Reviewer #3 (Public review):

Summary:

Butler et al. investigated how different force mechanisms influence Arp2/3-related branched actin networks at the leading edge of lamellipodial protrusions in mouse dermal fibroblasts. In particular, their study aimed at characterizing the specific contribution and interplay between load force and adhesion signaling on the regulation of branched actin networks in live-cell experiments using endogenously one-labeled Arp2/3 subunit. A key finding of their work is that by plating fibroblasts on two different substrates supporting or not integrin engagement, they observe striking differences in branched network architectures that cannot be explained solely by integrin signaling. Instead, several of their results point to mechanical feedback resulting from changes in membrane tension during spreading, regulating the density of branched actin networks. Finally, by modifying the extracellular viscosity, the authors suggest that the stress generated at the actin-membrane interface would play a key role in regulating branched actin density in protrusions.

Major Strengths:

(1) The combination of methods used in this paper (endogenous labeling of Arp2/3, Arp2/3 genetic knockout, optogenetic activation of Rac) provides a unique opportunity to monitor spatial and temporal reorganization of endogenous branched networks generated by Arp2/3 in live cells in response to different biochemical and mechanical manipulations.

(2) The authors provide a deep characterization of the actin-network organization and dynamics observed when plating cells on different substrates, engaging or not integrins (Figure 1 and associated supplementary: intensity and width of the signal in protrusions, retrograde flow, incorporation of actin to the edge, nascent focal adhesions), which serves as a strong basis to build the rest of the paper. They also offer a comprehensive analysis of the different parameters that could explain the lack of dense branched actin network at the leading edge of fibroblasts grown on PLL-coated surfaces (they exclude the contribution of reduced branch nucleation by NPF or insufficient branch stabilization in Figure 2, the insufficient integrin-mediated signaling activating NPF in Figure 2).

(3) After having ruled out the influence of adhesion signaling in the regulation of branched actin-network density at the leading edge of the cells, the authors demonstrate that the enrichment of Arp2/3 at the leading edge is evolving together with cell spreading, suggesting a possible role of membrane tension in the process (Figure 3 and associated supplementary). To prove their point, they tested numerous methods to promote adhesion-independent cell spreading (Figures 4 to 6), while describing well the limitations of each of these techniques. These methods included promoting rapid spreading on PLL-coated substrate using blebbistatin or physical compression under agarose, and finally increasing extracellular viscosity by treating cells with methylcellulose. All of these treatments led to very consistent results upon the increase in membrane tension, supporting the idea of membrane tension controlling the branched actin organization of cells. This conclusion was further supported by an experiment (Figure 4 S1) in which a hyper-osmotic shock was performed, increasing the actin-membrane interface stress while keeping the spreading area of cells, which led to a drastic increase in Arp2/3 density at the protrusions.

(4) By activating Rac optogenetically in cells plated on PLL treated with methylcellulose (Figure 8), the authors observe the formation of robust protrusions enriched in Arp2/3, showing that increased extracellular viscosity can bypass the requirement for ECM proteins to activate protrusion driven by signaling.

Weaknesses:

(1) Although the lamellipodial architecture in cells plated on PLL appears very different from the one developed by cells grown on fibronectin (Figure 1, wider and less homogenous), the branched network is still present, and one may wonder how these differences can affect the functionality of the lamellipodia (for example, by measuring the impact on migration in 2D and 3D systems).

(2) To explain the differences observed in the branched actin networks developed by cells on PLL and FN, the authors envision several hypotheses, among which signaling factors or branched-promoting factors would be decreased in the absence of integrin adhesions. They could have, in addition, assessed actin network dynamics and turnover (we could imagine that competition between Arp2/3- and non-Arp2/3- driven structures could be different in the presence or absence of adhesions, the competition being nicely visible from Figure 2B and 2C, where, in the absence of Arp2/3, cells form prominent filopodia).

(3) All of the methods used to apply physical forces on barbed ends have their own caveats and alter not only membrane tension (but the limitations are discussed in the paper). The paper may have benefited from micropatterning the cells to either reduce or force the spreading of cells in a controlled fashion. In addition, the conclusions on levels of interface stress between plasma-membrane and the barbed-ends of actin lamellipodial networks rely on an estimate of the effect of perturbations rather than on actual measurements of these stress levels.

Likely impact of the work on the field, and the utility of the methods and data to the community:

Although the finding that branched actin networks respond to the application of physical force by increasing their density was already known from previous in vitro studies, this paper offers a detailed and compelling characterization of the reorganization of endogenously labelled branched actin networks upon different mechanical perturbations. In addition to showing the effect of increased extracellular viscosity on promoting branched actin network densification in the absence of ECM, this paper sheds new light on the interplay between signaling and mechanics in regulating protrusion and spreading. While the authors show that both signaling and mechanical feedback are important regulators of branched actin regulation and cell spreading, they demonstrate that optogenetic Rac activation is not sufficient to trigger branch network formation in the absence of sufficient mechanical support. They thus propose that biochemical signaling would act at a different level than mechanics by promoting protrusion persistence and coherence. This work will therefore impact the field of cell biology in offering a new perspective to understand the interplay between mechanical and biochemical feedback in 2D and 3D migration. It may also have broader implications as the formation of branched actin networks under the regulation by mechanical loads has been shown to be involved in other processes such as endocytosis.

Reviewer #3 (Public review):

Summary:

In their manuscript, Huifang Yan and colleagues perform RNA-seq (CEL-seq) and ATAC-seq experiments to profile the transcriptome and chromatin accessibility of sponge larvae across larval competence, settlement and early postlarval development. Amphimedon, the sponge species that they use, is amenable to lab experiments and can therefore be a convenient model for experimenting with this otherwise difficult to assay ecological parameters and cues. They had previously observed that light conditions (diminished light) at sunset are critical for larvae to enter a pre-settlement stage and prime them for settlement and metamorphosis. In this paper, they report that these conditions induce a gain of accessibility in many genes, including transcription factors, and that altering these conditions by providing continuous light at sunset affects this reprogramming event.

Strengths:

The above is a very interesting observation, one that the authors speculate could have a broader significance and be a theme in many more larvae. I agree with the authors that this is an important finding, and I think that the paper will be interesting for a broad readership. If this is the case, the authors open up a new theme of chromatin regulation, extensively studied in mammalian contexts, but severely understudied in pretty much every other context.

Weaknesses:

I think, however, that their paper often reports the data in a difficult-to-follow way, and that other sorts of analyses would have made the results more accessible for a broad readership. Here, I present some suggestions that the authors might want to take into account to improve their results.

Reviewer #3 (Public review):

Summary:

Dancausse et al. examine behavioral responses to nicotine administration in larvae. The study first distinguishes between spasms and extreme hyperexcitability elicited at high doses from a hyperactivity state triggered at lower (~1 mM feeding) doses. They then focus on the hyperactivity state and examine if dopaminergic neuron function is involved (via transgenic and pharmacological manipulations). Next, the role of the Mushroom body, a site of integration in the larval brain, is interrogated. In these studies, the authors use multiple approaches to draw complementary conclusions. The last section examines the effect of repeated nicotine exposure and of nicotine preference following repeated exposure. The findings are foundational for future studies looking to use Drosophila larvae as a system to study nicotine addiction.

Strengths:

Overall, I think the study is of broad importance. The neurogenetics community gets valuable insight into how ACh excitation interplays with DA signaling to regulate movement. For the addiction community, the work describes a valuable system to further interrogate genetic and environmental factors potentially driving addiction under well-controlled conditions. The quantitative analysis is generally well done, and the use of multiple experimental strategies to buttress conclusions is commendable.

Weaknesses:

(1) Conceptual point. Insects use ACh as the primary excitatory neurotransmitter, with nAChRs broadly expressed, while vertebrates use Glutamate in this role. (Arguably, nicotine expression in tobacco plants evolved as an insecticide, broadly disrupting the central excitatory neurotransmitter.) In vertebrates, central ACh neurons are relatively sparse - primarily originating from the basal forebrain.

Based on these distinctions, it is important to consider/contrast nicotine-driven hyperexcitation from other methods to produce broad hyperexcitation (e.g., inhibition of GABA, high K+, elevated temperature, etc). Many of these methods to induce hyperexcitability would also modulate DA circuitry.

A discussion of the role of ACh in insect vs. vertebrate brains is necessary to interpret the experimental design and findings with regard to addiction. These points should be addressed in the intro and discussion.

(2) (Figure 1) Relatedly, how do the behaviors elicited in Figure 1B (30 or 60 mM) compare to the convulsions described following electroshock stimulation to induce a seizure? My suspicion is that you're essentially triggering a seizure (or seizures) in these larvae.

(3) (Figure 4) Is a statistical analysis between the CS, Ple>Kir, Ple, and Kir locomotion at baseline done? Presumably, these manipulations would alter the intrinsic activity levels of the larvae?

(4) (General quantitative question) How do the parameters co-vary across individuals following nicotine admin? Crawl speed and peristalsis frequency are analyzed. Turning doesn't seem to be considered. Do individuals that show large increases in velocity also show the largest reductions in turn rate? Are these relations preserved following the DA metabolism and MB function interventions?

(5) (Discussion / general question) Beyond DA, other monoamines are involved in regulating larval locomotion - OA and TA are a clear example from Fox et al. (2006). Could the authors comment on whether they would expect similar findings in other neurotransmitter systems or if these neurotransmitter systems are involved in the ACh -> DA interplay studied here?

(6) (Discussion) Following the establishment of nicotine preference, do larvae exhibit signs of 'withdrawal' or changes in baseline behavior when deprived of nicotine? For example, in Figure 6, does the speed following nic administration ever 'go below' the H2O line?

Reviewer #3 (Public review):

Summary:

Cuentas-Condori et al. generate cell-specific tools for visualizing the endogenous expression of, as well as knocking out, four different classes of neurotransmitter vesicular transporters (glutamatergic, cholinergic, gabaergic and monoaminergic) in C. elegans. They then use these tools in an intersectional strategy to provide evidence for the co-expression of these transporters in individual neurons, suggesting co-transmission of the associated neurotransmitters.

Strengths:

A major strength of the work is the generation of several endogenous tools that will be of use to the community. Additionally, this adds to accumulating evidence of co-transmission of different classes of neurotransmitters in the nervous system.

Another strength is the comparison to previously published single cell sequencing data and other previously published data.

Weaknesses:

Co-expression of these transporters is not in and of itself sufficient to establish neurotransmitter co-release, but this caveat is acknowledged by the authors.

Comments on revisions:

The authors have addressed all of my previous concerns.

Reviewer #3 (Public review):

Summary:

In this study the authors use the zebrafish model and in vitro co-cultures with human cell lines, to study how keratinocytes modulate the early stages of melanoma development/migration. The authors demonstrate that keratinocytes undergo an EMT-like transformation in the presence of melanoma cells which lead to a reduction in melanoma cell migration. This EMT transformation occurs via Twist; and resulted in an improvement in OS in zebrafish melanoma models. Authors suggest that the limitation of melanoma cell migration by Twist-overexpressing keratinocytes was through altered cell-cell interactions (Jam3b) that caused a physical blockage of melanoma cell migration.

Strengths:

Authors describe a new cross-talk between melanoma and its major initial microenvironment: the keratinocytes and how instructed by melanoma cells keratinocytes undergo an EMT transformation, which then controls melanoma migration.<br /> Overall, the paper is very well written, and the results are clearly organized and presented.

Weaknesses:

(1) To really show their last point it would be important to CRISPR KO Jam3b in melanoma with twist OE keratinocytes, in vivo or in vitro.

(2) Use of patient biopsies from early-stage melanomas vs healthy tissue to assess if there is a similar alteration of morphology of adjacent keratinocytes and increase in vimentin in human samples would strengthen the author's findings.

(3) Characterise better the cell-cell junctions and borders between cells (melanoma/ keratinocytes) with cellular and sub-cellular resolution. Since melanocytes can "touch" with their dendrites ~40 keratinocytes - can authors expand and explain better their model? Can this explain that in some images we cannot observe a direct interface between the cells?

Comments on revisions:

The authors answered most of the concerns raised.

Reviewer #3 (Public review):

Primary taste cortex neurons show a variety of dynamic response profiles during taste decision making tasks, reflecting both sensory and decision variables. In the present study, Lang et al., set out to determine how neurons with distinct response profiles contribute to perceptual decisions about taste stimuli.

The methods with regard to the behavioral task and electrophysiological recordings/data analysis are straightforward, solid and appropriate. The computational model is presented in a clear and conceptually intuitive manner, although the details are outside of my area of expertise.

The experimental design features a simple 2-alternative forced choice task that yielded clear psychometric curves across a range of stimuli. In vivo recordings were performed using neuropixels and yielded an appropriate sample of single neuron responses. The strength of the model lies in the fact that it consists of single neurons whose response profiles mimic those recorded in vivo, and allows neuron-selective manipulation.

By virtually lesioning specific subsets of neurons in the network, the authors demonstrate that a relatively small populations of neurons with specific tuning profiles were sufficient to produce the observed neural dynamics and behavioral responses. This effect was selective as lesioning other responsive neurons did not affect overall response dynamics or performance.

These findings provide new insight into the relation between the response profiles of single neurons in sensory cortex, their population-level activity dynamics, and the perceptual decisions they inform.

The approach is particularly innovative as it uses computational modeling to target functionally-defined "cell types", which cannot necessarily be targeted by more conventional genetic approaches.

Reviewer #3 (Public review):

Summary:

Developing consistent and reliable biomarkers is critically important for developing new pharmacological therapies in autism spectrum disorders (ASDs). Altered sensory perception is one of the hallmarks of autism and has been recently added to DSM-5 as one of the core symptoms of autism. Touch is one of the fundamental sensory modalities, yet it is currently understudied. Furthermore, there seems to be a discrepancy between different studies from different groups focusing on tactile discrimination. It is not clear if this discrepancy can be explained by different experimental setups, inconsistent terminology, or the heterogeneity of sensory processing alterations in ASDs. The authors aim to investigate the interplay between tactile discrimination and cognitive processes during perceptual decisions. They have developed a forepaw-based 2-alternative choice task for mice and investigated tactile perception and learning in Fmr1-/y mice

Strengths:

There are several strengths of this task: translational relevance to human psychophysical protocols, including controlled vibrotactile stimulation. In addition to the experimental setup, there are also several interesting findings: Fmr1-/y mice demonstrated choice consistency bias, which may result in impaired perceptual learning, and enhanced tactile discrimination in low-salience conditions, as well as attentional deficits with increased cognitive load. The increase in the error rates for low salience stimuli is interesting. These observations, together with the behavioral design, may have a promising translational potential and, if confirmed in humans, may be potentially used as biomarkers in ASD.

Reviewer #3 (Public review):

Summary:

This manuscript by Bai et al. reports single-cell transcriptomics of the oyster mantle to elucidate the respective contributions of ancient conserved programmes and lineage-specific genes to the origin of the molluscan shell. The authors compare their dataset with other oyster larval datasets as well as data from other organisms (annelids, chaetognaths) and find evidence of evolutionary conservation and functional similarity with secretory cell types. They also observe that cells involved in secreting the larval skeleton express predominantly recent genes, whereas the adult skeleton-secreting programme is evolutionarily more conserved.

Strengths:

The manuscript is well written and clearly presented, and the results are interesting, particularly the distinction between larval and adult skeleton secretion, which is placed in a thoughtful evolutionary context.

Weaknesses:

(1) My main concern is that the authors rely primarily on previous studies for the experimental and functional characterisation of the identified cell types. The cited papers (Piovani, 2023 and de la Forest Divonne et al., 2025) deal with distinct stages or tissues (larvae and hemocytes, respectively), which limits their direct relevance. The authors also cite other papers for in situ expression data; it would be helpful to summarise somewhere (e.g. in a table) which genes have been experimentally characterised and what their expression domains are, or alternatively to provide HCR or in situ staining on the mantle. For instance, what is the rationale for the claim that proliferative cells give rise to the mantle? The trajectory inference approach used (Monocle) would likely yield a similar result regardless of the reference cell type, so additional justification is needed.

(2) More broadly, I find that the functional properties of the identified cell types and their relationship to the expressed genes deserve more detailed discussion. For example, at L100, several genes are mentioned, but their functional roles are not discussed. Similarly, the basis for annotating the proliferative cells is not explained. How was gene orthology assessed? Throughout the manuscript, vertebrate-style gene names are used without explicitly establishing orthology status in oyster, which should be addressed.

(3) More detail is needed on the methods and quality control for the single-cell data. The authors should clarify that the platform used (BMKMANU) is a droplet-based technology comparable in principle to Drop-seq. BMKMANU is not widely used in the field. How does it compare to 10x Genomics in terms of sensitivity and cell recovery? The authors appear to use the 10x Chromium cellranger pipeline for data analysis, which suggests compatibility, but this should be stated explicitly. Additionally, no information is provided on the number of sequencing runs or biological replicates, nor on how reproducible the results are across samples.

(4) A limitation of the phylostratigraphic analysis is that it is restricted to mantle tissue, making it difficult to place the results in a whole-organism context. How do the age profiles of mantle-expressed genes compare to those of more evolutionarily conserved tissues, such as the nervous system? I appreciate the methodological and experimental constraints, but this is a genuine limitation of the study. The authors could at least discuss it explicitly, and ideally consider generating a broader single-cell atlas of the oyster to provide this comparative baseline.

(5) Have the authors considered the potential importance of lineage-specific gene duplication? It is well established that spiralians, including oysters, have undergone extensive lineage-specific duplication of transcription factors such as homeobox genes, and many structural shell-associated proteins may similarly have been duplicated. This could be relevant to interpreting both the phylostratigraphic results and the expansion of secretory gene families.

Reviewer #3 (Public review):

Summary:

This work aims to understand the mechanisms that platelets use to interact with and compact fibrin fibers during clot formation. This is an important process during wound healing, and recent work has demonstrated that platelets play a critical role in generating the force required to drive the accumulation of fibrin. The authors argue that current models are insufficient to account for the observed reduction in clot volume and propose that platelets actively 'wind up' these fibers by undergoing myosin-dependent rotation. While interesting, the experiments performed by the authors do not directly test this mechanism, and further evidence is required to support their claims.

Weaknesses:

(1) The motivation to switch from the system used in Figures 1 and 2 to the '2D fiber-retraction assay' is not clear. While the authors state that this system has 'reduced complexity', the differences between these assays appear to disrupt the 'cage-like' organization of fibrin around platelets shown in Figures 1 and 2 (compare images in Figure 2 with those in Figure 4). An in-depth comparison of two methods is needed to support the conclusions from the 2D system. Furthermore, the change in plasma volume (Figure 2 vs Figure 7) should also be tested - the authors state that this increases fibrin fiber formation, but this is not quantified or demonstrated in the figures. Notably, this appears to change the morphology of the fibrin fibers shown (comparing Figure 2 and Figure 7).

(2) It is unclear how the classification of platelets as 'fiber-winding' versus 'fiber compaction' differs in Figure 2. The criteria used for these classifications should be stated. Further, it seems premature to characterize fibers as wound without having established this earlier in the manuscript.

(3) Is the 'gearwheel' different from the 'cage' of fibrin fibers? They appear similar, but it is difficult to distinguish between them with only qualitative descriptions of these phenotypes.

(4) The quantification of platelet extensions in Figure 9 is confusing. While those in 9A are clear, those in 9B are not. For instance, what is the difference between #7 and #8 in the middle panel of 9B? It does not seem like #8 is labeling an extension.

(5) It is unclear what the modeling accomplishes, as there is no comparison between the results of these simulations and their experiments.

(6) The data presented in Figure 12 provides the most direct support for their mechanism, but falls short of directly testing their claims. These experiments should be repeated to include blebbistatin to test the contribution of myosin and include quantitative rather than qualitative comparisons of these experiments.

Reviewer #3 (Public review):

Summary:

The Training Village (TV) is an open-source automated platform for continuous training and testing of group-housed mice and rats in cognitive tasks. Animals live in enriched multi-compartment home cages and access a single operant box individually through a sorting corridor controlled by RFID identification and real-time video analysis. A Raspberry Pi 5 runs the entire system, manages an adaptive training algorithm, monitors animal welfare, and allows remote supervision via a graphical interface and Telegram alarm system. The system is validated across 12 groups totaling 121 animals, three cognitive paradigms of varying complexity, and experiments lasting up to 12 months.

Strengths:

(1) The open-source implementation is probably the paper's strongest point. The authors provide not just code but 3D-printable designs, a full bill of materials with costs (~5500€ total), assembly instructions, and a dedicated website. The estimated build time of 2-7 days is credible. In the current landscape of methods papers, this level of documentation is the minimum necessary to allow other laboratories to actually adopt and propagate the system - and the authors deliver it fully. The compatibility with two operant box designs, three cognitively distinct tasks, and two species - demonstrated empirically rather than merely claimed - makes the modularity argument credible and distinguishes the TV from systems designed around a single paradigm. Finally, the combination of automatic weighing at each exit, temperature and humidity tracking, and a granular Telegram alarm system (Table S2) represents a meaningful practical contribution. For a system operating 24/7 without daily human supervision, this level of welfare monitoring is a necessity, and it seems well implemented here.

(2) With 121 animals across 12 groups, three distinct cognitive paradigms, two species, and longitudinal data spanning up to 12 months, the validation effort is substantial. The authors acknowledge the limitations of their comparisons - notably that the TV vs. manual training comparison is not a controlled experiment. The rat dataset is limited in scope, but the authors at least demonstrate that the system can be adapted to a second species, which is a useful proof of concept. The demonstration that task engagement increases progressively over 12 months (Fig. 3g) is a novel observation at this temporal scale, with practical implications for the design of long-term experiments.

(3) The demonstration that operant box usage is distributed nearly uniformly across animals (Gini < 0.15 in all groups) is carefully demonstrated and addresses a question that any laboratory considering this type of system will legitimately ask, e.g., whether dominant individuals monopolize access at the expense of subordinates. This has been shown before in comparable systems, but remains a necessary validation for each new implementation. The control condition removing temporal constraints (Figure S4) adds useful mechanistic insight into the role of the refractory interval. However, the interpretation of this result deserves more nuance than the authors provide - see Weaknesses.

Weaknesses:

(1) The TV is more than an automation tool; its architecture makes the most sense if one intends to study how spontaneous home cage behavior relates to individual cognitive performance, and the introduction and discussion explicitly frame this as a key application. Yet the analysis delivers only group-level descriptive results, and the cognitive data are presented almost exclusively as group averages. The individual-level questions that the system is uniquely positioned to address (do stable home cage behavioral profiles emerge across animals, do animals learn at the same rate and using the same strategies, and do these dimensions correlate with each other ) are never asked. This is particularly relevant given that enriched social environments are precisely the conditions under which stable inter-individual differences tend to emerge spontaneously, even among genetically identical animals (Freund et al., 2013, Science), and that comparable systems have already linked such profiles to cognitive and neurochemical phenotypes (Torquet et al., 2018, Nature Communications). The TV clearly has the data to begin exploring this - doing so would substantially strengthen the paper's scientific contribution beyond its methodological value.

(2) Sustained daytime operant box usage in nocturnal animals deserves more discussion: Box occupancy during the light phase remains around 75% - only modestly below the ~85% seen at night (Fig. S5a-b). The authors conclude this reflects "sustained engagement with the task throughout the circadian cycle," but other explanations are not considered: residual thirst driving animals to seek sucrose water during the day, and the refractory interval mechanically redistributing sessions into the light phase? A more explicit discussion of the consequences of 24/7 unsupervised testing for data quality (daytime sessions may yield noisier behavioral data?) would be useful.

(3) The finding that all animals access the operant box in roughly equal proportions (Gini < 0.15) is practically important and carefully demonstrated. However, the authors' interpretation that animals self-organize in an egalitarian manner despite known social hierarchies deserves a note of caution. The system design itself constrains monopolization: the refractory interval imposes the same waiting time on all animals regardless of social rank, and session duration determines how often the box becomes available. The no-constraint control (Figure S4) partially addresses this but was run on already-trained animals, limiting its interpretive value. The key practical message, that all animals can access the task regularly under the proposed design, is well supported. Whether this reflects genuine social tolerance or is primarily a consequence of system constraints is a subtler question that the current data cannot fully resolve.

(4) The rat cohort consists of a single group of 6 female Long-Evans rats, yet species comparisons are drawn across multiple dimensions (daily sessions, task engagement, performance...). Observed differences could reflect group size, sex, strain, reward calibration, or simple individual variability rather than species differences. These results should be presented for what they are: a useful proof of concept showing the system works with a second species, not a basis for comparative conclusions.

Reviewer #3 (Public review):

Summary:

The authors aim to compare proposal models of perceptual decision making using a joint modeling approach, where they fit models to both behavioral outcomes as well as CPP. Most notably, they compare a standard evidence accumulation model with models that track the evidence without integrating it over time (extrema detection). The authors report that the joint CPP-behavioral data do not discriminate between two of their proposals.

Strengths:

This is an interesting finding that reinforces the idea that what we believe to see based on aggregation over trials may not be what happens on every single trial. The models are creative, and the simulations are convincing, relating the models to multiple neural markers of decision formation. These include the CPP but also mu/beta power spectra.

Weaknesses:

The paper makes some strong points, and the work seems generally well-executed. The weaknesses that I identified are twofold:

(1) Embedding in the literature/exposition of the main argument.

The focus in the introduction is on the noise-free nature of the stimulus and the prolonged presentation time. However, after reading the paper, I felt these were mostly experimental design choices that enable comparison of the different models using the CPP. Perhaps my misreading of the goals of the paper stems from two other observations:

a) The fact that the stimulus is noise-free does not entail that perception is noise-free. Thus, the argument that using a noise-free stimulus precludes the necessity of temporal integration seems not completely valid. Of course, one could argue that noise is limited in this case, but that makes a noise-free stimulus more of a design choice.

b) The focus on prolonged stimulus presentation, but at the same time the contrast with expanded judgement, did not make sense to me. Perhaps, as a non-native speaker, I am misreading the subtle difference between "protracted sampling" and "longer sampling", but again, the longer duration seems mostly a design choice.

