discussion there was here of putative stem-squamates or lepidosauromorphs more broadly. A few targeted comparisons could really benefit the manuscript. It is currently unclear as to why Cryptovaranoides could not be a stem-lepidosaur, although I know that the lepidosaur total-group in these manuscripts lacks character sampling due to their scarcity.

(3) This manuscript can be improved by additional figures, such as the slice data of the humerus. The poor quality of the scan data for Cryptovaranoides is stated during this paper several times, yet the scan data is often used as evidence for the presence or absence of often minute features without discussion, leaving doubts as to what condition is true. Otherwise, several sections can be rephrased to acknowledge uncertainty, and probably change some character scorings to '?' in other studies.

Reviewer #3 (Public review):

Summary:

The study provides an interesting contribution to our understanding of Cryptovaranoides relationships, which is a matter of intensive debate among researchers. My main concerns are in regard to the wording of some statements, but generally, the discussion and data are well prepared. I would recommend moderate revisions.

Strengths:

(1) Detailed analysis of the discussed characters.

(2) Illustrations of some comparative materials.

Weaknesses:

Some parts of the manuscript require clarification and rewording.

One of the main points of criticism of Whiteside et al. is using characters for phylogenetic considerations that are not included in the phylogenetic analyses therein. The authors call it a "non-trivial substantive methodological flaw" (page 19, line 531). I would step down from such a statement for the reasons listed below:

(1) Comparative anatomy is not about making phylogenetic analyses. Comparative anatomy is about comparing different taxa in search of characters that are unique and characters that are shared between taxa. This creates an opportunity to assess the level of similarity between the taxa and create preliminary hypotheses about homology. Therefore, comparative anatomy can provide some phylogenetic inferences. That does not mean that tests of congruence are not needed. Such comparisons are the first step that allows creating phylogenetic matrices for analysis, which is the next step of phylogenetic inference. That does not mean that all the papers with new morphological comparisons should end with a new or expanded phylogenetic matrix. Instead, such papers serve as a rationale for future papers that focus on building phylogenetic matrices.

(2) Phylogenetic matrices are never complete, both in terms of morphological disparity and taxonomic diversity. I don't know if it is even possible to have a complete one, but at least we can say that we are far from that. Criticising a work that did not include all the possibly relevant characters in the phylogenetic analysis is simply unfair. The authors should know that creating/expanding a phylogenetic matrix is a never-ending work, beyond the scope of any paper presenting a new fossil.

(3) Each additional taxon has the possibility of inducing a rethinking of characters. That includes new characters, new character states, character state reordering, etc. As I said above, it is usually beyond the scope of a paper with a new fossil to accommodate that into the phylogenetic matrix, as it requires not only scoring the newly described taxon but also many that are already scored. Since the digitalization of fossils is still rare, it requires a lot of collection visits that are costly in terms of time.

(4) If I were to search for a true flaw in the Whiteside et al. paper, I would check if there is a confirmation bias. The mentioned paper should not only search for characters that support Cryptovaranoides affinities with Anguimorpha but also characters that deny that. I am not sure if Whiteside et al. did such an exercise. Anyway, the test of congruence would not solve this issue because by adding only characters that support one hypothesis, we are biasing the results of such a test.

To sum up, there is nothing wrong with proposing some hypotheses about character homology between different taxa that can be tested in future papers that will include a test of congruence. Lack of such a test makes the whole argumentation weaker in Whiteside et al., but not unacceptable, as the manuscript might suggest. My advice is to step down from such strong statements like "methodological flaw" and "empirical problems" and replace them with "limitations", which I think better describes the situation.

Reviewer #1 (Public review):

Summary:

By applying the laser scanning photostimulation (LSPS) approach to a novel slice preparation, the authors aimed to study the degree of convergence and divergence of cortical inputs to individual striatal projection neurons (SPNs).

Strengths:

The experiments were well-designed and conducted, and data analysis was thorough. The manuscript was well written and related work in the literature was properly discussed. This work has the potential to advance our understanding of how sensory inputs are integrated into the striatal circuits.

Reviewer #2 (Public review):

Summary:

How corticostriatal synaptic connectivity gives rise to SPN encoding of sensory information is an important and currently unanswered question. The authors utilize a clever slice preparation in combination with electrophysiology and glutamate uncaging to dissect the synaptic connectivity between barrel cortex and individual striatal SPNs. In addition to mapping connectivity across major anatomical axes and cortical layers, the authors provide data showing that SPNs uniquely integrate sparse input from variable stretches across barrel cortex.

Strengths:

The methodology shows impressive rigor and the data robustly support the authors conclusions. Overall, the manuscript addresses its core question, provides valuable insights into corticostriatal architecture, and is a welcomed addition to the field.

Reviewer #3 (Public review):

Summary:

The authors explored how individual dorsolateral striatum (DLS) spiny projection neurons (SPNs) receive functional input from whisker-related cortical columns. The authors developed and validated a novel slice preparation and method to which they applied rigorous functional mapping and thorough analysis. They found that individual SPNs were driven by sparse, scattered cortical clusters. Interestingly, while the cortical input fields of nearby SPNs had some degree of overlap, connectivity per SPN was largely distinct. Despite sparse, heterogeneous connectivity, topographical organization was identified. The authors lastly compared direct (D1) vs. indirect (D2) pathway cells, concluding that overall connectivity patterns were the same, but D1 cells received stronger input from L6 and D2 cells from L2/3. The paper thoughtfully addresses the question of whether barrel cortex broadly or selectively innervates SPNs. Their results indicate selective input that is loosely topographic. Their work deepens the understanding of how whisker-related somatosensory signals can drive striatal neurons.

Strengths:

Overall this is a carefully conducted study, and the major claims are well-supported. The use of a novel ex vivo slice prep that keeps relevant corticostriatal projections intact allows for careful mapping of the barrel cortex to dorsolateral striatum SPNs. Careful reporting of both columnar and layer position, as well as postsynaptic SPN type (D1 or D2) allows the authors to uncover novel details about how the dorsolateral striatum represents whisker-related sensory information.

Weaknesses:

Most technical weaknesses have now been addressed in the text.

Reviewer #1 (Public review):

Summary:

The authors note that while many software packages exist for spike sorting, these do not automatically differentiate with known accuracy between excitatory and inhibitory neurons. Moreover, most existing spike sorting packages are for in vivo use, where the majority of electrodes are separated from each other by several hundred microns or more. There is a need for spike sorting packages that can take advantage of high-density electrode arrays where all electrodes are within a few tens of microns from other electrodes. Here, the authors offer such a software package with SpikeMAP, and they validate its performance in identifying parvalbumin interneurons that were optogenetically stimulated.

Strengths:

The main strength of this work is that the authors use ground truth measures to show that SpikeMAP can take features of spike shapes to correctly identify known parvalbumin interneurons against a background of other neuron types. They use spike width and peak to peak distance as the key features for distinguishing between neuron types, a method that has been around for many years (Barthó, Peter, et al. "Characterization of neocortical principal cells and interneurons by network interactions and extracellular features." Journal of neurophysiology 92.1 (2004): 600-608.), but whose performance has not been validated in the context of high-density electrode arrays.

Another strength of this approach is that it is automated - a necessity if your electrode array has 4096 electrodes. Hand-sorting or even checking such a large number of channels is something even the cruellest advisor would not wish upon a graduate student. With such large channel counts, it is essential to have automated methods that are known to work accurately. Hence, the combination of validation and automation is an important advance.

A nice feature of this work is that with high-density electrode arrays, the spike waveforms appear on multiple nearby electrodes simultaneously. And since spike amplitudes fall off with distance, this allows triangulation of neuron locations within the regular electrode array. Thus, spike correlations between neuron types, or within neuron types, can be plotted as a function of distance. While SpikeMAP is not the first to do this (Peyrache, Adrien, et al. "Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep." Proceedings of the national academy of sciences 109.5 (2012): 1731-1736.), it is a welcome capability of this package.

It is also good that the code for this package is open-source, allowing a community of people (I expect in vitro labs will especially want to use this) to use the code and further improve it.

Weaknesses:

As this code was developed for use with a 4096-electrode array, it is important to be aware of double counting neurons across the many electrodes. I understand that there are ways within the code to ensure that this does not happen, but care must be taken in two key areas: First, action potentials traveling down axons will exhibit a triphasic waveform that is different from the biphasic waveform that appears near the cell body, but these two signals will still be from the same neuron (for example, see Litke et al., 2004 "What does the eye tell the brain: Development of a System for the Large-Scale Recording of Retinal Output Activity"; figure 14). I did not see anything that would directly address this situation, so it might be something for you to consider in updated versions of the code. Second, spike shapes are known to change when firing rates are high, like in bursting neurons (Harris, K.D., Hirase, H., Leinekugel, X., Henze, D.A. & Buzsáki, G. Temporal interaction between single spikes and complex spike bursts in hippocampal pyramidal cells. Neuron 32, 141-149 (2001)). I did not see this addressed in the present version of the manuscript.

Another area for possible improvement would be to build on the excellent validation experiments you have already conducted with parvalbumin interneurons. Although it would take more work, similar experiments could be conducted for somatostatin and vasoactive intestinal peptide neurons against a background of excitatory neurons. These may have different spike profiles, but your success in distinguishing them can only be known if you validate against ground truth, like you did for the PV interneurons.

Appraisal:

This work addresses the need for an automated spike sorting software package for high density electrode arrays. Although no spike sorting software is flawless, the package presented here, SpikeMAP, has been validated on PV interneurons, inspiring a degree of confidence. This is a good start, and further validation on other neuron types could increase that confidence. Groups doing in vitro experiments, where 4096 electrode arrays are more common, could find this system particularly helpful.

Comments on revised version:

I appreciate the dialogue that has occurred over this submission. I have seen how the authors have taken into account the issues that I have raised, as well as those brought up by reviewer 2. I am satisfied that the paper has improved and is now a novel and useful contribution in the area of spike sorting.

Reviewer #2 (Public review):

Summary:

In this paper, entitled "SpikeMAP: An unsupervised spike sorting pipeline for cortical excitatory and inhibitory 2 neurons in high-density multielectrode arrays with ground-truth validation", the authors are presenting spikeMAP, a pipeline for the analysis of large-scale recordings of in vitro cortical activity. According to the authors, spikeMAP not only allows for the detection of spikes produced by single neurons (spike sorting), but also allows for the reliable distinction between genetically determined cell types by utilizing viral and optogenetic strategies as ground-truth validation. While I find that the paper is nicely written, and easy to follow, I find that the algorithmic part of the paper is not really new and should have been more carefully compared to existing solutions. While the GT recordings to assess the possibilities of a spike sorting tool to distinguish properly between excitatory and inhibitory neurons is interesting, spikeMAP does not seem to bring anything new to state of the art solutions, and/or, at least, it would deserve to be properly benchmarked. This is why I would suggest the authors to perform a more intensive comparison with existing spike sorters.

Strengths:

The GT recordings with optogenetic activation of the cells, based on the opsins is interesting and might provide useful data to quantify how good spike sorting pipelines are, in vitro, to discriminate between excitatory and inhibitory neurons. Such an approach can be quite complementary with artificially generated ground truth.

Weaknesses:

The global workflow of spikeMAP, described in Figure 1, seems to be very similar to the one of [Hilgen et al, 2020, 10.1016/j.celrep.2017.02.038.]. Therefore, the first question is what is the rationale of reinventing the wheel, and not using tools that are doing something very similar (as mentioned by the authors themselves). I have a hard time, in general, believing that spikeMAP has something particularly special, given its Methods, compared to state-of-the-art spike sorters. This is why at the very least, the title of the paper is misleading, because it let the reader think that the core of the paper will be about a new spike sorting pipeline. If this is the main message the authors want to convey, then I think that numerous validations/benchmarks are missing to assess first how good spikeMAP is, w.r.t. spike sorting in general, before deciding if this is indeed the right tool to discriminate excitatory vs inhibitory cells. The GT validation, while interesting, is not enough to entirely validate the paper. The details are a bit too scarce to me, or would deserve to be better explained (see other comments after)

Regarding the putative location of the spikes, it has been shown that center of mass, while easy to compute, is not the most accurate solution [Scopin et al, 2024, 10.1016/j.jneumeth.2024.110297]. For example, it has an intrinsic bias for finding positions within the boundaries of the electrodes, while some other methods such as monopolar triangulation or grid-based convolution might have better performances. Can the authors comment on the choice of Center of Mass as a unique way to triangulate the sources?

Still in Figure 1, I am not sure to really see the point of Spline Interpolation. I see the point of such a smoothing, but the authors should demonstrate that it has a key impact on the distinction of Excitatory vs. Inhibitory cells. What's special with the value of 90kHz for a signal recorded at 18kHz? What is the gain with spline enhancement compared to without? Does such a value depend on the sampling rate, or is it a global optimum found by the authors?

Figure 2 is not really clear, especially panel B. The choice of the time scale for the B panel might not be the most appropriate, and the legend filtered/unfiltered with a dot is not clear to me in Bii. In panel E, the authors are making two clusters with PCA projections on single waveforms. Does this mean that the PCA is only applied to the main waveforms, i.e. the ones obtained where the amplitudes are peaking the most? This is not really clear from the methods, but if this is the case, then this approach is a bit simplistic and not really matching state-of-the-art solutions. Spike waveforms are quite often, especially with such high-density arrays, covering multiple channels at once and thus the extracellular patterns triggered by the single units on the MEA are spatio-temporal motifs occurring on several channels. This is why, in modern spike sorters, the information in a local neighbourhood is often kept to be projected, via PCA, on the lower dimensional space before clustering. Information on a single channel only might not be informative enough to disambiguate sources. Can the authors comment on that, and what is the exact spatial resolution of the 3Brain device? The way the authors are performing the SVD should be clarified in the methods section. Is it on a single channel, and/or on multiple channels in a local neighbourhood?

About the isolation of the single units, here again, I think the manuscript lacks some technical details. The authors are saying that they are using a k-means cluster analysis with k=2. This means that the authors are explicitly looking for 2 clusters per electrodes. If so, this is a really strong assumption that should not be held in the context of spike sorting, because since it is a blind source separation technique, one cannot pre-determine in advance how many sources are present in the vicinity of a given electrode. While the illustration on Figure 2E is ok, there is no guarantee that one cannot find more clusters, so why this choice of k=2? Again, this is why most modern spike sorting pipelines are not relying on k-means, to avoid any hard coded number of clusters. Can the authors comment on that?

I'm surprised by the linear decay of the maximal amplitude as a function of the distance from soma, as shown in Figure 2H. Is it really what should be expected? Based on the properties of the extracellular media, shouldn't we expect a power law for the decay of the amplitude? This is strange that up to 100um away from the some, the max amplitude only dropped from 260 to 240 uV. Can the authors comment on that? It would be interesting to plot that for all neurons recorded, in a normed manner V/max(V) as function of distances, to see what the curve looks like

In Figure 3A, it seems that the total number of cells is rather low for such a large number of electrodes. What are the quality criteria that are used to keep these cells? Did the authors exclude some cells from the analysis, and if yes, what are the quality criteria that are used to keep cells? If no criteria are used (because none is mentioned in the Methods), then how come so few cells are detected, and can the authors convince us that these neurons are indeed "clean" units (RPVs, SNRs, ...)

Still in Figure 3A, it looks like there is a bias to find inhibitory cells at the borders, since they do not appear to be uniformly distributed over the MEA. Can the authors comment on that? What would be the explanation for such a behaviour? It would be interesting to see some macroscopic quantities on Excitatory/Inhibitory cells, such as mean firing rates, averaged SNRs, ... Because again, in Figure 3C, it is not clear to me that the firing rates of inhibitory cells is higher than Excitatory ones, while it should be in theory.

For Figure 3 in general, I would have performed an exhaustive comparison of putative cells found by spikeMAP and other sorters. More precisely, I think that to prove the point that spikeMAP is indeed bringing something new to the field of spike sorting, the authors should have compared the performances of various spike sorters to discriminate Exc vs Inh cells based on their ground truth recordings. For example, either using Kilosort [Pachitariu et al, 2024, 10.1038/s41592-024-02232-7], or some other sorters that might be working with such large high-density data [Yger et al, 2018, 10.7554/eLife.34518]

Figure 4 has a big issue, and I guess the panels A and B should be redrawn. I don't understand what the red rectangle is displaying.

I understand that Figure 4 is only one example, but I have a hard time understanding from the manuscript how many slices/mice were used to obtain the GT data? I guess the manuscript could be enhanced by turning the data into an open access dataset, but then some clarification is needed. How many flashes/animals/slices are we talking about. Maybe this should be illustrated in Figure 4, if this figure is devoted to the introduction of the GT data.

