Reviewer #2 (Public review):

Summary:

This manuscript examined the impact of sulcal morphology on reading development. A very specific feature on the ventral surface of the brain was identified, namely the presence of an interruption in the posterior portion of the left occipitotemporal sulcus (pOTS). Compared to children with a continuous pOTS, children with an interruption at age 5 years had better reading ability at age 8. This was a large effect measured in 43 children. Surprisingly, this morphological feature was a better predictor of reading ability than measures of pre-literacy cognitive skills, such as phonological awareness. The effect was tested and reproduced across several different measures of reading ability. The authors hypothesised that the mechanism underlying this benefit related to greater local connectivity, which confers a computational advantage. This was demonstrated using analysis of diffusion-weighted imaging data in 29 of the children obtained at age 8.

Strengths:

The novelty of the manuscript is threefold: (i) the measure was made in children who were pre-literate (previous work was in older children and adults); (ii) longitudinal brain imaging and behavioural data were analysed; and (iii) diffusion data were analysed to test a hypothesis about the underlying mechanism.

The manuscript is exceptionally well written. The methods are detailed and easily reproduced. The approach is thoughtful and meticulous. All possible alternatives appear to have been considered. Where possible, further analyses have been done to address these alternatives. For example, the testing of the specificity of the sulcal interruption to left pOTS was an important addition. None predicted reading skills.

Reviewer #2 (Public review):

Summary:

This manuscript challenges the long-standing assumption that Yoda1 and Yoda2 are PIEZO1-selective activators. Using patch-clamp electrophysiology and calcium imaging in HEK293TΔPZ1 cells overexpressing PIEZO2, the authors demonstrate that Yoda1 potentiates PIEZO2 stretch-activated currents to a similar extent as PIEZO1 and slows PIEZO2 poking-current inactivation (albeit with lower efficacy). They further show that the more potent analog Yoda2 affects PIEZO2 at nanomolar concentrations and use mutagenesis and molecular dynamics simulations to propose that Yoda2's benzoic acid group forms a transient salt bridge with R1724 in the putative Yoda binding pocket, explaining its enhanced potency.

Strengths:

The authors are established Piezo/biophysics experts; the study is highly important, technically competent, and carries significant implications for the reinterpretation of prior work that used Yoda compounds as PIEZO1-selective probes.

The core finding that Yoda1 modulates PIEZO2 stretch currents is convincing and important.

Reviewer #2 (Public review):

Summary:

This is a very interesting paper bringing new and important information about the poorly understood rhodopsin 7 photoreceptive molecule. The very ancient origin of the gene is revealed in addition to data supporting a signaling pathway that is different from the one known for the canonical rhodopsins. Precise expression data, particularly in the optic lobe of the fly, as well as clear behavioral phenotypes in responses to light changes, make this study a strong contribution to the understanding of the still-debated function of rhodopsin 7.

Specific comments

(1) Title and abstract: Contribution of Rh7 to circadian clock regulation

a) It is not that clear to me what rhodopsin does in terms of circadian regulation (even though its function might be circadianly regulated). The clear role in the light/dark distribution of activity might not be circadian per se, but mostly light/dark- driven and there is no evidence here for a role in the entrainment of the clock.

b) The authors should cite Lazopulo, which nicely shows that Rh7 has an important role in peripheral neurons to allow flies to escape from blue light (see below).

(2) Figure 2 C

The finding showing that Galphaz but not Galphaq can trigger signaling from light-excited Rh7 is a very intriguing finding to better understand Rh7 function. Since Galphaz is related to Gi/o, it would be interesting to test those, for example, by expressing RNAi with Rh7-gal4 and testing the Light-dark or light-off response behavior.

(3) Figures 3-4

The change in the locomotor activity distribution between light and dark in LD conditions provides a nice assay for Rh7 function. Since Lazopulo et al. (2019) have shown that wild-type but not Rh7 mutants do escape from blue light, it would be important to compare and discuss these LD behavior data with the Lazopulo results. Precisely, is this nighttime preference linked to blue light?

The expression data are really nice and show that Rh7 is mostly a non-retinal photoreceptor. However, the paper would be strongly reinforced by correlating this with the LD behavior. The LD phenotype should be tested in flies with Rh7 expression rescued under Rh7gal4 control (as done for the startle response). This is important to show whether the expression pattern is likely responsible for the described Rh7 function in LD. If L5 and or M11 drivers are available, they should be used to rescue Rh7? Since expression in some clock neurons is shown, the rescue experiment should also be done with a clock neuron driver.

In the same line, can the LD phenotype (or startle response phenotype of Figure 4) be restored by expressing Rh7 under ppk control, as shown for the blue light avoidance phenotype by Lazopulo et al?

Finally, the Rh7 "darkfly" rescued flies should be tested in LD.

Reviewer #2 (Public review):

The authors aimed to understand how the transcription factor Erbb4 contributes local circuit dynamics and affects performance. This is an interesting question because Erbb4 is largely localized to PV cells in the olfactory bulb (OB) and upregulated following exposure to an olfactory discrimination task.

Strengths:

They have now validated their local, PV-specific knockout strategy, which more directly tests their hypothesis.

The inclusion of optogenetically evoked presynaptic responses from PV cells onto M/T cells also strengthened the conclusion that this synapse was altered.

The authors also now included slice recordings from PV cells, which helps to form a more comprehensive picture of the circuit.

Weaknesses:

(1) The data used to support their spike sorting approach is now included but is difficult to evaluate. In Figure 4 - supp 1B, they show what appears to be mean + sem of concatenated waveforms from one tetrode, but this is not explained anywhere. Moreover, the timescale seems to be off by an order of magnitude. It also seems that the spike widths in panel E are bimodally distributed, with many of the half-widths being more consistent with fast-spiking cells. There is a high density of PV cells in the same layer where they record M/T cells, so is it not possible that some of the units were PV cells (note: this was suggested in the previous review as a potential exciting direction to further validate their findings)? It is still unclear how the authors confidently record from only M/T cells when there is no mention of how other units (e.g., fast-spiking cells) are excluded. The methods state that only M/T cells can be recorded from this layer, which is an oversimplification and appears at odds with the apparent diversity in waveforms and responses.

(2) Upon review, the authors acknowledge that the PV-Erbb4+/+ and PV-Erbb4-/- mice were recorded in separate batches where the control mice were exposed to unwanted artefacts related to a solenoid valve. While we don't know how much that change influenced the results, even with careful analysis, it does draw attention to a major weakness in study design where controls and experimental mice are not run simultaneously. The presence of an artefact from the solenoid valve (perhaps a noise?) immediately before odor presentation for controls, but not experimental mice, should be considered by the reader when it comes to evaluating the odor-evoked responses.

Overall, I feel the manuscript is much improved and more solidly demonstrates that Erbb4 expression in PV cells plays a major role in regulating local OB circuit dynamics and influences odor processing. The remaining weaknesses surrounding the in vivo single unit recordings from PV-Erbb4+/+ versus PV-Erbb4-/- mice reduce my confidence in the validity of the results, but the general agreement of the in vitro work is more re-assuring.

Reviewer #2 (Public review):

Summary:

This study by Suzuki et al. reports an interesting stereo-selective role of D-serine in regulating one-carbon metabolism during neurodevelopment to adapt the functional transition, probably through the competition with mitochondrial transport of L-serine. The authors provide a multi-layered set of evidence, including metabolomics, enzyme assays, mitochondrial transport competition and functional assays in immature/neural progenitor cells, to build up a conceptual integration of D-serine as both a neurotransmitter and a metabolic regulator in central neural system, which raises a broad potential interest to the neuroscience and metabolism communities.

Strengths:

This work provides a conceptual advance that D-serine is not only serves as a traditional neurotransmitter in central neural system but also critically contributes to metabolic regulation of neural cells. The authors performed solid metabolomic assays to validate the suppressive effect of D-serine on one-carbon metabolic pathway, providing some evidence that D-serine competitively inhibits mitochondrial serine transport, but not directly impairs SHMT2 enzymatic activity. All these data indicate a critical role of D-serine synthesis during neural maturation and suggest a potential translational strategy for targeting serine metabolism in neural tumors.

Comments on previous version.

My previous concerns have been appropriately addressed or discussed in this revised version of manuscript. I have to say that, at this stage, I have no further questions.

Reviewer #2 (Public review):

Summary:

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

Strengths:

The study is overall well done and the paper clearly written. Particularly the work on identifying the two pairs of dopaminergic neurons involved in cardiac deceleration using a series of drivers and generating new ones is rigorous and extensive. Finally, the authors manipulate heartbeat to investigate how it influences threat responses. Although the evidence that heartbeat deceleration-induced increase in locomotion remains partial, the study reveals important insight in the relationship between locomotion, cardiac activity and neuromodulatory system during threat responses.

Reviewer #2 (Public Review):

The authors develop an experimental framework to precisely control temperature in an arena and make quantitative measurements of thermal avoidance behavior in Drosophila. They find that wild-type animals show robust heat avoidance behavior and suggest that antennal ablation reduces this heat avoidance. They also make a novel claim that antennal inputs do more than simple heat detection but play a role in navigation strategies like centrophobism and are needed for decision-making at thermal boundaries. The fundamental two-choice behavior paradigm or the use of peltier modules for temperature control have been employed previously in the field. The strength of this work lies in detailed behavioral measurements. However, the study falls short due to a lack of adequate experimental controls. The authors then model this behavior as a classical Braitenberg vehicle of sensory-motor transformation. Overall, the paper has some interesting insights into in-depth analysis of Drosophila navigation in thermal gradients, but their evidence only partially supports their claims.

Reviewer #3 (Public review):

Summary:

Ma et al. use human-chimpanzee tetraploid cells to examine species differences in DNA methylation. They identify differentially methylated regions under cis or trans regulation. Cis-DMRs are enriched near SNVs that disrupt or create CpGs, providing a plausible mechanism for cis changes in methylation. They also seek to identify transcription factors that might affect methylation in trans, as well as gene sets with evidence for consistent changes in methylation and expression between humans and chimpanzees, suggesting that they may underlie lineage-specific traits.

Strengths:

The authors have generated a new dataset across multiple different cell types examining differences in DNA methylation between humans and chimpanzees using human diploid cells, chimpanzee diploid cells, and human-chimpanzee tetraploid cells. Using this dataset, they identify that cis-DMRs are enriched near SNVs that disrupt or create CpGs compared to trans-DMRs and identify transcription factors as candidate trans-acting factors. Both identified SNVs and transcription factors are good candidates for future experimentation. The authors also find that cis-DMRs are more highly correlated with cis-expressed genes than trans-DMRs with trans-expressed genes, providing evidence that methylation and expression are linked genome-wide. Further, they apply a clever sign test approach to identify gene pathways with evidence for lineage-specific selection.

Comments on revised version.

They authors have addressed my concerns from their initial manuscript. In particular, the authors have noted the limitations of their study as appropriate, including the use of permissive FDR cut-offs.

Reviewer #2 (Public review):

Summary:

The revised manuscript by Nohara et al. presents a novel 3D assay that allows for stratification of epithelia, active EMT through small pores, and active MET. They show that 3um pores allow for keratinocytes to sample the pore through filopodia and up regulate EMT genes to transverse the pores to the other side of the membrane where EMT genes are down regulated as the cells re-establish stratified epithelia. The TGFbeta pathway and actin polymerization promotes movement of cells into the pores and Piezo1 and KRT6 actively block this movement. This work provides a novel 3D assay that is likely to become a benchmark to analyze these processes using a more complex system than other current culture-specific EMT and MET assays.

Strengths:

The strengths of the manuscript include the foundational analysis of the pathways involved in establishing the 3-compartment epithelium. The authors have incorporated live imaging, drug studies, KO analysis, and RNA-sequencing to show the relevant pathways involved. The authors were responsive to the original round of reviews and present a much-improved study with greater details on molecular markers, quantification, and clarity.

Reviewer #2 (Public review):

This study from de Boer, Lamme, Verdwaald and Schafer describes the de novo AI-guided design of miniproteins that target the chemokine CCL25, with the aim to modulate the activation and signalling of the chemokine receptors CCR9 and ACKR4. The study focuses on characterising four miniproteins that all bind CCL25 with good affinity. Three designs appear to prevent CCL25 binding to both CCR9 and ACKR4, with increasing concentrations of miniproteins resulting in decreased arrestin (both receptors) and mini G protein recruitment (CCR9), less inhibition of forskolin-stimulated cAMP (CCR9), and decreased GRK3 recruitment and receptor internalisation (CCR9). One miniprotein, VUP25111, changes the properties of CCL25 rather than preventing ligand/receptor interactions, resulting in greater selectivity for CCR9 over ACKR4 and a G protein-biased signalling profile (maintenance of mini G protein recruitment, GRK3 recruitment, inhibition of cAMP and receptor internalisation, but loss of arrestin recruitment). VUP25111 also maintained chemotactic migration in MOLT-4 T lymphoblast cells, whereas this response was lost in the presence of the other three miniproteins.

Overall, this is a very interesting application of AI-designed de novo miniproteins to modulate GPCR responses by directly binding the ligand rather than the receptor. This is a conceptually very intriguing approach that could, in principle, be extended to other GPCR systems beyond the chemokine family. The authors deploy an impressive array of assays spanning multiple signalling endpoints, providing a thorough picture of how each miniprotein influences receptor activation and downstream signalling. The presentation of concentration-response relationships for CCL25 alone and in the presence of each miniprotein is particularly informative, and the figures are very well constructed throughout. The inclusion of clear cartoons illustrating the basis of each assay is a nice touch that will help readers from outside the immediate field follow the logic of each experiment.

Reviewer #2 (Public review):

Summary:

In this manuscript, Hossaim and colleagues investigate the role of the ITK kinase in modulating inflammation in a pristane-induced lung hemorrhage model. Using a germline ITK KO mouse, they report that loss of ITK skews the T cell compartment toward a memory-like state, expanding Tregs, and conferring protection against alveolar hemorrhage, inflammatory monocyte recruitment, proteinuria, and systemic cytokine elevation. They further show that transfer of ITK-deficient Tregs into wild-type hosts with established disease attenuates injury and shifts the cytokine balance toward resolution, and that ITK-deficient Tregs carry a transcriptional signature enriched for OXPHOS, mTORC1, MYC, and cell-cycle programs. While these observations are interesting for the development of potential immunotherapies, there are several issues with the methodological approach that support the authors' claims, tempering my enthusiasm for this manuscript.

Strengths:

(1) The clinical motivation and potential targeted therapies are relevant.

(2) The murine phenotype seems robust.

Comments on revised version.

The authors have addressed my comments appropriately.

GroupID: 63

DiseaseAssertion: VHL

GroupSize: 3

GroupNumberMales: N/A

GroupNumberFemales: N/A

GroupEthnicity: Japanese

GroupAgeRange: N/A

GroupInfo: 3 individuals from a single family (ID 63).

GroupPresentingHPOs: HP:0001732; HP:0005584; HP:0010797 (Abnormality of the pancreas, Renal cell carcinoma, Hemangioblastoma)

GroupHPOFreeText: CNS hemangioblastoma (2/3), renal cell carcinoma (3/3) and pancreatic cyst or tumor (unspecified) (1/3).

GroupNotHPOs: HP:0009711 (Retinal capillary hemangioma)

GroupNotHPOFreeText: N/A

GroupPreviousTesting: N/A

PreviouslyPublished: N/A

SupplementalData: N/A

Variant: p.S168Tfs*2 (c.503_513del)

LegacyVariant: c.716_726del (c.716del11bp)

CaseProblemVariantFreeText: N/A

ClinVarID: N/A

CAID: CA2573131812, https://reg.clinicalgenome.org/redmine/projects/registry/genboree_registry/by_caid?caid=CA2573131812

gnomAD: N/A

VariantEvidence: N/A

MutationType: deletion; minus_2_frameshift_variant

CivicName: S168Tfs*2 (c.503_513del)

MultipleGeneVariants: N/A

PMID: 16409463

Notes: several variants, currently curating c.823T>C (p.Cys275Arg) and family 11 proband is compound het with p.Pro1337Leu. Nucleotide position are not provided and the family is asserted as type B but no GPIb, RIPA or VWF platelet binding assay data are reported.

Case# RQMR10_V-2

Demographics: Female, 20 yrs at report,

CasePresentingHPOs HP:0000729, HP:0001763, HP:0001761, HP:0001956, HP:0000577

CaseNotHPOs

CasePreviousTesting

CaseMethod1

CaseMethod2

CaseGenotypingMethod

Variant NM_017890.4:c.6879delT(p.Phe2293Leufs*24)

ClinVar

CAID CA4824093

gnomAD 0.00003250 (South Asian)

VariantEvidence The proband is homozygous for the variant. This variant introduces a frameshift, resulting in premature protein truncation. The variant was found to cosegregate with disease in the family and was not found in 293 healthy ethnicity-matched controls. It is reported in a heterozygous state at a frequency of 0.00003250 in South Asians in gnomAD.

CaseAddInfo

CasePMIDs

Reviewer #2 (Public review):

Summary:

The authors seek to understand the hierarchical temporal representation of social communicative actions in the posterior superior temporal sulcus and other regions of the lateral pathway. They apply an analysis framework in which they predict fMRI responses from EEG responses to the same stimuli. (The EEG stimuli were shorter versions of the same clips shown as fMRI stimuli.) They examine which temporal segments of EEG data are most predictive of responses in which part of the brain. They find that early (< 100 ms post-stimulus) EEG activity is predictive of responses in early visual cortex, and that later (~ 150 ms post-stimulus) EEG activity is predictive of responses in the lateral visual stream. They then model the fMRI responses predicted by the EEG signals - which they term the "EEG-relevant" portion of the fMRI signal - as a function of various social and visual factors. They conclude that social interactions elicit responses in aSTS-SI later than low-level visual features elicit responses in early visual cortex, and they provide some evidence that mid-level social primitives elicit responses in mid-level areas somewhere in between.

Strengths:

I find the question and the framework interesting. The extraction of "EEG-relevant" fMRI signal as a target for visual and social feature-based encoding models has potential, though the approach in this instance suffers from low signal-to-noise. The evidence that the anterior portion of the superior temporal sulcus responds to communicative actions is clear and supports prior work in this regard.

Weaknesses:

I think there are several important points that the authors need to address to strengthen their case, and I think that many points could use more explicit description, explanation, and discussion.

Major concerns:

First, the basis of the EEG predictions of fMRI activity is not clear. The paradigm relies on predicting fMRI responses at each voxel from a weighted combination of EEG responses measured anywhere over the scalp. However, the authors never show any kind of weight map to visualize which electrodes on the scalp were weighted to predict responses in each fMRI voxel or ROI. I see that the use of PCA to orthogonalize the EEG responses creates problems with localizing the predictive signal in EEG to particular electrodes. Is the PCA step necessary? In any case, some form of analysis to estimate where in the brain the predictive EEG signal is coming from seems important. It's fairly clear that the best signal quality in the EEG recordings is in the early visual cortex. Thus, it could be the case that electrodes over EVC provide the main basis for predictions across the entire brain, with different temporal phases of EVC activity associated with activity in different regions in the fMRI data. It could also be the case (tho less likely in my opinion) that activity in regions away from EVC on the scalp provides the basis for predicting more anterior brain regions in the fMRI data. These two possibilities each suggest a substantially different interpretation of the present results: in the former case, there may be some sort of feedback loop between early visual areas and lateral areas; in the latter case, the hierarchical interpretation the authors have laid out seems more plausible. It seems important to draw a distinction between these possibilities.

In any case, the authors should say more in the discussion about the sources of EEG signals and fMRI signals and the relationship between the two.

On a similar note: the authors show that their EEG data was reliable based on a split-half analysis of the data. However, the authors only show average split-half correlation across electrodes and subjects. It stands to reason that some electrodes were much more reliable than others. The authors should show a map of electrode reliability across the scalp. One relevant question is: are there reliable electrode locations throughout the visual cortex? Or are the EEG signals only, or by far most reliable, in EVC? (How reliable is the EEG signal in regions that are at least somewhat near to the location of the lateral regions that is the principal object of investigation here?)

The use of predicted fMRI signals (predicted by EEG) as a target for image & social feature encoding models is only valid to the extent that the EEG signals accurately predict the fMRI signals. The prediction accuracy going from EEG to fMRI is not very high - a correlation of less than 0.2 in most regions besides EVC and the anterior extent of the activity along the ventral temporal lobe. In the best-predicted regions, the activity only reaches a correlation of 0.3. The majority of the signal (> 90% of the variance) is unmodeled in this analysis. Thus, I would hesitate to characterize the evidence for activation by particular stimulus features at particular times in this analysis as "strong". The authors themselves are somewhat ambivalent on this point, as well. While they write in the conclusion: "Finally, we find strong evidence that social interactions are computed via hierarchical computation of low- and mid-level social visual features in the lateral visual pathway." However, This statement conflicts somewhat with other statements the authors make in the discussion section: "Together, these results suggest that lateral visual regions may not be organized in a strict feedforward hierarchy as has previously been suggested" Later in the discussion there is more ambivalence about the hierarchical nature of the representation: "Our findings suggest that the processing of social action features from posterior to anterior brain regions does not adhere to a strict feedforward hierarchy." The authors should present a more consistent interpretation of their findings.

Finally, the low performance of most of the mid-level models in mid-level regions is a concern; it points to poor signal overall. The common threads in the models that do predict responses above chance seem to be space (spatial expanse and agent distance) and salience (to the extent that arousal may constitute salience). This raises a question: Were the social model features correlated with either of the low-level model features?

I think that the overriding issue here is poor signal quality in the EEG data. The estimate of repeatability from the split-half analysis is not high, even on average for the whole brain; it is likely to be very poor for areas outside EVC. I think this matters. An important null hypothesis to consider is that there may be a few fairly low-level, or at least simple and straightforward, elements of the response that are reflected in the noisy EEG signal. Chief among these is likely to be that the most salient or high-contrast retinotopic regions in the image, which both AlexNet and motion energy models would likely pick up on, are likely to elicit responses in EVC. This is likely why EVC is by far the best-predicted region, both based on the raw EEG data and in the two-step regression analysis. There could be elements of spatial priority at all levels of the visual system. This could be what the authors are observing: there is some relatively small (in terms of variance explained) component of the signal that is common to many regions, and which emerges somewhat later in later regions due to conduction delays.

I think an important exception to this signal problem is the finding of communicative actions eliciting responses in aSTS-SI. That seems very robust and clear.

Medium comments:

There should be a section in the methods on how the ROIs were defined. (There is a brief description in the results section, but this is not sufficient). The ROI outlines should also be displayed somewhere in the paper (not just circles showing the general region as in Figure 1). Figure 3 would be a nice place for them, as would Figure S3.

It's also worth noting in the methods and discussion that the use of template ROIs for hMT in particular is potentially sub-optimal. hMT lies in anatomically variable locations across subjects. One reason for the seemingly weak signal in hMT may be that the voxels being averaged do not fall within the true hMT for each subject.

In the results (section 3.2.1), the authors write, "We scored the regression by predicting the EEG-predicted fMRI activity (estimated fMRItest) to the held-out test videos and then correlating the actual fMRI response (fMRItest) in the test set to this prediction at each time point (estimated fMRItest)." Unless I am missing something, I think the authors can safely say that they predicted responses to the withheld stimuli without specifying the intended prediction target (whether it is the fMRI data or the estimated fMRI data), because the prediction is simply based on weights, labeled or estimated image features, and stimuli. So, the prediction might as well be a direct prediction of the fMRI test data. It seems to me that mentioning the estimated fMRI test in this sentence only muddies the waters.

In the two-stage regression analysis, were all the visual and social features fit to the EEG-predicted fMRI data in a single regression? Was this regression regularized? How many parameters total were fit? (This is clear for the AlexNet and motion energy models, but not clear for the other features - were they all single-feature models?)

Reviewer #2 (Public review):

Summary:

Drosophila haematopoiesis has been shown to be governed by a number of signalling pathways such as JAK/STAT and Dpp. This important study shows a role for nutrient sensing and autophagy in determining blood cell differentiation. The authors show that General control non-derepressible 5 (Gcn5), a histone acetyltransferase affects blood cell differentiation. Gcn5 also negatively regulates autophagy through its effector TFEB which directly regulates autophagy genes. The authors also show that mTORC1 modulates Gcn5 levels and through it TFEB activity thus acting as a fine-tuning mechanism which maintains optimal levels of autophagy.

Strengths:

The main strength of the work lies in the interesting finding that cellular metabolic processes such as autophagy has a direct role in blood cell differentiation and has the potential to be of interest to those working on vertebrate haematopoiesis as well. The report has generated intriguing data, using promoters specific for sub sections of the lymph gland, that different cellular subsets of the lymph gland contribute differently towards haematopoiesis, but this is not followed up in detail and the final conclusions are derived from a combination of whole lymph gland perturbations as well as those from specific promoters.

Weakness:

(1) Gc5 seems to be expressed throughout the lymph gland but modulating it in the subsections do not have the same result. It is very striking that the knockdown of Gcn5 in the prohemocyte population does not have an effect on differentiation whereas overexpression does. And the modulations of Gcn5 in PSC also has variable effects across hemocyte subpopulations which is not explored in the manuscript. Interestingly, also the domain deletion constructs show differential effect on blood cell differentiation when altered solely in the prohemocytes which is not explained. While Gcn5 can be seen in all sections of the lymph gland in the first figure, under the HHLT-Gal4 and Hml-Gal4, Gcn5 looks cytoplasmic and almost completely excluded from the nucleus strikingly unlike Gcn5 expression under the Collier-Gal4 and Dome-Gal4. The rest of the experiments in the manuscript are done with multiple promoters, with autophagy flux measured by modulating Gcn5 with a pan hemocyte promoter, but the mTORC1-Gcn5 axis is explored using chemical modulators which affect the whole of the lymph gland (Fig7) or using two pro-hemocyte promoters (Fig8).

(2) The knockdown of Gcn5 seems to affect the gland size (A compared to B and C). Since mTORC1 is a central regulator of cell size, it is possible that some of the effects seen in these knockdowns are potentially through mTORC1 affecting size suggesting that the signalling axis between mTORC1 and Gcn5 might not be a one-way axis as suggested in Figure 9. Also, this would mean that in experiments where absolute cell counts of crystal cells or niche cells are used to assess blood cell differentiation, further analysis to consider total cell numbers in the lymph gland would strengthen the manuscript.

(3) A genetic manipulation of mTORC1 specifically in the pro hemocytes would strengthen the role of mTORC1 in the pathway rather than the chemical modulation which affects the whole of the lymph gland.

Comments on the revised manuscript:

Overall, the revisions make the narrative more coherent and pertinent to the results presented. The authors have also added new data and the analysis supports their conclusions.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

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

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

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

Reviewer #3 (Public review):

The manuscript investigates the coupling of saccadic eye-movements (S) with directed tail flips (T), and the remarkable discovery is that tail flips that are preceded by a conjugate sacced (S-T) can be credibly classified as specific "volitional" turns that are distinguished from the standard tail movements that seem to be more of a spontaneous and "impulsive" nature.

They show that 'turning intent', as indicated by a small increase in S, is elevated by aversive odors, while 'gliding intent', as indicated by a decrease in S and an increase in undulation cycles, is elevated by appetitive odors.

This is a very important finding, which is backed up by a thorough behavioral analysis, and the identification of neural populations in the pallium and sub-pallium that clearly distinguish between these kinds of turns is very promising. Here they identify neuronal populations that are preferentially active during - and predictive of - coupled (S-T) versus isolated (T) tail flips.

Especially the fact that S-T turns (but not T turns) can be predicted already by pre-event, ramping, pallial activity is intriguing.

The authors then go on and demonstrate that the frequency of (S-T) turns is modulated in fish exposed to appetitive or aversive odors.<br /> Specifically, they quantify the aversiveness and appetitive-ness of several odors in a free swimming assay, they select a couple of these odors based on their valence and they demonstrate that these odors induce moderate modulation in the frequency of eye saccades (S) and tail flips (T) and (S-T) turns.

The study is rigorous and thorough and the findings are informative and novel.

In important controls they confirm that brain wide imaging can distinguish between appetitive and aversive context, and they show that pallial activation by aversive odors is consistent with neural activity in the rhombencephalon that correlates with turning activity, whereas sub-pallial activation by appetitive odors correlates with rhombencephalic activity related to gliding.

It's all very good.

Comments on revised version.

The authors have addressed all my concerns.

Reviewer #2 (Public review):

Summary:

This study presents a fundamental new finding - the identification of a sensory-neuron itch receptor, MRGPRX4, as an unexpected melanoma oncogene through a mechanism of lineage-inappropriate expression rather than mutation. The evidence supporting the core observation (tumor-specific upregulation, restriction to invasive transcriptional states, and sufficiency to drive fully penetrant metastatic melanoma in vivo) is compelling, drawing on convergent human genomic datasets and a well-controlled genetic mouse model. However, several of the mechanistic and translational claims - particularly regarding causal drivers of invasion, the immunosuppressive tumor microenvironment, and in vivo pharmacological efficacy - rely on correlative evidence.

