Reviewer #2 (Public Review):

The manuscript "Optimal Cancer Evasion in a Dynamic Immune Microenvironment Generates Diverse Post-Escape Tumor Antigenicity Profiles" by George and Levine describes TEAL - a mathematical model for the dynamics of cancer evolution in response to immune recognition. The authors consider a process in which tumor cells from one clone are characterized by a set of neoantigens that may be recognized by the immune system with a certain probability. In response to the recognition, the tumor may adapt to evade immune recognition, by effective removal of recognizable neoantigens. The authors characterize the statistics of this adaptive process, considering, in particular, the evasion probability parameter, and a possibility of an adaptive strategy when this parameter is optimized in each step of the evolution. The dynamics of the latter process are solved with a dynamic programming approach. In the optimal case, the model captures the tradeoff between a cancer population's need for adaptability in hostile immune microenvironments and the cost of such adaptability to that population. Additionally, immune recognition of neoantigens is incorporated. These two factors, anti-tumor vs pro-tumor IME as quantified by the Beta penalty term, and the level of immune recognition as quantified by the rate q, form the basis of a characterization of tumors as 'hot' or 'cold'.

I think this framework is a valuable attempt to formally characterize the processes and conditions that result in immunologically hot vs cold tumors. The model and the analytical work are sound and potentially interesting to a major audience. However, certain points require clarification for evaluation of the relevance of the model:

1) Tumor clonality

My main concern is about the lack of representation of the evolutionary process in the model and that the heterogeneity of the tumor is just glossed over.

The single mention of the problem occurs in Section 2, p2: "Our focus is on a clonal population, recognizing that subclonal TAA distributions in this model may be studied by considering independent processes in parallel for each clone."

I don't think this assumption resolves the impact of tumor heterogeneity on the immune evasion process. Furthermore, I would claim that the process depicted in Fig 1A is very rare and that cancers rarely lose recognizable neoantigens - typically it would be realized via subclonal evolution, with an already present cancer clone without the neoantigens picking up. Similarly, the adaptation of a tumor clone is an evolutionary process - supposedly the subclones that manage to escape recognition via genetic or epigenetic changes are the ones that persist. It is not clear what the authors assume about the heterogeneity of the adapting/adapted population between different generations, n->(n+1). Is the implicit assumption that the n+1 generation is again clonal, i.e. that the fitness advantage of the resulting subclone was such that the remaining clones were eliminated? Or does the model just focuses on the fittest subclone? A discussion on whether these considerations are relevant to the result would clarify the relevance of the result.

2) Time scales

Section 2, p2: "We assume henceforth that the recognition-evasion pair consists of the T cell repertoire of the adaptive immune system and a cancer cell population, recognizable by a minimal collection of s_n TAAs present on the surface of cancer cells in sufficient abundance for recognition to occur over some time interval n.".

How do the results depend on the duration of interval n? The duration should be long enough to allow for recognition and, up to some limiting duration, proportional to the TAA recognition probability q. However, it should not be so long that the state of the system can change significantly. A clarification on this point is needed.

Reviewer #3 (Public Review):

Cancer cell populations co-evolve under the pressure exerted by the recognition of tumor-associated antigens by the adaptive immune system. Here, George and Levine analyze how cancers could dynamically adapt the rate of tumor-associated antigen loss to optimize their probability of escape. This is an interesting hypothesis that if confirmed experimentally could potentially inform treatments. The authors analyze mathematically how such optimally adapting tumors gain and lose tumor-associated antigens over time. By simplifying the complex interplay of immune recognition and tumor evolution in a toy model, the authors are able to study questions of practical interest analytically or through stochastic simulations. They show how different model parameters relating to the tumor microenvironment and immune surveillance lead to different dynamics of tumor immunogenicity, and more immunologically hot or cold tumors.

Simple models are important because they allow an exhaustive study of dynamical regimes for different parameters, such as has been done elegantly in this study. However, in this quest for simplification, the authors have not considered biological features that are likely to be of importance for understanding the process of cancer immune co-evolution in generality: tumor heterogeneity and immune recognition that only stochastically results in cancer elimination. In this sense, this paper might be seen as the opening act in a series of more sophisticated models, and the authors discuss avenues towards such further developments.

Reviewer #1 (Public Review):

N1-methyladenosine (m1A) is a rather intriguing RNA modification that can affect gene expression and RNA stability etc. The manuscript presented the exploration of RNAs m1A modification in normal and OGD/R-treated neurons and the effects of m1A on diverse RNAs. The authors showed that m1 modification can mediate circRNA/LncRNA-miRNA-mRNA mechanism and 3'UTR methylation of mRNAs can disturb miRNA-mRNA binding.

The manuscript provides evidence for the following,<br /> 1. The OGD/R can have impacts on various functions of m1A mRNAs and neuron fates.<br /> 2. The m1A methylation of mRNA 3'UTRs disturbs the miRNA-mRNA binding.<br /> 3. The authors identified three possible patterns of m1A modification regulation in neurons.

The main merit of the manuscript is that the authors identified some critical features and patterns of m1A modification and in neurons and OGD/R-treated neurons. Moreover, the authors identified m1A modifications on different RNAs and explored the possible effects of m1A modification on the functions of different RNAs and the overall posttranscriptional regulation mechanism via an integrated approach of omics and bioinformatics. The major weakness of the manuscript is that technique details for many results are missing. Moreover, language inconsistences can be found throughout the manuscript. My general feeling about the manuscript is that some conclusions are rather superficial and therefore require validation and discussion.

Reviewer #2 (Public Review):

In this manuscript, investigators explore the m1A modification, an important post-transcriptional regulatory mechanism, in primary normal neuron and OGD/R treated neuron. As far as I know, the regulatory m1A modification remains poorly characterized in neuron. This is an interesting topic in the context of epitranscriptomics. This paper not only provided us with a landscape of m1A modifications in neuron, but also explored the impact of m1A modifications on the biological functions of different RNA (mRNA, lncRNA, circRNA). In addition, the argument that m1A modification affects miRNA binding to other RNAs is of interest to reader, and the authors have performed a dual luciferase validation here to add feasibility to this conclusion.

Reviewer #3 (Public Review):

Overall, this is an interesting and well performed study that described a comprehensive landscape of m1A modification in primary neuron and investigated the role of m1A in the circRNA/lncRNA‒miRNA-mRNA regulatory network following OGD/R. The focus on the two different complex regulatory networks for differential expression and differential methylation is important and it will be a valuable resource for the research community that focuses on epitranscriptomics and central nerve system diseases. Collectively, the authors present an exciting piece of work that certainly adds to the literature regarding epitranscriptomic features in neuron. While interesting results obtained and the paper is nicely written, I have the following suggestions for minor revisions to improve the paper.

1. The authors have explored the role of m1A modification in neuron, but it would have been helpful if the authors described the significance of these findings in depth in some sections (Figure 5 and Figure 6) to enhance the value of the article.<br /> 2. The authors should describe in detail the current research state of m1A modification and the significance of this study to the field of epitranscriptomics in the introduction and discussion section.

Reviewer #1 (Public Review):

Here, the authors generated a CSAS-LexA driver line to investigate the expression pattern of CSAS and showed that CSAS expression is confined to glia and does not overlap with DSiaT expression. DSiaT expression is presumed to be in neurons, but this was not evaluated with specific markers in this study.

The authors showed that restoring CSAS expression specifically in glia but not neurons could rescue the mutant phenotype of temperature sensitive paralysis and confirmed that glial (and not neuronal) CSAS expression could rescue excitatory junction potentials at neuromuscular junctions in CSAS mutants. In addition to rescue experiments, the authors also performed RNAi knockdowns in glia vs. neurons to show that CSAS function is required in glia and DSiaT in neurons for the same paralysis phenotype.

Next, the authors performed mass spec to analyse sialylated proteins in larval brains and found that sialylated proteins could not be detected in DSiaT and CSAS mutants. However, sialylated proteins were only barely detectable in wildtypes.

Of note, the authors show that CSAS functions normally in glia and cannot function in neurons due to low endogenous NANS activity (sialic acid synthase).

Finally, the authors explore the hypothesis that the temperature-sensitive paralysis CSAS phenotype is due to oxidative stress with a paraquat exposure paradigm. This could be strengthened by examining ROS levels in vivo in CSAS or DSiaT mutants. The specific genetic background of these experiments seemed to be a major factor in the results obtained and more stringent controls or backcrossing to isogenize the genetic background would be required to be fully confident in the conclusions drawn from these experiments.

The authors also demonstrate a link between sialylation and Para (protein) expression. Although intriguing, there is very little data provided on this aspect of the story, though it does not detract from the broader message of the manuscript.

Reviewer #2 (Public Review):

The function of many proteins depends on posttranslational modifications. Protein glycosylation is widespread and glycosylated proteins are mostly found on the outer surface of cells, where it is frequently implicated in cell-to-cell adhesion. It involves the addition of often complex and branched sugar chains to a protein backbone. Sialic acid is a particular relevant sugar as it is negatively charged and occupies terminal positions at the glycan chain. The enzymatic cascade leading to sialylated proteins is known. Unlike mammals, flies have only one sialyltransferase (SiaT), thus, Drosophila is a particularly well-suited model to study protein sialylation. The penultimate enzymatic steps in sialylation are mediated by N-acetlyneuraminic acid synthetase (NANS) and sialic acid synthetase (CSAS).

Scott et al., start with careful and state-of-the-art dissection of the expression patterns of the relevant genes. They first generated transgenic flies harboring a BAC covering the CSAS gene - which was able to rescue the mutant phenotype. They then replaced the CSAS coding sequence with LexA and demonstrated that LexA expression was sufficient to drive LexAop-CSAS to a full rescue of the CSAS mutant. CSAS-LexA was found to be active only in Repo expressing glial cells. The authors performed further experiments employing another BAC harboring an HA-tagged SiaT gene and found complementary expression in neurons (here I missed a comment on why the endogenously tagged SiaT gene (Repnikova 2010) was not used).

To study cell-type specific requirements UAS-based rescue experiments were conducted. The CSAS mutant phenotype could be rescued not only by panglial expression of CSAS but also by expression exclusively in subperineurial or ensheathing glial cells. Whether astrocytes or cortex glial cells are similarly able to rescue the mutant phenotype has not been addressed. No rescue was observed when CSAS was expressed in neurons, but co-expression of CSAS and NANS led to a partial rescue, further validating the split of the biosynthetic pathway leading to sialylated proteins to glial and neuronal cells.<br /> In addition to the rescue experiment, the authors also performed RNAi-based knockdown experiments for both, CSAS and SiaT which together support the conclusion that sialylation requires a split of the biosynthesis pathway.

In a subsequent mass spec approach, the authors analyzed sialylated proteins in larval brains. Whereas in wild type brains sialylated proteins were barely detected, they could not be seen in SiaT or CSAS mutant brains. However, according to Flybase, the highest expression of both genes is in adult flies. Why not look at these stages? It would also be good to use the cell type-specific knockdown flies for such experiments to fully support the notion that sialylation requires a glia-neuron transfer of intermediates. Possibly, low (and thus undetected) levels of SiaT in glia could be sufficient for function. In this respect it is interesting that the presence of a UAS-SiaT element is sufficient to rescue the SiaT mutant phenotype, suggesting that only very low levels of SiaT are needed for function.

Subsequently, Scott et al., demonstrate that the paralysis phenotype of CSAS mutants is sensitive to gene dose and that CSAS activity protects flies from oxidative stress. Quite interesting, they also demonstrate that sialylation is required - directly or indirectly - to maintain protein expression of the voltage gate sodium ion channel Para.

Reviewer #3 (Public Review):

In this work, the authors find that similar to mammals, sialylation is critical in neurons within flies, yet in flies the critical substrate for sialylation, CMP-Neu5Ac, is 'outsourced' to glial cells. These findings are shown through an extensive array of knockout, knockdown, and transgenic flies where CMP-Neu5Ac biosynthesis and sialyltransferase expression is modulated in either glial cells or neurons. The importance of sialylation in neurons is demonstrated by showing that sialylation impacts the expression levels of a critical voltage-gated ion channel.

This elegant work dissecting sialylation in the fly brain convincingly demonstrates the requirement for glial cells in the process of sialylation of neurons and deserves to be published. The major unaddressed question remaining is precisely how the CMP-Neu5Ac is delivered from the glial cells to neurons with several possibilities that merit further discussion including (but not limited to): extracellular vesicles, receptor-mediated uptake (unlikely but can't be ruled out), or exocytosis. The authors could make the point stronger that CMP-Neu5Ac should not be able to cross the neuronal membrane (or the Golgi membrane for that matter), requiring specific transport mechanisms.

Reviewer #1 (Public Review):

High-throughput genetic screening is a powerful approach to elucidate genes and gene networks involved in a variety of biological events. Such screens are well established in single-celled organisms (i.e. CRISPR-based K/O in tissue culture or unicellular organisms; screens of natural variants in response to drugs). It is desirable to extend such methodology, for example to Arabidopsis where more than 1000 ecotypes from around the Northern hemisphere are available for study. These ecotypes may be locally adapted and are fully sequenced, so the system is set up for powerful exploration of GxE. But to do so, establishing consistent "in vitro" conditions that mimic ecologically relevant conditions like drought is essential.

The authors note that previous attempts to mimic drought response have shortcomings, many of which are revealed by 'omics type analysis. For example, three treatments thought to induce osmotic stress; the addition of PEG, mannitol, or NaCl, fail to elicit a transcriptional response that is comparable to that of bonafide drought. As an alternative, the authors suggest using a low water-agar assay, which in the things they measure, does a better job of mimicking osmotic stress responses. The major issues with this assay are, however, that it introduces another set of issues, for example, changing agar concentration can lead to mechanical effects, as illustrated nicely in the work of Olivier Hamant's group (e.g., https://elifesciences.org/articles/34460).

Reviewer #2 (Public Review):

The authors aim to make a reliable plate-based system for imposing drought stress (which for experiments like this would be better referred to as low water potential stress). This is an admirable goal as a reliable experimental system is key to conducting successful low-water potential experiments and some of the experimental systems in use have problems. They compare several treatments but seem to be unaware that such comparisons need to be based on the measurement of water potential as the fundamental measure of how severe the level of water limitation is. Only by comparing things at the same water potential can one determine if the methods used to impose the low water potential are introducing confounding factors. In this manuscript, they compare several agar-plate-based treatments to what they view as a baseline experiment of plants subjected to soil drying. However, that baseline soil drying (vermiculite drying, to be precise) experiment illustrates many of the problems present in the molecular drought literature in that they give no information on plant or soil water potential or water content. Thus, there is no way to know how severe the drought stress was in that experiment and no way for any other lab to reproduce it. It is directly akin to doing a heat stress experiment and not reporting the actual temperature.

They compare transcriptome data from this soil drying experiment to transcriptome data from agar plates with PEG, mannitol or salt added. However, this comparison is problematic, because none of the treatments being compared are at the same water potential (as mentioned above). Also, the PEG-infused agar plates have limitations in that no buffer is added and it is not clear that anything is done to check or control the pH. Adding PEG to the solution will reduce the pH. Thus, in their unbuffered PEG plates, the plants are almost certainly exposed to low pH stress and this can explain the supposed difference they observe between PEG and other treatments, especially since the plants are left on such stressful pH conditions for a relatively long period. It is also problematic that the comparison between soil drying and plate-based treatments is at different times (5 vs 14 days). They also show an over-reliance on the GO annotations of drought-induced gene expression. This GO annotation is based on experiments using very severe stress for a short time period. It is notorious for not accurately reflecting what happens on longer-term exposure to more moderate levels of low water potential stress. Thus, for example, we would not expect many of the canonical drought regulation genes (RD29A and similar genes) to be upregulated in the longer-term treatments as its expression is induced rapidly but also rapidly declines back to near baseline at the plant acclimates to the low water potential stress.

The authors have not always considered literature that would be relevant to their topic. For example, there is a number of studies that have reported (and deposited in the public database) transcriptome analysis of plants on PEG-plates or plants exposed to well-controlled, moderate severity soil drying assays (for the latter, check the paper of Des Marais et al. and others, for the former, Verslues and colleagues have published a series of studies using PEG-agar plates). They also overlook studies that have recorded growth responses of wild type and a range of mutants on properly prepared PEG plates and found that those results agree well with results when plants are exposed to a controlled, partial soil drying to impose a similar low water potential stress. In short, the authors need to make such comparisons to other data and think more about what may be wrong with their own experimental designs before making any sweeping conclusions about what is suitable or not suitable for imposing low water potential stress.

To solve the problem of using these other systems to impose low water potential stress, the authors propose the seemingly logical (but overly simplistic) idea of adding less water to the same mix of nutrients and agar. Because the increased agar concentration does not substantially influence water potential (the agar polymerizes and thus is not osmotically active), what they are essentially doing is using a concentrated solution of macronutrients in the growth media to impose stress. This is a rediscovery of an old proposal that concentrated macronutrient solutions could be used to study the osmotic component of salt stress (see older papers of Rana Munns). There are also effects of using very hard agar that is of unclear relationship to actual drought stress and low water potential. Thus, I see no reason to think that this would be a better method to impose low water potential.

Reviewer #3 (Public Review):

This work compares transcriptional responses of shoots and roots harvested from four plate-based assays that simulate drought and from plants subjected to water deficit in pots using the model plant Arabidopsis thaliana with the aim to select a plate-based assay that best recapitulates transcriptional changes that are observed during water-deficit in pots. Polyethylene glycol (PEG), mannitol, and sodium chloride (salt) treatments that are commonly used by molecular biologists to simulate drought were used for the plate-based assays as well as a new assay that uses increased concentrations of agar and nutrients to elicit drought which was developed by the authors and termed a 'low-water agar' assay since the amount of water added to the media mix and plates was lowered. Plants in pots were grown on vermiculite with the same nutrient mix as used in the plates and drought was induced by withholding watering for five days. Additionally, treatment with abscisic acid was conducted to study whether growth on plates itself led to artifacts compared to water deficit in pots. Shoot and root samples were harvested from all treatments for RNA sequencing analysis and differentially expressed genes were called against control samples.

The authors observed that gene expression responses of roots in their 'low-water agar' assay resembled more closely the water deficit in pots compared to the PEG, mannitol, and salt treatments (all at the highest dose). In particular, 28 % of PEG led to the down-regulation of many genes that were up-regulated under drought in pots. Through GO term analysis, it was pointed out that this may be due to the negative effect of PEG on oxygen solubility since downregulated genes were over-represented in oxygen-related categories. The data also shows that the treatment with abscisic acid on plates was very good at simulating drought in roots. Gene expression changes in shoots showed generally a high concordance between all treatments at the highest dose and water deficit in pots, with mannitol being the closest match. This is surprising, since plants grow in plates under non-transpiring conditions, while a mismatch between water loss by transpiration on water supply via the roots leads to drought symptoms such as wilting in pot and field-grown plants. The authors concluded that their 'low-water agar' assay provides a better alternative to simulate drought on plates.

Strengths:

The development of a more robust assay to simulate drought on plates to allow for high-throughput screening is certainly an important goal since many phenotypes that are discovered on plates cannot be recapitulated on the soil. Adding less water to the media mix and thereby increasing agar strength and nutrient concentration appears to be a good approach since nutrients are also concentrated in soils during water deficit, as pointed out by the authors. To my knowledge, this approach has not specifically been used to simulate drought on plates previously. Comparing their new 'low-water agar' assay to popular treatments with PEG, mannitol, salt, and abscisic acid, as well as plants grown in pots on vermiculite led to a comprehensive overview of how these treatments affect gene expression changes that surpass previous studies. It is promising that the impact of 'low-water agar' on the shoot size of 20 diverse Arabidopsis accessions shows some association with plant fitness under drought in the field. Their methodology could be powerful in identifying a better substitute for plate-based high-throughput drought assays that have an emphasis on gene expression changes.

Weaknesses:

While the authors use a good methodological framework to compare the different drought treatments, gene expression changes were only compared between the highest dose of each stress assay (Fig. 2B, 3B). From Fig. 1F it appears that gene expression changes depend significantly on the level of stress that is imposed. Therefore, their conclusion that the 'low-water agar' assay is better at simulating drought is only valid when comparing the highest dose of each treatment and only for gene expression changes in roots. Considering how comparable different levels of stress were in this study leads to another weakness. The authors correctly point out that PEG, mannitol, and salt are used due to their ability to lower the water potential through an increase in osmotic strength (L. 45/46). In soils, water deficit leads to lower water potential, due to the concentration of nutrients (as pointed out in L. 171), as well as higher adhesion forces of water molecules to soil particles and a decline in soil hydraulic conductivity for water, which causes an imbalance between supply and demand (see Juenger and Verslues, The Plant Cell 2022 for a recent review). While the authors selected three different doses for each treatment that are commonly used in the literature, these are not necessarily comparable on a physiological level. For example, 200 mM mannitol has an approximate osmotic potential of around -5 bar (Michel et al. Plant Physiol. 1983) whereas 28 % PEG has an osmotic potential closer to -10 bar (Michel et al. Plant Physiol. 1973). It also remains unclear how the increase in agar concentration versus the increase in nutrient concentration in the 'low-water agar' affect water potentials. For these reasons it cannot be known whether a better match of the 'low-water agar' at the 28% dose to water deficit in pots for roots in comparison to the other treatments is due to a good match in stress levels with the 'low-water agar' or adverse side-effect of PEG, mannitol, or and salt on gene regulation. Lastly, since only two biological replicates for RNA sequencing were collected per treatment, it is not possible to know how much variance exists and if this variance is greater than the treatments themselves.

Reviewer #1 (Public Review):

In this study, the authors found that the chromatin remodeling complex mutant isw1Δ of the fungal pathogen Cryptococcus neoformans is resistant to multiple different antifungal drugs. The mutant, however, is fully virulent in a mouse model. By comparing transcript changes of the wild type and the mutant when treated with antifungal fluconazole, they found that many transporter genes are differentially expressed in the isw1Δ mutant. Consistently, they showed reduced expression of genes involved metabolism of another antifungal 5-FC and a lower level of cellular accumulation of 5-FC in the isw1Δ mutant, which likely contributes to its 5-FC resistance. They found that the Isw1 protein is degraded mostly through ubiquitination and identified K97 deacetylation as being critical for drug resistance/protein degradation. Then they mutated nine E3 ubiquitination ligase genes and identified Cdc4 to be responsible for Isw1 degradation. Lastly, they showed that Isw1 is low in some clinical isolates that are modestly resistant to antifungals. The evidence of the interplay between acetylation status and ubiquitination of Isw1 is strong. The finding that reduced Isw1 increases drug resistance also fits the growing interest in studying epigenetic regulation of drug resistance in fungal pathogens. One area that needs to be strengthened is the potential clinical relevance of Isw1 reduction in drug resistance.

Reviewer #2 (Public Review):

Cryptococcus neoformans is an important human pathogen, particularly in immunocompromised individuals. Like many fungal pathogens, resistance to antifungal drugs can emerge quickly in Cryptococcus. Understanding the mechanisms by which fungi develop resistance to antifungals will support new treatment strategies and, potentially, identify new drug targets. In this manuscript, Meng et al. describe a novel role for the conserved ATP-dependent chromatin remodeling factor, Imitation Switch (Isw1) in responding to antifungals in Cryptococcus. The authors first find that loss of Isw1 increases resistance to multiple antifungals and changes expression levels of genes potentially involved in antifungal resistance using functional genetics and cell growth assays. Next, the authors use mass spectrometry data (data generated in this study and public data) to identify ubiquitinated and acetylated sites of Isw1. The authors use this information to carry out an extensive series of western blot experiments using point mutations and chemical perturbations to dissect the contribution of specific modified sites of Isw1. Here, they identify important roles for the acetylation of K97 and ubiquitination of K113 and K441 in Isw1 stability. Lastly, the authors present evidence that clinical isolates of Cryptococcus that have increased antifungal resistance may have defects in Isw1 stability and that overexpressing ISW1 reduces antifungal resistance.

Strengths:

The authors present novel data that Isw1 is involved in responding to antifungals and that changes in Isw1 stability may lead to antifungal resistance. These results are of particular interest to the fungal pathogen research community and add to the general understanding of antifungal resistance.

The authors present exciting data on post-translation modification (i.e., acetylation and ubiquitination) of Isw1, how those modifications contribute to Isw1 stability, and the regulatory interplay between modifications. Considering that Isw1 is broadly conserved across eukaryotes, these results are, potentially, of broad interest and raise questions outside of pathogen biology to be addressed in future research. For example, are the residues characterized in this study conserved in other Isw1 homologs, are they similarly modified, and is regulating the stability of Isw1 (or other chromatin remodeling factors) a general strategy for responding to external signals?

Weaknesses:

The authors demonstrate that Isw1 has a role in responding to antifungals in Cryptococcus. However, it is not clear if changes in Isw1 stability represent a general response to stress. This study would have benefited from experiments to test: (1) if levels of Isw1 change in response to other stressors (e.g., heat, osmotic, or oxidative stress) and (2) if loss of Isw1 impacts resistance to other stressors.

The authors demonstrate a critical role in the acetylation of K97 and ubiquitination of K441 in regulating Isw1 stability. Additionally, this study shows that K113 is also likely involved in this process. However, it appears that K113 can be either acetylated or ubiquitinated, and it is, thus, less clear if one of the two modifications or both modifications is critical at this residue. Additional experiments may be required to answer this question. This study would have benefited from an additional discussion on the results related to the modification of K113.

The authors demonstrate that overexpression of ISW1 in select clinical isolates of Cryptococcus increases sensitivity to antifungals. However, these experiments would have benefited from additional controls, such as including overexpression of ISW1 in the wild-type strain (H99) and antifungal-sensitive isolate (CDLC120).

Reviewer #3 (Public Review):

This study focuses on the role of the chromatin remodeller ISWI in Cryptococcus. The authors show that a) ISWI modulates Cryptococcus' ability to grow in the presence of antifungal drugs and b) ISWI post-translational modifications (Acetylation and Ubiquitination) regulate ISWI protein stability. The observation that post-translational modifications regulate ISWI activity and stability is exciting and it could unveil novel mechanisms to rapidly and reversibly regulate the response to antifungal drug treatments. However, the study lacks a fundamental characterisation of ISWI. This information is essential to understand the mechanistic regulations of ISWI in Cryptococcus and how it mediates drug response. The following are questions that should be addressed:

1. ISWI chromatin remodellers are well-characterised in many organisms. How many ISWI proteins does Cryptococcus contain? Why did the authors focus on ISWI?<br /> 2. What is the ISWI protein complex(es)? The Mass-Spec analysis should reveal this.<br /> 3. Is Cryptococcus ISWI a transcriptional activator or repressor?<br /> 4. Is ISWI function in drug resistance linked to its chromatin remodelling activity?<br /> 5. Does ISWI interact with chromatin? If so, which are ISWI-target genes? Does drug treatment modulate chromatin binding?

Reviewer #1 (Public Review):

In this manuscript the authors overproduce two M. smegmatis DNA polymerases, DinB2 and DinB3, as a way to determine whether they may contribute to DNA damage tolerance and/or mutagenesis; the roles of these DNA polymerases in DNA damage tolerance and mutagenesis is currently unknown. The authors show that overproduced levels of DinB2, but not DinB3, impeded growth, and this inhibition was relieved by the disruption of DinB2 catalytic activity using the DinB-D109A mutation. They further demonstrate that the overproduction of DinB2 contributed to frameshift mutagenesis, while DinB3 did not. The contribution of overproduced levels of DinB2 to frameshift mutagenesis was studied in a careful and systematic way, convincingly showing that frameshifts correlated with DinB2 slipping while replicating homopolymeric nucleotide runs during dNTP and not rNTP incorporation. The authors also show that the metal cofactor (Mn vs Mg) contributes to the mutagenic behavior of DinB2. While this work is mostly compelling, the major concern is it fails to address the contribution of DinB2 and DinB3 to DNA damage tolerance and mutagenesis when they are expressed at normal levels from their respective chromosomal loci.

Reviewer #2 (Public Review):

The role of the family IV polymerases in mycobacteria is only partly understood. In this work, the authors investigate the role of the M. smegmatis DinB2 and DinB3 polymerases by a combination of biochemical analysis of enzyme activity in vitro and mutational and phenotypic characterization of M. smegmatis strains during induced over-expression of these proteins. They show both polymerases to be mutagenic and uncover a distinct role for DinB2 in slippage on homopolymeric tracts that is dependent on manganese.

Previous work showed that DinB1 overexpression resulted in SOS induction. This work shows that DinB2 and DinB3 similarly increase RecA levels. Previous work also showed that DinB1 overexpression resulted in growth inhibition and loss of viability which was independent of its polymerase activity. In this work, overexpression on DinB2 but not DinB3 inhibits growth along with a loss in viability but in contrast to DinB1, this inhibitory effect is only seen with a polymerase-proficient enzyme and is even more enhanced in a steric gate mutant. Overexpression of DinB3 and DinB2 increases the frequency of Rif-resistant mutants independent of the SOS response and DnaE2. The mutation spectrum in DinB2-overexpressing cells was distinct from that caused by DinB1 or DinB3 overexpression. In vitro and in vivo experiments clearly demonstrate that DinB2 catalyzes frameshift mutagenesis on substrates with homopolymeric nucleotide stretches demonstrating enhanced slippage compared to the recent data with DinB1. Remarkably, this slippage is enhanced on homopolymeric runs of purines than pyrimidines in vitro. In vivo slippage by DinB2 was not enhanced by long G runs. The slippage in vitro was only evident in its DNA-dependent DNA polymerase mode and not during ribonucleotide incorporation. In addition, while magnesium alone was associated with mis-addition, the presence of manganese shifted the enzyme to slippage mode in vitro. The detrimental effect of DnaB2 over-expression on viability is, however, not related to its slippage activity since conditions that enhance slippage in vitro (specifically manganese) are associated with a greater detrimental effect on viability in vivo despite a lack of evidence of slippage using reporter constructs.

Reviewer #3 (Public Review):

The work from Dupuy et al aims to characterize the mutagenic effects of two DinB homologs of Mycobacteria, DinB2, and DinB3. The manuscript shows solid and convincing biochemical data about slippage promoted by DinB 2 on various homopolymeric templates. Overall, this study makes a solid contribution to the understanding of the properties of polymerases from the different DinB subfamilies of bacteria, although some points of the in vivo experiments should be critically evaluated by the readers as described below.

In vivo DinB2 is the more mutagenic of the two and is toxic when overexpressed. Nevertheless, these results are obtained with the overexpression of the polymerases and should be interpreted with caution. In this sense, it would have been interesting to have a quantification of how much overexpression the plasmids constructs achieve in the conditions used in the experiments, for a better assessment of the relevance of the data. For example, a physiological 10-fold increase in the expression of DinB2 is mentioned in the discussion - would that be close to what is achieved with plasmid-based overexpression?

The finding of kanR CFUs without any detectable mutations in the kan marker is worrisome and should be better discussed in the text. The same for sacB data in supplementary material. The explanation given in lines 216-218 does not make sense. Markers 7G and 8G clearly are barely measuring any mutagenesis. I think that the experiments in which most of the supposed KanR revertants actually have no Kan mutation should either be removed from the manuscript or better discussed, because it is uncertain what they are measuring, therefore no conclusion can be drawn from them. For the Kan markers, one possible explanation is that translational frameshifts are occurring and allow residual growth of some of the cells. Gene amplification as seen in the lac system of Cairns and Foster in E. coli could also promote growth without actual mutations. Is the KanR phenotype of these colonies heritable and stable?

Also, spontaneous mutagenesis should have been more precisely measured by using fluctuation analysis of larger sample sizes. In many instances, the results shown are the means of a few cultures with very large differences in mutant frequencies (several hundred-fold - e.g. Figures 4C, D and E, 5C and F, S3). Authors could discuss/explain their choice of statistical analysis and sample sizes.

Reviewer #1 (Public Review):

Damon-Soubeyrand and colleagues use 3DISCO tissue clearing and light-sheet microscopy to provide a detailed atlas of the blood and lymphatic circulating networks of the mouse epididymis. While this manuscript does not address the function of these networks during the development or homeostasis of the epididymis, it is an outstanding example of a descriptive study that paves the way towards functional investigations of the role of epididymal vasculature in the post-testicular maturation of spermatozoa.

Strengths: The authors used a wide range of markers to carefully assess the differential patterns of epididymal blood and lymphatic vasculature, and elegantly describe each image in great detail. Where possible, the authors used appropriate quantitative methods to support their descriptive data, which are useful metrics for readers seeking to characterize vascular and lymphatic networks in disease models.

Weaknesses: In its current form, it is unclear which of the elements presented in the manuscript are novel discoveries about the blood and lymphatic networks of the epididymis, as the text lacks concise and precise statements about the major findings of the study. In addition, the authors frame this study of the vasculature as a way to understand the immune context of spermatozoa in the epididymis but do not integrate their data on blood and lymphatic networks with the immune system.

Reviewer #2 (Public Review):

This manuscript illustrates a vascular network in the postnatal developing and adult epididymis using high-resolution three-dimensional (3D) imaging and organ clearing coupled with multiplex immunodetections of lymphatic and blood markers.

Strengths:<br /> The cutting-edge imaging technique to visualize the three-dimensional vascular network.<br /> The images and videos were of great quality.<br /> The authors were very cautious and careful when interpreting the results of marker immunostaining.

Weaknesses:<br /> 1. Although the images and videos were of great quality, the results derived from them provided little new knowledge and few conceptual insights into male reproductive tract biology and basically confirmed what has been published using traditional methods. For example, the high intensity of the vascular network in the initial segment was previously reported by Abe in 1984 and Suzuki in 1982; the pattern of the major lymphatic vessel and drainage was beautifully depicted by Perez-Clavier, 1982.

2. The authors were very cautious when interpreting the results of marker immunostaining however these markers were not specific for a definite cell type. For example, as the authors stated, VEGFR3 marks both lymphatic vessels and fenestrated blood vessels. how could the authors claim the VEGFR3+ network was lymphatic? The authors claimed that they used three markers for the lymphatic vessel. But staining results of the networks were very different. How could the author make conclusions about the network of lymphatic vessels in the epididymis?

3. To understand the vascular network development in the epididymis, would the authors please look at the fetal stage when the vascular network is established in the first place? Wolffian duct tissues are much smaller and thinner and would be amenable for 3D imaging probably even without clearing.

4. Immunofluorescence staining of VEGF factors was not convincing. As a secreted factor, VEGF will be secreted out of the cells, would it be detected more in the interstitium? I am always skeptical about the results of immunostaining secreted growth factors. Would it be possible to perform in situ or RNAscope to confirm the spatial expression pattern of VEGFs?

5. The study is descriptive and does not provide functional and mechanistic insights. Maybe, the combination of 3D imaging with lineage tracing of endothelium cells or ligation study (removal/ligation of the certain vessel) would help better understand how the vascular network is established and their functional significance.

6. Immune response is among many physiological processes in which vascular networks play significant roles. Discussion would be needed in other physiological processes, such as tissue metabolism and stem/progenitor cell niche microenvironment.

7. How could the author determine the Cd-A labeled vessel in Fig 1 was an artery, not a vein? This leads to another critical question. Would it be possible to stain with artery and vein markers to help illustrate the blood flow directions of the vessel?

Reviewer #1 (Public Review):

This study aims at investigating temporal variation in patterns of germline mutation during the evolution of human populations. For this purpose, the authors analyzed polymorphism data from the 1000 Genomes project. They inferred the age of each derived variant using Relate, a newly developed method that reconstructs local genealogies based on phased haplotype sequences and estimates allele ages (Speidel et al. 2019).

Speidel et al. (2019) already had used their method to explore temporal variation in mutation patterns. Their analysis had confirmed the transient elevation in non-CpG C>T mutations in Europeans compared to African and Est Asians previously described by Harris (2015). However, Speidel et al. did not push their study very far, notably because of the difficulty of distinguishing the effects of changes in mutation patterns from those of GC-biased gene conversion (gBGC).

Here Gao et al. carefully accounted for gBGC to further explore variation in mutation patterns. As expected, they confirmed the previously described European-specific mutational shift. In addition, they identified two novel interpopulation differences in the mutation spectrum. This suggests that shifts in mutation spectra occur frequently, over a few thousands of generations. The reasons (environmental or genetic) for these recurrent shifts are not known, but the authors convincingly show that they cannot be explained by changes in the age of reproduction over time.

I found this manuscript very well written and very interesting. There is however an important point in their results that seems very puzzling. Indeed, the authors report that among mutations that are estimated to be old (>28800 generations), the ratio of T>C over T>G differs significantly in African samples compared to non-African samples (Fig. 2A). This difference is unexpected given that these old mutations largely predate the out-of-Africa migration (<3000 generations), and hence are a priori expected to be largely shared across populations. Curiously, this pattern is driven by variants for which the derived allele is observed in both Neanderthals and Denisovans (ND11 variants) (while ND01 and ND10 variants do not contribute to this pattern; Fig. 2D, SupFig 2.8). The authors hypothesize that the T>C/T>G ratio was higher in one or more populations in the remote past and those ancient groups contribute variable amounts of ancestry to contemporary populations. However, I do not understand how this model can account for the fact that ND10 and ND01 variants behave differently from ND11 variants (ND10 and ND01 variants are also expected to be emerged prior to the split of modern humans and archaic hominins).

It is possible that I misunderstood something, but in any case, there are several points in the methodology that have to be clarified. Notably, it is not clear to me if the reported pattern is driven by variants that are specific to the African samples, or if it is also observed among variants that are shared across populations. Furthermore, I suspect that polarization errors (notably at CpG sites) might be responsible for this pattern.

In summary, this manuscript reports very interesting observations, but several additional tests have to be done to check whether they are real or if they might result from methodological artefacts.

Reviewer #2 (Public Review):

This manuscript reassesses the strength of evidence for rapid human germline mutation spectrum evolution, using high coverage whole genome sequencing data and paying particular attention to the potential impact of confounders like biased gene conversion. The authors also refute some recently published arguments that historical changes in the age of reproduction might explain the existence of such mutation spectrum changes. My overall impression is that the paper presents a useful new angle for studying mutation spectrum evolution, and the analysis is nicely suited to addressing whether a particular model such as the parental age model can explain a set of observed polymorphism data. My main criticism is that the paper overstates certain weaknesses of previously published papers on mutation spectrum evolution as well as the generation time hypothesis; correcting these oversimplifications would more accurately capture what the paper's new analyses add to the state of knowledge in these areas.

As part of the motivation for the current study, the introduction states in lines 97-99 that "it thus remains unclear if the numerous observed [mutation spectrum] differences across human populations stem from rapid evolution of the mutation process itself, other evolutionary processes, or technical factors." This seems to overstate the uncertainty that existed prior to this study, given that Speidel, et al. 2021 found elevated TCC>TTC fractions in ancient genomes from a specific ancient European population, which seems like pretty airtight evidence that this historical mutation rate increase really happened. In addition, earlier papers (Harris 2015, Mathieson & Reich 2016, Harris & Pritchard 2017) already presented analyses rejecting the hypothesis that biased gene conversion or genetic drift could explain the reported patterns-in fact, the Mathieson & Reich paper reports one mutation spectrum difference between populations that they conclude is an artifact caused by the Native American population bottleneck, but they conclude that other mutation spectrum differences appear more robust. As the authors acknowledge in the discussion of their own results, biased gene conversion and non-equilibrium demography are difficult confounders to deal with, and neither previous papers nor the current paper are able to do this in a way that is 100% foolproof. The current manuscript makes a valuable contribution by presenting new ways of dealing with these issues, particularly since previous papers' work on this topic was often confined to supplementary material, but it seems appropriate to acknowledge that earlier papers discussed the potential impacts of biased gene conversion and demographic complexity and presented their own analyses arguing that these phenomena were poor explanations for the existence of mutation spectrum differences between populations.

For the most part, I found the paper's introduction to be a useful summary of previous work, but there are a few additional places where the limitations of previous work could be described more clearly. I'd suggest noting that the data artifacts discovered by Anderson-Trocmé, et al. were restricted to a few old samples and that the large differences the current manuscript focuses on were never implicated as potential cell line artifacts. In addition, when the authors mention that their new approach includes "minimiz[ing] confounding effects of selection by removing constrained regions and known targets of selection" (lines 106-107), they should note that earlier papers like Harris & Pritchard 2017 also excluded conserved regions and exons.

One innovative aspect of the current paper's approach is the use of allele ages inferred by Relate, which certainly has advantages over using allele frequencies as a proxy for allele age. Though the authors of Relate previously used this approach to study mutation spectrum evolution, they did not perform such a thorough investigation of ancient alleles and collapsed mutation type ratios. I like the authors' approach of building uncertainty into the use of Relate's age estimates, but I wonder about the validity of assuming that the allele age posterior probability is distributed uniformly between the upper and lower confidence bounds. Can the authors address why this is more appropriate than some kind of peaked distribution like a beta distribution?<br /> I would also argue that the statement on line 104 about Relate's reliability is not yet supported by data-there is certainly value in using Relate ages to investigate mutation spectrum change over time and compare this to what has been seen using allele frequencies, but I don't think we know enough yet to say that the Relate ages are definitely more reliable. Relate's estimates might be biased by the same processes like selection and demography that make allele frequencies challenging to interpret. The paper's statements about the limitations of allele frequencies are fair, but there is always a tradeoff between the clear drawbacks of simple summary statistics and the more cryptic possible blind spots of complicated "black box" algorithms (in the case of Relate, an MCMC that needs to converge properly). DeWitt, et al. 2021 noted that the demographic history inferred by Relate doesn't accurately predict the underlying data's site frequency spectrum, indicating that the associated allele ages might have some problems that need to be better characterized. While testing Relate for biases is beyond the scope of this work, the introduction should acknowledge that the accuracy and precision of its time estimates are still somewhat uncertain.

The paper's results on C>T mutations in Europeans versus Africans are a nice confirmation of previous results, including the observation from Mathieson & Reich that neither SBS7 nor SBS11 is a good match for the mutational signature at play. More novel is the ancient mutational signature enriched in Africa and the interrogation of the ability of parental age to explain the observed patterns. I just have a few minor suggestions regarding these analyses:

1. I like the idea of using maternal age C>G hotspots to test the plausibility of the maternal age as an explanatory factor, but I think this would be more convincing with the addition of a power analysis. Given two populations that have average maternal ages of 20 and 40, and the same population sample sizes available from 1000 Genomes, can the authors calculate whether the results they'd predict are any different from what is observed (i.e. no significant differences within the maternal hotspots and significant differences outside of these regions)?

2. Is it possible that the T>C/T>G ratio is elevated in all variants above a certain age but shows up as an African-specific signal because the African population retains more segregating variation in this age range, whereas non-African populations have fixed or lost more of this variation? Since Durvasula & Sankararaman identified putative tracts of of super-archaic introgression within Africans, is it possible to test whether the mutation spectrum signal is enriched within those tracts?

3. Although Coll Macià, et al. argued that generation time is capable of explaining all mutation spectrum differences between populations, including the excess of TCC>TTC in Europeans, Wang et al. argue something slightly different. They exclude TCC>TTC and the other major components of the European signature from their analysis and then argue that parental age can explain the rest of the differences between populations. I think the analysis in this paper convincingly refutes the Coll Macià, et al. argument, but refuting the Wang, et al. version would require excluding the same mutation types that are excluded in that paper.

Reviewer #1 (Public Review):

Inter-cellular mitochondria transfer has been observed in many systems but the role or relevance of transferred mitochondria in recipient cells is poorly defined in contexts where recipient cells have intact functional networks. This manuscript directly addresses this important question and present a model in which transferred mitochondria act as signaling organelles to increase cancer cell proliferation.

The authors present compelling evidence that macrophages transfer mitochondria to cancer cells. Activated macrophages transfer mitochondria more effectively than non-activated macrophages, and this increased transfer is at least in part due to enhanced mitochondrial fragmentation in activated macrophages. Probing the significance of mitochondrial transfer, the authors find that transferred mitochondria remain distinct from endogenous mitochondrial networks and do not exhibit the polarization that traditionally characterizes functional mitochondria. The transferred mitochondria have features consistent with elevated oxidative stress and/or ROS production. A series of elegant imaging experiments demonstrate that mitochondrial transfer is associated with increased growth in daughter cells that inherit transferred mitochondria. Mechanistically, the authors propose that ROS produced by transferred mitochondria stimulate ERK signaling to induce a proliferation advantage.

Overall the work addresses an important question regarding the functional role of mitochondria transferred to cancer cells. The data largely support the model that transferred mitochondria are defective and induce proliferation in recipient cells. Some clarification on the effect timescales and the role role of ROS and ERK signaling in cell proliferation in cells that do not receive mitochondria is warranted. Overall this work provides an important new view for how mitochondrial transfer affects cell biology and provides a suite of tools and protocols for quantifying the impact of mitochondrial transfer on recipient cells.

Reviewer #2 (Public Review):

In these studies, the authors make the observation that macrophages transfer their mitochondria to cancer cells. The authors claim that these mitochondria are dysfunctional and release reactive oxygen species (ROS) in the recipient cancer cells. Further, the authors illustrate that the mitochondrial-derived ROS activates proliferative ERK signaling. Macrophage mitochondria exhibit fragmentation, the extent of which promotes their transfer to cancer cells resulting in a functional increase in cancer cell proliferation. The authors initiated this work based on their previous findings where they illustrated the ability of macrophages to transfer cytosolic contents to recipient cancer cells.

The observations made in this manuscript, if further substantiated, are of interest in the field of cancer immunotherapy, metabolism, and basic cancer biology.