While there is no doubt that GT data as the ones recorded here by the authors are the most interesting data from a validation point of view, the pretty low yield of such experiments should not discourage the use of artificially generated recordings such as the ones made in [Buccino et al, 2020, 10.1007/s12021-020-09467-7] or even recently in [Laquitaine et al, 2024, 10.1101/2024.12.04.626805v1]. In these papers, the authors have putative waveforms/firing rates patterns for excitatory and inhibitory cells, and thus the authors could test how good they are in discriminating the two subtypes

Comments on revised version:

While I must thank the authors for their answers, I still think that they miss an important one, and only partially answering some of my concerns.

I truly think that SpikeMAP would benefit with a comparison with a state-of-the-art spike sorting pipeline, for example Kilosort. The authors said that they made the sorter modular enough such that only the E/I classification step can be compared. I think this would be worth it, just to be sure that SpikeMAP spike sorting, which might be more simple than other recent solution (with template matching), is not missing some cells, and thus degrading the E/I classification performances. I know that such a comparison is not straightforward, because there is no clear ground truth, but I would still need to be convinced that the sorting pipelines is bringing something, on its own. While there is no doubt that the E/I classification layer can be interesting, especially given the recordings shared by the authors, I'm still a bit puzzled by the sorting step. Thus maybe either a Table, a figure, or even as Supplementary one. Or the authors could try to generate fake GT data with MEArec for example, with putative E/I cells (discriminated via waveforms and firing rates) and show on such (oversimplified) data that SpikeMAP is performing similarly to modern spike sorters. Otherwise, this is a bit hard to judge...

Reviewer #1 (Public review):

Summary

This article is about the neural representation of odors in the early olfactory system of insects, fish, and rodents. Specifically, it regards the transformation that occurs between the olfactory sensory cells and the second-order neurons (projection neurons in insects, mitral/tufted cells in vertebrates). The central question is how the nervous system can encode both the identity of an odor and its concentration over many log units. The authors reanalyze data from experimental studies of odor responses in primary and secondary neurons, and test a range of computational models as to whether they match the observed transformation. They focus on two aspects of the second-order neuron response to odor concentration: the average activity across all neurons varies only a little with odor concentration, and different neurons have concentration-response curves with different shapes. They conclude that a model of divisive normalization can account for these effects, whereas two alternative models fail the test. A second observation is that tufted cells in the rodent system seem to undergo less normalization than mitral cells, and some reasons for this difference are proposed.

Strengths:

(1) The work compares different models for normalization, rather than simply reporting success with one.

(2) The analysis is applied to very diverse species, potentially revealing a common principle of olfactory processing.

Weaknesses:

(1) It is unclear that animals actually have a need to represent odor concentration over many log units in support of olfactory behaviors.

(2) The stimuli used in the chosen experiments, and the measure of neural response, are only weakly related to any ecological need, e.g., during odor tracking.

(3) Some of the comparisons between receptors and second-order neurons also compare across evolutionarily distant insect species that may not use the same coding principles.

(4) The analysis ignores the dynamics of odor responses, which figure prominently in previous answers to the question of identity/intensity coding.

(5) There is considerable prior consensus in the literature on the importance of normalization from primary to secondary neurons.

Elaboration of my comments:

(1) Motivation

The article starts from the premise that animals need to know the absolute concentration of an odor over many log units, but the need for this isn't obvious. The introduction cites an analogy to vision and audition. These are cases where we know for a fact that the absolute intensity of the stimulus is not relevant. Instead, sensory perception relies on processing small differences in intensity across space or time. And to maintain that sensitivity to small differences, the system discards the stimulus baseline. Humans are notoriously bad at judging the absolute light level. That information gets discarded even before light reaches the retina, namely through contraction of the pupil. Similarly, it seems plausible that a behavior like olfactory tracking relies on sensing small gradients across time (when weaving back and forth across the track) or space (across nostrils). It is important that the system function over many log units of concentration (e.g., far and close to a source) but not that it accurately represents what that current concentration is [see e.g., Wachowiak et al, 2025 Recalibrating Olfactory Neuroscience..].

Still, many experiments in olfactory research have delivered square pulses of odor at concentrations spanning many log units, rather than the sorts of stimuli an animal might encounter during tracking. Even within that framework, though, it doesn't seem mysterious anymore how odor identity and odor concentration are represented differently. For example, Stopfer et al 2003 showed that the population response of locust PNs traces a dynamic trajectory. Trajectories for a given odor form a manifold, within which trajectories for different concentrations are distinct by their excursions on the manifold. To see this, one must recognize that the PN responds to an odor pulse with a time-varying firing rate, that different PNs have different dynamics, and that the dynamics can change with concentration. This is also well recognized in the mammalian systems. Much has been written about the topic of dynamic coding of identity and intensity - see the reviews of Laurent (2002) and Uchida (2014).

(2) Conceptual

Given the above comments on the dynamics of odor responses in first- and second-order neurons, it seems insufficient to capture the response of a neuron with a single number. Even if one somehow had to use a single number, the mean firing rate during the odor pulse may not be the best choice. For example, the rodent mitral cells fire in rhythm with the animal's sniffing cycle, and certain odors will just shift the phase of the rhythm without changing the total number of spikes (see e.g., Fantana et al, 2008). During olfactory search or tracking, the sub-second movements of the animal in the odor landscape get superposed on the sniffing cycle. Given all this, it seems unlikely that the total number of spikes from a neuron in a 4-second period is going to be a relevant variable for neural processing downstream.

Much of the analysis focuses on the mean activity of the entire population. Why is this an interesting quantity? Apparently, the mean stays similar because some neurons increase and others decrease their firing rate. It would be more revealing, perhaps, to show the distribution of firing rates at different concentrations and see how that distribution is predicted by different models of normalization. This could provide a stronger test than just the mean.

The question "if concentration information is discarded in second-order neurons, which exclusively transmit odor information to the rest of the brain, how does the brain support olfactory behaviors, such as tracking and navigation?" is really not an open question anymore. For example, reference 23 reports in the abstract that "Odorant concentration had no systematic effect on spike counts, indicating that rate cannot encode intensity. Instead, odor intensity can be encoded by temporal features of the population response. We found a subpopulation of rapid, largely concentration-invariant responses was followed by another population of responses whose latencies systematically decreased at higher concentrations."

(3) Methods

It would be useful to state early in the manuscript what kinds of stimuli are being considered and how the response of a neuron is summarized by one number. There are many alternative ways to treat both stimuli and responses.

"The change in response across consecutive concentration levels may not be robust due to experimental noise and the somewhat limited range of concentrations sampled": Yes, a number of the curves just look like "no response". It would help the reader to show some examples of raw data, e.g. the time course of one neuron's firing rate to 4 concentrations, and for the authors to illustrate how they compress those responses into single numbers.

"We then calculated the angle between these two slopes for each neuron and plotted a polar histogram of these angles." The methods suggest that this angle is the arctan of the ratio of the two slopes in the response curve. A ratio of 2 would result from a slope change from 0.0001 to 0.0002 (i.e., virtually no change in slope) or from 1 to 2 (a huge change). Those are completely different response curves. Is it reasonable to lump them into the same bin of the polar plot? This seems an unusual way to illustrate the diversity of response curve shapes.

The Drosophila OSN data are passed through normalization models and then compared to locust PN data. This seems dangerous, as flies and locusts are separated by about 300 M years of evolution, and we don't know that fly PNs act like locust PNs. Their antennal lobe anatomy differs in many ways, as does the olfactory physiology. To draw any conclusions about a change in neural representation, it would be preferable to have OSN and PN data from the same species.

(4) Models of normalization

One conclusion is that divisive normalization could account for some of the change in responses from receptors to 2nd order neurons. This seems to be well appreciated already [e.g., Olsen 2010, Papadopoulou 2011, minireview in Hong & Wilson 2013].

Another claim is that subtractive normalization cannot perform that function. What model was used for subtractive normalization is unclear (there is an error in the Methods). It would be interesting if there were a categorical difference between divisive and subtractive normalization.

Looking closer at the divisive normalization model, it really has two components: (a) the "lateral inhibition" by which a neuron gets suppressed if other neurons fire (here scaled by the parameter k) , and (b) a nonlinear sigmoid transformation (determined by the parameters n and sigma). Both lateral inhibition and nonlinearity are known to contribute to decorrelation in a neural population (e.g., Pitkow 2012). The "intraglomerular gain control" contains only the nonlinearity. The "subtractive normalization" we don't know. But if one wanted to put divisive and subtractive inhibition on the same footing, one should add a sigmoid nonlinearity in both cases.

The response models could be made more realistic in other ways. For example, in both locusts and fish, the 2nd order neurons get inputs from multiple receptor types; presumably, that will affect their response functions. Also, lateral inhibition can take quite different forms. In locusts, the inhibitory neurons seem to collect from many glomeruli. But in rats, the inhibition by short axon cells may originate from just a few sparse glomeruli, and those might be different for every mitral cell (Fantana 2008).

(5) Tufted cells

There are questions raised by the following statements: "traded-off energy for faster and finer concentration discrimination" and "an additional type of second-order neuron (tufted cells) that has evolved in land vertebrates and that outperforms mitral cells in concentration encoding" and later "These results suggest a trade-off between concentration decoding and normalization processes, which prevent saturation and reduce energy consumption.". Are the tufted cells inferior to the mitral cells in any respect? Do they suffer from saturation at high concentration? And do they then fail in their postulated role for odor tracking? If not, then what was the evolutionary driver for normalization in the mitral cell pathway? Certainly not lower energy consumption (50,000 mitral cells = 1% of rod photoreceptors, each of which consumes way more energy than a mitral cell).

Reviewer #2 (Public review):

Summary:

The main goal of this study is to examine how information about odor concentration is encoded by second-order neurons in the invertebrate and vertebrate olfactory system. In many animal models, the overall mean firing rates across the second-order neurons appear to be relatively flat or near constant with increasing odor intensity. While such compression of concentration information could aid in achieving concentration invariant recognition of odor identity, how this observation could be reconciled with the need to preserve information about the changes in stimulus intensity is a major focus of the study. The authors show that second-order neurons have 'diverse' dose-response curves and that the combinations of neurons activated (particularly the rank-order) differ with concentration. Further, they argue that a single circuit-level computation, termed 'divisive normalization,' where the individual neural response is normalized by the total activity across all neurons, could help explain the coding properties of neurons at this stage of processing in all model organisms examined. They present approaches to read out the concentration information using spike rates or timing-based approaches. Finally, the authors reveal that tufted cells in the mouse olfactory bulb provide an exception to this coding approach and encode concentration information with a monotonic increase in firing rates.

Strengths:

(1) Comparative analysis of odor intensity coding across four different species, revealing the common features in encoding stimulus-driven features, is highly valuable.

(2) Showing how mitral and tufted cells differ in encoding odor intensity is potentially very important to the field.

(3) How to preserve concentration information while compressing the same with divisive normalization is also a novel and important problem in the field of sensory coding.

Weaknesses:

(1) The encoding problem:

The main premise that divisive normalization generates this diversity of dose-response curves in the second-order neurons is a little problematic. The authors acknowledge this as part of their analysis in Figure 3.

"Therefore, divisive normalization mostly does not alter the relative contribution (rank order) of each neuron in the ensemble." (Page 4, last paragraph, lines 6-8).

The analysis in this figure indicates that divisive normalization does what it is supposed to do, i.e., compresses concentration information and not alter the rank-order of neurons or the combinatorial patterns. Changes in the combinations of neurons activated with intensity arise directly from the fact that the first-order neurons did not have monotonic responses with odor intensity (i.e., crossovers). This was the necessary condition, and not the divisive normalization for changes in the combinatorial code.

There seems to be a confusion/urge to attribute all coding properties found in the second-order neurons to 'divisive normalization.' If the input from sensory neurons is monotonic (i.e., no crossovers), then divisive normalization did not change the rank order, and the same combinations of neurons are activated in a similar fashion (same vector direction or combinatorial profile) to encode for different odor intensities. Concentration invariance is achieved, and concentration information is lost. However, when the first-order neurons are non-monotonic (i.e., with crossovers), that causes the second-order neurons to have different rank orders with different concentrations. Divisive normalization compresses information about concentrations, and rank-order differences preserve information about the odor concentration. Does this not mean that the non-monotonicity of sensory neuron response is vital for robustly maintaining information about odor concentration?

Naturally, the question that arises is whether many of the important features of the second-order neuron's response simply seem to follow the input. Or is my understanding of the figures and the write-up flawed, and are there more ways in which divisive normalization contributes to reshaping the second-order neural response? This must be clarified.

Lastly, the tufted cells in the mouse OB are also driven by this sensory input with crossovers. How does the OB circuit convert the input with crossovers into one that is monotonic with concentration? I think that is an important question that this computational effort could clarify.

(2) The decoding problem.

The way the decoding results and analysis are presented does not add a lot of information to what has already been presented. For example, based on the differences in rank-order with concentration, I would expect the combinatorial code to be different. Hence, a very simple classifier based on cosine or correlation distance would work well. However, since divisive normalization (DN) is applied, I would expect a simple classification scheme that uses the Euclidean distance metric to work equally as well after DN. Is this the case?<br /> Leave-one-trial/sample-out seems too conservative. How robust are the combinatorial patterns across trials? Would just one or two training trials suffice for creating templates for robust classification? Based on my prior experience (https://elifesciences.org/reviewed-preprints/89330), I do expect that the combinatorial patterns would be more robust to adaptation and hence also allow robust recognition of odor intensity across repeated encounters.

Lastly, in the simulated data, since the affinity of the first-order sensory neurons to odorants is expected to be constant across concentration, and "Jaccard similarity between the sets of highest-affinity neurons for each pair of concentration levels was > 0.96," why would the rank-order change across concentration? DN should not alter the rank order.

If the set of early responders does change, how will the decoder need to change, and what precise predictions can be made that can be tested experimentally? The lack of exploration of this aspect of the results seems like a missed opportunity.

(3) Analysis of existing data.

I had a couple of issues related to the presentation and analysis of prior results.

i) Based on the methods, for Figures 1 and 2, it appears the responses across time, trials, and odorants were averaged to get a single data point per neuron for each concentration. Would this averaging not severely dilute trends in the data? The one that particularly concerns me is the averaging across different odorants. If you do odor-by-odor analysis, is the flattening of second-order neural responses still observable? Because some odorants activate more globally and some locally, I would expect a wide variety of dose-response relationships that vary with odor identity (more compressed in second-order neurons, of course). It would be good to show some representative neural responses and show how the extracted values for each neuron are a faithful/good representation of its response variation across intensities.

ii) A lot of neurons seem to have responses that flat line closer to zero (both firing rate and dF/F in Figure 1). Are these responsive neurons? The mean dF/F also seems to hover not significantly above zero. Hence, I was wondering if the number of neurons is reducing the trend in the data significantly.

iii) I did not fully understand the need to show the increase in the odor response across concentrations as a polar plot. I see potential issues with the same. For example, the following dose-response trend at four intensities (C4 being the highest concentration and C1 the lowest): response at C3 > response at C1 and response at C4 > response at C2. But response at C3 < response at C2. Hence, it will be in the top right segment of the polar plot. However, the responses are not monotonic with concentrations. So, I am not convinced that the polar plot is the right way to characterize the dose-response curves. Just my 2 cents.

(4) Simulated vs. Actual data.

In many analyses, simulated data were used (Figures 3 and 4). However, there is no comparison of how well the simulated data fit the experimental data. For example, the Simulated 1st order neuron in Figure 3D does not show a change in rank-order for the first-order neuron. In Figure 3E, temporal response patterns in second-order neurons look unrealistic. Some objective comparison of simulated and experimental data would help bolster confidence in these results.

Reviewer #3 (Public review):

Summary:

In their study, Shen et al. examine how first- and second-order neurons of early olfactory circuits among invertebrates and vertebrates alike respond to and encode odor identity and concentration. Previously published electrophysiological and imaging data are re-analyzed and complemented with computational simulations. The authors explore multiple potential circuit computations by which odor concentration-dependent increases in first-order neuron responses transform into concentration-invariant responses on average across the second-order neuron population, and report that divisive normalization exceeds subtractive normalization and intraglomerular gain control in accounting for this transformation. The authors then explore how either rate- or timing-based schemes in third-order neurons may decode odor identity and concentration information from such concentration-invariant mean responses across the second-order neuron population. Finally, the results of their study of second-order neurons (invertebrate projection neurons and vertebrate mitral cells) are contrasted with the concentration-variant responses of second-order projection tufted cells in mammals. Overall, through a combination of neural data re-analysis, computational simulation, and conceptual theory, this study provides important new understanding of how aspects of sensory information are encoded through the actions of distinct components of early olfactory circuits.

Strengths:

Consideration of multiple evolutionarily disparate olfactory circuits, as well as re-analysis of previously published neural data sets combined with novel simulations guided by those sets, lends considerable robustness to some key findings of this study. In particular, the finding that divisive normalization - with direct inspiration from established circuit components in the form of glomerular layer short-axon cells - accounts more thoroughly for the average concentration invariance of second-order olfactory neurons at a population level than other forms of normalization is compelling. Likewise, demonstration of the required 'crossover' of first-order neuron concentration sensitivity for divisive normalization to achieve such flattening of concentration variance across the second-order population is notable, with simulations providing important insight into experimentally observed patterns of first-order neuron responses. Limited clarity in other aspects of the study, in particular related to the consideration of neural response latencies and enumerated below, temper the overall strength of the study.

