Reviewer #2 (Public Review):

Slusarczyk et al. investigate the functional impairment of red pulp macrophages (RPMs) during aging. When red blood cells (RBCs) become senescent, they are recycled by RPMs via erythrophagocytosis (EP). This leads to an increase in intracellular heme and iron both of which are cytotoxic. The authors hypothesize that the continuous processing of iron by RPMs could alter their functions in an age-dependent manner. The authors used a wide variety of models: in vivo model using female mice with standard (200ppm) and restricted (25ppm) iron diet, ex vivo model using EP with splenocytes, and in vitro model with EP using iRPMs. The authors found iron accumulation in organs but markers for serum iron deficiency. They show that during aging, RPMs have a higher labile iron pool (LIP), decreased lysosomal activity with a concomitant reduction in EP. Furthermore, aging RPMs undergo ferroptosis resulting in a non-bioavailable iron deposition as intra and extracellular aggregates. Aged mice fed with an iron restricted diet restore most of the iron-recycling capacity of RPMs even though the mild-anemia remains unchanged.

Overall, I find the manuscript to be of significant potential interest. But there are important discrepancies that need to be first resolved. The proposed model is that during aging both EP and HO-1 expression decreases in RPMs but iron and ferroportin levels are elevated. In their model, the authors show intracellular iron-rich proteinaceous aggregates. But if HO-1 levels decrease, intracellular heme levels should increase. If Fpn levels increase, intracellular iron levels should decrease. How does LIP stay high in RPMs under these conditions? I find these to be major conflicting questions in the model.

Reviewer #3 (Public Review):

This is a comprehensive study of the effects of aging of the function of red pulp macrophages (RPM) involved in iron recycling from erythrocytes. The authors document that insoluble iron accumulates in the spleen, that RPM become functionally impaired, and that these effects can be ameliorated by an iron-restricted diet. The study is well written, carefully done, extensively documented, and its conclusions are well supported. It is a useful and important addition for at least three distinct fields: aging, iron and macrophage biology.

The authors do not explain why an iron-restricted diet has such a strong beneficial effect on RPM aging. This is not at all obvious. I assume that the number of erythrocytes that are recycled in the spleen, and are by far the largest source of splenic iron, is not changed much by iron restriction. Is the iron retention time in macrophages changed by the diet, i.e. the recycled iron is retained for a short time when diet is iron-restricted (making hepcidin low and ferroportin high), and long time when iron is sufficient (making hepcidin high and ferroportin low)? Longer iron retention could increase damage and account for the effect. Possibly, macrophages may not empty completely of iron before having to ingest another senescent erythrocyte, and so gradually accumulate iron.

Reviewer #1 (Public Review):

The study by Xie et al., investigates whether the entorhinal-DG/CA3 pathway is involved in working memory maintenance. The main findings include a correlation between stimulus and neural similarities that was specific for cued stimulus and entorhinal-DG/CA3 locations. The authors observed similar results (cuing and region specificity) using inverted encoding modeling approach. Finally, they also showed that trials in which participants made a smaller error showed a better reconstruction fidelity on the cued side (compared to un-cued). This effect was absent for larger-error trials.

The study challenges a widely held traditional view that working memory and episodic memory have largely independent neural implementations with the MTL being critical for episodic memory but not for working memory. The study adds to a large body of evidence showing involvement of the hippocampus across a range of different working memory tasks and stimuli. Nevertheless, it still remains unclear what functions may hippocampus play in working memory.

Reviewer #2 (Public Review):

Xie et al. investigated the medial temporal lobe (MTL) circuitry contributions to pattern separation, a neurocomputational operation to distinguish neutral representations of similar information. This presumably engages both long-term memory (LTM) and working memory (WM), bridging the gap between the working memory (WM) and long-term memory (LTM) distinction. Specifically, the authors combined an established retro-cue orientation WM task with high-resolution fMRI to test the hypothesis that the entorhinal-DG/CA3 pathway retains visual WM for a simple surface feature. They found that the anterior-lateral entorhinal cortex (aLEC) and the hippocampal DG/CA3 subfield both retained item-specific WM information that is associated with fidelity of subsequent recall. These findings highlight the contribution of MTL circuitry to item-specific WM representation, against the classic memory models.

I am a long-term memory researcher with expertise in representational similarity analysis, but not in inverted encoding modeling (IEM). Therefore, I cannot verify the correctness of these models and I will leave it to the other reviewers and editors. However, after an in-depth reading of the manuscript, I could evaluate the significance of the present findings and the strength of evidence supporting these findings. The conclusions of this paper are mostly well supported by data, but some aspects of image acquisition and data analysis need to be clarified. I would like to list several strengths and weaknesses of this manuscript:

Strengths:<br /> • Methodologically, the authors addressed uncertainty in previous research resulting from several challenges. Namely, they used a high-resolution fMRI protocol to infer signals from the MTL substructures and an established retro-cue orientation WM task to minimize the task load.<br /> • The authors selected a control ROI - amygdala - irrelevant for the experimental task, and at the same time adjacent to the other MTL ROIs, thus possibly having a similar signal-to-noise ratio. The reported effects were observed in the aLEC and DG/CA3, but not in the amygdala.<br /> • Memory performance, quantified as recall errors, was at ceiling - an average recall error of 12 degrees was only marginally away from the correct grating towards the closest incorrect grating (predefined with min. 20 degrees increments). However, the authors controlled for the effects of recall fidelity on MTL representations by comparing the IEM reconstructions between precise recall trials and imprecise recall trails (resampled to an equal number of trials). The authors found that precise recall trails have yielded better IEM reconstruction quality.<br /> • The author performed a control analysis of time-varying IEM to exclude a possibility that the mid-delay period activity in the aLEC-DG/CA3 contains item-specific information that could be attributed to perceptual processing. This analysis showed that the earlier TR in the delay period contains information for both cued and uncued items, whereas the mid-delay period activity contains the most information related to the cued, compared to uncued, item.

Weaknesses:<br /> • The authors formulate their main hypothesis building on an assumption related to the experimental task. This task requires correctly selecting the cued grating orientation while resisting the interference from internal representations of the other orientation gratings. The authors hypothesize that if this post-encoding information selection function is supported by the MTL-s entorhinal-DG/CA3 pathway, the recorded delay-period activity should contain more information about the cued item that the uncued item (even if both are similarly remembered). Thus, the assumption here is that resolving the interference would be reflected by a more distinct representation in MTL for the cued item. Could it be the opposite, namely the MTL could better represent the unresolved interference, for example by the mechanism of hippocampal repulsion (Chanales et al., 2017). It could strengthen the findings if the authors comment on the contrary hypothesis as well.<br /> • It is not clear for me why the authors chose the inverted encoding modelling approach and what is its advantage over the others multivoxel pattern analysis approaches, for example representational similarity analysis also used in this study. How are these two complementary? Since the IEM is still a relatively new approach, maybe a little comment in the manuscript could help emphasizing the strength of the paper? Especially that this paper is of interest to researchers in the fields of both working memory and long-term memory, the latter being possibly not familiar with the IEM.

Overall, this work can have a substantial impact of the field due to its theoretical and conceptual novelty. Namely, the authors leveraged an established retro-cue task to demonstrate that a neurocomputational operation of pattern separation engages both working-memory and long-term memory, both mediated by the MTL circuitry, beyond the distinction in classic memory models. Moreover, on the methodological side, using the multivariate pattern analyses (especially the IEM) to study neural computations engaged in WM and LTM seems to be a novel and promising direction for the field.

Reviewer #3 (Public Review):

This work addresses a long-standing gap in the literature, showing that the medial temporal lobe (MTL) is involved in representing simple feature information during a low-load working memory (WM) delay period. Previously, this area was suggested to be relevant for episodic long-term memory, and only implicated in working memory under conditions of high memory load or conjunction features. Using well-rounded analyses of task-dependent fMRI data in connection with a straightforward behavioural experiment, this paper suggests a more general role of the medial temporal lobe in working memory delay activity. It also provides a replication of previous findings on item-specific information during working memory delay in neocortical areas.

Strengths:<br /> The study has strengths in its methods and analyses. Firstly, choosing a well-established cueing paradigm allows for straightforward comparison with past and future studies using similar paradigms. The authors themselves show this by replicating previous findings on delay-period activity in parietal, frontal, and occipito-temporal areas, strengthening their own and previous findings. Secondly, they use a template with relatively fine-grained MTL-subregions and choose the amygdala as a control area within the MTL. This increases confidence in the finding that the hippocampus in particular is involved in WM delay-period activity. Thirdly, their combined use stimulus-based representational similarity analysis as well as Inverted Encoding Modeling and the convergence on the same result is encouraging. Finally, despite focusing on the delay period in their main findings, extensive supplementary materials give insight into the time-course of processing (encoding) which will be helpful for future studies.

Weaknesses:<br /> While the evidence generally supports the conclusions, there are some weaknesses in behavioural data analysis. The authors demonstrated fine stimulus discrimination in the neural data using Inverted Encoding Modeling (IEM), however the same standard is not applied in the behavioural data analysis. In this analysis, trials below 20 degrees and trials above 20 degrees of memory error are collapsed to compare IEM decoding error between them. As a result, the "small recall error" group encompasses a total range of 40 degrees and includes neighbouring stimuli. While this is enough to demonstrate that there was information about the remembered stimulus, it does not clarify whether aLEC/CA3 activity is associated with target selection only or also with reproduction fidelity. It leaves open whether fine-grained neural information in MTL is related to memory fidelity.

Moreover, the authors could be more precise about the limitations of the study and their conclusions. In particular, the paper at times suggests that the results contribute to elucidating common roles of the MTL in long-term memory and WM, potentially implementing a process called pattern separation. However, while the paper convincingly shows MTL-involvement in WM, there is no comparison to an episodic memory condition. It therefore remains an open question whether it fulfils the same role in both scenarios. Moreover, the paradigm might not place adequate pattern separation demands on the system since information about the un-cued item may be discarded after the cue.

Reviewer #1 (Public Review):

In this study, they demonstrate that neonatal mice produce more CD43- B cell-derived IL-10 following anti-BCR stimulation than adult mice. This is due to autocrine mechanisms whereby anti-BCR stimulation leads to pSTAT5 upregulation and production of IL-6 which then enhances IL-10 production via pSTAT3. These are interesting results for the regulatory B cell field, demonstrating that signaling is different in adult vs neonatal B cells and in particular for researchers studying the mechanisms underpinning the enhanced susceptibility to infection. The authors in the main achieved their aim and the results support their conclusions. However, considering that other studies have previously addressed the mechanisms contributing to enhanced IL-10 production in neonates, in the manuscript, there are some experimental decisions and data presentation decisions that at times need more explanation. An important additional comment is that the introduction/discussion is at times insufficiently referenced to put the data in context for non-experts in this field and that numbers in general are low for an in vitro study.

