Reviewer #3 (Public review):

Summary:

The authors construct a computational chimera by attaching a C. elegans connectome to a Drosophila body biomechanical model and use deep reinforcement learning to link neural activity to motor output. The model is able to produce walking, but is considered a priori to be scientifically meaningless, and the work is treated as a cautionary tale in complex interpretation layers unconstrained by experiment or data.

Strengths:

In a period of increasing excitement about linking AI and neuroscience, I respect very much that the authors work through a nontrivial example of nonsense results, rather than just making a theoretical case. It offers a clear and memorable existence proof that matching outputs of complex trained networks does not mean the internal dynamics are themselves emulated.

Weaknesses:

While I understand that the work was a rapidly produced comment on science-by-press-release, the message seems too important to be treated in quite as pithy a manner as it is. In particular, because the computational experiment is so memorable, it is worth getting the message right to avoid a set of readers who take from it that they should dismiss this category of neuroAI wholesale (which the authors absolutely do not imply!).

One part of me reads this work and thinks that by intentionally wiring up the sensory feedback in a particularly nonsense way, the authors have just made a bad model, and sometimes bad models can still generate sensible outputs, especially when expressive models are optimized to fit those sensible outputs. But I think this work is trying to say something more specific than this, and I would like it to be a bit clearer about that. The authors do a fairly good job of sharing a view about what should have been done instead, but this message would benefit from having some more concrete suggestions to avoid a simplistic interpretation. A few thoughts:

(1) It's not entirely obvious to me that the model is "scientifically meaningless." As the authors know extremely well, Drosophila walking is thought to be driven by simple central pattern generators coupled to leg-specific implementations. The C. elegans neural circuit is clearly capable of producing rhythmic activity as well. A version of the model they ran could have identified biologically valid rhythmic activity in the C elegans circuit and mapped it via the DRL to the right locomotor behavior in the fly. While this would not be a good emulation of the fly, it's not a concept devoid of scientific meaning. Similarly, if the ANN is converting a rhythmic signal to coordinated walking, it's not obvious to me that there aren't useful principles to identify in how it achieves this - it's basically the equivalent of that post-CPG circuitry, no?

(2) Similarly, is this outcome going to be relatively specific to rhythmic behaviors? I suspect that it would be harder to push the C. elegans connectome to produce some behaviors than others - for example, adding in visual navigation and other motor patterns, or a ring attractor. Rhythmic circuits arise in many places, and both biology and dynamical systems tell us they can come from numerous configurations of elements and interactions.

(3) Aside from the nonsense formulation of the problem, I would have liked to know more about what the authors should have done to know their model was useless. Put another way, if the authors hadn't known that their model was bad from the beginning (e.g., if they had stuck a fly brain in the middle of it, gotten the sensory feedback right), would there have been some way to figure out if it was meaningful or meaningless based on the results of the trained model itself?

Reviewer #1 (Public review):

This study by Alonso-Calleja and colleagues aimed to determine whether TGR5 regulates hematopoiesis and the bone marrow microenvironment under steady-state conditions and following transplantation. The revised manuscript substantially improves upon the original submission by providing additional characterization of TGR5 expression in hematopoietic and stromal populations, incorporating analyses in female mice, and expanding the investigation of bone marrow adipose tissue under aging and high-fat diet conditions. These additions more convincingly establish TGR5 as a regulator of bone marrow adipose tissue and stromal composition.

Major strengths of the study include the comprehensive characterization of the bone marrow adipose tissue phenotype across multiple experimental settings and the demonstration that TGR5 deficiency consistently alters the stromal compartment. The strongest and most convincing aspect of the work is the identification of TGR5 as a regulator of bone marrow adipose tissue and the bone marrow microenvironment. These findings provide useful insights into how metabolic signaling pathways influence the hematopoietic niche.

However, the evidence supporting a direct role for TGR5 in hematopoietic recovery following transplantation remains limited. Although reciprocal transplantation experiments and peripheral blood recovery analyses strengthen the manuscript, the conclusions regarding hematopoietic regeneration continue to rely largely on correlative observations. The study does not directly demonstrate that expansion of adipocyte progenitors is responsible for the enhanced recovery phenotype, nor does it establish improved regeneration of hematopoietic stem or progenitor cells within the bone marrow. Overall, the revised work addresses many of the concerns raised in the original review and provides useful new insights into the regulation of the bone marrow microenvironment by TGR5. Nevertheless, the conclusions regarding hematopoietic recovery should remain appropriately tempered, as the mechanistic basis linking the stromal phenotype to enhanced regeneration has not been directly demonstrated.

Reviewer #2 (Public review):

Summary:

The authors showed the expression of TGR5 in hematopoietic compartments and that loss of TGR5 doesn't impair steady-state hematopoiesis. Notably, TGR5 knockout significantly decreases BMAT, increase the APC population and accelerate the recovery upon bone marrow transplantation.

Strengths:

The role of TGR5 is interesting.

Weaknesses:

Additional mechanistic studies would further strengthen the work and provide deeper insight into how TGR5 regulates the bone marrow microenvironment.

Reviewer #1 (Public review):

Summary:

This manuscript combined rat fMRI, optogenetics and electrophysiology to examine the large-scale functional network of the olfactory system as well as its alteration in an aged rat model.

Strengths:

Overall methodology is very solid and the results provided an interesting perspective on large-scale functional network perturbation of the olfactory system.

Weaknesses:

The biological relevance and validation of the current results can be improved.

Comment on revised version.

Authors made satisfactory revision and I have no further comments.

Reviewer #2 (Public review):

Summary:

Ma and colleagues presented a study on the characterization of brain-wide spatio-temporal impact of olfactory cortical outputs. They take advantage of multi-modal techniques on rats: fMRI, optogenetics and electrophysiology. In addition, they used cutting-edge analytical techniques and modeling to support and interpret their data. The main findings of the study are:

(1) Neurons in Olfactory Bulb (OB) predominantly activate primary olfactory network regions, while stimulation of OB afferents in Anterior Olfactory Nucleus (AON) and Piriform Cortex (Pir) primarily orthodromically activates hippocampal/striatal and limbic networks, respectively.<br /> (2) Non-specified adaptation or habituation mechanisms may play a significant role in modulating olfactory outputs over subsequent fMRI sessions.

(3) Artificially induced aging in rats induces profound modification in the functional interaction between olfactory cortices and multiple brain regions.

The results on AON are of particular interest because of the lack of functional information on this region, despite its recognized importance in shaping OB output and behavior (odor localization tasks).

Strengths:

The manuscript is very accurate. The figures are well-crafted, clear and provide much information with the most appropriate plots and graphics. The study's amount and data quality are remarkable, and the experimental size adequately addresses the scientific questions. I particularly appreciated the details in the description of the methods regarding the missing data and the size of the different animal groups. The supplementary data complete the leading figures and provide information at a single animal level.

Weaknesses:

(1) One of the main reasons the Piriform Cx is understudied in rodents is because of the proximity to air, which creates artifacts in fMRI images. This issue becomes more critical at ultra-high magnetic fields, but I would expect it also at 7T. One main achievement of this study is, indeed, the acquisition of fMRI data from Piriform, and this point should be highlighted by showing raw functional data from a rat. The best would be if an fMRI data sample for a rat, no matter which stimulation, is shared on a public repository, like Zenodo or similar. I am curious to check the quality of the BOLD data from such an 'enormous' field of view, particularly in the OB, with a single-shot sequence. Also, the visual inspection of raw data is essential to appreciate how many 0.5 x 0.5 x 1 mm voxels fit into AON, and others analyzed small brain structures, like the amygdala, etc. Was the amygdala entirely visible in BOLD, or did the air in the ear channel make an artifact partially shadowing it?

(2) Surprisingly, the only information missing in the methods is the post-surgery period and the time between two consecutive fMRI sessions. How much time was accorded to rats to recover from the surgeries, and what time interval between two scans? This information is crucial for interpreting the decrease in most BOLD responses in subsequent recordings. The supposed adaptation should fit into the known time frames for odor adaptation. Usually, fast adaptation does not last for days (and it should be measured within a single experiment: is it the case?), while for long-lasting adaptation the stimulus (odor or opto) should be maintained constantly ON. This does not seem to be the case in this study. The hypothesis, alternative to adaptation, of a less efficient light activation, for example, due to gliosis around the fiber tips, should be discarded with more evidence than the preservation of OB > Pir responses or acknowledged in the manuscript.

(3) The D-galactose experiments were conducted only after administering the aging molecule, with no baseline/reference data on the same animals. Then, comparisons were made with healthy rats, but the two groups not only can be discriminated with respect to D-galactose administration but also with age (10 VS 18 weeks). A control group for 18-weeks-old rats with no D-galactose treatment would better compare the D-galactose effect and avoid any potential bias from group comparisons of rats at different ages. Do you confirm that D-galactose was injected into each rat 56 times/days in a raw, or am I mistaken?

The updated version of the manuscript partially addresses the flaws of the original submission. Here are my general concerns:

(1) Overall, the revised version comes with a few modifications/additions and no new data. Apart from a new correlation analysis, the improvements are mainly discursive, often non-convincing, justifications of the authors' choices. This may reflect a lack of interest in a publication that, in the meantime, lost its original peer-review value. However, it should be acknowledged that the Authors made an effort to partially address the concerns raised by the reviewers.

(2) My main concern was the quality of fMRI recordings. In the revised version, the Authors provided a new figure with an example single-mouse fMRI data. However, the depicted regions of interest (ROIs) mostly cover the brain spots that I expected to be the most impacted by the BOLD artifacts caused by the proximity of the air and the big field-of-view. In addition, these ROIs do not appear to match the mouse anatomy shown above the functional data. As an example, the EPI images in the OB are almost entirely covered by the colored mask. The feeling is that the fMRI data was indeed poor, as I worried, and the lack of any public repository of raw data reinforces that feeling. To make this point clear: I do not think the findings are not true, but poor fMRI data quality might have hidden more insightful results and does not foster the use of fMRI to monitor the olfactory pathway, which lowers the impact of this article.

Reviewer #2 (Public review):

Summary:

Overall, the authors aimed to provide evidence that clarifies two debates within metacognition research concerning subjective confidence reports:

(1) Does the post-decision confidence report arise from the same process that drives the initial decision, or does a separate, independent process support confidence computation?

(2) How do we stop accumulating evidence for the post-decision confidence report? Is it based on a self-imposed time limit, or on accumulated evidence crossing a boundary?

For the investigation, the authors constructed four models (2 × 2 factorial) to compare each combination of processes to account for random-dot motion tasks data with speed/accuracy manipulations. The models are generally embedded in the drift diffusion model framework, retaining basic parameters such as drift rate, boundary separation, starting point, and non-decision time, while adding linearly collapsing boundaries to model the initial choice. For the single vs. distinct process dimension, the difference lies in whether post-decision evidence accumulation is referenced to the endpoint of the initial decision process or restarts from a new, freely estimated starting point. For the time- vs. boundary-based stopping rule dimension, the key difference is that post-decision evidence accumulation stops either at a deadline sampled from a normal distribution or when the accumulated evidence hits a collapsing boundary.

Based on model comparison, the boundary-based stopping rule clearly outperformed the time-based stopping rule. However, models with the boundary-based stopping rule performed similarly regardless of whether a single or distinct process was used. Here, the authors drew additional insights from EEG recordings during the task, focusing on the centro-parietal positivity (CPP), which has been proposed as a neural correlate of the evidence accumulation process. By simulating evidence accumulation trajectories (with additional assumptions) and comparing the patterns of those trajectories with observed ERP waveforms, the authors argued that the single-process model provided a better match to the CPP findings and was therefore preferred. This was specifically demonstrated by the model's superior ability to match the pre-response CPP amplitude differences conditioned on the post-decision confidence-related variables.

Strengths:

(1) The authors translated existing theories into computational models of decision-making and systematically compared different cognitive processes by assessing model fits to the data. This provides strong evidence supporting the idea that post-decision confidence reports could be better explained by boundary crossing rather than a self-imposed deadline to respond.

(2) Beyond model evidence, an important result is that CPP amplitude predicted confidence before the initial choice was reported, which is a unique prediction of the single-process model. The use of EEG as an independent validation measure provided additional evidence in favour of this model.

(3) Combining points 1 and 2, this study successfully addressed the two key debates with solid evidence to favour one theory over another.

(4) Another strength of this study is the data quality. The high number of trials provided a strong foundation for model inference as well as ERP analysis. The experiment also contained a speed-accuracy manipulation to evaluate model performance across diverse situations.

Weaknesses:

I have two main concerns around the modelling work and neural analyses, which in my opinion could have limited the interpretation of the findings. My responses here will be lengthier, but this reflects the nature of the modelling work rather than implying stronger criticisms than those suggested by the strengths discussed above.

(1) There are a few assumptions in the models that lack psychologically meaningful interpretations, and this study placed more effort into model comparison while lacking discussion of the cognitive processes inferred from parameter estimates.

To start, I think some of the parameterisations were not properly justified. For the boundary models, it is not very clear why the upper and lower boundaries were different and collapsed at different rates for confidence decisions, given that a single boundary parameter and collapse rate were used for the initial decision. This allows more flexible shifts in the model's predictions of confidence ratings without strong justification. Specifically, it is unclear why the boundary-single model has such an implementation while the boundary-distinct model was only equipped with one boundary parameter (a2, compared to a2up and a2down).

Similarly, the inclusion of metacognitive noise creates another layer of flexibility in the predictions of confidence ratings. In most existing evidence accumulation models with a diffusion process, noise comes from two sources: within-trial noisy evidence accumulation and across-trial variability (e.g., drift rate variability). Beyond these, such models almost always assume that the decision is made deterministically once the evidence reaches a specific boundary. The inclusion of metacognitive noise here sounds more like a noisy decision-to-action mapping.

I also have similar doubts about allowing the non-decision time parameter for confidence accumulation in the distinct model to be negative. The authors argued that confidence accumulation may begin during initial evidence accumulation. However, this is a flawed implementation, as the non-decision time was simply added to the evidence accumulation time rather than being incorporated within it. Allowing negative non-decision times may achieve similar predictions, but it is ad hoc.

The inclusion of a collapsing boundary mechanism in the post-decision confidence accumulator helped the model reach more diverse levels of accumulated evidence and ultimately improved predictions of confidence ratings. However, no strong argument is presented for this implementation beyond the observation that the model performs worse without it. The collapsing boundary mechanism has traditionally been interpreted as reflecting a sense of urgency. For the boundary models, I noticed that the collapse rate of the upper boundary differed significantly between speed and accuracy conditions, which is consistent with the urgency interpretation. Overall, I would like to see more discussion of the specific model mechanisms included by the authors, interpreted in light of parameter estimates.

(2) While the ERP findings provided external evidence and validation of the modelling results, I find the simulation practices not particularly useful and potentially misleading for naive readers. Specifically, the authors attempted to draw a parallel between patterns of simulated evidence accumulation traces and observed CPP waveforms. While the CPP has received support as a correlate of the evidence accumulation process, the DDM is by no means a neural model capable of generating predictions of neural observations. To my understanding, the superior fit of the boundary-single model was primarily due to the fact that pre-response CPP amplitude predicts post-decision confidence ratings. Therefore, as the boundary-distinct model did not connect the two phases of evidence accumulation, it would fail to account for this observation. I think this point could be clearly demonstrated without the need to introduce additional assumptions into the model simulations in order to directly compare averaged trajectories with averaged ERP waveforms. While the authors did not explicitly claim otherwise, this approach creates an illusion that the model can mechanistically account for ERP data. I would like the authors to provide explicit clarification on this point.

Appraisal:

Overall, the authors have provided solid evidence in support of their research aims. The findings contribute to longstanding debates with insights from model mechanisms and neural findings that should not be overlooked by future studies on this topic. This study also offers a good starting point for future model development and refinement in broader contexts of confidence reporting, such as paradigms involving simultaneous initial decisions and confidence judgements. The high quality of the behavioural and EEG data will make a valuable contribution to future research.

Reviewer #1 (Public review):

Summary:

A central question in decision-making is whether confidence arises from the same evidence-accumulation process that led to the choice or from a separate second process. The manuscript addresses this question using a random-dot motion (RDM) task, with an initial choice followed by a confidence report, and a time-pressure manipulation on the confidence report. The authors fit a family of four models differing on two dimensions: the source of confidence (a continuation of the choice accumulator vs. a distinct accumulation process) and the stopping rule (time-based vs. boundary-based). The models are fitted to behavior, and then their simulated dynamics are compared with the CPP signal associated with evidence accumulation (not included in the fit). The main methodological contribution is the use of a neural signal to decide between the two boundary-based models, which are nearly indistinguishable behaviorally. The authors conclude that boundary-based stopping rules outperform time-based rules, and that the Boundary-Single model reproduces the certainty-related CPP dynamics better than the Boundary-Distinct model, supporting a single accumulation process for both choice and confidence.

Strengths:

The main strength is methodological: using a neural signal (CPP) as an out-of-sample arbiter between the two boundary-based models (which are nearly equivalent behaviorally). This addresses the model-identifiability problem: when behavior does not distinguish between competing models, a neural signal not included in the fit can provide external evidence.

The work is also thorough and empirically rigorous. The finding that CPP amplitude predicts subsequent certainty ratings several hundred milliseconds before the initial choice response is statistically supported and interesting in its own right, independent of its interpretation. The small-N/many-trials design (2,160 trials per participant) is well suited to capturing change-of-mind trials and is accompanied by thorough model and parameter recovery, with the generating model recovered in most simulations. The study also includes preregistration of the design and planned behavioral and neural analyses, and it replicates a previously reported behavioral pattern.

Weaknesses:

The central conclusion may well be correct, but in my view the current evidence does not fully support it. The behavioral comparison between the competing models did not resolve the issue and in fact showed a slight preference for the distinct-process model, so the weight of the decision falls mainly on the neural comparison.

(1) The neural evidence supports access to pre-choice variation, but does not necessarily establish a single continuous process. The critical difference between the single and distinct models is the presence of trial-to-trial variation in the evidence at choice commitment, which is inherited by the confidence process: the single model preserves it (the confidence process begins from the trial-specific endpoint of the choice DV), while the distinct model does not inherit it (z2 fixed). Thus, the neural test examines whether confidence has access to the state of evidence accumulation before the choice response but does not establish that the same accumulation process must continue seamlessly to determine confidence. The authors also test a model in which a distinct post-choice process is initialized using information from the endpoint of the choice process. However, its failure rules out one specific implementation of information transfer, rather than the broader class of two-stage models in which a distinct confidence process receives a readout of the decision state and may additionally integrate other metacognitive cues.

(2) A broader class of two-stage metacognitive models that combine a decision-state readout with additional cues is not tested. The distinct-process model implemented here captures only a limited subset of possible metacognitive architectures. Its starting point is independent of the choice-DV endpoint, and it remains driven by the available sensory evidence, potentially within a different reference frame. It does not capture second-order accounts in which confidence combines a readout of the decision state with additional cues not explicitly represented in the choice accumulator, such as response time, motor conflict, subjective stimulus clarity or attention. Moreover, the paradigm used in the manuscript provides few independently manipulated information sources that would allow such a process to be identified separately from the choice accumulator.

(3) The neural distinction between models is not quantitatively evaluated. The claim that the Boundary-Single model better reproduces the CPP rests primarily on a visual/qualitative comparison, without a numerical measure of the discrepancy between each model and the neural data. Although the plotted β coefficients (Figure 4D) provide estimates of the neural effects, no scalar summary of model-to-CPP fit is reported. Because the neural comparison carries much of the inferential weight, a quantitative comparison would strengthen the conclusion substantially.

(4) The fitted parameters raise a question about the post-choice process. Confidence responses were very fast (average median confidence RT = 243 ms), with no minimum RT threshold. The Boundary-Single model estimated a post-choice drift rate more than twice the pre-choice rate (1.69 vs. 0.75). In the model, confidence accumulation begins at commitment, before the initial response is executed. The measured confidence RT therefore does not capture the full accumulation window, which also includes the motor delay. Still, the sharp rise in drift rate at commitment requires an explanation. It may reflect stronger weighting of the still-available evidence, as the authors suggest. But it is also consistent with a fast readout of a decision state that was largely set before the initial response. The authors could compare the current model against a readout model, or against an intermediate variant that permits only a brief, bounded period of post-choice accumulation.

(5) The participant-level distribution of model preferences would clarify the comparison. Models were fit separately per participant and condition, but the comparison is summarized as mean BIC (a small average preference for Boundary-Distinct). A mean cannot distinguish two different situations: a consistent, weak preference for one model across all participants, versus a mixture in which some participants clearly favor one architecture and others the opposite. Reporting the distribution of per-participant ΔBIC and the number of participants favoring each model would clarify the result.

Reviewer #1 (Public review):

This manuscript describes a multi-modal study of associative learning and memory in humans, that combines scalp EEG, pupillometry and behavioral analysis to explore the construct of mnemonic prediction errors (MPEs), in terms of their relationship to attention and cognitive control. Across two pooled studies, participants performed associative memory tasks in which they learned the relationship between a cue word (action verb) and subsequent picture (animate or inanimate) with a strong vs. weak (4 or 1 repetitions) encoding manipulation. At test, participants were encouraged to generate a prediction following the cue word to determine whether the subsequently presented picture was a match or mismatch. The timecourse of pupillary responses during match decisions were decomposed using temporal principal components analysis, which identified 6 distinct and overlapping processes. Some of the components (PC3/PC4) exhibited sensitivity to both the strength and mismatch conditions, as well as behavior (both RT and accuracy) and retrieval success on the subsequent trial. Furthermore, relationships were also observed between pupillary responses (specifically for PC4) and both frontal theta and posterior alpha power measures obtained from scalp EEG in Experiment 2, as well as for frontal theta and subsequent learning from mismatch stimuli (assessed using subsequent memory findings from a surprise recognition test). The authors suggest the findings indicate that MPEs elicit changes in attention, arousal and cognitive control which impact subsequent learning.

Strengths:

This manuscript has many strengths, including a clever study design, thoughtful integration of multiple neurocognitive measures, and a set of rigorous and technically sophisticated analyses, which reveal a large set of relationships among the measures and behavior. The findings demonstrating brain/physiology-behavior relationships are particularly important, in that they point to potential functional consequences of MPEs.

Weaknesses:

The technical proficiency and complexity of the study and analysis also presents a clear limitation and challenge for interpretation. It is likely that readers, even those that are quite knowledgeable about the methods, constructs, and questions being addressed will often struggle (as this reviewer did) to keep the large set of findings in mind and gain understanding of how they all fit together.

Indeed, it seems like there many threads running together in the paper which make it challenging to find the through-line of the key findings. The authors do address some of the key questions motivating the paper in the Introduction, but the results are somewhat ambiguous with regard to the primary question of the study as to whether the detection of MPEs leads to interaction among cognitive control, attention, and arousal. To their credit, the authors tackle this question through both cross-correlation and formal mediation analyses, and summarize these in diagrammatic figures (Figure 3, Figure 6). Yet it is not resolved whether the results represent a clear answer pointing to independence, or rather a lack of statistical power, or ill-resolved formulation of the mediational relationship. In particular, the cross-correlation suggests that posterior alpha suppression in response to MPEs does precede frontal theta, yet this indirect relationship does not explain the variation in trial-by-trial RTs on mismatches. This suggests a potential model misspecification.

In addition to the primary interaction issue mentioned above (between cognitive control, attention & arousal), the Introduction lays out a number of claims: 1) that pupil size will be more sensitive to strong than weak MPEs; 2) that MPE-linked increases in attention (indexed with posterior alpha suppression) and arousal (indexed with pupil size) will be linked to learning; and 3) MPE learning will vary as a function of prediction strength. Given the focus on learning, it is somewhat surprising that learning is not included in the mediation models. As the authors indicate in the Discussion, the use of trial-by-trial RT variation to drive the mediation model might be problematic, given that the RTs are sensitive to a range of factors beyond mnemonic prediction strength and also are under competing pressures (longer for mismatches than matches, due to surprise-linked slowing, but also faster following stronger rather than weaker mnemonic predictions). Thus, an alternative possibility might be to use trial-by-trial recognition of mismatches as the outcome variable in mediation models rather than trial-by-trial RT as the independent variable.

A large component of the results (Sections 2 and 3) is devoted to analyses of cue-linked pupil and EEG processes that putatively reflect mnemonic predictions (i.e., occurring before picture probes are presented and match/mismatch detection, i.e., MPEs occur). Yet these Results and the subsequent pupillary PCA components (PC1 and PC5) that are elicited are not well-integrated with the primary themes of the paper or the causal hypotheses. One finding that does seem to figure prominently (in that it is mentioned in Abstract, Introduction & Discussion) relates to the amount of attention allocated to the mnemonic prediction generation. Yet this finding is not well emphasized in the Results themselves. Possibly it refers to the negative relationship between posterior alpha during memory retrieval and the magnitude of pupillary PC3 component, described in Section 3. But it was quite challenging to identify amongst the wealth of results described in this Section as well as the others. More generally, the large amount of findings described across all four lengthy Results sections makes it challenging for readers to discern what are the key ones that the authors would like to highlight.

It is recommended that the authors do another pass through the paper to better highlight the most critical findings that they want to emphasize or which are most interpretable from a mechanistic and causal flow perspective and then de-emphasize or move other findings to the Supplemental Materials. Although the authors are to be commended for such a rigorous and comprehensive set of analyses, there are so many of them and findings, that the key points get buried and the reader needs to struggle potentially unnecessarily to identify the key take-away points.

Reviewer #2 (Public review):

Summary:

The authors studied cognitive control and attention in response to mnemonic prediction errors (MPEs): situations in which the external reality violates internal memory-based predictions. The behavioral task first established strong versus weak predictions, and then either confirmed or violated these predictions. The authors examined markers of cognitive control (frontal theta) and attention (posterior alpha suppression, pupil response) while strong and weak predictions were confirmed or violated. They found increased cognitive control (frontal theta) for strong MPEs, which correlated with subsequent memory. Markers of attention (alpha suppression, pupil response) also accompanied strong MPEs but did not correlate with subsequent memory. Pupil response was investigated using an interesting approach that decomposes the response into different components, finding that different components respond earlier or later and show different correlations with MPEs and their strength. The authors also investigated how EEG, reaction time, and pupil responses correlated with one another, providing further insight into the mechanism underlying the response to MPEs. Together, the study points toward multiple control and attention mechanisms involved in MPE response and memory.

Strengths:

The study has a clear behavioral paradigm with multiple measures - behavioral, EEG, and pupillometry that offer an investigation into different aspects of MPE response and memory.

The study is also very comprehensive in looking at multiple phases in processing MPEs: the prediction phase (prior to the violation), the response to MPEs, and subsequent memory of MPEs, all within one study. Specifically, the link between neural mechanisms and subsequent memory is a major advancement, as most prior studies did not include this component. Mechanisms underlying subsequent memory of MPEs are theoretically important, as a primary function of MPEs is to promote learning and memory. As the authors mention, the different neural and pupillary signals are not robustly correlated, suggesting multiple mechanisms underlying MPE detections, which is interesting, offers avenues for future research, and can facilitate a better theory of how MPEs are processed in the brain. Finally, the decomposition of pupil response into different components and their correlation with behavior (RT during match/MPE detection) is interesting.

Weaknesses:

The methods are rigorous, and the data support the claims. The weaknesses are minor and are offered here as avenues for future research.

(1) The relationships the authors find between brain measures and pupil components were largely not specific to mismatches/matches. Thus, the specificity of this relationship is untested.

(2) The results with subsequent memory are important and address a major gap in the field that largely did not relate neural effects of MPE to subsequent memory. However, one major limitation of the study is that the authors did not test memory for matches. I understand the logic of avoiding testing matches. Because matches were repeated more times in the study, it's not a fair comparison and could change participants' overall criterion for old/new decisions. Future research could address this, e.g., by testing weak matches or potentially using a between-subject design.

Comments on revised version.

The authors addressed all my concerns. I appreciate the authors' thoughtful and detailed response.

Reviewer #1 (Public review):

Summary:

The uniqueness of this paper is the study of the formation of temporal binding-dependent memories in the cntnap2 mouse, a long-standing mouse model of autism that has been used to test therapeutic modalities.

Strengths:

I liked the combination of optical recordings and interventions and the backup of primary observations with control experiments.

Weaknesses:

(1) Fiber photometry recordings are too coarse to give salient clues to the underlying mechanism.

(2) Are perturbed pyramidal cells causally responsible for the altered trace? What can be concluded about the possible role of inhibitory interneurons as potential drivers? The observations focus on abnormal regional activity as observed with fiber photometry and manipulated by optogenetics. The authors should state clearly the limits of their conclusions.

(3) I found the "trace" nomenclature confusing. "....in which mice are required to memorize the association between a tone (Conditioned Stimulus) and a mild electric foot-shock (Unconditioned Stimulus), separated by a time interval called Trace (Sellami et al., 2017)." It seems that the conceptual model invokes the creation of an [eligibility] trace, characterized by its progressive disappearance over time. It may be a convention in the field or a matter of language, but it seems perverse to use "trace" to label the time interval rather than the entity that is decaying. If this is an accepted convention going back to Howard Eichenbaum, the authors should cite the paper that first introduced the convention.

(4) I would advocate for the addition of some discussion points for the authors to consider.

a) Is the retention of activity in CA1 related to phenomena at the cellular or subcellular level in CA1 pyramidal cells? I'm thinking of dendritic, delayed, and stochastic CaMKII activation (DDSC) as defined by Yasuda's group or short-term and associative plasticity of calcium dynamics (STAPCD) as delineated by Caya-Bissonette and Beique.

b) Was the optogenetic intervention ever administered in a delayed fashion, capitalizing on the temporal advantages of optogenetics to probe dynamics?

c) Is the newfound reliance on corticostriatal pathways something more than compensation at the behavioral level? Could it be driven in part by the ASD-related genetic changes?

Reviewer #2 (Public review):

The authors investigate the contribution of dorsal CA1 hippocampal dysfunction to cognitive impairments in the Cntnap2 knockout mouse model of autism spectrum disorder. Building on previous evidence implicating the hippocampus in episodic and relational memory processes, they combine trace fear conditioning, fiber photometry, optogenetic manipulation, a relational/declarative memory radial maze task, and cFos mapping to test whether altered CA1 function contributes to deficits in temporal binding and memory flexibility.

The study has several important strengths. First, the work addresses a relatively understudied aspect of autism-related cognition, namely hippocampal-dependent memory processes, whereas much of the literature has focused on social behavior, cortical circuits, or striatal dysfunction. Second, the authors employ multiple complementary approaches that converge on a coherent mechanistic hypothesis. The behavioral data demonstrate a reduced ability of Cntnap2 knockout mice to retain associations across long temporal gaps. Fiber photometry recordings reveal reduced dorsal CA1 activity during conditions that challenge temporal binding, and optogenetic activation of dorsal CA1 neurons during the trace interval is sufficient to rescue memory performance. Together, these findings provide strong support for a causal contribution of dorsal CA1 activity to temporal binding deficits in this model.

The second major strength of the manuscript is the extension of these findings to a more complex hippocampus-dependent memory task. The radial maze experiments indicate that Cntnap2 knockout mice show impaired memory flexibility and a greater reliance on egocentric learning strategies. The accompanying cFos analyses suggest altered recruitment of hippocampal and striatal networks during learning, providing a systems-level framework that may explain the observed behavioral phenotype.

Overall, the main conclusions regarding impaired temporal binding and reduced dorsal CA1 engagement are well supported by the data. The optogenetic rescue experiments are particularly compelling because they move beyond correlation and directly test causality. The manuscript therefore makes a meaningful contribution to our understanding of how hippocampal dysfunction may contribute to cognitive abnormalities associated with autism.

Weaknesses:

Some conclusions are necessarily more inferential than others. In particular, the interpretation that the observed behavioral phenotype reflects a broader shift from hippocampal-dependent declarative memory toward striatum-dependent procedural learning is supported primarily by cFos activity patterns and behavioral strategy measures. While the data are consistent with this interpretation, they do not directly demonstrate a causal reorganization of memory systems. Similarly, although the findings identify a mechanism in the Cntnap2 model, caution is warranted when extrapolating these conclusions to autism spectrum disorder more broadly; but I believe this caution is addressed in the discussion.

Despite these limitations, the study presents a coherent and well-executed body of work that provides novel mechanistic insight into hippocampal contributions to cognitive dysfunction in a widely used autism model. The findings should be of considerable interest to researchers studying hippocampal function, memory systems, and neurodevelopmental disorders.

Reviewer #3 (Public review):

Summary:

The manuscript evaluated behavioral phenotypes in the Cntnap2 knockout mouse using two behavioral paradigms: trace fear conditioning and a radial maze task. The trace fear conditioning training is normal, but memory generalization is impaired. The inflexibility is suggested to be related to low activity in dCA1 neurons, which can be rescued by ChR2. The radial maze task data suggested a similar conclusion. Brain-wide cFos mapping indicated impairments in the Cntnap2 knockout mouse. The brain-wide cFos mapping does not show direct correlations with Cntnap2, limiting the interpretation of these data in the context of this paper.

Strengths:

The behavior data are solid.

Weaknesses:

The underlying mechanism is not fully investigated.

Major points：

(1) The authors should thoroughly check their manuscript as there are many typos in the current version that affect the readability.

(2) In trace fear conditioning, the tone test impairment can be rescued by ChR2. Have the authors tried rescue experiments with Cntnap2? Rescue experiments in the radial maze task are also essential, either with ChR2 or Cntnap2.

(3) The quality of the cFos example image in Figure 3 is too low. The authors should also provide example images for the other brain regions in the supplementary data, if possible.

(4) The causal link between the brain-wide cFos mapping and the Cntnap2 knockout is weak. How to explain the increase of cFos cell densities in some brain regions, but the decrease in others?

Reviewer #1 (Public review):

This manuscript describes a novel downstream mechanism of mTORC1 deficiency-mediated lifespan extension in C. elegans. The authors demonstrated that the biosynthesis and the nuclear hormone receptor daf-12 binding of a bile acid-like hormone, dafachronic acid (DA), are essential for TORC1 mutant raga-1 to extend lifespan. Through RNA-seq and RNAi lifespan screen, they also discovered that a dehydrogenase, dhs-26, which is expressed in the canal-associated neurons, is regulated by DA/daf-12 and downstream of the mTORC1-DA signaling for lifespan extension. The authors also explored the conservation of mTOR/DA/daf-12/dhs-26 signaling in the mouse model. This work demonstrates significant findings that will advance the aging field and will be of interest to many researchers in this field. The conclusions are mostly well supported by data with proper controls.

Some suggestions to strengthen the manuscript include:

(1) Other mTOR activity perturbation or mutants should be used to support some of the core lifespan experiments. It will strengthen the conclusions made from raga-1 mutant only, although there is evidence from TOR RNAi in Figure 1g to support the daf-12 data in Figure 1d.

(2) The authors showed in Figure 1h and 1i that DA supplementation rescued the shortened lifespan of raga-1;daf-9 but not raga-1;daf-12; and also rescued the shortened lifespan of raga-1; dnh-26 in Fig. 5e. Does DA supplementation itself extend lifespan? If its level is increased by mTORC1 inhibition and it is downstream of mTORC1 inhibition, it should theoretically extend lifespan. But from the reported publications, it seems that the DA supplementation lifespan modulation is highly dependent on genetic backgrounds. It will strengthen the conclusions if the authors provide the wild-type condition DA supplementation lifespan data and also related discussions about it.

Reviewer #2 (Public review):

Summary

This manuscript by Schilling et al. presents an important advancement in our understanding of how mTOR signaling regulates organismal aging. While the longevity-promoting effects of reduced mTOR activity have been extensively documented across species, the mechanisms by which mTOR communicates systemic metabolic information to regulate lifespan remain unclear. In this study, the authors provide strong evidence that longevity induced by reduced TORC1 signaling requires the bile acid-like steroid hormone dafachronic acid (DA) and its cognate nuclear receptor DAF-12. Furthermore, through a combination of transcriptomics and functional genomics, they identify the conserved short-chain dehydrogenase DHS-26/DHRS1 as a previously unrecognized downstream effector of this pathway. The work integrates genetics, lifespan analyses, sterol measurements, transcriptomics, proteomics, endogenous genome engineering, and comparative mammalian datasets. The resulting model, in which mTOR influences lifespan through regulation of endocrine steroid signaling, represents a conceptual advance that links nutrient sensing, metabolism, and organismal aging. Although several mechanistic questions remain unresolved, the study is comprehensive, technically rigorous, and likely to be of broad interest to investigators studying aging, metabolism, endocrine signaling, and cellular stress responses.

Strengths:

One of the major strengths of this manuscript is its conceptual novelty. Rather than reinforcing the well-established role of mTOR as a longevity regulator, the study proposes a specific endocrine mechanism that links reduced mTOR activity to increased lifespan through steroid hormone signaling. This advances the field beyond descriptive observations of mTOR-dependent longevity and introduces a model in which bile acid-like hormones function as systemic mediators of nutrient-sensing pathways. The idea that endocrine steroid signaling may serve as a downstream effector of mTOR provides a new perspective on how longevity signals are coordinated at the organismal level.

The genetic evidence supporting this model is particularly strong. In Figure 1, the authors use a series of epistasis experiments to demonstrate that mutations in daf-36, daf-9, and daf-12 suppress lifespan extension in raga-1 mutants. The DA supplementation experiments further strengthen the pathway ordering by rescuing longevity in hormone-deficient backgrounds while failing to restore lifespan in receptor-deficient animals. Importantly, the direct quantification of endogenous DA levels elevates the study by providing biochemical support for the proposed model.

The transcriptomic analyses presented in Figure 2 provide a valuable systems-level perspective on the interaction between mTOR and steroid signaling pathways. The observation that DAF-12 profoundly reshapes the RAGA-1 transcriptional program highlights the importance of steroid signaling in mediating the physiological consequences of reduced mTOR activity. The enrichment of metabolic, lysosomal, and peroxisomal pathways is consistent with established longevity-associated programs and generates a valuable resource for future mechanistic studies.

Figure 3 effectively integrates discovery-driven and hypothesis-driven biology. The authors use transcriptomic information to prioritize candidate genes and then perform a functional genomic screen to identify factors required for RAGA-1-mediated lifespan extension. This approach converges on DHS-26, which subsequently emerges as a central mechanistic component of the study. The progression from transcriptomics to functional validation is well executed.

In Figure 4, the generation of CRISPR-engineered dhs-26 deletion mutants and endogenous tagged reporter strains provides strong validation for DHS-26 function. The demonstration that dhs-26 deletion selectively abolishes RAGA-1-dependent longevity without substantially affecting wild-type lifespan strongly supports its role as a context-dependent mediator of mTOR signaling. Furthermore, the conservation analyses linking DHS-26 to mammalian DHRS1 provide biological context and enhance the broader significance of the findings.

In Figure 5, multiple independent experimental approaches converge on the conclusion that DHS-26 participates in DA-dependent lifespan regulation. The rescue of lifespan by DA supplementation, reductions in DA levels in raga-1;dhs-26 mutants, reporter-based analyses of DAF-12 activity, and proteomic profiling collectively support a mechanistic model. The proposed positive feedback relationship between DA/DAF-12 signaling and DHS-26 is intriguing and offers a plausible explanation for how endocrine signaling may amplify longevity-promoting responses. Finally, the incorporation of mammalian datasets showing regulation of DHRS1 by rapamycin and FXR signaling provides a promising avenue for future studies investigating conservation of this pathway.

Weaknesses:

Despite the many strengths of the study, important mechanistic questions remain unresolved. The most significant limitation is that the precise molecular connection between reduced mTOR activity and increased DA production remains unclear. While the genetic and biochemical data convincingly place DA/DAF-12 signaling downstream of mTOR, the study does not establish whether mTOR regulates DA biosynthesis, degradation, intracellular trafficking, sterol uptake, or hormone availability. The observed increase in endogenous DA levels is statistically significant but relatively modest, and the mechanistic basis for this increase remains speculative. Additional experiments examining sterol flux, enzyme activity, or intracellular sterol trafficking would substantially strengthen the proposed model.

The transcriptomic analyses in Figure 2 are informative but correlative. Because the RNA-sequencing was performed at a single adult time point, it remains difficult to distinguish primary transcriptional responses from secondary adaptive changes. Similarly, while pathway enrichment analyses identify plausible processes, they do not establish direct regulatory relationships. Additional temporal analyses or direct assessment of DAF-12 occupancy at candidate loci would strengthen mechanistic interpretations and help distinguish direct from indirect targets.

A major unresolved question concerns the biochemical function of DHS-26 itself. While the genetic evidence clearly establishes DHS-26 as an important regulator of RAGA-1-mediated longevity, its endogenous substrate and enzymatic activity remain unknown. The manuscript presents evidence linking DHS-26 to sterol metabolism, but direct biochemical characterization is lacking. Thus, the mechanistic model remains somewhat incomplete. Defining the substrates and products of DHS-26 activity would greatly strengthen the study and provide important insight into how this enzyme influences DA availability.

Another area requiring additional clarification is the proposed neuroendocrine role of DHS-26. The expression of DHS-26 in canal-associated neurons is interesting and raises the possibility that these cells participate in systemic longevity regulation. However, the current data do not establish whether DHS-26 functions autonomously within these neurons or whether expression in other cell types contributes to the observed phenotypes. Tissue-specific rescue or depletion experiments would strengthen the neuroendocrine model and help establish physiological sites of action.

Finally, the mammalian data presented in Figure 5 are supportive and suggestive of evolutionary conservation, but they remain correlative. While regulation of DHRS1 expression by rapamycin and FXR signaling is interesting, these observations do not yet demonstrate functional conservation of the longevity mechanism itself. Additional studies directly testing DHRS1 function in mammalian systems will be required before stronger conclusions regarding conservation can be drawn.

In summary, this manuscript provides a significant contribution to the aging field and introduces a model linking mTOR signaling, endocrine steroid hormones, and longevity. The study is comprehensive, technically sophisticated, and supported by multiple complementary approaches. Although some mechanistic questions remain open regarding the precise regulation of DA production, the biochemical function of DHS-26, and the extent of conservation, these limitations represent opportunities for future investigation. Overall, the work substantially advances our understanding of how nutrient-sensing pathways regulate aging and is likely to stimulate considerable interest within the fields of aging biology, metabolism, and endocrine signaling.

Reviewer #3 (Public review):

Summary:

This interesting manuscript provides evidence that the well-established consequences of (reduced) mTOR activity on longevity are, at least in part, mediated by regulation of dafachronic acid (DA) availability and its signalling via its nuclear receptor DAF-12 in C.elegans, with some supporting evidence derived from mouse studies that similar processes may be functional in mammalian systems, i.e., be evolutionarily conserved. Earlier studies by the group have established that DA/DAF-12 signaling promotes adult longevity in several contexts. DA is a bile acid look-alike, and DAF-12 is a homolog of mammalian bile acid-activated nuclear receptors FXR and VDR: recent experimental studies and human cohort studies have indicated a role of (specific) bile acids in mammalian longevity.

The hypothesis that mTOR and DA/DAF-12 signaling interact to modulate longevity in C.elegans is novel and of great potential interest. The hypothesis has rigorously been tested in a series of well-performed experiments employing mutant strains, functional genomic screens, and DA exposures, etc.. It is convincingly demonstrated that DA/DAF-12 does not directly impact mTOR (assayed on AMPK phosphorylation) and acts downstream of the pathway. The short-chain hydrogenase DHS-26 (mammalian homologue DHRS1) was identified as a downstream target and modulator of this mTOR-DA-DAF12 axis by modulating the lifespan of the mTOR regulator raga-1. As the components of this axis are expressed in different cell types of the worms, this finding indicates a neuroendocrine mode of action. Mode of action of DHS-26 appears to be based on modulation of cholesterol and lathosterol, i.e., substrate availability for DA production.

Strengths:

Overall, the manuscript is well-written and builds up the story in a clear fashion. The conclusions are based on solid data and of relevance for ageing research, also because the mechanism identified appears to be evolutionary conserved.

Weaknesses:

No overt weaknesses were identified by this reviewer.

Reviewer #1 (Public review):

Summary:

The authors used FBDD screening to identify numerous compounds interacting with the ORF9b dimer. They expanded the original fragment hit, soaked the derivatives into the crystals and confirmed their binding poses, and showed that the derivatives bind the target with higher affinity. The authors further targeted the ORF9b binding site on TOM70, and used a fluorescence polarization-based (FP) assay to screen a compound library and obtained several hits. Structure-activity relationship (SAR) optimization yielded hit analogs that have higher binding affinity to TOM70.

Strengths:

(1) The study adopted novel drug design strategies, including stabilizing ORF9b homodimer to prevent it from binding TOM70, and blocking ORF9b from binding TOM70 by screening compounds that compete with ORF9b for binding TOM70.

(2) The work established a feasible high-throughput screening assay. This FP-based assay screened ~50,000 compounds, from which two hit compounds were further optimized to yield analogs with higher binding affinity.

Weaknesses:

(1) The study lacks functional assays to evaluate whether the ORF9b-stabilized compounds or TOM70 binding compounds could affect IFN inhibition caused by ORF9b or virus infection.

(2) There is a lack of experimental evidence to reveal the binding mode of lipidated-compounds with ORF9b homodimer.

(3) There is a lack of experimental evidence to reveal the binding mode of HTS hits or analogs for TOM70.

(4) Overall, none of the compounds shown in the paper have promising potency warranting further development; their binding affinity is limited to the micromolar range.

Reviewer #2 (Public review):

Summary:

The authors investigate chemical strategies to disrupt the interaction between the SARS-CoV-2 accessory protein Orf9b and the host mitochondrial receptor Tom70, an interaction implicated in suppression of type-I interferon responses. They employ two discovery approaches: a crystallographic fragment screen against the Orf9b homodimer and a high-throughput fluorescence polarization screen for compounds that compete with Orf9b binding to Tom70. The study identifies fragment-binding hotspots on Orf9b, develops lipidated analogs that stabilize the Orf9b homodimer, and discovers Tom70-binding compounds with low micromolar activity that inhibit Orf9b binding in vitro.

