Reviewer #2 (Public review):

In this manuscript, Chen and colleagues describe a novel means of labeling two RNA binding proteins, G3BP1 and TDP-43, using genetic code expansion. Overexpressed constructs that incorporate the intrinsically-fluorescent non-canonical amino acid Anap redistribute to cytoplasmic granules upon application of external stressors such as sodium arsenite. Similar labeling and redistribution of overexpressed G3BP1 and TDP-43 was observed in cultures of mouse primary neurons.

Genetic code expansion and non-canonical amino acid labeling have many advantages over traditional fusion proteins for tracking protein redistribution in living cells. The authors show that they are able to label exogenous G3BP1 and TDP-43 with the non-canonical amino acid Anap, and follow labeled proteins in living cells with and without stress.

I suspect that this method could be incredibly valuable to many investigators studying the dynamics and interactions of proteins that are difficult to label or detect by conventional methods.

Comment on revised version:

The revised manuscript is significantly improved, with added controls and experiments to confirm expression and Anap labeling of G3BP1 and TDP-43.

Reviewer #2 (Public review):

Summary:

This manuscript extends previous work from Calvin et al. and examines hippocampal representations during approach-avoidance conflict in a robotic predator foraging task. The paradigm itself is very interesting and addresses an important but relatively understudied question in the navigation and foraging literature: how the brain balances risk versus reward during goal-directed behavior. While hippocampal representations of positively valenced goals and future intentions have been extensively studied, much less is known about how these representations evolve during risk-reward tradeoffs involving threat.

The authors use a relatively simple and interpretable decoding approach together with thoughtful behavioral comparisons to ask whether future behavioral outcomes can be read out from hippocampal activity before behavior diverges. The most compelling comparison is between mid-track aborts (MTAs) and mid-track continues (MTCs), where the animals initially exhibit very similar pause behavior but ultimately either abort or continue the trajectory. The authors show that decoded location during these pauses differs prior to the overt manifestation of the behavioral decision, suggesting that hippocampal representations may reflect evolving internal evaluation processes during approach-avoidance conflict.

Strengths:

A major strength of the work is the behavioral paradigm itself. This type of risk-reward conflict task is relatively uncommon in the hippocampal navigation literature and provides a rich framework for examining defensive decision-making during naturalistic foraging behavior.

The decoding analyses are also relatively simple and easy to interpret. Rather than relying on highly complex modeling approaches, the authors use straightforward comparisons of decoded spatial representations across behavioral conditions, making the results accessible and conceptually clear.

Another strength is the use of behavioral controls to isolate comparisons between related behaviors. In particular, the comparison between MTAs and MTCs is compelling because the animals exhibit similar pause states before the behavioral outcomes diverge. This provides a useful framework for asking whether hippocampal activity reflects future behavioral outcome before the decision is overtly expressed.

Overall, the study asks an interesting question using a novel paradigm and provides evidence that hippocampal representations during approach-avoidance conflict may reflect future behavioral trajectory.

Weaknesses:

The main weakness is that many of the reported effects are relatively subtle and are not sufficiently controlled for differences in speed, trajectory structure, and other behavioral variables across conditions. While the subtraction plots (green versus purple decoding differences) appear visually striking, the actual effect sizes are fairly small, making it difficult to assess how robust or behaviorally meaningful these differences are.

Relatedly, many of the most interesting questions in this task concern how behavior unfolds dynamically within a trial, yet much of the analysis averages across events and trajectories. As a result, potentially important aspects of the behavior may be obscured.

In particular, the manuscript would benefit from richer characterization of the animals' actual movement trajectories and spatial strategies. Because the analyses rely heavily on linearized position, it is difficult to determine whether animals behave differently in two-dimensional space across conditions. For example, during continued approaches, do animals preferentially hug the wall opposite the robot? Do different behavioral conditions show distinct lateral occupancy or trajectory structure? These types of analyses would make the behavioral interpretation substantially more compelling.

More generally, while the results are suggestive and interesting, the relatively small decoding differences and substantial behavioral confounds make it difficult to conclude that the observed effects reflect distinct internal evaluative or threat-related states.

Reviewer #2 (Public review):

Summary:

This manuscript presents a broad survey of glutamate receptor composition at the neuromuscular junction in Drosophila across developmental stages and muscle types. The topic is clearly important, and the central observation-that adult muscles differ substantially from the canonical larval NMJ-is interesting and potentially impactful. The dataset is extensive and will likely be of value to the community. However, in my view, there are significant limitations in how the data are generated and interpreted, which at present reduce the strength of the conclusions.

Strengths:

The study addresses a relevant and timely question and provides a large and systematic dataset. The finding that adult muscles diverge from larval NMJ organization is compelling and challenges a widely held assumption in the field. The breadth of approaches, including genetic reporters, immunohistochemistry, endogenous tagging, and transcriptomic data, is, in principle, a strong aspect of the work and allows for a broad overview of receptor expression across tissues and developmental stages. Even in its current form, the manuscript provides useful descriptive information that will be of interest to the community.

Weaknesses:

A major concern is the reliance on a heterogeneous combination of detection methods (GAL4 reporters, antibody staining, endogenous tagging, and RNA), which are treated largely as equivalent lines of evidence. These approaches differ substantially in what they measure and in their sensitivity and specificity. While convergence across methods can in principle be convincing, here this convergence is often inferred from the shared absence of signal. This is problematic because all methods used are susceptible to false negatives for different reasons. As a result, the repeated conclusion that specific GluR subunits are "absent" from adult muscles, including those previously considered essential, is not fully justified by the data presented.

This issue is not only theoretical. The manuscript itself seemingly contains examples where methods disagree, demonstrating that detection is incomplete and method-dependent. These discrepancies could be better integrated into the interpretation. Instead, negative results across methods are often taken as strong evidence for absence, which overstates the certainty of the findings.

In addition, antibody validation appears to rely largely on prior work in larval tissue. Given the structural and biochemical differences in adult muscles, it is not clear that staining performance is equivalent, particularly in cases where the signal is weak or undetected. This further complicates the interpretation of negative results.

More generally, the manuscript moves in several places from descriptive observations to functional or mechanistic implications that are not directly supported. The suggestion that adult muscles operate with fundamentally different receptor assemblies is intriguing, but remains speculative without functional validation. At a minimum, the distinction between observation and interpretation should be made more explicit.

I thus think that the current conclusions need to be more carefully constrained. Ideally, the study would be strengthened by at least one functional experiment, such as electrophysiological recordings from adult NMJs or perturbation of candidate receptors like GluClα or Clumsy. This would help to anchor the expression data in synaptic function.

In summary, this is an interesting and potentially important study, but the current manuscript somewhat overinterprets heterogeneous and partly indirect evidence. It will already be useful in its present form, but could be more convincing if the authors more rigorously account for methodological limitations and moderate their claims accordingly.

DOI: 10.1016/j.isci.2026.116206

Resource: (ZFIN Cat# ZDB-ALT-030919-2,RRID:ZFIN_ZDB-ALT-030919-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-030919-2

What is this?

Reviewer #2 (Public review):

Summary:

In this study, the authors ask whether spatial cognition is under sexual selection in mountain chickadees. To do so, the authors examined a large dataset that includes a) spatial cognition data for both males and females (obtained via use of a clever RFID-based feeder system) and b) social and extra-pair paternity nesting data. As predicted, males with higher spatial cognition sired more extra-pair offspring, and extra-pair sires had, on average, higher spatial cognition scores than the males they cuckolded. Interestingly, females with lower spatial cognition scores were more likely to seek extra-pair copulations, potentially to compensate for their own low spatial cognition. Surprisingly, there was no difference in spatial cognition scores between males that sired their own offspring and those that lost paternity at the nest. Also surprising was the fact that there were no differences in patterns of extra-pair paternity and spatial cognition between high- and low-elevation sites. The latter is particularly surprising in that spatial cognition should be under stronger selection at the high elevation site. Overall, this is a fascinating study that demonstrates that spatial cognition - a trait under natural selection as it directly impacts winter foraging and survival behaviour -is also under sexual selection.

Strengths:

The authors have a robust dataset (n = 732 offspring sampled over 3 years), high-quality spatial cognition data collected with a procedure that has been well-honed over the years, and couple the data with solid statistical procedures that address many potential covariates and potentially confounding factors. In addition, the authors are careful in the discussion to elaborate on the many potential alternative explanations from the results and questions that are likely to arise in the minds of readers (e.g., how are females assessing male spatial ability?)

Weaknesses:

Overall, no major weaknesses were identified in this study. As always, there are editorial issues that I would encourage the authors to consider, including presentation of data/results and clarification on some statistical issues. Overall, however, this is an excellent study that will make an important contribution to our understanding of the evolution of cognition and targets of sexual selection.

Reviewer #2 (Public review):

Summary:

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 the use of shared resources.

Strengths:

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 job of demonstrating the potential conservation impacts of their research.

Weaknesses:

What the authors tested is really cool, but their claims go far beyond what they can say based on their experimental design. For example, the authors claim to show that pika impacts on yaks display density-dependent transitions from competition to facilitation. However, their experiment only looked at the presence (at moderate densities) and absence of pikas, and they only tested for facilitation, not competition.

The paper would also benefit from changes to the framing in the introduction and discussion. For example, the authors pitch the work as a test of the stress-gradient hypothesis. However, there is no abiotic stress gradient in the study, which is an essential component of the SGH. They also pitch the work in terms of density dependence, but there is no significant variation in population densities beyond the presence-absence binary. The paper would be stronger if they focused their framing around the literature on facilitative interactions across mammals of different size classes, especially indirect facilitation via use of shared resources, which is what this paper is really about.

Finally, the paper has significant weaknesses in the experimental and statistical methodology. Most importantly, there are inconsistencies in what is visualized in the figures compared to the model results. For example, the results section in several places notes a lack of significant interaction terms in the model but shows interactions in the p-values on the figures. The authors also plot smoothed lines rather than their model results and then draw interpretations from those lines that cannot be tested in the models that they used. There are also missing details that are important for model interpretation, including the distributions used and the sample sizes. Another major concern with experimental design is in the forage nutrient analyses. The authors picked plants along a grazing trail, then measured nutrient content without standardizing based on plant species, so any differences across treatments could be because of what they happened to grab rather than overall forage quality.

Reviewer #2 (Public review):

The authors address an important challenge in developmental biology: the quantitative description of tissue deformation during organogenesis. They have developed a new pipeline to quantify early heart tube morphogenesis in the mouse, with cellular resolution. They adopt an elegant approach by integrating multiple 3D time-lapse datasets into a dynamic atlas of cardiac morphogenesis in order to compute spatio-temporal deformation patterns. The main findings highlight a strong compartmentalization of cell behaviors, with tissue growth and anisotropy exhibiting complementary and spatially segregated patterns. Using these data, the authors developed an in-silico fate mapping tool to interrogate cell displacement within the myocardium. This virtual model provides new mechanistic insights into how the bilateral cardiac primordia converge and transform into a three-dimensional heart tube. The authors identify "belt-like" constraints at the arterial and venous poles that prevent tissue expansion and thus shape the ventricular barrel morphology.

The computational framework is highly innovative and impressive, providing an unprecedented 3D model of tissue deformation during heart morphogenesis. It also opens avenues for testing hypotheses regarding tissue growth and the forces that cause cell motion.

Overall, this carefully performed study provides a new model for exploring tissue deformation during organogenesis and will be of broad interest to computational and developmental biologists.

Reviewer #2 (Public review):

In this paper, Rayan et al. report that RNA influences cytotoxic activity of the staphylococcal secreted peptide cytolysin PSMalpha3 versus human cells and E. coli by impacting its aggregation. The authors used sophisticated methods of structural analysis and describe the associated liquid-liquid phase separation. They also compare to the influence of RNA on aggregation and activity of LL-37, which shows differences to that on PSMalpha3.

Major comments on the previous version:

(1) The premise, as stated in the introduction and elsewhere, that PSMalpha3 amyloids are biologically functional, is highly debatable and has never been conclusively substantiated. The property that matters most for the present study, cytotoxicity, is generally attributed to PSM monomers, not amyloids. The likely erroneous notion that PSM amyloids are the predominant cytotoxic form is derived from an earlier study by the authors that has described a specific amyloid structure of aggregated PSMalpha3. Other authors have later produced evidence that, quite unsurprisingly, indicated that aggregation into amyloids decreases, rather than increases, PSM cytotoxicity. Unfortunately, yet other groups have in the meantime published in-vitro studies on "functional amyloids" by PSMs without critically challenging the concept of PSM amyloid "functionality". Of note, the authors' own data in the present study that show strongly decreased cytotoxicity of PSMalpha3 after prolonged incubation are in agreement with monomer-associated cytotoxicity as they can be easily explained by the removal of biologically active monomers from the solution.

In their revision and in the rebuttal, the authors have further described their concept regarding what they call "functionality" of PSMalpha3 amyloids. They now admit that monomers are the active cytolytic form, like other researchers have stressed, whereas amyloids are not. This represents a considerable difference to earlier papers in which they ascribed functionality, i.e. cytolytic capacity, to PSMalpha3 amyloids, a claim that has raised considerable controversy. Now, they use the term "functional " to describe that PSMalpha3 amyloids, while not cytolytic, can be reversed to a cytolytic monomeric state, calling them a "dynamic reservoir". There is no evidence that such a reservoir is necessary for the cytolytic activity of the monomers to be established; also, there is no evidence that in a biological system, such an amyloid reservoir exists. To continue calling PSMalpha3 amyloids "functional" based on this - considerably changed - concept of the authors appears inappropriate, given the finally admitted absence of cytolytic activity of the PSM amyloids in addition to the continuing complete lack of evidence of any biological relevance of PSM amyloid formation.

(2) That RNA may interfere with PSM aggregation and influence activity is not very surprising, given that PSM attachment to nucleic acids - while not studied in as much detail as here - has been described. Importantly, it does not become clear whether this effect has biologically significant consequences beyond influencing, again not surprisingly, cytotoxicity in vitro. The authors do show in nice microscopic analyses that labeled PSMalpha3 attaches to nuclei when incubated with HeLa cells. However, given that the cells are killed rapidly by membrane perturbation by the applied PSM concentrations, it remains unclear and untested whether the attachment to nucleic acids in dying cells makes any contribution to PSM-induced cell death or has any other biological significance.

Reviewer #2 (Public review):

Summary:

The functional parcellation of cortical areas is a critical question in neuroscience. This is particularly true in frontal areas in mice. While sensory areas are relatively well characterized by their tuning to sensory stimuli, the situation is much less clear for motor areas. This has become even more ambiguous since recent studies using large-scale neuronal recordings consistently report mixed sensory and motor-related activity throughout the brain and motor mapping studies have shown that movements evoked by cortical stimulation are by no means limited to motor areas alone. Here, the authors use a correlation approach combining large-scale functional imaging at cellular-resolution with movement-tracking in mice executing a reaching task. Across multiple recording sessions in the same animals, the authors have imaged a large portion of the sensorimotor cortex at cellular resolution in mice performing a reaching task, recording the activity of nearly 40,000 neurons. By aligning the calcium signal of each neuron to three task events-the Go cue triggering the reach, the onset of paw lift, and the contact between the paw and the target-for different target positions, the authors identified different response patterns distributed differently across cortical areas. They defined a set of features that describe the neurons' response pattern, representing the temporal dynamics and tuning properties for the different target positions. These features were used to construct cortical maps, and the authors show that, interestingly, gradient maps obtained from the first derivative of the feature maps reveal sharp discontinuities at the boundaries between anatomically defined cortical areas. Using dimensionality reduction of the neuronal response features, the authors found that, despite clear differences in their average response properties, individual neurons from the same cortical areas do not form distinct clusters in the reduced-dimensional space. In fact, most areas contain heterogeneous neuronal populations, and most neuronal populations are present in multiple areas, albeit in different proportions. Interestingly, the authors identified four neuronal subpopulations based on the distance between the components of the Gaussian mixture model used to model the distribution of neurons within each area. One of these subpopulations is almost exclusively represented in the anterior M2 cortex, while another is broadly distributed across the different areas.

Strengths:

This article is based on an impressive dataset of nearly 40,000 neurons covering a large portion of the sensorimotor cortex and on innovative analytical approaches. This study is likely the first to clearly demonstrate boundaries between cortical areas defined based on the responses of individual neurons. This innovative approach to functional mapping of cortical areas potentially opens up new perspectives for higher-resolution mapping of frontal cortical areas, using a broader repertoire of sensory and motor evoked responses.

Weaknesses:

One limitation of this study - inherent in most cell imaging studies - is that it only takes into account the activity of neurons in superficial cortical layers. One might think that taking into account neuronal activity across the different layers would allow for an even finer functional cortical segmentation.

Comments on revised version:

The authors have answered all my questions and this new version has largely improved in clarity.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

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

Weaknesses:

Throughout much of the paper, the authors refer to the distinctions between environments primarily as differences in "initial learning rates" or "environment-specific learning rates." The optimal initial learning rate did indeed differ across environments -- the result of differences in underlying task statistics. These differences in task statistics result in distinct optimal initial learning rates and also vary with aspects of spatial position (e.g. vertical position in the example figure). The authors convincingly show that OFC activity increasingly reflects these variables throughout task experience. Given that these variables vary together, future work will be needed to distinguish whether particular variables drive these dynamics, or whether together they combine to evoke the representational differences.

The current work is also quite suggestive of meaningful individual differences in both local and global adaptive learning, in line with other prior work on predictive inference. This is perhaps underexplored in this data set, but certainly leaves the topic ripe for follow up going forward.

Finally, more information on all clusters that survived multiple comparisons correction would be useful, even in the absence of a priori hypotheses. For instance, there is commentary in the discussion section on the ACC, but this is not mentioned in the results, and it is unclear whether there were other undescribed clusters that survived correction.

Reviewer #2 (Public review):

Summary:

Liu et al. record intracranial EEG from the hippocampus and lateral temporal lobe in thirteen neurosurgical patients while they perform a delayed match-to-sample visual short-term memory task. The central question is whether hippocampal sharp-wave ripples (brief high-frequency oscillations well established in the long-term memory consolidation literature) also contribute to the active maintenance of visual representations over a short delay. The authors report three main findings: hippocampal ripple rates progressively ramp up across the 7-second maintenance period, hippocampal ripples temporally co-occur with ripples in the lateral temporal lobe, and these coupled events coincide with above-chance category-level decoding of the memorized stimulus in the lateral temporal lobe. The findings are interpreted within the dynamic coding framework of working memory, which predicts discrete reactivation bursts rather than sustained firing during maintenance. The question is timely, and the use of intracranial recordings affords a level of temporal and spatial resolution unavailable to non-invasive methods.

Strengths:

The study addresses a genuinely important and underexplored question: whether a neural mechanism best characterized in the context of offline memory consolidation is also engaged during active online maintenance. The use of intracranial recordings in humans is well suited to this question, providing the millisecond temporal resolution and regional specificity needed to detect transient high-frequency events. The dissociation from long-term memory, tested by splitting remembered trials according to whether the item was later recalled in a cued-recall test, directly addresses what would otherwise be a significant confound, and the finding that ripple dynamics during maintenance are unrelated to subsequent long-term memory performance adds specificity to the interpretation. The coupled ripple analysis is methodologically grounded, and the finding that coupled but not isolated ripples coincide with elevated memory decoding is mechanistically informative. The multivariate decoding approach applied to lateral temporal lobe spectral power provides a meaningful index of memory reactivation that goes beyond simple univariate rate measures. The control analysis and the alternative ripple detection method provide useful robustness checks. The public availability of preprocessed data and analysis code on OSF is commendable.

Weaknesses:

(1) Theoretical motivation for examining ripples in visual short-term memory.

A fundamental question that the paper does not adequately address is why hippocampal ripples, a mechanism strongly associated with offline memory consolidation during sleep, where they coordinate the transfer of hippocampal representations to cortex through temporally compressed replay, should be recruited for the online maintenance of visual information over a seconds-long delay. The Introduction acknowledges this gap but does not close it. The dynamic coding framework is used to motivate the ramping-up prediction, but this framework is agnostic about the specific neural mechanism responsible for reactivation bursts. In particular, the literature cited by the authors predicts high-frequency population activity or gamma bursts, but not specifically hippocampal ripples. The reasoning that "ripples share key properties with postulated reactivation bursts" risks being circular: it amounts to saying that ripples could be the relevant mechanism because the relevant mechanism has properties that ripples also have. A stronger theoretical motivation would require either evidence that the replay or reactivation computations that ripples support during offline states are also engaged during active short-term maintenance, or a mechanistic account of how the circuit processes underlying ripple generation are recruited differently across these two contexts.

This concern is compounded by what the authors present as one of their main controls. The finding that ripple dynamics during maintenance are not associated with subsequent long-term memory performance is treated as a reassurance that the observed effects are specific to short-term memory. But if ripples are canonically a long-term memory consolidation mechanism, the observation that they are engaged by a short-term memory task while appearing disengaged from concurrent long-term memory encoding is itself a finding that demands explanation. Resolving this tension is important for the paper's contribution to be correctly interpreted by the field.

(2) Ripple detection and specificity.

Even granting that ripples could in principle contribute to short-term memory maintenance, the study does not establish that the detected events are physiological sharp-wave ripples rather than broadband high-frequency activity. The detection band (70-180 Hz) substantially overlaps with the high-gamma range, which is a well-established proxy for local neural population activity and coding, and is broader than the 80-120 Hz band used by several of the cited papers, including Vaz et al. (2019), Ngo et al. (2020), Chen et al. (2021), Staresina et al. (2023), and Kunz et al. (2024). Without demonstrating that detected events have the hallmark features of physiological sharp-wave ripples, a clear narrowband spectral peak, and characteristic waveform morphology, it is difficult to conclude that the observed effects reflect a ripple-specific mechanism rather than a more general high-frequency population activity phenomenon. The reported mean rate of 0.29 Hz is somewhat higher than rates reported in some recent work, such as Chen et al. (2021, ref 74) and Kunz et al. (2024, ref 15). It is worth noting that van Schalkwijk and Helfrich (2026, Nature Communications) demonstrated that a large proportion of awake ripple detections in the human medial temporal lobe reflect false positives arising from aperiodic 1/f noise, with task-related modulations of this noise floor producing spurious detections. The authors present an 80-120 Hz control analysis as a robustness check, but this inverts the appropriate logic: if 80-120 Hz is the more validated band, as the cited literature suggests, it should serve as the primary analysis rather than a supplementary one.

(3) Internal inconsistency with the dynamic coding framework.

The authors invoke the dynamic coding framework, which predicts that reactivation bursts should ramp up toward the end of the retention interval in the region where memory representations are actively maintained. The hippocampal ramping-up result is presented as confirming this prediction. However, the lateral temporal lobe, the region where above-chance category decoding is found and memory reactivation is attributed, shows no corresponding ramp-up. The authors acknowledge this asymmetry but do not offer a mechanistically satisfying explanation, and the suggestion that the effect might exist in unsampled subregions cannot be evaluated with the current data. This leaves the framework's core prediction unconfirmed in the region that is claimed to maintain the representations.

(4) Coupled ripples, directionality of hippocampal-lateral temporal coupling, and the ramping-up paradox.

The conclusion that coupled hippocampal-lateral temporal ripples coordinate memory reactivation creates a logical tension that the paper does not resolve. If hippocampal ripples drive lateral temporal reactivation only when co-occurring with lateral temporal ripples, and hippocampal ripples ramp up in a memory-predictive fashion, then the absence of lateral temporal ripple ramping up implies that the hippocampal ramp-up is not primarily expressed through the coupled ripple mechanism, undermining the coherence of the two main findings. The coupled ripple analysis further quantifies only temporal co-occurrence and provides no evidence about the direction of influence. Without demonstrating that hippocampal ripples systematically precede lateral temporal ripples (i.e., the expected signature of hippocampus-to-cortex information flow), the central claim that hippocampal ripples drive lateral temporal reactivation remains an interpretive assumption. Directly testing whether lateral temporal ripples specifically coupled to hippocampal ripples show a ramping temporal profile during maintenance (even if overall lateral temporal ripple rates do not) is necessary to establish whether the lateral temporal lobe engages in hippocampally-gated reactivation bursts in the manner the framework predicts. Additionally, reporting the distribution of peak lags between hippocampal and lateral temporal ripple peaks, and testing whether hippocampal ripples systematically precede lateral temporal ripples, is similarly necessary to support the directional interpretation.

(5) Trial-level analysis clarity.

The paper reports that ripples occurred in 54%, 79%, and 27% of trials during encoding, maintenance, and retrieval, respectively, but does not state whether subsequent analyses were conducted on trials thresholded by ripple occurrence. Given that occurrence rates vary substantially across stages and conditions, this inclusion criterion has implications for interpreting rate differences and should be stated explicitly.

(6) Statistical model specification.

The methods describe the ramping-up analysis using both a "logistic" link function and a "Poisson link function" in different places, with the dependent variable described inconsistently as ripple occurrence and ripple count. These are not equivalent, and the distinction matters for interpreting the reported coefficients. Additionally, the regional dissociation in Figure 3 appears to be assessed by fitting separate models to each region and comparing results informally. This does not constitute a direct test of whether slopes differ between regions and risks the well-known error of inferring a difference based on one p-value being significant while another is not. A direct region × time interaction test would more cleanly support the claimed dissociation.

Reviewer #2 (Public review):

Summary:

Carricarte and colleagues set out to identify and functionally characterize feedforward (FF) and feedback (FB) information flow during object perception in humans, a question that has been difficult to address non-invasively because FF and FB signals overlap rapidly in time and across regions. The authors capitalize on the canonical cortical microcircuit-FF terminations primarily in middle layers, FB terminations primarily in superficial and deep layers, to spatially separate these signals using sub-millimeter (0.9 mm isotropic) GE-BOLD fMRI at 7T in early visual cortex (EVC) and lateral occipital complex (LOC). They combine these layer-resolved fMRI patterns with millisecond-resolution EEG (from a previously published dataset using the same 24 images) via representational similarity analysis-based EEG-fMRI fusion, and use a Vision Transformer (DeiT) trained on ImageNet to characterize the feature complexity of the resulting spatiotemporal signatures.

The authors first review their approach at the macroscale, replicating the expected EVC-then-LOC temporal hierarchy and the EVC-low/LOC-high feature complexity gradient. They then apply the same framework at the mesoscale of cortical layers, reporting: (a) early middle-layer signals in both EVC (~100 ms) and LOC (~160 ms) consistent with FF processing, (b) a later superficial-layer signal in LOC (~400 ms) interpreted as FB; (c) a layer-uniform feature-complexity profile in EVC (peaking at low-mid DNN layers across all depths); and (d) a feature-complexity dissociation in LOC, where middle-layer signals correspond to mid-to-high DNN layers and superficial-layer signals to high DNN layers. They argue that this complexity shift, combined with the timing difference, indicates interareal FB into LOC.

Strengths:

(1) The combination of layer-fMRI at 7T, EEG, and DNN-based representational analysis is well motivated through RSA. Each modality compensates for a known limitation of the others (fMRI: poor temporal resolution; EEG: poor spatial resolution; DNN: surrogate for representational format), and the RSA framework provides a principled common currency. Relatedly, the two-step macroscale-then-mesoscale design, in which the macroscale fusion replicates established findings before the same approach is applied at the layer level, is a sound and welcome scientific strategy that strengthens confidence in the combined-modality inferences.

(2) The authors include multiple complementary controls: partialing out lower layers to mitigate vascular draining, voxel-count matching across layers, an alternative DNN (AlexNet), an alternative time-window definition based on between-layer differences, and time-resolved commonality analyses. The convergence across these analyses is reassuring.

(3) Methodological transparency: The authors are forthright about partial-volume effects, foveal-confluence aggregation, and the indirect nature of the temporal estimates derived from EEG-fMRI fusion.

Weaknesses:

The central interpretive claim-that the late (~400 ms), superficial-layer LOC signal indexes interareal feedback that increases representational complexity-is intriguing, but in my view it is not yet fully supported by the evidence presented based on the following context.