Strengths:

The authors propose that MRGPRX4, normally restricted to a subset of peripheral sensory neurons, is aberrantly re-expressed in melanoma rather than through mutational mechanisms, and that this re-expression is sufficient to drive tumorigenesis through basal, ligand-independent GPCR signaling. This is a genuinely novel model for oncogenesis, and the manuscript deploys an impressive range of approaches - bulk and single-cell transcriptomics, spatial transcriptomics, proteomics, phosphoproteomics, and pharmacology - to support it.

The claim that MRGPRX4 is selectively upregulated in melanoma and confined to neural-crest-like/invasive transcriptional states is well supported, with consistent results across multiple independent human scRNA-seq datasets. The claim that ectopic MRGPRX4 is sufficient to drive melanoma is convincingly demonstrated by the fully penetrant, metastatic phenotype in the Tyr-CreER;MRGPRX4-LSL model, with appropriate specificity controls showing that MRGPRX1, MRGPRX2, and MRGPRX3 do not phenocopy this effect.

The claim that MRGPRX4 signals through basal, ligand-independent activity is reasonably well supported by bile-acid quantification showing endogenous ligand concentrations well below the EC50 required for activation.

Overall assessment:

The manuscript's central, most novel claim - that lineage-inappropriate expression of a sensory GPCR is sufficient to drive melanoma - is compellingly supported. The secondary mechanistic and translational claims built around this finding are convincing and consistent with the broader literature but are currently supported by correlative rather than causal or functional evidence.

Reviewer #2 (Public review):

Summary:

In this manuscript Zhu, Emanuelli and colleagues describe a novel pharmacological activator of the Integrated Stress Response kinase GCN2. The work is conclusive and biochemically solid. This work significantly adds to the pharmacological arsenal targeting the ISR and in particular GCN2.

Strengths:

Strong biochemistry, novel molecular activator of GCN2 (GCN1 independent).

Weaknesses:

Rationale for the screen not exploited in the results (e.g. pathogenic GCN2 mutants), lots of cell-based read-outs not endogenous.

Comments on revised version.

The authors did a great job at addressing my initial critique on their manuscript and consequently I have no further comment.

Reviewer #2 (Public review):

Summary:

This manuscript, by Kang and colleagues, examines the activity of the Gasdermin (GSDM) family of proteins in epidermal skin cells called keratinocytes. Gasdermins are a relatively recently discovered family of pore-forming proteins that are generally studied for their role in inflammasome activation and pyroptosis. The role and functions of GSDMs in keratinocytes are not fully understood, but programmed cell death is important to keratinocytes as part of the cornification process. Keratinocyte pyroptosis associated with GSDMD and GSDME has been linked to the pathogenesis of psoriasis, and GSDMA-mediated pyroptosis in keratinocytes is responsive to Staphylococcus aureus infection. These data suggest that defining the role of GSDM in keratinocytes could provide valuable insight into the treatment of skin pathologies, particularly those involving inflammation.

Strengths:

In this study, the authors use two human keratinocyte cell lines to focus primarily on the biology of GSDM in keratinocytes, particularly A, B and C, both during homeostatic conditions and in response to some pathogenic stimuli. The data indicate that the full-length GSDMA and B proteins are colocalizing not to the plasma membrane, but to the cytoskeletal, cytoplasmic or membrane fractions, in direct contrast to a number of other studies. Further, there are data showing the GSDM are not cleaved by the same proteases as in other studies, and that GSDMA is not involved control of Vaccinia virus skin infection. The controls for cleavage done in HeLa cells with a number of inhibitors were particularly strong, and the overall finding is interesting and potentially important, providing novel insight in the biology of GSDM proteins in keratinocytes. The second half of the paper focuses on treatment with calyculin A, a protein phosphatase inhibitor that the authors report recapitulates acantholysis, i.e. the loss of cell-cell junctions between keratinocytes. Calyculin A altered the localization of GSDM but increased cell death and keratin release irrespective of GSDM activity, suggesting that acantholytic cell death does not involve GSDM. Overall, these data are an interesting study showing differences in GSDM biology in keratinocytes and indicating that GSDM are not involved in a number of pathogenic processes associated with skin diseases.

Weaknesses:

Unfortunately, the data supporting these findings are not particularly strong and are somewhat confusing, and the study is not well organized. One of the major weaknesses is that most work was done in two human keratinocyte cell lines, and subcellular localization subcellular localization results were not consistent between the two lines. This is not hugely surprising, as cell lines even from the same cell type are very different, and the authors note that there may be some variability associated with the model used. However, the inconsistency between the two lines, alongside the lack of comparison to primary cells or patient data, makes it very difficult to determine which, if any, result accurately recapitulates the biology in human cells. The authors do suggest that the N-TERT line is more similar to primary cells but rely on a single study and no other research or data to support this. Several other experiments compare the keratinocyte lines to a colorectal adenocarcinoma cell line, showing the keratinocytes are also different from these cells, but it is not clear that they support the information about GSDM in either keratinocyte line other than showing that keratinocytes are distinct from colorectal cells.

There are several specific concerns regarding the data, particularly the western blotting. In a number of the blots, there are bands which are not mentioned. For example, in Figure 3A, "GSDMA and GSDMB translocated from the cytoskeleton equally to both the cytoplasmic and membrane fractions upon calyculin A treatment". But in the western blot shown, GSDMA moves from being entirely in the cytoskeleton to having lighter bands in the cytoskeleton and chromatin-bound regions and heavier bands in the membrane and cytoplasm. GSDMB shows heavier bands in the membrane and cytoplasm after Calyculin A, but the heaviest band remains in the chromatin-bound region. In addition, the western blots have no associated N or pooled data graph with statistics, so it is not clear how many studies it represents or if there were significant changes. A lack of statistical analysis was also seen in the Yoyo-1, membrane ballooning and cell death analyses in 3A, 3C, 4B and 4C, with the membrane ballooning finding being entirely qualitative and not using any image analysis - comparison of P2A or cell area could resolve this. The methods do note that unpaired T-tests and 2-way ANOVAs were performed in some places, so the results of these should be clearly shown on the graphs. Beyond this, all of the localization studies rely on a single modality, western blotting. This finding should be supported by orthogonal approaches, ideally immunofluorescence. Finally, the presence of GAPDH in the supernatant (5A) raises an important issue regarding nonspecific intracellular release. If the GAPDH is present in the sup that is representative of cell damage and does not correlate with the absence of other intracellular markers - such as those found in lysates - in the supernatant.

The final weakness of the paper was that it is not really clear what the study is trying to say, and it was difficult to follow the logic and data, with several figures being out of order. There are a number of statements that feel overinterpreted, such as stating that clathrin-mediated endocytosis is required for calyculin A mediated acantholysis. While it is clear that Calyculin A recapitulates features similar to acantholysis, it is a broad perturbation of the cell, and it is not clear that it is the same process. Further, the pharmacological evidence is not strong enough to make such a clear claim about the role of clathrin-mediated endocytosis. Similarly, the statement that "GSDMs can induce pyroptosis not only in their cleaved forms but also as full-length proteins through specific post-translational modifications" is much too strong for these data. The terminology surrounding "pyroptosis" should be used more carefully, as yoyo-1 uptake and membrane ballooning demonstrate membrane permeability and morphological changes but are not sufficient for cell death.

Reviewer #3 (Public review):

Summary:

The present work was aimed at investigating the specific contributions of thalamic nuclei to associative threat learning and extinction. Using fMRI, it examined activation patterns across pulvinar divisions, the lateral geniculate nucleus (LGN), and the mediodorsal thalamus (MD) during threat acquisition, extinction, and recall. It goals was to uncover whether distinct thalamic systems support different modes of learning-automatic survival mechanisms versus more deliberate processes-and to propose a hierarchical pulvinar model of fear conditioning. The manuscript also tried to refine current neuroanatomical models of threat learning and memory, highlighting the role of thalamic nuclei in it.

Strengths:

(1) Valuable theoretical elaboration and modeling regarding the differential role of pulvinar subdivisions on feedforward (inferior, lateral) and higher-order integration (anterior), and their functional interplay with other relevant subcortical and cortical structures in associative threat and extinction learning.

(2) Large sample sizes and multipronged analytical approaches were used for hypothesis testing.

(3) Exhaustive literature review in the field of associative threat, as well as regarding the role of thalamic nuclei and other brain structures in it.

Comments on revised version:

My three main concerns remain insufficiently addressed or only partially addressed in the study limitations. First, using 3 mm isotropic voxels limits anatomical and functional specificity when investigating small thalamic nuclei. Higher-resolution fMRI protocols and probabilistic thalamic atlases that allow subject-specific segmentation have been available for several years and could have been used to improve anatomical precision. Second, although analyses in normalized space are widely accepted for group-level analyses, normalization can distort the size and boundaries of small thalamic nuclei. Analyses in native space, using subject-specific segmentation, would help address this limitation, particularly given the relatively low functional resolution. Finally, standard preprocessing procedures and the inclusion of motion parameters as covariates do not fully exclude residual motion-related effects or potential differences in motion across experimental conditions. The authors do not report additional motion quality-control procedures, such as excluding participants with excessive motion or substantial translational or rotational drift. Taken together, these concerns prevent me from upgrading my assessment.

PatientID: None

KindredID: 1

Case: M, 2Y0M, Danish

DiseaseAssertion:Asymptomatic

FamilyInfo: The patient's father had the p.P2P VHL mutation as well as another VHL mutation (p.P81S) and presented with renal cell carcinoma. The patient's uncles had this p.P81S mutation but were both asymptomatic. The patient's paternal grandmother also had this mutation and had breast cancer. See Figure 1 for family pedigree.

CasePresentingHPOs: N/A

CaseHPOFreeText:N/A

CaseNotHPOs: N/A

CaseNotHPOFreeText: N/A

CasePreviousTesting: N/A

PreviouslyPublished: N/A

SupplementalData: N/A

Variant: p.P2= (c.6C>G)

LegacyVariant: N/A

CaseProblemVariantFreeText: N/A

ClinVarID: 456593 https://www.ncbi.nlm.nih.gov/clinvar/variation/456593/

CAID: N/A

gnomAD: 0.000 https://gnomad.broadinstitute.org/variant/3-10183537-C-G

VariantEvidence: N/A

MutationType: synonymous_variant

CivicName: P2= (c.6C>G)

MultipleGeneVariants: N/A

Reviewer #2 (Public review):

Summary:

This work by Hayford, Baleami, Stauffer et al. addresses the standard view of drug-tolerant persister (DTP) cells in cancer. The current model treats each DTP as a uniform, single-cell phenotype that survives therapy. Building on the authors' earlier "idling" DTP concept (Paudel et al. 2018), this study instead provides evidence that DTPs are heterogeneous populations of multiple co-existing sub-states whose individual division/death rates balance out to produce near-zero net population growth.

Using BRAF-mutant melanoma (SKMEL5) cells treated with the BRAF inhibitor PLX4720, the authors combine single-cell RNA-seq, bulk RNA-seq, ATAC-seq, DNA lineage barcoding (ClonMapper), calcium-flux assays, and ferroptosis drug-response assays to show:

(1) Idling DTP populations show fewer, but still multiple, distinct transcriptomic sub-states compared to untreated cells (Figure 1).

(2) They arise from nearly all pre-existing cell lineages rather than from selective expansion of a resistant clone (Figure 2).

(3) Transcriptomic and epigenomic data converge on altered ion-channel/calcium-handling genes; functional calcium-flux assays confirm reduced store-operated calcium entry (SOCE) in these cells (Figure 3).

(4) These cells are around 3-fold more sensitive to the ferroptosis inducer RSL3 (GPX4 inhibitor), an effect reversible by the ferroptosis inhibitor ferrostatin-1 (Figure 4).

The authors propose targeted landscaping, in which drug treatment reshapes a cell population's phenotypic landscape rather than selecting a single resistant subtype, implying that eradicating DTPs will require sequential interventions that progressively narrow the landscape, rather than targeting a presumed uniform persister state.

Strengths:

(1) scRNA-seq, bulk RNA-seq, ATAC-seq, lineage barcoding, calcium-flux assays, and drug-response assays all point toward the same conclusions, strengthening confidence in the findings.

(2) The ClonMapper approach directly tests (and rules out) clonal selection as the origin of DTPs - a key alternative explanation the authors address rigorously.

(3) The calcium-flux assay and RSL3/ferrostatin-1 ferroptosis experiments provide direct functional confirmation of transcriptomic/epigenomic predictions, moving beyond correlative sequencing data.

(4) The "bulk" vs. "single-cell-state" and the "targeted landscaping" analogy is useful for framing future treatment strategies.

(5) Discussion explicitly flags that the data are not time-resolved single-cell trajectories, and that the ferroptosis vulnerability's exact molecular driver is not identified.

Weaknesses:

(1) All experiments use one melanoma cell line (SKMEL5 and its subclones), so generalizability to other BRAF-mutant melanomas or other cancer types/drugs is unproven.

(2) As the authors acknowledge, the study lacks time-lapse/lineage-resolved tracking of individual cells transitioning between sub-states.

(3) Much of the ion-channel and metabolic pathway evidence relies on gene set enrichment, which is suggestive but not itself mechanistic; the calcium-flux assay has the main functional role.

(4) The paper demonstrates increased RSL3 sensitivity and rescue by ferrostatin-1, but doesn't identify the specific molecular driver linking ion-channel remodeling to ferroptosis susceptibility.

Reviewer #2 (Public review):

Liu, He, et al. record intracranial EEG from the hippocampus and lateral temporal lobe in thirteen neurosurgical patients performing a delayed match-to-sample visual short-term memory task. They ask whether hippocampal sharp-wave ripples, well established in long-term memory consolidation, also contribute to the active maintenance of visual representations. The authors report that hippocampal ripple rates ramp up across the 7-second maintenance period, that hippocampal ripples co-occur with lateral temporal ripples, and that these coupled events coincide with above-chance category decoding in the lateral temporal lobe. These findings are interpreted within the dynamic coding framework of working memory. In this revision, the authors have been very responsive and have added ripple validation, aperiodic and high-frequency activity controls, direct interaction tests, and peak-lag analyses, which have strengthened the manuscript considerably. The study now provides solid evidence that hippocampal high-frequency activity ramps up during maintenance in a manner that scales with short-term memory success. Some of the more specific interpretations remain open: whether the effect is ripple-specific, whether it is specific to short-term memory, and whether coupled ripples link the hippocampal ramp to cortical reactivation. The work will be of considerable interest to researchers studying working memory and human ripples and opens promising directions for future studies with parametric manipulations and broader cortical coverage.

Reviewer #2 (Public review):

Summary:

This work highlights a new approach to build an array of highly stable peptide MHC-I using temperature exchange, which can be used to identify antigen specific CD8+ T cells.

Strengths:

In this work, the authors have proposed an alternative method to reload the peptide MHC-I molecule. Their temperature-exchange approach is distinct from current reloadable peptide MHC technologies involving photolabile peptide, empty MHC-I (Nat Commun 11, 1314 (2020). https://doi.org/10.1038/s41467-020-14862-4), tapasin/TAPBPR chaperone-assisted (eLife 7:e40126.), enzyme exchangeable (https://pubmed.ncbi.nlm.nih.gov/31201791/) and small alcohol (Curr Res Immunol. 2022 Aug 18;3:167-174. doi: 10.1016/j.crimmu.2022.08.002) approaches

Reviewer #2 (Public review):

The revised manuscript has improved substantially. The authors have addressed the main concerns raised during the previous round by introducing an IVT-based calibration framework, evaluating background calls across probability thresholds, examining basecalling mismatches, and replacing "stoichiometry" with the more appropriate term "predicted modification proportion." The expanded discussion of non-DRACH predictions is also useful and appropriately balanced. Overall, the study now provides a valuable practical framework for Dorado-based RNA modification analysis.

I have only a few remaining comments, all of which should be addressable through clarification and minor textual revisions.

(1) The comparison between NHDF and HD10.6 cells does not appear to include independent biological replicates. The observed differences in predicted m6A/A proportions should therefore be presented as differences between the two datasets rather than as general cell-type-specific effects. The interpretation is already cautious in the Discussion, but similar wording should be used in the Abstract, Results heading, and title if applicable.

(2) The direction of the comparison in Figure 3D should be clarified. The percentages represent the fraction of GLORI sites that is also detected by Dorado. Thus, the analysis shows that Dorado increasingly recovers high-proportion GLORI sites. It does not directly demonstrate that high-proportion Dorado predictions are increasingly validated by GLORI. The text should reflect this distinction, and the figure should clearly identify which circle represents each method.

(3) The ratio of calls detected in IVT relative to biological samples is a useful empirical measure of background, but it is not formally a false-positive rate because the true status and testability of every site are not known. A term such as "IVT-to-sample call ratio" or "empirical background-call ratio" would be more precise and would not alter the main conclusion regarding the value of stringent filtering.

(4) The description of the change-point framework still contains the placeholder "at least X-fold more signal." The actual enrichment threshold should be provided. The authors should also ensure that a log2 ratio threshold of 1.5 is not described as 1.5-fold enrichment, since these values are not equivalent.

These are relatively minor issues. The authors have responded constructively to the previous reviews, and I do not consider further experimental work necessary.

Reviewer #2 (Public review):

The manuscript is extremely clear and well written, the experimental evidence is strong and the methods are clearly described, so I do not have major issues to raise. A few minor issues are listed below. Some of these were presented in my first review but were not addressed by the authors.

(2) In Section 3.3 the authors say that direct paths connecting Class I and Class IV behave similarly to indirect paths, despite having lower scores according to the RBM. How generic is this? Does it also happen for other classes? This might be an important point to address as direct paths are easier to sample.

(3) The path shown in Fig.4 goes through a region of non-functionality around sequences 18-19. It seems that the sample path is basically exploring the functional regions for Class I and Class II/III separately trying to approach the other class, but then it can't really do the switch.

By contrast the path going from Class I to Class IV seems able to perform the functional switch in a single step (20-21) without losing too much of the function.

Perhaps the authors could better comment on this? Is this a limitation of the sampling method, or a fundamental biological fact?

(4) On page 12 it is stated that temperature was chosen to 1/3 to maximize the score. This is important and should be mentioned earlier (I didn't notice it until that point).

(5) On page 13 it is stated that "However, the scores of the ancestral sequences along the phylogenetic pathways assigned by the RBM are significantly lower than the ones of the RBM-designed sequences. This result is expected as ASR reconstruction does not take into account epistasis, differently from RBM, and we expect ASR sequences to generally be of lesser quality."

I was very surprised by this result. My own experience with ASR shows that on the contrary, sequences found by ASR (via maximum likelihood) tend to have high scores in the (R)BM and tend to be more stable than extant sequences. I attribute this to the fact that ASR typically finds a "consensus" sequence that maximizes the contribution to the score coming from the fields (the profile), which is typically dominant over the epistatic signal, resulting in a bigger score. Maybe the authors did not use maximum likelihood in the ASR? Some clarification might be useful here.

Reviewer #2 (Public review):

Summary:

The revised manuscript by Nohara et al. presents a novel 3D assay that allows for stratification of epithelia, active EMT through small pores, and active MET. They show that 3um pores allow for keratinocytes to sample the pore through filopodia and up regulate EMT genes to transverse the pores to the other side of the membrane where EMT genes are down regulated as the cells re-establish stratified epithelia. The TGFbeta pathway and actin polymerization promotes movement of cells into the pores and Piezo1 and KRT6 actively block this movement. This work provides a novel 3D assay that is likely to become a benchmark to analyze these processes using a more complex system than other current culture-specific EMT and MET assays.

Strengths:

The strengths of the manuscript include the foundational analysis of the pathways involved in establishing the 3-compartment epithelium. The authors have incorporated live imaging, drug studies, KO analysis, and RNA-sequencing to show the relevant pathways involved. The authors were responsive to the original round of reviews and present a much-improved study with greater details on molecular markers, quantification, and clarity.

Reviewer #2 (Public review):

Enhancer elements are regulatory DNA sequences that are capable of driving specific expression patterns. As these elements are generally short and context-independent, enhancers can be used in expression vectors (e.g., packaged in an adeno-associated virus, AAV) to limit expression to target cell populations. This approach was identified as a major strategy for cell-type-specific manipulation in the brain and has been pursued by both standard research studies as well as large-scale efforts led by the BRAIN Initiative. This manuscript describes a major effort to generate enhancer-AAVs targeting astrocytes and oligodendrocytes orchestrated by a large research team led by the Allen Institute for Brain Science. This manuscript parallels other recent publications describing sets of enhancer-AAVs, following rigorous, similar methods with relatively broad testing and application.

To identify and screen candidate enhancers, the scientists prioritized candidates via analysis of single-nucleus accessibility and methylation datasets (i.e., snATAC-seq) and tested them in mice. The scientists prioritized candidate enhancers that exhibited specificity of accessibility in the target cell type. Following selection, the scientists cloned the candidate sequences into AAV vectors with a minimal promoter and reporter gene, packaged the virus, delivered it to the mouse brain, and screened for activity based on reporter expression. Candidates that passed initial screening were further characterized via imaging and sorting, followed by single-cell RNA-seq. This process had around a 50% success rate and yielded 25 astrocyte and 21 oligodendrocyte enhancer-AAVs with the targeted cell-type-specific expression patterns.

The scientists went on to test for subtype-specific activity patterns, finding wide diversity in astrocyte activities across sub-populations and conversely, homogenous oligodendrocyte activation. They optimized a few of these via concatenating the enhancer core sequence to increase expression levels of the reporter gene and showed strong specificity and completeness of cell targeting for a set of these enhancer-AAVs. Following characterization and validation, they then deployed these enhancer-AAVs in a number of demonstration applications to show the utility for basic and translational science. All the constructs developed here are available for public use via Addgene, ensuring that these new tools can be used by other researchers.

There really are no obvious weaknesses in the work presented here, from the generation of the enhancer-AAVs to use in sophisticated validation studies. The enhancer-AAV testing is rigorous and provides critical information necessary for other scientists to select and use these constructs. The applications demonstrate the power of enhancer-AAV approaches. The toolbox presented here may not enable specific targeting of all relevant cellular subtypes or activity states for astrocytes and oligodendrocytes, and future work will be needed to fully understand the activity of the enhancers, identity of the target cell types, and context-dependent utility of these constructs. However, the set of enhancer-AAVs developed here should be transformative for researchers working on accessing and manipulating these cell types and have a major impact on the field.

Reviewer #2 (Public review):

Summary:

This study aims to assess the drivers of the interspecific distribution of transposable elements (TEs), using genomes from the Sauropsids clade. In particular, the authors test the mutational hazard hypothesis (MHH), which stipulates that slightly deleterious TEs should preferentially accumulate in populations with smaller population sizes, where purifying selection is weaker.

Strengths:

The manuscript is clearly written, and the underlying concepts (MHH, Simpson's paradox) are well introduced and discussed. In that respect, the manuscript is highly didactic and an enjoyable read. The authors employ state-of-the-art phylogeny-aware statistical procedures, including structural equation modeling to infer causal relationships. The results are of high interest: the authors report that correlations between TE frequencies and proxies of population size (life-history traits - such as body mass, and molecular traits - such as the dN/dS ratio), which appear at large evolutionary scales, disappear when within-clade relationships are examined. The authors discuss these results and propose that at shorter time scales, clade- and TE family-specific bursts dominate the dynamics of TE dynamics.

Weaknesses:

While I generally agree with the authors' interpretation and find these results of great interest, I have several concerns about the underlying statistical analyses and think that some possible alternative explanations of a methodological nature should be checked to confirm the results.

(1) Three types of statistical analyses are employed: Pearson correlation coefficient, phylogenetic generalised least-squares (PGLS), and structural equation modelling (SEM). Both the Pearson correlation test and the PGLS assume normality (binormality in the case of Pearson's correlation) and linearity (and this is very likely the case for SEM as well, although it depends on the exact method employed). Deviation from these hypotheses can create erroneous P-value computations (in both directions). I could not find any assessment of how well the data fit these hypotheses in the manuscript; differences in significance could reflect differences in how the data fulfil these assumptions across time scales. Figure 2a suggests at least a deviation from normality, be it because of the Aves clade alone. A non-parametric correlation (such as Kendall's tau) should be used instead of Pearson's correlation, as it assumes neither normality nor linearity. The PGLS analysis is trickier in that respect: the authors should check the normality and independence of the model residuals (and likely homoscedasticity too, unless the underlying model allows for lineage-specific variance). An alternative would be to transform the variables using phylogenetic independent contrasts (PIC, which still assumes a Brownian model - as does the PGLS) and use a non-parametric correlation on the resulting PICs. I don't have expertise in SEM, but a quick search suggests that non-parametric (or at least non-linear) SEM implementations are available.

(2) The positive correlation (l208, l299) with generation time should be further commented on, as it has the opposite direction as expected. Following the proposed rationale, species with shorter generation times should have more TEs, shouldn't they? Unless "generation time" here actually refers to the number of generations per year, instead of the duration of a generation, as the text suggests?

Reviewer #2 (Public review):

Summary:

Negreira et al. present an application of a novel single-cell genomics approach to investigate the genetic heterogeneity of Leishmania parasites. Leishmania, while also representing a major global disease with hundreds of thousands of cases annually, serves as a model to test the rigor of the sequencing strategy. Its complex karyotypic nature necessitates a method that is capable of resolving natural variation to better understand genome dynamics. Importantly, an earlier single-cell genomics platform (10x Chromium) is no longer available, and new methods need to be evaluated to fill in this gap.

The study was designed to evaluate whether a capsule-based cell capture method combined with primary template-directed amplification (PTA) could maintain levels of genomic heterogeneity represented in an equal mixture of two Leishmania strains. This was a high bar, given the relatively small protozoan genome and prior studies that showed limitations of single-cell genomics, especially for gene-level copy number changes. Overall, the study found that semi-permeable capsules (SPC) are an effective way to isolate high-quality single cells. Additionally, short reads from amplified genomes effectively maintained the relative levels of variation in the two strains on the chromosome, gene copy, and individual base level. Thus, this method will be useful to evaluate adaptive strategies of Leishmania. Many researchers will also refer to these studies to set up SPC collection and PTA methods for their organism of choice.

Strengths:

(1) The use of SPC and PTA in a non-bacterial organism is novel. The study displays the utility of these methods to isolate and amplify single genomes to a level that can be sequenced, despite being a motile organism with a GC-rich genome.

(2) The authors clearly outlined their optimization strategy and provided numerous quality-control metrics that inspire confidence in the success of achieving even chromosomal coverage relative to ploidy.

(3) The use of two distinct Leishmania strains with known clonal status provided strong evidence that PTA-based amplification could reflect genome differences and displayed the utility of the method for studies of rare genotypes.

(4) Evaluating the SPCs pre- and post-amplification with microscopy is a practical and robust way of determining the success of SPC formation and PTA.

(5) The authors show that the PTA-based approach easily resolved major genotypic ploidy in agreement with a prior 10x Chromium-based study. The new method had improved resolution of drug resistance genotypes in the form of both copy-number variations and single-nucleotide polymorphisms.

(6) In general, the authors are very thorough in describing the methods, including those used to optimize PTA lysis and amplification steps (fresh vs frozen cells, naked DNA vs sorted cells, etc). This demonstrates a depth of knowledge about the procedure and leaves few unanswered questions.

(7) The custom, multifaceted, computational assessment of coverage evenness is a major strength of the study and demonstrates that the authors acknowledge potential computational factors that could impact the analysis.

Reviewer #2 (Public review):

Summary:

In this manuscript, Hossaim and colleagues investigate the role of the ITK kinase in modulating inflammation in a pristane-induced lung hemorrhage model. Using a germline ITK KO mouse, they report that loss of ITK skews the T cell compartment toward a memory-like state, expanding Tregs, and conferring protection against alveolar hemorrhage, inflammatory monocyte recruitment, proteinuria, and systemic cytokine elevation. They further show that transfer of ITK-deficient Tregs into wild-type hosts with established disease attenuates injury and shifts the cytokine balance toward resolution, and that ITK-deficient Tregs carry a transcriptional signature enriched for OXPHOS, mTORC1, MYC, and cell-cycle programs. While these observations are interesting for the development of potential immunotherapies, there are several issues with the methodological approach that support the authors' claims, tempering my enthusiasm for this manuscript.

Strengths:

(1) The clinical motivation and potential targeted therapies are relevant.

(2) The murine phenotype seems robust.

Comments on revised version.

The authors have addressed my comments appropriately.

PMID: 41513982

Gene: RNU4-2

HGNC: 10193

Disease: inherited retinal dystrophy

MONDO: 0019118

InheritancePattern: autosomal dominant

Prevalence: The prevelance of retinitis pigmentosa in general is between 1 in 750 and 1 in 9,000 individuals (PMID: 37108642). The proportion of those cases caused by RNU4-2 variants is not well known.

Penetrance: Penetrance appears to be incomplete, with family pedigrees including apparently unaffected carriers.

GroupID: 1

DiseaseAssertion: VHL

Group size: 1

GroupNumberMales: N/A

GroupNumberFemales: N/A

GroupEthnicity: N/A

GroupAgeRange: N/A

Group Info: Retrospective analysis of a longitudinal cohort of 21 VHL germline mutation carriers and retinal haemangioblastomas. All patients were categorized in two genotypic categories: missense mutations and truncating mutations. 