Strengths:

An impressive amount of work using a variety of complementary approaches and methods to validate binding (FP, SPR, and computational modelling and SAR). The combination of crystallographic fragment screening on Orfb9 and HTS on Tom70 provides two independent routes for perturbing the Orf9b-Tom70 interaction. The structural work seems to be of very high-quality. The fragment campaign is extensive, yielding a substantial number of fragment-bound structures and identifying biologically meaningful binding hotspots on Orf9b.

Finally, the screen results in reporting useful chemical starting points. Although potency remains modest, the study provides tractable scaffolds and a clear framework for future optimization.

Weaknesses:

General comment:

(1) Although there is already an incredible amount of data presented, one limitation of this study is the lack of cellular validation - do these drugs enter cells, restore interferon signalling, reduce viral loads, or alter Orfb9 localization?

(2) The logic of locking Orfb9 as a dimer is that the monomer binds Tom70 - thus, a more stable dimer means less monomer. In Figure 2, the Orfb9 homerdimer stabilization by compounds should reduce binding affinity to Tom70. A direct binding experiment measuring reduced Tom70 binding with compound treatment would better strengthen this claim.

Reviewer #3 (Public review):

Summary:

This paper attempts to and succeeds in demonstrating that Orf9b is able to bind small molecules using X-ray fragment screening, SPR and FP assays. Exploration of sites from the fragment screening is performed along with fragment linking with inter-dimer lipid moieties.

Strengths:

The experimental work looks strong and well performed. The interpretation of the data is appropriate and was often validated through orthogonal methods and follow-up compounds. The use of Tom70 to find binders that might disrupt interactions between Orf9b and Tom70 is elegant.

Weaknesses:

The use of Chai-1 to predict co-folded structures with binding molecules was not properly described - no mention of this in the methods. It was not commented on whether the compounds which were found were attempted to be co-crystallised. If they were but negative data was collected (didn't crystallise, didn't diffract or no additional density was found), then this needs to be stated.

Reviewer #1 (Public review):

Summary:

Using sequences of short videos to elicit emotional changes in participants, Malamud and Huys demonstrate how a brief, controlled emotion regulation intervention (distancing) can effectively alter subsequent emotion ratings. A novel computational approach based on state-space models captures the trajectories of emotion ratings and leverages tools from control theory to quantify the intervention's impact on emotion dynamics.

Strengths:

The experiment is well designed and tailored to the computational modeling approach advanced in the paper. It also relies on a selection of previously validated stimuli. Within the constraints of a controlled experiment, the intervention successfully implements a relatively common tool used in psychotherapeutic treatment, supporting its clinical relevance.

The computational modeling is grounded in the well-established framework of dynamical systems and control theory. This foundation offers a conceptually clear formalization, along with powerful quantification tools that go beyond previous, more data-driven approaches.

Overall, this timely study presents a coherent approach that bridges concepts from clinical psychology and computational theory, providing a stepping stone toward more quantified, evidence-based psychological interventions targeting emotion control.

Weaknesses:

A limitation of this study is that the data were acquired online, resulting in some heterogeneity in the measured effects and reduced statistical power when testing for complex interactions. While the current data are sufficiently solid to demonstrate the validity of the general concept and computational approach, future work should aim to replicate these results in a more controlled laboratory setting.

Additionally, the repeated reminders of the distancing instruction during the second phase of the experiment raise questions about the generalizability of the findings to longer-term remediation strategies, as typically implemented in clinical settings.

Reviewer #3 (Public review):

Summary:

The manuscript takes a dynamical systems perspective on emotion regulation, meaning that rather than a simplistic model conceptualising regulation as applying to a single emotion (e.g. regulation of sadness), emotion regulation could cause a shift in the dynamics of a whole system of emotions (which are linked mathematically to one another). This builds on the idea that there are 'attractor states' of emotions between which people transition, governed by both the system's intrinsic characteristics (e.g. temporal autocorrelation of a particular emotion/person) and external driving forces (having a stressful week). Conceptually this is a very useful advance because it is very unlikely that emotions are elicited (or reduced) singly, without affecting other emotions. This paper is a timely implementation of these ideas in the context of a psychotherapeutic intervention, distancing, which participants were trained (randomised) to perform while watching emotion-inducing videos.

The authors' main conclusion is that distancing both stabilises specific emotional patterns and reduces the impact of external video clips. I would consider these results strong and believable, and to have the potential to impact models of emotion regulation as well as the field's broader views on the mechanisms of psychological therapies.

Strengths:

This paper has very many strengths: I would especially note the authors' very-well-matched active control condition and the robustness of their model comparison approach. One feature of the authors' approach in is that they explicitly add noise - not what you typically see in an emotion time-series analysis - which allows for participants to make errors in their own subjective ratings (a reasonable thing to assume); this noise can then be smoothed during filtering. In their model comparison approach, they explicitly test whether a true dynamical system explains emotion change/emotion regulation effect on emotions - demonstrating that both intrinsic dynamics and external inputs were needed to explain subjective emotion. Powerfully, they also used this approach to test the differential effects of the treatment groups (see below).

The main result seems quite robust statistically. Verifying the effects of the distancing intervention on emotion, the authors found an interaction between time (pre- to post-intervention) and intervention group (distancing vs. relaxation) suggesting that distancing (but not relaxation) reduced ratings of almost all emotions. Participants allocated to the distancing intervention also showed decreased variability of emotion ratings compared to those in the relaxation intervention (though note this interaction was not significant).

Using a model comparison approach, the authors then demonstrated that whilst the control group was best-explained by a model that did not change its dynamics of emotions, the active intervention (distancing) group was best-explained by a model that captured both changing emotion dynamics and a changing input weights (influence of the videos) - results confirmed in follow-up analyses. This is convincing evidence that emotion regulation strategies may specifically affect the dynamics of emotions - both their relationships to one another and their susceptibility to changes evoked by external influences.

The authors also perform analyses that suggest their result is not attributable to a demand effect (finding that participants were quicker during the control intervention, which one would expect if they had already decided how to respond in advance of the emotion question). I personally also think a demand effect is unlikely given the robustness of their control intervention (which participants would be just as likely to interpret as a mental health-enhancing training as distancing) and am convinced by the notion that demand effects would be unlikely to elicit their more specific effects on the dynamic quality of emotions.

Weaknesses:

The authors use an active control - a relaxation intervention - which is extremely closely matched with their active intervention (and a major strength). However, there was an additional difference between the groups: "in the group allocated to the distancing intervention, the phrasing of the question about their feelings in the second video block reminded participants about the intervention, stating: "You observed your emotions and let them pass like the leaves floating by on the stream." Therefore, some of the effects of distancing may have also been driven by different emotion regulation strategies, i.e. reappraisal, since this reminder might have evoked retrospective changes in ratings.

An unanswered question is exactly how distancing is producing these effects. As the authors point out, there is a possibility that eye-movement avoidance of the more emotionally-salient aspects of scenes could be changing participants' exposure to the emotions somewhat, which could vary by emotion, as the authors now discuss in their limitations.

Comments on revised version.

The authors have addressed my concerns.

Reviewer #1 (Public review):

Summary:

The study investigates how learning with combined visual and olfactory cues strengthens memory in fruit flies. It demonstrates that pairing colours with odours improves later memory performance, even when only one of the two cues is presented during testing. The authors show that multisensory learning recruits visually responsive Kenyon cells in the mushroom body into memory representations that would otherwise primarily encode odours. Their experiments indicate that the serotonergic DPM neuron links sensory representations that are normally separated, while the APL neuron regulates local GABAergic inhibition of separated learning subcircuits. Together, these findings provide a mechanistic explanation for how a single sensory cue can retrieve a broader memory of a multisensory experience.

Strengths:

A major strength of the paper is its integration of behavioural experiments, targeted neuronal manipulations, and detailed anatomical analysis to address a clear mechanistic question. The findings are supported by multiple complementary experiments showing that multisensory learning enhances memory and recruits visual pathways into olfactory memory representations. Overall, the work provides a coherent mechanistic framework for how multisensory experiences strengthen subsequent memory.

Weaknesses:

A limitation of the paper is that it represents an unusual case, as substantial parts of the broader study were previously published in Nature and subsequently retracted because the physiological findings could not be reproduced. Those physiological experiments would have helped resolve several mechanistic questions raised by the behavioural results and directly test how multisensory information is integrated within the fruit-fly learning circuit. Presenting only the reproducible behavioural and anatomical findings is therefore appropriate and preserves the reliable contribution of the work. Nevertheless, the absence of reproducible physiological evidence makes the mechanistic model less complete and more inferential than it would be in a fully comprehensive study. The conclusions should consequently be framed as a well-supported circuit model rather than a direct demonstration of the underlying physiological processes.

Reviewer #2 (Public review):

Okray et al. identify a novel form of multisensory memory in Drosophila, where pairing reward with a color+odor together gives a stronger memory than color alone or odor alone. Remarkably, this multisensory enhancement occurs even if only one modality is used during testing (i.e. training color+odor, then testing odor alone gives a stronger memory than training odor alone, then testing odor alone), showing that the two modalities are persistently linked following training. The manuscript presents compelling behavioural genetic evidence that the normally visual-selective gamma-d Kenyon cells acquire a functional role in the retrieval of odor memories following odor+color training, and that this occurs via transfer from gamma-main KCs via the serotonergic interneuron DPM.

The key pieces of evidence supporting this conclusion are that olfactory retrieval of multisensory memories requires:

(1) synaptic output from gamma-d KCs during retrieval (but not training);

(2) synaptic output from gamma-main KCs during training and retrieval (whereas it's only required during retrieval, not training, for pure-olfactory memory);

(3) synaptic output from DPM during training and retrieval, and expression of the serotonin receptor 5HT2A in gamma-d KCs.

In the absence of physiological data, the exact nature of the gamma-d KCs' participation in olfactory retrieval following odor+color training remains unclear. For example, do the gamma-d KCs encode the odor identity (i.e., is there an odor-specific pattern of gamma-d KCs activated for a particular odor+color combination), or does their activity provide a general activity boost to other neurons (e.g. gamma-m) that encode odor identity? This will be interesting to address in future studies.

That being said, the behavioural data are clear and back up the authors' conclusion that signaling between KC subtypes via DPM underlies multisensory integration for multimodal memories in the fly mushroom body.

Reviewer #1 (Public review):

Summary:

The authors sought to understand the impact of the decreased expression of the G-protein-coupled receptor GPR34 in Alzheimer´s disease (AD). They analyzed the transcriptional impact of GPR34 deficiency in mice and found that it induced a DAM-like phenotype in control mice and enhanced the DAM signature in the AD model 5xFAD, although it did not result in amyloid plaque clearance or gross changes in microglia or astrocytes. Next, the authors developed an in vitro model of GPR34 deficiency using a CRISPR/Cas9 strategy in human iPSCs to introduce functional mutations that resulted in GPR34 protein deficiency in induced microglial cells. In this model, the authors identified myelin as a ligand of GPR34 and showed that GPR34 deficiency resulted in reduced myelin debris engulfment and transcriptional changes related to lysosomal pathways.

Strengths:

The combined strategy of using in vivo and in vitro models of GPR34 depletion is robust, and the transcriptional analyses are thoroughly performed.

Weaknesses:

The paper´s two main findings related to the lack of GPR34 (enhancement of DAM signature in vivo and reduced myelin engulfment in vitro) are disconnected. At the very least, the authors should discuss what the relevance of myelin clearance in AD is, but the paper would strongly benefit from a more thorough assessment of the impact of GPR34 deficiency in vivo, particularly because no effects on amyloid clearance were observed. The authors could assess whether GPR34-deficient 5xFAD mice have reduced cognitive performance, which, based on their in vitro findings, could be related to the myelin pathology in AD (previously described: see PMID 36284351). The analysis showing reduced myelin content in GPR34-deficient microglia in vitro is superficial and does not allow for identifying whether GPR34 is related to reduced engulfment or increased degradation, which could be related to the changes in the lysosomal gene CD68 identified in vivo. In addition, it would be interesting to compare the transcriptional profile induced by myelin phagocytosis with that of 5xFAD or AD patients, to gain insight into the impact of the signature. Finally, the transgenic approach to delete GPR34 in vivo could have been complemented with experiments with the GPR34 antagonists (YL-365 or S-E49) or agonist (Compound 4B), possibly helping in identifying the source of discrepancy with previous papers showing that GPR34 promotes amyloid clearance.

Reviewer #2 (Public review):

Summary:

Using the 5xFAD model in combination with GPR34 mice, the authors explore the function of microglia in the context of neurodegeneration. Using a broad spectrum of methodology, they show that DAM signatures are increased in KO 5xFAD mice. Using several KO clones of GPR34 KO iMGLs and another set of broad methodologies, the authors show that GPR34 is important for microglia homeostasis,<br /> phagocytosis, specifically of myelin. GPR34 KO iMGLs also show a distinct transcriptional response to myelin. Together, they propose that GPR34 limits microglial activation in neurodegeneration.

Strengths:

All methods are state-of-the-art, and the combination of mouse and human microglia responses is a particular strength.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #1 (Public review):

"Learning is a fundamental source of individuality," by Manna and colleagues, interrogates different sources of variation in individual behavior. The authors place individual flies in a Y-shaped arena, which is a common design in the field, and illuminate the arms of the Y with blue versus green light. They track the color preference of individual animals and also perform operant conditioning, meaning that they teach the fly to avoid a particular color/arm by generating a foot shock when the fly enters that arm. There are a number of things that are impressive about this setup: The authors are able to collect data on thousands of individual flies of many different strain backgrounds, and they demonstrate a strong change in color preference after conditioning. This is nice, because in past papers visual learning ability has been modest and difficult to study. To put a number on it, in this paper animals on average don't show a color preference at the start of the assay, spending around 30% of their time in the one arm illuminated green, and the remaining time in the two arms illuminated blue. After conditioning, the average animal spends only 23% of its time in the green arm.

The authors run 64 animals through the assay for each of 88 wild type strains (maybe? see Major Point 1 below) and see considerable strain-specific (genetic) variation in the change in time spent in the shocked color after conditioning. Some strains show no learning, while others spend <10% of their time in the shocked color after conditioning. They also, I believe, see that some strains have more variability across individuals, which would suggest that some strains have stronger canalization at the development or circuit function level than others-i.e. some genotypes produce more consistent copies of the individual, others less consistent copies. (Or, some genotypes produce robust circuits, and others produce noisy circuits.)

Finally, the authors argue statistically that learning itself increases variability in individual performance. This makes a lot of sense to me intuitively. Learning changes the physical/chemical properties of circuits in the brain, and because it evolves over time and interacts with environmental variables, it seems like it should send different animals down different channels. Or, at a conceptual level, if I learn to play the piano and my sister doesn't (because of some genetic difference between us or something stochastic), this learning experience will cause all sorts of other differences in our behavior as time passes. I also think the authors do have enough data to be able to make this finding. However, the presentation of the argument in this portion of the paper is hard for me to understand, and I am not an expert in statistics, so the strength of the result is difficult for me to evaluate.

Major points:

(1) It's difficult to track through the paper the number of animals tested for different assays. At the beginning, it says N=5632, which works out to 64 flies for each of the 88 DGRP strains. 64 happens to be the number of parallel Y arenas they have. Later in the methods, there's description of more variation within the set of 64 for each strain-two different parent sets per strain, different sexes, conditioned and un-conditioned. And, while the results text focuses on the color learning, the methods discuss additional assays (place learning, multi-day learning).

Given the numbers, does each run of the 64 mazes include all the tested flies of one strain, or are flies of many strains included in each batch? Do different flies do different assays (color, place, multi-day) or do they all do all the assays? Perhaps there is a table including this information already in the supplement, but I recommend making it much clearer in the main results text and methods. While the dataset is large, if it is split over many conditions and/or if batch and genotype confound each other, this will affect the robustness of the results and how strong the conclusions can be.

(2) The data presentation in Figure 1 is elegant and easy to follow, but getting into Figure 2 and subsequently, I get lost in the statistics and have trouble understanding what is being measured. My understanding of the big picture is that while genetics and individual randomness contribute a lot to behavior, the evidence for learning as an amplifier of individuality is that variance in behavior among animals of the same strain increases over time in the conditioned group (i.e. the group that is doing the most learning, or a specific kind of learning), but not in the control group. This idea is illustrated in the flattening distributions in the cartoons in Figure 1A. The authors should include graphs of the real data that use the same format as in that cartoon. Instead, the graphs present "residuals," and I don't know what those are. I suspect it's "variation left over after accounting for effects of strain and individual stochasticity." I see the residuals being tracked per strain over time in Figure 2H, but I don't see the change over time in other graphs. I'm looking for something simple like, "variation within the strain at the beginning of learning and at later time points in learning." (But I'm not sure exactly what instantaneous measurement would be the focus in longitudinal analyses of learning behavior.)

(3) Figure 3 is a cool stab at tracking down the precise mechanism by which stochastic environment interacts with learning to send individuals along different behavioral routes. But again, like in Figure 2, I don't have the sophisticated understanding of statistics to understand exactly what the graphs are telling me, or how they relate to the underlying measurements. I'm relying on the results text alone to reach a conceptual understanding and just taking the graphs on trust.

So, overall, the authors have a very nice body of work here and with the potential to add a new facet to our understanding of the origins of diversity in animal behavior. In addition to the interpretations they focus on here, this dataset also represents an advance in studying visual associative learning in general, and quite an amazing ability to make longitudinal measurements of many behavioral decisions within the same animals. Improving the data presentation to make it easier to follow for a larger swathe of researchers, especially in figures 2 and 3, will increase its potential impact.

Comment on revised version:

The authors have addressed my main points, including adding description of their statistical analyses and providing more detail about the different assays run and which animals were included in the same assay batches.

Reviewer #2 (Public review):

Summary:

The authors set out to test the extent to which differences in learning capacity and experience contribute to behavioural variation in a genetically identical population under identical environmental conditions.

Strengths:

The authors developed and used a scaled-up version of a simple two-choice behavioural paradigm allowing them to test thousands of individuals across multiple genotypes. They then deployed clever and powerful statistical analysis methods and provided compelling evidence for a role of variability in learning in the expression of behavioural variation.

Weaknesses:

There are no major weaknesses, although some level of longitudinal analysis to strengthen the evidence for a strict definition of individuality would be a welcome extension of a future study. In addition, it would have been very interesting, although understandably beyond the current scope, to delineate a potential source of learning variability in the brain.

Reviewer #1 (Public review):

The authors demonstrate an innovative approach to investigate the effect of cone dropout on visual acuity using their newly developed Oz platform. By systematically reducing the coverage of real-world input to the cone photoreceptor mosaic ("cone dropout condition"), the authors are able to assess how having less cones leads to reduced vision, in comparison to existing approaches ("pixel dropout condition").

The observation of visual acuity maintenance with cone dropout has been a longstanding mystery since the 2013/2018 papers by Ratnam and Foote. The authors should be commended for their approach to address this important question. However, there are some simplifications and assumptions being applied to make this jump (i.e. that a 50% reduction in cone stimulation in a healthy eye is comparable to a 50% reduction in cone density in a patient). It seems unlikely that in a patient eye, with cone dropout, that there will be gaps in the mosaic. Not considering any other non-photoreceptor related reasons for visual acuity loss which can occur in patients, the cone aperture acceptance angle may be different due to changes in cone size or packing; the sensitivity of individual cones may also be reduced due to deficits in the visual cycle recovery which could be affected in disease. Some of these limitations could be addressed and acknowledged more explicitly.

The capture of a rich dataset including both cone imaging and eye motion is valuable. Since the C stimulus test relies on foveal fixation, and there is a high degree of subject-to-subject variation in peak cone density, the authors may wish to report on peak cone density measurements of the subjects being included in this study. In addition, evaluating whether the eye motion is affected by simulated cone dropout condition can help to rule out whether these observed effects can be attributed to eye motion.

Overall, this is an impressive study incorporating state-of-the-art technology to probe the fundamental limits of human vision.

Comments on revised version.

The authors have nicely addressed my concerns. The additional clarifications and revised text have strengthened the paper. Thank you also for pointing out the inaccuracy of referring to the system as the olo system; this has been corrected.

Reviewer #1 (Public review):

Summary:

This is important and significant work because it helps describe the complexity of interactions between system components where 2 herbivores interact with vegetation. Whereas other studies have shown that the larger ungulate (yaks, Bos grunniens, in this case) can facilitate the abundance and population growth of the smaller (the semi-fossorial lagomorph, Ochotona curzoniae, plateau pika hereafter), this study flips the tables, and shows that, at least under some conditions, moderate densities of the plateau facilitate the nutritional condition of yaks.

Strengths:

Notably, the strong inference the authors can claim for their results is supported by the careful experimental design. A weaker paper would have simply noted correlations between pika burrow density and yak feeding efficiency without experimental removal. This paper, to its credit, not only used experimental removals but also documented the various intermediary results that support the ultimate conclusions. The statistical approaches used appear to be appropriate. (Readers are encouraged to read the full Materials and Methods, which are available in the Supplementary Materials section).

Weaknesses:

Although the study was well designed and executed, and its conclusions appear strongly supported, readers interested in the management implications on the Qinghai-Tibetan Plateau should be mindful of its limitations. First, the study site, at approximately 3,200 m elevation, was relatively low by Qinghai-Tibetan Plateau standards. Stellera chamaejasme becomes less common at elevations > 4,000 m, where a majority of livestock grazing occurs. Thus, it would be instructive to learn, through follow-up studies, whether similar facilitation occurs where unpalatable (and mildly poisonous) species in such genera as Astragalus, Oxytropis, and Thermopsis replace S. chamaejasme as the problematic plant for pastoralists. Second, the authors make no mention of wild ungulates, so it is unclear what, if any, role they may have played in this system. At least one study in Qinghai Province, albeit at a slightly higher elevation, showed that not only pikas, but also Tibetan gazelles (Procapra picticaudata), which were commonly observed on grazed pastures, grazed more frequently on some dicots avoided by domestic sheep than did the livestock themselves (Harris et al. 2015). It would also be instructive to learn if similar facilitation as observed here applied to the other principal livestock species in the area, domestic sheep (which are often herded together with smaller numbers of domestic goats). Finally, as suggested by this study, the interactions between all components of the system are complex and interactive. If pika facilitation of yak nutrition at the densities documented results in herders increasing yak density, might the increased herbivory from the domestic animals provide the conditions for the pika population to increase beyond the densities observed here, and thus toward the levels where facilitation yields to competition?

Reviewer #2 (Public review):

This study uses a combination of field sampling and manipulative experiments to test for facilitative impacts of pikas on yaks via suppression of a poisonous forb. The authors found that, when Stellera forbs were present, yak weight increases over the growing season were greater in the presence of pikas compared to in their absence. This occurred because, although pikas do not consume Stellera, they clip it and use it in nest/burrow construction, thereby decreasing its relative abundance in the plant community. Thus, overall, the study contributes to our understanding of how herbivores of different size classes indirectly affect each other via use of shared resources.

It is well known that large herbivores on grasslands impact smaller animals, but the reciprocal interaction is rarely tested. Thus, this study asks a valuable question, and the experiment is well-designed to test it. The authors also do a good of demonstrating the potential conservation impacts of their research.

Reviewer #1 (Public review):

I thank the authors for the revised manuscript and for the detailed responses.

I think the main points raised in the review have now been addressed. In particular, the new experiment with TbPLK inhibition and mass spectrometry is an important addition, as it provides direct evidence that phosphorylation of KIN-G at Thr301 and Ser569 depends on TbPLK activity in cells.

I also appreciate that the authors have toned down the interpretation of the Golgi phenotype. The revised text now makes clear that the fluorescence data show altered Golgi/ERES organization or duplication, but do not prove a structural defect in Golgi biogenesis.

The added discussion of the T301A result is also helpful. The finding that only a small fraction of KIN-G is phosphorylated at Thr301 in asynchronous cells makes the lack of a strong T301A phenotype more understandable.

Overall, I am happy with the revision of the beautiful manuscript.

Reviewer #2 (Public review):

Summary:

The authors identify KIN-G as an in vitro substrate for phosphorylation by TbPLK and show that several of the in vitro P-ated sites, including T310, overlap with P-ation sites seen in live cells. The authors further show that PLK-mediated P-ation inhibits KIN-G binding to microtubules in vitro, as does a KIN-G-T301D mutant, and that expression of a KIN-G-T301D Phospho-mimic in T. brucei phenocopies KIN-G RNAi knockdowns, producing defects in cell division, morphogenesis of the centrin arm, FAZ and other cellular structures, as well as misplaced cytokinesis furrow.

Understanding cytoskeletal rearrangements that drive cell division in T. brucei is an important and unresolved problem, so the work addresses important questions that are of great interest. PLK and KIN-G have previously been shown to be important for cell division and morphogenesis of cytoskeletal structures that drive cell division in T. brucei. The current work advances our understanding by suggesting a potential mechanism by which PLK and KIN-G might participate, namely through PLK-dependent P-ation to control KIN-G MT binding activity.

Strengths:

The authors use a rigorous combination of biochemistry, phosphoproteomics, cell biology, and mutant analysis to support their conclusion that PLK-mediated P-ation of KIN-G negatively regulates KIN-G microtubule binding and this may explain the observation that a KIN-G T301 phosphomimic mutant blocks cell division and perturbs biogenesis of cytoskeletal structures that drive cell division and morphogenesis. Combining rigorous and informative in vitro studies with mutant analysis in live cells is a great strength. The work is solid and important, though a few pieces are needed to fully connect the in vitro findings with the in vivo observations, as detailed below.

Weaknesses:

Overall, I find this work to be solid, and to provide an important addition to our understanding of mechanisms controlling cell division in T. brucei. The biochemistry, in particular, is rigorous and convincingly demonstrates PLK can P-ate KIN-G, altering its MT-binding ability. Analysis of phospho-mutants of KIN-G in live T. brucei support the conclusion that P-ation of KIN-G at T301 negatively affects KIN-G function in vivo. I think, however, that the results fall short of supporting the title, because, although the data convincingly show that PLK can phosphorylate KIN-G at T301 in vitro, and that T301 is P-ated in vivo, they do formally demonstrate (nor even test) whether PLK is the kinase responsible for this phosphorylation in vivo (experiments to address this seem quite feasible). I also do not see where the authors try to reconcile the absence of phenotype for KIN-G-T301A with the implied importance of KIN-G phosphorylation by PLK in cell division, which calls into question the need for P-ation of KIN-G-T301 in cell division. Suggestions for addressing these concerns are provided below.

My two main questions are:

(1) What is the biological relevance of KIN-G P-ation at T301?<br /> a. The authors report no defect for the KIN-G-T301A mutant, so what then is the need for T301 P-ation, if the cell gets along fine without it? One step toward addressing this would be to ask what fraction of KIN-G shows P-ation at T301. Although published studies indicate P-ation at T301, it isn't known what percentage of KIN-G in the cell is P-ated. One might anticipate, for example, that T301-P is a small minority of the population in asynchronous cultures and that T301 P-ation increases at specific cell cycle stages.<br /> b. Published work links PLK to cell division, FAZ elongation, etc... The current work suggests that one role of PLK is to P-ate KIN-G at T301. In contrast, however, the current work also indicates that P-ation of KIN-G at T301 is unnecessary for normal cell division, FAZ elongation, etc....<br /> c. Some experiments or at least commentary on points a and b above would strengthen the paper.<br /> - The authors have now addressed this question by assessing what % of KING is phosphorylated at T301 and adding commentary on this point in the revised paper.<br /> - I would suggest that the model (new figure 8) include a dephosphorylation step, as that is proposed by the authors in the text. Also include in the legend some commentary on the role of phosphorylation, which is the center point of this paper, but not currently mentioned.

(2) Is PLK the kinase that P-ates Kin-G T301 in vivo?<br /> a. The authors show PLK P-ates T301 (and other residues) in vitro, and that T-301 is P-ated in vivo. To bring the analysis full circle, it would be informative to examine KIN-G P-ation in a PLK mutant or upon inhibition of PLK with published inhibitors. This seems to be a very doable experiment with the tools available.<br /> - The authors have addressed this question by demonstrating that T301 phosphorylation is reduced upon treatment with a PLK inhibitor, thus supporting that PLK phosphorylated T301 in vivo. It is noted that one might consider an alternate kinase is also able to phosphorylate T301 in absence of PLK activity, as that could explain the relatively low (~27%) reduction in phosphorylation by PLK inhibitor treatment.

Reviewer #3 (Public review):

Summary:

Here the authors investigate the role of the Trypanosoma brucei polo-like kinase TbPLK in the function of flagellum-associated cellular structures in trypanosomes. They set out to test the hypothesis that a key substrate of TbPLK is the kinesin protein KIN-G, and that TbPLK phosphorylation of KIN-G regulates its functions in cells.

Strengths:

Using in vitro biochemistry with purified proteins, the authors convincingly demonstrate that TbPLK phosphorylates KIN-G at 29 sites. Moreover, they convincingly show that phosphorylation at one site, T301, impairs the binding of purified KIN-G to purified microtubules. They further confirm that inhibition of TbPLK in cells reduces KIN-G phosphorylation at T301 (and S569). Using immunofluorescence-based imaging approaches, they also show that TbPLK colocalizes with KIN-G at centrin arms during early S-phase of the cell cycle. Centin arms are structures that are located near the basal body and flagellum and are important for new flagellum biogenesis, Golgi positioning, and cell division. To evaluate the function of KIN-G phosphorylation in cells, they depleted KIN-G by RNAi, simultaneously expressed phospho-mimetic (T301D) and phospho-ablative mutant proteins, and used immunofluorescene to examine the impact on flagellum-associated cellular structures. They show that expression of the phospho-mimetic mutant KIN-G-T301D causes the following defects: reduced cell proliferation, disruption of centrin arm and Golgi biogenesis, impairment of FAZ elongation and flagellum positioning, and misplacement of the cell division plane. The data convincingly support the conclusion that KIN-G phosphorylation on T301 plays an important role in regulating the cellular functions of this kinesin motor protein.

Weaknesses:

The authors have addressed prior weaknesses in the manuscript through additional experimentation and rewording of the conclusions.

Reviewer #1 (Public review):

Summary:

This study demonstrates that nutrient resorption efficiency (NuRE) in Phragmites australis is genetically canalized rather than plastic to salt stress. Using 110 genotypes in a common garden, the authors show that intraspecific variation in NuRE is explained by phylogeographic lineage, ecotype, and latitude, not by effective salinity. Element-specific regulatory strategies further reveal how N, P, and K resorption are differentially controlled. At the population level, this is an important study that fundamentally advances our understanding of plant functional trait evolution and its implications for ecosystem nutrient dynamics under global change.

Strengths:

This study is the first to demonstrate genetic determination of a key nutrient conservation trait under effective salt stress in a widespread macrophyte, directly testing the 'plastic acclimation versus inherent conservatism' paradigm in a non-nutrient stress context. The experimental design is rigorous: each genotype was paired across control and salt treatments, and multilevel stress effectiveness (metabolomics, biomass, Na accumulation) was confirmed before evaluating NuRE. The large sample size of a macrophyte and dual classification (phylogeography + ecotype) allow robust disentangling of genetic versus plastic sources of variation.

The analysis comprehensively tests three resorption control hypotheses using appropriate SMA regression, revealing element-specific and condition-dependent patterns. The latitudinal gradient and variation partitioning provide strong evidence that genetic origin and geographic context outweigh short-term plasticity, with important implications for predicting ecosystem nutrient cycling under global change. This study provides a clear empirical demonstration that a key nutrient conservation trait can remain homeostatic under non-nutrient stress, and that intraspecific variation is primarily a product of population differentiation rather than short-term plasticity.

Weaknesses:

First, the salinity treatment spanned only one growing season. The conclusion of genetic canalization therefore specifically refers to the absence of plasticity to an acute salt shock. Whether long-term, multigenerational chronic salinity could act as a selective agent or induce transgenerational plasticity remains an open and interesting question for further research. Likewise, the physiological mechanisms underlying the observed lack of plastic increase in NuRE (for example, phloem loading or senescence gene expression) are not directly resolved, leaving some inference about trade-offs versus true unresponsiveness. These points do not weaken the study's main conclusion. Instead, they suggest productive future directions, such as longer-term field manipulations and targeted molecular investigations.

Second, the test of nutrient limitation control relies on resorbed N:P and N:K ratios as proxies, an established but indirect approach. Direct nutrient addition experiments would provide stronger causal evidence. Also, the metabolomic analysis is used primarily to validate stress effectiveness; deeper integration of specific metabolites with NuRE variation across genotypes could have offered mechanistic insights but was not pursued. Additionally, the potential collinearity between ecotype and phylogeographic lineage among Chinese populations is not quantitatively addressed. None of these considerations undermines the main finding, which is supported by a robust experimental design and widely accepted analytical approaches.

Reviewer #2 (Public review):

Summary:

The study finds that nutrient resorption efficiency in Phragmites australis shows no plastic response to salinity stress but is canalized by phylogeographic lineage, ecotype, and latitude. In a common garden with 110 genotypes, salinity induced stress, yet no plastic change occurred for N, P, or K resorption. The authors conclude that intraspecific variation is historical and geographic; thus, predictions of wetland nutrient cycling need to account for phylogeographic composition.

Strengths:

The core finding that NuRE shows no plastic response to salinity, but is instead evolutionarily canalized by lineage and latitude, challenges a key assumption of broad trait plasticity. This conclusion is firmly supported by a robust common garden design with 110 genotypes, rigorous multi-level stress validation, and element-specific resorption analyses. The work provides compelling evidence that intraspecific variation in this critical nutrient cycling trait is shaped by phylogeographic history rather than short-term acclimation. The implications for predicting wetland responses to salinization are significant, as ecosystem-level nutrient dynamics may be constrained by the genetic composition of plant populations.

Weaknesses:

The experiment covers only one growing season, with salinity applied in June and measurements in December. While the stress is clearly effective, longer-term or multi-year stress might reveal acclimation or epigenetic effects that are not captured. Given the author team's expertise in parental and transgenerational effects in clonal plants, this limitation is particularly relevant and warrants more thorough discussion in the manuscript.

The salinity treatment uses a single moderate level of 10 ppt, which does not allow assessment of whether more extreme stress might trigger a plastic response. A dose-response design across a gradient would have provided stronger inference about the threshold at which NuRE canalization might be overcome. Additionally, the ecotype analysis in Figure 4 applies only to Chinese populations, as classification was not available for non-Chinese populations, which should be stated more explicitly in the Results.

The variation partitioning shows latitude as a significant predictor, but the R² values are relatively low, indicating that much variance remains unexplained. The manuscript should avoid overinterpreting latitude's explanatory power and more openly acknowledge the role of unmeasured factors. The interpretation of slopes greater than 1 for the resorbed N:P versus green N:P relationship, labeled as "inverted limitation", also needs further explanation regarding its functional significance.

Reviewer #1 (Public review):

Summary:

The manuscript introduces cuBNM, a GPU‑accelerated Python package for whole‑brain modeling. The authors demonstrate that running simulations on GPUs provides substantial benefits in computational speed, cost-efficiency, and scalability compared to traditionally used CPUs, making large‑scale and individualized brain network modeling computationally feasible. The usage of cuBNM has been demonstrated by running optimization of group-level and individualized low- and high-dimensional models. By investigating the test-retest reliability and heritability of simulated and empirical measures in the Human Connectome Project dataset, the authors showed that simulated features were fairly reliable and significantly heritable.

Strengths:

This study is timely and presents an important contribution to the field of whole-brain computational modeling. A major strength is that the authors go beyond introducing a GPU-accelerated framework by demonstrating its utility through comprehensive benchmarking and biologically relevant applications, including individualized model fitting, comparisons of homogeneous and heterogeneous models, and analyses of test-retest reliability and heritability.

The computational performance is evaluated comprehensively, assessing speed, computational cost, energy consumption, and scalability across different simulation settings. The Human Connectome Project dataset is used to demonstrate that the software enables individualized whole-brain modeling in large datasets.

Finally, the software is modular, open-source, and well-documented, and can facilitate the broader adoption of GPU-accelerated whole-brain modeling within the neuroscience community.

Weaknesses:

The test-retest reliability and heritability are estimated using high-quality Human Connectome Project data. The manuscript would benefit from discussion and/or demonstrations regarding how the software performs under more challenging conditions, such as clinical datasets, shorter data acquisitions, higher-motion datasets, or multi-site datasets.

Apart from demonstrating the benefits of GPUs over CPUs, the manuscript would benefit from a more direct comparison between cuBNM and other whole-brain modeling software, such as The Virtual Brain.

The manuscript demonstrates that heterogeneous models improve the fit to empirical functional connectivity. However, the biological interpretation of this improvement could be expanded. The heterogeneous models are also more complex than homogeneous models, and some improvement in model fit may be explained by the increased model flexibility.

In whole-brain brain network modeling, different parameter combinations can result in similar empirical functional connectivity measures. The manuscript would benefit from a discussion of how this influences the interpretation of individualized parameter estimates.

Reviewer #2 (Public review):

Summary:

The authors aim to address a major problem in brain network modeling: the high computational cost of simulating and fitting brain activity models, particularly for large samples, individualized models, and broad parameter searches. They introduce cuBNM, an open software package that uses graphics processing units to accelerate model simulation, fitting, and calculation of simulated brain activity features.

The manuscript is primarily a methods and software contribution, rather than a paper providing novel neurobiological insights. The authors demonstrate the tool using human imaging data, showing examples of group-level and individualized model fitting, comparisons between homogeneous and heterogeneous model parameterizations, and analyses of repeated-measurement stability and genetic influences of simulated features. They also provide speed and scaling tests to support the claim that the software can make large-scale and individualized brain network modeling more practical for the field.

Strengths:

A major strength of this work is that it addresses a clear computational bottleneck in brain network modeling. The authors provide an open software package that combines a user-friendly Python interface with an accelerated back-end, making large numbers of simulations and model fits more practical for other researchers.

The demonstrations are broad and relevant to real use cases. The authors show group-level and individualized model fitting, different optimization strategies, and comparisons between homogeneous and heterogeneous models, rather than limiting the paper to a narrow technical benchmark. The benchmarking and openness of the work further increase its value. The comparisons across hardware and network sizes give readers a practical sense of the tool's speed and scalability, while the availability of code, documentation, tutorials, and containers should make the method easier for the community to test and adopt.

Weaknesses:

(1) The benchmarking provides solid evidence for substantial speed improvements within the authors' implementation, but the generality of the performance claims is more limited. The largest reported speed-ups are measured relative to a single central processing unit thread, and the study does not fully benchmark cuBNM against other optimized brain modeling frameworks. This makes the results useful as evidence of strong acceleration in the tested setting, but less definitive as a general comparison across available implementations.

(2) The comparison between homogeneous and heterogeneous models is informative, but it is not fully controlled for model complexity. The best-fitting node-based heterogeneous model has more free parameters than the homogeneous and map-based alternatives, so its improved fit may partly reflect greater flexibility rather than a more biologically valid parameterization. As a result, the model comparison supports the conclusion that this parameterization fits better under the current setup, but not necessarily that it is generally superior or more biologically realistic.

(3) The reliability and heritability analyses are valuable demonstrations of what scalable individualized modeling can enable, but they do not establish the simulated features as validated biological mechanisms. Because these simulated features are derived from individualized structural and functional imaging data, their stability across repeated measurements and genetic influences may partly reflect information already present in the empirical inputs or fitting targets. These results therefore support a more cautious conclusion: the simulated features retain stable and genetically structured variation, but their biological interpretation remains model dependent.

(4) The empirical demonstrations are narrower than some of the broader claims made in the manuscript. Most analyses rely on one human imaging dataset, one cortical parcelation, one main brain model, and a specific fitting objective, while broader claims refer to diverse populations, dense networks, high-dimensional models, and biological applications. The current results show that cuBNM is a useful and scalable tool in the tested setting, but the extent to which the findings generalize across datasets, model classes, network resolutions, or clinical contexts remains to be established.

Reviewer #1 (Public review):

Summary:

This study revisits an important and controversial question in brain repair: whether NeuroD1 can convert brain immune cells into nerve cells in vivo. Using a virus-free genetic system, in vivo imaging, injury experiments, and single-cell profiling, the authors provide convincing evidence that NeuroD1-expressing cells do not become nerve cells under the tested conditions. Instead, these cells largely retain their original immune-cell identity, and some appear to undergo cellular stress or loss.

Strengths:

The main strength of the work is that it tests this question with a cleaner genetic strategy, avoiding some of the concerns associated with viral delivery and unintended cell labeling. Although the overall conclusion is consistent with the authors' previous work, the current study adds useful independent evidence, particularly through the virus-free fate-mapping system and live imaging in the brain.

Weaknesses:

There are some limitations. In the injury experiment, the labeled cells may include both resident brain immune cells and blood-derived immune cells recruited after injury, so the authors should be cautious when referring to all labeled cells as microglia. The level of NeuroD1 expression achieved by the genetic system is also not fully defined, which matters because the effects of such a cell-fate regulator may depend on expression level. Finally, the tested time window may not fully address very delayed or incomplete neuronal differentiation.

Overall, this is a useful and careful study that supports the conclusion that NeuroD1 does not drive brain immune cells to become nerve cells in the tested settings. It should be valuable for researchers studying brain repair, cell fate conversion, and genetic fate mapping, and it provides a clear caution against overinterpreting reprogramming results based only on viral labeling.

Reviewer #2 (Public review):

Summary:

In vivo glia-to-neuron conversion emerges as a potential regeneration-based therapeutic strategy for neural injuries and diseases. However, controversies exist in this exciting field, largely arising from the non-stringent methods employed for analyzing in vivo neuronal conversions. The study by Li et al. directly addressed this controversy regarding Neurod1-mediated microglia-to-neuron conversion. They took advantage of two transgenic mouse lines to specifically express Neurod1 in the microglia of adult mouse brains. Results from immunohistochemistry, in vivo live-cell imaging, and scRNA-seq convincingly demonstrate that microglia cannot be converted in vivo to neurons by ectopic Neurod1 expression under both normal and injury conditions. Instead, it induces microglia death, consistent with their earlier findings. These solid results, though negative, are critical additions to the field and further support that stringent lineage tracing methods are essential for studying in vivo cell reprogramming. Overall, the studies are rigorously designed and executed. Only minor issues need to be dealt with.

Joint Public Review:

The revised manuscript is much clearer, and the additional analyses address several of the original concerns. RAIN analyses (Rhythmicity Analysis Incorporating Nonparametric methods) now detects circadian rhythmicity in 7/11 recordings under light-dark conditions and 8/12 recordings in constant darkness, compared with 2/12 following treatment with the Orco antagonist. This supports circadian modulation of spontaneous firing and a role for Orco in its normal expression. The expanded qPCR analysis of Orco also supports the conclusion that Orco transcript abundance is not circadian, and the cAMP experiment shows that cAMP can modulate Orco-dependent activity.

The remaining issue concerns the mechanistic interpretation. The lack of rhythmic Orco transcript abundance does not distinguish an autonomous post-translational feedback-loop (PTFL) clock from a model in which the canonical transcriptional-translational (TTFL) clock acts upstream through cAMP, calcium, kinases, phosphatases, channel trafficking, or related pathways to regulate Orco.

Similarly, the new Figure 10 provides a useful representation of the authors' hypothesis, but the proposed delayed feedback and coupling mechanisms are not experimentally demonstrated.

We do not think any further experiments are necessary for the present study. Instead, we recommend that the manuscript should clearly distinguish between what the data show and what remains proposed. The data support circadian modulation of ORN firing a role for Orco in its normal expression, non-circadian Orco transcript abundance, and cAMP-sensitive modulation of Orco-dependent activity. The proposal that an Orco-centred membrane feedback loop generates the rhythm is intriguing and may remain a hypothesis generated through this study that needs formal testing in the future. This should be explicitly stated. While this has been done in the discussion section, elsewhere, including in the abstract and elsewhere, the original claim remains.

Reviewer #1 (Public review):

Summary:

In this study, the authors investigate the physiological role of the Type VI secretion system (T6SS) in a naturally evolved gut microbiome derived from wild mice (the WildR microbiome). Focusing on Bacteroides acidifaciens, the authors use newly developed genetic tools and strain replacement strategies to test how T6SS-mediated antagonism influences colonization, persistence, and fitness within a complex gut community. They further show that the T6SS resides on an integrative and conjugative element (ICE), is distributed among select community members, and can be horizontally transferred, with context-dependent effects on colonization and persistence. The authors conclude that the T6SS stabilizes strain presence in the gut microbiome while imposing ecological and physiological constraints that shape its value across contexts.

This study is likely to have significant impact on the microbiome field by moving experimental tests of T6SS function out of simplified systems and into a naturally co-evolved gut community. The WildR system, together with the strain replacement strategy, ICE-seq approach, and genetic toolkit, represents a powerful and reusable platform for future mechanistic studies of microbial antagonism and mobile genetic elements in vivo.

The datasets-including isolate genomes, metagenomes, and ICE distribution maps-will be valuable community resources, particularly for researchers interested in strain-resolved dynamics, horizontal gene transfer, and ecological context dependence. Even where mechanistic resolution is incomplete, the work provides a strong experimental foundation upon which such questions can be directly addressed.

Overall, this study occupies a space between system building and mechanistic dissection. The authors demonstrate that the T6SS influences persistence and community structure in vivo, but the physiological basis of these effects remains unresolved. Interpreting the results as evidence of fitness costs or selective advantage therefore requires caution, as multiple ecological and host-mediated processes could produce similar abundance trajectories.

Placing the findings within the broader literature on microbial antagonism, particularly work emphasizing measurable costs, benefits, and tradeoffs, would help readers better contextualize what is directly demonstrated here versus what remains an open question. Viewed in this light, the principal contribution of the study is to show that such questions can now be addressed experimentally in a realistic gut ecosystem.

