Reviewer #1 (Public Review):

Summary:<br /> The authors present a detailed study of a nearly complete Entomophthora muscae genome assembly and annotation, along with comparative analyses among related and non-related entomopathogenic fungi. The genome is one of the largest fungal genomes sequenced, and the authors document the proliferation and evolution of transposons and the presence/absence of related genetic machinery to explore how this may have occurred. There has also been an expansion in gene number, which appears to contain many "novel" genes unique to E. muscae. Functionally, the authors were interested in CAZymes, proteases, circadian clock related genes (due to entomopathogenicity/ host manipulation), other insect pathogen-specific genes, and secondary metabolites. There are many interesting findings including expansions in trahalases, unique insulinase, and another peptidase, and some evidence for RIP in Entomophthoralean fungi. The authors performed a separate study examining E. muscae species complex and related strains. Specifically, morphological traits were measured for strains and then compared to the 28S+ITS-based phylogeny, showing little informativeness of these morpho characters with high levels of overlap.

This work represents a big leap forward in the genomics of non-Dikarya fungi and large fungal genomes. Most of the gene homologs have been studied in species that diverged hundreds of millions of years ago, and therefore using standard comparative genomic approaches is not trivial and still relatively little is known. This paper provides many new hypotheses and potential avenues of research about fungal genome size expansion, entomopathogenesis in zygomycetes, and cellular functions like RIP and circadian mechanisms.

Strengths:<br /> There are many strengths to this study. It represents a massive amount of work and a very thorough functional analysis of the gene content in these fungi (which are largely unsequenced and definitely understudied). Too often comparative genomic work will focus on one aspect and leave the reader wondering about all the other ways genome(s) are unique or different from others. This study really dove in and explored the relevant aspects of the E. muscae genome.

The authors used both a priori and emergent properties to shape their analyses (by searching for specific genes of interest and by analyzing genes underrepresented, expanded, or unique to their chosen taxa), enabling a detailed review of the genomic architecture and content. Specifically, I'm impressed by the analysis of missing genes (pFAMs) in E. muscae, none of which are enriched in relatives, suggesting this fungus is really different not by gene loss, but by its gene expansions.

Analyzing species-level boundaries and the data underlying those (genetic or morphological) is not something frequently presented in comparative genomic studies, however, here it is a welcome addition as the target species of the study is part of a species complex where morphology can be misleading and genetic data is infrequently collected in conjunction with the morphological data.

Weaknesses:<br /> The conclusions of this paper are mostly well supported by data, but a few points should be clarified.

In the analysis of Orthogroups (OGs), the claim in the text is that E. muscae "has genes in multi-species OGs no more frequently than Enotomophaga maimaiga. (Fig. 3F)" I don't see that in 3F. But maybe I'm really missing something.

Also related, based on what is written in the text of the OG section, I think portions of Figure 3G are incorrect/ duplicated. First, a general question, related to the first two portions of the graph. How do "Genes assigned to an OG" and "Genes not assigned to an OG" not equal 100% for each species? The graph as currently visualized does not show that. Then I think the bars in portion 3 "Genes in species-specific OG" are wrong (because in the text it says "N. thromboides had just 16.3%" species-specific OGs, but the graph clearly shows that bar at around 50%. I think portion 3 is just a duplicate of the bars in portion 4 - they look exactly the same - and in addition, as stated in the text portion 4 "Potentially species-specific genes" should be the simple addition of the bars in portion 2 and portion 3 for each species.

In the introduction, there is a name for the phenomenon of "clinging to or biting the tops of plants," it's called summit disease. And just for some context for the readers, summit disease is well-documented in many of these taxa in the older literature, but it is often ignored in modern studies - even though it is a fascinating effect seen in many insect hosts, caused by many, many fungi, nematodes (!), etc. This phenomenon has evolved many times. Nice discussions of this in Evans 1989 and Roy et al. 2006 (both of whom cite much of the older literature).

Reviewer #2 (Public Review):

In their study, Stajich and co-authors present a new 1.03 Gb genome assembly for an isolate of the fungal insect parasite Entomophthora muscae (Entomophthoromycota phylum, isolated from Drosophila hydei). Many species of the Entomophthoromycota phylum are specialised insect pathogens with relatively large genomes for fungi, with interesting yet largely unexplored biology. The authors compare their new E. muscae assembly to those of other species in the Entomophthorales order and also more generally to other fungi. For that, they first focus on repetitive DNA (transposons) and show that Ty3 LTRs are highly abundant in the E. muscae genome and contribute to ~40% of the species' genome, a feature that is shared by closely related species in the Entomophthorales. Next, the authors describe the major differences in protein content between species in the genus, focusing on functional domains, namely protein families (pfam), carbohydrate-active enzymes, and peptidases. They highlight several protein families that are overrepresented/underrepresented in the E. muscae genome and other Entomophthorales genomes. The authors also highlight differences in components of the circadian rhythm, which might be relevant to the biology of these insect-infecting fungi. To gain further insights into E. muscae specificities, the authors identify orthologous proteins among four Entomophthorales species. Consistently with a larger genome and protein set in E. muscae, they find that 21% of the 17,111 orthogroups are specific to the species. To finish, the authors examine the consistency between methods for species delineation in the genus using molecular (ITS + 28S) or morphological data (# of nuclei per conidia + conidia size) and highlight major incongruences between the two.

Although most of the methods applied in the frame of this study are appropriate with the scripts made available, I believe there are some major discrepancies in the datasets that are compared which could undermine most of the results/conclusions. More precisely, most of the results are based on the comparison of protein family content between four Entomophthorales species. As the authors mention on page 5, genome (transcriptome) assembly and further annotation procedures can strongly influence gene discovery. Here, the authors re-annotated two assemblies using their own methods and recovered between 30 and 60% more genes than in the original dataset, but if I understand it correctly, they perform all downstream comparative analyses using the original annotations. Given the focus on E. muscae and the small sample size (four genomes compared), I believe performing the comparisons on the newly annotated assemblies would be more rigorous for making any claim on gene family variation.

The authors also investigate the putative impact of repeat-induced point mutation on the architecture of the large Entomophthorales genomes (for three of the eight species in Figure 1) and report low RIP-like dinucleotide signatures despite the presence of RID1 (a gene involved in the RIP process in Neurospora crassa) and RNAi machinery. They base their analysis on the presence of specific PFAM domains across the proteome of the three Entomophthorales species. In the case of RID1, the authors searched for a DNA methyltransferase domain (PF00145), however other proteins than RID1 bear such functional domain (DNMT family) so that in the current analysis it is impossible to say if the authors are actually looking at RID1 homologs (probably not, RID1 is monophyletic to the Ascomycota I believe). Similar comments apply to the analysis of components of the RNAi machinery. A more reliable alternative to the PFAM analysis would be to work with full protein sequences in addition to the functional domains.

Reviewer #1 (Public Review):

The authors set out to illuminate how legumes promote symbiosis with beneficial nitrogen-fixing bacteria while maintaining a general defensive posture towards the plethora of potentially pathogenic bacteria in their environment. Intriguingly, a protein involved in plant defence signalling, RIN4, is implicated as a type of 'gatekeeper' for symbiosis, connecting symbiosis signalling with defence signalling. Although questions remain about how exactly RIN4 enables symbiosis, the work opens an important door to new discoveries in this area.

Strengths:<br /> The study uses a multidisciplinary, state-of-the-art approach to implicate RIN4 in soybean nodulation and symbiosis development. The results support the authors' conclusions.

Weaknesses:<br /> No serious weaknesses, although the manuscript could be improved slightly from technical and communication standpoints.

Reviewer #2 (Public Review):

Summary:<br /> The study by Toth et al. investigates the role of RIN4, a key immune regulator, in the symbiotic nitrogen fixation process between soybean and rhizobium. The authors found that SymRK can interact with and phosphorylate GmRIN4. This phosphorylation occurs within a 15 amino acid motif that is highly conserved in N-fixation clades. Genetic studies indicate that GmRIN4a/b play a role in root nodule symbiosis. Based on their data, the authors suggest that RIN4 may function as a key regulator connecting symbiotic and immune signaling pathways.

Overall, the conclusions of this paper are well supported by the data, although there are a few areas that need clarification.

Strengths:<br /> • This study provides important insights by demonstrating that RIN4, a key immune regulator, is also required for symbiotic nitrogen fixation.<br /> • The findings suggest that GmRIN4a/b could mediate appropriate responses during infection, whether it is by friendly or hostile organisms.

Weaknesses:<br /> • The study did not explore the immune response in the rin4 mutant. Therefore, it remains unknown how GmRIN4a/b distinguishes between friend and foe.

Reviewer #3 (Public Review):

Summary:<br /> This manuscript by Toth et al reveals a conserved phosphorylation site within the RIN4 (RPM1-interacting protein 4) R protein that is exclusive to two of the four nodulating clades, Fabales and Rosales. The authors present persuasive genetic and biochemical evidence that phosphorylation at the serine residue 143 of GmRIN4b, located within a 15-aa conserved motif with a core five amino acids 'GRDSP' region, by SymRK, is essential for optimal nodulation in soybean. While the experimental design and results are robust, the manuscript's discussion fails to clearly articulate the significance of these findings. Results described here are important to understand how the symbiosis signaling pathway prioritizes associations with beneficial rhizobia, while repressing immunity-related signals.

Strengths:<br /> The manuscript asks an important question in plant-microbe interaction studies with interesting findings.

Overall, the experiments are detailed, thorough, and very well-designed. The findings appear to be robust.

The authors provide results that are not overinterpreted and are instead measured and logical.

Weaknesses:<br /> No major weaknesses. However, a well-thought-out discussion integrating all the findings and interpreting them is lacking; in its current form, the discussion lacks 'boldness'. The primary question of the study - how plants differentiate between pathogens and symbionts - is not discussed in light of the findings. The concluding remark, "Taken together, our results indicate that successful development of the root nodule symbiosis requires cross-talk between NF-triggered symbiotic signaling and plant immune signaling mediated by RIN4," though accurate, fails to capture the novelty or significance of the findings, and left me wondering how this adds to what is already known. A clear conclusion, for eg, the phosphorylation of RIN4 isoforms by SYMRK at S143 modulates immune responses during symbiotic interactions with rhizobia, or similar, is needed.

Reviewer #1 (Public Review):

Summary:<br /> The authors demonstrated that carbon depletion triggers the autophagy-dependent formation of Rubisco Containing Bodies, which contain chloroplast stroma material, but exclude thylakoids. The authors show that RCBs bud directly from the main body of chloroplasts rather than from stromules and that their formation is not dependent on the chloroplast fission factor DRP5. The authors also observed a transient engulfment of the RBCs by the tonoplast during delivery to the vacuolar lumen.

Strengths:<br /> The authors demonstrate that autophagy-related protein 8 (ATG8) co-localizes to the chloroplast demarking the place for RCB budding. The authors provide good-quality time-lapse images and co-localization of the markers corroborating previous observations that RCBs contain only stroma material and do not include thylakoid. The text is very well written and easy to follow.

Weaknesses:<br /> A significant portion of the results presented in the study comes across as a corroboration of the previous findings made under different stress conditions: autophagy-dependent formation of RCBs was reported by Ishida et all in 2009. Furthermore, some included results are not of particular relevance to the study's aim. For example, it is unclear what is the importance of the role of SA in the formation of stromules, which do not serve as an origin for the RCBs. Similarly, the significance of the transient engulfment of RCBs by the tonoplast remained elusive. Although it is indeed a curious observation, previously reported for peroxisomes, its presentation should include an adequate discussion maybe suggesting the involved mechanism. Finally, some conclusions are not fully supported by the data: the suggested timing of events poorly aligns between and even within experiments mostly due to high variation and low number of replicates. Most importantly, the discussion does not place the findings of this study into the context of current knowledge on chlorophagy and does not propose the significance of the piece-meal vs complete organelle sequestration into the vacuole under used conditions, and does not dwell on the early localization of ATG8 to the future budding place on the chloroplast.

Reviewer #2 (Public Review):

This manuscript proposed a new link between the formation of chloroplast budding vesicles (Rubisco-containing bodies [RCBs]) and the development of chloroplast-associated autophagosomes. The authors' previous work demonstrated two types of autophagy pathways involved in chloroplast degradation, including piecemeal degradation of partial chloroplast and whole chloroplast degradation. However, the mechanisms underlying piecemeal degradation are largely unknown, particularly regarding the initiation and release of the budding structures. Here, the authors investigated the progression of piecemeal-type chloroplast trafficking by visualizing it with a high-resolution time-lapse microscope. They provide evidence that autophagosome formation is required for the initiation of chloroplast budding, and that stromule formation is not correlated with this process. In addition, the authors also demonstrated that the release of chloroplast-associated autophagosome is independent of a chloroplast division factor, DRP5b.

Overall, the findings are interesting, and in general, the experiments are very well executed. Although the mechanism of how Rubisco-containing bodies are processed is still unclear, this study suggests that a novel chloroplast division machinery exists to facilitate chloroplast autophagy, which will be valuable to investigate in the future.

Reviewer #3 (Public Review):

Summary:<br /> Regulated chloroplast breakdown allows plants to modulate these energy-producing organelles, for example during leaf aging, or during changing light conditions. This manuscript investigates how chloroplasts are broken down during light-limiting conditions.

The authors present very nice time-lapse imaging of multiple proteins as buds form on the surface of chloroplasts and pinch away, then associate with the vacuole. They use mutant analysis and autophagy markers to demonstrate that this process requires the ATG machinery, but not dynamin-related proteins that are required for chloroplast division. The manuscript concludes with a discussion of an internally-consistent model that summarizes the results.

Strengths:<br /> The main strength of the manuscript is the high-quality microscopy data. The authors use multiple markers and high-resolution time-lapse imaging to track chloroplast dynamics under light-limiting conditions.

Weaknesses:<br /> The main weakness of the manuscript is the lack of quantitative data. Quantification of multiple events is required to support the authors' claims, for example, claims about which parts of the plastid bud, about the dynamics of the events, about the colocalization between ATG8 and the plastid stroma buds, and the dynamics of this association. Without understanding how often these events occur and how frequently events follow the manner observed by the authors (in the 1 or 2 examples presented in each figure) it is difficult to appreciate the significance of these findings.

Reviewer #1 (Public Review):

Summary:<br /> The authors recently reported a scRNA-seq-based study focused on synovial fibroblasts using a mouse model of post-traumatic OA (Ref. 21). In the present manuscript, they reanalyzed the scRNA-seq data to investigate the diversity and roles of macrophages. In addition to their original scRNA-seq data (Ref. 21), they utilized the deposited data of other OA or RA models (Ref. 25-27) and compared cell types in the synovium. The authors extracted the macrophage/monocyte group, compared differentially expressed genes (DEGs) between OA and RA synovium, and analyzed macrophage subsets, including trajectory analysis. They further estimated the crosstalk between stromal and immune cells via M-CSF signaling, and transcription factors for monocyte differentiation.

Strengths:<br /> The descriptions are comprehensive, based on the scRNA-seq data including the original and other independent studies.

Weaknesses:<br /> Meanwhile, methods of sample preparation must be different, for example, the extent and location of excised synovium. The comparison with other studies is meaningful and informative; however, caution should be exercised regarding the potentially significant impact of methodological differences on the analysis results.

The various data obtained from these technologies are comprehensive and useful; however, they are just estimates. Without confirmation by experiments, it is impossible to determine how much of it can be believed. This issue is not limited to this paper.

Most of all signaling pathways and molecules described in the latter part of this study are previously known.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Knights et al set out to identify the specific immune cells and their contribution to the development of osteoarthritis. They performed a comprehensive analysis of scRNA-seq and flow cytometry using different stages of the PTOA model and sought to identify specific synovial macrophages in OA. Computational analysis revealed that M-CSF signaling in synovium plays an important role in stromal-immune crosstalk in OA. They also found that four transcription factors including Pu.1, Cebp-alpha, Cebp-beta, and Jun regulate the differentiation of monocytes into pro-inflammatory synovial macrophages in OA.

Strengths:<br /> The main strength of this study is the profiling of immune cells which will be a valuable resource for better understanding the pathogenesis of OA. The work is technically sound, and the level of analysis of gene expression, clustering, cell-cell communication, and dynamic changes in gene modules over time is state-of-the-art.

The reviewer appreciates that the authors uncovered the transcriptional network that regulates the differentiation of synovial macrophages in OA. In addition, the identification of M-CSF signaling as a major crosstalk axis in OA development is also intriguing.

Weaknesses:<br /> Although the scRNA-seq analysis of immune cells in OA is quite convincing, the data has been rather descriptive and superficial at this stage. The authors did not show the in vivo significance of their findings in OA development.

Reviewer #1 (Public Review):

Summary: The goal of this study was to develop and validate novel molecules to selectively activate a cell signaling pathway, the Wnt pathway in this case, in target cells expressing a specific receptor. This was achieved through a two-component system that the authors call BRAID, where each component simultaneously binds the target cell-specific marker BKlotho and a Wnt co-receptor. These components, called SWIFT molecules, bring together the Wnt co-receptors LRP and FZD, activating the pathway specifically in cells that express BKlotho.

Results presented in the study demonstrate the desired activity of SWIFT molecules; the binding assays support simultaneous association of SWIFT with BKlotho and a Wnt co-receptor, and the Wnt reporter and qPCR assays support pathway activation in cell lines and primary cells in a BKlotho-dependent manner. In the future, the BRAID approach could be applied to activate Wnt signaling or another pathway initiated by a co-receptor complex in a cell type-specific manner, and/or in a FZD subtype-specific manner to activate distinct branches of Wnt signaling.

Strengths:

• This study successfully demonstrates a novel way to activate Wnt signaling in target cells expressing a specific marker. Given the role of the Wnt signaling pathway in key processes such as cell proliferation and tissue renewal and the value of modulating cell signaling in a cell type-specific manner, the cell targeting system developed here holds great therapeutic and research potential. It will be curious to see whether the BRAID design can be applied to other cell surface markers for Wnt activation, or for activation of other signaling pathways that require co-receptor association.

• Octet assay results show simultaneous binding of SWIFT molecules to both the Wnt co-receptor FZD/LRP and BKlotho, while negative control molecules without the FZD/LRP or BKlotho-binding module show neither receptor binding nor Wnt pathway activation. These results indicate that SWIFT molecules function through the intended mechanism.

• Exposure of two cell types simultaneously exposed to the SWIFT molecules in 2-layer cell culture demonstrated the ability of the molecules to activate Wnt signaling in a cell type- and BKlotho expression-specific manner.

Weaknesses:

• The study does not address whether the targeted cells express FGFR1c/2c/3c and whether the FGF21 full length moiety or the 39F7 IgG moiety of SWIFT molecules could unintentionally activate FGF signaling in these cells.

Reviewer #2 (Public Review):

Summary:

The study introduces BRAID, a novel approach for targeting drugs to specific cell types, addressing the challenges of pleiotropic drug actions. Unlike existing methods, this one involves breaking a protein drug molecule into inactive parts that are then put back together using a bridging receptor on the target cell. The individual components of this assembly are not required to be together, thereby affording it a degree of flexibility. The authors applied this idea to the WNT/-catenin signaling pathway by splitting a WNT mimic into two parts with FZD and LRP binding domains and bridging receptors. This combined method, which is called SWIFT, showed that WNT signaling was turned on in target cells, showing that cell-specific targeting is. The technique shows promise for the development of therapeutics, as it provides a way to more precisely target signaling pathways.

The authors have effectively elucidated their strategy through visually appealing diagrams, providing clear and thorough visual aids that facilitate comprehension of the concept. In addition, the authors have provided convincing evidence that the C-terminal region of FGF21 is essential for the binding process. Their meticulous and thorough presentation of experimental results emphasizes the significance of this specific binding domain and validates their findings.

Strengths:

BRAID, a novel cell targeting method, divides an active drug molecule into inactive components formed by a bridging receptor. This novel approach to cell-specific drug action may reduce systemic toxicity.

The SWIFT approach successfully targets cells in the WNT/β-catenin signaling pathway. The approach activates WNT signaling only in target cells (hepatocytes), proving its specificity.

The study indicates that the BRAID approach can target various signaling systems beyond WNT/β-catenin, indicating its versatility. Therapeutic development may benefit from this adaptability.

Weaknesses:

The study shows the SWIFT approach works in vitro using cell lines, primary human hepatocytes, and human intestinal organoids, but it lacks in vivo animal model or clinical validation. I believe future studies will determine this aspect.

The success of SWIFT depends on the presence and expression of the bridging receptor (βKlotho) on target cells. The approach may fail if the target receptor is not expressed.

Reviewer #1 (Public Review):

Summary:<br /> "Expanding the Drosophila toolkit for dual control of gene expression" by Zirin et al. aims to develop resources for simultaneous independent manipulation of multiple genes in Drosophila. The authors use CRISPR knock-ins to establish a collection of T2A-LexA and T2A-QF2 transgenes with expression patterns in a number of commonly studied organs and tissues. In addition to the transgenic lines that are established, the authors describe a number of plasmids that can be used to generate additional transgenes, including a plasmid to generate a dual insert of LexA and QF that can be resolved into a single insert using FLP/FRT-mediated recombination, and plasmids to generate RNAi reagents for the LexA and QF systems. Finally, the authors demonstrate that a subset of the LexA and QF lines that they generated can induce RNAi phenotypes when paired with LexAop or QUAS shRNA lines. In general, the claims of the paper are well supported by the evidence and the authors do a thorough job of validating the transgenic lines and characterizing their expression patterns.

Strengths:<br /> -Numerous Gal4 lines allow for highly specific genetic manipulation in a wide range of organs and tissues, however, similar tissue-specific drivers using alternative binary expression systems are not currently well developed. This study provides a large number of tissue and organ-specific LexA and QF2 driver lines that should be broadly useful for the Drosophila community.<br /> -While a minority of the driver lines do not express the expected pattern (likely due to cryptic regulatory elements in the LexA or QF2 sequences), the ability to generate drivers using two different Gal4 alternatives mitigates this issue (as in nearly all cases at least one of the two systems produces a clean driver line with the expected expression pattern).<br /> -The use of LexA-GAD provides an additional degree of control as it is subject to Gal80 repression. This could prove to be particularly useful in cases where a researcher wishes to manipulate multiple genes using Gal4 and LexA-GAD drivers as the Gal80(ts) system could be used for simultaneous temporal control of both constructs.<br /> -The use of Fly Cell Atlas information to generate novel oenocyte-specific driver lines provides a useful proof-of-concept for constructing additional highly tissue-specific drivers.

Weaknesses:<br /> -Since these reagents will most commonly be paired with existing Gal4 lines, adding information about corresponding Gal4 lines targeting these tissues and how faithfully the LexA and QF2 lines recapitulate these Gal4 patterns would be highly beneficial.<br /> -It is not stated in the manuscript if these transgenic lines and plasmids are currently publicly available. Information about how to obtain these reagents through Bloomington, Addgene, or TRiP should be added to the manuscript.

Reviewer #2 (Public Review):

Zirin, Jusiak, and Lopes et al presented an efficient pipeline for making LexA-GAD and QF2 drivers. The tools can be combined with a large collection of existing GAL4 drivers for a dual genetic control of two cell populations. This is essential when studying inter-organ communications since most of the current genetic drivers are biased toward the expression of the central nervous system. In this manuscript, the authors described the methodology for efficiently generating T2A-LexA-GAD and T2A-QF2 knock-ins by CRISPR, targeting a number of genes with known tissue-specific expression patterns. The authors then validated and compared the expression of double as well as single drivers and found the tissue-specific expression results were largely consistent as expected. Finally, a collection of plasmids for LexA-GAD and QF,2 as well as the corresponding LexAop and QUAS plasmids were generated to facilitate the expansion of these tool kits. In general, this study will be of considerable interest to the fly community and the resources can be readily generalized to make drivers for other genes. I believe this toolkit will have a significant, immediate impact on the fly community.

Reviewer #1 (Public Review):

The findings in this paper can be split into three parts.

1) Processing of ITS2

Firstly, the authors identify two sites on ITS2 which are cleaved by the ScLas1-Grc3 complex, as part of 25S ribosomal RNA maturation.

For a smaller segment of ITS2 (33nt), the two sites separate out 3 parts with sizes of 10nt, 14nt, and 9nt (Figure 1C). However, bands in mass spec. occur at 23nt (14nt+9nt) and 14nt, but not 9nt alone. Additional bands can be seen at 22nt and 21nt. Hence the evidence for these two specific sites seems somewhat uncertain. It is not clear if there is an experimental limitation in terms of accuracy, or that the cleavage is perhaps somewhat approximate at the two sites. The authors may try to clarify these results a bit further.

For a larger ITS2 (81nt), similar support is found for the two cleavage sites, but now the possible fragments are 14nt, 30nt, 37nt, and 44nt (14nt+30nt) (Figure 1E). The observed bands match these fragments at 44nt, 37nt, 30nt, but again there are additional bands at 36nt, 28nt, and 13nt, which are not fully explained. It may be useful for explain or discuss these discrepancies.

Another useful result of these experiments is to confirm that Las1 alone has only weak activity against ITS2, but very strong activity when it is part of the Las1-Grc3 complex.

2) Structure of Las1, and Las1-Grc3 complexes

A second important contribution of this work are X-ray and cryoEM structures of Las1-Grc3 from Sc and Cj. It is interesting that even though the complexes are very similar, CjGrc3 shows weak activation of CjLas1, whereas ScGrc3 more strongly activates ScLas1. The X-ray and cryoEM structures are very similar. However, the X-ray structures also show an additional (CC) domain from Las1 not resolved in the cryoEM map. This difference is significant, because it suggests the CC domain may remain more flexible in solution, but stabilizes in the crystal. Also interestingly, the CC domains have different structures and are in different positions in ScLas1-Grc3 vs CjLas1-Grc3, again hinting that they are more dynamic. Further experiments described by the authors confirm the CC domain is indeed important in RNA binding and activity. Whether they are only implicated in binding RNA or both binding and cleavage is somewhat unclear.

The structure of Las1-Grc3 is described as resembling a butterfly, with Las1 being the body and Grc3 the wings. While this is a useful description, it may be a bit misleading. The complex has C2 symmetry, with one Las1-Grc3 unit related to the other by about ~180 rotation around a vertical axis parallel to the body of the butterfly as proposed. To use the butterfly analogy, one half of the body and one wing faces the opposite way as the other, not a mirror symmetry as a real butterfly would have.

Both Cj and Sc structures show the C-terminal of Grc3 binding to the active pocket of Las1, explaining its effect on activity. Mutation experiments also further show the importance of these residues on activity. Reciprocally, a region in Las1, LCT, inserts into Grc3, forming a stable complex. Again mutating these residues affects activity, strengthening their importance and the evidence for how the stable complex forms.

Finally, an X-ray structure of dimeric Las1 in Cj, without Grc3 is presented. Truncating CC and LCT appeared to be necessary to allow the dimer to crystallize. Superposition with Las1 dimer in Las1-Grc3 shows a conformational difference, and different distances between residues in the active pocket, explaining the change in activity with and without Grc3. Interestingly, the Las1 domains themselves do not change too much, i.e. both domains can be matched with less than 1Å Ca-RMSD, so the difference may be more of a repositioning of the two domains for the active conformation.

One notable strength of this study is the use of both X-ray and cryoEM to obtain structures of the Las1-Grc3 and dimeric Las1 complexes. Typically structures of cryoEM at ~3Å are sufficient for reliable modeling; for example, the backbone and side chains of residues in the active site are well resolved. However, in this case, a cryoEM model of the Las1 dimer was not obtained, so it was important to show first that the Las1-Las1 conformation in the Las1-Grc3 complex is the same in both X-ray and cryoEM models. Otherwise, there may remain doubt whether the X-ray model of Las1 dimer could be compared to the cryoEM map of Las1-Grc3, as crystallization conditions could potentially influence conformation and arrangement. It would be interesting to know whether a cryoEM structure of the Las1 dimer alone was attempted - perhaps it was too small to be reliably seen in micrographs. Having had such a model could avoid the need of X-ray structures, although of course more experimental data are always useful.

3) Mechanism of ITS2 cleavage

The proposed mechanism shown in Figure 8 seems to be well supported by the obtained structures and biochemical experiments. A question that remains is why it is proposed that both C2 and C2' cleavage can be performed upon a single binding of the ITS2 RNA, i.e. seeming to suggest there are two binding sites. This would seem to directly generate 3 fragments, without any other intermediate products. Mass spec. seemed to show the intermediate products, perhaps indicating two binding events for each cleavage process. Perhaps the authors could discuss this more. Also perhaps can be good to discuss whether it would be possible to obtain a structure with the bound RNA, further giving structural information of how the exact cleavage process is performed.

Reviewer #2 (Public Review):

In this manuscript, Chen et al. determined the structural basis for pre-RNA processing by Las1-Grc3 endoribonuclease and polynucleotide kinase complexes from S. cerevisiae (Sc) and C. jadinii (Cj). Using a robust set of biochemical assays, the authors identify that the sc- and CjLas1-Grc3 complexes can cleave the ITS2 sequence in two specific locations, including a novel C2' location. The authors then determined X-ray crystallography and cryo-EM structures of the ScLas1-Grc3 and CjLas1-Grc3 complexes, providing structural insight that is complimentary to previously reported Las1-Grc3 structures from C. thermophilum (Pillon et al., 2019, NSMB). The authors further explore the importance of multiple Las1 and Grc3 domains and interaction interfaces for RNA binding, RNA cleavage activity, and Las1-Grc3 complex formation. Finally, evidence is presented that indicates Las1 undergoes a conformational change upon Grc3 binding that stabilizes the Las1 HEPN active site, providing a possible rationale for the stimulation of Las1 cleavage by Grc3.

In the revised manuscript, the authors have made significant efforts towards addressing initial reviewer comments. This includes further clarification for key biochemical experiments, significant improvement in structural model quality, and additional structural analysis that further strengthens major conclusions in the manuscript. Overall, the authors conclusions are now well supported by the biochemical and structural data provided.

Reviewer #1 (Public Review):

Summary:<br /> Ellis et al. investigated the functional and topographical organization of the visual cortex in infants and toddlers, as evidenced by movie-viewing data. They build directly on prior research that revealed topographic maps in infants who completed a retinotopy task, claiming that even a limited amount of rich, naturalistic movie-viewing data is sufficient to reveal this organization, within and across participants. Generating this evidence required methodological innovations to acquire high-quality fMRI data from awake infants (which have been described by this group, and elsewhere) and analytical creativity. The authors provide evidence for structured functional responses in infant visual cortex at multiple levels of analyses; homotopic brain regions (defined based on a retinotopy task) responded more similarly to one another than to other brain regions in visual cortex during movie-viewing; ICA applied to movie-viewing data revealed components that were identifiable as spatial frequency, and to a lesser degree, meridian maps, and shared response modeling analyses suggested that visual cortex responses were similar across infants/toddlers, as well as across infants/toddlers and adults. These results are suggestive of fairly mature functional response profiles in the visual cortex in infants/toddlers and highlight the potential of movie-viewing data for studying finer-grained aspects of functional brain responses, but further evidence is necessary to support their claims and the study motivation needs refining, in light of prior research.

Strengths:<br /> - This study links the authors' prior evidence for retinotopic organization of visual cortex in human infants (Ellis et al., 2021) and research by others using movie-viewing fMRI experiments with adults to reveal retinotopic organization (Knapen, 2021).

- Awake infant fMRI data are rare, time-consuming, and expensive to collect; they are therefore of high value to the community. The raw and preprocessed fMRI and anatomical data analyzed will be made publicly available.

Weaknesses:<br /> - The Methods are at times difficult to understand and in some cases seem inappropriate for the conclusions drawn. For example, I believe that the movie-defined ICA components were validated using independent data from the retinotopy task, but this was a point of confusion among reviewers. In either case: more analyses should be done to support the conclusion that the components identified from the movie reproduce retinotopic maps (for example, by comparing the performance of movie-viewing maps to available alternatives (anatomical ROIs, group-defined ROIs). Also, the ROIs used for the homotopy analyses were defined based on the retinotopic task rather than based on movie-viewing data alone - leaving it unclear whether movie-viewing data alone can be used to recover functionally distinct regions within the visual cortex.

- The authors previously reported on retinotopic organization of the visual cortex in human infants (Ellis et al., 2021) and suggest that the feasibility of using movie-viewing experiments to recover these topographic maps is still in question. They point out that movies may not fully sample the stimulus parameters necessary for revealing topographic maps/areas in the visual cortex, or the time-resolution constraints of fMRI might limit the use of movie stimuli, or the rich, uncontrolled nature of movies might make them inferior to stimuli that are designed for retinotopic mapping, or might lead to variable attention between participants that makes measuring the structure of visual responses across individuals challenging. This motivation doesn't sufficiently highlight the importance or value of testing this question in infants. Further, it's unclear if/how this motivation takes into account prior research using movie-viewing fMRI experiments to reveal retinotopic organization in adults (e.g., Knapen, 2021). Given the evidence for retinotopic organization in infants and evidence for the use of movie-viewing experiments in adults, an alternative framing of the novel contribution of this study is that it tests whether retinotopic organization is measurable using a limited amount of movie-viewing data (i.e., a methodological stress test). The study motivation and discussion could be strengthened by more attention to relevant work with adults and/or more explanation of the importance of testing this question in infants (is the reason to test this question in infants purely methodological - i.e., as a way to negate the need for retinotopic tasks in subsequent research, given the time constraints of scanning human infants?).

Reviewer #2 (Public Review):

Summary:<br /> This manuscript shows evidence from a dataset with awake movie-watching in infants, that the infant brain contains areas with distinct functions, consistent with previous studies using resting state and awake task-based infant fMRI. However, substantial new analyses would be required to support the novel claim that movie-watching data in infants can be used to identify retinotopic areas or to capture within-area functional organization.

Strengths:<br /> The authors have collected a unique dataset: the same individual infants both watched naturalistic animations and a specific retinotopy task. These data position the authors to test their novel claim, that movie-watching data in infants can be used to identify retinotopic areas.

Weaknesses:<br /> To claim that movie-watching data can identify retinotopic regions, the authors should provide evidence for two claims:

- Retinotopic areas defined based only on movie-watching data, predict retinotopic responses in independent retinotopy-task-driven data.

- Defining retinotopic areas based on the infant's own movie-watching response is more accurate than alternative approaches that don't require any movie-watching data, like anatomical parcellations or shared response activation from independent groups of participants.

Both of these analyses are possible, using the (valuable!) data that these authors have collected, but these are not the analyses that the authors have done so far. Instead, the authors report the inverse of (1): regions identified by the retinotopy task can be used to predict responses in the movies. The authors report one part of (2), shared responses from other participants can be used to predict individual infants' responses in the movies, but they do not test whether movie data from the same individual infant can be used to make better predictions of the retinotopy task data, than the shared response maps.

So to be clear, to support the claims of this paper, I recommend that the authors use the retinotopic task responses in each individual infant as the independent "Test" data, and compare the accuracy in predicting those responses, based on:

- The same infant's movie-watching data, analysed with MELODIC, when blind experimenters select components for the SF and meridian boundaries with no access to the ground-truth retinotopy data.<br /> - Anatomical parcellations in the same infant.<br /> - Shared response maps from groups of other infants or adults.<br /> - (If possible, ICA of resting state data, in the same infant, or from independent groups of infants).

Or, possibly, combinations of these techniques.

If the infant's own movie-watching data leads to improved predictions of the infant's retinotopic task-driven response, relative to these existing alternatives that don't require movie-watching data from the same infant, then the authors' main claim will be supported.

The proposed analysis above solves a critical problem with the analyses presented in the current manuscript: the data used to generate maps is identical to the data used to validate those maps. For the task-evoked maps, the same data are used to draw the lines along gradients and then test for gradient organization. For the component maps, the maps are manually selected to show the clearest gradients among many noisy options, and then the same data are tested for gradient organization. This is a double-dipping error. To fix this problem, the data must be split into independent train and test subsets.

Reviewer #3 (Public Review):

The manuscript reports data collected in awake toddlers recording BOLD while watching videos. The authors analyse the BOLD time series using two different statistical approaches, both very complex but do not require any a priori determination of the movie features or contents to be associated with regressors. The two main messages are that 1) toddlers have occipital visual areas very similar to adults, given that an SRM model derived from adult BOLD is consistent with the infant brains as well; 2) the retinotopic organization and the spatial frequency selectivity of the occipital maps derived by applying correlation analysis are consistent with the maps obtained by standard and conventional mapping.

Clearly, the data are important, and the author has achieved important and original results. However, the manuscript is totally unclear and very difficult to follow; the figures are not informative; the reader needs to trust the authors because no data to verify the output of the statistical analysis are presented (localization maps with proper statistics) nor so any validation of the statistical analysis provided. Indeed what I think that manuscript means, or better what I understood, may be very far from what the authors want to present, given how obscure the methods and the result presentation are.

In the present form, this reviewer considers that the manuscript needs to be totally rewritten, the results presented each technique with appropriate validation or comparison that the reader can evaluate.

Reviewer #1 (Public Review):

Summary:

The paper of Mao et al. expands the genetic toolset that was previously developed by the Rao lab (Denfg et al 2019) to introduce the conditional KO or downregulation of neurotransmission components in Drosophila. The authors then use these tools to investigate neurotransmission in the clock neurons of the Drosophila brain. They first test some known components and then analyze the contribution of the CNMa neuropeptide and its receptor to the circadian behavior. The results indicate that CNMA acts from a subset of DN1ps (dorsal clock neurons) to set the phase of the morning peak of locomotor activity in light:dark cycles, with an advanced morning activity in the absence of the neuropeptide. Interestingly, the receptor for the PDF neuropeptide appears to be acting in some of the CNMa neurons to control morning activity.

Strengths/weaknesses:

This is clearly a very useful new set of tools to restrict the manipulation of these components to specific neuronal populations, and overall (see specific points below), the paper is convincing to show that the tools indeed allow to efficiently and specifically eliminate neuropeptides/receptors from subsets of neurons. The analysis of the CNMa function in the clock network reveals a new and interesting function for CNMa. but this part needs to be improved. Some of the behavioral data (PDF/PDFR) do not fit with published work with the mutants. This should be clarified by providing more data comparing the described genotypes with the classical mutants. Some conclusions also need to be toned down.

Reviewer #2 (Public Review):

In this study, Mao and co-workers deliver a substantial suite of genetic tools in support of the senior author's recent proposal to create a "chemoconnectomic" tool kit for the expression mapping and conditional disruption of specific neurotransmitter systems with fly neurons of interest. Specifically, they describe the creation of two toolsets for recombination-based and CRISPR/Cas9-based conditional knockouts of genes supporting neurotransmitter and neuromodulator function and Flp-Out and Split-LexA toolkit for the examination of gene expression within defined subsets of neurons. The authors report the creation of conditional genetic tools for the disruption/mapping of approximately 200 chemoconnectomic gene products, an examination of the general effectiveness of these tools in the fly brain, and apply them to the circadian clock network in an attempt to reveal new information regarding the transmitter/modulator systems involved in daily behavioral timing. The authors provide clear evidence of the effectiveness of the new methods along with a transparent assessment of the variability of the tools. In addition, they present evidence that the neuro peptide CNMa influences the morning peak of daily activity in the fly by regulating the timing of activity increases in anticipation of dawn.

A major strength of the study is the transparent assessment of the effectiveness and variability of the conditional genetic approaches developed by the authors. The authors have largely achieved their aims and the study therefore represents a major delivery on the promise of chemoconnectomics made by the senior author in 2019 (Neuron, Vol. 101, p. 876). Though there are some concerns about the variability of knockout effectiveness, off-target effects of the knockout strategies, and (especially) the accuracy of the gene expression approach, the tools created for this study will almost certainly be useful for the field and support a great deal of future work.

Reviewer #3 (Public Review):

Summary:

Mao and colleagues generated powerful reagents to genetically analyse chemical communication (CCT) in the brain, and in the process uncovered a function for the CNMa neuropeptide expressed in a subset of DN1p neurons that contributes to the temporal organization of locomotor activity, i.e., the timing of morning anticipation.

Strengths:

The strength of the manuscript relies on the generation/characterization of new tools for conditional targeting a well-defined set of CCT genes along with the design and testing of improved versions of Cas9 for efficient knockout. Such invaluable resources will be of interest to the whole community. The authors employed these tools and intersectional genetics to provide an alternative profiling of clock neurons, which is complementary to the ones already published. Furthermore, they uncovered a role for CNMamide, expressed in two DN1ps, in the timing of morning anticipation.

Weaknesses:

They targeted an extensive set of candidate genes putatively involved in communication (transporters, receptor subunits, neuropeptides, neurotransmitter synthesis, etc); they provide a list of efficient gRNAs to target even a longer list of candidate genes, however, it is not clear if all of those made it into transgenic lines that effectively mediate targeting all chemical transmission genes (as suggested by the authors).

Reviewer #1 (Public Review):

Characterizing gene-by-environment interactions has been of great interest for quite some time, as these effects are believed (based on plausible hypotheses and some data) to have importance for the interpretation of complex disease risk. Here, a major class of variants of interest is genetic regulatory variants where e.g. binding of context-specific regulators (TFs, etc) provides a plausible mechanism. However, these variants have been difficult to identify in eQTL and other studies.

This study leverages the MPRA approach to screen for many thousands of constructs of putative regulatory variants for their effects on vascular endothelial cells with and without caffeine. They identify thousands of sequences that are differentially regulated between the conditions, and with motif enrichment approaches and comparisons to prior studies, identify some TFs (including novel ones) that may have a role in how these cells respond to caffeine. Next, by allele-specific expression analysis, they identify thousands of variants that are not only regulatory (having a different activity from the two allelic versions of the construct) but also a major subset that has different regulatory activity following the caffeine treatment. Again, motif analysis indicates potential mechanisms, and the eQTL comparison nicely demonstrates the value of these discoveries. The MRPA approach is clearly fruitful and informative, and identifying many context-specific regulatory variants is informative for people working on genetic regulatory variation.

The part of the paper that felt underwhelming and not so well-founded was the link to complex disease. I was somewhat surprised to see caffeine experiments in vascular endothelial cells being so strongly framed in terms of CAD. This cell type (and potentially also caffeine) is relevant in many biological processes and diseases. More importantly, given the strongly disease-focused framing, I was surprised to find few results that would actually link the regulatory variant data here to CAD via GWAS overlap or other analyses. Maybe the results were slim here with little overlap, but the results provided do not really justify the implications that disease-relevant findings are being made.

Specifically, the evidence of PIP4K2B let alone the studied cASE variant having a causal role in CAD is weak. This is based on a previously published pTWAS paper, but the variant itself is not a significant GWAS variant. TWAS is known to easily suffer from non-causal hits due to LD and other complications, and hence this link should be taken with a heavy grain of salt. I would be more convinced if the variant was a significant GWAS hit, and even more so if it was a fine-mapped variant, but it is neither of them. As such, the language (Discussion) is not justified by the data: "By studying different environmental contexts, we can identify that, in this instance, caffeine can reduce the risk of poor cardiovascular health outcomes. If the environmental context was not considered and this work was conducted solely in the control condition, the decreased risk induced by caffeine would not have been observed." the decreased [CAD] risk induced by caffeine would not have been observed." Has to be softened. Furthermore, Figure 5 is an illustration but very little data (cASE p-values, etc) is provided here and in the text.

Furthermore, I find some of the suggested links to CAD via lipid biology and related TFs quite speculative; are these processes really taking place in vascular endothelian cells? The paper that is being referred to seems to focus on the liver.

Finally, the analyses seem carefully done, but in Figure 2A the systematic inflation of p-values seems concerning. This could be the real biology of broadly distributed response to caffeine, but it's also consistent with a bias that is unaccounted for and inflates p-values across the board. And do we really expect all these elements to respond to caffeine (more or less)? It is difficult to say what exactly this might be, but the caffeine libraries seem to have a higher sequencing coverage (SFig 5). How does this affect the results? Can it bias the DE results via different overdispersion, or the ASE or cASE estimation when the caffeine condition is a higher power (which ASE analysis is typically sensitive to)?

The library included negative controls of variants that are not believed to be regulatory variants, but I don't see a systematic presentation of the null obtained from these presented in the paper.

Reviewer #2 (Public Review):

In "Characterization of caffeine response regulatory variants in vascular endothelial cells", Boye et. al. employ a massively parallel reporter assay, bi-allelic targeted STARR-seq (BiT-STARR-seq), to characterize how non-coding variants affect gene expression in HUVECs after treatment with caffeine. After measuring the differential activity of the individual MPRA constructs in their cells, they test for both allele-specific effects (ASE) in each condition. They likewise test for conditional allele-specific effects (cASE). The authors identify an enrichment cASE variants with stronger allelic effects in caffeine vs control conditions and use a combination of transcription factor motif identification, open chromatin enrichment, caffeine response factor binding site identification, and eQTL fine-mapping to identify 25 SNPs that meet their selection criteria. The authors finally highlight one example SNP from this set, rs22871, as a potential candidate for further analysis.

Reviewer #3 (Public Review):

Though it is speculated that gene-environment interactions (GxE) contribute to disease heritability, they remain challenging to detect. Here, the authors use a massively parallel reporter assay in vascular endothelial cells treated with or without caffeine to explore context-specific gene regulation. They use a library of 200-bp candidate regions selected from a variety of genetic studies (eQTL, GWAS) and demonstrate allelic bias in activity across a large proportion, including variants with caffeine-specific allelic effects. The described assays represent a useful approach for examining GxE in complex traits, thus these results are of broad appeal. I have great enthusiasm for the experimental design, including the large library and sample size, testing the MPRA in an appropriate cell type with a relevant stimulus, and interesting functional analyses including transcription factor motif enrichment and comparison to GTEx data. My main critique is that the description of analyses and results lacks the clarity that would aid the reader in interpretation.

1. The abstract states that >43k variants are tested in the library while the methods section states that >43k constructs were tested. Because you tested allele pairs, my expectation is that you would have used ~86k constructs, and at various points, you mention denominators that are higher than 43k. Please address this discrepancy.<br /> 2. Previously, you reported allele-specific expression analysis across many conditions, including caffeine treatment. In that study, you observed high levels of differential expression induced by caffeine treatment (on the order of thousands of genes) with only a modest number of SNPs with allele-specific expression after caffeine treatment. In the current study, you report that only ~800 constructs are differentially active after caffeine treatment which you state as evidence that "caffeine overall increases the activity of the regulatory elements," but this is quite a small number given that you tested tens of thousands of constructs. Later you describe >8k constructs with conditional allele-specific expression. Do you mean that the former subset only displays caffeine effects without allele-specific expression? And taking both studies into account, what do you think accounts for the seeming discrepancies between the relative amount of conditional allele-specific expression measured by RNA-seq vs BiT-STARR-seq?<br /> 3. Your transcription factor motif enrichment analyses are interesting, and would benefit from a further grounding in the biology of the cells you're working with. To this end, what proportion of the transcription factor sets that you use for enrichment are expressed in your cell model? For those that are enriched, are they highly expressed, and does that expression vary with caffeine treatment? You provide some of this information for a specific example (rs228271), but a broader discussion is warranted.<br /> 4. I suggest elaborating on the choice of treatment conditions to provide valuable context. Acute responses to caffeine exposure may vary from chronic exposure. In this study, I think a single acute exposure is more than appropriate for reasons of feasibility and many of the regulatory pathways will be shared between acute and long-term; however, given that CAD is a chronic disease that develops over many years, it would be worthwhile to speculate on longer term effects of caffeine exposure in your model system.

Reviewer #1 (Public Review):

In this manuscript, the authors investigate whether the effects of the BCG vaccine on immunity to Mtb infection could be improved by inhibiting amidation of the peptidoglycan sidechains to allow for recognition by NOD-1. This is a very important area and an interesting new approach to improve vaccination for TB. The authors find that CRISPRi knockdown of murT-gatD causes rather dramatic cell wall defects, more accessible cell wall labeling, and results in attenuated growth in macrophages and mice. There is some data presented to support that the murT-gatD KD strain may be more protective in the animal model, but most comparisons made are not significant and some interpretations stated in the results section do not reflect the data in the figures. It seems that the most important comparisons are between WT BCG+Dox and rBCG+Dox, and the manuscript would be clearer if this comparison was focused on specifically.