GroupPresentingHPOs: HP:0009711 (retinal capillary hemangioma)

GroupHPOFreeText: The patient presented with retinal capillary hemangioma.

GroupNotHPOs: N/A

GroupNotHPOFreeText: N/A

GroupPreviousTesting: N/A

PreviouslyPublished: N/A

SupplementalData: Table S1

Variant: Exon 1-2 deletion

CaseProblemVariantFreeText: N/A

LegacyVariant: N/A

ClinVarID: N/A

CAID: N/A

gnomAD: N/A

VariantEvidence: Chr3(GRCh37): g.10182301_10188764del; c.-1231_463+444del6464

MutationType: exon_loss_deletion

CivicName: Exon 1-2 deletion

MultipleGeneVariants: N/A

Reviewer #2 (Public review):

The authors tackle the question of whether conceptual absence is neurally encoded in the same way as perceptual absence. On one hand, the neural bases of perceptual absence have been largely investigated, as exemplified by the study of neural correlates of perception of aware vs. unaware stimuli, and on the other hand, the overlapping neural encoding of symbolic ('0') and non-symbolic (number of dots) formats of numerical absence has been previously established (Barnett & Fleming, 2024). However, the direct comparison of neural representations between numerical and perceptual absences remained to be investigated.

This article fills this gap by designing a Magneto-EncephaloGraphy (MEG) study using multi-voxel pattern analysis (MVPA) and temporal generalization to probe the similarity of neural patterns across the representation of perceptual absence (lack of stimuli), symbolic ('0'), and non-symbolic (number of dots) formats of numerical absence. They confirmed previously obtained evidence for shared neural representation across both formats of numerical absence. They report evidence for an absence of shared representation between both formats of numerical absence on one hand and perceptual absence on the other hand, while controlling for the confounding effect of low-level visual features. Their results overall support the conclusion that conceptual and perceptual absence are neurally encoded in a distinct way and speak in favour of a boundary between the representation of the concepts and the percept of absence.

Major strengths:

(1) Behavioral and neuroimaging results convincingly demonstrate that neural encoding of symbolic and non-symbolic absences is shared and situated on a graded, abstract neural number line, replicating previous results, notably from the authors themselves (Barnett & Fleming, 2024).

(2) They show that neural encoding of perceptual and numerical absence do not generalise across each other, while controlling for spurious confounds due to visual stimuli that are commonly shared in the cases of non-symbolic numerical absence and perceptual absences.

(3) They adequately use Bayesian analysis to distinguish absence of evidence vs. evidence of absence to support their claim.

(4) The interpretation of numerical absence as a representation of the concept of "nothingness" is adequate, although it might be further discussed by distinguishing the concept of "zero" on a number line from the concept of nothingness and that of an empty set (Nieder, 2016).

(5) The discussion about development and metacognition paves an interesting road for further investigation on the acquisition of the concept of zero, especially in light of debates on the progressive development of metacognitive abilities in children (Goupil & Kouider, 2019).

(6) Data and code are published with open-source access, allowing the community to further investigate the points as major weaknesses evoked below, if desired.

Comments on revised version.

I appreciated the authors' responses as they provided important additions to their discussion and supplementary information.

Reviewer #2 (Public review):

The authors document that GM-CSF prevents the loss of ILC3 populations and inhibits pro-inflammatory cytokine production during gut inflammation. They combine a preclinical model of gut inflammation in zebrafish with spatial transcriptomic analysis of samples from Crohn's disease patients. The data show that GM-CSF ameliorates gut inflammation by (1) curtailing the differentiation of disease-associated ILC1 and (2) by "boosting" the tissue repair function of ILC3.

The topic of the manuscript is interesting and highly relevant to the question of how intestinal inflammation is initiated or perpetuated. However, there are some limitations. Specifically, it remains unclear through which distinct molecular programs macrophages maintain "beneficial" ILC3 and how they prevent the differentiation or expansion of disease-associated ILC1. Based on previous data, these processes may be interconnected as ILC3 show remarkable plasticity and can convert into ILC1-like cells ("ex-ILC3").

Reviewer #2 (Public review):

Summary:

This work presents a valuable resource by generating a comprehensive bulk RNA sequencing catalogue of gene expression in the mouse duodenum and ileum during the first postnatal month. The central findings of this work are based on analysis of this dataset. Specifically, the authors characterized molecular shifts that occur as the intestine matures from an immature to an adult-like state, investigating both temporal changes and regional differences between the proximal and distal small intestine. They also sought to validate key findings through complementary methods and to provide comparative context with human intestinal samples derived from premature infants. This study will provide a solid reference dataset for the community of researchers studying postnatal gastrointestinal development and diseases that arise during these stages. However, the study lacks in functional validation of the interpretations.

Strengths:

(1) The inclusion of numerous time points (day 0 through 4 weeks) and comparative analyses throughout the first postnatal month.

(2) Validation of key interpretations of RNA-seq data by other methods

(3) Linking mouse postnatal development to human premature infant development, enhancing its clinical relevance. The inclusion of human intestinal biopsy and organoid data for comparison further strengthens this link.

(4) The investigation covers a wide array of developmental gene categories with known significance, including epithelial differentiation markers (e.g., Vil1, Muc2, Lyz1), intestinal stem cell markers (e.g., Lgr5, Olfm4, Ascl2), mesenchymal markers (e.g., Pdgfra, Vim), Wnt signaling components (e.g., Wnt3, Wnt5a, Ctnnb1), and various immune genes (e.g., defensins, T cell, B cell, ILC, macrophage markers).

Weaknesses:

(1) The primary limitation is that there is no functional validation. The study primarily focuses on interpretation of RNA expression. This is a common limitation of transcriptomic "atlas" studies, but the functional and mechanistic relevance of these interpretations remains to be determined.

(2) The data are derived from bulk RNA-Seq of full-thickness intestinal tissue. While this approach helps capture rare cell types and both epithelial and mesenchymal components simultaneously, it does not provide cell-type specific gene expression profiles, which might obscure important nuances. Future investigations using single-cell sequencing would be a logical follow-up.

(3) The day 4 samples were omitted due to quality issues, which might have led to missing some dynamic changes, especially given that some ISC genes show dynamic changes around day 6.

Reviewer #2 (Public review):

Summary:

This is a technically sophisticated effort to visualize the nanoscale organization of native surface AMPA receptors in adult mouse brain. The authors combine chemical labeling of endogenous AMPARs with 3D, 2-color super-resolution microscopy. They use this to compare hippocampal and cortical AMPAR organization and to characterize changes in a P301S mutant mouse tauopathy model. The authors report a greater density of surface AMPAR localizations in hippocampal CA1 than in two examined cortical regions, as well as a smaller fraction of AMPARs organized in synaptic clusters. They report that PS19 tauopathy is associated with reduced hippocampal AMPAR localization density, a relative depletion of the population classified as extrasynaptic, and reduced organization of receptors within synaptic nanodomains.

Strengths:

The methodological contribution is substantial. The authors developed a valuable set of methods for examining endogenous surface AMPAR organization in brain tissue. The imaging methodology could be useful across many questions in synaptic neuroscience.

Weaknesses:

While the biological observations are interesting, several aspects of the neuroscience interpretation require clarification, and some of the biological conclusions should be weakened unless the measurements can better justify them. I provide several comments intended to strengthen this interesting manuscript.

(1) The authors measure fluorescent localization counts but often interpret these as AMPAR numbers. I believe that individual fluorophores can generate multiple localizations and that labeling efficiency can vary, challenging their frequent interpretation as AMPAR numbers. Importantly, AMPARs are tetrameric complexes with potentially different subunit compositions, so one AMPAR could be observed as 0-4 fluorescent localizations. The uncertainty is particularly important for comparisons of AMPAR abundance per cluster and nanodomain. The authors should consistently distinguish localization density from receptor number and clarify which conclusions depend on assumptions about labeling and the assumptions of photophysical equivalence between experimental conditions. The paper would be more rigorous and the claims more legitimate if the authors were disciplined throughout about using empirical descriptors instead of interpretations.

(2) The distinction between synaptic and extrasynaptic receptors is central to the manuscript. The authors identify synaptic receptors either through density-based clustering or proximity to PSD-95 clusters. However, neither measure directly establishes anatomical synaptic localization. This is particularly consequential because only ~1/3 of morphologically identified YFP-positive spines contain PSD-95 clusters that meet the detection criteria. It seems to me that AMPARs associated with weakly-labeled or undetected synapses could then be erroneously classified as extrasynaptic. PSD-95 detection should be assessed across regions and genotypes, and a sensitivity analysis undertaken to ensure that the conclusions are robust to the choices of clustering and distance thresholds.

(3) The authors suggest that the larger extrasynaptic AMPAR population in the hippocampus provides a reserve for rapid synaptic recruitment and that its reduction in tauopathy constrains plasticity. This is plausible, but receptor abundance does not establish receptor mobility or availability for synaptic capture. The importance of AMPAR surface diffusion for hippocampal LTP and contextual learning has been demonstrated (see Penn et al., Nature, 2017), but the current study does not measure diffusion or receptor exchange. The authors should temper their interpretation and explain the caveats.

(4) The authors infer that there has been a preferential extrasynaptic AMPAR loss from the reduced total AMPAR localization density and the increased synaptic fraction they measured. Because both synaptic and extrasynaptic populations could decrease even when the synaptic fraction increases, they should calculate the synaptic and extrasynaptic localization densities separately for each mouse. This would clarify the magnitude and selectivity of the changes that they report and help to reconcile the potentially contradictory increased synaptic fraction with reduced AMPAR localization density within synaptic clusters and nanodomains.

(5) The authors adjust DBSCAN parameters for PS19 hippocampus in response to reduced overall localization density. Because the resulting clustering measurements are themselves used to establish genotype differences, it is important to demonstrate that the conclusions are robust to parameter selection. Analyses using identical parameter sets across conditions, together with simulations in which WT localization patterns are subsampled to match PS19 localization density, would help determine whether the reported changes reflect biological reorganization rather than density-dependent detection effects. Similar validation is important for the nanodomain analysis. I found that, in general, the authors should have made a greater effort to ensure that their measurements are robust to the parameter choices.

(6) CA1 comparisons with motor/somatosensory cortex require more precise anatomical characterization. What cortical layers and hippocampal dendritic compartments are being compared? These are not homogeneous structures in almost any regard. AMPAR or, more empirically, fluorescence localization density per imaging field is not necessarily equivalent to AMPAR density per dendritic membrane area or per synapse. Differences in neuropil composition, spine density, and synapse density could contribute to the observed differences when there is, in fact, no AMPAR density per dendritic membrane area. Proper anatomical normalization or a stricter comparison of equivalent neuronal compartments would strengthen the interpretation. I believe that the authors are aware of the issue, as they did an analysis along a single dendrite, but this has to be done more thoroughly for them to justify the conclusions they assert.

(7) The main comparisons involve three mice per group, and the authors correctly analyze mouse-level averages. Nonetheless, the small number of biological replicates limits the strength of the conclusions about regional specificity and disease effects. The test statistics and degrees of freedom, not just the p-values, should be presented. In addition, effect sizes and individual mouse values should be presented. The absence of a statistically significant cortical effect does not establish that cortex is unaffected. There could be genotype-by-region interactions, which the authors should check.

(8) The authors describe that 6-mo PS19 mice precede neurodegeneration and they assert that the observed AMPAR-associated changes relate to the onset of synaptic dysfunction. However, Yoshiyama et al., Neuron, 2007 demonstrate hippocampal synapse loss and impaired synaptic function at three months in PS19 mice, before detecting fibrillary tau. The present observations likely occur after synaptic pathology in thePS19 mice. The authors should distinguish structural correlates of established dysfunction from the mechanisms that may have initiated that dysfunction. More importantly, the assertion that neuronal or synaptic anatomy is preserved in the PS19 mice should be supported by appropriate measurements.

(9) The reduction of the AMPAR nanodomain organization is potentially important. However, the authors do not measure presynaptic release sites or trans-synaptic alignment. To be rigorous, the data establish altered postsynaptic receptor organization but do not demonstrate disruption of trans-synaptic nanocolumns or establish the functional consequences for quantal transmission. If the authors do not provide evidence for these claims they should not make them and make it clear these are (reasonable) speculations for which they did not collect the data.

Reviewer #2 (Public review):

Summary:

The manuscript investigates whether transglutaminase 2 (TG2) released from intestinal epithelial cells (IECs) can trigger anti-TG2 autoantibody production by activating TG2-specific B cells that recognize the TG2-gluten complex, with assistance from gluten-specific T cells.

Strengths:

The main study is well designed and mechanistically important, providing direct evidence for a longstanding hypothesis in celiac disease pathogenesis. The mouse model, which uses an inducible TG2 fusion to a deamidated gluten peptide sequence (TG2-DGP fusion) in IECs, is elegant and directly tests a mechanism that could previously only be inferred indirectly. The Sollid group previously showed TG2 is present in the gut lumen (Meling et al., 2024) and can facilitate B-T cell collaboration in vitro (Iversen et al., 2020). This paper closes the loop by showing IEC-restricted TG2-DGP expression drives autoantibody formation in vivo. The experiments include appropriate controls, including the use of a Tgm2-/- B cell donor to avoid confounding by TG2 shed during cell isolation. The paper also shows that B cell expansion occurs in Peyer's patches (PP), whereas class-switching involves mesenteric lymph nodes (MLN).

Weaknesses:

Some suggestions for revision:

(1) Physiological relevance of antigen dose and kinetics. The dox-induced pulse of TG2-DGP expression is a bolus-like event, whereas endogenous IEC shedding and TG2 release presumably occur continuously at low levels. The author should discuss more how the kinetics and magnitude of the induced signal compare with estimated physiological TG2 derived from IEC turnover, and whether the strong immunogenicity observed here could overstate the potency of the natural process.

(2) The translational value of the model may hinge on epitope relevance. Celiac anti-TG2 antibodies preferentially target a small number of conformational epitopes on open TG2, and this restriction is tied to how TG2 is captured and presented. A central claim of the paper is that IEC-derived TG2 is the relevant source for autoantibody elicitation, rather than extracellular matrix-associated TG2 or other pools. If IEC-derived TG2 only activates non-human-relevant epitopes, that would undermine the claim that this is the mechanism responsible for the actual patient autoantibody repertoire. Whether IEC-derived TG2-DGP would recruit a broader range of TG2 epitopes seen in human disease, or only the subset compatible with this conformational epitope, is worth examining.

Reviewer #2 (Public review):

Summary:

This study examines how genes involved in cellular recycling (autophagy) influence lifespan under different experimental conditions. The findings help clarify why previous studies have reported conflicting results about whether blocking autophagy shortens or extends lifespan. The work will be of interest to researchers studying aging and cellular stress responses, particularly those using model organisms.

Strengths:

The findings are valuable, as they help resolve inconsistencies within a specific subfield of aging research. The evidence presented is solid, as the data broadly support the primary claims of the study. In addition, the discussion is thorough and thoughtfully integrates the findings within the broader context of the field.

Reviewer #2 (Public review):

Summary:

In this MEG study, the authors assess the oscillatory dynamics of object naming in 27 participants. The authors observed two distinct theta-coupled networks: a ventral occipito-temporal network linked to object recognition and a medial fronto-temporal network linked to lexico-semantic retrieval. Moreover, when contrasting object naming to a scrambled image control task, they observed increased theta-gamma phase amplitude coupling in the LIFG and fusiform gyrus. The authors conclude that theta-coupling ensures the binding of recognition and retrieval processes in object naming, while the theta-gamma phase amplitude coupling ensures local computations within those networks.

Strengths:

Addressing the question 'which oscillatory dynamics govern speech planning' is interesting and timely, since not much empirical evidence on this topic is currently available. Whole-brain MEG assessing phase and phase-amplitude coupling for object naming are not commonly attempted, and the study boosts a lot of power. Furthermore, the writing is concise and clear, making the paper easy to follow and pleasant to read.

Weaknesses:

My overall assessment is that the claims and conclusions of this study are stronger than the evidence supports, and this is because of several design weaknesses which I will detail below. Moreover, this study gives too one-sided a perspective on the current state-of-the-art in the neurophysiology of speech planning/object naming, and the presented conclusions can solely be understood from that narrow theoretical perspective.

(1) One of my bigger concerns is the lack of any language-related manipulations that allows to independently isolate the processing components the authors discuss. Object naming (speech planning) is a complex, multifaceted mental operation where different representations and processing levels can overlap in time and space (e.g., Indefrey, 2011; Hickok, 2012; Dell et al., 2013; Munding et al., 2016; Strijkers & Costa, 2016; Nozari & Pinet, 2020; De Zubicaray, 2023; Pickering & Strijkers, 2025). Without attempting to isolate those specific representations and/or processes and solely looking at object naming in general, the only manner to interpret the data is by relying on a priori defined and often one-to-one mappings of space and function in light of a specific theoretical perspective. This is indeed what the authors do in the current study, by linking certain brain regions they observe (e.g., more occipital for object recognition and more fronto-temporal for lexico-semantics) to the function assigned in dual-route models (note also that the authors rely on the dual-route models of comprehension, not production per se; see below). While doing so is of course legitimate, it does strongly reduce the impact of the results since they do not concern a direct empirical demonstration of the authors' conclusions, but an indirect inference based on a single theoretical framework. To give a few examples of how this weakens the conclusions: How do the authors know the fronto-temporal network is lexico-semantic and not phonological, as many other models would pose (e.g., Indefrey & Level, 2004; 2011; Hickok, 2012; 2014; De Zubicaray, 2023)? Similarly, theta-coupling in production is not uniquely related to lexico-semantics, but could also be linked to the syllabic rate (speech rate; e.g., Giraud & Poeppel, 2012; Meyer, 2018), or to working memory in production (e.g., Piai & Zheng, 2019). The occipito-temporal network may be involved in object recognition, but it may also reflect (embodied) word semantics, in particular for imageable objects as in the current study (e.g., Strijkers & Costa, 2016; Pulvermuller, 2018). In my opinion, given the lack of independent manipulations, these (and other) alternative conclusions are equally plausible, and the authors' interpretations of the data pattern are tentative at best; this is a substantial limitation in my view.

(2) The authors may object that they do have a contrast, namely the control condition. However, the control condition and the object naming condition differ in so many crucial aspects that it does not serve as a reliable control to address the above issues with interpretational strength. The control condition solely deals with low-level visual input, but not with conceptual and language-related processing, nor with motor preparation and articulation (since repeatedly saying 'no' is a flawed control for the more complex and variable naming responses required in object naming).

(3) Moreover, the timings between the control and object naming conditions are confounded: Naming and control RTs differ by 270 ms. The 1 s pre-response window therefore spans 0.08 to 1.08 s post-stimulus in naming, but −0.19 to +0.81 s in control. Hence, part of the contrast may rather reflect 'when' something was measured rather than 'what' processing differences it is supposed to reflect.

(4) As far as I can tell, the authors did not assess potential pre-articulation motor confounds in the data via ICA and/or recorded EMG activity (e.g., Vos et al., 2010; Porcaro et al., 2015; Strijkers et al., 2017; Abbasi et al., 2021). This may be relevant since motor preparatory activity is known to emerge well before actual articulation (e.g., Ries et al., 2013), and may (pre-)track articulatory speech rate in the theta range (e.g., Abbasi et al., 2023).

(5) Why are items not modelled as random intercepts/slopes? With 200 different stimuli, item variability seems relevant to include in the statistical models.

(6) I found that the authors gave a very restricted and not up-to-date literature overview on the neural basis of object naming. They mainly consider a dual route architecture. I understand that the authors don't wish (nor have to) give a complete overview of the different neurobiological models of object naming. However, the dual route model they cite is the speech processing and comprehension model (e.g., Hickok & Poeppel, 2004; 2007), which has no clear link to object naming, and they don't mention the production versions of a dual route architecture (e.g., Ueno et al., 2011; Hickok, 2012). Moreover, as mentioned also in comment 1, several other critical models (and more relevant for object naming specifically) are neglected: classical sequential object naming models (e.g., Indefrey & Levelt, 2004; Indefrey, 2011; Roelofs, 2014), interactive models (e.g., Dell et al., 2013; Dell & Chang, 2014), or more recent parallel models (e.g., Strijkers & Costa, 2016; Pickering & Costa, 2025) (for recent reviews see: e.g., Nozari & Pinet, 2020; Kerr et al., 2023; de Zubicaray, 2023). If the authors prefer to solely focus on a single class of models, that's fine (though a model that is more tightly linked to object naming would have been more relevant), but that limited scope should at the very least be acknowledged.

Reviewer #2 (Public review):

Summary:

In this study, the authors investigate luminal architectural reorganization during embryo implantation. Using 3D histological reconstruction, single-cell RNA sequencing, spatial transcriptomics, and genetic mouse models, the authors the uterine luminal epithelium undergoes rapid morphogenetic remodeling during the implantation. They further propose that a stress-responsive p38α signaling program coordinates epithelial remodeling and epithelial-stromal communication to establish an implantation-competent uterine lumen.

Strengths:

This study clearly described the changes of luminal architecture during implantation. Moreover, they also used integration of multiple advanced techniques, including 3D tissue reconstruction, single-cell transcriptomics, spatial transcriptomics, and genetic mouse models, which together provide a detailed description of the molecular characters of the uterine architecture during implantation.

Weaknesses:

The principal knockout model uses Pgr-Cre, which deletes p38α in both epithelial and stromal compartments. Importantly, they also added epithelial-specific knockout (Ltf-Cre) demonstrates that epithelial deletion alone produces only mild abnormalities and does not recapitulate the severe implantation phenotype observed in the uterine knockout. These data suggest that the critical function of p38α likely resides in stromal cells or in epithelial-stromal interactions. Consequently, the manuscript's title and central conclusion currently a little bit overstate epithelial regulation.

The data of this study are now support the main conclusions.

Impact of the work on the field:

This work will likely become an important resource for researchers studying embryo implantation because it provides a detailed atlas of peri-implantation uterine morphology integrated with spatial and single-cell transcriptomic datasets. The imaging methodology and quantitative description of luminal remodeling are likely to be widely useful.

Reviewer #2 (Public review):

Summary:

Rezaei et al. determined the cryo-electron microscopy structure of human ABCG5/8 in complex with ergosterol, revealing a conserved sterol-binding site at the transmembrane domain (TMD) interface, signifying the role of a specific key residue, Y432, in the interaction. ATPase assays showed the effective stimulation of ATPase activity of ABCG5/8 in the presence of ergosterol than cholesterol and CHS. The role of Y432 in the interaction with the transporter and stimulation of its ATPase activity was further supported by a mutagenesis study.

Strengths:

The study has been supported well with the structural and biochemical data combined with molecular dynamics simulations. The methods are very well detailed, which is helpful for the reader.

Weaknesses:

A few more experiments along similar lines (structures and mutagenesis and ATPase experiments) would have strengthened the molecular mechanism of sterol transport. Also, the physiological relevance of this sterol-ABCG5/8 structure has not been elaborated or connected via any experiments.

Reviewer #2 (Public review):

The authors developed a reinforcement learning framework to identify stimulation patterns that produce a predefined activity pattern in neuronal networks cultured on a microelectrode array. The main contribution of this study lies in the development and validation of the experimental framework, rather than in providing new insight into neuronal response properties or the biological mechanisms underlying network activity.

A major strength of the work is that the framework is implemented using open-source software. Electrical stimulation through microelectrode arrays is already widely used, and the proposed approach is therefore likely to be of interest to researchers seeking to automate the exploration and optimization of stimulation parameters. The authors also provide the software, hardware configuration, and experimental data, which substantially improves transparency and should facilitate reproduction and further development of the system by other laboratories.

The experiments provide a useful proof of concept showing that the framework can search for stimulation patterns associated with the predefined task in the tested neuronal network. The evidence is solid for demonstrating the feasibility of the approach within this specific experimental configuration. In particular, the closed-loop integration of stimulation, recording, evaluation of the neuronal response, and subsequent selection of stimulation patterns is clearly implemented and experimentally tested.

An important limitation is that the framework was evaluated using a relatively small and highly structured network. Four stimulation electrodes were positioned around a single network, and stimulation was delivered to microchannels in which axons were concentrated. This configuration is well suited to the initial demonstration, but it remains uncertain whether the same approach will perform similarly in conventional monolayer dissociated cultures, larger networks, or systems with different numbers and spatial arrangements of electrodes. Additional validation across a broader range of network structures and experimental configurations would therefore be needed to establish the general applicability of the framework.

Overall, this study presents a valuable and reproducible methodological framework for the closed-loop optimization of electrical stimulation in cultured neuronal networks. Its likely impact lies primarily in providing an accessible experimental and computational platform that can be adapted for studies requiring systematic exploration of stimulation patterns, although the extent to which the current findings generalize beyond the tested network configuration remains to be determined.

Reviewer #2 (Public review):

Summary:

The authors build on previous work demonstrating that the microtubule-associated protein CKAP2 functions at the mitotic spindle to regulate microtubule dynamics for proper chromosome segregation. Here they show that CKAP2 and its paralog CKAP2L also function at the cilium to regulate cilium formation and Hedgehog signaling.

Strengths:

A major strength of the manuscript is the use of GFP-CKAP2 knockin cells and the examination of single and double CKAP2 and CKAP2L knockout cells. The authors use live imaging of the knockin cells to show that CKAP2 localizes to the primary cilium of RPE1 cells after assembly.

The authors use the knockout cells to show that loss of either CKAP2 or CKAP2L results in longer cilia and a dampened Hedgehog response, whereas loss of both CKAP2 and CKAP2L results in shorter cilia and an even more dampened Hedgehog response.

Weaknesses:

The data are compelling and support the conclusions. However, the findings would be significantly strengthened by using additional markers of the cilium, not just acetylated tubulin.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors report on how a prion protein originating in yeast inside the gut interacts with a host insect's physiology to influence organismal traits important to fitness. Other recent studies have strongly found that interactions between gut yeasts and their Drosophila hosts are important to feeding behaviour, growth performance, and cold tolerance. They even point to the gut-brain axis. The authors extended from this work, combining screening approaches, genome-wide association studies, and forward genetics to determine that octopamine and serotonin signalling are important to these effects.

Strengths:

The data represent a valuable addition to the literature. There is a growing appreciation that host-microbiome interactions are important to animal performance and fitness, and this work provides strong evidence for a causal role of a prion protein in such a relationship. The high-throughput screening approaches and sequencing were, to the best of my knowledge, completed correctly. The idea that prion proteins could influence host physiology in beneficial ways is compelling, and the authors have done a great job of walking the reader through their reasoning and the multiple lines of evidence that support their reasoning.

Weaknesses:

While this is an interesting topic that is worthy of investigation, and identifying a causal role for a prion protein in host effects is a significant advance, other aspects of the novelty of the work are strongly overstated. No clear justification is provided for why cold tolerance was a particular trait of interest in the present study. Yet, a recent publication in the Journal of Experimental Biology clearly demonstrated a role of gut yeasts in Drosophila cold tolerance and is not cited or otherwise acknowledged [see: Jiménez-Padilla and Sinclair. Gut yeasts accelerate chill coma recovery in Drosophila melanogaster. J Exp Biol (2026) 229 (9): jeb251533]. I trust this is an oversight and will be corrected. Integrating this knowledge into the narrative of the paper would strongly improve the reasoning for this study.

The authors rely on a review of chill coma mechanisms from 2011 as their primary source of information for interpreting their results in relation to insect cold tolerance. A great deal of work has been done by several research groups on the physiology of cold tolerance in chill-susceptible insects in the past 15 years, much of which has relied on Drosophila as a model system. I think this is important and could inform the conclusions being reached and future work that extends from this work. For example, it is now well established that the renal system is centrally important to insect cold tolerance, including chill coma recovery phenotypes, but this is not being acknowledged or integrated into the narrative here and should be. Many of the mechanisms of interest highlighted in the present work are also directly relevant to other ionoregulatory epithelia beyond nervous tissue, and global knockdowns using the UAS/Gal4 approach will impact all tissues in which these genes are expressed. Figure 2D shows how these genes are enriched in the CNS, but also the midgut and Malpighian tubules, two ionoregulatory organs of import to cold tolerance.

The method the authors used to assess cold tolerance is not typically preferred by researchers who commonly study these phenotypes. Holding flies continuously at 4-6{degree sign}C and assessing whether or not they remain active is a reasonable approach, and I am confident that the differences observed among groups are real. However, chill coma onset and recovery are thought to be mediated by a complex combination of temperature effects on nervous, muscle, and renal function, and this assay method may conflate chill coma resistance with the capacity for chill coma recovery. This means variation in this assay could arise from differences in initial CTmin, plasticity in CTmin induced by RCH before chill coma is induced, or plasticity in neurumuscular/renal function that allows for recovery during the assay. I say this in part because Drosophila are documented to enter chill coma and then recover while the assay is ongoing, particularly around temperatures close to the CTmin [see: MacMillan et al., The capacity to maintain ion and water homeostasis underlies interspecific variation in Drosophila cold tolerance. Sci. Rep. 5:18607]. Therefore, flies active after 2 hours may never have entered chill coma, or may have entered and then recovered. Serotonin and octopamine signalling may be critical to one or both of these possibilities, and maybe in different ways, especially because these hormones play critically important roles in both renal and nervous function.

