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 #2 (Public Review):

The authors provide solid molecular and cellular evidence that ULK4 and STK36 not only interact, but that STK36 is targeted (transported?) to the cilium by ULK4. Their data helps generate a model for ULK4 acting as a scaffold for both STK36 and its substrate, Gli2, which appear to co-localise through mutual binding to ULK4. This makes sense, given the proposed role of most pseuodkinases as non-catalytic signaling hubs. There is also an important mechanistic analysis performed, in which ULK4 phosphorylation in an acidic consensus by STK36 is demonstrated using IP'd STK36 or an inactive 'AA' mutant, which suggests this phosphorylation is direct.

The major strength of the study is the well-executed combination of logical approaches taken, including expression of various deletion and mutation constructs and the careful (but not always quantified in immunoblot) effects of depleting and adding back various components in the context of both STK36 and ULK3, which broadens the potential impact of the work. The biochemical analysis of ULK4 phosphorylation appears to be solid, and the mutational study at a particular pair of phosphorylation sites upstream of an acidic residue (notably T2023) is further strong evidence of a functional interaction between ULK4/STK36. The possibility that ULK4 requires ATP binding for these mechanisms is not approached, though would provide significant insight: for example it would be useful to ask if Lys39 in ULK4 is involved in any of these processes, because this residue is likely important for shaping the ULK4 substrate-binding site as a consequence of ATP binding; this was originally shown in PMID 24107129 and discussed more recently in PMID: 33147475 in the context of the large amount of ULK4 proteomics data released.

The discussion is excellent, and raises numerous important future work in terms of potential transportation mechanisms of this complex. It also explains why the ULK4 pseudokinase domain is linked to an extended C-terminal region. Does AF2 predict any structural motifs in this region that might support binding to Gli2?

A weakness in the study, which is most evident in Figure 1, where Ulk4 siRNA is performed in the NIH3T3 model (and effects on Shh targets and Gli2 phosphorylation assessed), is that we do not know if ULK4 protein is originally present in these cells in order to actually be depleted. Also, we are not informed if the ULK4 siRNA has an effect on the 'rescue' by HA-ULK4; perhaps the HA-ULK4 plasmid is RNAi resistant, or if not, this explains why phosphorylation of Gli2 never reaches zero? Given the important findings of this study, it would be useful for the authors to comment on this, and perhaps discuss if they have tried to evaluate endogenous levels of ULK4 (and Stk36) in these cells using antibody-based approaches, ideally in the presence and absence of Shh. The authors note early on the large number of binding partners identified for ULK4, and siRNA may unwittingly deplete some other proteins that could also be involved in ULK4 transport/stability in their cellular model.

The sequence of ULK4 siRNAs is not included in the materials and methods as far as I can see.

Reviewer #3 (Public Review):

In this manuscript, Zhou et al. demonstrate that the pseudokinase ULK4 has an important role in Hedgehog signaling by scaffolding the active kinase Stk36 and the transcription factor Gli2, enabling Gli2 to be phosphorylated and activated.<br /> Through nice biochemistry experiments, they show convincingly that the N-terminal pseudokinase domain of ULK4 binds Stk36 and the C-terminal Heat repeats bind Gli2.

Lastly, they show that upon Sonic Hedgehog signaling, ULK4 localizes to the cilia and is needed to localize Stk36 and Gli2 for proper activation.

This manuscript is very solid and methodically shows the role of ULK4 and STK36 throughout the whole paper, with well controlled experiments. The phosphomimetic and incapable mutations are very convincing as well.<br /> I think this manuscript is strong and stands as is, and there is no need for additional experiments.

Overall, the strengths are the rigor of the methods, and the convincing case they bring for the formation of the ULK4-Gli2-Stk36 complex. There are no weaknesses noted. I think a little additional context for what is being observed in the immunofluorescence might benefit readers who are not familiar with these cell types and structures.

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 #2 (Public Review):

This study explores the breadth of effects of one important metabolite, azelaic acid, on marine microbes, and reveals in-depth its pathway of uptake and catabolism in one model bacterial strain. This compound is known to be widely produced by phytoplankton and plants, and to have complex effects on associated microbiomes.

This work uses transcriptomics to assay the response of two strains that show contrasting responses to the metabolite: one catabolizes the compound and assimilates the carbon, while the other shows growth inhibition and stress response. A highly induced TRAP transporter, adjacent to a previously identified regulator, is inferred to be the specific uptake system for azelaic acid. However the transport function was not directly tested via genetic or biochemical methods. Nevertheless, this is a significant finding that will be useful for exploring the distribution of azelaic acid uptake capability across metagenomes and other bacteria.

The authors use pulse-chase style metabolomics experiments to beautifully demonstrate the fate of azelaic acid through catabolic pathways. They also measure an assimilation rate per cell, though it remains unclear how this measured rate relates to natural systems. The metabolomics approach is an elegant way to show carbon flux through cells, and could serve as a model for future studies.

The study seeks to extend the results from two model strains to complex communities, using seawater mesocosm experiments and soil/Arabidopsis experiments. The seawater experiments show a community shift in mesocosms with added azelaic acid. However, the mechanisms for the shift were not determined; further work is necessary to demonstrate which community members are directly assimilating the compound vs. benefitting indirectly or experiencing inhibition. In my opinion the soil and Arabidopsis experiments are quite preliminary. I appreciate the authors' desire to broaden the scope beyond marine systems, but I believe any conclusions regarding different modes of action in aquatic vs terrestrial microbial communities are speculative at this stage.

This work is a nice illustration of how we can begin to tease apart the effects of chemical currencies on marine ecosystems. A key strength of this work is the combination of transcriptomics and metabolomics methods, along with assaying the impacts of the metabolite on both model strains of bacteria and whole communities. Given the sheer number of compounds that probably play critical roles in community interactions, a key challenge for the field will be navigating the tradeoffs between breadth and depth in future studies of metabolite impacts. This study offers a good compromise and will be a useful model for future studies.

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 #2 (Public Review):

The authors set out to show how hibernation is linked to brain size in frogs. If there were broader aims it is hard to decipher them. The authors present an extremely impressive dataset and a thorough set of cutting-edge analyses. However not all details are well explained. The main result about hibernation and brain size is fairly convincing, but it is hard to think of broader implications for this study. Overall, the manuscript is very confusing and hard to follow.

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 #2 (Public Review):

The research study presented by Rice et al. set out to further profile the host defense properties of the mitochondrial protein MOTS-c. To do this they studied i. the potential antimicrobial effects of MOTS-c on common bacterial pathogens E.coli and MRSA, ii. the effects of MOTS-c on the stimulation and differentiation of monocytes into macrophages. This is a well performed study that utilizes relevant methods and cell types to base their conclusions on. However, there appear to be a few weaknesses to the current study that hold it back from more broad application.

Comment 1: From reading the manuscript methods and results, it is unclear exactly what the synthetic MOTS-c source is. Therefore it is hard to determine whether there may be any impurities in the production of this synthetic protein that may interfere with the results presented throughout the manuscript. Though, the data presented in Supplemental Figure 4F, where E.coli expressing intracellular MOTS-c inhibited bacterial growth certainly support MOTS-c specific effects. Similarly with the experiments showing endogenous MOTS-c levels rising in stimulation and differentiated macrophages (Figure 3).

Comment 2: It is interesting that the mice receiving bacteria coupled with MOTS-c lost about 10% of their body weight. It would have been interesting to demonstrate the cause of this weight loss since the effect appears to be separate from mere PAMPs as shown by using heat-killed MRSA in Supplemental Figure 5. Was inflammation changed? Is this due to changes in systemic metabolism? Would have been interesting to have seen CRP levels or circulating liver enzymes.

Despite these concerns, the data are well suited to answering their research question, and they open up the door to studying how mitochondrial peptides like MOTS-c could have roles outside of the mitochondria.

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 #2 (Public Review):

The introduction is plotted with two parallel stories about PfKBP35 and FK506, with ribosome biogenesis as the central question at the end. In its current form, the manuscript suffers from two stories that are not entirely interconnected, unfinished, and somewhat confusing. I recommend focusing only on one story - either characterizing PfBP35 and its role in Plasmodium falciparum biology - future investigation of PfBP35 control of cellular processes or focusing on the actual targets of the FK506 drug (identified in figure 4). Both stories need additional experiments to make the manuscript(s) more complete. The results from PfFBP35 need more evidence for the proposed ribosome biogenesis pathway control. On the other hand, the results from the drug FK506 point to different targets with lower EC50, and other follow-up experiments are needed to substantiate the authors' claims. The strengths of the manuscript are the figures and experimental design. The combination of omics methods is informative and gives an opportunity for follow-up experiments.

Reviewer #3 (Public Review):

The study by Thommen et al. sought to identify the native role of the Plasmodium falciparum FKBP35 protein, which has been identified as a potential drug target due to the antiplasmodial activity of the immunosuppressant FK506. This compound has multiple binding proteins in many organisms; however, only one FKBP exists in P. falciparum (FKBP35). Using genetically-modified parasites and mass spectrometry-based cellular thermal shift assays (CETSA), the authors suggest that this protein is in involved in ribosome homeostasis and that the antiplasmodial activity of FK506 is separate from its activity on the FKBP35 protein. The authors first created a conditional knockdown using the destruction domain/shield system, which demonstrated no change in asexual blood stage parasites. A conditional knockout was then generated using the DiCre system. FKBP35KO parasites survived the first generation but died in the second generation. The authors called this "a delayed death phenotype", although it was not secondary to drug treatment, so this may be a misnomer. This slow death was unrelated to apicoplast dysfunction, as demonstrated by lack of alterations in sensitivity to apicoplast inhibitors. Quantitative proteomics on the FKBP35KO vs FKBP35WT parasites demonstrated enrichment of proteins involved in pre-ribosome development and the nucleolus. Interestingly, the KO parasites were not more susceptible to cycloheximide, a translation inhibitor, in the first generation (G1), suggesting that mature ribosomes still exist at this point. The SunSET technique, which incorporates puromycin into nascent peptide chains, also showed that in G1 the FKBP35KO parasites were still able to synthesize proteins. But in the second generation (G2), there was a significant decrease in protein synthesis. Transcriptomics were also performed at multiple time points. The effects of knockout of FKBP35 were transcriptionally silent in G1, and the parasites then slowed their cell cycles as compared to the FKBP35WT parasites.

The authors next sought to evaluate whether killing by FK506 was dependent upon the inhibition of PfKBP35. Interestingly, both FKBP35KO and FKBP35WT parasites were equally susceptible to FK506. This suggested that the antiplasmodial activity of FK506 was related to activity targeting essential functions in the parasite separate from binding to FKBP35. To identify these potential targets, the authors used MS-CETSA on lysates to test for thermal stabilization of proteins after exposure to drug, which suggests drug-protein interactions. As expected, FK506 bound FKBP35 at low nM concentrations. However, given that the parasite IC50 of this compound is in the uM range, the authors searched for proteins stabilized at these concentrations as putative secondary targets. Using live cell MS-CETSA, FK506 bound FKBP35 at low nM concentrations; however, in these experiments over 50 ribosomal proteins were stabilized by the drug at higher concentrations. Of note, there was also an increase in soluble ribosomal factors in the absence of denaturing conditions. The authors suggested that the drug itself led to these smaller factors disengaging from a larger ribosomal complex, leading to an increase in soluble factors. Ultimately, the authors conclude that the native function of FKBP35 is involved in ribosome homeostasis and that the antiplasmodial activity of FK506 is not related to the binding of FKBP35, but instead results from inhibition of essential functions of secondary targets.

Strengths<br /> This study has many strengths. It addresses an important gap in parasite biology and drug development, by addressing the native role of the potential antiplasmodial drug target FKBP35 and whether the compound FK506 works through inhibition of that putative target. The knockout data provide compelling evidence that the KBP35 protein is essential for asexual parasite growth after one growth cycle. Analysis of the FKBP35KO line also provides evidence that the effects of FK506 are likely not solely due to inhibition of that protein, but instead must have secondary targets whose function is essential. These data are important in the field of drug development as they may guide development away from structure-based FK506 analogs that bind more specifically to the FKBP35 protein.

Weaknesses:<br /> There are also a few notable weaknesses in the evidence that call into question the conclusion in the article title that FKBP35 is definitely involved in ribosomal homeostasis. While the proteomics supports alterations in ribosome biogenesis factors, it is unclear whether this is a direct role of the loss of the FKBP35 protein or is more related to non-specific downstream effects of knocking down the protein. The CETSA data clearly demonstrate that FK506 binds PfKB35 at low nM concentrations, which is different than the IC50 noted in the parasite; however, the evidence that the proteins stabilized by uM concentrations of drug are actual targets is not completely convincing. Especially, given the high uM amounts of drug required to stabilize these proteins. This section of the manuscript would benefit from validation of a least one or two of the putative candidates noted in the text. In the live cell CETSA, it is noted that >50 ribosomal components are stabilized in drug treated but not lysate controls. Similarly, the authors suggest that the -soluble fraction of ribosomal components increases in drug-exposed parasites even at 37{degree sign}C and suggests that this is likely from smaller ribosomal proteins disengaging from larger ribosomal complexes. While the evidence is convincing that this protein may play a role in ribosome homeostasis in some capacity, it is not sure that the title of the paper "FKBP secures ribosome homeostasis" holds true given the lack of mechanistic data. A minor weakness, but one that should nonetheless be addressed, is the use of the term "delayed death phenotype" with regards to the knockout parasite killing. This term is most frequently used in a very specific setting of apicoplast drugs that inhibit apicoplast ribosomes, so the term is misleading. It is also possible that the parasites are able to go through a normal cycle because of the kinetics of the knockout and that the time needed for protein clearance in the parasite to a level that is lethal.

Overall, the authors set out to identify the native role of FKB35 in the P. falciparum parasites and to identify whether this is, in fact, the target of FK506. The data clearly demonstrate that FKBP35 is essential for parasite growth and provide evidence that alterations in its levels have proteomic but not transcriptional changes. However, the conclusion that FKBP35 actually stabilizes ribosomal complexes remains intermediate. The data are also very compelling that FK506 has secondary targets in the parasite aside from FKBP35; however, the high uM concentrations of the drug needed to attain results and the lack of biological validation of the CETSA hits makes it difficult to know whether any of these are actually the target of the compound or instead are nonspecific downstream consequences of treatment.

Reviewer #2 (Public Review):

In this study, the investigators describe an unbiased phosphoproteomic analysis of cardiac-specific overexpression of adenylyl cyclase type 8 (TGAC8) mice that was then integrated with transcriptomic and proteomic data. The phosphoproteomic analysis was performed using tandem mass tag-labeling mass spectrometry of left ventricular (LV) tissue in TGAC8 and wild-type mice. The initial principal component analysis showed differences between the TGAC8 and WT groups. The integrated analysis demonstrated that many stress-response, immune, and metabolic signaling pathways were activated at transcriptional, translational, and/or post-translational levels.

The authors are to be commended for a well-conducted study with quality control steps described for the various analyses. The rationale for following up on prior transcriptomic and proteomic analyses is described. The analysis appears thorough and well-integrated with the group's prior work. Confirmational data using Western blot is provided to support their conclusions. Their findings have the potential of identifying novel pathways involved in cardiac performance and cardioprotection.

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 #2 (Public Review):

Among ionotropic glutamate receptors, kainate receptors (KAR) are still the object of intense investigation to understand their role in normal and pathological excitatory synaptic transmission. Like other receptors, KAR appear under different splicing variants and their respective physiological function is still debated. In this manuscript Dhingra et al explored the impact of the presence and of the absence of Exon9 of the GluK1 receptors on the pharmacological, biophysical and structural properties of the receptors. They further investigated how it is impacted by the association of KAR with their cognate auxiliary subunit Neto 1 and 2. This study represents a large body of work and data. The authors addressed the issue in a very systematic and rigorous manner.

First, by exploring RNAseq database, authors showed that GluK1 transcripts containing the exon 9 are present in many brain structures and especially in the cerebellum suggesting that a large part of GluK1 contains effectively this exon9.<br /> Using HEK cells as an expression system, they characterized many gating and biophysical properties of GluK1 receptors containing or not the exon9. Evaluated parameters were desensitization, relative potency of glutamate versus kainite, polyamine block.

It is known that the association of GluK1 with auxiliary proteins Neto1/2 modulate the properties of the receptors. Authors investigated systematically whether Neto1 and 2 similarly alter GluK1 properties in function of the presence of exon9. This study provides many quantitative data that could be reused for modeling the role of kainate receptors. Given the change shown by the authors, the presence of exon in GluK1 is noticeable and likely should have an impact of synaptic transmission.<br /> Interestingly, authors used a mutational approach to identify critical residue encoded by exon9 that are responsible for the functional differences between the two splice variants. In many cases, the replacement of a single amino acid lead to the absence of current confirming the crucial role of the segment of the receptor. However, it made the comparison and the identification of critical residues more challenging.<br /> Authors attempted to establish the structure GluK1 receptors comprising the exon9 using different preparation methods. They succeeded in obtaining structures with equivalent or lower resolution compared with previous report on GluK1 and GluK2 receptors. However, the organization of the peptide coded by exon is poorly defined and limited possible analyses. Despite this they could observe that the presence of the exon9 does not alter significantly the structure of GluK1.

Reviewer #3 (Public Review):

GluK1 forms glutamate-gated ion channels with an important function in synaptic transmission and neuron excitability. Particularly, a GluK1 splice-variant (Gluk1-1) with significant expression in different regions of the brain has not been characterized before. The paper of Dhingra et al. aims to evaluate the role of the Exon 9 splice insert in GluK1 on channel function. This study relies mainly on electrophysiological approaches to determine the effect of the splice insert on GluK1 gating properties, including desensitization, agonist efficacy, recovery, and rectification. Overall, this work provides two major milestones: 1) the first functional characterization of the Gluk1-1a variant and 2) the first structure of this channel. The functional data supporting the role of the insert on channel properties are solid, although the current data does not provide significant insights about the mechanisms behind this. Also, the little information associated with the resolved structure precludes providing further insights about the structural basis that account for the impact of the insert on channel function. Overall, I consider this an interesting paper that represents an important advance in the study of glutamate receptors.

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 #2 (Public Review):

In this manuscript, the authors present a novel interactome focused on human and fly alpha-arrestin family proteins and demonstrate its application in understanding the functions of these proteins. Initially, the authors employed AP/MS analysis, a popular method for mapping protein-protein interactions (PPIs) by isolating protein complexes. Through rigorous statistical and manual quality control procedures, they established two robust interactomes, consisting of 6 baits and 307 prey proteins for humans, and 12 baits and 467 prey proteins for flies. To gain insights into the gene function, the authors investigated the interactors of alpha-arrestin proteins through various functional analyses, such as gene set enrichment. Furthermore, by comparing the interactors between humans and flies, the authors described both conserved and species-specific functions of the alpha-arrestin proteins. To validate their findings, the authors performed several experimental validations for TXNIP and ARRDC5 using ATAC-seq, siRNA knockdown, and tissue staining assays. The experimental results strongly support the predicted functions of the alpha-arrestin proteins and underscore their importance.

Reviewer #3 (Public Review):

Lee, Kyungtae and colleagues have discovered and mapped out alpha-arrestin interactomes in both human and Drosophila through the affinity purification/mass spectrometry and the SAINTexpress method. They found the high confident interactomes, consisting of 390 protein-protein interactions (PPIs) between six human alpha-arrestins and 307 preproteins, as well as 740 PPIs between twelve Drosophila alpha-arrestins and 467 prey proteins. To define and characterize these identified alpha-arrestin interactomes, the team employed a variety of widely recognized bioinformatics tools. These included protein domain enrichment analysis, PANTHER for protein class enrichment, DAVID for subcellular localization analysis, COMPLEAT for the identification of functional complexes, and DIOPT to identify evolutionary conserved interactomes. Through these analyses, they confirmed known alpha-arrestin interactors' role and associated functions such as ubiquitin ligase and protease. Furthermore, they found unexpected biological functions in the newly discovered interactomes, including RNA splicing and helicase, GTPase-activating proteins, ATP synthase. The authors carried out further study into the role of human TXNIP in transcription and epigenetic regulation, as well as the role of ARRDC5 in osteoclast differentiation. This study holds important value as the newly identified alpha-arrestin interactomes are likely aiding functional studies of this group of proteins. Despite the overall support from data for the paper's conclusions, certain elements related to data quantification, interpretation, and presentation demand more detailed explanation and clarification.

1) In Figure 1B, it is shown that human alpha-arrestins were N-GFP tagged (N-terminal) and Drosophila alpha-arrestins were C-GFP (C-terminal). However, the rationale of why the authors used different tags for human and fly proteins was not explained in the main text and methods.<br /> 2) In Figure 2A, there seems to be an error for labeling the GAL4p/GAL80p complex that includes NOTCH2, NOTCH1 and TSC2.<br /> 3) In Figure 5, given that knockdown of TXNIP did not affect the levels and nuclear localization of HDAC2, the authors suggest that TXNIP might modulate HDAC2 activity. However, the ChiP assay suggest a different model - TXNIP-HDAC2 interaction might inhibit the chromatin occupancy of HDAC2, reducing histone deacetylation and increasing global chromatin accessibly. The authors need to propose a model consistent with these sets of all data.<br /> 4) The authors showed that ectopic expression of ARRDC5 increased osteoclast differentiation and function. Does loss of ARDDC5 lead to defects in osteoclast function and fate determination?<br /> 5) From Figure 6D, the authors argued that ARRDC5 overexpression resulted in more V-ATPase signals: however, there is no quantification. Quantification of the confocal images will foster the conclusion. Also, western blots for V-ATPase proteins will provide an alternative way to determine the effects of ARRDC5.<br /> 6) The results from Figure 6D did not support the authors' argument that ARRDC5 might control the membrane localization of the V-ATPase, as bafilomycin is the V-ATPase inhibitor. ARRDC5 knockdown experiments will help to determine whether ARRDC5 can control the membrane localization of the V-ATPase in osteoclast.

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.

Reviewer #2 (Public Review):

Tuller et al. first made the curious observation, that the first ∼30-50 codons in most organisms are encoded by scarce tRNAs and appear to be translated slower than the rest of the coding sequences (CDS). They speculated that this has evolved to pace ribosomes on CDS and prevent ribosome collisions during elongation - the "Ramp" hypothesis. Various aspects of this hypothesis, both factual and in terms of interpretating the results, have been challenged ever since. Sejour et al. present compelling results confirming the slower translation of the first ~40 codons in S. cerevisiae but providing alternative explanation for this phenomenon. Specifically, they show that the higher amino acid sequence divergence of N-terminal ends of proteins and accompanying lower purifying selection (perhaps the result of de novo evolution) is sufficient to explain the prevalence of rare slow codons in these regions. These results are an important contribution in understanding how aspects of evolution of protein coding regions can affect translation efficiency on these sequences and directly challenge the "Ramp" hypothesis proposed by Tuller et al.

I believe the data is presented clearly and the results generally justify the conclusions. I do have one specific concern related to interpretating the data. The authors show that the conservation score of the last 40 codons is not dissimilar to the conservation score of the first 40 (Fig. 4 A & C). They also show that the calculated translational speed of the first 40 codons is significantly lower than the rest of the CDS. At the same time, they show lack of statistically significant decrease of calculated translational speed for the last 40 codons (Figure S1). If the poor conservation of the first 40 codon explains the slower speed of their translation what is the authors' explanation for the absence of statistically significant reduction of calculated translational speed for the last 40 codons?

"Although the reporter is GFP, the N- terminal region of this particular protein is derived from yeast HIS3, not GFP, and has little if any effect on the fluorescence of the GFP fused downstream."

The statement above is logical and reasonable; however, it is not supported by any reference or control experiments. At the very least this fact should be explicitly acknowledged. Also, the RNA levels of reporters were not measured, which means it cannot be categorically concluded that the observed effect is due to changes of translational efficiency. This is an important caveat.

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 #2 (Public Review):

In this manuscript, the authors generated a novel transgenic C. elegans model with inducible expression and secretion of human GFP-tagged human Aβ1-42. Using this model, they investigated the role of ECM in the aggregation of Aβ. They identified collagens that regulate Aβ aggregate formation, and found the metalloproteases ADM-2 modulates ECM and assist in the removal of extracellular Aβ aggregates. The results suggest that ECM composition is critical for Aβ aggregate and removal. These data add in an interesting way to the ongoing discussion on the aggregation and clearance of amyloid through the extracellular matrix. However, some issues remain to be addressed.

1) The authors developed a novel C.elegans model for studying extracellular amyloid beta aggregation and is therefore likely to be taken up broadly by the field. However, the new model should be fully characterized. Throughout the manuscript, the only method to detect amyloid deposition was the GFP fluorescence intensity and morphology, while direct characterization of amyloid aggregates is lacking.

2) A targeted RNA interference (RNAi) screen was used to identify the key regulators of Aβ aggregation and clearance, which is one of the strengths of the study. There should be evidence that RNAi works to knockdown the specific genes. Similarly, there should be evidence indicating that ADM-2 is indeed expressed in the overexpression experiments.

3) It remains unknown whether ADM-2 directly degrades Aβ or facilitates the clearance of Aβ by remoulding the ECM. The effect of ADM-2 on ECM remodeing should be examined.

Reviewer #3 (Public Review):

The authors generated a novel sfGFP::Aβ C. elegans models of AD that expresses Abeta aggregates extracellularly; using this worm model, they identified that a disintegrin and metalloprotease ADM-2, an ortholog of human ADAM9, participated in removing these extracellular aggregates. This worm model may be very useful to the AD field after further characterization.

A novelty of this paper is the generation of a worm model of AD that produces extracellular Abeta aggregates, mimicking one of the two disease-defining pathological features of AD. The authors have also identified a protein which inhibits Abeta aggregation in the AD worm model; if these data are relevant to humans, they may reveal a new druggable target against AD.

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 #2 (Public Review):

During meiosis, mitotic cohesin complexes are replaced by meiosis-specific cohesins to enable a stepwise loss of sister chromatid cohesion. The identity of the cohesin complex is defined by its kleisin subunit. In the early meiotic prophase, the mitotic kleisin Scc1 is replaced by a meiotic counterpart Rec8. C. elegans expresses two additional meiotic kleisins, COH-3 and COH-4; however, how meiotic cohesin complexes differ in their loading and function has been unclear. In this paper, Castellano-Pozo and colleagues unveil their differential dynamics and functions using elegant approaches that include auxin-mediated depletion and TEV-mediated removal of meiotic kleisins. The association of COH-3/4 with chromosomes is dynamic and is under the control of two cohesin regulators, WAPL-1 and SCC-2, while REC-8 remains more stably associated. The authors established that COH-3/4 is involved in maintaining the structural integrity of chromosome axes, whereas the REC-8 cohesin is solely responsible for sister chromatid cohesion throughout meiosis. They further demonstrated the role of REC-8 in the repair of meiotic DSBs.