Strengths:

A major strength of this study is that it directly interrogates the physiological role of the T6SS in a naturally evolved gut microbiome, rather than relying on simplified pairwise or in vitro systems. By working within the WildR community, the authors advance beyond descriptive surveys of T6SS prevalence and address function in an ecologically relevant context.

The authors provide clear genetic evidence that Bacteroides acidifaciens uses a T6SS to antagonize co-resident Bacteroidales, and that loss of T6SS function specifically compromises long-term persistence without affecting initial colonization. This temporal separation is well designed and supports the conclusion that the T6SS contributes to maintenance rather than establishment within the community.

Another strength is the identification of the T6SS on an integrative and conjugative element (ICE) and the demonstration that this element is distributed among, and exchanged between, community members. The use of ICE-seq to track distribution and transfer provides strong support for horizontal mobility and adds mechanistic depth to the study.

Finally, the transfer of the T6SS-ICE into Phocaeicola vulgatus and the observation of context-dependent colonization benefits followed by decline is a compelling result that moves the study beyond simple "T6SS is beneficial" narratives and highlights ecological contingency.

Weaknesses:

Despite these strengths, there is a mismatch between the precision of the claims and the precision of the measurements, particularly regarding fitness costs, physiological burden, and mechanistic role of the T6SS.

First, while the authors conclude that the T6SS "stabilizes strain presence" and that its value is constrained by fitness costs, these costs are not directly measured. Persistence, abundance trajectories, and eventual loss are informative outcomes, but they do not uniquely identify fitness tradeoffs. Decline could arise from multiple non-exclusive mechanisms, including community restructuring, host-mediated effects, incompatibilities of the ICE in new hosts, or ecological retaliation, none of which are disentangled here.

Second, the manuscript frames the T6SS as having a defined physiological role, yet the data do not resolve which physiological processes are under selection. The experiments demonstrate that T6SS activity affects persistence, but they do not distinguish whether this occurs via direct killing, resource release, niche modification, or higher-order community effects. As a result, "physiological role" remains underspecified and risks being conflated with ecological outcome.

Third, although the authors emphasize context dependence, the study offers limited quantitative insight into what aspects of context matter. Differences between native and recipient hosts, or between early and late colonization phases, are described but not mechanistically interrogated, making it difficult to generalize beyond the specific cases examined.

Fourth is the lack of engagement with recent experimental literature demonstrating functional roles of the T6SS beyond simple interference competition. While the authors focus on persistence and competitive outcomes, they do not adequately situate their findings within recent work demonstrating that T6SS-mediated antagonism can serve additional physiological functions, including resource acquisition and DNA uptake, thereby linking killing to measurable benefits and tradeoffs. The absence of this literature makes it difficult to place the authors' conclusions about physiological role and fitness cost within the current conceptual framework of the field. Without this context, the physiological interpretation of the results remains incomplete, and alternative functional explanations for the observed dynamics are underexplored.

A further limitation concerns the taxonomic scope of the functional analysis. The authors state the role of the T6SS in the murine environment is functionally investigated using genetically tractable Bacteroides species, citing lack of genetic tools for Mucispirillum schaedleri. While this is a reasonable practical choice, it means that a substantial fraction of T6SS-encoding species in the WildR community are not experimentally interrogated. Consequently, conclusions about the role of the T6SS in the murine gut necessarily reflect the subset of taxa that are genetically accessible and may not fully capture community-level or niche-specific functions of T6SS activity. Given that M. schaedleri is represented as a metagenome-assembled genome, its isolation and genetic manipulation would be technically challenging. Nonetheless, explicitly acknowledging this limitation and slightly tempering claims of generality would strengthen the manuscript.

Finally, several interpretations would benefit from more cautious language. In particular, claims invoking fitness costs, selective advantage, or physiological burden should be explicitly framed as inferences from persistence dynamics, rather than as direct measurements, unless supported by additional quantitative fitness or growth assays.

Comments on revised version.

The authors have addressed my main concerns by more clearly distinguishing ecological outcomes from directly measured physiological mechanisms. They have moderated claims about fitness costs and benefits, replaced "physiological" with "ecological" where appropriate, expanded the discussion of potential downstream benefits of T6SS-mediated killing, and acknowledged the limited taxonomic scope of the functional analyses. The persistence trajectories support context-dependent relative fitness effects, although they do not identify the specific physiological basis of those effects. The revised manuscript now generally maintains this distinction. These revisions substantially improve the precision and balance of the manuscript.

Reviewer #2 (Public review):

Summary:

In this study, the authors set out to determine how a contact-dependent bacterial antagonistic system contributes to the ability of specific bacterial strains to persist within a complex, native gut community derived from wild animals. Rather than focusing on simplified or artificial models, the authors aimed to examine this system in a biologically realistic setting that captures the ecological complexity of the gut environment. To achieve this, they combined controlled laboratory experiments with animal colonization studies and sequencing-based tracking approaches that allow individual strains and mobile genetic elements to be followed over time.

Strengths:

A major strength of the work is the integration of multiple complementary approaches to address the same biological question. The use of defined but complex communities, together with in vivo experiments, provides a strong ecological context for interpreting the results. The data consistently show that the antagonistic system is not required for initial establishment but plays a critical role in long-term strain persistence, an insight that moves beyond traditional invasion-based views of microbial competition. The observation that transferable genetic elements can confer only temporary advantages, and may impose longer-term costs depending on community context, adds important nuance to current understanding of microbial fitness.

Weaknesses:

Overall, the study is not a lack of evidence, but a deliberate trade-off between ecological realism and mechanistic resolution, which leaves some causal pathways open to interpretation.

Comments on revised version.

The authors have addressed all previous concerns thoroughly and satisfactorily.

Reviewer #3 (Public review):

Summary:

In this work, the authors investigate the contribution of the type VI secretion system of Bacteroidales to gut microbiome assembly and the targeting of closely related species. They demonstrate that B. acidifaciens relies on T6SS-mediated antagonism to prevent displacement by co-resident Bacteroidales and other members of the microbiome, allowing it to persist in the gut. They also developed new tools for analyzing the distribution of mobile genetic elements. This study advances our understanding of how molecular systems contribute to shaping complex microbial communities.

Strengths:

The use of a gnotobiotic model colonized with a wild-mouse microbiome is a significant strength of this study. This approach allows tracking of microbiome changes over time and evaluating the targeting by Bacteroidales carrying T6SS in a more natural setting. The development of ICE-seq for mapping the distribution of the T6SS in the microbiome is remarkable, enabling the study of how this bacterial weapon is transferred between microbiome members without requiring long-read metagenomics methods.

Weaknesses:

Some conclusions are based on a limited number of mice per condition. This could be due to the complexity of using a gnotobiotic mouse model, but this should be considered when interpreting the data.

Overall, the authors successfully achieved their objectives, and their experimental design and results support their findings. As mentioned in the discussion, it would be important to investigate the role of the T6SS in resilience to microbiome disturbances, such as antibiotics, diet, or pathogen invasion. This work represents a step forward in understanding how contact-dependent competition influences the gut microbiome in relevant ecological contexts.

Reviewer #1 (Public review):

[Editor's Note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. When experimentally feasible, the authors have adequately addressed the concerns of the reviewers in the revised manuscript to support the conclusions of the study.

Summary:

The study by Akita B. Jaykumar et al. explored an interesting and relevant hypothesis whether serine/threonine With-No-lysine (K) kinases (WNK)-1, -2, -3, and -4 engage in insulin-dependent glucose transporter-4 (GLUT4) signaling in the murine central nervous system. The authors especially focused on the hippocampus as this brain region exhibits high expression of insulin and GLUT4. Additionally, disrupted glucose metabolism in the hippocampus has been associated with anxiety disorders, while impaired WNK signaling has been linked to hypertension, learning disabilities, psychiatric disorders or Alzheimer's disease. The study took advantage of selective pan-WNK inhibitor WNK 643 as the main tool to manipulate WNK 1-4 activity both in vivo by daily, per-oral drug administration to wild-type mice, and in vitro by treating either adult murine brain synaptosomes, hippocampal slices, primary cortical cultures, and human cell lines (HEK293, SH-SY5Y). Using a battery of standard behavior paradigms such as open field test, elevated plus maze test, and fear conditioning, the authors convincingly demonstrate that the inhibition of WNK1-4 results in behavior changes, especially in enhanced learning and memory of WNK643-treated mice. To shed light on the underlying molecular mechanism, the authors implemented multiple biochemical approaches including immunoprecipitation, glucose-uptake assay, surface biotylination assay, immunoblotting, and immunofluorescence. The data suggest that simultaneous insulin stimulation and WNK1-4 inhibition results in increased glucose uptake and the activity of insulin's downstream effectors, phosphorylated Akt and phosphorylated AS160. Moreover, the authors demonstrate that insulin treatment enhances the physical interaction of the WNK effector OSR1/SPAK with Akt substrate AS160. As a result, combined treatment with insulin and the WNK643 inhibitor synergistically increases the targeting of GLUT4 to the plasma membrane. Collectively, these data strongly support the initial hypothesis that neuronal insulin- and WNK-dependent pathways do interact and engage in cognitive functions.

In response to our initial comments, the authors mildly revised the manuscript, which did not improve the weaknesses to a sufficient level. Our follow-up comments are labeled under "Revisions 1".

Strengths:

The insulin-dependent signaling in the central nervous system is relatively understudied. This explorative study delves into several interesting and clinically relevant possibilities, examining how insulin-dependent signaling and its crosstalk with WNK kinases might affect brain circuits involved in memory formation and/or anxiety. Therefore, these findings might inspire follow-up studies performed in disease models for disorders that exhibit impaired glucose metabolism, deficient memory, or anxiety, such as Diabetes mellitus, Alzheimer's disease, or most of psychiatric disorders.

The graphical presentation of the figures is of high quality, which helps the reader to obtain a good overview and to easily understand the experimental design, results, and conclusions.

The behavioral studies are well conducted and provide valuable insights into the role of WNK kinases in glucose metabolism and their effect on learning and memory. Additionally, the authors evaluate the levels of basal and induced anxiety in Figures 1 and 2, enhancing our understanding of how WNK signaling might engage in cognitive function and anxiety-like behavior, particularly in the context of altered glucose metabolism.

The data presented in Figures 3 and 4 are notably valuable and robust. The authors effectively utilize a variety of in vivo and in vitro models, combining different treatments in a clear manner. The experimental design is well-controlled, efficiently communicated, and well-executed, providing the reader with clear objectives and conclusions. Overall, these data represent particularly solid and reproducible evidence on the enhanced glucose uptake, GLUT4 targeting, and downstream effectors' activation upon insulin and WNK/OSR1 signaling crosstalk.

Weaknesses:

(1) The study used a WNK643 inhibitor as the only tool to manipulate WNK1-4 activity. This inhibitor seems selective; however, it has been reported that it exhibits different efficiency in inhibiting the individual WNK kinases among each other (e.g. PMID: 31017050, PMID: 36712947). Additionally, the authors do not analyze nor report the expression profiles or activity levels of WNK1, WNK2, WNK3, and WNK4 within the relevant brain regions (i.e. hippocampus, cortex, amygdala). Combined, these weaknesses raise concerns about the direct involvement of WNK kinases within the selected brain regions and behavior circuits. It would be beneficial if the authors provided gene profiling for WNK1, 2, 3, and -4 (e.g. using Allen brain atlas). To confirm the observations, the authors should either add results from using other WNK inhibitors or, preferentially, analyze knock-down or knock-out animals/tissue targeting the single kinases.

Revisions 1: The authors added Fig. S1A during the revisions to show expression of Wnt1-4. While the expression data from humans is interesting, the experimental part of the study is performed in mice. It would be more informative for the authors to add expression profiles from mice or overview the expression pattern with suitable references in the introduction to address this point. The authors did not add data from knock down or knockout tissue targeting the single kinases.

(2) The authors do not report any data on whether the global inhibition of WNKs affects insulin levels as such. Since the authors demonstrate the synergistic effect of simultaneous insulin treatment and WNK1-4 inhibition, such data are missing.

Revisions 1: The authors added Fig. S5A to address this point. It is appreciated that authors performed the needed experiment. Unfortunately, no significant change was found, therefore, the authors still cannot conclude that they demonstrate a synergistic effect of simultaneous insulin treatment and WNT1-4 inhibition. It is a missed opportunity that the authors did not measure insulin in the CSF or tissue lysate to support the data.

(3) The study discovered that the Sortilin receptor binds to OSR1, leading the authors to speculate that Sortilin may be involved in the insulin-dependent GLUT4 surface trafficking. The authors conclude in the result section that "WNK/OSR1/SPAK influences insulin-sensitive GLUT4 trafficking by balancing GLUT4 sequestration in the TGN via regulation of Sortilin with GLUT4 release from these vesicles upon insulin stimulation via regulation of AS160." However, the authors do not provide any evidence supporting Sortilin's involvement in such regulation, thus, this conclusion should be removed from the section. Accordingly, the first paragraph of the discussion should be also rephrased or removed.

Revisions 1: The authors added Fig. 5M-N to address this point. The new experiment is appreciated. However, the authors still do not show that sortilin is involved in insulin or WNK-dependent GLUT4 trafficking in their set up since the authors do not demonstrate any changes in GLUT4 sorting or binding. The conclusions should therefore be rephrased or included purely in the discussion. Moreover, the discussion was not adjusted either, leading to over interpretation based on the available data.

(4) The background relevant to Figure 5, as well as the results and conclusions presented in Figure 5 are quite challenging to follow due to the lack of a clear introduction to the signaling pathways. Consequently, understanding the conclusions drawn from the data is also difficult. It would be beneficial if the authors addressed this issue with either reformulations or additional sections in the introduction. Furthermore, the pulldown experiments in this figure lack some of the necessary controls.

Revisions 1: The Authors insufficiently addressed this point during the revisions and did not rewrite the introduction as suggested.

(5) The authors lack proper independent loading controls (e.g. GAPDH levels) in their immunoblots throughout the paper, and thus their quantifications lack this important normalization step. The authors also did not add knock-out or knock-down controls in their co-IPs. This is disappointing since these improvements were central and suggested during the revision process.

(6) The schemes that represent only hypotheses (Fig. 1K, 4A) are unnecessary and confusing and thus should be omitted or placed at the end of each figure if the conclusions align.

(7) Low-quality images, such as Fig. 5H should be replaced with high-resolution photos, moved to the supplementary, or omitted.

Reviewer #2 (Public review):

This study by Jaykumar and colleagues seeks to expand the field's appreciation of insulin responses in the brain, specifically by implicating WNK kinase function in various neuronal responses, ranging from behavioral / memory changes to GLUT4 trafficking to the cell surface with subsequent glucose uptake. This revised study is now comprehensive and presents a logical and reasonably documented cascade of molecular interactions responsible in part for GLUT4 trafficking under the regulation of WKK and insulin. Additional data allow the authors to dissect a plausible WNK/OSR1/SPAK-sortilin pathway for the modulation of GLUT4 trafficking, in part by capitalizing on an overlay of various techniques and systems. The data - much of it in vivo or ex vivo - showing a potential role for WNK function in brain glucose utilization remains a compelling part of the story, with the dissection of the signaling cascade and a potential role for sortilin in mediating WNK function via effects on GLUT4 cellular localization now more convincing.

Initially, the group shows that oral WNK463 treatment - an inhibitor of WNKs broadly - in mice augments a number of memory readouts. These findings fit within the context of the overall story the authors present: that WNK function is critical to brain glucose utilization, which impacts learning. Multiple approaches are used to show that WNK463 treatment, i.e. inhibition of WNKs, increases glucose uptake, including labeled 2-deoxyglucose uptake in vivo in the brain and in isolated synaptosome, and uptake in ex vivo hippocampal slices. These findings are solid and consistent. With the exception of some relatively minor comments regarding the data presentation made to the authors and now fully addressed, the findings showing that WNK463 treatment increases GLUT4-mediated glucose uptake and surface localization of GLUT4 are reasonable, with the hippocampal slice data being particularly relevant.

While the details of the WNK signaling cascade is dense, in the revised application one clearly appreciates the molecular interrogation and interactions the group is dissecting, supported by the use of multiple models. With the additional findings, these systems and the data now reinforce each other, presenting a strongly documented overall story.

A limitation of the study with the initial submission was the authors' reliance upon a single pharmacological tool (WNK463) to inhibit WNK kinases. WNK463 apparently has substantial specificity for WNKs and WNK463 treatment lessened OSR1 phosphorylation (a WNK substrate). Nevertheless, the cohesiveness of the findings in terms of the broader pathway engagement (GLUT4 trafficking, glucose uptake) is consistent with the author's proposed mechanisms and conclusions. The authors have additionally addressed this concern in the revised manuscript with more information supporting the specificity of WNK463 as well as the multiple approaches to confirm the effect of WNK463 on the WNK signaling pathway of interest.

The final few paragraphs of the discussion that weave the author's findings into the field more broadly, including Sortilin function and neurological disorders, are appreciated. Additional clarity in the Methods section is also helpful.

Joint Public Reviews:

This manuscript presents an algorithm for identifying network topologies that exhibit a desired qualitative behaviour, with a particular focus on oscillations. The approach is first demonstrated on 3-node networks-where results can be validated through exhaustive search-and then extended to 5-node networks, where the search space becomes intractable. Network topologies are represented as directed graphs, and their dynamical behaviour is classified using stochastic simulations based on the Gillespie algorithm. To efficiently explore the large design space, the authors employ reinforcement learning via Monte Carlo Tree Search (MCTS), framing circuit design as a sequential decision-making process.

This work meaningfully extends the range of systems that can be explored in silico to uncover non-linear dynamics and represents a valuable methodological advance for the fields of systems and synthetic biology.

Strengths:

The evidence presented is strong and compelling. The authors validate their results for 3-node networks through exhaustive search, and the findings for 5-node networks are consistent with previously reported motifs, lending credibility to the approach. The use of reinforcement learning to navigate the vast space of possible topologies is both original and effective and represents a novel contribution to the field. The algorithm demonstrates convincing efficiency, and the ability to identify robust oscillatory topologies is particularly valuable. Expanding the scale of systems that can be systematically explored in silico marks a significant advance for the study of complex gene regulatory networks.

Weaknesses:

Although the proposed approach substantially expands the scale of tractable searches, the systems explored remain relatively small, being limited to five-node networks. The authors now discuss possible avenues for improving scalability, but extending the framework to substantially larger networks remains an important future challenge.

Another important limitation concerns the assumption of identical reaction rates for all circuit connections. As the authors' own analysis shows, relaxing this assumption leads to significant qualitative and quantitative changes in oscillatory dynamics. Consequently, it remains unclear how the properties of the identified fault-tolerant oscillators translate to more biologically realistic regulatory circuits, where kinetic parameters vary across interactions.

The conclusions should also be interpreted within the chosen modelling framework and parameter space. In particular, the sampled parameter ranges and restriction to relatively low Hill coefficients define the subset of regulatory architectures explored. Whether broader parameter regimes, including higher Hill coefficients, would reveal additional oscillatory architectures remains unclear.

Reviewer #1 (Public review):

Summary:

The ciliary photoreceptor cells and its downstream neurons of larval annelid must be orchestrated in a specific pattern to promote downward swimming in response to long duration of UV exposure. The authors first conducted neuroanatomical examination of the circuit to identify NOS-expression neurons (INNOS) that are immediately downstream to the ciliary photoreceptor cells. The INNOS is activated by UV and produce NO. The NOS is required for UV avoidance by Platynereis larvae and neural dynamics of the photoreceptor cells and their downstream circuit. Following up the RNA-seq data with in-situ hybridization experiments, the authors found that two unconventional guanylate cyclases, NIT-GC1 and NIT-GC2, are expressed and localized in different subcellular domain of the photoreceptor cells. Experiments using the culture cells ang genetically encoded sensors demonstrated that NIT-GC1 can generate cGMP in response to nitric oxide. Finally, authors build mathematical model that fit the live imaging data and used it to predict how the magnitude of the photoreceptor activation varied by intensity and duration of UV light.

Strengths:

The authors conducted comprehensive interrogations of the UV avoidance pathway at the molecular and circuit levels and constructed mathematical model. The main conclusions are supported with layers of evidence from different assays.

Weaknesses:

The authors addressed these weaknesses in the previous version of the manuscript. Statistics are missing in both figure legends and methods. The perturbations of genes and molecules were not cell-type-specific and therefore the observed behavioral defect could be attributed to the malfunction of the circuit elsewhere not examined in this study. I suggest adding more explanation about the functions of other NOS-expressing cells and conducting a control experiment to test behavioral response to a non-visual stimulus.

Reviewer #2 (Public review):

Summary:

This study is quite thorough, tackling this NO-dependent UV avoidance circuit with both breadth and depth. There are several novel discoveries throughout, but the whole package represents perhaps even more than the sum of these parts.

Strengths:

The presentation of the work is compelling. The introduction sets up the question and the state of the field very nicely. The discovery of the non-canonical NO receptor pathway in the ciliary photoreceptors is fascinating and will likely open up new avenues for future research into NO-pathways in different species. The use of genetic and pharmacological manipulations of circuit components was well thought-out. The authors applied different experimental techniques expertly throughout the study so that they could develop a comprehensive view from the molecular to the behavioral levels.

Weaknesses:

The authors have done an excellent job revising and explaining their model. No important weaknesses remain, in my opinion.

Reviewer #3 (Public review):

The transition from planktonic to benthic depends upon several physical and chemical cues. Nitric oxide (NO) is known as a critical player in the induction of larval metamorphosis in several invertebrates. Although NO is a widespread signalling molecule in a broad range of organisms regulating key physiological processes, internal regulatory mechanisms studies are scarce. While the UV sensing in larvae of the annelid Platynereis dumerilii using ciliary photoreceptors has been studied, the neuronal signalling mechanism remains unknown. In this study, Kei Jokura et al. investigated how annelid Platynereis dumerilii larvae detect UV sensing and modulate swimming behaviour through nitric oxide feedback. Using existing resources of Platynereis larval connectome/volume EM data, they identified NOS-expressing interneurons within the ciliary photoreceptors circuit (cPRCs). They demonstrated that NO is produced in cPRCs during UV/violet stimulation by using a fluorescent NO-reporter line. Further, they demonstrated that Nitric oxide signalling mediates UV-avoidance behaviour by using NOS-mutant larvae. Finally, they mapped out the signalled mechanisms of the cPRC circuit using published spatially mapped single-cell transcriptome data of Platynereis larvae, the Ca sensor lines, in situ HCR, and immunostaining. Additionally, by using their findings from Ca imagining data of cPRC, INNOS and INRGWa cells collected in wild-type, NOS knockout and NIT-GC2 morphant larvae, Kei Jokura et al. developed a mixed cellular-circuit-level mathematical model. However, my expertise in mathematical modelling is limited, so I cannot comment on this section.

Comments on revised version.

Thank you for the opportunity to re-evaluate this manuscript. I have reviewed the authors' responses and the revised manuscript. The authors have carefully and satisfactorily addressed all of my previous comments and concerns. The revisions have strengthened the paper, and I have no further suggestions.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

The authors point out that the fitness estimates obtained from different experimental assays (monoculture, pairwise competition or bulk competition) are not generally equivalent, not even with regard to the fitness ranking of different genotypes. Using a computational model based on experimentally measured growth phenotypes for knockout strains in yeast, as well as data from Lenski's Long Term Evolution Experiment (LTEE), they derive a set of best practice rules aimed at extracting the optimal amount of information from such experiments.

The study is very complete on a technical level, and the conceptual weaknesses raised in the first round of reviews have been fully addressed in the revision.

Reviewer #2 (Public review):

Summary:

The manuscript "Quantifying microbial fitness in high-throughput experiments" provides a comprehensive analysis of the various approaches to quantifying fitness in microbial evolution, focusing on three primary factors: encoding of relative abundance, time scale of measurement, and the choice of reference subpopulation. The authors systematically explore how these choices impact fitness statistics and provide recommendations aimed at standardizing practices in the field. This manuscript aims to highlight the impact of differing fitness definitions and the methodologies utilized for analysis and how that can significantly alter interpretations of mutant fitness, affecting evolutionary predictions and the overall understanding of genetic interactions in the experiments.

Strengths:

The choices for quantifying fitness in evolution experiments are critical and highly relevant given the increasing prevalence of high-throughput experiments in evolutionary biology. The authors methodically categorize fitness statistics and their implications, providing clarity on a complex subject. This structured approach aids in understanding the nuances of fitness measurement. The manuscript effectively highlights how different choices in fitness measurement can influence fitness rankings and the understanding of epistasis, which is important for modeling evolutionary dynamics.

Comments on revisions:

The authors have comprehensively addressed all previous comments and suggestions. In particular, the addition of the new methods section: 'A guide to calculate pairwise relative fitness under the logit encoding from bulk competition data' - significantly improves the clarity of the implementation and helps in the overall interpretation of the framework.

Reviewer #3 (Public review):

Summary:

The authors present analyses of different fitness measures derived from empirical data from yeast knock-out mutants and the long-term evolution experiment (LTEE) with Escherichia coli to explore discrepancies and identify preferred methods to estimate relative fitness in high-throughput experiments. Their work has three components. They first discuss the different "encodings" of relative abundance data and conclude that logit-transformations are preferred, because they transform nonlinear abundance trajectories into linear trajectories with greater predictive power. Next, they compare per-generation with per-growth cycle relative fitness estimates inferred from simulations of pairwise competitions based on published growth traits for the yeast strains and on published pairwise competition measurements for the LTEE data. Both data sets show quantitative and qualitative (i.e. rank order) discrepancies of estimates across different time scales, which are highlighted by considering possible underlying causes (i.e. trade-offs between growth traits) and consequences (i.e. epistasis among mutations affecting different growth traits). Finally, the authors compare simulated pairwise and bulk (i.e. where many mutants compete during a growth cycle in a single environment) competition assays based on the yeast knock-out mutants and demonstrate an optimal ratio of collective mutants to wild-type strains that minimizes both sampling error and overestimation of fitness estimates when compared with pairwise competitions.

Strengths:

The study deals with a highly relevant topic. Fitness is central to general evolutionary theory, but also poorly defined and implies different traits for different organisms and conditions. For microbes, which are often used in evolution experiments, high-throughput experiments may yield different measures to quantify abundance over time, from individual growth traits to bulk competition experiments. Hence, it is relevant to consider discrepancies among those measures and identify preferred measures with respect to predicting population dynamic and evolutionary processes. The present study contributes to this aim by (i) making readers aware of differences among commonly used fitness estimates, (ii) showing that simulated (yeast) and calculated (E. coli) competitive fitness may differ across time scales, and (iii) showing that bulk competitions may yield relative fitness estimates that are systematically higher than pairwise competitions. The study is rather thorough on the theory side, with extensive derivations and analyses of various fitness measures using their resource competition model in the Supplementary Information. The study ends with a few practical recommendations for preferred methods to infer relative fitness estimates, that may be useful for experimentalists and stimulate further investigations.

Comments on revisions:

I appreciate the thorough and effective response to all recommendations and have no further comments.

Reviewer #1 (Public review):

Summary:

This is a careful, well-powered treatment of age effects in resting-state MEG. Rather than extracting (say) complex connectivity measures, the authors look at the 'simplest possible thing' : changes in the overall power spectrum across age.

Strengths:

They find significant age-related changes at different frequency bands: broadly: attenuation at low-frequency (alpha) and increased beta. These patterns are identified in a large dataset (CamCAN) and then verified in other public data.

Weakness:

Some secondary interpretations (what is "unique" to age vs global anatomy) maybe go beyond what the statistics strictly warrant in the current form, but these can be tightened with (I think pretty quick) additions already foreshadowed by the authors' own analyses.

Aims:

The authors set out to replace piecemeal, band-by-band ageing claims with t-maps, and Cohen's f2 over sensors×frequency ("GLM-Spectrum").

On CamCAN, six spatio-spectral peaks survive relatively strict statistical controls. The larger effects are in low-frequency and upper-alpha/beta ranges (f2 approx. 0.2-0.3), while lower-alpha and gamma reach significance but with small practical impact (f2 < 0.075). A nice finding is that the same qualitative profile appears in three additional independent datasets.

Two analyses are especially interesting. First, the authors show a difference between absolute and relative spectral magnitude (basically within-subject normalization). Relative scaling sharpens spectral specificity of the spatial maps while absolute magnitude is dominated by a broad spatial mode that correlates positively across frequencies, likely reflecting head-position/field-spread factors. The replication of the main age profile is robust to preprocessing decisions (e.g. SSS movement compensation choices) - the bigger determinant of the effect is whether they apply sensor normalization (relative vs absolute).

Second, lots of brain-related things might be related to age and the authors spend some time trying to back out confounds / covariates. This section is handled transparently (in general I found the writing style very clear throughout) - they examine single covariates (sex, BP, GGMV, etc.) and compare simple vs partial age effects. For example, aging is correlated with reductions in global grey-matter volume (GGMV) but it would be nice to find a measure that is independent of this : Controlling for GGMV (via a linear model) reduces age-related effect sizes heterogeneously across space/frequency but does not eliminate them, a nuance the authors treat carefully.

This is a nice paper and I have only a few concrete suggestions:

(1) High-gamma<br /> There can be a lot of EMG / eye movement contamination (I know these were RS eyes closed data but still...) above 30-40 Hz and these effects are the weakest anyway. Could you add an analysis (e.g. ICA/label-based muscle component removal) and show the gamma band's sensitivity to that step. Or just note this point more clearly?

(2) GGMV confound control<br /> Controlling for GGMV reduces, but does not eliminate, age effects. I have a few questions about this: a) Could we see the residuals as a function of age? I wonder if there are non-linear effects or something else that the regression is not accounting for. Also, b) GGMV and age are highly colinear - is this an issue? Can regression really split them apart robustly? I think by some cunning orthogonalisation you can compute the effect of age independent of GGVM. I don't think this is the same as the effect 'adjusted' for GGMV (which is what is shown here if I'm reading it correctly). Finally, of course, GGMV might actually be the thing you want to look at (because it might more accurately reflect clinical issues) - so strong correlations are not really a problem: I think really the focus might even be on using MEG to predict GGMV and controlling for age.

Minor presentation edits:

It would be handy to see a single table listing each tested "analysis family" (e.g., sensors×frequency, source parcels×frequency), the multiple control used, and the permutation count. I kept wanting to see this as I was reading to compare back and fore.

I loved the power-planning content (section 3.2, the table with peak f2, CIs, contour plot). I think you could somehow make this even more explicit because people will use it a lot - both for this age/MEG domain and more generally as a template for other types of power planning in the field. Perhaps a "How to use this paper to plan N" guide in a paragraph? Power analysis is surely both "important and difficult" - but also not impossible. A flowchart?

Comments on the latest version:

The authors address all my initial points in their revisions and I have no further comments.

Reviewer #2 (Public review):

This paper describes application of the "GLM-Spectrum" mass univariate approach to examine the effects of age on M/EEG power spectra. Its strengths include promotion of the unbiased approach, suitable for future meta/mega-analyses, and the provision of effect sizes for powering future studies. These are useful contributions to the literature. What is perhaps lacking is discussion of limitations of this approach, in comparison to other methods.

An analogy is the mass univariate approach to spatial localisation of effects in fMRI/PET images. This approach is unbiased by prior assumptions about the organisation of the brain, but potentially also less sensitive, by ignoring that prior knowledge. For example, a voxelwise univariate approach is less sensitive to detecting effects in functionally homogeneous brain regions, where SNR can be increased by averaging over voxels. In the context of power spectra, the authors' approach deliberately ignores knowledge about the dominant frequency bands / oscillations in human power spectra. This is in contrast to approaches like FOOOF and IRASA, that explicitly parametrise frequency components. I am not saying these methods are better; I just think that the authors should acknowledge that these approaches have advantages over their mass univariate approach (in sensitivity and interpretation; see below). I guess it is a type of bias-sensitivity trade-off: the authors want to avoid bias, but they should acknowledge the corresponding loss of sensitivity, as well as loss of interpretation compared to model-based approaches (i.e., models that parameterise frequency; I don't mean the statistical models for each frequency separately).

An example of the interpretational loss can be seen in the authors' observation of opposite-signed effects of age around the alpha peak. While the authors acknowledge that this pattern can arise from a reduction in alpha frequency with age, this is an indirect inference, and a direct (and likely much more sensitive) approach would be to parametrise and estimate the peak alpha frequency directly for each participant, as done with FOOOF for example (possibly with group priors, as in Medrano et al, 2025, EJN). The authors emphasise the nonlinear effects of age in Fig 2A, but their approach cannot test this directly (e.g. in terms of plotting effects of age on frequency, magnitude, width for each participant), so for me, this figure illustrates a weakness of their approach, not a strength.

Then I think the section "Two dissociable and opposite effects in the alpha range" in the Discussion section is confusing, because if there is a single reduction in alpha peak frequency and magnitude with age, then there is only one "effect", not "two dissociable" ones. If the authors do want to claim that there are two dissociable age effects within the alpha range, then they need to do a statistical test, e.g., that the topographies of low and high alpha are significantly different. This then reveals another limitation of the mass univariate approach - that space (channel) is not parametrised either - so one cannot test for significant channel x effect interactions within this framework, as necessary to really claim a dissociation (e.g., in underlying neural generators).

While the authors show that normalisation of each person's power spectra by the sum across frequencies helps improve some statistics, they might want to say more about disadvantages of this approach, e.g., loss of sensitivity to any effects (e.g. of age) that are broadly distributed across majority of frequencies, loss of real SI units (absolute effect sizes) (as well as problems if normalisation were used for techniques like FOOOF, where the 1/f exponent would be affected).

Please give more information how artifactual ICs were defined. This may be important for cardiac artefacts, since Schmidt et al (2004, eLife) have pointed out how "standard" ICA thresholds can fail to remove all cardiac effects. This is very important for effects of age, given that age affects cardiac dynamics (even though the focus of Schmidt et al is the 1/f exponent, could residual cardiac effects cause artifactual age effects in current results, even above ~1Hz?).

Please could the authors clarify the precise maxfilter arguments, and explain what "reference" was used for the "trans" option - e.g., did the authors consider transforming the data to match a sphere at the centre of the helmet, which might not only remove some of the global power differences due to different head positions, but also be best for generalisation of the effect sizes they report to future studies (assuming the centre of the helmet is the most likely location on average)? And on that matter, did head positions actually differ by age at all?

Comments on the latest version:

I am happy with their revised version.

Reviewer #1 (Public review):

Summary:

This manuscript investigates how rhythmically presented stimuli support working memory by using task-trained recurrent neural networks (RNNs) endowed with short-term synaptic plasticity. RNNs trained with rhythmic sequences have a marginal performance increase (0.4%) over models trained with jittered input and show increased phase-locking and oscillatory organisation during the sample period. While the question addressed in this paper is highly relevant, the core conclusion that regular temporal structures provide a functional scaffold for sequence working memory lacks evidence. The extensive post-hoc filtering pipeline obscures whether there is phase coding or not, and whether or not the found oscillatory phenomena are truly emergent or a mathematical artefact of the analytical selection criteria.

Strengths:

(1) The manuscript addresses a highly relevant question.

(2) The introduction is nicely written and presents relevant background work.

(3) The authors' results are robust in the sense that they analysed and trained an ensemble of models instead of single networks.

Weaknesses:

(1) Misalignment between analysis epoch and core claims. The manuscript argues that temporal regularity supports sequence working memory. However, the majority of analyses focus on the sample/encoding period rather than the delay period during which memory maintenance occurs.

(2) Ambiguity in the neural code (rate vs. phase). The decoding accuracies suggest that the memory can be well decoded from the instantaneous activity, implying a rate (not a phase) code. This raises two questions:<br /> a) Can memory-related information be decoded directly from the oscillatory phase, particularly during the delay period?<br /> b) What would be the mechanism with which the increase in phase organisation improves a representation that seems otherwise decoded/represented from activity levels?

(3) Absence of any RNN activity plots. The manuscript would benefit from showing, e.g., single neuron response plots, raster plots, phase histograms of units, etc. Are there actually spontaneous oscillatory dynamics as the paper writes (line 243)? Can the authors show baseline activity (which is also supposed to be oscillatory, line 219)?

(4) Potential concerns in the analysis pipeline: The data undergo an intensive, selective pipeline that might be susceptible to introducing circularity and selection bias. I highlighted some points here:<br /> a) Many analyses are performed on (summed) data projected on demixed PCs (extracted from time-warped data). Crucially, dPCAs are not unsupervised; they already explicitly maximise the variance of interest.<br /> b) For the phase extraction during sample encoding: after dPCA percentile clipping, z-scoring, and z-score clipping are applied (lines 762-764), low-amplitude trials are excluded (lines 779-781), and there is further selection based on a valid-point criterion and r2 thresholding (lines 804-805). Do all of these selection criteria risk introducing bias?<br /> c) Some statistical assumptions are not explicitly evaluated. E.g., the sign-flip permutation test (lines 735-742) relies on sign-exchangeability.<br /> d) For selectivity analysis of oscillatory organisation (Figure 4C, lines 893-896): Units are first selected by ANOVA, and then on the selected units further stats (Power and PLV) are computed. Unless the further stats are completely independent of the ANOVA, this may introduce selection bias.<br /> e) The finding of stronger power around f0 given rhythmic inputs of that exact frequency seems somewhat circular (Figure 3A)?<br /> f) The dPCA description seems a little odd, e.g., line 674, for the ordinal component you would normally actually average (i.e., marginalise out) everything except the ordinal axis.

(5) Conflation of RNN learning dynamics with working memory mechanisms. The authors show that rhythmic input makes learning marginally easier, but in principle, both RNNs reach full performance (so working memory can be done as well with either case). To avoid the findings depending on learning dynamics, it could be of interest to test the RNNs trained with jittered input on fixed input (or retrain RNNs with both jittered and non-jittered input). It is also unclear if the small increase in performance (0.4%) can be expected to hold across different initialisations and/or learning rate /regularisation strengths.

(6) STSP. It is unclear if the findings rely on STSP being present or not (or what the role of STSP is in the model at all currently). Note that in Liebe et al. 2025, RNNs were trained on an almost identical task without STSP, and phase-coding was demonstrated in the models.

(7) Writing redundancy. The methods subsections "Population signal construction for oscillatory analysis" and "Oscillatory phase organization during sample encoding" seem to define exactly the same quantity with different characters (activity projected in dPCA space), which leads to confusion (in one section, z is the PC component, in another, it's the complex signal). There are also slightly different definitions of the wavelets in either section, for which the reasoning is unclear.

Reviewer #2 (Public review):

Summary:

The authors train E-I recurrent networks with short-term synaptic plasticity on a sequential delayed match-to-sample task, comparing regular versus jittered sample timing. They report a small accuracy gain under rhythmic input, a more separable population geometry during encoding, organization of internal oscillations around the dominant input frequency, a preference for temporal order over feature encoding, and improved decodability and persistence of stimulus information in both activity and synaptic efficacy. A delay-period perturbation shows synaptic efficacy contributes more than activity to maintenance.

Strengths:

The model is well-specified. Dale's law, the STSP formulation, the training objective, and the hyperparameters are all reported clearly enough to reproduce, and code is shared. The statistical machinery is appropriate, with cluster-permutation tests for the spectral analyses and across-network sign-flip tests rather than naive pooling. The temporal-order versus stimulus-direction dissociation in Figure 4C is the most interesting result. The negative association between phase locking and direction selectivity is non-trivial and argues against a simple global-gain reading, and it connects to Liebe et al. 2025. The serial-position decoding curves and the synaptic-versus-neuronal perturbation are well-motivated tests of the maintenance claim.

Weaknesses:

The behavioral effect is very small. Match accuracy is 0.991 versus 0.987, and non-match is 0.973 versus 0.969, on networks already at the ceiling. The entire mechanistic analysis is built to explain a roughly 0.4 percentage point difference, and the paper does not establish that this difference is functionally meaningful rather than a marginal byproduct of the timing manipulation. The IOI-dependence result meant to support it is weak, with an R-squared of 0.071 at a p-value of 0.029 on n of 67.

The core spectral and phase results are close to definitional and should be framed that way. The regularity index R is computed from IOI variability, the dominant frequency f0 is computed from the same IOIs, and the oscillatory metrics in Figures 3 and 4 are then measured relative to f0 and correlated against R. This shows that more regular input produces internal phase progression closer to the input-derived reference frequency, partly restating the input statistics rather than uncovering an independent network mechanism. The phase-locking-increases-with-regularity finding is the clearest case. This does not invalidate the analyses, but the manuscript currently reads them as a mechanism when much of the signal is built into the measurement.

The only genuinely causal manipulation is the delay-period shuffle, and it is underpowered at n of 15. Its main conclusion, that synaptic efficacy matters more than activity for maintenance, largely recovers prior STSP results (Mongillo et al. 2008, Masse et al. 2019) rather than establishing something specific to rhythm. The result the authors most want, that disrupting synaptic state removes the rhythmic advantage, is predicted in the Discussion but not tested.

The authors should add a control that breaks the circularity (a held-out f0/phase reference, or shuffling R against the metric) and run the causal STSP-disruption test that is mentioned in the Discussion.

The oscillatory framing is stronger than the model supports. Phase locking to a periodic input can reflect temporal predictability or repeated preparation without self-sustained entrainment, and the authors acknowledge this once but then use entrainment-style language throughout. The signals are extracted from firing-rate units and should not be read as LFP or EEG oscillations.

The authors should show raw single-unit and population activity so readers can verify the oscillations before the filtered pipeline. The delay perturbation largely recovers Mongillo 2008 / Masse 2019 rather than anything rhythm-specific, and the relationship to Liebe et al. 2025 should be addressed in the Results.

Appraisal and impact:

The authors largely achieve their stated aim of describing how temporal regularity constrains recurrent dynamics in this model, and the temporal-order preference is a useful prediction. The reach of the conclusions exceeds the evidence in two places: the functional importance of the behavioral effect and the degree to which the phase results are independent of the input construction. With the framing corrected and one causal test added, this would be a useful contribution to the modeling literature on timing and working memory rather than a definitive account.

Reviewer #1 (Public review):

Summary:

The authors explore how temporal information and decision-related dynamics are represented across FOF and ADS in rats. The authors used Neuropixels to record neurons simultaneously from FOF and ADS during a free-response auditory change-detection task. They then applied single-trial temporal decoding to estimate both the time elapsed since stimulus onset and the time remaining until movement initiation. When neurons in both FOF and ADS were sorted based on decoder weights, they showed ramping and transient bump-like dynamics aligned to stimulus onset. However, around the decision report, FOF showed a clearer ramping signal and stronger movement-aligned population reorganization than ADS. These results suggest that FOF and ADS share similar temporal dynamics during evidence evaluation, but that FOF undergoes a stronger reorganization near decision commitment.

Strengths:

(1) The authors recorded large-scale neural populations simultaneously from FOF and ADS, allowing direct and fair comparison between them in the same sessions.

(2) The free-response auditory change-detection task, which requires rats to evaluate sensory evidence over time and initiate a decision report, is suited to address the question. The behavioral results support that rats used sensory evidence to guide their choices.

(3) The authors used multiple approaches, including single-trial temporal decoding, decoder-weight PCA, PC loading trajectory, and population-geometry analyses, to explore the FOF and ADS dynamics. These methods provide converging evidence supporting that FOF and ADS share similar temporal dynamics during evidence evaluation but diverge around movement/decision commitment.

(4) The population-geometry analysis is quite strong and interesting because it compares epoch-specific neural subspaces and quantifies dimensionality and subspace alignment, showing stable subspaces during evidence evaluation and stronger subspace reorganization in FOF near movement initiation.

Weaknesses:

(1) The manuscript failed to include histological confirmation of probe placement.

(2) The direct FOF-ADS decoding comparison in fig 3f and 4f includes only 16 of 61 sessions because of imbalanced unit counts. While controlling for unit number is important, excluding most sessions may waste data. Restricting analyses to only 16 sessions questions the generalizability of the result.

(3) Fitted regression curves, and ideally confidence intervals, were missing from Figures 3c and 4c. Also, the confusion matrices in Figures 3a/b and 4a/b show a strong preference for predictions in the first and last time bins. The authors did not explain whether this reflects meaningful event-aligned neural activity or an endpoint artifact from decoding time as bounded discrete classes.

(4) The interpretation of the neuron groups defined by PCA on the decoder-weight matrix was confusing. The authors perform PCA on an N units by T time-bin matrix of LDA decoder weights, then group neurons according to their PC1 and PC2 scores. This is an interesting approach, but the current wording could make readers think that neurons at the extremes of PC1 or PC2 are necessarily the most important neurons for temporal decoding. In fact, these groups appear to represent neurons whose decoder-weight profiles project strongly onto the dominant weight-space patterns. They are not necessarily the neurons that contribute most strongly to decoding accuracy, nor are they necessarily the most common firing-rate dynamics in the raw neural population.

(5) Discussion is missing some needed context. First, given the causal role of ADS in evidence-accumulation-based choices (Yartsev et al., 2018), and its position as a key node that may integrate input from FOF (Brody & Hanks, 2016), the weaker decision-aligned transition in ADS compared with FOF should have been further discussed. If ADS contributes causally to the decision process, why does it show a much weaker population-state transition near decision commitment in the present data? Second, in DePasquale et al. (2024), more extensive choice vacillation was found in ADS, while greater choice certainty was found in FOF. Does this follow the same principle as the current manuscript, where FOF shows stronger reorganization near decision commitment compared to ADS?

(6) Current analyses do not fully exploit the simultaneous nature of the recordings. Apart from the comparison of decoding accuracy, most analyses could have been performed and compared based on the data collected independently from two regions.

(7) Figures 3-10 are hard to read and unpolished. Fonts are too small, and legends/labels are redundant.