Infectious prions are known to cross gut epithelia and the Drosophila gut is known to take up large organic molecules via endocytosis and exocytosis, so I am not entirely convinced that the effects observed necessarily result through the gut-brain axis. Future studies could be focused on observing whether these prion proteins remain in the gut, or act directly on other tissues via the hemolymph.

The CAFÉ assay used in the study is a tried and true way to measure feeding behaviour in Drosophila. The data in Figure 1H look like a single measurement per individual, unless they have been modified for presentation. From the text provided, it is not clear to me how the authors accounted for changes in feeding behaviour at different times of day. Feeding behaviour is regulated by the circadian rhythm in digestive tissues. If activity is enhanced at night, as in the present study, are MRPL10+ flies also feeding during the night more than the WT flies?

Reviewer #2 (Public review):

Summary:

The author uses multinomial logistic regression to assign fitness values to SARS-CoV-2 and Influenza H3N2 clades. The mean population fitness, referred to as the cumulative fitness flux, is tracked throughout evolution to describe the rate of adaptation. The author shows a clear decrease in the rate of adaptation for SARS-CoV-2 after mid 2022, coinciding with the emergence of the Omicron clades; the rate of adaptation of H3N2 is much slower, but inferred to be constant in time. With this analysis, the author confirms Fisher's fundamental theorem of natural selection. Using a fine-grained clade definition scheme, the author attributes changes in fitness to substitutions in different genomic regions, highlighting the importance of the spike and, in particular, the RBD region of spike. A fitness model based on a multinomial regression is shown to perform better than EvEscape and protein-language-model semanticity to predict changes in fitness.

Strengths:

The author provides an interactive reading experience on the github.io hosted website, which has additional interactivity with figures and offers simple manipulation of what is shown. The methods are well explained and are easy to follow. The conclusions of the study- the decrease in the rate of adaptation of SARS-CoV-2, mutations in Spike S1 as the main driver of evolution, and the Spike S1 mutational count as predictor of fitness change - are clear to follow.

Weaknesses:

(1) The main conclusions that (i) the rate of adaptation of SARS-CoV-2 has decreased after Jun 2022 and is lower than in Influenza H3N2 and (ii) that mutations in Spike S1 are the main drivers of evolution are not novel.

(2) At the core, the multinomial logistic regression assumes a time-independent fitness value for each viral clade, while the clade fitness is not constant and depends on the population immunity, which will change over time. As a result, the fitness increase can be wrongly interpreted: The author observes a doubling of the mean fitness every 6 months from Jan 2021 to Jun 2022, and every 2.4 years from July 2022 to Dec 2025. We would then expect that the mean fitness in Dec 2025, \bar{f}_t, has increased more than 2 to the power 4, as compared to the mean fitness in Jan 2021, \bar{f}_0 (\bar{f}_t > 2^{4} \bar{f}_0). Is the mean viral fitness in Dec 2025 more than 16 times higher than in Jan 2021? Fitness differences between viral populations at different times cannot be meaningfully compared because the fitness landscape is time-dependent. The increase in fitness, without accounting for the change in population immunity, is in part an artifact of the multinomial regression.

(3) The multinomial regression treats the fitness landscape on which the virus evolves as static. However the fitness landscape is actually time-dependent, as population immunity changes. For a static fitness landscape, the cumulative fitness flux is the mean population fitness. However, for a dynamic fitness landscape, this is no longer the case: the (mean population) fitness changes both because of adaptation, and because changes of changes in the fitness landscape, i.e., an increase in population immunity will decrease fitness non-uniformly, also affecting individual selection coefficients s_i(t). This leads to cases where the cumulative fitness flux between t_0 and t_n can be positive, but the fitness remains the same. This study neglects the time-dependence of the fitness landscape and therefore incorrectly calculate the fitness flux, leading to an overestimation of the rate of adaptation.

Reviewer #2 (Public review):

Summary:

This manuscript by Lahne et al. connects retinoic acid (RA) signaling regulation to the length of outer segments (OS) in UV-sensitive cone photoreceptors located in a specialized region of the zebrafish retina called the high acuity zone (AZ). The authors provide potentially compelling, but as it stands, incomplete, evidence that the Müller glia (possibly through expression of the Cyp26 RA-degrading enzyme) may act to regulate spatially restricted RA availability to UV cones in the AZ, supporting these UV cones to grow longer OS. While the evidence presented is indeed suggestive, it is not directly linked and is insufficient to support the full conclusions presented.

Strengths:

(1) The experiments make outstanding use of the strengths offered by the zebrafish model, including excellent reporter lines for RA signaling and Müller glia visualization, fluorescent in situ hybridization, high resolution fluorescent microscopy, and pharmacological manipulations; tools which are all well selected and utilized.

(2) The pharmacological manipulations allow temporally controlled modulation of RA signaling and Cyp26 enzyme activity at developmental timepoints of interest.

(3) Manipulation of RA signaling impacts cone OS lengths.

(4) The Müller glia express mRNA transcripts encoding the RA-degrading enzyme Cyp26 and respond to RA signaling modulation in a manner consistent with a role in regulating RA signaling.

(5) The use of DAPT to prevent Müller glia differentiation in the retina is used to attempt to implicate the Muller glia in the regulation of RA signaling and UV cone OS length.

Weaknesses:

(1) The experiments cannot rule out a role for other cell types in driving the outcomes studied (UV cone OS length). The expression of Cyp26 transcripts by the Müller glia does not specifically align with the high acuity zone, and other cell types show signal for these transcripts. Impacts on RA signaling, as well as Cyp26 expression can be detected in regions potentially implicating cell types other than the Müller glia. One region of Cyp26 transcript signal is consistent with the Retinal Pigment Epithelium (RPE), and cells in this same region also show RA signaling changes in response to pharmacological treatments.

(2) The analysis following pharmacological manipulations lacks quantification of UV cone numbers and their morphology after the treatments, which could indicate broader impacts on cones that may manifest in their outer segments.

(3) The cell-type assignment of RARE:GFP-positive ONL cells is central to the proposed non-cell-autonomous mechanism, yet representative colocalization images supporting the quantification are not fully shown.

(4) The SyGCaMP6f activity reporter experiments are a nice addition, but the results are not necessarily directly connected to OS length or to the Müller glia.

(5) The experiments using DAPT to prevent Muller glia development are not sufficiently executed to support the conclusions presented, and their efficacy in inhibiting Müller glia development, as well as UV-cone-specific impacts, are not demonstrated. These treatments could have broad impacts on photoreceptor maturation, retinal organization, or trophic support.

(6) The title refers to glial regulation of "local retinoic acid levels." However, the study measures: A synthetic RARE reporter, RA-responsive genes and pharmacological responses.

Reviewer #3 (Public review):

Summary:

This work shows experimentally and computationally that single CA1 neurons can perform mismatch detection on patterned CA3 inputs and that STP and EI balance underlie this detection.

Strengths:

It has been known that STP can enhance the EPSP when the corresponding presynaptic input exhibits abrupt changes in firing rate. This work provides experimental evidence and further computational support for the hypothesis that the basic computation through STP is useful for detecting abrupt changes in the spatial pattern of synaptic inputs at the Schaffer collaterals. Further, their results indicate the novel view that mismatch detection is most efficient when gamma-frequency bursting inputs exhibit mismatches between theta cycles. The authors included novel results in the revised manuscript to show that the effective frequency range of gamma oscillation is broad, including both slow and fast gamma bands.

In the initial submission, the dependence of mismatch detection performance on model parameters and experimental settings, such as pattern overlaps and other network parameters, was not sufficiently explored. In the revised manuscript, the authors extensively studied these points and summarized the novel results in Fig. 9. Furthermore, the authors clarified that jitters in input spikes can improve detection performance in some cases. These results show the robustness of their results against variations in external and internal conditions.

Weaknesses:

While this study shows an intriguing example of combined experimental and computational studies, the results of experiments and those of the computational model display relatively large deviations in some conditions. The cause of this discrepancy needs further clarification.

Reviewer #2 (Public review):

Summary

The paper investigates whether the real-time physical experience of the body shapes high-level physical reasoning. Participants played a set of computerized tool-use reasoning games (the Virtual Tools paradigm) in which they must use knowledge of physical laws - including gravity, collisions, and inertia - to guide a ball into a target area. In Study 1, participants played the games under terrestrial gravity while receiving either Galvanic Vestibular Stimulation (GVS), which introduces noise into the vestibular organ and disrupts gravitational signalling, or a Sham condition with matched skin sensation. In Study 2, a separate cohort played the same games redesigned under hypogravity (0.5 g - half Earth g) or hypergravity (2 g - double Earth g), again with concurrent GVS or Sham stimulation. Performance was assessed through success rate, number of attempts, and time per attempt; strategy was assessed through the spatial distance between successive tool placements and the frequency of tool switching across attempts. A post-hoc gravity-weighted index (GWI) was computed to compare the effect of vestibular perturbation across the two studies. The main finding is that GVS impairs performance in gravity-dependent games under terrestrial gravity, yet the same perturbation appears to be neutral or even beneficial when the game environment involves non-terrestrial gravity - a result the authors interpret as evidence for an adaptable, body-grounded internal model of physics.

Strengths

One of the most notable strengths of this work is its conceptual positioning at the intersection of embodied cognition and physical reasoning. Rather than treating the human body either as an abstract information-processing device or as a purely biomechanical system, the authors take seriously the idea that cognition is scaffolded by ongoing sensorimotor state - and they test this idea with a paradigm that is both tractable and theoretically motivated. The use of the Virtual Tools paradigm is well-suited to this goal: the games vary systematically in their reliance on gravitational predictions, allowing selective impairment (rather than general disruption) to serve as a signature of embodied physical reasoning.

The dual-study design is another strength. Testing the same vestibular perturbation under terrestrial and altered game-gravity conditions and observing a reversal in its effect depending on context, provides a form of internal control that is conceptually compelling. The additional clustering analyses (Dirichlet Process Gaussian Mixture Model and leave-one-out kernel density classification) strengthen the strategy results beyond raw distance measures, confirming that GVS systematically shifts participants' spatial exploration strategies.

The paper is also clearly written and engages meaningfully with relevant theoretical frameworks - predictive coding, embodied cognition, and stochastic resonance - making it accessible and stimulating for a broad audience.

Weaknesses

(1) Absence of multiple-comparisons correction. A large number of game-level pairwise t-tests are conducted in both studies (upward of twenty per study) without correction for familywise error rate. The game-level effects that anchor the main narrative - in Study 1 alone: Remove, GoalMove, Spiky, Falling_A, Shafts_B, Gap, and Chaining - arise from an uncorrected pool of comparisons. The probability that some of these constitute false positives is non-trivial. The authors should apply a correction (e.g., Benjamini-Hochberg) or at minimum discuss this limitation explicitly.

(2) The facilitation claim rests on a post-hoc and arbitrarily parameterized index. The gravity-weighted index (GWI), which drives the central cross-study comparison uses integer coefficients (1, 2, 3) to weight games by gravity dependency level. These coefficients are entirely arbitrary and bear no principled relationship to the actual gravitational magnitudes used in the study. Why not use the gravity dependency ratings themselves, or the empirically estimated gravity impact scores from the computational modelling mentioned in the Methods? The choice of weights should be either principled or tested across a range of values to demonstrate robustness. Furthermore, the notation in equation (1) as currently typeset reads as "Gravity minus Weighted Index" rather than "Gravity-Weighted Index"; this should be corrected.

(3) The "facilitation" interpretation exceeds what the data in Study 2 directly support. Across all games in Study 2, GVS versus Sham differences in absolute performance are non-significant in all directions. The facilitation claim derives entirely from the GWI being higher in Study 2 than in Study 1 - a between-subjects comparison involving different participant groups and a non-pre-registered metric. The language of "facilitation" should be tempered accordingly, or the authors should provide additional analyses to support this framing.

(4) Gravitational manipulation is visual only, and the vestibular system is only one component of the gravity-sensing network. Gravity perception results, as the authors very well know, from a distributed multisensory integration process that involves, in addition to the vestibular system, visual, proprioceptive, and visceral inputs. The present paradigm manipulates gravitational context solely through visual cues and targets the vestibular system through GVS - a point the authors acknowledge but do not discuss in sufficient depth. It is important to distinguish clearly between real gravitational alterations (as achieved in parabolic flight or centrifuge environments, where the entire body is physically subjected to a different gravitational vector) and virtually altered gravity, where only one sensory modality is targeted while others remain anchored to 1 g. The scope of the conclusions should reflect this distinction.

(5) The choice of 0.5 g and 2 g may lack sensitivity. Combining the two altered-gravity conditions in Study 2 because no significant effect of hypo versus hypergravity was found is statistically pragmatic but conceptually unsatisfying. There is evidence in the space physiology literature that gravitational processing is not linearly symmetric around 1 g: threshold effects exist below and above terrestrial gravity that may not be captured by modest deviations (half and double g) - see refs below. It is worth discussing whether the absence of a hypo/hyper distinction in Study 2 reflects a genuine equivalence or a lack of sensitivity, and whether more extreme conditions (e.g., near-zero g or 4-5 g) might reveal different processing regimes. Whether 0.5 g and 2 g were sufficient to saturate the system or merely insufficient to perturb it remains an open question with direct implications for the interpretation of the null GWI effects on strategy measures.

Lee SMC, Ribeiro LC, Martin DS, Zwart SR, Feiveson AH, Laurie SS, Macias BR, Crucian BE, Krieger S, Weber D, Grune T, Platts SH, Smith SM, and Stenger MB. Arterial structure and function during and after long-duration spaceflight. J Appl Physiol (1985) 129: 108-123, 2020.

de Winkel KN, Clément G, Groen EL, and Werkhoven PJ. The perception of verticality in lunar and Martian gravity conditions. Neurosci Lett 529: 7-11, 2012.

Clément G, Moore ST, Raphan T, and Cohen B. Perception of tilt (somatogravic illusion) in response to sustained linear acceleration during spaceflight. Exp Brain Res 138: 410-418, 2001.

Benson AJ, Kass JR, and Vogel H. European vestibular experiments on the Spacelab-1 mission: 4. Thresholds of perception of whole-body linear oscillation. Exp Brain Res 64: 264-271, 1986.

(6) High-level reasoning is not defined with sufficient precision. The term "high-level reasoning" appears from the title onward and in the heading of the Study 1 results section (line 138), but it is never formally defined. The reader needs a clearer account of what distinguishes high-level physical reasoning from low-level sensorimotor prediction, and where the games used here fall along that continuum. What specific physical competencies - ballistic trajectories, free-fall predictions, collision dynamics, frictional forces, inertial effects - are required across the game set? When describing the subset of games that drive key effects, this information is critical for evaluating whether effects are specific to gravity reasoning or to some other physical concept.

(7) Performance measures are disconnected from underlying kinematics. The performance measures (success rate, number of attempts, time per attempt) are coarse, high-level summaries. Time per attempt is used as a proxy for performance efficiency, yet participants received no instructions regarding speed, and different individuals may have adopted systematically different speed-accuracy trade-offs. It would be valuable to know whether time per attempt correlates with attempt number within a given game (which would indicate within-game learning) and whether mouse movement data - trajectory, velocity, hesitation - were recorded and could be analysed to provide more mechanistic insight into strategy formation.

Comments on revised version.

My overall assessment remains positive. I consider the findings important and the evidence solid. The study uses an original experimental approach to address a theoretically significant question, and I consider the data sufficient to support the more constrained conclusion that ongoing vestibular information can influence physical reasoning in a context-dependent manner.

I agree with the other reviewers that the game-level multiple comparisons require appropriate statistical treatment and that the Gravity-Weighted Index should not carry more inferential weight than justified by its construction. I also agree that some of the broader mechanistic interpretations should be presented cautiously. These issues concern primarily the strength and scope of interpretation rather than the validity of the experimental findings themselves.

I do not, however, consider the absence of preregistration of the exploratory analyses to constitute a methodological weakness in itself. Preregistration is desirable when analyses are intended to provide confirmatory evidence, but it is not a prerequisite for a scientifically informative exploratory analysis. The important distinction is that exploratory analyses should be transparently identified as such and their conclusions should be appropriately qualified. In my view, the relevant concern here is therefore the inferential weight placed on these analyses, rather than the fact that they were not preregistered.

I would consequently retain a rating of solid for the strength of evidence rather than incomplete. The main claims are supported provided they are formulated at an appropriate level of specificity; the evidence is less secure for the broader mechanistic claims concerning general recalibration of internal models of gravity.

Reviewer #2 (Public review):

Summary:

Wenke et al. investigated the role of vlPAG somatostatin-expressing neurons in the mediation of analgesia during defensive states. A newly developed paradigm of cued fear-conditioned analgesia, which consists of a combination of an auditory fear retrieval session and a pain test, was used to evaluate this cell population's contribution to fear-mediated analgesia. Optogenetic manipulation of vlPAG SST+ neurons modulated the responses to a nociceptive cue (Hot Plate) presented concomitantly with an aversively conditioned tone. At the same time, alterations in the freezing levels could be observed during optogenetic activation of vlPAG SST+ neurons. In order to disentangle the impact of these cells on analgesia from their impact on the expression of defensive behaviors, the authors performed electrophysiological recordings from the dorsal horn in the spinal cord of anesthetized mice. A vlPAG-RVM-DH pathway was identified to trigger nociceptive C-fibers upon optic activation of the RVM. Finally, pathway-specific activation of SST+ vlPAG-RVM neurons could abolish CS-induced analgesia.

Strengths:

The study addresses a relevant topic, that is, brainstem circuits for pain-modulatory mechanisms as part of defensive states evoked by threat. This is important because the circuit mechanisms underlying pain are still not fully understood, and defining molecular markers of cellular circuit substrates may support the identification of potential pharmaceutical targets in treating pain. The authors confirm a previous study in that a somatostatin-positive cellular population presents a crucial vlPAG circuit element mediating anti-nociceptive effects. Key novelty aspects of the present study are the demonstration that these neurons seem to play a role specifically in threat-induced analgesia. This was possible by the elegant design and application of a novel fear analgesia paradigm, combined with cell- and pathway-specific optogenetics.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Reviewer #2 (Public review):

Summary:

Harduf et al. investigated the mechanisms underlying the sense of agency (SoA), the feeling of being in control of our actions. SoA is thought to arise from the comparison of action outcome predictions, emerging from forward models, with the actually observed outcomes. Current literature has not elucidated how cues from different forward models predicting different features of actions are combined to form a final agency judgement. The authors investigate this gap by introducing various combinations of spatial and temporal mismatches during a virtual reality (VR) based reaching task, and recording participants' agency judgements. Then, they fit different models of cue combination to behavioural data and compare them. The authors find compelling evidence that a model in which the effects of spatial and temporal incongruence are multiplied provides the best fit, outperforming models that do not combine spatial and temporal incongruences, and one model that combines them by averaging rather than multiplying. The authors argue that sense of agency emerges from the domain-general integration of different forward models.

Strengths:

The experiments are well designed, and model validation is compelling thanks to the comparison with an appropriate set of alternative models and replication of the results in a first exploratory cohort and in a pre-registered validation cohort. The writing is generally very clear, and the presentation of the results is exhaustive yet compact, making the paper very readable. I also commend the authors for empirically addressing whether and how the integration of different cues contribute to the sense of agency, an idea which has been more widely proposed conceptually than empirically investigated.

Weaknesses:

(1) The terminology is not always clearly defined, and the authors are not sufficiently rigorous in specifying what gap their study aims to address exactly, and to what extent it succeeds. The authors acknowledge that the idea that cue combination underlies sense of agency was already proposed, yet frame their contribution as showing that "integration" underlies sense of agency. However, it is unclear what the authors exactly mean by "integration". Also, it is not well specified what a domain-general representation is exactly, and how such a notion is not already included in the previously proposed cue combination theory. Overall, the authors seem to imply the main contribution of the paper is to elucidate both whether and how cue integration is performed. However, the most novel contribution of the study appears to concern the "how" rather than the "whether".

(2) The authors should have discussed their findings in light of Bayesian inference, which has already been proposed as the basis of cue combination for sense of agency. The fact that a multiplication of the different cues fits the data better than an averaging makes it too tempting to think about Bayesian inference to ignore such idea. Indeed, if cues are assumed to provide statistically independent evidence about the probability of self-causation, then they should combine in a multiplicative way rather than by averaging. Even if the experimental design or pre-registration constraints preclude a formal Bayesian analysis, this point should be discussed and related to previous literature.

Reviewer #2 (Public review):

Summary:

This paper investigates how curricula should be structured to support learning in complex tasks, focusing on the distinction between concept discovery (learning new components) and concept implementation (learning how to use them in increasingly complex sequences). The main finding is that participants tend to prioritize discovering new concepts, even though learning outcomes are better when they instead practice implementing known concepts in increasingly complex ways.

Strengths:

A major strength of the paper is the experimental paradigm. The grid-world task is a novel way of independently manipulating two dimensions of complexity that are often confounded: the number of concepts that need to be learned and the complexity with which those concepts must be deployed. Participants have to learn how the underlying tools operate and use them in different configurations, which provides an interesting setting for studying variability in how individuals structure their own learning. Additionally, the experiments progress from correlational analyses of self-directed learning, to randomized experimental comparisons of curricula, and finally to transfer and adaptive curriculum tests. Overall, this paper addresses an important question about whether the complexity of concept discovery versus implementation is better for structuring learning.

Weaknesses:

The main concern is that the paper's conclusions do not seem to fully match the evidence. The authors present the results as identifying a relatively general principle of complex learning, such that we should master a concept's implementation before introducing new concepts. There was no implementation-first advantage in the transfer phase, and when testing was delayed by one week, the curriculum difference was no longer detectable. Although the implementation-first curriculum improves immediate performance within the constraints of these experimental paradigms, it is less clear that it produces more generalizable learning. Additionally, it is not sufficiently clear how the "flow" measure was created, what "flow" means in the context of subjective task experience, or more broadly in everyday learning, and what inferential role the measure plays in the paper.

Reviewer #2 (Public review):

Summary:

The authors set out to characterize how four studied components of executive function (Inhibitory Control, Shifting, Updating, and Working Memory) are organized in rhesus monkeys, using a battery of four touchscreen tasks performed repeatedly across many sessions. They report that individual monkeys fall into distinct "cognitive phenotypes," and that statistical modeling of the task scores reveals four separable latent factors, which they interpret as evidence against a single shared "general" executive function factor in this species. The four latent factors (Shifting/Learning, Exogenous Inhibitory Control, Endogenous Inhibitory Control, and Working Memory Updating) are slightly different from those mentioned in the introduction, which is of interest to refine the definition and assessment of executive functions in non-human primates.

Strengths:

(1) The multi-task, repeated-measures design is a substantial undertaking and yields a large, richly characterized behavioral dataset from the same animals across many sessions. The use of 4 different cognitive tasks is uncommon in this field and a real asset to the study.

(2) The overall approach of testing the reliability of each behavioral measure before including it in further analysis is a sound and appreciated practice, backed up with overall appropriate data analysis strategies.

(3) The attempt to move beyond single-task designs, which dominate the non-human primate literature, toward a broader profiling of executive sub-functions is conceptually valuable and timely.

Weaknesses:

(1) The four factors introduced early in the paper are not the same four factors that emerge from the final statistical analysis, and this shift is not clearly flagged or explained to the reader, which is likely to cause confusion about what the paper is actually demonstrating. In addition, working memory vs. updating are not clearly defined or distinguished. The introduction and discussion sometimes treat "updating" and "working memory" as separate executive functions, but the literature the authors themselves cite (e.g., Friedman & Miyake, 2017) treats updating as a component nested within working memory rather than a fully independent dimension. This conceptual ambiguity carries through the paper and affects how the reported four-factor structure should be interpreted.

(2) The grouping of very different types of behavioral measures (accuracy-based executive function metrics, reaction-time/processing-speed metrics, and reward/loss motivational-effect metrics) under a single "cognitive metric" label in Figure 2 is potentially misleading, since these are not conceptually equivalent, and the paper later treats them separately in supplementary analyses. This could give readers the impression that all reported metrics are equally informative tests of the four-domain hypothesis, when only a subset actually is.

(3) The rationale for how motivational & valence effects (gains/losses) were analyzed is not well supported. Effects of loss versus gain may reflect risk attitudes or loss-aversion documented in the primate literature, rather than motivation per se, and this alternative explanation is not discussed.

(4) The Discussion would benefit from acknowledging other factors that could influence performance and phenotype membership across sessions (such as age, sex, social rank, or stress) and from clarifying whether the identified cognitive phenotypes are expected to be stable traits or could shift with further training. Additionally, some interpretive statements appear in the Results section rather than the Discussion, which is inconsistent with how results and interpretation are normally separated, and could give undue weight to preliminary interpretations.

(5) Concerns about the reliability of some response-time-based measures. In the Continuous Updating task, reaction times appear to drop unexpectedly at the highest difficulty levels for each animal, which may reflect animals disengaging from the task (e.g., random choice heuristic) rather than a genuine cognitive effect, and could also reflect thinner, unevenly sampled data at these levels rather than a true average. If so, the slope measures derived from these data may need to be recalculated after appropriately filtering the trials. Related to this, individual differences in hand-resting position in front of the screen are acknowledged by the authors as a source of noise in reaction times across tasks, but no correction or normalization for this is applied.

(6) The Supplementary Methods referenced in the text (e.g., for the variance decomposition and additional factor analyses) do not appear to be included anywhere in the submitted materials, only Supplementary Results, making some analyses difficult to fully evaluate. There is no information about the subject age, sex, species, etc.

Achievement of aims and impact:

The authors largely achieve their stated aim of demonstrating that monkeys' executive function performance is reliable and structured in different dimensions rather than guided by a single factor, and the discovery of stable individual "cognitive phenotypes" across sessions is a genuinely interesting and useful contribution. However, the conceptual framework of the study needs to be better clarified (i.e. are working memory and updating truly separable?) and enriched with relevant inputs from the fields of comparative cognition and cognitive ethology in non-human primates and beyond. The conclusions about Response Times (RT) may change after proper or further analysis of these metrics; additional relevant dimensions may then be disentangled by considering the sources of variability in these RT data (i.e., motivation, emotional state). With clarification of these points, this work has the potential to become a useful reference and methodological resource for the field of comparative cognition and non-human primate cognitive neuroscience.

Reviewer #2 (Public review):

Summary:

The authors investigate the causal role of the LC in mobilizing cognitive resources. Two rhesus macaques expressing TH-dependent inhibitory (hM4Di) DREADDs in the LC, alongside one non-transduced control, performed a spatial foraging task under visible (transparent) and hidden (opaque) reward conditions. The authors report that systemic DCZ (0.1 mg/kg) administration selectively impaired performance in the memory-guided opaque condition in the DREADD animals, concluding that LC activity is causally required for allocating cognitive effort

Strengths:

(1) Achieving cell-type-specific, reversible chemogenetic inhibition in the macaque LC is a substantial technical achievement that successfully bypasses the limitations of systemic noradrenergic pharmacology.

(2) The experimental setup elegantly isolates memory-guided action selection from global arousal, sedation, or motivational deficits, providing a useful quantitative constraint for LC functional models.

(3) The introduction and discussion clearly identify the limitations of using indirect pupillometry to assess locus coeruleus activity. The authors thoughtfully frame their targeted manipulation as a critical complement to existing literature without overstating their claims.

(4) Demonstrating that the DREADD animals navigated the board similarly regardless of reward visibility is a valuable null finding. This specific result provides crucial evidence for addressing the memory-versus-effort debate. Elevating this insight from a subordinate clause to a more prominent position will further maximize the paper's impact.

Weaknesses:

(1) The current behavioral metrics-specifically mean inter-choice distance and success rates bounded by a "random agent" baseline-cannot distinguish between a working memory deficit and a fundamental shift in search strategy. Because performance is a many-to-one function of memory quality and search policy, animals could achieve the observed outcomes either by executing a tight local sweep with "leaky" memory for visited wells, or by abandoning tight spatial locality for a dispersed, near-random walk with intact memory. Without a detailed characterization of the animals' actual step-by-step visit sequences and spatial consistency across conditions, the data cannot adjudicate whether LC inhibition degrades an existing habitual policy (a memory/maintenance deficit) or impairs the adoption of a structured policy (an effort-mobilization/task-set deficit). Clarifying this is critical, as the two interpretations engage distinctly different LC-prefrontal mechanisms.

(2) The analyses of within-session behaviors rely on arbitrary reward-inclusion thresholds (e.g., 20 or 23 rewards) and censoring rules that obscure the temporal dynamics of the task. Because wells are depleted without replacement, the difficulty and working memory burden rise steeply as the board empties. A constant, time-invariant memory deficit could readily produce the observed early vs. late performance pattern, as the detectable cost of forgetting accrues heavily after the 20th reward. Furthermore, early-session failures to maintain an effective policy naturally lead to continued responding after depletion. However, the imposed time caps compress variance exactly where this within-session causal chain would be most evident, leaving the claimed independence of the early and late deficits unsupported.

