Reviewer #1 (Public Review):

As a pathogen, S. aureus has evolved strategies to evade the host's immune system. It effectively remains 'under the radar' in the host until it reaches high population densities, at which point it triggers virulence mechanisms, enabling it to spread within the host. The agr quorum sensing system is central to this process, as it coordinates the pathogen's virulence network in response to its cell density. When a threshold cell density (quorum) is reached, individual cells in the population detect the quorum peptide (AgrD). This activates the Agr two-component system comprising of a histidine kinase (AgrC) and response regulator (AgrA), leading to the expression of exoenzymes and toxins that facilitate the pathogenicity of S. aureus.<br /> However, previous research has indicated that oxidative stress can possibly bypass agr quorum signaling and inhibit agr-dependent gene expression. Specifically, when exposed to high concentrations of hydrogen peroxide (H2O2), the redox-sensitive cysteines in AgrA can form an intramolecular disulfide bond, preventing AgrA from binding to DNA and initiating gene expression. Moreover, another study has shown that cells which have responded to the quorum peptide are vulnerable to oxidative damage. This damage is mediated by PSM toxins that are produced by quorum peptide responders. Consequently, this oxidative stress leads to the selection of agr mutants that are better adapted to growth in oxygen-rich environments and can exploit the benefits of the products released by the quorum-responding cells.

Given this sensitivity of the agr system to oxidative modification and the fitness cost arising from PSM expression, it raises a pertinent question: how do cells with a functional agr quorum sensing system persist within the population under conditions of oxidative stress without being overtaken by agr mutants? The current study by Podkowik et al. offers a plausible explanation. It suggests that cells that respond to the quorum peptide may be primed against oxidative stress by activating intrinsic mechanisms that reduce not only the endogenous production of harmful ROS but also mitigate their adverse effects on the cell, thus providing a unique benefit to cells that maintain an active agr system. Interestingly, these protective mechanisms are long-lived, and safeguard the cells against external oxidative stressors such as H2O2, even after the agr system has been deactivated in the population.

In their study, the authors present compelling evidence that supports the role of agr in shielding S. aureus from lethal H2O2 stress, and they establish that this protection is connected to the activation of agr-dependent RNAIII and the subsequent block of Rot translation. Importantly, the protective mechanisms that are activated persist throughout growth and provide S. aureus with defense against the host's ROS in a murine intraperitoneal infection model.

However, the study falls short in elucidating the specific intrinsic mechanisms responsible for this long-lasting protection against external ROS. While the authors infer that agr mutants, which are more vulnerable to external H2O2, display an increased respiratory activity and gene expression profile associated with aerobic fermentation, it remains ambiguous whether controlling these mechanisms alone can confer extended protection from external H2O2 in an aerobic environment. Further research is needed to confirm this hypothesis.

In summary, this study reveals that the agr quorum sensing system's role in relation to ROS is multifaceted and more complex than previously thought, as it orchestrates a balance between mechanisms that both induce and mitigate endogenous ROS, ultimately contributing to the pathogenesis of S. aureus.

Reviewer #1 (Public Review):

Sun and co-authors have determined the crystal structures of EHEP with/without phlorotannin analog, TNA, and akuBGL. Using the akuBGL apo structure, they also constructed model structures of akuBGL with phlorotannins (inhibitor) and laminarins (substrate) by docking calculation. They clearly showed the effects of TNA on akuBGL activity with/without EHEP and resolubilization of the EHEP-phlorotannin (eckol) precipitate under alkaline conditions (pH >8). Based on this knowledge, they propose the molecular mechanism of the akuBGL-phlorotannin/laminarin-EHEP system at the atomic level. Their proposed mechanism is useful for further understanding of the defensive-offensive association between algae and herbivores. However, there are several concerns, especially about structural information, that authors should address.

1. TNA binding to EHEP<br /> The electron densities could not show the exact conformations of the five gallic acids of TNA, as the authors mentioned in the manuscript. On the other hand, the authors describe and discuss the detailed interaction between EHEP and TNA based on structural information. The above seems contradictory. In addition, the orientation of TNA, especially the core part, in Fig. 4 and PDB (8IN6) coordinates seem inconsistent. The authors should redraw Fig. 4 and revise the description accordingly to be slightly more qualitative.

2. Two domains of akuBGL<br /> The authors concluded that only the GH1D2 domain affects its catalytic activity from a detailed structural comparison and the activity of recombinant GH1D1. That conclusion is probably reasonable. However, the recombinant GH1D2 (or GH1D1+GH1D2) and inactive mutants are essential to reliably substantiate conclusions. The authors failed to overexpress recombinant GH1D2 using the E. coli expression system. Have the authors tried GH1D1+GH1D2 expression and/or other expression systems?

3. Inhibitor binding of akuBGL<br /> The authors constructed the docking structure of GH1D2 with TNA, phloroglucinol, and eckol because they could not determine complex structures by crystallography. The molecular weight of akuBGL would also allow structure determination by cryo-EM, but have the authors tried it? In addition, the authors describe and discuss the detailed interaction between GH1D2 and TNA/phloroglucinol/eckol based on docking structures. The authors should describe the accuracy of the docking structures in more detail, or in more qualitative terms if difficult.

Reviewer #1 (Public Review):

This manuscript tried to answer a long-standing question in an important research topic. I read it with great interest. The quality of the science is high, and the text is clearly written. The conclusion is exciting. However, I feel that the phenotype of the transgenic line may be explained by an alternative idea. At least, the results should be more carefully discussed.

Specific comments:

1) Stability or activity (Fv/Fm) was not affected in PSII with the W14F mutation in D1. If W14F really represents the status of PSII with oxidized D1, what is the reason for the degradation of almost normal D1?

2) To focus on the PSII in which W14 is oxidized, this research depends on the W14F mutant lines. It is critical how exactly the W-to-F substitution mimics the oxidized W. The authors tried to show it in Figure 5. Because of the technical difficulty, it may be unfair to request more evidence. But the paper would be more convincing with the results directly monitoring the oxidized D1 to be recognized by FtsH.

3) Figure 3. If the F14 mimics the oxidized W14 and is sensed by FtsH, I would expect the degradation of D1 even under the growth light. The actual result suggests that W14F mutation partially modifies the structure of D1 under high light and this structural modification of D1 is sensed by FtsH. Namely, high light may induce another event which is recognized by FtsH. The W14F is just an enhancer.

Reviewer #1 (Public Review):

In this study, the authors aimed to investigate how cells respond to dynamic combinations of two stresses compared to dynamic inputs of a single stress. They applied the two stresses - carbon stress and hyperosmotic stress - either in or out of phase, adding and removing glucose and sorbitol.

Both a strength and a weakness, as well as the main discovery, is that the cells' hyperosmotic response strongly requires glucose. For in-phase stress, cells are exposed to hyperosmotic shock without glucose, limiting their ability to respond with the well-studied HOG pathway; for anti-phase stress, cells do have glucose when hyperosmotically shocked, but experience a hypo-osmotic shock when both glucose and sorbitol are simultaneously removed. Responding with the HOG pathway and so amassing intracellular glycerol amplifies the impact of this hypo-osmotic shock. Counterintuitively then, it is the presence of glucose rather than the stress of its absence that is deleterious for the cells.

The bulk of the paper supports these conclusions with clean, compelling time-lapse microscopy, including extensive analysis of gene deletions in the HOG network and measurements of both division and death rates. The methodology the authors develop is powerful and widely applicable.

Some discussion of the value of applying periodic inputs would be helpful. Cells are unlikely to have previously seen such inputs, and periodic stimuli may reveal behaviours that are rarely relevant to selection.

The authors' findings demonstrate the tight links that can exist between metabolism and the ability to respond to stress. Their study appears to have parted somewhat from their original aim because of the HOG pathway's reliance on glucose. It would be interesting to see if the cells behaviour is simpler in periodically varying sorbitol and a stress where there is little known connection to the HOG network, such as nitrogen stress.

Reviewer #1 (Public Review):

Li, Fan et al. designed and evaluated a reinforcement learning (RL) based model to automate the planning of an optimal path for the collection of data for single particle cryo-electron microscopy. The goal was to maximize the quality of the data while minimizing the time required for acquisition. They use a deep regressor (DR) to rank all the targets in the grid based on their quality as predicted from low-magnification images. In the cryo-RL model, the prediction of the DR is modified by the result of a deep Q-network (DQN) driven by a reward based on the real-time assessment of newly acquired images and a penalty based on the time required to move the microscope stage to explore new areas of the specimen. The DR and the DQN are trained on a set of low-magnification preview images and their corresponding high-magnification recordings labeled based on the quality of fit of the contrast transfer function (the CTFMaxRes parameter). The distribution of quality of a series of non-ranked trajectories was used as a snowball baseline (SB). Importantly, all tests in this paper were performed on four datasets collected by an exhaustive sampling of the grid. Thus, all data is available to all protocols.

When trained on a subset of squares from the same grid, DR+DQN outperforms DR which in turn outperforms SB. To improve transferability between specimens, both DR and DQN were trained with a large dataset sourced from a variety of samples and grid types imaged at the Cianfrocco Lab. Comparison of the performance of Cryo-RL (DR+DQN), DR, SB and of human subjects with different levels of expertise indicates shows that Cryo-RL yields the most high-resolution images in the shortest time. Further, the quality of the maps obtained from subsets of data selected using Cryo-RL is on par with the best datasets collected manually, although the latter showed marked variability.

The demonstration that a low-magnification image contains sufficient information to predict the quality of high magnification counterpart is very encouraging. However, the authors show that this translates into a high-resolution structure for one of the four datasets. The use of CTFMaxRes, although prevalent in the field, is an incomplete estimator of the quality of micrographs. Even though both the DQN and DR can be trained using different criteria, it is not clear how strong a correlation between alternative parameters and the low-magnification images would be.

This study concentrates on three "well-behaved" samples that tend to distribute evenly in the holes. The behavior of many macromolecules, e.g. orientation bias and stability, correlates with ice thickness in convoluted ways. Since ice thickness can vary drastically throughout a single hole, the overall appearance may not be sufficient to ensure a recording of the region where "good particles" concentrate. In these cases, sub-hole characterization from the low-magnification images will be necessary to target the appropriate areas. However, the feasibility of such an approach is yet to be determined. All that said, this is a timely publication that is likely to have a positive impact on the efficiency of data collection for cryo-EM.

Reviewer #1 (Public Review):

The Hedgehog (HH) protein family is important for embryonic development and adult tissue maintenance. Deregulation or even temporal imbalances in the activity of one of the main players in the HH field, sonic hedgehog (SHH), can lead to a variety of human diseases, ranging from congenital brain disorders to diverse forms of cancers. SHH activates the GLI family of transcription factors, yet the mechanisms underlying GLI activation remain poorly understood. Modification and activation of one of the main SHH signalling mediators, GLI2, depends on its localization to the tip of the primary cilium. In a previous study the lab had provided evidence that SHH activates GLI2 by stimulating its phosphorylation on conserved sites through Unc-51-like kinase 3 (ULK3) and another ULK family member, STK36 (Han et al., 2019). Recently, another ULK family member, ULK4, was identified as a modulator of the SHH pathway (Mecklenburg et al. 2021). However, the underlying mechanisms by which ULK4 enhances SHH signalling remained unknown. To address this question, the authors employed complex biochemistry-based approaches and localization studies in cell culture to examine the mode of ULK4 activity in the primary cilium in response to SHH. The study by Zhou et al. demonstrates that ULK4, in conjunction with STK36, promotes GLI2 phosphorylation and thereby SHH pathway activation. Further experiments were conducted to investigate how ULK4 interacts with SHH pathway components in the primary cilium. The authors show that ULK4 interacts with a complex formed between STK36 and GLI2 and hypothesize that ULK4 functions as a scaffold to facilitate STK36 and GLI2 interaction and thereby GLI2 phosphorylation by STK36. Furthermore, the authors provide evidence that ULK4 and STK36 co-localize with GLI2 at the ciliary tip of NIH 3T3 cells, and that ULK4 and STK36 depend on each other for their ciliary tip accumulation. Overall, the described ULK4-mediated mechanism of SHH pathway modulation is based on detailed and rigorous Co-IP experiments and kinase assays as well as confocal imaging localization studies. The authors used various mutated and wild-type constructs of STK36 and ULK4 to decipher the mechanisms underlying GLI2 phosphorylation at the tip of the primary cilium. These novel results on SHH pathway activation add valuable insight into the complexity of SHH pathway regulation. The data also provide possible new strategies for interfering with SHH signalling which has implications in drug development (e.g., cancer drugs).

However, it will be necessary to explore additional model systems, besides NIH3T3, HEK293 and MEF cell cultures, to conclude on the universality of the mechanisms described in this study. Ultimately, it needs to be addressed whether ULK4 modulates SHH pathway activity in vivo. Is there evidence that genetic ablation of ULK4 in animal models leads to less efficient SHH pathway induction? It also remains to be resolved how ULK3 and ULK4 act in distinct or common manners to promote SHH signalling. Another remaining question is, whether cell type- and tissue-specific features exist, that play a role in ULK3- versus ULK4-dependent SHH pathway modulation. In particular for the studies on ciliary tip localization of factors, relevant for SHH pathway transduction, a higher temporal resolution will be needed in the future as well as a deeper insight into tissue/ cell type-specific mechanisms. These caveats, mentioned here, don't have to be addressed in new experiments for the revision of this manuscript but could be discussed.

Reviewer #1 (Public Review):

Summary:<br /> Shibl et al., studied the possible role of dicarboxylate metabolite azelaic acid (Aze) in modulating the response of different bacteria, it was used as a carbon source by Phycobacter and possibly toxic for Alteromonas. The experiments were well conducted using transcriptomics, transcriptional factor coexpression networks, uptake experiments, and chemical methods to unravel the uptake, catabolism, and toxicity of Aze on these two bacteria. They identified a putative Aze TRAP transporter in bacteria and showed that Aze is assimilated through fatty acid degradation in Phycobacter. Meanwhile, in Alteromonas it is suggested that Aze inhibits the ribosome and/or protein synthesis, and that efflux pumps shuttles Aze outside the cytoplasm. Further on, they demonstrate that seawater amended with Aze selects for microbes that can catabolize Aze.

Major strengths:<br /> The manuscript is well written and very clear. Through the combination of gene expression, transcriptional factor co-expression networks, uptake experiments, and chemical methods Shibl et al., showed that Aze has a different response in two bacteria.

Major weakness:<br /> There is no confirmation of the Aze TRAP transporters through mutagenesis.

Impact on the field:<br /> Metabolites exert a significant influence on microbial communities in the ocean, playing a crucial role in their composition, dynamics, and biogeochemical cycles. This research highlights the intriguing capacity of a single metabolite to induce contrasting responses in distinct bacterial species, underscoring its role in shaping microbial interactions and ecosystem functions.

Reviewer #1 (Public Review):

The authors have conducted lots of field work, lab work and statistical analysis to explore the effect of brumation on individual tissue investments, the evolutionary links between the relative costly tissue sizes, and the complex non-dependent processes of brain and reproductive evolution in anuran. The topic fits well within the scope of the journal and the manuscript is generally written well. The different parameters used in the present study will attract a board readership across ecology, zoology, evolution biology, and global change biology.

Reviewer #1 (Public Review):

In this work, the authors examine the mechanism of action of MOTS-c and its impact on monocyte-derived macrophages. In the first part of the study, they show that MOTS-c acts as a host defense peptide with direct antibacterial activity. In the second part of the study, the authors aim to demonstrate that MOTS-c influences monocyte differentiation into macrophages via transcriptional regulation.

Major strengths. Methods used to study the bactericidal activity of MOTS-c are appropriate and the results are convincing.

Major weaknesses. Methods used to study the impact on monocyte differentiation are inappropriate and the conclusions are not supported by the data shown. A major issue is the use of the THP-1 cell line, a transformed monocytic line which does not mimic physiological monocyte biology. In particular, THP-1 differentiation is induced by PMA, which is a completely artificial system and conclusions from this approach cannot be generalized to monocyte differentiation. The authors would need to perform this series of experiments using freshly isolated monocytes, either from mouse or human. The read-out used for macrophage differentiation (adherence to plastic) is also not very robust, and the authors would need to analyze other parameters such as cell surface markers. It is also not clear whether MOTS-c could act in a cell-intrinsic fashion, as the authors have exposed cells to exogenous MOTS-c in all their experiments. The authors did not perform complementary experiments using MOTS-c deficient monocytes. The authors have also analyzed the transcriptomic changes induced by MOTS-c exposure in macrophages derived from young or old mice. While the results are potentially interesting, the differences observed seem independent from MOTS-c and mainly related to age, therefore the conclusions from this figure are not clear. Another concern is the reproducibility of the experiments, as the authors do not indicate the number of biological replicates analyzed nor the number of independent experiments performed.

The different parts of the manuscript do not appear well connected and it is not clear what the main message from the manuscript would be. The physiological relevance of this study is also unclear.

Reviewer #1 (Public Review):

In this manuscript, the authors propose a new codon adaptation metric, Codon Adaptation Index of Species (CAIS), which they present as an easily obtainable proxy for effective population size. To permit between-species comparisons, they control for both amino acid frequencies and genomic GC content, which distinguishes their approach from existing ones. Having confirmed that CAIS negatively correlates with vertebrate body mass, as would be expected if small-bodied species with larger effective populations experience more efficient selection on codon usage, they then examine the relationship between CAIS and intrinsic structural disorder in proteins.

The idea of a robust species-level measure of codon adaptation is interesting. If CAIS is indeed a reliable proxy for the effectiveness of selection, it could be useful to analyze species without reliable life history- or mutation rate data (which will apply to many of the genomes becoming available in the near future).

A key question is whether CAIS, in fact, measures adaptation at the codon level. Unfortunately, CAIS is only validated indirectly by confirming a negative correlation with body mass. As a result, the observations about structural disorder are difficult to evaluate.

A potential problem is that differences in GC between species are not independent of life history. Effective population size can drive compositional differences due to the effects of GC-biased gene conversion (gBGC). As noted by Galtier et al. (2018), genomic GC correlates negatively with body mass in mammals and birds. It would therefore be important to examine how gBGC might affect CAIS, and to what extent it could explain the relationship between CAIS and body mass.

Suppose that gBGC drives an increase in GC that is most pronounced at 3rd codon positions in high-recombination regions in small-bodied species. In this case, could observed codon usage depart more strongly from expectations calculated from overall genomic GC in small vertebrates compared to large ones? The authors also report that correcting for local intergenic GC was unsuccessful, based on the lack of a significant negative relationship with body mass (Figure 3D). In principle, this could also be consistent with local GC providing a relatively more appropriate baseline in regions with high recombination rates. Considering these scenarios would clarify what exactly CAIS is capturing.

Given claims about "exquisitely adapted species", the case for using CAIS as a measure of codon adaptation would also be stronger if a relationship with gene expression could be demonstrated. RSCU is expected to be higher in highly expressed genes. Is there any evidence that the equivalent GC-controlled measure behaves similarly?

The manuscript is overall easy to follow, though some additional context may be helpful for the general reader. A more detailed discussion of how this work compares to the approach taken by Galtier et al. (2018), which accounted for GC content and gBGC when examining codon preferences, would be appropriate, for example. In addition, it would have been useful to mention past work that has attempted to explicitly quantify selection on codon usage.

Reviewer #1 (Public Review):

In this study, the authors investigate the biological function of the FK506-binding protein FKBP35 in the malaria-causing parasite Plasmodium falciparum. Like its homologs in other organisms, PfFKBP35 harbors peptidyl-prolyl isomerase (PPIase) and chaperoning activities, and has been considered a promising drug target due to its high affinity to the macrolide compound FK506. However, PfFKBP35 has not been validated as a drug target using reverse genetics, and the link between PfFKBP35-interacting drugs and their antimalarial activity remains elusive. The manuscript is structured in two parts addressing the biological function of PfFKBP35 and the antimalarial activity of FK506, respectively.

The first part combines conditional genome editing, proteomics and transcriptomics analysis to investigate the effects of FKBP35 depletion in P. falciparum. The work is very well performed and clearly described. The data provide definitive evidence that FKBP35 is essential for P. falciparum blood stage growth. Conditional knockout of PfFKBP35 leads to a delayed death phenotype, associated with defects in ribosome maturation as detected by quantitative proteomics and stalling of protein synthesis in the parasite. The authors propose that FKBP35 regulates ribosome homeostasis but an alternative explanation could be that changes in the ribosome proteome are downstream consequences of the abrogation of FKBP35 essential activities as chaperone and/or PPIase. It is unclear whether FKBP35 has a specific function in P. falciparum as compared to other organisms. The knockdown of PfFKBP35 has no phenotypic consequence, showing that very low amounts of FKBP35 are sufficient for parasite survival and growth. In the absence of quantification of the protein during the course of the experiments, it remains unclear whether the delayed death phenotype in the knockout is due to the delayed depletion of the protein or to a delayed consequence of early protein depletion. This limitation also impacts the interpretation of the drug assays.

In the second part, the authors investigate the activity of FK506 on P. falciparum, and conclude that FK506 exerts its antimalarial effects independently of FKBP35. This conclusion is based on the observation that FK506 has the same activity on FKBP35 wild type and knock-out parasites, suggesting that FK506 activity is independent of FKBP35 levels, and on the fact that FK506 kills the parasite rapidly whereas inducible gene knockout results in delayed death phenotype. However, there are alternative explanations for these observations. As mentioned above, the delayed death phenotype could be due to delayed depletion of the protein upon induction of gene knockout. FK506 could have a similar activity on WT and mutant parasites when added before sufficient depletion of FKBP35 protein. In some experiments, the authors exposed KO parasites to FK506 later, presumably when the KO is effective, and obtained similar results. However, in these conditions, the death induced by the knockout could be a confounding factor when measuring the effects of the drug. Furthermore, the authors show that FK506 binds to FKBP35, and propose that the FK506-FKBP35 complex interferes with ribosome maturation, which would point towards a role of FKBP35 in FK506 action. In summary, the study does not provide sufficient evidence to rule out that FK506 exerts its effects via FKBP35.

Reviewer #1 (Public Review):

Kainate receptors play various important roles in synaptic transmission. The receptors can be divided into low affinity kainate receptors (GluK1-3) and high affinity kainate receptos (GluK4-5). The receptors can assemble as homomers (GluK1-3) or low-high affinity heteromers (GluK4-5). The functional diversity is further increased by RNA splicing. Previous studies have investigated C-terminal splice variants of GluK1, but GluK1 N-terminal (exon 9) insertions have not been previously characterized. In this study Dhingra et al investigate the functional implications of a GluK1 splice variant that inserts a 15 amino acid segment into the extracellular N-terminal region of the protein using whole-cell and excised outside-out electrophysiology.<br /> The authors produce solid data to show that the insertion profoundly impacts the function of GluK1-1a - the channels that have the insertion are slower to desensitize. The data also shows that the insertion changes the modulatory effects of Neto proteins, resulting in altered rates of desensitization and recovery from desensitization. To determine the mechanism by which the insertion exerts these functional effects, the authors perform pull-down assays of Neto proteins, and extensive mutagenesis on the insert.

The electrophysiological part of the study is very rigorous and meticulous.

The biggest weakness of the manuscript is the structural work. Due to issues with preferred orientation (a common problem in cryo-EM), the 3D reconstructions are at a low resolution (in the 5-8 Å range) and cannot offer much mechanistic insight into the effects of the insertion. Based on the available data, the authors posit that the insertion does not change the arrangement of the subunits in the desensitized state. However, there is no comparison with a structure that does not contain the insertion, so while the statement may well be true, no data is shown to support it.

Overall, the cryo-EM contributes little and distracts from the good parts of the manuscript.

Another part that does not contribute much is the RNAseq data that has been pulled from a database and analyzed for the paper. It is being used to show that the exon 9 insertion variant is predominantly expressed in the cerebellar cortex at early stages of brain development. The methods do not describe in detail how the data has been analyzed (e.g., is the data scaled per sample/gene or globally?) so it is hard to know what we can compare in the heat plots. In Figure 1- supplement 1 there aren't striking differences in expression (at least not obviously visible in the current illustration).

Despite these weaknesses, the study is an important contribution to the field because it characterizes a GluK1 variant that has not been studied before and highlights the functional diversity that exists within the kainate receptor family.

Reviewer #1 (Public Review):

The study provides a complete comparative interactome analysis of α-arrestin in both humans and drosophila. The authors have presented interactomes of six humans and twelve Drosophila α-arrestins using affinity purification/mass spectrometry (AP/MS). The constructed interactomes helped to find α-arrestins binding partners through common protein motifs. The authors have used bioinformatic tools and experimental data in human cells to identify the roles of TXNIP and ARRDC5: TXNIP-HADC2 interaction and ARRDC5-V-type ATPase interaction. The study reveals the PPI network for α-arrestins and examines the functions of α-arrestins in both humans and Drosophila.

Comments<br /> I will like to congratulate the authors and the corresponding authors of this manuscript for bringing together such an elaborate study on α-arrestin and conducting a comparative study in drosophila and humans.

Introduction:<br /> The introduction provides a rationale behind why the comparison between humans and Drosophila is carried out.<br /> - Even though this is a research manuscript, including existing literature on similar comparison of α-arrestin from other articles will invite a wide readership.

Results:<br /> The results cover all the necessary points concluded from the experiments and computational analysis.<br /> • The authors could point out the similarity of the α-arrestin in both humans and Drosophila.<br /> • Citing the direct connecting genes from the network in the text will invite citations and a wider readership.

Figures:<br /> The images are elaborate and well-made.<br /> • The authors could use a direct connected gene-gene network that pointing interactions. This can be used by other readers working on the same topic and ensure reproducibility and citations.<br /> • The blot/gel images can be of higher resolution.

Discussion:<br /> The authors have utilized and discussed the conclusion they draw from their study. But could highlight more on ARRDCs and why it was selected out of the other arrestins. The authors have provided future work directions associated with their work.

Supplementary figures:<br /> The authors have a rigorous amount of work added together for the success of this manuscript.

Reviewer #1 (Public Review):

The manuscript by Sejour et al. is testing "translational ramp" model described previously by Tuller et al. in S. cerevisiae. Authors are using bioinformatics and reporter based experimental approaches to test whether "rare codons" in the first 40 codons of the gene coding sequences increase translation efficiency and regulate abundance of translation products in yeast cells. Authors conclude that "translation ramp" model does not have support using new set of reporters and bioinformatics analyses. The strength of bioinformatic evidence and experimental analyses of the rare codons insertion in the reporter make compelling case for authors claims. However major weakness of the manuscript is that authors do not take in account other confounding effects in their analyses as well as multiple previous studies that argue with "translation ramp" model. The existence of the early elongation ramp with "rare codons" was previously contested with local mRNA structure at the start codon, peptidyl-tRNA drop-off or interactions of the nascent peptide chain with exit channel of the ribosome models. All of these effects are not considered or discussed in the manuscript at this point. Such an authors approach makes the manuscript rather biased and short on discussing multiple other possible conclusions on reasons of slow translation elongation at the beginning of the protein synthesis.

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Reviewer #1 (Public Review):

In the current study, the authors employed C elegans transgenic line of sfGFP::Abeta worms to investigate molecules implicated in Abeta aggregation and clearance. They conduct siRNA knockdown, RNA deletion, and overexpression experiments to demonstrate that collagens and ADM-2 play critical roles in aggregate formation and clearance, respectively. Basically, the data support the main claims and key conclusions. However, the impact and significance of the findings are considered average at this time. Additional work is necessary for strengthening the research and supporting the major conclusions. For instance, it remains unclear how ADM-2 removes extracellular aggregates. The work is also missing studies to assess whether collagen escalation increases aggregate formation. These two biological processes are critical for understanding the balance in Abeta aggregate formation.

Reviewer #1 (Public Review):

Meiosis uses distinct cohesin complexes for chromosome morphogenesis and segregation such as cohesins with meiosis-specific REC-8 and COH-3/4 in the nematode. In this important paper, by using stage-specific depletion of the cohesin component, the authors nicely showed that REC-8-cohesin stably binds to meiotic chromosomes and plays an essential role in sister chromatid cohesion in diakinesis and meiosis I. Moreover, COH-3/4-cohesin, whose chromosome binding is stabilized by the SCC-2 cohesin regulator, is more dynamic than Rec8-cohesin in prophase I and plays a role in loop-axis formation.

Reviewer #1 (Public Review):

The manuscript describes a multi-ancestry meta-analysis of genome-wide association studies of tuberculosis risk from case-control cohorts across several European, Asian, and African countries.