Reviewer #3 (Public Review):

In this manuscript, Kidwell & Casalini, et al. use cell biology and functional approaches to investigate the dynamics and consequences mitochondrial transfer from macrophages to breast cancer cells. Unlike prior studies that emphasize the metabolic benefits of mitochondrial reconstitution in cells with defective mitochondrial DNA, they ask how mitochondrial transfer affects breast cancer cells with intact mitochondria. They observe that macrophage co-culture or "bathing" breast cancer cells in isolated mitochondria from macrophages results in low frequency mitochondrial transfer, which increases cell cycling, ERK signaling, and cell proliferation rate of recipient cells. Interestingly, fluorescent dyes and sensors were used to determine that transferred mitochondria had low mitochondrial membrane potential and were highly oxidized, suggesting dysfunctional mitochondria with elevated ROS. In addition, activation of mitochondrial ROS by photobleaching a region of mitochondria in cells expressing mito-KillerRed was sufficient to similarly increase cell cycling, and mitochondrial targeted antioxidants could mitigate the proliferative benefits of mitochondrial transfer. Finally, the authors used several in vitro and in vivo models to demonstrate that M2-like macrophages had more fragmented mitochondria, had higher mitochondrial transfer rates, and promoted cell cycling in tumors.

Overall, a strength of the study is the usage of creative cell biology techniques and rigorous mouse models to provide compelling support for their primary claims, many of which go against the grain of current thinking in mitochondrial transfer research. While the discrepancies with the literature are by no means the fault of the authors, this study could nonetheless improve its reach by directly seeking resolution to these differences. In addition, the study raises some important questions how mitochondrial ROS from transferred dysfunctional mitochondria might be beneficial and at what doses, which should be further investigated to contextualize the findings.

Reviewer #1 (Public Review):

The present study investigates the anatomical connectivity between Mu opioid receptor (MOR) expressing neurons of the pontine respiratory group with down-stream targets of the respiratory network in the medulla oblongata. The study employs a variety of viral tracing approaches, optogenetic stimulation of pre-synapses of descending pontine projection neurons, and patch clamp electrophysiology. Overall the study is well conducted and the authors show that MOR expressing excitatory glutamatergic pontine neurons project to the medullary respiratory rhythm generator and adjacent ventral respiratory group. The study implies that opioids act on MOR-located somata and dendrites of the pontine and medullary respiratory groups. Importantly MOR are expressed on the pre-synapses of the descending pontine projections neurons. The authors, therefore, propose that opioids mediate respiratory depression via distinct pre- and post- synaptic mechanisms across inter-connected ponto-medullary respiratory neurons. The study advances our knowledge of network mechanisms that mediate opioid respiratory depression and may provide interesting frameworks for the development of therapies to counteract or prevent opioid respiratory depression. The study is of broad interest to the respiratory control research community, as well as medically relevant.

Reviewer #2 (Public Review):

This study identifies the neural circuits inhibited by activation of opioid receptors using complex experimental approaches such as electrophysiology, pharmacology, and optogenetics and combined them with retrograde and anterograde tracings. The authors characterize two key regions of the brainstem, the preBötzinger Complex, and the Kolliker-Fuse, and how these neuronal populations interact. Understanding the interactions of these circuits substantially increases our understanding of the neural circuits sensitive to opioid drugs which are critical to understand how opioids act on breathing and potentially design new therapies.

Major strengths.<br /> This study maps the excitatory projections from the Kolliker-Fuse to the preBötzinger Complex and rostral ventral respiratory group and shows that these projections are inhibited by opioid drugs. These Kolliker-Fuse neurons express FoxP2, but not the calcitonin gene-related peptide, which distinguishes them from parabrachial neurons. In addition, the preBötzinger Complex is also hyperpolarized by opioid drugs. The experiments performed by the authors are challenging, complex, and the most appropriate types of approaches to understanding pre- and post-synaptic mechanisms, which cannot be studied in vivo. These experiments also used complex tracing methods using adenoassociated virus and cre-lox recombinase approaches.

Limitations.<br /> (1) The roles of the mechanisms identified in this study have not been established in models recording opioid-induced respiratory depression or respiratory activity. This study does not record, modulate, or assess respiratory activity in-vitro or in-vivo, without or with opioid drugs such as fentanyl or morphine.<br /> (2) Experiments are performed in-vitro which do not mimic the effects of opioids observed in-vivo or in freely-moving animals. However, identification of pre- and post- synaptic mechanisms, as well as projections, cannot be performed in-vivo, so the authors use the right approaches for their experiments.<br /> (3) The type of neurons projecting from KP to preBötzinger Complex or ventral respiratory group have not been identified. Although some of these cells are glutamatergic, optogenetic experiments could have been performed in other cre-expressing cell populations, such as neurokinin-1 receptors.

This study provides new insights into the types of circuits inhibited by opioid drugs, and the site of actions of inhibition, such as pre- or post-synaptic, and proposes how inhibition by opioids acts at multiple sites in the brainstem through various mechanisms.

Although many studies have recently explored the types of neurons and sites in the brain sensitive to opioids, the present study is the first to provide a clear picture of the neuronal mechanism underlying inhibition by opioids. Importantly, it provides a link between two sites known to inhibit breathing when inhibited by opioids. The results provided here combined with a complex methodology support the various conclusions reached by the authors.

Reviewer #3 (Public Review):

This manuscript reveals opioid suppression of breathing could occur via multiple mechanisms and at multiple sites in the pontomedullary respiratory network. The authors show that opioids inhibit an excitatory pontomedullary respiratory circuit via three mechanisms: 1) postsynaptic MOR-mediated hyperpolarization of KF neurons that project to the ventrolateral medulla, 2) presynaptic MOR mediated inhibition of glutamate release from dorsolateral pontine terminals onto excitatory preBötC and rVRG neurons, and 3) postsynaptic MOR-mediated hyperpolarization of the preBötC and rVRG neurons that receive pontine glutamatergic input.

This manuscript describes in detail a useful method for dissecting the relationship between the dorsolateral pons and the rostral medulla, which will be useful for various researchers. It's also great to see how many different methods have been applied to improve the accuracy of the results.

1. Relationship between the dorsolateral pons and rostral ventrolateral medulla.

The method of this paper is a good paper to show a very precise relationship between the presence of opioid receptors and the dorsolateral pons and rostral ventrolateral medulla, and for opioid receptors, based on the expression of Oprm1, the use of genetically modified mice with anterograde or retrograde viruses with additional fluorescent colors showed both anterograde and retrograde projections, revealing a relationship between the dorsolateral pons and rostral ventrolateral medulla.

For example, to visualize dorsal pontine neurons expressing Oprm1, Oprm1Cre/Cre mice were crossed with Ai9tdTomato Cre reporter mice to generate Ai9tdT/+ oprm1Cre/+ mice (Oprm1Cre/tdT mice) expressing tdTomato on neurons that also express MOR at any point during development, and the retrograde virus encoding Cre-dependent expression of GFP (retrograde AAV-hSIN-DIO-eGFP was injected into the respiratory center of Oprm1Cre/+ mice and into the ventral respiratory neuron group, showing that KF neurons expressing Oprm1 project to the respiration-related nucleus of the ventrolateral medulla.

However, although the authors have also corrected it, the virus may spread to other places as well as where they thought it would be injected, and it is important to note that it is injected accordingly to mark the injection site with an anterograde virus encoding a different fluorescent color mCherry, and the extent of the injection is quantified, which is excellent as a control experiment.

In addition, the respiratory center seems to be related not only to preBötC but also to pFRG recently, so if the relation with it is described, it is important from the viewpoint of the effect on the respiratory center and the effect on the rhythm.

2. Electrophysiological approaches and useful methods for target neurons

Oprm1Cre/+ mice), the authors found abundant Oprm1 + projections in the preBötC region of the medulla oblongata (respiratory center) and sought to determine whether presynaptic opioid receptors inhibit glutamate release from KF terminals to excitatory preBötC and rVRG neurons, since KF neurons in the dorsolateral pons projecting to the ventrolateral medulla oblongata had been shown to be glutamatergic and to have opioid receptors. The authors injected a channelrhodopsin-2-encoding virus (AAV2-hSin-hChR2 (H134R) -EYFP-WPRE-PA) into the dorsolateral pontine KF of vglu2Cre / tdT mice and performed whole-cell voltage-clamp recordings from td tomato-expressing, excitatory vglu2-expressing preBötC and rVRG neurons, contained in acute brain slices. Moreover, both opioid-sensitive and opioid-insensitive KF neurons that project to preBötC and rVRG were visible and recorded using FluoSpheres which are much more visible in acute brain sections than retrograde tracers of viruses.

1) Optogenetic stimulation of the KF terminus was blocked by the AMPA-type glutamate receptor antagonist DNQX. In excitatory pre-BötC and rVRG neurons, the terminals from the dorsal pontine KF were activated by optogenetic stimulation, and the KF synapses to the medullary respiratory neurons were found to be monosynaptic because oEPSCs(optical stimulated EPSCs) were removed by TTX but were subsequently restored by the application of K-channel blocker 4AP. Thus, KF neurons have been shown to send monosynaptic glutamatergic projections to excitatory ventrolateral medullary neurons using terminal optogenetic stimulation and receptor and channel inhibitors.

2) To determine whether opioids inhibit glutamate release from KF terminals to medullary respiratory neurons, we recorded a pair of oEPSCs (50 ms stimulus interval) from excitatory preBötC and rVRG neurons and applied an endogenous opioid agonist, [Met5] enkephalin (ME), to the perfusion solution. ME is preBötC and rVRG neurons, indicating inhibition of glutamate release by presynaptic MOR PPR. Thus, presynaptic opioid receptors have been shown electrophysiologically to inhibit glutamate release from KF terminals to excitatory pre-BötC and rVRG neurons.

3) Whether excitatory pre-BötC or rVRG neurons themselves receiving opioid-sensitive glutamatergic synaptic inputs from KF are hyperpolarized by opioids can be determined by monitoring their retention currents.

4) Since FluoSpheres are much more visible in acute brain sections than retrograde tracers of viruses and do not spread to injection sites, they chose to record from retrogradely labeled KF neurons with FluoSpheres injected into preBötC or rVRG in wild-type mice, allowing us to label KF neurons regardless of Oprm1 expression status and determine the projection patterns of both Oprm1 + and Oprm1- neurons. Whole-cell voltage-clamp recordings from fluorescent KF neurons contained in acute brain slices show that the presence of ME-mediated outward current can identify KF neurons that express functional MORs and are opioid-sensitive compared to neurons that lack ME-mediated outward current (insensitive). This suggests that both opioid-sensitive and opioid-insensitive KF neurons project to preBötC and rVRG.

Although much has been written about the relationship between KF neurons and medulla oblongata neurons and their being glutaminergic neurons, detailed descriptions of the recorded neuronal firing patterns are lacking. You should describe what firing pattern the recorded neurons had. If we don't do that, we won't be able to tell whether it's a respiratory neuron or another tonic firing neuron, so I don't think we can discuss whether it's involved in the respiratory rhythm.

3. Compare the distribution of neurons

To examine the distribution of Oprm1 + and Oprm1- dorsolateral pontine neurons projecting to the ventrolateral medulla, we injected retrograde AAV-hSin-DIO-eGFP and retrograde AAV-hSin-mCherry into preBötC and rVRG of Oprm1Cre/+ mice and found a neuronal distribution in which Oprm1-expressing projection neurons expressed GFP and mCherry, but not Oprm1-expressing projection neurons expressed only mCherry.

In addition, rostral glutamatergic KF neurons express FoxP2, while MOR-expressing glutamatergic neurons in the lateral parabrachial region that project to the forebrain express the CGRP-encoding gene, Calca. In view of this, the authors performed immunohistochemistry for FoxP2 and CGRP on Oprm1 + KF neurons projecting to the ventrolateral medulla, and Oprm1 + medulla oblongata projecting KF neurons expressed FoxP2 but not CGRP. The expression of CGRP was not observed in rostral KF and medullary projection Oprm1 + neurons and neurites but was strong in lateral parabrachial neurons and their axonal fiber projections. Can you describe the relationship between CGRP and FoxP2 and recorded neurons?

Reviewer #1 (Public Review):

Many previous studies have examined the regulation of hyphal growth in vitro, and have identified about 1,000 genes capable of influencing this process. However, a weakness is that most of these genes have weak effects and are not important in vivo. Therefore, it is very significant that this is the first large-scale study to examine the regulation of hyphal growth in vivo by analyzing a set of 156 transcription factor mutants in mice. A strength of these innovative studies is that mutant strains were injected into a mouse ear, which permitted the use of high-resolution microscopy to quantify the fraction of cells forming hyphae and the rate of hyphal elongation. Furthermore, wild-type cells were co-injected to serve as an internal control, which enhanced the rigor of these studies.

One major conclusion is that three core transcription factors were identified as being important in vivo (Rob1, Brg1, and Efg1) and two negative regulators (Tup1 and Efg1). Previously, many transcription factors were found to be important in vitro, so this is important for focusing future studies on the key regulators. Nanostring gene expression studies verified that these core factors regulate overlapping but distinct sets of genes in vitro and in vivo, which reinforces the importance of carrying out studies in vivo. Additional mutants were discovered to have minor defects in filamentous growth and were considered to be ancillary factors that act in concert with the core regulators.

Another innovative aspect of the manuscript is that they examined the rate of hyphal elongation in vivo. This is an understudied area both in vitro and in vivo. Transcription factors UME6, LYS14, and HMS1 were shown to regulate the elongation rate, which opens up new opportunities to study the mechanisms. Consistent with this, these transcription factors were shown to regulate a set of genes that is distinct from those regulated by transcription factors that control the initiation of hyphal growth.

Genetic approaches (complex haploinsufficiency) were used to examine the relationship between the core factors and the ancillary factor TEC1. Interestingly, these results revealed genetic interactions between TEC1 and the core factors EFG1 and BRG1, including their ability to regulate other transcription factors. This shows how these complex networks are functioning in vivo.

Another major advance was that the in vivo analysis of the two negative regulators of hyphal growth (Nrg1 and Tup1) revealed a new model for how they interact with the master transcriptional regulator Efg1. The results indicate that the major function of Efg1 in vivo is to mediate relief of Nrg1 repression. It was not needed to regulate the expression of hypha-induced genes.

Reviewer #2 (Public Review):

This manuscript is focused on the identification and characterization of transcriptional networks that control the major Candida albicans virulence property of filamentation during infection in vivo. Using an intravital imaging assay, the authors have screened a C. albicans transcription factor mutant library to identify factors important for controlling both filament initiation and elongation in vivo. They also perform Nanostring experiments to identify the in vivo transcriptional profiles of genes controlled by specific key factors in the network. Overall, the authors identify three positive and two negative core factors important for the initiation of filamentation and several factors specifically important for filament elongation (including 4 factors whose mutants have no in vitro elongation phenotypes). Target genes associated with filament initiation and elongation were shown to be mostly distinct. Unexpectedly, the authors also show that the main role of Efg1, a major positive regulator of filamentation, is to mediate relief of repression by Nrg1.

Overall, the manuscript is well-written and the data are clearly presented. In addition, the authors clearly appear to have achieved their Aim of identifying and characterizing transcriptional networks that regulate C. albicans morphogenesis during infection in vivo. In general, the conclusions of this paper are well-supported by the results. The results of this study are likely to have a significant impact on the field for several reasons: 1) new and valuable information will be provided about transcriptional networks that control C. albicans filamentation in vivo, 2) this study describes an important distinction between genes associated with filament initiation and elongation and will be the first to systematically analyze C. albicans genes associated with filament elongation, 3) while there are similarities, the authors also observe several important differences between transcriptional networks that control C. albicans filamentation in vivo vs. in vitro, which will help to clarify regulation that actually occurs during infection, 4) as indicated above, a new and surprising role for the C. albicans master regulator of filamentation, Efg1, is reported, 5) because filamentation is an important C. albicans virulence property, several of the target genes of transcription factor networks identified by this study (and the factors themselves) could serve as potential targets for new antifungals. As a consequence, this study is likely to provide information that opens up new and useful lines of research for the field.

Strengths:<br /> 1. Intravital imaging allows for the identification of transcription factors specifically important for C. albicans filamentation during infection.<br /> 2. Distinct sets of C. albicans genes and factors associated with filament initiation vs. elongation are identified.<br /> 3. Key differences between in vivo and in vitro transcriptional regulation of C. albicans filamentation are demonstrated, which in some cases challenge current paradigms. This also highlights the effect of the environment in determining target genes.<br /> 4. Evidence is presented to suggest that Efg1 promotes C. albicans filamentation primarily through relief of Nrg1 repression.

Weaknesses:<br /> 1. Nanostring does not profile the complete set of C. albicans genes, but rather a subset that is pre-selected. Therefore, defining proportions of genes and gene classes controlled by specific transcription factors may not give the complete picture and may not be accurate with respect to the transcriptome as a whole.<br /> 2. As the authors have noticed, transcription factors and target genes associated with C. albicans filamentation may vary significantly depending on the environment. It is therefore unclear whether the in vivo gene expression patterns observed in this study apply to other host niches besides the ear.<br /> 3. Similarly, variations in filamentation-associated transcription factors and target genes may occur in the "in vitro" conditions used by the authors. RPMI + 10% serum is the main "in vitro" condition but many other conditions are known to drive C. albicans filamentation.<br /> 4. Lines 361-366: A clear rationale for additional TFs to study in more detail was not provided.<br /> 5. Post-translational mechanisms, particularly septin phosphorylation, are likely to have an important effect on filament elongation (see work from Yue Wang's lab), which was not discussed.<br /> 6. Many Nrg1 targets are known to also be Tup1 targets (Kadosh & Johnson, 2005), which counters the argument that this corepressor and DNA-binding protein function separately.<br /> 7. While useful, examining genetic interactions using haploinsufficiency has several limitations and certain interactions may escape detection.

Reviewer #1 (Public Review):

Taking advantage of a publicly available dataset, neuronal responses in both the visual and hippocampal areas to passive presentation of a movie are analyzed in this manuscript. Since the visual responses have been described in a number of previous studies (e.g., see Refs. 11-13), the value of this manuscript lies mostly on the hippocampal responses, especially in the context of how hippocampal neurons encode episodic memories. Previous human studies show that hippocampal neurons display selective responses to short (5 s) video clips (e.g. see Gelbard-Sagiv et al, Science 322: 96-101, 2008). The hippocampal responses in head-fixed mice to a longer (30 s) movie as studied in this manuscript could potentially offer important evidence that the rodent hippocampus encodes visual episodes.

The analysis strategy is mostly well designed and executed. A number of factors and controls, including baseline firing, locomotion, frame-to-frame visual content variation, are carefully considered. The inclusion of neuronal responses to scrambled movie frames in the analysis is a powerful method to reveal the modulation of a key element in episodic events, temporal continuity, on the hippocampal activity. The properties of movie fields are comprehensively characterized in the manuscript.

Although the hippocampal movie fields appear to be weaker than the visual ones (Fig. 2g, Ext. Fig. 6b), the existence of consistent hippocampal responses to movie frames is supported by the data shown. Interestingly, in my opinion, a strong piece of evidence for this is a "negative" result presented in Ext. Fig. 13c, which shows higher than chance-level correlations in hippocampal responses to same scrambled frames between even and odd trials (and higher than correlations with neighboring scrambled frames). The conclusion that hippocampal movie fields depend on continuous movie frames, rather than a pure visual response to visual contents in individual frames, is supported to some degree by their changed properties after the frame scrambling (Fig. 4). However, there are two potential issues that could complicate this main conclusion.

One issue is related to the effect of behavioral variation or brain state. First, although the authors show that the movie fields are still present during low-speed stationary periods, there is a large drop in the movie tuning score (Z), especially in the hippocampal areas, as shown in Ext. Fig. 3b (compared to Ext. Fig. 2d). This result suggests a potentially significant enhancement by active behavior.

Second, a general, hard-to-tackle concern is that neuronal responses could be greatly affected by changes in arousal or brain state (including drowsy or occasional brief slow-wave sleep state) in head-fixed animals without a task. Without the analysis of pupil size or local field potentials (LFPs), the arousal states during the experiment are difficult to know. Many example movie fields in the presented raw data (e.g., Fig. 1c, Ext. Fig. 4) are broad with low-quality tuning, which could be due to broad changes in brain states. This concern is especially important for hippocampal responses, since the hippocampus can enter an offline mode indicated by the occurrence of LFP sharp-wave ripples (SWRs) while animals simply stay immobile. It is believed that the ripple-associated hippocampal activity is driven mainly by internal processing, not a direct response to external input (e.g., Foster and Wilson, Nature 440: 680, 2006). The "actual" hippocampal movie fields during a true active hippocampal network state, after the removal of SWR time periods, could have different quantifications that impact the main conclusion in the manuscript.

Another issue is related to the relative contribution of direct visual response versus the response to temporal continuity in movie fields. First, the data in Ext. Fig. 8 show that rapid frame-to-frame changes in visual contents contribute largely to hippocampal movie fields (similarly to visual movie fields). Interestingly, the data show that movie-field responses are correlated across all brain areas including the hippocampal ones. This could be due to heightened behavioral arousal caused by the changing frames as mentioned above, or due to enhanced neuronal responses to visual transients, which supports a component of direct visual response in hippocampal movie fields. Second, the data in Ext. Fig. 13c show a significant correlation in hippocampal responses to same scrambled frames between even and odd trials, which also suggests a significant component of direct visual response.

Is there a significant component purely due to the temporal continuity of movie frames in hippocampal movie fields? To support that this is indeed the case, the authors have presented data that hippocampal movie fields largely disappear after movie frames are scrambled. However, this could be caused by the movie-field detection method (it is unclear whether single-frame field could be detected). Another concern in the analysis is that movie-fields are not analyzed on re-arranged neural responses to scrambled movie frames. The raw data in Fig. 4e seem quite convincing. Unfortunately, the quantifications of movie fields in this case are not compared to those with the original movie.

Reviewer #2 (Public Review):

Purandare and Mehta investigated the neural activities modulated by continuous and sequential visual stimuli composed of natural images, termed "movie-tuning," measured along the visuo-hippocampal network when the animals passively viewed a movie without any task demand. Neurons selectively responded to some specific parts of the movie, and their activity timescales ranged from tens of milliseconds to seconds and tiled the entire movie with their movie-fields. The movie-tuning was lost in the hippocampus but not in the visual cortices when the image frames were temporally scrambled, implying that the rodent hippocampus encoded the specific sequence of images.

The authors have concluded that the neurons in the thalamo-cortical visual areas and the hippocampus commonly encode continuous visual stimuli with their firing fields spanning the mega-scale, but they respond to different aspects of the visual stimuli (i.e., visual contents of the image versus a sequence of the images). The conclusion of the study is fairly supported by the data, but some remaining concerns should be addressed.

1) Care should be taken in interpreting the results since the animal's behavior was not controlled during the physiological recording. It has been reported that some hippocampal neuronal activities are modulated by locomotion, which may still contribute to some of the results in the current study. Although the authors claimed that the animal's locomotion did not influence the movie-tuning by showing the unaltered proportion of movie-tuned cells with stationary epochs only, the effects of locomotion should be tested in a more specific way (e.g., comparing changes in the strength of movie-tuning under certain locomotion conditions at the single-cell level).

2) The mega-scale spanning of movie-fields needs to be further examined with a more controlled stimulus for reasonable comparison with the traditional place fields. This is because the movie used in the current study consists of a fast-changing first half and a slow-changing second half, and such varying and ununified composition of the movie might have largely affected the formation of movie-fields. According to Fig. 3, the mega-scale spanning appears to be driven by the changes in frame-to-frame correlation within the movie. That is, visual stimuli changing quickly induced several short fields while persisting stimuli with fewer changes elongated the fields. The presentation of persisting visual input for a long time is thought to be similar to staying in one place for a long time, and the hippocampal activities have been reported to manifest in different ways between running and standing still (i.e., theta-modulated vs. sharp wave ripple-based). Therefore, it should be further examined whether the broad movie-fields are broadly tuned to the continuous visual inputs or caused by other brain states.

3) The population activities of the hippocampal movie-tuned cells in Fig. 3a-b look like those of time cells, tiling the movie playback period. It needs to be clarified whether the hippocampal cells are actively coding the visual inputs or just filling the duration. The scrambled condition in which the sequence of the images was randomly permutated made the hippocampal neurons totally lose their selective responses, failing to reconstruct the neural responses to the original sequence by rearrangement of the scrambled sequence. This result indirectly addressed that the substantial portion of the hippocampal cells did not just fill the duration but represented the contents and temporal order of the images. However, it should be directly confirmed whether the tiling pattern disappeared with the population activities in the scrambled condition (as shown in Extended Data Fig. 11, but data were not shown for the hippocampus).

Reviewer #3 (Public Review):

In their study, Purandare & Mehta analyze large-scale single unit recordings from the visual system (LGN, V1, extrastriate regions AM and PM) and hippocampal system (DG, CA3, CA1 and subiculum) while mice monocularly viewed repeats of a 30s movie clip. The data were part of a larger release of publicly available recordings from the Allen Brian Observatory. The authors found that cells in all regions exhibited tuning to specific segments of the movie (i.e. "movie fields") ranging in duration from 20ms to 20s. The largest fractions of movie-responsive cells were in visual regions, though analyses of scrambled movie frames indicated that visual neurons were driven more strongly by visual features of the movie images themselves. Cells in the hippocampal system, on the other hand, tended to exhibit fewer "movie fields", which on average were a few seconds in duration, but could range from >50ms to as long as 20s. Unlike the visual system "movie fields" in the hippocampal system disappeared when the frames of the movie were scrambled, indicating that the cells encoded more complex (episodic) content, rather than merely passively reading out visual input.

The paper is conceptually novel since it specifically aims to remove any behavioral or task engagement whatsoever in the head-fixed mice, a setup typically used as an open-loop control condition in virtual reality-based navigational or decision making tasks (e.g. Harvey et al., 2012). Because the study specifically addresses this aspect of encoding (i.e. exploring effects of pure visual content rather than something task-related), and because of the widespread use of video-based virtual reality paradigms in different sub-fields, the paper should be of interest to those studying visual processing as well as those studying visual and spatial coding in the hippocampal system. However, the task-free approach of the experiments (including closely controlling for movement-related effects) presents a Catch-22, since there is no way that the animal subjects can report actually recognizing or remembering any of the visual content we are to believe they do. We must rely on above-chance-level decoding of movie segments, and the requirement that the movie is played in order rather than scrambled, to indicate that the hippocampal system encodes episodic content of the movie. So the study represents an interesting conceptual advance, and the analyses appear solid and support the conclusion, but there are methodological limitations.

Major concerns:

1) A lot hinges on hinges on the cells having a z-scored sparsity >2, the cutoff for a cell to be counted as significantly modulated by the movie. What is the justification of this criterion? It should be stated in the Results. Relatedly, it appears the formula used for calculating sparseness in the present study is not the same as that used to calculate lifetime sparseness in de Vries et al. 2020 quoted in the results (see the formula in the Methods of the de Vries 2020 paper immediately under the sentence: "Lifetime sparseness was computed using the definition in Vinje and Gallant").

To rule out systematic differences between studies beyond differences in neural sampling (single units vs. calcium imaging), it would be nice to see whether calculating lifetime sparseness per de Vries et al. changed the fraction "movie" cells in the visual and hippocampal systems.

2) In Figures 1, 2 and the supplementary figures-the sparseness scores should be reported along with the raw data for each cell, so the readers can be apprised of what types of firing selectivity are associated with which sparseness scores-as would be shown for metrics like gridness or Raleigh vector lengths for head direction cells. It would be helpful to include this wherever there are plots showing spike rasters arranged by frame number & the trial-averaged mean rate.

3) The examples shown on the right in Figures 1b and c are not especially compelling examples of movie-specific tuning; it would be helpful in making the case for "movie" cells if cleaner / more robust cells are shown (like the examples on the left in 1b and c).

4) The scrambled movie condition is an essential control which, along with the stability checks in Supplementary Figure 7, provide the most persuasive evidence that the movie fields reflect more than a passive readout of visual images on a screen. However, in reference to Figure 4c, can the authors offer an explanation as to why V1 is substantially less affected by the movie scrambling than it's main input (LGN) and the cortical areas immediately downstream of it? This seems to defy the interpretation that "movie coding" follows the visual processing hierarchy. Relatedly, the hippocampal data do not quite fit with visual hierarchical ordering either, with CA3 being less sensitive to scrambling than DG. Since the data (especially in V1) seem to defy hierarchical visual processing, why not drop that interpretation? It is not particularly convincing as is.

5) In the Discussion, the authors argue that the mice encode episodic content from the movie clip as a human or monkey would. This is supported by the (crucial) data from the scrambled movie condition, but is nevertheless difficult to prove empirically since the animals cannot give a behavioral report of recognition and, without some kind of reinforcement, why should a segment from a movie mean anything to a head-fixed, passively viewing mouse? Would the authors also argue that hippocampal cells would exhibit "song" fields if segments of a radio song-equally arbitrary for a mouse-were presented repeatedly? (reminiscent of the study by Aronov et al. 2017, but if sound were presented outside the context of a task). How can one distinguish between mere sequence coding vs. encoding of episodically meaningful content? One or a few sentences on this should be added in the Discussion.

I don't have an intuitive understanding of logs, I should spend sometime revisiting this.

Consensus Public Review:

Ottenheimer et al., present an interesting study looking at the neural representation of value in mice performing a pavlovian association task. The task is repeated in the same animals using two odor sets, allowing a distinction between odor identity coding and value coding. The authors use state-of-the-art electrophysiological techniques to record thousands of neurons from 11 frontal cortical regions to conclude that 1) licking is represented more strongly in dorsal frontal regions, 2) odor cues are represented more strongly in ventral frontal regions, 3) cue values are evenly distributed across regions. They separately perform a calcium imaging study to track coding across days and conclude that the representation of task features increments with learning and remains stable thereafter.

Overall, these conclusions are interesting and mostly well supported by the data, although there are some doubts about their definition of value coding. One limitation is the lack of focus on population-level dynamics from the perspective of decoding, with the analysis focusing primarily on encoding analyses within individual neurons.

Some specific comments:

The authors use reduced-rank kernel regression to characterize the 5332 recorded neurons on a cell-by-cell basis in terms of their responses to cues, licks, and reward, with a cell characterized as encoding one of these parameters if it accounts for at least 2% of the observed variance. At least 50% of cells met this inclusion criterion in each recorded area. 2% feels like a lenient cutoff, and it is unclear how sensitive the results are to this cutoff, though the authors argue that this cutoff should still only allow a false positive rate of 0.02% (determined by randomly shuffling the onset time of each trial).

Having identified lick, reward, and cue cells, the authors next select the 24% of "cue-only" neurons and look for cells that specifically encode cue value. Because the animal's perception of stimulus value can't be measured directly, the authors created a linear model that predicts the amount of anticipatory licking in the interval between odor cue and reward presentations. The session-average-predicted lick rate by this model is used as an estimate of cue value and is used in the regression analysis that identified value cells. (Hence, the authors' definition of value is dependent on the average amount of anticipatory behavior ahead of a reward, which indicates that compared to the CS+, mice licked around 70% as much to the CS50 and 10% as much to the CS-.) The claim that this is an encoding of value is strengthened by the fact that cells show similar scaling of responses to two odor sets tested. Whereas the authors found more "lick" cells in motor regions and more "cue" cells in sensory regions, they find a consistent percentage of "value" cells (that is, cells found to be cue-only in the initial round of analysis that is subsequently found to encode anticipatory lick rate) across all 11 recorded regions, leading to their claim of a distributed code of value.

In subsequent sections, the authors expand their model of anticipatory-licking-as-value by incorporating trial and stimulus history terms into the model, allowing them to predict the anticipatory lick rate on individual trials within a session. They also use 2-photon imaging in PFC to demonstrate that neural coding of cue and lick are stable across three days of imaging, supported by two lines of evidence. First, they show that the correlation between cell responses on all periods except for the start of day 1 is more correlated with day 3 responses than expected by chance (although the correlation is still quite low, for example, 0.2 on day 2). Second, they show that cue identity is able to capture the highest unique fraction of variance (around 8%) in day 3 cue cells across three days of imaging, and similarly for lick behavior in lick cells and cue+lick in cue+lick cells. Nonetheless, their sample rasters for all imaged cells also indicate that representations are not perfectly stable, and it will be interesting to see what *does* change across the three days of imaging.

Importantly, the authors do not present evidence that value itself is stably encoded across days, despite the paper's title. The more conservative in its claims in the Discussion seems more appropriate: "these results demonstrate a lack of regional specialization in value coding and the stability of cue and lick [(not value)] codes in PFC."

Reviewer #1 (Public Review):

The authors use a model of neonatal E.coli pneumonia to study differences between early neonates ad juvenile animals. They observe increased monocyte derived macrophage recruitment in juveniles compared to neonates as well as an increase in IFNG related genes. The data are of potential interest but in its current form it is unclear how well the experiments were controlled for confounders, such as sex and CFU.

1. This paper conducted research to identify the window of susceptibility to pneumonia due to E. coli, a bacteria that most often causes pneumonia in the neonatal period. This is an understudied area and thus the research is significant.

2. The paper provides evidence of differences in immune response in neonatal mice vs juvenile mice. However, it is unclear if the data are controlled adequately for the bacterial burden in the lung, which would be a crucial control to control for epi-phenomena. Additionally, it is unclear if the molecules that regulate macrophage recruitment are defective in neonatal mice or if it is an issue of macrophage progenitor cells.

Reviewer #2 (Public Review):

The authors have provided important detailed information on the inflammatory response to live E. coli infection in neonatal and juvenile mouse lungs. They have delineated key distinctions in these two periods and the potential impact on lung development. The study will inform future lines of investigation on the impact of bacterial infections on lung development.

Reviewer #1 (Public Review):

Synapses are modulated by neural activity on a variety of timescales. Typical neural network models primarily consider long-lasting changes to synaptic strengths, applied while the network is learning, with synaptic strengths then being fixed after learning. However, shorter-term plasticity mechanisms are ubiquitous in the brain and have been shown to have significant computational and information-storage capabilities. Here the authors study these mechanisms in the context of the integration of information tasks. Their two primary contributions are to analyze these short-term mechanisms separately from recurrent connections to isolate the specific ways these might be useful and to apply ideas from population data analysis to dissect how their networks solve the tasks.

I thought this was a clear, well-written, and well-organized paper, tackling an important problem. I also found that the conclusions were adequately supported by the simulations and analyses shown. I particularly appreciated the careful analysis of how the different networks solved the task and found the distinction between hidden neurons reflecting accumulated evidence (attractor architecture) vs. reflecting inputs (MPN architecture) very interesting and potentially very useful for thinking about experimental observations. My comments are primarily about the connection to biology/biological interpretability as well as how this study relates to prior work.

1) I was confused about the nature of the short-term plasticity mechanism being modeled. In the Introduction, the contrast drawn is between synaptic rewiring and various plasticity mechanisms at existing synapses, including long-term potentiation/depression, and shorter-term facilitation and depression. And the synaptic modulation mechanism introduced is modeled on STDP (which is a natural fit for an associative/Hebbian rule, especially given that short-term plasticity mechanisms are more often non-Hebbian). On the other hand, in the network models the weights being altered by backpropagation are changes in strength (since the network layers are all-to-all), corresponding more closely to LTP/LTD. And in general, standard supervised artificial neural network training more closely resembles LTP/LTD than changing which neurons are connected to which (and even if there is rewiring, these networks primarily rely on persistent weight changes at existing synapses). Moreover, given the timescales of typical systems neuroscience tasks with input coming in on the 100s of ms timescale, the need for multiple repetitions to induce long-term plasticity, and the transient nature/short decay times of the synaptic modulations in the SM matrix, the SM matrix seems to be changing on a timescale faster than LTP/LTD and closer to STP mechanisms like facilitation/depression. So it was not clear to me what mechanism this was supposed to correspond to.

2) A number of studies have explored using short-term plasticity mechanisms to store information over time and have found that these mechanisms are useful for general information integration over time. While many of these are briefly cited, I think they need to be further discussed and the current work situated in the context of these prior studies. In particular, it was not clear to me when and how the authors' assumptions differed from those in previous studies, which specific conclusions were novel to this study, and which conclusions are true for this specific mechanism as opposed to being generally true when using STP mechanisms for integration tasks.

Reviewer #2 (Public Review):

Most neuronal computations require keeping track of the inputs over temporal windows that exceed the typical time scales of single neurons. A standard and relatively well-understood way of obtaining time scales longer than those of the "microscopic" elements (here, the single neurons) is to have appropriate recurrent synaptic connectivity. Another possibility is to have a transient, input-dependent modulation of some neuronal and/or synaptic properties, with the appropriate time scale. Indeed, there is ample experimental evidence that both neurons and synapses modify their dynamics on multiple time scales, depending on the previous history of activation. There is, however, little understanding of the computational implications of these modifications, in particular for short-term memory.

Here, the authors have investigated the suitability of a class of transient synaptic modulations for storing and processing information over short-time scales. They use a purely feed-forward network architecture so that "synaptic modulation" is the only mechanism available for temporarily storing the information. The network is called Multi-Plasticity Network (MPN), in reference to the fact that the synaptic connectivity being transiently modulated is adjusted via standard supervised learning. They find that, in a series of integration-based tasks of varying difficulty, the MPN exhibits performances that are comparable with those of (trained) recurrent neuronal networks (RNNs). Interestingly, the MPN consistently outperforms the RNNs when only the read-out is being learned, that is in a minimal-training condition.

The conclusions of the paper are convincingly supported by the careful numerical experiments and the analysis performed by the authors, mostly to compare the performances of the MPN against various RNN architectures. The results are intriguing from a "classic" neuroscience perspective, providing a computational point of view to rationalize the various synaptic dynamics observed experimentally on largely different time scales, and are of certain interest to the machine learning community.

On the other hand, the general principle appears (perhaps naively) very general: any stimulus-dependent, sufficiently long-lived change in neuronal/synaptic properties is a potential memory buffer. For instance, one might wonder whether some non-associative form of synaptic plasticity (unlike the Hebbian-like form studied in the paper), such as short-term synaptic plasticity which depends only on the pre-synaptic activity (and is better motivated experimentally), would be equally effective. Or, for that matter, one might wonder whether just neuronal adaptation, in the hidden layer, for instance, would be sufficient. In this sense, a weakness of this work is that there is little attempt at understanding when and how the proposed mechanism fails.

Reviewer #3 (Public Review):

The authors study the performance, generalization, and dynamics of artificial neural networks trained on integration tasks. These types of tasks were studied theoretically in the past, and comparisons have also been made between artificial and biological networks. The authors focus on the effect of short-term plasticity on the networks. This is modeled as a multiplicative modulation of synaptic strengths that decays over time. When not decaying, this modulation is driven by Hebbian (or anti-Hebbian) activity-dependent terms. To isolate the effects of this component of the networks, the authors study a feedforward architecture, thereby rendering the synaptic modulations the only dynamical variables in the system. The authors also compare their network (MPN) with RNNs (gated and vanilla).

Perhaps not surprisingly, the information on the integration task is encoded in the dynamic variables of the networks - which are hidden units for RNNs and synaptic modulations for MPNs. The authors also study the dynamics of MPNs in the presence of noise or longer-than-trained input sequences. Finally, context-dependent integration is also studied.<br /> Biological neurons are far more complex than their artificial counterparts. This implies that there are computations that can be "outsourced" to these complexities, instead of being handled by a vanilla-rnn-like network that only has connectivity and hidden states. Given the recent rise in applications of trained RNNs as models of biological systems, it is thus timely to ask what are the consequences of integrating some of these complexities. The current study falls under this broad question, with a focus on short-term synaptic plasticity.<br /> I am worried, however, by two issues: the relation between integration tasks and the plasticity mechanism introduced, and the relation to existing work.

Because the MPN is essentially a low-pass filter of the activity, and the activity is the input - it seems that integration is almost automatically satisfied by the dynamics. Are these networks able to perform non-integration tasks? Decision-making (which involves saddle points), for instance, is often studied with RNNs.

The current work has some resemblance to reservoir computing models. Because the M matrix decays to zero eventually, this is reminiscent of the fading memory property of reservoir models. Specifically, the dynamic variables encode a decaying memory of the input, and - given large enough networks - almost any function of the input can be simply read out. Within this context, there were works that studied how introducing different time scales changes performance (e.g., Schrauwen et al 2007).

Another point is the interaction of the proposed plasticity rule with hidden-unit dynamics. What will happen for RNNs with these plasticity rules? I see why introducing short-term plasticity in a "clean" setting can help understand it, but it would be nice to see that nothing breaks when moving to a complete setting. Here, too, there are existing works that tackle this issue (e.g., Orhan & Ma, Ballintyn et al, Rodriguez et al).

One point regarding biological plausibility - although the model is abstract, the fact that the MPN increases without bounds are hard to reconcile with physical processes.<br /> To summarize, the authors show that plastic synapses can perform integration tasks in a manner that is dynamically distinct from RNNs - thereby strengthening the argument to include such synapses in models. This can be of interest to researchers interested in biologically plausible models of neural circuits.

Schrauwen, Benjamin, Jeroen Defour, David Verstraeten, and Jan Van Campenhout. "The Introduction of Time-Scales in Reservoir Computing, Applied to Isolated Digits Recognition." In Artificial Neural Networks - ICANN 2007, edited by Joaquim Marques de Sá, Luís A. Alexandre, Włodzisław Duch, and Danilo Mandic, 471-79. Lecture Notes in Computer Science 4668. Springer Berlin Heidelberg, 2007. http://link.springer.com/chapter/10.1007/978-3-540-74690-4_48.

Orhan, A. Emin, and Wei Ji Ma. "A Diverse Range of Factors Affect the Nature of Neural Representations Underlying Short-Term Memory." Nature Neuroscience 22, no. 2 (February 2019): 275-83. https://doi.org/10.1038/s41593-018-0314-y.

Ballintyn, B., Shlaer, B. & Miller, P. Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity. J Comput Neurosci 46, 279-297 (2019). https://doi.org/10.1007/s10827-019-00717-5

Rodriguez, H.G., Guo, Q. & Moraitis, T.. (2022). Short-Term Plasticity Neurons Learning to Learn and Forget. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:18704-18722 Available from https://proceedings.mlr.press/v162/rodriguez22b.html.

Reviewer #1 (Public Review):

This paper identifies an intracellular O-GlcNAc glycosylation of specific proteins in the control of bone formation and bone marrow adiposity. Compelling evidence is provided for the role of OGT-mediated O-GlcNAc glycosylation of RUNX2 in osteogenic differentiation versus OGT-mediated O-GlcNAc glycosylation of C/EBPβ in bone marrow adipogenesis.

Overall, the experiments have been done with great rigor, and sufficient details are provided for reproducibility. The authors developed a novel concept in the control of bone formation and bone marrow adiposity.

Reviewer #2 (Public Review):

Here I will mainly comment on the biology of adipocytes, which is my specialty.

In this manuscript, it has been very convincingly shown that O-GlcNAc acts as an important regulator of MSC differentiation in mice, and given previous studies in which O-GlcNAc is regulated by aging and nutritional status, it makes sense that this PTM determines differentiation and BM niche.

The point that O-GlcNAc regulates adipocyte differentiation is convincing, but there are already previous studies using 3T3-L1 (e.g., Biochemical and Biophysical Research Communications 417 (2012) 1158-1163), and a more step-by-step demonstration of the molecular mechanism would make this an excellent paper that can be extended to adipocyte research in general, not just BM.

It is somewhat unclear whether or not the authors' in vitro experiments using 10T1/2 cells accurately reflect what is happening in vivo in knockout mice. The PDGFRa+VCAM1+ population of adipocyte progenitors shown by the authors is upregulated by about 30% by knockout of Ogt (Figure 4C). How significant is this difference? Rather, might the expression of Pparg, which indicates lineage commitment, be the underlying mechanism? In any case, this manuscript is highly impactful in the sense that the differentiation of adipocytes forming the BM niche can be controlled using tissue-specific knockouts of the Ogt gene.

Reviewer #3 (Public Review):

This study has the strengths of novelty and significance across multiple fields, including bone marrow biology, skeletal health, hematopoiesis, and protein posttranslational modification (PTM). It establishes the role of protein O-GlcNAcylation in bone development and bone marrow niche. The cooperative O-GlcNAcylation on Runx2 and C/EBPb to prime BMSCs toward osteoblast differentiation over adipogenesis is a very interesting and sounding molecular mechanism. The employment of an inducible OGT conditional knockout mouse model with appropriate Osx-Cre controls is conclusive and rigorous. The in vitro experiments were carefully designed in support of strong rationales. The overall flow of the story is logical and clear. Last, the conclusions are drawn from concrete evidence in an accurate way.

Reviewer #1 (Public Review):

The authors made some biologically reasonable approximations of the Pump and Leak model. e.g., assuming the alpha_0 parameter to be zero. These approximations significantly simplify the model and make the results much more intuitive, e.g., Eq. 4 in the main text. The authors proposed an interesting and simple model of amino acid production, which is argued to be the primary determinant of cell volume. Combined with the gene expression model proposed recently by Lin and Amir, their model can nicely explain the homeostasis of protein density. Furthermore, by considering the saturation of DNA and mRNA by RNA polymerase and ribosome, the authors extended Lin and Amir's model by introducing protein degradation, which I think is the key to explaining cytoplasm dilution. The authors also discussed other applications of their model, including mitotic swelling and nuclear scaling. Below are my major comments:

1. Eq. 2 is valid for stationary states where the cell volume is constant with time. However, many cells grow and divide, including yeast cells. I think the authors have implicitly neglected the effects of cell growth. The authors may want to mention this explicitly to avoid confusion.

2. It's unclear how the authors go from Eq. S.21 to Eq. 2, although the authors mentioned it is straightforward. I think the dilute solution assumption is used without explicit mention, at least in section A of the SI.

3. A slight deviation from equilibrium is implicitly assumed in Eq. S.22 I think since the flow is linearly proportional to the chemical potential difference. The authors may want to mention this explicitly since the linear assumption is not necessarily true for biological systems.

4. A more general gene expression model is recently proposed by some of the authors of Ref. 30, in which the saturation of DNA by RNAPs is due to a high free RNAP concentration near the promoter (Wang and Lin, Nature Communications, 2021). I think the exact saturation mechanism is not very important to the conclusions. Still, I think it's good to let readers be aware that there are biologically more realistic saturation mechanisms.

