Reviewer #2 (Public review):

Summary:

The question of how caloric and taste information interact and consolidate remains both active and highly relevant to human health and cognition. The authors of this work sought to understand how nutrient sensing of glucose modulates sweet sensation. They found that glucose intake activates hugin signaling to AstA neurons to suppress feeding, which contributes to our mechanistic understanding of nutrient sensation. They did this by leveraging the genetic tools of Drosophila to carry out nuanced experimental manipulations, and confirmed the conservation of their main mechanism in a mammalian model. This work builds on previous studies examining sugar taste and caloric sensing, enhancing the resolution of our understanding.

Strengths:

Fully discovering neural circuits that connect body state with perception remains central to understanding homeostasis and behavior. This study expands our understanding of sugar sensing, providing mechanistic evidence for a hugin/AstA circuit that is responsive to sugar intake and suppresses feeding. In addition to effectively leveraging the genetic tools of Drosophila, this study further extends their findings into a mammalian model with the discovery that NMU neural signaling is also responsive to sugar intake.

Weaknesses:

The effect of Glut1 knockdown on PER in hugin neurons is modest in both fed and starved flies, suggesting that glucose intake through Glut1 may only be part of the mechanism. The authors address this in their discussion.

Reviewer #3 (Public review):

The DNA and RNA binding protein TDP-43 has been pathologically implicated in a number of neurodegenerative diseases including ALS, FTD, and AD. Normally residing in the nucleus, in TDP-43 proteinopathies, TDP-43 mislocalizes to the cytoplasm where it is found in cytoplasmic aggregates. It is thought that both loss of nuclear function and cytoplasmic gain of toxic function are contributors to disease pathogenesis in TDP-43 proteinopathies. Recent studies have demonstrated that depletion of nuclear TDP-43 leads to loss of its nuclear function characterized by changes in gene expression and splicing of target mRNAs. However, to date, most readouts of TDP-43 loss of function events are dependent upon PCR based assays for single mRNA targets. Thus, reliable and robust assays for detection of global changes in TDP-43 splicing events are lacking. In this manuscript, Xie, Merjane, Bergmann and colleagues describe a biosensor that reports on TDP-43 splicing function in real time. Overall, this is a well-described unique resource that would be of high interest and utility to a number of researchers validated in multiple cell types as a sensitive readout of TDP-43 loss of function. Future studies validating the utility of this biosensor in models of TDP-43 loss of function (e.g. disease iPSNs) that do not rely on TDP-43 knockdown will be of further interest.

Reviewer #2 (Public review):

Summary:

The paper proposes a workflow to accelerate EM connectomics by combining multi-scale imaging with image processing and analysis (image alignment, registration, neuron tracing, automated segmentation and synapse prediction, proof-reading) to derive a brain region connectome. The paper argues and (partially) demonstrates that this approach facilitates comparative connectomics.

The data acquisition pipeline uses a well-established sample preparation protocol, uCT guided acquisition, and SBEM imaging at cellular and synaptic resolution.

Data processing and analysis combine existing state-of-the-art components and focus on the alignment and complementary analysis of the two SBEM resolution levels. The paper applies the workflow to the central complex of six different insects and performs some preliminary analysis based on this (which is acceptable for a resource/tool).

Disclaimer for the rest of the review: I am an expert in image analysis and segmentation, so I have mainly focused on these aspects as I am not qualified to analyze the details of image acquisition.

Strengths:

The paper addresses an important problem and promises an acceleration and democratization of comparable connectomics. The time savings of the imaging approach are well-motivated and derived. The methods used for image alignment, segmentation, synapse detection, and proofreading are state-of-the-art.

Weaknesses:

I see two major weaknesses in the paper:

(1) The paper introduces the (approximate) equivalence of the projectome and connectome in the insect brain very prominently in the introduction and uses this as a central motivation for the multi-resolution image acquisition protocol. But - to me - it is unclear how this principle is really used in the analysis presented in the last results and if this assumption is evaluated at all. Specifically, Figure 4 a shows the anatomical neuron reconstructions (from cellular resolution SBEM), d-g show connectome-level analysis from the synaptic resolution data. The only link I can see between the two is that the neural processes in the synapse-resolution data can be mapped to the neurons from the cellular resolution data, thanks to the image alignment. This is certainly important, BUT it is only tangentially related to the projectome vs. connectome claim from the introduction. This claim implies that a tentative connectome is derived from projectome-level data (e.g. by assuming a uniform probability of synapse-formation given surface or distance between projections) that is then validated by the "true" connectome data from synaptic resolution. Instead, what is actually solved - to my understanding - is mapping the local connectome to the projectome. While related, these are different things and the current framing of the paper and the quite brief description of the section on comparative connectomics (also no corresponding Methods section) make this claim inadequately supported.

(2) Reporting on segmentation and proofreading is purely qualitative. Given that this is claimed as a core contribution of the paper (e.g. statement in line 497 and following), I would expect substantially more reporting and evaluation of this claim:<br /> a) Report the actual time needed for proofreading the segmentations in CAVE. I could not find any numbers on this.<br /> b) Report the initial segmentation quality of the model: How many errors does it make? Note: There is a brief mention of VoI-based quantification in Methods (around line 1060), but the results are not reported.

What should be done: Report the error rates (with an accurate measure such as skeleton VoI) independently for all 6 volumes. Given that the authors have the proofread versions, this is feasible. Only then can the claims be made here be evaluated. Note that the F1-score of synapse prediction is quantified. This is a good starting point, but could also be extended to further species in order to assess the actual transferability. Furthermore, none of the data from the study seems to be available. The training data of the network has to be made available. If possible, high-resolution data should be proofread too.

Further points:

(1) Why isn't reconstruction at the cellular level addressed with ML? This is surely possible and should be easier than the full connectome analysis. Similar to before, the actual times needed for tracing with CATMAID are not reported; the manuscript only states that this can be done in minutes for a neuron, but it's unclear if this is the best or average case. It would help to have quantitative numbers to assess whether automation would bring any benefits.

(2) Finally, regarding the underlying software. I did not try this myself due to time constraints, but did check the repositories. They seem to be in an ok state with some documentation in a README. However, given the central role of the software contribution, I would expect a centralized doc page that explains how to use the different parts of the software, including a full example with sample data. Without this, application by other labs - a central claim - will be difficult.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors test the hypothesis that whole-brain functional magnetic resonance imaging in behaving mice, coupled with reinforcement-learning modeling, can dissociate neural substrates of initial cue-reward acquisition versus contingency reversal, and potentially reveal underappreciated contributors to cognitive flexibility. Using a head-fixed go/no-go odor discrimination task with subsequent rule reversal in a subset of mice, they model trial-by-trial state-action values with a model-free Q-learning algorithm (hierarchical Bayesian fit) and use the model-derived decision variable as a parametric regressor in whole-brain analyses. They report that acquisition-related signals prominently involve ventral and dorsal striatal regions, whereas reversal learning additionally recruits the periaqueductal gray (negative correlation with the decision variable) and shows an apparent double dissociation between nucleus accumbens and periaqueductal gray responses for hit versus correct-rejection outcomes during reversal.

Strengths:

(1) The reversal manipulation is implemented without explicit punishment, targeting suppression of previously rewarded actions under reward omission - an underexplored regime for midbrain contributions beyond canonical threat/pain framing.

(2) The manuscript provides a credible MR-compatible olfactory/licking platform with synchronized sniff/lick/valve/reward timing and high-field imaging, supporting feasibility and broader utility for mesoscale systems neuroscience in rodents.

(3) Trial-by-trial value estimates from a Q-learning variant are fit via hierarchical Bayesian inference and explicitly integrated into subject-level general linear models with a mouse hemodynamic response function, which is appropriate for leveraging within-subject dynamics in small-N rodent fMRI.

(4) The decision-variable maps during acquisition recover expected basal ganglia involvement (including nucleus accumbens and dorsal striatum), providing face validity; the reversal-stage map yields an interpretable set of cortical/striatal/pallidal regions plus periaqueductal gray/hippocampus.

(5) The finite impulse response analysis stratified by behavioral outcomes (hit, false alarm, correct rejection, miss) adds interpretability beyond the model regressor alone, and the reported crossover interaction between nucleus accumbens and periaqueductal gray is potentially impactful if robust.

Weaknesses:

(1) The core claim regarding selective periaqueductal gray engagement rests on a subset of n = 6 mice for reversal. With permutation-based whole-brain inference and very small cluster sizes, the robustness of the periaqueductal gray effect to reasonable analytic perturbations is not yet convincing. I would suggest providing leave-one-animal-out analyses for the periaqueductal gray cluster/ROI effects and reporting how often the key findings survive.

(2) The authors note that due to temporal resolution and hemodynamics, they cannot separate stimulus, choice, and feedback and therefore model "whole trials." This limitation creates ambiguity about whether periaqueductal gray signals reflect value updating, action inhibition (no-lick), reward omission, autonomic arousal, or motor preparation/withholding, especially given the strong hit versus correct-rejection opponency. I would suggest adding targeted analyses that disambiguate "withholding" from "reversal-related updating".

(3) ROIs are defined from the whole-brain decision-variable maps and then interrogated by outcome types; the manuscript acknowledges non-independence. This can inflate apparent dissociations. It would be better if the authors define ROIs independently (anatomical periaqueductal gray/nucleus accumbens masks, or split-half ROI definition with held-out data) and repeat the key ROI conclusions.

(4) The reversal group is a subset of the acquisition cohort and also experiences a different task phase structure and additional sessions; the paper attempts to address exposure differences descriptively. I would suggest that the authors formally test whether periaqueductal gray effects are explained by session count, time-in-scanner, or learning rate differences (e.g., include these as covariates, or match sessions more strictly).

(5) The platform records sniffing and licking, but the imaging models described include motion, global, and ventricle regressors and do not clearly include trialwise lick/sniff covariates. Given the periaqueductal gray's known autonomic and defensive coordination roles, physiological state confounding is a major concern. Could the authors incorporate sniff and lick metrics (and their derivatives) as nuisance regressors and show whether the periaqueductal gray effects persist?

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 of 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 findings rely heavily on public dataset analyses.

Reviewer #2 (Public review):

Summary:

Bhattacharya and colleagues here use cell culture, single-cell RNA and ATACseq sequencing of such in vitro cultures and of ex vivo isolated B-lineage cells to infer an ontogeny for extra-germinal centre B cell differentiation. The manuscript presents a useful potential ontogeny for plasma cells, wherein in vitro cultured naïve human B cells enter a CD30+ intermediate state before moving in subsequent days through a CD44v9+ state before ultimately obtaining a 'mature' antibody-secreting plasma cell phenotype. Ex vivo isolated germinal centre B cells obtain the plasma cell state without expressing CD30 in their development. Phenotype analysis of tonsillar B-lineage cells supports the same phenotype conversion in vivo, although the intermediate cell population was smaller in vivo. The link to CD44v9 expression on developing plasma cells is inferred to be for extra-GC (T-independent) responses, but the data presented leave this equivocal, and the functional importance of developing via a CD30+CD44v9+ intermediate is not investigated.

Strengths:

The article presents a solid potential ontogeny for PC development, wherein some differentiating B cells acquire a CD30+ state, transition through a CD44v9+CD30+ state, then downmodulate CD30 before obtaining canonical CD38+ 'PC' status. A strength is the integration of in vitro cultured B cell results with tonsillar B-lineage cell data sets, and careful flow cytometry of the in vitro cultures over several days to infer lineage. The data provide reasonable support for the concept. CD30+ cells are shown to develop readily from naïve B cells in culture, but uncommonly from GC B cell cultures. A nice piece of data is Figure 6B, which shows reasonably strong correlative changes in phenotype through the assumed ontogeny, and this fits with the expected trajectory of maturation.

Weaknesses:

The most important weakness throughout is the non-absolute nature of the relationship. An example is seen in that the sorted ex vivo GC B cells also give rise to the 'extra-GC' phenotype of plasma cell, suggesting that while the profile is enriched, it is not absolute. There is a further weakness, as while cultures are run for several days, division-associated shifts in PC phenotype are not mapped; such would greatly strengthen the weight of the argument, and show conditional shifts in phenotype associated with division, an uncontrolled parameter in the mix. For example, for the MEF2C A388 inhibition experiments, it would be strong evidence of the pathway/process contributing if a by-division peak increase in CD30+ population was demonstrated in the early days of culture.

There are some basic sort experiments performed (e.g. 3C-3F), which show that the CD30+ cells do give rise to PC preferentially, but what is missing is the step-wise phenotype shifts in these sorted populations, which should support the trajectory shown in Figure 3B and (the in vitro equivalent of) 6B. It would emphatically support the trajectory to show the cellular phenotypes on the PC with sorting based on CD30, CD44v9, CD27, and CD20 expression, and following outcome phenotypes 24-48 hours later, if the inferred maturation trajectory is true.

There are also specific weaknesses with the bioinformatics, in that, while the analyses are likely appropriate, unpresented data is necessarily used to shape the argument. For example, Figure 1C shows bubble plots for two plasma cell sets, yet, of archetypal PC-expressed genes, only IRF4 is demonstrated to confirm they are true PC, and the gene is not universally expressed in cells in the clusters. For this figure, it would help to expand the bubble plot to show J-CHAIN, XBP-1, CIITA and PRDM1 or other appropriate PC demarcating molecules. Similarly, in Fig 2B, more evidence of a bifurcation in state is needed than that CD44v9 distinguishes PC1 from PC2 clusters-this is the stated conclusion, but 2A depicts that 50% of PC1 relatively weakly express CD44, while <25% of PC2 express it. Demonstrating additional molecules or genes distinguishing the clusters would improve veracity. Figure 2F shows clonal lineages, but it would be helpful to see somatic hypermutation burdens and learn if they differ between the demarcated subsets. I also find the pseudotime analyses of limited value, as some of the branches follow trajectories that are unrealistic biologically, so less weight should be placed on the pathways to which they do or do not point (i.e., the notion that GC B cells do or do not give rise to particular PC subsets).

Statistically, some of the experiments are single wells from single donors, so there is a low level of confidence and no reproducibility demonstrated for some aspects of the study, which is a weakness.

Paradoxical to the argument that it is the TI response process being modelled, it is presented that CpG stimulation, plus proxy T cell help (CD40L), drives the CD30+ phenotype best with the addition of the GC-associated cytokine IL-21. This should be carefully considered and discussed.

Overall, in addition to presenting more contextual information from the bioinformatics, the best way to solidify the data set, in my vie,w would be to revisit the hypothesis with two additional experimental approaches: (1) to incorporate division tracing into the ontogeny studies and (2) to perform lineage tracing on sort-purified populations at different stages of the maturation process.

cephalexin, dicloxacillin, penicillin VK, amoxicillin/clavulanate, or clindamycin (for penicillin-allergic patients). [1-2] These beta-lactam antibiotics provide excellent coverage against streptococci and methicillin-susceptible S. aureus (MSSA

endovascular stenting emerging as first-line therapy for rapid symptom relief, while definitive treatment targets the underlying cause

Reviewer #2 (Public review):

Summary:

In this study, the authors took advantage of a semi-intact ex vivo somatosensory preparation that includes hindlimb skin to characterize the response of projection neurons in the dorsal horn of the spinal cord to peripheral stimulation, including cold thermal stimuli. The main aim was to characterize the connectivity between peripheral afferents expressing the cold sensing receptor TRPM8 and a set of genetically tagged neurons of the anterolateral system (ALS). These ALS neurons expressed high levels of the calcium binding protein calbindin 1.

In addition, combining different viral tracing methods, the authors could identify the anatomical targets of this specific subset of projection neurons within the brainstem and diencephalon.

Strengths:

The use of a relatively new (seldom used previously) transgenic line to label TRPM8-expressing afferents, combined with the genetic characterization of a previously identified subset of projections neurons add specificity to the characterization. The transgenic line appears to capture well the subpopulation of Trpm8-expressing neurons.

In addition, the use of electron microscopy techniques makes the interpretation of the structural contacts more compelling

The writing is clear and the presentation of findings follows a logical flow.

Overall, this study provides solid, novel information about the brain circuits involved in cold thermosensation.

Weaknesses:

In the characterization of recorded neurons in close contact or in the absence of this contact with TRPM8 afferents, the number of recordedd neurons is relatively low. In addition, the strength of thermal stimuli is not very well controlled, preventing a more precise characterization of the connectivity.

The authors acknowledge that, technically, this is a very difficult preparation with very low yield as far as obtaining successful recordings. Moreover, the tissue needs to be maintained at room temperature which is obviously not ideal when characterizing cold thermoreceptors due to the unavoidable effects of low temperature on cold-activated receptors.

Reviewer #2 (Public review):

Summary:

In this study, the authors took advantage of a semi-intact ex vivo somatosensory preparation that includes hindlimb skin to characterize the response of projection neurons in the dorsal horn of the spinal cord to peripheral stimulation, including cold thermal stimuli. The main aim was to characterize the connectivity between peripheral afferents expressing the cold-sensing receptor TRPM8 and a set of genetically tagged neurons of the anterolateral system (ALS). These ALS neurons expressed high levels of the calcium-binding protein calbindin 1.

In addition, combining different viral tracing methods, the authors could identify the anatomical targets of this specific subset of projection neurons within the brainstem and diencephalon.

Strengths:

The use of a relatively new (seldom used previously) transgenic line to label TRPM8-expressing afferents, combined with the genetic characterization of a previously identified subset of projection neurons, adds a specificity to the characterization. The transgenic line appears to capture well the subpopulation of Trpm8-expressing neurons

In addition, the use of electron microscopy techniques makes the interpretation of the structural contacts more compelling.

The writing is clear, and the presentation of findings follows a logical flow.

Overall, this study provides solid, novel information about the brain circuits involved in cold thermosensation.

Weaknesses:

In the characterization of recorded neurons in close contact or in the absence of this contact with TRPM8 afferents, the number of recorded neurons is relatively low. In addition, the strength of thermal stimuli is not very well controlled, preventing a more precise characterization of the connectivity.

The authors could provide some sense of the effort needed to record from the 6 cold-activated neurons described. How many preparations were needed, etc?

Reviewer #2 (Public review):

Summary:

In this study, the authors have demonstrated, through a comprehensive approach combining electrophysiology, chemogenetics, fiber photometry, RNA interference, and multiple behavioral tasks, the necessity of projections from CCK+ CAMKIIergic neurons in the hippocampal CA3 region to the CA1 region for regulating spatial memory in mice. Specifically, authors have shown that CA3-CCK CAMKIIergic neurons are selectively activated by novel locations during a spatial memory task. Furthermore, authors have identified the CA3-CA1 pathway as crucial for this spatial working memory function, thereby suggesting a pivotal role for CA3 excitatory CCK neurons in influencing CA1 LTP. The data presented appear to be well-organized and comprehensive.

Strengths:

(1) This work combined various methods to validate the excitatory CCK neurons in the CA3 area; these data are convincing and solid.

(2) This study demonstrated that the CA3-CCK CAMKIIergic neurons are involved in the spatial memory tasks; these are interesting findings, which suggest that these neurons are important targets for manipulating the memory-related diseases.

(3) This manuscript also measured the endogenous CCK from the CA3-CCK CAMKIIergic neurons; this means that CCK can be released under certain conditions.

Weaknesses:

In summary, this work can be formally accepted after the revision. For the limitations of the revision, the distinct neural effects of cholecystokinin (CCK) receptors (CCK-1R, CCK-2R, and CCK-3R) on hippocampal function have not been fully elucidated. Recent studies indicate that CCK-2R can modulate hippocampal activity at CA3-Schaffer collateral synapses; however, the roles of CCK-1R and CCK-3R in hippocampal function remain poorly characterized, with limited experimental evidence supporting their involvement. Overall, this study provides an interesting and novel perspective on the role of excitatory CCK signaling in hippocampus-dependent navigation learning.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigate the response of the amphibian respiratory rhythm generator under varying excitability conditions. They use pharmacological agents to increase and/ or decrease synaptic excitability and demonstrate the resilience of buccal rhythms under different conditions. They employ these results to formulate their primary thesis, that there is no obligatory locus of the buccal respiratory rhythm in the frog, and that their respiratory rhythmogenic mechanisms should be considered diffuse and anatomically distributed across a larger brainstem region.

Strengths:

This manuscript is well written, with a sufficiently large number of experiments, for which the authors should be congratulated.

Weaknesses:

The presented results don't support the authors' main conclusions, and the interpretation of the data is heavily biased toward their hypothesis. This impregnates an unsubstantiated narrative in the Abstract, Introduction, and Discussion of this manuscript, which must be reexamined with the following points in consideration:

(1) The authors seem to confuse degeneracy with redundancy. For instance, at line 54, they state, "These findings support the broader hypothesis that respiratory rhythm-generating circuits can switch to being diffuse and redundant, with discrete oscillators quickly drowning in a sea of excitations."

Redundancy means having the same component repeated multiple times to buffer the failure of any single component, whereas degeneracy means different functional components that compensate for one another under perturbations (Goaillard and Marder, ARN 2021)

Since the premotor-lung units get converted to buccal units under high excitability, this suggests a degenerate mechanism for respiratory rhythm generation- rather than a redundant mechanism, where there should be multiple buccal units that get recruited under different excitability conditions.

(2) Line 83, "but the essential requirement for a discrete, rudimentary buccal oscillator is also lost".

This statement is not supported by the data presented in this study. How does the expansion of the buccal unit imply that the essential requirement for discreteness is lost? Under increased excitability, does the burst/rhythm initiation zone also expand? Or does it still remain centered around the location of buccal units under physiological conditions? Increased excitability can lead to recruitment of a larger area, without a change in the location of the rhythmogenic kernel.

(3) Line 86, "... oscillators should be viewed as promiscuous flexible functional entities that expand or contract...".

Oscillators can be regarded as promiscuous only if, under physiological conditions, they switch positions. Under high excitability, only the flexibility argument holds, which has been established in mammals before (e.g., CA Del Negro, K Kam, JA Hayes, JL Feldman, The Journal of physiology 587 (6), 1217-1231; CA Del Negro, C Morgado-Valle, JL Feldman,Neuron 34 (5), 821-830; NA Baertsch, LJ Severs, TM Anderson, JM Ramirez, Proceedings of the National Academy of Sciences 116 (15), 7493-7502; NA Baertsch, HC Baertsch, JM Ramirez Nature communications 9 (1), 843).

Results:

(4) Interpretation of data in Figure 6.

How does the Buccal activity and L2 Power stroke change with 60nm AMPA (in CN5)? Does the increase in the Buccal neurons and decrease in power stroke neurons also reflect in the CN5 activity? Also see comments on Figure 9 data below.

(5) Interpretation of data in Figure 7.

Here, classifying buccal neurons solely by spiking may obscure the fact that the 'silent' neurons under baseline conditions were part of the rhythmic network but could not spike due to subthreshold inputs. 60 nM AMPA increased their firing in response to previously subthreshold synchronous inputs during the buccal burst. Intracellular recordings are required to negate this possibility and establish that the neuronal classification is robust.

(6) Interpretation of data in Figure 8.

"Lung units can transform into buccal units under excitation".<br /> CN5 buccal and lung bursts need to be compared before and after AMPA injection. From Figure 8 A-D, it is apparent that the example Unit2's activity increases during the buccal bursts, after AMPA injection. However, they are also present in buccal burst pre-AMPA, albeit with less frequency.

It is striking that the pre-AMPA epoch (panel A) is less than half of the post-AMPA epoch. This would, in itself, lead to a biased estimate of lung units that are active under the baseline condition during the buccal bursts.

Figure 8G, meta-analysis of lung units spiking during the baseline buccal bursts is warranted to interpret the main claim of this figure. Similarly, analysis of spiking per lung burst for the post-AMPA condition is essential for comparing the lung unit's contribution under high excitability.

(7) Interpretation of data in Figure 9

"Buccal area loses importance under increased excitation."

This interpretation is not fully supported by the data presented in this manuscript. Under 60 nm AMPA, does the ratio of lung burst to buccal burst change in CN5? This analysis is crucial for determining whether the lung units are indeed converted into buccal bursts at the expense of lung activity or whether their appearance during buccal bursts is incidental due to increased excitability. In the baseline, there are 4-5 buccal bursts per lung burst, whereas under high excitability, there are 2-3 buccal bursts per lung burst (Figure 9 A-B). This seems inconsistent with the conclusion that increased excitability converts lung units into buccal units (Figures 6 &7).

Could the authors comment on the connectivity between the lung and the buccal units? Results in Figure 9A-B indicate that lung units may receive an efference copy of buccal units, and under high excitability, their spikes may generate negative feedback onto the buccal units, terminating their bursts. This could explain the decrease in the buccal-to-lung burst in high-AMPA conditions. This type of circuit interaction resembles the mammalian breathing CPG, in which the parafacial/RTN (which controls the abdominal muscles) and preBötC (which controls the diaphragm) interact and cross-inhibit each other.

(8) Line 382.

"Buccal-like bursting produced from two independent slices".

The two "independent" slices have portions of the same anatomical kernel, the buccal rhythm generator. This experiment is like the sandwich slice preparation of preBötC (Del Negro Lab), in which two thinner slices exhibit rhythmic activity. Thus, the two slices are not independent; they are anatomically adjacent and functionally overlapping.

Reviewer #2 (Public review):

Summary:

This study by Choucri and Treiber aims to directly address a recent critique regarding the role of transposable elements (TEs) in diversifying the neural transcriptome of Drosophila. The authors seek to demonstrate that TEs are not merely genomic "noise" but are frequently and reliably "exonized" into brain-specific mRNA. By introducing an upgraded computational pipeline, TEChim, and conducting precise experimental validations, the authors set out to show that TE-mediated splicing represents a genuine biological phenomenon that expands the molecular repertoire of the nervous system.