Weaknesses:

(1) While the authors focus on concentration-dependent increases in first-order neuron activity, reflecting the majority of observed responses, recent work from the Imai group shows that odorants can also lead to direct first-order neuron inhibition (i.e., reduction in spontaneous activity), and within this subset, increasing odorant concentration tends to increase the degree of inhibition. Some discussion of these findings and how they may complement divisive normalization to contribute to the diverse second-order neuron concentration-dependence would be of interest and help expand the context of the current results.

(2) Related to the above point, odorant-evoked inhibition of second-order neurons is widespread in mammalian mitral cells and significantly contributes to the flattened concentration-dependence of mitral cells at the population level. Such responses are clearly seen in Figure 1D. Some discussion of how odorant-evoked mitral cell inhibition may complement divisive normalization, and likewise relate to comparatively lower levels of odorant-evoked inhibition among tufted cells, would further expand the context of the current results. Toward this end, replication of analyses in Figures 1D and E following exclusion of mitral cell inhibitory responses would provide insight into the contribution of such inhibition to the flattening of the mitral cell population concentration dependence.

(3) The idea of concentration-dependent crossover responses across the first-order population being required for divisive normalization to generate individually diverse concentration response functions across the second-order population is notable. The intuition of the crossover responses is that first-order neurons that respond most sensitively to any particular odorant (i.e., at the lowest concentration) respond with overall lower activity at higher concentrations than other first-order neurons less sensitively tuned to the odorant. Whether this is a consistent, generalizable property of odorant binding and first-order neuron responsiveness is not addressed by the authors, however. Biologically, one mechanism that may support such crossover events is intraglomerular presynaptic/feedback inhibition, which would be expected to increase with increasing first-order neuron activation such that the most-sensitively responding first-order neurons would also recruit the strongest inhibition as concentration increases, enabling other first-order neurons to begin to respond more strongly. Discussion of this and/or other biological mechanisms (e.g., first-order neuron depolarization block) supporting such crossover responses would strengthen these results.

(4) It is unclear to what degree the latency analysis considered in Figures 4D-H works with the overall framework of divisive normalization, which in Figure 3 we see depends on first-order neuron crossover in concentration response functions. Figure 4D suggests that all first-order neurons respond with the same response amplitude (R in eq. 3), even though this is supposed to be pulled from a distribution. It's possible that Figure 4D is plotting normalized response functions to highlight the difference in latency, but this is not clear from the plot or caption. If response amplitudes are all the same, and the response curves are, as plotted in Figure 4D, identical except for their time to half-max, then it seems somewhat trivial that the resulting second-order neuron activation will follow the same latency ranking, regardless of whether divisive normalization exists or not. However, there is some small jitter in these rankings across concentrations (Figure 4G), suggesting there is some randomness to the simulations. It would be helpful if this were clarified (e.g., by showing a non-normalized Figure 4D, with different response amplitudes), and more broadly, it would be extremely helpful in evaluating the latency coding within the broader framework proposed if the authors clarified whether the simulated first-order neuron response timecourses, when factoring in potentially different amplitudes (R) and averaging across the entire response window, reproduces the concentration response crossovers observed experimentally. In summary, in the present manuscript, it remains unclear if concentration crossovers are captured in the latency simulations, and if not, the authors do not clearly address what impact such variation in response amplitudes across concentrations may have on the latency results. It is further unclear to what degree divisive normalization is necessary for the second-order neurons to establish and maintain their latency ranks across concentrations, or to exhibit concentration-dependent changes in latency.

(5) How the authors get from Figure 4G to 4H is not clear. Figure 4G shows second-order neuron response latencies across all latencies, with ordering based on their sorted latency to low concentration. This shows that very few neurons appear to change latency ranks going from low to high concentration, with a change in rank appearing as any deviation in a monotonically increasing trend. Focusing on the high concentration points, there appear to be 2 latency ranks switched in the first 10 responding neurons (reflecting the 1 downward dip in the points around neuron 8), rather than the 7 stated in the text. Across the first 50 responding neurons, I see only ~14 potential switches (reflecting the ~7 downward dips in the points around neurons 8, 20, 32, 33, 41, 44, 50), rather than the 32 stated in the text. It is possible that the unaccounted rank changes reflect fairly minute differences in latencies that are not visible in the plot in Figure 4G. This may be clarified by plotting each neuron's latency at low concentration vs. high concentration (i.e., similar to Figure 4H, but plotting absolute latency, not latency rank) to allow assessment of the absolute changes. If such minute differences are not driving latency rank changes in Fig. 4G, then a trend much closer to the unity line would be expected in Figure 4H. Instead, however, there are many massive deviations from unity, even within the first 50 responding neurons plotted in Figure 4G. These deviations include a jump in latency rank from 2 at low concentration to ~48 at high concentration. Such a jump is simply not seen in Figure 4G.

(6) In the text, the authors state that "Odor identity can be encoded by the set of highest-affinity neurons (which remains invariant across concentrations)." Presumably, this is a restatement of the primacy model and refers to invariance in latency rank (since the authors have not shown that the highest-affinity neurons have invariant response amplitudes across concentration). To what degree this statement holds given the results in Figure 4H, however, which appear to show that some neurons with the earliest latency rank at low concentration jump to much later latency ranks at high concentration, remains unclear. Such changes in latency rank for only a few of the first responding neurons may be negligible for classifying odor identity among a small handful of odorants, but not among 1-2 orders of magnitude more odors, which may feasibly occur in a natural setting. Collectively, these issues with the execution and presentation of the latency analysis make it unclear how robust the latency results are.

(7) Analysis in Figures 4A-C shows that concentration can be decoded from first-order neurons, second-order neurons, or first-order neurons with divisive normalization imposed (i.e., simulating second-order responses). This does not say that divisive normalization is necessary to encode concentration, however. Therefore, for the authors to say that divisive normalization is "a potential mechanism for generating odor-specific subsets of second-order neurons whose combinatorial activity or whose response latencies represent concentration information" seems too strong a conclusion. Divisive normalization is not generating the concentration information, since that can be decoded just as well from the first-order neurons. Rather, divisive normalization can account for the different population patterns in concentration response functions between first- and second-order neurons without discarding concentration-dependent information.

(8) Performing the same polar histogram analysis of tufted vs. mitral cell concentration response functions (Figure 5B) provides a compelling new visualization of how these two cell types differ in their concentration variance. The projected importance of tufted cells to navigation, emerging directly through the inverse relationship between average concentration and distance (Figure 5C), is not surprising, and is largely a conceptual analysis rather than new quantitative analysis per se, but nevertheless, this is an important point to make. Another important consideration absent from this section, however, is whether and how divisive normalization may impact tufted cell activity. Previous work from the authors, as well as from Schoppa, Shipley, and Westbrook labs, has compellingly demonstrated that a major circuit mediating divisive normalization of mitral cells (GABA/DAergic short-axon cells) directly targets external tufted cells, and is thus very likely to also influence projection tufted cells. Such analysis would additionally provide substantially more justification for the Discussion statement "we analyzed an additional type of second-order neuron (tufted cells)", which at present instead reflects fairly minimal analysis.

Reviewer #1 (Public review):

Summary:

Zhang et al. used a conditional knockout mouse model to re-examine the role of the RNA-binding protein PTBP1 in the transdifferentiation of astroglial cells into neurons. Several earlier studies reported that PTBP1 knockdown can efficiently induce the transdifferentiation of rodent glial cells into neurons, suggesting potential therapeutic applications for neurodegenerative diseases. However, these findings have been contested by subsequent studies, which in turn have been challenged by more recent publications. In their current work, Zhang et al. deleted exon 2 of the Ptbp1 gene using an astrocyte-specific, tamoxifen-inducible Cre line and investigated, using fluorescence imaging and bulk and single-cell RNA-sequencing, whether this manipulation promotes the transdifferentiation of astrocytes into neurons across various brain regions. The data strongly indicate that genetic ablation of PTBP1 is not sufficient to drive efficient conversion of astrocytes into neurons. Interestingly, while PTBP1 loss alters splicing patterns in numerous genes, these changes do not shift the astroglial transcriptome toward a neuronal profile.

Strengths:

Although this is not the first report of PTBP1 ablation in mouse astrocytes in vivo, this study utilizes a distinct knockout strategy and provides novel insights into PTBP1-regulated splicing events in astrocytes. The manuscript is well written, and the experiments are technically sound and properly controlled. I believe this study will be of considerable interest to a broad readership.

Weaknesses:

(1) The primary point that needs to be addressed is a better understanding of the effect of exon 2 deletion on PTBP1 expression. Figure 4D shows successful deletion of exon 2 in knockout astrocytes. However, assuming that the coverage plots are CPM-normalized, the overall PTBP1 mRNA expression level appears unchanged. Figure 6A further supports this observation. This is surprising, as one would expect that the loss of exon 2 would shift the open reading frame and trigger nonsense-mediated decay of the PTBP1 transcript. Given this uncertainty, the authors should confirm the successful elimination of PTBP1 protein in cKO astrocytes using an orthogonal approach, such as Western blotting, in addition to immunofluorescence. They should also discuss possible reasons why PTBP1 mRNA abundance is not detectably affected by the frameshift.

(2) The authors should analyze PTBP1 expression in WT and cKO substantia nigra samples shown in Figure 3 or justify why this analysis is not necessary.

(3) Lines 236-238 and Figure 4E: The authors report an enrichment of CU-rich sequences near PTBP1-regulated exons. To better compare this with previous studies on position-specific splicing regulation by PTBP1, it would be helpful to assess whether the position of such motifs differs between PTBP1-activated and PTBP1-repressed exons.

(4) The analyses in Figure 5 and its supplement strongly suggest that the splicing changes in PTBP1-depleted astrocytes are distinct from those occurring during neuronal differentiation. However, the authors should ensure that these comparisons are not confounded by transcriptome-wide differences in gene expression levels between astrocytes and developing neurons. One way to address this concern would be to compare the new PTBP1 cKO data with publicly available RNA-seq datasets of astrocytes induced to transdifferentiate into neurons using proneural transcription factors (e.g., PMID: 38956165).

Reviewer #2 (Public review):

Summary:

The manuscript by Zhang and colleagues describes a study that investigated whether the deletion of PTBP1 in adult astrocytes in mice led to an astrocyte-to-neuron conversion. The study revisited the hypothesis that reduced PTBP1 expression reprogrammed astrocytes to neurons. More than 10 studies have been published on this subject, with contradicting results. Half of the studies supported the hypothesis while the other half did not. The question being addressed is an important one because if the hypothesis is correct, it can lead to exciting therapeutic applications for treating neurodegenerative diseases such as Parkinson's disease.

In this study, Zhang and colleagues conducted a conditional mouse knockout study to address the question. They used the Cre-LoxP system to specifically delete PTBP1 in adult astrocytes. Through a series of carefully controlled experiments, including cell lineage tracing, the authors found no evidence for the astrocyte-to-neuron conversion.

The authors then carried out a key experiment that none of the previous studies on the subject did: investigating alternative splicing pattern changes in PTBP1-depleted cells using RNA-seq analysis. The idea is to compare the splicing pattern change caused by PTBP1 deletion in astrocytes to what occurs during neurodevelopment. This is an important experiment that will help illuminate whether the astrocyte-to-neuron transition occurred in the system. The result was consistent with that of the cell staining experiments: no significant transition was detected.

These experiments demonstrate that, in this experimental setting, PTBT1 deletion in adult astrocytes did not convert the cells to neurons.

Strengths:

This is a well-designed, elegantly conducted, and clearly described study that addresses an important question. The conclusions provide important information to the field.<br /> To this reviewer, this study provided convincing and solid experimental evidence to support the authors' conclusions.

Weaknesses:

The Discussion in this manuscript is short and can be expanded. Can the authors speculate what led to the contradictory results in the published studies? The current study, in combination with the study published in Cell in 2021 by Wang and colleagues, suggests that observed difference is not caused by the difference of knockdown vs. knockout. Is it possible that other glial cell types are responsible for the transition? If so, what cells? Oligodendrocytes?

Mention any security breaches avoided or user trust gained due to these measures.

Explain how this modularity improved the system's performance or maintainability.

Include user engagement metrics or feedback to demonstrate the success of the feature.

Quantify the reduction in response times or manual tasks to emphasize the achievement.

Quantify the time saved or efficiency gained from implementing CI/CD pipelines.

Provide metrics or examples of performance improvements achieved through this optimization.

Clarify what specific improvements were made as a result of these insights.

Explain how these QA outcomes benefited the team or project to show the impact.

Quantify the previous search time to highlight the improvement more effectively.

Specify how this precision impacted the project or client outcomes for better context.

Specify the metrics used to measure effectiveness and how they compare to competitors.

Explain how the system's accuracy was measured and its potential impact on user safety.

Include user feedback or adoption rates to show the app's effectiveness and relevance.

Mention the total number of events held and any feedback received to illustrate success.

Provide a baseline number of participants to demonstrate the actual increase in engagement.

Clarify the impact of these recommendations, such as time saved or improved accuracy.

Include the number of devices analyzed and how the findings influenced decision-making.

Quantify the time saved for partners due to this simplification to highlight efficiency gains.

Specify the number of pilot ideas developed and their impact on patient care or efficiency.

Reviewer #1 (Public review):

Summary:

This paper presents maRQup, a Python pipeline for automating the quantitative analysis of preclinical cancer immunotherapy experiments using bioluminescent imaging in mice. maRQup processes images to quantify tumor burden over time and across anatomical regions, enabling large-scale analysis of over 1,000 mice. The study uses this tool to compare different CAR-T cell constructs and doses, identifying differences in initial tumor control and relapse rates, particularly noting that CD19.CD28 CAR-T cells show faster initial killing but higher relapse compared to CD19.4-1BB CAR-T cells. Furthermore, maRQup facilitates the spatiotemporal analysis of tumor dynamics, revealing differences in growth patterns based on anatomical location, such as the snout exhibiting more resistance to treatment than bone marrow.

Strengths:

(1) The maRQup pipeline enables the automatic processing of a large dataset of over 1,000 mice, providing investigators with a rapid and efficient method for analyzing extensive bioluminescent tumor image data.

(2) Through image processing steps like tail removal and vertical scaling, maRQup normalizes mouse dimensions to facilitate the alignment of anatomical regions across images. This process enables the reliable demarcation of nine distinct anatomical regions within each mouse image, serving as a basis for spatiotemporal analysis of tumor burden within these consistent regions by quantifying average radiance per pixel.

Weaknesses:

(1) While the pipeline aims to standardize images for regional assessment, the reliance on scaling primarily along the vertical axis after tail removal may introduce limitations to the quantitative robustness of the anatomically defined regions. This approach does not account for potential non-linear growth across dimensions in animals of different ages or sizes, which could result in relative stretching or shrinking of subjects compared to an average reference.

(2) Furthermore, despite excluding severely slanted images, the pipeline does not fully normalize for variations in animal pose during image acquisition (e.g., tucked body, leaning). This pose variability not only impacts the precise relative positioning of internal anatomical regions, potentially making their definition based on relative image coordinates more qualitative than truly quantitative for precise regional analysis, but it also means that the bioluminescent light signal from the tumor will not propagate equally to the camera, as photons will travel differentially through the tissue. This differing light path through tissues due to variable positioning can introduce large variability in the measured radiance that was not accounted for in the analysis algorithm. Achieving more robust anatomical and quantitative normalization might require methods that control animal posture using a rigid structure during imaging.

Reviewer #2 (Public review):

Summary:

The authors developed a method that automatically processes bioluminescent tumor images for quantitative analysis and used it to describe the spatiotemporal distribution of tumor cells in response to CD19-targeting CAR-T cells, comprising CD28 or 4-1BB costimulatory domains. The conclusion highlights the dependence of tumor decay and relapse on the number of injected cells, the type of cells, and the initial growth rate of tumors ( where initial is intended from the first day of therapy). The authors also determined the spatiotemporal analysis of tumor response to CAR T therapy in different regions of the mouse body in a model of acute lymphoblastic leukemia (ALL).

Strengths:

The analysis is based on a large number of images and accounts for many variables. The results of the analysis largely support their claims that the kinetics of tumor decay and relapse are dependent on the CAR T co-stimulatory domain and number of cells injected and tumor growth rates.

Weaknesses:

The study does not specify how a) differences in mouse positioning (and whether they excluded not-aligned mice) and b) tumor spread at the start of therapy influenced their data. The study does not take into account the potential heterogeneity of CAR T cells in terms of CAR T expression or T cell immunophenotype ( differentiation, exhaustion, fitness...).