A main finding is that there is substantial common variant heritability of tuberculosis risk is well established. However, this analysis needs to be adjusted for differing case-control ratios in order to put the heritability estimates onto the liability scale so that variation across countries/cohorts can be properly assessed.

The authors find the strongest statistical evidence for association at a HLA locus. However, because of the complexity of this region and the diversity across ancestries, interpretation of this association is difficult.

This manuscript shows that there is potential to identify heritable sources of tuberculosis risk across ancestries. However, better genotyping of the HLA region and larger sample sizes will be needed to make further progress.

Reviewer #1 (Public Review):

Liu et al present a very interesting manuscript investigating whether there are distinct mechanisms of learning in children with ASD. What they found was that children with ASD showed comparable learning to typically developing children, but that there was a difference in learning strategy, with less plasticity and more stable learning representations in children with ASD. In other words, children with ASD showed similar learning performance to typically developing children but were more likely to use different learning rules to get there. Interestingly greater fMRI-measured brain plasticity was associated with learning gains in typically developing children, whereas more stable (less plasticity) neural patterns were associated with learning gains in autistic children. This was mediated by insistence on sameness (from the RRIB) in the ASD group.

This is a good paper, well reasoned and with strong methods. The biggest issue is related to subject numbers and possibly the conceptualization of ASD. With n=35 it is only possible to make a generalized statement about autism. For example, take the following statement from the results: "while most TD children used the memory-based strategy most frequently following training, nearly half of the children with ASD used rule-based strategies most frequently for trained problems." Is this the heterogeneity of autism at play, or the noisiness of the task and measures? Conceptually, is it realistic to expect a unitary learning strategy in all of autism? Lastly, the task itself can only be solved in a subset of autistic children and therefore presents a limited view of the condition.

Reviewer #1 (Public Review):

As part of a special issue on COVID-19 and cancer, Fuzzell and colleagues report findings from their mixed method study on the impact of the pandemic on cervical cancer screening and colposcopies, consisting of a national (United States) survey (March-August 2021) of 1251 clinicians (675 perform colposcopy) and qualitative interviews (June-December 2021) with 55 of these clinicians. The study looked specifically at perceived pandemic-related practice changes and disruptions over one year into the pandemic after the lockdowns had been lifted.

The overall focus is on three pandemic-related questions (impact on cervical cancer screening practice, colposcopy practice, ability to provide LEEP) that were asked as part of a larger survey related to cervical cancer screening and management of abnormal results, details of which are however not fully described in terms of the survey's general aim and items, but seem to have been designed within the context of adherence to guidelines (following Cabana's Guideline Based Practice Improvement Framework).

Reviewer #1 (Public Review):

The strength of this work is the quality and quantity of data, which identify a critical histidine residue, His12 of SsrB, that is responsible for the allosteric, pH-dependent conformational change in SsrB and for phosphorylation of SsrB. That is the fundamental question to the field: the low pH response when Salmonella invades host cells and utilizes acidification within a Salmonella-containing vacuole as a signal to initiate the expression of virulence genes from the Salmonella pathogenicity island 2 (SPI-2) suite of virulence genes, which encode specific effector proteins and a unique secretion injectisome that has, to date, eluded purification. The SsrB protein will activate the transcription of non-SPI-2 genes at neutral pH in the regulation of biofilm formation. The low pH, phosphorylated SsrB structure allows for cooperative binding to DNA that is necessary for SPI-2 gene activation. Remarkably, the substitution of the single His12 residue of SsrB is enough to eliminate its activity at acidic pH, but not at normal pH. The authors employ a clever and exceptional single-molecule DNA unzipping assay for their DNA affinity measurements. Another major strength of this work is the logical flow of the results section and the lucidity of the written presentation. This work will guide the field in allowing for the expression of SPI-2 in the lab for mechanistic studies that would be otherwise impossible to do within a vacuole.

The first chapter of the results section includes the demonstration that acid pH increases SsrB affinity for SPI-2 promoter DNA. The authors employed a sophisticated single molecule DNA unzipping to measure the effects of pH on SsrB affinity to the DNA target. The DAN affinity was ~32-fold higher at acid pH (6.1) than at neutral pH (7.4). At both acidic and neutral pH conditions DNA binding was highly cooperative.

In the second results chapter, the authors investigated whether the DAN binding domain of SsrB was responsible for low pH-stimulated DNA binding. SsrB is a classic two-component regulatory protein with an N-terminal receiver domain that gets phosphorylated during activation and a C-terminal DNA binding domain to affect the regulation of gene expression in response to phosphorylation. Again, the single molecule DNA unzipping assay was employed to characterize pH effects on just the C-terminal binding domain (SsrB-C). The isolated C-terminal domain bound DNA with a 4-fold lower affinity as compared to the full-length protein. Cooperativity was also reduced. SsrB-C was shown to be unable to support acid-stimulation of SPI-2 transcription using both in vivo and in vitro transcriptional assays. The data is quite solid.

The third results chapter is a comparison of SsrB to other members of the NarL/FixJ subfamily of response regulators. SsrB is the only member to have known pH dependence on its activity. The authors found SsrB to have the highest pI of the subfamily and the second-greatest number of histidine residues. Of four histidine residues in the receiver domain His12 was conserved in the subfamily, while His28, His34, and His 72 were unique to SsrB and thus initially investigated. Since histidine residues are known to play a role in pH sensing, the three histidine residues in the receiver domain were extensively characterized for a potential role in pH-dependent transcriptional activation. The experiments ruled out the role of the three unique histidine residues in the SsrB receiver domain in pH sensing.

The fourth research chapter demonstrated that it is the conserved His14 of SsrB that is responsible for pH sensing. A striking result was the finding that the H12Q substitution retained full DNA binding activity at neutral pH, but at acidic pH, the H12Q allele was unable to activate SPI-2 transcription. Further analysis showed the mutant allele was defective in subunit cooperativity.

The fifth research chapter characterized other amino acid substitutions at His12 of SsrB. Positively charged substitutions were employed to mimic the protonated state of His12 and aromatic substitutions were chosen to mimic the aromatic nature of the imidazole ring of histidine. H12Y and H12F substitutions had substantially reduced activity but retained pH sensing. Charged substitutions were defective for both binding and pH sensing. These results support the conclusion that the aromatic nature of the histidine imidazole role was important for pH sensing.

In the final research chapter, the authors characterized His 12 substitutions for effects on SsrB phosphorylation at Asp56. The results of these assays showed that substitution at His12 reduced both SsrB phosphorylation at neutral pH and abolished pH-dependent changes in SsrB phosphorylation consistent with conformational changes in SsrB as a result of substitution at His12.

Overall, a solid study that defines the essential role of His12 in SsrB activation at low pH. His12 is critical for pH sensing, SsrB phosphorylation, SsrB oligomerization, and in vivo Salmonella virulence.

Reviewer #1 (Public Review):

This study uses electrophysiological techniques in vitro to address the role of the Na+ leak channel NALCN in various physiological functions in cartwheel interneurons of the dorsal cochlear nucleus. Comparing wild type and glycinergic neuron-specific knockout mice for NALCN, the authors show that these channels 1) are required for spontaneous firing, 2) are modulated by noradrenaline (NA, via alpha2 receptors) and GABA (through GABAB receptors), 3) how the modulation by NA enhances IPSCs in these neurons.

This work builds on previous results from the Trussell's lab in terms of the physiology of cartwheel cells, and from other labs in terms of the role of NALCN channels, that have been characterized in more and more brain areas somewhat recently; for this reason, this study could be of interest for researchers that work in other preparations as well. The general conclusions are strongly supported by results that are clearly and elegantly presented.

I have a few comments that, in my opinion, might help clarify some aspects of the manuscript.

1) It is mentioned throughout the manuscript, including the abstract, that the results suggest a closed apposition of NALCN channels and alpha2 and GABAB receptors. From what I understand, this conclusion comes from the fact that GABAB receptors activate GIRK channels through a membrane-delimited mechanism. Is it possible that these receptors converge on other effectors, for example adenylate cyclase (see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374141/).

2) In Figure 2G, the neurons from NALCN KO mice appear to reach a significantly higher frequency than those from WT (figure 2E, 110 vs. 70 spikes/s). Was this higher frequency a feature of all experiments? The results mention a rundown of peak firing rate due to whole-cell dialysis, but, from what I understand, the control conditions should be similar for all experiments.

3) Also in Figure 2, the firing patterns for neurons from WT and NALCN KO mice appear to be quite different, with spikes appearing to be generated during the hyperpolarization of the bursts in the second half of the current step for WT neurons but always during the depolarization in KO neurons. Was this always the case? If so, could NALCN channels be involved in this type of firing? Along these lines, it would be interesting to show an example of a firing pattern of neurons from WT mice in the presence of NA, which inhibits NALCN channels.

4) It might be interesting to discuss how the hyperpolarization induced by the activation of GIRK channels and inhibition of NALCN channels could have different consequences due to their opposite effect on the input resistance.

Reviewer #1 (Public Review):

Wang and colleagues recently demonstrated the essential role of RBM24 (RNA-binding motif protein 24a) in the development of mouse hair cells (source: https://doi.org/10.1002/jcp.31003). In this study, they further expand on their findings by revealing that Rbm24 expression is absent in Pou4f3 mutant mice but not in Gfi1 mutant mice. This observation suggests that POU4F3 acts as an upstream regulator of Rbm24. The researchers effectively demonstrate that POU4F3 can bind to and regulate Rbm24 through three distant enhancers, which are located in open chromatin regions and are bound by POU4F3. Lastly, Wang and colleagues discovered that ectopic expression of Rbm24 was unable to prevent the degeneration of POU4F3 null hair cells.

The findings in this manuscript hold great significance as they provide additional insights into the transcriptional cascades crucial for hair cell development. The discovery of enhancers capable of driving transgene expression specifically in hair cells holds promising therapeutic implications. The figures presented in the study are of excellent quality, the employed techniques are state-of-the-art, the data are accurately represented without exaggeration, and the study demonstrates a high level of rigor.

Reviewer #1 (Public Review):

This manuscript by Kelly et al. reports results from single-cell transcriptomic analysis of spinal neurons in zebrafish. The work builds on a strong foundation of literature and the objective, to discern gene expression patterns specializing on functionally distinct motor circuits, is well rationalized. Specifically, they compared the transcriptomes in the escape and swimming circuits.

The authors discovered, in the motor neurons of the escape circuit, two functional groups or "cassettes" of genes related to excitability and vesicle release, respectively. Expression of these genes makes sense for a "fast" circuit. This finding will be important to the field and form the basis for subsequent studies differentiating the escape circuit from others.

Unfortunately, efforts to identify a counterpart cassette in the SMns of the swimming pathway were unsuccessful. Instead, they found an abundance of transcription factors and ribosomal proteins; 1/3 were reported as other proteins, although it wasn't clear whether those were genes mediating excitability or transmitter release. Further analysis was not reported, and the authors speculate that the neurons in that pathway may not yet be born.

Reviewer #1 (Public Review):

SUMMARY:

In this paper, Luppi et al., apply the recently developed integrated information decomposition to the question how the architecture of information processing changes when consciousness is lost. They explore fMRI data from two different populations: healthy volunteers undergoing reversible anesthesia, as well as from patients who have long-term disorders of consciousness. They show that, in both populations, synergistic integration of information is disrupted in common ways. These results are interpreted in the context of the SAPHIRE model (recently proposed by this same group), that describes information processing in the brain as being composed of several distinct steps: 1) gatekeeping (where gateway regions introduce sensory information to the global synergistic workspace where 2) it is integrated or "processed" before 3) by broadcast back to to the brain.

I think that this paper is an excellent addition to the literature on information theory in neuroscience, and consciousness science specifically. The writing is clear, the figures are informative, and the authors do a good job of engaging with existing literature. While I do have some questions about the interpretations of the various information-theoretic measures, all in all, I think this is a significant piece of science that I am glad to see added to the literature.

One specific question I have is that I am still a little unsure about what "synergy" really is in this context. From the methods, it is defined as that part of the joint mutual information that is greater than the maximum marginal mutual information. While this is a perfectly fine mathematical measure, it is not clear to me what that means for a squishy organ like the brain. What should these results mean to a neuro-biologist or clinician?

Right now the discussion is very high level, equating synergy to "information processing" or "integrated information", but it might be helpful for readers not steeped in multivariate information theory to have some kind of toy model that gets worked out in detail. On page 15, the logical XOR is presented in the context of the single-target PID, but 1) the XOR is discrete, while the data analyzed here are continuous BOLD signals w/ Gaussian assumptions and 2) the XOR gate is a single-target system, while the power of the Phi-ID approach is the multi-target generality. Is there a Gaussian analog of the single-target XOR gate that could be presented? Or some multi-target, Gaussian toy model with enough synergy to be interesting?

I think this would go a long way to making this work more accessible to the kind of interdisciplinary readership that this kind of article with inevitably attract.

STRENGTHS

The authors have a very strong collection of datasets with which to explore their topic of interest. By comparing fMRI scans from patients with disorders of consciousness, healthy resting state, and various stages of propofol anesthesia, the authors have a very robust sample of the various ways consciousness can be perturbed, or lost. Consequently, it is difficult to imagine that the observed effects are merely a quirk of some biophysical effect of propofol specifically, or a particular consequence of long-term brain injury, but do in fact reflect some global property related to consciousness. The data and analyses themselves are well-described, have been previously validated, and are generally strong. I have no reason to doubt the technical validity of the presented results.

The discussion and interpretation of these results is also very nice, bringing together ideas from the two leading neurocognitive theories of consciousness (Global Workspace and Integrated Information Theory) in a way that feels natural. The SAPHIRE model seems plausible and amenable to future research. The authors discuss this in the paper, but I think that future work on less radical interventions (e.g. movie watching, cognitive tasks, etc) could be very helpful in refining the SAPHIRE approach.

Finally, the analogy between the PID terms and the information provided by each eye redundantly, uniquely, and synergistically is superb. I will definitely be referencing this intuition pump in future discussions of multivariate information sharing.

WEAKNESSES

I have some concerns about the way "information processing" is used in this study. The data analyzed, fMRI BOLD data is extremely coarse, both in spatial and temporal terms. I am not sure I am convinced that this is the natural scale at which to talk about information "processing" or "integration" in the brain. In contrast to measures like sample entropy or Lempel-Ziv complexity (which just describe the statistics of BOLD activity), synergy and Phi are presented here as quasi-causal measures: as if they "cause" or "represent" phenomenological consciousness. While the theoretical arguments linking integration to consciousness are compelling, is this is right data set to explore them in?

For example, the work by Newman, Beggs, and Sherril (nee Faber), synergy is associated with "computation" performed in individual neurons: the information about the future state of a target neuron that is only accessible when knowing both inputs (analogous to the synergy in computing the sum of two dice). Whether one thinks that this is a good approach neural computation or not, it fits within the commonly accepted causal model of neural spiking activity: neurons receive inputs from multiple upstream neurons, integrate those inputs and change their firing behavior accordingly.

In contrast, here, we are looking at BOLD data, which is a proxy measure for gross-scale regional neural activity, which itself is a coarse-graining of millions of individual neurons to a uni-dimensional spectrum that runs from "inactive to active." It feels as though a lot of inferences are being made from very coarse data.

REFERENCES:

1. Newman, E. L., Varley, T. F., Parakkattu, V. K., Sherrill, S. P. & Beggs, J. M. Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition. Entropy 24, 930 (2022).

Reviewer #1 (Public Review):

In this study, the authors investigate where and when brain activity is modulated by incoming linguistic cues during sentence comprehension. Sentence stimuli were designed such that incoming words had varying degrees of constraint on the sentence's structural interpretation as participants listened to them unfolding, i.e. due to varying degrees of verb transitivity and the noun's likelihood of assuming a specific thematic role. Word-by-word "online" structural interpretations for each sentence were extracted from a deep neural network model trained to reproduce language statistics. The authors relate the various metrics of word-by-word predicted sentence structure to brain data through a standard RSA approach at three distinct points of time throughout sentence presentation. The data provide convincing evidence that brain activity reflects preceding linguistic constraints as well as integration difficulty immediately after word onset of disambiguating material.

The authors confirm that their sentence stimuli vary in degree of constraint on sentence structure through independent behavioral data from a sentence continuation task. They also show a compelling correlation of these behavioral data with the online structure metric extracted from the deep neural network, which seems to pick up on the variation in constraints. In the introduction, the authors argue for the potential benefits of using deep neural network-derived metrics given that it has "historically been challenging to model the dynamic interplay between various types of linguistic and nonlinguistic information". Similarly, they later conclude that "future DLMs (...) may provide new insights into the neural implementation of the various incremental processing operations(...)".

By incorporating structural probing of a deep neural network, a technique developed in the field of natural language processing, into the analysis pipeline for investigating brain data, the authors indeed take an important step towards establishing advanced machine learning techniques for researching the neurobiology of language. However, given the popularity of deep neural networks, an argument for their utility should be carefully evidenced. However, the data presented here don't directly test how large the benefit provided by this tool really is. In fact, the authors show compelling correlations of the neural network-derived metrics with both the behavioral cloze-test data as well as several (corpus-)derived metrics. While this is a convincing illustration of how deep language models can be made more interpretable, it is in itself not novel. The correlation with behavioral data and corpus statistics also raises the question of what is the additional benefit of the computational model? Is it simply saving us the step of not having to collect the behavioral data, not having to compute the corpus statistics or does the model potentially uncover a more nuanced representation of the online comprehension process? This remains unclear because we are lacking a direct comparison of how much variance in the neural data is explained by the neural network-derived metrics beyond those other metrics (for example the main verb probability or the corpus-derived "active index" following the prepositional phrase).

With regards to the neural data, the authors show convincing evidence for early modulations of brain activity by linguistic constraints on sentence structure and importantly early modulation by the coherence between multiple constraints to be integrated. Those modulations can be observed across bilateral frontal and temporal areas as well as parts of the default mode network. The methods used are clear and rigorous and allow for a detailed exploration of how multiple linguistic cues are neurally encoded and dynamically shape the final representation of a sentence in the brain. However, at times the consequences of the RSA results remain somewhat vague with regard to the motivation behind different metrics and how they differ from each other. Therefore, some results seem surprising and warrant further discussion, for example:

Why does the neural network-derived parse depth metric fit neural data before the V1 uniqueness point if the sentence pairs begin with the same noun phrase? This suggests that the lexical information preceding V1, is driving the results. However, given the additional results, we can already exclude an influence of subject likelihood for a specific thematic role as this did not model the neural data in the V1 epoch to a significant degree. Relatedly, In Fig 2C it seems there are systematic differences between HiTrans and LoTrans sentences regarding the parse depth of determiner and subject noun according to the neural network model, while this is not expected according to the context-free parse.

"The degree of this mismatch is proportional to the evidence for or against the two interpretations (...). Besides these two measures based on the entire incremental input, we also focused on Verb1 since the potential structural ambiguity lies in whether Verb1 is interpreted as a passive verb or the main verb."

The neural data fits in V1 epoch differ in their temporal profile for the mismatch metrics and the Verb 1 depth respectively. I understand the "degree of mismatch" to be a measure of how strongly the neural network's hidden representations align with the parse depth of an active or passive sentence structure. If this is correct, then it is not clear from the text how far this measure differs from the Verb 1 depth alone, which is also indicating either an active or passive structure.

In previous studies, differences in neural activity related to distinct amounts of open nodes in the parse tree have been interpreted in terms of distinct working memory demands (Nelson et al. pnas 2017, Udden et al tics 2020). It seems that some of the metrics, for example the neural network-derived parse depth or the V1 depth may be similarly interpreted in the light of working memory demands. After all, during V1 epoch, the sentences do not only differ with respect to predicted sentence structure, but also in the amount of open nodes that need to be maintained. In the discussion, however, the authors interpret these results as "neural representations of an unfolding sentence's structure".

Reviewer #1 (Public Review):

The authors' aim was to test to what extent atypical organization of language is associated with a mirrored brain organization of other cognitive functions. In particular, they focused on the inferior frontal gyri (IFG) by studying the inhibitory control network. This allowed them to directly test the support for the Causal hypothesis of hemispheric specialization, arguing for fast sequences of cognitive processes being better performed by a single hemisphere, versus the Statistical hypothesis of lateralization, postulating an independent lateralization of each cognitive function.

Previous studies on this topic did not focus on functions involving homotopic language regions. This limitation is bypassed in this study by assessing inhibition with a Stop-Signal Task which also engages the IFG in the contralateral site to the verb generation task. By studying a combination of structural and functional information, in addition to the activation contrasts, the authors are able to test whether atypical organization is accompanied by stronger interhemispheric connectivity. Although relying mainly on correlations and lacking important methodological information that may be critical to understand the reported effects, the results are quite straightforward. However the bilingual/monolingual status and gender of the participants is not reported which might affect the relationship between language and inhibitory control.

The conclusions of the paper are supported by the data. With their design, the authors observed that, as a group, individuals with atypical organization show a mirror organization of the whole inhibitory network to the contralateral site, supporting the Causal hypothesis at the group level. However, individual data support the Statistical hypothesis, since the segregation between language and inhibition was not observed in all individuals and a variety of configurations in bilateral and bilateral organisation of language and inhibition were also observed.

The results of this study have important implications for our understanding of the independence between different cognitive functions which is crucial when addressing brain damage and rehabilitation. This aspect also indirectly speaks to researchers interested in evolution and in bilingualism and its relation to cognitive control. These aspects are not discussed but incorporating them would broaden the interest of the paper beyond the current implications mentioned.

Reviewer #1 (Public Review):

This study provides a novel in vitro model for the study of retinol transport across the human BBB by pairing iPSC-derived BMECs with the use of recombinant vitamin A serum transport proteins, RBP and TTR. Key findings of the paper include 1) the observation that the delivery mode of retinol affects its intracellular accumulation at the BBB but not its permeation across the BBB, 2) further highlighting that intracellular concentrations of retinol are also ensured by its efflux via its receptor STRA6 and 3) a potential novel role for TTR in retinol transport by upregulating LRAT mRNA expression, independently of RBP. Notably, the model appears to be more accurate than ones previously used (primary porcine BMECs) to study retinol delivery at the BBB, and could be used to study the retinol dysregulation at the BBB in neurodegenerative diseases (e.g. by using iPSC lines from NDD patients), something that miss in the paper.

Indeed, the major disappointment of this work is the clinical relevance that was highlighted in the Introduction but was never really studied in the end. iPSC from patients could be added to the study.

As a general comment, the study is well done however the introduction and the discussion as a bit long and do not get to the point of the work easily. Even sometimes losing the reader in many details (necessary here?). Less abbreviations would be appreciated for general readers.

Reviewer #1 (Public Review):

This study provides insights into the early detection of malignancies with noninvasive methods. The study contained a large sample size with external validation cohort, which raises the credibility and universality of this model. The new model achieved high levels of AUC in discriminating malignancies from healthy controls, as well as the ability to distinguish tumor of origin. Based on these findings, prospective studies are needed to further confirm its predictive capacity.

However, there are several concerns about the manuscript, which needs to be clarified or modified.

First, the use of "multimodal model" will definitely increase workload of the testing. From the results of this manuscript, the integration of multimodal data did not significantly outperform the EM-based model. Is this kind of integration necessary? Is that tool really cost-effective? The authors did not convince me of its necessity, advantages, and clinical application.

Second, the baseline characteristics of part of the enrolled patients are not clear. It seems that some of the cancer patients were diagnosed only by imaging examinations. The manuscript described "staging information was not available for 25.7% of cancer patients, who were confirmed by specialized clinicians to have non-metastatic tumors". I have no idea how did this confirmation make? According to clinicians' experience only?

Third, it seems that one of the important advantages of this new model is the low depth coverage in comparing to previous screening models for cancer. The authors should discuss more on the reason why the new model could achieve comparable predictive accuracy with an obviously lower sequencing depth.

Lastly, the readability of this manuscript needs to be improved. The focus of the background section is not clear, with too much detail of other studies and few purposeful summaries. You need to explain the goals and clinical significance of your study. In addition, the results section is too long, and needs to be shortened and simplified. Move some of the inessential results and sentences to supplementary materials or methods.

Reviewer #1 (Public Review):

The manuscript addresses a fundamental question about how different types of communication signals differentially affect brain states and neurochemistry. In addition, the manuscript highlights the various processes that modulate brain responses to communication signals, including prior experience, sex, and hormonal status. Overall, the manuscript is well-written and the research is appropriately contextualized. The authors are thoughtful about their quantitative approaches and interpretations of the data.

That being said, the authors need to work on justifying some of their analytical approaches (e.g., normalization of neurochemical data, dividing the experimental period into two periods (as opposed to just analyzing the entire experimental period as a whole)) and should provide a greater discussion of how their data also demonstrate dissociations between neurochemical release in the basolateral amygdala and behavior (e.g., neurochemical differences during both of the experimental periods but behavioral differences only during the first half of the experimental period). The normalization of neurochemical data seems unnecessary given the repeated-measures design of their analysis and could be problematic; by normalizing all data to the baseline data (p. 24), one artificially creates a baseline period with minimal variation (all are "0"; Figures 2, 3 & 5) that could inflate statistical power.

The Introduction could benefit from a priori predictions about the differential release of specific neuromodulators based on previous literature.

The manuscript would also benefit from a description of space use and locomotion in response to different valence vocalizations.

Nevertheless, the current manuscript seems to provide some compelling support for how positive and negative valence vocalizations differentially affect behavior and the release of acetylcholine and dopamine in the basolateral amygdala. The research is relevant to broad fields of neuroscience and has implications for the neural circuits underlying social behavior.

Reviewer #1 (Public Review):

In this manuscript, the authors described a computational method catELMo for embedding TCR CDR3 sequences into numeric vectors using a deep-learning-based approach, ELMo. The authors applied catELMo to two applications: supervised TCR-epitope binding affinity prediction and unsupervised epitope-specific TCR clustering. In both applications, the authors showed that catELMo generated significantly better binding prediction and clustering performance than other established TCR embedding methods. However, there are a few major concerns that need to be addressed.

1. There are other TCR CDR3 embedding methods in addition to TCRBert. The authors may consider incorporating a few more methods in the evaluation, such as TESSA (PMCID: PMC7799492), DeepTCR (PMCID: PMC7952906) and the embedding method in ATM-TCR (reference 10 in the manuscript). TESSA is also the embedding method in pMTnet, which is another TCR-epitope binding prediction method and is the reference 12 mentioned in this manuscript.

2. The TCR training data for catELMo is obtained from ImmunoSEQ platform, including SARS-CoV2, EBV, CMV, and other disease samples. Meanwhile, antigens related to these diseases and their associated TCRs are extensively annotated in databases VDJdb, IEDB and McPAS-TCR. The authors then utilized the curated TCR-epitope pairs from these databases to conduct the evaluations for eptitope binding prediction and TCR clustering. Therefore, the training data for TCR embedding may already be implicitly tuned for better representations of the TCRs used in the evaluations. This seems to be true based on Table 4, as BERT-Base-TCR outperformed TCRBert. Could catELMo be trained on PIRD as TCRBert to demonstrate catELMo's embedding for TCRs targeting unseen diseases/epitopes?

3. In the application of TCR-epitope binding prediction, the authors mentioned that the model for embedding epitope sequences was catElMo, but how about for other methods, such as TCRBert? Do the other methods also use catELMo-embedded epitope sequences as part of the binding prediction model, or use their own model to embed the epitope sequences? Since the manuscript focuses on TCR embedding, it would be nice for other methods to be evaluated on the same epitope embedding (maybe adjusted to the same embedded vector length). Furthermore, the authors found that catELMo requires less training data to achieve better performance. So one would think the other methods could not learn a reasonable epitope embedding with limited epitope data, and catELMo's better performance in binding prediction is mainly due to better epitope representation.

4. In the epitope binding prediction evaluation, the authors generated the test data using TCR-epitope pairs from VDJdb, IEDB, McPAS, which may be dominated by epitopes from CMV. Could the authors show accuracy categorized by epitope types, i.e. the accuracy for TCR-CMV pair and accuracy for TCR-SARs-CoV2 separately?

5. In the unsupervised TCR clustering evaluation, since GIANA and TCRdist direct outputs the clustering result, so they should not be affected by hierarchical clusters. Why did the curves of GIANA and TCRdist change in Figure 4 when relaxing the hierarchical clustering threshold?