Reviewer #2 (Public Review):

This paper reports that neonatal CD43- B cells produce IL-10 upon BCR stimulation, which inhibits TNF-alpha secretion from the peritoneal macrophage. In the neonatal CD43- B cells, the BCR-mediated signal transmitted Stat5 activation and induced IL-6 production, and subsequently, the secreted IL-6 activated Stat3 finally leading to IL-10 production. The authors identified a unique signaling pathway leading to IL-10 production and revealed the different responses between CD43+ and CD43- B cells against BCR crosslinking. A weakness of this study is that the neonatal CD43- B cell subset secreting IL-10 has not been characterized and discussed as well. BCR expression levels between adult CD43- B cells and neonatal CD43- B cells have been overlooked to explain the different reactivity. Clarity on these points would substantially enhance the impact of the manuscript.

Reviewer #1 (Public Review):

In this study, the protein composition of exocytotic sites in dopaminergic neurons is investigated. While extensive data are available for both glutamatergic and GABA-ergic synapses, it is far less clear which of the known proteins (particularly proteins localized to the active zone) are also required for dopamine release, and whether proteins are involved that are not found in "classical" synapses. The approach used here uses proximity ligation to tag proteins close to synaptic release sites by using three presynaptic proteins (ELKS, RIM, and the beta4-subunit of the voltage-gated calcium channel) as "baits". Fusion proteins containing BirA were selectively expressed in striatal dopaminergic neurons, followed by in-vivo biotin labelling, isolation of biotinylated proteins and proteomics, using proteins labelled after expression of a soluble BirA-construct in dopaminergic neurons as reference. As controls, the same experiments were performed in KO-mouse lines in which the presynaptic scaffolding protein RIM or the calcium sensor synaptotagmin 1 were selectively deleted in dopaminergic neurons. To control for specificity, the proteomes were compared with those obtained by expressing a soluble BirA construct. The authors found selective enrichments of synaptic and other proteins that were disrupted in RIM but not Syt1 KO animals, with some overlap between the different baits, thus providing a novel and useful dataset to better understand the composition of dopaminergic release sites.

Technically, the work is clearly state-of-the-art, cutting-edge, and of high quality, and I have no suggestions for experimental improvements. On the other hand, the data also show the limitations of the approach, and I suggest that the authors discuss these limitations in more detail. The problem is that there is very likely to be a lot of non-specific noise (for multiple reasons) and thus the enriched proteins certainly represent candidates for the interactome in the presynaptic network, but without further corroboration it cannot be claimed that as a whole they all belong to the proteome of the release site.

Reviewer #2 (Public Review):

The Kaiser lab has been on the forefront in understanding the mechanism of dopamine release in central mammalian neurons. assessing dopamine neuron function has been quite difficult due to the limited experimental access to these neurons. Dopamine neurons possess a number of unique functional roles and participate in several pathophysiological conditions, making them an important target of basic research. This study here has been designed to describe the proteome of the dopamine release apparatus using proximity biotin labeling via active zone protein domains fused to BirA, to test in which ways its proteome composition is similar or different to other central nerve terminals. The control experiments demonstrating proper localization as well as specificity of biotinylation are very solid, yielding in a highly enriched and well characterized proteome data base. Several new proteins were identified and the data base will very likely be a very useful resource for future analysis of the protein composition of synapse and their function at dopamine and other synapses.

Major comment:

The authors find that loss of RIM leads to major reduction in the number of synaptically enriched proteins, while they did not see this loss of number of enriched proteins in the Syt1-KO's, arguing for undisrupted synaptome. Maybe I missed this, but which fraction of proteins and synaptic proteins are than co-detected both in the Syt1 and control conditions when comparing the Venn diagrams of Fig2 and Fig 3 Suppl. 2? This analysis may provide an estimate of the reliability of the method across experimental conditions.

Reviewer #3 (Public Review):

In this study Kershberg et al use three novel in vivo biotin-identification (iBioID) approaches in mice to isolate and identify proteins of axonal dopamine release sites. By dissecting the striatum, where dopamine axons are, from the substantia nigra and VTA, where dopamine somata are, the authors selectively analyzed axonal compartments. Perturbation studies were designed by crossing the iBioID lines with null mutant mice. Combining the data from these three independent iBioID approaches and the fact that axonal compartments are separated from somata provides a precise and valuable description of the protein composition of these release sites, with many new proteins not previously associated with synaptic release sites. These data are a valuable resource for future experiments on dopamine release mechanisms in the CNS and the organization of the release sites. The BirA (BioID) tags are carefully positioned in three target proteins not to affect their localization/function. Data analysis and visualization are excellent. Combining the new iBioID approaches with existing null mutant mice produces powerful perturbation experiments that lead and strong conclusions on the central role of RIM1 as central organizers of dopamine release sites and unexpected (and unexplained) new findings on how RIM1 and synaptotagmin1 are both required for the accumulation of alpha-synuclein at dopamine release sites.

It is not entirely clear how certain decisions made by the authors on data thresholds may affect the overall picture emerging from their analyses. This is a purely hypothesis-generating study. The authors made little efforts to define expectations and compare their results to these. Consequently, there is little guidance on how to interpret the data and how decisions made by the authors affect the overall conclusions. For instance, the collection of proteins tagged by all three tagging strategies (Fig 2) is expected to contain all known components of dopamine release sites (not at all the case), and maybe also synaptic vesicles (2 TM components detected, but not the most well-known components like vSNAREs and H+/DA-transporters), and endocytic machinery (only 2 endophilin orthologs detected). Whether or not a more complete collection the components of release sites, synaptic vesicles or endocytic machinery are observed might depend on two hard thresholds applied in this study: (a) "Hits" (depicted in Fig 2) were defined as proteins enriched {greater than or equal to} 2-fold (line 178) and peptides not detected in the negative control (soluble BirA) were defined as 0.5 (line 175). How crucial are these two decisions? It would be great to know if the overall conclusions change if these decisions were made differently.<br /> Given the good separation of the axonal compartment from the somata (one of the real experimental strengths of this study), it is completely unexpected to find two histones being enriched with all three tagging strategies (Hist1h1d and 1h4a). This should be mentioned and discussed.

It would also help to compare the data more systematically to a previous study that attempted to define release sites (albeit not dopamine release sites) using a different methodology (biochemical purification): Boyken et al (only mentioned in relation to Nptn, but other proteins are observed in both studies too, e.g. Cend1).

Reviewer #1 (Public Review):

This is an exciting paper describing the development of a robust differentiation of the common marmoset induced pluripotent stem cells (iPSCs) into primordial germ cell-like cells and subsequently into spermatogonia-like cells when combined with testis somatic cells. The work is of high quality, but some experimental details and protocols are missing which are necessary for a new protocol development - for example, reconstitution methods and protocols are missing completely in the manuscript and additional details in various aspects of the differentiation and cell maintenance are missing. Despite this, the work is valuable and would be of interest to the germ cell and in vitro gametogenesis communities. The data suggest that marmosets are very similar to humans and macaques, and indeed previously established protocols for PGCLC induction and likely previously published testis reconstitution methods/differentiation were employed here to generate the spermatogonia-like cells.

Reviewer #2 (Public Review):

This paper identifies the need for improved pre-clinical models for the study of human primordial germ cells (PGCs) and suggests the common marmoset (Callithrix jacchus) as a suitable primate model. In vitro gametogenesis offers an alternative method to generate germ cells from pluripotent stem cells for study and potential pre-clinical applications. Therefore, the authors aimed to take the first steps toward developing this technology for the marmoset. Here, iPSCs have been derived from the marmoset and differentiated to PGC like-cells (PGCLCs) in vitro that have similarities in gene expression with PGCs identified from single-cell studies of marmoset embryos, as demonstrated through immunofluorescence and RT-qPCR approaches, as well as RNA-sequencing.

The authors have successfully developed a protocol that produces PGCLCs from marmoset iPSCs. These are shown to express key germline gene markers and are further shown to correlate in gene expression with PGCs from the marmoset. This study uses a 2D culture system for further expansion of the PGCLCs. When cultured with mouse testicular cells in a xenogeneic reconstituted testis culture, evidence is provided that cjPGCLCs have the capacity to develop further, expressing marker genes for later germline differentiation. However, the efficiency of generating these prospermatogonia-like cells in culture is unclear. Nonetheless, with the importance of developing protocols across species for in vitro gametogenesis, this paper takes a key step towards generating a robust preclinical system for the study of germ cells in the marmoset.

The claims of the authors are generally justified by the data provided; however, some conclusions should be clarified. In particular, the authors have failed to show convincingly that cjPGCLCs are a distinct cell type to the iPSCs that generated them. cjiPSCs cultured in feeder conditions (OF) with IWR1 are reported to cluster closely with the derived cjPGCLCs using principal component analysis of RNA-Seq data. This contrasts with the cjiPSCs cultured in feeder-free (FF) conditions which maintain a more undifferentiated/less primed state, and are not capable of differentiating to the germline lineage. Therefore, the OF/IWR1 cjiPSCs could rather be an intermediate cell-state between iPSCs and cjPGCLCs.

The reasons behind improved germline competence of iPSCs in the different media conditions are unclear. The authors reject the idea that this is due to the presence of IWR1, since this condition has not affected FF iPSCs. However, the efficiency of differentiation was greatly increased in OF conditions when IWR1 was used, indicating inhibition of WNT does indeed have a positive effect on induction to the germline lineage. This area requires further clarification.

Another area requiring clarification is the reporting of RNA sequencing data as representative of a developmental trajectory, without defining which cell lines produced clusters, or defining the stages of this trajectory. The authors refer to the identification of four clusters representative of a developmental trajectory, however, they provide unclear information as to what this refers to. Importantly, detailed transcriptomic comparisons between in vivo-derived PGCs and in vitro PGCLCs are not provided.

Functional validation of iPSC lines generated in the study is not provided besides confirming that the cells express pluripotency markers OCT3/4, SOX2, and NANOG. It is important to confirm tri-lineage differentiation of iPSCs, e.g., through an embryoid body assay. Since FF cjiPSCs were unable to differentiate into cgPGCLCs, it is even more important to confirm cells are genuine iPSCs.

In summary, although there are issues surrounding clarity, this paper is generally justified in its conclusions. The authors present an optimised protocol for the derivation of PGCLCs from marmoset iPSC-like cells, with defined expansion conditions and evidence of further differentiation to prospermatogonia-like cells.

Reviewer #1 (Public Review):

Sayin et al. sought to determine if bacterial drug resistance has impact on drug efficacy. They focused on gemcitabine, a drug used for pancreatic cancer that is metabolized by E. coli. Using an innovative combination of genetic screens, experimental evolution, and cancer cell co-cultures to reveal that E. coli can evolve resistance to gemcitabine through loss-of-function mutations in nupC, with potential downstream consequences for drug efficacy.