5. The success of the fitting in Figure 2E is intriguing but may not be a smoking gun evidence of the model's validity. All one needs is a protein number proportional to cell volume for tt**, as far as I understand. Alternative models incorporating the above features will be able to reproduce the fitting of Figure 2E as well, I think. For example, instead of adding protein degradation, one can alternatively assume that protein translation becomes much slower for t>t**, but amino acids are still produced at a constant rate. The time-dependences of amino acids and cell volume may not be important if one just wants to fit the data in Figure 2E since the cell volume dynamics are extracted from Figure 2B. The authors may want to discuss this point.

6. On line 752, the estimation of the average charge of proteins is unclear to me. How did the authors obtain z_p = 0.8?

Reviewer #2 (Public Review):

The manuscript proposes a theoretical framework for the size scaling of cells. The main predictions are (1) the application of a nested pump-leak model to explain cell size scaling through an osmotic balance, (2) the role of metabolites in maintaining electroneutrality, and (3) the breakdown of this scaling law during specific phases of cell growth and senescence.

Although the overall topic and approach are of significant interest, there are several issues with the presentation and claimed scope, detailed below.

Major comments:

1. The manuscript claims to provide a unified theory of cell size scaling, but quantitative agreement is only shown in a few specific cases (non-dividing yeast cells, mitotic swelling in mammalian cells, nuclear size scaling). Given the significant number of adjustable parameters in the model, the claim of a unified theory seems to be somewhat of a stretch. In addition, many of the approximations used (such as turgor pressure being negligible on p. 5) are valid in mammalian cells, but not in plant or yeast cells. For example, in walled cells, the rate of volume growth is dictated largely by cell-wall synthesis and turgor pressure (Rojas and Huang, 2018).

2. The paper claims to supersede previous work: "Many theoretical papers have assumed a priori a linear phenomenological relation between volume and protein number in order to study cell size [30],[31],[32]. Our results instead emphasize that the proportionality is indirect, only arising from the scaling between amino-acid and protein numbers." However, the conclusions reached (e.g. NC1 in eq. 15) appear to recover those of previous work, at least in certain limiting cases. Moreover, this is not a fully accurate description of the previous work, since in some of the previous works the osmotic balance is given in terms of general macromolecules, not necessarily proteins, and the linear relationship was not assumed but rather derived based on osmotic balance. The authors should carefully explain the relationship of their work to the previous studies.

3. The role of metabolites is an important point that should be further clarified. The authors state that "As a key consequence, we find that the NC ratio would be four times larger in the absence of metabolites". However, the formula obtained in the metabolite-dominated limit for NC1 in eq. 15 recovers previous results which were based solely on osmotic balance, without accounting for electroneutrality via metabolites. Why is electroneutrality violated in the absence of metabolites? Does this remain true if the chromatin and counterions are considered to be polyelectrolytes?

4. Appendix H on the extension to scaling of other organelles contains no comparison to data. Is the size control of all membrane-bound organelles expected to behave according to the same principles, or is the theory applicable to a particular subset of organelles?

5. It is stated several times that the size cell is "tightly regulated by active processes". The authors should define what they mean by "control" and "active" in this context. For example, one interpretation of the NC ratio size scaling result is that it is not under direct control, but rather is a consequence of the ratio of nuclear-bound proteins and is only controlled indirectly. (The authors themselves state that the relationship between volume and protein number is indirect.) If the NC ratio is actively controlled, this suggests that its maintenance at a certain value is important for the proper functioning of the cell. Is there evidence of this, or would the cell continue to function if the nuclear size could hypothetically be perturbed independently of the protein ratio?

Reviewer #1 (Public Review):

This work applies duplex sequencing to study point mutations in mice across tissues in young (4.5 months) and old mice (26 months). In this study, they identified 89,000 independent somatic mtDNA mutations representing the largest collection of somatic 'point' mtDNA mutation (not considering mtDNA deletions). They find that mtDNA mutations accumulate linearly with age in a clock-like manner but are not uniformly represented in all tissues. This indicates a likely constant 'clock-like' accumulation analogous to what is seen in the nuclear genome. This part of the paper is a comprehensive extension of work done by Arbeithuber et al., 2020. They also find variability between tissues of the ROS-linked (transversions) mutations. Similar to prior work by Kennedy and Loeb (2013 Plos Genetics) they conclude that ROS-linked mutations do not accumulate significantly with age. Lastly, the authors apply this knowledge and technique to interrogate whether mtDNA mutations are affected by two known treatments, elimipretide and nicotinamide mononucleotide, that have been shown to improve mitochondrial function and reverse apparent aging phenotypes. Here they demonstrate that these treatments reduced the low level of ROS accumulated mtDNA mutations seen in untreated tissues.

Comments:<br /> The paper states that they observed a combined total of 77,017 single-nucleotide variants (SNVs) and 12,031 insertion/deletions (In/Dels) across all tissue, age, and intervention groups. Collectively, these data represent the largest collection of somatic mtDNA mutations obtained in a single study to date. However, A study with more somatic mtDNA mutations by the LostArc method (PMID 32943091) revealed 35 million deletions (~ 470,000 unique spans) in skeletal muscle from 22 individuals with and 19 individuals without pathogenic variants in POLG. Thus, the authors should reword this part to say that this study represents the largest collections of mouse mtDNA point mutations detected, but not the largest amount of mutations (deletions exceed this number).

What is the theoretical limit of pt mutations in the mitochondrial genome, assuming only one pt mutation per genome? Doesn't 77000 detected independent pt mutations approach that limit? Can the authors estimate how many molecules contained two or more pt mutations? Did the analysis reveal any un-mutated regions implying an essential function? For example, on p.9 can the authors provide an explanation of why OriL and other G/C-rich regions were not uniformly covered as compared to the rest of the genome?

Given that mitochondrial disease usually doesn't present until >60% of the genomes are affected, the very low level of detected pt mutations observed in the mouse (and presumably similar to human) would mean that they are well below a physiological level. Thus, these low-level pt mutations are well tolerated. Can the authors estimate a theoretical age of the mouse (well beyond their life span) where over 50% of the genomes carry at least one pt mutation?

Also, the problem with this low level of pt mutations is that they are not physiological, the effect of the drug treatment causing a reduction in ROS-mediated transversions would not be expected to have a detectable effect on mitochondria. The improvement on mitochondrial seen by others is most likely independent of the mutations in the genome. There needs to be a cause and effect here and I don't see one.

There's no mention in this paper and methodology about how point mutations in nuclear-encoded mtDNA (NUMTs) are excluded from the reads and I'm worried that these errors are being read as rare errors in the mtDNA genome. While NUMTs have been documented for decades, a recent report in Science (PMID: 36198798) documents how frequently and fluidly NUMTs occur. Can the authors provide a clear explanation of how mutations in NUMTs are excluded?

Reviewer #2 (Public Review):

A common problem in mutation analysis is that DNA damage (present in one strand) is difficult to separate from real mutations (present in both strands). One of the approaches to solve this problem based on independent tagging of the two strands by different unique molecular identifiers was developed by the authors about 10 years ago. This study summarizes the application of this method to a wide range of mouse tissues, ages, and drug treatment regimes. Much of the results confirm previous conclusions from this laboratory. This involves overall mutational levels of somatic mtDNA mutations (~10-6-10-5), their accumulation with age, the prevalence of GA/CT transitions, and their clonality. Although these results were not new, it is important that these were confirmed in a single study with high confidence in a huge number of independent mutations.

What really sets this study apart from other studies is the detection of a large proportion of transversion mutations, primarily of the C>A/G>T and C>G/G>C types. Transversions are traditionally considered 'persona non grata' in mtDNA mutational spectra and are typically associated with errors of mutational analysis (which they in fact are). The presence of these mutations in both strands of the duplex makes a good case that these mutations are real, rather than converted damage. However, because this is such a novel discovery and because regular controls do not work (I mean, for example, that these mutations never clonally expand. If there is a clonal expansion, then the mutation is real, only real mutation can expand. But in the case of non-expandable C>A/G>T and C>G/G>C this control does not help to validate these mutations), it would be nice to provide extra assurances that this is not some kind of artifact that somehow slipped through the ds sequencing procedure. I would recommend including in the supplement the data on the abundance of single-stranded base changes as detected by ds sequencing (i.e., changes confirmed in one and not in the other strand of a given molecule). An unusually high presence of such single-stranded changes of the C>A/G>T and C>G/G>C type would be a red flag for me. If ratios of single and double-stranded mutations were similar for transitions and transversions - that would reassure me and hopefully the reader.

Furthermore, a similar excess of C>A/G>T and C>G/G>C has been observed in a recent paper by Abascal 2021 (cited in the manuscript). In that paper, a UMI- free, but otherwise very similar ds sequencing approach in nuclear DNA (BotSeqS) was demonstrated to suffer from an artifact causing (among other effects) an excess of C>A/G>T and C>G/G>C transversions. This artifact is related to end repair and nick-translation of DNA fragments during library preparation. Because BotSeqS is very similar to ds sequencing, we expect that same artifact may be taking place in the study under review. We recommend running checks similar to those undertaken by Abascal et al (which include, at the very minimum, checking the distribution of the C>A/G>T and C>G/G>C transversions within the reads (artifacts tend to be concentrated towards the ends of the reads).

Of note, even if transversions detected in this study prove to be artifacts of the Abascal type (likely) they still may reflect real ss damage in mtDNA (not instrumental artifacts, like sequencing errors or in vitro DNA damage). This is supported by the strong variation in the levels of transversions across tissues and as a result of the ameliorating drug intervention. Artifacts, in contrast, would be expected to be at a constant level. This logic, however, does not differentiate between real ds mutations and ss damage. So UMI-based ds sequencing evidence remains the only (though very strong) independent proof. So, in my view, whereas the jury may be still out on whether the observed transversions are true ds mutations or some kind of single-stranded damage, this is a critically important observation. The evidence of ss damage greatly varied between tissues and detected with such precision on a single molecule level is a very important finding as well.

Out of caution, I would recommend mentioning the above-stated uncertainty and noting that more research is needed to fully confirm that C>A/G>T and C>G/G>C changes detected in this study are indeed double-stranded mutations.

Reviewer #1 (Public Review):

The manuscript by Hekselman et al presents analyses linking cell-types to monogenic disorders using over-expression of monogenic disease genes as the signal. The manuscript analyses data from 6 tissues (bone marrow, lung, muscle, spleen, tongue and trachea) together with ~1,000 rare diseases from OMIM (with ~2,000 associated genes) to identify cell-type of interest for specific disease of choice. The signal used by the approach is the relative expression of OMIM-genes in a particular cell type relative to the expression of the gene in the tissue of interest identifying cell-type-disease pairs that are then investigated through literature review and recapitulated using mouse expression. A potentially interesting finding is that disease genes manifesting in multiple tissues seem to hit same cell-types. Overall this important study combines multiple data analyses to quantify the connection between cell types and human disorders. However whereas some of the analyses are compelling, the statistical analyses are incomplete as they don't provide full treatment of type I error.

Reviewer #2 (Public Review):

This study identifies 110 disease-affected cell types for 714 Mendelian diseases, based on preferential expression of known disease-associated genes in single-cell data. It is likely that many or most of the results are real, and the results are biologically interesting and provide a valuable resource. However, updates to the method are needed to ensure that inference of statistical significance is appropriately stringent and rigorous.

Strengths: a systematic evaluation of disease-affected cell types across Mendelian diseases is a valuable addition to the literature, complementing systematic evaluations of common disease and targeted analyses of individual Mendelian diseases. The validation via excess overlap with disease-cell type pairs from literature co-appearance provides compelling evidence that many or most of the results are real. In addition, many of the results are biologically interesting. In particular, it is interesting that diseases with multiple affected tissues tend to affect similar cell types in the respective tissues.

Limitations: the main limitation of the study is that, although many or most of the results are likely to be real, the criteria for statistical significance is probably not stringent enough, and is not well-justified. For diseases with only 1 disease-associated gene, the threshold is a z-score>2 for preferential expression in the cell type, but this threshold is likely to be often exceeded by chance. (For diseases with many disease-associated genes, the threshold is a median (across genes) z-score>2 for preferential expression in the cell type, which is less likely to occur by chance but still an arbitrary threshold.) Thus, there is a good chance that a sizable proportion of the reported disease-affected cell types might be false positives. The best solution would be to assess statistical significance via empirical comparison with results for non-disease-associated control genes, and assess the statistical significance of the resulting P-values using FDR.

The re-analysis using mouse single-cell data adds an interesting additional dimension to the study, with the small caveat that mouse single-cell data does not provide statistically independent information across genes (for the same reason that adding data from independent human individuals would not provide statistically independent information across genes, given that human and mouse expression are partially correlated).

Reviewer #3 (Public Review):

The authors describe the method, PrEDiCT, which helps identify disease affected cell types based on gene sets. As I understand it, the method is based on finding which "disease genes" (from an annotation) are relatively highly expressed. The idea is nice, however, I have concerns about how "significance" is assessed and the relative controls.

Overall, I find the idea interesting, but the execution raises some concerns.

1. From a causal perspective, there is an association of high expression of these genes within these cell types, but without also assessing individuals with those specific diseases, I do not it is fair to say "disease affected" cell types. It is possible that these genes might behave completely fine but are highly expressed in those cell types while being affected another in other cell types.

2. It is unclear to me what the "null" comparison is in the method and if there is one. For example, by chance, would I expect this gene to be highly expressed because other genes are also highly expressed in this cell type? Some way to assess "significance" or "enrichment" beyond simply using ranks and thresholds would be helpful in deciding whether these associations are robust.

3. Additionally, it is unclear to me, but I suspect that there are unequal cell numbers in the scores computed as well as between relevant tissues. This is related to point (2) above, but as a result, the estimates of the scores will inherently have different variances, thus making comparisons between them difficult/unreliable unless accounted for. If I understand correctly, the score is first the average expression within a tissue, _then_, the Z-score? If so, my comment applies.

4. There is a large set of work done in gene enrichment sets which appears to not be mentioned (e.g. GSEA and other works by the Price group). It would be helpful for the authors to summarize these methods and how their method differs.

5. Additionally, it should be noted that a caveat of this analysis is that the comparisons are all done only relative to the cell types sampled and the diseases which have Mendelian genes associated with them. I would expect these results to change, possibly drastically, if the sampled cell types and diseases were to be changed.

6. Finally, I would appreciate a more detailed explanation in the methods of how the score is computed. Some equations and the data they are calculated from would be helpful here.

In summary, the general idea is an interesting one, but I do think the issues above should be addressed to make the results convincing.

Reviewer #1 (Public Review):

This is a carefully performed and well-documented study to indicate that the FUS protein interacts with the GGGGCC repeat sequence in Drosophila fly models, and the mechanism appears to include modulating the repeat structure and mitigating RAN translation. They suggest FUS, as well as a number of other G-quadruplex binding RNA proteins, are RNA chaperones, meaning they can alter the structure of the expanded repeat sequence to modulate its biological activities.

Overall this is a nicely done study with nice quantitation. It remains somewhat unclear from the data and discussions in exactly what way the authors mean that FUS is an RNA chaperone: is FUS changing the structure of the repeat or does FUS binding prevent it from folding into alternative in vivo structure?

Reviewer #2 (Public Review):

Fuijino et al. provide interesting data describing the RNA-binding protein, FUS, for its ability to bind the RNA produced from the hexanucleotide repeat expansion of GGGGCC (G4C2). This binding correlates with reductions in the production of toxic dipeptides and reductions in toxic phenotypes seen in (G4C2)30+ expressing Drosophila. Both FUS and G4C2 repeats of >25 are associated with ALS/FTD spectrum disorders. Thus, these data are important for increasing our understanding of potential interactions between multiple disease genes. However, further validation of some aspects of the provided data is needed, especially the expression data.

Some points to consider when reading the work:

The broadly expressed GMR-GAL4 driver leads to variable tissue loss in different genotypes, potentially confounding downstream analyses dependent on viable tissue/mRNA levels.

The relationship between FUS and foci formation is unclear and should be interpreted carefully.

Reviewer #3 (Public Review):

In this manuscript Fujino and colleagues used C9-ALS/FTD fly models to demonstrate that FUS modulates the structure of (G4C2) repeat RNA as an RNA chaperone, and regulates RAN translation, resulting in the suppression of neurodegeneration in C9-ALS/FTD. They also confirmed that FUS preferentially binds to and modulates the G-quadruplex structure of (G4C2) repeat RNA, followed by the suppression of RAN translation. The potential significance of these findings is high since C9ORF72 repeat expansion is the most common genetic cause of ALS/FTD, especially in Caucasian populations and the DPR proteins have been considered the major cause of the neurodegenerations.

1) While the effect of RBP as an RNA chaperone on (G4C2) repeat expansion is supposed to be dose-dependent according to (G4C2)n RNA expression, the first experiment of the screening for RBPs in C9-ALS/FTD flies lacks this concept. It is uncertain if the RBPs of the groups "suppression (weak)" and "no effect" were less or no ability of RNA chaperone or if the expression of the RBP was not sufficient, and if the RBPs of the group "enhancement" exacerbated the toxicity derived from (G4C2)89 RNA or the expression of the RBP was excessive. The optimal dose of any RBPs that bind to (G4C2) repeats may be able to neutralize the toxicity without the reduction of (G4C2)n RNA.

2) In relation to issue 1, the rescue effect of FUS on the fly expressing (G4C2)89 (FUS-4) in Figure 4-figure supplement 1 seems weaker than the other flies expressing both FUS and (G4C2)89 in Figure 1 and Figure 1-figure supplement 2. The expression level of both FUS protein and (G4C2)89 RNA in each line is important from the viewpoint of therapeutic strategy for C9-ALS/FTD.

3) While hallmarks of C9ORF72 are the presence of DPRs and the repeat-containing RNA foci, the loss of function of C9ORF72 is also considered to somehow contribute to neurodegeneration. It is unclear if FUS reduces not only the DPRs but also the protein expression of C9ORF72 itself.

4) In Figure 5E-F, it cannot be distinguished whether FUS binds to GGGGCC repeats or the 5' flanking region. The same experiment should be done by using FUS-RRMmut to elucidate whether FUS binding is the major mechanism for this translational control. Authors should show that FUS binding to long GGGGCC repeats is important for RAN translation.

5) It is not possible to conclude, as the authors have, that G-quadruplex-targeting RBPs are generally important for RAN translation (Figure 6), without showing whether RBPs that do not affect (G4C2)89 RNA levels lead to decreased DPR protein level or RNA foci.

Reviewer #1 (Public Review):

This manuscript by Mahlandt, et al. presents a significant advance in the manipulation of endothelial barriers with spatiotemporal precision, and in the use of optogenetics to manipulate cell signaling in vascular biology more generally. The authors establish the role of Rho-family GTPases in controlling the cytoskeletal-plasma membrane interface as it relates to endothelial barrier integrity and function and adequately motivate the need for optogenetic tools for global and local signaling manipulation to study endothelial barriers.

Throughout the work, the optogenetic assays are conceptualized, described, and executed with exceptional attention to detail, particularly as it relates to potential confounding factors in data analysis and interpretation. Comparison across experimental setups in optogenetics is notoriously fraught, and the authors' control experiments and measurements to ensure equal light delivery and pathway activation levels across applications are very thorough. In demonstrating how these new opto-GEFs can be used to alter vascular barrier strength, the authors cleverly use fluorescent-labeled dextran polymers of different sizes and ECIS experiments to demonstrate the physiological relevance of BOEC monolayers to in vivo blood vessels. Of particular note, the resiliency of the system to multiple stimulation cycles and longer time course experiments is promising for use in vascular leakage studies.

Given that dozens of Rho GTPase-activating GEFs exist, an expanded rationale for the selection of p63, ITSN1, and TIAM1 in the form of discussion and literature citations would be helpful to motivate their selection as protein effectors in the engineered tools. Extensive tool engineering studies demonstrate the superiority of iLID over optogenetic eMags or rapamycin-based chemogenetic tools for these purposes. However, as the utility of iLID and eMags has been demonstrated for the manipulation of a variety of signaling pathways, the iSH-Akt demonstration does not seem necessary for these systems.

The demonstration of orthogonality in GTPase- and VE-cadherin-blocking antibody-mediated barrier function decreases and is compelling, even without full elucidation of the role of cell size or overlap in barrier strength. The discussion section presents a mature and thoughtful description of the limitations, remaining questions, and potential opportunities for the tools and technology developed in this work. Importantly, this manuscript demonstrates a commitment to scientific transparency in the ways in which the data are visualized, the methods descriptions, and the reagent and code sharing it presents, allowing others to utilize these tools to their full potential.

Reviewer #2 (Public Review):

This manuscript reports on the use of Optogenetics to influence endothelial barrier integrity by light. Light-induced membrane recruitment of GTPase GEFs is known to stimulate GTPases and modulate cell shape, and here this principle is used to modulate endothelial barrier function. It shows that Rac and CDc42 activating constructs enhance barrier function and do this even when a major junctional adhesion molecule, VE-cadherin, is blocked. Activation of Rac and Cdc42 enhanced lamellipodia formation and cellular overlaps, which could be the basis for the increase in barrier integrity.

The authors aimed at developing a light-driven technique with which endothelial barrier integrity can be modulated on the basis of activating certain GTPases. They succeeded in using optogenetic tools that recruit GEF exchange domains to membranes upon light induction in endothelial cell monolayers. Similar tools were in principle known before to modulate cell shape/morphology upon light induction but were used here for the first time as regulators of endothelial barrier integrity. In this way, it was shown that the activation of Cdc42 and Rac can increase barrier integrity even if VE-cadherin, a major adhesion molecule of endothelial junctions, is blocked. Although it was shown before that stimulation of the S1P1 receptor or of Tie-2 can enhance endothelial barrier integrity in dependence on Cdc42 or Rac1 and can do this independent of VE-cadherin, the current study shows this with tools directly targeting these GTPases.

Furthermore, this study presents very valuable tools. The immediate and repeatable responses of barrier integrity changes upon light-on and light-off switches are fascinating and impressive. It will be interesting to use these tools in the future in the context of analyzing other mechanisms which also affect endothelial barrier function and modulate the formation of endothelial adherens junctions.

Reviewer #3 (Public Review):

Mahlandt et al. report the design and proof of concept of Opto-RhoGEF, a new set of molecular tools to control the activation by light of the three best-known members of the Rho GTPase family, RhoA, Rac1, and Cdc42.

The study is based on the optogenetically-controlled activation of chimeric proteins that target the plasma membrane guanine nucleotide exchange factors (GEFs) domains, which are natural activators specific for each of these three Rho GTPases. Membrane-targeted GEFs encounter and activate endogenous Rho proteins. Further investigation into the effect of these tools on RhoGTPase signaling would have strengthened the report.

These three Opto-RhoGEFs are reversible and enable the precise spatiotemporal control of Rho-regulated processes, such as endothelial barrier function, cell contraction, and plasma membrane extension. Hence, these molecular tools will be of broad interest to cell biologists interested in this family of GTPases.

Mahlandt et al. design and characterize three new optogenetic tools to artificially control the activation of the RhoA, Rac1, and Cdc42 by light. These three Rho GTPases are master regulators of the actin cytoskeleton, thereby regulating cell-cell contact stability or actin-mediated contraction and membrane protrusions.

The main strength of this new experimental resource lies in the fact that, to date, few tools controlling Rho activation by reversibly targeting Rho GEFs to the plasma membrane are available. In addition, a comparative analysis of the three Opto-RhoGEFs adds value and further strengthens the results, given the fact that each Opto-GEF produces different (and somehow expected) effects, which suggest specific GTPase activation. The design of the tools is correct, although the membrane targeting could be improved, since the Lck N-terminus used to construct the recombinant proteins contains myristoylation and palmitoylation sites, which have the potential to target the chimeric protein to lipid rafts. As a consequence, this may not evenly translocate these Rho-activating domains.

An additional technical feature that must be highlighted is an elegant method to activate Opto-RhoGEFs in cultured cells, independent of laser and microscopes, by using led strips, which notably expands the possibilities of this resource, potentially allowing biochemical analyses in large numbers of cells.

The experimental evidence clearly indicates that the authors have achieved their aim and designed very useful tools. However, they should have taken more advantage of this remarkable technical advance and investigated in further detail the spatiotemporal dynamics of Rho-mediated signaling. Although the manuscript is a "tool and resource", readers may have better grasped the potential benefits of tuning GTPase activity with this tool by learning about some original and quantitative insights of RhoA, Rac1, and Cdc42 function.

One of such insights may have come from the set of data regarding the contribution of adherens junctions. The effect of other endothelial cell-cell junctions, such as tight junctions, may also contribute to barrier function, as well as junctional independent, cell-substratum adhesion. These optogenetic tools will undoubtedly impact these future studies and help decipher whether these other adhesion events that are important for endothelial barrier integrity are also under the control of these three GTPases. Overall, the manuscript is sound and presents new and convincing experimental strategies to apply optogenetics to the field of Rho GTPases.

Reviewer #1 (Public Review):

This is a well-conceived and well-executed investigation of how activation loop autophosphorylation and IN-box autophosphorylation synergistically activate AURKB/INCENP. An elegant chemical ligation strategy allowed construction of the intermediate phospho-forms so that the contributions of each phosphorylation event to structure, dynamics, and activity could be dissected. Autophosphorylation at both sites serves to rigidify both AURKB and the IN-box, and to coordinate opening, twisting, and activation loop movements. Consistent with previous findings, both sites are necessary for enzymatic activity; further, this work finds that activation loop autophosphorylation occurs slowly in cis while IN-box autophosphorylation occurs quickly in trans.

Due to abundant previous work in the field, many of the conclusions of this paper were expected. However, that does not diminish the quality of the work, and the addition of how kinase dynamics contribute to activation is important for AURKB and many other kinases. The experimental results are clear and interpreted appropriately, with good controls. The computational work is also clearly explained and directly tied to the function of the enzyme, making it highly complementary to the experimental findings and to previously published structures.

Some minor limitations of the study:

1. Of note when interpreting the HDX data, there is no coverage of the peptide containing the activation loop autophosphorylation site T248 (Fig S2A), and as mentioned in the Discussion, the time scale of HDX is not able to capture differences in exchange in very flexible regions like the activation loop.

2. Some data lack robust statistical analysis, which would make the findings more compelling.

3. One point that might be clarified is how the occupancy of T248 was confirmed to be either fully phosphorylated in the [AURKB/IN-box]IN-deltaC or fully dephosphorylated in the IN-box K846N/R827Q mutant. Especially because T248 autophosphorylation is found to occur in cis, it is unclear how incubating the [AURKB/IN-box]IN-deltaC with traces of wild-type [AURKB/IN-box]all-P would ensure that T248 is phosphorylated.

Reviewer #2 (Public Review):

This study presents a dynamic, multi-step model for the activation of Aurora-B kinase through the interaction with INCENP and autophosphorylation. This interaction is critical to the proper execution of chromosome segregation, and key details of the mechanism are not resolved. The study is an advance on previous studies on Aurora-B and the related kinase Aurora-C, primarily because it clarifies the roles of the different phosphorylation sites. However, major differences in the details of the molecular interactions are presented that are not clearly backed up by the evidence due to limitations in the approach, when compared to previous work based on crystal structures.

Strengths. The experimental approach to the analysis of the Aurora-B/INCENP interaction is sound and novel and it is striking example of preparation of proteins in specific phosphorylation states, and of using HDX to characterise localised changes in the structural dynamics of a protein complex. The authors have generated two intermediate phosphorylation states of the complex, enabling them to dissect their contributions to the regulation of structural dynamics and activity of the complex.

Weaknesses. The major weakness of the study is the molecular dynamics simulation. The resulting model of the complex differs from the crystal structure of the Aurora-C/IN-box structure in key details, and these are neither described clearly nor explained. The challenges/limitations of simulation of phosphorylated proteins should be described.

Reviewer #3 (Public Review):

The chromosomal passenger complex (CPC) is an important regulator of mitotic progression, e.g. controlling kinetochore-microtubule attachment and cytokinesis. In this manuscript, Segura-Peña and colleagues investigated how the enzymatic core complex of the CPC, Aurora B and IN-box (the C-terminal part of INCENP), is structurally and functionally regulated by multiple (auto)phosphorylations. By doing so they are providing an insightful, dynamic picture of how the coordinated phosphorylations of the Aurora B T-loop and two serines in IN-box act cooperatively in order to fully activate the kinase.

Previously, several structures of Aurora B/IN-Box (missing the C-terminus of IN-box with two important phosphorylation sites or being unstructured, Sessa et al. 2005, Sessa and Villa et al. 2015, Elkins et al., 2012) and phosphorylated Aurora C/IN-Box (Abdul Azeez et al., 2019) had provided numerous structural insights and highlighted the role of the phosphorylated residues in T-loop and IN-box. Here, the authors now reveal the dynamic dimension of how the activity of this complex is regulated by using a compelling combination of H/D exchange mass spectrometry (HDX), molecular dynamics simulation and elegant biochemistry. Using HDX they demonstrate that upon Aurora B/IN-box autophosphorylation several regions of the complex become more structured. Using molecular dynamics, they explore the different conformational states of the complex and in particular how the phosphorylation and interactions of the phosphorylated C-terminal tail of IN-box coordinates and rigidifies Aurora B. To dissect the contributions of the phosphorylations on T-loop and IN-box, the authors create differentially phosphorylated versions of the complex using a sophisticated, intein-based protein engineering approach. The biochemical assays performed with these versions reveal not only the synergistic nature of these phosphorylation sites but also establish the nature of the autophosphorylation (cis for Aurora B, trans for IN-box) and show that Aurora B autophosphorylation in cis is rate-limiting. The data is convincing and intriguing, and remaining criticisms have been addressed extensively during the rewriting of the manuscript. In my opinion no additional experiments are required.

In summary, this is a well-executed study that provides new detailed molecular insights into the regulation of an important cell cycle complex. The findings and approaches will be of great interest to both the kinase and the cell cycle community.

Reviewer #1 (Public Review):

This paper by Melo et al. is a technically elegant study investigating the important emerging hypothesis that the brainstem preBötzinger complex (preBötC) region - a critical nuclear structure where the rhythm of breathing in mammals originates - has segregated subgroups of output neurons that modulate specific behaviors coordinated with breathing, in this study the orofacial muscle activity. The preBötC has been under intense investigation for several decades but the subregional neuronal subtype composition and organization are not fully understood. Understanding this organization and how breathing modulates specific behaviors has many implications for normal brain function and pathophysiology.

Strengths of the paper include:<br /> 1) The authors use an effective combinatorial dual viral transgenic approach for Cre-dependent expression of the chloride channel (GtACR2) and labeling of neurons projecting to the facial motor nucleus controlling orofacial muscle activity, for optogenetic photoinhibition of these preBötC neurons in vivo.<br /> 2) The experimental results presented convincingly support the authors' conclusion that a subgroup of preBötC neurons provides inspiratory modulation of facial motoneurons that appear to be distinct from other output neurons that drive inspiratory activity to bulbospinal neurons and neurons projecting to autonomic nervous system circuits.<br /> 3) These results advance our understanding of preBötC circuit organization that coordinates and integrates breathing with different motor and physiological behaviors.

Weaknesses:<br /> There are a few technical issues related to the photoinhibition paradigm used and the patterns of neuronal transduction with the dual viral transgenic approach used that the authors need to clarify.

Reviewer #2 (Public Review):

By using elegant optogenetic viral transgenic approaches the authors show that subgroups of neurons located in the preBötzinger region of the brainstem and projecting to the facial nucleus are involved in controlling orofacial activity while being minimally implicated in breathing behavior. The experiments are properly performed, and technically challenging with several physiological parameters measured in vivo allowing the monitoring of several functions simultaneously (breathing, heart rate, blood pressure, orofacial muscle activity). They also demonstrate that the type of anesthetic used and the state of consciousness are important for the effects of their photoinhibition. While this study is particularly interesting for a better understanding of the coordination between breathing and other behaviours controlled by neurons located in the brainstem, the identification of the neurons of interest here as components of the preBötC network requests clarification and the interpretation of the effects of photo-inhibiting both excitatory and inhibitory neurons remain difficult.

Reviewer #3 (Public Review):

Melo et. al. sought to characterize the neuronal basis for the breathing modulation of nasal dilation (mystacial pad activity). The hypothesis is that a subset of breathing pacemaker neurons (preBötC) are specialized to relay a breathing signal to modulate the nares instead of contributing to pacing breathing. The authors identify that a subset of neurons within the anatomical region of the preBötC project to the facial motor nucleus and are required for the respiratory modulation of the nares. Furthermore, they show these neurons are partially required for breathing. The authors do this by using an intersectional genetic approach to selectively inhibit the preBötC neurons that project to the facial motor nucleus while measuring the impact of this manipulation on the breathing-related movement of the nares and breathing. As a control, the authors broadly silence the preBötC. The simplicity of the experiments makes the results robust and the correct positive control is used. The manuscript's conclusion contributes to the logic for the breathing modulation of the nares and the notion that subsets of neurons in the preBötC play distinct roles in breathing-related behaviors. Although the data are compelling for this conclusion, alternative models cannot be completely ruled out, like that these neurons are important for breathing rhythm generation and a secondary cell type from other premotor centers (Kurnikova 2019) are those that relay this signal to the motor neurons for the nares. The role of the preBötC as a "master clock" for orofacial activity (nose movement, swallowing, chewing, vocalizing; Kurnikova 2017) is an important line of research and this work contributes to understanding the cellular mechanisms.

Reviewer #1 (Public Review):

This research tackles an important question in evolutionary biology that has long stood on theory, with little experimental evidence to support this big idea. This paper provides a large natural dataset on several morphometric factors that allow a robust testing of the "handicap principle". The strength in this dataset comes from extensive field observations not only on morphology, but also fecundity and pairing behavior. The manuscript could use a little tightening up in prose, but the statistics and results are well explained. As the discussion mostly focuses on shrimp, generalizable principles are somewhat unclear. Overall, the research is an important finding that could one day be incorporated into undergraduate textbooks.

Reviewer #2 (Public Review):

This study presents important findings on trade-offs in investment in costly traits related to survival and reproduction. The evidence supporting the claims of the authors is convincing with an exceptional sample size, the inclusion of three species, and measurement of numerous traits. The authors do not incorporate genetics or use experimentation, but they do use an elegant observational approach to glean the likely presence of trade-offs and improve understanding of investment in crucial life-history traits. The work will be of interest to evolutionary biologists, researchers working in the field of animal behavior, and those specializing in sexual selection.

The extent to which individuals should invest in costly traits is an ongoing puzzle to evolutionary biologists. Why is there a limit to investment in traits that enhance survival or mating? Why do some individuals invest so much less than others in traits that should boost fitness? In this manuscript, Dinh and Patek use a strong sample size of snapping shrimp to investigate this question. They examine three species and measure numerous traits. The approach they use to deduce trade-offs is to examine residuals. Specifically, they plot the traits of interest against body size generating a regression for the population. Then, for each individual, they extract a residual value that is how much more or less they invest in a trait for a given body size. For example, some individuals might grow a big claw, but also express a small abdomen relative to others of the same size. The authors measure the extent to which each individual invests in a number of traits to investigate resource allocation trade-offs and reproductive benefits and costs.

This is an elegant and thorough study that thoughtfully examines how animals invest in their bodies and with what potential costs. They even look at male pairing success and the size of his mate to better understand the reproductive benefits of growing a larger claw in snapping shrimp. For females, they examine if growing a larger claw might lead to reduced reproduction because such females cannot care for as many eggs. The strengths of this study are many. It would, of course, be helpful to more thoroughly understand the costs and benefits of investment in claws, but the authors did an excellent job with what was possible. The current version of the manuscript would benefit from a discussion of the pros and cons of their approach of using residuals versus other approaches to measure resource allocation trade-offs.

Overall, this is such a nice study with excellent writing, and it will likely inspire others to examine trait investment in a myriad of other animals. It helps the field of sexual selection better understand the costs and benefits of growing a big (or small) weapon. And, more generally, it addresses the important question of why animals cannot have it all.

Reviewer #1 (Public Review):

Kim et al carried out a genetic screening using Drosophila lines to identify genetic modifiers of ubiquilin 2 mutations associated with ALS/FTD. They generated Drosophila lines expressing wild-type or various mutations of ubiquilin 2 and used the rough eye phenotype as the primary screening criterion. They used the deficiency library in the screening and subsequently attempted to narrow down to single genes. They identified multiple suppressors and enhancers from the deficiency lines and carried out further studies on an endosomal gene rab5, an axon guidance gene unc5 and its co-receptor frazzled, and another axon guidance gene beat-1b. Critical findings were also confirmed in iPSC and induced motor neurons (iMNs), supporting the relevance of the findings in human neurons. The study is important as it provides compelling evidence linking axon guidance/synaptic maintenance to ubiquilin 2-mediated neurotoxicity.

With the above strengths and impact, there are several weaknesses. First, the heat shock effect in the drosophila lines was not understood in the study. Why did some lines show phenotypes only at 29C but not 22C? The study showed data that ubiquilin 2 expression was not impacted by 29C, then what caused the phenotypic differences? In addition, the method section did not describe clearly whether a temperature sensitive promoter was used in the flies. Second, the study showed data on male and female flies separately in some but not all experiments. In addition, the manuscript largely avoided discussing whether there was a sex difference in those experiments. Third, some data appear to be peripheral with no significant contribution to the main findings. Moreover, some data were introduced but were not explained. For instance, the RNA-Seq analysis (Fig 2) did not contribute much to the study. The rescue effect of UBA* (F594A mutant) in Fig 1-Supplemental 1B was interesting but was not elaborated or followed up. FUS flies in Fig 6-Supplement 2 were abrupted introduced with little discussion. Fourth, the main quadrupole (4xALS) mutation used in the study was not found in patients. The relevance of the findings needs to be thoroughly justified. Lastly, ALS and FTD are age-related neurodegenerative diseases, whereas the involvement of axon guidance genes in indicative of disruptions during the developmental stage. The manuscript did not discuss this potential caveat.

Overall, this study identified several potential genetic modifiers of ubiquilin 2 in the context of ALS/FTD. It represents a significant advancement of our understanding of ubiquilin 2-mediated ALS/FTD and related neurodegenerative diseases.

Reviewer #2 (Public Review):

In the present article, the author aimed at finding disease-modifier for a disease that still nowadays is incurable. To do so the authors decided to employ a drosophila model of ALS, bearing four mutations on the Ubiquilin gene. The model displays eye and motoneuron phenotypes serving as a valuable platform for genetic screenings. The screening performed in the present work shows many suppressors and enhancers of the toxicity associated with the presence of the 4 Ubiquilin mutations. The authors then strengthen the findings of the screening by validating some hits and by studying more in details one involved in the axon guidance signaling. They found that suppressing Unc5 and DCC leads to a less severe phenotype in the flies. They then suppress the ligand of the Unc5 receptor and found that also this approach relieves the phenotype. They then confirmed this results in iPSCs by creating a new cell line harboring the four mutations. They found that the neurites defects found in the mutated UBQLN iPSC was rescued by suppressing Unc5 and DCC. This study has relevance to the ALS field as many of the findings can be harnessed to develop drugs suited for ALS patient bearing Ubiquilin mutations. I think that the major weaknesses of this paper are (i) the fact that they focus on just one mutation, which is pretty rare, while probably most of findings should be also validated in models of sporadic ALS (iPSCs lines). (ii) The amount of data presented, for as much as it is technically well-performed, does not help the reader to focus the attention of the main point which is Unc5 signaling relevance in Ubiquilin associated ALS.

Reviewer #1 (Public Review):

The manuscript by Kschonsak et al. describes the rational structure-based design of novel hybrid inhibitors targeting human Nav1.7 channel. CryoEM structure of arylsulfonamide (GNE-3565) - VSD4 NaV1.7-NaVPas channel complex confirmed binding pose observed in x-ray structure GX-936 - VSD4 Nav1.7-NavAb channel. Remarkably, cryoEM structure of acylsulfonamide (GDC-0310) - VSD4 NaV1.7-NaVPas channel complex revealed a novel binding pocket between the S3 and S4 helices, with the S3 segment adopting a distinct conformation compared to the arylsulfonamide (GNE-3565) - VSD4 NaV1.7-NaVPas channel complex. Creatively, the authors designed a novel class of hybrid inhibitors that simultaneously occupy both the aryl- and acylsulfonamide binding pockets. This study underscores the power of structure-guided drug design to target transmembrane proteins and will be useful to develop safer and more effective therapeutics.

Reviewer #2 (Public Review):

In this manuscript, the authors identify a critical unmet need for the (structure-based) drug design of human Nav channels, which are of clinical interest. They cleverly rationalized a hybrid strategy for developing target-specific small molecule inhibitors, which integrate binding mechanisms of two drug candidates that act orthogonally on the VSD4 of Nav 1.7. Thus, the authors illustrate a promising outlook on pharmaceutical intervention on Nav channels.

Overall, the cryo-EM structures of the ligand-bound Nav channels are convincing, with a clear indication of the site-specific, distinct density of the small molecules. At the moment, it is difficult to tell how innovative the pipeline is compared to conventional cryo-EM structure determination.

Reviewer #3 (Public Review):

This is an excellent manuscript, describing a few lines of discoveries:<br /> 1. Establishment of a structural biological pipeline for iterative structural determination of an engineered Nav1.7;<br /> 2. Illumination of the novel compound binding mode;<br /> 3. Structure-based development of the hybrid compounds, which led to the novel Nav1.7 inhibitor;

The cryo-EM study on the engineered Nav1.7 consistently reveals the map at the mid to low 2 Å range, which is unprecedented and impressive, thus, demonstrating the high value of this workflow. The further strength of this study is that the authors were able to develop a new compound by combining structural information gained from the two Nav1.7 structures complexed to two different compounds with different binding modes. Overall, the depth and quality of this study are excellent.

Reviewer #1 (Public Review):

In this manuscript, Chure and Cremer first provide a broad panorama of the different sector models for resource allocation in biosynthesis and how they provide an explanation of cellular growth physiology; then they formalise how optimal flux balance (flux parity) can reproduce many different physiological observables in a quantitative manner.

The first part of this study comprises a valuable synthesis of many literature results, which are here gathered together and clearly reformulated. The authors also assembled a rich and impressive collection of experimental published datasets in E. coli from several sources, which are then extensively compared with the outcomes of the models. In my view, these points are the main strengths of the manuscript.

The flux-parity regulation introduced in the second part emerges from the balance of metabolic and biosynthesis fluxes, which have to be mutually optimised in the authors' framework. Those ingredients are often found in the literature, and the reader has sometimes the impression that novelty is lacking. Although flux balance and optimisation are often assumed in modelling resource allocation, the authors have the merit of formalising the approach in a clearer way than was done before, making an extensive comparison with data.

Reviewer #2 (Public Review):

The authors propose a proteome allocation model which includes a ribosomal and metabolic sector (and an additional sector in the case of nutrient upshift or downshift), and they consider the effect of tRNA charging on translation. It appears that the rate of protein generation via translation by ribosomes and the rate of tRNA charging via metabolic proteins are mutually maximized (the so-called "flux-parity regulation"). Based on this principle, one can reproduce many aspects of bacterial growth both in and out of a steady state, without having to consider other processes.

A major strength of this article is that the authors include many different E. coli datasets. From the figures presented, the model appears to agree well with the data. If the model can indeed predict bacterial growth out of a steady state, then it will be useful in understanding how tRNA charging affects the bacterial response to environmental fluctuations.

To improve the manuscript, units and typical values in E. coli should be provided in the main text as parameters are introduced, to give the reader some benchmark numbers and physical intuition. Furthermore, how proteins are assigned to metabolic, ribosomal, or other proteome sectors can be better explained in the main text, i.e. based on the dependence of their respective abundances on the growth rate. It would also help the reader to explicitly state which parameters are being adjusted and which are fixed (four are mentioned in Section 8 of the appendix but there are many others defined in the text). Finally, whether v_max (max metabolic rate) and tau (uncharged-to-charged tRNA ratio) take on physically reasonable values is not clear, e.g. values for v_max span 4 orders of magnitude. These are essential parameters to the model, and without a sense of how they compare to real values, it is difficult to judge the robustness of the results.

Some specific questions follow:

- Are there experimental data to verify the charging sensitivity parameter tau?<br /> - Which molecules, other than charged tRNAs, are considered 'precursors', and are these neglected or accounted for in the model? For example, the other components of the ternary complex, e.g. GTP and EF-Tu, are not mentioned.<br /> - What is the yield coefficient Y in Eqs. 10, 55, Fig. S2,A(iii)? No value appears in the text or supplemental tables.<br /> - Why is the inactive fraction of ribosomes considered a puzzle? Bremer & Dennis and Metzl-Raz et al. have provided polysomal profiling data in E. coli and in S. cerevisiae, respectively. In E. coli it is ~85% but can be considerably lower in S. cerevisiae. Furthermore, it seems unphysical that 100% of ribosomes would be active at all times; it takes time for a ribosome to find and bind to mRNA.<br /> - (p)ppGpp binds to molecules other than tRNAs, e.g. RNA polymerase. Shouldn't this be accounted for in, e.g., Eq. 3?

Reviewer #1 (Public Review):

Pyrin domains (PYD) in inflammasome proteins oligomerize into filamentous assemblies and mediate inflammasome formation. Mammalian pyrin-only-proteins (POPs) exert inhibitory effects on inflammasome as they mimic the pyrin domains while lacking the effector domain. In this manuscript, Mazanek and colleagues combined computational prediction with cellular and in vitro experiments to investigate the mechanism and target specificity for three POPs, POP1, POP2, and POP3, in inflammasome activation.