6. In the unsupervised TCR clustering evaluation, the authors examined the TCR related to the top eight epitopes. However, there are much more epitopes curated in VDJdb, IEDB and McPAS-TCR. In real application, the potential epitopes is also more complex than just eight epitopes. Could the authors evaluate the clustering result using all the TCR data from the databases?

7. In addition to NMI, it is important to know how specific each TCR cluster is. Could the authors add the fraction of pure clusters in the results? Pure cluster means all the TCRs in the cluster are binding to the same epitope, and is a metric used in the method GIANA.

Joint Public Review

The manuscript by Mitra and coworkers analyses the functional role of Orai in the excitability of central dopaminergic neurons in Drosophila. The authors show that a dominant-negative mutant of Orai (OraiE180A) significantly alters the gene expression profile of flight-promoting dopaminergic neurons (fpDANs). Among them, OraiE180A attenuates the expression of Set2 and enhances that of E(z) shifting the level of epigenetic signatures that modulate gene expression. The present results also demonstrate that Set2 expression via Orai involves the transcription factor Trl. The Orai-Trl-Set1 pathway modulates the expression of VGCC, which, in turn, are involved in dopamine release. The topic investigated is interesting and timely and the study is carefully performed and technically sound; however, there are several major concerns that need to be addressed:

1- In Figure S2E, STIM is overexpressed in the absence of Set2 and this leads to rescue. It is presumed that STIM overexpression causes excess SOCE, yet this is rarely the case. Perhaps the bigger concern, however, is how excess SOCE might overcome the loss of SET2 if SET2 mediates SOCE-induced development of flight. These data are more consistent with something other than SET2 mediating this function.

2- In Figure 3, data is provided linking SET2 expression and Cch-induced Ca2+ responses. The presentation of these data is confusing. In addition, the results may be a simple side effect of SET2-dependent expression of IP3R. Given that this article is about SOCE, why isn't SOCE shown here? More generally, there are no measurements of SOCE in this entire article. Measuring SOCE (not what is measured in response to Cch) could help eliminate some of this confusion.

3- A significant gap in the study relates to the conclusion that trl is a SOCE-regulated transcription factor. This conclusion is entirely based on genetic analysis of STIMKO heterozygous flies in which a copy of the trl13C hypomorph allele is introduced. While these results suggest a genetic interaction between the expression of the two genes, the evidence that expression translates into a functional interaction that places trl immediately downstream of SOCE is not rigorous or convincing. All that can be said is that the double mutant shows a defect in flight which could arise from an interruption of the circuit. Further, it is not clear whether the trl13C hypomorph is only introduced during the critical 72-96 hour time window when the Orai1E180E phenotype shows up. The same applies to the over-expression of Set2 and the other genes. If the expression is not temporally controlled, then the phenotype could be due to the blockade of an entirely different aspect of flight neuron function.

4- In Figure 4, data is shown that SOCE compensates for the loss of Trl, the presumed mediator of SOCE-dependent flight. The fact that flight deficits are rescued by raising SOCE in the absence of Trl is very inconsistent with this conclusion.

5- In Figure 5 (A-C), data is provided that Trl transcripts are unaffected by loss of SOCE and that overexpression cannot rescue flightlessness. From this, the authors conclude that this gene "must" be calcium responsive. While that is one possibility, it is also possible that these genes are not functionally linked.

6- There is no characterization of SOCE in fpDANs from flies expressing native Orai or the dominant negative OraiE180A mutant. While the authors refer to previous studies, as the manuscript is essentially based on Orai function thapsigargin-induced SOCE should be tested using the Ca2+ add-back protocol in order to assess the release of Ca2+ from the ER in response to thapsigargin as well as the subsequent SOCE.

7- In the experiments performed to rescue flight duration in Set2RNAi individuals the authors overexpress STIM and attribute the effect to "Excess STIM presumably drives higher SOCE sufficient to rescue flight bout durations caused by deficient Set2 levels.". This should be experimentally tested as the STIM:Orai stoichiometry has been demonstrated as essential for SOCE.

8- The authors show that overexpression of OraiE108A results in Stim downregulation at a mRNA level. What about the protein level? And more important, how does OraiE108A downregulate Stim expression? Does it promote Stim degradation? Does it inhibit Stim expression?

9- Lines 271-273, the authors state "whereas overexpression of a transgene encoding Set2 in THD' neurons either with loss of SOCE (OraiE180A) or with knockdown of the IP3R (itprRNAi), lead to significant rescue of the Ca2+ response". This is attributed to a positive effect of Set2 expression on IP3R expression and the authors show a positive correlation between these two parameters; however, there is no demonstration that Set2 expression can rescue IP3R expression in cells where the IP3R is knocked down (itprRNAi). This should be further demonstrated.

10- The data presented in Figure 3E should be functionally demonstrated by analyzing the ability of CCh to release Ca2+ from the intracellular stores in the absence of extracellular Ca2+.

11- The conclusion that SOCE regulates the neuronal excitability threshold is based entirely on either partial behavioral rescue of flight, or measurements of KCl-induced Ca2+ rises monitored by GCaMP6m in DAN neurons. The threshold for neuronal excitability is a precise parameter based on rheobase measurements of action potentials in current-clamp. Measurements of slow calcium signals using a slow dye such as GCaMp6m should not be equated with neuronal excitability. What is measured is a loss of the calcium response in high K depolarization experiments, which occurs due to the loss of expression of Cav channels. Hence, the use of this term is not accurate and will confuse readers. The use of terms referring to neuronal excitability needs to be changed throughout the manuscript. As such, the conclusions regarding neuronal excitability should be strongly tempered and the data reinterpreted as there are no true measurements of neuronal excitability in the manuscript. All that can be said is that expression of certain ion channel genes is suppressed. Since both Na+ channels and K+ channel expression is down-regulated, it is hard to say precisely how membrane excitability is altered without action potential analysis.

12- Related, since trl does not contain any molecular domains that could be regulated by Ca2+ signaling, it is unclear whether trl is directly regulated by SOCE or the regulation is highly indirect. Reporter assays evaluating trl activation upon Ca2+ rises would provide much stronger and more direct evidence for the conclusion that trl is a SOCE-regulated TF. As such the evidence is entirely based on RNAi downregulation of trl which indicates that trl is essential but has no bearing on exactly what point of the signaling cascade it is involved.

13- Are NFAT levels altered in the Orai1 loss of function mutant? If not, this should be explicitly stated. It would seem based on previous literature that some gene regulation may be related to the downregulation of this established Ca2+-dependent transcription factor. Same for NFkb.

14- Does over-expression of Set2 restore ion channel expression especially those of the VGCCs? This would provide rigorous, direct evidence that SOCE-mediated regulation of VGCCs through Set2 controls voltage-gated calcium channel signaling.

15- All 6 representative panels from Figure 3B are duplicated in Figure 4G. Likewise, 2 representative panels from Figure 5H are duplicated in Figure 6D. Although these panels all represent the results from control experiments, the relevant experiments were likely not conducted at the same time and under the same conditions. Thus, control images from other experiments should not be used simply because they correspond to controls. This situation should be clarified.

16- The figures are unusually busy and difficult to follow. In part this is because they usually have many panels (Fig. 1: A-I; Fig. 2, A-J, etc) but also because the arrangement of the panels is not consistent: sometimes the following panel is found to the right, other times it is below. It would help the reader to make the order of the panels consistent, and, if possible, reduce the number of panels and/or move some of the panels to new figures.

17- As a final recommendation, the reviewers suggest that the authors a- Reword the text that refers to membrane excitability since membrane excitability was not directly measured here. b-Explain why STIM1 rescues the partial loss of flight in Set2 RNAi flies (Fig. S2E); and c- Explain how/why trl is calcium regulated and test using luciferase (or other) reporter assays whether Orai activation leads to trl activation.

Reviewer #1 (Public Review):

The authors show that concurrently presenting foreign words and their translations during sleep leads to the ability to semantically categorize the foreign words above chance. Specifically, this procedure was successful when stimuli were delivered during slow oscillation troughs as opposed to peaks, which has been the focus of many recent investigations into the learning & memory functions of sleep. Finally, further analyses showed that larger and more prototypical slow oscillation troughs led to better categorization performance, which offers hints to others on how to improve or predict the efficacy of this intervention. The strength here is the novel behavioral finding and supporting physiological analyses, whereas the biggest weakness is the interpretation of the peak vs. trough effect.

Major importance:

I believe the authors could attempt to address this question: What do the authors believe is the largest implication of this studies? How far can this technique be pushed, and how can it practically augment real-world learning?

Lines 155-7: How do the authors argue that the words fit well within the half-waves when the sounds lasted 540 ms and didn't necessarily start right at the beginning of each half-wave? This is a major point that should be discussed, as part of the down-state sound continues into the up-state. Looking at Figure 3A, it is clear that stimulus presented in the slow oscillation trough ends at a time that is solidly into the upstate, and would not neurolinguists argue that a lot of sound processing occurs after the end of the sound? It's not a problem for their findings, which is about when is the best time to start such a stimulus, but it's a problem for the interpretation. Additionally, the authors could include some discussion on whether possibly presenting shorter sounds would help to resolve the ambiguities here.

Medium importance:

Throughout the paper, another concern relates to the term 'closed-loop'. It appears this term has been largely misused in the literature, and I believe the more appropriate term here is 'real-time' (Bergmann, 2018, Frontiers in Psychology; Antony et al., 2022, Journal of Sleep Research). For instance, if there were some sort of algorithm that assessed whether each individual word was successfully processed by the brain during sleep and then the delivery of words was subsequently changed, that could be more accurately labeled as 'closed-loop'.

Figure 5 and corresponding analyses: Note that the two conditions end up with different sounds with likely different auditory complexities. That is, one word vs. two words simultaneously likely differ on some low-level acoustic characteristics, which could explain the physiological differences. Either the authors should address this via auditory analyses or it should be added as a limitation.

Line 562-7 (and elsewhere in the paper): "episodic" learning is referenced here and many times throughout the paper. But episodic learning is not what was enhanced here. Please be mindful of this wording, as it can be confusing otherwise.

Reviewer #1 (Public Review):

The Ras/MEK/Erk signaling cascade is a ubiquitous pathway activated by many extracellular signals and is critical for a wide variety of cell function. In this manuscript, the authors generate Erk1/2 double knockouts specifically in Nkx2.1-derived cells (basically MGE/POA-derived cells in the forebrain) and explore changes in oligodendrocyte number and cortical interneuron function. They observe a striking loss of Nkx2.1-lineage oligos (and astrocytes) in the anterior commissure, although the mechanism for this specific loss is unclear. While there is no significant change in the number of cortical interneurons, the authors do note a decrease in SST+/Calb- INs in the mutant. The authors then use DREADDs to manipulate activity in Nkx2.1-lineage cells. Surprisingly, chemogenetic activation of Nkx2.1-lineage KO cells led to an upregulation of SST protein in SST+ INs, while other characteristics in KO mice (cFos expression, open field locomotion) were not changed (or altered at much lower levels) in KOs compared to similar stimulation in control mice. Overall, the paper contains numerous insightful observations, but a coherent, overall theme for what Erk1/2 is doing in Nkx2.1-lineage cells at different development timepoints is somewhat lacking. For example, the authors focus on changes in SST levels in the KO mice, justifiably because that is where they see the biggest difference, yet they perform e-phys experiments only on PV+, fast spiking cells in Figure 5. While it may be more challenging to find SST+ cells in the KO, the logic of recording from PV cells was not clear. Sometimes this paper reads as a series of data points where the overall theme of the story is not always evident.

More importantly, the authors use heterozygous ERK1/2 mice as 'het controls' throughout the manuscript. However, they have not sufficiently demonstrated that the ERK levels in hets are similar to WT. Figure 1B-J purports to show that ERK1/2 levels in a handful of cells from heterozygous mice are equivalent to WT, but there is no quantification of this observation. It is unconventional to use heterozygous mice as controls without clearly demonstrating that they are similar/identical to controls. Especially in a scenario such as this, where one would expect to see 50% of protein levels in hets compared to WT mice. As such, readers are cautioned for how to interpret some of these findings. For example, there may be instances where there is no significant difference between KO and 'het controls', but if they had compared to true WT controls, then it's possible some differences could emerge.

Reviewer #1 (Public Review):

This paper describes a novel and important role for IP3 receptors (IP3R) in the control of store-operated calcium entry (SOCE) in neurons. The authors provide strong evidence that in human neural progenitor cells before and after differentiation in vitro, as well as a neuroblastoma cell line (SH-SY5Y), knockdown of the IP3R1 isoform significantly diminishes SOCE triggered by ER calcium store depletion. Interestingly, SOCE is fully restored in these cells by overexpressing WT IP3R1 or a mutant that cannot conduct Ca2+ but is not restored by an IP3R1 mutant that cannot bind IP3. Based on these results the authors conclude that IP3-bound IP3R1 enhances SOCE not by depleting ER Ca2+ but through an as yet uncharacterized physical interaction.

The authors propose that resting levels of IP3 are sufficient for this activity, based on the ability of a Gq inhibitor to mimic the effect of IP3R1 knockdown on SOCE. Importantly, the inhibitor does not affect SOCE in cells lacking IP3R1, arguing against a nonspecific effect of the drug. The ability of partial binding of low levels of IP3 to support this activity is somewhat surprising, and further studies will be needed to test whether the enhancing effect is amplified by receptor-driven elevation of IP3.

An important question is how the IP3R1 acts to enhance SOCE. A proximity ligation assay clearly showed that IP3R1 knockdown disrupted STIM1 and Orai1 colocalization after store depletion, supporting the notion that IP3R1 acts to enhance STIM1-Orai1 interactions. How might this occur? The authors suggest that IP3R1 enhances the formation or stability of ER-plasma membrane (ER-PM) junctions where STIM1 and Orai1 combine to trigger SOCE, based on the rescue of SOCE by overexpression of STIM1 or E-syt1, both of which promote ER-PM junction formation or stability. However, this is indirect evidence, and a more direct demonstration of how IP3R1 affects ER-PM junction abundance and size would add stronger support for this hypothesis.

The authors suggest that the effects of IP3R1 described here may serve to selectively promote SOCE in response to stimuli that generate IP3 as opposed to other signals that release ER Ca2+. This proposal and its functional impact need further study, including why it appears to be cell-specific, occurring in neurons but not HEK 293 cells and other cell types.

Reviewer #1 (Public Review):

Schmid et al. investigate the question of how sensory learning in animals and artificial networks is driven both by passive exposure to the environment (unsupervised) and from reinforcing feedback (supervised) and how these two systems interact. They first demonstrate in mice that passive exposure to the same auditory stimuli used in a discrimination task modifies learning and performance in the task. Based on this data, they then tested how the interaction of supervised and unsupervised learning in an artificial network could account for the behavioural results.

Strengths :<br /> The clear behavioural impact of passive exposure to sounds on accelerating learning is a major strength of the paper. Moreover, the observation that passive exposure had a positive impact on learning whether it was prior to the task or interleaved with learning sessions provides interesting constraints for modelling the interaction between supervised and unsupervised learning. A practical fallout for labs performing long training procedures is that the periods of active learning that require water-restriction could be reduced by using passive sessions. This could increase both experimental efficiency and animal well-being.

The modelling section clearly exhibits the differences between models and the step-by-step presentation building to the final model provides the reader with a lot of intuition about how supervised and unsupervised learning interact. In particular, the authors highlight situations in which the task-relevant discrimination does not align with the directions of highest variance, thus reinforcing the relevance of their conclusions for the complex structure of sensory stimuli. A great strength of these models is that they generate clear predictions about how neural activity should evolve during the different training regimes that would be exciting to test.

Weaknesses :<br /> The experimental design presented cannot clearly show that the effect of passive exposure was due to the specific exposure to task-relevant stimuli since there is no control group exposed to irrelevant stimuli. Studies have shown that exposure to a richer sensory environment, even in the adult, swiftly (ie within days) enhances responses even in the adult and even when the stimuli are different from those present in task1-3. Since the authors conclude that their network models "build latent representations of features that are determined by statistical properties of the input distribution, as long as those features aid the decoding of task-relevant variables" (line 339, my emphasis). This conclusion, and therefore the link of behaviour to the models, is weakened by the lack of direct testing of the need for task-relevant stimuli to be presented.

The conclusion that "passive exposure influences responses to sounds not used during training" (line 147) does not seem fully supported by the authors' analysis. The authors show that there is an increase in accuracy for intermediate sweep speeds despite the fact that this is the first time the animals encounter them in the active session. However, it seems impossible to exclude that this effect is not simply due to the increased accuracy of the extreme sounds that the animals had been trained on. For example, simply prolonging learning in stage 3 is likely to increase accuracy across sounds at stage 4, passive sessions may be mimicking this effect. Moreover, the authors point out that there is no effect on the slope of the psychometric curve. Such a sharpening would be predicted if the passive presentations were indeed enhancing intermediate sound representations, making them more precise and more discriminable.

In the modelling section, the authors adjusted the hyper-parameters to maximize the difference between pure active and passive/active learning. This makes a comparison of learning rates between models somewhat confusing, raising the question of whether the differences highlight an interaction between the two types of learning or simply parameter choice. For example:

- Figure 5: although in model 3 passive listening enhances learning relative to the pure active condition, learning is overall much slower in the active condition compared to model 2. This raises the question of whether the addition of unsupervised rules makes the models more apt at exploiting passive exposure but at the cost of efficient active learning.

- Figure 6 & 7: model 5 only differs from model 4 by the addition of supervised learning at layer 1 and the use of what should be a harder task (stimuli spread over the first PCs) however model 5 clearly has much better performance for the P: A condition which is surprising given that the unsupervised and supervised learning periods are clearly separated.

1. Mandairon, N., Stack, C. & Linster, C. Olfactory enrichment improves the recognition of individual components in mixtures. Physiol. Behav. 89, 379-384 (2006).<br /> 2. Alwis, D. S. & Rajan, R. Environmental enrichment and the sensory brain: The role of enrichment in remediating brain injury. Front. Syst. Neurosci. 8, 1-20 (2014).<br /> 3. Polley, D. B., Kvašňák, E. & Frostig, R. D. Naturalistic experience transforms sensory maps in the adult cortex of caged animals. Nature 429, 67-71 (2004).

Reviewer #1 (Public Review):

Summary of what the authors were trying to achieve.

This paper studies the possible effects of tACS on the detection of silence gaps in an FM-modulated noise stimulus. Both FM modulation of the sound and the tACS are at 2Hz, and the phase of the two is varied to determine possible interactions between the auditory and electric stimulation. Additionally, two different electrode montages are used to determine if variation in electric field distribution across the brain may be related to the effects of tACS on behavioral performance in individual subjects.

Major strengths and weaknesses of the methods and results.

The study appears to be well-powered to detect modulation of behavioral performance with N=42 subjects. There is a clear and reproducible modulation of behavioral effects with the phase of the FM sound modulation. The study was also well designed, combining fMRI, current flow modeling, montage optimization targeting, and behavioral analysis. A particular merit of this study is to have repeated the sessions for most subjects in order to test repeat-reliability, which is so often missing in human experiments. The results and methods are generally well-described and well-conceived. The portion of the analysis related to behavior alone is excellent. The analysis of the tACS results is also generally well described, candidly highlighting how variable results are across subjects and sessions. The figures are all of high quality and clear. One weakness of the experimental design is that no effort was made to control for sensation effects. tACS at 2Hz causes prominent skin sensations which could have interacted with auditory perception and thus, detection performance.

Appraisal of whether the authors achieved their aims, and whether the results support their conclusions.

Unfortunately, the main effects described for tACS are encumbered by a lack of clarity in the analysis. It does appear that the tACS effects reported here could be an artifact of the analysis approach. Without further clarification, the main findings on the tACS effects may not be supported by the data.

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

The central claim is that tACS modulates behavioral detection performance across the 0.5s cycle of stimulation. However, neither the phase nor the strength of this effect reproduces across subjects or sessions. Some of these individual variations may be explainable by individual current distribution. If these results hold, they could be of interest to investigators in the tACS field.

The additional context you think would help readers interpret or understand the significance of the work.

The following are more detailed comments on specific sections of the paper, including details on the concerns with the statistical analysis of the tACS effects.

The introduction is well-balanced, discussing the promise and limitations of previous results with tACS. The objectives are well-defined.

The analysis surrounding behavioral performance and its dependence on the phase of the FM modulation (Figure 3) is masterfully executed and explained. It appears that it reproduces previous studies and points to a very robust behavioral task that may be of use in other studies.

There is a definition of tACS(+) vs tACS(-) based on the relative phase of tACS that may be problematic for the subsequent analysis of Figures 4 and 5. It seems that phase 0 is adjusted to each subject/session. For argument's sake, let's assume the curves in Fig. 3E are random fluctuations. Then aligning them to best-fitting cosine will trivially generate a FM-amplitude fluctuation with cosine shape as shown in Fig. 4a. Selecting the positive and negative phase of that will trivially be larger and smaller than a sham, respectively, as shown in Fig 4b. If this is correct, and the authors would like to keep this way of showing results, then one would need to demonstrate that this difference is larger than expected by chance. Perhaps one could randomize the 6 phase bins in each subject/session and execute the same process (fit a cosine to curves 3e, realign as in 4a, and summarize as in 4b). That will give a distribution under the Null, which may be used to determine if the contrast currently shown in 4b is indeed statistically significant.

Results of Fig 5a and 5b seem consistent with the concern raised above about the results of Fig. 4. It appears we are looking at an artifact of the realignment procedure, on otherwise random noise. In fact, the drop in "tACS-amplitude" in Fig. 5c is entirely consistent with a random noise effect.

To better understand what factors might be influencing inter-session variability in tACS effects, we estimated multiple linear models ..." this post hoc analysis does not seem to have been corrected for multiple comparisons of these "multiple linear models". It is not clear how many different things were tried. The fact that one of them has a p-value of 0.007 for some factors with amplitude-difference, but these factors did not play a role in the amplitude-phase, suggests again that we are not looking at a lawful behavior in these data.

"So far, our results demonstrate that FM-stimulus driven behavioral modulation of gap detection (FM-amplitude) was significantly affected by the phase lag between the FM-stimulus and the tACS signal (Audio-tACS lag) ..." There appears to be nothing in the preceding section (Figures 4 and 5) to show that the modulation seen in 3e is not just noise. Maybe something can be said about 3b on an individual subject/session basis that makes these results statistically significant on their own. Maybe these modulations are strong and statistically significant, but just not reproducible across subjects and sessions?

"Inter-individual variability in the simulated E-field predicts tACS effects" Authors here are attempting to predict a property of the subjects that was just shown to not be a reliable property of the subject. Authors are picking 9 possible features for this, testing 33 possible models with N=34 data points. With these circumstances, it is not hard to find something that correlates by chance. And some of the models tested had interaction terms, possibly further increasing the number of comparisons. The results reported in this section do not seem to be robust, unless all this was corrected for multiple comparisons, and it was not made clear?

"Can we reduce inter-individual variability in tACS effects ..." This section seems even more speculative and with mixed results.

Given the concerns with the statistical analysis above, there are concerns about the following statements in the summary of the Discussion:

"2) does modulate the amplitude of the FM-stimulus induced behavioral modulation (FM-amplitude)"<br /> This seems to be based on Figure 4, which leaves one with significant concerns.

"4) individual variability in tACS effect size was partially explained by two interactions: between the normal component of the E-field and the field focality, and between the normal component of the E-field and the distance between the peak of the electric field and the functional target ROIs."<br /> The complexity of this statement alone may be a good indication that this could be the result of false discovery due to multiple comparisons.

For the same reasons as stated above, the following statements in the Abstract do not appear to have adequate support in the data:<br /> "We observed that tACS modulated the strength of behavioral entrainment to the FM sound in a phase-lag specific manner. ... Inter-individual variability of tACS effects was best explained by the strength of the inward electric field, depending on the field focality and proximity to the target brain region. Spatially optimizing the electrode montage reduced inter-individual variability compared to a standard montage group."<br /> In particular, the evidence in support of the last sentence is unclear. The only finding that seems related is that "the variance test was significant only for tACS(-) in session 2". This is a very narrow result to be able to make such a general statement in the Abstract. But perhaps this can be made more clear.

Reviewer #1 (Public Review):

Kerkoerle and colleagues present a very interesting comparative fMRI study in humans and monkeys, assessing neural responses to surprise reactions at the reversal of a previously learned association. The implicit nature of this task, assessing how this information is represented without requiring explicit decision-making, is an elegant design. The paper reports that both humans and monkeys show neural responses across a range of areas when presented with incongruous stimulus pairs. Monkeys also show a surprise response when the stimuli are presented in a reversed direction. However, humans show no such surprise response based on this reversal, suggesting that they encode the relationship reversibly and bidirectionally, unlike the monkeys. This has been suggested as a hallmark of symbolic representation, that might be absent in nonhuman animals.

I find this experiment and the results quite compelling, and the data do support the hypothesis that humans are somewhat unique in their tendency to form reversible, symbolic associations. I think that an important strength of the results is that the critical finding is the presence of an interaction between congruity and canonicity in macaques, which does not appear in humans. These results go a long way to allay concerns I have about the comparison of many human participants to a very small number of macaques.

I understand the impossibility of testing 30+ macaques in an fMRI experiment. However, I think it is important to note that differences necessarily arise in the analysis of such datasets. The authors report that they use '...identical training, stimuli, and whole-brain fMRI measures'. However, the monkeys (in experiment 1) actually required 10 times more training. More importantly, while the fMRI measures are the same, group analysis over 30+ individuals is inherently different from comparing only 2 macaques (including smoothing and averaging away individual differences that might be more present in the monkeys, due to the much smaller sample size).

Despite this, the results do appear to show that macaques show the predicted interaction effect (even despite the sample size), while humans do not. I think this is quite convincing, although had the results turned out differently (for example an effect in humans that was absent in macaques), I think this difference in sample size would be considerably more concerning.

I would also note that while I agree with the authors' conclusions, it is notable to me that the congruity effect observed in humans (red vs blue lines in Fig. 2B) appears to be far more pronounced than any effect observed in the macaques (Fig. 3C-3). Again, this does not challenge the core finding of this paper but does suggest methodological or possibly motivational/attentional differences between the humans and the monkeys (or, for example, that the monkeys had learned the associations less strongly and clearly than the humans).

This is a strong paper with elegant methods and makes a worthwhile contribution to our understanding of the neural systems supporting symbolic representations in humans, as opposed to other animals.

Reviewer #1 (Public Review):

Bull et al aimed to use data from observational studies and mendelian randomisation to explore if changes in circulating metabolites are associated with colorectal cancer development. As Mendelian randomisation uses information on genetic variations which are fixed at birth, it is less vulnerable to confounding than standard observational studies.

Overall, a major strength of the study is that it uses data from large cohort studies, one from childhood, adolescence, and early adulthood when the incidence of colorectal cancer is very low (reducing the likelihood of reverse causation) and before medication (such as statins which have the potential to affect metabolite levels) has been initiated.

This study has some weaknesses which have been acknowledged by the authors. Although the findings of this study indicate the potentially significant role that polyunsaturated fatty acids may have in colorectal cancer risk, the genes and therefore also the genetic variations (SNPs) associated with fatty acids often produce an effect for more than one fatty acid which may introduce bias. This together with the fact that there was limited information available on many specific fatty acids which are known causative metabolites for colorectal cancer, makes it difficult to establish with confidence which specific classes of fatty acids could potentially play a causative role in these associations. Also, the study populations are majority white European descent which may limit the applicability of these findings to other populations.

The methodology used was largely acceptable to achieve the aims set out and the findings have shown an association between polyunsaturated fat and colorectal cancer. However, I feel that the conclusion should be tempered slightly as although this study alongside other similar MR studies provides evidence of an association between genetic liability to CRC and levels of metabolites at certain ages, I do not think there is enough evidence at this stage to say that genetic liability for CRC actually alters the levels of metabolites.

Overall, this is an important piece of work that has the potential to contribute to our understanding of the causal relationship between circulating metabolites at different stages of the life cycle and colorectal cancer risk as it would be extremely difficult to gather such evidence using other study designs. It opens the door for future research aiming to better understand the role that these metabolites could play in colorectal cancer risk prediction and in turn help identify groups of individuals who would benefit most from prevention and early detection interventions.

This work will be of interest not only to epidemiologists working in the area of GI tract cancers but also those interested in the different applications for mendelian randomisation within cancer epidemiology research.