Major strengths include:<br /> • Paired use of genetic screens and experimental evolution<br /> • The spheroid model is a creative approach to modeling the tumor microbiome that I hadn't seen before<br /> • Rigorous microbiology, including accounting for mutation rate in both selective and non-selective conditions<br /> • Timely research question

Major weaknesses of the methods and results include the following:

1. Limited scope of the current work. Just a single drug-bacterial pair is evaluated and there are no experiments with microbial communities, animal models, or attempts to test the translational relevance of these findings using human microbiome datasets.

2. No direct validation of the primary genetic screen. The authors use a very strict cutoff (16-fold-change) without any rationale for why this was necessary. More importantly, a secondary screen is necessary to evaluate the reproducibility of the results, either by testing each KO in isolation or by testing a subset of the library again.

3. Some methodological concerns about the spheroid system. As I understood it, these cells are growing aerobically, which may not be the best model for the microbiome. Furthermore, bacterial auxotrophs are used and only added for 4 hours, which will really limit their impact. It also was unclear if the spheroids are truly sterile. Finally, the data lacks statistical analysis, making it unclear which KOs are meaningful. Delta-cdd looks clearly distinct by eye, but the other two genes are more subtle.

Despite these concerns, this paper is a valuable addition to the growing literature on interactions between cancer chemotherapy and the microbiome, which will definitely inspire follow-up work in complex microbial communities, animal models, and human cohorts.

Reviewer #2 (Public Review):

Many cancers, including pancreatic tumors, host microbes that have the ability to metabolize anti-cancer drugs, thus altering cancer response to these treatments. However, many anti-cancer drugs also are quite toxic to bacteria. Thus, the authors first investigate how a model bacterium that could live pancreatic tumors can become resistant to the pancreatic chemotherapy gemcitabine. Second, they investigate how bacteria that are resistant to gemcitabine impact cancer cell response to this therapy compared to bacteria that are not resistant. By answering these two questions, the authors hope to determine how bacterial evolution to chemotherapy can impact how well chemotherapy works in pancreatic cancer.

To answer the first question, the authors perform both genetic screens and laboratory evolution experiments of E. coli bacteria exposed to gemcitabine. Both the genetic screen and laboratory evolution experiments identified mutation of the bacterial protein nupC as mediating bacterial resistance to gemcitabine. NupC is the transporter protein that bacteria use to take up gemcitabine. Thus, the authors conclude that loss of ability to take up gemcitabine would likely underlay bacterial evolution to gemcitabine in pancreatic tumors.

To answer the second question, the authors take either control of nupC mutant bacteria and expose these to gemcitabine. They then take the bacterial media with its residual gemcitabine and treat mouse colorectal cancer cells with these media. They find the amount of gemcitabine is higher in nupC mutant media and media from these mutants cause correspondingly higher killing of cancer cells.

Thus, the authors conclude that bacteria become resistant to gemcitabine by not taking it up, leaving more gemcitabine around in tumors to kill the cancer cells. The findings of the first question are a major strength of the manuscript - the complementary genetic screen and laboratory evolution experiment convincingly show that loss of nupC is likely a major genetic route for bacteria to become resistant to gemcitabine. Excellent biochemical studies delineate mechanistically how the different mutations including nupC contribute to gemcitabine resistance in the bacteria.

However, a major weakness of the manuscript is the extension to how this laboratory evolved nupC resistance to gemcitabine influences tumor response to gemcitabine. The only experiments done to assess this are performed in colorectal cell culture models in vitro. Importantly, these in vitro models do not recapitulate chemotherapy resistance observed in pancreas cancer and utilize levels of bacteria and gemcitabine that are likely not relevant to tumor physiology. Thus, additional experiments assessing in vivo if nupC mutations become prevalent in the pancreatic tumor microbiome and how much mutations affect tumor gemcitabine levels and response will be necessary to fully answer the authors second question of how bacterial evolution to gemcitabine affects tumor response to this agent.

If you don't refer back to your notes for any reason, why bother taking them? Were they so boring? Was there nothing of surprise in them for having taken them in the first place?

Often note taking (writing) for understanding can be initially useful, but reviewing over these can be less useful in a larger corpus of notes. File the boring and un-useful things away. Center the important and the surprising.

Reviewer #1 (Public Review):

Monfared et al. construct a three-dimensional phase-field model of cell layers and use it to examine cellular extrusion by independently tuning cell-substrate and cell-cell adhesion. In line with earlier studies (in some of which some of the authors were involved), they find that extrusion is linked to topological defects in cellular arrangement and relieving stress.<br /> The authors claim that their development of the three-dimensional phase field model is crucial for understanding cell extrusion (which I agree with the authors is inherently three-dimensional). However, I don't think the data they currently present clearly demonstrate that the three-dimensional model adds significantly more to our understanding of extrusion events than earlier two-dimensional models.

In the end, I think that the more important achievement of this work -- and one that is likely to be more influential -- is developing a three-dimensional phase field model for cell monolayers rather than any specific result regarding extrusion.

Reviewer #2 (Public Review):

The paper provides a natural extension of 2D multiphase field models for cell monolayers to 3D, addressing cell deformations, cell-cell interaction, cell-substrate interactions and active components for the cells. As known from 2D, the cell arrangement leads to positional (hexatic) defects and if the elongation of the cells is coarse-grained to define a global nematic order also to orientational (nematic) defects. These defects are characterized, see Figure 2. However, this is done in 2D and it remains unclear if the projected basal or apical side is considered in this figure and the following statistics. The authors identify correlations between orientational defects and extrusion events. In terms of positional defects such statistics seem not to be considered and the relation between positional defects and cell extrusion events remains vague. Also in-plane and out-of-plane stresses are computed. These results confirm a mechanical origin for cell extrusions. However, these are the only 3D information provided. The final claim that the results clearly demonstrate the existence of a mechanical route related with hexatic and nematic disclinations is not clear to me. 3D vertex models for such systems e.g. showed the importance of different mechanical behavior of the apical and basal side and identified scutoids as an essential geometric 3D feature in cell monolayers. These results are not discussed at all. A comparison of the 3D multiphase field model with such results would have been nice.

Reviewer #3 (Public Review):

In this paper, the authors studied the influence of topological defects on extrusion events using 3D multi-phase field simulations. By varying cell-cell and cell-substrate parameters, this study helps to better understand the influence of mechanical and geometrical parameters on cell extrusion and their linkage to topological defects.

First the authors show that extrusion events and topological defects of nematic and hexatic order are typically found in their system, and then that extrusions occur, on average, at a distance of a few cell sizes from a + and - 1/2 defects. Next, the author analyse at extrusion events the temporal evolution of the local isotropic stress and the local out-of-plane shear stress, showing that near the instant of extrusion, the isotropic stresses relax and the shear stresses fluctuate around a vanishing value. Finally, the authors analyse both the distribution of isotropic stress and the average isotropic stress pattern near +1/2 defects.

Reviewer #1 (Public Review):

Junctophilin is mostly known as a structural anchor to keep excitation-contraction (E-C) proteins in place for healthy contractile function of skeletal muscle. Here the authors provide a new interesting role in skeletal muscle for Junctophilin (44 kD segment, JPh44), where it translocates to the nuclei and influences gene transcription. Also, the authors have shown that Calpain 1 can digest junctophilin to generate the 44 kDa segment. The field of skeletal muscle generally knows little about how E-C coupling proteins have dual role and influence gene regulation that subsequently may alter the muscle function and metabolism. This part of the manuscript is solid, informative, and novel. The authors use advanced imaging and genetic manipulations of junctophilin etc to support their hypothesis. The authors then also aim to link this mechanism to hyperglycemia in individuals susceptible for malignant hyperthermia as they have elevated levels of the 44kDa segment. However, the power of the analyses are low and the included data comparisons complicates the possibility to interpret the results and its relevance. Nevertheless, the data supporting the novel dual role of junctophilin would likely be appreciated and gain attention to the muscle field.

Reviewer #2 (Public Review):

Skeletal muscle is the main regulator of glycemia in mammals and a major puzzle in the field of diabetes is the mechanism by which skeletal muscle (as well as other tissues) become insensitive to insulin or decrease glucose intake. the authors had proposed in a previous publication that high intracellular calcium, by means of calpain activation, could cleave and decrease the availability of GLUT4 glucose transporters. In this manuscript, the authors identify two additional targets of calpain activation. One of them is GSK3β, a specialized kinase that when cleaved, inhibits glycogen synthase and impairs glucose utilization. The second target is junctophilin 1, a protein involved in the structure of the complex responsible for E-C coupling in skeletal muscle. The authors succeeded in showing that a fragment of junctophilin1 (JPh44) moves from the triad to other cytosolic regions including the nuclei and they show changes in gene expression under these conditions, some of them linked to glucose metabolism.

Overall, the manuscript shows a novel and audacious approach with a careful treatment of the data (that was not always easy nor obvious) that allow sensible conclusions and definitively constitutes a step forward in this field.

Reviewer #3 (Public Review):

The manuscript reports two separate lines of evidence whereby in individuals with Malignant Hyperthermia susceptibility, the increased cytosolic calcium levels caused by leaky RYR1 mutant channels boost Calpain1 activity resulting in the activation of two different pathways, where one results into impaired glucose metabolism, while the other is expected to stimulate glucose utilization by skeletal muscles.

In the first set of data, the authors report evidence that muscles fibers of MHS patients contain increased levels of the 40kDa activated form of GSK3ß, which is generated by Calpain1-mediated cleavage of 47kDa full length GSK3ß protein. The activation of GSKß activity is associated to impaired glucose utilization by skeletal muscle and fits well with previous data on alterations of glucose storage in MHS patients reported by the same authors in a previous paper (Tamminemi et al., 2020).

In the second set of data, the authors report evidence indicating that skeletal muscles from individuals with MHS present reduced levels of JPH1 in the presence of a 44 kDa fragment of JPH1 (JPH44) that corresponds to the C-terminal region of JPH1, a cleavage again generated by the calcium-induced activation of Calpain1 proteolytic activity. They then go on to present data indicating that the JPH44 fragment, although expected to contain the transmembrane segment of JPH1, migrates to the nucleus where it activates the transcription of genes correlated with increased glucose metabolism, an activity that would oppose the effect of GSK3ß activation. These data on JPH44 show some analogy with the reported calcium-induced cleavage of JPH2 in cardiomyocytes, where a fragment of JPH2 translocate to the nucleus, where it activates a protective program to counteract cardiac stress conditions (Guo et al., 2018).