Reviewer #2 (Public review):

Summary:

This work investigated differences in the temporal dynamics of neural populations in frontal orienting fields (FOF) and anterior dorsal striatum (ADS) in rodents during an auditory change detection task. The relative roles of these two regions have been studied previously and have been shown to play a role in the accumulation of evidence, with FOF converting this evidence into a categorical decision. By focusing on the temporal dynamics of neurons in these regions, the authors identified a subpopulation of neurons within FOF that displayed an abrupt ramping of activity near the time of decision commitment. Both FOF and ADS contained subpopulations exhibiting ramping activity aligned to stimulus onset. This is an interesting finding, suggesting that FOF contains a subpopulation of neurons that transforms accumulating evidence from other subpopulations in ADS and FOF into an action.

Strengths:

The conclusions of this paper are mostly well supported by data.

Weaknesses:

(1) In the neural analysis, the authors use a technique in which the weights of a linear decoder are used to define a feature vector for each neuron. These weights are used to measure the overall contribution of a neuron in decoding time (from stimulus or decision commitment). Interpreting decoding weights in this way is technically not correct (Kriegeskorte and Douglas, "Interpreting encoding and decoding models"), as a large weight in a decoder is not necessarily indicative of a large effect. Weights in decoding models can become large in order to cancel noise. Alternative analyses, for instance, treating the time series of each neuron as a feature vector, could have supported the conclusions from this technique.

(2) In this same analysis, it appears that the abrupt change in response in FOF at the time of decision commitment is coming from a single subpopulation of about 130 neurons. In the example session (Figure 8J), there is a clear outlier (the neuron in the top right corner). A closer inspection of the single neuron responses in this group would strengthen the results to confirm the abrupt change in mean population response is not coming from a relatively small number of neurons and sessions.

(3) The significance of the dynamical motif corresponding to transient bumps was unclear. For example, when looking at Figure 6K-L, I do not see any neuron groups that exhibit a clear transient bump. I would characterize all groups as ramping, with some groups showing steeper ramps. It would be helpful if the figure displayed the fraction of variance explained by PC2 so that it would be clear how much variance the bump motif is contributing. Given that there was no discussion of the functional relevance of this second motif, interpretation of this result is unclear.

(4) The finding that FOF contains subpopulations which slowly ramp during the trial as well as a subpopulation which acts like a switch that abruptly turns on at the time of decision commitment is interesting and significant and presents several computational questions. For example, is this subpopulation a non-linear readout of the more slowly ramping populations? The approach based on constructing a feature vector for each neuron, projecting these vectors into a low-dimensional subspace, and partitioning into subpopulations is insightful and allowed distinguishing these different computational functions within a single region (FOF). However, I found this particular result to not be clearly stated and obscured by other seemingly less significant results (e.g., existence of the transient bump motif) and other less interpretable analyses (e.g., subspace re-alignment).

Reviewer #3 (Public review):

Summary:

This study investigates how frontostriatal circuits encode elapsed time and exhibit decision-related dynamics during an auditory change-detection task. Using population-level temporal decoding and analyses of low-dimensional neural dynamics, the authors compare activity in the frontal orienting field (FOF) and anterior dorsal striatum (ADS). The manuscript addresses an important question in systems neuroscience: how cortical and striatal circuits represent elapsed time and signal action initiation during decision-making.

The results suggest that FOF and ADS differ in how they represent decision-related information near decision commitment or behavioral report. In particular, FOF shows greater movement-aligned changes in temporal decoding and population geometry than ADS. These findings are potentially important because they may help clarify how cortical and striatal circuits contribute to timing, decision formation, and action initiation.

Strengths:

A major strength of the study is its use of population-level analyses to identify temporal structure and movement-aligned changes in neural dynamics. The analyses provide evidence that neural dynamics and low-dimensional population geometry change around the time of behavioral report, especially in FOF. This provides a useful population-level description of decision-related dynamics beyond what could be inferred from average firing rates alone.

Another strength is that FOF and ADS activity were recorded simultaneously during the same auditory change-detection task. This design strengthens the regional comparison by minimizing confounds related to session-to-session variability, including differences in task engagement, decision accuracy, or other behavioral variables across recordings. The simultaneous recordings therefore provide a strong basis for comparing temporal decoding and population dynamics between cortical and striatal circuits.

Weaknesses:

One limitation is that the physiological interpretation of the population-geometry analyses remains somewhat abstract. Concepts such as low-dimensional subspaces, subspace alignment, and subspace rotation are potentially powerful, but it is not always clear what specific changes in neural activity give rise to these effects. For example, it is difficult to tell whether changes in population geometry primarily reflect recruitment of different neurons, or changes in the dominant temporal profiles of the same neurons. This limits the physiological interpretability of the population-level findings.

A second limitation is that the mechanistic interpretation of the FOF-ADS difference remains underdeveloped. The observed differences could reflect an internally generated transition in frontostriatal dynamics, similar to the dynamical-regime and neural-mode transition described by Luo et al. (2025). Alternatively, they could reflect a circuit-readout process, analogous to the framework proposed by Stine et al. (2023), in which cortical activity drives threshold crossing in a downstream circuit, triggering orienting or motor signals that terminate the decision process. The current manuscript describes the regional differences clearly, but it does not fully discuss these mechanistic interpretations.

Finally, the strength of the evidence would be easier to evaluate if the manuscript more clearly reported the number of animals contributing to each major analysis and the consistency of the main effects across animals. Because many analyses are performed across sessions, the absence of this information makes it difficult to assess whether the key findings are robust across animals or could be influenced by one or a small number of animals.

Reviewer #1 (Public review):

Summary:

Here, the authors examine how CRH neurons in the PVN track social behaviours. They use fiber photometry to record the bulk activity of PVN CRH neurons during the resident-intruder test. They find that PVN CRH activity increases when the intruder enters, and also when mice make movements to approach the intruder. They further show that the magnitude of this response differs depending on the familiarity of the mouse. Specifically, if the intruding mouse is unfamiliar, there is a greater PVN CRH response relative to a familiar mouse. The authors argue that this is specific to social familiarity, as they do not see the same differentiation in the PVN CRH response when mice approach a familiar or unfamiliar object. Finally, the authors conduct optogenetic experiments and show that inhibition of PVN CRH neurons reduces social investigative behaviour. The authors then conclude that PVN CRH neurons are a part of a decision-making circuit to influence behaviour in ambiguous settings, specifically that they are a "key component of the neural circuitry underlying rapid social appraisal, linking endocrine regulation to real-time behavioural decision making".

The data are interesting and novel. They help us understand the dynamics and range of situations in which PVN CRH neurons are activated. There is some overinterpretation of the data and restriction of what this signal means (i.e., specifically driven by unfamiliar social situations), which doesn't seem to be supported by the data. Indeed, PVN CRH neurons are robustly activated by scenarios outside unfamiliar social ones.

Strengths:

The experiments are run and presented very beautifully in a sophisticated way. The data are novel and interesting. They help us understand the time course of PVN CRH responding in social and object settings, and how this differs with the familiarity of a social stimulus.

The optogenetic manipulation is also very nice. The authors optically inhibit during just the first 20 seconds of the resident-intruder test. They find that this inhibition results in a long-term reduction in social behaviours. To me, this supports an idea that the PVN CRH signal triggers a cascade of behaviours, but is not necessarily driving these behaviours per se.

Weaknesses:

It would be great to see more sophisticated analysis of the fiber photometry data, which may reveal interesting effects that are currently being occluded by static AUC analysis. One pipeline that is freely available that could be used is found in Jean-Richard-dit-Bressel, Clifford, and McNally (2020) Frontiers in Molecular Neuroscience. Referred to as waveform analysis, this would allow the authors to examine the significance of their data across time. There are multiple points at which this would be interesting. For example, in Figure 3F, it is possible that differences between the familiar and unfamiliar objects emerge. Also, there seems to be one outlier in this figure in the familiar object group. What happens if it is removed (Figure 3H)?

Similarly, what do these signals look like when aligned with making contact with the social or object stimuli? It is possible that the objects do not elicit a difference depending on familiarity when approaching because: (1) they are not moving, and (2) it is unclear whether they are familiar or not until contact is made, consistent with the object recognition literature. What would inhibition of the PVN CRH signal do to investigative behaviours directed towards objects?

Finally, given the robust nature of the response to the approach to the objects and familiar mouse, why is this signal being argued to predominantly act in unfamiliar social settings? The lack of difference between the familiar and unfamiliar objects doesn't negate the importance of this signal. To me, this is the most interesting finding: PVN CRH neurons that are usually activated in stressful situations can also be robustly activated by familiar objects. Relatedly, while the authors argue that inhibition of PVN CRH neurons only reduces social behaviours in the unfamiliar case, there is likely a floor effect in the behaviours that they are looking at, which occludes observation of a reduction via optical inhibition.

Reviewer #2 (Public review):

Summary:

The authors investigated the role of hypothalamic CRH neurons in social behavior. They performed fiber photometry recordings in mice from CRH neurons and showed that novel conspecifics trigger stronger and more prolonged responses compared to familiar conspecifics and objects. The activity of CRH neurons appears to be related to risk assessment, as interactions with juvenile unfamiliar mice (lower-risk conspecifics) trigger responses similar to those of familiar adult mice. Behaviorally, CRH neurons were linked to increased anogenital investigation of unfamiliar compared to familiar mice. Optogenetic suppression of CRH neurons decreased anogenital sniffing of unfamiliar conspecifics.

Strengths:

The manuscript is elegant, and the results are compelling. The approaches are well justified, and the methods are validated (eg: Arch inhibition).

The findings substantiate the role of CRH neurons in responses to stress and uncover the involvement of these neurons in the assessment of social risk.

Weaknesses:

These are not weaknesses, just some observations: It is somewhat surprising that CRH neurons respond similarly to familiar and unfamiliar objects; it would be good to have more insights into that aspect.

Similarly, the novel context by itself is expected to lead to increased activity of CRH neurons (based on data from the last author's lab as well as other labs in the field). It is somewhat surprising (and interesting) that the novel environment did not affect the magnitude of CRH responses to unfamiliar conspecifics.

Reviewer #1 (Public review):

[Editors' note: the authors have revised the work in response to the original reviews.]

Summary:

This paper describes experiments with alpha-synuclein (aS) with acetylated lysines (acK) at various positions. Their findings on how to use non-canonical amino acid (ncAA) mutagenesis to generate aS with acetylated lysines are valuable. The paper then continues with a range of experiments to characterise the acetylated alpha-synuclein constructs at different positions, with the aim of providing insights into which sites are relevant to disease or their function inside cells. The paper concludes these experiments with the suggestion that inhibiting the Zn2+-dependent histone deacetylase HDAC8 to potentially increase acetylation at lysine 80 may have therapeutic benefit. However, the relevance of most of these experiments is unclear, mainly as the filaments that form from these constructs are different from those observed in human disease (but see below for more details). Moreover, using the recombinantly produced acetylated versions of alpha-synuclein to normalise mass-spectrometry data, the authors themselves report that acetylation of alpha-synuclein does not differ between individuals with Parkinson's disease or healthy controls.

Strengths:

The authors report difficulties with chemical synthesis and then decide to make these constructs using non-canonical amino acid (ncAA) mutagenesis, which seems to work reasonably well (yields vary somewhat). In the Conclusion section, the authors report that they used these recombinant proteins to obtain quantitative insights into the levels of acetylation of lysines in individuals with PD versus healthy controls, for which they find no significant differences. This part of the work is valuable.

Weaknesses:

The authors then use circular dichroism to show that aSyn with acK at position 43 has less alpha-helical content. From this result, they deduce that "only this site could potentially perturb aS function in neurotransmitter trafficking", but no experiments on neurotransmitter trafficking were performed.

Reviewer #2 (Public review):

Summary:

Shimogawa et al. studied the effect of lysine acetylation at different sites in the alpha-synuclein (aS) sequence on the protein-membrane affinity, seeding capacity in the test tube and in cells, and on the structure of fibrils, using a range of biophysical methods. They use non-canonical amino acid (ncAA) mutagenesis to prepare aS lysine acetylated variant at different sites.

Strengths:

The major strength of this paper is the approach used for the production of site-specific lysine acetylated variants of aS using ncAA mutagenesis, as well as the combination of a range of biophysical methods together with cellular assays and structure biology to decipher the effect of lysine acetylation on aS-membrane binding, seeding propensity, and fibril structure. This approach allowed the author to find that lysine acetylation at positions 12, 43, and 80 led to lower seeding capacity of aS in the test tube and in cells, but only acetylation at lysine 80 did not affect aS-membrane interaction. These results suggest that lysine acetylation at position 80 may be protective against aggregation without perturbing the proposed functional role of aS in synaptic plasticity.

Weaknesses:

SDS is not a good membrane model to investigate the effect of lysine acetylation on aS membrane-binding because it is a harsh detergent and solubilizes membranes. Negatively charged vesicles or vesicles made of a mixture of lipids mimicking the lipid composition of synaptic vesicles are more accepted in the field to study aS-membrane interactions. The authors used such vesicles for the FCS experiments, and they could be used for the initial screening of the 12 lysine acetylated variants of aS.

Reviewer #3 (Public review):

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

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

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

Reviewer #1 (Public review):

In the manuscript by Fabian-Fine et al., the authors employ neuroanatomy to investigate aquaporin-4 expression in cells they consider tanycytes and their supposed involvement in tau tangles and amyloid-beta plaques in the hippocampus. This study includes samples from three mice and two Alzheimer's disease (AD) patients.

My key concern and question is whether the cells presented in the manuscript are tanycytes. Tanycytes are specialized ependymoglial cells located in the circumventricular organs and are known to express specific markers. Importantly, they are not myelinated cells, which is a crucial distinction that the authors do not address.

Additionally, the methodologies described in the manuscript lack clarity and controls. For instance, the use of Cdh5-GCaMP882 mice is not adequately justified. It is unclear what these mice contribute to the study's objectives, particularly concerning the aim of investigating waste removal processes in the brain. Moreover, the rationale behind the purported "fluorophore uptake experiments" is unclear and appears to involve the uptake of fluorophore-labeled goat anti-rabbit secondary antibody, which seems implausible to me.

The hypotheses and claims presented in this manuscript are not sufficiently substantiated and are conceptually unclear. The notion that amyloid beta and tau proteins play structural roles in a hypothesized "tanycytes"-derived canal network is not sufficiently supported by the evidence. Furthermore, the study lacks rigorous data to convincingly establish the proposed interactions between these proteins and the processes of waste internalization.

In conclusion, due to conceptual and methodological issues, I consider the current evidence as inadequate to support the primary claims.

Reviewer #2 (Public review):

Summary:

In this study, the authors propose the existence of an AQP4-positive tanycyte-associated canal system in the hippocampus and suggest that this system participates in waste clearance and contributes to Alzheimer's disease pathology. Using histological, ultrastructural, immunohistochemical, and RNA-based approaches, the manuscript attempts to reinterpret amyloid-β plaques and tau-associated structures as components of a tanycyte-derived waste-internalization system. The work is conceptually ambitious and raises observations that may stimulate discussion regarding glial organization and waste clearance in the diseased brain.

Strengths:

A strength of the manuscript is the combination of imaging modalities and anatomical observations across mouse and human tissue. Some of the reported morphological features are intriguing and may warrant additional investigation. The study also attempts to integrate structural observations with broader hypotheses regarding neurodegeneration and Alzheimer's disease.

Weaknesses:

The central interpretation depends almost entirely on identifying the observed hippocampal structures as tanycytes, and the evidence supporting this conclusion remains insufficient. Tanycytes are classically associated with ventricular regions in circumventricular organs, particularly in the third ventricle and median eminence region, yet the manuscript does not provide sufficiently specific anatomical or molecular evidence to convincingly distinguish the described structures from astrocytic, ependymal, radial glial-like, oligodendroglial, myelin-associated, vascular-associated, or degenerative elements. The marker profile used throughout the study, particularly the reliance on AQP4 labeling and Luxol-positive structures, is not sufficiently selective to establish tanycyte identity, especially in pathological tissue where reactive glial changes may occur.

This becomes particularly important because the manuscript repeatedly interprets Luxol-positive and myelin-associated structures as tanycytic processes or "myelin-derived tanycyte protrusions," despite tanycytes not being known to produce myelin. Alternative explanations are not sufficiently explored. Some of the canal-like structures shown in Figure 4 also resemble vascular profiles, and additional vessel markers would be necessary to exclude this possibility.

Several of the proposed structures and mechanisms are also difficult to reconcile with established cell biology and neuroanatomy. The introduction of new terminology such as "tanysomes," "waste receptacles," and "toroids" further extends the interpretation beyond what is currently demonstrated experimentally.

The discussion and integration of the existing literature on tanycytes are also insufficient. Tanycytes themselves are not clearly introduced; the manuscript does not adequately discuss what is currently established regarding tanycyte anatomy, ventricular localization, morphology, and function. Foundational literature defining tanycyte biology, including work from the Prévot group or others, is largely absent despite its central importance to the field. Because the manuscript proposes a substantial departure from established neurobiological concepts, it is particularly important that previous literature be discussed comprehensively and critically. The current version does not sufficiently contextualize the proposed model within the existing literature on tanycyte, AQP4, glymphatic, and Alzheimer's disease, making it difficult to evaluate what is genuinely novel versus what is merely being reinterpreted. It is also not entirely clear what is genuinely new here compared with the authors' previous work, particularly reference 11, which appears to present a highly similar conceptual framework.

More broadly, several of the manuscript's mechanistic conclusions extend well beyond the available evidence. The proposal that amyloid-β plaques and tau pathology represent hypertrophic tanycyte-derived waste structures is provocative and potentially interesting, but currently remains largely correlative and speculative. At several points, it becomes difficult to distinguish direct observations from broader mechanistic interpretation. The manuscript itself acknowledges that the proposed glial-canal hypothesis contradicts the current understanding of nervous system organization and states that ultrastructural serial-section analysis would be required to unambiguously determine the origin of the myelinated profiles described. This point is critical because the study's central conclusions depend on the assumption that these structures are tanycyte-derived. At present, this interpretation remains insufficiently demonstrated, which substantially limits the strength of the broader pathological and mechanistic conclusions proposed throughout the manuscript.

Although access to human material is understandably limited, the study appears to include only one male and one female AD patient, making it difficult to assess the reproducibility or frequent these structures are across individuals and pathological conditions. The manuscript would benefit from clearer characterization of prevalence, reproducibility, and variability across samples.

Overall, the manuscript presents an unconventional and thought-provoking model that may stimulate discussion. However, the evidence currently provided does not convincingly establish tanycyte identity for the described hippocampal structures, and several of the broader disease-related interpretations would require substantially stronger anatomical and molecular evidence before the proposed model can be convincingly supported.

Reviewer #1 (Public review):

Summary:

This manuscript describes the development and validation of a low-cost device to identify viruses from saliva samples of animals non-invasively. This device was tested under laboratory conditions to assess whether viruses could be recovered in different environmental conditions and after different durations of time. The devices were then used to sample mice and cats in shelters to assess utility.

Strengths:

Sampling animals is cost-effective and highly labour-intensive, and this device has the potential to substantially improve surveillance. The device is relatively low-cost, and the authors demonstrate that the virus can be obtained from these filter papers after different durations of time and in different environmental conditions.

Weaknesses:

The authors do not discuss if different volumes were obtained from different animals (for example, due to different behaviours or attractiveness of the odour baits). Additionally, it appears the virus results were cross-validated using the serological status of the animals. While I am not an expert on FIV, there seems that there could be potential for different levels of viral shedding, and it would be more prudent to cross-validate against blood or another gold standard sample. Finally, the statistical analysis could be improved as there appear to be relatively few replicates and limited analysis conducted.

Reviewer #2 (Public review):

Summary:

The study introduces an innovative device designed to collect non-invasive saliva samples from animals using disposable cassettes with odor attractants and filter paper. The authors aimed to validate this tool for pathogen monitoring, specifically by detecting pathogen RNA in animal models. While the concept is compelling and the problem statement well-framed, the validation of the device for pathogen detection was not achieved. For example, the rabies virus was not detected in the chosen model, and results were limited primarily to FeLV. The work highlights the potential of saliva-based sampling for microbiota analysis, but the rationale for virus selection and the experimental design require further clarification. Overall, the study presents a novel approach with promise, though its current scope is better suited to microbiota monitoring rather than pathogen surveillance.

Strengths:

The innovative design of the device, which enables non-invasive saliva collection through disposable cassettes with odor attractants, represents a creative and practical advance in sampling methodology. The authors undertook an extensive experimental effort, generating a substantial amount of data that highlights the feasibility of saliva-based monitoring. The rationale for exploring saliva as a medium is valid, and the work successfully shows that the device can be applied to microbiota profiling, where the strongest results were obtained. This methodological innovation could be valuable for expanding non-invasive approaches to animal health monitoring.

The authors acknowledge that metabarcoding sequencing has limitations; however, the study could be refocused on the microbiota in general rather than on pathogen detection. They could give greater prominence to the taxonomic composition of microorganisms in saliva using high-throughput sequencing. That is where they obtained the most results.

Weaknesses:

Despite the enormous experimental effort undertaken, the results fall short of the expected success of the proposed test. The rationale and criteria for virus selection are not clearly explained, leaving the experimental design insufficiently justified.

The central aim of validating the device for pathogen detection was not achieved, particularly in the case of the rabies virus. The mouse infection model used for the rabies virus does not seem to adequately replicate the natural course of the disease. This could explain, at least in part, the negative results obtained.

Of the three viruses evaluated, satisfactory results were obtained only for FeLV, and the sample size remains limited. According to the literature reviewed, this virus is not common in wild cats, so the applicability of the results would appear to be limited primarily to domestic cats.

The collected samples were stored at −80 {degree sign}C for later analysis, which likely contributed to the high Ct values observed with the device. The need to store samples at low temperatures may be a limitation to applying this technique in wildlife sampling scenarios where access to dry ice or liquid nitrogen tanks may be difficult.

Stating that the device can be used for pathogen monitoring in wild animals is not desirable, since the viruses for which results were obtained are not relevant in wild animals. On the other hand, claiming that this is a tool for monitoring diseases in endangered species is also misleading. Endangered species are typically scarce and therefore would not be the reservoirs that these surveillance efforts should target. In fact, groups such as wild rodents would be a better target for monitoring zoonotic pathogens.

Reviewer #1 (Public review):

Summary:

This carefully executed study uncovers the functional relevance of curl signals that impinge on the retina every time an observer's gaze direction and movement direction are not aligned. This finding is important, highlighting the functional role of an abundant incidental signal (curl in retinal motion) that has thus far believed to be a nuisance that needs to be filtered out of the retinal motion stream. As such, the study forms an important contribution to the emerging recognition that incidental sensory signals are not a challenge to the sensorimotor system, but contain functionally relevant and effectively used visual signals. The study's evidence is compelling: A combination of psychophysical experiments and critical manipulations, control theory and neural modeling makes an internally consistent and biologically plausible case for the role of curl signals in estimating heading direction. The experimental and modeling results clearly go beyond previous studies and significantly advance our understanding of vision-based navigation.

Strengths:

The study has its strengths in the combination of psychophysical experiments and critical manipulations, control theory and neural modeling, which together make an internally consistent and biologically plausible case for the role of curl signals in estimating heading direction.

This study uncovers the functional relevance of curl signals that occur on the retina when an observer is moving and gaze is not straight ahead. The experimental and modeling results clearly go beyond previous studies and significantly advance our understanding of vision-based navigation.

Another clear strength is that the study uses tightly controlled experimental manipulation to provide strong test cases for the hypothesis that curl is used for visual navigation. These conditions are important to constrain the proposed model (and future models) of heading control.

The modeling is very clearly described and the modeling and analysis code is published and freely available. The authors go beyond a back-of-the-envelope control model and show how it might be implemented at the neural-circuit level. The model is biologically plausible.

Weaknesses:

I see no major weaknesses of the study. I expect it to inspire future research that extends these findings to a wider range of visual environments (including walking in natural scenes), motion speeds and kinds of movements.

Comments on revised version.

I have no additional comments for the authors.

Reviewer #2 (Public review):

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

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

Major issues:

(1) The manuscript contains inconsistent, if not misleading, messaging about what information retinal curl does, and does not, provide regarding heading estimation. In the Abstract, the authors state: "We propose an alternative: the visual system utilizes retinal curl directly to estimate heading, rendering the explicit recovery of the FOE unnecessary." Based on my understanding of the rest of the manuscript, I find this statement to be a misrepresentation for two main reasons:<br /> a. To "directly estimate heading" relative to what? When not qualified, most people interpret "heading" to mean an observer's heading relative to the world (or some allocentric reference frame). But retinal curl only gives information about an observer's heading relative to the point on which their eyes are fixated. Moreover, that point of fixation will change every few hundred milliseconds in natural viewing, so the retinal curl will change with each new fixation even as heading relative to the world remains unchanged. So, I think most readers would grossly misinterpret the claim that retinal curl can be used "directly to estimate heading". Indeed, in the authors' controller model, the initial heading needs to be given and then the controller can work. But from where does the visual system get the initial heading, since it does not come from curl? These issues are left hanging. Thus, while curl can provide a very useful input for steering toward a fixated target, other signals are needed to estimate heading relative to the world. This has to be made much clearer early on, and a conceptual schematic diagram might help. Also, the authors generally do not specify the reference frame of the variables they are talking about, leaving lots of room for misinterpretations. It should be clear each time they are talking about a variable, such as heading, whether it is relative to the fixation target, body, world, etc.<br /> b. It seems to me that retinal curl will depend on other variables, in addition to heading relative to the fixation target. For example, it seems to me that the magnitude of retinal curl will depend on self-motion speed, the depth structure of the scene, the angle of elevation of the fixated target, and perhaps others. This is not discussed at all, and many readers would get the misguided impression that there is a 1:1 mapping from curl to heading (relative to fixation). If I am right that this is not correct, it means that retinal curl can tell the observer whether to steer right or left to move toward the fixated target, but it cannot tell them how much to steer. Indeed, in the authors' controller model, there is a free parameter that calibrates curl to angle. It makes sense that this works to fit trajectory data that are given from a fixed environment, but it is unclear how the brain would use retinal curl to control steering when these other variables are uncertain or changing unpredictably. Moreover, how does the system change the mapping from curl to steering command as the location of fixation changes relative to the current heading? These are issues that need to be brought up in framing the problem and discussed at some length. If the authors can show mathematically that retinal curl is only dependent on heading (relative to fixation) and not any of these other variables, it would be very valuable to show the equations for this relationship.

(2) The description of the behavioral experiment and presentation of behavioral data leaves a lot to be desired.<br /> a. First, it is stated (line 158) that "Participants continuously reported their perceived direction of self-motion while maintaining fixation on the yellow dot." Again, reference frame is completely unspecified. Participants were reporting their perceived heading relative to what? The fixation target? The world? What exactly were the instructions given to the subjects to perform the task? Based on the description of how perceived paths are computed (line 166-), it seems to be presumed that subjects are reporting their heading relative to the world because those angles are then converted into x and z coordinates in what I presume is a world-centered reference frame. But how do we know that subjects are accurately reporting their heading relative to the world? What if they are biased in their reports by the location of the fixation target relative to the scene, or by some other reference signal? Is it possible for the authors to rule out the possibility that perceptual biases seen in the unaltered curl condition result from observers not fully adopting the assumed reference frame of the task? If this cannot be firmly excluded, it seems to create problems for the rest of the study.<br /> b. I also feel that there is a mismatch between what the behavioral task requires and what the controller model does. Subjects are apparently asked to report their heading relative to the world, but the controller model only controls their heading relative to the point that they are fixating. I understand how this is resolved in the model, but I think this type of distinction is buried and will not be apparent to most readers. Again, the reference frames of what is being measured and controlled need to be specified explicitly in all parts of the paper, and the authors needs to explain how the system would combine curl-based control with some other measures of (at least initial) heading for world-centered heading to be computed. All of the assumptions need to be clearly specified.<br /> c. Second, I found it frustrating that the authors never present raw perceptual data from the observers. Rather, in Figure 2, we see reconstructed trajectories that are perfectly smooth with no indications of noise whatsoever. Since these paths are computed from the perceptual reports, there must be some noise inherent in them. The figures should represent this uncertainty somehow, and it should be explained how these perfectly smooth trajectories are obtained.

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

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

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

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

Comments on revised version.

Overall, the authors have done a responsible job of responding to the comments of my previous review, and the manuscript is substantially improved. There are a few points on which I still do not completely agree with the authors, and I think these are important to document for the record:

(1) Introduction: "Pure visual decomposition should function regardless of 3D depth or whether the rotation stems from an active eccentric fixation." Perhaps in a world of noiseless perfect computation, this might be true. But I generally disagree. When there is more depth structure in an environment, then translation of the observer is generally going to create greater motion parallax. That is a fact that I don't think can be disputed. And greater motion parallax should help to decompose optic flow into components related to translation and rotation (the latter of which is not depth dependent), especially when there is noise in estimating location motion vectors.

(2) Related to point #9 of my previous review: I had asked why the authors believed that retinal curl was computed in area MSTd. Their response is that previous studies (i.e., Graziano et al. 1994) show selectivity to spiral motion stimuli in MSTd. That is true, but those studies typically placed the spiral stimulus centered on the MSTd receptive field, hence they were not presenting something like retinal curl as defined here. So, I think it is still an open question as to where in the brain retinal curl is encoded, and from which areas it would be possible to decode retinal curl from population responses.

(3) Related to point #10 of my previous review: I had asked about biological plausibility of the gaze-centered inhibition signal in the model. The authors' response is that parietal neurons show gain fields in which response depends (usually monotonically) on eye position. This is true, but it is not a trivial jump from gain fields in individual neural responses to a gaze-centered inhibition signal, and I think the authors should have been more forthcoming about the lack of an established neural signal that directly signals what they want in their model.

(4) The authors point out that the perceptual biases they measure take a few seconds to emerge and they attribute this to temporal integration. But in their curl manipulations, they temporally average over a 2.4 second window in computing the curl signals that they use to cancel or over-cancel curl. So, it is not clear whether some of the delay in the behavioral effects might result from their computations.

(5) Related to point #13 of my previous review: I had asked about empirical evidence for the assumption of a relationship between the heading preferences of MSTd neurons and their receptive field locations. In response, the authors state that such a relationship is built into the Layton and Browning (2014) model. While that is a precedent, citing another model as a response to a question about empirical evidence is not a convincing response. If there is no empirical evidence to support such a relationship, it would have been better for the authors to acknowledge this.<br /> Given the way that the eLife review model works, it is not necessary for the authors to address these comments, but I think they should be included in the public review record.

Reviewer #3 (Public review):

Major strengths include the use of realistic retinal motion recorded during virtual walking, an elegant manipulation of curl, converging behavioral and modeling evidence, and grounding in control theory. This provides a novel and important contribution to our understanding of how the brain processes motion information and intuition about how that information might be used to guide steering. In addition, they provide a computational mechanism by which retinal flow curl can be used as a control signal.

The revised ms has been strengthened by more explicit discussion of the literature where there has been mixed evidence for the use of the Focus of Expansion. Since the ms is a strong test of the use of curl as a heading signal, this allows a deeper understanding of the importance of the finding and historical context. The ms has also been strengthened by a more explicit discussion of integration of the time-varying signal over periods of several seconds, which is an important demonstration. The implications of the ms are still a little unclear, as the results involve visual judgements in seated subjects. The use of different sources of information when humans walk from one place to another in real life may be complex and involve a variety of different sources of information.

Reviewer #1 (Public review):

Summary:

Poh and colleagues investigate dopamine signaling in the nucleus accumbens (ventromedial striatum) in rats engaged in several forms of go/no-go tasks, that differed in reward controllability (self-initiated reward seeking or cue-evoked/quasi-pavlovian), and in the specific timing of the action-reward contingencies. They analysis dopamine recordings made with fast scan cyclic voltammetry and find that dopamine signals vary most consistently to cues that signal a required action (go cues) vs cue signaling action withholding (no go cues). Through various analysis they report that dopamine signals align most clearly with action initiation and with the approach to the reward-delivery location. Collectively these data support aspects of a variety of frameworks related to accumbens dopamine signaling in movement, action vigor, approach, etc.

Strengths:

These studies use several task variants that consolidate a few different components of dopamine signal functions and allow for a broad comparison of many psychological and behavioral aspects. The behavioral analysis is detailed. These results touch on many previous findings, larger showing consistent results with past studies.

Weaknesses:

The paper is dense and could benefit from some revision to increase clarity of the figures, the methods and analysis. The inclusion of many tasks is a strength but also somewhat overshadows specific points in the data, which could be improved with some revision to focus. There is a lack of strong connection between some of the findings, which if revised would help to emphasize the impact of the work.

Reviewer #2 (Public review):

Here, the authors record dopamine release using fast-scan cyclic voltammetry in the nucleus accumbens/ ventromedial striatum (VMS) while rats perform variants of a go/no-go task. Two versions are self-paced, in that the rat can initiate a trial by nosepoking at the odor port at any time once the ITI had elapsed, whereas the other two require the rat to wait for a cue-light before responding. Two "long" variants also require either more lever-presses on go trials, or a longer nosepoke time for no-go trials, and also incorporate "free" trials in which the rat is rewarded for just heading straight to the food tray. The authors find that dopamine levels increase more during the response requirement for go than no-go trials, indicating a role for invigorating to-be-rewarded actions. Dopamine levels also steadily increased as rats approached the site of reward delivery, and the authors demonstrate quite elegantly that this was not due to orientation to the food tray, or time-to-reward, or action initiation, but instead reflects spatial proximity to the rewarded location. Contrary to previous reports, the authors did not discern any differences in dopamine dynamics depending on whether the trials were cue- or self-paced, and dopamine release did not scale with effort requirements.<br /> The manuscript is well-written and the authors use figures to great effect to explain what could otherwise be a hard-to-parse set of data. The authors make good use of the richness of their behavioral data to justify or negate potential conclusions.

Reviewer #1 (Public review):

This study by Gangadharan and colleagues provides significant progress towards a quantitative biochemical mechanism for Stu2 polymerase activity. A key conceptual advance is the novel application of an enzyme-like model, initially developed for the actin polymerase Ena/VASP, to Stu2.

Strength:

New refined affinity measurements for a Stu2 TOG domain using Bio-layer interferometry show more than an order of magnitude higher affinity of TOG domains to tubulin compared to previously published reports.

The findings reinforce the "concentrating reactants" or, more specifically, for TOG-domain proteins, the "tubulin-shuttling antenna" model, compared to the "polarized unfurling" model, a more speculative structural hypothesis.

The manuscript builds upon a series of previous manuscripts that showcase the profound intellectual engagement with microtubule polymerization mechanisms by TOG-domain proteins from the Rice lab, a thought leader in microtubule polymerization for over a decade.

Minor weakness:

The affinity discrepancy is not fully resolved by side-by-side measurements, which seem to be not feasible as not all buffer conditions are compatible with all assays.

Reviewer #2 (Public review):

Summary:

The manuscript from the Rice lab by Gangadharan et al., submitted to eLife, investigates the polymerization mechanism of the yeast microtubule polymerase Stu2. The lab has published a number of articles demonstrating the structural basis by which the two TOG domains of Stu2 each bind free tubulin heterodimers and has developed a tethered polymerization model by which the TOG domains drive polymerization by shuttling those tubulin subunits onto the microtubule plus end. A second model was proposed by Nithianantham et al. (eLife, 2018) based on a closed - to - open transitional state in which Stu2 unfurls and loads two longitudinal associated tubulin heterodimers onto the microtubule plus end. While the second model is not directly tested, the current work aims to further characterize/model the tethered polymerization model using a kinetic framework developed by developed by Breitsprecher et al. for Ena/VASP actin polymerization activity, using a model that is enzymatic (EMBO J., 2011). The general architecture and function of Ena/VASP on actin polymerization versus Stu2 on microtubule polymerization is a reasonable relation and hits upon, as the authors note, potential convergent mechanistic evolution across distinct cytoskeletal networks. The model effectively treats tubulin as the substrate, and the polymerized microtubule plus end as the product. If Stu2 is "enzymatic" in this framework, the model predicts it would behave with Michaelis-Menten kinetics, that there would a Vmax, and polymerase activity would either be "affinity limited" by TOG:tubulin affinity (KD) and/or "kinetically limited" by TOG:tubulin association (Kon) and transfer of tubulin to the microtubule plus end (Kt). The authors find that the Brietsprecher model works well for Stu2 activity, and that Stu2 best aligns with a "kinetically limited" model. The work is interesting and adds to the growing elucidation of the Stu2 microtubule polymerase model. While yeast microtubule polymerases are somewhat distinct in their architecture, there is significant overlap that findings from the manuscript can be utilized to inform the mechanisms of larger, more complex microtubule polymerases such as human ch-TOG.

Strengths:

The manuscript invokes the enzymatic model of Breitsprecher et al. used for Ena/VASP and conducts an elegant series of (mostly established) experiments to determine whether Stu2 microtubule polymerase activity aligns with the model - which they conclude does align, supported by the data/results obtained.

Weaknesses:

The authors used biolayer interferometry to measure TOG:tubulin affinity. The affinities obtained were significantly higher affinities than the lab obtained in an earlier publication using analytical ultracentrifugation. While differences in buffer and salt conditions may underlie these differences, additional runs using comparable buffer systems, or use of a third independent assay to measure affinities would have added rigor.

The discussion could be expanded to better compare and contrast the results with both existing polymerase models introduced in the introduction, as well as expanded to look at reversible enzymatic activity (microtubule depolymerization at low to zero tubulin concentrations) and microtubule plus versus minus end activity.

Comments on revised version.

The revised submission has addressed these comments adequately.

Reviewer #3 (Public review):

Summary:

This study by Gangadharan and colleagues seeks to establish a quantitative biochemical model for the microtubule polymerase activity of Stu2. Stu2 is the budding yeast member of the XMAP215 protein family, which is broadly conserved across eukaryotes. XMAP215 proteins play a wide variety of important roles in cells, and these are attributes to effects on microtubule dynamics. Many studies over the last ~20 years have shown that XMA215 proteins selectively associate with microtubule ends where they increase rates of microtubule assembly and disassembly. More recently, structural biology and biochemical studies by the authors and other groups have shown that the multiple TOG domains on XMAP215 proteins are tubulin-binding domains that selectively bind to curved tubulin, which is present in solution and at microtubule ends, but not to straight tubulin which is present in the walls of the microtubule lattice. This has led to the general model that XMAP215 proteins promote polymerization by delivering soluble tubulin to the growing plus end, and two distinct models have been proposed to explain the mechanism. The 'concentrating reactants' model proposed previously by the authors suggests that TOG domains grab hold of tubulin in solution and concentrate at the microtubule end. The 'polarized unfurling' model proposed by the Al Bassam lab suggests that XMAP215 delivers multiple tubulins to the end, using a stepwise mechanism involving different roles for each TOG domain. The current study seeks to improve our understanding of the mechanism by developing a quantitative model to explain the binding and release of tubulins, the number of Stu2 molecules at the end, and the overall rate of tubulin addition. The authors accomplish this goal using new experimental data. The final model fills in new details of the mechanism. The authors draw a comparison between Stu2 and the actin polymerase which bears similarity to the Ena/VASP and suggest a convergent strategy for cytoskeletal polymerases.

Strengths:

This is a focused and clearly written study that incorporates prior knowledge of XMAP215 and draws inspiration from the actin field. The data are clear and convincing, and the study accomplishes its goal of generating a new, quantitative model for Stu2. The model will be important for microtubule researchers to predict and test key points for altering XMAP215 activity across different organisms and potentially for different tubulin substrates. The comparison to Ena/VASP may also inspire similar comparisons across other microtubule and actin regulators, which could lead to new insights across cytoskeletal fields.

Weaknesses:

The study is without major weaknesses.

Reviewer #1 (Public review):

Summary:

The factors that create and maintain diversity in host-associated microbiomes remain poorly understood. A better understanding of these factors will help in the efforts to leverage the adaptive potential of the microbiome to help solve pressing problems in health and agriculture.

Experimental evolution provides a promising path forward as we can track the causes and consequences in the emergence of novel variants, but experimental evolution remains underutilized in host-microbiome interactions. Here, Gracia-Alvira utilizes a long-term experimental evolution study in Drosophila simulans under hot and cold temperature regimes to identify strain-level variation in an important fly bacterium, Lactiplantibacillus plantarum. They identify three strains of L. plantarum, which are most prevalent in their respective three temperature regimes, suggesting that these are locally adapted bacteria. Then, using a combination of genomics, in vitro, and in vivo, Gracia-Alvira et al attempt to understand the factors that led to the differentiation of the hot and cold L. plantarum and their impacts on the fly host.

Strengths:

This is an excellent use of experimental evolution to track the emergence of novelty in the microbiome. The genomic analyses are all solid and appropriate for the data sets. It is especially striking that the comparisons with the other, independent experimental evolution studies in different labs (and across continents between Portugal and South Africa) show a consistent response to temperature. Many have disregarded the microbiome as it is something that is too sensitive to seemingly innocuous variables (particularly in the fly microbiome), such that we cannot find generalities. However, this finding highlights the potential for experimental evolution to uncover these dynamics. The question of how strains emerge and are maintained is timely and is one of the key open questions in host-microbiome evolution currently.

Comments on revised version:

I thank the authors for their thoughtful responses to my concerns, and I appreciate the additional experiments to help resolve the questions about subspecies competition. The manuscript remains strongest in the genomic assessment of changes in the L. plantarum genomes, and it is striking and noteworthy that the isolates across multiple countries but same temperature conditions group together phylogenetically.

I appreciate the additional clarity also incorporated in this revision, but there are still a few key concerns that are unresolved about the microbial ecology described here. I will also note that I apologize if I missed something in the text as no line numbers were provided to point me to where the changes were incorporated in the revised manuscript.

(1) Competition has many different meanings and many different measurements (see Hart 2018 https://doi.org/10.1111/1365-2745.12954) -and incorporating the effects of competition in shaping an ecological community is, has been, and will continue to drive much research in community ecology. Measuring strain level competition is one of the major questions in host-associated microbiomes, and it is difficult-though there have been significant advances in doing so (see isogenic barcodes, e.g., Daniel 2024 doi: https://doi.org/10.1038/s41564-024-01634-9b, Ordon 2024 https://doi.org/10.1038/s41564-024-01619-8, as well as my previous suggestion to track the outcomes of competition). The inability to directly track and measure competition of the isolates remains a limitation of this manuscript. The authors' explanation of measuring competition is unusual, simplistic, and at times inconsistent.

They need to be crystal clear about their definitions, logic for making these inferences, and weaknesses in their approach. I think what the authors mean is that competition between the unevolved and C or H in their respective regimes leads to the decrease of the U clade over experimental evolution. But it is not clear how the authors are thinking about competition between C and H clades in the different temperatures.

The authors state that competition is inferred because changes in relative abundance across the time series-and this is unusual because there are alternative explanations that require no ecological interactions among sub-strains, as I described in my comments on the prior version. This is then combined with in vitro work that shows that the H and C clades can both grow in their mismatched temperature regimes-and thus I think it is to be inferred that because they can grow alone in vitro (and C isolates show lower growth than H isolates in hot temperature), then changes in the relative abundance over fly generations can be attributed to competitive interactions among C and H clades. But then the logic is inconsistent because then the authors just say that in vitro growth curves don't support the differences in relative abundance observed in the flies (lines 224-225). Then the authors argue is it about a combination of diet/sugar metabolism and temperature (line 373), which doesn't make any sense because temperature previously didn't matter (lines 224-225).

All of this is to say is that the authors need to make clear their logic to the readers-and explain these inconsistencies appropriately. To me, it suggests that there are clear methodological weaknesses that inhibit the ability to track competitive microbial dynamics. Because you can't really assess the microbial dynamics in vivo, it remains further unresolved why clade C isolates have such strong negative fitness effects on the fly but reach such high relative abundances in the C evolving flies. I find that this series of logical inconsistencies (and see my point #2) distracts from the important finding that the C and H clades evolved to utilize sugars differently from the U clade, which is an interesting finding!

(2) There are also inconsistencies in the patterns observed between the text and the figures. Some of this arises because the authors are not clear what comparisons they are making. For example, line 450 says that clade C outcompeted the other clades, which I presume means only in the cold temperature. Line 456 says that C and H isolates grow faster in the sugar-rich lab diet, but that is not really true because U and C have similar growth rates in Fig. 5, and U and H have similar growth rates in Fig. S4. The text about microbial load is a bit misleading (lines 271-273), as it is confusing that clade C is significantly higher load in both hot and cold temperatures (Fig. S6), which is counterintuitive given Fig. 4, 5, S4. But it is also overly speculative to say that these results suggest that fitness effects depend on microbial load of clade C without connecting the load to the fly fitness measures (and also given the inconsistency with the time series data from evolving lines). Please take care to more carefully phrase these statements to ensure the inference is supported by the experiment design (e.g., clarifying comparison) and statistics (e.g., ensuring agreement with what the figure shows).

(3) I understand the concern about focusing the reader on the L. plantarum strains. However, it should be clear to the readers that you did not examine the other parts of the microbiome, and that L. plantarum is often very rare in lab and wild fly populations. The data presented on Table S4 (cited line 552, I think citation at line 176 is incorrect) is confusing. If these were colonies picked and then identified, this should be explicit. If it is based off on colonies, then please clarify if this was sampled randomly or occurred when trying to enrich/focus on L. plantarum isolates. If the data was computational (e.g., Kraken to classify), then only taxa richness is not necessarily relevant, but please also include to the relative abundance of each taxa.

To me, this is relevant information to contextualize these results, particularly because you test this in both D. mel and D. simulans (apologies for the confusion over Mazzucco & Schlotterer 2021), and we have insight into how combinations of Lactobacillus and other taxa impact fitness (Gould PNAS 2018). If the results from D. melanogaster are not applicable to D. simulans, then the authors need to explain this. I understand if incorporating analysis of the broader microbiome is beyond the scope of this manuscript, but at least acknowledging the general rarity in Lactobacillus frequency in Drosophila microbiome and variation in fitness effects will more accurately contextualization these results.