The authors first modeled the structures of complex formed by POPs with inflammasomal PYDs, including ASCPYD, AIM2PYD, IFI16PYD, NLRP6PYD, and NLRP3PYD, then calculated their Rosetta interface energies(∆Gs). By comparing the ∆Gs of inflammasomal PYD(∆GPYD•PYD) with inflammasomal PYD/POPs complex (∆GPOP•PYD), they defined favorable and unfavorable interaction surfaces (∆∆G = ∆GPYD•PYD- ∆GPOP•PYD ). Their initial computational model indicates POP1 may have the strongest inhibitory effect on ASC, as it exhibits the most favorable interfaces. But the experiment results showed otherwise, with POP2 and POP3, which contain both favorable and unfavorable interfaces, exhibiting stronger inhibitory effects. They then revised the model and proposed the combination of favorable (recognition) and unfavorable interfaces (repulsion) is necessary for POPs to interfere with the assembly of inflammasome PYDs, which was further tested by other inflammasomal PYDs.

This is a timely study that enhanced our current understanding of inflammasome regulation by POPs, it is also interesting as it combined the newest computational prediction method with biological experimental validation. The explanations on 1.) sequence homology may not dictate the target specificity of POPs, and 2.) excess POPs are required to inhibit the polymerization of inflammasome assembly, are well supported; however, some questions about the target specificity need to be addressed/clarified:

1. The authors showed MBP tag affected the oligomerization of POPs, while the POPs used in Figures 2A, 3A, and 4A contain a GFP tag. It should be considered GFP may affect the property of POPs, such may change the inhibitory effect of POPs on ASC filament formation.

2. The authors take the reduction of PYD filamentation as an indication of inhibition, but it was not clear how they ruled out the possibility that POP1 co-assembles into ASCPYD filaments and inhibits inflammasome formation by repressing the recruitment of Caspase-1, as it lacks CARD the effector domain. Especially the model predicted comparable energy between POP1 and ASC, which could indicate POP1 co-assembled into ASC filament.

3. Further computational analysis should be performed to evaluate the interpretation of Rosetta interface energies. Could the "combination of favorable and unfavorable interfaces" theory apply to other PYD/PYD interactions and CARD/CARD interactions?

Reviewer #2 (Public Review):

In this manuscript, Mazanek et al use Rosetta to calculate the relative binding energies of the six distinct PYD/PYD interactions between the pyrin-only proteins (POPs) and the pyrin domains (PYD) of various inflammasome components. Following these calculations, the authors measure the ability of the POPs to disrupt PYD spec formation or disrupt PYD oligomerization. From these experiments the authors propose that the POPs do not simply disrupt ASC oligomerization, but instead that each POP has unique specificity for the various PYDs and can thusly act upstream of ASC filamentation through their direct interactions with the inflammasome PYDs. Furthermore, the authors propose the ability of the POPs to inhibit PYD filament formation is not solely dictated by sequence similarity between the POP and the PYDs, but instead that a combination of both strong and weak interactions between the POP and PYD is required to disrupt PYD filament formation. These observations help to elucidate the individual roles of the different POPs.

In total this manuscript presents a rigorous and careful biochemical analysis of how the POPs act to modulate PYD oligomerization. However, there are several weaknesses that need to be addressed. First, while the authors propose that the combination of strong and weak interactions dictates the ability of the POPs to disrupt PYD oligomerization this hypothesis is not directly tested. Second, while the author's careful examination demonstrates the ability of the POPs to disrupt PYD spec formation in a reconstituted system, they do not confirm that their in vitro measurements correlate with the ability to restrict inflammasome activity in an endogenous system and as such the physiological consequences of their measurements remain unclear.

Reviewer #3 (Public Review):

The authors use a combination of computational and experimental analyses to study how Pyrin-only proteins (POPs) could regulate either the abundant ASC effector protein or the PYDs of ALRs AIM2 and IFI16 or NLRs NLRP3 and NLRP6. This systematic approach shows differences in the free energy of binding interfaces within the potential filament assemblies. Fluorescence anisotropy experiments are performed on PYD filament formation, using FRET-donor and -acceptor labeled recombinant PYDs (e.g., ASC) and increasing concentrations of unlabeled POPs. These experiments indicate how the lag phase of PYD nucleation and the kinetics of the filament elongation phase is perturbed. Fluorescence microscopy images of HEK cells co-transfected with, e.g., mCherry-tagged ASC-PYD and eGFP-labelled POPs indicate co-localization and overall filament content (as % puncta). Finally, negative stain EM imaging shows assemblies into ordered filaments or aggregates for the recombinant PYD proteins in the presence or absence of POPs. In conclusion, the authors propose a decoy receptor mechanism for the POPs and NLRs/ALRs with different specificities for each individual PYD.

Reviewer #1 (Public Review):

In this manuscript, McQuate et al. use serial block face SEM to provide a high resolution, 3D analysis of mitochondrial structure in hair cells and surrounding supporting cells of the zebrafish lateral line. They first demonstrate that hair cells have a higher mitochondrial volume as compared to supporting cells, which likely reflects the high metabolic load of these sensory cells. Their deeper analysis of mitochondrial morphology in hair cells reveals that the base of the hair cell - near the presynapse is dominated by a large, networked mitochondrion, while the apex of the cell is dominated by many small mitochondria. By examining hair cells at different stages of development, the authors show that specialized features of hair cell mitochondria are gradually established over the course of development. Finally, by examining hair cells in mutants that lack mechanosensation or presynaptic calcium responses, McQuate et al. reveal that cellular activity contributes to the development of appropriate mitochondrial morphology and localization within hair cells. This dataset, which will be made publicly available, is an immense resource to the community and will facilitate the generation of novel hypotheses about hair cell mitochondrial function in health and disease.

Strengths:<br /> 1. The painstaking acquisition and analysis of hair cell EM data in a genetically tractable system that is easily accessible for in vivo functional experiments to address hypotheses that emerge from this work.<br /> 2. The use of multiple datasets and analysis methods to cross-validate results.<br /> 3. The thoughtful, careful analysis of the data highlights the richness of the dataset.<br /> 4. The use of both wild-type and mutant animals substantially adds to the manuscript, providing significantly more insight than wild-type data alone.

Weaknesses:<br /> 1. The manuscript could more strongly highlight the utility of this dataset and facilitate its future use by providing a summary table that lists each sample together with salient details.<br /> 2. The authors examine an opa-1 mutant with altered mitochondrial fission (which consequently has changes in mitochondrial morphology and organization) to suggest that aberrant mitochondrial architecture negatively impacts mitochondrial function. However, mitochondrial fusion is thought to be critical for mitochondrial health beyond just altered architecture. Because fusion has other roles, it is difficult to use this manipulation to conclude that it is simply disruptions in mitochondrial architecture that alters function.

3. Although the work of acquiring and reconstructing EM data is labor-intensive, ideally, multiple fish would be examined for each genotype. Readers should take into consideration that one of the mutant datasets is derived from just one animal.

Reviewer #2 (Public Review):

Sensory hair cells have high metabolic demands and rely on mitochondria to provide energy as well as regulate homeostatic levels of intracellular calcium. Using high-resolution serial block face SEM, the authors examined the influences of both developmental age and hair cell activity on hair cell mitochondrial morphology. They show that hair cell mitochondria develop a regionally specific architecture, with the highest volume mitochondria localized to the basolateral presynaptic region of hair cells. Data obtained from mutants lacking either mechanotransduction or presynaptic calcium influx provide evidence that hair cell activity shapes regional mitochondrial morphology. These observed specializations in mitochondrial morphology may play an important role in mitochondrial function, as mutants showing disrupted hair cell mitochondrial architecture showed depolarized mitochondrial potentials and impaired evoked mitochondrial calcium influx.

This work provides novel and intriguing evidence that mechanotransduction and presynaptic calcium influx play important roles in shaping subcellular mitochondrial morphology in sensory hair cells. Yet there was a lack of consistency in the analysis and presentation of the data which made it difficult to contextualize and interpret the results. This study would be greatly strengthened by i) consistent definitions for hair cell maturation, ii) comparable data analysis of cav1.3a mutant and cdh23 mutant mitochondrial morphologies, and iii) more detailed descriptions and interpretations of the UMAP analysis.

Reviewer #3 (Public Review):

McQuate et al have succeeded in reconstructing 3D images of mitochondria and discovered unique structural features of mitochondria in zebrafish hair cells. Compared to the other cell types, such as central and peripheral support cells, Hair cells have many elongated and connected mitochondria and they seem to be involved in hair cell and ribbon synapses development. These findings will contribute to understanding the mechanisms for mitochondrial network regulation.

Using the SBFSEM technique, the authors provide clear 3D images of hair cells and the technique improves the resolution of the image to understand the structural parameters of not only mitochondria but also ribbon synapses compared to typical fluorescent imaging. These results are very attractive and have the high potential to broadly apply to 3D imaging of any type of organelles, cells, and tissues. On the other hand, however, the authors provide the data from a small sample size, and the functional experiments to make a conclusion are lacking. Some missing representative images and the nonunified methods of grouping for the analysis make the reviewer concerned.

Reviewer #1 (Public Review):

The article from Dumoux et al. shows the use of plasma-based focused ion beams for volume imaging on cryo-preserved samples. This exciting application can potentially increase the throughput and quality of the data acquired through serial FIB-SEM tomography on cryo-preserved and unstained biological samples. The article is well-written, and it is easy to follow. I like the structure and the experimental description, but I miss some points in the analyses, without which the conclusions are not adequately supported.

The authors state the following:<br /> "the application of serial FIB/SEM imaging of non-stained cryogenic biological samples is limited due to low contrast, curtaining, and charging artefacts. We address these challenges using a cryogenic plasma FIB/SEM (cryo-pFIB/SEM)".<br /> Reading the article, I do not find that the challenges are addressed; it appears that some of these are evaluated when the samples are prepared using plasma-based beams. To support the fact that charging, contrast, and curtaining are addressed, a comparison should be made with the current state of the art, or it is otherwise impossible to determine whether these systems bring any advantage.

Charging is an issue that is not described in detail, nor has it been adequately analysed. The effect of using plasma beams is independent of the presented algorithm for charging suppression, which is purely image processing based, although very interesting. Given that the focus of the work is on introducing the benefit of using plasma ion beams (from the title) and given that a great deal of data is presented on the effect of the multiple ion sources, one would expect to have comparable images acquired after the surfaces have been prepared with the different beams. This should also be compared against the current state-of-the-art (gallium) to provide a baseline for different beams' benefits. I realise that this requires access to another microscope and that this also imposes controls on the detector responses on each instrument to have a normalised analysis. Still, it also provides the opportunity to quantify the benefits of each instrumentation.

The curtaining scores. This is a good way to explain the problem, though a few aspects need to be validated. For example, curtains appear over time when milling, and it would be useful to understand how different sources behave over time in FIB/SEM tomography sessions. The score is currently done from individual windows milled, which gives a good indication of the performance. However, it would make sense to check that the behaviour remains identical in an imaging setting and with the moving milling windows (or lines). This will show the counteracting effect to the redeposition and etching effect reported when imaging with the E-beam the milled face.

No detail about the milling resolution has been reported. Since different currents and beams have different cross-sections, it is expected to affect the z-resolution achievable during an imaging session. It would be useful to have a description of the beam cross-sections at the various conditions used and how or whether these interfere with the preparation.

Contrast. No analysis of plasma FIBs' benefits on image contrast compared to the current state of the art has been provided. Measuring contrast is complex, especially when this value can change in response to the detector settings. Still, attempts can be made to quantify it through the FRC and through the analysis of the image MTF (amplitude and fall off), given that membranes are the only most prominent and visible features in cryoFIB/SEM images of biological samples.

Figure S4 points out that electrons that hit the sample at normal incidence give better signal/contrast or imaging quality than when the sample is imaged at a tilt. This fact is expected to significantly affect large areas as the collection efficiency will vary across the sample, particularly as regions get further away from the optimal location. The dynamic focusing option available on all SEM will compensate for the focal change but not the collection efficiency. Even though this is a fact, the authors show a loss of resolution, which is not explained by the tilt itself. In particular, the generation of secondary electrons is known to increase with the increased tilt, and to consider that the curtains (that are the prominent feature on the surface) are running along the tilt direction, it would be expected to see no contrast difference between the background and the edge of each curtain as the generation of secondary electrons will increase with tilt for both the edges and the background. Therefore, the contrast should be invariant, at least on the curtains.

Looking at the images presented in the figure, they appear astigmatic and not properly focused when imaged at a tilt. As evidence of this claim, the cellular features do not measure the same, and the sharpness of the edge of the curtains is gone when tilted. This experience comes from improper astigmatism correction, which in turn, in scanning systems, leads to the impossibility of focusing. The tilt correction provides not only dynamic focusing but also corrects for the anisotropy in the sampling due to the tilt. If all imaging is set up correctly, the two images should show the imaged features with the exact sizes regardless of the resolution (which, in the presented case, is sufficient), and the sharpness of the curtain edges should be invariant regardless of the tilt, at least while or where in focus. Only at that point, the comparison will be fair.

Finally, the resolution measurements presented in the last supplementary figures have no impact or relation to the use of plasma FIB/SEM. It is an effect related to the imaging conditions used in the SEM regardless of the ion beam nature. The distribution of the resolution within images appears predominantly linked to local charging and the local sample composition (from fig8). Given the focus is aimed at introducing or presenting the use of the plasma-based beams the results should be presented in that optic in mind with a comparison between beams.

Reviewer #2 (Public Review):

The authors present a manuscript highlighting recent advancements in cryo-focused ion beam/scanning electron microscopy (cryo-FIB) using plasma ion sources as an alternative to positively-charged gallium sources for cryo-FIB milling and volumetric SEM (cryo-FIB/SEM) imaging. The authors benchmark several sources of plasma and determine argon gas is the most suitable source for reducing undesirable curtaining effects during milling. The authors demonstrate that milling with an argon source enables volumetric imaging of vitrified cells and tissue with sufficient contrast to gleam biological insight into the spatial localization of organelles and large macromolecular complexes in both vitrified human cells and in high-pressure frozen mouse brain tissue slices. The authors also show that altering the sample angle from 52 to 90 degrees relative to the SEM beam enhances the contrast and resolution of biological features imaged within the vitrified samples. Importantly, the authors also demonstrate that the resolution of SEM images after serial milling with argon and nitrogen plasma sources does not appear to significantly affect resolution, suggesting that resolution does not vary over an acquisition series. Finally, the authors test and apply a neural network-based approach for mitigating image artifacts caused by charging due to SEM imaging of biological features with high lipid content, such as lipid droplets in yeast, thereby increasing the clarity and interpretability of images of samples susceptible to charging.

Strengths and Weaknesses:<br /> The authors do a fantastic job demonstrating the utility of plasma sources for increased contrast of biological features for cryo-FIB/SEM images. However, they do not specifically address the lingering question of whether or not it is possible to use this plasma source cryo-FIB/SEM volumetric imaging for the specific application of localizing features for downstream cryo-ET imaging and structural analyses. As a reader, I was left wondering whether this technique is ideally suited solely for volumetric imaging of cryogenic samples, or if it can be incorporated as a step in the cellular cryo-ET workflow for localization and perhaps structure determination. Another biorxiv paper (doi.org/10.1101/2022.08.01.502333) from the same group establishes a plasma cryo-FIB milling workflow to generate lamella of sufficient quality to elucidate sub-nanometer reconstructions of cellular ribosomes. However, I anticipate the real impact on the field will be from the synergistic benefits of combining both approaches of volumetric cryo-FIB/SEM imaging to localize regions of interest and cryo-ET imaging for high-resolution structural analyses.

Another weakness is the lack of demonstration that the contrast gained from plasma cryo-FIB/SEM is sufficient to apply neural network-based approaches for automated segmentation of biological features. The ability to image vitrified samples with enhanced contrast is huge, but our interpretation of these reconstructions is still fundamentally limited in our ability to efficiently analyze subcellular architecture.

Reviewer #3 (Public Review):

The authors present analyses of cryo-plasma FIB/SEM hardware for practical use in the field of cell and tissue biology at microscopic resolutions. The results include several practical analyses and considerations for structural biologists when imaging their specimens; details are provided for optimizing imaging parameters and some image processing. Several examples of pFIB-milling cells and tissues are shown. The authors also introduce a method for quantifying curtaining, one of the major artifacts in FIB/SEM imaging, and software for reducing streaking artifacts in images. The analyses in the manuscript appear to come to conclusions that are experimentally justified. I see no major weaknesses in this manuscript.

Reviewer #1 (Public Review):

Taliani et al. have studied the role of the lncRNA pCharme during cardiac development. pCharme knockout-mice present hyperplastic hearts and the authors attempt to decipher whether this cardiac phenotype result from a developmental alteration during heart formation. They showed that pCharme is specifically expressed in the heart from early stage of development at heart tube stage and persists until birth while its expression decreases after birth. The expression of pCharme in early cardiac progenitors is regulated by the transcription factor Tbx5. Several genes and signaling pathways are differentially affected in pCharme mutant hearts and most of them affected cell cycle activity and cardiac differentiation. pCharme is required to form chromatin aggregates including the MATR3 protein and this sounds important to regulate cardiac gene transcription.

One issue concerns the description of the cardiac phenotype in pCharme mutant embryos as immunofluorescent data are difficult to interpret. A deeper investigation of the level of compaction and hypotrabeculation is required to affirm that pCharme plays a role in the ventricular wall differentiation/maturation.

Another issue is that the cardiac phenotype in pCharme is not directly related to that observed in MATR3 mutants.

Reviewer #2 (Public Review):

Charme is a long non-coding RNA reported by the authors in their previous studies. Their previous work, mainly using skeletal muscles as a model, showed the functional relevance of Charme, and presented data demonstrating its nuclear role, primarily via modulating the sub-nuclear localization of Matrin 3 (MATR3). Their data from skeletal muscles suggested that loss of the intronic region of Charme affects the local 3D genome organization, affecting MATR3 occupancy and this gene expression. Loss of Charme in vivo leads to cardiac defects. In this manuscript, they characterize the cardiac developmental defects and present molecular data supporting how the loss of Charme affects the cardiac transcriptome repertoire. Specifically, by performing whole transcriptome analysis in E12.5 hearts, they identify gene expression changes affected in developing hearts due to loss of Charme. Based on their previous study in skeletal muscles, they assume that Charme regulates cardiac gene expression primarily via MATR3 also in developing cardiomyocytes. They provide CLIP-seq data for MATR3 (transcriptome-wide footprinting of MATR3) in wild-type E15.5 hearts and connect the binding of MATR3 to gene expression changes observed in Charme knockout hearts. I credit the authors for providing CLIP seq data from in vivo embryonic samples, which is technically demanding.

Major strengths:

Although, as previously indicated by the authors in Charme knockout mice, the major strength is the effect of Charme on cardiac development. While the phenotype might be subtle, the functional data indicate that the role of Charme is essential for cardiac development and function. The combinatorial analysis of MATR3 CLIP-seq and transcriptional changes in the absence of Charme suggests a role of Charme that could be dependent on MATR3.

Weakness:

(i) Nuclear lncRNAs often affect local gene expression by influencing the local chromatin. Charme locus is in close proximity to MYBPC2, which is essential for cardiac function, sarcomerogenesis, and sarcomere maintenance. It is important to rule out that the cardiac-specific developmental defects due to Charme loss are not due to (a) the influence of Charme on MYBPC2 or, of that matter, other neighboring genes, (b) local chromatin changes or enhancer-promoter contacts of MYBPC2 and other immediate neighbors (both aspects in the developmental time window when Charme expression is prominent in the heart, ideally from E11 to E15)

(ii) The authors provide data indicating cardiac developmental defects in Charme knockouts. Detailed developmental phenotyping is missing, which is necessary to pinpoint the exact developmental milestones affected by Charme. This is critical when reporting the cell type/ organ-specific developmental function of a newly identified regulator.

(iii) Along the same line, at the molecular level, the authors provide evidence indicating a change in the expression of genes involved in cardiogenesis and cardiac function. Based on changes in mRNA levels of the genes affected due to loss of Charme and based on immunofluorescence analysis of a handful of markers, they propose a role of Charme in cell cycle and maturation. Such claims could be toned down or warrant detailed experimental validation.

(iv) Authors extrapolate the mechanistic finding in skeletal muscle they reported for Charme to the developing heart. While the data support this hypothesis, it falls short in extending the mechanistic understanding of Charme beyond the papers previously published by the authors. CLIP-seq data is a step in the right direction. MATR3 is a relatively abundant RBP, binding transcriptome-wide, mainly in the intronic region, based on currently available CLIP-seq data, as well as shown by the authors' own CLIP seq in cardiomyocytes. It is also shown to regulate pre-mRNA splicing/ alternative splicing along with PTB (PMID: 25599992) and 3D genome organization (PMID: 34716321). In addition, the authors propose a MATR3 depending molecular function for Charme primarily dependent on the intronic region of Charme and due to the binding of MATR3. Answering the following question would enable a better mechanistic understanding of how Charme controls cardiac development. (i) what are the proximal genomic regions in the 3D space to Charme locus in embryonic cardiomyocytes? Authors can re-analysis published Hi-C data sets from embryonic cardiomyocytes or perform a 4-C experiment using Charme locus for this purpose. (ii) does the loss of Charme affect the splicing landscape of MATR3 bound pre-mRNAs in E12.5 ventricles in general and those arising from the NCTC region specifically? (iii) MATR3 binds DNA, as also shown by authors in previous studies. Is the MATR3 genomic binding altered by Charme loss in cardiomyocytes globally, as well as on the loci differentially expressed in Charme knockout heart? Overlapping MATR3 genomic binding changes and transcriptome binding changes to differentially expressed genes in the absence of Charme would better clarify the MATR3-centric mechanisms proposed here. Further connecting that to 3D genome changes due to Charme loss could provide needed clarity to the mechanistic model proposed here.

Reviewer #3 (Public Review):

With this work, the authors build on their previous findings on the role of the long non-coding RNA, Charme. Here, the authors show that the nuclear isoform of Charme ncRNA, pCharme, is specifically expressed in cardiac myocytes from the earliest stages of cardiac development and persists in postnatal life too. The authors perform phenotypic and molecular analysis on Charme knockout hearts to demonstrate abnormal cardiogenesis in the form of cardiac hyperplasia during development which persists postnatally. pCharme also localizes with the nuclear matrix protein MATR3 to form puncta in cardiomyocytes during development, similar to what was observed in skeletal muscle and the authors provide data to show that this punctated form of MATR3 is lost in Charme KO hearts. Finally, by CLIP-seq, the authors identify other transcripts that can interact with MATR3, including pCharme, and a percentage of these are involved in cardiac development. This paper is of interest since it highlights a new non-coding player in cardiac development which could further inform how non-coding RNAs govern gene expression during specific developmental processes. However, the authors have previously shown similar studies identifying the role of pCharme and its interaction with MATR3 in skeletal muscle. While it is important to show that a similar process is occurring in a different muscle cell-type, a more in-depth analysis and discussion especially of the CLIP-seq data would further elevate the paper. Overall, these findings do extend the authors' previous work. However, the manuscript would greatly benefit from a more nuanced and in-depth discussion of their findings as to how this non-coding RNA is regulating cardiac development at a more mechanistic level.

Reviewer #1 (Public Review):

Plasmodium falciparum must decide how much resources it will invest in within-host proliferation, or divert into gametocytogenesis to ensure onward transmission to the mosquito. The authors present here an interesting new perspective to this question using longitudinal data from a single study site over 18 years which covers three distinct transmission phases: pre-decline, decline and post-decline (which reflects a high malaria transmission setting declining to a low transmission setting). Laboratory studies in gametocyte commitment are having a renaissance in recent years, however in vivo studies have lagged behind. To address this knowledge gap, the authors have quantified the transcript levels in patient samples of ap2-g (a key player in gametocyte commitment), and PfSir2a (a gene hypothesised to sense various cellular processes via metabolic regulation). They found that transcripts of both genes increase (indicating increased investment in gametocyte production) as transmission declines. Using the Luminex platform, they were then able to link gene expression directly to key inflammatory markers within the patient, showing that as malaria transmission declines, host inflammatory response changes. Adding greater depth, using unbiased lipidomics the authors then went on to link identified inflammatory response phenotypes to specific lipid species. Excitingly, they link depleted levels of host lysophosphatidylcholine (LPC) with a defined immune response state and increased gametocyte commitment in the low transmission setting. Taken together, this gives strong in vivo support for LPC as a key modulator in both parasite development and host immune response, something that to date has been mostly characterised in vitro.

Reviewer #2 (Public Review):

The manuscript by Abdirahman I. Abdi et al. examines markers of host immunity and metabolism and markers of the malaria parasite (Plasmodium falciparum) growth and transmission. As the transmission of the malaria disease is governed by the sexual forms, (gametocytes), understating the commitment process represents a major step towards the global elimination of malaria. While the study focuses on a sound, very important topic in malaria research, its findings are partially based on rather weak evidence. In particular, in some parts there is a lack of adequate correlations, inaccurate statistics and misleading statistical tests. Moreover, these analyses are poorly explained, to a degree that some conclusions seem a bit enforced. In addition, the multitude of terms used makes it hard for the reader to follow the text. The appeal of this study lies in its potential relevance to the global public health drive to eliminate malaria.

Reviewer #3 (Public Review):

The authors present an association study geared to examine how epigenetic regulation of sexual commitment, immune responses and parasite growth change within a region that has undergone dramatic changes in transmission patterns over time. The work builds on previous epidemiological studies suggesting lower transmission settings result in parasites increasing sexual commitment, and most notably, examines mechanisms underlying these trends. The work shows the first in vivo association between LysoPC and gametocyte commitment (previously shown in vitro) in a large patient cohort. It also shows some very interesting trends relating LPC and parasite epigenetic markers to patient immune reactions.

The strengths of this paper include the use of a large patient cohort from a single geographic region, across distinct transmission intensities - an intrinsically exciting way of studying.<br /> The combination and integration of Luminex, RT-PCR, lipidomics, and clinical data provide a rich dataset for understanding host and parasite factors and provide novel in vivo evidence to support a role for LysoPC in commitment to gametocytogenesis.

In terms of weaknesses, by its nature as an association study it is difficult to ascribe causation to the patterns of seen. However, the work is built around testing of clearly defined hypothesis (based on both in vitro and clinical data) and has enabled the development of sound and exciting models for testing in future work.

The work is well-designed and written, and the conclusions fully align with the data presented. The one minor contention with the description of data is the discussion of Fig 4C-E. The manuscript states "Indeed, LPC species showed a negative association with both ap2-g and Pfsir2a transcription levels (Fig.4C-E). The association was only significant in our data when inflammation is highest (and LPC level lowest), which is at low transmission (i.e., post decline)." There is in fact only an association in post decline samples and very clearly no association pre decline. This could be made clearer here and also in the discussion (L217). This is a minor point of clarity - the work remains a compelling addition to our understanding of sexual commitment of malaria parasites.

Reviewer #1 (Public Review):

In this manuscript, Wei & Robles et al seek to estimate the heritability contribution of Neanderthal Informative Markers (NIM) relative to SNPs that arose in modern humans (MH). This is a question that has received a fair amount of attention in recent studies, but persistent statistical limitations have made some prior results difficult to interpret. Of particular concern is the possibility that heritability (h^2) attributed to Neanderthal markers might be tagging linked variants that arose in modern humans, resulting in overestimation of h^2 due to Neanderthal variants. Neanderthal variants also tend to be rare, and estimating the contribution of rare alleles to h^2 is challenging. In some previous studies, rare alleles have been excluded from h^2 estimates.

Wei & Robles et al develop and assess a method that estimates both total heritability and per-SNP heritability of NIMs, allowing them to test whether NIM contributions to variation in human traits are similar or substantially different than modern human SNPs. They find an overall depletion of heritability across the traits that they studied, and found no traits with enrichment of heritability due to NIMs. They also developed a 'fine-mapping' procedure that aims to find potential causal alleles and report several potentially interesting associations with putatively functional variants.

Strengths of this study include rigorous assessment of the statistical methods employed with simulations and careful design of the statistical approaches to overcome previous limitations due to LD and frequency differences between MH and NIM variants. I found the manuscript interesting and I think it makes a solid contribution to the literature that addresses limitations of some earlier studies.

My main questions for the authors concern potential limitations of their simulation approach. In particular, they describe varying genetic architectures corresponding to the enrichment of effects among rare alleles or common alleles. I agree with the authors that it is important to assess the impact of (unknown) architecture on the inference, but the models employed here are ad hoc and unlikely to correspond to any mechanistic evolutionary model. It is unclear to me whether the contributions of rare and common alleles (and how these correspond with levels of LD) in real data will be close enough to these simulated schemes to ensure good performance of the inference.

In particular, the common allele model employed makes 90% of effect variants have frequencies above 5% -- I am not aware of any evolutionary model that would result in this outcome, which would suggest that more recent mutations are depleted for effects on traits (of course, it is true that common alleles explain much more h^2 under neutral models than rare alleles, but this is driven largely by the effect of frequency on h^2, not the proportion of alleles that are effect alleles). Likewise, the rare allele model has the opposite pattern, with 90% of effect alleles having frequencies under 5%. Since most alleles have frequencies under 5% anyway (~58% of MH SNPs and ~73% of NIM SNPs) this only modestly boosts the prevalence of low frequency effect alleles relative to their proportion. Some selection models suggest that rare alleles should have much bigger effects and a substantially higher likelihood of being effect alleles than common alleles. I'm not sure this situation is well-captured by the simulations performed. With LD and MAF annotations being applied in relatively wide quintile bins, do the authors think their inference procedure will do a good job of capturing such rare allele effects? This seems particularly important to me in the context of this paper, since the claim is that Neanderthal alleles are depleted for overall h^2, but Neanderthal alleles are also disproportionately rare, meaning they could suffer a bigger penalty. This concern could be easily addressed by including some simulations with additional architectures to those considered in the manuscript.

Reviewer #2 (Public Review):

The goal of the work described in this paper is to comprehensively describe the contribution of Neanderthal-informative mutations (NIMs) to complex traits in modern human populations. There are some known challenges in studying these variants, namely that they are often uncommon, and have unusually long haplotype structures. To overcome these, the authors customized a genotyping array to specifically assay putative Neanderthal haplotypes, and used a recent method of estimating heritability that can explicitly account for differences in MAF and LD.

This study is well thought-out, and the ability to specifically target the genotyping array to the variants in question and then use that information to properly control for population structure is a massive benefit. The methodology also allowed them to include rarer alleles that were generally excluded from previous studies. The simulations are thorough and convincingly show the importance of accounting for both MAF and LD in addition to ancestry. The fine-mapping done to disentangle effects between actual Neanderthal variants and Modern human ones on the same haplotype also seems reasonable. They also strike a good balance between highlighting potentially interesting examples of Neanderthal variants having an effect on phenotype without overinterpreting association-based findings.

The main weakness of the paper is in its description of the work, not the work itself. The paper currently places a lot of emphasis on comparing these results to prior studies, particularly on its disagreement with McArthur, et al. (2021), a study on introgressed variant heritability that was also done primarily in UK Biobank. While they do show that the method used in that study (LDSR) does not account for MAF and LD as effectively as this analysis, this work does not support the conclusion that this is a major problem with previous heritability studies. McArthur et al. in fact largely replicate these results that Neanderthal variants (and more generally regions with Neanderthal variants) are depleted of heritability, and agree with the interpretation that this is likely due to selection against Neanderthal alleles. I actually find this a reassuring point, given the differences between the variant sets and methods used by the two studies, but it isn't mentioned in the text. Where the two studies differ is in specifics, mainly which loci have some association with human phenotypes; McArthur et al. also identified a couple groups of traits that were exceptions to the general rule of depleted heritability. While this work shows that not accounting for MAF and LD can lead to underestimating NIM heritability, I don't follow the logic behind the claim that this could lead to a false positive in heritability enrichment (a false negative would be more likely, surely?). There are also more differences between this and previous heritability studies than just the method used to estimate heritability, and the comparisons done here do not sufficiently account for these. A more detailed discussion to reconcile how, despite its weaknesses, LDSR picks up similar broad patterns while disagreeing in specifics is merited.

In general this work agrees with the growing consensus in the field that introgressed Neanderthal variants were selected against, such that those that still remain in human populations do not generally have large effects on phenotypes. There are exceptions to this, but for the most part observed phenotypic associations depend on the exact set of variants being considered, and, like those highlighted in this study, still lack more concrete validation. While this paper does not make a significant advance in this general understanding of introgressed regions in modern populations, it does increase our knowledge in how best to study them, and makes a good attempt at addressing issues that are often just mentioned as caveats in other studies. It includes a nice quantification of how important these variables are in interpreting heritability estimates, and will be useful for heritability studies going forward.

Reviewer #1 (Public Review):

Overall, this is a well-written and well-executed study that addresses the in vivo and in vitro functions of PCM1, a key component and regulator of centriolar satellites previously implicated in centrosome and ciliary biogenesis and function. The authors first generated mice lacking PCM1 and through careful phenotypic characterization, they demonstrate a tissue- and cell-type specific role for PCM1 in ciliogenesis in vivo, including a role in ciliogenesis in multiciliated ependymal cells but not airway epithelial cells. Consistently, Pcm1-/- mice were demonstrated to display perinatal lethality and select ciliopathy phenotypes such as hydrocephalus. Using high resolution immunofluorescence imaging and electron microscopy, the authors provide evidence that PCM1 promotes early stages of ciliogenesis, specifically removal of the CP110 capping protein from the distal end of (mother) centrioles. They go on to investigate this in more detail using cultured mouse embryonic fibroblasts (MEFs) and RPE1 cells lacking PCM1. Intriguingly, they find that PCM1 is required for ciliogenesis in RPE1 cells but not in MEFs, even though CP110 levels at the mother centriole are elevated in both cell types when PCM1 is depleted. The authors propose that PCM1 promotes ciliogenesis in select cell types by "wicking away" CP110 from the mother centriole at the onset of ciliogenesis, and provide some additional evidence (e.g. co-immunoprecipitation and live cell imaging analysis) to support this model. The manuscript represents a significant amount of high-quality work, and most of the claims are justified by the data. However, the manuscript would be strengthened by addressing the following points:

1) Based on their results, including the observation that CP110 and CEP97 centrosomal levels are increased in PCM1-/- cells, the authors propose that PCM1 promotes ciliogenesis by mediating removal/"wicking away" of CEP97 and CP110 from the mother centriole at the onset of ciliogenesis (Figure 9). Although this model could explain the authors' observations, alternative models should be considered. For example, an equally plausible mechanism is that PCM1 promotes centrosome/mother centriole recruitment of an E3 ligase that (negatively) regulates CP110. Indeed, the authors show in Fig. 4 that MEFs lacking PCM1 display reduced centrosome levels of the E3 ligase MIB1. This raises the question if MIB1 is also reduced at the centrosome in RPE1 cells lacking PMC1, and whether other E3 ligases known to promote CP110 removal/degradation are also decreased at the mother centriole of PCM1-/- cells. This includes EDD1/UBR5, which was previously implicated in CP110 removal from the mother centriole of RPE1 cells (Hossain et al. 2017; Goncalves et al., 2021), and which may be linked to centriolar satellites via CSPP-L (Shearer et al. 2018). Other relevant CP110 regulators to check include LUBAC and PRPF8, which may act in parallel with UBR5 to mediate CP110 removal from the mother centriole (Shen et al., 2021). The authors should at least discuss the possibility that PCM1 might affect the centrosome localization of these known CP110 regulators, if not address it experimentally. Finally, to confirm that reduced ciliogenesis in PCM1-/- cells is indeed due to increased levels of CP110 at the mother centriole, the authors could (partially) deplete CP110 from PCM1-/- RPE1 cells to investigate if this rescues the ciliogenesis phenotype of the mutant cells, e.g. as done recently by Goncalves et al. for CEP78-/- cells.

2) Figure 5 supplement 1A, B; lines 232-242; 430-439: the authors report that Talpid3 localization at the centrosome in PCM1 mutant cells is equivalent to that of controls. However, when looking at Figure 5 supplement 1B it seems that Talpid3 levels at the centrosome may be slightly elevated at the centrosome in the mutant cells although the change is not statistically significant. I suggest the authors specifically state this in the text, given that previous work by Wang et al. (2016) indicated that PCM1 does have an effect on centrosomal Talpid3 levels. A change in Talpid3 centrosomal level could be very small, requiring larger sample size to reach statistical significance, and different experimental conditions (fixation, permeabilization, antibody dilution etc.) could also influence the results and explain the discrepancy between the authors' observations and those of Wang et al. (2016).

3) Figure 5 supplement 1C, D: given that the authors´ results are in contrast to those of Wang et al. (2016), they should measure the actual fluorescence intensity of Centrobin at the mother centriole rather than just counting number of Centrobin foci, as they have done for e.g. CP110.

4) The observed requirement for PCM1 in promoting ciliogenesis in RPE1 cells and not MEFs is puzzling, given that the authors still observed increased CP110 levels at the mother centriole in the Pcm1-/- MEFs. In the discussion (lines 464-473), the authors suggest that CP110 removal from the mother centriole may be more important for ciliogenesis in cells using the "extracellular" pathway of ciliogenesis compared to cells forming cilia via the "intracellular" pathway. However, mouse fibroblasts and RPE1 cells were shown to both form cilia via the "intracellular" pathway (e.g. see Ganga et al. 2021) thus this explanation seems insufficient to explain the observed differences between RPE1 cells and MEFs lacking PCM1. It would be helpful if the authors could comment on this.

Reviewer #2 (Public Review):

Centriole satellites are membraneless granules that surround the centrosome. Some proteins localize exclusively to centriole satellites, while others are present at both satellites and the centrosome. The function of centriole satellites is somewhat mysterious, but they have been implicated in ciliogenesis, autophagy, and mediating cellular stress responses. PCM1 is a core scaffolding protein essential for the assembly of centriole satellite and many studies have examined the role of centriole satellites in PCM1 depleted cell lines. However, the role of centrosome satellites at the organismal level has not been examined, and it remains unclear if the effects observed in cell lines are present across diverse cell types found in vivo.

In this manuscript, Hall et al., examine the effect of PCM1 knockout in mice. Surprisingly, Pcm1-/- mice are viable but exhibit increased perinatal lethality. Mice lacking PCM1 also have many interesting phenotypes, including dwarfism, male infertility, hydrocephaly, and hydronephrosis. These phenotypes are consistent with defects occurring in both primary and motile cilia. The ciliogenesis deficits in Pcm1-/- mice must be relatively mild, as severe defects in cilia assembly result in embryonic lethality. Thus, centriole satellites are not required for cilia assembly in most cell types. Consistently, the authors show that Pcm1-/- MEFs have no apparent phenotypes in cilia assembly. Pcm1-/- multiciliated ependymal cells have a delay in ciliogenesis and defects in cilia beating. Surprisingly, given the array of interesting phenotypes to examine in the mice, the authors switch to characterizing PCM1-/- RPE1 cells. Unlike primary MEFs, PCM1-/- RPE1 cells show reduced ciliogenesis. The authors show that in RPE1 cells, PCM1 promotes the recruitment of preciliary vesicles to the mother centriole and helps remove the CP110/CEP97 centriole capping complex. The authors propose that CP110 and CEP97 are transported away from mother centrioles by centriole satellites. However, Pcm1-/- MEFs also fail to remove CP110 from the mother centriole, despite having no defects in ciliogenesis. Thus, CP110 removal is not universally required for ciliogenesis.

This is an excellent manuscript that thoroughly examines the role of PCM1 both in vivo and in vitro. In my view, the major strength of this work lies in the examination of the impact of PCM1 loss in vivo. As a result, I was a little surprised the authors didn't focus more attention on the interesting phenotypes that arise in the Pcm1-/- mouse. The switch over to RPE1 cells is abrupt. Moreover, the phenotypes observed in this cell line are likely not occurring in most cell types in vivo, or else the expected organismal phenotypes would probably be even more severe. That notwithstanding, the RPE1 cell biology is rigorous, high quality, and the conclusions are well-justified. Overall, the work will be of broad interest to the centrosome/cilia community.

Reviewer #3 (Public Review):

The manuscript by Hall et al., first describes the global and multi-organs phenotype of PCM1-/- mice and then focus on the role of PCM1 in the process of basal body production/maturation in multiciliated cells and finally on the role of PCM1 in primary ciliogenesis on RPE1 and MEF cells. In multiciliated cells, they show that the absence of PCM1 delays basal body formation and that PCM1 is required for the formation of structurally normal cilia, and for their consecutive coordinated beating. As regards to primary ciliogenesis, they show that PCM1 is required to allow efficient ciliation in RPE1 but not in MEF cells. Notably, they reveal defects in the formation of the preciliary vesicle in RPE1 cells and propose that PCM1 restricts CP110 and Cep97 at the centrosomal centriole in both MEFs and RPE1.

The study presented here represents a lot of nice work and highlights original data. However, in its present form, the study, which covers many aspects of the PCM1 mouse phenotype, is too fragmentary and does not allow to have, either a global view of the diversity of the phenotypes, or give mechanistic insight into one of the phenotypes. I would recommend the authors make two different papers on multiciliation and primary ciliogenesis, or try to test whether both type of ciliation are affected in a common way by the absence of PCM1. For instance, the title focuses only on the last part of the paper. Below are my comments.

Global phenotype

The authors convincingly show that the absence of PCM1 during development leads to perinatal lethality, hydrocephalus, cerebellar hypoplasia, oligospermia and cystic kidneys.

Role of PCM1 in multiciliation

The authors convincingly show that the absence of PCM1delays centriole amplification and therefore multiciliation which has never been shown before to my knowledge.

They also propose that the basal bodies produced in absence of PCM1 show a problem of rotational polarity. This is not fully supported by the data. To confirm this observation, the authors should look at later time points as P3 is very early and the rotational polarity is progressively established after BB docking and the beginning of cilia beating. Also many more cells should be analyzed. Since this is a lot of work by EM, one should consider doing it by immunostainings as done in some other papers. Same comment for the absence of ciliary pocket in PCM1 KO. P3 is too early and since some cilia do not show a clear ciliary pocket, one should look in a sufficient number of EM sections.

The defect in translational polarity is interesting and has never been described before. This phenotype is analyzed at P5 and should also be confirmed at later time point since the delay in multiciliation in the PCM1 KO may affect the number of cells with a terminal differentiated state and therefore bias the result. In fact, migration of BB is the last event occurring during multiciliation.

The phenotype of cilia beating uncoordination is convincing and confirms what has been also described by Zhao et al., in 2021. The authors seem to propose a causality link between this phenotype and the proteomic study between WT and PCM1 KO in another MCC cell type: mTEC at ALID7. Since the difference resolve in these mTEC at ALID21, do the authors think the delay in cilia motility protein expression could explain a consecutive permanent problem of cilia beating coordination seen at later stages ? Also it is difficult to link these results with motility since motility is assessed in ependymal cilia and proteomic study in mTEC. One would like to know if motility is also affected in mTEC. And to use the proteomic study to propose an additional explanation of the one proposed by Zhao et al. showing that PCM1 depletion also deregulates the centriolar and ciliary targeting of satellites client proteins, a process that could affect cilia beating. The structural defects of cilia seen by the authors and by Zhao et al., are also one important piece of explanation.

In vitro, MCC in PCM1 KO seem to display less cilia. Is this true in vivo in the brain? Since it is not obvious in vivo in the trachea, it would be nice to just address qualitatively whether this is the case in vivo in the brain. Also, are the number of BB affected ? Zhao et al., counted the number of BB in PCM1 siRNA treated cells and show no difference. If one would address how PCM1 affect the number of cilia, this is important to know whether less centrioles are produced or whether they fail to dock correctly at the plasma membrane. Since formation of the preciliary vesicle is affected in in RPE1 cells, it is tempting to speculate that a similar defect could arise in MCC and affect motile ciliogenesis. If the « number of cilia » phenotype is not true in vivo, one should also consider a culture artefact.

Altogether, the phenotype on multiciliation needs to be strengthened to confirm the original results and to be put into the context of the previous study done in vitro (Zhao et al., 2021).

Role of PCM1 in primary ciliogenesis

Knockdown of different satellite components have been shown to affect primary ciliogenesis (Conkar et al., 2017; Kim et al., 2008; Klinger et al., 2014; Lee and Stearns, 2013; Mikule et al., 2007; Staples et al., 2014, Kurtulmus et al., 2016). More particularly cell type dependent variability of PCM1 suppression on ciliogenesis has previously been described (Odabasi et al., 2019; Wang et al., 2016). It appears necessary to clarify in one paragraph in the introduction this bibliographic context and to put forward the unresolved questions the present study proposes to address as well as the new insights it provides on the question.

First, the two main phenotypes described here, e.g. defect in ciliary vesicle formation and defect in CP110 and Cep97 removal from the mother centrioles, are very similar to the phenotype described in WDR8 knock down (Kurtulmus et al., 2016). Is there any reason why the authors did not cite this study ? If not, and since WDR8 and PCM1 are interacting partners and are interdependent for their localization, I would suggest assessing whether PCM1 acts upstream or downstream of the WDR8-Cep135 axis. For example, I would suggest testing if WDR8 expression in PCM1 KO rescue the ciliary vesicle and CPP110/Cep97 phenotypes.

The phenotype of preciliary vesicle formation defect in PCM1 KO is convincing in RPE1 cells. I would suggest to reproduce the MyoVa staining in MEFs to detect whether, in cells forming cilia in the absence of PCM1, the ciliary vesicles are forming properly. It may be a good control and also give insight into how PCM1 affects differentially ciliogenesis in different cell types. Also, the extent of TEM analysis is difficult to assess (I did not find the « n »). TEM is important to confirm the phenotype since MyoVa is an actin-based molecular motor that plays several roles in the final stages of secretory pathways.

Then the authors propose that PCM1 promotes the transition zone formation and IFT recruitment. The data presented here support that PCM1 promotes TZ formation. However, since PCM1 absence compromises preciliary vesicle formation, one could conclude that TZ alterations are just a consequence of this defect. This needs to be discussed. Regarding recruitment of IFT and TZ components, the data presented here do not support that PCM1 promotes TZ components and IFT recruitment. In fact, TZ components are not absent in non ciliated RPE1 KO cells, just decreased, and they are present at normal levels in ciliated MEFs in absence of PCM1.

