Reviewer #3 (Public review):

Summary:

The authors performed single-cell RNA sequencing of adult zebrafish hearts and identified markers for distinct cardiomyocyte subpopulations. One marker, phlda2, marks primordial cardiomyocytes. They generated transgenic reporter lines to characterize phlda2 expression patterns and a phlda2-NTR ablation line to determine the functional requirement of primordial cardiomyocytes during heart regeneration. They found that phlda2+ primordial cardiomyocytes are essential for myocardial morphogenesis and coronary vessel development. Interestingly, when phlda2+ primordial cardiomyocytes are ablated during heart regeneration, gata4+ cortical cardiomyocytes, coronary vessel revascularization, and scar tissue formation are not affected.

Strengths:

The authors identified a new primordial cardiomyocyte marker, phlda2. They further demonstrated that primordial cardiomyocytes are important for heart morphogenesis but dispensable for heart regeneration. Their findings reveal a potential difference between heart development and regeneration programs.

Weakness:

Despite the interesting findings, the authors did not provide supplemental data for their scRNAseq to demonstrate the data quality and support their conclusions, and some results are not well described.

Reviewer #3 (Public review):

This paper reveals that the neuronal protein PRRT2, previously known for its association with paroxysmal dyskinesia and infantile seizures, modulates the slow inactivation of voltage-gated sodium ion (Nav) channels, a gating process that limits excitability during prolonged activity. Using electrophysiology, molecular biology, and mouse models, the authors show that PRRT2 accelerates entry of Nav channels into the slow-inactivated state and slows their recovery, effectively dampening excessive excitability. The effect seems evolutionarily conserved, requires the C-terminal region of PRRT2, and is recapitulated in cortical neurons, where PRRT2 deficiency leads to hyper-responsiveness and reduced cortical resilience in vivo. These findings extend the functional repertoire of PRRT2, identifying it as a physiological brake on neuronal excitability. The work provides a mechanistic link between PRRT2 mutations and episodic neurological phenotypes.

Comments:

(1) The precise structural interface and the molecular basis of gating modulation remain inferred rather than demonstrated.

(2) The in vivo phenotype reflects a complex circuit outcome and does not isolate slow-inactivation defects per se.

(3) Expression of PRRT2 in muscle or heart is low, so the cross-isoform claims are likely of limited physiological significance.

(4) The mechanistic separation between trafficking of PRRT2 and its gating effects is not clearly resolved.

(5) Additional studies with Nav1.6 should be carried out.

Comments on revised version.

These comments have been addressed in the revised version.

Reviewer #3 (Public review):

Summary:

This study tries to identify the genetic signatures of adaptation to starvation conditions. For this, outbred populations of Drosophila melanogaster were selected for starvation resistance by using the 20% surviving adults after starvation to start the next generation. This was done for 60 generations while parallel populations were kept under control conditions. At the end of the experiment, starvation-selected flies showed increased survival, longevity, and TGA storage. DNA poolseq data from control and starvation populations were compared to identify genomic regions with low heterozygosity and signatures of selective sweeps, and SNPs with differences in allele frequency. The candidate regions point to mitochondrial and metabolic pathways as the targets of selection for starvation resistance.

The authors replicate the experimental design, selection approach, data collection, and analyses from Hardy et al 2018 (https://doi.org/10.1093/molbev/msx254), which also investigated adaptation to starvation conditions but used a different Drosophila melanogaster population. In this sense, the current study recapitulates most of the findings from Hardy et al. (2018). The analyses of the mitochondrial results, including the overlap with human data, are the novelty of this paper. However, those analyses are not very well justified. The fact that this study is almost identical to Hardy et al is not clearly stated nor discussed in the manuscript.

Strengths:

The authors made use of an experimental evolution approach to identify the genetic basis underlying adaptation. This is a powerful approach that has proven very successful in the past. They used a good number of replicates (four per condition), an appropriate depth of sequencing, and quantified higher-order phenotypes to validate the claim that the populations had evolved increased starvation resistance.

Weaknesses :

Although the findings of this study seem credible based on the known biology of starvation resistance, there are several aspects of the experimental design that weaken my confidence in the results. The points below should be clarified, and the limitations of the experimental design and analyses need to be included in the discussion.

(1) Pooled genomic data were collected for the four replicates at the end of 60 generations of selection, and four replicates were kept under control conditions. No data were collected at the beginning of the experiment, which is the current standard in Evolve and Resequence experiments. To infer the genomic regions underlying adaptation to starvation, evolved control and starved cages are compared. Although this will identify regions that are possibly truly caused by adaptation to starvation stress, the available data doesn't allow to determine, for example: a) whether the differences between control and starvation regimes are due to changes in control cages relative to the starting population, combined with no changes in starvation cages relative to the starting population; b) whether the differences across replicates are due to different genomic composition at the start of the experiment that could have been amplified by drift.

(2) Selection was applied by starving flies until ~80% of the population died. The 20% surviving flies were used to seed the next generation. The control populations, on the other hand, were propagated using the whole population. Given that only the starvation populations were subject to such a strong bottleneck, it is not possible to disentangle whether the genomic signatures at the end of the experiment are due to this, and not necessarily to starvation resistance. For example, the low heterozygosity blocks and the very great changes in allele frequency could be a natural result of such a bottleneck. A proper comparison would have been to select a random 20% of the control individuals to seed every generation.

(3) The analyses that involve human populations are poorly justified, and the enrichment tests are not clearly explained. There is no evidence of signatures of selection for starvation resistance in human datasets (as mentioned in the text, line 112), and yet the authors claim that their analyses that identify branch-specific alleles for a set of four human populations serve as a dataset for it. I don't think the results of this analysis and further overlap with candidate genes identified in the Drosophila experiment support the conclusion that polygenic adaptation of metabolic pathways is conserved across species (line 303).

(4) The conclusion that adaptation to starvation conditions is repeatable is not justified by the data. The overlap across replicates is very low in every metric.

(5) The methods are poorly described. In most of the sections, there is not enough information to be able to replicate the experiments or the analyses. Several of the analyses presented in the results are not described in the methods. Without this information, it is very difficult to assess whether the analyses were correctly done or whether the results are robust.

Reviewer #3 (Public review):

Summary:

PLCβ3 is activated by both Gαq and Gβγ subunits. This paper follows previous solution and cryoEM studies of the PLCβ3 / Gβγ complex to delineate the molecular details of activation using cellular BRET assays and cryoEM.

Strengths:

The authors find evidence for multiple binding sites on PLCβ3 for Gβγ and suggest that Gβγ is not bone fide activator per se but enhances Gαq activation by positioning the catalytic site towards substrate. The authors also find that this activation is not through recruitment of the enzyme to the membrane by Gβγ released upon G protein activation in accord with other PLCβ enzymes.

Weaknesses:

(1) The main issue is that the author's mechanism does not fully explain how Gβγ activation occurs for PLCβ2 in reconstituted systems in the absence of Gαq subunits but will be investigating this in future studies.

Reviewer #3 (Public review):

Summary:

Drosophila, like other animals, use sophisticated taste systems with specialized chemoreceptors to identify gustatory cues in their environment. Multiple gustatory cues associated with a food source are often encountered simultaneously, but our understanding of how this sensory information is detected and integrated remains incompletely understood. This valuable study investigates how salt, amino acids, or their combination are detected by specific combinations of peripheral Ionotropic Receptors, leading to behavioral attraction. The authors show that distinct combinations of IR76b, IR25a, and IR20a confer sensitivity to salt, arginine, or both. They also show striking evidence that cells co-expressing IR25a/IR20a display a synergistic response to a mixture of sub-activating concentrations of these tastants. Together, these experiments lead to the conclusion that combinatorial expression of different subunits and synergistic responses to taste mixtures facilitates integration of taste cues beginning in the periphery. However, in its current form, key methodological details are missing or inadequately described, which complicates interpretation. Additionally, characterization is heavily focused on the population of IR20a+ neurons in the tarsi, while the response properties of the newly-identified, functionally distinct population in the labellum are investigated only through behavioral analysis, limiting the description of potentially additional IR20a complexes. Ultimately, more in-depth biochemical characterization of the IR complexes described will be required to fully support the conclusion that combinatorial assembly of distinct IR20a receptors enables peripheral integration of taste mixtures.

Strengths:

The authors characterize the expression pattern of IR20a in the tarsi as well as in the labellum, a tissue for which IR20a expression has been a point of debate. Multiple levels of analysis, including behavioral assays, physiological recordings, as well as ectopic and heterologous expression systems, are used to characterize the response properties of different combinations of IR subunits, demonstrating remarkably consistent behavior of the IR-complexes across cell types. Well-controlled genetic analysis and the use of multiple behavioral assays provide additional support for their results, including the surprising demonstration of synergistic responses to mixtures of tastants that supports the idea of peripheral integration of gustatory inputs. This report also identifies a distinct IR, IR56b, required for starvation-enhanced responses to salt.

Weaknesses:

(1) The title states that IR20a integrates L-arginine and salt signals via distinct subunit assemblies, though the paper lacks direct evidence that IR20a serves as a multimodal tuning receptor in distinct functional assemblies. Heterologous expression shows that co-expression of IR20a/IR25a confers sensitivity to Arg, IR76b confers sensitivity to NaCl, and IR20a/IR25a/IR76b co-expression confers sensitivity to both Arg and NaCl. This seems to be interpreted to mean that all three subunits are assembling into a single complex. However, current results do not show any difference in salt response when IR76b is expressed alone compared to alongside IR25a+IR20a. Without more direct evidence for co-assembly of all three subunits, it is equally plausible that the responses observed represent activity of distinct IR25a/IR20a and IR76b receptors for Arg and salt, respectively. In this model, genetic disruption resulting in expression of either IR25a or IR20a alone with IR76b could disrupt its activity or membrane trafficking (as seen here and in previous studies) while co-expression of both IR20a and IR25a relieves this inhibition by sequestering IR20a/IR25a into a distinct complex from IR76b. Direct biochemical characterization, for instance in the form of co-immunoprecipitation or FRET, will be required to differentiate between these possibilities.

(2) Key methodological details are missing throughout the manuscript. For instance, incomplete genotype and staining information is provided for images in Figure 1, making it difficult to interpret what is being shown. Additionally, for the calcium imaging methods, what is the imaging speed? How are max values calculated (is this the average of several images or just a single maximum)? How are ligands diluted and delivered to cells, and were they applied in a manner that allowed for subsequent washout?

(3) The composition of the S2 imaging bath buffer requires clarification. As described, the bath buffer appears to lack any Ca2+ or other IR-permeable cations. If this is indeed the case, more detail should be provided about why this bath buffer was selected and what this means for the source and mechanism of calcium responses observed, since it would not reflect direct IR-mediated transduction. It is also notable that addition of water gives such a detectable change in the tarsal preps.

(4) Visualization of IR20a driver activity in the labellum is interesting. Previous descriptions of labellar expression of IR20a range from no expression to expression in bitter neurons, so the current data linking IR20a to a different population of IR76b+ neurons warrants careful analysis in light of this discrepancy. However, some of the strongest presented evidence for expression is found in Figure 1, where the images are quite small, making it difficult to distinguish the morphology and sensillar innervation pattern of the cells labeled by the IR20a driver. In Figure 1A, several of the arrows do not appear to be associated with any visible fluorescence. It is similarly difficult to assess overlap. Including higher-resolution images and/or validating labellar expression, using antibodies, in situ hybridization, RT-PCR, or transcriptomics would strengthen these claims.

(5) Similarly, Figure 2 shows that IR20a is not required for Ca2+ responses to AAs or KCl in the legs, but is required for behavioral preferences and PER responses in the labellum. This suggests that IR20a receptors may function differently in different tissues, though direct evidence is lacking. Calcium imaging from a weakly expressed driver may be difficult, but electrophysiological recordings from relevant labellar sensilla or ectopic/heterologous reconstitution of the molecular receptors found there would give important insights into the response properties of these other IR20a receptor type(s) and could provide evidence for additional IR20a-containing complexes. The current paper focuses exclusively on IR25a/IR20a/IR76b, which do seem to reliably reproduce the Arg/NaCl responses observed in the tarsi, but even for the tarsal neurons it is unclear that this represents an exhaustive list of all the relevant IR20a-interacting subunits coexpressed in these cells. For instance, Koh et al., 2014 (PMID: 25123314) found several additional IR driver lines, including IR56b, were active in the 5v/s tarsal sensilla.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Reviewer #3 (Public review):

Summary:

The authors aimed to test whether spontaneous gamma-band oscillations over the parieto-occipital region can be volitionally upregulated using EEG neurofeedback, and whether this upregulation reduces subsequent pain perception and nociceptive-evoked brain responses. Gamma-band activity has been repeatedly associated with pain processing, but most available evidence remains correlational, and previous attempts to modulate pain-related gamma activity using non-invasive stimulation have not produced robust analgesic effects. The present study therefore addresses an important question: whether real-time neurofeedback may provide a more effective way to train endogenous gamma activity and thereby influence pain.

Strengths:

A major strength of the study is the use of an active/sham neurofeedback design. The authors also combine subjective pain ratings with laser-evoked potentials, which provides converging behavioural and neurophysiological outcome measures. The manuscript is clearly written overall, and the study addresses a question of broad interest for pain neuroscience and neurofeedback research.

Weaknesses:

A number of aspects limit the strength of the conclusions. The first and most important issue concerns the interpretation of scalp gamma-band activity. Gamma-band oscillations recorded with scalp EEG are difficult to measure reliably, are not observable in all participants, and can be strongly affected by muscle activity. The authors acknowledge this issue and include posterior neck EMG, but the control remains limited. A lack of correlation between one posterior neck EMG channel and Pz gamma power is not sufficient to exclude muscle contamination, especially because gamma-band artifacts can arise from multiple muscle groups and may not be well captured by a single EMG channel. This is particularly important because changes in posture, facial tension, breathing, and arousal could all influence high-frequency scalp activity.

Second, the evidence for a causal relationship between parieto-occipital gamma activity and pain perception should be interpreted cautiously. The authors show that gamma power increased in approximately half of the active neurofeedback participants and that these responders showed reduced pain ratings and laser-evoked potentials. However, because the main analgesic effect is tied to responder classification, it remains difficult to separate the specific effect of gamma upregulation from broader individual differences in task engagement, suggestibility, relaxation ability, attentional state, or neurofeedback learning capacity.

A third limitation concerns the control condition and blinding. Participants were reportedly blinded to group allocation, and the credibility ratings appear similar between groups, which is reassuring. However, it is not clear whether the experimenters were also blinded during data collection and interaction with participants. This matters because neurofeedback studies are particularly vulnerable to expectancy.

The choice of the two neurofeedback scenarios requires clearer justification. The manuscript describes a deep ocean scene followed by a seaside scene with relaxation instructions, but it is not clear why these two scenarios were selected, and whether they were matched for attentional engagement and affective content. This is not a minor point, because both groups showed reductions in pain ratings after the entire neurofeedback procedure.

The comparison with tACS is interesting but currently underdeveloped. The authors suggest that neurofeedback may succeed where gamma-frequency tACS failed because it allows real-time, personalized, self-regulatory modulation of ongoing activity. This is plausible, but the manuscript should discuss this distinction more deeply. Neurofeedback may not simply be a different way of modulating gamma; it may recruit volitional control, attentional engagement, immersion, expectation, etc. These mechanisms could be central to the observed pain reduction and may partly explain why neurofeedback effects differ from those of externally applied stimulation.

Overall, this is an interesting study that introduces a promising neurofeedback approach for experimental pain modulation. The findings are encouraging, especially the convergence between subjective ratings and laser-evoked potentials in responders. However, the conclusions should be tempered. The current evidence supports the feasibility of training gamma-band activity in a subset of participants and suggests that successful training is associated with reduced experimental pain.

Reviewer #3 (Public review):

Summary:

This manuscript reports that RNAi depletion of the inner-ring nucleoporin NPP-3/NUP205 in Caenorhabditis elegans embryos causes nuclear envelope rupture, premature chromatin condensation, and relocalization of condensed prophase chromosomes to the nuclear periphery. Through a candidate epistasis screen, the authors argue that this relocalization requires spindle assembly checkpoint (SAC) components (MDF-1, MDF-2, SAN-1), inner kinetochore proteins (HCP-3, HCP-4, and partially KNL-1), and NE rupture-repair factors (BAF-1, LEM-2), but not the CEC-4 heterochromatin- or SUN-1/POT-1 telomere-anchoring pathways. They further show that NPP-3 loss extends prophase and the NEBD-to-anaphase interval in a SAC-dependent manner, redistributes MDF-1/MDF-2, and reduces import of KNL-1/BUB-1/HCP-1. Co-depletion of NPP-3 with MDF-1 abolishes both the arrest and the peripheral localization while increasing lagging chromosomes, HUS-1 foci, micronuclei, and lethality, which the authors interpret as evidence that peripheral positioning is protective.

Weaknesses:

(1) The "protective" conclusion is largely correlative. The protective claim rests on the observation that co-depleting MDF-1 (or MDF-2) with NPP-3 removes the peripheral localization and simultaneously increases DNA damage, micronuclei, and lethality. However, depleting a SAC component removes at least three things at once: the peripheral localization, the prophase extension, and the NEBD-to-anaphase arrest. Because loss of the SAC independently causes premature anaphase and genomic instability through well-established mechanisms unrelated to chromosome positioning, the current design cannot separate damage caused by loss of a protective peripheral location from damage caused by checkpoint bypass. As presented, the increased damage is at least as consistent with simple SAC bypass. To support the protective model, the authors should provide a manipulation that disrupts peripheral positioning without abrogating the SAC-dependent arrest (for example, via the BAF-1/LEM-2 or kinetochore depletion) and show that damage still increases. The LEM-2 co-depletion, which partially suppresses positioning, is a natural place to test whether micronuclei and HUS-1 foci also rise.

(2) Knockdown efficiency of the partner gene in double RNAi is not verified. The double depletions are performed by cloning both gene fragments into a single vector. This risks reducing the effective dose of each dsRNA, so an apparent suppression in an npp-3; gene X (RNAi) condition could reflect weaker NPP-3 knockdown rather than a true epistatic relationship. The authors partially address this by showing that NPP-3::mCherry is still reduced in npp-3;mdf-1 (Figure S4A/B), which is helpful, but they do not demonstrate efficient knockdown of the partner genes in any double condition. For the key epistasis conclusions (MDF-1, MDF-2, HCP-3, HCP-4 suppressions), the knockdown of the second gene should be independently validated with a reporter strain for the second protein.

(3) Alternative explanations for the transcriptomic and H3K9me3 data are not excluded. NPP-3 depletion blocks nuclear import of molecules smaller than ~70 kDa and arrests development at early gastrulation. Both the RNA-seq changes (30% of genes downregulated) and the increased H3K9me3 signal could therefore be secondary consequences of nucleocytoplasmic transport failure and developmental arrest rather than evidence of position-dependent transcriptional repression. Notably, the authors' own finding that up- and down-regulated genes show no chromosomal positional bias (Figure S2C/D) argues against a model in which peripheral repositioning drives silencing of specific chromatin domains. This section should be reframed more cautiously, with the transport/arrest confound explicitly discussed, and RNA-seq replicate number and differential-expression thresholds reported.

(4) Evidence for SAC "activation in prophase" is indirect, and the effect is small. The claim of a novel prophase role for the SAC rests on MDF-1/MDF-2 intensity changes that are repeatedly described as "modest," "slight," or "mild," measured with small n and Student's t-tests, together with phenotypic suppression of prophase extension. There is no direct readout of SAC catalytic activity (for example, MCC assembly). The prophase-extension suppression by MDF-1 is the strongest evidence; the intensity data are weak support. I recommend tempering "the SAC is activated in prophase" to a hypothesis, and strengthening it with a more direct assay if feasible.

(5) The BAF-1 arm of the model is inferred rather than demonstrated. The authors state that baf-1(RNAi) and npp-3;baf-1 produced clustering too severe for epistasis, so BAF-1's requirement for peripheral localization is not actually established genetically; it rests on increased BAF-1 accumulation (correlative) plus the LEM-2 partial suppression. The proposed BAF-1/CENP-C bridge is extrapolated from Drosophila (ref. 71). This is reasonable as a discussion hypothesis but should not be presented in the abstract or summary model as an established dependency.

Reviewer #3 (Public review):

Summary:

TDP-43 proteinopathy is broadly found in neurodegenerative diseases. This manuscript investigates how nuclear export influences the biophysical properties of TDP-43. The authors use a combination of chemical screening and genome-wide siRNA screening to identify pathways that modulate TDP-43 liquid-to-solid transitions. Overall, the study employs a broad array of approaches and addresses an important question in TDP-43 pathobiology. The identification of nuclear export as a central regulator is compelling and conceptually aligns with the emerging view that TDP-43 nucleocytoplasmic trafficking is a major defect in neurodegeneration.

Strengths:

This work integrates chemical and genetic screening to identify novel modifiers. The candidates were validated in both reporter cell lines and iPS-differentiated organoids. The findings support the nucleocytoplasmic transport is important for the biophysical properties of TDP-43.

Comments on revised version.

The manuscript has been improved with more data and clarification. The RNase T1 treatment experiment suggests that RNA is required for anisosome integrity. However, this does not directly demonstrate LMB increases nuclear RNA availability as changes in protein composition or other RNA-dependent mechanisms may also contribute. The conclusion and discussion need to be edited to consider these alternative scenarios. Overall, as most of the evidence remains indirect, the manuscript should avoid overinterpretation regarding the mechanisms underlying TDP-43 phase transition and aggregation.

i got doberman, and i feel like that is true to my personality, cause im very efficient i try to do as good as a job as needed to do or complete, i do disagree about the impatient part cause my patient level is pretty good and i will prove that, or i will at least try to meet that expectation as much as i can, and i do take risks cause without risk there is no reward or close to it in my mind.

Reviewer #3 (Public review):

Summary:

The authors aimed to overcome the challenges associated with complex, conventional prokaryotic cell-free protein synthesis (CFPS) systems, which require up to thirty-five components, by developing a streamlined and efficient E. coli CFPS platform to encourage broader adoption. The main objective was to reduce the number of reaction components from thirty-five to seven, while also developing an accessible 'fast lysate' preparation protocol that eliminates time-consuming runoff and dialysis steps. The authors also sought to demonstrate the robustness and translational quality of this streamlined system by efficiently synthesising challenging functional proteins, including the cytotoxic restriction endonuclease BsaI and the self-assembling intermediate filament protein vimentin.

Strengths:

This study presents several key strengths of the optimised E. coli cell-free protein synthesis system in terms of its design, performance and accessibility.<br /> - The reaction mixture has been dramatically simplified, with the number of essential core components successfully reduced from up to thirty-five in conventional systems to just seven.<br /> - The "fast lysate" protocol is a significant advance in terms of procedure.<br /> - The system's ability to synthesise challenging, functional proteins is evidence of its robustness.

Comments on previous version.

The authors have adequately addressed my previous concerns.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors investigated TBRS etiology by using new human pluripotent stem cell models, modeling varying levels of TBRS-associated loss of DNMT3A function. They identified increased lineage-specific proliferation of precursors in TBRS ventral MGE-like progenitors, which they propose was related to increased signaling through the PIK3/AKT/mTOR pathway. Furthermore, they show that reduced DNA methylation during MGE-like progenitor differentiation into GABAergic interneurons can cause a premature expression of neuronal and synaptic genes, triggering precocious neuronal maturation. In conclusion, they propose that TBRS-derived GABAergic neurons exhibit hyperactivity that can alters the development and structure of neuronal networks.

Strengths:

Overall, the data presented is convincing, from an early developmental point of view, given that the iPSC-derived 2D cultures or organoids used do not get to reach a mature state. Nonetheless, the data clearly show the effects that deleterious mutations in TBRS can cause during the period of neurogenesis, which was missing in the field.

Comments on revised version.

The authors have responded to the reviewer's comments satisfactorily, and the manuscript has been much improved.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors aimed to assess the mechanisms of social influence on charitable giving, particularly by separating the role of donation magnitude and variability in others' donations, and by examining the role of incremental social information in a learning framework. They additionally investigated individual differences in the magnitude effects in relation to self-reported psychopathy and empathy. The main findings suggest that magnitude and variability of others' donation impacted the magnitude and variability of the participants' donations, respectively, and that the weight of social information on individual decisions correlates positively with psychopathy, but not with empathy.

Strengths:

(1) The findings extend previous evidence for social influence on charitable giving to contexts where social information is provided incrementally, and to effects on the variability in social information (in addition to the mean).

(2) Individual differences suggest a role for psychopathy, but not empathy.