1) Figure 1 A and B show a western blot of proteins isolated from muscles of MHN and MHS individuals decorated with two different antibodies directed against JPH1. According to the manufacturer, antibody A is directed against the JPH1 protein sequence encompassing amino acids 387 to 512 while antibody B is directed against a no better specified C-terminal region of JPH1. Surprisingly, antibody B appears not to detect the full-length protein in lysates from human muscles, but recognizes only the 44 kDa fragment of JPH1. However, to the best of the reviewer's knowledge, antibody B has been reported by other laboratories to recognize the full-length JPH1 protein.<br /> Thus, is not obvious why here this antibody should recognize only the shorter fragment. In addition, in MHS individuals there is no direct correlation between reduction in the content of the full-length JPH1 protein and appearance of the 44 kDa JPH1fragment, since, as also reported by the authors, no significant difference between MHN and MHS can be observed concerning the amount of the 44 kDa JPH1.<br /> Based on the data presented, it is very difficult to accept that antibody A and B have specific selectivity for JPH1 and the 44 kDa fragment of JPH1.

2) In Figure 2B staining of a nucleus is shown only with antibody B against the 44 kDa JPH1 fragment, while no nucleus stained with antibody A is shown in Fig 2A. Images should all be at the same level of magnification and nuclear staining of nuclei with antibody A should be reported.<br /> In Figure 2Db labeling of JPH1 covers both the nucleus and the cytoplasm, does it mean that JPH1 also goes to the nucleus? One would rather think that background immunofluorescence may provide a confounding staining and authors should be more cautious in interpreting these data.<br /> Images in 2D and 2E refer to primary myotubes derived from patients. The authors show that RyR1 signals co-localizes with full-length JPH1, but not with the 44 kDa fragment, recognized by antibody B. How do the authors establish myotube differentiation?

3. Figure 3 A-C. The authors show images of a full-length JPH1 tagged with GFP at the N-terminus and FLAG at the C-terminus. In Figure 3Ad and Cd the Flag signal is all over the cytoplasm and the nuclei: since these are normal mouse cells and fibers, it is surprising that the FLAG signal is in the nuclei with an intensity of signal higher than in patient's muscle.<br /> Can the authors supply images of entire myotubes, possibly captured in different Z planes? How can they distinguish between the cleaved and uncleaved JPH1 signals, especially in mouse myofibers, where calpain is supposed not to be so active as in MHS muscle fibers?

4. If the 44 kDa JPH1 fragment contains a transmembrane domain, it is difficult to understand the dual sarcoplasmic reticulum and nuclear localization. To justify this the authors, in the Discussion session, mention a hypothetical vesicular transport of the 44 kDa JPH1 fragment by vesicles. Traffic of proteins to the nucleus usually occurs through the nuclear pores and does not require vesicles. Even if diffusion from the SR membrane to the nuclear envelope occurs, the protein should remain in the compartment of the membrane envelope. There is no established evidence to support such an unusual movement inside the cells.

5. In Figure 5, the authors show the effect of Calpain1 on the full-length and 44 kDa JPH1 fragment in muscles from MHS patients. Can the authors repeat the same analysis on recombinant JPH1 tagged with GFP and FLAG? Can the authors provide images from MHN muscle fibers stained with JPH1 and Calpain1.

6. In Figure 6, the authors show images of MHS derived myotubes transfected with FLAG Calpain1 and compare the distribution of endogenous JPH1 and RYR1 in two cells, one expressing FLAG Calpain1 (cell1) and one not expressing the recombinant protein. They state that cell1 shows a strong signal of JPH1 in the nucleus, while this is not observed in cell2. Nevertheless, it is not clear where the nucleus is located within cell2 since the distribution of JPH1 is homogeneous across the cell. Can the authors show a different cell?

7. In Figure 7, panels Bb and Db: nuclei appear to stain positive for JPH1. It is not clear why in panels Ac, Bc they show a RYR1 staining while in panels Cc and Dc they show N-myc staining. The differential localization to nuclei appears rather poor also in these panels.

8. The strong nuclear staining in Figure 8, panels C and D is very different from the staining observed in Fig. 2 and Fig. 3. Transfection should not change the ratio between nuclear and cytoplasmic distribution.

Reviewer #1 (Public Review):

Xin Gao et al. have performed mouse and human studies on the role of Tfh17 cells in the maintenance and function of central memory (Tcm). The authors conclude that antigen-specific Tfh17 cells outcompete Tfh1 or Tfh2 cells for persistence in the memory phase. Overall, the manuscript is well written and addresses an important issue in the field of Tfh biology. However, further investigation is warranted to understand how the CCR6 expressing Tfhcm contributes to the recall of humoral responses.

The strength of this manuscript is the experimental system in the mouse model, indicating that the adoptive transfer of the in vitro induced Tfh17-like cells induced higher antibody responses and more GC responses than those received Tfh1 or Tfh2 cells. Another strength is the analysis of multiple human cohorts indicating that cTfh17 cells are superior in memory maintenance for HBV, influenza virus, and tetanus toxin vaccines.

The weakness of this manuscript is not clear enough about how the CCR6 expressing Tfhcm contributes to the humoral responses. CCR6 controls mainly the localization of T cells into the inflammatory site but not into the GC site. Therefore, I could not understand the advantage of cTfh17 cells for memory maintenance in vaccination.

Reviewer #2 (Public Review):

This paper investigates the maintenance and function of memory follicular helper T (Tfh) cell subsets using in vitro approaches, murine immunization models and vaccine-challenged humans. Murine Tfh cell subsets (Tfh1, Tfh2, Tfh17) were generated using in vitro polarization (iTfh1, iTfh2, iTfh17), and then tested for support of humoral response following adoptive transfer or adoptive transfer with resting in vivo for 35 days. iTfh17 cells were statistically better than iTfh1 and iTfh2 cells in promoting GC B cell and plasma cell maturation after resting in vivo, although all 3 populations were capable of B cell help. Tfh17 cells were comparatively enriched among blood borne Tfh central memory cells in humans, and were enriched at the memory phase of vaccination with hepatitis B and influenza vaccines, compared to effector phase, suggesting the possibility they are comparatively superior in Tfh cell memory formation, with greater persistence in aged individuals.

Significance<br /> The enrichment of Tfh17 cells in Tfh cell central memory compartment and the dominance of Tfh17 cell population and the Tfh17 transcriptional signature in circulating Tfh cells at the memory phase are nicely demonstrated, and may well be helpful for understanding the heterogeneity of memory Tfh cells and potentially providing clues for vaccine design. The in vitro differentiation system for mouse Tfh cells also provides a strategy for others to build upon in dissection of Tfh cell development and function.

Points to consider<br /> 1. Even though Tfh17 cells are more likely to persist at memory timepoints in mice and in humans, or produce more GC B cells or plasma cells following transfer, all subsets can do this. Is GC output otherwise distinguishable following transfer of the individual subsets, or is their effect (cytokine related perhaps) pre-GC with differential CSR? It is also not clear if the individual subsets populate the GC and assuming they do so, if their respective phenotypes persist when they become GC Tfh cells.

2. iTfh17 cells induce more GC B cells and antibodies after resting and antigen challenge (Figures 1, 2). However, it's not clear whether this effect is a consequence of comparatively enhanced iTfh17 survival during resting (as suggested by latter figures), or better expansion or differential skewing to Tfh differentiation during challenge (as suggested by Figure 1 J,K). The total number of remaining adoptively-transferred cells right before challenge and 7 days post challenge will be helpful to understand that.

3. The authors tried to address whether Tfh17 cells have better ability to survive till memory phase or Tfh17 cells with memory potential are generated at higher frequency at the effector phase of vaccination (Figure 5); however, the experiment is not conclusive. The cTfh population 7 days post vaccination is a mixed population with effector Tph cells and Tfh memory precursors. The increased frequency of Th17 cells at day 28 compared to day 7 could be a consequence of superior survival ability, or Tfh memory precursors with Tfh17 signature are better generated.

4. Experiments to confirm expansion ability of the human subsets or their B cell helper ability were not performed.

Reviewer #3 (Public Review):

In order to study memory Tfh cell subsets the authors develop an in vitro assay to generate Ovalbumin (OVA) specific Tfh1, Tfh2 and Tfh17 cells. In vitro, these subsets express the expected hallmarks of successful differentiation. These subsets are able to mostly maintain their phenotype upon adoptive transfer and reactivation (by immunisation) in vivo providing an experimental system to test their function. The transferred cells can support germinal centres and antibody production, with iTfh17 having a larger effect after a long in vivo rest period, proposed to be due to enhanced expression of CCR7.

The authors then focus on human CXCR5+CD45RA-CD4+ cells that they call circulating Tfh-like (cTfh) cells, and divide these into CCR7+PD-1- TfhCM and PD-1+CCR7low TfhEM. RNAseq shows that there are different pathways enriched in these groups, with TfhCM having superior survival and proliferation in vitro as compared to TfhEM. The authors then further subdivide TfhCM and TfhEM into Tfh1/2/17 and show that there are differences in the ratios of these subgroups, and that the TfhEM have more pronounced effector characteristics that are typically associated with Th1/2/17 cells. In an HBV vaccination cohort, antigen specific cTfh17 cells were expanded in people who produced an early antibody response to HBV, but not in those who responded later. The authors then used a publicly available dataset of scRNAseq of HA-specific CD4+ T cells to identify an enrichment of T cells Tfh17 signature prior to vaccination and with a Tfh1 signature 12 days after vaccination, the latter finding is consistent with previous reports. Finally, the authors examine long term immunity by focusing on antigen-specific cells that likely were generated during childhood vaccination. cTfh17 cells were the most abundant cTfh subset recalled. Further these appear to accumulate with increasing age, indicating that these cells are likely retained as memory. Together, this body of work makes the case that CCR6+CXCR3-CXCR5+CD45RA-CD4+ cells (cTfh17 cells) are memory cells that are recalled upon challenge.

Reviewer #1 (Public Review):

This paper provides biochemical and structural evidence for how two different phage proteins inhibit the RecBCD system. The paper provides interesting new insights into the battle that takes place between bacteria and phages and shows how convergent evolution has led to two different phages inhibiting RecBCD in two manners.

Reviewer #2 (Public Review):

This study addresses the ways in which bacteriophages antagonize or coopt the DNA restriction or recombination functions of the bacterial RecBCD helicase-nuclease.

The strength of the paper lies in the marriage of biochemistry and structural biology.

A cryo-EM structure of the RecBCD•gp5.9 complex establishes that gp5.9 is a DNA-mimetic dimer composed of an acidic parallel coiled coil that occupies the dsDNA binding site on the RecB and RecC subunits. The structure of gp5.9 is different from that of the RecBCD-inhibiting DNA mimetic protein phage λ Gam.

Cryo-EM structures of Abc2 are solved in complex with RecBCD bound to a forked DNA duplex, revealing that Abc2 interacts with the RecC subunit. A companion structure is solved containing PPI that copurifies with RecBCD•Abc2.