One small point is that line 452 the citations are OK, but there are fly-specific examples to support this statement, like Gould PNAS 2018, Henry Proceedings B 2025.

Reviewer #2 (Public review):

Summary:

In this manuscript, Gracia-Alvira et al. investigated how environmental temperature affects competition among members of the microbiome, with a focus on intraspecific diversity, using the Drosophila model.

Notably, the authors identified three clades of Lactiplantibacillus plantarum from a natural population of Drosophila simulans collected in Florida. They tracked the dynamics of these three bacterial clades under two temperature conditions over the course of more than ten years. Using comparative genomics and phylogeny, they showed that these three bacterial clades likely adapted to their host independently in a temperature-specific manner. Further, by combining in vitro culture and in vivo mono-association assays, they demonstrated the functional divergence of these three bacterial clades phenotypically, including their growth dynamics and effects on host fitness. Lastly, they performed pathway analysis and speculated on key genomic variance supporting such functional divergence.

Strengths:

The laboratory evolutionary experiment in response to cold or hot environmental temperature is impressive, given its more than ten years of experimental time period. This collection of achieved microbiome samples paired with the fly host data can be a valuable resource for the field.

Comments on revised version:

The revised version has addressed my major points raised in the original review.

Reviewer #3 (Public review):

Summary:

The study presents an analysis of 297 pangenomes derived from 20 populations of Drosophila simulans, at 19 time points for fast-reproducing individuals in a hot environment, or at 10 time points for slow-reproducing individuals in a cold environment, over a period of more than 10 years. The authors select a particular microbial component of the pangenomes and study the dynamics of Lactiplantibacillus plantarum strains in two environments. They discover that the revealed operational taxonomic units could be divided into three phylogenetic clades, which have their own genomic and genetic features, different adaptive capabilities that depend on the environment, and have a distinct impact on the fitness of the host.

Strengths:

The authors prove that bacterial microbiome components are sensitive to the environment and could rapidly (years) be fixed in eukaryotic populations. This study establishes a tractable model that potentially enables the study of variability of the physiological influence of distinct strains of an important commensal species, Lactiplantibacillus plantarum, on the Drosophila host. It is clearly shown that this single species consists of several phylogenetically and functionally diverse strains. The authors did not limit their interest to their own model, but rather they have integrated a comparative approach by analysing phylogenetic relationships among 92 described L. plantarum strains.

Overall, the study is novel and delivers important discoveries of a longitudinal, well-replicated experiment, generating a substantial amount of genomic data. It highlights an important dimension of research that environmental selection operates at the subspecies level.

Weaknesses:

Even though the authors show only one particular example by conducting their longitudinal experiment, they honestly acknowledge failures important for interpretation of the biological significance of the results (gnotobiotic mono-association experiments was done with D. melanogaster, but not D. simulans) and therefore they state limitations of their conclusions (weaker effects in the non-axenic flies are due to the presence of other taxa or to higher-order interactions with other members of the microbiome). These interactions could significantly affect bacterial growth, metabolism, and physiological influence on the host.

The authors exploit the results of their experiment to speculate about a wide range of evolutionary phenomena, like within-species competition, ecological adaptation and evolution of the host, fitness advantage of bacteria to the host, the benefits of parasitism or mutualism, the domestication of the microbiome, etc. At the end, they conclude that their study "highlights that even subspecies diversity plays a key role in adaptation to environmental temperature". However, the potential mechanisms of such adaptation are barely discussed, so that the focus of the study shifts from the temperature-induced changes in microbial population structures toward metabolism-related adaptations of clade representatives that enable them to diversify their carbon and nitrogen sources. The role of the temperature factor remains elusive.

In addition to that, the paper has a clearly minimalistic experimental approach to address functional properties of the revealed L. plantarum strains, so that their own fitness, or their relationship with the Drosophila host, is characterised superficially. Therefore, the authors' discourse can be speculative rather than factual (especially when the authors use the expression "likely" to share their guesses in the "Results" section). Nevertheless, these minor drawbacks do not underscore the novelty of the discovered phenotypes and the importance of their further investigation.

Comments on revised version:

I have read the authors revisions and find them compelling and they address fully the minor points raised in my review.

Reviewer #1 (Public review):

Summary:

In this manuscript, Uphoff et al. propose a structural and mechanistic model in which the multidomain ECM protein SVEP1 enables Angiopoietin (ANG) binding to the orphan receptor TIE1, thereby promoting downstream receptor phosphorylation and signaling. Using AlphaFold-based modeling, the authors predict that the CCP20 domain of SVEP1 binds to TIE1, creating a composite surface that facilitates Angiopoietin association and TIE1 activation. The resulting ternary model (SVEP1-TIE1-ANG) offers a structural rationale for how SVEP1 converts TIE1 into a functional, ligand-responsive receptor. Additional models and biological assays suggest roles for other domains of SVEP1, such as CCP5-EGF-L7, although these interactions are predicted with low confidence. The authors interpret these findings as the first structural framework for how SVEP1 enables ANG-TIE1 signaling.

Strengths:

(1) The central hypothesis - that SVEP1 enables ANG binding to the orphan receptor TIE1 - is biologically compelling and addresses an important question in vascular biology.

(2) The AlphaFold-predicted ternary complex (SVEP1-TIE1-ANG) is plausible, high-confidence, and structurally consistent with prior functional data (e.g., poly-Ala scanning from Sato-Nishiuchi et al.).

(3) The authors' model offers a potential explanation for the previously observed role of SVEP1 in enhancing ANG signaling through TIE1 and may represent the first structural insight into TIE1's transition from orphan to ligand-activated receptor.

(4) The potential clinical implication - that a combinatorial ligand (ANG+SVEP1) can activate TIE1- could have translational relevance for vascular leak and inflammatory disease.

Comments on revised version:

The authors have adequately addressed my concerns.

Reviewer #2 (Public review):

Uphoff and colleagues present the results of a study focused on characterizing the binding of SVEP1 to TIE1 along with Angiopoietin-2. Starting with computational prediction of SVEP1 binding to TIE1, the authors identify the region of SVEP1 that serves as a high-affinity ligand for TIE1. Advanced studies identify a weak secondary binding site within SVEP1 that appears to be sufficient but not necessary for its interaction with TIE1 based on in vivo rescue experiments. The most novel contribution of the manuscript seems to be the identification of angiopoietin-1 and -2 as co-factors that seem to enhance the binding of SVEP1 with TIE1 and impact downstream AKT signaling. They propose a complex in which SVEP1 binds to TIE1 and ANG2.

Although the first set of results is essentially confirmatory, the identification of ANG-2 as a "co-factor" enhancing the binding of SVEP1 to TIE1 and associated downstream signaling (i.e., Figures 3 and 4) is novel and is of interest. However, the manuscript and its conclusions would greatly benefit from some clarifying details and additional experiments to ensure rigor and support specific claims.

Comments on revised version:

I have no further comments. The authors have addressed my concerns.

Reviewer #1 (Public review):

Summary:

This manuscript describes a study examining the relationship between microsaccades and covert attention. This question has been widely investigated, with numerous studies showing that during sustained fixation, when subjects covertly attend to a peripheral stimulus, microsaccades tend to be biased toward the attended location. Here, the authors ask whether this microsaccade bias reflects a shift of covert attention or the maintenance of covert attention. They conclude that the bias is primarily driven by attention shifts, a finding that also helps reconcile the seemingly conflicting results of prior research, where the bias was questioned in paradigms that largely involved attention maintenance rather than shifting.

Strengths:

A large sample size was used.

Weaknesses:

The main weakness is that the authors' response does not adequately resolve concerns about the robustness of the microsaccade analyses. The newly reported event counts reveal that the number of microsaccades per participant is very low, especially in Experiment 2, and highly variable across subjects. Because the key analyses rely on proportions of microsaccades toward versus away from the attended location, estimates based on so few events are likely unstable and may not provide reliable subject-level measures.

A second major concern is that several additional analyses introduced in the revision appear to suffer from the same limitation. The permutation analyses and angle-partition analyses may give the impression of statistical rigor, but if the underlying averages are based on very few microsaccadic events, the resulting probabilities are difficult to interpret. Further subdividing already sparse data into narrower angular bins likely makes the estimates even less reliable.

A third concern is that the authors have not fully addressed issues related to microsaccade detection and fixation control. The presence of very small-amplitude events with relatively high velocities raises the possibility that some detected microsaccades may be artifacts. The authors also did not implement the requested exclusion of microsaccades smaller than 5 arcmin or the suggested reanalysis using stricter fixation criteria. These omissions leave open the possibility that the reported effects are influenced by detection errors.

A fourth weakness is that some of the requested analyses or clarifications were addressed only superficially. The comparison with Brandolani et al. remains minimal, despite being highly relevant to interpreting whether the observed microsaccade-direction effect is transient or sustained. Similarly, the gaze-density plots do not show the raw gaze-position distributions that were requested and may therefore be misleading, because difference maps cannot determine whether subjects were actually fixating centrally.

Overall, the revision raises additional concerns rather than resolving the original ones. The main conclusions remain insufficiently supported unless the authors can demonstrate that the effects are robust at the individual-subject level, based on adequate numbers of microsaccadic events, reliable detection criteria, and appropriate controls for fixation behavior.

Reviewer #2 (Public review):

Summary:

This study aims to test the hypothesis that microsaccades are linked to the shifting of spatial attention, rather than the maintenance of attention at the cued location. In two experiments, participants were required to judge an orientation change at either a validly cued location (80% of the time) or an invalidly cued location (20% of the time). This change was presented at varying intervals (ranging from 500 to 3,200 ms) after cue onset. Accuracy and reaction times both showed attentional benefits at the valid versus invalid location across the different cue-target intervals. In contrast, microsaccade biases were time-dependent. The authors report a directional bias primarily observed around 400 ms after the cue, with later intervals (particularly in Experiment 2) exhibiting no biases in microsaccade direction towards the cued location. Noteworthy, it would have been interesting to observe whether directional biases in microsaccades are also evident when compared to a neutral condition. The authors argue that this finding supports their initial hypothesis that microsaccade biases reflect shifts in attention, but that maintaining attention at the cued location after an attention shift is not correlated with microsaccade direction.

Strengths:

The results are straightforward given the chosen experimental design. The manuscript is clearly written, and the presentation of the study and its visualisations are of a high standard.

Weaknesses:

The link between attention and microsaccades has been the subject of extensive research over the past two decades. The authors present a potential solution to the conflicting past findings, arguing that attention should be considered a dynamic process that can be broken down into an attention shift and a sustained attention phase. To differentiate between the two components, the authors varied the interval between the onset of the attention cue and the test stimulus. It would have been nice to use a theory-driven criterion (or an independent measure), in addition to their data-driven approach, to distinguish between these components of a dynamic attention concept. Moreover, it is important to note that the current experiments take a purely correlational approach.

Reviewer #1 (Public review):

In this article, the authors investigate how glutamate transporter function regulates excitability and synaptic coding in T-stellate cells in the mouse ventral cochlear nucleus. They test this in acute brain slices using whole-cell electrophysiology and artificially raise the relative local concentration of glutamate via pharmacological inhibition of transporter proteins. The main finding is that when sub-saturating doses of DL-TBOA are applied, cells become much more sensitive to synaptic input, diminishing the normally high fidelity of EPSP-spike coupling in these neurons. Notably, high-frequency stimulation in the presence of DL-TBOA reveals a large and slowly decaying AMPA receptor component that underlies persistent/rebound firing in earlier recordings. These effects are not seen in other ventral cochlear neurons, suggesting that rapid glutamate clearance in T-stellate cells, particularly, is important for auditory intensity coding. Overall, these experiments are well-performed, and the findings are robust, though there are some aspects that could be expanded to make the work more impactful. These include a better understanding of the relative contribution of neuronal vs glial transporters and an ability to separate the relative contributions of tonic glutamate concentrations in the cleft vs changes in membrane potential in action potential output. Additionally, there were some minor issues of clarity in both the figure presentation and the main text language that should be addressed.

Major Points:

(1) Given the dramatic effect of saturating DL-TBOA on tonic leak/RMP and that the sub-maximal concentration used in most of the experiments still varied between 25-50 uM, Figure 1 would be strengthened substantially by a dose-response curve. Ideally, 5 or 6 concentrations, plotting the effect on tonic current or RMP increase.

(2) Examining the contribution of glial (EAAT1/2) vs. neuronal (EAAT3) transporters (Fig 8) is intriguing but comes across as incomplete here, especially given the small number of recordings. Using a different non-selective EAAT inhibitor (TFB-TBOA) to chase the EAAT1/2 blocker combo seems like an odd choice, given that you have already characterized the effects of DL-TBOA well. One could also try a lower concentration (~50-100 nM) of TFB-TBOA since it is somewhat selective itself for glial EAAT1/2. Given the data presented, neuronal transporters (presumably EAAT3) appear to dominate the rapid clearance of glutamate at this synapse, but this point isn't emphasized or explored sufficiently.

(3) Separating the effects of depolarization vs. glutamate clearance was never explored. What effect does depolarizing the cell ~10 mV in control conditions (i.e., without TBOA) have on AP number/fidelity during synaptic stimulation experiments? The authors state that submaximal DL-TBOA generally causes no more than a 5 mV change in RMP, but tonic depolarization could also influence spike fidelity. This experiment could demonstrate that the increase in excitability during/after stimulation is not due to increased engagement of voltage-gated channels.

Reviewer #2 (Public review):

Summary:

This manuscript addresses an important and mechanistically interesting question: whether plasma membrane glutamate transporters contribute only to slow clearance of ambient glutamate or whether they can rapidly shape synaptic signaling during high-frequency auditory activity. This manuscript provides important evidence that EAAT-mediated glutamate uptake is not merely a slow background clearance mechanism but is essential for maintaining reliable synaptic transmission and linear stimulus-intensity coding in ventral cochlear nucleus T-stellate cells during sustained auditory nerve activity.

Strengths:

The finding that EAATs may be required for rapid, local control of glutamate during high-frequency auditory nerve activity is interesting and could have broad relevance to auditory processing. The electrophysiological evidence is generally strong, particularly the use of patch-clamp recordings, stimulus trains, partial versus complete EAAT blockade, and comparison with bushy cell/endbulb synapses. The comparison between T-stellate cells and bushy cells/endbulb synapses strengthens the manuscript. The authors demonstrate that EAAT blockade disrupts coding in T-stellate cells but has little effect on bushy cell spike transmission, supporting a cell-type- and synapse-specific role of glutamate uptake.

Weaknesses:

However, some mechanistic conclusions, especially the specific contribution of neuronal versus glial EAATs and the absence of glutamate crosstalk between auditory nerve inputs, rely mainly on pharmacological and indirect electrophysiological inference and would be strengthened by additional anatomical, genetic, or direct glutamate-sensing evidence.

(1) Clarification of DL-TBOA concentration.

The authors used bath application of 200 µM TBOA and 25-50 µM in the other experiments, stating that "sub-maximal concentrations (25-50 µM)". The authors should provide a clearer rationale for why different concentrations were used across experiments rather than a fixed concentration.

The reversibility of DL-TBOA effects should be demonstrated by washout experiments. In addition, potential off-target effects of DL-TBOA on postsynaptic receptors, intrinsic membrane excitability, or presynaptic release (e.g., PPR measurement) should be carefully considered. It would also be useful to test the effects of the submaximal DL-TBOA concentrations (25-50 µM) on membrane potential and inward currents, shown in Figure 1, to determine whether these concentrations depolarize the membrane potential in current-clamp mode or induce inward currents under voltage-clamp conditions.

(2) Potential contribution of altered intrinsic excitability.

In Figures 3B and 3C, DL-TBOA appears to induce additional action potentials even immediately after the first stimulation, whereas Figures 6 and 7 suggest that the first EPSC is not substantially altered. This raises the possibility that the enhanced firing may partly result from a modest depolarization caused by background glutamate accumulation or from other changes in intrinsic membrane properties after drug treatment. To address this, the authors should provide a quantitative analysis of physiological parameters under submaximal DL-TBOA conditions, including spontaneous action potential frequency, resting membrane potential, input resistance, and spike threshold.

(3) Spillover/ crosstalk between AN-fiber-synpases.

The authors should provide more explanation of how altering the number of active auditory nerve fibers demonstrates the absence of glutamate spillover/crosstalk between bouton synapses. Strong stimulation likely recruits more AN fibers, but it may also change release probability, axonal synchrony, or stimulation spread. The authors should more clearly justify the interpretation that strong stimulation recruits additional independent AN fibers rather than altering release probability or activating fibers with different intrinsic properties.

(4) Interpretation of glial versus neuronal EAAT contributions.

The authors claim that both neuronal and glial transporters contribute to rapid uptake using pharmacological approaches. The pharmacological data demonstrate that glial EAATs play a major role in glutamate clearance at T-stellate cell synapses. The strong increase in EPSC decay time and synaptic charge after UCPH-101/DHK application supports the conclusion that glial transporters contribute substantially to limiting glutamate accumulation during sustained auditory nerve activity. However, the conclusion that neuronal EAATs contribute directly should be stated with some caution. The evidence for neuronal EAAT involvement is indirect and depends on the pharmacological specificity and completeness of glial EAAT blockade. The conclusion would be strengthened by additional evidence, such as EAAT subtype expression/localization in T-stellate cells or auditory nerve terminals, transporter current recordings, immunohistochemistry, or genetic manipulation of neuronal EAATs. In addition, fitting the decay phase with a double-exponential model may help determine whether glial and neuronal EAATs contribute over distinct temporal windows.

Joint Public Review:

Summary:

This manuscript couples a 32-parameter model with simulation-based inference (SBI) to identify parameter changes that can compensate for three canonical hyperexcitability perturbations (interneuron loss, recurrent-excitatory sprouting, and intrinsic depolarisation). The study demonstrates a careful implementation of SBI and offers a practical ranking of "compensatory levers" that could, in principle, guide therapeutic strategies for epilepsy and related network disorders.

Strengths:

(1) By analysing three mechanistically distinct hyper-excitable regimes within the same modelling and inference framework, the work reveals how different perturbations require different compensatory interventions.

(2) The authors adopt posterior estimation to systematically rank the efficiency of different mechanisms in balancing hyperexcitability.

(3) Code and data are available.

Comments on revised version:

I appreciate the authors' extensive efforts in revising the manuscript and responding to the previous review. The revised version is substantially improved in clarity, organization, and presentation. In particular, the addition of schematic figures, the reorganization of the Methods section, the improved explanation of the model, and the inclusion of replication analyses all strengthen the manuscript.

The manuscript remains entirely computational, and therefore its conclusions should be interpreted as predictions generated by a specific model rather than validated biological mechanisms. I believe the work has the potential to make a useful methodological contribution. However, several concerns remain regarding validation, interpretation of inferred posteriors, organization of the manuscript, and presentation.

Major comments:

(1) The manuscript states that simulation-based calibration showed the amortized posterior estimator was unreliable (85-88), but these results are not shown. The manuscript explicitly states that simulation-based calibration demonstrated substantial failures of the amortized posterior estimator, yet the corresponding analyses are not presented. Since these results motivate the transition to sequential NPE and are central to assessing inference reliability, they should be reported quantitatively, either in the main text or supplementary material.

(2) The authors present two independently trained estimators and show strong agreement between them. This is a useful robustness analysis. However, the rebuttal occasionally presents this as addressing concerns regarding cross-validation and generalization. The new analysis does not constitute cross-validation in the usual sense and does not directly assess generalization to held-out targets or posterior accuracy.<br /> I recommend that the authors explicitly describe Figure 4 as a reproducibility analysis and avoid presenting it as a substitute for validation.

(3) Posterior correlations are useful for generating hypotheses about compensatory mechanisms, but they should not be interpreted as direct evidence of compensation. The compensatory interpretation should instead be supported by the perturbation analyses (e.g., Figure 6), which provide mechanistic validation.

The manuscript consistently treats posterior correlations and conditional posterior shifts as direct evidence of compensatory mechanisms. These are consistent with compensatory mechanisms, but they do not by themselves establish that the corresponding biological parameters causally compensate for the perturbation. I recommend clarifying this distinction and emphasizing that the conditional posterior analyses generate hypotheses regarding compensation, which are then partially supported by the perturbation experiments shown later in the manuscript.

The language throughout the manuscript should therefore be softened.

(4) The manuscript repeatedly suggests that the inferred conditional distributions may be useful for identifying precise interventions or guiding personalized treatments (examples include lines 24-29, lines 217-223, lines 242-246, lines 277-282, lines 283-286). These claims go beyond what is directly demonstrated.

The study does not evaluate treatment outcomes, patient-specific inference, intervention efficacy, or clinical decision-making. Rather, it demonstrates differences in inferred parameter distributions within a computational model. While these results are valuable and may generate clinically relevant hypotheses, they do not yet establish predictive utility for treatment selection or precision medicine. I therefore recommend substantially softening these translational claims and emphasizing that the current findings generate hypotheses that could be tested experimentally in future work.

(5) The revised manuscript still mixes presentation of findings with interpretation.

For example, lines 217-226 largely continue to describe findings from Figure 6 and would fit better in the Results section. The Discussion would be strengthened by focusing more exclusively on biological implications, limitations, and future directions.

A similar issue appears later in the discussion comparing posterior correlations and conditional distributions. Much of this section effectively reinterprets Figures 2 and 3 rather than discussing broader implications.

(6) The discussion around lines 271-282 overstates what can be concluded from the inferred posteriors.<br /> The statement that correlations "discover broadly applicable mechanisms" whereas conditionals "identify specific mechanisms" is stronger than the presented evidence supports. Likewise, the conclusion that conditional distributions are more useful for precision treatments is speculative and not directly demonstrated.

I recommend reformulating these statements as interpretations or hypotheses rather than conclusions.

(7) Around line 84, the manuscript introduces q(theta|x) without clearly defining θ, x, or q. Readers unfamiliar with SBI may struggle to follow the notation. All quantities should be defined when first introduced.

(8) The manuscript equates larger KS distances between conditional posteriors with greater compensatory potential. While KS distance provides a useful measure of posterior redistribution, it is not obvious that it should be interpreted as a measure of biological efficacy.

(9) The manuscript would benefit from a discussion of parameter identifiability. The inference problem maps 32 model parameters to 7 summary statistics, implying substantial non-identifiability. While complete identifiability analysis is likely beyond the scope of the current work, this limitation should be discussed explicitly.

All in all, the revised manuscript is significantly improved and addresses several concerns raised in the previous review. However, important issues remain as discussed above.

Reviewer #1 (Public review):

This work evaluates the impact of reproductive history on growth, body weight and body composition in mammals. In mice, somatic growth is stimulated by the first pregnancy while the second pregnancy increases body weight mainly by increasing adiposity. To probe the role of pituitary growth hormone (GH), the key regulator of somatic growth in these processes, was addressed by comparing the impact of reproduction on growth in normal ("wild type") and genetically GH-deficient females and by detailed characterization of the profile of fluctuations in circulating GH levels in both types of animals. Additional studies addressed the possible role of other endocrine pathways (ghrelin and estrogen) in the pregnancy-related growth. Surprisingly, reproduction-related growth was independent of GH, ghrelin and estrogen. To determine whether these results may apply ("translate") to human physiology, data on various parameters of somatic growth were collected from women with hereditary GH deficiency. The findings indicate that GH-independent stimulation of growth by reproductive events also occurs in women.

Use of multiple animal models, rigorous characterization of GH levels in normal and GH-deficient females, and inclusion of data derived from a unique and well-characterised population of people with hereditary isolated GH deficiency and no GH replacement therapy are important strengths of these elegant and innovative studies. The results address a broader and clinically significant issue of permanent changes in body size, composition and function that result from pregnancy and lactation. This work also provides important background for further studies aimed at the identification of the mechanism involved and the role of specific reproductive events in the regulation of growth.

Reviewer #2 (Public review):

This manuscript describes the fascinating phenomenon of growth hormone (GH)-independent growth occurring in the mother during pregnancy. This growth was most pronounced in dwarf mice that are lacking the receptor for growth hormone-releasing hormone (GHRH) and therefore showing isolated GH deficiency. However, the pregnancy-induced growth could also be observed in wild-type mice, suggesting that it is a normal part of the maternal adaptation to pregnancy. The study falls short of identifying the mechanism(s) driving this pregnancy-induced growth response, but it certainly reveals a novel insight into maternal physiology. The authors have completed a range of experiments in mice to prove that, as well as being GH independent, the pregnancy-induced growth also did not require GH signaling in the liver (i.e. not another pregnancy-specific ligand operating through the GHR to promote IGF). They also provided complementary data from a population of humans with untreated isolated GH deficiency that are broadly consistent with the hypothesis. While it is important to consider the significant species differences between rodents and humans, both in terms of growth physiology and also in terms of evolution of placental somato-mammotrophic hormones, this unique population are a valuable resource and adds credence to the study. Overall, I find this a compelling research story, but disappointingly unfinished. There are some areas where additional information could improve the ability to interpret the data, and some additional concepts that could be considered in the discussion. There are also areas where additional experiments might provide important insights. However, I think that such suggestions can be considered as appropriate for future research, rather than delaying consideration of the current manuscript.

Main comments:

(1) Data in Figure 1 are remarkable - not so much the growth in pregnancy in the wildtype mice, because while elevated GH is well known in pregnancy, but growth in the dwarf mice is indicative of GH-independent growth. From these data, it seems that there is good evidence that growth in pregnancy is an adaptive function. However, it is possible that growth is achieved in dwarf mice and that in wildtype mice may have been mediated through different mechanisms. The dwarf mice showed an increase in liver and plasma IGF1, suggestive of an additional ligand driving IGF in pregnancy. One could hypothesize that such an effect could be mediated by an additional pregnancy-specific ligand activating the GH receptor. In humans, placental growth hormone could be such a ligand, but as far as we know, there is no placental GH in mice. In contrast, the wildtype animals showed suppression of liver and circulating IGF1, and low levels of pSTAT5 in the liver during pregnancy. These data (in Figure 5) are very surprising. Given the high circulating GH in pregnancy, as well as high placental lactogen (which would be expected to activate STAT5 in the liver through the Prlr), the low levels of pSTAT5 are unexpected and would seem to indicate some sort of acquired insensitivity to GH. Is this entirely driven by down-regulation of STAT5b protein, or could there be activation of other, negative regulators of STAT signalling, such as SOCS? What is causing such a profound suppression of STAT5? Regardless of the mechanism, this suggests that pregnancy-induced growth in wildtype mice is independent of circulating IGF1 (potentially a different mechanism or in addition to that seen in IGHD mice).

The data shown in Figure 6 are a major strength of the study, showing that the pregnancy-induced changes are not specific to one particular transgenic model, but still occur in a variety of models affecting GH through different approaches. Given the pregnancy-specific nature of the changes, however, it seems an oversight not to have evaluated the role of placental lactogens. Prlr is highly expressed in the liver, but the function of this hormone in the liver is not well established. Could the extremely high levels of PL be mediating this growth response? Given the low expression of STAT5 in the liver and the fact that plasma IGF1 is not markedly elevated, it seems more likely that this growth response may be mediated by locally produced IGF1 in target tissues.

I think these possibilities could be addressed by an expanded discussion of species variation in placental hormones, to highlight that humans have expansion of the GH locus, but rodents have expansion of the prolactin axis (see Soares, M. J. The prolactin and growth hormone families: pregnancy-specific hormones/cytokines at the maternal-fetal interface. Reprod Biol Endocrinol 2, 51, 2004). Importantly, placental GH and chorionic somatomammotropins (CSM) in humans are all variants of the GH gene, but CSM have preferential activity at Prlr. This seems to be a fundamental species difference in pregnancy biology, but has been interpreted as an example of convergent evolution, with conservation of prolactin and GH-like functions at the maternal-fetal interface, mediated by different mechanisms, likely contributing to the metabolic adaptations of the mother (see Newbern D, Freemark M. Placental hormones and the control of maternal metabolism and fetal growth. Curr Opin Endocrinol Diabetes Obes. 2011; 18: 409-416). While the preceding function has focused on explaining the evolution of placental lactogens (either prolactin or GH variants), the present data suggest that there are also mechanisms to maintain growth in pregnancy, independent of GH (even in the absence of a placental GH).

(2) The human data are very interesting, and my initial impression was that it seemed unlikely to be the same phenomenon. Was there any real evidence for "growth" in pregnancy? Pubertal maturation of long bone growth might be expected to prevent further growth in adulthood. However, these issues were appropriately discussed, and it seems well justified to evaluate this unique population of women with IGHD who underwent pregnancy. It would be very interesting to know if these women experienced elevated IGF1 during pregnancy, indicative of placental GH contributing to growth. Mechanistically, this might be more like the dwarf mouse situation of IGHD, that the situation in wildtype mice (associated with liver insensitivity to GH and low IGF1).

(3) It would be useful to include investigations that isolate the effects of pregnancy and the placental hormones. Such studies could include evaluating growth in pseudopregnant mice with IGHD (pregnancy-like changes in hormones but lacking the placental contribution) and in IGHD animals that experience pregnancy but not lactation (pups removed at birth). I accept that this might be too large an additional study to add for the present manuscript.

(4) It is an important and translationally relevant observation that pregnancy increased the risk of long-term weight gain, and that after the first pregnancy, the pregnancy-induced growth response was more directed to promoting fat deposition. Does this provide any mechanistic insight? Could a metabolic adaptation result in growth?

Reviewer #3 (Public review):

Summary:

The study describes an increase in body growth and body composition in both mice and women. In mice, the impact on growth is mainly seen during the first pregnancy, and the changes postpartum on body composition are also different during the first and second pregnancies. The study has used various knock-out models in the growth hormone axis to understand these changes as well as some gene expression analysis related to GH, IGF-1 and estrogen signalling pathways.

Strengths:

(1) The inclusion of various knock-out mouse models that allow for exploration of mechanisms related to the above-mentioned changes.

(2) The investigation of gene expression of GHR, IGF-1R and ER pathways.

Weaknesses:

The human findings are dependent on the patient's recollection of bodily changes after their pregnancies.

Conclusion:

The authors have partly achieved their aim of describing changes in growth and body composition that remain after pregnancy and the mechanisms behind these changes. This study may have importance for a wide variety of research areas as well as in the clinical setting. The study is also unique in its attempt to bridge findings in mice to a unique human model of congenital GH deficiency.

Reviewer #1 (Public review):

Summary:

Zhang et al. investigated EEG neurofeedback as a method to modulate brain activity prior to painful stimulation and its effect on pain perception. Neurofeedback was designed to train participants to upregulate alpha power contralateral to the site of painful stimulation. Real or sham neurofeedback was administered to two independent groups. Each group performed two tasks: one in which participants were asked to modulate their brain signals (training task) and another in which they were asked to passively watch the feedback (non-training task). The authors reported an increase in alpha power during real neurofeedback training compared with sham training and non-training conditions. The authors also reported a decrease in pain perception during the training task, both in the real and sham neurofeedback groups. Additionally, in an offline analysis, the authors investigated brain dynamics with microstate analysis during the neurofeedback training. Also, they implemented a mediation analysis to infer which brain responses to neurofeedback training mediated changes in pain perception.

Strengths:

(1) The research question is licit and sound. EEG neurofeedback is a promising non-invasive technique with the potential to alleviate at least the sensory component of pain. The rationale for applying neurofeedback at the alpha band in the somatosensory cortex is well justified by the alpha-gating theory in pain modulation.

(2) The sample size is adequate to capture neurofeedback effects. The effort to conduct a double-blind study with a complex design paradigm and an adequate sample size is valuable and appreciated.

Weaknesses:

(1) Reported behavioral effects on pain reduction might be due to the placebo effect rather than neurofeedback, as pain ratings were reduced both in the real and sham neurofeedback groups during training. It is important that authors report this effect appropriately and disclose which information was given to the participants when they enrolled in the study, i.e., whether the paradigm was designed to reduce pain perception.

(2) The utility of training effects, especially in the sham group, is unclear. I understand that including the non-training condition allows the distinction between neurofeedback effects and arousal effects. However, interpreting training effects should not be the point of this study. What does it tell us that participants who received sham stimulation increased or decreased alpha power in the training session vs the non-training session?

(3) There might be hidden time effects (habituation/sensitization) on pain responses and/or on brain responses to neurofeedback. A within-session analysis comparing the first half of the training with the second half should be conducted to discard them.

(4) Connectivity analysis reflects spurious effects. In EEG, deriving phase-based functional connectivity at the sensor level is problematic due to volume conduction effects. EEG functional connectivity should be performed after source reconstruction, and measures discarding instantaneous phase lags should be preferred, which is not the case with magnitude-squared coherence. See (Bastos and Schoffelen, 2015).

Although neurofeedback is a promising technique for modulating pain perception, the current study adds limited novelty to the field, as its design could not disentangle whether behavioral effects (reductions in pain intensity and unpleasantness) were specific to neurofeedback training or due to non-specific effects (e.g., placebo). Nevertheless, the authors corroborated that brain states before painful stimuli could be modulated with neurofeedback (enhancement of alpha power).

Reviewer #2 (Public review):

Summary:

This study uses neurofeedback to modulate alpha-band activity and examines how this influences pain-related processing. The question is timely and methodologically elegant, because it addresses whether noninvasive modulation of ongoing oscillatory activity can causally shape pain perception and/or expectation-related processes.

Strengths:

The use of neurofeedback as a tool to modulate alpha activity is a major strength, because it provides a noninvasive and conceptually clean approach to probing the functional role of oscillatory brain activity. The design is also attractive because it links neurophysiological regulation to a psychologically meaningful outcome, namely pain processing. Further, the induced changes were also related to different EEG microstates and ERP components during the processing of the pain stimulus, and therefore the authors demonstrate a clear relation between preparatory prestimulus states and stimulus processing.

The manuscript appears to address an important and clinically relevant question, and the idea of testing whether alpha regulation can alter pain-related responses is of high interest for systems neuroscience and pain research.

Weaknesses:

Methodologically, it is unclear what alpha values were used in the analyses. It is stated that alpha was extracted within 2s windows of the 16s long feedback period. However, the values change across this period. Which value is used for the correlation with the pain ratings and all other analyses? Using the average across the 16s could reflect large values in the first half and low values in the final half, but for the relationship between alpha and pain, the last segments should be more relevant. If the initially elevated alpha activity subsides several seconds before the onset of the pain stimulus, it is difficult to see how it could influence subsequent pain processing.

Related, after the 16s feedback period, a fixation period is used with a 3-5s length. If alpha band activity is relevant for the consecutive pain processing, the amount of alpha in this period should be relevant. The authors should demonstrate that the induced alpha change during the feedback period remains stable during the fixation period and that the activity in this period is related to pain processing.

Further, it should be noted that the alpha band modulations related to alpha band training were accompanied by significant effects in other frequencies. Therefore, a clear relationship between alpha and behavioral pain ratings is not the only interpretation. Correlations with other frequencies or combinations of frequency band modulations should be incorporated to allow a more precise interpretation. Furthermore, in the sham feedback group, an increase in alpha band activity was observed (p=0.06), and the small difference in the pain intensity rating may be related to a clear outlier in the Sham group (Figure 4a).

In both groups, a main effect of training, regardless of sham or real feedback, was reported with a small difference between groups. But the main modulator seems to be related to the instruction to modulate the neural activity, and this large effect should be discussed in more detail regarding, for example, possible attentional processes.

A further central concern is that the visual feedback signal (the ball movement) may generate expectations that are not specific to alpha activity and that these expectation processes modulate the pain processing (ball down may indicate more pain). It is well known that intensity cues can generate expectations about upcoming perceptions, and the used feedback signal with an increasing or decreasing visual curve clearly signals what intensity should be expected. Therefore, it is important to show that the amount of positive (ball up) and negative visual displays is matched between the sham and real feedback group. Further, the authors should report whether the final ball position can predict the latter pain rating in both groups or differentially. Following this interpretation, alpha band activity is not directly related to pain processing but only serves as a signal that is transformed to a visual stimulus that then generates expectations.

Finally, the manuscript would benefit from a more explicit analysis of whether individual alpha changes are related to pain ratings within each subject. If higher alpha is truly linked to reduced pain perception, this should be visible at the participant level during learning of the neurofeedback procedure. Relatedly, there is no learning period incorporated, and usually participants are not able to regulate their alpha activity from the first trial on. The authors should include an analysis of the development of alpha band activity over learning and a relation of these individual alpha values and the corresponding pain ratings.

I cannot find a link to the preregistration in the current manuscript.

In summary, a "causal" relation of alpha activity with pain perception -that is mentioned several times in the manuscript- is not fully supported by the present results

Reviewer #1 (Public review):

Summary:

This manuscript investigates whether the human brain contains a shared category-general representation of gender across faces, bodies, and gender-associated objects. The authors acquired fMRI data while participants viewed male and female stimuli from three categories in a one-back task. They then used searchlight MVPA, cross-category decoding, regression-based RSA, CNN vs. brain representational comparisons, and PPI analyses. Their main finding is that gender information could be decoded from distributed occipitotemporal regions within each category, whereas a cluster in the rMTG showed convergence across cross-category decoding and RSA. The authors concluded that this rMTG representation resembles intermediate layers of fine-tuned CNNs and that face and body gender processing share similar functional connectivity patterns.

Strengths:

The question is potentially important, particularly for social cognition, object recognition, and the use of neural network models to interpret high-level visual representations. Previous behavioral studies have shown cross-category adaptation between bodies and faces, and even between gender-associated objects and faces, so the attempt to test for a neural counterpart using fMRI is well motivated. The use of multiple complementary analyses including within-category decoding, cross-category decoding, regression RSA, CNN comparisons, and effective connectivity analyses is also a strength. The convergence of cross-category MVPA and RSA in a right MTG cluster is potentially interesting and deserves attention.

Weaknesses:

The largest problem is conceptual. The term gender is used as if it refers to the same construct across faces, bodies, and objects. This is not self-evident. In faces and bodies, the stimuli seem to contain visual cues from which observers infer binary gender categories. In objects, however, the relevant information is almost gender stereotype, cultural association, or learned semantic association. These are not equivalent constructs. The manuscript therefore needs to distinguish much more carefully between perceived gender, biological sex cues, gender-associated visual features, and gender stereotypes. Without this distinction, the title and main conclusion are too broad. The object condition is particularly problematic. Javadi & Wee (2012) showed that gender-associated objects can bias subsequent judgments of ambiguous face gender, and they discussed two possible mechanisms, including shared neural substrates or top-down modulation induced by the gender concept. However, their behavioral adaptation study does not directly demonstrate that objects, faces, and bodies are encoded in the same neural representational format. The present manuscript treats these object stimuli as if they provide evidence about the same kind of gender representation as faces and bodies, but that step requires additional empirical support. Independent ratings of object gender association, cultural familiarity, visual similarity, and semantic category are essential here.

A second major concern is stimulus control. The face images were taken from Chinese male and female actors, the body images were headless bodies in underwear, and the object images were selected because of prior gender associations. This design introduces many possible confounds: hairstyle, makeup, skin texture, body shape, clothing, color, luminance, object category, object function, curvature, spatial frequency, and cultural familiarity. Cross-category decoding can be significant even when a classifier relies on shared visual statistics rather than an abstract gender code. For example, female-associated stimuli may differ from male-associated stimuli in color, shape, brightness, texture, or semantic category in ways that are consistent across faces, bodies, and objects. The present analyses do not adequately rule out these alternatives. Foster et al. (2019) are especially relevant in this respect. They reported that body sex could be decoded from both body- and face-responsive regions. However, the sex of well-controlled faces, for example faces excluding hairstyle cues, could not be decoded from face- or body-responsive regions. This finding should make the authors more cautious. The fact that the present study used more ecological face stimuli may increase sensitivity to gender-related cues, but it also increases the possibilities that decoding is driven by uncontrolled external features rather than by an abstract gender representation. Accordingly, because no additional visual, semantic, or stereotype-based model RDMs were included in the RSA analysis, this result alone cannot establish an abstract, category-independent gender representation. Any systematic difference between male- and female-associated images will load onto the gender RDM. At least, the authors should include additional model RDMs for low-level visual features. In addition, the current RSA analysis has another limitation. The neural RDMs are based on only six condition-level patterns, producing a 6 × 6 matrix. The theoretical model includes only binary gender and category RDMs. This is too coarse to support the claim of category-independent gender representation. Ideally, all the RSA analysis should be performed at the item level rather than at the condition level.

The cross-category decoding result in rMTG is promising but not yet conclusive. The authors identify a right MTG cluster by overlapping thresholded maps from three cross-category decoding analyses. This is useful descriptively, but it does not by itself establish a common representational code. The overlap of thresholded maps depends on the chosen threshold. If the authors want to make a formal conjunction claim, they should use a valid conjunction-null approach such as a minimum-statistic conjunction evaluated under the appropriate conjunction null, rather than simply displaying the intersection of thresholded maps. Even if this approach cannot be adopted in this study, the issue should be included as a limitation.

In the PPI analysis, the reported similarity between face and body connectivity matrices is a little bit small (r = 0.08). The claim of a shared functional network should therefore be softened unless the authors test whether this correlation is significantly larger than the face-object and body-object correlations, correct for multiple comparisons, account for the non-independence of matrix elements, and report participant-level distributions and confidence intervals.

Reviewer #2 (Public review):

Summary:

The study tests whether male/female-related information is represented in a form that generalizes across faces, bodies, and gender-associated objects. Using within- and cross-category MVPA, regression RSA, comparisons with fine-tuned CNNs, and connectivity analyses, the authors identify a right middle temporal gyrus region whose patterns generalize across the three stimulus classes. They conclude that this region provides a category-general, mid-level representation of gender and acts as a neural hub.

Strengths:

The question is novel and important, while the logic of the study is straightforward. Examining faces, bodies, and objects within the same participants provides a useful extension beyond the predominantly face-based literature. Cross-category decoding is also a stronger test of shared information than simple anatomical overlap between within-category maps. The combination of MVPA, RSA, computational modelling, and connectivity analysis is ambitious, and the replication of the CNN layer profile with both AlexNet and VGG16 is a useful characterization of relevant information.

Weaknesses:

(1) The construct labelled "gender" is not equivalent across stimulus classes. For faces and bodies, the male/female label is intended to track a property of the depicted person, albeit one inferred imperfectly from appearance; for objects, masculinity or femininity is not an intrinsic property of the object but a culturally contingent association that may vary across observers and contexts. Treating both as levels of a single binary factor risks conflating person-category information with gender-stereotypic object associations and interpreting their common neural discriminability as evidence for one abstract concept of gender. The term "object gender" could also be confused with grammatical gender in some languages (e.g., French or German).

(2) The CNN analysis does not isolate the shared male/female component. The authors correlate the complete six-condition neural RDM with the complete CNN RDM. However, rMTG also carries substantial information about whether an image is a face, body, or object. Consequently, the peak correspondence with Conv4 may reflect category structure rather than the representation that supports cross-category male/female decoding. The current analysis does not establish that shared gender-related information specifically depends on mid-level features.

(3) The connectivity interpretation is overstated. PPI measures task-dependent covariance; it does not establish information transmission, directionality, or an upstream-to-downstream processing sequence. The reported face-body connectivity similarity is also small (r=.08). Also, describing rMTG as a "hub" is not justified without network-centrality measures, lesion evidence, or causal perturbation.

The authors partly achieve their aims. The results provide credible evidence that patterns in rMTG contain information that generalizes across binary male/female-labelled faces and bodies and masculine/feminine-associated objects. They do not yet establish a genuinely abstract representation of gender, a specifically gender-related correspondence with intermediate CNN layers, or a neural hub that transmits information through a directed network. With more precise framing and targeted reanalysis, the study could make a useful contribution to research on social vision and cross-category representation.

Reviewer #3 (Public review):

Summary:

In this work, the authors investigate whether gender information is encoded in the brain in a way that is invariant to the object being perceived. They design an fMRI experiment in which 22 participants perform a one-back repetition detection task in a block design. Images shown are of three types (faces, objects, and bodies) and of two perceived genders, male and female. They perform MVPA, RSA, and functional connectivity analyses to determine whether gender information is invariant to the type of image being perceived. They report an area in the posterior right middle temporal gyrus (rMTG) that is found in their gender decoding analysis across categories. To confirm that this area encodes gender information, they perform a regression-based RSA with category and gender model RDMs, and report that the gender model RDM is significantly correlated with brain representations in that area. Finally, to further investigate the representations in this area, they perform a model-based RSA in which they first fine-tune a deep neural network for gender classification, and then study the correlation between model RDMs and brain RDMs. Consistent with a previous report in face processing (Jiahui et al., 2023), they find that gender information is more consistent with representations in middle-to-late layers of the networks. Additional functional connectivity and PPI analyses are reported to reveal differences in co-fluctuation of brain activity within occipital and parietal nodes when perceiving different types of male/female images. Based on these results, the authors conclude that rMTG represents gender information invariant of the category perceived, although rMTG also afforded decoding of category information.

Strengths:

Whether perceived gender is represented in a manner invariant to the category of the stimulus is a legitimate and interesting question, and one of relevance particularly to the face and person perception literature.

The model-based RSA, in which RDMs from networks fine-tuned for gender classification are compared against brain RDMs, is an interesting approach, and the layer-wise profile the authors obtain converges with a previous report in the face processing literature (Jiahui et al., 2023).

Weaknesses:

A substantial number of inferences are drawn on the basis of weak statistical methods and a suboptimal design. My concerns are set out below, ordered by severity.