(1) Eye movements as a possible confound for late signals. Stimuli were presented for 1 second, and fixation was enforced only behaviorally via a color-change task on a central cross. No eye-tracking is reported for either the fMRI or EEG datasets. While this approach is not uncommon, the absence of gaze monitoring introduces ambiguity when the goal is to decouple feedforward and feedback contributions at fine temporal resolution in EEG recordings. Under these conditions, multiple image-driven saccades within a trial are plausible, and saccade patterns are likely to be systematically image-specific, given the small (n = 24) and heterogeneous naturalistic stimulus set. Critically, the temporal window over which RDM correlations are interpreted as feedback coincides with the period during which observers typically make 2-4 fixations (average fixation durations of ~250-330 ms; Rayner, 1998; Henderson, 2003), meaning the late EEG-fMRI fusion peaks fall in a window where image-locked saccadic activity and successive foveation-driven feedforward responses would be expected to accumulate. Late peaks could therefore reflect cumulative feedforward responses across successive foveations rather than top-down feedback. The manuscript would be strengthened by providing eye-tracking data (if available), control analyses leveraging post-hoc indicators, or a discussion citing prior evidence that EEG/fMRI response profiles in this paradigm are robust to such eye movements.

(2) Decoding accuracy along the visual hierarchy raises questions about whether LOC is adequately engaged. Pairwise decoding accuracy is substantially higher in EVC than in LOC (Figure 1D), and the noise ceiling for LOC RDMs is markedly lower than for EVC across all layers (Supplementary Figure 4D-F). This pattern inverts the canonical hierarchical gradient of progressively stronger object decoding along the ventral visual stream, as well as the analogous gradient observed in DNN late layers that underlies the commonality analyses. As written, it is unclear how the manuscript reconciles this with its emphasis on LOC's role in higher-order, feedback-modulated representations with greater tolerance or increased complexity--unless decoding accuracies should be understood as image-level discrimination rather than at the level of object-category discrimination. A parsimonious alternative is that the 24-image set is too small or too coarse to reveal category-level representations in LOC robustly, such that LOC RDMs may be driven by lower-level or background/contextual variance and noise. This concern has direct bearing on the mesoscale commonality analyses supporting the "feedback transmits high-complexity features" conclusion. I would encourage the authors to (a) report split-half reliability of LOC RDMs alongside the commonality analyses, and either (b) acknowledge that the feature-complexity inferences are conditional on LOC RDMs faithfully capturing object structure rather than residual contextual/low-level variance, or (c) discuss how replication with a richer stimulus set might bear on the feedback-content interpretation.

(3) The interareal feedback interpretation could be more robustly defended against intra-areal alternatives. In EVC, the authors carefully consider non-feedback explanations for layer-specific dynamics, including lateral connections modulating gain and superficial GE-BOLD bias, and conclude these are sufficient. The same skepticism is not extended to LOC, where the corresponding superficial-layer signal is interpreted as interareal feedback, with speculative sourcing to DLPFC. Slow (unmyelinated) horizontal/lateral propagation in superficial cortical layers (e.g., Davis et al., 2024) can, in principle, produce delayed superficial-layer signals on the timescale observed here without any interareal contribution. This asymmetry is compounded by the treatment of the absence of sustained EVC activity following the middle-layer peak, which is dismissed as a "limitation of the spatial and temporal sensitivity of our measurements" (lines 388-390). If feedback to EVC truly cannot be resolved with this method, the corresponding feedback claim in LOC-imaged with the same protocol warrants comparable caution. The manuscript would benefit from either presenting positive evidence that distinguishes interareal feedback from intra-areal recurrence (e.g., frequency-band signatures, source-resolved EEG, or coupling with frontal regions), or qualifying the conclusion to "delayed superficial-layer activity consistent with either interareal feedback or intra-areal recurrence."

(4) The predictive coding framing is invoked but not well-grounded. The Discussion (lines 349-357) includes a theoretical implication of predictive coding. Predictive coding makes content-specific claims-feedback carries predictions, feedforward carries error signals relative to those predictions, and dissociating these requires manipulations of expectation, congruence, or predictability, none of which are present in the current design. The observed layer-wise timing differences do not bear evidence for rejecting non-predictive accounts. I would suggest either removing this framing or explicitly noting that the present data neither support nor refute predictive coding.

Reviewer #2 (Public review):

Summary:

This study advances our understanding of the neuronal basis of the circadian clock in pancrustaceans. It extends our knowledge on the pigment-dispersing hormone system and provides links to information on the expression of core clock components, cryptochrome 2, and period. The data are sound and well-documented.

Comments:

The neuronal components of the arthropod circadian clock system have been analysed extensively in insects. Much less information on this system is available on malacostraca crustacea crustaceans. However, considering that malacostracan crustaceans and insects go back to a common pancrustacean ancestor and considering that we know that the brain architecture in these two groups shares many commonalities (see, e. g., extensive reviews by N. J. Strausfeld), we have to expect that crustaceans and insects share many of the characteristics of the circadian system. This is the case, e. g., for the network of pigment-dispersing hormone-positive neurons. The authors cite these studies, although late in the paper (discussion, line 339ff), and I suggest to move this info into the introduction: "339 ff: The arborization pattern of the PDH-network has been described in various malacostracan crustaceans, including Carcinus maenas (Alexander et al., 2020; Mangerich & Keller, 1988; Mangerich et al., 1987), Cancer productus (Hsu et al., 2008), Orconectes limosus (de Kleijn et al., 1993; Mangerich & Keller, 1988; Mangerich et al., 1987), Homarus americanus (Harzsch etal., 2009), Cherax destructor, Procambarus clarkii (Sullivan et al., 2009), and Procambarus virginalis (Luna et al., 2010)."

The strength of this paper is that it extends our knowledge on the PDH system and brings together neuroanatomical information on PDH-positive neurons with information on the expression of core clock components, cryptochrome 2, and period. That way, it advances our understanding of the neuronal basis of the circadian clock in pancrustaceans. The data are sound and well documented, and the authors are to be applauded for the superb dissection presented in Figure 1.

Below, please find some essential suggestions on how to further improve the paper.

(1) Framing of the study:

I know that krill is a key element of the Southern Ocean's food webs, but my sense is that discussing the current findings in a context of resilience of this species to global ocean change means largely overselling this study:

- Lines 47, 48: "and the resilience of this key species in a rapidly changing Southern Ocean."

- Lines 70 ff: "Hence, understanding the mechanisms of adaptation, including biological clocks, is crucial for predicting how species, populations, and whole ecosystems will respond to climate change."

- 154 ff: "The Southern Ocean environment experiences rapid change (Abram et al., 2025; Meredith et al., 2019; Thomalla et al., 2023). To assess krill's resilience to environmental changes, understanding the mechanisms that govern daily and seasonal timing in krill is essential."

- 325 ff: "The rhythmic adaptation of krill to its high-latitude environment is key to its success in the Southern Ocean, which in turn represents a cornerstone for the well-being of the whole krill centred ecosystem. To predict krill's resilience to rapid environmental changes, it is essential to understand the mechanisms that govern daily and seasonal timing in krill."

- 597 ff: "A detailed mechanistic understanding of the flexibility of clock-based processes is therefore essential to predict krill resilience in a changing Southern Ocean."

My understanding is that duration of day length is one of the most predictable environmental drivers, and - despite the seasonal changes of day length - nevertheless a very stable one compared to fluctuations of environmental drivers such as temperature or salinity (see, e.g. this recent review on environmental driver fluctuations on nervous system functioning in crustaceans: Stein W, Harzsch S (2021) The Neurobiology of Ocean Change - insights from decapod crustaceans. Zoology: 125887. https://www.sciencedirect.com/science/article/pii/S094420062030146X).

I do not see how global ocean change may significantly change day length, and what this study has to do with understanding this species' resilience against ocean change. I suggest that you explain in more detail why the light day length will change in the future or strongly tone this aspect. Statements such as Line 76 ff: "Due to their disproportionate importance for ecosystem function, understanding the resilience of ecological key species is essential in assessing the fate of ecosystems in the future." are completely out of focus here and, again, trying to oversell the current study.

(2) Uncited essential studies of crustacean neuroanatomy, missing connection to contemporary crustacean neurobiology:

- Line 157: "despite the ecological importance of E. superba, only very little is known about its neurobiology".

- Line 329: "However, so far, little was known about the neurobiology of krill in general."

I agree that this species' brain is understudied, but this makes it even more important to cite the little information that IS available. Please consider this essential reading for any crustacean neurobiologist: "Sandeman, D.C., Scholtz, G., Sandeman, R.E., 1993. Brain evolution in decapod crustacea. J. Exp. Zool. 265, 112-133." to find information on the basic brain anatomy in E. superba.

The manuscript in many places seems to reinvent the wheel and raises the impression that our knowledge of crustacean brain morphology is close to zero. The authors in places seem to operate in a vacuum, and I find it disturbing that in a study on the crustacean brain, very few references are provided to studies on crustacean brain anatomy, such as the following essential book chapter: "Schmidt, M., 2016. Malacostraca. In: Schmidt-Rhaesa, A., Harzsch, S., Purschke, G. (Eds.), Structure & Evolution of Invertebrate Nervous Systems. Oxford University Press, Oxford, pp. 529-582. https://www.researchgate.net/publication/315366157"

In terms of brain anatomy, I would like to know if the authors have a hypothesis on whether and how their target species' brain structure may be similar or different to the brains of other "shrimps" as described, e. g., in the following studies. If so, please elaborate in the introduction:

Krieger J, Hörnig MK, Sandeman RE, Sandeman DC, Harzsch S (2020), Masters of communication: The brain of the banded cleaner shrimp Stenopus hispidus (Olivier, 1811) with an emphasis on sensory processing areas. Journal of Comparative Neurology 528(9): 1561-1587.

Meth R, Wittfoth C, Harzsch S (2017) Brain architecture of the Pacific White Shrimp Penaeus vannamei Boone, 1931 (Malacostraca, Dendrobranchiata): correspondence of brain structure and sensory input? Cell and Tissue Research 369(2): 255-271.

(3) Lacking rigor and command of crustacean brain nomenclature

I suggest that for their brain nomenclature, the authors should rigorously stick to that laid out by Sandeman et al. 1992 (not yet cited in the ms): Sandeman, D.C., Sandeman, R.E., Derby, C.D., Schmidt, M., 1992. Morphology of the brain of crayfish, crabs, and spiny lobsters: a common nomenclature for homologous structures. Biol. Bull. 183, 304-326.

More specifically, in lines 41, 163, 199, 204, 207, and throughout the paper, the authors use the terms "Optic lobes" or "optic lobe neuropils". To the best of my knowledge, "optic lobe" is not a term used in crustacean neuroanatomy at all (as opposed to insects). Lamina, medulla, and lobula are collectively referred to as "visual neuropils" (see Krieger, J., Hörnig, M. K., Sandeman, R. E., Sandeman, D. C., & Harzsch, S. (2020). Masters of communication: The brain of the banded cleaner shrimp Stenopus hispidus (Olivier, 1811) with an emphasis on sensory processing areas. Journal of Comparative Neurology, 528(9), 1561-1587. https://doi.org/10.1002/CNE.24831). The medulla terminalis and mushroom bodies are referred to as "lateral protocerebrum". All afore-mentioned neuropils are summarized as "eyestalk neuropils" (compare nomenclature in Schmidt 2016 as referenced above).

Line 170, 172, 175 ff, and Figure 1. "abdomen", "abdominal ganglia": Contra the book chapter by Siegel 2016 "Introducing Antarctic Krill Euphausia superba Dana, 1850", his Fig. 1.2, the "tail" of crustaceans in most books on crustacean anatomy is not called "abdomen" but instead "pleon"; hence the name "pleopods" for the appendages of the pleon (instead of "abdomipods"). What is more, I suggest using the terms "pleon ganglia" instead of "abdominal ganglia", following the terminology suggested in "Harzsch S, Sandeman D, Chaigneau J (2012) Morphology and development of the central nervous system. In: Forest J and von Vaupel Klein JC (Eds.). Treatise on Zoology - Anatomy, Taxonomy, Biology. The Crustacea Vol. 3. Brill, Leiden pp. 9-236."

Line 174: "thoracic ganglia". In Figure 1, there is a labelling mistake as these ganglia are named "thoracaic ganglia".

Line 176, and throughout the paper: "supraesophageal ganglion". Following the standard nomenclature for crustaceans (see, e. g., Schmidt, M., 2016. Malacostraca. In: Schmidt-Rhaesa, A., Harzsch, S., Purschke, G. (Eds.), Structure & Evolution of Invertebrate Nervous Systems. Oxford University Press, Oxford, pp. 529-582. https://www.researchgate.net/publication/315366157", this structure (as in insects) is typically called a "brain". For terminology, also consult the following nomenclature paper: "Richter, S., Loesel, R., Purschke, G., Schmidt-Rhaesa, A., Scholtz, G., Stach, T., Vogt, L., Wanninger, A., Brenneis, G., Döring, C., Faller, S., Fritsch, M., Grobe, P., Heuer, C. M., Kaul, S., Møller, O. S., Müller, C. H. G., Rieger, V., Rothe, B. H., Stegner, M., Harzsch, S. (2010). Invertebrate neurophylogeny: Suggested terms and definitions for a neuroanatomical glossary. Frontiers in Zoology, 7. https://doi.org/10.1186/1742-9994-7-29".

Line 212, and throughout the paper - hemielliposoid body: please refer to Harzsch Krieger 2011 and the numerous references to studies by Strausfeld cited therein in crustaceans. Strausfeld has provided compelling evidence that the crustacean hemiellipsoid body is equivalent to the insect mushroom body, so this term should be replaced. Harzsch, S., & Krieger, J. (2021). Genealogical relationships of mushroom bodies, hemiellipsoid bodies, and their afferent pathways in the brains of Pancrustacea: Recent progress and open questions. Arthropod Structure & Development, 65, 101100. HYPERLINK "https://doi.org/10.1016/J.ASD.2021.101100" https://doi.org/10.1016/J.ASD.2021.101100.

Legend, figure 2, and others, and throughout the paper: "The olfactory neuropiles comprise the lateral antennal neuropile (LAN, ochre), the olfactory lobes (OL, yellow), and the antennal neuropile (AnN, green)." This is a strange terminological mix that you should urgently revise according to the standard terminology by Sandeman et al. 1992 (as referenced above). The LAN is the lateral antenna 1 neuropil. The AnN is the antenna 2 neuropil. The AnN is NOT deutocerebral but tritocerebral.

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:

It is not obvious whether the selected labeling strategy avoids labeling Layer 6 CT neurons, which would contaminate dendritic recordings. The images provided suggest enrichment in L5, but a discussion of this important potential caveat is warranted, especially since within-cell comparisons of apical dendrites to somata were not performed.

The application of DeepInterpolation to dendritic data appears to be novel, and little detail or vetting is provided. The reader is left guessing: Was the model retrained or fine-tuned on dendritic data? How does the denoising affect the resulting segmentation and activity traces? Is denoising necessary for this workflow?

The activity patterns of the recorded cells appear to lack the characteristic ramping during the delay epoch previously reported in both calcium imaging and electrophysiology studies. Given that a major contribution to the significance of the work is to constrain models of ALM function, a discussion of how the data aligns with previous measurements in the same circuit would improve the work.

It would be very informative to compare differences in signals between dendrites and somata of the same cells. Consistently tracing dendrites to their respective somata would assuage worries of potential contamination from dendrites of deeper cells and enable more direct comparisons of signal transformations between dendrites and somata. It would be good to understand the relationship between dendritic calcium signals and backpropagating action potentials in this task. The authors detect less frequent calcium events in tufts versus somata; is this due to selective backpropagation of action potentials? The dynamics of this process were recently investigated by Adam Cohen's group in vivo and in vitro, and measurements in the present settings could be compared to such work.

The Coding Direction analyses presented in this work, while consistent with previous literature on population codes in ALM, are at odds with the nature of the measurements here. The changes in representation that occur between the dendrites and soma of an individual cell are probably best thought of in terms of the dynamics of signals themselves within individual neurons, rather than in the information encoded across a population.

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 #2 (Public review):

Summary:

This paper investigates risk and cooperation decisions by integrating computational modeling with event-related potential (ERP) measures. Participants completed two tasks involving financial risk and cooperation under possible betrayal. The comparison between social and non-social decision-making is interesting and potentially valuable. However, the conceptual framing, theoretical grounding, and modeling rationale require substantial clarification.

Strengths:

(1) The paper introduces comparable tasks to probe social vs. non-social decision making.

(2) The authors use a model to identify a psychological distinction and test its validity using neural data.

Weaknesses:

(1) Conceptual framing and theoretical clarity

The primary theoretical contribution of the paper is currently unclear. Specifically, it is not clear what key difference the authors hypothesize between risk and cooperation conditions. This distinction should be grounded in prior literature.

The manuscript states: "Indeed, mutual cooperation maximizes social welfare, whereas betrayal benefits the trustee but comes at the trustor's expense in the Trust Game (Joyce et al., 1995)." However, the authors do not discuss the substantial literature on the Trust Game, which is used here but not explicitly acknowledged.

• The original Trust Game framework and behavior in one-shot settings (e.g., Berg et al., 1995).

• The persistence of cooperation even when defection is economically optimal (e.g., Berg et al., 1995; Fehr & Fischbacher, 2003).

• The influence of trustworthiness of the partner on cooperation decisions has been previously studied (Ma et al., 2022).

• Differences between social and non-social decision-making contexts have also been reported with matched tasks (Liu et al., 2024).

(2) Distinction between constructs (risk, loss aversion, betrayal aversion)

The introduction introduces multiple related constructs-risk aversion, loss aversion, and betrayal aversion-but does not clearly differentiate them. A theoretically grounded distinction is needed.

In particular:

• The manuscript introduces multiple related constructs, or maybe the terms are used interchangeably? The distinction between risk aversion, loss aversion, defection aversion, and betrayal aversion should be clearly defined.

• Betrayal aversion versus loss aversion is introduced but not clearly differentiated. Importantly, it should be clarified that this distinction is not experimentally manipulated but instead inferred through computational modeling. This point is currently not made explicit, which leads to confusion in the introduction

• The computational model should be introduced clearly in the introduction. Without explaining how these constructs are operationalized in the model, the framework is difficult to follow.<br /> The statement "In the risk task, losses were solely impersonal" is also unclear. It seems the authors may mean "personal or non-social" rather than "impersonal" as rewards are always personally relevant.

(3) Hypotheses and preregistration

The manuscript would benefit from more theoretical rationale for hypotheses. For example:

• What is the basis for hypothesizing that financial loss aversion and betrayal aversion independently affect cooperation choices?

• Why should these constructs be separable and modeled independently?

• Additionally, the absence of preregistration is a limitation that should be acknowledged even more.

• Given the flexibility of the modeling approach and number of parameters, this is particularly important.

• For instance, the rationale for focusing on decision times is also not clearly explained and should be better motivated.

(4) Computational modeling

There are several concerns regarding the modeling approach:

• The choice of model comparison metric should be justified. Why is AIC used rather than BIC, which penalizes model complexity more strongly? This is particularly relevant given the inclusion of additional parameters to capture processes not directly measured by the task.

• Full model recovery analyses are missing. A full model recovery is necessary to demonstrate that competing models produce distinguishable behavioral patterns. This needs to be shown in order to justify the specificity of the winning model

• How correlated are the parameters across participants, particularly loss and betrayal parameters?

• More broadly, it is unclear how well loss aversion and betrayal aversion can be differentiated based on behavior alone. If these constructs are separable, they should predict distinct aspects of behavior.

(5) ERP analyses

The ERP results (e.g., P300 and LPP) seem to suggest that betrayal aversion is relevant in both time periods and similarly.

• Do neural signals differentially reflect betrayal aversion versus loss aversion earlier and later on?

• Are there significant interaction effects between betrayal and loss aversion for each ERP component?

Reviewer #2 (Public review):

Summary:

The authors report the presence of extrachromosomal circular DNAs (eccDNAs) within the core of stress granules purified from both yeast and mammalian cells.

Strengths:

This study is important for understanding the molecular mechanisms underlying stress granules containing eccDNAs and is likely to have a major impact on future research. A major strength of the study is the extensive experimental validation performed in yeast cells. In particular, cytoplasmic CRISPR-mediated targeting of eccDNAs suppresses stress granule formation and impairs recovery from hypoxic stress in yeast cells.

Weaknesses:

The conclusions would be further strengthened by validating the functional findings in an additional model system, such as mammalian cells.

Comments:

(1) Section: "Stress granule cores contain eccDNA"

a) The presence of eccDNAs would be more convincingly demonstrated using an orthogonal validation approach, such as DNA FISH targeting MYC and Centromere 8 (CEN8) on metaphase spreads from HEK293T cells (as performed in PMID: 34819668).

b) The study would also benefit from assessing the presence of eccDNAs in the extracellular medium. For example, DNA could be extracted from conditioned media and analyzed by PCR using primers spanning eccDNA breakpoint junctions (as performed in PMID: 40074906; PMID: 36123406).

(2) Section: "eccDNA-CRISPR abrogates stress granules"

These findings should be further validated under additional stress conditions, such as drug-induced stress (like methotrexate) or nutrient deprivation in the cell medium.<br /> In addition, the same set of experiments should be performed in HEK293T cells to support the broader relevance of the observations.

Reviewer #2 (Public review):

Summary:

The manuscript by Bai and colleagues investigates how Escherichia coli navigates and explores agar gels through chemotaxis and what parameters of bacterial swimming are tuned under selection pressure for rapid migration (i.e., reaching the edge of the agar plate quickly). Prior studies have examined related questions to a substantial degree. Examples include "Migration of Chemotactic Bacteria in Soft Agar: Role of Gel Concentration" (https://pmc.ncbi.nlm.nih.gov/articles/PMC3145277) and numerous other studies in this area (e.g., "Migration of bacteria in semi-solid agar" https://www.pnas.org/doi/10.1073/pnas.86.18.6973). From such studies has emerged the paradigm/model that reorientation (i.e., tumbling) is essential when bacteria navigate agar, which is considered a model for "complex" environments, because run-only bacteria become trapped in the agar matrix and are unable to migrate far. This new manuscript provides some evidence that this paradigm may be overly simplified or incomplete. As I understand it, the authors propose that migration is influenced to a greater extent by bias in the chemotactic run, where runs up attractant gradients are longer. The authors incorporate these data into a new model for chemotactic navigation and claim that this work establishes a general principle for how bacteria optimize active transport through complex environments.

I will first note to the editor and authors that I am not qualified to assess the detailed mathematics of the model, and my review therefore focuses on the biology and phenotypes described. Nevertheless, in my view, this manuscript, in its current form, has several important limitations. For each point, I provide suggestions for additional experiments that could strengthen the rigor of the work and clarify the claims.

Strengths:

A strength of this work is the use of microscopy and automated methods to characterize an extremely large number of bacterial cells, which strengthens the authors' claims. However, substantially greater detail on these approaches is needed for the analysis to be reproducible and to allow verification that the analyses were performed correctly.

Weaknesses:

Major concerns

(1) Claims are overly broad, and the experimental system is too artificial to support general conclusions about bacteria, chemotaxis, or evolution.

E. coli MG1655 is a longstanding model organism in the chemotaxis field, and agar chemotaxis assays are also widely used. However, the authors make very broad claims about how phenotypic changes observed during selection in 0.2% or 0.3% agar relate to bacterial chemotaxis and evolution more generally. In essence, the experimental foundation on which the authors build a complex theoretical framework is limited to a domesticated laboratory strain of E. coli and a highly artificial environment consisting of agar in a Petri dish. Although E. coli is well studied, its motility and taxis behaviors are not necessarily representative of bacteria across nature. In addition, natural environments are dynamic, and bacteria rarely experience stable gradients for extended periods, such as the 24-hour time-frame used here. The authors have also only focused on responses to attractant gradients with undefined complex growth media, and not assessed if this is also true for repellent gradients. This is important to consider because E. coli also generates repellent gradients (indole) that are not considered here. E. coli also generates AI-2, sensed as an attractant, that would be an opposing force for migration. For these reasons, it is not clear that the data and theory presented here generalize to diverse bacterial species, to natural environments, or to chemotaxis broadly.

The authors should acknowledge that further work is needed to generalise their findings by testing additional organisms, such as non-laboratory E. coli isolates, other enteric bacteria, and species with fundamentally different motility systems (e.g., Campylobacter jejuni). Further work could also expand beyond agar by examining chemotaxis in a biological matrix such as mucin, as well as testing responses to defined attractants and repellents.

(2) No genetic component is identified, so claims about evolution are not supported.

Evolution requires heritable genetic changes that produce phenotypes advantageous under a given selection pressure. The authors state that bacteria were selected for rapid migration and that this selection produced progressively more efficient migrators. However, no sequencing analyses of the evolved isolates were performed, no genetic changes were identified, and no mechanism underlying this phenotypic shift was described. Without identifying genetic alterations, they cannot substantiate the claim that evolution occurred. Whole-genome sequencing of the evolved isolates is necessary to determine whether specific mutations underlie the observed phenotypes.

(3) The predictive power of the model is not tested.

The authors develop a model with post-dictive capability, meaning the model reproduces behaviors similar to those observed in the data used to construct it. However, the manuscript does not demonstrate that the model has predictive power. Demonstrating predictive performance would substantially increase the value of the model. For example, the authors could perform an additional round of selection and predict the resulting bacterial behavior under a condition not used during model construction (such as a different agar concentration or predicting the behavior of different bacteria). Otherwise, the authors should tone down the claims.

(4) Limited novelty and impact of the environmental difference studied.

A central point of the manuscript is the difference between evolution in 0.2% versus 0.3% agar and how this difference relates to the proposed model. However, this represents a relatively minor change in the environment experienced by the bacteria. Developing an extensive theoretical framework and proposing that bacterial evolution is highly sensitive to these parameters based on this narrow experimental system may be premature. This would be addressed by the suggested broadening of experiments described above.

(5) The manuscript is too brief, and some data and methods are insufficiently described, particularly related to the machine learning analysis.

The manuscript addresses a complex topic, yet the main text, methods, and figures are very brief, which need not be the case. As a result, it is often difficult to understand exactly what was done and how the data support the authors' claims. More detailed descriptions of the experimental approaches and analyses are necessary.

One example is the machine learning approach used for cell tracking. This method is only briefly described, and no validation data are presented that would allow readers to evaluate whether the approach performs accurately. If the method is robust, it would be a powerful analytical tool, but the current description does not provide sufficient information to evaluate the reliability of the results. This issue is particularly important because the authors conclude that tumbles account for less than 3% of escape events, which contrasts with previous paradigms. Automated tracking methods can be susceptible to artifacts, and therefore, rigorous validation of the tracking pipeline, supported by appropriate figures and benchmark data, is essential.

Reviewer #2 (Public review):

Overall, we found the responses to be quite recalcitrant.

We have one remaining composite concern about the comparison between observed expression patterns with the new strains versus published data.

First, the authors only report patterns for one stage while it should be not too much effort to image the different life stages. However, since this is a revision, we are not formally requesting they do this.

Second, in the now provided Table (thank you) 'observed expression' (last column) is lacking for 9 of the 30 proteins, and for 6 of these the procedure was not successful. Why not report patterns for the other three? It is confusing also because on page 5, the authors say that "overall, 24 of 30 tags ...all of which were visible with fluorescence stereomicroscopy" - are we missing something? Also, they then said that they "obtained 6/9 of the originally failed tags"; why are the corresponding patterns not included in table 1, and are 9 proteins still labeled as "no" in the "success?" Column?

Third, we strongly feel that the response to our comments about expression patterns is not adequate. On page 5 the authors say that "all proteins were expected to be ubiquitously expressed" and that "scRNA-seq indicated that transcript abundance was ubiquitous and without strong tissue-specific enrichment with few exceptions". However, in their rebuttal, the authors now argue for tissue-specific expression for proteins with paralogs, turning around their own argument! Moreover, their Table indicates that many genes show tissue-enriched expression by RNA-seq while many of their tagged proteins exhibit ubiquitous expression.

Overall, this indicates that both the overall accomplishment of generating tagged protein strains and analyzing their expression is oversold.