Reviewer #3 (Public review):

Summary:

The paper "The 1000+ mouse project: large-scale spatiotemporal parametrization and modeling of preclinical cancer immunotherapies" is focused on developing a novel methodology for automatic processing of bioluminescence imaging data. It provides quantitative and statistically robust insights into preclinical experiments that will contribute to optimizing cell-based therapies. There is an enormous demand for such methods and approaches that enable the spatiotemporal evaluation of cell monitoring in large cohorts of experimental animals.

Strengths:

The manuscript is generally well written, and the experiments are scientifically sound. The conclusions reflect the soundness of experimental data. This approach seems to be quite innovative and promising to improve the statistical accuracy of BLI data quantification.

This methodology can be used as a universal quantification tool for BLI data for in vivo assessment of adoptively transferred cells due to the versatility of the technology.

Weaknesses:

No weaknesses were identified by this Reviewer.

Reviewer #1 (Public review):

In this study, Ramirez-Diaz and coworkers address an important and lingering question in the bacterial cell division field, i.e., whether FtsZ polymers bend the cell membrane inwards, using an elegant and innovative approach. The key cell division protein FtsZ is a homolog of tubulin and forms curved polymers in the presence of GTP. It has long been hypothesized that this curvature provides the force to bend the cell membrane inwards, thereby triggering septal synthesis. Several in vitro studies have shown that purified FtsZ, when attached to the membrane, can indeed deform artificial membranes. However, other studies favor the view that only septal peptidoglycan synthesis drives cell division. Ramirez-Diaz has tried to address the membrane deformation theory in vivo by developing a mutant that synthesizes extra lipids. In this way, the membrane tension is lowered, which would facilitate cell division if deformation of the cell membrane by curved FtsZ polymers is a crucial step in cell division. Surprisingly, they showed that this mutant overcomes the cell division block in a sepF ezrA double mutant. In addition, they carefully characterize the membrane characteristics of the mutant and the effect on FtsZ ring formation. With this work, they have set up a very useful model system to study the role of the cell membrane in cell division, and also a new tool to better study the function of the cell division proteins EzrA and SepF. Overall, this is a very important study for the bacterial cell division field with interesting findings and ideas.

Nevertheless, the authors jump to a conclusion that I cannot yet share. The main issue I have is that they focus on membrane tensions, yet what they seem to modulate is membrane fluidity. Both are clearly related but not the same. I think that it is important to extensively address this issue in the manuscript. They (also) use Laurdan generalized polarization as an indication of membrane tension (Figure 1F), but this method is primarily used in the literature to measure membrane fluidity. In addition, they explain the occurrence of strong local fluorescent membrane signals as the occurrence of double membranes (Figure S1D), whereas others have shown that such fluorescent hot spots can, in theory, also be formed by local accumulation of fluid lipids (PMID: 24603761). The reason why it is so important to distinguish fluidity from tension is that for the attachment of FtsZ polymers, the cell makes use of anchor proteins like FtsA that contain an amphipathic alpha helix, which inserts into the inner leaflet of the lipid bilayer. Importantly, this insertion only works when the fatty acids can be "pushed apart", and this is stimulated by unsaturated and short-chain fatty acids that make the membrane more fluid (PMID: 12676941). If a membrane is "more fluid", then it can more easily accommodate an amphipathic helix. Thus, the production of extra membrane material may increase the fluidity of the cell membrane, as the Laurdan GP measurements indicated, which can then facilitate the attachment of FtsA, including the attached FtsZ polymers, to the membrane. In other words, what the authors have observed may not be a stimulation of Z-ring formation due to lowering membrane tension, but rather because of stimulated binding of FtsZ polymers to the cell membrane. It might be that the attachment of late cell division to the Z-ring, which is all transmembrane proteins, is also facilitated in a more fluid lipid environment. The authors have not excluded the latter (by using a mutant depleted for one of the late cell division proteins).

Finally, the authors performed EM studies to measure septa thickness, and surprisingly, they did not seem to observe deformed septa in a sepF-ezrA double mutant, when overexpressing accDA, while it has been shown before that the absence of SepF leads to strongly deformed septa. Since this finding nuances the mode of action of SepF polymers, it should be discussed.

In conclusion, this is an important and interesting study, but it seems crucial for the interpretation of the findings to include a clear discussion on membrane fluidity and its consequences.

Reviewer #2 (Public review):

Summary:

In this manuscript, Ramirez-Diaz and colleagues set out to examine key physical mechanisms of bacterial cell division, using the Gram-positive model Bacillus subtilis. Specifically, they investigate the hypothesis that condensation of polymers of the master regulator of division FtsZ can deform membranes to initiate division, but that this is limited by membrane tension. They test this by modulating both membrane tension and FtsZ condensation genetically. To modulate membrane tension, they overexpress accDA to increase the rate of phospholipid synthesis and increase the "hidden membrane reservoir", thereby decreasing membrane tension. To modulate FtsZ condensation, they deplete the bundling protein EzrA in a background lacking a second bundling protein, SepF. They confirm the effects of accDA overexpression on membrane tension using two different sensors before assessing the relationship between membrane tension, FtsZ condensation, and division. They demonstrate that cells with excess membrane (reduced membrane tension) can divide with reduced bundling protein abundance, suggesting that FtsZ condensation driven by ZBPs normally serves to overcome membrane tension to initiate division. In addition, they find an inverse relationship between membrane tension and FtsZ ring constriction rate, but no effect of membrane tension on FtsZ treadmilling. Estimation of physical parameters leads them to conclude that very small membrane fluctuations are sufficient to initiate division in unperturbed cells and that the membrane contributes only ~0.1% of the total surface tension strength, maintaining cell shape.

Strengths:

The highly quantitative approach of this work is a strength, as is the rigorous assessment of membrane tension with multiple sensors. The model proposed is largely consistent with existing data and provides a mechanism for further study and validation. The study tackles a major outstanding question in bacterial cell biology, and provides a potential mechanism for a key step in replication with broad implications in other organisms.

Weaknesses:

The authors only use one method (overexpression of accDA) to perturb membrane tension, which could influence division in unanticipated ways (e.g., metabolic adaptations and/or activation of signaling pathways). The proposed model for initiation of division posits that FtsZ condensation bends membranes, which is supported by in vitro evidence, but there is no in vivo evidence that FtsZ condensation can bend membranes in cells. It remains possible that the function of FtsZ condensation is to localize sufficient cell wall synthetic activity to build peptidoglycan that rectifies membrane fluctuations.

Reviewer #1 (Public review):

Summary:

This manuscript describes the use of computational tools to design a mimetic of the interleukin-7 (IL-7) cytokine with superior stability and receptor binding activity compared to the naturally occurring molecule. The authors focused their engineering efforts on the loop regions to preserve receptor interfaces while remediating structural irregularities that destabilize the protein. They demonstrated the enhanced thermostability, production yield, and bioactivity of the resulting molecule through biophysical and functional studies. Overall, the manuscript is well written, novel, and of high interest to the fields of molecular engineering, immunology, biophysics, and protein therapeutic design. The experimental methodologies used are convincing; however, the article would benefit from more quantitative comparisons of bioactivity through titrations.

Reviewer #2 (Public review):

Summary:

This manuscript presents the computational design and experimental validation of Neo-7, an engineered variant of interleukin-7 (IL-7) with improved folding efficiency, expression yield, and therapeutic activity. The authors employed a rational protein design approach using Rosetta loop remodeling to reconnect IL-7's functional helices through shorter, more efficient loops, resulting in a protein with superior stability and binding affinity compared to wild-type IL-7. The work demonstrates promising translational potential for cancer immunotherapy applications.

Strengths:

(1) The integration of Rosetta loop remodeling with AlphaFold validation represents an established computational pipeline for rational protein design. The iterative refinement process, using both single-sequence and multimer AlphaFold predictions, is methodologically sound.

(2) The authors provide thorough characterization across multiple platforms (yeast display, bacterial expression, mammalian cell expression) and assays (binding kinetics, thermostability, bioactivity), strengthening the robustness of their findings.

(3) The identification of the critical helix 1 kink stabilized by disulfide bonding and its recreation through G4C/L96C mutations demonstrates deep structural understanding and successful problem-solving.

(4) The MC38 tumor model results show clear therapeutic advantages of Neo-7 variants, with compelling immune profiling data supporting CD8+ T cell-mediated anti-tumor mechanisms.

(5) The transcriptomic profiling provides valuable mechanistic insights into T cell activation states and suggests reduced exhaustion markers, which are clinically relevant.

Weaknesses:

(1) While computational predictions are extensive, the manuscript lacks experimental structural validation of the designed Neo-7 variants. The term "Structural Validation" should not be used in the header.

(2) The authors observe slower on/off-rates for Neo-7 variants compared to wild-type IL-7. Could the authors speculate about the potential biological impacts of the slow off-rate, especially focusing on downstream signaling pathways that might be differentially affected by the altered binding kinetics of Neo-7 variants?

(3) While computational immunogenicity prediction is provided, these methods are very limited.

Reviewer #1 (Public review):

This study aims to elucidate the mechanisms by which stress-induced α2A-adrenergic receptor (α2A-AR) internalization leads to cytosolic noradrenaline (NA) accumulation and subsequent neuronal dysfunction in the locus coeruleus (LC). While the manuscript presents an interesting but ambitious model involving calcium dynamics, GIRK channel rundown, and autocrine NA signaling, several key limitations undermine the strength of the conclusions.

First, the revision does not include new experiments requested by reviewers to validate core aspects of the mechanism. Specifically, there is no direct measurement of cytosolic NA levels or MAO-A enzymatic activity to support the link between receptor internalization and neurochemical changes. The authors argue that such measurements are either not feasible or beyond the scope of the study, leaving a significant gap in the mechanistic chain of evidence.

Second, the behavioral analysis remains insufficient to support claims of cognitive impairment. The use of a single working memory test following an anxiety test is inadequate to verify memory dysfunction behaviors. Additional cognitive assays, such as the Morris Water Maze or Novel Object Recognition, are recommended but not performed.

Third, concerns regarding the lack of rigor in differential MAO-A expression in fluorescence imaging were not addressed experimentally. Instead of clarifying the issue, the authors moved the figure to supplementary data without providing further evidence (e.g., an enzymatic assay or quantitative reanalysis of Western blot, or re-staining of IF for MAO-A) to support their interpretation.

Fourth, concerns regarding TH staining remain unresolved. In Figure S7, the α2A-AR signal appears to resemble TH staining, and vice versa, raising the possibility of labeling errors. It is recommended that the authors re-examine this issue by either double-checking the raw data or repeating the immunostaining to validate the staining.

Overall, the manuscript offers a potentially interesting framework but falls short in providing the experimental rigor necessary to establish causality. The reliance on indirect reasoning and reorganizing of existing data, rather than generating new evidence, limits the overall impact and interpretability of the study.

Reviewer #2 (Public review):

Summary:

This manuscript investigates the mechanism by which chronic stress induces degeneration of locus coeruleus (LC) neurons. The authors demonstrate that chronic stress leads to the internalization of α2A-adrenergic receptors (α2A-ARs) on LC neurons, causing increased cytosolic noradrenaline (NA) accumulation and subsequent production of the neurotoxic metabolite DOPEGAL via monoamine oxidase A (MAO-A). The study suggests a mechanistic link between stress-induced α2A-AR internalization, disrupted autoinhibition, elevated NA metabolism, activation of asparagine endopeptidase (AEP), and Tau pathology relevant to Alzheimer's disease (AD). The conclusions of this paper are largely well-supported by the data, but some aspects of image acquisition require further examination.

Strengths:

This study clearly demonstrates the effects of chronic stimulation on the excitability of LC neurons using electrophysiological techniques. It also elucidates the role of α2-adrenergic receptor (α2-AR) internalization and the associated upstream and downstream signaling pathways of GIRK-1, using a range of pharmacological agents, highlighting the innovative nature of the work. Additionally, the study identifies the involvement of the MAO-A-DOPEGAL-AEP pathway in this process. The topic is timely, the proposed mechanistic pathway is compelling, and the findings have translational relevance, particularly about therapeutic strategies targeting α2A-AR internalization in neurodegenerative diseases.

Weaknesses:

(1) The manuscript reports that chronic stress for 5 days increases MAO-A levels in LC neurons, leading to the production of DOPEGAL, activation of AEP, and subsequent tau cleavage into the tau N368 fragment, ultimately contributing to neuronal damage. However, the authors used wild-type C57BL/6 mice, and previous literature has indicated that AEP-mediated tau cleavage in wild-type mice is minimal and generally insufficient to cause significant behavioral alterations. Please clarify and discuss this apparent discrepancy.

(2) It is recommended that the authors include additional experiments to examine the effects of different durations and intensities of stress on MAO-A expression and AEP activity. This would strengthen the understanding of stress-induced biochemical changes and their thresholds.

(3) Please clarify the rationale for the inconsistent stress durations used across Figures 3, 4, and 5. In some cases, a 3-day stress protocol is used, while in others, a 5-day protocol is applied. This discrepancy should be addressed to ensure clarity and experimental consistency.

(4) The abbreviation "vMAT2" is incorrectly formatted. It should be "VMAT2," and the full name (vesicular monoamine transporter 2) should be provided at first mention.

Comments on revisions:

The authors have addressed all of the reviewers' comments.

Reviewer #3 (Public review):

Summary:

The authors present a technically impressive data set showing that repeated excitation or restraint stress internalises somatodendritic α2A adrenergic autoreceptors (α2A ARs) in locus coeruleus (LC) neurons. Loss of these receptors weakens GIRK-dependent autoinhibition, raises neuronal excitability, and is accompanied by higher MAO A, DOPEGAL, AEP, and tau N368 levels. The work combines rigorous whole-cell electrophysiology with barbadin-based trafficking assays, qPCR, Western blotting and immunohistochemistry. The final schematic is appealing and in principle, could explain early LC hyperactivity followed by degeneration in ageing and Alzheimer's disease.

Strengths:

Multi-level approach - The study integrates electrophysiology, pharmacology, mRNA quantification, and protein-level analysis.

Use of barbadin to block β-arrestin/AP-2-dependent internalisation is both technically precise and mechanistically informative

Well-executed electrophysiology

translation relevance

converges to a model that peers discussed (scientists can only discuss models - not data!)

Weaknesses:

Nevertheless, the manuscript currently reads as a sequence of discrete experiments rather than a single causal chain

Reviewer #1 (Public review):

Summary:

This study investigates how the brain processes facial expressions across development by analyzing intracranial EEG (iEEG) data from children (ages 5-10) and post-childhood individuals (ages 13-55). The researchers used a short film containing emotional facial expressions and applied AI-based models to decode brain responses to facial emotions. They found that in children, facial emotion information is represented primarily in the posterior superior temporal cortex (pSTC) - a sensory processing area - but not in the dorsolateral prefrontal cortex (DLPFC), which is involved in higher-level social cognition. In contrast, post-childhood individuals showed emotion encoding in both regions. Importantly, the complexity of emotions encoded in the pSTC increased with age, particularly for socially nuanced emotions like embarrassment, guilt, and pride. The authors claim that these findings suggest that emotion recognition matures through increasing involvement of the prefrontal cortex, supporting a developmental trajectory where top-down modulation enhances understanding of complex emotions as children grow older.

Strengths:

(1) The inclusion of pediatric iEEG makes this study uniquely positioned to offer high-resolution temporal and spatial insights into neural development compared to non-invasive approaches, e.g., fMRI, scalp EEG, etc.

(2) Using a naturalistic film paradigm enhances ecological validity compared to static image tasks often used in emotion studies.

(3) The idea of using state-of-the-art AI models to extract facial emotion features allows for high-dimensional and dynamic emotion labeling in real time.

Weaknesses:

The study has notable limitations that constrain the generalizability and depth of its conclusions. The sample size was very small, with only nine children included and just two having sufficient electrode coverage in the posterior superior temporal cortex (pSTC), which weakens the reliability and statistical power of the findings, especially for analyses involving age. Electrode coverage was also uneven across brain regions, with not all participants having electrodes in both the dorsolateral prefrontal cortex (DLPFC) and pSTC, and most coverage limited to the left hemisphere-hindering within-subject comparisons and limiting insights into lateralization. The developmental differences observed were based on cross-sectional comparisons rather than longitudinal data, reducing the ability to draw causal conclusions about developmental trajectories. Moreover, the analysis focused narrowly on DLPFC, neglecting other relevant prefrontal areas such as the orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC), which play key roles in emotion and social processing. Although the use of a naturalistic film stimulus enhances ecological validity, it comes at the cost of experimental control, with no behavioral confirmation of the emotions perceived by participants and uncertain model validity for complex emotional expressions in children. A non-facial music block that could have served as a control was available but not analyzed. Generalizability is further limited by the fact that all participants were neurosurgical patients, potentially with neurological conditions such as epilepsy that may influence brain responses. Additionally, the high temporal resolution of intracranial EEG was not fully utilized, as data were downsampled and averaged in 500-ms windows. Finally, the absence of behavioral measures or eye-tracking data makes it difficult to directly link neural activity to emotional understanding or determine which facial features participants attended to.