(3) Findings are replicated across all 4 (or for some findings 3 out of the 4) experiments, which helps strengthen the claims.

(4) Multiple experiments are a strength, especially Experiment 4, which helped address concerns/potential confounds in the previous experiments, increase representativeness of the sample, add incentive compatibility, and generalize to another task domain (perceptual).

(5) For modelling, strong model and parameter recovery was obtained, thus validating the modelling pipelines.

(6) The experiments were pre-registered, though it's unclear whether only planned analyses were pre-registered, or specific directional hypotheses. It would help if the manuscript took the reader through the pre-registration (and any deviation from it), instead of expecting the reader to do the comparison between the pre-registrations and actual manuscripts.

(7) The studies are appropriately powered, and power analyses are provided.

Weaknesses

(1) Lack of rationale and justification for the between-subjects design.

While this design may be appropriate in some cases (for example, for the generalization of donation to new charities or as a potential "intervention"), it would have been great to know if the findings related to social influence extend to a within-subjects design, especially given the weak results related to the effects of standard deviation in others' donations. It is possible that variability in others' responses would have a stronger effect if manipulated within individuals, since the same individual exposed to both high-SD and low-SD social information may weight low-SD information more, but this effect may lack when individuals are only exposed to the same variability across trials.

(2) Motivation for the RL framework.

The use of reinforcement learning (RL) isn't very well motivated, both in the introduction and methods/results (given the task). In particular, why is RL relevant to studying the problem of social influence, which isn't inherently a learning problem? This should be better motivated in the introduction. Second, when taking the task into account, it's unclear why RL is an appropriate model, given that from the perspective of the participant, the 5 others are different individuals, so the model shouldn't assume that predicting an individual's donation should be related to the previous individual's donation. Unless participants are informed that there is some dependency between the 5 donors they observe on each trial? If so, this should be made clear.

(3) Specifics of modelling analyses, and separability between prediction and second donation data.

Does the RL-based model (either prediction-only or hybrid) explain more variance in second donations than a simple linear regression model predicting second donation from initial donation and the mean of others' donations (or each individual other's donation)? It could be helpful to add some models that include social influence (i.e., integration of social and individual information) but no learning mechanisms per se. If this is not done, I do not believe that current results show that participants combine "their initial self-donation tendencies with their predictions of observed others' giving to guide their second individual donations". While participants may update their predictions, the authors should test multiple models of prediction update (fit only on the prediction data to understand the specific mechanisms of prediction update independently of second donation - for example, is it RL, or could it just be a running average, or some other heuristic? In parallel, it would be helpful to test whether it's the learned predictions (or whatever other prediction update mechanism was found to best explain the prediction data) or the actual others' donation information that best explains second donation - when combined with initial donation. These latter models would be fit on second donation data only in order to be comparable. If it's not possible to separate people's predictions from the actual social information (others' donations) then this should be acknowledged as a limitation. Ultimately, separating the modelling by data type (prediction only vs second donation data only) would help provide more insights into the learning mechanisms (if any) and whether it's learned prediction, or just social information, which influences second donation.

(4) Missing statistics in generalization to novel donation results.

On page 13, in the generalization effect, the authors mention that "Compared with participants exposed to High-SD social information, those exposed to Low-SD social information exhibited less variability in their novel donations, with this effect being especially pronounced in the Low-Mean condition." Was this supported by a significant interaction between SD and Mean condition? If so, please report the statistics of the interaction; if not, it's probably better to refrain from making this claim.

(5) Behavioral index of social influence individual differences.

For the first analysis reported on the association with psychopathy (Figure S9), as well as empathy (Figure S10), the absolute change between first and second donation does not seem like the appropriate marker of social influence. While I understand from Figure 2 that most participants changed their donation in a direction consistent with the social information, it would appear more appropriate to calculate an index of donation change consistent with influence, so calculated as D2 - D1 for the high mean groups and D1 - D2 for the low mean groups. This would be a better measure to interpret high values as an index of social influence.

(6) Interpretation of psychopathy effects.

a) The general idea that high psychopathy would be associated with increased social influence seems counterintuitive. While I appreciate that the authors controlled for additional variables such as age, gender, condition, and other model parameters, is it possible that this effect could be instead explained by the availability heuristic (the social information is more readily available to participants than their individual choice from the baseline trials), lower memory for their own choice, or lower IQ/cognitive abilities? These appear to be important confounds to address to be able to interpret the findings.

b) Related to this, and given that psychopathy/empathy were negatively/positively related to baseline donation amounts, it would be good to account for baseline mean donation amount in the individual difference analyses.

c) Finally, the authors interpret this association in line with other studies that have shown strategic social blending in psychopathy - while this seems possible in contexts where others are present, it doesn't really seem to be the case in this task. Did participants believe the other donors were watching them somehow? It also appears contradictory for the incentivized experiment, whereby if high psychopathy participants would no longer be able to "maintain a favorable social image while still pursuing their own self-interests" (p.23), since as soon as incentivization is added, participants' own self-interests are directly in conflict with the social image. Was participants' understanding of the incentive compatibility tested in Experiment 4?

(7) Asymmetry between generous vs stingy social influence and link with psychopathy.

a) Was such an asymmetry present - in other words, were people more strongly influenced by generous others or stingy others, or were the two comparable? I believe some analyses could be added to test this, and this is also where a within-subject design could help (e.g., different parameters for the two directions of social influence at the individual levels).

b) Related to that, does the correlation with psychopathy vary between conditions? It appears important to test if the increased social susceptibility is general or specific to increases (~high mean group, generous social influence) or decreases (~low mean group, stingy social influence) in donation. I understand that the main effect of psychopathy survived controlling for conditions, but it would still be interesting to test for an interaction between psychopathy and condition in predicting donation changes (calculated as suggested in point 5 above) or social influence weight.

(8) Perceptual task in Experiment 4.

a) While it is good to show that there was no correlation between psychopathy and initial estimate in the perceptual task, were there differences in initial estimate accuracy (i.e., difference between initial estimate and correct answer) along psychopathology? If so, this should be controlled for in the analyses. Given that social influence is always in the direction of the true value, the proportional deviations between initial estimate and social information could yield larger numerical differences and induce larger changes in estimate.

b) Even if previous studies have excluded rounds in which participants update their estimate in the opposite direction of the social information or move beyond it, I believe analyses that include those rounds should be included, especially in the context of individual difference analyses. Could it be that individuals who are high in psychopathy or low in empathy have a higher proportion of rounds where they go against the social influence? The same question applies to the main 4 experiments (in case this criterion was applied to) as well as the perceptual task.

c) Because the perceptual task was completed by the same participants as Experiment 4, were the two social influence measures correlated across tasks? Was psychopathy better predicted by a combination of predictors across the two tasks?

(9) Were individual difference measures examined in relation to the variability effect?

(10) Discussion.

The authors argue against a role for opportunistic conformity. While I tend to agree with their interpretation, I believe that it could be strengthened as follows:

a) First, it relies on a null result (the absence of a difference in decreases between low-mean low-SD and low-mean high-SD groups), which I do not believe was explicitly tested; and even if it was, it should ideally be corroborated by Bayesian statistics to provide strength of evidence for the null effect.

b) Second, this could be a great opportunity to dive into the mechanisms of social influence in the model, by testing the theory that only the lowest (or highest) donation from the group (rather than the mean, or the learned prediction) influences donation. Could a subset of participants be better fitted by such a model?

(11) Methods. Maybe I missed it, but it's unclear what participants were told about the other donors they are observing. It is mentioned that they were fully debriefed after the experiment, but what they were told in the instructions appears important. Was believability tested (this also relates to my comment #1 about the rationale for a between-subjects design, which creates fairly biased sets of social information from the perspective of a single participant)? And related to my comment #2, what participants were told about the donors could help justify the rationale for the RL framework.

Reviewer #3 (Public review):

Summary:

Chovanec and Yin used their newly developed sci-L3-Strand-seq powerful method to characterize SCE after Cas9 cleavage in a human cell line, using either a single target site or an element repeated 237 times in the genome. SCE are often neglected in DNA repair analyses since they are “genetically silent”. Interestingly, the authors found enrichment of SCE at unique Cas9 sites, but only a modest enrichment of SCE when Cas9 targets 237 sites in the genome. The genetic control of SCE formation at Cas9 sites is not deliberately addressed in this paper. However, the authors found that targeted SCE seem to be enriched in a subpopulation of cells, particularly “permissive” for SCE, but the determinants of such a population are unknown. Finally, the power of the sci-L3-Strand-seq allowed the authors to characterize a specific type of SCE based on the analysis of reciprocal daughter-cell pairs' genomes that is associated with a specific type of chromosomal rearrangement compatible with the ones observed in HR defective BRCA1/2 deficient cells.

Strengths:

This is an interesting paper that molecularly explores sister chromatid exchanges, which represent an important challenge in molecular biology since they are genetically silent.

Weaknesses:

A complexity of the current paper is that it heavily relies on a recently published paper (Chovanec et al 2026, NAR) describing the powerful but complex technique sci-L3-Strand-seq. Knowledge of this paper is a prerequisite to understanding the current manuscript because no reminder is provided. In addition, the current manuscript presents the use of the sci-L3-Strand-seq technique in the study of SCE after Cas9-induced DSBs, while a companion study is referred to several times for containing results about SCE in XRCC1 KO. At some point, one questions the relevance of splitting the use of sci-L3-Strand-seq in different papers instead of making a single integrated one.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

The behavior data are solid.

Weaknesses:

The underlying mechanism is not fully investigated.

Major points：

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

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

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

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

Reviewer #3 (Public review):

Summary:

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

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

Strengths:

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

Weaknesses:

No overt weaknesses were identified by this reviewer.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

Reviewer #3 (Public review):

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

Comments on revised version.

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Comments on revisions:

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

Reviewer #3 (Public review):

Summary:

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

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

Strengths:

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

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

Weaknesses:

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

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

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

Reviewer #3 (Public review):

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

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

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

Reviewer #3 (Public review):

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

The study is without major weaknesses.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

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

Comments on revised version:

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

Conclusion:

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

Weaknesses:

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

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

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public review):

Summary

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

Strengths

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

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

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

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

Weaknesses

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

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

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

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

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

Weaknesses:

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

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

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

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

Reviewer #3 (Public review):

Summary:

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

Strength:

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

Weaknesses:

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

Conclusion:

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

No major weaknesses were identified.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

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

Weaknesses:

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

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

Weaknesses:

(1) Data deposition (major):

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

(2) LLM agent capabilities underspecified:

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

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors describe the results of a high throughput screen for small molecule activators of GCN2. Ultimately, they find 3 promising compounds. One of these three, compound 20 (C20) is of the most interest both for its potency and specificity. The major new finding is that this molecule appears to activate GCN2 independent of GCN1, which suggests that it works by a potentially novel mechanism. Biochemical analysis suggests that each bind in the ATP binding pocket of GCN2, and that at least in vitro C20 is a potent agonist. Structural modeling provides insight into how the three compounds might dock in the pocket and generates testable hypotheses as to why C20 perhaps acts through a different mechanism than other molecules.

Strengths:

Of the 3 compounds identified by the authors, C20 is of the most interest, not just for its intriguing mechanistic distinction as being GCN1-independent (shown genetically in two distinct cell lines, CHO and 293T, and in contrast to other GCN2 activators) but also for its potency. Ultimately, C20 might be a tool for providing mechanistic insight into the details of GCN2 activation and regulation and could be exploited therapeutically.

Weaknesses:

The chief limitation of this work is that the experiments exploring the effects of C20 on ISR output in cells are limited, so how useful these compounds are both experimentally and therapeutically remains to be determined.

Comments on revised version.

The authors have satisfactorily addressed my comments. A more extensive analysis of UPR signaling in cells (transcription and cell death in particular) would have further strengthened the paper, but that can be left to future work.

Reviewer #3 (Public review):

Summary:

The present paper by Shinoda et al. from the Miura group builds upon findings reported in an earlier study by the same team (Shinoda et al., PNAS, 2019), which identified a non-apoptotic role for the Drosophila executioner caspase Dcp-1 in promoting wing tissue growth. That earlier work attributed this function primarily to Dcp-1 and to Decay, a caspase structurally related to executioner caspases, but not to DrICE, the principal apoptotic executioner caspase. The authors further proposed that this non-apoptotic caspase activity operates independently of the initiator caspase Dronc.

In the current study, the authors both corroborate aspects of their previous findings and extend the investigation to mechanisms regulating Dcp-1 in this context. They identify roles for the giant IAP Bruce, two BCL-2 family members, and autophagy-related components in modulating non-apoptotic Dcp-1 activity. Moreover, they show that Bruce binds to a BIR-like peptide exposed upon Dcp-1 cleavage, but not to DrICE. The study further suggests that low levels of Dcp-1 activity promote wing tissue growth, whereas excessive activity induces cell death, as evidenced by impaired wing development following Dcp-1 overexpression. Overall, the manuscript provides several intriguing insights into the non-apoptotic regulation of the comparatively weak apoptotic executioner caspase Dcp-1 and complements the group's earlier work. However, several concerns remain regarding certain interpretations of the data and the experimental rigour of some of the results.

Strengths:

A major strength of the work is its systematic genetic and biochemical approaches, which combine tissue-specific manipulation with protein interaction mapping to explore how Dcp-1 is regulated. The identification of several regulatory factors, including an inhibitor of cell death protein and components linked to autophagy, provides a coherent framework for understanding how Dcp-1 activity might be tuned.

Weaknesses:

The evidence supporting some key claims remains incomplete. In particular, the type of cell death form induced when Dcp-1 is overexpressed is not clearly established, and additional tests would be needed to distinguish between the different cell death types.

Likely impact:

The study contributes to a growing body of work showing that proteins traditionally associated with cell death can have broader roles in tissue development. This conceptual advance is likely to be of interest to researchers studying growth control and tissue maintenance.

Specific points:

(1) Nature of the wing ablation phenotype<br /> A central concern is whether the wing ablation phenotype observed upon Dcp-1 overexpression truly reflects apoptotic cell death. The authors show in Fig. 1c that nuclei in cells overexpressing Dcp-1, but not DrICE, zymogens are highly condensed, which is suggestive of apoptosis. However, it is equally plausible that this phenotype reflects a form of non-apoptotic, Dcp-1-dependent cell death (e.g. autophagy-dependent cell death). This distinction could be readily addressed using TUNEL labelling and direct caspase activity assays. The latter would be particularly informative, as it remains unclear whether zymogen Dcp-1 is capable of cleaving standard effector caspase reporters in vivo. Does the anti-cleaved Dcp-1 antibody detect Dcp-1 activation following overexpression of the Dcp-1 zymogen?

(2) Role of Decay<br /> In their earlier study, the authors identified Decay as another caspase influencing wing growth, albeit more modestly than Dcp-1. It is therefore unclear why this line of investigation was not pursued further in the current work. This omission is notable, as Decay is not implicated in apoptosis and, to date, no substantial physiological function has been assigned to this caspase in any system. At minimum, this point should be discussed explicitly.

(3) Fig. 2: Proximity labelling analysis<br /> The authors use TurboID-mediated proximity labelling to reveal distinct Dcp-1- and DrICE-associated proteomes across tissues, with a particular focus on the wing disc. They further demonstrate that RNAi-mediated knockdown of the Dcp-1-associated proteins Sirt1 and Fkbp59 suppresses the wing ablation phenotype induced by Dcp-1 overexpression, suggesting that these factors are required for Dcp-1 activity. However, it should be clarified whether Bruce was identified as a Dcp-1 interactor in the proximity labelling dataset, given its proposed central regulatory role. In addition, further discussion of Fkbp59, its known functions and how it might mechanistically influence Dcp-1 activity, would be valuable.

(4) Fig. 3: Autophagy-related factors<br /> Given that Sirt1 is known to promote autophagy, the authors next examine autophagy-related proteins and identify roles for Atg2, Atg8a, Debcl, and Buffy in Dcp-1 activation. Notably, these proteins do not promote cell death in the Hid-induced canonical apoptotic pathway. However, it is important to determine whether knockdown of Debcl, Buffy, Atg2, or Atg8a alone affects wing development in the absence of Dcp-1 overexpression, to exclude the possibility that these perturbations independently impair wing formation.

(5) Evidence for canonical autophagy<br /> The involvement of autophagy would be more convincingly demonstrated by testing additional core autophagy genes, such as Atg7, Atg5, and Atg12, as well as performing a combined knockdown of Atg8a and Atg8b. Moreover, direct assessment of autophagy at the cellular level using established genetic reporters would substantially strengthen the conclusions.

(6) Figs. 4-5: Functional consequences<br /> It would be informative to determine whether Synr, Debcl, or Buffy influence wing size on their own and whether their overexpression enhances wing growth.

(7) Terminology and interpretation of cell death<br /> Taken together, the results suggest that Dcp-1 zymogen overexpression induces a form of non-apoptotic cell death, potentially autophagy-dependent or related. The reviewer does not understand the authors' insistence on referring to this process as apoptosis. The authors should be more cautious in their terminology: there is no canonical versus non-canonical apoptosis, there is simply apoptosis. Without stronger evidence, these effects should not be described as apoptotic cell death.

Comments on revised version.

In the revised manuscript, the authors addressed each of my concerns in good faith and, in my opinion, responded to them thoroughly and satisfactorily. I have no further concerns.

Reviewer #3 (Public review):

Sutlief and colleagues report behavioral and neural results from mice performing a patch foraging task. Behaviorally, they argue that time since last reward is a major determinant of when mice decide to leave a patch. In the brain, they find neurons in the dorsomedial striatum that show step-like changes in their firing rate at a range of times following reward. Population analyses show that the cumulative fraction of neurons that have undergone such a step-like change in firing rate can be used to predict patch-leaving times with impressive accuracy.

Overall, this is an interesting set of results that has been analyzed in a principled way. The manuscript is well written, the results are explained clearly, and the evidence supporting the authors' conclusions is strong. The manuscript is therefore a potentially valuable contribution to the growing literature assessing how the brain solves stopping problems like the patch foraging scenario. I have suggestions for the authors to consider that might further increase the rigor of their results, and a few suggestions for improving the clarity of the work for readers.

(1) I don't quite understand how the behavioral task works. Are mice rewarded for making discrete nose poke responses in the investment and context ports? Or are they required to nose poke and hold? Is reward given with some probability per response (which decreases with time in the patch), or is the reward probability a function of elapsed time in the patch, time since last response, or dwell time in the port? Also, exactly what equation defines how reward probability changes over time for the high- and low-value contexts? I couldn't find these details anywhere in the manuscript, and they would be helpful for better understanding the behavior and the later neural results.

(2) How was the optimal strategy determined? Several features of the author's task violate the assumption of the marginal value theorem, so computing the optimal residence time is not a straightforward application of the classic model. There's a diagram in Figure 1h that depicts an MVT-like graphical solution, but the conventions of the plot are not familiar to me, and there's no description of how it works in the results or methods. More detail here would be much appreciated. In a similar vein, the authors report that mice generally exceeded optimal residence times in patches, but no statistical comparison is provided to back up that statement. There should be some formal test of this if it is to be included in the results.

(3) The authors argue that time since last reward is the predominant determinant of patch leaving time. However, as the authors note, time since last reward is correlated with other task variables (patch reward rate, time in patch, etc.). I don't trust that SVM coefficients can be interpreted as straightforward measures of a variable's importance for classification performance in the case of correlated predictors. A better approach would be to assess how well the model performs as subsets of variables are added or removed from the model.

(4) For the SVM analysis, I'm not quite understanding how or why the authors are using 5 s after mice left the patch as additional "Leave" examples. For instance, is time since entry computed for the investment patch, or the context patch that mice enter after they leave the investment patch? Similarly, is the time since the last reward relative to the investment patch, or the reward the mouse is likely to receive at the context patch? Moreover, I'm not sure it's safe to assume that because the mouse left at time t, time t+1 necessarily reflects conditions on which the mouse would definitely leave again. If we're thinking about the stay/leave decision as something that is being repeated sequentially on a fast time scale to determine how long mice stay in the patch, it doesn't follow that observing a mouse leave means that any patch conditions after that would necessarily result in the same decision. If that were the case, it would mean that seeing a mouse leave a patch after 2 s would preclude ever observing a residence time longer than 2 s, which is clearly not compatible with the authors' data. Ultimately, it's only possible to observe one decision to leave per trial; including data points beyond that as additional leave examples seems overly speculative to me.

(5) The authors validate their approach for quantifying step-like changes in firing rate using simulations of constant-rate Poisson spiking and observe a low false positive rate. This is encouraging, but it doesn't seem like the only way in which their method could go awry, or even the most concerning way. I would be much more interested in seeing the false positive rate for continuous, ramp-like changes in firing rate, which would be much more likely to trip up the authors' approach and are also the major relevant alternative hypothesis to step-like changes in firing rate. Random walks in firing rate might also be worth testing.

(6) The finding that cumulative "transitioned" neurons is predictive of patch leaving is interesting. However, I can't help but wonder how truly informative this variable is for predicting patch leaving. It seems as though neurons can only transition firing rates one time. That means that as time in the patch increases, the fraction of transitioned neurons naturally increases. Similarly, all visits must eventually end with the mouse leaving the patch, so the hazard rate of leaving increases with time in the patch. Given that, can the authors be certain that the cumulative transitioned neurons are really what's predicting patch leaving time, or would any generically increasing function perform roughly the same? An interesting test would be to mismatch the neural predictor and behavior at the level of trials. If this mechanism is really specific, rather than something that captures the general structure of an increasing hazard rate of leaving, then prediction of leaving time should work substantially better when the neural predictor is correctly matched to behavior on the trial for which it was recorded.

Reviewer #3 (Public review):

Summary:

In this manuscript, Duan et al perform a combinatorial TF overexpression screen combined with single-cell RNA-seq (Reprogram-Seq) to extract general principles of how combinatorial TF interactions drive distinct gene regulatory networks in reprogrammed cells. Using a library of 105 TFs, they induce different cell fates, many of which resemble in vivo cell identities. They observe that combinations of TFs have better reprogramming results than inductions driven by a single TF. By looking at gene expression enrichment/depletion in different reprogrammed cell clusters, they infer functional GRNs induced by specific TF combinations and identify GRNs specific for certain cell types. They also identify TFs that could improve known TF cocktails for the induction of certain cell fates. They observe that TFs with cooperative interactions regarding the regulation of gene expression may lead to better reprogramming results, and finally, they build a bottom-up approach that utilizes the single-cell transcriptomes to predict TFs driving certain reprogrammed fates.

Strengths:

Reprogram-Seq is not new, but the strength of the study lies in the fact that the authors assess the induction outcome from a large number of different TF combinations. The authors are thus able to make broad observations, such as the modularity of GRNs and TF cooperativity, as well as propose new TFs and TF interactions to be tested for the induction of certain cell fates. The manuscript is well written, and the conclusions are, in general, supported well by the authors' analyses and data.

Weaknesses:

The study would benefit from some further analysis and discussion to better tighten the conclusions:

While both expression enrichment and depletion were used to define perturbation clusters, the authors then focused on analyzing functional gene groups only for the enriched genes. Are there any functional relations between the repressed genes within a perturbation cluster? Do the authors observe the same modularity (in terms of regulation by TFs) for repressed genes as they do for induced genes?

Can the authors give a description of how neomorphic TF interactions work? How would gene expression be affected in single vs double perturbation in those cases?

What does it mean functionally when a TF pair shows more than one type of interaction (as shown in Supplementary Table 6), and how do such interactions correlate with successful transcriptional reprogramming?

In the last Results section, the authors are able to use the transcriptome to predict the TF that was used for the induction. Could the authors discuss some plausible applications of this TF prediction method? For example, could they use it on in vivo single-cell RNA-seq of a certain cell type to predict candidate TFs for the induction of that cell type?

It would help the reader if, at the end of each Results section, the authors add a concluding paragraph, highlighting the most important conclusions and findings (same as they have done in the section titled "Combinatorial TF over-expression reprograms MEFs to diverse states").

Reviewer #3 (Public review):

This manuscript studies the connection between neural activity collected through electrocorticography and hidden vector representations from autoregressive language models, with the specific aim of studying the influence of language model size on this connection. Neural activity was measured from subjects that listened to a segment from a podcast, and the representations from language models were calculated using the written transcription as the input text. The ability of vector representations to predict neural activity was evaluated using 10-fold cross-validation with ridge regression models.

The main results are that (as well summarized in section headings):<br /> (1) Larger models predict neural activity better.

(2) The ability of language model representations to predict neural activity differs across electrodes and brain regions.

(3) The layer that best predicts neural activity differs according to model size, with the "SMALL" model showing a correspondence between layer number and the language processing hierarchy.