Reviewer #2 (Public review):

Summary:

This paper starts with a large-scale yeast two-hybrid (Y2H) screen using Set1 (full-length and smaller parts) and other Set1C/COMPASS subunits as bait. There are hundreds possible interactions identified, but only a small number are given any follow-up. While it's useful to document all the possible interactions, the unfocused and preliminary nature of the results makes the paper feel scattered and incomplete.

Strengths:

The Y2H screen was very comprehensive, producing lots of interesting possible leads for further experiments.

Weaknesses:

Most interactions were not further tested, and even in the case of those that were, the experiments are often inconclusive or incomplete.

Reviewer #2 (Public review):

This is a very interesting study from Vandendoren and colleagues examining the role of PVN oxytocin neurons during thermoregulatory behaviors, in particular during thermoregulatory huddling. The findings are important and have implications for the thermoregulation field as well as the social/naturalistic behavior field. The findings are compelling and use a combination of state-of-the-art tools (photometry, optogenetics, automated behavior tracking, thermal imaging, and core body temperature measurement), often in combination with each other, to produce a rigorous and high-dimensional dataset.

Comments on revised version.

I appreciate the effort the authors have put into addressing all of my questions, and I have no remaining concerns.

Reviewer #2 (Public review):

Summary:

The manuscript "Canonical and phosphoribosyl ubiquitination coordinate to stabilize a proteinaceous structure surrounding the Legionella-containing vacuole" by Steinbach et al. is well written and presents strong evidence that satisfactorily supports the main hypothesis and research objectives. The authors have clearly demonstrated the presence of cloud-like, detergent-resistant GTPase Rab5 surrounding the LCV, and formation of the structure is dependent on the SidE family of effectors. The study provides insights into the relevant (associated with described phenotype) ubiquitination pathways. The findings advance our understanding of Legionella pneumophila vacuole remodeling during intracellular infection and open directions for future research to establish broader implications of this structure on Legionella pathogenesis.

Strengths:

The manuscript convincingly demonstrates the presence of a cloud-like, detergent-resistant GTPase Rab5 surrounding the LCV through elegant microscopy. The experimental evidence about the dependence of the observed phenotype on the SidE family of effectors is compelling and presented with strong scientific rigor. The introduction is well-written, and the discussion is thorough and satisfactory. The article is thought-provoking and shows preliminary evidence for ubiquitin-mediated protection and spatial organization of the LCV.

Reviewer #2 (Public review):

Summary:

Huang et al recorded anterior cingulate cortex activity in mice while they performed a shuttle escape task. The task utilized two auditory cues, each of which informed the mice to stay or escape depending on which side they were on, and incorrect responses were punished by shock administration. Analyses focused on ACC neurons that fired when mice crossed the shuttle box in either direction (A-->B or B-->A), coined "action state", or when mice crossed in one direction but not the other, coined "action content". The authors characterized these populations, and ACC firing changes mostly occurred around the time of shuttle crossing. This work will likely be of broad interest to those who are interested in neocortical neurophysiology broadly, anterior cingulate cortex specifically, and their contributions to learning about actions. The task is well-designed and provides a nice background for neurophysiological recordings. The authors leveraged these strengths in characterizing the neural populations that fire to shuttle crossings in both directions vs one direction.

Strengths:

The factorial design nicely controls for sensory coding and value coding, since the same stimulus can signal different actions and values.

The figures are well presented, labeled, and easy to read.

Additional analyses, such as the 2.5/7.5s windows and place-field analysis, are nice to see and indicate that the authors were careful in their neural analyses.

The n-trial + 1 analysis where ACC activity was higher on trials that preceded correct responses is a nice addition, since it shows that ACC activity predicts future behavior, well before it happens.

The authors identified ACC neurons that fire to shuttle crossings in one direction or to crossings in both directions. This is very clear in the spike rasters and population scaled color images. While other factors such as place fields, sensory input, and their integration can account for this activity, the authors discuss this and provide additional supplemental analyses.

Reviewer #2 (Public review):

Summary:

Learning in dynamic, stochastic environments is difficult, and neuromodulatory systems may shape where learning signals appear in the brain. Using fMRI from four probabilistic learning studies and a Bayesian ideal observer model, the authors examined latent variables driving learning, such as confidence and surprise. They found that brain activity related to confidence, and to a lesser degree surprise, is highly spatially invariant across tasks and modalities, suggesting a stable cortical organization. This invariant pattern aligns with PET-derived maps of receptors and transporters, implicating catecholamine and opioid systems, and supporting a neuromodulatory account of adaptive learning with receptor-level hypotheses.

Strengths:

(1) Elegant combination of computational modelling, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET).

(2) The authors describe results of four separate experiments, with very similar results, in effect providing internal replications.

(3) Cross-validated results compared against a meaningful null model.

Weaknesses:

(1) Unclear rationale for using one-sided statistics (e.g., in Figure 3). One-sided tests appear to be invalid, given that the Introduction lacks a preregistered directional hypothesis at an operationalised level. This may have consequences for the following statement in the Discussion: "The associations between receptor architecture and functional topography were substantially weaker for the language network, which is not thought to rely strongly on neuromodulatory systems."

(2) Limited computational modelling. Since learning rates probably differ across subjects, I wonder if they have considered fitting the "volatility" instead of using the generative one. Would that give more meaningful fMRI maps, and better explained variance when correlating these to the PET-based predictors? I was also wondering how their surprise measure relates to "change-point probability" (e.g., Murphy et al., Nat Neurosci, 2021). Finally, I think it would be helpful to show average time courses of surprise and confidence time-locked to state changes.

(3) Lack of GLM validation. It would help to show that the model fits the data well. This is important given the many underlying assumptions (shape of the HRF, linear effects of variables, etc). For example, one could show average insula activity time-locked to state changes, as well as the model-predicted activity, and separately for three strata defined by how surprising the state change was (according to the ideal observer model). Related, the authors use a substantial number of predictors in their GLM, and the language in the Methods is a little casual. It would help to show part of a design matrix, and clearly describe the following: were (occasional) questions and responses modelled by separate stick functions? Which predictors (stimulus, questions, response) varied parametrically with which variables?

Reviewer #2 (Public review):

Summary:

This study by Mentch et al. uses naturalistic-movie fMRI and grayordinate-level stacked encoding models to test preregistered hypotheses about low/high-level and audio/visual feature encoding in autism and adolescence from openly available Healthy Brain Network data. Null results reported that autism was not linked to increased low-level encoding in primary sensory cortices. Exploratory analyses showed participants with autism showed reduced high-level visual encoding in social regions (pSTS, face areas), with the high-low feature shift tracking social responsiveness scale (SRS) scores. Age and laterality effects were also found.

Strengths:

(1) This study and hypotheses were preregistered.

(2) The study utilised proper variance partitioning, split-half noise ceilings, FD-threshold sensitivity analyses, and an explicit modelling framework that recovers known sensory hierarchies in the aggregated sample. The developmental sampling adds to the interest.

(3) The manuscript is written clearly, laying out the background and theories to be tested with encoding models. The analyses and reporting of results are clear.

Weaknesses:

(1) If I understand correctly, by only averaging the grayordinates that already passed a significance threshold, the resulting parcel value is guaranteed to look stronger than if all grayordinates had been included. This has been raised in neuroimaging (Kriegeskorte et al., 2009; Vul et al., 2009). Can the authors justify these choices?

(2) I assume that the phrase "temporally permuting the order of observations" on Page 22 means random shuffling of time points. The details of this exact permutation are not specified. Both the fMRI BOLD signal and movie features have strong temporal autocorrelation, and random shuffling will destroy this structure. This is important as grayordinate-level survivors will propagate to parcel pools. Circular shifting or phase randomization preserving the autocorrelation spectrum is appropriate.

(3) In the movie feature selection, the low-level visual model contains only two scalars: mean perceptual brightness and a single averaged value across 2,139 motion-energy filters. With only two low-level visual features, the low-level visual model potentially would underestimate low-level visual encoding. The H1.1 toward the null perhaps suggests to this. Principal components of the motion-energy outputs, as was done for the cochleagram, could be used.

(4) The pilot sample composition is not described. Features were selected based on their performance on an independent set of 54 pilot subjects. Please provide age, sex, and diagnostic composition of the pilot sample. The main point being whether the selected features were optimised for a population that differs from the subject studied.

(5) The authors acknowledge the lack of eye-tracking in theory study. I think this should be elaborated, especially why this modality is important for answering sensory and perceptual encoding. Face encoding may not be degraded, but just that faces are not being attended to.

(6) I think a more nuanced distinction about the representational nature of encoding-model R² should be mentioned, especially when the interpretation of findings is related to perceptual functioning (EPF theory). R² measures how well a feature set predicts brain activity, not perceptual function or cognitive integration.

(7) The literature also includes evidence for no Colavita effect, not just reverse Colavita in autism, and the framing should reflect this more even-handedly.

(8) The 0.2 mm per-volume threshold is quite strict. The 40%/60%/80% sensitivity analyses partially address this, but a brief justification for the choice of 0.2 mm would strengthen the Methods.

(9) Figure 1 seems confusing and would benefit from more information or text in the figure.

(10) Figure 2 supplement has caption A labelled twice; please correct.

(11) Acronyms. Please spell out MSI on first mention (page 2) and ISC/ISFC on first mention (page 4).

Reviewer #2 (Public review):

Summary:

The authors present a creative approach using visual anagrams matched on low-level image statistics to isolate animacy from low-level visual features and report consistent effects of animacy on visual working memory and attention. While this is a thoughtful design and is well executed across seven pre-registered experiments, it remains unclear whether the reported effect is truly driven by animacy, as opposed to broader differences in ensemble statistics or semantic structure across the "mixed animacy" versus "uniform animacy" conditions. As such, the interpretation of a "pure" animacy effect may be overstated.

Strengths:

(1) An important methodological advance in controlling low-level confounds that have historically complicated the study of animacy.

(2) The converging effects across multiple experiments, together with the pre-registered design, strengthen the reliability of the reported findings.

Weaknesses:

(1) Specificity of the animacy effect vs. category-level ensemble structure

The central claim is that animacy itself drives the observed effects. However, the key manipulation ("mixed animacy" versus "uniform animacy") also introduces differences in category-level ensemble structure. For example, in Experiments 1-2, cross-category change detection (e.g., dog to chair) may be easier not because of animacy per se, but because of a change in overall ensemble statistics (Brady & Alvarez, 2011, 2015). In addition, since each display contains five objects (two in one category and three in the other category), cross-category changes may also alter category balance in a way that further facilitates detection. In contrast, within-category changes preserve both ensemble structure and category composition, making them more difficult to detect.

Brady, T. F., & Alvarez, G. A. (2011). Hierarchical encoding in visual working memory: Ensemble statistics bias memory for individual items. Psychological Science.

Brady, T. F., & Alvarez, G. A. (2015). Contextual effects in visual working memory reveal hierarchically structured memory representations. Journal of Vision.

(2) Limited stimulus set and potential learning effects

The relatively small stimulus set (six anagram pairs) and repeated exposure raise the possibility of learning or familiarity effects. Does performance change over time? e.g., are there meaningful differences between early and late trials (e.g., first 10% vs. last 10%)? If such differences are present, they could suggest the development of task-specific strategies or increased efficiency with repeated exposure, rather than stable effects driven by the experimental manipulation itself.

(3) Role of semantics

Although the anagram paradigm effectively controls low-level visual features, it still relies on high-level semantics (e.g., "dog" vs. "boot"). These stimuli differ not only in animacy but also along other semantic dimensions such as natural versus manmade categories. From a semantic standpoint, it remains unclear whether the observed effects can be uniquely attributed to animacy or whether they reflect broader conceptual distinctions.

Reviewer #2 (Public review):

Summary:

Understanding the factors and mechanisms underlying the deleterious effects of distraction, and protection from distraction, in working memory is an important question that has a long and rich history in psychology and neuroscience, and continues to be highly relevant. In this study, the authors recorded the EEG while subjects viewed the initial presentation of two oriented-grating stimuli, aligned on either side of fixation along the horizontal meridian (memory array), followed by a 70%-valid cue, then one of three distractor conditions (overlapping cued item (40%), opposite cued item (40%), no distraction (20%)), followed by recall ("delayed estimation"). The behavioral and EEG results from this procedure are complemented with computational modeling with a two-tier bump-attractor model.

Weaknesses:

Interpretation of the results is complicated by several factors. One is the non-consideration of a considerable amount of extant research that is highly relevant to the question of interest (these include seminal studies from Gi-Yeul Bae and from Tatiana Pasternak). Relatedly, the manuscript emphasizes biasing effects of distractors to the exclusion of a conceptually distinct effect: degradation of representational precision. (For example, the actual focus of the study of Wimmer et al. (2014) that the manuscript cites with reference to bias is the degradation of precision; one only has to read the title of this paper to know this.) Also relatedly, the authors are aware of the possibility of misbinding (a.k.a. "swap") errors, in which subjects mistakenly recall a high-fidelity representation of a foil (in this case, the distractor) rather than the target, but they (1) fail to cite any of the extensive literature on this topic and (2) seem to erroneously attribute what their analyses would seem to identify as misbinding errors as "antagonistic bias" exerted by the distractor on the target item.

A second concern relates to the interpretation of patterns in the empirical results. In particular, Figure 1G is interpreted as displaying a pattern of repulsive bias exerted by the distractor on trials when the distractor appeared at the location opposite to the cued item. However, it is not clear that this is a repulsive bias. Rather, what the plot shows is that report error is attracted to "near" distractors with a positively signed offset but repelled by "near" distractors with a negatively signed offset. Stated another way, when one applies a model-free assessment of the influence of the distractor on the memorandum, there is no systematic bias: the AOC of positively signed offset values from 0 to +45 deg is roughly the same as the AOC negatively signed offset values from 0 to -45 deg. The same also seems to be true, albeit with a smaller magnitude, for trials featuring "stronger mnemonic neural representation" that are illustrated in Figure 2. And so it's unclear that the effect of the "Dist. Opp" distractor is indeed a repulsive bias, rather than a loss of precision.

The third primary concern is that the results from simulations from the two-tier bump-attractor modeling are difficult to interpret due to several poorly motivated and seemingly "hand-coded" assumptions. These include the (seemingly arbitrary) strengthening of HCVC feedback connections by 20% for cued vs. uncued items; and the choice to "transiently block[ed] feedforward connections from the VC to the HC during the maintenance epoch" as a consequence of cuing. There is frankly no evidence that the latter phenomenon actually happens in primate brains performing comparable tasks, including in papers (such as from Xu and from Rademaker) that are cited in this manuscript. The current consensus is that priority-related rotations of representational geometry are the scheme employed by mammalian nervous systems to control the otherwise deleterious effects of distraction.

Reviewer #2 (Public review):

The manuscript by Ono et al compares two prime editing strategies in zebrafish, one based on a nickase and the other on a nuclease, and evaluates their performance for introducing substitutions, short insertions, and transmission to the next generation. The study aims to clarify the relative strengths of these approaches and to extend their use for inserting short DNA sequences in vivo.

The study provides a useful and well-executed comparison of two editing strategies in a vertebrate model. In particular, the finding that the nuclease-based approach shows higher efficiency for short insertions is of practical interest for functional studies. The authors also present convincing evidence supporting their conclusions, including sequencing and phenotypic validation at selected loci. These results support the reliability of the approach in this system.

The overall conceptual advance remains somewhat limited, as the general strategy of delivering prime editing components in zebrafish has been described previously. The present study extends this work by comparing two editing modes and exploring insertion efficiency, which represents a useful but incremental advance.

Regarding the comparison between the two systems, the authors have made efforts to address concerns about generalizability by adding data from additional loci and by refining the scope of their conclusions. These additions strengthen the manuscript. However, the comparison is still based on a relatively small number of loci, and the conclusions may therefore remain somewhat context-dependent.

Overall, the authors largely achieve their stated aims of comparing two editing strategies and demonstrating their applicability in zebrafish. The data generally support the conclusions, particularly within the tested loci. The work provides practical value to the community, especially for researchers seeking efficient strategies for short sequence insertion in this model system, although its broader impact is somewhat limited by its incremental nature.

Reviewer #2 (Public review):

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

Strengths:

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

Reviewer #2 (Public review):

Summary:

The goal of this proposal was to understand how two separate projection neurons from the medial prefrontal cortex, those innervating the basolateral amygdala (BLA) and nucleus accumbens (NAc), contribute to the encoding of emotional behaviors. The authors record the activity of these different neuron classes across three different behavioral environments. They propose that, although both populations are involved in emotional behavior, the two populations have diverging activity patterns in certain contexts. A subset of projections to the NAc appear particularly important for social behavior. They then attempt to link these changes to the emotional state of the animal and changes in synaptic connectivity.

Strengths:

The behavioral data builds on previous studies of these projection neurons supporting distinct roles in behavior and extend upon previous work by looking at the heterogeneity within different projection neurons across contexts, this is important to understand the "neural code" within the PFC that contributes to such behaviours and how it is relayed to other brain structures.

Weaknesses:

The diversity of neurons mediating these projections and their targeting within the BLA and NAc is not explored. These are not homogeneous structures and so one possibility is that some of the diversity within their findings may relate to targeting of different sub-structures within BLA or NAc or the diversity of projection neuron subtypes that mediate these pathways. This is an important future direction for this work but does not detract from the main finding as reported.

Reviewer #2 (Public review):

The authors have made several corrections to the original manuscript. For example, they revised the bootstrapping analysis to avoid arbitrarily inflating the degrees of freedom. However, most substantive concerns remain inadequately addressed.

(1) The primary issue is still the lack of baseline models against which to benchmark the predictive performance of the proposed DenseNet model. This concern was raised independently by two reviewers. Without such benchmarks, it is difficult to interpret the reported results in the context of prior work on MRI-based cognition prediction.

Notably, the authors state: "While we compared our model with the connectome predictive modeling (CPM) approach and observed better performance with our deep learning framework, we did not conduct a comprehensive benchmark across all available machine learning methods, nor was this the aim of the present study."

However, I could NOT find any discussion or results related to the CPM model in the manuscript. It is therefore unclear whether the DenseNet model was actually statistically compared with CPM, and, if so, how the comparison was conducted.

Note that the statement, "While Vieira et al. show that the majority (76%) of prior studies used linear modeling approaches, including CPM and penalized regressions, these models are often vulnerable to overfitting, especially when applied to high-dimensional fMRI data," is not entirely accurate. Linear models typically have far fewer parameters than deep-learning models and are therefore often less prone to overfitting. In fact, it is well established that deep-learning models are particularly susceptible to overfitting and usually require substantially larger sample sizes to achieve stable and reliable performance. Although deep-learning models may outperform shallower models once sufficient data are available and training is well controlled, this does not justify the authors' claim as stated. I therefore disagree with the argument put forward by the authors.

The authors further justify the absence of benchmarking by stating: "In this context, deep learning was employed as a flexible framework capable of modelling high-dimensional functional connectivity patterns across cognitive states, rather than as a claim of inherent methodological superiority. Thus, our goal was not to propose a universally superior prediction model, but rather to test how brain state influences predictive utility for WM and EM using a deep learning approach." However, most shallow models can likewise be applied across different brain states and cognitive targets. This rationale does not establish deep learning as a uniquely appropriate or necessary choice. If deep learning is indeed a better approach in this context, the authors should demonstrate this empirically through appropriate benchmarking against established baseline models.

(2) Additional analysis shows that "BCG is not significantly associated with cognition itself". This is the most perplexing result. This is like saying Brain Age Gap is not related to chronological Age. It is counterintuitive since the Brain Age Gap is calculated by chronological age minus actual age, and most research has shown a strong relationship between the Brain Age Gap and age.

If the brain cognition gap is not related to cognition, is it possible that the results found are mainly due to the predictive model not fitting well with another dataset? Regardless, the lack of association between BCG and cognition deserves a discussion.

(3) I still do not fully understand the rationale of the mediation analysis. The analysis and findings are still not related to aims 1 and 2, since DA and entropy are not part of the prediction models. But I appreciate the explanation that this part is related to the authors' previous work, and that the authors attempted to link to them somehow.

Reviewer #2 (Public review):

Summary:

Shapiro et al. set out to verify the American Beefalo Association's claim that Beefalo cattle possess 37.5% bison ancestry. They employ a comprehensive range of well-established population genomics methods to estimate ancestry in these hybrid populations, including PCA, ADMIXTURE, D and F statistics, and local ancestry inference. Their findings conclusively demonstrate that most Beefalo lack the claimed bison ancestry, with only 8 out of 47 samples showing any detectable bison ancestry, ranging from 2-18%.

Strengths:

The primary strength of this analysis lies in the comprehensive dataset available to the authors, which includes important foundational Beefalo individuals and various reference populations. The rigorous and multi-faceted methodological approach employs several well-established techniques in population genomics for detecting and measuring admixture. Each method used has a firm basis in the field, providing consistent and robust results. The authors' approach of using PCA to initially assess the data within a global context, followed by more specific analyses using ADMIXTURE and D-statistics, provides a clear and logical progression of evidence. The presentation of these results in figures is particularly effective, clearly illustrating the key findings of the study. Additionally, the examination of both autosomal and sex chromosome ancestry offers a more complete understanding of Beefalo genetic composition and the mechanics of bison-cattle hybridisation.

Weaknesses:

One limitation of this analysis is the relatively low coverage (~2x) of many Beefalo samples. However, the authors have taken steps to mitigate biases that may arise from this, and their downsampling experiment demonstrates that this level of coverage is appropriate for summarising species-level ancestry across Bos. Another potential weakness is the limited sampling of contemporary Beefalo populations, as the study focuses primarily on historical samples. The authors have justified this choice on the grounds that contemporary Beefalo breeding involves no further bison input, so founder-era individuals are the most informative samples for addressing the study's central question.

Appraisal:

The authors have clearly achieved their primary aim using a rigorous and comprehensive methodology. Their extensive dataset and multi-faceted analytical approach provide strong support for their conclusions. The study not only addresses its main research question but also reveals unexpected insights into Beefalo genetics, particularly the presence of zebu ancestry, predominantly from Brahman cattle.

Discussion:

This study is valuable for several reasons beyond its primary findings. First, it definitively addresses and refutes the claim of 37.5% bison ancestry in Beefalo, providing crucial information for those studying these interspecies hybrids and the viability of their offspring. Second, it reveals the unexpected presence of zebu ancestry, predominantly from Brahman cattle, in many Beefalo, raising intriguing questions about the breed's development and the potential role of zebu cattle in achieving desired traits. This finding suggests that the distinctive appearance of Beefalo may be due in part to zebu admixture rather than bison ancestry. Third, the study highlights the significant barriers to admixture between bison and cattle, both in controlled breeding programs and potentially in wild populations. This has important implications for conservation genetics and our understanding of gene flow between these species. Lastly, the study demonstrates the power of genomic analysis in verifying breed claims and understanding the complex history of domestic animal breeds. These findings open new avenues for research in bovine genomics, breed development, and the dynamics of interspecies hybridisation.

Comments on revised version:

Thanks for the responses, which address my comments in full. I have no further concerns.

Reviewer #2 (Public review):

Summary

The authors investigate how parallel olfactory pathways contribute to CO₂ valence processing in Drosophila. By combining multiple approaches, the study identifies LN23 as a previously unrecognized component of the CO₂ circuit and proposes a model in which distinct downstream pathways contribute to aversive and attractive behavioral responses. More broadly, the work aims to connect circuit organization with context-dependent sensory processing and behavioral valence.

Strengths

A major strength of the study is the integration of multiple complementary approaches spanning anatomy, circuit analysis, and behavior. This combination provides a rich and valuable framework for understanding how CO₂ information may be processed across different levels of the olfactory system. The identification of LN23 as an important component of the CO₂ pathway is particularly interesting and will likely be useful for future studies investigating olfactory processing, behavioral state modulation, and valence coding. The connectomic and anatomical analyses also provide a valuable resource for the community.

Another strength of the manuscript is its conceptual ambition. The work moves beyond a simple labeled-line view of olfactory processing and proposes that flexible behavioral responses may emerge from interactions between parallel downstream pathways and multimodal integration centers. The behavioral manipulations further support an important role for LN23 in CO₂-related behaviors.

Weaknesses

Several aspects of the conceptual interpretation would benefit from additional clarification or more cautious framing relative to the current experimental evidence. In particular, the distinction between atmospheric versus experimentally elevated CO₂ conditions, as well as the interpretation of chronic exposure in terms of habituation, remains somewhat unclear throughout the manuscript.

Some conclusions regarding valence coding and multimodal integration also appear more inferential than directly demonstrated experimentally, especially when moving from anatomical connectivity to functional interpretation.

Reviewer #2 (Public review):

This study uses monkey single-unit recordings to examine the role of the STN in combining noisy sensory information with reward bias during decision-making between saccade directions. Using multiple linear regressions and clustering approaches, the authors overall show that a highly heterogeneous activity in the STN reflects almost all aspects of the task, including choice direction, stimulus coherence, reward context and expectation, choice evaluation, and their interactions. The authors report in particular how three classes of neurons map to different decision processes evaluated via the fitting of a drift-diffusion model. Overall, the study provides evidence for functionally diverse and anatomically intermingled populations of STN neurons, supporting multiple roles in perceptual and reward-based decision-making.

This study follows up on work conducted in previous years by the same team and complements it. Extracellular recordings in monkeys trained to perform a complex decision-making task remain a remarkable achievement, particularly in brain structures that are difficult to target, such as the sub-thalamic nucleus. The authors conducted numerous analyses of STN activities, using sophisticated statistical approaches and functional computational modeling.

One criticism that I would still make in the revised version of the paper concerns the description of the behavior of the two monkeys which is still minimal, while acknowledging differences in their choice and RT performance that reflect "individual differences in sensitivity to motion stimulus and a common heuristic-based satisficing strategy". This sentence is not clear to me. Moreover, the potential consequences of these differences on neuronal activity are only considered in the cluster analysis done for each of the two animals separately and for which it turns out there is no notable difference.

Compared to the first version of the paper, the cluster analysis in this revised version yields three distinct populations instead of the previous four. While the authors suggest that these subpopulations play important roles in encoding different aspects of decision-making, the identification of three rather than four subpopulations seems to me an important update that warrants discussion.

Finally, I think it would have been interesting to identify the level of collinearity in the model proposed by the authors (equation 7). Indeed, one can expect significant collinearity between some of the proposed explanatory factors of neuronal activity, such as choice and coherence level, for example. Similarly, for the analysis relating neuron activity to decision evaluation signals (p 16), firing rates calculated using sliding averages with 1-ms steps are compared, but the method does not specify controls for multiple comparisons or for non-independent data.

Reviewer #2 (Public review):

Summary:

Many insects possess extremely sensitive olfactory systems that can detect chemical signals from distances of several kilometers. For decades, the arms race between bats and insects has served as a prime example of acoustic co-evolution. The auditory adaptations of insects to echolocation have been well documented. Cricket has a multi-sensory predator recognition system with keen olfactory, tactile, and auditory senses. However, whether crickets can use the scent of bats to avoid them remains unknown at present. The authors hypothesized that cricket prey (Loxoblemmus equestris) might eavesdrop on predator bat (Scotophilus kuhlii) VOCs as an early warning. L. equestris is one of the prey species of S. kuhlii, and the authors demonstrated that the body odor of the insectivorous bat S. kuhlii triggers robust avoidance and electrophysiological responses in the cricket L. equestris, and that a single compound, (-)-limonene, is sufficient to elicit this avoidance in the laboratory and suppress calling in the field. Overall, this paper has a complete chain of evidence and should be a highly praised study.

Comments:

(1) Olfactory eavesdropping can transcend the evolutionary divide between vertebrate predators and invertebrate prey, enabling invertebrates to trigger defensive avoidance behaviors in response to predator-derived volatile odors. This phenomenon is empirically well-documented and requires no excessive emphasis.

(2) Without quantitative analysis and without knowing the relative content of this key substance limonene, I don't quite understand how to determine the concentration of limonene standard for EAD, as well as the concentration in field experiments. How is the concentration of limonene determined in field spraying, and is this actually the case in the wild environment?