More could be said about the optimality of the extrema detection methods. In particular, decades of work (centuries?) have shown that evidence integration is an optimal decision-making procedure: For example, the Sequential Probability Ratio Test is Bayes-optimal wrt mean RT (Wald, 1946); evidence accumulation together with collapsing threshold serves to maximize rewards in repeated choices (e.g., Bogacz et al., PsychRev, 2006; Boehm et al. APP, 2020). Given all this work, why would the brain have evolved to adopt a different mechanism? I realize that the paper is not about optimal decision making, but some discussion of this point seems warranted.

(2) Modeling choices.

The authors introduce a parameter, sampT, that represents uncertainty in the sampling onset time. It was not clear to me whether this parameter represented an offset of all trials, or a distribution (probably the latter). I wonder how exactly this parameter was integrated into the models, and in particular, if and how it interacts with the starting-point parameters. My intuition is that on a single-trial, IF early sampling occurs, you can model that with either a negative sampT and z at 0, or with sampT at 0 but a shift in z. This would suggest trade-offs between these parameters, making them hard to estimate independently. Since the paper does not depend on the identification of parameter estimates, this may not be a huge problem, but nevertheless it is good to explore the consequences.

The way the Bounded Integration model (BIntg) is formulated seems very close to the EZ-diffusion model (Wagenmakers et al., PBR, 2007). This model states that the proportion of correct responses Pc = 1/(1+exp(-B*D/s^2), with B and D the bound and drift rate parameters, respectively. However, filling in the numbers for the high contrast condition from Table 2, and assuming that s=2 (because the model description states that dt=2, with s undefined), I get a Pc of 80% for the 1.6H condition. This seems substantially less than what Figure 2 suggests.

On some occasions, it is unclear to me what modeling choices are being made:

a) It seems as if the models are fit on accuracy data alone (before introducing the neural data). This seems suboptimal given that the authors do report differences in RT.

b) Are the models fit on all data combined, or on the data of individual participants? Fitting individual participant data is preferred, as combined or aggregated data may be distorted by individual differences.

c) The authors seem to suggest that the diffusion coefficient s is estimated (in the section "Integration models"). Most likely, however, this is set to a fixed value. Obviously, it matters for the model comparison using AIC whether this parameter was freely estimated or not.

Not really a weakness, but I wondered about the effect of stimulus duration on RT. In particular, what hypothesis (or post hoc explanation) do the authors have for these RT effects? I could think of at least three hypotheses that are consistent with the behavioral data:

a) H1: The shorter the evidence duration, the more likely participants are to require a double-check before response execution, reflecting their uncertainty about their decision.<br /> b) H2: There is a collapsing threshold that initiates at stimulus offset, leading to quicker responses on trials where there is more evidence.<br /> c) H3: motor preparation is correlated with the evidence signal, which leads to faster responses on trials with more evidence.

Reviewer #3 (Public review):

Vasilevskaya and Keller set out to experimentally distinguish between two variants of predictive processing: a hierarchical and a non-hierarchical variant. The hierarchical variant assumes a hierarchical organization in which internal representation neurons (believed to be a subset of layer 5 excitatory neurons) serve as a source of a teaching signal for local prediction error neurons as well as for the next higher level of the hierarchy, while simultaneously providing prediction signals to the preceding lower level. In contrast, the non-hierarchical variant posits that these layer 5 internal representation neurons provide local predictions to layer 2/3 prediction error neurons.

The interaction between internal representation neurons and prediction error neurons differs fundamentally between the two variants. In the hierarchical variant, internal representation neurons excite positive prediction error neurons and inhibit negative prediction error neurons, while at the same time being inhibited by positive prediction error neurons and excited by negative prediction error neurons. In the non-hierarchical variant, this pattern of connectivity is reversed.

This work is very exciting, timely, and carefully executed. The authors functionally, and later molecularly, identify layer 2/3 prediction error neurons in V1 and probe their interactions with genetically defined neuron types in cortical layers 5 and 6 using optogenetics. They demonstrate that the functional influence of putative prediction error neurons in layer 2/3 onto layer 5 is incompatible with the hierarchical variant, whereas the influence of layer 5 onto putative prediction error neurons in layer 2/3 is incompatible with the non-hierarchical variant. They then test an alternative hypothesis, in which layer 2/3 responses resemble prediction errors with respect to perturbations of artificial layer 5 activity patterns. To investigate this, they designed an experiment in which optogenetic activation of L5 IT neurons was closed-loop coupled to the mouse's locomotion speed in the absence of visual feedback, allowing them to probe the causal influence of L5 activity on layer 2/3 responses.

Finally, the authors hypothesize that their data are more consistent with a joint embedding predictive architecture (JEPA) and outline experimentally testable predictions arising from this framework.

While the work is overall convincing and significantly advances our understanding of the circuit-level implementation of predictive processing, there are a few weaknesses that should be addressed or discussed:

(1) The authors define putative positive prediction error neurons as the 15% of neurons most responsive to grating onset and putative negative prediction error neurons as the 15% most responsive to visuomotor mismatch. While this selection would be expected to overlap with negative and positive prediction error neurons, the criterion is not sufficiently stringent (independent of the exact percentage chosen). In particular, classification of a neuron as a prediction error neuron should ideally be accompanied by evidence that it does not exhibit a significant increase in activity when the prediction matches the sensory input or teaching signal.

(2) The authors "speculate that the prediction error responses in layer 2/3 may not be computed with respect to sensory input, but with respect to layer 5 activity as a teaching signal." However, it is unclear how this perspective differs from earlier statements in the manuscript. In the Introduction, the authors note that "these signals, typically referred to as sensory signals, we will refer to as teaching signals," and later describe the hierarchical variant as one "in which internal representation neurons act as a source of the teaching signal." Given this framing, it is difficult to identify what is conceptually novel in the updated view. Is the key distinction that layer 2/3 neurons are now proposed to generate predictions in an internal representation space rather than in sensory input space, as briefly suggested in the Discussion? Or are the authors introducing a distinction between an external (sensory) and an internal (cortical) teaching signal? If so, this distinction should be made explicit. Clarifying this point would considerably strengthen the manuscript.

(3) The authors propose that "L2/3 neurons predict L5 activity, hence making predictions in the internal representation space rather than the input space," and further suggest that, since both deep and superficial cortical layers receive thalamic input, the cortex may function like a JEPA. This idea appears closely related to the model introduced by Nejad et al. (2025), which effectively implements a JEPA-like architecture: L5 activity serves as a target against which L2/3 predictions are compared in a self-supervised manner, with both L5 and L2/3 (via L4) receiving thalamic input. It would be helpful for the authors to clarify how their framework differs from that model, and to specify the key conceptual or mechanistic distinctions between the present proposal and the approach described by Nejad et al..

Reviewer #3 (Public review):

Summary:

This revised study analyzes the somatic mutational profiles and transcriptomic expression of three zinc-finger genes (ZNF217, ZNF703, ZNF750) in 23 Kenyan women with breast cancer, using whole-exome sequencing and RNA-sequencing of paired tumor-normal tissues. A total of 358 somatic mutations were detected, and all three genes were significantly upregulated in tumors compared to normal tissues (ZNF217 showing the most prominent difference). Higher expression was observed in HER2-positive tumors, though mutation burden for each gene did not correlate significantly with HER2 status or cancer stage. The findings provide preliminary evidence for the idenfication of diagnostic/prognostic biomarkers or therapeutic targets in sub-Saharan African populations.

Strengths:

The study's key strengths lie in its focus on an underrepresented Kenyan cohort, addressing a critical gap in sub-Saharan African breast cancer genomic research. It integrates DNA-level mutation analysis with RNA-level expression data, leveraging standardized bioinformatics pipelines (e.g., Mutect2 for variant calling, DESeq2 for differential expression) and rigorous quality control to deliver detailed insights into mutation types, functional impacts, and amino acid changes. Additionally, it explores gene expression patterns across different cancer stages and HER2 status subgroups, generating targeted hypotheses for future validation and enhancing the reliability of its findings.

Weaknesses:

The author has enhanced the descriptive depth of the study by adding details on mutations, expression subgroup analyses, and functional annotations but has not addressed the core weaknesses of small cohort size and lack of functional validation. While the revised version is more comprehensive in cataloging molecular alterations, it remains confined to descriptive analysis, with no substantial improvement in the reliability or generalizability of its conclusions.

【洞察】120 倍的人机差距意味着：当前 AI 推理能力的提升是「在已知模式上的优化」，而非「真正的归纳推理泛化」。这对所有声称「AI 已接近人类」的产品宣传都是正面挑战——AGI 时间线的预期需要重新校准，而非渐进式调整。

⚠️【令人震惊的数字】最强前沿模型得分低于 0.5%——而非专业人类轻松超过 60%，差距超过 120 倍。这是继 ARC-AGI-2 之后最彻底的「AI 能力幻觉清醒剂」。推理能力的提升并未自动迁移到「新颖抽象推理」，当所有人在讨论 AGI 即将到来时，这份数据是最直接的反驳。

Reviewer #3 (Public review):

Summary:

Using the approach of Myomatrix recording, the authors report that 1) motor units are recruited differently in the two types of muscles and 2) individual units are probabilistically recruited during the locomotion strides, whereas the population bulk EMG has a more reliable representation of the muscle. Third, the recruitment of units was proportional to walking speed.

Strengths:

The new technique provides a unique dataset, and the data analysis is convincing and well-executed.

Weaknesses:

After the revision, I no longer see any apparent weaknesses in the study.

Reviewer #3 (Public review):

The manuscript of Haertter and coworkers studied the variation of the length of a single sarcomere and the response of microfibrils made by sarcomeres of cardiomyocytes on soft gel substrates of varying stiffness.

The measurements at the level of a single sarcomere are an important new result of this manuscript. They are done by combining the labeling of the sarcomeres z line using genetic manipulation and a sophisticated tracking program using machine learning. This single sarcomere analysis shows strong heterogeneities of the sarcomeres that can show fast oscillations not synchronized with the average behavior of the cell and what the authors call popping eveents which are large amplitude oscillations. Another important result is the fact that cardiomyocyte contractility decreases with the substrate stiffness, although the properties of single sarcomeres do not seem to depend on substrate stiffness.

The authors suggest that the cardiomyocyte cell behavior is dominated by sarcomere heterogeneity. They show that the heterogeneity between sarcomere is stochastic and that the contribution of static heterogeneity (such as composition differences between sarcomeres) is small.

Strengths:

All the results are, to my knowledge, new and original. The authors also made a theoretical model where each sarcomere is described by a Langevin equation based on a non-linear coupling between force and velocity of the sarcomeres. This model accounts well for the experimental results including the observation of what the authors call popping events.

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 Drsosophila 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.

Reviewer #3 (Public review):

Summary:

Eukaryotic gene transcription requires a large assemblage of protein complexes that govern the molecular events required for RNA Polymerase II to produce mRNAs. One of these complexes, TFIIH, comprises two modules, one of which promotes DNA unwinding at promoters, while the other contains a kinase (Kin28 in yeast) that phosphorylates the repeated motif at the C-terminal domain (CTD) of the largest subunit of Pol II. Kin28 phosphorylation of Ser5 in the YSPTSPS motif of the CTD is normally highly localized at promoter regions, and marks the beginning of a cycle of phosphorylation events and accompanying protein association with the CTD during the transition from initiation to elongation.

The two modules of TFIIH are linked by Tfb3. Tfb3 consists of two globular regions, an N-terminal domain that contacts the Core module of TFIIH and a C-terminal domain that contacts the kinase module, connected by a linker. In this paper, Giordano et al. test the role of Tfb3 as a connector between the two modules of TFIIH in yeast. They show that while no or very slow growth occurs if only the C-terminal or N-terminal region of Tfb3 is present, near normal growth is observed when the two unlinked regions are expressed. Consistent with this result, the separate domains are shown to interact with the two distinct TFIIH modules. ChIP experiments show that the Core module of TFIIH maintains its localization at gene promoters when the Tfb3 domains are separated, while localization of the kinase module, and of Ser5 phosphorylation on the CTD of Pol II, is disrupted. Finally, the authors examine the effect of separating the Tfb3 domains on another function of TFIIH, namely nucleotide excision repair, and find little or no effect when only the N-terminal region of Tfb3 or the two unlinked domains are present.

Strengths:

Experiments involving expression of Tfb3 domains in yeast are well-controlled and the data regarding viability, interaction of the separate Tfb3 domains with TFIIH modules, genome-wide localization of the TFIIH modules and of phosphorylated Ser5 CTDs, and of effects on NER, are convincing. The experiments are consistent with current models of TFIIH structure and function and support a model in which Tfb3 tethers the kinase module of TFIIH close to initiation sites to prevent its promiscuous action on elongating Pol II.

Weaknesses:

The work is limited in scope and does not provide major insights into the mechanism of transcription. The main addition to current models of transcription is that tethering of Kin28 to Tfb3 may limit kinase action from occurring downstream from the initiation site.

The first described experiment, which purports to show that three kinases cannot function in place of Kin28 when tethered (by fusion) to Tfb3 is missing the crucial control of showing that Kin28 can support viability in the same context. This result also does not connect with the rest of the manuscript, although the experiment apparently motivated the subsequent studies reported here.

Finally, the authors present the interesting and reasonable speculation that the TFIIH complex and connecting Tfb3 found in mammals and yeast may have evolved from an earlier state in which the two TFIIH subdomains were present as unconnected, distinct enzymes. It will be interesting to have this idea tested more thoroughly as more molecular evolutionary data becomes available.

Comments on revisions:

For the most part, the authors have satisfactorily addressed my previous critique. In particular, they have added to their discussion of evolutionary implications, and performed an experiment casting doubt on the assertion of a dominant negative effect, and as a consequence removed this claim from the manuscript. I also pointed out that the fusion experiments that lead off the Results section are missing the crucial control of including a Tfb3-Kin28 fusion. The authors have elected not to perform this control experiment, pointing out that even this control would be imperfect in some respects, and agreeing that this experiment is somewhat disconnected from the rest of the paper. The reason for including it, in spite of its somewhat tangential nature, is that it provides something of a rationale for the experiments that follow. I don't so much mind their retaining the experiment, as the absence of this control (and indeed, the results) does not so much impact the later results. However, I think if it is to be included, this shortcoming should be explicitly recognized, especially as a service to younger scientists who could benefit from an exposition that includes a thorough consideration of potential control experiments.

Reviewer #3 (Public review):

Summary:

In this manuscript, Strzelczyk, Vetsch, and Langer tackle an incredibly important question in clinical neuroscience: the use of the theta/beta ratio as a biomarker of attention deficit hyperactivity disorder (ADHD). The theta/beta ratio is argued to be so reliable as an ADHD biomarker that, in the United States, the Food and Drug Administration has approved its use as a biomarker for ADHD diagnosis. However, there is mounting evidence that the theta/beta ratio is likely not really measuring the relative power between two oscillations - the theta rhythm and the beta rhythm - but rather reflects differences in a singular, non-oscillatory aperiodic process. In this very convincing study, Strzelczyk and colleagues take a "multiverse" analysis approach to show that aperiodic activity differences between healthy controls and people with ADHD are driving the apparent theta/beta ratio differences. While in a vacuum, where a measure is a measure and if it's related to a diagnosis it's still useful no matter what, this distinction might not seem important, from a neuroscientific perspective this is a critical distinction, because the ratio between two oscillations has fundamentally very different underlying physiological mechanisms than aperiodic differences, and this framing has a major impact on guiding research on the diagnosis and treatment of ADHD.

Strengths:

While smaller studies and analyses have already hinted at similar results as shown here, the current study's multiverse analysis approach is comprehensive, convincing, and very well done. The large sample size of 1,499 participants is very impressive, as is the use of an independent validation sample of 381 participants.

Overall, the technical and statistical aspects are very well done: the multiverse approach, the validation set, the resampling methods, and even the shiny apps. The authors should be applauded for being so thorough and making their data and analyses publicly accessible.

Weaknesses:

To be clear, I see no breaking weaknesses in the theoretical foundations, methods, statistical analyses, or interpretations. All of my recommendations below are for the sake of clarity, which I believe is especially important because this is such an important paper that many people should read.

Comments:

(1) Some figures are mislabeled. For example, Supplementary Figure 1 says (C) are scalp topographies, but those are (A), while (C) shows power spectra, but it's unclear what (C) is. I assume it's only the aperiodic part of the spectrum (oscillations removed)? But it would be better to plot on a log-log scale if so.

In fact, I recommend showing all spectra on a log-log scale.

(2) Supplementary Figure 6 is also mislabeled, saying (A) shows age (it does not) and so on.

(3) In Supplementary Figure 7, is (B) the aperiodic-removed spectrum? The authors are very inconsistent with what they're showing in these spectral plots, and not actually explaining what they're showing: raw spectra, semi-logged or not, aperiodic-removed or oscillations-removed, etc.

(4) For the HBN data, it is said that, "electrode impedances were kept below 40 kΩ, lower than EGI's standard recommendation of 50 (Net Station Acquisition Technical Manual)." For the validation data: "... electrode impedances were maintained below 5 kΩ." These are big impedance threshold differences. Of course, these recommendations differ by recording system, the use of active electrodes, and so on. But such differences can certainly influence signal-to-noise. The fact that the results are so consistent between them is a strength that perhaps should be explicitly called out.

(5) The authors cite a lot of foundational / related work here, such as Finley et al, but they should also cite several other highly relevant ones:

- Saad et al., "Is the Theta/Beta EEG Marker for ADHD Inherently Flawed?", J Atten Disord, 2015

- Donoghue, Dominguez, Voytek, "Electrophysiological frequency band ratio measures conflate periodic and aperiodic neural activity", eNeuro, 2020

- Karalunas et al., "Electroencephalogram aperiodic power spectral slope can be reliably measured and predicts ADHD risk in early development", Develop Psychobiol, 2022

- Donoghue, "A systematic review of aperiodic neural activity in clinical investigations", Eur J Neurosci 2025

Reviewer #3 (Public review):

Summary:

In this study, Cherian and colleagues compare visual perception between humans and monkeys using a common oddball visual search task across a battery of perceptual phenomena. By keeping the task constant and varying only the stimulus sets, the authors aim to isolate perceptual similarities and differences between species. Across six experiments, they report that monkeys and humans share similarities in coarse object representations, Weber's law, and amodal completion, but differ in mirror confusion and global/local processing.

Strengths:

A major strength of the study is the unified experimental framework. The authors designed the experiments such that the task procedures are identical across conditions and species, differing only in the images shown. This is a significant methodological advantage, as it minimizes task-related confounds that often complicate cross-species and cross-experiment comparisons. As a result, observed similarities and differences can be more directly attributed to perceptual processes rather than differences in training or task demands. This allows for a more comprehensive evaluation of visual perception than is typical in the literature, where individual studies often focus on a single effect with specialized training. The data are carefully collected, and the analyses are systematic and appropriate for the questions posed.

Weaknesses:

Despite its strengths, the study is largely descriptive and provides limited mechanistic or theoretical explanation for the observed similarities and differences. While the authors document several convergences and divergences between humans and monkeys, there is relatively little discussion of why these patterns arise or how they relate to existing theories of visual processing. As a result, it is difficult to assess the broader implications or generalizability of the findings beyond the specific task and stimuli used.

Relatedly, the rationale for selecting the particular set of perceptual phenomena is not fully developed. Some tasks appear motivated by prior work comparing humans and deep neural networks, but it is unclear whether this set constitutes a representative or theoretically grounded sampling of visual perception. Without a clearer justification, it is difficult to interpret the absence or presence of specific effects (e.g., mirror confusion or global advantage) as reflecting fundamental species similarities/differences.

Reviewer #3 (Public review):

Summary:

Malhotra and colleagues present evidence that the integrin LFA-1 on NK cells is a ligand for the Plasmodium falciparum protein GBP130 on the infected erythrocyte surface and that this interaction plays a role in the clearance of infected erythrocytes by NK cells.

The authors first select a subdomain contained within the CD11a subunit of LFA-1 as a probe to discover possible binding proteins on the infected erythrocyte surface. Parasite-infected erythrocytes stained positively with this probe; the level of staining increased as the parasites progressed through the life cycle. Using the LFA-1-based probe in cross-linking pull-down experiments, GBP130 was identified by mass spectrometry as a co-purifying parasite protein. The N-terminal portion of GBP130 was recombinantly expressed and shown to interact with LFA-1 alpha-I by biolayer interferometry experiments. The full-length extracellular domain of GBP130 was then recombinantly expressed and used to stain primary human NK cells and THP-1 cells. Knocking down LFA-1 by siRNA reduced staining by GBP130. To assess the contribution of GBP130 to the activation of NK cells, CHO cells exogenously expressing GBP130 were incubated with primary NK cells. Transfecting CHO cells with GBP130 led to increased activation of co-incubated NK cells compared to mock-transfected and compared to GBP130 transfected cells, with the inclusion of anti-CD11a to block NK cell adhesion. Finally, CHO cells expressing GBP130 led to increased activation of NK cells compared to mock-transfected CHO cells.

Overall, although the authors present data from NK cell killing assays that include appropriate controls, the data suggesting a direct interaction between PfGBP-130 and LFA-1 does not include the same necessary controls, for example, the use of blocking antibodies. Most critically, the biolayer interferometry experiments use a recombinant fragment of PfGBP-130, which does not include the residues predicted to be important for mediating specific interaction with LFA1. The biolayer interferometry data instead suggest non-specific interactions between PfGBP-130 and LFA1, as binding does not reach saturation.

Comments on revised version:

The authors have addressed all minor concerns, however the major point regarding the biophysical data supporting direct interaction between PfGB130 and LFA-1, in my opinion, has not been satisfactorily addressed. Biophysical data supporting the interaction was generated using a fragment of PfGB130, which does not include residues that the authors predict by structural modelling to be important for the interaction. The authors argue that PfGB130 is a repeat containing protein and may have multiple binding sites for LFA-1. If this is the best mechanistic hypothesis given the current data, the authors need to explain this in the results section.

Overall though, I agree with Reviewer#1 that the structural modelling results are not convincing and given that the modelling data do not straightforwardly agree with the experiment, the clarity of the manuscript would benefit from their omission.

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.

Weaknesses:

No major weaknesses were identified by this reviewer.

Reviewer #3 (Public review):

Summary:

Su et al. sought to understand how the opportunistic pathogen Staphylococcus aureus responds to multiple selection pressures during infection. Specifically, the authors were interested in how the host environment and antibiotic exposure impact the evolution of both virulence and antibiotic resistance in S. aureus. To accomplish this, the authors performed an evolution experiment where S. aureus were fed to Caenorhabditis elegans as a model system to study the host environment and then either subjected to the antibiotic oxacillin or not. Additionally, the authors investigated the difference in evolution between an antibiotic-resistant stain MRSA and an isogenic susceptible strain MSSA. They found that MRSA strains evolved in both antibiotic and host conditions became more virulent and that strains evolved outside these conditions lost virulence. Looking at the strains evolved in just antibiotic conditions, the authors found that S. aureus maintained its ability to lyse blood cells. Mutations in codY, gdpP and pbpA were found to be associated with increased virulence. Additionally, these mutations identified in these experiments were found in S. aureus strains isolated from human infections.

Strengths:

The data are well-presented, thorough, and are an important addition to the understanding of how certain pathogens might adapt to different selective pressures in complex environments.

Comments on revisions:

For the most part, my comments have been addressed. It seems that the authors have not addressed my comments about quantifying population sizes in order to understand mutation supply, particularly in light of which experimental phase exhibits the strongest selection and possible increases in mutation rates. While I think this information would be very useful if they had collected it during the experiment, I don't think it is important enough to require additional experiments. I am therefore satisfied with the current state of the manuscript.

Reviewer #3 (Public review):

Summary:

This is an interesting paper which asks a compelling and translationally relevant question: since the firing rate of GABAergic PV+ interneurons (which powerfully control pyramidal cell excitability) increases prior to and during seizures, why doesn't this increase in inhibition do more to prevent seizure propagation? The authors hypothesize that increased PV+ spiking might lead to spike propagation failures in the axon.

To test this hypothesis, the authors conduct paired electrophysiological recordings from PV+ neurons in acute barrel cortex slices of mice and from a handful of human neurosurgical samples. They use patch clamp recordings to measure the membrane potential of PV+ neurons at the soma, while simultaneously measuring spike propagation with a recording electrode in the axon of the same neuron.

After a variety of elegant experiments and modeling, the authors conclude that extracellular K+ accumulation around the axon during high-frequency firing might be causing propagation failures.

Strengths:

Overall, the paper is nicely written, the experiments are technically challenging, and the figures are, for the most par,t well laid out. The topic will be of broad interest for the neuroscience field, given the relevance of PV+ interneurons to cortical circuit function, plasticity across development, and disease.

Weaknesses:

In addition to the strengths here, I feel the need to highlight a few weaknesses which, if rectified, could improve the work.

(1) The key hypothesis in this paper is that extended periods of somatic spiking lead to progressive decreases in the axonal AP amplitude, which eventually lead to failures, potentially (but not necessarily) at branchpoints. Two comments here.

It would be helpful for the authors to show us examples of the axonal spike waveforms at a faster time base (along with the somatic recording) so that we can really understand what's happening to the spike in the axon.

Their data are also compatible with failures of spike initiation at the AIS. Could the authors show us the first derivative of somatic voltage for successes and failures, and maybe show us some phase plots of Vm vs dV/dt for the failures, successes, and attenuated spikes? Effectively, what I'm asking is whether the changes they see in the distal axon are downstream of the initiation zone. It's very possible that extended spiking is simply depolarizing the AIS and inactivating Na+ channels there. In which case, the authors should be able to pull this out in a phase plot.

(2) There's no baseline period for their calcium fluorescence signals, which is necessary to compare their "signal" magnitude to frame-by-frame variance of dG/R. Could the authors correct this issue in Figure 6B?

(3) Some of their stats are a bit unorthodox. Why are they doing two separate Wilcoxon tests in 6D and 6E? Why not throw all that into a one-way ANOVA model followed by appropriate post hoc tests?