The authors propose that centriolar satellites restrict CP110 and Cep97 levels at centrioles, which promotes ciliogenesis. Defect in the removal of CP110 and Cep97 from the mother centriole are very convincing in PCM1 KO both in RPE1 and MEFs. However, the causality link between this mother centriole maturation and ciliogenesis still needs to be tested since MEFs are able to ciliate in the absence of PCM1 and in the presence of CP110. Knock down of CP110 in PCM1 KO would be needed to accurately test this hypothesis. For example, in absence of WDR8, CP110 knock down does not rescue ciliogenesis defect probably because of the upstream defect of preciliary vesicle docking (Kurtulmus et al., 2016). This could be the case also here.

Finally, the authors propose that PCM1 satellites transport CP110 and Cep97 away from the centriole. They nicely show that CP110 colocalize with satellites. By IP, they suggest that PCM1 and CP110 coIP which need to be further confirmed by another IP since the signal is really weak. They show that CP110 does not colocalize anymore to the satellites as soon as 1h after serum deprivation. If satellites were involved in removing CP110 from the mother centriole for ciliation, I would expect to see an increase in CP110 localization to the satellites, and not a decrease at this time point. The authors also measure an increase of CP110 and Cep97 at the centrioles in PCM1 KO, which would go in line with their hypothesis. However, this phenotype is the opposite of what was shown in Quarantotti 2019 in the same cell type where they show that upon PCM1 loss, CP110 was decreased at the centrosome. Together with the fact that the overaccumulation of CP110 and Cep97 illustrated by IF and measured is weak, more data are needed to support this phenotype. Altogether, the hypothesis that satellites are transporting CP110 and Cep97 away from the centrioles needs more data to be convincing.

Reviewer #1 (Public Review):

In the present manuscript, the authors investigated circuits mechanisms that underlie habituation of visually evoked escape behaviors in larval zebrafish. For eliciting escape behaviors, the authors used dark looming stimuli. Larvae habituate to repeated stimulation with dark looming stimuli. The authors decomposed a dark looming stimulus into two independent components: one that is characterized by an overall spatial expansion, and the other that represents an overall dimming within the whole visual field. The authors found that pre-exposure to just the dimming component habituates responsiveness to dark looming in a comparable fashion than repeated exposure to the full dark looming. They investigated neural mechanisms that account for this using two photon calcium imaging experiments. Based on the results, the authors propose a circuits model where a subset of inhibitory DS (dimming sensitive) neurons are incrementally potentiated by repetitive stimulation and where these neurons serve to locally depress the looming selective relay pathway.

There are two caveats in the present study. First, there exists another independent habituation pathway as habituation also occurs for spatial expansion stimuli that do not accompany dimming (checkerboard stimuli). This manuscript does not investigate neural mechanisms of this habituation pathway at all. Second, the authors performed no experiment that supports the validity of the model (i.e., no ablation experiment). These two caveats reduce the impact of the manuscript. Nevertheless, I think the manuscript is worth publishing, as the model the authors propose is interesting. The model generates a series of predictions about behavior, neural response properties and synaptic connectivity, which, I hope, will be tested in future experiments.

Reviewer #2 (Public Review):

Overall, the greatest value of this article lies in the discovery and statistics of the inhibitory components that increased in response to continuous repetitive visual stimuli and suppressed responses of the critical neurons that transmit looming information to elicit escape. Although the author proposes a possible mechanism for visual habituation in larva zebrafish, there are still some shortcomings in the circuitry level proof and data interpretation, most conclusions in Figures 1-5 have been drawn in other work and lack certain innovations. In general, the overall logic of this article is relatively complete and the content is substantial, many data are very interesting and worth further interpretation.

Reviewer #1 (Public Review):

The study by Scinicariello et al. set out to identify novel factors that controlled TTP stability and identified HUWE1 by CRISPR screening in macrophages. HUWE1 phosphorylated TTP on residues distinct from those phosphorylated by MAPKs and regulated TTP protein stability. Overall, the biochemical and cellular signaling experiments were thoughtfully designed and well executed, leading to the discovery of HUWE1 as a TTP regulator.

Reviewer #2 (Public Review):

TPP is critical for regulating the mRNA abundance of proinflammatory cytokines. Sara Scinicariello et al., identified ubiquitin E3 ligase HUWE1 function as a key regulator of the TPP degradation, which could direct the related immune responses. However, the physiological importance and their major conclusions were not fully clarified or supported by the experimental data.

Reviewer #3 (Public Review):

The manuscript by Scinicariello and collaborators examines the mechanisms regulating the cellular accumulation of the RNA-binding protein Tristetraprolin (TTP). This factor is a well-described regulator of mRNA stability. TTP binds to RNA AU-rich sequences localized in mRNA 3'Untranslated regions. As AU-rich elements are abundant in mRNA encoding pro-inflammatory factors, TTP has been described as a negative regulator of the inflammatory response.

Previous reports have described that the cellular level of TTP is modulated by phosphorylation and proteasome-dependent process (see several references in the introduction of the manuscript). Non-degradative phosphorylation-dependent ubiquitination of TTP has also been reported (Schichl et al. 2011 JBC 286:38466). This publication is not cited in the current version of the manuscript. The results of Schichl et al. seem particularly relevant for the interpretation of some of the results presented here and should be considered in the final discussion and conclusions of the present work.

In the first part of the results section, Scinicariello et al. evaluate the degradation and ubiquitination of TTP and conclude that TTP is degraded in a ubiquitin-dependent manner. By a pharmacological approach, they observed, as previously shown, that endogenous TTP is degraded by the proteasome (Fig1a). They also show that an overexpressed tagged version of TTP is degraded by the proteasome and ubiquitinated on lysine residues (Fig. 1B, C). The general conclusion of this paragraph seems premature in relation to the results presented. The ubiquitination of endogenous TTP has not been demonstrated. The type of ubiquitination detected on the overexpressed version of TTP is not characterized. This seems important in view of the results of Schichl et al. who showed non-degradative ubiquitination (K63) of TTP. The half-life of the non-ubiquitinated mutant of TTP (K→R) was not precisely compared to the half-life of the wild-type TTP protein (similar to the experiment presented in 1B). The effect of the E1 ubiquitin ligase TAk-243 on endogenous TTP levels was not tested.

In the second part, the authors identified the E3 ligase HUWE1 as a major determinant of cellular TTP protein abundance. This demonstration is first based on the identification of HUWE1 in an unbiased CRISPR/cas9 screen to identify modulators of mCherry-TTP fusion reporter accumulation upon activation of RAW 264.7 cells by LPS. While they demonstrate that TTP-HA is efficiently degraded after 3 to 7h of LPS stimulation (Fig 1B) and that the stronger decrease in mCherry-TTP fusion level occurs between 4 and 6h of LPS stimulation the screen for identification of TTP modulators is performed 16h of LPS stimulation (Fig 2A). The rationale behind this experimental setting is not explicitly described. Nevertheless, the authors convincingly demonstrate that HUWE1 is involved in the controls of TTP cellular abundance. This demonstration mainly relies on the fact that HUWE1 inactivation induced a strong increase of both mCherry-TTP fusion and endogenous TTP (Fig. 2B and C). Ablation of HUWE1 selectively decreases the abundance of a limited number of proteins including TTP (Fig. 5A). The specificity of Huwe1 effect is confirmed by the detection of a constant level of the co-expressed BFP protein upon HUWE1 depletion (fig sup. 2E). The effect of HUWE1 depletion on TTP accumulation is observed in different cell lines and primary cells (murine, human) (Fig. sup. 2G, Fig2F).<br /> In this paragraph, the demonstration that Huwe1 specifically affects the stability of TTP protein appears less robust. The authors did not directly test the effect of HUWE1 inactivation on endogenous TTP accumulation after blocking protein synthesis. This control seems important as data presented in figure 2E could result both from an effect of Huwe1 level on LPS-induced TTP synthesis and TTP degradation.

In the data presented in figure 2, it is not entirely clear what exactly the authors are referring to as "endogenous TTP". In Figure 2C endogenous TTP is detected by western blot on cells transfected with an mCherry-TTP fusion. In this case, the size difference allows unambiguous identification of the endogenous form of TTP (although one could not exclude that overexpressing a TTP fusion protein might affect the level of the endogenous protein). However, TTP and mCherry-TTP cannot be distinguished by FACS (Fig2 D and E). If cells used in the experiments shown in 2C and 2D-E are distinct, this should be mentioned more explicitly in the legend of Fig. 2. Otherwise, the detection of endogenous TTP should be performed on cells that do not express mCherry-TTP.

The third part of the manuscript aims to demonstrate that loss of Huwe1 decreases the half-life of pro-inflammatory mRNAs controlled by TTP. In my opinion, this conclusion is reliably supported by the data presented in Figure 3 and Supplementary Figure 3. As the conclusion of this paragraph refers to the effect of TTP on the stability of these mRNAs, the measurement of TNF mRNA stability (Fig. sup. 3C) should be presented in the main part of Fig. 3.

The authors then aim to demonstrate that HUWE1 regulates TTP phosphorylation and its increase is responsible for increased TTP stability. Taken together, data from fig. 1F, 2C, and 2F clearly show that a phosphorylated form of TTP is accumulated in Huwe1 deficient cells. The authors state that Fig 4E aims to identify kinases and phosphatases potentially involved in TTP stability (line 277, line 298). However, the approach used here (a measure of intracellular TTP level) cannot distinguish between increased production of TTP or a decrease in TTP degradation. Also, the result presented in fig. 4E, are not totally consistent with the results presented in 4A. Fig4D shows a similar level of endogenous TTP accumulating after 2h of LPS stimulation in Huwe1 KO and control cells while a clear difference in TTP level is observable in the same condition in fig. 4A. Could the difference in the TTP detection method (Western vs intracellular FACS) be responsible for this discrepancy? In addition, the absence of positive control for the various pharmacological treatments renders difficult the interpretation of these results, especially when the inhibitor shows no effect on TTP level (ex: CalyculinA). On this basis, the authors' conclusions for this paragraph seem partially over-interpreted.

From the data presented in figure 5, the authors conclude that HUWE1 controls only a small fraction of proteasome targets and regulates the stability of TTP paralog ZFP36L1.<br /> A comparison of protein levels in Huwe1 and Psmb7 Ko cells reveals that Huwe1 ablation significantly changes the concentrations of only a limited number of proteins (Fig. 5A). The reliability of these data is confirmed by the identification as increased proteins in the huwe1 ko of factors previously identified as targets of HUWE1 (Fig. sup. 5C). These experiments and data presented in Fig.5D show that the level of the TTP paralog ZFP36L1 accumulates in huwe1 KO cells but do not demonstrate that HUWE1 affects ZFP36L1 protein stability.

The next conclusion of the manuscript describes residues in the TTP234-278 region as important for their stability. Based on data presented in fig. 6 B and sup. 6B the authors conclude that residues S52 and 178, previously identified as regulators of TTP stability, are unlikely to be involved in HUWE1-dependent TTP accumulation. The data are only based on 2 independent experiments, one of which (fig 6B) shows a difference in TTP S52/S178 mutant in Huwe1 deficient cells as compared to wt TTP. These results seem therefore too preliminary to reliably exclude the implication of S52 and 178 on the HUWE1 accumulation of TTP.

Other data from Fig. 6 further analyze the effect of deleting different regions of the TTP protein on the accumulation of this factor in HUWE1 KO and control cells. From these data, the authors conclude (line 416) that N-terminal deletion does not affect the TTP protein level. However, TTP accumulation in Huwe1 KO cells seems mostly lost in mutant N4. As mentioned above the limited number of replicates (n=2) and the absence of a statistical test makes the interpretation of this result difficult.

Several TTP C-terminal mutants show a HUWE1-independent accumulation when compared to the wt protein (Fig6. D). Is this region identical to the unstructured region identified by Ngoc (line 1255) as a potent regulator of TTP degradation? If relevant this point should be discussed.

Reviewer #1 (Public Review):

Understanding the evolution of nitrogenases is a very important problem in the field of evolutionary biogeochemistry. Ancestral sequence reconstruction at least in theory could offer insights into how this planet alerting activity evolved from ancestors that did not reduce nitrogen. But the very many components of the nitrogenase enzyme system make this a very challenging question to answer.

This paper now demonstrates the first empirical resurrection of functional ancestral nitrogenases both in vivo and in vitro. The nodes that are resurrected are very shallow in the nitrogenase tree and do not help answer how these proteins evolved. The authors' reasoning for choosing these nodes is that they are likely compatible with the metal cluster assembly machinery of their chosen host organism, A. vinelandii. The reader is left to wonder if deeper, more interesting nodes were tried but didn't yield any activity. As the paper stands, it proves that relatively shallow nitrogenase ancestors can be resurrected, but these nodes do not yet teach us anything very fundamental about how these enzymes evolved.

Technically, this work was no doubt challenging. Genome engineering in A vinelandii is very difficult and time-consuming. This organism was chosen because it is an obligate aerobe, which makes it easier to handle than the many anaerobic bacteria and archaea that harbor nitrogenases. It does make one wonder if this choice of organism is wise: the authors themselves note that it probably has a set of specialized proteins that allow the nitrogenase to be assembled and function in the presence of oxygen. This may limit A. vinelandii's potential future ancestral reconstructions deeper in the tree, which according to the authors' reasoning probably requires different assembly machinery.

The ancestral sequence reconstruction is done in two different ways: Two out of three reconstructions are carried out with what appears to be an incorrect algorithm implemented in older versions of RaxML. This algorithm is not a full marginal reconstruction, because it only considers the descendants of the node of interest for the reconstruction. The full algorithm (implemented e.g. in PAML and the newest versions of RaxML) considers all tips for a marginal reconstruction. The fact that this was called a marginal ancestral sequence reconstruction in RaxML's manual is unfortunate - as far as I understand it is in fact just the internal labelling of nodes produced by the pruning algorithm, which is not equivalent to a marginal reconstruction. In this specific case, it is unlikely that this has led to any fundamental issues with the reconstructions (as all are functional nitrogenases, which is to be expected in this part of the tree). For the shallower of the two nodes, the authors in fact verify that they get the same experimental results if they use PAML's full implementation of a marginal reconstruction (which yields a somewhat different sequence for this node). It would have been helpful to point this RaxML-related issue out in the methods, so as to prevent others from using this incorrect implementation of the ASR algorithm.

One other slightly confusing aspect of the paper is that it contains two different maximum likelihood trees, which were apparently inferred using the same dataset, model, and version of RaxML. It is unclear why they have different topologies. This probably indicates a lack of convergence. Again, this does not cast any doubt on the uncontroversial findings of this paper that shallow nodes within the nitrogenases are also nitrogenases.

Reviewer #2 (Public Review):

The authors convincingly show that their reconstructed ancestral nitrogenases are active both in vivo and in vitro, and show similar inhibitory effects as extant/wild-type enzymes.

The conclusion that, evolutionarily, there is a "single available mechanism for dinitrogen reduction" is not well explored in the paper. This suggests a limitation of using ancestral sequence reconstruction in this instance.

Reviewer #3 (Public Review):

In this work, the authors attempt to probe the constraints on the early evolution of nitrogen fixation, the development of which presented a key metabolic transition. Given that life on Earth evolved only once (to our knowledge) which aspects were necessary and which may have taken a different course are open questions. Are there alternative forms of life, metabolic networks, or even enzymatic mechanisms that could have replaced the ones we see today, or is the space of possible biologies limited? This manuscript tests the ability of ancestrally-reconstructed molybdenum-dependent nitrogenase complexes to support diazotrophic growth in Azotobacter vinelandii, as well as in vivo and in vitro activity, which all point towards a conserved mechanism for nitrogen reduction at least since proteobacteria divergence.

This is an ambitious project, requiring multiple techniques, systems, and approaches, and the successful combination of these is one of the major strengths of this work. Using parallel techniques is an important way to be certain that the overall results are robust, and an appropriate mix of in vivo and in vitro experiments is chosen here. The manuscript should serve as a useful model for how to combine phylogenetics and biochemistry.

The nature of ASR means that a solid phylogeny and/or understanding of how robust the results are to uncertainty in reconstructed states is essential since all results flow from there. The overall phylogenetic methods used are appropriate and the system is an apt one for the technique, but there is not quite enough detail in the methods to be certain of the results. Given that only the single maximum a posteriori sequence is assayed at every 3 nodes, this may have compounding results in that the sensitivity to uncertainty in the reconstruction is increased. The authors appropriately make qualitative rather than quantitative inferences, but some hesitation towards the overall results still exists.

The assumption that the Anc1A/B and Anc2 nodes correspond to ancestral states might be undermined by horizontal gene transmission, which has been reported for nif clusters. In particular, there may be different patterns of transmission for each element of the cluster. By performing reconstruction with a concatenated alignment, the phylogenetic signal is potentially maximized, but with the assumption that each gene has an identical history. Discordant transmission may cause an incorrect topology to be recovered.

Finally, I am unsure if ASR is the most appropriate approach to answer questions of contingency and alternative pathways for protein evolution. ASR may tell what nitrogenase millions or billions of years ago looked like, but it can only say what has already existed. If there are different mechanisms or metabolic pathways enabling nitrogen fixation that simply never came to pass, via contingency and entrenchment or simple chance, ASR would say nothing about them. It is true that a conserved mechanism would point towards a constrained space for evolving nitrogen fixation, but that does not directly address it.

Overall, despite these issues, the manuscript is compellingly written and the figures are attractive and clear, and help get the major narrative across. This work will be of interest to protein biochemists of evolutionary bent and microbial physiologists with an interest in the origins of life.

Reviewer #1 (Public Review):

Because of the importance of brain and cognitive traits in human evolution, brain morphology and neural phenotypes have been the subject of considerable attention. However, work on the molecular basis of brain evolution has tended to focus on only a handful of species (i.e., human, chimp, rhesus macaque, mouse), whereas work that adopts a phylogenetic comparative approach (e.g., to identify the ecological correlates of brain evolution) has not been concerned with molecular mechanism. In this study, Kliesmete, Wange, and colleagues attempt to bridge this gap by studying protein and cis-regulatory element evolution for the gene TRNP1, across up to 45 mammals. They provide evidence that TRNP1 protein evolution rates and its ability to drive neural stem cell proliferation are correlated with brain size and/or cortical folding in mammals, and that activity of one TRNP1 cis-regulatory element may also predict cortical folding.

There is a lot to like about this manuscript. Its broad evolutionary scope represents an important advance over the narrower comparisons that dominate the literature on the genetics of primate brain evolution. The integration of molecular evolution with experimental tests for function is also a strength. For example, showing that TRNP1 from five different mammals drives differences in neural stem cell proliferation, which in turn correlate with brain size and cortical folding, is a very nice result. At the same time, the paper is a good reminder of the difficulty of conclusively linking macroevolutionary patterns of trait evolution to molecular function. While TRNP1 is a moderate outlier in the correlation between rate of protein evolution and brain morphology compared to 125 other genes, this result is likely sensitive to how the comparison set is chosen; additionally, it's not clear that a correlation with evolutionary rate is what should be expected. Further, while the authors show that changes in TRNP1 sequence have functional consequences, they cannot show that these changes are directly responsible for size or folding differences, or that positive selection on TRNP1 is because of selection on brain morphology (high bars to clear). Nevertheless, their findings contribute strong evidence that TRNP1 is an interesting candidate gene for studying brain evolution. They also provide a model for how functional follow-up can enrich sequence-based comparative analysis.

Reviewer #2 (Public Review):

In this paper, Kliesmete et al. analyze the protein and regulatory evolution of TRNP1, linking it to the evolution of brain size in mammals. We feel that this is very interesting and the conclusions are generally supported, with one concern.

The comparison of dN/dS (omega) values to 125 control proteins is helpful, but an important factor was not controlled. The fraction of a protein in an intrinsically disordered region (IDR) is potentially even more important in affecting dN/dS than the protein length or number of exons. We suggest comparing dN/dS of TRNP1 to another control set, preferably at least ~500 proteins, which have similar % IDR.

Reviewer #3 (Public Review):

In this work, Z. Kliesmete, L. Wange and colleagues investigate TRNP1 as a gene of potential interest for the evolution of the mammalian cortex. Previous evidence suggests that TRNP1 is involved in self-renewal, proliferation and expansion in cortical cells in mouse and ferret, making this gene a good candidate for evolutionary investigation. The authors designed an experimental scheme to test two non-exclusive hypotheses: first, that evolution of the TRNP1 protein is involved in the apparition of larger and more convoluted brains; and second, that regulation of the TRNP1 gene also plays a role in this process alongside protein evolution.

The authors report that the rate of TRNP1 protein evolution is strongly correlated to brain size and gyrification, with species with larger and more convoluted brains having more divergent sequences at this gene locus. The correlation with body mass was not as strong, suggesting a functional link between TRNP1 and brain evolution. The authors directly tested the effects of sequence changes by transfecting the TRNP1 sequences from 5 different species in mouse neural stem cells and quantifying cell proliferation. They show that both human and dolphin sequences induce higher proliferation, consistent with larger brain sizes and gyrifications in these two species. Then, the authors identified six potential cis-regulatory elements around the TRNP1 gene that are active in human fetal brain, and that may be involved in its regulation. To investigate whether sequence evolution at these sites results in changes in TRNP1 expression, the authors performed a massively parallel reporter assay using sequences from 75 mammals at these six loci. The authors report that one of the cis-regulatory elements drives reporter expression levels that are somewhat correlated to gyrification in catarrhine monkeys. Consistent with the activity of this cis-regulatory sequence in the fetal brain, the authors report that this element contains binding sites for TFs active in brain development, and contains stronger binding sites for CTCF in catarrhine monkeys than in other species. However, the specificity or functional relevance of this signal is unclear.

Altogether, this is an interesting study that combines evolutionary analysis and molecular validation in cell cultures using a variety of well-designed assays. The main conclusions - that TRNP1 is likely involved in brain evolution in mammals - are mostly well supported, although the involvement of gene regulation in this process remains inconclusive.

Strengths:<br /> - The authors have done a good deal of resequencing and data polishing to ensure that they obtained high-quality sequences for the TRNP1 gene in each species, which enabled a higher confidence investigation of this locus.<br /> - The statistical design is generally well done and appears robust.<br /> - The combination of evolutionary analysis and in vivo validation in neural precursor cells is interesting and powerful, and goes beyond the majority of studies in the field. I also appreciated that the authors investigated both protein and regulatory evolution at this locus in significant detail, including performing a MPRA assay across species, which is an interesting strategy in this context.

Weaknesses:<br /> - The authors report that TRNP1 evolves under positive selection, however this seems to be the case for many of the control proteins as well, which suggests that the signal is non-specific and possibly due to misspecifications in the model.<br /> - The evidence for a higher regulatory activity of the intronic cis-regulatory element highlighted by the authors is fairly weak: correlation across species is only 0.07, consistent with the rapid evolution of enhancers in mammals, and the correlation in catarrhine monkeys is seems driven by a couple of outlier datapoints across the 10 species. It is unclear whether false discovery rates were controlled for in this analysis.<br /> - The analysis of the regulatory content in this putative enhancer provides some tangential evidence but no reliable conclusions regarding the involvement of regulatory changes at this locus in brain evolution.

Reviewer #1 (Public Review):

The study's primary motivating goal of understanding how nutrigenomic signaling works in different contexts. The authors propose that OGT- a sugar-sensing enzyme- connects sugar levels to chromatin accessibility. Specifically, the authors hypothesize that the OGT/Plc-PRC axis in sweet taste neurons interprets the sugar levels and alters chromatin accessibility in sugar-activated neurons. However, the detailed model presented by authors on OGT/PRC/Pcl Rolled in regulating nutrigenomic signaling relies on pharmacological treatments and overexpression of transgenes to derive genetic interactions and pathways; these approaches provide speculative rather than convincing evidence. Secondly, evidence is absent to show that PRC occupancy remains the same in other neurons (non-sweet taste neurons) under varied sugar levels or OGT manipulations. Hence, the claim that OGT-mediated access to chromatin via PRC-Plc is a key regulatory arm of nutrigenomic signaling needs further substantiation.

Reviewer #2 (Public Review):

Nutrigenomics has advanced in recent years, with studies identifying how the food environment influences gene expression in multiple model organisms. The molecular mechanisms mediating these food-gene interactions are poorly understood. Previous work identified the enzyme O-GlcNAC (OGT) in mediating the decreased sensitivity in sweet-taste cells when exposed to a high-sugar diet. The present study, using fly gustatory neurons as a model, provides mechanistic insight into how nutrigenomic signaling encodes nutritional information into cellular changes. The authors expand previous work by showing that OGT is associated with neural chromatin at introns and transcriptional start sites, and that diet-induced changes in chromatin accessibility were amplified at loci with presence of both OGT and PRC2.1. The work also identifies Mitogen Activated Kinase as a critical mediator in this pathway. This is an elegant group of experiments revealing mechanisms for how nutrigenomic signaling triggers cellular responses to nutrients.

Reviewer #3 (Public Review):

This paper dissects the molecular mechanisms of diet induced taste plasticity in Drosophila. The authors had previously identified two proteins essential for sugar-diet derived reduction of sweet taste sensitivity - OGT and PRC2.1. Here, they showed that OGT, an enzyme implicated in metabolic signaling with chromatin binding functions, also binds a range of genomic loci in the fly sweet gustatory receptor neurons where binding in a subset of those sites is diet composition dependent. Furthermore, a minority of OGT binding sites overlapped with PRC2.1 recruiter Pcl, where collectively binding of both proteins increased under sugar-diet while chromatin accessibility decreased. The authors demonstrate, that the observed taste plasticity requires catalytic activity of OGT, which impacts chromatin accessibility at shared OGT x Pcl but not diet induced occupancy. In an effort to identify transcriptional mechanisms that instantiate the plastic changes in sensory neuron functions the authors looked for transcription factors with enriched motifs around OGT binding sites and identified Stripe (Sr) as a transcription factor that yielded sugar taste phenotypes upon gain and loss of function experiments. In follow-up overexpression experiments, they show that this results in reduced taste sensitivity and reduced taste evoked spiking in gustatory receptor neurons. Notably the effects of Sr on taste sensitivity also depend on OGT catalytic activity as well as PRC2.1 function. Finally, they explore the function of rolled (rl) - an extracellular-signal regulated kinase (ERK) ortholog in Drosophila, suggested to function upstream of Sr - in diet induced gustatory plasticity. The authors showed that the overexpression of the constitutively active form of rl kinase results in reduced neuronal and behavioral responses to sucrose which was dependent on OGT catalytic activity. In sum, these findings reveal several new players that link dietary experience to sensory neuron plasticity and open up clear avenues to explore up- and downstream mechanisms mediating this phenomenon.

Strengths:<br /> • Good genetically targeted interventions<br /> • Thorough exploration of the epistatic relationships between different players in the system<br /> • Identification of several new signaling systems and proteins regulating diet derived gustatory plasticity

Weaknesses:<br /> • The GO term enrichment analyses with little functional follow up has limited explanatory power<br /> • ERK/rl data is a bit hard to interpret since any imbalance in this system appears to reduce gustatory sensitivity.

The conclusions in this manuscript are mostly well or at least reasonably supported by data. Below are a few recommendations for improvement:<br /> • The paper claims to address cell-type-specific nutrigenomic regulatory mechanisms. However, this work only explores nutrigenomic mechanisms in a single cell type (Gr5a+ sweet sensing cells) and we don't really learn whether these nutrigenomic mechanisms exist in all other cell types or just Gr5a+ cells. It would be valuable to see how specific OGT and PRC2.1 binding locations and effects on chromatin accessibility are in a different cell type - e.g. bitter sensing Gr66a. This would reveal how global in nature these findings are and or which aspects of nutrigenomic signaling are specific for sweet sensory cells.<br /> • Behavioral data from the screen identifying Sr is missing. Which other candidates were screened and what were the phenotypes?

Reviewer #1 (Public Review):

In this study, the authors introduced a new mathematical model of coarsening of protein ensembles between chromosome axes and nucleoplasm to explain the random distribution of the complexes including Hei10 in a chromosome synapsis-defective, zyp1a/zyp1b double mutant. Although the modeling of the new regulatory mechanism of the crossover (CO) control during meiosis (nucleoplasmic coarsening model and/or trans-interference), which seems to be validated by the super-resolution imaging results, is intriguing, it incrementally contributes to our understanding of the molecular mechanism of CO control during "wild-type" meiosis, since the new model only explains the distribution of COs only in the synapsis-defective mutant (little implication of CO patterning in wild-type).

Reviewer #2 (Public Review):

The authors address a very old question: what is the mechanism that controls genetic exchanges (crossovers) between the maternal and paternal chromosomes during sexual reproduction (meiosis). Specifically, what could account for two crucial aspects of the non-random distribution of crossovers: the lower-than-expected rate of non-exchange chromosomes, and the larger-than-expected distance between adjacent crossovers on the same chromosome. Despite the great progress that was made in the last few decades in understanding the molecular details crossover formation, the mechanism accounting for their non-random distribution remains a matter of heated debate. Hence, an ability to provide new insight into this question will be of interest to the wide chromosome biology community.

In this work, the authors combine two important findings/resources. The first is their own modeling of a biophysical framework called 'coarsening'. Coarsening relates to the well-described behavior of liquid compartments, which tend to get larger with time, at the expense of smaller compartments. As the authors note, their coarsening work builds on research by many labs, and on the recent understanding of the role of condensates in cell biology in general, and the liquid nature of the synaptonemal complex - a conserved meiotic chromosomal interface. In their previous paper, the authors found that coarsening could account for multiple cytological aspects of crucial regulators of crossovers - a conserved protein called HEI10. Their modeling was able to recapitulate temporal changes in HEI10 distribution and to account for changes that occur upon changes to HEI10 expression levels (halving of expression and over-expression). The second is the recent analysis of plant strains lacking the synaptonemal complex (zyp1). In that mutant, crossovers do occur (this is different than in some organisms), but the non-random distribution of crossovers is mostly lost: both crossover interference and the paucity of non-exchange chromosomes fit mostly random distribution.

Here, the authors combine these resources and adjust their modeling to account for the lack of the synaptonemal complex. A crucial difference is that instead of diffusing inside the SC (which spans each chromosome pair end-to-end), HEI10 now diffuses in the nucleoplasm. With this modified simulation they mostly account for crossover distribution in zyp1 mutants, using both published and new data they have acquired.

Despite the very limited amount of new data included in this manuscript, the clever combination of these two sources of data manages to add yet another layer of evidence to the idea that coarsening can explain crossover distribution. The main concern regarding the manuscript is that most of the aspects of crossover distribution that the model reproduces are quite trivial - for example, the resulting random distribution of the number of crossovers per chromosome. Some of the non-trivial aspects of the distribution - for example, the telomere enrichment - were built into the simulation as an explicit parameter. The only aspect that would be considered truly non-trivial is the narrower-than-expected number of total crossovers, despite the random distribution of crossovers per chromosome (Fig. 2A). Indeed, the modeling recapitulates this parameter, albeit to a much stronger degree than the in vivo data.

The ability of the model to recreate one non-trivial aspect of the crossover distribution is not sufficient to rule out other possible models, which would be necessary to consider this work a significant advance. However, if the authors are able to provide additional, non-trivial predictions relating to this and to other experimental conditions, this would dramatically elevate their ability to claim that a coarsening-based mechanism is indeed the most plausible one to explain crossover distribution. Some of these conditions could involve experimental perturbation of key parameters in the model: HEI10 levels, the number of DSBs or recombination intermediates (the 'substrate' that ends up resulting in crossovers), the length of time coarsening is allowed to proceed, or the volume of the nucleus.

Reviewer #3 (Public Review):

Fozard et al. presented a new model explaining the distribution of the pro-crossover factor HEI10 and its effect on the formation of crossovers in the absence of a functional synaptonemal complex (SC). The creation of such a model is important considering recent results showing that in Arabidopsis and possibly many other plants (perhaps all plants), the major crossover pathway may function independently of the SC. Crossover modeling can help to better understand crossover formation dynamics and facilitate the prediction of crossover distribution.

The new model assumes the possibility of loading HEI10 directly from the nucleoplasm, which of course is logical considering the phenotype of the zyp1 mutant in Arabidopsis. However, in a situation where the SC is fully functional, should not we expect some level of nucleoplasmic coarsening in addition to the dominant SC-mediated coarsening? Should the original model not be corrected, and if it is not necessary (e.g., because it included this effect from the very beginning, or the effect is too weak and therefore negligible), the authors should discuss it. With reference to this observation, it would be worthwhile to compare different characteristics of both types of coarsening (e.g., time course).

Recently, a preprint from the Raphael Mercier group has been released, in which the authors show a massive increase in crossover frequency in zyp1 mutants overexpressing HEI10. I think this is a great opportunity to check to what extent the parameters adopted by the authors in the nucleoplasmic coarsening model are universal and can correctly simulate such an experimental set-up. Therefore, can the authors perform such a simulation and validate it against the experimental data in Durand et al. doi.org/10.1101/2022.05.11.491364? Can CO sites identified by Durand et al. be used instead of MLH1 foci for the modeling?

Reviewer #1 (Public Review):

In this work, the authors investigate a means of cell communication through physical connections they call membrane tubules (similar or identical to the previously reported nanotubes, which they reference extensively). They show that Cas9 transfer between cells is facilitated by these structures rather than exosomes. A novel contribution is that this transfer is dependent on the pair of particular cell types and that the protein syncytin is required to establish a complete syncytial connection, which they show are open ended using electron microscopy.

The data is convincing because of the multiple readouts for transfer and the ultrastructural verification of the connection. The results support their conclusions. The implications are obvious, since it represents an avenue of cellular communication and modifications. It would be exciting if they could show this occurring in vivo, such as in tissue. The implication of this would be that neighboring cells in a tissue could be entrained over time through transfer of material.

Reviewer #2 (Public Review):

There is a lot of interest in how cells transfer materials (proteins, RNA, organelles) by extracellular vesicles (EV) and tunneling nanotubes (TNTs). Here, Zhang and Schekman developed quantitative assays, based on two different reporters, to measure EV and direct contact-dependent mediated transfer. The first assay is based on transfer of Cas9, which then edits a luciferase gene, whose enzymatic activity is then measured. The second assay is based on a split-GFP system. The experiments on EV trafficking convincingly show that purified exosomes, or any other diffusible agent, are unable to transfer functional Cas9 (either EV-tethered or untethered) and induce significant luciferase activity in acceptor cells. The authors suggest a plausible model by which Cas9 (with the gRNA?) gets "stuck" in such vesicles and is thus unable to enter the nucleus to edit the gene.

To test alternative pathways of transfer, e.g. by direct cell-cell contact, the authors co-cultured donor and acceptor cells and detect significant luciferase activity. The split GFP assay also showed successful transfer. The authors further characterize this process by biochemical, genetic and imaging approaches. They conclude that a small percentage of cells in the population produce open-ended membrane tubules (which are wider and distinct from TNTs) that can transfer material between cells. This process depends on actin polymerization but not endocytosis or trogocytosis. The process also seems to depend on endogenously expressed Syncytin proteins - fusogens which could be responsible for the membrane fusion leading to the open ends of the tubules.

The paper provides additional solid evidence to what is already known about the inefficiency of EV-mediated protein transport. Importantly, it provides an interesting new mechanism for contact-dependent transport of cellular material and assigns valuable new information about the possible function of Syncytins. However, the evidence that the proteins and vesicles transfer through the tubules is incomplete and a few more experiments are required. In addition, certain inconsistencies within the paper and with previous literature need to be resolved. Finally, some parts of the text, methods and the figures require re-writing or additional information for clarity.

Major comments<br /> 1. In Figure 1F, the authors compare the function of exosome-transported SBP-Cas9-GFP vs. transient transfection of SBP-Cas9-GFP. It is not clear if the cells in the transiently transfected culture also express the myc-str-CD63 and were treated with biotin. It is important to determine if CD63-tethering itself affects Cas9 function.<br /> 2. The authors do not rule out that TNTs are a mode of transfer in any of their experiments. Their actin polymerization inhibition experiments are also in-line with a TNT role in transfer. This possibility is not discussed in the discussion section.<br /> 3. Issues with the Split GFP assay:<br /> a. On page 4, line 176, the authors claim that "A mixture of cells before co-culture should not exhibit a GFP signal". However, this result is not presented.<br /> b. The authors show in Figure 2C and F that in MBA/HEK co-culture or only HEK293T co-culture, there are dual-labeled, CFP-mCherry, cells. First - what is the % of this sub-population? Second, the authors dismiss this population as cell adhesion (Page 5, line 192) - but in the methods section they claim they gated for single particles (page 17, line 642), supposedly excluding such events. There is a simple way to resolve this - sort these dual labeled cells and visualize under the microscope. Finally - why do the authors think that the GFP halves can transfer but not the mature CFP or mCherry?<br /> c. In the Cas9 experiments - the authors detect an increase in Nluc activity similar in order of magnitude that that of transient transfection with the Cas9 plasmid - suggesting most acceptor cells now express Nluc. However, only 6% of the cells are GFP positive in the split-GFP assay. Can the authors explain why the rate is so low in the split-GFP assay? One possibility (related to item #2 above) is that the split-GFP is transferred by TNTs.<br /> 4. The membrane tubules, the membrane fusion and the transfer process are not well characterized:<br /> a. The suggested tubules are distinct from TNTs by diameter and (I presume, based on the images) that they are still attached to the surface - whereas TNTs are detached. However, how are these structures different from filopodia except that they (rarely) fuse?<br /> b. Figure 5E shows that the acceptor cells send out a tubule of its own to meet and fuse. Is this the case in all 8 open-ended tubules that were imaged? Is this structure absent in the closed-ended tubules (e.g. as seen in Figures 6 & 8)?<br /> c. The authors suggest a model for transport of the proteins tethered to vesicles (via CD63 tethering). However, the data is incomplete.<br /> i. They show only a single example of this type of transport, without quantification. How frequent is this event?<br /> ii. Furthermore, the labeling does not conclusively show that these are vesicles and not protein aggregates. Labeling of the vesicle - by dye or protein marker will be useful to determine if these are indeed vesicles, and which type.<br /> iii. The data from Figure 2 suggest (if I understand correctly) transfer of the CD63-tethered half-GFP, further strengthening the idea of vesicular transfer. However, the authors also show efficient transfer of untethered Cas9 protein (Figure 2A and other figures). Does this mean that free protein can diffuse through these tubules? The Cas9 has an NLS so the un-tethered versions should be concentrated in the nucleus of donor cells. How, then, do they transfer? The authors do not provide visual evidence for this and I think it is important they would.<br /> iv. In Figures 6 & 8, where transfer is diminished, there are still red granules in acceptors cells (representing CD63-mcherry). Does this mean that vesicles do transfer, just not those with Cas9-GFP? Is this background of the imaging? The latter case would suggest that the red granule moving from donor to acceptor cells in figure 4 could also be "background". This matter needs to be resolved.<br /> 5. Why do HEK293T do not transfer to HEK293T?<br /> a. A major inexplicable result is that HEK293T express high levels of both Syncytin proteins (Figure 7 - supp figure 1A) yet ectopic expression of mouse Syncytin increases transfer (Figure 7E). Why would that be? In addition, Fig 3A shows high transfer rates to A549 cells - which express the least amount of Syncytin. The authors suggest in the discussion that Syncytin in HEK293T might not be functional without real evidence.<br /> b. In addition - previous publications (e.g. PMID: 35596004; 31735710) show that over expression of syncytin-1 or -2 in HEK293T cells causes massive cell-cell fusion. The authors do not provide images of the cells, to rule out cell-cell fusion in this particular case.

Reviewer #3 (Public Review):

In this manuscript, Zhang and Schekman investigated the mechanisms underlying intercellular cargo transfer. It has been proposed that cargo transfer between cells could be mediated by exosomes, tunneling nanotubes or thicker tubules. To determine which process is efficient in delivering cargos, the authors developed two quantitative approaches to study cargo transfer between cells. Their reporter assays showed clearly that the transfer of Cas9/gRNA is mediated by cell-cell contact, but not by exosome internalization and fusion. They showed that actin polymerization is required for the intercellular transfer of Cas9/gRNA, the latter of which is observed in the projected membrane tubule connections. The authors visualized the fine structure of the tubular connections by electron microscopy and observed organelles and vesicles in the open-ended tubular structure. The formation of the open-ended tubule connections depends on a plasma membrane fusion process. Moreover, they found that the endogenous trophoblast fusogens, syncytins, are required for the formation of open-ended tubular connections, and that syncytin depletion significantly reduced cargo Cas9 protein transfer.

Overall, this is a very nice study providing much clarity on the modes of intercellular cargo transfer. Using two quantitative approaches, the authors demonstrated convincingly that exosomes do not mediate efficient transfer via endocytosis, but that the open-ended membrane tubular connections are required for efficient cargo transfer. Furthermore, the authors pinpointed syncytins as the plasma membrane fusogenic proteins involved in this process. Experiments were well designed and conducted, and the conclusions are mostly supported by the data. My specific comments are as follows.

1. The authors showed that knocking down actin (which isoform?) in both donor and acceptor cells blocked transfer, and more so in the acceptor cells perhaps due to the greater knockdown efficiency in these cells. However, Arp2/3 complex knockdown in donor cells, but not recipient cell, reduced Cas9 transfer. It would be good to clarify whether the latter result suggests that the recipient cells use other actin nucleators rather than Arp2/3 to promote actin polymerization in the cargo transfer process. Are formins involved in the formation of these tubular connections?<br /> 2. The authors provided convincing evidence to show that the tubular connections are involved in cargo transfer. Intriguingly, in Figure 4-figure supplement video (upper right), protein transfer appeared to occur along a broad cell-cell contact region instead of a single tubular connection. How often does the former scenario occur? Is it possible that transfer can happen as long as cells are contacting each other and making protrusions that can fuse with the target cell?<br /> 3. The requirement of MFSD2A in both donor (HEK293T) and recipient (MDA-MB-231) cells is consistent with a role for syncytin-1 or 2 in both types of cells. Since HEK293T cells contain both syncytins and MFSD2A but cargo transfer does not occur among these cells, does this suggest that syncytins and/or MFSD2A are only trafficked to the HEK293T cell membrane in the presence of MDA-MB-231 cells?

Reviewer #1 (Public Review):

The authors conducted a case-control study in the NHANES database and found that women who tested positive for HPV infection had lower bone mineral density (BMD) measures at the spine and at the hip. A major strength is the novelty of the association that they are reporting. Major weaknesses include not controlling for covariates that might account for the association between HPV and osteoporosis; unclear definition of the hip (described as "leg") BMD; and unclear methodology used for the propensity score matching and correlations. These weaknesses mean that it is unclear whether the authors' results support their conclusions. The impact of the work on the field and the utility of the methods and data to the community is therefore limited.

Reviewer #2 (Public Review):

To explore their dataset, the authors first identify all eligible women (n = 4673) in the database queried and use propensity score matching (PSM) to match group A (not infected by HPV) with group B (infected by HPV) for several covariates thought to affect bone mineral density (e.g.: age, smoking, alcohol). After PSM, no significant difference for selected covariates can be detected between the two groups.

Because they add matched their groups for relevant covariates possibly affecting bone mineral density, the authors then use Welch two-sample t-test to compare bone mineral densities of leg and lumbar spine between group A and group B, and detect significantly lower bone mineral densities for participants infected by HPV, group B. Here, the statistical approach chose by the author seems limited, and although PSM had been applied to match group earlier in the analysis pipeline, the reader could expect the statistical approach to be more robust, i.e. accounting for other covariates, like a linear mixed model.

Then, the authors analyse each HPV subtype independently and use Kendall's tau-b correlation test to estimate a correlation between a given HPV subtype and bone mineral density. To apply this test, the authors had to transform the bone mineral density to a binary variable, i.e. greater or equal to 1. Here again, the statistical approach does not control for any of the bone mineral density potentially affecting covariates. Also, the authors' study performed 32 Kendall's tau-b correlation tests and did not seem to correct for multiple testing.

Finally, the authors use the Restricted cubic spline model to establish a non-linear relationship between the number of infected HPV subtypes and bone mineral density.

The authors had set the aim to explore the association between HPV and bone mineral density. Unfortunately, due to possibly not high enough robustness of statistical approaches used in this manuscript, it does not seem sufficient to establish a clear association between HPV infection status and a lower bone mineral density. However, given the database the authors have created, it is believed that they have all the tools needed to pursue their aim.

Reviewer #1 (Public Review):

This study investigated how changes in spatial stimulus statistics affect neuronal tuning properties in the barn owl, a well-studied model organism of spatial processing and sound localization. The authors utilized the fact that the owls' facial ruff significantly affects the reliability of binaural cues at specific frequencies. To this end, they compared the tuning to frequency and interaural time differences (ITD) of midbrain (ICX) neurons in adult owls with intact or removed ruff and juvenile owls (with undeveloped ruff). They find that frequency preference is lowered at frontally tuned neurons in the absence of intact /fully developed facial ruff, in accordance with the notion that ITD reliability is lowered for higher frequencies by the lack of ruff. Likewise, they find that ITD tuning width is increased in juvenile and ruff-removed owls, providing further indications for a lowered frequency preference (because ITD tuning width is correlated with wavelength). While the authors cannot provide causal evidence that ITD reliability is the driver for these experience-dependent changes, the data is very consistent with this interpretation. Thus, the conclusions are mostly well supported and will add interesting aspects to our understanding of spatial (ITD) coding and the role of stimulus statistics in general. Nonetheless, a few questions should be clarified that would strengthen the conclusions in my opinion:

1) It would be helpful to include some sort of comparison in Fig. 4, e.g. the regressions shown in Fig 3, to indicate to what extent the ICCl data corresponds to the "control range" of frequency tuning.<br /> 2) A central hypothesis of the study is that the frequency preference of the high-frequency neurons is lower in ruff-removed owls because of the lowered reliability caused by a lack of the ruff. Yet, while lower, the frequency range of many neurons in juvenile and ruff-removed owls seems sufficiently high to be still responsive at 7-8 kHz. I think it would be important to know to what extent neurons are still ITD sensitive at the "unreliable high frequencies" even if the CFs are lower since the "optimization" according to reliability depends not on the best frequency of each neuron per se, but whether neurons are less ITD sensitive at the higher, less reliable frequencies.<br /> 3) It would be interesting to have an estimate of the time scale of experience dependency that induces tuning changes. Do the authors have any data on this question? I appreciate the authors' notion that the quantifications in Fig 7 might indicate that juvenile owls are already "beginning to be shaped by ITD reliability" (line 323 in Discussion). How many days after hearing onset would this correspond to? Does this mean that a few days will already induce changes?