(3) Figures 1C and D should compare the GC-EAD response of L. equestris to the odor of bat body and the odor of bat nasal secretions. It should not be compared with the air control group. Figure 1D has the same problem.

Reviewer #2 (Public review):

Summary:

To investigate the detachment and reattachment kinetics of kinesin-1, 2 and 3 motors against loads oriented parallel to the microtubule, the authors used a DNA tensiometer approach comprising a DNA entropic spring attached to the microtubule on one end and a motor on the other. They found that for kinesin-1 and kinesin-2 the dissociation rates at stall were smaller than the detachment rates during unloaded runs. With regard to the complex reattachment kinetics found in the experiments, the authors argue that these findings were consistent with a weakly-bound 'slip' state preceding motor dissociation from the microtubule. The behavior of kinesin-3 was different and (by the definition of the authors) only showed prolonged "detachment" rates when disregarding some of the slip events. The authors performed stochastic simulations which recapitulate the load-dependent detachment and reattachment kinetics for all three motors. They argue that the presented results provide insight into how kinesin-1, -2 and -3 families transport cargo in complex cellular geometries and compete against dynein during bidirectional transport.

Strengths:

The present study is timely, as significant concerns have been raised previously about studying motor kinetics in optical (single-bead) traps where significant vertical forces are present. Moreover, the obtained data are of high quality and the experimental procedures are clearly described.

Reviewer #2 (Public review):

Summary:

Using E. coli K-12 as a model system, the authors investigated how phosphate (Pi) depletion induces polymyxin resistance in Enterobacteriaceae, which notably lack the canonical phospholipid remodeling pathways commonly associated with phosphate starvation responses. They demonstrated that low-phosphate conditions promote L-Ara4N modification of lipid A, thereby enhancing polymyxin resistance. Proteomic analyses revealed significant upregulation of the arn operon and ugd under phosphate-limited conditions, and promoter activity assays further confirmed that both promoters are strongly induced during Pi depletion. Through gene deletion experiments, the authors showed that arn expression is regulated by the PmrAB two-component system, whereas ugd is controlled by PhoBR under low-phosphate conditions. Using ICP-MS analysis, they further found that phosphate limitation increases cell-associated Fe levels, and that reducing Fe availability abolishes PmrAB-dependent activation of the arn operon. Finally, the study demonstrated that Mg supplementation and Fe chelation can suppress polymyxin resistance, highlighting the critical role of metal homeostasis in phosphate depletion-induced antimicrobial resistance.

Strengths:

Overall, I found this study to be well conducted, with convincing results that strongly support the proposed model. Through comprehensive genetic analyses and detailed characterization of metal ion homeostasis and membrane lipid modifications, the authors uncovered a novel regulatory connection among Mg²⁺, Fe³⁺, and the PmrAB pathway, a key driver of polymyxin resistance. These findings are highly interesting and have important implications for understanding the evolution of the Fe-sensing PmrAB system, as well as the broader role of nutrient availability in shaping antibiotic resistance.

Weaknesses:

I did not identify any particular weaknesses.

Reviewer #2 (Public review):

Synesthesia is a neurological condition where stimulation of one sensory channel leads to involuntary, automatic, and consistent experience of another, unrelated percept. For example, Sir Francis Galton (1880, Nature) famously described the robust tendency of some individual (synesthetes) to associate numerals with a distinct color. Ever since, synesthesia keeps attracting a broad interest in the cognitive neurosciences in light of its implications for the study of domains such as perception, consciousness, and brain connectivity, among others.

Strauch, Leenaars, and Rouw measured pupil size in a group of 16 grapheme-color synesthetes and two matched control groups. The participants were presented with gray digits - that is, visual stimuli having identical physical properties in terms of brightness. Each participant subsequently rated the corresponding evoked color and brightness: unlike controls, synesthetes did so in a very consistent and reliable fashion. Accordingly, this was also shown in their pupils: despite the same objective luminance, digits associated with brighter percepts caused their pupils to constrict and digits associated with darker percepts caused their pupils to dilate more than controls. These results highlight how crossmodal correspondences are deeply rooted in synesthetes, and puts forward pupillometry as a particularly appealing biomarker for some phenomenological experience (at least those grounded in "brightness").

Further strengths of the technique are its temporal resolution and its responsiveness to several constructs. Across several tasks, the authors show for example that responses to synesthetic light are somewhat slower than responses to real light (i.e., they are likely mediated), but at the same time faster than responses to mental imagery. The role of mental imagery can also be reasonably dismissed when considering the second feature of pupil size: its responsiveness to mental effort and cognitive load. The pupils tend to dilate with demanding, challenging tasks, and this was the case when control participants were asked to report the color of a digit for which they did not consistently experience a synesthetic association. The same task was, instead, seemingly effortless for synesthetes, again speaking in favor of the automaticity of number-color correspondences in their case.

Overall, the findings by Strauch, Leenaars, and Rouw are highly significant for the field and likely to be impactful. The strength of their evidence, when accounting for the relatively small sample size and the inherent variability of both phenomenology (color perception and subjective reporting) and physiology (pupil size), is adequate and sufficiently convincing.

Reviewer #2 (Public review):

Summary:

The study offers a thorough analysis of the prevalence of pain in women with polycystic ovary syndrome (PCOS) and its associations with health outcomes across various racial groups. Furthermore, the research investigates the prevalence of PCOS and pain among different racial demographics, as well as the increased risk of developing various conditions in comparison to individuals who have PCOS alone.

Strengths:

The study emphasizes pain as a significant comorbidity of PCOS, an area that is critically underexplored in existing literature. The findings regarding the increased prevalence of some of the diseases in the PCOS + pain group provide valuable direction for future research and clinical care. I believe physicians should incorporate pain score assessments into their clinical practice to improve patients' quality of life and raise awareness about pain management. If future research focuses on the mechanisms of pain, it would provide a better understanding of pain and allow for a focus on the underlying causes rather than just symptomatic management. The study also highlights the association between PCOS+pain and various comorbidities, such as obesity, hypertension, and type 2 diabetes, as well as conditions like infertility and ovarian cysts, offering a holistic view of the burden of PCOS.

Weaknesses:

Due to the nature of retrospective design, some data may not be readily available in the EHR system. Diagnosis of PCOS, pain is based on ICD codes, which may lead to misclassification and may not capture symptom severity or patient-reported experiences.

Reviewer #2 (Public review):

Since its original discovery, the mechanistic basis for TCT-mediated pathogenesis of Bordetella pertussis has been a moving target and difficult to uncouple from confounding variables. The current study provides some exciting data that suggest PGLYRP-1 modulates host responses upon 'activation' by TCT. While there are some strengths associated with the unbiased approaches and collective data to support the claims associated with TCT and PGLYRP-1's function in this system, caution should be used when interpreting and extrapolating some the information provided. While many of the initial concerns were addressed, one concern remains: using whole, intact PG sacculi from other species for comparative studies with a fragment of released PG (i.e., TCT).

Comments on revised version.

I have no further comments.

Reviewer #2 (Public review):

Summary:

The JAK-STAT pathway (JSP) exhibits cell-type-specific functional heterogeneity in breast cancer. This study investigates the JSP in breast cancer and its response to anti-PD‑1 immunotherapy. JSP displays distinct cell‑type heterogeneity: it promotes malignant phenotypes and immunosuppression in tumor cells, while enhancing cytotoxicity and reducing exhaustion in T cells. Elevated JSP expression correlates with improved immunotherapy responses, especially in triple‑negative breast cancer. These findings highlight the paradoxical roles of JSP, indicating that broad inhibition may compromise anti‑tumor immunity.

Strengths:

The major strengths of this study include the comprehensive characterization JSP heterogeneity across epithelial, tumor, and T cells in breast cancer. The identification of JSP and STAT4 as predictive biomarkers for immunotherapy response, particularly in triple‑negative breast cancer, provides clinically relevant insights for patient stratification.

Weaknesses:

The corresponding content has been revised.

Reviewer #2 (Public review):

Summary:

Savage et al. investigate the synchronization of retinal Ca2+ waves with developmental cell death, microglia activation, and vascular outgrowth. These developmental processes occur through a mechanism where apoptotic cells release ATP through Panx-1 channels to stimulate both Ca2+ retinal waves and microglia activation. Using scRNAseq, the authors classify autofluorescence cell clusters (ACCs) at the leading edge of vasculature outgrowth as Hmox-1+ microglia. From here, they show microglia engulfment of apoptotic RGCs, and the potential release of ATP may contribute to Ca2+ wave generation. The authors demonstrate these mechanisms through the use of two pharmacological agents to either block the ATP release from Panx-1 or block receptor binding to ATP. Furthermore, while previous studies have described the site of initiation of retinal Ca2+ waves as random, this study shows that the initiation of Ca2+ waves is biased to the leading edge of vascular growth in the developing retina. To do this, the authors use a combination of wide-field Ca2+ imaging and multi-electrode arrays to pinpoint the sites of Ca2+ wave initiation in the developing retina.

Strengths:

The authors use several techniques to interrogate these mechanisms, including single-cell RNAseq, wide-field Ca2+ imaging, and multi-electrode arrays. With these experiments, this manuscript proposes several novel ideas, such as ATP as the Ca2+ wave-initiating cue, and the localization of the Ca2+ wave initiation to the leading edge of vascular growth.

Weaknesses:

The main weakness of the manuscript is the overreliance on only two pharmacological agents to test the central hypotheses. These conclusions would be strengthened if, in addition to their pharmacological manipulations, they used genetic knockout models to perturb programmed cell death or ATP release (i.e., BAX-KO, Panx-1 KO).

Reviewer #2 (Public review):

Summary:

In this paper, the authors set out to better understand the genetic mechanisms underlying thermal adaptation in insects. They experimentally evolved diamondback moth (Plutella xylostella) populations - a pest species with a wide distribution - under both hot (12h:12h 32{degree sign}C/27{degree sign}C) and cold (15{degree sign}C/10{degree sign}C) thermal conditions, and conducted phenotypic assays and metabolic and transcriptomic profiling to analyze how populations changed to deal with this thermal stress compared to the nonevolved ancestral population (constant 26{degree sign}C). Phenotypic assays showed that evolved hot populations had increased survival at high temperatures (42-43{degree sign}C) while evolved cold populations had lower freezing points compared to the ancestral population. When measured at the constant 26{degree sign}C conditions, metabolic and transcriptomic profiles of 3rd instar larvae from the evolved population were distinctive from the ancestral population, with a set of overlapping metabolic and transcriptomic pathways that were significantly differentially expressed in both hot and cold evolved populations compared to the ancestral. The authors narrowed down this set of candidate genes further by focusing on genes with high expression levels overall, whose expression profile was correlated with differentially expressed metabolites, and that contained mutants in both hot and cold strains. From this set, they chose the PxSODC gene for further functional validation, as it has previously been shown to be involved in the response of insects to abiotic stress with its antioxidative role in cellular defense. At the constant 26{degree sign}C, this gene showed lower expression across development in evolved strains compared to the ancestral population, while it showed similar expression patterns under thermal stress. Knockdown of PxSODC resulted in decreased survival rates at high temperatures and higher freezing points compared to the ancestral population. Based on this validation, the authors hypothesize that the non-synonymous mutation in the PxSODC gene that they found in the cold and hot evolved populations might alter the conformation of the PxSODC protein, increasing enzyme capacity. Their experimental evolution experiment furthermore indicates the capacity of the pest species, the diamondback moth, to adapt to a wide range of temperatures, providing insights into its capacity for global dispersal.

Strengths:

(1) The authors did a tremendous amount of work to characterize the mechanisms underlying thermal adaptation in the diamondback moth, artificially selecting populations for three years in the lab and characterizing how they evolved as a result at different biological levels: from phenotypes in different life stages, to larval metabolites and gene transcription, to functionally validating how one of the resulting gene candidates influences the capacity to deal with thermal stress.

(2) The paper identifies and provides further evidence for candidate genetic mechanisms that might be particularly important for thermal adaptation in insects, including lipid metabolism, oxidoreductase activity, and DNA methylation. It is furthermore interesting that the authors found similar mechanisms to be involved in both the adaptation to cold and hot environments. Their functional validation of some of the genes involved in these mechanisms is very useful to understand how these genes might be causally involved in insect thermal adaptation.

(3) The paper also has applied value: the diamondback moth is a pest species with a wide distribution, so understanding its adaptive capacity to different thermal environments is important for predicting the prevalence and potential further range expansion of this species under future climate change.

Reviewer #2 (Public review):

Summary:

Cong et al. investigated the regulatory effects of ABHD6 on AMPARs. The authors performed adequate electrophysiology recordings to show the exact pattern of this regulation and covered major critical points.

Strengths:

The authors have performed high-quality ephys recordings and examined all potential regulatory aspects of ABHD6 on AMPARs. This is important to understand the AMPAR functions.

Weaknesses:

(1) The authors discussed CNIH-2 extensively from line 92-110 in the introduction, however, they did not perform related experiments. I suggest they move this part to the discussion where they also discussed the roles of CNIH.

(2) The authors need to report the "n" for all the experiments they have presented in this manuscript. How many cells were recorded in each condition? How many batches? This information has to be in all of the figure legends, but it is missing except Fig. 4.

(3) One question is what the physiological meanings of this regulatory effect are. The authors may consider adding some discussions.

(4) About statistics. The authors need to add more details and make sure their statistics sound. For example, they also need to check the equality of variances. In their Table EVs, where the P values are reported, the authors need to report which statistics they have used, one-way ANOVA, K-W test, or others, and the exact post-hoc test type for each comparison. For one-way ANOVA, report the F values simultaneously with the P values in all figure legends.

(5) Fig. 3J, the authors need to correct the label of the Y axis. It is shifted.

Comments on revised version.

In the revised manuscript, the authors have addressed all my concerns. The manuscript has been substantially strengthened by additional data and discussion.

Reviewer #2 (Public review):

In this manuscript, Burnsdon et al. aim to study PIK3CA-related overgrowth spectrum (PROS) by establishing a mosaic zebrafish model with overexpression of pik3ca carrying hotspot mutations, coupled with an mScarlet+ reporter. Using fluorescence microscopy, the authors demonstrated that overexpression of pik3ca with a number of hotspot mutations led to mesodermal and particularly vascular malformations in the zebrafish model. Interestingly, they found a paucity of mScarlet+ mutant cells in the vascular lesions, consistent with the finding of low PIK3CA mutation burden in PROS tissue. Such data suggest a non-cell-autonomous effect of PIK3CA mutation. Following this logic, the authors performed single-cell RNA-Sequencing on zebrafish overexpressing WT pik3ca and mutant pik3ca at 19 hpf, and demonstrated widespread transcriptomic perturbations across multiple lineages, including lineage frequencies, key cell pathways, and cell-cell interactions. Importantly, they demonstrate that mScarlet+ cells carrying mutant pik3ca cluster separately from other cell types, do not demonstrate clear lineage identity, and have a general downregulation in signaling components.

Overall, the conclusions in the manuscript are well-supported by the presented data. The imaging studies are particularly convincing. The transcriptomic analysis generated a list of potential pathways to further investigate and potentially target with future therapeutic interventions. Importantly, this study provides a valuable in vivo model of PROS that: 1) recapitulates key features of PROS (e.g., multiple mesodermal defects, paucity of mutation burden in lesions suggesting non-cell-autonomous interactions); 2) is scalable; and 3) offers direct visualization of lesion development, compatible with time-course live imaging. This model will be valuable to further understand PROS and potentially study other diseases where the PIK3CA pathway is altered (e.g., certain cancers).

The following are not necessarily weaknesses of the data, but rather suggestions where the manuscript could be further strengthened:

(1) The model recapitulates the variability of mesodermal lesions in PROS. It would be valuable to utilize this model to further study factors that are associated with the development of more severe lesions (e.g., by comparing samples with more severe lesions to those unaffected despite carrying the mutations, Figure 1F).

(2) ScRNA-seq analysis could be enriched with a comparison between cells overexpressing mutant pik3ca vs. those overexpressing WT pik3ca.

(3) In the scRNA-Seq analysis, it is curious that the C0 cluster, enriched with mScarlet+ cells, is found to have downregulated signaling interactions (Fig. 5C), yet exerts a widespread non-cell-autonomous effect. Meanwhile, there is also a noticeable loss of certain lineages (e.g., notochord, Figure 4E) and related cell-cell interactions (e.g., notochord-related interaction, Figure 5A). A deeper exploration of the basis of the non-cell-autonomous effect would be valuable.

(4) The scRNA-Seq analysis was performed at one time point (19 hpf). Additional analysis (not necessarily by scRNA-Seq) at other time points to study whether findings at 19 hpf are persistent throughout development or undergo dynamic changes (e.g., cell fate/state of mSc+ mutant cells) would be helpful.

(5) The scRNA-Seq analysis provides a valuable list of perturbed interactions that could be targeted by future therapeutic approaches. Validation of the scRNA-Seq findings with protein-level analysis, and studying the effect of targeting some of the pathways on the disease phenotype, would offer valuable data for the community.

Reviewer #2 (Public review):

Summary:

This manuscript presents an ambitious and technically challenging spatial-transcriptomic atlas of 26 gastruloids using seqFISH. The authors introduce quantitative metrics (mixing score, exposure index, L-metric / scL-metric, spatial L-metric, triplets) to characterize spatial organization at multiple scales. The dataset is valuable, and several analyses are original, particularly the rank-based L-metric family for mutual exclusivity.

Strengths:

The authors generate one of the most detailed spatial transcriptomic datasets of gastruloids to date. They propose creative computational metrics (L-metric/scL-metric) to quantify mutual exclusivity of gene expression without predefined thresholds, and they explore organizational principles from single-cell topology to cluster-level structure. Many observations align well with known gastruloid biology, such as posterior robustness and anterior variability. The writing is generally clear, and the figures are rich.

Weaknesses:

Several central claims rely on metrics whose computation and justification are insufficiently explained, making it difficult to assess how robust or interpretable the results are. Many choices in the analysis appear arbitrary or are insufficiently motivated (normalization schemes, choice of parameters such as the number of neighbors, the distance cutoffs, hierarchical clustering setup, and so on). The interpretations of spatial consistency, gene-program inference, and endothelial heterogeneity are plausible but might be stronger than the evidence currently supports.

The manuscript would benefit from stronger benchmarking, quantification of uncertainty, and explicit controls for known artifacts in spatial transcriptomics (e.g., spillover, 2D slicing, cell type assignment entropy). The biological insights are promising, but since several depend on methodological assumptions that have not yet been demonstrated to be stable, they would benefit from clearer methodological explanation.

The work is rich and could become a reference dataset. Then, clarifying and validating the quantitative methods will considerably strengthen the impact and reliability of the conclusions.

Reviewer #2 (Public review):

Summary:

This is an extensive study using phylogenetic comparison across multiple plasmodium species to gain new insights in relation to their evolutionary pathways and the potential function of pir. In addition to establishing a framework to identify related orthologues across species as well as expanding paralogues families within a species, the work also focuses on understanding loss and gain of different PIRs and how this indicates a relative lack of functional constraints and essentiality for most members of the gene family.

The authors provide evidence that at least pirC has a conserved function and plays an important role in parasite growth in multiple species.

While this study represents a significant effort and does provide interesting new insights that would help our understanding of this complex gene family in the future, it has a number of limitations.

Strengths:

Extensive and thorough phylogenetic analysis that is supported by some biological validation. Provides an indication that the PIR gene family has limited biological constraints and evolved independently across different species, leading to rapid expansion and deletion of orthologous groups. Identified pirC as a functional and important member of the family that is conserved across the species.

Weaknesses:

The phylogenetic tree is based on a truncated sequence that focuses on the more conserved parts of the pir sequence. This could potentially lead to missing the key functional drivers of evolution. The biological validation of the role of pirC has some inconsistencies that need to be addressed.

Reviewer #2 (Public review):

The study addresses the long-standing question in molecular biology and genetics: why has nature selected the current genetic code (SGC, or standard genetic code)? The authors have tested 'error minimization theory', one of the prevailing hypotheses to explain this. Their approach is to create a minimum genetic code (MGC) and its variants (3^9 theoretical possible codes). Using three parameters to quantify the effect of mutations (Polarity, volume, and hydropathy), they computationally test the cost of these genetic codes (3^9) by simulations. Finally, they test this cost experimentally using an in vitro translation system with 10 select genetic code variants with a range of costs (low to high). They use three randomly mutated reporter genes for this purpose - beta-galactosidase, luciferase, and mSG. They find no correlation between the cost of the genetic code and the reporters' output. Based on these observations, they suggest that error-minimization theory may not explain the current egocentric code.

The question they are asking is very exciting, and their approach is solid. The authors are very careful in their analyses and conclusions.

Reviewer #2 (Public review):

Summary:

The authors of this study are trying to resolve how cellular infection by enteropathogenic E. coli (EPEC) subverts cellular signaling pathways to promote infection and dampen immune responses. Specifically, alteration in calcium dynamics has been evidenced in the prior literature as a potential initiator of these adaptions, and this study provides ideas and mechanistic detail as to how cellular calcium dynamics may be subverted by pathogens.

Strengths:

The clear strengths of this paper relate to the new ideas inherent in the proposed hypothesis and their support from the experimental approaches used. Overall, the proposed work provides new ideas in this area, which will benefit from further investigation. Certainly, this is an interesting and challenging paradigm to pick apart mechanistically, and is important for improving treatments from intestinal infections. The authors have provided additional data to clarify and expand on concerns raised during the original review, and these additions are helpful.

Comments on revised version.

Thorough response to original review. No further comments.

Reviewer #2 (Public review):

Summary:

In this study, Zhang and colleagues examine the role of participant selection in creating and using functional templates to improve analyses using hyperalignment. Hyperalignment aligns participants' functional MRI data to a shared functional template, analogous to the anatomical templates used to bring anatomical MRI data into a shared space (e.g., MNI152). The question of appropriate template creation is especially pressing for population-level analyses, where a large number of demographic groups (e.g., different age ranges, clinical statuses) may be included in the same analysis. These different demographic groups may have differences in their functional organization that complicate the creation of a single study-specific functional template.

To provide an initial investigation of the potential effect of demographic-specific templates, the authors use the publicly available Cam-CAN dataset which contains participants from 18 to 87 years of age. They define a young adult (< 45 years of age) and an older adult group (> 65 years of age) from this dataset with approximately the same number of participants. They investigate whether "age-congruent" templates (i.e. defined in the same age group they are used) improve three analyses where hyperalignment has been previously shown to boost performance: inter-subject correlation, predicting individual connectomes, and predicting individual functional responses. Using the Cam-CAN derived older adult template, they then replicate the ISC analyses using the publicly available Dallas Lifespan Brain Study (DLBS).

Overall, the presented results are highly suggestive that age-congruent templates consistently improve performance, though the absolute effects are small.

Strengths:

The use of a separate validation sample-re-using the same template calculated with Cam-CAN-highlights the potential of developing independent templates for individual demographic groups and then distributing these for wider use, analogous to the MNI templates that are widely used throughout the field of neuroimaging. This suggests that the potential impact of this framework is significant.

Weaknesses:

In their revision, the authors have addressed the previously raised "weaknesses" by providing guidance for researchers interested in using age-specific hyperalignment templates in practice.

Impact:

Overall, this work is likely to encourage future development of age-specific functional templates in the imaging community.

Reviewer #2 (Public review):

Summary:

The authors describe a tunable Bessel beam two-photon microscope (tBessel-TPFM) designed to overcome a common limitation of Bessel-based volumetric imaging: axial shifts of the effective focus during Bessel beam parameter tuning. Their optical design allows independent control of axial beam length and resolution while keeping the axial center fixed. This is extensively validated through simulations and experiments.

Strengths:

A major strength of the work is the breadth of validation combined with the level of technical detail provided. The authors carefully characterize the optical performance of the system and clearly explain the design choices and underlying derivations, which will make it easier for others to understand and implement. The authors demonstrate the utility of the method across several in vivo applications, including neurovascular imaging, blood flow measurements, optogenetic stimulation, and microglial dynamics.

Weaknesses:

In the in vivo demonstrations, the authors employ different Bessel beam configurations across experiments, but the beam parameters are not dynamically tuned during live imaging. A video example showing continuous or interactive tuning of the Bessel beam within a single in vivo imaging sequence would further highlight the practical advantages of this platform and strengthen the case for its potential applications. In addition, while excitation powers are reported, the manuscript does not place these values in the broader context of known photodamage thresholds for two-photon microscopy, which would be helpful to the readers. Denoising/image restoration are applied in one of the in vivo examples, but it is unclear why this step was used specifically for this dataset and whether it was necessary to achieve adequate SNR or primarily included as an additional demonstration.

Reviewer #2 (Public review):

Summary:

The authors investigated the effect of prolonged iron limitation (which does stop growth but does not lead to cell death) alters central metabolism in M. tuberculosis. The major tool they used is metabolomics combined with stable isotope tracing. They show that the Krebs cycle is still active, despite the fact that it is dependent on some iron-dependent enzymes. They show that carbon flux through the oxidative branch of the Krebs cycle is stalled, resulting in the accumulation of metabolites, such as malate and alpha-ketoglutarate that are partially secreted. Apparently, the carbon flux from glycolysis is partially diverted to the reductive branch of the Krebs cycle. This is not achieved by using the glyoxylate shunt but probably through the GABA shunt. This unprecedented split of the Krebs cycle and malate secretion allows a continuous flow of carbon through the core of carbon metabolism, overcoming the metabolic stalling triggered by iron starvation.

Strengths:

Novel insight in the central metabolism of a major pathogen and its adaptation to iron starvation. Carefully conducted experimentation. Paper ends with a clear and helpful model.

Weaknesses:

The authors show some surprising and important findings, but would need a little more effort to really substantiate this. Especially the role of the GABA shunt should be genetically tested, as they did for ICL and the glyoxylate shunt.

Also, the dataset 1 is not very convincing, it is only based on transcriptomics and shown with up or down, hardly a strong base for major conclusions. The very least you want is actual differences, preferable on the protein level, where it really counts....

Comments on the revised version:

In the revised version all these points were appropriately dealt with and discussed, although some of them textually and not experimentally, but for reasons that are logical.

Reviewer #2 (Public review):

Summary:

The authors design a custom Bayesian model to estimate the probabilities of access, use and use given access of insecticide-treated nets in six African countries, providing sub-national estimates and inferring the average duration of ITN use and access. An individual-based model was employed to simulate malaria epidemics and estimate the effectiveness of different ITN distribution strategies. The study finds that the mean probability of use or access did not reach 80% (a universal coverage formerly targeted by WHO) for any of the regions even for biennial campaigns, demonstrates that switching from triennial to biennial distribution campaigns increases population use by 7.9%, and evaluates the impact of employing more efficient ITNs on P. falciparum prevalence.

Strengths:

The authors developed a data-driven model that accounts for data collection imperfections and sources of uncertainty while differentiating between ITN use and access. They developed a methodology to infer the timing of mass campaign from publicly available data instead of assuming fixed dates. The probability of use given access allows determining the regions where ITN distribution is least effective. This work can help better inform future interventions by identifying regions where increasing mass campaign frequency or employing better ITNs are most effective. Finally, in addition to insights on ITN access and use for the six countries analyzed, the paper contributes with a methodological framework that can likely be extended to other countries.

Weaknesses:

Since the models employed are rather complex, the methodology description may be hard to follow for some readers. In addition, the models assume many hypotheses, including exponential decay of ITN use/access and narrow prior distributions. It is worth noting that, in the revised version of the manuscript, the authors justified the choice of exponential decay and narrow prior distributions, and made a significant effort to clarify the methodology and the model equations.

Comments on revised version:

I appreciate the improvements made to the text. The methodology description is much clearer now. I have no further suggestions.

Reviewer #2 (Public review):

Summary:

Programmed DNA elimination is increasingly recognised as an important phenomenon across many species, including in animals. Exactly how widespread is still unclear, and the function of PDE is even more mysterious in most species where it has been described. PDE has been discovered in several nematode species, and in this manuscript, the authors carry out a more extensive search for PDE. They find PDE in many species, indicating that it is widespread across the phylum.