Overall, this solid work unequivocally establishes the distinct regulation and requirements for REC-8 and COH-3/4 cohesin complexes during C. elegans meiosis. However, as the authors acknowledged, the role of REC-8 cohesins in sister chromatid cohesion has been shown previously using genetic mutants (Crawley et al., 2016 eLife). While the authors highlighted the advantages of removing cohesin subunits in establishing their distinct requirements, many of the results were recapitulated from their previous work (e.g. rec-8; spo-11 and coh-3/4; spo-11). It might be helpful for the readers to compare the results between the two studies and point out uniquely illuminating results.

The role of REC-8 in DNA repair has also been shown in different contexts. Chromosomes fragmentation and DNA bridges are observed in rec-8; syp-1 or rec-8; syp-2 (RNAi) animals (Colaiacovo et al., 2003 Dev Cell; Crawley et al., 2016 eLife), suggesting a role of REC-8 in inter-sister repair. Persistent RAD-51 foci are also observed on asynapsed chromosomes in rec-8 mutants, suggesting a role for REC-8 in DNA repair (Cahoon et al., 2019 Genetics). The authors must cite these papers and discuss the results in the context of prior work.

Reviewer #3 (Public Review):

The study, performed in the animal model C. elegans, aims at characterizing functional differences in the meiosis-specific kleisins, REC-8 and COH-3/4.<br /> The authors conclude that in worms the identity of the kleisin subunit of the cohesin complex determines whether cohesin promotes cohesion, or controls higher-order chromosome structure. COH-3/4 is highly abundant and dynamic and responds to SCC-2 and WAPL-1. In contrast, REC-8 complexes associate stably and in low abundance and are resistant to SCC-2 and WAPL-1 perturbations.

Main points:

This study is a continuation and partially a repeat of a study Castellano-Pozo & Martinez-Perez published in Nat. Comm. 2020, in which they depleted COH-3/4 and REC-8 by injecting TEV and cleaved artificially engineered TEV sites in these kleisins.The results were slightly different though, as the authors concluded: "Disassembly of axial elements requires simultaneous removal of REC-8 and COH-3/4."

The current study uses a degron instead of TEV and SIM to revisit the same result. This time, degradation of COH-3/4 alone, but not of Rec8 alone completely eliminates axial elements. It seems that, if the conclusion is now correct, the previous headline must be incorrect, showing that more care has to be taken in the conclusions.

One new experiment in this study is the degradation of scc-2::AID::GFP. The authors treat the germline with auxin for 14 hours. How long scc-2::AID actually needs for degradation and thus, how long cells actually remain without SCC-2, is unknown. What is definitely needed is a serious analysis of the speed of degradation of Scc2 in the various stages.

It is currently not possible to estimate, as the authors do, how long cells have been without SCC-2. This estimation assumes an immediate depletion of SCC-2.<br /> If this were indeed the case, then depletion intervals should be much shorter, because the important primary phenotypes occur immediately after depletion, not 14 hours later.

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 #2 (Public Review):

This manuscript tackles the important and vexing problem of mapping alleles for TB. It is a really important problem, and this paper presents the largest genetic data set. It does so by amalgamating data from multiple cohorts. The manuscript rightly points out that many studies have not produced reproducible results, and most alleles are population specific, and rarely seen in multiple studies.

1. Authors find a strong HLA associated SNP. They do conduct HLA imputation, but there is little effective fine-mapping. Authors should report which classical alleles are consistent with this allelic association (e.g. which classical alleles are in phase with it). Authors comment on DQA1-0301, but it isn't clear in the main text how significant it is. I think the authors should dig a little deeper. Imputing amino acids and assessing association might be useful. Finding classical alleles that explain the SNP associations and are seen across populations might be useful. If the authors think that the SNP might be a regulatory allele, the authors should make a case for that based on genomic annotations, eQTL analyses etc.<br /> 2. The authors comment on ancestry. Are ancestry components disease associated in any cohort? It might be interesting to demonstrate this.

Reviewer #3 (Public Review):

This paper was a significant and commendable effort, given all the challenges in TB genetics research. It was generally well written and analyses well done. Analytical methods were appropriate. The inclusion of polygenic heritability estimates is also nice to have within this large work. There is also a wealth of supplemental data provided, which will be useful to the field.

However, there are a number of important weaknesses that need to be addressed. These are listed here, and recommended revisions are addressed in the recommendations section:<br /> 1. As the authors point out, one of the challenges in this work is the varying phenotype definitions (diagnosis of TB cases, definition of controls) across all the included genetic studies. Table S1 is critical for this, however it is missing information, and some of the information is unclear. More importantly, the authors state multiple times that there is no evidence of heterogeneity due to these variable phenotype definitions, and that genetic ancestry contributes more to differences in effect sizes between GWAS than study design. However, these two things are confounded - different study designs / phenotype definitions were used in studies of different ancestry.<br /> 2. The polygenic heritability analysis table is not explained very well.<br /> 3. The supplemental data file is not very helpful without some sort of guide. It isn't clear whether the wealth of candidate genes that have been studied in TB were examined in these data. That would be a great benefit of this work.<br /> 4. There needs to be clarity on how unpublished works were sought. In non-genetic meta-analyses, there is usually some detail about a process of contacting authors, etc. There needs to be some assurance that every attempt was made to collect all the relevant data. It is also not clear why family-based analyses could not be included considering that summary statistics were the basis of analysis.<br /> 5. It is rather surprising that only one locus meets genome-wide significance. The authors do explain this well in terms of the ancestry-specific effects driving these results, but it is also surprising that no candidate genes (that had not been discovered in GWAS studies, but were rather studied separately) did not rise to some higher significance threshold.

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 #2 (Public Review):

- Overall, the authors sought to determine whether children with autism spectrum disorder (ASD) or typical development (TD) would both benefit from a 5-day intervention designed to improve numerical problem-solving. They were particularly interested in how learning across training would be associated with pre-post intervention changes in brain activity, measured with functional magnetic resonance imaging (fMRI). They also examined whether brain-behavior associations driven by learning might be moderated by a classic cognitive inflexibility symptom in ASD ("insistence on sameness").

- The study is reasonably well-powered, uses a 5-day evidence-based intervention, and uses a multivariate correlation-based metric for examining neuroplastic changes that may be less susceptible to random variation over time than conventional mass univariate fMRI analyses.

- The study did have some weaknesses that draw into question the specific claims made based on the present set of analyses, as well as limit the generalizability of the findings to the significant proportion of individuals with ASD that are outside of the normative range of general cognitive functioning. The study also found minimal evidence for transfer between trained and untrained mathematical problems, limiting enthusiasm for the intervention itself.

- The majority of the authors' claims were rooted in the data and the team was generally able to accomplish their aims. I am sensitive to the fact that one of the main limitations I noted would have significant ethical implications-i.e. NOT offering potentially beneficial numerical training to children randomized to a sham or control group.

- I think the authors' work will represent a welcome addition to a growing corpus of studies showing similar neuropsychological test performance across several cognitive domains (e.g. learning, memory, proactive cognitive control, etc.) in ASD and TD. However, these relatively preserved cognitive functions still appear to be implemented by unique neural systems and demonstrate unique correlations to clinical symptoms in youth with ASD relative to TD, which may have implications for both educational and clinical contexts.

Reviewer #3 (Public Review):

Liu and colleagues examined learning and brain plasticity in neurotypical children and children with autism. The main findings include autistic children relying more on rule-based versus memory-based learning strategies, altered associations between learning gains and brain plasticity in children with autism, and insistence on sameness as a moderator between brain plasticity and learning in autism. Although the sample size is limited in this study, the findings provide a significant contribution to the field.

The major strengths of this paper include an extensive pre and post training protocol, a detailed methods section, rationale behind the study, investigation of a potential moderator of learning gains and neural plasticity, and investigation of "neural plasticity" in association to learning in autism.

Weaknesses of the study include a small sample size, and some missing information/analyses from the study.

The authors laid out four clear aims of the study. They investigated these aims and the analytic approaches were appropriate.

The paper included significant findings toward better understanding the mechanisms underlying differences in learning strategies and behavior in children diagnosed with autism spectrum disorder. This holds significant value in educational and classroom settings. Further, the investigation of a potential moderator of learning gains and neural plasticity provides a potential mechanism to improve the relationship. Overall, this is a significant contribution to the field.

The autism literature is limited in understanding differences in learning styles and the underlying neural mechanisms of these differences.

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 #2 (Public Review):

Lindsay Fuzzell and her team of researchers have performed an extremely well-executed survey study, which captures a wide spectrum of providers who perform cervical cancer screening in the US. The researchers have captured a vast amount of demographic data in this study in attempting to determine whether cervical cancer screening continued to be reduced in the year immediately after the lockdown period caused by the COVID-19 pandemic.

The authors have uncovered some important and revealing concerns regarding the current state of cancer screening during the public health crisis caused by the COVID-19 pandemic. The most notable implication from their survey was a statistically higher reported reduction in cervical cancer screening in Internal medicine and family medicine providers as well as for community health and safety net clinics. These findings are important as they represent a large portion of primary care and a vulnerable patient population that has been shown to have worse cancer-related outcomes.

This study is more sobering information about the magnitude of ramifications of the COVID-19 pandemic on the US public health system. Decreases in cancer screening may have lasting implications for cancer-related mortality for many years to come. The implications of not going back to pre-pandemic cancer screening rates are daunting, to say the least.

The scope of this survey, the amount of data attained, and the sound methodology of the data acquisition and statistical analysis are the strengths of this study. Weaknesses are inherent to the study relying on survey answers rather than data from cervical cancer screening registries. Reporting biases are complex in surveys and answers given may not reflect the true rates of screening. The authors have also reported a disproportionate and statistically significant reduction in cervical cancer screening for Black and Asian providers. I would conclude more cautiously here with confidence intervals crossing one in both for this statistical analysis.

Overall, this is a survey study with a great magnitude, which has important implications for cancer screening and public health in the US.

Reviewer #3 (Public Review):

In this paper, the authors report cervical cancer screening practice during the covid pandemic in the US from the perspective of health professionals (HPs). Two methods were used: survey and regression analysis, and qualitative interviews. Analyses indicated that older, non-White, internal medicine, and family medicine clinicians and those practicing in community health centers had higher odds of reporting reduced screening. Interviews highlighted disruptions of services and a lack of tracking systems.<br /> The strengths of the paper are mainly i) using three different sources of HPs' recruitment and ii) being able to recruit a large number of participants in both survey and interviews and iii) the demographic characteristics of the interviewees were similar to those of the participants of the survey.

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 #2 (Public Review):

The authors seek to explore the mechanistic basis for enhancement binding to DNA by SsrB at lower pH. Their evidence supports the conclusions listed in the Evaluation Summary. Multiple additional conclusions are not supported by the data as described below:

1. The experiment displayed in Figure 5 is deeply flawed for multiple reasons and should be removed from the manuscript entirely. A Michaelis-Menton plot compares the initial rate of a reaction versus substrate concentration. Instead, the authors plotted the fraction of SsrB that is phosphorylated after 10 minutes at various substrate concentrations. Such a plot must reach saturation because the enzyme is limiting, whereas it is not always possible to achieve saturation in a genuine Michaelis-Menton plot. Because no reaction rates were measured, it is not possible to derive kcat values from the data. There are also at least three potential problems with the reaction conditions themselves: (i) Increasing the concentration of the phosphoramidite substrate increased ionic strength. Response regulator active sites contain many charged moieties and autophosphorylation of at least one response regulator (CheY) is inhibited by increasing ionic strength (PMID 10471801). (ii) Autophosphorylation with phosphoramidite is pH dependent because the nitrogen on the donor must be protonated to form a good leaving group (PMID 9398221). The pKa of phosphoramidite is ~8. Therefore, the fraction of phosphoramidite that is reactive (i.e., protonated) will be very different at pH 6.1 and 7.4. (iii) Response regulator autophosphorylation absolutely depends on the presence of a divalent metal ion (usually Mg2+) in the active site (PMID 2201404). There is no guarantee that the 20 mM Mg2+ included in the reaction is sufficient to saturate SsrB. Furthermore, as the authors themselves note, the amino acid at SsrB position 12 is likely to affect the affinity of Mg2+ binding. Therefore, the fraction of SsrB that is reactive (i.e. has Mg2+ bound) may differ between wildtype and the H12Q mutant, and/or between wildtype at different pHs (because the protonation state of His12 changes).

2. The data in Figures 1abcd and 3de are clearly sigmoidal rather than hyperbolic, indicating cooperativity. However, there are insufficient data points between the upper and lower bounds to accurately calculate the Hill coefficient or KD values. This limitation of the data means that comparisons of apparent Hill coefficient or KD values under different conditions cannot be the basis of credible conclusions.

3. There are hundreds of receiver domain structures in PDB. There is some variation, but to a first approximation receiver domain structures, all exhibit an (alpha/beta)5 fold. The structure of SsrB predicted by i-TASSER breaks the standard beta-2 strand into two parts, which throws off the numbering for subsequent beta strands. Given the highly conserved receiver domain fold, I am skeptical that the predicted i-TASSER structure is correct or adds any value to the manuscript. If the authors wish to retain the structure of the manuscript, then they should point out the unusual feature and the consequence of strand numbering.

4. The detailed predictions of active site structure in Supplementary Figure 5 are not physiologically relevant because Mg2+ was not included in the simulation. The presence of a divalent cation binding to Asp10 and Asp11 is likely to substantially alter interactions between Asp 10, Asp11, His12, and Lys109.

5. The authors present an AlphaFold model of an SsrB dimer, and note that His12 is at the dimer interface. However, the authors also believe that a higher-order oligomer of SsrB binds to DNA in a pH-dependent manner. Do the authors have any suggestions or informed speculation about how His12 might affect higher-order oligomerization than dimerization?

Reviewer #3 (Public Review):

Once inside a cellular vacuole, Salmonella senses the low pH and activates the transcriptional regulator SsrB to induce expression of the Salmonella pathogenicity island 2 genes that are essential for intracellular survival and replication inside the host. This study investigates the mechanisms by which SsrB senses the pH changes, and with a series of elegant experiments identify a conserved residue in the receiver domain, His12, as essential for pH sensing and Salmonella virulence.

Overall, this study identifies an important mechanism of pathogen virulence, which could be targeted to control intracellular replication of the pathogen. The experiments are well conducted, the manuscript is clearly written, and the data are convincing and well presented. The authors perform a logical and detailed analysis of several portions of SsrB to finally identify His12 as a key residue for pH sensing. This was not an easy task. Moreover, the fact that a single amino acid appears to be so important for SsrB pH sensing and SsrB phosphorylation is an important finding for potentially targeting SsrB and inhibiting Salmonella virulence.

Reviewer #3 (Public Review):

The work proposes a model of neural information processing based on a 'synergistic global workspace,' which processes information in three principal steps: a gatekeeping step (information gathering), an information integration step, and finally, a broadcasting step. The authors determined the synergistic global workspace based on previous work and extended the role of its elements using 100 fMRI recordings of the resting state of healthy participants of the HCP. The authors then applied network analysis and two different measures of information integration to examine changes in reduced states of consciousness (such as anesthesia and after-coma disorders of consciousness). They provided an interpretation of the results in terms of the proposed model of brain information processing, which could be helpful to be implemented in other states of consciousness and related to perturbative approaches. Overall, I found the manuscript to be well-organized, and the results are interesting and could be informative for a broad range of literature, suggesting interesting new ideas for the field to explore. However, there are some points that the authors could clarify to strengthen the paper. Key points include:

1. The work strongly relies on the identification of the regions belonging to the synergistic global workspace, which was primarily proposed and computed in a previous paper by the authors. It would be great if this computation could be included in a more explicit way in this manuscript to make it self-contained. Maybe include some table or figure being explicit in the Gradient of redundancy-to-synergy relative importance results and procedure.

2. It would be beneficial if the authors could provide further explanation regarding the differences in the procedure for selecting the workspace and its role within the proposed architecture. For instance, why does one case uses the strength of the nodes while the other case uses the participation coefficient? It would be interesting to explore what would happen if the workspace was defined directly using the participation coefficient instead of the strength. Additionally, what impact would it have on the procedure if a different selection of modules was used? For example, instead of using the RSN, other criteria, such as modularity algorithms, PCA, Hidden Markov Models, Variational Autoencoders, etc., could be considered. The main point of my question is that, probably, the RSN are quite redundant networks and other methods, as PCA generates independent networks. It would be helpful if the authors could offer some comments on their intuition regarding these points without necessarily requiring additional computations.

3. The authors acknowledged the potential relevance of perturbative approaches in terms of PCI and quantification of consciousness. It would be valuable if the authors could also discuss perturbative approaches in relation to inducing transitions between brain states. In other words, since the authors investigate disorders of consciousness where interventions could provide insights into treatment, as suggested by computational and experimental works, it would be interesting to explore the relationship between the synergistic workspace and its modifications from this perspective as well.

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 #2 (Public Review):

The authors analysed functional MRI recordings of brain activity at rest, using state-of-the-art methods that reveal the diverse ways in which the information can be integrated in the brain. In this way, they found brain areas that act as (synergistic) gateways for the 'global workspace', where conscious access to information or cognition would occur, and brain areas that serve as (redundant) broadcasters from the global workspace to the rest of the brain. The results are compelling and consisting with the already assumed role of several networks and areas within the Global Neuronal Workspace framework. Thus, in a way, this work comes to stress the role of synergy and redundancy as complementary information processing modes, which fulfill different roles in the big context of information integration.

In addition, to prove that the identified high-order interactions are relevant to the phenomenon of consciousness, the same analysis was performed in subjects under anesthesia or with disorders of consciousness (DOC), showing that indeed the loss of consciousness is associated with a deficient integration of information within the gateway regions.

However, there is something confusing in the redundancy and synergy matrices shown in Figure 2. These are pair-wise matrices, where the PID was applied to identify high-order interactions between pairs of brain regions. I understand that synergy and redundancy are assessed in the way the brain areas integrate information in time, but it is still a little contradictory to speak about high-order in pairs of areas. When talking about a "synergistic core", one expects that all or most of the areas belonging to that core are simultaneously involved in some (synergistic) information processing, and I do not see this being assessed with the currently presented methodology. Similarly, if redundancy is assessed only in pairs of areas, it may be due to simple correlations between them, so it is not a high-order interaction. Perhaps it is a matter of language, or about the expectations that the word 'synergy' evokes, so a clarification about this issue is needed. Moreover, as the rest of the work is based on these 'pair-wise' redundancy and synergy matrices, it becomes a significative issue.

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 #2 (Public Review):

This is a very interesting paper with several important findings related to the working mechanism of the cartwheel cells (CWC) in the dorsal cochlear nucleus (DCN). These cells generate spontaneous firing that is inhibited by the activation of α2-adrenergic receptors, which also enhances the synaptic strength in the cells, but the mechanisms underlying the spontaneous firing and the dual regulation by α2-adrenergic receptor activation have remained elusive. By recording these cells with the NALCN sodium-leak channel conditionally knocked, the authors discovered that both the spontaneous firing and the regulation by noradrenaline (NA) require NALCN. Mechanistically, the authors found that activation of the adrenergic receptor or GABAB receptor inhibits NALCN. Interestingly, these receptor activations also suppress the low [Ca2+] "activation" of NALCN currents, suggesting crosstalk between the pathways. The finding of such dominant contribution of the NALCN conductance to the regulation of firing by NA is somewhat surprising considering that NA is known to regulate K+ conductances in many other neurons.

The studies reveal the molecular mechanisms underlying well known regulations of the neuronal processes in the auditory pathway. The results will be important to the understanding of auditory information processing in particular, and, more generally, to the understanding of the regulation of inhibitory neurons and ion channels. The results are convincing and are clearly presented.

Reviewer #3 (Public Review):

The study by Ngodup and colleagues describes the contribution of sodium leak NALCN conductance on the effects of noradrenaline on cartwheel interneurons of the DCN. The manuscript is very well-written and the experiments are well-controlled. The scope of the study is of high biological relevance and recapitulates a primary finding of the Khaliq lab (Philippart et al., eLife, 2018) in ventral midbrain dopamine neurons, that Gi/o-coupled receptors inhibit NALCN current to reduce neuronal excitability. Together these studies provide unequivocable evidence for NALCN as a downstream target of these receptors. There are no major concerns. I have only minor suggestions:

Minor<br /> 1. As introduced in the introduction, NALCN is inhibited by extracellular calcium which has led to some discourse of the relevance of NALCN when recorded in 0.1 mM calcium. A strength of this study is the effect of NA on NALCN is recorded in physiological levels of calcium (1.2 mM). I suggest including the concentration of extracellular calcium in the aCSF in the Results section instead of relying on the reader to look to the Methods.

2. It would be interesting to include the basal membrane properties of the KO compared to wildtype, including membrane resistance and resting membrane potential. From the example recording in Figure 2, one might think that the KOs have lower membrane resistance, so it is interesting that the 2 mV hyperpolarization produced similar effects on rheobase. In addition, from the example in Figure 2G, it appears that NA has an effect on firing frequency with large current injection in the KO. Is this true in grouped data and if so, is there any speculation into how this occurs?

3. Please expand on the rationale for why GABAB and alpha2 must be physically close to NALCN. To my knowledge, the mechanism by which these receptors inhibit NALCN is not known. Must it be membrane-delimited?

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 #2 (Public Review):

Previous studies have shown that two hair cell transcription factors, Pou4f3 and Gfi1, are both necessary for the survival of cochlear hair cells, and that Gfi1 is regulated by Pou4f3. The authors have previously also shown that mosaic inactivation of the RNA-binding protein RBM24 leads to outer hair cell death.

In the present study, the authors show that hair cells die in Pou4f3 and Gfi1 mutant mice. They show that Gfi1 is regulated by Pou4f3. Both these observations have been published before. They then show that RBM24 is absent in Pou4f3 knockouts, but not Gfi1 knockouts. They ectopically activate RMB24 in the hair cells of Pou4f3 knockouts, but this does not rescue the hair cell death. Finally, the authors validate three RMB24 enhancers that are active in young hair cells and which have been previously shown to bind Pou4f3.

The experiments are well-executed and the data are clear. The results support the conclusions of the paper.

Much of the work in the paper has been reported before. The result that hair cell transcription factors operate in a network, with some transcription factors activating only a subset of hair cell genes, is an expected result. Since RMB24 is only one of many genes regulated directly by Pou4f3, it is not surprising that it cannot rescue the Pou4f3 knockout hair cell degeneration.

The identification of new hair cell enhancers may be of use to investigators wishing to express genes in hair cells.

In sum, this work, although carefully performed, does not shed significant new light on our understanding of hair cell development or survival.

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 #2 (Public Review):

Kelly et al. strategically leverage state-of-the art scRNA-seq methods combined with unique strengths of the zebrafish larval model to identify gene expression patterns that underlie the different functional output of different neuronal circuits that converge on similar muscle groups. The results lead to the identification of ion channel and synapse associated genes that distinguish the neuronal components of a fast circuit mediating escape behavior from a rhythmic circuit mediating graded swimming.

The authors develop methods for isolation of single spinal cord neurons from 4 day post fertilization (dpf) zebrafish larvae. The 4 dpf neuronal circuits mediating escape vs. rhythmic swimming behavior have been extensively characterized allowing knowledge of the specific motor neuron and interneuron populations involved in one vs. the other circuit. (Work from the authors' research group has contributed to this strong starting point for this study.)

The transcriptomic analyses lead to the identification of clusters of cells sharing significant gene expression that distinguishes them from other clusters. Using well-known neuron subtype specific markers, the authors are able to assign a specific neuronal identity to about 2/3 of the cluster. Moreover, one other cluster results in the recognition in zebrafish of a neuronal cell type identified in the mammalian spinal cord, v0c, that they confirm to be present in zebrafish using solid markers. In addition, the results show that the zebrafish v0c population expressed markers of both cholinergic and glutamatergic neurons, while the mammalian v0c population is known to be cholinergic. (It is not clear whether the possibility that mammalian v0c neurons also express glutamatergic markers has been specifically tested, but it seems, at present, there is no evidence to suggest that might be the case.)

To zoom in on the question of molecular differences between the fast vs. rhythmic circuits, the authors focus on motor neurons as two different populations of neurons are involved in each circuit. (Along the way, they also identify markers that mark different subtypes of motor neurons.) They find that primary motor neurons (PMNs) involved in the fast circuit express a distinguishing cassette of ion channel and synapse associated genes. Moreover, the cassette of genes also is expressed by interneurons that function in the fast circuit. The results are illuminating and set the stage for many future exacting experiments.

As is true for significant work, the results open up and permit yet more rigorous and strategic analyses, running the gamut from specific molecules to behavior, of the circuit mechanisms underlying unique behaviors.

Overall, the work is carried out to high rigorous standards and the vast majority of conclusions are strongly supported by the results. However, there are a few instances of potential over-interpretation and points that could be further clarified/discussed:

1 - lines 412-414. The authors conclude that "Most importantly, and as detailed below, our scRNA seq revealed the ion channel and synaptic genes that serve to match specific neuronal function to behavior." That the authors have identified a gene cassette that distinguishes neurons of the fast escape circuit is a laudable finding. However, at this stage, to say that this gene cassette is the basis for unique circuit function and resultant behavior is a well-supported hypothesis that requires rigorous testing and not yet a solid conclusion. (Maybe that is what the authors meant, and I have misinterpreted the sentence.)

2 - lines 323-324: Given that ~ 6 hrs separates PMN from SMN birthdates (Myers et al. 1986) and that the study was done using 4dpf larval tissue, the possibility that the higher level of expression of transcription factors and RNA-biding factors in SMNs reflects "the less well differentiated state that accompanies the later birthdate of the SMns" seems unlikely.

3 - Fig 5 and Sup Fig 1:The authors mention that the unidentified cluster in the motor neuron set shares markers with non-skeletal muscle. I realize that this cluster is tangential to their focus. However, given that this cluster predominantly arises from the FACS sorted cells, it is worth considering that the cells might correspond to the pancreas.

4 - lines 113-115 and Fig. 1: The authors indicate that three clusters reflect cells that have mixed glial and neuronal cell expression. Is there any possibility that in a few instances, in the final single cell capture, that two rather than one cell were collected? (Again, not a major focus of the study but the cluster is commented on.)

Finally, as the transcriptomic information about glial cells will be of interest to many in the field, the authors are to be commended for depositng the data in congratulations to the authors for depositing the data in the publicly accessible Gene Expression Omnibus.

Reviewer #3 (Public Review):

Functional and anatomical studies of spinal circuitry in vertebrates have formed the basis of our understanding of neuronal control of movements. Larval zebrafish provide a simplified system for deciphering spinal circuitry. In this manuscript, the authors performed scRNAseq on spinal cord neurons in larval zebrafish, identifying major classes of neuronal and glial types. Through transcriptome analysis, they validated several key interneuron types previously implicated in zebrafish locomotion circuitry. The authors went beyond identifying transcriptional markers and explored synaptic molecules associated with the strength of motor output. They discovered molecular distinctions causally related to the unique physiology of primary motoneuron (PMn) function, which involves providing strong synaptic outputs for escapes and fast swimming. They defined functional 'cassettes' comprising specific combinations of voltage-dependent ion channel types and synaptic proteins, likely responsible for generating maximal motor outputs.