(1) The statistical tests are not appropriate for classification and RSA, and are prone to false positives. Classification accuracies and RSA correlations may be positively biased, and the true null distribution may therefore be centered above the nominal chance level, or above zero in the case of RSA. Testing against a theoretical value with a one-sample t-test under these conditions inflates the false positive rate, especially with few test samples per classification, and does not afford valid population inference for information-like measures (Combrisson & Jerbi, 2015; Allefeld et al., 2016). The concern applies to every inferential claim in the manuscript, including the identification of the rMTG cluster on which the paper's central conclusion rests. The established remedy is permutation testing, in which the labels are randomly permuted and the full analysis, including cross-validation, is re-computed so that any bias is captured in the empirical null distribution (Stelzer et al., 2013; Etzel & Braver, 2013). This approach has been applied in comparable face-decoding studies using both classification and RSA (Guntupalli et al., 2017). I raise this methodological concern here because it is the clearest way to convey why the reported statistics cannot be safely interpreted at face value.

(2) The decoding analyses do not appear to test generalization to left-out stimuli. From my reading of the design, each run contained all six conditions presented three times in random order, with each block containing 12 images (10 unique plus two repetitions serving as catch trials). If all images were presented in every run, the same images would be present in both the training and test sets of the cross-validation. Under these conditions, the interpretation of a general "gender" code is difficult to justify: the classifier may be exploiting low-level image features specific to the particular exemplars rather than gender per se. This bears directly on the paper's central claim, which concerns an abstract, category-invariant representation of gender, a claim that requires decoding to generalize to stimuli the classifier has not encountered.

(3) There is no evidence that participants perceived the stimuli's gender as the authors assumed. Perceived gender may be subject-specific, yet no norming is reported establishing that participants actually rated or processed the stimuli according to the gender the authors assigned to each image. Some images are likely to be more ambiguous than others. This is a construct validity issue rather than an analysis issue: the class labels used throughout the decoding analyses, and the gender model RDM used in the RSA, both rest on an assumption about the participants' percepts that is never tested against the participants themselves.

(4) The rMTG ROI reported in Figure 2c appears to overlap almost perfectly with the motion-sensitive area hMT+. The reported effects may therefore be driven, at least in part, by low-level motion signals arising from the rapid on/off changes of the stimuli and the associated optic flow. I am not claiming that the results are fully driven by this, but no control reported in the manuscript rules it out, and this region is the centerpiece of the paper's conclusion.

(5) Stimulus size is confounded with category in the functional connectivity analyses. The authors report that functional connectivity differed between faces and objects, and between bodies and objects. However, faces and bodies were shown with the same visual extent, while objects were larger. Given that the nodes being investigated are in visual areas, it is unclear how these differences can be attributed to category rather than to the low-level difference in stimulus size. The same confound bears on the behavioral task performed within the scanner: participants can perform the one-back task more easily, simply by detecting size differences, since two images of different sizes are clearly not the same image, rather than by processing the image content. This affects what can be assumed about participants' attention to the stimulus category or gender.

(6) No motion quality control is reported for the functional connectivity analyses. Functional connectivity is well known to be highly susceptible to head motion, yet the manuscript reports no summary of how much subject motion there was, no indication of whether volumes with excessive motion were removed or censored, and no account of quality control on the measured data more generally.

(7) The use of famous faces introduces an avoidable confound. The face stimuli were famous faces. Famous and familiar faces are known to recruit substantially more widespread activity than unfamiliar faces, extending well beyond the core visual system (Gobbini & Haxby, 2007; Natu & O'Toole, 2011; Visconti di Oleggio Castello et al., 2017; Kovacs, 2020). For a study focused specifically on gender, this introduces a source of variance that unfamiliar faces would have avoided, and it complicates the comparison of the face conditions against the body and object conditions.

(8) The rationale and benefit of fine-tuning the deep neural networks are not established. The manuscript does not report the original, non-fine-tuned accuracy of the models that required fine-tuning, so the benefit of the procedure cannot be assessed; given that the final validation accuracy is low, it is unclear that fine-tuning actually helped. AlexNet and VGG are trained for object classification on large datasets, and fine-tuning with 2,000 training images may not be sufficient to genuinely shift the objective. Whether the activation patterns and RDMs changed in any significant manner after fine-tuning is not reported, and the rationale for selecting the specific layers used is not stated.

(9) Taken together, the analyses as presented do not establish the paper's central claim. My concern is not that the reported effects are necessarily absent, but that the combination of statistical tests that do not account for possible positive bias, a cross-validation scheme that may not guarantee generalization across stimuli, a key region that coincides with a motion-sensitive area, and gender labels that were never validated against participants' own perception leaves too many open questions for the results to be evaluated as they stand.

(10) I would add one broader consideration. Perceived gender is likely to depend on culture and to vary across individuals. A binary male/female contrast in 22 participants, without evidence that those participants perceived the stimuli as the authors intended, is a narrow operationalization of a construct that is unlikely to be so simple. Even if the analyses were fully sound, caution would be warranted in generalizing from this design to claims about how the brain universally represents gender.

Reviewer #1 (Public review):

In this paper, Pal and colleagues propose a mechanistic unification of two influential accounts of inter-areal communication: communication through coherence and communication subspaces. A major strength of the paper is that it does not treat coherence and communication subspaces as independent phenomena, as typically done, but instead derives both from the same circuit with divisive normalization. In this framework, noise-driven fluctuations around the normalized fixed point determine covariance and cross-power structure (which, in retrospect, makes so much sense to be related). Then, they show how these determine linear prediction performance and the effective dimensionality of the communication subspace. They also show (however not very visually, see recommendation below for a figure) how divisive normalization is crucial to shape inter-areal coherence and the dimensionality of communication.

I found this conceptual contribution potentially very influential, but somewhat obscured by the technical complexity of the model. The central intuition (I think) is that recurrent normalization can organize cross-area fluctuations, both frequency-specific correlations and cross-covariances. Took me a while to grasp this insight, mostly because I was stuck with the model details. Note that I have some experience with network dynamics, but not with this particular model.

Reviewer #2 (Public review):

Summary:

The authors extend the ORGaNICs framework (a recurrent circuit that dynamically implements divisive normalization) to connected cortical areas with explicit top-down feedback. Because the network has a known analytical fixed point that coincides with (or closely approximates) the normalization equation, the authors can linearize about that fixed point and derive closed-form expressions for the power spectral density, inter-areal coherence, and communication subspaces. Using a two-area instantiation (V1 & V2) with a single fixed parameter set and no data fitting, they show the model reproduces: (i) contrast-response functions with steeper slope V2; (ii) gamma-band power and coherence peaks that shift to higher frequency with contrast; and (iii) a low-dimensional inter-areal communication subspace that is lower-dimensional than the within-area subspace. They derive parallel predictions of what happens by changing model parameters: feedback gain enhances inter-areal and suppresses within-area communication, and normalization is necessary for both the oscillatory dynamics and the reduced subspace dimensionality. A three-area extension (V1&V4, V1&V5/MT) is used to argue that differential top-down feedback can dynamically route functional connectivity.

Strengths:

(1) Analytical tractability: Deriving power spectra, coherence, and communication-subspace structure in closed form from a known fixed point is genuinely valuable.

(2) Conceptual unification: Framing coherence and communication subspaces as arising from the same normalization-driven dynamics is an elegant and useful contribution.

(3) Breadth from few assumptions: A large range of phenomena (contrast gain, gamma dynamics) emerges from normalization-based model assumptions.

(4) Biological grounding: The mapping of model variables onto identified cell types connects the abstract computation to known cortical microcircuitry.

(5) The prediction that input-gain versus feedback-gain modulation produce distinct spectral signatures gives experimentalists a clear way to test the framework.

Weaknesses:

(1) Comparisons are qualitative, not quantitative: The theory/experiment panels are visual side-by-side comparisons. There is no quantitative goodness-of-fit for any predictions.

(2) The simulations use τ ≈ 1 ms for all cell types, which the authors acknowledge is unrealistically short; realistic values would shift the gamma peaks to lower frequencies.

(3) Divisive normalization is a special case and is recovered exactly only for the identity recurrent matrix (self-normalization). Some statements that the circuit implements divisive normalization exactly need softening.

(4) The element-wise (multiplicative) interaction in the modulator dynamics is not tied to a specific cellular mechanism.

Reviewer #3 (Public review):

Summary

The work of Pal and colleagues considers a hierarchical and multi-population version of the "oscillatory recurrent gated neural integrator circuits" (ORGaNICs) model, showing through analytics that the model captures multiple relevant experimental results: first of all, its oscillatory dynamics produce a profile with high resemblance to experimental results, both in terms of decay of power at high frequency and in terms of shifting peak as a function of stimulus contrast. Second, inter-areal communication subspace dimensionality is lower than within-area dimensionality. The authors then proceed to further characterize the model's response properties as a function of input and feedback gain. In particular, they find that frequencies transmitted with higher strength also carry more information, that changing gain modifies the dimensionality of communication subspaces, and that these properties can be used in a three-layer model, where an upstream area can select which downstream area to communicate to, based on the strength of feedback gain.

Strengths

This work demonstrates that a single-circuit model with normalization properties can capture both the oscillatory dynamics and the inter-areal communication properties measured in cortical circuits, matching multiple experimental results. The full analytical tractability of the model is highly advantageous, allowing for easier exploration of parameters, replicability, and effective interpretations of results compared to purely numerical approaches.

The work also makes a useful conceptual link between normalization, coherence-based communication, and subspace-based communication. In particular, it shows how both phenomena can emerge from the same circuit dynamics, where normalization is a key factor.

Interestingly, the model is also extended to multiple areas, showing how attention (in the form of changes in feedback gain) can synchronize the activity of a downstream area with one of two upstream areas, thus effectively selecting which area to communicate with.

In general, this is an interesting computational framework and a useful starting point for future modeling work. A particular strength is that it connects normalization, oscillatory dynamics, coherence, and communication subspaces within one analytically tractable model, making it possible to generate mechanistic hypotheses about when inter-areal communication should be stronger, lower-dimensional, or preferentially routed through feedback.

Weaknesses

Although I see the analytic approach as a strength, at the same time I regard the lack of any numerical comparison as a big weakness. Circuit simulations would not only confirm the correctness of the analytics, but also offer further insights on the error margins and on the regimes where the analytics are valid. This is because, to my understanding, the analytics are based on a linear approximation around the operating regime, which means deviations might be expected, especially for high gain levels in the input, or in the feedforward and feedback pathways.

Another problem is that the analytically tractable model seems to rely on effective connectivity weights that break Dale's law. Numerical simulations with explicitly modeled excitatory and inhibitory units might give insights into effects due, e.g., to the additional transmission delays mentioned in the Discussion.

Another weakness is the use of the term "predictions" to indicate features of the model dynamics that are purely described in the context of the model parameters. Although the model's response properties may certainly lead to predictions, I think the term requires a better contextualization in terms of neurophysiology and experimental neuroscience. The Discussion draws very interesting and valuable bridges between neuron morphology, interneuron types, and model parameters. But it seems it's left to the reader to backtrack and figure out which biological mechanisms or experimental manipulations should correspond to changes in input or feedback gain, and how these should be distinguished from possible changes in feedforward gain.

Relatedly, the manuscript places substantial emphasis on modulation of feedback gain, but does not comparably explore modulation of the feedforward gain, β2, which regulates the V1-to-V2 drive. This seems important because changes in feedforward gain could also influence communication subspace dimensionality and oscillatory dynamics. Therefore, predictions related to top-down feedback modulations should be taken with a grain of salt.

Last but not least, the model dynamics are split among multiple elements and nonlinear interactions, reaching a level of complexity far higher than the other ORGaNICs formulations present in the literature. The authors derive these dynamics in the supplementary material, as a dynamical system that converges to a fixed-point solution that includes "exact divisive normalization". I wonder, however, if there could be simpler solutions that also produce normalization, either approximate or in a different form than the one proposed by the authors. Note also that the designation of "excitatory neurons" is misleading: despite the presence of two explicitly inhibitory populations, the "excitatory" units also interact with negative effective weights both recurrently and in the inter-areal interactions, thus breaking Dale's law.

Reviewer #1 (Public review):

Summary:

Pang et al. investigated the expression pattern of the transcription factor foxQ2II in an adult beetle brain. They find nine distinct clusters, with many neurons expressing Glut/ChaT and dopamine. Some of the dopamine neurons resemble cell types described in Drosophila. Several neurons seem to project to prominent higher brain regions such as the MB and CX, and might even connect to both.

Strengths:

The authors use state-of-the-art labeling techniques for the analysis of individual cell types, such as beetle brainbow, to investigate the until now unknown expression of the transcription factor in the adult beetle brain.

Rigorous cell reconstruction and image analysis revealed a better understanding of the anatomy of the labeled cells.

Weaknesses:

The brainbow labeling seems to include all cells labeled by the enhancer trap line, as well as the ones not expressing foxQ2II. Thus, it is unclear how useful this data is to compare individual cells to other insects.

The functional relevance of this transcription factor in the adult brain cell is still unknown. It is therefore unclear if the described neurons have any specific function and if they require this transcription factor for normal function.

Overall, the neural reconstructions are missing single-neuron details; it is difficult to compare the shown cell types to specific cell types in Drosophila based on the presented data, and this finding remains speculative.

Reviewer #2 (Public review):

Summary:

The authors provide the first thorough profiling of neurons in Tribolium characterized by the expression of the transcription factor foxQ2, which will be useful for developmental neurobiology. They use state-of-the-art methods convincingly to not only identify the neurons, but also to further characterize them anatomically and neurochemically.

Strengths:

Thorough and meticulous application of state-of-the-art anatomical methods in a non-standard laboratory organism.

Weaknesses:

No weaknesses were identified by this reviewer.

Comments:

I don't really have any major suggestions at all. Loved the work.

There is only one tiny nitpicking aspect:

P21: "Biogenic amines are involved in learning and memory and setting arousal threshholds (Davis, 2023), which are functions performed by the mushroom bodies and related to the function of the central complex in goal directed navigation, respectively."

MBs mainly process olfactory memory. At least in Drosophila, most other kinds of memories are being supported elsewhere.

https://pubmed.ncbi.nlm.nih.gov/10454381/

such as, e.g., visual pattern learning in the CX

https://pubmed.ncbi.nlm.nih.gov/16452971/

or motor learning in motor neurons

https://pubmed.ncbi.nlm.nih.gov/38779314/

or ventral ganglion, antennal lobes, and median bundle for place learning:

https://pubmed.ncbi.nlm.nih.gov/10706599/

If the authors focus on MBs, this sentence ought to reflect the fact that the function of the MBs is much narrower than the current sentence appears to suggest.

Reviewer #1 (Public review):

Summary:

Esfahany et al. describe a new platform (Toothy) to identify and analyze dentate spikes and sharp wave ripples from silicon probe electrophysiology data. The goal is to facilitate and standardize the extraction of DS1 and DS2 events, which have highly variable properties across recordings from different labs. The manuscript describes the basic workflow of the Toothy pipeline, including loading data, assigning channels along a linear probe, customizing parameters, selecting ideal channels for analysis, and classifying DS1 and DS2 events.

Strengths:

The manuscript is clear and easy to follow and does a good job of describing the platform. Overall, this will be a useful analysis pipeline that can help to standardize DS analysis across labs and datasets.

Weaknesses:

The current version has several bugs that prevent analysis, and the documentation of analysis parameters needs to be improved.

(1) In limited testing, the pipeline had several bugs, and I was not able to complete the full analysis of a dataset. Loading data from .mat or .npy files gave errors (it seemed that the metadata was not loaded correctly from the pop-up window). I was able to load a .nwb file, which worked well. The probe configuration tool was a bit difficult to understand, and there was not much documentation to help, although it worked when simply entering the x-y coordinates of the channels. It also crashed several times while trying to make a probe configuration due to it trying to save when a small typo was briefly entered. The initial analysis worked well, and the auto-selected channels matched our recording notes and seemed appropriate. DSs and ripples were extracted. An error came when trying to classify DSs, and the program repeatedly crashed across a variety of parameters. Overall, parts of the pipeline worked well, but others had significant bugs that need to be addressed.

(2) The authors should provide test data that can be run through the pipeline. Ideally, this could use a variety of data types, probes, and conditions so that it is clear how they differ.

(3) There are a lot of parameters that can be adjusted, but very little information about how they are chosen and what goes into parameter selection for a dataset. Additional documentation with more information on adjustable parameters, channel selection, and best practices would help improve the utility of the tool. Ideally, this could also integrate citations (either in the manuscript or documentation) to support some of the choices made during parameter selection.

(4) There is no validation presented against other analysis methods or datasets. While there is no ground truth of when DSs occur, this may limit the ability of this tool to become the standard for DS analysis. A section comparing the analysis used in the pipeline to other published analyses would be helpful.

(5) In the manuscript, it would be helpful to further describe the rationale for initially detecting DSs and SPW-Rs on all channels, when they are network events that occur across channels.

(6) A section on what hardware and software are necessary to run the pipeline should be added.

Reviewer #2 (Public review):

Summary:

This work provides an open-source, Python-based, graphical user interface for curating the detection and classification of dentate spikes (DSs) from hippocampal local field potential (LFP) recordings. The tool may also be used to detect, but not classify, sharp wave-ripples (SPW-Rs). The tool utilizes previously published Python packages for loading LFP files and creating experiment-specific probe objects. Detection and classification parameters are clearly defined and logged in a parameter file before starting processing. Once LFP data has been mapped to the probe object, event detection occurs across all channels. DSs are detected as qualifying peaks in the filtered DS band LFP, while SPW-Rs are detected as qualifying peaks in the filtered ripple band amplitude envelope. An initial curation step allows visualization of the LFP, instantaneous current source density (CSD), and depth-by-frequency band power plots for determining the approximate channel locations of key anatomical regions (i.e., CA1, the hippocampal fissure, and the hilus of the dentate gyrus). The optimal channel for detection is further refined in the next step by comparing event waveforms and quality metrics across channels. Artifacts and noisy waveforms can also be manually excluded during this step. Finally, DSs detected from the optimal channel are classified by computing the CSD profile around events and then clustering the first two principal components of all CSDs. The authors claim that this customizable tool will standardize DS detection and classification.

Strengths:

Toothy's detection and classification algorithms are appropriate and well-validated in the literature. The ability to change many parameters, the CSD calculation method, and clustering algorithm is helpful for precise replication of methodology that has varied previously. Default parameters optimized for mouse recordings provide a standardized starting point for rodent researchers.

The authors' commitments to transparency and user-friendliness are to be commended (e.g., clear instructions, defined and logged parameters, multiple visualization options, etc.) and are likely to be appreciated by new users. Researchers with little-to-no coding experience should find this tool especially powerful for jumpstarting their own DS analyses.

While not the focus of the paper, the capability to detect SPW-Rs provides an additional use case for Toothy and streamlines simultaneous analysis of SPW-Rs and DSs.

Weaknesses:

I encountered unexpected errors while trying to load LFP data into Toothy for testing, indicating that the "data ingestion" stage of Toothy requires minor code revision.

Toothy's utility for recordings that do not produce an LFP depth profile is unclear. According to the authors, Toothy allows probe designs with irregular spatial sampling (e.g., tetrodes) to be used. However, recording from a linear probe with electrodes spanning from approximately the hippocampal fissure to the hilus of the dentate gyrus is required for Toothy's full functionality. For example, Toothy uses a DS type classification algorithm that relies on sufficiently sampled CSD depth profiles that tetrode recordings cannot provide. As such, usage is currently restricted to detection only for certain recording setups.

The documentation on Toothy's output could be improved. Specifically, the work does not state which files different data are saved to or list the properties saved per detected event. Furthermore, the work does not discuss the potential importance of DS properties that are saved besides those related to the timing of the DS and its type.

Reviewer #3 (Public review):

Summary:

Esfahany et al present a novel, UI-based tool to detect dentate spikes from hippocampal local field potential recordings, called Toothy. Toothy is easily accessible, compatible with many popular recording formats, and guides users entirely via UI through the dentate spike curation and analysis process. The functional and interactive visualizations enable users to gain a detailed understanding of their data and rigorously analyze dentate spike phenomena. This tool will be broadly useful for anyone who studies hippocampal electrophysiology. Furthermore, by expanding access to dentate spike analysis, it may encourage more scientists to explore this understudied but critical phenomenon.

Strengths:

(1) Toothy provides several ways for users to interact directly with parameters, revealing the ramifications of these choices. Most parameters are adjustable and made obvious via a UI panel. Their effects are then visualized across channels and individual events. This will help users think critically when selecting parameters.

(2) Toothy is fully UI-based and pip-installable, lowering the barrier to entry far below what most electrophysiology analysis tools offer.

(3) The channel selection tool is broadly useful for identifying DG hilus and CA1 pyramidal locations. Since subregional and laminar localization of electrode sites is critical to correctly interpret hippocampal recordings, this tool could be more generally used to identify site locations across the hippocampus.

Weaknesses:

(1) The rationale behind parameter choices is not explained. In order to function "not as a black-box detector", as the authors state, all initial parameter choices should be explained with citations. If possible, these citations would also be available from Toothy directly, alongside citations describing alternative parameter choices. This will help users make informed choices. For instance, a user analyzing data from rats would need to adjust the default ripple frequency band upwards (150-250Hz), and would benefit from guidance to adjust this properly.

(2) The Results describe the functions of Toothy from the perspective of the user, but there is no Methods section describing what Toothy does between UI displays. This would allow readers to compare the tool directly to analysis pipelines as described in the Methods sections from other papers. Particular attention should be paid to justifying the analysis decisions that cannot be changed by the user, such as detecting events off of a single representative channel instead of across a consensus of multiple channels.

(3) It's unclear whether or how Toothy evaluates data quality to confirm that its analyses return interpretable results. At a minimum, the tool should confirm adequate sampling rate (e.g. <=1kHz) and inter-site spacing for CSD (e.g. <=50um).

(4) The paper does not put Toothy into context among the other common open-source electrophysiology analysis toolboxes. Consider Rippl-AI (Navas-Olive & Rubio et al, 2024) or pynapple (Viejo et al, 2023), to give a few examples. The paper would be strengthened by addressing how Toothy extends beyond the capacities of these other tools and how Toothy can be integrated into a workflow that also uses these other tools.

Reviewer #2 (Public review):

Summary:

In natural visual behavior, such as when one is looking for a face in the crowd, the eyes are moved from site to site, seeking possible matching targets. This involves attention both to the current view at center of vision (the foveal location) as well as to upcoming views via attention to targets in the periphery. While it has been established that attention generally enhances neuronal response (compared to simple visual activation) at the attended spatial location, this study provides solid evidence that attention during active visual search leads to neuronal response enhancement only when the eye moves towards targets that exhibit the desired feature and category. This study thus moves the field towards understanding the neural encoding of active vision.

This study examines the neuronal basis of feature selective attention during active, freely behaving visual search. Traditional electrophysiological studies on visual attention in monkeys commonly used an eye fixation with covert attention paradigm, but have not sufficiently addressed the roles of both foveal and peripheral attention in play during natural looking behavior. Here, the authors present a novel paradigm in which, during eye movement mediated search neuronal receptive fields are recorded in multiple cortical areas (sensory V4, temporal and prefrontal areas). In this manner, as the eye foveates, items in the array fall into foveal or non-foveal recorded sites. Thus, the experimental paradigm is elegant, offering the opportunity to make multiple types of comparisons: target/distractor, towards/away from fovea, areal. Specifically, following a category cue (face, house, hand, flower), freely initiated saccades are made to locate a categorically matching 'target' in an array of distractors. Feature attention is assessed by comparing eye saccades made to targets vs to distractors. Spatial attention is assessed by comparing saccades made 'towards' vs 'away' from targets. Statistics are rigorous and nicely designed. Detailed association of simultaneously obtained eye movement sequences and neural parameters are well done. These are valuable data which will contribute to our understanding of attentional modulation in visual search.

The significance of these findings is fundamental. Decades of attention research in vision have been based on the paradigm of visual fixation and covert peripheral attention. However, increasingly the field has moved towards understanding how the visual system works during active vision. Here, the authors use an active visual search paradigm and record from key mid-tier (V4) and higher order (IT, PFC) areas. They find enhancement of attention both in the foveal and peripheral locations, and, furthermore, marked by a high degree of feature and categorical specificity. That is, while attention generally enhances neuronal response (compared to simple visual activation) at the attended spatial location, this study provides solid evidence that attention during active visual search leads to neuronal response enhancement only when the eye moves towards targets that exhibit the desired feature and category. This provides valuable data for the concept of a foveal-peripheral spatiotemporal attentional window in natural vision. The controls (comparisons of neuronal response during looks to targets vs distractors and looks towards and away from the target) and statistical rigor make these findings compelling. There will likely be additional future impacts of this study. For example, the eye movement patterns collected in this study may also provide a valuable dataset for future study of understanding search strategies. Goal-directed vs non-goal-directed task comparisons could be designed to test possible circuit models. Although much remains unknown regarding how and where frontal and temporal signals are integrated during active search, these data contribute important guideposts for future models of active visual search.

Reviewer #3 (Public review):

In this manuscript, the authors investigate the role of attention in foveal processing during a naturalistic task. They record neural activity from extrastriate visual areas V4 and inferotemporal cortex, as well as from the lateral prefrontal cortex, in macaques performing a free-gaze visual search task. In this task, animals searched for a face or house target among multiple complex stimuli, with no constraints on eye movements. Unlike classic studies of visual attention, which often rely on controlled fixation, this work examines neural activity in both foveal and peripheral receptive fields during naturalistic eye movements.

The main question addressed by the authors is how feature-based attention is distributed and coordinated across foveal and peripheral visual fields during active search, and how this attentional processing influences saccade behavior. The authors show that foveal units in visual areas exhibit feature-based attentional enhancement, with stronger responses when a fixated stimulus is a target compared to when the same stimulus serves as a distractor. Peripheral units in visual and prefrontal areas show both feature-based and spatial attentional modulation, consistent with prior work. Finally, the authors show that attentional modulation depends primarily on stimulus category rather than response magnitude, with neurons showing similar enhancement for all images within the target category regardless of how strongly individual images drive the cell.

There are several notable strengths of this paper including:

(1) Disentangling feature-based and spatial attention during naturalistic vision remains a central challenge. This paper tackles both simultaneously, parsing neural populations by object selectivity (face-selective, house-selective, non-selective) and RF position (foveal vs. peripheral).

(2) The unconstrained search task (Fig. 1A) moves beyond the dominant fixed-gaze, cued-attention designs (Zhou & Desimone, 2011) to study attention as it operates during natural behavior, with sequential fixations and voluntary saccades.

(3) The scale of the multi-area recordings is a major strength and is well aligned with current trends in primate and human neuroscience toward large-scale, multi-area recordings. Simultaneous recordings from visual and prefrontal areas, comprising over 4,900 foveal units and more than 1,500 peripheral units, enable meaningful cross-area latency comparisons and area-specific analyses of attentional modulation. This study builds on the authors' previous analyses of this dataset by expanding the scope to show that feature-based attention generalizes across neuronal classes and operates on categorical identity rather than response magnitude.

(4) The combination of simultaneous multi-area recordings and a rich behavioral paradigm provide a dataset that is well suited for population decoding, cross-area interaction analyses, and trial-by-trial prediction of saccade choices, which could substantially deepen mechanistic understanding beyond the largely univariate comparisons presented here.

While the data broadly support the paper's main conclusions, several issues limit the strength of the mechanistic interpretation and should be taken into consideration:

(1) Receptive field size is not explicitly quantified and may confound foveal-peripheral comparisons. Units are classified as foveal or peripheral based on responsiveness to the cue versus the search array (Methods, p. 17), but the manuscript lacks essential information about receptive field sizes, eccentricities, and the number of search stimuli falling within each receptive field and related proper controls. This is critical because receptive fields in visual area V4 at foveal eccentricities are relatively small (Gattass et al., 1988; Desimone & Schein, 1987), whereas receptive fields in inferotemporal cortex can span several degrees to tens of degrees and often include the fovea (Op de Beeck & Vogels, 2000; DiCarlo & Maunsell, 2003; Zoccolan et al., 2007). Given the 2{degree sign} × 2{degree sign} stimulus size, multiple search items could potentially fall simultaneously within peripheral receptive fields. This introduces a potential confound, as attentional modulation is known to be strongest when multiple stimuli appear within a single receptive field (Reynolds et al., 1999). Although the authors acknowledge this issue for visual area V4 (p. 17), it is neither quantified nor controlled for. Without explicit receptive field mapping relative to the search array, comparisons between foveal and peripheral units, as well as between visual areas, are difficult to interpret cleanly.

(2) Attentional modulation is difficult to dissociate from saccade planning and decision-related signals. The free-gaze paradigm enhances ecological validity but introduces a temporal confound: mean distractor fixation durations are approximately 156 ms (p. 9), while attentional effects emerge between 137 and 170 ms after fixation onset (Fig. 2). As a result, the reported attentional modulation coincides with preparation of the subsequent saccade. Neural activity measured in the primary analysis window (150-225 ms; p. 19) therefore likely reflects a mixture of visual, attentional, motor planning, target recognition, and behavioral relevance signals, all of which are known to modulate responses in visual areas at similar latencies (e.g., Chelazzi et al., 1998). Moreover, target fixations (~257 ms) and distractor fixations (~156 ms) occur on fundamentally different behavioral timescales, which may inflate apparent foveal attentional effects. While the authors suggest that these timing differences support the idea that foveal feature-based attention facilitates prolonged fixation on target stimuli, this interpretation is not fully supported by the current analyses. That said, the saccade-aligned analyses of peripheral units (Fig. S3) partially mitigate this concern by demonstrating that feature-based modulation persists through saccade execution.

(3) The "attention-out" condition for spatial attention lacks directional control. In the spatial attention analyses (Fig. 4D-F), the "attention-out" condition appears to include all fixations followed by saccades directed away from the receptive field, regardless of saccade direction. This differs from classic spatial attention designs, which typically use controlled anti-saccades or saccades to fixed locations opposite the receptive field (e.g., Moore & Armstrong, 2003; Gregoriou et al., 2009). Saccades directed toward locations adjacent to, but outside, the receptive field may still partially engage spatial attention mechanisms near the receptive field via broad attentional fields or motor preparation gradients (Bisley & Goldberg, 2010). In addition, the "attention-out" condition likely contains a heterogeneous mixture of trials in which the stimulus in the receptive field is either a target or a distractor, since feature-based attention effects are derived from this same pool of trials. As a result, spatial and feature attention effects are not fully orthogonal, and variance related to feature attention may already be embedded in the spatial attention baseline.

[Editors' note: the authors have provided responses to each of these points.]

Reviewer #1 (Public review):

Summary:

Based on previous work showing that viral evolution follows reproducible patterns in diverse animals, the authors sought to examine whether the antibody response operates under similar constraints. By analyzing over 17,000 B cells isolated from 6 monkeys at 3 different time points, the authors convincingly show that the immune response does follow specific patterns of responses to different classes of epitopes based on the infecting virus. Moreover, each of these clusters has characteristic (cross-) binding and neutralization properties. Importantly, these classes are independent of the underlying immunogenetics, which (as expected) vary significantly between monkeys. This last point is particularly relevant for vaccine design, as it means that immunogens may not need to be as narrowly focused on specific germline genes as previously thought.

Strengths:

The large number of B cells cultured for this study is a particular strength, as is the fact that they were isolated in an antigen-unbiased fashion. The experiments are well-designed and comprehensive.

Weaknesses:

The genetic element is a relatively minor component overall and more qualitative than quantitative. It would be nice to investigate other properties of the repertoire like CDRH3 length and possible public clones, as well.

Reviewer #2 (Public review):

Summary:

Song et al. comprehensively analyzed the SHIV-infected macaque B cell repertoires and commonalities among their antibody responses, despite their diverse genetic background. They suggest these studies would inform HIV-1 vaccine design.

Strengths:

This study is well-designed and used proper analysis methods, and the figures are clear and effectively presented.

Weaknesses:

However, it tends to overstate its novelty and significance, emphasizing points that are relatively obvious (e.g., different classes of antibodies can recognize a common epitope) and appears to have been overwritten and unnecessarily fancy ("conceptually analogous to ecomorph evolution", "epitopic convergence"). Moreover, some limitations of the rhesus macaque model and the differences between bnAbs and nAbs should be discussed. That said, the underlying data are solid and important in their detail, and the manuscript will be a useful resource for HIV-1 vaccine and pathogen studies.

Reviewer #1 (Public review):

Summary:

Foik et al. report that hypochlorous acid, a reactive chlorine species generated during host defense, activates the transcription of the froABCD in P. aeruginosa. This gene cluster had previously been associated with a potential role during flow of fluids and appears to be regulated by the sigma factor FroR and its anti-sigma factor FroI. In the present study, the authors show that froABCD is expressed both in neutrophils and macrophages, which they claim is likely a result of HOCl but not H2O2 production. Fro expression is also induced in a murine model of corneal infection, which is characterized by immune cells invasion. Expression of the fro system can be quenched by several antioxidants, such as methionine, cysteine, and others. FroR-deficient cells that lack froABCD expression during HOCl stress, appear more sensitive to the oxidant.

Strengths:

The authors provide a number of data supporting their claim that transcription of the froABCD system is induced by reactive chlorine species. This was shown by RNAseq, qRT-PCR, and through microscopy using a transcriptional reporter fusion. Likewise, elevated expression of froABCD was shown in vitro and in vivo, excluding potential in vitro artifacts. The manuscript, while mostly descriptive, is easy to follow and the data were presented clearly and convincingly. The authors have also been responsive to concerns from the previous review.

Weaknesses:

(1) Line 10: "HOCl preferentially oxidizes....". Please consider modifying the language to: "the second-order rate constant of HOCl is significantly higher with Met/Cys compared to other aa."

(2) I am not sure I completely understand Fig 1B. Is the promoter right upstream of yfp or is yfp located downstream of froA? If the latter is the case, wouldn't this be a translational fusion?

(3) My previous comment regarding why fro expression is higher during phagocytosis in macrophages compared to neutrophils has been somewhat (albeit not convincingly), addressed by the authors in the response to the reviewer, but this discussion should be part of the manuscript as the macrophage data were shown.

(4) Line 122: The statement "The degree of fro inhibition by 4-ABAH...." is incorrect unless the authors can provide experimental evidence. Fro expression is not upregulated because MPO is inhibited by 4-ABAH, which results in less hOCL production.

(5) Can Supp Fig. 1 be quantified in a similar way it was done for HOCl to allow for a better comparison if HOCl or flow is the more potent inducer?

(6) Overall, the fro expression (YFP/mCherry) seems highly variable for treatment with HOCl (Fig. 2C: ~65; 2D: ~20; why is fro expression 3x lower?

(7) The authors should provide evidence that N-chlorotaurine can activate fro expression also. They said they weren't able to obtain chlorinated taurine, but this is quite simple to produce: PMCID: PMC1219228

(8) Fig. 4 supplement 1: Please provide concentrations for the oxidants used in these experiments.

(9) Lines 251/252: change to: upregulation of instead of in

(10) Chaperones and other heat-shock genes are more upregulated in ∆froR, indicating elevated HOCl-mediated oxidative damage, which supports their findings.

(11) Complementation of ∆froR is missing

(12) Line 198: The growth experiment at 4 uM shows differences between WT and mutant, but at 2 uM cells showed already low fro expression due to cell death (which has not been proven by CFU counts). This discrepancy should at least be discussed.

(13) The critical in vitro experiment is missing: does purified FroI get oxidized by HOCl and dissociated from FroR?

(14) Lines: 350-355: The claim that the fro system is the first-line defense is unproven.

Reviewer #2 (Public review):

Summary:

Foik et al. studied the regulation of the fro operon in response to HOCl, an oxidant derived from immune cells, especially neutrophils. They use a transcriptional fusion of YFP to the froA promotor in an mCherry expressing P. aeruginosa strain to determine fro-induction under the microscope. They use this system to study fro expression in medium, in the presence of neutrophils and macrophages, neutrophil-conditioned medium, and several chemical stimuli, including NaCl, HOCl, hydrogen peroxide, nitric acid, hydrochloric acid, and sodium hydroxide. They also use a corneal infection model to demonstrate that froA is upregulated in P. aeruginosa 20 h post infection and perform transcriptional analyses in WT and a froR mutant in response to HOCl.

Strengths:

Their data clearly shows that HOCl is a strong inducer of the fro Operon. Addition of HOCl-quenching chemicals together with HOCl abrogates the response. They also show that a froR mutant is more susceptible to HOCl than WT. Their transcriptomic data reveals genes under control of the FroR/FroI sigma factor/anti sigma factor system.

Weaknesses:

Although the presented evidence is mostly solid, some of their findings need to be evaluated more carefully; explaining the rationale behind some of the experiments might enhance the article; and some of the models proposed by the authors seem far-fetched, as outlined below:

Unexpected outcomes and open questions for future research:

(1) As outlined above, HOCl seems to be the main inducer of the fro operon. Interestingly, during interaction with immune cells, macrophages and neutrophils seem to induce a reporter gene under fro control in a similar manner, although macrophages are generally thought to produce less HOCl, when compared to neutrophils. May be this view needs to be revised, or another reactive species, produced by macrophages, can activate the fro operon as well.

(2) HOCl is typically unstable in the presence of biomolecules. Nevertheless, medium conditioned by activated neutrophils is a strong inducer of the fro operon. The medium used by the authors for this experiment contains taurine, and, as the authors acknowledge, this taurine will likely react with HOCl to form the more stable taurine N-chloramine. Similarly, the MinA bacterial medium used to treat P. aeruginosa with HOCl directly also contains ammonium ions at mM concentrations, which could potentially react with HOCl to form monochloramine. It could be speculated that taurine N-chloramine and other chloramines are as effective as HOCl in activating the fro-operon.

(3) The fro operon was originally described to be activated by shear stress ("flow-regulated operon"). How shear stress and HOCl-stress are related, or if fro activation by both stimuli is a coincidence, remains unclear. The authors propose a model, in which flow transports oxidizing molecules, which ultimately activate the fro operon. However, the initial work by Sanfilippo et al. (2019, Nat Microbiol) used plain LB medium in a fluidic chamber to induce the shear stress, which should be free of oxidants, and certainly of HOCl.

Comments on revised version:

The authors have addressed my concerns appropriately.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have considered and discussed the comments raised in the previous round of review.]

Summary:

The authors aim to use state-of-the art behaviour, imaging and connectome techniques to identify the neural interaction between sleep and long-term memory consolidation in the PAM-DPM circuits, a well-known dopaminergic pathway within Drosophila Mushroom Body.

Strengths:

The investigation follows a logical strategy to collect huge dataset of sleep, appetitive memory and live imaging. The authors identified and showed that activation of a PAM subset: alpha-1 reduces sleep quality and memory consolidation in a starvation dependant manner. The author also convincingly demonstrated the corresponding neuronal responses of DPM neurons following PAM alpha-1 activation, and the positive role of DPM neural activity in sleep and memory consolidation. Moreover, the new data provide TRIC-LUC provided better temporal resolution of neural activity correlates for PAMalpha1-DPM inhibition. Importantly, the author demonstrated that memory loss derived from PAM alpha 1 activation can be partly restored by ectopic sleep enhancement via feeding THIP at the memory consolidation period after training.

Weaknesses:

Although the revised version carries arguments to satisfy the reviewers' concern, the writing is now less cohesive. Crucially an explanation however remains required for the following experimental contradiction: the central observation of the study indicates that PAM alpha1 activation cause DPM inhibition which disrupt sleep and memory consolidation. Therefore, one would expect a reduced PAMalpha1 and increased DPM activities after memory training, but the authors found the opposite is true from now enhanced TRIC-LUC dataset. The authors indicate this data reinforce the inhibitory nature of PAM-alph1-DPM, but it does not explain why such a reduced DPM activity is observed after training.

Reviewer #2 (Public review):

Summary:

Sleep plays a critical role in memory consolidation, but the neural mechanisms underlying this relationship remain incompletely understood. The authors examined a specific subset of PAM dopaminergic neurons, PAM-α1, and DPM neurons in Drosophila. These neurons have previously been implicated in memory, and DPM neurons have also been linked to sleep. The study explores whether this circuit provides a mechanistic link between sleep and memory consolidation.

Strengths:

The authors report several novel findings. Brief activation or inhibition of PAM-α1 neurons, or brief inhibition of DPM neurons during the first few hours after training, impairs 24-hour LTM. Notably, these brief manipulations disrupt sleep for many hours afterward, particularly during the night. The authors further show that perturbation of PAM-α1 and DPM neurons impairs sleep and appetitive memory consolidation under starvation conditions, and that pharmacological sleep induction during the night rescues the LTM defects. Together, these findings suggest that PAM-α1 and DPM neurons are involved in sleep regulation and LTM consolidation under starvation. These are important observations that advance our understanding of the circuits regulating sleep and memory consolidation.

Weaknesses:

Some claims require additional evidence or clarification.

(1) Previous studies linking impaired memory to reduced sleep have primarily examined conditions involving severe sleep deprivation. In contrast, this manuscript argues that relatively modest decreases in total sleep, accompanied by sleep fragmentation, are sufficient to impair memory consolidation. It remains unclear whether sleep fragmentation of this magnitude is itself critical for LTM consolidation. An independent method for inducing comparably mild sleep loss and fragmentation would be needed to directly test this interpretation.

(2) It is unclear why both activation and inactivation of PAM-α1 neurons produce similar effects on sleep and memory. In addition, MB299B-labeled neurons exert stronger effects on memory than MB043B-labeled neurons, whereas MB043B-labeled neurons have stronger effects on sleep. If sleep disruption is the primary driver of impaired memory consolidation, a stronger correspondence between the sleep and memory phenotypes might be expected. The authors speculate that MB043B may affect sleep through non-PAM neurons, but without identifying the relevant neurons, this remains speculative.

(3) The complex schematic model (Fig. 12), with parallel circuits and unidentified neuronal groups, underscores the difficulty of interpreting the current data. In the "less activity" arm of the model, distinct circuits are proposed to regulate sleep and LTM, respectively, and DPM neurons are not included. This makes it difficult to reconcile the model with the central claim that the PAM-α1-to-DPM microcircuit links sleep and LTM consolidation.

(4) The TRIC-LUC reporter system is not ideal for resolving dynamic changes in neuronal activity. Activity-dependent Ca²⁺ signaling must first reconstitute the TRIC transcriptional system, which then drives luciferase transcription, translation, and accumulation. The original characterization of TRIC indicates that TRIC signals accumulate and decay over several hours. Thus, the kinetics of the TRIC-LUC reporter should be interpreted cautiously, particularly when inferring transient or precisely timed changes in neuronal activity.

(5) Including data from training under fed conditions would provide a more complete understanding of state-dependent neural activity and would help distinguish starvation-specific effects from more general circuit mechanisms.

Reviewer #3 (Public review):

Summary:

Understanding the neural circuits that link sleep and memory remains a fundamental challenge in neuroscience. In this study, Lin Yan and colleagues investigate how dopamine signaling in Drosophila regulates long-term memory (LTM) formation in the context of sleep. They identify a specific microcircuit between protocerebral anterior medial dopamine neurons (PAM-DANs) and dorsal paired medial (GABAergic DPM) neurons that modulates memory consolidation. Their findings suggest that disrupting the basal activity of PAM-α1 neurons during early consolidation impairs LTM, with particularly pronounced effects under starvation conditions. Notably, sleep fragmentation caused by this disruption can be pharmacologically rescued, restoring LTM. These results provide compelling evidence how dopamine signaling plays a crucial role in linking sleep and memory, offering new insights into the underlying mechanisms.

Strength:

This study presents a well-executed investigation into sleep-memory interactions, utilizing a combination of connectomics, behavioral assays, functional imaging, and pharmacological manipulations. The authors convincingly demonstrate that the PAM-α1 and DPM circuit interact, highlighting a potential mechanism by which sleep influences memory consolidation. The anatomical and functional dissection of this circuit is of high interest to the field, and the study's integration of sleep and memory processes contributes significantly to our understanding of the role of dopamine in cognitive functions. Additional experiments investigating the contribution of MBON-α1 to the circuit, connectomic analysis together with a dissection of dopamine receptor function further strengthen the proposed circuit motif and its biological relevance.

Weaknesses:

While the study is well designed, presents compelling findings and has been further strengthened by additional experiments, some aspects remain unclear. The role of DPM neurons in memory consolidation seems not yet fully resolved, as different genetic approaches yield variable results. Furthermore, some manipulations impair memory without affecting sleep fragmentation - or vice versa, suggesting that the observed memory deficits cannot be explained solely by impaired sleep-dependent consolidation. It would also have been interesting to discuss potential mechanisms by which dopamine receptor-mediated cAMP signaling could lead to a reduction in Ca²⁺ signals. I am confident that these questions can be addressed in future studies.

Conclusion:

Overall, this study provides valuable new insights into how sleep and dopaminergic circuits interact to regulate memory consolidation in Drosophila and may reveal general principles underlying the neural regulation of memory.

Reviewer #2 (Public review):

Summary:

This manuscript presents the "NoSeMaze", a novel automated platform for studying social behavior and cognitive performance in group-housed male mice. The authors report that mice form robust, transitive dominance hierarchies in this environment and that individual social rank remains largely stable across multiple group compositions. They further demonstrate that social dominance and aggressive behaviors, like chasing, are partially dissociable and that dominance traits are independent of non-social cognitive performance. The study includes a genetic manipulation of oxytocin receptor expression in the anterior olfactory nucleus, which showed only transient effects on social rank.

Strengths:

(1) Innovative Methodology:<br /> The NoSeMaze platform is a technically elegant and conceptually well-integrated system that enables fully automated, long-term monitoring of both social and cognitive behaviors in large groups of group-housed mice. It combines tube-test-like dominance contests, voluntary chase-escape interactions, and an embedded operant olfactory discrimination task within a single, ethologically relevant environment. This modular design allows for high-throughput, minimally invasive behavioral assessment without the need for repeated handling or artificial isolation.

(2) Experimental Scale and Rigor:<br /> The study includes 79 male mice and over 4,000 mouse-days of observation across multiple group reshufflings. The use of RFID-based identification, automated data logging, and longitudinal design enables robust quantification of individual trait stability and group-level social structure.