Reviewer #1 (Public Review):

The manuscript by Zheng, et al., is focused on assessing the role of deletion of PTPMT1, a mitochondria-based phosphatase, in mitochondrial fuel selection. Authors show that the utilization of pyruvate, a key mitochondrial substrate derived from glucose, is inhibited, whereas fatty acid utilization is enhanced. Importantly, while the deletion of PTPMT1 does not impact development of skeletal muscle or heart, the metabolic inflexibility leads to muscular atrophy, heart failure, and sudden death. Mechanistically, authors claim that the prolonged substrate shift from carbohydrates to lipids causes oxidative stress and mitochondrial dysfunction, leading to accumulation of lipids and muscle cell and CM damage in the KO. Interestingly, PTPMT1 deletion from the liver or adipose tissue does not generate any local or systemic defects. Authors conclude that PTPMT1 plays an important role in maintaining mitochondrial flexibility and that the balanced utilization of carbohydrates and lipids is essential for skeletal muscle and heart.

The following issues remain:

1) Authors have not alleviated the concern regarding the fact that CKMM- and the MYHC-Cre express early, during development ; even if the effects are not grossly apparent during development, many developmental issues progress over time and manifest later in adulthood, particularly those concerning cardiac function and development (ie adult congenital disease). As such, the authors explanation that they don't observe differences does not suffice; detailed developmental assessment by histology at the various developmental stages (by timed mating) are needed to validate the study and conclusions of the authors. Alternatively, as mentioned previously, authors could utilize inducible cre drivers, expressing the gene only in adulthood to prove that the effects are or not developmental in nature. Similarly, the authors new assertion that late-onset phenotypes observed in the knockout mice over time is attributed to the metabolic defects arising from the loss of PTPMT1 in the embryos needs to be validated- therefore the developmental effects are in fact critical to the phenotype and should be demonstrated in the paper.

2) Quantification of ALL western blot data is an absolute necessity and speaks to the rigor and reproducibility of the study. I do not agree that this is unnecessary or that it would take up too much space.

Reviewer #1 (Public Review):

The Eph receptor tyrosine kinase family plays a critical function in multiple physiological and pathophysiological processes. Hence, understating the regulation of these receptors is a highly important question. Through extensive experiments in cell lines and cultured neurons, Chang et.al show that the signaling hub protein, MYCBP2 positively regulates the overall stability of a specific member of the family, EPHB2, and by that the cellular response to ephrinBs. Overall, this work sheds light on the divergence in the regulatory mechanisms of the Eph receptors family. The physiological importance of this new regular mechanism awaits discovery.

Reviewer #1 (Public Review):

In this manuscript, the authors identified and characterized the five C-terminus repeats and a 14aa acidic tail of the mouse Dux protein. They found that repeat 3&5, but not other repeats, contribute to transcriptional activation when combined with the 14aa tail. Importantly, they were able to narrow done to a 6 aa region that can distinguish "active" repeats from "inactive" repeats. Using proximal labeling proteomics, the authors identified candidate proteins that are implicated in Dux-mediated gene activation. They were able to showcase that the C-terminal repeat 3 binds to some proteins, including Smarcc1, a component of SWI/SNF (BAF) complex. In addition, by overexpressing different Dux variants, the authors characterized how repeats in different combinations, with or without the 14aa tail, contribute to Dux binding, H3K9ac, chromatin accessibility, and transcription. In general, the data is of high quality and convincing. The identification of the functionally important two C-terminal repeats and the 6 aa tail is enlightening. The work shined light on the mechanism of Dux function.

Reviewer #1 (Public Review):

In mammals, a large methyltransferase complex (including METTL3, METTL14 and WTAP) deposits m6A across the transcriptome, and METTL3 serves as its catalytic core component. In this manuscript, the authors identified two cleaved forms of METTL3 and described the function of METTL3a (residues 239-580) in breast tumorigenesis. METTL3a mediates the assembly of METTL3-METTL14-WTAP complex, the global m6A deposition and breast cancer progression. Furthermore, the METTL3a-mTOR axis was uncovered to mediate the METTL3 cleavage, providing potential therapeutic target for breast cancer. This study is properly performed and the findings are very interesting; however, some problems with the model and assays need to be modified.. It is widely known that METTL3 and METTL14 form a stable heterodimer with the stoichiometric ratio of 1:1 (Wang X et al. Nature 534, 575-578 (2016), Su S et al. Cell Res 32(11), 982-994 (2022), Yan X et al. Cell Res 32(12), 1124-1127 (2022)), the numbers of METTL3 and METTL14 in the model of Fig 7P are not equivalent and need to be modified.

Reviewer #1 (Public Review):

Peng et al develop a computational method to predict/rank transcription factors (TFs) according to their likelihood of being pioneer transcription factors--factors that are capable of binding nucleosomes--using ChIP-seq for 225 human transcription factors, MNase-seq and DNase-seq data from five cell lines. The authors developed relatively straightforward, easy to interpret computational methods that leverage the potential for MNase-seq to enable relatively precise identification of the nucleosome dyad. Using an established smoothing approach and local peak identification methods to estimate positions together with identification of ChIP-seq peaks and motifs within those peaks which they referred to as "ChIP-seq motifs", they were able to quantify "motif profiles" and their density in nucleosome regions (NRs) and nucleosome free regions (NFRs) relative to their estimated nucleosome dyad positions. Using these profiles, they arrived at an odd-ratio based motif enrichment score along with a Fisher's exact test to assess the odds and significance that a given transcription factor's ChIP-seq motifs are enriched in NRs compared to NFRs, hence, its potential to be a pioneer transcription factor. They showed that known pioneer transcription factors had among the highest enrichment scores, and they could identify 32 relatively novel pioneer TFs with high enrichment scores and relatively high expression in their corresponding cell line. They used multiple validation approaches including (1) calculating the ROC-AUC associated with their enrichment score based on 16 known pioneer TFs among their 225 TFs which they used as positives and the remaining TFs (among the 225) as negatives; (2) use of the literature to note that known pioneer TFs that acted as key regulators of embryonic stem cell differentiation had a highest enrichment scores; (3) comparison of their enrichments scores to three classes of TFs defined by protein microarray and electromobility shift assays (1. strong binder to free and nucleosomal DNA, 2. weak binder to free and nucleosomal DNA, 3. strong binding to free but not nucleosomal DNA); and (4) correlation between their calculated TF motif nucleosome end/dyad binding ratio and relevant data from an NCAP-SELEX experiment. They also characterize the spatial distribution of TF motif binding relative to the dyad by (1) correlating TF motif density and nucleosome occupancy and (2) clustering TF motif binding profiles relative to their distance from the dyad and identifying 6 clusters.

The strengths of this paper are the use of MNase-seq data to define relatively precise dyad positions and ChIP-seq data together with motif analysis to arrive at relatively accurate TF binding profiles relative to dyad positions in NRs as well as in NFRs. This allowed them to use a relatively simple odds ratio based enrichment score which performs well in identifying known pioneer TFs. Moreover, their validation approaches either produced highly significant or reasonable, trending results.

The weaknesses of the paper are relatively minor. The most significant one is that they used ROC-AUC to assess the prediction accuracy of their enrichment score on a highly imbalanced dataset with 16 positives and 209 negatives. ROC-AUC is known to be a misleading prediction measure on highly imbalanced data. This is mitigated by the fact that they find an AUC = 0.94 for their best case. Thus, they're likely to find good results using a more appropriate performance measure for imbalanced data. Another minor point is that they did not associate their enrichment score (focus of Figure 2) with their correlation coefficients of TF motif density and nucleosome occupancy (focus of Figure 3). Finally, while the manuscript was clearly written, some parts of the Methods section could have been made more clear so that their approaches could be reproduced. The description of the NCAP-SELEX method could have also been more clear for a reader not familiar with this approach.

Reviewer #1 (Public Review):

This study addresses the fundamental question of how the nucleotide, associated with the beta-subunit of the tubulin dimer, dictates the tubulin-tubulin interaction strength in the microtubule polymer. This problem has been a topic of debate in the field for over a decade, and it is essential for understanding microtubule dynamics.

McCormick and colleagues focus their attention on two hypotheses, which they call the "self-acting" model and the "interface-acting" model. Both models have been previously discussed in the literature and they are related to the specific way, in which the GTP hydrolysis in the beta-tubulin subunit exerts an effect on the microtubule lattice. The authors argue that the two considered models can be discriminated based on a quantitative analysis of the sensitivity of the growth rates at the plus- and minus-ends of microtubules to the concentration of GDP-tubulins in mixed nucleotide (GDP/GMPCPP) experiments. By combing computational simulations and in vitro observations, they conclude that the tubulin-tubulin interaction strength is determined by the interfacial nucleotide.

The major strength of the paper is a systematic and thorough consideration of GDP as a modulator of microtubule dynamics, which brings novel insights about the structure of the stabilizing cap on the growing microtubule end.

I think that the study is interesting and valuable for the field, but it could be improved by addressing the following critical points and suggestions. They concern (1) the statistical significance of the main experimental finding about the distinct sensitivity of the plus- and minus-ends of microtubules to the GTP-tubulin concentration in solution, and (2) the validity of the formulation of the "self-acting" model with an emphasis solely on the longitudinal bonds.

Reviewer #1 (Public Review):

This cross-sectional study examined the results of a survey about cancer treatment disruption during June-August 2020 in 82 counties located in Missouri and Illinois in the U.S. The main outcome was disruption in cancer care. Authors reported that higher education, being a female, experiencing more discrimination in healthcare settings, and having scheduled a telehealth appointment were associated with higher odds of care disruption. Lack of a research focus, lack of following any conceptual framework, the cross-sectional nature of the study, and the small sample size were the noted shortcomings of the manuscript.

Reviewer #1 (Public Review):

This work serves to fill an important gap in our understanding of the control of insect walking: characterization of the structure of inter-individual variability. The authors use an extensive novel dataset to exhaustively test across models. Such integration of mechanistic theory and experimental analyses is both crucial and not seen enough in the literature.

In this study, the authors perform experiments using external electrical muscle stimulation in intact, immobilised animals and measure joint torques in three muscles: the retractor coax (which is involved in propulsion and joint stiffness), the protractor coxae (which is involved in joint stiffness and in the swing-stance transition), and the levator trochanteris (which is involved in the swing stance transition). These experiments quantify the relationship between electrical stimulus and torque generated in each joint. Because these experiments are performed on many animals, the authors are able to investigate how this relationship varies between (and within) each individual. The results of these experiments are then interpreted in the context of a hierarchical Bayesian model.

The results of this work are helpful towards our understanding of the role of inter-individual variation in the control of insect walking. Proper links between such variation observed in biomechanical studies in freely walking animals will require an understanding of how the variability characterized in this study interplays with other behavioural factors. The authors make note of this: their work takes place in immobilised animals, and thus cannot explicitly test the predictions of their model parameters on performance in freely-behaving insects. They outline a possible path forward to this end, which involves using their previously presented Motion Hacking method in unrestrained locomotion. This is an exciting future direction that is set up by the results here, but is outside the scope of the current work; the authors are upfront and reasonable about the limitations of their study.

The clarity of this work suffers from its structure: the models (and the parameters within) are central to the results of this study. The integration of data-driven modelling and experiment is a main reason this work is exciting! Yet, these are introduced far after the results are presented. While this is partially due to the section structure set forward, some basic aspects of the models and experimental system should be introduced prior to delineating the Results.

Reviewer #1 (Public Review):

Castano et al. report a screening to search for selective CDKL5 inhibitors. After profiling an extensive library of selective cyclin-dependen kinase inhibitors, the authors synthesized and characterized high-affinity selective inhibitors of CDKL5. Kinome-wide studies were performed to verify selectivity. Preliminary PK studies were realized in rodents, including the determination of total brain/plasma ratio associated with two dose levels and microsomal stability. When applied directly to rat hippocampal brain slices, one of the inhibitors (CAF-382) reduced post-synaptic function of AMPA-type glutamate receptors dose-dependently, and also reduced hippocampal long-term potentiation. CAF-382 could be a valuable tool to further investigate the role of CDKL5 in disease, although some potential applications may be limited by the seemingly low brain bioavailability of this compound.

The conclusions of this paper are in general terms well supported by data, but some aspects of the discussion of the results could be extended.

Reviewer #1 (Public Review):

ONC201/TIC10 refers to the imipridone class of inhibitors which is currently being evaluated in clinical trials for solid tumors. The present manuscript explored the combination treatment of ONC201/TIC10 with everolimus in ER+ breast cancer cell lines. The authors demonstrated the increased therapeutic response by ONC201/TIC10 in primary patient cells progressing on everolimus. The authors show that ONC201/TIC10, in metastatic ER+ breast cancer cells, mechanistically involves oxidative phosphorylation inhibition and stress response activation.

The manuscript provides evidence for the following:

1. ONC201/TIC10 inhibits the proliferation of breast cancer cell lines sensitive and resistant to everolimus.<br /> 2. ONC201/TIC10 increased therapeutic response in primary patient cells progressing on everolimus.<br /> 3. ONC201/TIC10, in metastatic ER+ breast cancer cells, mechanistically involves oxidative phosphorylation inhibition and stress response activation<br /> The main merit of the manuscript is that the authors demonstrated that the combination treatment of ONC201/TIC10 with everolimus might be a therapeutic choice for ER+ breast cancer, particular for those resistant to everolimus. This is rather interesting with potential translational impact for breast cancer patients. The major weakness of the manuscript is that some conclusions of the manuscript require rigorous validation. In particular, the therapeutic potential of the combination treatment of ONC201/TIC10 with everolimus needs to be further explored. Some serious work should be done to amend the manuscript before any further consideration.

Reviewer #1 (Public Review):

This study demonstrates that a hybrid measurement method increases 3 fold the resolution of mouse USV localization. This increased resolution enables to revise previous occurrence frequency measures for female vocalizations and establishes the existence of vocal dominance in tryadic interactions. The method is well described and its efficiency is carefully quantified. A limitation of the study is the absence of ground truth data, which may have been generated eventually with miniaturized loudspeakers in mouse puppets. However, a careful error estimation partially compensates for the absence of these likely challenging calibrations. In addition, the conclusions take into account this uncertainty. The gain in accuracy with respect to previous methods is clear and the impact of localisation accuracy on biological conclusions about vocalisation behavior is clearly exemplified. This study demonstrates the impact of the new method for understanding vocal interactions in the mouse model, which should be of tremendous interest for the growing community studying social interactions in mice.

Reviewer #1 (Public Review):

Castano et al. report a screening to search for selective CDKL5 inhibitors. After profiling an extensive library of selective cyclin-dependen kinase inhibitors, the authors synthesized and characterized high-affinity selective inhibitors of CDKL5. Kinome-wide studies were performed to verify selectivity. Preliminary PK studies were realized in rodents, including the determination of total brain/plasma ratio associated with two dose levels and microsomal stability. When applied directly to rat hippocampal brain slices, one of the inhibitors (CAF-382) reduced post-synaptic function of AMPA-type glutamate receptors dose-dependently, and also reduced hippocampal long-term potentiation. CAF-382 could be a valuable tool to further investigate the role of CDKL5 in disease, although some potential applications may be limited by the seemingly low brain bioavailability of this compound.

The conclusions of this paper are in general terms well supported by data, but some aspects of the discussion of the results could be extended.

Reviewer #1 (Public Review):

The authors present a back-of-the-envelope exploration of various possible resource allocation strategies for ITNs. They identify two optimal strategies based on two slightly different objective functions and compare 3 simple strategies to the outcomes of the optimal strategies and to each other. The authors consider both P falciparum and P vivax and explore this question at the country level, using 2000 prevalence estimates to stratify countries into 4 burden categories.

This is a relevant question from a global funder perspective, though somewhat less relevant for individual countries since countries are not making decisions at the global scale. The authors have made various simplifications to enable the identification of optimal strategies, so much so that I question what exactly was learned. It is not surprising that strategies that prioritize high-burden settings would avert more cases. Generally, I found much of the text confusing and some concepts were barely explained, such that the logic was difficult to follow.

I am not sure why the authors chose to stratify countries by 2000 PfPR estimates and in essence explore a counterfactual set of resource allocation strategies rather than begin with the present and compare strategies moving forward. I would think that beginning in 2020 and modeling forward would be far more relevant, as we can't change the past. Furthermore, there was no comparison with allocations and funding decisions that were actually made between 2000 and 2020ish so the decision to begin at 2000 is rather confusing.

I realize this is a back-of-the-envelope assessment (although it is presented to be less approximate than it is, and the title does not reveal that the only intervention strategy considered is ITNs) but the number and scope of modeling assumptions made are simply enormous. First, that modeling is done at the national scale, when transmission within countries is incredibly heterogeneous. The authors note a differential impact of ITNs at various transmission levels and I wonder how the assumption of an intermediate average PfPR vs modeling higher and lower PfPR areas separately might impact the effect of the ITNs. Second, the effect of ITNs will differ across countries due to variations in vector and human behavior and variation in insecticide resistance and susceptibility to the ITNs. The authors note this as a limitation but it is a little mind-boggling that they chose not to account for either factor since estimates are available for the historical period over which they are modeling. Third, the assumption that elimination is permanent and nothing is needed to prevent resurgence is, as the authors know, a vast oversimplification. Since resources will be needed to prevent resurgence, it appears this assumption may have a substantial impact on the authors' results.

The decision to group all settings with EIR > 7 together as "high transmission" may perhaps be driven by WHO definitions but at a practical level this groups together countries with EIR 10 and EIR 500. Why not further subdivide this group, which makes sense from a technical perspective when thinking about optimal allocation strategies?

The relevance of this analysis for elimination is a little questionable since no one eliminates with ITNs alone, to the best of my understanding.

Reviewer #1 (Public Review):

In this manuscript, Marmor and colleagues reanalyze a previously published dataset of chronic widefield Ca2+ imaging from the dorsal cortex of mice as they learn a go/no-go somatosensory discrimination task. Comparing hit trials that have a distinct history (i.e. are preceded by distinct trial types), the authors find that hit trials preceded by correct rejections of the non-target stimulus are associated with larger subsequent neural responses than trials precede by other hits, across the cortex. The authors analyze the time course over which this effect emerges in the barrel cortex (BC) and the rostrolateral visual area (RL), and find that its magnitude increases as the animals become expert task performers. Although the findings are potentially interesting, I, unfortunately, believe that there are important methodological concerns that could put them into question. I also disagree with the rationale that singles out BC and RL as being especially important for the emergence of trial history effects on neural responses during decision-making. I detail these points below.

1) The authors did not perform correction for hemodynamic contamination of GCaMP fluorescence. In widefield imaging, blood vessels divisively decrease neural signals because they absorb green-wavelength photons, which could lead to crucial confounds in the interpretation of the main results because of neurovascular coupling, which lags neural activity by seconds. For example, if a reward response from the previous trial is associated with a lagged hemodynamic contamination that artificially decreases the signal in the following trial, one could get artificially higher activity in trials that were not preceded by a reward (i.e. CR), which is what the authors observed. Ideally, the experiments would be repeated with proper hemodynamic correction, but at the very least the authors should try to address this with control analyses. For example, what is the time course of reward-related responses in BC and elsewhere? Do hemodynamics artifacts have a trial-by-trial correlation with the subsequent trial history effect? What is the learning time course of reward responses? Note that I don't believe the FA-Hit condition analysis that the authors have already presented provides adequate control, as punishment responses are also pervasive in the cortex and therefore suffer from the same interpretational caveat. Unfortunately, I believe this is a serious methodological issue given the above. However, I will proceed to take the reported results at face value.

2) The statistics used to assess the effect of trial history over learning are inadequate (e.g., Fig 2b). The existence of a significant effect in one condition (e.g., CR-Hit vs. Hit-Hit in expert) but not in another (e.g., same comparison in naive) does not imply that these two conditions are different. This needs to be tested directly. Moreover, the present analysis does not account for the fact that measures across learning stages are taken from the same animals. Thus, the appropriate analysis for these cases would be to first use a two-way ANOVA with repeated measures with factors of trial history and learning stage (or equivalent non-parametric test) and then derive conclusions based on post hoc pairwise tests, corrected for multiple comparisons.

3) I am not convinced that BC and RL are especially important for trial-history-dependent effects. Figures 4 and 5 suggest that this modulation is present across the cortex, and in fact, the difference between CR-Hit and Hit-Hit in some learning stages appears stronger in other areas. BC and RL do have the highest absolute activity during the epochs in Figs 4 and 5, but I would argue that this is likely due to other aspects of the task (e.g., touch) and therefore is not necessarily relevant to the issue of trial history.

4) Because of similar arguments to the above, and because this was not directly assessed, I do not believe the conclusion that history information emerges in RL and is transferred to BC is warranted. For instance, there is no direct comparison between areas, but inspection of the ROC plots in Fig 6b suggests that history information emerges concomitantly across cortical areas. I suggest directly comparing the time course between these (and other areas).

5) How much is task performance itself modulated by trial history? How does this change over the course of learning? These behavioral analyses would greatly help interpret the neural findings and how this trial history might be used behaviorally.

Reviewer #1 (Public Review):

In their manuscript titled "A human mitofusion 2 mutation causes mitophagic cardiomyopathy", Franco et al suggest that a rare mutation in MFN2 (R400Q) is over-represented in patients with cardiomyopathy, causes loss of conformational malleability, leading to mitochondrial fusion defects, impaired Parkin recruitment to mitochondria, and suppressed MFN2-Parkin mediated mitophagy. This work is an extension of previous work from the same group that found the MFN2 R400Q mutation is loss of function in a Drosophila model. Unlike MFN2 R94Q and T105M that cause Charcot-Marie-Tooth disease type 2 A, the MFN2 R400Q mutant has normal GTPase activity and mitochondrial electrochemical integrity, motility, and respiration. MFN2 R400Q knock-in mice exhibit cardiac-specific phenotypes.

Strengths include detailed characterization of the MFN2 R400Q variant in variety of models, including cell models and novel knock-in mouse model.<br /> However, there are some weaknesses. The central claim that the R400Q mutation causes cardiomyopathy in humans and the claim that the pathogenetic mechanism is decreased mitophagy require additional support.

First, the claim of an association between the R400Q variant (identified in three individuals) and cardiomyopathy has some limitations based on the data presented. The initial association is suggested by comparing the frequency of the mutation in three small cohorts to that in a large database gnomAD, which aggregates whole exome and whole genome data from many other studies including those from specific disease populations. Having a matched control population is critical in these association studies. For instance, according to gnomAD the MFN2 Q400P variant, while not observed in those of European ancestry, has a 10-fold higher frequency in the African/African American and South Asian populations (0.0004004 and 0.0003266, respectively). If the authors data in table one is compared to the gnomAD African/African American population the p-value drops to 0.029262, which would not likely survive correction for multiple comparison (e.g., Bonferroni). (The source and characteristics of the subjects used by the authors in Table 1 is not clear from the methods.)

Relatedly, evaluation in a knock-in mouse model is offered as a way of bolstering the claim for an association with cardiomyopathy. Some caution should be offered here. Certain mutations have caused a cardiomyopathy in mice when knocked in have not been observed in humans with the same mutation. A recent example is the p.S59L variant in the mitochondrial protein CHCHD10, which causes cardiomyopathy in mice but not in humans (PMID: 30874923). While phenocopy is suggestive there are differences in humans and mice, which makes the correlation imperfect.

Additionally, the argument that the Mfn2 R400Q variant causes a dominant cardiomyopathy in humans would be better supported by observing of a cardiomyopathy in the heterozygous Mfn2 R400Q mice and not just in the homozygous Mfn2 R400Q mice. Relatedly, it is not clear what the studies in the KI mouse prove over what was already known. Mfn2 function is known to be essential during the neonatal period and the authors have previously shown that the Mfn2 R400Q disrupts the ability of Mfn2 to mediate mitochondrial fusion, which is its core function. The results in the KI mouse seem consistent with those two observations, but it's not clear how they allow further conclusions to be drawn.

Additionally, the authors conclude that the effect of R400Q on the transcriptome and metabolome in a subset of animals cannot be explained by its effect on OXPHOS (based on the findings in Figure 4H). However, an alternative explanation is that the R400Q is a loss of function variant but does not act in a dominant negative fashion. According to this view, mice homozygous for R400Q (and have no wildtype copies of Mfn2) lack Mfn2 function and consequently have an OXPHOS defect giving rise to the observed transcriptomic and metabolomic changes. But in the rat heart cell line with endogenous rat Mfn2, exogenous of the MFN2 R400Q has no effect as it is loss of function and is not dominant negative. Additionally, as the authors have shown MFN2 R400Q loses its ability to promote mitochondrial fusion, and this is the central function of MFN2, it is not clear why this can't be the explanation for the mouse phenotype rather than the mitophagy mechanism the authors propose.

Finally, it is asserted that the MFN2 R400Q variant disrupts Parkin activation, by interfering with MFN2 acting a receptor for Parkin. The support for this in cell culture however is limited. Additionally, there is no assessment of mitophagy in the hearts of the KI mouse model.

Reviewer #1 (Public Review):

MCM8 and MCM9 are paralogues of the eukaryotic MCM2-7 proteins. MCM2-7 form a heterohexameric complex to function as a replicative helicase while MCM8-9 form another hexameric helicase complex that may function in homologous recombination-mediated long-tract gene conversion and/or break-induced replication. MCM2-7 complex is loaded during the low Cdk period by ORC, CDC6, and Cdt1, when the origin DNA may intrude into the central channel via the MCM2-MCM5 entry "gate". In the S phase, MCM2-7 complex is activated as CMG helicase with the help of CDC45 and GINS complex. On the other hand, it still remains unclear how MCM8-9 complex is loaded onto DNA and then activated.

In this study, the authors first investigated the cryo-EM structure of chicken MCM8-9 (gMCM8-9) complex. Based on the data obtained, they suggest that the observed gMCM8-9 structure might represent the structure of a loading state with possible DNA entry "gate". The authors further investigated the cryo-EM structure of human MCM8-9 (hMCM8-9) complex in the presence of the activator protein, HROB, and compared the structure with that obtained without HROB1, which the authors published previously. As a result, they suggest that MCM8-9 complex may change the conformation upon HROB binding, leading to helicase activation. Furthermore, based on the structural analyses, they identified some important residues and motifs in MCM8-9 complex, mutations of which actually impaired the MCM8-9 activity in vitro and in vivo.

Overall, the data presented would support the authors' conclusions and would be of wide interest for those working in the fields of DNA replication and repair. One caveat is that most of the structural data are shown only as ribbon model without showing the density map data obtained by cryo-EM, which makes accurate evaluation of the data somewhat difficult.

Addition after review of the revised manuscript: The authors have made a reasonable attempt to address the points raised by the reviewers, by which the paper is significantly improved.

• Title
  • Cap top 20% of energy users to reduce carbon emissions

• Publication

  • https://www.sciencedaily.com/releases/2023/07/230717175940.htm

• Summary -Consumers in the richer, developed nations will have to accept restrictions on their energy use

  • if international climate change targets are to be met, warn researchers.
  • The big challenge is to identify the fairest and most equitable way
  • that governments can curtail energy use,
    • a process known as energy demand reduction. -The research team analyzed several scenarios to identify a potential solution.

Reviewer #1 (Public Review):

Detection of early-stage colorectal cancer is of great importance. Recently, both laboratory scientists and clinicians have reported different exosomal biomarkers to identify colorectal cancer patients.

Here, the authors exhibited a full RNA landscape for plasma exosomes of 60 individuals, including 31 colorectal cancer (CRC) patients, 19 advanced adenoma (AA) patients, and 10 noncancerous controls. RNAs with high fold change, high absolute abundance, and various module attribution were used to construct RT-qPCR-based RNA models for CRC and AA detection.

Overall, this is a well-performed proof-of-concept study to highlight exosomal RNAs as potential biomarkers of early-stage colorectal cancer and its precancerous lesions.

Depicting the full RNA landscape of circulating exosomes is still quite challenging. The authors annotated 58,333 RNA species in exosomes, most of which were lncRNAs, but the authors do not explain how they characterized those RNAs.

The authors tested their models in a medium size population of 124 individuals, which is not enough to obtain an accurate evaluation of the specificity and sensitivity of the biomarkers proposed here. External validation would be required.

Reviewer #1 (Public Review):

In this manuscript, the authors investigated the roles of the target of rapamycin (TOR) pathway in various pathobiological processes of Aspergillus flavus. They found that rapamycin treatment affects the growth, sporulation, sclerotia, and aflatoxin synthesis of A. flavus. The authors identified four immunophilin genes (FKBP1 -4), among which FKBP3 is involved in both rapamycin and FK506 resistances, with K19 residue being essential for succinylation. The authors identified a single Tor kinase and characterized its function. Subsequently, the authors analyzed a series of downstream effectors of the TOR pathway, including Sch9, TapA, SitA, Ppg1, and Spot7/Nem1, in terms of vegetative growth, sexual development, stress responses, and aflatoxin production.