Reviewer #2 (Public review):

Summary:

In this paper, Fan et al. aim to characterize how neural representations of facial emotions evolve from childhood to adulthood. Using intracranial EEG recordings from participants aged 5 to 55, the authors assess the encoding of emotional content in high-level cortical regions. They report that while both the posterior superior temporal cortex (pSTC) and dorsolateral prefrontal cortex (DLPFC) are involved in representing facial emotions in older individuals, only the pSTC shows significant encoding in children. Moreover, the encoding of complex emotions in the pSTC appears to strengthen with age. These findings lead the authors to suggest that young children rely more on low-level sensory areas and propose a developmental shift from reliance on lower-level sensory areas in early childhood to increased top-down modulation by the prefrontal cortex as individuals mature.

Strengths:

(1) Rare and valuable dataset: The use of intracranial EEG recordings in a developmental sample is highly unusual and provides a unique opportunity to investigate neural dynamics with both high spatial and temporal resolution.

(2) Developmentally relevant design: The broad age range and cross-sectional design are well-suited to explore age-related changes in neural representations.

(3) Ecological validity: The use of naturalistic stimuli (movie clips) increases the ecological relevance of the findings.

(4) Feature-based analysis: The authors employ AI-based tools to extract emotion-related features from naturalistic stimuli, which enables a data-driven approach to decoding neural representations of emotional content. This method allows for a more fine-grained analysis of emotion processing beyond traditional categorical labels.

Weaknesses:

(1) The emotional stimuli included facial expressions embedded in speech or music, making it difficult to isolate neural responses to facial emotion per se from those related to speech content or music-induced emotion.

(2) While the authors leveraged Hume AI to extract facial expression features from the video stimuli, they did not provide any validation of the tool's accuracy or reliability in the context of their dataset. It remains unclear how well the AI-derived emotion ratings align with human perception, particularly given the complexity and variability of naturalistic stimuli. Without such validation, it is difficult to assess the interpretability and robustness of the decoding results based on these features.

(3) Only two children had relevant pSTC coverage, severely limiting the reliability and generalizability of results.

(4) The rationale for focusing exclusively on high-frequency activity for decoding emotion representations is not provided, nor are results from other frequency bands explored.

(5) The hypothesis of developmental emergence of top-down prefrontal modulation is not directly tested. No connectivity or co-activation analyses are reported, and the number of participants with simultaneous coverage of pSTC and DLPFC is not specified.

(6) The "post-childhood" group spans ages 13-55, conflating adolescence, young adulthood, and middle age. Developmental conclusions would benefit from finer age stratification.

(7) The so-called "complex emotions" (e.g., embarrassment, pride, guilt, interest) used in the study often require contextual information, such as speech or narrative cues, for accurate interpretation, and are not typically discernible from facial expressions alone. As such, the observed age-related increase in neural encoding of these emotions may reflect not solely the maturation of facial emotion perception, but rather the development of integrative processing that combines facial, linguistic, and contextual cues. This raises the possibility that the reported effects are driven in part by language comprehension or broader social-cognitive integration, rather than by changes in facial expression processing per se.

Joint Public Review:

Summary:

This study investigates plasticity effects in brain function and structure from training in navigation and verbal memory.

The authors used a longitudinal design with a total of 75 participants across two sites. Participants were randomised to one of three conditions: verbal memory training, navigation training, or a video control condition. The results show behavioural effects in relevant tasks following the training interventions. The central claim of the paper is that network-based measures of task-based activation are affected by the training interventions, but structural brain metrics (T2w-derived volume and diffusion-weighted imaging microstructure) are not impacted by any of the training protocols tested.

Strengths:

(1) This is a well-designed study which uses two training conditions, an active control, and randomisation, as appropriate. It is also notable that the authors combined data acquisition across two sites to reach the needed sample size and accounted for it in their statistical analyses quite thoroughly. In addition, I commend the authors on using pre-registration of the analysis to enhance the reproducibility of their work.

(2) Some analyses in the paper are exhaustive and compelling in showcasing the presence of longitudinal behavioural effects, functional activation changes, and lack of hippocampal volume changes. The breadth of analysis on hippocampal volume (including hippocampal subfields) is convincing in supporting the claim regarding a lack of volumetric effect in the hippocampus.

Weaknesses:

(1) The rationale for the study and its relationship with previous literature is not fully clear from the paper. In particular, there is a very large literature that has already explored the longitudinal effects of different types of training on functional and structural neuroimaging. However, this literature is barely acknowledged in the Introduction, which focuses on cross-sectional studies. Studies like the one by Draganski et al. 2004 are cited but not discussed, and are clumped together with cross-sectional studies, which is confusing. As a reader, it is difficult to understand whether the study was meant to be confirmatory based on previous literature, or whether it fills a specific gap in the literature on longitudinal neuroimaging effects of training interventions.

(2) The main claim regarding the lack of changes in brain structure seems only partially supported by the analyses provided. The limited whole-brain evidence from structural neuroimaging makes it difficult to confirm whether there is indeed no effect of training. Beyond hippocampal analyses, many whole-brain analyses of both volumetric and diffusion-weighted imaging metrics are only based on coarse ROIs (for example, 34 cortical parcellations for grey matter analyses). Although vertex-wise analyses in FreeSurfer are reported, it is unclear what metrics were examined (cortical thickness? area? volume?). Diffusion-weighted imaging seems to focus on whole-tract atlas ROIs, which can be less accurate/sensitive than tractography-defined ROIs or voxel-wise approaches.

(3) Quality control of images is only mentioned for FA images in subject space. Given that most analyses are based on atlas ROIs, visual checks following registration are fundamental and should be described in further detail.

Reviewer #1 (Public review):

Summary:<br /> The authors strived for an inventory of GPCRs and GPCR pathway component genes within the genomes of 23 choanoflagellates and other close relatives of metazoans.

Strengths:<br /> The authors generated a solid phylogenetic overview of the GPCR superfamily in these species. Intriguingly, they discover novel GPCR families, novel assortments of domain combinations, novel insights into the evolution of those groups within the Opisthokonta clade. A particular focus is laid on adhesion GPCRs, for which the authors discover many hitherto unknown subfamilies based on Hidden Markov Models of the 7TM domain sequences, which were also reflected by combinations of extracellular domains of the homologs. In addition, the authors provide bioinformatic evidence that aGPCRs of choanoflagellates also contained a GAIN domain, which are self-cleavable thereby reflecting the most remarkable biochemical feat of aGPCRs.

Weaknesses:<br /> The chosen classification scheme for aGPCRs may require reassessment and amendment by the authors in order to prevent confusion with previously issued classification attempts of this family.

Reviewer #2 (Public review):

Summary:<br /> The authors set out to characterise the GPCR family in choanoflagellates (and other unicellular holozoans). GPCRs are the most abundant gene family in many animal genomes, playing crucial roles in a wide range of physiological processes. Although they are known to evolve rapidly, GPCRs are an ancient feature of eukaryotic biology. Identifying conserved elements across the animal-protist boundary is therefore a valuable goal, and the increasing availability of genomes from non-animal holozoans provides new opportunities to explore evolutionary patterns that were previously obscured by limited taxon sampling. This study presents a comprehensive re-examination of GPCRs in choanoflagellates, uncovering examples of differential gene retention and revealing the dynamic nature of the GPCR repertoire in this group. As GPCRs are typically involved in environmental sensing, understanding how these systems evolved may shed light on how our unicellular ancestors adapted their signalling networks in the transition to complex multicellularity.

Strengths:<br /> The paper combines a broad taxonomic scope with the use of both established and recently developed tools (e.g. Foldseek, AlphaFold), enabling a deep and systematic exploration of GPCR diversity. Each family is carefully described, and the manuscript also functions as an up-to-date review of GPCR classification and evolution. Although similar attempts of understanding GPCR evolution were done over the last decade, the authors build on this foundation by identifying new families and applying improved computational methods to better predict structure and function. Notably, the presence of Rhodopsin-like GPCRs in some choanoflagellates and ichthyosporeans is intriguing, even though they do not fall within known animal subfamilies. The computational framework presented here is broadly applicable, offering a blueprint for surveying GPCR diversity in other non-model eukaryotes (and even in animal lineages), potentially revealing novel families relevant to drug discovery or helping revise our understanding of GPCR evolution beyond model systems.

Weaknesses:<br /> While the study contributes several interesting observations, it does not radically revise the evolutionary history of the GPCR family. However, in an era increasingly concerned with the reproducibility of scientific findings, this is arguably a strength rather than a weakness. It is encouraging to see that previously established patterns largely hold, and that with expanded sampling and improved methods, new insights can be gained-especially at the level of specific GPCR subfamilies. Then, no functional follow ups are provided in the model system Salpingoeca rosetta, but I am sure functional work on GPCRs in choanoflagellates is set to reveal very interesting molecular adaptations in the future.

Comments on the latest version:

The authors have done a good job answering my questions and suggestions.

Reviewer #2 (Public review):

Summary:

CTCF is one of the most well-characterized regulators of chromatin architecture in mammals. Given that CTCF is an essential protein, understanding how its binding is regulated is a very active area of research. It has been known for decades that CTCF is sensitive to 5-cystosine DNA methylation (5meC) in certain contexts. Moreover, at genomic imprints and in certain oncogenes, 5meC-mediated CTCF antagonism has very important gene regulatory implications. A number of labs (eg, Schubeler and Stamatoyannopoulos) have assessed the impact of DNA methylation on CTCF binding, but it is important to also interrogate the effect on chromatin organization (ie, looping). Here, Roseman and colleagues used a DNMT1 inhibitor in two established human cancer lines (HCT116 [colon] and K562 [leukemia]), and performed CTCF ChIPseq and HiChIP. They showed that "reactivated" CTCF sites-that is, bound in the absence of 5meC-are enriched in gene bodies, participate in many looping events, and intriguingly, appear associated with nuclear speckles. This last aspect suggests that these reactivated loops might play an important role in increased gene transcription. They showed a number of genes that are upregulated in the DNA hypomethylated state actually require CTCF binding, which is an important result.

Strengths:

Overall, I found the paper to be succinctly written and the data presented clearly. The relationship between CTCF binding in gene bodies and association with nuclear speckles is an interesting result. Another strong point of the paper was combining DNMT1 inhibition with CTCF degradation.

Weaknesses:

The most problematic aspect of the original version was the insufficient evidence for the association of "reactivated" CTCF binding sites with nuclear speckles. This has been more diligently assessed in the revised version.

Comments on revisions:

The authors have adequately addressed my points in this revised version.

Reviewer #1 (Public review):

Summary:

This manuscript investigates beta burst dynamics in the primate motor cortex during movement and recovery from stroke. The authors differentiate between "global" beta bursts, which are synchronous across cortical and often subcortical regions, and more spatially confined "local" bursts. Global bursts are associated with reduced spiking variability, slower movements, and are more frequent after stroke, while local bursts increase during recovery and grasp execution. The study provides compelling evidence that beta bursts with different spatial and temporal characteristics may play distinct roles in motor control and recovery.

Strengths:

The major strength of this paper lies in its conceptual advance: the identification and characterization of distinct global and local beta bursts in the primate motor cortex. This distinction builds upon and considerably extends previous work on the heterogeneity of beta bursts. The paper is methodologically rigorous, using simultaneous cortical and subcortical recordings, detailed behavioral tracking, and thorough analyses of spike-LFP interactions. The use of stroke models and neurotypical animals provides converging evidence for the functional dissociation between burst types. The observation that local bursts increase with motor recovery and occur during grasping is particularly novel and may prove valuable for developing biomarkers of motor function.

Weaknesses:

There are several conceptual and methodological limitations that should be addressed. First, the burst detection method relies on an amplitude threshold (median + 1 SD), which is susceptible to false positives and variability (Langford & Wilson, 2025). The classification into global or local bursts then depends on the number of co-bursting channels, compounding the arbitrariness. Second, the imposition of a minimum of three co-bursting cortical channels may bias against the detection of truly local bursts. Third, the classification is entirely cortical; subcortical activity is considered post hoc rather than integrated into the classification, despite the key role of subcortical-cortical synchrony in motor control. Fourth, the apparent dissociation between global and local bursts raises important questions about their spatial distribution across areas like M1 and PMv, which are not thoroughly analyzed. Finally, while the authors interpret local bursts during grasping as novel, similar findings have been reported (e.g., Szul et al., 2023; Rayson et al., 2023), and a deeper discussion of these precedents would strengthen the argument.

Impact:

This work is likely to have a substantial impact on the field of motor systems neuroscience. The distinction between global and local beta bursts offers a promising framework for understanding the dual roles of beta in motor inhibition and sensorimotor computation. The findings are relevant not only for basic research but also for translational efforts in stroke rehabilitation and neuromodulation, particularly given the emerging interest in beta burst-based biomarkers and stimulation targets. The dataset and analytical framework will be useful to researchers investigating beta dynamics, spike-field relationships, and recovery from neural injury.

Langford, Z.D., Wilson, C.R.E., 2025. Simulations reveal that beta burst detection may inappropriately characterize the beta band. https://doi.org/10.1101/2023.12.15.571838.

Rayson, H., Szul, M.J., El-Khoueiry, P., Debnath, R., Gautier-Martins, M., Ferrari, P.F., Fox, N., Bonaiuto, J.J., 2023. Bursting with potential: How sensorimotor beta bursts develop from infancy to adulthood. J. Neurosci. https://doi.org/10.1523/JNEUROSCI.0886-23.2023.

Szul, M.J., Papadopoulos, S., Alavizadeh, S., Daligaut, S., Schwartz, D., Mattout, J., Bonaiuto, J.J., 2023. Diverse beta burst waveform motifs characterize movement-related cortical dynamics. Prog. Neurobiol. 228, 102490.

Reviewer #2 (Public review):

Summary:

The paper by Khanna et al. describes global vs local beta synchrony between a cortical premotor area (PMv) and subcortical structures during motor tasks in the non-human primate, specifically investigating the progression following M1 injury. They found that increases in global beta synchrony between PMv and subcortical structures during the sub-acute phase of injury, and that global synchrony was associated with relatively slower motor movements. As recovery progressed, they report a shift from global synchrony to local synchrony and a subsequent reduction in the movement time. The authors suggest that global changes in subcortical and cortical beta synchrony may generally underpin a variety of movement disorders, including Parkinson's disease, and that shifting from global to local (or reducing global synchrony) might improve functional outcomes.

Strengths:

Ischemic insults and other acquired brain injuries have a significant public health impact. While there is a large body of clinical and basic science studies describing the behavioral, neurophysiological, and mechanistic outcomes of such injury, there is a significant lack studies looking at longitudinal, behaviorally-related neurophysiological measures following cortical injury, so any information has outsized contribution to understanding how brain injury disrupts underlying neural activity and how this may contribute to injury presentation and recovery.

A significant percentage of pre-clinical stroke studies tend to focus on peri-infarct or other cortical structures and their role in recovery. The addition of subcortical recordings allows for the investigation of the role of thalamo-basal gangliar-cortical loops that may be contributing to the degree of impairment or to the recovery process is important for the field. Here, there are longitudinal (up to 3 months post-injury) recordings in the ventral premotor area (PMv) and either the internal capsule or sensorimotor thalamus that can be synchronized with phases of behavioral recovery.

The methods are well described and can act as a framework for assessing synchrony across other data sets with similar recording locations. Limitations in methodology, recordings, and behavior were noted.

Weaknesses:

A major limitation of this paper is that it is a set of case studies rather than a well-designed, well-controlled study of beta synchrony following motor cortex injury. While non-human primate neurophysiological studies are almost always limited by extremely low animal numbers, they are made up for by the fact that they can acquire significant numbers of units or channels, and in the case of normal behavior, can obtain many behavioral trials over months of individual sessions. Here, there were two NHPs used, but they had different subcortical implant locations (thalamus vs internal capsule). They had different injury outcomes, with one showing a typical recovery curve following injury while one had complications and worsening behavior before ultimately recovering. Further, there were significant differences in the ability to record at different times, with one NHP having poor recordings early in the recovery process while one had poor recordings late in the process. Due to the injury, the authors report sessions in which they were not able to record many trials (~10). Assuming that recovery after a cortical injury is an evolving process, breaking analysis into "Early" and "Late" phases reduces the interpretation of where these shifts occur relative to recovery on the task, especially given different thresholds for recovery were used between animals. Because of this, despite a careful analysis of the data and an extensive discussion, the conclusions derived are not particularly compelling. To overcome this, the authors present data from neurotypical NHPs, but with electrodes in M1 rather than PMv, doing a completely different task with no grasping component, again making accurate conclusions about the results difficult. Even with low numbers, the study would have been much stronger if there were within-animal longitudinal data prior to and after the injury on the same task, so the impact of M1 injury could be better assessed.