Reviewer #2 (Public Review):

This study investigates whether frequency tuning in the avian auditory midbrain is changed by the reliability of a key sound localization cue (Interaural Time Differences, ITDs) during development. It tests whether auditory neurons become more sensitive to sound frequencies that provide more reliable information about ITDs.

To manipulate the reliability of ITDs in a frequency-specific way, the authors removed the facial ruff of barn owls during development, which alters the acoustical input available to the animal in a number of important ways. When these animals reached adulthood, electrophysiological recordings were performed in the external nucleus of the inferior colliculus (ICx). Compared to control animals, these recordings revealed a weaker relationship between the best-frequency and best-ITD of individual neurons. A similarly weak relationship was observed in young animals whose ruff had not yet fully developed.

These results arise partly because animals without a facial ruff possess neurons with a best ITD of 0 that are tuned to unusually low frequencies. Having considered a number of possible explanations, the authors argue that this occurs because facial ruff removal reduces the reliability of high-frequency ITDs for frontal locations. Consequently, neurons tuned to frontal locations shift their frequency sensitivity to lower frequencies, which provides more reliable information about ITD. This shift toward lower frequencies is also thought to partly explain changes in tuning width that are observed in the absence of a facial ruff.

The study concludes that these results collectively provide evidence that the brain learns to implement probabilistic coding of sound location during development. However, although the study clearly shows changes in neural tuning in the absence of a fully developed facial ruff, the causal link with ITD reliability is complicated by a number of technical issues. The most important of these include a tendency to ignore the rear hemifield for some analyses but not others, the complex acoustical effects of facial ruff removal, and a model of IPD reliability that may or may not accurately reflect real-world listening. Nevertheless, the study presents an interesting set of results and shows an innovative approach in a number of places.

ACOUSTICS: A key strength of the study is its attempt to quantify the reliability of ITDs, which forms the foundation for the rest of the study. However, it is not entirely clear whether the method used for calculating ITD reliability is the most appropriate, and the way the data are presented raises a number of questions.<br /> 1) Why is IPD variability plotted instead of ITD variability (or indeed spatial reliability)? The relationship between these measures is likely to vary across frequency, which makes it difficult to compare ITD variability across frequency when IPDs are plotted. Normalizing data across frequencies also makes it difficult to compare different locations and acoustical conditions. For example, in Fig.1a and Fig.1b, the data shown for 3 kHz at ~160 degrees seems quantitatively and visually quite different, but the difference (in Fig.1c) appears to be negligible.

2) How well do the measures of ITD reliability used reflect real-world listening? For example, the model used to calculate ITD reliability appears to assume the same (flat) spectral profile for targets and distractors, which are presented simultaneously with the same temporal envelope, and a uniform spatial distribution of sounds across space. It is therefore unclear how robust the study's results are to violations of these assumptions.

3) Does facial ruff removal produce an isolated effect on ITD variability or does it also produce changes in directional gain, and the relationship between spatial cues and sound location? Although the study considers this issue in some places (e.g. Fig.2, Fig.5), a clearer presentation of the acoustical effects of facial ruff removal and their implications (for all locations, not just those to the front), as well as an attempt to understand how these acoustical changes lead to the observed changes in ITD reliability, would greatly strengthen the study. In addition, Fig.1 shows average ITD reliability across owls, but it would be helpful to know how consistent these measures are across owls, given individual variability in Head-Related Transfer Functions (HRTFs). This potentially has implications for the electrophysiological experiments, if the HRTFs of those animals were not measured. One specific question that is potentially very relevant is whether the facial ruff attenuates sounds presented behind the animal and whether it does so in a frequency-dependent way. In addition, if facial ruff removal enables ILDs to be used for azimuth, then ITDs may also become less necessary at higher frequencies, even if their reliability remains unchanged.

ELECTROPHYSIOLOGY: The electrophysiological recordings in young owls are impressive, particularly since they were done longitudinally (although the follow-up data in adults is not shown). The decision to look at the relationship between different tuning properties following different types of developmental experience (e.g. relationship between best ITD and best frequency in the absence/presence of a fully developed facial ruff) is also a major strength, particularly in light of the very interesting results observed. The authors have succeeded in identifying clear evidence for the importance of acoustical input for determining frequency-tuning properties in the auditory midbrain. However, a number of points remain unclear.

1) It is unclear why some analyses (Fig.5, Fig.7) are focused on frontal locations and frontally-tuned neurons. It is also unclear why neurons with a best ITDs of 0 are described as frontally tuned since locations behind the animal produce an ITD of 0 also. Related to this, in Fig.1, facial ruff removal appears to reduce IPD variability at low frequencies for locations to the rear (~160 degrees), where the ITD is likely to be close to 0. Neurons with a best ITD of 0 might therefore be expected to adjust their frequency tuning in opposite directions depending on whether they are tuned to frontal or rearward locations.

2) The study suggests that information about high-frequency ITDs is not passed on to the ICX if the ICX does not contain neurons that have a high best frequency. However, neurons might be sensitive to ITDs at frequencies other than the best frequency, particularly if their frequency tuning is broader. It is also unclear whether the best frequency of a neuron always corresponds to the frequency that provides the most reliable ITD information, which the study implicitly assumes.

Joint Public Review

Although several biochemical pathways have been proposed for doxorubicin-induced cardiotoxicity, the exact causal mechanisms remain elusive. Enhanced knowledge of these mechanisms would allow the identification of new therapeutic targets to prevent doxorubicin cardiac adverse effects and thus, extend its use in cancer treatment. Mazevet et al. investigated the role of the exchange protein directly activated by cAMP (EPAC) in doxorubicin-induced cardiotoxicity. The authors found that doxorubicin elicited an increase in EPAC1 isoform expression and activity in neonatal cardiac myocytes and that EPAC1 genetic and pharmacological inhibition successfully reduced doxorubicin-induced DNA damage, mitochondrial dysfunction, and apoptotic cell death. These findings were confirmed in in vivo studies using EPAC1 KO mice, which did not show the deteriorated cardiac function observed in WT mice after doxorubicin treatment. Moreover, the authors showed that doxorubicin-induced cytotoxicity in two cancer cell lines was not altered or even potentiated by pharmacological EPAC1 inhibition. Overall the results of this paper suggest that EPAC1 inhibition is a novel strategy to alleviate doxorubicin-induced cardiotoxicity.

Reviewer #1 (Public Review):

This manuscript presents a fascinating "connectome" dataset of the Octopus vulgaris vertical lobe (VL), a brain region involved in learning and memory with a unique structure. It presents the cell types and connectivity of several major classes of cells in this region. One of the most notable findings is that the most numerous neurons, the SAMs, receive only one synaptic input, while another much less numerous class, the CAMs, receive many. Both of these feed onto an output layer of neurons named LNs. This organization is strikingly different from many other associative learning areas in other species.

Overall, the paper presents an interesting and important collection of anatomical results that will be of interest to those working on this system, as well as (at least at first glance) related systems like the insect mushroom body or mammalian cerebellum. The authors do a good job of highlighting the key properties of this system and contrasting them to other systems. My detailed suggestions are largely about the presentation, but I do have some conceptual comments.

This paper raises an interesting question about learning signals. The most intriguing property of this system is the one-to-one convergence, plasticity, and apparently linear input/output function of the SFL-to-SAM relay. These properties suggest that, unlike structures like the insect mushroom body or mammalian cerebellum, in which the intermediate layer is thought to increase the dimensionality of the representation, the SAMs should be thought of more like the weights of a linear readout of the SFL inputs by the LNs. What learning signal guarantees appropriate weight changes? In a few places (the section on "associativity" and the section on AFs), it is suggested that SAMs can themselves, through coordinated local activity, cause LTP, which the authors call "self LTP-induction." But what is the purpose of such plasticity? It doesn't seem like it would permit, for example, LTP which associates a pattern of SFL activity with the appropriate LNs for the correct vs. the incorrect action. Presumably, appropriately routed information from the NMs and AFs sends the appropriate learning signals to the right places. Does the pattern of innervation of NMs and AFs reveal how these signals are distributed across association modules? Does this lead to a prediction for the logic of the organization of the association modules?

One challenge for a reader who is not an expert on the VL is that the manuscript in its present form lacks discussion about the impact (or hypothesized impact) of the VL on behavior. There is a reference to a role for LNs suppressing attack behavior, but a more comprehensive picture of what the readout layer of this system is likely controlling would be helpful.

The authors do a thorough job of characterizing the "fan-out" architecture from SFL axons to SAMs and CAMs. A few key numbers remain to characterize the "fan-in" architecture of LNs. There appears to be a 400:1 convergence from AMs to LNs. Is it possible to estimate the approximate number of presynaptic inputs per LN? The text around Figure 7 states a median of 162 sites per 100μm dendrite length. One could combine this with an estimate of the total dendritic length for one of these cells from previously available data to estimate the number of inputs per LN. This would help determine the degree of overlap of different association modules in Figure 11, which would be interesting from a computational perspective.

This is an exciting and intriguing set of results that contributes significantly to our knowledge about the brain regions that control learning and memory.

Reviewer #2 (Public Review):

Octopuses are known for their abilities in solving complex tasks and numerous apparently complex cognitive behaviours such as astonishment at octopuses learning how to open jars by watching others and the mind-boggling camouflage. They are very clever molluscs. The octopus shows the famously advanced brain plan but it is one that has little research progress due to its large size and structural complexity. This was originally recognised by the work of BB Boycott, JZ Young, EG Gray, and others in mid last century. Since then, however, little progress has been achieved towards a modern-day description of the octopus neural network particularly in the higher-order brain lobe, despite intense interest and indeed research progress concerning their complex behavioural and cognitive abilities.

This study applied a combination of EM-based imaging, neural tracing, and analyses to start revealing a further detailed view of a part of the lateral gyrus of the vertical lobe (learning and memory centre) of the common European octopus. It is a long overdue contribution and starts to bring octopus neuroscience a step close to the details of some vertebrates achieved. The new findings of neurons and the associated network provide new insights into this very complex but unfamiliar brain, allowing to propose a functional network that may link to the octopus memory formation. Also, this work could be of potential interest to a broad audience of neuroscientists and marine biologists as well as those in bio-imaging and deep-learning fields.

Strengths:<br /> Current knowledge of the neuroanatomy and the associating network of the octopus vertical lobe (learning and memory centre) remains largely based on the pioneering neuroanatomical studies in the '70s, this work indeed provides a rich and new dataset using modern-day imaging technology and reveals numerous previously-unknown neuron types and the resulting further complex network than we thought before. This new dataset reveals hundreds of cell processes from seven types of neurons located in one gyrus of the vertical lobe and can be useful for planning further approaches for advanced microscopy and other approaches including electrophysiological and molecular studies.<br /> Another strength of this study is to apply the current fashion of the deep learning technique to accelerate the imaging process on this octopus complex neural network. This could trigger some inventions to develop new algorithms for further applications on those non-model animals.

Weakness/limitations:<br /> In an effort to match the key claims of the first connectome of the octopus vertical lobe, mapping up an entire vertical lobe is essential. However, also understandably, given challenges in imaging a large-sized brain region, this study managed to image a very small proportion of the anterior part of the lateral gyrus. Along with the current limited dataset, a partially reconstructed neural network of one gyrus, it is unclear whether the wiring pattern found in this study would appear as a similar arrangement throughout an entire lateral gyrus. Furthermore, it is also unknown if another 4 gyri might keep a similar pattern of neural network as it found in the lateral gyrus. Considering some recent immunochemistry evidence that showed distinct different signals in different gyri in terms of heterogeneity of neuron types amongst gryi, to assume this newly-discovered network can represent the wiring pattern across an entire 5-gyrus vertical lobe is inadequate. As this study is the first big step to reveal the complex network in the octopus vertical lobe system, the title may be changed to "Toward connectomics of the Octopus vulgaris vertical lobe - new insights of memory acquisition network".

Reviewer #3 (Public Review):

The manuscript 'Connectomics of the Octopus vulgaris vertical lobe provides insight into conserved and novel principles of a memory acquisition network' by Bidel et al. uncovers the connectivity of the vertical lobe (VL) of the octopus' central brain. Using serial section electron microscopy, the authors report several cell types and connectivity patterns consistent with their previous work and the classic work of Young and Gray. They also uncover novel cell types, including a set of complex amacrine cells (CAMs), with far less abundance compared to simple amacrine cells (SAMs). Importantly, CAMs are proposed to be GABAergic and inhibitory and plausibly suggested to be involved in pattern sharpening - while SAMs are cholinergic and excitatory. SAMs receive single inputs from diverging SFL input, while CAMs receive multiple afferent inputs and additionally pool inputs from SAMs. Both SAMs and CAMs converge onto LNs that form the output layer of the VL. Finally, the authors describe putative neuromodulatory connections.

This study is equally impressive as important - using high-resolution anatomy it uncovers putative computational motifs at high resolution. The described network reveals a novel computational logic and highlights how different biological computational networks can be made up. Indeed, comparison to the Drosophila mushroom bodies - a structure following a fan-out, fan-in logic - will allow more in-depth cross-species comparisons in the future, both regarding commonalities and differences in network architecture. Importantly, this study additionally describes, at high resolution, synaptic motifs (palms) that appear quite different from motifs in other systems, including putative direct feedforward connections via SAMs to CAMs and organelle distributions.

Peer review report

Title: Crossref as a source of open bibliographic metadata

version: 2

Referee: Silvio Peroni

Institution: University of Bologna

email: silvio.peroni@unibo.it

ORCID iD: 0000-0003-0530-4305

General assessment

This article describes an analysis of the Crossref dataset to assess if it can be a rich and reliable source for open bibliographic metadata, considering its central role as a primary source of several Open Science infrastructures such as OpenCitations and OpenAlex. The article is very well-written and addresses an important topic for the community. The analysis focuses mainly on the availability of some metadata, namely reference lists, abstracts, ORCIDs, author affiliations, funding information, and license information. Both data and interactive versions of the figures in the article are openly available online, shared in open formats and appropriately cited, supporting well the reproducibility of the analysis. However, it would be crucial to clarify a few aspects, listed in the section below.

Essential revisions that are required to verify the manuscript

Only a few aspects may need to be clarified in the paper.

1. In Section 3.2, there is a link between the fact that large publishers support I4OC and the fact that we have reached a tipping point of one billion open citations available. However, in the cited paper by Hutchins (2021), while there is a strict reference to I4OC, the reaching of the tipping point was not computed directly using Crossref (which is the dataset discussed in the present article) but by combining the data contained OpenCitations’ COCI (derived from Crossref data) and the NIH Open Citation Collection (derived from PubMed data). Thus, while indeed this result has been reached thanks to the enormous contribution of Crossref data, it was necessary to involve other open collections that do not necessarily involve the same publishers participating in I4OC and releasing their bibliographic references via Crossref.

2. In the case of ORCIDs (page 9), it would be good to clarify whether the availability of such identifiers (and other metadata) in Crossref is full responsibility of the publisher or Crossref makes some inference (e.g. using specific tools and/or external sources) either to fill in fields that are not specified or to validate them (at least at a first syntactic level, e.g. by seeing if the check digit of the ORCID identifier is correct or not).

Other suggestions to improve the manuscript

There are two aspects that, if included, would make the analysis even more robust.

First, the authors explicitly tell us that IEEE references have yet to be considered in the analysis since the Crossref dump they have used precedes the release of IEEE bibliographic references in Crossref. It would be great to re-run everything with a newer dump to address this lack, if feasible, considering the publisher's importance and dimension in terms of publications.

Second, it is clear that the authors have developed some tools (e.g. scripts, software, queries) they used to parse the Crossref dump and extract the relevant information from it. However, there is no mention of such tools in the paper. Having them available as open-source material would be ideal since they implement the methods that have been used for processing data and gathering the statistics introduced in the article. Indeed, such a code's availability would increase the analysis's reproducibility. Therefore, even if it is not mandatory for the narrative of the article, I would suggest (if feasible and legally allowed) publishing such sources with a readme file that explains how to run them, providing them with a persistent identifier (either Software Heritage or GitHub+Zenodo can be used for it), and to cite them properly in the article.

Decision

Verified: The content is scientifically sound, only minor amendments (if any) are suggested.

https://www.thecrimson.com/article/2023/2/2/donovan-forced-leave-hks/

This is a massive loss for HKS, but a potential major win for the school that picks the project up.

It seems to be a sad use of "rules" to shut down a project which may not jive with an administrations' perspective/needs.

Read on Fri 2023-02-03 at 7:14 PM

Reviewer #1 (Public Review):

Insect chemosensory receptors function as ligand-gated ion channels, while vertebrate and nematode chemoreceptors are G-protein coupled receptors. This difference led to multiple questions. One was whether there are vertebrate homologs of insect chemosensory receptors or receptor-like proteins. This manuscript of Benton and Himmel titled "Structural screens identify candidate human homologs of insect chemoreceptors and cryptic Drosophila gustatory receptor-like proteins" addressed this key question. First, it showed consistent results using the new tool for protein structure prediction, AlphaFold2, and confirmed the previously identified OR, GR, GRL, and DUF proteins in the 7TMIC superfamily as structural homologs of Orco. Then the authors identified human/vertebrate homologs: PHTF, but the function of this protein is not clear. Finally, they further expanded drosophilid-specific GRL proteins. It is great to see new members of the 7TMIC superfamily!

Reviewer #2 (Public Review):

The chemosensory systems of vertebrates and insects share a lot of structural and functional similarities. However, looking deeper into their molecular components reveals that these similarities likely represent remarkable examples of convergent evolution. For instance, receptor molecules that detect odors are unrelated between vertebrates and insects - vertebrates use G-protein coupled receptors while insects use ligand-gated ion channels. The latter was long regarded as specific to insects, but later studies identified putative homologs in other animals, (but not in vertebrates), some unicellular eukaryotes, and plants, raising the possibility that it is an ancient family. Still, the evolution of this protein family is notoriously difficult to analyze due to a high degree of sequence divergence between the genes despite the shared structural features of the proteins they encode. Here, the authors make use of the recent explosion of high-quality structural predictions produced by AlphaFold to conduct a deep search for previously undiscovered homologs of insect odorant and gustatory receptors.

The study describes two major findings:<br /> 1. In contrast to the previous idea that vertebrates lack any homologs of the insect receptors, two proteins in vertebrates turn out to display a similar structure (Fig. 2B).<br /> 2. The authors describe a previously uncharacterized family of Drosophila "gustatory receptor-like" proteins with a putative function in chemoreception as suggested by expression data (Fig 3A, G).

All analyses are extremely thorough, the logic of the narrative is very clear, and I find all conclusions well supported by data. The authors clearly favor a hypothesis that the family that includes insect odorant and gustatory receptors has a very deep evolutionary origin, and the homologous genes in other animals and non-animals have strongly diverged at the level of the sequence but retained detectable structural homology. However, they also acknowledge the limitations of some of their arguments and they discuss an alternative whereby the observed structural similarity is the result of convergence (which would be equally interesting). Overall, this study represents a major advance in our understanding of protein evolution and opens several avenues of research into the question of how functional demands steer the preservation of structural features of proteins while allowing their amino acid sequences to diverge.

Reviewer #3 (Public Review):

The Odorant Receptor and Gustatory Receptor families of 7 Transmembrane domain Ion channels were previously believed to have no family members in vertebrates. This paper uses the recent advances in protein folding prediction tools to first validate previous discoveries and confirm their approach with genes of known function. They then search for new family members and discover additional related genes in insects, where both ORs and IRs were previously known to exist. The most striking finding of the paper is that they identify genes related to these protein families in vertebrates, including humans. They propose a model for the evolution of this gene family based on their data.

Overall, the data in this paper is strong, the data presentation is clear and the text is well-written and scholarly. The main weaknesses of the paper are that they have no functional analysis of any of their newly discovered proteins. This paper would benefit from experimental evidence that these are functional ligand-gated ion channels. The authors discuss this limitation at the end of the paper and note the challenges that conducting a functional analysis of these channels would represent. We agree that this could take years and that it is beyond the scope of the current paper, although we eagerly await a follow-up study where those experiments might be done.

Reviewer #1 (Public Review):

The authors sequence some of the oldest maize macroremains found to date, from lowland Peru. They find evidence that these specimens were already domesticated forms. They also find a lack of introgression from wild maize populations. Finally, they find evidence the Par_N16 sample already carried alleles for lowland adaptation.<br /> Overall I think this is an interesting topic, the study is well-written and executed for the most part.

I have a variety of comments, most important of which revolve around methodological clarity. I will give those comments first.

The authors should say in the Results section how "alleles previously reported to be adaptive to highlands and lowlands, specifically in Mesoamerica or South America" were identified in Takuno et al. 2015. What method was used? I see this partly comes in the Discussion eventually, but it would help to have it in the Results with more detail. The answer to this question would help a skeptical reader decide the appropriateness of the resource, given that many selection scans have been performed on maize genomes, the choice would ideally not be arbitrary.

How were the covered putative adaptive SNPs distributed in the genome? Were any clustered and linked? The random sampled SNPs should be similarly distributed to give an appropriate null.

How is genetic similarity calculated? It should be briefly described in the Results.

It would help for the authors to state why they focus on Par_N16, I did not see this in my reading. Presumably, the analyses done are because of the higher quality data, but it would also help to mention why Par_N16 was sequenced in an additional run.

In the sections on phylogenetic analysis, introgression, and D statistics, the authors could do a better job specifically indicating how the results support their conclusions.

Reviewer #2 (Public Review):

In this foundational article, the authors conduct an ancient DNA characterization of maize unearthed in archaeological contexts from Paredones and Huaca Prieta in the Chicama river valley of Peru. These maize specimens were recovered by painstakingly controlled excavation. Their context would appear to be beyond reproach though the individual radiocarbon determinations should be subject to further scrutiny.

Radiocarbon determination for at least one of the maize cobs analyzed for aDNA is not a direct date, but dates associated material. The authors should provide a table of the direct dates on the specimens that were analyzed for ancient DNA. They should also specify the type and quantity of material sent and whether the cob, glumes, pith, or husks were submitted for dates. Include δ13C determinations for each cob with laboratory analysis numbers because there is justifiable concern that at least one of these cob dates has a δ13C value suggesting the material dated is not maize. Generally, the δ13C for maize ranges from -14 to -7. One or more of the specimens subjected to ancient DNA analysis in this paper have δ13C values far outside of this confidence interval.

From the perspective of future scientists being able to repeat the analyses performed here, I would hope that all details of specimen treatment, extraction methods, read length and quality would need to be assiduously described. Routine analytical results should be reported so that comparisons with earlier and future results are facilitated, and not made difficult to decipher or search for.

The aDNA analysis may or may not be affected by the anomalous δ13C values but one would anticipate that standard aDNA extraction and analysis protocols would provide a means by which the specimen's preservation of the specimens could be ascertained, for example, perhaps deamination and fragmentation rates could be compared or average read length evaluated with modern-contemporary materials so that preservation of the Paredones samples relative to that of maize in the CIMMYT germplasm bank and the San Marcos specimens investigated by the same researchers can be evaluated.

The size and shape of the cobs depicted are similar to specimens occurring much later in Mesoamerican assemblages. For example, the approximate rachis diameter of the San Marcos specimens depicted by Valle-Bueno et al. (2016: Fig.1) averages less than 0.5cm while the specimens depicted in Valle-Bueno et al. (this manuscript) average 1.0 cm. The former - San Marcos - specimens are dated at 5300-4970 BP cal while the larger - Paredones - specimens date roughly 6777 - 5324 BP cal. The considerable disparity among the smaller more recent specimens compared to the very much larger putatively older specimens suggests the Paredones specimen's radiocarbon determinations are equivocal. The authors point this out but repeatedly state these cobs are the most ancient; a conundrum that should be resolved.

I would suggest the authors consider redating these three specimens and if they do, hope that they will prepare the laboratory personnel with depositional environment information. MacNeish was skeptical about late dates on maize at Tehuacan, at first. Adovasio was initially certain about maize's associated dates from Meadowcroft. One would prefer to be reasonably certain the foundation this article creates is solid; the author's repeated reference to these cobs as the most ancient in the Americas should be reaffirmed so retraction will not be necessary.

Reviewer #1 (Public Review):

The authors investigated state-dependent changes in evoked brain activity, using electrical stimulation combined with multisite neural activity across wakefulness and anesthesia. The approach is novel, and the results are compelling. The study benefits from an in-depth sophisticated analysis of neural signals. The effects of behavioral state on brain responses to stimulation are generally convincing.

It is possible that the authors' use of "an average reference montage that removed signals common to all EEG electrodes" could also remove useful components of the signal, which are common across EEG electrodes, especially during deep anesthesia. For example, it is possible (in fact from my experience I would be surprised if it is not the case) that under isoflurane anesthesia, electrical stimulation induces a generalized slow wave or a burst of activity across the brain. Subtracting the average signal will simply remove that from all channels. This does not only result in signals under anesthesia being affected more by the referencing procedure than during waking but also will have different effects on different channels, e.g. depending on how strong the response is in a specific channel.

Reviewer #2 (Public Review):

This study reports a novel role of thalamic activity in the late components of a cortical event-related potential (ERP). To show this association, the authors used high-density EEG together with multiple deep electrophysiological recordings combined with electrical stimulation of superficial and deep cortical layers. Stimulation of deep layers elicits a late ERP component that is closely related to bursts of thalamic activity during quiet wakefulness. This relationship is quite noticeable when deep layers of the cortex are stimulated, and it does depend on the arousal state, being maximal during quiet wakefulness, diminished during active wakefulness, and absent during anesthesia.

The study is very well performed, with a high number of subjects and appropriate methodology. Performing simultaneous recording of EEG and several neuropixels probes together with cortical microstimulation is no small feat considering the size of the mouse head and the fact that mice are freely behaving in many of the experiments. It is also noticeable how the authors use a seemingly outdated technique (electrical microstimulation) to produce compelling and significant research. The conclusions regarding the thalamic contributions to the ERP components are strongly supported by the data.

The spatiotemporal complexity is almost a side point compared to what seems to be the most important point of the paper: showing the contribution of thalamic activity to some components of the cortical ERP. Scalp ERPs have long been regarded as purely cortical phenomena, just like most EEGs, and this study shows convincing evidence to the contrary.

The data presented seemingly contradicts the results presented by Histed et al. (2009), who assert that cortical microstimulation only affects passing fibers near the tip of the electrodes, and results in distant, sparse, and somewhat random neural activation. In this study, it is clear that the maximum effect happens near the electrodes, decays with distance, and is not sparse at all, suggesting that not only passing fibers are activated but that also neuronal elements might be activated by antidromic propagation from the axonal hillock. This appears to offer proof that microstimulation might be much more effective than it was thought after the publication of Histed 2009, as the uber-successful use of DBS to treat Parkinson's disease has also shown.

Reviewer #1 (Public Review):

This manuscript studies the representation by gender and name origin of authors from Nature and Springer Nature articles in Nature News. The representation of author identities is an important step towards equality in science, and the authors found that women are underrepresented in news quotes and mentions with respect to the proportion of women authors.

Strengths:

The research is rigorously conducted. It presents relevant questions and compelling answers. The documentation of the data and methods is thoroughly done, and the authors provide the code and data for reproduction.

Weaknesses:

The article is not so clearly structured, which makes it hard to follow. A better framing, contextualization, and conceptualization of their analysis would help the readers to better understand the results. There are some unclear definitions and wrong wording of key concepts.

Reviewer #2 (Public Review):

This paper set out to investigate disparities in how authors of scientific papers are quoted in the context of science journalism. Quotations, the authors argue, reveal who a science journalist approaches as a source and thus who is considered an expert. At the same time, quotation in the news legitimizes experts and signals the importance of their perspective and opinions. It is therefore important to identify disparities in a quotation, both as a matter of justice and to ensure the representation of diverse viewpoints in journalism.

Here, the authors investigate disparities in quotation based on the gender and national origin of experts. They focus on science journalism in non-research articles published in the journal Nature. Articles are scraped from the Nature website and using established NLP tools the article content is parsed for quotations and the names of scientists being quoted. The gender and national origin of scientists are inferred based on their names and gendered pronouns used in the text. The rates of quotation based on gender/national origin are then compared to the demographics of authors (also inferred) of research articles published in Nature; this establishes a baseline to compare who is quoted vs. who is actually doing research. Based on these data, a variety of analyses are presented showing various aspects of bias and disparity in who is quoted in science journalism.

From their analysis, the authors make the following claims:

• Authors inferred as men were over-represented in quotations in journalistic Nature articles relative to their share of first and last authors in Nature.

• A quotation is sharply trending towards gender parity, with variation by the type of article.

• Authors with names inferred as originating from Celtic/English regions were over-represented, whereas authors with names inferred as originating from East Asia were heavily under-represented in quotations.

• The representation of authors with inferred East Asian names has increased faster among the last authors of research articles in Nature than it has in a journalistic quotation.

Claims 2-4 are solidly supported by the evidence presented in the manuscript. Claim 1 is supported by the evidence, but with some caveats. Support for Claim 1 depends on whether Nature's first or last authors are the most appropriate comparison set; if the last authors are the most appropriate, then Claim 1 only holds for 2005 through 2010. I expand on this point below.

I praise the manuscript and the authors for their commitment to reproducibility. Supplied with the paper is all the data (where possible) and code necessary to reproduce the results, as well as a Docker image that ensures that it can be re-executed far into the future.

The analyses conducted are methodologically rigorous. The authors provide bootstrapped confidence intervals for all analyzed values, choose appropriate baselines, and validate their name inference approach. In addition, I found their analysis comprehensive. By this I mean that they sufficiently explored their data to support their claims; nearly every caveat or limitation I could think of while reading was appropriately addressed either in the main or in a supplemental figure or table.

While a good paper, it is not without weaknesses. The paper is generally well-written, and the visualizations do a good job of communicating results. There is, of course, room to improve on both. In some cases, the manuscript lacks consistency in terminology, and uses word choice that is strange (e.g., "enrichment" and "depletion" when discussion representation). While this paper is methodologically rigorous and professional in its presentation, I feel that the authors could have done a better job of interpreting and contextualizing their findings. Specifically, readers should be aware of the caveats regarding Claim 1 (listed above), the limits of generalizing these findings to other areas of science journalism, and a somewhat shallow discussion section that I believe detracts from the study's significance. I outline these points in more detail below.

Despite these quibbles, the authors find solid support for their claims and achieve their goals. This paper, I believe will be of general interest to scientists and science communicators, to those interested in science communication as a field, to meta-scientists, and to those aiming to improve diversity and equity in the scientific process.

Caveats to Claim Claim 1:

One of the claims made by the authors (Claim 1) is that quotations in the dataset skew towards men. I find this true, but with two related caveats: that it depends on the choice of comparator set, and that it changes over time.

The authors assess the representation of quotation by comparison to either Nature's first authors, or last authors. However, the authors do not discuss whether one is more appropriate, and what is implied if, say, quotations match the last author but not the first authors. In most scientific fields, the last author corresponds to the conceptual lead of a paper and is often the corresponding author who is most likely to be contacted to discuss the paper's significance. First authors, in contrast, will often represent the "driver" of the project-basically the person doing most of the actual work and is usually a student or more junior researcher. This distinction is important because cases could be made for either being a more appropriate comparator - last authors due to their seniority, first authors due to their closeness to the study, and (typically) greater diversity.

The choice of comparator set becomes an issue because, as per Claim 2, the representation of women is increasing over time. Claim 1 only holds for the last authors from 2005 through 2010, and after 2018 women have higher representation given the demographics of the last authors. For the first authors, Claim 1 holds through 2017, after which they are representative or slightly over-representative of women authors.

So while Claim 1 holds, it does not hold for all comparator sets and for all years. I don't think this is critical of the paper-the authors do discuss the trend in Claim 2-but interpretation of this claim should take care of these caveats, and readers should consider the important differences in first and last authorship.

Generalizability to other contexts of science journalism:

Journalistic articles in Nature may not be representative of all contexts of science journalism. Nature has a unique readership, consisting of scientists from many disciplines who have not only a generalist interest in science but also an interest in aspects of science as a profession. Science journalism as a whole, however, is part of the broader landscape of mainstream media, consisting of outlets such as ABC, BBC, and Scientific American. The audiences for these outlets will be more general, less interested in science as a career, and will likely have a different appetite for direct quotations and for more technical topics.

This does not make the study bad. On the contrary, the author's focus on Nature allowed for many interesting analyses-but their findings should still be understood as coming from a specific context. While the authors outline many limitations of their study, they do not grapple with the limits of its generalizability, and what aspects of their analysis might translate to other contexts of science journalism. For example, part of the trend towards gender parity in a quotation is explained by the higher representation of women in the "Career Feature" article type. However, this article type will likely not be present in more general-interest contexts, which would affect the representation of women.

Shallow discussion:

I feel that the authors missed an opportunity to use their discussion to not only properly contextualize their results, but also explore their significance. In broad terms, there is literature on science journalism, its consequences for science, and the impact on public perceptions, as well as a continuous meta-discourse on journalistic ethics and best practices. The authors pay lip service to some of these themes but do little to actually place their findings in the broader discourse. Below, I provide a few specific points that could be further discussed:

What might be the downstream impacts on the public stemming from the under-representation of scientists with East Asian names?

The authors highlight gender parity in career features, but why exactly is there gender parity in this format of Representation in quotations varies by first and last author, most certainly as a result of the academic division of labor in the life sciences. However, what does it say about the scientific quotation that it appears first authors are more often to be quoted? Does this mean that the division of labor is changing such that the first authors are the lead scientists? Or does it imply that senior authors are being skipped over, or giving away their chance to comment on a study to the first author?

Moreover, there are several findings in the study which are notable but don't seem to have been mentioned at all in the discussion.

Below I highlight a few:

• According to Figure 3d, not only are East Asian names under-represented in quotations, but they are becoming more under-represented over time as they appear as authors in a greater number of Nature publications.

• Those with European names are proportionately represented in quotations given their share of authors in Nature. Why might this be, especially seeing as Anglo names are heavily over-represented?

Reviewer #3 (Public Review):

In this study, the authors present the first comprehensive transcriptome map of the human locus coeruleus using two independent but complementary approaches, spatial transcriptomics and single nucleus RNA sequencing. Several canonical features of locus coeruleus neurons that have been described in rodents were conserved, but potentially important species differences were also identified. This work lays the foundation for future descriptive and experimental approaches to understand the contribution of the locus coeruleus to healthy brain function and disease.

This study has many strengths. It is the first reported comprehensive map of the human LC transcriptome, and uses two independent but complementary approaches (spatial transcriptomics and snRNA-seq). Some of the key findings confirmed what has been described in the rodent LC, as well as some intriguing potential genes and modules identified that may be unique to humans and have the potential to explain LC-related disease states. The main limitations of the study were acknowledged by the authors and include the spatial resolution probably not being at the single cell level and the relatively small number of samples (and questionable quality) for the snRNA-seq data. Overall, the strengths greatly outweigh the limitations. This dataset will be a valuable resource for the neuroscience community, both in terms of methodology development and results that will no doubt enable important comparisons and follow-up studies.

Major comments:

Overall, the discovery of some cells in the LC region that express serotonergic markers is intriguing. However, no evidence is presented that these neurons actually produce 5-HT.

Concerning the snRNA-seq experiments, it is unclear why only 3 of the 5 donors were used, particularly given the low number of LC-NE nuclear transcriptomes obtained, why those 3 were chosen, and how many 100 um sections were used from each donor. It is also unclear if the 295 nuclei obtained truly representative of the LC population or whether they are just the most "resilient" LC nuclei that survive the process.

The LC displays rostral/caudal and dorsal/ventral differences, including where they project, which functions they regulate, and which parts are vulnerable in neurodegenerative disease (e.g. Loughlin et al., Neuroscience 18:291-306, 1986; Dahl et al., Nat Hum Behav 3:1203-14, 2019; Beardmore et al., J Alzheimer's Dis 83:5-22, 2021; Gilvesy et al., Acta Neuropathol 144:651-76, 2022; Madelung et al., Mov Disord 37:479-89, 2022). It was not clear which part(s) of the LC was captured for the SRT and snRNAseq experiments.

The authors mention that in other human SRT studies, there are typically between 1-10 cells per expression spot. I imagine that this depends heavily on the part of the brain being studied and neuronal density, but it was unclear how many LC cells were contained in each expression spot.

Regarding comparison of human LC-associated genes with rat or mouse LC-associated genes (Fig. 2D-F), the authors speculate that the modest degree of overlap may be due to species differences between rodents and human and/or methodological differences (SRT vs microarray vs TRAP). Was there greater overlap between mouse and rat than between mouse/rat and human? If so, that is evidence for the former. If not, that is evidence for the latter. Also would be useful for more in-depth comparison with snRNA-seq data from mouse LC: https://www.biorxiv.org/content/10.1101/2022.06.30.498327v1.

The finding of ACHE expression in LC neurons is intriguing, especially in light of work from Susan Greenfield suggesting that ACHE has functions independent of ACH metabolism that contributes to cellular vulnerability in neurodegenerative disease.

High mitochondrial reads from snRNA-seq can indicate lower quality. It was not clear why, given the mitochondrial read count, the authors are confident in the snRNA-seq data from presumptive LC-NE neurons.

Reviewer #1 (Public Review):

Weber et al. collect locus coeruleus (LC) tissue blocks from 5 neurotypical European men, dissect the dorsal pons around the LC and prepare 2-3 tissue sections from each donor on a slide for 10X spatial transcriptomics. From three of these donors, they also prepared an additional section for 10x single nucleus sequencing. Overall, the results validate well-known marker genes for the LC (e.g. DBH, TH, SLC6A2), and generate a useful resource that lists genes which are enriched in LC neurons in humans, with either of these two techniques. A comparison with publicly available mouse and rat datasets identifies genes that show reliable LC-enrichment across species. Their analyses also support recent rodent studies that have identified subgroups of interneurons in the region surrounding the LC, which show enrichment for different neuropeptides. In addition, the authors claim that some LC neurons co-express cholinergic markers, and that a population of serotonin (5-HT) neurons is located within or near the LC. These last two claims must be taken with great caution, as several technological limitations restrict the interpretation of these results. Overall, there is limited integration between the spatial and single-nucleus sequencing, thus the data does not yet provide a conclusive list of bona fide LC-specific genes. The authors transparently present limitations of their work in the discussion, but some points discussed below warrant further attention.

Specific comments:

1) snRNAseq:

a. Major concerns with the snRNAseq dataset are A) the low recovery rate of putative LC-neurons in the snRNAseq dataset, B) the fact that the LC neuron cluster is contaminated with mitochondrial RNA, and C) that a large fraction of the nuclei cannot be assigned to a clear cell type (presumably due to contamination or damaged nuclei). The authors chose to enrich for neurons using NeuN antibody staining and FACS. But it is difficult to assess the efficacy of this enrichment without images of the nuclear suspension obtained before FACS, and of the FACS results. As this field is in its infancy, more detail on preliminary experiments would help the reader to understand why the authors processed the tissue the way they did. It would be nice to know whether omitting the FACS procedure might in fact result in higher relative recovery of LC-neurons, or if the authors tried this and discovered other technical issues that prompted them to use FACS.

b. It is unclear what percentage of cells that make up each cluster.

c. The number of subjects used in each analysis was not always clear. Only 3 subjects were used for snRNAseq, and one of them only yielded 4 LC-nuclei. This means the results are essentially based on n=2. The authors report these numbers in the corresponding section, but the first sentence of the results section (and Figure 1C specifically!) create the impression that n=5 for all analyses. Even for spatial transcriptomics, if I understood it correctly, 1 sample had to be excluded (n=4).

2) Spatial transcriptomics:

a. It is not clear to me what the spatial transcriptomics provides beyond what can be shown with snRNAseq, nor how these two sets of results compare to each other. It would be more intuitive to start the story with snRNAseq and then try to provide spatial detail using spatial transcriptomics. The LC is not a homogeneous structure but can be divided into ensembles based on projection specificity. Spatial transcriptomics could - in theory - offer much-needed insights into the spatial variation of mRNA profiles across different ensembles, or as a first step across the spatial (rostral/caudal, ventral/dorsal) extent of the LC. The current analyses, however, cannot address this issue, as the orientation of the LC cannot be deduced from the slices analyzed.

b. Unfortunately, spatial transcriptomics itself is plagued by sampling variability to a point where the RNAscope analyses the authors performed prove more powerful in addressing direct questions about gene expression patterns. Given that the authors compare their results to published datasets from rodent studies, it is surprising that a direct comparison of genes identified with spatial transcriptomics vs snRNAseq is lacking (unless this reviewer missed this comparison). Supplementary Figure 17 seems to be a first step in that direction, but this is not a gene-by-gene comparison of which analysis identifies which LC-enriched genes. Such an analysis should not compare numbers of enriched genes using artificial cutoffs for significance/fold-change, but rather use correlations to get a feeling for which genes appear to be enriched in the LC using both methods. This would result in one list of genes that can serve as a reference point for future work.

c. Maybe the spatial transcriptomics could be useful to look at the peri-LC region, which has generated some excitement in rodent work recently, but remains largely unexplored in humans.

3) The comparison of snRNAseq data to published literature is laudable. Although the authors mention considerable methodological differences between the chosen rodent work and their own analyses, this needs to be further explained. The mouse dataset uses TRAPseq, which looks at translating mRNAs associated with ribosomes, very different from the nuclear RNA pool analyzed in the current work. The rat dataset used single-cell LC laser microdissection followed by microarray analyses, leading to major technical differences in terms of tissue processing and downstream analyses. The authors mention and reference a recent 10x mouse LC dataset (Luskin et al, 2022), however they only pick some neuropeptides from this study for their analysis of interneuron subtypes (Figure S13). Although this is a very interesting part of the manuscript, a more in-depth analysis of these two datasets would be very useful. It would likely allow for a better comparison between mouse and human, given that the technical approach is more similar (albeit without FACS), and Luskin et al have indicated that they are willing to share their data.

4) Statements in the manuscript about the unexpected identification of a 5-HT (serotonin) cell-cluster seem somewhat contradictory. Figure S14 suggests that 5-HT markers are expressed in the LC-regions just as much as anywhere else, but the RNAscope image in Figure S15 suggests spatial separation between these two populations. And Figure S17 again suggests almost perfect overlap between the LC and 5HT clusters. Maybe I misunderstood, in which case the authors should better clarify/explain these results.

Reviewer #2 (Public Review):

The data generated for this paper provides an important resource for the neuroscience community. The locus coeruleus (LC) is the known seed of noradrenergic cells in the brain. Due to its location and size, it remains scarcely profiled in humans. Despite the physically minute structure containing these cells, its impact is wide-reaching due to the known neuromodulatory function of norepinephrine (NE) in processes like attention and mood. As such, profiling NE cells has important implications for most neurological and neuropsychiatric disorders. This paper generates transcriptomic profiles that are not only cell-specific but which also maintain their spatial context, providing the field with a map for the cells within the region.

Strengths:

Using spatial transcriptomics in a morphologically distinct region is a very attractive way to generate a map. Overlaying macroscopic information, i.e. a region with greater pigmentation, with its corresponding molecular profile in an unbiased manner is an extremely powerful way to understand the specific cellular and molecular composition of that brain structure.

The technologies were used with an astute awareness of their limitations, as such, multiple technologies were leveraged to paint a more complete and resolved picture of the cellular composition of the region. For example, the lack of resolution in the spatial transcriptomic platform was compensated by complementary snRNA-seq and single molecule FISH.

This work has been made publicly available and accessible through a user-friendly application such that any interested researcher can investigate the level of expression of their gene of interest within this region.

Two important implications from this work are 1) the potential that the gene regulatory profiles of these cells are only partially conserved across species, humans, and rodents, and 2) that there may be other neuromodulatory cell types within the region that were otherwise not previously localized to the LC

Weaknesses:

Given that the markers used to identify cells are not as specific as they need to be to definitively qualify the desired cell type, the results may be over-interpreted. Specifically, TH is the primary marker used to qualify cells as noradrenergic, however, TH catalyzes the synthesis of L-DOPA, a precursor to dopamine, which in turn is a precursor for epinephrine and norepinephrine suggesting some of the cells in the region may be dopaminergic and not NE cells. Indeed, there are publications to support the presence of dopaminergic cells in the LC (see Kempadoo et al. 2016, Takeuchi et al., 2016, Devoto et al. 2005). This discrepancy is further highlighted by the apparent lack of overlap per given Visium spots with TH, SCL6A2, or DBH. While the single-nucleus FISH confirms that some of the cells in the region are noradrenergic, others very possibly represent a different catecholamine. As such it is suggested that the nomenclature for the cells be reconsidered.

The authors are unable to successfully implement unsupervised clustering with the spatial data, this greatly reduces the impact of the spatial technology as it implies that the transcriptomic data generated in the study did not have enough resolution to identify individual cell types.

The sample contribution to the results is highly unbalanced, which consequently, may result in ungeneralizable findings in terms of regional cellular composition, limiting the usefulness of the publicly available data.

This study aimed to deeply profile the LC in humans and provide a resource to the community. The combination of data types (snRNA-seq, SRT, smFISH) does in fact represent this resource for the community. However, due to the limitations, of which, some were described in the manuscript, we should be cautious in the use of the data for secondary analysis. For example, some of the cellular annotations may lack precision, the cellular composition also may not reflect the general population, and the presence of unexpected cell types may represent the accidental inclusion of adjacent regions, in this case, serotonergic cells from the Raphe nucleus.

Nonetheless having a well-developed app to query and visualize these data will be an enormous asset to the community especially given the lack of information regarding the region in general.