Strengths:

The study's primary strength lies in its rigorous technical "forensic" analysis of previous failed replication attempts. The authors convincingly demonstrate that the lack of signal in the opposing study stemmed from a fundamental methodological mismatch: the software used by the critics (TIDAL) is logically incapable of detecting splice sites located within TE sequences. Importantly, the authors complement this computational clarification with definitive experimental evidence through an effective "experimental rescue." By employing correctly designed primers and matching the genetic backgrounds of the fly strains, thereby accounting for genomic polymorphisms, they successfully validated all seven loci that were previously reported as undetectable. This dual-pronged strategy, addressing both algorithmic bias and experimental design, establishes a more robust technical benchmark for the detection and validation of TE-derived exons in neural tissues.

Weaknesses:

While the technical rebuttal is highly convincing, the scope of the study remains primarily defensive. As a response to a prior critique, the work focuses on establishing the existence and detectability of chimeric TE-derived transcripts rather than exploring their broader functional consequences. As a result, there is limited new insight into how these TE-modified isoforms influence neural circuit function or organismal behavior. In addition, the detection and validation of these events remain technically demanding, requiring deep sequencing and specialized bioinformatic expertise, which may limit broader adoption by laboratories without dedicated computational resources.

Reviewer #3 (Public review):

Xu, Cao and colleagues aimed to overcome the obstacles of high-resolution imaging of intact liver tissue. They report successful modification of the existing CUBIC protocol into Liver-CUBIC, a high-resolution multiplex 3D imaging method that integrates multicolor metallic compound nanoparticle (MCNP) perfusion with optimized liver tissue clearing, significantly reducing clearing time and enabling simultaneous 3D visualization of the portal vein, hepatic artery, bile ducts, and central vein spatial networks in the mouse liver. Using this novel platform, the researchers describe a previously unrecognized perivascular structure they termed Periportal Lamellar Complex (PLC), regularly distributed along the adult liver portal veins.<br /> Using available scRNAseq data, the authors assessed the CD34⁺Sca-1⁺ cells' expression profile, highlighting mRNA presence of genes linked to neurodevelopment, bile acid transport, and hematopoietic niche potential. Different aspects of this analysis were then addressed by protein staining of selected marker proteins in the mouse liver tissue. Next, the authors addressed how the PLC and biliary system react to CCL4-induced liver fibrosis, implying PLC dynamically extends, acting as a scaffold that guides the migration and expansion of terminal bile ducts and sympathetic nerve fibers into the hepatic parenchyma upon injury.

The work clearly demonstrates the usefulness of the Liver-CUBIC technique and the improvement of both resolution and complexity of the information, gained by simultaneous visualization of multiple vascular and biliary systems of the liver. The identification of PLC and the interpretation of its function represent an intriguing set of observations that will surely attract the attention of liver biologists as well as hepatologists. The importance of the CD34+/Sca1+ endothelial cell population and claims based on transcriptomic re-analysis require future assessment by functional experimental approaches to decipher the functional molecules involved in PLC formation, maintenance, and the involvement in injury response before establishing their role in biliary, arterial, and neural liver systems.

Strengths:

The authors clearly demonstrate an improved technique tailored to the visualization of the liver vasulo-biliary architecture in unprecedented resolution.<br /> This work proposes a new morphological feature of adult liver facilitating interaction between the portal vein, hepatic arteries, biliary tree, and intrahepatic innervation, centered at previously underappreciated protrusions of the portal veins - PLCs.

Weaknesses:

The importance of CD34+Sca1+ endothelial cell sub-population for PLC formation and function was not tested and warrants further validation.

Reviewer #2 (Public review):

In this study, the authors investigate how increasing cognitive demand shapes activity patterns in the dorsal dentate gyrus (DG). Using a touchscreen-based TUNL task combined with TRAP/c-Fos tagging, birth-dating of adult-born granule cells (abDGCs), and chemogenetic inhibition, they show that higher task demand increases mature granule cell (mGC) recruitment and enhances suprapyramidal (SB) versus infrapyramidal (IB) blade bias. Functionally, mGC inhibition reduces overall activity and impairs performance without disrupting blade bias, whereas inhibition of {less than or equal to}7-week-old abDGCs increases mGC activity, abolishes blade bias, and impairs discrimination under high-demand conditions. These findings suggest that effective pattern separation depends not only on overall DG activity levels but also on the spatial organization of recruited ensembles.

The integration of touchscreen TUNL with temporally controlled activity tagging and birth-dated cohorts is technically strong. Quantification of SB-IB bias and radial/apical distributions adds anatomical precision beyond bulk activity measures. The comparison between mGC and abDGC inhibition is conceptually compelling and supports dissociable functional roles. Overall, the data convincingly demonstrate that increasing cognitive demand amplifies blade-biased DG recruitment and that mGCs and abDGCs differentially contribute to both behavioral performance and network organization.

However, how abDGCs are integrated into the mGC network under high cognitive demand remains unresolved. Additional experiments are needed to clarify how abDGCs shape spatial recruitment patterns and whether they directly inhibit or indirectly regulate mGC activity to maintain high performance.

Furthermore, the authors frame "high cognitive demand" as a multidimensional construct encompassing broad behavioral challenge. It would strengthen the work to delineate how local abDGC-mGC circuit interactions regulate specific task components in real time. This will require higher temporal resolution approaches, as TRAP and c-Fos labeling integrate activity over prolonged windows and primarily reflect sustained engagement rather than moment-to-moment computations.<br /> The central conclusion that dentate function depends on coordinated spatial recruitment rather than total activity magnitude is supported by the data, although mechanistic interpretations should be tempered given methodological limitations.<br /> Overall, this work advances models of adult neurogenesis by emphasizing a critical-period modulatory role of abDGCs in organizing DG network activity during high-demand discrimination. The combined behavioral and circuit-level framework is likely to be influential in the field.

Reviewer #2 (Public review):

Summary:

The authors present a new method, Unbend, for measuring motion in cryo-EM images, with a particular emphasis on more challenging in situ samples such as lamella and whole cells (that can be more prone to overall motion and/or variability in motion across a field of view). Building on their previous approach of full-frame alignment (Unblur), they now perform full-frame alignment followed by patch alignment, and then use these outputs to generate a 3D model of the motion. This model allows them to estimate a continuous, per-pixel shift field for each movie frame that aims to better describe complex motions and so ultimately generate improved motion-corrected micrographs. Performance of Unbend is evaluated using the 2D template matching (2DTM) method developed previously by the lab, and results are compared to using full-frame correction alone and to the leading local motion correction methods. Several different in situ samples are used for evaluation covering a broad range that will be of interest to the rapidly growing in situ cryo-EM community.

Strengths:

The method appears an elegant way of describing complex motions in cryo-EM samples and the authors present sound data that Unbend generally improves SNR of aligned micrographs as well as increases detection of particles matching the 60S ribosome template when compared to using full-frame correction alone and since review to the leading local motion correction methods. The authors also give interesting insights into how different areas of a lamella behave with respect to motion by using Unbend on a montage dataset collected previously by the group. There is growing interest in imaging larger areas of in situ samples at high resolution and these insights contribute valuable knowledge. Additionally, the availability of data collected in this study through the EMPIAR repository will be much appreciated by the field.

Weaknesses:

A major weakness was comparing this method to full-frame approaches only but this has since been addressed by the authors during review and Unbend is compared to MotionCor2, 3, CryoSPARC and Warp. The improvements here are smaller, generally it seems to perform on par with the above methods, but there are significant gains for certain samples (e.g. the M. pneumoniae sample). A comment from this reviewer about using an adaptive approach to decide if/when to proceed to the full Unbend pipeline, over full-frame alone, has been addressed by the authors.

Reviewer #2 (Public review):

Summary:

Yamashiro et al. investigated how transient absence of visual input (i.e. darkness) impacts tactile neural encoding in the rat primary somatosensory cortex (S1). They recorded local field potentials (LFPs) using a 32-channel array implanted in forelimb and hindlimb primary somatosensory cortex while rats walked on smooth or rough textures under illuminated and dark conditions. Employing a convolutional neural network (CNN), they successfully decoded both texture and lighting conditions from the LFPs. The authors conclude that the subtle differences in LFP patterns underlie tactile representation surface roughness and become more distinct in darkness, suggesting a rapid cross-modal reorganization of the neural code for this sensory feature.

Strengths:

• The manuscript addresses a valuable question regarding how sensory cortices dynamically adapt to changes in sensory context.<br /> • The use of machine learning (CNNs) enables the analysis to go beyond conventional amplitude-based metrics, potentially uncovering subtle but meaningful effects.<br /> • The authors have substantially improved the manuscript with clearer figures, additional statistical analyses (including permutation tests and cross-validation), and greater methodological transparency.

Weaknesses:

• The new analyses (grand-average LFPs, correlation maps, wavelet decompositions, attribution-score correlations) improve transparency but do not yet clarify which specific neural features the CNN exploits, leaving the central interpretability question unresolved.<br /> • A plausible alternative explanation for the increased discriminability in darkness remains insufficiently ruled out: visually driven activity in the light condition (e.g., ambient illumination changes or self-motion-induced visual input) could contaminate S1 LFPs and account for the effect without reflecting a true neural representational change.<br /> • Behavioural and order controls have been improved but remain somewhat limited in sample size.

Overall assessment:

The revised manuscript is clearer, more transparent, and technically strengthened. However, the true nature of the signal changes underlying the observed differences in discriminability remains unclear, limiting the scientific strength of the conclusions. The possibility that visual interference contributes to the observed effects remains a plausible and untested alternative interpretation. Additional experiments or analyses quantifying visually evoked activity in S1 would be required to confirm the claim of genuine reorganization of neural representation depending on the illumination condition.

Reviewer #3 (Public review):

The paper identifies effects of gonadal hormones within hormone-responsive GABAergic neurons in the MPOA. Although it is not surprising that hormones have effects on neurons that express hormone receptors, the current paper adds insights with higher cellular and spatial resolution than previous work and focuses on adolescence period. The paper also identifies a major role for Esr1-dependent mechanisms on behavior using an intersectional genetic strategy to ablate Esr1 in GABAergic or glutamatergic neurons in the MPOA.

The authors have thoughtfully addressed the reviews, in particular by focusing quantitative analyses on Vgat+Esr1+ clusters and adding important technical and conceptual considerations in the limitations section.

I have one remaining minor concern. I appreciate that the text now defines "transcriptional maturation". However, the term seems inappropriate when describing the "minimal transcriptional changes" in Vgat+hormone RLow clusters, which implies that they are transcriptionally immature. Do the authors mean to imply that transcriptional maturation is observed in Vgat+Esr1+ clusters but not Vgat+hormone RLow clusters? The authors also use the term "hormone-dependent transcriptional dynamics", which I think is more appropriate. For example, hormone-dependent transcriptional dynamics are observed in Vgat+Esr1+ clusters but not Vgat+hormone RLow clusters.

Reviewer #2 (Public review):

In this manuscript, the authors test growth, behavior, and gene expression in pairs of clownfish as they establish social dominance hierarchies, examining patterns of gene expression in these pairs after dominance has been established. The authors show solid evidence that emerging dominant clownfish show increased growth, aggression, and food consumption compared to their submissive or solitary counterparts, eventually adopting distinct gene expression profiles.

Major Comments:

(1) The Introduction is comprehensive, but it could be condensed. Likewise, the discussion could be condensed. There is considerable redundancy between the methods, the results, and the legend in Figure 1. The authors should consolidate and remove the redundancy.

(2) For Figure 3, the authors are showing PC2 and PC3; why is PC1 not shown? There is so much overlap between the three groups in PC2 vs PC3; it seems unlikely that researchers could conclusively identify any individual as belonging to a group based on the expression profile. The ovals shown do not capture all the points within each of the groups, and particularly the grey S oval seems misaligned with the datapoints shown.

(3) The authors indicate that the 15 replicates exhibiting the greatest size difference between P1 and P2 were selected for gene profiling. Does this mean that each of the P1 and P2 were pairs with each other? Have the authors tried examining the gene expression patterns in a paired manner? E.g., for the pairs that showed the greatest size differences, do they also show the greatest differences in gene expression? Do the P1s show the most extreme differences from P2s that also show the most extreme P2 differences? Perhaps lines on Figure 3A connecting datapoints from the P1 and P2 pairs would be informative.

(4) For the specific target pathways that are up- and downregulated in the different backgrounds, I recommend that the authors include boxplots (or heatmaps) showing the actual expression values for these targets. Figure 6 shows a heatmap for appetite-related genes, and it would be great to see a similar graph for the metabolism and glycolysis genes; it would also be informative to see similar graphs for hormonal and sexual maturation pathways as well.

(5) Particularly given that there is a relatively small number of genes enriched in the different rank conditions, I did not understand the need to do the WGCNA module analysis. I thought that an analysis of GO terms across the dataset would have been more meaningful than the GO term analysis shown in Figure 4, which considers only genes assigned to the "brown WGCNA module". This should be simplified or clarified.

(6) The authors say that they have identified coordinated changes in behaviors and the "underlying gene expression, leading to the emergence" of social roles. This is a little bit misleading, since the gene expression analysis occurred well after the behavioral and phenotypic differences emerged. Presumably, the hormonal and genetic shifts that actually caused the behavioral and phenotypic difference occurred during the weeks during which the experiment was underway, and earlier capture of the transcriptome would presumably reveal different patterns, and ones that would be considered more causative. The authors acknowledge this in 434-435, but it could be emphasized further.

(7) The authors have measured a number of differences between the different dominance classes of fish. All these differences were measured relative to the other classes, but in my view, the Solitary group was the closest to a baseline control. So I'm not sure that it is fair to say that "P2 and S individuals showed consistent downregulation of these genes and pathways" (line 401). I encourage the authors to emphasize the differences in gene expression from the "perspective" of the P1 individuals compared to the baseline of P2 and S individuals. Line 474 says that "P2 fish showed significant upregulation" of a number of pathways. It should be very clear what that is compared to (compared to P1, presumably?)

(8) Along the same lines, the authors say in line 514 that subordinates and solitaries strategically downregulate their growth. I'm not convinced that this is the case: I would consider this growth trajectory to be the default and the baseline. I would interpret that under certain social conditions, a P1 dominant pattern of growth, behavior, and gene expression is allowed to emerge.

Reviewer #2 (Public review):

Summary:

The authors use a combination of genomics, genome conformation assays, and CRISPR-mediated deletion to study the transcriptional regulation of the SOX2 gene in human neural stem cells (hNSCs).

Strengths:

The authors show that two distal elements, located ~550kb downstream of the SOX2 gene, are important for SOX2 transcription in hNSC. They investigate both the deletion of these elements in established hNSCs and in hNSCs generated by differentiation of human pluripotent stem cells, suggesting these elements are important in both the establishment and maintenance of SOX2 expression in hNSCs.

Weaknesses:

Homologous elements have been studied in the mouse genome and have conserved function in mouse NSCs, yet these findings are not mentioned. Inclusion of biological replicates for the scRNA-seq and replicate CRISPR-deleted clones would strengthen the study.

Reviewer #2 (Public review):

Summary:

Silva and co-workers exploit their previously established methods of analyzing release events at single parallel fiber to molecular layer interneuron synapses. They observed synaptic depression at low transmission frequencies (< 5 Hz), which rapidly recovers during high-frequency transmission. Analysis of the time course of low-frequency depression revealed an initial rapid and a slow linearly increasing time course. Strikingly, the initial depression occurred even in the absence of preceding release, arguing against vesicle depletion as the underlying mechanism.

Strengths:

The main strength of the study is the careful demonstration of an interesting synaptic phenomenon challenging the classical vesicle-centered interpretation of synaptic depression.

Weaknesses:

No major weaknesses were identified by this reviewer.

The finding of release-independent synaptic depression is important and would have widespread implications. Therefore, some more analyses to increase the confidence in these findings could be performed.

My concern is whether rundown could explain the findings. If the rate of failures in s1 increases and at the same time the amplitude decreases during the experiments, an apparent depression in s2 could arise. The Supplementary Figure 5A addresses run-down, but the figure is not easy to understand, and, as far as I understood, it does not address the question of whether the release-independent depression could be caused by a rundown. To address this, the analysis of Figure 5 could be repeated by investigating the failure rate and amplitude separately or by analyzing the 1st and 2nd half of the recordings separately.

Reviewer #2 (Public review):

Summary:

Prior work identified TMEM30B (knockout mice) as well as ATP8B1 (human genetics and mouse model), ATP8A2 (knockout mice), and ATP811A (human genetics) as relevant for hearing. The authors also reasoned that, given the recent discovery of TMC1 and TMC2's dual function as mechanotransduction channels of the inner ear and as lipid scramblases, a counterpart flippase should be in the sensory hair-cell stereocilia bundle where mechanotransduction happens. They use CRISPR/CAS to modify the endogenous mouse genes and add an HA tag at the N-terminus of the ATP8B1, ATP8A1, ATP8A2, and ATP11A proteins. Their experiments with these mice unambiguously localized ATP8B1 at the base of outer hair cell stereocilia bundles. Knockout of ATP8B1 results in loss of outer hair cells, deficient auditory function (ABR), and degeneration of outer hair cell stereocilia bundles. Similarly, hair cells from genetically modified mice with endogenous HA-tagged TMEM30B proteins show localization of this protein to outer hair cell stereocilia bundles. TMEM30B knock-out mice phenocopy the ATP8B1 knock-out model. Interestingly, the authors show that annexing V staining precedes hair cell loss in ATP8B1 and TMEM30B knockout mice and that proper localization of these proteins is lost in mice that lack CIB2, a protein essential for hair cell mechanotransduction.

Strengths:

(1) Use of knock-in HA-tagged proteins, rather than antibody staining, to unambiguously localize ATP8B1 and TMEM30B.

(2) Systematic characterization of auditory function (ABR), hair cell loss, and hair-cell stereocilia bundle morphology.

(3) Advances our understanding of the role played by lipid homeostasis in auditory function.

(4) Reports on mouse models that will be helpful to further understand the mechanistic role played by ATP8B1 and TMEM30B in normal hearing and hereditary deafness.

Weaknesses:

(1) Are the HA tags causing any functional issues? Function and localization of tagged proteins can sometimes be compromised. It would be good to know, for each knock-in model (TMEM30B, ATP8B1, ATP8A1, ATP8A2, and ATP11A ), whether the HA-tagged protein is causing any issues with the mice and particularly with hearing (ABRs). Are these mice normal? Can they hear? These data are missing.

(2) Following on the point above, is it possible that ATP8B1-HA is well localized, but localization for the other three flippases (ATP8A1-HA, ATP8A2-HA, and ATP11A-HA) is compromised by the tag? Is this potential mislocalization causing any functional phenotypes? (ABRs of point 1). I find it surprising that there are flippases only in outer hair cells, and only formed by ATP8B1. A possible explanation is that the tag is interfering with trafficking. If so, there should be a phenotype (ABRs), although this might be masked by redundancy among these flippases or caused by systemic issues (admittedly difficult to sort out). Given that this manuscript will likely become foundational, and that there is evidence that at least two of the other flippases are involved in hearing loss, it would be good to provide more information about the mice and HA-tagged proteins in the other knock-ins (ATP8A1-HA, ATP8A2-HA, and ATP11A-HA). Depending on the data available for the knock-ins, the authors may want to discuss these scenarios and soften the statement indicating that inner-hair cells may lack flippase activity altogether.

(3) Expression of ATP8B1 at P0 (Figure 1D), when there should not be protein in outer hair cells yet, seems high. Does this mean that other cells in the cochlea also express ATP8B1? Is this a concern?

(4) Fluorescence scales in Figure 6 B and D and Figure 7 B and D are very different. So are the values for WT. One would expect that the WT would be similar in all cases (at least within the same compartments), given that the methods section indicates that "All images were collected using identical acquisition parameters, including zoom and laser power, across genotypes". If WT shows such variability, how can we compare?

Reviewer #3 (Public review):

Summary:

This study addresses a long-standing question in visual neuroscience concerning how the human visual system balances stability and plasticity when sensory input is altered from early in life. Using achromatopsia as a model of lifelong cone deprivation, the authors examine whether early visual cortex undergoes retinotopic reorganization to compensate for the absence of foveal cone input, or whether canonical retinotopic organization is largely preserved. By combining fMRI-based population receptive field (pRF) mapping with connective field (CF) modelling, the authors characterize changes across multiple hierarchical stages of visual processing.

The main findings indicate that primary visual cortex (V1) shows no systematic remapping of the foveal projection zone, whereas extrastriate cortex, particularly V3, exhibits altered patterns of sampling from V1. The authors interpret these results as evidence for hierarchical adaptation, whereby downstream readout mechanisms adjust to make more efficient use of degraded rod-mediated input while preserving early-stage retinotopic organization.

Strengths:

A major strength of this work is the use of silent substitution to generate rod-selective stimuli. This approach enables a principled comparison between achromats and typically sighted controls by isolating rod-driven responses in both groups. In doing so, the study overcomes a key limitation of prior work, where differences in cortical organization could often be confounded by differences in photoreceptor class rather than reflecting neural reorganization per se. The inclusion of a rod-driven baseline in controls provides an important reference for distinguishing long-term adaptation from transient or stimulus-driven effects.

Another notable strength is the integration of CF modelling alongside conventional pRF mapping. While pRF analyses alone suggest enlarged receptive fields in V1, consistent with reduced spatial resolution, the CF analysis offers a more mechanistic account by revealing changes in how V3 samples information from the V1 surface. This multi-level modelling approach moves beyond descriptive accounts of cortical map structure and provides a framework for interpreting how downstream areas may adjust their integration strategies under conditions of altered input.

Weaknesses:

Although the study is methodologically strong, the central claims regarding stability and compensatory plasticity require clearer conceptual framing and stronger empirical support. Stability is primarily defined as the absence of large-scale retinotopic remapping in V1, yet the presence of significantly enlarged V1 pRFs indicates substantial tuning-level plasticity at the input stage; distinguishing topographic stability from functional reorganization would therefore strengthen the interpretation. Moreover, the proposed compensatory mechanism raises a signal-processing concern, as reduced downstream sampling (smaller CFs in V3) cannot restore spatial information lost due to coarse upstream representations, and may instead limit integration. The mechanistic link between altered CF properties and normalization of extrastriate pRFs is not directly tested, as group differences are not shown to covary across individuals or visual field locations. Finally, the interpretation of these changes as compensatory implies functional benefit, yet no behavioral or performance measures are provided to establish that the observed reorganization preserves or enhances visual function, leaving open whether these effects reflect adaptive optimization or passive downstream consequences of altered input.

Reviewer #2 (Public review):

The paper proposes a hierarchically layer approach to larval locomotion, chemotaxis and learning. The model consists of a basic locomotor layer with two coupled oscillators, one for crawls and one for turns. The intermediate layer modulates the frequency and amplitude of tunings to enables chemotaxis. The higher layer, integrates a spiking neural network model of the Mushroom Body to modify the door valence in response to experience as during learning.

The model is compared to experimental data with a good degree of agreement. This modular framework provides a valuable advance for modeling larva behavior.

Strengths:

A novel multilayer level model that reflects current thinking of the neuronal organisation of motor control. The model is very useful to investigate the neuronal architecture of central pattern generators<br /> and higher order motor control circuits that could be linked to larval connectome data.

Weaknesses:

All the limitations of the model are discussed and therefore the paper perfectly fits its purpose.

Reviewer #2 (Public review):

This study investigates the visual information that is used for the recognition of faces. This is an important question in vision research and is critical for social interactions more generally. The authors ask whether our ability to recognise faces, across different viewpoints, varies as a function of the orientation information available in the image. Consistent with previous findings from this group and others, they find that horizontally filtered faces were recognised better than vertically filtered faces. Next, they probe the mechanism underlying this pattern of data by designing two model observers. The first was optimised for faces at a specific viewpoint (view-selective). The second was generalised across viewpoints (view-tolerant). In contrast to the human data, the view-specific model shows that the information that is useful for identity judgements varies according to viewpoint. For example, frontal face identities are again optimally discriminated with horizontal orientation information, but profiles are optimally discriminated with more vertical orientation information. These findings show human face recognition is biased toward horizontal orientation information, even though this may be suboptimal for the recognition of profile views of the face.

One issue in the design of this study was the lowering of the signal-to-noise ratio in the view-selective observer. This decision was taken to avoid ceiling effects. However, it is not clear how this affects the similarity with the human observers.

Another issue is the decision to normalise image energy across orientations and viewpoints. I can see the logic in wanting to control for these effects, but this does reflect natural variation in image properties. So, again, I wonder what the results would look like without this step.

Despite the bias toward horizontal orientations in human observers, there were some differences in the orientation preference at each viewpoint. For example, frontal faces were biased to horizontal (90 deg) but other viewpoints had biases that were slightly off horizontal (e.g. right profile: 80 deg, left profile: 100 deg). This does seem to show that differences in statistical information at different viewpoints (more horizontal information for frontal and more vertical information for profile) do influence human perception. It would be good to reflect on this nuance in the data.

Comments on revisions:

I am happy with the response and changes to the comments in my review. The key findings from this study are: (1) that there is bias toward the use of horizontal information across all viewpoints for face recognition in humans using an old-new recognition task. (2) In contrast, the optimal information for matching faces varies as a function of viewpoint. The view-selective model shows horizontal information is dominant for frontal views and vertical information is dominant for profile views.