Whereas the gp5.9 structure fully rationalizes the effect of gp5.9 on RecBCD activity, the Abc2 structure - while illuminating the docking site on RecBCD, a clear advance - does not clarify how Abc2 impacts RecBCD function.

The authors speculate that Abc2 binding prevents RecA loading on the unwound DNA 3' strand while favoring the loading of the phage recombinase Erf.

Does the structure provide impetus and clues for further experiments to elaborate on that question and, if so, how?

Reviewer #3 (Public Review):

Wilkinson et al. report the biochemical and structural characterization of two bacteriophage-encoded modifiers of E. coli RecBCD, which has both helicase and nuclease activities. In addition to a function in double-stranded DNA break repair, RecBCD also degrades the genomic DNA of an invading phage and generates phage DNA fragments to be incorporated into CRISPR-based defense systems. Bacteriophages often encode inhibitors to block the RecBCD nuclease activity as the first line of defense. Furthermore, some bacteriophages also encode modifiers of RecBCD to hijack it for phage propagation. The phenomena and effects of phage-encoded Abc2 and Gam were characterized and reported in a series of papers by KC Murphy in the 1990s, of which the 1994 JBC paper is specifically cited as Reference 15.

In this paper, the authors chose to study phage T7 encoded RecBCD inhibitor gp5.9 and Salmonella phage P22 encoded RecBCD modifier Abc2. Based on prior knowledge and amino acid composition, it was proposed that gp5.9 is a DNA mimic and blocks DNA binding and hence the enzymatic activity of RecBCD. The authors verified these properties, which are similar to the phage lambda encoded RecBCD inhibitor Gam, whose structure in complex with RecBCD is known. However, gp5.9 shares no sequence similarity with Gam. The cryoEM structure of RecBCD-gp5.9 was thus determined by the authors and reveals that gp5.9 dimerizes to generate a pair of parallel negatively charged alpha helices that mimic a DNA substrate and block DNA binding by RecBCD. Meanwhile, GamS dimerizes in an orthogonal fashion, and only one GamS subunit extends an alpha helix into the DNA binding site of RecBCD. This study shows the diversity in biology and convergent evolution of bacteriophage in blocking RecBCD.

Interestingly, Abc2 cannot be purified by itself alone but is stable only in complexes with RecBCD. Because of a Proline residue (Pro68) in Abc2, which is a substrate of prolyl-isomerase (PPI), WT Abc2 is tightly associated with PPI, but the mutant Abc2P68A can be separated from PPI. Therefore, the authors have prepared both RecBCD- Abc2P68A and RecBCD- Abc2-PPI. The biochemical characterization of the effects of Abc2 on RecBCD is a repeat of KC Murphy's paper, but different from KC Murphy's in the effects of Abc2 on dsDNA-end binding (2-4 fold increase, by Murphy) and helicase activity (3-4 fold reduced, by Murphy) of RecBCD (reference 15). Here, both RecBCD- Abc2P68A and RecBCD- Abc2-PPI have comparable enzymatic activities as RecBCD alone and both can be blocked by gp5.9 as by Gam (Murphy). The cryoEM structures reveal Abc2 binds the Chi-recognition RecC subunit and potentially modifies RecBCD in response to the Chi sequence. But in the absence of DNA, the structure does not explain the in vivo function of Abc2 hijacking RecBCD, nor how Abc2 alters dsDNA binding and helicase activity of RecBCD as reported by Murphy.

The biochemical experiments are expertly carried out. The cryoEM structures are of good quality. While the RecBCD-gp5.9 structure explains the inhibiting mechanism of gp5.9, the lack of functional effects of Abc2 on RecBCD in the in vitro assays is peculiar.

Reviewer #1 (Public Review):

This study developed a novel model of accelerated tendon extracellular matrix (ECM) aging via depletion of Scleraxis-lineage (ScxLin) cells in young mice (DTR). The authors found the depletion reduced cell numbers to similar baselines as aged tendons, indicating that a minimum cell number threshold exists to maintain tendon. This cell loss coincided with disrupted ECM organization and reduced mechanical properties. The DTR and aged tendons had similar protein composition with the main difference compared to young healthy tendons being a loss of high turnover ECM proteins. Via scRNA-seq, DTR and aged tendon had fewer biosynthetic cells, correlating with loss of certain ECM proteins. Interestingly, the remaining cells in the DTR model differed from aged tendons. While somewhat artificial, this depletion model system is an interesting way to investigate mechanisms that lead to reduced ECM turnover and matrix degeneration, and may have inform the mechanisms by which aging affects the maintenance of dense connective tissues.

Reviewer #2 (Public Review):

The molecular changes of the aged tendon are not well understood. Loiselle et al previously established a mouse model that mimics aging tendon, where they depleted Scleraxis lineage (Scxlin) cells from tendon by injecting diptheria toxin (DT) in mice expressing the DT receptor under the control of the Scx promoter (DTR mice). In this manuscript, the authors demonstrate that the tendons from DTR mice resemble tendons from aged WT mice, in that they both have decreased cellularity, altered collagen organization (via SHG imaging), and impaired biomechanical properties. Proteomic analysis of WT, DTR, and aged WT tendons show that both DTR and aged WT tendons have decreased expression of extracellular matrix proteins (ECM). Corresponding with this, single RNA seq analysis of tendons from these three groups of mice showed that while WT tendons are enriched for genes related to collagen and ECM synthesis and also inflammation, DTR tendons express genes associated with ECM organization and structure and aged tendons express genes that regulate inflammation. The authors point out that this supports designing therapies to prevent tendon cell death to prevent the changes seen in aging tendon.

These data enhances the understanding of the protein and gene changes associated with aging in the tendon and in particular characterizes the importance of Scx+ cells to tendon organization and the aging process. The conclusions are supported by the data presented.

The manuscript would be strengthened by:<br /> 1) Improved clarity of figures presented<br /> 2) More details on the methodology used for biomechanical testing<br /> 3) Clarification if the decrease in ECM protein expression is due to decreased cellularity in the tendons of the DTR and aged mice, or decreased expression per cell<br /> 4) Providing more details on genes that are downregulated in comparison between groups

Reviewer #1 (Public Review):

Zhang et al. have submitted a manuscript demonstrating that STAT3 regulates RNA polymerase III transcription in human tumor cell lines. They present several lines of evidence for this proposal. They show that short hairpin (sh)RNAs that repress STAT3 inhibit Pol III transcription and limit proliferation in HepG2, HuH-7, and 293T cells. Accordingly, overexpression of STAT3 enhances Pol III transcription and increases proliferation in the same cell lines. STAT3-dependent EdU incorporation into synthesized DNA confirmed the proliferation results. The Pol-III transcription inhibitor ML-60218 reversed the positive proliferation effects of STAT3 overexpression. In a mouse xenograft model, overexpression of STAT3 enhanced tumor growth of HepG2 cells, whereas suppression of STAT3 inhibited its growth. Consistent with these results, overexpression of STAT3 enhanced colony formation of HepG2 cells in soft agar, whereas STAT3 suppression inhibited it. ChIP data suggest that STAT3 shRNAs reduce the presence of TBP, BRF1, TFIIIC subunits, and POLR3A at Pol III genes regulated by gene-internal promoters. However, STAT3 does not bind to 5S, 7SL, U6, and tRNA Met genes. In addition, STAT3 does not affect the expression of various Pol III transcription factors. RNA-seq in STAT3-shRNA-expressing HepG2 cells and in shRNA-expressing control cells revealed upregulation of 356 and downregulation of 590 Pol II-transcribed genes. None of the Pol III transcription factors were affected. Among the genes whose expression was enhanced by silencing STAT3 was TP73. Accordingly, overexpression of STAT3 decreased mRNA expression of TP73. To show that TP73 acts downstream of STAT3, the authors demonstrated that HepG2 cells expressing both STAT3 shRNAs and TP73 shRNAs did not exhibit decreased Pol III transcription or proliferation. Consistent with these results, TP73 shRNAs enhance Pol III transcription in HepG2 cells, and overexpression of endogenous TP73 represses Pol III transcription. This inhibition of TP73 is caused by the disruption of TFIIIB assembly. Consequently, TP73shRNAs increase the presence of Pol III factors at Pol III genes without affecting their expression. However, co-IP with alphaTP73 antibodies detected TBP, TFIIIC2, and TFIIICC3 but not BRF1, and vice versa. Moreover, shTP73 enhanced the co-IP of TBP with antiBRF1 antibodies. To discover molecular mechanisms explaining how TP73 expression is indirectly regulated by STAT3, the authors identified miR-106a-5p as a potential regulator. In agreement with a regulatory role, miR-106a-5p mimics reduce TP73 expression and enhance Pol III transcription. Finally, Zhang et al. show that STAT3 binds to the miR-106a-5p promoter and activates miR-106a-5p promoter transcription in a luciferase assay.

Overall, the data presented in this manuscript is clean and convincing and clearly supports the proposed model.

Reviewer #2 (Public Review):

These discoveries are strongly supported by a large amount of clear and convincing data. Thus, the expression of seven distinct pol III-transcribed genes covering all types of promoter is shown to increase in three cell lines when STAT3 is overexpressed and to decrease when endogenous STAT3 is depleted. The proliferation of HepG2 liver cancer cells can be increased by STAT3 overexpression and decreased by STAT3 depletion. Crucially, proliferative induction by STAT3 is dependent on increased pol III activity, as it can be blocked using a pol III-specific inhibitor at a concentration that allows normal levels of pol III activity, but prevents further elevation. Growth of HepG2 xenograft tumours in mice is also slowed significantly when STAT3 is depleted. The effects of STAT3 on pol III output are indirect, mediated by miR-106a-5p. Thus, the knockdown of miR-106a-5p reverses the drop in pol III product expression following STAT3 depletion; conversely, pol III output is stimulated by a miR-106a-5p mimic. Elevated levels of miR-106a-5p correlate with significantly worse prognosis for patients with liver cancer. A key target for miR-106a-5p is a sequence in the 3'-UTR of the mRNA encoding TP73. Complementarity to this sequence allows miR-106a-5p to deplete the expression of TP73 and this is shown to be crucial for STAT3 to regulate the proliferation of HepG2 cells. Furthermore, TP73 is revealed to be a direct repressor of pol III-mediated transcription, an activity not previously known. TP73 is shown to inhibit the assembly of TFIIIB, the factor that is responsible for recruiting pol III to all of its genetic templates. A clear and convincing causal flow can therefore be traced: STAT3 induces miR-106a-5p, which depletes TP73, thereby removing a brake that limits pol III output and cell proliferation.