While the authors provided a large amount of data regarding the genes involved in the TOR pathway, it is highly descriptive and mostly confirmative data, as numerous papers have already shown that the TOR pathway plays essential roles in a myriad of biological processes in multiple fungi. The authors seemed to perform a series of parallel studies in several genes involved in the TOR pathway in other fungi. However, their data are not properly interconnected to understand the TOR signaling pathway in this fungal pathogen. The authors frequently drew premature conclusions from basic phenotypic observations. For instance, based on their finding that sch9 mutant showed high calcium stress sensitivity, they concluded that Sch9 is the element of the calcineurin-CrzA pathway. Furthermore, based on their finding that the sch9 mutant show weak rapamycin sensitivity and increased Hog1 phosphorylation, they concluded that Sch9 is involved in TOR and HOG pathways. To make such conclusions, the authors should provide more detailed mechanistic data.

In the section "Tor kinase plays important roles in A. flavus", some parts of their data are confusing. The authors said they identified a single Tor kinase ortholog, which is orthologous to S. cerevisiae Tor2. And then, they said failed to obtain a null mutant, but constructed a single copy deletion strain delta Tor1+/Tor2-. What does this mean? Does this mean A. flavus diploid strain? So is this heterozygous TOR/tor mutant? Otherwise, does the haploid A. flavus strain they used contain multiple copies of the TOR gene within its genome? What is the real name of A. flavus Tor kinase (Tor1 or Tor2?). "tor1+/tor2-" is the wrong genetic nomenclature. What is the identity of detalTor1+/Tor2-? Please provide detailed information on how all these mutants were generated. A similar issue was found in the analysis of TapA, which is speculated to be essential (what is the deltaTapA1+/TapA2-?). I couldn't find any detailed information even in Materials and Methods. The authors should provide southern blot data to validate all their mutants.

How were the FRB domain deletion mutants constructed? If the FKBP12-rapamycin binding (FRB) domain is specifically deleted in the Tor kinase allele, should it be insensitive and resistant to rapamycin? However, the authors showed that the FRB domain deleted TOR allele was indeed non-functional.

In Figure 4C, the authors should monitor Hog1 phosphorylation patterns under stressed conditions, such as NaCl treatment, and provide quantitative measurements. Similar issues were found in the western blot analysis of Slt2 (Fig. 8D).

For all the deletion mutants generated in this study, the authors should generate complemented strains to validate their data.

Reviewer #1 (Public Review):

In this study, the authors strive to characterize the role of protein arginine methyltransferase 5 (Prmt5) in chromatin organization and transcriptional regulation during adipogenesis. Their main aim is to delve into Prmt5's function during the differentiation of preadipocytes by conducting genome-wide analyses, including ChIP-Seq, RNA-Seq, and Hi-C experiments. They hypothesize and present evidence for Prmt5's broad regulatory effect on gene expression and its role in maintaining topologically associating domain (TAD) boundaries and overall chromatin organization.

Strengths of the study include its genome-wide approach, which provides a comprehensive perspective on Prmt5's potential involvement in adipogenesis. These methods yield a large dataset and offer novel insights into Prmt5's possible function in adipogenesis.

However, there are a few areas where the methodology and interpretation of results fall short. One noticeable gap is the absence of a comprehensive control in the ChIP-Seq experiments. Specifically, a control such as ChIP in Prmt5 knockdown cells would have been valuable to account for potential non-specific binding. This lack of a robust control raises questions about the confidence in the detected Prmt5 peaks.

Moreover, the knockdown experiments predominantly employ a single siRNA. Given the well-known off-target effects of siRNA-mediated RNA interference, this approach might cast doubts on the reliability of the results derived from these experiments. Therefore, the inclusion of multiple siRNAs in each assay would greatly strengthen the data.

Lastly, the authors' assertion of Prmt5's influence on the weakening of TAD boundaries and transcriptional dysregulation could benefit from further experimentation. As it stands, this finding is merely correlative, not causative, and represents a very minor fraction of Prmt5 occupied sites. Hence, the evidence provided does not overwhelmingly support the authors' conclusions regarding Prmt5's role in chromatin organization.

This research is potentially important for our understanding of adipogenesis and the role of Prmt5, which is already known for its diverse roles in cellular processes. However, while the authors have taken on an ambitious research question, there are some areas where the study falls short in substantiating the broad conclusions it draws.

Reviewer #1 (Public Review):

The regulation of motor autoinhibition and activation is essential for efficient intracellular transport. This manuscript used biochemical approaches to explore two members in the kinesin-3 family. They found that releasing UNC-104 autoinhibition triggered its dimerization whereas unlocking KLP-6 autoinhibition is insufficient to activate its processive movement, which suggests that KLP-6 requires additional factors for activation, highlighting the common and diverse mechanisms underlying motor activation. They also identified a coiled-coil domain crucial for the dimerization and processive movement of UNC-104. Overall, these biochemical and single-molecule assays were well performed, and their data support their statements. The manuscript is also clearly written, and these results will be valuable to the field.

Ideally, the authors can add some in vivo studies to test the physiological relevance of their in vitro findings, given that the lab is very good at worm genetic manipulations. Otherwise, the authors should speculate the in vivo phenotypes in their Discussion, including E412K mutation in UNC-104, CC2 deletion of UNC-104, D458A in KLP-6.

While beyond the scope of this study, can the author speculate on the candidate for an additional regulator to activate KLP-6 in C. elegans?

The authors discussed the differences between their porcine brain MTs and chlamydonomas axonemes in UNC-104 assays. However, the authors did not really retest UNC-104 on axonemes after more than two decades, thereby not excluding other possibilities.

Reviewer #1 (Public Review):

This manuscript explores physiological properties of Purkinje-to-nuclear synapses. The report provides largely incremental advances over what has already been discovered about this synaptic relationship. The main findings, as articulated by the authors, are that Purkinje-to-nuclear synaptic strength is variable, with a few very strong inputs to the cerebellar nuclei. They show that single inputs effectively inhibit nuclear firing and that the diversity of synaptic strength influences nuclear neuron responsivity to inputs by enhancing synaptic variance. In addition, while not necessarily surprising, it's nice to see that stronger inputs would have a stronger influence on a postsynaptic cell, both in terms of rates and temporal coding transfer. Overall, as it stands, the manuscript is not very scholarly, overstates the novelty of findings, and frames a straw-man. That said, buried in here are some potentially interesting observations.

Reviewer #1 (Public Review):

The authors have employed a digital twin approach to show that depending on the underlying disease mechanism, a digital replica constructed from human data can both recapitulate clinical findings, but also provide important insights into the fundamental disease state by revealing underlying contributing mechanisms. Moreover, the authors are able to show that a disease state caused by two different underlying genetic anomalies exhibit different electrical and morphological profiles.

This is important information as it allows for potential stratification of treatment approaches in future cases based on underlying phenotype by linking it to specific genotype properties. One of the most innovative aspects of the study is the mismatch switching between personalized structure, remodelling and genotype specific electrophysiological properties. The approach is elegant and allows for further exposure of the key mechanisms that contribute to the development of ventricular tachycardia circuits. One addition that could add more insight is to predict the effect of structural remodelling alone well, considering only normal electrophysiological models. Another interesting approach would be a sensitivity analysis, to determine how sensitive the VT circuits are to the specific geometry of the patient and remodelling that occurs during the disease, such an approach could also be used to determine how sensitive the outputs are to electrophysiological model inputs.

Reviewer #1 (Public Review):

Establishing direct links between the neuronal connectivity information of connectomics datasets with circuit physiology and behavior and exciting current research area in neurobiology. Until recently, studies of aggression in Drosophila had been conducted largely in males, and many of the neurons involved in this behavior are male-specific clusters. Since the currently available fly brain connectomes come from female brains, their applicability for the study of the circuitry underlying aggressive behavior is very limited.

The authors have previously used the Janelia hemibrain connectome paired with behavior analysis to show that activating either the aIPg or pC1d cell types can induce short-term aggression in females, while activation of other PC1 clusters (a-c and e) does not. Here they expand on those findings, showing that optogenetic stimulation of aIPg neurons was sufficient to promote an aggressive internal state lasting at least 10 minutes following a 30-second activation. In addition, the authors show that while stimulation of PC1d alone is not sufficient to induce this persistent aggressive state, simultaneous activation of PC1d + PC1e is, suggesting a synergistic effect. Connectomics analysis performed in the authors' previous study had shown that PC1d and aIPg are interconnected. However, silencing pC1d neuronal activity did not reduce aIPg-evoked persistent aggression, indicating that the aggressive state did not depend on pC1d-aIPg recurrent connectivity.

The conclusions are well supported by the data, and the results presented in this manuscript represent an important contribution to our understanding of the neuronal circuitry underlying female aggression.

Reviewer #1 (Public Review):

In this study, Clark et. al. uncovered an association between the positional encoding of grid cell activity with good performance in spatial navigation tasks that requires path integration, highlighting the contribution of grid firing to behavior. Using electrophysiology approaches, the authors measured MEC neuron activity while mice performed a spatial memory task in one-dimensional (1D) virtual tracks, where the mice must stop in a specific reward zone for a reward. Individual trials either had a visual cue at the reward location (beaconed trials), no cue at the reward location (non-beaconed trials), or no cue and no reward regardless of stopping (probe trials). The authors identified that grid cells could encode track position or distance traveled, which were distinguished on a per-session or per-trial basis by calculating whether cell firing was periodic with respect to track length (firing at the same location on each trial) or periodic with respect to distance traveled (firing locations drift across trials), respectively. While some behavioral sessions had stable coding of either position or distances, other sessions exhibited coding schemes that switched between these two modes. The behavioral performance in beaconed trials was comparable when grid cells showed position or distance coding. In contrast, mice perform better on non-beaconed trials when grid cells showed position coding. The authors concluded that position coding in grid cells may enhance performance when tasks require path integration (non-beaconed condition).

The conclusions of this paper are mostly well supported by data, the finding about the association between grid cell encoding and behavior in spatial memory tasks is important. However, some aspects of the analysis need to be clarified or extended.

1. While the current dataset aims to demonstrate a "correlation" between grid cell encoding and task performance, the other variables that could confound this correlation should be carefully examined.<br /> (1) The exact breakdown of the fraction of beaconed/non-beaconed/probe trials is never shown. if the session makeup has a significant effect on the coding scheme or other results, this variable should be accounted for.<br /> (2) The manuscript did not provide information about whether individual mice experienced sessions with different combinations of the three trial types, and whether they show different preferences in position or distance encoding even in comparable sessions. This leads to the question of whether different behavior and activity encoding were dominated by experimental or natural differences between individual mice. Presenting the data per mouse will be helpful.<br /> (3) Related to the above point, in Figure 5, the mice appeared to behave worse in probe trials than non-beaconed trials. If the mouse did not know if a trial is a probe or a non-beacon trial, they should behave equivalently until the reward location and thus should stop an equal amount. If this difference is because multiple probe trials are placed consecutively, did the mouse learn that it will not get a reward and then stop trying to get rewards? Did this affect switching between position and distance coding?<br /> (4) It is not shown how the behaviors (e.g., running speed away from the reward zone, licking for reward) in beaconed/non-beaconed/probe trials were different and whether the difference in behaviors led to the different encoding schemes.<br /> 2. Regarding the behavior and activity encoding on a trial-by-trial basis, did the behavioral change occur first, or did the encoding switch occur first, or did they happen within the same trial? This analysis will potentially determine whether the encoding is causal for the behavior, or the other way around.<br /> 3. The author determined that the grid cell coding schemes were limited to distance encoding and position encoding. However, there could be other schemes, such as switching between different position encodings (with clear spatial fields but at different locations), as indicated by Low et. al., 2021, and switching between different distant encodings (with different distance periods). If these other schemes indeed existed in the data, they might contribute to the variation of the behaviors.<br /> 4. The percentage of neurons categorized in each coding scheme was similar between non-grid and grid cells. This implies that non-grid cells might switch coding schemes in sync with grid cells, which would mean the whole MEC network was switching between distance and position coding. This raises the question of whether the grid cell coding scheme was important per se, or just the MEC network coding scheme.<br /> 5. In Figure 2 there are several cell examples that are categorized as distance or position coding but have a high fraction of the other coding scheme on a per-trial basis. Given this variation, the full session data in F should be interpreted carefully, since this included all cells and not just "stable" coding cells. It will be cleaner to show the activity comparison only between the stable cells.<br /> 6. The manuscript is not well written. Throughout the manuscript, there are many unexplained concepts (especially in the introduction) and methods, mis-referenced figures, and unclear labels.

Reviewer #1 (Public Review):

Aging is associated with a number of physiologic changes including perturbed circadian rhythms. However, mechanisms by which rhythms are altered remain unknown. Here authors tested the hypothesis that age-dependent factors in the sera affect the core clock or outputs of the core clock in cultured fibroblasts. They find that both sera from young and old donors are equally potent at driving robust ~24h oscillations in gene expression, and report the surprising finding that the cyclic transcriptome after stimulation by young or old sera differs markedly. In particular, genes involved in the cell cycle and transcription/translation remain rhythmic in both conditions, while genes associated with oxidative phosphorylation and Alzheimer's Disease lose rhythmicity in the aged condition. Also, the expression of cycling genes associated with cholesterol biosynthesis increases in the cells entrained with old serum. Together, the findings suggest that age-dependent blood-borne factors, yet to be identified, affect circadian rhythms in the periphery. The most interesting aspect of the paper is that the data suggest that the same system (BJ-5TA), may significantly change its rhythmic transcriptome depending on how the cells are synchronized. While there is a succinct discussion point on this, it should be expanded and described whether there are parallels with previous works, as well as what would be possible mechanisms for such an effect.

Major points:<br /> Fig 1 and Table S1. Serum composition and levels of relevant blood-borne factors probably change in function of time. At what time of the day were the serum samples from the old and young groups collected? This important information should be provided in the text and added to Table S1.

Fig 2A. Luminescence traces: the manuscript would greatly benefit from inclusion of raw luminescence traces.

Fig 2. Of the many genes that change their rhythms after stimulation with young and old sera, what are the typical fold changes? For example, it would be useful to show histograms for the two groups. Does one group tend to have transcript rhythms of higher or lower fold changes?

Fig. 2 Gene expression. Also here, the presentation would benefit from showing a few key examples for different types of responses.

What was the rationale to use these cells over the more common U2OS cells? Are there similarities between the rhythmic transcriptomes of the BJ-5TA cells and that of U2OS cells or other human cells? This could easily be assessed using published datasets.

For the rhythmic cell cycle genes, could this be the consequence of the serum which synchronizes also the cell cycle, or is it rather an effect of the circadian oscillator driving rhythms of cell cycle genes?

While the reduction of rhythmicity in the old serum for oxidative phosphorylation transcripts is very interesting and fits with the general theme that metabolic function decreases with age, it is puzzling that the recipient cells are the same, but it is only the synchronization by the old and young serum that changes. Are the authors thus suggesting that decrease of metabolic rhythms is primarily a non cell-autonomous and systemic phenomenon? What would be a potential mechanism?

The delayed shifts after aged serum for clock transcripts (but not for Bmal1) are interesting and indicate that there may be a decoupling of Bmal1 transcript levels from the other clock gene phases. How do the authors interpret this? could it be related to altered chronotypes in the elderly?

Reviewer #1 (Public Review):

Yu et al. investigated Fusarium oxysporum f. sp. lycopersici SIX effectors structure using experimental and computational approaches, and while doing so, the authors identified several SIX effectors as member of the FOLD family, and expanded the known diversity of the SIX effectors. A very interesting and novel finding is the presence of FOLD putative effectors in other Ascomycetes secretome, sharing structural similarities with SIX effectors Avr1, Avr3 and SIX6.

By performing technically sound predictions and experimental confirmation, the authors also confirmed co-operative interactions between Fol effectors, something that was previously known for different pairs of proteins, expanding this observation for new SIX effectors. In addition, the authors contributed to the understanding of the interaction Fol effectors, specifically FOLD and LARS effectors, - I receptors to suppress immunity by structurally similar effectors.

The conclusions of this paper are supported by data and I think it is a pioneer study analyzing the correspondence between AlphaFold predictions and experimentally derived structures, highlighting that models can answer the scientific questions in some cases but could not be enough in others.

Reviewer #1 (Public Review):

Warnhoff et al present a genetic investigation of the response of C. elegans to high dietary cysteine. Using a Pcdo-1::CDO-1::GFP reporter (for a cysteine dioxygenase gene) and unbiased mutagenesis, they identify multiple alleles, including nonsense alleles, in egl-9 and rhy-1, which they validate with reference alleles. Further mutational analysis shows that hif-1 and cysl-1, components of the same established genetic regulatory pathway, also act in cdo-1 regulation. High dietary levels of cysteine activate cdo-1 expression, but loss of cdo-1 does not cause sensitivity to excess dietary cysteine, whereas cysl-1 and hif-1 are completely inviable in these conditions. Using sulfite oxidase suox-1 mutant and double and triple mutant analysis the authors show that the defects caused by suox-1 deletion (which causes sulfite accumulation) are exacerbated by loss of egl-9, which is alleviated by concomitant loss of enzymes cdo-1 / cth-2 or regulators rhy-1 / hif-1, demonstrating that the key issue is cysteine derived sulfites. Further genetic analysis shows that although egl-9 is required for cdo-1 induction, this is only partially dependent on its hydroxylase activity and the egl-9 partner vhl-1 is also only partially involved.

The significance of the findings is that they describe a regulatory pathway by which organisms might respond to high levels of cysteine in vivo.

Strengths<br /> - The genetic analysis is generally well done and convincing, with multiple alleles identified for each gene, several reporters used for cdo-2, etc.<br /> - Genetic analysis using site-directed mutagenesis of egl-9 and cdo-1 with point mtuations is especially nice.<br /> - The data are analyzed and represented properly, and microscopy data have been quantified.<br /> - The paper is also written quite clearly and the figures are easy to understand.

Weaknesses<br /> - The relevance is somewhat unclear. High cysteine levels can be achieved in the laboratory, but, is this relevant in the life of C. elegans? Or is there physiological relevance in humans, e.g. a disease? The authors state "cells and animals fed excess cysteine and methionine", but is this more than a laboratory excess condition? SUOX nonfunctional conditions in humans don't appear to tie into this, since, in that context, the goal is to inactivate CDO or CTH to prevent sulfite production. The authors also mention cancer, but the link to cysteine levels is unclear. In that sense, then, the conditions studied here may not carry much physiological relevance.<br /> - The pathway is described as important for cysteine detoxification, which is described to act via H2S (Figure 6). Much of that pathway has already been previously established by the Roth, Miller, and Horvitz labs as critical for the H2S response. While the present manuscript adds some additional insight such as the additional role of RHY-1 downstream on HIF-1 in promoting toxicity, this study therefore mainly confirms the importance of a previously described signalling pathway, essentially adding a new downstream target rhy-1 -> cysl-1 -> egl-9 -> hif-1 -> sqrd-1/cdo-1. The impact of this finding is reduced by the fact that cdo-1 itself isn't actually required for survival in high cysteine, suggesting it is merely a maker of the activity of this previously described pathway.

Reviewer #1 (Public Review):

The mutation rate and spectrum have been found to differ between populations as well as across individuals within the same population. Hypothesizing that some of the observed variation has a genetic basis, the authors of this paper have made important contributions in the past few years in identifying genetic variants that modify mutation rate or spectrum in natural populations. This paper makes one significant step further by developing a new method for mapping genetic variants associated with the mutation spectrum, which reveals new biological insights.

Using traditional quantitative trait locus (QTL) mapping in the BXD mouse recombinant inbred lines (RILs), the authors of this paper previously identified a genetic locus associated with C>A mutation rate. However, this approach has limited power, as it suffers from multiple testing burden as well as noise in the "observed mutation rate/spectrum phenotype" due to rarity and randomness of mutation events. To overcome these limitations, the authors developed a new method that they named "inter-haplotype distance" (IHD), which in short measures the difference in the aggregate mutation spectrum between two groups of individuals with distinct genotypes at a specific genomic locus. With this new approach, they recover the previously reported candidate mutator locus (near Mutyh gene) and identify a new candidate variant that modifies the C>A mutation rate on only one genetic background. Using more rigorous statistical testing, the authors show convincingly synergistic epistatic effects between the mutator alleles at the two loci.

Overall, the analyses presented are well done and provide convincing evidence for the major findings, including the new candidate mutator locus and its epistatic interaction with the Mutyh locus. The new IHD method introduced is innovative and outperforms traditional QTL mapping under certain conditions, but some of its statistical properties and limitations are not fully described. The part that describes how the method works is a little hard to follow (partially due to the confusing name; see comments below), but the rest of the paper is very well written. Below are my comments and suggestions on how to improve, but I identify no major issues.

The name of the new method "inter-haplotype distance" is more confusing than helpful, as the haplotype information is not critical for implementing this method. First, the mutation spectrum is aggregated genome-wide regardless of the haplotypes where the mutations are found. Second, the only critical haplotype information is that at the focal site (i.e., the locus that is tested for association): individuals are aggregated together when they belong to the same "haplotype group" at the focal site. However, for the classification step, haplotype information is not really necessary: individuals can be grouped based on their genotypes at the given locus (e.g., AA vs AB). As the authors mentioned, this method can be potentially applied to other mutation datasets, where haplotype information may well be unavailable. I hope the authors can reconsider the name and remove the term "haplotype" (perhaps something like "inter-genotype distance"?) to avoid giving the wrong impression that haplotype information is critical for applying this method.

The biggest advantage of the IHD method over QTL mapping is alleviation of the multiple testing burden, as one comparison tests for any changes in the mutation spectrum, including simultaneous, small changes in the relative abundance of multiple mutation types. Based on this, the authors claim that IHD is more powerful to detect a mutator allele that affects multiple mutation types. Although logically plausible, it is unclear under what quantitative conditions IHD can actually have greater power over QTL. It will be helpful to support this claim by providing some simulation results.

The flip side of this advantage of IHD is that, when a significant association is detected, it is not immediately clear which mutation type is driving the signal. Related to this, it is unclear how the authors reached the point that "...the C>A mutator phenotype associated with the locus on chromosome 6", when they only detected significant IHD signal at rs46276051 (on Chr6), when conditioning on D genotypes at the rs27509845 (on Chr4) and no significant signal for any 1-mer mutation type by traditional mapping. The authors need to explain how they deduced that C>A mutation is the major source of the signal. In addition, beyond C>A mutations, can mutation types other than C>A contribute to the IHD signal at rs46276051? More generally, I hope the authors can provide some guidelines on how to narrow a significant IHD signal to specific candidate mutation type(s) affected, which will make the method more useful to other researchers.

To account for differential relatedness between the inbred lines, the authors regressed the cosine distance between the two aggregate mutation spectra on the genome-wide genetic similarity and take the residual as the adjusted test metric. What is the value of the slope from this regression? If significantly non-zero, this would support a polygenic architecture of the mutation spectrum phenotype, which could be interesting. If not, is this adjustment really necessary? In addition, is the intercept assumed to be zero for this regression, and does such an assumption matter? I would appreciate seeing a supplemental figure on this regression.

Reviewer #1 (Public Review):

Solution state 15N backbone NMR relaxation from proteins reports on the reorientational properties of the N-H bonds distributed throughout the peptide chain. This information is crucial to understanding the motions of intrinsically disordered proteins and as such has focussed the attention of many researchers over the last 20-30 years, both experimentally, analytically and using numerical simulation.

This manuscript proposes an empirical approach to the prediction of transverse 15N relaxation rates, using a simple formula that is parameterised against a set of 45 proteins. Relaxation rates measured under a wide range of experimental conditions are combined to optimize residue-specific parameters such that they reproduce the overall shape of the relaxation profile. The purely empirical study essentially ignores NMR relaxation theory, which is unfortunate, because it is likely that more insight could have been derived if theoretical aspects had been considered at any level of detail.

Despite some novel aspects, in particular the diversity of the relaxation data sets, the residue-specific parameters do not provide much new insight beyond earlier work that has also noted that sidechain bulkiness correlated with the profile of R2 in disordered proteins.

Nevertheless, the manuscript provides an interesting statistical analysis of a diverse set of deposited transverse relaxation rates that could be useful to the community.<br /> Crucially, and somewhat in contradiction to the authors stated aims in the introduction, I do not feel that the article delivers real insight into the nature of IDP dynamics. Related to this, I have difficulty understanding how an approximate prediction of the overall trend of expected transverse relaxation rates will be of further use to scientists working on IDPs. We already know where the secondary structural elements are (from 13C chemical shifts which are essential for backbone assignment) and the necessary 'scaling' of the profile to match experimental data actually contains a lot of the information that researchers seek.

1. The introduction is confusing, mixing different contributions to R2 as if they emanated from the same physics, which is not necessarily true. 15N transverse relaxation is said to report on 'slower' dynamics from 10s of nanoseconds up to 1 microsecond. Semi-classical Redfield theory shows that transverse relaxation is sensitive to both adiabatic and non-adiabatic terms, due to spin state transitions induced by stochastic motions, and dephasing of coherence due to local field changes, again induced by stochastic motions. These are faster than the relaxation limit dictated by the angular correlation function. Beyond this, exchange effects can also contribute to measured R2. The extent and timescale limit of this contribution depends on the particular pulse sequence used to measure the relaxation. The differences in the pulse sequences used could be presented, and the implications of these differences for the accuracy of the predictive algorithm discussed.

2. Previous authors have noted the correlation between observed transverse relaxation rates and amino acid sidechain bulkiness. Apart from repeating this observation and optimizing an apparently bulkiness-related parameter on the basis of R2 profiles, I am not clear what more we learn, or what can be derived from such an analysis. If one can possibly identify a motif of secondary structure because raised R2 values in a helix, for example, are missed from the prediction, surely the authors would know about the helix anyway, because they will have assigned the 13C backbone resonances, from which helical propensity can be readily calculated.

3. Transverse relaxation rates in IDPs are often measured to a precision of 0.1s-1 or less. This level of precision is achieved because the line-shapes of the resonances are very narrow and high resolution and sensitivity are commonly measurable. The predictions of relaxation rates, even when applying uniform scaling to optimize best-agreement, is often different to experimental measurement by 10 or 20 times the measured accuracy. There are no experimental errors in the figures. These are essential and should be shown for ease of comparison between experiment and prediction.

4. The impact of structured elements on the dynamic properties of IDPs tethered to them is very well studied in the literature. Slower motions are also increased when, for example the unfolded domain binds a partner, because of the increased slow correlation time. The ad hoc 'helical boosting' proposed by the authors seems to have the opposite effect. When the helical rates are higher, the other rates are significantly reduced. I guess that this is simply a scaling problem. This highlights the limitation of scaling the rates in the secondary structural element by the same value as the rest of the protein, because the timescales of the motion are very different in these regions. In fact the scaling applied by the authors contains very important information. It is also not correct to compare the RMSD of the proposed method with MD, when MD has not applied a 'scaling'. This scaling contains all the information about relative importance of different components to the motion and their timescales, and here it is simply applied and not further analysed.

5. Generally, the uniform scaling of all values by the same number is serious oversimplification. Motions are happening on all timescales they are giving rise to different transverse relaxation. It is not possible to describe IDP relaxation in terms of one single motion. Detailed studies over more than 30 years, have demonstrated that more than one component to the autocorrelation function is essential in order to account for motions on different timescales in denatured, partially disordered or intrinsically unfolded states. If one could 'scale' everything by the same number, this would imply that only one timescale of motion were important and that all others could be neglected, and this at every site in the protein. This is not expected to be the case, and in fact in the examples shown by the authors it is also never the case. There are always regions where the predicted rates are very different from experiment (with respect to experimental error), presumably because local dynamics are occurring on different timescales to the majority of the molecule. These observations contain useful information, and the observation that a single scaling works quite well probably tells us that one component of the motion is dominant, but not universally. This could be discussed.

6. With respect to the accuracy of the prediction, discussion about molecular detail such as pi-pi interactions and phase separation propensity is possibly a little speculative.

7. The authors often declare that the prediction reproduces the experimental data. The comparisons with experimental data need to be presented in terms of the chi2 per residue, using the experimentally measured precision which as mentioned, is often very high.