Strengths:

The large number of species across many different clades provides good evidence that the phenomenon has evolved many times independently. The work will therefore prompt many further studies characterising individual species, and potentially linking the evolution of the phenomenon to other features of these species' ecological characteristics.

Weaknesses:

The major technical weakness of this project is the assay that is used to evaluate PDE. First, this assay is clearly insensitive, as the authors acknowledge, O. tipulae, which has PDE, does not appear in their screen. Second, the assay gives no information about breakpoints and only limited, non-quantitative information about how much DNA is eliminated. Thus, their data really is only a preliminary screen, which would need to be confirmed by genomic assays.

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 #2 (Public review):

This manuscript presents a substantial technical advance for the genetic manipulation of Blastocystis by establishing an integrated workflow for stable episomal transgenesis, antibiotic selection, clonal recovery, and reporter-based imaging in the ST7-B subtype. The study is particularly valuable because it combines multiple previously fragmented approaches into a coherent and practically applicable toolkit, including endogenous regulatory elements, optimized electroporation conditions, selectable markers, and anaerobic compatible fluorescent reporters. This methodological work greatly expands the molecular toolbox and future studies focused on both basic and infection biology can now build on the ability to express and localize proteins in fixed as well as live cells.

The microscopy data are convincing and clearly demonstrate functional reporter expression and successful recovery of stable transgenic lines. Nevertheless, because this is primarily a methodological paper, the study would be further strengthened by the inclusion of Western blot validation of reporter expression and bicistronic constructs. In particular, biochemical analysis of the P2A-containing constructs would help assess the efficiency of ribosomal skipping and exclude the possible presence of uncleaved fusion proteins, thereby providing stronger support for the interpretation of the imaging data and the functionality of the expression system.

Reviewer #2 (Public review):

Summary:

Nian and colleagues comprehensively apply metabolomics, molecular, and genetic approaches to demonstrate that CLas hijacks the DA/DcDop2-miR-31a-AKH-JH signaling cascade to enhance lipid metabolism and fecundity in D. citri, while concurrently promoting its own replication.

Strengths:

These findings provide solid evidence of a mutualistic interaction between CLas proliferation and ovarian development in the insect host. This insight significantly advances our understanding of the molecular interplay between plant pathogens and vector insects and offers novel targets and strategies for HLB field management.

Weaknesses:

While the article investigates the involvement of dopamine signaling and specific microRNAs in enhancing fecundity and pathogen proliferation, it still needs to provide a detailed mechanistic understanding of these interactions. The precise molecular pathways and feedback mechanisms by which CLas manipulates dopamine signaling in Diaphorina citri remain unclear.

Reviewer #3 (Public review):

Summary:

Ioakeimidis and colleagues studied miscrostructural abnormalities in N=56 Huntington's disease (HD) patients compared to N=57 normative controls. The authors used a powerful MRI Connectom scanner and applied the SANDI model to estimate the soma size, neurite size, soma density, and extracellular fraction in key subcortical nuclei related to HD. In the striatum, they found decreased soma density and increased soma size, which also seemed to become more pronounced in advanced HD individuals in the final exploratory analyses. The authors conducted important analyses to find whether the SANDI measures correlate with clinical scores (i.e., QMotor) and whether the variance of the striatal volume is explained by the SANDI measures. They found a relationship of SANDI measures to both.

Strengths:

The study is both innovative and of high interest for the HD community. The authors provide a rich pool of statistical analyses and results which anticipate the questions that may emerge in the HD research community. Statistics are carefully chosen and image processing is done with state-of-the-art methods and tools. The sample size gives sufficient credibility to the findings. Altogether, I think this study sets a milestone in the attempts of the HD community to understand neuropathological processes with non-invasive methods, and extends the current knowledge of microstructural anomalies identified in HD with diffusion MRI. More importantly, the newly identified anomalies in soma size and soma density open new avenues for studying these biological effects further, and perhaps develop these biomarkers for use in clinical trials.

Weaknesses:

(1) An important question is whether the SANDI measures, which require an expensive scanner and elaborate processing, are better biomarkers than the more traditional DTI measures. Can the authors compare the effect size of FA/MD with SANDI measures. In some of the plots and tables, FA/MD seem to have comparable, if not higher, correlations with QMotor or CAP scores. On the same vein, it is unclear whether DTI measures were included in hierarchical stepwise regression. I wonder if the stepwise models may have picked up FA/MD instead of SANDI measures if they are given a chance. Overall, I hope the authors can discuss their findings also in this light of cost vs. benefit of adopting SANDI in future studies, which is an important topic for clinical trials.

(2) Similar to the above point, it is very important to consider how strong the biomarking signal is from SANDI measures compared to the good old striatal volume. Some plots seem to indicate that volumes still have the highest correlation with QMotor, and highest effect size in group comparisons. It would be helpful for the community to know where do the new SANDI measures stand compared to the most typically used volumes in terms of effect size.

(3) The diffusion measures are inevitably correlated to some degree. Please provide a correlation matrix in supplementary material including all DWI measures to enable readers to understand better how similar SANDI measures are between each other or vs. other DTI measures. Perhaps adding volumes to this correlation matrix may also be a good future reference.

(4) ISS stages:

(a) The online ISS calculator requires cut-offs derived from the longitudinal Freesurfer pipeline, while the authors do not have longitudinal data. Thus, the ISS classification might be inaccurate to some degree if the authors used the FS cross-sectional pipeline. Please review this issue and see if updated cut-offs should be used to classify participants.<br /> (b) Were there really no participants with ISS 0 among 56 HD individuals, please clarify in the manuscript?<br /> (c) A note on terminology that might be confusing to some readers. According to the creators of ISS, the ISS stages are created for research only, they are not used or applied in the clinic. On the other hand, the terms "premanifest" and "manifest" have a clinical meaning, typically based on the diagnostic confidence level. The assignment of ISS0-1 to premanifest and ISS2-3 to manifest may create some non-trivial confusion, if not opposition, in some segments the HD community. The authors can keep their current terminology but will need to at least clarify to the reader that this assignment is speculative, does not fully match the clinically-based categories, and should not be confused with similarly named groups in the previous literature.

Comments on revised version.

The authors have moved to address many points from reviewers. The manuscript had indeed become more objective, transparent, and to the point. The amount of information and analyses is large, which perhaps is inevitable when new methods are being tested for the first time in a neurodegenerative disease.

Reviewer #2 (Public review):

Summary

This study uses functional MRI to evaluate visual contrast sensitivity across the visual field at the level of the visual cortex, testing the method as a proof of principle in a small group of normally sighted individuals, modelling both normal vision and simulated vision loss, as well as a patient with independently verified vision loss. The results suggest a promising technique to measure vision objectively across the visual field and overcomes the requirement for careful fixation which is often challenging in those with low vision or sight loss.

Strengths

• Objective measure of central vision: The proposed method may provide a more comprehensive and objective assessment of residual visual function in individuals with sight loss. This may be particularly useful for those with central visual field loss without the requirement of stable fixation or subjective motor responses.

• More sensitive measure: The use of slope to calculate contrast sensitivity across a range of contrasts within the brain is clever and likely more sensitive than single threshold measurements or standard clinical measures of visual acuity using letter charts. Standard supra-threshold (high contrast) tests are not ideal for capturing residual vision or partial vision loss.

• Good agreement with standard atlas: The Benson atlas provides a good estimate of visual field maps within V1 based on anatomical landmarks, and the authors take steps to refine this informed by cortical magnification and V1 surface area (brain size) for each individual participant. This could allow the technique to be generalised without the need to collect lengthy individual mapping data from every participant.

• Within-subject reproducibility: The measurements appear to be sensitive and reproducible, particularly in those with normal vision, and are consistent with known features of visual sensitivity differences in different parts of the visual field.

• Potential tool to measure visual field sensitivity in controls: Even if the proposed methods are not ideal for widespread clinical translation, they do offer an exciting tool to test hypotheses about visual field differences in healthy controls. For example, there seems to be an increase in sensitivity on either side of the simulated ring scotoma (Fig 6 - perhaps due to the release of lateral inhibition?). Reliability measures suggest that individual differences are consistent in healthy controls (although not tested statistically, perhaps due to the small sample size?). Whether they reflect behaviourally meaningful differences in visual field sensitivity could be tested in individuals by comparing them to behavioural measures across the visual field.

• Potential tool to test novel treatments: The proposed techniques could be used to test within-subject changes in visual function in environments that are equipped to measure and analyse fMRI data, including clinical trials aimed at determining the success of novel treatments. Preliminary testing in healthy controls with eye movements also suggests that the method is suitable for testing low vision patients with unstable fixation (e.g., nystagmus), and the authors have modelled the effects of varying amounts and types of eye movements on functional outcome measures.

Weaknesses

• Questionable sensitivity to differences in patients. The variability in heat maps across healthy control participants is somewhat surprising, and it is uncertain whether they represent actual visual sensitivity differences or an artifact of the measurement technique, e.g., due to signal-to-noise differences introduced by local variations in brain anatomy. Thus, it is uncertain whether the substantial variance across controls will allow for a sufficiently stable baseline to detect meaningful differences in individual patients. Also, as the authors rightly point out, Benson atlas does not model differences along meridians, so that upper/lower field differences might not be detectable. However, the authors acknowledge that this is a pilot study, and further testing a wider range of scotoma types in patients and simulated in controls will only improve the methods. Furthermore, the ability to capture visual field representations in human visual cortex is also likely to improve with computational advances, making the use of atlases more feasible, obviating the need for individualised population receptive field mapping.

• Potential for clinical translation. Although it is a sensitive measure, functional MRI is costly, is not available in all clinical settings, requires significant post-processing analyses, and may be contraindicated in some individuals due to safety (e.g., metallic implants) or other concerns (e.g., claustrophobia). These could present significant barriers to widespread clinical translation, if this were the ultimate goal of the study.

• Limited range of spatial frequencies. The spatial frequencies tested were still quite low (0.3 and 3cpd) compared to measures such a visual acuity. Extending the measurements to higher spatial frequencies could allow better characterization of central vision, although necessarily for peripheral vision. However, this may depend on the typical visual abilities of the patient population of interest.

Appraisal and Impact:

The authors used appropriate and robust methods to assess and model known features of visual sensitivity differences across the visual field in sighted controls. In addition, the assessment technique successfully captured sensitivity changes due to simulated and actual partial field loss but was also fairly resilient to eye movements and fixation instability, typical of patients with sight loss. Although currently providing a proof of principle, the method is likely to improve with further testing and increasing normative sample sizes, and as computational methods continue to advance visual field map predictions. Although it may not be adopted widely as a standard clinical assessment technique due to the expense and other obstacles, it would provide a valuable tool in assessing clinical populations, for example in the context of clinical trials to assess suitability for treatment interventions or monitor treatment outcomes.

Reviewer #2 (Public review):

Summary:

This manuscript addresses a clear and widely relevant question: how ongoing fluctuations in alertness during wakefulness relate to large scale patterns of coordinated brain activity. The authors combine high field magnetic resonance imaging with simultaneous pupil measurements, and they compute an edgewise measure of arousal-related coupling for every pair of regions. Their main contribution is to show that arousal-related coupling is low dimensional and organized into seven reproducible "connectivity communities", each with characteristic network pair compositions. A secondary contribution is the observation that these communities exhibit systematic but community-specific hemispheric asymmetries, including a striking left/right dissociation within the ventral attention network, where the left side participates broadly across communities while the right side forms a more cohesive, segregated arousal responsive module. A final contribution is cross-context generalization: the same organizational structure and lateralization signatures are largely preserved during naturalistic movie watching.

Strengths:

(1) The paper moves beyond state contrasts and quantifies arousal related modulation continuously within wakefulness, directly addressing a gap highlighted in the Introduction.

(2) The hemispheric asymmetry result is not framed as a crude global dominance effect; the authors explicitly test and argue that the key signal lies in structured spatial heterogeneity rather than mean shifts.

(3) The cross-paradigm replication in movie watching is a strong design choice and supports the claim that the organizational motifs are not limited to unconstrained rest.

(4) Arousal effects on BOLD signals and on pupil size can have different delays. The authors have now tested lagged relationships (for example shifting the pupil series forward and backward) to show that the main community structure and lateralization results are not sensitive to an arbitrary temporal alignment.

(5) Time resolved connectivity results are now shown to be robust to changes in parameters.

Reviewer #2 (Public review):

In their manuscript "TGF-β drives the conversion of conventional NK cells into uterine tissue-resident NK cells to support murine pregnancy", Yokoyama and colleagues investigate the role of Tgfbr2 expression by NK cells in the formation of tissue-resident uterine NK cells and subsequent importance in murine pregnancy. By transferring congenic splenic conventional NK cells into pregnant mice, they show conversion of circulating NK cells into uterine ivCD45 negative tissue-resident NK cells. When interfering with the formation of uterine trNK cells, spiral artery remodelling was impaired, fetal resorption rates were increased, and litter sizes were reduced.

Generally, this is a research topic of high interest, yet the manuscript is lacking detailed mechanistical insights and some questions remain open. At the current state, the data represent an interesting characterisation of the Tgfbr2-fl/fl Ncr1-Cre mice in pregnancy, but considering 1) the recent publication by the group (Ref#17) on the role of Eomes+ cNK cells during pregnancy, 2) the previously described role of Tgfbr2 and autocrine TGFb expression for uterine NK cell differentiation in virgin mice (also cited by the authors), and 3) the well-known relevance of uterine NK cells during pregnancy, additional experiments addressing the specific role of Tgfb during pregnancy would help to improve novelty and significance of the manuscript.

Comments on revised version:

In their revised version of the manuscript and their point-by-point response, the authors have very carefully addressed and discussed all of our concerns and suggestions.

Reviewer #2 (Public review):

Summary:

This study identified U2AF1/2 as a regulator of pre-mRNA splicing that either promotes or supresses the splicing of introns on different genes. The authors then focused on two genes PURPL and MALAT1 that U2AF1/2 can promote intron retention of specific introns, and characterized the biological implications of these introns regulated by U2AF1/2.

Strengths:

(1) The experiments in this manuscript are relatively rigorously designed and performed, often with validation checks such as verifying the knockout, verifying the treatment itself doesn't have an effect, etc.

(2) The experiments provided comprehensive support for the claims that these specific introns are important for the stability or nuclear localization of the RNA, as well as that U2AF1/2 suppresses the splicing of these introns.

(3) The writing of the manuscript is very clear and doesn't overstate the conclusions that can be drawn from the experiments.

Weaknesses:

I think one main weakness of this study is the lack of a deeper analysis of the mechanisms. Whether studying the mechanism is within the scope of this paper is probably debatable, but with the current experiment setup and data, I believe there are some analyses that can be relatively easily done to enhance the value or significance of this study. My detailed questions and suggestions are listed below:

(1) Line 194-195 and Figure 2A: How many RBPs are included in "other RBPs" in line 194? Does "other RBPs" only include PTBP1, PRPF8 and SRSF1 in Figure 2A, or do they include all the ~100 RBPs with HepG2 eCLIP data available on ENCODE? If U2AF1/2 have the highest occupancy around the intron 2 region among the ~100 RBPs, it would be nice to visualize it.

(2) Figure 2A and 2B: Why didn't U2AF2 show interaction with exon 2 and 3 in RNA-IP but showed enrichment over exon 2 and exon 3 regions in the eCLIP data?

(3) Figure 3C - 3F: Maybe I misinterpreted the experiments, but to my understanding, these experiments showed that the exogenous PURPL with intron 2 promoted cell proliferation compared to when the exogenous PURPL wasn't induced, but didn't compare to the effect of the same amount of PURPL with intron 2 removed. Wouldn't it be clearer to compare the effects of exogenous PURPL with intron 2 and exogenous PURPL without intron 2 to pinpoint whether the effect is related to intron 2? Without an intron 2 specific experiment, these current experiments don't seem to provide much added value than "PURPL promotes cell proliferation".

(4) It's not very clear what proportion of these introns are retained in the endogenous PURPL and MALAT1 in various tissues, cell types and conditions. I think it will be valuable to provide this background (either from previous research, public database or data from this study).

(5) Since U2AF1/2 have a wide range of targets as demonstrated by Figure 4A, I think it would be valuable to have some experiments that directly disrupt the interaction between U2AF1/2 and PURPL and MALAT1 and test the effect on splicing outcomes, such as by mutating the sequence that U2AF1/2 bind to. The section on the weak py-tract of PURPL touched upon this topic but focused more on how the weak py-tract causes the intron 2 retention in the background rather than how U2AF1/2 binding and action were affected by sequence mutations. I think experiments on disrupting the direct binding between U2AF1/2 on targets can provide valuable mechanistic insights.

(6) Across all the target genes of U2AF1/2, it might be feasible to do some systematic analysis to find what correlates with whether U2AF1/2 have a promoting or suppressing effect on intron splicing. For example, do genes with decreased IR after U2AF2 depletion systematically have a weak py-tract compared to genes with increased IR? This dataset can potentially provide many hypotheses for understanding the dual role of U2AF1/2.

Reviewer #2 (Public review):

The authors describe the first deep neurological characterization of WAC mutation in two vertebrate species (zebrafish and mouse). They examine these at various levels, guided by the work in humans that has associated a heterozygous WAC mutation with DeSantos Shinawi Syndrome (DESSH). Therefore, they investigate the animals for a variety of phenotypes, following a template for what is seen when characterizing a new mouse/fish model of a developmental disability gene. Investigations include analysis of skull and jaw for abnormalities(both species), MRI of brain structure(in mice), electrophysiology(mice), assessment of signaling pathways (by Western blot, in mice), cell counts (both, more in mice), transcriptomics (mice), and behavior (both).

Generally, this describes an important first characterization of the consequences of the mutation. Most of the studies appear well-conducted and reasonably powered, thus solid or convincing.

Reviewer #2 (Public review):

Summary:

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

Strengths:

(1) Very detailed analysis of experiments to systematically characterize and quantify differences between mammary and bronchial epithelia.

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

Reviewer #2 (Public review):

Summary:

Gaurav et al. investigate residue-level interactions within the MUT-16 FFR condensate using all-atom molecular dynamics simulations. The authors first argue, based on sequence analysis, that MUT-16 FFR is more representative than the widely studied FUS LCD. They then characterize the UCST phase behavior of MUT-16 FFR experimentally, followed by a detailed analysis of residue-level contact frequencies and lifetimes. In addition, the manuscript examines ion-residue interactions and water-mediated interactions. Overall, this work provides a comprehensive view of the dynamic interactions within the MUT-16 FFR condensate.

Strengths:

Large-scale all-atom molecular dynamics simulations have been performed to investigate dynamical interactions within condensates. The analysis is comprehensive and rigorous, and the claims are strongly justified by the data.

Weaknesses:

The large amount of detail in the results section sometimes makes it difficult to identify the central take-home messages. I encourage the authors to more clearly highlight the principal findings and the physical insights that may generalize to other condensate-forming systems. The authors may also consider streamlining parts of the Results section to improve focus and readability.

Reviewer #2 (Public review):

Overall, this is a solid manuscript that delivers an important community resource. The execution is relatively simple, but the value is real, the work is rigorously performed, and the open dissemination through Zenodo, the F1000Research YCharOS Gateway and OGA is well executed. The effort invested in generating the knockout lines for validation experiments is a clear strength of the study. I have a number of comments that I think would strengthen the resource and the conclusions drawn from it.

Below, I list specific points.

(1) The rationale for the selection of these 33 genes is insufficient. The authors lean on the Nijs & Van Damme classification and on PubMed entry counts, but the number of PubMed entries is not a meaningful criterion for what constitutes an important ALS protein - some of the most disease-relevant genes are precisely those with fewer publications, while heavily cited genes such as CAV1 carry weak ALS-specific evidence. The authors should provide a more transparent and biologically motivated rationale for inclusion and exclusion (ClinGen evidence tier, replicated GWAS signals, large meta-analyses, ALSoD) and explain why specific risk genes outside this list were not part of ALS-RAP.

(2) "107 of 231 (46%) demonstrated specific target staining in IF." The criteria used to define "specific target staining" at the IF level are not stated. From the Galectin-1 example, the mosaic WT/KO strategy provides a binary readout, but for proteins with low expression, weak punctate staining or unusual subcellular distributions, a single threshold is unlikely to capture specificity uniformly across 231 antibodies.

(3) Several claims in the manuscript depend on differential protein abundance across cell types. As presented, these claims are supported by qualitative Western blot images only. They should be substantiated by quantification across multiple biological replicates.

(4) This manuscript represents a unique opportunity to address antibody recognition of splicing variants, which is something of of considerable value to the community. For each target, the predicted isoforms in Ensembl could be cross-referenced against the observed bands, and the pattern of bands compared across cell types could be informative about which isoforms each antibody captures. This would convert ambiguous "extra bands" into useful biological information and would substantially increase the value of the resource. I strongly encourage the authors to include this analysis.

(5) The iPSC-derived microglia receive a comprehensive QC panel (IBA1/PU.1 IF, CD45/CD11b flow, qRT-PCR for nine canonical markers; Figure S4), which allows the reader to assess culture purity. The other iPSC-derived lineages - motor neurons, dopaminergic neurons, oligodendrocytes and astrocytes - are validated by a single marker each in WB (Figure S3) without purity quantification. Given that several conclusions of the manuscript rest on the cell-type-specific detection of ALS-associated proteins, equivalent quality control should be performed for the other lineages so that the reader can evaluate the purity of each preparation.

(6) The robustness of the resource would be substantially increased by validating at least a subset of the targets in a second iPSC background, in at least some of the cell types analysed.

(7) The newly developed SGC scFv antibodies are arguably the most novel reagent contribution of this manuscript, yet they receive a single sentence in the body of the paper. A more thorough description is warranted.

(8) Accessibility of the resource through Zenodo is not straightforward - the reader currently has to navigate to individual antibody characterization reports one by one to extract recommendations for a given target. While the use of an established public repository is important for permanence, a dedicated ALS-RAP website with an interactive, searchable interface - filterable by target, application, host species and clonality - would meaningfully improve uptake. The relationship between such a portal and the existing OGA platform should also be clarified.

Reviewer #2 (Public review):

While the importance of asparagine in the differentiation and activation of CD8 T cells has been previously reported, its role in CD4 T cells remained unclear. Using culture media containing specific amino acids, the authors demonstrated that extracellular asparagine promotes CD4 T cell proliferation. Consistent with this, depletion of extracellular asparagine using PEG-AsnASE suppressed CD4 T cell activation. Proteomic analysis focusing on asparagine content revealed that, during the early phase of T cell activation, most asparagine incorporated into proteins is derived from extracellular sources. The authors further confirmed the importance of extracellular asparagine in vivo, demonstrating improved EAE pathology.

While the data are well organized and convincing, the mechanism by which asparagine deficiency leads to altered T cell differentiation remains unclear. It is also necessary to investigate the transporters involved in asparagine uptake. In particular, elucidating whether different T cell subsets utilize the same or distinct transport mechanisms would provide important insight into the immunoregulatory role of asparagine.

Comments on revised version:

The authors have addressed the previous concerns, and the manuscript has been significantly improved.

Reviewer #2 (Public review):

This study by Jonker et al., examines how the metabolic adaptations to the microenvironment by pancreatic ductal adenocarcinomas (PDAC) present vulnerabilities that could be used for therapeutic purposes. The evidence supporting the claims of the authors is mostly solid, and the multiplicity of models used, as well as the combination of in vitro and in vivo work are appreciated, but some conclusions would benefit from additional substantiation. This work would be of interest to biologists working on the impact of microenvironment and metabolism in cancer, and especially those investigating pancreatic cancer.

In this study, the authors use mostly "doublings per day" as an indicator of cell death, notably for figures 4 to 6. However, proliferative arrest (or a decrease in the proliferative rate) is not necessarily synonymous with cell death. It might be nice to complement these experiments with a true measure of cell death (e.g. PI uptake).

Reviewer #2 (Public review):

Summary:

This manuscript presents a comparative analysis of optomotor behavior in zebrafish and medaka larvae. Using multiple behavioral paradigms, the authors argue that the two species differ in both the spatial and temporal integration of visual motion. They further decompose turning behavior into large- and small-turn components and use a simple mechanistic model to capture several of the main response features. Overall, the study addresses an interesting question, and the comparative framework gives the work a clear conceptual appeal.

Strengths:

A major strength of the manuscript is the breadth of the behavioral analysis. The authors use several stimulus paradigms to probe spatial extent, temporal persistence, and response dynamics, which makes the cross-species comparison richer and more informative than a single-assay study. The decomposition into large and small turn components is also a useful feature of the work, as it provides a more structured account of where the species differences may arise. The modeling further helps organize the results and offers a useful framework for interpreting the behavioral differences.

Weaknesses:

The main limitations are in presentation and clarity rather than in the overall motivation or approach. In several places, it is difficult to determine exactly how some quantities are summarized statistically, and some figures and legends would benefit from clearer explanations. In addition, a few of the more specific interpretive claims would be strengthened by more explicit statistical framing and slightly clearer presentation. These issues appear addressable and do not detract from the overall interest of the study.

Reviewer #2 (Public review):

Summary:

This manuscript, "Modality-Specific and Amodal Language Processing by Single Neurons," presents an intracranial electrophysiology study investigating how language is represented in the human brain across spoken and written modalities. The authors analyze activity from over one thousand single neurons and local field potentials recorded in twenty-one neurosurgical patients while participants read and listened to sentences. Using encoding models based on temporal receptive fields, they examine whether neural responses track modality-specific features, such as phonological and orthographic information, as well as higher-level linguistic features. The results are interpreted as evidence for a dissociation between modality-specific processing in sensory regions and modality-independent ("amodal") representations in temporal and frontal cortices, supporting a two-stage model of language processing.

Strengths:

This study uses a rare and valuable dataset, combining single-neuron recordings with broader field potential measures in human participants. The large-scale recording, in terms of both neuron count and anatomical coverage across multiple regions and individuals, represents a significant technical achievement for intracranial research.

The use of encoding models to relate neural activity to multiple levels of linguistic representation is methodologically rigorous and provides a unified framework to compare phonological, orthographic, and higher-level features. This approach allows the authors to systematically test how different aspects of language are represented across neurons and regions.

Another key strength is the attempt to directly link concepts from Linguistics to neural data. By framing the results in terms of modality-specific versus amodal representations, the study engages with longstanding theoretical questions and offers a potential bridge between linguistic theory and systems neuroscience.

The manuscript is also very well written, and the data are presented clearly and effectively. The inclusion of raw data and raster plots is particularly valuable, as it allows readers to directly assess the neural responses and strengthens the transparency of the analyses.

Weaknesses:

Despite these strengths, the central claims of the paper are not fully supported by the analyses presented, and several key issues limit the strength of the conclusions.

A primary concern is the lack of clear reporting and statistical characterization of the proportion of neurons that significantly encode the tested linguistic features. While the paper presents illustrative examples and regional patterns of encoding, it does not systematically quantify how many neurons exhibit significant effects across conditions, nor does it provide formal statistical comparisons of these proportions across brain regions or feature types. As a result, it is difficult to determine whether the reported dissociations reflect robust population-level phenomena or relatively sparse subsets of neurons identified through model fitting. Figure 2H offers a visual depiction of the distribution of Brain-Score (a measure of model evaluation) across the fusiform gyrus and superior temporal gyrus, but it falls short of providing formal statistical testing or quantitative summaries, limiting its interpretability in supporting the authors' claims. Given that the authors employ temporal receptive field (TRF) analyses, the framework naturally allows for straightforward quantification of the proportion of neurons that significantly encode any linguistic features in the model, which could be reported by region as well as by stimulus condition (auditory vs. visual). Including such analyses would further strengthen the population-level interpretation of the results.

Relatedly, the interpretation of "amodal" neurons is not sufficiently substantiated. The classification of neurons as modality-independent relies on encoding model performance across conditions, but the statistical criteria for establishing cross-modal generalization are not always clearly defined or rigorously tested. Without explicit comparisons (e.g., testing whether the same neurons significantly encode features in both modalities above chance, and whether this exceeds what would be expected under appropriate null models), the claim of modality-independent representation remains somewhat underdetermined.