Reviewer #1 (Public Review):

LIS1 is a key dynein regulator and mutations in LIS1 cause the human brain developmental disease lissencephaly. The authors have previously reported a 3.1Å structure of yeast dynein bound to Pac1 (budding yeast LIS1) (Gillies et al., 2022, Elife). However, mutations they designed using the yeast dynein-PAC1 structure had mild effects on human dynein activation in vitro. Here they reported cryo-EM structures of human dynein-LIS1 complexes. While LIS1 and Pac1 bind to roughly the same sites (ring and stalk) of the dynein motor domains at the level of the 2D class averages, their current 3D cryo-EM structures of human dynein bound to one and two human LIS1 beta-propeller domains (4.0 Å and 4.1 Å resolution respectively) have revealed interesting similarities and differences in the interaction sites. In addition, they have provided the locations of missense mutations of LIS1 and dynein that cause lissencephaly and other human brain developmental or neurodegenerative disorders in the context of the human dynein-LIS1 structure. Overall, this first detailed structural analysis on human dynein-LIS1 interaction is well presented and will be important to the dynein field as well as people interested in lissencephaly and/or other neurodevelopmental disorders.

Methods are convincing. I do think it is important to point out that the dynein motor domain rather than full length dynein was used in this study. A relative weakness is the lack of functional analyses on the involved amino acids in the dynein-LIS1 and LIS1-LIS1 interaction interfaces. This is in contrast to the Gillies et al., 2022 paper, in which multiple functional assays were presented. However, knowing that functional assays are much more difficult to perform in human cells than in budding yeast, functional tests can be done in the future after this structural work is published.

Reviewer #2 (Public Review):

Summary:<br /> Dominant mutations in the gene encoding LIS1 cause lissencephaly, a severe developmental brain disorder. LIS1 regulates the multisubunit microtubule motor, cytoplasmic dynein 1, which can exist in an autoinhibited (closed) form and an activatable (open) form. Dynein is only active when bound to another complex, dynactin, and one of several known cargo adaptors. Because dynactin and cargo adaptors only interact with the open form, the local ratio of open to closed dynein can potentially dictate the proportion of "activatable" motors. The current view is that LIS1 stimulates dynein by reducing the autoinhibited closed form, and by recruiting two dynein motors to the active complex, which is thought to increase speeds and run lengths. LIS1 is highly conserved across animal and fungal species. The budding yeast LIS1 ortholog, called Pac1, is around 43% identical to human LIS1. Both Pac1 and LIS1 regulate dynein but there have been intriguing differences in their effect on dynein processivity in assays with purified proteins. The authors of the current manuscript recently published high resolution cryo-EM studies of Pac1 bound to yeast dynein (Gilles 2022). Based on their models they tested several mutations predicted to impact Pac1 binding to dynein and showed these mutations disrupted the single dynein dependent process in budding yeast, translocation of the mitotic spindle. However, mutations in yeast dynein that impacted LIS1 binding apparently only modestly impacted human dynein, prompting the current study that compares cryo-EM studies of human LIS1 bound to human dynein with the previously published studies using yeast proteins. The work points to subtle differences in how yeast and human LIS1 interact with the stem and loop regions of yeast and human dynein heavy chains and reveal an intriguing difference in the residues predicted to be important for the interaction between the two LIS1 propeller structures in the LIS1 dimer. They also report map known disease causing mutations in LIS1 and dynein on the structures and find three that might impact residues involved in protein-protein interaction.

Methods:<br /> This group has been able to use innovative methods to increase the resolution of CryoEM images to 3-4 Å, allowing them to make more accurate predictions about residues involved in protein-protein interactions. They have substantial expertise in the analysis of the resultant data, as demonstrated by past peer review studies. These are very labor-intensive experiments that allow a level of detail not possible with standard biochemical or cell biological analyses.

Results:<br /> The studies revealed subtle, but potentially important, differences between the yeast and human proteins.

Based on their analyses, the authors predicted specific residues that are likely to be important for human LIS1-dynein interactions with the stem region of dynein (sitestem) and with dyneins AAA domain containing ring (sitering). They also predicted specific residues that are likely to be important for an interaction between the two LIS1 beta-propellers, which in the yeast protein is apparently critical for dynein regulation.

The prediction that K147 could be important in the interaction between beta-propellers is very intriguing, given evidence that a K147A mutation disrupts LIS1 binding to dynein, but not to NDEL1, another interacting protein.

Impact

The predictions set out in this manuscript, if they hold up, could inform the design of tools to study LIS1 in the context of human disease. It seems likely from the data that at least some of the indicated residues will be important for human LIS1/dynein interactions

Reviewer #3 (Public Review):

The lissencephaly 1 protein, LIS1, is key regulator of cytoplasmic dynein-1. Gillies et al., (2022) had previously reported a 3.1 Å structure of yeast dynein bound to Pac1, the yeast homologue of LIS1. This structure revealed the details of their interactions but mutational studies based on sequence homology indicated that it did not completely represent how Lis1 binds to human dynein. To mitigate this lack of knowledge, in this manuscript, the authors have solved the structure of Lis1 bound to human cytoplasmic dynein-1 using cryo-EM.

The authors solved structures of human dynein bound to one and two LIS1 β-propellers to 4.0 Å and 4.1 Å, respectively. These structures revealed that while the overall structure of dynein's interaction with LIS1/Pac1 is conserved from yeast to humans, there are important differences in the specifics of the dynein-LIS1/Pac1 and LIS1/Pac1-LIS1/Pac1 interactions. The authors further suggest residues/interfaces that can be targeted in the future to probe the role of LIS1 in promoting the assembly of active dynein complexes.<br /> This structure is an important piece in the puzzle of how LIS1 activates human dynein. The information on how to better disrupt the human dynein-LIS1 interface and where the human disease-causing mutations lie will be very important for future studies.

Reviewer #1 (Public Review):

This paper provides de novo assembly of full-length 18S and 28S rRNA sequences from 33 mosquito species for whom no genome sequence exists. This is a very useful approach and dataset and provides a new tool by which wild-caught mosquitoes can be species-identified. Additionally, the existence of rRNA reference sequences will allow more effective depletion of these hyperabundant species of RNA prior to investing in RNA-seq of other cellular RNAs from a given sample. It is interesting how phylogenetic trees constructed using 28s rRNA compare to the more standard mitochondrial cytochrome c oxidase I gene. The availability of these data will be very useful for field entomologists and the method by which the rRNAs were obtained may be broadly useful for scientists contemplating a similar approach in less-studied species of medical or biological importance.

Reviewer #2 (Public Review):

The authors have provided a large dataset of ribosomal RNA sequences to assist in the molecular identification of rare and unstudied medically important mosquitoes in four locations with high biodiversity and mosquito-borne virus circulation, Cambodia, French Guiana, Madagascar, and the Central African Republic. This was accomplished using a non-traditional approach, rRNA seq, which could help in the identification of novel potential vectors of disease in hotspots of transmission. Their method uses previously published insect and non-insect-specific rRNA sequences from multiple locations to perform "depletion" of interfering rRNA. This method allowed the authors to create both 28s and 18s sequences for the identification of novel species of mosquito vectors with high reliability based on phylogenetic analysis and utility where traditional cytochrome oxidase subunit I sequences are not available for systematics.

Strengths:<br /> The non-traditional approach used is well described and provides novel guidance for researchers undertaking similar studies.

The depletion method described allowed the authors to identify mosquito rRNA sequences even in the instance of non-target RNA being present.

Weaknesses:<br /> The author's approach, as with traditional approaches to molecular identification of vector species, relies on expert entomologists capable of identifying mosquitoes in the field which is rare in most places. The authors do not provide citations for the taxonomic keys used for morphological identification, which in many places are outdated or unavailable for specific locations.

While next-generation sequencing is becoming more available, it is still largely unobtainable for researchers lacking resources and infrastructure which is common in locations similar to those the authors provide these data for.

The authors give no explanation as to why they chose rRNA-seq as their method of next-generation sequencing, which is most commonly used for transcriptomics, instead of traditional DNA-based metagenomics which is more commonly used to define community relationships as would be more appropriate for this study.

Reviewer #3 (Public Review):

This manuscript generates a valuable new genetic resource for mosquito research. The ribosomal RNA (rRNA) data generated for 33 mosquito species will ultimately enable physical subtraction of rRNA from mosquito RNA preps prior to sequencing, something that has not been possible for most mosquito species. This will dramatically improve the power of RNA sequencing in the mosquito field. Since mosquitoes harbor many RNA viruses, this is very important and removes a major roadblock to the study of mosquitoes and their viruses.

In addition, the authors seem to show that rRNA-based taxonomical identification of mosquitoes is superior to traditional COI-based taxonomy. This would be a very important finding if true, but the authors never unequivocally conclude this.

Reviewer #1 (Public Review):

The author's findings are as follows:<br /> (A) S.pombe Rlc1 is highly phosphorylated at Ser35 during the mitotic phase under respiratory metabolism.<br /> (B) This phosphorylation promotes the assembly and contraction of the contractile actomyosin ring (CAR).<br /> (C) This mechanism sustains CAR assembly and contraction under the respiratory metabolism, generating ROS. The ROS activates SAPK, which inhibits F-actin formation by reducing For3 expression.

They are important findings explaining the robustness of proliferative and regenerative activity of the eukaryotic cells.<br /> The data presented in the paper support the author's model.

Although there are controversial reports on Rlc1 phosphorylation, whether it activates or inactivates type II myosin in S.pombe, this paper does not terminate the debate. Inhibitory phosphorylation on Rlc1 was reported by Mohan Balasubramanian laboratory and Susan Lowey laboratory before, as the authors referred to in this paper. In contrast, the author's model showed that Rlc1 is phosphorylated to facilitate cell division. Since the molecular mechanism of the CAR assembly and contraction is still not defined well yet, the research field should welcome this study to facilitate the discussion in the future.

Reviewer #2 (Public Review):

I agree with the authors (line 421) that their "findings provide a remarkable example of how carbohydrate metabolism dictates the relative importance of different sources of actin filaments for CAR dynamics during cellular division." The scope of the work is very broad, and the manuscript reports dozens of interesting phenotypes with high-quality experimental data. However, most points are not investigated in depth.

My main reservation is the presentation of the work. The writing style is conversational and expansive, which makes it challenging for the reader. Furthermore, long paragraphs shift from one topic to the next rather than using separate paragraphs with strong topic sentences to cover each topic. I suggested a few places to start new paragraphs, but many more paragraphs could be divided. I edited one paragraph to illustrate how the text might be cut in half.

Most of the figures are also overly complicated. I did not attempt to edit one of them, but I am sure that findings will be much clearer with about half of the panels moved to supplemental materials, so the reader can concentrate on the most important data.