(4) There seems to be a similar relationship between the time lag and the ability of language model representations to predict neural activity across models.

Strengths:

(1) The experimental and modeling protocols generally seem solid, which yielded results that answer the authors' primary research question.

(2) Electrocorticography data is especially hard to collect, so these results make a nice addition to recent functional magnetic resonance imaging studies.

Weaknesses:

(1) The interpretation of some results seems unjustified, although this may just be a presentational issue.

a) Figure 2B: The authors interpret the results as "a plateau in the maximal encoding performance," when some readers might interpret this rather as a decline after 13 billion parameters. Can this be further supported by a significance test like that shown in Figure 4B?

b) Figure S1A: It looks like the drop in PCA max correlation is larger for larger models, which may suggest to some readers that the same trend observed for ridge max correlation may not hold, contra the authors' claim that all results replicate. Why not include a similar figure as Figure 2B as part of Figure S1?

(2) Discussion of what might be driving the main result about the influence of model size appears to be missing (cf. the authors aim to provide an explanation of what seems to drive the influence of the layer location in Paragraph 3 of the Discussion section). What explanations have been proposed in the previous functional magnetic resonance imaging studies? Do those explanations also hold in the context of this study?

(3) The GloVe-based selection of language-sensitive electrodes (at least to me) isn't explained/motivated clearly enough (I think a more detailed explanation should be included in the Materials and Methods section). If the electrodes are selected based on GloVe embeddings, then isn't the main experiment just showing that representations from larger language models track more closely with GloVe embeddings? What justifies this methodology?

(4) (Minor weakness) The main experiments are largely replications of previous functional magnetic resonance imaging studies, with the exception of the one lag-based analysis. Is there anything else that the electrocorticography data can reveal that functional magnetic resonance imaging data can't?

Comments on revised version.

I reread the manuscript, my previous review, and the authors' response to it. I thank the authors for clarifying any misunderstanding from my end (e.g. the different LLM tokenizers) and feel that the authors addressed my concerns very carefully.

Reviewer #3 (Public review):

Summary:

This study investigates the molecular underpinnings of immune responses in the leptomeninges in neonatal bacterial meningitis. Bacterial meningitis is a major disease burden, particularly for neonates, and it has previously been noted that the meningeal immune environment in infants is permissive to opportunistic infection (Kim et al., Sci Immunol, 2023). There is less known about the contribution of the stromal compartment to meningeal immune responses. Seegren et al. interrogate the role of leptomeningeal endothelium in host defense in E. coli infected neonatal mice using mouse genetic tools to delete the LPS receptor Tlr4 from either endothelial cells/stromal cells (using Cdh5-CreER) or myeloid cells (using LysM-Cre). The authors use snRNAseq, cleared cortical mounts, and in vitro work to define the impact of E. coli infection on leptomeningeal endothelial cells. This study uses a range of innovative techniques to probe the role of the stromal compartment in meningitis. With additional experiments to confirm the specificity of their Cre models, this strengthens the interpretation of the study significantly. The only major weakness is the inability to confirm TLR4 knockout in myeloid cells.

Strengths:

This study makes excellent use of cleared cortical mounts to examine the biology of the leptomeninges, in particular, changes to the endothelium, with unprecedented detail. In combination with high-quality sequencing data provide new insights into the impact of meningitis on the leptomeninges. The data presented by the authors is of very high quality.

The authors have also done substantial work to address my two major comments regarding 1) the specificity of their Cre systems and 2) peripheral impacts of the interventions.

(1) The authors identified and acknowledged some impacts in the leptomeningeal stroma (the relatively high level of recombination in ECs vs FBs presumably reflects a single low dose being given, where other groups have done more aggressive tamoxifen regimens that drive recombination in FBs as well). Given the incomplete recombination in the leptomeningeal FBs, I agree with their conclusion that it is probably endothelial driven. Acknowledging the contributions of other myeloid cells with the L. The Cre-NLS experiments with nuclear markers provided excellent data and had beautiful staining.

(2) The authors did not observe differences in bacterial burden in peripheral organs in either CKO model, suggesting that CNS impacts are not downstream of peripheral bacterial control.

Weaknesses:

(1) While the inducible Cre lines used by the authors target both peripheral and CNS tissues, this potential confound is mitigated by the lack of impact on peripheral disease burden.

(2) The authors were not able to confirm TLR4 knockout in myeloid cells, and this caveat is acknowledged. The lack of response in TLR4 VEKO mice strongly suggests successful conditional knockout.

(3) The cell line model (bEnd.3) is a relatively low fidelity model of BBB endothelial cells. The authors acknowledge this, and it is likely that endothelial cell responses to LPS are highly conserved.

(4) It is perhaps not surprising that Tlr4 is required for meningitis responses with E. coli. However, it is unclear if these findings can be generalised to other, more common, meningitis infections (streptococcal/pneumococcal).

Reviewer #3 (Public review):

Summary:

The study provides an in-depth phenotyping of a novel zebrafish larval model of ADHD. This topic is interesting, and the model and the approach are relevant and well-justified. While the paper has a massive amount of high-quality data, the general structure and presentation of this material lack focus and a clearly articulated rationale.

Strengths:

The paper is methodologically sound, well-presented, and well- illustrated. It has a clear logical rationale and reasonable experimental design.

Weaknesses:

The amount of high-quality data is impressive, yet the general structure and presentation of this material lack focus and a clearly articulated rationale.

(1) First, it is unclear why VR is necessary here. It needs a better explanation in both the abstract and the intro section of the manuscript.

(2) Second, data need to be better presented (most important things first, least important - shorter or move to the Supplementary materials). Currently, it is too much to be clear and easy to follow.

(3) Discussion needs to better state the novelty and the significance of these findings. What does the study offer that is new? Why was it important to perform? What big questions does it address?

(4) The authors should better discuss the study limitations and future directions of research.

(5) There should be a stronger conclusion with a take-home message to emphasize what new information the study brings and why it is important.

(6) The overall style of the paper should be improved. Currently, it reads like a dry bulleted CRO report, not a usual scholarly paper.

(7) Optimize the text flow. Currently, the overall flow of the discussion needs to be smoother - it now reads as a selection of bulleted paragraphs, with few connections between them.

Reviewer #3 (Public review):

This study makes clever use of generative AI to create stimuli that are pixel-for-pixel identical but which have radically different meanings depending on their orientation, to investigate the perception of animacy while retaining control over low-level image features (so-called 'anagram' stimuli).

The authors present seven elegantly designed experiments in a commendably compact format.

Experiments 1 and 2 involved a working memory paradigm in which participants had to spot which of five objects in an array changed after a pause. Importantly, the changed object was an anagram stimulus that in one orientation matched the animacy/inanimacy of the changed object, and in the other orientation was the opposite (e.g., a rabbit is replaced by either a dog or a boot, where the dog and boot stimuli are actually identical, just rotated by 90 degrees). They found a difference in accuracy depending on whether the animacy of the objects matched.

Experiments 3 and 4 used a visual search task in which the participants had to localize the target, and the distractors were anagrams that either matched the target in terms of animacy or did not. There was a significant cost in terms of response time when the animacy of the target was the same as that of the distractors. Experiments 5 and 6 also used a similar visual search design, except that the task was to determine if the target was present or absent from the display, and the distractors again either matched or differed from the target in terms of animacy. Again, the authors found slower responses when the distractor arrays matched the animacy of the target than when they differed.

An obvious potential concern about the studies is addressed by Experiment 7. It is unclear if the observed effects are related to the specific orientations of the target and distractor stimuli selected in each condition. For example, it could be that all the animate versions of the anagrams involved tall and skinny shapes, while all the inanimate versions involved wide and short objects, due to the 90-degree rotational difference between the two versions of the stimuli. To control for this, the authors repeated the visual search experiment but with convex-hull silhouettes of each of the stimuli. In other words, all targets and distractors from each trial were replaced by a black splotch with approximately the same overall outline (envelope) as the corresponding stimulus. Importantly, in contrast to the anagram stimuli, the silhouettes had had no meaningful semantic interpretation, and their animacy did not change depending on their orientation.

Reviewer #3 (Public review):

Summary:

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

Strength:

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

Weaknesses:

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

Conclusion:

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

Reviewer #3 (Public review):

In this manuscript, Wang et al employ a chemical biology approach to investigate the differences between the enzymatic and scaffolding roles of tankyrase during Wnt β-catenin signalling. It was previously established that, in addition to its enzymatic activity, tankyrase 1/2 also plays a scaffolding function within the destruction complex, a property conferred by SAM-domain-dependent polymerization (PMID: 27494558). It is also known that TNKS1/2 is an autoregulated protein and that its enzymatic inhibition leads to accumulation of total TNKS proteins and stabilization of Axin punctae (through the scaffolding function of TNKS1/2), leading to rigidification of the DC and decreased β-catenin turnover. The authors surmised that this could, in part, explain the limited efficacy of TNKS1/2 catalytic inhibition for the treatment of colorectal cancers. To test this hypothesis, they evaluated a series of PROTAC molecules promoting the degradation of TNKS1/2 to block both the catalytic and scaffolding activities. They show that IWR1-POMA (their most active molecule) promotes more efficient suppression of beta-catenin-mediated transcription and is more active in inhibiting colorectal cancer cell and CRC patient-derived organoids growth. Mechanistically, the authors used FRAP to demonstrate that catalytic inhibitors of TNKS led to a reduced dynamic assembly of the DC (rigidification), whereas IWR1-POMA did not affect the dynamics.

Overall, this is an interesting study describing the design and development of a PROTAC for TNKS1/2 that could have increased efficacy where catalytic inhibitors have displayed limited activity. Knowing the importance of the scaffolding role of TNKS1/2 within the destruction complex, targeting both the catalytic and scaffolding roles certainly makes sense. The manuscript contains convincing evidence of the different mechanisms of the PROTAC vs catalytic inhibitors. Some additional efforts to quantify several of the experiments and to indicate the reproducibility and statistical analysis would strengthen the manuscript. Ultimately, it would have been great to evaluate the in vivo efficacy of IWR1-POMA in an in vivo CRC assay (APCmin mice or using PDX models); however, I realize that this is likely beyond the scope of this manuscript.

Reviewer #3 (Public review):

Summary:

The authors investigated the role of the zona incerta in motivation and cue-reward associations. Using chemogenetic and optogenetic manipulations of the ZI, they altered motivation in cued and uncued variants of the progressive ratio task and rescued deficits in motivation induced by chronic stress. They further use fiber photometry to demonstrate that the ZI tracks the formation of cue-reward associations.

Strengths:

(1) The authors fill an important gap in the literature linking sensory input to motivation via the zona incerta.

(2) The authors demonstrate that ZI tracks cue value rather than just tracking sensory input.

(3) The authors demonstrate that the ZI excitation rescues stress-induced suppression of motivation.

(4) The authors perform several important control tasks, demonstrating that their findings are not a result of alterations in locomotor activity, food consumption, or memory.

Weaknesses:

In Figure 1D and E (inhibitory vs excitatory DREADDS), the control groups in the Gi group appear to have more elevated breakpoints than the control groups in the Gq group, although a statistical comparison between the two is not reported. It is not clear if this is because the two groups were given a different reinforcement schedule, this should be made clearer.

In Figure 1E, it is important to note that although the authors found a significant planned comparison between Gq VEH and Gq CNO, the interaction was not significant, nor were comparisons to mice injected with control virus. Thus, activation of ZI GABA neurons appears to be a relatively weak effect.

In Figure 5, the authors see what is likely a significant difference in lever presses during acclimation between the Gi and GFP groups, which they state is an expected difference. However, it is difficult to see why this would be expected. While Gi:CNO manipulation yielded lower breakpoints in Figure 1D, it did not yield lower FR1 responding for food in Fig S3 (although this was FR1 for food dispenser visits rather than lever press). One reason I ask is that the authors highlight the differences in CS+/CS- between groups, but the biggest difference between groups appears to be in acclimation, which may be driving the group x block interaction.

In Figure 6, the authors demonstrate that optogenetic stimulation during cue light increases the breakpoint in females, but not in males. They suggest that this may be because the males did not sufficiently discriminate the cue light before optogenetic manipulation began. If this were the case, then the authors would need to use "cue discrimination" as a factor to determine if it is a better predictor than sex.

The authors' work demonstrates that chemogenetic inhibition of GABAergic ZI cells reduces uncued motivation for reward but enhances cued responses under extinction. The authors state that this is a paradoxical finding that suggests that the ZI operates within a redundant motivation network. However, a critical difference between the two tasks is that one measures motivation for food while the other measures persistent responding under food extinction, which are not the same process. Thus, a simpler explanation is that ZI inhibition reduces motivation and impairs extinction.

Reviewer #3 (Public review):

This is a methodologically sound manuscript and provides reasonably interpretable results. While being appropriate, they do not seem to bring entirely novel concepts; nevertheless, most of my comments concern the calibration of the interpretive claims rather than the quality of the data.

Strengths:

(1) Longitudinal within-subject imaging:<br /> Tracking the same layer 2/3 neurons across learning allows the bidirectional effect (enhancement in R+, suppression in R-) to be measured within identified cells rather than inferred across cohorts.

(2) Appropriate behavioural controls:<br /> The R+/R- design controls for repeated sensory exposure, and maintaining rewarded auditory trials in both groups controls for engagement and arousal, arguing against disengagement as the source of the R- effect.

(3) Convergent causal manipulations:<br /> Muscimol and optogenetic inactivation both abolish acquisition and include an adjacent control region (fpS1); the temporally restricted optogenetic result partially addresses the concern (Hong et al., 2018) that sustained inactivation may destabilise downstream circuits.

(4) Convergent analyses:<br /> Single-cell learning modulation indices, population similarity measures, and a trial-resolved decoder projection onto a naïve-to-expert axis provide consistent evidence that representational change is concurrent with behavioural acquisition.

(5) Projection-specific resolution:<br /> Retrograde labelling shows learning-related changes in wS2-projecting, but not wM1-projecting neurons, consistent with preferential routing of task-relevant signals through the wS1 to wS2 pathway.

(6) Mechanistically motivated reactivation analysis:<br /> Relating rapid, reward-dependent plasticity to spontaneous reactivations on a timescale of minutes is an original use of the single-session paradigm.

Weaknesses and points requiring clarification

(1) The stimulus is not strictly novel: Passive whisker stimulations were delivered on pre-training Days -2 and -1, so what changes on Day 0 is the stimulus-reward contingency rather than the stimulus itself. This resembles contingency reassignment with reversal-like properties (and possible habituation or latent inhibition) rather than de novo learning, and the licking response is already established during auditory training. The framing should be qualified accordingly.

(2) Barrel cortex dependence should be stated more narrowly: The data show that acute wS1 suppression prevents acquisition of this task, not that whisker detection in general requires barrel cortex; cortical dependence varies with task and manipulation (Hong et al., 2018; Ryan et al., 2022 vs Miyashita and Feldman, 2013). The near-threshold explanation would require psychometric or stimulus-intensity data.

(3) The passive block carries confounds: It is acquired after task disengagement, when satiety, arousal, and reward history differ across groups and days. The authors should report within-block response adaptation and the robustness of the main results to early versus late passive trials. Additionally, could passive presentation of the stimulus without reward delivery lead to devaluation of the stimulus, leading to additional behaviour and plasticity changes which are not addressed?

(4) Some statistics appear to be neuron-level rather than animal-level:<br /> Very small p-values (e.g., the LMI-participation correlation r = 0.24, p on the order of 10^-41) suggest thousands of non-independent neurons treated as independent samples, risking pseudoreplication. Central claims should rest on hierarchical or animal-level statistics with effect sizes.

(5) The cosine-similarity decrease in R- animals needs clarification:<br /> Because cosine similarity is scale-invariant, uniform suppression would leave it largely unchanged; the observed decrease therefore implies heterogeneous suppression, reduced signal-to-noise, or increased variability, and the favoured interpretation should be stated.

(6) The decoder requires cautious interpretation:<br /> Training on passive trials and applying to active Day 0 trials could introduce a behavioural-state domain shift. The meaning of positive and negative values in Figure 4C should be defined, and near-zero early projections reflect the classifier boundary rather than a biological baseline.

(7) The reactivation analysis is the least conclusive and is susceptible to circularity:<br /> The template and the LMI are both derived from the passive whisker response, predisposing responsive neurons to register as reactivation participants, and the Day 0 template is obtained after learning.

Leave-one-cell-out and pre-learning templates, cell-specific templates, and tests of whether reactivations predict subsequent trial responses would strengthen the claim; causal disruption would ultimately be required.

(8) Figure, sample-size, and specificity points:<br /> The positive LMI shift in R+ animals is less visible than the R- shift in Figure 3F; the optogenetic cohort is small (n = 6 per group); and confirming that auditory detection was preserved during wS1 inactivation would establish whisker-specificity.

(9) The comparison to prior work is overly broad:<br /> Banerjee et al. (2020) and Chéreau et al. (2020) are reversal learning and discrimination paradigms and may not be equated with simple whisker detection; the defensible novelty claim is the trial-resolved tracking within the first session and its concurrence with online reactivations.

Reviewer #3 (Public review):

Summary:

In this manuscript, Le and Wei proposed a new method to identify differential correlations in real recordings (and simulations) that is based on splitting the simultaneously recorded population of neurons into two disjoint subpopulations. The method is based on evaluating the correlation between the decoded stimulus for each sub-population across trials. The authors validate their method on simulations and find the magnitude of differential correlations on three different publicly available datasets.

Strengths:

We think that this is a solid and relevant study for the computational neuroscience community, especially for the originality of the method and the fact that it seems to bypass the problem of very large populations to identify differential correlations. Overall, the results are novel and significant, and it addresses an important gap in the field. The main results are presented clearly and are easy to follow.

Weaknesses:

However, we believe that there are some additional analyses and clarifications that should be made to increase the clarity and impact of this study. In general, we believe that the authors should make a better effort to explain how their novel method depends on the number of trials and the number of neurons. More specifically:

Major

(1) The authors should show a realistic case for the covariance matrix in Figure 1. Currently, they are showing only Poisson noise (Figure 1c-e), only gain + Poisson (Figure 1f-h), and only differential correlations + Poisson (Figure 1i-k). They should show these same plots with a biologically realistic non-differential correlation structure (limited-range correlations, see Kanitscheider PNAS 2015). Perhaps even show the case for limited-range + gain + differential correlations. They should do the same for Figure 2.

(2) Throughout the manuscript, the role of population size (N) on the method is a bit confusing. Figures 1 and 2 give the impression that N is not particularly important, which is counterintuitive and surprising. We understand that that is one of the strengths of the split-trial method, but the authors should explain in much more detail in the results and methods the role of population size on their novel method. Why is large N crucial for the other methods, but not for them? There is a little bit of population-size dependency on Figures 3-5, especially on Figure 4g. The authors should explain in more detail those effects.

(3a) For dataset [27], the stimulus density was ~12 samples per deg for uniform sampling and ~1000 samples per deg for dense sampling. Figure S12 shows an overestimate of information-limiting noise when the number of trials used was significantly downsampled, which is, first of all, in disagreement with simulation results showing "when only a small number of trials are available to infer a large d-prime, split-trial analysis exhibits an under-estimation". It is true that we are not strictly in a binary classification task setting, but we are wondering if the authors have any justification for this result for [27].

(3b) Related to this point, the estimated info-limiting noise was 0.26 deg with all neurons and 0.6 deg with downsampling (we guess that is the first value of red lines in Figure S12). The only difference here, if we understand correctly, is the number of trials used. Otherwise, it's exactly the same neural responses used for estimation. So, a similar magnitude should be expected. If the latter is due to an insufficient number of trials used, would the same problem apply to the uniform sampling dataset? In other words, if there were more trials recorded with uniformly sampled stimuli, would the authors expect to see a further and significant decrease of sigma as well?

Reviewer #3 (Public review):

Summary:

Rahul Nagvekar et al. generated a novel genetic model (SP-oScarlet) to label brain macrophages via their engulfment activity in the naturally short-lived African turquoise killifish. They found that these brain phagocytes exhibit transcriptional features resembling mammalian BAMs/MDMs and provided evidence that their engulfment capacity declines with age. The model and topic are interesting, but some of the central conclusions require more precise calibration to match the strength of the supporting evidence.

Major comments:

The SP-oScarlet model enriches cells based on phagocytic capacity - by design, any phagocytic cell, including microglia, can be labeled. Only 0.5% of oScarlet^LOW cells were myeloid cells, confirming that this method captures virtually the entire myeloid population. The transcriptional resemblance to BAMs/MDMs is therefore a post hoc characterization of brain phagocytes broadly, rather than evidence for a selectively labeled subset. The authors show examples of apoeb⁺ cells near vasculature (Fig. 3b), but do not provide a comprehensive quantification of the full spatial distribution of oScarlet^HIGH cells. Importantly, neither the SP-oScarlet macrophages nor previously published wild-type killifish brain macrophages could be transcriptionally separated into three subgroups analogous to mammalian microglia, BAMs, and MDMs by PCA. This suggests that fish brain macrophages may not exist as subpopulations that correspond with their mammalian counterparts. The authors should therefore describe these cells as brain myeloid cells that exhibit BAM/MDM-like transcriptional characteristics, rather than implying they are a population equivalent to mammalian BAMs/MDMs.

The age-related decline in oScarlet fluorescence in oScarlet^HIGH cells in vivo could reflect either reduced phagocytic capacity of macrophages, or reduced oScarlet secretion by neurons, as the authors have discussed (Fig. 5a). The ex vivo assay addresses this by standardizing substrate concentration, which is a strength, but an in vivo functional assessment would provide a more physiologically relevant complement. The authors have already established the methodology for in vivo substrate injection (Fig. 3a, dextran). A similar experiment comparing substrate uptake in young and old fish would circumvent potential artifacts of the ex vivo approach, such as enzymatic dissociation altering surface receptor availability, and would directly test whether engulfment declines in the native brain environment.

Significance:

General assessment: This study presents a novel genetic model (SP-oScarlet) for visualizing chronic engulfment by brain macrophages in a short-lived vertebrate. The finding that killifish brain phagocytes exhibit BAM/MDM-like transcriptional features is interesting. Leveraging the killifish's naturally short lifespan, the authors further provide functional evidence that brain macrophage engulfment capacity declines with age. However, the authors should exercise caution when defining these cells as a distinct population specialized for engulfment of material from the brain extracellular space, since the SP-oScarlet model labels nearly the entire myeloid population in the brain.

Advance: This study establishes a novel genetic model for chronic, in vivo visualization of engulfment in a vertebrate brain. The conceptual insight that killifish brain phagocytes transcriptionally resemble BAMs/MDMs rather than classical microglia is novel and may reflect evolutionary differences in brain clearance strategies.

Audience: This research will interest a broad audience across developmental biology, genetics, neuroimmunology, aging research, and evolutionary biology.

reply to u/NoFace125 at https://www.reddit.com/r/typewriters/comments/1vb701u/thoughts_on_3d_printed_parts_on_typewriters/

I've heard mixed things on 3-D printed platens and there aren't many out there from what I've seen/heard.

I've seen lots of broken spacebars, though never run across a 3-D printed one.

As for the platen knobs on the Hermes 3000, they're probably one of the most commonly broken parts of any typewriter out there, and I've seen dozens of variations of printed knobs for those. It's almost like you don't own a real Hermes 3000 unless you've replaced the knobs on it.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors investigate the dynamics of distal visceral endoderm (DVE) migration during early anterior-posterior axis formation in the mouse embryo. Using long-term light-sheet imaging combined with geodesic projections and quantitative motion analysis, they characterize DVE migration at both the cellular and tissue levels. The study identifies three distinct phases of DVE migration, describes the intermittent "stop-and-go" nature of DVE movement, and quantifies coordinated tissue behaviors within the visceral endoderm. The authors further report a previously unrecognized posterior movement of the underlying epiblast that occurs concomitantly with anterior DVE migration. Finally, they develop a two-dimensional vertex model to investigate the mechanical basis of the observed intermittent migration, proposing that cycles of stress accumulation and T1-mediated stress relaxation within the surrounding visceral endoderm account for the observed dynamics

Strengths:

Overall, this is a very interesting study combining state-of-the-art live imaging with an impressive quantitative image analysis framework. The imaging quality is excellent, and the authors provide one of the most detailed quantitative descriptions of visceral endoderm (VE) dynamics to date. In particular, the combination of whole-embryo light-sheet imaging, geodesic projections and quantitative analysis provides a rich dataset that will undoubtedly be valuable for the community. The model is also informative and provides a mechanistic hypothesis for the start and stop motion of the VE.