Reviewer #2 (Public Review):

In this manuscript, Shaku and colleagues investigated if the deletion of the enzymatic pair MurT-GatD from Mycobacterium bovis BCG leads to more effective immune activation and protection against tuberculosis disease. MurT-GatD are enzymes implicated in the amidation of peptidoglycan sidechains, an immune evasion mechanism used by virulent mycobacteria to avoid recognition by the pathogen recognition receptor NOD-1.<br /> Using CRISPRi, the authors show that D-glutamate diaminopimelate (iE-DAP) gets unmasked in BCG when MurT-GatD are deleted. They call the resulting recombinant BCG strain in which the induction of the CRISPRi construct is achieved via anhydrotetracycline, BCG::iE-DAP.<br /> Subsequently, the authors characterize the growth kinetics of the strain and show that MurT-GatD deletion results in cell wall defects (as expected) and increased susceptibility to antibiotics. They use in vitro assays with bone marrow-derived macrophages to show that rBCG::iE-DAP leads to an enhanced 'training effect' of the macrophages and increased killing of subsequent Mtb infection. They go on to show that the growth of the rBCG strain can be inhibited both in vitro and in vivo via the addition of doxycycline. Finally, the authors vaccinate Balb/c mice with wildtype BCG or their rBCG strain, deliver doxycycline via oral gavage, and challenge mice with Mycobacterium tuberculosis 6 weeks later. At 4 and 8 weeks after M. tuberculosis infection the mice get assessed for bacterial burden and histopathology. They show that rBCG::iE-DAP leads to reduced bacterial burden, but increased pathology in the lung compared to parental BCG.

The conclusions of this paper are mostly supported by data, but the in vivo protection results against TB need to be clarified and extended.

Strength:<br /> The authors demonstrate an important new pathway by which to improve immunogenicity of BCG - the unmasking of DAP. This is an exciting finding and could lead to the improvement of multiple existing rBCG strains.<br /> The authors also show a rigorous characterization of the rBCG strain and robust in vitro data, demonstrating the effect of MuRT-GatD deletion on cell wall morphology, antibiotic susceptibility and immune training of macrophages.

Weaknesses:<br /> The in vivo part of the manuscript is much weaker than the in vitro findings, and the in vivo experiments are only performed with 5 mice per group and time-point in one single experiment. Scientific standards require that each experiment is repeated at least once to show reproducibility and robustness. The low number of mice for the in vivo experiments also don't allow for strong statistical power.

Reviewer #3 (Public Review):

The authors inactivated the MurT-GatD of Mycobacterium bovis BCG using CRISPR interference and found that loss of these enzymes curbs growth but also alters the cell envelope and cell wall composition. As MurT-GatD are required for amidation of D-glutamate residues in the cell wall and amidation is required for cell wall crosslinking, depletion of MurT-GatD leads to envelope defects and increased sensitivity to cell wall-targeting antibiotics. Loss of D-glutamate amidation also leads in the accumulation of cell wall components that are detected by the cellular NOD1-innate immune surveillance system that is normally blind to amidated cell wall components. Such MurT-GatD-depleted BCG cells are more effective in protecting host cells towards infection by Mycobacterium tuberculosis (Mtb) in an in vitro monocyte model, but also in a murine lung infection model of Mtb.

The use of the cellular and animal models gave consistent findings for the recombinant BCG mutant strain in its protective effect against subsequent Mtb infections, importantly occurring in a concentration-dependent manner that correlates with the levels of CRISPR-mediated inhibition.

As no efficient vaccine exists against Mtb and the authors showed the potency of the mutant BCG over WT BCG to vaccinate mice against Mtb, this work identifies MurT-GatD-depleted BCG as a strong new and effective vaccine candidate against Mtb. It is possible that enhanced NOD1-signaling caused by loss of D-glutamate has a general self-adjuvanting effect on BCG bacteria and its conserved surface antigens towards Mtb. Alternatively, Mtb bacteria could alter their cell envelope structure during the course of an infection, rendering them more susceptible to immune responses already entrained by MurT-GatD-depleted BCG.

Reviewer #1 (Public Review):

Predator-prey interactions often involve one predator and one prey. Where more than one predator hunts a single prey, a key question is whether the predators involved are cooperating in some manner. Where this has been observed in biology, it has been suggested that complex cognitive processes may be needed to support the cooperation, such as each predator representing the intention of other predators. In this study the authors ask whether cooperation can emerge in a highly idealized scenario with little more than a basic reinforcement learning approach. Due to the size of the resulting state space, computing the value function becomes computationally cumbersome, so a function approximation method using a variant of a deep Q-network (DQN) is used. The authors have successfully shown that cooperation, here operationalized as a higher success rate with two predators in the context of sharing of the reward (prey that's captured), can emerge in this context. Further, they show that a cluster-based analysis of the DQN can guide the generation of a short description length rule-based approach that they also test and show qualitative agreement with the original DQN results.

Strengths of the work include providing a demonstration proof that cooperation can emerge with simple rules in a predator-prey context, suggesting that its emergence over phylogenetic time within certain clades of animals may not require the complex cognitive processes that prior work has suggested may be needed. Given the simplicity of the rules, one possible outcome could be a widening of investigation into cooperative hunting beyond the usual small number of species in which this has been observed, such as chimpanzees, seals, dolphins, whales, wild dogs, and big cats. The authors have done well to show how, with a variety of adjustments, a DQN network can be used to gain insights into a complex ethological phenomena.

One weakness of the work is the simplicity of the environment, a 2D plane that is 10 body lengths in each dimension, with full observability and no limitations to movement besides the boundaries of the space. Recent literature suggests that more complex phenomena such as planning may only evolve in the context of partial observability in predator-prey interactions. Thus the absence of more advanced tactics on the part of the predator agents may reflect limitations due to the simplicity of the behavioral arena, or limitations of associative learning alone to drive the emergence of these tactics. Another is that although correlations between network activity are discussed, and used to generate a rule-based approach that succeeds in replicating some of the results, there is no further analysis that may go beyond correlation to a causal analysis.

Reviewer #2 (Public Review):

This paper demonstrates that model-free reinforcement learning, with relatively small networks, is sufficient to observe collaborative hunting in predator prey environments. The paper then studies the conditions under which collaborative hunting emerges (namely, difficulty of hunting and sharing of the spoils) which is an interesting question to study and the paper contains a fascinating study in which a human is tasked with controlling the prey. However, the simplicity of the environment, a 2-d particle world with simple dynamics, makes it unclear how generalizable the results are and the results rely heavily on visual interpretation of t-SNE plots rather than more direct metrics.

Strengths:<br /> - The distinct behaviors uncovered between the predators in shared vs. not-shared reward are quite interesting!<br /> - The realization that the ability of deep RL models to solve predator-prey problems has implication for models of what is needed for collaborative hunting is clever.

Weaknesses:<br /> - The paper seems to make a claim that since this problem is solvable with model-free learning or a model-free decision tree, complicated cognition is not needed for collaborative hunting. However, the settings under which this hunting is done is exceedingly simple and it is possible that in more complex settings such as more partially observable settings or settings where the capabilities of the partners are unknown then more complicated forms of cognition might still be needed.<br /> - The problem is fully observed (I think), so there may be one uniquely good strategy that the predators can use that will work successfully against all prey. If this is the case, the human studies are of limited value, they are just confirming that the problem has a near-deterministic solution on the part of the predators.

Reviewer #3 (Public Review):

This paper aims to understand the nature of collaborative hunting. It sets out by first defining simple conditions under which collaborative hunting emerges, which leads to the emergence of a toy environment. The environment itself is simple, K prey chasing a single predator with no occlusions. I find this a little strange, since it was my understanding that collaborative hunting emerges in part because the presence of occlusions allows for more complex strategies that require planning.

That being said, I do think the environment is sufficient for this paper, and I quite enjoyed using it to run some toy experiments. It reminds me of some of the simpler environments from Petting Zoo, a library for multi-agent learning in reinforcement learning.

Once a simple environment was established, the authors fit a reinforcement learning model to the environment. In this case, the model is Q-learning. The predator and prey are treated as separate agents in the environment, each with their own independent Q functions. Each agent gets full observability of the surroundings. As far as I understand, the predators do not share an action space, and so they can only collaborate implicitly by inferring the actions of the other predators. However, there is a version of these experiments wherein the reward function is shared, all agents receiving a 1 when the prey is caught. One limitation of the current work is that it does not consider reinforcement learning methods methods wherein a value function is shared. This is a current dominant strategy in multi-agent RL. See for example OpenAI Five and also Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. Missing these algorithms limits the scope of the work.

Having fit an RL model, the next order of business is to try and search for internal representations in the agent's model that correspond to collaboration. The author's use t-SNE embeddings of the agents last hidden layers in the policy network.

Analyzing these embeddings in Figure 3, we see that there are some representations that correspond to specific types of collaborative behavior, which indicates that the model is indeed learning to encode collaboration. I should note that this is not surprising from an RL perspective. Certainly, we are aware that Multi-Agent actor critic methods can exhibit cooperative behavior. See Emergent tool use from multi-agent interaction where agents jointly learn to push a table together. It is true that earlier work didn't specifically identify the units responsible for this behavior, and I think this work should be lauded for the novelty it brings to this discussion.

A large underlying point of this paper seems to be that we we need to consider these simple toy environments where we can easily train Q-learning, because it is impossible to analyze the behaviors that emerge from real animal behavior. See lines 187-189. This makes sense on the surface, because there are no policy weights in the case of real-world behavior. However, it is unfortunately misleading. It is entirely possible to take existing animal behavior, fit a linear model (or a deep net) to this behavior, and then do t-SNE on this fit model. This is referred to as behavioral cloning. What's more, offline RL makes it entirely possible to fit a Q-function to animal behaviors, in which case the exact same t-SNE analysis can be carried out without ever running Q-learning in the environment. From my perspective, the fact that RL is not needed to carry out the paper's main analysis is the biggest weakness of the paper.

Meanwhile, I do think the comparisons with human players was exceptionally interesting, and I'm glad it was included in this work.

Finally, I would like to speak to the reinforcement learning sections of this paper, as this is my personal area of expertise. I will note that the RL used in this paper is all valid and correct. The descriptions of Q-learning and its modifications are technically accurate. It's worth noting that the performance offered by the Q-learning methods in this paper is not particularly close to optimal. I mean this in two ways. First, cooperative RL methods are much more performant. Second, the Q-learning implementation considered by the author's is far below state of the art standards.

I will also note that, from the perspective of RL, there is no novelty in the paper. Indeed, many Deep Mind papers, including the original Q-learning paper, have similar t-SNE embeddings to try and understand the action space. And works such as Sentiment Neuron and Visualizing and Understanding Recurrent Networks, among many many others, have focused on the problem of understanding the correspondence between network weights and behaviors. Thus, the novelty must come from a biological perspective. Or perhaps from a perspective at the intersection of biology and RL. I do believe this is an area worth further studying.

Joint Public Review:

LD Score regression (LDSC) is a software tool widely used in the field of genome-wide association studies (GWAS) for estimating heritabilities, genetic correlations, the extent of confounding, and biological enrichment. LDSC is for the most part not regarded as an accurate estimator of \emph{absolute} heritability (although useful for relative comparisons). It is relied on primarily for its other uses (e.g., estimating genetic correlations). The authors propose a new method called \texttt{i-LDSC}, extending the original LDSC in order to estimate a component of genetic variance in addition to the narrow-sense heritability---epistatic genetic variance, although not necessarily all of it. Epistasis in quantitative genetics refers to the component of genetic variance that cannot be captured by a linear model regressing total genetic values on single-SNP genotypes. \texttt{i-LDSC} seems aimed at estimating that part of the epistatic variance residing in statistical interactions between pairs of SNPs. To simplify, the basic model of \texttt{i-LDSC} for two SNPs $X_1$ and $X_2$ is<br /> \begin{equation}\label{eq:twoX}<br /> Y = X_1 \beta_1 + X_2 \beta_2 + X_1 X_2 \theta + E,<br /> \end{equation}<br /> and estimation of the epistatic variance associated with the product term proceeds through a variant of the original LD Score that measures the extent to which a SNP tags products of genotypes (rather than genotypes themselves). The authors conducted simulations to test their method and then applied it to a number of traits in the UK Biobank and Biobank Japan. They found that for all traits the additive genetic variance was larger than the epistatic, but for height the absolute size of the epistatic component was estimated to be non-negligible. An interpretation of the authors' results that perhaps cannot be ruled out, however, is that pairwise epistasis overall does not make a detectable contribution to the variance of quantitative traits.

Major Comments

This paper has a lot of strong points, and I commend the authors for the effort and ingenuity expended in tackling the difficult problem of estimating epistatic (non-additive) genetic variance from GWAS summary statistics. The mere possibility of the estimated univariate regression coefficient containing a contribution from epistasis, as represented in the manuscript's Equation~3 and elsewhere, is intriguing in and of itself.

Is \texttt{i-LDSC} Estimating Epistasis?

Perhaps the issue that has given me the most pause is uncertainty over whether the paper's method is really estimating the non-additive genetic variance, as this has been traditionally defined in quantitative genetics with great consequences for the correlations between relatives and evolutionary theory (Fisher, 1930, 1941; Lynch & Walsh, 1998; Burger, 2000; Ewens, 2004).

Let us call the expected phenotypic value of a given multiple-SNP genotype the \emph{total genetic value}. If we apply least-squares regression to obtain the coefficients of the SNPs in a simple linear model predicting the total genetic values, then the partial regression coefficients are the \emph{average effects of gene substitution} and the variance in the predicted values resulting from the model is called the \emph{additive genetic variance}. (This is all theoretical and definitional, not empirical. We do not actually perform this regression.) The variance in the residuals---the differences between the total genetic values and the additive predicted values---is the \emph{non-additive genetic variance}. Notice that this is an orthogonal decomposition of the variance in total genetic values. Thus, in order for the variance in $\mathbf{W}\bm{\theta}$ to qualify as the non-additive genetic variance, it must be orthogonal to $\mathbf{X} \bm{\beta}$.

At first, I very much doubted whether this is generally true. And I was not reassured by the authors' reply to Reviewer~1 on this point, which did not seem to show any grasp of the issue at all. But to my surprise I discovered in elementary simulations of Equation~\ref{eq:twoX} above that for mean-centered $X_1$ and $X_2$, $(X_1 \beta_1 + X_2 \beta_2)$ is uncorrelated with $X_1 X_2 \theta$ for seemingly arbitrary correlation between $X_1$ and $X_2$. A partition of the outcome's variance between these two components is thus an orthogonal decomposition after all. Furthermore, the result seems general for any number of independent variables and their pairwise products. I am also encouraged by the report that standard and interaction LD Scores are ``lowly correlated' (line~179), meaning that the standard LDSC slope is scarcely affected by the inclusion of interaction LD Scores in the regression; this behavior is what we should expect from an orthogonal decomposition.

I have therefore come to the view that the additional variance component estimated by \texttt{i-LDSC} has a close correspondence with the epistatic (non-additive) genetic variance after all.

In order to make this point transparent to all readers, however, I think that the authors should put much more effort into placing their work into the traditional framework of the field. It was certainly not intuitive to multiple reviewers that $\mathbf{X}\bm{\beta}$ is orthogonal to $\mathbf{W}\bm{\theta}$. There are even contrary suggestions. For if $(\mathbf{X}\bm{\beta})^\intercal \mathbf{W} \bm{\theta} = \bm{\beta}^\intercal \mathbf{X}^\intercal \mathbf{W} \bm{\theta} $ is to equal zero, we know that we can't get there by $\mathbf{X}^\intercal \mathbf{W}$ equaling zero because then the method has nothing to go on (e.g., line~139). We thus have a quadratic form---each term being the weighted product of an average (additive) effect and an interaction coefficient---needing to cancel out to equal zero. I wonder if the authors can put forth a rigorous argument or compelling intuition for why this should be the case.

In the case of two polymorphic sites, quantitative genetics has traditionally partitioned the total genetic variance into the following orthogonal components:<br /> \begin{itemize}<br /> \item additive genetic variance, $\sigma^2_A$, the numerator of the narrow-sense heritability;<br /> \item dominance genetic variance, $\sigma^2_D$;<br /> \item additive-by-additive genetic variance, $\sigma^2_{AA}$;<br /> \item additive-by-dominance genetic variance, $\sigma^2_{AD}$; and<br /> \item dominance-by-dominance genetic variance, $\sigma^2_{DD}$.<br /> \end{itemize}<br /> See Lynch and Walsh (1998, pp. 88-92) for a thorough numerical example. This decomposition is not arbitrary or trivial, since each component has a distinct coefficient in the correlations between relatives. Is it possible for the authors to relate the variance associated with their $\mathbf{W}\bm{\theta}$ to this traditional decomposition? Besides justifying the work in this paper, the establishment of a relationship can have the possible practical benefit of allowing \texttt{i-LDSC} estimates of non-additive genetic variance to be checked against empirical correlations between relatives. For example, if we know from other methods that $\sigma^2_D$ is negligible but that \texttt{i-LDSC} returns a sizable $\sigma^2_{AA}$, we might predict that the parent-offspring correlation should be equal to the sibling correlation; a sizable $\sigma^2_D$ would make the sibling correlation higher. Admittedly, however, such an exercise can get rather complicated for the variance contributed by pairs of SNPs that are close together (Lynch & Walsh, 1998, pp. 146-152).

I would also like the authors to clarify whether LDSC consistently overestimates the narrow-sense heritability in the case that pairwise epistasis is present. The figures seem to show this. I have conflicting intuitions here. On the one hand, if GWAS summary statistics can be inflated by the tagging of epistasis, then it seems that LDSC should overestimate heritability (or at least this should be an upwardly biasing factor; other factors may lead the net bias to be different). On the other hand, if standard and interaction LD Scores are lowly correlated, then I feel that the inclusion of interaction LD Score in the regression should not strongly affect the coefficient of the standard LD Score. Relatedly, I find it rather curious that \texttt{i-LDSC} seems increasingly biased as the proportion of genetic variance that is non-additive goes up---but perhaps this is not too important, since such a high ratio of narrow-sense to broad-sense heritability is not realistic.

How Much Epistasis Is \texttt{i-LDSC} Detecting?

I think the proper conclusion to be drawn from the authors' analyses is that statistically significant epistatic (non-additive) genetic variance was not detected. Specifically, I think that the analysis presented in Supplementary Table~S6 should be treated as a main analysis rather than a supplementary one, and the results here show no statistically significant epistasis. Let me explain.

Most serious researchers, I think, treat LDSC as an unreliable estimator of narrow-sense heritability; it typically returns estimates that are too low. Not even the original LDSC paper pressed strongly to use the method for estimating $h^2$ (Bulik-Sullivan et al., 2015). As a practical matter, when researchers are focused on estimating absolute heritability with high accuracy, they usually turn to GCTA/GREML (Evans et al., 2018; Wainschtein et al., 2022).

One reason for low estimates with LDSC is that if SNPs with higher LD Scores are less likely to be causal or to have large effect sizes, then the slope of univariate LDSC will not rise as much as it ``should' with increasing LD Score. This was a scenario actually simulated by the authors and displayed in their Supplementary Figure~S15. [Incidentally, the authors might have acknowledged earlier work in this vein. A simulation inducing a negative correlation between LD Scores and $\chi^2$ statistics was presented by Bulik-Sullivan et al. (2015, Supplementary Figure 7), and the potentially biasing effect of a correlation over SNPs between LD Scores and contributed genetic variance was a major theme of Lee et al. (2018).] A negative correlation between LD Score and contributed variance does seem to hold for a number of reasons, including the fact that regions of the genome with higher recombination rates tend to be more functional. In short, the authors did very well to carry out this simulation and to show in their Supplementary Figure~S15 that this flaw of LDSC in estimating narrow-sense heritability is also a flaw of \texttt{i-LDSC} in estimating broad-sense heritability. But they should have carried the investigation at least one step further, as I will explain below.

Another reason for LDSC being a downwardly biased estimator of heritability is that it is often applied to meta-analyses of different cohorts, where heterogeneity (and possibly major but undetected errors by individual cohorts) lead to attenuation of the overall heritability (de Vlaming et al., 2017).

The optimal case for using LDSC to estimate heritability, then, is incorporating the LD-related annotation introduced by Gazal et al. (2017) into a stratified-LDSC (s-LDSC) analysis of a single large cohort. This is analogous to the calculation of multiple GRMs defined by MAF and LD in the GCTA/GREML papers cited above. When this was done by Gazal et al. (2017, Supplementary Table 8b), the joint impact of the improvements was to increase the estimated narrow-sense heritability of height from 0.216 to 0.534.

All of this has at least a few ramifications for \texttt{i-LDSC}. First, the authors do not consider whether a relationship between their interaction LD Scores and interaction effect sizes might bias their estimates. (This would be on top of any biasing relationship between standard LD Scores and linear effect sizes, as displayed in Supplementary Figure~S15.) I find some kind of statistical relationship over the whole genome, induced perhaps by evolutionary forces, between \emph{cis}-acting epistasis and interaction LD Scores to be plausible, albeit without intuition regarding the sign of any resulting bias. The authors should investigate this issue or at least mention it as a matter for future study. Second, it might be that the authors are comparing the estimates of broad-sense heritability in Table~1 to the wrong estimates of narrow-sense heritability. Although the estimates did come from single large cohorts, they seem to have been obtained with simple univariate LDSC rather than s-LDSC. When the estimate of $h^2$ obtained with LDSC is too low, some will suspect that the additional variance detected by \texttt{i-LDSC} is simply additive genetic variance missed by the downward bias of LDSC. Consider that the authors' own Supplementary Table~S6 gives s-LDSC heritability estimates that are consistently higher than the LDSC estimates in Table~1. E.g., the estimated $h^2$ of height goes from 0.37 to 0.43. The latter figure cuts quite a bit into the estimated broad-sense heritability of 0.48 obtained with \texttt{i-LDSC}.

Here we come to a critical point. Lines 282--286 are not entirely clear, but I interpret them to mean that the manuscript's Equation~5 was expanded by stratifying $\ell$ into the components of s-LDSC and this was how the estimates in Supplementary Table~S6 were obtained. If that interpretation is correct, then the scenario of \texttt{i-LDSC} picking up missed additive genetic variance seems rather plausible. At the very least, the increases in broad-sense heritability reported in Supplementary Table~S6 are smaller in magnitude and \emph{not statistically significant}. Perhaps what this means is that the headline should be a \emph{negligible} contribution of pairwise epistasis revealed by this novel and ingenious method, analogous to what has been discovered with respect to dominance (Hivert et al., 2021; Pazokitoroudi et al., 2021; Okbay et al., 2022; Palmer et al., 2023).

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Reviewer #1 (Public Review):

In this work, 40 healthy volunteers underwent a placebo followed by a ketamine infusion during a resting state fMRI scan. The authors use principal components analysis (PCA) of the difference in global brain connectivity (GBC) between the ketamine and placebo infusions as their summary neural measure. First, a GBC map is computed after processing with the HCP minimal pipeline and removal of the global brain signal for each scan (~4.5 min, TR=700ms). Then the significant PCA components of difference between ketamine and placebo GBC maps are taken as the neural effect of interest and compared to the mean delta GBC. The first two principal components account for 24.5% of the variance of the data and had correlations with SST and PVALB cortical gene expression patterns that were above chance. No significant correlations were found between mean change in GBC and these genes. Additionally, in comparison with the mean GBC the PCs were found to better correlate with behavioral measures.

To further support their aim to establish the multi-dimensionality of the ketamine response using their neural measure, the PCA dimensionality was estimated in external datasets that used psilocybin and LSD with sample size matching using identical processing and found lower dimensionality in these datasets.

Effective dimensionality was calculated using the participation ratio and dataset re-sampling was used to control for sample size in this calculation, but dimensionality is also affected by motion within the sample among other noise sources, which are not well discussed. In particular, each drug may affect physiological noise in different ways and this may in turn affect their dimensionality measurement.

A PCA decomposition of the changes (ketamine-placebo) in 31 measured behavioral variables was also performed which resulted in two major PCs which accounted for 41.4% of the variance. Following prior work, behavioral PCs were mapped onto the neural PCs to create neuro-behavioral PCs. The weighing of the PCs at the individual level was explored to compare inter-individual variability.

In an earlier fMRI study of the timeseries response to ketamine (De Simoni, 2013) it was clear that there are both individual and regional brain response differences. Behaviorally, there is known individual variability in the response to ketamine insofar as only about 60% of depressed people will experience symptom improvement and even then to varying levels. Thus, it is good to see that the compound summary measure of the PCA of the change in GBC after ketamine follows this pattern and shows inter-individual differences.

A strength of this paper is that it brings together multimodal and external datasets and combines them in a linked analysis to support their investigational aims. Several sets of analyses are used to draw relations between fMRI results, genetics, and behavioral measures but the range of conclusions is limited by the understandably small sample size for this kind of drug challenge study. A weakness is that the chosen summary measure (delta GBC of ketamine-placebo, followed by a group-level PCA) that has been principally developed by this lab and has not seen wide replication. The presentation of the analyses could be simplified to increase readability and impact. Nevertheless, this study provides informative steps toward the development of markers for individualized drug response.

Reviewer #2 (Public Review):

In this interesting work on the neuropharmacological effects of ketamine, the authors conducted a pharmacological functional magnetic resonance imaging (fMRI) study in 40 healthy participants receiving bolus and constant infusion of ketamine during resting-state fMRI. Data were preprocessed with the human connectome-based standard pipeline previously successfully used by the lab (FS parcellation and application of an atlas published by the group, HCP pipeline, FSL, global brain connectivity with and without global-signal regression). Briefly, GBC and principle component maps of the positive and negative syndrome scale (PANSS) were related to somatostatin and parvalbumin cortical gene expression patterns. In addition, the authors compared the effective dimensionality, i.e. eigenvalues of covariance matrices of drug vs. placebo, and found higher complexity of responses in ketamine vs. LSD and psilocybin, which is very interesting. Also, there was substantial inter-individual variation in behavioral and neurobehavioral results, which was captured by PC and GBC maps. In supplementary results, the authors also showed that the principle component PS1 highly correlated with the fMRI global signal.

Although a complex set of analyses is presented, the paper is written very clearly and understandable. The authors did a good job of outlining the steps of their analyses in supplemental diagrams and the source code is provided. As a general remark, I consider the main strength of this work, to acknowledge the very diverse inter-individual variation of ketamine's effects and to use advanced methodological approaches to disentangle these.

Since the drug also exhibits strong variation in clinical antidepressant responses, the methodology applied here will very likely yield interesting results applied in clinical datasets of patients with major depressive disorder.

Reviewer #3 (Public Review):

The authors elucidated the roles of cholecystokinin (CCK)-expressing excitatory neurons, which project from the rhinal cortex to the motor cortex, in motor skill learning. The authors found CCK knock-out mice exhibited learning defects in the pellet reaching task while the baseline success rate of the knock-out mice was similar to that of the wild-type mice. Application of a CCK B receptor (CCKBR) antagonist into the motor cortex lowered the success rate in the motor task. The authors found the population activity which was observed in the in vivo calcium imaging during motor learning was elevated after motor learning, but this increase disappeared in CCK knock-out mice and animals with CCKBR antagonist administration. Anterograde and retrograde viral tracing revealed that CCK-expressing excitatory neurons in the rhinal cortex projected to the motor cortex. Chemogenetic inhibition of the CCK-expressing neurons in the rhinal cortex lowered the ability for motor learning. The application of a CCKBR agonist increased the motor learning ability of CCK knock-out animals as well as long-term potentiation (LTP) observed in the slice of the motor cortex.

However, the manuscript contains several shortcomings:

First, the "Discussion" has several statements that are only supported weakly by the results, for example, ll. 429-431, ll. 432-433, and ll. 447-448. In addition, most of the sentences in this section are not divided into subsections. The paragraphs should be composed in multiple subsections with appropriate subheadings, even though the initial section summarizing the results can lack a subheading.

Second, it would be important that the authors showed which area(s) of the brain is affected by the CCKBR antagonist in the experiments described in ll. 166-206 and Fig. 2. The authors injected the drug into the motor cortex, but the chemical can spread to neighboring cortical areas (e.g. somatosensory cortex) or wider brain regions. If so, the blockade of the CCKBR in the brain areas other than the motor cortex could cause the defects of the motor task learning observed in these experiments. I think it is desirable that such a possibility should be excluded. Conversely, it is possible that the antagonist had an effect on a limited subarea of the motor cortex (e.g. only the primary motor cortex (M1)). In this case, the information about the field altered by the CCKBR blocker would be useful to interpret the results of the learning defects.

Third, the authors need to show bilateral data about their anterograde and retrograde tracking of CCK-expressing neurons in the rhinal cortex. In ll. 290-292, they described as follows: "Both anterograde and retrograde tracking results indicated that CCK-expressing neurons in the rhinal cortex projecting to the motor cortex were asymmetric, showing a preference for the ipsilateral hemisphere." However, they provided only unilateral data for the anterograde (Fig. 4B) and the retrograde (Fig. 4D) experiments.

Fourth, unilateral (contralateral to the dominant forelimb) experiments are needed in the chemogenetic inhibition of the CCK neurons. In ll. 301-338 and Fig. 5, the authors inhibited the CCK -expressing neurons in both hemispheres by injecting the virus into both sides. However, the CCKBR antagonist injection into the motor cortex contralateral to the dominant forelimb caused defects in motor learning ability, as described in ll. 166-206. The authors also observed that the population neuronal activity in the motor cortex contralateral to the dominant forelimb changed in accordance with the improvement of the motor skill in ll. 208-269. Therefore, it may be the case that inhibition of CCK neurons only in the side contralateral to the dominant forelimb - not bilaterally, as the authors did - could cause the lowered ability of motor learning. Such unilateral inhibition can be carried out by unilateral injection of the virus.

In relation to the point above, in the chemogenetic inhibition experiments, it would be important to show which neurons in which cortical area is inhibited. This could be done by examining the distributions of the mCherry-labeled somata in the rhinal cortex using histochemistry.

Fifth, it would be valuable to further examine differences in task performance across sessions and groups. The paragraph in ll. 138-153 needs a comparison of the "miss" rates of CCK-/- animals between Day 1 vs. Day 6 (related to ll. 429- 431). This paragraph also needs comparisons of the "no-grasp" and "drop" rates of CCK-/- animals between Day 1 vs. Day 6 (related to ll. 432- 433). The paragraph in ll. 175-190 needs comparisons of success rates between Day 1 and Day 5/6 within the antagonist group (related to ll. 447-448).

Reviewer #2 (Public Review):

The authors set out to study the potent HIV capsid inhibitor lenacapavir (LEN) and how it alters capsid stability. They use a previously developed single-molecule fluorescence imaging assay to take two measurements of individual viral particles over time: 1) they track the release of GFP from GFP-loaded particles to determine whether the capsid is intact or open, and 2) they track the disassembly of the capsid lattice by measuring the signal intensity of a capsid binding fluorophore (AF568-CypA), which diminishes as the capsid lattice subunits disassociate.

As in their previous work, the authors report that most of their capsids are "leaky" and rapidly lose GFP after the viral membrane is permeabilized, followed by disassembly of the capsid lattice. A subset of capsids maintain GFP signal for various periods of time until they spontaneously "open," and a smaller subset remains closed for the entire length of the imaging experiment (typically 30 min). Interestingly, the authors find that LEN has two effects in this assay: it not only promotes a more rapid release of GFP (interpreted to mean loss of capsid integrity), but it also prevents the capsid lattice from disassembling after opening. As expected, the cellular cofactor IP6 (which stabilizes capsids in cells and in vitro) was found to protect against capsid rupture and counteracted the effects of LEN (although high concentrations of LEN could override any protective effects of IP6).

Their single-molecule experiments are nicely buttressed by in vitro assembly reactions of purified CA protein, with IP6 promoting cone formation and LEN promoting aberrant assembly into tubes. The authors go further to test the kinetics of LEN's effects on HIV infection and reverse transcription, and they perform experiments in comparison to other factors that target the FG binding pocket (BI-2, PF-74, and a peptide from the host factor CPSF6). They find that LEN works differently than these other capsid binders, and stabilizes the lattice structure much more effectively, which the authors suggest is due to how well LEN bridges between CA-CA monomers and rigidifies CA hexamers.

It's particularly interesting that the results of their kinetic studies indicate that LEN's effects on capsid strain (which may ultimately promote rupture) may not happen immediately, but instead, take time to build as the drug occupies more and more binding sites. The authors estimate that roughly 30% of binding sites need to be occupied by LEN to reach half-maximal inhibition of infection, and based on their binding curves, it may take ~20h to reach this level of occupancy in the presence of sub nM concentrations of LEN. Although other mechanisms in addition to catastrophic rupture of capsids are likely at play during inhibition of infection (such as inhibition of host factor binding), these kinetics support previous reports that the most potent functions of capsid inhibition occur at or between the steps of nuclear entry and integration.

It is important to note that although in vitro uncoating assays can help us understand the physical nature of HIV capsid and capsid inhibitor interactions, the assays in this paper might not accurately model the capsid dynamics that are experienced in a cell during infection. The authors report that more than half of their capsids are "leaky" at the start of their assay, but this could be an artifact of the experimental system. Several groups have now demonstrated that capsids remain intact or largely intact for several hours after infection. Thus, while their method is valuable to the research community and can provide insight into capsid stability (and how it can be influenced by capsid binding factors), the authors should be cautious about using pore-forming proteins to permeabilize the virion and interpreting the release of GFP in their single-molecule fluorescence system as an accurate reflection of HIV dynamics in vivo.

In this regard, it would be helpful to establish whether the pore-forming proteins used in vitro to permeabilize the virus membrane have an impact on capsid integrity. It's possible that the concentration of pore-forming proteins used in this paper (200nM) actually promotes "leaky" capsids and rapid opening of capsids in vitro, whereas capsids in their native state in the cytoplasm could remain mostly intact until disrupted by host factors and/or small molecules. Determining whether lower concentrations of DLY/SLO (or PFO as used in Marquez et al., 2018) change the ratio of leaky to closed capsids, or delay the time to capsid opening (either in the presence of IP6 or in the presence of LEN) would be informative. It may be possible to optimize the concentration of pore-forming proteins (and other buffer constituents) to achieve permeabilization of the membrane with minimal disruption to capsid integrity, which could approximate conditions within the cell.

Experiments with capsid mutations that stabilize or destabilize the lattice structure (and exhibit different sensitivities to IP6) could help support the authors' conclusions, as would testing mutations that confer resistance to LEN (e.g. Q67H+N74D, M66I, etc...). It would be of great interest to find if CA mutations affect either GFP release or the CypA paint signal, and whether resistance mutations mitigate the effects of LEN in single-molecule experiments.

The discussion section of this paper is expertly written and places the work into the larger context of HIV research. The authors have thoughtfully analyzed their experiments with capsid inhibitors in relation to kinetics, occupancy, the potential for rigidification, and cofactor binding. They offer reasonable explanations for how LEN exhibits opposing effects on the HIV capsid at high occupancy through inducing capsid rupture while simultaneously preventing the dissociation of CA subunits. Many lines of evidence are now converging on the concept that the capsid evolved to be stable enough to protect its contents, yet flexible enough to navigate the steps of reverse transcription, nuclear entry, and uncoating. With this paper, the authors make a strong case that LEN functions as an antiviral, at least in part, through engaging "lethal hyperstabilization" of the capsid, promoting rigid lattice formations that are incompatible with closed cone structures.

Reviewer #1 (Public Review):

This thorough study expands our understanding of BMP signaling, a conserved developmental pathway, involved in processes diverse such as body patterning and neurogenesis. The authors applied multiple, state-of-art strategies to the anthozoan Nematostella vectensis in order to first identify the direct BMP signaling targets - bound by the activated pSMAD1/5 protein - and then dissect the role of a novel pSMAD1/5 gradient modulator, zwim4-6. The list of target genes features multiple developmental regulators, many of which are bilaterally expressed, and which are notably shared between Drosophila and Xenopus. The analysis identified in particular zswim4-6 a novel nuclear modulator of the BMP pathway conserved also in vertebrates. A combination of both loss-of-function (injection of antisense morpholino oligonucleotide, CRISPR/Cas9 knockout, expression of dominant negative) and gain-of-function assays, and of transcriptome sequencing identified that zwim acts as a transcriptional repression of BMP signaling. Functional manipulation of zswim5 in zebrafish shows a conserved role in modulating BMP signaling in a vertebrate.<br /> The particular strength of the study lies in the careful and thorough analysis performed. This is solid developmental work, where one clear biological question is progressively dissected, with the most appropriate tools. The functional results are further validated by alternative approaches. Data is clearly presented and methods are detailed.

I have a couple of comments.<br /> 1) I was intrigued - as the authors - by the fact that the ChiP-Seq did not identify any known BMP ligand bound by pSMAD1/5. Are these genes found in the published ChiP-Seq data of the other species used for the comparative analysis? One hypothesis could be that there is a change in the regulatory interactions and that the initial set-up of the gradient requires indeed a feedback loop, which is then turned off at later gastrula. In this case, immunoprecipitation at early gastrula, prior to the set-up of the pSMAD1/5 gradient, could reveal a different scenario. Alternately, the regulation could be indirect, for example, through RGM, an additional regulator of BMP signaling expressed on the side of lower BMP activity, which is among the targets of the ChiP-Seq. This aspect could be discussed. Additionally, even if this is perhaps outside the scope of this study, I think it would be informative to further assess the effect of ZSWIM manipulation on RGM (and vice versa).<br /> 2) I do not fully understand the rationale behind the choice of performing the comparative assays in zebrafish: as the conservation was initially identified in Xenopus, I would have expected the experiment to be performed in frog. Furthermore, reading the phylogeny (Figure 4A), it is not obvious to me why ZSWIM5 was chosen for the assay (over the other paralog ZSWIM6). Could the Authors comment on this experiment further?

Reviewer #1 (Public Review):

In several developmental systems, the core Planar Cell Polarity (PCP) pathway organises the dynamics of cellular behaviours underlying morphogenesis. During pupal stages, the Drosophila wing undergoes a complex morphogenetic process that results in the simultaneous elongation and narrowing of the wing blade along the proximal-distal and anterior-posterior axes, respectively. It was proposed that this dynamic process is driven by mechanical stress that results in cell deformations and cell rearrangements. However, prior work by Etournay et al. (eLife 2015) shows that mutants that reduce of mechanical stresses do not completely eliminate oriented cell rearrangements. Here, Piscitello-Gomez et al. use imaging techniques previously developed by them and others, combined with a computational analysis of a rheological model, to evaluate the role of the core-PCP pathway as a possible patterning cue that could orient cell rearrangements in this system. Surprisingly, the authors found that core-PCP mutants only affect an early retraction velocity upon laser ablation, but do not seem to drive overall morphogenesis in this system. Therefore, the original question of the work, namely, identifying the patterning cues that establish oriented cell rearrangements in this system, remains unanswered.

The work exemplifies how the integration of mechanical perturbations, image analysis, and computational modelling could be used to investigate the contribution of a specific patterning cue in morphogenesis. While the conclusions of the manuscript are solid and the data support the conclusion that core-PCP pathway mutants do not display an altered cell dynamic or cell elongation phenotype relative to wild-type controls, one challenge of the approach is that the time-lapse imaging technique is done only in a handful of pupal wings. This does not permit to conclude whether subtle changes in cell elongation or cell rearrangements could account for observed changes in the shape of adult wings (that are more round in these mutants). Other patterning and polarity cues such as Fat-Daschous or Toll-like signalling are suggested by the authors, but their examination is left for future studies.

Reviewer #2 (Public Review):

The core planar cell polarity (PCP) pathways are known to control tissue morphogenesis in vertebrates and also in a number of developing tissues in the fruitfly Drosophila. However, it has long been observed that beyond effects on hair polarity, core PCP activity does not have dramatic effects on Drosophila wing morphogenesis. Here the authors carry out detailed quantitative studies of cell behaviors in flies mutant for core PCP genes during pupal wing morphogenesis between about 16 to 32 hours of pupal life to further try to determine if core PCP activity affects cell behaviors in the wing.

Their overall conclusion is that there is no effect on tissue morphogenesis. However, the number of wings looked at for each genotype is low due to the enormous amount of work required to analyze the cell behaviors on an entire wing surface over 16 hours of development. Thus, rigorous statistics cannot be applied to support the statement that there is no change in morphogenesis. Moreover, by eye, the average cell behaviors do appear different and the authors themselves say there are subtle differences. They also note that adult wings have a change in size. Also, a previous publication suggested a change in cell arrangements at the late stages of the period studied (Sugimura & Ishihara 2013).

Interestingly, the authors do report a change in local mechanical properties of the tissue in flies with altered core PCP pathway activity, by using laser ablation to study tissue rheology. This seems to support the view that there could be a subtle change in tissue morphogenesis.

Ultimately, this is a valuable set of results that help to clarify core PCP pathway function in Drosophila tissues. It clearly demonstrates effects on tissue mechanics, but also indicates that this does not result in gross changes in tissue morphogenesis - the latter being consistent with previous observations.

Reviewer #2 (Public Review):

In the present study, Liu et al present an analysis of benign and HCC liver samples which were subjected to a new technology (LOOP-Seq) and paired WES. By integrating these data, the authors find isoforms, fusions and mutations which uniquely cluster within HCC samples, such as in the HLA locus, which serve as candidate leads for further investigation. The main appeal of the study is in the potential of LOOP-Seq as a method to present isoform-resolved data without actually performing long-read sequencing.

Comments on revised version:

I made several comments on the previous version which have been adequately addressed.

Reviewer #1 (Public Review):

In the manuscript "Long‐read single‐cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells", S. Liu et al present a protocol combining 10x Genomics single-cell assay with Element LoopSeq synthetic long-read sequencing to study single nucleotide variants (SNVs) and gene fusions in Hepatocellular carcinoma (HCC) at single‐cell level. The authors were the first to combine LoopSeq synthetic long‐read sequencing technology and 10x Genomics barcoding for single cell sequencing. For each cell and each somatic mutation, they obtain fractions of mutated transcripts per gene and per each transcript isoform. The manuscript states that these values (as well as gene fusion information) provide better features for tumor-normal classification than gene expression levels. The authors identified many SNVs in genes of the human major histocompatibility complex (HLA) with up to 25 SNVs in the same molecule of HLA‐DQB1 transcript. The analysis shows that most mutations occur in HLA genes and suggests evolution pathways that led to these hypermutation clusters.

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.

In this revised submission, the authors addressed all my questions. This is very interesting work that could be of interest for researchers working in other brain areas as well.

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.

In this revision, the authors have satisfactorily addressed all my previous comments.

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.

In re-review of this study, the authors have addressed the concerns sufficiently. This is a very nice study.

Reviewer #1 (Public Review):

Summary:<br /> The paper investigates the physiological and neural processes that relate to infants' attention allocation in a naturalistic setting. Contrary to experimental paradigms that are usually employed in developmental research, this study investigates attention processes while letting the infants be free to play with three toys in the vicinity of their caregiver, which is closer to a common, everyday life context. The paper focuses on infants at 5 and 10 months of age and finds differences in what predicts attention allocation. At 5 months, attention episodes are shorter and their duration is predicted by autonomic arousal. At 10 months, attention episodes are longer, and their duration can be predicted by theta power. Moreover, theta power predicted the proportion of looking at the toys, as well as a decrease in arousal (heart rate). Overall, the authors conclude that attentional systems change across development, becoming more driven by cortical processes.

Strengths:<br /> I enjoyed reading the paper, I am impressed with the level of detail of the analyses, and I am strongly in favour of the overall approach, which tries to move beyond in-lab settings. The collection of multiple sources of data (EEG, heart rate, looking behaviour) at two different ages (5 and 10 months) is a key strength of this paper. The original analyses, which build onto robust EEG preprocessing, are an additional feat that improves the overall value of the paper. The careful consideration of how theta power might change before, during, and in the prediction of attention episodes is especially remarkable. However, I have a few major concerns that I would like the authors to address, especially on the methodological side.

Points of improvement<br /> 1. Noise<br /> The first concern is the level of noise across age groups, periods of attention allocation, and metrics. Starting with EEG, I appreciate the analysis of noise reported in supplementary materials. The analysis focuses on a broad level (average noise in 5-month-olds vs 10-month-olds) but variations might be more fine-grained (for example, noise in 5mos might be due to fussiness and crying, while at 10 months it might be due to increased movements). More importantly, noise might even be the same across age groups, but correlated to other aspects of their behaviour (head or eye movements) that are directly related to the measures of interest. Is it possible that noise might co-vary with some of the behaviours of interest, thus leading to either spurious effects or false negatives? One way to address this issue would be for example to check if noise in the signal can predict attention episodes. If this is the case, noise should be added as a covariate in many of the analyses of this paper.<br /> Moving onto the video coding, I see that inter-rater reliability was not very high. Is this due to the fine-grained nature of the coding (20ms)? Is it driven by differences in expertise among the two coders? Or because coding this fine-grained behaviour from video data is simply too difficult? The main dependent variable (looking duration) is extracted from the video coding, and I think the authors should be confident they are maximising measurement accuracy.

2. Cross-correlation analyses<br /> I would like to raise two issues here. The first is the potential problem of using auto-correlated variables as input for cross-correlations. I am not sure whether theta power was significantly autocorrelated. If it is, could it explain the cross-correlation result? The fact that the cross-correlation plots in Figure 6 peak at zero, and are significant (but lower) around zero, makes me think that it could be a consequence of periods around zero being autocorrelated. Relatedly: how does the fact that the significant lag includes zero, and a bit before, affect the interpretation of this effect?

A second issue with the cross-correlation analyses is the coding of the looking behaviour. If I understand correctly, if an infant looked for a full second at the same object, they would get a maximum score (e.g., 1) while if they looked at 500ms at the object and 500ms away from the object, they would receive a score of e.g., 0.5. However, if they looked at one object for 500ms and another object for 500ms, they would receive a maximum score (e.g., 1). The reason seems unclear to me because these are different attention episodes, but they would be treated as one. In addition, the authors also show that within an attentional episode theta power changes (for 10mos). What is the reason behind this scoring system? Wouldn't it be better to adjust by the number of attention switches, e.g., with the formula: looking-time/(1+N_switches), so that if infants looked for a full second, but made 1 switch from one object to the other, the score would be .5, thus reflecting that attention was terminated within that episode?

3. Clearer definitions of variables, constructs, and visualisations<br /> The second issue is the overall clarity and systematicity of the paper. The concept of attention appears with many different names. Only in the abstract, it is described as attention control, attentional behaviours, attentiveness, attention durations, attention shifts and attention episode. More names are used elsewhere in the paper. Although some of them are indeed meant to describe different aspects, others are overlapping. As a consequence, the main results also become more difficult to grasp. For example, it is stated that autonomic arousal predicts attention, but it's harder to understand what specific aspect (duration of looking, disengagement, etc.) it is predictive of. Relatedly, the cognitive process under investigation (e.g., attention) and its operationalization (e.g., duration of consecutive looking toward a toy) are used interchangeably. I would want to see more demarcation between different concepts and between concepts and measurements.

General Remarks<br /> In general, the authors achieved their aim in that they successfully showed the relationship between looking behaviour (as a proxy of attention), autonomic arousal, and electrophysiology. Two aspects are especially interesting. First, the fact that at 5 months, autonomic arousal predicts the duration of subsequent attention episodes, but at 10 months this effect is not present. Conversely, at 10 months, theta power predicts the duration of looking episodes, but this effect is not present in 5-month-old infants. This pattern of results suggests that younger infants have less control over their attention, which mostly depends on their current state of arousal, but older infants have gained cortical control of their attention, which in turn impacts their looking behaviour and arousal.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript explores infants' attention patterns in real-world settings and their relationship with autonomic arousal and EEG oscillations in the theta frequency band. The study included 5- and 10-month-old infants during free play. The results showed that the 5-month-old group exhibited a decline in HR forward-predicted attentional behaviors, while the 10-month-old group exhibited increased theta power following shifts in gaze, indicating the start of a new attention episode. Additionally, this increase in theta power predicted the duration of infants' looking behavior.

Strengths:<br /> The study's strengths lie in its utilization of advanced protocols and cutting-edge techniques to assess infants' neural activity and autonomic arousal associated with their attention patterns, as well as the extensive data coding and processing. Overall, the findings have important theoretical implications for the development of infant attention.

Weaknesses:<br /> Certain methodological procedures require further clarification, e.g., details on EEG data processing. Additionally, it would be beneficial to eliminate possible confounding factors and consider alternative interpretations, e,g., whether the differences observed between the two age groups were partly due to varying levels of general arousal and engagement during the free play.

Reviewer #3 (Public Review):

Summary:<br /> Much of the literature on attention has focused on static, non-contingent stimuli that can be easily controlled and replicated--a mismatch with the actual day-to-day deployment of attention. The same limitation is evident in the developmental literature, which is further hampered by infants' limited behavioral repertoires and the general difficulty in collecting robust and reliable data in the first year of life. The current study engages young infants as they play with age-appropriate toys, capturing visual attention, cardiac measures of arousal, and EEG-based metrics of cognitive processing. The authors find that the temporal relations between measures are different at age 5 months vs. age 10 months. In particular, at 5 months of age, cardiac arousal appears to precede attention, while at 10 months of age attention processes lead to shifts in neural markers of engagement, as captured in theta activity.

Strengths:<br /> The study brings to the forefront sophisticated analytical and methodological techniques to bring greater validity to the work typically done in the research lab. By using measures in the moment, they can more closely link biological measures to actual behaviors and cognitive stages. Often, we are forced to capture these measures in separate contexts and then infer in-the-moment relations. The data and techniques provide insights for future research work.