Line 873: Fig 1C and many other figures. The legend says the error bars are SD's but they include far less than 2/3 of the measurements, so something is wrong. In Fig 4A and other figures, three data points are insufficient to verify a normal distribution, a prerequisite for using the Student's T-test. Furthermore, the T-test requires equal SD's.

Reviewer #3 (Public Review):

In this study, the authors provide the first molecular clue to the apparent dispensability of RLC phosphorylation at S35,S36 (equivalent of RLC T18,S19 in non-muscle myosin II) for cytokinesis in Schizosaccharomyces pombe. Using point-mutant alleles, they successfully demonstrated that the S35 residue of Rlc1 is phosphorylated during cytokinesis in cells growing on glucose and that a mutant expressing the Rlc1-S35A allele is inviable on glycerol. The mutant cells exhibit slow CAR constriction and disassembly, multi-septated phenotype, and occasional cell lysis.

Rlc1 phosphorylation at S35 increases glycerol, which requires either Pak1 or Pak2. Although the localization of endogenously tagged Pak2-GFP was not detectable, the authors showed that Pak2-GFP expressed from the pak1+ promoter can localize to the division site in both glucose and glycerol conditions. Next, the authors elucidated the physiological significance of Rlc1 phosphorylation by looking at the regulation of formin For3. Previously, the authors showed that For3 is downregulated at the protein level (probably through degradation) in response to latrunculin A treatment in a Sty1-dependent manner. Similarly, the shift from glucose to glycerol caused phosphorylation of Sty1 and concomitant downregulation of For3 protein levels, which in turn caused a reduced actin cable-to-path ratio. Because expression of For3-DAD (a constitutively active allele) or a lack of For3 downregulation was sufficient to fully rescue mutants in which Rlc1-S35 phosphorylation is impaired in glycerol conditions, the authors concluded that this phosphorylation compensates for the reduced actin-cable nucleation.

Finally, the authors hypothesized that ROS production during respiratory growth is responsible for Sty1-dependent For3 downregulation, and showed that the addition of the antioxidant GSH was sufficient to rescue the reduction in For3 levels and (as expected) the inviability of mutants lacking Rlc1-S35 phosphorylation in glycerol.

This will be the first report on the cellular response in the regulation of cytokinesis to a shift from fermentative to respiratory growth. It provides a new and important context to the value of fission yeast as a model to study animal cytokinesis and the effects of oxidative stress during the process. Data are generally well presented and clear-cut, and the components of two molecular pathways involved (SAPK-For3 and PAK-Rlc1) appear to behave in manners consistent with the authors' conclusions.

Some areas of weakness are as follows:<br /> (1) Lack of use of phosphomimetic Rlc1 alleles (e.g., Sladewski et al., MBoC 2009) to strengthen the author's conclusions.<br /> (2) It is not very clear how the two pathways (SAPK-For3 and PAK-Rlc1) interact with each other. Fig. S6 suggests that the authors favor the model they are regulated independently under respiratory conditions. However, alternative models are possible and testable.<br /> (3) The authors conclude that oxidative stress causes Sty1 phosphorylation and that this phosphorylation is ultimately responsible for For3 downregulation and dependency on phosphorylation at Rlc1-S35. However, it is formally possible that all of these are independent events, which could easily be tested by using the sty1∆ mutant that the authors have used in publication.

Reviewer #1 (Public Review):

Starrett, Gabriel et al. investigated 43 bladder cancers (primary tumors), 5 metastases and 14 normal tissues from 43 solid organ transplant recipients of 5 Transplant Cancer Match Study participating registries (US) for the presence of viral genetic signatures, their host genome integration and possible contribution in carcinogenesis. They isolated DNA and RNA from FFPE tissues to perform state of the art whole genome and transcriptome sequencing. They find that 20 of the primary tumors, 3 of the metastases and 7 of the normal tissues harbor viral signatures with BKPyV and JCPyV being the most prevalent viruses detected. The bulk of the experiments focuses on the 9 BKPyV-positive primary tumors. They report that several of the BKPyV-positive tumors show host genome integration of BKPyV with associated focal amplifications of adjacent host chromosome regions, with chromosome 1 being the most prevalent. Furthermore, BKPyV-positive tumors show a distinct transcriptomic signature with gene expression changes related to DNA damage responses, cell cycle progression, angiogenesis, chromatin organization, mitotic spindle assembly, chromosome condensation/separation and neuronal differentiation. The authors only touch the features of other virus-positive tumors, e.g. those with JCPyV and HPV signals, without offering further detail or thought. The overall mutation signature analysis reveals no clear correlation between presence of viral sequences and tumor mutation burden suggesting that many different, virus-unrelated, factors possibly contribute to bladder cancer genesis and progression. Most striking are cases potentially linked to aristolochic acid, ABOBUCK3 and SBS5. Thus, while the approach is state-of-the-art, the causality of viral signatures and oncogenesis and vice versa remains unsolved.

Strengths:<br /> 1) The study assesses 43 primary tumors, 5 metastases and 14 normal tissues from 43 solid organ transplants of different kinds (24x kidney, 4x liver, 14x heart and/or lung, 1x pancreas) rather than just focusing on a particular organ.

2) The study makes use of whole genome sequencing and transcriptomics and the assayed material is extracted from FFPE tissue, which shows a high level of practical, technical and computational skills and expertise.

Weaknesses:<br /> 1) There have been multiple inconsistencies in sample number and figure references throughout the publication. Is it 19 or 20 cases that have viral sequences detected? A comprehensive checker board table showing all cases, the available tissue samples and respective analyses would be in order.

2) The overall low coverage of the whole genome sequencing, which the authors mention, and the relatively big variation in coverage in both datasets (WGS, transcriptomics) are major limitations of the study. Possibly, this was done to increase specificity, but sorting out and discarding reads may also be problematic. Please comment.

Reviewer #2 (Public Review):

Starrett et al performed whole genome and transcriptome sequencing of bladder cancers from 43 organ transplant recipients. They found that most of these tumors contained DNA from one of four viruses (BKPyV, JCPyV, HPV, and TTV). Viral genomes are most often integrated into the genomes of these tumor cells and the authors provide evidence that the integration utilized the POL theta-mediated end joining pathway. In most cases, viral RNA was detected in tumors with viral DNA. This suggests that the viruses are actively altering the cellular environment. Frequently, this resulted in similarities for overall gene expression patterns in the tumors that were grouped by the type of virus present in the tumor. Moreover, the changes in expression linked with viral gene expression were found in genes relevant to tumorigenesis. Immunohistochemical detection of viral proteins in these tumors also demonstrated active viral gene expression. However, the presence of viral proteins was heterogenous within the tumor, with between 1 and 100% of the tumor staining positive for BKPyV large T antigen. An analysis of mutational signatures in these tumors indicate that the viruses are also shaping the tumor genome by inducing mutations. Evidence that specific viruses are contributing to tumorigenesis in organ transplant patients has fundamental implications for preventing tumorigenesis in these patients.

The conclusions of this paper are generally well supported by the data provided. Indeed, there is little doubt that viral infections are more likely in these tumors. However, there are aspects of the paper that could be improved and or clarified. Most importantly, despite the strong evidence that the viruses are altering the tumor cell environment, it is unclear if these changes are necessary for tumorigenesis or less excitingly the result of an even more immune suppressive environment within the tumor. The heterogeneity of the LT expression suggests that the presence of the viral DNA and RNA may not be enough to assess whether it is actively contributing to the tumor. Is an increased frequency of viral protein staining linked with any evidence of an active contribution to tumorigenesis (fewer tumor-suppressor/oncogene mutations). that they reduced mutations in tumor suppressors. This might be easiest to assess with the tumors that have oncogenic HPV DNA. If those tumors lacked p53 and RB mutations, it would support a causative role for the virus.

Reviewer #1 (Public Review):

In the manuscript Malagon et al. investigate the nano-organization of asynchronous release at glutamatergic synapses. The authors conduct near-TIRF imaging to probe the localization of synchronous and asynchronous release sites at a single synapses using vGlut-pHluorin. Recent work in the field of synaptic neurobiology has focused on investigating how different modes of neurotransmission are organized in the presynaptic bouton, however, discrepancy remains on the sub-synaptic localization of asynchronous release sites and whether these are independent from synchronous release locations. While a variety of techniques including flash-freeze EM and super resolution microscopy have been employed, the use of live imaging by Malagon et al. provides further insight at the single synapse level.

With an impressive resolution of 27nm in live synapses the authors are able delineate synchronous and asynchronous release events within the same active zone. Furthermore, beyond the pure localization of release sites, how the vGlut-pHluorin fluorescent signal decays following fusion provides insight into distinct endocytic mechanisms. The authors delineate two populations of asynchronous events - one located within the active zone center and one ectopically outside this map (as defined by synchronous release sites). Synchronous and asynchronous demonstrate similar kinetics for the ultra-fast component of endocytosis with major differences in the fast component, which is calcium dependent for synchronous release. The authors demonstrate a consistent pattern in the localization and kinetics of release across multiple types of experiments with both EGTA and Sr2+ manipulation.

Inclusion of further analyses on already acquired data would greatly strengthen the paper, such as if single synapses preference one type of release over another. While this paper reconciles differences in the field major questions still remain; what is the mechanism for calcium independent and calcium dependent endocytosis and how does this differ between synchronous and asynchronous release. This paper sets the stage for further work probing what presynaptic machinery drives the segregation of release, what proteins mediate the differences in exocytosis-endocytosis coupling, and how the nano-organization of asynchronous release sites imparts autonomous roles for asynchronous release.

Reviewer #2 (Public Review):

Synaptic transmission is a fundamental process of communication in the brain. How and where neurotransmitter release occurs is still an open question. This study addresses an interesting question about the spatiotemporal location of neurotransmitter release in a synapse. This has important implications for postsynaptic signaling and neural excitability in general. The work provides valuable additions to recently uncovered discrepancies in the nanometer scale organization of the two primary forms of evoked vesicle fusion (synchronous and asynchronous) in the synapse from two very different methods. Essentially demonstrating convincingly that synchronous and asynchronous release sites are unshared. The study utilizes tools of super-resolution measurements of synaptic transmission that were previously developed in their lab, which help bridge the discrepancies using a convincing number of experimental paradigms. Limits in the speed of optical resolution opened a few questions of interpretation. However, this study greatly expands our knowledge of synaptic architecture and function of different forms of release. A further claim of different coupling of vesicle fusion and retrieval kinetics is made that at present seems incomplete due to temporal limitation of the super-resolution method.

Reviewer #3 (Public Review):

This important study uses high resolution imaging of single synaptic vesicle fusion events to look at the localization of individual vesicle vGlut-pHluuorin fusion events. Using this approach, the authors were able to determine with high resolution the location of single vesicle fusion. The authors find that a significant percentage of asynchronous events occur ectopically outside the synapse, but that most still fuse within the synapse and that the fluorescent decay rates, as a proxy for vesicle endocytosis change with localization within the synapse.