Reviewer #1 (Public Review):

Ibar and colleagues investigate the function of spectrin in Drosophila wing imaginal discs and its effect on the Hippo pathway and myosin activity. The authors find that both βH-Spec and its canonical binding partner α-Spec reduce junctional localization of the protein Jub and thereby restrict Jub's inhibitory effect on Hippo signaling resulting in activation of the Hippo effector Yorkie regulating tissue shape and organ size. From genetic epistasis analysis and analysis of protein localization, the authors conclude that βH-Spec and α-Spec act independently in this regulation. The major point of this study is that the apical localization of βH-Spec and myosin is mutually exclusive and that the proteins antagonize each other's activity in wing discs. In vitro co-sedimentation assays and in silico structural modeling suggest that this antagonization is due to a competition of βH-Spec and myosin for F-actin binding.

The study's strengths are the genetic perturbation that is the basis for the epistasis analysis which includes specific knockdowns of the genes of interest as well as an elegant CRISPR-based overexpression system with great tissue specificity. The choice of the model for such an in-depth analysis of pathway dependencies in a well-characterized tissue makes it possible to identify and characterize quantitative differences between closely entangled and mutually dependent components. The method of quantifying protein localization and abundance is common for multiple figures which makes it easy to assess differences across experiments.

A weakness in the methodology is the link to tissue tension and conclusions about tissue mechanics. Methods that directly affect tissue tension and a more thorough and systematic application of laser ablation experiments would be needed to profoundly investigate mechanosensation and consequential effects on tissue tension by the various genetic perturbations. While the in-silico analysis of competing for F-actin binding sites for βH-Spec and myosin appears logical and supports the authors' claims, no point mutation or truncations were used to test these results in vivo. In its current structure the manuscript's strength, the genetic perturbations, is compromised by missing clear assessments of knockdown efficiencies early in the manuscript and other controls such as the actual effect on myosin by ROCK overactivation.

The flow of experiments is logical and in general, the author's conclusions are supported by the presented data. The findings are very well embedded into the context of relevant literature and both confronting and confirming literature are discussed.

The study shows how components of the cytoskeleton are directly involved in the regulation of the mechanosensitive Hippo pathway in vivo and thus ultimately regulate organ size supporting previous data in other contexts. The molecular mechanism regulating myosin activity by out-competing it for F-actin binding has been observed for small actin-binding proteins such as cofilin but is a new mode for such a big, membrane-associated actin-binding protein. This may inspire future experiments in different morphogenetic contexts for the investigation of similar mechanisms. For example, the antagonistic activity of βH-Spec and myosin in this tissue context might help explain phenomena in other systems such as spectrin-dependent ratcheting of apical constriction during mesoderm invagination (as the authors discuss). Against the classical view, the work shows that βH-Spec can act independently of α-Spec. Together the results will be of interest to the cell biology community with a focus on the cytoskeleton and mechanotransduction.

Reviewer #2 (Public Review):

Ibar and colleagues address the role of the spectrin cytoskeleton in the regulation of tissue growth and Hippo signaling in an attempt to elucidate the underlying molecular mechanism(s) and reconcile existing data. Previous reports in the field have suggested three distinct mechanisms by which the Spectrin cytoskeleton regulates Hippo signaling and this is, at least in part, due to the fact that different groups have mainly focused on different spectrins (alpha, beta, or beta-heavy) in previous reports.

The authors start their investigation by trying to reconcile their previous data on the role of Ajuba in the regulation of Hippo signaling via mechanotransduction and previous observations suggesting that Spectrins affect Hippo signaling independently of any effect on myosin levels or Ajuba localization. Contrary to previous reports, the authors reveal that, indeed, depletion of alpha- and beta-heavy-spectrin leads to an increase in myosin levels at the apical membrane. Moreover, the authors also reveal that the depletion of spectrins leads to an increase in Ajuba levels.

The authors suggest that Ajuba is required for the effect of beta-heavy spectrin. However, it is still formally possible that this could be a parallel pathway that is being masked by the strong phenotype of Ajuba RNAi flies.

One of the major points of the manuscript is the observation that alpha- and beta-heavy-spectrin are potentially working independently and not as part of a spectrin tetramer. This is mostly dependent on the observation that alpha- and beta-heavy-spectrin appear to have non-overlapping localizations at the membrane and the fact that alpha- and beta-heavy-spectrin localize at the membrane seemingly independently. It is not entirely obvious that a potential lack of colocalization and the fact that protein localization at the membrane is not affected when the other partner is absent is sufficient to argue that alpha- and beta-heavy-spectrin do not form a complex. Moreover, it is possible that the spectrin complexes are only formed in specific conditions (e.g. by modulating tissue tension).

If indeed spectrins function independently, would it not be expected to see additive effects when both spectrins are depleted?

Related to the two previous points, the fact that the authors suggest that both alpha- and beta-heavy-spectrin regulate Hippo signaling via Ajuba would be consistent with the necessity of an alpha- and beta-heavy-spectrin complex being formed. How would the authors explain that both spectrins require Ajuba function but work independently?

Another major point of the manuscript is the potential competition between beta-heavy-spectrin and myosin for F-actin binding. The authors suggest that there is a mutual antagonism between the two proteins regarding apical F-actin. However, this has not been formally assessed. Moreover, despite the arguments put forward in the discussion, it seems hard to justify a competition for F-actin when beta-heavy-spectrin seems to be unable to compete with myosin. Myosin can displace beta-heavy-spectrin from F-actin but the reciprocal effect seems unlikely given the in vitro data.

Reviewer #1 (Public Review):

The authors sought to assess how not only RNA but also protein changes across the developmental time course of cortical organoid development. The methods used included reporter lines to label progenitor and neuronal populations, RNA-sequencing, protein quantification using mass spectrometry, and analysis of these results. The primary findings included the identification of RNA sequences that impact translation, the most significant of which was a 5'-TOP cassette that is mediated by mTOR.

Strengths of the paper include strong experimental design, replicates, and images to show the quality of the organoids used in the studies. Additionally, the analysis of elements regulating translation was strong, and the polysome experiments exploring an impact when TSC is deleted were interesting.

Potential limitations include technical challenges related to the specificity of the reporters, ambiguity about the impact of normalization on the actual protein/RNA data, and potential over-interpretation of the TSC result to encompass all of the mTOR signalings.

The paper validates already observed and documented results in translational regulation whereby RNA does not fully predict protein levels. The impact of the specific examples upon functional significance in cortical development is currently unclear but this work could set the stage for additional future impactful work.

Reviewer #2 (Public Review):

This work by Sidhaye, Trepte et al. systematically investigates the relationship between transcript and protein abundance across the genome in human neurogenesis. Through analysis of the transcriptome and proteome in brain organoids, they find that for specific gene modules, transcript and protein abundance are highly disconnected. While there are already several anecdotic examples of this phenomenon in the literature, highlighting the role of post-transcriptional gene regulation in corticogenesis, Sidhaye, Trepte et al. for the first time systematically explore the pervasiveness of this phenomenon in a genome-wide manner at different stages of human neurogenesis using a dual reporter cell line to isolate neural progenitor cells and neurons.

The authors then focus on one of the modules that is characterized by the enrichment of the 5'TOP (terminal oligopyrimidine) motif in the 5'UTR of transcripts and enriched in ribosomal proteins and translation initiation factors. The authors show that partial inhibition of the translation of ribosomal genes in neural progenitor cells inhibits the translation of differentiation genes, a process that involves mTOR-mediated regulation.

Strength:

The integration of transcriptome and proteome data enables an unbiased systemic analysis revealing gene modules that follow similar trajectories, and as such may share common regulatory principles. For one of the modules, the authors dissect the posttranscriptional regulatory cascade using an elegant combination of fluorescent reporter human pluripotent stem cell lines in combination with gene knockouts.

Overall, the data presented in this work is of a very high standard and supports the conclusions put forward by the authors. The processed omics data sets are made available via a Shiny app web interface for easy access and therefore promote exploration by the scientific community.

Limitations:

This study uses a large range of specific reporter and knockout hPSC lines generated in the context of this work, however, very limited information is provided on these lines. For example, do the lines remain karyotypically normal throughout the targeting procedure? Does reporter gene expression faithfully recapitulate the activity of the promoters controlling their expression? Specifically, it appears that a significant GFP signal is detected within the neuronal layer (Figure 1B) and that there is a much larger double reporter-positive population than expected (Figure S2A).

The authors propose that stress-associated translational regulation takes place in early neural progenitors, involving the sequestration of transcripts in stress granule-like structures. However, given that at least some human brain organoid protocols have been reported to lead to ectopic activation of cellular stress pathways (Bhaduri et al., Nature 2019), it would be desirable to see this aspect of the study confirmed in primary tissue (mouse or human).

Reviewer #3 (Public Review):

The manuscript by Sidhaye et al. aims to integrate proteomic and transcriptomic analyses of human stem cell-derived cortical brain organoids to identify post-transcriptional regulatory mechanisms during human cortical development. The authors use an innovative and useful dual-reporter strategy to isolate NPCs and neurons separately and integrate proteomic and transcriptomic analyses in each cell type. The data analysis is robust and identifies gene modules with cell class specificity.

While there is no large overlap between the proteomic and transcriptomic datasets, the authors focus additional experiments on one candidate pathway, mTOR-mediated regulation of translation in progenitors, and validate this pathway's role in progenitor development.

The authors also identified a stress-related role for processes in corticogenesis, although, without comparison to human tissue, it's possible that some of the results are due to the artificial nature of the organoids as they have been reported to have elevated stress (Bhaduri et al.,).

The data is from organoids from one human stem cell line, the female H9 human embryonic stem cell line and so it is critical to validate the results on 1-2 additional stem cell lines, to rule out the possibility that these results are unique to this one cell line.

The major concerns in this paper can be addressed through validation of the results in other systems (e.g. human tissue) or in additional cell lines.

The results provide a valuable resource and address some of the limitations of current organoid and tissue single-cell data by focusing on proteomics.

Reviewer #1 (Public Review):

This paper presents the results of two fragment screens of PTP1B using room-temperature (RT) crystallography, and compares these results with a previously published fragment screen of PTP1b using cryo-temperature crystallography. The RT screen identified fewer fragment hits and lower occupancy compared to the cryo screen, consistent with prior publications on other proteins. The authors attempted to identify additional hits by applying two additional layers of data processing, which resulted in a doubling in the number of possible hits in one of the screens. Because I am not an expert in panDDA modeling, however, I am unable to evaluate the reproducibility and potential potency of these fragment hits as protein binders or their potential use as starting points for follow-up chemistry.

The fragment library used in this study was larger than those used in previously published RT crystallography experiments. Among the cryo hits that bound in RT, most fragments bound in the same manner as they did in cryo, while some bound in altered orientations or conformations, and two bound at different locations in RT compared to cryo. This level of variability is not surprising. However, one fragment was observed to bind covalently to lysines in RT, even though it showed no density in the cryo crystallization attempt. It is unclear from the provided information whether this fragment decayed during storage or if the higher temperatures accelerated the covalent chemistry. The authors also observed temperature-dependent changes in the solvation shell, and modifications to the protein structure upon fragment binding, including a distal modification.

The current version of the paper is somewhat repetitive in its presentation of the results and could be clearer in its presentation of the variations and comparisons of the two different protocols. It would be helpful to have a more concise summary of the differences between the two protocols in the current paper, as well as a discussion of how they compare to the protocol used in the previously published cryo-temperature fragment screen.

While I appreciate the speculative nature of the discussion at the end of the paper, the evidence presented by the authors does not instil confidence that these results will correspond to meaningful binders that could be used to train future machine learning models. However, depending on the intended use, it may be acceptable to train ML models to predict expected densities under typical experimental conditions.

Reviewer #2 (Public Review):

The authors set out to understand how a room-temperature X-Ray crystallography-based chemical-fragment screen against a drug target may differ from a cryo screen. They carried out two room-temperature screens and compared the results with that of a cryo screen they previously performed. With a substantial set of crystallographic evidence they showed that the modes of protein-fragment binding are affected by temperature. The conclusion of the work is compelling. It suggests that temperature provides another dimension in X-ray crystallography-based fragment screening. In a practical sense, it suggests that room-temperature fragment screen is a promising new avenue for hit identification in drug discovery and for obtaining insights into the fragment binding. Room-temperature screening carries unique advantage over cryo screening. This work is confirmative to the notion, which seems not yet universally considered, that very weak protein-small molecule binding may be inherently fluid structurally, and that crystal structures of such weak binding, especially cryo structures, cannot be taken for granted without cross validation.

Reviewer #1 (Public Review):

Autoantibodies to nuclear proteins are commonly associated with autoimmune conditions. Since their discovery, several reports have suggested that T-follicular regulatory cells (Tfr) Tfr cells have the capacity to preferentially suppress autoimmune antibody responses. Tfr have a TCR repertoire strongly skewed to self-antigens and in this report Ke et al. probe the idea that Tfr directly recognize nuclear proteins and inhibit nuclear protein specific B-cells. They find that vaccination of mice with an ongoing GC reaction to a foreign antigen using nuclear proteins causes expansion of Tfr and a Tfr dependent inhibition of the germinal center. Overall, this is a well written paper that significantly advances the idea that Tfr can control autoreactive B-cells in a selective manner. Most experiments are convincing. Some of the novel methods regarding the use of nuclear proteins during sequential vaccinations in mice or Tfr-B-cell doublet formation will be of interest to members of the same fields.

A primary weakness of the paper is that despite detailed analysis of cells involved in antibody production, there is very little analysis of the antibodies themselves. Particularly when Tfr deficient mice are used in figure 5 analysis of both anti-SA and anti-NucPr antibodies between the Tfr cKO and other groups would significantly advance the findings.

Reviewer #2 (Public Review):

In this study, the authors developed a mouse model to specifically investigate whether GC B cells that present nuclear protein (NucPr) could be specifically suppressed by Tfr cells. Most current mouse models that have been used in investigating Tfr functions are based on the overall readout of autoantibody production in the scenario of loss-of-function of Tfr cells. The proposed model of gain-of-function of Tfr cells is novel and valuable.

The authors mainly compared two boosting immunizations by Strepatividin (SA) alone or SA-conjugated with nuclear proteins (SA-NucPr) and demonstrated SA-NucPr boosting immunization was able to expand Tfr cells, suppress overall and SA-specific GC/memory/plasma cell responses. The results are mostly convincing.

One major concern is the conditions and controls used in the study. The control group (SA boosting immunization) would have enhanced T and B cell responses by this boosting. Unfortunately, there was no non-boosting control group so the level was unclear. It is therefore to strictly match such boosting condition in the SA-NucPr group. Notably, both SA and SA-NucPr were used at 10ug for boosting immunization. Considering NucPr were comparable or much larger (Nucleosome, about 200KDa) than SA (about 60KDa), the dose of SA in the SA-NucPr group was far less than that in the SA group. Due to this cavity, it is difficult to judge the difference between two groups was due to less SA boosting immunization or NucPr-induced Tfr function. This was a fundamental issue weakens the conclusion.

The single cell analyses clearly demonstrated the expansion of Tfr clones. It remains unclear why other Treg populations other than Tfr cells were not expanded? The Treg cells in the CXCR5intPD-1int population were recently activated and should be able to respond to the boosting immunization. On an alternative explanation, the changes in Tfr cells could be indirectly driven by the changes in Tfh cells. For example, Tfh can produce IL-21 and restrict Tfr expansion (Jandl C, et al.2017). This could be the case of the reduction in Tfr cells in the SA-OVA group as compared to the SA group.

Reviewer #1 (Public Review):

The authors have succeeded in demonstrating that they can further extend the methodology and value of Mendelian randomization by combining their two recently developed novel approaches to Mendelian randomization studies (1) Lifecourse MR which relates the genetic instruments to the outcome, eg obesity, at different stages of life eg childhood and adulthood and (2) Tissue partitioned MR to determine if the genetic instruments have different effects on different tissues such as the brain and adipose tissue. They have successfully combined these two to investigate the influence of adiposity on circulating leptin to demonstrate the value/proof of concept of these techniques in extending the use of MR.

This is a very clearly presented and well-conducted work showing both new methodology and clear-cut results on the impact of adiposity at age 10 and in middle life and the weight gain in between on leptin levels and that the effect is mediated via the brain. They show that childhood obesity has a direct effect on leptin levels at age 10 years and an indirect effect on adult leptin along a causal pathway involving adulthood body size. They also show that BMI exerts its effect on leptin levels at both life stages via brain-tissue-mediated pathways.

Major strengths are the well-characterized data sets used and in particular, having a comprehensive data set for children and the successful use of a new approach to address a complex issue. There are no major weaknesses

The authors have achieved their two aims - the use of the new methodology and its application to the specific issue to demonstrate how it works ie proof of concept. Their results support their conclusions.

The main advance here is a demonstration of a new further enhanced approach to Mendelian randomization. This is likely to end up being used by other researchers to address complex questions.

Reviewer #2 (Public Review):

In this proof-of-concept study, Richardson et al explore lifecourse effects of adiposity on leptin levels using life course Mendelian randomization and perform a tissue-partitioned MR to study the effects of tissue-specific BMI genetic instruments on leptin levels. The methods are solid and they have been nicely applied in the context of the present study. The results are important, revealing differences in the impact of adiposity on leptin levels in childhood vs adulthood, and highlighting the importance of the adipose-brain pathway in leptin homeostasis.

Additional MR analyses are suggested to explore bidirectional associations between leptin levels and adiposity, due to the interrelation of these two markers. Also, the fact that the MR instruments for childhood adiposity are based on self-reported body size, while the MR instruments for adult adiposity are based on measured adult BMI should be highlighted in the manuscript, and the possible impact of this in the findings should be discussed.

In summary, this important study is a proof of concept of life course and tissue-partitioned MR, while providing interesting insights into the regulation of leptin homeostasis by adiposity in different life stages.

Reviewer #1 (Public Review):

This work endeavours to delineate the relationship between IL-7R+ and IL-7R- ILC1 in the liver. They elegantly utilize a PLZF reporting system to identify the progenitor/product relationship between ILC subsets and show that ILC1s emerge separately from NK cells and LTi cells.

Furthermore, ILC1 are enriched in the liver. Extending this work in Rora-deficient mice, they demonstrate that over time, these cells are poorly replaced in the liver, and that IL-7R+ cells did not convert into IL-7R- cells at steady-state. Fetal liver IL-7R+ ILC1s were shown to partially contribute to mature ILC1s. Interestingly, they show that there were localization changes between ILC1 precursors and mature ILC1s in the liver. They then analysed the factors that might underpin these different localizations by examining IL-15 which is highly produced by macrophages and endothelial cells. They identify that hepatocyte-derived IL-15 supports the development of 7R− ILC1s in the parenchyma to maintain adult 7R− ILC1s within the sinusoids. Finally, the authors addressed the discrepancy in understanding of cytotoxicity expressed by ILC1s and identify that constitutive expression of mTOR was necessary to effect this function, thereby providing a mechanistic explanation for variable cytotoxicity observed in other studies. Overall, this study advances our knowledge of how ILC1 are generated and maintained in the liver, and how they acquire their effector functions.

Reviewer #2 (Public Review):

The authors are aiming to characterize the developmental process and functional heterogeneity of liver ILC1s. The role of liver ILC1's in human health is still unknown. ILC1's are abundant in fetal liver and decline throughout development into adults.

The authors have gone to great lengths to establish the relationship between IL-7R expression and ILC1 ontogeny.

The study provides insight into the complex ILC1 ontogeny by revealing relationships among heterogenous ILC1 subsets.

The data suggests intrinsic cytotoxic programs of 7R− ILC1s differ to NK cells, proposing them as critical steady-state sentinels against infection prevention and tumor surveillance.

The big unresolved question is why ILC1 dominate fetal innate lymphocytes versus NK cells in adult life.

Reviewer #1 (Public Review):

In this manuscript, Scagliotti and colleagues investigate the role of Dlk1 in regulating pituitary size in multiple mouse models with different Dlk1 gene dosages in order to understand the mechanisms of organ size control. They find that overexpression of Dlk1 leads to pituitary overgrowth and loss of Dlk1 causes undergrowth. Authors find two compartments of Dlk1 expression in the pituitary, in the marginal zone stem cell compartment and the parenchymal differentiated cell compartment, and by combing genetic mouse models show that a specific interaction of Dlk1 expression in both regions is necessary to affect pituitary organ size. They present to suggest that Dlk1 may repress Wnt signaling during development to control a shift from progenitor proliferation to differentiation. The data are meticulous, high quality, and clear.

I have some questions about the interpretation of their data regarding the mechanism of Dlk1 regulation of pituitary organ size, as I believe there could be potential alternative explanations for their observations:

I was wondering about the cause of the enlargement of the pituitary gland in Fig 1E, and whether it is caused by an increased number of cells (hyperplasia), an increased cell size (hypertrophy), or both. Line 104 states it is hyperplasia, and that cell size was not affected in WT-TG ('not shown', line 121). However, line 444 says the TG is hypertrophic. It would be good if the authors could elaborate on this and show or state how cell size was determined. Figs 5/6 show that WT-Tg proliferation is generally similar to WT, which suggests the increased size is not hyperplasia. It would be good to know whether this is correct. Some previous studies have shown that in pregnancy, lactotroph hypertrophy can be responsible for pituitary enlargement without hyperplasia (Castrique 2010, Hodson 2012).

Related to the organ size question above, I had a question about the cell number and proportions in Fig 1D/E/F, which shows the maintenance of endocrine cell proportions and an increase in the volume of ~30% in WT-Tg. For the cell proportions to be maintained, I thought the increase in volume per cell type (Fig 1G) would therefore have to also increase proportionally in every cell type, while 1G appears to show an increase in GH (sig) and PRL/TSH cells (ns). It would be good if the authors could discuss this briefly.

This study is impactful and will be of interest to several research communities, including those interested in pituitary development and function, organ size control, and gene imprinting mechanisms.

Reviewer #2 (Public Review):

Scagliotti et al address how organ size is regulated by imprinted genes. Using a series of mouse models to modulate the dosage of the paternally expressed gene, Dlk1, the authors demonstrate that DLK1 is important for the maintenance of the stem cell compartment leading to the growth of the pituitary gland and the expansion of growth hormone-producing cells. The authors show that overexpression of Dlk1 leads to pituitary hyperplasia while deletion of the paternal allele leads to reduced pituitary size. Reduced pituitary size is accompanied by reduced cell proliferation in the cleft at e13.5 and an increase in the number of POU1F1+ cells, suggesting that loss of Dlk1 alters the balance between the number of cells remaining in the replicating stem cell pool and those differentiating into the POU1F1 lineage. An elegant caveat of this paper is the rescue of Dlk1 expression in the population of cells expressing Pou1f1 but not in SOX2+ stem cells. Expression of Dlk1 only in POU1F1+ cells is not sufficient to rescue pituitary size. The authors suggest that this is because DLK1 must be present in stem cells which then activate paracrine WNT signaling to promote cell proliferation in POU1F1+ cells.

Strengths:

This is an important study that provides a mechanistic understanding of how the imprinted gene, Dlk1, regulates organ size. The study employs an elegant experimental design to address the dosage requirement for Dlk1 in regulating pituitary gland size. Rescuing Dlk1 in the POU1F1+ cells, but not the marginal zone SOX2+ cells provides intriguing results about a possible role for DLK1 in paracrine signaling between these different pituitary cell types. The study uses publicly available scRNAseq and ChIPseq data to further support their findings and identify Dlk1 as a likely target of POU1F1.

Weaknesses:

The study only analyzes females for the adult time point. For embryonic and postnatal time points sexes are pooled. Gender differences in pituitary gene expression embryonically or postnatally could potentially affect experimental outcomes.

The authors employ a mouse model that rescues Dlk1 expression starting at e15.5 in POU1F1+ parenchymal cells but not in marginal zone stem cells. Rescuing Dlk1 expression in a specific population of cells is one of the strengths of this study. Based on this information and the fact that overexpression of Dlk1 leads to increased pituitary size, the authors suggest that DLK1+ marginal zone stem cells and DLK+ parenchymal cells may interact to promote postnatal proliferation. However, the ability to more carefully parse out the complex spatial and temporal contributions of DLK1 to pituitary size would be enhanced by the addition of a mouse model that rescues Dlk1 expression only in SOX2+ cells and a model that rescues expression in both stem cells and POU1F1+ cells.

Reviewer #1 (Public Review):

The authors of this study sought to test whether the optogenetic induction of context-related freezing behavior could be enhanced by synchronizing light pulses to the ongoing hippocampal theta rhythm. Theta is a hippocampus-wide oscillation that strongly modulates almost every cell in this structure, which suggests that causal interventions locked to theta could have a more pronounced impact than open-loop ones. Indeed, the authors found that activating engram-associated dentate gyrus (DG) neurons at the trough of theta resulted in an increase in freezing relative to baseline when averaging across all stimulation epochs. In contrast, open-loop stimulation and peak-locked stimulation had weaker effects. Analysis of local field potentials showed that only the theta-locked stimulation facilitated coupling between theta and mid-gamma, indicating that this manipulation likely enhances the flow of activity from DG to CA1 via CA3 (as opposed to promoting transmission from entorhinal cortex to CA1). Previous results from mice, rats, and humans support the hypothesis that memory encoding and recall occur at distinct phases of theta. This work further strengthens the case for phase-specific segregation of memory-related functions and opens up a path toward more precise clinical interventions that take advantage of intrinsic theta rhythm.

Strengths:

This study recognizes that, when artificially reactivating a context-specific memory, the brain's internal context matters. In contrast to previous attempts at optogenetically inducing recall, this work adds an additional layer of precision by synchronizing the light stimulus to the ongoing theta rhythm. This approach is more challenging, because, in addition to viral expression and bilateral optical fibers, it also requires a recording electrode and real-time signal processing. The results indicate that this additional effort is worth it, as it results in a more effective intervention.

The findings on theta-gamma cross-frequency coupling suggest a possible mechanism underlying the observed behavioral effects: trough stimulation enhances DG to CA1 interactions via CA3. LFP recordings showed that stimulation increases the coupling between theta and mid-gamma (though not in all mice), and the percentage of freezing during reactivation is correlated with the gamma modulation index.

Weaknesses:

Given the precision of the intervention being performed, one might expect to see a stronger behavioral impact. Instead, the overall effect is subtle, and quite variable across mice. Looking at individual data points, the biggest overall increase in freezing actually occurred in 2 mice during the 6 Hz stimulation condition. Furthermore, trough stimulation decreased freezing in 3 mice This is not a weakness in itself; rather, the weakness lies in the lack of an attempt to make sense of this variability. There are a number of factors that could explain these differences, such as viral expression levels, electrode/fiber placement, and behavior during baseline. There is of course a risk of over-interpreting results from a few mice, but there is also a chance that the results will appear more consistent after accounting for these additional sources of variation.

While trough-locked optogenetic stimulation significantly increases freezing, the effects are much weaker than placing the mouse in the actual fear-conditioned context (average time freezing of 15% vs. 50%). The discussion would benefit from additional treatment of ways to further increase the specificity and effectiveness of artificial memory reactivation.

Using an open-source platform (RTXI) for real-time signal processing is commendable; however, more work could be done to make it easier to adopt these methods and make them compatible with other tools. The RTXI plugin used for closed-loop stimulation should be fully documented and publicly available, to allow others to replicate these results.

Reviewer #2 (Public Review):

In this manuscript, Rahsepar et al. test the hypothesis that the precise timing of engram cell activation in relation to the phase of hippocampal theta oscillations plays a causal role in recall. This hypothesis is derived from theories (e.g. the SPEAR model) positing that the hippocampus segregates information for memory encoding and retrieval in time and that separation is organized across the many neurons and subregions of the hippocampus by theta oscillations. They test this hypothesis using stimulation of dentate gyrus neurons active during the encoding of fear memory. Using closed-loop stimulation that they developed, the authors stimulate these dentate engram cells at different phases of theta to measure freezing behavior to determine if the fear memory is recalled. They compare this stimulation to stimulation at the same average frequency regardless of theta phase, or at a constant 20Hz, in line with prior research, as control conditions. The authors use an elegant within animal design. They find that stimulating at the theta phase when CA3 inputs most strongly influence CA1 leads to significant increases in freezing (relative to baseline), while none of the other stimulation conditions have significant effects on freezing. They then show that this stimulation also causes increases in gamma modulation by theta, which is correlated with learning in prior work. However, the gamma that is theta-modulated appears to be medium gamma which is not associated with CA3 inputs to CA1. Overall, the study is well-designed and well-controlled. The stimulation effects at the "best" theta phase are modest but do appear different than the other conditions. It is unclear why the authors chose to stimulate in dentate and not CA3 as the SPEAR hypothesis centers around CA3 and EC inputs to CA1. Furthermore, I wonder if the freezing behavior itself confounds the detection of the theta phase. Finally, some of the statistical analyses require controlling for multiple comparisons.

Reviewer #3 (Public Review):

The paper by Rahsepar et al. employed a closed-loop optogenetic approach to stimulate mouse dentate gyrus (DG) 'engram cells' at different phases of the ongoing theta rhythm. While stimulation of DG engram cells in fear conditioning paradigms has been conducted several times before (with similar results to those presented here), the current approach constitutes a significant methodological improvement over typical 'open loop' designs. The authors first characterize the performance of their closed-loop theta phase prediction method and show that it outperforms constant frequency stimulation in achieving a theta phase-specific stimulation, albeit with some limitations. A prominent theory in the field has proposed that memory encoding and recall preferentially take place at the peak and trough of theta respectively. Based on this framework, the authors compared the behavioral and physiological effects of stimulating engram cells at either the theta peak or trough as well as with constant frequencies. They found that, as predicted by the theory, stimulation at the theta through was the most effective in inducing enhanced fear memory recall (measured as freezing during re-exposure to a neutral context). Finally, the authors examined theta-gamma hippocampal LFP dynamics to provide physiological support for the observed behavioral differences of the different stimulation patterns.

Overall, this work illustrates an interesting methodological development that will be of relevance for future studies conducting manipulations of engram cells and provides additional experimental support for an influential theory in the memory field. Experiments are well conducted and the results presented support the main interpretation of the authors, but several aspects of the interpretation and discussion of the work need to be improved. Likewise, several aspects of data analysis and interpretation, in particular in reference to hippocampal oscillations and regional differences need to be improved.

Reviewer #1 (Public Review):

In this article, Prassad and colleagues describe a new mechanism involved in the elimination of misspecified/mislocated cells in the wing imaginal disc. This study follows a previous study from the same group (Bilmeier et al. Curr Biol 2016) which showed that a large panel of genetic backgrounds changing locally cell fate can trigger aberrant sorting of the misspecified cells triggered by the increased of contractility at clone interfaces. This process was suggested to directly participate to clone elimination below a certain clone size. However, the mechanism involved in apoptosis induction was not really studied per se. Here, they use similar genetic backgrounds and showed that JNK activation occurs specifically at the interface of the misspecified clones on both side (inside and outside the clone) hence leading to a local increase of cell death both in the WT and misspecified cells. This local activation of cell death participates to clone elimination, although the authors also delineate an alternative mechanism of death induction in the center of the clone that may correlate with the local buckling and the deformation. Importantly, this mechanism seems quite specific of these misspecified backgrounds and is unrelated to other more classical cell competition scenarios which trigger the elimination of Minute mutant (affecting ribosomes) or based on differential levels of Myc.<br /> The model proposed is interesting and clearly delineate a distinctive feature of this quality control mechanism which triggers local JNK activation. It is based on solid genetic evidences and use a large panel of genetic backgrounds and careful quantifications. The demonstration is overall very convincing. Moreover, these results provide a novel perspective for the field of cell competition and quality control mechanism which has been dominated by the concept of absolute fitness, which is not at all required in this context (where both WT or altered cells can be eliminated provided they are in minority in the tissue).

Admittedly, the unicity and novelty is bit tuned down by former studies showing similar patterns of JNK activity upon local distortion of morphogens (so called morphogenetic apoptosis, Adachi-Yamada and O'Connor Dev Biol 2002), or the pattern of JNK activation observed near polarity mutant clones (Ohsawa et al, Dev Cell 2011) suggesting that this bilateral JNK activation might not be completely unique to these contexts. But non of these studies characterised such large range of genetic backgrounds and this study clearly provide new mechanistic insights.

It is important to note that at this stage, it is not clear whether there is any link between the sorting behaviour and the activation of JNK (they could be both activated by unknown upstream factors), while the terminology "interfacial contractility" used to define this type of clone elimination may convey the idea that this is the most upstream factor in the process. Also further quantifications may be required to see to which extend JNK activation is indeed restricted to cell directly contacting clone border and also to support the final proposed model suggesting that the number of contact could influence the levels of JNK (actually alternative models could also explain why smaller clones get eliminated). Finally, while the JNK levels clearly influence death in the clone, further experiments may be required to test how the line of JNK activation in WT cells contribute to their death and their elimination similar to mispecified cells, specially in the context where the majority of tissue is covered by mispecified clones.

Reviewer #2 (Public Review):

Prasad et al investigate mechanisms of interface contractility that occur at borders between cells of different specification states. Cells of different specification states typically sort out, minimizing interface contact, in association with increased junctional contractility that can be visualized by phalloidin labeling. Here, Prasad et al show, for multiple different examples of specification and/or signaling states, that bilateral activation of JnK flanks these interfaces, which are associated with elevated rates of Jnk-dependent apoptosis. Blocking Jnk activity does not seem to affect phalloidin labeling, however, placing interphase contractility upstream or parallel to Jnk activity and apoptosis. Interestingly, activated Ras[V12] is an exceptional case where interphase contractility and bilateral Jnk activation occur without elevated apoptosis. Indeed, RasV12 can suppress apoptosis associates with interfaces between other distinct cell types. Prasad et al suggest that this property of Ras[V12] activated cells may underlie their oncogenic potential in mammals. These are potentially interesting observations that address what happens when cell of disparate signaling and/or specification states are opposed. In principle, they could be of interest both to developmental biologists, from the perspective of correction of developmental errors, and to cancer biologists, from the perspective of eliminating precancerous cells.

It is not clear how much advance is represented over the prior description of 'morphogenetic apoptosis', in which bilateral Jnk activity was also an integral part (Adachi-Yamada and O'Connor, Devl Biol vol251 pp74-90 2002). There is little new mechanistic insight provided here. As such, the observations seem preliminary and to represent only a limited advance.

Reviewer #3 (Public Review):

Elimination of aberrant cells from epithelial tissues is important for normal tissue physiology. Here the authors study a specific type of cell elimination that is dedicated to the removal of miss-specified cells. This type of elimination is dependent on interface contractility. The authors now identified an important role for JNK signaling, which is activated at this interface, where contractility is highest.

Strength: The authors use a large variety of cell specification mutants and different drivers to manipulate cell specification. Together, this shows that the observed phenotypes are of a general nature and not dependent on single signaling pathways.<br /> Weakness: Quantitative characterization of much of the data is missing. Only single representative images are shown for many of the experiments. The manuscript would strengthen massively when these images are supported with a quantitative measurement. For example (but not limited to), TRE-GFP in correctly vs mis-specified clones in Figure 2K-L, TRE-GFP intensity in Figure 3, clonal analysis in Figure 5.

Type of elimination:<br /> The authors describe a very distinct and specific phenotype of smooth rounded clones with high contractility. It is obvious that this is, on a phenotypic scale, different from other types of cell elimination, such as live extrusion and cell-cell competition. Throughout the manuscript the authors emphasize that the underlying nature of interface contractility is different to cell competition. Because cell competition "responds to a clearly defined fitness gradient between two neighbouring cells, which ensures that always the aberrant loser cell dies, independent of spatial context." And "linking apoptosis to a fixed loser genotype". However, this only holds true for the classical types of cell competition (e.g. Minute), while many examples of cell competition have been reported where elimination of cells is not set in stone, but also highly context dependent. For example, HRasV12 expressing cells are eliminated from epithelia in mice on a normal diet, while a high fat diet prevents their elimination (Sasaki et al, Cell Reports 2018). Without the experimental support that relative differences in cell specification do not cause a difference in cellular fitness it is hard to grasp the conceptual difference. Instead, the concept reported by the authors is better described as a variety of cell competition.

Clone size<br /> The authors claim that remove aberrant cells by interface contractility is dependent on clone size and only occurs when aberrant cells are the minority compared to the surrounding tissue. Currently, there is no data in the manuscript that supports this claim. The only analysis of tissues containing a majority of miss-specified cells (Figures 2I-2J) shows a bilateral activation of JNK, similar to a minority of miss-specified cells. To support the claim that the phenotype is size dependent further analysis of clone size in relation to apoptosis and JNK activation is essential.

JNK and cell autonomous regulation:<br /> The authors validate that expression of TRE-GFP is dependent on JNK signaling, through over-expression of a dominant negative variant of the JNK kinase (BSKDN) in clones of miss-specified cells (ey or tkv). This experiment nicely shows that activation of JNK in surrounding WT cells is not altered. This furthermore illustrates that JNK signaling in the miss-specified cells is not needed for activation of JNK in their neighbors. However, this does not support the conclusion that JNK is activated in a cell autonomous fashion in either of these populations. The interaction of the two cell types can still cause signaling, but through inhibition of one of the kinases within the pathway, this just does not lead to downstream activation of TRE-GFP. In fact, one could argue that the expression of TRE-GFP is not cell-autonomous, because tkvCA clones that are not mis-specified (within dad4-LacZ regions) do not show induction of TRE-GFP (Fig 2L). The only way to untangle cell autonomous vs non-autonomous effects is through manipulation of upstream communication between the different cell populations. Such experiments, for example manipulation of contractility, are likely beyond the scope of this study. Therefore, I would suggest rephrasing this paragraph.

Apoptosis:<br /> A large part of the manuscript is dedicated to the characterization of elimination of miss-specified cells through apoptosis. This process is important for maintenance of tissue integrity and a crucial part of the manuscript. Some conclusions are not fully supported by the data represented in the current form of this manuscript;<br /> The authors claim that fkh- and ey-expressing cells are not eliminated when apoptosis is blocked by expression of p35. This is based on analysis of apical vs basal clone count (Figure 1T). This analysis reflects a combination of induction efficiency and clone retention. Therefore, information on the cellular behavior within clones is lacking and only provides information on survival of cells when complete clones are eliminated. The conclusion should be supported by additional analysis on clone size and total clone area, ideally based on cell number. In addition, statistical analysis of conditions with and without expression of p35 should be included.<br /> Furthermore, the analysis of apoptosis at clonal interfaces does not support the conclusion that "many, but not all apoptotic events occur at interfaces". Overall, there is increased apoptosis within clones compared to wild-type tissue. However, the rates of apoptosis are higher (ey, Fig S5B) or similar (fkh and tkvCA, Fig 5B-C) in clonal cells compared to clonal interface cells. The authors should revise these statements or provide more compelling analysis.

Joint Public Review:

Hepatitis E virus (HEV) causes over 20 million infections per year. The open reading frame 1 (ORF1) is responsible for genome replication, however very little is known about the structure and functions of several of the components. The author use a diverse a diverse number of techniques (molecular virology, structure prediction using AlphaFold, site directed mutagenesis and biochemistry) to probe ORF1 activity. The work is thorough, well prepared, and discusses the strength and weakness of the structural information. Interestingly, AlphaFold prediction of the papain-like cysteine protease domain did not identify a classic papain-like fold. Lastly, the authors demonstrate the necessity of six conserved cysteines within the putative PCP domain.

The presence and necessity of proteolysis for genome replication or cleavage of other host factors still remains an uncharacterized problem, which is beyond the scope of this manuscript. My only concern relates to the presence of a zinc ion in ORF1.<br /> The authors use extensive triplet alanine scanning to test for virus replication capacity and in some cases see gains above WT (Figure 3). Do these patterns match natural variation observed in comparisons of HEV sequences un any way?

Overall, the study presents an intriguing hypothesis for HEV ORF1 function not involving protease processing as assumed by early bioinformatic analysis. The alternate hypothesis of metal ion coordination is supported by increasingly sophisticated structural modeling tools and related experiments. However, a lack of direct evidence leaves, as the authors note, alternate hypotheses such as disulfide bond coordination or protease functions that occur intramolecularly within ORF1.

The study will likely have an impact on the field, especially if evidence builds in the future directly supporting the mechanism proposed. HEV is an impactful pathogenic virus that is relatively underappreciated. In addition to a major revision in HEV biology, the idea that many proteins initially annotated with canonical functions might instead have different mechanisms is also of high interest beyond the field of virology.

Reviewer #1 (Public Review):

Of course, many of the most important aspects of feeding happen post-ingestion. As digested food moves through the intestines specialized epithelial cells (called Enterochromaffin Cells or EECs) sense and respond to the constituent chemicals. The function of EECs initiates physiological responses to facilitate nutrient absorption, protect from toxins and encourage proper waste removal. EECs are sparse and heterogenous and release a variety of transmitters and diffusible signaling molecules that signal to peripheral neurons and the brain. Their collective activity slows or speeds gut transit and promotes feelings of satiety or malaise. The current work by Liberles and colleagues seeks to provide deeper insight into the function of EECs. They build on previous work by further categorizing these cells by their unique gene expression signatures. The work utilizes single-cell transcriptomic analyses and intersectional approaches to define and genetically manipulate subsets of EECs. A key aspect of the study is behavioral assays used to investigate how direct stimulation of EEC subtypes influences key aspects of feeding, specifically gut transit, ingestion, and food preference.

The work has several strengths. A new mouse line (Villin-flp) is developed and used intersectionally with Cre mouse lines to manipulate different subsets of epithelial cells. The authors characterize these compound mouse strains and how the labeled cells map onto transcriptomic class. These data are reasonably comprehensive and show the exclusion of marker expression from the central nervous system, important controls. The chemogenetic activation strategy is an elegant way to probe the consequences of EEC stimulation by Gq coupled GPCR signalling. The gut transit experiments show clear effects.

The weakness is it remains unclear whether stimulation of the DREADD receptor outside the intestinal EECs really has consequences (e.g. in the tongue), the behaviors tested are somewhat limited, the responses to CNO administration variable between animals, and the effect sizes are small.

Overall, this is an interesting study and provides useful tools for the field.

Reviewer #2 (Public Review):

Enteroendocrine cells (EEC) line the gut and prior evidence suggest that they are primary sensors of gut contents. In turn, these cells release transmitters that regulate gut function, including gut motility, enzyme secretion, and gut permeability. More recent studies have also found synaptic connections between EEC and neural sensory fibers that connect the gut to the brain, implicating this pathway in taste learning. Thus, EEC signals can be integrated with sensory signals originating in more distal areas of the alimentary canal.

EECs express a variety of receptors and transmitters that are hypothesized to contribute to the diversity of sensing and motor functions. In this report, Hayashi et al develop a novel transgenic mouse that permits manipulation of EEC subtypes via intersectional methods. Using this approach, they identify differential roles for EEC subtypes in controlling gut motility and taste learning.

Strengths

• The authors supplement existing single-cell RNA sequencing of the proximal intestine.<br /> • A Vil1-2a-Flp mouse was generated, which exhibits highly selective expression in the gut epithelium. This mouse line can be used to manipulate EEC subtypes when bred with other Cre driver lines and double conditional (Flp/Cre) mice.<br /> • Using the above tool, different EEC subtypes were histologically characterized along the alimentary canal. Additionally, other tissues were examined, including the brain, pancreas, and lungs to demonstrate the gut specificity of their approach. The intersectional approach yield sparse recombination in the pancreas, therefore the authors included controls in their gut motility and feeding studies to account for this.<br /> • In probing the function of distinct EECs, it was found that Cck(cholecystokinin) and Gcg (GLP-1) expressing EECs slow down gut motility, whereas Tac1 (substance P) and Pet1(serotonin) expressing cells increase motility.<br /> • Food intake studies revealed several subpopulations that decrease feeding (Pet1, Npy1r, Cck, Gcg).<br /> • A conditioned flavor preference assay suggests that some of the above EEC subtypes (Pet1, Tac1, Npy1r, Gcg) decrease feeding in part through conditioned flavor avoidance.

Reviewer #3 (Public Review):

This manuscript describes a villin-2a-Flp-based intersectional strategy for selectively targeting EEC in the intestine and uses it to examine the function of subsets. The approach for targeting select subsets of enteroendocrine cells described here will be important for neuroscientists, endocrinologists, microbiologists, and other scientists studying nutritional biology. Here single-cell sequencing is used, primarily, to confirm what was already known about EEC classes at a transcriptomic level. The intersectional approach described here has the potential to provide broad access to EECs. However, from the relatively limited characterization of targeted EEC cells, it appears that the genes that have been combined with the villin driver largely fail to selectively target transcriptomically defined cell types. Thus, at present, this manuscript fails to convincingly target transcriptome-defined enteroendocrine cell types, and conclusions on gut motility, feeding behavior, and flavor avoidance are overstated.

Some aspects of the study are compelling including the use of villin drivers as a means to restrict recombination to the epithelium containing EECs. The single-cell data (although not unique to this study) proved a basis for a better understanding of EECs and also their developmental specification. The charcoal-based gut motility assay appears valuable (although the results are perhaps not surprising given what was already known). In addition, some of the care taken characterizing extra-EEC expression is commendable. However, the manuscript is difficult to read with important details scattered in different figures and text (e.g., the characterization of expression patterns of the various lines). Moreover, whereas some things like the genetic makeup of the lines are always specified in full (excruciating) details, the expression patterns of the various lines are often casually dealt with e.g., describing separate targeting of L and I cells despite no evidence that this is actually being done. I would hope that the authors will address these issues and devote significant attention to making the paper more accessible to its readers.