The data from the view-tolerant model is less easy to interpret as it doesn't fit with any theoretically plausible model of face recognition. It might be a useful model for a face matching task in which participants had to match unfamiliar faces across viewpoints. This might be a possible extension of the current work.

Nonetheless, I still think this is an interesting contribution to the literature.

Reviewer #2 (Public review):

Summary:

This article is a useful compendium of advice for MD/PhD students (and research-focused MD students) to consider when it is time to decide on a clinical field for residency training. The authors are a distinguished group of physician-scientists and program directors who are drawing on published data and their own experience as mentors to provide advice and resources to students about to make what can be a career-defining choice. It makes an effective argument for considering important differences between clinical fields in their ability to sustain research integration, provide mentorship, meet lifestyle expectations, and foster a long-term career as a research-focused physician-scientist.

Strengths:

(1) A lot has been written about physician-scientists as an endangered species. Given the important role that physician-scientists can play if they engage in research that is informed by experience in patient care, not nearly enough has been written about the choices that students make during training that can keep them on track or throw them off.

(2) The article provides not only general advice, but specific information in the 2 tables that can help trainees to weigh their priorities and consider their options.

(3) Among the best advice is to weigh clinical demands, maintenance of procedural skills, recognition of the impact of research time on salary, and the impact of high salaries on the tension between research effort and clinical effort in clinical departments, which is where most physician-scientists in academia are employed.

Areas for potential improvement:

(1) Some of the most useful pieces of advice are scattered through the text when they might be more impactful if focused. For example, what are the 4 or 5 most essential factors that someone in an MD/PhD or an MD program should weigh when they are deciding between clinical disciplines? There are also published data on the experience of past graduates in achieving a research-focused career in each clinical discipline. How should that data be applied by trainees? What are the factors that should be weighed in deciding where to work as a research-focused physician once training has been completed?

(2) Some clinical fields at academic institutions have proved to be much more hospitable to careers as research-focused physicians than others. Published data highlight the challenges. I believe the authors have tried very hard to present a balanced perspective, but in the process, they have, I believe, missed an opportunity to guide trainees and make them aware of what they should look for to avoid making a decision that may prove incompatible with their long-term goals.

(3) An issue that hasn't been raised: Where will be the jobs for physician-scientists who have an MD {plus minus} PhD and want to do research and discovery? How many openings will there be for physician-scientists in academia 5-10 years from now? In industry? How are recent events in Washington affecting the continuation of those jobs? Unfortunately, I am not aware of labor statistics for physician-scientists, but perhaps the authors can find them.

(4) Additional questions that can be raised and addressed in the article: Should one of the "smart choices" in the article's title be where you do the residency, and not just which residency you do? How important is it to be at a successful, research-intensive medical center/university, both during and after residency and fellowship training? If being in an institution where there are numerous very successful physician-scientists and scientists improves the likelihood of being able to sustain a physician-scientist career, how should graduating students improve their chances of being at one of those institutions?

(5) In every clinical discipline, there are departments that value physician-scientists more than other departments and invest accordingly. What advice would the authors give to help graduating students identify those departments?

Reviewer #2 (Public review):

I think this paper is an excellent and timely contribution. It clearly shows that learning overlapping relationships in a disjoint training schedule (where the overlaps are not encountered close together in time) appears to aid the formation of an integrated associative memory structure (a cognitive map) and supports generalisation. I believe the methods are sound and the results are clear. I only have a couple of methodological questions that may not warrant any changes to the paper (or only very minor changes/additions):

(1) The mixed effects models did not include random slopes for the within-subject factors ("spatial manipulation" and "block"), and so the corresponding fixed effect inferences may be unsafe. Having said that, it is likely that including these slopes may not be warranted given their contribution to the model's fit. I recommend that the authors check this.

(2) The mixed effects models for accuracy appear to model average performance across trials rather than using a generalised linear model with a (e.g.) logit link function and the binomial distribution to characterise performance. I think this is a little sub-optimal, as the latter is often more sensitive. Nonetheless, it is not in any way wrong; the results are clear enough as is, and there may be a good reason to avoid a non-linear link function, which can alter the interpretation of effects close to the ceiling and floor.

I think the introduction and/or discussion would benefit from contrasting their results with Berens & Bird (2022, PLOS Comp Bio). In this paper, it is shown that blocking the training of discriminations in a linear hierarchy (what we call progressive training) substantially benefited transitive inference performance. This seems at odds with the author's finding that "participants struggle to integrate information across rows and columns, i.e. across groups of transitions that were trained separately in time".

I would really like to know what the authors think about this discrepancy (or, indeed, whether they think there is one at all). Is it possibly because "progressive" learning is some combination of "grouping", "blocking" and "chaining" (where there is a structured overlap between adjacently trained relationships)? Or is it something else, e.g., that there is a fundamental difference between learning associations and discriminations (personally, I lean on this explanation)?

Relevant to this, the authors note that their "findings do contradict recent reports from the category learning literature, where blocking seems to help learning and generalisation (Dekker et al., 2022; Flesch et al., 2018; Noh et al., 2016). It may be that where the goal is not to learn a complex knowledge structure - like a map - but simply to compress exemplars by mapping them onto a smaller number of labels - the benefits of blocking emerge." However, the benefit of progressive (blocked) training in my own work was observed in a task that required learning a complex/relational structure in the form of a transitive hierarchy, which theoretical accounts suggest depends on learning map-like representations (Whittington et al., 2020).

Reviewer #2 (Public review):

Summary:

The manuscript by Puno and colleagues investigates the impact of hypoxia on cortical interneuron migration and downstream signaling pathways. They establish two models to test hypoxia, cortical forebrain assembloids and primary human fetal brain tissue. Both of these models provide a robust assay for interneuron migration. In addition, they find that ADM signaling mediates the migration deficits and rescue using exogenous ADM. The findings are novel and very interesting to the neurodevelopmental field, revealing new insights into how cortical interneurons migrate and as well, establishing exciting models for future studies.The authors use sufficient iPSC lines including both XX and XY, so analysis is robust. In addition, the RNAseq data with re-oxygenation is a nice control to see what genes are changed specifically due to hypoxia. Further, the overall level of valiation of the sequencing data and involvement of ADM signaling is convincing, including the validation of ADM at the protein level. Overall this is a very nice manuscript. I have a few comments and suggestions for the authors.

Strengths/Weaknesses:

(1) Can they comment on the possibility of inflammatory response pathways being activated by hypoxia - has this been shown before? While not the focus of the manuscript, it would be discussed in the Discussion as an interesting finding and potential involvement of other cells in the Hypoxic response.

(2) Can they comment on the mechanism at play here with respect to ADM and binding to RAMP2 receptors - is this a potential autocrine loop, or is the source of ADM from other cell types besides inhibitory neurons? Given the scRNA-seq data, what cell-to-cell mechanisms can be at play? Since different cells express ADM, there could be different mechanisms at place in ventral vs dorsal areas.

(3) For data from Figure 6 - while the ELISA assays are informative to determine which pathways (PKA, AKT, ERK) are active, there is no positive control to indicate these assays are "working" - therefore, if possible, western blot analysis from assembloid tissue could be used (perhaps using the same lysates from Fig 3) as an alternative to validate changes at the protein level (however, this might prove difficult); further to this, is P-CREB activated at the protein level using WB?

(4) Can the authors comment further on the mechanism and what biological pathways and potential events are downstream of ADM binding to RAMP2 in inhibitory neurons? What functional impact would this have linked to the CREB pathway proposed? While the link to GABA receptors is proposed, CREB has many targets beyond this.

(5) Does hypoxia cause any changes to inhibitory neurogenesis (earlier stages than migration?) - this might always be known but was not discussed.

(6) In the Discussion section - it might be worth detailing to the readers what the functional impact of delayed/reduced migration of inhibitory neurons into the cortex might results in, in terms of functional consequences for neural circuit development

Comments on revisions:

The authors have addressed my comments thoroughly. I have no further comments or suggestions

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors report that Nora virus, a natural Drosophila pathogen that also persistently infects many laboratory fly stocks, infects intestinal stem cells (ISCs), leading to a shorter life span and increased sensitivity to intestinal infection with the Pseudomonas bacterium. Nora virus infection was associated with an increased proliferation of ISC and disrupted gut barrier function. Genetically, the authors show that increased ISC division in Nora virus and Pseudomonas coinfected flies is driven by signaling through the JAK-STAT pathway and apoptosis.

Accordingly, blocking apoptosis and JAK-STAT signaling reduces viral load, suggesting that in this context the JAK-STAT pathway is proviral in contrast to other previous observations in systemically infected flies. This work adds to the findings of another recent paper showing that another persistent fruit fly virus, Drosophila A virus, also increases ISC proliferation and decreases gut barrier function. Intestinal viruses should therefore be considered confounders in studies of fly intestinal physiology.

Strengths:

Overall, the data are convincing and robust, starting with two wildtype fly stocks (Ore-R strain) that differ in their Nora virus infection status, followed by experiments in which cleared stocks are reinfected with a purified Nora virus stock preparation. The conclusions of the paper will be of interest to scientists working on insect physiology, virology, and immunology, but should also serve as a warning for scientists that use the fly as a model to study gut physiology.

Reviewer #3 (Public review):

Summary:

The authors present a new computational method (OPT) for predicting off-target probe binding in the commercial 10X Xenium spatial transcriptomics platform. They identified 28 genes in the 10x xenium human breast cancer gene panel (280 genes) that are not accurately detected at the single-molecule level. They validated the predicted off-target binding using reference data from single-cell RNA-seq and 3'-sequencing-based Visium RNA-seq. This work provides a practical resource and will serve as a valuable reference for future data interpretation.

Strengths:

(1) Provides a toolbox for the community to identify off-target probes.

(2) Validates the predictions using single-cell RNA-seq and sequencing-based Visium RNA-seq datasets.

Comments on revision:

The authors state that OPT is a new software tool and have posted example code on GitHub. However, the Jupyter notebook does not display any figures or workflows that would allow the process to be replicated. Please provide documentation and code that can reproduce the results/figures presented in the paper.

Reviewer #2 (Public review):

Summary:

This paper examines resting-state electroencephalography (EEG), the electrophysiological underpinnings of the temporal integration window in perception, and its modulation by priors (serial dependence) as measured through the perceptual fusion point of two continuous alternating stimuli. The study also includes a measure of perceptual confidence. Separating periodic from aperiodic EEG activity, the results show that the faster the individual alpha-frequency at rest and the steeper the aperiodic slope (previously linked to higher sampling/ lower noise), the lower the perceptual fusion point (corresponding to narrower integration windows), with independent contributions of the period and aperiodic activity to the integration window. The data also reveal that the point of fusion depends on prior history, and that the strength of this effect depends on individual alpha frequency and aperiodic slope: the lower the individual alpha frequency and the aperiodic slope, the stronger the serial dependence, with the two contributions being again independent. Higher alpha frequency also led to higher confidence. The results are interpreted to suggest that speed of alpha oscillations and aperiodic slope of the power spectrum (presumably reflecting rate/fidelity of visual sampling and the level of background noise) jointly shape the perceptual measure under study: high rate/ fidelity and low noise promote temporal precision in integration, while lower rate/fidelity and higher noise lead to a higher reliance on prior history. It is concluded that it is the interaction between two EEG features that shapes temporal integration and hence perceptual fusion.

Strengths:

The strength lies in the use of a continuous visual stream of two alternating stimuli whose timing shapes fusion or separation of the two stimulus precepts, avoiding some of the pitfalls of previous fusion probes through discrete (not continuous) stimulus pairs (missed detection of one stimulus of the pair may be misinterpreted as fusion). The results seem robust (based on n=83 participants), the results are interesting, and the interpretations are sound.

Weaknesses:

The main weakness lies in the reliance on resting state EEG for correlation with the behavioural measures. This captures trait-based relationships, but does miss out on the brain activity dynamics within/across trials, which could be used for a direct readout of evidence accumulation to a decision, for capturing spontaneous fluctuations of the processes under study, etc. Also, in terms of resting state EEG, both eyes-closed (EC) and eyes-open (EO) data have been recorded, but their links to perceptual fusion point/ confidence seem somewhat inconsistent across the results. This is a bit confusing. Are the EO and EC signals in any way related/ correlated, and if not, what are they supposed to represent? Would an analysis of these EEG measures during task performance (e.g., in a pre-stimulus = baseline time window) provide more consistent results? These points could be resolved by additional analyses and/or more elaborate discussions.

Reviewer #2 (Public review):

Summary:

Using a public dataset of retinotopic mapping and resting-state data, the authors find that the default mode network has voxels that respond (positively or negatively) to visual stimulation at specific retinotopic positions, and that resting-state activity in these voxels is correlated with activity in more traditional sensory voxels with the same visual-location preference. The retinotopic specificity is bidirectional, such that high activity in default mode voxels drives activity only in voxels with matching receptive fields in sensory cortex, and vice versa. These findings are at odds with traditional views of the default mode network as having abstract (non-retinotopic) representations and competing (rather than cooperating) with external sensory representations.

Strengths:

This study continues an intriguing line of research about how default mode regions interact with the sensory cortex. Demonstrating that there are structured interactions between these regions at rest, and that these interactions are in fact organized according to retinotopic location (as opposed to traditional views of representational format in the default mode network), provides a new framework for thinking about large-scale internal and external brain networks. The authors make use of a well-powered public dataset that allows for precise estimates of pRFs and individual-specific resting-state networks, and develop a number of interesting analyses that characterize the relationships between DN and dATN voxels. The findings are exciting and could have a major impact on future studies in cognitive neuroimaging.

The authors mention that these findings could shed light on internal/external interactions such as "anticipatory saccades or memory-guided attention," which is true, though I would argue that constructing DN representations of external stimuli is in fact even more fundamental than these specific cases (e.g., see Barnett and Bellana, 2025, "Situation models and the default mode network"). The "highways" identified in this study could play a vital role in real-world perceptual processes that are constantly translating external input into internal mental models.

Weaknesses:

(1) The criterion used for defining voxels as retinotopic seems very liberal. The authors show that only 5% of voxels have R^2>0.14 in a null analysis, and therefore define voxels with R^2>0.14 as retinotopic. Although all the networks in 1C show voxel distributions that differ from the null, the number of false positives above R^2>0.14 seems problematic, especially for the DN positive pRFs (red distribution) and to a lesser extent the DN negative pRFs (blue distribution). From visual inspection of the plot, the false discovery rate (fraction of voxels labeled as retinotopic that are false positives) looks like it would be greater than 50% for the DN-positive pRFs. The authors do show that the positive pRF voxels have above-chance consistency across runs, again providing evidence that there are true positive voxels in this set, but perhaps a stricter criterion (such as having consistent negative fits across runs) would provide more targeted identification of the DN voxels with true retinotopic sensitivity.

(2) The claim that "opponency at rest between the DN and dATN appears to be driven by the subset of DN voxels with negative retinotopic tuning" is not well supported. The fraction of DN voxels with negative pRFs is small: 9.42% of DN voxels have pRFs, and 58.77% are negative, so about 6% of DN voxels have negative pRFs. The fact that any DN voxels have negative pRFs is notable, but the authors do not provide evidence that these 6% are driving the overall behavior of the DN. They do show (e.g., in Figure 2B) that negative and positive pRFs have opposing influences, but the overall correlation with dATN does not look similar to the negative pRF connectivity. I'm also unsure whether "opponency" is a reasonable description for two networks that are "independent (i.e., not correlated)" in this analysis.

(3) The event-triggered analysis is effective at testing the bidirectional relationship between DN and dATN, with high activity in either network triggering a response in the other network. However, it would be helpful to show more validation that these "events" are meaningful windows of time to study. First, is 13 TRs a typical length of time that activity is elevated during one of these events? Second, the top-down and bottom-up terminology is perhaps too loaded and not well-justified; if the negative pRFs in the DN reflect a meaningful coding system, then couldn't low (rather than high) activity indicate a top-down event?

(4) The framing of this paper relative to the authors' past week, such as Steel et al. 2024 ("A retinotopic code structures the interaction between perception and memory systems"), could be improved. The existence of negative pRFs in the DN and a functional relationship between these pRFs and the sensory pRFs have already been described in prior work. My understanding of the primary novelty here is that this paper examines resting-state data, showing that there are widespread spontaneous interactions between broad internal and external networks, but this distinction is not made explicit in the Introduction.

(5) The definition of the default mode (DN) in this study aligns with past research, but the definition of the dorsal attention network (dATN) seems at odds with standard terminology. For example, the authors cite Fox et al. 2006, which depicts the dATN as including regions such as IPS, FEF, SMA, and MT+. Here, however, the "dATN" seems to be primarily lateral and ventral visual cortex (e.g., Figure S5). The exact location of these sensory pRFs is not critical to the authors' claims, but this labeling seems incorrect, and the motivation for defining/selecting the sensory network in this way is not described.

Reviewer #2 (Public review):

Summary:

The manuscript by Garcia-Alcala et al. reports an interesting paradox: the cost of gene expression slows the population-average growth rate, whereas at the single-cell level, expression levels from these genes positively correlate with the growth rate. The effect is observed in the expression of flagellar genes and a gene under a synthetic promoter in E. coli. The findings are explained by the inheritance of growth factors, including ribosomes, during asymmetric division.

Strengths:

(1) The manuscript adds strength to an emerging body of literature showing that the population-level bacterial growth laws do not match correlations based on single-cell data. The evidence presented here is more striking than in previous works (such as Pavlou et al., Nat. Commun. 2025), as the trends in population-level data and single-cell data are reversed.

(2) A relatively simple model correctly explains the trends in the data.

Weaknesses:

(1) It is not clear whether flagellar proteins are expressed proportionally to the reporter signal. Furthermore, it is questionable if E. coli bacteria in the mother machine channels are flagellated. If they are, they could potentially swim out of the channels, which is not the case when they do not carry the MotA E98K mutation. The authors should provide some evidence that E. coli expresses the actual filament proteins in the channels.

(2) It is unclear what fraction of the total proteome mVenus represents in different measurements. Some quantification is needed (for example, using the Coomassie staining). Using f_U as high as 14.4% in simulations is questionable.

(3) The data from the MC4100 strain does not directly match the trends of MG1655. The justification for filtering out the low-frequency components of MC4100 is not particularly convincing. It appears unlikely that ribosomes or other growth factors partition significantly differently in the MC4100 strain than in the MG1655 strain. Further discussion and a plot similar to Figure 1 (Left) for this strain are warranted.

(4) The model needs to be described in more detail. A closed set of equations that has been simulated must be presented, along with all values of the model parameters and their sources. The authors should consider depositing their code on GitHub or another publicly accessible repository.

Reviewer #2 (Public review):

McGaughey & Gold trained rhesus macaque monkeys to perform a motion-direction discrimination task in which a behaviorally irrelevant adapting stimulus with either fast or slow direction alternations preceded a variable-duration test stimulus, while simultaneously recording single-unit activity in area MT and pupil diameter. They report that adaptation to the more rapidly changing stimulus was associated with reduced behavioral sensitivity, attenuated test-evoked MT responses, and larger pupil-linked arousal signals. The authors interpret these behavioral changes as evidence for a more "leaky" evidence-accumulation process, and argue that this apparent leak is implemented in part through context-dependent sensory adaptation in MT and in part through arousal-related mechanisms. More broadly, they conclude that flexible evidence accumulation in dynamic environments arises from distributed adjustments across sensory encoding and neuromodulatory systems rather than solely from changes within a downstream accumulator. If correct, this interpretation has significant implications not only for our understanding of the neural mechanisms of perceptual decision-making but also for broader theories concerning the functional role of sensory adaptation.

The conclusions of the paper are mostly well supported by the data. Evidence for robust adaptation-induced changes in sensory encoding, behavior, and pupil dynamics is convincing, but further clarification and refinement are needed to establish a clear mechanistic link between these effects and decision-making processes.

Aspects of the behavioral analysis would benefit from a tighter connection between theoretical claims about evidence accumulation and the empirical features of the psychometric functions. For example, the rightward shifts observed across adapting conditions are interpreted as consistent with a reset of accumulation on switch trials, but similar patterns could also arise from failures to detect the test stimulus on a subset of trials, leading responses to default to the final adaptor direction. Likewise, changes in psychometric slope and asymptote are attributed to differences in evidence accumulation without explicit modelling or consideration of alternative explanations. Clarifying how specific features of the psychometric functions map onto distinct components of the decision process will strengthen the link between the theoretical framework and the behavioral data.

A slight concern is the lack of a consistent analytical approach for relating behavioral changes to neural and pupil-linked measures. Different sections of the manuscript rely on different behavioral metrics-such as differences in accuracy within a selected stimulus-duration range (e.g., Figure 5C) or psychometric slope differences (Figure 6C) - without clear justification for these choices. The analytical approach likewise varies between simple correlational analyses (Figure 5C, Figure 6C), pseudo-experimental group comparisons (Figures 5D, E), and the inclusion of neural or pupil terms in the behavioral psychometric regression model (Figure 7B). While each metric and approach may be defensible in isolation, adopting a more consistent framework will help convince readers that the reported effects are robust and not contingent on the selective choice of metric or analysis.

Reviewer #2 (Public review):

Summary:

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

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

Strengths:

The significance of these findings is fundamental. Decades of attention research in vision have been based on the paradigm of visual fixation and covert peripheral attention. However, increasingly, the field has moved towards understanding how the visual system works during active vision. Here, the authors use an active visual search paradigm and record from multiple areas (V4, IT, PFC). They find enhancement of attention both in the foveal and peripheral locations, and, furthermore, a high degree of feature and categorical specificity. This provides valuable data for the concept of a foveal-peripheral attentional window in natural vision. The controls (comparisons of neuronal response during looks to targets vs distractors, and looks towards and away from the target) and statistical rigor make these findings quite compelling.

Weaknesses:

While the study is generally quite strong, there are a few weaknesses to be addressed.

(1) Little rationale is provided for recording in the selected areas, V4, IT, and PFC. Given the respective roles in sensory, object recognition, and goal-directed behavior, some rationale for this design should be offered, and commonalities/distinctions between these areas should be discussed.

(2) Given the reliance of all analyses on saccadic behavior (towards target/distractor, towards/away from target), additional description and summaries of eye movement behavior during single trials and across trials should be provided.

(3) The dependency of findings on top-down (categorical & feature-specific) task design should be discussed.

Reviewer #2 (Public review):

Summary:

Spike sorting, that is, assigning events detected in extracellular electrophysiology data to the firing of individual neurons, is an inherently difficult computational problem involving multiple steps. The difficulty arises from low signal-to-noise, instability in signal due to the relative motion of the tissue and recording sites, and large volumes of data. Experimental ground truth data - where the correct assignment of spikes is known - is not available in large enough quantities to test algorithms. This paper describes a tool for creating fully synthetic ground truth data and benchmarking the individual steps of spike sorting to dissect the impact of signal-to-noise, firing rate, and motion correction on each step. This information is used to construct an optimized algorithm for sorting the ground truth data. One result of particular interest is the dominant role of motion correction in degrading accuracy. Another important technical result is that motion correction via interpolation of the voltage traces yields similar accuracy to interpolation of the spike templates.

Strengths:

The paper clearly shows the benefits of analyzing the complex process of spike sorting step by step. While this analysis has also been done in papers presenting spike sorters (for example, reference [32]), the tools presented here allow users and developers to do similar studies for their own work. This toolset will be very useful to many labs, especially those working in less studied brain areas or model systems, cases where the tuning of standard spike sorting tools is not a good match to the data.

Weaknesses:

The model ground truth data used in the paper does not need to be a perfect match to experimental data to provide useful benchmarking. However, as with all measurements of spike sorting accuracy, extrapolation to experimental data can be complicated. Users of these tools will need to assess how well the simulated data matches their recordings.

Reviewer #2 (Public review):

Summary:

In this study, Yang et al. develop a real-time system for automatic face detection and identification of multiple unrestrained common marmosets in a home cage setting.

Strengths:

The study aims to address an unmet need in behavioral neuroscience: the ability to non-invasively identify animals is crucial to the automated and rigorous study of neural behaviors; this is especially true for common marmosets, which are rapidly becoming a model system of choice for the study of complex social cognition. By using a YOLOv8 backbone, the study achieve human level performance, both in terms of precision and recall of the trained models.

Weaknesses:

The robustness of the system is not clear from the limited datasets presented. The use of color-coded beads undercuts the study's premise that the system achieves truly non-invasive tracking. Although the system achieves good performance in face detection, it does not perform as well for classification using faces alone (especially when the faces are similar, as in twin animals). Here, too, the color-coded beads play a key role in identity discrimination. The stated goals of the study and the actual results presented are therefore at odds.

Reviewer #2 (Public review):

Summary:

The authors used an LFA-1 αI-Fc fusion protein to pull down potential ligands and LC-MS/MS, leading to the selection of PfGBP-130 as a potential membrane protein on the surface of infected cells. PfGBP-130 antibodies were raised and used to support the surface localization. This putative ligand interacted strongly with LFA-1 (Kd = 15 nM). A presumed PfGBP-130 ectodomain interacts with monocytes and NK cells but not cells that lack LFA-1. PfGBP-130 antibodies also interfered with NK cell-mediated infected cell killing; the effect, although statistically significant, is modest. The authors propose that NK cells recognize infected cells via LFA-1 interaction with PfGBP-130 exposed on the host cell and that this interaction is critical to initiation of NK cell activation and killing of infected cells.