Weaknesses:

(1) Clarification of the Superpixel-based image analysis

The image analysis pipeline is impressive but could be explained more clearly for readers unfamiliar with the authors' previous work. In particular, the manuscript relies extensively on superpixel tracking, but it remains unclear what advantages this approach offers over more conventional Lagrangian particle image velocimetry (PIV). Since this paper should be self-contained, it would be helpful if the authors briefly explained the rationale for choosing superpixel tracking rather than referring readers to their previous eLife publication.

Related to this point, the manuscript appears to use two different levels of coarse-graining. Motion is initially estimated from thousands of superpixels (1000-5000 according to the Methods), whereas the quantitative analyses are ultimately averaged over only 32 spatial sectors. The relationship between these two levels of representation is not entirely clear and would benefit from clarification. Why use such a dense superpixel seeding, which seems oversampled, if the intent is to eventually bin the result?

Relatedly, how was the number of superpixels chosen? What is their effective size relative to the size of a VE or epiblast cell? This information is important because the analysis appears to be oversampled. This is particularly evident in Movie S14/Figure 7, where numerous superpixels appear to span a single epiblast cell. At this spatial scale, the measured motion is likely to include intracellular or subcellular movements, such as interkinetic nuclear migration or transient cell-shape changes, rather than pure tissue displacement. This may be somewhat misleading, as the visual impression is that the tissue itself is moving, whereas in some instances this reflects cellular/subcellular fluctuations. A discussion of the spatial scale of the superpixel analysis, together with a demonstration that the conclusions are robust to the degree of coarse-graining, would greatly strengthen the manuscript, especially regarding he movement of the epiblast (see point 4).

(2) Use of the term "ratchet-like"

We would recommend avoiding the term ratchet-like and instead using start-stop or stop-and-go migration throughout the manuscript. While these terms describe the same observed behavior, ratchet-like implicitly suggests an irreversible mechanism underlying the motion, whereas the present study primarily documents an intermittent migration pattern. In my opinion, stop-and-go is a more descriptive and mechanistically neutral terminology, leaving the mechanistic interpretation to the modelling section.

(3) Mechanistic interpretation of the stop-and-go behavior

The vertex model constitutes the principal mechanistic component of the study and provides an interesting explanation for intermittent DVE migration through stress accumulation followed by T1-mediated stress relaxation. However, the comparison between the model and the experimental data reveals an important discrepancy. As acknowledged by the authors, the model predicts a broader distribution of T1 transitions than observed experimentally, whereas in vivo T1 events appear largely confined to the embryonic visceral endoderm ahead of the migrating DVE.

This discrepancy suggests that an important aspect of junctional mechanics may be missing from the current formulation. Have the authors considered whether an asymmetric constitutive description, in which junctions remodel more readily under compression than under tension, could better account for the observed spatial restriction of T1 events? Such constitutive asymmetry may provide a more biologically realistic mechanism for intermittent migration while preserving the overall framework proposed here.

Overall, we find the modelling direction promising, but at present the model appears somewhat premature or overly simplified relative to the experimental observations. The simulations convincingly demonstrate that T1-mediated stress relaxation can generate intermittent migration, but they do not yet quantitatively, if not qualitatively, reproduce the spatial distribution of T1 events observed in vivo. Since the authors have segmented some samples, could all the cells then provide a movie with T1 annotated? That would be helpful to get an intuition on the level of performance of the model compared to experimental data.

Related to this point, the stop-and-go behavior shown in Figure S7 is not immediately obvious. It would be helpful to display the instantaneous DVE velocity together with the timing of T1 transitions, allowing the proposed correlation to be appreciated more directly. In addition, in Figure 5E, the lower panel appears to be labelled "DVE position", whereas the text suggests that DVE velocity is intended. This should be clarified.

(4) Motion of the epiblast

The observation of coordinated epiblast motion is intriguing. However, it would be helpful if the authors quantified the magnitude of the net displacement. From the movies, the overall displacement appears relatively modest, perhaps on the order of one cell diameter. Is this indeed the case?

More generally, we have some concerns regarding the quantification and representation of epiblast motion. As discussed above, the superpixel analysis appears to operate at a subcellular scale, with many superpixels spanning the apico-basal extent of individual epiblast cells. Consequently, the measured motion may partly reflect transient cell deformations, for example during mitosis or interkinetic nuclear migration, rather than displacement of the tissue itself. Finally, we wonder whether the flattened representation is the most appropriate way to present the epiblast data. Such projections are clearly helpful for analyzing the whole VE motion over a curved epithelial surface. However, the epiblast motion described here is essentially linear, and it is therefore less obvious how the flattening affects the apparent displacement. It would be helpful if the authors could also present the epiblast movement in the original, non-flattened imaging data (e.g. using an optical transverse section through the embryo). At present, the motion is only shown either as a geodesic projection or as a flattened transverse view, such that the reader never directly observes the movement in its native three-dimensional geometry.

Reviewer #3 (Public review):

Summary:

Shpektor et al. investigate how hierarchical sequence structure is represented in the entorhinal cortex (EC) and medial temporal lobe (MTL) using a combination of single-unit recordings and fMRI. In the single-unit recordings, they find abstract representations of ordinal position within short sequences in both the EC and the hippocampus. Next, they use two fMRI datasets to examine representations of hierarchical sequence structure in EC. They find that these representations (1) are organized along a posterior-to-anterior hierarchy, with finer sequence structure represented in posterior EC and coarser structure in anterior EC, and (2) generalize across sensory features, suggesting an abstract representation of sequence position. The authors take these findings as evidence of a non-spatial hierarchical coordinate system in the human EC, analogous to grid cells in rodents.

Strengths:

The methodological approach presented in this study is commendable, combining single-unit recordings in the MTL with two fMRI datasets. The finding of hierarchical and abstract sequence representations in the EC is compelling and is replicated across these datasets and modalities. The manuscript addresses important questions about how the MTL abstracts across experiences that share hierarchical structure, a topic of considerable current interest. As such, the work is likely to be of broad interest to researchers studying these processes in both rodents and humans.

Weaknesses:

In my view, the main weaknesses concern the interpretation of the results, as well as several areas where additional analyses and methodological clarification would strengthen the manuscript. My point-by-point comments are as follows:

(1) I found the evidence for hierarchical and abstract sequence-position representations interesting. However, I am less convinced by the stronger claim that these findings demonstrate a coordinate system analogous to grid-cell coding. The current results appear to provide stronger support for abstract sequence-position coding than for grid-like coding per se. In particular, it is not clear to me that hierarchical sequence representations necessarily imply a grid-like representational format or a coordinate system. Many neural systems exhibit gradients of representational scale along the anterior-posterior axis, both within and across brain regions, without being considered grid-like. I would encourage the authors to clarify why it should be interpreted specifically in terms of a coordinate system rather than more general hierarchical sequence representations. The manuscript would benefit either from a more explicit justification of this link to grid-cell coding or from a more cautious framing of the conclusions.

(2) Relatedly, the emphasis on grid-cell-like coding naturally centers the story on entorhinal cortex (EC). Yet, the single-neuron results indicate that the hippocampus contained a comparable number of position-selective cells. In addition, a large body of literature has implicated the hippocampus in hierarchical representations of memories, sequences, and relational structure. For completeness, I encourage the authors to repeat the key fMRI analyses within the hippocampus, rather than focusing exclusively on EC.

(3) I have some concerns regarding the amount of information available to distinguish representations at different levels of the sequence hierarchy. As I understand the design, each 113-tone sequence was associated with only eight images, meaning there were approximately 14 tones between successive image events. It would be helpful to provide additional detail regarding how image coordinates were assigned and selected, how many observations contributed to each hierarchical level, and how much statistical power was available to distinguish representations at different scales.

(4) I was also uncertain about the potential influence of visual similarity in Dataset 1. My understanding is that the images were not entirely unique but instead consisted of rotated versions of the same images. If so, this visual similarity could potentially complicate the interpretation of representational structure. It would therefore be useful to clarify whether repeated images occurred within the same or different locations in the hierarchy and to provide analyses demonstrating that the reported effects cannot be explained by visual similarity. This seems particularly important given that the corresponding effects in Dataset 2 were weaker.

(5) The rationale for using a custom orderness metric could be explained more clearly. It would be helpful to understand why a custom metric was preferred over rank-order measures such as Kendall's tau or Spearman's rho. I would be interested in seeing whether the orderness results replicate using one of these more conventional metrics.

(6) I had difficulty reconciling the finding that sequence representation effects are stronger across rather than within sequences. Intuitively, I would have expected representations within a sequence to reflect both shared hierarchical position and sensory experience, thus yielding stronger within-sequence effects than across sequences. The opposite pattern seems somewhat counterintuitive. I would appreciate additional discussion of this pattern and what it implies about the nature of the underlying representation. It would also be informative to know whether similar effects are observed elsewhere in the brain, and why EC might preferentially express a purely abstract representation more strongly than representations that additionally share sensory features.

(7) The authors' theory is that hierarchical representations of sequences in EC are used as a scaffold for memory, yet the current paper does not link their behavioural results to their neural ones. I think making such a link would greatly strengthen the results presented here. For example, is displacement error or sequence memory related to ordered representations of the sequence structure?

(8) I thought the manuscript would benefit from a broader discussion of prior work on (1) sequence representations and (2) hierarchical representations in the hippocampus and related regions. As it stands, the manuscript does a good job of situating its findings within the literature on grid cells in the EC but gives comparatively little attention to the literature on sequence representations in the hippocampus. Placing the current findings within this broader body of work would help clarify which aspects of the results are specific to a grid-like interpretation and which may instead reflect more general principles of hierarchical representation in the MTL or across the brain.

Reviewer #3 (Public review):

Summary and Significance:

In this work, Cary and Hayashi address the important question of when, in evolution, certain mobile genetic elements (Ty3/gypsy-like non-LTR retrotransposons) associated with certain membrane fusion proteins (viral glycoprotein F or B-like proteins), which could allow these mobile genetic elements to be transferred between individual cells of a given host. It is debated in the literature whether the acquisition of membrane fusion proteins by non-LTR retrotransposons is a rather recent phenomenon that separately occurred in the ancestors of certain host species or whether the association with membrane fusion proteins is a much more ancient one, pre-dating the Cambrian explosion. Obviously, this question also touches upon the origin of the retroviruses, which can spread between individuals of a given host but seem restricted to vertebrates. Based on convincing data, Cary and Hayashi argue that an ancient association of non-LTR retrotransposons with membrane fusion proteins is most probable.

Strengths:

The authors take the smart approach to systematically retrieve apparently complete, intact, and recently functional Ty3/gypsy-like non-LTR retrotransposons that, next to their characteristic gag and pol genes, additionally carry sequences that are homologous to viral glycoprotein F (env-F) or viral glycoprotein B (env-B). They then construct and compare phylogenetic trees of the host species and individual encoded proteins and protein domains, where 3D-structure calculations and other features explain and corroborate the clustering within the phylogenetic trees. Congruence of phylogenetic trees and correlation of structural features is then taken as evidence for an infrequent recombination and a long-term co-evolution of the reverse transcriptase (encoded by the pol gene) and its respective putative membrane fusion gene (encoded by env-F or env-B). Importantly, the env-F and env-B containing retrotransposons do not form a monophyletic group among the Ty3/gypsy-like non-LTR retrotransposons, but are scattered throughout, supporting the idea of an originally ancient association followed by a random loss of env-F/env-B in individual branches of the tree (and rather rare re-associations via more recent recombinations).

Reviewer #3 (Public review):

Summary:

In "A‬‭ whole-animal‬‭ phenotypic‬‭ drug‬‭ screen‬‭ identifies‬‭ suppressors‬‭ of‬‭ atherogenic‬ lipoproteins", Kelpsch et al seek to identify new, chemically targetable pathways that regulate ApoB function and could ultimately serve as treatments for elevated lipid disorders and/or cardiovascular disease. Given the interconnected nature of lipid regulation in the whole organism with interdependent organs and secreted components (i.e. lipoproteins), they use the vertebrate model zebrafish to screen a large library of ~3000 compounds for their ability to lower the important ApoB-containing lipoproteins. They find 49 hits with 19 compounds passing a higher level of scrutiny, and focus on the role of enoxolone in modulating B-Ip levels at least partly through the HNF4alpha transcription factor and, putatively, through downstream cholesterol/lipid biosynthetic pathways.

Strengths:

The study uses a well-validated in vivo stain (LipoGlo) for measuring lipoproteins in the context of a developing whole organism with a quantitative read-out on a high-throughput platform, allowing for screening of thousands of compounds altering the complex metabolic/physiologic functions necessary for lipoprotein production.

The use of genetic mutant HNF4alpha to assign the mechanism of action to the prime candidate compound studied (enoxolone) is a powerful approach for this challenging aspect of chemical genetics studies.

Reviewer #3 (Public review):

Summary:

Jiang et al. described findings aimed at interrogating the interactions of the antibiotic polymyxin B with human kidney proteins that mediate nephrotoxicity. Their findings using both computational molecular dynamics simulations and experimental approaches illustrate the importance of aspartic acid residues (D215) in mediating the antibiotic uptake into the cells, and upon mutagenesis with Alanine, the effects are less pronounced. Further, they could modify the antibiotic units interacting with proteins into less toxic peptides with retained antibacterial properties.

Strengths:

I was impressed by this text, which advances the knowledge of how the antibiotic causes human nephrotoxicity and how this could be exploited into less problematic antibiotic peptides.

Weaknesses:

Interactions of Polymyxin B with kidney proteins were not demonstrable in vivo, and with reliable technologies such as X-ray or NMR.

Reviewer #3 (Public review):

Aly et al investigate the potential for a single N6-methyladenosine RNA modification in the context of the 5' UTR sequence of SARS-CoV-2 to regulate translation of a downstream luciferase reporter transfected into cells. They show using meRIP (m6A RNA IP) that this site is methylated in the plasmid-driven transcript, and convincingly show it mediates reporter translational efficiency using knockdown of the m6A methyltransferase METTL3 and mutation of the modified UTR site together with analysis of the transcript's association with polyribosomes. They suggest that the benefit to translation conferred by the modification is through its effect on the secondary structure of the 5' UTR, based on an RT-PCR-based assay in control and METTL3 knockdown cells linking RT processivity to translation (luciferase) output. They also extend their conclusions to two cellular mRNA 5' UTRs, also reported to contain a single m6A modification, and show METTL3-dependent changes in RNA structure stability, hinting at a broader significance of this mechanism of m6A control of gene expression.

The conclusions of the paper are mostly well supported by the data presented, though validation of knockdown of METTL3 (and reader proteins) is absent.

A major limitation of the work is the exclusive use of the reductionist artificial reporter system in uninfected cells. Though the 5' UTR site they identify is methylated in the context of a transcript generated in the nucleus (where the m6A installing complex is mainly localized, and believed to act exclusively in uninfected cells), how frequently this site is modified, if at all, on viral RNAs generated within cytoplasmic membrane-bound replication organelles. Similarly, whether the translation regulation by a single m6A modification identified here occurs within the context of an infected cell, in which there are many changes to the RNA and translational regulatory landscape, also remains to be tested.

How this work can be reconciled with others that have concluded either little potential for translational regulation by 5' UTR modification (Guca et al 2024; PMID: 38244546) or that an eIF3-mediated mechanism is responsible (Meyer et al, 2015 PMID: 26593424) is not addressed in the discussion.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Rademacher and colleagues examined the effect on the integrity of the dopamine system in mice of chronically stimulating dopamine neurons using a chemogenetic approach. They find that one to two weeks of constant exposure to the chemogenetic activator CNO leads to a decrease in the density of tyrosine hydroxylase staining in striatal brain sections and to a small reduction of the global population of tyrosine hydroxylase positive neurons in the ventral midbrain. They also report alterations in gene expression in both regions using a spatial transcriptomics approach. Globally, the work is well done and valuable and some of the conclusions are interesting. However, the conceptual advance is perhaps a bit limited in the sense that there is extensive previous work in the literature showing that excessive depolarization of multiple types of neurons associated with intracellular calcium elevations promotes neuronal degeneration. The present work adds to this by showing evidence of a similar phenomenon in dopamine neurons. In terms of the mechanisms explaining the neuronal loss observed after 2 to 4 weeks of chemogenetic activation, it would be important to consider that dopamine neurons are known from a lot of previous literature to undergo a decrease in firing through a depolarization-block mechanism when chronically depolarized. Is it possible that such a phenomenon explains much of the results observed in the present study? It would be important to consider this in the manuscript. The relevance to Parkinson's disease (PD) is also not totally clear because there is not a lot of previous solid evidence showing that the firing of dopamine neurons is increased in PD, either in human subjects or in mouse models of the disease. As such, it is not clear if the present work is really modelling something that could happen in PD in humans.

Comments on the introduction:

The introduction cites a 1990 paper from the lab of Anthony Grace as support of the fact that DA neurons increase their firing rate in PD models. However, in this 1990 paper, the authors stated that: "With respect to DA cell activity, depletions of up to 96% of striatal DA did not result in substantial alterations in the proportion of DA neurons active, their mean firing rate, or their firing pattern. Increases in these parameters only occurred when striatal DA depletions exceeded 96%." Such results argue that an increase in firing rate is most likely to be a consequence of the almost complete loss of dopamine neurons rather than an initial driver of neuronal loss. The present introduction would thus benefit from being revised to clarify the overriding hypothesis and rationale in relation to PD and better represent the findings of the paper by Hollerman and Grace.

It would be good that the introduction refers to some of the literature on the links between excessive neuronal activity, calcium, and neurodegeneration. There is a large literature on this and referring to it would help frame the work and its novelty in a broader context.

Comments on the results section:

The running wheel results of Figure 1 suggest that the CNO treatment caused a brief increase in running on the first day after which there was a strong decrease during the subsequent days in the active phase. This observation is also in line with the appearance of a depolarization block.

The authors examined many basic electrophysiological parameters of recorded dopamine neurons in acute brain slices. However, it is surprising that they did not report the resting membrane potential, or the input resistance. It would be important that this be added because these two parameters provide key information on the basal excitability of the recorded neurons. They would also allow us to obtain insight into the possibility that the neurons are chronically depolarized and thus in depolarization block.

It is great that the authors quantified not only TH levels but also the levels of mCherry, co-expressed with the chemogenetic receptor. This could in principle help to distinguish between TH downregulation and true loss of dopamine neuron cell bodies. However, the approach used here has a major caveat in that the number of mCherry-positive dopamine neurons depends on the proportion of dopamine neurons that were infected and expressed the DREADD and this could very well vary between different mice. It is very unlikely that the virus injection allowed to infect 100% of the neurons in the VTA and SNc. This could for example explain in part the mismatch between the number of VTA dopamine neurons counted in panel 2G when comparing TH and mCherry counts. Also, I see that the mCherry counts were not provided at the 2-week time point. If the mCherry had been expressed genetically by crossing the DAT-Cre mice with a floxed fluorescent reported mice, the interpretation would have been simpler. In this context, I am not convinced of the benefit of the mCherry quantifications. The authors should consider either removing these results from the final manuscript or discussing this important limitation.

Although the authors conclude that there is a global decrease in the number of dopamine neurons after 4 weeks of CNO treatment, the post-hoc tests failed to confirm that the decrease in dopamine number was significant in the SNc, the region most relevant to Parkinson's. This could be due to the fact that only a small number of mice were tested. A "n" of just 4 or 5 mice is very small for a stereological counting experiment. As such, this experiment was clearly underpowered at the statistical level. Also, the choice of the image used to illustrate this in panel 2G should be reconsidered: the image suggests that a very large loss of dopamine neurons occurred in the SNc and this is not what the numbers show. A more representative image should be used.

In Figure 3, the authors attempt to compare intracellular calcium levels in dopamine neurons using GCaMP6 fluorescence. Because this calcium indicator is not quantitative (unlike ratiometric sensors such as Fura2), it is usually used to quantify relative changes in intracellular calcium. The present use of this probe to compare absolute values is unusual and the validity of this approach is unclear. This limitation needs to be discussed. The authors also need to refer in the text to the difference between panels D and E of this figure. It is surprising that the fluctuations in calcium levels were not quantified. I guess the hypothesis was that there should be more or larger fluctuations in the mice treated with CNO if the CNO treatment led to increased firing. This needs to be clarified.

Although the spatial transcriptomic results are intriguing and certainly a great way to start thinking about how the CNO treatment could lead to the loss of dopamine neurons, the presented results, the focussing of some broad classes of differentially expressed genes and on some specific examples, do not really suggest any clear mechanism of neurodegeneration. It would perhaps be useful for the authors to use the obtained data to validate that a state of chronic depolarization was indeed induced by the chronic CNO treatment. Were genes classically linked to increased activity like cfos or bdnf elevated in the SNc or VTA dopamine neurons? In the striatum, the authors report that the levels of DARP32, a gene whose levels are linked to dopamine levels, are unchanged. Does this mean that there were no major changes in dopamine levels in the striatum of these mice?

The usefulness of comparing the transcriptome of human PD SNc or VTA sections to that of the present mouse model should be better explained. In the human tissues, the transcriptome reflects the state of the tissue many years after extensive loss of dopamine neurons. It is expected that there will be few if any SNc neurons left in such sections. In comparison, the mice after 7 days of CNO treatment do not appear to have lost any dopamine neurons. As such, how can the two extremely different conditions be reasonably compared?

Comments on the discussion:

In the discussion, the authors state that their calcium photometry results support a central role of calcium in activity-induced neurodegeneration. This conclusion, although plausible because of the very broad pre-existing literature linking calcium elevation (such as in excitotoxicity) to neuronal loss, should be toned down a bit as no causal relationship was established in the experiments that were carried out in the present study.

In the discussion, the authors discuss some of the parallel changes in gene expression detected in the mouse model and in the human tissues. Because few if any dopamine neurons are expected to remain in the SNc of the human tissues used, this sort of comparison has important conceptual limitations and these need to be clearly addressed.

A major limitation of the present discussion is that it does not discuss the possibility that the observed phenotypes are caused by the induction of a chronic state of depolarization block by the chronic CNO treatment. I encourage the authors to consider and discuss this hypothesis. Also, the authors need to discuss the fact that previous work was only able to detect an increase in the firing rate of dopamine neurons after more than 95% loss of dopamine neurons. As such, the authors need to clearly discuss the relevance of the present model to PD. Are changes in firing rate a driver of neuronal loss in PD, as the authors try to make the case here, or are such changes only a secondary consequence of extensive neuronal loss (for example because a major loss of dopamine would lead to reduced D2 autoreceptor activation in the remaining neurons, and to reduced autoreceptor-mediated negative feedback on firing). This needs to be discussed.

There is a very large, multi-decade literature on calcium elevation and its effects on neuronal loss in many different types of neurons. The authors should discuss their findings in this context and refer to some of this previous work. In a nutshell, the observations of the present manuscript could be summarized by stating that the chronic membrane depolarization induced by the CNO treatment is likely to induce a chronic elevation of intracellular calcium and this is then likely to activate some of the well-known calcium-dependent cell death mechanisms. Whether such cell death is linked in any way to PD is not really demonstrated by the present results.

The authors are encouraged to perform a thorough revision of the discussion to address all of these issues, discuss the major limitations of the present model, and refer to the broad pre-existing literature linking membrane depolarization, calcium, and neuronal loss in many neuronal cell types.

Reviewer #3 (Public review):

Summary:

Barrett et al. compare the responses of different parts of the mouse primary and secondary motor cortex in the context of a task where the animals manipulate and eat food using either or both hands. They find that roughly half the activity is conserved when reaching with one hand vs. the other hand, or with both. Similarity of activity was somewhat higher in the "lateral oral and manual" (LOM) part of the motor cortex, consistent with notions of a more generalized oromanual function there.

Strengths:

This work aims at addressing two worthwhile questions in a mouse model of motor control: (1) what specializations do we have for controlling feeding movements, and (2) how are the arms and hands coordinated with one another? The authors develop a simple but innovative apparatus to block either hand during food handling, track the behavior at high temporal fidelity, and record a sizable neural dataset. The analyses come from numerous angles to take good advantage of the data, and succeed in showing multiple lines of evidence for greater invariance in LOM than in the forelimb parts of M1 and M2.

Weaknesses:

There are several limitations of the current study. Most importantly, the behavior presents an inherent challenge: there is only one type of movement for each of the three conditions (contra hand, ipsi hand, and bimanual). This is entirely reasonable from the perspective that this is the ethological behavior when feeding, but it limits what analyses are possible. In particular, it precludes disentangling the neural relationship with many correlated aspects of behavior, and limits identifying population-level features of the neural activity meaningfully. This means that there are a number of alternative possible sources of the neuron-level area differences found here, and the population-level features may not be reliable. Second, the behavior tracking was used at a relatively coarse level, and thus the relationships to various behavioral variables were left less distinguishable than they might have been. Finally, there may be an issue with the coordinates of what is being called forelimb M1 here, which may include some hindlimb M1.