(4) Why don't the authors observe washout of their effect after high K+ application? This concerns me that their high K+ application is having secondary and long-lasting effects on PV excitability, which mimic (but are not necessarily identical) to their hypothesized mechanism of axonal failures.

Reviewer #3 (Public review):

Summary:

The authors are trying to demonstrate how astrocytes influence the connections within neural circuits that control behavior.

Strengths:

The data presented in the manuscript are thorough and well-executed, using advanced Drosophila approaches (Ca2+ imaging, GRASP, clonal analysis, trans-Tango) in new ways (PEAPODS) and with new tools (pyk mutants, anti-pyk Ab, LexAop2-pykRNAi). Use of two RNAi lines for each of three glycolytic enzymes is strong evidence that perturbation of glycolysis is responsible, though it does not rule out that inappropriate build-up of intermediates, or shunting to alternative pathways, may play a role here. Subsequent focus on Pyk alone is understandable.

Weaknesses:

As strong as the data is, it does not always support some of the stated claims, and this should be addressed in any revision. In addition, there seems to be an oversimplification of the possible effects of Pyk RNAi, and some missing pieces that could fill in gaps and align the proposed mechanism with observed phenotypes.

Where the data does not support stated claims:

(1) The authors claim larvae executed more reorientation actions during locomotion "as a result" of attenuated astrocyte-to-DAergic neuron signaling through neuroligin 2 (astrocyte) and neurexin 1 (DA Neuron). They correlated these, but did not make a direct connection.

(2) There is a claim that "at the circuit level, behavioral alterations were found to arise from increased DAergic neuronal synaptogenesis and DAergic-motor connection" (sic). However, the work does not build a causal relationship between behavior and synaptogenesis or connectivity. At present, the manuscript does not directly address whether increased DA-motor neuron synapses are sufficient to explain the increased orientation reactions observed.

(3) It is asserted that (line 182, and elsewhere) "astrocyte glycolysis deficiency increased PEAPODs and DAergic neuron synaptogenesis". While astrocyte Pyk KD increased PEAPODS (Figure 2), and it also increased endogenous Brp-GFP in DA neurons (via STaR, Figure 3F), the added Brp-GFP was not localized to synapses under these conditions (pyk KD), to unequivocally demonstrate that the increased PEAPODS are at the sites of DAergic synapses. Also seen in 6I-J.

(4) It may be premature to refer to this strictly as synaptogenesis, as alternative explanations (e.g., stabilization or impaired pruning) could also account for the observations.

(5) The use of trans-Tango is an elegant way to support the idea that extra DAergic synapses are formed onto motor neurons, with potential impact on motor circuits. But again, the claim (line 215, and elsewhere) that this "Biased DAergic-motor wiring" is what "alters motor output", would benefit from additional evidence.

(6) Oversimplification of the possible effects of Pyk RNAi: Because Pyk knockdown is likely to alter glycolytic flux rather than abolish glycolysis entirely, it may be clearer to describe the manipulation as 'Pyk loss' rather than 'glycolysis-deficient' in most contexts.

(7) Filling gaps to align the proposed mechanism with observed phenotypes:

a) Figure 6K-M - the ER retention of Nlg2 should also be tested using Pyk-RNAi, in addition to the pyk mutants shown. This would confirm the astrocyte-specific nature of this effect and close the loop to align the phenotypes.

b) From the mechanism proposed (ER retention of Nlg, presumably leading to loss of Nlg function in astrocytes), one might expect that the effects of loss of Nlg2 from astrocytes could phenocopy the behavioral deficits seen in pyk KD (from astrocytes). Ackerman et al (2021) knocked down Nlg2 from astrocytes and examined motor behavior with FIMTrack. They saw increased accumulated distance but did not see the effects seen upon pyk KD in this manuscript (increased pausing, sweeping). The authors could perform this experiment themselves or alternatively should address this inconsistency in the discussion.

Reviewer #3 (Public review):

Summary:

Gosselin et al. use the sybody technology to study effects of in vivo inhibition of the Bacillus subtilis SMC complex. Smc proteins are central DNA binding elements of several complexes that are vital for chromosome dynamics in almost all organisms. Sybodies are selected from three different libraries of the single domain antibodies, using the "transition state" mutant Smc. They identify 14 such mutant sybodies that are lethal when expressed in vivo, because they prevent proper function of Smc. The authors present evidence suggesting that all obtained sybodies bind to a coiled-coil region close to the Smc "neck", and thereby interfere with the Smc activity cycle, as evidenced by defective ATPase activity when Smc is bound to DNA.

The study is well done and presented and shows that the strategy is very potent in finding a means to quickly turn off a protein's function in vivo, much quicker than depleting the protein.

The authors also draw conclusions on the molecular mode of action of the SMC complex. The provide a number of suggestive experiments, but in my view mostly indirect evidence for such mechanism.

My main criticism is that the authors have used a single - and catalytically trapped form of SMC. They speculate why they only obtain sybodies from one library, and then only identify sybodies that bind to a rather small part of the large Smc protein. While the approach is definitely valuable, it is biassed towards sybodies that bind to Smc in a quite special way, it seems. Using wild type Smc would be interesting, to make more robust statements about the action of sybodies potentially binding to different parts of Smc.

Line 105: Alternatively, the other libraries did not produce good binders or these sybodies were 106 not stably expressed in B. subtilis. This could be tested using Western blotting - I am assuming sybody antibodies are commercially available. However, this test is not important for the overall study, it would just clarify a minor point.

Fig. 2B: is odd to count Spo0J foci per cells, as it is clear from the images that several origins must be present within the fluorescent foci. I am fine with the "counting" method, as the images show there is a clear segregation defect when sybodies are expressed, I believe the authors should state, though, that this is not a replication block, but failure to segregate origins.

Testing binding sites of sybodies to the SMC complex is done in an indirect manner, by using chimeric Smc constructs. I am surprised why the authors have not used in vitro crosslinking: the authors can purify Smc, and mass spectrometry analyses would identify sites where sybodies are crosslinked to Smc. Again, I am fine with the indirect method, but the authors make quite concrete statements on binding based on non-inhibition of chimeric Smc; I can see alternative explanations why a chimera may not be targeted.

Smc-disrupting sybodies affect the ATPase activity in one of two ways. Again, rather indirect experiments. This leads to the point Revealing Smc arm dynamics through synthetic binders in the discussion. The authors are quite careful in stating that their experiments are suggestive for a certain mode of action of Smc, which is warranted.

In line 245, they state More broadly, the study demonstrates how synthetic binders can trap, stabilize, or block transient conformations of active chromatin-associated machines, providing a powerful means to probe their mechanisms in living cells. This is off course a possible scenario for the use of sybodies, but the study does not really trap Smc in a transient conformation, at least this is not clearly shown.

Overall, it is an interesting study, with a well-presented novel technology, and a limited gain of knowledge on SMC proteins.

Significance:

The work describes the gaining and use of single-binder antibodies (sybodies) to interfere with the function of proteins in bacteria. Using this technology for the SMC complex, the authors demonstrate that they can obtain a significant of binders that target a defined region is SMC and thereby interfere with the ATPase cycle.

The study does not present a strong gain of knowledge of the mode of action of the SMC complex.

Reviewer #3 (Public review):

Original review:

The paper by Maggi et al. builds on earlier work by the team (Paatero et al., 2018) on oriented junction-based lamellipodia (JBL). They validate the role of JBLs in guiding endothelial cell rearrangements and utilise high-resolution time-lapse imaging of novel transgenic strains to visualise the formation of distal junctions and their subsequent fusion with proximal junctions. Through functional analyses of Arp2/3 and actomyosin contractility, the study identifies JBLs as localized mechanical hubs, where protrusive forces drive distal junction formation, and actomyosin contractility brings together the distal and proximal junctions. This forward movement provides a unique directionality which would contribute to proper lumen formation, EC orientation, and vessel stability during these early stages of vessel development.

Time-lapse live imaging of VEC, ZO-1, and actin reveals that VEC and ZO-1 are initially deposited at the distal junction, while actin primarily localizes to the region between the proximal and distal sites. Using a photoconvertible Cdh5-mClav2 transgenic line, the origin of the VEC aggregates was examined. This convincingly shows that VE-cadherin was derived from pools outside the proximal junctions. However, in addition to de novo VEC derived from within the photoconverted cell, could some VEC also be contributed by the neighbouring endothelial cell to which the JBL is connected?

As seen for JAILs in cultured ECs, the study reveals that Arp2/3 is enhanced when JBLs form by live imaging of Arpc1b-Venus in conjunction with ZO-1 and actin. Therefore Arp2/3 likely contributes to the initial formation of the distal junction in the lamellopodium.

Inhibiting Arp2/3 with CK666 prevents JBL formation, and filopodia form instead of lamellopodia. This loss of JBLs leads to impaired EC rearrangements.

Is the effect of CK666 treatment reversible? Since only a short (30 min) treatment is used, the overall effect on the embryo would be minimal, and thus washing out CK666 might lead to JBL formation and normalized rearrangements, which would further support the role of Arp2/3.

From the images in Figure 4d it appears that ZO-1 levels are increased in the ring after CK666 treatment. Has this been investigated, and could this overall stabilization of adhesion proteins further prevent elongation of the ring?

To explore how the distal and proximal junctions merge, imaging of spatiotemporal imaging of Myl9 and VEC is conducted. It indicates that Myl9 is localized at the interjunctional fusion site prior to fusion. This suggests pulling forces are at play to merge the junctions, and indeed Y 27632 treatment reduces or blocks the merging of these junctions.

For this experiment, a truncated version of VEC was use,d which lacks the cytoplasmic domain. Why have the authors chosen to image this line, since lacking the cytoplasmic domain could also impair the efficiency of tension on VEC at both junction sites? This is as described in the discussion (lines 328-332).

Since the time-lapse movies involve high-speed imaging of rather small structures, it is understandable that these are difficult to interpret. Adding labels to indicate certain structures or proteins at essential timepoints in the movies would help the readers understand these.

Reviewer #3 (Public review):

Summary:

Type II IRES, such as those from encephalomyocarditis virus (EMCV) and foot-and-mouth disease virus (FMDV), mediate cap-independent translation initiation by using the full complement of eukaryotic initiation factors (eIFs), except the cap-binding protein eIF4E. The molecular details of how IRES type II interacts with the ribosome and initiation factors to promote recruitment have remained unclear. Das and Hussain used cryo-electron microscopy to determine the structure of a translation initiation complex assembled on the EMCV IRES. The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Strengths:

The structure reveals a direct interaction between the IRES and the 40S ribosomal subunit, offering mechanistic insight into how type II IRES elements recruit the ribosome.

Comments on revised version:

The revised manuscript does not improve the resolution; however, the authors provide a detailed and well-reasoned rationale that directly addresses the concerns I raised about their structural interpretation. In addition, two independent preprints have been released since the initial submission. In one case, the authors report a higher-resolution, and importantly, all three studies present consistent assignments and interpretations. Together, these observations strengthen confidence in the authors' conclusions. I therefore do not have major concerns regarding the publication of this revised manuscript.

Reviewer #3 (Public review):

Summary:

The authors use single-cell transcriptomic analysis to identify distinct cell types in the zebrafish inner ear. They identify markers of hair cells and supporting cells associated with sensory patches, cells that generate the semicircular canals, endolymphatic duct and sac, and periotic mesenchymal cells.

Strengths:

The computational analysis is thorough, and the findings are clearly described. In situ hybridization provides corroboration of cell identities in many cases. This resource atlas will be of particular interest for studies of inner ear morphogenesis. Indeed, the identification of a smooth muscle marker in the endolymphatic sac suggests future analysis of the degree to which this structure undergoes contraction. Identification of cell signaling components in BMP, Wnt, FGF, and other signaling pathways will also provide a resource for understanding signals coordinating ear development.

Weaknesses:

The manuscript is incomplete. Important details that would allow replicable analysis are not provided, with notebooks not available on the referenced GitHub site, and additional files are missing.

The authors make a detailed description of hair cells and supporting cells that are consistent with previous findings (Figures 2 and 3). By contrast, the analysis of distinct cell types that have not been previously well characterized in zebrafish is somewhat incomplete. Markers are described for cells forming the semicircular canals, including ccn1l1 (Figure 4). The authors report an intriguing pattern of its expression before overt bud formation; however, they provide no detailed expression analysis to support this assertion.

The authors also identify new markers for subsets of periotic mesenchyme (Figure 6). These include epyc and otos, which mark distinct populations within the mammalian inner ear - cochlea supporting cells, spiral limbus, and ligament, respectively. Identification of the equivalent of the spiral ligament would be of particular interest. However, the expression analysis is not of sufficient resolution to identify which cell types these represent in the zebrafish inner ear.

Differences in gene expression are reported for lmx1bb mutants. However, none of the single-cell data for mutants is provided, and the table (S8) of differential gene expression is missing. Significantly more detail would be needed to interpret these findings.

Reviewer #3 (Public review):

Summary:

The manuscript by Dearborn et al investigates the kinetics of intron splicing in inflammation-associated transcripts after TNF-stimulation of macrophages, using targeted sequencing of chromatin-associated RNA to obtain high coverage across a focused set of induced genes. The authors' main conclusion is that splicing kinetics are heterogeneous across these transcripts, and that delayed introns (which they term "bottleneck introns") are associated with weak donor sequences. Using a deep learning approach, they have also identified additional sequence features that might contribute to intron splicing kinetics.

Overall, I think the findings in the manuscript are very intriguing and will be of interest to readers working on RNA biology. The changes the authors have made to the manuscript in response to some very valid comments from reviewers have strengthened the manuscript. While the existing data might not be sufficient to directly address some of the broader mechanistic claims made by the authors, I think the findings are nonetheless very interesting and should contribute towards a better understanding of the post-transcriptional regulation of gene expression.

Strengths:

A strength of the manuscript is the experimental design. The targeted capture approach is innovative and well-suited to the goal of measuring intron-specific splicing behaviour across time. The inclusion of experimental validation in minigene assays of some of the computational predictions also strengthens the claims made by the authors.

The authors have made a constructive effort to address some of the concerns raised in a previous round of review. The revised manuscript reads as a balanced text.

Weaknesses:

The study still does not fully resolve the downstream consequences of delayed splicing. In particular, it remains unclear whether the bottleneck introns lead primarily to delayed production of mature transcripts, reduced productive transcript output, or some combination of the two.

On a related point, the minigene reporter assays measure a steady-state level of the transcript and don't provide insights into the kinetics directly.

Lastly, given that the detailed analyses were performed on a selected subset of (inflammation-induced) transcripts, a broader evolutionary interpretation needs to be restrained given the current data.

Reviewer #3 (Public review):

Summary:

Zacharia et al report on the molecular function of the C-terminal domain of the intraflagellar transport IFT-B complex component IFT172 by structure determination and biochemical in vitro and cell culture-based assays. The authors identify an IFT-A binding site that mediates a mutually exclusive interaction to two different IFT-A subunits, IFT144 and IFT140, consistent with interactions suggested in anterograde and retrograde IFT trains by previous cryo-electron tomography studies. Additionally, the authors identify a U-box-like domain that binds ubiquitin and conveys ubiquitin conjugation activity in the presence of the UbcH5a E2 enzyme in vitro. RPE1 cell lines that lack the U-box domain show a reduction in ciliation rate with shorter cilia, and heterozygous cells manifest TGF-beta signaling defects, suggesting an involvement of the U-box domain in cilium-dependent signaling.

Strengths:

(1) The structural analyses of the C-terminal domain of IFT172 combine crystallography with structure prediction using state-of-the-art algorithms, which gives high confidence in the presented protein structures. The structure-based predictions of protein interactions are validated by further biochemical experiments to assess the specific binding of the IFT172 C-terminal domains with other proteins.

(2) The finding that the IFT172 C-terminus interactions with the IFT-A components IFT140 and IFT144 appear mutually exclusive confirm a suggested role in mediating the binding of IFT-B to IFT-A in anterograde and retrograde IFT trains, which is of very high scientific value.

(3) The suggested molecular mechanism of IFT train coordination explains previous findings in Chlamydomonas IFT172 mutants, in particular an IFT172 mutant that appeared defective in retrograde IFT, as well as mutations identified in ciliopathy patients.

(4) The identification of other IFT172 interactors by unbiased mass spectrometry-based proteomics is very exciting. Analysis of stoichiometries between IFT components suggests that these interactors could be part of IFT trains, either as cargos or additional components that may fulfill interesting functions in cilia and flagella.

(5) The authors unexpectedly identify a U-box-like fold in the IFT172 C-terminus and thoroughly dissect it by sequence and mutational analyses to reveal unexpected ubiquitin binding and potential intrinsic ubiquitination activity.

(6) The overall data quality is very high. The use of IFT172 proteins from different organisms suggests a conserved function.

Overall, the authors achieved to characterize an understudied protein domain of the ciliary intraflagellar transport machinery and gained important molecular insights into its role in primary cilia biology, beyond IFT. By identifying an unexpected functional protein domain and novel interaction partners the work makes an important contribution to further our understanding of how ciliary processes might be regulated by ubiquitination on a molecular level. Based on this work it will be important for future studies in the cilia community to consider direct ubiquitin binding by IFT complexes.

Conceptually, the study highlights that protein transport complexes can exhibit additional intrinsic structural features for potential auto-regulatory processes. Moreover, the study adds to the functional diversity of small U-box and ubiquitin-binding domains, which will be of interest to a broader cell biology and structural biology audience.

Reviewer #3 (Public review):

Summary:

In this manuscript by Jaiswal et al., the authors used structural biology combined with cellular assays to determine the molecular basis underlying the neutralizing ability of the SARS-CoV-2 antibodies. The authors compared the binding mode of the neutralizing antibodies that have two distinct binding interfaces and identified key sites that determine their vulnerability to virus evolution. Interestingly, the author also demonstrated that the trimer-disrupting antibody has the broadest activity as the variations at the trimer interface are limited in evolution.

Strengths:

This manuscript reported a large collection of structures and covered a broad range of binding modes and mechanisms of action. Many of the cryo-EM structures are of good quality. The authors' hypothesis regarding the molecular determinants of evolution vulnerability is solid.

Weaknesses:

However, in my opinion, several points listed below need to be addressed.

(1) At the beginning of the results section, the authors started by determining the structures of Fab PVI.V3-9 and Fab PVI.V6-4 in complex with the ancestral SARS-CoV-2 spike. However, the readers could benefit from a brief introduction of the Fabs PVI.V3-9 and PVI.V6-4. The same applies to the anti-NTD Fabs.

(2) In Figure 1A and E, the spike protein is shown with two different views. It is best to show the same view for comparison.

(3) Throughout the manuscript, the map quality of some Fabs (e.g., V6-11, V6-7, V6-2) is suboptimal. Does the map support the claims on the residues that form the interface? The authors should provide a figure showing the cryo-EM density for all side-chain residues involved at the interface.

(4) Line 152, the terminology "NTD top binders" could be ambiguous, as it could mean those Fabs have the strongest binding affinity. Maybe the authors can change the "top" to "tip".

(5) The authors described the interface between the spike protein and the Fabs in great detail. However, it would be nice if the authors could summarize the common binding strategy for each group of antibodies that utilize the same binding surface.

(6) Line 275, the authors should define what strain of Omicron is in Figure 4. The authors should also explain that the strains in Figure 4A are ordered by evolutionary age.

(7) Lines 286-287, isn't this conclusion already made from the cell-based flow cytometry binding assay? This sentence could be deleted.

(8) In both Figures S10 and S11, the readers could benefit from an additional row highlighting the residues interacting with ACE2.

(9) Lines 298-301, based on Figure S11, no contact is made between the N2 loop and the Fab. The authors may elaborate on why the mutations observed in the N2 loop indirectly influenced Fab recognition.

(10) Lines 321-323, even though this is a well-established assay, it is probably better to clearly explain that one pool of cells expresses the spike and the other pool of cells expresses ACE2.

Reviewer #3 (Public review):

In this study, the authors aim to establish a calibrated framework for identifying RNA chemical marks from direct RNA sequencing data using a modification-aware basecalling workflow, with a particular focus on N6-methyladenosine. By combining native RNA sequencing with an unmodified control transcriptome, enzyme inhibition, comparison across multiple software versions, and orthogonal validation using an independent mapping approach, the authors seek to define a best-practice pipeline for reducing false-positive calls and improving confidence in quantitative interpretation across cell types.

A major strength of the work is the rigor of the benchmarking strategy. In particular, the inclusion of an unmodified control transcriptome is both important and useful, and the study provides compelling evidence that this control remains necessary for robust interpretation, despite being omitted in many current workflows. The comparison across software versions and the matched analysis with an independent sequencing-based approach also substantially strengthen the evidence presented. The work therefore makes a valuable contribution to the community by offering a more stringent analytical framework that will likely be broadly useful to groups applying native RNA sequencing to study RNA chemical marks.

The evidence supporting the main conclusions is solid overall. The authors convincingly show that stringent filtering substantially reduces false-positive calls and improves agreement with orthogonal approaches, particularly at highly modified sites. The observation that many sites are conserved across cell types, while showing differences in relative modification levels, is also supported by the presented analyses.

At the same time, several conceptual issues limit the strength of some downstream interpretations. Most importantly, the manuscript repeatedly refers to the reported values as "stoichiometry," whereas the underlying software output is more appropriately interpreted as a statistical estimate of the proportion of aligned reads classified as modified. This distinction is important because the conclusions regarding cell-type differences rely on quantitative comparisons of these values. In addition, the current calling framework depends on successful canonical base assignment before modification calling, which raises an important limitation: sites with the strongest signal deviations may be underrepresented if they are more likely to be miscalled during basecalling. This issue may be especially relevant for RNA marks that induce stronger mismatch signatures than N6-methyladenosine and should be more explicitly discussed.

Overall, the authors largely achieve their primary aim of establishing a more rigorous and broadly applicable analytical framework for direct RNA sequencing-based modification detection. The work is likely to have a meaningful impact on the field, particularly by reinforcing the importance of appropriate negative controls and benchmarking standards. With clearer framing of the quantitative outputs and explicit discussion of current software limitations, this study will serve as a highly useful resource for the community.

Reviewer #3 (Public review):

Zhang et al. investigated how germline tumors influence the development of neighboring wild-type (WT) germline stem cells (GSC) in the Drosophila ovary. They report that germline tumors generated by differentiation-arrested mutations (bam and bgcn) inhibit the differentiation of neighboring WT GSCs by arresting them in an undifferentiated state, resulting from reduced expression of the differentiation-promoting factor Bam. They find that these tumor cells produce low levels of the niche-associated signaling molecules Dpp and Gbb, which suppress bam expression and consequently inhibit the differentiation of neighboring WT GSCs non-cell-autonomously. Based on these findings, the authors propose that germline tumors mimic the niche to suppress the differentiation of the neighboring wild-type germline stem cells.

Strengths:

The study uses a well-established in vivo model to addresses an important biological question concerning the interaction between germline tumor cells and wild-type (WT) germline stem cells in the Drosophila ovary. If the findings are substantiated, this study could provide valuable insights that are applicable to other stem cell systems.

Weaknesses:

The authors have addressed some of my concerns in the revised submission. However, the data presented do not allow the authors to distinguish whether the failed differentiation of WT stem cells/germline cells results from "arrested differentiation due to the loss of the differentiation niche" or from "direct inhibition by tumor-derived expression of niche-associated molecules Dpp and Gbb". The critical supporting data, HCR in situ results, are not sufficiently convincing.

Reviewer #3 (Public review):

Summary:

The authors employed the BindCraft platform to develop binders targeting the chemokine CCL25, a selective activator of the chemokine receptor CCR9. They successfully generated two classes of binders: one that inhibits all CCL25-mediated CCR9 activation, and another that permits CCR9 G protein signaling while simultaneously preventing arrestin recruitment. These tools will enable the dissection of arrestin involvement in regulating cell migration.

My comments, in the order of reading:

(1) Title: I strongly recommend removing the term "biasing" from the title. In this context, it does not convey a specific mechanistic concept. The term "biased signaling" has been used for a very broad range of mechanistically distinct pharmacological phenomena, and without a precise definition, it adds more confusion than clarity. I therefore suggest refraining from using it in the title.

(2) Abstract, line 34: The term "bias" should be replaced. As currently used, it appears to suggest a dichotomy between G protein signaling and arrestin recruitment. However, arrestin recruitment is a consequence of G protein signaling, and it is not conceptually sound to compare a signaling event mediated by one protein family with a protein-protein interaction involving another protein family. A meaningful comparison requires experimental paradigms that differ by a single variable; in this case, there are two - distinct protein families and fundamentally different types of readouts (signaling versus protein-protein interaction).

(3) Abstract, line 34: The term "balanced agonist" should be removed. Any chosen reference ligand is, by definition, the "balanced" agonist for that analysis, regardless of its actual signaling profile. Consequently, the expression "balanced agonist" adds no mechanistic information beyond "the agonist used as reference in a particular bias calculation" and is potentially misleading, as it implies that this ligand possesses a uniquely unbiased, system‑independent signaling profile, which is not the case.

(4) Abstract, line 36: I also recommend removing the term "bias" at this point. The concept of bias typically arises from experiments that quantitatively compare more than one variable. As currently written, the phrasing suggests a dichotomy between G protein- and arrestin-mediated signaling, yet the study does not assess arrestin signaling, only arrestin recruitment. Under these conditions, the use of "bias" is not appropriate. The data are clear and compelling on their own without the need for this potentially misleading terminology.

(5) Introduction: This is interesting to read and generally well written, though certain statements would benefit from improved semantic precision. For example, in lines 110-111, the phrase "G protein-biased complex" should be reconsidered, as it relies on the notion of G protein- versus arrestin-mediated signaling. Arrestins themselves do not signal; what is measured here is their recruitment. Comparing G protein signaling with arrestin recruitment is therefore conceptually unsound, since arrestin engagement is a downstream consequence of G protein activation. Comparisons become meaningful only when designed to differentiate between G protein-dependent and G protein-independent arrestin recruitment, which is not the case in this study.