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 #2 (Public Review):

This article is focused on investigating incremental speech processing, as it pertains to building higher-order syntactic structure. This is an important question because speech processing in general is lesser studied as compared to reading, and syntactic processes are lesser studied than lower-level sensory processes. The authors claim to shed light on the neural processes that build structured linguistic interpretations. The authors apply modern analysis techniques, and use state-of-the-art large language models in order to facilitate this investigation. They apply this to a cleverly designed experimental paradigm of EMEG data, and compare neural responses of human participants to the activation profiles in different layers of the BERT language model.

Strengths:

[1] The study aims to investigate an under-explored aspect of language processing, namely syntactic operations during speech processing

[2] The study is taking advantage of technological advancements in large language models, while also taking linguistic theory into account in building the hypothesis space

[3] The data combine EEG and MEG, which provides a valuable spatio-temporally resolved dataset

[4] The use of behavioural validation of high/low transitive was an elegant demonstration of the validity of their stimuli

Weaknesses:

[1] The manuscript is quite hard to understand, even for someone well-versed in both linguistic theory and LLMs. The questions, design, analysis approach, and conclusions are all quite dense and not easy to follow.

[2] The analyses end up seeming overly complicated when the underlying difference between sentence types is a simple categorical distinction between high and low transitivity. I am not sure why tree depth and BERT are being used to evaluate the degree to which a sentence is being processed as active or passive. If this is necessary, it would be helpful for the authors to motivate this more clearly.

[3] The main data result figures comparing BERT and the EMEG brain data are hard to evaluate because only t-values are provided, and those, only for significant clusters. It would be helpful to see the full 600 ms time course of rho values, with error bars across subjects, to really be able to evaluate it visually. This is a summary statistic that is very far away from the input data

[4] Some details are omitted or not explained clearly. For example, how was BERT masked to give word-by-word predictions? In its default form, I believe that BERT takes in a set of words before and after the keyword that it is predicting. But I assume that here the model is not allowed to see linguistic information in the future. How were the auditory stimuli recorded? Was it continuous speech or silences between each word? How was prosody controlled? Was it a natural speaker or a speech synthesiser?

It is difficult for me to fully assess the extent to which the authors achieved their aims, because I am missing important information about the setup of the experiment and the distribution of test statistics across subjects.

Reviewer #3 (Public Review):

Syntactic parsing is a highly dynamic process: When an incoming word is inconsistent with the presumed syntactic structure, the brain has to reanalyze the sentence and construct an alternative syntactic structure. Since syntactic parsing is a hidden process, it is challenging to describe the syntactic structure a listener internally constructs at each time moment. Here, the authors overcome this problem by (1) asking listeners to complete a sentence at some break point to probe the syntactic structure mentally constructed at the break point, and (2) using a DNN model to extract the most likely structure a listener may extract at a time moment. After obtaining incremental syntactic features using the DNN model, the authors analyze how these syntactic features are represented in the brain using MEG.

Although the analyses are detailed, the current conclusion needs to be further specified. For example, in the abstract, it is concluded that "Our results reveal a detailed picture of the neurobiological processes involved in building structured interpretations through the integration across multifaceted constraints". The readers may remain puzzled after reading this conclusion.

Similarly, for the second part of the conclusion, i.e., "including an extensive set of bilateral brain regions beyond the classical fronto-temporal language system, which sheds light on the distributed nature of language processing in the brain."<br /> The more extensive cortical activation may be attributed to the spatial resolution of MEG, and it is quite well acknowledged that language processing is quite distributive in the brain.

The authors should also discuss:

(1) individual differences (whether the BERT representation is a good enough approximation of the mental representation of individual listeners).

(2) parallel parsing (I think the framework here should allow the brain to maintain parallel representations of different syntactic structures but the analysis does not consider parallel representations).

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 #2 (Public Review):

Language skills are traditionally associated with a network of brain regions in the left hemisphere. In this intriguing study, Esteban Villar-Rodríguez and collaborators examined if atypical hemispheric lateralization for language determines the functional and structural organisation of the network for inhibitory control as well as its relationship with schizotypy and autistic spectrum traits. The results suggest that individuals who have atypical lateralisation of the language function have also an atypical (mirrored) lateralisation of the inhibitory control network, compared to the typical group (individuals with left-lateralised language function). Furthermore, the atypical organization of language production is associated with a greater white matter volume of the corpus callosum, and atypical lateralization of inhibitory control is related to a higher interhemispheric functional coupling of the IFC, suggesting a link between atypical functional lateralisation (language and inhibitory control) and structural and functional changes in the brain.

This study also provides interesting evidence on how atypical language lateralisation impacts some aspects of language behaviour (reading), i.e., atypical lateralization predicts worse reading accuracy. Furthermore, the results suggest an association between atypical lateralization and increased schizotypy and autistic traits.

The strength of this work is that it presents a collection of measurements on the same individuals (including task-related behavioural, functional and structural neuroimaging measures) to reveal if (and how) atypical language lateralisation might be associated with: (1) atypical neural organisation of other non-linguistic cognitive systems, (2) behavioural performance associated with language tasks, and finally (3) personality traits. As such the results presented in this manuscript have the potential to be informative for various disciplines. For instance, if clarifications/corrections are provided (see below), the results might provide some insight into the role of the right hemisphere for language processing in healthy individuals as well as patient populations with acquired linguistic impairment including stroke and dementia.

One important weakness of this manuscript is that several areas, including the characteristics of participants tested, and the hypotheses/predictions, are underspecified or incomplete. Furthermore, in some cases the types of analysis do not seem to be appropriate for addressing the questions of the present study and very little explanation for those choices is provided.

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 #2 (Public Review):

The manuscript by Est and Murphy tested the feasibility of using brain microvascular endothelial-like cells (BMECs) derived from induced pluripotent stem cells (iPSCs) as a model for studying retinoid uptake and transport across the blood-brain barrier (BBB). Establishing this experimental model is an important step towards obtaining greater mechanistic insight into the specificity of retinol trafficking between blood and retinoid-dependent tissues. The authors validated the iPSC-derived BMECs by detecting the expression of specific protein markers for BBB. They also demonstrated that BMECs form a tight barrier when cultured in a Transwell chamber, allowing for the quantification of permeability across the cells rather than through paracellular leakage. Finally, they confirmed the expression of the transporter (STRA6), binding protein (CRBP1), and enzyme (LRAT), which are key elements of the molecular machinery involved in the cellular uptake of circulating retinol. The carefully established model of the human BBB served as an experimental platform for the authors to investigate the uptake and transcellular transport of retinol. For this purpose, they compared the kinetics and efficiency of retinoid accumulation delivered to the cell as free retinol, retinol bound to serum retinol-binding protein (RBP), or retinol-RBP in complex with transthyretin (TTR), a physiological binding partner for retinol-loaded RBP.

Although the development and thorough characterization of the experimental model of the BBB have great value and meaningfully contribute to ongoing efforts to better understand the mechanisms of retinoid homeostasis, the premise and interpretation of cellular uptake appear controversial. In particular:

1. The authors assume that there is a significant fraction of free ROL, 20% for ROH/RBP and 7% for RBP/TTR complexes (summarized in Table 1). This implies that at the physiological concentration of ROH/RBP in the plasma of 2 uM, free ROL represents 0.4 uM. However, the concentration of free ROL is limited by its poor solubility in the aqueous phase, which is around 0.06 uM (Szuts EZ, 1991, Arch Biochem Biophys). Moreover, taking into account the large concentration of other potential nonspecific carriers for lipids, it is safe to assume that there is virtually no free ROH in the plasma. There is also an important physiological reason for the limited amount of free ROL. Its rapid and nonspecific partition into cells (also observed in this study) would work against the highly specific RBP/STRA6-dependent ROH uptake pathway, undermining its physiological function.

2. The advantage of the experimental system used in this report is that it allows for the assessment of the permeability across BMECs. Interestingly, the basolateral accumulation of ROH represented only a small fraction (1 - 1.5%) of the total ROH taken up by the cells. Moreover, the overall permeability was comparable regardless of the source of ROL added at the apical side. However, a question remains: would the outcome of the experiment be different if the basolateral chamber contained an ROH acceptor (retinol-binding proteins) rather than Hank's balanced salt solution, to which the partition of ROL is limited by its water solubility? In fact, the maximum concentration of ROH on the basolateral side did not exceed 40 nM (Fig 5D and 7C), which is roughly the maximum water solubility of ROH. Thus, this experimental design limits extrapolation of the data to in vivo conditions.

3. The authors claim that transthyretin (TTR) increases BMECs permeability when compared to ROH/RBP. However, the mechanistic explanation for this phenomenon remains unclear. Do the authors imply the presence of a putative TTR receptor whose signaling could affect the efflux of ROL at the basolateral side of BMECs? TTR is an ubiquitous plasma protein. The concentration of TTR is tightly regulated and maintained between 300 - 330 mg/L. Therefore, it is questionable how TTR can serve as a signaling molecule modulating retinoid homeostasis in the brain.

4. Although overexpression of LRAT in response to increased uptake of ROH is well-documented, the postulate that TTR stimulates the expression of LRAT in an RBP-independent manner is puzzling, for the reasons mentioned in point 3. Moreover, LRAT is a highly efficient enzyme that operates under physiological conditions with substrate concentrations below the Km value. The rate of esterification is primarily limited by the intracellular transport of ROH to the ER. Therefore, without kinetic studies, it is unclear whether an increased number of LRAT copies (x2) would have a significant effect on the rate of accumulation of retinyl esters (REs).

5. The conclusion that cellular uptake of ROH is biphasic appears to be correct. However, the proposed interpretation of the mechanistic principles of this phenomenon is oversimplified. It assumes that loading CRBP1 with ROL to its capacity triggers the synthesis of REs. However, the saturation of CRBP1 with ROH is not required for REs formation. In fact, studies on CRBP1-deficient mice indicate that this protein is not necessary for the efficient esterification of ROL but rather affects the intracellular turnover of retinoids. It is likely that with increasing concentration of ROH, the specific and controlled mechanism of intracellular retinoid transport becomes saturated, allowing for spontaneous diffusion-driven partitioning of retinoids within cells.

Additional technical issues that could affect the experimental outcomes:

1. The formation of the ROH/RBP-TTR complex should be confirmed and purified using gel filtration to separate free TTR and ROH/RBP. Only fractions containing the complex should be used in the experiments. Assuming that the complex is formed with 100% efficiency is overly optimistic.

2. Reloading RBP with isotopically labeled ROH requires an additional purification step. Stripping ROL from the ROH/RBP complex with organic solvent (diethyl ether) is appropriate but relatively harsh, causing partial unfolding of a fraction of RBP. Therefore, assuming that 100% of stripped RBP remains functional and can be reloaded with ROH is inaccurate. Reloading apo-RBP with a stoichiometric amount of ROH without an additional purification step (e.g., ion exchanger) leads to an excess of free ROL and/or its nonspecific association with nonfunctional RBP fractions. Measuring absorbance at 330 nm is not sufficient proof of binding since free ROH also absorbs at the same wavelength.

Reviewer #3 (Public Review):

Vitamin A is critical for the development of the brain and for neuronal function and plasticity, however the mechanisms responsible for the uptake of retinol across the blood brain barrier (BBB) are currently not known. The authors investigate vitamin A uptake across the blood brain barrier using an in vitro model based on endothelial cells differentiated from human derived induced pluripotent stem cells. Using recombinant cargo proteins and radioactive tracers the authors then propose a mechanism and a kinetic model for the uptake of retinol across the BBB that requires serum retinol binding protein 4 (RBP4 or RBP) and its receptor stimulated by retinoic acid 6 (STRA6). The results support a concentration dependent mechanism of transport combining a rapid fluid-phase retinol and a slower directed RBP-complexed retinol across the BBB. The data also hint at the potential regulatory roles of TTR on this process independent of its interaction with RBP.

Strengths:<br /> The studies are rigorous and careful and the authors consider free retinol uptake from the fluid-phase in addition to evaluating RBP-TTR and RBP-STRA6 interactions.<br /> The antibody to STRA6 is validated.<br /> The experiments performed are clearly described.

Weaknesses:<br /> The results presented do not offer significant new information regarding the uptake of retinol by tissues beyond what is known and published using genetic, structural and biochemical approaches.<br /> The use of the iPSC-derived BBB model is potentially interesting but this could have been complemented by a thorough genetic dissection of the cellular factors required for the uptake, transcellular transport, and secretion of retinol by the brain endothelial cells.<br /> The conclusions derived are not well supported by the data presented.<br /> It is difficult to infer a mechanism or to derive a meaningful conclusion regarding the in vivo relevance of the results presented.

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 #2 (Public Review):

The authors tried to diagnose cancers and pinpoint tissues of origin using cfDNA. To achieve the goal, they developed a framework to assess methylation, CNA, and other genomic features. They established discovery and validation cohorts for systematic assessment and successfully achieved robust prediction power.

Still, there are places for improvement. The diagnostic effect can be maximized if their framework works well in early stage cancer patients. According to Table 1, about 10% of the participants are stage I. Do these cancers also perform well as compared to late stage cancers?

Can authors show a systematic comparison of their method to other previous methods to summarize what their algorithm can achieve compared to others.

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 #2 (Public Review):

Ghasemahmad et al. report findings on the influence of salient vocalization playback, sex, and previous experience, on mice behaviors, and on cholinergic and dopaminergic neuromodulation within the basolateral amygdala (BLA). Specifically, the authors played back mice vocalizations recorded during two behaviors of opposite valence (mating and restraint) and measured the behaviors and release of acetylcholine (ACh), dopamine (DA), and serotonin in the BLA triggered in response to those sounds.

Strength: The authors identified that mating and restraint sounds have a differential impact on cholinergic and dopaminergic release. In male mice, these two distinct vocalizations exert an opposite effect on the release of ACh and DA. Mating sounds elicited a decrease of Ach release and an increase of DA release. Conversely, restraint sounds induced an increase in ACh release and a trend to decrease in DA. These neurotransmission changes were different in estrus females for whom the mating vocalization resulted in an increase of both DA and ACh release.

Weaknesses: The behavioral analysis and results remain elusive, and although addressing interesting questions, the study contains major flaws, and the interpretations are overstating the findings.

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.

Reviewer #2 (Public Review):

In the manuscript, the authors highlighted the importance of T-cell receptor (TCR) analysis and the lack of amino acid embedding methods specific to this domain. The authors proposed a novel bi-directional context-aware amino acid embedding method, catELMo, adapted from ELMo (Embeddings from Language Models), specifically designed for TCR analysis. The model is trained on TCR sequences from seven projects in the ImmunoSEQ database, instead of the generic protein sequences. They assessed the effectiveness of the proposed method in both TCR-epitope binding affinity prediction, a supervised task, and the unsupervised TCR clustering task. The results demonstrate significant performance improvements compared to existing embedding models. The authors also aimed to provide and discuss their observations on embedding model design for TCR analysis: 1) Models specifically trained on TCR sequences have better performance than models trained on general protein sequences for the TCR-related tasks; and 2) The proposed ELMo-based method outperforms TCR embedding models with BERT-based architecture. The authors also provided a comprehensive introduction and investigation of existing amino acid embedding methods. Overall, the paper is well-written and well-organized.

The work has originality and has potential prospects for immune response analysis and immunotherapy exploration. TCR-epitope pair binding plays a significant role in T cell regulation. Accurate prediction and analysis of TCR sequences are crucial for comprehending the biological foundations of binding mechanisms and advancing immunotherapy approaches. The proposed embedding method presents an efficient context-aware mathematical representation for TCR sequences, enabling the capture and analysis of their structural and functional characteristics. This method serves as a valuable tool for various downstream analyses and is essential for a wide range of applications.

Reviewer #3 (Public Review):

Here, the authors trained catElMo, a new context-aware embedding model for TCRβ CDR3 amino acid sequences for TCR-epitope specificity and clustering tasks. This method benchmarked existing work in protein and TCR language models and investigated the role that model architecture plays in the prediction performance. The major strength of this paper is comprehensively evaluating common model architectures used, which is useful for practitioners in the field. However, some key details were missing to assess whether the benchmarking study is a fair comparison between different architectures. Major comments are as follows:

- It is not clear why epitope sequences were also embedded using catELMo for the binding prediction task. Because catELMO is trained on TCRβ CDR3 sequences, it's not clear what benefit would come from this embedding. Were the other embedding models under comparison also applied to both the TCR and epitope sequences? It may be a fairer comparison if a single method is used to encode epitope sequence for all models under comparison, so that the performance reflects the quality of the TCR embedding only.<br /> - The tSNE visualization in Figure 3 is helpful. It makes sense that the last hidden layer features separate well by binding labels for the better performing models. However, it would be useful to know if positive and negative TCRs for each epitope group also separate well in the original TCR embedding space. In other words, how much separation between these groups is due to the neural network vs just the embedding?<br /> - To generate negative samples, the author randomly paired TCRs from healthy subjects to different epitopes. This could produce issues with false negatives if the epitopes used are common. Is there an estimate for how frequently there might be false negatives for those commonly occurring epitopes that most populations might also have been exposed to? Could there be a potential batch effect for the negative sampled TCR that confounds with the performance evaluation?<br /> - Most of the models being compared were trained on general proteins rather than TCR sequences. This makes their comparison to catELMO questionable since it's not clear if the improvement is due to the training data or architecture. The authors partially addressed this with BERT-based models in section 2.4. This concern would be more fully addressed if the authors also trained the Doc2vec model (Yang et al, Figure 2) on TCR sequences as baseline models instead of using the original models trained on general protein sequences. This would make clear the strength of context-aware embeddings if the performance is worse than catElmo and BERT.

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 #2 (Public Review):

In this project, Schmidig, Ruch and Henke examined whether word pairs that were presented during slow-wave sleep would leave a detectable memory trace 12 and 36 hours later. Such an effect was found, as participants showed a bias to categorize pseudowords according to a familiar word that they were paired with during slow-wave sleep. This behavior was not accompanied by any sign of conscious understanding of why the judgment was made, and so demonstrates that long-term memory can be formed even without conscious access to the presented content. Unconscious learning occurred when pairs were presented during troughs but not during peaks of slow-wave oscillations. Differences in brain responses to the two types of presentation schemes, and between word pairs that were later correctly- vs. incorrectly-judged, suggest a potential mechanism for how such deep-sleep learning can occur.

The results are very interesting, and they are based on solid methods and analyses. Results largely support the authors' conclusions, but I felt that there were a few points in which conclusions were not entirely convincing:

1) As a control for the critical stimuli in this study, authors used a single pseudoword simultaneously played to both ears. This control condition (CC) differs from the experimental condition (EC) in a few dimensions, among them: amount of information provided, binaural coherence and word familiarity. These differences make it hard to conclude that the higher theta and spindle power observed for EC over CC trials indicate associative binding, as claimed in the paper. Alternative explanations can be made, for instance, that they reflect word recognition, as only EC contains familiar words.

2) The entire set of EC pairs were tested both following 12 hours and following 36 hours. Exposure to the pairs during test #1 can be expected to have an effect over memory one day later, during test #2, and so differences between the tests could be at least partially driven by the additional activation and rehearsal of the material during test #1. Therefore, it is hard to draw conclusions regarding automatic memory reorganization between 12 and 36 hours after unconscious learning. Specifically, a claim is made regarding a third wave of plasticity, but we cannot be certain that the improvement found in the 36 hour test would have happened without test #1.

3) Authors claim that perceptual and conceptual processing during sleep led to increased neural complexity in troughs. However, neural complexity was not found to differ between EC and CC, nor between remembered and forgotten pairs. It is therefore not clear to me why the increased complexity that was found in troughs should be attributed to perceptual and conceptual word processing, as CC contains meaningless vowels. Moreover, from the evidence presented in this work at least, I am not sure there is room to infer causation - that the increase in HFD is driven by the stimuli - as there is no control analysis looking at HFD during troughs that did not contain stimulation.

Reviewer #3 (Public Review):

The study aims at creating novel episodic memories during slow wave sleep, that can be transferred in the awake state. To do so, participants were simultaneously presented during sleep both foreign words and their arbitrary translations in their language (one word in each ear), or as a control condition only the foreign word alone, binaurally. Stimuli were presented either at the trough or the peak of the slow oscillation using a closed-loop stimulation algorithm. To test for the creation of a flexible association during sleep, participant were then presented at wake with the foreign words alone and had (1) to decide whether they had the feeling of having heard that word before, (2) to attribute this word to one out of three possible conceptual categories (to which translations word actually belong), and (3) to rate their confidence about their decision.

The paper is well written, the protocol ingenious and the methods are robust. However, the results do not really add conceptually to a prior publication of this group showing the possibility to associate in slow wave sleep pairs of words denoting large or small object and non words, and then asking during ensuing wakefulness participant to categorise these non words to a "large" or "small" category. In both cases, the main finding is that this type of association can be formed during slow wave sleep if presented at the trough (versus the peak) of the slow oscillation. Crucially, whether these associations truly represent episodic memory formation during sleep, as claimed by the authors, is highly disputable as there is no control condition allowing to exclude the alternative, simpler hypothesis that mere perceptual associations between two elements (foreign word and translation) have been created and stored during sleep (which is already in itself an interesting finding). In this latter case, it would be only during the awake state when the foreign word is presented that its presentation would implicitly recall the associated translation, which in turn would "ignite" the associative/semantic association process eventually leading to the observed categorisation bias (i.e., foreign words tending to be put in the same conceptual category than their associated translation). In the absence of a dis-confirmation of this alternative and more economical hypothesis, and if we follow Ocam's razor assumption, the claim that there is episodic memory formation during sleep is speculative and unsupported, which is a serious limitation irrespective of the merits of the study. The title and interpretations should be toned down in this respect

Other remarks:

Lines 43-45 : the assumption that the sleeping brain decides whether external events can be disregarded, requires awakening or should be stored for further consideration in the waking state is dubious, and the supporting references date from a time (the 60') during which hypnopedia was investigated in badly controlled sleep conditions (leaving open the doubt about the possibility that it occurred during micro awakenings)

1st paragraph, lines 48-53 , the authors should be more specific about what kind of new associations and at which level they can be stored during sleep according to recent reports, as a wide variety of associations (mostly elementary levels) are shown in the cited references. Limitations in information processing during sleep should also be acknowledged.

The authors ran their main behavioural analyses on delayed retrieval at 36h rather than 12h with the argument that retrieval performance was numerically larger at 36 than 12h but the difference was non-significant (line 181-183), and that effects were essentially similar. Looking at Figure 2, is the trough effect really significant at 12h ? In any case, the fact that it is (numerically) higher at 36 than 12h might suggest that the association created at the first 12h retrieval (considering the alternative hypothesis proposed above) has been reinforced by subsequent sleep.

In the discussion section lines 419-427, the argument is somehow circular in claiming episodic memory mechanisms based on functional neuroanatomical elements that are not tested here, and the supporting studies conducted during sleep were in a different setting (e.g. TMR)

Supplementary Material: in the EEG data the differentiation between correct and incorrect ulterior classifications when presented at the peak of the slow oscillation is only significant in association with 36h delayed retrieval but not at 12h, how do the authors explain this lack of effect at 12 hour?

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 #2 (Public Review):

Knowles et al. investigated the developmental roles of Erk1/2 expression in cells from the Nkx2.1-lineage, which includes the PV and SST classes of cortical inhibitory interneurons (CINs) and glial subtypes. They find that embryonic expression of Erk1/2 regulates the number of Nkx2.1-derived oligodendrocytes and astrocytes, but not CINs, observed in postnatal mice. However, Erk1/2 is necessary for the expression of SST in subset of Nkx2.1-derived CINs, which can be partially rescued by postnatal depolarization via chemogenetic stimulation with DREADDs. Finally, loss of Erk1/2 from these cells impairs activity-dependent expression of FOSB. Collectively, this revised paper demonstrates differential roles of Erk1/2 for the development of glia and neurons. Furthermore, it suggests SST CINs may be particularly vulnerable to loss of Erk1/2 signaling during both early embryonic and later postnatal developmental stages.

Strengths:<br /> This paper uses multiple transgenic mouse lines to investigate the contributions of Erk1/2 loss and over-expression and MEK overexpression for interneuron and glial development. Furthermore, they consider how Erk1/2 signaling may evolve over the course of development from embryonic to postnatal juvenile and adult stages. Thus, they investigate Erk1/2's early role in cell differentiation and its later role in activity dependent signaling. This approach to studying gene function throughout development is important but not often attempted within a single study.

The authors investigate Erk1/2 using several techniques, including immunohistochemistry, sequencing of translated genes using the Ribotag method, electrophysiology, and chemogenetic stimulation using DREADDs. Thus, they aim to apply a comprehensive battery of approaches to assay Erk1/2 signaling in Nkx2.1-derived cells throughout development.

Weaknesses:<br /> This paper describes a series of mostly separate observations that are not directly linked. The mechanisms underlying their observations and the significance of the findings are often unclear.

The authors use Erk1-/-; Erk2fl/wt; Nkx2.1Cre as "het" controls throughout the manuscript. However, there is no explanation for why this is a valid control except for a statement that they are "grossly intact", without elaboration. It is unclear why the authors did not use Nkx2.1Cre mice for their control. Figure 1 - Supplemental Figure 1 provides the only comparison between Erk1-/-; Erk2fl/wt; Nkx2.1Cre and Erk1-/-; Erk2wt/wt; Nkx2.1Cre mice. This figure shows a single example of immune staining for Erk2, but it is not obvious that Nkx2.1 control or "het control" cells even express Erk2 in this image. There is no quantification. Thus, their choice of control condition is not obviously appropriate.

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 #2 (Public Review):

Chakraborty et al. present a comprehensive analysis of the role of the IP3R in regulating SOCE in neuronal cells starting with human neurons derived from stem cells and continuing with SH-SY5Y cells after careful characterization of the maintenance of the inhibitory role of IP3R. They also show differential effects in non-neuronal cell lines. The work is careful and the data convincing. The conclusion that IP3Rs somehow stabilize ER-PM MCS to enhance SOCE is supported by the findings especially the surprising finding that the IP3R effect does not require a functional pore but does require IP3 binding to IP3R. Overall this is a careful, well-done analysis. However, the conclusion that IP3R stabilizes ER-PM MCS is mostly inferred from the current data. The authors need to extend the finding by directly assessing the size, density, and the number of ER-PM MCS using endogenous STIM1 (there are reliable antibodies for STIM1) to confirm their conclusion that when IP3R is knocked down ER-PM MCS are smaller/less dense. Another interesting experiment that would support their conclusion is expressing tagged STIM1 and Orai1 and observing their interaction in real time after store depletion. These experiments would need to be carefully controlled to select cells with low levels of expression of STIM1-Orai1 as there are hints from their current data that high expressors would not exhibit the IP3R dependence on SOCE. So, some independent experimental evidence that IP3R knockdown is affecting ER-PM MCS and not STIM1-Orai1 interaction directly to support the presented PLA data would greatly support the final conclusion of the paper. From the PLA assay alone it is difficult to differentiate between poor direct STIM1-Orai1 interaction versus stability of ER-PM MCS.