It is unclear to what extent the subpial aspiration used is a stroke model. While it is much more difficult to perform a pure ischemic motor injury using electrocoagulatory methods in animal models that do not have a lissencephalic cortex, the suction ablation method that the authors use leads to different outcomes than an ischemic injury alone. For instance, in rat models, ischemic vs suction ablation leads to very different electrophysiological profiles and differences in underlying anatomical reorganization (see Carmichael and Chesselet, 2002), even if the behavioral outcomes were similar. There is a concern that the effects shown may be an artifact of the lesion model rather than informing underlying mechanisms of recovery.

The injury model leads to seemingly mild impairments in grasp (but not reach), with rapid and complete recovery occurring within 2-3 weeks from the time of injury. Because of the rapid recovery, relating the physiological processes of recovery to beta synchronization becomes challenging to interpret - Are the global bursts the result of the loss of M1 input to subcortical structures? Are they due to the lack of M1 targets, so there is a more distributed response? Is this due to other post-injury sub-acute mechanisms? How specific is this response - is it limited to peri-infarct areas (and to what extent is the PMv electrode truly in peri-infarct cortex), or would this synchrony be seen anywhere in the sensorimotor networks? Are the local bursts present because global synchrony wanes over time as a function of post-injury homeostatic mechanisms, or is local beta synchrony increasing as new motor plans are refined and reinforced during task re-acquisition? How coupled are they related to recovery - if it is motor plan refinement, the shift from global to local seemingly should lag the recovery? While the study has significant limitations in design that reduce the impact of the results, it should act as a useful baseline/pilot data set in which to build a more complete picture of the role of subcortical-cortical beta synchrony following cortical injury.

Reviewer #3 (Public review):

Summary:

Khanna et al. use a well-conceived and well-executed set of experiments and analyses primarily to document the interaction between neural oscillations in the beta range (here, 13-30 Hz) and recovery of function in an animal model of stroke. Specifically, they show that cortical "beta bursts", or short-term increases in beta power, correlate strikingly with the timeline of behavioral recovery as quantified with a reach-to-grasp task. A key distinction is made between global beta bursts (here, those that synchronize between cortical and subcortical areas) and local bursts (which appear on only a few electrodes). This distinction of global vs. local is shown to be relevant to task performance and movement speed, among other quantities of interest.

A secondary results section explores the relationship between beta bursts and neuronal firing during the grasp portion of the behavioral task. These results are valuable to include, though mostly unsurprising, with global beta in particular associated with lower mean and variance in spike rates.

Last, a partial recapitulation of the primary results is offered with a neurologically intact (uninjured) animal. No major contradictions are found with the primary results.

Highlights of the Discussion section include a thoughtful review of atypical movements executed by individuals with Parkinson's disease or stroke survivors, placing the current results in an appropriate clinical context. Potential physiological mechanisms that could account for the observed results are also discussed effectively.

Strengths:

Overall, this is a very interesting paper. The ultimate impact will be enhanced by the authors' choice to analyze beta bursts, which remain a relatively under-explored aspect of neural coding.

The reach-and-grasp task was also a well-considered choice; the combination of a relatively simple movement (reaching towards a target in the same location each time) and a more complex movement (a skilled object-manipulation grasp) provides an internal control of sorts for data analysis. In addition, the task's two sub-movements provide a differential in terms of their likelihood to be affected by the stroke-like injury: proximal muscles (controlling reach) are likely to be less affected by stroke, while distal muscles (controlling grasp) are highly likely to be affected. Lastly, the requirement of the task to execute an object lift maximizes its difficulty and also the potential translational impact of the results on human injury.

The above comments about the task exemplify a strength that is more generally evident: a welcome awareness of clinical relevance, which is in evidence several times throughout the Results and Discussion.

Weaknesses:

The study's weaknesses are mostly minor and, for the most part, correctable.

One concern that may not be correctable in this study: the results about the spatial extent of beta activity seem constrained by relatively poor-quality data. It seems half or more of the electrodes are marked as too noisy to provide useful data in Figure 3. If this reflects the wider reality for all analyses, as mentioned, it may not be correctable for the present study. In that case, perhaps some of the experiments or analyses can be revisited or expanded for a future study, when better electrode yields are available.

Other concerns:

In some places, there is a lack of clarity in the presentation of the results. This is not serious but should be addressed to aid readers' comprehension.

Lastly, given the central role of beta oscillations within the study, it would be better for completeness to include even a brief exploration of sustained beta power (rather than bursts), and the modulation of sustained beta (or lack thereof) in the study's areas of concern: behavioral recovery, task performance, etc.

Reviewer #1 (Public review):

The manuscript by Butler et al. explores a novel physiological role for connexin 32 (Cx32) hemichannels in Schwann cells at peripheral nerves. Building on the authors' prior work on CO₂-sensitive gating of connexins, this study proposes that mitochondrial CO₂ production dependent on neuronal activity promotes the opening of Cx32 hemichannels in the paranode, which in turn modulates neuronal activity by reducing conduction velocity. This hypothesis is addressed using a multifaceted approach that includes immunofluorescence microscopy, dye uptake assays, calcium imaging, computational modeling, and extracellular recordings in isolated sciatic nerves.

Among the strengths of the study are the interdisciplinary integration of imaging, in silico approaches, and functional data. Also, this study proposes a new mechanism with profound physiological relevance. Specifically, Butler et al. provide new insights into glial modulation of electrical conduction in sensory/motor myelinated nerves.

In the current state, the study has some limitations. The evidence linking Cx32 to the observed dye uptake and conduction velocity changes relies primarily on pharmacological inhibition with carbenoxolone, which lacks specificity. The imaging data show overlapping marker signals that preclude the anatomical distinction between nodes and paranodes. FITC uptake, while convincing to test Cx32 hemichannel gating, lacks spatial-temporal information and validation of distribution and localization to viable intracellular compartments. Moreover, while the findings are intriguing, functional proof that Cx32 regulates conduction velocity through ATP release or other downstream effects remains incomplete. Further work using targeted genetic tools, live-tissue imaging, and additional controls would strengthen the mechanistic conclusions.

Overall, the manuscript offers compelling preliminary evidence that supports a new role for Cx32 in peripheral nerve physiology and raises important questions for future investigation.

Reviewer #2 (Public review):

Summary:

This article aims to demonstrate that local production of CO₂ at the axonal node opens Cx32 hemichannels in the Schwann cell paranode, and that CO₂ diffuses through the AQP1 channel to reach Cx32 and trigger its opening. The authors also present evidence supporting a physiological role for this regulatory mechanism. They propose that CO₂-dependent Cx32 activation mediates activity-dependent Ca²⁺ influx into the paranode, and by increasing the leak current across the myelin sheath, it contributes to a slowing of action potential conduction velocity.

The study presents a very interesting and novel mechanism for the physiological regulation of Cx32 hemichannels. The findings are relevant to the field, and the methods and results are of good quality, with some improvements in interpretation and explanation required, and some minor experimental suggestions.

Strengths:

The article is solid in terms of the novelty of the findings and relevance for the physiology of myelinated axons. In addition, it is of major interest for the Connexin field because it explores a physiological way to open Cx32 hemichannels. The experiments are well elaborated, and most of them are sufficient for the main points described by the authors. The finding that nervous activity will trigger the mechanism of hemichannel opening by CO2 is probably the most relevant biological mechanism derived from this article.

Weaknesses:

Throughout the manuscript, the authors interpret their findings as if the described mechanism specifically occurs in the node and paranode regions. However, there is no direct evidence identifying the precise site of CO₂ production or the activation site of Cx32 hemichannels. Therefore, statements such as the one in the title ("activity-dependent CO₂ production in the axonal node opens Cx32 in the Schwann cell paranode") should be reconsidered or removed, as they may be misleading and are not essential to the interpretation of the data. In addition, the participation of aquaporin AQP1 as the main conduit for CO2 diffusion through the plasma membrane could have another interpretation.

Reviewer #1 (Public review):

Summary:

This article presents a study consisting of two experiments, which aim to dissociate and quantify the distinct motivational functions of phasic and tonic pain within a naturalistic and immersive VR setting. Specifically, the authors test two hypotheses: (i) that phasic pain acts as a punishment signal that drives avoidance learning; (ii) that tonic pain reduces motivational vigor, promoting energy conservation and recuperation. In both experiments, participants performed a free-operant foraging task, where they collected virtual pineapples to earn points.

In Experiment 1, phasic pain was delivered as a brief electric shock to the grasping hand when picking up green pineapples. As phasic pain intensity increased, participants were less likely to choose painful fruits. A reinforcement learning model that incorporated reward, pain cost, and effort cost was able to successfully capture behavior.

Experiment 2 combined the effects of phasic and tonic pain. Tonic pain was induced by a pressure cuff on the non-dominant arm, simulating sustained discomfort. Interestingly, tonic pain did not affect the perceived intensity or avoidance of phasic pain. However, it significantly reduced movement velocity and pineapple collection rate, interpreted as a reduction of motivational vigor. A temporal decision model incorporating vigor cost successfully captured these effects.

Concomitant EEG recordings showed that tonic pain was associated with reduced alpha and beta power in parietal and temporal areas. Phasic pain ratings and decision values distinctively correlated with skin conductance responses.

Overall, these findings indicate that phasic and tonic pain have distinct and dissociable motivational effects.

Strengths:

This is an ambitious study that provides a quantitative dissociation of the roles of phasic and tonic pain in adaptive behavior, by integrating ecological neuroscience, motivational theory, and computational modeling. The use of immersive VR combined with a free-operant foraging task offers a more ecologically valid context to study pain-related behavior compared to traditional paradigms. Furthermore, the study employs a multimodal approach by combining behavioral data, computational frameworks, physiological signals, and EEG. In particular, one of the main strengths of the study is the use of sophisticated computational modeling to capture phasic and tonic pain effects. The experiment codes are available on GitHub, increasing reproducibility.

Weaknesses:

The main limitations of this article are that it provides insufficient detail on VR implementation. The design of the VR environment is, at this stage, under-described. Crucial information is missing, such as the number of pineapples per block, timing precision, details on how motion is mapped to the virtual movement, etc. This aspect strongly limits the reproducibility of the experiments. A second limitation lies in the lack of clarity regarding the study hypotheses. Although two overarching hypotheses can be inferred, they are not explicitly formulated. To this end, it is unclear which analyses were merely exploratory, especially for physiological and EEG outcomes.

In Experiment 2, the reduction in vigor during tonic pain could plausibly reflect attentional load rather than pain per se. As recognized by the authors, there is no control condition involving an innocuous salient stimulus to rule out non-specific effects of distraction. Perhaps a tonic non-painful but salient somatosensory stimulus (e.g., a strong vibrotactile stimulus applied on the same arm) could have been used as a control stimulus.

Reviewer #2 (Public review):

Summary:

The study investigated the distinct roles of phasic and tonic pain in adaptive behavior. Phasic pain was proposed to function as a teaching signal, promoting avoidance of further injury, while tonic pain was hypothesized to support recuperative behavior by reducing motivational vigor. This hypothesis was tested using an immersive virtual reality (VR) EEG foraging task, in which participants harvested fruit in a forest environment. Some fruits triggered brief phasic pain to the grasping hand, which in turn reduced the likelihood of choosing those fruits. Concurrently, tonic pressure pain applied to the contralateral upper arm was associated with reduced action velocities. The authors employed a free-operant computational framework to quantify how phasic and tonic pain modulate motivational vigor and decision value. Importantly, model parameters were found to correlate with EEG responses, providing neurophysiological support for the hypothesized functional distinctions.

Strengths:

Overall, this study aims to address an important topic and is generally well written.

Weaknesses:

Two critical issues require clarification or justification.

First, phasic pain was induced using electrical stimulation, which typically elicits somatosensory evoked potentials (SEPs). These responses may not reflect pain-specific processes and thus complicate interpretation. This issue bears directly on the study's conclusions, especially when discussing interactions between phasic and tonic pain. For example, tonic pain is known to reduce perceived intensity or cortical responses to phasic pain stimuli delivered elsewhere on the body - an effect not expected for SEPs elicited by electrical stimuli.

Second, additional control experiments are necessary to rule out alternative explanations. For instance, the authors are suggested to deliver phasic pain to the contralateral arm (e.g., at 1-2 Hz), which might also reduce action velocity. Similarly, tonic pain applied to the grasping hand should be tested to disentangle hand-specific effects.

Reviewer #3 (Public review):

Summary:

This study investigates how phasic and tonic pain modulate behaviour in a free-operant foraging paradigm. The authors apply a computational modeling approach to the behavioural data to quantify the decision value of phasic pain, as well as the degree to which tonic pain reduces motivational vigour. EEG assessments showed, e.g., reduced signal power at alpha and beta frequencies in tonic pain conditions compared to no-tonic-pain conditions, but no association between these neural measures and motivational vigour. The authors conclude that tonic and phasic pain serve different motivational functions, with phasic pain acting as a punishment signal promoting avoidance and tonic pain reducing motivational vigour.

Strengths:

The experimental paradigm is highly innovative. Assessing human behaviour in a naturalistic yet highly controlled setting represents a promising approach to pain research. Notably, assessing pain magnitude implicitly, via its motivational value, offers insights about the overall pain experience that are not usually accessible via common pain ratings.

Weaknesses:

Despite these strengths, the manuscript would benefit significantly from more precise definitions of key concepts and an overall clearer, more coherent presentation of its main arguments. The writing, in its current form, often presents claims that are too vague or insufficiently connected with the experimental findings. Moreover, certain aspects of the computational modeling and statistical analysis appear flawed or inadequately justified.

Reviewer #1 (Public review):

Summary

This manuscript describes a haemogenic gastruloid system that the authors claim recapitulates early mouse embryonic development to produce sequential waves of yolk sac and AGM-like haematopoiesis, with spatial and temporal accuracy. The model claims to reproduce mouse development to 'beyond' the E9.0 stage and apply its use to the aetiology of infant leukaemia.

Strengths

Gastruloids models are useful systems for studying early embryonic development, recapitulating aspects of gastrulation, anteroposterior regionalisation and somitogenesis. Gastruloid models that specifically mimic particular regions of the embryo could provide insights into how these regions form during development.

Weaknesses

There are a couple of major issues with this manuscript that I feel need to be addressed.

Firstly, the authors acknowledge that the proportion of blood cells that are produced by their haemogenic gastruloid system is very low - there are fewer than 2% of either blood or endothelium produced. The authors argue however, that this is because they have developed a hematopoietic organoid that captures much more of the essence of the developing embryo and therefore has a broader tissue representation and a more relevant spatial representation.

In order to prosecute this argument, this reviewer needs to understand how the differentiation protocol achieves this end, ie what is notable about the combination of factors and other media components. Also, they need to know what the evidence is to support this claim, in other words, what are the tissues that make up the organoid and is it truly representative of what would be expected in a developing embryo over this time. Does it pass from epiblast to primitive streak and then to cells of the germ layers? And how do haemGXs at different times map onto the developing mouse embryo?

Secondly, the point is repeatedly made by the authors that the distinction between non-engrafting yolk sac hematopoiesis and AGM-like hematopoiesis from which repopulating HSCs first derive is not really possible without spatial cues. This is really not true. It has been shown by a number of investigators, and summarised in a recent review (Abuhantash et al 2021), that the expression of HOXA cluster genes - most prominently HOXA9 - clearly distinguishes AGM-derived, from yolk sac derived cells. In this manner, it is evident from the UMAP provided that the is no HOXA9 expressed in either endothelium or blood cells. This argues very strongly against the proposition that AGM-type hematopoiesis is generated. Indeed, given the duration of the organoid culture of only 9 days (216hrs), it would be highly unlikely that development would even reach the stage of AGM hematopoiesis (E11.5 in the mouse), even with a 1:1 concordance between embryonic time and in vitro differentiation. Finally, if there is recapitulation of the normal pattern of embryogenesis, it would be expected that there would be a prominent phase of yolk sac hematopoiesis antedating AGM-associated hematopoiesis, which should be observed in the haemGx.

I feel that these are major conceptual points that need to be addressed in this manuscript.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors describe the development of a new hemogenic gastruloid (hGX) system, which they claim recapitulates the sequential generation of various hematopoietic cell types. A key proposed advantage of this system is its ability to more faithfully model the spatiotemporal emergence of hematopoietic progenitors within a physiologically relevant niche, as compared to existing in vitro platforms. While the authors provide some initial characterisation and demonstrate the utility of the system in studying infant leukemia, the presented data are not fully conclusive and fall short of robustly supporting several of their key assertions.

Strengths:

The development of a novel in vitro system to model hematopoietic development is innovative and could potentially address important limitations of existing platforms.

Weaknesses:

The characterization of the hematopoietic progenitors generated by the hGX system is not fully convincing. The evidence supporting the emergence of late yolk sac (YS) progenitors, including lymphoid cells, and AGM-like pre-hematopoietic stem cells (pre-HSCs), is incomplete and relies heavily on transcriptomic profiling and a limited set of markers.

The identification of lymphoid or pre-HSC-like populations is primarily inferred from scRNA-seq data. The lack of robust functional validation (e.g., lymphoid differentiation assays or long-term repopulation experiments) significantly weakens the manuscript's main claims.