Reviewer #3 (Public review):

In this manuscript, the authors propose a product-dependent negative-feedback mechanism of human glutamine synthetase, whereby the product glutamine facilitates filament formation, leading to reduced catalytic specificity for ammonia. Using time-resolved cryo-EM, the authors demonstrate filament formation under product-rich conditions. Multiple high-quality structures, including decameric and di-decameric assemblies, were resolved under different biochemical states and combined with MD simulations, revealing that the conformational space of the active site loop is critical for the GS catalysis. The study also includes extensive steady-state kinetic assays, supporting the view that glutamine regulates GS assembly and its catalytic activity. Overall, this is a detailed and comprehensive study. However, I would advise that a few points be addressed and clarified.

Comments on revised version.

The revision addresses several reviewer concerns: the authors add sharpened maps, ligand-density panels, symmetry expansion/focused classification, biochemical blank/substrate/TCEP controls, and E305-loop focused classification. The E305-loop part is stronger now: turnover decamer recovers partial E-flap density in few classes, while turnover filament does not.

My only remaining comment is that - as also the authors agree on the need to integrate density with biochemical data and that local resolution/averaging complicates modeling - I would advise softening the claim regarding glutamine from "glutamine binds" to "density consistent with glutamine/product-associated density". In general, it would be best to avoid overstating atomic certainty at the filament interface and the safest framing is the observed interface density is compatible with glutamine but not independently conclusive.

Reviewer #3 (Public review):

Summary:

This paper by Esmaeili and co-authors presents a connectome prediction study to predict episodic memory and relate prediction errors to other phonotypic variables.

Strengths:

(1) A primary and external validation dataset.

(2) Novel use of prediction errors (i.e., brain-cognitive gap).

(3) A wide range of data was investigated.

Reviewer #3 (Public review):

Summary:

This study investigates the molecular underpinnings of immune responses in the leptomeninges in neonatal bacterial meningitis. Bacterial meningitis is a major disease burden, particularly for neonates, and it has previously been noted that the meningeal immune environment in infants is permissive to opportunistic infection (Kim et al., Sci Immunol, 2023). There is less known about the contribution of the stromal compartment to meningeal immune responses. Seegren et al. interrogate the role of leptomeningeal endothelium in host defense in E. coli infected neonatal mice using mouse genetic tools to delete the LPS receptor Tlr4 from either endothelial cells/stromal cells (using Cdh5-CreER) or myeloid cells (using LysM-Cre). The authors use snRNAseq, cleared cortical mounts, and in vitro work to define the impact of E. coli infection on leptomeningeal endothelial cells. This study uses a range of innovative techniques to probe the role of the stromal compartment in meningitis. With additional experiments to confirm the specificity of their Cre models, this strengthens the interpretation of the study significantly. The only major weakness is the inability to confirm TLR4 knockout in myeloid cells.

Strengths:

This study makes excellent use of cleared cortical mounts to examine the biology of the leptomeninges, in particular, changes to the endothelium, with unprecedented detail. In combination with high-quality sequencing data provide new insights into the impact of meningitis on the leptomeninges. The data presented by the authors is of very high quality.

The authors have also done substantial work to address my two major comments regarding 1) the specificity of their Cre systems and 2) peripheral impacts of the interventions.

(1) The authors identified and acknowledged some impacts in the leptomeningeal stroma (the relatively high level of recombination in ECs vs FBs presumably reflects a single low dose being given, where other groups have done more aggressive tamoxifen regimens that drive recombination in FBs as well). Given the incomplete recombination in the leptomeningeal FBs, I agree with their conclusion that it is probably endothelial driven. Acknowledging the contributions of other myeloid cells with the L. The Cre-NLS experiments with nuclear markers provided excellent data and had beautiful staining.

(2) The authors did not observe differences in bacterial burden in peripheral organs in either CKO model, suggesting that CNS impacts are not downstream of peripheral bacterial control.

Weaknesses:

(1) The inducible Cre lines used by the authors target peripheral tissues as well as CNS tissues. Although this is mollified by the lack of impact on peripheral disease burden.

(2) The authors were not able to confirm TLR4 knockout in myeloid cells, and this caveat is acknowledged. The lack of response in TLR4 VEKO mice strongly suggests successful conditional knockout.

(3) The cell line model (bEnd.3) is a relatively low fidelity model of BBB endothelial cells. The authors acknowledge this, and it is likely that endothelial cell responses to LPS are highly conserved.

(4) It is perhaps not surprising that Tlr4 is required for meningitis responses with E. coli. However, it is unclear if these findings can be generalised to other, more common, meningitis infections (streptococcal/pneumococcal).

Reviewer #3 (Public review):

Summary:

Hossain et al. investigate the role of ITK as a central regulator of autoimmune lung injury. They used ITK-deficient mice and the pristane-induced pulmonary hemorrhage (PH) model to show that ITK deficiency confers protection against PH. The adoptive cell transfer experiment suggests a possible role for altered Treg cells in ITK-deficient mice in regulating the inflammatory response in the lungs of pristane-injected mice. This study shows that targeting the ITK axis may be beneficial by reducing systemic inflammatory injury that contributes to poor outcomes in PH.

Strengths:

This study highlights the importance of ITK in regulating pulmonary hemorrhage. The enrichment of Treg cells is known to confer protection in autoimmunity-mediated alveolar damage. However, ITK's involvement in regulating Treg cell function is interesting and could be explored as a novel therapeutic approach for chronic inflammation.

Weaknesses:

The novelty of this study lies in the association between ITK-deficient Tregs and pulmonary hemorrhage in autoimmunity. The weakness of the manuscript is the lack of sufficient experiments to support the claim that ITK-deficient mice show protection specifically mediated by Treg cells, and to demonstrate that ITK-deficient Treg cells are more efficient than WT Treg cells in regulating other immune cells that drive pulmonary damage. The authors performed all the experiments in ITK global knockout mice, in which not only T cells but all other cell types are deficient in ITK. Furthermore, they have not performed any functional analysis to demonstrate the functional differences between WT Treg and ITK-deficient Treg cells, undermining the novelty of this study.

Reviewer #3 (Public review):

Summary:

Alonso-Caraballo et al. use behavioral testing and ex vivo patch-clamp electrophysiology combined with circuit-specific optogenetic stimulation of PVT terminals to examine how oxycodone self-administration and abstinence duration shape cue-induced relapse and PVT-NAcSh synaptic transmission in male and female rats. In the revision, the authors reanalyzed intrinsic excitability using nested hierarchical GLMMs, acknowledged the low power in the male prolonged-abstinence group, and expanded the discussion of relevant PVT-NAc literature. These changes improve the manuscript. That said, most of the revisions are textual and the main experimental gap remains. Both sexes show increased oxycodone seeking compared to saline at 14 days, but only females show a time-dependent incubation from 1 to 14 days, and the PVT-NAcSh synaptic strengthening is the same in both sexes. Nothing in the revision brings those two observations closer together. The excitability data also come from NAcSh MSNs with no confirmation of PVT connectivity, which limits what circuit-specific conclusions can be drawn. The study is a solid characterization of abstinence-related synaptic changes in this pathway, but some of the conclusions still go further than the data allow.

Strengths:

The behavioral characterization is thorough and well-executed, covering self-administration, somatic withdrawal, and cue-induced relapse across two abstinence durations in both sexes. The sex-specific escalation in oxycodone seeking from 1 to 14 days in females but not males is a clear and compelling finding. The use of circuit-specific ex vivo optogenetics to isolate PVT terminal inputs onto NAcSh neurons is a genuine methodological strength, and the demonstration of feedforward inhibitory recruitment through local GABAergic interneurons adds meaningful novelty to the circuit characterization. The reanalysis of intrinsic excitability using nested hierarchical GLMMs appropriately accounts for the non-independence of cells recorded within the same animal and is a real improvement over the original approach. The expanded discussion of prior PVT-NAc work, particularly the more accurate treatment of Keyes et al. (2020) and Paniccia et al. (2024), better situates the findings within the existing literature.

Reviewer #3 (Public review):

Summary:

The authors set off with an analysis of the lysosomal integrity upon knockdown of genes of the sphingolipid metabolic pathway that they identified in a previous work of an RNA screen using a new C.elegans Tau model. They then used cell culture and C.elegans experiments to study the link between lysosomal rupture and Tau propagation.

Strengths:

The authors use two complementary model systems and used probes to assess membrane rigidity that allow a quick assessment of the membrane dynamics and offer the opportunity to treat the cells with lipids, RNAi. Tau seeds etc.

Comments on revised version:

The authors have addressed the majority of my critical comments and thus I support the manuscript.

They have still not analysed the knockdown efficiencies of their RNAi experiments. But this is their choice.

The other publication establishing their Tau model is meanwhile published and there is no disconnect anymore between the model their analysis builds on.

Reviewer #3 (Public review):

Summary:

This paper addresses a fundamental gap in bone biology: our near-complete ignorance of the in vivo dynamics of calvarial bone marrow adiposity (BMA) at population scale. The authors developed an elegant artificial neural network trained on simulated data to automatically localize and quantify the bone marrow layer within standard T1-weighted MRI head scans; scans originally acquired to study the brain but harboring rich, unexploited information about adjacent bone. Applying this method to over 33,000 individuals from the UK Biobank, they accomplished three things that had never been done before: (1) they precisely quantified the sex-dimorphic age trajectory of calvarial BMA, including the dramatic post-menopausal rise and the protective role of hormone replacement therapy; (2) they performed the first well-powered GWAS of this trait, identifying 41 genome-wide significant loci including six sex-specific ones, with SNP heritability of 31.5%; and (3) they revealed significant genetic correlations and overlap between BMA and traits including bone mineral density, Parkinson's disease, and general cognitive ability, a finding made all the more intriguing by the recently described direct vascular channels connecting calvarial bone marrow to the meninges. Integration of GWAS genes with single-cell RNA-sequencing data from mesenchymal lineage cells further illuminated which genes govern lineage commitment to the adipogenic pathway versus lipid loading in mature adipocytes.

Comments on revised version.

The reviews raised substantive points across three domains, and the authors engaged with every one of them seriously and thoroughly.

On the validation of T1-weighted MRI as a measure of BMA: Reviewer 2 raised the strongest concern, arguing that T1-weighted signal intensity had never been formally validated as a quantitative fat-fraction measure in the calvarium. The authors responded with both a principled scientific argument and new data. They assembled existing literature demonstrating that T1-weighted signal is an established semi-quantitative proxy for marrow fat in multiple skeletal sites (Loevner et al. 2002, Shen et al. 2013, Zhang et al. 2020), and provided additional comparative analyses against quantitative T1 relaxation maps, multiple intensity normalization strategies (KDE, WhiteStripe, GMM, FCM, Z-score), DEXA-derived bone mineral density, and osteoporosis status. The biological coherence of their findings, recapitulating known sex and age profiles, identifying genes already established in cell and animal models of BMA biology, and estimating heritabilities consistent with twin data constitutes powerful, convergent evidence for construct validity. Their point that a semi-quantitative measure of a highly variable, well-demarcated biological signal can outperform a perfectly precise measure of a poorly defined entity is methodologically sound and well-argued.

On sex differences and the role of Hyperostosis frontalis interna: Reviewer 1 raised the clinically astute concern that Hyperostosis frontalis interna (HFI), a condition of inner table thickening prevalent in up to 49% of postmenopausal women, could confound calvarial BMA measurements and drive apparent sex differences. The authors performed a dedicated new analysis, stratifying BMA-BMD associations by sex and age group. They demonstrated that (1) the BMA-BMD association is robust in both males and females, (2) it remains stable across age groups, and (3) the neural network trained on simulations incorporating wide anatomical variation including inner table thickness is inherently resistant to moderate inner table thickening. Given that HFI is restricted to the frontal bone, which represents only a fraction of the calvarial surface, and that severe cases are rare (ICD-10 prevalence ~0.02% in the UK Biobank), the authors make a convincing case that this does not materially bias their results. Their suggestion that the method could itself be used in future work to study the genetic architecture of HFI is a nice forward-looking addition.

On genetic correlation interpretation and cross-trait pleiotropy: Reviewer 1 asked for clarification of the vertical versus horizontal pleiotropy distinction and for formal Mendelian randomization to support the possible causal effect of BMA on cognition. The authors appropriately clarified the conceptual framework in the revised text and, rather than overstating a causal claim without the supporting analysis, responsibly softened the language to "may be consistent with the hypothesis that BMA could have a causal effect on cognition." This is scientifically honest and appropriate.

On mouse scRNAseq and its relevance to humans: The authors acknowledged that the results section had not explicitly stated the mouse origin of the scRNAseq data, corrected this, and provided a well-justified rationale for the relevance of mouse mesenchymal lineage data to human BMA biology, which is a well-established and widely accepted model system in this field.

On GWAS replication: The claim that the study lacked replication was addressed by clarifying the a priori separation of discovery (white British, n=33,042) and replication (non-white British, n=4,958) samples, with 62% of significant discovery SNPs and 95% of lead SNPs replicating in the correct direction.

Overall Assessment:

This is a technically innovative, scientifically rigorous, and biologically meaningful paper. The method is genuinely novel, the sample size is among the largest ever applied to this phenotype, the genetic findings are well-powered and well-replicated, and the integration across imaging, genetics, and single-cell transcriptomics is exemplary. The authors have engaged with every substantive reviewer criticism in good faith, producing new analyses where appropriate and defending, and convincingly, with findings that were challenged without adequate basis. The revised manuscript is strengthened throughout.

This paper opens a new window quite literally, through the skull - into bone marrow biology at a scale and resolution that has never been achieved before.

Reviewer #3 (Public review):

Summary:

This manuscript presents a comprehensive and well-executed investigation into the metabolic role of D-serine in the central nervous system. The authors provide solid evidence that D-serine competitively inhibits mitochondrial L-serine transport, thereby impairing one-carbon metabolism. This stereoselective mechanism reduces glycine and formate production, suppresses cellular proliferation, and induces apoptosis in immature neural cells and glioblastoma stem cells. Developmental analyses further reveal a physiological enantiomeric shift in serine metabolism during neurogenesis, aligning with the transition from proliferation to maturation. Overall, the study bridges developmental neurobiology, cancer metabolism, and amino acid transport, uncovering a previously unrecognized metabolic function of D-serine beyond its role in neurotransmission.

Strengths:

(1) The discovery that D-serine inhibits one-carbon metabolism by competing for mitochondrial L-serine transport-rather than through enzymatic inhibition or receptor-mediated signaling-represents a significant and previously underappreciated mechanism. This finding has broad implications for understanding metabolic regulation during neurodevelopment and offers potential relevance for targeting metabolic vulnerabilities in cancer.

(2) The authors integrate metabolomics, mitochondrial transport assays, molecular dynamics simulations, genetic and pharmacologic perturbations, transcriptomics, and both in vitro and ex vivo models. The breadth of experimental approaches, combined with the coherence of the findings across systems, provides strong support for the central conclusions and enhances the overall impact of the study.

(3) The temporal shift in D-/L-serine levels during neurodevelopment is elegantly linked to the transition from proliferative to mature neuronal states. The selective vulnerability of neural progenitors and tumor cells-contrasted with the resistance of mature neurons-highlights a biologically meaningful and potentially targetable metabolic distinction.

Weaknesses:

(1) While the authors attribute D-serine's metabolic effects to competition with mitochondrial L-serine transport, the specific identity of the transporter(s) mediating this process remains undefined. This represents a meaningful mechanistic gap, as the central conclusion depends on D-serine limiting mitochondrial L-serine availability to inhibit one-carbon metabolism.

(2) The effective concentrations of D-serine used in vitro (IC₅₀ ≈ 1-2 mM) exceed typical brain levels (~0.3 mM). While the authors acknowledge this, a more focused discussion on whether higher local D-serine concentrations could arise in specific microenvironments-such as synaptic compartments, tumor niches, or pathological states-would help contextualize the in vitro findings and strengthen their physiological relevance. For example, disruptions in D-serine clearance or altered expression of serine racemase and transporters in disease contexts could lead to localized accumulation. Moreover, differences between extracellular and intracellular D-serine pools-and the mechanisms governing their regulation-may further influence its metabolic impact in vivo.

(3) While the manuscript focuses on neural stem/progenitor cells and neural tumors, it remains unclear whether the anti-proliferative effects of D-serine are specific to neural lineages or extend to other highly proliferative non-neural cell types. A brief discussion addressing this point would help clarify the scope of D-serine's metabolic impact and whether its mechanism of action reflects a unique vulnerability in neural cells or a more general feature of proliferative metabolism. This distinction is particularly relevant for assessing the broader therapeutic potential of targeting mitochondrial L-serine transport.

Reviewer #3 (Public review):

Summary:

Submitted to the Tools and Resources series, this study reports on the use of a single-domain antibody targeting the nucleoporin Nup84 to probe and track NPCs in budding yeast. The authors demonstrate their ability to rapidly label or pull down NPCs by inducing the expression of a tagged version of the nanobody (Fig. 1).

Strengths:

This tool's main strength is its versatility as an inexpensive, easy-to-set-up alternative to metabolic labelling or optical switching. This same rationale could, in principle, be applied to the study of other multiprotein complexes using similar strategies, provided that single-chain antibodies are available.

Weaknesses:

This approach has no inherent weaknesses, but it would be useful to verify in the future that this pulse labelling strategy can also be used to detect assembly intermediates, structural variants, or damaged NPCs, e.g. NPC clusters formed in some nucleoporin mutants.

Overall, the data clearly shows that Nup84 nanobodies are a valuable tool for imaging NPC dynamics and investigating their interactomes through affinity purification.

Comments on revised version.

None at this stage.

Reviewer #3 (Public review):

Summary:

The study aims to elucidate the dual molecular mechanisms of the RNA-binding protein MATR3 in oocyte growth and maturation. The authors propose that MATR3, highly expressed in growing oocytes (GOs), regulates oocyte quality through two pathways: epigenetically, by recruiting KDM3B to remove the repressive H3K9me2 mark at the Gdf9 locus to activate transcription; and post-transcriptionally, by binding Rdx mRNA to maintain microvillus structure for GDF9 secretion. This mechanism ensures oocyte-granulosa cell communication and female fertility. The study also explores the link between MATR3 and human oocyte maturation arrest (OMA).

Strengths:

The study proposes an innovative dual-mechanism model encompassing "epigenetic transcriptional activation and cytoskeletal regulation," which not only expands the functional understanding of RNA-binding proteins in chromatin regulation but also reveals the coordination between nuclear transcription and organelle structure. By integrating scRNA-seq and LACE-seq, the authors constructed a comprehensive regulatory network for MATR3, identifying both key targets and numerous potential molecules, thereby providing rich resources for future mechanistic studies. Furthermore, the inclusion of oocyte samples from human OMA patients directly links the basic findings to clinical reproductive disorders. Despite the limited sample size, this approach demonstrates strong translational potential.

Weaknesses:

The partial phenotypic improvement achieved by exogenous GDF9 supplementation suggests that the downstream effector pathways may involve a more complex network regulation, implying that the current interpretation of GDF9 central role could be further explored. Regarding the developmental abnormalities of granulosa cells in the conditional knockout model, their pathological origins require in-depth analysis to determine whether they represent primary alterations or secondary adaptive responses resulting from the loss of oocyte signaling.

Reviewer #3 (Public review):

Summary:

This paper focused on how to navigate the complex decision-making process of whether to go into human trials. This is a critical topic considering the well-documented challenges in replicating and translating findings. While these are two distinct topics (i.e., replication and translation), they are related, and the authors simulated many conditions to assess the utility of replication assessment metrics.

Strengths:

A major strength of the study is the detailed approach to identifying relevant conditions and metrics, and to providing rich results that outline the strengths and weaknesses of each metric. Any simulation study is challenged by trying to identify the most relevant variables of interest, and this study provided sound justification for its chosen variables of interest. While this study does not make a strong recommendation (which I see as a strength), it does provide a comprehensive overview of the various metrics and conditions that were investigated.

Conclusion:

This paper provides a much-needed investigation and discussion of how decisions are made when assessing whether to go into human trials. This is an important topic that productively challenges the status quo, considering documented challenges in replication and translation in biomedical research.

Reviewer #3 (Public review):

Summary:

In this manuscript, Flamholz et al. sought to determine whether consistent and significant interactions exist between the gut microbiome and disease pathology in sickle cell disease (SCD). By sequencing and analysing metagenomes from faecal samples collected from 98 SCD patients and 46 control subjects, they identified community-level shifts in both the bacterial and proviral gut microbiome of SCD patients. They further reported correlations between the proviral microbiome and multiple blood cytokines, whereas similar associations were not observed for the bacterial microbiome. Based on these findings, the authors propose the existence of a viral-immune axis in SCD pathophysiology and targetable functional alterations in the gut microbiome.

Strengths:

This work includes the largest SCD cohort analysed to date, enabling analysis with relatively strong statistical power. In addition to profiling the bacterial microbiome, the study also examines the gut proviral microbiome, thereby providing a more comprehensive investigation of the topic. The newly generated metagenomic dataset will also be valuable for further meta-analysis by the wider community. Overall, the authors have largely achieved their aims.

Weaknesses:

However, this study represents a single-centre cross-sectional investigation, and most findings remain correlative in nature. In particular, the claim that the study identifies targetable functional alterations in the gut microbiome for disease treatment may be somewhat overstated. Although the reported functional module changes in SCD patients are intriguing, additional mechanistic and/or longitudinal evidence would be required before these features can realistically be considered targetable.

Reviewer #3 (Public review):

In this manuscript, Wang et al employ a chemical biology approach to investigate the differences between the enzymatic and scaffolding roles of tankyrase during Wnt β-catenin signalling. It was previously established that, in addition to its enzymatic activity, tankyrase 1/2 also plays a scaffolding function within the destruction complex, a property conferred by SAM-domain-dependent polymerization (PMID: 27494558). It is also known that TNKS1/2 is an autoregulated protein and that its enzymatic inhibition leads to accumulation of total TNKS proteins and stabilization of Axin punctae (through the scaffolding function of TNKS1/2), leading to rigidification of the DC and decreased β-catenin turnover. The authors surmised that this could, in part, explain the limited efficacy of TNKS1/2 catalytic inhibition for the treatment of colorectal cancers. To test this hypothesis, they evaluated a series of PROTAC molecules promoting the degradation of TNKS1/2 to block both the catalytic and scaffolding activities. They show that IWR1-POMA (their most active molecule) promotes more efficient suppression of beta-catenin-mediated transcription and is more active in inhibiting colorectal cancer cell and CRC patient-derived organoids growth. Mechanistically, the authors used FRAP to demonstrate that catalytic inhibitors of TNKS led to a reduced dynamic assembly of the DC (rigidification), whereas IWR1-POMA did not affect the dynamics.

Overall, this is an interesting study describing the design and development of a PROTAC for TNKS1/2 that could have increased efficacy where catalytic inhibitors have displayed limited activity. Knowing the importance of the scaffolding role of TNKS1/2 within the destruction complex, targeting both the catalytic and scaffolding roles certainly makes sense. The manuscript contains convincing evidence of the different mechanisms of the PROTAC vs catalytic inhibitors. Some additional efforts to quantify several of the experiments and to indicate the reproducibility and statistical analysis would strengthen the manuscript. Ultimately, it would have been great to evaluate the in vivo efficacy of IWR1-POMA in an in vivo CRC assay (APCmin mice or using PDX models); however, I realize that this is likely beyond the scope of this manuscript.

Reviewer #3 (Public review):

Summary:

This manuscript presents openretina, a Python-based platform designed to facilitate collaborative retinal modeling across datasets, laboratories, species, and recording modalities. The package provides standardized model architectures, evaluation metrics, and analysis tools, while also integrating several publicly available retinal datasets. The authors further demonstrate the platform through examples of in silico analyses and model benchmarking.

Strengths:

(1) Emphasis on standardization and reproducibility. Retinal modeling has become increasingly dependent on deep learning approaches, yet datasets and evaluation procedures remain fragmented across laboratories. By providing a unified framework, the authors lower barriers to entry and create opportunities for more systematic comparisons of models and datasets.

(2) The manuscript is clearly written, and the examples effectively illustrate the range of analyses supported by the platform.

(3) The benchmarking results are useful, particularly because they reveal substantial remaining gaps between current model performance and explainable variance ceilings.