Reviewer #3 (Public Review):

SOCE is a ubiquitous cell signalling pathway that sustains long-lasting Ca2+ elevations required for the proliferation of T cells and the differentiation and contractility of skeletal muscle. Patients with loss of function mutations in either STIM1 or ORAI1 suffer from severe combined immunodeficiency while patients with gain-of-function mutations suffer from muscle weakness. The report that an intracellular calcium channel acts as a tether at membrane contact sites to regulate the activity of STIM/ORAI channels is thus relevant for health and disease, given the essential role of the SOCE pathway for immune and muscle cell function.

The IP3R is the major Ca2+ release pathway that initiates the STIM/ORAI activation cascade and the group of Colin Taylor (coauthor of the present study) showed that a pool of immobile receptors licensed to respond to physiological stimuli localizes near STIM-ORAI interaction sites at ER-PM junctions DOI: 10.1016/j.ceb.2018.10.001. This group further showed that IP3Rs are tethered to PM-bound actin by the KRas-induced actin-interacting protein (KRAP) DOI: 10.1038/s41467-021-24739-9 while the group of Indu Ambudkar showed that IP3R is juxtaposed to immobile STIM2 clusters within ER-PM junctions DOI: https://doi.org/10.1073/pnas.2114928118 The mechanism by which IP3R impinges on SOCE at ER-PM contact sites remains unclear, however.

The present study provides an important clue by showing that IP3Rs themselves can act as tethering proteins independently of their calcium release function. However, several important questions remain unanswered. Are the native and mutated receptors recruited differentially to ER-PM junctions? If so, what interacting partner(s) and mechanisms enable IP3-bound receptors to enhance the interactions between STIM1 and ORAI1? And why is this effect restricted to neuronal cells?

Previous studies indicate that IP3R can interact with actin via KRAP, with STIM proteins, with ORAI channels, and with phosphoinositides. The authors point to phosphoinositides as a potential target that could explain the need for IP3, but this possibility has not been experimentally addressed. They should establish whether phosphoinositides are involved in the recruitment of IP3R receptors and provide additional mechanistic insight by documenting whether IP3R depletion impacts the stability of contact sites or their ability to exchange lipids between membranes. Another unresolved question relates to the observation that the phenotype is restricted to neuronal cell types and absent in HEK-293 cells typically used for electrophysiological recordings of CRAC currents. The authors should attempt to clarify the molecular basis of this difference between cell types.

From a methodological standpoint, one limitation is that the functional assays used are quite indirect. One critical SOCE determinant is the filling state of intracellular calcium stores, which was estimated indirectly by measuring the amplitude of the Ca2+ elevation evoked by the addition of the SERCA inhibitor thapsigargin. Although this method is widely used it does not directly reflect the key parameter driving STIM1 activation which is the free calcium concentration within the ER lumen. Direct ER [Ca2+] recordings are required to clarify this critical point.

Reviewer #2 (Public Review):

McCormick, Cleary et al., explore the question of how the nucleotide state of the tubulin heterodimer affects the interaction between adjacent tubulins.

(1) The setup of the authors' model, which attributes the dynamic properties of the growing microtubule only to the differences in interface binding affinities, is unrealistic. They excluded the influence of the nucleotide-dependent global conformational changes even in the 'Self-Acting Nucleodide' model (Fig. 1A). As the authors have found earlier, tubulin in its unassembled state may be curved irrespective of the species of the bound nucleotide (Rice et al., 2008, doi: 10.1073/pnas.0801155105), but at the growing end of microtubules, the situation could be different. Considering the recently published papers from other laboratories, it may be more appropriate to include the nucleotide-dependent change in the tubulin conformation in the Self-Acting Nucleotide model.

(2) The result that the minus end is insensitive to GDP (Fig. 2) was previously published in a paper by Tanaka-Takiguchi et al. (doi: 10.1006/jmbi.1998.1877). The exact experimental condition was different from the one used in Fig. 2, but the essential point of the finding is the same. The authors should cite the preceding work, and discuss the similarities and differences, as compared to their own results.

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):

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 #2 (Public Review):

Schmid et al present a lovely study looking at the effect of passive auditory exposure on learning a categorization task.

The authors utilize a two-alternative choice task where mice have to discriminate between upward and downward-moving frequency sweeps. Once mice learn to discriminate easy stimuli, the task is made psychometric and additional intermediate stimuli are introduced (as is standard in the literature). The authors introduce an additional two groups of animals, one that was passively exposed to the task stimuli before any behavioral shaping, and one that had passive exposure interleaved with learning. The major behavioral finding is that passive exposure to sounds improves learning speed. The authors show this in a number of ways through linear fits to the learning curves. Additionally, by breaking down performance based on the "extreme" vs "psychometric" stimuli, the authors show that passive exposure can influence responses to sounds that were not present during the initial training period. One limitation here is that the presented analysis is somewhat simplistic, does not include any detailed psychometric analysis (bias, lapse rates etc), and primarily focuses on learning speed. Ultimately though, the behavioral results are interesting and seem supported by the data.

To investigate the neural mechanisms that may underlie their behavioral findings, the authors turn to a family of artificial neural network models and evaluate the consequences of different learning algorithms and schedules, network architectures, and stimulus distributions, on the learning outcomes. The authors work through five different architectures that fail to recapitulate the primary behavior findings before settling on a final model, utilizing a combination of supervised and unsupervised learning, that was capable of reproducing the key aspects of the experiments. Ultimately, the behavioral results presented are consistent with network models that build latent representations of task-relevant features that are determined by statistical properties of the input distribution.

Reviewer #3 (Public Review):

Summary of Author's Results/Intended Achievements<br /> The authors were trying to ascertain the underlying learning mechanisms and network structure that could explain their primary experimental finding: passive exposure to a stimulus (independent of when the exposure occurs) can lead to improvements in active (supervised) learning. They modeled their task with 5 progressively more complex shallow neural networks classifying vectors drawn from multi-variate Gaussian distributions.

Account of Major Strengths:<br /> Overall, the experimental findings were interesting, albeit not necessarily novel. The modelling was also appropriate, with a solid attempt at matching the experimental condition to simplified network models.

Account of Major Weaknesses:<br /> I would say there are two major weaknesses of this work. The first is that even Model 5 differs from their data. For example, the A+P (passive interleaved condition) learning curve in Figure 7 seems to be non-monotonic, and has some sort of complex eigenvalue in its decay to the steady state performance as trials increase. This wasn't present in their experimental data (Figure 2D), and implies a subtle but important difference. There also appear to be differences in how quickly the initial learning (during early trials) occurs for the A+P and A:P conditions. While both A+P and A:P conditions learn faster than A only in M5, A+P and A:P seem to learn in different ways, which isn't supported in their data. The second major weakness is that the authors also don't generate any predictions with M5. Can they test this model of learning somehow in follow-up behavioural experiments in mice?

Discussion of Likely Impact:<br /> Without follow-up experiments to test their mechanism of why passive exposure helps in a schedule-independent way, the impact of this paper will be limited.

Additional Context:<br /> I believe the authors need to place this work in the context of a large amount of existing literature on passive (unsupervised) and active (supervised) learning interactions. This field is broad both experimentally and computationally. For example, there is an entire sub-field of machine learning, called semi-supervised learning that is not mentioned at all in this work.

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 #2 (Public Review):

In "Behavioral entrainment to rhythmic auditory stimulation can be modulated by tACS depending on the electrical stimulation field properties" Cabral-Calderin and collaborators aimed to document 1) the possible advantages of personalized tACS montage over standard montage on modulating behavior; 2) the inter-individual and inter-session reliability of tACS effects on behavioral entrainment and, 3) the importance of the induced electric field properties on the inter-individual variability of tACS.

To do so, in two different sessions, they investigated how the detection of silent gaps occurring at random phases of a 2Hz- amplitude modulated sound could be enhanced with 2Hz tACS, delivered at different phase lags. In addition, they evaluated the advantage of using spatially optimized tACS montages (information-based procedure - using anatomy and functional MRI to define the target ROI and simulation to compare to a standard montage applied to all participants) on behavioral entrainment. They first show that the optimized and the standard montages have similar spatial overlap to the target ROI. While the optimized montage induced a more focal field compared to the standard montage, the latter induced the strongest electric field. Second, they show that tACS does not modify the optimal phase for gap detection (phase of the frequency-modulated sound) but modulates the strength of behavioral entrainment to the frequency-modulated sound in a phase-lag specific manner. However, and surprisingly, they report that the optimal tACS lag, and the magnitude of the phasic tACS effect were highly variable across sessions. Finally, they report that the inter-individual variability of tACS effects can be explained by the strength of the inward electric field as a function of the field focality and on how well it reached the target ROI.

The article is interesting and well-written, and the methods and approaches are state-of-the-art.

Strengths:<br /> - The information-based approach used by the authors is very strong, notably with the definition of subject-specific targets using a fMRI localizer and the simulation of electric field strength using 3 different tACS montages (only 2 montages used for the behavioral experiment).<br /> - The inter-session and inter-individual variability are well documented and discussed. This article will probably guide future studies in the field.

Weaknesses:<br /> - The addition of simultaneous EEG recording would have been beneficial to understand the relationship between tACS entrainment and the entrainment to rhythmic auditory stimulation.<br /> - It would have been interesting to develop the fact that tACS did not "overwrite" neural entrainment to the auditory stimulus. The authors try to explain this effect by mentioning that "tACS is most effective at modulating oscillatory activity at the intended frequency when its power is not too high" or "tACS imposes its own rhythm on spiking activity when tACS strength is stronger than the endogenous oscillations but it decreases rhythmic spiking when tACS strength is weaker than the endogenous oscillations". However, it is relevant to note that the oscillations in their study are by definition "not endogenous" and one can interpret their results as a clear superiority of sensory entrainment over tACS entrainment. This potential superiority should be discussed, documented, and developed.<br /> - The authors propose that "by applying tACS at the right lag relative to auditory rhythms, we can aid how the brain synchronizes to the sounds and in turn modulate behavior." This should be developed as the authors showed that the tACS lags are highly variable across sessions. According to their results, the optimal lag will vary for each tACS session and subtle changes in the montage could affect the effects.<br /> - In a related vein, it would be very useful to show the data presented in Figure 3 (panels b,d,e) for all participants to allow the reader to evaluate the quality of the data (this can be added as a supplementary figure).

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 #2 (Public Review):

In their article titled "Brain mechanisms of reversible symbolic reference: a potential singularity of the human brain", van Kerkoerle et al address the timely question of whether non-human primates (rhesus macaques) possess the ability for reverse symbolic inference as observed in humans. Through an fMRI experiment in both humans and monkeys, they analyzed the bold signal in both species while observing audio-visual and visual-visual stimuli pairs that had been previously learned in a particular direction. Remarkably, the findings pertaining to humans revealed that a broad brain network exhibited increased activity in response to surprises occurring in both the learned and reverse directions. Conversely, in monkeys, the study uncovered that the brain activity within sensory areas only responded to the learned direction but failed to exhibit any discernible response to the reverse direction. These compelling results indicate that the capacity for reversible symbolic inference may be unique to humans.

In general, the manuscript is skillfully crafted and highly accessible to readers. The experimental design exhibits originality, and the analyses are tailored to effectively address the central question at hand. Although the first experiment raised a number of methodological inquiries, the subsequent second experiment thoroughly addresses these concerns and effectively replicates the initial findings, thereby significantly strengthening the overall study. Overall, this article is already of high quality and brings new insight into human cognition.

I identified three weaknesses in the manuscript:<br /> - One major issue in the study is the absence of significant results in monkeys. Indeed, authors draw conclusions regarding the lack of significant difference in activity related to surprise in the multi-demand network (MDN) in the reverse congruent versus reverse incongruent conditions. Although the results are convincing (especially with the significant interaction between congruency and canonicity), the article could be improved by including additional analyses in a priori ROI for the MDN in monkeys (as well as in humans, for comparison).<br /> - While the authors acknowledge in the discussion that the number of monkeys included in the study is considerably lower compared to humans, it would be informative to know the variability of the results among human participants.<br /> - Some details are missing in the methods.

Reviewer #3 (Public Review):

This study investigates the hypothesis that humans (but not non-human primates) spontaneously learn reversible temporal associations (i.e., learning a B-A association after only being exposed to A-B sequences), which the authors consider to be a foundational property of symbolic cognition. To do so, they expose humans and macaques to 2-item sequences (in a visual-auditory experiment, pairs of images and spoken nonwords, and in a visual-visual experiment, pairs of images and abstract geometric shapes) in a fixed temporal order, then measure the brain response during a test phase to congruent vs. incongruent pairs (relative to the trained associations) in canonical vs. reversed order (relative to the presentation order used in training). The advantage of neuroimaging for this question is that it removes the need for a behavioral test, which non-human primates can fail for reasons unrelated to the cognitive construct being investigated. In humans, the researchers find statistically indistinguishable incongruity effects in both directions (supporting a spontaneous reversible association), whereas in monkeys they only find incongruity effects in the canonical direction (supporting an association but a lack of spontaneous reversal). Although the precise pattern of activation varies by experiment type (visual-auditory vs. visual-visual) in both species, the authors point out that some of the regions involved are also those that are most anatomically different between humans and other primates. The authors interpret their finding to support the hypothesis that reversible associations, and by extension symbolic cognition, is uniquely human.

This study is a valuable complement to prior behavioral work on this question. However, I have some concerns about methods and framing.

Methods - Design issues:

1. The authors originally planned to use the same training/testing protocol for both species but the monkeys did not learn anything, so they dramatically increased the amount of training and evaluation. By my calculation from the methods section, humans were trained on 96 trials and tested on 176, whereas the monkeys got an additional 3,840 training trials and 1,408 testing trials. The authors are explicit that they continued training the monkeys until they got a congruity effect. On the one hand, it is commendable that they are honest about this in their write-up, given that this detail could easily be framed as deliberate after the fact. On the other hand, it is still a form of p-hacking, given that it's critical for their result that the monkeys learn the canonical association (otherwise, the critical comparison to the non-canonical association is meaningless).

2. Between-species comparisons are challenging. In addition to having differences in their DNA, human participants have spent many years living in a very different culture than that of NHPs, including years of formal education. As a result, attributing the observed differences to biology is challenging. One approach that has been adopted in some past studies is to examine either young children or adults from cultures that don't have formal educational structures. This is not the approach the authors take. This major confound needs to minimally be explicitly acknowledged up front.

3. Humans have big advantages in processing and discriminating spoken stimuli and associating them with visual stimuli (after all, this is what words are in spoken human languages). Experiment 2 ameliorates these concerns to some degree, but still, it is difficult to attribute the failure of NHPs to show reversible associations in Experiment 1 to cognitive differences rather than the relative importance of sound string to meaning associations in the human vs. NHP experiences.

4. More minor: The localizer task (math sentences vs. other sentences) makes sense for math but seems to make less sense for language: why would a language region respond more to sentences that don't describe math vs. ones that do?

Methods - Analysis issues:

5. The analyses appear to "double dip" by using the same data to define the clusters and to statistically test the average cluster activation (Kriegeskorte et al., 2009). The resulting effect sizes are therefore likely inflated, and the p-values are anticonservative.

Framing:

6. The framing ("Brain mechanisms of reversible symbolic reference: A potential singularity of the human brain") is bigger than the finding (monkeys don't spontaneously reverse a temporal association but humans do). The title and discussion are full of buzzy terms ("brain mechanisms", "symbolic", and "singularity") that are only connected to the experiments by a debatable chain of assumptions.

First, this study shows relatively little about brain "mechanisms" of reversible symbolic associations, which implies insights into how these associations are learned, recognized, and represented. But we're only given standard fMRI analyses that are quite inconsistent across similar experimental paradigms, with purely suggestive connections between these spatial patterns and prior work on comparative brain anatomy.

Second, it's not clear what the relationship is between symbolic cognition and a propensity to spontaneously reverse a temporal association. Certainly, if there are inter-species differences in learning preferences this is important to know about, but why is this construed as a difference in the presence or absence of symbols? Because the associations aren't used in any downstream computation, there is not even any way for participants to know which is the sign and which is the signified: these are merely labels imposed by the researchers on a sequential task.

Third, the word "singularity" is both problematically ambiguous and not well supported by the results. "Singularity" is a highly loaded word that the authors are simply using to mean "that which is uniquely human". Rather than picking a term with diverse technical meanings across fields and then trying to restrict the definition, it would be better to use a different term. Furthermore, even under the stated definition, this study performed a single pairwise comparison between humans and one other species (macaques), so it is a stretch to then conclude (or insinuate) that the "singularity" has been found (see also pt. 2 above).

7. Related to pt. 6, there is circularity in the framing whereby the authors say they are setting out to find out what is uniquely human, hypothesizing that the uniquely human thing is symbols, and then selecting a defining trait of symbols (spontaneous reversible association) *because* it seems to be uniquely human (see e.g., "Several studies previously found behavioral evidence for a uniquely human ability to spontaneously reverse a learned association (Imai et al., 2021; Kojima, 1984; Lipkens et al., 1988; Medam et al., 2016; Sidman et al., 1982), and such reversibility was therefore proposed as a defining feature of symbol representation reference (Deacon, 1998; Kabdebon and Dehaene-Lambertz, 2019; Nieder, 2009).", line 335). They can't have it both ways. Either "symbol" is an independently motivated construct whose presence can be independently tested in humans and other species, or it is by fiat synonymous with the "singularity". This circularity can be broken by a more modest framing that focuses on the core research question (e.g., "What is uniquely human? One possibility is spontaneous reversal of temporal associations.") and then connects (speculatively) to the bigger conceptual landscape in the discussion ("Spontaneous reversal of temporal associations may be a core ability underlying the acquisition of mental symbols").

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 #2 (Public Review):

The manuscript by Bull et al investigates the relationship between metabolic features, in particular different lipoproteins and fatty acids, and colorectal cancer. They combine different data sources to analyze forward and reverse Mendelian Randomization associations in children and adults. Their results indicate that polyunsaturated fatty acids may be implicated in the risk for colorectal cancer.

Overall, the paper is well-written, and the methods used are solid. The use of different data (cohort individual data and summary stats) and stratifications strengthens the analyses. The conclusions drawn from the results are balanced and supported by the data although the novelty of the findings is modest.

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 #2 (Public Review):

This study presents novel findings on the metabolic fuel preference shift regulated by PTPMT1, a target of interest, in skeletal and cardiac muscle cells.

Zheng et al. have investigated the effects of PTPMT1 Knock-out on cellular metabolic flexibility. Since the authors used several types of appropriate tissue-specific mouse models, it seems to be a broad significance at the first glance. However, most of the data lack the quantification, consequently they don't provide statistical significance. In addition, the functional data such as echocardiography shows partial and limited data.<br /> Therefore, it is only a matter of speculation that the absence of PTPMT1 inhibits glucose (pyruvate) utilization and promotes FAO.

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 #2 (Public Review):

Members of the EphB family of tyrosine kinase receptors are involved in a multitude of diverse cellular functions, ranging from the control of axon growth to angiogenesis and synaptic plasticity. In order to provide these diverse functions, it is expected that these receptors interact in a cell-type-specific manner with a diverse variety of downstream signalling molecules.

The authors have used proteomics approaches to characterise some of these molecules in further detail. This molecule, myc-binding protein 2 (MYCBP2) also known as highwire, has been identified in the context of establishment of neural connectivity. Another molecule coming up on this screen was identified as FBXO45.

The authors use classical methods of co-IP to show a kinase-independent binding of MYCBP2 to EphB2. They further showed that FBXO45 within a ternary complex increased the stability of the EphB2/MYCBP2 complex.

To define the interacting domains, they used clearly designed swapping experiments to show that the extracellular and transmembrane domains are necessary and sufficient for the formation of the ternary complex.

Using a cellular contraction assay, the authors showed the necessity of MYCBP2 in mediating the cytoskeletal response of EphB2 forward signalling. Furthermore, they used the technically challenging stripe assay of alternating lanes of ephrinB-Fc and Fc to show that also in this migration-based essay MYCBP2 is required for EphB mediated differential migration pattern.

MYCBP2 in addition is necessary to stabilize EphB2, that is in the absence of MYCBP2, EphB2 is degraded in the lysosomal pathway.

Interestingly, the third protein in this complex, Fbxo45, was further characterized by overexpression of the domain of MYCBP2, known to interact with Fbxo45. Here the authors showed that this approach led to the disruption of the EphB2 / MYCBP2 complex, and also abolished the ephrinB-mediated activation of EphB2 receptors and their differential outgrowth on ephrinB2-Fc / Fc stripes.

Finally, the authors demonstrated an in vivo function of this complex using another model system, C elegans where they were able to show a genetic interaction.

Data shows in a nice set of experiments a novel level of EphB2 forward signalling where a ternary complex of this receptor with multifunctional MYCBP2 and Fbxo45 controls the activity of EphB2, allowing a further complex regulation of this important receptor. Additionally, the authors challenge pre-existing concepts of the function of MYCBP2 which might open up novel ways to think about this protein.

Of interest is this work also in terms of the development of the retinotectal projection in zebrafish where MYCBP2/highwire plays a crucial role, and thus might lead to a better understanding of patterning along the DV axis, for which it is known that EphB family members are crucial.

Overall, the experiments are classical experiments of co-immunoprecipitations, swapping experiments, collapse assays, and stripe assays which all are well carried out and are convincing.

Reviewer #3 (Public Review):

In this manuscript, Chang et al set out to find direct interactions with the Eph-B2 receptor, as our knowledge of its function/regulation is still incomplete. Using proteomic analysis of Hela cells expressing EPHB2, they identified MYCBP2 as a potential binder, which they then confirm using extensive biochemical analyses, an interaction that seems to be negatively affected by the binding of ephrin-B2 (but not B1). Furthermore, they find that FBXO45, a known MYCBP2 interaction, strongly facilitates its binding to EPHB2. Intriguingly, these interactions depend on the extracellular domains of EPHB2, something that is surprising given the fact that MYCBP2 is an intracellular protein. Finally, they find that, in contrast to what could be expected given the known function of MYCBP2 as a ubiquitin E3 ligase, it actually positively regulates EPHB2 protein stability, and function.

The strength of this manuscript is the extensive biochemical analysis of the EPHB2/MYCBP2/FBXO43 interactions. Most of the conclusions are warranted although I do not understand the physiological interpretation of how these proteins could interact in the extracellular space.

The attempt to extend the study to an in vivo animal using the worm is important. However, I find the results in the worm confusing and overly interpreted in their current form.

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 #2 (Public Review):

In this manuscript, Smith et al. delineated novel mechanistic insights into the structure-function relationships of the C-terminal repeat domains within the mouse DUX protein. Specifically, they identified and characterised the transcriptionally active repeat domains, and narrowed down to a critical 6aa region that is required for interacting with key transcription and chromatin regulators. The authors further showed how the DUX active repeats collaborate with the C-terminal acidic tail to facilitate chromatin opening and transcriptional activation at DUX genomic targets.

Reviewer #3 (Public Review):

Dux (or DUX4 in human) is a master transcription factor regulating early embryonic gene activation and has garnered much attention also for its involvement in reprogramming pluripotent embryonic stem cells to totipotent "2C-like" cells. The presented work starts with the recognition that DUX contains five conserved c. 100-amino acid carboxy-terminal repeats (called C1-C5) in the murine protein but not in that of other mammals (e.g. human DUX4). Using state-of-the-art techniques and cell models (BioID, Cut&Tag; rescue experiments and functional reporter assays in ESCs), the authors dissect the activity of each repeat, concluding that repeats C3 and C5 possess the strongest transactivation potential in synergy with a short C-terminal 14 AA acidic motif. In agreement with these findings, the authors find that full-length and active (C3) repeat containing Dux leads to increased chromatin accessibility and active histone mark (H3K9Ac) signals at genomic Dux binding sites. A further significant conclusion of this mutational analysis is the proposal that the weakly activating repeats C2 and C4 may function as attenuators of C3+C5-driven activity.

By next pulling down and identifying proteins bound to Dux (or its repeat-deleted derivatives) using BioID-LC/MS/MS, the authors find a significant number of interactors, notably chromatin remodellers (SMARCC1), a histone chaperone (CHAF1A/p150) and transcription factors previously (ZSCAN4D) implicated in embryonic gene activation.

The experiments are of high quality, with appropriate controls, and thus provide a rich compendium of Dux interactors for future study. Indeed, a number of these (SMARCC1, SMCHD1, ZSCAN4) make biological sense, both for embryonic genome activation and for FSHD (SMCHD1).

The central question raised by this study, however, concerns the function of the Dux repeats, apparently unique to mice. While it is possible, as the authors propose, that the weak activating C1, C2 C4 repeats may exert an attenuating function ("sub-functionalization") on activation mediated by C3 and/or C5, it could similarly be argued that the different repeats are indeed expected to display different activation potentials, chromatin opening, cofactor recruitment, due to, simply, the differences in their sequences. The argument for an active attenuating function would have been strengthened, for example, by the finding of repressor recruitment by C1/C2/C4 (and not just less of everything). The possible biological relevance of these repeats thus remains to be established.

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 #2 (Public Review):

In this study, Yan et al. report that a cleaved form of METTL3 (termed METTL3a) plays an essential role in regulating the assembly of the METTL3-METTL14-WTAP complex. Depletion of METTL3a leads to reduced m6A level on TMEM127, an mTOR repressor, and subsequently decreased breast cancer cell proliferation. Mechanistically, METTL3a is generated via 26S proteasome in an mTOR-dependent manner.

The manuscript follows a smooth, logical flow from one result to the next, and most of the results are clearly presented. Specifically, the molecular interaction assays are well-designed. This model represents a significant addition to the current understanding of m6A-methyltransferase complex formation.

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 #2 (Public Review):

In this study, the authors utilize a compendium of public genomic data to identify transcription factors (TF) that can identify their DNA binding motifs in the presence of nuclosome-wrapped chromatin and convert the chromatin to open chromatin. This class of TFs are termed Pioneer TFs (PTFs). A major strength of the study is the concept, whose premise is that motifs bound by PTFs (assessed by ChIP-seq for the respective TFs) should be present in both "closed" nucleosome wrapped DNA regions (measured by MNase-seq) as well as open regions (measured by DNAseI-seq) because the PTFs are able to open the chromatin. Use of multiple ENCODE cell lines, including the H1 stem cell line, enabled the authors to assess if binding at motifs changes from closed to open. Typical, non-PTF TFs are expected to only bind motifs in open chromatin regions (measured by DNaseI-seq) and not in regions closed in any cell type. This study contributes to the field a validation of PTFs that are already known to have pioneering activity and presents an interesting approach to quantify PTF activity.