(3) The experimental timeline heavily confounds chemogenetic manipulation with elapsed time. Test weeks 1-2 were exclusively saline, and weeks 3-4 were exclusively DCZ, with treatment alternation only occurring in weeks 5-6. Consequently, the critical contrast between opaque saline and DCZ conditions is largely a between-block comparison across a month of testing. Plausible time-varying factors-such as habituation, declining novelty of a daily home-cage task, and accumulating satiety-all directionally align with the reported DCZ impairment, undermining the isolation of the chemogenetic effect.

Reviewer #2 (Public review):

In the present study, the authors report the role of virus-induced apoptosis in positively regulating innate immune response. Upon infection, host cell apoptosis is triggered as a defense mechanism against virus replication. Culmination of infected-cell death impairs replicative potential for viruses hence attenuating virus propagation. Reports exist denoting inhibitory effect of apoptosis upon innate immune signaling. Contrary to that, findings of this manuscript underscore possible role of apoptosis in enhancing innate immune signaling and effector response. Infection-induced activation of caspases (3, 6, 7 and 8) has been demonstrated to cleave DRP1 protein. DRP1, acting as positive regulator for mitochondrial fission, upon degradation leads to altered mitochondrial morphology (elongation).

Mitochondria being a hub for innate immune signaling (via operation of RLR-MAVS-downstream effector molecule-axis), upon elongation as a result of DRP1 depletion results in greater innate immune signal flux and interferon induction. Increased interferon induction thus acts to inhibit virus propagation, as demonstrated by the authors using cell-culture models.

Strengths:

(1) The findings presented by the authors have been validated by employing elaborate biochemical experimental approaches. The study entails extensive biochemical characterization of DRP1 residues targeted by activated caspases, in vitro assays validating caspase-mediated DRP1 cleavage & caspase-DRP1 interaction.

(2) This study possesses broad implications since authors demonstrate role of caspase-mediated DRP1 cleavage in promoting innate immunity, in context of infection by diverse viruses (both RNA and DNA viruses).

Weakness:

Although the authors undertook a thorough experimental approach attempting to validate their findings, all the experiments were performed using either cell-culture models for infection or in vitro biochemical assays (cleavage and protein-protein interaction). Additional experimentation using animal models (in vivo) will further help strengthen biological significance of their findings under more physiological settings.

Comments on revised version.

I am satisfied with the revised version of the manuscript.

Reviewer #2 (Public review):

Summary:

This work presents interesting findings where addition of exogenous ATP extends replicative lifespan of yeast cells in a way that seems uncorrelated with actual increased intracellular ATP levels or mitochondria. To be clear, 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. I would not state that the data support a role for ATP availability regulating aging because human cells do not import ATP from the environment; maybe for yeast or microbes that import ATP unlike normal human cells that make all their own. I would not state that the data support a role for ATP in increasing replicative lifespan since there is no correlation between increased lifespan an increased ATP- rather the opposite is shown - you got no correlation between intracellular ATP at replicative lifespan. What you show is that you can increase replicative lifespan when you increase extracellular ATP in a way that doesn't matter if you further increase intracellular ATP with NTT1, but maybe you were saturated. But then, intracellular ATP doesn't correlate either. So, it doesn't make sense to say that the paper shows that increasing ATP increases replicative lifespan. You gave the cells what seems like an osmotic stress and it didn't even matter if they had the mitochondria to respond. Lifespan didn't correlate with more ATP at all. So, it seems incorrect to indicate that ATP regulates lifespan in yeast, when you haven't shown that it just wasn't osmotic stress and you have shown that it doesn't really have anything to do with how much ATP each cell has. Rather, it seems more like it's the robustness of some other response to the ATP and not how much they imported.

The authors point out in their rebuttal that, paraphrased, with the QUEEN reporter gene that quantifies ATP via ratiometric fluorescence, some cells increase atp with age, some cells decrease it and others show no age related changes, so it would not be meaningful to make an ATP measurement in old cells. Essentially, that line of reasoning further supports that, while adding extracellular atp seems to increase replicative lifespan, the lack of correlation between intracellular ATP and replicative aging suggest that they are eliciting a stress response to having high extracellular ATP. The increased replicative lifespan seems to be caused by some kind of stress response to the extracellular ATP, possibly osmotic. So, this is an interesting result that suggests that there are mechanisms to respond to high extracellular ATP that result in increased replicative lifespan that aren't caused by the ATP per say, but by having a high concentration of ATP, outside the cell. This paper seems like it might relate to the Kaeberlein paper about high osmolarity increasing replicative lifespan, but I don't know if this condition qualifies as high osmolarity.

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.

Strengths:

Longitudinal imaging of single cells. Analyzed ATP levels with two approaches. Creative approach to use NTT1 transporter to increase intracellular ATP levels (that did nothing for lifespan). Solid replicative lifespan data. Seemingly clear lack of correlation between ATP and replicative lifespan among individual cells strongly pointing so some kind of stress response, possibly some kind of osmolarity response like Kaeberlein 2002.

Weaknesses/inconclusive data:

The LCMS data seems better than the microscopy data on ATP because it does not suffer from signal interference/befuddlement from autofluorescence or pH or age-related changes in either of those parameters. The authors seem to have subtracted an average autofluorescence from each cell in their revised analysis, but it is unclear if this value changes with age to account for age related changes in autofluorescence; it is also unclear if signal attenuation as a function of age-related intracellular pH change was accounted for. They may have age-related values for autofluorescence or age-related changes in intracellular pH that affect fluorescence of GFP if they go back to their raw data, which might be useful for signal cleanup.

Reviewer #2 (Public review):

The remarkable evolvability of the olfactory system enables animals to rapidly adapt to dynamic and chemically complex environments. Over the past two decades, substantial effort has been devoted to uncovering the evolutionary principles that drive the diversification of odorant receptors (ORs), yielding key insights into the forces shaping their striking variability in both vertebrates and insects. In this manuscript, Zhang and colleagues analyze the OR repertoires of over 100 insect species, leveraging sequence and structural similarity to infer patterns of gene family evolution within this diverse and ecologically important clade. By integrating sequence-based and structure-based comparisons, their study builds on a compelling and recently emerging line of research made possible by the advent of AlphaFold, which has previously clarified the phylogenetic relationship between insect Ors and the gustatory receptor gene family and revealed the unexpectedly deep evolutionary origins of this ancient structural fold.

Applying this approach to a large set of ORs derived from species throughout the insect phylogeny, the authors confirm many previously reported patterns of OR evolution. More importantly, with their large-scale approach the authors found new structural features of different Or clades, resulting in intriguing testable hypotheses about Or evolution that have the potential to support future directions in studying this important gene family. For example, the authors identify a structural feature mostly unique to the OR co-receptor ORco, a beta-sheet in EL2, which they functionally show reduces odorant binding affinity - a key aspect of ORco, which does not bind ligands in the ancestral ligand-biding site. This is a particularly strong part of the manuscript, since the authors support their in silico-derived hypothesis with functional data.

In an attempt to assess the relationship between sequence identity and structure on one hand and function on the other, the authors perform an in-silico structure prediction and chemical docking analysis. While not suitable to assess the actual function of individual receptors without functional testing, this strategy revealed potential receptor clades with differences in the number and type of docked chemicals. As a response to the previous reviews, the authors now changed their writing to refer to these results as 'docking-derived function' instead of just 'function'. While I appreciate that the authors changed their wording, this semantic change still can be read as referring to docking-derived predictions as conclusions of actual biological function. However, this is not the case. Any functional claims based solely on the docking approach need to be interpreted with caution, as in silico-only approaches are currently not well enough predictors for actual receptor function. Accordingly, I strongly recommend to remove the parts that connect receptor function to structure or ecology.

Assessing their docking analyses with respect to species ecology, the authors identify correlations suggesting that Or repertoires might follow predictable evolutionary trajectories broadly aligned with species ecology. These are interesting hypotheses. However, due to the inability to predict actual receptor function the author's claims about ecology-function relationships are not well supported. Accordingly, the authors should change the respective parts of the manuscript in way that it is clear these are currently untested hypotheses. This includes adjusting the title, since the work does not find any experimental support for ecological tuning.

I believe that future studies combining functional testing with an explicit evo-eco framework could benefit from this work and the explicit hypotheses it generates. But the authors should be careful to not overstate their in-silico derived results.

Reviewer #2 (Public review):

Summary:

The study from Maigler et al investigates how between- and within-animal differences in taste preference relate to differences in neural responsiveness. The experiments rely on an elegant combination of behavioral assays to measure preference (e.g., repeated brief access testing, BAT) and electrophysiological recordings to monitor the activity of ensembles of neurons in the gustatory cortex (GC) of rats.

BAT with distinct batteries of tastants revealed pronounced variability in preference (measured as licking bout size) across individuals. This variability across individuals persisted after repeated testing. Repeated BAT also revealed that each individual rat's preference for different tastants changed across time.

Electrophysiological responses of GC neurons to batteries of tastants showed that firing in the "late epoch" of taste processing (i.e., 500ms post taste delivery) correlated more strongly with the individualized rat's BAT preference rather than with a canonical preference ranking. Importantly, this correlation was stronger for the last BAT session compared to the first. Finally, the authors show that the correlation disappeared in a second, consecutive recording session, indicating that exposure to tastants reconfigure preferences.

Strengths:

(1) The experimental design allows for an unprecedented look at the relationship between individual variability in taste preferences and neural processing.

(2) The study demonstrates that taste preference variability is not mere experimental noise but reflects the dynamic nature of taste. A key strength is the clear evidence that behavioral variability is reflected in neural activity patterns, establishing a strong correlation between brain and behavior.

(3) The evidence that simple exposure to familiar tastes can reconfigure preferences and taste representations is interesting.

Reviewer #2 (Public review):

Summary:

The authors aim to demonstrate skin inflammation is associated with fat pad atrophy and lymph node expansion. They further propose that these phenotypes are driven by the recruitment and lipid metabolism of CCR2-independent macrophages.

The authors took advantage of two skin inflammation models, fight-induced and imauimod-induced skin inflammation and analyzed multiple tissues, including skin, fat pads, and lymph nodes. Using a macropahge-depletion method (e.g., CSF-1R inhibitor), the authors further suggest the inverse correlation between fat pads atrophy and lymph node expansion is macropahge-dependent.

Comments on revised version:

The authors improved their manuscript with additional data and discussion. However, the authors need to clarify their terminology in the experiments. To strengthen their conclusion, authors need some additional experiments addressed below.

Clarify cohousing conditions in Fig. 1 through 3: the authors used 'resting' ,'alone', 'together'. These are unclear. Please state whether 'resting' mice were singly housed or co-housed in Fig. 1. If they singly house, specify when they were separated from their littermate or cage mates. In Fig. 1-3. If they were co-housed ('together'), specify whether their cage mates were littermate or non-littermate Ccr2-/- mice in the manuscript, including line 107.

Strengthen the link between fighting and the observed phenotypes: Co-housed Ccr2-/- males had increased lymph node (LN) weight and reduced fat pad weight. To test whether these changes are associated with fighting-induced skin inflammation, please consider comparing mice that are singly housed after three weeks of co-housing with mice that are singly housed throughout the same period.

Clarify the monocyte analysis: At line 141, please use 'monocytes' rather than 'inflammatory monocytes' if the analysis does not include inflammatory markers/phenotypes. Please also label the monocytes and macrophage populations in Fig. 3Di and 3Dii as was done for neutrophils and eosinophils in Fig. 3B.

Recruitment vs. Self-renewal: At line 144, CD11b expression alone does not distinguish recruited cells from cells expanded by local proliferation; Inflammation itself can alter CD11b expression. Please consider providing the evidence that distinguish these possibilities. Fate mapping would be an ideal approach, while Ki67 staining or BrdU incorporation assay could alternatively assess proliferation.

Reviewer #2 (Public review):

Summary:

This is a theory/computational paper that explores a model of a two-dimensional tissue undergoing homeostatic turnover. The motivation is to explore how the rates of cell division and cell loss in a tissue can be precisely balanced to maintain tissue integrity. It is already well appreciated that this can be achieved by a feedback mechanism whereby cells tune their division or loss rate in response to a change in cell density relative to a homeostatic set point (e.g. PMID 30269903 and other papers cited in the introduction to this new manuscript). Cell density can be sensed locally by mechanical pressure.

Here, the question asked is whether pressure-dependent homeostatic control can maintain not only cell density (as previously known) but also cell type proportions when a tissue has a proliferative hierarchy. The specific example considered is a 3-tier hierarchy with stem cells (S) that give rise to transit-amplifying cells (T) that give rise to terminally differentiated cells (D), which are then lost. Next, the authors notice in simulations that in such a model of density-dependent cell division, stem cells undergo spontaneous clustering without any need for cell adhesion. Thus, the key result of the paper is that a simple pressure-based homeostatic feedback control leads to spontaneous formation of stem cell clusters, which the authors call "niche-like". They characterize these clusters numerically.

Overall, the work is elegant and presented in a pedagogical manner. But I think its central result has already been previously described (DOI 10.1103/PhysRevE.77.031907), albeit without the use of terms such as "niche-like" that increase the relevance of the work, and using a different detailed choice of model. Essentially, clustering is the outcome of a log-divergence of the density of random walkers undergoing a critical birth-death process in two dimensions, as discussed in the 2008 paper. Essentially, once stem cell division is coupled to total density (rather than just stem cell density), then the stem cells follow a critical birth-death process in 2D, and the clustering is an inevitable outcome. This is the precise point of the previous paper. As such, my review will focus on novelty compared to prior work.

Strengths:

The first strength of the paper is that it gives a clear and biologically oriented presentation of how pressure-dependent (or more simply, density-dependent) feedback can be sufficient to maintain steady-state homeostasis with defined proportions of stem, transit-amplifying, and differentiated cells. The authors scan parameters in numerical simulations using an established agent-based model of cells under mechanical tension, and show that tissue can be maintained provided that stem cell division rates are able to match loss rates. This is not very surprising, but I believe it has not been laid out before in this way, and it is clearly presented.

A second strength is that the paper may make a useful contribution by drawing attention to the connection between mechanical homeostatic feedback and the spatial organization of stem/progenitor cells. Even though there is a precedent for this point (see overview above), the work is more biologically oriented than the prior paper, and may therefore be useful to readers interested in how simple local rules can shape tissue organization. The current paper uses a more biophysically informed particle-based model for simulations than the prior work, and it provides a useful framework for studying the interaction between mechanical feedback, tissue homeostasis, and cell-type hierarchy that could be generalized. The simulations are well motivated, and the results are presented in a pedagogical manner. More on the comparison to the 2008 paper below.

Weaknesses (or rather, comparison to prior work):

The main weakness is that the central result of the paper appears to have already been previously described, albeit using different terminology and a different detailed choice of model. I will describe the similarities and differences between the two papers below.

The 2008 paper...

• ...is motivated by asking how a critical birth-death process can maintain uniform density in 2D. This is different from the current paper, which asks whether a birth-death process can be tuned by total density and still maintain cell type proportions. As such, the questions motivating the work are subtly different, although both worry about how homeostasis is maintained.

• ...considers a 2-tier model consisting of progenitor and differentiated cells (A->B->0). This is different from the current paper, which considers a 3-tier model (S->T->D->0). But the current paper is aiming to be general rather than specific, and I would propose that complexifying the model from a 2-tier hierarchy to a 3-tier hierarchy doesn't make the new paper qualitatively different.

• ...shows that "the population of progenitor cells should cluster over time" (quoted from the paper). This is shown for A cells in the 2008 paper, and is shown for stem (S) cells in this new paper. As such, the prior paper already shows the key result of this new paper.

• ...uses slightly different rules, which are clunkier: it allows for symmetric and asymmetric division of A cells, does not allow direct A cell differentiation (no A->B allowed), and it uses a hard-core lattice-based model with hole-diffusion to model density-dependent cell division. By contrast, the current paper uses a more biophysically informed two-particle agent-based model for simulations, and it only considers symmetric divisions and direct differentiation. As such, the current paper is a more realistic treatment than a lattice-based model, and simply different (not more or less realistic) in terms of ignoring epigenetic heritable sister cell correlations. But since clustering is a large-scale phenomenon, as is often the case in statistical physics, the microscopic implementations should not be important. As such, I would not expect the difference in rules to be important to the clustering.

• ...derives a long-term asymptotic form for the clustering dynamics of progenitor cells (Equation 15 of that paper, and the unnumbered equation at the top of column 2 of page 7 of the paper). This goes beyond the new paper, which only describes the phenomenon numerically. The 2008 paper also explains the phenomenon in terms of prior work on random walkers undergoing birth-death processes, which better links the phenomenon to prior statistical literature.

• ...shows experimental evidence for clustering of progenitors in mouse epidermis. This goes substantially beyond the current paper, which is entirely computational. That said, despite the excellent fit of data to the theory, I am myself not convinced that the clustering seen in epidermis is the result of this phenomenon alone.

• ...studies clonal dynamics and shows that it is consistent with a voter-model-like behavior, with clone size growing as t/log(t). The scaling law is different from that reported in the current paper.<br /> Thus, the key result that progenitor cells undergo clustering seems to have been mathematically described in the 2008 paper and even shown in that paper to be consistent with experimental data in one tissue.

That said, I do think that this new paper may make a useful contribution. It is certainly more biologically oriented (despite lacking experimental data, which the prior paper has). And it may be that the quantitative dynamics of clustering and clonal competition are subtly different in this model. So, with certain revisions, I am supportive of this work.

Specific suggestions:

(1) The new paper should appropriately cite precedent. The paper should avoid making claims that progenitor clustering in this model of birth-death with spatial density control is a major novel discovery. This was a key result of the previous paper. The new paper should also cite the 2008 paper for evidence that clustering of this type has been proposed to be seen experimentally. The differences between the papers discussed above could be used as needed in highlighting the contribution of the new work.

(2) There is still a question about whether microscopic details of the simulation may alter the asymptotic dynamics of the clusters and clones. The authors could compare the dynamics of clustering between their simulations and the 2008 lattice-based result (Equation 15 of that paper, the "1/log(t)" behavior of the density radial correlation function) derived in the original paper, and similarly compare clonal dynamics in that paper to this new paper. Whatever they find is interesting: if they confirm that the same dynamic behaviours occur, it will reinforce the results of the prior work, rephrase the problem in more relevant terms, and help make it accessible to a broader audience. If they show that their model leads to different quantitative dynamics, they could explore (in a supplement perhaps?) what is the origin of the difference, given that the models are superficially similar.

Reviewer #2 (Public review):

Summary:

Dinucleoside polyphosphates have been found to increase in responses to cellular stress and are involved in the regulation of transcription, antibiotic resistance, DNA replication and innate immunity in metazoa. Increased Ap4A levels have been previously found to regulate the cGAS/STING pathway, leading to the proposal that modulation of Ap4A levels may be a suitable pathway to pharmacologically modulate cGAS/STING signalling (Guerra et al., 2020). Here, Weiland et al. present compelling biochemical, biophysical and structural evidence that dinucleoside polyphosphates (Ap3/4A and Ap4G) do not interact with STING nor are likely to affect cGAS signalling in vivo.

The evidence that they present evaluates the interactions of Ap3/4A and Ap4G s with cGAS, STING and TBK1 kinase as key signalling molecules in the cGAS/STING innate immune response. Previous work has identified Ap4A as a regulator of cGAS/STING signalling, and through precise characterisation of the biophysical interaction, crystallographic as well as modelling data provide evidence that these effects are too weak to affect these processes in vivo. Their work identifies that there are no direct interactions between STING and dinucleoside polyphosphates at physiological concentrations and at hyperactivated levels in NUDT2 mutants. There are detectable interactions of both Ap4A/and Ap4G with cGAS. In the presence of DNA, cGAS shows either ATP when bound with Ap4A or an intermediate of the cGAMP synthesis when crystallised with Ap4G. This is an interesting observation, showing Ap4G appears to serve as a weak substrate for canonical cGAMP production in the presence of DNA. Finally, the effect of Ap4A on the catalytic activity of TBK1 was evaluated and showed very weak effects. Competition assays in the presence of ATP and Ap4A/Ap4G showed a higher inhibitory effect for Ap4G, but only at concentrations 5x or greater than intracellular ATP levels and much weaker effects for Ap4A (~32x ATP levels).

Strengths:

The approaches used provide robust data for a subset of physiological Ap4Ns. The strength of their evidence comes from the complementary structural and biophysical approaches investigating the interactions of cGAS, STING and TBK1 independently. These data, including the dissociation constants for Ap4A and Ap4G with cGAS, allow for comparison with physiological levels of the dinucleoside polyphosphate in vivo. The use of quantitative mass spectrometry provides some increased rigor to the conclusions by integrating the in vitro and in vivo studies.

Weaknesses:

The data presented for Ap4G and Ap4A are convincing, and conclusions are consistent with the evidence presented. However, there are other Ap4Ns (Ap4C/U) that may be active against cGAS/STING/TBK1 that are not included in this study, which weakens the conclusions. This could be rectified relatively simply. The data would be more robust if the effects of Ap4C and Ap4U were included for cGAS using ITC, and also the additive effect of competition assays with physiological ATP and the levels of all Ap4Ns in the same reaction as in Figure 3A. At present, each Ap4N is investigated in isolation. To determine the effect of physiological Ap4N levels, reactions with all 4 Ap4Ns at maximum physiological levels as determined from NUDT2 KO cells would provide a robust test of their activities.

Overall, the in vivo data suggest that Ap4A and Ap4G can reduce cGAS activity, but only weakly and only at levels that are unachievable even where NUDT2 is inactivated. The data suggest that Ap4A/Ap4G do not affect cGAS/STING/TBK1 signalling at physiological levels and that NUDT2 targeting would be ineffective for the pharmacological modulation of cGAS/STING/TBK1 signalling. This conclusion is accurate only if Ap4C/U do not contribute to this inhibition. At present in the absence of these data, this conclusion cannot be made, as their activity could in principle affect cGAS/STING signaling.

Reviewer #3 (Public review):

The authors suggest that the small release of DA may be due to a release of glutamate from DRN 5-HT neurons to the VTA that stimulates weakly and in a transient fashion the VTA DA neurons, which in the end, produce a transient and small release of DA in the NAc.

Their findings give more information on the previously reported complex and partial known crosstalk between 5-HT and DA in the NAc.

Comments on revised version.

The authors have added several new experiments that address some of the prior concerns. However, there are still points of confusion. For example, the authors repeatedly argue that 5HT or DA are synaptic when in fact most of the transduction mechanisms for these neuromodulators and their transporters are extra synaptic, as indicative of volume transmission. This is not well articulated in the authors' interpretative scheme.

Reviewer #2 (Public review):

Summary:

To discover peptides that interact with autophagy-related protein LC3B and profile the key binding determinants, the authors screened a library of ~500,000 36-residue peptides derived from the human proteome using bacterial cell-surface display. Analysis of the screening data revealed exceptions to the reported LIR motif and a strong preference for negatively charged residues adjacent to the LIR.<br /> These results support a refinement the LIR motif definition and expand the network of candidate LC3B interaction partners.

Strengths:

High-throughput approach

Weaknesses:

Lack of in vitro data and molecular dynamics simulations

Comments on revised version.

In the revised manuscript the authors did not provide in vitro and molecular dynamics simulations data.

Reviewer #2 (Public review):

Summary:

This is a highly competent and very throough phylogenomic study of the position of neobodonids within the Kinetoplastid phylogeny. The addition of new transcriptomic data for diverse neobodonids allows their relationships to be better understood, leading to convincing changes to neobodonid taxonomy.

Strengths:

The generation of transcriptomic data produces more phylogenetic characters than ever before from several, diverse neobodonid lineages. This represents a substantial increase in the data available for this relatively unstudied group. The methodology used to define the character set and then estimate the phylogeny is conventional, very thoroughly described and expertly executed. The phylogeny is highly robust, and we can be very confident of the result.

Weaknesses:

These changes to neobodonid taxonomy, or essentially the discovery that some 'neobodonids' actually represent distinct lineages at the base of the Metakinetoplastida, are not surprising. So undersampled are the Kinetoplastida, both in terms of molecular markers and phyletic diversity, that an investment in sampling on this scale is always likely to bring about changes. The significance of these changes is not immediately apparent. Since they do not concern the Eubodonidae, they have no implications for the trypanosomatids and the new Metakinetoplastid lineages are very distant from the origins of parasitism, such that comments on "preadaptations for disease" are not compelling.

Reviewer #2 (Public review):

Summary:

The authors aimed to develop and validate a preclinical computational framework for assessing functional recovery after aversive learning. They argue that a reduction in defensive behavior (e.g., freezing) is insufficient to conclude recovery, and that a comprehensive assessment must also track the re-emergence of exploration and goal-directed action. To test this, they adapted a multimodal paradigm in rats, measuring freezing, 22-kHz and 50-kHz ultrasonic vocalizations (USVs), and food-reinforced lever pressing. Using a theory-guided hidden Markov model (HMM) with four states derived from tri-reference-point theory (Fear/Danger, Quiescent, Exploratory, Goal-directed), they decoded behavioral trajectories across fear conditioning, extinction, delay, and contextual relapse experiments.

Strengths:

(1) The study tackles a significant gap in animal models of fear and trauma-related disorders. It correctly identifies that a focus on a single defensive response like freezing may be misleading and that a broader behavioral assessment is necessary to understand the impact of interventions.

(2) The application of a hidden Markov model to combine multiple behavioral channels into a cohesive, theory-driven framework is a strength. This approach moves beyond simple summary statistics to capture the temporal dynamics of behavior and allows for the identification of distinct, recurring states.

(3) The authors demonstrate commendable rigor in their model selection and validation. They employ discrete-aware scoring to address the boundary issues of count data and conduct extensive stability analyses across restarts, variance floors, and studies. The association of the Fear/Danger mode occupancy with fecal boli, an independent autonomic measure, provides a convincing external validation.

(4) The study provides several important findings. For instance, it shows that during the first extinction session, although Fear/Danger occupancy declined, the dominant state was Quiescent, rather than Exploratory or Goal-directed. This directly supports the core premise that the absence of fear does not equal functional recovery.

Weaknesses:

(1) The authors transparently report that their results are not emission-family invariant. The failure of count-native models (e.g., negative binomial) to converge or produce the expected four-state solution is a major limitation. This suggests the theory-guided state structure is tied to the specific Gaussian representation chosen and may not be a robust and universal feature of the behavioral data. This limits the strength of the claim that the data naturally resolve into these four modes.

(2) The exclusive use of male rats from a single laboratory limits the generalizability of the findings to female animals and other experimental settings. The cross-study recurrence analysis is a helpful sensitivity check but does not substitute for true model transfer to an independent, out-of-sample dataset.

(3) Testing K=4 vs. K=8 raises the question of whether the four-state solution is the most appropriate level of abstraction for the questions being asked.

(4) The interpretation of the 50-kHz USV band as an Exploratory state is somewhat broad. 50-kHz calls are emitted in a variety of contexts, including social interaction and reward. While the use of the band is justified, it remains an indirect measure of exploration and should be interpreted with caution. The interpretation and link to the exploratory state should be toned down and discussed.

Reviewer #2 (Public review):

Summary:

In their manuscript "Penumbria: Advanced 3D cell segmentation for biomedical imaging", the authors describe their 3D nuclei segmentation pipeline and compare it against state-of-the-art methods for nuclei and cell segmentation.

The introduction is clear, with state-of-the-art methods, for instance, segmentation of cells from microscopy images, and motivates the method with science that can be done with cell segmentation. Related work is appropriately mentioned with the caveat that it conflates cell and nuclei segmentation, and some related work is missing.

The Results section describes the loss, the architecture novelties, and evaluation results on nuclei segmentation, not cell segmentation. Here it becomes clear that Penumbria addresses 3D nuclei segmentation, i.e., instance segmentation of medium-sized, mostly convex-ish and well-separated objects in single-channel 3D volumes.

Penumbria combines several choices and innovations:

- loss: boundary distance regression<br /> - architecture<br /> - 3D U-VixLSTM<br /> - Zernike Layer<br /> - SGG<br /> - combine multiple scales in SGG<br /> - sampling strategy to look for bright and dark objects.

The authors show on four nuclei segmentation benchmarks from confocal images that Penumbria outperforms CellPose-SAM (in either zero-shot or fine-tuned mode) and Stardist-3D in most cases, except for some where Stardist-3D performs better. They report results of an IoU threshold sweep to measure instance segmentation performance.

Penumbria is available on GitHub under a non-commercial license. I have not tested it yet, and therefore assume that the code is in full working condition and reproduces the reported results.

Strengths:

(1) The method may outperform state-of-the-art methods for cell segmentation as shown in three of four 3D benchmarks.

(2) The code is available.

Weaknesses:

Major

(1) Similar to the established confusion across the literature, all the authors' evaluation datasets do not label cells, but cellular nuclei. This is important to understand because the methods they compare against, CellPose-SAM and Stardist-3D, demonstrate their performance on a variety of datasets, some of which label cellular membranes instead of nuclei. The cell segmentation task is often considered harder than the nuclei segmentation task, because objects consistently touch each other, the separation may have noise-induced holes, and object shapes may be more diverse than for nuclei.