Weaknesses:

The sample is relatively modest, although this is somewhat balanced by the sheer number of data points generated by the moment-to-moment analyses. In addition, the study is cross-sectional, so the data cannot capture true change over time. Larger samples, followed over time, will provide a stronger test for the robustness and reliability of the preliminary data noted here. Finally, while the method certainly provides for a more active and interactive infant in testing, we are a few steps removed from the complexity of daily life and social interactions.

Reviewer #1 (Public Review):

In this study, authors performed multiple sets of mesoscale chromatin simulations at nucleosome resolution to study the effects of TF binding on chromatin structures. Through simulations at various conditions, authors performed systemically analysis to investigate how linker histone, tail acetylation, and linker DNA length can operate together with TFs to regulate chromatin architecture. Using gene Eed as one example, authors found that binding of Myc:Max could repress the gene expression by increasing fiber folding and compaction and this repression can be reversed by the linker histone. Understanding how transcription factors bind to regulatory DNA elements and modulate chromatin structure and accessibility is an essential question in epigenetics. Through modelling of TF binding to chromatin structures at nucleosome levels, authors demonstrated that TF binding could create microdomains that are visible in the ensemble-based contact maps and short DNA linkers prevent the formation microdomains. It has also been shown that tail acetylation and TF binding have opposite effects on chromatin compaction and linker histone can compete for the linker DNA with TF binding to impair the effect of TF binding. This study improves our knowledge on how TFs collaborate with different epigenetic marks and chromatin features to regulate chromatin structure and accessibility, which will be of broad interest to the community.

Reviewer #2 (Public Review):

In this paper, Portillo-Ledesma et al. study chromatin organization in the length scale of a gene, simulating the polymer at nucleosome resolution. The authors have presented an extensive simulation study with an excellent model of chromatin. The model has linker DNA and nucleosomes with all relevant interactions (electrostatics, tails, etc). Authors simulate 10 to 26 kb chromatin with varying linker lengths, linker histones (LH), and acetylated tails. The authors then study the effect of a transcription factor (TF) Myc: Max binding. The critical physical feature of the TF in the model is that it binds to the linker region and bends the DNA to make loops/intra-chromatin contacts. Authors systematically investigate the interplay between different variables such as linker DNA length, LH density, and the TF concentration in determining chromatin compaction and 3D organization.

The manuscript is well-written and is a relevant study with many useful results. The biggest strength of the work is the fact that the authors start with a relevant model that incorporates well-known biophysical properties of DNA, nucleosomes, linker histones, and the transcription factor Myc:Max. One of the novel results is the demonstration of how linker lengths play an important role in chromatin compaction (measured by computing packing ratio) in the presence of DNA-bending TFs. As the TF concentration increases, chromatin with short linker lengths does not compact much (only a small change in packing ratio). If the linker lengths are long, a higher percentage of TFs leads to an increase in packing ratio (higher compaction). Authors further show that TFs are able to compact Life-like chromatin fiber with linker length taken from a realistic distribution. The authors compute inter-nucleosomal contact maps from their simulated configurations and show that the map has features similar to what is observed in Hi-C/Micro-C experiments. Authors study the compaction of the Eed gene locus and show that TF binding leads to the formation of small domains known as micro-domains. Authors have predicted many relevant and testable quantities. Many of the results agree with known experiments like the formation of the micro-domains. Hence, the conclusions made in this study are justified - they follow from the simulation results.

Reviewer #1 (Public Review):

In this very strong and interesting paper the authors present a convincing series of experiments that reveal molecular mechansism of neuronal cell type diversification in the nervous system of Drosophila. The authors show that a homeodomain transcription factor, Bsh, fulfills several critical functions - repressing an alternative fate and inducing downstream homeodomain transcription factors with whom Bsh may collaborate to induce L4 and L5 fates (the author's accompanying paper reveals how Bsh can induce two distinct fates). The authors make elegant use of powerful genetic tools and an arsenal of satisfying cell identity markers.

I believe that this is an important study because it provides some fundamental insights into the conservation of neuronal diversification programs. It is very satisfying to see that similar organizational principles apply in different organism to generate cell type diversity. The authors should also be commended for contextualizing their work very well, giving a broad, scholarly background to the problem of neuronal cell type diversification.

My one suggestion for the authors is to perhaps address in the Discussion (or experimentally address if they wish) how they reconcile that Bsh is on the one hand: (a) continuously expressed in L4/L4, (b) binding directly to a cohort of terminal effectors that are also continuously expressed but then, on the other hand, is not required for their maintaining L4 fate? A few questions: Is Bsh only NOT required for maintaining Ap expression or is it also NOT required for maintaining other terminal markers of L4? The former could be easily explained - Bsh simply kicks of Ap, Ap then autoregulates, but Bsh and Ap then continuously activate terminal effector genes. The second scenario would require a little more complex mechanism: Bsh binding of targets (with Notch) may open chromatin, but then once that's done, Bsh is no longer needed and Ap alone can continue to express genes. I feel that the authors should be at least discussing this. The postmitotic Bsh removal experiment in which they only checked Ap and depression of other markers is a little unsatisfying without further discussion (or experiments, such as testing terminal L4 markers). I hasten to add that this comment does not take away from my overall appreciation for the depth and quality of the data and the importance of their conclusions.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, the authors explore the role of the Homeodomain Transcription Factor Bsh in the specification of Lamina neuronal types in the optic lobe of Drosophila. Using the framework of terminal selector genes and compelling data, they investigate whether the same factor that establishes early cell identity is responsible for the acquisition of terminal features of the neuron (i.e., cell connectivity and synaptogenesis).

The authors convincingly describe the sequential expression and activity of Bsh, termed here as 'primary HDTF', and of Ap in L4 or Pdm3 in L5 as 'secondary HDTFs' during the specification of these two neurons. The study demonstrates the requirement of Bsh to activate either Ap and Pdm3, and therefore to generate the L4 and L5 fates. Moreover, the authors show that in the absence of Bsh, L4 and L5 fates are transformed into a L1 or L3-like fates.

Finally, the authors used DamID and Bsh:DamID to profile the open chromatin signature and the Bsh binding sites in L4 neurons at the synaptogenesis stage. This allows the identification of putative Bsh target genes in L4, many of which were also found to be upregulated in L4 in a previous single-cell transcriptomic analysis. Among these genes, the paper focuses on Dip-β, a known regulator of L4 connectivity. They demonstrate that both Bsh and Ap are required for Dip-β, forming a feed-forward loop. Indeed, the loss of Bsh causes abnormal L4 synaptogenesis and therefore defects in several visual behaviors.

The authors also propose the intriguing hypothesis that the expression of Bsh expanded the diversity of Lamina neurons from a 3 cell-type state to the current 5 cell-type state in the optic lobe.

Strengths:<br /> Overall, this work presents a beautiful practical example of the framework of terminal selectors: Bsh acts hierarchically with Ap or Pdm3 to establish the L4 or L5 cell fates and, at least in L4, participates in the expression of terminal features of the neuron (i.e., synaptogenesis through Dip-β regulation).

The hierarchical interactions among Bsh and the activation of Ap and Pdm3 expression in L4 and L5, respectively, are well established experimentally. Using different genetic drivers, the authors show a window of competence during L4 neuron specification during which Bsh activates Ap expression. Later, as the neuron matures, Ap becomes independent of Bsh. This allows the authors to propose a coherent and well-supported model in which Bsh acts as a 'primary' selector that activates the expression of L4-specific (Ap) and L5-specific (Pdm3) 'secondary' selector genes, that together establish neuronal fate.

Importantly, the authors describe a striking cell fate change when Bsh is knocked down from L4/L5 progenitor cells. In such case, L1 and L3 neurons are generated at the expense of L4 and L5. The paper demonstrates that Bsh in L4/L5 represses Zfh1, which in turn acts as the primary selector for L1/L3 fates. These results point to a model where the acquisition of Bsh during evolution might have provided the grounds for the generation of new cell types, L4 and L5, expanding lamina neuronal diversity for a more refined visual behaviors in flies. This is an intriguing and novel hypothesis that should be tested from an evo-devo standpoint, for instance by identifying a species when L4 and L5 do not exist and/or Bsh is not expressed in L neurons.

To gain insight into how Bsh regulates neuronal fate and terminal features, the authors have profiled the open chromatin landscape and Bsh binding sites in L4 neurons at mid-pupation using the DamID technique. The paper describes a number of genes that have Bsh binding peaks in their regulatory regions and that are differentially expressed in L4 neurons, based on available scRNAseq data. Although the manuscript does not explore this candidate list in depth, many of these genes belong to classes that might explain terminal features of L4 neurons, such as neurotransmitter identity, neuropeptides or cytoskeletal regulators. Interestingly, one of these upregulated genes with a Bsh peak is Dip-β, an immunoglobulin superfamily protein that has been described by previous work from the author's lab to be relevant to establish L4 proper connectivity. This work proves that Bsh and Ap work in a feed-forward loop to regulate Dip-β expression, and therefore to establish normal L4 synapses. Furthermore, Bsh loss of function in L4 causes impairs visual behaviors.

Weaknesses:<br /> ● The last paragraph of the introduction is written using rhetorical questions and does not read well. I suggest rewriting it in a more conventional direct style to improve readability.

● A significant concern is the way in which information is conveyed in the Figures. Throughout the paper, understanding of the experimental results is hindered by the lack of information in the Figure headers. Specifically, the genetic driver used for each panel should be adequately noted, together with the age of the brain and the experimental condition. For example, R27G05-Gal4 drives early expression in LPCs and L4/L5, while the 31C06-AD, 34G07-DBD Split-Gal4 combination drives expression in older L4 neurons, and the use of one or the other to drive Bsh-KD has dramatic differences in Ap expression. The indication of the driver used in each panel will facilitate the reader's grasp of the experimental results.

● Bsh role in L4/L5 cell fate:<br /> o It is not clear whether Tll+/Bsh+ LPCs are the precursors of L4/L5. Morphologically, these cells sit very close to L5, but are much more distant from L4.<br /> o Somatic CRISPR knockout of Bsh seems to have a weaker phenotype than the knockdown using RNAi. However, in several experiments down the line, the authors use CRISPR-KO rather than RNAi to knock down Bsh activity: it should be explained why the authors made this decision. Alternatively, a null mutant could be used to consolidate the loss of function phenotype, although this is not strictly necessary given that the RNAi is highly efficient and almost completely abolishes Bsh protein.<br /> o Line 102: Rephrase "R27G05-Gal4 is expressed in all LPCs and turned off in lamina neurons" to "is turned off as lamina neurons mature", as it is kept on for a significant amount of time after the neurons have already been specified.<br /> o Line 121: "(a) that all known lamina neuron markers become independent of Bsh regulation in neurons" is not an accurate statement, as the markers tested were not shown to be dependent on Bsh in the first place.<br /> o Lines 129-134: Make explicit that the LPC-Gal4 was used in this experiment. This is especially important here, as these results are opposite to the Bsh Loss of Function in L4 neurons described in the previous section. This will help clarify the window of competence in which Bsh establishes L4/L5 neuronal identities through ap/pdm3 expression.

● DamID and Bsh binding profile:<br /> ○ Figure 5 - figure supplement 1C-E: The genotype of the Control in (C) has to be described within the panel. As it is, it can be confused with a wild type brain, when it is in fact a Bsh-KO mutant.<br /> ○ It Is not clear how L4-specific Differentially Expressed Genes were found. Are these genes DEG between Lamina neurons types, or are they upregulated genes with respect to all neuronal clusters? If the latter is the case, it could explain the discrepancy between scRNAseq DEGs and Bsh peaks in L4 neurons.

● Dip-β regulation:<br /> ○ Line 234: It is not clear why CRISPR KO is used in this case, when Bsh-RNAi presents a stronger phenotype.<br /> ○ Figure 6N-R shows results using LPC-Gal4. It is not clear why this driver was used, as it makes a less accurate comparison with the other panels in the figure, which use L4-Split-Gal4. This discrepancy should be acknowledged and explained, or the experiment repeated with L4-Split-Gal4>Ap-RNAi.<br /> ○ Line 271: It is also possible that L4 activity is dispensable for motion detection and only L5 is required.

● Discussion: It is necessary to de-emphasize the relevance of HDTFs, or at least acknowledge that other, non-homeodomain TFs, can act as selector genes to determine neuronal identity. By restricting the discussion to HDTFs, it is not mentioned that other classes of TFs could follow the same Primary-Secondary selector activation logic.

Reviewer #1 (Public Review):

Like the "preceding" co-submitted paper, this is again a very strong and interesting paper in which the authors address a question that is raised by the finding in their co-submitted paper - how does one factor induce two different fates. The authors provide an extremely satisfying answer - only one subset of the cells neighbors a source of signaling cells that trigger that subset to adopt a specific fate. The signal here is Delta and the read-out is Notch, whose intracellular domain, in conjunction with, presumably, SuH cooperates with Bsh to distinguish L4 from L5 fate (L5 is not neighbored by signal-providing cells). Like the back-to-back paper, the data is rigorous, well-presented and presents important conclusions. There's a wealth of data on the different functions of Notch (with and without Bsh). All very satisfying.

I have again one suggestion that the authors may want to consider discussing. I'm wondering whether the open chromatin that the author convincingly measure is the CAUSE or the CONSEQUENCE of Bsh being able to activate L4 target genes. What I mean by this is that currently the authors seem to be focused on a somewhat sequential model where Notch signaling opens chromatin and this then enables Bsh to activate a specific set of target genes. But isn't it equally possible that the combined activity of Bsh/Notch(intra)/SuH opens chromatin? That's not a semantic/minor difference, it's a fundamentally different mechanism, I would think. This mechanism also solves the conundrum of specificity - how does Notch know which genes to "open" up? It would seem more intuitive to me to think that it's working together with Bsh to open up chromatin, with chromatin accessibility than being a "mere" secondary consequence. If I'm not overlooking something fundamental here, there is actually also a way to distinguish between these models - test chromatin accessibility in a Bsh mutant. If the author's model is true, chromatin accessibility should be unchanged.

I again finish by commending the authors for this terrific piece of work.

Reviewer #2 (Public Review):

Summary:

In this work, the authors explore how Notch activity acts together with Bsh homeodomain transcription factors to establish L4 and L5 fates in the lamina of the visual system of Drosophila. They propose a model in which differential Notch activity generates different chromatin landscapes in presumptive L4 and L5, allowing the differential binding of the primary homeodomain TF Bsh (as described in the co-submitted paper), which in turn activate downstream genes specific to either neuronal type. The requirement of Notch for L4 vs. L5 fate is well supported, and complete transformation from one cell type into the other is observed when altering Notch activity. However, the role of Notch in creating differential chromatin landscapes is not directly demonstrated. It is only based on correlation, but it remains a plausible and intriguing hypothesis.

Strengths:<br /> The authors are successful in characterizing the role of Notch to distinguish between L4 and L5 cell fates. They show that the Notch pathway is active in L4 but not in L5. They identify L1, the neuron adjacent to L4 as expressing the Delta ligand, therefore being the potential source for Notch activation in L4. Moreover, the manuscript shows molecular and morphological/connectivity transformations from one cell type into the other when Notch activity is manipulated.

Using DamID, the authors characterize the chromatin landscape of L4 and L5 neurons. They show that Bsh occupies distinct loci in each cell type. This support their model that Bsh acts as a primary selector gene in L4/L5 that activates different target genes in L4 vs L5 based on the differential availability of open chromatin loci.

Overall, the manuscript presents an interesting example of how Notch activity cooperates with TF expression to generate diverging cell fates. Together with the accompanying paper, it helps thoroughly describe how lamina cell types L4 and L5 are specified and provides an interesting hypothesis for the role of Notch and Bsh in increasing neuronal diversity in the lamina during evolution.

Weaknesses:<br /> Differential Notch activity in L4 and L5:<br /> ● The manuscript focuses its attention on describing Notch activity in L4 vs L5 neurons. However, from the data presented, it is very likely that the pool of progenitors (LPCs) is already subdivided into at least two types of progenitors that will rise to L4 and L5, respectively. Evidence to support this is the activity of E(spl)-mɣ-GFP and the Dl puncta observed in the LPC region. Discussion should naturally follow that Notch-induced differences in L4/L5 might preexist L1-expressed Dl that affect newborn L4/L5. Therefore, the differences between L4 and L5 fates might be established earlier than discussed in the paper. The authors should acknowledge this possibility and discuss it in their model.<br /> ● The authors claim that Notch activation is caused by L1-expressed Delta. However, they use an LPC driver to knock down Dl. Dl-KD should be performed exclusively in L1, and the fate of L4 should be assessed.<br /> ● To test whether L4 neurons are derived from NotchON LPCs, I suggest performing MARCM clones in early pupa with an E(spl)-mɣ-GFP reporter.<br /> ● The expression of different Notch targets in LPCs and L4 neurons may be further explored. I suggest using different Notch-activity reporters (i.e., E(spl)-GFP reporters) to further characterize these differences. What cause the switch in Notch target expression from LPCs to L4 neurons should be a topic of discussion.

Notch role in establishing L4 vs L5 fates:<br /> ● The authors describe that 27G05-Gal4 causes a partial Notch Gain of Function caused by its genomic location between Notch target genes. However, this is not further elaborated. The use of this driver is especially problematic when performing Notch KD, as many of the resulting neurons express Ap, and therefore have some features of L4 neurons. Therefore, Pdm3+/Ap+ cells should always be counted as intermediate L4/L5 fate (i.e., Fig3 E-J, Fig3-Sup2), irrespective of what the mechanistic explanation for Ap activation might be. It's not accurate to assume their L5 identity. In Fig4 intermediate-fate cells are correctly counted as such.<br /> ● Lines 170-173: The temporal requirement for Notch activity in L5-to-L4 transformation is not clearly delineated. In Fig4-figure supplement 1D-E, it is not stated if the shift to 29{degree sign}C is performed as in Fig4-figure supplement 1A-C.<br /> ● Additionally, using the same approach, it would be interesting to explore the window of competence for Notch-induced L5-to-L4 transformation: at which point in L5 maturation can fate no longer be changed by Notch GoF?

L4-to-L3 conversion in the absence of Bsh<br /> ● Although interesting, the L4-to-L3 conversion in the absence of Bsh is never shown to be dependent on Notch activity. Importantly, L3 NotchON status is assumed based on their position next to Dl-expressing L1, but it is not empirically tested. Perhaps screening Notch target reporter expression in the lamina, as suggested above, could inform this issue.<br /> ● Otherwise, the analysis of Bsh Loss of Function in L4 might be better suited to be included in the accompanying manuscript that specifically deals with the role of Bsh as a selector gene for L4 and L5.

Different chromatin landscape in L4 and L5 neurons<br /> ● A major concern is that, although L4 and L5 neurons are shown to present different chromatin landscapes (as expected for different neuronal types), it is not demonstrated that this is caused by Notch activity. The paper proves unambiguously that Notch activity, in concert with Bsh, causes the fate choice between L4 and L5. However, that this is caused by Notch creating a differential chromatin landscape is based only in correlation (NotchON cells having a different profile than NotchOFF). Although the authors are careful not to claim that differential chromatin opening is caused directly by Notch, this is heavily suggested throughout the text and must be toned down.<br /> e.g.: Line 294: "With Notch signaling, L4 neurons generate distinct open chromatin landscape" and Line 298: "Our findings propose a model that the unique combination of HDTF and open chromatin landscape (e.g. by Notch signaling)" . These claims are not supported well enough, and alternative hypotheses should be provided in the discussion. An alternative hypothesis could be that LPCs are already specified towards L4 and L5 fates. In this context, different early Bsh targets in each cell type could play a pioneer role generating a differential chromatin landscape.

● The correlation between open chromatin and Bsh loci with Differentially Expressed genes is much higher for L4 than L5. It is not clear why this is the case, and should be discussed further by the authors.

Reviewer #1 (Public Review):

Summary:

This study aims to provide imaging methods for users of the field of human layer-fMRI. This is an emerging field with 240 papers published so far. Different than implied in the manuscript, 3T is well represented among those papers. E.g. see the papers below that are not cited in the manuscript. Thus, the claim on the impact of developing 3T methodology for wider dissemination is not justified. Specifically, because some of the previous papers perform whole brain layer-fMRI (also at 3T) in more efficient, and more established procedures.

The authors implemented a sequence with lots of nice features. Including their own SMS EPI, diffusion bipolar pulses, eye-saturation bands, and they built their own reconstruction around it. This is not trivial. Only a few labs around the world have this level of engineering expertise. I applaud this technical achievement. However, I doubt that any of this is the right tool for layer-fMRI, nor does it represent an advancement for the field. In the thermal noise dominated regime of sub-millimeter fMRI (especially at 3T), it is established to use 3D readouts over 2D (SMS) readouts. While it is not trivial to implement SMS, the vendor implementations (as well as the CMRR and MGH implementations) are most widely applied across the majority of current fMRI studies already. The author's work on this does not serve any previous shortcomings in the field.

The mechanism to use bi-polar gradients to increase the localization specificity is doubtful to me. In my understanding, killing the intra-vascular BOLD should make it less specific. Also, the empirical data do not suggest a higher localization specificity to me.

Embedding this work in the literature of previous methods is incomplete. Recent trends of vessel signal manipulation with ABC or VAPER are not mentioned. Comparisons with VASO are outdated and incorrect.

The reproducibility of the methods and the result is doubtful (see below).

I don't think that this manuscript is in the top 50% of the 240 layer-fmri papers out there.

3T layer-fMRI papers that are not cited:<br /> Taso, M., Munsch, F., Zhao, L., Alsop, D.C., 2021. Regional and depth-dependence of cortical blood-flow assessed with high-resolution Arterial Spin Labeling (ASL). Journal of Cerebral Blood Flow and Metabolism. https://doi.org/10.1177/0271678X20982382

Wu, P.Y., Chu, Y.H., Lin, J.F.L., Kuo, W.J., Lin, F.H., 2018. Feature-dependent intrinsic functional connectivity across cortical depths in the human auditory cortex. Scientific Reports 8, 1-14. https://doi.org/10.1038/s41598-018-31292-x

Lifshits, S., Tomer, O., Shamir, I., Barazany, D., Tsarfaty, G., Rosset, S., Assaf, Y., 2018. Resolution considerations in imaging of the cortical layers. NeuroImage 164, 112-120. https://doi.org/10.1016/j.neuroimage.2017.02.086

Puckett, A.M., Aquino, K.M., Robinson, P.A., Breakspear, M., Schira, M.M., 2016. The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. NeuroImage 139, 240-248. https://doi.org/10.1016/j.neuroimage.2016.06.019

Olman, C.A., Inati, S., Heeger, D.J., 2007. The effect of large veins on spatial localization with GE BOLD at 3 T: Displacement, not blurring. NeuroImage 34, 1126-1135. https://doi.org/10.1016/j.neuroimage.2006.08.045

Ress, D., Glover, G.H., Liu, J., Wandell, B., 2007. Laminar profiles of functional activity in the human brain. NeuroImage 34, 74-84. https://doi.org/10.1016/j.neuroimage.2006.08.020

Huber, L., Kronbichler, L., Stirnberg, R., Ehses, P., Stocker, T., Fernández-Cabello, S., Poser, B.A., Kronbichler, M., 2023. Evaluating the capabilities and challenges of layer-fMRI VASO at 3T. Aperture Neuro 3. https://doi.org/10.52294/001c.85117

Scheeringa, R., Bonnefond, M., van Mourik, T., Jensen, O., Norris, D.G., Koopmans, P.J., 2022. Relating neural oscillations to laminar fMRI connectivity in visual cortex. Cerebral Cortex. https://doi.org/10.1093/cercor/bhac154

Strengths:

See above. The authors developed their own SMS sequence with many features. This is important to the field. And does not leave sequence development work to view isolated monopoly labs. This work democratises SMS.<br /> The questions addressed here are of high relevance to the field: getting tools with good sensitivity, user-friendly applicability, and locally specific brain activity mapping is an important topic in the field of layer-fMRI.

Weaknesses:

1. I feel the authors need to justify why flow-crushing helps localization specificity. There is an entire family of recent papers that aim to achieve higher localization specificity by doing the exact opposite. Namely, MT or ABC fRMRI aims to increase the localization specificity by highlighting the intravascular BOLD by means of suppressing non-flowing tissue. To name a few:

Priovoulos, N., de Oliveira, I.A.F., Poser, B.A., Norris, D.G., van der Zwaag, W., 2023. Combining arterial blood contrast with BOLD increases fMRI intracortical contrast. Human Brain Mapping hbm.26227. https://doi.org/10.1002/hbm.26227.

Pfaffenrot, V., Koopmans, P.J., 2022. Magnetization Transfer weighted laminar fMRI with multi-echo FLASH. NeuroImage 119725. https://doi.org/10.1016/j.neuroimage.2022.119725

Schulz, J., Fazal, Z., Metere, R., Marques, J.P., Norris, D.G., 2020. Arterial blood contrast ( ABC ) enabled by magnetization transfer ( MT ): a novel MRI technique for enhancing the measurement of brain activation changes. bioRxiv. https://doi.org/10.1101/2020.05.20.106666

Based on this literature, it seems that the proposed method will make the vein problem worse, not better. The authors could make it clearer how they reason that making GE-BOLD signals more extra-vascular weighted should help to reduce large vein effects.

The empirical evidence for the claim that flow crushing helps with the localization specificity should be made clearer. The response magnitude with and without flow crushing looks pretty much identical to me (see Fig, 6d).<br /> It's unclear to me what to look for in Fig. 5. I cannot discern any layer patterns in these maps. It's too noisy. The two maps of TE=43ms look like identical copies from each other. Maybe an editorial error?

The authors discuss bipolar crushing with respect to SE-BOLD where it has been previously applied. For SE-BOLD at UHF, a substantial portion of the vein signal comes from the intravascular compartment. So I agree that for SE-BOLD, it makes sense to crush the intravascular signal. For GE-BOLD however, this reasoning does not hold. For GE-BOLD (even at 3T), most of the vein signal comes from extravascular dephasing around large unspecific veins, and the bipolar crushing is not expected to help with this.

2. The bipolar crushing is limited to one single direction of flow. This introduces a lot of artificial variance across the cortical folding pattern. This is not mentioned in the manuscript. There is an entire family of papers that perform layer-fmri with black-blood imaging that solves this with a 3D contrast preparation (VAPER) that is applied across a longer time period, thus killing the blood signal while it flows across all directions of the vascular tree. Here, the signal cruising is happening with a 2D readout as a "snap-shot" crushing. This does not allow the blood to flow in multiple directions.<br /> VAPER also accounts for BOLD contaminations of larger draining veins by means of a tag-control sampling. The proposed approach here does not account for this contamination.

Chai, Y., Li, L., Huber, L., Poser, B.A., Bandettini, P.A., 2020. Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. NeuroImage 207, 116358. https://doi.org/10.1016/j.neuroimage.2019.116358

Chai, Y., Liu, T.T., Marrett, S., Li, L., Khojandi, A., Handwerker, D.A., Alink, A., Muckli, L., Bandettini, P.A., 2021. Topographical and laminar distribution of audiovisual processing within human planum temporale. Progress in Neurobiology 102121. https://doi.org/10.1016/j.pneurobio.2021.102121

If I would recommend anyone to perform layer-fMRI with blood crushing, it seems that VAPER is the superior approach. The authors could make it clearer why users might want to use the unidirectional crushing instead.

3. The comparison with VASO is misleading.<br /> The authors claim that previous VASO approaches were limited by TRs of 8.2s. The authors might be advised to check the latest literature of the last years.<br /> Koiso et al. performed whole brain layer-fMRI VASO at 0.8mm at 3.9 seconds (with reliable activation), 2.7 seconds (with unconvincing activation pattern, though), and 2.3 (without activation).<br /> Also, whole brain layer-fMRI BOLD at 0.5mm and 0.7mm has been previously performed by the Juelich group at TRs of 3.5s (their TR definition is 'fishy' though).

Koiso, K., Müller, A.K., Akamatsu, K., Dresbach, S., Gulban, O.F., Goebel, R., Miyawaki, Y., Poser, B.A., Huber, L., 2023. Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset. Aperture Neuro 34. https://doi.org/10.1101/2022.08.19.504502

Yun, S.D., Pais‐Roldán, P., Palomero‐Gallagher, N., Shah, N.J., 2022. Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols. Human Brain Mapping. https://doi.org/10.1002/hbm.25855

Pais-Roldan, P., Yun, S.D., Palomero-Gallagher, N., Shah, N.J., 2023. Cortical depth-dependent human fMRI of resting-state networks using EPIK. Front. Neurosci. 17, 1151544. https://doi.org/10.3389/fnins.2023.1151544

The authors are correct that VASO is not advised as a turn-key method for lower brain areas, incl. Hippocampus and subcortex. However, the authors use this word of caution that is intended for inexperienced "users" as a statement that this cannot be performed. This statement is taken out of context. This statement is not from the academic literature. It's advice for the 40+ user base that wants to perform layer-fMRI as a plug-and-play routine tool in neuroscience usage. In fact, sub-millimeter VASO is routinely being performed by MRI-physicists across all brain areas (including deep brain structures, hippocampus etc). E.g. see Koiso et al. and an overview lecture from a layer-fMRI workshop that I had recently attended: https://youtu.be/kzh-nWXd54s?si=hoIJjLLIxFUJ4g20&t=2401

Thus, the authors could embed this phrasing into the context of their own method that they are proposing in the manuscript. E.g. the authors could state whether they think that their sequence has the potential to be disseminated across sites, considering that it requires slow offline reconstruction in Matlab?<br /> Do the authors think that the results shown in Fig. 6c are suggesting turn-key acquisition of a routine mapping tool? In my humble opinion, it looks like random noise, with most of the activation outside the ROI (in white matter).

4. The repeatability of the results is questionable.<br /> The authors perform experiments about the robustness of the method (line 620). The corresponding results are not suggesting any robustness to me. In fact, the layer profiles in Fig. 4c vs. Fig 4d are completely opposite. The location of peaks turns into locations of dips and vice versa.<br /> The methods are not described in enough detail to reproduce these results.<br /> The authors mention that their image reconstruction is done "using in-house MATLAB code" (line 634). They do not post a link to github, nor do they say if they share this code.

It is not trivial to get good phase data for fMRI. The authors do not mention how they perform the respective coil-combination.<br /> No data are shared for reproduction of the analysis.

5. The application of NODRIC is not validated.<br /> Previous applications of NORDIC at 3T layer-fMRI have resulted in mixed success. When not adjusted for the right SNR regime it can result in artifactual reductions of beta scores, depending on the SNR across layers. The authors could validate their application of NORDIC and confirm that the average layer-profiles are unaffected by the application of NORDIC. Also, the NORDIC version should be explicitly mentioned in the manuscript.

Akbari, A., Gati, J.S., Zeman, P., Liem, B., Menon, R.S., 2023. Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD (preprint). Neuroscience. https://doi.org/10.1101/2023.04.06.535924

Knudsen, L., Guo, F., Huang, J., Blicher, J.U., Lund, T.E., Zhou, Y., Zhang, P., Yang, Y., 2023. The laminar pattern of proprioceptive activation in human primary motor cortex. bioRxiv. https://doi.org/10.1101/2023.10.29.564658

Reviewer #2 (Public Review):

This study developed a setup for laminar fMRI at 3T that aimed to get the best from all worlds in terms of brain coverage, temporal resolution, sensitivity to detect functional responses, and spatial specificity. They used a gradient-echo EPI readout to facilitate sensitivity, brain coverage and temporal resolution. The former was additionally boosted by NORDIC denoising and the latter two were further supported by parallel-imaging acceleration both in-plane and across slices. The authors evaluated whether the implementation of velocity-nulling (VN) gradients could mitigate macrovascular bias, known to hamper the laminar specificity of gradient-echo BOLD.

The setup allows for 0.9 mm isotropic acquisitions with large coverage at a reasonable TR (at least for block designs) and the fMRI results presented here were acquired within practical scan-times of 12-18 minutes. Also, in terms of the availability of the method, it is favorable that it benefits from lower field strength (additional time for VN-gradient implementation, afforded by longer gray matter T2*).

The well-known double peak feature in M1 during finger tapping was used as a test-bed to evaluate the spatial specificity. They were indeed able to demonstrate two distinct peaks in group-level laminar profiles extracted from M1 during finger tapping, which was largely free from superficial bias. This is rather intriguing as, even at 7T, clear peaks are usually only seen with spatially specific non-BOLD sequences. This is in line with their simple simulations, which nicely illustrated that, in theory, intravascular macrovascular signals should be suppressible with only minimal suppression of microvasculature when small b-values of the VN gradients are employed. However, the authors do not state how ROIs were defined making the validity of this finding unclear; were they defined from independent criteria or were they selected based on the region mostly expressing the double peak, which would clearly be circular? In any case, results are based on a very small sub-region of M1 in a single slice - it would be useful to see the generalizability of superficial-bias-free BOLD responses across a larger portion of M1.

As repeatedly mentioned by the authors, a laminar fMRI setup must demonstrate adequate functional sensitivity to detect (in this case) BOLD responses. The sensitivity evaluation is unfortunately quite weak. It is mainly based on the argument that significant activation was found in a challenging sub-cortical region (LGN). However, it was a single participant, the activation map was not very convincing, and the demonstration of significant activation after considerable voxel-averaging is inadequate evidence to claim sufficient BOLD sensitivity. How well sensitivity is retained in the presence of VN gradients, high acceleration factors, etc., is therefore unclear. The ability of the setup to obtain meaningful functional connectivity results is reassuring, yet, more elaborate comparison with e.g., the conventional BOLD setup (no VN gradients) is warranted, for example by comparison of tSNR, quantification and comparison of CNR, illustration of unmasked-full-slice activation maps to compare noise-levels, comparison of the across-trial variance in each subject, etc. Furthermore, as NORDIC appears to be a cornerstone to enable submillimeter resolution in this setup at 3T, it is critical to evaluate its impact on the data through comparison with non-denoised data, which is currently lacking.

The proposed setup might potentially be valuable to the field, which is continuously searching for techniques to achieve laminar specificity in gradient echo EPI acquisitions. Nonetheless, the above considerations need to be tackled to make a convincing case.

Reviewer #3 (Public Review):

Summary:<br /> The authors are looking for a spatially specific functional brain response to visualise non-invasively with 3T (clinical field strength) MRI. They propose a velocity-nulled weighting to remove the signal from draining veins in a submillimeter multiband acquisition.

Strengths:<br /> - This manuscript addresses a real need in the cognitive neuroscience community interested in imaging responses in cortical layers in-vivo in humans.<br /> - An additional benefit is the proposed implementation at 3T, a widely available field strength.

Weaknesses:<br /> - Although the VASO acquisition is discussed in the introduction section, the VN-sequence seems closer to diffusion-weighted functional MRI. The authors should make it more clear to the reader what the differences are, and how results are expected to differ. Generally, it is not so clear why the introduction is so focused on the VASO acquisition (which, curiously, lacks a reference to Lu et al 2013). There are many more alternatives to BOLD-weighted imaging for fMRI. CBF-weighted ASL and GRASE have been around for a while, ABC and double-SE have been proposed more recently.<br /> - The comparison in Figure 2 for different b-values shows % signal changes. However, as the baseline signal changes dramatically with added diffusion weighting, this is rather uninformative. A plot of t-values against cortical depth would be much more insightful.<br /> - Surprisingly, the %-signal change for a b-value of 0 is not significantly different from 0 in the gray matter. This raises some doubts about the task or ROI definition. A finger-tapping task should reliably engage the primary motor cortex, even at 3T, and even in a single participant.<br /> - The BOLD weighted images in Figure 3 show a very clear double-peak pattern. This contradicts the results in Figure 2 and is unexpected given the existing literature on BOLD responses as a function of cortical depth.<br /> - Given that data from Figures 2, 3, and 4 are derived from a single participant each, order and attention affects might have dramatically affected the observed patterns. Especially for Figure 4, neither BOLD nor VN profiles are really different from 0, and without statistical values or inter-subject averaging, these cannot be used to draw conclusions from.<br /> - In Figure 5, a phase regression is added to the data presented in Figure 4. However, for a phase regression to work, there has to be a (macrovascular) response to start with. As none of the responses in Figure 4 are significant for the single participant dataset, phase regression should probably not have been undertaken. In this case, the functional 'responses' appear to increase with phase regression, which is contra-intuitive and deserves an explanation.<br /> - Consistency of responses is indeed expected to increase by a removal of the more variable vascular component. However, the microvascular component is always expected to be smaller than the combination of microvascular+macrovascular responses. Note that the use of %signal changes may obscure this effect somewhat because of the modified baseline. Another expected feature of BOLD profiles containing both micro- and microvasculature is the draining towards the cortical surface. In the profiles shown in Figure 7, this is completely absent. In the group data, no significant responses to the task are shown anywhere in the cortical ribbon.<br /> - Although I'd like to applaud the authors for their ambition with the connectivity analysis, I feel that acquisitions that are so SNR starved as to fail to show a significant response to a motor task should not be used for brain wide directed connectivity analysis.

The claim of specificity is supported by the observation of the double-peak pattern in the motor cortex, previously shown in multiple non-BOLD studies. However, this same pattern is shown in some of the BOLD weighted data, which seems to suggest that the double-peak pattern is not solely due to the added velocity nulling gradients. In addition, the well-known draining towards the cortical surface is not replicated for the BOLD-weighted data in Figures 3, 4, or 7. This puts some doubt about the data actually having the SNR to draw conclusions about the observed patterns.

Reviewer #1 (Public Review):

Summary:<br /> Based on a "dichoptic-background-movie" paradigm that modulates ocular dominance, the present study combines fMRI and TMS to examine the role of the frontoparietal attentional network in ocular dominance shifts. The authors claimed a causal role of FEF in generating the attention-induced ocular dominance shift.

Strengths:<br /> A combination of fMRI, TMS, and "dichoptic-background-movie" paradigm techniques is used to reveal the causal role of the frontoparietal attentional network in ocular dominance shifts. The conclusions of this paper are mostly well supported by data.

Weaknesses:<br /> The relationship between eye dominance, eye-based attention shift, and cortical functions remains unclear and merits further delineation. The rationale of the experimental design related to the hemispheric asymmetry in the FEF and other regions should be clarified.

Theoretically, how the eye-related functions in this area could be achieved, and how it interacts with the ocular representation in V1 warrant further clarification.

Reviewer #2 (Public Review):

Summary<br /> Song et al investigate the role of the frontal eye field (FEF) and the intraparietal sulcus (IPS) in mediating the shift in ocular dominance (OD) observed after a period of dichoptic stimulation during which attention is selectively directed to one eye. This manipulation has been previously found to transiently shift OD in favor of the unattended eye, similar to the effect of short-term monocular deprivation. To this aim, the authors combine psychophysics, fMRI, and transcranial magnetic stimulation (TMS). In the first experiment, the authors determine the regions of interest (ROIs) based on the responses recorded by fMRI during either dichoptic or binocular stimulation, showing selective recruitment of the right FEF and IPS during the dichoptic condition, in line with the involvement of eye-based attention. In a second experiment, the authors investigate the causal role of these two ROIs in mediating the OD shift observed after a period of dichoptic stimulation by selectively inhibiting with TMS (using continuous theta burst stimulation, cTBS), before the adaptation period (50 min exposure to dichoptic stimulation). They show that, when cTBS is delivered on the FEF, but not the IPS or the vertex, the shift in OD induced by dichoptic stimulation is reduced, indicating a causal involvement of the FEF in mediating this form of short-term plasticity. A third control experiment rules out the possibility that TMS interferes with the OD task (binocular rivalry), rather than with the plasticity mechanisms. From this evidence, the authors conclude that the FEF is one of the areas mediating the OD shift induced by eye-selective attention.

Strengths<br /> 1. The experimental paradigm is sound and the authors have thoroughly investigated the neural correlates of an interesting form of short-term visual plasticity combining different techniques in an intelligent way.

2. The results are solid and the appropriate controls have been performed to exclude potential confounds.

3. The results are very interesting, providing new evidence both about the neural correlates of eye-based attention and the involvement of extra-striate areas in mediating short-term OD plasticity in humans, with potential relevance for clinical applications (especially in the field of amblyopia).

Weaknesses<br /> 1. Ethics: more details about the ethics need to be included in the manuscript. It is only mentioned for experiment 1 that participants "provided informed consent in accordance with the Declaration of Helsinki. This study was approved by the Institutional Review Board of the Institute of Psychology, Chinese Academy of Sciences". (Which version of the Declaration of Helsinki? The latest version requires the pre-registration of the study. The code of the approved protocol together with the code and date of the approval should be provided.) There is no mention of informed consent procedures or ethics approval for the TMS experiments. This is a huge concern, especially for brain stimulation experiments!

2. Statistics: the methods section should include a sub-section describing in detail all the statistical analyses performed for the study. Moreover, in the results section, statistical details should be added to support the fMRI results. In the current version of the manuscript, the claims are not supported by statistical evidence.

3. Interpretation of the results: the TMS results are very interesting and convincing regarding the involvement of the FEF in the build-up of the OD shift induced by dichoptic stimulation, however, I am not sure that the authors can claim that this effect is related to eye-based attention, as cTBS has no effect on the blob detection task during dichoptic stimulation. If the FEF were causally involved in eye-based attention, one would expect a change in performance in this task during dichoptic stimulation, perhaps a similar performance for the unattended and attended eye. The authors speculate that the sound could have an additional role in driving eye-based attention, which might explain the lack of effect for the blob discrimination task, however, this hypothesis has not been tested.

4. Writing: in general, the manuscript is well written, but clarity should be improved in certain sections.

a. fMRI results: the first sentence is difficult to understand at first read, but it is crucial to understand the results, please reformulate and clarify.

b. Experiment 3: the rationale for experiment one should be straightforward, without a long premise explaining why it would not be necessary.

c. Discussion: the language is a bit familiar here and there, a more straightforward style should be preferred (one example: p.19 second paragraph).

5. Minor: the authors might consider using the term "participant" or "observer" instead of "subject" when referring to the volunteers who participated in the study.

Reviewer #3 (Public Review):

Summary:<br /> This study studied the neural mechanisms underlying the shift of ocular dominance induced by "dichoptic-backward-movie" adaptation. The study is self-consistent.

Strengths:<br /> The experimental design is solid and progressive (relationship among three studies), and all of the raised research questions were well answered.

The logic behind the neural mechanisms is solid.

The findings regarding the cTMS (especially the position/site can be useful for future medical implications).

Weaknesses:<br /> Why does the "dichoptic-backward-movie" adaptation matter? This part is severely missing. This kind of adaptation is neither intuitive like the classical (Gbison) visual adaptation, nor practical as adaptation as a research paradigm as well as the fundamental neural mechanism. If this part is not clearly stated and discussed, this study is just self-consistent in terms of its own research question. There are tons of "cool" phenomena in which the neural mechanisms are apparent as "FEF controls vision-attention" but never tested using TMS & fMRI, but we all know that this kind of research is just of incremental implications.

Reviewer #1 (Public Review):

Summary:<br /> The authors of this study investigated the development of interoceptive sensitivity in the context of cardiac and respiratory interoception in 3-, 9-, and 18-month-old infants using a combination of both cross-sectional and longitudinal designs. They utilised the cardiac interoception paradigm developed by Maister et al (2017) and also developed a new paradigm to investigate respiratory interoception in infants. The main findings of this research are that 9-month-old infants displayed a preference for stimuli presented synchronously with their own heartbeat and respiration. The authors found less reliable effects in the 18-month-old group, and this was especially true for the respiratory interoceptive data. The authors replicated a visual preference for synchrony over asynchrony for the cardiac domain in 3-month-old infants, while they found inconclusive evidence regarding the respiratory domain. Considering the developmental nature of the study, the authors also investigated the presence of developmental trajectories and associations between the two interoceptive domains. They found evidence for a relationship between cardiac and respiratory interoceptive sensitivity at 18 months only and preliminary evidence for an increase in respiratory interoception between 9 and 18 months.

Strengths: The conclusions of this paper are mostly well supported by data, and the data analysis procedures are rigorous and well-justified. The main strengths of the paper are:<br /> - A first attempt to explore the association between two different interoceptive domains. How different organ-specific axes of interoception relate to each other is still open and exploring this from a developmental lens can help shed light into possible relationships. The authors have to be commended for developing novel interoceptive tasks aimed at assessing respiratory interoceptive sensitivity in infants and toddlers, and for trying to assess the relationship between cardiac and respiratory interoception across developmental time.<br /> - A thorough justification of the developmental ages selected for the study. The authors provide a rationale behind their choice to examine interoceptive sensitivity at 3, 9, and 18 months of age. These are well justified based on the literature pertaining to self- and social development. Sometimes, I wondered whether explaining the link between these self and social processes and interoception would have been beneficial as a reader not familiar with the topics may miss the point.<br /> - An explanation of the direction of looking behaviour using latent curve analysis. I found this additional analysis extremely helpful in providing a better understanding of the data based on previous research and analytical choices (though see comment under weaknesses). As the authors explain in the manuscript, it is often difficult to interpret the direction of infant-looking behaviour as novelty and familiarity preferences can also be driven by hidden confounders (e.g. task difficulty). The authors provide some evidence that analytical choices can explain some of these effects. Beyond the field of interoception, these findings will be relevant to development psychologists and will inform future studies using looking time as a measure of infants' ability to discriminate among stimuli.<br /> - The use of simulation analysis to account for the small sample size. The authors acknowledge that some of the effects reported in their study could be explained by a small sample size (i.e. the 3-month-olds and 18-month-olds data). Using a simulation approach, the authors try to overcome some of these limitations and provide convincing evidence of interoceptive abilities in infancy and toddlerhood (but see also my next point).

Weaknesses:<br /> - The authors should carefully address the potential confounding of not counterbalancing the conditions of the first trial in both interoceptive tasks for the 9-month and 18-month age groups. The results of these groups could indeed be driven by having seen the synchronous trial first.<br /> - The conclusion that cardiac interoception remains stable across infancy is not fully warranted by the data. Given the small sample size of 18-month-old toddlers included in the final analyses, it might be misleading to state this without including the caveat that the study may be underpowered. In other words, the small sample size could explain the direction of the results for this age group.

Reviewer #2 (Public Review):

Summary:<br /> This study by Tünte et al. investigated the development of interoceptive sensitivity in the first year of life, focusing specifically on cardiac and respiratory sensitivity in infants aged 3, 9, and 18 months. The research employed a previously developed experimental paradigm in the cardiac domain and adapted it for a novel paradigm in the respiratory domain. This approach assessed infants' cardiac and respiratory sensitivity based on their preferential-looking behavior toward visuo-auditory stimuli displayed on a monitor, which moved either in sync or out of sync with the infants' own heartbeats or breathing. The results for the cardiac domain showed that infants, across all age groups, preferred stimuli moving synchronously rather than asynchronously with their heartbeat, suggesting the presence of cardiac sensitivity as early as 3 months of age. However, it is noteworthy that the direction of this preference contradicts a previous study, which found that 5-month-old infants looked longer at stimuli moving asynchronously, rather than synchronously, with their heartbeat (Maister et al., 2017). In the respiratory domain, only the younger age group(s) of infants showed a preference for stimuli presented synchronously with their breathing, unlike the 18-month-olds. The authors conducted various statistical analyses to thoroughly examine the obtained data, an effort that provides deeper insights and is valuable for future research in this field.

Strengths:<br /> Few studies have explored the early development of interoception, making the replication of the original study by Maister et al. (2017) particularly valuable. Beyond replication, this study expands the investigation into the respiratory domain, significantly enhancing our understanding of interoceptive development. The provision of longitudinal and cross-sectional data from infants at 3, 9, and 18 months of age is instrumental in understanding their developmental trajectory.

Weaknesses:<br /> (1) My primary concern is that this study did not counterbalance the conditions of the first trial in both iBEAT and iBREATH tests for the 9-month and 18-month age groups. In these tests, the first trial invariably involved a synchronous stimulus. I believe that the order of trials can significantly influence an infant's looking duration, and this oversight could potentially impact the results, especially where a marked preference for synchronous stimuli was observed among infants.<br /> (2) The analysis indicated that the study's sample size was too small to effectively assess the effects within each age group. This limitation fundamentally undermines the reliability of the findings.<br /> (3) The authors attribute the infants' preferential-looking behavior solely to the effects of familiarity and novelty. However, the meaning of "familiarity" in relation to external stimuli moving in sync with an infant's heartbeat or breathing is not clearly defined. A deeper exploration of the underlying mechanisms driving this behavior, such as from the perspectives of attention and perception, is necessary.