For this reviewer, there were a few notable limitations. One was the uncertainty regarding whether expression of the respective TFs across cell types was taken into account. This would help inform if a TF would be able to open chromatin. Another limitation was the cell types used. While understandable that these cell types were used, because of their deep epigenetic phenotyping and public availability, they are mostly transformed and do not bear close similarity to lineages in a healthy organism. Next, the methods used to identify PTFs were not made available in an easy-to-use tool for other researchers who may seek to identify PTFs in their cell type(s) of interest. Lastly, some terms used were not defined explicitly (e.g., meaning of dyads) and the language in the manuscript was often difficult to follow and contained improper English grammar.

Reviewer #3 (Public Review):

Peng et al. designed a computational framework for identifying pioneer factors using epigenomic data from five cell types. The identification of pioneer factors is important for our understanding of the epigenetic and transcriptional regulation of cells. A computational approach toward this goal can significantly reduce the burden of labor-intensive experimental validation. Nevertheless, there are several caveats in the current analysis which may require some modification of the computational methods and additional analysis to maximize the confidence of the pioneer factor prediction results.

A key consideration that arises during this review is that the current analysis anchors on H1 ESC and therefore may have biased the results toward the identification of pioneer factors that are relevant to the four other differentiated cell types. The low ranking of Yamanaka factors and known pioneer factors of NFYs and ESRRB may be due to the setup of the computational framework. Analysis should be repeated by using each of every cell type as an anchor for validating the reproducibility of the pioneer factors found so far and also to investigate whether TFs related to ESC identity (e.g. Yamanaka factors, NFYs and ESRRB) would show significant changes in their ranking. Given the potential cell type specificity of the pioneer factors, the extension to more cell types appears to be important for further demonstrating the utility of the computational framework.

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 #2 (Public Review):

Dr. Kia Davis and colleagues present a thoughtful analysis of disruptions to cancer care during COVID-19 in the article, "Understanding disruptions in cancer care to reduce increased cancer burden: a cross-sectional study." The article is based on an online survey of 680 residents in the Siteman Cancer Center catchment area in Summer 2020. The authors aim to characterize demographic differences in cancer care disruptions. Information about the causes and distribution of care disruption can help reduce the impacts of COVID-19 and guide the recovery of programs and services. The article provides a clear and detailed assessment of factors associated with care disruption and return to care during the first six months of the pandemic.

A strength of the study is the focus on the catchment area of the cancer center during a period of dramatic change. The results would provide timely and actionable data to address emerging barriers to care and associated social or contextual factors. This information helps the Community Outreach and Engagement efforts to be responsive to community priorities despite rapidly evolving circumstances.

The analysis would benefit from greater detail in three areas. First, it would be helpful to have more information about how the outcome measures were originally developed or tested. Second, for the regression analysis, it would be helpful to show the demographic characteristics of the two strata to better understand the sample composition. Third, the authors should demonstrate that the data do not violate the assumptions for conducting logistic regression to improve confidence in the findings.

COVID-19 affected all aspects of the cancer continuum. The study reports factors associated with postponing or canceling cancer-related appointments during the pandemic. It will be of great interest to researchers and practitioners in cancer prevention and control.

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 #2 (Public Review):

In this manuscript, the authors study the generation of joint torques in stick insects under external electrical excitation. The goal of this paper is to develop a model for the relationship between torque and excitation period, with a specific focus on accounting for inter-individual variances in the model. The long-term motivation for this work is to be able to generate controlled external excitation of insect muscle to create "cyborg" systems where computer-controlled electronics generate movement of living systems.

The authors performed measurements of joint torque generated from three different muscles across two excitation parameters (voltage and excitation time). The authors study the relationship between excitation parameters and muscle torque comparing a linear relationship, and a non-linear (power-law) relationship between torque and voltage. In addition, the authors also compare a hierarchical version of the model which includes inter-individual differences, with a pooled model that ignores individual differences. The authors use an information criteria metric to then identify the best model.

I believe that the methods of this paper and the findings are all sound; however, I have the following comments and questions.

Main questions:<br /> 1. It is interesting to find that inter-individual differences are important in the torque output from the joint. However, in some sense, this is what I would have expected. I am curious if these inter-individual differences can be related to any distinct differences among the insects studied: for example body mass, limb length, cross-sectional muscle area, and age all would likely influence torque. Now I am not advocating that all of the above parameters (age, size, etc) be added into a more complex model because I don't think that is necessarily the right path. However, I do think it would be beneficial to present the known information about the variance in individual size/age/etc, some of which may be unknown.

2. Line 145 states that "Models 1-2 and 2-1 most accurately predicted the posterior predictive distribution.", but is this not a typo? I thought Models 1-2 and 2-2 are the best as they are the linear and nonlinear models with hierarchical slopes.

In the paragraph starting at line 147 and the subsequent paragraph it is argued that while the nonlinear model 2-2 worked well, the linear model is still better. "The comparison of the linear model (model 1-2) with the nonlinear model (model 2-2) using the WAIC for all conditions (muscle type and applied voltage) resulted in lower values for the linear model." But certainly, both are quite close in WAIC, and my question is, might there be reasons from muscle physiology on stick insects to expect a non-linear model? While the linear model had the lowest WAIC (marginally from looking at Fig 2) without any prior assumptions about the torque-duration curve, certainly much is known about the effect of stimulation on force production, and might including that information validate the non-linear model over linear?

Alternatively, if the goal is to just model the data under 500ms stimulation because this is the relevant timescale for walking behavior (line 181) then the linear model is fine. But reading the manuscript I got the impression the goal was to best model the torque-voltage relationship, which I would think includes the full excitation range and incorporates known information from muscle physiology.

3. Fig 3 is a bit confusing as this is meant to compare the experimental data with the hierarchical model distribution. However, all the model distributions across the 10 insects look identical. I thought the point of the hierarchical model is that the slope parameter varies across individuals (isn't this what Fig 4 demonstrates?). So shouldn't the distributions and green fit lines all be different for the individuals?

I have some questions that should be clarified about the methods:<br /> 4. It is stated that 20 insects were tested, but all the plots show only 10. Is this just because the other 10 were not presented? Or were observations discarded from the other 10 insects for some reason? This is important to describe so that readers can assess the results.

5. More information should be provided about the ordering of the different excitation experiments. The methods do not describe what the time duration between excitations was, how many were performed over what time period, etc. Additionally, it looks like four different voltage amplitudes were performed which I could only observe from figures 2 and 4. It would be beneficial to describe in detail the full sequence of data collection on an insect.

6. What is the order of presentation of different voltages? It is stated that muscle fatigue should be negligible for under 50 stimulations, but the range of the 2V experiments alone was between 49-79 stimulations. So were another ~50 stimulations performed at the three other voltages? And if so was fatigue a possible issue?<br /> Also, were there "warm up" effects too where the muscle force increased with subsequent stimulations? It would be useful to provide some characterization of this.

Reviewer #3 (Public Review):

This paper combines experiments and simple modeling to try to identify the relationship between external muscle torque vs. a stimulus burst duration on several leg muscles of a stick insect. The authors created a setup to input PWM and voltage values and measured the output torque through load cells. They found an appropriate model for estimating muscle torque through different PWM burst durations and voltage values by comparing WAIC values for each modeling equation. They found that the linear hierarchical model relating burst duration and joint torque and a nonlinear hierarchical model relating burst duration and joint torque to a power function represent the muscle torque activation the best.

The problem that the study tries to address is of great importance to the field of cyborg, biomechanics, neuromechanics, mechano-sensing, and animal locomotion (see below). There have been very few studies that tried to quantify how muscle activation in invertebrates affects force/torque output, which is important for understanding the dynamics of their movement, and this is one of the first to investigate this. The approach is technically sound, and the experimental data and modeling analyses are solid and support the conclusions drawn.

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 #2 (Public Review):

In the present study, Castano et al. discovered a chemical inhibitor that is specifically effective against the kinase activity of CDKL5 and applied it in the in vitro and the brain slice culture to reveal the acute effects of the loss of function (LOF) of CDKL5. LOF has been modeled in gene knockout mice, but these are loss-of-function models with the added developmental time effects of the absence of CDKL5 from developmental stages. The present authors' approach is the fastest timescale study to date, examining CDKL5 LOF effects in seconds to minutes.

The authors showed that chemical inhibition of CDKL5 kinase activity suppresses postsynaptically derived LTP in rat brain slice experiments, indicating that the previously controversial results of CDKL5 LOF on LTP in knockout mice and rats are possibly due to combined effects of the loss of the kinase and compensation by other factors.

The authors employed state-of-the-art methodologies and presented their data clearly and convincingly.

Reviewer #3 (Public Review):

In this manuscript, Castano et al generate and test a small molecule inhibitor of CDKL5, an X-linked kinase whose loss-of-function is the cause of a severe neurodevelopmental disorder. Since the current knowledge of CDKL5 functions mainly rely on genetic models it is still unclear which effects are caused directly by CDKL5 loss and which can be ascribed to indirect effects. A specific inhibitor would therefore be an important tool for the field.

Castano and colleagues therefore tested a panel of twenty kinase inhibitors for their capacity to block phosphorylation of a EB2, a bona fide CDKL5 substrate, in rat neurons. Among the three that could inhibit EB2 phosphorylation at low concentrations, one was found to inhibit CDKL5 while not affecting GSK3 kinases, which share significant homology to CDKL5. Considering that genetic studies have previously linked CDKL5 to excitatory synaptic transmission, acute hippocampal slices were exploited to test the consequences of CDKL5 inhibition. While CDKL5 loss in the past was found to affect both AMPA- and NMDA-Rs, the small molecule-based inhibition affected only AMPA-R responses at the post-synaptic level. Since pharmacokinetic analyses showed that the inhibitor has a low capacity for brain penetration the molecule remains limited for testing the acute inhibition of CDKL5 in vitro and ex vivo. Such a tool represents an important aspect in the CDKL5 field and the findings suggesting a direct role of CDKL5 in regulating AMPA-R functions are interesting. However, the manuscript could be improved to render it more readable.

The description of the binding and orthogonal assays, which are the basis for the selection of the small molecule inhibitor, is not straightforward to understand for non-expert readers and could be improved.

While the in vitro and ex vivo assays are well presented, it is not clear why the myelin basic protein is used as a substrate for CDKL5 in the in vitro kinase assays. Does this protein contain a CDKL5 consensus site?

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 #2 (Public Review):

In this work, the authors examine the antineoplastic effects of a combined treatment with the impridone ONC201/Tic10 and everolimus against ER+ breast cancer models. The combination was shown to have enhanced activity against everolimus resistant cells especially in 3D models as well as against primary cells derived from patients that have received treatment with everolimus in the past.

The authors address the important issue of drug resistance in ER+ breast cancer by using resistant cell models. Moreover, patient-derived cells were used in this work. From a molecular point of view, current mechanisms of action of ONC201/Tic10 were explored including effects on ERK/AKT pathways, integrated stress response and oxphos. Overall, this interesting work opens a venue for further exploration of imipridones in ER+ breast cancer resistant to current first- and second-line therapies.

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 #2 (Public Review):

Past systems for identifying and tracking rodent vocaliztions have relied on triangulating positions using only a few high-quality ultrasonic microphones. There are also large arrays of less sensitive microphones, called acoustic cameras that don't capture the detail of the sounds, but do more accurately locate the sound in 3D space. Therefore the key innovation here is that the authors combine these two technologies by primarily using the acoustic camera to accurately find the emitter of each vocalization, and matching it to the high-resolution audio and video recordings. They show that this strategy (HyVL) is more accurate than other methods for identifying vocalizing mice and also has greater spatial precision. They go on to use this setup to make some novel and interesting observations. The technology and the study are timely, important, and have the potential to be very useful. As machine learning approaches to behavior become more widespread in use, it is easy to imagine this being incorporated and lowering entry costs for more investigators to begin looking at rodent vocalizations. I have a few comments.

1) What is the relationship of the current manuscript to this: https://www.biorxiv.org/content/10.1101/2021.10.22.464496v1 which has a number of very similar figures and presents a SLIM-only method that reportedly has lower precision than the current HyVL approach. Is this superseded by the submitted paper?

2) Can the authors provide any data showing the accuracy of their system in localizing sounds emitted from speakers as a function of position and amplitude? I am imagining that it would be relatively easy to place multiple speakers around the arena as ground truth emitting devices to quantify the capabilities of the system.

3) How is the system's performance affected by overlapping vocalizations? It might be useful to compare the accuracy of caller identification for periods where only one animal is calling at a time vs. periods where multiple animals are simultaneously calling.

4) Can the authors comment on how sound shadows cast by animals standing between the caller and a USM4 affect either the accuracy of identification or the fidelity of the vocal recording?

5) I'm a bit confused about how the algorithm uses the information from the video camera. Reading through the methods, it seems like they primarily calculate competing location estimates by the two types of microphone data and then make sure that a mouse is in close proximity to one location, discarding the call if there isn't. Why did the authors choose this procedure rather than use the tracked position of the snouts as constrained candidate locations and use the microphone data to arbitrate between them? Do they think that their tracking data are not reliable or accurate enough?

6) I guess the authors have code that we can run, but I couldn't access it. The manuscript describes the algorithms and equations that are used to calculate the location, but this doesn't really give me a feel for how it works. If you want to have the broadest impact possible, I think you would do well to make the code user-friendly (maybe it is, I don't know). In pursuit of that goal, I would suggest that the authors devote some of the paper to a guided example of how to use it.

Reviewer #3 (Public Review):

The present manuscript describes a new method to identify the emitter of ultrasonic vocalisations during social interactions between 2 or 3 mice. The method combines two technologies (an "acoustic camera" and a set of four microphones) and succeeds in increasing the spatial precision and the attribution of USV emission to one of the mice. The manuscript describes the characteristics and advantages of each method and the advantages of using both to optimize the identification of USV emitter. The authors used the method to confirm that females are also vocalising during male-female interactions and that females emit USV mostly during nose-nose contact while this was not the case for males. Interestingly, the authors identified that the vocal behaviour of two competing males was strongly asymmetric when facing a female. This was not the case for two females facing one male.

The method is really promising since the identification of the emitter of USVs during mouse social interactions is a necessary step to speed up our understanding of this communication modality. The increase in spatial precision and in the proportion of attributed vocalisations is non-negligible and will be of great utility in the future.

Generally, the statistical analyses should be adjusted. Indeed, the statistical analyses do not consider the fact that the same individuals were recorded several times (if we understood well the methods). Each point was considered independent (in non-parametric Wilcoxon tests), while this is not the case given the repetitions with the same individuals (the number of repeated encounters per individual should be given in the methods section, by the way). We strongly recommend revising the statistical analyses of the results in Figures 4 and 5. In addition, it could be interesting to check whether the vocal behaviour is stable within each individual (i.e., a male that is vocalising frequently in one situation vocalises always frequently in other situations).

It is not easy to understand the rationale behind testing animals in pairs and in triads from the beginning of the manuscript. The authors should better introduce this aspect in the manuscript, especially given the fact that biological results deal with this aspect in Figure 5. The authors might strengthen the parts on the biological results extracted from their new method.

More specifically, the fact that one male takes over the vocal behaviour within a triad is of high interest. Nevertheless, some behavioural data would be needed to strengthen these findings.

A small proportion of USVs was not assigned. The authors did not discuss the potential reason for this failure (Were the USVs too soft? Did they include specific acoustic characteristics that render them difficult to localise?). These points could be of interest when testing other mouse strains or other species.

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 #2 (Public Review):

In the present study, Castano et al. discovered a chemical inhibitor that is specifically effective against the kinase activity of CDKL5 and applied it in the in vitro and the brain slice culture to reveal the acute effects of the loss of function (LOF) of CDKL5. LOF has been modeled in gene knockout mice, but these are loss-of-function models with the added developmental time effects of the absence of CDKL5 from developmental stages. The present authors' approach is the fastest timescale study to date, examining CDKL5 LOF effects in seconds to minutes.

The authors showed that chemical inhibition of CDKL5 kinase activity suppresses postsynaptically derived LTP in rat brain slice experiments, indicating that the previously controversial results of CDKL5 LOF on LTP in knockout mice and rats are possibly due to combined effects of the loss of the kinase and compensation by other factors.

The authors employed state-of-the-art methodologies and presented their data clearly and convincingly.

Reviewer #3 (Public Review):

In this manuscript, Castano et al generate and test a small molecule inhibitor of CDKL5, an X-linked kinase whose loss-of-function is the cause of a severe neurodevelopmental disorder. Since the current knowledge of CDKL5 functions mainly rely on genetic models it is still unclear which effects are caused directly by CDKL5 loss and which can be ascribed to indirect effects. A specific inhibitor would therefore be an important tool for the field.

Castano and colleagues therefore tested a panel of twenty kinase inhibitors for their capacity to block phosphorylation of a EB2, a bona fide CDKL5 substrate, in rat neurons. Among the three that could inhibit EB2 phosphorylation at low concentrations, one was found to inhibit CDKL5 while not affecting GSK3 kinases, which share significant homology to CDKL5. Considering that genetic studies have previously linked CDKL5 to excitatory synaptic transmission, acute hippocampal slices were exploited to test the consequences of CDKL5 inhibition. While CDKL5 loss in the past was found to affect both AMPA- and NMDA-Rs, the small molecule-based inhibition affected only AMPA-R responses at the post-synaptic level. Since pharmacokinetic analyses showed that the inhibitor has a low capacity for brain penetration the molecule remains limited for testing the acute inhibition of CDKL5 in vitro and ex vivo. Such a tool represents an important aspect in the CDKL5 field and the findings suggesting a direct role of CDKL5 in regulating AMPA-R functions are interesting. However, the manuscript could be improved to render it more readable.

The description of the binding and orthogonal assays, which are the basis for the selection of the small molecule inhibitor, is not straightforward to understand for non-expert readers and could be improved.

While the in vitro and ex vivo assays are well presented, it is not clear why the myelin basic protein is used as a substrate for CDKL5 in the in vitro kinase assays. Does this protein contain a CDKL5 consensus site?

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 #2 (Public Review):

Schmit et al. analyze and compare different strategies for the allocation of funding for insecticide-treated nets (ITNs) to reduce the global burden of malaria. They use previously published models of Plasmodium falciparum and Plasmodium vivax malaria transmission to quantify the effect of ITN distribution on clinical malaria numbers and the population at risk. The impact of different resource allocation strategies on the reduction of malaria cases or a combination of malaria cases and achieving pre-elimination is considered to determine the optimal strategy to allocate global resources to achieve malaria eradication.

Strengths:<br /> Schmit et al. use previously published models and optimization for rigorous analysis and comparison of the global impact of different funding allocation strategies for ITN distribution. This provides evidence of the effect of three different approaches: the prioritization of high-transmission settings to reduce the disease burden, the prioritization of low-transmission settings to "shrink the malaria map", and a resource allocation proportional to the disease burden.

Weaknesses:<br /> The analysis and optimization which provide the evidence for the conclusions and are thus the central part of this manuscript necessitate some simplifying assumptions which may have important practical implications for the allocation of resources to reduce the malaria burden. For example, seasonality, mosquito species-specific properties, stochasticity in low transmission settings, and changing population sizes were not included. Other challenges to the reduction or elimination of malaria such as resistance of parasites and mosquitoes or the spread of different mosquito species as well as other beneficial interventions such as indoor residual spraying, seasonal malaria chemoprevention, vaccinations, combinations of different interventions, or setting-specific interventions were also not included. Schmit et al. clearly state these limitations throughout their manuscript.

The focus of this work is on ITN distribution strategies, other interventions are not considered. It also provides a global perspective and analysis of the specific local setting (as also noted by Schmit et al.) and different interventions as well as combinations of interventions should also be taken into account for any decisions. Nonetheless, the rigorous analysis supports the authors' conclusions and provides evidence that supports the prioritization of funding of ITNs for settings with high Plasmodium falciparum transmission. Overall, this work may contribute to making evidence-based decisions regarding the optimal prioritization of funding and resources to achieve a reduction in the malaria burden.

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 #2 (Public Review):

Marmor et al. mine a previously published dataset to examine whether recent reward/stimulus history influences responses in sensory (and other) cortices. Bulk L2/3 calcium activity is imaged across all of the dorsal cortex in transgenic mice trained to discriminate between two textures in a go/no-go behavior. The authors primarily focus on comparing responses to a specific stimulus given that the preceding trial was or was not rewarded. There are clear differences in activity during stimulus presentation in the barrel cortex along with other areas, as well as differences even before the second stimulus is presented. These differences only emerge after task learning. The data are of high quality and the paper is clear and easy to follow. My only major criticism is that I am not completely convinced that the observed difference in response is not due to differences in movement by the animal on the two trial types. That said, the demonstration of differences in sensory cortices is relatively novel, as most of the existing literature on trial history effect demonstrates such differences only in higher-order areas.

Major:

1a. The claim that body movements do not account for the results is in my view the greatest weakness of the paper - if the difference in response simply reflects a difference in movement, perhaps due to "excitement" in anticipation of reward after not receiving one on CR-H vs. H-H trials, then this should show up in movement analysis. The authors do a little bit of this, but to me, more is needed.

First, given the small sample size and use of non-parametric tests, you will only get p<.05 if at least 6 of the 7 mice perform in the same way. So getting p>.05 is not surprising even if there is an underlying effect. This makes it especially important to do analyses that are likely to reveal any differences; using whisker angle and overall body movement, which is poorly explained, is in my opinion insufficient. An alternative approach would be to compare movements within animals; small as the dataset is, it is feasible to do an animal-by-animal analysis, and then one could leverage the large trial count to get much greater statistical power, foregoing summary analyses that pool over only n=7.

The authors only consider a simple parametrization of movement (correlation across successive frames), and given the high variability in movement across animals, it is likely that different mice adopt different movements during the task, perhaps altering movement in specific ways. Aggregating movement across different body parts after an analysis where body parts are treated separately seems like an odd choice - perhaps it is fine, but again, supporting evidence for this is needed. As it stands, it is not clear if real differences were averaged out by combining all body parts, or what averaging actually entails.

If at all possible, I would recommend examining curvature and not just the whisker angle, since the angle being the same is not too surprising given that the stimulus is in the same place. If the animal is pressing more vigorously on CR-H trials, this should result in larger curvature changes.

Finally, the authors presumably have access to lick data. Are reaction times shorter on CR-H trials? Is lick count or lick frequency shorter?

If movement differs across trial types, it is entirely plausible that at least barrel cortex activity differences reflect differences in sensory input due to differences in whisker position/posture/etc. This would mitigate the novelty of the present results.

1b. Given the importance of this control to the story, both whisker and body movement tracking frames should be explicitly shown either in the primary paper or as a supplement. Moreover, in the methods, please elaborate on how both whisker and body tracking were performed.

2. Did streak length impact the response? For instance, in Fig. 1f "Learning", there is a 6-trial "no-go" streak; if the data are there, it would be useful to plot CR-H responses as a function of preceding unrewarded trials.

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 #2 (Public Review):

In this manuscript, Franco et al show that the mitofusin 2 mutation MFN2 Q400 impaires mitochondrial fusion with normal GTPase activity. MFN2 Q400 fails to recruit Parkin and further disrupts Parkin-mediated mitophagy in cultured cardiac cells. They also generated MFN2 Q400 knock-in mice to show the development of lethal perinatal cardiomyopathy, which had an impairment in multiple metabolic pathways.

The major strength of this manuscript is the in vitro study that provides a thorough understanding in the characteristics of the MFN2 Q400 mutant in function of MFN2, and the effect on mitochondrial function. However, the in vivo MFN2 Q/Q400 knock-in mice are more troubling given the split phenotype of MFN2 Q/Q400a vs MFN2 Q/Q400n subtypes. Their main findings towards impaired metabolism in mutant hearts fail to distinguish between the two subtypes.

While the data support the conclusion that MFN2 Q400 causes cardiomyopathy, several experiments are needed to further understand mechanism. This manuscript will likely impact the field of MFN2 mutation-related diseases and show how MFN2 mutation leads to perinatal cardiomyopathy in support of previous literature.

Reviewer #2 (Public Review):

MCM8 and MCM9 together form a hexameric DNA helicase that is involved in homologous recombination (HR) for repairing DNA double-strand breaks. The authors have previously reported on the winged-helix structure of the MCM8 (Zeng et al. BBRC, 2020) and the N-terminal structure of MCM8/9 hexametric complex (MCM8/9-NTD) (Li et al. Structure, 2021). This manuscript reports the structure of a near-complete MCM8/9 complex and the conformational change of MCM8/9-NTD in the presence of its binding protein, HROB, as well as the residues important for its helicase activity.

The presented data might potentially explain how MCM8/9 works as a helicase. However, additional studies are required to conclude this point because the presented MCM8/9 structure is not a DNA-bound form and HROB is not visible in the presented structural data. Taking into these accounts, this work will be of interest to biologists studying DNA transactions.

A strength of this paper is that the authors revealed the near-complete MCM8/9 structure with 3.66A and 5.21A for the NTD and CTD, respectively (Figure 1). Additionally, the authors discovered a conformational change in the MCM8/9-NTD when HROB was included (Figure 4) and a flexible nature of MCM8/9-CTD (Figure S6 and Movie 1).

The revised version of "Structural and mechanistic insights into the MCM8/9 helicase complex" by Weng et al. includes only very minor changes in the text and incorporates two additional supplementary figures (S8 and S11) illustrating the size of MCM8/9 mutants.

In the previous version, I raised two important concerns that required addressing. 1) The presented structures exclusively depicted the unbound forms of DNA. It is crucial to elucidate the structure of a DNA-bound form. 2) The MCM8/9 activator, HROB, was not visible in the structural data. Although HROB induced a conformational change in MCM8/9-NTD, it is essential to visualize the structure of an MCM8/9-HROB complex.

The authors neither addressed nor provided new data in response to these issues. Consequently, I maintain my initial stance and have no further comments on the revised version.

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.

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 #2 (Public Review):

The authors present an important study on the potential of small extracellular vesicle (sEV)-derived RNAs as biomarkers for the early detection of colorectal cancer (CRC) and precancerous adenoma (AA). The authors provide a detailed analysis of the RNA landscape of sEVs isolated from participants, identifying differentially expressed sEV-RNAs associated with T1a stage CRC and AA compared to normal controls. The paper further categorises these sEV-RNAs into modules and constructs a 60-gene model that successfully distinguishes CRC/AA from NC samples. The authors also validate their findings using RT-qPCR and propose an optimised classifier with high specificity and sensitivity. Additionally, the authors discuss the potential of sEV-RNAs in understanding CRC carcinogenesis and suggest that a comprehensive biomarker panel combining sEV-RNAs and proteins could be promising for identifying both early and advanced CRC patients. Overall, the study provides valuable insights into the potential clinical application of sEV-RNAs in liquid biopsy for the early detection of CRC and AA.

Major strengths:<br /> 1. Comprehensive sEV RNA profiling: The study provides a valuable dataset of the whole-transcriptomic profile of circulating sEVs, including miRNA, mRNA, and lncRNA. This approach adds to the understanding of sEV-RNAs' role in CRC carcinogenesis and facilitates the discovery of potential biomarkers.