Weaknesses:

Not a weakness per se, but rather a limitation, is that the manuscript focuses on software infrastructure rather than new biological or computational insights. While this is appropriate for a resource paper, some of the scientific examples, such as the gradient-field analysis of ON-OFF cells, function more as demonstrations than as rigorous validations of novel hypotheses. It might be useful to add a few sentences discussing potential scientific projects that can be immediately facilitated by the openretina (the current text in the Discussion focuses more on advancements in the technical/social aspects of science that will be supported by openretina).

Overall, this is a valuable and timely resource that is likely to benefit the retinal and computational neuroscience communities.

Reviewer #3 (Public review):

Summary:

In this manuscript, Williams et al. combine optogenetics, whole-cell electrophysiology, local field potential recordings, large-scale voltage imaging, and computational modeling to investigate the cellular and circuit mechanisms underlying theta-nested gamma oscillations in superficial medial entorhinal cortex (mEC). The authors propose that fast-spiking interneurons receive strong gamma-frequency excitatory drive and provide rhythmic inhibition onto principal neurons, supporting a pyramidal-interneuron network gamma (PING) mechanism. They further report cell-type-specific differences in gamma phase locking, spatial clustering of subthreshold voltage signals, and a network model reproducing several observed features, including interneuron bursting and gamma-cycle skipping in excitatory neurons.

Strengths:

The study is technically sophisticated and addresses an important question in entorhinal circuit function. The combination of intracellular recordings, voltage imaging, and computational modeling is a clear strength.

Weaknesses:

Several key conclusions developed from experimental results require additional raw data, statistical support, clearer methodological description, and more cautious interpretation. The computational modeling focuses primarily on stellate cells, whereas the experimental results suggest an important role for pyramidal neurons in PING dynamics. This creates inconsistency between theory and experiments.

Reviewer #3 (Public review):

Summary:

Following previous work that demonstrated a relationship between higher homeostatic cytosolic calcium and lower retinal ganglion cell (RGC) apoptosis following injury to their axons, McCracken et al. investigated whether homeostatic calcium levels of the endoplasmic reticulum (ER) or mitochondria provide additional insights into the mechanisms by which calcium influences RGC survival. Their study reveals that homeostatic mitochondrial calcium shows a similar positive correlation with RGC survival. Despite that correlation, pharmacologic or genetic methods to lower mitochondrial calcium improved, rather than reduced, the survival of injured RGCs, while a genetic approach intended to increase mitochondrial calcium resulted in more RGC loss. These findings highlight the complexities of calcium regulation in modulating neuronal survival and raise important questions of how homeostatic levels of mitochondrial calcium affect stress responses that themselves can be either neuroprotective or neurodegenerative.

Strengths:

This study tackles an intriguing hypothesis that differences in calcium ion homeostasis in specific organelles may contribute to differences in survival of various RGC subtypes after optic nerve injury. This is a technically demanding question, and a primary strength of this work is its attention to, and meticulous reporting of, appropriate controls and, where applicable, seemingly contradictory results. Among these are careful evaluation of the effects of drug (or vehicle) delivery and genetic manipulations with and without injury and over extended time courses. The combination of thoughtful pharmacologic and genetic approaches makes for a thorough analysis of a challenging set of questions. The result is a study that provides a helpful perspective on the complicated roles that calcium, and especially mitochondrial calcium, can play across neuronal insults, neuronal types, and neuronal subtypes.

Weaknesses:

Given the paradoxical results, it would be helpful to have a clearer picture of how strongly the overexpression and knockdown of MCU altered the mitochondrial calcium levels. There may be potential for extraordinarily strong effects that would need to be tuned by using different shRNAs or promoters to more closely align with the observed differences between surviving RGCs and those that die. The investigation includes a relatively small number of resilient RGC subtypes, using the markers SPP1 and TBR2, raising questions of how generalizable the trend is between mitochondrial calcium levels and RGC resilience. The analysis and implications of Figure 3D might benefit from including not only the provided 50:50 split between "high" and "low" but also views of the data after splitting into thirds, fourths, and perhaps even fifths. The authors' inference that higher homeostatic calcium in more resilient RGCs may result in chronic mitochondrial stress is intriguing and worthy of more experimental investigation than is currently provided.

Reviewer #3 (Public review):

Summary:

The article by Chen et al. describes the comprehensive metabolic profiling of DP16 mice, a Down syndrome model that carries a duplicated segment of the mouse chromosome syntenic to human chromosome 21. The authors note that this model is superior to previously used models, based on genetics, as ~65% of the chromosome 21 orthologues. The metabolic phenotypes also appear to be more consistent with those observed in humans with Down Syndrome. The study lays the groundwork for a more detailed genetic dissection of dosage-sensitive genes that contribute to the metabolic deficits observed in Down Syndrome.

Strengths:

There is an enormous amount of data in this manuscript, and the methods are described with adequate attention to detail. A strength of the manuscript is that both male and female mice were analyzed, so that concordant and discordant phenotypes were identified. Both males and females had evidence of insulin resistance. Transcriptomic and metabolomic data revealed impaired pathways for lipid metabolism, a pro-inflammatory state, reduced mitochondrial health and oxidative stress. Although the effects of a high-fat diet on weight gain were divergent, this diet caused worsened insulin resistance in both males and females.

The discussion is excellent. Limitations of the study are well described. This reviewer does not identify any critical missing data.

Reviewer #3 (Public review):

This is a short and punchy manuscript that nicely summarises the 4 structures that are determined and provides a basis for the differences seen for acetylation sites shown for RNAPII activity.

The authors build on previous biochemical work that determined the functional outcomes of H3 core acetylation, adapting an assay they have previously used extensively to investigate RNAPII transcription on nucleosomes and, indeed, even H3 N-terminal tail acetylation. This assay is as such well set up and has a wealth of confirmatory previous studies from this lab and the authors are careful not to overanalyse their results, leading to robust and well-considered results. The structures are determined to a high resolution, allowing the interpretation put forward about side chain orientations, with clear densities shown for the regions of interest.

Further discussion or experiments would strengthen the conclusions further:

(1) The conclusion on the role of H3K56Acetylation could be strengthened, especially as the results are somewhat counterintuitive. It is conceptually surprising that acetylation near the entry/exit DNA that destabilises this region also leads to a reduced stall propensity at the dyad but has a limited effect at SHL5? While it can be explained by the clash at the dyad pause being reduced, the more direct effect of DNA breathing amplification would be expected to have a larger effect at SHL 5. Indeed, the density for DNA at SHL5 appears to be weaker in Figure 2A, suggesting the entry/exit DNA flexibility is amplified past this region.

Perhaps another assay that looks more directly at the flexibility of the entry/exit DNA would be useful, either through restriction enzyme-mediated cleavage or FRET (DNA ends and H2AK119 labels), providing stronger evidence of this effect. MNase is rather indirect and similar to the RNAPII assay itself.

Similarly, were the authors surprised by the modest effect (less than 2-fold) in transcriptional pause at SHL 0 for the K122Ac? Presumably, based on the model in Figure 4, this would be expected to be the area with the largest effect? The results of K56Ac and K122Ac almost seem swapped to what would be expected in Figure 1H. Further discussion of this observation would be useful.

(2) Could the local weakening of DNA, especially at the dyad, be observed in the cryo-EM structures? Perhaps comparison of local resolution estimation differences in this region compared to unmodified would be useful.

(3) Caution should be taken, and discussion should include that the structural data presented is after extensive processing. Many nucleosome averaging classes were discarded in the 3D classification steps (nicely summarised in Table 1 as "particles for 3d classification" and "particles in final map"). Indeed, it is likely that higher DNA flexibility particles would be thrown away during this processing step. This can be observed for K56Ac DNA ordering, for example, in Supplementary Figure S4, yellow and cyan classes from the round of 3D classification look to be high resolution and have a higher order of DNA, so there has been some selection here. How was this done? While this is not fully quantifiable, it gives an idea of the extent of wrapping. We would suggest discussing the methodological limitations and showing the models after the first auto refinement to see if the features discussed on end flexibility and dan ordering are retained.

(4) Di Cerbo et al. (reference 13) showed acetylation at K64 alters salt stability and affects transcription. Why do the authors think there is a discrepancy, albeit with different assays? Direct reference and discussion of this in the text should be included.

(5) Why was H3.2 used, while this is relatively abundant in mouse cells, human protein was used, and this appears to be less common than H3.1 and H3.3. We are sure that the effect is not likely to be substantive on structure (as shown by the Kurumizaka lab previously), but should be addressed in the text

Reviewer #3 (Public review):

Summary:

In this work, the authors set out to characterise how encounter states between antibodies and antigens evolve during affinity maturation through molecular dynamics simulations and Markov state modeling. They demonstrate how early glycan-mediated interactions increased association rates rather than modifying the final bound state.

Strengths:

The computational approach is backed up by experimental results and allows for visualising otherwise too short-lived association states, thus allowing to discriminate between different lineages.

Weaknesses:

The figures and captions are not always clear about what they are trying to show. The choice of CVs is not sufficiently discussed.

Milton Solomon reports having used a resin 3D printed platen at 95A duro.

Reviewer #3 (Public review):

In this manuscript, the authors strived to develop a highly efficient drug survival assay for in vitro cultured human malaria parasites P. falciparum. This was done by generating a transgenic P. falciparum line using a creLox strategy that allows detection of (presumably) viable parasites by a β-lactamase assay. To estimate the Limit of quantification of the recombined P. falciparum NF54i-lacZ, the authors ultimately designed a protocol in which viable parasites are detected by the luminescence of β-D-galactoside generated by β-lactamase within the transgenic parasites. For this, the parasite must be incubated with rapamycin for 120 hours to induce CreLox recombinase, which places β-lactamase under an active promoter. Using this assay, termed MULTI-i2, the author shows interactions between two antimalarial drug pairs that were previously demonstrated by another assay. In the case of pyronaridine and piperaquine pair, the NULT-i2 assay generated some additional insights compared to the previous assay, presumably by virtue of including more concentration datapoints. In conclusion, the authors argue that the MULTI-i2 assay is much less resource-intensive and time-consuming and can be applied on a large scale at a much lower cost and with the highest efficiency.

Overall, the data generated in this manuscript are clear and well represented, and I am convinced that MULTI-i2 provides yet another of many drug assays for malaria parasites and could be put to good use. However, I struggle to fully appreciate the merit of his study, as the manuscript reads more like a technical document than a scientific study.

I particularly lack an understanding of the strengths and weaknesses/limitations of the MULTI-i2 methodology and, thus, its applicability. I also do not fully appreciate the need for such an elaborate luminescence-based experimental setup. It would be good if some of these issues were addressed.

Specifically:

(1) The whole assay is based on detecting parasites by luminescence after 120 hr (5 days) after drug exposure. During that time, presumably the parasites that survived the drug pressure regrow to a detectable level and, at the same time, perform efficacious CreLox-based recombination to produce β-D-galactoside for detection. Is this necessary? How superior is this detection method to other methods, such as Fluorescence-assisted Cell Sorting (FACS), etc? Moreover, the 5-day growth-CreLox-β-D-galactoside production could introduce a series of confounding effects. In my view, more studies (beyond comparisons with a single existing method) would be useful for understanding this entire process.

(2) Related to that above, how would MULTI-i2 perform in case of drugs that do not necessarily kill all parasites, such as artemisinin? In the case of artemisinin, it is becoming evident that at least a small fraction of the parasite revives after treatment via a temporary dormancy state. This has, in fact, also been shown for other drugs such as mefloquine, pyrimethamine, etc. Would such a situation produce a range of false readings? In general, in its current state, it is hard to see what the limitations of this method are, which makes it hard to decide whether to use it for a particular application.

(3) Given the stated cost and labor efficiency of MULTI-i2, it is disappointing to see only two applications for two drug pairs: atovaquone/proguanil and piperquine/pyronaridine, for both of which their interactions were already known. The manuscript would benefit greatly if the authors demonstrated more drug interactions and identified (and ultimately validated) new ones. This would certainly make MULT-i2 method more attractive. In particular, it would be nice to see if one could use MULTI-i2 for studies of triple combinations as enthusiastically suggested.

(4) Throughout the manuscript, the authors claim that MULTI-i2 is considerably less expensive and can be done much faster than previous methods. In my view, this is not exactly a scientific argument. The cost of an assay depends heavily on the cost of reagents and labor, which are subject to market price fluctuations. The efficiency and time consumption can very much depend on laboratory organization, etc. Unless the author could specifically demonstrate where and how these assays are cheaper and faster, I suggest not discussing this.

Reviewer #3 (Public review):

Solyga, Zelechowski, and Keller present a concise report of an innovative study demonstrating clear visuomotor mismatch responses in ambulating humans, using a mobile EEG setup and virtual reality. Human subjects walked around a virtual corridor while EEGs were recorded. Occasionally, motion and visual flow were uncoupled, and this evoked a mismatch response that was strongest in occipitally placed electrodes and had a considerable signal to noise ratio. It was robust across participants and could not be explained by the visual stimulus alone.

This is an important extension of their prior work in mice and represents an elegant translation of those previous findings to humans, where future work can inform theories of e.g. psychiatric diseases that are believed to involve disordered predictive processing. For the most part, the authors are appropriately circumspect in their interpretations and discussions of the implications. The paper in its current form represents an important addition to the literature.

The authors have included analyses of the auditory mismatch using temporal electrodes, referenced to Cz (and therefore should exhibit a mismatch positivity). This added data clearly and convincingly shows that the sensorimotor mismatch is, indeed, stronger than the passive auditory MMN.

Comments on latest version:

The authors added useful points to the discussion and also included time frequency analyses to the paper formally, which strengthens the translational potential, in addition to the bolstering their claims slightly.

Reviewer #3 (Public review):

Summary

This manuscript reports that protocells derived from wall-deficient B. subtilis proliferate well when densely packed but fail to divide and eventually lyse when isolated. The authors attribute this density-dependent proliferation to mechanical shearing between growing neighbors, which deforms cells and increases the likelihood of membrane stalk formation and subsequent scission, enabling division without any dedicated molecular machinery. Through a combination of quantitative imaging, membrane tension measurements, and Cellular Potts Model simulations, the authors make a compelling case that self-generated mechanical stresses are critical for sustaining population growth in protocolonies. The findings have implications for understanding the lifestyles of primitive life forms, L-form bacterial pathogenesis, and the design of synthetic cells.

Strengths

The central finding is both surprising and counterintuitive: crowding is not just tolerated by protocells but is required for sustained population growth. The mechanism the authors propose is interesting: mechanical shearing between growing neighbors deforms cells, increasing the likelihood of membrane stalk formation and thus division, all without dedicated molecular machinery. Conceptually, this is a type of biophysical "scaffold" (Jacobeen et al. 2018, Nat. Phys.; Day et al. 2022, Biophys. Rev.) in which key elements of a Darwinian loop, namely a life cycle involving growth and reproduction, are provided "for free" by physics, enabling open-ended Darwinian evolution that can eventually bring these life cycle components under developmental control. Such scaffolds, both biophysical and ecological (Black et al. 2020, Nat. Ecol. Evol.; Libby & Rainey 2013, Phys. Biol.), are likely key mechanisms in the origin of life and in evolutionary transitions in individuality, and this paper provides a nice example of how they can work in a protocell context.

The combination of experiments and modeling works well. The membrane tension measurements are the strongest piece of evidence for the proposed mechanism, showing directly that tension is elevated in protocolonies and concentrated at cell-cell interfaces. The Cellular Potts Model captures the key experimental features. The discussion is nicely balanced, particularly the note about Gram-negative L-forms, whose rigid outer membrane may preclude this mechanism, which is a testable prediction for future work. I would suggest the authors also discuss the connection to biophysical scaffolding, as I think this is conceptually important and would help situate their work within a broader framework for understanding how primitive life cycles can arise from physical processes (see also Zamani-Dahaj et al. 2023, Genes; Hammerschmidt et al. 2014, Nature).

Weaknesses

The surface-volume balance analysis is central to the argument, and it depends on the assumption that cells have a fixed thickness of 0.8 µm, taken from the width of walled cells. But these are wall-deficient cells, which are mechanically quite different, and their thickness could plausibly vary during growth or under compression. I think the paper would benefit from either a direct measurement of cell thickness or a sensitivity analysis showing how η responds to plausible variation in this parameter. If the results are robust, that would put the analysis on much firmer ground.

The positive correlation between cell shape deformation and division rate (Figure 3C) is central to the proposed mechanism, but I think the paper needs to be more careful about the jump from correlation to causation. The authors propose that deformation increases the likelihood of membrane stalk formation, leading to scission. That is plausible, but an alternative is that cells with higher local growth rates both deform more and divide more frequently, with the two outcomes driven independently by the same underlying cause. The paper does show that average volume growth rates are indistinguishable between aggregated and isolated cells, which argues against a simple "faster growth explains everything" interpretation, but this does not rule out local variation within protocolonies driving the correlation. I think the most convincing experiment would be to apply external mechanical stress to isolated cells and see if that alone can drive division, decoupling deformation from growth. I realize that this may be technically very difficult, but at a minimum, the paper should acknowledge this as an alternative hypothesis.

The Cellular Potts Model has quite a few free parameters (Table S1), and it is not clear how tightly these are constrained by the data. A sensitivity analysis would go a long way toward showing that the results are robust and not overly dependent on specific parameter choices.

In any case, this is a strong paper with a cool finding and an interesting mechanistic explanation. I think it will be of broad interest, particularly to people thinking about the origins of life and synthetic cell design.

Reviewer #3 (Public review):

Summary:

The study by Bhojappa et al. brings new and interesting elements about the stability of the septin ring and the crosstalk between septin and actomyosin ring assemblies. The study focuses on the four kinases associated with the septin ring, Elm1p, Gin4p, Hsl1p and Kcc4p. Elm1 and Gin4 show the strong knock-out phenotypes, whereas Hsl1p and Kcc4p show the weak knock-out phenotypes. The Elm1p/Kccp1p and Gin4p/Hsl1p pairs show similar timing at the bud neck. While these kinases share redundant functions, Gin4 appears to have a unique interaction with the BAR domain protein Hof1, revealing a novel direct interaction between the septin and actomyosin rings. Interestingly, the kinase activity of Gin4 is not required for its role in septin organisation and AMR constriction. The last part of the manuscript shows an original protein tethering protocol used to show that Hsl1 and its membrane binding ability are required for phenotype rescue of gin4null cells.

Comments on revised version:

I thank the authors for their thorough and thoughtful response to my review. The revised manuscript clearly reflects their efforts to provide rigorous and high-quality science. Addressing the concerns raised in the review required significant effort, but the improvements in the manuscript make it clear that the work was well worth it.

Reviewer #3 (Public review):

Summary:

This manuscript provides a comprehensive characterization of the Plasmodium falciparum protein LSA3, combining biochemical, genetic, and in vivo approaches. The authors convincingly demonstrate that LSA3 is expressed during liver stage infection and that disruption of the gene leads to a modest but reproducible reduction in liver stage parasite load in humanized mice.

Strengths:

Their biochemical and cell biological analysis of blood stages provides strong evidence that LSA3 is exported to the infected erythrocyte, and the detailed analysis of its PEXEL motif processing is well executed.

Weaknesses:

The study suggests LSA3 as one of only two known P. falciparum PEXEL proteins contributing to this stage, although there is no evidence for the export beyond the vacuolar membrane. Several key conclusions, particularly regarding antibody specificity, localization in liver stage parasites, and the interpretation of the phenotypic data, are not fully supported by the current experiments.

Comments on revised version.

I appreciate the authors' efforts to revise the manuscript and to clarify several aspects of the study.

However, I remain concerned that some conclusions extend beyond the data presented. In particular, the authors acknowledge in their rebuttal letter that antibody specificity in liver stages could not be validated and that cross-reactivity cannot be excluded. Consequently, the localization data shown in Figure 5 cannot currently be considered definitive evidence for liver stage localization of LSA3 itself.

Similarly, the revised manuscript appropriately states that LSA3 was not detected beyond the PVM in liver stages and that export into the hepatocyte remains unresolved. Nevertheless, several statements continue to imply a role for liver stage protein export. At present, the possibility that a domain of LSA3 may face the host-cell side of the PVM remains speculative and is not supported by direct experimental evidence.

The liver stage fitness phenotype is convincing and supports the conclusion that LSA3 contributes to normal liver stage development. However, the current data do not establish the developmental process affected nor connect the phenotype to export beyond the PVM.

I therefore recommend that the manuscript consistently distinguish between (i) demonstrated export of LSA3 during blood stage infection and (ii) the unresolved localization and trafficking of LSA3 during liver stage infection. I would also encourage the authors to consider revising the title to better reflect the findings presented, as the current title may be interpreted as demonstrating liver stage export, which has not been shown.

Reviewer #3 (Public review):

Summary:

Tan et al demonstrated the importance of ALDH-high cells in the epithelial development in the mouse endometrium, and these cells displayed properties of stem cells.

Strengths:

The findings are solid, supported and validated through a combination of technical methods. I appreciated this combined use of mouse and human endometrial cells to strengthen the findings. Genomic results from a single-cell sequencing dataset were informative as they depicted the different stages of the estrus cycle during the regeneration process. Verification with immunostainings with various markers made it convincing for readers to visualize the cell's location, progression, and status at different timepoints. Utilizing human endometrial cells further demonstrated that the phenomenon observed in mice can be translated to humans.

This work will greatly advance the understanding of endometrial regeneration for reproductive biologists.

Comments on revised version.

The authors have answered the questions in the revised manuscript, no further comments.

Reviewer #3 (Public review):

Summary:

The manuscript by Zilinskas et al seeks to understand the mechanisms underlying the ability of Mtb to suppress Th17 differentiation. As Th17 responses are needed for protective immunity against TB, this is an important topic of investigation. They use Mtb mutants that lack eccC1 (from ESX-1 locus) and fadD28 (encoding PDIM) and implicate a Tbet-dependent pathway by which Mtb modulates Th17 differentiation. The mechanism by which ESX-1/PDIM function to impact Th17 differentiation is, however, unclear, which limits the novelty of the results.

Strengths:

Understanding how Mtb limits Th17 differentiation has implications for vaccine development. Comparative study of KO mice and Mtb mutants is a strength.

Weaknesses:

(1) Addressing several questions related to the Tbet KO mouse experiments would strengthen the study. Do the Tbet KO mice have elevated IL-4/5/13 (which has been previously reported in non-TB studies) in addition to IL-17? The lack of Th17 cells in the IFNg KO compared to the Tbet KO may reflect a difference in timing, since only 3-week data are shown; earlier and later time points would provide a better interpretation. The authors do not present any data on neutrophil infiltration in WT vs Tbet KO vs IFNg KO mice. Since IL-17 is known to be important for recruiting neutrophils to the lung, neutrophil data are important for clarifying the mechanism underlying the CFU outcomes.

(2) While IL-23 is important for sustaining IL-17 production, IL-6, TGF-b and/or IL-1β are necessary for Th17 polarization. What were the levels of these cytokines in DCs in the lung? (Fig 5). Additionally, Tbet-deficient DCs exhibit impaired activation of antigen-specific Th1 cells and have reduced IL-12 production. Given the data showing higher IL-17 levels in Tbet KO mice, the authors should provide information on the DC phenotype (IL-23, IL-6 etc) in the Tbet KO experiments.

(3) The mechanism by which ESX-1/PDIM function to impact Th17 differentiation is not clear. While data showing a role for ESX-1 and PDIMs in inhibiting Th17 responses is interesting, there is no insight into the potential mechanism of action. Fig 3 showing reduction in IFNg+ CD4 T cells after infection with eccC1 and fadD28 mutants suggests that this outcome is due to a lower bacterial load relative to WT Mtb at the 3-week time point. Since IFNg is known to suppress IL-17, the higher levels of Th17 cells could be due to the reduction in IFNg due to the attenuated growth of the mutants.

Reviewer #3 (Public review):

Strengths:

Mahieu and colleagues address an interesting and underexplored question: whether non-pathogenic variation in the mitochondrial genome contributes to the inter-individual variability of human telomere length (TL). Using a Belgian Flow-FISH reference cohort (n=491) to identify donors at TL extremes, they generate transmitochondrial cybrids from platelets of seven donors of distinct mtDNA subhaplogroups and characterize the resulting cells with a broad and well-executed toolkit (TRF, TeSLA, ddTRAP, EPR-based mitoROS, Seahorse with permeabilized-cell ETC dissection, LC-MS metabolomics, telomeric PAR-FISH). The most compelling finding is that cybrids derived from donors with low complex I (CI) activity undergo rapid telomere shortening during the glycolysis-to-OXPHOS transition of cybrid formation, and that this is largely prevented by co-treatment with NAC and the NAD⁺ precursor nicotinamide riboside, supporting a model in which CI sustains the NAD⁺ pool required for PARP1-mediated repair of oxidative damage at telomeres. The authors further report an inverse correlation between donor lymphocyte TL and mitoROS in the corresponding cybrids, and provide preliminary evidence that the K1a-defining ATP6 A177T variant (m.G9055>A) may be enriched in long-telomere individuals.