In the revised manuscript, the authors incorporate single-cell RNA-seq analyses indicating that their cells resemble AGM-derived endothelial-to-hematopoietic transition (EHT) populations. However, they do not test whether these cells might more closely resemble YS-derived EHT, which remains an unresolved and critical question. Additionally, the claim in line 263 that Cluster 8 (CD45⁺ cells at 192-216 h) expresses lymphoid markers is not clearly supported by the provided supplemental data (Supplemental File S1-S2).

While the authors respond that they did not claim to generate bona fide HSCs, they do state at the end of the Introduction (lines 116-121) that their system captures AGM hematopoiesis. The current data do not support this conclusion and instead suggest that the system recapitulates the generation of multipotent lymphoid progenitors (MLPs) akin to those found in the YS.

The engraftment data presented are not particularly convincing. It is unclear why the analysis was terminated at 8 weeks post-transplant, especially given that a minimum of 12 weeks is generally required to meaningfully assess the presence of pre-HSCs or bona fide HSCs with long-term repopulating potential.

Given the uncertainties discussed above, the interpretability of the MNX1 overexpression study is limited.

The authors could have more directly tested their claim of capturing multiple hematopoietic waves by performing kinetic analyses of colony-forming potential, with the expectation that more multipotent colonies would emerge at later time points. Additionally, isolating and characterizing the potential of hemogenic endothelium at different time points corresponding to the putative waves would have strengthened their conclusions. In the absence of such data, it remains unclear whether the system recapitulates sequential waves of hematopoiesis or merely reflects the progressive maturation of cells originating from a single wave.

Reviewer #3 (Public review):

The authors present a revised version of their manuscript (Ragusa et al.) describing a hemogenic gastruloid (haemGx) model, used to investigate stages of blood production in vitro and for modeling a rare type of infant leukemia. The revisions address several major concerns raised during the initial round of review, and new data have been provided that overall improve the clarity and rigour of the study. In particular, the additional flow cytometry, single-cell RNA-seq analyses, and benchmarking against in vivo datasets help, to some extent, to substantiate the claims of developmental relevance of haemGx to yolk sac (YS)- and AGM-like hematopoietic waves. Nonetheless, some issues remain, particularly regarding the claims of short-term engraftment, novelty of the model, and the extent to which AGM-like HSPC are truly captured.

Major Points:

(1) The authors have clarified the novelty of their haemGx protocol relative to existing gastruloid models, including the importance of the Activin A pulse and protocol extension to 216h. Flow cytometry and scRNA-seq analyses support the emergence of endothelial and hematopoietic populations with dynamic marker expression. However, direct side-by-side comparisons with previously published protocols (e.g., Rossi et al., 2022) remain limited. The claim of "spatio-temporal accuracy" should be more cautiously phrased.

(2) The characterization of the identity of the hematopoietic waves generated in the haemGx system has been improved in the revised manuscript. Flow cytometry analysis now includes CD31/CD34 co-expression in CD41+ and CD45+ subsets, and scRNA-seq re-clustering supports two hematopoietic waves with distinct marker sets (e.g., Gata2/Myb vs. Hoxa9/Ikzf1). Projection onto multiple embryonic reference datasets (Hou et al., Zhu et al., Thambyrajah et al.) is a valuable addition. The case for YS-like EMP and AGM-like HSPC precursors is reasonably made, though further functional distinctions (e.g., lineage output differences) would strengthen the claims.

(3) The authors have now provided additional evidence for low-level engraftment following adrenal implantation of whole haemGx. Although technically demanding, this in vivo result remains marginal and should be interpreted with caution. Crucially, this still does not demonstrate HSC-level repopulation capacity. The revised manuscript has softened the claims accordingly, now referring to "progenitor" activity rather than "pre-HSC." We agree that this adjusted claim is more suitable, though the reproducibility of this experiment is still unclear.

(4) The MNX1 overexpression experiments are generally convincing in showing early expansion of a putative HE-to-EMP-like population and transcriptional resemblance to MNX1-r AML. However, the evidence for transformation is still solely based on in vitro data and lacks any evidence of in vivo leukaemia engraftment. The ability to perturb the system would add translational value to the haemGx platform, although future studies are needed to better define transformation dynamics and leukemogenic progression.

Reviewer #1 (Public review):

Summary:

The manuscript aims to elucidate the impact of a prophage within the genome of Shewanella fidelis on its interaction with the marine tunicate Ciona robusta. The authors made a deletion mutant of S. fidelis that lacks one of its two prophages. This mutant exhibited an enhanced biofilm phenotype, as assessed through crystal violet staining, and showed reduced motility. The authors examined the effect of prophage deletion on several genes that could modulate cyclic-diGMP levels. While no significant changes were observed under in vitro conditions, the gene for one protein potentially involved in cyclic-diGMP hydrolysis was overexpressed during microbe-host interactions. The mutant was retained more effectively within a one-hour timeframe, whereas the wild-type (WT) strain became more abundant after 24 hours. Fluorescence microscopy was used to visualize the localization patterns of the two strains, which appeared to differ. Additionally, a significant difference in the expression of one immune protein was noted after one hour, but this difference was not evident after 23 hours. An effect of VCBC-C addition on the expression of one prophage gene was also observed.

Strengths:

I appreciate how the authors integrate diverse expertise and methods to address questions regarding the impact of prophages on gut microbiome-host interactions. The chosen model system is appropriate, as it allows for high-throughput experimentation and the application of simple imaging techniques.

Weaknesses:

My primary concern is that the manuscript primarily describes observations without providing insight into the molecular mechanisms underlying the observed differences. It is particularly unclear how the presence of the prophage leads to the phenotypic changes related to bacterial physiology and host-microbe interactions. Which specific prophage genes are critical, or is the insertion at a specific site in the bacterial genome the key factor? While significant effects on bacterial physiology are reported under in vitro conditions, there is no clear attribution to particular enzymes or proteins. In contrast, when the system is expanded to include the tunicate, differences in the expression of a cyclic-diGMP hydrolase become apparent. Why do we not observe such differences under in vitro conditions, despite noting variations in biofilm formation and motility? Furthermore, given that the bacterial strain possesses two prophages, I am curious as to why the authors chose to target only one and not both.

Regarding the microbe-host interaction, it is not clear why the increased retention ability of the prophage deletion strain did not lead to greater cell retention after 24 hours, especially since no differences in the immune response were observed at that time point.

Concerning the methodological approach, I am puzzled as to why the authors opted for qPCR instead of transcriptomics or proteomics. The latter approaches could have provided a broader understanding of the prophage's impact on both the microbe and the host.

Comments on revisions:

While the authors were able to solve some of my issues, I see that other questions were not tackled.

Reviewer #2 (Public review):

Summary:

In the manuscript, "Prophage regulation of Shewanella fidelis 3313 motility and biofilm formation: implications for gut colonization dynamics in Ciona robusta", the authors are experimentally investigating the idea that integrated viruses (prophages) within a bacterial colonizer of the host Ciona robusta affect both the colonizer and the host. They found a prophage within the Ciona robusta colonizing bacterium Shewanella fidelis 3313, which affected both the bacteria and host. This prophage does so by regulating the phosphodiesterase gene pdeB in the bacterium when the bacterium has colonized the host. The prophage also regulates the activity of the host immune gene VCBP-C during early bacterial colonization. Prophage effects on both these genes affect the precise localization of the colonizing bacterium, motility of the bacterium, and bacterial biofilm formation on the host. Interestingly, VCBP-C expression also suppressed a prophage structural protein, creating a tripartite feedback loop in this symbiosis. This is exciting research that adds to the emerging body of evidence that prophages can have beneficial effects not only on their host bacteria but also on how that bacteria interacts in its environment. This study establishes the evolutionary conservation of this concept with intriguing implications of prophage effects on tripartite interactions.

Strengths:

This research effectively shows that a prophage within a bacterium colonizing a model ascidian affects both the bacterium and the host in vivo. These data establish the prophage effects on bacterial activity and expand these effects to the natural interactions within the host animal. The effects of the prophage through deletion on a suite of host genes are a strength, as shown by striking microscopy.

Weaknesses:

Unfortunately, global transcriptomics of the bacteria and the host during colonization by the prophage-containing and prophage-deleted bacteria (1 hour and 24 hours) would be suggested to better understand the tripartite interactions.

Impact:

The authors are correct to speculate that this research can have a significant impact on many animal microbiome studies, since bacterial lysogens are prevalent in most microbiomes. Screening for prophages, determining whether they are active, and "curing" the host bacteria of active prophages are effective tools for understanding the effects these mobile elements have on microbiomes. There are many potential effects of these elements in vivo, both positive and negative, this research is a good example of why this research should be explored.

Context:

The research area of prophage effects on host bacteria in vitro has been studied for decades, while these interactions in combination with animal hosts in vivo have been recent. The significance of this research shows that there could be divergent effects based on whether the study is conducted in vitro or in vivo. The in vivo results were striking. This is particularly so with the microscopy images. The benefit of using Ciona is that it has a translucent body which allows for following microbial localization. This is in contrast to mammalian studies where following microbial localization would either be difficult or near impossible.

Comments on revisions:

I am satisfied with the great amount of work that went into the comments provided by the reviewers. The figure presentations are more compelling for the story, and this latest revision is a very interesting read that should be considered for future microbiome studies.

Reviewer #3 (Public review):

In this manuscript, Natarajan and colleagues report on the role of a prophage, termed SfPat, in the regulation of motility and biofilm formation by the marine bacterium Shewanella fidelis. The authors investigate the in vivo relevance of prophage carriage by studying the gut occupation patterns of Shewanella fidelis wild-type and an isogenic SfPat- mutant derivative in a model organism, juveniles of the marine tunicate Ciona robusta. The role of bacterial prophages in regulating bacterial lifestyle adaptation and niche occupation is a relatively underexplored field, and efforts in this direction are appreciated.

Comments on revisions:

The authors have addressed my main concerns. While some responses remain somewhat ambiguous or defer key clarifications to future studies, I appreciate that not everything can be resolved within a single manuscript.

Reviewer #1 (Public review):

Summary:

The study by Li and coworkers addresses the important and fundamental question of replication initiation in Escherichia coli, which remains open, despite many classic and recent works. It leverages single-cell mRNA-FISH experiments in strains with titratable DnaA and novel DnaA activity reporters to monitor DNA activity peaks versus size. The authors find oscillations in DnaA activity and show that their peaks correlate well with the estimated population-average replication initiation volume across conditions and imposed dnaA transcription levels. The study also proposes a novel extrusion model where DNA-binding proteins regulate free DnaA availability in response to biomass-DNA imbalance. Experimental perturbations of H-NS support the model validity, addressing key gaps in current replication control frameworks.

Strengths:

I find the study interesting and well conducted, and I think its main strong points are:

(1) the novel reporters obtained with systematic synthetic biology methods, and combined with a titratable dnaA strain.

(2) the interesting perturbations (titration, production arrest, and H-NS).

(3) the use of single-cell mRNA FISH to monitor transcripts directly.

The proposed extrusion model is also interesting, though not fully validated, and I think it will contribute positively to the future debate.

Weaknesses and Limitations:

(1) A relevant limitation in novelty is that DnaA activity and concentration oscillations have been reported by the cited Iuliani and coworkers previously by dynamic microscopy, and to a smaller extent by the other cited study by Pountain and coworkers using mRNA FISH.

(2) An important limitation is that the study is not dynamic. While monitoring mRNA is interesting and relevant, the current study is based on concentrations and not time variations (or nascent mRNA). Conversely, the study by Iuliani and coworkers, while having the drawback of monitoring proteins, can directly assess production rates. It would be interesting for future studies or revisions to monitor the strains and reporters dynamically, as well as using (as a control) the technique of this study on the chromosomal reporters used by Iuliani et al.

(3) Regarding the mathematical models, a lot of details are missing regarding the definitions and the use of such models, which are only presented briefly in the Methods section. The reader is not given any tools to understand the predictions of different models, and no analytical estimates are used. The falsification procedures are not clear. More transparency and depth in the analysis are needed, unless the models are just used as a heuristic tool for qualitative arguments (but this would weaken the claims). The Berger model, for example, has many parameters and many regimes and behaviors. When models are compared to data (e.g., in Figure 2G), it is not clear which parameters were used, how they were fixed, and whether and how the model prediction depends on parameters.

(4) Importantly, the main statement about tight correlations of peak volumes and average estimated initiation volume does not establish coincidence, and some of the claims by the authors are unclear in these respects (e.g., when they say "we resolve a 1:1 coupling between DnaA activity thresholds and replication initiation", the statement could be correct but is ambiguous). Crucially, the data rely on average initiation volumes (on which there seems to be an eternally open debate, also involving the authors), and the estimate procedure relies on assumptions that could lead to biases and uncertainties added to the population variability (in any case, error bars are not provided).

(5) The delays observed by the authors (in both directions) between the peaks of DnaA-activity conditional averages with respect to volume and the average estimated initiation volumes are not incompatible with those observed dynamically by Iuliani and coworkers. The direct experiment to prove the authors' point would be to use a direct proxy of replication initiation, such as SeqA or DnaN, and monitor initiations and quantify DnaA activity peaks jointly, with dynamic measurements.

(6) While not being an expert, I had some doubt that the fact that the reporters are on plasmid (despite a normalization control that seems very sensible) might affect the measurements. Also, I did not understand how the authors validated the assumptions that the reporters are sensitive to DnaA-ATP specifically. It seems this assumption is validated by previous studies only.

Overall Appraisal:

In summary, this appears as a very interesting study, providing valuable data and a novel hypothesis, the extrusion model, open to future explorations. However, given several limitations, some of the claims appear overstated. Finally, the text contains some self-evaluations, such as "our findings redefine the paradigm for replication control", etc., that appear exaggerated.

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 Figure 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.

Reviewer #1 (Public review):

Summary:

The authors describe the degradation of an intrinsically disordered transcription factor (LMO2) via PROTACs (VHL and CRBN) in T-ALL cells. Given the challenges of drugging transcription factors, I find the work solid and a significant scientific contribution to the field.

Strengths:

(1) Validation of LMO2 degradation by starting with biodegraders, then progressing to chemical degrades.

(2) interrogation of the biology and downstream pathways upon LMO2 degradation (collateral degradation and apoptotic markers).

(3) Cell line models that are dependent/overexpression of LMO2 vs LMO2 null cell lines.

(4) CRBN and VHL-derived PROTACs were synthesized and evaluated.

Weaknesses:

(1) The conventional method used to characterize PROTACs in the literature is to calculate the DC50 and Dmax of the degraders, I did not find this information in the manuscript.

(2) The proteomics data is not very convincing, and it is not clear why LMO2 does not show in the volcano plot (were higher concentrations of the PROTAC tested? and why only VHL was tested and not CRBN-based PROTAC?).

(3) The correlation between degradation potency and cell growth is not well-established (compare Figure 4C: P12-Ichikawa blots show great degradation at 24 and 48 hrs, but it is unclear if the cell growth in this cell line is any better than in PF-382 or MOLT-16) - Can the authors comment on the correlation between degradation and cell growth?

(4) The PROTACs are not very potent (double-digit micromolar range?) - can the authors elaborate on any challenges in the optimization of the degradation potency?

(5) The authors mentioned trying six iDAb-E3 ligase proteins; I would recommend listing the E3 ligases tried and commenting on the results in the main text.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

The overall degradation is relatively weak, and the mechanism of potential collateral degradation is not thoroughly evaluated. In addition, experiments comparing the authors' prior work with their anti-LMO2 iDAb or Abl-L are lacking, which would improve our understanding of the potential advantages of a degrader strategy for LMO2.

Reviewer #1 (Public review):

M. tuberculosis exhibits metabolic flexibility, enabling it to adapt to various environmental stresses, including antibiotic treatment. In this manuscript, Serafini et al. investigate the metabolic remodeling of M. tuberculosis used to survive iron-limited conditions by employing LC-MS metabolomics and 13C isotope tracing experiments. The results demonstrate that metabolic activity in the oxidative branch of the TCA cycle slows down, while the reductive branch is reverted to facilitate the biosynthesis of malate, which is subsequently secreted.

Overall, this study is experimentally well-designed, particularly the use of 13C isotope tracing to monitor TCA cycle remodeling under iron-limited conditions. The findings are valuable as they offer potential new targets for antibiotics aimed at non-replicating M. tuberculosis occurring in the hosts. However, despite these strengths, the reviewer has concerns regarding the mechanistic basis underlying the observed metabolic remodeling and its role in M. tuberculosis pathogenesis.

Major Comments:

The authors argue that iron starvation is a physiologically relevant stressor encountered by M. tuberculosis post-infection. Using Erdman and H37Rv strains under DFO conditions, Erdman loses viability, whereas H37Rv maintains it. Nonetheless, both strains exhibit similar metabolic remodeling in the TCA cycle based upon metabolomics and isotope tracing data. The authors should clarify the specific metabolic adaptations in H37Rv that enable it to sustain viability under DFO conditions.