2. Detection of early-stage CRC and AA: The developed 60-gene t-SNE model successfully differentiated T1a stage CRC/AA from normal controls with high specificity and sensitivity, indicating the potential of sEV-RNAs as diagnostic markers for early-stage colorectal lesions.

3. Independent validation cohort: The study combines RNA-seq, RT-qPCR, and modelling algorithms to select and validate candidate sEV-RNAs, maximising the performance of the developed RNA signature. The comparison of different algorithms and consideration of other factors enhance the robustness of the findings.

Major weaknesses:<br /> 1. Lack of analysis on T1-only patients in the validation cohort: While the study identifies key sEV-RNAs associated with T1a stage CRC and AA, the validation cohort is only half of the patients in T1(25 out of 49). It would be better to do an analysis using only the T1 patients in the validation cohort, so the conclusion is not affected by the T2-T3 patients.

2. Lack of performance analysis across different demographic and tumor pathology factors listed in Supplementary Table 12. It's important to know if the sEV-RNAs identified in the study work better/worse in different age/sex/tumor size/Yamada subtypes etc.

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 #2 (Public Review):

In this study, the authors identified the complex TOR, HOG, and CWI signaling networks-involved genes that relatively modulate the development, aflatoxin biosynthesis and pathogenicity of A. flavus by gene deletions combined with phenotypic observation.

They also analyzed the specific regulatory process and proposed that the TOR signaling pathway interacts with other signaling pathways (MAPK, CWI, calcineurin-CrzA pathway) to regulate the responses to various environmental stresses. Notably, they found that FKBP3 is involved in sclerotia and aflatoxin biosynthesis and rapamycin resistance in A. flavus, and that the conserved site K19 of FKBP3 plays a key role in regulating the aflatoxin biosynthesis. In general, there is a heavy workload task carried in this study and the findings are interesting and important for understanding or controlling aflatoxin biosynthesis. However, findings have not been deeply explored and conclusions mostly are based on parallel phenotypic observations. In addition, there are some concerns that exist surrounding the conclusions.

Reviewer #3 (Public Review):

The paper by Li et al. describes the role of the TOR pathway in Aspergillus flavus. The authors tested the effect of rapamycin in WT and different deletion strains. This paper is based on a lot of experiments and work but remains rather descriptive and confirms the results obtained in other fungi. It shows that the TOR pathway is involved in conidiation, aflatoxin production, pathogenicity, and hyphal growth. This is inferred from rapamycin treatment and TOR1/2 deletions. Rapamycin treatment also causes lipid accumulation in hyphae. The phenotypes are not surprising as they have been shown already for several fungi. In addition, one caveat is in my opinion that the strains grow very slowly and this could cause many downstream effects. Several kinases and phosphatases are involved in the TOR pathway. They were known from S. cerevisiae or filamentous fungi. The authors characterized them as well with knock-out approaches.

As for many results, I miss the re-complementation of the created mutants throughout the manuscript. This is standard praxis.

Fig. 1: cultures were grown for 48 h before measuring the transcript level. The authors show that brlA, abaA, and some sexual regulators are less expressed. In my opinion, this does not allow the conclusion that there is a direct control through rapamycin. Since the colonies grow very slowly in the presence of rapamycin, the authors should add rapamycin and follow gene expression after 15, 30, 60, 90 min. The figure legend needs to be more detailed. Which type of cultures were used, liquid, solid medium? Etc.

Why in chapter one Fig. 9 is already cited? Those data should then be included in Fig. 1 for the general phenotype.

The authors wrote that radial growth and conidiation were gradually reduced with increasing rapamycin concentrations. This is not true. There is no gradient! However, it should be tested if there is a gradient if lower concentrations are used. The current data imply that there is a threshold concentration, so either there is 100 % growth or a reduction to 25 %. This looks strange.

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 #2 (Public Review):

This study by Syed et al identifies Prmt5 as a novel and broad modulator of gene expression and genome architecture during the early stages of adipogenesis. Specifically, Prmt5 is reported to be required to maintain strong insulation at TAD boundaries.

This is a logically and clearly conducted study that relies on the integration of public datasets (PCHi-C) to identify chromatin loops, with its own new genomics datasets, including Prmt5 ChIPseq and Hi-C data in control and Prmt5 kd cells. Despite showing relatively model effects of Prmt5 kd on genome architecture, the results are informative and contribute to advancing our knowledge of chromatin-linked processes during early adipogenesis.

The manuscript would benefit from incorporating ATACseq data (public or own) to better appreciate binding profiles of Prmt5 at H3K27ac sites. A more detailed analysis of these relative enrichments would also be useful, particularly if linked to a transcription factor footprint from ATAC data.

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 #2 (Public Review):

The Kinesin superfamily motors mediate the transport of a wide variety of cargos which are crucial for cells to develop into unique shapes and polarities. Kinesin-3 subfamily motors are among the most conserved and critical classes of kinesin motors which were shown to be self-inhibited in a monomeric state and dimerized to activate motility along microtubules. Recent studies have shown that different members of this family are uniquely activated to undergo a transition from monomers to dimers.

Niwa and colleagues study two well-described members of the kinesin-3 superfamily, unc104 and KLP6, to uncover the mechanism of monomer to dimer transition upon activation. Their studies reveal that although both Unc104 and KLP6 are both self-inhibited monomers, their propensities for forming dimers are quite different. The authors relate this difference to a region in the molecules called CC2 which has a higher propensity for forming homodimers. Unc104 readily forms homodimers if its self-inhibited state is disabled while KLP6 does not.

The work suggests that although mechanisms for self-inhibited monomeric states are similar, variations in the kinesin-3 dimerization may present a unique form of kinesin-3 motor regulation with implications on the forms of motility functions carried out by these unique kinesin-3 motors.

Reviewer #3 (Public Review):

In this work, Kita et al., aim to understand the activation mechanisms of the kinesin-3 motors KLP-6 and UNC-104 from C. elegans. As with many other motor proteins involved in intracellular transport processes, KLP-6 and UNC-104 motors suppress their ATPase activities in the absence of cargo molecules. Relieving the autoinhibition is thus a crucial step that initiates the directional transport of intracellular cargo. To investigate the activation mechanisms, the authors make use of mass photometry to determine the oligomeric states of the full-length KLP-6 and the truncated UNC-104(1-653) motors at sub-micromolar concentrations. While full-length KLP-6 remains monomeric, the truncated UNC-104(1-653) displays a sub-population of dimeric motors that is much more pronounced at high concentrations, suggesting a monomer-to-dimer conversion. The authors push this equilibrium towards dimeric UNC-104(1-653) motors solely by introducing a point mutation into the coiled-coil domain and ultimately unleashing a robust processivity of the UNC-104 dimer. The authors find that the same mechanistic concept does not apply to the KLP-6 kinesin-3 motor, suggesting an alternative activation mechanism of the KLP-6 that remains to be resolved. The present study encourages further dissection of the kinesin-3 motors with the goal of uncovering the main factors needed to overcome the 'self-inflicted' deactivation.

Reviewer #2 (Public Review):

Yu et al. investigated the structural landscape of 'secreted in xylem' (SIX) effector (virulence and avirulence) proteins from the plant-pathogenic fungus, Fusarium oxysporum f. sp. lycopersici (Fol), with the goal of better understanding effector function and recognition by host (tomato) immune receptors. In recent years, several experimental and computational studies have shown that many effector proteins of plant-associated fungi can be assigned to one of a few major structural families. In the study by Yu et al., X-ray crystallography was used to show that two avirulence effectors of Fol, Avr1 (SIX4) and Avr3 (SIX1), which are recognized by the tomato immune receptors I and I-3, respectively, form part of a new structural family, the Fol dual-domain (FOLD) family, found across three fungal divisions. Using AlphaFold2, an ab initio structural prediction tool, the authors then predicted the structures of all proteins within the Fol SIX effector repertoire (and other effector candidates) and provided evidence that two other effectors, SIX6 and SIX13, also belong to this family.

In addition to identifying members of the FOLD family, structural prediction revealed that proteins of the Fol effector repertoire can largely be classified into a reduced set of structural families. Examples included four members of the ToxA-like family (including Avr2 (SIX3) and SIX8), as well as four members of a new family, Family 4 (including SIX5 and PSL1). Given previous studies had demonstrated that Avr2 (ToxA-like) and SIX5 (Family 4) interact and function together and that the genes encoding these proteins are divergently transcribed, and because homologues of SIX8 (ToxA-like) and PSL1 (Family 4) from another Fusarium pathogen are functionally dependent on each other and, in the case of Fol, are encoded by genes that are next to each other in the genome, the authors hypothesized that SIX8 and PSL1 may also physically interact. In line with this, co-incubation of the SIX8 and PSL1 proteins, followed by size exclusion chromatography (SEC), gave elution and gel migration profiles consistent with interaction in the form of a heterodimer. AlphaFold2-Multimer modelling then suggested that this interaction was mediated through an intermolecular disulfide bond. Such a prediction was subsequently confirmed through mutational analysis of the relevant cysteine residue in each protein in conjunction with SEC.

Finally, using a variant (homologue) of Avr1 from another Fusarium pathogen, as well as chimeric forms of this protein that integrated regions of Avr1 from Fol, Yu et al. determined through co-expression assays in Nicotiana benthamiana with the I immune receptor, as well as subsequent ion leakage assays, that the C-domain of Avr1 is recognized by the I immune receptor. Furthermore, through these assays, the authors were also able to show that surface-exposed residues in the C-domain enable Avr1 to evade recognition by a variant of the I receptor in Moneymaker tomato that does not provide resistance to Fol.

Overall, the manuscript presents a large body of work that is well supported by the data. A key strength of the manuscript is the validation (benchmarking) of protein structures predicted using AlphaFold2, which is a first for largescale effector structure prediction papers published to date. Another key strength is the use of largescale effector structure predictions to make hypotheses about functional relationships or interactions that are then tested (i.e. the SIX8-PSL1 protein interaction and recognition of Avr1 by the I immune receptor). This testing again goes above and beyond the large scale effector structure prediction papers published to date. Taken together, this showcases how experimental and computational experiments can be effectively combined to provide biologically relevant data for the plant protection and molecular plant-microbe interactions fields.

In terms of weaknesses, the manuscript could have validated the SIX8-PSL1 protein interaction with in planta experiments, such as co-immunoprecipitation assays or co-localization experiments in conjunction with confocal microscopy, to provide support for the interaction in a plant setting. However, given what is already known about the Avr2-SIX5 interaction, these additional experiments are not crucial and could instead form part of a follow-up study. With regards to the Agrobacterium tumefaciens-mediated transient expression assays involving co-expression of the Avr1 effector and I immune receptor, the authors need to make clear how many biological replicates were performed as this information is only provided for the ion leakage assay.

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 #3 (Public Review):

In this work, the authors shed light onto the structures of Fusarium oxysporum f.sp. lycopersici proteins involved in the infection of tomato. They unravelled several new secreted effector protein structures and additionally used computational approaches to structurally classify the remaining effectors known from this pathogen. Through this they uncovered a new and unique structural class of proteins which they found to be present and widely distributed in fungal plant pathogens and plant symbiotic fungi. The authors further predicted structural models for the full SIX effector set revealing that genome-proximal effector pairs share similar structural classes. Building on their Avr1 structure, the authors also define the C-terminal domain and specific amino acid residues that are essential to Avr1 detection by its cognate immune receptor.

A major strength of this work is a portfolio of several (Avr1, Avr3, SIX6, SIX8) new structurally resolved proteins which led to the discovery that several of them fall into the same structural class. These findings are supported by strong results.

The experiments addressing the structure-function relationship of Avr1's avirulence activity are highly relevant to our understanding of disease resistance mechanisms against Fusarium, but will require additional controls to allow for solid conclusions to be drawn. In particular, it needs to be demonstrated that specific I gene alleles are at all required for FonSIX4's cell death activity in N.benthamiana leaves or whether FonSIX4 and those of some chimeric proteins is independent of the tomato I receptor. Complementary work in Fusarium mutants lacking Avr1 and expressing chimeric versions would document that the observations from transient expressions in Nicotiana benthamiana are relevant in the biological context of a Fusarium/tomato interaction.

The discovered solvent-exposed residues conditioning Avr1 recognition by the I receptor seem to be positioned in an area of the protein which had previously been highlighted as being highly variable in FOLD proteins of symbiotic fungi but it is not clear from the work whether this is indeed the case or whether Avr1 differs significantly in its structure from FOLD proteins found in other fungi.<br /> It also remains to be addressed whether the residues conditioning avirulence activity is also crucial for virulence activity in Fusarium?

This work uncovered a new structural class of proteins with critical roles in plant-pathogen interactions. Structure-based predictions and genome-wide comparisons have emerged as a new approach enabling the identification of similar proteins with divergent sequences. The work undertaken by the authors adds to a growing body of work in plant-microbe research, predominantly from pathogenic organisms, and more recently in symbiotic fungi.

Reviewer #3 (Public Review):

This is an interesting and carefully done study that will be of considerable value to the field of cortical interneurons. The main result is the development of a novel intersectional genetic strategy to identify and manipulate neurogliaform cells (NGFCs), an interneuron subtype that has been somewhat under-explored to date (but perhaps not quite as enigmatic as implied by the authors). The new strategy, using Id2-CreER transgenic mice crossed with a pan-interneuronal Flp line, appears to label all interneurons which do not express PV, Sst, or VIP, and thus defines a fourth subclass of interneurons. The main members of this subclass are NPY-expressing NGFCs. The strategy allows the targeting of NGFCs in all cortical layers, in contrast to previous strategies using the NDNF-Cre mice which target mostly Layer 1 NGFCs (and possibly also other Layer 1 subtypes). The same strategy also labels a relatively small population of non-NGF Id2 cells belonging to the CCK-expressing subtype(s).

In the first stage of the study, the authors characterize the labeled neurons by their expression of protein markers (most notably NPY and CCK), by their dendritic and axonal morphology, and by their electrophysiological properties. This characterization is detailed and rigorous and the observed characteristics are consistent with what is already known about the properties of NGFCs. The weaknesses here are that the morphological features are not analyzed quantitatively, the definition of electrophysiological subtypes remains somewhat subjective, and the authors do not attempt a multivariate analysis that could provide a data-driven parcellation into subtypes.

The authors then go two steps further. First, they use ex-vivo recordings to demonstrate that presumed CCK+ neurons (identified by their firing pattern as "non-late-spiking), but not NGFCs (identified by their "late-spiking" phenotype), are sensitive to endocannabinoids released from postsynaptic pyramidal cells upon depolarization of the latter. This DSI ("depolarization suppression of inhibition") is a well-studied property of hippocampal CCK+ basket cells, so its demonstration adds to the validation of the intersectional strategy in targeting this subtype in the neocortex. Somewhat surprisingly, the authors do not attempt to demonstrate in their ex-vivo experiments what may be the best-known property of NGFCs - their propensity to preferentially activate GABAB receptors.

The authors then perform in-vivo silicon probe recordings in which Id2 cells are "optotagged" with ChR2 and can thus be identified in extracellular recordings. These in-vivo recordings are probably the first ever from identified NGFCs below layer 1, although some uncertainty remains about the identification of optotagged cells as NGFCs vs CCK-expressing interneurons. They find several differences between firing patterns of NGFCs and other interneurons or pyramidal cells (identified by their extracellular spike waveforms), the most dramatic being a pronounced "rebound" of NGFC firing during slow-wave sleep immediately after a DOWN-to-UP state transition. While the functional significance of these findings is not clear, these experiments provide proof of concept that this fourth (and last?) interneuron subclass can be identified, recorded, and manipulated in freely behaving animals.

In summary, while adding only modestly to our knowledge of NGFCs and CCK-expressing interneurons per se, this work provides an important new tool that will no doubt be used in future studies to target cortical NGFCs and CCK interneurons for in-vivo and ex-vivo recordings, for optogenetic manipulations and for calcium or voltage imaging using genetically-encoded probes. In this sense, the current study is a breakthrough into what may truly be "the last frontier" of cortical interneurons.

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 #2 (Public Review):

Machold and colleagues develop and describe an intersectional genetic mouse (Id2Cre:Dlx5/6FlpE) that allows for the targeting of a cortical interneuron subpopulation predominantly consisting of the neurogliaform cell subtype (NGFCs). The strategy is a modification of that previously published by the authors (Id2cre:Nkx2-1Flpo; Valero et al., 2021) in which a subset of deep layer 6 NGFCs with distinct embryonic origins were targeted. Conversely, using the NDNF transgenic mouse lines previous studies, including those from the Rudy laboratory, have clearly shown the prevalence of NGFCs in the outermost cortical Layer 1 region. Thus, the Id2Cre:Dlx5/6FlpE mouse poses an advantage over these previous approaches permitting the targeting of NGFCs in Layers 2-5. NGFCs in these regions have been hitherto difficult to study in an expedited manner.

The manuscript is of the resource/toolbox type and the authors are thorough in their description of the distribution and molecular characteristics of the ID2 neurons labelled by this intersectional approach. Furthermore, the authors perform a series of in vivo experiments. These entail the identification of NGFCs, the assessment of their influence on other neuronal populations, and the ability to delineate their activity during various network and behavioral states. Indeed, the authors reveal an activity pattern that is unique to NGFCs across epochs of specific network states. Therefore, this clearly demonstrates the applicability of the ID2Cre:Dlx5/6Flpe mouse to study the role of L2-5 NGFCs in a whole brain setting and these in vivo experiments constitute a major strength of the current study.

However, as with many transgenic mice, they are not always perfect, and the authors are very transparent regarding the additional, albeit a relatively smaller number of reported non-NGFCs particularly those of the CCK IN subtype. Indeed, clear morpho-functional divergence is revealed by the authors between these ID2 IN subpopulations. Furthermore, it is possible that this variability may differ across varying cortical regions. Thus, careful consideration of this caveat is necessary when using this mouse for future in vitro and in vivo studies. Related to this matter is a concern regarding the framing of the manuscript. The authors term the ID2 mixed population as the "4th group" since they do not express PV, SST, and VIP. One could argue this is a matter of semantics but to combine IN types that display distinct morphological and physiological properties into a single "group" based on one molecular feature is not consistent with that proposed by the widely accepted Petilla terminology (Ascoli et al., 2008).

Of interest to many who investigate cortical INs is the ability to genetically target specific subtypes during development. To this end, a potential and welcome addition to the manuscript would be an analysis (perhaps restricted to distribution/molecular characterization) highlighting whether the Id2cre:Dlx5/6Flpe strategy allows genetic access to layer 2-5 NGFCs during postnatal development following maternal tamoxifen administration.

Regardless, the experiments in the current study are, in general, well performed and clearly presented with the authors' conclusions supported by the results. Thus, it is clear that further refinements to genetic strategies are obviously required to exclusively target NGFCs throughout the cortical depth. Nevertheless, in the interim, the approach described in this current manuscript will be of use to the neuroscience community and help to further unravel the physiological role of this relatively understudied neuronal subtype.

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 #2 (Public Review):

In this manuscript, the authors address how cerebellar Purkinje cells (PC) control the firing of nuclear cells (CbN), the output stage of the cerebellar. They used patch-clamp recordings in acute cerebellar slices, and combined dynamic clamp with simulations of nuclear cell firing rate.

This article addresses one of the most fundamental unresolved question of the cerebellar physiology: how inhibitory PCs control the output stage of the cerebellum?<br /> They first described a developmental evolution of the that PC-CbN synapses. Inhibitory synaptic weights become highly variable after three weeks of age, with a group of very large PC inputs. They used dynamic clamp to examine the influence of these variable inputs on CbN firing rate. They demonstrate that while all input size affect CbN discharge, larger ones can stop them for a few milliseconds. Using a distribution of variable input size, they showed that increasing the variability of PC inputs favor CbN discharge, while increasing the magnitude of a constant inhibitory conductance decrease their firing rate. By varying the frequency of PC inputs, they suggest that CbNs faithfully transmit rate code, but larger inputs are more effective to decrease their firing rate. Finally, addressing how synchrony of variable PC inputs influence CbN discharge, dynamic clamp studies and simulations showed that input synchronization enhance firing, but driven by the total charge of the inhibitory input.

The keystone observations that PC inputs are highly variable is very interesting and convincing and open new questions about PC-CbN plasticity. More importantly the combination of dynamic clamp and simulations is a real strength of the study, allowing the authors to test many combinations of inputs in real cells and extrapolating their hypotheses in silico. Weaknesses result from the assumptions made on the construction of the distribution of inputs and the many different conditions explored. The organization of the article could be difficult to read for a non-specialist of cerebellar physiology.

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 #2 (Public Review):

The authors of this paper use a "digital twin" computational model of electrophysiology to investigate the pathology of Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) in several patients undergoing Electro-Physiological Studies (EPS) to treat Ventricular Tachycardias (VTs). The digital twin computational models are customised to the individual patient in two ways. Firstly, information on the patient's heart geometry and muscle/fibrous structure is extracted from Late Gadolium-Enhanced Magnetic Resonance Image (LGE-MRI) scans. Secondly, information from the patient's genotype is used to decide the particular electrophysiological cell model to use in the computational model. The two patient genotypes investigated include a Gene Ellusive (GE) group characterised by abnormal fibrous but normal cell electrical physiology and a palakophilin-2 (PKP2) group in which patients have abnormal fibrotic remodelling and distorted electrical conduction. The computational model predicts the locations and pathways of re-entrant circuits that cause VT. The model results are compared to previous recordings of induced VTs obtained from EPS studies.

The paper is very well written, and the modelling study is well thought out and thorough and represents an exemplar in the field. The major strengths of the paper are the use of a personalised patient model (geometry, fibrous structure and genotype) in a clinically relevant setting. Such a comprehensive personal model puts this paper at the forefront of such models in the field. The main weaknesses of the paper are more of a reflection on what is required for creating such models than on the study itself. As the authors acknowledge, the number of patients in each group is small. Additional patients would allow for statistical significance to be investigated.

The paper's authors set out to demonstrate the use of a "digital twin" computational model in the clinical setting of ARVC. The main findings of the paper were threefold. Firstly, the locations of VTs could be accurately predicted. There was a difference in the abnormal fibrous structure between the two genotype groups. Finally, there was an interplay between the fibrous structure of the heart and the cellular electrophysiology in that the fibrous remodelling was responsible for VTs in the GE group, but in the PKP2 group VTs were caused by slowed electrical conduction and altered restitution. The study successfully met the aims of the paper.

The major impact of the paper will be in demonstrating that a personalised computational model can a) be developed from available measurements (albeit at the high end of what would normally be measured clinically) and b) generate accurate results that may prove helpful in a clinical setting. Another impact is the finding in the paper that the cause of VTs may be different for the two genotypes investigated. The different interplay between fibrous and electrophysiology suggested by the modelling results may provide insights into different treatments for the different genotypes of the pathology. The authors use open-source software and have deposited all non-confidential data in publically available repositories.

Reviewer #3 (Public Review):

Overview: The authors propose a personalized ventricular computational model (Geno-DT) that incorporates the patient's structural remodeling (fibrosis and scar locations based on LGE-CMR scans) as well as genotyping (cell membrane kinetics based on genetic testing results) to predict VT locations and morphologies in ARVC setting.<br /> To test the model, the authors conducted a retrospective study using 16 ARVC patient data with two genotypes (PKP2, GE) and reported high degree of sensitivity, specificity, and accuracy. In addition, the authors determined that in GE patients, VT was driven by fibrotic remodeling, whereas, in PKP2 patients, VT was associated with a combination of structural and electrical remodeling (slowed conduction and altered restitution).<br /> Based on the findings, the authors recommend using Geno-DT approach to augment therapeutic accuracy in treatment of ARVC patients.

Critiques:

1. The small sample size is a limitation but has already been acknowledged and documented by the authors.

2. Another limitation is the consideration of only two of the possible genotypes in developing the cell membrane kinetics, but again has been acknowledged by the authors.

Final Thoughts: The authors have done a commendable job in targeting a disease phenotype that is relatively rare, which constrains the type of data that can be collected for research. Their personalized computational model approach makes a valuable contribution to furthering our understanding of ARVC mechanisms.

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 #2 (Public Review):

The mechanisms that mediate female aggression remain poorly understood. Chiu, Schretter, and colleagues, employed circuit dissection techniques to tease apart the specific roles of particular doublesex and fruitless expressing neurons in the fly Drosophila in generating a persistent aggressive state. They find that activating the fruitless positive alPg neurons, generated an aggressive state that persisted for >10min after the stimulation ended. Similarly, activating the doublesex positive pC1de neurons also generated a persistent state. Activating pC1d or pC1e individually did not induce a persistent state. Interestingly, while neural activation of alPGs and pC1d+e neurons induced persistent behavioural states it did not induce persistent activity in the neurons being activated.

The conclusions of this paper are well supported by the data, there were only a few points where clarification might help:

1) Figure 3 is a little confusing. This is a circuit behavioural epistasis experiment where the authors activate alPg with CsChrimson while inhibiting pC1d with Kir2.1. In Fig. 2 flies were separated for 10 min following stimulation which allowed for identification of a persistent state. However, in Fig 3 it appears as if flies were allowed to freely interact during and immediately post-stimulation. It is unclear why flies were not separated as in Fig. 2, which makes it difficult to compare the two results. Some discussion of this point would help. Also, from the rasters it appears as if inhibition of pC1d reduced aggression induced by alPg during the stimulation period. Is this true?

2) pC1e neurons also have recurrent connectivity with alPg neurons. It might help to also discuss the potential role of this arm of the microcircuit.

Reviewer #3 (Public Review):

Two studies published in 2020 independently identified the alPg, pC1d, and pC1e neurons to be involved in initiating and maintaining a state of aggression in female Drosophila. Both studies combined behavioural analyses, optogenitic manipulation of neurons, and connectomics. One of these studies proposed that the extensive interconnections seen between the alPg and pC1d+e neurons might represent a recurrent motif known to support persistent behvioural states in other systems. In this manuscript, the authors test this idea and report that their data do not support it. Specifically, they report that alPg or pC1d+e (but not pC1d alone) can initiate a persistent state of aggression. But they find that the persistent aggressive state is maintained even when the pC1d neurons are inactivated. Finally, they show that neither of these neurons themselves sustains neuronal activity upon stimulation, nor do either of them induce a persistent activity in the other. Together, their data suggest that the recurrent connection between alPg and pC1d is not what supports the persistent state. The data underlying these claims are convincing. A possibility to explore before ruling out recurrent motifs (at this circuit level) in maintaining aggression is that the connections between alPg and pC1e can compensate for the loss of pC1e. Overall, the study is important and will be of interest to those who study the circuit basis of persistent behavioural states, but also to neuroscientists in general.

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 #2 (Public Review):

Clark and Nolan's study aims to test whether the stability of grid cell firing fields is associated with better spatial behavior performance on a virtual task. Mice were trained to stop at a rewarded location along a virtual linear track. The rewarded location could be marked by distinct visual stimuli or be unmarked. When the rewarded location was unmarked, the animal had to estimate its distance run from the beginning of the trial to know where to stop. When the mouse reached the end of the virtual track, it was teleported back to the start of the virtual track.