Weaknesses:

(1) Statistical support and donor sampling for the central in vivo correlation (Figure 4C).

The inverse correlation between donor lymphocyte TL and cybrid mitoROS (R²=0.794, p=0.007) is the principal in vivo claim of the paper, but it is built on seven donors deliberately selected from the extremes of the Flow-FISH distribution. Sampling at the tails of the outcome variable can substantially inflate apparent correlation strength and significance. I would encourage the authors to (i) explicitly state this sampling structure where the correlation is introduced, (ii) report a leave-one-out sensitivity analysis to confirm the relationship is not driven by one or two donors (Cyb3 and Cyb6 appear to anchor the line), and (iii) where feasible, extend the analysis to additional donors with intermediate TL to test whether the relationship holds across the full distribution. Even a modest expansion (e.g., 4 to 5 additional donors at P25 to P75) would substantially strengthen this central claim.

(2) Reconciling the cybrid CI / TL relationship (Fig 3B) with the absence of a CI / TL relationship in donor lymphocytes (Figure 4A).

Figure 3B shows a strong correlation between CI activity and TL in cybrids (R²=0.87), while Figure 4A shows no correlation between donor CI activity (measured in the same cybrids) and donor lymphocyte TL. The authors acknowledge this, but the manuscript subsequently builds toward a CI-centric model of in vivo TL regulation, which seems to outrun the data. The most internally consistent interpretation is that the cybrid CI phenotype reports a sensitized in vitro response to the acute oxidative stress of the metabolic shift, rather than a steady-state determinant of leukocyte TL. I would suggest reframing the abstract, significance statement, and Discussion to make this distinction clearer. The in vitro CI / NAD⁺ / PARP1 axis is a strong finding on its own, while the in vivo role of CI activity (as opposed to ROS more broadly) is not yet established here. Donor #1's profile (very long lymphocyte TL, low CI activity, severe shortening in cybrids, no telomere inheritance in offspring) is informative in this regard and could be discussed more directly as a case that helps delineate where the cybrid model does and does not recapitulate in vivo biology.

(3) The K1a / ATP6 A177T inheritance claim.

The proposal that K1a (and specifically ATP6 A177T) contributes to maternal inheritance of long telomeres is intriguing but currently rests on three pedigrees (one of which, donor #1, does not support the hypothesis) and a chi-square test that does not reach significance (p=0.153, Figure 4F). The supporting evidence is also limited by the fact that platelet-mediated mitochondrial transfer delivers donor mitochondrial proteins, lipids, and residual mtRNA in addition to mtDNA, making it difficult to attribute the cybrid phenotype of donor #6 specifically to the ATP6 A177T variant. I would recommend either: (a) extending the genotyping screen to additional unrelated donors and, if feasible, confirming the effect of ATP6 A177T through an isogenic approach (e.g., mtDNA base editing in a clean background), or (b) softening the relevant statements to "suggestive trend warranting larger studies," and presenting the K1a observation as hypothesis-generating rather than supportive. The Ashkenazi-centenarian connection raised in the Discussion is an excellent direction for follow-up and could be framed accordingly.

Reviewer #3 (Public review):

In this manuscript, the authors aim to understand the function of the transcription factor CHOP, which is known to promote cell death during severe stress in the ER. The authors note that CHOP is induced during less severe stress, but its functional output is not well understood in these cases. Here, they study the effects of conditional knockouts of CHOP in hepatocytes of mice challenged with chemical inducers of ER stress.

Tunicamycin (an ER stress inducer) injection leads to the upregulation of CHOP and lipid accumulation in the liver, but no significant cell death in the experiments outlined here. Conditional knockout of CHOP results in a number of differences in the way hepatocytes respond to stress, notably resulting in lower steatosis.

There are two main findings supported by the data presented here. First, the authors show that CHOP suppresses the expression of ONECUT, a master regulator of hepatocyte differentiation and metabolism, during ER stress. They show by ChIP-seq that CHOP binds to the promoter region of this gene, and by RNA-seq that ONECUT expression is suppressed by ER stress in a CHOP-dependent manner. Many predicted targets of ONECUT1 were also suppressed by ER stress in a CHOP-dependent manner, though they were not bound directly by CHOP. The data support a model where CHOP down-regulates hepatocyte metabolism and identity via regulation of ONECUT1. This is a new and interesting finding, perhaps explaining the steatosis phenotype of livers that accompanies ER stress, although this was not tested directly.

The second main finding of this paper is that CHOP deletion leads to an interesting assortment of effects on genes related to the ER stress response and integrated stress response (ISR). As expected, based on prior work, CHOP deletion led to more phosphorylation of eIF2alpha (CHOP is known to upregulate the phosphatase for this translation factor). However, unexpectedly, this did not cause increased expression of ATF4 (a transcription factor whose upregulation during stress is dependent on eIF2alpha phosphorylation) and its downstream targets; in fact, CHOP deletion had the opposite effect on these. In other words, CHOP seems to both turn off the initiating signal for the ISR (namely, eIF2alpha phosphorylation) and also promote the downstream signaling events that rely on this initiating signal. It makes sense that cells would do this, as restoring translation would be important for realizing the effects of the massive changes in gene expression initiated by ER stress, and yet this would exacerbate stress in the short term, so it would be counterproductive to also turn off the entire stress-regulated program. Having a factor (perhaps CHOP) that coordinates these two events makes sense. It will be interesting in future work to understand the mechanisms behind this regulation.

Finally, CHOP deletion led to less activity of other aspects of the ER stress response, notably IRE1 (determined through measurement of XBP1 splicing and RIDD of Bloc1s1). This is explained by the continued phosphorylation of eIF2alpha in these knockouts, as the continued attenuation of translation would lessen the burden of misfolded proteins in the ER. Somewhat confusingly, the same pattern is not seen in downstream targets of XBP1. Less splicing, coupled with perhaps less translation of the spliced mRNA, should result in less active transcription factor and lower expression of its target genes in the CHOP KO. This is not observed in Figure 2, although the more global gene expression analysis suggests that all stress-dependent gene expression changes were weaker in the CHOP KO livers.

The authors characterize the effects of CHOP, promoting restoration of protein synthesis and the accompanying exacerbation of stress while preserving the signaling that should relieve ER stress, as a switch from an acute to chronic phase of ER stress. This is mirrored in their analysis of ATF6 in a similar series of experiments. Although this is an interesting framework for thinking about the stress response, whether CHOP is the key factor or a supporting actor in regulating this transition will require a better understanding of the mechanisms involved.

Reviewer #3 (Public review):

Summary:

In this article, the authors performed targeted lesions in cortical areas involved in forelimb control of rhesus macaques. Using a reaching task with kinematic tracking, they compared kinematic variability (as a proxy for dexterity) and reaching speed (as a proxy for strength) before and after cortical (n=7) and magnocellular red nucleus (RNm) (n=2) lesion. Changes in these movement metrics were related to the location and extent of the lesions, reconstructed from histology. The authors report that lesions with a large component in New M1 had a pronounced effect on kinematic variability, whereas lesions with a large component in posterior Old M1 primarily affected reaching speed. Lesions of the RNm were performed in two animals approximately seven to eight weeks before the cortical lesion. By themselves, RNm lesions produced a significant but small reduction in reach speed. They also magnified the effect of the subsequent cortical lesions.

Strengths:

(1) For non-human primate (NHP) research, this is a large cohort.

(2) The behavioural analyses are clear and precise.

(3) The additional red nucleus lesions in two monkeys provide unique complementary information.

Weaknesses:

(1) Description of injuries. As described and reported in the current result section and figures, readers do not have a clear understanding of the lesion extent and location.

(2) Lack of formal correlative analyses between lesion characteristics and behaviour. Currently, it seems that the conclusions are based on impressions between some aspects of the lesions and precise kinematic measures.

(3) The data will be of interest to a large community of researchers working on brain injury and stroke recovery, as well as cortical motor control. There are, however, some major methodological issues in the current version of the manuscript that prevent a clear evaluation of the findings and potential contribution of the work in the field. These issues need to be addressed to support the conclusions.

Reviewer #3 (Public review):

This manuscript addresses an interesting and timely question: whether regional glymphatic clearance in the human cortex is spatially coupled to neural activity and whether a mismatch between activity and clearance may help explain regional vulnerability to amyloid-β deposition. The authors use intrathecal gadolinium-based glymphatic MRI in 96 participants, derive cortical influx and clearance maps, integrate these with Allen Human Brain Atlas transcriptomic data, and then relate regional clearance to resting-state fMRI measures in a smaller subgroup. They further compare the resulting activity-clearance mismatch map with an open-source ¹¹C-PiB amyloid PET dataset. The overall concept is attractive because it attempts to connect glymphatic physiology, neuronal activity, and proteopathy at the regional level of the human brain, an important and understudied area.

The main strength of the study is the use of direct intrathecal contrast-enhanced MRI to generate cortical maps of glymphatic tracer dynamics. This is a technically demanding approach and provides a richer spatial readout than indirect MRI proxies of glymphatic function. The authors show that the cortical tracer signal increases from 4.5 h to 15 h and then decreases by 39 h, allowing them to interpret the early signal as reflecting influx and the persistent signal at 39 h as impaired clearance. They further identify regional patterns, with faster influx in medial prefrontal/insular areas and slower clearance in dorsal prefrontal and parietal surface regions. The analysis is visually clear, and the use of cortical gradients is a useful way to reduce complex regional data into interpretable spatial axes.

The multimodal integration is also interesting. The transcriptomic analysis suggests that regions with faster glymphatic clearance are enriched for synaptic organisation and neuronal activity-related pathways, while regions with slower clearance show enrichment for metabolic and mitochondrial pathways. The cell-type enrichment analysis further implicates excitatory and inhibitory neurons, oligodendrocyte lineage cells, microglia and, to a lesser extent, astrocytes. This provides a plausible biological bridge between regional neural activity and clearance function, and the sensitivity analysis using ReHo in addition to fALFF is a useful robustness check.

However, the manuscript should be more careful in its causal interpretation. The study is cross-sectional and largely correlative in space. The finding that regions with higher spontaneous neural activity tend to show better glymphatic clearance is intriguing, but it does not establish that neural activity drives clearance in these participants. Conversely, it remains possible that better tissue integrity, vascular function, CSF access, cortical geometry, vascular density, or disease composition jointly influence both fMRI measures and tracer clearance. The authors do acknowledge some of these limitations, but the abstract and discussion should more consistently frame the findings as associations rather than evidence of an activity-clearance mechanism in humans.

The most important limitation is the small size of the fMRI subgroup. Although the whole glymphatic MRI cohort includes 96 participants, the key activity-clearance analysis is based on only 15 individuals, including 11 with peripheral neuropathy and 4 with motor neuron disease. This is a very small and clinically heterogeneous sample on which to build a central conclusion about regional neural activity and glymphatic clearance. The authors show that the 39 h PC map in the fMRI subgroup resembles the whole-cohort map, which is helpful, but this does not address whether the fALFF-clearance relationship is robust at the individual level. The paper would be strengthened by reporting subject-level stability, leave-one-out analyses, and whether the association persists after excluding the four motor neuron disease cases.

A second major concern is the interpretation of the amyloid analysis. The ¹¹C-PiB map is derived from an external open-source Alzheimer's disease dataset, not from the same participants who underwent glymphatic MRI and fMRI. Therefore, the association between activity-clearance mismatch and amyloid burden is a spatial correspondence across group-average maps, not an individual-level relationship. This is valuable for hypothesis generation, but should not be presented as evidence that a mismatch in the present cohort predicts amyloid deposition. The authors should clearly state that this analysis tests whether mismatch regions overlap with known amyloid-prone cortical regions, rather than directly linking mismatch to amyloidosis in individual participants.

The definition of "mismatch" also needs clarification. The text defines the mismatch index as the negative absolute difference between z-fALFF and z-39h PC, and states that higher scores indicate greater mismatch. Because the index is negative, values closer to zero would normally indicate a smaller absolute difference rather than a greater mismatch. This should be checked carefully and corrected if necessary. More broadly, because a higher 39 h PC indicates worse clearance, the interpretation of match and mismatch categories is not intuitive. The authors should provide a clearer schematic and ensure that the mathematical definition, biological interpretation and figure labelling are fully aligned.

Several technical confounds require more attention. Intrathecal gadolinium MRI is influenced by CSF dynamics, posture, sleep, circadian timing, renal clearance, age, intracranial pathology, and potentially diagnosis-specific differences. The authors acquired scans at fixed time points and noted that patients slept as usual, but individual sleep duration, sleep quality, posture, and daytime activity were not objectively measured. Given that the central claim concerns glymphatic clearance, these are not minor confounders. The authors should consider adjusting for age, sex, diagnosis, vascular risk factors, and relevant clinical variables where possible, and be more explicit about how heterogeneous disease indications may influence cortical tracer kinetics.

The statistics are generally good. However, many correlations are performed across 400 cortical parcels, which are not independent biological samples. The paper would benefit from clearer separation between participant-level inference and region-level spatial inference. For example, the fALFF-clearance and mismatch-amyloid analyses are regional map correlations, not correlations across individuals. This should be clearly stated throughout. The authors should also report effect sizes and confidence intervals more consistently, and explain how multiple comparisons were controlled across transcriptomic, cell-type, fMRI, ReHo and amyloid analyses.

The transcriptomic analysis is useful but should be presented as indirect. AHBA data come from six post-mortem brains; only the left hemisphere was used, and the donors were healthy and younger than the clinical cohort. Therefore, these data capture intrinsic regional gene-expression patterns rather than disease-state expression in the same individuals. The authors should avoid implying that the transcriptomic findings directly explain glymphatic function in their participants. The current discussion partly acknowledges this, but the framing in the abstract and results could be more cautious.

There are also several points of presentation that should be improved. The manuscript should consistently distinguish glymphatic influx, glymphatic clearance, CSF tracer retention, and waste clearance. A 39 h residual gadolinium signal is a useful proxy for delayed clearance, but it is not the same as direct measurement of amyloid or tau clearance. The language around "waste clearance" and "amyloidosis" should therefore be precise. The authors should also clarity whether "higher clearance" corresponds to lower 39 h PC across all analyses, as this inversion is easy for readers to misinterpret.

Reviewer #3 (Public review):

Summary:

In this manuscript, Noirot-Gross et al take an open-ended approach to elucidate the c-diGMP-associated protein complexes in Pseudomonas fluorescens. Starting with 10 cyclic d-GMP putative proteins, they use a combination of genome-wide two-hybrid system followed by CRISPRi-mediated exploration of phenotypes to describe the cyclic di-GMP-associated regulation of biofilm formation, and how it relates to other functions. Overall, this work presents an excellent example of how genome annotations can be further confirmed with the use of integrated functional genomic approaches. Some areas of improvement can be applied to this manuscript to enhance readability and provide a clearer distinction between confirmatory results and new findings, which are provided below:

Strengths:

(1) The authors have explored their findings extensively and provide a comprehensive view of the topic.

(2) The combination of genome-wide explorations of protein-protein interactions with the more focused phenotypic exploration of the interactions found provides a solid framework for the work presented.

Weaknesses:

(1) Overall goal of the work:

While articles that describe open-ended approaches can be comprehensive and descriptive in nature, the authors should have a main overall goal, which can guide the reader through the main and most compelling findings at the end. As written, the overall goal is not clear. The network perspective is interesting, and the focus on biofilm formation appears in the title. Why P. fluorescens? How is cyclic di-GMP-mediated regulation of biofilm formation in P. fluorescens different from P. aeruginosa? Why would it be studied?

(Positive or negative regulation of biofilm formation?)

(2) Abstract:

The abstract is very well written and guides the reader to the DipA as a hub protein in the network. From further reading, the article could clarify whether this finding is confirmatory or novel (does DipA play a similar role in P. aeruginosa?) It would be appropriate to mention the role of DipA in other Pseudomonas species from the beginning, and not only in the discussion session.

(3) Introduction:

The introduction is nicely written. An area of improvement could be giving more attention to protein interactions as relevant to c-di-GMP. The authors could consider an independent paragraph starting with line 84-85 "Protein-protein interactions involving DGCs, PDEs, and target effectors are crucial in establishing localized signalling through the generation of local pools of c-di-GMP", expanding on this particular aspect with an example of localized signal, after explaining that localization could help decipher specific function within the network of DGCs and PDEs. Then go into connecting biofilms with c-di-GMP and protein-protein interactions, using the example of GcbC and LapD.

(4) The rationale of choosing 10 PDEs could be clarified. The nice diagrams shown in the supplementary table could be used as part of Figure 1, so the reader understands why these proteins were used, and what is known about them (for example, add them as Figure 1a).

(5) Figures 1b and 2 convey the same information as in Figure 1a. They could be removed without affecting the understanding of the article.

(6) CRISPRi and Figure 3. Figure 3 shows the methodology of CRICPR phenotypic screening. A diagram showing the CRISPRi system in P. fluorescens could help the non-expert reader. While the choice of 23 proteins related to the emerging hub DipA is clear, the choice of the other 33 genes could be better explained. Are these proteins already related to biofilm formation? Where are they part of the network detected? How about the other 14 SBW25 genes? The authors could clarify the rationale of the choices. Figure 4 could be combined with Figure 3 or moved to the supplementary material.

(7) Figures 5, 6 and 7 represent solid network analysis of the findings. Still, they could be improved in clarity on the main findings. The authors conclude at the end of section 3.2.3 that there are networks that exert a "positive role" and a "negative role". The authors could show that in the figures, explaining what those roles are: more biofilm structural coding genes? positive or negative regulation of biofilm formation?)

Reviewer #3 (Public review):

Summary:

Hall et al. use a multi-omics approach to investigate the responses of bovine alveolar macrophages to Mycobacterium tuberculosis infections and the underlying mechanisms that are shared between bacterial family members. The study is of particular importance for multiple reasons, including the impact of bovine infections on food supply (and resulting economic impacts) as well as the use of bovines as a large animal model to investigate the effects of M. tuberculosis infection. The authors isolated bovine alveolar macrophages and exposed them to infection with M. bovis, M. bovis BCG, irradiated M. bovis, or M. tuberculosis. 24 hours post-infection, samples were analyzed by RNAseq, ChIP-seq, and ATAC-seq to look at alterations in the transcriptome and epigenome. Through fluorescence imaging-based analysis, the authors show equivalent infection (bacterial uptake) in all groups, except for the irradiated M. bovis. Principal component analysis of the transcriptomic data demonstrated strong segregation for the M. bovine-infected cells compared to the other groups, which had some intermixing in the PCA. Among the differentially expressed genes, the cytokine IL36G was significantly upregulated across all four groups. This is significant as this cytokine enhanced autophagy in macrophages and subsequent M. tuberculosis killing activity. To further investigate the transcriptional changes, ChIP-seq and ATACseq were utilized to investigate chromatin changes in the form of differential affinity binding sites (DABS) and differential open chromatin regions (DOCR). TPMRSS2, a protease that plays an important role in multiple types of infections (tuberculosis, COVID, etc.), was found to be a significant DOCR in both the M. bovis and M. tuberculosis challenge groups, conferring enhanced TPMRSS2 expression in both of these groups. Using the integrative approach between all the omics data, the authors found that CD274, which encodes the PD-L1 protein, was upregulated in all four groups. PD-L1 is known to play an immunosuppressive role, and PD-L1+ macrophages have been shown to create "cold" microenvironments that would likely favor the mycobacterium. Lastly, SNP analysis found variants in four genes (ERBB4, LRCH1, MRTFA, RNPC3) that could serve as susceptibility genes.

Strengths:

Overall, this study demonstrates that challenge with M. bovis elicits a more extensive remodeling of chromatin and subsequent gene expression changes in macrophages compared to the other closely related strains. The work demonstrates the power of functional genomics and its utility in investigating the underlying changes that can affect responses to infections and subsequent outcomes. While the study lacks functional validation, the cohesive dataset is quite compelling, and from it, the authors draw conservative conclusions and are frank about their study limitations.

Weaknesses:

Lack of functional validation of some of the targets found.

Reviewer #3 (Public review):

Summary:

This work takes a precision neuroscience approach to examining how different task demands elicit lateralized vs. bilateral activity in large-scale brain networks. Using ~35-150+ minutes of resting state data per person, the authors identify individualized networks and map their degree of laterality. They find that all association networks are bilateral, with some networks. such as the language network (left) and fronto-parietal B network (right), showing some marked degree of laterality. A sentence reading task evokes activity bilaterally in the language networks, while working memory load during an n-back task evokes bilateral activity largely in fronto-parietal, dorsal attention, and salience networks. Interestingly, though, the authors find that when digging into the N-back task and contrasting blocks that have rhyming words vs. faces, this elicits a more lateralized pattern of activity; left activation for rhyming and right activation for faces. This lateralization is seen across multiple association networks, not just the language or fronto-parietal networks. These findings are then replicated in another precision data set.

Strengths:

This work has several notable strengths. This study boasts a lot of data for each individual, allowing them to examine individualized functional networks and task activations. Given the marked individual differences in laterality (see Figures S4 and S5), a group-averaged network atlas and group-averaged activation maps would likely muddle some of the interesting effects.

The authors also use an elegant approach for individualized network estimation, multisession hierarchical Bayesian modeling, or MSHBM. MSHBM allows vertex-based functional connectivity to steer individualized networks while also incorporating meaningful priors to identify comparable networks across individuals. This is a nice approach combining elements of a common atlas across individuals with data-driven approaches.

The authors have a rich working memory N-back task with four different stimuli types with which they can examine processing different types of stimuli.

A major strength of this work is the replication. The authors take their surprising findings and replicate the entire experiment in another sample. The replication sample notably has even more data per person than the discovery sample, allowing for robust and precise estimation of individual networks and task activity.

Weaknesses:

I'd like to frame this section as 'unsolved challenges' rather than 'weaknesses'.

One of the strengths of this paper is that the working memory task that incorporates so many different stimuli and conditions also poses a caveat for interpreting the task stimuli effects. The 'word' condition requires multiple cognitive demands - working memory and rhyming/phonological processing. This makes it a little hard to draw complete parallels between the word and face conditions. It does, however, provide a useful backdrop to explain why there are such strong left laterality effects for the word condition. I would expect that explicit phonological processing that is required for a rhyming task would elicit more left lateralized activation than the sentence processing task, as fluent readers are likely not using explicit phonological processing for sentence processing. This could explain why, for the sentence reading task, they find more bilateral activation, but a task that specifically targets phonological processing (i.e., the 2-back word condition) would evoke more left lateralized activation above and beyond the activation supporting working memory, which is held constant across both the 'words' and 'faces' conditions. Ultimately, I believe this supports the broad idea of this work - that large-scale association networks can show lateralization, even beyond language network boundaries. However, the fact that there is a dual-task load that evokes phonological processing for the word condition is important for contextualizing these findings.

When looking at plots of individual differences, it is clear that some people are more bilateral than others in certain networks (and sometimes overall). Because of the amount of data per person in this precision study, it naturally limits the overall sample size (N=29). This makes it difficult to interrogate individual differences in laterality and what might predict this effect across people.

Reviewer #3 (Public review):

Summary:

The manuscript by Murrell et al. describes a high-throughput approach for evaluating food foraging strategies in mice. Building on their prior publication describing the technical aspects of the Feeding Experimentation Device 3 (FED3), this study demonstrates the utility of the FED3 in evaluating decision-making in mice. The authors identify key differences in male and female foraging strategies that could not be accounted for by total food consumption or overall food motivation. Given the rapid adoption of the open source FED3 platform, this work is likely to be of broad interest and utility in the field.

Strengths:

(1) The use of cost-effective, open-source devices like FED3 provides substantial value to the scientific community. Validation of appropriate conditions for using this equipment is an important step toward broad adoption.

(2) The authors implement a simple but elegant experimental design for studying food-motivated decision-making behavior. This approach could be applied to a wide range of preclinical disease models in future studies.

(3) The study is well-powered to evaluate sex as the primary experimental factor (62 females, 74 males), allowing the authors to make convincing claims about differences in strategy. Additionally, the dataset provides a useful benchmark for power analyses in future studies involving more complex experimental designs.

(4) The figures are clear and generally easy to interpret the primary findings.

(5) The conclusions are appropriate and not overstated

Weaknesses:

(1) A major strength of this study is the potential utility for new investigators trying to implement cognitive behavioral tasks in mice. However, the present version provides limited background on the rationale for selecting the bandit task and on prior work applying it in similar contexts. Including additional background and discussion would better contextualize the approach for other groups considering adopting it in their own studies.