More generally, the reliance on encoding models introduces some interpretational ambiguity. Although the observed dissociation between fusiform and superior temporal regions is consistent with orthographic and phonological processing, respectively, the feature spaces used in the models are partially linked to lower-level sensory properties (e.g., visual form and acoustic features). The authors' single-neuron results suggest these effects reflect genuine linguistic selectivity, but the findings do not uniquely distinguish between linguistic and perceptual explanations. While fully disentangling these factors may be beyond the scope of the current study, the manuscript could benefit from a brief discussion acknowledging these correlations or clarifying how lower-level sensory contributions were considered.

Another limitation is that the proposed two-stage model of language processing is not directly tested against competing hypotheses. While the dissociation between modality-specific and amodal representations is consistent with this model, the authors note that higher-level features, such as syntax, may be encoded in a distributed or overlapping manner. These possibilities are not systematically tested, so the conclusions risk overinterpreting correlational patterns as evidence for a specific processing hierarchy. A more explicit discussion or quantitative consideration of these alternative accounts would strengthen the interpretation, while still allowing the two-stage model to be presented as a plausible framework.

Reviewer #2 (Public Review):

This paper is an interesting conceptual work where certain hotspot areas were found to induce unique gait patterns. These patterns differed from a classic change in speed or gait pattern from a walk to a gallop. From this, a hypothesis was formed that these areas could be important for possible alternative walking patterns seen, for example, during pathologies such as Parkinson's disease or perhaps related to stalking behaviors.

While I liked the work and found it interesting, it remains descriptive in that the actual behaviors observed can't be causally related to a particular behavior such as stalking or shuffling. If the necessity or sufficiency of this region was related to a specific hunting behavior, for example, its interest to the field would be greater.

Nevertheless, this paper does contribute to growing evidence that specific behaviors can be triggered by specific neuronal populations within the brainstem.

Reviewer #2 (Public review):

This manuscript aims to elucidate the mechanistic basis for the long-standing observation that DNA methylation and the histone variant H2A.Z occupy mutually exclusive genomic regions. The authors test two hypotheses: (i) that DNA methylation intrinsically destabilizes H2A.Z nucleosomes, thereby preventing H2A.Z retention, and (ii) that DNA methylation suppresses H2A.Z deposition by ATP-dependent chromatin-remodelling complexes. The revised manuscript addresses a number of previous concerns, and the manuscript has therefore improved accordingly. However, several limitations remain.

Comments on revisions:

The authors have addressed a number of my previous concerns, and the manuscript has improved accordingly. However, several limitations remain that, in my view, constrain the strength of the conclusions. In particular, the absence of a direct comparison with a canonical nucleosome assembled on the same DNA template. This control is essential to determine whether the observed effects are specific to H2A.Z or reflect more general properties of methylated DNA-nucleosome interactions. Notably, even within the authors' own data, there is a trend suggesting that methylated canonical H2A nucleosomes may also exhibit increased accessibility. Although this does not reach statistical significance, the authors themselves argue that subtle differences can be biologically meaningful; it is therefore plausible that extended digestion conditions (e.g., longer HinfI exposure) could reveal a significant effect. Unless a direct structural comparison with a canonical nucleosome is performed, the possibility that the reported phenomenon is not specific to H2A.Z remains. This is compounded by the reliance on a single restriction enzyme-based assay, which represents a limited experimental approach. Such an approach is insufficient to unequivocally support the central claim that DNA methylation increases accessibility of H2A.Z-containing nucleosomes. Additional orthogonal assays would be required to substantiate this conclusion. With respect to the cryo-EM analysis of methylated and unmethylated 601L H2A.Z nucleosomes, and in general, the authors still do not adequately consider the positional context of CpG methylation. Extensive literature demonstrates that the effects of DNA methylation on canonical nucleosome structure and stability are highly position-dependent. Without accounting for the location of methylated CpGs relative to key DNA-histone contact sites, the structural data remain difficult to interpret mechanistically. Overall, while the manuscript has improved, it remains a relatively limited study that draws broad mechanistic conclusions from a minimal experimental data.

Reviewer #2 (Public review):

Summary:

The authors investigated the effects of a low-protein diet (LPD) and a high sugar- and fat-rich diet (Western diet, WD) on paternal metabolic and reproductive parameters and feto-placental development and gene expression. They did not observe significant effects on fertility; however, they reported gut microbiota dysbiosis, alterations in testicular morphology, and severe detrimental effects on spermatogenesis. In addition, they examined whether the adverse effects of these diets could be prevented by supplementation with methyl donors. Although LPD and WD showed limited negative effects on paternal reproductive health (with no impairment of reproductive success), the consequences on fetal and placental development were evident and, as reported in many previous studies, were sex-dependent.

Strengths:

This study is of high quality and addresses a research question of great global relevance, particularly in light of the growing concern regarding the exponential increase in metabolic disorders, such as obesity and diabetes, worldwide. The work highlights the importance of a balanced paternal diet in regulating the expression of metabolic genes in the offspring at both fetal and placental levels. The identification of genes involved in metabolic pathways that may influence offspring health after birth is highly valuable, strengthening the manuscript and emphasizing the need to further investigate long-term outcomes in adult offspring.

The histological analyses performed on paternal testes clearly demonstrate diet-induced damage. Moreover, although placental morphometric analyses and detailed histological assessments of the different placental zones did not reveal significant differences between groups, their inclusion is important. These results indicate that even in the absence of overt placental phenotypic changes, placental function may still be altered, with potential consequences for fetal programming.

Comments on revised version:

The authors have adequately addressed all my previous comments.

Reviewer #2 (Public review):

Summary:

Muscle hypertrophy is a major regulator of human health and performance. Here, van der Pilj and colleagues assess the role of the giant elastic protein, titin, in regulating the longitudinal hypertrophy of diaphragm muscles following denervation. Interestingly, the authors find an early hypertrophic response, with 30% new serial sarcomeres added within 6 days, followed by subsequent muscle atrophy. Using RBM20 mutant mice, which express a more compliant titin, the authors discovered that this longitudinal hypertrophy is mediated via titin mechanosensing. Through an omics approach, it is suggested that the Muscle ankyrin proteins may regulate this approach. Genetic ablation of MARPs 1-3 blocks the hypertrophic response, although single knockouts are more variable, suggesting extensive complementation between these titin binding proteins. Finally, it is found through the administration of rapamycin that the mTOR signalling pathway plays a role in longitudinal hypertrophic growth.

Strengths:

This paper is well written and uses an impressive suite of genetic mouse models to address this interesting question of what drives longitudinal muscle growth.

Weaknesses:

While the findings are of interest, they lack sufficient mechanistic detail in the current state to separate cross-sectional versus longitudinal hypertrophy. The authors have excellent tools such as the RBM20 model to functionally dissect mTOR signalling to these processes. It is also unclear if this process is unique to the diaphragm or is conserved across other muscle groups during eccentric contractions.

Reviewer #2 (Public review):

Summary:

This work identifies a previously unknown way that red light can slow ageing. The authors show that red light lowers the level of a protein called SIRT4 in skin cells. Reducing SIRT4 boosts fatty acid use and increases a type of histone modification that keeps genes active. These changes help cells clear away signs of ageing, reduce inflammation, and restore normal metabolism. The findings open the possibility of developing new treatments that target SIRT4 to reverse age‑related decline.

Strengths:

The evidence is solid because the authors use several complementary methods. They test red light in both cultured cells and naturally aged mice, and they confirm the key role of SIRT4 by silencing its gene. Measurements of metabolism, protein changes, and ageing markers all point in the same direction. However, the exact way red light lowers SIRT4 levels is not fully explained, which leaves a minor gap. Overall, the conclusions are well supported and convincing.

Weaknesses:

The paper does not evolve to use the mechanistic discoveries of the manuscript to help our community to identify the mechanism of photobiomodulation, which is not known so far.

I would like to draw attention to a recently published paper by Herrera et al. (FEBS Letters 2025, doi:10.1002/1873-3468.70195), which shows that red light (660 nm) stimulates mitochondrial fatty acid oxidation in keratinocytes via AMPK‑dependent phosphorylation of ACC, without altering expression of electron transport chain complexes. I believe this paper is highly complementary to the current study.

Herrera et al. demonstrate that red light increases basal, ATP‑linked, and maximal oxygen consumption rates in keratinocytes specifically through enhanced fatty acid oxidation (inhibited by etomoxir). This independently validates the central finding of the current manuscript, i.e., red light boosts lipid metabolism, strengthening the robustness of this concept.

While the current manuscript focuses on the SIRT4‑MCD axis, Herrera et al. identify AMPK phosphorylation and ACC inhibition as key effectors. The authors can integrate and expand their discussion, since SIRT4 downregulation may converge on AMPK activation, or they may represent parallel, reinforcing mechanisms. This would enrich the mechanistic model and open new hypotheses.

The mechanism of photobiomodulation: Herrera et al. explicitly challenge the prevailing paradigm that red light acts solely via cytochrome c oxidase (by showing long‑lasting effects, unchanged OXPHOS protein levels, and no difference in permeabilised cells). The current finding (red light acts through SIRT4 downregulation, i.e., not direct enzymatic activation) aligns perfectly with Herrera´s critique.

Long‑term metabolic effects - Herrera et al. show that a single red light exposure elevates oxygen consumption for up to 2 days. The current study focuses on changes at 12‑24 h. Their data extend the time window and suggest that the metabolic reprogramming you describe may persist longer than currently discussed, which is clinically relevant.

Discussing Herrera et al.'s results would not only acknowledge independent, corroborating evidence but would also allow the authors to position their SIRT4‑centric mechanism within a broader, emerging understanding of red‑light photobiomodulation.

Reviewer #2 (Public review):

The authors investigate the impact of the deletion of the small GTPase regulator ARHGEF6 on the development and physiology of interneurons. Using public databases, they first show that ARHGEF6 is enriched in interneurons or in areas that give rise to them, both in development and adulthood, in humans and mice. Using a complete KO mouse previously reported, and using a GAD67-GFP reporter mice line, they show that in the adult mouse cortex and hippocampus, there is a notorious reduction GFP+ cells. These mice show increased apoptotic cells at different timepoints and areas of the brain during development. In the developing cortex of ARHGEF6-KO mice, there are fewer IN in all layers of the developing cortex, and cells present processes not correctly oriented. IN from the hippocampus in culture show reduced excitability and impaired neurite branching. The authors then established isogenic hiPSCs lines to study ARHGEF6 deletion in human cells and differentiated ventral forebrain neurons, to find interneuron-related and non-related phenotypes. Most importantly, human interneurons grown in organoids show reduced branching and altered growth cone morphology. The authors claim that the novel interneuron phenotypes found in these models can explain, in part, the human intellectual disabilities associated with mutations in this protein. The study is well conducted and opens new avenues of research not only for the role of small GTPases regulation in early nervous system development, but also for how interneuron deficiencies impact a wider range of intellectual disability syndromes found in humans.

However, most conclusions of the present version would be strengthened after considering the following comments:

Major comments

(1) The reported biological processes evaluated at different developmental stages may be directly or indirectly related to ARHGEF6 function itself. As a model of a hereditary disease, full organism gene deletion is valid, since the human patients suffer from that condition as well. However, to investigate the roles of a protein, complete deletions may not be very accurate since they can give rise to phenotypes that are only indirectly related to the protein function itself. Most conclusions of the present manuscript should either be discussed in this regard or add evidence for a direct role of the protein. One such evidence is typically performed with acute knockdowns in culture, or in developing brains by in utero electroporation. For example, Figure 1C shows that the principal excitatory neurons in the hippocampus do not express ARHGEF6. However, most electrophysiological and behavioral evidence of defects in ARHGEF6-KO mice arises from evaluating these cells (Remakers et al., 2012). I am not suggesting that either previous or actual evidence is wrong. But I believe readers would benefit from a clear distinction (or add caution notes) between a functional consequence of the deletion (that can be months away and in other cells than the actual molecular defect) and a true cell biological function of the protein under study. In favor of the authors, this is a concern with most conclusions derived from KO organisms.

(2) Figure 1E-G H I. All conclusions are made with a GAD67-GFP reporter, which is a very powerful and reliable tool for large-scale screening. All the conclusions of the paper would be strengthened if some immunohistochemical staining in the same areas of specific markers for interneurons would be added as supporting complementary evidence.

(3) Cell death in development: It is surprising that the high amount of TUNEL staining during development does not translate into gross histological changes in the adult brain (studied elsewhere). Can authors discuss possible explanations?

(4) Section 4 (Figures 2F-J) - The authors present this staining as an analysis of migration. Normally, migration studies are performed with a "pulse-chase" paradigm, where a single cohort is labeled and then followed over time (normally by in utero electroporation of a fluorescent protein). Tissue is then fixed at different time points, and migration can be followed. On the contrary, the evidence is from a single point, in an experimental setting in which all Gad67 IN are stained, and hence, one cannot imply a defect in migration. The differences between WT and ARHGEF6-KO are obvious and interesting; it is just that they cannot be solely attributed to a problem in migration.

Also, a true phenotype of migration in the current setting should have found that the cells that failed to migrate are accumulated in deeper layers. My impression is that the changes in IN per layer are easier explained by total cell number, rather than migration. Perhaps evaluating earlier timepoints could clarify this.

(5) It is known that ARHGEF6 deletion produces severe F-actin phenotypes in neurons. Have the authors confirmed in their hippocampal cultures GAD67 cells ALSO have these phenotypes? Stress fibers in somas, growth cones, and actin patches along neurites.

(6) Section 4. The authors present data for deficient migration of the GFP-labeled interneurons. Is it possible to assess, in the same sections, whether other cell types are also affected? Although the hypothesis that ARHGEF6 deletion will have an impact in IN is well rooted in expression data, by assessing other cell types, one can even include a positive control or evidence for a cell-autonomous phenotype.

(7) ARHGEDF6 deletion has an important impact on organoid development (size, shape, etc). Have the authors analysed whether these organoids produced fewer interneurons?

(8) In assembloids, the differences in migration parameters are very small between WT and ARHGEF6-KO, which reinforces that perhaps what is observed in the different layers of cortex during mouse development is likely not entirely due to migration, as concluded.

(9) To properly weigh the present evidence -interneuron deficits- using the ARHGEF6-KO model, authors should include a deeper discussion in light of much work that has been done using these mice. How does the finding of a diminished IN population in the brain of these mice explain the large amount of electrophysiological and behavioral evidence produced before with these animals? Perhaps the most important work to discuss these aspects is the initial ARHGEF6-KO report by Ramakers and colleagues (2012), but there are others.

Minor comments

(1) Figure 1A. It looks clear that the GE shows the highest expression of ARHGEF6; however, the reader needs the reference levels where the log2 expression is calculated. What are the reference levels?

(2) Have the authors compared the number of GAD67-eGFP cells in the hippocampal cultures between WT and ARHGEF6-KO mice?

(3) Section 3, as a caution note, authors should mention that it is not possible to know from the evidence provided which cells are dying.

(4) In the dorsal-ventral assembloids, it is expected that the ventral organoid would contain lots of GFP expression compared to the dorsal, but in the image shown (Figure 5A) both parts of the assembloid seem to have the same amount and distribution of GFP. How is that possible?

Reviewer #2 (Public review):

Summary:

This manuscript describes an investigation into the effect of diet and exercise interventions in WT and transgenic (male and female) mice who are exposed to either a high-fat or a low-fat diet. The outcome variables include MRI volume and brain morphology, as well as memory performance. First, this study measured the impact of genotype (WT vs 3xTgAD mice), then examined the impact of a high-fat or low-fat diet in each group, and finally examined the impact of a low-fat diet, exercise, or a combined low-fat diet and exercise intervention. This is an important study as it allows us to better understand how changes to lifestyle can affect neurocognitive function and potentially change a person's AD risk.

Strengths:

(1) The study uses a well-controlled longitudinal design, allowing the authors to track how diet and exercise interventions influence brain and behaviour over time.

(2) The integration of multiple levels of analysis (brain imaging, behaviour, and multivariate modelling) provides a rich and comprehensive assessment of intervention effects.

(3) The inclusion of both genotype and sex as key variables strengthens the relevance and interpretability of the findings, given known differences in risk and response across groups.

Weaknesses:

There are a lot of analyses in this paper, and I had a little bit of trouble distilling the major take-home messages. For example, I was left wondering:

(1) If the effect of genotype and the effect of the high-fat diet were consistent in the current study compared to the authors' previous work (e.g. Rollins et al., 2019). A more direct report on the consistency of these findings (maybe even an overlap map, if possible) would benefit the reader.

(2) How consistent/different are the volumetric and morphometric (DBM) results from each other? Especially in the regions of interest (hippocampus and cerebellum), are increases in volumes always related to "expansion" of a given region using DBM? Some of the similarities are reported in the results, but for transparency, a side-by-side table comparing the results across techniques for each effect of interest might provide more clarity.

(3) I was interested in the Partial Least Squares approach that the authors used to investigate how patterns of brain measures relate to the behavioral variables. Because they are presented mostly in the supplement (except for Figure 6E), it's difficult to map the LVs described onto the univariate contrasts in Figures 2-5. In general, greater clarity is needed regarding how the PLS-derived latent variables relate to the univariate findings, and whether the emphasis on LV3 reflects a principled selection or post hoc interpretation.

(4) If I understand the results correctly, there were only modest differences in behavior reported, and the patterns were somewhat inconsistent across sex and genotype. In fact, the authors report that the high-fat diet alone did not impair memory on the Morris Water maze (line 323). The discrepancy between robust neuroanatomical effects and relatively modest behavioural changes raises important questions about the functional significance of the observed structural alterations.

(5) On line 507, the authors state, "Notably, 3xTgAD mice already show smaller brain volumes at baseline, which may constrain the detectable impact of the diet." Is this true for the entire brain or just the hippocampus and cerebellum? Would a global reduction in brain volume due to the 3xTgAD AD model affect the interpretation of the intervention effects?

Reviewer #2 (Public review):

Summary:

The authors develop a miniaturized MR1 construct (SMART-MR1) in which the α1/α2 platform is stabilized by a synthetic domain, and show that it can bind ligands, engage a cognate TCR, and recapitulate native-like recognition by cryo-EM.

Strengths:

The work is well-written, technically strong and carefully executed. The authors combine biochemical, biophysical and structural approaches, including ITC, NMR and cryo-EM, to show that SMART-MR1 behaves in a manner closely resembling native MR1. The reduction in size and the demonstration of solution NMR are clear practical advantages for certain types of mechanistic studies.

Weaknesses:

The main limitation is that the manuscript does not clearly establish a practical advantage over existing MR1 formats, such as single-chain MR1-β2M or previously described stabilized constructs. The comparison is largely framed against native MR1, which risks overstating the problem, and on the basis of the data presented, it is unlikely that other researchers will adopt this system. In addition, the choice of the A-F7 TCR as a validation reagent may overestimate the generality of the approach, as this receptor is known to exhibit relatively broad ligand tolerance, including recognition of MR1 presenting vitamin B6 metabolites (PDB 9CGR) and structurally diverse synthetic ligands. The extent to which SMART-MR1 supports recognition by a broader range of MR1-restricted TCRs is not addressed.

Reviewer #2 (Public review):

Summary:

The authors aimed to evaluate whether integrating genomic (SNP) and transcriptomic information with machine learning can improve phenotypic prediction of polygenic traits across environments. The manuscript explored not only the predictability across models and predictor feature sets, but also attempted to identify meaningful genes and interactions underlying trait variation.

Strengths:

The main strength of the manuscript is its integration of SNP, transcriptomic, and phenotype datasets for 426 sorghum genotypes between Texas and Michigan. It provides a systematic comparison of predictor types (SNP versus transcriptomic abundance) and model strategies to integrate them.

Weaknesses:

(1) Experimental Design

The experimental design raises several concerns that should be clarified before strong biological conclusions are drawn from the transcriptomic analyses.

First, the transcriptomic sampling is not well aligned with the developmental stages most relevant to the phenotypes being modeled. Leaf tissue was collected at a single time point in each environment, whereas traits such as flowering time, biomass, tiller count, and panicle height arise from developmental processes occurring over extended and potentially distinct temporal windows. Consequently, the measured expression profiles are likely to reflect physiological states specific to the sampling dates (May 5-6 in Texas and June 22-24 in Michigan) rather than the regulatory processes underlying the target phenotypes.

Second, the phrase "haphazardly randomized" is questionable for a field experiment. It is unclear whether the design included formal randomization, blocking, row/column structure, or spatial correction. Without explicit accounting for spatial field heterogeneity, environmental variation within sites may confound genotype and transcriptomic effects.

Third, the Methods do not clearly describe biological replication for RNA-seq. If each genotype-by-environment combination were represented by a single transcriptomic sample, then within-genotype expression variance cannot be estimated. This is important because transcript abundance is highly sensitive to microenvironment, sampling time, tissue status, developmental stage, and technical variation. The absence of replication significantly weakens confidence in gene-level feature importance and gene-gene interaction claims.

Four, the analysis of expression differences across environments is based on a simple subtraction (TX - MI) followed by correlation with genetic similarity. This approach is not standard in transcriptomic analysis and does not account for variability, replication, or statistical uncertainty. Conventional methods for assessing differential expression and genotype-by-environment interactions rely on model-based frameworks that explicitly estimate variance components and test for interaction effects. Without such modeling, the observed expression differences may reflect noise or confounding factors rather than genotype-driven responses.

(2) SHAP contribution values

Although SHAP is a well-established framework for decomposing model predictions into feature-level contributions, its use in this manuscript raises several concerns regarding interpretation, statistical validity, and biological inference.

First, SHAP values quantify the contribution of features within the fitted model, conditional on the joint distribution of inputs and the model structure. They do not represent causal effects or direct biological importance. There is a difference where SHAP values are often in log-odds and the regression model uses absolute units. Without a fair evaluation of model fit, the interpretation of SHAP values needs to take a cautious step because a model could fit poorly when a feature shows very high SHAP values.

In genomic data, where features are highly correlated due to linkage disequilibrium and co-expression, SHAP values can distribute contribution values across correlated variables in ways that are not uniquely identifiable. As a result, features highlighted as "important" may reflect correlation structure rather than true functional relevance.

This correlative structure can be exacerbated in this manuscript because of the use of TPM-normalized transcript abundances as predictor variables without biological replicates. Assume the estimates of transcript abundances are robust, TPM values are compositional, with a constant-sum constraint that creates dependencies among all genes that induce negative correlations. This issue is particularly relevant for the interpretation of gene importance and interaction effects, where correlated predictors can lead to unstable and non-unique attributions. This biological interpretation of transcript-based features remains uncertain.

(3) Result interpretation

For example, in page 11, "plasticity SNP- and transcriptomic-based models generally outperformed single-environment models for traits with low cross-environment correlation, such as green-up (Fig. 2c, r = -0.13, p < 8.3 × 10⁻³) and tiller count (Fig. 2f, r = -0.08, p = 0.1) (Supplementary Fig. S1).", is too broad. For green-up, the Diff model appears much better than MI, but not clearly better than TX.

And, same page 11, "...Diffexp was more predictive than SNPs for trait plasticity in biomass, flowering time, and tiller count..." only holds true for biomass, not flowering time, or tiller count.

The aspect of "complementary information" between SNP and transcriptomic models in page 12 is stronger than what is supported by Figure 2. Figure 2 shows different predictive performance, but it does not by itself demonstrate complementarity. Establishing complementarity requires evidence that combining SNP+T improves prediction consistently or captures distinct, non-overlapping signals. Yet the preceding section says SNP+T outperformed either single data type in only 15% of cases, with modest gains. This is confusing. Also, there was not G+T in Figure 2; it is SNP+T.

Reviewer #2 (Public review):

Summary:

This review is valuable in principle because circadian rhythms in zebrafish are unexplored and therefore this degree is valuable in principle. There are a number of significant weaknesses that should be addressed for it to have an impact. First, while the review covers a broad range of topics in chronobiology, it does not put them in context. Placing zebrafish work in the context of other model organisms that are better understood and other fish species would broaden the appeal. The review could also expand to a discussion of sleep, where the understanding in zebrafish is much more advanced. Critically, providing a novel framework, identifying new areas of opportunity and limitations of the system would expand the interest to non-zebrafish research groups. In addition, there are a number of misstatements/mis-citations that are critical to correct. Therefore, I find this review potentially impactful, but its current form is likely to limit its impact.

Strengths:

Focusing on decentralized photo sensing is a strength because it is relatively unique to zebrafish.

The breadth of discussion in zebrafish is a strength.

Weaknesses:

It might be helpful to reorganize the review with an introduction on what is known in other better studied systems to be highly conserved, then to focus in on the components of zebrafish that are discussed here.

A weakness is the lack of integration with other model organisms and other fish systems. Therefore, the narrow focus on zebrafish is unlikely to appeal to broader audiences.

It's surprising that there is not more discussion of sleep, which has been studied in detail, and its relationship to the clock.

Discussions of limitations of the model, including adult vs larval analysis and challenges performing long-term behavioral analysis in fish, would be valuable.

Reviewer #3 (Public review):

Sheidaei et al. report how chromosomes are favourably positioned to facilitate kinetochore-microtubule interactions during early mitosis. Studying kinetochore capture during early prophase is extremely difficult due to kinetochore crowding, but the team has taken up the challenge by classifying types of kinetochore movements, carefully marking kinetochore positions in early mitosis, and linking these to map their fate/next positions over time. The work is an excellent addition to the chromosome segregation field, as most of the literature has thus far focused on tracking kinetochores at slightly later stages of mitosis. The authors show that PANEM facilitates chromosome positioning toward the interior of the newly forming spindle, which in turn promotes chromosome congression. In the absence of PANEM, chromosomes end up in unfavourable locations and fail to form proper kinetochore-microtubule interactions. The work highlights the perinuclear actomyosin network in early mitosis (PANEM) as a key spatial and temporal element of chromosome congression, a step that precedes the segregation process.

Comments on revised version:

The authors' revisions have brought clarity to the description of movements in many of the figures. The manuscript ties a fundamental process to differences in cancer cell lines.

The work extends their published discovery that an actomyosin network forms on the cytoplasmic side of the nuclear envelope during prophase. The current manuscript explains how this network facilitates chromosome capture and congression by tracking the motions of individual kinetochores during early mitosis. The findings are broadly useful for the cell division and cytoskeletal fields.

Reviewer #2 (Public review):

Summary:

Here, the authors performed a phylogenetic analysis of mitochondrial ATP/ADP carrier (AAC) proteins. They also performed a structure-based screen for remote homologs, seeking to reveal their evolutionary origins. The authors claim that AACs are found at the root of their family tree, and through a structure-based homolog search protocol, identify putative prokaryotic homologs.

The proposed evolutionary history of AACs is bold and complicated, but the phylogenetic methodology and the way in which the tree is interpreted are incomplete and unconvincing. Further, the structure-based search strategy uses very relaxed cutoffs for fold similarity, which may be fine, but it does not clearly justify this decision. This is potentially very problematic, as I did not find the quantitative or qualitative assessments of fold similarity particularly compelling.

In summary, the authors have presented a bold and extremely interesting hypothesis for the evolution of these proteins, but there is insufficient support for their claims.

Strengths:

(1) The authors are presenting a very interesting hypothesis about the birth of these proteins, including that they may have undergone a radical rearrangement in their sequence at some point in evolution.

(2) The paper makes use of appropriate tools for structure-based homolog identification.

(3) Identification of a conserved sequence motif in these twilight zone proteins would be a rare and interesting occurrence, and could be consistent with their proposed homology.

Weaknesses:

(1) The phylogenetic analysis and its interpretations are incomplete. The authors regularly refer to the root of the tree, and its placement is given central importance. However, the methodology by which they selected the root is unexplained. This is notable, as the proposed root is curious and quite confusing. It implies that (at least) yeast and Paramecium AACs are independently paraphyletic. While certainly not impossible, this evokes quite a complicated evolutionary history. The taxonomy of this gene family, when rooted this way, does not seem to echo the phylogeny of species, suggesting an extremely complex history of duplication/loss and horizontal gene transfer, none of which the authors discuss in detail. Perhaps more clearly and specifically: I'm very surprised by the branching order at the root, where there are three independent branches of fungal proteins, followed by the excavate proteins in a monophyletic clade, followed by several independent branches of the Paramecium proteins. I very much expect incomplete lineage sorting at this evolutionary depth, but this seems extreme to the point that I question if it is accurately placed. More directly: this very much looks like an unrooted tree, presented radially.