The authors report no significant changes in NAD/NADH and ATP levels in H37Rv and Erdman exposed to DFO conditions. They observe TCA cycle remodeling, particularly the reversal of the reaction between OAA and MAL, catalyzed by malate dehydrogenase, an enzyme that uses NAD+ and NADH as cofactors. The directionality of this reaction likely depends on the relative levels of NAD+ and NADH. Additionally, other dehydrogenases, such as pyruvate DH and aKG DH, also require NAD+/NADH cofactors. In Figure 1I, NAD+ and NADH levels are monitored only at day 3 post-exposure to DFO conditions. Since Erdman loses viability after 2-3 weeks, the authors should include measurements of NAD+, NADH, and ATP levels at weekly intervals up to 3 weeks. Furthermore, glycine levels - which are linked to NAD+ recycling via the conversion of glyoxylate - should be measured under both HI and DFO conditions as an indirect indicator of the NAD+/NADH ratio.

In Figure 2A, it is unclear why a 100-fold accumulation of aKG does not correspond proportionally to the accumulation of (iso)citrate.

The authors state that fumarate, aKG, (iso)citrate, malate, and pyruvate are secreted under DFO conditions. While the secretion of aKG and pyruvate makes sense, given their marked intracellular accumulation, it is puzzling why (iso)citrate, malate, and fumarate are secreted even though there are no changes in their intracellular abundance. To rule out the possibility that these metabolites are released due to bacterial lysis rather than active secretion, the authors should analyze the 13C-labeled fractions of these metabolites in the culture filtrate using the M. tuberculosis culture in media containing 13C glycerol.

To validate the role of the PCK-mediated reductive TCA cycle in malate biosynthesis and secretion under DFO conditions, the authors should generate a malate dehydrogenase (MDH) knockdown strain, considering that MDH is essential, and examine the 13C labeling patterns and NAD/NADH under DFO conditions.

The authors also observe decreased GABA abundance and overall 13C labeling in DFO conditions, suggesting that the GABA shunt is the primary route for Succinate biosynthesis under DFO conditions. Thus, it is strongly recommended that the authors perform a 13C glutamate tracing experiment to directly track labeling in aKG and GABA shunt metabolites, providing more definitive evidence for the involvement of the GABA shunt.

Reviewer #2 (Public review):

Summary:

The authors investigated the effect of prolonged iron limitation (which does stop growth but does not lead to cell death), altering central metabolism in M. tuberculosis. The major tool they used is metabolomics combined with stable isotope tracing. They show that the Krebs cycle is still active, despite the fact that it is dependent on some iron-dependent enzymes. They show that carbon flux through the oxidative branch of the Krebs cycle is stalled, resulting in the accumulation of metabolites, such as malate and alpha-ketoglutarate, that are partially secreted. Apparently, the carbon flux from glycolysis is partially diverted to the reductive branch of the Krebs cycle. This is not achieved by using the glyoxylate shunt but probably through the GABA shunt. This unprecedented split of the Krebs cycle and malate secretion allows a continuous flow of carbon through the core of carbon metabolism, overcoming the metabolic stalling triggered by iron starvation.

Strengths:

Novel insight into the central metabolism of a major pathogen and its adaptation to iron starvation. Carefully conducted experimentation. The paper ends with a clear and helpful model.

Weaknesses:

The authors show some surprising and important findings, but they would need a little more effort to really substantiate these. Especially the role of the GABA shunt should be genetically tested, as they did for ICL and the glyoxylate shunt.

Also, dataset 1 is not very convincing, it is only based on transcriptomics and shown with up or down; this is not a strong base for major conclusions. As a minimum, one would want actual differences, preferably on the protein level, where it really counts.

Reviewer #1 (Public review):

To elucidate the mechanisms and evolution of animal biomineralization, Voigt et al. focused on the sponge phylum-the earliest branching extant metazoan lineages exhibiting biomineralized structures-with a particular emphasis on deciphering the molecular underpinnings of spicule formation. This study centered on calcareous sponges, specifically Sycon ciliatum, as characterized in previous work by Voigt et al. In S. ciliatum, two morphologically distinct spicule types are produced by set of two different types of cells that secrete extracellular matrix proteins, onto which calcium carbonate is subsequently deposited. Comparative transcriptomic analysis between a region with active spicule formation and other body regions identified 829 candidate genes involved in this process. Among these, the authors focused on the calcarine gene family, which is analogous to the Galaxins, the matrix proteins known to participate in coral calcification. The authors performed three-dimensional structure prediction using AlphaFold, examined mRNA expression of Calcarin genes in spicule-forming cell types via in situ hybridization, conducted proteomic analysis of matrix proteins isolated from purified spicules, and carried out chromosome arrangement analysis of the Calcarin genes. Based on these analyses, it was revealed that the combination of Calcarin genes expressed during spicule formation differs between the founder cells-responsible for producing diactines and triactines-and the thickener cells that differentiate from them, underscoring the necessity for precise regulation of Calcarin gene expression in proper biomineralization. Furthermore, the observation that 4 Calcarin genes are arranged in tandem arrays on the chromosome suggests that two rounds of gene duplication followed by neofunctionalization have contributed to the intricate formation of S. ciliatum spicules. Additionally, similar subtle spatiotemporal expression patterns and tandem chromosomal arrangements of Galaxins during coral calcification indicate parallel evolution of biomineralization genes between S. ciliatum and aragonitic corals.

Strength:

The study presents detailed and convincing insights that point to parallel evolution of biomineralization in calcitic sponges and corals. This is supported by a comprehensive analysis employing a wide range of experimental approaches including protein tertiary structure predictions, gene expression profiling during calcification (RNA seq and Whole-mount in situ hybridization), and chromosomal sequence analysis.

An integrative research approach, encompassing transcriptomic, genomic, and proteomic analyses as well as detailed FISH.

High-quality FISH images of Calcarin genes, along with a concise summary clearly illustrating their expression patterns, is appreciated.

It was suggested that thickener cells originate from founder cells. To the best of my knowledge, this is the first study to demonstrate trans-differentiation of sponge cells based on the cell-type specific gene expression, as determined by in situ hybridization.

Overall, this is a high-quality piece of work that proposes a compelling scenario for biomineralization.

Weaknesses:

I found no significant weakness in this manuscript.

Comments on revisions:

The authors have addressed all of the questions and recommendations from the prior review.

Reviewer #2 (Public review):

Summary:

This paper reports on the discovery of calcarins, a protein family that seems involved in calcification in the sponge Sycon ciliatum, based on specific expression in sclerocytes and detection by mass spectrometry within spicules. Two aspects stand out: (1) the unexpected similarity between Sycon calcarins and the galaxins of stony corals, which are also involved in mineralization, suggesting a surprising, parallel co-option of similar genes for mineralization in these two groups; (2) the impressively cell-type-specific expression of specific calcarins, many of which are restricted to either founder or thickener cells, and to either diactines, triactines, or tetractines. The finding that calcarins likely diversified at least partly by tandem duplications (giving rise to gene clusters) is a nice bonus.

Strengths:

I enjoyed the thoroughness of the paper, with multiple lines of evidence supporting the hypothesized role of calcarins: spatially and temporally resolved RNAseq, mass spectrometry, and whole-mount in situ hybridization using CISH and HCR-FISH (the images are really beautiful and very convincing). The structural predictions and the similarity to galaxins are very surprising and extremely interesting, as they suggest parallel evolution of biomineralization in sponges and cnidarians during the Cambrian explosion by co-option of the same "molecular bricks".

Weaknesses:

I did not detect any major weakness, beyond those inherent to working with sponges (lack of direct functional inhibition of these genes) or with fast-evolving gene families with complex evolutionary histories (lack of a phylogenetic tree that would clarify the history of galaxins/calcarins and related proteins).

Comments on revisions:

I am fully satisfied with the revision, and notably with the new Figure 3 which is now extremely informative and readable. Congratulations on a job well done.

Reviewer #3 (Public review):

Summary:

Voigt et al. present a comprehensive study exploring the molecular mechanisms and evolution of biomineralization in the calcareous sponge Sycon ciliatum. Using a multi-omics approach, including comparative transcriptomics, proteomics, genomic analyses, and high-resolution in situ hybridization, the authors identify 829 candidate biomineralization genes, with a special focus on the calcarin gene family. These calarains, structurally analogous to galaxin in stony corals, show cell-type- and spicule-type-specific expression patterns, revealed through meticulous FISH imaging. Chromosomal analysis further uncovers that several calcarin genes are arranged in tandem arrays, suggesting diversification via gene duplication and neofunctionalization. Notably, the study finds striking parallels between the calcarins of S. ciliatum and galaxins of aragonitic corals in terms of gene arrangement, tertiary structure predictions, and expression dynamics, pointing to a remarkable case of parallel evolution during the emergence of biomineralized skeletons in early metazoans.

Strengths:

The study is methodologically robust, integrating transcriptomic, proteomic, and genomic data with detailed cell biological analysis.

High-quality, carefully annotated FISH images convincingly demonstrate the spatial expression patterns of calcarins.

Novel evidence of sponge cell trans-differentiation is presented through cell-type-specific gene expression.

The comparative perspective with coral galaxins is well-executed and biologically insightful, supported by structural predictions and chromosomal data.

Figures and supplementary materials are thoughtfully revised for clarity and accessibility, addressing reviewer feedback.

Weaknesses:

Direct functional validation of calcarin roles in biomineralization is lacking, a limitation acknowledged by the authors and inherent to sponge models.

The evolutionary history of calcarins and galaxins remains only partially resolved due to challenges in reconstructing phylogenies of fast-evolving gene families.

Some initial figure annotations and definitions (e.g., "radial tube") required clarification, although these were addressed in revision.

Overall, the work significantly advances our understanding of biomineralization´s molecular basis and its parallel evolution in early diverging metazoans.

Comments on revisions:

I would like to thank the authors for addressing all my comments/suggestions. I am OK with the revised version of the manuscript

Reviewer #1 (Public review):

Summary:

Migration of the primordial germ cells (PGCs) in mice is asynchronous, such that leading and lagging populations of migrating PGCs emerge. Prior studies found that interactions between the cells the PGCs encounter along their migration routes regulates their proliferation. In this study, the authors used single cell RNAseq to investigate PGC heterogeneity and to characterize their niches during their migration along the AP axis. Unlike prior scRNAseq studies of mammalian PGCs, the authors conducted a time course covering 3 distinct stages of PGC migration (pre, mid, and post migration) and isolated PGCs from defined somite positions along the AP axis. In doing so, this allowed the authors to uncover differences in gene expression between leading and lagging PGCs and their niches and to investigate how their transcript profiles change over time. Among the pathways with the biggest differences were regulators of actin polymerization and epigenetic programming factors and Nodal response genes. In addition, the authors report changes in somatic niches, specifically greater non-canonical WNT in posterior PGCs compared to anterior PGCs. This relationship between the hindgut epithelium and migrating PGCs was also detected in reanalysis of a previously published dataset of human PGCs. Using whole mount immunofluorescence, the authors confirmed elevated Nodal signaling based on detection of the LEFTY antagonists and targets of Nodal during late stage PGC migration. Taken together, the authors have assembled a temporal and spatial atlas of mouse PGCs and their niches. This resource and the data herein provide support for the model that interactions of migrating mouse PGCs with their niches influences their proliferation, cytoskeletal regulation, epigenetic state and pluripotent state.

Overall, the findings provide new insights into heterogeneity among leading and lagging PGC populations and their niches along the AP axis, as well as comparisons between mouse and human migrating PGCs. The data are clearly presented, and the text is clear and well-written. This atlas resource will be valuable to reproductive and developmental biologists as a tool for generating hypotheses and for comparisons of PGCs across species.

Strengths:

(1) High quality atlas of individual PGCs prior to, during and post migration and their niches at defined positions along the AP axis.

(2) Comparisons to available datasets, including human embryos, provide insight into potentially conserved relationships among PGCs and the identified pathways and gene expression changes.

(3) Detailed picture of PGC heterogeneity.

(4) Valuable resource for the field.

(5) Some validation of Nodal results and further support for models in the literature based on less comprehensive expression analysis.

Reviewer #2 (Public review):

Summary:

Germ cells go on to form sperm and eggs and are, therefore, critical for the survival of the species. This work addresses the question of how 'leading' and 'lagging' PGCs differ, molecularly, during their migration to the mouse genital ridges/gonads during fetal life (E9.5, E10.5, E11.5), and how this is regulated by different somatic environments encountered during the process of migration. E9.5 and E10.5 cells differed in expression of genes involved in canonical WNT signaling and focal adhesions. Differences in cell adhesion, actin cytoskeletal dynamics were identified between leading and lagging cells, at E9.5, before migration into the gonads. At E10.5, when some PGCs have reached the genital ridges, differences in Nodal signaling response genes and reprogramming factors were identified. This last point was verified by whole mount IF for proteins downstream of Nodal signaling, Lefty1/2. At E11.5, there was upregulation of genes associated with chromatin remodeling and oxidative phosphorylation. Some aspects of the findings were also found to be likely true in human development, established via analysis of a dataset previously published by others.

Strengths:

The work is strong in that a large number of PGCs were isolated and sequenced, along with associated somatic cells. The authors dealt with the problem of a very small number of migrating mouse PGCs by pooling cells from embryos (after ascertaining age matching using somite counting). 'Leading' and 'lagging' populations were separated by anterior and posterior embryo halves and the well-established Oct4-deltaPE-eGFP reporter mouse line was used.

The most likely possible use of this fundamental information will be the incorporation of some aspects (e.g. the potential importance of Nodal signaling) into protocols for generation of in vitro derived gametes.

Reviewer #3 (Public review):

Summary:

The migration of primordial germ cells (PGCs) to the developing gonad is a poorly understood yet essential step in reproductive development. Here, the authors examine whether there are differences in leading and lagging migratory PGCs using single-cell RNA sequencing of mouse embryos. Cleverly, the authors dissected embryonic trunks along the anterior-to-posterior axis prior to scRNAseq in order to distinguish leading and lagging migratory PGCs. After batch corrections, their analyses revealed several known and novel differences in gene expression within and around leading and lagging PGCs, intercellular signaling networks, as well as number of genes upregulated upon gonad colonization. The authors then compared their datasets with publicly available human datasets to identify common biological themes. Altogether, this rigorous study reveals several differences between leading and lagging migratory PGCs, hints at signatures for different fates among the population of migratory PGCs, and provides new potential markers for post-migratory PGCs in both humans and mice. While many of the interesting hypotheses that arise from this work are not extensively tested, these data provide a rich platform for future investigations.

Strengths:

The authors have successfully navigated significant technical challenges to obtain a substantial number of mouse migratory primordial germ cells for robust transcriptomic analysis. Here, the authors were able to collect quality data on ~13,000 PGCs and ~7,800 surrounding somatic cells, which is ten times more PGCs than previous studies.

The decision to physically separate leading and lagging primordial germ cells was clever and well-validated based on expected anterior-to-posterior transcriptional signatures.

Within the PGCs and surrounding tissues, the authors found many gene expression dynamics they would expect to see both along the PGC migratory path as well as across developmental time, increasing confidence in the new differentially expressed genes they found.

The comparison of their mouse-based migratory PGC datasets with existing human migratory PGC datasets is appreciated.

The quality control, ambient RNA contamination elimination, batch correction, cell identification and analysis of scRNAseq data were thorough and well-done such that the new hypotheses and markers found through this study are dependable.

The subsetting of cells in their trajectory analysis is appreciated, further strengthening their cell terminal state predictions.

Weaknesses:

There were a few validation experiments within this study. For one such experiment, whether there is a difference in pSMAD2/3 along the AP axis is unclear and not quantified, as was nicely done for Lefty1/2.

Reviewer #2 (Public review):

In this manuscript, Tiedje and colleagues longitudinally track changes in parasite number across four time points as a way of assessing the effect of malaria control interventions in Ghana. Some of the study results have been reported previously, and in this publication, the authors focus on age-stratification of the results. Malaria prevalence was lower in all age groups after IRS. Follow-up with SMC, however, maintained lower parasite prevalence in the targeted age group but not the population as a whole. Additionally, they observe that diversity measures rebound more slowly than prevalence measures. This adds to a growing literature that demonstrates the relevance of asymptomatic reservoirs.

Overall, I found these results clear, convincing, and well presented. There is growing interest in developing an expanded toolkit for genomic epidemiology in malaria, and detecting changes in transmission intensity is one major application. As the authors summarize, there is no one-size-fits-all approach, and the Bayesian MOIvar estimate developed here has the potential to complement currently used methods, particularly in regions with high diversity/transmission. I find its extension to a calculation of absolute parasite numbers appealing as this could serve as both a conceptually straightforward and biologically meaningful metric.

As the authors address, their use of the term "census population size" is distinct from how the term is used in the population genetics literature. I therefore anticipate that parasite count will be most useful in an epidemiological context where the total number of sampled parasites can be contrasted with other metrics to help us better understand how parasites are divided across hosts, space, and time.