Reviewer #1 (Public Review):

A subclass of inhibitory heterotrimeric guanine nucleotide-binding protein subunits, GNAI, has been implicated in sensory hair cell formation, namely the establishment of hair bundle (stereocilia) orientation and staircase formation. However, the former role of hair bundle orientation has only been demonstrated in mutants expressing pertussis toxin, which blocks all GNAI subunits, but not in mutants with a single knockout of any of the Gnai genes, suggesting that there is a redundancy among various GNAI proteins in this role. Using various conditional mutants, the authors concluded that GNAI3 is the primary GNAI proteins required for hair bundle morphogenesis, whereas hair bundle orientation requires both GNAI2 and GNAI3.

Strength<br /> Various compound mutants were generated to decipher the contribution of individual GNAI1, GNAI2, GNAI3 and GNAIO in the establishment of hair bundle orientation and morphogenesis. The study is thorough with detailed quantification of hair bundle orientation and morphogenesis, as well as auditory functions.

Weakness<br /> While the hair bundle orientation phenotype in the Foxg1-cre; Gnai2-/-; Gnai3 lox/lox (double mutants) appear more severe than those observed in Ptx cKO mutants, it may be an oversimplification to attribute the differences to more GNAI function in the Ptx cko mutants. The phenotypes between the double mutants and Ptx cko mutants appear qualitatively different. For example, assuming the milder phenotypes in the Ptx cKO is due to incomplete loss of GNAI function, one would expect the Ptx phenotype would be reproducible by some combination of compound mutants among various Gnai genes. Such information was not provided. Furthermore, of all the double mutant specimens analyzed for hair bundle orientation (Fig. 8), the hair bundle/kinocilium position started out normally in the lateral quadrant at E17.5 but failed to be maintained by P0. This does not appear to be the case for Ptx cKO, in which all affected hair cells showed inverted orientation by E17.5. It is not clear whether this is the end-stage of bundle orientation in Ptx cKO, and the kinocilium position started out normal, similar to the double mutants before the age of analysis at E17.5. Understanding these differences may reveal specific requirements of individual GNAI subunits or other factors are being affected in the Ptx mutants.

Reviewer #1 (Public Review):

Aw et al. have proposed that utilizing stability analysis can be useful for fine-mapping of cross populations. In addition, the authors have performed extensive analyses to understand the cases where the top eQTL and stable eQTL are the same or different via functional data.

Major comments:

1. It would be interesting to see how much fine-mapping stability can improve the fine-mapping results in cross-population. One can simulate data using true genotype data and quantify the amount the fine-mapping methods improve utilizing stability idea.

2. I would be very interested to see how other fine-mapping methods (FINEMAPE, SusiE, and CAVIAR) perform via the stability idea.

3. I am a little bit concerned about the PICS's assumption about one causal variant. The authors mentioned this assumption as one of their method limitations. However, given the utility of existing fine-mapping methods (FINEMAPE and SusiE), it is worth exploring this domain.

• Title
  • Big Oil v the World Series 1 03 Delay
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  • BBC

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  • Big Oil v the World Series 1 02 Doubt
• Author BBC

• Title
  • Big Oil v the World Series 1 01 Denial
• Author BBC

Reviewer #1 (Public Review):

The study by Hjaltelin, Novitski, and colleagues analyses clinical records of people with pancreatic cancer in the 5 years prior to their diagnosis, aiming to determine patterns of symptoms and disease trajectories that precede the pancreatic cancer diagnosis. The authors use established methodology to identify temporal disease patterns from the Danish National Patient Registry, covering >22,700 patients with pancreatic cancer and 8.1 million controls. They also apply a text-mining approach to extract potential symptoms from free-text clinical notes of a subset of individuals (>4,400 people with pancreatic cancer and >44,000 controls).

The large datasets used in this study present a very clear strength, and the results are presented quite clearly.

Weaknesses of the study include the relatively low sensitivity of the text-mining approach to identify symptoms (83.4%) and the comparison of findings from datasets including different individuals (rather than a comparison of findings based on free-text entries and diagnosis codes from specific entries for the same patients). It is also not clear which proportion of patients is captured by the symptom and disease trajectories catalogued in this work. The different average survival times associated with different trajectories are very interesting, and it would be helpful to examine whether these are due to differences in cancer stage at diagnosis.

Reviewer #1 (Public Review):

The paper describes a very interesting public health experience. The Danish breast cancer screening program is one of the few programs that never suspended its activity during the pandemic.

In general, in the discussion, I miss two of the main points that led to suspend screening programs in most countries during the pandemic: 1) protecting women from the risk of infection linked to attending a clinic during pandemic when health facilities were mostly attended by symptomatic people seeking care for Covid-19; 2) the of health professionals because they were mostly involved in covid related activities: lack of radiologists (addressed to the emergency department to assure diagnoses of pneumonia), lack of anesthesiologists (due to the expansion of intensive care), thus risking not having timely surgical treatment; lack of screening organization personal for invitations and phone calls (working on contact tracing). Lacking the rationale for suspending screening, it is not clear to the reader how the Danish program afforded these issues and was able to maintain open the program.

Reviewer #1 (Public Review):

The authors set out to further probe how mTORC signaling can impact metabolism by modulating the function of the APA machinery. The major strength of the paper is the identification of a 'twin UGUA' motif that governs PAS selection as dictated by the CFIm complex. Further, the authors show that the twin UGUA motif is not just necessary but is sufficient to confer sensitivity to mTORC activity and the CFIm complex regulation. The weaknesses of the paper include a tenuous connection between mTORC signaling and CFIm as it was not rigorously established how CFIm gets activated/deactivated when mTORC is modulated.

Reviewer #1 (Public Review):

Hoang, Tsutsumi and colleagues use 2-photon calcium imaging to study the activity of Purkinje cells during a Go/No-go task and related this activity to their location in Aldolase-C bands. Tensor component analysis revealed that a substantial part of the calcium responses can be linked to four functional components. The manuscript addresses an important question with an elegant technical approach and careful analysis. There are a few points that I think could be addressed to further improve the quality of the manuscript.

1. The authors should be careful not to overstate the goal and results. For instance, in the abstract it is stated that dynamical functional organization is necessary for dimension reduction. However, the statement that the 4 TCs together account for about half of the variance (line 220) indicates that dimensionality may not be reduced that much. I would suggest revising the first and last sentence of the abstract accordingly.<br /> At the end of the introduction, the authors refer to "the first evidence supporting the two major theories of cerebellar function" but which two theories is referred to and how this manuscript support them is not very obvious. Similarly, they state that "This study unveiled the secret of cerebellar functional architecture", which I would consider to be an unnecessary overstatement of the impact of the work described.<br /> In the title, the authors use the word modular. In the consensus paper on cerebellar modules (Apps et al., 2018) an attempt is made to unify the terms used to describe cerebellar anatomical structures. Here "module" is used for the longitudinal zone of interconnected PCs, CN neurons and olivary neurons. As the authors only studied PC activity (and indirectly the IO), I would suggest using band, stripe or subpopulation instead.<br /> Finally, the term "CF firing" or "CF activity" is used when referring to the recorded signals. However, the authors measure postsynaptic calcium responses that are indeed likely driven by CF inputs, but could also be influenced by PF inputs. At the very least, because Purkinje cells and not climbing fibers are being imaged, "complex spike" should be used instead. It would be more accurate still to use the more general "calcium response" and make less of an assumption about the origin of the calcium response.

2. For some figure panels and statements in the manuscript error bars or confidence intervals and statistics are missing. This is the case for, for example, the changes in fraction correct, lick latency, fraction incorrect, etc. (Fig 1B, 2E-F, TC levels in 3, 4D-E and 5A-C). Including these is particularly relevant in Fig 4E as this is a key result, mentioned also in the abstract. Please indicate clearly if these plots are cumulative for all mice or per mouse and averaged. I advise the authors to statistically support the claim that the changes are significant and in opposite direction as this element of the study is referred to in the abstract and discussion (summary).

3. Data presentation sometimes does not do the work justice. For example, the data in Figure 6 are very interesting, but hard to read because of the design of the figure. It is clear how the components are mostly confined to Aldolase-C domains, but within the domains the distribution is not clear. I would advise to also more clearly indicate what the locations of the colors within the bands refers to. The spatial distribution of the selected top 300 cells for each TC could be added.

Reviewer #1 (Public Review):

The authors investigate the mechanistic underpinning of paradoxical activation (PA) of RAF by small molecule kinase inhibitors using mathematical modeling. The main novelty of the study is the consideration of RAF conformational autoinhibition by its N-terminal regulatory domains as a new determinant of PA. This mechanism has not been explicitly considered in previous theoretical studies, which are based on two other mechanisms: drug-induced RAF oligomerization into active dimers (dimer potentiation DP) and negative cooperativity (NC) of inhibitor binding by a second monomer in the inhibitor-induced RAF kinase dimerization. An important discovery of this study is that conformational autoinhibition is a critical determinant of PA and that in some cases, it can contribute to PA in the absence of DP and NC. Another novelty is the consideration of RAF interaction with 14-3-3 proteins, as a determinant of PA. The 14-3-3 dimeric scaffolds play an important role in the regulation of both autoinhibited and active states of RAF and thus understanding how their interaction with RAF influences PA by RAF inhibitors is important. Using mathematical modeling the authors show that 14-3-3 binding does indeed enhance PA in response to a spectrum of RAF inhibitors.

Strengths<br /> The overall strength of this study is that it increases the mechanistic understanding of how PA of RAF originates in response to its inhibitors. Consideration of the effect that the inhibitors play in breaking the autoinhibited conformation has been overlooked by previous mathematical analyses of PA, and this study bridges this gap. By doing so, the authors discover that breaking that autoinhibited state is in fact the biggest contribution to PAB by RAF inhibitors. In my opinion, this is the most impactful finding of this study, which additionally speaks to how important are the autoinhibitory mechanisms for constraining basal RAF signaling in cells. The presented analysis also shows that consideration of conformational autoinhibition can explain PA by all different types of RAF inhibitors (1, 1.5, and 2), which until now has been difficult to reconcile.

Another important contribution of this study is the investigation of how the 14-3-3 scaffold proteins can further contribute to PA. This is exciting, especially in light of recent elegant structural studies that unveiled complex regulation of RAF by 14-3-3 (which are both important for RAF inhibition and stabilization of the active dimers). The authors dissect these opposing roles of 14-3-3 in their model and show the autoinhibitory interaction with 14-3-3, but not the activating one, significantly increases the PA response. Their findings that an increase in the 14-3-3 levels amplifies PA is very interesting and somewhat provocative as it is unclear how much 14-3-3 levels in cells can oscillate. To this end, the authors show that elevated 14-3-3 levels are observed with increased time of RAF inhibitor treatment, which might point to a new mechanism of resistance to RAF inhibitors.

Weaknesses<br /> The main weakness of the study is the limited experimental analysis conducted to test the predictions that arise from the mathematical models. While some of these predictions might be challenging to test, the one which is tested is not tested rigorously. The experiments focus on 14-3-3-based regulation and are conducted in cells by observing the effect of 14-3-3 overexpression on the inhibition of RAF signaling by its different kinase inhibitors. While the authors acknowledge that too, 14-3-3 overexpression will have a multifaceted effect on signaling as these scaffold proteins participate in the regulation of almost all signaling events. Thus, the proposed experiments are not sufficient to conclude that the observed effects are in fact a result of 14-3-3/RAF interaction.

The authors consider conformational autoinhibition and 14-3-3 stabilization of autoinhibited RAF as two different mechanisms. While it is not a weakness, I am curious how accurate is the consideration of the autoinhibited state of RAF in the absence of 14-3-3. Is it known how the proportion of RAF in cells in its inactive state exists while not bound to 14-3-3?

Reviewer #1 (Public Review):

This manuscript reports a study to investigate the reporting practices in three top cardiovascular research journals for articles published in 2019. The study was preregistered, which makes the intent and methodology transparent, and the authors also make their materials, data, and code open. While the preregistration and sample strategy is a strength, it suffers from a higher than expected number of non-empirical articles decreasing the sample size and thus inference that can be drawn. The author's focus was mainly on transparency of reporting and not on the actual reproducibility or replicability of the articles; however, the accessibility of data, code, materials, and methods is a prerequisite. While the authors were still able to draw inferences to their main objectives, they could not perform some of their proposed analyses because of a small sample size (due partly to the less than half empirical articles in their sample as well as the low number of papers with accessible information to code). One of the descriptive analyses they performed, the country level scores (Figure 6), in particular suffers from the small sample size and while the authors state indicates this in their manuscript I do not think it would be reasonable to include as it has the potential to be misinterpreted since so many are based on an n=1. Overall, I found the authors presentation and discussion clear and concise; however, a lack of a more in-depth discussion is an area to improve the current manuscript. The manuscript outlines opportunities for researchers, journals, funders, and institutions to improve the way cardiovascular research is reported to enable discovery, reuse, and reproducibility.

Reviewer #1 (Public Review):

The COVID-19 pandemic strained population-level mammography screening programs, but to what quantitative degree is unclear. Through a rapid review, the authors quantified the changes in breast screening volume and uptake during the first year of the COVID-19 pandemic, compared to a prior year.

A major strength of this rapid review is that the detail provided by the authors makes this rapid review easily replicated. The detail provided in the time frames used as comparison and the added rigor of using grey literature make this a strong study. The authors nonetheless note that a limitation of this review is that the production of articles is rapid and that newly published articles relevant to the topic could have been missed. However, the authors lay out well how to replicate and strengthen this rapid review to replicate the findings.

The authors found evidence supporting the concern that the COVID-19 pandemic disrupted breast mammography screening on a global scale. They conclude that overall, there were global volume and uptake reductions in breast cancer screening. The volume and uptake reductions varied regionally and there was compelling evidence that these reductions were in part due to health care coverage.

What I found most compelling about this rapid review is the thorough assessment of the included articles and the detailed accounting of the limitations of these articles. This rapid review revealed major deficits in the evidence quality in global assessments of breast mammography screening uptake and volume and future studies that include common and rigorous measures are needed.

In conclusion, the implications of the findings suggest that monitoring patient volume and uptake could be early warning signs to determine if prevention services need strengthening. Especially for those with public vs private insurance and additional markers of social determinants of health.

Reviewer #1 (Public Review):

TP73 is a member of the p53 family of tumor suppressors and is expressed as TAp73 and DNp73 and multiple C-terminal isoforms as a result of alternative splicing. In this study, the authors used isoform-specific disruption of the TP73 gene to investigate the physiological functions of p53 C-terminal isoforms, focussing on p73a and p73g. They identify an oncogenic role of TAp73-γ in tumorigenesis via regulating the expression of a novel target Leptin. Furthermore, they generated and characterized a mouse mode that expresses the TAp73 isoform γ but not α and shows how this splicing switch has oncogenic effects and causes metabolic defects. Overall, this is an important and well-done study uncovering a key role of TAp63-g in tumorigenesis via regulating Leptin expression.

Reviewer #1 (Public Review):

The authors have previously suggested that virtually all cortical interneurons are positive for one of three markers: Pvalb, Sst, or Htr3. Here they present convincing evidence that the Htr3a group, includes a significant fraction that does not in fact express this marker in the adult and has distinct properties. By mining existing single-cell RNAseq results, the authors conclude that these cells express the marker Id2. Because some excitatory neurons and non-neuronal cells also express this marker, they selectively target these Id2+ interneurons by combining a pan-interneuron driver and an Id2-creER driver. They show that these neurons are present across cortical layers and account for about 18% of all interneurons. Based on morphological and electrophysiological analyses, this group includes non-VIP neurons in layer 1, neurogliaform cells in layers 2-6, and a small population of CCK basket cells. These in vitro characterizations are well done and will make it straightforward for others to adopt the same or related genetic strategies to target these cells for other functional or mechanistic studies. Since it was not previously possible to genetically target most of these cells, they have been less studied than the other populations of interneurons, so the present study fills an important gap in the field.

The authors also use optogenetics and silicon probe multielectrode recordings to characterize the state-dependent firing and impact of these neurons in vivo. By monitoring sleep, and wake state, the authors show convincingly that these cells reduce their firing during NREM sleep and show a rebound increase in firing after this reduction. The authors then stimulate these neurons optogenetically and show that the firing of other neurons is more likely to be reduced than increased, as expected if these neurons provide broad inhibitory output. The magnitude of this effect is a bit difficult to assess from the current presentation of these data and so it is not clear whether the authors' suggestion that recruitment of these neurons "might drive a widespread switch in the activity of all other cortical neurons" is supported, or whether the effects on circuit activity are more subtle. Regardless of this concern, this is an important study for our understanding of the properties and functions of cortical interneurons.

Reviewer #1 (Public Review):

In their manuscript entitled "Scavenger receptor endocytosis controls apical membrane morphogenesis in the Drosophila airways," Pinheiro and colleagues identify a requirement for Epithelial membrane protein (Emp), a Drosophila CD36 homolog, in embryonic viability and show that mutant embryos display tracheal tube elongation and gas-filling defects. The authors first generate a null allele of emp. The authors validate gene-specific defects by the transgenic rescue of a deletion allele of emp, and further show partial rescuing activity of human CD36. The authors generate and validate an Emp antibody. In mutant trachea, the authors determine there is defective internalization of Serpentine and Vermiform from the tube lumens. Endocytic defects appear selective as GASP internalization does not appear affected. Crb is also found to accumulate to higher levels. The affected process is not clathrin-dependent, as disruption of clathrin function blocks endocytosis of GASP but not Serp or Verm. Emp localizes to apical/adherens junction membranes in epithelia. Actin bundles regulate endocytosis and affect Emp internalization as seen by disruption of actin bundles with Ptp mutants or expression of DAAM dominant negative. Some Emp-GFP colocalized with Rab and Rab7 endosomal markers. A fraction of Srp-GFP co-localized with early and late endosomes and that colocalization was decreased in emp mutants. The LDLr domain was identified as responsible for Emp-dependent endocytosis. In Srp-GFP overexpression, Emp and Crb accumulate on the apical membrane; in serp, verm double mutants Emp and Crb levels on the apical membrane are decreased. These data are consistent with ligand clustering driving Emp and Crb apical localization. Overall Emp and Crb protein levels did not change, arguing for a role in subcellular distribution rather than protein stability. In emp; SerpGFP embryos, DT length was decreased relative to SerpGFP alone; authors suggest this implies that SerpGFP increase in length is partially dependent upon Emp.

In emp mutants, DE-Cad and Crb accumulate along longitudinal junctions, whereas only DE-Cad shows increased accumulation at transverse junctions. Western blots indicate no change in overall protein levels for DE-Cad or Crb. alpha-Catenin (adherens junction component) levels were indistinguishable from wt.

MoeGFP distribution in emp embryos is altered compared to wild-type, with a diffuse appearance. The formin, DAAM, accumulates apically in emp mutant embryos as compared to wt.

A yeast 2 hybrid screen revealed a physical interaction with beta-heavy spectrin. Co-IP experiments in S2 cells support this interaction. kst mutants show tube elongation defects suggesting that the two proteins function in the same process. Kst levels were reduced near the apical membrane in emp mutants. Emp localization was not notably altered in kst mutant embryos.

In emp mutants, pSrc levels are higher. Also seen in western blot. Embryos mutants for src have a shorter dorsal trunk. Double mutant embryos (emp; src42A) showed significant suppression of the emp phenotype. Crb and DE-Cad accumulation could be suppressed by the expression of an srcDN transgene.

The authors propose that Emp affects pSrc levels to regulate tube size and possibly other morphogenetic processes.

The manuscript makes excellent use of genetic and cell biological approaches to provide insight into the regulation of tube length during embryonic tracheal development. Many genes and pathways have been implicated in this process and this study begins to make some connections. A weakness of the manuscript is the lack of a molecular mechanism linking Emp to pSrc distribution.

Reviewer #1 (Public Review):

The authors have developed a new method to measure brain activity in the developing chick embryo. Thereby they have provided convincing evidence of asymmetry in the chick embryo and shown how it is influenced by exposure of the embryo to light. This is an important step forward in understanding the development of visual lateralization of behaviour and asymmetry of the thalamofugal visual pathway. Although asymmetry of the thalamofugal visual projections to the Wulst in newly hatched chicks has been well-documented previously, until now, it has not been possible to obtain such direct evidence of lateralized neural activity in the embryo.

The method that the authors have developed has potential for future research. It could now be applied at other times during embryonic development and to other species. In fact, since the tectofugal system is asymmetrical in the pigeon, it would be interesting to use the technique in pigeon embryos, as a comparison.

Reviewer #1 (Public Review):

This study aims to compare the impact on KCNQ1/ KCNE1 channel complexes of localizing PKA components in distinct ways: by targeting of PKA domains to the C-terminus of KCNQ1 or KCNE1 or overexpression of an untargeted catalytic domain. The evidence is compelling that targeting PKA domains to the C-terminus of KCNQ1 causes distinct phosphorylation as well as decreasing channel conductance and channel protein at the cell surface when compared to overexpression of PKA subunits with other constructs. The study effectively deploys a symbiotic combination of techniques to link electrophysiology, surface expression, and phosphorylation changes responding to targeted recruitment. Support seems incomplete for the minor claims of retention specifically to the ER/Golgi, and that targeted recruitment of PKA domains to KCNE1 was successful and distinct from untargeted overexpression. This study demonstrates the potential for engineering submolecularly-targeted phosphorylation to post-translationally modify a single protein in multiple ways with the same kinase. That distinct intramolecular patterns of phosphorylation can be encoded by the recruitment point of a kinase is very interesting and expected to be of value to the studies of ion channel modulation, kinase activity, and the development of related biotechnology.

Reviewer #1 (Public Review):

An interesting combination of simultaneous broadband NIRS and EEG was acquired in 5-month-old infants (N=42) while they watched social and non-social videos. This substantial undertaking yielded a valuable dataset. The analysis was well developed, including a metabolic measure (COO) as well as haemoglobin measures; localisation of the NIRS signal; and an investigation of the EEG frequency bands correlated with the NIRS. The results, that the temporoparietal junction is engaged by social stimuli, are consistent and reassuring.

The contributions of the manuscript seem largely methodological, which is valuable, but in places the authors oversell the implications of the work - both theoretically and methodologically.

Reviewer #1 (Public Review):

Qing et al. hypothesize that CD8+ tissue-resident memory T (Trm) cells contribute to the pathogenesis of oral lichen planus. They compare oral mucosal lesions from patients with non-erosive oral lichen planus (NEOLP; n=3) and erosive oral lichen planus (EOLP; n=1) using single-cell RNA-sequencing and spatial transcriptomics and report that CD8+ Trm is enriched and more functionally active in EOLP. Their principal findings are 1) increased proportion of CD8+ Trm in EOLP (vs NEOLP), 2) CD8 Trm in OLP lesions produce TNF, IFNg, and IL-17, and 3) CD8 Trm exist in the healthy epithelium and in lamina propria adjacent to the damaged epithelium in NEOLP/EOLP. The strength of evidence for findings reported in the manuscript is weak.

Strengths:<br /> The pathogenic CD8+ T cell response in lichen planus is a relatively unexplored topic and oral lichen planus is a debilitating disease, thus advancements in its understanding are impactful. The authors' approach is innovative; the manuscript's spatial transcriptomics data are completely novel. The logistical regression analyses that tie CD8+ T cell transcriptional signatures to clinical outcomes are compelling.

Weaknesses:<br /> The authors' data do not firmly support their conclusions. The methods section and figures/figure legends lack important details and labels which makes it difficult to interpret the data. For instance, it is unclear to me how the authors have defined CD8+ tissue-resident memory T (Trm) cells. Human CD8 Trm expresses specific surface markers (CD69, CD103, and CD49a) and a core transcriptional signature (Kumar et al Cell Rep 2017; Cheuk et al Immunity 2017; Fonseca et al Nat Immunol 2022) that are not described herein. In Figures 1-2, the authors do not describe how they annotated their scRNA-seq data, what samples they are including/comparing, criteria used to identify relevant gene expression changes, and they report T cell phenotypes that are inconsistent with published reports and make me question the validity of their T cell/NK cell cluster annotation. Double positive (CD4+CD8+) T cells are not thought to exist outside of the thymus. Human CD8+ Trm has been described to express Itga1 (CD49a), Itgae (CD103), and granzymes yet the authors' CD8 Trm cluster (Fig 2B) exhibits little to no expression of these genes. Also, the authors report il23a expression by CD8 Trm when T cells are not a recognized source of IL-23.

Impact<br /> In my opinion, the main comparison made (NEOLP vs EOLP) is not meaningful. The authors' main conclusion is that there is more CD8+ Trm-mediated inflammation occurring in erosive OLP compared to non-erosive OLP. This is in line with what one would predict, as erosive OLP is a more severe form of the disease. Thus, I don't believe this manuscript significantly advances the understanding of lichen planus immunopathogenesis. The utility of exploring the pathogenesis of human disease is it may identify new targets of intervention and lead to better treatments. The methods used within the manuscript (scRNA-seq, spatial transcriptomics) have the potential to yield significant insights into OLP however in their present form, the authors' analyses do not support the premise that CD8 Trm is the pathogenic cell type in OLP. Thus I do not feel that the authors achieved their central aim.

Reviewer #1 (Public Review):

Romagosa, Nieukirk et al. present an interesting approach and interpretation to what is assumed to be a learned animal behavior. In this case, the observed behavior is fin whale (Balaenoptera physalus) singing and the analyses provide results indicating spatio-temporal variation in three fin whale song features at distinct locations within the Central and Northeast North Atlantic Ocean (ONA) region within a two-decade time period. The data set is a non-standardized collection of acoustic recordings obtained from multiple research scientists. Most of the acoustic recording samples are very sparse, with the majority of data coming from an area around the Azores and collected by Okeanos scientists. The senior author undertook the enormously demanding task of analyzing the acoustic data using non-automatic, standardized techniques and protocols. Songs from individual periods of singing on any given day were selected for analysis based on song quality. Song measurements included interval of time between successive 20-Hz song notes (INI), peak frequencies of those 20-Hz notes and peak frequencies of higher frequency notes (HF note). The resultant units of analysis are daily measures of INI (average and s.d.), 20-Hz note peak frequencies (average and s.d.), and HF note peak frequencies (average and s.d.). Several of the figures are confused by not representing the time axis in a typical, uniformly linear way (Fig. 2A and Fig. 3). This form of dynamic time warping smooths and distorts the time-varying features of the results and obscures the inherent sparseness of and high variability in the durations and locations of recordings in available data set. This fundamental characteristic of the available data (see Fig. S1), represents a form of sample aliasing, is not adequately addressed in the paper in terms of how it influences or restricts interpretation of the results. Another possible over-interpretation of results involves misrepresentation of the actual areas sampled. For example, data were collected on Dec 2007-Feb 2008 and Oct 2015 March from a recorder location off the southwest of the Iberian Peninsula. The acoustic sampling detection space is restricted to the ocean within some tens of kilometers of a single sensor, a very small dot on the maps in the manuscript, yet the data from this recorder are assigned to the relatively very large region referred to as the "Bay of Biscay & Iberian Coast". Within the two-decade period of the study (ca. 120 months), recordings were collected at this site (E in Figure 1) for 9 months (7.5%), and the two sampling periods occurred within the December 2007 through March 2018 time span (see Fig S1). It is scientifically inappropriate to translate this as data representing the Bay of Biscay & Iberian Coast as this kind of misrepresentation can lead to misinterpretation of the results.

Despite these spatial and temporal sampling issues, the analyses reveal several important features (Fig. 2 and Fig. 3) about fin whale song in the ONA. The import of the analytical results is that the time span and spatial scale over which recordings were collected provide a unique opportunity to observe whether or not there were variations in fin whale song features within a large ocean region, across a span of two decades. One can consider these spatial and temporal scales appropriately matched to the known scales of fin whale natural history and ecology. Thus, the study results, although confronted by some sampling issues, are not biased by inappropriately sized spatial and temporal scales.

This MS joins a small but growing list of papers documenting variability in baleen whale acoustic behaviors over ecologically appropriate spatial and temporal scales. These papers are primarily focused on singing, an acoustically obvious male reproductive display. As with several recent papers, the author takes advantage of a growing body of data collected during previous studies. The actual measurements utilized several established acoustic analysis software tools. The interpretation of the results focuses on evidence of vocal learning in fin whale singers (i.e. males performing reproductive displays) and wisely remains tangential to interpreting fin whale song through a cultural lens.