Reviewer #1 (Public Review):

This study examined a universal fractal primate brain shape. However, the paper does not seem well structured and is not well written. It is not clear what the purpose of the paper is. And there is a lack of explanation for why the proposed analysis is necessary. As a result, it is challenging to clearly understand what novelty in the paper is and what the main findings are. Additionally, several terms are introduced without adequate explanation and contextualization, further complicating comprehension. Does the second section, "2. Coarse-graining procedure", serve as an introduction or a method? Moreover, the rationale behind the use of the coarse-graining procedure is not adequately elucidated. Overall, it is strongly recommended that the paper undergoes significant improvements in terms of its structure, explanatory depth, and overall clarity to enhance its comprehensibility.

Reviewer #2 (Public Review):

In this manuscript, Wang and colleagues analyze the shapes of cerebral cortices from several primate species, including subgroups of young and old humans, to characterize commonalities in patterns of gyrification, cortical thickness, and cortical surface area. The work builds on the scaling law introduced previously by co-author Mota, and Herculano-Houzel. The authors state that the observed scaling law shares properties with fractals, where shape properties are similar across several spatial scales. One way the authors assess this is to perform a "cortical melting" operation that they have devised on surface models obtained from several primate species. The authors also explore differences in shape properties between the brains of young (~20 year old) and old (~80) humans. My main criticism of this manuscript is that the findings are presented in too abstract a manner for the scientific contribution to be recognized.

1. The series of operations to coarse-grain the cortex illustrated in Figure 1, constitute a novel procedure, but it is not strongly motivated, and it produces image segmentations that do not resemble real brains. The process to assign voxels in downsampled images to cortex and white matter is biased towards the former, as only 4 corners of a given voxel are needed to intersect the original pial surface, but all 8 corners are needed to be assigned a white matter voxel (section S2). This causes the cortical segmentation, such as the bottom row of Figure 1B, to increase in thickness with successive melting steps, to unrealistic values. For the rightmost figure panel, the cortex consists of several 4.9-sided voxels and thus a >2 cm thick cortex. A structure with these morphological properties is not consistent with the anatomical organization of a typical mammalian neocortex.

2. For the comparison between 20-year-old and 80-year-old brains, a well-documented difference is that the older age group possesses more cerebral spinal fluid due to tissue atrophy, and the distances between the walls of gyri becomes greater. This difference is born out in the left column of Figure 4c. It seems this additional spacing between gyri in 80-year-olds requires more extensive down-sampling (larger scale values in Figure 4a) to achieve a similar shape parameter K as for the 20-year-olds. A case could be made that the familiar way of describing brain tissue - cortical volume, white matter volume, thickness, etc. - is a more direct and intuitive way to describe differences between young and old adult brains than the obscure shape metric described in this manuscript. At a minimum, a demonstration of an advantage of the Figure 4a and 4b analyses over current methods for interpreting age-related differences would be valuable.

3. In Discussion lines 199-203, it is stated that self-similarity, operating on all length scales, should be used as a test for existing and future models of gyrification mechanisms. First, the authors do not show, (and it would be surprising if it were true) that self-similarity is observed for length scales smaller than the acquired MRI data for any of the datasets analyzed. The analysis is restricted to coarse (but not fine)-graining. Therefore, self-similarity on all length scales would seem to be too strong a constraint. Second, it is hard to imagine how this test could be used in practice. Specific examples of how gyrification mechanisms support or fail to support the generation of self-similarity across any length scale, would strengthen the authors' argument.

Some additional, specific comments are as follows:

4. The definition of the term A_e as the "exposed surface" was difficult to follow at first. It might be helpful to state that this parameter is operationally defined as the convex hull surface area. Also, for the pial surface, A_t, there are several who advocate instead for the analysis of a cortical mid-thickness surface area, as the pial surface area is subject to bias depending on the gyrification index and the shape of the gyri. It would be helpful to understand if the same results are obtained from mid-thickness surfaces.

5. In Figure 2c, the surfaces get smaller as the coarse-graining increases, making it impossible to visually assess the effects of coarse-graining on the shapes. Why aren't all cortical models shown at the same scale?

6. Text in Section 3.2 emphasizes that K is invariant with scale (horizontal lines in Figure 3), and asserts this is important for the formation of all cortices. However, I might be mistaken, but it appears that K varies with scale in Figure 4a, and the text indicates that differences in the S dependence are of importance for distinguishing young vs. old brains. Is this an inconsistency?

Reviewer #3 (Public Review):

Summary:

Through a detailed methodology, the authors demonstrated that within 11 different primates, the shape of the brain matched a fractal of dimension 2.5. They enhanced the universality of this result by showing the concordance of their results with a previous study investigating 70 mammalian brains, and the discordance of their results with other folded objects that are not brains. They incidentally illustrated potential applications of this fractal property of the brain by observing a scale-dependent effect of aging on the human brain.

Strengths:

- New hierarchical way of expressing cortical shapes at different scales derived from the previous report through the implementation of a coarse-graining procedure.<br /> - Positioning of results in comparison to previous works reinforcing the validity of the observation.<br /> - Illustration of scale-dependence of effects of brain aging in the human.

Weaknesses:

- The impact of the contribution should be clarified compared to previous studies (implementation of new coarse graining procedure, dimensionality of primate brain vs previous studies, and brain aging observations).<br /> - The rather small sample sizes, counterbalanced by the strength of the effect demonstrated.<br /> - The use of either averaged or individual brains for the different sub-studies could be made clearer.<br /> - The model discussed hypothetically in the discussion is not very clear, and may not be state-of-the-art (axonal tension driving cortical folding? cf. https://doi.org/10.1115/1.4001683).

Reviewer #1 (Public Review):

The major aim of the paper was a method for determining genetic associations between two traits using common variants tested in genome-wide association studies. The work includes a software implementation and application of their approach. The results of the application of their method generally agree with what others have seen using similar AD and UKB data.

The paper has several distinct portions. The first is a method for testing genetic associations between two or more traits using genome-wide association tests statistics. The second is a python implementation of the method. The last portion is the results of their method using GWAS from AD and UK Biobank.

Regarding the method, it seems like it has similarities to LDSC, and it is not clear how it differs from LDSC or other similar methods. The implementation of the method used python 2.7 (or at least was reportedly tested using that version) that was retired in 2020. The implementation was committed between Wed Oct 3 15:21:49 2018 to Mon Jan 28 09:18:09 2019 using data that existed at the time so it was a bit surprising it used python 2.7 since it was initially going to be set for end-of-life in 2015. Anyway, trying to run the package resulted in unmet dependency errors, which I think are related to an internal package not getting installed. I would expect that published software could be installed using standard tooling for the language, and, ideally, software should have automated testing of key portions.

Regarding the main results, they find what has largely been shown by others using the same data or similar data, which add prima facie validity to the work The portions of the work dealing with AD subgroups, pathology, biomarkers, and cognitive traits of interest. I was puzzled why the authors suggested surprise regarding parental history and high cholesterol not associated with MCI or cognitive composite scores since the this would seem like the likely fallout of selection of the WRAP cohort. The discussion paragraph that started "What's more, environmental factors may play a big role in the identified associations." confused me. I think what the authors are referring to are how selection, especially in a biobank dataset, can induce correlations, which is not what I think of as an environmental effect.

Overall, the work has merit, but I am left without a clear impression of the improvement in the approach over similar methods. Likewise, the results are interesting, but similar findings are described with the data that was used in the study, which are over 5 years old at the time of this review.

Reviewer #2 (Public Review):

Summary:<br /> Yan, Hu, and colleagues introduce BADGERS, a new method for biobank-wide scanning to find associations between a phenotype of interest, and the genetic component of a battery of candidate phenotypes. Briefly, BADGERS capitalizes on publicly available weights of genetic variants for a myriad of traits to estimate polygenic risk scores for each trait, and then identify associations with the trait of interest. Of note, the method works using summary statistics for the trait of interest, which is especially beneficial for running in population-based cohorts that are not enriched for any particular phenotype (ie. with few actual cases of the phenotype of interest).

Here, they apply BADGERS on Alzheimer's disease (AD) as the trait of interest, and a battery of circa 2,000 phenotypes with publicly available precalculated genome-wide summary statistics from the UK Biobank. They run it on two AD cohorts, to discover at least 14 significant associations between AD and traits. These include expected associations with dementia, cognition (educational attainment), and socioeconomic status-related phenotypes. Through multivariate modelling, they distinguish between (1) clearly independent components associated with AD, from (2) by-product associations that are inflated in the original bivariate analysis. Analyses stratified according to APOE inclusion show that this region does not seem to play a role in the association of some of the identified phenotypes. Of note, they observe overlap but significant differences in the associations identified with BADGERS and other Mendelian randomization (MR), hinting at BADGERS being more powerful than classical top variant-based MR approaches. They then extend BADGERS to other AD-related phenotypes, which serves to refine the hypotheses about the underlying mechanisms accounting for the genetic correlation patterns originally identified for AD. Finally, they run BADGERS on a pre-clinical cohort with mild cognitive impairment. They observe important differences in the association patterns, suggesting that this preclinical phenotype (at least in this cohort) has a different genetic architecture than general AD.

Strengths:<br /> BADGERS is an interesting new addition to a stream of attempts to "squeeze" biobank data beyond pure association studies for diagnosis. Increasingly available biobank cohorts do not usually focus on specific diseases. However, they tend to be data-rich, opening for deep explorations that can be useful to refine our knowledge of the latent factors that lead to diagnosis. Indeed, the possibility of running genetic correlation studies in specific sub-settings of interest (e.g. preclinical cohorts) is arguably the most interesting aspect of BADGERS. Classical methods like LDSC or two-sample MR capitalize on publicly available summary statistics from large cohorts, or having access to individual genotype data of large cohorts to ensure statistical power. Seemingly, BADGERS provides a balanced opportunity to dissect the correlation between traits of interest in settings with small sample size in which other methods do not work well.

Weaknesses:<br /> However, the increased statistical power is just hinted, and for instance, they do not explore if LDSC would have identified these associations. Although I suspect that is the case, this evidence is important to ensure that the abovementioned balance is right. Finally, as discussed by the authors, the reliance on polygenic risk scoring necessarily undermines the causality evidence gained through BADGERS. In this sense, BADGERS provides an alternative to strict instrumental-variable based analysis, which can be particularly useful to generate new mechanistic hypotheses.

In summary, after 15 years of focus on diagnosis that would require having individual access to large patient cohorts, BADGERS can become an excellent tool to dig into trait heterogeneity, especially if it turns out to be more powerful than other available methodologies.

Reviewer #1 (Public Review):

Summary:<br /> Frey et al. report the structures of aSyn fibrils that were obtained under a variety of conditions. These include the generation of aSyn fibrils without seeds, but in different buffers and at different pH values. These also include the generation of aSyn fibrils in the presence of seeding fibrils, again performed in different buffers and at different pH values, while the seeds were generated at different conditions. The authors find that fibril polymorphs primarily correlate with fibril growth buffer conditions, and not such much with the type of seed. However, the presence of a seed is still required, likely because fibrils can also seed along their lateral surfaces, not only at the blunt ends.

Strengths:<br /> The manuscript includes an excellent review of the numerous available structures of aSyn. As the authors state, "it seems that there are about as many unique atomic-resolution structures of these aggregates as there are publications describing them."

The text is interesting to read, figures are clear and not redundant.

Weaknesses:<br /> The manuscript is excellently written, but sometimes a few commas are lacking.

Reviewer #2 (Public Review):

Summary:<br /> This is an exciting paper that explores the in vitro assembly of recombinant alpha-synuclein into amyloid filaments. The authors changed the pH and the composition of the assembly buffers, as well as the presence of different types of seeds, and analysed the resulting structures by cryo-EM.

Strengths:<br /> By doing experiments at different pHs, the authors found that so-called type-2 and type-3 polymorphs form in a pH-dependent manner. In addition, they find that type-1 filaments form in the presence of phosphate ions. One of their in vitro assembled type-1 polymorphs is similar to the alpha-synuclein filaments that were extracted from the brain of an individual with juvenile-onset synucleinopathy (JOS). They hypothesize that additional densities in a similar place as additional densities in the JOS fold correspond to phosphate ions.

Weaknesses:<br /> The paper contains multiple instances of non-scientific language, as indicated below. It would also benefit from additional details on the cryo-EM structure determination in the Methods and inclusion of commonly accepted requirements for cryo-EM structures, like examples of 2D class averages, raw micrographs, and FSC curves (between half-maps as well as between rigid-body fitted (or refined) atomic models of the different polymorphs and their corresponding maps). In addition, cryo-EM maps for the control experiments F1 and F2 should be presented in Figure 9.

Reviewer #3 (Public Review):

Summary:<br /> The high heterogeneity nature of α-synuclein (α-syn) fibrils posed significant challenges in structural reconstruction of the ex vivo conformation. A deeper understanding of the factors influencing the formation of various α-syn polymorphs remains elusive. The manuscript by Frey et al. provides a comprehensive exploration of how pH variations (ranging from 5.8 to 7.4) affect the selection of α-syn polymorphs (specifically, Type1, 2, and 3) in vitro by using cryo-electron microscopy (cryo-EM) and helical reconstruction techniques. Crucially, the authors identify two novel polymorphs at pH 7.0 in PBS. These polymorphs bear resemblance to the structure of patient-derived juvenile-onset synucleinopathy (JOS) polymorph and diseased tissue amplified α-syn fibrils. The manuscript supports the notion that seeding is non-polymorph-specific in the context of secondary nucleation-dominated aggregation, underscoring the irreplaceable role of pH in polymorph formation. Nevertheless, certain areas within the manuscript would benefit from further refinement and elaboration to more robustly substantiate this hypothesis.

Strengths:<br /> This study systematically investigates the effects of environmental conditions and seeding on the structure of α-syn fibrils. It emphasizes the significant influence of environmental factors, especially pH, in determining the selection of α-syn polymorphs. The high-resolution structures obtained through cryo-EM enable a clear characterization of the composition and proportion of each polymorph in the sample. Collectively, this work provides strong support for the pronounced sensitivity of α-syn fibril structures to environmental conditions and systematically categorizes previously reported α-syn fibril structures. Furthermore, the identification of JOS-like polymorph also demonstrates the possibility of in vitro reconstruction of brain-derived α-syn fibril structures.

Weaknesses:<br /> 1. The authors reveal that both Type 1 monofilament fibril polymorph (reminiscent of JOS-like polymorph) and Type 5 polymorph (akin to tissue-amplified-like polymorph) can both form under the same condition. Additionally, this condition also fosters the formation of flat ribbon-like fibril across different batches. Notably, at pH 5.8, variations in experimental groups yield disparate abundance ratios between polymorph 3B and 3C, indicating a degree of instability in fibrillar formation. The variability would potentially pose challenges for replicability in subsequent research. In light of these situations, I propose the following recommendations:

(1) An explicit elucidation of the factors contributing to these divergent outcomes under similar experimental conditions is warranted. This should include an exploration of whether variations in purified protein batches are contributing factors to the observed heterogeneity.

(2) To enhance the robustness of the conclusions, additional replicates of the experiments under the same condition should be conducted, ideally a minimum of three times.

(3) Further investigation into whether different polymorphs formed under the same buffer condition could lead to distinct toxicological and pathology effects would be a valuable addition to the study.

2. The cross-seeding study presented in the manuscript demonstrates the pivotal role of pH conditions in dictating conformation. However, an intriguing aspect that emerges is the potential role of seed concentration in determining the resultant product structure. This raises a critical question: at what specific seed concentration does the determining factor for polymorph selection shift from pH condition to seed concentration? A methodological robust approach to address this should be conducted through a series of experiments across a range of seed concentrations. Such an approach could delineate a clear boundary at which seed concentration begins to predominantly dictate the conformation, as opposed to pH conditions. Incorporating this aspect into the study would not only clarify the interplay between seed concentration and pH conditions, but also add a fascinating dimension to the understanding of polymorph selection mechanisms.

Furthermore, the study prompts additional queries regarding the behavior of cross-seeding production under the same pH conditions when employing seeds of distinct conformation. Evidence from various studies, such as those involving E46K and G51D cross-seeding, suggests that seed structure plays a crucial role in dictating polymorph selection. A key question is whether these products consistently mirror the structure of their respective seeds.

3. In the Results section of "The buffer environment can dictate polymorph during seeded nucleation", the authors reference previous cell biological and biochemical assays to support the polymorph-specific seeding of MSA and PD patients under the same buffer conditions. This discussion is juxtaposed with recent research that compares the in vivo biological activities of hPFF, ampLB as well as LB, particularly in terms of seeding activity and pathology. Notably, this research suggests that ampLB, rather than hPFF, can accurately model the key aspects of Lewy Body Diseases (LBD) (refer to: https://doi.org/10.1038/s41467-023-42705-5). The critical issue here is the need to reconcile the phenomena observed in vitro with those in in-vivo or in-cell models. Given the low seed concentration reported in these studies, it is imperative for the authors to provide a more detailed explanation as to why the possible similar conformation could lead to divergent pathologies, including differences in cell-type preference and seeding capability.

4. In the Method section of "Image processing", the authors describe the helical reconstruction procedure, without mentioning much detail about the 3D reconstruction and refinement process. For the benefit of reproducibility and to facilitate a deeper understanding among readers, the authors should enrich this part to include more comprehensive information, akin to the level of detail found in similar studies (refer to: https://doi.org/10.1038/nature23002).

5. The abbreviation of amino acids should be unified. In the Results section "On the structural heterogeneity of Type 1 polymorphs", the amino acids are denoted using three-letter abbreviation. Conversely, in the same section under "On the structural heterogeneity of Type 2 and 3 structures", amino acids are abbreviated using the one-letter format. For clarity and consistency, it is essential that a standardized format for amino acid abbreviations be adopted throughout the manuscript.

Reviewer #1 (Public Review):

Summary:<br /> UGGTs are involved in the prevention of premature degradation for misfolded glycoproteins, by utilizing UGGT-KO cells and a number of different ERAD substrates. They proposed a concept by which the fate of glycoproteins can be determined by a tug-of-war between UGGTs and EDEMs.

Strengths:<br /> The authors provided a wealth of data to indicate that UGGT1 competes with EDEMs, which promotes glycoprotein degradation.

Weaknesses:<br /> Less clear, though, is the involvement of UGGT2 in the process. Also, to this reviewer, some data do not necessarily support the conclusion.

Major criticisms:

1. One of the biggest problems I had on reading through this manuscript is that, while the authors appeared to generate UGGTs-KO cells from HCT116 and HeLa cells, it was not clearly indicated which cell line was used for each experiment. I assume that it was HCT116 cells in most cases, but I did not see that it was clearly mentioned. As the expression level of UGGT2 relative to UGGT1 is quite different between the two cell lines, it would be critical to know which cells were used for each experiment.

2. While most of the authors' conclusion is sound, some claims, to this reviewer, were not fully supported by the data. Especially I cannot help being puzzled by the authors' claim about the involvement of UGGT2 in the ERAD process. In most of the cases, KO of UGGT2 does not seem to affect the stability of ERAD substrates (ex. Fig. 1C, 2A, 3D). When the author suggests that UGGT2 is also involved in the ERAD, it is far from convincing (ex. Fig. 2D/E). Especially because now it has been suggested that the main role of UGGT2 may be distinct from UGGT1, playing a role in lipid quality control (Hung, et al., PNAS 2022), it is imperative to provide convincing evidence if the authors want to claim the involvement of UGGT2 in a protein quality control system.

In fact, it was not clear at all whether even UGGT1 is also involved in the process in Fig. 2D/E, as the difference, if any, is so subtle. How the authors can be sure that this is significant enough? While the authors claim that the difference is statistically significant (n=3), this may end up with experimental artifacts. To say the least, I would urge the authors to try rescue experiments with UGGT1 or 2, to clarify that the defect in UGGT-DKO cells can be reversed. It may also be interesting to see that the subtle difference the authors observed is indeed N-glycan-dependent by testing a non-glycosylated version of the protein (just like NHK-QQQ mutants in Fig. 2C).

To this reviewer, it is still possible that the involvement of UGGT1 (or 2, if any) could be totally substrate-dependent, and the substrates used in Fig 2D or E happen not to be dependent to the action of UGGTs. To the reviewer, without the data of Fig. 2D and E the authors provide enough evidence to demonstrate the involvement of UGGT1 in preventing premature degradation of glycoprotein ERAD substrates. I am just afraid that the authors may have overinterpreted the data, as if the UGGTs are involved in stabilization of all glycoproteins destined for ERAD.

3. I am a bit puzzled by the DNJ treatment experiments. First, I do not see the detailed conditions of the DNJ treatment (concentration? Time?). Then, I was a bit surprised to see that there were so little G3M9 glycans formed, and there was about the same amount of G2M9 also formed (Figure 1 Figure supplement 4B-D), despite the fact that glucose trimming of newly syntheized glycoproteins are expected to be completely impaired (unless the authors used DNJ concentration which does not completely impair the trimming of the first Glc). Even considering the involvement of Golgi endo-alpha-mannosidase, a similar amount of G3M9 and G2M9 may suggest that the experimental conditions used for this experiment (i.e. concentration of DNJ, duration of treatment, etc) is not properly optimized.

Reviewer #2 (Public Review):

In this study, Ninagawa et al., shed light on UGGT's role in ER quality control of glycoproteins. By utilizing UGGT1/UGGT2 DKO cells, they demonstrate that several model misfolded glycoproteins undergo early degradation. One such substrate is ATF6alpha where its premature degradation hampers the cell's ability to mount an ER stress response.

While this study convincingly demonstrates early degradation of misfolded glycoproteins in the absence of UGGTs, my major concern is the need for additional experiments to support the "tug of war" model involving UGGTs and EDEMs in influencing the substrate's fate - whether misfolded glycoproteins are pulled into the folding or degradation route. Specifically, it would be valuable to investigate how overexpression of UGGTs and EDEMs in WT cells affects the choice between folding and degradation for misfolded glycoproteins. Considering previous studies indicating that monoglucosylation influences glycoprotein solubility and stability, an essential question is: what is the nature of glycoproteins in UGGTKO/EDEMKO and potentially UGGT/EDEM overexpression cells? Understanding whether these substrates become more soluble/stable when GM9 versus mannose-only translation modification accumulates would provide valuable insights.

The study delves into the physiological role of UGGT, but is limited in scope, focusing solely on the effect of ATF6alpha in UGGT KO cells' stress response. It is crucial for the authors to investigate the broader impact of UGGT KO, including the assessment of basal ER proteotoxicity levels, examination of the general efflux of glycoproteins from ER, and the exploration of the physiological consequences due to UGGT KO. This broader perspective would be valuable for the wider audience. Additionally, the marked increase in ATF4 activity in UGGTKO requires discussion, which the authors currently omit.

The discussion section is brief and could benefit from being a separate section. It is advisable for the authors to explore and suggest other model systems or disease contexts to test UGGT's role in the future. This expansion would help the broader scientific community appreciate the potential applications and implications of this work beyond its current scope.

Reviewer #3 (Public Review):

This manuscript focuses on defining the importance of UGGT1/2 in the process of protein degradation within the ER. The authors prepared cells lacking UGGT1, UGGT2, or both UGGT1/UGGT2 (DKO) HCT116 cells and then monitored the degradation of specific ERAD substrates. Initially, they focused on the ER stress sensor ATF6 and showed that loss of UGGT1 increased the degradation of this protein. This degradation was stabilized by deletion of ERAD-specific factors (e.g., SEL1L, EDEM) or treatment with mannose inhibitors such as kifunesine, indicating that this is mediated through a process involving increased mannose trimming of the ATF6 N-glycan. This increased degradation of ATF6 impaired the function of this ER stress sensor, as expected, reducing the activation of downstream reporters of ER stress-induced ATF6 activation. The authors extended this analysis to monitor the degradation of other well-established ERAD substrates including A1AT-NHK and CD3d, demonstrating similar increases in the degradation of destabilized, misfolding protein substrates in cells deficient in UGGT. Importantly, they did experiments to suggest that re-overexpression of wild-type, but not catalytically deficient, UGGT rescues the increased degradation observed in UGGT1 knockout cells. Further, they demonstrated the dependence of this sensitivity to UGGT depletion on N-glycans using ERAD substrates that lack any glycans. Ultimately, these results suggest a model whereby depletion of UGGT (especially UGGT1 which is the most expressed in these cells) increases degradation of ERAD substrates through a mechanism involving impaired re-glucosylation and subsequent re-entry into the calnexin/calreticulin folding pathway.

I must say that I was under the impression that the main conclusions of this paper (i.e., UGGT1 functions to slow the degradation of ERAD substrates by allowing re-entry into the lectin folding pathway) were well-established in the literature. However, I was not able to find papers explicitly demonstrating this point. Because of this, I do think that this manuscript is valuable, as it supports a previously assumed assertion of the role of UGGT in ER quality control. However, there are a number of issues in the manuscript that should be addressed.

Notably, the focus on well-established, trafficking-deficient ERAD substrates, while a traditional approach to studying these types of processes, limits our understanding of global ER quality control of proteins that are trafficked to downstream secretory environments where proteins can be degraded through multiple mechanisms. For example, in Figure 1-Figure Supplement 2, UGGT1/2 knockout does not seem to increase the degradation of secretion-competent proteins such as A1AT or EPO, instead appearing to stabilize these proteins against degradation. They do show reductions in secretion, but it isn't clear exactly how UGGT loss is impacting ER Quality Control of these more relevant types of ER-targeted secretory proteins.

Lastly, I don't understand the link between UGGT, ATF6 degradation, and ATF6 activation. I understand that the idea is that increased ATF6 degradation afforded by UGGT depletion will impair activation of this ER stress sensor, but if that is the case, how does UGGT2 depletion, which only minimally impacts ATF6 degradation (Fig. 1), impact activation to levels similar to the UGGT1 knockout (Fig 4)? This suggests UGGT1/2 may serve different functions beyond just regulating the degradation of this ER stress sensor. Also, the authors should quantify the impaired ATF6 processing shown in Fig 4B-D across multiple replicates.

Ultimately, I do think the data support a role for UGGT (especially UGGT1) in regulating the degradation of ERAD substrates, which provides experimental support for a role long-predicted in the field. However, there are a number of ways this manuscript could be strengthened to further support this role, some of which can be done with data they have in hand (e.g., the stats) or additional new experiments.

Reviewer #1 (Public Review):

It is known that aberrant habit formation is a characteristic of obsessive-compulsive disorder (OCD). Habits can be defined according to the following features (Balleine and Dezfouli, 2019): rapid execution, invariant response topography, action 'chunking' and resistance to devaluation.<br /> The extent to which OCD behavior is derived from enhanced habit formation relative to deficits in goal-directed behavior is a topic of debate in the current literature. This study examined habit-learning specifically (cf. deficits in goal-directed behavior) by regularly presenting, via smartphone, sequential learning tasks to patients with OCD and healthy controls. Participants engaged in the tasks every day over the course of a month. Automaticity, including the extent to which individual actions in the sequence become part of a unified 'chunk', was an important outcome variable. Following the 30 days of training, in-laboratory tasks were then administered to examine 1) if performing the learned sequences themselves had become rewarding 2) differences in goal-directed vs. habitual behavior.

Several hypotheses were tested, including:<br /> Patients would have impaired procedural learning vs. healthy volunteers (this was not supported, possibly because there were fewer demands on memory in the task used here)<br /> Once the task had been learned, patients would display automaticity faster (unexpectedly, patients were slower to display automaticity)<br /> Habits would form faster under a continuous (vs. variable) reinforcement schedule

Exploratory analyses were also conducted: an interesting finding was that OCD patients with higher self-reported symptoms voluntarily completed more sessions with the habit-training app and reported a reduction in symptoms.

Strengths

This paper is well situated theoretically within the habit learning/OCD literature.<br /> Daily training in a motor-learning task, delivered via smartphone, was innovative, ecologically valid and more likely to assay habitual behaviors specifically. Daily training is also more similar to studies with non-humans, making a better link with that literature. The use of a sequential-learning task (cf. tasks that require a single response) is also more ecologically valid.<br /> The in-laboratory tests (after the 1 month of training) allowed the researchers to test if the OCD group preferred familiar, but more difficult, sequences over newer, simpler sequences.

Weaknesses

The authors were not able to test one criterion of habits, namely resistance to devaluation, due to the nature of the task.<br /> The sample size was relatively small. Some potentially interesting individual differences within the OCD group could have been examined more thoroughly with a bigger sample (e.g., preference for familiar sequences). A larger sample may have allowed the statistical testing of any effects due to medication status.

The authors achieved their aims in that two groups of participants (patients with OCD and controls) engaged with the task over the course of 30 days. The repeated nature of the task meant that 'overtraining' was almost certainly established, and automaticity was demonstrated. This allowed the authors to test their hypotheses about habit learning. The results are supportive of the author's conclusions.

This article is likely to be impactful -- the delivery of a task across 30 days to a patient group is innovative and represents a new approach for the study of habit learning that is superior to an in-laboratory approach.

An interesting aspect of this manuscript is that it prompts a comparison with previous studies of goal-directed/habitual responding in OCD that used devaluation protocols, and which may have had their effects due to deficits in goal-directed behavior and not enhanced habit learning per se.

Reviewer #2 (Public Review):

I would like to express my appreciation for the authors' dedication to revising the manuscript. It is evident that they have thoughtfully addressed numerous concerns I previously raised, significantly contributing to the overall improvement of the manuscript.

My primary concern regarding the authors' framing of their findings within the realm of habitual and goal-directed action control persists. I will try explain my point of view and perhaps clarify my concerns.<br /> While acknowledging the historical tendency to equate procedural learning with habits, I believe a consensus has gradually emerged among scientists, recognizing a meaningful distinction between habits and skills or procedural learning. I think this distinction is crucial for a comprehensive understanding of human action control. While these constructs share similarities, they should not be used interchangeably. Procedural learning and motor skills can manifest either through intentional and planned actions (i.e., goal-directed) or autonomously and involuntarily (habitual responses).

Watson et al. (2022) aptly detailed my concerns in the following statements: "Defining habits as fluid and quickly deployed movement sequences overlaps with definitions of skills and procedural learning, which are seen by associative learning theorists as different behaviours and fields of research, distinct from habits."<br /> "...the risk of calling any fluid behavioural repertoire 'habit' is that clarity on what exactly is under investigation and what associative structure underpins the behaviour may be lost."<br /> I strongly encourage the authors, at the very least, to consider Watson et al.'s (2022) suggestion: "Clearer terminology as to the type of habit under investigation may be required by researchers to ensure that others can assess at a glance what exactly is under investigation (e.g., devaluation-insensitive habits vs. procedural habits)", and to refine their terminology accordingly (to make this distinction clear). I believe adopting clearer terminology in these respects would enhance the positioning of this work within the relevant knowledge landscape and facilitate future investigations in the field.

Regarding the authors' use of Balleine and Dezfouli's (2018) criteria to frame recorded behavior as habitual, as well as to acknowledgment the study's limitations, it's important to highlight that while the authors labeled the fourth criterion (which they were not fulfilling) as "resistance to devaluation," Balleine and Dezfouli define it as "insensitive to changes in their relationship to their individual consequences and the value of those consequences." In my understanding, this definition is potentially aligned with the authors' re-evaluation test, namely, it is conceptually adequate for evaluating the fourth criterion (which is the most accepted in the field and probably the one that differentiate habits from skills). Notably, during this test, participants exhibited goal-directed behavior.

The authors characterized this test as possibly assessing arbitration between goal-directed and habitual behavior, stating that participants in both groups "demonstrated the ability to arbitrate between prior automatic actions and new goal-directed ones." In my perspective, there is no justification for calling it a test of arbitration. Notably, the authors inferred that participants were habitual before the test based on some criteria, but then transitioned to goal-directed behavior based on a different criterion. While I agree with the authors' comment that: "Whether the initiation of the trained motor sequences in experiment 3 (arbitration) is underpinned by an action-outcome association (or not) has no bearing on whether those sequences were under stimulus-response control after training (experiment 1)." they implicitly assert a shift from habit to goal-directed behavior without providing evidence that relies on the same probed mechanism.<br /> Therefore, I think it would be more cautious to refer to this test as solely an outcome revaluation test. Again, the results of this test, if anything, provide evidence that the fourth criterion was tested but not met, suggesting participants have not become habitual (or at least undermines this option).

Reviewer #1 (Public Review):

Summary:<br /> Ger and colleagues address an issue that often impedes computational modeling: the inherent ambiguity between stochasticity in behavior and structural mismatch between the assumed and true model. They propose a solution to use RNNs to estimate the ceiling on explainable variation within a behavioral dataset. With this information in hand, it is possible to determine the extent to which "worse fits" result from behavioral stochasticity versus failures of the cognitive model to capture nuances in behavior (model misspecification). The authors demonstrate the efficacy of the approach in a synthetic toy problem and then use the method to show that poorer model fits to 2-step data in participants with low IQ are actually due to an increase in inherent stochasticity, rather than systemic mismatch between model and behavior.

Strengths:<br /> Overall I found the ideas conveyed in the paper interesting and the paper to be extremely clear and well-written. The method itself is clever and intuitive and I believe it could be useful in certain circumstances, particularly ones where the sources of structure in behavioral data are unknown. In general, the support for the method is clear and compelling. The flexibility of the method also means that it can be applied to different types of behavioral data - without any hypotheses about the exact behavioral features that might be present in a given task.

Weaknesses:<br /> That said, I have some concerns with the manuscript in its current form, largely related to the applicability of the proposed methods for problems of importance in computational cognitive neuroscience. This concern stems from the fact that the toy problem explored in the manuscript is somewhat simple, and the theoretical problem addressed in it could have been identified through other means (for example through the use of posterior predictive checking for model validation), and the actual behavioral data analyzed were interpreted as a null result (failure to reject that the behavioral stochasticity hypothesis), rather than actual identification of model-misspecification. I expand on these primary concerns and raise several smaller points below.

A primary question I have about this work is whether the method described would actually provide any advantage for real cognitive modeling problems beyond what is typically done to minimize the chance of model misspecification (in particular, post-predictive checking). The toy problem examined in the manuscript is pretty extreme (two of the three synthetic agents are very far from what a human would do on the task, and the models deviate from one another to a degree that detecting the difference should not be difficult for any method). The issue posed in the toy data would easily be identified by following good modeling practices, which include using posterior predictive checking over summary measures to identify model insufficiencies, which in turn would call for the need for a broader set of models (See Wilson & Collins 2019). Thus, I am left wondering whether this method could actually identify model misspecification in real world data, particularly in situations where standard posterior predictive checking would fall short. The conclusions from the main empirical data set rest largely on a null result, and the utility of a method for detecting model misspecification seems like it should depend on its ability to detect its presence, not just its absence, in real data.

Beyond the question of its advantage above and beyond data- and hypothesis-informed methods for identifying model misspecification, I am also concerned that if the method does identify a model-insufficiency, then you still would need to use these other methods in order to understand what aspect of behavior deviated from model predictions in order to design a better model. In general, it seems that the authors should be clear that this is a tool that might be helpful in some situations, but that it will need to be used in combination with other well-described modeling techniques (posterior predictive checking for model validation and guiding cognitive model extensions to capture unexplained features of the data). A general stylistic concern I have with this manuscript is that it presents and characterizes a new tool to help with cognitive computational modeling, but it does not really adhere to best modeling practices (see Collins & Wilson, eLife), which involve looking at data to identify core behavioral features and simulating data from best-fitting models to confirm that these features are reproduced. One could take away from this paper that you would be better off fitting a neural network to your behavioral data rather than carefully comparing the predictions of your cognitive model to your actual data, but I think that would be a highly misleading takeaway since summary measures of behavior would just as easily have diagnosed the model misspecification in the toy problem, and have the added advantage that they provide information about which cognitive processes are missing in such cases.

As a more minor point, it is also worth noting that this method could not distinguish behavioral stochasticity from the deterministic structure that is not repeated across training/test sets (for example, because a specific sequence is present in the training set but not the test set). This should be included in the discussion of method limitations. It was also not entirely clear to me whether the method could be applied to real behavioral data without extensive pretraining (on >500 participants) which would certainly limit its applicability for standard cases.

The authors focus on model misspecification, but in reality, all of our models are misspecified to some degree since the true process-generating behavior almost certainly deviates from our simple models (ie. as George Box is frequently quoted, "all models are wrong, but some of them are useful"). It would be useful to have some more nuanced discussion of situations in which misspecification is and is not problematic.

Reviewer #2 (Public Review):

SUMMARY:<br /> In this manuscript, Ger and colleagues propose two complementary analytical methods aimed at quantifying the model misspecification and irreducible stochasticity in human choice behavior. The first method involves fitting recurrent neural networks (RNNs) and theoretical models to human choices and interpreting the better performance of RNNs as providing evidence of the misspecifications of theoretical models. The second method involves estimating the number of training iterations for which the fitted RNN achieves the best prediction of human choice behavior in a separate, validation data set, following an approach known as "early stopping". This number is then interpreted as a proxy for the amount of explainable variability in behavior, such that fewer iterations (earlier stopping) correspond to a higher amount of irreducible stochasticity in the data. The authors validate the two methods using simulations of choice behavior in a two-stage task, where the simulated behavior is generated by different known models. Finally, the authors use their approach in a real data set of human choices in the two-stage task, concluding that low-IQ subjects exhibit greater levels of stochasticity than high-IQ subjects.

STRENGTHS:<br /> The manuscript explores an extremely important topic to scientists interested in characterizing human decision-making. While it is generally acknowledged that any computational model of behavior will be limited in its ability to describe a particular data set, one should hope to understand whether these limitations arise due to model misspecification or due to irreducible stochasticity in the data. Evidence for the former suggests that better models ought to exist; evidence for the latter suggests they might not.

To address this important topic, the authors elaborate carefully on the rationale of their proposed approach. They describe a variety of simulations - for which the ground truth models and the amount of behavioral stochasticity are known - to validate their approaches. This enables the reader to understand the benefits (and limitations) of these approaches when applied to the two-stage task, a task paradigm commonly used in the field. Through a set of convincing analyses, the authors demonstrate that their approach is capable of identifying situations where an alternative, untested computational model can outperform the set of tested models, before applying these techniques to a realistic data set.

WEAKNESSES:<br /> The most significant weakness is that the paper rests on the implicit assumption that the fitted RNNs explain as much variance as possible, an assumption that is likely incorrect and which can result in incorrect conclusions. While in low-dimensional tasks RNNs can predict behavior as well as the data-generating models, this is not *always* the case, and the paper itself illustrates (in Figure 3) several cases where the fitted RNNs fall short of the ground-truth model. In such cases, we cannot conclude that a subject exhibiting a relatively poor RNN fit necessarily has a relatively high degree of behavioral stochasticity. Instead, it is at least conceivable that this subject's behavior is generated precisely (i.e., with low noise) by an alternative model that is poorly fit by an RNN - e.g., a model with long-term sequential dependencies, which RNNs are known to have difficulties in capturing.

These situations could lead to incorrect conclusions for both of the proposed methods. First, the model misspecification analysis might show equal predictive performance for a particular theoretical model and for the RNN. While a scientist might be inclined to conclude that the theoretical model explains the maximum amount of explainable variance and therefore that no better model should exist, the scenario in the previous paragraph suggests that a superior model might nonetheless exist. Second, in the early-stopping analysis, a particular subject may achieve optimal validation performance with fewer epochs than another, leading the scientist to conclude that this subject exhibits higher behavioral noise. However, as before, this could again result from the fact that this subject's behavior is produced with little noise by a different model. Admittedly, the existence of such scenarios *in principle* does not mean that such scenarios are common, and the conclusions drawn in the paper are likely appropriate for the particular examples analyzed. However, it is much less obvious that the RNNs will provide optimal fits in other types of tasks, particularly those with more complex rules and long-term sequential dependencies, and in such scenarios, an ill-advised scientist might end up drawing incorrect conclusions from the application of the proposed approaches.

In addition to this general limitation, the paper also makes a few additional claims that are not fully supported by the provided evidence. For example, Figure 4 highlights the relationship between the optimal epochs and agent noise. Yet, it is nonetheless possible that the optimal epoch is influenced by model parameters other than inverse temperature (e.g., learning rate). This could again lead to invalid conclusions, such as concluding that low-IQ is associated with optimal epoch when an alternative account might be that low-IQ is associated with low learning rate, which in turn is associated with optimal epoch. Yet additional factors such as the deep double-descent (Nakkiran et al., ICLR 2020) can also influence the optimal epoch value as computed by the authors.

An additional issue is that Figure 4 reports an association between optimal epoch and noise, but noise is normalized by the true minimal/maximal inverse-temperature of hybrid agents (Eq. 23). It is thus possible that the relationship does not hold for more extreme values of inverse-temperature such as beta=0 (extremely noisy behavior) or beta=inf (deterministic behavior), two important special cases that should be incorporated in the current study. Finally, even taking the association in Figure 4 at face value, there are potential issues with inferring noise from the optimal epoch when their correlation is only r~=0.7. As shown in the figures, upon finding a very low optimal epoch for a particular subject, one might be compelled to infer high amounts of noise, even though several agents may exhibit a low optimal epoch despite having very little noise.

APPRAISAL AND DISCUSSION:<br /> Overall, the authors propose a novel method that aims to solve an important problem, but whose generality might be limited only to special cases. In the future, it would be beneficial to test the proposed approach in a broader setting, including simulations of different tasks, different model classes, different model parameters, and different amounts of behavioral noise. Nonetheless, even without such additional work, the proposed methods are likely to be used by cognitive scientists and neuroscientists interested in assessing the quality and limits of their behavioral models.

Reviewer #1 (Public Review):

The authors present a model for multisensory correlation detection that is based on the neurobiologically plausible Hassenstein Reichardt detector. It modifies their previously reported model (Parise & Ernst, 2016) in two ways: a bandpass (rather than lowpass) filter is initially applied and the filtered signals are then squared. The study shows that this model can account for synchrony judgment, temporal order judgment, etc in two new data sets (acquired in this study) and a range of previous data sets.

Strengths:<br /> 1. The model goes beyond descriptive models such as cumulative Gaussians for TOJ and differences in cumulative Gaussians for SJ tasks by providing a mechanism that builds on the neurobiologically plausible Hassenstein-Reichardt detector.<br /> 2. This modified model can account for results from two new experiments that focus on the detection of correlated transients and frequency doubling. The model also accounts for several behavioural results from experiments including stochastic sequences of A/V events and sinewave modulations.

Additional thoughts:<br /> 1. The model introduces two changes: bandpass filtering and squaring of the inputs. The authors emphasize that these changes allow the model to focus selectively on transient rather than sustained channels. But shouldn't the two changes be introduced separately? Transients may also be detected for signed signals.

2. Because the model is applied only to rather simple artificial signals, it remains unclear to what extent it can account for AV correlation detection for naturalistic signals. In particular, speech appears to rely on correlation detection of signed signals. Can this modified model account for SJ or TOJ judgments for naturalistic signals?

Even Nidiffer et al. (2018) which is explicitly modelled by the authors report a significant difference in performance for correlated and anti-correlated signals. This seems to disagree with the results of study 1 which is reported in the current paper and the model's predictions. How can these contradicting results be explained? In case the brain performs correlation detection on signed and unsigned signals, is a more complex mechanism needed to arbitrate between those two mechanisms?

3. The number of parameters seems quite comparable for the authors' model and descriptive models (e.g. PSF models). This is because time constants require refitting (at least for some experimental data sets) and the correlation values need to be passed through a response mode (i.e. probit function) to account for behavioural data. It remains unclear how the brain adjusts the time constants to different sensory signals.

4. Fujisaki and Nishida (2005, 2006) proposed mechanisms for AV correlation detection based on the Hassenstein-Reichardt motion detector (though not formalized as a computational model).

Reviewer #2 (Public Review):

Summary:<br /> This is an interesting and well-written manuscript that seeks to detail the performance of two human psychophysical experiments designed to look at the relative contributions of transient and sustained components of a multisensory (i.e., audiovisual) stimulus to their integration. The work is framed within the context of a model previously developed by the authors and is now somewhat revised to better incorporate the experimental findings. The major takeaway from the paper is that transient signals carry the vast majority of the information related to the integration of auditory and visual cues, and that the Multisensory Correlation Detector (MCD) model not only captures the results of the current study but is also highly effective in capturing the results of prior studies focused on temporal and causal judgments.

Strengths:<br /> Overall the experimental design is sound and the analyses are well performed. The extension of the MCD model to better capture transients makes a great deal of sense in the current context, and it is very nice to see the model applied to a variety of previous studies.

Weaknesses:<br /> My one major issue with the paper revolves around its significance. In the context of a temporal task(s), is it in any way surprising that the important information is carried by stimulus transients? Stated a bit differently, isn't all of the important information needed to solve the task embedded in the temporal dimension? I think the authors need to better address this issue to punch up the significance of their work.

In a more minor comment, I think there also needs to be a bit more effort into articulating the biological plausibility/potential instantiations of this sustained versus transient dichotomy. As written, the paper suggests that these are different "channels" in sensory systems, when in reality many neurons (and neural circuits) carry both on the same lines.

Reviewer #1 (Public Review):

Summary:<br /> This paper identifies GABA cells in the preoptic hypothalamus which are involved in REM sleep rebound (the increase in REM sleep) after selective REM sleep deprivation. By calcium photometry, these cells are most active during REM, and show more claim signals during REM deprivation, suggesting they respond to "REM pressure". Inhibiting these cells ontogenetically diminishes REM sleep. The optogenetic and photometry work is carried out to a high standard, the paper is well-written, and the findings are interesting.

Points that could be addressed or discussed:<br /> 1. The circuit mechanism for REM rebound is not defined. How do the authors see REM rebound as working from the POAGAD2 cells? Although the POAGAD2 does project to the TMN, the actual REM rebound could be mediated by a projection of these cells elsewhere. This could be discussed.

2. The "POAGAD2 to TMN" name for these cells is somewhat confusing. The authors chose this name because they approach the POAGAD2 cells via retrograde AAV labelling (rAAV injected into the TMN). However, the name also seems to imply that neurons (perhaps histamine neurons) in the TMN are involved in the REM rebound, but there is no evidence in the paper that this is the case. Although it is nice to see from the photometry studies that the histamine cells are selectively more active (as expected) in NREM sleep (Fig. S2), I could not logically see how this was a relevant finding to REM rebound or the subject of the paper. There are many other types of cells in the TMN area, not just histamine cells, so are the authors suggesting that these non-histamine cells in the TMN could be involved?

3. It is a puzzle why most of the neurons in the POA seem to have their highest activity in REM, as also found by Miracca et al 2022, yet presumably some of these cells are going to be involved in NREM sleep as well. Could the same POAGAD2-TMN cells identified by the authors also be involved in inducing NREM sleep-inhibiting histamine neurons (Chung et al). And some of these POA cells will also be involved in NREM sleep homeostasis (e.g. Ma et al Curr Biol)? Is NREM sleep rebound necessary before getting REM sleep rebound? Indeed, can these two things (NREM and REM sleep rebound) be separated?

4. Is it possible to narrow down the POA area where the GAD2 cells are located more precisely?

5. It would be ideal to further characterize these particular GAD2 cells by RT-PCR or RNA seq. Which other markers do they express?

Reviewer #2 (Public Review):

Maurer et al investigated the contribution of GAD2+ neurons in the preoptic area (POA), projecting to the tuberomammillary nucleus (TMN), to REM sleep regulation. They applied an elegant design to monitor and manipulate the activity of this specific group of neurons: a GAD2-Cre mouse, injected with retrograde AAV constructs in the TMN, thereby presumably only targeting GAD2+ cells projecting to the TMN. Using this set-up in combination with technically challenging techniques including EEG with photometry and REM sleep deprivation, the authors found that this cell-type studied becomes active shortly (≈40sec) prior to entering REM sleep and remains active during REM sleep. Moreover, optogenetic inhibition of GAD2+ cells inhibits REM sleep by a third and also impairs the rebound in REM sleep in the following hour. Despite a few reservations or details that would benefit from further clarification (outlined below), the data makes a convincing case for the role of GAD2+ neurons in the POA projecting to the TMN in REM sleep regulation.

The authors found that optogenetic inhibition of GAD2+ cells suppressed REM sleep in the hour following the inhibition (e.g. Fig2 and Fig4). If the authors have the data available, it would be important to include the subsequent hours in the rebound time (e.g. from ZT8.5 to ZT24) to test whether REM sleep rebound remains impaired, or recovers, albeit with a delay.

REM sleep is under tight circadian control (e.g. Wurts et al., 2000 in rats; Dijk, Czeisler 1995 in humans). To contextualize the results, it would be important to mention that it is not clear if the role of the manipulated neurons in REM sleep regulation hold at other circadian times of the day.

The effect size of the REM sleep deprivation using the vibrating motor method is unclear. In FigS4-D, the experimental mice reduce their REM sleep to 3% whereas the control mice spend 6% in REM sleep. In Fig4, mice are either subjected to REM sleep deprivation with the vibrating motor (controls), or REM sleep deprivations + optogenetics (experimental mice). The control mice (vibrating motor) in Fig4 spend 6% of their time in REM sleep, which is double the amount of REM sleep compared to the mice receiving the same treatment in FigS4-D. Can the authors clarify the origin of this difference in the text?