Reviewer #1 (Public Review):

To explore possible functions of SA proteins in the absence of cohesin, authors use conditional AII-dependent proteins SA1 and SA2, after whose degradation they observe the phenotypes just indicated. 3D analysis shows that SA proteins cluster at specific regions. In addition, it is shown that SA proteins not only interact with CTCF after RAD21 degradation but with other F/YXF-motif containing proteins such as CHD6, MCM3 or HRNPUL2 as determined by ChIP. Mass spectrometry of proteins co-immunoprecipitated with SA1 reveals 136 interactor proteins that include a number of chromatin remodeling factors, transcription factors and RNA binding proteins including factors involved in RNA processing and modification, ribosome biogenesis and translation. After these results, authors perform CLIP to show that SA1 protein binds RNA in the absence of cohesin. Different analysis using RNH, mainly IF and IP and the S9.6 antibody, are used to conclude that SA1 binds to R-loop regions. The authors conclude that SA proteins are loaded to chromatin via NIPBL/mMAu complex at RNA:DNA hybrid regions. Further analyses suggest that SA proteins stabilize RNA via interaction with other RNA-binding proteins, some of which have been shown by other authors to be enriched at R loop-containing regions, a property that localizes to exon 32 in SA2. The manuscript provides a large amount of work that has been put together in a large collaboration to bring new roles for SA in RNA metabolism, even though this is not investigated.

Reviewer #2 (Public Review):

In the manuscript by Porter et al., the authors describe a putative role for the STAG proteins (SA1 and SA2), not as part of the cohesin complex, but in isolation and in particular at R-loops where they contribute to R-loop regulation, linking chromatin structure and cohesin loading.

My major concern is rather general: " the role of SA1 and SA2 proteins (or cohesion subunits) its only highlighted upon acute depletion of RAD21 (cohesin subunit that holds together the complex)". I am not sure that this context is recapitulated in living cells. I.e is there a particular phase of the cell cycle where RAD21 is acutely depleted or targeted for specific degradation? How do we know that we are not looking at remnants of a complex (cohesin) that has been partially targeted by IIA mediated degradation? Is the RNA binding of SA1 an SA2 CTCF-independent (as CTCF encompasses an RNA binding domain?).

Is there any proof that in untreated cells (ie before depletion) SA1 AND SA2 are chromatin bound independently of Rad21 (and /or SMC1-3)? Overall, it is a nice manuscript, but I am not sure whether the IAA-dependent degradation of a single subunit of pentameric complex is the right tool to assess whether other subunits of the same complex work independently.

Reviewer #3 (Public Review):

Porter, Li et al. investigate the roles of SA1 and SA2 in cohesin loading, and as well as roles that are independent of the cohesin ring. Using co-IP and imaging approaches, they show that both SA1 and SA2 interact with CTCF and they use auxin-induced degradation of Rad21 to show that this is only partially dependent on cohesin. The authors next use IP followed by mass spectrometry to identify additional SA binding partners, which include many RNA binding proteins including factors involved in RNA modification, export, splicing, and translation. Unlike the interaction with CTCF, these interactions are enhanced in cohesin depletion conditions. In fact, CLIP experiments show that SA binds RNA directly, in an R-loop-dependent manner. This co-localisation of SA with R-loops is confirmed by STORM.

To address whether SA proteins are involved in cohesin loading, the authors measure chromatin-bound cohesin levels after auxin washoff in the presence and absence of NIPBL and SA. They find that SA knockdown has a comparable impact on cohesin binding to chromatin compared to NIPBL knockdown, and that combining the knockdowns reduces cohesin loading further. This newly synthesised cohesin co-localises with R-loop domains by STORM, and this localisation is sensitive to RNAse H. The authors propose that SA promotes cohesin loading at R-loops, and that SA1 is the main contributor to this. Finally, they provide evidence that differential usage of a conserved exon between SA1 and SA2 may be responsible for differences between SA1 and SA2 in this system, as SA2 with this exon included has higher RBP binding and is more enriched at R-loops.

This paper provides convincing evidence that SA proteins associate with R-loops and various RNA-binding proteins, suggesting that they may have a cohesin-independent role related to RNA processing or R-loops specifically. Additionally, the paper provides evidence for a NIPBL-independent role of SA proteins at cohesin loading, which may occur at R-loops. These results will be of broad interest in relation to chromatin organisation and the role of SA proteins/cohesin in cancer.

Overall, the experiments are thorough and well-controlled, including some nice validations such as the use of siRNA-mediated cohesin depletion and a different cell line to confirm the SA-CTCF interactions. In many cases STORM imaging is used to provide complementary evidence to western blots / IP experiments.

However, one weakness is that imaging approaches can only address co-localisation. Although the vast majority of cohesin complexes will be bound to DNA, imaging approaches cannot distinguish between chromatin-bound and unbound nuclear proteins. For example, although cohesin co-localises with R-loops and SA after auxin washoff, and this is dependent on R-loops, it is not possible to tell from imaging whether this cohesin is chromatin bound and whether this is bound to specific genomic loci that contain R-loops or just associated with them in 3D space. Therefore it would be preferable to have a clearer distinction in terminology depending on whether the evidence discussed can demonstrate chromatin binding (e.g. chromatin fractionation experiments), or just co-localisation.

Reviewer #1 (Public Review):

This manuscript by Koropouli et al. is a much-needed study that provides novel mechanistic insight of how signaling receptors can be targeted to distinct subcellular domains or membrane locations that, in part, confer their functional specificity. It is well-established that members of the class 3 secreted semaphorins guidance cues can bind to the receptors the neuropilins (Nrp1 and Nrp2) to elicit numerous cellular processes important for circuit assembly. Previously, it was demonstrated that Sema3F signaling with Nrp2 and its co-receptor Plexin-A3 is required for the removal of excess excitatory synaptic spines on the apical dendrite of layer V cortical neurons, while the closely related member Sema3A signaling with Nrp1/Plexin-A4 promotes the elaboration of the basal dendritic arbor on the same neuron. The question is then how do the two different signaling pathways convey such precise and opposite cellular function of eliminating spines and promoting dendritic elaboration in distinct subcellular compartments of the same neuron? While some hints were provided that the Nrp2 receptor is localized to the apical dendrite and Nrp1 is distributed widely along all dendrites on the same cortical neuron in vitro, this has not been shown in vivo and the mechanism of such targeted subcellular localization is not known. In the current study, the authors used biochemical, cellular, and molecular assays in combination with mouse genetics and live-cell imaging to demonstrate that the post-translational modification of S-palmitoylation dictates the proper subcellular localization and trafficking of Nrp2, but not Nrp1, and is required for Sema3F-dependent pruning of spines on the apical dendrites of layer V cortical neurons. The following are the strength and novel findings of this study.

1. This study confirms previous findings and adds new information by mapping the specific locations of the cysteine amino acid residues to the transmembrane/juxtamembrane region of neuropilin receptors for palmitoylation, which confers the subcellular localization specificity for Nrp2 but not Nrp1, in cortical neurons and non-neuronal cells.<br /> 2. The study also found that select cysteine residues on Nrp2 are palmitoylated by the palmitoyltransferase DHHC15, and palmitoylation of these sites are required for the homo-oligomerization of the Nrp2 receptor but not for the association with the co-receptor Plexin-A3.<br /> 3. The authors demonstrated that Sema3F signaling itself seems to enhance the level of Nrp2 palmitoylation in some sort of positive feedback loop. It would be interesting for future experiments to determine how Sema3F signaling promotes this palmitoylation.

Although most of the key claims are supported by data presented in the paper, clarification of the following concerns would further strengthen the overall conclusion of the study.

1. While some of the qualitative micrograph images are very convincingly in illustrating the drastic difference in Nrp2 versus Nrp1 expression patterns/cell-surface localization, such as Fig. 1A and 1D, many of the quantitative analyses have a low n number and/or low sample size, with only 2 replicate experiments or only 2 brains/animals per genotype analyzed. To increase the rigor of this study, the authors should add a few more replicates to the experiments with low n numbers.<br /> 2. The substitutions of C878, C885, and C887 to serines caused an ~80%, ~50%, and ~60% reduction, respectively, in Nrp2 palmitoylation compared to WT neuroblastoma-2a cells (as show in Fig. 2D and 2E). However, when mutating all three of these cysteine sites (the TCS plasmid), there is only ~80% total reduction in Nrp2 palmitoylation (Fig. 2F and 2G), just about equal to the C878S substitution alone. One would expect that the reduction in palmitoylation to be more severe with the TCS plasmid, but might this be due to the low n number in quantifications shown in Fig. 2E and 2G. It would add substantially to support the specificity of these cysteine residues' function if the single C878 was demonstrated to be required for either subcellular localization of Nrp2 leading to the rescue of the dendritic spine phenotype in Nrp2-/- primary neurons or in an in utero experiment.

Reviewer #2 (Public Review):

The study of Koropouli is a tour the force investigation of the Semaphorins receptor, Neuropilin-2, modification by Palmitoylation. The work consists of biochemical, cellular and in vivo experiments and overall underscore an interesting layer of regulation of axonal guidance receptors membrane localization and function by lipid modification.

Reviewer #3 (Public Review):

Although initially discovered as axon guidance molecules in the nervous system, Semaphorins, signaling through their receptors the Neuropilins and Plexins, regulate a variety of cell-cell signaling events in a variety of cell types. In addition, cells often express multiple Semas and receptors. Thus, one important question that has yet to be adequately understood about these important signaling proteins is: how does specificity of function arise from a ubiquitously expressed signaling family?

This study addresses that important question by investigating the role of cysteine palmitoylation on the localization and function of the Neuropilin-2 (Nrp-2) receptor. It was already known that Sema3F signaling through a complex of Nrp-2 and Plexin-A3 regulates pruning of dendritic spines in cortical neurons while Sema3A signals through Nrp-1/PlexA4 to regulate dendritic arborization. The major finding of this study which is well-supported by the data is that palmitoylation of Nrp-2 regulates its cell surface clustering and dendritic spine pruning activity in cortical neurons. Interestingly, palmitoylation of Nrp-1 at homologous residue does not appear to regulate its localization or known neuronal function.

A clear strength of this manuscript is the many techniques that are utilized to examine the question: this study represents a tour de force of biochemical, molecular, genetic, pharmacological and cell biological assays performed both in vitro and in vivo. The authors carefully dissect the function of distinct palmitoylated cysteine residues on Nrp-2 localization and function, concluding that palmitoylation of juxtamembrane cysteines predominates over C-terminal palmityolyation for the Nrp-2 dependent processes assayed in this study. The authors also demonstrate that a specific palmityl transferase (DHHC15) acts on Nrp-2 but not Nrp-1 and is required for Nrp-2 clustering and dendritic spine pruning. These findings are important because they demonstrate one mechanism by which different signaling pathways, even from a related family of proteins, can achieve signaling specificity in the cell.

A minor weakness of the paper is that one would like to see a connection between palmitoylation-dependent cell membrane clustering of Nrp-2 on the cell surface and Nrp-2 regulation of dendritic spine pruning. Although the two phenotypes frequently correlate in the data presented, there are a few notable exceptions: e.g. Nrp-2TCS forms larger clusters in cortical neurons while Nrp-2FullCS is diffuse on the cell surface; both mutants affect spine pruning. In the future, it would also be interesting to know if increased clustering of Nrp-2 was observed at spines that were eliminated, for example. Nonetheless this manuscript represents an important advance in our understanding of synaptic pruning and cellular mechanisms that constrain protein surface localization and signaling pathways.

Reviewer #1 (Public Review):

The manuscript by Curtis et al. reports the interaction between CaMKII and alpha-actinin-2. The authors found that the interaction was elevated after NMDA receptor activation in dendritic spines. In addition, this study reveals NMDA receptor binding to CaMKII facilitates alpha-actinin-2 access to the CaMKII regulatory segment, indicating that the NMDA receptor is involved in this interaction. The authors identified the EF1-4 motifs mediated this interaction, and overexpression of this motif inhibited structural LTP. Moreover, biochemical measurements of affinities from various combination of protein fragments including autoinhibited CaMKII 1-315, regulatory segments of CaMKII, and the EF-hand motif reveals that autoinhibited CaMKII has limited access to alpha-actinin-2. The authors also solved the structure of the interaction, supporting their finding in neurons at the molecular level. The authors claim that the interaction between CaMKII and alpha-actinin-2 is essential for structural LTP through cooperative action by the NMDA receptor and actin cytoskeleton.

Overall, the experiments are well-designed and the results are largely convincing and well-interpreted. But some aspects of the experiments need to be clarified.

1. Time resolution of the interaction analysis appears to be poor, as calcium elevation in a dendritic spine would be at milli-second order. What is the time window to interact alpha-actinin-2 with CaMKII during NMDA receptor activation or LTP?<br /> 2. The authors analyzed the binding of CaMKII and alpha-actinin-2 with partial fragments. It remains to be unknown whether CaMKII can form a protein complex with GluN2B and alpha-actinin-2 in a single CaMKII protomer.<br /> 3. Besides synaptic localization, the effect of the interaction on the enzymatic activity of CaMKII is not known.<br /> 4. Although the authors quantify the effect of the EF-hand disruptor by measuring numbers of the dendritic spine by its shape, the specificity of the EF-hand disruptor needs to be clarified.

Reviewer #2 (Public Review):

Gold and his colleagues first ectopically expressed aACTN2 constructs with various deletions and determine the spatial proximity to CaMKII by PLA. Chemical LTP induced by brief glycine application in hippocampal cultures strongly augmented the PLA puncta density in spines (postsynaptic sites). This interaction specifically depended on the 4 EF hands near the C-terminus of aACTN. At the same time expression of the 4 EF hands (plus the C-terminal PDZ ligand) impaired the formation of larger mushroom spines under unstimulated conditions and the increase in mushroom spines seen after chemLTP when compared to non-transfected conditions or transection of the EF hands with a point mutation (L854R) that disrupted binding to CaMKII.

To further define the interaction between aACTN and CaMKII the authors then solved a crystal structure formed by the aACTN EF3/4 and regulatory segment of CaMKII. This structure confirmed the role of L854 in the interaction. It also explained earlier results that phosphorylation of threonine in position 306 but not of threonine 305 of the CaMKII regulatory domain impaired aACTN binding as T306 but not T305 is engaged in critical interactions. This contrasts with Ca/CaM binding to CaMKII, which engages both threonines and is blocked by the phosphorylation of either residue. Consistently, earlier structures of Ca/CaM with the CaMKII regulatory domains showed respective differences to the new aACTN-CaMKII structure.

Additional analysis of these data indicated that the association of the regulatory domain with the kinase domain occludes access to aACTN EF3/4. This is an important finding because it implies that only active CaMKII like T286 autophosphorylated CaMKII or bound to GluN2B would be able to effectively interact with aACTN in intact cells.

Finally, and remarkably, binding was augmented by a protein fragment of the GluN2B C-terminus that contains the binding site for CaMKII even when Ca/CaM was still present. This result suggests that with GluN2B present aACTN can bind to CaMKII even though in the absence of GluN2B Ca/CaM occludes this binding. This finding opens up new research directions.

Reviewer #3 (Public Review):

This manuscript builds upon prior work showing that alpha-actinin-2 binds to the regulatory domain of the major postsynaptic protein kinase, CaMKII. The authors report the structure of a complex between the relevant domain in alpha-actinin-2 and a peptide based on the CaMKII regulatory domain. Data are presented indicating that the interaction of the NMDA receptor GluN2B subunit with the CaMKII catalytic domain stabilizes the complex with alpha-actinin-2. Furthermore, the authors present proximity ligation assay (PLA) data obtained in cultured neurons demonstrating that NMDA receptor activation strongly enhances the colocalization of CaMKII with alpha-actinin-2. Data obtained using mutated proteins indicate that this co-localization is mediated by the interaction characterized structurally.

Strengths:

Significant strengths of this work are:<br /> 1. The high-quality structures of the complex that are reported.<br /> 2. Integration of these findings with the much better-studied complex of CaMKII and GluN2B.<br /> 3. The convincing PLA analyses show that NMDA receptor activation increases CaMKII colocalization with alpha-actinin-2.<br /> 4. The careful comparisons of data from these new studies with data reported in previous publications.

Weaknesses:

Despite the significant strengths of the work, there are some gaps/weaknesses.<br /> 1. Although there is abundant published evidence that activated CaMKII colocalizes with NMDA receptors, the evidence for the involvement of GluN2B in the CaMKII-alpha-actinin-2 complex in neurons is lacking.<br /> 2. The evidence supporting a role for the EF1 and EF2 domains of alpha-actinin-2 in binding to CaMKII is not very convincing.<br /> 3. CaMKII autophosphorylation at multiple sites plays an important role in regulating the subcellular localization of CaMKII, but the role of autophosphorylation is not explored here.

Taken to together the manuscript describes novel data that provide a significant extension to prior work, and the data convincingly, but perhaps only partially, support an interesting proposed model for the control of CaMKII targeting in spines.

This more sophisticated delineation of the mechanisms underlying CaMKII targeting synapses will be of interest to the broader field of investigators studying the molecular basis for the regulation of excitatory synaptic transmission, learning, and memory.

Reviewer #1 (Public Review):

The study provides mechanistic insight into molecular events occurring at the onset of differentiation mediated by the kinase PASK. Specifically, the work focuses on the multiple steps that converge on post-translational modifications of PASK and its translocation to the nucleus during myogenesis. The authors present evidence that glutamine-fueled, CPB/EP300-mediated acetylation of PASK is required for its nuclear translocation. This allows (nuclear) PASK to interact with Wdr5 and consequently disrupt its association with the anaphase-promoting complex/cyclosome and inhibit Pax7 transcription, marking the onset of muscle differentiation. The conclusions are supported by an analysis of the effects of glutamine modulation on differentiation and maintenance of stemness in primary muscle stem cells; PASK localization in myoblasts and primary muscle stem cells as well as detailed biochemistry with modified forms of PASK to interrogate molecular interactions. C2C12 myoblast cells and primary muscle stem cells are cellular systems employed in the study with observations confirmed in cells derived from mice with genetic ablation of PASK. The study provides molecular detail on events linking glutamine metabolism to the transcriptional control of lineage differentiation, through the regulation of PASK. The analysis of these events in other systems would be of value to understanding their broader applicability.

Reviewer #2 (Public Review):

In this paper, Xiao et al. suggest that PASK is a driver for stem cell differentiation by translocating from the cytosol to the nucleus. This phenomenon is dependent on the acetylation of PASK mediated by CBP/EP300, which is driven by glutamine metabolism. Furthermore, this study showed that PASK interferes/weakens the Wdr5-APC/C interaction, where PASK interacts with Wdr5, resulting in repression of Pax7, leading to stem cell differentiation.

There exist huge interest in maintaining adult stem cells and ES cells in their pluripotent form and the work painstakingly perform several experiments to present that PASK is a good target to achieve that goal.

However, the work on the paper relies mostly on data from C2C12 cells as adult muscle stem cell models, in vivo experimental data, and primary myoblasts from mice. Using these models makes the story contextual in muscle stem cells. Authors have not tried to extrapolate similar claims in other adult stem cell models. This severely restricts the claim to muscle stem cells even though PASK is required for the onset of embryonic and adult stem cell differentiation in general. Their work could be much strengthened if it is also tried on mesenchymal stem cells as these cells are also as metabolically active as muscle cells.

Reviewer #3 (Public Review):

This manuscript entitled "PASK relays metabolic signals to mitotic Wdr5-APC/C complex to drive exit from self-renewal" by Xiao et al presents an interesting story on the role of PASK in the control of muscle stem cell fate by controlling the decision between self-renewal and differentiation. While the biochemistry presented is fairly compelling, the experiments revolving around the myogenic cells are lacking in quality and data.

Major concerns:

1. The isolation method used by this group to isolate muscle stem cells is inappropriate for the experiments used and may contribute to the misinterpretation of some of the results. It is simply a preplating method that results in a very heterogenous cell population in terms of cell type, comprised of numerous fibroblasts. While preplating can be used to isolate muscle stem cells and culture them as myoblasts, it takes days of growth and multiple rounds of passaging that are not used in this paper in order to get a more pure population of myogenic cells. This would also explain the high number of Pax7 negative cells in their primary myoblast experiments (~50% in some conditions) as they are most likely fibroblasts, which the authors could show by staining for fibroblast markers. The increase in Pax7 cells in certain conditions could also simply be due to the loss of contaminating cell types due to the treatment. Every single experiment that was performed on myoblasts must be redone using a more appropriate cell isolation method (i.e. FACS) or by culturing these isolated cells for a much longer period of time to eventually get a more pure cell population. As it stands, none of the data from the primary myoblast experiments are trustworthy.<br /> 2. The authors possess a genetic mouse model where PASK is knocked out. However, the mouse model is never described and the paper that is referenced also does not describe it. Please detail your mouse model.<br /> 3. The majority of experiments are performed on C2C12 cells. While C2C12s are adequate for biochemistry and proof of concepts, when it comes to biological significance primary myoblasts should be used. While the authors try to explain this use by claiming that primary myoblasts undergo precocious differentiation that can be avoided by using an appropriate growth media (F10, 20% FBS, 1% P/S, 5ng/mL of bFGF).<br /> 4. The authors possess a genetic mouse model, yet performed RNA-Seq on C2C12 myoblasts that were either untreated or treated with a PASK inhibitor. It would be much more informative and valuable to sequence the primary myoblasts from WT and PASK KO mice, thereby providing a more biologically relevant model.<br /> 5. The KO mouse model is rarely used and the cells isolated from it would be very useful in determining the biological role of PASK in muscle cells. The authors should isolate WT and KO cells and perform basic muscle functional experiments such as EDU incorporation for proliferation, and fusion index for differentiation to see whether the loss of PASK has an effect on these cells.<br /> 6. The authors never look at quiescent muscle stem cells and early activated muscle stem cells in terms of PASK protein expression and dynamics. The authors should isolate EDL myofibers and stain for PASK and PAX7 at 0, 24, 48, and 72-hour post isolation. This would allow the authors to quantify the changes in PASK expression and cell localization, as well as confirm the number of muscle stem cells in WT and KO mice, during quiescence and during the process of muscle stem cell activation, proliferation, and differentiation in a near in vivo context.<br /> 7. Contrary to their claim, MyoD is not a stemness/self-renewal gene.<br /> 8. The authors state that PASK is necessary for exit from self-renewal and establishment of a progenitor population but this is a vast overstatement. In the genetic KO mouse model, the mice are able to regenerate their muscle after injury, therefore PASK cannot be a necessary protein for the formation of progenitor cells.<br /> 9. In numerous figure panels, the y-axis represents the # of cells, rather than a percentage or ratio. This is uninformative as the number of cells will never be the same between conditions and experiments. These panels need to be replaced with a more appropriate y-axis.

Reviewer #1 (Public Review):

The authors of this study used SMART-seq to study differentiating B cells. Then they performed extensive in silico analyses to validate that a subset of the cells mimicked human antibody-secreting cells. For example, they compared gene expression profile of each cluster in B cell developmental trajectory (Figs 1, 2), investigated gene enrichment in ASC-like cluster (Fig 3), adopted independent dataset (Fig 3), and compared gene expression signatures of their cells to those of GC ASCs (Fig 4). Overall, the results from these analyses are convincing and valuable, but still do not seem to be a big leap from their Unger 2021 paper and therefore making this study preliminary.

The methodology that they established should be described more clearly so that it can be shared with the research community. For example, they say cells how many donors were recruited for this experiment? are there differences in efficiency in B cell differentiation by individual?

Also, it would be important to assay for antibodies in the culture media. How would you suggest to improve the culture system to be used to model diseases?

At the beginning the largest contributing factor for cell culstering was cell cycle. But B cell differentiation may also influence to cell cycle regulation. Rather than normalize its effect, can authors analyze effect of cell cycle in B cell differentiation? For example, identify sub-clusters shown in supple Fig 1g.

Reviewer #2 (Public Review):

In this work, Verstegen and colleagues try to delineate human B cell differentiation trajectories by using in vitro differentiation culture of human naive B cells. The authors adopted a protocol of B cell stimulation with CD40L-expressing fibroblasts and IL-4/IL-21, and cultured B cells were analyzed by single-cell transcriptome analysis. Five distinct clusters were identified with features of memory B cells, germinal center-like B cells, ASCs, pre-ASCs, or post-GC B cells. This work provides a precise description of gene expression profiles of activated B cell populations and some insight into the pathways of effector B cell differentiation. This work will be a solid basis for human B cell study using in vitro culture of target B cell populations, providing an excellent experimental protocol.

Reviewer #1 (Public Review):

Doostani et al. present work in which they use fMRI to explore the role of normalization in V1, LO, PFs, EBA, and PPA. The goal of the manuscript is to provide experimental evidence of divisive normalization of neural responses in the human brain. The manuscript is well written and clear in its intentions; however, it is not comprehensive and limited in its interpretation. The manuscript is limited to two simple figures that support its concussions. There is no report of behavior, so there is no way to know whether participants followed instructions. This is important as the study focuses on object-based attention and the analysis depends on the task manipulation. The manuscript does not show any clear progression towards the conclusions and this makes it difficult to assess its scientific quality and the claims that it makes.

Strengths:<br /> The intentions of the paper are clear and the design of the experiment itself is simple to follow. The paper presents some evidence for normalization in V1, LO, PFs, EBA, and PPA. The presented study has laid the foundation for a piece of work that could have importance for the field once it is fleshed out.

Weakness:<br /> The paper claims that it provides compelling evidence for normalization in the human brain. Very broadly, the presented data support this conclusion; for the most part, the normalization model is better than the weighted sum model and a weighted average model. However, the paper is limited in how it works its way up to this conclusion. There is no interpretation of how the data should look based on expectations, just how it does look, and how/why the normalization model is most similar to the data. The paper shows a bias in focusing on visualization of the 'best' data/areas that support the conclusions whereas the data that are not as clear are minimized, yet the conclusions seem to lump all the areas in together and any nuanced differences are not recognized. It is surprising that the manuscript does not present illustrative examples of BOLD series from voxel responses across conditions given that it is stated that that it is modeling responses to single voxels; these responses need to be provided for the readers to get some sense of data quality. There are also issues regarding the statistics; the statistics in the paper are not explicitly stated, and from what information is provided (multiple t-tests?), they seem to be incorrect. Last, but not least, there is no report of behavior, so it is not possible to assess the success of the attentional manipulation.

Reviewer #2 (Public Review):

My main concern is in regards to the interpretation of these results has to do with the sparseness of data available to fit with the models. The authors pit two linear models against a nonlinear (normalization) model. The predictions for weighted average and summed models are both linear models doomed to poorly match the fMRI data, particularly in contrast to the nonlinear model. So, while I appreciate the verification that responses to multiple stimuli don't add up or average each other, the model comparisons seem less interesting in this light. This is particularly salient of an issue because the model testing endeavor seems rather unconstrained. A 'true' test of the model would likely need a whole range of contrasts tested for one (or both) of the stimuli, Otherwise, as it stands we simply have a parameter (sigma) that instantly gives more wiggle room than the other models. It would be fairer to pit this normalization model against other nonlinear models. Indeed, this has been already been done in previous work by Kendrick Kay, Jon Winawer and Serge Dumoulin's groups. So far, may concern above has only been in regards to the "unattended" data. But the same issue of course extends to the attended conditions. I think the authors need to either acknowledge the limits of this approach to testing the model or introduce some other frameworks.

Reviewer #3 (Public Review):

In this paper, the authors model brain responses for visual objects and the effect of attention on these brain responses. The authors compare three models that have been studied in the literature to account for the effect of attention on brain responses to multiple stimuli: a normalization model, a weighted average model, and a weighted sum model.

The authors presented human volunteers with images of houses and bodies, presented in isolation or together, and measured fMRI brain activity. The authors fit the fMRI data to the predictions of these three models, and argue that the normalization model best accounts for the data.

The strengths of this study include a relatively large number of participants (N=19), and data collected in a variety of different visual brain regions. The blocked design paradigm and the large number of fMRI runs enhance the quality of the dataset.

Regarding the interpretation of the findings, there are a few points that should be considered: 1) The different models that are being studied have different numbers of free parameters. The normalization model has the highest number of free parameters, and it turns out to fit the data the best. Thus, the main finding could be due to the larger number of parameters in the model. The more parameters a model has, the higher "capacity" it has to potentially fit a dataset. 2) In the abstract, the authors claim that the normalization model best fits the data. However, on closer inspection, this does not appear to be the case systematically in all conditions, but rather more so in the attended conditions. In some of the other conditions, the weighted average model also appears to provide a reasonable fit, suggesting that the normalization model may be particularly relevant to modeling the effects of attention. 3) In the primary results, the data are collapsed across five different conditions (isolated/attended for preferred and null stimuli), making it difficult to determine how each model fares in each condition. It would be helpful to provide data separately for the different conditions.

Reviewer #1 (Public Review):

The article by Mann et al. describes a knockin (KI) mouse model of mitofusin 2- related lipodystrophy, in mice carrying MFN2 R707W. The mice recapitulate some but not all aspects of the human phenotype, as summarized in Table 2. The phenotypic characterization is extensive and is generally well done. There was an adipose-specific alteration of mitochondrial morphology, accompanied by activation of the integrated stress response and reduced adipokine secretion. These findings are consistent with the human phenotype. The alteration in fat distribution that is present in humans with this mutation was not observed, and the mice did not have the insulin resistance seen in humans. The transcriptome analyses revealed a reduced epithelial-mesenchymal transition (EMT) in the KI mice, suggesting possible involvement of TGF-beta related pathways. There was also upregulation of the mTorc signaling pathway, suggesting that a possible therapeutic approach in humans may involve the mTORC1 inhibitor sirolimus. The reason for the largely adipose -specific effect of the mutation remains unexplained. As well, the hypothesis that changes in EMT pathways reflect altered activity of TGF-beta pathways must remain somewhat speculative at this point. Notwithstanding these weaknesses, the manuscript provides an important advance in understanding this lipodystrophy (and potentially other lipodystrophies), and the model that has been generated will enable further studies to further characterize the pathophysiology.

Reviewer #2 (Public Review):

This study generated a valuable preclinical model of patients with Mfn2-related lipodistrophy (R707W). Such a mouse model enables the understanding the pathogenic mechanism causing this lipodistrophy and testing specific therapeutic approaches for these patients.

The strengths are the thorough phenotypic characterization of the mice and the clear decrease in circulating leptin and adiponectin levels in the absence of changes in fat mass observed in Mfn2 R707W/R707W mice. This partially recapitulates one of the key phenotypes of human patients with these mutations.

The major weakness is the conclusion that the integrated stress response is activated in white adipose tissue is not supported by the data and the phenotype. The ISR caused by primary insults to mitochondria was defined as a response that decreases the translation of mitochondrial proteins, thus decreasing mitochondrial respiratory function via ATF4 without engaging ATF5 (Quiros et al., JCB 2016). In addition, the increase in ATF4 caused by phosphorylation of eif2alpha is in ATF4 translation and translocation to the nucleus, not in ATF4 transcription. It is a possibility that it is a selective increase in ER stress that is responsible for defective leptin secretion, as Mfn2 R707W/R707W adipose tissue shows no mitigation of mitochondrial function as expected from ATF4-ISR activation.

Reviewer #3 (Public Review):

Mann and colleagues have generated a knock-in mouse model carrying a recently identified mutation in the Mfn2 gene that leads to a syndrome of severe upper body adipose overgrowth in humans (Mfn2R707W). The goal was to gain a better mechanistic understanding on how this mutation leads to such a dramatic phenotype in humans. The authors consistently demonstrate how the knock-in mutation leads to abnormalities in mitochondrial shape, mtDNA content, as well as in the abundance of some mitochondrial proteins, most notably in brown adipose tissue. The authors detect some stress response signatures, which could explain the decreased leptin and adiponectin levels observed in the knockin mice.

The authors have to be praised for their effort in trying to provide mechanistic insights to such a rare condition. This work constitutes a real tour de force in the characterization of Mfn2R707W mice. The path, however, was full of surprises. On one side, the knockin mouse model fails to recapitulate multiple aspects of the human syndrome. This is, of course, beyond the control of the researchers, but somehow tells us that there are some elements missing in our understanding of the effects of this Mfn2 mutation at the cellular level (not just organismal), and on why it impacts so much adipose tissues. A second layer of complexity is that the authors find an interesting connection between Mfn2R707W, the integrated stress response and a severe decrease in the expression of leptin and adiponectin. However, whether these elements have any causal role in the human syndrome or in the phenotypes observed in the mice, remains an open question.

Reviewer #1 (Public Review):

In this study, Barthe et al. developed an approach to selectively activate beta-adrenergic receptors in the sarcolemma of ventricular myocytes. The approach involved the linking of a 5Kd PEG chain to the beat agonist isoprenaline. This prevents the agonist from entering transverse tubules. Using this approach, the authors find that activation of beta-adrenergic receptors in the surface sarcolemma of ventricular myocytes leads to lower cytosolic cAMP levels but longer-lasting effects on EC coupling than when TT receptors were activated.

Strengths of the study:<br /> 1) The PEG-ISO, size exclusion approach is very interesting and useful.<br /> 2) The observation that activation of beta-adrenergic receptors in the surface sarcolemma of ventricular myocytes leads to lower cytosolic cAMP levels, but longer-lasting effects on EC coupling than when TT receptors were activated is interesting.<br /> 3) The observation that beta-adrenergic receptors in the TT lead to stronger nuclear activation of nuclear cAMP/PKA signaling is interesting.

Weaknesses of the study:<br /> 1) There seems to be a paucity of mechanistic insights into the study.<br /> 2) It is unclear what would be the ideal control for these experiments. Would the addition of the PEG chain, by itself, alter the binding of and activation of beta-adrenergic receptors regardless of their location?<br /> 3) The novelty of the findings is unclear, as other studies have suggested differential effects of beta-adrenergic receptors in membrane compartments.

Impact on the field:<br /> 1) PEG-ISO may become a useful strategy to selectively activate surface sarcolemmal beta-adrenergic receptors.

Reviewer #2 (Public Review):

Barthé et al. present a manuscript examining membrane-domain specific signaling by βAR stimulation in cardiomyocytes. Specifically, the authors seek to use a size exclusion approach using PEGylated-isoproterenol to allow only surface sarcolemmal βAR receptor stimulation without T-tubule βAR stimulation. This innovative approach was advanced using confocal microscopy to determine the accessibility of the PEGylated substrates to the T-tubule network. The authors show comparable responses of L-type Ca channels, Ca transients, and contraction using equipotent doses of PEG-Iso and Iso, but differences in nuclear and cytoplasmic cAMP responses based on FRET reporters.

Strengths<br /> 1. The size exclusion strategy using PEGylation technology is well rationalized and well supported by the physicochemical characterization of PEGylated Iso. This represents a novel strategy to decipher cardiomyocyte cell surface signaling from T-tubule network signaling resulting from the stimulation of β-adrenergic receptors. This approach can be used to study the compartmentalization of various signaling pathways in cardiomyocytes as well as in other cell types that exhibit complex cytoarchitecture. The authors use multiple cAMP FRET sensors as well as assay a number of relevant physiological cellular responses to assess the effect of Iso vs. PEGylated Iso which are informative.

Weaknesses<br /> 1. The authors' evidence that PEG-FITC does not penetrate the TT network is not convincing as presented in Figure 1. A single confocal image from one cell showing a lack of fluorescence (Figure 1A) could be due to an outlier cell or lack of penetration to more central regions of the cell where images are taken from. More convincing would be a confocal Z-scan series comparing PEG-FITC and FITC in ARVM. Some form of quantification of T-tubule network density from multiple cells would provide even more robust evidence, similar to the many studies that have done this characterization in models of dilated cardiomyopathy showing a loss of TT network. This exclusion of PEG-FITC provides the critical foundation for the paper and it is somewhat unanticipated given the large dimensions of the t-tubules relative PEG-Iso, so strong data here are particularly important.

2. The conclusion on line 160 that 'the maximal efficacy of PEG-Iso was significantly lower by 30% than that of Iso,' may be overstated. What approach was used to conclude significantly differently as this implies a statistical comparison? Were the concentration-response curves fit to determine maximal responses? In the examples given, the responses are continuing to increase at the highest concentrations tested, so it is difficult to simply compare the responses to the highest doses tested.

3. For experiments using adenovirus delivery of FRET-based sensor, the culture of ARVM is required which may impact the biology. Such culture is known to result in changes in cell structure and physiology with loss of the TT network over time. It is essential for the authors to demonstrate that under the conditions of their FRET experiments, the cells continue to exhibit a robust TT network.

4. As pointed out by the authors, the interpretation of OSM/TTM adrenergic receptor functions in this study is limited by the fact that the relative contributions of β-adrenergic receptor subtypes had not been assessed. This particularly complicates the interpretation of their results in that the authors demonstrate in Figure 2 that PEGylation increases the Ki for Iso for β1 receptors by 700-fold whereas the increase for β2 receptors is about 200-fold. Thus, the relative contribution of β1 and β2 receptors to a 'comparable' dose of Iso and PEGylated Iso will potentially be different. Could that difference in relative β1/β2 receptors be the cause of the different 'efficacy of nuclear and cytoplasmic' cAMP changes between the two tested ligands in Figure 8 and supplemental Figure 3? This would fundamentally alter the conclusions of the paper.

5. The equipotent doses of Iso and PEG-Iso were initially defined based on their ability to elevate global [cAMP]i. The authors then further demonstrated that such equipotent doses of Iso and PEG-Iso also had equal effects on ICa,L amplitude, Ca2+ transient parameters, and cellular contractility (shortening), presumably because they raised global [cAMP]i to the same levels. These findings seem to defy the importance of nanodomain organization and local [cAMP]i in the regulation of LTCCs, Ca2+ cycling proteins, and contractile machinery. The authors argued that "Since OSM contributes to ~60% of total cell membrane in ARVMs, either β-ARs and ACs are more concentrated in OSM than TTM, or they are in large excess over what is needed to activate PKA phosphorylation of proteins involved in EC coupling. Also, cAMP produced at OSM must diffuse rapidly in the cytosol in order to activate PKA phosphorylation of substrates located deep inside the cell, such as LTCCs in TTM" (lines 336-341). Although this argument may be valid at high concentrations of Iso and PEG-Iso when PKA activation is saturated, it also implies that discrepancy could be detectable at lower (non-saturating) doses of Iso and PEG-Iso. Thus, additional experiments using lower Iso and PEG-Iso doses are required to support this notion.

6. The size excluded compartment for PEG-Iso proposed by the authors is the TT network, but this ignores other forms of sarcolemmal nanodomains such as caveolae, which include β2 receptors and AC, and may exhibit similar if not great sensitivity to the size exclusion approaches pioneered by the authors.

Reviewer #3 (Public Review):

The manuscript by Barthe et al compares the effects derived from the application of isoprenaline (Iso) or isoprenaline covalently linked to PEG (PEG-Iso) on adult rat ventricular myocytes (ARVM). Iso is a well-characterized β-AR agonist and the authors work under the assumption that PEGylation of Iso prevents it from accessing the T-tubules. Therefore, due to its larger size, PEG-Iso is only able to activate β-ARs located on the outer surface membrane (OSM), and any additional effect observed by Iso stimulation is attributed to the activation of β-ARs located in T-tubules. First, the authors determined that the affinity of PEG-Iso for β-ARs is about 100 times lower than the one of Iso. Then, they analyze the effects of Iso (10 nM) and PEG-Iso (1 µM) on calcium channel currents, contractility, calcium transients, and cytosolic and nuclear PKA activity. They only found a stronger effect of Iso on nuclear pKA activity. Therefore they conclude that, while OSM β-ARs stimulation mainly results in positive inotropy and lusitropy, T-tubules ARs stimulation mainly results in increased nuclear pKA activity.

Overall the manuscript is well written and the findings are biologically important from the perspective of understanding the mechanism of β-AR stimulation as well as in assigning the functional contribution of β-ARs in the OSM and in the T-tubules. However, the major conclusion is not strongly supported by the data. The interpretation of the results is all based on the assumption that PEG-Iso is excluded by the T-tubules, but no experiment presented here rigorously demonstrates this.

1. The only indication that PEG-Iso may be excluded by the T-tubules is one confocal image in which FITC or PEG-FITC were applied on ARVM. No experiment has been performed to assess if PEG-Iso is indeed not able to enter the T-tubules.<br /> The treatment of ARVM with neuraminidase made the T-tubules accessible to PEG-FITC. If the authors could demonstrate that neuraminidase treatment followed by PEG-Iso would result in similar nuclear pKA activity as Iso, this would strengthen their conclusion.<br /> 2. The fact that PEG-Iso treatment resulted in a lower increase of intracellular cAMP (Figure 3) could also be due to the activation of a smaller fraction of β-ARs, independent of their localization.

Reviewer #1 (Public Review):

In this manuscript, Braet et al provide a rigorous analysis of SARS-CoV-2 spike protein dynamics using hydrogen/deuterium exchange mass spectrometry. Their findings reveal an interesting increase in the dynamics of the N-terminal domain that progressed with the emergence of new variants. In addition, the authors also observe an increase in the stabilization of the spike trimeric core, which they identify originates from the early D614G mutation.

Overall this is a timely and interesting exploration of spike protein dynamics, which have so far remained largely unexplored in the literature.<br /> What I find a bit missing in this manuscript is a link between how the identified changes in protein dynamics lead to increased viral fitness. While there are some possibilities listed in the discussion, I think these should be elaborated upon further. In addition, it should also be discussed how understanding the changes in the spike protein dynamics could have implications for the development of small molecule inhibitors for the virus.

Reviewer #2 (Public Review):

The study systematically looks at dynamic differences across variants longitudinally and the authors appropriately only limit their analyses to peptides that are conserved across the different variants.

There are some concerns listed below, particularly related to the ensemble heterogeneity that is reported and need considerable revision.

1) The authors explain that cold-temperature treatment of the S trimer ectodomain constructs has been shown to lead to instability and heterogeneity. They also show this with a comparison of untreated vs. 3-hour 37 C treated samples. I'm confused as to why "During automated HDXMS experiments protein samples were stored at 0 degrees". Will this not cause issues in protein heterogeneity, where the longer the protein sits at 0 C the more potential heterogeneity there will be, and thus greatly confound the analysis?

2) The authors presume that the bimodal spectra that are observed reflect EX1 kinetics, however, there can be multiple reasons for an apparent bimodal distribution in the spectra. I agree that some of the spectra indicate that more than a single species is present, but what the two populations represent is murky. In Figure 2D, the apparent size of the highly deuterated population gets larger going from the 60 sec to the 600-sec spectra, as expected for an EX1 transition. However, in Figure 3D the WT highly deuterated population gets smaller going from the 60-sec to the 600-sec spectra. Were bimodal examples observed beyond those shown in Figure 2?

3) How were the spectra that appeared broadened analyzed? There is no description of this in the methods, and the only data shown for this is in table 1. The left/right percentages are reported without any description of how they were obtained. Are these solely from a single spectrum? The most alarming issue is that Table 1B reports 9.4% for the right population of the 988-998 peptide, but the corresponding spectra in Figure 3D doesn't seem to have any highly deuterated population at all.

4) The authors state on page 12: "Replicate analysis of stabilized S trimers with incubation at 4C prior to deuterium exchange (see methods) showed a time-dependent reversal of stabilization as reported previously (Costello et al., 2022), most evident at the same peptides." Is this data shown anywhere? If not then it should be included somewhere, possibly in table 1 as I would expect the cold treatment to offset the left/right population sizes.

5) The authors state that peptide 899-913 'exhibits a slow conformational interconversion (time scale ~ 15-30 min)'. Where did this estimated rate come from? From the data shown and the limited number of time points, I don't think there is sufficient sampling of this conformational transition to really narrow down the exact timescale, especially since the ratio of left/right populations is so dependent on the pre-treatment of the sample prior to deuterium exchange. (See 1st comment)

6) The woods plots presented in the Supporting information: (Figures 2-S4, 2-S5, 3-S4, 4-S2, 5-S2, 6-S2) are not conventional Woods plots. Normally the plots would indicate a global threshold for what is deemed to be significant based on the overall error in the dataset. From what I gather the authors used error within an individual peptide to establish significance for each specific peptide, which would be okay, but the authors don't describe the number of replicates or how the p-value was calculated. I would strongly recommend that the authors instead rely on a hybrid significance testing approach, as described recently: (PMID 31099554). What's really alarming with the current approach is that several of the Woods plots shown have data points found to be significantly different that are right at zero on the y-axis.

7) Table 1: The summary of the peptides with observed bimodal behavior should include data from the replicates, particularly for assessment of how consistent the left/right population sizes are across replicates. Instead of just a percentage, the table should report an average and the standard deviation from the replicate measurements. Furthermore, the table should also include peptides that are overlapping with those presented. Based on Figure 2-figure supplement 1, there are at least two other peptides that cover the 899-913 region. These additional peptides should show a similar trend with bimodal profiles and will be important for showing how reproducible the apparent EX1 kinetics are in the dataset.<br /> All available replicates and overlapping peptides should be analyzed to ensure that these percentages reported are consistent across the data. It is also odd that the authors choose to use the 3+ charge state of the WT, but the 2+ for the D614G mutant. If both charge states were present, then both of them should be analyzed to ensure the population distributions are consistent within different charge states.

8) The method for calculating p-values used to assess the significance of a difference in observed deuterium uptake is not described. The manuscript mentions technical replicates, but no specific information as to how many replicates were collected for each time point. These details should be included as they are also part of the summary table that is recommended for the publication of HDX data.

Reviewer #3 (Public Review):

The authors use hydrogen-deuterium exchange mass spectrometry (HDXMS) to assess the dynamics of several relevant mutant forms of SARS-CoV 2 Spike protein including the most recent Omicron variant. The Spike protein is heavily glycosylated and is a trimer so is a very difficult protein to study by HDXMS. The authors confirm the glycosylation sites, which can't be covered by the HDXMS experiment, yet they still manage to cover nearly 50% of the sequence revealing many interesting changes in dynamics in the prevalent circulating mutant forms. The beautiful HDXMS data reveal consistent trends as SARS-CoV2 mutates to survive including stabilization of the stalk and increased dynamics of the N-terminal domain where ACE2 receptor binding occurs. The authors incubate the protein at 37C and discover additional stabilization of the trimer occurs under these conditions explaining a lot of conflicting data in the literature done at different temperatures. These results have profound implications for the development of small molecule inhibitors of the Spike protein-ACE2 interaction.