Reviewer #1 (Public Review):

The hippocampus is a structure in the cerebral cortex known to be compartmentalised into regions with different functions. Dorsal hippocampus is involved in cognitive functions such as declarative memory and spatial navigation and interconnects chiefly with the neocortex. Ventral hippocampus interconnects with limbic structures such as amygdala and hypothalamus and is involved in affective states and anxiety. What specifies this functional regionalisation during development is not well understood. The present study focuses on the role of transcription factors COUPTFI and COUPTFII, confirming a previously observed dorsal to ventral gradient of expression of COUPTFI in both embryonic and adult mouse hippocampus, and reporting that expression of COUPTFII is strongest in ventral hippocampus. The aim of the authors was then to probe the role of these transcription factors with the use of conditional knockout of one or both factors using RxCre+ mice (sometimes Emx1Cre+ for comparison). As predicted, COUPTFI insufficiency resulted in failure of the CA1 subregion of the dorsal hippocampus to develop properly (with concomitant loss of performance in a spatial memory task) COUPTFII knockdown had even more marked effects upon the ventral hippocampus with ectopic CA1/CA3 domains forming, while a double knockout lead to a drastic reduction in size of the hippocampus with subsequent effects upon the appearance of hippocampal synaptic circuitry and the capacity for adult neurogenesis (a feature of rodent hippocampus). In order to help explain the role of COUPTFI/II a role in regulating expression of two transcription factors LHX2 and LHX5, known to be crucial to hippocampal development, was tested by examining gene and protein expression. Changes in LHX2 and LHX5 was observed and a role for COUPTFI/II in regulating expression of these genes was postulated.

I believe the authors have largely achieved their aims and the results mostly support the conclusions, but, as discussed further below, there are some weaknesses in the data and some areas that could be expanded upon and improved. The methods are mostly appropriate. The use of the transgenic mice and the application of histological methods, especially tyramide amplified immunohistochemistry, is exemplary. However, I'm not sure a wide enough range of tests to explore the phenotype of the transgenic mice was employed to back the conclusions drawn by the authors. The introduction and discussion are nicely written and explain the general concepts and conclusions well. The work makes an important contribution to our understanding of brain development in general and hippocampal development in particular.

Turning to more specific comments, I must first point out that specification of the ventral hippocampus by expression of COUPTFII is not an entirely original finding, as it was suggested for the developing human hippocampus following immunohistochemical experiments illustrating COUPTFII expression to be confined to the ventral hippocampal structures of the medial temporal cortex (doi: 10.1093/cercor/bhx185). Of course, this study, unlike the present study, was restricted to fetal cortex, not adult, and also reported expression of COUP-TFI throughout dorsal and ventral hippocampal structures but without observing any dorsal to ventral gradient, however I feel its contribution to the field has been overlooked by the present study, and should be incorporated into the introduction and/or discussion.

More information about Rx-cre mice would be informative and could help explain the different phenotype observed when EMX1-cre mice were used to conditionally knock down COUPTFI/II expression.

The demonstration of antagonistic gradients of COUP-TFI and -TFII across the hippocampus is more convincing in the immunohistochemical preparations than in the western blots. The qualitative data presented in Fig.1p does not convincingly represent the quantitative data presented in Fig.1q. There seem to be multiple bands for COUP-TFII and I wonder exactly how quantifying this was approached?

Behavioural testing is limited to one test of dorsal hippocampus function. other tests for non-spatial memory, e.g. novel object recognition, or ventral hippocampus function, e.g. step through passive avoidance, might have lead to some interesting discriminations between the various knock down animals (see doi: 10.3389/fnagi.2018.00091).

Abnormalities in the trisynaptic circuit. No studies of actual synapses, either physiological or morphological, were carried out. I wonder to what extent these immunohistochemical studies just further reflect the abnormalities in hippocampal morphology presented earlier in the manuscript without specifically telling us about synaptic circuits? Although the immunohistochemical preparations are beautiful, they are inadequate on their own in telling us much about what sort of synaptic circuitry exists in the transgenic animals.

LHX2/LHX5 interaction. The immunohistochemical study, which shows clear differences in LHX5 and LHX2 protein expression at E14.5 in double knockdown mice is more convincing than the qPCR study at E11.5, which show surprisingly small differences in mRNA expression. Could the authors expand upon whether this is due to stage of development, or differences between mRNA and protein expression? Why hasn't both mRNA and protein expression data at both time points been presented?

Response to the re-submission

I am happy that the western blot presentation has been improved, and my minor comments attended to. It is disappointing, although understandable given the timeframe, that the lack of qPCR data at 14.5 ED has not been rectified. The immunohistochemical data alone is qualitative and only indicative of LHX5 expression remaining depressed and LHX2 expression possibly increasing between E11.5 and E14.5. In the absence of qPCR data, a more quantitative immunohistochemical study, such as counting blind the number of LHX5+ Cajal-Retzius cells, or measuring optical density of LHX2 expression under rigorous experimental conditions regarding image collection and processing, would be required to support the hypothesis that COUPTFI/II expression modulates the LHX2/LHX5 axis.

Reviewer #1 (Public Review):

Barlow et al performed a viral insertion screen in larval zebrafish for sleep mutants. They identify a mutant named dreammist (dmist) that displayed defects in sleep, namely, decreased sleep both day and night, accompanied by increased activity. They find that dmist encodes a previously uncharacterized single-pass transmembrane protein that shows structural similarity to Fxyd1, a Na+K+-ATPase regulator. Disruption of fxyd1 or atp1a3a, a Na+,K+-ATPase alpha-3 subunit, decreased night-time sleep. By staining for sodium levels, the authors uncover a global increase of sodium in both dmist and atp1a3a mutants following PTZ treatment, consistent with defects in Na+K+-ATPase function. These genetic data from multiple mutant lines help establish the importance of sodium and/or potassium homeostasis in sleep regulation.

The conclusions of this paper are mostly well supported by data, with the following strengths and weaknesses as described below.

Strengths:<br /> Elegant use of CRISPR knockout methods to disrupt multiple genes that help establish the importance of regulating Na+K+-ATPase function in sleep.<br /> Data are mostly clearly presented.<br /> Double mutant analysis of dmist and atp1a3a help establish an epistatic relationship between these proteins.

Weaknesses:<br /> The authors emphasize the role of increased cellular sodium, but equally plausibly, the phenotypes could be due to decreased cellular potassium. The potassium channel shaker has been previously identified as a critical sleep regulator in Drosophila.<br /> Although the increased sleep rebound after PTZ treatment in the dmist mutant is interesting, I find it difficult to understand, especially in the context of the dmist mutant having decreased sleep.

The similar phenotype between dmist and Fxyd1 in sleep reduction yet very different expression patterns, with dmist being mostly neuronal while fxyd1 being mostly non-neuronal, raise many possible questions: 1) are the sleep phenotypes due to neuronal Na/K imbalance? Or 2) Are the sleep phenotypes due to extracellular Na/K imbalance? Or 3) both? Some feasible experiments may help achieve a better mechanistic understanding of the observed sleep defects.

Reviewer #1 (Public Review):

In this manuscript, the authors present a valuable new method to represent animal behavior from video data using a variational autoencoder framework that disentangles individual-specific and background variance from variables that can be more reliably compared across individuals. They achieve this aim through the use of a novel Cauchy-Schwatz (C-S) regularization term in their loss function that leads to latents that model continuously varying features in the images. The authors present a variety of validations for the method, including testing across sessions and individuals for a head-fixed task. They also show how the methods could be used for behavioral decoding from neural data, quantifying social behavior in mice, demonstrating the applicability of the method outside of head-fixed environments and for different measurement modalities. While some areas of confusion and questions about the validation exist, this is an overall strong paper and an important contribution to this field.

Strengths:

- The use of the C-S regularizer is novel approach that has potential for wide use across experimental paradigms and model organisms<br /> - The extent of the validations performed was solid, although perhaps not as convincing in a couple of cases as might be ideal<br /> - The GitHub code demo worked well, and the code appears to be accessible and well-written

Weaknesses:

- Some of the validation figures were a bit unclear in their presentation, making it difficult to assess exactly what had been tested<br /> - It is possible that I missed this, but the authors didn't really provide a sense of how to pick a particular distribution to match using the CS term for a specific paradigm/modality and how the choice affects the results<br /> - While the authors' statements about individual training vs. transfer learning accuracy and efficiency in Figure 6 are technically true, the effect size is rather small ( a few percent at most in each case), thus I don't know how much of a big deal I would want to make out of these results<br /> - In general, I would have liked to have seen the Discussion section speak more to the choices and limitations inherent in applying the method. How does the choice of prior/metaparameters/architecture/etc affect the results? In what situations would this method to fail? What are the next advances that are necessary for the field to progress?

Reviewer #1 (Public Review):

In the manuscript by Urban et al., the authors attempt to further delineate the role which non-neuronal CNS cells play in the development of ALS. Toward this goal, the transmembrane signaling molecule ephrinB2 was studied. It was found that there is an increased expression of ephrinB2 in astrocytes within the cervical ventral horn of the spinal cord in a rodent model of ALS. Moreover, the reduction of ephrinB2 reduced motoneuron loss and prevented respiratory dysfunction at the NMJ. Further driving the importance of ephrinB2 is an increased expression in the spinal cords of human ALS individuals. Collectively, these findings present compelling evidence implicating ephrinB2 as a contributing factor towards the development of ALS.

Reviewer #1 (Public Review):

Wheeler et al. have discovered a new RNA circuit that regulates T-cell function. They found that the long non-coding RNA Malat1 sponges miR-15/16, which controls many genes related to T cell activation, survival, and memory. This suggests that Malat1 indirectly regulates T-cell function. They used CRISPR to mutate the miR-15/16 binding site in Malat1 and observed that this disrupted the RNA circuit and impaired cytotoxic T-cell responses. While this study presents a novel molecular mechanism of T-cell regulation by Malat1-miR-15/16, the effects of Malat1 are weaker compared to miR-15/16. This could be due to several reasons, including higher levels of miR-15/16 compared to Malat1 or Malat1 expression being mostly restricted to the nucleus. Although the role of miR15/16 in T-cell activation has been previously published, if the authors can demonstrate that miR15/16 and/or Malat1 affect the clearance of Listeria or LCMV, this will significantly add to the current findings and provide physiological context to the study.

Reviewer #1 (Public Review):

In this manuscript, the authors investigated the role of Elg1 in the regulation of telomere length. The main role of the Elg1/RLC complex is to unload the processivity factor PCNA, mainly after completion of synthesis of the Okazaki fragment in the lagging strand. They found that Elg1 physically interacts with the CST (Cdc13-Stn1-Ten1) and propose that Elg1 negatively regulates telomere length by mediating the interaction between Cdc13 and Stn1 in a pathway involving SUMOylation of both PCNA and Cdc13. Accumulation of SUMOylated PCNA upon deletion of ELG1 or overexpression of RAD30 leads to elongated telomeres. On the other hand, the interaction of Elg1 with Sten1 is SIM-dependent and occurs concurrently with telomere replication in late S phase. In contrast Elg1-Cdc13 interaction is mediated by PCNA-SUMO, is independent on the SIM of Elg1 but still dependent on Cdc13 SUMOylation. The authors present a model containing two main messages 1) PCNA-SUMO acts as a positive signal for telomerase activation 2) Elg1 promotes Cdc13/Stn1 interaction at the expense of Cdc13/Est1 interaction thus terminating telomerase action.

The manuscript contains a large amount of data that make a major inroads on a new type of link between telomere replication and regulation of the telomerase. Nevertheless, the detailed choreography of the events as well as the role of PCNA-SUMO remain elusive and the data do not fully explain the role of the Stn1/Elg1 interaction. The data presented do not convincingly support the claim that SUMO-PCNA is a positive signal for telomerase activation. This was partially addressed in the current version.

Reviewer #1 (Public Review):

More than ten years ago, it was shown that activity in the primary visual cortex of mice substantially increases when mice are running compared to when they are sitting still. This finding 'revolutionised' our thinking about the visual cortex, turning away from it being a passive image processor and highlighting the influence of non-visual factors. The current study now for the first time repeats this experiment in a primate (the marmoset). The authors find that in contrast to mice, marmoset V1 activity is slightly suppressed during running, and they relate this to differences in gain modulations of V1 activity between the two species.

Strengths:

- Replication in primates of the original finding in mice partly took so long, because of the inherent difficulties with recording from the brain of a running primate. The treadmill for the marmosets in the current study is a very elegant solution to this problem. It allows for true replication of the 'running vs stationary' experiment and undoubtedly opens up many possibilities for other experiments recording from a head-fixed but active marmoset.<br /> - In addition to their own data on the marmoset, the authors run their analyses on a publicly available data set on the mouse. This allows them to directly compare mouse and marmoset findings, which significantly strengthens their conclusions.

Weaknesses:

- The main thing that is missing from the study is a good explanation as to why running has such a different effect on marmoset V1 compared to mouse V1. Differences in neuromodulatory inputs are cited in the discussion as a possible cause for the discrepancy, but an obvious influencing factor that the authors could investigate in their own data set is the retinal input. In Fig1b, the authors even show these data in the form of gaze and pupil size. In these example data, by eye, it looks like the pupil size is positively correlated with the run speed. This would of course have large consequences on the activity in V1, but the authors do not do anything with these data. The study would improve substantially if the authors would correlate their run speed traces with other factors that they have recorded too, such as pupil size and gaze.

- Fig2a shows the 'most correlated mouse session', i.e. the session where the relation between visual cortex activity and running speed was strongest. Looking at the raster plot, however, shows that this strong positive correlation must be due entirely to the lower half of the neurons significantly increasing their firing rate as the mouse starts to run; in fact, the upper 25% or so of the neurons show exactly the opposite (strong suppression of the neurons as the mouse starts running). It would be more balanced if this heterogeneity in the response is at least mentioned somewhere in the text.

Significance:

The paper provides interesting new evidence to the ongoing discussion about the influence of non-visual factors in general, and running in particular, on visual cortex activity. As such, it helps to pull this discussion out of the rodent field mainly and into the field of primate research. The elegant experimental set-up of the marmoset on a treadmill will certainly add new findings to this issue also in the years to come.

Reviewer #1 (Public Review):

The study titled "Distinct states of nucleolar stress induced by anti-cancer drugs" by Potapova and colleagues demonstrates that different chemotherapeutic agents can induce nucleolar stress, which manifests with varying cellular and molecular characteristics. The study also proposes a mechanism for how a novel type of nucleolar stress driven by CDK inhibitors may be regulated. As a reviewer, I appreciate the unbiased screening approach and I am enthusiastic about the novel insights into cell biology and the implications for cancer research and treatment. The study has several significant strengths: i) it highlights the understudied role of nucleolar stress in the on- and off-target effects of chemotherapy; ii) it defines novel molecular and cellular characteristics of the different types of nucleolar stress phenotypes; iii) it proposes novel modes of action for well-known drugs.

However, there are several important points that should be addressed:<br /> • The rationale behind choosing RPE cells for the screen is unclear. It might be more informative to use cancer cells to study the effects of chemotherapeutic agents. Alternatively, were RPE cells selected to evaluate the side effects of these agents on normal cells? Clarifying these points in the introduction and discussion would guide the reader.<br /> • Figure 2F indicates that DLD1 and HCT116 cells are less sensitive to nucleolar changes induced by several inhibitors, including CDK inhibitors. It would be crucial to correlate these differences with cell viability. Are these differences due to cell-type sensitivity or variations in intracellular drug levels? Assessing cell viability and intracellular drug concentration for the same drugs and cells would provide valuable insights.<br /> • Have the authors interpreted nucleolar stress as the primary cause of cell death induced by these drugs? When cells treated with CDK inhibitors exhibit the dissociated nucleoli phenotype, is this effect reversible? Is this phenotype indicative of cell death commitment? Conducting a washout experiment to measure the recovery of nucleolar function and cell viability would address these questions.<br /> • The correlation between the loss of Treacle phosphorylation and nucleolar stress upon CDK inhibition is intriguing. However, it remains unclear how these two events are related. Would Treacle knockdown yield the same nucleolar phenotype as CDK inhibition? Moreover, would point mutations that abolish Treacle phosphorylation prevent its interaction with Pol-I? Experiments addressing these questions would enhance our understanding of the correlation/causation between Treacle phosphorylation and the effects of CDK inhibition on nucleolar stress.

Overall, this study is significant and novel as it sheds light on the importance of nucleolar stress in defining the on-target and off-target effects of chemotherapy in normal and cancer cells.

Reviewer #1 (Public Review):

In this paper by Lui and colleagues, the authors examine the role of locus coeruleus (LC)-noradrenaline (NA) neurons in the extinction of appetitive instrumental conditioning. They report that optogenetic activation of global LC-NA neurons during the conditioned stimulus (CS) period of extinction enhances long-term extinction memory without affecting within-session extinction. In contrast, LC-NA activation during the intertrial interval doesn't affect extinction and long-term memory. They then show that optogenetic activation of LC-NA neurons doesn't induce conditioned place preference/avoidance. Finally, they assess the necessity of LC-NA neurons in appetitive extinction and find that optogenetic inactivation of LC-NA neurons during the CS period results in the enhancement of within-session extinction. The experiments are well-designed, including offset control in the optogenetic activation study. I think this study adds new insight into the LC-NA system in the context of appetitive extinction.

Strengths:<br /> ・These studies identify that the artificial activation of LC-NA neurons enhances long-term memory of appetitive extinction, while this activation can't induce long-term conditioned place aversion. Thus, optogenetic activation of LC-NA neurons can inhibit spontaneous recovery of appetitive extinction without causing long-term aversive memory.<br /> ・Optoinhibition study demonstrates the reduction of a conditioned response of within-session extinction. Therefore, LC-NA neuronal activity at the CS period of extinction could act as anti-extinction or be important for the expression of the conditioned response.

Weaknesses:<br /> ・It is unclear how LC-NA neurons behave during the CS period of appetitive extinction from this study. This weakens the importance of the optogenetic inactivation result.<br /> ・While authors manipulate global LC-NA neurons, many people find functionally heterogeneous populations in the LC. It remains unsolved if there is a specific LC-NA subpopulation responsible for appetitive extinction.

Reviewer #1 (Public Review):

This is my first review of the article entitled "The canonical stopping network: Revisiting the role of the subcortex in response inhibition" by Isherwood and colleagues. This study is one in a series of excellent papers by the Forstmann group focusing on the ability of fMRI to reliably detect activity in small subcortical nuclei - in this case, specifically those purportedly involved in the hyper- and indirect inhibitory basal ganglia pathways. I have been very fond of this work for a long time, beginning with the demonstration of De Hollander, Forstmann et al. (HBM 2017) of the fact that 3T fMRI imaging (as well as many 7T imaging sequences) do not afford sufficient signal to noise ratio to reliably image these small subcortical nuclei. This work has done a lot to reshape my view of seminal past studies of subcortical activity during inhibitory control, including some that have several thousand citations.

In the current study, the authors compiled five datasets that aimed to investigate neural activity associated with stopping an already initiated action, as operationalized in the classic stop-signal paradigm. Three of these datasets are taken from their own 7T investigations, and two are datasets from the Poldrack group, which used 3T fMRI.

The authors make six chief points:<br /> 1. There does not seem to be a measurable BOLD response in the purportedly critical subcortical areas in contrasts of successful stopping (SS) vs. going (GO), neither across datasets nor within each individual dataset. This includes the STN but also any other areas of the indirect and hyperdirect pathways.<br /> 2. The failed-stop (FS) vs. GO contrast is the only contrast showing substantial differences in those nodes.<br /> 3. The positive findings of STN (and other subcortical) activation during the SS vs. GO contrast could be due to the usage of inappropriate smoothing kernels.<br /> 4. The study demonstrates the utility of aggregating publicly available fMRI data from similar cognitive tasks.<br /> 5. From the abstract: "The findings challenge previous functional magnetic resonance (fMRI) of the stop-signal task"<br /> 6. and further: "suggest the need to ascribe a separate function to these networks."

I strongly and emphatically agree with points 1-5. However, I vehemently disagree with point 6, which appears to be the main thrust of the current paper, based on the discussion, abstract, and - not least - the title.

To me, this paper essentially shows that fMRI is ill-suited to study the subcortex in the specific context of the stop-signal task. That is not just because of the issues of subcortical small-volume SNR (the main topic of this and related works by this outstanding group), but also because of its limited temporal resolution (which is unacknowledged, but especially impactful in the context of the stop-signal task). I'll expand on what I mean in the following.

First, the authors are underrepresenting the non-fMRI evidence in favor of the involvement of the subthalamic nucleus (STN) and the basal ganglia more generally in stopping actions.<br /> - There are many more intracranial local field potential recording studies that show increased STN LFP (or even single-unit) activity in the SS vs. FS and SS vs. GO contrast than listed, which come from at least seven different labs. Here's a (likely non-exhaustive) list of studies that come to mind:<br /> o Ray et al., NeuroImage 2012<br /> o Alegre et al., Experimental Brain Research 2013<br /> o Benis et al., NeuroImage 2014<br /> o Wessel et al., Movement Disorders 2016<br /> o Benis et al., Cortex 2016<br /> o Fischer et al., eLife 2017<br /> o Ghahremani et al., Brain and Language 2018<br /> o Chen et al., Neuron 2020<br /> o Mosher et al., Neuron 2021<br /> o Diesburg et al., eLife 2021<br /> - Similarly, there is much more evidence than cited that causally influencing STN via deep-brain stimulation also influences action-stopping. Again, the following list is probably incomplete:<br /> o Van den Wildenberg et al., JoCN 2006<br /> o Ray et al., Neuropsychologia 2009<br /> o Hershey et al., Brain 2010<br /> o Swann et al., JNeuro 2011<br /> o Mirabella et al., Cerebral Cortex 2012<br /> o Obeso et al., Exp. Brain Res. 2013<br /> o Georgiev et al., Exp Br Res 2016<br /> o Lofredi et al., Brain 2021<br /> o van den Wildenberg et al, Behav Brain Res 2021<br /> o Wessel et al., Current Biology 2022<br /> - Moreover, evidence from non-human animals similarly suggests critical STN involvement in action stopping, e.g.:<br /> o Eagle et al., Cerebral Cortex 2008<br /> o Schmidt et al., Nature Neuroscience 2013<br /> o Fife et al., eLife 2017<br /> o Anderson et al., Brain Res 2020

Together, studies like these provide either causal evidence for STN involvement via direct electrical stimulation of the nucleus or provide direct recordings of its local field potential activity during stopping. This is not to mention the extensive evidence for the involvement of the STN - and the indirect and hyperdirect pathways in general - in motor inhibition more broadly, perhaps best illustrated by their damage leading to (hemi)ballism.

Hence, I cannot agree with the idea that the current set of findings "suggest the need to ascribe a separate function to these networks", as suggested in the abstract and further explicated in the discussion of the current paper. For this to be the case, we would need to disregard more than a decade's worth of direct recording studies of the STN in favor of a remote measurement of the BOLD response using (provably) sub ideal imaging parameters. There are myriads of explanations of why fMRI may not be able to reveal a potential ground-truth difference in STN activity between the SS and FS/GO conditions, beginning with the simple proposition that it may not afford sufficient SNR, or that perhaps subcortical BOLD is not tightly related to the type of neurophysiological activity that distinguishes these conditions (in the purported case of the stop-signal task, specifically the beta band). But essentially, this paper shows that a specific lens into subcortical activity is likely broken, but then also suggests dismissing existing evidence from superior lenses in favor of the findings from the 'broken' lens. That doesn't make much sense to me.

Second, there is actually another substantial reason why fMRI may indeed be unsuitable to study STN activity, specifically in the stop-signal paradigm: its limited time resolution. The sequence of subcortical processes on each specific trial type in the stop-signal task is purportedly as follows: at baseline, the basal ganglia exert inhibition on the motor system. During motor initiation, this inhibition is lifted via direct pathway innervation. This is when the three trial types start diverging. When actions then have to be rapidly cancelled (SS and FS), cortical regions signal to STN via the hyperdirect pathway that inhibition has to be rapidly reinstated (see Chen, Starr et al., Neuron 2020 for direct evidence for such a monosynaptic hyperdirect pathway, the speed of which directly predicts SSRT). Hence, inhibition is reinstated (too late in the case of FS trials, but early enough in SS trials, see recordings from the BG in Schmidt, Berke et al., Nature Neuroscience 2013; and Diesburg, Wessel et al., eLife 2021).<br /> Hence, according to this prevailing model, all three trial types involve a sequence of STN activation (initial inhibition), STN deactivation (disinhibition during GO), and STN reactivation (reinstantiation of inhibition during the response via the hyperdirect pathway on SS/FS trials, reinstantiation of inhibition via the indirect pathway after the response on GO trials). What distinguishes the trial types during this period is chiefly the relative timing of the inhibitory process (earliest on SS trials, slightly later on FS trials, latest on GO trials). However, these temporal differences play out on a level of hundreds of milliseconds, and in all three cases, processing concludes well under a second overall. To fMRI, given its limited time resolution, these activations are bound to look quite similar.

Lastly, further building on this logic, it's not surprising that FS trials yield increased activity compared to SS and GO trials. That's because FS trials are errors, which are known to activate the STN (Cavanagh et al., JoCN 2014; Siegert et al. Cortex 2014) and afford additional inhibition of the motor system after their occurrence (Guan et al., JNeuro 2022). Again, fMRI will likely conflate this activity with the abovementioned sequence, resulting in a summation of activity and the highest level of BOLD for FS trials.

In sum, I believe this study has a lot of merit in demonstrating that fMRI is ill-suited to study the subcortex during the SST, but I cannot agree that it warrants any reappreciation of the subcortex's role in stopping, which are not chiefly based on fMRI evidence.

A few other points:<br /> - As I said before, this team's previous work has done a lot to convince me that 3T fMRI is unsuitable to study the STN. As such, it would have been nice to see a combination of the subsamples of the study that DID use imaging protocols and field strengths suitable to actually study this node. This is especially true since the second 3T sample (and arguably, the Isherwood_7T sample) does not afford a lot of trials per subject, to begin with.<br /> - What was the GLM analysis time-locked to on SS and FS trials? The stop-signal or the GO-signal?<br /> - Why was SSRT calculated using the outdated mean method?<br /> - The authors chose 3.1 as a z-score to "ensure conservatism", but since they are essentially trying to prove the null hypothesis that there is no increased STN activity on SS trials, I would suggest erring on the side of a more lenient threshold to avoid type-2 error.<br /> - The authors state that "The results presented here add to a growing literature exposing inconsistencies in our understanding of the networks underlying successful response inhibition". It would be helpful if the authors cited these studies and what those inconsistencies are.

Reviewer #1 (Public Review):

In this manuscript, the authors describe an improved miniscope they name "E-scope", combining in vivo calcium imaging with electrophysiological recording. They use it to examine neural correlates of social interactions with respect to cerebellar and cortical circuits. Through correlations between electrophysiological single units of Purkinje cells and dentate nucleus neurons as well as with calcium signals imaging of neurons from the anterior cingulate cortex, the authors provide correlative data supporting the view that intracerebellar circuits and cerebello-cortical communications take part in the modulation of social behavior. In particular, the electrophysiological dataset reflects the PC-DN connection and strongly suggests its involvement in social interactions. Cross-correlations analyses between PC / DN single units and ACC calcium signals suggest that the recorded cerebellar and cortical structures both take part in the brain networks at play in social behavior.

Strengths:<br /> - This is a timely and important study with solid evidence for correlative conclusions that are not overstated in the manuscript, which is commendable.<br /> - Despite the technical challenge, the experiments presented in this study seem well performed and the quality of the dataset is appropriate.

Weaknesses:<br /> - While the novelty of the device is strongly emphasized, I find that its value is somewhat diminished by the wire-free device developed by the same group as it should thus be possible to perform calcium imaging wire-free and electrophysiological recording via a single conventional cable (or also via wireless headstages).<br /> - The role of the identified network activations in social interactions is not touched upon.

Reviewer #1 (Public Review):

This study conducted a series of experiments to comprehensively support the allocentric rather than egocentric visual spatial reference updating for the path-integration mechanism in the control of target-oriented locomotion. Authors firstly manipulated the waiting time before walking to tease apart the influence from spatial working memory in guiding locomotion. They demonstrated that the intrinsic bias in perceiving distance remained constant during walking and that the establishment of a new spatial layout in the brain took a relatively longer time beyond the visual-spatial working memory. In the following experiments, the authors then uncovered that the strength of the intrinsic bias in distance perception along the horizontal direction is reduced when participants' attention is distracted, implying that world-centered path integration requires attentional effort. This study also revealed horizontal-vertical asymmetry in a spatial coding scheme that bears a resemblance to the locomotion control in other animal species such as desert ants.