(2) The addition of U-VixLSTM, SGG, multi-scale input, Zernike layers, and distance transform regression sounds great, but a comparison to architectures or conventional losses used to train any combination is missing. Five innovations/choices combined simultaneously without an attempt to attribute.

(3) There is no clear delineation of 2D and 3D approaches. E.g. CellPose-SAM is not 3D, but sequential 2D execution with optional averaging of doing this along all axes.

(4) For the 3-fold cross-validated evaluation: It sounds like they were all trained for each benchmark independently? Did Stardist-3D and CellPose SAM start from their existing 'general' weights or from scratch (not possible with SAM, I know). Not described at all.

(5) Experiments to evaluate generalization performance are missing (train on all datasets, compare across datasets, ...).

(6) The Zernike layer optimizes only coefficients {3,4,12}; the evaluation could be described as rudimentary. Table 3 is missing cross-validation. Since mouse_skull is (a lot) different and differences for the others are minor, I would be curious how significant these results are. The following text is unclear: "This reflects how we developed these modifications in turn." How so?

(7) Training and inference: input and output block size missing, GPU memory requirements undefined, discuss block size vs. batch size. 50% overlap block-inference with feathering not compared to other overlap ratios, just "works best". Test-time augmentation 8x rotation (what are those rotations? Why 8? How much does it contribute? Compare the cost for 8xs test time augmentation against skipping "background" heuristic.)

Minor

(1) What is the incoming contrast range and bit depth?

(2) Stardist-3D's weakness is not that it requires convexity (it requires star-convexity) but that it picks one hypothesis leading to low-resolution outlines. This has been overcome by Starfinity (https://linkinghub.elsevier.com/retrieve/pii/S0092867421013398; https://github.com/multiFISH/EASI-FISH).

(3) Heatmap regression is not the same as distance regression; the manuscript does not explain what it is and uses terms interchangeably. This should be clarified. The reference for heatmap is 14; for distances, there are others, e.g. doi:10.1007/978-3-030-00934-2_36.

(4) Only one evaluation example compares against CellPose-SAM zero-shot; I would add this to all of them. The mentioned place switch would be interesting to see in a plot instead of text.

(5) "We replicated this failure on multiple machines." How does the machine matter here? Did the authors do cross-validation across different computers, instead of different random seeds? This sounds expensive!

(6) Brightness-based sampling could be achieved by augmentation?

(7) In the text "We use the following bicubic upsampling factors[...]", I think the authors mean tricubic, or cubic (because they upscale only one axis). Bicubic makes no sense, but people say this in the literature; we can do better. Why integer upscaling factors instead of exact ones? Are the authors concerned about that one incomplete border pixel?

(8) "Furthermore, it is not confined to Linux-based environments such as Segmamba [32], an important consideration given that biologists often work with Windows machines."<br /> This is confusing. Is this a comment about somebody's publicly available repository? This is not relevant to evaluate the method and should be discussed separately. Does it work in WSL?

(9) Downsampling by factors >2 with bicubic interpolation sounds wrong; should the authors use averaging?

Reviewer #2 (Public review):

Summary:

The authors investigated the phosphorylation- and ligand-dependent regulation of HDAC6 condensates using explicit-solvent coarse-grained simulations. They found these regulations can be attributed to the reorganization of the interaction networks between multiple domains within the condensate.

Strengths:

Large-scale condensate simulations with multiple domains using explicit-solvent models are nontrivial, impressive, and provide a useful example of what can currently be studied using such simulation models.

Comparison between simulations with/without ligand and with/without phosphorylation at different salt concentrations provides a comprehensive framework for understanding conditional residue- and domain-level condensate organization.

The simulation results are mostly consistent with previous experimental observations and offer potential mechanistic understandings that extend those findings.

The proposed FD3-centered interaction network for phosphorylation- and ligand-dependent regulations is a novel hypothesis.

The study generates a few experimentally testable predictions regarding the IDR- and FD3-mediated interactions.

Weaknesses:

When going through the manuscript, especially the result section, I sometimes found it challenging to distinguish what has already been reported experimentally (mostly from Lu et al., Nature Cancer) from what is newly learned from the simulations. I do appreciate that the authors mentioned which results reproduce experimental trends; however, this is often after all simulations. It may help if each subsection first briefly states the known experimental observation and then explains the specific question addressed by or new mechanisms learned by the simulation. For instance, experiments have shown that phosphorylation enhanced and ligand suppressed phase separation.

The authors directly used Automartini to generate the ligand parameters and then checked the contact fraction between the ligand and the pocket. Such analysis only tells whether the ligand visits the pocket but does not test either specificity or binding affinity, and therefore does not support the claim of ligand pocket recognition. The simulation concentration is also in the millimolar regime, probably much higher than the experimental range, so it's not surprising to see ~10% to 20% contact fraction. Considering that no native interactions are used to restrain the ligand-pocket interactions, I suspect the ligand might also have similar contacts to the other regions of HDAC. The authors might want to be careful about their discussions.

The claim regarding the IDR dimension might be problematic. It's still an open question whether a coarse-grained model, or even current all-atom force fields, can reliably resolve the effect of a single phosphorylation on IDR dimensions. I agree that all current results, including the error estimate, support that Rg do not change that much, but the authors might still want to be careful about such claim considering such small perturbation in the sequence.

The proposed electrostatic rewiring is based on the difference between the contact maps of WT and phosphorylation variant. It looks like the difference is relatively small to the contact probabilities. There is also no error estimation or pattern validation from multiple simulations. The authors might want to quantify the uncertainties of such patch-level relative changes by either checking some independent simulations or time-block variations.

A similar comment applies to the claims on the charged-contact reorganization mechanism of two small inhibitors on LLLPS. Error estimates should be provided for these contacts when comparing different variant. In cases when errors are large (e.g. contact number with errors reported in Fig. 4K to N), statistical analysis (p-values) is needed to show the significance of the results.

Regarding the proposed "phospho-S22-centered" contact network, I had some difficulty recognizing the specific regions discussed in Figures 2 and 3. It might be helpful to mark these regions directly on the contact maps. The authors may also wish to consider whether "S22-centered" is the most accurate description, or whether the observed changes are better described as involving a broader segment spanning several residues around S22. And since phosphorylation is introduced at S22, observing changes near S22 is not unexpected and may not be sufficient to establish phospho-S22-centered network.

Within the last result section, the authors argued that FD3 plays an important role in both phosphorylation- and ligand-regulated HDAC assembly. However, only contact-based analysis instead of saturation concentration was presented. I understand the substantial challenge of obtaining converged saturation concentration for such system with multiple domains, if so, the authors need to discuss such caveats.

Though direct experimental evidence of such FD3-centered interaction network does not exist, the experimental paper did provide measurements for a couple domain-deletion constructs, including the deletion of the FD3 (BUZ) region. This might be worth discussing, considering the manuscript focuses on the role of FD3 region. In particular, deletion of the BUZ region did not appear to affect LLPS. This does not necessarily exclude a role for FD3 in phosphorylation- or ligand-dependent regulation. However, it may complicate the description of FD3 as the center of the condensate network. The authors might want to discuss how their model can be reconciled with these domain-deletion results and consider their corresponding conclusion.

Finally, the authors describe their mechanism as coupled networks. They might want to clarify the coupling they meant. If this refers to the coupling between different regions (e.g. S22 and FD3) or domains, they might want to consider more quantitive analysis, for instance by conditional/correlated contacts between domains. Changes in contact patterns at different conditions do not necessarily show the coupling between domains.

Reviewer #2 (Public review):

Summary:

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

Strengths:

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

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

Reviewer #2 (Public review):

Summary:

In this paper, Bo et al. study the spatiotemporal organization of BOLD responses in the amygdala. They apply FIR modelling to a large sample (two cohorts tested at the same site and scanner, totalling >350 individuals) who completed a negative/neutral picture viewing and cognitive reappraisal task. Averaging voxelwise BOLD responses across two distinct probabilistic atlases of the amygdala, the authors found that a corresponding centromedial region showed sustained activation to negative stimuli beyond the stimulus period. Laterobasal and superficial subregions peaked earlier, within the stimulus presentation period. In all cases, reappraisal did not change these profiles. Lateralization effects (left-side dominance) were found in the CM region at later time points, and to a lesser extent in the superficial nucleus area at earlier time points. Data-driven clustering of voxelwise time courses could partly recover findings from probabilistic atlases, but nonetheless showed partial decoupling of anatomical and functional boundaries in the amygdala.

Strengths:

This is a well-written paper with a clear objective addressed by sound methods. The authors set out to address two main questions: (1) "do amygdala subregions exhibit distinct temporal dynamics in response to negative emotional stimuli?" and (2) "does cognitive reappraisal differentially modulate these temporal profiles across subregions?" Regarding question 1, FIR modelling results indicate strong differentiation of the centromedial region of the amygdala from other subdivisions, with strong and late responses to presented negative stimuli. On question 2, cognitive reappraisal does not seem to play a major role here, although I raise a question on this in the next section. Overall, all presented conclusions are supported by study findings. I would add that lateralization effects are not mentioned in the main objectives/questions of the paper, but this analysis represents a large section of the results that may warrant mention in the introduction as well.

Weaknesses:

(1) An important concern when looking at mesiotemporal regions such as the amygdala is signal-to-noise. Yet, this challenge in analyzing data from the amygdala was not addressed here but represents a potential confound if SNR varies within the amygdala along its major subdivisions.

(2) The authors analyze BOLD evoked response time courses, but I did not find a GLM analysis showing that the task reliably engaged the amygdala, either when analyzed with a canonical HRF or with the FIR model used here. We can see from Figure 2 that the task may not engage the entire amygdala (e.g. LB/LA subregions seem to show lower amplitude evoked-responses), yet the authors focus equally on time courses of subregions engaged and not engaged in the task. As such, differences observed between subregions may reflect different underlying processes (e.g. different processes of engagement in the task, or high/low engagement).

(3) The study uses two different probabilistic atlases to parse BOLD response time courses in the amygdala. However, methodological details on the processing of the atlas data are missing from the text. For example, it is unclear if the subregions were thresholded or overlap in any way, which would affect the strength of detected effects if there is substantial overlap between areas.

(4) The authors found no effect of reappraisal on BOLD response time courses. However, if I understand correctly, not all participants showed successful reappraisal according to their behavioural ratings (lines 125-126: "Eighty-one percent of participants showed a regulation effect in the same direction as the group-level effect"). Were these participants excluded from the reappraisal analysis on evoked response time courses? This is currently unclear, but holds significance in reliably answering question 2.

Reviewer #2 (Public review):

The manuscript by Wang, Nathans, and colleagues presents a compelling investigation into the regulatory crosstalk between Wnt/beta-catenin and VEGF signaling within central nervous system (CNS) endothelial cells. Overall, it is a very solid paper from a very respected research lab. The use of in vivo genetic mosaics to directly compare endothelial cells (ECs) with differing beta-catenin states within the same tissue is a particularly elegant approach to mapping receptor dynamics.

Major Comments:

(1) Distinguishing Receptor Abundance from Functional Signaling:

The study relies heavily on immunohistochemistry to assess total VEGFR1 and VEGFR2 levels, inferring that changes in receptor abundance directly equate to altered VEGF signaling. Because immunofluorescence does not establish plasma membrane localization, receptor phosphorylation, or downstream pathway activation, the definitive conclusion that beta-catenin "acts at the receptor level to reduce VEGF signaling" is currently overstated.

Recommendation: The authors should tone down the functional claims throughout the text (e.g., state that the receptor changes are "predicted to reduce VEGF signaling") or provide direct functional readouts of VEGFR2 activation in vivo, such as phospho-VEGFR2, phospho-PLCγ, ERK, or AKT.

(2) Limitations of the In Vitro HEK293T Model:

The HEK293T STF reporter system is utilized to argue for a "post-receptor physiological suppression" of VEGF signaling. While experimentally practical, this non-endothelial system does not definitively establish a physiological mechanism for CNS ECs. Furthermore, NFAT-luciferase is not uniquely specific to VEGFR2 and can be activated by broader calcium/calcineurin pathways. The authors also show that high Wnt1 expression reduces total VEGFR2 protein in these cells, but only at plasmid concentrations much higher than those required to suppress the VEGFR2/NFAT reporter.

Recommendation: Explicitly decouple the in vitro observations from the physiological in vivo claims. Remove the term "physiological" unless you validate post-receptor suppression in a primary endothelial system under endogenous signaling conditions.

(3) Regional Heterogeneity (Brain vs. Retina):

The retinal vascular mosaic data reveal that while VEGFR1 is reduced in BRB- ECs, VEGFR2 shows no obvious difference. The authors correctly acknowledge that Müller glial VEGFR2 expression complicates endothelial quantification, rendering Figure 4C a negative result with uncertain sensitivity. Consequently, the broad generalization that an "increase in Wnt/beta-catenin signaling produces an increase in VEGFR1 levels and a decrease in VEGFR2 levels" across all CNS ECs is not entirely accurate.

Recommendation: Embrace this biological nuance. Revise the generalized claims to reflect that beta-catenin regulates VEGFR1 consistently across the brain and retina, but VEGFR2 regulation appears context-dependent and is not clearly demonstrated in the retina. To resolve the Müller glia interference, consider orthogonal approaches to isolate the EC signal, such as EC-specific RNAscope or flow cytometry on FACS-sorted ECs.

(4) Statistical Rigor and Experimental Units:

The figure legends (e.g., Figure 1) explicitly state that data points represent the mean values from single confocal images. If multiple images were obtained from the same animal, treating them as independent units. Furthermore, the authors utilized unpaired statistics (Wilcoxon rank-sum) to compare BBB+ and BBB- ECs within the same mosaic tissue sections.

Recommendation: Reanalyze the data using the animal as the primary biological replicate if such data are available (averaging all image measurements within each animal). For mosaic comparisons within the same tissue image/animal, utilize paired statistical tests (e.g., paired t-test or paired Wilcoxon signed-rank test). Figure legends should clearly report the exact N (animals) and n (images/cells), the specific statistical tests used, and confidence intervals.

Reviewer #2 (Public Review):

Summary:

This study proposes that the microRNA cluster miR-277/34 controls the generation of sexual dimorphism in Drosophila melanogaster during metamorphosis by acting on specific hormonal and developmental gene pathways.

Strengths:

Using a combination of mRNA and small RNA sequencing together with genome-wide in silico and in vitro analyses the authors identified a microRNA cluster that may be involved in metamorphosis and the generation of sexual dimorphism in Drosophila melanogaster.

Weaknesses:

Biological validation of the identified sexually dimorphic genes and a detailed understanding of how the microRNA cluster miR-277/34 might be involved in the regulation of sesquiterpenoids are needed.

Major suggestions:

(1) If AstC-R1 and Kr-h1 are targets of the miR-277/34 cluster and cause their downregulation, it is not clear why there would also be a decrease in the levels of these genes in the miR-277/34 mutants. This would suggest that the mechanism is not straightforward and that further epistatic experiments should be carried out in order to clarify this issue.

(2) The changes in the expression levels of AstC-R1 in pupae of miR-277-KO and mir-34-KO flies must be accompanied by photos of the respective larvae and pupae, as well as an analysis of the larvae-pupa transition on the mutants by gender.

(3) Biological validation of the identified sexually dimorphic genes in vivo will be necessary for the support of this work.

Reviewer #2 (Public review):

Summary:

In this manuscript, Hassan et al. examine dormancy in fission yeast spores and killifish embryos in diapause. The authors first performed a genetic screen in yeast spores to identify genetic modulators of spore longevity and resilience to heat stress, finding that there is a trade-off between stress resilience and longevity. Next, the authors performed transcriptomic and proteomic profiling of yeast spores and killifish diapause embryos, comparing active and dormant states, early and late dormancy, and with and without heat stress in each model. Comparison between the two models identified a shared set of gene expression changes in dormancy. Lastly, the authors tested whether stress in dormancy is able to reshape the transcriptome or proteome following exit from dormancy, finding that heat stress altered gene expression post-dormancy in both yeast spores and killifish diapause embryos.

This study is interesting because it provides valuable transcriptomic and proteomic datasets across a large number of conditions in both yeast spores and killifish embryos. Furthermore, these datasets reveal several interesting insights about dormancy, including shared gene expression changes in dormancy across diverse organisms, a trade-off between stress resilience and longevity in yeast spores, and memory of heat stress following exit from dormancy.

This interesting manuscript would be strengthened by additional characterization of the functional consequences of these gene expression changes.

Major comments:

(1) The finding that a set of gene expression changes in dormancy is shared between yeast spores and killifish diapause embryos is very interesting and raises the question of whether the genetic determinants of stress resilience or longevity in dormant states are also shared. For instance, do any genes identified in the genetic screen as critical for resilience to heat stress in yeast spores also show a similar phenotype in killifish diapause embryos?

(2) The authors show that gene expression changes in dormancy (old vs. young spores and late vs. early diapause) are poorly correlated with aging in stationary-phase yeast cells (Figure 4E) and killifish brain (Figure 5E). The authors suggest that this poor correlation might reflect a lack of aging during dormancy. Interestingly, the authors also show that in both yeast and killifish models, both time and stress in dormancy result in gene expression changes following exit from dormancy. Are these post-dormancy differences correlated with aging in stationary-phase yeast cells and adult killifish? Additionally, could the authors compare these changes with age-related changes in different adult killifish organs?

(3) In Figures 1C, 4F, and 5F, the authors find significant correlations between two comparisons with a shared condition (e.g. 2-week unstressed spores in Figure 4F). Using the same samples in both comparisons can inflate the correlation between the two sets of fold-changes because the two comparisons are not made independently. The authors should ensure that the correlations found in these figures remain significant even when different samples are used for the shared condition in the two comparisons.

Significance:

This study is interesting because it provides a comparison between different states of dormancy and identify shared pathways. This paper should appeal to a broad audience, including aging, stress pathways, and quiescence/dormancy.

Reviewer #2 (Public review):

This study by Masser et al. analyzes global replication timing and gene expression in rif-1 null zebrafish. This work is an extension of their previous report of the normal replication timing pattern during wild-type zebrafish development. The major valuable finding here is that Rif1 is not essential for viability in zebrafish, and - counter to expectation from studies in cultured cells and other species - late replication does not strongly depend on Rif1. Instead, the data suggest that Rif1 subtly sharpens replication timing pattern during normal development rather than function generally to delay replication timing. In the absence of Rif1, the normal pattern establishment is somewhat delayed. The authors also document some changes in expression during development with more genes being repressed by Rif1 than activated at some early stages.

The study and analysis are generally rigorous, and the conclusions are supported by convincing data. Given the strong link between replication timing and cell type/development, studying timing in a whole developing organism is important. The experimental approach is technically challenging, particularly the bioinformatic analysis. The scientific advance here is largely confined to documenting the timing of Rif1-affected transcription, the unanticipated effect of the rif1 deletion on replication timing and on sex determination, though the latter is not explored. The difference in timing of the transcription phenotypes and replication phenotypes suggests they may be very distinct Rif1 roles. The overall study a useful set of findings and detailed data for future work.

Loss of Rif1 did not affect viability, but it did strongly influence sex determination, resulting in a lower population of females. This effect is the strongest organismal phenotype, but the study provides no mechanistic explanation for the loss of females from the data gathered here.

Comments on revised version:

We are generally satisfied with the revised version of this manuscript.

Reviewer #2 (Public review):

The manuscript by Desingu et al., explores the role of SIRT2 in regulating Japanese Encephalitis Virus (JEV) replication and disease progression in rodent models. Using both an in vitro and an in vivo approach, the authors demonstrate that JEV infection leads to decreased SIRT2 expression, which they hypothesize is exploited by JEV for viral replication. To test this hypothesis, the authors utilize SIRT2 inhibition (via AGK2 or genetic knockout) and demonstrate that it leads to increased viral load and worsens clinical outcomes in JEV-infected mice. Conversely, SIRT2 overexpression via an AAV delivery system reduces viral replication and improves survival among infected mice. The study proposes a mechanism in which SIRT2 suppresses JEV-induced autophagy and inflammation by deacetylating NF-κB, thereby reducing Beclin-1 expression (an NF-κB-dependent gene) and autophagy, which the authors consider a pathway that JEV exploits for replication. Transcriptomic analysis further supports that SIRT2 deficiency leads to NF-κB-driven cytokine hyperactivation. Additionally, pharmacological inhibition of NF-κB using Bay 11 (an IKK inhibitor) results in reduced viral load and improved clinical pathology in WT and SIRT2 KO mice. Overall, the findings from Desingu et al. are generally supported by the data and suggest that targeting SIRT2 may serve as a promising therapeutic approach for JEV infection and potentially other RNA viruses that SIRT2 helps control.

Comments on revised version.

We believe the authors of Desingu et al. have adequately addressed the major concerns of each reviewer. Thus, we believe the current version of the manuscript is significantly improved, and the data more strongly support the authors' conclusions.

We would like to point out that the statistics in Figure 8 are difficult to interpret and should be improved to make them useful to the reader and strengthen the paper. Also, line 70 has an extra period that should be removed.

Reviewer #2 (Public review):

Summary:

The article from Zheng et al. proposes that the Med16 subunit of Mediator can dissociate from the complex, associate with transcription factor UBP1, and contribute to activation or repression of specific sets of genes in human cells. The question is potentially interesting, and the possibility of a distinct role for Med16 in UBP1-dependent transcription could be of interest to the field. However, I have substantial concerns about the experimental design, reproducibility, and interpretation of the data. In particular, several of the central conclusions rely on single experiments, indirect assays, or perturbations that are expected to cause broader changes in Mediator structure and transcription. In my view, the current data therefore do not provide sufficient evidence to support several of the principal mechanistic conclusions of the manuscript.

Strengths:

The authors provide preliminary evidence that Med16 may exist in an altered or partially dissociated Mediator complex and may be associated with specific transcriptional effects. The use of biochemical assays, reporter assays, RNA-seq, and Med16 knockout/rescue experiments provides several potentially useful approaches to investigating this question. In particular, the Med16 knockout/rescue RNA-seq experiment described in the rebuttal is a potentially informative experiment. Making the underlying IP-MS abundance and peptide-count data available is also an improvement over the previous version.

However, important limitations of these experiments need to be addressed before the proposed mechanism can be established.

Comments on the revised version:

In the previous review, I raised concerns that the evidence for a specific UBP1-Med16 interaction and for a mechanistic role of this interaction was limited. The revised manuscript does not resolve these concerns. In particular, whether UBP1 interacts specifically with Med16, whether this interaction is required for activation of UBP1 target genes, and whether UBP1 represses HIV-1 transcription through Med16 remain unresolved.

A stronger case for a specific UBP1-Med16 interaction would require more direct biochemical, structural, or biophysical evidence. The current experiments do not distinguish sufficiently between a specific Med16-dependent mechanism and broader consequences of perturbing Mediator structure or transcription.

The revision also does not include the additional biological replicates needed to assess the reproducibility of several foundational experiments. Consequently, a number of the central conclusions remain based on single experiments, which substantially limits the strength of the conclusions that can be drawn.

Major concerns:

Figure 1:

All of the data in Figure 1 are from single experiments without biological replicates. Because Figure 1 provides an important foundation for the proposed mechanism, this limits confidence in the reproducibility of the observations. Western blot experiments can show substantial experiment-to-experiment variability, and such variability appears relevant to several of the observations presented here.

A key experiment is the western blot analysis of the gel-filtration experiment in Figure 1D. Rather than migrating with Mediator, the majority of the detected Med16 appears to elute at a lower molecular weight. The extract was maintained at super-physiological salt concentrations from preparation through column loading and elution, which could potentially contribute to this result. Other possibilities include experiment-to-experiment variation or detection of a cross-reacting protein by the Med16 antibody. The latter possibility warrants consideration because the bands detected in fractions 28-31 appear to migrate at a different molecular weight from the Med16 bands in fractions 16-19. Additional replicated experiments and appropriate controls would be needed to establish that the lower-molecular-weight signal represents Med16 in a distinct complex.

Figure 2:

The data in Figure 2 are also based on single experiments without biological replicates. The biochemical experiments use protein fragments that do not occur as such in the native protein. Such experiments can be useful for mapping possible protein-protein interaction surfaces, but they can also generate false-positive or false-negative interactions depending on the experimental configuration.

Some of the results raise additional questions about the proposed interaction model. For example, the observations involving TFCP2 in panel B and Med1 and Med23 in panel F appear difficult to reconcile with a simple model in which UBP1 interacts with Mediator through Med16. Panel 2F is particularly relevant because Med1 and Med23 are not detected despite the proposed model involving UBP1 interaction with Mediator. These observations should be addressed when interpreting the specificity and mechanism of the proposed UBP1-Med16 interaction.

Figure 3:

The reporter assays provide potentially useful preliminary evidence for factors that influence transcription. However, the relatively long experimental timeframes (36-48 hours after transfection), use of artificial promoters, and overexpression of proteins introduce important limitations.

The data are consistent with the authors' model, but they are also consistent with a simpler explanation in which Med16 depletion broadly affects Mediator structure and function. Previous work from the Gross, Robert, and Roeder laboratories has shown that loss of Med16 can result in dissociation of Med23, Med24, and Med25 from Mediator. Importantly, the authors' own data in the rebuttal document appear to show similar effects following Med16 loss. These observations are important because Med23, Med24, and Med25 provide major interfaces for DNA-binding transcription factors, and Med16, Med23, Med24, and Med25 together represent a substantial fraction of the molecular mass of Mediator. The functional properties and stability of the resulting Mediator complex therefore need to be considered when interpreting Med16 depletion experiments.

The authors' data also suggest broader transcriptional effects: Med16 knockout causes loss of basal transcription in panel 3E. Conversely, overexpression of Med16 or Med16 fragments can cause broad defects in transcriptional activation, including through squelching. The relatively modest effects observed despite substantial increases in protein abundance are therefore also consistent with non-specific consequences of overexpression. These alternative explanations need to be considered when interpreting the reporter assays.

Figure 4:

RNA-seq is an appropriate approach for investigating possible effects of Med16 on gene expression. However, the experimental design has important limitations because Med16 depletion is expected to affect the composition of Mediator, including loss of Med23, Med24, and Med25. The knockdown also occurs over an extended timeframe (>24 hours), making it difficult to distinguish direct effects of Med16 from secondary effects arising from altered Mediator structure or broader changes in transcription.

The RNA-seq results could therefore reflect Med16-specific functions, but they are also consistent with a model in which loss of Med16 compromises Mediator structure and function and produces downstream gene-expression changes.

The presentation of the RNA-seq data in Figure 4E is also difficult to interpret. The volcano plot shows UBP1 overexpression versus the shCTRL vector, whereas the comparison central to the authors' interpretation concerns the effects of shMed16. Showing the volcano plot for the shMed16 comparison would allow readers to assess the statistical significance and magnitude of the relevant gene-expression changes directly. Instead, the authors superimpose "x" symbols for genes that "failed to be activated by UBP1 in the shMed16 cell line." Because the underlying Med16 comparison is not displayed, the p-values and fold changes for the comparison of interest cannot be evaluated. Moreover, positioning the "x" symbols according to the coordinates of the UBP1-overexpression volcano plot does not appear to represent their corresponding positions in a volcano plot of the Med16 comparison. This makes the figure difficult to interpret and could give a misleading visual impression of the underlying data.

Finally, the RNA-seq data are not spike-in normalized. This is potentially important because Med16 depletion is expected to cause broad effects on transcription and could result in an overall reduction in transcription across the genome. Appropriate normalization is therefore important for distinguishing gene-specific effects from global changes. I also did not identify information in the manuscript indicating where the RNA-seq data have been deposited in a public repository.

Figure 5:

The reporter assays in Figure 5 are subject to similar concerns. The authors' model may explain the observations, but the data are also consistent with broader disruption of Mediator structure and function following Med16 depletion, or with more general transcriptional effects caused by overexpression of Med16 or Med16 fragments.

A separate concern is the proposed mechanism for HIV-1 repression. The authors propose that UBP1 interacts with Med16/Mediator and that overexpression of Med16 enhances Mediator recruitment, although Mediator recruitment is not directly measured. The proposed increase in Mediator recruitment would, in isolation, be expected to promote transcription rather than repression. The authors instead invoke a UBP1-dependent mechanism to explain the repression. However, a simpler explanation is that overexpression of Med16 or selected Med16 fragments perturbs normal transcription through mechanisms including squelching. The authors themselves invoke a Med16 squelching model to explain reduced transcription at the UBP1 reporter in Figure 3G-I. The possibility that the same mechanism contributes to the results in Figure 5 should therefore be addressed.

Finally, a central component of the proposed model is recruitment of Mediator by UBP1 through Med16, but neither UBP1 occupancy nor Mediator occupancy at the relevant promoters is directly measured. These experiments would provide important evidence for the proposed mechanism. There is also no legend for Figure 5G,H, and the legend for Figure 5C,D appears to be incorrect.

Figure 6:

The title of Figure 6 states that "The position of the UBP1 binding site relative to the TSS dictates whether MED16-UBP1 activates or represses transcription." The data presented do not establish this conclusion.

Only one alternative location of the UBP1 binding site is tested, and the analysis in panel D compares only the 50 most up- or down-regulated genes in UBP1-overexpression experiments. The correlations shown are relatively weak, and a rigorous statistical analysis is not presented.