Major points:

(1) PfGBP-130 is proposed to be a membrane protein based on a single predicted transmembrane domain. Figures 2b and 3a show ribbon schematics with this TM domain at residues 51-68, in agreement with TM prediction algorithms such as TMHMM 2.0 and Phobius. However, this predicted TM is upstream of the PEXEL motif (residues 84-88, sequence RILAE), a conserved sequence for parasite protein export to host cytosol that is proteolytically processed at its 4th residue. Thus, residues 1-87 are removed from PfGBP-130 prior to export, yielding a mature protein without predicted TMs. Prior studies have determined that the mature PfGBP-130 lacks TMs and is retained as a soluble protein in host cell cytosol (PMID: 19055692, 35420481). Thus, the authors' model of PfGBP-130 as a surface-exposed membrane protein conflicts with both computational analysis of the mature protein and these prior reporter studies. An important simple experiment would be to evaluate PfGBP-130 membrane association in immunoblots using the authors' PfGBP-130 antibody after hypotonic lysis (PMID: 19055692) and after alkaline extraction (e.g. 100 mM NaCO3, pH 11 as frequently used, PMID: 33393463). If the prior studies and computational analyses are correct, the protein will be predominantly in the soluble and/or alkaline supernatant fractions.

(2) Many findings rely on the specificity of antibodies generated against PfGPB-130 or NK cell receptors. Although the authors have included key controls (use of isotype control antibodies, lack of anti-PfGBP-130 binding to uninfected cells), cross-reactivity between P. falciparum antigens is well-recognized and could significantly undermine the interpretation of experiments (PMID: 2654292 and 1730474 provide key examples of antigens recognized by antibodies raised against other proteins). For example, the surface localization in IFA experiments (Figure 2B(iii)) could reflect anti-PfGBP-130 binding to an unrelated parasite surface antigen, a possibility not addressed by any of the authors' controls. As another example, the iRBC lysate immunoblot using this antibody in Fig. 2B(iv) suggests a MW of 95 kDa, which corresponds to the unprocessed pre-protein before export; cleavage in the PEXEL motif yields a processed mature protein of 85 kDa, which should be readily resolved from the pre-protein in immunoblots (PMID: 19055692). A better immunoblot using immature infected cell stages might show both the pre-protein and the mature protein as a doublet band.

(3) PfGBP-130 is not essential for in vitro cultivation (PMID: 18614010 and MIS of 1.0 in the piggyBac mutagenesis screen as tabulated on plasmodb.org, indicating a highly dispensable gene). The authors should use the knockout line as a control in their IFA localization experiments to address antibody specificity. More fundamentally, their model predicts that NK cells should not recognize or kill infected cells from the knockout line when compared to their untransfected parent. Such results with the knockout line would compellingly support the authors' model without reliance on antibodies that may cross-react with other parasite antigens. PMID: 18614010 reported that the PfGBP-130 knockout exhibited increased membrane rigidity, suggesting an intracellular scaffolding protein rather than a surface localization and use as a ligand for LFA-1 interaction and NK cell-mediated killing.

(4) PfGBP-130 non-essentiality raises the question of why the gene would be retained if it triggers NK cell-mediated killing of infected cells in vivo. Presumably, this killing would pose strong selective pressure against retention of PfGBP-130. Some speculation is warranted to support the model.

Reviewer #2 (Public review):

In 'Developmental constraints mediate the summer solstice reversal of climate effects on European beech bud set [their original title]' Rebindaine and co-authors report on two experiments on Fagus sylvatica where they manipulated temperatures of saplings between day and night and at different times of year. I think the experiments are interesting, but I found the exact methods of them somewhat extreme compared to how the authors present them. Further, given that much of the experiment happened outside, I am not sure how much we can generalize from one year for each experiment, especially when conducted on one population of one species. I was also very concerned by the revisions.

I expand briefly on these concerns and a few others for readers of the paper (see `The below comments relate to my original review'). Subsequent edits to the paper addressed some of these by providing a new figure and moving around the methods. Further, I am at a loss about their hypothesis, when they write in their letter: "Importantly, the Solstice-as-Phenology-Switch hypothesis does not assume that the reversal is fixed to June 21." Why on earth reference the solstice if the authors do not mean to exactly reference the solstice?

The comments below relate to my original review with many of them still applying.

Methods: As I read the Results I was surprised the authors did not give more info on the methods here. For example, they refer to the 'effect of July cooling' but never say what the cooling was. Once I read the methods I feared they were burying this as the methods feel quite extreme given the framing of the paper. The paper is framed as explaining observational results of natural systems, but the treatments are not natural for any system in Europe of which I have worked in. For example a low of 2 deg C at night and 7 deg C during the day through end of May and then 7/13 deg C in July is extreme. I think these methods need to be clearly laid out for the reader so they can judge what to make of the experiment before they see the results.

I also think the control is confounded with growth chamber experience in Experiment 1. That is, the control plants never experience any time in a chamber, but all the treatments include significant time in a chamber. The authors mention how detrimental chamber time can be to saplings (indeed, they mention an aphid problem in experiment 2) so I think they need to be more upfront about this. The study is still very valuable, but -- again -- we may need to be more cautious in how much we infer from the results.

Also, I suggest the authors add a figure to explain their experiments as they are very hard to follow. Perhaps this could be added to Figure 1?

Finally, given how much the authors extrapolate to carbon and forests, I would have liked to see some metrics related to carbon assimilation, versus just information on timing.

Fagus sylvatica: Fagus sylvatica is an extremely important tree to European forests, but it also has outlier responses to photoperiod and other cues (and leafs out very late) so using just this species to then state 'our results likely are generalisable across temperate tree species' seems questionable at best.

Measuring end of season (EOS): It's well known that different parts of plants shut down at different times and each metric of end of season -- budset, end of radial expansion, leaf coloring etc. -- relate to different things. Thus I was surprised that the authors ignore all this complexity and seem to equate leaf coloring with budset (which can happen MONTHS before leaf coloring often) and with other metrics. The paper needs a much better connection to the physiology of end of season and a better explanation for the focus on budset. Relatedly, I was surprised the authors cite almost none of the literature on budset, which generally suggests is it is heavily controlled by photoperiod and population-level differences in photoperiod cues, meaning results may different with a different population of plants.

Somewhat minor comments:<br /> (1) How can a bud type -- which is apical or lateral -- be a random effect? The model needs to try to estimate a variance for each random effect so doing this for n=2 is quite odd to me. I think the authors should also report the results with bud type as fixed, or report the bud types separately.<br /> (2) I didn't fully see how the authors results support the Solstice as Switch hypothesis, since what timing mattered seemed to depend on the timing of treatment and was not clearly related to solstice. Could it be that these results suggest the Solstice as Switch hypothesis is actually not well supported (e.g., line 135) and instead suggest that the pattern of climate in the summer months affects end of season timing?

Reviewer #2 (Public review):

Summary:

Protein tyrosine kinases are subject to diverse regulatory mechanisms controlling their activity in normal situations. The authors previously identified SLAP (Src-like adaptor protein), a negative regulator of receptor tyrosine kinase (RTK) signaling, as a key suppressor of the cytoplasmic tyrosine kinase SRC in the normal colon and demonstrated that SLAP is downregulated in a majority of colorectal cancers (CRCs).

In this study, the authors further explored SLAP functions in mouse models using constitutive and inducible epithelial-specific Slap deletion (villin-CreERT2 model). They found that loss of SLAP augments colonic epithelial cell proliferation and that induction of tumorigenesis by the AOM/DSS protocol mimicking CRC leads to more aggressive tumors in the absence of SLAP. This effect is apparently cell-autonomous as growth of normal and tumoral colonic organoids is SLAP-dependent in in vitro settings. Finally, the authors define that, in colon, SLAP represses EphB2, an RTK lying upstream of SRC, and show that inhibitors of EphB2 can partially limit tumorigenic development in vitro.

Strengths:

The manuscript is clearly and concisely written, making it easy to follow. The data obtained in the mouse models are very convincing.

Weaknesses:

Direct evidence that EphB2 is activated/phosphorylated in the absence of SLAP is lacking, as conclusions are only based on results obtained with inhibitors. Some other issues have to be addressed before acceptance, in particular, the relevance of the findings in CRC patients.

Reviewer #2 (Public Review):

Summary:

In this work, Xiong and colleagues examine the relationship between the profile of the morphogen Shh and the resulting cell fate decisions in the zebrafish neural tube. For this, the authors combine high-resolution live imaging of an established Shh reporter with reporter lines for the different progenitor types arising in the forming neural tube. One of the key observations in this manuscript is that, while, on average, cells respond to differences in Shh activity to adopt distinct progenitor fates, at the single cell level there is strong heterogeneity between Shh response and fate choices. Further, the authors showed that this heterogeneity was particularly prominent for the pMN fate, with similar Shh response dynamics to those observed in neighboring LFP progenitors.

Strengths:

It is important to directly correlate Shh activity with the downstream TFs marking distinct progenitor types in vivo and with single cell resolution. This additional analysis is in line with previous observations from these authors, namely in Xiong, 2013. Further, the authors show that cells in different anterior-posterior positions within the neural tube show distinct levels of heterogeneity in their response to Shh, which is a very interesting observation and merits further investigation.

Weaknesses:

This is a convincing work, however, adding a few more analyses and clarifications would, in my view, strengthen the key finding of heterogeneity between Shh response and the resulting cell fate choices.

Reviewer #3 (Public review):

Summary:

This study addresses a central question in genome organization: whether the positions of chromosomal domain boundaries are functionally coupled to gene activity. The authors use high-throughput imaging to simultaneously measure distances between boundary markers and nascent RNA production in thousands of individual cells, enabling direct comparison of boundary positions and transcriptional status at single chromosomal copies. This approach is applied across multiple loci, genes, and cell types, and is combined with acute transcriptional perturbations and depletion of architectural proteins to test the relationship between chromosome structure and gene activity in both directions.<br /> This work makes a meaningful contribution by providing direct, single-cell evidence that domain boundary positions and gene activity are largely uncoupled in this system.

Strengths:

A major strength of the work is its single-cell, single-allele resolution, which overcomes the averaging inherent to population-based assays. The authors consistently find that boundary proximity is largely independent of transcriptional status: active and inactive alleles have similar boundary distances, transcriptional perturbations do not shift boundary distributions, and depletion of the boundary factor CTCF does not alter gene expression, whereas cohesin depletion affects both boundary organization and transcription. These conclusions are supported by large numbers of alleles, multiple loci and cell types, and internal controls that distinguish boundary-specific effects from broader chromatin influences. The study offers a robust, scalable imaging pipeline that will be valuable for future studies linking genome organization and transcription at single-cell resolution.

Weaknesses:

The study has important limitations that are acknowledged by the authors. Measurements are restricted to distances between flanking boundaries and do not capture internal domain architecture, sub-domain structure, or finer-scale regulatory contacts. Resolution is limited by probe size and imaging, potentially masking subtle positional changes, and only a small set of loci is examined, leaving open how broadly the uncoupling generalizes. Some perturbation effects, particularly for RAD21, may involve mechanisms beyond boundary disruption.

Reviewer #2 (Public review):

The goal of the study was to uncover the mechanisms mediating tactile-context-dependent locomotion modulation in C. elegans, which represents an interesting model of behavioral plasticity. Starting from a candidate genetic screen focusing on guanylate cyclase (GCY) mutants, the authors identified the AFD-specific gcy-18 gene as essential for tactile-context-dependent locomotion modulation. AFD has been primarily characterized as a thermosensory neuron. However, key thermosensory transduction genes and the sensory ending structure of AFD were shown here to be dispensable for tactile-context locomotion modulation. AFD actuates tactile-context locomotion modulation via the cell-autonomous actions of GCY-18 and the CNG-3 cyclic nucleotide-gated channel, and via AFD's connection with AIB interneurons through electrical synapses. At the circuit level, AIB also receive inputs from the mechanosensory neuron FLP, which was also shown to be relevant for tactile-context-dependent locomotion modulation.

For this study, the authors combined a very clever microfluidic-based behavioral assay with a large set of genetic manipulations to dissect the molecular and cellular pathways involved. Rescue experiments with single-copy transgenes are particularly convincing. The study is very clearly written, and the figures are nicely illustrated with diagrams that effectively convey the authors' interpretation. Overall, the convergence of behavioral assays, genetics, and circuit analysis provides convincing support for the proposed role of the AFD-AIB connection, potentially downstream of FLP via synapic and of other mechanosensory neurons via extra-synaptic communication.

The facts that AFD mediates tactile-context locomotion modulation, that this role relies on GCY-18, and on electrical synapses linking AFD to AIB are new, somewhat unexpected, and interesting. The study raises intriguing and addressable questions about the role of innexin-based cellular communication in a multimodal sensory-behavior microcircuit, including the direction and nature of the signal(s) transmitted through these electrical synapses. These questions remain difficult to address in most experimental systems. The compact and genetically tractable nervous system of C. elegans provides a powerful entry point for addressing them in the context of an intact in vivo circuit.

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.

Weaknesses:

(1) The paper in its current form is hard to digest and would benefit from improved clarification of the storyline, as well as a tighter integration between the phenotypic, omics, and functional validation data. Currently, it is not always clear what the relevance is of all the reported results, nor why certain decisions were made, or how all the different methods the authors used fit together. For example, the authors functionally validated a second gene, PxDnmt1, but it is unclear why this particular gene was chosen, nor how it relates to their selection regimes when looking at the results obtained with the phenotyping and omics data collection. Seeing how much work the authors did, this makes the paper overwhelming and difficult to read.

(2) The authors at times stretch their results too far, as the ecological relevance of their study design and results is not clear, limiting the generalizability and value of the results for understanding species' adaptive potential under climate change. For example, the selection regimes used present the minimum and maximum known temperatures at which the species can survive and develop, but it is unclear how the temperatures relate to the natural environment of the source population, to what extent wild populations might experience these temperatures, and whether they would experience them at the extended duration used (12h at max/min temperature). Moreover, I wonder whether the comparisons made would identify the genes that matter under natural conditions, as unevolved populations were kept under constant conditions compared to 12h:12h temperature regimes for the evolved populations, and the metabolic and transcriptomic profiling was done under a constant favorable 26{degree sign}C rather than under thermal stress in a, as far as I can tell, randomly chosen life stage (larval stage).

(3) The paper in its current form does not adequately describe the statistical analyses underlying the results, nor do the authors share their code, making it very hard to judge whether the analyses used are appropriate and the results trustworthy. I have concerns about the inappropriate use of t-tests, the lack of correcting for confounding variables, and the need for multiple testing corrections.

Reviewer #2 (Public review):

In this study, the authors investigate the spatial organization of direction and orientation selectivity in the mouse superior colliculus (SC) and its retinal inputs. By combining two-photon imaging of retinal boutons and SC neurons with Neuropixels recordings, they assess whether tuning preferences form structured maps or are arranged in a salt-and-pepper fashion. They further compare SC tuning organization to previously described retinal geometric models to determine the extent to which collicular responses inherit retinal topography. The authors conclude that SC inherits a cardinally biased topographic scaffold from the retina, which progressively weakens with depth, and that strong global maps are absent.

A major strength of the study is the impressive combination of methodologies, including imaging of retinal boutons, imaging of SC neurons, and large-scale electrophysiological recordings across SC depth. The direct comparison to retinal geometric models is particularly valuable, as it situates the SC within a broader framework of retinotopic information transfer and advances our understanding of how retinal computations are transformed in downstream targets.

A limitation of the study, however, is that the imaging experiments sample only a relatively small and spatially homogeneous region of the visual field, whereas the electrophysiological recordings cover a different portion of SC. This separation makes it difficult to form a fully integrated, global picture of the spatial organization of direction and orientation selectivity across the entire collicular map.

Reviewer #2 (Public review):

This well-written manuscript proposes to use attractors in space and time (STA) as a mechanistic explanation for planning in the prefrontal cortex. The main conceptual hypothesis is that planning is implemented as attractor dynamics in a representation that encodes states at each time step jointly. Depending on inputs, the network relaxes to a trajectory that already contains future states that will be visited at each time step, rather than computing a scalar value at each point in time and space like other classical approaches from RL. The authors compare this approach to implementations such as TD learning and successor representation, and further show that trained recurrent neural networks on specific tasks involving planning develop structured subspaces resembling the ones postulated in STA.

The idea of treating attracting trajectories unfolding in time as the computational substrate for planning is very interesting and potentially important. The explicit construction of a state x time representational space and its implementation via recurrent dynamics are appealing and convincing in the idealized tasks considered. I found the manuscript to be refreshingly explicit regarding several of the assumptions and limitations of the models, for example, the fact that certain advantages can be viewed as properties of the state space itself and not necessarily of a fundamentally new planning mechanism.

Overall, the manuscript presents a cool attractor model that extends in time and explores its performance in a subset of illustrative tasks involving planning. My doubts concern mostly the interpretation and scope of the claims made in the manuscript. Here are a few comments where I detail my questions/concerns:

(1) The authors nicely discuss that much of the difference between STA and classical TD or SR agents is "in some sense a property of the state space rather than the decision making algorithm," and that TD and SR could in principle be implemented in a comparable space x time representation. This is fair, but it also suggests that the central contribution of the manuscript lies primarily in the representational factorization (state x time tiling) and its dynamical implementation via attractors, rather than in a fundamentally new planning algorithm or theory, mechanistic or not. I think theory should be distinguished from mechanism, and it would therefore help the reader to describe the conceptual advancement more as a novel mechanism or implementation than a novel (mechanistic) theory for decision/planning.

(2) Related to my previous point, I think it would be helpful to position STA more explicitly relative to computational/theoretical literature in which attractor networks encode temporally ordered patterns (so effectively including future times). For example, classical extensions of Hopfield networks with asymmetric connectivity implement retrieval of sequences and ordered transitions between patterns (Sompolinsky & Kanter, 1986). More recently, sequential attractors and limit-cycle dynamics have been constructed in structured recurrent networks by the Morrison group (Parmelee et al., 2021). These works do not implement an explicit discretized state x future-time tiling as in STA and do not specifically discuss the usage for planning. However, they do provide concrete precedents for attractor dynamics over temporally structured trajectories in terms of mechanism. It would be useful to discuss this literature and clarify a little what's new mechanistically in the view of the authors.

(3) A central claim of the manuscript is that space-time trajectories are attractors of the STA dynamics. The manuscript does provide empirical evidence consistent with attractor-like behavior. However, it is not explicitly shown whether trajectory representations persist in the absence of sustained external inputs. So it's not clear to me whether the trajectories should be interpreted as intrinsic attractors of the recurrent system, which can be selected by delivering transient inputs, or whether they must be stabilized by a specific continuous external drive. It would be useful if the author could clarify/discuss this point.

As far as I understand it, reward information is provided as input to specific populations encoding future time steps, and that's essential for rapid adaptation without rewiring connectivity. How such future-time-specific reward inputs would be generated and routed to distinct neural populations isn't entirely clear to me. Since this seems to be an essential component of the model, I think it would be important to discuss more deeply the source and plausibility of these reward signals related to different timesteps.

(4) The authors note that vanilla STA scales linearly with planning horizon, and discuss potentially hierarchical extensions for longer horizons. They acknowledge that learning abstractions remains an open challenge, yet the examples of planning in the manuscript are restricted to very short temporal horizons and limited branching complexity. It is not obvious to me in what cases the current implementation and interpretation of STA remains viable (for example, in terms of relaxation iterations) as the horizon and branching factor increase. Relatively simple planning can be managed by simpler, less costly models/algorithms, whereas complex planning is a lot harder to deal with, and it's something that a mechanistic "theory" should address. In the context of the claims of the paper in its present form, I think this is possibly the most important conceptual and practical limitation in the manuscript.

(5) The RNN analyses show that trained networks develop structured subspaces aligned with future time indices and exhibit perturbation behavior consistent with attractor-like dynamics. The manuscript also explicitly notes differences between the trained RNN and the handcrafted STA (e.g., long-range couplings between subspaces and differences in behavior of lower-value trajectories under perturbation), which I much appreciated. My doubt is on the specificity of this result, as trained RNNs on fixed-horizon tasks can develop latent dimensions correlated with temporal progress within a trial or time-to-goal. I think it would help the reader to clarify whether the results demonstrate that STA-like computations emerge in RNNs trained on planning tasks, or that RNNs generally develop some kind of structured spacetime representations when tasks involve future timesteps and some degree of flexibility in the decisions.

A few more minor points, mainly concerning clarity:

(1) The main dynamical equation combines a log-domain recurrent term, a floor operation, and a log-sum-exp normalization step, followed by exponentiation. The intuition/logic behind this specific formulation could be clarified for the reader. For example tt would be helpful to explain why the recurrent input appears inside a log, and also whether/how these operations relate to any multiplicative constraint.

(2) While the computational cost of successor representation in an expanded NT x NT representation is discussed, the corresponding scaling of STA in terms of number of units and connections (as a function, for example, of the planning horizon) isn't clear to me. Perhaps the authors could compare costs more explicitly.

(3) In the RNN analyses, structured subspaces aligned with future time indices are shown. I couldn't find a quantification of how much variance is captured by the subspaces, relative to other latent dimensions. Adding it would help get a feeling for the strength of the alignment.

Reviewer #2 (Public review):

In this manuscript, Aguilera et al. investigate the mechanisms underlying transcriptional repression of constitutively expressed genes during heat stress. While the activation of heat-shock genes has been extensively studied, the mechanisms responsible for widespread transcriptional repression remain poorly understood. The authors propose that the GA-binding transcription factor CLAMP acts as a major regulator of heat-stress-induced transcriptional repression in Drosophila. Using nascent RNA-sequencing approaches, they report that CLAMP contributes to the repression of a large fraction of genes whose transcription decreases upon heat stress. In addition, the authors generate high-resolution Micro-C datasets to analyze changes in chromatin architecture during heat stress and report widespread alterations in chromatin looping associated with transcriptional changes. Based on these results, the study proposes that CLAMP regulates repression through both direct transcriptional mechanisms and modulation of local 3D genome architecture.

The study addresses an important question in gene regulation: how transcription is rapidly repressed during environmental stress. The work is timely because most previous studies have focused on transcriptional activation of heat-shock genes, whereas repression mechanisms remain comparatively less understood. The integration of transcriptional profiling with high-resolution chromatin conformation data is a major strength of the manuscript and provides a valuable resource for the community studying genome organization and stress responses.

The nascent RNA-sequencing experiments appear carefully designed and allow the authors to capture rapid transcriptional responses to heat stress. These data provide convincing evidence that heat stress leads to widespread transcriptional repression of constitutive genes and that CLAMP contributes substantially to this process. The genomic analyses linking CLAMP binding to repressed genes are also informative and support the idea that CLAMP plays a direct regulatory role at many loci.

Another strength of the study is the generation of Micro-C datasets under heat stress conditions. These datasets provide a high-resolution view of chromatin architecture and reveal dynamic changes in local chromatin looping associated with transcriptional responses. The authors' analysis suggests that heat stress induces widespread reorganization of chromatin contacts, and that CLAMP may contribute to these structural changes. This dataset is likely to be useful for future studies exploring how environmental cues influence genome organization.

Despite these strengths, several aspects of the study would benefit from further clarification. First, the mechanism by which CLAMP mediates transcriptional repression remains insufficiently defined. While the data support a role for CLAMP in the repression of a subset of genes during heat stress, the molecular basis of this effect is not fully explored. Second, although the Micro-C dataset represents a valuable resource for studying chromatin architecture during heat stress, the functional interpretation of the observed structural changes could be further developed. In particular, it would be helpful to better establish the relationship between the identified chromatin loops and gene regulation, and to clarify whether these structural changes play a causal role in transcriptional repression or instead reflect broader chromatin reorganization associated with the stress response.

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Venoactive drugs (diosmin, hesperidin, horse chestnut seed extract) may be considered as adjuncts to compression for symptomatic relief in countries where available

Endovenous ablation is contraindicated or relatively unsuitable when venous anatomy precludes catheter-based treatment, specifically: aneurysmal dilation of the GSV close to the saphenofemoral junction, subcutaneous location of truncal veins above the saphenous fascia and close to the skin, and significant tortuosity of the GSV or SSV. [1] In these scenarios, high ligation and stripping is recommended as the preferred alternative (grade 1 strong recommendation

Reviewer #3 (Public review):

I have carefully reviewed the manuscript, the two referee reports, and the authors' detailed responses. I appreciate the substantial effort the authors have invested in addressing the reviewers' comments, and I also recognize the strength and ambition of the work. This is a technically sophisticated study that integrates coarse-grained modeling with live-cell imaging to address an important and timely question regarding HIV-1 capsid inhibition by lenacapavir.

Embedded within Reviewer #2's report are several substantive points that warrant careful consideration, particularly with respect to framing, terminology, and engagement with the broader literature. I view my role here is to distinguish those issues from claims that I do not find to be supported.

First, I do not agree with Reviewer #2's central assertion that the manuscript lacks novelty or fails to present meaningful new findings. While individual elements of the system studied here-capsid docking at the NPC, lenacapavir-induced capsid hyperstabilization, capsid rupture, and competition with FG- nucleoporins-have been observed previously, this work provides a coherent, mechanistic account of how these elements are coupled. In particular, the proposed sequence linking LEN-induced lattice hyperstabilization, preferential pentamer loss at the narrow end, NPC-induced mechanical stress, and failure of nuclear import represents a nontrivial integration that goes beyond prior phenomenological observations. I therefore do not view this work as redundant with existing literature.