The authors found that grid cells could fire in at least two modes. In the "virtual position" mode, grid firing fields had stable positions relative to the virtual track. In the "distance run" mode, grid fields were decoupled from the virtual cues and appeared to be located as a function of distance run on the running wheel. Importantly, on trials in which the rewarded location was unmarked, the behavioral performance of mice was better when grid cells fired in the "virtual position" mode.

This study is very timely as there is a pressing need to identify/delimitate the contribution of grid cells to spatial behaviors. More studies in which grid cell activity can be associated with navigational abilities are needed. The link proposed by Clark and Nolan between "virtual position" coding by grid cells and navigational performance is a significant step toward better understanding how grid cell activity might support behavior. It should be noted that the study by Clark and Nolan is correlative. Therefore, the effect of selective manipulations of grid cell activity on the virtual task will be needed to evaluate whether the activity of grid cells is causally linked to the behavioral performance on this task. In a previous study by the same research group, it was shown that inactivating the synaptic output of stellate cells of the medial entorhinal cortex affected mice's performance of the same virtual task (Tennant et al., 2018). Although this manipulation likely affects non-grid cells, it is still one of the most selective manipulations of grid cells that are currently available.

When interpreting the "position" and "distance" firing mode of grid cells, it is important to appreciate that the "position" code likely involves estimating distance. The visual cues on the virtual track appear to provide mainly optic flow to the animal. Thus, the animal has to estimate its position on the virtual track by estimating the distance run from the beginning of the track (or any other point in the virtual world).

It is also interesting to consider how grid cells could remain anchored to virtual cues. Recent work shows that grid cell activity spans the surface of a torus (Gardner et al., 2022). A run on the track can be mapped to a trajectory on the torus. Assuming that grid cell activity is updated primarily from self-motion cues on the track and that the grid cell period is unlikely to be an integer of the virtual track length, having stable firing fields on the virtual track likely requires a resetting mechanism taking place on each trial. The resetting means that a specific virtual track position is mapped to a constant position on the torus. Thus, the "virtual position" mode of grid cells may involve 1) a trial-by-trial resetting process anchoring the grid pattern to the virtual cues and 2) a path integration mechanism. Just like the "virtual position" mode of grid cell activity, successful behavioral performance on non-beaconed trials requires the animal to anchor its spatial behavior to VR cues.

One main conclusion of this study is that better performance on the VR task was observed when the grid cells were anchored to the reference frame that was the most behaviorally relevant.

Reviewer #3 (Public Review):

This study addresses the major question of 'whether and when grid cells contribute to behavior'. There is no doubt that this is a very important question. My major concern is that I'm not convinced that this study gives a significant contribution to this question, although this study is well-performed and potentially interesting. This is mainly due to the fact that the relation between grid cell properties and behavior is exclusively correlative and entirely based on single cell activity, although the introduction mentions quite often the grid cell network properties and dynamics. In general, this study gives the impression that grid cells exclusively support the cognitive processes involved in this task. This problem is in part related to the text. However, it would be interesting to look at the population level (even beyond grid cells) to test whether at the network level, the link between behavioral performance and neural activity is more straightforward compared to the single-cell level. This approach could reconcile the present results with those obtained in their previous study following MEC inactivation.

The authors used a statistical method based on the computation of the frequency spectrum of the spatial periodicity of the neural firing to classify grid cells as 'position-coding' (with fields anchored to the virtual track) and 'distance-coding' (with fields repeating at regular intervals across trials). This is an interesting approach that has nonetheless the default to be based exclusively on autocorelograms. It would be interesting to compare with a different method based on the similarities between raw maps. Beyond this minor point, cell categorization is performed using all trial types. Each trial type (i.e. beacon or non-beacon) is supposed to force mice to use different strategies and should induce different spatial representations within the entorhinal-hippocampal circuit (and not only in the grid cell system). In that context, since all trials are mixed, it is difficult to extrapolate general information. On page 5 the authors state that 'Since only position representations should reliably predict the reward location, ..., we reasoned that the presence of positional coding could be used to assess whether grid firing contributes to the ongoing behaviour'.

I do not agree with this statement. First of all, position coding should be more informative only in a cue-guided trial. Second, distance coding could be as informative as position coding since at the network level may provide information relevant to the task (such as distance from the reward). This possibility is not tested here. Third, position-coding is interpreted as more relevant because it predominates in correct trials. However, this does not imply that this coding scheme is indeed used to perform correct trials. It could be more informative to push forward the correlative analysis by looking at whether behavioral performance can be predicted by the coding scheme on a trial-by-trial basis. This analysis would not provide a causal relation between cell activity and behavior, but could strengthen the correlation between the two.

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 #2 (Public Review):

Schwarz et al. have presented a study aiming to investigate whether circulating factors in sera of subjects are able to synchronize depending on age, circadian rhythms of fibroblast. The authors used human serum taken from either old (age 70-76) or young (age 25-30) individuals to synchronise cultured fibroblasts containing a clock gene promoter driven luciferase reporter, followed by RNA sequencing to investigate whole gene expression.

This study has the potential to be very interesting, as evidence of circulating factors in sera that mediate peripheral rhythms has long been sought after. Moreover, the possibility that those factors are affected by age which could contribute to the weaken circadian rhythmicity observed with aging.

Here, the authors concluded that both old and young sera are equally competent at driving robust 24 hour oscillations, in particular for clock genes, although the cycling behaviour and nature of different genes is altered between the two groups, which is attributed to the age of the individuals. This conclusion could however be influenced by individual variabilities within and between the two age groups. The groups are relatively small, only four individual two females and two males, per group. And in addition, factors such as food intake and exercise prior to blood drawn, or/and chronotype, known to affect systemic signals, are not taken into consideration. As seen in figure 4, traces from different individuals vary heavily in terms of their patterns, which is not addressed in the text. Only analysing the summary average curve of the entire group may be masking the true data. More focus should be attributed to investigating the effects of serum from each individual and observing common patterns. Additionally, there are many potential causes of variability, instead or in addition to age, that may be contributing to the variation both, between the groups and between individuals within groups. All of this should be addressed by the authors and commented appropriately in the text.

The authors also note in the introduction that rhythms in different peripheral tissues vary in different ways with age, however the entire study is performed on only fibroblast, classified as peripheral tissue by the authors. It would be very interesting to investigate if the observed changes in fibroblast are extended or not to other cell lines from diverse organ origin. This could provide information about whether circulating circadian synchronising factors could exert their function systemically or on specific tissues. At the very least, this hypothesis should be addressed within the discussion.

In addition to the limitations indicated above I consider that the data of the study is an insufficiently analysis beyond the rhythmicity analysis. Results from the STRING and IPA analysis were merely descriptive and a more comprehensive bioinformatic analysis would provide additional information about potential molecular mechanism explaining the differential gene expression. For example, enrichment of transcription factors binding sites in those genes with different patters to pinpoint chromatin regulatory pathways.

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 #2 (Public Review):

The authors investigate the transcriptional regulation of cysteine dioxygenase (CDO-1) in C. elegans and its role in maintaining cysteine homeostasis. They show that high cysteine levels activate cdo-1 transcription through the hypoxia-inducible transcription factor HIF-1. Using transcriptional and translational reporters for CDO-1, the authors propose a negative feedback pathway involving RHY-1, CYSL-1, EGL-9, and HIF-1 in regulating cysteine homeostasis.

Genetics is a notable strength of this study. The forward genetic screen, gene interaction, and epistasis analyses are beautifully designed and rigorously conducted, yielding solid and unambiguous conclusions on the genetic pathway regulating CDO-1. The writing is clear and accessible, contributing to the overall high quality of the manuscript.

Addressing the specifics of cysteine supplementation and interpretation regarding the cysteine homeostasis pathway would further clarify the paper and strengthen the study's conclusions.

First, the authors show that the supplementation of exogenous cysteine activates cdo-1p::GFP. Rather than showing data for one dose, the author may consider presenting dose-dependency results and whether cysteine activation of cdo-1 also requires HIF-1 or CYSL-1, which would be important data given the focus and major novelty of the paper in cysteine homeostasis, not the cdo-1 regulatory gene pathway. While the genetic manipulation of cdo-1 regulators yields much more striking results, the effect size of exogenous cysteine is rather small. Does this reflect a lack of extensive condition optimization or robust buffering of exogenous/dietary cysteine? Would genetic manipulation to alter intracellular cysteine or its precursors yield similar or stronger effect sizes?

Second, there remain several major questions regarding the interpretation of the cysteine homeostasis pathway. How much specificity is involved for the RHY-1/CYSL-1/EGL-9/HIF-1 pathway to control cysteine homeostasis? Is the pathway able to sense cysteine directly or indirectly through its metabolites or redox status in general? Given the very low and high physiological concentrations of intracellular cysteine and glutathione (GSH, a major reserve for cysteine), respectively, there is a surprising lack of mention and testing of GSH metabolism. In addition, what are the major similarities and differences of cysteine homeostasis pathways between C. elegans and other systems (HIF dependency, transcription vs post-transcriptional control)? These questions could be better discussed and noted with novel findings of the current study that are likely C. elegans specific or broadly conserved.

Reviewer #3 (Public Review):

There has been a long-standing link between the biology of sulfur-containing molecules (e.g., hydrogen sulfide gas, the amino acid cysteine, and its close relative cystine, et cetera) and the biology of hypoxia, yet we have a poor understanding of how and why these two biological processes and are co-regulated. Here, the authors use C. elegans to explore the relationship between sulfur metabolism and hypoxia, examining the regulation of cysteine dioxygenase (CDO1 in humans, CDO-1 in C. elegans), which is critical to cysteine catabolism, by the hypoxia inducible factor (HIF1 alpha in humans, HIF-1 in C. elegans), which is the key terminal effector of the hypoxia response pathway that maintains oxygen homeostasis. The authors are trying to demonstrate that (1) the hypoxia response pathway is a key regulator of cysteine homeostasis, specifically through the regulation of cysteine dioxygenase, and (2) that the pathway responds to changes in cysteine homeostasis in a mechanistically distinct way from how it responds to hypoxic stress.

Briefly summarized here, the authors initiated this study by generating transgenic animals expressing a CDO-1::GFP protein chimera from the cdo-1 promoter so that they could identify regulators of CDO-1 expression through a forward genetic screen. This screen identified mutants with elevated CDO-1::GFP expression in two genes, egl-9 and rhy-1, whose wild-type products are negative regulators of HIF-1, raising the possibility that cdo-1 is a HIF-1 transcriptional target. Indeed, the authors provide data showing that cdo-1 regulation by EGL-9 and RHY-1 is dependent on HIF-1 and that regulation by RHY-1 is dependent on CYSL-1, as expected from other published findings of this pathway. The authors show that exogenous cysteine activates cdo-1 expression, reflective of what is known to occur in other systems. Moreover, they find that exogenous cysteine is toxic to worms lacking CYSL-1 or HIF-1 activity, but not CDO-1 activity, suggesting that HIF-1 mediates a survival response to toxic levels of cysteine and that this response requires more than just the regulation of CDO-1. The authors validate their expression studies using a GFP knockin at the cdo-1 locus, and they demonstrate that a key site of action for CDO-1 is the hypodermis. They present genetic epistasis analysis supporting a role for RHY-1, both as a regulator of HIF-1 and as a transcriptional target of HIF-1, in offsetting toxicity from aberrant sulfur metabolism. The authors use CRISPR/Cas9 editing to mutate a key amino acid in the prolyl hydroxylase domain of EGL-9, arguing that EGL-9 inhibits CDO-1 expression through a mechanism that is largely independent of the prolyl hydroxylase activity.

Overall, the data seem rigorous, and the conclusions drawn from the data seem appropriate. The experiments test the hypothesis using logical and clever molecular genetic tools and design. The sample size is a bit lower than is typical for C. elegans papers; however, the experiments are clearly not underpowered, so this is not an issue. The paper is likely to drive many in the field (including the authors themselves) into deeper experiments on (1) how the pathway senses hypoxia and sulfur/cysteine/H2S using these distinct mechanisms/modalities, (2) how oxygen and sulfur/cysteine/H2S homeostasis influence one another, and (3) how this single pathway evolved to sense and respond to both of these stress modalities.

Major strengths of the paper include (1) the use of the powerful whole animal C. elegans model to reveal results that have meaning in vivo, (2) the careful demonstration through mutant rescue experiments that key transgenes have functional activity, (3) the use of CRISPR/Cas9 editing to mutate a critical residue in the catalytic domain of the EGL-9 prolyl hydroxylase, (4) transgenic rescue experiments that show that CDO-1 operates in the hypodermis with regard to the larval arrest phenotype, and (5) the thorough epistatic analysis of different pathway mutants.

Major weaknesses of the paper include (1) the over-reliance on genetic approaches, (2) the lack of novelty regarding prolyl hydroxylase-independent activities of EGL-9, and (3) the lack of biochemical approaches to probe the underlying mechanism of the prolyl hydroxylase-independent activity of EGL-9.

Major Issues We Feel the Authors Should Address:

1. One particularly glaring concern is that the authors really do not know the extent to which the prolyl hydroxylase activity is (or is not) impacted by the H487A mutation in egl-9(rae276). If there is a fair amount of enzymatic activity left in this mutant, then it complicates interpretation. The paper would be strengthened if the authors could show that the egl-9(rae276) eliminates most if not all prolyl hydroxylase activity. In addition, the authors may want to consider doing RNAi for egl-9 in the egl-9(rae276) mutant as a control, as this would support the claim that whatever non-hydroxylase activity EGL-9 may have is indeed the causative agent for the elevation of CDO-1::GFP. Without such experiments, readers are left with the nagging concern that this allele is simply a hypomorph for the single biochemical activity of EGL-9 (i.e., the prolyl hydroxylase activity) rather than the more interesting, hypothesized scenario that EGL-9 has multiple biochemical activities, only one of which is the prolyl hydroxylase activity.

2. The authors observed that EGL-9 can inhibit HIF-1 and the expression of the HIF-1 target cdo-1 through a combination of activities that are (1) dependent on its prolyl hydroxylase activity (and subsequent VHL-1 activity that acts on the resulting hydroxylated prolines on HIF-1), and (2) independent of that activity. This is not a novel finding, as the authors themselves carefully note in their Discussion section, as this odd phenomenon has been observed for many HIF-1 target genes in multiple publications. While this manuscript adds to the description of this phenomenon, it does not really probe the underlying mechanism or shed light on how EGL-9 has these dual activities. This limits the overall impact and novelty of the paper.

3. Cysteine dioxygenases like CDO-1 operate in an oxygen-dependent manner to generate sulfites from cysteine. CDO-1 activity is dependent upon availability of molecular oxygen; this is an unexpected characteristic of a HIF-1 target, as its very activation is dependent on low molecular oxygen. Authors neither address this in the text nor experimentally, and it seems a glaring omission.

4. The authors determined that the hypodermis is the site of the most prominent CDO-1::GFP expression, relevant to Figure 4. This claim would be strengthened if a negative control tissue, in the animal with the knockin allele, were shown. The hypodermal specific expression is a highlight of this paper, so it would make this article even stronger if they could further substantiate this claim.

Minor issues to note:

Mutants for hif-1 and cysl-1 are sensitive to exogenous cysteine levels, yet loss of CDO-1 expression is not sufficient to explain this phenomenon, suggesting other targets of HIF-1 are involved. Given the findings the authors (and others) have had showing a role for RHY-1 in sulfur amino acid metabolism, shouldn't the authors consider testing rhy-1 mutants for sensitivity to exogenous cysteine?

The cysteine exposure assay was performed by incubating nematodes overnight in liquid M9 media containing OP50 culture. The liquid culture approach adds two complications: (1) the worms are arguably starving or at least undernourished compared to animals grown on NGM plates, and (2) the worms are probably mildly hypoxic in the liquid cultures, which complicates the interpretation.

An easily addressable concern is the wording of one of the main conclusions: that cdo-1 transcription is independent of the canonical prolyl hydroxylase function of EGL-9 and is instead dependent on one of EGL-9's non-canonical, non-characterized functions. There are several points in which the wording suggests that CDO-1 toxicity is independent of EGL-9. In their defense, the authors try to avoid this by saying, "EGL-9 PHD," to indicate that it is the prolyl hydroxylase function of EGL-9 that is not required for CDO-1 toxicity. However, this becomes confusing because much of the field uses PHD and EGL-9/EGLN as interchangeable protein names. The authors need to be clear about when they are describing the prolyl hydroxylase activity of EGL-9 rather than other (hypothesized) activities of EGL-9 that are independent of the prolyl hydroxylase activity.

The authors state in the text, "the egl-9; suox-1 double mutants are extremely sick and slow growing." We appreciate that their "health" assay, based on the exhaustion of food from the plate, is qualitative. We also appreciate that it is a functional measure of many factors that contribute to how fast a population of worms can grow, reproduce, and consume that lawn of food. However, unless they do a lifespan assay and/or measure developmental timing and specifically determine that the double mutant animals themselves are developing and/or growing more slowly, we do not think it is appropriate to use the words "slow growing" to describe the population. As they point out, the rate of consumption of food on the plate in their health assay is determined by a multitude and indeed a confluence of factors; the growth rate is one specific one that is commonly measured and has an established meaning.

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 #2 (Public Review):

In this paper Sasani, Quinlan and Harris present a new method for identifying genetic factors affecting germline mutation, which is particularly applicable to genome sequence data from mutation accumulation experiments using recombinant inbred lines. These are experiments where laboratory organisms are crossed and repeatedly inbred for many generations, to build up a substantial number of identifiable germline mutations. The authors apply their method to such data from mice, and identify two genetic factors at two separate genetic loci. Clear evidence of such factors has been difficult to obtain, so this is an important finding. They further show evidence of an epistatic interaction between these factors (meaning that they do not act independently in their effects on the germline mutation process). This is exciting because such interactions are difficult to detect and few if any other examples have been studied.

The authors present a careful comparison of their method to another similar approach, quantitative trait locus (QTL) analysis, and demonstrate that in situations such as the one analysed it has greater power to detect genetic factors with a certain magnitude of effect. They also test the statistical properties of their method using simulated data and permutation tests. Overall the analysis is rigorous and well motivated, and the methods explained clearly.

The main limitation of the approach is that it is difficult to see how it might be applied beyond the context of mutation accumulation experiments using recombinant inbred lines. This is because the signal it detects, and hence its power, is based on the number of extra accumulated mutations linked to (i.e. on the same chromosome as) the mutator allele. In germline mutation studies of wild populations the number of generations involved (and hence the total number of mutations) is typically small, or else the mutator allele becomes unlinked from the mutations it has caused (due to recombination), or is lost from the population altogether (due to chance or perhaps selection against its deleterious consequences).

Nevertheless, accumulation lines are a common and well established experimental approach to studying mutation processes in many organisms, so the new method could have wide application and impact on our understanding of this fundamental biological process.

The evidence presented for an epistatic interaction is convincing, and the authors suggest some plausible potential mechanisms for how this interaction might arise, involving the DNA repair machinery and based on previous studies of the proteins implicated. However as with all such findings, given the higher degree of complexity of the proposed model it needs to be treated with greater caution, perhaps until replicated in a separate dataset or demonstrated in follow-up experiments exploring the pathway itself.

Reviewer #3 (Public Review):

Sasani et al. develop and implement a new method for mutator allele discovery in the BXD mouse population. This new "IHD" method carries several notable strengths, including the ability to aggregate de novo mutations across individuals to reduce data sparsity and to combine mutation rate frequencies across multiple nucleotide contexts into a single estimate. These advantages may render the IHD method better suited to mutator discovery under certain scenarios, as compared to conventional QTL or association mapping. Overall, the theoretical premise of the IHD method is judged to be both strong and innovative, and careful simulation studies benchmark its power.

The authors then apply their method to the BXD mouse recombinant inbred mapping population. As proof-of-principle, they first successfully re-identify a known mutator locus in this population on chr4. Next, to assess possible genetic interactions involving this known mutator, Sasani et al. condition on the chr4 mutator genotype and reimplement the IHD scan. This strategy led them to identify a second locus on chr6 that interacts epistatically with the chr4 locus; mice with "D" alleles at both loci exhibit a significantly increased burden of C>A de novo mutations, even though mice with the D allele at the chr6 locus alone show no appreciable increase in the C>A mutation fraction. This exciting discovery not only adds to the catalog of known mutator alleles, but also reveals key aspects of mutator biology. Notably, this finding reinforces the hypothesis that segregating variants in genes associated with DNA repair influence germline mutation spectra. Further, Sasani et al.'s findings suggest that some mutators may lie dormant until recombined onto a permissive genetic background. This discovery could have intriguing implications for the evolution of mutators in natural populations.

Despite a high level of overall enthusiasm for this work, some weaknesses are identified in the IHD method, approach for nominating candidate genes within the newly identified chr6 locus, and the authors' conclusions.

Under simulated scenarios, the authors' new IHD method is not appreciably more powerful than conventional QTL mapping methods. While this does not diminish the rigor or novelty of the authors findings, it does temper enthusiasm for the IHD method's potential to uncover new mutators in other populations or datasets. Further, adaptation of this methodology to other datasets, including human trios or multigenerational families, will require some modification, which could present a barrier to broader community uptake. Notably, BXD mice are (mostly) inbred, justifying the authors consideration of just two genotype states at each locus, but this decision prevents out-of-the-box application to outbred populations and human genomic datasets. Lastly, some details of the IHD method are not clearly spelled out in the paper. In particular, it is unclear whether differences in BXD strain relatedness due to the breeding epoch structure are fully accounted for in permutations. The method's name - inter-haplotype distance - is also somewhat misleading, as it seems to imply that de novo mutations are aggregated at the scale of sub-chromosomal haplotype blocks, rather than across the whole genome.

Nominating candidates within the chr6 mutator locus requires an approach for defining a credible interval and excluding/including specific genes within that interval as candidates. Sasani et al. delimit their focal window to 5Mb on either side of the SNP with the most extreme P-value in their IHD scan. This strategy suffers from several weaknesses. First, no justification for using 10 Mb window, as opposed to, e.g., a 5 Mb window or a window size delimited by a specific threshold of P-value drop, is given, rendering the approach rather ad hoc. Second, within their focal 10Mb window, the authors prioritize genes with annotated functions in DNA repair that harbor protein coding variants between the B6 and D2 founder strains. While the logic for focusing on known DNA repair genes is sensible, this locus also houses an appreciable number of genes that are not functionally annotated, but could, conceivably, perform relevant biological roles. These genes should not be excluded outright, especially if they are expressed in the germline. Further, the vast majority of functional SNPs are non-coding, (including the likely causal variant at the chr4 mutator previously identified in the BXD population). Thus, the author's decision to focus most heavily on coding variants is not well-justified. Sasani et al. dedicate considerable speculation in the manuscript to the likely identity of the causal variant, ultimately favoring the conclusion that the causal variant is a predicted deleterious missense variant in Mbd4. However, using a 5Mb window centered on the peak IHD scan SNP, rather than a 10Mb window, Mbd4 would be excluded. Further, SNP functional prediction accuracy is modest [e.g., PMID 28511696], and exclusion of the missense variant in Ogg1 due its benign prediction is potentially premature, especially given the wealth of functional data implicating Ogg1 in C>A mutations in house mice. Finally, the DNA repair gene closest to the peak IHD SNP is Rad18, which the authors largely exclude as a candidate.

Additionally, some claims in the paper are not well-supported by the author's data. For example, in the Discussion, the authors assert that "multiple mutator alleles have spontaneously arisen during the evolutionary history of inbred laboratory mice" and that "... mutational pressure can cause mutation rates to rise in just a few generations of relaxed selection in captivity". However, these statements are undercut by data in this paper and the authors' prior publication demonstrating that a number of candidate variants are segregating in natural mouse populations. These variants almost certainly did not emerge de novo in laboratory colonies, but were inherited from their wild mouse ancestors. Further, the wild mouse population genomic dataset used by the authors falls far short of comprehensively sampling wild mouse diversity; variants in laboratory populations could derive from unsampled wild populations.

Finally, the implications of a discovering a mutator whose expression is potentially conditional on the genotype at a second locus are not raised in the Discussion. While not a weakness per se, this omission is perceived to be a missed opportunity to emphasize what, to this reviewer, is one of the most exciting impacts of this work. The potential background dependence of mutator expression could partially shelter it from the action of selection, allowing the allele persist in populations. This finding bears on theoretical models of mutation rate evolution and may have important implications for efforts to map additional mutator loci. It seems unfortunate to not elevate these points.

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 #2 (Public Review):

Qin, Sanbo and Zhou, Huan-Xiang created a model, SeqDYN, to predict nuclear magnetic resonance (NMR) spin relaxation spectra of intrinsically disordered proteins (IDPs), based primarily on amino acid sequence. To fit NMR data, SeqDYN uses 21 parameters, 20 that correspond to each amino acid, and a sequence correlation length for interactions. The model demonstrates that local sequence features impact the dynamics of the IDP, as SeqDYN performs better than a one residue predictor, despite having similar numbers of parameters. SeqDYN is trained using 45 IDP sequences and is retrained using both leave-one-out cross validation and five-fold cross validation, ensuring the model's robustness. While SeqDYN can provide reasonably accurate predictions in many cases, the authors note that improvements can be made by incorporating secondary structure predictions, especially for alpha-helices that exceed the correlation length of the model. The authors apply SeqDYN to study nine IDPs and a denatured ordered protein, demonstrating its predictive power. The model can be easily accessed via the website mentioned in the text.

While the conclusions of the paper are primarily supported by the data, there are some points that could be extended or clarified.

1. The authors state that the model includes 21 parameters. However, they exclude a free parameter that acts as a scaling factor and is necessary to fit the experimental data (lambda). As a result, SeqDYN does not predict the spectrum from the sequence de-novo, but requires a one parameter fitting. The authors mention that this factor is necessary due to non-sequence dependent factors such as the temperature and magnetic field strength used in the experiment. Given these considerations, would it be possible to predict what this scaling factor should be based on such factors?

2. The authors mention that the Lorentzian functional form fits the data better than a Gaussian functional form, but do not present these results.

3. The authors mention that they conducted five-fold cross validation to determine if differences between amino acid parameters are statistically significant. While two pairs are mentioned in the text, there are 190 possible pairs, and it would be informative to more rigorously examine the differences between all such pairs.

Reviewer #3 (Public Review):

The manuscript by Qin and Zhou presents an approach to predict dynamical properties of an intrinsically disordered protein (IDP) from sequence alone. In particular, the authors train a simple (but useful) machine learning model to predict (rescaled) NMR R2 values from sequence. Although these R2 rates only probe some aspects of IDR dynamics and the method does not provide insight into the molecular aspects of processes that lead to perturbed dynamics, the method can be useful to guide experiments.

A strength of the work is that the authors train their model on an observable that directly relates to protein dynamics. They also analyse a relatively broad set of proteins which means that one can see actual variation in accuracy across the proteins.