(6) Results, 122,123: The authors should consider being more precise; possibly, the truncated CCL25 is somewhat less potent on CCR9. The authors should make a statistical test and then decide whether to rephrase or not for enhanced precision.

(7) Figure S5: This figure is currently confusing and needs clarification. The authors state in the main text that CXCR4 is stimulated with CXCL12, yet the figure legend refers to CCL25; this discrepancy should be corrected to ensure consistency. In addition, inhibition of CXCR4 by the miniprotein binders should be analyzed and presented with normalization to CXCR4 responses, not to CCL25-stimulated CCR9. To avoid misinterpretation, inhibition by the miniproteins should be quantified separately for CCR9 and CXCR4, each normalized to its own receptor-specific and functionally equivalent stimulation condition, rather than to the "other" receptor.

(8) Results, lines 211-213: The authors should be more semantically precise. They state that no binder has any effect on arrestin recruitment to CXCR4. If I see the data, this is not really true, as 25101 and 25107 inhibit arrestin recruitment by about 50 % or more at the highest applied concentrations; only 111 and 112 are completely inactive. As already commented, normalization should be done to arrestin recruitment of CXCR4 and not CCR9.

The Repair Shop, Season 4 Episode 21<br /> Ventriloquist's Dummy & Running Trophy <br /> and a Corona 3 typewriter

There are some hints about how to recreate the gold leaf lettering on vintage typewriters here.

Reviewer #3 (Public review):

Summary

GPR30 responds to bicarbonate and plays a role in regulating cellular pH and ion homeostasis. However, the molecular basis of bicarbonate recognition by GPR30 remains unresolved. This study reports the cryo-EM structure of GPR30 bound to a chimeric mini-Gq in the presence of bicarbonate, revealing mechanistic insights into its G-protein coupling. Nonetheless, the study does not identify the bicarbonate-binding site within GPR30.

Strengths

The work provides strong structural evidence clarifying how GPR30 engages and couples with Gq.

Weaknesses

Several GPR30 mutants exhibited diminished responses to bicarbonate, but their expression levels were also reduced. As a result, the mechanism by which GPR30 recognizes bicarbonate remains uncertain.

"I would have everyone have a typewriter so they could feel the spirit of this"<br /> by [[Dippold Ádám]], [[Kun Zsuzsi]] for Qubit<br /> accessed on 2026-05-02T20:27:58

Reviewer #3 (Public review):

Summary:

This manuscript describes a study into the functional organization of the forceps major (FMA). The authors present a Bayesian population receptive field (pRF) analysis of group-averaged HCP fMRI retinotopic mapping data, focusing on voxels within the FMA. This is unconventional because pRF modelling is usually limited to gray matter voxels, where synaptic activity underlying neural computation is the highest. Nevertheless, some previous work suggests that meaningful fMRI signals can also be gleaned from white matter voxels, where the signals are thought to reflect metabolic activity from action potentials that travel along axons. However, these signals are generally much noisier, and possible confounding effects due to partial voluming, draining veins, and different hemodynamics must be carefully ruled out. Based on the Bayesian pRF analysis, the authors claim evidence of segregated contralateral and ipsilateral representations of the visual field in the FMA. Anatomical tract tracing based on HCP diffusion MRI data from seeds identified using the pRF analysis further suggests that these representations are underpinned by separate fiber bundles, which also appear to be consistent with the results of viral tracing in mice. The results of this study could mean an important step forward in understanding transcallosal signaling.

Strengths:

The study treads uncharted territory, leveraging multiple data modalities across species and advanced analytical approaches.

Weaknesses:

The study does not address potential confounds related to BOLD imaging in white matter structures. If the fMRI results can be explained based on neighboring grey matter responses, the evidence that remains is limited to an apparent anatomical segregation of white matter bundles that appear to be present in both mice and humans.

Further details are also missing regarding the Bayesian pRF approach, including the priors used for the pRF model. These are important as they will dominate the estimates when the data are very noisy, and the authors have adopted unconventional, more complex pRF models compared with earlier work employing Bayesian pRF analyses.

It appears that the authors have not applied any statistical thresholding to ensure that only good-quality model fits are entered into subsequent analyses (i.e., the reported probabilities pertain to model comparisons, not goodness of fit). From Figure 2, it appears that the majority of the FMA voxels, barring those adjacent to visual gray matter, do not exhibit more than a few percentage points of explained variance (EV). In fact, a common threshold is >15% EV, but it looks like none of the FMA voxels exceed this threshold.

Reviewer #3 (Public review):

Summary

This study aimed to investigate whether the development of functional connectivity (FC) is modulated by early physical growth, and whether these might impact cognitive development in childhood. This question was investigated by studying a large group of infants (N=204) assessed in Gambia with fNIRS at 5 visits between 5 and 24 months of age. Given the complexity of data acquisition at these ages and following data processing, data could be analyzed for 53 to 97 infants per age group. FC was analyzed considering 6 ensembles of brain regions and thus 21 types of connections. Results suggested that: i) compared to previously studied groups, this group of Gambian infants have different FC trajectory, in particular with a change in frontal inter-hemispheric FC with age from positive to null values; ii) early physical growth, measured through weight-for-length z-scores from birth onwards, is associated with FC at 24 months. Some relationships were further observed between FC during the first two years and cognitive flexibility, in different ways between 4- and 5-year-old preschoolers, but results did not survive corrections for multiple comparisons.

Strengths

The question investigated in this article is important for understanding the role of early growth and undernutrition on brain and behavioral development in infants and children. The longitudinal approach considered is highly relevant to investigate neurodevelopmental trajectories. Furthermore, this study targets a little studied population from a low-/middle-income country, which was made possible by the use of fNIRS outside the lab environment. The collected dataset is thus impressive and it opens up a wide range of analytical possibilities.

Weaknesses

Data analyses were constrained by the limited number of children with longitudinal data on NIRS functional connectivity. Applying advanced statistical modeling approaches such as structural equation modelling would provide further insights on neurodevelopmental trajectories and relationships with early growth and later cognitive development.

Reviewer #3 (Public review):

Summary:

The manuscript by Zhang et al. demonstrates that MORC2 undergoes liquid-liquid phase separation (LLPS) to form nuclear condensates critical for transcriptional repression. Using a combination of in vitro LLPS assays, cellular studies, NMR spectroscopy, and crystallography, the authors show that a dimeric scaffold formed by CC3 drives phase separation, while multivalent interactions between an intrinsically disordered region (IDR) and a newly defined IDR-binding domain (IBD) further promote condensate formation. Notably, LLPS enhances MORC2 ATPase activity in a DNA-dependent manner and contributes to transcriptional regulation, establishing a functional link between phase separation, DNA binding, and transcriptional control.

Strengths:

The manuscript is well organized and logically structured. It provides valuable mechanistic insights into MORC2 function, and the majority of the conclusions are well supported by the data presented.

Reviewer #3 (Public review):

Summary:

This study examines how ingestive-related orofacial movements relate to ensemble dynamics in gustatory cortex (GC) during taste processing. Previous work has shown that GC activity evolves through a sequence of population states following taste delivery, culminating in a transition to palatability-related firing that precedes rejection-related orofacial movements (e.g., gaping). Importantly, perturbing GC activity around the time of this transition alters the timing of gaping, suggesting that these ensemble dynamics play a functional role in linking taste evaluation to behavioral responses. The present study asks whether similar neural dynamics are also associated with ingestive-related orofacial movements that occur during the consumption of palatable stimuli.

To address this question, the authors develop a machine-learning classifier to identify distinct orofacial movements from anterior digastric EMG recordings. Using a set of labeled EMG waveforms obtained from video-scored trials, a gradient-boosted (XGBoost) classifier is trained to detect gapes, mouth/tongue movements (MTMs), and periods of no movement. Applying this classifier to a larger EMG dataset reveals that ingestive-related MTMs cluster into three distinct movement subtypes whose frequencies change systematically within trials.

The authors then relate these behavioral dynamics to previously described GC ensemble transitions identified using changepoint modeling. They report that changes in MTM subtype frequencies tend to occur shortly after the transition to palatability-related activity in GC. These results suggest that GC population dynamics are temporally associated not only with rejection-related behaviors but also with ingestive motor patterns that occur as animals prepare to consume palatable tastants.

Strengths:

The study introduces an innovative framework for extracting intricate orofacial movement information from EMG recordings. The machine-learning classifier provides a scalable method for identifying specific orofacial movements and performs better than previously published algorithms designed for gape detection. This approach allows the authors to examine movement microstructure at a temporal resolution that cannot be achieved with video scoring in freely moving animals.

A second strength is the integration of orofacial movement analysis with neural population dynamics. By relating EMG-derived movement subtypes to ensemble state transitions in GC, the study builds on a substantial body of work examining the temporal evolution of taste responses in cortex. The finding that ingestive-related movement dynamics occur shortly after the emergence of palatability-related firing provides an interesting extension of previous observations linking GC state transitions to rejection behavior.

The manuscript is also commendable in its commitment to data accessibility. By providing clear information about how the datasets can be accessed and making training data for the classifier publicly available, the authors make it possible for other researchers to examine the analytical pipeline and apply similar approaches to their own datasets. This transparency provides a useful framework for extending and building upon the methods presented here.

Weaknesses:

Some aspects of the EMG-based movement classification pipeline warrant careful interpretation. The training dataset used for classifier development is relatively small and is derived from a subset of trials in which mouth movements were clearly visible in video recordings. While the classifier performs well on this labeled dataset, it is not entirely clear how representative these labeled examples are of the full range of EMG signals present in the larger dataset.

The interpretation of the three identified MTM subtypes also remains somewhat tentative. The study convincingly demonstrates that distinct waveform-defined clusters exist in the EMG data, but the functional significance of these clusters as ingestive "behaviors" is less clear. As acknowledged by the authors, the specific roles of these movement patterns in the ingestion process remain speculative.

Finally, several conclusions in the Discussion rely on relatively strong mechanistic language when describing the relationship between GC dynamics and ingestive behavior. The data clearly demonstrate a temporal association between GC state transitions and changes in the frequencies of the different MTM subtypes. However, the results primarily support the interpretation that similar cortical dynamics are associated with ingestive and rejection-related behaviors rather than definitively establishing that these behaviors "are governed by the same underlying neural mechanisms".

Reviewer #3 (Public review):

Summary:

In this paper by Jones and colleagues, a non-human primate model is described in which wild-type TDP-43 is expressed in the cervical spinal cord. This gave rise to loss of motor neurons in the ventral horn at that level in the cervical spinal cord. MRI of the muscles allowed to see increased intensity in the mostly affected brachioradialis muscle, suggesting this muscle becomes denervated. At the neuropathological level, TDP-43 and pTDP-43 staining in the cytoplasm is increased, not only at the specific level of the cervical spinal cord, but also at a distance.

Strengths:

A clear strength is the state-of-the art focal expression of the TDP-43 transgene at a focal site in the cervical spinal cord. This is achieved by combining a general expression of a flipped loxP flanked TDP-43 vector using AAV9 intrathecal administration, followed by an intramuscular AAV2 hSyn CRE-TdTomato vector in the brachioradialis muscle in order to induce focal recombination and expression of TDP-43 in motor neurons innervating this muscle on one side.

Another strength is the non-human primate background, which is much closer to the human situation.

Weaknesses:

Given the complexity and cost of the model, the n is very low.

The design of the experiments and the results shown about the toxicity induced by this focal TDP-43 expression do not allow us to conclude that it is a good model for ALS for several reasons. It is not clear that the TDP-43 overexpression results in spreading weakness or in spreading motor neuron loss. The neuropathological changes described suggest that there is a kind of stress response, which extends to regions away from the site of primary damage, but more is needed to provide convincing evidence that there is spreading of disease pathology reminiscent of human ALS.

https://www.facebook.com/groups/705152958470148/posts/1255009756817796/

Reviewer #3 (Public review):

Summary:

This study sought to explore how neural representations during encoding change with expertise or proficiency in the method of loci (MoL). To do this, the authors compared three groups: memory athletes (experts in MoL), naive controls, and naive participants before and after 6 weeks of MoL training and analyzed how similar their encoding-related activity patterns were across groups and training. They found that in lateral prefrontal, inferior temporal, and posterior parietal regions, pattern similarity decreased with expertise and training. They also found that changes in similarity between pre- and post-training were associated with improvements in memory performance measured 4 months later. Additionally, in a follow-up exploratory analysis on the temporal order recognition task, neural patterns were more similar for those proficient in MoL - a contrast to the decrease seen at encoding. Taken together, the authors interpret these findings as evidence that proficiency with the method of loci is associated with distinct encoding representations: Broadly, the findings suggest that greater representational differentiation at encoding may be associated with better memory.

Strengths:

(1) The manuscript is impressively rich with analyses. Their general claim that neural differentiation increases between individuals with MoL experience is thus addressed in this work. Specifically, the authors effectively explore different levels of granularity to tackle the question of whether a participant's neural representation (with MoL experience) looks more similar to that of another (with less experience) during encoding.

(2) The authors connect their hypotheses about neural representational differences caused by training to behavioral data (and 4 months later at that).

(3) Although exploratory, they not only look at encoding-related differences, but also retrieval-related differences.

(4) The authors provide many supplementary figures with complementary and interesting findings. As I read, I found myself curious about exploratory analyses, which were then addressed in supplementary figures.

Weaknesses:

(1) The manuscript is impressively rich, but the number of analyses and levels of comparison (and how they are presented) made it difficult to follow. The paper would benefit from an anticipatory introductory paragraph (or an introductory Results paragraph) that explicitly states the hypotheses and which sections of the results addressed them. Additionally, given how this is a Methods-last formatted paper, the manuscript would benefit from a few introductory sentences at each Results section describing the methodology.

(2) One of the motivations needs strengthening. Given the introduction, the manuscript seems to be motivated by two complementary questions: (i) whether neural differentiation effects reported with short-term MoL training (as done in Liu et al., 2022) extend with longer-term training and expertise and (ii) whether training might lead individuals towards a canonical "expert" representation that can only be acquired through training as has been previously shown in other work (e.g., Meshulam et al., 2021).

The first motivation is clear and compelling. The second one, however, does not feel as well grounded. In studies like Meshulam et al., alignment is expected because participants are exposed to the same stimulus or concept. In contrast, as the authors note, a user of the method of loci is encouraged to create unique, vivid representations of their loci and to-be-remembered items - here, neural alignment is at odds with the premise of the technique. As such, the described tension between increased pattern similarity across the studies cited in the second paragraph of the introduction and individuals proficient with MoL feels underdeveloped (despite the reference-rich second paragraph).

The authors would benefit from articulating why the counterfactual of "increased neural alignment" might be expected, specifically, in the method of loci. In other words, why should we expect trainees to become more similar to experts when the strategy itself promotes idiosyncratic representations? Perhaps, the authors could distinguish between alignment at the level of knowledge representations vs the process of encoding (e.g., the act of placing items into loci).

(3) Relatedly, terminology referencing the employed methodology is a bit unclear. In some of the papers cited that look at pattern similarity across people (like Meshulam et al., or Koch et al.), the spatial patterns of individuals are compared with 'template' patterns that reflect the canonical representation of a concept or episode. However, the manuscript does not include this type of template-based comparison. This is understandable because there may not be a representative canonical pattern when each participant has their own idiosyncratic palace. In this case, a pairwise comparison may be more fitting as it focuses on the distances between people's representations instead of the distances between them and a group template. Although both comparisons (pairwise and template-based similarities) are related, they have different interpretations. A clearer justification for why pairwise similarity, instead of template-based similarity (as in many of the cited papers), is the more appropriate metric in this paradigm early on would add to the clarity of the work. Additionally, this slight difference in methodology was confusing because some portions of the text (including the figures) say "group average", but in others, we see "pairwise".

Minor Comments:

A recent paper (Masis-Obando et al., 2026, Nat Hum Behav) shows that stable and distinctive spatial representations can support later reinstatement of items placed within those contexts. Their conclusions seem to support your hypotheses and results here. In parallel, prior work (like Robin et al., 2018, J Neurosci) emphasizes the importance of spatial contexts for the representation of events. Given how MoL encoding relies on vivid context-item binding, including these perspectives in the Introduction (and/or discussion) may help frame the current findings within the broader memory literature.

Overall, this work provides rich and valuable contributions to the field.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

The authors could greatly improve the manuscript by focusing on two issues.

(1) Strengthening their cis/trans analysis, including:<br /> a) only showing or analyzing genomic regions that pass FDR correction;<br /> b) clarifying how cis genes are defined (Figure 2B shows some genes labeled as cis where the direction-of-effect differs between hybrid and parent cells);<br /> c) assessing how well powered they are to perform each analysis.

(2) Softening claims about human evolution or human specificity for several reasons:<br /> a) Their comparison lacks tetraploid controls (e.g. human-human tetraploids and chimp-chimp tetraploids) or experimental follow-up in diploid cells, making it hard to be certain that observed effects are not due to ploidy.<br /> b) There are no outgroup species included in the analysis.<br /> c) The use of no or very loose FDR corrections with the sign test makes it difficult to draw conclusions.<br /> d) Experimental data to link SNVs to changes in cis methylation or identified transcription factors to changes in trans methylation would be needed to validate the authors' predictions.

Reviewer #3 (Public review):

Summary:

The authors describe a new structural biology framework termed "in extracto cryo-EM," which aims to bridge the gap between single-particle cryo-EM of purified complexes and in situ cryo-electron tomography (cryo-ET). By utilizing high-resolution 2D template matching (2DTM) on mammalian cell lysates, the authors sought to visualize the translational apparatus in a near-native environment while maintaining near-atomic resolution. The study identifies elongation factor 2 (eEF2) as a major hibernation factor bound to both 60S and 80S particles and describes a variety of hibernation scenarios involving factors such as SERBP1, LARP1, and CCDC124.

Strengths:

(1）The use of 2DTM effectively overcomes the signal-to-noise challenges posed by the dense and viscous nature of cellular extracts, yielding maps as high as 2.2 Å.<br /> (2）The discovery of eEF2-GDP as a ubiquitous shield for ribosomal functional centers, particularly its unexpected stabilization on the 60S subunit, provides a compelling model for ribosome preservation during stress.

Weaknesses:

(1) Representative nature of cell samples and lower detection limit

The cells used in this study (MCF-7, BSC-1, and RRL) are either fast-growing cancer cell lines or specialized protein-synthetic systems. For cells with naturally low ribosomal abundance (such as quiescent primary cells), achieving the target concentration (e.g., A260 > 1000 ng/uL) would require an exponentially larger starting cell population.

Is there a defined lower limit of ribosomal concentration in the raw lysate below which the 2DTM algorithm fails to yield high-resolution classes? In ribosome-sparse lysates, A260 becomes an unreliable proxy for ribosome density due to the high background of other RNA species and proteins. How do the authors estimate specific ribosome abundance in such heterogeneous fields?

(2) Quantitation in heterogeneous lysates and crowding effects

The authors utilize A260 as a key quality control measure before grid preparation. However, if extreme physical concentration is required to see enough particles, the background concentration of other cytoplasmic components also increases. This may lead to molecular crowding or sample viscosity that interferes with the formation of optimal thin ice. How do the authors calculate or estimate the specific abundance of ribosomes in the cryo-EM field of view when they represent a much smaller percentage of the total cellular content?

(3) Optimization of sample preparation

The authors describe lysates as dense and viscous, requiring multiple blotting steps (2-3 times) for 3-8 seconds. Have the authors tested whether a larger molecular weight cutoff (e.g., 100 kDa) during concentration could improve the ribosome-to-background ratio without losing small factors like eIF5A (approx. 17 kDa)? Could repeated blotting of a concentrated, viscous lysate introduce shearing forces or increased exposure to the air-water interface that perturbs the native conformation of the complexes?

(4) The regulatory switch and mechanism of eEF2

The finding that eEF2-GDP occupies dormant ribosomes is striking. What drives eEF2 from its canonical role in translocation to this hibernation state? Is this transition purely driven by stoichiometry (lack of mRNA/tRNA) and the GDP/GTP ratio, or is there a role for post-translational modifications? How do these eEF2-bound dormant ribosomes rapidly re-enter the translation pool upon stress relief?

(5) Hibernation diversity and LARP1 contextualization

The study reveals that hibernation strategies vary across cell types. Does the high hibernation rate in RRL reflect a physiological state, or does it hint at "preparation-induced stress" due to resource exhaustion or mRNA degradation in the cell-free system? How do the authors reconcile their discovery of LARP1 on 80S particles with recent 2024 reports that primarily describe LARP1 as an SSU-bound repressor?

Comments on revisions:

The authors have addressed the issues I had raised in my initial review. The additional data and clarifications provided in the revision are satisfactory. I have no further recommendations.<br /> Thanks to the authors for their efforts.

Reviewer #3 (Public review):

Summary:

Using realistic mathematical models, Cebrián-Lacasa et al. address the relationship between waves of activation of Cyclin B-Cdk1 that propagate through the cytoplasm of large (~1 mm) oocytes and fertilized eggs and surface contraction waves (SCWs) driven by Rho GTPase activity in the cell cortex. They present numerical simulations of the underlying reaction-diffusion equations that account in broad strokes for both the expected behavior of 'fronts' of Cdk1 activation (that propagate at constant velocity from the nucleus-the source of Cdk1 activity-to the cell cortex) and the unusual behavior of 'backs' of Cdk1 inactivation (that may propagate either away from or towards the nucleus, or exhibit simultaneous inactivation throughout the cytoplasm). They also model Rho GTPase activity in the cortex as an excitable system that propagates SCWs (target patterns, spiral waves, and more complicated patterns). When Cdk1 is activated in the cortex, it phosphorylates and inhibits the RhoGEF, Ect1, which suppresses SCWs by reducing Rho GTPase activity. As the wave-back of Cdk1 inactivation moves across the cortex, Rho GTPase activity recovers abruptly, and SCWs reappear as 'phase waves' whose speed and directionality are determined by the underlying cytoplasmic Cdk1 signal.

Strengths:

As a theoretical examination of an interesting and puzzling aspect of early embryonic development, this study shares the same strengths and weaknesses as all mathematical and computational approaches to molecular cell biology. The mathematical models are precise formulations of the underlying assumptions of the authors (which are quite reasonable in this reviewer's opinion), and the analysis and computational results are dependable consequences of the molecular mechanisms the authors have in mind. The model is expertly analyzed, and the results are both reliable and intriguing. The results are discussed in light of experimental evidence. Because the authors' methods and results suggest novel-and sometimes counterintuitive-avenues for experimental research, this paper is likely to have a significant impact on the field of Rho GTPase signaling in oocytes and early embryos, and perhaps in other cells as well.

Weaknesses:

Like all mathematical models, the underlying assumptions can be critiqued as neglecting this -or-that 'crucial' effect (e.g., mechanical coupling via cortical tension or cytoplasmic flow, as the authors acknowledge), and the highly technical methods of analysis and simulation can be unfamiliar and off-putting to experimental cell biologists. The paper is a difficult read, even for an experienced theoretician. For those who take the time to understand this paper, it may change the way they think about the coupling of cell cycle control (Cdk1 activation and inactivation) and cell surface contraction waves.

Reviewer #3 (Public review):

Summary:

This paper addresses the controversial internal relationships within the Spiralia, a major clade of invertebrate animals including molluscs, annelids, brachiopods and flatworms.

Strengths:

Performs a range of empirical analyses and simulations that address the core question. Although a favoured unrooted topology finds some support, this is not strongly endorsed in the paper.

Weaknesses:

(1) Only considers a subset of relevant phyla (e.g. gastrotrichs are relevant to the phylogenetic position of Platyhelminthes), although how this would change the scale of the analyses (i.e. number of topologies) is addressed in the paper.

(2) Discussion of Spiralia evolution and broader context, particularly the relevance for the fossil record. Line 448: our current understanding of the early spiralian fossil record is quite consistent with the main results of this paper. For example, there are very few claims for fossils that sit on the short branch leading to Spiralia (or Lophotrochozoa as defined here) that this paper discusses. Many of the key fossils that inform on the characters discussed in the introduction, which have unusual character combinations, have an apomorphy of one of the phyla discussed, and so are resolved as members of the stem lineages of particular phyla.

(3) This is what you would expect with long phylum stem lineages (line 148) and a short spiralia stem lineage. For example, the mollusc Wiwaxia has chaetae, but a mollusc like Radula (Smith 2012), the conchiferan mollusc Pelagiella has chaetae and a coiled shell (Thomas et al. 2020). The only fossil groups that are routinely discussed as belonging to the stem lineage of more than one phylum are the tommotiids, which have chaetae, segmentation and a complex mineralised skeleton (but not shells in the brachiopod/mollusc sense, see Guo et al 2023) but they sit on the lophophorate stem lineage, a synapomorphy rich group the monophyly of which the present paper endorses (e.g. line 435). The fossil record is consistent with the scenario presented in line 442, e.g. convergent loss or reduction of chaetae and segmentation and convergent evolution of shells in molluscs and brachiopods.

https://www.facebook.com/groups/1794856020751839/posts/4430134563890625

Saving for the commentary on the Brooksaw Antiques platen recovery using 3-D printing.

https://www.ebay.com/itm/318200812265

Reviewer #3 (Public review):

Most of the data are based on measurements of the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) measured by the Seahorse analyser in control and ATP5l KO cells. However, these measurements are conducted by a single injection of a biguanide, followed over time and presented as fold change. By doing so, the individual information of the effect to of metformin and derivate on control and KO cells are lost. In addition, the usual measurement of OCR is coupled with certain inhibitors and uncouplers, such as oligomycin, FCCP and Antimycin A/rotenone, to understand the contribution of individual complexes to the respiration. Since biguanides and ATP5l KO affect protein levels of components of complex I and IV, it would be informative to measure their individual contributions/effects in the Seahorse. To further strengthen the data, it would be helpful to obtain measurements of actual ATP levels in these cells, as this would explain the activation of AMPK.

The authors report on alterations in mitochondrial morphology upon ATP5l KO, which is measured by subjective quantifications of filamentous versus puncta structures. Fiji offers great tools to quantify the mitochondrial network unbiased and with more accuracy using deconvolution and skeletonization of the mitochondria, providing the opportunity to measure length, shape and number quantitatively. This will help to understand better, whether mitochondria are really fragmented upon ATP5l KO and rescued by its re-introduction.