That said, Reviewer #2 is correct to note that the manuscript would benefit from broader and more explicit engagement with recent independent studies, including computational and hybrid modeling efforts that address capsid mechanics, nuclear entry, and LEN effects using different frameworks. While the authors' bottom-up coarse-grained approach is clearly distinct and, in many respects, more systematically derived, eLife readers would benefit from a clearer discussion of how the present results relate to, complement, or differ from these other approaches. I strongly encourage the authors to add a short discussion paragraph situating their work within this broader context, without disparaging alternative models.

Second, I find that some mechanistic claims in the manuscript would benefit from more careful language distinguishing model-conditioned interpretation from de novo prediction. This applies in particular to discussions of LEN binding heterogeneity and stoichiometry, as well as to conclusions drawn from biased enhanced-sampling simulations. While I agree with the authors that parameterization does not invalidate mechanistic insight, it is important to be precise about what aspects of the behavior emerge from the simulations versus what is constrained by prior experimental knowledge. Modest tightening/revising of language (e.g., "suggests," "is consistent with," "within the model") would address this concern without weakening the scientific conclusions.

Third, Reviewer #2 raises a legitimate semantic issue regarding the use of the term "elasticity." The manuscript infers changes in capsid mechanical response using heterogeneous elastic network models, which quantify effective stiffness and deformability rather than elasticity in the macroscopic materials sense. I recommend that the authors clarify this definition explicitly in the text to avoid confusion and unnecessary debate.

Finally, I note that several of Reviewer #2's objections-particularly those asserting circular reasoning, misuse of enhanced sampling methods, or invalidity of coarse-grained predictions-reflect a misunderstanding of contemporary bottom-up coarse-grained modeling rather than genuine methodological flaws. I do not believe these points require further rebuttal or revision beyond what the authors have already provided.

In summary, in my view, the manuscript represents a solid contribution to the field, provided that the authors undertake a limited set of targeted revisions aimed at improving framing, clarity, and engagement with the broader literature. Addressing these points will strengthen the manuscript and ensure that its contributions are clearly and fairly communicated to the community.

Reviewer #2 (Public review):

Summary:

The authors are trying to understand why certain mutants of O-GlcNAc transferase (OGT) appear to cause developmental disorders in humans. As an important step towards that goal, the authors generated a mouse model with one of these mutations that disrupts OGT activity. They then go on to test these mice for behavioral differences, finding that the mutant mice exhibit some signs of hyperactivity and differences in learning and memory. They then examine alterations to the structure of the brain and skull, and again find changes in the mutant mice that have been associated with developmental disorders. Finally, they identify proteins that are up or down regulated between the two mice as potential mechanisms to explain the observations.

Strengths:

The major strength of this manuscript is the creation of this mouse model, as a key step in beginning to understand how OGT mutants cause developmental disorders. This line will prove important for not only the authors but other investigators as well, enabling the testing of various hypotheses and potentially treatments. The experiments are also rigorously performed and the conclusions are well supported by the data.

Weaknesses:

The only weakness is a lack of mechanistic insight. However, this certainly may come in the future through more targeted experimentation using this mouse model. I do not recommend that these experiments need to be performed in this manuscript.

Comments on revisions:

The authors have addressed all of my suggestions proactively.

Reviewer #2 (Public review):

Summary:

In this study the authors follow up on a previous suppressor screen of a temperature-sensitive allele of mis4 (mis4-G1487D), the cohesin loading factor in S. pombe, and identify additional suppressor alleles tied to the S. pombe TORC1 complex. Their analysis suggests that these suppressor mutations attenuate TORC1 activity while enhanced TORC1 activity is deleterious in this context. Suppression of TORC1 activity also ameliorates chromosome segregation and spindle defects observed in the mis4-G1487D strain, although some more subtle effects are not reconstituted. The authors provide evidence that this genetic suppression is also tied to the reconstitution of cohesin loading. Moreover, disrupting TORC1 also enhances Mis4/cohesin association with chromatin (likely reflecting enhanced loading) in WT cells while rapamycin treatment can enhance the robustness of chromosome transmission. These effects likely arise directly through TORC1 or its downstream effector kinases as TORC1 co-purifies with Mis4 and Rad21; these factors are also phosphorylated in a TORC1-dependent fashion. Disrupting Sck2, a kinase downstream of TORC1, also suppresses the mis4-G1487D allele while simultaneous disruption of Sck1 and Sck2 enhances cohesin association with chromatin, albeit with differing effects on phosphorylation of Mis4 and Psm1/Scm1. Phosphomutants of Mis4 and Psm1 that mimic observed phosphorylation states identified by mass spectrometry that are TORC1-dependent also suppressed phenotypes observed in the mis4-G1487D background. Lastly, the authors provide evidence that the mis4-G1487D background and TORC1 mutant backgrounds display an overlap in the dysregulation of genes that respond to environmental conditions.

Overall, the authors provide compelling evidence from genetics, biochemistry and cell biology to support a previously unknown mechanism by which nutrient sensing regulates cohesin loading with implications for the stress response. The technical approaches are generally sound, well-controlled, and comprehensive.

The specific points that I raised in the first review have been addressed by changes/additions to the manuscript or have been determined to be beyond the scope of the study by the authors.

One major question that remains open is the relationship between local changes in cohesin loading and gene expression through this TORC1 regulatory signaling pathway and the details of the underlying mechanisms.

Reviewer #2 (Public review):

The work by Spokaite et al describes the discovery of a novel Rab5 binding site present in complex II of class III PI3K using a combination of HDX and Cryo EM. Extensive mutational and sequence analysis define this as the primordial Rab5 interface. The data presented are convincing that this is indeed a biologically relevant interface, and is important in defining mechanistically how vps34 complexes are regulated.

This paper is a very nice expansion of their previous cryo-ET work from 2021, and is an excellent companion piece on high resolution cryo-EM of the complex I class III complex bound to Rab1 from the Hurley lab in 2025. Overall, this work is of excellent technical quality, and answers important unexplained observations on some unexpected mutational analysis from the previous work.

They used their increased affinity vps34 mutant to determine the 3.2 ang structure of Rab5 bound to vps34-CII. Clear density was seen for the original Rab5 interface, but an additional site was observed. Based on this structure they mutated out the vps34 interface, allowing for a high resolution structure of the Rab5 bound at the Vps15 interface.

They extensively validated the vps15 interface in the yeast variant of vps34, showing that the Vp215-Rab5 (Vps21) interface identified is critical in controlling complex II vps34 recruitment.

The major strengths of this paper are that the experiments appear to be done carefully and rigorously and I have very few experimental suggestions.

Here is what I recommend based on some very minor weaknesses I observed

(1) My main concern has to do a little bit with presentation. My main issue is how the authors use mutant description. They clearly indicate the mutant sequence in the human isoform (for example see Fig 2A, Vps15 described as 579-SHMIT-583>DDMIE), however, when they shift to the yeast version they shift to saying vps15 mutant, but don't define the mutant, Fig 2G). I would recommend they just include the same sequence numbering and WT to mutant replacement every time a new mutant (or species) is described. It is always easier to interpret what is being shown when the authors are jumping between species when the exact mutant is included. This is particularly important in this paper, where we are jumping between both different subunits and different species, so clear description in figure/figure legends makes it much easier to read for non-specialists.

(2) The HDX data very clearly shows that Rab5 is likely able to bind at both sites, which back ups the cryo EM data nicely. I am slightly confused by some of the HDX statements described in the methods.

(3) The authors state "Only statistically significant peptides showing a difference greater than 0.25 Da and greater than 5% for at least two timepoints were kept." This seems to be confusing why they required multiple timepoints, and before they also describe that they required a p value of less than 0.05. It might be clearer to state that significant differences required a 0.25 Da, 5%, and p value of <0.05 (n=3). Also what do they mean by kept? Does this mean that they only fully processed the peptides with differences.

(4) They show peptide traces for a selection in the supplement, but it would be ideal to include the full set of HDX data as an excel file, including peptides with no differences as there is a lot of additional information (deuteration levels for everything) that would be useful to share, as recommended from the Masson et al 2019 recommendations paper. This may be attached but this reviewer could not see an example of it in the shared data dropbox folder.

Comments on revisions:

The authors have addressed all of my issues.

Reviewer #2 (Public review):

This manuscript describes experiments characterising how malaria parasites respond to physiologically relevant heat-shock conditions. The authors show, quite convincingly, that moderate heat-shock appears to increase cytoadherance, likely by increasing trafficking of surface proteins involved in this process.

While generally of a high quality and including a lot of data, I have a few small questions and comments, mainly regarding data interpretation.

(1) The authors use sorbitol lysis as a proxy for trafficking of PSAC components. This is a very roundabout way of doing things and does not, I think, really show what they claim. There could be a myriad of other reasons for this increased activity (indeed, the authors note potential PSAC activation under these conditions). One further reason could be a difference in the membrane stability following heat shock, which may affect sorbitol uptake, or the fragility of the erythrocytes to hypotonic shock. I really suggest that the authors stick to what they show (increased PSAC) without trying to use this as evidence for increased trafficking of a number of non-specified proteins that they cannot follow directly.

(2) Supplementary Figure 6C/D: The KAHRP signal does not look like it should. In fact, it doesn't look like anything specific. The HSP70-X signal is also blurry and overexposed. These pictures cannot be used to justify the authors' statements about a lack of colocalisation in any way.

(3) Figure 6: This experiment confuses me. The authors purport to fractionate proteins using differential lysis, but the proteins they detect are supposed to be transmembrane proteins and thus should always be found associated with the pellet, whether lysis is done using equinatoxin or saponin. Have they discovered a currently unknown trafficking pathway to tell us about? Whilst there is a lot of discussion about the trafficking pathways for TM proteins through the host cell, a number of studies have shown that these proteins are generally found in a membrane-bound state. The authors should elaborate, or choose an experiment that is capable of showing compartment-specific localisation of membrane-bound proteins (protease protection, for example).

(4) The red blood cell contains, in addition to HSP70-X, a number of human HSPs (HSP70 and HSP90 are significant in this current case). As the name suggests, these proteins non-specifically shield exposed hydrophobic domains revealed upon partial protein unfolding following thermal insult. I would thus have expected to find significantly more enrichment following heat shock, but this is not the case. Is it possible that the physiological heat shock conditions used in this current study are not high enough to cause a real heat shock?

Comments on Revision:

Although in any study there are going to be residual weaknesses, this reviewer is happy to see that the authors have gone to lengths to address many of my main concerns, and also those of other reviewers.

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. The electrophysiological data in Figure 7 have significant experimental confounds that makes their interpretation challenging.

Reviewer #2 (Public review):

Summary:

The authors attempt to address the issue of high rates of translation failure from animal studies to humans in the literature, where promising results in animal studies fail when conducting human clinical trials. Using parameters from a previous meta-analysis on prenatal amino acid supplementation and the effects it has on maternal blood pressure, the authors assessed the performance of the metrics used and whether they can quantify translation success. Performing a simulation study, the authors compared nine translation success metrics and found that no one method was uniformly optimal. The authors list several limitations of the study, such as comparability of effect sizes between animal and human studies, different goals of animal studies versus human studies, and the focus of the study on one aspect (statistics of translation) is part of a broader, more complex decision-making process before proceeding to human trials. The authors recommend using multiple metrics in combination while taking into consideration their strengths and weaknesses to assess the translation of animal studies to human outcomes. The paper achieves the aim of providing a model with several metrics to evaluate translation success from animal studies to humans.

Strengths:

(1) Utilizing 9 different translation success metrics in combination provides strong flexibility in evaluating whether results in animal studies can translate to humans. This would allow researchers to evaluate translation success using multiple different metrics according to the context of the study.

(2) The authors accommodated for the limited sample size in animal studies, which are typically underpowered, and also caution that special attention should be given to heterogeneity when interpreting translation results.

(3) Overall, this approach has the potential to be applied to other biomedical studies, provided the limitations for each of the metrics are considered. It would provide a useful tool in assessing translation from animals to humans, in addition to other factors such as safety, pharmacokinetics, etc.

Weaknesses:

While the study has several strengths, there are some limitations.

(1) Preclinical animal study sizes tend to be much smaller than human studies, which results in underpowered results. The authors adjusted for this by pooling animal study data. However, high heterogeneity in the animal studies can affect translation results.

(2) The study focuses only on evaluating the statistical component of translation, which is only one aspect of the decision-making process to move on to human trials. The study does not take into account safety and toxicological profiles, pharmacokinetics, or genetics, which are important considerations that influence the overall effect in humans.

Reviewer #2 (Public review):

Summary:

The study by Milton et al titled "Human CD1c-autoreactive T cells recognise Mycobacterium tuberculosis-infected antigen-presenting cells and display cytotoxic effector programmes" characterises CD1c-restricted autoreactive T cells and their potential role in controlling Mtb infection. The authors develop a well-controlled system to assay for the functioning/activation of autoreactive T cells. They report the presence of CD1c-restricted autoreactive T cells in the circulating blood of healthy donors. They show that these T cells respond to CD1c and get activated even in the absence of any exogenous antigen. They next show that CD1c, along with CD1a and b, are typically downregulated on APCs during Mtb infection. These autoreactive T cells are cytotoxic, indicating they respond to Mtb treatment and/or to changes in the T cell ratio. The autoreactive T cells could effectively lyse Mtb-infected or PAMP-stimulated CD1c+APCs. Next, using TCR sequencing, they show that T cell responses were mediated by specific TCR clones with common sequence features. They show that these autoreactive T cells could curtail Mtb growth as measured by luminescence. Finally, using scRNAseq, they selectively identify the CD1c-reactive T cell pool and detect enrichment of typical effector memory CD4 and CD8 cells expressing cytolytic markers such as Granzyme, granulolysin, etc. The lung biopsy staining, along with the other data presented here, suggests that while CD1c-restricted T cells could have potential anti-bacterial roles, Mtb downregulation effectively shuts down this mechanism for TB control.

Strengths:

The study is designed well and has developed many exciting tools to generate specific information.

Weaknesses:

The study has weaknesses in two important parameters - novelty and relevance in controlling TB. Further, the results could be better presented and discussed to allow easy understanding of the experimental design. For example, at several places, UV-killed or live Mtb were used. What is the rationale behind that? Why use irradiated THP1-CD1c cells for activating T cells?

While functional assays identified only CD4+ cells as CD1c-restricted, scRNAseq shows that both CD4+ and CD8+ cells exhibit this phenotype. Identifying the specific lipid antigen presented by CD1c could add greater value to the study.

Since autoreactivity was independent of exogenous antigen, the cytotoxic activity should also be independent of exogeneous antigens? What additional signal a THP1-CD1c cells treated with UV-killed Mtb express that is absent from the untreated cells?

The relative Mtb growth assay is confusing. CD1c cells with Mtb infection triggers massive lytic response, as shown in Figure 4. Under similar conditions, in Figure 6, the authors report a significant decline in Mtb growth in these cells. The problem is that with the kind of lytic response observed, a lot more Mtb could be present extracellularly and would evade killing. How do we reconcile the two observations?

Reviewer #2 (Public review):

Summary:

In the current study the authors attempt to identify correlates of protection for improved outcomes following re-challenge with ASFV. An advantage is the study design which compares the responses to a vaccine like mild challenge and during a virulent challenge months later. It is a fairly thorough description of the immune status of animals in terms of T cell responses, antibody responses, cytokines and transcriptional responses and the methods appear largely standard. The comparison between SPF and farm animals is interesting and probably useful for the field in that it suggests that SPF conditions might not fully recapitulate immune protection in the real world. I thought some of the conclusions were over-stated and there are several locations where the data could be presented more clearly.

Strengths:

The study is fairly comprehensive in the depth of immune read-outs interrogated. The potential pathways are systematically explored. Comparison of farm animals and SPF animals gives insights into how baseline immune function can differ based on hygiene, which would also likely inform interpretation of vaccination studies going forward.

Weaknesses:

There are limited numbers of animals assessed.

Comments on revisions:

The authors mostly addressed my comments to the previous version. However, in the discussion they added comments relating to and an interpretation based on their own unpublished data and I think that those statements should be removed because the data are not included in this publication and cannot be cited.

Reviewer #2 (Public review):

Summary:

The authors tested an interesting hypothesis that white flies and planthoppers independently evolved salivary proteins to dampen plant immunity by targeting a receptor like protein. Unlike previously reported receptor like proteins with large ligand-binding domains, the NtRLP4 here has a malectin LRR domain. Interestingly, it also associates with the adaptor SOBIR1. While the function of this protein remains to be further explored, the authors provide strong evidence showing it's the target of salivary proteins as the insects' survival strategy.

The authors have nicely addressed the questions I raised.

I noticed two small points the authors may modify:<br /> - Line 16: delete "on"<br /> - Line 185: Replace "is resistant to B. tabaci infestation" with "confers resistance against B. tabaci".

Reviewer #2 (Public review):

This paper investigates the latent dynamic brain states that emerge during memory encoding and predict later memory performance in children (N = 24, ages: 8 -13 years). A novel computational approach (Bayesian Switching Dynamic Systems, BSDS) discovers latent brain states from fMRI data in an unsupervised and parameter-free manner that is agnostic to external stimuli, resulting in 4 states: an active-encoding state, a default-mode state, an inactive state, and an intermediate state. The key finding is that the percentage of time occupied in the active-encoding state (characterized by greater activity in hippocampal, visual, and frontoparietal regions), as well as greater transitions to this state, predicts memory accuracy. Memory accuracy was also predicted by the mean lifetime and transitions to the default-mode state (characterized by greater activity in medial prefrontal cortex and posterior cingulate cortex) during post-encoding rest. Together, the results provide insights into dynamic interactions between brain regions that may be optimal for encoding novel information and consolidating memories for long-term retention.

The approach is interesting and important for our understanding of neural mechanisms of memory during development, as we know less about dynamic interactions between memory systems in development.

Moreover, the novel methodology may be broadly useful beyond the questions addressed in this study. The manuscript is well-written and concise. Nonetheless, there are several areas for improvement:

(1) The study focuses on middle childhood, but there is a lack of engagement in the Introduction or Discussion about what is known about memory development and the brain during this period. Many of the brain regions examined in this study, particularly frontoparietal regions, undergo developmental changes that could influence their involvement in memory encoding and consolidation. The paper would be strengthened by more directly linking the findings to what is already known about episodic memory development and the brain.

(2) A more thorough overview of the BSDS algorithm is needed, since this is likely a novel method for most readers. Although many of the nitty-gritty details can be referenced in prior work, it was unclear from the main text if the BSDS algorithm discovered latent states based on activation patterns, functional connectivity, or both. Figure 1F is not very informative (and is missing labels).

(3) A further confusion about the BSDS algorithm was whether it necessarily had to work on the rest data. Figure 4A suggests that each TR was assigned one of the four states based on the maximum win from the log-likelihood estimation. Without more details about how this algorithm was applied to the rest data, it is difficult to evaluate the claim on page 14 about the spontaneous emergence of the states at rest.

(4) Although the BSDS algorithm was validated in prior simulations and task-based fMRI using sustained block designs in adults, it is unclear whether it is appropriate for the kind of event-related design used in the current study. Figure 1G shows very rapid state changes, which is quantified in the low mean lifetime of the states (between 1-3 TRs on average) in Figure 4C. On the one hand, it is a strength of the algorithm that it is not necessarily tied to external stimuli. On the other hand, it would be helpful to see simulations validating that rapid transitions between states in fMRI data are meaningful and not due to noise.

(5) The Methods section mentions that participants actively imagined themselves within the encoded scenes and were instructed to memorize the images for a later test during the post-encoding rest scan. This detail needs to be included in the main text and incorporated into the interpretation of the findings, as there are likely mechanistic differences between spontaneous memory replay/reinstatement vs. active rehearsal.

(6) Information about the general linear model used to discover the 16 ROIs that showed a subsequent memory effect are missing, such as: covariates in the model (motion, etc.), group analysis approach (parametric or nonparametric), whether and how multiple-comparisons correction was performed, if clusters were overlapping at all or distinct, if the total number of clusters was 16 or if this was only a subset of regions that showed the effect.

Reviewer #3 (Public review):

Summary:

Lin et al report on the dynamic localization of EXOC6A and Myo-Va at pre-ciliary vesicles, ciliary vesicles, and ciliary sheath membrane during ciliogenesis using three-dimensional structured illumination microscopy and ultrastructure expansion microscopy. The authors further confirm the interaction of EXOC6A and Myo-Va by co-immunoprecipitation experiments and demonstrated the requirement of EHD1 for the EXOC6A-labeled ciliary vesicles formation. Additional experiments using gene-silencing by siRNA and pharmacological tools identified the involvement of dynein-, microtubule-, and actin in the transport mechanism of EXOC6A-labeled vesicles to the centriole, as they have previously reported for Myo-Va. Notably, loss of EXOC6A severely disrupts ciliogenesis, with the majority of cells becoming arrested at the ciliary vesicle (CV) stage, highlighting the involvement of EXOC6A at later stages of ciliogenesis. As the authors observe dynamic EXOC6A-positive vesicle release and fusion with the ciliary sheath, this suggests a role in membrane and potentially membrane protein delivery to the growing cilium past the ciliary vesicle stage. While CEP290 localization at the forming cilium appears normal the recruitment of other transition zone components, exemplified by several MKS and NPHP module components, was also impaired in EXOC6A-deficient cells.

Strengths:

- By applying different microscopy approaches, the study provides deeper insight into the spatial and temporal localization of EXOC6A and Myo-Va during ciliogenesis.

- The combination of complementary siRNA and pharmacological tools targeting different components strengthens the conclusions.

- This study reveals a new function of EXOC6A in delivering membrane and membrane proteins during ciliogenesis, both to the ciliary vesicle as well as to the ciliary sheath.

- The overall data quality is high. The investigation of EXOC6A at different time points during ciliogenesis is well schematized and explained.

- The authors confirmed central antibody reagents used in this study and validated key experiments by using two independent knockout clones (for which sequencing information was provided).

Weaknesses:

- The precise molecular function of EXOC6A remains open, as the presented data suggests no involvement of other exocyst components.

Taken together, the authors achieved their goal to elucidate the role of EXOC6A in ciliogenesis, demonstrating its involvement in vesicle trafficking and membrane remodeling in both early and late stages of ciliogenesis. Their findings are supported by experimental evidence. This work is likely to have an impact on the field by expanding our understanding of the molecular machinery underlying cilia biogenesis, particularly the coordination between exocyst components and cytoskeletal transport systems. The methods and data presented offer valuable tools for dissecting vesicle dynamics and cilium formation, providing a foundation for future research into ciliary dysfunction and related diseases. By connecting vesicle trafficking to structural maturation of an organelle, the study adds important context to the broader description of cellular architecture and organelle biogenesis.

Comments on revisions:

We very much appreciate the extra work you put into improving your manuscript and want to congratulate you on your important discovery. We encourage you to keep up the good work!

Reviewer #2 (Public review):

Summary:

Lipid transfer between membranes is essential for lipid biosynthesis across different organelle membranes. Ups1-Mdm35 is one of the best-characterized lipid transfer proteins, responsible for transferring phosphatidic acid (PA) between the mitochondrial outer membrane (OM) and inner membrane (IM), a process critical for cardiolipin (CL) synthesis in the IM. Upon dissociation from Mdm35, Ups1 binds to the intermembrane space (IMS) surface of the OM, extracts a PA molecule, re-associates with Mdm35, and moves through the aqueous IMS to deliver PA to the IM. Here, the authors analyzed the early steps of this PA transfer - membrane binding and PA extraction - using a combination of in vitro biochemical assays with lipid liposomes and purified Ups1-Mdm35 to measure liposome binding, lipid transfer between liposomes, and lipid extraction from liposomes. The authors found that membrane curvature, a previously overlooked property of the membrane, significantly affects PA extraction but not PA insertion into liposomes. These findings were further supported by MD simulations.

Strengths:

The experiments are well-designed, and the data are logically interpreted. The present study provides an important basis for understanding the mechanism of lipid transfer between membranes.　

Weaknesses:

The physiological relevance of membrane curvature in lipid extraction and transfer still remains open.

Comments on revisions:

The authors have addressed most of my previous concerns, and the manuscript now looks much stronger.

Reviewer #2 (Public review):

Summary:

Kim and Parsons reviewed the nitroreductase (NTR)/prodrug system: when engineered cells expressing the enzyme NTR are treated with prodrug (e.g. metronidazole), NTR converts the prodrug into a cytotoxic compound that kills these cells. The review covers how the system has been developed, spatiotemporal control of targeted cell ablation, and its broad utility to study regenerative mechanisms, model human diseases, and screen chemicals to discover pro-regenerative and protective compounds. They further discussed the newer version of NTR, a more potent prodrug, and experimental design, which not only expands the possible utility of the NTR/prodrug system, but also allows the research community to develop a precise, reproducible and versatile platform.

Strengths:

The review summarized landmark work application of the NTR/prodrug system, and recent studies, with focus on the model organism zebrafish. The review provides a good gateway to understanding the system and considering regenerative studies.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #2 (Public review):

Summary:

Human DS is associated with metabolic dysfunction in humans, but the precise details of this have not been studied in detail. Here, the authors use a mouse model of DS to study systemic metabolic and transcriptional responses in key metabolic tissues to provide a deep understanding of the metabolic changes associated with DS. As part of his work, the authors also aimed to help inform the selection of a mouse model that best reflects the metabolic profile of DS, through comparison with other DS model metabolic data.

The data presented in this model will be of interest to those in the field of metabolism. The immediate impact is unclear, but the breadth of data presented makes this a very useful resource.