The authors cite Duttke et al. (Nature 2024) in support of the concept that transcription-factor position can influence regulatory activity. That study used genome-wide mapping of native TSSs, MPRA analysis of approximately 2,000 promoter variants, and targeted follow-up experiments. By contrast, Figure 6 relies on an artificial reporter and a single change in UBP1 binding-site position. The current data therefore support the possibility that binding-site position influences the observed reporter activity, but they do not establish the broader conclusion that the position of the UBP1 binding site relative to the TSS dictates whether Med16-UBP1 activates or represses transcription.

Figure 7:

The title of Figure 7 states that Med16 "interferes with HIV-1 PIC assembly," but the data presented do not establish this mechanism.

First, the proposed mechanism is described as acting through interaction with UBP1, but UBP1 is not measured in this experiment.

Second, Med16 knockout or overexpression has relatively modest effects on the estimated levels of bound PIC components, and these changes do not consistently correspond to the transcriptional changes shown in panel C. For example, in the WT versus Med16 KO comparison, there is no apparent difference in the levels of Pol II, Med1, IIB, or IIH based on the western blot data. The reduction in Med16 and Med23 is expected following Med16 knockout and therefore does not by itself establish altered PIC assembly. In the WT versus Med16 overexpression comparison, panel C suggests a meaningful reduction in transcription, whereas the changes in PIC-associated proteins do not follow the direction predicted by the proposed model: Pol II and IIB increase with Med16 overexpression, while Med1, Med23, Med16, and IIH appear similar.

Third, Cdk8 and Med12 are not PIC components. They are subunits of the CDK8 module, which can inhibit PIC formation. Even under a simplified interpretation in which reduced Cdk8 and Med12 levels were relevant to PIC formation, the direction of these changes is opposite to that expected from the "relative mRNA level" plots in panel C. Reduced levels of these proteins in the Med16 overexpression condition would, under this interpretation, be expected to promote rather than reduce transcription.

Finally, this is a single-replicate experiment, and quantitative comparisons are made across immobilized-template pulldowns using different nuclear extracts (WT, Med16 KO, and Med16 OE). Differences in protein concentration between extracts, differences in pulldown efficiency, and experiment-to-experiment variation could all contribute to the observed differences. Replicated experiments with appropriate normalization would therefore be needed to support the proposed PIC mechanism.

Figure 8:

Panel F presents a model that, in my view, goes beyond what is supported by the experimental data. In particular, there is currently insufficient evidence to establish the proposed Med16-UBP1-TFCP2 complex as a distinct functional complex.

The model also depicts the Mediator CDK8 module bound at promoters together with Pol II. The manuscript does not provide evidence supporting this arrangement, and there is substantial prior evidence that the CDK8 module can inhibit or prevent PIC formation. This aspect of the model therefore requires either experimental support or clarification.

Finally, the model depicts a separate Med16-UBP1-TFCP2 complex. This appears inconsistent with the authors' statements in the rebuttal that they did not intend to propose such an unsupported isolated complex. If this is not the intended model, Figure 8 should be revised to avoid conveying that interpretation.

Figure S1: IP-MS analysis:

The IP-MS data raise substantial concerns regarding the specificity of the proposed UBP1-Med16 interaction.

The authors state that they "have now reanalyzed the IP-MS data using a more precise quantification approach." However, the filtering procedure described in the Methods is difficult to evaluate and requires substantially more justification. The procedure includes multiple sequential criteria: detection in at least two of three replicates of the target bait; a minimum unique peptide count; detection in at least one additional Mediator bait; exclusion of Mediator/Pol II subunits, ribosomal proteins, histones, keratins, and common contaminants; and annotation with transcriptional or DNA-binding functions using GO terms. The rationale for this particular combination of filters and its impact on the candidate list should be explained and justified.

The resulting "bait-preferential score" is also difficult to assess. The reported mean_target and mean_other values are frequently negative, with some positive values, and the method appears to derive a positive score from the difference between these values after taking an absolute value. The biological and statistical interpretation of this score is therefore unclear. The reported scores are generally clustered around relatively narrow values, and the analysis does not appear to provide the statistical framework normally used to assess enrichment and reproducibility in IP-MS experiments.

Importantly, the candidate proteins identified by this approach do not appear to overlap with proteins identified in other published Med16 IP-MS analyses in human cell lines, including HCT116, MCF7, HEK293T, and HeLa. I was unable to identify a protein among the 37 Med16 candidates that was consistently observed in the cited resources, including BioPlex (Huttlin et al., Cell 2021) and Malovannaya et al. (Cell 2011). This lack of concordance raises questions about the specificity and physiological relevance of the proposed Med16-associated proteins and, in particular, about the evidence for a specific UBP1-Med16 interaction.

The peptide counts for UBP1 also illustrate the limitation of the current analysis. The total UBP1 peptide counts across the Cdk8, Med1, and Med16 IP-MS experiments are 12, 10, and 14, respectively, yielding bait-preferential scores of 1.07, 0.76, and 1.85. Given these relatively small differences and the absence of statistical significance measures, it is difficult to determine whether these observations provide evidence for a specific biological interaction.

The authors have now made the raw abundance values and peptide counts available as supplementary tables, which is useful. However, standard statistical measures for evaluating MS data, including FDR, p-values, and adjusted p-values, are not provided, nor is sufficient information given about how the reported scores were calculated. These additions would be important for readers to assess the robustness of the conclusions.

Concerns about the rebuttal document:

Several statements in the rebuttal appear inconsistent with the data shown in the revised manuscript or with the interpretation of the authors' own experiments. I highlight specific examples below.

(1) The proposed Med16-UBP1 complex:

I previously raised concerns that the manuscript overstated the evidence for a separate Med16 complex associated with UBP1 and lacked rigorous biochemical characterization of such a complex. The authors responded that the wording of the original manuscript may have created this interpretation and stated that this was not the mechanistic model they intended to convey. However, Figure 8 of the revised manuscript still depicts a separate Med16-UBP1-TFCP2 complex. The figure therefore appears to retain the interpretation that the authors stated they did not intend to convey. This should be clarified.

(2) Replication and experimental rigor:

I previously raised concerns about the absence of replication and the limitations of the experimental approaches used to infer roles for Med16 and UBP1. These concerns remain because no additional replicate experiments were performed for several of the key experiments. As a result, important conclusions continue to depend on single western blots, including foundational experiments in Figures 1, 2, and 7.<br /> Examples include the variable quantification in Figure 1A; the absence of detectable Med23 and Med1 in the UBP1 IP in Figure 2F despite the proposed interaction between UBP1 and Mediator; and the variable association of TFCP2 with Flag-MED16 fragments in Figure 2B, including lanes 7-9. Replication would be important for determining whether these observations are reproducible.

(3) IP-MS analysis:

The revised IP-MS analysis provides additional information compared with the previous version, but important concerns remain regarding the processing and interpretation of the data. The "bait-preferential score" approach is not sufficiently justified, and standard measures such as p-values, adjusted p-values, or FDR are not provided.

The analysis also identifies no proteins among the 37 Med16 candidates that appear to be consistently observed in other published Med16 IP-MS analyses in human cell lines. This is important when assessing the specificity of the proposed interaction.

The peptide counts for UBP1 are similarly low and do not show a clear Med16-specific enrichment: 12 peptides in the Cdk8 IP, 10 in the Med1 IP, and 14 in the Med16 IP, corresponding to scores of 1.07, 0.76, and 1.85, respectively. These observations do not by themselves provide strong evidence for a specific UBP1-Med16 interaction.<br /> In addition, the Med16 IP experiments identify approximately 800 proteins per replicate. This broad set of associated proteins highlights the importance of appropriate controls and statistical analysis when interpreting a co-IP as evidence for a specific protein-protein interaction.

(4) Interpretation of the 1,6-HD experiments:

I previously raised concerns that the IP-western data in Figure 1 did not establish a Med16-specific effect. The rebuttal states that "MED16 exhibits a uniquely pronounced dissociation by 1,6-HD that is consistent across three independent IPs using anti-CDK8, anti-MED1, and anti-MED12 antibodies." However, the quantitative data shown do not clearly support this interpretation.

In the CDK8 IP, CDK8 and MED23 appear to be lost to a similar extent as Med16. In the Med1 IP, Med12 shows a similar loss to Med16, with Med23 also showing a comparable trend. In the Med12 IP, CDK8 and Med23 show changes comparable to Med16. There are also unexplained increases in some Mediator subunits with increasing 1,6-HD concentrations. For example, Med12 and Med1 are variable across the 1,6-HD conditions, and Med1 and Med24 increase at higher concentrations. Similar trends are present in the CDK8 IP.

The Figure 1B data raise related concerns. Several Mediator subunits show substantial increases following the 2,5-HD condition compared with 1,6-HD, in some cases greater than the change observed for Med16. Thus, the available data do not clearly establish that Med16 exhibits a uniquely pronounced dissociation from Mediator.

(5) Evidence for the proposed mechanism:

I previously suggested that more rigorous approaches would be needed to distinguish a specific Med16-dependent mechanism from broader effects of Med16 perturbation. The rebuttal instead emphasizes that the authors' conclusion that the UBP1-TFCP2 heterodimer acts through Med16 is supported by multiple independent lines of evidence.

However, the different experiments do not independently establish the same mechanistic conclusion. Many rely on long experimental timeframes, overexpression, reporter assays, limited controls, or lack of biological replication. These approaches can provide useful preliminary evidence for effects on transcription but do not establish that a specific UBP1-Med16 interaction is required for UBP1 target-gene expression.

Additional approaches that directly test the proposed interaction and its requirement for transcriptional regulation would therefore be needed to establish this mechanism.

(6) Med16 rescue experiment:

The authors performed an RNA-seq comparison of WT, Med16 KO, and Med16 KO cells rescued by re-expression of WT Med16. This is a potentially informative experiment, and I appreciate that it was performed.

However, the heatmap shown in the rebuttal does not appear to demonstrate a clear rescue of UBP1 target-gene expression. The figure contains approximately 60 genes but provides no legend beyond "Gene profiling of MED16 KO and rescue effect." The authors describe the result as showing that re-expression of Med16 can largely restore expression of UBP1 target genes, but this conclusion is not readily apparent from the figure as presented. More information about the analysis, normalization, and comparisons would be needed to evaluate the claimed rescue. It would also be useful to include the relevant analysis in the revised manuscript rather than only in the rebuttal.

(7) Consequences of Med16 depletion:

A continuing concern is that the manuscript does not adequately account for the broader consequences of Med16 depletion or knockout. Loss of Med16 is known to cause dissociation of other Mediator subunits, including Med23, Med24, and Med25. The authors acknowledge this in the rebuttal and state that their new Co-IP and IP-MS experiments confirm partial dissociation of these subunits following Med16 knockout. They also acknowledge that this structural perturbation must be considered when interpreting phenotypes from Med16 depletion and that long-term knockdown or knockout approaches are susceptible to secondary effects and cellular adaptation.<br /> These are important caveats, but they are not adequately incorporated into the interpretation of the revised manuscript, and the new data demonstrating loss of Med23, Med24, and Med25 are not shown in the article.

This issue is particularly important for interpreting the transcriptional experiments. Loss of Med16 is expected to disrupt Mediator more broadly, including loss of Med23, Med24, and Med25, which provide important transcription-factor interaction surfaces. These changes could produce widespread transcriptional effects independently of a specific UBP1-Med16 mechanism. Given that the authors' own experiments now provide evidence for this structural perturbation, the possibility of indirect effects and altered Mediator function should be explicitly incorporated into the experimental interpretation.

Overall assessment:

The manuscript addresses an interesting question and presents several potentially useful observations concerning Med16, Mediator, and UBP1. However, the central mechanistic conclusions are currently not supported by sufficiently rigorous or independent evidence. The principal concerns are the extensive reliance on single-replicate experiments; the use of overexpression, artificial reporters, and long depletion timeframes; the absence of direct measurements of UBP1 and Mediator occupancy; the broad structural consequences of Med16 depletion; and the limited statistical support for the IP-MS analysis.

Taken together, the current data do not distinguish adequately between the proposed specific UBP1-Med16 mechanism and alternative explanations involving broader disruption of Mediator structure or transcription. Addressing this distinction is important because it affects the interpretation of much of the manuscript, including the RNA-seq and reporter-assay results and the proposed HIV-1 mechanism.

The authors have generated several useful datasets, including the Med16 knockout/rescue experiment and additional IP-MS information, but the manuscript would need to incorporate the associated caveats and provide stronger experimental evidence before the proposed mechanistic model can be established.

Reviewer #2 (Public review):

The study by Angla et al. proposes a model in which NT-3 produced by motor neurons regulates interneuron numbers and distribution in a non-cell autonomous manner. The authors demonstrate that ablation of motor neurons (MNs) and global and conditional deletion of OC transcription factors lead to changes in interneuron distribution. They identify that NT3 is upregulated after MN-specific OC deletion in RNA-seq experiments and show that olig2-cre mediated NT3 deletion leads to increased ventral interneuron numbers, altered distribution and defects in locomotor behavior. The authors conclude that MN-derived NT3, under OC control, regulates interneuron development. While this is an intriguing hypothesis, the study does not consolidate the results from the different experimental models in a cohesive model to provide any mechanistic insights into how NT3 may accomplish this function.

(1) The authors utilize different experimental models (OC transcription factor global and conditional knockouts, DTA ablation and NT3 deletion) to show that MNs may influence interneuron numbers and positioning, but there doesn't seem to be a direct link into how each manipulation relates to the others in an informative way. For example, the relationship between OC and NT3 deletion data is not entirely clear, even though NT3 is downstream of OC transcription factors. Both deletions lead to changes in interneuron distribution but there doesn't seem to be any correlation between the two that relates to relative changes in NT3 levels. Although it is understandable that transcription factor manipulations may have additional effects, it is hard to derive any mechanistic insights into how exactly MNs influence interneuron differentiation or migration from the data presented here. Similarly, while the rationale for following up on NT3 seems to be the chick electroporation experiments, only a very minor decrease in Chx10 interneurons is shown in those experiments. Shouldn't NT3 overexpression lead to substantial decreases in IN numbers according to the authors' model?

(2) The study primarily quantifies interneuron numbers and distribution at different levels of the spinal cord and under different genetic manipulations. In different figures, the values and distributions shown for controls vary quite a lot. For example, in figure 2B vs figure 4B, the number of FoxP2+ V1 neurons at brachial levels is ~350 vs 125. Similarly, the control distributions in 2I and 4I are quite different. While this can be partially explained by technical issues, it remains challenging to determine whether the conclusions regarding the impact of each genetic manipulation on interneuron numbers and distribution are valid.

(3) It is not clear that the behavioral phenotypes seen in the olig2-cre mediated deletion of NT3 can be attributed to specific changes in interneuron development, connectivity or function. While the behavioral phenotype is in theory consistent with defects in motor circuits, there is a big gap between the embryonic changes shown here and behavior, with no in between circuit level changes in locomotor circuits shown. Thus, the behavioral experiments are not very informative as to how exactly components of motor circuits might be altered with the NT3 deletion.

Reviewer #2 (Public review):

Summary:

This is a significant study with implications for signal transduction, breast cancer, and metastasis research. The study uses cell lines from the innovative HER2BOW model mouse to compare and contrast properties of mouse mammary tumors induced by wt HER2, dex16 HER2, and p95 HER2. The linkage between p95 Y1139 and TGFB1I1, as well as MRTFA, is very interesting. The study involves mechanistic approaches to distinguish signaling from dex16 HER2 and p95 HER2 that will be well received by the field.

Strengths:

The study describes the development of novel cell lines for mouse mammary tumors induced by different forms of HER2. In vitro analysis includes a wide variety of assays, including high-resolution fluorescent imaging and tracing of cell movement/ECM degradation, and single-cell RNA-seq analysis. Several approaches are used to confirm p95 HER2 pY1139-induced signaling through nuclear MRTFA and TGFB1I1. shRNA and CRISPR/Cas9 were used to confirm signaling through this pathway.

Weaknesses:

The authors have only described one cell line for each HER2 form, a weakness which they acknowledge. The fact that these lines mimic characteristics that were described in their previous work on tumors induced by wt HER2 vs. dex16 HER2 vs. p95 HER2 is, however, supportive of their conclusions. In addition, the specific connection between p95 and nuclear MRTFA is confirmed in HEK cells. The manuscript could be improved with some discussion on how p95 p-Y1139 is likely to signal (what binds to this site?)

Reviewer #2 (Public review):

Summary:

The manuscript by Jepson and colleagues presents the structural and biochemical study of the outer membrane LolB in complex with LolA in its apo and lipoprotein-bound states. The Lol system (LolABCDE) is essential in the translocation of lipoproteins to the outer membrane. Several studies have looked at the inner membrane complex. This study captured the last step of the process, the translocation of lipoproteins to the outer membrane via the LolB and LolA proteins. The crystal structures of LolB in complex with LolA revealed key interactions via the hook and charged belt motifs. Mutagenesis was not able to fully dissociate the complex or prevent the transfer of lipoprotein. The complex of LolB with LolA and lipoprotein revealed the 'final' step of engaging the complex for lipoprotein insertion into the outer membrane.

Overall, this is an excellent and exciting study for the Lol field as it captures the events happening at the outer membrane.

Strengths:

The authors have determined several high-resolution structures and validated them with excellent mutagenesis experiments. The key structure is that of LolB with LolA and the lipoprotein (even partially resolved).

Weaknesses:

The only 'weakness' is that the authors have used mutants of either LolB or LolA to capture the stable states, and the discussion section would benefit from commenting if these states could be dead-end states, as validation of the mutants has been done with a WT background. In addition, can they discuss whether a2 is likely to undergo further conformational changes to open a lateral gate to the OM for the acyl chains of the lipoprotein to insert?

Reviewer #2 (Public review):

Summary:

The authors have used a previously published SBEM dataset of the adult zebrafish olfactory bulb and carefully reconstructed the fine structure of a large number (459) of neurons. Through manual classification of cells into 13 different anatomical classes, they provide a carefully annotated library of cells. Classifications have been validated by comparing the labeling of 5 human annotators, which revealed a large degree of reproducibility. Synaptic identification enables assignment of connectivity, revealing a major contribution of reciprocal connections (two-way synapses) that may support key computational features during olfactory processing. In general, the work provides a clean descriptive account of the organization of the zebrafish olfactory bulb, which should help to better understand principles of olfactory processing in vertebrates.

Strengths:

The authors provide a library of neurons in the olfactory bulb of adult zebrafish that will be highly useful to the broader circuit neuroscience community. Figures of classified cell types have been convincingly arranged to appreciate the high quality of the EM volume and reconstructions. Cells are easily accessible through a web interface to facilitate the assessment of classifications. An important result is that anatomical structures in zebrafish clearly follow the glomerular arrangement known in mammals, which argues against a microglomerulus model previously proposed for the zebrafish olfactory bulb.

Weaknesses:

The paper uses Wanner et al. 2016 as a main base dataset to generate a descriptive library of cells, in a limited volume (18%) of the zebrafish olfactory bulb. Annotations of cell types and synapses are largely human-based. Using a multi-human validation strategy helps to assess labeling, but it would benefit from more rigorous statistics, in particular for the synapse annotations. In general, the paper would be easier to read with a glossary of cell types. Some features are quite intriguing, like the cilia structures in some of the somata or the ruffs, but discussion of the potential computational roles of these fine structures is missing.

Reviewer #2 (Public review):

Summary:

The authors claim to present a generally applicable tissue processing pipeline for optical tissue clearing and labelling of whole human organs. To achieve this, they combine multiple previously published tissue pretreatment steps to enhance tissue permeabilization, and hence label penetration, as well as optical transparency. Most notably, the authors include LED photobleaching, or photoclearing as they call it here, to reduce both autofluorescent background and increase tissue transparency. Extending their previously published method towards a wide variety of large human samples encompassing multiple organs and tissues, they combine their clearing protocol with several small molecule dyes for general overview stains as well as immunofluorescence labelling. For some of the processed samples, they introduce a novel multimodal imaging pipeline, which includes photogrammetry, MRI, and 3D histology/microscopy. Both the multimodal pipeline and the described tissue clearing and labelling protocols seem highly useful for both pathology as well as fundamental research, especially since the modularity of tissue-clearing protocols allows for the integration of individual steps, such as photoclearing, into already established methods.

Strengths:

The transparency of the large, archival human samples is very impressive and, to my knowledge, constitutes the best human tissue clearing to date. The positive effects of photobleaching/clearing on autofluorescence reduction as well as transparency are obvious and highly interesting for the field, as this method can easily be implemented into any tissue clearing approach. It is highly appreciated that notes are provided in both the methods and results, on which steps of the pipeline had to be adjusted for different tissue types and sizes. However, this could be presented in a more structured way, by showing a graphical representation or at least a table of all used tissues and all the steps of the pipeline, with indications for which steps were skipped/adjusted per tissue type as well as the incubation times for every step. Table 2 already does that for light exposure and decalcification times, but it would be beneficial to do this for all steps to get a better overview. This would also aid replication and implementation of their method, as other groups can just look up the closest matching tissue type in the table and go from there rather than piecing this information together with the in-text notes.

The combination of different small molecule dyes, while in itself not new, has the potential to provide 3D alternatives for conventional histological overview stains. Other groups have already shown 3D H&E and Nissl stains, and it would be interesting what other routinely used stains could be mimicked in that way. Here, the combination of Eosin Y and Fast Green seems especially interesting.

The quality of the immunofluorescence labelling is extremely good, especially given the age of some of the samples (e.g. very high-quality SMA staining in 15-year-old material). It would be interesting to know if the authors encountered antibodies that were not compatible with their archival tissue and, if so, which ones.

Weaknesses:

It is not exactly clear to me how some of the image volumes were acquired. For example, the panels 1f-h: The regions these volumes originate from are indicated in the cleared heart (1e), but were these regions blocked out or was the entire heart somehow immersed in the imaging chamber? The latter would be, to the best of my knowledge, a novel scale of light sheet imaging and should be mentioned. If the regions were somehow blocked out, this should be described. In general, a lot of the imaging volumes seemed to have been acquired mainly towards the periphery of the sample, especially the higher magnification inserts. This immediately raises the question of whether this is because imaging was not possible in the centre and why (limitation of the imaging chamber size, light penetration, label penetration, or a combination of those?). While it is understandable that it is currently not possible to image, e.g. entire kidneys in light-sheet set-ups, the limits of feasible sample sizes should be mentioned somewhere to give the reader a better appreciation of the usefulness of the method. The authors demonstrate that it is possible to clear whole organs, but is it also possible to label whole organs? Is that possible with antibodies as well, or only with small-molecule dyes? And how deep can samples be imaged in the best-case scenario? Right now, the paper uses a common scheme in the tissue clearing literature, of showing a very large cleared sample and then imaging a smaller sub-volume while implying that, in principle, it is possible to do that for the entire organ, if the objective working distance and imaging chamber would allow. However, to scale their method up, the authors would first need to show that their labels would penetrate throughout the entire sample, and this is currently not done in an unambiguous manner.

The biggest limitation in the manuscript for me is the authors' claim: "We demonstrate the voxel-by-voxel alignment between MRI and microscopy datasets, merging cellular-scale detail and protein-specific labelling with macroscopic medical imaging." While technically true, the authors only show actual overlays of microscopy and MRI data for the inner ear sample in Figure 5. No overlays between MRI and the microscopy volumes are shown for the heart and finger data, which is a missed opportunity. Similarly, no overlays between all three acquired modalities (photogrammetry, MRI, 3D microscopy) are shown for any of the samples. This is a pity, as the authors put a lot of emphasis on this novel multimodal pipeline, even though side-by-side demonstrations of MRI and 3D microscopy in a similar manner have been shown already by other groups and a truly multimodal dataset with the different modalities, e.g. as different channels in a volume, would be very exciting.

Reviewer #2 (Public review):

Summary:

The manuscript by Conessa and colleagues tests whether movement time and decision time are co-regulated in humans using a novel harvesting paradigm and computational modelling of the behavioral data. The data contradict the common utility model and are better supported by an alternative model where the costs of the harvesting and movement phases are optimized separately - rather than jointly.

Strengths:

The major strength of the study is the development of the novel paradigm where the forces applied during the distinct phases of the task (reaching and harvesting) can be perfectly controlled and measured.

This elegant manipulation allows precise estimation of subjective effort costs, and later can be used to better understand how these costs shape behavior.

The novel separate-cost model is also an interesting development.

Weaknesses:

In my opinion, the manuscript suffers from too many weaknesses to solidly support the general conclusions:

(1) The manuscript oversells the significance of the results;

(2) The novel model appears to be built ad hoc based on the results, and its ingredients are poorly motivated

(3) There are important methodological shortcomings. I develop all of these points below. The paper also lacks clarity, particularly when presenting the key concepts of the novel model.

Importantly, I also recommend that the authors follow open science policy and publish their data and code on a public repository.

(1) Significance

As mentioned above, the question of the co-regulation of the vigor of different behaviors is a very interesting topic, but I believe the significance of the results is overstated, for different reasons:

a) This is a harvesting task, which means that decisions are specifically about the duration of the reward period. In other words, more time directly provides more (immediate) reward, whereas in other types of tasks more time allows gathering and processing more information, and so reaching a more informed decision, which is a more indirect way of securing better outcomes - see e.g. Drugowitsch, Moreno-Bote et al. JNeurosci 2012 for optimizing decision time in this most common setting. This major difference is not discussed in the manuscript, and it is not clear whether the results found here would hold for the most common type of decisions. This is also different from the time to initiate a response, as explored by Morvan et al. in their cited preprint.

b) The title of the study mentions "flexible" co-regulation, but it's hard to see direct evidence for flexibility in the results. This flexibility is extrapolated in the Discussion, but in order to make such a statement, it is necessary to show that the co-regulation differs between different conditions, as predicted from the model.

c) The lack of impact of the harvesting effort on harvest time is very surprising, and the null value of the sensitivity to effort (d=0) in the model is intellectually uncomfortable. As some value of effort, it SHOULD matter. I am not very satisfied with the explanation provided in the Discussion ("possible hypothesis would be that the perceived effort of harvest was outweighed by the reward value"): this trade-off should be precisely dealt with by the model.

(2) Separate-Cost Model

The separate-cost model is poorly motivated and not clearly presented, in particular the indirect coupling between the two phases.

a) Some key components are motivated by the constraint to obtain a well-behaved optimization problem ('we need to find a cost that increases with time' ) rather than based on cognitive principles. Some other key components seem to have been placed there just to fit the data better, but their plausibility is not even discussed. Notably:<br /> i- The fact that the offset o_h includes the reaching delay is key to the coupling between t_h and t_r, but is not properly motivated. Why should this offset include all preceding intervals within the trial (leading to double counting of the cost of the reaching phase, in both G_r and G_d)? In this cyclic paradigm where participants aim at maximizing cumulative reward, it is not even clear that they parse behavior into different trials starting as they need to reach for a new target.<br /> ii- Why is there a free parameter d for the cost of harvest effort? If time and motor costs are converted into the same currency, and that conversion holds for all types of phases, then I believe we should have d=1. This free parameter seems to have been introduced a posteriori to accommodate the lack of effect of harvest effort on harvest time, but is not motivated.

b) The two models differ in two fundamental aspects: whether optimization is run separately on the different phases of the trial, or jointly; and how the different costs and rewards are assembled in the to-be-optimized metric. Which aspect is more important to capture behavior is not assessed.

c) The presentation of the "Separate-cost model" is very obscure. For example, the notion of a `cost of reward` is rather odd, and consequently the formula for R(t_h) appears arbitrary. I understand that this is the difference between the maximum reward that can be collected within a trial and the actual reward that is collected, but it is not even presented as such. The effort cost of harvesting seems to be discarded in the model presentation but does appear in equation 14.

(3) Methodological Weaknesses

I believe that there are numerous methodological weaknesses that really limit the inferences that can be drawn from the experimental and modelling results.

a) Sample sizes are small, especially for control experiments, and it is not clear how they have been selected. This is especially problematic because the only manipulation that shows no significant effect on behavior (harvest duration) is only tested with the smallest sample size (n=10), severely limiting the significance of this negative result.

b) The rationale for the different manipulations in different experiments is not clear. Notably, why were modulations of the harvest effort not included in the main experiment? Because not a single participant sees a modulation of both reach and harvest efforts, we cannot evaluate whether the model can capture both effects at the same time (i.e., with the same parameter values). The number of data points used for fitting directly depends on the number of conditions performed by the participant, and is very low compared to the number of parameters to be fitted. For example, the model used very different shapes for the costs of time (Figure 8) for different sets of conditions, but this cost of time should be fixed, so it's not clear whether a single cost of time function can accommodate all manipulations.

c) Best practice in behavioral modelling is to model single-subject data, as fitting the model jointly to all subjects completely obliterates subject heterogeneity. Fitting on single-subject data would also allow investigating whether the models reproduce the consistency of harvest durations and reach durations across conditions, and most importantly the absence of correlation across subjects between reach and harvest duration. At the moment, we're left wondering what the implications of these analyses for the original question are.

d) What is the rationale for comparing reach duration in one condition to harvest duration in another condition?

e) The study does not take into account the likely impact of delays on muscular fatigue and perceived effort: the effort associated with maintaining the cursor on target during harvest might be perceived as less effortful after a longer break, partially explaining the impact of delay on harvest duration.

f) I am confused by the reference to indirect effects in the separate-cost model (L692-7, Table 2). To fit this model, was the experimental value or theoretical value of the reach duration t_d used to determine the optimal harvest value (from Equation 14)? In both cases, the reach duration DOES increase with reach effort, so we should expect an impact on the harvest duration. This is indeed seen in Figure 7A. So why does Table 2 suggest that reach effort should not impact harvest duration in this model?

g) Could the effect of pre-reach delay on reach time be alternatively explained simply by longer reaction times as reported in Figure S2, e.g., a longer period to reactivate the muscles after a longer break? In other words, as far as I understood, here reaction times are subsumed in the motor time, against the tradition of splitting them into two independent (non-overlapping) behavioral metrics.

h) The EMG-time integral in the harvest phase should be largely proportional to the harvest duration, given that the applied torque is constrained to remain on target. Is this the case? If so, then how to interpret the significant effect of reach effort on this value, given that the main analyses did not identify an effect of reach effort on harvest duration?