Reviewer #1 (Public Review):

Summary:<br /> The study by Liff et al significantly advances our understanding of transgenerational olfactory changes resulting from fear conditioning, particularly in revealing elevated odor-encoding neurons in both conditioned mice (F0) and their progeny (F1). The authors attribute F0 increases to biased stem cell receptor selection, building upon the seminal work of Dias and Ressler (2014). While the dedication and use of novel histological techniques add strength to the study, there are notable weaknesses, including the need for clarification on discrepancies with previous findings, the decision to modify paradigms, and the presentation of behavioral data in supplementary materials.

Overall, the manuscript has strong potential but would benefit from addressing these weaknesses and minor recommendations to enhance its quality and contribution to the field.

Strengths:<br /> - Significant contribution to understanding transgenerational olfactory changes induced by fear conditioning.<br /> - Use of novel histological techniques and exploration of stem cell involvement adds depth to the study.

Weaknesses:<br /> Discrepancies with previous findings need clarification, especially regarding the absence of similar behavioral effects in F1. Lack of discussion on the decision to modify paradigms instead of using the same model. Presentation of behavioral data in supplementary materials, with a recommendation to include behavioral quantification in main figures. Absence of quantification for freezing behavior, a crucial measure in fear conditioning.

Reviewer #2 (Public Review):

Summary:<br /> The authors examined inherited changes to the olfactory epithelium produced by odor-shock pairings. The manuscript demonstrates that odor fear-conditioning biases olfactory bulb neurogenesis toward more production of the olfactory sensory neurons engaged by the odor-shock paring. Further, the manuscript reveals that this bias remains in first-generation male and female progeny produced by trained parents. Surprisingly, there was a disconnect between the increased morphology of the olfactory epithelium for the conditioned odor and the response to odor presentation. The expectation based on previous literature and the morphological results was that F1 progeny would also show an aversion to the odor stimulus. However, the authors found that F1 progeny were not more sensitive to the odor compared to littermate controls.

Strengths:<br /> The manuscript includes conceptual innovation and some technical innovation. The results validate previous findings that were deemed controversial in the field, which is a major strength of the work. Moreover, these studies were conducted using a combination of genetically modified animals and state-of-the-art imaging techniques, highlighting the rigorous nature of the research. Lastly, the authors provide novel mechanistic details regarding the remodeling of the olfactory epithelium, demonstrating that biased neurogenesis, as opposed to changes in survival rates, account for the increase in odorant receptors after training.

Weaknesses:<br /> The main weakness is the disconnect between the morphological changes reported and the lack of change in aversion to the odorant in F1 progeny. The authors also do not address the mechanisms underlying the inheritance of the phenotype, which may lie outside of the scope of the present study.

Reviewer #3 (Public Review):

In their paper entitled "Fear conditioning biases olfactory stem cell receptor fate" Liff et al. address the still enigmatic (and quite fascinating) phenomenon of intergenerationally inherited changes in the olfactory system in response to odor-dependent fear conditioning.

In the abstract / summary, the authors raise expectations that are not supported by the data. For example, it is claimed that "increases in F0 were due to biased stem cell receptor choice." While an active field of study that has seen remarkable progress in the past decade, olfactory receptor gene choice and its relevant timing in particular is still unresolved. Here, Liff et al., do not pinpoint at what stage during differentiation the "biased choice" is made.

Similarly, the concluding statement that the study provides "insight into the heritability of acquired phenotypes" is somewhat misleading. The experiments do not address the mechanisms underlying heritability.

The statement that "the percentage of newborn M71 cells is 4-5 times that of MOR23 may simply reflect differences in the birth rates of the two cell populations" should, if true, result in similar differences in the occurrence of mature OSNs with either receptor identity. According to Fig. 1H & J, however, this is not the case.

An important result is that Liff et al., in contrast to results from other studies, "do not observe the inheritance of odor-evoked aversion to the conditioned odor in the F1 generation." This discrepancy needs to be discussed.

The authors speculate that "the increase in neurons responsive to the conditioned odor could enhance the sensitivity to, or the discrimination of, the paired odor in F0 and F1. This would enable the F1 population to learn that odor predicts shock with fewer training cycles or less odorant when trained with the conditioned odor." This is a fascinating idea that, in fact, could have been readily tested by Liff and coworkers. If this hypothesis were found true, this would substantially enhance the impact of the study for the field.

Reviewer #1 (Public Review):

Summary:<br /> The authors examined whether archerfish have the capacity for motor adaptation in response to airflow perturbations. Through two experiments, they demonstrated that archerfish could adapt. Moreover, when the fish flipped its body position with the perturbation remaining constant, it did not instantaneously counteract the error. Instead, the archerfish initially persisted in correcting for the original perturbation before eventually adapting, consistent with the notion that the archerfish's internal model has been adapted in egocentric coordinates.

Evaluation:<br /> The results of both experiments were convincing, given the observable learning curve and the clear aftereffect. The ability of these fish to correct their errors is also remarkable. Nonetheless, certain aspects of the experiment's motivation and conclusions temper my enthusiasm.

1. The authors motivated their experiments with two hypotheses, asking whether archerfish can adapt to light refractions using an innate look-up table as opposed to possessing a capacity to adapt. However, the present experiments are not designed to arbitrate between these ideas. That is, the current experiments do not rule out the look-up table hypothesis, which predicts, for example, that motor adaptation may not generalize to de novo situations with arbitrary action-outcome associations. Such look-up table operations may also show set-size effects, whereas other mechanisms might not. Whether their capacity to adapt is innate or learned was also not directly tested, as noted by the authors in the discussion. Could the authors clarify how they see their results positioned in light of the two hypotheses noted in the Introduction?

2. The authors claim that archerfish use egocentric coordinates rather than allocentric coordinates. However, the current experiments do not make clear whether the archerfish are "aware" that their position was flipped (as the authors noted, no visual cues were provided). As such, for example, if the fish were "unaware" of the switch, can the authors still assert that generalization occurs in egocentric coordinates? Or simply that, when archerfish are ostensibly unaware of changes in body position, they continue with previously successful actions.

3. The experiments offer an opportunity to examine whether archerfish demonstrate any savings from one session to another. Savings are often attributed to a faster look-up table operation. As such, if archerfish do not exhibit savings, it might indicate a scenario where they do not possess a refined look-up table and must rely on implicit mechanisms to relearn each time.

4. The authors suggest that motor adaptation in response to wind may hint at mechanisms used to adapt to light refraction. However, how strong of a parallel can one draw between adapting to wind versus adapting to light refraction? This seems important given the claims in this paper regarding shared mechanisms between these processes. As a thought experiment, what would the authors predict if they provided a perturbation more akin to light refraction (e.g., a film that distorts light in a new direction, rather than airflow)?

5. The number of fish excluded was greater than those included. This raises the question as to whether these fish are merely elite specimens or representative of the species in general.

Reviewer #2 (Public Review):

Summary:<br /> The work of Volotsky et al presented here shows that adult archerfish are able to adjust their shooting in response to their own visual feedback, taking consistent alterations of their shot, here by an air flow, into account. The evidence provided points to an internal mechanism of shooting adaptation that is independent of external cues, such as wind. The authors provide evidence for this by forcing the fish to shoot from 2 different orientations to the external alteration of their shots (the airflow). This paper thus provides behavioral evidence of an internal correction mechanism, that underlies adaptive motor control of this behavior. It does not provide direct evidence of refractory index-associated shoot adjustance.

Strengths:<br /> The authors have used a high number of trials and strong statistical analysis to analyze their behavioral data.

Weaknesses:<br /> While the introduction, the title, and the discussion are associated with the refraction index, the latter was not altered, and neither was the position of the target. The "shot" was altered, this is a simple motor adaptation task and not a question related to the refractory index. The title, abstract, and the introduction are thus misleading. The authors appear to deduce from their data that the wind is not taken into account and thus conclude that the fish perceive a different refractory index. This might be based on the assumption that fish always hit their target, which is not the case. The airflow does not alter the position of the target, thus the airflow does not alter the refractive index. The fish likely does not perceive the airflow, thus alteration of its shooting abilities is likely assumed to be an "internal problem" of shooting. I am sorry but I am not able to understand the conclusion they draw from their data.

Reviewer #1 (Public Review):

Summary:<br /> Huang and Luo investigated whether regularities between stimulus features can be exploited to facilitate the encoding of each set of stimuli in visual working memory, improving performance. They recorded both behavioural and neural (EEG) data from human participants during a sequential delayed response task involving three items with two properties: location and colour. In the key condition ('aligned trajectory'), the distance between locations of successively presented stimuli was identical to their 'distance' in colour space, permitting a compression strategy of encoding only the location and colour of the first stimulus and the relative distance of the second and third stimulus (as opposed to remembering 3 locations and 3 colours, this would only require remembering 1 location, 1 colour, and 2 distances). Participants recalled the location and colour of each item after a delay.

Consistent with the compression account, participants' location and colour recall errors were correlated and were overall lower compared to a non-compressible condition ('misaligned trajectory'). Multivariate analysis of the neural data permitted decoding of the locations and colours during encoding. Crucially, the relative distance could also be decoded - a necessary ingredient for the compression strategy.

Strengths:<br /> The main strength of this study is a novel experimental design that elegantly demonstrates how we exploit stimulus structure to overcome working memory capacity limits. The behavioural results are robust and support the main hypothesis of compressed encoding across a number of analyses. The simple and well-controlled design is suited to neuroimaging studies and paves the way for investigating the neural basis of how environmental structure is detected and represented in memory. Prior studies on this topic have primarily studied behaviour only (e.g., Brady & Tenenbaum, 2013).

Weaknesses:<br /> The main weakness of the study is that the EEG results do not make a clear case for compression or demonstrate its neural basis. If the main aim of this strategy is to improve memory maintenance, it seems that it should be employed during the encoding phase. From then on, the neural representation in memory should be in the compressed format. The only positive evidence for this occurs in the late encoding phase (the re-activation of decoding of the distance between items 1 and 2, Fig. 5A), but the link to behaviour seems fairly weak (p=0.068). Stronger evidence would be showing decoding of the compressed code during memory maintenance or recall, but this is not presented. On the contrary, during location recall (after the majority of memory maintenance is already over), colour decoding re-emerges, but in the un-compressed item-by-item code (Fig. 4B). The authors suggest that compression is consolidated at this point, but its utility at this late stage is not obvious.

Impact:<br /> This important study elegantly demonstrates that the use of shared structure can improve capacity-limited visual working memory. The paradigm and approach explicitly link this field to recent findings on the role of replay in structure learning and will therefore be of interest to neuroscientists studying both topics.

Reviewer #2 (Public Review):

Summary:<br /> In this study, the authors wanted to test if using a shared relational structure by a sequence of colors in locations can be leveraged to reorganize and compress information.

Strength:<br /> They applied machine learning to EEG data to decode the neural mechanism of reinstatement of visual stimuli at recall. They were able to show that when the location of colors is congruent with the semantically expected location (for example, green is closer to blue-green than purple) the related color information is reinstated at the probed location. This reinstatement was not present when the location and color were not semantically congruent (meaning that x displacement in color ring location did not displace colors in the color space to the same extent) and semantic knowledge of color relationship could not be used for reducing the working memory load or to benefit encoding and retrieval in short term memory.

Weakness:<br /> The experiment and results did not address any reorganization of information or neural mechanism of working memory (that would be during the gap between encoding and retrieval). There was also a lack of evidence to rule out that the current observation can be addressed by schematic abstraction instead of the utilization of a cognitive map.<br /> The likely impact of the initial submission of the study would be in the utility of the methods that would be helpful for studying a sequence of stimuli at recall. The paper was discussed in a narrow and focused context, referring to limited studies on cognitive maps and replay. The bigger picture and long history of studying encoding and retrieval of schema-congruent and schema-incongruent events is not discussed.

Reviewer #1 (Public Review):

Padamsey et al. followed up on their previous study in which they found that male mice sacrifice visual cortex computation precision to save energy in periods of food restriction (Padamsey et al. 2021, Neuron). In the present study, the authors find that female mice show much lower levels of adaptation in response to food restriction on the level of metabolic signaling and visual cortex computation. This is an important finding for understanding sex differences in adaptation to food scarcity and also impacts the interpretation of studies employing food restriction in behavioral analyses and learning paradigms.

The manuscript is, in general, very clear and the conclusions are straightforward. The main limitation, that the number of experiments is insufficient to compare the effects of food restriction in males and females directly, is discussed by the authors: to address this point they use Bayes factor analysis to provide an estimate of the likelihood that females and males indeed differ in terms of energy metabolism and sensory processing adaptions during food restriction.

The following points are not entirely clear yet.<br /> 1. For a number of experiments the authors use their new data set on females and compare that with the data set previously published on males. In how far are these data sets comparable? Have they been performed originally in parallel for example using siblings of different sexes or have the experiments been conducted several years apart from each other? What is the expected variability, if one repeated these experiments with the same sex considering the differences/similarities between experimental setups, housing conditions, interindividual differences, etc.?

2. Energy consumption and visual processing may differ between periods in which animals are in different behavioral states. Is there a possibility that male and female mice differed in behavioral state during measurements? Were animals running or resting during visual stimulation and during ATP measurements?

3. Related to the previous point: the authors show that ATP consumption was reduced in male mice during visual stimulation. What about visual cortex ATP consumption in the absence of visual stimulation? Do food-deprived males and/or females show lower ATP consumption in the visual cortex e.g. during sleep?

Reviewer #2 (Public Review):

Summary:<br /> Padamsey et al build up on previous significant work from the same group which demonstrated robust changes in the visual cortex in male mice from long-term (2-3 weeks) food restriction. Here, the authors extend this finding and reveal striking sex-specific differences in the way the brain responds to food restriction. The measures included the whole-body measure of serum leptin levels, and V1-specific measures of activity of key molecular players (AMPK and PPARα), gene expression patterns, ATP usage in V1, and the sharpness of visual stimulus encoding (orientation tuning). All measures supported the conclusion that the female mouse brain (unlike in males) does not change its energy usage and cortical functional properties on comparable food restriction.

While the effect of food restriction on more peripheral tissue such as muscle and bones has been well studied, this result contributes to our understanding of how the brain responds to food restriction. This result is particularly significant given that the brain consumes a large fraction of the body's energy consumption (20%), with the cortex accounting for half of that amount. The sex-specific differences found here are also relevant for studies using food restriction to investigate cortical function.

Strengths:<br /> The study uses a wide range of approaches mentioned above which converge on the same conclusion, strengthening the core claim of the study.

Weaknesses:<br /> Since the absence of a significant effect does not prove the absence of any changes, the study cannot claim that the female mouse brain does not change in response to food restriction. However, the authors do not make this claim. Instead, they make the well-supported claim that there is a sex-specific difference in the response of V1 to food restriction.

Reviewer #3 (Public Review):

Summary:<br /> The authors food-deprived male and female mice and observed a much stronger reduction of leptin levels, energy consumption in the visual cortex, and visual coding performance in males than females. This indicates a sex-specific strategy for the regulation of the energy budget in the face of low food availability.

Strengths:<br /> This study extends a previous study demonstrating the effect of food deprivation on visual processing in males, by providing a set of clear experimental results, demonstrating the sex-specific difference. It also provides hypotheses about the strategy used by females to reduce energy budget based on the literature.

Weaknesses:<br /> The authors do not provide evidence that females are not impacted by visually guided behaviors contrary to what was shown in males in the previous study.

Reviewer #1 (Public Review):

Summary:<br /> Zhang et al. provide valuable data for understanding molecular features of the human spinal cord. The authors made considerable efforts to acknowledge and objectively address the limitations of Visium while attempting to overcome them by utilizing single-nucleus RNA sequencing (snRNA-seq) from the same tissue. By mapping snRNA-seq clusters to Visium data, they offer spatial information, complemented by RNA-ISH and immunofluorescence (IF) validation. They also discuss gender-related differences and the similarities between human and mouse data, aiming to establish a crucial foundation for experimental research. However, I have some comments below.

1. The observation of gender-related differences is interesting. The authors reported that SCN10A, associated with nociceptos, exhibited stronger expression in females. While they intend to validate this finding through IF, the quantitative difference is not clearly observed in the IF data (Figure 5f). It would be essential to provide validation through DAPI-based cell counts, demonstrating the difference in CHAT/SCNA10A co-expression.

2. It is meritorious that in novel features of the transcriptomic study, the authors considered gender-related differences and similarities between humans and mice. Nevertheless, despite the extensive bioinformatics-based analyses performed, the results mostly confirm what has been previously reported (Nguyen et al. 2021; Yadav et al. 2023; Jung et al. 2023).

3. The study did not perform snRNA-seq in the DRG. The limitations of Visium in cell type separation are acknowledged, and the authors are aware that Visium alone has limitations in describing cell expression patterns. The authors need to validate their findings via analyses of public DRG snRNA-seq data (Jung et al. 2023 Ncom; Nguyen et al. 2021eLife) before drawing broad conclusions.

4. Figure 7's comparison between human Visium spot data and Renthal et al.'s mouse snRNA-seq may have limitations as Visium spot data could not provide a transcriptional profile at the single cell resolution. The authors need to clarify this point.

5. Recent findings indicate that type 2 cytokines can directly stimulate sensory neurons. This includes the expression of IL-4RA, IL31RA, and IL13RA in DRG. These findings support the role of JAK kinase inhibitors in mediating chronic itch. Demonstrating the expression of these itch receptors in DRG would be valuable.

6. Given that juxtacrine and paracrine signals operate from 0 to 200 um, spatial information is vital to understanding intercellular communication. The presentation of spatial information using Visium is meaningful, and more comprehensive analyses of potential interaction based on distance should be provided, beyond the top 10 interactions (Figure 8).

7. The gender-related differences are interesting and, if possible, it would be interesting to explore whether age-related differences or degeneration-related factors exist. Using public data could allow the examination of age-related changes.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, the authors generated a comprehensive dataset of human spinal cord transcriptome using single-cell RNA sequencing and the Visium spatial transcriptomics platform. They employed Visium data to determine the spatial orientation of each cell type. Using single-cell RNA sequencing data, they identified differentially expressed genes by comparing human and mouse samples, as well as male and female samples.

Strengths:<br /> This study offers a thorough exploration of both cellular and spatial heterogeneity within the human spinal cord. The resulting atlas datasets and analysis findings represent valuable resources for the neuroscience community.

Weaknesses:<br /> The analysis of spatial transcriptomics data was conducted as it is single-cell RNAseq data. However, there are established tools for effectively integrating these two types of data. The incorporation of deconvolution methods could enhance the characterization of each spot's cell type composition.

Reviewer #3 (Public Review):

Summary:<br /> Zhang et al sought to use spatial transcriptomics and single-nucleus RNA sequencing to classify human spinal cord neurons. The authors reported 17 clusters on 10x Visium slides (6 donors) and 21 clusters by single-nucleus sequencing (9 donors). The authors tried to compare the results to those reported in mice and claimed similar patterns with some differing genes.

Strengths:<br /> The manuscript provides a valuable database for the molecular and cellular organization of adult human spinal cords in addition to published datasets (Andersen, et al. 2023; Yadav, et al. 2023).

Weaknesses:<br /> The results are largely observatory and lack quantitative analysis. Moreover, the assertions regarding the sex differences in motor neurons and the potential interactions between DRG and spinal cord neuronal subclusters appear preliminary and necessitate more rigorous validation.

Reviewer #1 (Public Review):

Summary and strengths<br /> The authors tried to address why only a subset of genes are highlighted in many publications. Is it because these highlighted genes are more important than others? Or is it because there are non-genetic reasons? This is a critical question because in the effort to discover new genes for drug targets and clinical benefit, we need to expand a pool of genes for deep analyses. So I appreciate the authors' efforts in this study, as it is timely and important. They also provided a framework called FMUG (short for Find My Understudied Gene) to evaluate genes for a number of features for subsequent analyses.

Weaknesses<br /> Many of the figures are hard to comprehend, and the figure legends do not sufficiently explain them.<br /> # For example, what was plotted in Fig 1b? The number of articles increased from results -> write-ups -> follow-ups in all four categories with different degrees. But it does not seem to match what the authors meant to deliver.<br /> # Fig 4 is also confusing. It appears that the genes were clustered by many features that the authors developed. But does it have any relationship with genes being under- or over-studied?

Reviewer #2 (Public Review)

Summary and strengths<br /> In this manuscript the authors analyse the trajectory of understudied genes (UGs) from experiment to publication and study the reasons for why UGs remain underrepresented in the scientific literature. They show that UGs are not underrepresented in experimental datasets, but in the titles and abstracts of the manuscripts reporting experimental data as well as subsequent studies referring to those large-scale studies. They also develop an app that allows researchers to find UGs and their annotation state. Overall, this is a timely article that makes an important contribution to the field. It could help to boost the future investigation of understudied genes, a fundamental challenge in the life sciences. It is concise and overall well-written, and I very much enjoyed reading it. However, there are a few points that I think the authors should address.

Weaknesses<br /> The authors conclude that many UGs "are lost" from genome-wide assay at the manuscript writing stage. If I understand correctly, this is based on gene names not being reported in the title or abstract of these manuscripts. However, for genome-wide experiments, it would be quite difficult for authors to mention large numbers of understudied genes in the abstract. In contrast, one might highlight the expected behaviour of a well-studied protein simply to highlight that the genome-wide study provides credible results. Could this bias the authors' conclusions and, if so, how could this be addressed? For example, would it be worth to normalise studies based on the total number of genes they cover?

Figure 1B is confusing in its present form. I think the plot and/or the legend need revising. For example, what "numbers to the right of each box plot" are the authors referring to? Also, I assume that the filled boxes are understudied genes and the empty/white box is "all genes", but that's not explained in the legend. In the main text, the figure is referred to with the sentence "we found that hit genes that are highlighted in the title or abstract are strongly over-represented among the 20% highest-studied genes in all biomedical literature ". I cannot follow how the figure shows this. My interpretation is that the y-axis is not showing the number of articles, but represents the percentage of articles mentioning a gene in the title/abstract, displayed on a log scale. If so, perhaps a better axis labels and legend text could be sufficient. But then one would also need to somehow connect this to the statement in the main text about the 20% highest-studied genes (a dashed line?). Alternatively, the authors could consider other ways of plotting these data, e.g. simply plotting the "% of publication in which a gene appears" from 0-100% or so.

Reviewer #3 (Public Review):

Summary and strengths<br /> The manuscript investigated the factors related to understudied genes in biomedical research. It showed that understudied are largely abandoned at the writing stage and identified biological and experimental factors associated with selection of highlighted genes.

It is very important for the research community to recognize the systematic bias in research of human genes and take precautions when designing experiments and interpreting results. The authors have tried to profile this issue comprehensively and promoted more awareness and investigation of understudied genes.

Weaknesses<br /> Regarding result section 1 "Understudied genes are abandoned at synthesis/writing stage", the figures are not clear and do not convey the messages written in the main text. For example, in Figure 1B, figure S5 and S6,<br /> - There is no "numbers to the right of each box plot".<br /> - Do these box plots only show understudied genes? How many genes are there in each box plot? The definition and numbers of understudied genes are not clear.<br /> - "We found that hit genes that are highlighted in the title or abstract are strongly over-represented among the 20% highest-studied genes in all biomedical literature (Figure 1B)". This is not clear from the figure.

Regarding result section 2 "Subsequent reception by other scientists does not penalize studies on understudied genes", the authors showed in figure 2 that there is a negative correlation between articles per gene before 2015 and median citations to articles published in 2015. Another explanation could be that for popular genes, there are more low-quality articles that didn't get citations, not necessarily that less popular genes attract more citations.

Regarding result section 3 "Identification of biological and experimental factors associated with selection of highlighted genes", in Figure 3 and table s2, the author stated that "hits with a compound known to affect gene activity are 5.114 times as likely to be mentioned in the title/abstract in an article using transcriptomics", The number 5.144 comes out of nowhere both in the figure and the table. In addition, figure 4 is not informative enough to be included as a main figure.

Reviewer #1 (Public Review):

Summary:<br /> The type I ABC importer OpuA from Lactococcus lactis is the best-studied transporter involved in osmoprotection. In contrast to most ABC import systems, the substrate binding protein is fused via a short linker to the transmembrane domain of the transporter. Consequently, this moiety is called the substrate binding domain (SBD). OpuA has been studied in the past in great detail and we have a very detailed knowledge about function, mechanisms of activation and deactivation as well as structure.

Strengths:<br /> Application of smFRET to unravel transient interactions of the SBDs. The method is applied at a superb quality and the data evaluation is excellent.

Weaknesses:<br /> The proposed model is not directly supported by experimental data. Rather all alternative models are excluded as they do not fit the obtained data.

Reviewer #2 (Public Review):

Summary:<br /> In this report, the authors used solution-based single-molecule FRET and low-resolution cryo-EM to investigate the interactions between the substrate-binding domains of the ABC-importer OpuA from Lactococcus lactis. Based on their results, the authors suggest that the SBDs interact in an ionic strength-dependent manner.

Strengths:<br /> The strength of this manuscript is the uniqueness and importance of the scientific question, the adequacy of the experimental system (OpuA), and the combination of two very powerful and demanding experimental approaches.

Weaknesses:<br /> A demonstration that the SBDs physically interact with one another and that this interaction is important for the transport mechanism will greatly strengthen the claims of the authors. The relation to cooperativity is also unclear.

Reviewer #1 (Public Review):

This is an interesting manuscript that extends prior work from this group identifying that a chemovar of Cannabis induces apoptosis of T-ALL cells by preventing NOTCH1 cleavage. Here the authors isolate specific components of the chemovar responsible for this effect to CBD and CBDV. They identify the mechanism of action of these agents as occurring via the integrated stress response. Overall the work is well performed but there are two lingering questions that would be helpful to address as follows:

-Exactly how CBD and CBDV result in the upregulation of the TRPV1/integrated stress response is unclear. What is the most proximal target of these agents that results in these changes?

-Related to the above, all experiments to confirm the mechanism of action of CBD/CBDV rely on chemical agents, whose precise targets are not fully clear in some cases. Thus, some use of genetic means (such as by knockout of TRPV1, ATF4) to confirm the dependency of these pathways on drug response and NOTCH cleavage would be very helpful.

Reviewer #2 (Public Review):

Summary:<br /> The Meiri group previously showed that Notch1-activated human T-ALL cell lines are sensitive to a cannabis extract in vitro and in vivo (Ref. 32). In that article, the authors showed that Extract #12 reduced NICD expression and viability, which was partially rescued by restoring NICD expression. Here, the authors have identified three compounds of Extract #12 (CBD, 331-18A, and CBDV) that are responsible for the majority of anti-leukemic activity and NICD reduction. Using a pharmacological approach, the authors determined that Extract #12 exerted its anti-leukemic and NICD-reducing effects through the CB2 and TRPV1 receptors. To determine the mechanism, the authors performed RNA-seq and observed that Extract #12 induces ER calcium depletion and stress-associated signals -- ATF4, CHOP, and CHAC1. Since CHAC1 was previously shown to be a Notch inhibitor in neural cells, the authors assume that the cannabis compounds repress Notch S1 cleavage through CHAC1 induction. The induction of stress-associated signals, Notch repression, and anti-leukemic effects were reversed by the integrated stress response (ISR) inhibitor ISRIB. Interestingly, combining the 3 cannabinoids gave synergistic anti-leukemic effects in vitro and had growth-inhibitory effects in vivo.

Strengths:<br /> 1. The authors show novel mechanistic insights that cannabinoids induce ER calcium release and that the subsequent integrated stress response represses activated NOTCH1 expression and kills T-ALL cells.

2. This report adds to the evidence that phytocannabinoids can show a so-called "entourage effect" in which minor cannabinoids enhance the effect of the major cannabinoid CBD.

3. This report dissects the main cannabinoids in the previously described Extract #12 that contribute to T-ALL killing.

4. The manuscript is clear and generally well-written.

5. The data are generally high quality and with adequate statistical analyses.

6. The data generally support the authors' conclusions. The exception is the experiments related to Notch.

7. The authors' discovery of the role of the integrated stress response might explain previous observations that SERCA inhibitors block Notch S1 cleavage and activation in T-ALL (Roti Cancer Cell 2013). The previous explanation by Roti et al was that calcium depletion causes Notch misfolding, which leads to impaired trafficking and cleavage. Perhaps this explanation is not entirely sufficient.

Weaknesses:<br /> 1. Given the authors' previous Cancer Communications paper on the anti-leukemic effects and mechanism of Extract #12, the significance of the current manuscript is reduced.

2. It would be important to connect the authors' findings and a wealth of literature on the role of ER calcium/stress on Notch cleavage, folding, trafficking, and activation.

3. There is an overreliance on the data on a single cell line -- MOLT4. MOLT4 is a good initial choice as it is Notch-mutated, Notch-dependent, and representative of the most common T-ALL subtype -- TAL1. However, there is no confirmatory data in other TAL1-positive T-ALLs or interrogation of other T-ALL subtypes.

4. Fig. 6H. The effects of the cannabinoid combination might be statistically significant but seem biologically weak.

5. Fig. 3. Based on these data, the authors conclude that the cannabinoid combination induces CHAC1, which represses Notch S1 cleavage in T-ALL cells. The concern is that Notch signaling is highly context-dependent. CHAC1 might inhibit Notch in neural cells (Refs. 34-35), but it might not do this in a different context like T-ALL. It would be important to show evidence that CHAC1 represses S1 cleavage in the T-ALL context. More importantly, Fig. 3H clearly shows the cannabinoid combination inducing ATF4 and CHOP protein expression, but the effects on CHAC1 protein do not seem to be satisfactory as a mechanism for Notch inhibition. Perhaps something else is blocking Notch expression?

6. Fig. 4B-C/S5D-E. These Western blots of NICD expression are consistent with the cannabinoid combination blocking Furin-mediated NOTCH1 cleavage, which is reversed by ISR inhibition. However, there are many mechanisms that regulate NICD expression. To support their conclusion that the effects are specifically Furin-medated, the authors should probe full-length (uncleaved) NOTCH1 in their Western blots.

7. Fig. S4A-B. While these pharmacologic data are suggestive that Extract #12 reduces NICD expression through the CB2 receptor and TRPV1 channel, the doses used are very high (50uM). To exclude off-target effects, these data should be paired with genetic data to support the authors' conclusions.

Joint Public Review:

Summary:

The manuscript of Heydasch et al. addresses the spatiotemporal regulation of Rho GTPase signaling in living cells and its coupling to the mechanical state of the cell. They focus on a GAP of RhoA, the Rho-specific GAP Deleted in Liver Cancer 1 (DLC1). They first show that removing DLC1 either by a CRISPR KO or by downregulation using siRNA leads to increased contractility and globally elevated RhoA activity, as revealed by a FRET biosensor. This result was expected, since DLC1 is deactivating RhoA its absence should lead to increasing amounts of active RhoA. To go beyond global and steady levels of RhoA activity, the authors developed an acute optogenetic system to study transient RhoA activity dynamics in different genetic and subcellular contexts. In WT cells, they found that pulses of activation lead to an increased RhoA activity at focal adhesions (FA) compared to plasma membrane (PM), which suggests that FAs contain less RhoA GAPs, more RhoA, or that FAs involve positive feedback implying other GEFs for example. In DLC1 KO cells, they found that the RhoA response upon pulses of optogenetic activation was increased (higher peak) both at FA and PM, which could be expected since less GAP should increase the amount of active RhoA. But surprisingly, they observed a higher rate of RhoA deactivation in DLC1 KO cells, which is counterintuitive: less GAP should result in a slower rate of deactivation. Less GAP should also lead to a lower rate of observed RhoA activation (smaller koff) and delayed peak. From the data, it seems hard to conclude on these two expectations since the initial rates (slopes right after the activation) and times at peak appear similar in both WT and DLC1 KO cells. Further on, the authors study the dynamics of DLC1 on FAs depending on the mechanical state and nicely show a causal decrease of DLC1 enrichment at FA upon FA reinforcement, hereby probing a positive feedback where RhoA activation is further amplified as the force exerted at FA is increasing.

Strengths:

- Experiments are precise and well done.<br /> - Technically, the work brings original and interesting data. The use of transient optogenetic activation within focal adhesions together with a biosensor of activity is new and elegant.<br /> - The link between DLC1 and global contractility/RhoA activity is clear and convincing.<br /> - The surprisingly higher rate of RhoA deactivation in DLC1 KO cells is convincing, as well as the differences in the dynamics of RhoA between focal adhesions and plasma membrane.<br /> - The correlation between DLC1 enrichment and focal adhesion dynamics is very clear.

Weaknesses:

- There is no explanation for the higher rate of RhoA deactivation in DLC1 KO cells.<br /> - For the optogenetic experiments, it is not clear if we are looking at the actual RhoA dynamics of the activity or at the dynamics of the optogenetic tool itself.<br /> - There is no model to analyze transient RhoA responses, however, the quantitative nature of the data calls for it. Even a simple model with linear activation-deactivation kinetics fitted on the data would be of benefit for the conclusions on the observed rates and absolute amounts.

Reviewer #1 (Public Review):

Summary:<br /> This study uses whole genome sequencing to characterise the population structure and genetic diversity of a collection of 58 isolates of E. coli associated with neonatal meningitis (NMEC) from seven countries, including 52 isolates that the authors sequenced themselves and a further 6 publicly available genome sequences. Additionally, the study used sequencing to investigate three case studies of apparent relapse. The data show that in all three cases, the relapse was caused by the same NMEC strain as the initial infection. In two cases they also found evidence for gut persistence of the NMEC strain, which may act as a reservoir for persistence and reinfection in neonates. This finding is of clinical importance as it suggests that decolonisation of the gut could be helpful in preventing relapse of meningitis in NMEC patients.

Strengths:<br /> The study presents complete genome sequences for n=18 diverse isolates, which will serve as useful references for future studies of NMEC. The genomic analyses are high quality, the population genomic analyses are comprehensive and the case study investigations are convincing.

Weaknesses:<br /> The NMEC collection described in the study includes isolates from just seven countries. The majority (n=51/58, 88%) are from high-income countries in Europe, Australia, or North America; the rest are from Cambodia (n=7, 12%). Therefore it is not clear how well the results reflect the global diversity of NMEC, nor the populations of NMEC affecting the most populous regions.

The virulence factors section highlights several potentially interesting genes that are present at apparently high frequency in the NMEC genomes; however, without knowing their frequency in the broader E. coli population it is hard to know the significance of this.

Reviewer #2 (Public Review):

Summary:<br /> In this work, the authors present a robust genomic dataset profiling 58 isolates of neonatal meningitis-causing E. coli (NMEC), the largest such cohort to be profiled to date. The authors provide genomic information on virulence and antibiotic resistance genomic markers, as well as serotype and capsule information. They go on to probe three cases in which infants presented with recurrent febrile infection and meningitis and provide evidence indicating that the original isolate is likely causing the second infection and that an asymptomatic reservoir exists in the gut. Accompanying these results, the authors demonstrate that gut dysbiosis coincides with the meningitis.

Strengths:<br /> The genomics work is meticulously done, utilizing long-read sequencing.<br /> The cohort of isolates is the largest to be sampled to date.<br /> The findings are significant, illuminating the presence of a gut reservoir in infants with repeating infection.

Weaknesses:<br /> Although the cohort of isolates is large, there is no global representation, entirely omitting Africa and the Americas. This is acknowledged by the group in the discussion, however, it would make the study much more compelling if there was global representation.

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, Schembri et al performed a molecular analysis by WGS of 52 E. coli strains identified as "causing neonatal meningitis" from several countries and isolated from 1974 to 2020. Sequence types, virulence genes content as well as antibiotic-resistant genes are depicted. In the second part, they also described three cases of relapse and analysed their respective strains as well as the microbiome of three neonates during their relapse. For one patient the same E. coli strain was found in blood and stool (this patient had no meningitis). For two patients microbiome analysis revealed a severe dysbiosis.

Major comments:<br /> Although the authors announce in their title that they study E. coli that cause neonatal meningitis and in methods stipulate that they had a collection of 52 NMEC, we found in Supplementary Table 1, 29 strains (threrefore most of the strains) isolated from blood and not CSF. This is a major limitation since only strains isolated from CSF can be designated with certainty as NMEC even if a pleiocytose is observed in the CSF. A very troubling data is the description of patient two with a relapse infection. As stated in the text line 225, CSF microscopy was normal and culture was negative for this patient! Therefore it is clear that patient without meningitis has been included in this study.

Another major limitation (not stated in the discussion) is the absence of clinical information on neonates especially the weeks of gestation. It is well known that the risk of infection is dramatically increased in preterm neonates due to their immature immunity. Therefore E. coli causing infection in preterm neonates are not comparable to those causing infection in term neonates notably in their virulence gene content. Indeed, it is mentioned that at least eight strains did not possess a capsule, we can speculate that neonates were preterm, but this information is lacking. The ages of neonates are also lacking. The possible source of infection is not mentioned, notably urinary tract infection. This may have also an impact on the content of VF.

Sequence analysis reveals the predominance of ST95 and ST1193 in this collection. The high incidence of ST95 is not surprising and well previously described, therefore, the concluding sentence line 132 indicating that ST95 E. coli should exhibit specific virulence features associated with their capacity to cause NM does not add anything. On the contrary, the high incidence of ST1193 is of interest and should have been discussed more in detail. Which specific virulence factors do they harbor? Any hypothesis explaining their emergence in neonates? In the paragraph depicted the VF it is only stated that ST95 contained significantly more VF than the ST1193 strains. And so what? By the way "significantly" is not documented: n=?, p=?<br /> The complete sequence of 18 strains is not clear. Results of Supplementary Table 2 are presented in the text and are not discussed.

46 years is a very long time for such a small number of strains, making it difficult to put forward epidemiological or evolutionary theories. In the analysis of antibiotic resistance, there are no ESBLs. However, Ding's article (reference 34) and other authors showed that ESBLs are emerging in E. coli neonatal infection. These strains are a major threat that should be studied, unfortunately, the authors haven't had the opportunity to characterize such strains in their manuscript.

Second part of the manuscript:<br /> The three patients who relapsed had a late neonatal infection (> 3 days) with respective ages of 6 days, 7 weeks, and 3 weeks. We do not know whether they are former preterm newborns (no term specified) or whether they have received antibiotics in the meantime.

Patient 1: Although this patient had a pleiocytose in CSF, the culture was negative which is surprising and no explanation is provided. Therefore, the diagnosis of meningitis is not certain. Pleiocytose without meningitis has been previously described in neonates with severe sepsis.

Line 215: no immunological abnormalities were identified (no details are given).

Patient 2: This patient had a recurrence of bacteremia without meningitis (line 225: CSF microscopy was normal and culture negative!). This case should be deleted.

Patient 3: This patient had two relapses which is exceptional and may suggest the existence of a congenital malformation or a neurological complication such as abscess or empyema therefore, "imaging studies" should be detailed.

The authors suggest a link between intestinal dysbiosis and relapse in three patients. However, the fecal microbiomes of patients without relapse were not analysed, so no comparison is possible. Moreover, dysbiosis after several weeks of antibiotic treatment in a patient hospitalized for a long time is not unexpected. Therefore, it's impossible to make any assumption or draw any conclusion. This part of the manuscript is purely descriptive. Finally, the authors should be more prudent when they state in line 289 "we also provide direct evidence to implicate the gut as a reservoir [...] antibiotic treatment". Indeed the gut colonization of the mothers with the same strain may be also a reservoir (as stated in the discussion line 336).

Finally, the authors do not discuss the potential role of ceftriaxone vs cefotaxime in the dysbiosis observed. Ceftriaxone may have a major impact on the microbiota due to its digestive elimination.

Joint Public Review:

Summary:

Cincotta et al set out to investigate the presence of glucocorticoid receptors in the male and female embryonic germline. They further investigate the impact of tissue specific genetically induced receptor absence and/or systemic receptor activation on fertility and RNA regulation. They are motivated by several lines of research that report inter and transgenerational effects of stress and or glucocorticoid receptor activation and suggest that their findings provide an explanatory mechanism to mechanistically back parental stress hormone exposure induced phenotypes in the offspring.

Strengths:

- A chronological immunofluorescent assessment of GR in fetal and early life oocyte and sperm development.<br /> - RNA seq data that reveal novel cell type specific isoforms validated by q-RT PCR E15.5 in the oocyte.<br /> - 2 alternative approaches to knock out GR to study transcriptional outcomes. Oocytes: systemic GR KO (E17.5) with low input 3-tag seq and germline specific GR KO (E15.5) on fetal oocyte expression via 10X single cell seq and 3-cap sequencing on sorted KO versus WT oocytes - both indicating little impact on polyadenylated RNAs -<br /> - 2 alternative approaches to assess the effect of GR activation in vivo (systemic) and ex vivo (ovary culture): here the RNA seq did show again some changes in germ cells and many in the soma.<br /> - They exclude oocyte specific GR signaling inhibition via beta isoforms<br /> - Perinatal male germline shows differential splicing regulation in response to systemic Dex administration, results were backed up with q-PCR analysis of splicing factors.

Weaknesses:

- Sequencing techniques used are not Total RNA but either are focused on all polyA transcripts (10x) - effects on non-polyA-transcripts are left unexplored.<br /> The number of replicates in the low input seq is very low and hence this might be underpowered. Since Dex treatment showed some (modest) changes in oocyte RNA effects of GR depletion might only become apparent upon Dex treatment as an interaction. Meaning GR activation in the presence of GR shows changes, upon GR depletion those changes are abolished --> statistically speaking an interaction --> conclusion: there are germline GR effects that get abolished when there is no GR hinting on germline GR autonomous effects.<br /> - Effects in oocytes following systemic Dex might be indirect due to GR activation in the soma. The changes observed might be irrelevant to meiosis and thus in the manuscript are deemed irrelevant, but they could still lead to settle consequences. in other terms.

Even though ex vivo culture of ovaries shows GR translocation to nucleus it is not sure whether the in vivo systemic administration does the same. The authors argue in their rebuttal that GR is already nuclear in fetal oocytes hence the<br /> conclusion that fetal oocytes are resistant to GR manipulation is understandable, at least for the readouts that were considered. Yet the question arises: If GR is already nuclear (active) in the absence of additional Dex treatment why does GR knock out not elicit any changes in the considered readouts -> what are we missing.

This work is a good reference point for researchers interested in glucocorticoid hormone signaling fertility and RNA splicing. It might spark further studies on germline-specific GR functions and the impact of GR activation on alternative splicing.<br /> The study provides a characterization of GR and some aspects of GR perturbation, and the negative findings in this study do help to rule out a range of specific roles of GR in the germline. This will help the study of unexplored options. The authors do acknowledge the unexplored options in their discussion.<br /> The intro of the study eludes to implications for intergenerational effects via epigenetic modifications in the germline and points out additional potential indirect effects of reproductive tissue GR signaling on the germline. Future studies might hence focus on further exploration of epigenetic modifications and/or indirect effects.

Reviewer #1 (Public Review):

The work by Debashish U. Menon, Noel Murcia, and Terry Magnuson brings important knowledge about histone H3.3 dynamics involved in meiotic sex chromosome inactivation (MSCI). MSCI is unique to gametes and failure during this process can lead to infertility. Classically, MSCI has been studied in the context of DNA Damage repair pathways and little is known about the epigenetic mechanisms behind maintenance of the sex body as a silencing platform during meiosis. One of the major strengths of this work is the evidence provided on the role of ARID1A, a BAF subunit, in MSCI through the regulation of H3.3 occupancy in specific genic regions.

Using RNA seq and CUT&RUN and ATAC-seq, the authors show that ARID1A regulates chromatin accessibility of the sex chromosomes and XY gene expression. Loss of ARID1A increases promoter accessibility of XY linked genes with concomitant influx of RNA pol II to the sex body and up regulation of XY-linked genes. This work suggests that ARID1A regulates chromatin composition of the sex body since in the absence of ARID1A, spermatocytes show less enrichment of H3.3 in the sex chromosomes and stable levels of the canonical histones H3.1/3.2. By overlapping CUT&RUN and ATAC-seq data, authors show that changes in chromatin accessibility in the absence of ARID1A are given by redistribution of occupancy of H3.3. Gained open chromatin in mutants corresponds to up regulation of H3.3 occupancy at transcription start sites of genes mediated by ARID1A.

Interestingly, ARID1A loss caused increased promoter occupancy by H3.3 in regions usually occupied by PRDM9. PRDM9 catalyzes histone H3 lysine 4 trimethylation during meiotic prophase I, and positions double strand break (DSB) hotspots. Lack of ARID1A causes reduction in occupancy of DMC1, a recombinase involved in DSB repair, in non-homologous sex regions. These data suggest that ARID1A might indirectly influence DNA DSB repair on the sex chromosomes by regulating the localization of H3.3. This is very interesting given the recently suggested role for ARID1A in genome instability in cancer cells. It raises the question of whether this role is also involved in meiotic DSB repair in autosomes and/or how this mechanism differs in sex chromosomes compared to autosomes.

The fact that there are Arid1a transcripts that escape the Cre system in the Arid1a KO mouse model might difficult the interpretation of the data. The phenotype of the Arid1a knockout is probably masked by the fact that many of the sequencing techniques used here are done on a heterogeneous population of knockout and wild type spermatocytes. In relation to this, I think that the use of the term "pachytene arrest" might be overstated, since this is not the phenotype truly observed. Knockout mice produce sperm, and probably litters, although a full description of the subfertility phenotype is lacking, along with identification of the stage at which cell death is happening by detection of apoptosis.<br /> It is clear from this work that ARID1a is part of the protein network that contribute to silencing of the sex chromosomes. However, it is challenging to understand the timing of the role of ARID1a in the context of the well-known DDR pathways that have been described for MSCI. Staining of chromosome spreads with Arid1a antibody showed localization at the sex chromosomes by diplonema, however, analysis of gene expression in Arid1a ko was performed on pachytene spermatocytes. Therefore, is not very clear how the chromatin remodeling activity of Arid1a in diplonema is affecting gene expression of a previous stage. CUTnRUN showed that ARID1a is present at the sex chromatin in earlier stages, leading to hypothesize that immunofluorescence with ARID1a antibody might not reflect ARID1a real localization.

Reviewer #2 (Public Review):

The authors tried to characterize the function of the SWI/SNF remodeler family, BAF, in spermatogenesis. The authors focused on ARID1A, a BAF-specific putative DNA binding subunit, based on gene expression profiles. The study has several serious issues with the data and interpretation. The conditional deletion mouse model of ARIDA using Stra8-cre showed inefficient deletion; spermatogenesis did not appear to be severely compromised in the mutants. Using this data, the authors claimed that meiotic arrest occurs in the mutants. This is obviously a misinterpretation. In the later parts, the authors performed next-gen analyses, including ATAC-seq and H3.3 CUT&RUN, using the isolated cells from the mutant mice. However, with this inefficient deletion, most cells isolated from the mutant mice appeared not to undergo Cre-mediated recombination. Therefore, these experiments do not tell any conclusion pertinent to the Arid1a mutation. Furthermore, many of the later parts of this study focus on the analysis of H3.3 CUT&RUN. However, Fig. S7 clearly suggests that the H3.3 CUT&RUN experiment in the wild-type simply failed. Thus, none of the analyses using the H3.3 CUT&RUN data can be interpreted. Overall, I found that the study does not have rigorous data, and the study is not interpretable. If the author wishes to study the function of ARID2 in spermatogenesis, they may need to try other cre-lines to have more robust phenotypes, and all analyses must be redone using a mouse model with efficient deletion of ARID2.

In this revised manuscript, the authors did not make any efforts to address my major criticisms, and I do not see any improvement. I only found the responses to 4 points, but I do not see any response to other major and minor comments. I understand the challenge (~70 deletion efficiency in the mutants) in this study. However, the inefficient deletion of ARID1A in this mouse model does not allow any detailed analysis in a quantitative manner.

Reviewer #3 (Public Review):

In this manuscript, Magnuson and colleagues investigate the meiotic functions of ARID1A, a putative DNA binding subunit of the SWI/SNF chromatin remodeler BAF. The authors develop a germ cell specific conditional knockout (cKO) mouse model using Stra8-cre and observe that ARID1A-deficient cells fail to progress beyond pachytene, although due to inefficiency of the Stra8-cre system the mice retain ARID1A-expressing cells that yield sperm and allow fertility. Because ARID1A was found to accumulate at the XY body late in Prophase I, the authors suspected a potential role in meiotic silencing and by RNAseq observe significant misexpression of sex-linked genes that typically are silenced at pachytene. They go on to show that ARID1A is required for exclusion of RNA PolII from the sex body and for limiting promoter accessibility at sex-linked genes, consistent with a meiotic sex chromosome inactivation (MSCI) defect in cKO mice. The authors proceed to investigate the impacts of ARID1A on H3.3 deposition genome-wide. H3.3 is known be regulated by ARID1A and is linked to silencing, and here the authors find that upon loss of ARID1A, overall H3.3 enrichment at the sex body as measured by IF failed to occur, but H3.3 was enriched specifically at transcriptional start sites of sex-linked genes that are normally regulated by ARID1A. The results suggest that ARID1A normally prevents H3.3 accumulation at target promoters on sex chromosomes and based on additional data, restricts H3.3 to intergenic sites. Finally, the authors present data implicating ARID1A and H3.3 occupancy in DSB repair, finding that ARID1A cKO leads to a reduction in focus formation by DMC1, a key repair protein. Overall the paper provides new insights into the process of MSCI from the perspective of chromatin composition and structure, and raises interesting new questions about the interplay between chromatin structure, meiotic silencing and DNA repair.