(3) Multidimensional Behavioral Profiling:<br /> The integration of social (tube dominance, proactive chasing), physical (body weight), and cognitive (olfactory learning task) measures offers a rich, multi-dimensional profile of each individual mouse. The authors' finding that social dominance traits and non-social cognitive performance are largely uncorrelated reinforces emerging models of orthogonal behavioral trait axes or "animal personalities".

(4) Clarity and Data Analysis:<br /> The analytical framework is well-suited to the study's complexity, with appropriate use of dominance metrics, mixed-effects models, and permutation tests. The analyses are clearly explained, statistically rigorous, and supported by transparent supplementary materials.

Weaknesses:

(1) Scope Limitations (Sex):<br /> The study is limited to male mice, which represents a common but problematic bias.

(2) Ambiguity of Dominance as a Construct:<br /> While the study robustly quantifies social rank and hierarchy structure, the broader functional meaning of "dominance" remains unclear.

Reviewer #2 (Public review):

Summary:

This convincing study builds on previously published findings in both mice and humans to advance quantitative insights into the coupling between noradrenergic activity fluctuations during mouse NREM sleep and heart rate fluctuations. The work reaffirms the presence of coordinated infraslow fluctuations in sigma power and heart rate during NREM sleep and that this coordination is enabled by noradrenaline-releasing neurons in the locus coeruleus. Also supporting previously published work in mice and humans, the authors describe a link between the strength of these infraslow fluctuations and memory consolidation in mice and humans.

Strengths:

A major finding of this study is the mechanistic insight it provides into the regulation of the previously understudied very-low-frequency (0-0.15 Hz) component of heart rate variability, and the demonstration, through elegant optogenetic bidirectional interference, that infraslow noradrenergic fluctuations are an underlying driving force. This finding will promote recognition of heart rate variability in sleeping mice as a read-out of neuronal activity patterns that control autonomic balance.

Another strength of the study is its translational part, whereby the sigma power-heart rate coupling in mouse is used to identify a previously unrecognized correlation between such coupling and memory consolidation in humans. This widens the applicability of heart rate variability measures, highlighting their use as biomarkers for noradrenergic fluctuations and associated sleep-dependent memory consolidation.

Weaknesses:

The study impresses by the thorough parallel analysis of both mouse and human correlational data between electrophysiological and fluorescent activity measures of the sleeping brain. Further work will be needed to disentangle the mechanisms by which heart rate is regulated, notably the contribution of parasympathetic and sympathetic nervous systems, to establish the very low frequency heart rate variability in mice as a novel biomarker for noradrenergic dynamics in the sleeping brain.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

Summary:

In this manuscript, Scheib et al. identify distinct calcium dynamics in the somata and tuft dendrites of layer 5 pyramidal cells in mice performing a licking task. Animals are trained to lick water ports on the left or right following an acoustic cue, and can adjust their targeting when the ports are displaced. For tongue premotor cortical neurons projecting to the ventromedial thalamus, calcium transients in tuft dendrites are tightly locked to the direction-instructive cue, while somatic calcium signals are more broadly dispersed and more frequently synchronized with tongue motion and port contact. Finally, when the targets are shifted, tufts exhibit a sparse but large corrective signal on an improperly-targeted first lick, and the changes in population activity in the tufts and somata differ after adaptation to the new port locations.

Strengths:

In my opinion, this is a very strong manuscript which reports several novel and significant observations, contains high-quality data and (for the most part) reasonable analyses, and is clear and well-written. Most prior studies of cortical sensorimotor processing have measured the output of neurons using extracellular recording - an approach which obscures potentially important signaling differences between neuronal compartments. This study leverages cutting-edge imaging techniques in mice to document large, time-dependent differences between calcium signals at cortical somata and tuft dendrites. This phenomenon could have major implications at the cellular level for synaptic plasticity, and at the systems and behavioral levels for motor adaptation.

Weaknesses:

At a conceptual level, the authors may wish to elaborate a bit on what sensorimotor computation they think the circuit is implementing, and how their results help explain this implementation. Several possibilities are raised: tuft activation could "prime" the pyramidal cells in advance of movement initiation (line 319ff), or could track errors to engage plasticity (line 351ff) and solve the credit assignment problem (line 362ff). It might be helpful to make one of these proposals more concrete with a computational model, but this is not strictly necessary. [The authors explain that they will address this with modeling work in subsequent research.]

Reviewer #2 (Public review):

Summary:

The authors set out to compare functional encoding in the tuft dendrites and somata of a specific cortical cell type during motor planning and learning.

Strengths:

The investigation of a specific projection type (L5 ET) is a strength that aids reproducibility and interpretation. The elegant approach to increasing the depth of field of dendritic imaging is another strength. The data analyses are largely clear in their methods, scope, and interpretation. The writing is extremely clear and appropriately referenced, with an excellent Introduction, in particular.

Weaknesses:

This work is largely observational, describing signals that might reflect computational transformations and/or instruct plasticity, but those possibilities have not yet been deeply investigated. The manuscript does a good job of laying out these as future directions.

Reviewer #3 (Public review):

Summary:

This article by Scheib et al. investigates how layer 5 extratelencephalic (ET) neurons in the frontal cortex encode sensorimotor information during motor learning, focusing on differences between their apical tuft dendrites and somas. The authors alternated recordings among these ET neuronal compartments in the mouse anterior lateral motor cortex (ALM) during a cued directional licking task with a target port shift. They found that while tuft dendrites predominantly encode sensory cues, with a subset selectively active during corrective actions, somatic activity was more strongly associated with action timing. Additionally, learning induced divergent plasticity: tuft dendrites increased their selectivity but decreased response gain, maintaining stable net selectivity, whereas somas showed increased net selectivity early in learning. Together, these findings reveal distinct sensorimotor representations and learning-related plasticity in dendritic and somatic compartments, providing insight into how compartment-specific activity in the frontal cortex may contribute to motor skill acquisition.

Strengths:

The authors developed an innovative imaging approach and a comprehensive data analysis pipeline to address a knowledge gap in the literature. By alternating imaging of dendritic tufts and somas in the same animals, they compare compartment-specific activity during motor learning and identify distinct encoding of task variables and learning-related plasticity across these compartments. Interestingly, a subset of dendritic tufts shows activity associated with corrective actions. The findings are discussed in the context of current theories of dendritic computation, credit assignment, and motor learning, providing a useful foundation for future mechanistic studies.

Weaknesses:

No major weaknesses were identified.

Reviewer #1 (Public review):

Summary:

The paper investigates how AVP modulates pancreatic alpha and beta cell activity using acute mouse pancreatic tissue slices, calcium imaging, hormone secretion assays, RNAscope, and newly synthesized receptor-selective ligands. The Authors report that AVP regulates islet cell activity in a glucose- and state-dependent manner, with maximal effects occurring within physiological AVP concentrations and a bell-shaped concentration-response profile. They conclude that V1b receptors are the principal mediators of these effects and propose that IP3 receptor-dependent signaling underlies the observed nonlinear responses.

Strengths:

The use of fresh pancreatic tissue slices preserves islet architecture and cell-cell interactions, providing a physiologically relevant experimental model compared with isolated islets or immortalized cell lines.

The combination of live calcium imaging, hormone secretion measurements, RNAscope, and pharmacological characterization of newly synthesized receptor-selective ligands represents a technically comprehensive experimental approach that addresses AVP signaling from multiple complementary perspectives.

Weaknesses:

(1) The central mechanistic model of the manuscript is not supported by the experimental data. Although the Authors repeatedly attribute the observed bell-shaped responses to IP3 Receptor activation and inactivation, no direct mechanistic evidence is provided to implicate IP3 receptors. Experiments assessing IP3 receptor function using genetic manipulation and direct measurements of IP3 signaling are necessary before such mechanistic conclusions can be drawn.

(2) The Authors should directly demonstrate V1b receptor expression in β cells using complementary approaches, since the RNAscope data indicate broader expression but do not convincingly establish receptor localization within specific endocrine populations.

(3) In my opinion, the central conclusion that V1b receptors are the predominant mediators of the observed effects is insufficiently supported because definitive loss-of-function experiments are lacking. Genetic deletion or selective silencing of V1b receptors should be provided to validate the proposed mechanism.

(4) The heterogeneous responses observed among islets substantially weaken the proposed mechanistic model. Data should be provided to identify the determinants responsible for activation, absence of response, or inhibition in individual islets.

(5) Please explain why the marked changes in alpha-cell calcium activity were not accompanied by corresponding alterations in glucagon secretion. This apparent discrepancy requires additional experimental evidence.

(6) The Authors need to provide stronger evidence linking the observed calcium dynamics with insulin secretion, since calcium measurements alone cannot establish the proposed functional consequences.

(7) Proper assays should be provided to assess whether the newly synthesized ligands exhibit comparable selectivity and efficacy at murine receptors rather than relying primarily on pharmacological characterization performed using human receptor-expressing cell lines.

(8) The proposed absence of V1a receptor involvement is based primarily on pharmacological inhibition. Independent experimental approaches should be provided to exclude a contribution of this receptor subtype.

(9) They must provide additional quantitative analyses demonstrating that the reported bell-shaped concentration-response relationship is robust across individual experiments rather than reflecting substantial biological variability.

(10) The Authors should include experiments evaluating endogenous AVP signaling under more physiological conditions instead of relying predominantly on exogenous agonist administration.

(11) I believe the role of forskolin deserves further clarification because many conclusions were obtained under cAMP-permissive conditions that may substantially influence AVP responses. Additional experiments without pharmacological cAMP stimulation should be presented.

(12) Please clarify how beta cells and alpha cells were identified exclusively from functional activity patterns during calcium imaging and provide independent validation of cell identity within the analyzed recordings.

(13) In my opinion, the manuscript relies heavily on changes in intracellular calcium activity as a surrogate for endocrine function, whereas the secretion data do not consistently support the proposed functional conclusions. Additional evidence is needed to establish a direct relationship between the observed calcium dynamics and hormone release.

(14) The Authors should better reconcile their findings with previous reports showing minimal or absent AVP receptor expression in β cells and explain how the current data resolve these discrepancies rather than adding another possible interpretation.

Reviewer #2 (Public review):

Summary:

In this paper Drs. Kercmar, Murko and Bombek make a series of observations related to the role of AVP in pancreatic islets. They use the pancreatic slice preparation that their group is well known for. The observations on the slide physiology are technically impressive. However, I am not convinced by the conclusions of this manuscript for a number of reasons. At the core of my concern is perhaps that this manuscript appears to be motivated to resolve 'controversies' surrounding the actions of AVP on insulin and glucagon secretion. This manuscript adds more observations, but these do not move the field forward in improving or solidifying our mechanistic understanding of AVP actions on islets. A major claim in this manuscript is the beta cell expression of the V1b Receptor for AVP, but the evidence presented in this paper fall short of supporting this claim. Observations on the activation of calcium in alpha cells via V1b receptor align with prior observations to this effect and can explain the effects of beta cell calcium and insulin secretion better than an explanation where beta cells express functional V1BR, for which direct evidence is lacking.

I have focused my main concerns below. I hope the authors will consider these suggestions carefully - please be assured that they were made with the intent to support the authors and increase the impact of this work.

Strengths:

The main strength of this paper is the technical sophistication of the approach and the analysis and representation of the calcium traces from alpha and beta cells.

Weaknesses:

(1) There are excellent data that indicate that the actions of AVP are mediated via V1bR on alpha cells and that V1bR is 1) not expressed by beta cells and 2) does not activate beta cell calcium at all at 10 nM - which is the same concentration used in this paper (Figure 4G) for peak alpha cell Ca2+ activation (see https://doi.org/10.1016/j.cmet.2017.03.017; cited as ref 30 in the current manuscript). Any published stimulatory actions of AVP on insulin secretion can be explained by the potentiating effects of glucagon, released in response to AVP stimulation of alpha cells.

(2) The RNAscope data offered in the revision as a second line of evidence for the expression of the V1bR in beta cells do not convince. I applaud the authors for trying as these are hard experiments to do well, as evidenced from the Gcg RNAscope signal that is not at all concentrated in the islet periphery, and in fact both color puncta occur outside of the islet at similar density. Absent a convincing concentration of Gcg signal (which is a very abundant transcript in alpha cells), it is hard to depend on these results. They certainly do not substitute experiments to determine cell autonomous activation of isolated beta cells by AVP. Claim of beta cell expression of V1br, require a more direct demonstration by staining (if appropriate antibodies exist), by beta cell-specific deletion of V1br, or by documenting the direct calcium activation in isolated beta cells in the absence of alpha cells. This should include a demonstration of Gaq-dependence in isolated beta cells.

(3) We know from bulk RNAseq data on purified alpha, beta, and delta cells from both the Huising and Gribble groups that there is no expression of V2a. I will point you to the data from the Huising lab website published almost a decade ago (http://dx.doi.org/10.1016/j.molmet.2016.04.007) - which is publicly available and can be used to generate figures (https://huisinglab.com/data-ghrelin-ucsc/index.html). They indicate the absence of expression of not only AVP2 receptors anywhere in the islet - but the lack of expression of V1bra, V1brb, and Oxtr in beta cells. These AVP/OXT receptor expression data are largely and helpfully confirmed by the efforts in this paper that involved the generation of the V1aR agonist and V2R antagonist.

(4) Importantly, the lack of V1br from beta cells does not invalidate observations that AVP affects calcium in beta cells, but it does indicate that these effects are mediated 1) indirectly, downstream of alpha cell V1br or 2) via an unknown off-target mechanism (less likely). The different peak efficacies in Figure 4G would also suggest they are not mediated by the same receptor. The recent work by Huixia Ren and colleagues (PMID: 41916313) that demonstrates that glucagon accelerates the frequency of beta cell calcium is in line with such a scenario.

(5) The use of forskolin across almost all traces complicates the interpretation of the results. The design does not account for the elevation of cAMP in alpha cells and subsequent release of glucagon - particularly upon co-stimulation with AVP which permits glucagon release by activating a calcium response in alpha cells. This glucagon then could activate beta cells. If resolving the mechanism of action is the goal, often less is more. The activation of Gaq-mediated calcium is not cAMP dependent (although the downstream hormone secretion clearly often is). As was shown, AVP does not activate calcium in beta cells in the absence of cAMP. The experiments should have been completed in the absence of cAMP/forskolin, which would likely have had different outcomes on the beta cell responses and to the hormone secretion.

(6) It is motivated by a desire to 'study the AVP dependence of both alpha and beta cells at the same time'. As best as I can determine, the design choice to conduct most studies under sustained forskolin stimulation is related to the permissive actions of AVP on hormone secretion in response to cAMP-generating stimuli. The permissive actions by AVP that are cited are on hormone secretion - which in many cell types requires activation of both calcium and cAMP signaling. Whether the activation of V1br and subsequent calcium responsive is permitted by cAMP is unclear. I believe the argument the authors are making here is that the activation of beta cell calcium by AVP is permitted by forskolin. i.e. the cAMP stimulated by it in beta cells.

(7) Figure 9 suggests a pharmacological activation of beta cell V1bR in the low pM range. How do the authors reconcile this compare with the apparent absence of an effect of AVP stimulation at low pM to low nM doses in beta cells (Figure 5A). I note that there are changes over time with sustained beta cell stimulation with 8 mM glucose, but these changes are relatively subtle, gradual and quite likely represent the progression of calcium behaviors that would have occurred under sustained glucose irrespective of these very low AVP concentrations. I will note that the Kd of the V1bR for AVP is around 1 nM, with tracer displacement starting around 100 pM according to the data in figure 6B, which is hard to reconcile with changes in beta cell calcium by AVP doses that start 10-100-fold lower than this dose at 1 and 10 pM (Figure 9).

Reviewer #3 (Public review):

Summary:

This work aims to better understand the role of arginine vasopressin (AVP) in the control of islet hormone secretion. This builds on previous literature in this area reporting on the actions of AVP to stimulate islet hormones. The gap in literature being addressed by these studies is primarily focused on the glucose-dependency of AVP on both insulin and glucagon secretion. A secondary objective is to explore the role of individual receptors with the use of newly generated peptides and existing tools. The methods include the use of Ca2+ imaging in pancreas slices from mice, with additional outcomes including insulin secretion in some areas. The conclusions presented are that AVP acts through V1b receptors in both alpha- and beta-cells, that this activity occurs in the high cAMP environment, and is glucose dependent.

Strengths:

The area of research is emerging with plenty of room for new contributions. The concept of AVP stimulating islet hormone secretion is important and deserving of further insight. The use of pancreas tissue to image primary cells makes the experiments physiologically relevant. The advancement of novel tools in this area should be helpful to other groups investigating the actions of AVP.

Comments on revised version:

Overall, the authors have modified their conclusions to more accurately capture the results of this manuscript. They also add the significant limitations outlined in the review process to the Discussion. With the addition of new data, however, a few concerns have emerged.

(1) The rational for showing somatostatin staining in Figure 1 a is unclear. It also does not appear to be the same region of interest as in panel B.

(2) It is difficult to assess the success of the RNAscope with the representative image used in Figure 1b. It is surprising how low the Gcg signal is in this image, suggesting some optimization is required. Additionally, how many mice were used (biological replicates) and how many V1b receptor+ and Gcg+ cells per mouse were quantified for the RNAscope images? This must be indicated in the methods section and should be sufficiently powered to make a conclusion.

(3) While the addition of insulin and glucagon secretions with AVP ramp provide a functional output to the calcium imaging, it is unclear why the measurements are sometimes log10 transformed (Figure 5 K and L) but not always (Figure 5E). It is difficult to interpret negative glucagon values. What is the functional output of the dose-dependent calcium response to AVP in alpha cells if it is not glucagon?

(4) Finally, the highlights section has not been refined to the revised interpretations of the manuscript.

Reviewer #1 (Public review):

Summary:

This paper develops a formalism for quantifying epidemic dynamics in terms of relative fitnesses of circulating variants, uses the formalism to elucidate fundamental tradeoffs of epidemics driven by variants with increased transmissibility versus immune escape capability, shows the formalism implies a natural quantity measuring the impact of selection on epidemic growth, and demonstrates that the formalism enables a decomposition of epidemic dynamics into circulation among different immunity groups. The relative fitness formalism enables these analyses to be performed with genetic sequence data only, a major benefit of the model given the relatively high availability of sequence data compared to other data streams such as case counts and titers.

Strengths:

Linking epidemic dynamics to pathogen evolution is a fundamental problem in studies of antigenically variable pathogens, with models of epidemic dynamics and immune-driven evolution going back decades in applications to respiratory pathogens such as influenza. The COVID-19 pandemic heightened the urgency for developing methods for quantifying epidemic growth in contexts where novel variants emerge, leading to differential susceptibility among individuals with diverse exposure histories with implications for vaccination strategies. Real-world data streams such as case counts and immunological measurements have a variety of shortcomings that pose major challenges for quantitative models aiming to inform policy. In recent years, genetic sequencing data has become widely available for pathogens including SARS-CoV-2 and influenza, allowing tracking of pathogen evolution at unprecedented detail in real time, yet biases in the collection of sequence data across different populations make connections between absolute epidemic size and variant frequencies from sequence data not immediately transparent.

This paper's contributions are exciting because they demonstrate new ways to link pathogen evolution and epidemic dynamics using very accessible data. From a theoretical perspective, the model is appealing because of its simple derivation in terms of compartmental models of epidemics, which are standard in the literature, and its clear extension to populations with heterogeneous immune histories. The latter extension leads directly to new methods for inferring immune groups with differential susceptibility to antigenically distinct variants in populations with heterogeneous immune histories without access to immunological data such as titers, an important advance given the wide applicability of quantification of antigenic relationships among variants in real populations.

Weaknesses:

While the demonstrated methods for forecasting short-term epidemic growth and for quantifying population immunity using sequence data are exciting as proofs of principle, the validation and statistical support provided in the analyses have drawbacks that are not fully addressed in the manuscript, weakening the evidence for the usefulness of the methods in their current form.

The analyses forecasting epidemic growth using Gaussian process models are justified using Pearson correlation coefficients whose values are extremely low for the test data period. The explanation given for this is that the case data used to validate the predictions has worse ascertainment over time, but it is not shown directly that the model may be working well despite the low correlations. Whereas, by eye, the predicted epidemic growth curves appear to capture features of the observed epidemic growth curves, the computed metrics don't support the claim of success of the predictions. Additionally, nearly all the model fits lack estimates of uncertainty, so it is not possible to discern the significance of departures between the model and data, or subtle differences in relative fitness calculations across geographies.

The analysis of latent pseudo-immune components also suffers drawbacks that render it more of an interesting proof of principle than a convincing tool for prediction at this point. In particular, in figures S18 and S19, metrics meant to quantify the statistical significance of the results show no difference from null models computed by permuting variants and their escape vectors, yet no interpretation is given for the lack of significance. Moreover, the model fits relating titer distance to pseudo escape distance seem unsuccessful for JN.1 infection and XBB infection histories, which is not adequately accounted for in the text, which cites just "weaker correlations" in these cohorts.

In several instances, the evidence for the new data analyses is weakened by a lack of clarity in the presentation of the technical details of the methods. For example, in the discussion of the Gaussian process models, it was not clear what features of the problem inform the choice of kernel (Matern 5/2), which hyperparameters were used, and how novel this use of Gaussian processes is. In the section describing methods for predicting epidemic growth rate from selective pressure, the discussion of the gradient boosting regressor model provided no intuition as to why this method performed better than the others tested or whether this was particularly important to the conclusions, and the lack of discussion of uncertainty or variability in the model predictions makes it difficult to assess the significance of the time series estimates alone. In the discussion of the latent immune factor model, the mismatch between the notation used in Equation 5 compared to that in Equation 18 made the derivations more difficult to follow. Subsequently, the explanation of the fitting of the pseudo-immune model left out details, such as an explicit definition of distance in pseudo-escape space, to what extent the group-level mean aggregated titer measurement captured features of the titer data (despite ignoring interindividual variability), and a thorough discussion of the successes and shortcomings of the fits in different scenarios. More explicit presentation of the mathematical choices going into the methods, sources and quantification of uncertainty, and cases where the model performs well or poorly could significantly bolster the case for the usefulness of sequence data in quantitatively predicting epidemic growth and antigenic relationships among variants in practice, in more general settings than those carried out here.

Reviewer #2 (Public review):

Summary:

The authors first introduce a framework to understand how different phenotypic drivers of viral evolution, i.e., changes in transmissibility versus immune escape, complicate epidemic forecasting using only genetic data. To overcome these complications, they advance an evolutionary "selective pressure" metric to predict population-wide epidemic growth from genetic data alone. Separately, they introduce a latent space model to infer a "pseudo" population immune structure from geographic variation in viral lineage dynamics, and find that the inferred pseudo-structure predicts human serological data.

Strengths:

This paper begins with a useful pedagogical exposition on the connection between fitness-driven frequency dynamics and underlying mechanisms of viral-immune co-evolution. A major contribution of this paper - a method to infer variant-specific escape properties from geographically non-uniform variant frequency dynamics alone - is an interesting and potentially timely one, given the advance of sequencing-based surveillance.

Weaknesses:

The logical flow of the pedagogy part of the text works against the reader, which is problematic since it motivates the rest of the text. Moreover, some important modelling choices and procedures, particularly with respect to the selective pressure metric, are only cursorily described in the methods section. The lack of explanation and detail, especially relative to more simple choices that are seemingly motivated by the authors' own theory, makes it difficult to understand and therefore assess their validity and/or necessity.

Reviewer #3 (Public review):

Summary:

This study introduces a new analytical framework to analyze how viral variant frequencies change over time and in different locations. Two examples are given that demonstrate where this approach can be useful and where other approaches can be ambiguous in characterizing novel variants. The authors then demonstrate that the spatiotemporal dynamics of variant frequencies can be used to predict future epidemic growth rates and to investigate how variants differ in immune escape.

Strengths:

(1) Examples are provided that make the study accessible for a general audience.

(2) The authors demonstrate that their approach is predictive both of overall epidemic growth rates and immunological distance between variants.

(3) The approach introduced in this study can be readily applied to current and future epidemiological challenges that are similar to SARS-CoV-2 with respect to the relative evolutionary timescales wherever there is spatiotemporal heterogeneity in the susceptible population.

Weaknesses:

(1) The authors conclude their abstract claiming that their method provides an early signal of epidemic growth. Can this be quantified? Could the authors perform retrospective analyses for sequences available through various cutoff times, identify how early significant new variants are detected, and compare this to other detection methods?

(2) Analysis depicted in Figure 4 and Figure S9 could be explored further than speculatively attributing weak correlation to declining reporting rates for US states. Exploring how correlation between data and prediction varies over time during the test period might identify periods/events that explain weak correlation overall. The authors could explore predicting growth rates for estimated state prevalences rather than reported cases.

Reviewer #1 (Public review):<br /> <br /> Summary:

This tumour type is missing from the big pan-cancer databases, so none of the popular online analysis tools works for it. That's a real gap, and it's the right one to go after. The authors build an online resource that gathers the scattered public molecular datasets for this disease, adds three of their own patient cohorts, ties everything to clinical data, and exposes interactive tools, downloads, and programmatic access so other people can build on it. To show what it does, they take one gene through the whole platform - clinical, gene-expression, protein, single-cell, immune, and drug-response and then test that gene in cell lines. So there are really two things on offer here: a resource and a practical example of using it. They land very differently.

Strengths:

The resource is the real contribution, and it's done with care. It covers 37 centres and nearly 2,000 samples across five kinds of molecular data, and the authors are honest about provenance: how they screened datasets in or out, where they recorded the diagnostic codes, and why they dropped ambiguous mixed-tumour collections. The key methodological decision is the right one; every analysis runs inside its own cohort, and the cross-cohort views are explicitly "for looking, not for combining." That's exactly how you should treat heterogeneous public data, and they say so plainly instead of quietly pooling everything. Their three pathologist-confirmed cohorts add genuine independent material, so this isn't a re-skin of data that already existed. And because the code and a public access point are actually available, the reuse claim holds.

The example is internally consistent, which is what makes it persuasive. The gene reads higher in higher-risk patients across several independent cohorts and in their own protein data, tracks with the disease spreading and recurring, and lines up with worse survival. The single-cell data put it in the dividing cells; the pathway analysis points to proliferation. Three independent data types landing on the same proliferation story are the strongest part of the biology.

Weaknesses:

The honest problem is that the entire biological story rests on one gene, tested one way. The lab work is two cell lines with the gene knocked down, showing less growth and migration: there is no rescue to confirm the effect is real, no second gene to show the approach generalises, nothing in a living animal. That earns the modest claim: the resource can point you at a candidate worth testing. It does not earn the headline claim that the platform reliably generates good target hypotheses, because we only ever watch it succeed once. One example illustrates a workflow; it doesn't establish a method.

Some of the statistics won't survive scrutiny. The clearest case is a perfect separation between treatment-resistant and treatment-sensitive cases from a single immune cell population, reported with no error bars, no check for information leakage, and apparently from very few samples. A perfect result in that setting is almost always overfitting or a small-sample artefact, not a strong classifier. The same pattern shows up elsewhere: small groups, p-values with no effect sizes or error bars, and no correction for the enormous number of features and cohorts being tested across the whole platform. Separately, one drug result is a correlation against a predicted sensitivity score from a model.

The AI assistant gets far more weight than the evidence supports. Credit where due: the authors are clear and consistent that it only helps interpret and navigate, and never touches the data, the statistics, or the results. That's the correct line to draw, and they hold it. But the assistant itself is never tested, no accuracy numbers, no benchmark, no error analysis, no described way for a human to check what it produces. Calling it something that "fundamentally transforms the user experience" is an assertion, not a finding. And since even the literature feature is admitted not to be a proper systematic review, the prominence of the artificial-intelligence framing runs ahead of what's been shown.

Reviewer #2 (Public review):

Summary:

dbGIST appears to be the first dedicated multi-omics resource worldwide that is specifically focused on GIST.

Strengths:

The main value of the paper is not simply that the authors collected datasets, but that they built a usable resource around them, with cohort-aware analyses, curated clinical labels, interactive visualizations, downloadable results, selected API access, and an optional LLM-assisted interface. The work is solid, and the database is likely to be useful for GIST researchers interested in target discovery, cross-dataset validation, drug-response hypotheses, and translational follow-up.

The MCM7 analysis is a reasonable use case. It shows how a user can start from one candidate gene and then move across transcriptomic, proteomic, clinical, single-cell, immune-related, drug-response, and experimental evidence. I do not see this as the main discovery of the paper, but rather as a practical demonstration of what the database can do. That is appropriate for a resource manuscript.

Weaknesses:

(1) The authors should make the organization of the platform a little easier to follow. The manuscript refers to five primary omics layers, six omics-focused pages, and eight analytical modules. This structure is understandable after reading the relevant sections, but it may not be immediately obvious to readers. A brief clarification of how the omics layers, web pages, and analytical modules relate to each other would help.

(2) Since dbGIST is a live web resource, the authors should provide a clear versioning statement. The manuscript should indicate which version of the database corresponds to the analyses and figures reported in the paper, and how future updates will be distinguished from the version evaluated here. This is a small point, but it matters for reproducibility.

(3) The API function is a strength of the resource, but it is still described rather generally. The authors should give more concrete documentation of what can be accessed through the API, what inputs are required, and what type of output is returned. This could be placed in the supplementary materials. It would make the database more useful for computational users.

(4) The manuscript should clarify the status of downloadable data. It is clear that figures, source-data tables, and selected derived outputs are available, but it is less clear whether the full processed matrices used internally by the platform are downloadable or only maintained for deployment. This distinction should be stated plainly.

(5) The statistical reporting in the MCM7 clinical-association analyses needs a little more care. Several p-values are shown across different cohorts and clinical variables. The authors should state whether these are nominal p-values or adjusted p-values. If they are nominal, that is acceptable for a resource demonstration, but the exploratory nature of the analyses should be made clear.

(6) The ROC analyses for imatinib response should include sample sizes, and confidence intervals for AUC values would be useful if available. Some of the AUC values are high, and without group sizes, it is difficult to judge how stable those estimates are. The authors should avoid implying that these ROC results are validated predictive models.

(7) The interpretation of MCM7 should be slightly more cautious. MCM7 is a well-known DNA replication and cell-cycle gene, and the single-cell analyses seem to support its association with proliferative cell states. This is biologically consistent, but it also means that MCM7 expression should not be presented as tumour-cell-specific without qualification. The manuscript should frame it mainly as a proliferation-associated signal in the current analysis.

(8) The drug-response section would benefit from a clearer explanation of the response metric. The authors report correlations between MCM7 expression and predicted response to C6-ceramide, but readers need to know whether the predicted value represents IC50, AUC, sensitivity score, or another metric. The direction of interpretation should also be made explicit, since a negative correlation can mean different things depending on the scoring system.

(9) The single-cell annotation would be more convincing if the authors provided a compact marker-gene summary for the major cell types in each single-cell cohort. The current description of annotation by source labels, marker inspection, and manual curation is reasonable, but users of the database would benefit from seeing the marker evidence behind the labels.

(10) The experimental validation section should include a few routine details that are currently not easy to find. The siRNA sequences or target regions, number of biological replicates, statistical tests for the CCK-8 and wound-healing assays, and details of wound-closure quantification should be reported. These additions would make the in vitro part more reproducible.

(11) The wound-healing result should be interpreted with caution. Since MCM7 knockdown reduces proliferation, reduced wound closure could reflect changes in proliferation, migration, or both. Unless proliferation was controlled during the wound-healing assay, the authors should avoid describing this result as purely migratory.

(12) The LLM-related claims should remain conservative. The assistant is a useful feature for navigation, plain-language explanation, and user support, especially for clinicians or wet-lab researchers. However, the strongest statements about the LLM transforming interpretation or automating analysis should be toned down. The important point is that the LLM layer helps users interact with the resource, while the numerical analyses come from predefined dbGIST modules.

Reviewer #3 (Public review):

Summary:

The dbGist dataset/tool would provide substantial value to the cancer research community.

Strengths:

The manuscript presents dbGIST, a dedicated GIST-focused multiomics resource integrating data from 37 centers and ~2k samples across genomics, transcriptomics, proteomics, phosphoproteomics, and single-cell transcriptomics. Given that GIST is virtually absent from major cancer genomics consortia (TCGA, ICGC), this resource fills a genuine gap and represents a valuable contribution to the GIST research community.

(1) The MCM7 case study effectively demonstrates the platform's utility, linking a resource-derived candidate to survival outcomes.

(2) The LLM-assisted interface (dbGIST Assistant) is a reasonable addition for accessibility, lowering the barrier for clinicians and wet-lab researchers, who may not always have the skill set required for proper data analysis, especially for a rich and wide dataset like the dataset in question.

Weaknesses:

(1) Data deposition (major):

While the manuscript references public accessions for raw source datasets and provides a GitHub repository for code, it remains unclear where the **curated, harmonized data matrices** - which represent the core value-add of this work - are independently deposited. Access to these processed data appears to depend entirely on the dbGIST web interface and API. The authors should deposit the harmonized matrices in a persistent, general-purpose repository to ensure long-term availability independent of the web platform.

(2) LLM agent capabilities underspecified:

The manuscript would benefit from a clearer description of the assistant's capabilities and boundaries. Specifically, what tools or actions are available to the LLM agent? Can it execute code against the underlying data, trigger analytical modules programmatically, or is it limited to natural-language explanation of pre-computed results? Clarifying this would help readers assess the scope of the AI layer and distinguish it from agentic platforms that perform computation on behalf of the user.

Reviewer #1 (Public review):

Summary:

The manuscript by Waterman et al. describes the development of a mathematical model that quantifies plant volatile emissions dynamics in response to mechanical/biotic stress. Model outputs were based on volatile emission measurements from maize plants using PTR-MS. Modeling revealed differences in emission patterns dependent on the intensity of wounding damage, application of herbivore oral secretions, age of leaf, circadian clock, and genotype. Differences were also observed between different types of volatiles, and the response curves somewhat correlated with expression patterns of biosynthetic genes. Moreover, the model showed priming effects from overlapping response curves upon multiple wounding events.

Strengths:

As a non-expert in modeling, this reviewer assesses the work from a broader point of view. Overall, I consider this model to be useful for other researchers to quantify volatile emission dynamics for their plant system. Generating the models does not seem to be overly complicated as long as emissions can be measured with a real-time system such as PTR-MS, which is costly and not available to every lab. The advantage of this approach is that it does not rely on parameters of underlying enzymatic pathways or transport processes. The authors claim that it can be easily applied to other biological responses.

Weaknesses:

The manuscript lacks a deeper discussion of how the model can help make predictions of volatile emission dynamics from plants in the greenhouse or field. Can the model be trained and validated with volatile measurements from plants under different environmental conditions? How realistic is this approach given the complexity of a field environment? It would be helpful to provide a better outlook of the application of the model for scientists in the field of plant volatile biology and beyond.

The authors state that "emissions can be regulated independently of each other" (Line 359). I would assume that regulatory mechanisms in different genotypes are similar but show genotype-specific variation.

Reviewer #2 (Public review):

This is a study of the dynamics of plant volatile emissions, using a curve-fitting approach to describe salient properties of the dynamics of plant volatile chemicals. The study is interesting and unique in taking this approach. Some of the dynamics uncovered (e.g. lagged emission of many sesquiterpenes) are already well known using less sophisticated approaches, while other properties (diurnal cycles in emission dynamics) are newly uncovered. The approach in general is new for the topic of plant volatile emissions, but curve-fitting is widely used to describe the dynamics or function-valued responses of plants and other organisms. The study thus reads as rather methods-focused, giving tidbits of interesting properties of the dynamics of plant VOCs rather than being structured strongly around clear biological hypotheses. The method seems like a logical and robust way to analyze the dynamics of plant VOCs. I believe the impact of the work will largely depend on whether there are substantial and meaningful outcomes (for herbivores, downstream processes of induction, etc) due to the differences in VOC dynamics described via these methods that would be hard to observe in other ways. If so, there will be a need to adopt robust methods such as this to describe the salient features of those dynamics. At present, I do not believe there is evidence one way or another as to whether the subtle differences in VOC dynamics have large consequences.

The paper sells itself as describing a new technique for describing response curves generally across biological systems, but it only uses this technique to look at the dynamics of induced plant volatiles. I believe to show general utility of this approach, a wider range of examples of plastic responses to stimuli across organismal groups would be needed. I am, however, convinced that this approach is both novel and useful within the scope in which the examples are shown (i.e. in describing the dynamics of induced plant responses). Some of the text purporting novelty in uncovering shared and divergent responses across the tree of life seems pretty overstated.

Much of the introductory and discussion text is quite broad, and I wonder if the technique is really meant to be applicable to the specific case that is described (repeated measures of an induced volatile response). Likewise, there has been considerable work in such realms as behavioral science, function-valued traits (e.g. Stinchcombe et al 2012), performance curves (Kingsolver various papers), etc to describe dynamic or variable responses phenomenologically, and there are approaches including GAMs, parametric curve fitting, and other techniques that probably report the same salient features as the approach here. Indeed, there are already statistical techniques to assess the macroevolution of response curves (e.g. Goolsby 2015) and wide discussions as to how to compare function-based responses among organisms (The Functional Phylogenies Group 2012). So in the broad scheme of biology, I am not sure I'm convinced of the novelty of the approach. However, I believe it is novel within the context in which it is used here. The salient part of the methods is that it uses predefined attributes of dynamics (onset, duration, etc) based on a gamma distribution that the researchers (with good reason) believe to be biologically meaningful. This is in contrast to multivariate approaches (e.g. Izem et al 2005) that attempt to find salient dynamic features in a less constrained way.

I would have liked to see a clear description of model fits (e.g., how much of the variation in the real data is described by the fitted model). This seems important because there are quite a number of constraints placed on model fitting - so presumably when a model blind to those constraints picks unrealistic parameters, that would suggest that the constrained model probably does not fit the data all that well.

I am curious about the normalization process in the 'normalized emission' that is analyzed throughout the study. Normalization to leaf size makes sense, though I was less clear about L459: "Additionally, values were normalized to the maximum response observed in each experiment, yielding a range of positive values < 1." Why was this needed? Is the 'maximum response observed in each experiment' across all plants/compounds/treatments or within a single plant? In general, is there a way of reporting VOC emission rates in absolute values (e.g. umol / Liter air)? Normalization would presumably not impact most curve properties very much, but it could have effects on 'integral', and the need for within-experiment normalization would suggest a lack of transferability or comparability among datasets from different experiments (at least as regards 'integral'), which is suggested as a major advantage of this approach in the discussion.

A practical look at how to handle early-stage visual concepting when a team needs quick, varied drafts rather than a single polished asset.

A common situation for anyone doing early creative work: a small team needs to pitch three ad directions, or a founder needs a rough product mockup for a deck, and there's no time or budget for a full design pass. The bottleneck usually isn't taste, it's speed — you need to see ten mediocre options to find the one worth refining.

The practical approach here is to separate divergent exploration from convergent polish. In the divergent phase, the goal is volume and variation: different compositions, color moods, framing, and subject placement, judged quickly and discarded fast. Only after narrowing to one or two directions does it make sense to slow down and refine details like lighting consistency, brand color accuracy, or typography.

This is where prompt-based AI image tools fit as one option among several, alongside sketching, stock photo collage, or hiring a designer for quick roughs. If your workflow involves swapping a reference object into different scenes — say, a product bottle mocked up against several backgrounds, or a storyboard frame reused with variations — a tool built around object-reference workflows can shortcut some of that manual compositing. Nano Banana 2 Lite is one independent, third-party site set up for that kind of rapid visual exploration: prompt-driven generation plus reference-based editing for things like ad concepts, mockups, and early social graphics. It's not affiliated with Google or DeepMind, just a separate tool built for this stage of work.

The limitation worth naming: none of this replaces a real design or photography pass for anything customer-facing or brand-critical. AI-generated drafts are useful for internal alignment and direction-finding, not for final assets, and results can vary depending on the reference material and prompt clarity. Treat the output as a sketch, not a deliverable, and budget real design time once the direction is chosen.

Reviewer #1 (Public review):

Summary:

This manuscript describes a chemical screen for activators of the eIF2 kinase GCN2 (EIF2AK4) in the integrated stress response (ISR). Recently, reported inhibitors of GCN2 and other protein kinases have been shown at certain concentrations to paradoxically activate GCN2. The study uses CHO cells and ISR reporter screens to identify a number of GCN2 activator compounds, including a potent "compound 20." These activators have implications for the development of new therapies for ISR-related diseases. For example, although not directly pursued in this study, these GCN2 activators could be helpful for the treatment of PVOD, which is reported for patients with certain GCN2 loss-of-function mutations. The identified activators are also suggested to engage with the GCN2 directly and can function devoid of GCN1, a co-activator of GCN2.

Strengths:

The manuscript appears to be a largely rigorous study that flows in a logical manner. The topic is interesting and significant.

Weaknesses:

Portions of the manuscript are not fully clear. There are some experimental presentation and design concerns that should be addressed to support the stated conclusions.

Reviewer #2 (Public review):

Summary:

In this manuscript Zhu, Emanuelli and colleagues describe a novel pharmacological activator of the Integrated Stress Response kinase GCN2. The work is conclusive and biochemically solid. This work significantly adds to the pharmacological arsenal targeting the ISR and in particular GCN2.

Strengths:

Strong biochemistry, novel molecular activator of GCN2 (GCN1 independent).

Weaknesses:

Rationale for the screen not exploited in the results (e.g. pathogenic GCN2 mutants), lots of cell-based read-outs not endogenous.

Comments on revised version.

The authors did a great job at addressing my initial critique on their manuscript and consequently I have no further comment.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors describe the results of a high throughput screen for small molecule activators of GCN2. Ultimately, they find 3 promising compounds. One of these three, compound 20 (C20) is of the most interest both for its potency and specificity. The major new finding is that this molecule appears to activate GCN2 independent of GCN1, which suggests that it works by a potentially novel mechanism. Biochemical analysis suggests that each bind in the ATP binding pocket of GCN2, and that at least in vitro C20 is a potent agonist. Structural modeling provides insight into how the three compounds might dock in the pocket and generates testable hypotheses as to why C20 perhaps acts through a different mechanism than other molecules.

Strengths:

Of the 3 compounds identified by the authors, C20 is of the most interest, not just for its intriguing mechanistic distinction as being GCN1-independent (shown genetically in two distinct cell lines, CHO and 293T, and in contrast to other GCN2 activators) but also for its potency. Ultimately, C20 might be a tool for providing mechanistic insight into the details of GCN2 activation and regulation and could be exploited therapeutically.

Weaknesses:

The chief limitation of this work is that the experiments exploring the effects of C20 on ISR output in cells are limited, so how useful these compounds are both experimentally and therapeutically remains to be determined.

Comments on revised version.

The authors have satisfactorily addressed my comments. A more extensive analysis of UPR signaling in cells (transcription and cell death in particular) would have further strengthened the paper, but that can be left to future work.

Reviewer #1 (Public review):

The authors show that during prophase I of male meiosis, nucleoli disassemble and nucleolar components relocalize to the sex chromosome (XY) body. They further demonstrate that this process is regulated by the ATR-dependent signaling pathway that mediates meiotic sex chromosome inactivation (MSCI). Pharmacological disruption of pre-rRNA synthesis using the RNA polymerase I inhibitor BMH-21 leads to the recruitment of RNA polymerase II to the sex chromosomes and ectopic expression of sex chromosome-linked genes. These findings uncover a previously unrecognized role for pre-rRNAs in maintaining transcriptional silencing during meiosis. The study employs a combination of cell biology, genetics, and genomics approaches, and the conclusions are supported by compelling, well-organized data.

Comments:

(1) The current study focuses on transcriptional regulation of the sex chromosomes. It would be interesting to know whether perturbation of pre-rRNA synthesis also affects transcription of autosomal genes.

(2) Is ribosome biogenesis still active during prophase I of male meiosis? Additional discussion of the timing and extent of rRNA synthesis at this stage would help place the findings in a broader biological context.

(3) A recent preprint reports active RNA polymerase II-mediated transcription of Y chromosome genes within nucleolus-like bodies (NLBs) during prophase I of meiosis in Drosophila male germ cells (https://doi.org/10.64898/2026.05.20.726666). These findings suggest that the meiotic nucleolus may have species-specific roles in regulating sex chromosome gene expression. It would be valuable for the authors to discuss how their findings compare with these observations and the potential evolutionary implications.

Reviewer #2 (Public review):

Summary:

The authors showed the localization pattern of nucleolus components, including Pre-rRNA, a precursor of rRNAs, changes during meiotic prophase I, particularly with the localization of these nucleolar components to the X-Y body, which shows inactivation of RNA polymerase II transcription, during pachynema. The localization of Pre-rRNA depends on ATR kinase and gammaH2AX. The chemical inhibition of rRNA transcription disrupts the binding of pre-rRNA to the X-Y body and suppresses the inhibition of the RNA polymerase II-mediated transcription on the sex chromosomes.

Strengths:

The cytological analysis, combined with the chemical inhibition, provided solid evidence to support the idea that, together with the remodeling of the nucleolus structure, pre-rRNA is an essential component of sex chromosome inactivation in male mouse meiosis. The role of pre-rRNA in sex chromosome inactivation in male meiosis helps our understanding of how the X-Y body, which would be a biological condensate, would be formed; e.g. for example, this Pre-rRNA may promote phase separation.

Weaknesses:

However, there is limited information on how Pre-rRNA is recruited to only sex chromosomes and how the RNA promotes the inactivation of sex chromosomes. Of course, these will be a target of future study. One major weakness of this paper is a poor description of the results, with fair presentation and interpretation of the data.

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. Primary responses, including germinal centers, may still be ongoing at 3 weeks after the initial immunization and defects in GCs may contribute to the findings. Nonetheless, the authors do check antibody levels prior to the boost, and it is likely that most of the observed defects in Gls/Mpc2-deficiency are driven by faulty recall responses.

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.

Comments on revised version.

Authors extensively modified the text with great care and provided new data e.g. Fig. 5. Collectively, this is convincing and hence, I have no further comments.