The overall design of the behavioral experiments is elegant and statistics are well performed to support the authors' viewpoint in the allocentric rather than egocentric visual spatial coding scheme for distance perception along the horizontal line.

It is however worth noting the statement from Gibson in 1979 that for egocentric distances, tangible information arises from the effort required to walk a distance, thus, effort becomes associated through experience with visual distance cues. Accordingly, visual information alone is insufficient to support the awareness of distance. Perceived distance is rather specified by an invariant relationship between distal extent and a persons' potential to perform gross motion actions such as walking. This view is supported later by Proffitt et al. (2003) in which participants wore backpacks and their perceived distance increased compared with the baseline condition. Authors need to acknowledge the physical effort in addition to visual information for the spatial coding and may consider the manipulation of physical efforts in the future to support the robustness of constant intrinsic bias in ground-based spatial coding during walking.

Furthermore, it would be more comprehensive and fit into the Neuroscience Section if the authors can add in current understandings of the spatial reference frames in neuroscience in the introduction and discussion, and provide explanations on how the findings of this study supplement the physiological evidence that supports our spatial perception as well. For instance, world-centered representations of the environment, or cognitive maps, are associated with hippocampal formation while self-centered spatial relationships, or image spaces, are associated with the parietal cortex (see Bottini, R., & Doeller, C. F. (2020). Knowledge Across Reference Frames: Cognitive Maps and Image Spaces. Trends in Cognitive Sciences, 24(8), 606-619. https://doi.org/10.1016/j.tics.2020.05.008 for details)

Reviewer #1 (Public Review):

The manuscript by Lin et al describes a wide biophysical survey of the molecular mechanisms underlying full-length BTK regulation. This is a continuation of this lab's excellent work on deciphering the myriad levels of regulation of BTKs downstream of their activation by plasma membrane localised receptors.

The manuscript uses a synergy of cryo EM, HDX-MS and mutational analysis to delve into the role of how the accessory domains modify the activity of the kinase domain. The manuscript essentially has three main novel insights into BTK regulation.

1. Cryo EM and SAXS show that the PHTH region is dynamic compared to the conserved Src module.<br /> 2. A 2nd generation tethered PH-kinase construct crystal of BTK reveals a unique orientation of the PH domain relative to the kinase domain, that is different from previous structures.<br /> 3. A new structure of the kinase domain dimer shows how trans-phosphorylation can be achieved.

Excitingly these structural works allow for the generation of a model of how BTK can act as a strict coincidence sensor for both activated BCR complex as well as PIP3 before it obtains full activity. To my eye the most exciting result of this work is describing how the PH domain can inhibit activity once the SH3/SH2 domain is disengaged, allowing for an additional level of regulatory control.

I have very few experimental concerns as the methods and figures are well-described and clear. As the authors are potentially saying that the previously solved PH domain-kinase interface is artefactual, additional evidence strengthening their model would be helpful to resolve any possible controversies.

Reviewer #1 (Public Review):

First, I agree with the authors of this manuscript that conformational changes in the XFEL structures with 2.8 A resolution are not reliable enough for demonstrating the subtle changes in the electron transfer events in this bacterial photosynthesis system. Actually, the data statistics in the paper by Dods et al. showed that the high-resolution range of some of the XFEL datasets may include pretty high noise (low CC1/2 and high Rsplit) so the comparison of the subtle conformational changes of the structures is problematic.

The manuscript by Gai Nishikawa investigated time-dependent changes in the energetics of the electron transfer pathway based on the structures by Dods et al. by calculating redox potential of the active and inactive branches in the structures and found no clear link between the time-dependent structural changes and the electron transfer events in the XFEL structures published by Dods, R.et al. (2021). This study provided validation for the interpretation of the structures of those electron-transferring proteins.

The paper was well prepared.

Reviewer #1 (Public Review):

Here, the authors describe, in detail, the transition between the summer form and the winter form of the pear psyllid, Cacopsylla chinensis. While the authors explore many components of this transition, the central hypotheses they seek to test are (i) that a protein they deem CcTRPM is a cold-sensitive Transient Receptor Potential Melastatin (TRPM) channel, and (ii) that this channel is involved in the summer-to-winter transition, in response to cold.

The authors demonstrate that: both cold and menthol can initiate the summer-to-winter transition; that the protein of interest is required for the summer-to-winter transition (in vivo); that the protein of interest is involved in menthol-dependent Ca2+ transients (in vitro); that miR-252 expression is temperature-dependent, modulates the seasonal transition, and affects the expression of the transcript of interest; and finally, somewhat separately, that the chitin biosynthesis pathway is linked to the summer-to-winter transition.

Although I generally found the evidence to be convincing, I note a few critical weaknesses in the manuscript as it is currently presented. Firstly, there is insufficient methodological detail to understand how the genes/transcripts/proteins in this work were identified. Further, the structural and phylogenetic analyses are incompletely described and the results are inconsistent with our previous understanding of the structure and evolution of TRPMs. It is thus possible (although unlikely) that this protein has been misidentified. Alternatively, this could be a structurally aberrant TRPM from a lineage previously presumed to be lost in insects, but there is not sufficient evidence to conclude this. Perhaps more importantly, the authors conclude that the protein of interest is cold sensitive (i.e., a "temperature receptor") primarily based on menthol sensitivity. Although menthol and cold activate the same receptors in other species, there is no demonstrated reason to think that menthol sensitivity necessitates cold sensitivity, or vice-versa. Thus, the authors' conclusions are, in my opinion, incomplete and overstated. Below are specific comments giving further context to the criticisms summarized above:

1. The method used to identify the various genes/proteins described herein is not described. Relatedly, the alignment in Figure S1 lacks Trpms from non-hemipteran taxa, making it difficult to judge sequence similarity to other more well-characterized Trpms (e.g., from human, mouse, fly, nematode, etc.), and thus difficult to assess homology from the manuscript alone.

2. The authors suggest that the CcTrpm has ankyrin repeats. To my knowledge, this would be the first description of ankyrin repeats in TRPM. It's not stated how the authors identified these putative ankyrin repeats. There's also no description of the absence or presence of previously identified Melastatin Homology Regions (MHRs), a C-terminal coiled-coil that is typically present, other C-terminal domain motifs, or the TRP domain. In the absence of methodological detail, and given the proposed presence of ankyrin repeats, it seems possible that this may not be TRPM.

3. The authors suggest that, because mRNA abundance for CcTRPM is increased in response to cold, it is cold-sensitive. However, this says nothing as to whether cold actually activates the ion channel -- a critical distinction. The authors finally conclude that CcTRPM encodes a cold-sensitive ion channel because menthol elicits Ca2+ activity in vitro. However, this experiment only demonstrates that the protein is likely menthol sensitive. This experiment does not support the authors' conclusion that this is a cold-sensitive receptor (although their later knockdown experiments do, albeit indirectly).

4. The lack of taxonomic representation in the phylogenetic analysis makes it difficult to interpret, especially in the context of methodological detail concerning the initial identification of the gene/transcript/protein of interest. Further, it's not stated if the tree is rooted (if it is, the rooting methodology is not described), the branch lengths are not shown, and the branch support methodology is not described. Many previous phylogenetic analyses have concluded--implicitly or explicitly--that there are at least two ancestral animal TRPM paralogs. From the perspective of vertebrates, one ancestral copy went on to diversify into TRPMs 1, 3, 6, and 7, and the other ancestral copy went on to diversify into TRPMs 2, 8, 4, and 5. The insect Trpms are generally thought to be more closely related to vertebrate TRPMs 1,3,6, and 7. If this phylogeny is rooted, it implies that the hemipteran Trpms are more closely related to vertebrates 2, 8, 4, and 5 (or at least 8, since that is all that is present here), and quite distantly related to other insect Trpms (and presumably, to vertebrates 1,3,6, and 7, which are not present). To my knowledge, this would be the first description of this Trpm subfamily in insects, but there is insufficient evidence or phylogenetic rigor here to conclude that. The most likely explanation is that the tree is unrooted, incorrectly rooted, or that the protein of interest is not TRPM.

Reviewer #1 (Public Review):

The authors aimed to establish a cell culture system to investigate muscle tissue development and homeostasis. They successfully developed a complex 3D cell model and conducted a comprehensive molecular and functional characterization. This approach represents a critical initial step towards using human cells, rather than animals, to study muscular disorders in vitro. Although the current protocol is time-consuming and the fetal cell model may not be mature enough to study adult-onset diseases, it nonetheless provides a valuable foundation for future disease modelling studies using isogenic iPSC lines or patient-derived cells with specific mutations. The manuscript does not explore whether or how this stem cell model can advance our understanding of muscular diseases, which would be an exciting avenue for future research. Overall, the detailed protocol presented in this paper will be useful for informing future studies and provides an important resource to the stem cells community. The inclusion of data on disease modelling using isogenic iPSC lines or patient-derived cells would further enhance the manuscript's impact.

Joint Public Review:

Throughout the study, there is insufficient information about how experiments were performed and how often (imaging, pull-downs etc), how data was acquired, modified and analysed (especially imaging data, see below), how statistical analyses were done and what is presented in the figures (single planes or maximum intensity projections etc). This makes it difficult to evaluate the data and results.

There is insufficient information about tools and reporters used. This is misleading and impacts the conclusions that can be made from the results presented. To give an example, in Figure 1D-F, the authors present data that HDA-1::GFP and LIN-53::mNeonGreen (both components of the nucleosome remodeling and deacetylation complex) but not the histone acetyltransferase MYS-1::GFP are 'asymmetrically segregated' during QR.a division. However, the authors do not mention that HDA-1::GFP and LIN-53::mNeonGreen are expressed at endogenous levels (they are CRISPR alleles) whereas MYS-1::GFP is overexpressed (integration of a multi-copy extrachromosomal array). The difference in 'segregation' could therefore be a consequence of different levels of expression rather than different modes of segregation ('asymmetric' versus 'symmetric').

There is insufficient information about the phenotypes of the animals used (RNAi knock-downs of hda-1, lin-53 RNAi, pig-1 etc). Again this is misleading and impacts the conclusions that can be made. To give some examples, (1) in Figure 3A-G, control RNAi embryos are compared to hda-1 RNAi and lin-53 RNAi embryos. What the authors do not mention is that hda-1 RNAi and lin-53 RNAi embryos have severe developmental defects and essentially cannot be compared to control RNAi embryos. The differences between the embryos can be seen in Figure S7B where bright-field images of control RNAi, hda-1 RNAi and lin-53 RNAi embryos are shown. At the 350 min time point, a normal embryo is visible for the control, a 'ball of cells' embryo for hda-1 RNAi and an embryo that seems to have arrested at an earlier developmental stage (and therefore have much larger cells) for lin-53 RNAi. Because of these pleiotropic phenotypes, it is unclear whether differences seen for example in sAnxV::GFP positive cells (Figure 3A) are the result of a direct effect of hda-1(RNAi) on cell death or whether they are the result of global changes in development and cell fate induced by hda-1(RNAi). hda-1(RNAi) and lin-53(RNAi) embryos are also used for the data shown in Figures S6 and S7, raising the same concerns; (2) the authors do not mention what the impact of Baf A1 treatment is on animals; however, the images provided in Figure 5E indicate that Baf A1 treatment causes pleiotropic effects in L1 larvae.

There is a lack of adequate controls. Because of this, some of the data presented must be considered as preliminary. To give some examples: (1) controls are lacking for the data shown in Figure 3D-G (i.e. genes other than egl-1). Since hda-1 RNAi has a pleiotropic effect and most likely affects H3K27 acetylation genome-wide, this is critical. Based on what is shown, it is unclear whether the results presented are specific to egl-1 or not; (2) the co-IP and mass spec data shown in Figure 4A, C and Figure S8 also lack a critical control, which is GFP only. Because of this, it is unclear whether subunits of the V-ATPase bind to HDA-1 or GFP. The co-IP and mass spec data forms the basis of Figures 5 and 6 as well as Figure S9. Data presented in these figures therefore has to be considered preliminary as well.

Inappropriate methods are used. For this reason, some of the data again must be considered preliminary. To give some examples: (1) in Figure 5A, B, the authors used super-ecliptic pHluorin to look at changes in pH in the daughter cells. However, the authors used quenching of super-ecliptic pHluorin fluorescence rather than a ratio-metric method to 'measure' changes in pH. Because of this, it is unclear whether the changes in fluorescence observed are due to changes in pH or changes in the amount of pHluorin protein. Figure 5A, B forms the basis for the experiments presented in the remaining parts of Figure 5 as well as in Figure 6 and Figure S9; (2) the authors' description of how some images were modified before quantitative analysis raises concerns. The figures of concern are particularly Figure 1 and Figure S4, where background subtraction with denoising and deconvolution was used. Background subtraction, with denoising and deconvolution is an image manipulation that enhances the contrast between background and what looks like foreground. Therefore, background subtraction should be applied primarily in experiments involving image segmentation not fluorescence intensity measurement. Not being provided any information by the authors about the kind of subtraction that was made, this processing could lead to an uneven subtraction across the image, which can easily lead to artefacts. Since the fluorescence intensity in the smaller daughter cell is lower, and thus closer to background, the algorithm the authors used may have misinterpreted the grey value information in the smaller daughter cell pixels. This could have led to an asymmetric subtraction of background in the two daughter cells, leading to a stronger subtraction in the smaller daughter cell. Ultimately, their processing could have artificially increased the intensity asymmetry between the two daughter cells in all their results.

The imaging data is of low quality (for example Figures 1, 2, 5, 6; Figures S2, S3, S5, S6, S9). Since much of the study and the findings are based on imaging, this is a major concern. Critical parameters are not mentioned (number of sections in z-stack, size of the field-of-view, laser power used etc), which makes it difficult to understand what was done and what one is looking at. To give some specific examples, (1) the images shown in Figure 2B are of very low quality with severe background from neighbouring cells. In addition, the outline of the cells (plasma membrane) or the nuclei of the daughter cells is unknown. Based on this it is not clear how the authors could have measured 'Fluorescence intensity ratio between sister nuclei' in an accurate and unbiased way (what is clear from these images is that there is an increase in HDA-1::GFP signal in ALL surviving daughters (asymmetric and symmetric divisions) post cytokinesis but not in the daughter cell that is about to die (asymmetric and unequal division); (2) the images in Figure 6A and Figure S9A on VHA-17 segregation and its colocalization to ER and lysosome segregation during QR.a division are of very low quality and it is unclear to the reviewer how such images were used to obtain the quantitative data shown.

In some cases, there is a discrepancy between what is shown in figures and what the authors state in the text. To give some examples: (1) on page 7, the authors state "..., we found that nuclear HDA-1 or LIN-53 asymmetry gradually increased from 1.1-fold at the onset of anaphase to 1.5 or 1.8-fold at cytokinesis, respectively (Figure 1D-E)." Looking at the images for HDA-1 and LIN-53 in Figure 1D, the increase in the ratio mainly occurs between 4 min and 6 min, which is post cytokinesis and NOT prior to cytokinesis; (2) these images (Figure 1D) also show that there is an increase in the HDA-1 and LIN-53 signals in the larger daughter cells (QR.ap), which suggests that the increase in ratios (Figure 1E) is the result of increased HDA-1 and LIN-53 synthesis post cytokinesis. However, on top of page 8, the authors state "The total fluorescence of HDA-1, LIN-53 and MYS-1 remained constant during ACDs, suggesting that protein redistribution may establish NuRD asymmetry (Figure S4C)." In Figure S4C, the authors present straight lines for 'relative total fluorescence' for imaging (probably z-stacks) that was done every min over the course of 7 min. If there was no increase in material as the authors claim, they should have seen significant photobleaching over the course of the 7 min and therefore reduced level of 'relative total fluorescence' over time. How the data presented in Figure S4C was generated is therefore unclear. (Despite the fact that the authors claim that the asymmetry seen is not due to new synthesis in the larger daughter cell post cytokinesis, it would be more consistent with the first experiment presented in this study (Figure S1) that shows that there is more hda-1 mRNA in egl-1(-) cells compared to egl-1(+) cells); (3) On page 12, the authors state "..., in Baf A1-treated animals, QRaa inherited similar levels of HDA-1::GFP as its sister cell,...". However, looking at the image provided in Figure 5E (0 min), there seems to be a similar ratio of HDA-1::GFP between the daughter cells in DMSO and Baf A1-treated animals.

Reviewer #1 (Public Review):

The study as a concept is well designed, although there are two issues I see in the methodology (these may be just needing further explanation or if I am correct in my interpretation of what was done, may need reanalysis to take into account). Both issues relate to the data that was extracted from the published literature on zoonotic malaria prevalence in the study area.

1. No limit was set on the temporal range<br /> With no temporal limit on the range of studies, the landscape in many cases will have changes between the study being conducted and the spatial data. This will be particularly marked in areas where there has been clearing since the zoonotic malaria prevalence study. Also, population changes (either through population growth, decline or movement) will have occurred. All research is limited in what it can do with the available data, so I realise that there may not be much the authors can do to correct this. One possible solution would be to look at the land use change at each site between the prevalence study and the remote sensing data. I'm not sure if this is feasible, but if it is I would recommend the authors attempt this as it will make their results stronger.

2. Most studies only gave a geographic area or descriptive location.<br /> The spatial analysis was based on a 5km and 20km radius of the 'study site' location, but for many of the studies the exact site is not known. Therefore the 'study site' was artificially generated using a polygon centroid. Considering that the polygon could be an administrative boundary (ie district/state/country), this is an extremely large area for which a 5km radius circle in the middle of the polygon is being taken as representative of the 'study site'. This doesn't make sense as it assumes that the landscape is uniform across the district, which in most cases it will not be (in rural areas it is going to be a mixture of villages, forest, plantation, crops etc which will vary across the landscape). This might just be a case of misunderstanding what was done (in which case the text needs rewording to make it clearer) or if I have interpreted it correctly the selection of the centroid to represent the study area does not make sense. I am not sure how to overcome this as it probably not possible to get exact locations for the study sites. One possibility could be to make the remote sensing data the same scale as the prevalence data ie if the study site is only identifiable at the polygon level, then the remote sensing data (fragmentation, cover and population) is used at the polygon level.

Both these issues could have an impact on the study's findings. I would think that in both cases it might make the relationship between the environmental variables and prevalence even clearer.

Reviewer #1 (Public Review):

The authors investigated the metabolic effects of ∆9-THC, the main psychoactive component of cannabis, on early mouse embryonic cell types. They found that ∆9-THC increases proliferation in female mouse embryonic stem cells (mESCs) and upregulates glycolysis. Additionally, primordial germ cell-like cells (PGCLCs) differentiated from ∆9-THC-exposed cells also show alterations to their metabolism. The study is valuable because it shows that physiologically relevant ∆9-THC concentrations have metabolic effects on cell types from the early embryo, which may cause developmental effects. However, the claim of "metabolic memory" is not justified by the current data, since the effects on PGCLCs could potentially be due to ∆9-THC persisting in the cultured cells over the course of the experiment, even after the growth medium without ∆9-THC was added.

The study shows that ∆9-THC increases the proliferation rate of mESCs but not mEpiLCs, without substantially affecting cell viability, except at the highest dose of 100 µM which shows toxicity (Figure 1). Treatment of mESCs with rimonabant (a CB1 receptor antagonist) blocks the effect of 100 nM ∆9-THC on cell proliferation, showing that the proliferative effect is mediated by CB1 receptor signaling. Similarly, treatment with 2-deoxyglucose, a glycolysis inhibitor, also blocks this proliferative effect (Figure 4G-H). Therefore, the effect of ∆9-THC depends on both CB1 signaling and glycolysis. This set of experiments strengthens the conclusions of the study by helping to elucidate the mechanism of the effects of ∆9-THC.

Although several experiments independently showed a metabolic effect of ∆9-THC treatment, this effect was not dose-dependent over the range of concentrations tested (10 nM and above). Given that metabolic effects were observed even at 10 nM ∆9-THC (see for example Figure 1C and 3B), the authors should test lower concentrations to determine the dose-dependence and EC50 of this effect. The authors should also compare their observed EC50 with the binding affinity of ∆9-THC to cellular receptors such as CB1, CB2, and GPR55 (reported by other studies).

The study also profiles the transcriptome and metabolome of cells exposed to 100 nM ∆9-THC. Although the transcriptomic changes are modest overall, there is upregulation of anabolic genes, consistent with the increased proliferation rate in mESCs. Metabolomic profiling revealed a broad upregulation of metabolites in mESCs treated with 100 nM ∆9-THC.

Additionally, the study shows that ∆9-THC can influence germ cell specification. mESCs were differentiated to mEpiLCs in the presence or absence of ∆9-THC, and the mEpiLCs were subsequently differentiated to mPGCLCs. mPGCLC induction efficiency was tracked using a BV:SC dual fluorescent reporter. ∆9-THC treated cells had a moderate increase in the double positive mPGCLC population and a decrease in the double negative population. A cell tracking dye showed that mPGCLCs differentiated from ∆9-THC treated cells had undergone more divisions on average. As with the mESCs, these mPGCLCs also had altered gene expression and metabolism, consistent with an increased proliferation rate.

My main criticism is that the current experimental setup does not distinguish between "metabolic memory" vs. carryover of THC (or its metabolites) causing metabolic effects. The authors assume that their PGCLC induction was performed "in the absence of continuous exposure" but this assumption may not be justified. ∆9-THC might persist in the cells since it is highly hydrophobic. In order to rule out the persistence of ∆9-THC as an explanation of the effects seen in PGCLCs, the authors should measure concentrations of ∆9-THC and THC metabolites over time during the course of their PGCLC induction experiment. This could be done by mass spectrometry. This is particularly important because 10 nM of ∆9-THC was shown to have metabolic effects (Figure 1C, 3B, etc.). Since the EpiLCs were treated with 100 nM, if even 10% of the ∆9-THC remained, this could account for the metabolic effects. If the authors want to prove "metabolic memory", they need to show that the concentration of ∆9-THC is below the minimum dose required for metabolic effects.

Overall, this study is promising but needs some additional work in order to justify its conclusions. The developmental effects of ∆9-THC exposure are important for society to understand, and the results of this study are significant for public health.

Reviewer #1 (Public Review):

Trebino et al. investigated the BRAF activation process by analysing the interactions of BRAF N-terminal regulatory regions (CRD, RBD, and BSR) with the C-terminal kinase domain and with the upstream regulators HRAS and KRAS. To this end, they generated four constructs comprising different combinations of N-terminal domains of BRAF and analysed their interaction with HRAS as well as conformational changes that occur. By HDX-MS they confirmed that the RBD is indeed the main mediator of interaction with HRAS. Moreover, they observed that HRAS binding leads to conformational changes exposing the BSR to the environment. Next, the authors used OpenSPR to determine the binding affinities of HRAS to the different BRAF constructs. While BSR+RBD, RBD+CRD, and RBD bound HRAS with nanomolar affinity, no binding was observed with the construct comprising all three domains. Based on these experiments, the authors concluded that BSR and CRD negatively regulate binding to HRAS and hypothesised that BSR may confer some RAS isoform specificity. They corroborated this notion by showing that KRAS bound to BRAF-NT1 (BSR+RBD+CRD) while HRAS did not. Next, the authors analysed the autoinhibitory interaction occurring between the N-terminal regions and the kinase domain. Through pulldown and OpenSPR experiments, they confirm that it is mainly the CRD that makes the necessary contacts with the kinase domain. In addition, they show that the BSR stabilizes these interactions and that the addition of HRAS abolishes them. Finally, the D594G mutation within the KD of BRAF is shown to destabilise these autoinhibitory interactions, which could explain its oncogenic potential.

Overall, the in vitro study provides new insights into the regulation of BRAF and its interactions with HRAS and KRAS through a comprehensive in vitro analysis of the BRAF N-terminal region. Also, the authors report the first KD values for the N- and C-terminal interactions of BRAF and show that the BSR might provide isoform specificity towards KRAS. While these findings could be useful for the development of a new generation of inhibitors, the overall impact of the manuscript could probably be enhanced if the authors were to investigate in more detail how the BSR-mediated specificity of BRAF towards certain RAS isoforms is achieved. Moreover, though the very "clean" in vitro approach is appreciated, it also seems useful to examine whether the observed interactions and conformational changes occur in the full-length BRAF molecule and in more physiological contexts. Some of the results could be compared with studies including full-length constructs.

Reviewer #1 (Public Review):

As this experience as a reviewer has been unusual, it may be helpful to outline some relevant parameters of the task at the outset. While I was invited to review the Fuentes et al. study only, two additional papers concerning the claimed engravings and burials associated with Homo naledi by Berger and colleagues were also provided as components of the reviewer package. The two manuscripts presenting the archaeological evidence are accessible as preprints in bioRxiv, by Lee Berger and colleagues ('2023a, 2023b').

Unfortunately, the arguments in the Fuentes et al manuscript hinge entirely on the strength of archaeological evidence for engravings and intentional burial by Homo naledi (presented in the abovementioned two preprints). All inferences regarding hominin behaviour and biology of Homo naledi, discussed by Fuentes and colleagues, are wholly dependent on the evidence presented in the archaeology preprints being true.

Yet both of the archaeological manuscripts are unfortunately weak. In short, the claims for engravings depend on the demonstration of several elements of association that are rather standard for linking material traces found in the archaeological record with particular hominin behaviours. For the particular arguments by Berger and colleagues to be demonstrated, the traces on the rock surface need to be linked causally with hominin agency, in other words, their anthropogenic nature need to be established. The author of the engravings needs to be demonstrated as a particular hominin species (Homo naledi in this case), and the activity of engraving needs to have taken place ~241-335 kya. After reading the manuscript on the engravings, however, what is clear is that the scratches could as easily have been made by a modern-day farmer 50 years ago, as Homo naledi ~335 kya. Berger and colleagues do not present any evidence to the contrary, they simply describe their narrative as the most parsimonious scenario. A particularly curious piece of information presented as evidence is a list of individuals known to have entered the Dinaledi system in recent times (and known not to have scratched the walls, one presumes, though this is not stated).

The question of intentional burial is more complex. What we know from other widely accepted early burials is that documenting the geoarchaeological context of the hominin remains is critical to assess the likelihood of an intentional burial - this needs to be established at the outset through high quality fieldwork. Yet even the boundaries of the excavation presented in the burial manuscript appear so angled or skew relative to one another (Fig. 2a) that the individual squares look to be aligned with different XY grids, which does not instill confidence in the quality of field documentation. One can make out very little from the sediment section images - which are key to identifying intrusive features associated with burials - and the multivariate geochemical analysis of sediments is unconvincing: a scatterplot (not a biplot) should have been provided showing the geochemistry of the burial sediment samples relative to the immediately surrounding sediment characteristics. While one remains excited about the potential for a spectacular archaeological discovery within the Dinaledi cave system, unfortunately, the three manuscripts provided do not present convincing evidence to that effect.

Reviewer #1 (Public Review):

I think it is important to note up front that I recognize that the goal of this paper was to announce the discovery of what appear to be intentionally-made marks in Rising Star cave in South Africa. This was not meant to be an in-depth analysis or a declaration of definitive results. With this in mind, I appreciate that the authors did not try to overstate this new discovery, but instead simply reported what had been observed, provided a little bit of background on the current state of the field in regards to the evolution of hominin visual mark-making, made a few tentative identifications, but then clearly acknowledged that a lot more documentation, sampling, and study would be needed before we could understand the full scope and potential importance of this find.

This is a big claim. If it proves to be true, it has the potential to be paradigm-shifting as the identification of intentional engraved marks, made by a small-brained distant human cousin 200,000+ years ago in South Africa, would completely change our understanding of where, when and who made the first graphic marks. Twenty years ago, this claim would probably have been dismissed out of hand as being too far-fetched to be taken seriously, but there have been some major shifts in the field in recent years, in regard to the age of the art and the identity of the artists, that means this is a claim that should be approached with a scientifically cautious, but open mind. There is now mounting evidence for Neanderthals, and potentially other closely related species as well, to have been engaging in similar art-making practices to our own Homo sapiens ancestors. What makes this particular claim so extraordinary is that these marks are some of the oldest in the world and that Homo naledi is a more distant relation with a smaller brain. This is also what makes the further study of this discovery such a fascinating exercise in scientific inquiry.

From a technical and methodological perspective, there is an excellent range of tools and technologies that can be used to study these engravings, so I have no doubt that further studies will help answer some of the "nuts and bolts" questions. Then there is also the opportunity created by this discovery to really open a broader dialogue in the field about who were the first artists and at what point does the hominin brain become "primed" for making visual marks. I look forward to all sorts of lively debates in the future and to seeing the results of further in-depth studies.