A weakness of the work is that it is not always clear what the measured R2 rates mean. In some cases, these may include both fast and slow motions (intrinsic R2 rates and exchange contributions). This in turn means that it is actually not clear what the authors are predicting. The work would also be strengthened by making the code available (in addition to the webservice), and by making it easier to compare the accuracy on the training and testing data.

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 #2 (Public Review):

Jarysta and colleagues set out to define how similar GNAI/O family members contribute to the shape and orientation of stereocilia bundles on auditory hair cells. Previous work demonstrated that loss of particular GNAI proteins, or inhibition of GNAIs by pertussis toxin, caused several defects in hair bundle morphogenesis, but open questions remained which the authors sought to address. Some of these questions include whether all phenotypes resulting from expression of pertussis toxin stemmed from GNAI inhibition; which GNAI family members are most critical for directing bundle development; whether GNAI proteins are needed for basal body movements that contribute to bundle patterning. These questions are important for understanding how tissue is patterned in response to planar cell polarity cues.

To address questions related to the GNAI family in auditory hair cell development, the authors assembled an impressive and nearly comprehensive collection of mouse models. This approach allowed for each Gnai and Gnao gene to be knocked out individually or in combination with each other. Notably, a new floxed allele was generated for Gnai3 because loss of this gene in combination with Gnai2 deletion was known to be embryonic lethal. Besides these lines, a new knockin mouse was made to conditionally express untagged pertussis toxin following cre induction from a strong promoter. The breadth and complexity involved in generating and collecting these strains makes this study unique, and likely the authoritative last word on which GNAI proteins are needed for which aspect of auditory hair bundle development.

Appropriate methods were employed by the authors to characterize auditory hair bundle morphology in each mouse line. Conclusions were carefully drawn from the data and largely based on excellent quantitative analysis. The main conclusions are that GNAI3 has the largest effect on hair bundle development. GNAI2 can compensate for GNAI3 loss in early development but incompletely in late development. The Gnai2 Gnai3 double mutant recapitulates nearly all the phenotypic effects associated with pertussis toxin expression and also reveals a role for GNAIs in early movement of the basal body. Although these results are not entirely unexpected based on earlier reports, the current results both uncover new functions and put putative functions on more solid ground.

Based on this study, loss of GNAI1 and GNAO show a slight shortening of the tallest row of stereocilia but no other significant changes to bundle shape. Antibody staining shows no change in GNAI localization in the Gnai1 knockout, suggesting that little to no protein is found in hair cells. One caveat to this interpretation is that the antibody, while proposed to cross-react with GNAI1, is not clearly shown to immunolabel GNAI1. More than anything, this reservation mostly serves to illustrate how challenging it is to nail down every last detail. In turn, the comprehensive nature of the current study seems all the more impressive.

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.

Reviewer #2 (Public Review):

Aw et al presents a new stability-guided fine-mapping method by extending the previously proposed PICS method. They applied their stability-based method to fine-map cis-eQTLs in the GEUVADIS dataset and compared it against what they call residualization-based method. They evaluated the performance of the proposed method using publicly available functional annotations and claimed the variants identified by their proposed stability-based method are more enriched for these functional annotations.

While the reviewer acknowledges the contribution of the present work, there are a couple of major concerns as described below.

Major:

1. It is critical to evaluate the proposed method in simulation settings, where we know which variants are truly causal. While I acknowledge their empirical approach using the functional annotations, a more unbiased, comprehensive evaluation in simulations would be necessary to assess its performance against the existing methods.

2. Also, simulations would be required to assess how the method is sensitive to different parameters, e.g., LD threshold, resampling number, or number of potential sets.

3. Given the previous studies have identified multiple putative causal variants in both GWAS and eQTL, I think it's better to model multiple causal variants in any modern fine-mapping methods. At least, a simulation to assess its impact would be appreciated.

4. Relatedly, I wonder what fraction of non-matching variants are due to the lack of multiple causal variant modeling.

5. I wonder if you can combine the stability-based and the residualization-based approach, i.e., using the residualized phenotypes for the stability-based approach. Would that further improve the accuracy or not?

6. The authors state that confounding in cohorts with diverse ancestries poses potential difficulties in identifying the correct causal variants. However, I don't see that they directly address whether the stability approach is mitigating this. It is hard to say whether the stability approach is helping beyond what simpler post-hoc QC (e.g., thresholding) can do.

7. For non-matching variants, I wonder what the difference of posterior probabilities is between the stable and top variants in each method. If the difference is small, maybe it is due to noise rather than signal.

8. It's a bit surprising that you observed matching variants with (stable) posterior probability ~ 0 (SFig. 1). What are the interpretations for these variants? Do you observe functional enrichment even for low posterior probability matching variants?

Commentary: Causal Inference for Social Exposures

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 #2 (Public Review):

This manuscript reports on an important study that aims to identify symptom trajectories for the early detection of pancreatic cancer. The study's findings are based on the analysis of two complementary data sources: structured data obtained from the Danish National Patient Registry and unstructured information extracted from the free-text sections of patient notes. The researchers successfully identified various symptoms and disease trajectories that are strongly associated with pancreatic cancer, with compelling evidence from both data sources. Additionally, the study provides a detailed comparison and contrast of the results obtained from each data source, adding valuable insights into the strengths and limitations of each method.

Strengths:

The work is well motivated by the urgent need for early detection of pancreatic cancer, which is often difficult due to the lack of effective (computational) methods. The manuscript is generally well-written and includes relevant studies, providing a comprehensive overview of the current state of the field.

One of the unique contributions of this work is its use of both structured registry data and unstructured clinical notes to leverage complementary information. This approach enables a more nuanced and comprehensive understanding of the disease symptom trajectories, which is critical for improving early disease diagnosis and prognosis.

The methodology employed in this study is sound and robust, and the authors have candidly discussed its limitations. The results are significant and highlight previously unknown insights into symptom disease trajectories, which have important implications for the management of pancreatic cancer.

Overall, this is a well-designed and executed study that makes an important contribution to the field of cancer/informatics research, and it should be of great interest to both researchers and clinicians.

Weaknesses:

To complement the results in Figure 1, I'd also suggest that the authors compile a list of the most common (known) symptoms of pancreatic cancer as a reference. In other words, not only can you compare results found from the two sources but also compare them with existing knowledge. This is something you discussed partly in lines 245 but including this early as part of the results in Figure 1 would be more informative.

In terms of the text mining evaluation results, providing information on recall errors would be beneficial to better understand the performance of the method. Additionally, line 144 mentions 53 words, but it is still not clear to me what these words refer to. Could you please clarify this point or provide more context?

The disparities between Figure 2A and 2B are noteworthy, from very different initial symptoms to the proportion of short median survival dates (<=90 days), with much more pronounced differences than those observed in Figure 1 comparing two data sources. The highlighted trajectories are almost completely different. Should this be expected? I was hoping to see at least some overlap between the two results.

All trajectories shown in Figure 2 include three symptoms. Is this by design? Could there be meaningful trajectories with different numbers of symptoms (e.g. 4 or more)?

Considering those patients with both clinical notes and registry data, it may be beneficial to merge their symptoms to generate more informative trajectories.

Given that results from two sources are being compared in Figures 1 and 2, have you considered calculating the top 20 most significant symptoms from the registry data as well?

While there is a discussion related to cardiovascular diseases, I noticed no mention of cataracts or gonarthrosis, which were found to be prevalent among patients with short survival in Figure 2.

Ultimately, the goal of this research is to improve the early detection and prognosis of pancreatic cancer, thus it is important to discuss how the findings of this work could be applied in practice towards this goal (e.g. used by disease prediction algorithms?)

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 #2 (Public Review):

The manuscript "Nation-wide mammography screening participation in Denmark during the COVID-19 pandemic: An observational study" aims at assessing the impact of COVID-19 on the participation to the breast cancer national screening program in Denmark.

Using a cohort of almost one million women, the authors used ageneralised linear model to estimate the prevalence ratios of participation to the screening program within 3, 6, and 12 months since the start of the pandemic.

The high quality of the data used represents the strongest point of the study, which provided a strong, reliable basis on which conduct the analysis. Some limitations are related to the way the date of invitation (to the screening program) is handled, the vaccination status of the cohort of interest (information not available) and the transferability of the study to other countries, for different countries handled the pandemic in different ways.

The authors show that there was an overall slight decrease in screening participation despite the screening program remained open throughout the pandemic and discuss likely reasons of why that may have happened. Further, they identified that groups of women who were already characterised by low participation rates, experienced a further reduction in attending screening. Those were mostly composed by immigrants and low income individuals. They also discuss the barrier that language may have posed in relation to the distribution of guidelines form the government, as those were delivered in Danish.

In conclusion, the study indicates that social iniquity, which usually relates to disparity in screening participation, has been slightly exacerbated during the pandemic. Although the authors do not discuss in detail what the consequences of those findings can be, it would be interesting to assess (through a follow-up study) whether they will have an impact on the cancer incidence and, in particular, the staging of cancers at detection for the interested groups.

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 #2 (Public Review):

In this work, Herron et al. investigated the impact of mTORC1 and CFIm on the expression of the Trim9/TRIM9 isoforms in both mouse and human. They extend upon their cTAG-PAPERCLIP method and demonstrated that systemic AAV injection of cell type-specific Cre recombinases to cTag-PABP mice is a feasible method of APA profiling. From this they show that mTORC1 hyperactivation promotes a shift towards the long Trim9 isoform, Trim9-L. They further provide evidence that the mTORC1 signalling pathway controls Trim9/TRIM9 isoform usage in both human and mouse with high mTORC1 promoting usage of the long isoform and low mTORC1 favouring the short isoform. They also show that the CFIm subunits CPSF6 and NUDT21 play a crucial role in the use of the TRIM9-S/Trim9-S isoform and demonstrate the importance of a twin UGUA motif in this PAS for its regulation by CPSF6. Additionally, they find that this twin UGUA motif is functionally present in the human BMPR1B, MOB4 and BRD4 genes and that insertion of the twin UGUA motif into a heterologous PAS is enough to confer regulation by both CPSF6 and mTORC1. Critically, the position of the twin UGUA motif directs preferential cleavage and polyadenylation to generate an isoform, such that it's presence can result in the use of a short isoform (TRIM9) or a long isoform (BMPR1B, MOB4 and BRD4). The work expands upon the known cis-regulatory motifs for CPSF6 and provides further evidence of a connection between the mTORC1 signalling pathway and CPSF6-mediated alternative polyadenylation. The mechanistic connection between TORC1 signalling and CPSF6 function is, however, still opaque. An experiment probing the connection between TORC1 signalling and the nuclear-cytoplasmic shuttling of CPSF6 with its activity (regulating APA) would significantly strengthen the study. Most conclusions are well supported by the presented data.

Reviewer #3 (Public Review):

Alternative polyadenylation is an important aspect of RNA processing that can alter the type or amount of proteins that are produced from a gene, with consequences for many aspects of biology. Herron et al. set out to identify how the mTORC1 pathway, which regulates cellular metabolism, influences alternative polyadenylation in the mouse brain. They identified a novel mTORC1-regulated gene with alternative polyadenylation - TRIM9 - and convincingly demonstrate that its alternative polyadenylation is controlled by the CFIm complex of the cleavage and polyadenylation machinery. A major strength of these results is that the authors use multiple orthogonal methods - including PAPERCLIP, qPCR and western blotting, to demonstrate that TRIM9 is regulated by mTORC1 and CFIm. They also demonstrate that this regulation is conserved between mice and humans by using multiple different model systems, and use synthetic reporter constructs to identify the cis-regulatory elements that are responsible for TRIM9 regulation by CFIm. These results highlight the importance of alternative polyadenylation in controlling gene expression and are important for researchers wishing to understand how the mTORC1 pathway functions.

The authors also identify that a "twin" UGUA motif in the poly(A) site of the short form of TRIM9 is responsible for its regulation by CFIm. They show that this motif is conserved across mammals and suggest that the adjacent UGUA motifs are necessary for regulation by CFIm. The evidence supporting this aspect of the manuscript is incomplete because the authors only ever mutate both UGUA motifs of TRIM9, and so it is not possible to determine whether the full motif or only one of the UGUA motifs is necessary for regulation, nor whether the effect of the two UGUA motifs is simply additive. The only evidence for the necessity of the full twin motif comes from a synthetic JUNB reporter construct, where a single UGUA motif was insufficient to induce proximal polyadenylation. However, given that there is previous evidence that individual UGUA motifs can act as enhancers of polyadenylation, this may be due to context-specific issues with the JUNB reporter, and evidence from different contexts would make the authors conclusions more convincing.

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 #2 (Public Review):

Hoang, Tsutsumi et al provide a comprehensive functional mapping of cerebellar climbing fiber responses in Lobule Crus II. The study derives from analysis of a dataset originally published in Tsutsumi et al eLife 2019, using two photon Ca2+ imaging throughout the learning of a Go/No-go reward-driven licking behavior. Each recording session yielded data from a ~two-hundred micron patch of tissue, with neurons spatially localized relative the "zebrin" banding pattern of the cerebellar cortex as reported by an aldolaceC-tdTomato transgenic line. In the present work, complex spike times were extracted at higher temporal resolution using subframe raster line-scan timing information, and then decomposed at the trial-averaged population level using tensor component analysis.

The central conclusion is that the entirety of crus II climbing fiber responses decomposes into just a few patterns that capture key features of the behavior. Some of these patterns strengthen with learning, i.e., feature climbing fiber spiking that increases in frequency, while others decay with learning, i.e., feature climbing fiber responses that are prominent only in novice animals. These different climbing fiber activity components are in some cases associated with either positive or negative aldolace-C compartments of crus II. Finally, synchronization is concentrated among cells contributing to the same tensor components, and synchrony levels increase or decrease for different components over learning.

The analysis therefore suggests that distinct principles of climbing fiber function can be present simultaneously in distinct cerebellar modules (and, according to the TCA cell weightings, potentially simultaneously in individual climbing fibers). This conclusion is contrary to the implied dichotomy in the literature that climbing fibers either function as "error signals" or as "timing signals" in a particular behavioral context or cerebellar region. The authors speculate that resolution of this dichotomy could result from the biophysics of the inferior olive, in which flexibly coupled oscillators might self-organize into a low dimensional decomposition of task dynamics. Relatedly, the authors speculate that changes in synchronization that contrast between different components could serve to either regulate instructive signal dimensionality or climbing fiber timing functions, depending on each component's functional contribution. From a theoretical standpoint, this is a helpful new direction. The framework is more agnostic to the details of the activity profiles of any specific group of climbing fibers, but more attuned to the systems-level distribution of activity profiles and how these might collectively serve a behavior.

A valuable feature of the study is the simultaneous analysis of many imaging fields spanning 17 subjects and the entire dorsal surface of crus II. This bypasses some of the recurring interpretational issues with climbing fiber recordings that stem from their spatial organization across the cerebellar surface with often abrupt transitions at compartmental boundaries. By decomposing responses across many compartments simultaneously (at the trial-averaged level), the authors provide a quantitative estimate of the diversity of response patterns and their distribution across space and cells. It's worth noting that this approach is also a double-edged sword, as the trial-averaged decomposition does not depend on single-trial correlations between neurons, thus strictly speaking leaving it an open question whether apparently similar climbing fiber patterns present in distant imaging fields exhibit correlated variability either across trials or across learning.

The data convincingly show that several dominant tensor components explain a large amount of climbing fiber variance across crus II. The authors speculate that this reflects an olivary decomposition of task dynamics. Due to the nature of the analysis - TCA applied over an entire dataset - there is not a clear test of this hypothesis in the present manuscript.

The authors also present the interesting and compelling result that different CF response patterns undergo opposite learned changes in synchronization. They speculate that different trajectories of synchronization, specifically, increases for TC1 (hit) and decreases for TC2 (false alarm), could reflect different functional uses of TC1 and TC2, although it is difficult to assess the likelihood of this being true based on the data and analyses presented.

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 #2 (Public Review):

In this study, the authors set out to investigate factors that have been neglected in existing mathematical models for the paradoxical activation (PA) of RAF by pharmacological inhibitors. The PA phenomenon is well known and is thought to be an important factor in limiting the effectiveness of RAF inhibitors. The authors primarily use mathematical models, first to examine the importance of conformational autoinhibition of RAF monomers, and later to investigate the potential role played by binding of 14-3-3 proteins to either autoinhibited monomers or active dimers. The authors develop several model variants containing different candidate mechanisms and generate analytical solutions that demonstrate under which parameter conditions PA may occur within these models. The use of analytical solutions is a strong point of the paper, as it allows evaluation of the models independently of specific parameter values. This analysis suggests that conformational autoinhibition is a very strong contributor to paradoxical activation, as models that include this mechanism show substantially larger concentration ranges under which RAF is activated by inhibitors. Fitting the parameters of the model to a published dataset on multiple inhibitors further suggests that conformational activation is important, as models containing this mechanism can fit the dataset with significantly lower error. Another interesting observation is that the different types of RAF inhibitors (1, 1.5, 2) fit the data with parameter values that are reasonably similar within each type. A moderate weakness in this analysis is that all of these observations provide indirect evidence for the importance of conformational autoinhibition. A direct test of whether PA is reduced when conformational autoinhibition is removed would be more compelling, but such a test could be difficult to set up experimentally.

The authors then perform an analysis of how 14-3-3 binding to either autoinhibited monomers or active dimers might enhance PA. A new model is constructed that contains these binding events in the context of conformational activation, but without negative cooperativity or dimer potentiation included, for the sake of limiting complexity. These models implicate monomer binding, but not dimer binding as a contributor to PA. They follow up on this model result by overexpressing 14-3-3 proteins in two RAS-mutant cell lines, which leads to both higher baseline ERK phosphorylation and to a wider range of inhibitor-induced PA, as predicted by the model. A cell-based RAF dimerization assay also shows higher dimerization effects when 14-3-3 plasmids are transfected as well. This experimental evidence provides strong support for the model, although one drawback, which is noted by the authors in the discussion, is that 14-3-3 overexpression could potentially exert effects on RAF activity through pleiotropic effects other than the binding actions included in the model.

Overall, this study makes a strong contribution to understanding the paradoxical effects of RAF inhibitors on the RAS/ERK signaling pathway, which remains a significant problem in the use of targeted inhibitors for cancer. Demonstrating that both conformational activation and 14-3-3 binding strongly contribute to the PA effect is an important step forward, as it establishes that these mechanisms should not be overlooked when designing strategies to use Raf inhibitors.

Reviewer #3 (Public Review):

The authors describe a mathematical and computational modeling study of RAF paradoxical activation (PA), a phenomenon in which RAF inhibitors exhibit a bell-shaped dose-response curve of Erk phosphorylation - activating signaling through wild-type RAF at low drug concentrations before inhibiting it at higher concentrations. They explore three distinct mechanisms that may contribute to PA - conformational autoinhibition, negative cooperativity, and drug-induced dimerization - and conclude that all three are required to best fit published data that show the PA phenomenon. They explore the effect of 14-3-3 binding to RAF both computationally and experimentally and reach the conclusion that 14-3-3 can potentiate the PA phenomenon via stabilization of the autoinhibited conformation.

Strengths:

One key finding will be quite valuable to the field - that paradoxical activation can arise in the absence of negative cooperativity and without any effect of the inhibitor on the propensity of RAF to dimerize, provided that there exists a "conformationally autoinhibited" state that cannot dimerize and cannot bind inhibitor. This finding is important because negative cooperativity and dimer-induction have been a major focus - arguably the main focus - of prior studies of the phenomenon and also a source of considerable confusion. Inhibitors with very different chemical structures and binding properties - type 1.5 inhibitors that are dimer-breakers (and may or may not exhibit negative cooperativity) and type I and II inhibitors that can promote dimers (and almost certainly do not exhibit negative cooperativity) can nevertheless both exhibit PA. Thus the authors' modeling provides a unifying explanation - it is not dimer-induction or negative cooperativity that is at the root of PA, rather it is that there exists an autoinhibited state that can neither bind inhibitor nor dimerize. The authors further show that negative cooperativity and dimer-induction can act in concert with "conformational autoinhibition" to modify the PA response in a drug-specific manner.

Weaknesses:

Unfortunately, the authors don't really explain in a straightforward manner what is going on with the conformational autoinhibition model (Figure 2A). One has to read carefully and all the way to section 3 of appendix 1 to piece it together. In short, what the math shows is that at least for certain ranges of parameter values, the presence of an inhibitor can increase the concentration of dimers, even when it does not change the equilibrium constant for dimer formation, and some of those dimers will have an active, drug-free protomer. This is because the inhibitor effectively traps open monomers, which can then capture drug-free open monomers to form active dimers (active in one subunit, inactive and drug-bound in the other). As inhibitor concentration increases, the pool of autoinhibited RAF is diminished, and eventually, it is shifted completely to fully inhibited dimers. But at low concentrations of inhibitor, there is a net increase in dimerized (active) but drug-free protomers (see figure on page 27 of the appendix). Voila, paradoxical activation, with no need to invoke negative cooperativity.

Considering the potential for confusion around what is meant by "drug-induced dimerization" as an effect distinct from the effect of the drug in promoting RAF dimerization in their conformational autoinhibition model, it would have been helpful for the authors to explicitly address the distinction (drug-induced dimerization alters the equilibrium constant for dimerization; this is not a feature of the conformational autoinhibition model).

Also, I am confused by Figure 3C. The figure shows, and the authors state in the text, that for type II inhibitors an f > ~1 indicates a propensity to break dimers. But type 1.5 inhibitors should break dimers, and Type I and II inhibitors should promote dimers (at least some Type I and II drugs have been shown to promote kinase dimers). Seems that the predictions of the model are inconsistent with experimental data, at least for some inhibitors.

A large part of the paper deals with the effect of 14-3-3 binding. In my view, this part of the manuscript is not particularly helpful. There is no evidence (that I am aware of) that 14-3-3 concentrations vary significantly, or that their variation affects RAF activity/signaling. Considering their abundance relative to RAF, and relatively high affinity for RAF, it is likely that both autoinhibited and active RAF are saturated with 14-3-3. (RAF that is not 14-3-3-bound is likely mostly bound to chaperones and not active). That said, the authors' conclusion (based on modeling) that 14-3-3 can increase the extent of paradoxical activation by stabilizing the autoinhibited state seems sensible, but hard to reconcile with their experimental result where they find increased basal signaling with 14-3-3 over-expression. It is also difficult to understand how increased 14-3-3 binding to RAF could lead to active RAF dimers that are not inhibited at 10-100 uM concentrations of potent RAF dimer inhibitors like LY3009120 (Fig. 5C). It seems more likely that 14-3-3 overexpression is promoting Erk phosphorylation in a manner that is (at least partially) Raf-independent. To their credit, the authors acknowledge this concern.

Finally, one comment regarding the presentation. The authors discuss conformational inhibition and 14-3-3 binding as if they are promoting and/or inducing paradoxical activation. This is pervasive in the paper, including in the title, and is distracting and potentially will mislead some readers. Obviously, it is RAF inhibitor that induces or promotes paradoxical activation. Conformational autoinhibition - mediated by 14-3-3 - is a feature of the system that makes paradoxical activation possible.

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 #2 (Public Review):

This is a descriptive paper in the field of metascience, which documents levels of accessibility and reproducible research practices in the field of cardiovascular science. As such, it does not make a theoretical contribution, but it argues, first, that there is a problem for this field, and second, it provides a baseline against which the impact of future initiatives to improve reproducibility can be assessed. The study was pre-registered and the methods and data are clearly documented. This kind of study is extremely labour-intensive and represents a great deal of work.

I have a major concern about the analysis. It is stated that to be fully reproducible, publications must include sufficient resources (materials, methods, data and analysis scripts). But how about cases where materials are not required to reproduce the work? In line 128-129 it is noted that the materials criterion was omitted for meta-analyses, but what about other types of study where materials may be either described adequately in the text, readily available (eg published questionnaires), or impossible to share (e.g. experimental animals).

To see how valid these concerns might be, I looked at the first 4 papers in the deposited 'EmpricalResearchOnly.csv' file. Two had been coded as 'No Materials availability statement' and for two the value was blank.<br /> Study 1 used registry data and was coded as missing a Materials statement. The only materials that I could think might be useful to have might be 'standardized case report forms' that were referred to. But the authors did note that the Registry methods were fully documented elsewhere (I am not sure if that is the case).<br /> Study 2 was a short surgical case report - for this one the Materials field was left blank by the coder.<br /> Study 3 was a meta-analysis; the Materials field was blank by the coder<br /> Study 4 was again coded as lacking a Material statement. It presented a model predicting outcome for cardiac arrhythmias. The definitions of the predictor variables were provided in supplementary materials. I am not clear what other materials might be needed.<br /> These four cases suggest to me that it is rather misleading to treat lack of a Materials statement as contributing to an index of irreproducibility. Certainly, there are many studies where this is the case, but it will vary from study to study depending on the nature of the research. Indeed, this may also be true for other components of the irreproducibility index: for instance, in a case study, there may be no analysis script because no statistical analysis was done. And in some papers, the raw data may all be present in the text already - that may be less common, but it is likely to be so for case studies, for instance.

A related point concerns the criteria for selecting papers for screening: it was surprising that the requirement for studies to have empirical data was not imposed at the outset: it should be possible to screen these out early on by specifying 'publication type'; instead, they were included and that means that the numbers used for the actual analysis are well below 400. The large number of non-empirical papers is not of particular relevance for the research questions considered here. In the Discussion, the authors expressed surprise at the large number of non-emprical papers they found; I felt it would have been reasonable for them to depart from their preregistered plan on discovering this, and to review further papers to bring the number up to 400, restricting consideration to empirical papers only - also excluding case reports, which pose their own problems in this kind of analysis.

A more minor point is that some of the analyses could be dropped. The analysis of authorship by country had too few cases for many countries to allow for sensible analysis.

Overall, my concern is that the analysis presented here may create a backlash against metascientific analyses like this because it appears unfair on authors to use a metric based on criteria that may not apply to their study. I am strongly in favour of open, reproducible science, and agree it is important to document the state of the science for different disciplines. But what this study demonstrates to me is that if you are going to evaluate papers as to whether they include things like materials/data/ availability statements, then you need to have a N/A option. Unfortunately, I suspect it may not be possible to rely on authors' self-evaluation of N/A and that means that metascientists doing an evaluation would need to read enough of the paper to judge whether such a statement should apply.

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 #2 (Public Review):

The authors undertook a review of studies describing the effects of the COVID-19 pandemic on breast cancer screening in countries across the world. The major strengths of the study are its breadth and the rigour of the literature search and review. The volume of studies included, and their different contexts and designs, make it challenging to summarize succinctly and the authors have done a good job. The weakness of this review, or any like it, is that we have limited data to explain the findings which a likely a complex mix of societal, structural, and personal reasons. The importance of the findings lies in the consistency of the overall trend and what the implications of potential delayed/missed breast cancer screening are and how far into the future these implications will reach.

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.