(2) The Bayesian and ML trees seem quite incongruent, but this is not discussed. In fact, the text states that they "exhibit a similar tree topology." This is admittedly very difficult to assess without very carefully going over the tree, branch by branch, but there are nevertheless differences, the most obvious being paraphyly vs monophyly of taxon-specific AAC clades. Do the authors have any comments on this, and can they show some sort of consensus tree? How does this affect their interpretation?

(3) Presenting branch support as similarly-sized points makes it nearly impossible to actually judge the strength of support.

(4) The use of structure for remote homology detection is becoming increasingly popular, and in my opinion, is very powerful. But it is still much too early to be taken for granted. The methodology must be justified. Most importantly, the authors have not clearly described why they chose these quantitative cutoffs (I'm mostly thinking of the Dali Z-score cutoff, which here seems very low for a transmembrane protein of this size, as the Z-score is very dependent on alignment length). The authors reference categories defined by tool authors, but why a Z-score of 3, specifically? The same goes for TM scores. There are not yet any defined best practices, to my knowledge, so the authors should independently validate/justify their approach in some way and/or cite and discuss relevant literature (there have been a growing number of these screens using similar approaches in recent years).

(5) The proposed homologs have very little quantitative structural similarity to the query structure, or to each other, as shown in Figure 3 (and hence my concerns about the methodology). Also, I did not find the structural alignments in the supplement or Figure 4 to be qualitatively compelling. They simply appear too different, and I cannot discard this qualitative assessment because the quantitative similarities are likewise very weak. It's not clear to me if this is because the folds are in fact different, or if my view of them is a presentation issue (perhaps it could be improved by visualizing more angles, or more carefully cartooning the similarities and differences).

(6) The authors point out that the alpha-helices are ordered differently in YihY and CysZ, and that their membrane orientation is flipped. Taken at face value, I would view this as evidence against homology. This could perhaps be more reasonably explained as convergent global fold similarity resulting from different underlying structures. However, the authors imply that this may be the result of the transposition of the sequences encoding these alpha helices, yet there is no convincing description or argument concerning when and how this could have occurred. I think this would be a deeply interesting phenomenon, but there is insufficient evidence and discussion to seriously consider whether or not it is homology or convergence.

(7) Following up on comment #5, the authors did perform a very interesting in silico experiment by transposing sequences to reorder the helices. They then note that structural similarity improved. This is very, very interesting, but without other evidence of homology between the transposed alpha helices, I do not think this disproves alternative hypotheses. Does any such evidence exist?

(8) The authors show in Figure 5E-F that sequence transposition flips the membrane orientation, such that YihY and CysZ have extracellular termini (which you would expect from homologs, I suppose). But it is just cartooned and not discussed. Is this computationally or experimentally supported?

(9) The putative presence of a conserved motif would be a very compelling piece of evidence consistent with homology. However, it is not clear to me in the text which proteins actually have the repeats - is it truly just CysZ? What does this mean for YihY? Further, what specifically is being proposed to be homologous? Is SLC25 repeat 2 proposed to be homologous to CysZ repeat 2 (and the same for 3 to 3)? If so, this would seem to have implications for the transposition hypothesis. The helix nomenclature (e.g., H1-6) suggests homology across the proteins (i.e, H1 is homologous to H1); however, wouldn't the presence of these conserved domains instead, for example, suggest homology between SLC H3 and CysZ H2? The authors' conclusions are not clear, and it is difficult to interpret what the implications are for assessing homology.

(10) The sequence retrieval methods are incomplete, so it is impossible to reproduce the searches or to judge their accuracy and scope. What were the E-value cutoffs and other settings used in the searches?

(11) The phylogenetic methods are incomplete. What substitution models were used, and how were they chosen? What branch support method was used? What were the stop conditions of the Bayesian analysis (e.g. did the authors monitor for convergence, and how)? How much of the Bayesian analysis was considered burn-in, if any? And echoing points 1 & 2 above, how were these phylogenies rooted?

(12) Throughout, there is a distinct lack of careful, evolutionarily informative language.

(i) In reference to the phylogeny, the authors frequently refer to "grouping," but it's not entirely clear what this means. Referring to clades and their branching order would be more informative.

(ii) The authors refer to the excavate branch as the "most ancient." Whether or not excavates most closely resemble LECA is somewhat irrelevant, because the branch itself is not the most ancient - it is equally as ancient as its sister branch, which may be all other eukaryotes.

(iii) Likewise, the authors refer to bacterial proteins as "the evolutionary ancestor of mitochondrial AACs," and state that "AAC emerged from the conserved sulfat transporter CysZ." But extant bacteria are not the ancestors of mitochondria - nor are extant proteins descended from other extant proteins. They are, perhaps more accurately, cousins.

(iv) The authors refer to AACs as "evolutionarily founder member of the SLC25 carrier family," but I'm not sure that has a clear evolutionary meaning, unless the authors mean to say that the common ancestor was more AAC-like than anything-else-like. Even if the rooting is accurate, a basal branch does not necessarily reflect the ancestral state.

Reviewer #2 (Public review):

Summary:

Overall, this is an excellent paper, making use of a newly developed system for monitoring the behaviour of chromatophores in the skin of (mostly) free swimming bobtail squid and European cuttlefish. The manuscript is very well written, clearly presented and very well structured. The central finding, that individual chromatophores are connected to multiple motor neurones, is not new. Novelty instead comes from the ability to measure the actuation of chromatophore sections across wide areas of skin in free-swimming animals, showing the diversity of local motor units and reinforcing the notion that individual chromatophores are not necessarily the individual units of colour change, but rather local motor units that cover multiple neighbour and near neighbour chromatophore muscles. This is an excellent finding and one that will shape our understanding of the neural control of cephalopod skin colour. I have a number of minor points below that the authors will need to address before acceptance.

Strengths:

The methodological approach to collecting large amounts of data about local variations in the expansion of sections of chromatophores is exciting, and the analysis pipeline for clustering sections of chromatophores whose spontaneous activity correlated over time is powerful and exciting.

Comments on revisions:

All concerns have been addressed in the revised version of the manuscript.

Reviewer #3 (Public review):

Summary:

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

Strengths:

* This study presents a bold challenge to a literature trend that has touted similarities between Transformer models and human cognition based on representational correlations with brain activity. This challenge is substantiated by identifying sentences for which brain and model representations of sentences diverge.

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

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

Weaknesses:

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

* There may be confounds between the critical sentence structure manipulations and visual processing. This is inconvenient because activation in brain regions that process semantics tends to partially correlate with low-level representations of sentence surface features encoded in visual cortex. Although the study commendably controls for confounds associated with sentence length, correlations with the key sentence structure models are most salient in visual cortex and diminish in other brain networks when V1-V4 activation is controlled for.

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

Reviewer #2 (Public review):

Summary:

In this study, the authors investigate how cytosolic acetyl-CoA metabolism influences replicative aging in budding yeast. They propose that acetyl-CoA regulates aging through three major pathways: (1) mitochondrial transport to support mitochondrial function, (2) fatty acid synthesis, and (3) global protein acetylation. The data show that AMPK activation promotes mitochondrial import of acetyl-CoA and partially mitigates mitochondrial decline in a subset of aging cells.

Furthermore, the engineered A2A strain, which enhances mitochondrial acetyl-CoA utilization while relieving inhibition of fatty acid synthesis, increases the proportion of cells exhibiting a "low senescence" phenotype.

Overall, this is a thoughtful and potentially impactful study that advances our understanding of metabolic control of aging. Addressing the points below, particularly by refining interpretations and, where feasible, incorporating additional analyses, will further strengthen the manuscript and its conclusions.

Strengths:

The study has several notable strengths. It addresses an important question by shifting the focus from lifespan to preservation of late-life fitness, which is highly relevant to aging biology. The work integrates metabolic, genetic, and functional analyses to link cytosolic acetyl-CoA flux with distinct aging outcomes, and the engineering of the A2A strain provides a clear and elegant demonstration of how coordinated pathway modulation can improve cellular fitness.

Weaknesses:

(1) While the manuscript focuses on mitochondrial transport and fatty acid synthesis, cytosolic acetyl-CoA is also a key regulator of histone acetylation and chromatin silencing. It would strengthen the study to consider whether acetyl-CoA depletion contributes to improved fitness through enhanced rDNA silencing. Given the well-established role of rDNA instability in yeast aging, additional experiments examining rDNA silencing and stability would be valuable. For example, monitoring rDNA copy number changes (not necessarily ERCs) under AMPK activation, oleic acid supplementation, and in the A2A strain, similar to approaches used in the authors' prior work, would help clarify whether chromatin regulation contributes to the observed phenotypes.

(2) The current data do not fully distinguish whether AMPK activation and oleic acid supplementation act on distinct subpopulations of aging cells. An alternative explanation is that oleic acid supplementation enhances mitochondrial function and acts additively with AMPK activation, thereby increasing the fraction of cells in the "low senescence" state. Since this distinction is not central to the main conclusions, I suggest softening the language around subpopulation specificity. Emphasizing instead that the A2A strain coordinately modulates multiple branches of acetyl-CoA metabolism to improve late-life fitness would maintain the strength of the central message without overinterpretation.

(3) The manuscript proposes that lipid starvation and excess acetyl-CoA are major drivers of senescence in distinct subpopulations of wild-type aging cells. This conclusion is not yet fully supported by the presented data. Direct measurements of age-dependent divergence in acetyl-CoA and fatty acid levels at the single-cell level would be needed to substantiate this model. Based on the current evidence, a more conservative interpretation would be that aging cells exhibit differential sensitivity to perturbations in acetyl-CoA and lipid metabolism. Accordingly, I recommend revising the statement in the Abstract ("We further implicate lipid starvation and excess acetyl coenzyme A availability as major drivers of senescence...") and the corresponding discussion text to better align with the data.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Reviewer #2 (Public review):

This study explores the dynamic association between malate dehydrogenase (MDH1) and citrate synthase (CIT1) in Saccharomyces cerevisiae, with the aim of linking this interaction to respiratory metabolism. Utilizing a NanoBiT split-luciferase system, the authors monitor protein-protein interactions in vivo under various metabolic conditions.

Major Concerns:

(1) NanoBiT Signal May Reflect Protein Abundance Rather Than Interaction Strength<br /> In Figure 1C, the authors report increased MDH1-CIT1 interaction under respiratory (acetate) conditions and decreased interaction during fermentation (glucose), as indicated by NanoBiT luminescence. However, this signal appears to correlate strongly with the expression levels of MDH1 and CIT1, raising the possibility that the observed luminescence reflects protein abundance rather than specific interaction dynamics. To resolve this, NanoBiT signals should be normalized to the expression levels of both proteins to distinguish between abundance-driven and interaction-driven changes.

(2) Lack of Causal Evidence<br /> The study presents a series of metabolic perturbation experiments (e.g., arsenite, AOA, antimycin A, malonate) and correlates changes in metabolite levels with NanoBiT signals. However, these data are correlative and do not establish a functional role for the MDH1-CIT1 interaction in metabolic regulation. To demonstrate causality, the authors should implement approaches to specifically disrupt the MDH1-CIT1 interaction. One strategy could involve using a 15-residue peptide (Pept1) derived from the Pro354-Pro366 region of CIT1, previously shown to mediate the interaction or introducing the cit1Δ3 (Arg362Glu) mutation, which perturbs binding. Metabolic flux analysis using ^13C-labeled glucose and mitochondrial respiration assays (e.g., Seahorse) could then assess functional consequences.

(3) Absence of Protein Expression Controls Under Perturbation Conditions<br /> In experiments involving acetate, arsenite, AOA, antimycin A, and malonate, the authors infer changes in MDH1-CIT1 association based solely on NanoBiT signals. However, no accompanying data are provided on MDH1 and CIT1 protein levels under these conditions. This omission weakens the conclusions, as altered expression rather than interaction strength could underlie the observed luminescence changes. Immunoblotting or quantitative proteomics should be used to confirm constant protein expression across conditions.

Conclusion:

Although the central question is compelling and the use of NanoBiT in live cells is a strength, the manuscript requires additional experimental rigor. Specifically, normalization of interaction signals, introduction of causative perturbations, and validation of protein expression are essential to substantiate the study's claims.

Comments on revised version:

The manuscript is much improved.

Reviewer #2 (Public review):

Summary:

Castanheira et al. investigate the role of spatial attention for planning during three maze navigation experiments (one new experiment and two existing datasets). Effective planning in complex situations requires the construction of simplified representations of the task at hand. The authors find that these mental representations (as assessed by conscious awareness) of a given stimulus are influenced by (spatially) surrounding stimuli. Individual participants varied in the degree to which attention influenced their task representations, and this attentional effect correlated with the sparsity of representations (as measured by the range of awareness reports across all stimuli). Spatially grouping task-relevant information on either the left or right side of the maze led to mental representations more similar to optimal representations predicted by the value-guided construal (VGC) model - a normative model describing a theoretical approach to simplifying complex task information. Finally, the authors propose an update to this model, incorporating an attentional spotlight component; the revised descriptive model predicts empirical task representations better than the original (normative) VGC model.

Strengths:

The novelty of this study lies in the proposal and investigation of a cognitive mechanism through which a normative model like value-guided construal can enable human planning. After proposing attention as this mechanism, the authors make concrete hypotheses about mismatches between the VGC predictions and real human behavior, which are experimentally validated. Thus, not only does this study describe a possible mechanism for simplification of task information for planning, but the authors also propose a descriptive model, revising VGC to incorporate this attentional component.

A strength of this paper is the variety of investigative approaches: analysis of existing data, novel experiment, and a computational approach to predict experimental findings from a theoretical model. Analyzing pre-existing datasets increases the size of the participant cohort and strengthens the authors' conclusions. Meanwhile, comparing the predictions of the existing normative model and the authors' own refined model is a clever approach to substantiate their claims. In addition, the authors describe several crucial controls, which are key to the interpretability of their results. In particular, the eye tracking results were critical.

In summary, this paper constitutes an important step toward a more complete understanding of the human ability to plan.

Comments on revised version:

I am overall happy with the revision and agree that the authors have addressed most of the comments.

Reviewer #2 (Public review):

Summary:

Castanheira et al. investigate the role of spatial attention for planning during three maze navigation experiments (one new experiment and two existing datasets). Effective planning in complex situations requires the construction of simplified representations of the task at hand. The authors find that these mental representations (as assessed by conscious awareness) of a given stimulus are influenced by (spatially) surrounding stimuli. Individual participants varied in the degree to which attention influenced their task representations, and this attentional effect correlated with the sparsity of representations (as measured by the range of awareness reports across all stimuli). Spatially grouping task-relevant information on either the left or right side of the maze led to mental representations more similar to optimal representations predicted by the value-guided construal (VGC) model - a normative model describing a theoretical approach to simplifying complex task information. Finally, the authors propose an update to this model, incorporating an attentional spotlight component; the revised descriptive model predicts empirical task representations better than the original (normative) VGC model.

Strengths:

The novelty of this study lies in the proposal and investigation of a cognitive mechanism through which a normative model like value-guided construal can enable human planning. After proposing attention as this mechanism, the authors make concrete hypotheses about mismatches between the VGC predictions and real human behavior, which are experimentally validated. Thus, not only does this study describe a possible mechanism for simplification of task information for planning, but the authors also propose a descriptive model, revising VGC to incorporate this attentional component.

A strength of this paper is the variety of investigative approaches: analysis of existing data, novel experiment, and a computational approach to predict experimental findings from a theoretical model. Analyzing pre-existing datasets increases the size of the participant cohort and strengthens the authors' conclusions. Meanwhile, comparing the predictions of the existing normative model and the authors' own refined model is a clever approach to substantiate their claims. In addition, the authors describe several crucial controls, which are key to the interpretability of their results. In particular, the eye tracking results were critical.

In summary, this paper constitutes an important step toward a more complete understanding of the human ability to plan.

Weaknesses:

(1) There is a critical conceptual gap in the study and its interpretation, mainly due to the reliance on a self-report metric of awareness (rather than an objective measure of behavioral performance).

a. Awareness is tested by a 9-point self-report scale. It is currently unclear why awareness of task-irrelevant obstacles in this task would necessarily compromise optimal planning. There is no indication of whether self-reported awareness affects performance (e.g., navigation path distance, time to complete the maze, number of errors). Such behavioral evidence of planning would be more compelling.

b. Relatedly, it would have been more convincing to have an objective measure of awareness, for instance, how the presence or absence of a "task-irrelevant" obstacle affects performance (e.g., change navigation path distance or time to complete the maze), or whether participants can accurately recall the location of obstacles.

c. Consequently, I'm not sure that we can conclude that the spatial context does impact participants' ability to plan spatial navigation or to "incorporate task-relevant information into their construal". We know that the spatial context affects subjective (self-reported) awareness, but the authors do not present evidence that spatial context affects behavioral performance.

d. Another concern that may complicate interpretation is the following: Figure 3c shows improved VGC model predictions (steeper slope) for mazes with greater lateralization. However, there are notable outliers in these plots, where a high lateralization index does not correspond to good model performance. There is currently no discussion/explanation of these cases.

(2) I noticed an issue with clarity regarding task-relevance. It is currently not fully clear which obstacles are "task irrelevant". Also, the term is used inconsistently, sometimes conflating with "awareness". For example, in the "Attentional spotlight model of task representations" section, the authors state that "task-relevant information becomes less relevant when surrounded by task-irrelevant information". But they really mean that participants become less aware of those task-relevant obstacles. I assume task-relevance is an objective characteristic related to maze organization, not to a participant's construal. Indeed, the following paragraph provides evidence of model predictions of awareness.

(3) The behavioral paradigm has some distinct disadvantages, and the validity of the task is not backed up by behavioral data.

a. I understand the need for central fixation, but it also makes the task less naturalistic.

b. The task with its top-down grid view does not seem to mimic real human navigation. Though this grid may be similar to mental maps we form for navigation, the sensory stimuli corresponding to possible paths and to spatial context during real-life navigation are very different.

c. Behavioral performance is not reported, so it is unknown whether participants are able to properly complete the task. The task seems pretty difficult to navigate, especially when the obstacles disappear, and in combination with the central fixation.

d. There is no discussion of whether/how this navigation task generalizes to other forms of planning.

Reviewer #2 (Public review):

Summary:

The authors aiming in developing a neural mass model characterized by few collective variables mimicking the dynamics of a network of Hodgkin - Huxley neurons encompassing ion-exchange mechanisms. They describe in details the derivation of the mean-field model , then they compare experimental results obtained for the hippocampus of a mice with the neural network simulations and the mean-field results. Furthermore, they report a bifurcation analysis of the developed model and simulation of a small network containing various coupled neural masses, somehow moving towards the simulation of an entire connectome.

Strengths:

The author attempts to develop a mean-field model for a globally coupled network of heterogeneous Hodgkin-Huxley neurons with explicit ion exchange mechanism between the cell interior and exterior.

Weaknesses:

(1) They do not employ the reduction methodology more suited for the single neuron model they consider.<br /> (2) Their derivation of the neural mass model is based on several assumptions, and not all well justified.<br /> (3) Their formulation of the mean-field derivation is unnecessary complicated, it can be strongly simplified by following previously published approaches to derive biologically realistic neural masses.<br /> (4) Their model seems to work only for highly synchronized situations and not for the standard asynchronous evolution usually observed in neural circuits.

General Statements:

The authors honestly declared the many limitations of their approach, once assumed this the results of the mean-field are somehow inconsistent with the neural network simulations as expected.

The authors suggest to employ this model for the simulations on the whole connectome to follow seizure propagation, however I believe that a simpler model, as the Epileptor, remains superior in this respect to this model. That indeed includes biophysical parameters but their correspondence with the ones employed in the network dynamics remain elusive, due to the many assumptions required to derive this mean field model. Furthermore it is more complicated than the Epileptor, I do not think that the present model will be largely employed by the community.

Comments on revisions:

The authors have corrected mistakes present in the manuscript and put a correct list of references.

However, they refuse

(1) To simplify the formulation of the model, the model contains unnecessary complications, as I have clearly written in my report, the authors agree, but they do not want to change the formulation;

(2) To derive the mean field model in a simpler way, as possible, and as I asked many times in my Referee report, this would help the readers to understand the important aspect of the derivation, without not needed and confusing complicated formulations;

(3) To compare direct simulations of the network with neural mass results in sub-section "Bifurcation analysis: emergent network states and multistability" to show bistability, as I asked.

As a matter of fact the performed modifications do not solve my previous doubts on the validity of the results reported in the manuscript.

Therefore, my previous assessments remain valid.

Reviewer #2 (Public review):

Summary:

This manuscript presents an elegant and innovative imaging approach to visualize DNase activity at the interface between macrophages and extracellular substrates. The platform is technically strong and enables the study of localized DNA degradation with high spatial resolution. The work is of clear interest and provides a useful framework to investigate how immune cells process extracellular DNA. However, several aspects of the mechanistic interpretation and conceptual framing would benefit from clarification.

Strengths:

(1) The study introduces a creative and well-designed imaging platform that allows visualization of localized DNase activity at cell-substrate interfaces.

(2) The approach is technically robust and represents a valuable tool that could be broadly useful to the field.

(3) The experiments are thoughtfully designed and address an important question regarding how immune cells interact with extracellular DNA.

(4) The work opens interesting avenues for studying DNA processing in contexts such as infection and inflammation.

Weaknesses:

While the experimental approach is strong, several key conclusions rely on interpretations that would benefit from further clarification:

(1) First, the conclusion that DNaseX is recruited to phagocytic cups from the "cytoplasm" appears conceptually imprecise. Given that DNaseX is a membrane-anchored protein, it is unlikely to exist as a freely soluble cytoplasmic pool. A more plausible interpretation is that DNaseX is supplied from intracellular membrane compartments. This interpretation would also be more consistent with the data showing dependence on a membrane anchor.

(2) Second, the interpretation that actin polymerization is not required for DNaseX recruitment raises concerns. Phagocytic cup formation is known to depend strongly on actin dynamics, and it is therefore unclear whether the structures observed under actin inhibition represent fully formed functional cups or partial cell-substrate contacts. This distinction is important for interpreting recruitment versus activity, particularly since enzymatic activity is reduced under these conditions.

(3) Third, the identification of DNaseX as the main nuclease responsible for the observed activity is not fully resolved. The conclusions rely primarily on gene silencing and staining approaches, but the specificity of these strategies relative to other nucleases is not addressed. It therefore remains possible that additional enzymes contribute to the observed activity.

(4) Finally, the interpretation of the biofilm experiments may be overstated. While the data clearly show localized DNA degradation in contact with macrophages, it is not fully established that this process depends specifically on phagocytic cup structures. An alternative explanation is that membrane-associated DNase activity more generally mediates this effect. In addition, the physiological relevance of this mechanism would benefit from further discussion.

Overall, the study is technically strong and introduces a valuable methodology, but several central conclusions are only partially supported by the current data and would benefit from more cautious interpretation and clearer conceptual framing.

Reviewer #2 (Public review):

In this study, the authors address the molecular mechanism underlying the transcriptional changes during erythroid differentiation from hematopoietic progenitor cells. The authors combine single-molecule live cell imaging and CUT&RUN to analyze the chromatin binding properties of the GATA2 transcription factor prior to and after initiation of differentiation into the erythroid cell lineage. Using three distinct cellular systems, the authors demonstrate that the chromatin binding of GATA2 is transiently increased early in the differentiation process, as evidenced by increased chromatin binding residence time and the emergence of new genomic binding sites identified by CUT&RUN. The strength of the study lies in the combination of single-molecule imaging, which reports on binding dynamics but is agnostic of the binding site, with CUT&RUN, which reports on the binding sites but does not provide dynamic information. The authors clearly demonstrate that chromatin binding of GATA2 is altered early in the differentiation process and is later displaced as cells switch to expression of GATA1, which has been previously observed. The use of three distinct cell lines, in particular the GATA2-SNAP mouse model, is a strength in principle; however, the results are not fully consistent between the different cell systems. A key difference is that the G1E-ER4 and HPC7 cell line models express HaloTagged GATA2 in addition to the endogenous GATA2 protein. The authors go through great lengths to control GATA2-HaloTag expression levels, but they use polyclonal cell lines and do not analyze expression levels of the GATA2-HaloTag transgene, which is a key variable in interpreting their experimental results. Finally, a key variable determined in their single-molecule analysis is the number of binding events observed during the distinct differentiation changes. The number of binding events observed is influenced by the expression level of the tagged protein, which in turn is controlled by the Shield-1 ligand, and the fraction of molecules labeled with the HaloTag ligand. Since transgene protein levels and the labeling efficiency were not determined, it is hard to assess how reliable the measurements of the number of binding events are across all cell lines.

To address the weaknesses summarized above the authors could take the following steps:

(1) Determine the expression levels of the GATA2-HaloTag transgene over the course of differentiation under the conditions used for single-molecule imaging. This will not only allow them to determine the expression of the transgene but also the endogenous untagged protein with which the GATA2-HaloTag fusion proteins compete for binding sites.

(2) To determine the fraction of molecules labeled during imaging, the authors could carry out a titration of the HaloTag ligand and compare the amount of labeled protein under single-molecule imaging conditions to that of saturating labeling of the HaloTag. This approach will ensure that the number of labeled molecules per cell is comparable across experimental conditions and allow the authors to draw more solid conclusions regarding the number of binding events.

(3) The analysis of residence times using single-molecule imaging requires robust single-particle tracking without gaps or interruptions of trajectories. The authors should show images of their particle trajectories to demonstrate that their tracking is robust. Or even better, movies superimposing the trajectories onto the imaging data.

Reviewer #2 (Public review):

Summary:

This manuscript addresses an important and timely question in the molecular simulation of biomolecular condensates. Most residue-level coarse-grained models used for IDP phase separation employ implicit solvent and represent effective interactions through relatively simple pairwise potentials. While these models have been very useful, they usually do not explicitly distinguish direct contacts from solvent-separated interactions, nor do they include an energetic barrier associated with water removal. This manuscript attempts to address that limitation by introducing desolvation-inspired terms into coarse-grained models and examining their consequences for phase behavior, chain conformations, dense-phase packing, and dynamics.

Strengths:

The central idea is physically well motivated. Using a simple homopolymer model, the authors show that increasing the desolvation barrier suppresses phase separation, whereas stabilizing solvent-separated contacts enhances phase separation. They further show that solvent-separated interactions can reduce dense-phase over-compaction, which is a meaningful result given the known challenges in obtaining both accurate single-chain dimensions and realistic dense-phase properties from the same coarse-grained model. The finding that desolvation-like terms can reshape dense-phase packing without simply rescaling the overall interaction strength is interesting and could be useful for future model development. I also found the attempt to connect conformational changes across dilute and dense phases with thermal distance from the critical point to be intriguing. The dynamic analysis, including the FRAP-like simulations and the discussion of kinetic arrest during coarsening, adds another useful dimension to the work.

Weaknesses:

At the same time, there are several places where the manuscript would benefit from more careful framing. First, the desolvation terms are still effective coarse-grained parameters rather than a direct representation of water molecules. The language sometimes gives the impression that desolvation is being treated explicitly, whereas the model introduces desolvation-inspired effective interactions into an implicit-solvent framework. Second, the conformational analysis is interesting, but the broader context of prior work on dilute-to-dense phase conformational reorganization of IDPs could be more clearly discussed. This would help clarify what is new in the present work, whether it is the conformational change itself, its dependence on desolvation terms, or the proposed scaling with distance from the critical point. Third, the dynamic results are potentially useful, but the manuscript should more clearly articulate what is nontrivial beyond the expected slowing of local rearrangements by an added barrier in the potential.

Overall, I think this is a useful and potentially important contribution.