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

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

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

Weaknesses:

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

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

Comments on revised version:

The authors have added some technical information, but have not made any major efforts to clarify some of the major points or to strengthen the paper. The degree of advance remains limited and several conclusions are not convincingly supported by the presented data.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Comments on revised version:

The authors have added some technical information and provided a better discussion of the data, but beyond that, they have not strengthened the conclusions. The observations are not supported by any perturbation assays.

Reviewer #1 (Public review):

The authors clearly demonstrate that overexpressed Dcp-1, but not Drice, is activated without canonical apoptosome components.

Using TurboID-based proximity labeling they revealed distinct proximal proteomes, among which Sirtuin 1, an Atg8a deacetylase, which promotes autophagy, was specifically required for Dcp-1 activation. Additionally, the show that autophagy-related genes, including Bcl-2 family members Debcl and Buffy, are required for Dcp-1 activation. Using structure-based prediction using AlphaFold3 they identified that Bruce, an autophagy-regulated inhibitor of apoptosis, as a Dcp-1-specific regulator acting outside the apoptosome-mediated pathway. Finally, they show that Bruce suppresses wing tissue growth. These findings indicate that non-lethal Dcp-1 activity is governed by the autophagy- Bruce axis, enabling distinct non-lethal functions independent of cell death.

Comments on revised version.

No further comments.

Reviewer #2 (Public review):

Summary:

The Drosophila executioner caspase Dcp-1 has established roles in cell death, autophagy, and imaginal disc growth. This study reports previously unrecognized factors that work together with Dcp-1. Specifically, the authors performed a turboID-based proximal ligation experiment to identify factors associated Dcp-1 and Drice. Dcp-1-specific interactors were further examined for their genetic interaction. The authors report autophagy-related genes, including Debcl and Buffy, to be required for Dcp-1 activation. In addition, the authors present evidence of an interaction between Bruce and Dcp-1. Bruce expression blocks the Dcp-1 overexpression phenotype. Inhibition of effector caspases or overexpression of Bruce commonly reduced wing growth, suggesting a relationship between the two proteins.

Strengths:

The study identifies new Dcp-1-interacting proteins and provides a functional link between Dcp-1 and Sirt1, Fkbp59, Debcl, Buffy, Atg2, and Atg8a. During the revision, the authors have also added convincing new data supporting the interaction between Dcp-1 and Bruce. They further make a strong case regarding the quality of the turboID-proteomics data. Overall, this is a strong manuscript supporting an interesting discovery.

Reviewer #3 (Public review):

Summary:

The present paper by Shinoda et al. from the Miura group builds upon findings reported in an earlier study by the same team (Shinoda et al., PNAS, 2019), which identified a non-apoptotic role for the Drosophila executioner caspase Dcp-1 in promoting wing tissue growth. That earlier work attributed this function primarily to Dcp-1 and to Decay, a caspase structurally related to executioner caspases, but not to DrICE, the principal apoptotic executioner caspase. The authors further proposed that this non-apoptotic caspase activity operates independently of the initiator caspase Dronc.

In the current study, the authors both corroborate aspects of their previous findings and extend the investigation to mechanisms regulating Dcp-1 in this context. They identify roles for the giant IAP Bruce, two BCL-2 family members, and autophagy-related components in modulating non-apoptotic Dcp-1 activity. Moreover, they show that Bruce binds to a BIR-like peptide exposed upon Dcp-1 cleavage, but not to DrICE. The study further suggests that low levels of Dcp-1 activity promote wing tissue growth, whereas excessive activity induces cell death, as evidenced by impaired wing development following Dcp-1 overexpression. Overall, the manuscript provides several intriguing insights into the non-apoptotic regulation of the comparatively weak apoptotic executioner caspase Dcp-1 and complements the group's earlier work. However, several concerns remain regarding certain interpretations of the data and the experimental rigour of some of the results.

Strengths:

A major strength of the work is its systematic genetic and biochemical approaches, which combine tissue-specific manipulation with protein interaction mapping to explore how Dcp-1 is regulated. The identification of several regulatory factors, including an inhibitor of cell death protein and components linked to autophagy, provides a coherent framework for understanding how Dcp-1 activity might be tuned.

Weaknesses:

The evidence supporting some key claims remains incomplete. In particular, the type of cell death form induced when Dcp-1 is overexpressed is not clearly established, and additional tests would be needed to distinguish between the different cell death types.

Likely impact:

The study contributes to a growing body of work showing that proteins traditionally associated with cell death can have broader roles in tissue development. This conceptual advance is likely to be of interest to researchers studying growth control and tissue maintenance.

Specific points:

(1) Nature of the wing ablation phenotype<br /> A central concern is whether the wing ablation phenotype observed upon Dcp-1 overexpression truly reflects apoptotic cell death. The authors show in Fig. 1c that nuclei in cells overexpressing Dcp-1, but not DrICE, zymogens are highly condensed, which is suggestive of apoptosis. However, it is equally plausible that this phenotype reflects a form of non-apoptotic, Dcp-1-dependent cell death (e.g. autophagy-dependent cell death). This distinction could be readily addressed using TUNEL labelling and direct caspase activity assays. The latter would be particularly informative, as it remains unclear whether zymogen Dcp-1 is capable of cleaving standard effector caspase reporters in vivo. Does the anti-cleaved Dcp-1 antibody detect Dcp-1 activation following overexpression of the Dcp-1 zymogen?

(2) Role of Decay<br /> In their earlier study, the authors identified Decay as another caspase influencing wing growth, albeit more modestly than Dcp-1. It is therefore unclear why this line of investigation was not pursued further in the current work. This omission is notable, as Decay is not implicated in apoptosis and, to date, no substantial physiological function has been assigned to this caspase in any system. At minimum, this point should be discussed explicitly.

(3) Fig. 2: Proximity labelling analysis<br /> The authors use TurboID-mediated proximity labelling to reveal distinct Dcp-1- and DrICE-associated proteomes across tissues, with a particular focus on the wing disc. They further demonstrate that RNAi-mediated knockdown of the Dcp-1-associated proteins Sirt1 and Fkbp59 suppresses the wing ablation phenotype induced by Dcp-1 overexpression, suggesting that these factors are required for Dcp-1 activity. However, it should be clarified whether Bruce was identified as a Dcp-1 interactor in the proximity labelling dataset, given its proposed central regulatory role. In addition, further discussion of Fkbp59, its known functions and how it might mechanistically influence Dcp-1 activity, would be valuable.

(4) Fig. 3: Autophagy-related factors<br /> Given that Sirt1 is known to promote autophagy, the authors next examine autophagy-related proteins and identify roles for Atg2, Atg8a, Debcl, and Buffy in Dcp-1 activation. Notably, these proteins do not promote cell death in the Hid-induced canonical apoptotic pathway. However, it is important to determine whether knockdown of Debcl, Buffy, Atg2, or Atg8a alone affects wing development in the absence of Dcp-1 overexpression, to exclude the possibility that these perturbations independently impair wing formation.

(5) Evidence for canonical autophagy<br /> The involvement of autophagy would be more convincingly demonstrated by testing additional core autophagy genes, such as Atg7, Atg5, and Atg12, as well as performing a combined knockdown of Atg8a and Atg8b. Moreover, direct assessment of autophagy at the cellular level using established genetic reporters would substantially strengthen the conclusions.

(6) Figs. 4-5: Functional consequences<br /> It would be informative to determine whether Synr, Debcl, or Buffy influence wing size on their own and whether their overexpression enhances wing growth.

(7) Terminology and interpretation of cell death<br /> Taken together, the results suggest that Dcp-1 zymogen overexpression induces a form of non-apoptotic cell death, potentially autophagy-dependent or related. The reviewer does not understand the authors' insistence on referring to this process as apoptosis. The authors should be more cautious in their terminology: there is no canonical versus non-canonical apoptosis, there is simply apoptosis. Without stronger evidence, these effects should not be described as apoptotic cell death.

Comments on revised version.

In the revised manuscript, the authors addressed each of my concerns in good faith and, in my opinion, responded to them thoroughly and satisfactorily. I have no further concerns.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

Summary:

In this study, the authors describe an early diverging vertebrate KCNE gene present in jawless lampreys that they denote KCNE0.

Three forms of the protein are isolated from different lampreys, which have 95% homology to each other, but only moderate homology to KCNE1-6.

Co-expression with lamprey KCNQ1 produced a non-inactivating current, whereas co-expression with mammalian KCNQ1 resulted in less modulation. Introduction of a tetra-leucine motif from KCNE4 into KCNE0 reduced current on co-expression with KCNQ1, conferring an inhibitory effect.

Strengths:

This is an interesting and uncontroversial report of a new KCNE isoform from lower vertebrates that gives insight into the evolutionary progression of the sequence and functional properties of the accessory protein.

Reviewer #2 (Public review):

Summary:

This study functionally characterizes a single KCNE-like gene, kcne0, from a jawless vertebrate. The authors conducted multiple experiments, including TEVC, VCF, RT-PCR, and RNA-seq to show that KCNQ1 and kcne0 exhibited a broadly overlapping organ distribution in lamprey species, and KCNE0 produced a constitutively active current when co-expressed with lamprey KCNQ1, similar to the effects of human KCNE3 on KCNQ1. This modulation was species-specific, as co-expression of KCNE0 with other species' KCNQ1 was less effective. Moreover, the authors found that truncating the N-terminal had a more significant reduction of the modulatory effects than truncating the C-terminal of KCNE0. Interestingly, the introduction of the tetra-leucine motif from human KCNE4 into KCNE0 conferred KCNE0 with comparable effects of human KCNE4 on KCNQ1.

Strengths:

The authors clearly introduced an early-diverging member of the KCNE family, and convincingly demonstrated the function of this gene, KCNE0. The results are supported by experiments of multiple approaches and are clearly written. The work is significant and will interest readers from the extended research area.

Weaknesses:

No major concerns were identified with the manuscript in general.

Reviewer #1 (Public review):

Summary:

This paper investigates whether semantic prioritization in visual working memory reflects pre-decisional access, evidence accumulation, or both, using drift diffusion modeling across a reanalysis of prior data and two new experiments. The core finding - that semantic information receives a robust pre-decisional access advantage that is amplified by attentional disruption rather than temporal delay alone - is novel and contributes meaningfully to ongoing debates about the format and accessibility of working memory representations.

Strengths:

The experimental approach is well-motivated, and the use of drift-diffusion modeling to decompose decision components adds analytical value beyond standard RT and accuracy measures. The two new experiments are pre-registered and address important questions. The broader theoretical conclusion - that working memory limits are shaped not only by storage capacity but by which representational formats remain accessible under attentional uncertainty - is an important and timely contribution to the field.

Weaknesses:

The central interpretive claims rely heavily on differences in non-decision time, a parameter that aggregates many processes unrelated to memory retrieval, making it rather difficult to uniquely attribute the observed effects to access or retrieval mechanisms specifically. Additionally, the characterization of the two memory conditions as genuinely perceptual versus semantic warrants further justification, as both may primarily require categorical rather than format-specific knowledge.

Reviewer #2 (Public review):

This manuscript aims to characterize how semantic information is prioritized relative to perceptual details in visual working memory. The central claim is that semantic judgements benefit from faster pre‑decisional access (shorter non‑decision time), and that advantages in evidence accumulation emerge under higher cognitive demands (e.g., when items are outside the focus of attention or must be maintained under interference). Based on this, the paper argues that unattended working‑memory contents are reformatted into more abstract, long‑term‑memory‑like semantic representations that remain more readily accessible than fine‑grained perceptual features.

Strengths:

(1) The question is timely and relevant to current research about the format of visual working memory.

(2) Behaviorally, the semantic advantage is carefully documented in many conditions across datasets.

(3) The use of hierarchical drift-diffusion modelling is helpful to decompose the semantic advantage into cognitive processes such as non‑decision time and drift‑rate components.

Weaknesses:

(1) The strong claims about visual working‑memory representation and "long‑term‑memory‑like" formats rest on an indirect inference from decision‑model parameters to representational content, and this link is not convincingly established. Non‑decision time, as implemented here, bundles many things, such as probe processing, cue processing, retrieval/access, and motor preparation, so reduced non‑decision time for semantic probes could reflect easier question reading, simpler response mapping, or more efficient decision preparation rather than a genuine advantage in accessing semantic memory representations. Although the manuscript acknowledges that non‑decision time includes multiple processes, it nonetheless treats this parameter as primary evidence for a retrieval‑stage semantic advantage, which overstates what the data can uniquely support.

(2) The modelling approach is relatively constrained and does not fully address the underdetermination inherent in mapping latent drift-diffusion parameters onto specific psychological mechanisms. The preferred model that allows multiple parameters (non‑decision time, drift rate, threshold) to vary provides only modest improvements in predictive accuracy over simpler models, and several key drift‑rate effects are present only in particular load or lag conditions. As a result, the theoretical interpretation that semantic prioritization primarily reflects faster access and secondarily more efficient accumulation under high demand appears rather post hoc, and alternative accounts focused on generic task efficiency or strategy differences remain plausible.

(3) The operationalization of "semantic" is narrow and largely categorical, focusing on animacy (animal/object) and a perceptual format dimension (photo/drawing), rather than richer semantic or associative relations among items. This makes it difficult to generalize the conclusions to broader claims about semantic structure and its integration into working‑memory representations. Important recent work on how semantic and associative relationships facilitate the formation, maintenance, and retrieval of visual working memory is not adequately integrated into the theoretical framing. Consequently, the discussion tends to generalize from a specific probe structure to a broader semantic prioritization theory without engaging fully with the existing literature on semantic facilitation and neural decoding of working‑memory content.

(4) The paper contrasts its behavioral/model‑based results with prior neural decoding findings, but the comparison is not fair. Neural decoding provides complementary evidence about the content and format of working memory representations, whereas drift-diffusion parameters reflect downstream decision dynamics given a probe. Because the current work does not include any direct representational or neural measure, its conclusions about representational "reformatting" and long‑term‑memory‑like access remain speculative and, in places, feel like a stretch.

(5) Overall, while the data show a semantic advantage in decision‑stage measures and the modelling provides an informative decomposition of this advantage, the manuscript does not fully achieve its stated aim of characterizing the representational format of visual working memory or demonstrating a mechanistic shift toward long‑term‑memory‑like semantic representations. The work primarily informs decision‑process analyses of the conditions under which semantic judgements are faster and more robust, rather than the nature of visual working‑memory representations themselves.

Reviewer #1 (Public review):

Summary:

This manuscript describes three conformers derived from a complex between ERK2-T185V, a variant of MEK1-DD with the KIM sequence replaced by the KIM from the p38 activator, GRA24, ADP, and AlF4-. The goal was to try to capture the complex in its active state. The results show contacts between the kinases between their N-lobe and their C-lobes that resemble MKK6-p38 complexes previously reported by the authors. Two MEK1-ERK2 conformers (States 1,3) are deemed inactive based on the lack of access of ERK-Y187 to the MEK1 active site, and the absence of ADP bound to MEK1 in State 3, while one conformer (State 2) is deemed active, but not fully active due to disorder in MEK1 activation loop (A-loop) and an essential salt bridge between strand beta3 and helix aC. HDX-MS and SAXS solution measurements and all-atom MD simulations are used to model the mutant complex and variants with WT ERK2. The study concludes that substrate recognition involves low-energy contacts with MEK, allowing substantial protein flexibility within the complex in a manner that may accommodate processive phosphorylation of ERK2.

Strengths:

The strengths of the work are that the findings provide important structural insights for MEK-ERK signaling and protein phosphorylation in general. These are valuable given that atomic resolution structures of kinase-substrate complexes are still limited in number. The authors succeeded in showing key contacts between subunits and conformational variations within the complex.

Weaknesses:

Weaknesses were that some of the conclusions about activity state, dynamics, and effects of ligand binding were less convincing. For example, that State 2 truly represents an active configuration seemed ambiguous, given the absence of Mg2+ and AlF4- in the cryoEM structure and disorder in the activation loop and the K97-E114 salt bridge. Conclusions by SAXS that ADP-AlF4 binding increases active site compaction while increasing local flexibility were not rigorously supported by HDX data, given that the latter were performed without ligand. Sections of the narrative and figures throughout were often confusing, and many assertions were made without clear explanation. Data shown in the supplementary materials were not always described in the Results, even those important for the conclusions. Figure legends and text lacked clear descriptions of specific complexes analyzed. Substantial changes are recommended to improve the readability and clarity of the work.

Reviewer #2 (Public review):

Summary:

The authors used Cryo-EM to obtain a complex between MEK1 and ERK2. They used the same method as previously used by the same authors to form a stable complex between MKK6 and p38, an extra-strong KIM replacing the wild-type KIM in MEK1. Three conformers were resolved, with the highest resolution of 3.0 Å. The multiple conformers indicate more flexibility in MEK2 than in ERK2. These data suggest that nucleotide exchange is possible while maintaining MEK1-ERK2 interactions. SAXS and HDX data reinforce the idea of flexibility. They point to interactions between the two N-terminal domains between histidines at the N-terminus of helix C and between the G helices that are maintained in each of the 3 conformers, and sequence and structure suggest these histidines may be a source of specificity in MEK1-ERK2 versus MKK6-p38 interactions. A 2.2 Å structure of a complex between ERK1 (88% identical to ERK2) and the docking peptide used was also presented. Molecular dynamics simulations suggest that the MEK1-ERK2 complex can assume a fully active configuration of MEK1.

Strengths:

This is the first structure of a MEK1-ERK2 complex. The structural data are valuable additions to our understanding of MAP2K-MAPK interactions. The discussion points offered in the results section are palatable. These include the origins of specificity and the idea of flexibility in the MAP2K in support of a processive mechanism for the dual phosphorylation activity of MAP2Ks.

Weaknesses:

(1) This reviewer considers that the abstract is overstated. Specifically, this paper does not reveal the molecular details of phosphoryl transfer, nor does it demonstrate that substrate binding releases the catalytic machinery.

(2) The discussion is in some places not supported by evidence and in others has superfluous text. Examples follow:

- "Once the αG-helix is docked, and the C-lobe histidine triad is in place, the N-lobe interactions must then be fulfilled." The data in this paper does not suggest an order of events.<br /> - "If the substrate MAPK is incorrect, the N-lobe interaction will not be stabilised, preventing alignment of the MAPK A-loop with the MAP2K active site." This statement could be described as obvious.

(3) Much of the discussion is embedded in the results, such that it is difficult to separate new facts offered by the paper from speculation.

Reviewer #1 (Public review):<br /> <br /> Summary:

In this study, Shuler and colleagues record neurons from the DMS in mice performing a patch foraging task. In this task, mice had the choice between harvesting rewards from 2 ports - one the time-investment port where the rate of reward declined over time and the other a context port where the rate of reward was either high or low. Mice performed the task appropriately, switching between ports as the rate of reward declined in the time-investment port and switching more rapidly when the context port delivered high versus low rewards. The behavior of the mice was also strongly driven by time since the most recent reward receipt, in conflict with normative accounts of patch foraging. Individual DMS neurons showed bistable firing patterns, transitioning to high rates of activity at various times from reward. Overall, the population tiled the temporal space, and the accumulation of the number of neurons in the high firing state was predictive of patch exit. The rate of accumulation varied with things that also affected behavior.

Strengths:

Overall, the aims of the study were clear and important, the experiment directly addresses them, and the results are clear and provide compelling support for the authors' conclusions.

Weaknesses:

I have only a few comments and questions to consider, none of which are criticisms of what was done, really.

(1) Probably my chief question, alluded to in the discussion, is what the evidence is that DMS plays a causal role in generating these correlates and the resulting behavior, in light of the lack of causal evidence here. What are other options? Could such information depend on upstream areas such as OFC or mPFC, with DMS just a pass-through? And while I would not ask for causal data, is there a specific prediction? That is, if the area were inactivated, would mice stay longer or shorter? Not do the task? If I wanted to do a causal test of the authors' idea regarding the contribution of DMS to this behavior, what would be predicted, and what result would invalidate the hypothesis? Speculating on this a bit, beyond just saying DMS is involved, would be useful.

(2) Not much is said about the suboptimal strategy. Would DMS continue to play the same role if the mice showed no effect of recent reward and instead performed appropriately? Or is some other area doing that job? Or is this not important? I thought it was interesting that the mice basically did not treat the game quite like they were supposed to. Is it important to go back and look at what is happening in DMS under normative conditions to really know how this area contributes to proper foraging?

(3) Do these neurons also track time in the context port? Or do they only exhibit this behavior in the port where rewards are depleting? This seems like an interesting question. Do they show the same profile in different ports, if so?

Reviewer #2 (Public review):

Summary:

Here, Sutlief et al. use a novel patch-foraging task to investigate the role of dorsomedial striatal (DMS) neurons in determining when animals disengage from a resource. They show that mice, contrary to canonical optimal-foraging predictions, adopt a strategy in which reward receipt resets timing behavior, with decisions further shaped by both cumulative time spent in a patch and the overall quality of the environment. The authors further demonstrate that a subset of DMS neurons exhibits step-like activity patterns during task performance. Importantly, the accumulation of these state transitions across the neuronal population predicts the timing of patch-leaving decisions on a trial-by-trial basis, providing a potential neural mechanism underlying decisions about when to abandon a currently exploited resource.

Strengths:

This study addresses an important question using a well-designed, interesting behavioral task. The finding that mice employ a reward-triggered exit-timing policy is particularly interesting, as it is pertinent to the many patch foraging-style tasks that have been developed for use in mice, where rewards are delivered as discrete events. The identification of step-like activity in DMS neurons is mostly compelling, and the authors' trial-by-trial analysis linking this activity to behavior provides some support for its relevance to patch-leaving decisions.

Weaknesses:

A key interpretational issue is whether the DMS signal reflects timing specifically, rather than movement initiation or other task-related factors. The authors argue that once a sufficient number of neurons transition, the animal exits the time-investment port. However, it remains unclear whether this population threshold reflects a timing computation that determines when to leave in the more abstract sense, or a signal more directly related to movement onset (that may also be initiated after some proportion of the population has changed its activity). An important control would be to examine neural activity while animals are engaged at the context port. In this epoch, animals presumably do not need to time their departure in the same way, but they still eventually initiate movement. If the DMS signal reflects timing rather than movement, one would not expect the same accumulation-to-threshold pattern of step-like transitions at the context port.

It would also be helpful for the authors to clarify the behavioral definition of the leaving decision. Can mice return to the time-investment port after exiting it if they do not subsequently enter the context port? How exactly is "exit" defined: as withdrawal from the time-investment port, entry into the context port, or some other behavioral event? Is there variability in the latency between time-investment port exit and context-port entry, and if so, is this latency related to DMS step-like activity? These details are important for interpreting whether the neural activity is aligned with a timing decision, movement initiation, or the execution of a transition between task states.

The classification approach for identifying step-like activity seems generally reasonable, and the low false-positive rate against homogeneous Poisson controls is reassuring. However, one potential issue is that the identification of trial-by-trial state transitions is not independent of the session-level characterization of each neuron. The algorithm first fits a sigmoid to the pooled session data and then uses the resulting high- and low-firing-rate states to constrain interval-level fits. This may bias the analysis toward finding step-like transitions in neurons whose activity is only approximately step-like at the session level, effectively reducing the space of alternative solutions available to the interval-level fits. As implemented, the approach therefore functions more as a detector of consistency with a session-defined step model than as an unbiased test of whether individual intervals are better described by discrete state transitions versus alternative dynamics such as ramps or gradual drifts. This concern could be addressed by comparing the constrained sigmoid model against alternatives, such as constant-rate or ramping models, on held-out intervals, or by deriving state parameters from an independent subset of trials and testing classification on the remaining trials.

The inclusion threshold for the accumulation analysis is difficult to evaluate. Sessions were included if they contained at least seven simultaneously recorded step-like units, but this number is hard to interpret without knowing the total number of recorded units per session and the fraction classified as step-like. Seven units may be sufficient for fitting a population accumulation trajectory, but because the cutoff is based on an absolute number rather than a proportion of the recorded population, it is unclear whether included sessions reflect robust population-level step-like dynamics or a relatively small selected subset of DMS activity. Reporting the number and fraction of step-like units per session, as well as the sensitivity of the accumulation results across different inclusion thresholds, would help clarify this point.

Reviewer #3 (Public review):

Sutlief and colleagues report behavioral and neural results from mice performing a patch foraging task. Behaviorally, they argue that time since last reward is a major determinant of when mice decide to leave a patch. In the brain, they find neurons in the dorsomedial striatum that show step-like changes in their firing rate at a range of times following reward. Population analyses show that the cumulative fraction of neurons that have undergone such a step-like change in firing rate can be used to predict patch-leaving times with impressive accuracy.

Overall, this is an interesting set of results that has been analyzed in a principled way. The manuscript is well written, the results are explained clearly, and the evidence supporting the authors' conclusions is strong. The manuscript is therefore a potentially valuable contribution to the growing literature assessing how the brain solves stopping problems like the patch foraging scenario. I have suggestions for the authors to consider that might further increase the rigor of their results, and a few suggestions for improving the clarity of the work for readers.

(1) I don't quite understand how the behavioral task works. Are mice rewarded for making discrete nose poke responses in the investment and context ports? Or are they required to nose poke and hold? Is reward given with some probability per response (which decreases with time in the patch), or is the reward probability a function of elapsed time in the patch, time since last response, or dwell time in the port? Also, exactly what equation defines how reward probability changes over time for the high- and low-value contexts? I couldn't find these details anywhere in the manuscript, and they would be helpful for better understanding the behavior and the later neural results.

(2) How was the optimal strategy determined? Several features of the author's task violate the assumption of the marginal value theorem, so computing the optimal residence time is not a straightforward application of the classic model. There's a diagram in Figure 1h that depicts an MVT-like graphical solution, but the conventions of the plot are not familiar to me, and there's no description of how it works in the results or methods. More detail here would be much appreciated. In a similar vein, the authors report that mice generally exceeded optimal residence times in patches, but no statistical comparison is provided to back up that statement. There should be some formal test of this if it is to be included in the results.

(3) The authors argue that time since last reward is the predominant determinant of patch leaving time. However, as the authors note, time since last reward is correlated with other task variables (patch reward rate, time in patch, etc.). I don't trust that SVM coefficients can be interpreted as straightforward measures of a variable's importance for classification performance in the case of correlated predictors. A better approach would be to assess how well the model performs as subsets of variables are added or removed from the model.

(4) For the SVM analysis, I'm not quite understanding how or why the authors are using 5 s after mice left the patch as additional "Leave" examples. For instance, is time since entry computed for the investment patch, or the context patch that mice enter after they leave the investment patch? Similarly, is the time since the last reward relative to the investment patch, or the reward the mouse is likely to receive at the context patch? Moreover, I'm not sure it's safe to assume that because the mouse left at time t, time t+1 necessarily reflects conditions on which the mouse would definitely leave again. If we're thinking about the stay/leave decision as something that is being repeated sequentially on a fast time scale to determine how long mice stay in the patch, it doesn't follow that observing a mouse leave means that any patch conditions after that would necessarily result in the same decision. If that were the case, it would mean that seeing a mouse leave a patch after 2 s would preclude ever observing a residence time longer than 2 s, which is clearly not compatible with the authors' data. Ultimately, it's only possible to observe one decision to leave per trial; including data points beyond that as additional leave examples seems overly speculative to me.

(5) The authors validate their approach for quantifying step-like changes in firing rate using simulations of constant-rate Poisson spiking and observe a low false positive rate. This is encouraging, but it doesn't seem like the only way in which their method could go awry, or even the most concerning way. I would be much more interested in seeing the false positive rate for continuous, ramp-like changes in firing rate, which would be much more likely to trip up the authors' approach and are also the major relevant alternative hypothesis to step-like changes in firing rate. Random walks in firing rate might also be worth testing.

(6) The finding that cumulative "transitioned" neurons is predictive of patch leaving is interesting. However, I can't help but wonder how truly informative this variable is for predicting patch leaving. It seems as though neurons can only transition firing rates one time. That means that as time in the patch increases, the fraction of transitioned neurons naturally increases. Similarly, all visits must eventually end with the mouse leaving the patch, so the hazard rate of leaving increases with time in the patch. Given that, can the authors be certain that the cumulative transitioned neurons are really what's predicting patch leaving time, or would any generically increasing function perform roughly the same? An interesting test would be to mismatch the neural predictor and behavior at the level of trials. If this mechanism is really specific, rather than something that captures the general structure of an increasing hazard rate of leaving, then prediction of leaving time should work substantially better when the neural predictor is correctly matched to behavior on the trial for which it was recorded.

Reviewer #1 (Public review):

Lohse et al. describe an open-source system for laser scanning photostimulation (LSPS) in head-fixed animals. Although similar systems have been developed and used by different groups, Zapit provides an open-source solution requiring few custom parts and minimal coding. This tool can clearly facilitate and speed the adoption of LSPS, particularly for the increasingly used purpose of mapping the effects of focal cortical silencing during behavior. Other potential uses include mapping optogenetically evoked movements and selectively activating genetically labeled neuronal subtypes of interest in the cortex. The design is well thought through, and the presentation is mostly clear and well written.

In general, the more modular such a system is, the better, in terms of compatibility with existing hardware and software that potential users may already have purchased - laser, galvo, and camera in particular. The system has struck a reasonable balance between allowing modularity and providing an integrated complete package, but even more flexibility would be welcome for potential users looking to cut costs, as would clearer presentation of such flexibility as already exists.

Comments and suggestions are mostly minor, as follows.

(1) Command signals:

How is the relationship between analog voltage commands and laser power determined? Is this assumed (or required) to be linear (as Figure 7F implies)? Usability and modularity would be improved by an option to measure or provide a calibration curve for systems with a nonlinear mapping between command voltage and laser power.

For the grid calibration step, how is the initial mapping from galvo voltage commands to image position determined? Presumably, some sort of initial guess or calculation based on the hardware specifications is needed for the grid calibration to be feasible. Also, how are the number of grid lines and the distance between them determined?

Why is the mapping between analog outputs and hardware (galvos, laser, masking light) fixed? This would be trivial to make configurable and allow labs with existing setups to adopt Zapit without rewiring existing hardware.

(2) Laser and optics:

In Figure 1, the authors should consider explaining the scanning principle schematically, i.e., depicting how tilting of the scan mirrors translates via the scan lens into beam displacement in the specimen plane. Perhaps Zemax can be used for accurate rendering.

Since the unexpanded beam greatly under-fills the back aperture of the lens, the z resolution is presumably terrible - which is good! That is, for the purposes of LSPS, this advantageously avoids focus-dependent effects, which might otherwise arise due to (e.g.) skull curvature. The authors should consider pointing this out, as well as providing an estimate of the z resolution.

What is the working distance?

Reviewer #2 (Public review):

Summary:

In this work, Lohse and colleagues develop a system for doing targeted photostimulation in mouse cortex. The system uses a camera image to target laser stimulation to stereotactically defined locations in mouse dorsal cortex.

Strengths:

The hardware is well designed, and the software is well documented and supported. The build guide and well-documented software package should allow for simple implementation of the technology. Without a doubt, this is a valuable community resource for the circuit neuroscience field.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #3 (Public review):

Zappit is an open-source implementation of arbitrary-access laser-scanning optogenetics for manipulation of neuronal activity in mice. As the method requires expertise ranging from optics, hardware control and programming, the authors make the point that this powerful strategy is underutilized in the field, and put forward a well-documented modular hardware and software platform aligned to the Allen Mouse Brain Atlas aimed at enabling the larger scientific community to use this approach (democratizing) for controlling cortical activity during behavior in mice.

The authors favor a galvanometric approach to laser targeting. The system is inexpensive, easy to build, well-documented and user friendly (Matlab based GUI and GitHub repository). The photo-stimulation laser is directed into an X-Y galvo scanner targeted to the specimen using a dichroic mirror and focused on the sample using a Plössl lens as scan lens which is also used as an objective. The scan lens/objective images the specimen onto a camera via tube lens (also a Plössl lens) in a 0.5X magnification ensuring to fit the extent of the mouse brain onto the camera sensor (USB-3 Basler acA120-40um).

The authors report short and reproducible onsite time (~ 0.5 ms) and block (mask) the stimulation source using the laser analog control (~0.5 ms). The system is reliable, aiming at up to 20 stimulation sites per sequence considered as quasi-simultaneous (10 ms). They minimize rebound by gentle ramping down of stimulation over 250 ms.

The system is fast to calibrate by mapping scanner positions to pixel space in the camera space and mapping stereotaxic coordinate onto the image of the exposed skull. The theoretical x-y PSF is 70 µm (measured ~90µm) while the authors make the point that due to scattering the photo-stimulation spot size (lateral extent) is about 1 mm in diameter. This is what they also observe in electrophysiological recordings using silicon probes. The effective radius of inactivation depends on laser power, but was about 1 mm for laser powers (1-2-4 mW) on which the authors observed significant behavioral perturbations - in several tasks: 1) a delayed response somatosensory discrimination, 2) a visual detection task assessing changes in temporal frequency of a drifting visual stimulus; and 3) a visual discrimination (International Brain Laboratory task) in which mice were tasked to report the location of visual stimuli by turning a wheel. As proof of principle, the authors used a photo-stimulation set composed of 52 bilateral sites positioned at 0.5 mm interval covering a large network of frontal, motor and somatosensory cortical areas. Indeed, photo-inhibition of frontal motor cortex sites produced robust increases in reaction time. In contrast, stimulation at other motor and somatosensory sites produced modest, but significant decreases in reaction times.

While the approach is not novel, it does serve the need of better disseminating this technique in the research community. Overall, the Zappit is well-documented and easy to build and use, and will have impact in increasing robust use of site directed photo-stimulation (exciting/inhibiting ensembles of neurons at particular ~1 mm size regions of interests across the dorsal surface of the brain). The authors also note that the axial resolution is ~1.5 mm.

Concerns & comments:

(1) While the authors argue that it offers the best utility to affordability trade-off - faster than motorized drivers and require much less power than DMDs (100X) and less expensive/easier to use compared to SLMs, in the current form, the manuscript does not clearly list the limitations of the approach. At such, in my opinion, the authors should include side by side comparisons (perhaps as a table). For example, clear statements should be included with respect to comparisons in lateral (x-y), axial (z) spatial resolution, as well as temporal sequential aspect of Zappit and other photo-stimulation techniques involving DMDs or SLMs.

(2) Is power really a limitation in terms of the laser sources? Or is this a disadvantage mainly because using less power has beneficial effects on the tissue health? It may be useful to provide metrics of comparisons along these lines between Zappit and DMD-based approaches.

(3) Arbitrary-scanning vs random scanning may be more appropriate to describe to strategy.

Reviewer #1 (Public review):

Summary:

This manuscript reports a prospective longitudinal study examining whether infants with high likelihood (HL) for autism differ from low-likelihood (LL) infants in two levels of word learning: brain-to-speech cortical entrainment and implicit word segmentation. The authors report reduced syllable tracking and post-learning word recognition in the HL group relative to the LL group. Importantly, both the syllable-tracking entrainment measure and the word recognition ERP measure are positively associated with verbal outcomes at 18-20 months, as indexed by the Mullen Verbal Developmental Quotient. Overall, I found this to be a thoughtfully designed and carefully executed study that tackles a difficult and important set of questions. With some clarifications and modest additional analyses or discussion on the points below, the manuscript has strong potential to make a substantial contribution to the literature on early language development and autism.

Strengths:

This is an important study that addresses a central question in developmental cognitive neuroscience: what mechanisms underlie variability in language learning, and what are the early neural correlates of these individual differences? While language development has a relatively well-defined sensitive period in typical development, the mechanisms of variability-particularly in the context of neurodevelopmental conditions-remain poorly understood, in part because longitudinal work in very young infants and toddlers is rare. The present study makes a valuable contribution by directly targeting this gap and by grounding the work in a strong theoretical tradition on statistical learning as a foundational mechanism for early language acquisition.

I especially appreciate the authors' meticulous approach to data quality and their clear, transparent description of the methods. The choice of partial least squares correlation (PLS-c) is well motivated, given the multidimensional nature of the data and collinearity among variables and the manuscript does a commendable job explaining this technique to readers who may be less familiar with it.

The results reveal interesting developmental changes in syllable tracking and word segmentation from birth to 2 years in both HL and LL infants. Simply mapping these trajectories in both groups is highly valuable. Moreover, the associations between neural indices of brain-to-speech entrainment and word segmentation with later verbal outcomes in the LL group support a critical role for speech perception and statistical learning in early language development, with clear implications for understanding autism. Overall, this is a rich dataset with substantial potential to inform theory.

Comment on revised version.

The revised manuscript has provided additional analyses that lead to critical clarification of the main findings, including the longitudinal nature of the relationship between neural tracking of speech and language, the role of sleep, and the potential modulation effect of stream structure on syllable-level neural tracking. The overall results highlight the robustness of the findings as well as the specific relevance of the structured speech tracking to verbal outcomes of infants with high likelihood (HL) of autism.

Reviewer #2 (Public review):

Summary:

This article looks at differences in how the brain entrains to, or tracks, the rhythmic presentation of syllables and words in speech in infants at increased likelihood versus low likelihood for autism. The authors first sought to characterize how brain responses are modulated by learning the statistical probability of a given syllable following the one before it over the first two years of life. They then sought to identify at which stages of word learning infants at increased likelihood for autism showed difficulties, and whether those difficulties worsened over time. Finally, they sought to indicate whether infants' statistical learning and word learning abilities could predict later verbal skills. The authors found similar developmental trajectories of neural entrainment to syllables in infants at high and low likelihood for autism, but infants at high likelihood for autism had overall weaker syllable-level entrainment. Infants at high versus low likelihood for autism showed different developmental trajectories for word entrainment. Lower syllable entrainment in high-likelihood infants corresponded with poorer verbal outcomes, but word entrainment was not associated with verbal outcomes. Event-related potential responses to words and part words were positively associated with verbal outcomes, however, but only in low-likelihood infants.

Strengths:

Overall, the article provides rigorous statistical analysis of longitudinal EEG data to provide strong support for the claims that neural entrainment to syllable and word features of speech may be a useful marker for language development difficulties, particularly in infants at increased likelihood for neurodevelopmental disorders. The EEG data collection and preprocessing procedures are well within standards within the field. Readers should take care to note that authors indexed neural entrainment to speech using phase-locking values instead of spectral power.

Comments on revised version.

While the statistical analyses are rigorous, there are a few potential confounds to the results. The authors now do a nice job addressing these limitations to the work. For example, sleep status may modulate some of the biomarkers relevant for language learning. Exposure to additional languages may influence performance on the verbal assessment, though the authors do clarify that participants came from majority French-speaking households. As a result, readers should be encouraged to interpret that neural entrainment to speech features is likely a useful mechanism to explain differences in language development, while taking this interpretation with some caution.

Reviewer #1 (Public review):

Summary:

GCPs, which drive postnatal cerebellar growth and can give rise to SHH-MB, are not uniform. The authors show that GCPs include a rare Nestin-expressing subpopulation with distinct molecular features. This subpopulation is spatially restricted, enriched for stem cell-like properties, and shows a high competency for tumor formation comparable to larger GCP pools, with tumors preferentially arising in the posterior-lateral cerebellum. Overall, the findings indicate that SHH-MB might originate preferentially from this small, tumor-competent Nestin-expressing GCP subset.

Strengths:

(1) The authors use a breadth of approaches from histology, mouse genetics, and single-cell RNA sequencing.

(2) Throughout, this paper uses very elegant genetic approaches, such as the double Nes-FlpoER; Atoh1-FSF-Cre; LSL-Smo-M2, to generate tumors only from Atoh1+; Nes+ double-positive cells. This intersectional genetic experiment makes for a very clear answer.

(3) The findings reported in this manuscript are valuable since they reveal a novel GCP subpopulation defined by spatial and molecular identity. Some of their experiments suggest that these cells could represent the main cell-of-origin of SHH MB. The experiments are carefully performed, and the evidence is convincing.

Weaknesses or elements that could be improved:

(1) A transgenic Nestin-CFP mouse is used in this study. However, it is not clear whether CFP accurately reflects the Nestin protein. Figure 1: After the promoter is turned off, these cells might remain positive for CFP for longer than they are positive for Nestin, due to CFP protein stability. Is the Nestin protein present in these cells? Nestin double immunofluorescence with CFP and Sox2 and Barhl1 could be performed to address this. Related to this comment, it is also important to note that this is a rat promoter transgene. So the transgene might not reflect exactly the endogenous Nestin expression.

(2) Could the posterior restriction of Nestin-CFP be due to the timing (P1) at which the authors looked? In other words, if they look earlier, would the authors see Nestin-CFP cells more anterior?

(3) Since only one medulloblastoma mouse model (Smo-M2) is used to conclude that "the Nes-expressing GCP population in the normal cerebellum is transcriptionally closer to SHH MB tumor cells than the remainder of the GCPs", the findings might not apply to other SHH-MB models. This should be mentioned.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors studied transgenic reporter mice to profile Nestin expression in the postnatal mouse cerebellum. They discovered a small population of Nestin+; Atoh1+ granule neuron precursors (GNPs) in the external granule cell layer (EGL). Using immunostaining, qPCR, and RNA-sequencing, the authors showed that these Nestin+ cells are not identical to Sox2+ cells (e.g., the majority of Nestin+ cells are Sox2-). Using various mouse genetic strategies, including an elegant intersectional strategy that specifically targets Nestin+; Sox2+ cells, the authors showed that Nestin+ cells are capable of initiating Sonic hedgehog (SHH) medulloblastoma when they express the SmoM2 allele that drives constitutively active SHH signaling. Lastly, the authors profiled the transcriptomes of these cells and showed that they display enriched stem cell genes and are closer to the transcriptomes of GNP-like cells in medulloblastoma compared to Nestin- GNPs in the developing cerebellum.

Strengths:

(1) The comprehensive mouse genetics experiments, in combination with immunostaining, lineage tracing, and RNA-seq studies, provided compelling evidence that rare Nestin+ cells are present in the EGL, predominantly at the posterior lateral cerebellum in early postnatal mice.

(2) The intersectional genetics experiment unequivocally show that Nestin+; Atoh1+ cells can be oncogenically transformed by SmoM2, leading to SHH medulloblastoma.

(3) The more stem cell-like transcriptomic features of the Nestin+ GNPs compared to Nestin- GNPs provide support for the heterogeneity of this transient progenitor cell population, with implications for development, congenital diseases, and tumors from the cerebellum.

Weaknesses:

Main comments:

My main concern relates to whether these Nestin+; Atoh1+ cells are restrictively localized in the EGL. Both the title "A Rare Nestin-Expressing Granule Cell Precursor Subpopulation Underlies SHH Medulloblastoma Formation" and what the authors described throughout the manuscript propose that Nestin+; Atoh1+ cells in the EGL are the cell-of-origin of SHH medulloblastoma. To definitively conclude this, the authors need to comprehensively analyze all regions of the developing cerebellum.

Most importantly, are Nestin+; Atoh1+ cells present in the rhombic lip? Are there any rhombic lip cells genetically labeled in their intersectional mouse mutants (e.g., the Atoh1Frt-Cre/+; Nes-FlpoER; R26LSL-SsmoM2/+ mice)?

If Nestin+; Atoh1+ cells are present at non-EGL regions in the developing cerebellum, the authors would have to reconsider many of their conclusions and also the title of this paper.

Additional comments:

(1) To investigate Nestin expression, the authors used Nes-CFP transgenic mice expressing CFP from promoter/enhancer sequences from the rat Nes gene (Encinas et al., 2006). Given that Nestin expression is of central importance for this study, it is important to validate that these reporter mice faithfully report Nestin protein expression (e.g., by co-labeling CFP with Nestin antibody and systemically comparing signals throughout the cerebellum, ideally in several developmental stages).

(2) The authors mostly presented immunostaining data of the cerebellum from P1 mice. It is important to systematically profile the appearance and disappearance of these Nestin+, Atoh1+ cells in mouse cerebellum across developmental stages (e.g., embryonic, early, and late postnatal stages).

(3) How different is the proliferative ability of the Nestin+ versus Nestin- GNPs at various developmental stages? Also, the difference between EdU+; Barhl1+; Nestin+ and EdU+; Barhl1+; Nestin- cells is quite small despite statistical difference (Figure 1N). Do the authors think this very small EdU incorporation difference can translate into a biological difference (in developmental and/or disease context)?

(4) Lines 145-147: "Compared to double-negative cells, Atoh1 and Nes were significantly higher in the double-positive fraction, supporting the identity of the cells as a previously unrecognized rare population of GCPs at P1 that expresses both the GCP marker Atoh1 and ventricular zone marker Nes." Nestin is not a ventricular zone marker. This should be rephrased.

(5) In Figure 3, the authors showed mouse survival data and concluded that Nes-driven and Atho1-driven SHH medulloblastoma models show similar tumor penetrance. This is not an entirely accurate description of their data. The Nes-SmoM2 mice displayed significantly longer survival compared to the Atoh1-SmoM2 mice (Figure 3B). This conclusion needs to be revised.

(6) In Figure 5, the authors showed that genes enriched in cluster 10 included Sox2, Nes, Wls, and Wnt1, while Neurod1 and Rbfox3 were preferentially expressed in the other GCP clusters. They conclude that cluster 10 represents a less differentiated, more stem-like GCP state, potentially positioned upstream in the lineage hierarchy. While these few markers are useful, it is more informative to formally support this conclusion by comparing the stem cell transcriptomic signature (using a larger gene list) between cluster 10 and other GCPs.

(7) In Figure 5, the authors performed gene ontology analysis and showed that cluster 10 is enriched for biological processes linked to WNT signaling and proposed that this molecular profile supports their identity as a transient, developmentally plastic population within the GCP lineage related to the rhombic lip. The authors are recommended to use an orthogonal approach (i.e., immunostaining to compare nuclear localization of beta-Catenin) to validate their transcriptome-based finding.