Strengths:

(1) This work builds on other comprehensive analyses that the authors have performed in other DS mouse models.

(2) The authors note common metabolic disturbances between male and female mice (e.g., insulin resistance) alongside clearly sexually dimorphic phenotypes (e.g., body weight). Studying both sexes in this context is important.

(3) The authors have written the paper in a way that integrates a large number of observations well. There is complex data, and a high degree of sexual dimorphism. The study has generated a valuable and wide-ranging dataset comprising molecular, biochemical, and physiological data that will be useful for further, more mechanistic studies of metabolism in DS.

(4) For specific observations, like the findings of altered body temperature in male and female mice, the authors undertake follow-up hypothesis-driven analyses of BAT mitochondria and specific hormones. Although these analyses do not explain the change in temperature, they ensure the study is not purely descriptive in nature.

Weaknesses:

(1) Assessing metabolism using dynamic testing is a strength. ITT, GTT and LTTs are included.

(2) The dosing for GTTs, ITTs and LTTs was performed per body weight. But the mice under chow and HFD had different body weights. This may compromise the interpretation of the data. Further, ITTs are presented as percentage change, and this can be heavily influenced by baseline glucose measures. The changes appear quite dramatic, so can the authors plot the raw data instead?

(3) In addition, throughout the manuscript, it is not clear which tissues are the most dominant in disrupting metabolism. The ITT and GTT are composite measures across tissues. Tissue-specific analyses using a clamp technique or isolated tissues may provide more clarity here.

(4) One of the aims of the study was "to help inform the selection of mouse model that best reflects the metabolic profile of DS". The discussion does not contain a comparison between the previous work on different strains and relative to known human data.

(5) Data availability. Raw metabolomic data should be made available.

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 that 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.

Weaknesses:

However, in the present version of the manuscript, the conclusions drawn from the experiments, the overall interpretation of the results, and the novelty over previous reports appear less clear.

Major comments:

(1) The use of the term "catch bond" is misleading, as the authors do not really mean consistently a catch bond in the classical sense (i.e., a protein-protein interaction having a dissociation rate that decreases with load). Instead, what they mean is that after motor detachment (i.e., after a motor protein dissociating from a tubulin protein), there is a slip state during which the reattachment rate is higher as compared to a motor diffusing in solution. While this may indeed influence the dynamics of bidirectional cargo transport (e.g., during tug-of-war events), the used terms (detachment (with or without slip?), dissociation, rescue, ...) need to be better defined and the results discussed in the context of these definitions. It is very unsatisfactory at the moment, for example, that kinesin-3 is at first not classified as a catch bond, but later on (after tweaking the definitions) it is. In essence, the typical slip/catch bond nomenclature used for protein-protein interaction is not readily applicable for motors with slippage.

(2) The authors define the stall duration as the time at full load, terminated by >60 nm slips/detachments. Isn't that a problem? Smaller slips are not detected/considered... but are also indicative of a motor dissociation event, i.e., the end of a stall. What is the distribution of the slip distances? If the slip distances follow an exponential decay, a large number of short slips are expected, and the presented data (neglecting those short slips) would be highly distorted.

(3) Along the same line: Why do the authors compare the stall duration (without including the time it took the motor to reach stall) to the unloaded single motor run durations? Shouldn't the times of the runs be included?

(4) At many places, it appears too simple that for the biologically relevant processes, mainly/only the load-dependent off-rates of the motors matter. The stall forces and the kind of motor-cargo linkage (e.g., rigid vs. diffusive) do likely also matter. For example: "In the context of pulling a large cargo through the viscous cytoplasm or competing against dynein in a tug-of-war, these slip events enable the motor to maintain force generation and, hence, are distinct from true detachment events." I disagree. The kinesin force at reattachment (after slippage) is much smaller than at stall. What helps, however, is that due to the geometry of being held close to the microtubule (either by the DNA in the present case or by the cargo in vivo) the attachment rate is much higher. Note also that upon DNA relaxation ,the motor is likely kept close to the microtubule surface, while, for example, when bound to a vesicle, the motor may diffuse away from the microtubule quickly (e.g., reference 20).

(5) Why were all motors linked to the neck-coil domain of kinesin-1? Couldn't it be that for normal function, the different coils matter? Autoinhibition can also be circumvented by consistently shortening the constructs.

(6) I am worried about the neutravidin on the microtubules, which may act as roadblocks (e.g. DOI: 10.1039/b803585g), slip termination sites (maybe without the neutravidin, the rescue rate would be much lower?), and potentially also DNA-interaction sites? At 8 nM neutravidin and the given level of biotinylation, what density of neutravidin do the authors expect on their microtubules? Can the authors rule out that the observed stall events are predominantly the result of a kinesin motor being stopped after a short slippage event at a neutravidin molecule?

(7) Also, the unloaded runs should be performed on the same microtubules as in the DNA experiments, i.e., with neutravidin. Otherwise, I do not see how the values can be compared.

(8) If, as stated, "a portion of kinesin-3 unloaded run durations were limited by the length of the microtubules, meaning the unloaded duration is a lower limit." corrections (such as Kaplan-Meier) should be applied, DOI: 10.1016/j.bpj.2017.09.024.

(9) Shouldn't Kaplan-Meier also be applied to the ramp durations ... as a ramp may also artificially end upon stall? Also, doesn't the comparison between ramp and stall duration have a problem, as each stall is preceded by a ramp ...and the (maximum) ramp times will depend on the speed of the motor? Kinesin-3 is the fastest motor and will reach stall much faster than kinesin-1. Isn't it obvious that the stall durations are longer than the ramp duration (as seen for all three motors in Figure 3)?

(10) It is not clear what is seen in Figure S6A: It looks like only single motors (green, w/o a DNA molecule) are walking ... Note: the influence of the attached DNA onto the stepping duration of a motor may depend on the DNA conformation (stretched and near to the microtubule (with neutravidin!) in the tethered case and spherically coiled in the untethered case).

(11) Along this line: While the run time of kinesin-1 with DNA (1.4 s) is significantly shorter than the stall time (3.0 s), it is still larger than the unloaded run time (1.0 s). What do the authors think is the origin of this increase?

(12) "The simplest prediction is that against the low loads experienced during ramps, the detachment rate should match the unloaded detachment rate." I disagree. I would already expect a slight increase.

(13) Isn't the model over-defined by fitting the values for the load-dependence of the strong-to-weak transition and fitting the load dependence into the transition to the slip state?

(14) "When kinesin-1 was tethered to a glass coverslip via a DNA linker and hydrodynamic forces were imposed on an associated microtubule, kinesin-1 dissociation rates were relatively insensitive to loads up to ~3 pN, inconsistent with slip-bond characteristics (37)." This statement appears not to be true. In reference 37, very similar to the geometry reported here, the microtubules were fixed on the surface, and the stepping of single kinesin motors attached to large beads (to which defined forces were applied by hydrodynamics) via long DNA linkers was studied. In fact, quite a number of statements made in the present manuscript have been made already in ref. 37 (see in particular sections 2.6 and 2.7), and the authors may consider putting their results better into this context in the Introduction and Discussion. It is also noteworthy to discuss that the (admittedly limited) data in ref. 37 does not indicate a "catch-bond" behavior but rather an insensitivity to force over a defined range of forces.

Reviewer #2 (Public 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 is somewhat lacking. A Table with all the observations must be included. This can replace the descriptions of the observations with the different lines, which could be somewhat laborious for the reader, and are often wrong. There are numerous mistaken expectations of protein expression here, but two examples include:

(1) The expectation that ACDH-10 is enriched in the intestine and epidermal tissues (hypodermis)<br /> There are multiple paralogs of this protein (see 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.

(2) The expectation that HXK-1 is ubiquitously expressed<br /> Three paralogous enzymes 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 show 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.

Reviewer #3 (Public review):

In this study the authors tested for alterations in selection intensity across ~13,000 protein coding genes along the gorilla lineage in order to test the hypothesis that the evolution of a polygynous social system resulted in relaxed selective constraint through a reduction in sperm competition. Of these genes, 578 exhibited signatures of relaxed purifying selection that were enriched for functions in male germ cells including meiosis and sperm biology. These genes were also more likely to be expressed in male germ cells and to contain deleterious mutations. Functional analysis of genes not previously implicated in male reproduction identified 41 new genes essential to male fertility in a Drosophila model. Moreover, genes under relaxed selective constraint in the gorilla lineage were more likely to contain loss of function variants in a cohort of infertile men. The authors conclude that their results support the hypothesis that the emergence of a polygynous social system may have reduced the degree of selective pressures exerted through sperm competition.

(1) The identification of novel genes involved in spermatogenesis using signatures of relaxed selective constraint coupled to in vivo RNAi in Drosophila offers a proof of principal as to the power of evolutionarily-informed functional genomics that has been largely underutilized.

(2) The analysis is restricted to protein-coding regions of genes that have single, orthologous sequences spanning 261 mammalian species, and as such is a non-random set of 13,310 genes that have higher evolutionary conservation. While this approach is necessary for the analyses being performed, it excludes non-coding regions, recently duplicated genes/gene families, and rapidly evolving genes, which are all likely subject to stronger selection as compared to evolutionarily conserved genes (and gene regions). Thus, the conclusions of relaxed selective constraint as being pervasive could be missing a large number of the most strongly selected genes, many of which may include sex and reproduction related genes.

(3) The identification of genes showing relaxed selection along the gorilla lineage, which are overrepresented in male reproduction, supports the hypothesis that the emergency of polygyny resulted in relaxed sperm competition and is the driving force behind their observations. To more fully test this hypothesis the authors contrast their findings to observations in elephant seals, however of the 573 genes under relaxed selection in gorillas only 14 show a similar pattern. These genes are not enriched for male reproductive function, and may be under-powered or result from variation in reproductive strategies in gorillas as compared to elephant seals that mate seasonally.

(4) The comparisons of human males with infertility to a large number of healthy males from a separate cohort can lead to genetic differences related to population structure or differences in study recruitment independent of infertility, and care must be taken to avoid confounding. Population structure is more likely to affect patterns of rare variation (including loss of function mutations), even when controls are ascertained using similar enrollment criteria, geographic regions, racial/ethnic and national identities. In this study, the MERGE cohort is largely recruited from Germany, vs. a geographically more broadly recruited control cohort gnomeAD. The authors performed a sub-cohort analysis among individuals identified as having predominantly European genetic ancestry within MERGE, to that of non-Finnish European individuals from genomeAD, and find similar results, thus strengthening their findings.

Reviewer #2 (Public review):

This study investigates how BG-induced myeloid reprogramming influences inflammatory bowel disease in a mouse model of DSS-induced colitis. The authors use in vivo functional experiments, adoptive transfer, and scRNA-seq to assess whether innate immune reprogramming can confer protection in colitis.

In the revised versions of the manuscript, the authors clarified the mechanistic scope of the study, softened the conclusions, and acknowledged the lack of direct epigenetic validation of trained immunity in this model. The manuscript now also better emphasizes the context-dependent nature of BG-induced reprogramming.

While some aspects remain correlative and will require further investigation, the central findings are well supported.

Overall, this work provides a meaningful contribution to the field, and I support its publication.

Comments on revisions:

No further comments.

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.

Comments on revision:

The authors extensively entered into a scientific debate with the reviewers in their Response Letter. This led to a few changes and some (limited) new data in the manuscript. This is great and did improve the manuscript.

However, in the view of this reviewer, (i) a significant number of responses fall short of actually addressing the concerns of the three reviewers (e.g. wrt using the same kinesin-1 neck-coil domains for all motors) and or (ii) a significant number of arguments now only occur in the response letter but not in the manuscript. The authors may check themselves critically for both. In principle, each longer discussion in the response letter warrants mentioning the appropriate facts and arguments in the main text of the manuscript.

Reviewer #2 (Public review):

Summary:

This manuscript describes the fully in silico design of a new variant of Staphylococcus aureus Cas9 (SaCas9) using an improved UniDesign workflow.

The design strategy consists of three sequential steps:<br /> (1) reducing positional bias at PAM position 3;<br /> (2) restoring DNA binding through nonspecific interactions;<br /> (3) combining individually favorable substitutions.

The overall pipeline is conceptually elegant and logically structured, and the genome-editing activity of the designed variants is comprehensively characterized. The resulting KRH variant exhibits relaxed PAM specificity, expanding the targeting range of SaCas9 across diverse cell types. Notably, the KRH variant demonstrates performance comparable to that of the evolution-derived KKH variant, underscoring the effectiveness of the proposed computational design framework.

Strengths:

The design pipeline is entirely computational and does not rely on experimental data for pretraining or iterative optimization.

Weaknesses:

The computationally generated KRH mutant differs from the experimentally evolved KKH variant by only a single residue, which may reflect insufficient exploration of the available sequence space.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors demonstrate strong correlations between a pro-inflammatory state, the activity of an intracrine hormone (3 beta-hydroxysteroid dehydrogenase, 3B-HSD), and the NAD co-factor. Specifically, in a 3B-HSD knockout mouse, there was an upregulation in pro-inflammatory cytokines and increased CD38+ cells (CD38 is an enzyme that depletes NAD, a necessary cofactor for 3B-HSD activity). Conversely, induction of inflammation in the eyelids resulted in reductions in 3B-HSD activity. Supplementation with 5 alpha-dihydrotestosterone (DHT) or the NAD precursor NMN, and inhibition of CD38 activity (78c), corrected the pathologies observed in both the 3B-HSD knockout mouse and the pro-inflammatory model (LPS injection into eyelids).

Strengths:

The experiments were performed with good rigor, assessing the impact of inflammation and 3B-HSD activity using multiple model systems. The endpoints represented a combination of transcriptional changes, protein quantification, enzymatic activity, and immunofluorescent microscopy. The authors use human tissue from both younger and older individuals to justify their hypotheses that increased CD38 + cells and reduced 3B-HSD quantity exist in older individuals. The data provide the foundation for assessing more global changes to the tear film and ocular surface.

Weaknesses:

The main weaknesses of the study include the following:

(1) An absence of information on meibomian gland health, tear film, and ocular surface.

(2) Too few human subjects to validate the hypotheses.

Conclusion:

Overall, this study demonstrates an important relationship that exists between intracrine signaling, inflammation, and cofactor signaling. It represents a novel approach in therapeutic design for patients with meibomian gland dysfunction.

Reviewer #2 (Public review):

Kim et al. investigate heterogeneity in aged muscle stem cells using a model that enables lifelong lineage tracing. The questions addressed in the paper are highly relevant to the fields of aging and stem cell biology, and the experimental approach overcomes some of the limitations of previous studies.

The study provides evidence for phenotypic and functional heterogeneity within the lineage-traced aged MuSC pool. However, the data as presented do not yet support the broader conclusions that MuSC abundance is maintained with age or that a previously unrecognized aged MuSC subpopulation has been identified. These claims would require stronger age-matched cohorts, absolute cell counts normalized to tissue mass, and direct comparison to previously described aged muscle stem cell states.

If the core observations were experimentally reinforced, this study could prompt the field to reassess muscle stem cell loss, heterogeneity, and age-associated changes in canonical marker expression in geriatric mice. However, because several of the central claims depend on analyses that are currently incomplete, the conceptual impact should be treated as provisional. The deposited bulk RNA-seq and scRNA-seq datasets should be useful for mapping these states to existing atlases and for re-analysis by groups interested in quiescent and senescent programs in geriatric muscle stem cells.

Reviewer #2 (Public review):

Summary:

In this study, Xu et al. present a transcriptome-wide, single-base resolution map of RNA pseudouridine modifications across evolutionarily diverse bacterial species using an adapted form of BID-Seq. By optimizing the method for bacterial RNA, the authors successfully mapped modifications in rRNA, tRNA, and, importantly, mRNA across both exponential and stationary growth phases. They uncover evolutionarily conserved Ψ motifs, dynamic Ψ regulation tied to bacterial growth state, and propose functional links between pseudouridylation and bacterial transcript stability, translation, and RNA-protein interactions. To extend these findings, they develop a deep learning model that predicts pseudouridine sites from local sequence and structural features.

Strengths:

The authors provide a valuable resource: a comprehensive Ψ atlas for bacterial systems, spanning hundreds of mRNAs and multiple species. The work addresses a gap in the field - our limited understanding of bacterial epitranscriptomics, by establishing both the method and datasets for exploring post-transcriptional modifications.

Weaknesses:

The main limitation of the study is that most functional claims (i.e. translation efficiency, mRNA stability, and RNA-binding protein interactions) are based on correlative evidence. While suggestive, these inferences would be significantly strengthened by targeted perturbation of specific Ψ synthases or direct biochemical validation of proposed RNA-protein interactions (e.g., with Hfq). Additionally, the GNN prediction model is a notable advance.

Reviewer #2 (Public review):

Summary:

In this manuscript by Laham and co-workers, the authors profiled structurally diverse LXR ligands via a coregulator TR-FRET (CRT) assay for their ability to recruit coactivators and kick off corepressors, while identifying coregulator preference and LXR isoform selectivity.

The relative ligand potencies measured via CRT for the two LXR isoforms were correlated with ABCA1 induction or lipogenic activation of SRE depending on cellular contexts (i.e, astrocytoma or hepatocarcinoma cells). While these correlations are interesting, there is some leg room to improve the quantitative presentation of these correlations. Finally, the CRT signatures were correlated with the structural stabilization of the LXR: coregulator complexes. In aggregate, this study curated a set of LXR ligands with disparate agonism signatures that may guide the design of future nonlipogenic LXR agonists with potential therapeutic applications for cardiovascular disease, Alzheimer's and type 2 diabetes, without inducing mechanisms that promote fat/lipid production.

Strengths:

This study has many strengths, from curating an excellent LXR compound set, to the thoughtful design of the CRT and cellular assays. The design of a multiplexed precision CRT (pCRT) assay that detects corepressor displacement as a function of ligand-induced coactivator recruitment is quite impressive as it allows measurement of ligand potencies to displace corepressors in the presence of coactivators, which cannot be achieved in a regular CRT assay that looks at coactivator recruitment and corepressor dissociation in separate experiments.

Comments on revisions:

These weaknesses have been satisfactorily addressed by the authors in the revised preprint.

Reviewer #2 (Public review):

In the current version, Zhang et al. have made substantial improvements to the manuscript. It is now easier to read, and the data are more solid compared with the previous version, supporting their conclusion that tumor GSCs secrete stemness factors (BMPs and Dpp) to suppress the differentiation of neighboring wild-type GSCs. This study should benefit a broad readership across developmental biology, germ cell biology, stem cell biology, and cancer biology.

However, the following suggestions may further improve the clarity and rigor of the research content:

(1) Clarification of sample size (n).<br /> Each germarium can contain highly variable numbers of SGCs, sometimes reaching 50-100. When reporting "n" values, the authors are encouraged to also indicate the number of germaria analyzed. For example, in lines 126-128:<br /> "Notably, 74% of SGCs (n = 132) were GFP-negative, while the remaining 26% were GFP-positive (Figure 2B, C). This suggests that SGCs can be categorized into two distinct groups: those resembling GSCs (GSC-like) and those resembling cystoblasts (cystoblast-like)."<br /> Please clarify how many germaria were examined to obtain n = 132. In addition, it is unclear whether the authors intend to suggest that the GFP-negative SGCs are GSC-like or cystoblast-like; this point should be clarified.

(2) Improvement of Fig. 6 in situ hybridization images.<br /> The in situ hybridization images in Fig. 6 are not fully convincing. The control images, in particular, would benefit from higher resolution and enlarged views of the germarium region. In panel C, abundant signals are also present outside the germarium, which may complicate interpretation and should be clarified or controlled for.

Alternatively, the authors could strengthen the in situ analysis by using bam mutants or bam dpp / bam gbb double mutants as controls to better define signal specificity.

Reviewer #2 (Public review):

Summary:

In this manuscript, van Dijk et al analyse the expression of the largely ignored paralogue of TNF in zebrafish, tnfb. They generate reporter transgenic lines and show that the reporter expression is consistent with endogenous mRNA expression in zebrafish larvae. Unlike its better-known paralogue tnfa, tnfb is constitutively expressed in mantle cells of neuromasts, and in a few leukocytes. It is also inducible in macrophages and some neutrophils upon wounding or detection of microbes, with faster kinetics than tnfa or il1b.

Strengths:

Generation and convincing validation of new transgenic reporter lines for tnfb with either green or red fluorescent proteins. Superb imaging and careful analysis of these lines crossed to complementary reporter transgenics, backed with in situ hybridization and qRT-PCR analysis of FACS-sorted cells. Excellent methods section.

Weaknesses:

Lack of functional analysis; these lines are a potentially valuable tool, but so far provide no clue regarding the role of tnfb. Is it a pro-inflammatory cytokine acting in synergy with tnfa, or is it an antagonist? What are its receptor(s)? What signalling pathways and downstream genes does it induce? Addressing at least some of these questions should greatly increase the impact of the paper.

Reviewer #2 (Public review):

Summary:

This article by Bhattacharya et al. investigates how neural stem cells (NSCs, NBs) in Drosophila integrate spatial and temporal cues to activate neuron-specific terminal selector (TS) genes. Prior to this work, it was understood that NSCs utilize spatial transcription factors (STFs) and temporal transcription factors (TTFs) to determine lineage identity and birth order, but the mechanisms of integration were not fully elucidated. The authors employed chromatin profiling techniques to analyze the binding of STFs and TTFs in two specific neuroblast lineages, NB5-6 and NB7-4. They found that Gsb (an STF) binds both accessible and less-accessible chromatin in NB5-6, while En (another STF) binds only to pre-accessible chromatin in NB7-4. The findings support an "STF code" where the combination of pioneer and non-pioneer spatial factors, along with temporal factors, triggers neuroblast-specific enhancer activation and determines lineage identity.

Strengths:

The experiments are well-executed, the interpretations are generally sound, and the figures are clear and elegant. However, some conclusions are drawn too broadly without essential functional data. Therefore, additional work is needed to more effectively convey the central message.

Weaknesses:

(1) Integration of TaDa and functional data on Gsb for the STF model

The authors demonstrate that TaDa profiling maps Gsb binding across the genome and identifies candidate chromatin-priming sites in NB5-6. Gsb LOF/GOF experiments reveal effects on NB identity. Combining TaDa data with LOF and GOF analyses indicates that Gsb influences NB5-6 specification by binding to both open and relatively closed chromatin, helping maintain NB5-6 identity while limiting NB3-5 fate.

However, the study does not establish a direct link between specific LOF/GOF phenotypes and particular genomic targets. For instance, analyzing Gsb occupancy at lineage-specific identity factors or terminal selector genes (such as Lbe, Ap, or Eya for NB5-6; and Ems, etc., for NB3-5) in wild-type and manipulated conditions (Gsb misexpression) would directly connect chromatin binding to the regulation of fate determinants. These investigations would strengthen the mechanistic connection between the correlative TaDa profiles and the observed identity changes, supporting the idea that Gsb functions as a context-dependent chromatin-priming factor within the STF code, rather than as a generic transcription factor.

(2) Gsb misexpression reveals bidirectional chromatin remodelling

Experiments with ectopic Gsb expression demonstrate bidirectional chromatin remodeling in NB7-4, showing decreases in accessibility at some binding sites and increases at others. While the authors show that Gsb can disrupt chromatin upon misexpression, interpreting its "pioneer-like" or chromatin-priming activity is complex due to several factors: the misexpression occurs in a non-native lineage, the direct versus indirect effects rely on whole-embryo Dam-Gsb peaks instead of NB7-4-specific binding, and heat-shock-induced chromatin changes are not fully accounted for. These issues make it challenging to definitively determine Gsb's role in chromatin priming.

A complementary approach would be to perform Gsb knockdown/loss-of-function in its native NB5-6 lineage and profile chromatin accessibility (TaDa or CATaDa). This would allow a cleaner, more physiologically relevant assessment of Gsb's contribution to priming, SoI establishment, and Hb recruitment. Such an experiment would strengthen the causal link between Gsb occupancy and chromatin state and clarify whether Gsb truly acts as a context-dependent pioneer in vivo, rather than producing indirect effects due to ectopic misexpression.

(3) En is not a pioneer factor

The authors conclude that Engrailed (En) is not a pioneer factor, based on the observation that En binding correlates with accessible chromatin and that En is not enriched at NB5-6-specific SOIs. However, this conclusion is not sufficiently supported by the functional data.

First, the absence of En binding at NB5-6-specific SOIs does not necessarily indicate an inability to engage closed chromatin. These regions were not selected for the presence of En consensus motifs, so their lack of occupancy may simply reflect the absence of En binding motifs rather than a lack of pioneering capacity. A systematic motif analysis at NB5-6-specific SOIs is needed to determine whether En binding sites are present but unoccupied.

Second, the claim that En lacks pioneer activity relies solely on a single steady-state TaDa/DamID occupancy assay at one developmental stage. Because pioneer factor interactions can be transient, low-affinity, and stage-specific, such binding may not be detected by TaDa, which also depends on local GATC density and methylation kinetics and may yield false negatives. Given these technical limitations, the absence of En binding at less accessible regions does not definitively rule out a priming role.

In the absence of direct functional assays (En LOF/GOF), the authors should explicitly acknowledge these technical and conceptual limitations and tone down the claim that "En lacks pioneer activity".