In general the data are convincing. The conditional KO mouse model has some inherent limitations due to incomplete recombination and the existence of 'escaper' cells that express ARID1A and progress through meiosis normally. This reviewer feels that the authors have addressed this point thoroughly and have demonstrated clear and specific phenotypes using the best available animal model. The data demonstrate that the mutant cells fail to progress past pachytene, although it is unclear whether this specifically reflects pachytene arrest, as accumulation in other stages of Prophase also is suggested by the data in Table 1. The western blot showing ARID1A expression in WT vs. cKO spermatocytes (Fig. S2) is supportive of the cKO model but raises some questions. The blot shows many bands that are at lower intensity in the cKO, at MWs from 100-250kDa. The text and accompanying figure legend have limited information. Are the various bands with reduced expression different isoforms of ARID1A, or something else? What is the loading control 'NCL'? How was quantification done given the variation in signal across a large range of MWs?

An additional weakness relates to how the authors describe the relationship between ARID1A and DNA damage response (DDR) signaling. The authors don't see defects in a few DDR markers in ARID1A CKO cells (including a low resolution assessment of ATR), suggesting that ARID1A may not be required for meiotic DDR signaling. However, as previously noted the data do not rule out the possibility that ARID1A is downstream of DDR signaling and the authors even indicate that "it is reasonable to hypothesize that DDR signaling might recruit BAF-A to the sex chromosomes." It therefore is difficult to understand why the authors continue to state that "...the mechanisms underlying ARID1A-mediated repression of the sex-linked transcription are mutually exclusive to DDR pathways regulating sex body formation" (p. 8) and that "BAF-A-mediated transcriptional repression of the sex chromosomes occurs independently of DDR signaling" (p. 16). The data provided do not justify these conclusions, as a role for DDR signaling upstream of ARID1A would mean that these mechanisms are not mutually exclusive or independent of one another.

A final comment relates to the impacts of ARID1A loss on DMC1 focus formation and the interesting observation of reduced sex chromosome association by DMC1. The authors additionally assess the related recombinase RAD51 and suggest that it is unaffected by ARID1A loss. However, only a single image of RAD51 staining in the cKO is provided (Fig. S11) and there are no associated quantitative data provided. The data are suggestive but it would be appropriate to add a qualifier to the conclusion regarding RAD51 in the discussion which states that "...loss of ARID1a decreases DMC1 foci on the XY chromosomes without affecting RAD51" given that the provided RAD51 data are not rigorous. In the long-term it also would be interesting to quantitatively examine DMC1 and RAD51 focus formation on autosomes as well.

Reviewer #1 (Public Review):

In the submitted manuscript, Port et al. investigated the host and viral factors influencing the airborne transmission of SARS-CoV-2 Alpha and Delta variants of concern (VOC) using a Syrian hamster model. The authors analyzed the viral load profiles of the animal respiratory tracts and air samples from cages by quantifying gRNA, sgRNA, and infectious virus titers. They also assessed the breathing patterns, exhaled aerosol aerodynamic profile, and size distribution of airborne particles after SARS-CoV-2 Alpha and Delta infections. The data showed that male sex was associated with increased viral replication and virus shedding in the air. The relationship between co-infection with VOCs and the exposure pattern/timeframe was also tested. This study appears to be an expansion of a previous report (Port et al., 2022, Nature Microbiology). The experimental designs were rigorous, and the data were solid. These results will contribute to the understanding of the roles of host and virus factors in the airborne transmission of SARS-CoV-2 VOCs.

Reviewer #2 (Public Review):

This manuscript by Port and colleagues describes rigorous experiments that provide a wealth of virologic, respiratory physiology, and particle aerodynamic data pertaining to aerosol transmission of SARS-CoV-2 between infected Syrian hamsters. The data is particularly significant because infection is compared between alpha and delta variants, and because viral load is assessed via numerous assays (gRNA, sgRNA, TCID) and in tissues as well as the ambient environment of the cage. The paper will be of interest to a broad range of scientists including infectious diseases physicians, virologists, immunologists and potentially epidemiologists.

Reviewer #1 (Public Review):

Summary and strengths<br /> This is an interesting, timely and informative article. The authors used publicly available data (made available by a funding agency) to examine some of the academic characteristics of the individuals recipients of the National Institutes of Health (NIH) k99/R00 award program during the entire history of this funding mechanism (17 years, total ~ 4 billion US dollars (annual investment of ~230 million USD)). The analysis focuses on the pedigree and the NIH funding portfolio of the institutions hosting the k99 awardees as postdoctoral researchers and the institutions hiring these individuals. The authors also analyze the data by gender, by whether the R00 portion of the awards eventually gets activated and based on whether the awardees stayed/were hired as faculty at their k99 (postdoctoral) host institution or moved elsewhere. The authors further sought to examine the rates of funding for those in systematically marginalized groups by analyzing the patterns of receiving k99 awards and hiring k99 awardees at historically black colleges and universities.

The goals and analysis are reasonable and the limitations of the data are described adequately. It is worth noting that some of the observed funding and hiring traits are in line with the Matthew effect in science (Merton, 1968: https://www.science.org/doi/10.1126/science.159.3810.56) and in science funding (Bol et al., 2018: https://www.pnas.org/doi/10.1073/pnas.1719557115). Overall, the article is a valuable addition to the research culture literature examining the academic funding and hiring traits in the United States. The findings can provide further insights for the leadership at funding and hiring institutions and science policy makers for individual and large-scale improvements that can benefit the scientific community.

Weaknesses<br /> The authors have addressed my recommendations in the previous review round in a satisfactory way.

Reviewer #2 (Public Review):

Summary and strengths<br /> Early career funding success has an immense impact on later funding success and faculty persistence, as evidenced by well-documented "rich-get-richer" or "Matthew effect" phenomena in science (e.g., Bol et al., 2018, PNAS). In this study the authors examined publicly available data on the distribution of the National Institutes of Health's K99/R00 awards - an early career postdoc-to-faculty transition funding mechanism - and showed that although 89% of K99 awardees successfully transitioned into faculty, disparities in subsequent R01 grant obtainment emerged along three characteristics: researcher mobility, gender, and institution. Men who moved to a top-25 NIH funded institution in their postdoc-to-faculty transition experienced the shortest median time to receiving a R01 award, 4.6 years, in contrast to the median 7.4 years for women working at less well-funded schools who remained at their postdoc institutions.

Amongst the three characteristics, the finding that researcher mobility has the largest effect on subsequent funding success is key and novel. Other data supplement this finding: for example, although the total number of R00 awards has increased, most of this increase is for awards to individuals moving to different institutions. In 2010, 60% of R00 awards were activated at different institutions compared to 80% in 2022. These findings enhance previous work on the relationship between mobility and ones' access to resources, collaborators, or research objects (e.g., Sugimoto and Larivière, 2023, Equity for Women in Science (Harvard University Press)).

These results empirically demonstrate that even after receiving a prestigious early career grant, researchers with less mobility belonging to disadvantaged groups at less-resourced institutions continue to experience barriers that delay them from receiving their next major grant. This result has important policy implications aimed at reducing funding disparities - mainly that interventions that focus solely on early career or early stage investigator funding alone will not achieve the desired outcome of improving faculty diversity.

The authors also highlight two incredible facts: No postdoc at a historically Black college or university (HBCU) has been awarded a K99 since the program's launch. And out of all 2,847 R00 awards given thus far, only two have been made to faculty at HBCUs. Given the track record of HBCUs for improving diversity in STEM contexts, this distribution of awards is a massive oversight that demands attention.

At no fault of the authors, the analysis is limited to only examining K99 awardees and not those who applied but did not receive the award. This limitation is solely due to the lack of data made publicly available by the NIH. If this data were available, this study would have been able to compare the trajectory of winners versus losers and therefore could potentially quantify the impact of the award itself on later funding success, much like the landmark paper by Bol et al. (PNAS; 2018) that followed the careers of an early career grant scheme in the Netherlands. Such an analysis would also provide new insights that would inform policy.

Although data on applications versus awards for the K99/R00 mechanism are limited, there exists data for applicant race and ethnicity for the 2007-2017 period, which were made available by a Freedom of Information Act request through the now defunct Rescuing Biomedical Research Initiative (https://web.archive.org/web/20180723171128/http://rescuingbiomedicalresearch.org/blog/examining-distribution-k99r00-awards-race/). These results are highly relevant given the discussion of K99 award impacts on the sociodemographic composition of U.S. biomedical faculty. During the 2007-2017 period, the K99 award rate for white applicants was 31% compared to 26.7% for Asian applicants and 16.2% for Black applicants. In terms of award totals, these funding rates amount to 1,384 awards to white applicants, 610 to Asian applicants, and 25 to Black applicants. However, the work required to include these data may be beyond the scope of the study.

The conclusions are well-supported by the data, and limitations of the data and the name-gender matching algorithm are described satisfactorily.

Reviewer #3 (Public Review):

Summary<br /> The researchers aim add to the literature on faculty career pathways with particular attention to how gender disparities persist in the career and funding opportunities of researchers. The researchers also examine aspects of institutional prestige that can further amplify funding and career disparities. While some factors about individuals' pathways to faculty lines are known, including the prospects of certain K award recipients, the current study provides the only known examination of the K99/R00 awardees and their pathways.

Strengths<br /> The authors establish a clear overview of the institutional locations of K99 and R00 awardees and the pathways for K99-to-R00 researchers and the gendered and institutional patterns of such pathways. For example, there's a clear institutional hierarchy of hiring for K99/R00 researchers that echo previous research on the rigid faculty hiring networks across fields, and a pivotal difference in the time between awards that can impact faculty careers. Moreover, there's regional clusters of hiring in certain parts of the US where multiple research universities are located. Moreover, documenting the pathways of HBCU faculty is an important extension of the study by Wapman et al. (2022: https://www.nature.com/articles/s41586-022-05222-x), and provides a more nuanced look at the pathways of faculty beyond the oft-discussed high status institutions. (However, there is a need for more refinement in this segment of the analyses). Also, the authors provide important caveats throughout the manuscript about the study's findings that show careful attention to the complexity of these patterns and attempting to limit misinterpretations of readers.

Weaknesses<br /> The authors have addressed my recommendations in the previous review round in a satisfactory way.

Reviewer #1 (Public Review):

Summary:

This study by He, Liu, and He et al. investigated the fundamental role of microglia in modulating general anesthesia. While microglia have been previously shown to regulate neuronal network activity, their role in the induction of (i.e., LORR) and emergence from (i.e., RORR) anesthesia has only recently been explored. Recently published work by Cao et al. reported that microglia modulate general anesthesia via P2Y12 receptor. The present study largely reproduces those findings and does so using an impressive array of techniques and clever approaches. Following the serendipitous discovery that microglia-depleted mice exhibit increased LORR and decreased RORR, the authors go on to demonstrate that microglia regulate neuronal activity in a region-specific manner during anesthesia via purinergic receptor-mediated calcium signaling. The manuscript is well written and the data are convincing, elegantly validated using several different methods and controls, and largely complete. Nevertheless, this Reviewer has a few minor comments and suggestions to further strengthen the manuscript.

Strengths:

Impressive number of genetic mouse models, techniques, controls, and methods of validation.

Weaknesses:

Some of the novelty of these findings may be reduced based on the recent publication of a similar study.

Reviewer #2 (Public Review):

In this manuscript, He et al. have found that delayed anesthesia induction and early anesthesia emergence were observed in microglia-depleted mice. They also showed that neuronal activities were differentially regulated by microglia depletion, possibly via suppressing the neuronal network of anesthesia-activated brain regions and activating emergence-activated brain regions. Mechanistically, this influence was found to be dependent on the activation of microglial P2Y12 receptors and subsequent calcium influx. These findings contribute to a better understanding of the role microglia play in regulating anesthesia and shed light on the underlying mechanisms involved. Nonetheless, there are still some aspects that require further investigation and clarification.

1. In Figure 3A the authors used IBA1 to represent microglia, and the corresponding description is 'brain microglia were not influenced'. However, IBA1 is not a specific biomarker for brain resident microglia. It's recommended to use other biomarkers, such as TMEM119 and P2RY12 to better examine the efficiency of microglial depletion.<br /> 2. In Figure 7, 8 and 9 the authors stated that they aim to investigate the impacts microglia exert on neuronal activity. However, using only c-Fos is not sufficient to represent neuron. The authors are supposed to combine c-Fos with other specific biomarkers for neuron to better validate their conclusions.<br /> 3. In Figure 11 the authors use C1qa-/- transgenic mice and draw the conclusion 'microglia mediated anesthesia modulation does not result from spine pruning'. However, as C1q contains multiple subtypes, I have some reservations regarding whether the authors' conclusion is entirely warranted based solely on the knockout of a single subtype of C1q.<br /> 4. In Figure 14E the authors showed that expression levels of Stim1 is significantly down-regulated in CX3CR1CreER::STIM1fl/fl mouse brains. While this is not incorrect, I would suggest the authors sort microglia with FACS or MACS to perform q-RT-PCR and examine the expression levels of Stim1 since the Cre-LoxP system here is microglia specific.<br /> 5. The flow of the manuscript should have been improved. For instance, the results of repopulated microglia in Figure 1B was described even after Figure 2 and 3, which makes the manuscript a little confusing. Additionally, in Figure 14, it would be beneficial to provide a more comprehensive introduction to molecules such as hM3Dq and Stim1 to improve the clarity and readability of the result descriptions.

Reviewer #3 (Public Review):

Summary:<br /> This work aims to understand the contribution of microglia to anesthesia induced by general anesthetics. The authors report that ablation of microglia shortens anesthesia, manifested by the delay of anesthesia induction and the early anesthesia emergence. They show that microglial depletion suppresses activity in the neuronal network of anesthesia-activated brain regions but enhances activity in emergence-activated brain regions. Based on these findings, the authors suggest microglia facilitate and stabilize the anesthesia status. To elucidate the underlying mechanism, they further tested the potential contribution of microglia-mediated dendritic spine plasticity and microglial P2Y12-Ca2+ signaling, and identified the latter as a critical pathway through which microglia regulate anesthesia.

Strengths:<br /> A major strength of this study is the systematic experimental design, which includes multiple anesthetics and complementary approaches, leading to very compelling data. As a result, a significant contribution of microglia in instating and maintaining the state of anesthesia is convincingly established. In addition, the results also shed light on the potential underlying microglial mechanistic. The findings are of relevance to both medical practice and basic understanding of microglial biology and neuron-glia interactions.

Weaknesses:<br /> The study produces a large amount of data that is in general cohesive and support the main conclusions, but more thorough considerations on some of their findings may be helpful, as exemplified by the following:

1) the effect of microglial ablation on chloral hydrate-induced RORR in Fig. 1B appears to be not the same as other anesthetics. what does this mean?

2) Macrophage ablation impedes anesthesia emergence from pentobarbital (Fig. 3C). how may this occur?

3) examination of the potential effect of microglial depletion on dendritic spine density is interesting but the experimental design does not seem to align well with the PPR and eEPSC data, which indicate a reduction in presynaptic release (Fig.10E) and increase of postsynaptic function (Fig. 10H), respectively. The PPR data seems to suggest a presynaptic effect of microglia; ablation.

Reviewer #2 (Public Review):

Summary:<br /> A bidirectional occasion-setting design is used to examine sex differences in the contextual modulation of reward-related behaviour. It is shown that females are slower to acquire contextual control over cue-evoked reward seeking. However, once established, the contextual control over behaviour was more robust in female rats (i.e., less within-session variability and greater resistance to stress) and this was also associated with increased OFC activation.

Strengths:<br /> The authors use sophisticated behavioural paradigms to study the hierarchical contextual modulation of behaviour. The behavioural controls are particularly impressive and do, to some extent, support the specificity of the conclusions. The analyses of the behavioural data are also elegant, thoughtful, and rigorous.

Weaknesses:<br /> My primary concern is that the authors' claim of sex differences in context-dependent discrimination behaviour is not fully supported by their data.

First, the basic behavioural effect does not seem to replicate across experiments. The authors first show sex differences in the % time in food port and the discrimination ratio (Figures 1 and 2) such that males show better context-dependent discrimination than females (group ctx-dep O1). However, this difference is not observed in the baseline condition group in the next experiment, which investigates the effect of acute stress on context-gated reward seeking: "In Figure 4, we observe no difference between males versus females in group "ctx-dep O1".

Second, I am not fully convinced by the authors' assertion that the results are specific to the contextual modulation process. The authors' main conclusions are derived from comparing a group trained with the differential outcome procedure (group cxt-dep O1/O2) and a group with the non-differential outcome procedure (group cxt-dep O1). However, importantly, a different number of training sessions was used for ctx-dep O1/O2 and ctx-dep O1. Is it not possible that sex differences could have emerged with additional training in the cxt-dep O1/O2 group? Moreover, the authors also seem to assume that rats are not using a contextual strategy in the context-dep O1/O2 condition (i.e., rats use instead distinct context-outcome associations) but what is the evidence for this? Also, the authors argue that the impact of stress is specific to the hierarchical contextual modulation of behaviour however inspection of Figure 4A suggests that there may also be an effect of stress on the context-dependent O1/O2 group.

I also had some minor issues with how the authors interpreted some of the findings. First, it is shown that recent rewards disrupt contextual control of reward seeking in male, but not female, rats. That is, in males, prior reward increased the probability of responding on subsequent non-rewarded trials but trial history had no effect in females. How do the authors reconcile this finding with the quicker acquisition and better discrimination that is observed in males? It is not evident to me how males can have difficulty inhibiting responding to non-rewarded cues following recent reward yet still show better discrimination throughout training.

Finally, the authors argue that the contextual control over behaviour was more robust in female rats as females show less within-session variability and greater resistance to stress. What evidence is there that the restraint stress procedure causes a similar stress response in both sexes?

Reviewer #1 (Public Review):

Summary:<br /> Peterson et al., present a series of experiments in which the Pavlovian performance (i.e. time spent at a food cup/port) of male and female rats is assessed in various tasks in which context/cue/outcome relationships are altered. The authors find no sex differences in context-irrelevant tasks and no such differences in tasks in which the context signals that different cues will earn different outcomes. They do find sex differences, however, when a single outcome is given and context cues must be used to ascertain which cue will be rewarded with that outcome (Ctx-dep O1 task). Specifically, they found that males acquired the task faster, but that once acquired, the performance of the task was more resilient in female rats against exposure to a stressor. Finally, they show that these sex differences are reflected in differential rates of c-fos expression in all three subregions of rat OFC, medial, lateral, and ventral, in the sense that it is higher in females than males, and only in the animals subject to the Ctx-dep O1 task in which sex differences were observed.

Strengths:<br /> • Well-written.<br /> • Experiments elegantly designed.<br /> • Robust statistics.<br /> • Behaviour is the main feature of this manuscript, rather than any flashy techniques or fashionable lab methodologies, and luckily the behaviour is done really well.<br /> • For the most part I think the conclusions were well supported, although I do have some slightly different interpretations to the authors in places.

Weaknesses:<br /> 1. With regards to the claim (page 4 of pdf), I think I can see what the authors are getting at when they claim "Only Ctx-dep.01 engages context-gated reward predictions", because the same reward is available in each context, and the animal must use contextual information to determine which cue will be rewarded. In other words, it has a discriminative purpose. In Ctx-dep.O1/O2, however, although the context doesn't serve a discriminative purpose in the sense that one cue will always earn a unique outcome, regardless of context, the fact that these cues are differentially rewarded in the different context means that animals may well form context-gated cue-outcome associations (e.g. CtxA-(CS1-O1), CtxnoA-(CS2-O2)). Moreover, the context is informative in this group in telling the animal which cue will be rewarded, even prior to outcome delivery, such that I don't think contextual information will fade to the background of the association and attention be lost to it in the way, say Mackintosh (1975) might predict. Therefore, I don't think this statement is correct.

2. I think the results shown in Figure 1 are very interesting, and well supported by the statistics. It's so nice to see a significant interaction, as so many papers try to report these types of effects without it. However, I do wonder how specific the results are to contextual modulation. That is, should a discriminative discrete cue be used instead of each context (e.g. CS1 indicates CS2 earns O1, CS3 indicates CS4 earns O1), would female rats still be as slow to learn the discrimination?

3. Pages 8-9 of pdf, where the biological basis or the delayed acquisition of contextual control in females is considered, I find this to be written from a place of assuming that what is observed in the males is the default behaviour. That is, although the estrous cycle and its effects on synaptic plasticity/physiology may well account for the results, is there not a similar argument to be made for androgens in males? Perhaps the androgens also somehow alter synaptic plasticity/physiology, leading to their faster speed, reduced performance stability, and increased susceptibility to stress.

4. In addition, the OFC - which is the brain region found to have differential expression of c-fos in males and females in Figure 5 - is not explicitly discussed with regard to the biological mechanisms of differences, which seems odd.

Reviewer #3 (Public Review):

Summary:<br /> This manuscript reports an experiment that compared groups of rats acquisition and performance of a Pavlovian bi-conditional discrimination, in which the presence of one cue, A, signals that the presentation of one CS, X, will be followed by a reinforcer and a second CS, Y, will be nonreinforced. Periods of cue A alternated with periods of cue B, which signaled the opposite relationship, cue X is nonreinforced, and cue Y is reinforced. This is a conditional discrimination problem in which the rats learned to approach the food cup in the presence of each CS conditional on the presence of the third background cue. The comparison groups consisted of the same conditional discrimination with the exception that each CS was paired with a different reinforcer. This makes the problem easier to solve as the background is now priming a differential outcome. A third group received simple discrimination training of X reinforced and Y nonreinforced in cues A and B, and the final group was trained with X and Y reinforced on half the trials (no discrimination). The results were clear that the latter two discrimination learning procedures resulted in rapid learning in comparison to the first. Rats required about 3 times as many 4-session blocks to acquire the bi-conditional discrimination than the other two discrimination groups. Within the biconditional discrimination group, female and male rats spent the same amount of time in the food cup during the rewarded CS, but females spent more time in the food cup during CS- than males. The authors interpret this as a deficit in discrimination performance in females on this task and use a measure that exaggerates the difference in CS+ and CS_ responding (a discrimination ratio) to support their point. When tested after acute restraint stress, the male rats spent less time in the food cup during the reinforced CS in comparison to the female rats, but did not lose discrimination performance entirely. The was also some evidence of more fos-positive cells in the orbitofrontal cortex in females, but this difference was of degree.

Overall, I think the authors were successful in documenting performance on the biconditional discrimination task. Showing that it is more difficult to perform than other discriminations is valuable and consistent with the proposal that accurate performance requires encoding of conditional information (which the authors refer to as "context"). There is evidence that female rats spend more time in the food cup during CS-, but I hesitate to agree that this is an important sex difference. There is no cost to spending more time in the food cup during CS- and they spend much less time there than during CS+. Males and females also did not differ in their CS+ responses, suggesting similar levels of learning. A number of factors could contribute to more food cup time in CS-, such as smaller body size and more locomotor activity. The number of food cup entries during CS+ and CS- was not reported here. Nevertheless, I think the manuscript will make a useful contribution to the field and hopefully lead readers to follow up on these types of tasks.

One area for development would be to test the associative properties of the cues controlling the conditional discrimination, can they be shown to have the properties of Pavlovian occasion-setting stimuli? Such work would strengthen the justification/rationale for using the terms "context" and "occasion setter" to refer to these stimuli in this task in the way the authors do in this paper.

Strengths:<br /> - Nicely designed and conducted experiment.<br /> - Documents performance difference by sex.

Weaknesses:<br /> - Overstatement of sex differences.<br /> - Inconsistent, confusing, and possibly misleading use of terms to describe/imply the underlying processes contributing to performance.

Reviewer #3 (Public Review):

Perrodin, Verzat and Bendor describe the response of female mice to the playback of male mouse ultrasonic songs. The experiments were performed in a Y-maze-like apparatus with two acoustically separate response chambers. Sounds were presented in 4 trials, alternating strictly between the left and right branches of the Y. Cumulative dwell time in the two chambers was measured, and used as an index of female preference. They first show, consistent with previous observations, that female mice will spend more time near a speaker playing a male mouse song than near a speaker playing nothing. They then performed several manipulations-time reversals, syllable order randomization, phase scrambled replacement, pure tone replacement, and 'hyper-regular' inter-syllable-intervals-which female mice did not discriminate from the normal song in this assay. Finally, they show that females spent more time near normal songs than near songs with more variable inter-syllable-intervals

The authors' approach to the problem was ethologically sensible -- females were tested in proestrus and estrus, the male odor was used to increase motivation, mouse handling was with tube transfers to reduce stress, mice were age-matched across conditions, and experiments were conducted in the dark (active) phase. In addition, animals were habituated to handling and to the apparatus.

The acoustics were very good. The acoustic structure of the vocal signals was well described. Specific ranges of dB SPL were reported, speaker flatness was evaluated, the sound amplitude was matched in manipulated and unmanipulated songs, and playback onset timing jittered randomly between manipulated and unmanipulated signals.

I think it is a reasonable result. My concerns are the following:

1) The authors use "approach" as it has been used in other publications, but what is actually measured is dwell time. Pomerantz et al, 1983 observed that female mice approached mute and singing males the same number of times (e.g. approached both at the same rate), but spent more time with the singing than the mute male. Their use of "approach" to describe dwell time was a bit confusing to me, but sticking with the way the literature is defensible. However, they also refer to the assay as a "place preference assay", which I found confusing.

2) I am a bit worried about their method of removing side bias (29% of trials). It certainly seems like a reasonable thing to exclude mice that simply picked one side or the other, but, because the stimulus always alternated between the sides, this exclusion of mice exhibiting a side bias is also excluding, specifically, behavior that would be incorrect.

3) Given the observation by Hammerschmidt et al, 2009, that female mice would only discriminate male songs in a playback assay on the first presentation, it is important to know whether females were used across the different manipulations. How many conditions did each female experience? How often did a female display positive discrimination in a condition after having displayed no discrimination?

Specific comments:

1) For Figure 2L

The heat map legend is labeled "Towards" indicating a motion towards either the speaker playing the song or the silent speaker. However, there is nothing in the methods that indicates that the direction of movement was ever measured. I may have missed it, but I can't figure out how this heat map was generated and what it represents. The figure legend states: "Normalized temporal profiles of approach behaviour to mouse songs vs silence over the course of 4 sound presentation trials (x-axis, coloured bars) for each of the behavioural sessions (y-axis, each animal is one line, n = 29), calculated as in I. Sessions (lines) are ordered by the amplitude of their last element." 2I states " I. Temporal profile of approach behaviour over the four sound presentation trials in the example session in C, calculated as the cumulative sum of time in the intact song playback (positively weighted) vs silent (negatively weighted) speaker zone." I interpret this to mean that "Towards" is an inaccurate description of what is being plotted, as there is no motion, only dwell time.

References

K. Hammerschmidt, K. Radyushkin, H. Ehrenreich & J. Fischer (2009) Female mice respond to male ultrasonic 'songs' with approach behavior. Biol. Lett. 5:589-592.

Pomerantz, S.M., Nunez, A.A. & Bean, J (1983) Female behavior is affected by male ultrasonic vocalizations in house mouse. Physiol. Behav. 31:91-96.

Reviewer #1 (Public Review):

Summary:

Given knowledge of the amino acid sequence and of some version of the 3D structure of two monomers that are expected to form a complex, the authors investigate whether it is possible to accurately predict which residues will be in contact in the 3D structure of the expected complex. To this effect, they train a deep learning model that takes as inputs the geometric structures of the individual monomers, per-residue features (PSSMs) extracted from MSAs for each monomer, and rich representations of the amino acid sequences computed with the pre-trained protein language models ESM-1b, MSA Transformer, and ESM-IF. Predicting inter-protein contacts in complexes is an important problem. Multimer variants of AlphaFold, such as AlphaFold-Multimer, are the current state of the art for full protein complex structure prediction, and if the three-dimensional structure of a complex can be accurately predicted then the inter-protein contacts can also be accurately determined. By contrast, the method presented here seeks state-of-the-art performance among models that have been trained end-to-end for inter-protein contact prediction.

Strengths:

The paper is carefully written and the method is very well detailed. The model works both for homodimers and heterodimers. The ablation studies convincingly demonstrate that the chosen model architecture is appropriate for the task. Various comparisons suggest that PLMGraph-Inter performs substantially better, given the same input than DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter. As a byproduct of the analysis, a potentially useful heuristic criterion for acceptable contact prediction quality is found by the authors: namely, to have at least 50% precision in the prediction of the top 50 contacts.

Weaknesses:

My biggest issue with this work is the evaluations made using *bound* monomer structures as inputs, coming from the very complexes to be predicted. Conformational changes in protein-protein association are the key element of the binding mechanism and are challenging to predict. While the GLINTER paper (Xie & Xu, 2022) is guilty of the same sin, the authors of CDPred (Guo et al., 2022) correctly only report test results obtained using predicted unbound tertiary structures as inputs to their model. Test results using experimental monomer structures in bound states can hide important limitations in the model, and thus say very little about the realistic use cases in which only the unbound structures (experimental or predicted) are available. I therefore strongly suggest reducing the importance given to the results obtained using bound structures and emphasizing instead those obtained using predicted monomer structures as inputs.

In particular, the most relevant comparison with AlphaFold-Multimer (AFM) is given in Figure S2, *not* Figure 6. Unfortunately, it substantially shrinks the proportion of structures for which AFM fails while PLMGraph-Inter performs decently. Still, it would be interesting to investigate why this occurs. One possibility would be that the predicted monomer structures are of bad quality there, and PLMGraph-Inter may be able to rely on a signal from its language model features instead. Finally, AFM multimer confidence values ("iptm + ptm") should be provided, especially in the cases in which AFM struggles.

Besides, in cases where *any* experimental structures - bound or unbound - are available and given to PLMGraph-Inter as inputs, they should also be provided to AlphaFold-Multimer (AFM) as templates. Withholding these from AFM only makes the comparison artificially unfair. Hence, a new test should be run using AFM templates, and a new version of Figure 6 should be produced. Additionally, AFM's mean precision, at least for top-50 contact prediction, should be reported so it can be compared with PLMGraph-Inter's.

It's a shame that many of the structures used in the comparison with AFM are actually in the AFM v2 training set. If there are any outside the AFM v2 training set and, ideally, not sequence- or structure-homologous to anything in the AFM v2 training set, they should be discussed and reported on separately. In addition, why not test on structures from the "Benchmark 2" or "Recent-PDB-Multimers" datasets used in the AFM paper?

It is also worth noting that the AFM v2 weights have now been outdated for a while, and better v3 weights now exist, with a training cutoff of 2021-09-30.

Another weakness in the evaluation framework: because PLMGraph-Inter uses structural inputs, it is not sufficient to make its test set non-redundant in sequence to its training set. It must also be non-redundant in structure. The Benchmark 2 dataset mentioned above is an example of a test set constructed by removing structures with homologous templates in the AF2 training set. Something similar should be done here.

Finally, the performance of DRN-1D2D for top-50 precision reported in Table 1 suggests to me that, in an ablation study, language model features alone would yield better performance than geometric features alone. So, I am puzzled why model "a" in the ablation is a "geometry-only" model and not a "LM-only" one.

Reviewer #2 (Public Review):

This work introduces PLMGraph-Inter, a new deep-learning approach for predicting inter-protein contacts, which is crucial for understanding protein-protein interactions. Despite advancements in this field, especially driven by AlphaFold, prediction accuracy and efficiency in terms of computational cost) still remains an area for improvement. PLMGraph-Inter utilizes invariant geometric graphs to integrate the features from multiple protein language models into the structural information of each subunit. When compared against other inter-protein contact prediction methods, PLMGraph-Inter shows better performance which indicates that utilizing both sequence embeddings and structural embeddings is important to achieve high-accuracy predictions with relatively smaller computational costs for the model training.

The conclusions of this paper are mostly well supported by data, but test examples should be revisited with a more strict sequence identity cutoff to avoid any potential information leakage from the training data. The main figures should be improved to make them easier to understand.

1) The sequence identity cutoff to remove redundancies between training and test set was set to 40%, which is a bit high to remove test examples having homology to training examples. For example, CDPred uses a sequence identity cutoff of 30% to strictly remove redundancies between training and test set examples. To make their results more solid, the authors should have curated test examples with lower sequence identity cutoffs, or have provided the performance changes against sequence identities to the closest training examples.

2) Figures with head-to-head comparison scatter plots are hard to understand as scatter plots because too many different methods are abstracted into a single plot with multiple colors. It would be better to provide individual head-to-head scatter plots as supplementary figures, not in the main figure.

3) The authors claim that PLMGraph-Inter is complementary to AlphaFold-multimer as it shows better precision for the cases where AlphaFold-multimer fails. To strengthen the point, the qualities of predicted complex structures via protein-protein docking with predicted contacts as restraints should have been compared to those of AlphaFold-multimer structures.

4) It would be interesting to further analyze whether there is a difference in prediction performance depending on the depth of multiple sequence alignment or the type of complex (antigen-antibody, enzyme-substrates, single species PPI, multiple species PPI, etc).

Reviewer #1 (Public Review):

Summary:<br /> This paper uses a high-throughput assay of transcription levels to (i) assess the potential of large numbers of Escherichia coli genomic sequences to function as promoters, and (ii) identify regulatory sequences in some of those promoters. This is a substantial undertaking, and while much of the work supports principles of transcription and transcription regulation described by many prior studies, there is considerable value in assessing promoters on such a large scale. The identification of putative regulatory sequences in larger numbers of promoters will likely be valuable to other groups studying transcription regulation in E. coli. And the analysis of antisense promoters provides some interesting new insight that goes beyond previous anecdotal studies.

Strengths:<br /> - The presentation of the work is very clear, and the conclusions are mostly well supported by the data.<br /> - The assays are rigorously controlled and analyzed.<br /> - Conclusions regarding the impact of antisense transcription on sense transcript levels provide new insight. While these data are consistent with previous anecdotal studies, to my knowledge this is the first large-scale analysis supporting a negative regulatory role for antisense transcription.<br /> - The putative regulatory elements mapped in the high-throughput mutagenesis experiments will be a valuable resource for the scientific community.

Weaknesses:<br /> (all minor)<br /> - There are some parts where the authors could clarify their arguments.<br /> - I'm not convinced that intragenic promoters impact codon usage rather than the other way around.<br /> - The authors should present a more nuanced discussion of promoters that avoids making yes/no calls (i.e., characterize sequences by promoter strength rather than a binary yes/no call of being a promoter).<br /> - Data relating to intragenic promoters should be presented and discussed for sense and antisense promoters separately.

Reviewer #2 (Public Review):

In this work, Urtecho et al. use genome-integrated massively parallel reporter assays (MPRAs) to catalog the locations of promoters throughout the E. coli genome. Their study uses four different MPRA libraries. First, they assayed a library containing 17,635 promoter regions having transcription start sites (TSSs) previously reported by three different sources. They found that 2,760 of these regions exhibited transcription above an experimentally determined threshold. Second, they assayed a library using sheared E. coli genome fragments. This library allowed the authors to systematically identify candidate promoter regions throughout the genome, some of which had not been identified before. Additionally, by performing experiments with this library under different growth conditions, the authors were able to identify promoters with condition-dependent activity. Third, to improve the resolution at which they were able to identify transcription start sites, the authors assayed a library that tiled all candidate promoter regions identified using the genomic fragments library. Data from the tiled library allowed the authors to identify minimal promoter regions. Fourth, the authors assayed a scanning mutagenesis library in which they systematically scrambled individual 10 bp windows within 2,057 previously identified active promoters at 5 bp intervals. After validation with known promoters, this approach allowed the authors to identify novel functional elements within regulatory regions. Finally, the authors fit multiple machine learning models to their data with the goal of predicting promoter activity from DNA sequences.

The work by Urtecho et al. provides an important resource for researchers studying bacterial transcriptional regulation. Despite decades of study, a comprehensive catalogue of E. coli promoters is still lacking. The results of Urtecho et al. provide a state-of-the-art atlas of promoters in the E. coli genome that is readily accessible through the website, http://ecolipromoterdb.com. The authors' work also provides an important demonstration of the power of genome-integrated MPRAs. Unlike many MPRA-based studies, the authors use the results of their initial MPRAs to design follow-up MPRAs, which they then carry out. Finally, the scanning mutagenesis MPRAs the authors perform provide valuable data that could lead to the discovery of novel transcription factor binding sites and other functional regulatory sequence elements.

Below I provide two major critiques and some minor critiques of the paper. The purpose of these critiques is simply to help the authors improve the quality of the manuscript.

Major points:<br /> 1. Ultimately, a comprehensive atlas of E. coli promoters should include nucleotide resolution TSS data, which is not present in the MPRA datasets reported by Urtecho et al.. The authors do use some methods to narrow down the positions of TSSs, but these methods do not provide the resolution one would ideally like to see in a TSS atlas. I understand that acquiring single-nucleotide-resolution data is beyond the scope of this manuscript, but it still might make sense for the authors to discuss this limitation in the Discussion section.

2. The authors should clarify which points in the Results section are novel conclusions or observations, and which points are simply statements that prior conclusions or observations were confirmed. This distinction can be unclear at times.

Minor points:<br /> 1. Line 200-203: "We conclude that inactive TSS-associated promoters lack -35 elements but may become active in growth conditions where additional transcription factors mobilize and facilitate RNAP positioning in the absence of a -35 motif." Making this type of mechanistic observations from the slight difference observed in the enrichment analysis seems too speculative to me. Also, I do not understand how the discrepancies can be explained in terms of transcription factor differences. If the previous studies from which the annotated TSS were extracted were also performed during the log phase in rich media, why would the transcription factors present be different?

2. Line 224-226: "Active TSSs not overlapping a candidate promoter region generally exhibited weak activity, which may indicate that greater sensitivity is achieved through testing of oligo-array synthesized regions (Figure S3)." The authors should clarify this statement. In particular, it is mechanistically unclear why one library would be more sensitive than another if they contain similar sequences.

3. Figure 2B. The authors should clarify that the heights of the arrows correspond to TSS activity as assayed by one library and that the pile-up plots represent promoter activity as assayed by a different library.

4. Line 255-257: "We also observed an enrichment for 150 bp minimal promoter regions, although these were generally weak indicating that our resolution is limited when tiling weaker promoters." The authors should clarify whether the peak at 150 bp is an artifact of using oligos containing 150 bp tiles to construct the library. Also, the authors should clarify why there are some minimal promoters with lengths > 150 bp when the length of the tiles was 150 bp.

5. Line 262 refers to "Supplementary Table 1", but I was not able to find this table in the supplement.

6. Line 324-325: "We used a σ70 PWM to identify the highest-scoring σ70 motifs within intragenic promoters and determined their relative coding frames". I find the term "relative coding frame" here to be unclear; the authors should clarify what they mean.

7. Figure 3 C , D: The authors should use the same terminology in the plots and the methods section describing them. They should also clarify how the values plotted in C and D were computed.

8. Line 329-332: "The observed depletion of -35 motifs positioned in the +2 reading frame and -10 motifs in the +1 reading frame is likely due to the fact that the canonical sequences for these motifs would create stop codons within the protein if placed at these positions." The definition of the reading frame here is unclear. Do the authors mean that the 0 frame is defined as occurring when the hexamer exactly overlaps 2 codons, the +1 frame is when the hexamer is shifted 1 nt downstream of that position, and the +2 frame is when the hexamer is shifted 2 nt downstream of that position?

9. Line 538-539: "We performed hyperparameter tuning for a three-layer CNN and achieved an AUPRC =0.44." The authors should explicitly describe the architecture used for the CNN, and perhaps include a diagram of this architecture. In addition, the authors should clarify the mathematical forms of the other methods tested.

10. Line 1204-1205: "We standardized all datasets as detailed above in 'Universal Promoter Expression Quantification and Activity Thresholding'". That title does not appear before in the text. I believe the appropriate subsection is called "Standardizing Promoter Expression Quantification and Activity Thresholding".

11. Line 1265-1266: "We include a k-mer if the absolute correlation with expression is greater than the 'random' k-mer frequency, resulting in 4800/5440 filtered k-mers." It is unclear to me which two correlations are being compared. Please clarify. For example, would this be accurate: "We include a k-mer if the absolute correlation of its frequency with expression is greater than the absolute correlation of its 'random' frequency with expression"?

Reviewer #3 (Public Review):

In this revised manuscript, Urtecho et al., present an updated version of their earlier submission. They characterized thousands of promoter sequences in E. coli using a massively-parallel reporter assay and built a number of computational models to classify active from inactive promoters or associate the sequence to promoter expression/strength. As eluded in the earlier review cycle, the amount of experimental, bioinformatics, and analytical work presented here is astounding.

Identifying promoters and associating genomic (or promoter) sequences to promoter strength is nontrivial. Authors report challenges in achieving this grand goal even with the state-of-the-art characterization technology used here. Nevertheless, the experimental work, analytic workflow, and data resource presented here will serve as a milestone for future researchers.

Reviewer #1 (Public Review):

In this work, Xie et al. developed SCA-seq, which is a multiOME mapping method that can obtain chromatin accessibility, methylation, and 3D genome information at the same time. SCA-seq first uses M.CviPI DNA methyltransferase to treat chromatin, then perform proximity ligation followed by long-read sequencing. This method is highly relevant to a few previously reported long read sequencing technologies. Specifically, NanoNome, SMAC-seq, and Fiber-seq have been reported to use m6A or GpC methyltransferase accessibility to map open chromatin, or open chromatin together with CpG methylation; Pore-C and MC-3C have been reported to use long read sequencing to map multiplex chromatin interactions, or together with CpG methylation. Therefore, as a combination of NanoNome/SMAC-seq/Fiber-seq and Pore-C/MC-3C, SCA-seq is one step forward. The authors tested SCA-seq in 293T cells and performed benchmark analyses testing the performance of SCA-seq in generating each data module (open chromatin and 3D genome). The QC metrics appear to be good and I am convinced that this is a valuable addition to the toolsets of multi-OMIC long-read sequencing mapping.

The revised manuscript addressed most of my questions except my concern about Fig. S9. This figure is about a theory that a chromatin region can become open due to interaction with other regions, and the author propose a mathematic model to compute such effects. I was concerned about the errors in the model of Fig. S9a, and I was also concerned about the lack of evidence or validation. In their responses, the authors admitted that they cannot provide biological evidence or validations but still chose to keep the figure and the text.

The revised Fig. S9a now uses a symmetric genome interaction matrix as I suggested. But Figure S9a still have a lot of problems. Firstly, the diagonal of the matrix in Fig. S9a still has many 0's, which I asked in my previous comments without an answer. The legend mentioned that the contacts were defined as 2, 0 or -2 but the revised Fig. S9a only shows 1,0, or -1 values. Furthermore, Fig. S9b,9c,9d all added a panel of CTCF+/- but there is no explanation in text or figure legend about these newly added panels. Given many unaddressed problems, I would still suggest deleting this figure.

In my opinion, this paper does not need Fig. S9 to support its major story. The model in this figure is independent of SCA-seq. I think it should be spinoff as an independent paper if the authors can provide more convincing analysis or experiments. I understand eLife lets authors to decide what to include in their paper. If the authors insist to include Fig. S9, I strongly suggest they should at least provide adequate explanation about all the figure panels. At this point, the Fig. S9 is not solid and clearly have many errors. The readers should ignore this part.

Reviewer #2 (Public Review):

In this manuscript, Xie et al presented a new method derived from PORE-C, SCA-seq, for simultaneously measuring chromatin accessibility, genome 3D and CpG DNA methylation. SCA-seq provides a useful tool to the scientific communities to interrogate the genome structure-function relationship.

The revised manuscript has clarified almost of the concerns raised in the previous round of review, though I still have two minor concerns,

1) In fig 2a, there is no number presented in the Venn diagram (although the left panel indeed showed the numbers of the different categories, including the numbers in the right panel would be more straightforward).

2) The authors clarified the discrepancy between sfig 7a and sfig 7g. However, the remaining question is, why is there a big difference in the percentage of the cardinality count of concatemers of the different groups between the chr7 and the whole genome?

Reviewer #1 Public Review

Anobile and colleagues present a manuscript detailing an account of numerosity processing with an appeal to a two-channel model. Specifically, the authors propose that the perception of numerosity relies on (at least) two distinct channels for small and large numerosities, which should be evident in subject reports of perceived numerosity. To do this, the authors had subjects reproduce visual dot arrays of numerosities ranging from 8 to 32 dots, by having subjects repetitively press a response key at a pre-instructed rate (fast or slow) until the number of presses equaled the number of perceived dots. The subjects performed the task remarkably well, yet with a general bias to overestimate the number of presented dots. Further, no difference was observed in the precision of responses across numerosities, providing evidence for a scalar system. No differences between fast and slow tapping were observed. For behavioral analysis, the authors examined correlations between the Weber fractions for all presented numerosities. Here, it was found that the precision at each numerosity was similar to that at neighboring numerosities, but less similar to more distant ones. The authors then went on to conduct PCA and clustering analyses on the weber fractions, finding that the first two components exhibited an interaction with the presented numerosity, such that each was dominant at distinct lower and upper ranges and further well-fit by a log-Gaussian model consistent with the channel explanation proposed at the beginning.

Overall, the authors provide compelling evidence for a two-channel system supporting numerosity processing that is instantiated in sensorimotor processes. A strength of the presented work is the principled approach the authors took to identify mechanisms, as well as the controls put in place to ensure adequate data for analysis. Some questions do remain in the data, and there are aspects of the presentation that could be adjusted.

-The use of a binary colormap for the correlation matrix seems unnecessary. Binary colormaps between two opposing colors (with white in the middle) are best for results spanning positive and negative values (say, correlation values between -1 and +1), but the correlations here are all positive, so a uniform colormap should be applied. I can appreciate that the authors were trying to emphasize that a 2+ channel system would lead to lower correlations at larger ratios, but that's emphasized better in the numerical ratio line plots.

-In Figure 1, the correlation matrices in Figure 1 appear blurred out. I am not sure if this was intentional but suspect it was not, and so they should appear like those presented in Figure 3.

-It's notable that the authors also collected data on a timing task to rule out a duration-based strategy in the numerosity task. If possible, it would be great to have the author also conduct the rest of the analyses on the duration task as well; that is, to look at WF correlation matrices/ratios as well as PCA. There is evidence that duration processing is also distinctly sensorimotor, and may also rely on similar channels. Evidence either for or against this would likely be of great interest.

-For the duration task, there was no fast tapping condition. Why not? Was this to keep the overall task length short?

-The number of subjects/trials seems a bit odd. Why did some subjects perform both and not others? The targets say they were presented "between 25 and 30 times", but why was this variable at all?

-For the PCA analysis, my read of the methods and results is that this was done on all the data, across subjects. If the data were run on individual subjects and the resulting PCA components averaged, would the same results be found?

-For the data presented in Figure 2, it would be helpful to also see individual subject data underlaid on the plots to get a sense of individual differences. For the reproduced number, these will likely be clustered together given how small the error bars are, but for the WF data it may show how consistently "flat" the data are. Indeed, in other magnitude reproduction tasks, it is not uncommon to see the WF decrease as a function of target magnitude (or even increase). It may be possible that the reason for the observed findings is that some subjects get more variable (higher WFs) with larger target numbers and others get less variable (lower WFs).

-Regarding the two-channel model, I wonder how much the results would translate to different ranges of numerosities? For example, are the two channels supported here specific to these ranges of low and high numbers, or would there be a re-mapping to a higher range (say, 32 to 64 dots) or to a narrower range (say 16 to 32 dots). It would be helpful to know if there is any evidence for this kind of remapping.

Reviewer #2 Public Review

The authors wish to apply established psychophysical methods to the study of number. Specifically, they wish to test the hypothesis - supported by their previous work - that human sensorimotor processes are tuned to specific number ranges. In a novel set of tasks, they ask participants to tap a button N times (either fast or slow), where N varies between 8 and 32 across trials. As I understood it, they then computed the Weber fraction (WF) for each participant for each number and correlated those values across participants and numbers. They find stronger correlations for nearby numbers than for distant numbers and interpret this as evidence of sensorimotor tuning functions. Two other analyses - cluster analyses and principal component analyses (PCA) - suggest that participants' performance relied on at least 2 mechanisms, one for encoding low numbers of taps (around 10) and another for encoding larger numbers (around 27).