(2) Some methodological details surrounding the initiation of the experiment could be clarified. Specifically, it is unclear if mice transitioned directly from standard housing conditions (group housed, standard chow) to the study conditions (single housed, FED3-based probabilistic learning), or intermediate acclimation/training steps were used, such as autoshaping, free access to new food pellets, or FR1 training. A more detailed experimental timeline (for example, see Figure 1 from PMID 39710132) would address this concern.

(3) The authors evaluated multiple probabilistic conditions (100%, 90%, 80%, 70%, 60%), but ultimately focused on the 80% condition for this study. A more detailed explanation for how this conclusion was reached would be useful for future researchers working under different experimental conditions (i.e., age, strain, genotype, disease model) where other probabilistic conditions may be more appropriate.

Reviewer #3 (Public review):

Summary:

In "Design of a minimal, allosteric, and ATPase-like machine using mechanical linkage," Omabegho introduces a simplified model of an allosterically-regulated molecular machine. The model machine takes inspiration from simple ATPase motors, specifically myosin or dynein, and is meant to capture the interactions between enzyme, ligand, and substrate that empower these enzymatic biomolecular machines. The model system attempts to specifically model the inhibitory allosteric regulation relevant to machine operation, following work on mechanical features of allostery in protein-analogue elastic network models. After the introduction of the model machine, the manuscript discusses the primary cycle by which the substrate serves to displace and then cyclically replace the ligand (roughly corresponding to a "recovery" and "power" stroke, respectively, in their biomolecular machine inspiration), as well as cataloguing other futile cycles or individual chemical pathways the machine may traverse. After an exhaustive enumeration of all possible states and transitions of the model machine, the actual mechano-chemical system is mapped to an abstracted stochastic chemical reaction network, which is finally studied numerically in more detail. We believe this model system is novel, interesting, and, importantly, interpretable and could prove to be useful in future modeling and rational design of simple bio-mimetic nanoscopic systems.

Strengths:

(1) Omabegho introduces a novel chemo-mechanical model system that captures the core behavior of a simple ATPase molecular machine. The model is relatively simple in construction, but, as the manuscript demonstrates, it can display very rich behavior depending on various competing chemical timescales and mechanisms. These features suggest that this system could, in fact, serve as a useful starting model if adopted by the wider community.

(2) A key feature to highlight is the mechanistic interpretability of the model. By cutting to the seemingly core functional details of an ATPase-like machine, Omebegho is able to algorithmically produce an exhaustive listing of all allowed chemical states and all possible transitions between them, enabling the study of all possible mechanistic pathways and the relative frequency of each observed path. Further, Omebegho produced very clear visualizations of these states, transitions, pathways, and cycles that again facilitate the interpretability and utility of the model. This interpretability is key to our suggestion that this model could be more widely adopted as a clear ATPase analogue model for study by the broader biophysical community.

Weaknesses:

There are some issues to note.

(1) First, although the model is inspired by ATPases, the cycle in question is not a model even for the significantly abstracted form presented in Supplemental Section 1.1, as noted in the manuscript. The allosteric regulation utilized in the model does not mandate that the ligand (a stand-in for actin, for example) displaces both products (stand-ins for ADP and P). Rather, one product (P) dissociates spontaneously, and the other, larger one (ADP) is allosterically displaced. We recognize mandating this small further detail would presumably complexify the model (i.e., it may require an additional tile in the enzyme construction), but it would also lead to a more accurate, while still simple and interpretable, picture of the biological molecular machines in question.

(2) Much as we recognize and applaud the model's structure as simple and interpretable, when trying to actually study the chemical dynamics and observed dynamical behaviors, individual chemical rates of various binding and unbinding processes appear to be quite finely tuned. There is some attempt at studying the behavior of this model in various parameter tuning regimes, but the large number of model parameters makes a truly complete numerical study prohibitive. Given this difficulty, it would be instructive to know if a comparison could be made to the actual motivating biological systems and any observed chemical rates in the experiment. Further, given the desired cyclic behavior, it would be quite interesting to see to what degree this behavior is robust to various parameter choices. Again, preliminary work was done in the manuscript by biasing rates or numerical siloing experiments, but a far more exhaustive study is certainly worthwhile.

(3) Perhaps our biggest critique of the manuscript is that, although it incorporates both mechanical and chemical aspects into the model system construction, all mechanical aspects of the model simply function to limit allowed state transitions. From our understanding, all mechanical aspects of the model are, in fact, abstracted away during any simulations. This modeling choice, of course, retains some mechanical inspiration while making the resulting system more tractable, but it is ultimately not an actual mechanical model. ATPases, especially motors such as myosin and dynein, are inherently mechanical, their fundamental features being the conversion of chemical fuel into mechanical motion. A fuller treatment, perhaps in future work, should include these physical degrees of freedom in simulation, thus truly tracking the interplay between physical enzyme mechanics and chemistry. The absence of actual mechanics as yet, beyond a strict allosteric restriction, is an inherent limitation of the model.

Reviewer #3 (Public review):

This paper, by Brian Chen and collaborators, adapts the highly alternatively spliced Dscam1 gene locus for use in a system for simultaneous multi-transgene expression in a variety of insect species. Specifically, they show that the hypervariable Dscam1 exon 4 region maintains its alternative splicing when placed in a UAS expression vector, and that each of the twelve exon 4 alternates can be replaced with an exogenous gene such that co-expression of up to twelve proteins can be achieved. Since the co-expression of more than a few proteins simultaneously is difficult, this represents a significant advance with multiple use cases. The authors validated the technique by assessing expression in vitro and in vivo, and by rewiring Drosophila sensory neuron axons by simultaneously expressing several cell surface receptors within the neuron. Overall, this is a clearly written paper that describes a potentially important new system. I have no major criticisms.

Reviewer #3 (Public review):

Summary:

The authors aimed to identify new molecular pathways that could enable long-range signaling through the cerebrospinal fluid (CSF), focusing on a specialized class of neurons called CSF-contacting neurons (CSF-cNs) in larval zebrafish.

Strengths:

Anatomical validation of transcriptomic candidates using HCR, providing high-resolution spatial mapping of chemoreceptor expression in CSF-contacting neurons and neighboring spinal cord cells. The work broadens the potential understanding of CSF-cNs and offers a resource for future functional investigations of CSF-mediated signaling.

Weaknesses:

The principal limitation of the study is that the conclusions remain largely transcriptomic and inferential. Although HCR convincingly validates mRNA expression, no protein-level evidence is provided to demonstrate receptor translation or subcellular localization, leaving uncertainty regarding functional receptor availability at the CSF interface. Moreover, the study does not establish whether the proposed ligands are present in the relevant CSF microenvironment or engage the identified receptors in vivo. As such, the functional significance of the proposed chemosensory pathways remains speculative.

Reviewer #3 (Public review):

This manuscript reports a striking sex-specific effect of the daf-2(e1370) mutation on C. elegans lifespan. The authors show that male daf-2 mutants exhibit dramatically extended lifespan relative to wild-type males, wild-type hermaphrodites, and daf-2 hermaphrodites. The study also demonstrates increased lipid accumulation in these long-lived males, which is increased further over time, improved late-life motility, enhanced oxidative stress resistance, and a requirement for the downstream effector of daf-2, daf-16, for the longevity phenotype.

The interest of the work is the magnitude and consistency of the lifespan effect. The authors report large increases in both median and mean lifespan in daf-2 males across independently replicated experiments. This is further supported by healthspan analyses and their finding that male daf-2 mutants maintain improved motility and stress resistance, which argues against the interpretation that lifespan extension merely reflects prolonged frailty. The genetic epistasis experiment demonstrating loss of the longevity phenotype in daf-2;daf-16 double mutants provides evidence that the effect depends on canonical insulin/IGF-1 signalling.

The main limitation is that at least the first figure is rather an incremental increase on previous work examining the lifespan of daf-2 males, although the authors do indeed show that the effects can be much larger (or more 'plastic') than those previously published. While these findings are potentially important, the manuscript would certainly benefit from a more extensive discussion of how the results compare with prior studies of daf-2 mutants and male longevity, including possible explanations for the apparent discrepancies.

The epistasis experiment shows that this exceptional longevity requires the expression of daf-16. However, in contrast to the initial experiments (Figure 1) that show three replicates of the lifespan experiment (the standard in lifespan work in this model), it appears that the daf-2;daf-16 experiment has only been performed once.

In addition, the lifespan data for hermaphrodite daf-2 mutants appear somewhat unusual. Although the mean lifespan is increased, the median lifespan is reported to be only modestly greater than that of wild-type hermaphrodites. I know that this mutant can give lifespan curves that look like this, but either the use of another allele or of the experimental conditions and how these values compare with previously published daf-2(e1370) datasets would help readers interpret the magnitude of the male-specific effect.

The lipid phenotype is intriguing. It would be interesting to expand this to examine somatic vs embryonic fat. In addition, I noted that in the methods section, the authors use palmitic acid to stop the male worms 'fleeing' the plates; is it possible to rule out the possibility that the daf-2 mutants are simply eating and metabolising/storing this fatty acid barrier differently than their wild-type counterparts? This would be worth considering and controlling for, particularly as male C. elegans have been shown to have dramatically altered metabolic transcriptional profiles. If indeed this increased lipid is responsible for the extreme longevity of the daf-2 mutant males, it would be desirable to try to link this mechanistically to the phenotype.

Overall, the evidence convincingly supports the conclusion that male daf-2(e1370) mutants are exceptionally long-lived under the conditions tested and that this phenotype requires DAF-16. The work has the potential to make an important contribution to understanding sex-specific regulation of ageing, although further contextualisation within the existing literature would strengthen the manuscript.

Reviewer #3 (Public review):

Summary:

The authors investigate the structural brain basis of congenital aphantasia, a condition characterised by a lifelong absence of voluntary mental imagery. They test two competing accounts: one predicting structural differences in early visual pathways, the other predicting differences in higher-order frontotemporal and cingulate systems. To do this, they combine four complementary structural imaging approaches: white-matter microstructure profiling along anatomically defined tracts, tractography seeded from functional regions of interest, whole-brain structural network analysis, and cortical thickness mapping. The main finding is that white-matter differences are selective for frontotemporal and cingulate pathways and absent in early visual pathways, which the authors interpret as support for the higher-order account.

Strengths:

The multi-modal design is a genuine strength: running four independent analyses increases the chance of detecting real effects and of identifying false positives that appear in only one stream. The statistical choices within each analysis are appropriate. Permutation-based correction with a threshold-free method is well-suited to the tract-level comparisons. The use of Bayes factors to quantify evidence for null results, rather than simply reporting non-significant tests, is particularly valuable here, since the absence of visual pathway differences is central to the argument. The robustness checks across multiple brain parcellations for the network analysis strengthen confidence in those findings.

Weaknesses:

The main limitation concerns the relationship between two of the analysis streams. The measure used to weight structural connections in the network analysis is calibrated to match fiber density estimates derived from the same diffusion signal that drives the white-matter microstructure differences. If the two groups differ in tissue organisation in certain pathways (which the microstructure analysis suggests they do), that difference will feed into both measures. The authors should acknowledge this dependency when discussing convergence across analyses.

More broadly, the imaging metrics used throughout (measures of fiber organisation and weighted connection counts) reflect what the diffusion model captures from the tissue and cannot be directly read as measures of axon number or connection strength. This is a known limitation of the field, but it is relevant to the strength of structural claims made in this paper.

The network analysis is presented without comparison to a null network. Without this, it is hard to know whether the node-level differences reflect specific network topology or simply follow from overall differences in connectivity weight or density between groups.

The study runs four separate discovery analyses on the same 36 participants, each corrected within itself but with no control across analysis streams. At 18 participants per group, this is exploratory work. Some of the language used in the abstract and discussion, like "first comprehensive characterization" and "selective structural phenotype", reads as more definitive than the data support at this sample size. Framing the results as hypotheses to be replicated would make the paper stronger.

The paper frames the results as distinguishing between two competing accounts. The positive evidence for the higher-order account is clear. The absence of differences in visual pathways is a different kind of result: it means such differences were not detected in this sample, not that visual pathways are uninvolved. The discussion at times moves toward that stronger conclusion, which the data do not support.

The cortical thickness analysis finds one cluster in the predicted direction, while the other analyses each return multiple effects. One cluster in a whole-brain search with 18 participants per group is not strong evidence and should not be presented as equivalent to the other results.

Effect sizes are reported without confidence intervals throughout. With 18 participants per group, the uncertainty around those estimates is large, and confidence intervals would give readers a more accurate sense of what can be concluded.

Reviewer #3 (Public review):

Summary:

In this study, the authors reanalyzed choice, RT and gaze datasets collected from human subjects performing a food-choice task. They show that models that posit a causal role for attention in shaping the decision-making process fail to account for empirical observations in the data. These include the attentional drift diffusion model (aDDM) and models that derive attention-choice associations from an optimal policy. The authors show that a model that assumes that gazes are directed towards the chosen option after decision commitment captures more (but not all) empirical findings, suggesting that attention may reflect decisions once they are made instead of contributing to their formation. However, this post-decision-gaze (PDG) model failed to capture all aspects of the data, suggesting that gaze may reflect both decisional and post-decisional operations, and existing models are still missing some features of the gaze-directing process. The authors provide convincing evidence that post-decision gaze explains a number of empirical findings in this task.

Strengths:

(1) The analyses are generally appropriate, and the conclusions are supported by the data.

(2) The study was rigorous, as the authors considered a number of alternative possible models for behavior, and evaluated their performance based on a wide range of qualitative predictions (as opposed to exclusively relying on model comparison).

(3) The proposal that gaze may largely reflect post-decisional processes is interesting, and as far as I am aware, novel.

Weaknesses:

There was limited discussion about why one might allocate attention post-decision. I would have appreciated more discussion on the potential functional consequences or implications of post-decision gaze.

Reviewer #3 (Public review):

Summary:

The authors studied social aspects of antibiotic resistance by co-cultivating antibiotic-resistant and sensitive Enterococcus faecalis (an important pathogen) as biofilms to assess the extent to which sensitive cells can take advantage of the protection provided by resistant cells against both a beta-lactam antibiotic and in the presence of a B-lacatamase inhibitor. By quantifying the proportion of each cell type using fluorescence microscopy, they conclude that protection is provided equally in the biofilm and planktonically, and that the biofilm is completely unstructured with regard to the locations of the two cell types. A mathematical model is then used to show that no spatial information is needed to recapitulate the results and that the protective effect can be described completely by the growth rates of the two cell types and the affinity of the β-lactamase to the antibiotic and inhibitor. The strength of evidence is difficult to assess due to unclear descriptions of some methods, and the significance of the findings is limited by the experimental setup, where antibiotics were added very close to the time of inoculation.

Strengths:

The co-cultivation of antibiotic-resistant and sensitive bacteria allows for exploration of the social aspects of antibiotic resistance. Fluorescently-tagged strains allow for unambiguous tracking of the two cell types. The simultaneous analysis of biofilm and planktonic cells enables insight into whether these different growth modalities are influenced by social aspects of antibiotic resistance. In analyzing the structure of the biofilm, the use of a null model with randomized cell positions allows for an accurate determination of whether the observed data are due to some effect; however, as noted below, there is a caveat to this analysis. The broad observation that biofilm and planktonic populations are linked is generally supported by the data; however, this result is closely tied to the experimental setup used. The development of a mathematical model that can recapitulate results from a second set of data with values obtained from fitting a different set of data shows robustness of the model for using it to explain the results.

Weaknesses:

The observed results are tied very closely to the experimental setup of adding antibiotics very close to the time of inoculation, but this connection is not discussed. The described 'population inversion' effect is better described as frequency-dependent selection for resistant cells, but frequency-dependent selection is not discussed. Confocal microscopy was used to quantify the relative proportion of antibiotic-resistant and sensitive cells in the biofilm; however, it is unclear if the entirety of the Z stacks was used to determine these proportions. This is also the case for the analysis of whether the sensitive/resistant cells are non-randomly distributed in the biofilm: it is unclear whether the vertical distance between cells was taken into account. The authors claim that biofilm and planktonic bacteria are protected equally by the presence of resistant bacteria; however, Figure 1a and b seem to clearly show that the proportion of sensitive cells is higher in the planktonic cells compared to biofilm cells when started from an equal frequency inoculum, meaning this is not always the case. The mathematical model is used to confirm the result that no spatial components are needed to describe the results; however, this is mostly linked to the initial setup of the experiment, where antibiotics are added at the time of inoculation, and no biofilm could form before the outcome of the antibiotic-cell interactions was concluded.

Reviewer #3 (Public review):

Summary:

The authors investigate mRNA targets of the nonsense-mediated decay (NMD) pathway in astrocytes and link the dysfunction of NMD in astrocytes to aberrant synaptic transmission that has downstream effects on behavior. Specifically, they find a link between the aberrant synaptic transmission with elevated spontaneous calcium signaling in astrocytes, and functionally they demonstrate that manipulating astrocyte calcium signaling with CalEx modulates astrocyte calcium signaling towards wildtype levels and improves anxiety behavior. They investigate the astrocyte calcium signaling changes in Upf2 conditional knockout mice in several brain regions that have been linked to anxiety behavior, including the hippocampus and prefrontal cortex. They also observe aberrant astrocyte calcium signaling in the visual cortex, demonstrating that dysfunction of the NMD pathway in astrocytes has widespread effects on synaptic transmission in various brain regions. This work identifies, through RNA-Sequencing, potential mRNA targets of NMD in astrocytes, and shows that pathway enrichment of these targets highlights calcium signaling. Altogether, this work highlights the importance of the basic cellular process of NMD in astrocytes, which are known to have extensive local translation of proteins in their perisynaptic processes. NMD may be particularly important in astrocytes due to their intimate association of processes with neuronal synapses, and the authors suggest that alterations to NMD function in astrocytes may be an important avenue for future investigation in neurodevelopmental disorders.

Strengths:

Altogether, this work is a critical foundation for future research into astrocyte contributions to neurodevelopmental disorders. The authors do a thorough characterization of astrocyte conditional Upf2 knockout mice in several brain regions. They present a complete story that connects molecular events (NMD pathway regulation of mRNA degradation) to astrocyte regulation of circuit activity to organismal behavior. The electrophysiological analysis is thorough, and the manipulation of calcium activity ties astrocyte calcium activity to anxiety behavior. The RNA-sequencing dataset is useful to the scientific community and provides a resource of candidate molecules that might be dysregulated in neurodevelopmental disorders.

Weaknesses:

The study suffers from some overstated claims and a lack of statistical rigor in some experiments, as detailed below.

(1) The title states that "Astrocytic Nonsense-mediated mRNA decay regulates calcium signaling to support synapse function and restrain anxiety". The term "restrain anxiety" implies that the NMD pathway has a direct effect on a molecular switch to control anxiety. Anxiety behavior is a complicated process, controlled by many biological phenomena and synaptic transmission in the circuit as a whole, and is not directly linked to a specific NMD mRNA target. This title is overstating the findings of the study.

(2) In general, the first figures (1-2) suffer from low power (N = 3) and statistical rigor. The statistics are inflated by analyzing individual fields of view and per-cell data rather than performing the statistics on the average of biological replicates. It is preferable to show the biological replicate data so that readers can observe the natural biological variability between replicates.

(3) The claim that astrocytes have decreased engulfment of synapses in the Upf2 conditional knockout mice is not strongly substantiated by the data. The resolution of confocal microscopy and the static nature of histological images make it difficult to measure synaptic engulfment as an active process. Additionally, the metric of quantifying the % occupancy of PSD95 puncta within the total astrocyte volume may be skewed due to overall differences in cell size (shown in Figure 1). There is not much discussion of how a decrease in astrocyte engulfment of synapses may lead to decreased synapse number. To the contrary, one might expect decreased engulfment to result in increased synapse density.

(4) The authors use Gfap as a marker to count astrocyte cell number and assess if there are changes in cell number between genotypes (Figure S6). However, Gfap does not label all astrocytes in the cortex and, in fact, is rather an aberrantly expressed marker in conditions of inflammation, as opposed to the hippocampus, where Gfap is basally expressed in all astrocytes. In the cortex, there seems to be a trend for reduced Gfap in the conditional knockout mice, which may suggest differences in astrocyte molecular signatures rather than cell numbers. Another astrocyte marker, like Aldh1L1, will be more accurate to assess this question histologically.

(5) The authors state that "Preventing abnormally high basal calcium activity in NMD-deficient astrocytes restores normal excitatory synapse function...". However, this claim is not substantiated by the data. CalEx manipulation certainly shifts the input-output curve but does not restore to wildtype baseline levels (Figure 6E). Additionally, synapse number does not appear to be restored to wildtype levels (Figure 6D - although the p-value for this comparison is now shown). The investigators do observe improvements in anxiety phenotypes, suggesting there is some modulation of circuit activity, but the claim that CalEx manipulation restores baseline synaptic transmission is not supported.

Reviewer #3 (Public review):

Summary:

In the presented work, the authors investigate spectral staining in axons of the sciatic nerve, where the MPS has been detected before using STED microscopy. They employ 3D-dSTORM in tissue sections and analyze the data, measuring localization of clusters on the axon perimeter and the relative distribution of those. From these data the conclude that large gaps in spectrum localizations exist and that clusters around the axon exist that are spaced at 200nm.

Major Comments:

(1) The presented data are at times overinterpreted, and the discussion lacks a critical view of the data. For example, the statement "...Unlike previous suggestions from qualitative evidence in cultured neurons (REfs), βII‑spectrin distribution in MPS segments of peripheral nerves is discontinuous, with extensive stretches of the perimeter lacking βII‑spectrin." is quite strong, given it is based on immunofluorescence staining and dSTORM microscopy in tissue. Absence of evidence of staining is not evidence of absence.

(2) The authors claim in the abstract that "The number of these clusters scales linearly with the axonal perimeter, maintaining a constant membrane occupancy of ~20% across varying axon diameters." Again, this is from a cut through an axon, while measuring the density of clusters on the perimeter. If they claim area occupancy, an area should be imaged, and the dots (clusters) should be measured in surface coverage in a 2D projection of the axonal surface.

(3) In general, this reviewer suggests being a bit more moderate in statements such as: "These findings challenge simplified models of the MPS based on cultured systems and demonstrate that the MPS in peripheral nerves is composed of discrete structural units." These statements are bold from the relatively few measurements in a single method and a single viewpoint. Especially when considering that techniques such as dSTORM depend extremely highly on labeling density, and apparent clustering of localization is highly prone to misinterpretation. If the authors desire to make such statements, working with endogenously labeled protein would be warranted. The authors should at least hedge such statements.

(4) If the authors want to make statements about general organization, why do they not compare adjacent cuts through the axon? If there are continuous spectrin filaments, the clusters should appear at the same site across repeated cuts through the axon.

Besides this, this reviewer welcomes the effort that has been made to establish dSTORM in tissue sections and to investigate the MPS in native tissue.

Reviewer #3 (Public review):

Marantos et al. showed that for some coliphages, the energetic state of the bacterial host cell has a strong impact on whether phage infection is initiated. The authors drew this conclusion from the observation that there are more free phages remaining in the medium after infection of arsenate-azide-treated cells as compared to after infection of untreated cells. These data were analyzed and reported both as ratios of the treated vs. untreated conditions and using a mass-action kinetic model of phage-cell collision in the infection mixture. The data supported the findings that for four phages infecting Escherichia coli bacteria, namely, phages λ, 𝜙80, m13, and T6, the phages are less likely to initiate infection if the host bacteria are energy depleted. However, for phage T5, the authors found that their infection propensity is not impacted.

As I have stated in the first submission of this manuscript, the data presented by the authors clearly supported the principal conclusion of the study. The five phages chosen by the authors represent different viral lifestyles and infection mechanisms, highlighting the potential applicability to other Escherichia coli phages. Finally, the authors successfully use a classic mass-action model of phage-cell collision to interpret their data. The simplicity of their experimental assay, combined with the use of this mathematical model, offers other investigators who study phage-bacterial interactions in other contexts a potentially useful toolkit to examine infection in general, and specifically, the dependence of phage infection on the host's metabolic state.

Comments on revised version.

In this revised version, the authors have successfully resolved all of my comments. I appreciate that the main text has been majorly revamped, which greatly helps the readers follow the motivation behind the experiment and analyses, and interpret the data. I agree that the revised terminology choice "commitment to infection", instead of the previous interchangeably used "adsorption"/"entry", is much more logical, considering the experimental data. I also commend the authors for writing the modeling part in a very clear, pedagogical, and instructive manner. Overall, I believe that this manuscript will be valuable to those who are interested in phage-bacterial interactions.