(4) Clarity of STF-code Model and Central Message

The manuscript begins by presenting two models, direct and epigenetic, but the central takeaway of the paper is not clear. Specifically, the nuanced roles of the spatial factors Gsb and En as chromatin-priming versus stabilizing/effector factors within an STF code, and the resulting division of labor, are not clearly illustrated. The distinction between Gsb as a chromatin-priming factor and En as a cofactor-dependent activator/stabilizer should be explicitly presented in a stepwise model for better clarity. The authors could strengthen this by providing a schematic with two sequential stages illustrating how neuroblast identity factors (STF code) change chromatin states to drive lineage-specific enhancer activation. The schematic can be shown from the neuroectoderm to individual NB lineages to make it more panoramic.

(5) Identification of Priming Factors in NB7-4

While the authors suggest that an unknown priming factor might be responsible for establishing sites of integration in NB7-4, they do not identify or explore potential candidates for this role. Further investigation into what factors might be involved in chromatin priming in NB7-4 could provide a more complete understanding of the mechanisms at play.

(6) Functional Validation of STF Code Components

The study proposes an STF code for each neuroblast lineage, but the specific components of these codes, beyond Gsb and En, are not fully explored. Identifying and validating additional factors that contribute to the STF code in each lineage could strengthen the conclusions.

Reviewer #2 (Public review):

Summary:

The authors present a computational framework for generating "cell-specific" digital twins of human iPSC-CMs from a single optimized voltage clamp recording. Using deep learning trained on > 1 million artificial cells, the authors demonstrate that the model can infer 52 biophysical parameters governing 6 major ionic currents, and the resulting digital twins can reproduce experimentally recorded action potentials.

Strengths:

The framework has clear potential for understanding cellular heterogeneity in iPSC-CMs, predicting individual drug responses, and reducing the experimental burden of multiple patch clamp protocols.

Weaknesses:

There are several concerns about the validation of the model and its clarity. First, the biological variability being modeled in this manuscript is not defined well. It is unclear whether the framework addresses cell-to-cell differences within a single differentiation batch, variability across iPSC lines, or donor-to-donor differences. This ambiguity makes it difficult to interpret what the "digital twin populations" actually represent biologically. Second, the main claim, "the digital twins enable drug testing and arrhythmia prediction that would be impractical experimentally", is not experimentally validated. For example, the E-4031 simulations predict EAD rates, but no direct experimental head-to-head comparison is provided to confirm that these predictions are accurate. Third, technical reproducibility and biological representativeness are not assessed. Single voltage clamp recordings are inherently noisy. Without knowing how much variability comes from the recording process (technical variation) vs true biological differences, it is difficult to judge whether observed "cell-specific" parameter differences are meaningful. In addition, the optimized protocol is claimed to be superior to conventional approaches, but again, no experimental comparison is shown.

The authors should address these concerns, with particular emphasis on clarifying the biological context and providing direct experimental validation. Below are detailed specific points:

(1) Ambiguous definition of iPSC-CM heterogeneity.

The authors model "typical iPSC-CM heterogeneity" by varying 52 parameters +/- 40% around a baseline model (Figure 1), generating > 1 million synthetic cells. However, the manuscript does not clearly state what biological variability this model is intended to capture. Is this modeling within-line, cell-to-cell variability (e.g., cells from the same dish or differentiation batch that differ due to stochastic gene expression or maturation state)? Or is this modeling between-line or between-donor variability (e.g., genetic background differences, reprogramming efficiency)? This distinction is critical for interpretation. If the goal is to understand why different cells in the same dish behave differently, then training data should reflect that. If the goal is to compare patient lines or disease models, the framework needs validation across multiple donors or lines.

For example, the experimental validation in Figure 5 uses a single iPSC line (iPS-6-9-9T.B), but how many differentiation batches or dishes were tested, or whether cells came from the same preparation are unclear. Another example is that the wide AP diversity in the training population (Figure 1A) is impressive, but there is no demonstration that real experimental cells actually fall within this assumption range of +/- 40%.

From a biological perspective, iPSC-CMs are known to be highly heterogeneous within lines (maturation state, metabolic differences, epigenetic variation, spatial differences within the same dish, etc) and between lines (different donor/genetic background). Thus, please explicitly state whether the +/- 40% variation is intended to model within-line or between-line heterogeneity, and justify this choice with wet experiment data (or reference to experimental literature on iPSC-CM variability). Please clarify how many dishes, differentiation batches, and time points post-differentiation were used for experimental recordings (Figures 5-6). If the framework is intended to generalize across lines from different donors, please test the model on multiple independent iPSC lines (from different donors).

(2) Biological representativeness of single-cell measurements.

The framework generates digital twins from single voltage clamp recordings. The patch clamp recordings in iPSC-CMs are subject to substantial technical variability. The manuscript does not address a fundamental question: "How representative are the measurements from a single cell on the dish (or line)?" In other words, if I measure one cell from a dish of a million cells, does that cell's digital twin tell me something about the dish as a whole, or just about that one cell? The manuscript presents Cell 1 and Cell 2 (Figures 5-6) as distinct individuals, but it's unclear whether these differences reflect true biological heterogeneity or simply sampling variability. I think the authors should perform replicate recordings on multiple cells (e.g., > 10 cells) from the same dish (same differentiation batch) and quantify how much the inferred parameters vary, and then compare between lines.

(3) No experimental validation of the main claim that in silico populations can replace wet experiments.

The most exciting claim in the manuscript is that digital twins enable drug testing and arrhythmia prediction "at scale" without requiring hundreds of patch clamp experiments. Specifically, the authors show that in silico populations derived from two experimental cells (Figure 6C) predict dose-dependent EAD incidence for the IKr blocker E-4031 (Figure 6D), with ~3% of cells showing EADs at 50 nM.

However, this prediction is not validated experimentally. If I actually patch 20-30 real iPSC-CMs and apply 50 nM E-4031, will ~3% of them show EADs, as the model predicts? Without this validation, I think the drug testing framework is purely hypothetical. The model may be internally consistent (e.g., Cell 1's twin behaves differently from Cell 2's twin), but there is no evidence that these in silico populations reflect real biological variability in drug response. Please provide experimental validation that justifies the prediction by digital twins.

(4) Experimental validation and head-to-head comparison of optimized protocol.

The authors claim that their deep learning-optimized voltage clamp protocol (Figure 3, Figure 4A) is superior to conventional approaches, but they have not validated this experimentally by doing a head-to-head comparison. The manuscript does not compare the optimized protocol to any published voltage clamp designs. If the optimized protocol is genuinely easier to implement and more informative than existing approaches, this would be a major practical advance. But without side-by-side comparison, it is impossible to judge whether the optimization made a real difference.

Reviewer #2 (Public review):

Summary:

The manuscript provides a very high-resolution crystal structure of the bacteriophage T4 spike gp5-gp5.4 complex and clear evidence of the importance of gp5.4 for the fitness of the phage and its necessity for successful infection of strains of Escherichia coli with truncated lipopolysaccharide. Evidence, or at least speculation, as to what bacterial compounds gp5.4 interacts with would have been welcome.

Strong points:

(1) Very high resolution detailed crystal structure of the gp5-gp5.4 complex.

(2) First proof of the importance of gp5.4 for bacteriophage T4 and by extension, of homologous proteins in other phages.

Weaker points:

(1) Localisation experiments were performed not with protein 5.4 but the homologous gpV from bacteriophage P2.

(2) The exact mechanism was not yet resolved, i.e. to which bacterial component gp5.4 binds.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Comments on revisions:

I would like to thank the authors for addressing all the concerns and providing additional details to improve the clarity of presentation.

Reviewer #2 (Public review):

Summary:

Structural Maintenance of Chromosome proteins (SMCs), a family of proteins found in almost all organisms, are organizers of DNA. They accomplish this by a process known as loop extrusion, wherein double-stranded DNA is actively reeled in and extruded into loops. Although SMCs are known to have several DNA binding regions, the exact mechanism by which they facilitate loop extrusion is not understood but is believed to entail large conformational changes. There are currently several models for loop extrusion, including one wherein the coiled coil (CC) arms open, but there is a lack of insightful experimentation and analysis to confirm any of these models. The work presented aims to provide much-needed new tools to investigate these questions: conformation-selective sybodies (synthetic nanobodies) that are likely to alter the CC opening and closing reactions.

The authors produced, isolated, and expressed sybodies that specifically bound to Bacillus subtilis Smc-ScpAB. Using chimeric Smc constructs, where the coiled coils were partly replaced with the corresponding sequences from Streptococcus pneumoniae, the authors revealed that the isolated sybodies all targeted the same 4N CC element of the Smc arms. This region is likely disrupted by the sybodies either by stopping the arms from opening (correctly) or forcing them to stay open (enough). Disrupting these functional elements is suggested to cause the Smc-dependent chromosome organization lethal phenotype, implying that arm opening and closing is a key regulatory feature of bacterial Smc-ScpAB.

Significance:

The authors present a new method for trapping bacterial Smc's in certain conformations using synthetic antibodies. Using these antibodies, they have pinpointed the (previously suggested) 4N region of the coiled coils as an essential site for the opening and closing of the Smc coiled coil arms and that hindering these reactions blocks Smc-driven chromosomal organization. The work has important implications for how we might elucidate the mechanism of DNA loop extrusion by SMC complexes.

Reviewer #2 (Public review):

This manuscript by Tkacik et al. uses in vitro reconstituted systems to examine paradoxical activation across RAF isoforms and inhibitor classes. The authors conclude that paradoxical activation can be explained without invoking negative allostery and propose a general model in which ATP displacement from an "open monomer" promotes dimerization and activation. The biochemical work is technically sound, and the systematic comparison across RAF paralogs (along with mutational/functional analysis) across inhibitor classes is a strength.

However, the central mechanistic conclusions are overgeneralized relative to the experimental systems, and several key claims, particularly the dismissal of negative allostery and the proposed unifying model in Figure 6, are not directly supported by the data presented. Most importantly, the absence of RAS, membranes, and relevant regulatory context fundamentally limits the physiological relevance of several conclusions, especially regarding the current clinical type I.5 RAF inhibitors and paradoxical activation.

Overall, this is a potentially valuable biochemical study, but the manuscript would benefit from more restrained interpretation, clearer framing of scope, and revisions to the model and title to better reflect what is actually tested.

(1) A central issue is that the biochemical system lacks RAS, membranes, 14-3-3 and endogenous regulatory factors that are known to be required for paradoxical RAF and MAPK activation in cells. As previous work has repeatedly shown and the authors also acknowledge, paradoxical activation by RAF inhibitors is RAS-dependent in cells, and this dependence presumably explains why full-length autoinhibited RAF complexes are refractory to activation in the authors' assays.

Importantly, the absence of paradoxical activation by type I.5 inhibitors in this system is therefore not mechanistically informative. Type I.5 inhibitors (e.g., vemurafenib, dabrafenib, encorafenib), but not Paradox Breakers (e.g., plixorafenib), robustly induce paradoxical activation in cells because binding of the inhibitor to inactive cytosolic RAF monomer promotes a conformational change that drives RAF recruitment to RAS in the membrane, promoting dimerization. The inability of the type 1.5 inhibitor to suppress the newly formed dimers is the basis of the pronounced paradoxical activation in cells. In the absence of RAS and membrane recruitment, failure to observe paradoxical activation in vitro does not distinguish between competing mechanistic models.

As a result, conclusions regarding inhibitor class differences, and especially the generality of the proposed model, should be substantially tempered.

(2) The authors argue that their data argue against negative allostery as a central feature of paradoxical activation. However, the presented data do not directly test negative allostery, nor do they exclude it. The biochemical assays do not recreate the cellular context in which negative allostery has been inferred. Further, structural data showing asymmetric inhibitor occupancy in RAF dimers cannot be dismissed on the basis of alternative symmetric structures alone, particularly given the dynamic nature of RAF dimers in cells.

Most importantly, negative allostery was proposed to explain paradoxical activation by Type I.5 RAF inhibitors, yet these inhibitors do not paradoxically activate in the assays presented here. The absence of paradoxical activation in this system, therefore, cannot be used to argue against a mechanism that is specifically invoked to explain cellular behavior not recapitulated by the assay.

(3) The model presented in Figure 6 is conceptually possible but remains speculative. Key elements of the model, including RAS engagement, membrane recruitment, 14-3-3 rearrangements, and the involvement of cellular kinases and phosphatases, are explicitly absent from the experimental system. Accordingly, the model is not tested by the data presented and should not be framed as a validated or general mechanism. The figure and accompanying text should be clearly labeled as a working or conceptual model rather than a mechanistically supported conclusion.

(4) The manuscript states that type I.5 inhibitors do not induce paradoxical activation in the biochemical assay because their C-helix-out binding mode disfavors dimerization. While this is true in isolation, it overlooks the well-established fact that type I.5 inhibitors (with the exception of paradox breakers) clearly promote RAS-dependent RAF dimerization in cells. This distinction is critical and should be explicitly acknowledged when interpreting the in vitro findings.

(5) The title suggests a general mechanism for paradoxical activation across RAF isoforms and inhibitor classes, whereas the data primarily address type I and type II inhibitors acting on isolated kinase-domain monomers. A more accurate framing would avoid the term "general" and confine the conclusions to C-helix-in (type I/II) RAF inhibitors in a reduced biochemical context.

Reviewer #2 (Public review):

This manuscript by Carmona, Zagotta, and Gordon is generally well-written. It presents a crude and incomplete structural analysis of the voltage-gated proton channel based on measured FRET distances. The primary experimental approach is Förster Resonance Energy Transfer (FRET), using a fluorescent probe attached to a noncanonical amino acid. This strategy is advantageous because the noncanonical amino acid likely occupies less space than conventional labels, allowing more effective incorporation into the channel structure.

Fourteen individual positions within the channel were mutated for site-specific labeling, twelve of which yielded functional protein expression. These twelve labeling sites span discrete regions of the channel, including P1, P2, S0, S1, S2, S3, S4, and the dimer-connecting coiled-coil domain. FRET measurements are achieved using acridon-2-ylalanine (Acd) as the acceptor, with four tryptophan or four tyrosine residues per monomer serving as donors. In addition to estimating distances from FRET efficiency, the authors analyze full FRET spectra and investigate fluorescence lifetimes on the nanosecond timescale.

Despite these strengths, the manuscript does not provide a clear explanation of how channel structure changes during gating. While a discrepancy between AlphaFold structural predictions and the experimental measurements is noted, it remains unclear whether this mismatch arises from limitations of the model or from the experimental approach. No further structural analysis is presented to resolve this issue or to clarify the conformational states of the protein.

The manuscript successfully demonstrates that Acd can be incorporated at specific positions without abolishing channel function, and it is noteworthy that the reconstituted proteins function as voltage-activated proton channels in liposomes. The authors also report reversible zinc inhibition of the channel, suggesting that zinc induces structural changes in certain channel regions that can be reversed by EDTA chelation. However, this observation is not explored in sufficient depth to yield meaningful mechanistic insight.

Overall, while the study introduces an interesting labeling strategy and provides valuable methodological observations, the analysis appears incomplete. Additional structural interpretation and mechanistic insight are needed.

Reviewer #3 (Public review):

I very much enjoyed reading Lingxiu Xu et al.'s paper "Temporally controlled nervous system-to-gut signaling bidirectionally regulates longevity in C. elegans," where they investigate the mechanisms by which motor neurons regulate lifespan in C. elegans worms. In this paper, they first discover that interfering with synaptic release in cholinergic motor neurons affects lifespan. Using mutants and gene knockdowns they show that these effects are due to the neurotransmitter acetylcholine. They show that the effects these motor neurons on lifespan are opposite, depending on timed genetic interventions promoting synaptic release. If these interventions occur during development, lifespan is shortened, but if they occur starting on day 7 of adulthood, then lifespan is lengthened. They then show that the transcription factor daf-16 is required for the former effect, while the transcription factor hsf-1 is required for the latter one. In addition, these early and late effects, they find, required the acetylcholine receptors acr-6 and gar-3, respectively, and intestinal expression of these genes rescues the respective phenotypes. Interestingly, tagging the endogenous acr-6 and gar-3 genes with mCherry, they find that the ACR-6 and GAR-3 proteins are expressed in the intestine, ACR-6 during development and GAR-3 during adulthood. Based on these findings they propose a model where acetylcholine from motor neurons regulates lifespan by modulating different receptors expressed at different times. These receptors, in turn, affect lifespan in opposing ways via different transcription factors.

Comments on revisions:

I am grateful to the authors for their effort to address my comments and suggestions, and for the thoughtful discussion of their efforts to strengthen the claims supporting their model.

Reviewer #2 (Public review):

Summary:

Joint analysis of multiple modalities in single cells will provide a comprehensive view of cell fate states. In this manuscript, Bhardwaj et al developed a single-cell multi-omics assay, T-ChIC, to simultaneously capture histone modifications and the full-length transcriptome and applied the method to early embryos of zebrafish. The authors observed a decoupled relationship between the chromatin modifications and gene expression at early developmental stages. The correlation becomes stronger as development proceeds, as genes are silenced by the cis-spreading of the repressive marker H3k27me3. Overall, the work is well performed, and the results are meaningful and interesting to readers in the epigenomic and embryonic development fields.

Strengths:

This work utilized a new single-cell multi-omics method and generated abundant epigenomics and transcriptomics datasets for cells covering multiple key developmental stages of zebrafish.

Weaknesses:

The data analysis was superficial and mainly focused on the correspondence between the two modalities. The discussion of developmental biology was limited.

Overall, the T-ChIC method is efficient and user-friendly, and the single-cell datasets for zebrafish early development are also valuable. Audiences in the field of epigenomic and embryonic development will benefit from this work.

Comments on revised version:

The authors have answered my previous concerns.

Reviewer #2 (Public review):

Summary:

In this paper the authors seek to disentangle brain areas that encode the subjective value of individual stimuli/items (input regions) from those that accumulate those values into decision variables (integrators) for value-based choice. The authors used a novel task in which stimulus presentation was slowed down to ensure that such a dissociation was possible using fMRI despite its relatively low temporal resolution. In addition, the authors leveraged the fact that gaze increases item value, providing a means of distinguishing brain regions that encode decision variables from those that encode other quantities such as conflict or time-on-task. The authors adopt a region-of-interest approach based on an extensive previous literature and found that the ventral striatum and vmPFC correlated with the item values and not their accumulation whereas the pre-SMA, IPS and dlPFC correlated more strongly with their accumulation. Further analysis revealed that the pre-SMA was the only one of the three integrator regions to also exhibit gaze modulation.

The study uses a highly innovative design and addresses an important and timely topic. The manuscript is well-written and engaging, while the data analysis appears highly rigorous.

Weaknesses:

With 23 subjects the study has relatively low statistical power for fMRI although the within-subjects design and relatively high trial count reduces these concerns.

Reviewer #2 (Public review):

Summary:

This work presents interesting findings where the addition of exogenous ATP extends the replicative lifespan of yeast cells in a way that seems uncorrelated with actual increased intracellular ATP levels or mitochondria. To be clear, the addition of ATP to yeast growth media increases the number of cell divisions per cell in yeast. Expression of the NTT1 ATP transporter gene increases intracellular ATP levels according to LCMS analysis, but the effect on replicative lifespan works without the NTT1 gene and without an intracellular increase in ATP (possibly with a decrease in intracellular ATP), so the effect appears to be independent of the effect on intracellular ATP levels or mitochondria, as mitochondria-less R0 yeast cells also have increased numbers of cell division when grown with extracellular ATP. The plots in Figure 5 make it seem like exogenous ATP addition lowers intracellular ATP for both the NTT1 cells and the wild-type cells, and that is not what the data in Figure 2d with LCMS shows.

As an aside, this seems like a better model for increased tumor cell growth in the presence of increased extracellular ATP, which happens in some cancers.

Restated, the data suggest they were successful in increasing intracellular ATP by LCMS, but not by queen reporter, and that the seemingly likely increased intracellular ATP was not causative, as cells that did not have an increase in intracellular ATP, but had the same exogenous ATP addition, also gained an increase in replicative lifespan. There could also be two distinct mechanisms extending replicative lifespan to the same degree in these two different strains. More measurements, controls, and analyses are needed to accurately determine what is happening with intracellular ATP levels with age. It is currently unknown if there is any correlation between ATP levels and replicative aging (with properly controlled longitudinal measurements).

Strengths:

Longitudinal imaging of single cells. Analyzed ATP levels with two approaches. Creative approach to use NTT1 transporter to increase intracellular ATP levels. Solid replicative lifespan data.

Weaknesses:

Mostly unclear about ATP levels with age and the relationship, or lack thereo,f between intracellular ATP levels and replicative lifespan. No idea what this effect depends on, but some ideas what it does not depend on (mitochondria or increased intracellular ATP). Experiments seem to lack biological controls (cells without gfp) for age related changes in autofluorescence (and pH that can affect gfp signal) for the fluorescent microscopy quantifying ATP with age using the QUEEN reporter (seems that way as written); conflicting evidence on ATP levels; lack of LC-MS measurements in old cells; no apparent correlation between ATP levels and replicative lifespan, but that could be wrong - just not apparent from the longitudinal data plots. The LCMS data seems better than the microscopy data on ATP because the microscopy approach seems to lack proper biological controls, and the selection of only the top 40% of pixels to quantify signal seems unjustified as written, and possibly prone to technical artifacts. Figure 2 B&C plots of ATP levels should show what the cells were normalized to. The figures also seem too diluted and should probably be combined or put in the supplements (hog1 western) if they do not relate to the lifespan effect. There seem to be some technical scientific editorial errors, like in Figure 7.

Reviewer #2 (Public review):

Summary

The authors introduce DNA O-MAP, a method that combines oligo-based in situ hybridization with peroxidase-mediated proximity biotinylation to profile proteins and DNA-DNA interactions linked to targeted genomic regions. In the revised manuscript, they expand the method beyond repetitive elements by profiling non-repetitive gene clusters (HOXA and HOXB), studying inhibitor-induced chromatin remodeling, and differentiating homolog-specific proteomes on both the active and inactive X chromosome. These additions considerably broaden the scope of the work and indicate that DNA O-MAP is currently most effective for analyzing gene-cluster size or domain-level chromatin environments, rather than focusing on individual promoters or cis-regulatory elements.

Strengths

The study demonstrates that DNA O-MAP can be applied to both repetitive domains and non-repetitive genomic regions, including gene clusters spanning 80 kilobases and larger single-copy chromosomal intervals, rather than isolated cis-regulatory elements.

Orthogonal validation using ENCODE ChIP-seq data supports several differentially enriched proteins observed between the HOXA and HOXB gene clusters proteomes.

The ability to detect quantitative changes in local protein environments after chemical perturbation demonstrates the method's sensitivity at the level of extended genomic domains.

Homolog-resolved analysis of the active and inactive X chromosome provides an additional demonstration of biological specificity and technical flexibility at the megabase scale.

The revised manuscript appropriately frames DNA O-MAP as a method for interrogating local domain-level genomic environments, rather than exhaustively defining the protein composition of individual regulatory elements.

Weaknesses

As with all proximity labeling approaches, the effective resolution of DNA O-MAP is constrained by the spatial distance of peroxidase-mediated labeling rather than by genomic distance. Consequently, for gene-cluster-scale targets, enrichment extends beyond the targeted interval into surrounding chromosomal regions, potentially limiting the method's specificity at the level of individual promoters, enhancers, or gene bodies.

Specificity is demonstrated through comparative and internally controlled analyses rather than through a quantitative estimate of false discovery rate for locus specificity. Readers should therefore interpret individual protein enrichments as indicative of local chromatin environments rather than definitive evidence of direct binding to a specific regulatory element.

Orthogonal validation is necessarily selective and hypothesis-driven. A broader validation would be required before newly enriched proteins can be interpreted as bona fide region-resident factors.

Comparisons to prior locus-proteomics methods remain indirect and should be interpreted primarily in terms of demonstrated feasibility, scalability, and reduced cell-number requirements rather than absolute performance or resolution.

Reviewer #2 (Public review):

Summary:

The manuscript submitted by Koch et al. describes a novel approach to collect budding yeast cells in metaphase I or metaphase II by synthetically activating the spinde checkpoint (SAC). The arrest is transient and reversible. This synchronization strategy will be extremely useful for studying meiosis I and meiosis II, and compare the two divisions. The authors characterized this so named syncSAC approach and could confirm previous observations that the SAC arrest is less efficient in meiosis I than in meiosis II. They found that downregulation of the SAC response through PP1 phosphatase is stronger in meiosis I than in meiosis II. The authors then went on to purify kinetochore-associated proteins from metaphase I and II extracts for proteome and phosphoproteome analysis. Their data will be of significant interest to the cell cycle community (they compared their datasets also to kinetochores purified from cells arrested in prophase I and -with SynSAC in mitosis).

Significance:

The technique described here will be of great interest to the cell cycle community. Furthermore, the authors provide data sets on purified kinetochores of different meiotic stages and compare them to mitosis. This paper will thus be highly cited, for the technique, and also for the application of the technique.