Reviewer #2 (Public review):

Summary:

Hann and colleagues introduce a gaze-based analytical framework designed to capture, on a trial-by-trial basis, how people form and revise their predictions during implicit probabilistic sequence learning. Using an eye-tracking adaptation of an alternating sequence task, they record the first anticipatory saccade during the response-stimulus interval and classify each such saccade along two dimensions: whether it was directed toward a high- or low-probability upcoming stimulus (the learning-dependent vs. not-learning-dependent distinction), and whether the anticipated location coincided with the stimulus that actually appeared. A complementary iterative-updating metric codes whether a participant's prediction for a given three-element context is repeated or revised on successive encounters of that context.

On the basis of these measures, the authors report that errors congruent with the inferred regularity - which they interpret as reflecting environmental noise - become progressively more frequent than errors reflecting an inaccurate internal model; that participants show a pronounced tendency to repeat their previous prediction rather than revise it; and that updates depend more on whether a prior belief is congruent with the task's statistical structure than on whether the previous prediction was confirmed. They interpret these results as evidence that statistical learning is less error-driven and more repetition-based (Hebbian in character) than is typically assumed.

Strengths:

The methodological ambition of the work is considerable, and the paper makes several contributions that are likely to be useful to the implicit-learning and predictive-processing communities. Using the first anticipatory saccade as a pre-response behavioral readout of prediction is conceptually well-motivated: it provides a trial-by-trial index of predictive orienting at a temporal resolution that manual reaction times cannot deliver, and it does so before the outcome of the trial is known. The explicit distinction between errors arising because the task's outcome is stochastic - that is, predictions congruent with the statistical structure but unconfirmed by the stochastic sample - and errors arising because the internal model is inaccurate is a theoretically meaningful move: predictive-coding and Bayesian accounts have long argued that these two sources of surprise should carry different weight for model revision, and the authors offer a behavioral operationalization of that distinction. The analytical pipeline is not tied to the specific paradigm used here and could be applied to other probabilistic sequence-learning tasks, which gives it broader methodological utility than a single-paradigm report. Finally, the demonstration that learners maintain their prior across successive occurrences of the same context, even when it has been disconfirmed by the most recent outcome, is a robust behavioral observation that speaks directly to an unresolved debate about whether statistical learning is dominantly error-driven.

Weaknesses:

The framework and the core behavioral observations are valuable, but several inferential steps - from the gaze signal to the cognitive constructs the authors invoke - are not fully supported by the present design, and these gaps affect how readers should interpret the stronger theoretical conclusions.

The "process-pure" framing conflates sensitivity with construct purity. The authors repeatedly describe the eye-tracking measure as providing a more process-pure index of statistical learning than manual-response paradigms. Anticipatory saccades are themselves a learned motor behavior - the oculomotor system is among the most plastic motor outputs the primate brain generates, and sequence learning in the saccadic system is well-documented. The present design does not dissociate learning of the statistical structure from learning of the oculomotor sequence that expresses it, so the measure is not, on its face, free from the motor-learning confound that the authors criticize in button-press paradigms. The framing should be read as aspirational rather than as demonstrated by the present data.

The oculomotor reaction-time data do not show the canonical signature of statistical learning. Reaction times for low-probability trials rise across epochs while those for high-probability trials remain approximately flat (Figure 5). The emerging difference between the two trial types, therefore, appears to be driven by a slowing of responses to low-probability stimuli rather than by a facilitation of responses to high-probability ones, and the authors do not rule out the alternative interpretations that this pattern reflects fatigue, a motor floor effect, or inhibition of unexpected locations. Because no fixation constraint is imposed during the response-stimulus interval, pre-stimulus gaze drift toward the anticipated location will artifactually reduce reaction time on precisely those trials the authors wish to treat as learning-driven; the fact that measured reaction times remain well above zero even on trials classified as correct anticipations is itself evidence that this contamination is present. The oculomotor reaction-time data, therefore, do not provide as clean a verification of learning as the manuscript implies.

The correct/error labeling of anticipatory saccades incorporates information that the participant did not have. Because the first saccade occurs during the response-stimulus interval - that is, before the upcoming stimulus is revealed - the participant's internal predictive state is identical whether the trial is subsequently classified as a learning-dependent correct response or a learning-dependent error. Any difference in the epochwise frequency of these two categories must therefore be driven, at least in part, by the external stochastic structure of the task rather than by a difference in the predictive process itself. In particular, the observation that learning-dependent errors are the most frequent saccade type (Figure 7) is predicted by the prior probabilities of the outcomes alone, given a high-probability prediction, without appeal to any difference in predictive state. Readers should recognize that the theoretically meaningful contrast is between learning-dependent and not-learning-dependent anticipations (two categories), and that the four-way split risks confounding predictive state with outcome stochasticity.

The iterative-updating metric does not distinguish prior revision from alternative processes. The binary update / no-update code, computed across non-contiguous occurrences of the same three-element context, does not discriminate between a genuine update of the internal model, simple episodic retrieval of a previously encountered triplet, and oculomotor perseveration. Without a formal generative model to anchor the interpretation, the central theoretical claim - that statistical learning is less error-driven than commonly assumed - is underdetermined by the data. The repetition pattern the authors observe is equally consistent with an error-driven model equipped with a low learning rate in a stable environment, an interpretation the authors themselves acknowledge in the Discussion. Adjudicating between these possibilities requires comparison against explicit computational models, which the present manuscript does not provide.

Data loss and the absence of fixation control. An interpretable saccade is detected on fewer than half of all trials (48.76%; line 889), and the manuscript does not report the distribution of saccade counts per interval, the per-condition trial counts after all exclusions, or the decomposition of the 20% missing-data threshold into its underlying causes. Given that the entire inferential apparatus rests on this subset of trials, the degree of data loss is a relevant context for the reader. Separately, no fixation constraint is imposed between trials: the participant's starting gaze position at the onset of each response-stimulus interval is whatever position was reached at the end of the preceding response, and this starting position carries trial-history information correlated with the upcoming stimulus. This leaves open the possibility that what is classified as predictive orienting partly reflects the mechanical consequences of where the eye happened to be at the end of the previous trial. The authors defend the absence of a fixation cross on the grounds that it would transform the transitional structure of the task, but this is an empirical claim presented without a supporting citation.

Heterogeneity within the high-probability condition is not addressed. The two routes to a high-probability triplet in the design - pattern-random-pattern (50% of trials) and random-pattern-random (12.5%) - differ both in their base rate and in the reliability of the contextual cue they provide. Collapsing across these subtypes is an analytical choice that may conceal heterogeneity in the underlying learning process.

Appraisal: Do the results support the authors' conclusions?

The framework succeeds in providing a trial-by-trial behavioral readout of predictive orienting that is more fine-grained than conventional reaction-time measures, and the behavioral dissociation between errors congruent with the regularity and errors reflecting an inaccurate internal model is a genuine empirical contribution. The conclusions about the mechanistic nature of statistical learning should be read as motivating hypotheses for future modeling work rather than as settled empirical claims.

Impact and utility:

The analytical framework introduced here is likely to be useful to researchers working on implicit learning, predictive processing, and Bayesian models of perception and cognition. The measure of predictive orienting and the iterative-updating code could be adapted to a range of probabilistic learning paradigms, and the behavioral dissociation between noise-driven and model-mismatch errors fills a methodological gap that the field has long acknowledged. The authors share their data and code openly, which will facilitate reuse. The most durable contribution of the paper is methodological; the theoretical claims about the nature of statistical learning will require additional computational modeling before they can be regarded as established.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors leverage a high-powered 7T fMRI dataset of subjects viewing naturalistic audiovisual movies to elucidate the topographic organization of the human auditory cortex. By applying a nonlinear pRF model, they successfully map tonotopic gradients extending beyond the auditory core into the STG and STS areas. A primary finding is a medial-to-lateral gradient of increasing response compressivity, which the authors claim mirrors the hierarchical cascade architecture of the visual system. Furthermore, the modeling reveals that regions exhibiting high speech selectivity predominantly occupy the low-frequency portions of non-primary tonotopic maps. The authors argue that this architecture reflects an efficient coding mechanism where the cortex magnifies specific spectral features to facilitate the transition from acoustic encoding to flexible speech representation.

Overall, the study presents concise analyses and compelling high-resolution results that advance our understanding of auditory cortical organization. However, the manuscript currently exhibits several significant theoretical and methodological gaps that temper its broader claims. Most notably, the authors' reliance on a spatial, retinotopic-like analogy overlooks the fundamentally temporal nature of audition. Decoding continuous, natural speech relies heavily on dynamic, full-spectrum temporal integration and contextual recurrent computations, which are difficult to reconcile with the purely static, low-frequency spatial tuning observed here.

Strengths:

(1) The utilization of ultra-high-field 7T functional imaging combined with large-scale, naturalistic continuous stimuli provides an excellent signal-to-noise ratio and captures cortical responses under ecologically valid conditions.

(2) The application of a non-linear pRF encoding model provides a robust, quantitative method for parameterizing and mapping tonotopic features across the cortex, moving beyond simple contrast-based parcellations.

(3) The manuscript effectively demonstrates the relationship between category selectivity (e.g., speech) and underlying tonotopy, drawing an elegant and structurally useful analogy to the well-established relationship between category selectivity and retinotopy in the visual cortex.

Weaknesses:

(1) While the PCA mapping of the functional and structural parameter space is visually compelling, the robustness of this representational geometry across varying acoustic contexts remains ambiguous. Because the model relies on the specific statistical regularities of a single naturalistic audiovisual stimulus set, it is unclear if this low-dimensional structure would hold when tested against isolated speech sounds, environmental noise, or spectrally matched non-speech control stimuli.

(2) The methodological descriptions currently lack the computational precision required for replication and deep evaluation. I would suggest that the exact mathematical formulation of the encoding model be fully specified in the Methods section. This should include an explicit definition of the objective function, a clear accounting of all terms and hyperparameters utilized during the fitting process, and the exact dimensionalities of both the input feature space and the resulting parameter space.

(3) There is a critical theoretical disconnect between the observed static, low-frequency tuning in the STG and the known acoustic requirements for continuous speech perception. Speech is a full-spectrum signal; while fundamental frequencies and formants dominate the lower spectrum (which is vital for processing dynamic pitch contours), high-frequency bands (>1 kHz) carry indispensable phonetic information, such as the rapid spectrotemporal dynamics of consonants, especially fricatives. If the speech-responsive cortex is primarily and statically tuned to a low-frequency spectrum, it is unclear how the dynamic, high-frequency spectral information required for semantic decoding is represented. A rich body of electrophysiological literature documents diverse spectrogram coding in the STG. For example, Mesgarani et al. (Science, 2014) demonstrated using spectrotemporal receptive field models that neural populations in the STG are tuned to both low and high-frequency spectrograms well above 1 kHz. The authors must address this discrepancy and attempt to reconcile their static tonotopic findings with the existing literature on dynamic speech encoding.

(4) While drawing parallels between visual and auditory processing hierarchies is conceptually attractive, the modalities face fundamentally different computational challenges. Vision is largely resolved in space, making a retinotopic spatial coding strategy ecologically and computationally sound. Audition, however, evolves continuously in time. Complex temporal structure, continuous temporal integration, and contextual recurrent computations are paramount for auditory processing, particularly for speech comprehension. In this sense, a purely spatial or tonotopic coding framework is insufficient to fully explain the complex temporal processing dynamics required in the higher-order auditory domain.

Reviewer #2 (Public review):

Summary:

The authors study cardiac deceleration during threat responses in Drosophila. Particularly, it focuses on identifying the neuronal control of this deceleration. Using behavioral and cardiac tracking and analysis, genetics, and calcium imaging, they identify two pairs of dopaminergic neurons involved in cardiac deceleration during air puff responses

Strengths:

The study is overall well done, and the paper is clearly written. Particularly, the work on identifying the two pairs of dopaminergic neurons involved in cardiac deceleration using a series of drivers and generating new ones is rigorous and extensive. Finally, the authors manipulate the heartbeat to investigate how it influences threat responses

Weaknesses:

There are, however, several points that need to be clarified, as some claims are not entirely supported by evidence.

The authors, for example, claim that dopaminergic neurons are responsible for cardiac deceleration (during the air puff, lines 182-3, page 9). However, based on the work in this study, it seems that other neurons could be involved in this control as well. In addition to dopaminergic neurons, the authors test serotonergic and octopaminergic neurons, which, based on silencing experiments, also show an implication in heart-beat deceleration. Furthermore, because they find that dopaminergic neurons are the only ones that, upon thermogenetic activation, lead to lower heart beat frequency, they conclude that the dopaminergic neurons are responsible for air -puff induced cardiac deceleration.

However, these activation experiments are done in a different context than the air puff experiments (at a higher temperature, which could have an effect on the heartbeat changes upon activation of different neuron groups), and because silencing of other monoaminergic neuron types during the air puff also resulted in less cardiac deceleration, one cannot exclude the implication of octopaminergic or serotonergic neurons in air-puff-induced deceleration.

Activation experiments without high temperatures (using, for example, optogenetics) and/or in the presence of the air puff would be important to determine that the dopaminergic neurons are the main type of monoaminergic neurons involved in air-puff-induced cardiac deceleration. Otherwise, the related claims should be rephrased in a way that clearly doesn't exclude a possible implication of other monoaminergic neurons.

Regarding the interactions between the cardiac deceleration and locomotion, the authors propose, based on the results, that the optogenetic cardiac deceleration is sufficient to induce an increase in locomotion, and that it is the decrease in heartbeat that would be responsible via interoceptive pathways to trigger an increase in locomotion. In the model they propose, the DA-WED neurons would induce a decrease in heartbeat that, in turn, would trigger an increase in locomotion. There is not enough proof that cardiac deceleration is the one that triggers an increase in locomotion during air puff responses. As the authors themselves state, the experiments that would demonstrate this would involve preventing cardiac deceleration while optogenetically activating DA-WED. It can therefore not be excluded that the DA-WED neurons trigger an increase in locomotion that is possibly modulated by the cardiac activity. Both alternatives should be considered (models in Figures 4 and 5).

Reviewer #2 (Public review):

This study addresses an important question regarding exercise-induced modulation of pain in women, but the conclusions appear to be based on relatively limited and selective evidence. The authors report an interaction between exercise intensity and stimulus intensity, which they interpret as evidence for exercise-induced hypoalgesia and conclude that fitness, but not sex, modulates this effect. However, this main result relies on a relatively small interaction that emerges only under specific conditions, with inconsistent findings across pain modalities and stimulus intensities, and an analysis approach that does not fully exploit the continuous pain ratings collected. The lack of a baseline condition further limits the interpretability of the findings as reflecting hypoalgesia, and overall, the data provide a rather constrained basis for drawing broader conclusions.

Strengths:

(1) The focus on women is important and timely, particularly given the ambiguity in prior findings and the historical bias toward male-dominated samples.

(2) The attempt to revisit previous findings in a new cohort is valuable in principle.

Weaknesses:

(1) The core interpretation may not be fully supported by the data

The central claim-that the results demonstrate exercise-induced hypoalgesia and its dependence on fitness but not sex-does not appear to be fully supported by the evidence presented.

1.1 Lack of baseline condition

The absence of a no-exercise baseline substantially limits interpretation. The study compares high- and low-intensity exercise, but without a baseline, it is not possible to determine whether either condition produces hypoalgesia or hyperalgesia relative to calibration. The observed HI-LI difference, therefore, reflects only a relative contrast between exercise intensities, not an absolute reduction in pain. As a result, attributing the findings to "hypoalgesia" may be difficult to justify fully.

1.2 Lack of internal replication across conditions

The reported effect is highly specific and does not clearly generalise across the experimental design. It emerges significantly only for heat pain at the highest stimulus intensity, with no clear effects for other intensities and for pressure pain. Moreover, the main statistical result is a relatively small interaction effect with a modest p value, which translates into a difference of approximately 6-8 VAS units on a 150 scale. This combination-a small effect size, limited statistical strength, and restriction to a single condition-substantially weakens the evidence for a robust or generalisable effect.

1.3 Deviations from the original study and selective use of data

Although framed as a follow-up to previous work, the current study introduces substantial methodological changes, particularly in the acquisition and scaling of pain ratings (continuous vs post-hoc ratings, modified VAS with sub-threshold range). Despite collecting rich continuous data, the analysis focuses on peak responses to approximate the previous study. While this may aid comparability, it results in a strong emphasis on a single data point (highest intensity), rather than leveraging the full dataset. This limits both interpretability and comparability.

1.4 Over-reliance on null results regarding sex differences

The conclusion that fitness, but not sex, modulates exercise-induced pain may not be directly supported by the data presented. The current study includes only highly fit women, and comparisons with men or less-fit women rely on non-significant differences in a previous cohort. The absence of a significant difference does not provide evidence for equivalence, and no formal statistical support for a null effect is provided. As such, conclusions about the absence of sex differences would unfortunately benefit from more cautious interpretation.

(2) Limited sample and lack of diversity

The dataset is narrow in scope, comprising a small sample (N = 21) of healthy, highly fit women. Key demographic characteristics (e.g. age range, BMI distribution) are not fully presented, explored or discussed. This limits generalisability and makes it difficult to draw broader conclusions about exercise-induced pain modulation in women, as the main focus of the study.

(3) Methodological choices limit the interpretability of the data

Several methodological decisions would benefit from stronger justification:

3.1 The use of a non-standard VAS scale (0-150 with a fixed pain threshold at 50) is unconventional and may influence how participants report pain, while limiting comparability with related literature.

3.2 Participants explicitly reported expecting exercise to reduce pain, introducing a potential confound that is not presently addressed.

3.3 A more comprehensive use of the full time series of pain ratings would provide a stronger and more transparent basis for interpretation of the present findings.

Reviewer #2 (Public review):

In the manuscript "Cancer cells differentially modulate mitochondrial respiration to alter redox state and enable biomass synthesis in nutrient-limited environments", Chang et al investigate how cancer cells respond to the limitation of certain environmental nutrients by regulating the cellular NAD+/NADH ratio. They focus on serine and lipid metabolism, pathways known to be controlled by the NAD+/NADH ratio, and propose that changes in mitochondrial respiration in response to deprivation of these nutrients can influence the NAD+/NADH ratio, thereby impacting biomass synthesis.

While the study is descriptive in nature and does not investigate specific molecular mechanisms that explain the crosstalk between nutrient availability and mitochondrial redox changes, the experimental component is robust, and the conclusions are well supported by the results. Some suggestions could further refine the conclusions and enhance the quality of the manuscript.

Comments on revised version:

The authors have provided a very comprehensive response. Their updated paper has improved, and the critiques have been mitigated.

Reviewer #3 (Public review):

Summary:

Core conclusions are well-supported by data: co-folding outperforms docking in known ligand pose/affinity prediction (validated by RMSD and IC₅₀ correlation), struggles with false positive discrimination in virtual screens (lower AUC values), and is complementary to docking (non-correlated errors, distinct strengths in drug discovery stages).

Strengths:

Unprecedented prospective design with 557 novel Mac1-ligand complexes ensures rigorous, independent evaluation of co-folding methods, provides an unbiased and rigorous benchmark dataset, which contains structures and compounds absent from the co-folding models training sets. Comprehensive comparison of 3 co-folding tools (AlphaFold3, Chai-1, Boltz-2) with DOCK3.7 across diverse targets and metrics enables nuanced performance assessment. The revised results clarify an intriguing finding: co-folding can predict correct ligand poses even when protein formations are mispredicted. The study clearly demonstrates complementary roles of co-folding (superior pose/affinity prediction for known ligands) and docking (better hit prioritization), and addresses deep learning memorization concerns via ligand similarity analysis.

Weaknesses:

The study identifies a major limitation of co-folding-failure to capture rare protein conformational changes, which deserve future investigation. The authors include uncalibrated Boltz-2 affinity data (addressing a prior comment) but note that large-scale free energy perturbation (FEP) comparisons are beyond their capabilities.

Appraisal of Aims Achieved:

The authors successfully achieved their primary aims and the results provide strong, well-supported evidence for their core conclusions. Key conclusions are grounded in the study's unbiased, training-set independent data, ensures the conclusions are not confounded by model memorization and are broadly applicable to the field's use of these co-folding models.

Field Impact:

This study provides a critical reality check for the field: co-folding models are powerful tools for pose prediction but are not yet standalone solutions for virtual screening, a key distinction that will prevent over-reliance on these models and guide more rational tool selection.

Reviewer #2 (Public review):

Original Review:

The manuscript by Eroglu and Hobert presents a set of strains each harboring up to three fluorescently tagged endogenous proteins. While there is technically nothing wrong with the method and the images are beautiful, we struggled to appreciate the advance of this work - who is this paper for?

As a technical method, the advance is minimal since the first author had already demonstrated that three mutations (fluorophore insertion and co-CRISPR marker) could be introduced simultaneously.

As a pilot for creating genome-scale resources, it is not clear whether three different fluorophores in one animal, while elegantly designed and implemented, will be desired by the broader community.

Finally, the interpretation of the patterns observed in the created lines leaves much to be desired. A Table with all the observations must be included and can replace the tedious (and often wrong) descriptions of the observations with the different lines. It would be too much to point out every mistaken expectation of protein expression. Two examples include:

The expectation that ACDH-10 is enriched in the intestine and epidermal tissues (hypodermis) is naïve - there are multiple paralogs of this protein (look at WormPaths or WormFlux) that may share functions in different tissues. There is also no reason to assume that fatty acid metabolism does not occur in other tissues (including the germline). Finally, there are no published studies about this enzyme, so we really don't know for sure what it's doing.

The expectation that HXK-1 is ubiquitously expressed is similarly naïve. There are three paralogous enzymes that are all associated with the same reaction, and we have shown that these three function redundantly in vivo, perhaps in different tissues (PMID: 40011787). Moreover, single cell RNA-seq data (PMID: 38816550) also shows enrichment of hxk-1 in gonadal sheath cells.

The table should have at least the following information: gene/protein name - Wormbase ID - TPM levels of single cell data assigned to tissues for L2, L4 and adult (all published) - tissues in which expression is observed in the lines presented by the authors.

Other points:

(1) We would encourage the authors to provide systematic validation of the reported insertions. The manuscript reports that 24 of 30 tags were isolated and visible but does not clearly state whether each isolated line was confirmed by sequence‑level validation to be correctly in‑frame and free of unintended mutations at the target locus.

(2) The manuscript presents aggregated success counts (e.g., 8/10 mTagBFP2 tags, 9/10 mStayGold, 7/10 mScarlet3) and useful narrative descriptions of injection outcomes. We suggest also to include per‑locus success rates.

(3) For pools that required re‑injection after initial failures, we would like to see a description of the specific changes that were made to the injection mixes or procedures (e.g., new repair template prep, different Cas9 reagent lot, guide redesign). This will be useful troubleshooting information for others.

(4) The authors states that the fluorophore sequences are codon-optimized for C. elegans. We suggest they provide the exact donor/tag sequences used specifically state whether the fluorophore sequences contain any synthetic/artificial introns or other sequence modifications (e.g., silent PAM‑disrupting mutations) were included in the donor templates.

(5) Page 3: Include a reference for "The C. elegans genome encodes around 20,000 genes"

We hope these comments are useful.

Comments on Revised Version:

Overall, we found the responses to be quite recalcitrant.

We have one remaining composite concern about the comparison between observed expression patterns with the new strains versus published data.

First, the authors only report patterns for one stage while it should be not too much effort to image the different life stages. However, since this is a revision, we are not formally requesting they do this.

Second, in the now provided Table (thank you) 'observed expression' (last column) is lacking for 9 of the 30 proteins, and for 6 of these the procedure was not successful. Why not report patterns for the other three? It is confusing also because on page 5, the authors say that "overall, 24 of 30 tags ...all of which were visible with fluorescence stereomicroscopy" - are we missing something? Also, they then said that they "obtained 6/9 of the originally failed tags"; why are the corresponding patterns not included in table 1, and are 9 proteins still labeled as "no" in the "success?" Column?

Third, we strongly feel that the response to our comments about expression patterns is not adequate. On page 5 the authors say that "all proteins were expected to be ubiquitously expressed" and that "scRNA-seq indicated that transcript abundance was ubiquitous and without strong tissue-specific enrichment with few exceptions". However, in their rebuttal, the authors now argue for tissue-specific expression for proteins with paralogs, turning around their own argument! Moreover, their Table indicates that many genes show tissue-enriched expression by RNA-seq while many of their tagged proteins exhibit ubiquitous expression.

Overall, this indicates that both the overall accomplishment of generating tagged protein strains and analyzing their expression is oversold.

Reviewer #2 (Public review):

Summary:

The authors aimed to determine Molidustat targets and the potential utility of these findings. They clearly demonstrate that Molidustat interferes with GSTP1 and some other proteins on top of PHD2. They also demonstrate that PHD2 deletion is not sufficient to recapitulate Molidustat effects in cells and proteomes. Finally, they demonstrate synthetic lethality in organoids for Molidustat and APC deletion.

Strengths:

The data on Molidustat proteomes, GSTP1 binding, inhibition and metabolic health of organoids is really clear. All biochemical, docking and omic data are really strong. The potential impact of these findings could be the use of Molidustat in APC null tumours and awareness of potential off-target effects.

Reviewer #2 (Public review):

Synesthesia is a neurological condition where stimulation of one sensory channel leads to involuntary, automatic, and consistent experience of another, unrelated percept. For example, Sir Francis Galton (1880, Nature) famously described the robust tendency of some individual (synesthetes) to associate numerals with a distinct color. Ever since, synesthesia keeps attracting a broad interest in the cognitive neurosciences in light of its implications for the study of domains such as perception, consciousness, and brain connectivity, among others.

Strauch, Leenaars, and Rouw measured pupil size in a group of 16 grapheme-color synesthetes and two matched control groups. The participants were presented with gray digits - that is, visual stimuli having identical physical properties in terms of brightness. Each participant subsequently rated the corresponding evoked color and brightness: unlike controls, synesthetes did so in a very consistent and reliable fashion. Accordingly, this was also shown in their pupils: despite the same objective luminance, digits associated with brighter percepts caused their pupils to constrict and digits associated with darker percepts caused their pupils to dilate more than controls. These results highlight how crossmodal correspondences are deeply rooted in synesthetes, and puts forward pupillometry as a particularly appealing biomarker for some phenomenological experience (at least those grounded in "brightness").

Further strengths of the technique are its temporal resolution and its responsiveness to several constructs. Across several tasks, the authors show for example that responses to synesthetic light are somewhat slower than responses to real light (i.e., they are likely mediated), but at the same time faster than responses to mental imagery. The role of mental imagery can also be reasonably dismissed when considering the second feature of pupil size: its responsiveness to mental effort and cognitive load. The pupils tend to dilate with demanding, challenging tasks, and this was the case when control participants were asked to report the color of a digit for which they did not consistently experience a synesthetic association. The same task was, instead, seemingly effortless for synesthetes, again speaking in favor of the automaticity of number-color correspondences in their case.

Overall, the findings by Strauch, Leenaars, and Rouw are highly significant for the field and likely to be impactful. The strength of their evidence, when accounting for the relatively small sample size and the inherent variability of both phenomenology (color perception and subjective reporting) and physiology (pupil size), is adequate and sufficiently convincing.

Comments on revisions:

I thank the authors for addressing all my comments in a satisfactory way. I think that the paper has improved, especially in terms of transparency of the reporting and clarity of the results.

Reviewer #2 (Public review):

Summary:

Chang et al. attempted to analyze a large number of ribo-seq datasets through a standardized pipeline, identifying novel non-canonical ORFs and elucidating their evolutionary and expression characteristics.

Strengths:

(1) The datasets analyzed by the authors are sufficiently comprehensive, and the use of standardized pipelines ensures excellent analytical consistency.

(2) Their analyses of ORF evolution and co-expression further deepen our understanding of these ORFs.

Weaknesses:

(1) The authors primarily conducted analyses through bioinformatics, lacking sufficient wet-lab experimental evidence.

(2) Some analytical methods and standards were not clearly presented in the manuscript.

Reviewer #2 (Public review):