Finally, the authors report in the last part of the paper a genetic CRISPR/Cas9 KO screen in NALM-6 cells cultured with high amounts of metformin to identify potential new mediators of metformin action. It is difficult to connect that to the rest of the paper, because a) different concentrations of metformin are used and b) the metabolic effects on energy consumption are not defined. They argue about molecular function of the obtained hits based on literature, and on comparison the pattern of genetic alterations based on treatments with known inhibitors such as oligomycin and rotenone. However, a direct connection is not provided, thus the interpretation at the end of the results that "the OMA1-DEL1-HRI pathway mediates the antiproliferative activity of both biguanides and the F1ATPase inhibitor oligomycin" while increasing glycolysis, needs to be tuned down. This is an interesting observation, but no causality is provided. In general, this part stands alone and needs to be better connected to the rest of the paper.

Comments on revisions:

Thanks to the authors for addressing the concerns raised during the review of the original manuscript. The data now include proper measurements of OCR and quantifications of the mitochondria network. The screening data is better connected to the rest of the paper and provide compelling evidence for mitochondria and in particular the ATP synthase as potential targets of metformin.

DOI: 10.1016/j.celrep.2026.117295

Resource: RRID:ZFIN_ZDB-ALT-200611-3

Curator: @nmaralla

SciCrunch record: RRID:ZFIN_ZDB-ALT-200611-3

What is this?

80% sind bei corona mitgelaufen<br /> 80% werden beim dritten weltkrieg mitlaufen<br /> menschliche dummheit ist eine naturkonstante

all wars are bankers wars

der krieg soll nicht gewonnen werden...<br /> das einzige ziel ist: weisse männer gehen in den fleischwolf

Reviewer #3 (Public review):

Summary:

The manuscript "Unreliable homeostatic action potential broadening in cultured dissociated neurons" by Ritzau-Jost et al. investigates action potential (AP) broadening as a mechanism underlying homeostatic synaptic plasticity. Given the existing variability in the literature concerning AP broadening, the authors address an important and timely research question of considerable interest to the field.

The study systematically demonstrates cell-type- and model-specific AP broadening in hippocampal neurons after chronic treatment with either tetrodotoxin (TTX) or glutamatergic transmission blockers. The findings indicate AP broadening in CA3 pyramidal neurons in organotypic cultures after TTX treatment, but notably not in dissociated hippocampal neurons under identical conditions. However, blocking glutamatergic neurotransmission caused AP broadening in dissociated hippocampal neurons. Moreover, extensive evaluations in neocortical dissociated cultures robustly challenge previous findings by revealing a lack of AP broadening following TTX treatment. Additionally, the proposed role of BK-type potassium channels in mediating AP broadening is convincingly questioned through complementary electrophysiological and voltage-imaging experiments.

Strengths:

The manuscript exhibits an outstanding experimental design, employing state-of-the-art techniques and a rigorous multi-lab validation approach that greatly enhances scientific reliability. The experimental results are meticulously illustrated, and the conclusions drawn are justified and supported by the presented data. Furthermore, the manuscript is comprehensively and clearly written.

Reviewer #3 (Public review):

Summary:

This work describes how two chemosensory neurons in C. elegans drive opposite behaviors in response to a volatile cue. Because they have different concentration dependencies, this leads to different behavioral responses (attraction at low concentration and repulsion at high concentration). It has been known that many odorants that are attractive at low concentrations are aversive at high concentrations, and the implicated neurons (at least AWC for attraction and ASH for repulsion) have been well established. None the less, by studying behavior and neural responses in a common context (odor pulses, as opposed to gradients) this provides a clear picture of how these sensory neurons may guide the dose dependent response by separately modulating odor entry and odor exit behaviors.

Strengths:

(1) This work provides good evidence that worms are attracted to low concentrations and repelled by high concentrations of 1-oct. Calcium imaging also makes it clear that dose-dependence of this response is stronger for ASH than AWC.

(2) This work presents calcium imaging and behavior with the same stimulus (sudden pulses in volatile odor concentration), while previous studies often focus on using neuronal responses to pulses to understand navigation of gentle gradients.

Weaknesses:

(1) As a whole it is not clear precisely how important AWC is (compared to other cells) for the attractive response (as the authors correctly acknowledge).

(2) The evidence that AIB minus AVA contains relevant information is weak. It appears the entrainment index in Fig. 6H for AIB-AVA could easily be explained by the negative entrainment between AVA and the stimulus (along with no effect or role for AIB). This is suggested by the similar p-values and similar distribution of random EIs (stretched and mirrored) between the first and last rows of this figure.

(3) The model in Figure 7 would be strengthened if it was demonstrated that IAA is attractive when worms are saturated in a 1/10^4 concentration. Panel 7G (and ref. 39) indicate that 10^-4 IAA activates ASH, which would suggest a different explanation for the change from attraction to repulsion in 7C.

Reviewer #3 (Public review):

I first want to state that I am not an expert in the field, making it hard for me to provide informed comments on the value of the scientific results. But from where I stand, the study seems very carefully designed, very well controlled, and the statistical methodology used across the manuscript is strong and sound.

Summary:

The authors investigated the role of PFC interneurons in cue-guided behaviour under threat. They designed a behavioural task with increasing levels of difficulty that allows them not only to correlate the activation of cortical interneurons with different parameters of the tasks, but also to assess if this correlation changes with increasing cognitive load. They carefully take into account confounding factors such as movement and show that indeed neuronal activity is strongly driven by movement. Using generalised linear models throughout their manuscript, the authors could include movement as a confounding factor in their statistical analysis, thus allowing them to next correlate interneuron activity with task-specific parameters. Using first fibre photometry to image bulk activity of the interneurons and by comparing the responses in the PFC and in the visual cortex, they identify that PFC neurons show stronger activation related to punishment compared to the sensory cortex. Interestingly, under high cognitive demand, PFC interneurons show cue-specific activation, which could reflect the involvement of the PFC in cue-selective action selection.

In a second set of experiments, they use Miniscope to image individual interneurons. They classified interneurons, not based on their expression of specific markers as usually done, but based on their correlation with movement. Using this classification, they identify clusters of neurons that show activity modulation related to various behavioural parameters.

Lastly, they performed optogenetic manipulations to silence the PFC during cue-guided behaviour and showed little behavioural effect of the manipulation, which they suggest means the PFC is not involved in taking action in this task.

Strengths:

The design of the study is backed by convincing arguments from the authors. The confounding factors are carefully taken into account and integrated into state-of-the-art statistics. The results thus appear robust and reliable. The authors do not overinterpret their results; quite the contrary, they are prone to toning down the interpretation of statistically significant results and they warn the readers about potential misinterpretation or confounding factors. The discussion makes for a very interesting and informative reading.

Weaknesses:

The main weakness, in my view, lies in the Results section. In the figures, the authors do not present any raw data, and the plots are shown as mean {plus minus} SEM without displaying the distribution of individual data points. It is both a strength and a weakness that the authors do not attempt to guide the reader through the Results section and instead present the findings with very little emphasis on the key outcomes of the GLM. While this approach is arguably the most transparent way to report results, it also makes the section quite difficult to follow and may discourage readers.

I would recommend rewriting the Results section to make it more accessible to a broader audience. A similar issue applies to the figures: presenting all plots reflects a commendable commitment to transparency, but it would greatly benefit from a clearer narrative. As it stands, it is difficult to grasp the message of each figure by simply browsing through them.

Reviewer #3 (Public review):

The paper is well written and well presented. The topic is important, and its significance is explained succinctly and accurately. I am only capable of reviewing the clinical aspects of this work which is very largely technical in nature. Several clinical points are worth considering:

(1) Tendons typically display large magic angle effects as a result of their highly ordered collagen structure (cortical bone much less so) and so it would have been of interest to know what orientation the tendons had to B 0 (in vitro and in vivo). This could affect the signal level at the longer echo time and thus the signal on the subtracted images.

(2) The in vivo transverse image looks about mid-forearm where tendons are not prominent. A transverse image of the lower forearm where there is an abundance of tendons might have been preferable.

(3) The in vivo images show the interosseous membrane as high signal on both the shorter and longer TE images. The structure contains ordered collagen with fibres at different oblique angles to the radius and ulnar and thus potentially to B 0. Collagen fibres may have been at an orientation towards the magic angle and this may account for the high signal on the longer TE image, and the low signal on the subtracted image.

(4) Some of the signals attributed to muscle may be from an attachment of the muscle to aponeurosis.

(5) There is significant collagen in subcutaneous tissues so the designation "skin" may more correctly be "skin and subcutaneous tissue".

(6) Cortical bone is very heterogeneous with boundaries between hard bone and soft tissue with significant susceptibility differences between the two across a small distance. This might be another mechanism for ultrashort T 2 * tissue values in addition to the presence of collagen. The two effects might be distinguished by also including a longer TE spin echo acquisition.

Solid cortical bone may also have an ultrashort T 2 * in its own right.

(7) It may be worth noting that in disease T 2 * may be increased. As a result, the subtraction image may make abnormal tissue less obvious than normal tissue. Magic angle effects may also produce this appearance.

(8) It may be worth distinguishing fibrous connective tissue (loose or dense) which may be normal or abnormal, from fibrosis which is abnormal accumulation of fibrous connective tissue in damaged tissue. Fibrosis typically has a longer T 2 initially and decreases its T 2 * over time. In places, the context suggests that fibrous connective tissue may be more appropriate than fibrosis.

Overall, the paper appears very well constructed and describes thoughtful and important work.

Comments on revisions:

The responses to my criticisms are well thought out and are fine as far as I am concerned.

I suggest in Figure 5 line 6 changing "trabecular bone" to "trabecular bone marrow".

Reviewer #3 (Public review):

This manuscript aims to investigate cell extrinsic mechanisms that regulate the differentiation and distribution of interneuron types in the spinal cord. The authors demonstrate that the loss of motor neurons leads to changes in the number and distribution of different interneuron types, specifically V0v, V1, and V2b (but not V2a). The authors then hypothesize that this phenotype may be controlled by the action of Onecut (OC) transcription factors in motor neurons. Conditional knockout of OC1 + OC2 in motor neurons using Olig2-Cre, however, does not lead to significant changes in the numbers of V1, V2a, and V2b interneurons, although there is a change in their spatial distribution. While the authors do not check V0v neurons in OC mutants, they do check V2c, which show a reduction in number and change in distribution. Why the same neurons are not checked across experiments is unclear. The authors then analyze existing RNA-seq data to identify factors that could be mediating the effects of the OC factors in motor neurons. They identify Ntf3 as a candidate and confirm that it is upregulated in OC mutants. Conditional loss of function of Ntf3 (Olig2-Cre) leads to increases in V1, V2a, and V2b (but not V2c) interneurons and changes in the distribution of all four interneuron types. Finally, the authors demonstrate that these Ntf3 conditional mutants have major defects in motor function.

The conclusions of this manuscript are not well supported by the data for the reasons listed below, making it difficult to assess the impact of this work on the field.

(1) The manuscript relies heavily on quantifying numbers and the spatial distribution of interneuron populations. However, these do not seem to be consistent in control animals across experiments, making it difficult to interpret any changes observed in genetic manipulations. Specifically, in Figures 2 and 4, the same markers are being used to quantify V1, V2a, V2b, and V2c interneurons in controls vs. OC (Figure 2) or Ntf3 (Figure 4) conditional knockouts, but the numbers of neurons and their distribution in control animals are variable between these two figures. For example, there seems to be a mean of >300 V1 neurons in E12.5 brachial sections of Fig. 2 controls, but this number is <150 in Fig. 4 controls. The cell distribution scoring is similarly variable between these controls without any explanation. The same is true for E14.5 controls used in Figure S1 vs. Figure S3.

(2) Neurotrophic factors generally promote neuronal survival. However, in this study, the loss of Ntf3 leads to increased numbers of interneurons. This finding is in disagreement with previous observations in slice cultures of spinal cords, as stated in the discussion. This discrepancy makes it even more important that the cell counts reported in the figures discussed above are robust.

(3) The claim that phenotypes are non-cell autonomously driven by motor neurons is not well supported. In Olig2-Cre conditional knockouts of Onecut and Ntf3, there is no confirmation that the loss of these factors is specific to motor neurons. Therefore, it cannot be ruled out that other cell populations may be mediating the phenotypes.

(4) The claim that interneuron development is regulated by OC control of Ntf3 expression in motor neurons is not well supported. The authors show that loss of OC1/2 leads to an increase in Ntf3 expression in motor neurons. If this pathway were controlling interneurons, loss of OC function and overexpression of Ntf3 would have the same phenotype, which is not the case. Additionally, it would also be expected that loss of OC function and loss of Ntf3 function would have inverse phenotypes, which is also not the case. The phenotypes from OC loss of function and Ntf3 loss of function seem distinct from one another. The authors state that too little and too much Ntf3 are both bad for interneuron development, but there is no data to support their claim that OC1/2 mutants have altered interneuron development because of higher Ntf3 expression.

(5) It is not clear that interneurons being studied express the Ntf3 receptor TrkC, which makes it difficult to assess whether changes in Ntf3 signaling are directly responsible for the phenotype.

(6) While the behavioral phenotypes are consistent with Ntf3 playing a role in motor circuits, there is no evidence to suggest that Ntf3's influence on premotor interneurons being studied is driving or contributing to this phenotype, as discussed by the authors.

Reviewer #3 (Public review):

Summary:

In this submission, Wang and colleagues jointly examine the association between depression and anxiety symptoms and individuals' affective reactivity to reward prediction errors in Ruttledge et al.'s gambling paradigm. Taking a bifactor approach to anxiety and depression in several non-clinical (and one clinical sample), the authors find that anxiety-specific symptoms relate to over-reactivity of mood to reward prediction errors (RPEs) as well as heightened mood variability, while depression-specific symptoms relate to blunted mood sensitivity to RPEs. These depression- but not anxiety-specific relationships replicated in patient samples.

Strengths:

I was impressed that the data-driven, transdiagnostic approach employed by the authors uncovered specific relationships between anxiety and depression-specific factors and RPE reactivity in a well characterized task and computational model, especially in a non-clinical sample. This sheds new light on how these affective processes may be perturbed-and importantly, in different ways-by anxiety and depression symptoms. Likewise, the replication of the depression-specific finding (RPE hypo-reactivity) in a clinical sample was nice to see.

Weaknesses:

(1) While the anxiety- and depression-specific factors had differential effects on mood variability (Figure 2A-D) and RPE reactivity (Figure 2E-G) in all samples, such that the correlations between the two factors and these mood parameters were significantly different, the anxiety factor was not consistently (significantly) associated with either mood-related parameter across samples. However, the authors resolve anxiety-specific predictive effects when they collapse across datasets. While it is intuitive that achieving a larger effective sample size would afford the power necessary to detect such individual differences, this struck me as a major caveat for this set of results.

(2) The authors observe associations between the 'common factor' of depression and anxiety and risk-attitude tendencies, presumably the alpha (exponent) parameter in a prospect theory-type subjective value model. But where is this analysis explained? (i.e. how was this model formulated and how were risk attitude parameters estimated?) And what is the interpretation of this finding - is there precedent for looking at risk attitudes in this task? And why would these predictive effects only be observed in relation to the common, but not unique, factors of anxiety and depression?

Reviewer #3 (Public review):

Summary:

An outstanding question in the field of high-frequency oscillations (HFOs) in the context of epilepsy is how these oscillations emerge, considering that they occur at such high frequencies, i.e., 250Hz, well above the firing ability of single neurons. One hypothesis that has been suggested in the past is that neurons that fire in an out-of-phase fashion, or rather at random intervals,s may contribute to a spectrum of HFOs ranging from 250-500Hz that are observed in epilepsy. However, how possible it is that random action potentials could aggregate to the extent that they could give rise to HFOs in the so-called fast ripple (FRs) frequency range (>200 according to the authors) remains unclear. To test this hypothesis, they used computational modeling to randomly insert action potentials in a signal, and they found that this approach is sufficient to generate FRs. Some of the predictors of whether FRs could occur were neuronal count, firing rate, and synchronization. Besides computational modeling, they used different model systems to test whether that would be possible to be observed in neuronal cultures, in epileptic rats (intrahippocampal kainic acid model), and human data. Neuronal cultures treated with picrotoxin did not show evidence that FRs could be generated beyond chance aggregation of action potentials. They then asked whether synchronization and firing rate could play a role in the emergence of FRs. They found that changes in neural firing and synchronization, such as those occurring during differences phase of the sleep-wake cycle, could affect the number of FRs occurring by chance aggregation, with more FRs seen during periods of wakefulness, a result that they replicated in human data.

The authors largely achieve their proposed aims of demonstrating that random neuronal firing can, in principle, generate FRs. Results from this study could influence current thinking around mechanisms generating FRs in epilepsy. The use of different computational approaches and model systems could offer new analytical methodologies for the study of FRs in the context of brain disease.

Strengths:

(1) The authors used a multi-level approach combining computational modeling with experimental datasets, including neuronal cultures, a rat model of temporal lobe epilepsy, and human data.

(2) Identification of key parameters such as neuronal count, firing rate, synchronization, and brain state in observed incidence of FRs generated through random aggregation of neural firing.

(3) Cross-species validation increases the likelihood of generalizability of the findings.

Weaknesses:

(1) Some of the simulated FRs appear short in duration and may not meet standard detection and definition criteria, potentially influencing validity.

(2) The neuronal culture approach does not directly test random insertion of action potentials, limiting interpretation.

(3) Sleep is treated as a homogeneous state in the rat dataset, without accounting for stage-specific differences in synchronization, which may affect the results and interpretation.

(4) The analyses conducted in human data lack direct comparison with sleep data.

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:

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:

In this work, Riegman et al. establish the promoter interactome of cerebellar granule cell progenitors (CGPs) and identify thousands of putative enhancers regulating key genes in this cell population. The authors isolate primary CGps cells from the mouse cerebellum and perform promoter capture Hi-C in order to reanalyse previously generated epigenomic datasets (ATAC-seq, H3K4me1/3, H3K27ac) in these cells. They identify 22'797 enhancers interacting with gene promoters. The authors then use CHD7 ChIP-seq experiments to better annotate regulatory regions linked to genes deregulated upon CHD7 loss of function. After observing that CHD7 is frequently co-bound with ATOH1, they compare the binding profiles of ATOH1 and CHD7 together with genes deregulated in loss-of-function datasets, and refine the regulatory elements associated with each of these proteins.

Strengths:

The work is well designed and carefully executed, leading to an enhancer-promoter (E-P) interaction cartography that largely surpasses the current standard in the field. The pc-HiC dataset enables a deeper analysis of previously generated datasets (ChIP-seq and loss-of-function), which clearly improves the understanding of the mechanisms underlying CGps proliferation and differentiation. Moreover, the integration of published loss-of-function datasets for CHD7 and ATOH1 is relatively novel in this type of study and helps reduce the purely descriptive nature of the work. In particular, the analysis sheds light on genes with potential functions in CGps that had not previously been identified, as well as their regulatory connections. Overall, the study is convincing and supports the conclusions presented by the authors.

Weaknesses:

(1) A substantial part of the manuscript focuses on E-P interactions in CGPs, which gives the impression that this is primarily a genome organisation study. However, in this regard the manuscript does not bring major conceptual novelties. In contrast, the biological insights related to CGPs and the identification of new candidate genes likely represent the most novel aspect of the work. The authors should clarify the central message of the manuscript and reorganise the presentation of the results accordingly.

(2) The numbers presented throughout the manuscript are sometimes confusing. For instance, the authors initially report 106'589 PIF (line 175), but later only 61'928 (line 243) when calling enhancers. The relationship between these numbers is not straightforward. More generally, simplifying the nomenclature used to describe interaction analyses would help emphasise the biological insights rather than the computational framework.

(3) ATAC-seq alone is a relatively poor predictor of enhancers. In this context, H3K27ac would provide a more accurate marker of enhancer activity. This point is particularly important because the authors' data suggest that CHD7 does not function as a pioneer factor capable of opening chromatin. Instead, this role appears to be more closely associated with ATOH1. Therefore, alterations in CHD7 are more likely to affect enhancer activity (reflected by H3K27ac) rather than chromatin accessibility itself. If the authors do not have access to H3K27ac ChIP-seq data, this limitation should be explicitly acknowledged.

(4) The authors do not functionally test most enhancers and instead discuss primarily putative enhancers (with the exception of VISTA-tested elements). Although the term "putative enhancer" appears in some subsections, it is not consistently applied throughout the manuscript. This limitation should be clearly stated early in the manuscript with a sentence such as: "As these regions have not been functionally validated, they should be considered putative enhancers. However, for simplicity, we will refer to them as enhancers throughout the manuscript."

(5) Where feasible, the enhancer identified at the Reln gene should be functionally tested to demonstrate the added value of the approach.

Reviewer #3 (Public review):

Original summary:

The manuscript of Kowalewski et al. titled "Machine learning of honey bee olfactory behavior identifies repellent odorants in free flying bees in the field" did machine learning to predict potential candidates for honeybee repellents, which may keep foraging bees from pesticides. This is a pilot research with strong significance in the research of olfactory behavior and in pest control.

Reviewer #3 (Public review):

Summary:

The manuscript by Goicoechea et al. assesses the influence of hippocampal-network targeted TMS to parietal cortex on episodic memory using a meta-analytic approach. This is an important contribution to the literature, as the number of studies using this approach to modulate memory/hippocampal function has clearly increased since the initial publication by Wang et al. 2014. This manuscript makes an important contribution to the literature. In general, the analysis is straightforward and the conclusions are well-supported by the results.

Strengths:

(1) A meta-analysis across published work is used to evaluate the influence of hippocampal-network-targeted TMS in parietal cortex on episodic memory. By pooling results across studies, the meta-analytic effects demonstrate an influence of TMS on memory across the diversity of many details in study design (specific tasks, stimuli, TMS protocols, study populations).

(2) Selectivity with regard to episodic memory vs. non-episodic memory tasks is evaluated directly in the meta-analysis.

(3) The investigation into supplemental factors as predictors of TMS's influence on memory was tested. This is helpful given the diversity of study designs in the literature. This analysis helps to shed light on which study designs, e.g., TMS protocols, etc., are most effective in memory modulation.

Weaknesses:

The authors thoroughly addressed and responded to the prior comments in the revision. The only minor weakness I see is acknowledged in terms of how null effects for particular design or TMS features should be interpreted (i.e., with caution given the regression approach used).

Reviewer #3 (Public review):

Summary:

In this important work, the authors use extensive MD simulations to study how the IRE1 protein can detect unfolded peptides. Their study consolidates contradictory experimental results and offers a unique view of the different sensing models proposed in the literature. Overall, it is an excellent study that is quite extensive. The research is solid, meticulous, and carefully performed, leading to convincing conclusions.

Strengths:

The strength of this work is the extensive and meticulous molecular dynamics simulations. The authors use and investigate different structural models, for example carefully comparing a model based a PDB structure with reconstructed loops with a AlphaFold 2 Multimer model. The authors also investigate a wide range of different protein structural models that probe different aspects of the peptide-sensing process. Additionally, the authors experimentally validate a part of the simulation results. These solid and meticulous MD simulations allow the authors to obtain convincing conclusions concerning the peptide-sensing process of the IRE1 protein.

Weaknesses:

A potential weakness of the study is the use of equilibrium (unbiased) molecular dynamics simulations, which means only processes and conformational changes on the microsecond timescale can be probed. Furthermore, there can be inaccuracies and biases in the description of unfolded peptides and protein segments due to the protein force fields. Here, it should be noted that the authors do acknowledge these possible limitations of their study in the conclusions. Furthermore, in the revised version, the authors partly address this weakness by employing orthogonal simulation methods and experimental techniques.

Comments on revisions:

The authors have addressed all the issues that I raised in my previous report.

Reviewer #3 (Public review):

Summary:

This work examined the transcriptional response to pain induction by CFA and/or morphine treatment in rat VTA at the level of single cells. This builds on prior work using bulk-tissue RNA-seq to evaluate response to SNI pain and/or oxycodone treatment. Here, authors find few lasting gene expression changes with CFA, but a robust transcriptional response to acute morphine, particularly in non-neuronal cells, where an increase in Fkbp5 stood out. The authors validated corticosterone-induced elevations in Fkbp5 in rat glial cell culture and human astrocyte cell culture, which are blocked by the GR antagonist mifepristone and inhibition of Nr3c1, but are not independently induced by the µOR agonist DAMGO.

Strengths:

The authors started with somewhat surprising transcriptional observations and followed the science appropriately to investigate the functional relevance of one particular finding. This work is well-powered and uses state-of-the-art snRNA-seq and CRISPR-based manipulations in both rat glia and human astrocyte cell preparations to determine the functional relevance of Fkbp5-regulated transcriptional activity.

Weaknesses:

(1) It was somewhat surprising that the CFA-Morphine group was not taken at a time point when the morphine treatment was found to be behaviorally effective.

(2) The final conclusion that Nr3c1 repression reduces the response to cort is not novel or surprising, even if it is within human astrocyte culture (which is cool).

(3) This work falls short of bringing the research full circle by applying their Nr3c1-CRISPRi approach in vivo to alter behavioral response to morphine and/or pain.

Reviewer #3 (Public review):

Summary:

This study reports KRH, a SaCas9 variant computationally engineered via UniDesign to recognize an expanded NNNRRT PAM with substantially enhanced editing efficiency at non-canonical sites. KRH achieves genome- and base-editing efficiencies comparable to or exceeding the evolution-derived KKH variant across multiple human cell types, demonstrating that computational design can effectively remodel PAM specificity while preserving nuclease activity.

Strengths:

The research follows a clear line of reasoning, and the results appear sound. The computational design strategy presented offers a valuable alternative to directed evolution, with potential applicability beyond Cas9 engineering.