Strengths

Individual differences can be a rich source of scientific insight and I applaud the authors for taking them seriously, and for exploring new avenues in the study of numerical cognition.

Weaknesses

Inter-subject-correlation<br /> The experiment "is based on the idea that interindividual variability conveys information that can reveal common sensory processes (Peterzell & Kennedy, 2016)" but I struggled to understand the logic of this technique. The authors explain it most clearly when they write "Regions of high intercorrelation between neighbouring stimuli intensity can be interpreted to imply that sets of stimuli are processed by the same (shared) underlying channel. This channel, while responding relatively more to its preferred stimulus, will also be activated by neighbouring stimuli that although slightly different from the preferred intensity, are nevertheless included in the same response distribution." As I understood it, the correlations are performed "between participants, for all targets values" - meaning that they are measuring the extent to which different participants' WFs vary together. But why is this a good measure of channels? This analysis seems to assume that if people have channels for numerical estimation, they will have the same channels, tuned to the same numerical ranges. But this is an empirical question - individual participants could have wildly different channels, and perhaps different numbers of channels (even in the tested range). If they do, then this between-subject analysis would mask these individual differences (despite the subtitle).

Different channels<br /> I had trouble understanding much of the analyses, and this may account for at least some of my confusion. That said, as I understand it, the results are meant to provide "evidence that tuned mechanisms exist in the human brain, with at least two different tunings" because of the results of the clustering analysis and PCA. However, as the authors acknowledge, "PCA aims to summarize the dataset with the minimal number of components (channels). We can therefore not exclude the possible existence of more than two (perhaps not fully independent) channels." So I believe this technique does not provide more evidence for the existence of 2 channels as for the existence of 4 or 8 or 11 channels, the upper bound for a task testing 11 different numbers. If we can conclude that people may have one channel per number, what does "channel" mean?

Several other questions arose for me when thinking through this technique. If people did have two channels (at least in this range), why would they be so broad? Why would they be centered so near the ends of the tested range? Can such effects be explained by binning on the part of the participants, who might have categorized each number (knowingly or not) as either "small" or "large"? Whereas the experiment tested numbers 8-32, numbers are infinite - How could a small number of channels cover an infinite set? Or even the set 8-10,000? More broadly, I was unsure what advantages channels would have - that is - how in principle would having distinct channels for processing similar stimuli improve (rather than impede) discrimination abilities?

No number perception<br /> I was uncertain about the analogy to studies of other continuous dimensions like spatial frequency, motion, and color. In those studies, participants view images with different spatial frequency, motion, or color - the analogy would be to see dot arrays containing different numbers of dots. Instead, here participants read written numerals (like "19"), symbols which themselves do not have any numerical properties to perceive. How does that difference change the interpretation of the effects? One disadvantage of using numerals is that they introduce a clear discontinuity: Our base-10 numerical system artificially chunks integers into decades, potentially causing category-boundary effects in people's reproductions.

Sensorimotor<br /> The authors wished to test for "sensorimotor mechanisms selective to numerosity" but it's not clear what makes their effects sensorimotor (or selective to numerosity, see below). It's true they found effects using a tapping task (which like all behavior is sensorimotor), but it's not clear that this effect is specific to sensorimotor number reproduction. They might find similar effects for numerical comparison or estimation tasks. Such findings would suggest the effect may be a general feature of numerical cognition across modalities.

Specific to numbers<br /> The authors argue that their effects are "number selective" but they do not provide compelling evidence for this selectivity. In principle, their main findings could be explained by the duration of tapping rather than the number of taps. They argue this is unlikely for two reasons. The first reason is that the overall pattern of results was unchanged across the fast and slow tapping conditions, but differences in duration were confounded with numerosity in both conditions, so the comparison is uninformative. (Given this, I am not sure what we stand to learn by comparing the two tapping speeds.) The second reason is that temporal reproduction was less precise in their control condition than numerical reproduction, but this logic is unclear: Participants could still use duration (or some combination of speed and duration) as a helpful cue to numerosity, even if their duration reproductions were imperfect.

If the authors wish to test the role of duration, they might consider applying the same analytical techniques they use for numbers to their duration data. Perhaps participants show similar evidence for duration-selective channels, in the absence of number, as they do for other non-numerical domains (like spatial frequency).

Theories of numerical cognition. An expansive literature on numerical cognition suggests that many animals, human children, and adults across cultures have two systems for representing numerosity without counting - one that can represent the exact cardinality of sets smaller than about 4 and another that represents the approximate number of larger sets (but see Cheyette & Piantadosi, 2020). The current paper would benefit from better relating its findings to this long lineage of theories and findings in numerical approximation across cultures, ages, and species.

Reviewer #1 (Public Review):

The study presents an extensive computational approach to identify the motor neuron input from the characteristics of single motor neuron discharge patterns during a ramp up/down contraction. This reverse engineering approach is relevant due to limitations in our ability to estimate this input experimentally. Using well-established models of single motor neurons, a (very) large number of simulations were performed that allowed identification of this relation. In this way, the results enable researchers to measure motor neuron behavior and from those results determine the underlying neural input scheme. Overall, the results are very convincing and represent an important step forward in understanding the neural strategies for controlling movement.

Reviewer #1 (Public Review):

Summary:<br /> In this study, Basha and colleagues aim to test whether the thalamic nucleus reuniens can facilitate the hippocampus/prefrontal cortex coupling during sleep. Considering the importance of sleep in memory consolidation, this study is important to understand the functional interaction between these three majorly involved regions. This work suggests that the thalamic nucleus reuniens has a functional role in synchronizing the hippocampus and prefrontal cortex.

Strengths:<br /> The authors performed recordings in naturally sleeping cats, and analysed the correlation between the main slow wave sleep oscillatory hallmarks: slow waves, spindles, and hippocampal ripples, and with reuniens' neurons firing. They also associated intracellular recordings to assess the reuniens-prefrontal connectivity, and computational models of large networks in which they determined that the coupling of oscillations is modulated by the strength of hippocampal-thalamic connections.

Weaknesses:<br /> The authors' main claim is made on slow waves and spindle coupling, which are recorded both in the prefrontal cortex and surprisingly in reuniens. Known to be generated in the cortex by cortico-thalamic mechanisms, the slow waves and spindles recorded in reuniens show no evidence of local generation in the reuniens, which is not anatomically equipped to generate such activities. Until shown differently, these oscillations recorded in reuniens are most likely volume-conducted from nearby cortices. Therefore, such a caveat is a major obstacle to analysing their correlation (in time or frequency domains) with oscillations in other regions.

Finally, the choice of the animal model (cats) is the best suited one, as too few data, particularly anatomical ones regarding reuniens connectivity, are available to support functional results.

Reviewer #2 (Public Review):

Summary:<br /> The interplay between the medial prefrontal cortex and ventral hippocampal system is critical for many cognitive processes, including memory and its consolidation over time. A prominent idea in recent research is that this relationship is mediated at least in part by the midline nucleus reuniens with respect to consolidation in particular. Whereas the bulk of evidence has focused on neuroanatomy and the effects of temproary or permanent lesions of the nucleus reuniens, the current work examined the electrophysiology of these three structures and how they inter-relate, especially during sleep, which is anticipated to be critical for consolidation. They provide evidence from intercellular recordings of the bi-directional functional connectivity among these structures. There is an emphasis on the interactions between these regions during sleep, especially slow-wave sleep. They provide evidence, in cats, that cortical slow waves precede reuniens slow waves and hippocampal sharp-wave ripples, which may reflect prefrontal control of the timing of thalamic and hippocampal events, They also find evidence that hippocampal sharp wave ripples trigger thalamic firing and precede the onset of reuniens and medial prefrontal cortex spindles. The authors suggest that the effectiveness of bidirectional connections between the reuniens and the (ventral) CA1 is particularly strong during non-rapid eye movement sleep in the cat. This is a very interesting, complex study on a highly topical subject.

Strengths:<br /> An excellent array of different electrophysiological techniques and analyses are conducted. The temporal relationships described are novel findings that suggest mechanisms behind the interactions between the key regions of interest. These may be of value for future experimental studies to test more directly their association with memory consolidation.

Weaknesses:<br /> Given the complexity and number of findings provided, clearer explanation(s) and organisation that directed the specific value and importance of different findings would improve the paper. Most readers may then find it easier to follow the specific relevance of key approaches and findings and their emphasis. For example, the fact that bidirectional connections exist in the model system is not new per se. How and why the specific findings add to existing literature would have more impact if this information was addressed more directly in the written text and in the figure legends.

Reviewer #1 (Public Review):

Summary: By elevating Ca influx and inducing PTP, the authors have maximised the release probability. In this condition, the release probability is nearly one. Under such a condition, the release site can release another vesicle in a short time. By analyzing mean, variance and covariance, the authors propose a release model that each release site contains a docking site and a replacement site. They excluded the LS-TS model (Neher and Brose) based on discrepancy between model and the data (mean and covariance).

Strengths: The authors have used a minimal stimulation and modelling nicely to look into stochastic nature of release sites with good resolution. This cannot be done at other synapses. Overall conclusions are reasonable and convincing.

Weaknesses: The interpretation is somewhat model-dependent, and it is unclear if the interpretation is unique. For example, it is unclear if the heterogeneous release probability among sites, silent sites, can explain the results. However, the authors discuss these potential caveats in a fair manner and argue that their model is very likely to be the best so far.

Reviewer #2 (Public Review):

Summary:<br /> Silva et al. describe an experimental study conducted on cerebellar parallel fiber-to-molecular interneuron synapses to investigate the size of the readily releasable pool (RRP) of synaptic vesicles (SVs) per docking site in response to trains of action potentials. The study aims to determine whether there are multiple binding sites for SVs at each docking site, which could lead to a higher RRP size than previously thought.

The researchers used this glutamatergic synapse to conduct their experiments. They employed various techniques and manipulations to enhance release probability, docking site occupancy, and synaptic depression. By counting the number of released SVs in response to action potential trains and normalizing the results based on the number of docking sites, they estimated the RRP size per docking site.

The key findings and observations in the manuscript are as follows:

Docking Site Occupancy and Release Probability Enhancement: The researchers used 4-amidopyridine (4-AP) and post-tetanic potentiation (PTP) protocols to enhance the release probability of docked SVs and the occupancy of docking sites, respectively.

Synchronous and Asynchronous Release: Synchronous release refers to SVs released in response to individual action potentials, while asynchronous release involves SVs released after the initial release response due to calcium elevation. The study observed changes in the balance between synchronous and asynchronous release under different conditions, revealing the degree of filling of the RRP.

Modeling of Release Dynamics: The researchers employed a modeling approach based on the "replacement site/docking site" (RS/DS) model, where SVs bind to a replacement site before moving to a docking site and eventually undergoing release. The model was adjusted to experimental conditions to estimate parameters like docking site occupancy and release probabilities.

Comparison of Different Models: The study compared the RS/DS model with an alternative model known as the "loosely docked/tightly docked" (LS/TS) model. The LS/TS model assumes that a docking site can only accommodate one SV at a time, while the RS/DS model considers the possibility of accommodating multiple SVs.

Maximum RRP Size: Through a combination of experimental results and model simulations, the study revealed that the maximum RRP size per docking site reached close to two SVs under certain conditions, supporting the idea that each docking site can accommodate multiple SVs.

Strengths:<br /> The study is rigorously conducted and takes into consideration previous work of RRP size and SV docking site estimation. The study addresses a long-standing question in synaptic physiology.

Weaknesses:<br /> It remains unclear how generalizable the findings are to other types of synapses.

Reviewer #1 (Public Review):

Activity has effects on the development of neural circuitry during almost any step of neuronal differentiation. In particular during specific time periods of circuit development, so called critical periods (CP), altered neural activity can induce permanent changes of neuronal and network excitability. In complex neural networks it is often difficult to pinpoint the specific network components that are permanently altered by activity, and it often remains unclear how activity is integrated during the CP to set mature network excitability. This study combines electrophysiology with pharmacological and optogenetic manipulation in the Drosophila genetic model system to pinpoint the neural substrate that is influenced by altered activity during a critical period (CP) of larval locomotor circuit development. Moreover, it is then tested whether and how different manipulations of synaptic input are integrated during the CP to tune network excitability.

Strengths: Based on previous work, during the CP network activity is increased by feeding the GABA-AR antagonist PTX. This results in permanent network activity changes as highly convincingly assayed by a prolonged recovery period following induced seizure and by altered intersegmental locomotor network coordination. This is then used to provide two important findings: First, compelling electro- and optophysiological as well as anatomical experiments track the site of network change down to the level of single neurons and pre- versus postsynaptic specializations. In short, increased activity during the CP increases both, the magnitude of excitatory and inhibitory synaptic transmission to the aCC motoneuron, but excitation is affected more strongly. This results in altered excitation inhibition ratios. Fine electrophysiology shows that excitatory synapse strengthening occurs postsynaptically. High quality anatomy shows that dendrite size and numbers of synaptic contacts remain unaltered. It is a major accomplishment to track the tuning of network excitability during the CP down to the physiology of specific synapses at identified neurons.<br /> Second, additional experiments with single neuron resolution demonstrate that during the CP different forms of activity manipulation are integrated so that opposing manipulations can rescue altered setpoints. This provides novel insight into how developing neural network excitability is tuned, and it indicates that during the CP training can rescue the effects of hyperactivity.

Weaknesses: There are no major weaknesses to the findings presented, but the molecular cause that underlies increased motoneuron postsynaptic responsiveness as well as the mechanism that integrates different forms of activity during the CP remain unknown. However, the discussion addresses this point adequately.

Reviewer #2 (Public Review):

SUMMARY: In this study, the authors use the tractable Drosophila embryonic/larval motor circuit to determine how manipulations to activity during a critical period (CP) modify the circuit in ways that persist into later developmental stages. Previously, this group demonstrated that manipulations to the aCC/MN-Ib neuron in embryonic stages enhance (or can rescue) susceptibility to seizures at later larval stages. Here, the authors demonstrate that following enhanced excitatory drive (by PTX feeding), the aCC neuron acquires increased sensitivity to cholinergic excitatory transmission, presumably due to increased postsynaptic receptor abundance and/or sensitivity, although this is not clarified. Although locomotion is not altered at later developmental larval stages, the authors suggest there is reduced "robustness" to induced seizures. The second part of the study then goes on to enhance inhibition during the CP in an attempt to counteract the enhanced excitation, and show that many aspects of the CP plasticity are rescued. The author conclude that "average" E/I activity is integrated during the CP to determine excitability of the mature locomotor network.

Overall, this study provides compelling mechanistic insight into how a final motor output neuron changes in response to enhanced excitatory drive during a CP to change functionality of the circuit at later mature developmental stages. The first part of this study is strong, clearly showing the changes in the aCC neuron that result from enhanced excitatory input. This includes very nice electrophysiology and imaging data that assess synaptic function and structure onto aCC neurons from pre-motor inputs resulting from PTX exposure during development. However, the later experiments in Figures 6 and 7 designed to counteract the CP plasticity are somewhat difficult to interpret. In particular, the specificity of the manipulations of the ch neuron intended to counteract the CP plasticity is unclear, given the complexities of how these changes impact excitability all neurons during development. It is clear that CP plasticity is largely rescued in later stages, but it is hard to know if downstream or secondary adaptations may be masking the PTX-induced plasticity normally observed. Nonetheless, this study provides an important advance in our understanding of what parameters change during CPs to calibrate network dynamics at later developmental stages.

Reviewer #3 (Public Review):

Summary:<br /> In Hunter, Coulson et al, the authors seek to expand our understanding of how neural activity during developmental critical periods might control the function of the nervous system later in life. To achieve increased excitation, the authors build on their previous results and apply picrotoxin 17-19 hours after egg-laying, which is a critical period of nervous system development. This early enhancement of excitation leads to multiple effects in third-instar larvae, including prolonged recovery from electroshock, increased synchronization of motor neuron networks, and increased AP firing frequency. Using optogenetics and whole-cell patch clamp electrophysiology, the authors elegantly show that picrotoxin-induced over-excitation leads to increased strength of excitatory inputs, and not loss of inhibitory inputs. To enhance inhibition, the authors chose an approach that involved stimulation of mechanosensory neurons; this counteracts picrotoxin-induced signs of increased excitation. This approach to enhancing inhibition requires further validation.

Strengths:<br /> • The authors confirm their previous results and show that 17-19 hours after egg laying is a critical period of nervous system development.<br /> • Using Ca2+/Sr2+ substitutions, the authors demonstrate that synaptic connections between A18a & aCC show increased mEPSP amplitudes. The authors show that this aCC input is what is driving enhanced excitation.<br /> • The authors demonstrate that the effects of over-excitation attributed to picrotoxin exposure are generalizable and also occur in bss mutant flies.

Weaknesses:<br /> • The authors build on their previous work and argue that the critical period (17-19h after egg-laying) is a uniquely sensitive period of development. Establishing the developmental window of the critical period is important for the present study. The present study would benefit from demonstrating that exposure to picrotoxin at L1 or L2 do not lead to changes in induced seizure at L3. This would further the authors hypothesis of the criticality of the 17-19h AEL period.<br /> • The ch-related experiments require further controls and explanation. Regarding experiments in Fig 6, what is the effect of ch neuron stimulation alone on time lag and AP frequency? The authors report related pilot experiments have been performed; the present study would be strengthened with inclusion of these data.

Reviewer #1 (Public Review):

The authors presented a new MNase-based proximity ligation method called MChIP-C, allowing for the measurement of protein-mediated chromatin interactions at single-nucleosome resolution on a genome-wide scale. With improved resolution and sensitivity, they explored the spatial connectivity of active promoters and identified the potential candidates for establishing/maintaining E-P interactions. Finally, with published CRISPRi screens, they found that most functionally verified enhancers do physically interact with their cognate promoters, supporting the enhancer-promoter looping model.

The study's experimental approach and findings are interesting. However, several issues need to be addressed.

1. The authors described that "the lack of interaction between experimentally-validated enhancers and their cognate promoters in some studies employing C-methods has raised doubts regarding the classical promoter-enhancer looping model", so it's intriguing to see whether the MChIP-C could indeed detect the E-P interactions which were not identified by C-methods as they mentioned (Benabdallah et al., 2019; Gupta et al., 2017). I agree that they identified more E-P interactions using MChIP-C, but specifically, they should show at least 2-3 cases. It's important since this is the main conclusion the authors want to draw.

2. The authors compared their data to those of Chen et al. (Chen et al., 2022), who used PLAC-seq with anti-H3K4me3 antibodies in K562 cells and standard Micro-C data previously reported for K562, concluding that "MChIP-C achieves superior sensitivity and resolution compared to C-methods based on standard restriction enzymes.". This is not convincing since they only compared their data to one dataset. More datasets from other cell lines should be included.

3. The reasons for choosing Chen's data (Chen et al., 2022) and CRISPRi screens (Fulco et al., 2019; Gasperini et al., 2019) should be provided since there are so many out there.

4. The authors identify EP300 histone acetyltransferase and the SWI/SNF remodeling complex as potential candidates for establishing and/or maintaining enhancer-promoter interactions, but not RNA polymerase II, mediator complex, YY1, and BRD4. More explanation is needed for this point since they're previously suggested to be associated with E-P interactions.

5. The limitations of the method should be discussed.

Reviewer #2 (Public Review):

Summary:<br /> Golov et al performed the capture of MChIP-C using the H3K4me3 antibody. The new method significantly increases the resolution of Micro-C and can detect clear interactions which are not well described in the previous HiChIP/PLAC-seq method. Overall, the paper represents a significant technological advance that can be valuable to the 3D genomic field in the future.

Strengths:<br /> 1. The authors established a novel method to profile the promoter center genomic interactions based on the Micro-C method. Such a method could be very useful to dissect the enhancer promoter interaction which has long been an issue for the popular HiC method.

2. With the MChIP-C method the authors are able to find new genomic interactions with promoter regions enriched in CTCF. The author has significantly increased the detection sensitivity of such methods as PLAC-seq, Micro-C, and HiChIP.

3. The authors identified a new type of interaction between the CTCF-less promoter and the CTCF binding site. This particular type of interaction could explain the CTCF's function in regulating gene transcription activity as observed in many studies. I personally think the second stripe model of P-CTCF interaction is more likely as this has been proposed for the super-enhancer stripe model before. The author should also discuss this part of the story more.

Weaknesses:<br /> 1. The data presentation should include the contact heat map. The current data presentation makes it hard for the readers to have a comprehensive view of pair-wise interactions between promoters and the PIR. In particular, these maps may directly give answers to the proposed model of promoter-CTCF interactions by the authors in Figure 3a.

2. In Fig 3D, there seems a very limited increase of power predicting MChIP-C signal for DHS-promoter pairs beyond the addition of CTCF. This figure could be simplified with fewer factors.

3. The current method seems to have a big fraction of unusable reads. How the authors process the data should be included to allow for future reproduction. Ideally, the authors should generate a package on R or Bioconda for this processing.

Reviewer #3 (Public Review):

Summary:<br /> This manuscript represents a technological development- specifically a micrococcal nuclease chromatin capture approach, termed MChIP-C to identify promoter-centered chromatin interactions at single nucleosome resolution via a specific protein, similar to HiChIP, ChIA-PET, etc.. In general, the manuscript is technically well done. Two major issues raise concerns that need to be addressed. First, it does not appear that novel chromatin interactions identified by MChIP-C which were missed by other approaches such as HiChIP, were validated. This is central to the argument of "improved" sensitivity, which is one of the key factors to assess sensitivity. Second is the question of resolution. Because the authors focus on a histone mark (H3K4me3) it is unclear whether the resolution of the assay truly exceeds other approaches, especially microC. These two issues are not completely supported by the data provided.

Strengths:<br /> 1) The method appears to hold promise to improve both the sensitivity and resolution of protein-centered chromatin capture approaches.

Weaknesses:<br /> 1) Specific validation experiments to demonstrate the identification of previously missed novel interactions are missing.

2) It is unclear if the resolution is really superior based on the data provided.

3) It is unclear how much advantage the approach has, especially compared to existing approaches such as HiChIP< since sequencing depth as a variable is not adequately addressed.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript considers a mechanistic extension of MacArthur's consumer-resource model to include chasing down food and potential encounters between the chasers (consumers) that lead to less efficient feeding in the form of negative feedback. After developing the model, a deterministic solution and two forms of stochastic solutions are presented, in agreement with each other. Finally, the model is applied to explain observed coexistence and rank-abundance data.

Strengths:<br /> - The application of the theory to natural rank-abundance curves is impressive.<br /> - The comparison with the experiments that reject the competitive exclusion principle is promising. It would be fascinating to see if in, e.g. insects, the specific interference dynamics could be observed and quantified and whether they would agree with the model.<br /> - The results are clearly presented; the methods adequately described; the supplement is rich with details.<br /> - There is much scope to build upon this expansion of the theory of consumer-resource models. This work can open up new avenues of research.

Weaknesses:<br /> - I am questioning the use of carrying capacity (Eq. 4) instead of using nutrient limitation directly through Monod consumption (e.g. Posfai et al. who the authors cite). I am curious to see how these results hold or are changed when Monod consumption is used.

- Following on the previous comment, I am confused by the fact that the nutrient consumption term in Eq. 1 and how growth is modeled (Eq. 4) are not obviously compatible and would be hard to match directly to experimentally accessible quantities such as yield (nutrient to biomass conversion ratio). Ultimately, there is a conservation of mass ("flux balance"), and therefore the dynamics must obey it. I don't quite see how conservation of mass is imposed in this work.

- These models could be better constrained by more data, in principle, thereby potential exists for a more compelling case of the relevance of this interference mechanism to natural systems.

- The underlying frameworks, B-D and MacArthur are not properly exposed in the introduction, and as a result, it is not obvious what is the specific contribution in this work as opposed to existing literature. One needs to dig into the literature a bit for that. The specific contribution exists, but it might be more clearly separated and better explained. In the process, the introduction could be expanded a bit to make the paper more accessible, by reviewing key features from the literature that are used in this manuscript.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Kang et al investigates how the consideration of pairwise encounters (consumer-resource chasing, intraspecific consumer pair, and interspecific consumer pair) influences the community assembly results. To explore this, they presented a new model that considers pairwise encounters and intraspecific interference among consumer individuals, which is an extension of the classical Beddington-DeAngelis (B-D) phenomenological model, incorporating detailed considerations of pairwise encounters and intraspecific interference among consumer individuals. Later, they connected with several experimental datasets.

Strengths:<br /> They found that the negative feedback loop created by the intraspecific interference allows a diverse range of consumer species to coexist with only one or a few types of resources. Additionally, they showed that some patterns of their model agree with experimental data, including time-series trajectories of two small in-lab community experiments and the rank-abundance curves from several natural communities. The presented results here are interesting and present another way to explain how the community overcomes the competitive exclusion principle.

Weaknesses:<br /> The authors only explore the case with interspecific interference or intraspecific interference exists. I believe they need to systematically investigate the case when both interspecific and intraspecific interference exists. In addition, the text description, figures, and mathematical notations have to be improved to enhance the article's readability. I believe this manuscript can be improved by addressing my comments, which I describe in more detail below.

1. In nature, it is really hard for me to believe that only interspecific interference or intraspecific interference exists. I think a hybrid between interspecific interference and intraspecific interference is very likely. What would happen if both the interspecific and intraspecific interference existed at the same time but with different encounter rates? Maybe the authors can systematically explore the hybrid between the two mechanisms by changing their encounter rates. I would appreciate it if the authors could explore this route.

2. In the first two paragraphs of the introduction, the authors describe the competitive exclusion principle (CEP) and past attempts to overcome the CEP. Moving on from the first two paragraphs to the third paragraph, I think there is a gap that needs to be filled to make the transition smoother and help readers understand the motivations. More specifically, I think the authors need to add one more paragraph dedicated to explaining why predator interference is important, how considering the mechanism of predator interference may help overcome the CEP, and whether predator interference has been investigated or under-investigated in the past. Then building upon the more detailed introduction and movement of predator interference, the authors may briefly introduce the classical B-D phenomenological model and what are the conventional results derived from the classical B-D model as well as how they intend to extend the B-D model to consider the pairwise encounters.

3. The notations for the species abundances are not very informative. I believe some improvements can be made to make them more meaningful. For example, I think using Greek letters for consumers and English letters for resources might improve readability. Some sub-scripts are not necessary. For instance, R^(l)_0 can be simplified to g_l to denote the intrinsic growth rate of resource l. Similarly, K^(l)_0 can be simplified to K_l. Another example is R^(l)_a, which can be simplified to s_l to denote the supply rate. In addition, right now, it is hard to find all definitions across the text. I would suggest adding a separate illustrative box with all mathematical equations and explanations of symbols.

4. What is the f_i(R^(F)) on line 131? Does it refer to the growth rate of C_i? I noticed that f_i(R^(F)) is defined in the supplementary information. But please ensure that readers can understand it even without reading the supplementary information. Otherwise, please directly refer to the supplementary information when f_i(R^(F)) occurs for the first time. Similarly, I don't think the readers can understand \Omega^\prime_i and G^\prime_i on lines 135-136.

Reviewer #3 (Public Review):

Summary:<br /> A central question in ecology is: Why are there so many species? This question gained heightened interest after the development of influential models in theoretical ecology in the 1960s, demonstrating that under certain conditions, two consumer species cannot coexist on the same resource. Since then, several mechanisms have been shown to be capable of breaking the competitive exclusion principle (although, we still lack a general understanding of the relative importance of the various mechanisms in promoting biodiversity).

One mechanism that allows for breaking the competitive exclusion principle is predator interference. The Beddington-DeAngelis is a simple model that accounts for predator interference in the functional response of a predator. The B-D model is based on the idea that when two predators encounter one another, they waste some time engaging with one another which could otherwise be used to search for resources. While the model has been influential in theoretical ecology, it has also been criticized at times for several unusual assumptions, most critically, that predators interfere with each other regardless of whether they are already engaged in another interaction. However, there has been considerable work since then which has sought either to find sets of assumptions that lead to the B-D equation or to derive alternative equations from a more realistic set of assumptions (Ruxton et al. 1992; Cosner et al. 1999; Broom et al. 2010; Geritz and Gyllenberg 2012). This paper represents another attempt to more rigorously derive a model of predator interference by borrowing concepts from chemical reaction kinetics (the approach is similar to previous work: Ruxton et al. 1992). The main point of difference is that the model in the current manuscript allows for 'chasing pairs', where a predator and prey engage with one another to the exclusion of other interactions, a situation Ruxton et al. (1992) do not consider. While the resulting functional response is quite complex, the authors show that under certain conditions, one can get an analytical expression for the functional response of a predator as a function of predator and resource densities. They then go on to show that including intraspecific interference allows for the coexistence of multiple species on one or a few resources, and demonstrate that this result is robust to demographic stochasticity.

Strengths:<br /> I appreciate the effort to rigorously derive interaction rates from models of individual behaviors. As currently applied, functional responses (FRs) are estimated by fitting equations to feeding rate data across a range of prey or predator densities. In practice, such experiments are only possible for a limited set of species. This is problematic because whether a particular FR allows stability or coexistence depends on not just its functional form, but also its parameter values. The promise of the approach taken here is that one might be able to derive the functional response parameters of a particular predator species from species traits or more readily measurable behavioral data.

Weaknesses:<br /> The main weakness of this paper is that it devotes the vast majority of its length to demonstrating results that are already widely known in ecology. We have known for some time that predator interference can relax the CEP (e.g., Cantrell, R. S., Cosner, C., & Ruan, S. 2004).

While the model presented in this paper differs from the functional form of the B-D in some cases, it would be difficult to formulate a model that includes intraspecific interference (that increases with predator density) that does not allow for coexistence under some parameter range. Thus, I find it strange that most of the main text of the paper deals with demonstrating that predator interference allows for coexistence, given that this result is already well known. A more useful contribution would focus on the extent to which the dynamics of this model differ from those of the B-D model.

The formulation of chasing-pair engagements assumes that prey being chased by a predator are unavailable to other predators. For one, this seems inconsistent with the ecology of most predator-prey systems. In the system in which I work (coral reef fishes), prey under attack by one predator are much more likely to be attacked by other predators (whether it be a predator of the same species or otherwise). I find it challenging to think of a mechanism that would give rise to chased prey being unavailable to other predators. The authors also critique the B-D model: "However, the functional response of the B-D model involving intraspecific interference can be formally derived from the scenario involving only chasing pairs without predator interference (Wang and Liu, 2020; Huisman and De Boer, 1997) (see Eqs. S8 and S24). Therefore, the validity of applying the B-D model to break the CEP is questionable.".

However, the way "chasing pairs" are formulated does result in predator interference because a predator attacking prey interferes with the ability of other predators to encounter the prey. I don't follow the author's logic that B-D isn't a valid explanation for coexistence because a model incorporating chasing pairs engagements results in the same functional form as B-D.

More broadly, the specific functional form used to model predator interference is of secondary importance to the general insight that intraspecific interference (however it is modeled) can allow for coexistence. Mechanisms of predator interference are complex and vary substantially across species. Thus it is unlikely that any one specific functional form is generally applicable.

Joint Public Review:

This paper explores how minimal active matter simulations can model tissue rheology, with applications to the in vivo situation of zebrafish morphogenesis. The authors explore the idea of active noise, particle softness and size heterogeneity cooperating to give rise to surprising features of experimental tissue rheologies (in particular an increase and then a plateau in viscosity with fluid fraction). In general, the paper is interesting from a theoretical standpoint, by providing a bridge between concepts from jamming of particulate systems and experiments in developmental biology. The idea of exploring a free space picture in this context is also interesting. It will be interesting in the future to see whether and how the findings change when considering 3D tissues with less size heterogeneity or how viscosity is impacted by the time scale of measurements.

Reviewer #1 (Public Review):

Gazula and co-workers presented in this paper a software tool for 3D structural analysis of human brains, using slabs of fixed or fresh brains. This tool will be included in Freesurfer, a well-known neuroimaging processing software. It is possible to reconstruct a 3D surface from photographs of coronal sliced brains, optionally using a surface scan as a model. A high-resolution segmentation of 11 brain regions is produced, independent of the thickness of the slices, interpolating information when needed. Using this method, the researcher can use the sliced brain to segment all regions, without the need for ex vivo MRI scanning.

The software suite is freely available and includes 3 modules. The first accomplishes preprocessing steps, for correction of pixel sizes and perspective. The second module is a registration algorithm that registers a 3D surface scan obtained prior to sectioning (reference) to the multiple 2D slices. It is not mandatory to scan the surface - a probabilistic atlas can also be used as a reference - however, the accuracy is lower. The third module uses machine learning to perform the segmentation of 11 brain structures in the 3D reconstructed volume. This module is robust, dealing with different illumination conditions, cameras, lenses, and camera settings. This algorithm ("Photo-SynthSeg") produces isotropic smooth reconstructions, even in high anisotropic datasets (when the in-plane resolution of the photograph is much higher than the thickness), interpolating the information between slices.

To verify the accuracy and reliability of the toolbox, the authors reconstructed 3 datasets, using real and synthetic data. Real data of 21 postmortem confirmed Alzheimer's disease cases from the Massachusetts Alzheimer's Disease Research Center (MADRC) and 24 cases from the AD Research at the University of Washington (who were MRI scanned prior to processing) were employed for testing. These cases represent a challenging real-world scenario. Additionally, 500 subjects of the Human Connectome project were used for testing error as a continuous function of slice thickness. The segmentations were performed with the proposed deep-learning new algorithm ("Photo-SynthSeg") and compared against MRI segmentations performed to "SAMSEG" (an MRI segmentation algorithm, computing Dice scores for the segmentations. The methods are sound and statistically showed correlations above 0.8, which is good enough to allow volumetric analysis. The main strengths of the methods are the datasets used (real-world challenging and synthetic) and the statistical treatment, which showed that the pipeline is robust and can facilitate volumetric analysis derived from brain sections and conclude which factors can influence the accuracy of the method (such as using or not 3D scan and using constant thickness).

Although very robust and capable of handling several situations, the researcher has to keep in mind that processing has to follow some basic rules in order for this pipeline to work properly. For instance, fiducials and scales need to be included in the photograph, and the slabs must be photographed against a contrasting background. Also, only coronal slices can be used, which can be limiting for certain situations.

The authors achieved their aims, and the statistical analysis confirms that the machine learning algorithm performs segmentations comparable to the state-of-the-art of automated MRI segmentations.

Those methods will be particularly interesting to researchers who deal with post-mortem tissue analysis and do not have access to ex vivo MRI. Quantitative measurements of specific brain areas can be performed in different pathologies and even in the normal aging process. The method is highly reproducible, and cost-effective since it allows the pipeline to be applied by any researcher with small pre-processing steps.

The paper is very interesting and well structured, adding an important tool for fixed and fresh brain analysis. The software tool is robust and demonstrated good and consistent results in the hard task of managing automated segmentation from brain slices. In the future, segmentation of the histological slices could be developed and histological structures added (such as small brainstem nuclei, for instance). Also, dealing with axial and sagittal planes can be useful to some labs.

Reviewer #2 (Public Review):

Summary:<br /> The authors developed a tool-set Photo-SynthSeg for the software FreeSurfer which performs 3D reconstruction and high-resolution 3D segmentation on a stack of dissection photographs of brain tissues. The tool-set consists of three modules: the pre-processing module, which performs dissection photography correction; the registration module, which registers corrected dissection photographs based on 3D surface scan, ex vivo MRI or probabilistic atlas; the segmentation module based on U-Net. To prove the performance of the tools, three experiments were conducted, including a volumetric comparison of brain tissues on AD and HC groups from MADRC, a quantitative evaluation of segmentation on UW-ADRC and a quantitative evaluation of 3D reconstruction on HCP digitally sliced MRI data.

Strengths:<br /> The quantitative evaluation of segmentation and reconstruction on synthetic and real data demonstrates the accuracy of the methodology. Also, the successful application of this toolset on two brain banks with different slice thicknesses, tissue processing, and photograph settings demonstrates its robustness. The toolset also benefits from its adaptability of different 3D references, such as surface scans, ex vivo MRI, and even probabilistic atlas, suiting the needs of different brain banks.

Weaknesses:<br /> 1) The current method could only perform accurate segmentation on subcortical tissues. It is of more interest to accurately segment cortical tissues, whose morphometrics are more predictive of neuropathology. The authors also mentioned that they would extend the toolset to allow for cortical tissue segmentation in the future.

2) Brain tissues are not rigid bodies, so dissected slices could be stretched or squeezed to some extent. Also, dissected slices that contain temporal poles may have several disjoined tissues. Therefore, each pixel in dissected photographs may go through slightly different transformations. The authors constrain that all pixels in each dissected photograph go through the same affine transform in the reconstruction step probably due to concerns of computational complexity. But ideally, dissected photographs should be transformed with some non-linear warping or locally linear transformations. Or maybe the authors could advise how to place different parts of dissected slices when taking dissection photographs to reduce such non-linearity of transforms.

3) For the quantitative evaluation of the segmentation on UW-ARDC, the authors calculated 2D Dice scores on a single slice for each subject. Could the authors specify how this single slice is chosen for each subject? Is it randomly chosen or determined by some landmarks? It's possible that the chosen slice is between dissected slices so SAMSEG cannot segment accurately. Also from Figure 3, it seems that SAMSEG outperforms Photo-SynthSeg on large tissues, WM/Cortex/Ventricle. Is there an explanation for this observation?

4) In the third experiment, quantitative evaluation of 3D reconstruction, each digital slice went through random affine transformations and illumination fields only. However, it's better to deform digital slices using random non-linear warping due to the non-rigidity of the brain as mentioned in 2). So, the reconstruction errors estimated here are quite optimistic. It would be more realistic if digital slices were deformed using random non-linear warping.

Overall, this is quite useful a toolset that could be widely used in many brain banks without MRI scanners.

Reviewer #1 (Public Review):

Gumaste et al studied if a parameter of odor plumes, the intermittency can be detected by an animal species, such as mice that heavily rely on olfaction to navigate and search for food and mates, among other behaviors. They also ask if the animals can extract information from this to gain knowledge about the odor source. Intermittency is defined as the fraction of time an odorant is present at a sampled point within the odor plume space. Their findings could be summarized as follows: they found that animals are capable of detecting differences in intermittency levels and suggest that this parameter of odor plumes is important for odor-based navigation in mammals, as it has been seen in other animals such as flying insects. The authors used a combination of behavioral training while concomitantly performing calcium imaging of olfactory receptor neurons (input to the olfactory bulb) and also mitral cells (output of the olfactory bulb). They found that mice are able to behaviorally discriminate between odor plumes of high and low intermittency. Interestingly, they found that the response of both input and output neurons of the olfactory bulb is capable to encode the intermittency experienced by the animals. The methods utilized in this work are very well suited for the kind of questions that the authors are asking. The combination of behavior and imaging, as opposed to only anesthetized imaging gives the authors a lot of power to interpret their data. A very relevant point is the generation of the olfactory stimuli that will be used to test the animals. The authors go to great lengths to generate more naturalistic odorant stimulations, as opposed to the typically used square pulses. Although there are some issues that can be addressed, the authors succeeded in answering the questions they set at the beginning of this work, and their conclusions are supported by their experiments. This work would generate interest among a relatively broad audience because the issue presented here (how the temporal structure of the odor plume affects the detection and encoding of an odorant) is novel in mice olfactory research.

Reviewer #2 (Public Review):

The study from Gumaste et al investigates whether mice can use changes of intermittency, a temporal odor feature, to locate an odor source. First, the study tries to demonstrate that mice can discriminate between low and high intermittency and that their performance is not affected by the odor used or the frequency of odor whiffs. Then, they show that there is a correlation between glomerular responses (OSNs and mitral cells) and intermittency. Finally, they conclude that sniffing frequency impacts the behavioral discrimination of intermittency as well as its neural representation. Overall, the authors seek to demonstrate that intermittency is an odor-plume property that can inform olfactory navigation.

The paper explored an interesting question, the use of intermittency of an odor plume as a behavioral cue, which is a new and intriguing hypothesis. However, it falls short in demonstrating that the animal is actually sensitive to intermittency but not other flow parameters, and is missing some important details.

Major concerns

1) One of the cornerstones of this paper consists in showing that mice are behaviorally able to distinguish among different intermittency values (high or low), across a variety of different stimuli and without confounds such as the number of whiffs or concentration. However, I could not find in the paper a convincing explanation of how these confounds were tested. It is clear that the authors repeat their measurements in different conditions (low or high concentration, and different whiff numbers) but it is not specified how: do the authors mix all stimuli in the same session, and so the animals simply generalize across all the stimuli and only consider intermittency for the behavioral choices? Or do authors repeat different sessions for different parameters? For example: do they perform two separate sessions with low concentration and high concentration? If this last one is the case, I would argue that this is not enough proof that animals generalize across concentrations, as the animals might simply use concentration as a cue and change the decision criteria at each session. Please clarify.

2) It looks to me that the measure of intermittency strongly depends on the set. What is the logic of setting a specific threshold? Do the results hold when this threshold changes within a reasonable range? The same questions (maybe even more important) go for the measure of glomerular intermittence. Unfortunately, a sensitivity analysis for both measures is missing, which makes it hard to interpret the results.

3) The logic of choosing the decision boundary for the discrimination task is not clear: low intermittency is considered to be below 0.15 and high intermittency is considered to be between 0.2 and 0.8. Do these values correspond to natural intermittency distribution? How were these values chosen?

4) Only 2 odors were used in the whole study and some results were in disagreement between the two odors. By looking at only two odors it is very difficult to make a general conclusion about intermittency encoding in the OB.

5) Assuming that all the above issues are resolved, one can conclude that intermittency can be perceived by an animal. The study puts a strong accent on the fact that this feature could be used for navigation. I understand that it is extremely hard to demonstrate that this feature is actually used for navigation, however, the analysis of relevance of this measure is missing. Even if it is used in navigation, most probably this would be in combination with other features, thus its relative importance needs to be discussed, or even better, established.

Reviewer #3 (Public Review):

In this study, Gumaste et al. aim to determine whether mice can discriminate odor intermittency and whether the olfactory bulb encodes intermittency. Using a Go/No-Go task, the study first showed that mice can be trained to discriminate odor stimuli with a low versus high intermittency value. Next, the authors demonstrated that early olfactory processing in the OSNs and mitral/tufted cells encodes intermittency. Through calcium imaging of olfactory bulb glomeruli, they obtained the glomerular response properties across intermittency and demonstrated the effects of sniff frequency on the glomerular representation of intermittency. Although the results are expected based on previous literature, they do lend support to the notion that intermittency can be used for odor-guided navigation.

Strengths:

The counterbalanced olfactometer used in this study keeps the air flow constant while odor concentration changes. This design is very useful for experiments in which odor delivery needs to be precisely controlled.

In a Go/No-Go task, mice were successfully trained to discriminate CS+ versus CS- odor stimuli with high versus low intermittency values in three different stimulus types (termed naturalistic, binary naturalistic, and square wave).

The olfactory bulb glomerular activity (from either olfactory sensory neurons or mitral/tufted cells) was monitored while mice performing the behavioral tasks, supporting that intermittency coding could arise from early olfactory processing.

Weaknesses:

Alternative interpretations of the behavioral outcome could be better discussed. For instance, the odors delivered with high intermittency values may lead to higher odor concentrations that olfactory sensory neurons encounter in the mucus. Mice might discriminate the total amount of odors present in the mucus rather than intermittency.

The conclusion that intermittency encoding is odor specific and depends on the spatial patterning/intrinsic glomerular properties is only based on two odorants used in this study.

Reviewer #1 (Public Review):

With MERGEseq, the authors sought to develop a scalable and accessible method for getting both projectome and transcriptome information at the single-cell level from multiple projection targets within a single animal. MERGEseq uses a retro rAAV2 to deliver a 15-nucleotide barcode driven by a CAG promoter with co-expression of eGFP to enrich barcoded cells using FACS. Injection of this rAAV2 in distinct regions (with each injection region distinguished by a unique barcode that is specific to the virus used) allows retrograde trafficking and expression of the barcodes in cells that project to the injected region. In this manuscript, rAAVs harboring 5 unique barcodes were stereotactically delivered to 5 targets of the mouse: dorsomedial striatum (DMS), mediodorsal thalamic nucleus (MD), basal amygdala (BLA), lateral hypothalamus (LH), and agranular insular cortex (AI). After a 6-week period to allow for viral transduction and expression, the ventromedial prefrontal cortex (vmPFC) was harvested for scRNAseq. vmPFC scRNAseq data were validated against previously published PFC datasets, demonstrating that MERGEseq does not disrupt transcript expression and identifies the same principal cell types as annotated in previous studies. Importantly, MERGEseq enabled the identification of cell types in the vmPFC that project to distinct areas, with separation occurring largely based on cell type and cortical layer. The application of stringent criteria for barcode index determination is rigorous and improves confidence that barcoded cells are correctly identified. The observation that all barcoded cells were excitatory is consistent with prior work, although it is not clear if viral tropism contributes to this in some way. In a parallel experiment, FAC-sorted cells (vmPFC cells expressing EGFP) were isolated as a comparison. Notably, EGFP+ cells were exclusively excitatory neurons, consistent with literature showing PFC projection neurons are excitatory. Next, barcode analysis was combined with transcriptional identification of neuronal subtypes to define general projection patterns and single-cell projection patterns, which were validated by the DMS and MD in situ using retrograde tracing in combination with RNA FISH. MERGEseq data were also used to identify transcriptional differences between neurons with dedicated and bifurcated projections. DMS+LH and DMS+MD projecting neurons had distinct transcriptional profiles, unlike cells with other targets. RNA FISH for marker gene Pou3f and retrograde tracing from DMS+LH projecting cells demonstrate enrichment of this gene in this projection population. Finally, machine-learning was used to predict projection targets based on transcriptional profiles. In this dataset, 50 highly variable genes (HVGs) were optimal for predicting projection patterns, though this might vary in different circuits. Overall, the results of this manuscript are well presented and include rigorous validation for select vmPFC targets with in situ techniques. The application of unique barcodes for retro-AAV delivery is an accessible tool that other labs can implement to study other brain circuits.

Ultimately, MERGEseq is a subtle conceptual advancement over VECTORseq (retro-AAV delivered transgenes rather than barcodes, in combination with scRNAseq) that offers higher confidence in the described projectome diversity in comparison. The use of a retrograde AAV inherently limits the number of projection areas that can be assessed, a weakness compared to anterograde approaches such as MAPseq/BARseq. However, BARseq demands more time and resources; further, the use of the highly toxic Sindbis virus limits the application of this technique. This manuscript builds upon previous work by utilizing machine learning to predict projection targets. BARseq2 could be used to rigorously validate predicted projectomes and gain single-cell information regarding target neurons. Overall, MERGEseq is an accessible technique that can be used across many animal models and serve as an important starting point to define circuits at the single-cell level.

Reviewer #2 (Public Review):

Investigating the relationship between transcriptomic profiles, their axonal projection and collateralization patterns will help define neuronal cell types in the mammalian central nervous system. The study by Xu et al. combined multiple retrograde viruses with barcodes and single-cell RNA-sequencing (MERGE-seq) to determine the projection and collateralization patterns of transcriptomically defined ventral medial prefrontal cortex (vmPFC) projection neurons. They found a complex relationship: the same transcriptomically defined cell types project to multiple target regions, and the same target region receives input from multiple transcriptomic types of vmPFC neurons. Further, collateralization patterns of vmPFC to the five target regions they investigated are highly non-random.

While many of the biological conclusions are not surprising given recent studies on the collateralization patterns of vmPFC neurons using single neuron tracing and other methods that integrate transcriptomics and projections, MERGE-seq provides validation, at the single cell level, collateralization patterns of individual vmPFC neurons, and thus offer new and valuable information over what has been published. The method can also be used to study collateralization patterns of other neuron types.

Some of the conclusions the authors draw depend on the efficiency of retrograde labeling, which was not determined. Without quantitative information on retrograde labeling efficiency, and unless such efficiency is close to 100%, these conclusions are likely misleading.

Reviewer #3 (Public Review):

This manuscript describes a multiplexed approach for the identification of transcriptional features of neurons projecting to specific target areas at the single-cell level. This approach, called MERGE-seq, begins with multiplexed retrograde tracing by injecting distinctly barcoded rAAV-retro viruses into different target areas. The transcriptomes and barcoding of neurons in the source area are then characterized by single-cell RNA sequencing (scRNAseq) on the 10xGenomics platform. The projection targets of barcoded neurons in the source area can be inferred by matching the detected barcodes to the barcode sequences to of rAAV-retro viruses injected into the target areas.