Reviewer #2 (Public review):

Summary:

The manuscript by Luden et al. investigates the molecular function and DNA-binding modes of AHL15, a transcription factor with pleiotropic effects on plant development. The results contribute to our understanding of AHL15 function in development specifically and transcriptional regulation in plants more broadly.

Strengths:

The authors developed a set of genetic tools for high-resolution profiling of AHL15 DNA binding and provide exploratory analyses of chromatin accessibility changes upon AHL15 overexpression. The generated data (CHiP-Seq, ATAC-Seq and RNA-Seq is a valuable resource for further studies. The data suggest that AHL15 does not operate as a pioneer TF, but is likely involved in gene looping.

Weaknesses:

The authors have extended the motif analysis to the top 1,000 shared peaks, addressing part of my previous concern. It did not erase my worries about overclaiming completely, but I think it can be considered as a terminological issue, rather than technical one. Therefore, it could be addressed through minor revision, without further analyses.

Specifically, the absence of a predominant enriched motif does not establish that AHL15 binds non-specifically or lacks sequence preferences. Low motif prevalence limits the proportion of peaks it could explain but does not exclude a genuine binding preference; a binding motif need not be rare in the genomic background (binding can be supported by other factors). Please revise the interpretation in lines 188-194, and the conclusion in lines 204-206 to state that the present analysis did not identify a strongly enriched motif accounting for a substantial proportion of AHL15-associated regions. Any equivalent claims of non-specific binding elsewhere in the manuscript should be toned down.

Additional minor points:

(1) Please provide the exact HOMER background-selection procedure, and definition of the 50-bp search windows.

(2) Figures 2B-C and the related supplementary figures, add the x axis label.

Reviewer #3 (Public review):

The Sustar et al. manuscript catalogs glutamate receptor composition across distinct Drosophila NMJs: larval and adult abdominal NMJs, as well as NMJs on adult leg and flight muscles. This work is important and probably overdue. The larval NMJ is the exemplar NMJ in this system, and the identity of "essential" and "alternative" subunits at this stage is assumed by many to hold across developmental stages and NMJ types. Here, the authors show that there is surprising diversification among NMJ types and that the notion of essential/alternative subunits only holds true at larval NMJs.

The study will generate interest in the Clumsy GluR subunit, which has not been well-characterized at all, but is widely expressed at adult NMJs. They also find striking extrasynaptic expression of glutamate-gated chloride channel GluRClalpha in adult leg and flight muscles, raising questions about its role. The study is interesting, logical, and well-written. The figures are clear, and the discussion was particularly thoughtful.

Comments on revised version.

This is a nicely revised manuscript that went a long way toward addressing the concerns in the first round. The response to the reviewers is clear and thoughtful. I think the question is framed very well and the authors do a much better job throughout making clear where the heterologous approaches agree and differ. I also think the Limitations section in the Discussion does a great job putting the work in context.

Reviewer #2 (Public review):

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

Comments on revised version.

I thank the authors for their responses. A number of the smaller points I raised are now resolved. However, I am disappointed to see that the more substantial concerns, which relate to the cell-type expression claims running through the Results and the Discussion, remain unaddressed. My original point was that these claims are not quantitatively supported and that the identity of the iPSC-derived cell types used to make them has not been documented, and I do not think this revision changes that. Most of what I asked for was quantification of experiments the authors have already performed, or quality control on the cell types themselves, and this has been answered with scope arguments rather than with data. The other points I originally raised remain.

I give further details on my two main remaining concerns below:

(1) The cell-type expression claims across the manuscript are still supported by qualitative Western blot images only. These include statements such as "higher signal in glial populations," "enriched in neuronal lineages," "predominantly detected in microglial populations," and the "convergence on multicellular disease mechanisms" narrative in the Discussion. I understand the authors' decision to keep ALS-RAP as a tool and resource paper rather than expand the experimental work. However, if that is the framing, the biological interpretations of these expression data need to be removed. In their response, the authors themselves acknowledge that "quantification across multiple biological replicates would be required to establish definitive differences in protein abundance between neuronal and glial cell types." As long as this quantification is not provided, every claim of cell-type-specific expression should be removed from the Abstract, Results and Discussion, and the data presented as a descriptive survey.

(2) Related to the previous point, I do not think that quality control of the non-microglia iPSC lineages is beyond the scope of the study, as argued in the response. For ALS-RAP to be adopted as a resource, other laboratories need to trust that the authors' iPSC-derived neurons, astrocytes and oligodendrocytes are really neurons, astrocytes and oligodendrocytes. The fact that the differentiation protocols have been previously published does not confirm that they have been correctly implemented here, and this is precisely the kind of quality control that should be documented in the paper. In their response, the authors list the single markers already shown in Figure S3 (ISL1, TH, MBP, GFAP, CD44) as the answer to my concern, but a single positive marker per lineage does not establish purity.

Reviewer #2 (Public review):

Summary:

This paper asks an important question that has not been discussed much in the extensive literature on the High Frequency Oscillations (HFOs) that have been extensively studied in patients with epilepsy and experimental models of epilepsy. The question is whether the Fast Ripples (FRs), the HFOs in the 250-500 Hz frequency band, represent a pathological phenomenon or represent a physiological phenomenon that occurs in the healthy brain but happens to be more frequent in epileptic tissue. It is an important question that has not been systematically addressed until now. The authors conclude, from very extensive simulations, from extensive experimental animal studies (the systemic kianate model of epilepsy in rats), and from a modest amount of human data, that FRs occur in healthy brains as a result of the chance occurrence of bursts of action potentials, and that in epileptic tissue, their frequency of occurrence is approximately 30% higher than what is expected by chance. They conclude that FRs are not a separate phenomenon of epileptic tissue. This finding is reinforced by the recent findings of FRs in experimental models of Alzheimer's disease.

Strengths:

This is a valuable study because it asks an important and original question and because it evaluates it from several angles (simulation, tissue culture, experimental animals, and human patients). The simulations and the analyses of real data are performed very carefully and with original and solidly documented approaches, using extensive simulations and extensive data sets in the cultured cell data and in the in vivo experiments. The paper is clearly written and well-illustrated.

Comments on revised version.

The authors have appropriately addressed the questions I raised in the first review

Reviewer #2 (Public review):

Summary:

The authors present an ambitious and large-scale reproducibility analysis of 400 articles on Drosophila immunity published before 2011. They extract major and minor claims from each article, assess their verifiability through literature comparison and, when possible, through targeted experimental re-testing, and synthesize their findings in an openly accessible online database. The goal is to provide clarity to the community regarding claims that have been contradicted, incompletely supported, or insufficiently followed up in the literature, and to foster broader community participation in evaluating historical findings. The manuscript summarizes the major insights emerging from this systematic effort.

Strengths:

(1) Novelty and community value: This work represents a rare example of a systematic, transparent, and community-facing reproducibility project in a specific research domain. The creation of a dedicated public platform for disseminating and discussing these assessments is particularly innovative.

(2) Breadth and depth: The authors analyze an impressive number of publications spanning multiple decades, and they couple literature-based assessments with new experimental data where follow-up is missing.

(3) Clarity of purpose: The manuscript carefully distinguishes between assessing evidential support for claims and judging the scientific merit of historical work. This helps frame the project as constructive rather than punitive.

(4) Metascientific relevance: The analysis identifies methodological and contextual factors that commonly underlie irreproducible claims, providing a useful guide for future study design and interpretation.

(5) Transparency: Supplementary datasets and the public website provide an exceptional degree of openness, which should facilitate community engagement and further refinement.

Comments on revised version:

In the revised manuscript the authors addressed in a satisfactory manner the weaknesses I previously highlighted in my initial review.

Reviewer #3 (Public review):

Summary:

The authors of this paper were trying to identify how reproducible, or not, their subfield (Drosophila immunity) was since its inception over 50 years ago. This required identifying not only the papers, but the specific claims made in the paper, assessing if these claims were followed up in the literature, and if so whether they supported or refuted the original claim. In addition to this large manually curated effort, the authors further investigated some claims that were left unchallenged in the literature by conducting replications themselves. This provided a rich corpus of the subfield that could be investigated into what characteristics influence reproducibility.

A major strength of this study is the focus on a subfield, the detailing of identifying the main, major, and minor claims - which is a very challenging manual task - and then cataloging not only their assessment of if these claims were followed up in the literature, but also what characteristics might be contributing to reproducibility, which also included more manual effort to supplement the data that they were able to extract from the published papers. While this provides a rich dataset for analysis, there is a major weakness with this approach, which is not unique to this study.

The main weakness is relying heavily on the published literature as the source of whether a claim was determined to be verified or not. Nonetheless it is understandable why the authors took this approach - it is the only way to get at a breadth of the literature. However, there are many documented issues with this stemming from every field of research - such as publication bias, selective reporting, all the way to fraud. Importantly, this limitation is highlighted throughout the paper. At the same time, it is not reasonable to expect this study to have conducted independent experimental replications for all 400 papers identified as this would have been a laborious and costly effort. To overcome this weakness, the authors leveraged their own expertise, and crowdsourcing within the Drosophila immunity community, to assess the validity of the claims. While this helps mitigate some of this weakness, it does introduce new challenges in interpretation, which is acknowledged in the limitations section of the discussion. Overall, this situates this study more as an expert assessment of the replicability of the published literature opposed to an assessment of independent experimental replications.

The authors should be applauded for the monumental effort they put into this project, which does a wonderful job of having experts within a subfield engage their community to understand the connectiveness of the literature and attempt to understand how reliable specific results are and what factors might contribute to them. This project provides a nice blueprint for others to build from as well as leverage the data generated from this subfield and thus should have an impact in the broader discussion on reproducibility and reliability of research evidence.

Reviewer #2 (Public review):

This is an excellent paper, well researched, correctly referenced and very well presented. The main addition to the existing literature, apart from supporting earlier results (see also Roberts et al. 2015), is the ability to detect the length of the upcoming conversational contribution from the neural signal. This is interesting because it shows very early and sustained planning of response during listening to the incoming turn from the interlocutor. Why is that interesting? Because this simultaneous listening and response planning is utilising the very same mental machinery - a kind of dual-tasking that humans generally find very hard, given extreme capacity constraints on short-term and working memory. This then talks to the issue of whether speech production and comprehension are more independent than current theory holds. Clearly, human language is one of the elite skills of the species, but this dual-task ability in just this domain may be a specialization evolved over deep evolutionary time. I would like to see this background sketched more clearly in the introduction - it would make the paper of more interest to the general reader.

The other main contribution is methodological, namely the ability to extract meaningful EEG out of unconstrained verbal interaction, using, e.g. machine learning to filter out movement artefacts. Earlier work was held back by scepticism that this was possible, but this paper points clearly to one way to do this. A further contribution is the suggestion that Beta modulation is associated with the duration of the turn under planning, while Alpha modulation was in addition associated with preparation for execution. This needs to be tested in further studies, but could be a valuable addition to the analytical armoury.

In the final discussion, a conceptual model is outlined in Figure 8. This looks very similar to that given in Levinson & Torreira 2015 and Levinson 2016 - at least if there is a difference, it would be good to see it clarified. Note that in that model we hypothesized that preparation of the upcoming turn was continued right up to completion and then held if necessary for ending cues from the interlocutor. Note that quite late delivery preparations have been noted both in Torreira et al. 2015 (breathing signal) and in Bögels & Levinson 2023; in the latter, there is evidence of tongue preparation sometimes occurring not tied time-wise to delivery (suggesting inhibition till incoming turn is ending). So our model already had an early and late component - the news here is that the early component can be robustly discerned in naturalistic verbal interaction.

Also, in the discussion, the Pickering & Garrod IAM model is favourably discussed, even though that model imagines the production machinery is used to predict the unfolding comprehension of the incoming turn: in that case, the production machinery would have to be doing two things at once - doing comprehension and planning one's own turn. In a way, the main lesson of the findings of the submission is precisely that this is implausible.

In general, I have no hesitation recommending publication of an important contribution to the neuroscience of human performance in ecologically more valid settings.

References:

Bögels S, Levinson SC. Ultrasound measurements of interactive turn-taking in question-answer sequences: Articulatory preparation is delayed but not tied to the response. PLoS One. 2023 Jul 5;18(7):e0276470. doi: 10.1371/journal.pone.0276470. PMID: 37405982; PMCID: PMC10321606.

Griffin, Z.M. and Bock, K. (2000) What the eyes say about speaking. Psychol. Sci. 4, 274-279.

Levinson SC. Turn-taking in Human Communication--Origins and Implications for Language Processing. Trends Cogn Sci. 2016 Jan;20(1):6-14. doi: 10.1016/j.tics.2015.10.010. Epub 2015 Dec 1. PMID: 26651245.

Levinson SC, Torreira F. Timing in turn-taking and its implications for processing models of language. Front Psychol. 2015 Jun 12;6:731. doi: 10.3389/fpsyg.2015.00731. PMID: 26124727; PMCID: PMC4464110.

Roberts SG, Torreira F, Levinson SC. The effects of processing and sequence organization on the timing of turn taking: a corpus study. Front Psychol. 2015 May 13;6:509. doi: 10.3389/fpsyg.2015.00509. PMID: 26029125; PMCID: PMC4429583.

Reviewer #2 (Public review):

Summary:

The paper by Yadav et al. provides compelling theoretical and experimental evidence for an aliphatic-to-threonine substitution (ATS) in the transmembrane helices (TMs) of membrane proteins residing in the inner mitochondrial membrane (IMM) across several species. Furthermore, using ATS, they designed variants of the human Atp6 subunit and of Atp6 subunits from other species that seem to be internalized into mitochondria.

Strengths:

In general, the results support the conclusions. The findings are particularly relevant to the field of allotopic expression and could have significant implications for the future development of gene therapies for mitochondrial disorders.

Weaknesses:

The manuscript could be substantially strengthened by the addition of further experimental evidence and by some minor revisions and adjustments to the interpretation and presentation of the results.

Major Points

(1) The protein product of atp6 is an essential component of the proton channel of ATP synthase, and more than a dozen pathogenic mutations in this gene have been associated with mitochondrial diseases. Accordingly, several studies have reported attempts to achieve allotopic expression of Atp6 in human cells. I would dedicate a pair of paragraphs in the Discussion to acknowledging the most relevant of these previous efforts. Here, in the work of Yadav et al., several Atp6 constructs were detected within mitochondria by fluorescence microscopy. However, no direct evidence was provided that the allotopically expressed Atp6 protein was incorporated into complex V. Localization within mitochondria, by itself, does not demonstrate successful allotopic expression in the functional sense, as the protein could remain unassembled, inserted in an incorrect topology, or in a process of degradation. In my view, this is the main weakness of the present study. A convincing demonstration of allotopic ATP6 expression should ideally be supported by biochemical evidence showing that the imported subunit is stably incorporated into ATP synthase and, most importantly, contributes to its function. This would require demonstrating at least partial restoration of the respiratory or OXPHOS defect.

I therefore encourage the authors to test their different constructs in cybrid cells carrying a pathogenic mutation in the mitochondrial atp6 gene and determine whether they can restore OXPHOS function or, at a minimum, respiratory capacity. Such an experiment would provide substantially stronger evidence that the imported Atp6 protein is not merely localized to mitochondria but is functionally incorporated into ATP synthase. Alternatively, Blue Native PAGE of mitochondria isolated after allotopic expression of the different constructs, followed by second-dimensional SDS-PAGE, could provide direct evidence that a tagged Atp6 subunit has been incorporated into the ATP synthase complex. Furthermore, this experiment could provide a valuable functional comparison of the three ATS constructs (T1, T2, and T3) and identify which one is most effective in restoring function. Importantly, the T3 construct, despite carrying the highest level of ATS, may not necessarily be the most effective in increasing the levels of assembled ATP synthase or in restoring ATP synthase activity.

(2) I do not agree with the interpretation of the results presented in Figure 7 - Figure Supplement 3, "Targeting tracks with hydrophobicity, not expression." I am concerned that the experimental design may be biased. The two most highly hydrophobic proteins (WT and T1), which are expected to have greater difficulty being imported into mitochondria, were selected for low-level expression. In contrast, the two constructs with extensive ATS (T2 and T3) were expressed at high levels. The hydrophobicity effect and the expression levels are confounded in this experiment, making it difficult to determine whether the observed targeting differences are attributable to one of these parameters. To disentangle these effects, this experiment should be repeated using both low and high expression levels for all four constructs, resulting in eight experimental conditions (four constructs x two expression levels).

Reviewer #2 (Public review):

Summary:

This work highlights a new approach to build an array of highly stable peptide MHC-I using temperature exchange, which can be used to identify antigen specific CD8+ T cells.

Strengths:

In this work, the authors have proposed an alternative method to reload the peptide MHC-I molecule. Their temperature-exchange approach is distinct from current reloadable peptide MHC technologies involving photolabile peptide, empty MHC-I (Nat Commun 11, 1314 (2020). https://doi.org/10.1038/s41467-020-14862-4), tapasin/TAPBPR chaperone-assisted (eLife 7:e40126.), enzyme exchangeable (https://pubmed.ncbi.nlm.nih.gov/31201791/) and small alcohol (Curr Res Immunol. 2022 Aug 18;3:167-174. doi: 10.1016/j.crimmu.2022.08.002) approaches

Weaknesses:

The temperature-exchange approach identifies antigen-specific T cells at the same frequency of the photolabile peptide MHC-I molecules. The time saved using temperature-exchange protocol may not be a pull factor as the photolabile peptide MHC-I approach is not unreasonably laborious.

As with other exchange systems, performance can vary by HLA allele and complex stability, so each new allele needs empirical optimization.

In short, temperature-based pMHC exchange is a practical and fast loading strategy with good utility for multimer generation, but its main limitation is dependence on thermal stability and careful template selection.

Reviewer #2 (Public review):

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

Reviewer #2 (Public review):

Summary:

Bussey-Sutton et al. investigate the RelB interactome in the presence and absence of the SMAC mimetic AZD5582. They combined RelB BioID with CRISPR screening and highlighted multiple potential transcriptional regulators that may contribute to the maintenance or reversal of HIV latency. However, there are several technical limitations and gaps in validation in the current version of the work.

Strengths:

The overall topic is of clear interest; the RelB interactome and CRISPR screens provide interesting hits.

Weaknesses:

From a technical standpoint, RelB is expressed using a doxycycline-inducible system, yet the fusion protein (Figure S1A) appears to be expressed at high levels even in the absence of doxycycline (and does not seem to respond to doxycycline supplementation). As this is the key model system used in the paper to identify RelB interactors, this limitation should be discussed and potentially addressed.

It would also be important to include some orthogonal validation (e.g., co-immunoprecipitation) of top novel RelB interactors identified by BioID.

In Figure 2C, two non-targeting controls appear among the most highly enriched hits. This raises the question that other top hits might reflect nonspecific effects or assay noise. Consistent with this concern, the effects observed following siRNA-mediated depletion of the top CRISPR-screen candidates in Figure 2D and Figure 3 appear relatively modest.

More broadly, the absence of validation in primary-cell models is a further limitation, as the chromatin environment and transcriptional regulation of HIV latency in the two cell-line models used here are likely to differ substantially from those in primary CD4+ T cells.

Minor point:

Endogenous RelB remains present in the RelB-BioID cells and could compete with the miniTurbo-RelB fusion protein for physiological binding partners. Moreover, the effect of two sources of constitutive RelB expression (see comment above) on the physiologic RelB interactome is not obvious. Controlling for this is not trivial, but this potential confounding factor should be acknowledged (and further reinforces the need for orthogonal targeted validations of interactors in cells with endogenous RelB).

Reviewer #2 (Public review):

Summary:

In this study, Serrano et al. employed a combination of cell biological and molecular approaches to investigate the localization and regulation of Casein Kinase CK1 during the cell cycle using U20S cells. They show that CK1 dynamically localizes between the centrosomes and the nucleus but can be sequestered away from the centrosomes upon overexpression of its binding partner PER2. They provide evidence that CK1 WT but not a phospho-null mutant strongly accumulates in a hyperphosphorylated form upon inhibition of phosphatases (using Calyculin and Okadaic Acid) and thus conclude that CK1 tail phosphorylation protects the kinase from degradation. Using synchronized cells, they show that CK1 accumulates unphosphorylated in S-phase (APC/Cdh1 inactive) but phosphorylated at the G2-M transition. Immunostaining shows that CK1 localizes to the centrosomes during mitosis.

Strengths:

The authors reveal that the activity and abundance of dephosphorylated and phosphorylated CK1δ are regulated in a cell cycle-dependent manner. This suggests that these different pools are associated with distinct physiological functions.

Comments on latest version:

Unfortunately, problems with controls remain in the revised manuscript. At the very least, the authors should include all the necessary controls in their study instead of referring to previously published papers. I have asked for a detailed description of the kinase construct in which the phospho-Serine and Threonine of the C-terminal tail are substituted, but I did not see any more information in the revised version.

Reviewer #2 (Public review):

Summary:

The manuscript by Nilssen et al. presents a comprehensive study of the circuitry linking the medial and lateral entorhinal cortices (MEC and LEC). Using a combination of anatomical tracing, optogenetics, and in vitro electrophysiology, the authors convincingly demonstrate that the MEC sends both glutamatergic and long-range inhibitory SST+ GABAergic projections to the LEC, with distinct laminar and cell-type-specific targeting. Notably, they reveal that SST+ inhibitory projections selectively suppress the activity of layer IIa neurons, whereas excitatory inputs preferentially engage neurons in layers IIb and III, thereby differentially modulating hippocampal-projecting populations.

Strengths:

The experiments are carefully executed, the results are compelling, and the conclusions are well supported by the data. This work will be of broad interest to researchers studying memory circuits, cortical inhibition, and the organization of long-range connectivity.

Weaknesses:

Although the in vivo relevance of these connections remains to be determined, this is an important and timely contribution to our understanding of entorhinal-hippocampal interactions.

Comments on revised version.

The authors have addressed my comments satisfactorily, and I am satisfied with the changes made in the revised version.

Reviewer #2 (Public review):

Summary:

The authors propose Hetlearn, a novel learning algorithm for unstable environments: instead of using statistically optimal methods to estimate the variability of the environment, simply have a range of "sub-learners" that learn at different rates, and rely more heavily on the sub-learners with a recent track record of success. This strategy is particularly helpful when the environment has unknown and fluctuating stochasticity (randomness around a constant mean reward) and volatility (the mean itself changes over time). For high stochasticity and low volatility, it's better to learn slowly to smooth out randomness; for low stochasticity and high volatility, it's better to learn quickly to catch onto the new trend. The authors show that existing methods work well when stochasticity and volatility are constant, or when methods can learn them over a long time, but that Hetlearn does better when stochasticity and volatility change over time or an agent has limited time or data to learn from.

They then propose that the fly mushroom body implements a form of Hetlearn, since it has different compartments that form parallel memory traces at different learning and forgetting rates. Here, the conclusions are less clear because their model doesn't appear to implement the key feature of Hetlearn (the differential weighting of sublearners based on recent success), and because their model has features that don't have an obvious biological equivalent in the real mushroom body.

Strengths:

The idea behind Hetlearn is clever and interesting, and it makes sense that a heuristic method like Hetlearn would outperform a theoretically optimal algorithm under conditions of limited data. The model is clearly defined, and they test it across a range of tasks and compare it to a range of different Kalman/particle filter learners. The application to the mushroom body is also an inspiring connection and takes advantage of the discovery of empirically described sublearners. The work will stimulate new ways of understanding how biological systems might implement heuristic tricks that may not be optimal under ideal conditions, but that succeed in "real world" conditions, and it will stimulate new thinking about the computational role of parallel memory traces in the mushroom body.

Weaknesses:

In the tests of Hetlearn vs. Kalman filters, it would be useful to more fully explore the parameter space. Specifically, in Figures 1-2, it would help define the boundaries of when Hetlearn is better or worse than Kalman filters, by systematically changing the parameters of the task (e.g. increasing/decreasing the amount of data available to the learner or the frequency of changing stochasticity/volatility), and changing the parameters of Hetlearn (the learning rates of the sublearners and the recency bias, i.e. how far back to track the recent success of the sublearners when weighting their outputs). For Figure 3, while they do check the performance when varying the recency bias, there are no conditions presented in which the particle filter does better. To define when Hetlearn is better, it is important to show when it is worse.

In the mushroom body model, there are several elements that have no apparent biological equivalent, and it's not clear whether they are incidental (in which case they could be removed to enhance biological realism) or essential to the model's performance (in which case the discrepancy should be discussed; perhaps the model can be reframed as a prediction for as-yet-undiscovered features). In addition, the model seems to lack the defining feature of Hetlearn (up/down-weighting sublearners according to their recent performance). Finally, the authors argue that Hetlearn explains experimental data better than existing models, but existing models could be easily and parsimoniously tweaked to accommodate these data, so the explanatory advantage of Hetlearn is unclear.

Reviewer #2 (Public review):

Summary:

This study extends previous work on the adaptive EPAS1 locus by examining the pleiotropic activity of the ENH5 enhancer in adipocytes and its potential role in metabolic and thermogenic responses. The authors progress from demonstrating enhancer activity and evolutionary conservation to in vivo phenotyping and environmentally dependent transcriptional responses in primary adipocytes. The work provides an interesting example of how pleiotropic regulatory effects can contribute to the complexity of adaptation, with a single adaptive regulatory locus influencing multiple biological processes in an environmentally dependent manner.

Strengths:

The study is well executed and is clearly presented, with a logical experimental progression. A particular strength is the genotype-by-treatment interaction analysis demonstrating that ENH5 loss alters metabolic and adipocyte-associated transcriptional programs following both hypoxia and beta-3-adrenergic stimulation. The convergence of these responses is particularly interesting in the context of regulatory pleiotropy and suggests that selection at the EPAS1 locus may have consequences extending beyond the canonical hypoxia response. The inclusion of negative findings, including the absence of an overt baseline metabolic phenotype and the lack of an additive response to combined stimulation, also provides a balanced presentation of the results.

Weaknesses:

The principal conclusions are generally supported by the data, and the weaknesses are relatively minor and primarily relate to the scope of interpretation. The study assesses transcriptional programs associated with thermogenic signaling using CL316243 rather than directly measuring physiological thermogenesis. Nonetheless, CL316243 is a rational and well-established approach for experimentally inducting beta-3-adrenergic thermogenic signaling. In addition, the murine ENH5 knockout is a useful model of reduced enhancer activity that phenocopies the Tibetan ENH5 haplotype, but it is not genetically equivalent to the naturally occurring Tibetan ENH5 haplotype. The authors generally recognize these limitations, and they do not substantially detract from the central findings.

Reviewer #2 (Public review):

In this paper, the authors study the role of Krüppel in regulating the termination and survival of mushroom body neuroblasts. They first confirm that adult wild-type brains have no proliferation and report that Kr mutants and Kr RNAi specifically in neuroblasts prolong MBNB lifespan, enabling continued neurogenesis in the adult brain. They further show that Kr acts during pupal stages to regulate the elimination to the mushroom body neuroblasts, as its depletion leads to neuroblast retention, revealing a previously unrecognised postembryonic function of Kr distinct from its established role in embryonic neurogenesis. Mechanistically, this is achieved by the regulation of the Imp/Syp transition and E93 expression. Finally, they show that Kr acts antagonistically to Kr-h1, which is expressed predominantly in larval stages. Together, these results identify Kr as a mushroom body neuroblast regulator coordinating intrinsic and extrinsic signal to regulate neuroblast termination.

Strengths:

The main strength of this paper is that it identified a novel regulator of Imp expression in the mushroom body neuroblasts. Imp is a conserved RNA binding protein that has been shown to regulate neural stem cell proliferation and survival in different animals.

Moreover, the authors provide a series of evidence that this regulator, Kruppel, acts by integrating extrinsic signals (ecdysone) with intrinsic timers (Imp-Syp transition) to regulate neuroblast termination.

Weaknesses:

No significant weaknesses were identified.