Reviewer #3 (Public review):

Previous studies have shown that language model embeddings of future words can predict brain responses to language. This has been interpreted as evidence for predictive representations in the brain. The primary finding of the present study is that this index of predictive processing is not consistent with a pre-activation account, but instead suggests continuously evolving representations. A strength of the manuscript is that it uses methods that build on previous studies and shows that previous results replicate in the current datasets, before testing new hypotheses. Addressing some minor weaknesses would further strengthen the results and ascertains that the conclusions are justified:

(1) When analyzing neural data, "words with multiple tokens assigned by the model were excluded" (11). I am wondering whether this could have had an influence on the results. I suspect that using only single token words would bias the dataset towards semantically light high frequency and function words. Pre-activation may be different for those words from more semantically rich, longer words.

(2) The study only used a context window of 50 tokens for language model predictions (11). This is less than in previous studies, and may constitute a confound when comparing results across studies. This may be particularly relevant in comparison to Caucheteux et al. (2003), whose results suggested more extensive predictions (9), which may require more extensive context.

(3) The manuscript is largely missing data on the reliability of the results. Some form of significance test, and indication of variability and/or the noise floor in the figures would be helpful.

A primary concern when analyzing naturalistic speech data is that different speech features are highly correlated across linguistic levels and across time. The manuscript makes a reasonable effort to control for stimulus autocorrelations. It is encouraging that the effect survived this correction. As the manuscript concedes, control is not perfect and controlling for "all regularities inherent to natural speech" remains a challenge (9). This should be kept in mind when interpreting the results.

Finally, the manuscript also argues that "we observed clear signatures of postdiction, with neural activity reflecting persistent encoding of prior words" (abstract). I did not follow this reasoning. The ostensible evidence for this is that "including the previous word ... improves encoding even after the current word's onset" (Figure 5). However, this is not further surprising, because the previous word can often only be recognized around the end of the word, corresponding to the time of the current word onset. Language model embeddings reflect a contextual semantic interpretation of the word, which likely requires further processing after word recognition. I would thus expect that the initial contextual interpretation of a word occurs during presentation of the subsequent word. Evidence for "persistent encoding" should include encoding beyond this point, i.e., over the course of several subsequent words. Contrary to this, Figure 5 a (left) suggests that the predictive effect of the previous word (d-1) stops around the offset of the current word (d). This suggests to me that, once controlling for subsequent embeddings, the embedding of a word disappears from the neural activity soon after word recognition.

Reviewer #3 (Public review):

Summary:

This excellent manuscript by Pinto, Sharp, and colleagues examines bovine tissue tropism for influenza viruses. They find that bovine flu, as well as other strains, has strong replication in mammary tissue. They also map the genetic changes to influenza that improve replication in bovine cells. Overall, the study is well designed and executed, and the results are very timely.

Strengths:

(1) The experiments are well-controlled.

(2) The figures are well-constructed and easy to follow.

(3) The Methods and legends are detailed, with sufficient information.

Weaknesses:

(1) A comparison to human cells would strengthen the overall impact of the results. Are human mammary cells also uniquely susceptible to influenza? Are bovine mammary cells special in some way?

(2) For the virus infection studies with segment 8 swaps, it should at least be noted that some of the phenotypes could be driven by NEP.

(3) The data demonstrating that bMEC can support co-infection are compelling and important, but would be strengthened with a comparison from a different cell type or species. Do mammary cells uniquely support higher co-infection?

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.

- 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.

Weaknesses:

(1) Title: "A simplified and highly efficient cell-free protein synthesis system for prokaryotes".<br /> - This title is misleading since one would expect a simplified and highly efficient cell-free protein synthesis system to yield similar protein levels compared to current cell-free protein synthesis systems. What this study shows is that the composition of cell-free protein synthesis systems can be simplified while maintaining a certain level of protein synthesis. Here, optimisation does not involve maintaining protein synthesis yield while simplifying the cell-free protein synthesis system; rather, it involves developing a simplified cell-free protein synthesis system. As mentioned in my comments below, this study lacks a comparison of protein levels with a typical cell-free protein synthesis system.<br /> - What do the authors mean by "highly efficient"? Highly efficient compared to what experimental conditions? If one is interested by the yield of protein synthesis, is this simplified system highly efficient compared to current systems?

(2) Figure 1, 3-5 :<br /> - What do relative luciferase units represent? How are these units calculated?<br /> - In this system, the level of expression depends mainly on the level of NLuc transcripts and the efficiency of NLuc translation. How did the authors ensure that the chemical composition of the different eCFPS buffers only affected protein translation and not transcript levels? In other words, are luciferase units solely an indicator of protein synthesis efficiency, or do they also depend on transcription efficiency, which could vary depending on the experimental conditions?<br /> - How long were the eCFPS reactions allowed to proceed before performing the luciferase activity measurement? Depending on the reaction time, the absence or presence of certain compounds may or may not impact NLuc expression. For example, it can be assumed that tRNA does not significantly affect NLuc levels over a short period of time, and that endogenous tRNA in the lysate is present at sufficient concentrations. However, over a longer period of time, the addition of tRNA could essential to achieve optimal NLuc levels.<br /> - The authors show that tRNA and amino acids are not strictly essential for the expression of NLuc, likely due to residual amounts within the cell lysate. However, are the protein levels achieved without added amino acids and tRNA sufficient for biochemical assays that require a certain amount of protein? It is important to note that the focus here is on optimising the simplicity of the buffer rather than the level of protein expression. In fact, the simplicity of the buffer is prioritised over the amount of protein produced. This should be made clear.<br /> - How would the NLuc level compare if all the components were optimised individually and present in an optimised buffer, compared to a buffer optimised for simplicity as described by the authors?

(3) Line 71, Streamlining eCFPS: removal of dispensable components. This title is misleading because it creates the false impression that proteins can be produced in vitro without the addition of certain compounds. While this is true, the level of protein produced may not be sufficient for subsequent biochemical analyses. This should be made clear.

(4) Figure 2: In the legend, change "(A) Protein expression levels of the eCFPS system measured at varying concentrations of KGlu and MgGlu2" to "(A) Protein expression levels of the eCFPS system using an Nanoluciferase (NLuc) reporter DNA measured at varying concentrations of KGlu and MgGlu2".

(5) Lanes 302-303: "The thorough optimization of the seven core components was a critical step in achieving high protein expression levels". What are "high expression levels"? Compared to what?

Comments on revisions:

The authors have adequately addressed the issues I had raised in my initial review.

Reviewer #3 (Public review):

Leshem et al have generated a transcriptional cell atlas of the human outflow tract at two developmental timepoints and its adult valvular derivatives. This carefully performed study provides a useful resource for the study of known genes implicated in outflow tract defects and potentially also to discover new disease genes. The authors reveal neural crest and mesodermal contributions to different outflow tract components and show that GATA6, known to play a role in arterial valve development, controls a set of genes expressed in endocardial derived cells during valve development. Interestingly the results reveal intersection with GLI3 and suggest lineage persistence of gene expression through to the adult timepoint, a main new finding of this study.

Comments on revisions:

The authors have carefully addressed previous comments, including the addition of new analysis pointing to potential cooperation between GATA6 and GLI3.

Reviewer #3 (Public review):

Summary:

The focus of the research is to understand how transcription factors with high expression in neural crest cell derived cancers (e.g., neuroblastoma) and roles in neural crest cell development function to promote malignancy. The focus is on the transcription factor FOXC1 and using murine cell culture, gain- and loss of function approaches and ChIP profiling, among other techniques, to place PKC inhibitor ARHGAP36 mechanistically between FOXC1 and another pathway associated with malignancy, Sonic Hedgehog (SHH).

Strengths:

Major strengths are the mechanistic approaches to identify FOXC1 direct targets, definitively showing that FOXC1 transcriptional regulation of ARHGAP36 leads to dysregulation of SHH signaling downstream of ARHGAP36 inhibition of PKC. Starting from a screen of Foxc1 OE to get to ARHGAP36 and then using genetic and pharmacological manipulation to work through the mechanism is very well done. There is data that will be of use to others studying FOXC1 in mesenchymal cell types, in particular the FOXC1 ChIP-seq.

Weaknesses:

Work is almost all performed in NIH3T3 or similar cells (mouse cells, not patient or mouse-derived cancer cells) so the link to neuroblastoma that forms the major motivation of the work is not clear. The authors look at ARHGAP36 levels in association the neuroblastoma patient survival however the finding, though interesting and quite compelling, is misaligned with what the literature shows about FOXC1 and SHH, their high expression is associated with increased malignancy (also maybe worse outcomes?). Therefore, ARHGAP36 expression may be more complicated in a tumor cell or may be unrelated to FOXC1 or SHH, leaving one to wonder what the work in NIH3T3 cells, though well done, is telling us about the mechanisms of FOXC1 as an oncogene in neuroblastoma cells or in any type of cancer cell. Does it really function as a SHH activator to drive tumor growth? The 'oncogenic relevance' and 'contribution to malignancy' claimed in the last paragraph of the introduction is currently weakly supported with the data as presented. This could be improved with studying some of these mechanisms in patient-derived neuroblastoma cells with high FOXC1 expression. Does inhibiting FOXC1 change SHH and ARHGAP36 and have any effect on cell proliferation or migration? Alternatively, does OE of FOXC1 in NIH3T3 cells increase their migration or stimulate proliferation in some way and is this dependent on ARHGAP36 or SHH? Application of their mechanistic approaches in cancer cells or looking for hallmarks of cancer phenotypes with FOXC1 OE (and dependent on SHH or ARHGAP36) could help to make a link with cellular phenotypes of malignant cells.

In the revised manuscript, the authors did not add studies in any malignant cell type (mouse or human, neuroblastoma or other) with Foxc1 overexpression to test if the mechanisms they identify in the mouse fibroblasts is present in cancer cells nor if this relates to cellular phenotypes of malignancy (migration or proliferation). Therefore strengths and weaknesses identified by this reviewer in the prior version are the same.

Reviewer #3 (Public review):

Summary:

The authors described an exciting 400-drug screening using a MMV pathogen box to select compounds that effectively affects the medically important Toxoplasma parasite bradyzoite stage. This work utilises a bradyzoites culture technique that was published recently by the same group. They focused on compounds that affected directly the mitochondria electron transport chain (mETC) bc1-complex and compared with other bc1 inhibitors described in the literature such as atovaquone and HDQs. They further provide metabolomics analysis of inhibited parasites which serves to provide support for the target and to characterise the outcome of the different inhibitors.

Strengths:

This work is important as, until now, there are no effective drugs that clear cysts during T. gondii infection. So, the discovery of new inhibitors that are effective against this parasite-stage in culture and thus have the potential to battle chronic infection is needed. The further metabolic characterization provides indirect target validation and highlight different metabolic outcome for different inhibitors. The latter forms the basis for new studied in the field to understand the mode of inhibition and mechanism of bc1-complex function in detail.

The authors focused in the function of one compound, MMV1028806, that is demonstrated to have a similar metabolic outcome to burvaquone. Furthermore, the authors evaluated the importance of ATP production in tachyzoite and bradyzoites stages and under atovaquone/HDQs drugs.

Reviewer #3 (Public Review):

Summary:

This study explores the dynamic reprogramming of histone modification H3K4me2 during the early stages of mammalian embryogenesis. Utilizing the advanced CUT&RUN technique coupled with high-throughput sequencing, the authors investigate the erasure and re-establishment of H3K4me2 in mouse germinal vesicle (GV) oocytes, metaphase II (MII) oocytes, and early embryos.

Strengths:

The findings provide valuable insights into the temporal and spatial dynamics of H3K4me2 and its potential role in zygotic genome activation (ZGA).

Weaknesses:

The study primarily remains descriptive at this point. It would be advantageous to conduct further comprehensive functional validation and mechanistic exploration.<br /> Key areas for improvement include enhancing the innovation and novelty of the study, providing robust functional validation, establishing a clear model for H3K4me2's role, and addressing technical and presentation issues. The text would benefit from the introduction of a novel conceptual framework or model that provides a clear explanation of the functional consequences and molecular mechanisms underlying H3K4me2 reprogramming in the transition from parental to early embryonic development.

While the findings are significant, the current manuscript falls short in several critical areas. Addressing major and minor issues will significantly strengthen the study's contribution to the field of epigenetic reprogramming and embryonic development.

Reviewer #3 (Public review):

Summary:

This study is significant as it established a protocol for the long-term culture of Schistosoma mansoni newly transformed cercariae, which developed in vitro into sexually dimorphic forms. The impact of two different sera, Fetal Bovine Serum (FBS) and Human Serum (HS), added to the culture medium supplemented with human red blood cells was evaluated. The authors demonstrated that HS-cultured parasites were able to digest red blood cells, a critical step for long-term parasite development. Furthermore, while most FBS-cultured parasites did not progress beyond an early liver stage, sexual dimorphism was clearly evident in the HS-cultured worms, albeit delayed compared to in vivo development.

Strengths:

This study could contribute to further in vitro studies for a better understanding of the unique sexual biology of Schistosoma mansoni and for screening novel schistosomicidal compounds. By increasing parasite development in in vitro studies, this protocol could have a positive impact on the principles of the 3Rs (Replacement, Reduction and Refinement) for animal research.

Weaknesses:

As the authors mentioned, "pairing between male and female parasites was rare. Pairing was observed in approximately ~7% of the experiments, usually after day ~ 80 in culture. Egg production was also not achieved with this protocol.

Reviewer #3 (Public review):

Summary:

In this study, the authors describe modeling of neuronopathic Gaucher disease (nGD) using midbrain-like organoids (MLOs) derived from hiPSCs carrying GBA1 L444P/P415R or L444P/RecNciI variants. These MLOs recapitulate several disease features, including GCase deficiency, reduced enzymatic activity, lipid substrate accumulation, and impaired dopaminergic neuron differentiation. Correction of the GBA1 L444P variant restored GCase activity, normalized lipid metabolism, and rescued dopaminergic neuronal defects, confirming its pathogenic role in the MLO model. The authors further leveraged this system to evaluate therapeutic strategies, including: (i) SapC-DOPS nanovesicles for GCase delivery, (ii) AAV9-mediated GBA1 gene therapy, and (iii) GZ452, a glucosylceramide synthase inhibitor. These treatments reduced lipid accumulation and ameliorated autophagic, lysosomal, and neurodevelopmental abnormalities.

Strengths:

This manuscript demonstrates that nGD patient-derived MLOs can serve as an additional platform for investigating nGD mechanisms and advancing therapeutic development.

Comments on revisions:

I have no further concerns regarding this manuscript.

Reviewer #3 (Public review):

In this work, Tsay et al. examine the challenge of inferring the ordering of amyloid fibrils. There is a clear need for such methodology. In their work, they computationally analyze the case of the expected decay in the DEER signal for spins randomly distributed in one, two, and three dimensions and show that (not unexpectedly) the decay is sensitive to dimensionality for a range of spin label concentrations. More intriguingly, they measure the dimensionality of tau amyloid labeled at several positions. Intriguingly, they show uniform (but unexpected) dimensionality when the label is in the fibril core. Through further simulations, they show that this anomalous dimensionality cannot arise from label attraction or repulsion (which can lead to deviations from random positions). Instead, this dimensionality is interpreted (again using compelling simulations) to arise from mis-registering due to changes in packing. Taken together, this paper convincingly shows that the DEER signal can be used to get site-specific information on amyloid dimensionality and can discriminate between regions of fibril core vs the "fuzz coat". Overall, this paper moves forward the methodology and opens up the technique to attractive applications in the areas of amyloid formation. More substantively, the field of DEER has been fixated on the dipolar modulation, and it is only once in a while now that one comes across a paper with a fresh breath of air - this paper certainly is!

Reviewer #3 (Public review):

Summary:

Various groups over the last several decades have provided many examples of proteins associating with nascent mRNA co-transcriptionally to influence gene expression at subsequent stages, including in the cytoplasm. In this and previously published works, the Choder group has described these events as "mRNA imprinting", which we know as a field that reflects the differential association of proteins with mRNAs in a gene-specific or environmentally induced fashion to regulate gene expression.

In this study, the authors use a proteomics-based approach termed PROFIT to identify factors associated with RNA Pol II in an RNA-dependent manner. The identified interactors have the potential to be part of mRNA-protein complexes (mRNPs) being formed co-transcriptionally with an "mRNA imprinting" function. PROFIT employs a pulldown of RNA Pol II via a tagged Rpb3 subunit, followed by RNase I-mediated elution to isolate proteins associated in an RNA-dependent manner. Proteomics analyses identified known mRNA-associated proteins that have previously been reported as imprinting factors, as well as other proteins involved in gene expression, including factors functioning in the cytoplasm. The authors suggest, based on the RNA-dependence and assumed formation of these interactions with RNA Pol II co-transcriptionally, that these novel hits could be mRNA imprinting factors. Although for most of these factors, it has not been determined whether they associate with RNA-Pol II in the context of transcription with nascent transcripts to contribute to the downstream regulations of these transcripts.

Strengths:

PROFIT successfully identified nuclear factors known to engage mRNA co-transcriptionally. This suggests that the method has the potential to detect imprinting factors. By employing a proximity-labeling technique, termed BioPROFIT, further evidence is provided for some of the novel interactors being in proximity to RNA Pol II. The authors further demonstrate that one of the factors, the eIF3 component Rpg1, exists in two fractions, with a soluble fraction that matures into a ribosome fraction, which is suggestive of Rpg1 traveling along the gene expression pathway with an mRNP to be engaged in translation. In addition, the authors showed that PROFIT detects changes in RNA Pol II associated factors in response to heat shock, consistent with gene expression reprogramming during stress. As such, these methods and proteomics data provide a starting point for a more detailed characterization of mRNP compositions formed in the nucleus and their impact on gene expression at later stages.

Weaknesses:

The authors interpret the interaction data from PROFIT and BioPROFIT under the assumption that this reflects interactions happening co-transcriptionally. There is no discussion of other ways these data may result, or more importantly, controls to prove these assumptions are true. Overall, these assays lack important controls and experimental validations by independent methods to demonstrate that the identified interactions occur co-transcriptionally within the nucleus and do not represent interactions occurring in the cytoplasm or artifacts related to experimental design. For example, the authors focus on Rpg1 as a potential imprinting factor, which would require this protein to shuttle and be localized at transcribing genes. Yet no evidence is presented that Rpg1 enters the nucleus or can be found in association with a transcribed gene, which leaves open the possibility that this interaction is occurring in the cytoplasm or forming post-lysis.

To the possibility of in vitro interactions, in the PROFIT assay, yeast collected from a 3L culture is cryo-ground and resuspended in 7 mL of lysis buffer. This ratio of cell material to buffer will create a highly concentrated cell lysate that is subsequently used over ~6.5 hours, which is the time for centrifugation, DNase I digestion, and immunoprecipitation. These conditions have a very high probability of promoting new interactions between RNA, RNA Poll II, other proteins, and/or RNA Pol II-associated nascent RNA complexes in vitro. Notably, the PROFIT assay detects many highly expressed proteins but does not identify many of the factors known to be loaded into nuclear mRNPs (e.g., Yra1, THO complex, Sub2, or Nab2). The BioPROFIT assay is used to try to address this issue, but biotinylation may occur post-lysis because the desalting process to remove biotin is performed just before the immunoprecipitation, providing ~2 hours for the reaction to happen in vitro. In addition, even if the biotinylation occurs in cells, nothing about this assay indicates this is occurring in the context of transcribing RNA Pol II or nascent transcripts. To address this major issue, the authors should add a mixing control to show that the detected interactions between RNA Pol II and the identified factors are produced in cells, not in the cell lysate. Specifically, mixing cell grindates from two independent yeast strains (e.g., RPB3-FLAG strain mixed with a TIF4631-HA strain) with the lysate used in the PROFIT assay with western blotting. In this case, if the interaction is detected, the interaction is produced in the cell lysate. To verify PROFIT hits associated with transcribing RNA Pol II and nascent transcripts, BIOPROFIT should be performed in cells treated with a transcription inhibitor (e.g., thiolutin) or mutants blocking transcription by Pol II. These types of verifications should be performed for the multiple novel hits reported in the manuscript.

Another in vitro issue must also be addressed. In the PROFIT assay, elution of RNA-associated factors from the immunoprecipitated material is performed by RNase I digestion, but the reaction time is very long (3 hours) at room temperature. During such a long incubation time and at higher temperature (i.e., above 4 Celsius), it is possible that non-RNA-mediated interactors dissociate from the beads and/or protein binding partners. This possibility is made more problematic by the fact that the authors define interactors using fold change over an Rpb3 no tag sample, where the sample does not contain isolated RNA Pol II complexes and their associated protein-binding partners. As such, even a small amount of non-RNA-mediated RNA Poll II interactors that elute would appear significantly enriched. For this point, a comparison of +/- RNase I elution in the Rpb3-FLAG pulldown sample should be performed using PROFIT.

Other points to address:

(1) The cartoon in Figure 1A should be corrected to present the PROFIT experiment as described in the text. Specifically, in the cartoon, UV is shown to be applied to cells, but this is done with cell grindate.

(2) The cartoon in Figure 2A should be corrected. In the cartoon, it shows the biotin ligase biotinylating proximal proteins during DNase digestion as well as on the Sepharose beads, but in theory, the majority of the biotinylation reaction occurs in cells. In addition, the cartoon depicts biotinylation of proximal proteins, but the system described uses wild-type BirA to specifically biotinylate an Avi-tag. To perform non-specific labeling of proximal proteins, BirA* would need to be used. Finally, the cartoon indicates mass spectrometry analysis of labeled proteins, but this is not done in the manuscript.

(3) In the text, the sentence "However, no bio-Spt6-Avi was released from the complexes containing Pol II mutants (Fig. 5C)" appears to have two errors. "Pol II mutants" should likely be "rpb4 mutant" and "Fig. 5C" is probably "Fig. 6C".

(4) In the Figure 6 legend, the sentence "The bulk Spt6 was detected by anti-HIS Abs that bound to (HIS)x6, which was placed upstream of the FLAG" suggests that "FLAG" should be "Avi-tag." Please correct it if necessary and accurately describe it in the strain list.

(5) On page 18, Npl3 is listed and discussed, but never mentioned anywhere prior in the paper. For example, the paragraph states "...our observation that it binds nascent RNA in an Rpb4-dependent manner...", but Npl3 is not listed in the supplemental Table 4, which lists PROFIT hits affected by rpb4∆. If Npl3 is to be discussed, the associated data needs to be properly presented.

Reviewer #3 (Public review):

Summary:

In this study, Zhu et al. use long-read transcriptomes, with correction using short-read RNA-seq, from 12 spider species that span the major evolutionary lineages to investigate the diversification of spider silk proteins (spidroins). Here, they identify 60 spidroin sequences and propose that two highly divergent sequences found in the basal Liphistius sp., where one is an alanine-serine-rich (AS-type), and one is a glycine-serine-rich (GS-type), represent ancestral templates from which all major spidroin families diversified. Using separate phylogenetic analyses for N-terminal domains, C-terminal domains, and repetitive domains, the authors argue that the AS-type lineage remained relatively conserved and gave rise to tubuliform spidroins (TuSp) used in eggcase silk, while the GS-type lineage evolved into minor ampullate spidroins (MiSp) and may have provided the substrate for major ampullate spidroins (MaSp). In addition, they describe a specific flagelliform-like (flag) transcript in a basal clade spider, with MaSp-like terminal domains, and propose that Flag was co-opted into ampullate silk glands before being progressively lost in more derived retrolateral tibial apophysis (RTA) lineages.

Strengths:

The taxon sampling is a strength of this study, covering representative species at key nodes across spider evolution, from the earliest-diverging Mesothelae through Mygalomorphae and into the most derived Araneomorphae lineages, which enables the authors to make comparative inferences about ancestral states. Also, the use of long-read sequencing is well-suited to the problem since spidroin genes contain highly repetitive coding sequences that would be very hard to resolve by short-read assembly alone. Thus, retrieving 30 full-length sequences in this context is notable, and the assembly quality appears reasonable for transcriptomic resources, with BUSCO completeness values reported between 85% and 93% across species.

The decision to analyse N-terminal, C-terminal, and repetitive domains in separate phylogenetic trees is methodologically sound and yields a biologically interesting result: terminal domains show greater diversification in basal lineages than repetitive regions, suggesting that specialisation of silk gland microenvironments preceded compositional innovation in the repetitive sequences.

Weaknesses:

While the paper has strengths in providing a useful comparative resource and generating interesting hypotheses, several of the central evolutionary conclusions are not directly supported by the current data. There are three main elements that require further attention:

(1) The GS-type Liphistius sequence (Liphistius sp._5400) is central to the manuscript's model for the origin of GA-rich ampullate spidroins, but the authors describe it as a spidroin-like transcript whose N- and C-terminal regions lack homology to known spidroins and may not support typical silk-fiber assembly. Since its terminal domains are excluded from the phylogenetic analyses, the proposed scenario, GS-type to MiSp to MaSp, rests largely on repeat-region similarity. Supplementary materials provided in this study further indicate no predicted signal peptide, although this feature alone is not unique among the annotated silk proteins. The manuscript should therefore either provide a stronger justification for treating Liphistius sp._5400 as an ancestral spidroin or more consistently frame it as a spidroin-like, repeat-based intermediate. The distinction between repeat-region clustering and full functional homology should also be made explicit.

(2) The whole-body transcriptome approach is an important sampling limitation that is acknowledged here, where the authors note that they were unable to recover complete spidroin repertoires for each species. Because the newly generated data are not silk-gland-specific, the absence of a transcript in a given species should be interpreted with caution and not equated directly with gene absence. This is particularly relevant to the manuscript's proposed loss of Flag during RTA evolution. In the focal taxa, the inference combines one positive transcript in H. davidbowie with non-detection in H. diardi, while broader support comes from limited synteny-based absence in a small number of external genomes. Therefore, while the Flag-loss scenario could be plausible, it remains suggestive rather than conclusive without more targeted silk-gland sampling or broader genomic validation.