The authors validated their approach by injecting five rAAV-retro GFP viruses, each encoding a different barcode, into five known targets of the ventromedial prefrontal cortex (vmPFC). The transcriptomes and barcoding of vmPFC neurons were then analyzed by scRNA-seq with or without enrichment of retrogradely labeled neurons based on GFP fluorescence. The authors confirmed the previously described heterogeneity of vmPFC neurons. In addition, they showed that most transcriptionally defined cell types project to multiple targets and that the five targets received projections from multiple transcriptomic types. The authors further characterized the transcriptomic features of barcoded vmPFC neurons with different projection patterns and defined Pou3f1 as a marker gene of neurons extending collateral branches to the dorsomedial striatum and lateral hypothalamus.

Overall, the results of the manuscript are convincing: the transcriptomic vmPFC cell types defined by scRNAseq in this study appear to correlate well with previous studies, the bifurcated projection patterns inferred by barcoding are validated using dual-color retro-AAV tracing, and marker genes for projection-specific cell subclasses are validated in retrogradely labeled vmPFC using RNA FISH for marker detection.

The concept of combining retrograde tracing and scRNAseq is not new. Previous studies have applied recombinase-expressing viruses capable of retrograde labeling, such as CAV, rabies virus, and AAV2-Retro, to retrogradely label and induce the expression of fluorescence markers in projection neurons, therefore facilitating enrichment and analysis of neurons projecting to a specific target. Multiplexed analysis can be achieved with the combination of different reporter viruses or viruses expressing different recombinases and appropriate reporter mouse lines. The advantages of MERGE-seq include that no transgenic lines are required and that it could be applied at even higher levels of multiplexity.

However, previously existing datasets that have already profiled this region with scRNAseq have not been utilized to their full extent. Therefore, for the proper context with prior literature, bioinformatic integration of these scRNAseq and prior scRNAseq data is needed.

Moreover, robust detection of barcodes in neurons labeled by barcoded AAV-retro viruses remains a challenge. The authors should clearly discuss the difficulties with barcode detection in this approach, as well as discuss potential solutions, which are important for others interested in its approach.

While this study is limited to the five known targets of vmPFC, the results suggest that MERGE-seq is a valuable tool that could be used in the future to characterize projection targets and transcriptomes of neurons in a multiplexed manner. As MERGE-seq uses AAVs to deliver barcodes, this method has the potential for application in model organisms for which transgenic lines are not available. Further improvements in experimental design and data analysis should be considered when applying MERGE-seq to poorly characterized source areas or with increased multiplexity of target areas.

In summary, this is a valuable approach, but the authors should clearly provide the context for their study within the existing literature, transparently discuss the limitations of MERGE-seq, as well as suggest improvements for the future.

Reviewer #1 (Public Review):

This study characterized the role of the Drosophila odorant-binding protein Obp56g in mediating post-mating responses in females. The authors show that this Obp56g is expressed in the male ejaculatory bulb, use genetic approaches to disrupt Obp56g, and show that males with disrupted Obp56g fail to form mating plugs in their mates.

Despite the fact that Obp56g deficient males generate and transfer sperm normally, the lack of formation of a functional mating plug leads to sperm loss in females mated to these males and thus a failure to induce normal post-mating responses.

This study identifies an unexpected role for an Obp and raises new questions about the function of this class of protein and the variety of roles that they may play in sensory function and reproduction.

Reviewer #2 (Public Review):

Here, Brown and colleagues report a valuable finding on the function and evolution of the seminal odorant-binding protein Obp56g in Drosophila melanogaster. Previous studies have shown that this family of proteins is highly expressed in olfactory tissues like the antennae and maxillary palps. Some of these proteins have been shown to mediate behavioural responses to specific odorants-hence the general moniker odorant binding proteins. This slightly misleading historical naming convention implies an exclusive role in olfaction-however, many of these proteins are expressed in other tissues of the animal, including the male reproductive system. In addition, seminal fluid proteins exhibit a fascinating evolutionary history, with rapid evolution and turnover across taxa.

The authors suggest that the Obp56g protein may have been co-opted for a reproductive role in Drosophila melanogaster during evolution. The authors show that Obp56g is required for male fertility and the induction of the post-mating response in females. Mutant males lacking Obp56g fail to form a mating plug in the female reproductive tract-leading to ejaculate loss and reduced sperm storage. The experimental evidence supporting the claims of the authors is solid and compelling. The data were collected and analyzed using solid and validated methodologies. The author's findings can be used as a starting point for understanding the mechanistic roles of this family of proteins in mating plug coagulation. The work will interest biologists studying non-sperm seminal fluid protein function and evolution.

Reviewer #3 (Public Review):

Male seminal fluid proteins play a crucial role in fertility and influence female physiology and behavior after mating. Brown et al. have discovered a new reproductive function for odorant-binding proteins (Obps) in Drosophila. The study shows that Obp56g is expressed in male reproductive tissues that produce seminal fluid proteins and is required for the formation of the mating plug in the mated female. The study demonstrates that RNAi knockdown and CRISPR/Cas9-generated mutations in Obp56g result in a defective mating plug, reduced sperm storage, and subsequent effects on female post-mating responses. The research also suggests that Obp56g has been co-opted for a reproductive function over evolutionary time, as supported by functional and comparative RNAseq data across Drosophila species. Finally, the study reports expression shifts, duplication, and divergence in the evolution of these seminal protein genes.

Overall, the study represents a significant contribution to our understanding of seminal proteins and their reproductive function. The creation of novel Obp mutants using CRISPR/Cas9 technology is a valuable resource for future research in the Drosophila community. The manuscript successfully conveys the key findings and their potential implications for the field. However, to reinforce the study's conclusions, more quantitative data is necessary. Furthermore, improving the statistical analysis and incorporating additional genetic controls would enhance the quality of the study and provide a stronger foundation for its conclusions.

Reviewer #1 (Public Review):

In this paper, the authors develop new models of sequential effects in a simple Bernoulli learning task. In particular, the authors show evidence for both a "precision-cost" model (precise posteriors are costly) and an "unpredictability-cost" model (expectations of unpredictable outcomes are costly). Detailed analyses of experimental data partially support the model predictions.

Strengths:<br /> - Well-written and clear.<br /> - Addresses a long-standing empirical puzzle.<br /> - Rigorous modeling.

Weaknesses:<br /> - No model adequately explains all of the data.<br /> - New empirical dataset is somewhat incremental.<br /> - Aspects of the modeling appear weakly motivated (particularly the unpredictability model).<br /> - Missing discussion of some relevant literature.

Reviewer #2 (Public Review):

This paper argues for an explanation of sequential effects in prediction based on the computational cost of representing probability distributions. This argument is made by contrasting two cost-based models with several other models in accounting for first- and second-order dependencies in people's choices. The empirical and modeling work is well done, and the results are compelling.

The main weaknesses of the paper are as follows:

1. The main argument is against accounts of dependency based on sensitivity to statistics (ie. modeling the timeseries as having dependencies it doesn't have). However, such models are not included in the model comparison, which makes it difficult to compare these hypotheses.

2. The task is not incentivized in any way. Since incentives are known to affect probability-matching behaviors, this seems important. In particular, we might expect incentives would trade off against computational costs - people should increase the precision of their representations if it generates more reward.

3. The sample size is relatively small (20 participants). Even though a relatively large amount of data is collected from each participant, this does make it more difficult to evaluate the second-order dependencies in particular (Figure 6), where there are large error bars and the current analysis uses a threshold of p < .05 across a large number of tests hence creating a high false-discovery risk.

4. In the key analyses in Figure 4, we see model predictions averaged across participants. This can be misleading, as the average of many models can produce behavior outside the class of functions the models themselves can generate. It would be helpful to see the distribution of raw model predictions (ideally compared against individual data from humans). Minimally, showing predictions from representative models in each class would provide insight into where specific models are getting things right and wrong, which is not apparent from the model comparison.

Reviewer #3 (Public Review):

This manuscript offers a novel account of history biases in perceptual decisions in terms of bounded rationality, more specifically in terms of finite resources strategy. Bridging two works of literature on the suboptimalities of human decision-making (cognitive biases and bounded rationality) is very valuable per se; the theoretical framework is well derived, building upon the authors' previous work; and the choice of experiment and analysis to test their hypothesis is adequate. However, I do have important concerns regarding the work that do not enable me to fully grasp the impact of the work. Most importantly, I am not sure whether the hypothesis whereby inference is biased towards avoiding high precision posterior is equivalent or not to the standard hypothesis that inference "leaks" across time due to the belief that the environment is not stationary. This and other important issues are detailed below. I also think that the clarity and architecture of the manuscript could be greatly improved.

1. At this point it remains unclear what is the relationship between the finite resources hypothesis (the only bounded rationality hypothesis supported by the data) and more standard accounts of historical effects in terms of adaptation to a (believed to be) changing environment. The Discussion suggests that the two approaches are similar (if not identical) at the algorithmic level: in one case, the posterior belief is stretched (compared to the Bayesian observer for stationary environments) due to precision cost, in other because of possible changes in the environment. Are the two formalisms equivalent? Or could the two accounts provide dissociable predictions for a different task? In other words, if the finite resources hypothesis is not meant to be taken as brain circuits explicitly minimizing the cost (as stated by the authors), and if it produces the same type of behavior as more classical accounts: is the hypothesis testable experimentally?

2. The current analysis of history effects may be confounded by effects of the motor responses (independently from the correct response), e.g. a tendency to repeat motor responses instead of (or on top of) tracking the distribution of stimuli.

3. The authors assume that subjects should reach their asymptotic behavior after passively viewing the first 200 trials but this should be assessed in the data rather than hypothesized. Especially since the subjects are passively looking during the first part of the block, they may well pay very little attention to the statistics.

4. The experiment methods are described quite poorly: when is the feedback provided? What is the horizontal bar at the bottom of the display? What happens in the analysis with timeout trials and what percentage of trials do they represent? Most importantly, what were the subjects told about the structure of the task? Are they told that probabilities change over blocks but are maintained constant within each block?

Reviewer #3 (Public Review):

In this study, Yang et al. address a fundamental question of the role of dorsal striatum in neural coding of gait. The authors study the respective roles of D1 and D2 MSNs by linking their balanced activity to detailed gait parameters. In addition, they put in parallel the striatal activity related to whole-body measures such as initiation/cessation of movement or body speed. They are using an elegant combination of high-resolution single-limb motion tracking, identification of bouts of movements, and electrophysiological recordings of striatal neurons to correlate those different parameters. Subpopulations of striatal output neurons (D1 and D2 expressing neurons) are identified in neural recordings with optogenetic tagging. Those complementary approaches show that a subset of striatal neurons have phase-locked activity to individual limbs. In addition, more than a third of MSNs appear to encode all three aspects of motor behavior addressed here, initiation/cessation of movement, body speed, and gait. This activity is balanced between D1 and D2 neurons, with a higher activity of D1 neurons only for movement initiation. Finally, alterations of gait, and the associated striatal activity, are studied in a mouse model of Parkinson's Disease, using 6-OHDA lesions in the medial forebrain bundle (MFB). In the 6OHDA mice, there is an imbalance toward D2 activity.

Strengths:<br /> There is a long-standing debate on the respective role of D1 and D2 MSNs on the control of movement. This study goes beyond prior work by providing detailed quantification of individual limb kinematics, in parallel with whole-body motion, and showing a high proportion of MSNs to be phase-locked to precise gait cycle and also encoding whole-body motion. The temporal resolution used here highlights the preferential activity of D1 MSN at the movement starts, whereas previous studies described a more balanced involvement. Finally, they reveal neural mechanisms of dopamine depletion-induced gait alterations, with a preponderant phase-locked activity of D2 neurons. The results are convincing, and the methodology supports the conclusions presented here.

Weaknesses:<br /> Some more detailed explanations would improve the clarity of the results in the corresponding section. Analysis of the 6OHDA experiments could be expanded to extract more relevant information.

Reviewer #1 (Public Review):

Summary:<br /> Yang et al combine high-speed video tracking of the limbs of freely moving mice with in vivo electrophysiology to demonstrate how striatal neurons encode single-limb gait. They also examine encoding other well-known aspects of locomotion, such as movement velocity and the initiation/termination of movement. The authors show that striatal neurons exhibit rhythmic firing phase-locked with mouse gait, while mice engage in spontaneous locomotion in an open field arena. Moreover, they describe gait deficits induced by severe unilateral dopamine neuron degeneration and associate these deficits with a relative strengthening of gait-modulation in the firing of D2-expressing MSNs. Although the source and function of this gait-modulation remain unclear, this manuscript uncovers an important physiological correlate of striatal activity with gait, which may have implications for gait deficits in Parkinson's Disease.

Strengths:<br /> While some previous work has looked at the encoding of gait variables in the striatum and other basal ganglia nuclei, this paper uses more careful quantification of gait with video tracking. In addition, few if any papers do this in combination with optically-labeled recordings as were performed here.

Weaknesses:<br /> The data collected has a great richness at the physiological and behavioral levels, and this is not fully described or explored in the manuscript. Additional analysis and display of data would greatly expand the interest and interpretability of the findings.

There are also some caveats to the interpretation of the analyses presented here, including how to compare encoding of gait variables when animals have markedly different behaviors (eg comparing sham and unilaterally 6-OHDA treated mice), or how to interpret the loss of gait modulation when single unit activity is overall very low.

1. The authors use circular analysis to quantify the degree to which striatal neurons are phase-locked to individual limbs during gait. The result of this analysis is shown as the proportion of units phase-locked to each limb, vector length, and vector angle (Fig 2H-K; Fig 4E-F; Fig 6E-F). Given that gait is a cyclic oscillation of the trajectories of all four limbs, one could expect that if one unit is phase-locked to one limb, it will also be phase-locked to the other three limbs but at a different phase. Therefore, it is not clear in the manuscript how the authors determine to which limb each unit is locked, and how some units are locked to more than one limb (Fig 2H). More methodological/analytical detail would be especially helpful.

2. In Figures 2 and 3, the authors describe the modulation of striatal neurons by gait, velocity, and movement transitions (start/end), with most of their examples showing firing rates compatible with rates typical of striatal interneurons, not MSNs. In order to have a complete picture of the relationship between striatal activity and gait, a cell type-specific analysis should be performed. This could be achieved by classifying units into putative MSN, FS interneurons, and TANs using a spike waveform-based unit classification, as has been done in other papers using striatal single-unit electrophysiology. An example of each cell type's modulation with gait, as well as summary data on the % modulation, would be especially helpful.

3. By normalizing limb trajectories to the nose-tail axis, the analysis ignores whether the mouse is walking straight, or making left/right turns. Is the gait-modulation of striatal activity shaped by ipsi- and contralateral turning? This would be especially important to understand changes in the unilateral disease model, given the imbalance in turning of 6-OHDA mice.

4. It looks like the data presented in Figure 4 D-F comes from all opto-identified D1- and D2-MSNs. How many of these are gait-modulated? This information is missing (line 110). Pooling all units may dilute differences specific to gait-modulated units, therefore a similar analysis only on gait-modulated units should be performed.

5. Since 6-OHDA lesions are on the right hemisphere, we would expect left limbs to be more affected than right limbs (although right limbs may also compensate). It is therefore surprising that RF and RR strides seem slightly shorter than LF and LR (Fig 5G), and no differences in other stride parameters (Fig 5H-J). Could the authors comment on that? It may be that this is due to rotational behavior. One interesting analysis would be to compare activity during similar movements in healthy and 6-OHDA mice, eg epochs in which mice are turning right (which should be present in both groups) or walking a few steps straight ahead (which are probably also present in both groups).

6. Multiple publications have shown that firing rates of D1-MSN and D2-MSN are dramatically changed after dopamine neuron loss. Is it possible that changes observed in gait-modulation might be biased by changes in firing rates? For example, dMSNs have exceptionally low overall activity levels after dopamine depletion (eg Parker...Schnitzer, 2018; Ryan...Nelson, 2018; Maltese...Tritsch, 2021); this might reduce the ability to detect modulation in the firing of dMSNs as compared to iMSNs, which have similar or increased levels of activity in dopamine depleted mice. Does vector length correlate with firing rate? In addition, the normalization method used (dividing firing rate by minimum) may amplify very small changes in absolute rates, given that the firing rates for MSN are very low. The authors could show absolute values or Z-score firing rates (Figure 6 A, D).

7. The analysis shown in Fig 3C should also be done for opto-identified D1- and D2-MSNs (and for waveform-based classified units as noted above).

8. Discussion: the origin of the gait-modulation as well as the possible mechanisms driving the alterations observed in 6-OHDA mice should be discussed in more detail.

Reviewer #2 (Public Review):

Summary:<br /> Yang et al. recorded the activity of D1- and D2-MSNs in the dorsal striatum and analyzed their firing activity in relation to single-limb gait in normal and 6-OHDA lesioned mice. Although some of the observations of striatal encoding are interesting, the novelty and implications of this firing activity in relation to gait behavior remain unclear. More specifically, the authors made two major claims. First, the striatal D1- and D2-MSNs were phase-locked to the walking gait cycles of individual limbs. Second, dopamine lesions led to enhanced phase-locking between D2-MSN activity and walking gait cycles. The second claim was supported by the increase of vector length in D2-MSNs after unilateral 6-OHDA administration to the medial forebrain bundle. However, for the first claim, the authors failed to convincingly demonstrate that striatal MSNs were more phase-locked to gait with single-limb and step resolution than to the global gait cycles.

Strengths:<br /> It is a technically advanced study.

Weaknesses:<br /> 1. The authors focused on striatal encoding of gait information in current studies. However, it remains unclear whether the part of the striatum for which the authors performed neuronal recording is really responsible for or contributing to gait control. A lesion or manipulation experiment disrupting the part of the striatum recorded seems a necessary step to test or establish its relationship to gait control.

2. The authors attributed one of the major novelties to phase-locking of striatal neural activities with single-limb gait cycles. The claim was not clearly supported, as the authors did not demonstrate that phase-locking to single-limb gaits was more significant than phase-locking to global walking gait cycles. In rhythmic walking, the LR and RF limbs were roughly anti-phase with the LF and RR limbs (Fig. 1D, E). In line with this relationship, striatal neurons were mainly in-phase with LR and RF limbs and anti-phase with LF and RR limbs (Fig. 2J, K). One could instead interpret this as the striatal neurons spanned all the phases of the global walking gait cycles (Fig. 3D). To demonstrate phase-locking with individual limb movements, the authors need to show that neural activities were better correlated with a specific limb than to the global gait cycles.

3. The observation of the enhancement of coupling between D2 MSN firing and the gait cycles was interesting, but the physiological interpretation was not clear (as the authors also noted in the Discussion), which hampers the significance of the observation.

4. Due to the lack of causality experiments as mentioned in the first comment above, the observations of coupling between striatal neuronal activity and gait control might well result from a third brain region/factor serving as the common source to both, whether in normal or dopamine lesioned brain. If this is the case, the significance and implications of current findings will be greatly limited.

Reviewer #1 (Public Review)

This work challenges previously published results regarding the presence and abundance of 6mA in Drosophila genome, as well as the claim that the TET or DMAD enzyme serves as the "eraser" of this DNA methylation mark and its roles in development. This information is needed to clarify these questions in the field. Generally speaking, the methods for fly husbandry and treatment seem to be in accordance with those established ones in the field.

Here are a couple of suggestions that could be discussed with the current work and addressed in the future, in order to better understand the roles of 6mA and TET.

1. Regarding the estimated "200 to 400 methylated adenines per haplogenome", some insights regarding where they are enriched in the genome could inform the potential target sites regulated by 6mA.

2. The TET-GFP and TET-CD-GFP knock-in lines give proper nuclear localization and could be used to identify genomic regions bound with full-length TET and TET-CD using anti-GFP for ChIP-seq or CUT&RUN (or CUT&TAG).

Reviewer #2 (Public Review)

DNA adenine methylation (6mA) is a rediscovered modification that has been described in a wide range of eukaryotes. However, 6mA presence in eukaryote remains controversial due to low abundance of its modification in eukaryotic genome. In this manuscript, Boulet et al. re-investigate 6mA presence in drosophila using axenic or conventional fly to avoid contaminant from feeding bacteria. By using these flies, they find that 6mA is rare but present in drosophila genome by performing LC/MS/MS. They also find that the loss of TET (also known as DMAD) does not impact on 6mA levels in drosophila, contrary to previous studies. In addition, the authors find that TET is required for fly development in its enzymatic activity-independent manner.

The strength of this study is, compared to previous studies of 6mA in drosophila, the authors employ axenic or conventional fly for 6mA analysis. These fly strains make it possible to analyze 6mA presence in drosophila without bacterial contaminant. This established method is valuable in this field.

Reviewer #1 (Public Review):

Summary:

Using chromaffin cells as powerful model systems for studying secretion, the authors study the regulatory role of complexin in secretion. Complexin is still enigmatic in its regulatory role, as it both provides inhibitory and facilitatory functions in release. The authors perform an extensive structure-function analysis of both the C- and N-terminal regions of complexin. There are several interesting findings that significantly advance our understanding of cpx/SNARe interactions in regulating release. C-terminal amphipathic helix interferes with SNARE complex assembly and thus clamps fusion. There are acidic residues in the C-term that may be seen as putative interaction partners for Synaptotagmin. The N-terminus of Complexin promoting role may be associated with an interaction with Syt1. In particular, the putative interaction with Syt1 is of high interest and supported by quite strong functional and biochemical evidence. The experimental approaches are state-of-the-art, and the results are of the highest quality and convincing throughout. They are adequately and intelligently discussed in the rich context of the standing literature. Whilst there are some concerns about whether the facilitatory actions of complexion have to be tightly linked to Syt1 interactions, the proposed model will significantly advance the field by providing new directions in future research.

I have only minor comments related to the interpretation of the data:

Fig 5 While the data very nicely show that CPX and Syt1 have interdependent interactions in the chromaffin neurons, this seems to be not the case in neurons, where the loss of complexins and synaptotagmins have additive effects, suggesting independent mechanisms (eg Xue et al., 2010). This would be a good opportunity to discuss some possible differences between secretion in endocrine cells vs neurons.

Fig 8 Shows an apparent shift in Ca sensitivity in N-terminal mutants suggesting a modification of Ca sensitivity of Syt1. Could there be also an alternative mechanism, that explains this phenotype which is based on a role of the n-term lowering the energy barrier for fusion, that in turn shifts corresponding fusion rates to take place at lower Ca saturation levels?

Reviewer #2 (Public Review):

Summary:

Complexin (Cplx) is expressed at nearly all chemical synapses. Mammalian Cplx comes in four different paralogs which are differentially expressed in different neuron types, either selectively or in combination with one or two other Cplx isoforms. Cplx binds with high affinity to assembled SNARE complexes and promotes AP-evoked release by increasing vesicle fusogenicity. Cplx is assumed to preclude premature SV fusion by preventing full SNARE assembly, thereby arresting subsequent SNARE-driven fusion ("fusion-clamp" theory). The protein has multiple domains, the functions of which are controversially discussed. Cplx's function has been studied in a variety of model organisms including mice, flies, worms, and fish with seemingly conflicting results which led to partly contradicting conclusions.

Makee et al. study the function of mammalian Cplx2 by making use of chromaffin cells derived from Cplx2 ko mice as a system to overexpress and functionally characterize mutant Cplx2 forms. This work is an important extension of previous studies of the same lab using similar techniques. The main conclusion of the present study are:

The hydrophobic character of the amphipathic helix in Cplx's C-terminal domain is essential for inhibiting premature vesicle fusion at a [Ca2+]i of several hundreds of nM (pre-flash [Ca2+]i). The Cplx-mediated inhibition of fusion under these conditions does not rely on the expression of either Syt1 or Syt7.

Slow-down of exocytosis by N-terminally truncated Cplx mutants in response to a [Ca2+]i of several µM (peak flash [Ca2+]i) occurs regardless of the presence or absence of Syt7 demonstrating that Cplx2 does not act as a switch favoring preferential assembly of the release machinery with Syt1,2 rather than the "slow" sensor Syt7.

Cplx's N-terminal domain is required for the Cplx2-mediated increase in the speed of exocytosis and faster onset of exocytosis which likely reflect an increased apparent Ca2+ sensitivity and faster Ca2+ binding of the release machinery.

Strengths:

The authors perform systematic truncation/mutational analyses of Cplx2 by making use of chromaffin cells derived from Cplx2 ko mice. They analyze the impact of single and combined deficiencies for Cplx2 and Syt1 to establish interactions of both proteins.

State-of-the-art methods are employed: Vesicle exocytosis is assayed directly and with high resolution using capacitance measurements. Intracellular [Ca2+] is controlled by loading via the patch-pipette and by UV-light-induced flash-photolysis of caged [Ca2+]. The achieved [Ca2+ ] is measured with Ca2+ -sensitive dyes.

The data is of high quality and the results are convincing.

Weaknesses:

The authors provide a "chromaffin cell-centric" view of the function of mammalian Cplx in vesicle fusion. With the exception of mammalian retinal ribbon synapses (and some earlier RNAi knockdown studies that had off-target effects), there is very little evidence for a "fusion-clamp"-like function of Cplxs in mammalian synapses. At conventional mammalian synapses, genetic loss of Cplx (i.e. KO) consistently decreases AP-evoked release, and generally either also decreases spontaneous release rates or does not affect spontaneous release, which is inconsistent with a "fusion-clamp" theory. This is in stark contrast to invertebrate (D. m. and C. e.) synapses where genetic Cplx loss is generally associated with strong upregulation of spontaneous release, providing support for Cplx acting as a "fusion-clamp".

The authors use a Semliki Forest virus-based approach to express mutant proteins in chromaffin cells. This strategy leads to a strong protein overexpression (~7-8fold, Figure 3 Suppl. 1). Therefore, experimental findings under these conditions may not necessarily be identical to findings with normal protein expression levels.

Measurements of delta Cm in response to Ca2+ uncaging by ramping [Ca2+ ] from resting levels up to several µM over a time period of several seconds were used to establish changes in the release rate vs [Ca2+ ]i relationship. It is not clear to this reviewer if and how concurrently occurring vesicle endocytosis together with a possibly Ca2+-dependent kinetics of endocytosis may affect these measurements.

It should be pointed out that an altered "apparent Ca2+ affinity" or "apparent Ca2+ binding rate" does not necessarily reflect changes at Ca2+-binding sites (e.g. Syt1).

There are alternative models on how Cplx may "clamp" vesicle fusion (see Bera et al. 2022, eLife) or how Cplx may achieve its regulation of transmitter release without mechanistically "clamping" fusion (Neher 2010, Neuron). Since the data presented here cannot rule out such alternative models (in this reviewer's opinion), the authors may want to mention and briefly discuss such alternative models.

Some parts of the Discussion are quite general and not specifically related to the results of the present study. The authors may want to consider shortening those parts.

Last but not least, the presentation of the results could be improved to make the data more accessible to non-specialists, this concerns providing necessary background information, choice of colors, and labeling of diagrams.

Reviewer #1 (Public Review):

The brain's code is not static. Neuronal activity patterns change as a result of learning, aging, and disease. Reliable tracking of activity from individual neurons across long time periods would enable detailed studies of these important dynamics. For this reason, the authors' efforts to track electrophysiological activity across days without relying on matching neural receptive fields (which can change due to learning, aging, and disease) are very important.

By utilizing the tightly-spaced electrodes on Neuropixels probes, they are able to measure the physical distance and the waveform shape 'distance' between sorted units recorded on different days. To tune the matching algorithm and validate the results, they used the visual receptive fields of neurons in the mouse visual cortex (which tend to change little over time) as ground truth. Their approach performs quite well, with a high proportion of neurons accurately matched across multiple weeks. This suggests that the method may be useable in other cases where the receptive fields can't be used as ground truth to validate the tracking. This potential extendibility to tougher applications is where this approach holds the most promise.

The main caveat (and disappointment) is that this paper does not address generalizability to other experimental conditions. Because it only looks at one brain area (visual cortex), in one species (mouse), using one type of spike sorter (Kilosort), and one type of behavioral prep (head-fixed), it is not clear if this approach is overfit to those conditions or if it will perform equally well in other conditions. Most importantly, in brain areas where neuronal receptive fields are more dynamic and can't be used as a ground truth diagnostic, it isn't clear how to apply the technique outlined in this study, since many of the parameters are tuned to a very specific set of conditions using visual receptive fields as ground truth.

Reviewer #2 (Public Review):

The manuscript presents a method for tracking neurons recorded with neuropixels across days, based on the matching of cells' spatial layouts and spike waveforms at the population level. The method is tested on neuropixel recordings of the visual cortex carried over 47 days, with the similarity in visual receptive fields used to verify the matches in cell identity.

This is an important tool as electrophysiological recordings have been notoriously limited in terms of tracking individual neuron's fate over time, unlike imaging approaches. The method is generally sound and properly tested but I think some clarifications would be helpful regarding the implementation of the method and some of the results.

1) Page 6: I am not sure I understand the point of the imposed drift and how the value of 12µm is chosen.<br /> Is it that various values of imposed drift are tried, the EMDs computed to produce histograms as in Fig2c, values of rigid drifts estimated based on the histogram modes, and then the value associated with minimum cost selected? The corresponding manuscript section would need some clarification regarding this aspect.

2) The EMD is based on the linear sum, with identical weight, of cell distance and waveform similarity measures. How performance is affected by using a different weighting of the 2 measures (for instance, using only cell distance and no waveform similarity)? It is common that spike waveforms associated with a given neuron appear differently on different channels of silicon probes (i.e. the spike waveform changes depending on the position of recording sites relative to the neuron), so I wonder if that feature is helping or potentially impeding the tracking.

3) Fig.5: I assume the dots represent time gaps for which cell tracking is estimated. The 3 different groups of colors correspond to the 3 mice used. For a given mouse, I would expect to always see 3 dots (for ref, putative, and mixed) for a given tracking gap. However, for mouse AL036 for instance, at a tracking duration of 8 days, a dot is visible for mixed but not for ref and putative. How come this is happening?

4) Matched visual responses are measured by the sum of the correlation of visual fingerprints, which are vectors of cells' average firing rate across visual stimuli, and the correlation of PSTHs, which are implemented over all visual stimuli combined. I believe that some information is lost from combining all stimuli in the implementation of PSTHs (assuming that PSTHs show specificity to individual visual stimuli). The authors might consider, as an alternative measure of matched visual responses, a correlation of the vector concatenations of all stimulus PSTHs. Such a simpler measure would contain both visual fingerprint and PSTH information, and would not lose the information of PSTH specificity across visual stimuli.

Reviewer #1 (Public Review):

Summary:<br /> The authors have nicely demonstrated the efficiency of the HCR v.3.0 using hr38 mRNA expression as a marker of neuronal activity. This is very important in the Drosophila neuroscience field as in situ hybridization in adult Drosophila brains has been so far very challenging to do and replicate. However, this method has been described before [Choi et al., (2018)] and, if I understand correctly, is now the property of the non-profit organization molecular Technologies, who are the ones responsible for designing the probes (the sequences are not provided). Here the authors present their work as a description of a new method, called HI-FISH. However, I do not consider this as a new method but rather an application, a "proof of principle" that HCR v.3.0 can be done even on challenging tissues such as the adult Drosophila brain. Hence, if HCR v3.0 is sensitive enough and powerful enough to be used as a marker of neuronal activity, we can use it, for other neurobiological purposes, using other gene probes.<br /> To demonstrate the efficiency of HI-FISH, the authors have addressed two biological questions. The first one addressed whether specific groups of neurons, known to trigger specific behaviours (here courtship and/or aggression) are indeed activated by the behavioural context they can promote. In other words: is the behavioural output of these neurons also a trigger for their activation? The second question addressed whether this method is powerful enough to distinguish two subgroups of a class of neurons called P1 known to be involved in the two behaviours considered. In other words, are the same P1 neurons that promote aggression and courtship?

Strengths: The demonstration of the efficiency of the method is very convincing and well-performed. It gives the will for the reader to apply the method to their own subject.

Weakness: The pictures provided for HI-FISH and catFISH do not corroborate with the quantification and therefore I am not convinced about the author's biological interpretation of their data. See below for details.

Previously, using a split-gal4 line to restrict the Gal4 expression to a subset of P1 neurons, the authors have shown that these particular neurons when activated can trigger both aggressivity and courtship behaviour [Hoopfer et al., 2015]. The P1 neurons are composed of about 20 FruM neurons/hemibrain but are part of a bigger group that comprises about the same number of Fru- neurons that seem to be exclusively aggression-promoting neurons [Koganezawa et al., 2016]. Hence, this group of 40 neurons (pC1 neurons) contains aggressive-promoting neurons, courtship-promoting neurons, and perhaps neurons that can do both. Therefore, to address the first question, the authors compared hr38 expression in different groups of neurons, with a focus on subgroups, under different social contexts. While there is no ambiguity concerning the function of the Tk neurons as being exclusively aggressive-promoting neurons [Asahina et al., 2014], it is less clear when we look at the pC1 neurons. This is particularly evident for the P1a neurons which have been shown to be ambiguous as they can promote both aggression and courtship. For example, while optogenetic activation of these neurons promotes hr38 expression (Fig. 3D and fig sup. 4), it is less clear in the pictures to determine whether these specific P1a neurons labeled by the split-gal4 line are specifically activated by an aggressive behavioural context or a courtship behavioural context (Fig1, supp. 2 and fig. 4). Furthermore, the pictures chosen do not reflect the reality of the quantification (Fig. 2 B-D compared to sup. 2 or fig. 4C compared to fig. 4D) and therefore the authors conclusion. Because the drivers used are only expressed by a small subset of a larger population, I believe it would be more informative to separate in the quantification between the Gal4-expressing neurons and the non-expressing ones. Notably, based on the pictures provided, it looks like more P1 neurons (or rather pC1 neurons) are activated by a male-male encounter than by a male-female encounter. On the other hand, the splitGal4+ P1a seem to be more responsive to a courtship behavioural context (2/6 P1a neurons express hr38 in a courtship behavioural context while 0/9 _if we mentally abstract the increase of the background signal compared to the control picture_ seem to express hr38 in an aggression behavioural context). Hence, while activation of this P1asplit-Gal4 can promote both aggressive behaviour and courtship behaviour [Hoopfer et al., 2015], the authors didn't provide clear evidence (pictures not corroborating the quantification) that these specific small subpopulation of neurons are activated by one or the other or both behavioural conditions. Therefore my suggestion of differentiating in the quantification between the Gal4+ neurons from the others in the same local area.

Fig. 3, suppl. 3: In this section the authors addressed the question of whether the HI-FISH can be used to identify the downstream targets of this subpopulation. As positive controls of known downstream targets, the authors looked at the pCd population which they recently published as being an indirect downstream target of the P1a neurons [Jung et al., Neuron 2020]. They identified the Kenyon cells and a group of dopaminergic neurons, the PAM neurons as being activated by the P1a neurons. To confirm the increase of hr38 expression is indeed the result of a neuronal response of these neurons to the P1a activation, the authors used a different strategy used by them and others before. Using Gcamp signal to monitor the neuronal response of the presumably downstream targets the authors activated the P1a neurons using optogenetic (chrimson). It is important to note that they have previously shown that depending on the frequency of the light pulses activation of the P1a neurons can trigger only aggression, both aggression and wing extension or only wing extension [Hoopfer et al., eLife 2015]. Here the authors use 50Hz which is a frequency that leads to wing extension during the stimulation and aggressive behaviour at the offset of the stimulus [Hoopfer et al., eLife 2015]. Interestingly, the Gcamp experiment shows activation of the Kenyon cells and the PAM neurons but this activity is not maintained when the stimulus is turned off, suggesting that these neurons are activated during a courtship context rather than an aggressive behavioural context. I think it would be nice to see in which behavioural context the Kenyon cells and PAM neurons are activated (hr38 expression in the different behavioural context using the corresponding Gal4).<br /> Fig.4 and supp.4: The demonstration that the catFISH can now be done in Drosophila brain with a new in situ method was nicely performed. Notably, the intronic Hr38 probe appears to be an excellent marker for recent neuronal activation. However, while the optogenetic activation of the P1a neurons used to quantify the time lapse for both probes nicely distinguishes between nuclear and cytoplasmic exonic hr38, it is very difficult to use the localization of this probe in the experimental setup the authors used. Also, With their setup, I would simply use the frequency of intronic hr38 as a marker of recent activation correlating or not with the frequency of exonic hr38 marker (present in both conditions first and second encounter). This is important as this experiment addresses the second biological question. Once again, the pictures chosen absolutely do not corroborate the quantification. For example, the picture of the double encounter with the same gender male-male context clearly shows a higher number of cells that are hr38INT positive (and therefore nuclear) than the picture of the female-female context (Fig. 4C), and thus even if we only considered the P1asplit-Gal4 positive cells. In the male-male picture, 5/6 P1a cells have the Hr38INT marker while the presence of this marker is debatable in the female-female context. Especially, in some of the cells these magenta dots appear to be localized in the cytoplasm, suggesting a non-specific signal. Therefore, I would suggest to quantify the percentage of Hr38INT positive cells as the only marker for recent activation and the relative level of Hr38EXN immunostaining, and this among the P1asplit-Gal4 positive cells and the gal4- ones. A high Hr38EXN level associated with the presence of hr38INT would indicate that the cell has been activated during both encounters, while a lower hr38EXN with no hr38INT would suggest only an activation during the 1st behavioural context. Finally, a lower hr38EXN associated with the presence of hr38INT would suggest the opposite, an activation only during the 2nd behaviour.<br /> Overall, by only looking at the pictures provided, I would conclude that the HCR applied with the hr38 probes seems to efficiently work and is usable to address the question of whether a specific group of neurons are indeed activated by a specific social behavioural context. However, I would also conclude that this technique nicely demonstrated that flies are not robots and that even in a "simple" organism model such as Drosophila melanogaster individual variability is present among a group of neurons. Hence, only the quantification of the gal4-expressing neurons in comparison with their neighbor neurons known to belong to the same functional group, would allow a conclusion toward a specificity of contextual response. Therefore, although activation of a small group of neurons can be enough to trigger a specific behaviour that shouldn't happen under a certain environmental context [Hoopfer et al., eLife 2015], the results presented here suggest that we should, using this method, considering the response of the neighbour cells of the Gal4+ ones. Although currently, the quantification of the author's data does not allow such analysis, to strengthen the author's argumentation, I would distinguish in their quantification between gal4+ from the others (Fig. 2 and 4). Furthermore, I am not certain that the distinction between cytoplasmic and nuclear hr38EXN is 100% feasible (based on the pictures provided). I would instead for the hr38EXN marker only use the relative intensity (Fig. 4D).

Reviewer #2 (Public Review):

Summary:<br /> Watanabe et al establish a novel method for the activity-dependent labeling of neural circuits in flies. While activity mapping of neurons that are active during specific behaviors is widespread in rodents, the application of this method to fly circuit neuroscience is limited, mainly due to technological challenges. Thus, the present study addresses a timely problem. To do so, they apply the in situ hybridization amplification method called Hybridization Chain Reaction v. 3.0 (Choi et al. 2018) to the adult fly brain in order to visualize the expression changes of the immediate early gene (IEG) Hr38 under different types of social contexts. The conclusions of this paper are mostly very well supported by data but it would strongly benefit from additional methodological details as well as additional controls, in particular for the HI-catFISH experiments.

Strengths:<br /> The major strength of this method is its versatility and sensitivity. It can be applied to a wide variety of biological questions and assess the dynamic transcriptional regulation of an unlimited number of genes with a high signal-to-noise ratio. It will be therefore useful to many research labs working on different biological questions.

Weaknesses:<br /> Although the paper has great strengths in principle, the major weakness is the calibration of the temporal resolution of HI-CatFISH in Figure 4 and Figure Supplement 4. According to Figure Supplement 4C, close to 100% of the Hr38-positive cells are already labeled with the exonic probe 30min post-stimulation, which is not reflected in Figure 4B (there, the expression level of the exonic probe peaks 60min post-induction) and may have profound implications for the interpretation of the results. The present manuscript would strongly benefit from additional controls, such as the quantification of the intronic and exonic Hr38 probes after either only the 1st or 2nd social context but at the same timepoint than if two consecutive social contexts were tested.

Reviewer #2 (Public Review):

Summary:

The large-conductance Ca2+ activated K+ channel (BK) has been reported to promote breast cancer progression, but it is not clear how. The present study carried out in breast cancer cell lines, concludes that BK located in mitochondria reprograms cells towards the Warburg phenotype, one of the metabolic hallmarks of cancer.

Strengths:

The use of a wide array of modern complementary techniques, including metabolic imaging, respirometry, metabolomics, and electrophysiology. On the whole, experiments are astute and well-designed and appear carefully done. The use of BK knock-out cells to control for the specificity of the pharmacological tools is a major strength. The manuscript is clearly written. There are many interesting original observations that may give birth to new studies.

Weaknesses:

The main conclusion regarding the role of a BK channel located in mitochondria appears is not sufficiently supported. Other perfectible aspects are the interpretation of co-localization experiments and the calibration of Ca2+ dyes. These points are discussed in more detail in the following paragraphs:

1. May the metabolic effects be ascribed to a BK located in mitochondria? Unfortunately not, at least with the available evidence. While it is clear these cells have a BK in mitochondria (characteristic K+ currents detected in mitoplasts) and it is also well substantiated that the metabolic effects in intact cells are explained by an intracellular BK (paxilline effects absent in the BK KO), it does not follow that both observations are linked. Given that ectopic BK-DEC appeared at the surface, a confounding factor is the likely expression of BK in other intracellular locations such as ER, Golgi, endosomes, etc. To their credit, authors acknowledge this limitation several times throughout the text ("...presumably mitoBK...") but not in other important places, particularly in the title and abstract.

2. MitoBK subcellular location. Pearson correlations of 0.6 and about zero were obtained between the locations of mitoGREEN on one side, and mRFP or RFP-GPI on the other (Figs. 1G and S1E). These are nice positive and negative controls. For BK-DECRFP however, the Pearson correlation was about 0.2. What is the Z resolution of apotome imaging? Assuming an optimum optical section of 600 nm, as obtained by a 1.4 NA objective with a confocal, that mitochondria are typically 100 nm in diameter and that BK-DECRFP appears to stain more structures than mitoGREEN, the positive correlation of 0.2 may not reflect colocalization. For instance, it could be that BK-DECRFP is not just in mitochondria but in a close underlying organelle e.g. the ER. Along the same line, why did BK-RFP also give a positive Pearson? Isn´t that unexpected? Considering that BK-DEC was found by patch clamping at the plasma membrane, the subcellular targeting of the channel is suspect. Could it be that the endogenous BK-DEC does actually reside exclusively in mitochondria (a true mitoBK), but overflows to other membranes upon overexpression? Regarding immunodetection of BK in the mitochondrial Percoll preparation (Fig. S5), the absence of NKA demonstrates the absence of plasma membrane contamination but does not inform about contamination by other intracellular membranes.

3. Calibration of fluorescent probes. The conclusion that BK blockers or BK expression affects resting Ca2+ levels should be better supported. Fluorescent sensors and dyes provide signals or ratios that need to be calibrated if comparisons between different cell types or experimental conditions are to be made. This is implicitly acknowledged here when monitoring ER Ca2+, with an elaborate protocol to deplete the organelle in order to achieve a reading at zero Ca2+.

4. Line 203. "...solely by the expression of BKCa-DECRFP in MCF-7 cells". Granted, the effect of BKCa-DECRFP on the basal FRET ratio appears stronger than that of BK-RFP, but it appears that the latter had some effect. Please provide the statistics of the latter against the control group (after calibration, see above).

Reviewer #1 (Public Review):

Bischoff et al present a carefully prepared study on a very interesting and relevant topic: the role of ion channels (here a Ca2+-activated K+ channel BK) in regulating mitochondrial metabolism in breast cancer cells. The potential impact of these and similar observations made in other tumor entities has only begun to be appreciated. That being said, the authors pursue in my view an innovative approach to understanding breast cancer cell metabolism.

Considering the following points would further strengthen the manuscript:

Methods:

1. The authors use an extracellular Ca2+ concentration (2 mM) in their Ringer's solutions that is almost twice as high as the physiologically free Ca2+ concentration (ln 473). Moreover, the free Ca2+ concentration of their pipette solution is not indicated (ln 487).

2. Ca2+I measurements: The authors use ATP to elicit intracellular Ca2+ signals. Is this then a physiological stimulus for Ca2+ signaling in breast cancer? What is the rationale for using ATP? Moreover, it would be nice to see calibrated baseline values of Ca2+i.

3. Membrane potential measurements: It would be nice to see a calibration of the potential measurements; this would allow us to correlate the IV relationship with membrane potential. Without calibration, it is hard to compare unless the identical uptake of the dye is shown.

Does paxilline or IbTx also induce depolarization?

4. Mito-potential measurements: Why did the authors use such a long time course and preincubate cells with channel blockers overnight? Why did they not perform paired experiments and record the immediate effect of the BK channel blockers in the mito potential?

5. MTT assays are also based on mitochondrial function - since modulation of mito function is at the core of this manuscript, an alternative method should be used.

Results:

1. Fig. 5G: The number of BK "positive" mitoplasts is surprisingly low - how does this affect the interpretation? Did the authors attempt to record mitoBK current in the "whole-mitoplast" mode? How does the mitoBK current density compare with that of the plasma membrane? Is it possible to theoretically predict the number of mitoBK channels per mitochondrion to elicit the observed effects? Can these results be correlated with the immuno-localization of mitoBK channels?

2. There are also reports about other mitoK channels (e.g. Kv1.3, KCa3.1, KATP) playing an important role in mitochondrial function. Did the authors observe them, too? Can the authors speculate on the relative importance of the different channels? Is it known whether they are expressed organ-/tumor-specifically?

Reviewer #3 (Public Review):

The original research article, titled "mitoBKCa is functionally expressed in murine and human breast cancer cells and promotes metabolic reprogramming" by Bischof et al, has demonstrated the underlying molecular mechanisms of alterations in the function of Ca2+ activated K+ channel of large conductance (BKCa) in the development and progression of breast cancer. The authors also proposed that targeting mitoBKCa in combination with established anti-cancer approaches, could be considered as a novel treatment strategy in breast cancer treatment.

The paper is clearly written, and the reported results are interesting.

Strengths:

Rigorous biophysical experimental proof in support of the hypothesis.

Weaknesses:

A combinatorial synergistic study is missing.

Reviewer #2 (Public Review):

Singh and colleagues employ a methodic approach to reveal the function of the transcription factors Rela and Stat3 in the regulation of the inflammatory response in the intestine.

Strengths of the manuscript include the focus on the function of these transcription factors in hepatocytes and the discovery of their role in the systemic response to experimental colitis. While the systemic response to induce colitis is appreciated, the cellular and molecular mechanisms that drive such systemic response, especially those involving other organs beyond the intestine are an active area of research. As such, this study contributes to this conceptual advance. Additional strengths are the complementary biochemical and metabolomics approaches to describe the activation of these transcription factors in the liver and their requirement - specifically in hepatocytes - for the production of bile acids in response to colitis.

Some weaknesses are noted in the presentation of the data, including a comprehensive representation of findings in all conditions and genotypes tested.

Reviewer #1 (Public Review):

Summary:

In this study, the authors showed that activation of RelA and Stat3 in hepatocytes of DSS-treated mice induced CYPs and thereby produced primary bile acids, particularly CDCA, which exacerbated intestinal inflammation.

Strengths:

This study reveals the RelA/Stat3-dependent gene program in the liver influences intestinal homeostasis.

Weaknesses:

Additional evidence will strengthen the conclusion.

1. In Fig. 1C, photos show that phosphorylation of RelA and Stat3 was induced in only a few hepatocytes. The authors conclude that activation of both RelA and Stat3 induces inflammatory pathways. Therefore, the authors should show that phosphorylation of RelA and Stat3 is induced in the same hepatocytes during DSS treatment.

2. In Fig. 5, the authors treated mice with CDCA intraperitoneally. In this experiment, the concentration of CDCA in the colon of CDCA-treated mice should be shown.

Reviewer #3 (Public Review):

Summary:

The authors try to elucidate the molecular mechanisms underlying the intra-organ crosstalks that perpetuate intestinal permeability and inflammation.

Strengths:

This study identifies a hepatocyte-specific rela/stat3 network as a potential therapeutic target for intestinal diseases via the gut-liver axis using both murine models and human samples.

Weaknesses:

1. The mechanism by which DSS administration induces the activation of the Rela and Stat3 pathways and subsequent modification of the bile acid pathway remains clear. As the authors state, intestinal bacteria are one candidate, and this needs to be clarified. I recommend the authors investigate whether gut sterilization by administration of antibiotics or germ-free condition affects 1. the activation of the Rela and Stat3 pathway in the liver by DSS-treated WT mice and 2. the reduction of colitis in DSS-treated relaΔhepstat3Δhep mice.

2. It has not been shown whether DSS administration causes an increase in primary bile acids, represented by CDCA, in the colon of WT mice following activation of the Rela and Stat3 pathways, as demonstrated in Figure 6.

3. The implications of these results for IBD treatment, especially in what ways they may lead to therapeutic intervention, need to be discussed.