Reviewer #3 (Public review):

In this study Hammond et al. investigated the role of Dual-specificity Tyrosine Phosphorylation regulated Kinase 1A (DYRK1) in G1/S transition. By exploiting Dependency Map portal, they identified a previously unexplored protein FAM53C as potential regulator of G1/S transition. Using RNAi, they confirmed that depletion of FAM53C suppressed proliferation of human RPE1 cells and that this phenotype was dependent on the presence protein RB. In addition, they noted increased level of CDKN1A transcript and p21 protein that could explain G1 arrest of FAM53C-depleted cells but surprisingly, they did not observe activation of other p53 target genes. Proteomic analysis identified DYRK1 as one of the main interactors of FAM53C and the interaction was confirmed in vitro. Further, they showed that purified FAM53C blocked the ability of DYRK1 to phosphorylate cyclin D in vitro although the activity of DYRK1 was likely not inhibited (judging from the modification of FAM53C itself). Instead, it seems more likely that FAM53C competes with cyclin D in this assay. Authors claim that the G1 arrest caused by depletion of FAM53C was rescued by inhibition of DYRK1 but this was true only in cells lacking functional p53. This is quite confusing as DYRK1 inhibition reduced the fraction of G1 cells in p53 wild type cells as well as in p53 knock-outs, suggesting that FAM53C may not be required for regulation of DYRK1 function. Instead of focusing on the impact of FAM53C on cell cycle progression, authors moved towards investigating its potential (and perhaps more complex) roles in differentiation of IPSCs into cortical organoids and in mice. They observed a lower level of proliferating cells in the organoids but if that reflects an increased activity of DYRK1 or if it is just an off-target effect of the genetic manipulation remains unclear. Even less clear is the phenotype in FAM53C knock-out mice. Authors did not observe any significant changes in survival nor in organ development but they noted some behavioral differences. Whether and how these are connected to the rate of cellular proliferation was not explored. In the summary, the study identified previously unknown role of FAM53C in proliferation but failed to explain the mechanism and its physiological relevance at the level of tissues and organism.

Comments on the previous version:

In the revised version of the manuscript, authors addressed most of the critical points. They now include new data with depletion of FAM53C using single siRNAs that show small but significant enrichment of population of the G1 cells. This G1 arrest is likely caused by a combined effects on induction of p21 expression and decreased levels of cyclin D1. Authors observed that inhibition of DYRK1 rescued cyclin D1 levels in FAM53 depleted cells suggesting that FAM53C may inhibit DYRK1. This possibility is also supported by in vitro experiments. On the other hand, inhibition of DYRK1 did not rescue the G1 arrest upon depletion of FAM53C, suggesting that FAM53C may have also DYRK1-independent role in G1. Functional rescue experiments with cyclin D1 mutants and detection of DYRK1 activity in cells would be necessary to conclusively explain the function of FAM53C in progression through G1 phase but unfortunately these experiments were technically not possible. Knock out of FAM53C in iPSCs and in mice suggest that FAM53C may have additional functions besides the cell cycle control and/or that adaptation may have occurred in these model systems. Overall, the study implicated FAM53C in fine tuning DYRK1 activity in cells that may to some extent influence the progression through G1 phase. In addition, FAM53C may also have DYRK1 and cell cycle independent functions that remain to be addressed by future studies.

Reviewer #2 (Public review):

Summary:

Measurements of the reward positivity, an electrophysiological component elicited during reward evaluation, have previously been used to understand how self-benefitting effort expenditure influences processing of rewards. The present study is the first to complement those measurements with electrophysiological reward after-effects of effort expenditure during prosocial acts. The results provide solid evidence that effort adds reward value when the recipient of the reward is the self but discounts reward value when the beneficiary is another individual.

Strengths:

An important strength of the study is that amount of effort, the prospective reward, the recipient of the reward, and whether the reward was actually gained or not were parametrically and orthogonally varied. In addition, the researchers examined whether the pattern of results generalized to decisions about future efforts. The sample size (N=40) and mixed-effects regression models are also appropriate for addressing the key research questions. Those conclusions are plausible and adequately supported by statistical analyses.

Reviewer #2 (Public review):

Summary:

This manuscript reports a novel and quite important study of chimerism among common marmosets. As the authors discuss, it has been known for years that marmosets display chimerism across a number of tissues. However, as the authors also recognize, the scope and details of this chimerism have been controversial. Some prior publications have suggested that the chimerism only involves cells derived from hematopoietic stem cells, while other publications have suggested more cell types can also be chimeric, including a wide range of cell types present in multiple organs. The present authors address this question and several other important issues by using snRNA-seq to track the expression of host and sibling-derived mRNAs across multiple tissues and cell types. The results are clear and provide convincing evidence that for the various organs analyzed, all chimeric cells are derived from hematopoietic cell lineages.

This work will have impact on studies using marmosets to investigate various biological questions, but will have biggest impact on neuroscience and studies of cellular function within the brain. The demonstration that microglia and macrophages from different siblings from a single pregnancy, with different genomes expressing different transcriptomes, are commonly present within specific brain structures of a single individual opens a number of new opportunities to study microglia and macrophage function as well as interations between microglia, macrophages and other cell types.

Strengths:

The paper has a number of important strengths. This analysis employs the first unambiguous approach providing a clear answer to the question of whether sibling-derived chimeric cells arise only from hematopoietic lineages or from a wider array of embryonic sources. That is a long-standing open question and these snRNA-seq data seem to provide a clear answer, at least for brain and liver and kidney. In addition, the present authors investigate quantitative variation in chimeric cell proportions across several dimensions, comparing the proportion of chimeric cells across individual marmosets, across organs within an individual and across brain regions within an individual. All these are significant questions, and the answers have important implications for multiple research areas. Marmosets are increasingly being used for a range of neuroscience studies, and a better understanding of the process that leads to chimerism of microglia and macrophages in the marmoset brain is a valuable and timely contribution. But this work also has implications for other lines of study such as defining embryological and development processes and the potential to track specific cell populations within genetically engineered marmosets. Third, the snRNA-seq data will be made available through Brain Initiative NeMO portal and the software used to quantify host vs. sibling cell proportions in different biosamples will be available through Github.

Comments on revisions:

Several minor weaknesses have been addressed by the authors in a revision of the original manuscript. Each of my concerns and perceived weaknesses regarding the initial submission have been satisfactorily addressed in the revision.

Reviewer #2 (Public review):

Summary:

Infants' auditory brain responses reveal processing of music (clearly different from shuffled music patterns) from the age of 3 months; however, they do not show related increase in spontaneous movement activity to music until the age of 12 months.

Strengths:

This is a nice paper, well designed, with sophisticated analyses and presenting clear results filling an important gap about early infant sensitivity, detection, and differentiation of musical sounds. The addition of EEG recordings (specifically ERPs) in response to music presentations at 3 different infant ages in the first postnatal year is important, and the manipulation of the music stimuli into shuffled, high and low pitch to capture differences in brain response processing and spontaneous movements is interesting. Further, the movement analysis based on Quantity of Movements (QoM) and movement subdivision into 10 distinct Principal Movements (PMs) is novel and creative.

Overall, results show that ERPs responses to music occurs earlier than QoM in early development, and that even at 12 months, motor responses to music remain coarse and not rhythmically aligned with the music tempo. This work increases our fundamental understanding of infants' early music perception in relation to auditory processing and motor response.

Comments on revisions:

The authors have addressed my questions in their revision. I have no other questions. Thanks again for the opportunity to read and evaluate this interesting work.

Reviewer #3 (Public review):

Summary and strengths:

In this manuscript, Grimes presents an extension of Ellipse of Insignificant (EOI) and Region of Attainable Redaction (ROAR) metrics to meta-analysis setting as metrics for fragility and robustness evaluation of meta-analysis. The author applies these metrics to three meta-analyses of Vitamin D and cancer mortality, finding substantial fragility in their conclusions. Overall, I think extension/adaption is a conceptually valuable addition to meta-analysis evaluation, and the manuscript is generally well-written.

Specific comments:

(1) The manuscript would benefit from a clearer explanation of in what sense EOIMETA is generalizable. The author mentions this several times, but without a clear explanation of what they mean here.

(2) The authors mentioned the proposed tools assume low between-study heterogeneity. Could the author illustrate mathematically in the paper how the between-study heterogeneity would influence the proposed measures? Moreover, the between-study heterogeneity is high in Zhang et al's 2022 study. It would be a good place to comment on the influence of such high heterogeneity on the results, and specifying a practical heterogeneity cutoff would better guide future users.

(3) I think clarifying the concepts of "small effect", "fragile result", and "unreliable result" would be helpful for preventing misinterpretation by future users. I am concerned that the audience may be confusing these concepts. A small effect may be related to a fragile meta-analysis result. A fragile meta-analysis doesn't necessarily mean wrong/untrustworthy results. A fragile but precise estimate can still reflect a true effect, but whether that size of true effect is clinically meaningful is another question. Clarifying the effect magnitude, fragility, and reliability in the discussion would be helpful.

Comments on revisions:

I am unable to find the author's responses to my previous round comments (Reviewer #3) in the revision package, though replies to the other reviewers are present. I will provide my updated feedback once these responses are available for review.

Reviewer #2 (Public review):

The authors begin by highlighting the importance of genome organisation in cellular compartmentalisation and identity. They focus their study on centromeres - key chromosomal features required for segregation-and aim to identify proteins responsible for their spatial distribution in interphase nuclei. However, none of the experimental data addresses broader aspects of genome architecture, such as individual chromosome territories or A/B compartments. As such, the title of the article may be misleading and would benefit from being more specific, for example: "Identification of factors influencing centromere positioning in interphase."

Strengths:

One of the strengths of the paper is the comprehensive CRISPR-based screening and the comparative analysis between two distinct cell lines.

Including further investigation into factors that behave differently across these cell lines - particularly in relation to expression levels or the unique "inverted architecture" of RPE cells-would have added valuable depth.

Comments on revisions:

From the previous review:<br /> The Authors have undertook a very minimal revision of the paper. The Authors have addressed some of the comments raised by rewarding the text and being slightly more critical in the interpretation of their results and added previously published literature.<br /> They have provided more details on the characterisation of the new cell lines and added some statistical analyses.

However, I still believe that the title does not reflect the finding, as it is all about centromere position rather than "interphase genome architecture" as claimed.<br /> As I said in my previous comments, this will make a precedent and will cause mis-interpretations in the field.

Changes from the previous version:

While in the new manuscript the Authors have discussed that degradation of NUF2 and SPC24 caused some aberrant nuclear phenotypes, this is at odd with the first screening where these morphologies were used as part of the exclusion criteria. Some comments would be required.

Reviewer #2 (Public review):

This manuscript investigates the neural mechanisms of anxiety and identifies the supramammillary nucleus (SuM) as a critical hub in mediating anxiety-related behaviors. The authors describe a population of neurons in the SuM that are activated by acute and chronic stress. While their activity is not required for fear memory recall, reactivation of these neurons after chronic stress robustly increases anxiety-like behaviors as well as physiological stress markers. Circuit analysis further shows that these stress-activated neurons are driven by inputs from the ventral, but not dorsal, subiculum, and inhibition of this pathway exerts an anxiolytic effect.

The study provides an elegant integration of techniques linking stress, neuronal ensembles, and circuit function, advancing our understanding of the neural substrates of anxiety. A particularly notable point is the selective role of these stress-activated neurons in anxiety, but not in associative fear memory, highlighting functional distinctions between neural circuits underlying anxiety and fear.

The recruited neuronal population is activated by acute and chronic stress, though the overlap across stress exposures is partial, suggesting that further studies will be important to define how these neurons respond under other stressors and conditions.

Overall, this work identifies SuM stress-activated neurons and their ventral subiculum inputs as central elements of anxiety circuitry, providing a valuable framework for future studies and potential targeted interventions for stress-related disorders.

Reviewer #2 (Public review):

Summary:

This is a very interesting study focusing on a remarkable oligomerization domain, the LisH-CTLH-CRA module. The module is found in a diverse set of proteins across evolution. The present manuscript focuses on the extraordinary elaboration of this domain in GID/CTLH RING E3 ubiquitin ligases, which assemble into a gigantic, highly ordered, oval-shaped megadalton complex with strict subunit specificity. The arrangement of LisH-CTLH-CRA modules from several distinct subunits is required to form the oval on the outside of the assembly, allowing functional entities to recruit and modify substrates in the center. Although previous structures had shown that data revealed that CTLH-CRA dimerization interfaces share a conserved helical architecture, the molecular rules that govern subunit pairing have not been explored. This was a daunting task in protein biochemistry that was achieved in the present study, which defines this "assembly specificity code" at the structural and residue-specific level.<br /> The authors used X-ray crystallography to solve high-resolution structures of mammalian CTLH-CRA domains, including RANBP9, RANBP10, TWA1, MAEA, and the heterodimeric complex between RANBP9 and MKLN. They further examined and characterized assemblies by quantitative methods (ITC and SEC-MALS) and qualitatively using nondenaturing gels. Some of their ITC measurements were particularly clever, and involved competitive titrations, and titrations of varying partners depending on protein behavior. The experiments allowed the authors to discover that affinities for interactions between partners is exceptionally tight, in the pM-nM range, and to distill the basis for specificity while also inferring that additional interactions beyond the LisH-CTLH-CRA modules likely also contribute to stability. Beyond discovering how the native pairings are achieved, the authors were able to use this new structural knowledge to reengineer interfaces to achieve different preferred partnerings.

Strengths:

Nearly everything about this work is exceptionally strong.<br /> -The question is interesting for the native complexes, and even beyond that has potential implications for design of novel molecular machines.<br /> -The experimental data and analyses are quantitative, rigorous, and thorough.<br /> -The paper is a great read - scholarly and really interesting.<br /> -The figures are exceptional in every possible way. They present very complex and intricate interactions with exquisite clarity. The authors are to be commended for outstanding use of color and color-coding throughout the study, including in cartoons to help track what was studied in what experiments. And the figures are also outstanding aesthetically.

Weaknesses:

There are no major weaknesses of note, and in the revision the authors addressed my minor suggestions for the text.

Reviewer #2 (Public review):

Summary:

Methods to infer action potentials from fluorescence-based measurements of intracellular calcium dynamics are important for optical measurements of activity across large populations of neurons. The variety of existing methods can be separated into two broad classes: a) model-independent approaches that are trained on ground truth datasets (e.g., deep networks), and b) approaches based on a model of the processes that link action potentials to calcium signals. Models usually contains parameters describing biophysical variables, such as rate constants of the calcium dynamics and features of the calcium indicator. The method presented here, PGBAR, is model-based and uses a Bayesian approach. A novelty of PGBAR is that static parameters and state variables are jointly estimated using particle Gibbs sampling, a sequential Monte Carlo technique that can efficiently sample the latent embedding space.

Strengths:

A main strength of PGBAR is that it provides probability distributions rather than point estimates of spike times. This is different from most other methods and may be an important feature in cases when estimates of uncertainty are desired. Another important feature of PGBAR is that it estimates not only the state variable representing spiking activity, but also other variables such as baseline fluctuations and stationary model variables, in a joint process. PGBAR can therefore provide more information than various other methods. The information in the github repository is well-organized. The authors demonstrate convincingly that PGBAR can resolve inter-spike intervals in the range of 5 ms using fluorescence data obtained with a very fast genetically encoded calcium indicator at very high sampling rates (line scans at >= 1 kHz).

Weaknesses:

The accuracy of spike train reconstructions is not higher than that of other model-based approaches, and lower than the accuracy of a model-independent approach based on a deep network in a regime of commonly used acquisition rates.

Comments on revisions:

I have no further comments on the manuscript.

Reviewer #2 (Public review):

Summary:

In this study, Serantes and colleagues analysed how sleep and anesthesia impact the processing of olfactory inputs, focusing on early sensory processing (occurring at the first or second synaptic contacts). First, they show that the transition to sleep has a major impact on breathing-dependent gamma activity. Second, they show that this decrease originates at the first synaptic contact and is independent of respiration itself. Third, they show a decrease in connectivity associated with neocortical slow waves. These results are very interesting and supported by a robust methodology. However, I have two major concerns regarding this work.

First, the authors fail to adequately contextualize their work. For example, the impact of sleep on respiration-locked gamma activity was reported several years ago and is, in fact, used in some laboratories to score sleep using data from the olfactory bulb.

Second, the authors should exercise much more caution when comparing the urethane anesthesia model with NREM/REM sleep cycles. There are very significant differences between the two. Yet, the title and abstract of the article mention only sleep and anesthesia. More concerningly, the results obtained under urethane anesthesia are uncritically generalized to sleep.

In conclusion, the first finding was already shown in previous studies, and the second and third results were obtained not during sleep but during an anesthetic state that only resembles certain aspects of sleep.

Strengths:

The authors deploy an interventional approach that allows them to determine with compelling evidence the relationship of the gamma activity time-locked to breathing and different aspects of breathing, proving in particular that the disconnection is independent of respiratory dynamics. They leveraged invasive recordings that allow them to pinpoint at which level the disconnection occurs.

Weaknesses:

(1) My first comment concerns how this work fits within the state of the art. The introduction of the article leaves out very important and highly relevant work.

(1a) First, "disconnection" is not a defining feature of sleep; "unresponsiveness" is. It is often assumed that this unresponsiveness (which can be directly measured, contrary to disconnection) is due to a form of disconnection, but there has been substantial work over the past decade showing that disconnection is not as extensive as initially expected. It is therefore incorrect, in my view, to state that "most models attribute sensory gating to thalamocortical mechanisms". Most models attribute sensory gating to a combination of thalamocortical and cortical mechanisms.

(1b) The rationale of the article appears unclear ("the olfactory system-bypassing the thalamus-offers a unique window into earlier stages of sensory disconnection"). If the idea is to investigate gating mechanisms before the thalamus, then any sensory modality would suffice, since even modalities that later relay through the thalamus involve pre-thalamic processing stages. I assume that the authors instead mean that, because olfactory information does not relay through the thalamus, gating mechanisms in the olfactory stream could occur very early. However, this also implies that focusing on olfactory processing would say little about other sensory modalities.

(1c) Key previous results have been completely overlooked. First, the impact of sleep on respiration-locked gamma activity was reported several years ago (Bagur et al., Plos Biology 2018). Second, important articles investigating olfactory processing during sleep have been overlooked (e.g., Arzi et al., Nature Neuroscience 2012; Arzi et al., Journal of Neuroscience 2014). I am not providing an exhaustive list here, but these articles are not only extremely relevant to the present study; they have also become classics in the sleep literature.

(2) For most of their findings (Figures 2 to 5), the authors used urethane anesthesia. They show that this pharmacological manipulation results in alternation between periods of high-amplitude delta waves (SWSt) and a desynchronized state (ASt). However, the parallel with NREM and REM sleep, respectively, is rough and insufficiently justified. Differences can already be noted by contrasting the short examples provided in the figures. While NREM and REM sleep differ in terms of muscle tone (EMG), no such difference is discernible between SWSt and ASt. In SWSt, the slow waves appear to overlap with fast activity at the cortical level (M1, S1), which is not typically the case during NREM sleep. In addition, because the time scale is not the same in Figures 1 and 2 (1 s vs 2 s), yet the slow waves appear to have similar durations, it is also possible that the slow waves generated during SWSt and NREM differ. To better support the proposed parallel between NREM and SWSt on the one hand, and ASt and REM on the other, the authors should provide a thorough comparison of these states (spectral features, properties of the slow waves, duration and frequency of each state, etc.). Without this, inferences from results obtained under urethane anesthesia to sleep are not warranted.

The authors acknowledge this issue in the Discussion ("These findings suggest that there is no functional equivalence between urethane-activated states and REM sleep"), but this caveat should be integrated from the very beginning (title, abstract, and introduction).

(3) In some graphs, the power spectrum is normalized. Under anesthesia, this normalization was performed "within each animal to the SWSt maximum for that signal". However, I could not find equivalent information for sleep. This is key information needed to correctly interpret the results shown in Figure 1.

(4) The authors should also clarify their criteria for concluding on the absence or presence of a given effect. For example, in the legend of Figure 1c, they write: "Note the presence of coherence during wakefulness, demonstrating the internalization of the respiratory signal, and its drop during sleep". Unless coherence is exactly zero, some degree of coherence is always "present". Figure 1 instead shows that coherence is modulated across frequencies during wakefulness, with peaks in the delta and theta ranges.

In Figure 2, they write: "PAC between respiration and OB gamma amplitude was present during ASt but disappeared during SWSt". Again, the authors should clarify what is meant by "disappeared", as they only tested for differences between ASt and SWSt.

Given that the authors implemented a strategy to test for above-chance coherence using surrogate datasets, they should consistently provide statistical tests showing which conditions or frequency bands exhibit coherence above chance in order to justify claims about the presence or absence of an effect.

(5) Likewise, comparisons across states should always be supported by statistical tests, for example, in Figure 4. In addition, despite the apparent absence of coherence during SWSt in Figures 4f and 4g (which again should be formally tested), Figure 4h shows an increase in coherence around 2 Hz, which suggests some degree of coherence between nasal airflow and the olfactory bulb.

(6) Figures should more clearly distinguish results based on a single "representative" animal from population averages. For example, were Figures 4g and 2h computed at the population level?

Reviewer #2 (Public Review):

Summary:

After manually labelling 144 human adult hemispheres in the lateral parieto-occipital junction (LPOJ), the authors 1) propose a nomenclature for 4 previously unnamed highly variable sulci located between the temporal and parietal or occipital lobes, 2) focus on one of these newly named sulci, namely the ventral supralateral occipital sulcus (slocs-v) and compare it to neighbouring sulci to demonstrate its specificity (in terms of depth, surface area, gray matter thickness, myelination, and connectivity), 3) relate the morphology of a subgroup of sulci from the region including the slocs-v to the performance in a spatial orientation task, demonstrating behavioural and morphological specificity. In addition to these results, the authors propose an extended reflection on the relationship between these newly named landmarks and previous anatomical studies, a reflection about the slocs-v related to functional and cytoarchitectonic parcellations as well as anatomic connectivity and an insight about potential anatomical mechanisms relating sulcation and behaviour.

Strengths:

- To my knowledge, this is the first study addressing the variable tertiary sulci located between the superior temporal sulcus (STS) and intra-parietal sulcus (IPS).

- This is a very comprehensive study addressing altogether anatomical, architectural, functional and cognitive aspects.

- The definition of highly variable yet highly reproductible sulci such as the slocs-v feeds the community with new anatomo-functional landmarks (which is emphasized by the provision of a probability map in supp. mat., which in my opinion should be proposed in the main body).

- The comparison of different features between the slocs-v and similar sulci is useful to demonstrate their difference.

- The detailed comparison of the present study with state of the art contextualises and strengthens the novel findings.

- The functional study complements the anatomical description and points towards cognitive specificity related to a subset of sulci from the LPOJ

- The discussion offers a proposition of theoretical interpretation of the findings

- The data and code are mostly available online (raw data made available upon request).

Reviewer #3 (Public review):

Summary:

Sleep affects cognition and metabolism, evolving throughout development. In mammals, infants have fast sleep-wake cycles that stabilize in adults via circadian regulation. In this study, the author performed a genetic screen for neurotransmitters/peptides regulating sleep and identified the neuropeptide Hugin and its receptor PK2-R1 as essential components for sleep in Drosophila larvae. They showed that IPCs express Pk2-R1 and silencing IPCs resulted in significant increase in the sleep amount, which was consistent with the effect they observed in PK2-R1 knock out mutants. They also showed that Hugin peptides, secreted by a subset of Hugin neurons (Hug-PC), activate IPCs through the PK2-R1 receptor. This activation prompts IPCs to release insulin-like peptides (Dilps), which are implicated in the modulation of sleep. They showed that Hugin peptides induce a PK2-R1 dependent calcium (Ca²⁺) increase in IPCs, which they linked to the release of Dilp3, showing a connection between Hugin signaling to IPCs, Dilp3 release and sleep regulation. Additionally, the activation of Hug-PC neurons reduced sleep amounts, while silencing them had the opposite effect. In contrast to the larval stage, the Hugin/PK2-R1 axis was not critical for sleep regulation in Drosophila adults, suggesting that this neuropeptidergic circuitry has divergent roles in sleep regulation across different stages of development.

Strengths:

This study used an updated system for sleep quantification in Drosophila larvae and this method allowed precise measurement of larval sleep patterns which is essential for the understanding of sleep regulation.

The authors performed unbiased genetics screening and successfully identified novel regulators for larval sleep, Hugin and its receptor PK2-R1, making a substantial contribution to the understanding of neuropeptidergic control of sleep regulation.

They clearly demonstrated the mechanism by which Hugin expressing neurons influence sleep through the activation of IPCs via PK2-R1 with Ca2+ responses and can modulate sleep.

Based on the demonstrated activation of PK2-R1 by the human Hugin orthologue Neuromedin U, research on human sleep disorders may benefit from the discoveries from Drosophila since sleep regulating mechanisms are conversed across species.

Weaknesses:

Previously identified weaknesses have been largely addressed by the authors.

Reviewer #2 (Public review):

The work by Henning et al. explores the role of feedback inhibition in motion vision circuits, providing the first identification of inhibitory inheritance in motion-selective T4 and T5 cells of Drosophila. Among the strengths of this work is the verification of the GABAergic nature of C2 and C3 with genetic and immunohistochemical approaches. In addition, double-silencing C2&C3 experiments help to establish a functional role for these cells. The authors holistically use the Drosophila toolbox to identify neural morphologies, synaptic locations, network connectivity, neuronal functions and the behavioral output.

A limitation of the study is that the mediating neural correlates from C2&C3 to T4&T5 are not clarified, rather Mi1 is found to be one of them. In the future, the same set of silencing experiments performed for C2-Mi1 could be extended to C2 &C3-Tm1 or Tm4 to find the T5 neural mediators of this feedback inhibition loop. Future experiments might also disentangle the parallel or separate function of C2 and C3 neurons.

In summary, this work advances our current knowledge in Drosophila motion vision and sets the way for further exploring the intricate details of direction selective computations.

Comments on revisions:

A label for T5 is missing from Figure 5b. Thank you for addressing our concerns and considering each of our suggestions.