(3) The Flag co-option model is interesting, but as presented now, it is based on limited evidence: a single Flag-like transcript in H. davidbowie, the absence of detection in H. diardi, restricted synteny comparisons, and terminal-domain similarity to ampullate spidroins. The manuscript does not present proteomic evidence that this Flag-like protein is incorporated into ampullate silk fibers, nor does it show a series of pseudogenized or truncated Flag loci across derived RTA lineages. This is a plausible and interesting scenario, but it should be framed more consistently as a testable hypothesis rather than as an established evolutionary pathway.

Reviewer #3 (Public review):

Neural activity in visual cortex has primarily been studied in terms of responses to external visual stimuli. While the variability of neural inputs to a visual area are known to also influence visual responses, the contribution of this stimulus independent component to overall visual responses has not been well characterized.

In this study, the authors analyze datasets from both mice (a previous V1 Ca++ imaging study) and monkeys (data from a previous study and new large-scale electrophysiological recordings from V1-V4). Using regression models, they examine the predictability of neural activity between Layer 4 and Layer 2/3 in mice and between V1 and V4 in monkeys. Their main finding is that significant predictions are possible even in the absence of visual input, highlighting the influence of stimulus independent downstream activity on neural responses. These findings can inform future modeling work of neural responses in visual cortex to account for such non-visual influences.

The authors perform a thorough analysis comparing regression-based predictions for a wide variety of combinations of stimulus conditions and directions of influence. While many of the predictability pattens are largely in line with expectations (eg., downstream layers/areas predicting upstream activity), it is valuable to have these relationships quantified as the authors have done here. Predictability also depended on stimulus type, but these dependencies were not consistent across animals, making it difficult to draw general conclusions. Finally, they show robust predictions even during spontaneous activity which are only partially accounted for by available behavioral metrics. Together, these analyses provide a valuable quantification of stimulus-independent components of visual cortical activity and their potential role in shaping sensory responses.

Reviewer #3 (Public review):

Overview and Strengths:

Accurate evaluation of threat levels allows animals to determine whether to escape. The precise mechanism underlying threat evaluation remains unclear. Smith et al. identified a cluster of neurons in the zebrafish rostrolateral dorsal pallium (Rl) that respond differentially to varying levels of negative-valence stimuli.

This work leverages the small size and optical transparency of the larval zebrafish, using two-photon selective plane illumination microscopy to assay the response of pallial neurons to various negative-valence stimuli. Interestingly, unlike the ventromedial pallium and habenula, which responded to all stimuli tested, neurons in the Rl were activated by a selection of stimuli representing relatively higher levels of threats. By leveraging a zebrafish brain atlas, the authors identified a transgenic line labeling a tiam2a+ cluster of neurons that appears to be the activated population in the Rl. Together, these results demonstrate a subpopulation of pallial neurons that likely categorizes the strength of negative valence in larval zebrafish.

The primary conclusions of this work are well supported by the data. The identification of a neuronal cluster that may underlie the categorization of threat-associated sensory stimuli is significant. Furthermore, this study generates a high-quality functional imaging dataset using cutting-edge microscopy, setting the foundation for understanding the neuronal encoding of emotions in zebrafish.

Results from this work set the stage to answer further exciting questions: How do tiam2a+ Rl neurons modulate the activity of the hindbrain escape circuit? What is the functional role of the Rl population inhibited by threat stimuli? Computationally, how does Rl integrate sensory signals and classify threat levels? How does the activity of Rl change in the context of habituation and conditioning? Future work may use more nuanced stimuli and combine new genetic tools, behavioral recording, and circuit-level analysis to systematically reveal how emotions modulate defensive behaviors.

Weaknesses:

The impact of this work could be further enhanced by incorporating more sophisticated data analysis and by more clearly anchoring the findings within the known framework of zebrafish defensive behavior.

(1) The authors performed statistical analyses across six ROIs per experiment in Figures 1E/J, 3E/J, and 6B/D/F. This increases the probability of Type I errors. Applying multiple comparison corrections would mitigate this concern. Given that most stimuli (except for the "IR heating") are non-directional, the authors may consider first testing for the response symmetry following each stimulus and then combining ROIs from the two hemispheres to calculate a single averaged measurement per region per fish for comparisons of regional dF/F.

(2) I found the topographical mapping of activated and inhibited ROIs very informative. There appear to be two subpopulations of Rl: a posterior-medial population often activated by negative valence stimuli, and an anterior-lateral population that is frequently inhibited. I wonder if it is possible to decode the valence or category of a stimulus based on the topography and response profiles of these neurons? These results would provide additional evidence for the Rl's roles of threat evaluation.

(3) Findings in this paper, especially differential responses of the Rl to full and partial looming, deserve an expanded discussion. The authors should better anchor these findings to established literature to emphasize their significance in the Discussion. For example, how might this potential categorization mechanism contribute to, or differ from, the mechanisms underlying habituation (Fotowat & Engert, 2023, eLife); what are the possible connections between the pallium and the hindbrain escape circuits that could relay these Rl signals (Kunst et al., 2019, Curr Biol)?

(4) The authors make conservative claims associating the tiam2a+ cluster with Rl neurons activated by noxious stimuli, and their data support this conclusion. However, this link could be further strengthened by testing whether the tiam2a+ cluster shows differential responses to full vs partial looming. This could be achieved by performing pERK staining following the stimulus paradigm. While future tools may allow for direct functional imaging of this population, I believe such experiments are beyond the scope of this paper.

(5) Figure 1E/J, Figure 3E/J: Please clarify whether the dashed red vertical lines indicate the onset or the offset of the stimuli. Additionally, different time windows were used for AUC calculations across these experiments; the authors should provide a rationale for these varying windows in the Results or Methods.

Reviewer #3 (Public review):

Summary:

This study provides direct neuroimaging evidence relevant to the integration-segregation theory of exogenous attention-a framework that has shaped behavioral research for more than two decades but has lacked clear neural validation. By combining an inhibition-of-return (IOR) paradigm with a modified Stroop task in an optimized event-related fMRI design, the authors examine how attentional integration and segregation processes are implemented at the neural level and how these processes interact with semantic and response conflicts. The central goal is to map the distinct neural substrates associated with integration and segregation and to clarify how IOR influences conflict processing in the brain.

Strengths:

The study is well-motivated, addressing a theoretically important gap in the attention literature by directly testing a long-standing behavioral framework with neuroimaging methods. The experimental approach is creative: integrating IOR with a Stroop manipulation expands the theoretical relevance of the paradigm, and the use of a genetic-algorithm-optimized fMRI design ensures high efficiency. Methodologically, the study is rigorous, with appropriate preprocessing, modeling, and converging analyses across multiple contrasts. The results are theoretically coherent, demonstrating plausible dissociations between integration-related activity in the fronto-parietal attention network (e.g., FEF, IPS, TPJ, dACC) and segregation-related activity in medial temporal regions (e.g., PHG, STG). Importantly, the findings provide much-needed neural support for the integration-segregation framework and clarify how IOR modulates conflict processing.

Revisions and Evaluation:

The authors have responded thoroughly and convincingly to the concerns raised in the previous round of review. In particular, issues related to the interpretation of dACC activity, the functional characterization of PHG and STG, and reporting clarity have been carefully addressed. The manuscript has been improved in terms of transparency, consistency of reporting, and overall readability.

As a result, I no longer see any major weaknesses. The study is now clearly presented, methodologically sound, and theoretically informative. It makes a valuable contribution to the literature on attention and cognitive control.

Comments on revisions:

I appreciate the authors' efforts in addressing the previous comments. They have responded thoroughly to the concerns raised in the prior round of review. The work is well executed and makes a meaningful contribution to the field.

Reviewer #3 (Public review):

Summary:

Razlan and colleagues provide a detailed anatomical characterization of lamina I projection neurons in the mouse spinal cord that are densely innervated by primary afferents activated by cooling of the skin. The authors validate a Trpm8-Flp mouse line, show synaptic contacts between Trpm8⁺ boutons and projection neurons at the ultrastructural level, and demonstrate at the physiological level that these neurons specifically respond to cooling stimuli. Next, by taking advantage of previous transcriptomic analysis of ALS neurons, the authors identify calbindin as a marker for cold activatetd lamina I projection neurons and map their ascending projections to the rostral lateral parabrachial area, caudal periaqueductal gray, and ventral posterolateral thalamus, well-known thermosensory and thermoregulatory centers. Altogether, these findings provide strong anatomical and functional evidence for a direct line of transmission from Trpm8⁺ sensory afferents through Calb1⁺ lamina I neurons to key supraspinal centers controlling perception of cold and thermoregulatory responses.

Strengths:

The combination of mouse genetics, electron microscopy, ex-vivo physiology, optogenetics and viral tracing provides convincing evidence for a direct cold pathway. The work validates the Trpm8-Flp line by extensive anatomical and molecular characterization. Integration with previous transcriptomic and anatomical data, neatly links the cold-selective lamina I neurons to a molecularly defined cluster of ALS neurons, strengthening the bridge between molecular identity, anatomy, and physiological function.

Weaknesses:

The main limitation remains the relatively small number of neurons that could be recorded electrophysiologically. While understandable given the complexity of the preparation, this necessarily limits generalization.

Reviewer #3 (Public review):

This study aimed to understand the depressing and sensitizing effects of adaptation in mice visual cortex during different behavioral states: locomotion and stationary. There is an impressive characterisation of the responses in different cortical cell types and with different optogenetic manipulations to the inhibitory populations. These form a very interesting dataset to understand the effects of the state on the circuits and gain insight into the mechanisms. This data is then used to constrain a model of the responses. Unfortunately, the model appears to be too flexible, and it was difficult to interpret the insights gained from the different model fits.

Strengths:

The data is impressive. There is a characterisation of responses of PCs and VIP, SST and PV interneurons. Additionally, there is the characterisation of some responses to specific optogenetic manipulations, VIP inactivation, SST or PV activation or inactivation. These data will help develop a good insight into the system. The principle of using the optigenetic manipulations to constrain model parameters is very interesting.

Weaknesses:

Many of the analyses have some concerns in the methodology used, which we list in detail below. Further, the model used to gain insight into the mechanism appears overly complicated and seems hard to gain clear insights from.

Major concerns:

(1) Key concern is the usage of dF/F signals for all analyses, especially when comparing responses.

1a) Figure 1G: Comparison of sensitisers and depressors. It is important to consider what the baseline rates are when making these comparisons, especially when comparing the degree of effects between different cell types. For example, if baseline rates for sensitizers were overall higher, it would mean the difference in gain of response would be lower, and could affect the results in the opposite direction of what is claimed. One option to account for this would be to z-score the overall responses, using the same normalization for locomotion and rest. We also suggest plotting differences in sensitisers, intermediates, and depressors as a function of firing rate. Matching for firing rate across each PC categorization and calculating delta AI for each matched firing rate bin.

1b) Figure 2A-F: The above is an even more significant issue when it comes to estimating spiking rates. The methods do not state how dF/F is calculated. If these are based on using the pre-stim as the reference, the algorithms for spike rate used might not be appropriate if this were used. Using pre-stimulus referencing could result in the estimate going into the wrong range in the calculation of the spike rate.

1c) In both cases above, it could be a problem if baseline firing rates are different between cell types, or states (locomotion/stationary). The latter is established to have effects on many cell types measured, and so needs to be accounted for very carefully.

1d) It would be informative to see per-neuron comparison for adaptive indices during rest and locomotion states. This could be visualized using a scatter plot with AI-rest vs. AI-locomotion for Figures 1D- 1F and 2J- 2L.

1e) Are neurons more strongly modulated between locomotion and rest, also more likely to experience a shift in AI indices (i.e. delta AI). Is there a correlation between the change in firing rate between behavioral states and Delta AI (Loco-Rest)? If so, is this present for all neuron subtypes (e.g. VIP, SST, and PV)?

1f) Optogenetic inhibition of VIP neurons on average abolished the slow depressive effects of adaptation in SST (Figure 3). The strength and prevalence of this effect are unclear. Perhaps one can perform a bootstrap control and opto AI indices and calculate whether AI was significantly reduced following optogenetics inhibition, and if so, on average, how likely was this to occur for the recorded SST neurons? This is important in knowing that the average effects (Figure 3D) aren't driven by a portion of SST neurons, especially as this is later used to confirm the region of parameter space and affects the subsequent results in Figure 4.

(2) Statistics for the effects. There is a mention of Liner mixed models, but no information is given on the actual models being used and tested. This is particularly for the case of Figure 1G, where there is a composition of effect sizes between different populations. What precise significance test is being used? Are the stats on paired cells when considering locomotion and rest?

(3) Model parameters: It is acknowledged that there is a large range of parameters that can model the responses effectively, up to 11% of initial conditions. At 9000 initial conditions, this is around 1000. The parameter estimates are then considered as the mean of each parameter. This seems like a strange choice for a few different reasons:

3a) A mean solution might not be one of the solutions. Let's say the parameters range over a large dimensional space. They could occupy non-overlapping / discontinuous subspaces. In that case, the mean parameters do not necessarily fall within the solution subspaces. Therefore, this reduction to means might not be valid.

3b) Compare distributions rather than means. There are multiple distributions of parameters between conditions. All stats should be on the comparison of distributions rather than just the means.

(4) Visualizing weight matrices: It is very challenging to interpret the weight matrices. Furthermore, it appears that the stationary and locomotion conditions fit independently, and given the large parameter spaces, it is even harder to interpret. Can the fitting instead be done by fitting on one and using those at the initial conditions for the other state? Figure 7 shows an initiative cartoon, but it is not clear how the matrices in Figures 5 and 6 lead to the summary shown in Figure 7. It is also not clear why the connections between inhibitory neurons are not shown in Figure 7. One option is to perhaps run some kind of dimensionality deduction on the parameter space to better interpret the data. When showing deltaWeights, was the model initialised with 'Rest' weights and allowed to change? It is not obvious what the difference is between 'relative change in connection weights' and 'relative change in synaptic weights'.This needs to be clarified.

4a) Model parameters were reduced differently for locomotion and rest (Figure 4). We suggest evaluating the results for locomotion and rest using the same chi-square value of 3 for both behavioral states (at least in controls).

Reviewer #3 (Public review):

Major Comments on first version:

- The manuscript presents a beautiful set of high-quality images showing expression of lens differentiation markers over time in the organoids. The set of experiments is very robust, with high numbers of organoids analysed and reproducible data. The mechanism by which lens specification is promoted in these organoids is, however, poorly analysed, and the reader does not get a clear understanding of what is different in these experiments, as compared to previous attempts, to support lens differentiation. There is a mention to HEPES supplementation, but no further analysis is provided, and the fact that the process is independent of ECM contradicts, as the authors point out, previous reports. The manuscript would benefit from a more detailed analysis of the mechanisms that lead to lens differentiation in this setting.

- The markers analysed to show onset of lens differentiation in the organoids seem to start being expressed, in vivo, when the lens placode starts invaginating. An analysis of earlier stages is not presented. This would be very informative, allowing to determine whether progenitors differentiate as placode and neuroepithelium first, to subsequently continue differentiating into lens and retina, respectively. Could early placodal and anterior neural plate markers be analysed in the organoids? This would provide a more complete sequence of lens vs retina differentiation in this model.

- The analysis of BMP and Fgf requirement for lens formation and differentiation is suggestive, but the source of these signals is not resolved or mentioned in the manuscript. Are BMP4 and Fgf8 expressed by the organoids? Where are they coming from?

- The fact that the lens becomes specified in the centre of the organoid is striking, but it is for me difficult to visualise how it ends up being extruded from the organoid. Did the authors try to follow this process in movies? I understand that this may be technically challenging, but it would certainly help to understand the process that leads to the final organisation of retinal and lens tissues in the organoid. There is no discussion of why the morphogenetic mechanism is so different from the in vivo situation. The manuscript would benefit from explicitly discussing this.

Significance:

This study describes a reproducible approach to differentiate ocular organoids composed of lens and retinal tissues. The characterisation of lens differentiation in this model is very detailed, and despite the morphogenetic differences, the molecular mechanisms show many similarities to the in vivo situation. The manuscript however does not highlight, in my opinion, why this model may be relevant. Clearly articulating this relevance, particularly in the discussion, will enhance the study and provide more clarity to the readers regarding the significance of the study for the field of organoid research, ocular research and regenerative studies.

Comments on revised version:

The authors presented substantial additional experimental evidence that further strengthens their manuscript and addressed with these experiments and their revised results/discussion in the manuscript the comments and suggestions from the reviewers. I think the manuscript has been greatly improved with the additions presented.

Reviewer #3 (Public review):

In this manuscript Moss et al. demonstrate that Hsp70 phosphorylation at a conserved threonine residue integrates DNA damage responses with cell-cycle control. The authors present unbiased biochemical, cell-based, and yeast genetic analyses showing that phosphorylation of human Hsp70 at T495 (and the analogous Ssa1 T492 in yeast) is triggered by base-excision-repair intermediates and downstream DDR kinase activity, leading to delayed G1/S progression after DNA damage. They used orthogonal approaches such as ATPase assays, phospho-specific detection, kinase-inhibition studies, synchronization experiments, and phenotypic analyses of phosphomutants. They presented robust data which collectively supported the conclusion that dynamic Hsp70 phosphorylation functions as a conserved "molecular brake" to prevent inappropriate S-phase entry under genotoxic stress.

Comments on revisions:

The authors have addressed all my questions and concerns.

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

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

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

Weaknesses:

(1) The manuscript has improved methodologically and analytically after the review. Several weaknesses remain, in my opinion, but still findings are valuable and the evidence can be considered as convincing.<br /> a) fMRI data have low resolution (3 cubic mm), which certainly limits the examination of small nuclei such as the ones investigated here, and especially the examination of the LGN and inferior pulvinar.<br /> b) fMRI was normalized to standard space. Analyzing the data in individual-subject space would have given you the options of avoiding altering every participant's brain and of using more precise atlases than the normalized AAL for ROI selection.<br /> c) Motion during scanning was poorly controlled. Including the motion parameters as covariates of no interest in the GLM/analysis does not fully guarantee that motion is not influencing the results, and that motion is not differentially influencing some experimental conditions more than others.

Reviewer #3 (Public review):

Summary:

This is an interesting paper. The process of tooth exfoliation and replacement in vertebrates remains an intriguing and fascinating subject of inquiry. As the scientists noted, there are no mammalian models that can be used to examine signaling pathways in real time.

Strengths:

This work integrates in vivo and high-resolution transcriptomics. The study confirms previous findings and emphasizes the need for additional research into the processes that drive the restoration of missing teeth for future therapeutic uses.

Weaknesses:

I disagree with the use of the phrase "plucking". Instead, the authors use tooth extraction or tooth removal, which is clinically more correct for the procedure they are doing.

The title is rather broad and appears to be more appropriate for a review than an original research work. I would advise specifying the species under research and/or the sort of damage model used in the transcriptome analysis.

It's uncertain whether the findings are exclusively based on regeneration. The presence of tooth remnants, as well as unintended harm to surrounding tissues, may have triggered repair mechanisms, thereby biasing the current data. How did the authors handle this issue? The oral cavity was under severe manipulation, increasing the inflammatory stimuli, a situation that does not take place in physiological exfoliation.

The authors indicated the use of microCT analysis; however, no such information appears in the main text. In fact, this manuscript lacks anatomical information. It is required to conduct histological examinations of the regenerated teeth at various time points.

Although the current findings confirm previously found and verified signaling pathways, the absence of functional data lends uniqueness to this work.

Reviewer #3 (Public review):

Summary:

The authors of this report wish to show that distinct populations of meningeal macrophages respond to cortical spreading depolarization (CSD) via unique calcium activity patterns depending on their location in the meningeal sub compartments. Perivascular macrophages display calcium signaling properties that are sometimes in opposition to non-perivascular macrophages. Many of the meningeal macrophages also displayed synchronous activity at variable distances from one another. Other macrophages were found to display calcium signals in response to dural vasomotion. CSD could induce variable calcium responses in both perivascular and non-perivascular macrophages in the meninges in part due to RAMP1 dependent effects. Results will inform future research on the calcium responses displayed by macrophages in the meninges under both normal and pathological conditions.

Strengths:

Sophisticated in vivo imaging of meningeal immune cells is employed in the study which has not been performed previously. A detailed analysis of the distinct calcium dynamics in various subtypes of meningeal macrophages is provided. Functional relevance of the responses are also noted in relation to CSD events.

Weaknesses:

Specificity of the methods used to target both meningeal macrophages and RAMP1 are limited. A discussion section on potential pitfalls is included to address this.

Reviewer #3 (Public review):

The authors build on the large body of their previous research, which showed that the mouse primary visual cortex is organised into two types of clusters, M2+ and M2-, which exhibit distinct input patterns from thalamus and higher visual cortical areas and distinct visual tuning preferences. The current study reveals that a like-to-like projection from within-cluster neurons to the areas that provide feedback projections and, furthermore, that neurons in the M2- clusters are more strongly affected by non-visual signals about the locomotion of the animal.

The study adds fundamental insights to our understanding of the principles of cortical organisation and computation, specifically how the cortex integrates sensory and action-related signals.

While the tracing data are very convincing, data analysis should be strengthened to support the claims:

(1) The locomotion modulation index (LMI) compares the mean activity during running and not running but does not seem to account for differences between visual stimuli, so that the LMI could be influenced by the neuron's visual tuning rather than its sensitivity to locomotion, e.g. if the mouse was running more when the neuron's preferred stimulus was presented. Trials should first be averaged per stimulus, and then across stimuli. Alternatively, only the preferred stimulus could be considered.

The significance test (unpaired t-test) suffers from the same flaw. Instead an ANOVA (with stimulus parameter as factor) would resolve the problem, or testing whether fitting the data with two tuning curves (one per locomotion state) or a single curve results in a lower error (using cross-validation).

Given that there is evidence that specific visual stimuli can induce more or less running in mice, this issue is very important to account for behavioural differences across stimuli.

(2) All bars in Figure 2b show a lower LMI than the reported mean LMI of 0.19. This should be checked.

(3) Correlation tests: Pearson correlation is only meaningful when applied to continuous data. A more suitable test for discrete data like the M2 patch quantile is a rank test like Kendall's coefficient of rank correlation. This applies to data in Figure 2b,c, 4j,k, Figure 2 - Supplement 2,1a, etc.

(4) How OSI was determined should be clarified. Specifically, were R_pref and R_ortho the mean responses to the two opposite movement directions? Similarly, how was the half-width at half-maximum of orientation determined? From the fits in Figure 2a, it looks like the widths of both Gaussians can be different.

(5) The correlation measures in Figure 3 would greatly benefit from additional analyses to help interpretation of the results.

a) Correlations between neurons typically increase with increasing firing rates (e.g., de la Rocha J, Doiron B, Shea-Brown E, Josić K, Reyes A. 2007. Correlation between neural spike trains increases with firing rate. Nature 448:802-6. doi:10.1038/nature06028). Could the higher correlations in M2+ pairs (Figure 3a) be explained by higher firing rates in M2+ compared to M2- neurons?

b) To determine correlations in Figure 3a, trials during locomotion and stationarity were pooled. As locomotion impacts the firing rate of the neurons, it would be helpful to separate correlations between the two states, locomotion vs stationarity, so the measures reflect something closer to "noise correlations" rather than tuning to locomotion.

c) Similarly, in Figure 3b, I wonder whether the large correlations in M2- pairs are driven by locomotion rather than functional connectivity. As suggested in b, a better test of noise correlations would be to account for locomotion, i.e., separate trials by stimulus identity and locomotion state. To prevent conditions with few trials from having greater weight in the overall noise correlations, I suggest the authors first z-score responses per condition, then determine noise correlations across all trials (as explained in Renart et al., 2010).

d) Correlations in Figure 3a,b should be tested with an ANOVA and a control for multiple tests.

(6) In plots like Figure 4j-l, it would be very informative to show individual measures (per ROI and mouse) in addition to mean +- SEM. As the counts are low (<10) it wouldn't obstruct the plot.

(7) The caption of Figure 4l says that most retrogradely labelled cells are located in L2/3. However, the plot only shows data from L2/3 and a single section of L4, so one cannot compare it to other layers. Can the authors corroborate the claim with data from other layers?

(8) Methods:<br /> The authors should provide more details on the visual stimuli: What was the background on which gratings were presented? How long was the inter-stimulus interval? What was presented during the inter-stimulus interval? How large were gratings used to map tuning to SF, TF, and orientation?

Reviewer #3 (Public review):

Summary:

This manuscript uses a novel paradigm to demonstrate that rotational motion patterns in the retinal image, called curl, directly influence perception of heading direction. This means that it is not necessary to recover the focus of expansion, defined by the point of zero motion when moving along a straight trajectory toward a target, as is commonly thought.

Strengths:

It has long been accepted that the focus of expansion of the optic flow field generated by self-motion is used to guide heading direction. While there have been many challenges to the need to recover the focus of expansion when gaze is not in the direction of travel, it is still not well understood how retinal motion patterns contribute to heading perception. Recent work has demonstrated the complexity of the retinal motion patterns during natural walking, where body motion adds a rotational component. A rotational component also results from curved paths as well as gaze off the direction of travel. This rotational component is called curl. The primary contribution of this manuscript is to demonstrate convincingly that curl influences perception of heading, and that it is not necessary to recover the focus of expansion.

A strength of the manuscript is that realistic retinal motion patterns are generated by recording the image sequences generated by a walker in a virtual environment, and then using those patterns as stimuli in the experiment. This allows the creation of the more complex flow patterns that are a consequence of the bob and sway of natural walking, which are often considered a minor factor. The elegant experimental design allows direct manipulation of the curl signal, and this in turn directly influences measured heading perception. Another strength is that the authors ground their findings in control theory and neural computations, using a model that produces human-like path trajectories.

The study is timely, given the long history of this question, together with the growing understanding of the complexity of naturally generated retinal motion and the absence of direct evidence for the way that these motion patterns are used in heading perception. It adds an important piece of evidence for how retina-centered optic flow may be used by the visual system, which is critical for our understanding of motion processing in the brain.

Weaknesses:

The primary limitation of the paper is that it avoids discussion of some of the inevitable complexities of heading perception. The main issue is what exactly is meant by heading. Different behaviors evolve over different timescales. The geometry of retinal motion defines instantaneous heading, which varies widely through the gait cycle. Time-varying information like this is known to be important in the momentary control of balance. Heading can also be thought of as steering the body toward a distant goal, which evolves over longer timescales. The current manuscript appears to be concerned with heading information integrated over a few seconds and seems to provide evidence that heading is indeed integrated over the gait cycle. The issue of the time scale of the computation is touched on, but it is not related to how it might be used in normal walking or what situations it might apply to. Steering toward a distant goal during walking is not a very difficult problem and may not require evaluation of retinal motion, but control of balance is more challenging and may depend critically on curl. Consequently, the timescale of the computation needs to be considered in order to understand what is meant by heading.

Reviewer #3 (Public review):

Summary:

This study used a pre-registered novel behavioural paradigm and computational modelling to investigate multi-sensory influences on detection and confidence. Participants performed amodal detection of auditory and visual stimuli (indicating that a stimulus was there when either an auditory stimulus or a visual stimulus or both were present), followed by amodal and unimodal confidence ratings. Detection was higher when both stimuli were present, and the presence of one modality increased the confidence in the presence of the other modality. In contrast to previous detection studies, confidence was higher for absent than for present judgements, but metacognitive efficiency was higher for present judgements. Metacognitive sensitivity was higher for bimodal stimuli, but this was not the case for metacognitive efficiency, suggesting that the sensitivity might be driven by first-order performance. The computational model showed that both detection and confidence in absence followed a disjunctive evidence integration rule, while confidence in presence followed a conjunctive integration rule.

Strengths:

The paper has several major strengths. Firstly, it addresses a novel research question using an innovative and well-controlled paradigm. Furthermore, the paradigm and analyses were pre-registered, and all effects that were interpreted were replicated in two independent samples. Finally, the paper uses an advanced computational model to capture counterintuitive patterns in the data.

Weaknesses:

The major weakness of the paper is the narrative structure. It is not always clear how the different analyses relate to the main research question. Many different effects are reported in terms of detection accuracy, bias, confidence and metacognition, as well as cross-modal and unimodal versus bimodal effects. It would help readability if the paper were streamlined in terms of the research question that is being answered, which I believe is specifically about multimodal absence judgements. Relatedly, for a reader not intimately familiar with the metacognition literature, the difference between MRatio, metacognitive sensitivity and metacognitive efficiency is not obvious. It would be good to clarify this more in the manuscript.

In general, the conclusions drawn by the authors seem to be supported by the results. However, I was missing quantitative model comparisons between the conjunctive and the disjunctive models and an explanation of why the models systematically overestimated the confidence ratings. Furthermore, the 'perceptual multisensory interference' section reports on very interesting effects, but these are not supported by statistical tests in the main text. It would help to assess the strength of the claims if the statistical evidence in favour of these claims were presented together in the main text.

One other concern is that in real-world multi-sensory perception, such as the mosquito example in the introduction, the auditory and visual signals have a strong natural association, which means that if you hear the auditory signal, you expect that you will see the visual signal soon and vice versa. As far as I understood, this association was not present in the current paradigm, which might influence the type of effects that one would expect to see.

Reviewer #3 (Public review):

Summary:

Sugarman et al. present a microCT scan of a hatchling octopus from the species Octopus bimaculoides. The scan is publicly available and poses as a valuable tool for the field of cephalopod biology. Using this scan, the authors uncover two undescribed neural pathways: the intermediate longitudinal tract (iLTs) in the axial nerve cord linking the suckers to the brain, and the arm-to-arm u-tracts (AAUTs) interconnecting neighboring arms. How the eight sucker-lined octopus arms are coordinated with one another and with the brain have been long standing questions in the octopus motor control field, and the results presented here have promise for addressing these questions. However, major weaknesses addressed below limit the interpretability of the dataset.

Strengths:

The authors have publicly published a scan of an entire hatchling octopus, with major organs and subdivisions of the nervous system already segmented. Accessing the data is straightforward, and the authors provide adequate instructions on how to navigate the dataset.

The authors provide validation of the AAUTs using lucifer yellow and wheat germ agglutinin. To overcome motion artifact in the hatchling dataset, the connections between the iLTs and the suckers are validated with a microCT scan of a distal section of adult arm.

Weaknesses:

Given the resolution of the dataset, neural connectivity is determined by texture differences alone, which can be misleading. As such, any claims of anatomical connectivity will need further validation, ideally with tracing techniques. While the authors investigated the AAUTs with other techniques, no such validation exists for the iLTs. Furthermore, the authors themselves state that as the iLTs converge with the brachial nerve, they become indistinguishable from other fibers. Any connections of the iLTs to the brain are only hypothesized, despite their claim of demonstrating a clear pathway from the suckers to the brain.

The relevant prior research on octopus neurobiology is not well explained, making it challenging to understand the significance of the results in a broader context.

Reviewer #3 (Public review):

Summary:

The manuscript by Patel et al investigates the hypothesis that CDHR1a on photoreceptor outer segments is the binding partner for PCDH15 on the calyceal processes, and the absence of either adhesion molecule results in separation between the two structures, eventually leading to degeneration. PCDH15 mutations cause Usher syndrome, a disease of combined hearing and vision loss. In the ear, PCDH15 binds CDH23 to form tip links between stereocilia. The vision loss in less understood. Previous work suggested PCDH15 is localized to the calyceal processes, but the expression of CDH23 is inconsistent between species. Patel et al suggest that CDHR1a (formerly PCDH21) fulfills the role of CDH23 in the retina.

The experiments are mainly performed using the zebrafish model system. Expression of Pcdh15b and Cdhr1a protein is shown in the photoreceptor layer through standard confocal and structured illumination microscopy. The two proteins co-IP and can induce aggregation in vitro. Loss of either Cdhr1a or Pcdh15, or both, results in degeneration of photoreceptor outer segments over time, with cones affected primarily.

The idea of the study is logical given the photoreceptor diseases caused by mutations in either gene, the comparisons to stereocilia tip links, and the protein localization near the outer segments. The work here demonstrates that the two proteins interact in vitro and are both required for ongoing outer segment maintenance. The major novelty for this paper would be the demonstration that Pcdh15 localized to calyceal processes interacts with Cdhr1a on the outer segment, thereby connecting the two structures. Unfortunately, the data as presented are inadequate proof of this model.

Strengths:

The in vitro data to support the ability of of Pcdh15b and Cdhr1a to bind is well done. The use of pcdh15b and cdhr1a single and double mutants is also a strength of the study, especially being that this would be the first characterization of a zebrafish cdhr1a mutant.

This is a large body of data.

Weaknesses:

(1) I have serious concerns about the quality of the imaging here. The premise that cdhr1a/pcdh15 juxtaposition is evidence for the two proteins mediating the connection between outer segments and calyceal processes requires very careful microscopy. The SIM images have two major issues - one being that the red and green channels are misaligned and the other being evidence of bleed through between the channels. This is obvious in Fig 2A but likely true across all the panels in Fig 2, and possibly applies to confocal images in Fig 1 as well. The co-labelling with actin shows very uneven, punctate staining for actin bundles.

(2) The newly added TEM and transverse sections include colored regions that obscure the imaging.

(3) The quantification should be done with averages from individual fish. Counting individual measurements as single data points artificially inflates the significance. Also, the cone subtypes are still lumped together for analysis despite their variable sizes.

(4) I highlighted previously that the measurement of calyceal processes was incorrect. The redrawn labels in Fig 7 are now more accurate, although still difficult to interpret. However, the quantification in Fig 7O is exactly the same. How can that be if the measurement region is now different?

(5) Lower magnification views would provide context for the TEM data.

(6) The statement describing the separation between calyceal processes and the outer segment in the mutants is still not backed up by the data.

(7) The authors state "from the fact that rod CPs are inherently much smaller than cone CPs". This is now referenced, but incorrectly. Also, the issue of pigment interference was not addressed.

(8) The images in panels B-F of the Supplemental Figure are uncannily similar, possibly even of the same fish at different focal planes.

Reviewer #3 (Public review):

Summary:

The authors proposed to use 5-layer systems level analysis (genomics, transcriptomics, proteomics / protein degradation, metabolomics, phosphoproteomics) to uncover how post-transcriptional mechanisms regulate stage differentiation in Leishmania donovani.<br /> This enabled the identification of several potential regulatory networks, including the regulation of stage-specific gene clusters by RNA stabilisation or decay, proteasomal degradation and protein phosphorylation.<br /> In the new version of this manuscript, the authors have addressed all questions raised by the reviewers.

Strengths:

Although some observations in this study have already been described in the literature, the integrated analysis applied here provides a novel view on how different levels of post-transcriptional networks regulate Leishmania differentiation. This "5-layer system" represents the first analysis of this depth in kinetoplastid parasites.<br /> The revised version with an increased sample number for the RNA-seq now made the authors assumptions adequate to their obtained data.<br /> The use of a proteasomal inhibitor adds an interesting insight in how protein degradation is involved in the parasite differentiation, confirming previous observations in the literature, and help to explain the discrepancies between mRNA and protein expression in the different stages.

Weaknesses:

While this work provides an impressive and foundational dataset, it opens the door for future research to rigorously validate these initial findings and conclusions.

Significance and Impact in the field.

The different datasets generated in this study will be of great interest to the parasitology community, either to be used for hypothesis generation, to validate data from other sources, etc.

The multi-layered analysis performed here identified a series of potential feedback loops and regulatory networks to be further explored in organisms that lack transcriptional control.

Reviewer #3 (Public review):

Summary:

This study addresses the role of MIRO1 in vascular smooth muscle cell proliferation, proposing a link between MIRO1 loss and altered growth due to disrupted mitochondrial dynamics and function. While the findings are useful for understanding the importance of mitochondrial positioning and function in this specific cell type, the main bioenergetic and mechanistic claims are not strongly supported.

Strengths:

- This study focuses on an important regulatory protein, MIRO1, and its role in vascular smooth muscle cell (VSMC) proliferation, a relatively underexplored context.<br /> - This study explores the link between smooth muscle cell growth, mitochondrial dynamics, and bioenergetics, which is a significant area for both basic and translational biology.<br /> - The use of both in vivo and in vitro systems provides a useful experimental framework to interrogate MIRO1 function in this context.

Weaknesses:

- Some key bioenergetic aspects may require further investigation.

Comments on revisions:

The authors have adequately addressed most of the concerns I raised. I would suggest adding some of the justifications provided to the reviewers to the manuscript to further clarify and aid interpretation of the data, especially for the bioenergetic part (e.g., the proposed interaction with CI components, which might otherwise appear implausible to readers).

Reviewer #3 (Public review):

Summary:

The authors investigate how HIV-1 Env engagement affects the nanoscale organization and dynamics of the CXCR4 coreceptor on target cells. Using single-particle tracking TIRF microscopy, they analyze CXCR4 distribution following exposure to gp120 or HIV virus-like particles, including both wild-type CXCR4 and the WHIM-associated CXCR4.R334X variant. The study further examines the role of CD4-CXCR4 heterodimerization and contrasts Env-induced receptor organization with that elicited by the natural ligand CXCL12.

Evaluation:

A major strength of this work is the integration of high-resolution imaging with functional and comparative analyses that distinguish Env-induced CXCR4 clustering from chemokine-driven effects. The experiments are clearly described, include appropriate controls, and are supported by quantitative analyses that are consistent across experiments. The revised manuscript appears to have addressed many of the technical and interpretive issues raised during initial review, improving clarity around data analysis and strengthening confidence in the conclusions.

I am not an expert in TIRF microscopy or single-molecule tracking and defer to other reviewers regarding limits of imaging and tracking methods. However, I did not identify major inconsistencies between the biological data presented and the conclusions drawn.

The authors data support the conclusion that HIV-1 Env, delivered as gp120 or virus-like particles, promotes CD4-dependent nanoscale clustering of CXCR4, including the CXCR4.R334X variant associated with WHIM syndrome, in a manner distinct from CXCL12-induced receptor organization. The authors are generally careful to frame their conclusions in proportion to the evidence and avoid overinterpretation.

Overall, this study builds on prior work on CXCR4 distribution and HIV entry by providing higher-resolution insight into receptor nanoclustering and its modulation by Env. The findings provide a mechanistic refinement rather than a conceptual paradigm shift but is a valuable dataset useful to researchers studying HIV entry, coreceptor biology, and membrane receptor organization.

Reviewer expertise: HIV-1 Envelope glycoproteins and entry assays, HIV broadly neutralizing antibodies, HIV vaccine design

Comments on revised version:

This reviewer has no further recommendations and thanks the authors for clarifying that the Env content in gp120-VLPs was lower than the NL4-3deltaIN particles but that the percentage of mature particles in the gp120-VLPs was higher.

Reviewer #3 (Public review):

Summary:

Yang et al sought to describe central brain circuits that underlie nociception-induced escape in Drosophila using a combination of neurogenetic tools to silence subsets of neurons and to trace their postsynaptic connections. They present interesting data that identify subsets of DANs and MBONs that are required for a jumping response to an aversive stimulus, but not for baseline locomotion, and present a model for linking peripheral nociception to MB- dependent escape behavior.

Strengths:

They use an innovative avoidance assay to elicit a robust behavioral response and use trans-tango to identify downstream targets of painless and TrpA1-expressing neurons.

Weaknesses:

This reviewer's enthusiasm for the study is lowered due to an incomplete description of methods, methods section, appropriate behavioral controls, immunohistochemistry data, and a complete behavioral screen of DANs and MBONs. Below I list my suggestions, questions, and criticisms.

(1) Behavioral studies are interesting. The assay is simple, yet innovative. However, there is no power analysis or explanation of how sample sizes were selected. I commend the authors for including a positive control; however, although UAS-controls are present, there are no GAL4-controls included in the study. Given that many of the lines used for behavior are split-GAL4's, it's unclear if the additional transgene influenced behavior. This should be addressed.

(2) It is also not clear from the methods how the behavior was run and how it was analyzed. Was baseline locomotion recorded before the laser was introduced? I assume this is the case; however, more importantly, how long after the flies were introduced to the arena were baseline recordings collected? How much data was used to calculate velocity? Were the experimenters blind to the conditions they were assessing? More detail in the methods is essential for understanding the data and providing an opportunity to replicate results.

(3) At times, the authors describe "locomotion velocity" as baseline locomotion, but other times, they describe it as escape velocity (see reference to Figure 1F). The authors should clarify whether escape velocity was calculated.

(4) Immunohistochemistry: There is a lack of detail regarding a description of the flies used for trans-tango experiments. How many brains were evaluated? Was there variability across brains? Were the flies males or females? This is an important detail as sex could impact the level of expression of the ligand and therefore the results. It is also not clear at what age these flies were dissected and at what temperature they were raised. This can also significantly affect the post-synaptic signal that is measured (see Talay et al 2017).

(5) Figure 2 shows the overlap of trans-tango and dopamine signal, but there is no signal for the GAL4-line to evaluate the overlap between presynaptic signal and postsynaptic signal. This expression is an important consideration and should be included.

(6) Expression of the GAL4 lines in the central brain is also important to show because the authors suggest that, because painless and TrpA1 expression does not fully overlap in peripheral tissue, it might converge in the central brain. Does that central brain expression of painless and TrpA1 overlap?

(7) Further, although the authors clearly label the different dopamine subsets (PPL1, PAL, and PAM), some orientation with regard to where these images were taken would be helpful. I recommend a stack showing the location of the cell bodies and then a zoom in to see the overlap.

(8) Behavioral data for DANs and MBONSs: I recommend that the authors discuss the results by the neurons that are targeted and not the driver lines. For instance, the authors suggest they get the largest effects for 433B, 434B, and 298B, but all of these lines target very similar neuronal subsets y4>y1y2. It's also not clear why different split-lines were selected. Several of the lines have overlapping expression, and other compartments were not included at all. In order to determine which MBONs and DANs are required for escape behavior, all MBONs and DANs should be included. See Aso et al for a list of recommended lines for behavior based on specificity and intensity.

(9) Based on trans-tango data, it is not clear why the authors focus exclusively on PPL1 and PAM when PAL, PPM1, 2, 3, and PPL2 also overlap with painless and trpA1. Certainly, PPL1 and PAM DANs innervate the MB, but so do some of the other DANs identified.

(10) For Figure 5, the titles of A and B are DANs and MBONs, but it is really showing the average jumping response when neurons that innervate MB compartments are silenced. Many DANs and MBONs innervate multiple compartments (PPL1-a`2a2, etc.); thus, if the intention is to identify neural circuits that modulate escape response, the analysis should focus on the neurons, not the MB compartments. I recommend reorganizing this data so it highlights the DANs and MBONs instead of the MB compartments. I also recommend showing error bars for averages and/or raw data and organizing the x-axes so DAN and MBON compartments can be easily compared.

(11) Lastly, nuance is lost here in the Behavioral Potency Level, given that some of these compartments are over-represented and not adjusted for the strength of expression in different split-GAL4 lines. Aso et al. (2014) recommended specific split-GAL4 lines based on specificity and intensity. Some of the lines that are included in the average Behavioral Potency are not recommended for behavior based on the intensity of expression, which could significantly influence the potency score.

Reviewer #3 (Public review):

Summary:

This study presents a novel device for wireless control of optogenetic stimulation of the mouse brain, the Blueberry, using Bluetooth Low Energy (BLE) communication for parallel activation of up to 4 devices through an Arduino interface. The authors also present two types of brain implants for light delivery that can be connected to the Blueberry: one using uLEDs for surface cortical stimulation, and another using optical fibers for intra- or sub-cortical implants. The architecture of the system, including electronics, communication, and programming, is thoroughly described. Because the system was especially designed to be integrated with existing software used for neuroscience behavioral experiment for closed-loop experiments, validation of the system is shown on two different scenarios: a learning task in a "infinite" Y-maze, where light delivery at precise locations conditions arm choice for navigation; and a social interaction analysis where 3 animals are simultaneously stimulated in order to alter social dynamics among the group.

Strengths:

(1) The full system can be built by individual labs with simple PCB printing, off-the-shelf components, and readily available hardware (Arduino) for widespread dissemination.

(2) Four headstages can be controlled in parallel for simultaneous experiments with multiple mice.

(3) Validation across different relevant behavioral tests, demonstrating the potential of integrating Bluberry in closed-loop setups.

Weaknesses:

(1) Some details in the manuscript regarding system characterization (latency, battery life, etc) are included only in the supplementary materials.

(2) The practical details of integration with other commercial and open-source software used for the closed-loop experiments, which could help third-party researchers interested in using the system, are lacking sufficient detail.

(3) System range (3 meters reported) is limited for a BLE device.

(4) Light output amplitude is not programmable, limiting the choice of stimulation protocols and LEDs used.

(5) Thermal modeling of the cortical surface stimulator was not performed, and it is unclear if the brain implant for this purpose is within the safety limits.

(6) The paper is missing a comparison with other state-of-the-art devices for wireless control of optogenetic stimulation in mice.

Reviewer #3 (Public review):

Summary:

This modelling study connects synaptic plasticity, connectivity motifs, and representational drift. The authors combine excitatory and inhibitory STDP with weight normalization and intrinsic plasticity in a recurrent spiking network of AdEx neurons. This combination generates heavy-tailed synaptic weight distributions and supports repeating spike sequences under both unstructured and structured inputs. While global network statistics stabilize over time, individual synapses continue to change, creating a form of drift. Structured inputs further stabilize sequences, yet the network retains flexibility to learn new patterns.

Strengths:

(1) Multi-scale turnover analysis:

The authors study the evolution of individual synapses, 3-neuron motifs, follower neurons, and entire neuronal sequences, revealing distinct turnover timescales.

(2) Fan-in/out motif analysis:

A specific connectivity motif (fan-in/out) is shown to be over-represented in the network and preferentially stabilised by the plasticity rules compared to other possible motifs. This generates interesting insights and testable predictions.

(3) Connection to representational drift:

The connection of ongoing synaptic plasticity to drift is timely and interesting, reproducing observations of macro-level stability and synapse-level turnover with a relatively simple mechanism.

(4) Rigour and thoroughness:

The overall quality of the numerical experiments performed in this study is high, with extensive supplementary material performing various controls to solidify the claims.

Weaknesses:

(1) Limited connection to network function:

Sequence detection relies on a rather artificial protocol (forced spiking of a single neuron 1,000 times), which I suspect mostly tests whether the lognormal tail of the weight distribution can propagate activity. This risks being circular. I think performing the same sequence analysis on a random network/a network with the same weight distribution but shuffled would help understand what comes from a generic heavy-tailed weight distribution and the particular weights potentiated by the plasticity rules used here.

The network, which would classically be evaluated as a memory network, is not assessed on this aspect. While the authors do not overclaim, this limits the impact.

Relatedly, the relearning experiment (Figure 5G) shows catastrophic forgetting. This is acknowledged in the discussion, but the suggested solutions (alternating patterns, plastic readout) are speculative without supporting simulations. This limits the applicability of the model as a memory model or, more broadly, as a model of a brain region/function.

Additionally, in the sequence learning experiments with structured input, the ability to learn seems tied to the very specific timescale of pattern presentation (~10 ms per pattern, comparable to the STDP kernel time constants), arguably faster than the timescale of external stimuli. The stability of sequences may also owe more to the normalization scheme than to STDP per se.

(2) Novelty claims and positioning within the literature:

On page 16, the authors write: "Our results demonstrate that spiking sequences can be generated in randomly connected networks trained by synaptic plasticity even under unstructured inputs, which supports STDP being the main actor, while stabilizing mechanisms such as weight normalization and intrinsic plasticity play a complementary role." (c1).

Several aspects of this work are less novel than the presentation suggests:

(a) The fact that STDP can create sequence-like dynamics/asymmetric connectivity matrices in recurrent networks has been studied theoretically [1,2] and in simulations [3,4,5]. While [3] is cited, the manuscript underplays the similarity. [4] (uncited) considers e+iSTDP with a different homeostatic term to represent sequential stimuli in large recurrent spiking networks. [5] (uncited) also considers a recurrent spiking network with several STDP-like rules and shows that many combinations can store and recall sequential inputs.

(b) Lognormal weight distributions emerging from STDP-based plasticity and the autonomous emergence of connectivity structures have extensive literature. While many of these articles are already cited in the manuscript, I fail to see what this work brings to this matter compared to existing work (particularly [6]).

(c) Several published works challenge the manuscript's implicit claim (c1) that sequences require their particular combination of rules. Many other plasticity mechanisms can create sequences [3,4,5,7,8,9]. Some interpretations may also need to be dialed down: [10] (uncited) showed that sequences can be stored and retrieved using EI and IE plasticity alone. iSTDP may be doing more computational work than acknowledged, which complicates the interpretation of which mechanisms are truly driving the phenomena.

Overall, most of the relevant work is already cited in the manuscript, but not necessarily acknowledged adequately.

(3) Justification of plasticity model/robustness analysis:

The parameters in Tables 1 and 2 are quite specific without strong justification (for instance, different sparsity values for each connection type and specific normalization factors). Without parameter sweeps, it is difficult to know whether the key findings are robust or overfit to this particular network configuration. Given the number of parameters, exhaustive sweeps are out of question, and the argument made previously would still prevent the rule combination proposed from being considered as more than one possible mechanism for sequence generation among many others. However, this deserves to be acknowledged, and potentially a few sweeps to be run (e.g., over LTP/LTD ratio, normalization threshold, and network size). I don't think that Figure S12, which shows that removing any component of the model causes it to break down in some way, is enough to cover alternative plasticity rules.

A related concern is that the network is small by current standards (1,200E + 240I neurons), especially with sparse connectivity (6-20%). Small networks with few connections are susceptible to synchronization (other studies typically consider networks of at least 10k neurons). The authors should discuss whether the phenomena they observe would persist at larger scales and under more biologically realistic connectivity. Specifically, are the intrinsic and normalization plasticity terms as crucial in this case?

(4) Fan-in/out motif evidence is correlational:

The evidence linking the fan-in/out motif to sequence stability appears to be correlational. Properly establishing causality would require targeted ablations or rewiring of fan-in/out connections. While designing a clean causal intervention may be difficult, the correlational nature of the evidence should be stated explicitly.

Conclusion:

To summarize, the manuscript would benefit from:

(1) Reframing the contribution:

Multi-scale turnover analysis and the discussion around representational drift as the core novelties. I would reposition sequence emergence and lognormal distributions as reproducing known results under a specific plasticity model and analysis method.

(2) Acknowledging that many rule combinations could produce equivalent outcomes, and not suggesting that the combination chosen here is special.

(3) Adding parameter sensitivity analysis or, at a minimum, discussing robustness.

References:

[1] Kempter, Gerstner and van Hemmen, Hebbian learning and spiking neurons, 1999, PRE

[2] Ocker, Litwin-Kumar and Doiron, Self-organization of microcircuits in networks of spiking neurons with plastic synapses, 2015, plos CB<br /> (Theoretical account of STDP in spiking networks and motifs, though it only looks at 2-synapse motifs (not fan-in/fan-out)).

[3] Fiete et al., Spike-Time-Dependent Plasticity and Heterosynaptic Competition Organize Networks to Produce Long Scale-Free Sequences of Neural Activity, 2010, Neuron

[4] Duarte and Morrison, Dynamic stability of sequential stimulus representations in adapting neuronal networks, 2014, Frontiers in Comp Neuro

[5] Confavreux et al., Memory by a thousand rules: Automated discovery of functional multi-type plasticity rules reveals variety and degeneracy at the heart of learning, 2025, bioRxiv

[6] Zheng, Dimitrakakis and Triesch , Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex, 2013, plos CB

[7] Zheng and Triesch, Robust development of synfire chains from multiple plasticity mechanisms, 2014, Front Comp Neuro

[8] Ravid Tannenbaum and Burak, Shaping Neural Circuits by High Order Synaptic Interactions, 2016, plos CB

[9] Bell, Duffy, and Fairhall, Discovering plasticity rules that organize and maintain neural circuits, 2024, NeurIPS

[10] Gong and Brunel, Inhibitory Plasticity Enhances Sequence Storage Capacity and Retrieval Robustness, 2024, bioRxiv