Reviewer #1 (Public Review):

In this manuscript, May et al use H2B overexpression driven by Keratin14 Cre-mediated excision of a loxP-stop cassette to quantify bulk chromatin dynamics in the live epidermis. They observe heterogeneity of H2B distribution within the basal stem cell layer and a change in distribution when the stem cells delaminate into the suprabasal layers. They further show that these chromatin rearrangements precede cell fate commitment, as detected by adding another Cre-mediated transgene on top (tetO-Cre mediated Keratin10 reporter). Finally, they generate an MST stem-loop transgene for the keratin 10 transcript and observe transcriptional bursting.

The manuscript uses elegant in vivo imaging approaches to describe a set of observations that are logically based on a panel of studies that have used genetic approaches to dissect the role of heterochromatin and histone/DNA modifications in epidermal state transitions (Aarenstrup et al., 2008; Driskell et al., 2012; Eckert et al., 2011; Ezhkova et al., 2011; Ezhkova et al., 2009; Fessing et al., 2011; Indra et al., 2005; Kashiwagi et al., 2007; Lien et al., 2011; Luis et al., 2011; Mejetta et al., 2011; Sen et al., 2010). In addition, the MST stem-loop analysis is a nice technical advance, confirming transcriptional bursting as a general phenomenon of how transcription is regulated in cells (see work from Daniel Larsson, Jonathan Chubb, Arjun Raj, and others). The value of the study in my view is recapitulating these known phenomena in a live tissue setting with high-quality imaging and careful quantification. Overall the analyses appear thorough, although the overall changes appear relatively minor, which is perhaps to be expected from imaging bulk H2B distribution as a proxy for chromatin states.

There is one major technical concern that might impact the interpretation of the data. The authors combine Cre lines for their key conclusions (Krt10 reporter and SRF KO) and analyze single cells that thus express very high levels of Cre. Knowing that Cre will target non-loxP sites and is genotoxic, it is possible that the effect of chromatin is due to high levels of Cre expression in single cells rather than specific effects due to cell state transitions. I would encourage the authors to carefully quantify the dose-dependent effects of the Cre protein (independent of the LoxP sites) on chromatin organization. Along these lines, is the phenotype of the SRF KO similar in the presence of two Cre alleles versus just one?

The second issue is the conclusion of "chromatin spinning". Concluding that chromatin is spinning would in my view require that the authors demonstrate that the nuclear envelope is not moving or is moving less than the chromatin. To support this conclusion the authors should do double imaging for example with LINC complex proteins, an ER/outer nuclear membrane marker, or equivalent.

Reviewer #2 (Public Review):

In this work entitled "Live imaging reveals chromatin compaction transitions and dynamic transcriptional bursting during stem cell differentiation in vivo" the authors use a combination of genetic and imaging tools to characterize dynamic changes in chromatin compaction of cells undergoing epidermal stem cell differentiation and to relate chromatin compaction to transcriptional regulation in vivo. They track this phenomenon by imaging the epithelium at the ear of live mice, thus in a physiological context. By following individual nuclei expressing H2B-GFP along time ranges of hours and up to 3 days, they develop a strategy to quantify the profile of chromatin compaction across different epidermal layers based on normalized intensity profiles of H2B-GFP. They observe that cells belonging to the basal stem cell layer display a considerable level of internuclear variability in chromatin compaction that is cell-cycle independent. Instead, intercellular variability in chromatin compaction appears more related to the differentiation status of the cells as it is stable in the hours range but dynamic in the days range. The authors show that differentiated nuclei in the spinous layer exhibit higher chromatin compaction. They also identified a subset of cells in the basal stem layer with an intermediate profile of chromatin compaction and with the dynamic expression of the early differentiation marker keratin 10. Lastly, they show that the expression of keratin-10 precedes the chromatin compaction establishing relevant temporal relationships in the process of epidermal differentiation.

This work includes a number of challenging approaches and techniques since it is carried out in living mice. Also, it provides nice tools and methods to study chromatin structure in vivo during multiple days and within a differentiation physiological system. On the other hand, the results are descriptive and, in some respect, expected in line with previous observations.

Reviewer #1 (Public Review):

The shift from outcrossing to selfing is one of the most prevalent evolutionary events in flowering plants. The ecological and genetic backgrounds of these transitions have been of major interest for decades, and one of the key questions was the dating of this transition. Timing of pseudogenization of the self-incompatibility (SI) genes has been used as a proxy for this transition because loss-of-function mutations of SI genes are often responsible for the evolution of predominant selfing. However, SI genes are identified only in a limited number of taxa, and in some cases, the evolution of selfing is not necessarily associated with loss of SI. Therefore, an independent time estimate of the evolution of selfing by genome-wide polymorphism data has been considered important in this field.<br /> <br /> This study provides two statistical methods: SMC-based and ABC-based methods. Both methods intend to detect the genome-wide signatures of the outcrossing-to-selfing transition that alters the ratio of population recombination rate and mutation rate. Authors validated these methods by using the simulated data, confirming that both methods can generally infer the timing of the outcrossing-to-selfing transition jointly with population size changes, although its precision depends on several population history settings.  <br /> <br /> This study would be an important contribution to the field of mating system evolution. By applying the proposed methods to many other selfing organisms, we may be able to see a general picture of the timescale of the outcrossing-to-selfing transition combined with population size dynamics. At the same time, this is one of the extensions of the SMC method, which has already been well utilized for various inferences, including population size and recombination rate heterogeneity.  <br /> <br /> I do not find a major weakness in the methodologies of this study, but I have a few comments on their applications to the data of Arabidopsis thaliana. It is important that these estimates largely depend on what input data is used, especially the mutation rate and recombination rate. While the authors claim that their estimate is older than Bechsgaard's estimate (<413 kyrs), these two studies used different mutation rates: the authors used Ossowski's mutation rate, and Bechsgaard used Koch's mutation rate (Koch et al. MBE 2010). To compare these two estimates, it is important to use the same mutation rate. Shimizu & Tsuchimatsu (2015; Ann Rev Eco Evo Syst) in detail discussed this point and showed that Bechsgaard's estimate becomes <1.48 myrs when Ossowski's mutation rate was used (see Figure 4). Then it happens to overlap with the estimate of this study.<br /> <br /> I am also concerned about the genomic regions of Arabidopsis thaliana used for this study. Authors chose specific five regions based on homogeneity of recombination rates and diversity, but how does the estimated change when randomly chosen genomic regions are used? If it is important to choose "preferable" regions according to the homogeneity of recombination rates and diversity, it may be useful to describe how these regions should be chosen for future applications of this method to other organisms.

Reviewer #2 (Public Review):

This submission seeks to detect changes in the rate of selfing through pairwise comparison of haplotypes sampled from a population. It begins, as did a previous paper by a subset of the authors (Sellinger et al. 2020), with the well-known theoretical finding that partial selfing increases the rate of coalescence and decreases the rate of crossing-over events in genealogical histories.

I am supportive of pitching this contribution as primarily theoretical, with the very short discussion of the Arabidopsis data provided as a worked example. This perspective increases my enthusiasm, compared to an initial reading. My comments are intended to encourage development.

Some thematic characteristics reduce the impact of the submission. Among these are:<br /> (1) a rather less than a scholarly perspective on previous literature;<br /> (2) tendency to avoid theoretical development in favor of computation;<br /> (3) little interpretation of results of their only analysis of real data.

Reviewer #1 (Public Review):

Landshammer et al. characterized the role of LNCSOX17, a previously not annotated lncRNA, in the regulation of human endoderm differentiation, further reinforcing the importance of lncRNAs in the regulation of human stem cells differentiation and embryonic development. LNCSOX17 is a unique lncRNA as it does not regulate neighboring SOX17 gene within the TAD.

Employing different loss-of-function methods (i.e. CRISPR-Cas9 MECP2, CRISPR-pAS), the authors manage to untangle the complexity of the LNCSOX17 locus, showing that it contains a distal enhancer of SOX17, a transcription factor crucial for the determination of endodermal cell fate, and, on the other hand, it operates as RNA transcript to guarantee endodermal cell differentiation.<br /> Although lncRNA LNCSOX17 does not regulate SOX17 levels and chromatin occupancy, the authors show that its loss leads to the impairment of definitive endodermal differentiation, in line with the downregulation of endoderm-related genes and markers (eg CXCR4). These data fit well with the LNCSOX17 expression profile, which indeed appears to be restricted to early human definitive endoderm. The combination of multiple genomic techniques to manipulate the LNCSOX17 locus, together with the evidence of a clear phenotype upon loss of this lncRNA, constitutes the strength of the paper.

The mechanism of how LNCSOX17 regulates endoderm differentiation is not clear and should be strengthened. The reader had a feeling that the identification of the LNCSOX17 molecular mechanism in definitive endoderm differentiation was not the focus of the work, but at the same time, it was also clear that the authors put a lot of effort to address this biological question by employing several-omics approaches (i.e. RNA pulldown, RNA-seq, CRISPR, HI-C).

Overall the conclusions are supported by the data but some methods used in this manuscript (eg RIP, Pulldown) should be strengthen with alternative tools. The manuscript is easy to read and the figures are nicely represented.

Reviewer #2 (Public Review):

The paper has two key messages: the discovery and the function of LncSox17. Claims of gene discovery are today untrivial, given the large number of genome-wide datasets. Of course, I understand the authors cannot check everything but I feel some more clear and deep analysis of current databases is lacking. Also, the exact coordinates of the lncRNA are not easy to find in the manuscript.

Many statistical analyses are rather lacking. In particular I did not find details of how the DEGs were identified during differentiation (FDR? How many replicates?).

The results of the smFISH are surprising, since the level of expression seems rather low in comparison to the qPCR (only 4 times less expressed than Sox17) or the RNA-seq.

Reviewer #3 (Public Review):

Definitive endoderm is an important transient, progenitor tissue formed in the embryo that gives rise to most of the internal organ systems. Studying how definitive endoderm arises in development is important for understanding several common diseases and also for improving methods to specialise pluripotent stem cells in culture towards functional cell types with applications in regenerative medicine. The aim of the current study was to identify and characterise new genetic factors that contribute to these processes. The authors identified a previously-overlooked gene that they named LNCSOX17 and showed that this gene is needed for cells in culture to maintain their definitive endoderm identity. Similar genes have been shown previously to function by controlling other nearby genes, but the authors showed that this is not what is happening for LNCSOX17. Instead, it is likely that LNCSOX17 affects other processes in the cell, beyond the nearby gene. This research provides a nice example of how a noncoding gene that is expressed in a very restricted developmental stage can have strong effects on cell lineage control. Because there are thousands of other long, noncoding transcripts, most of which are largely uncharacterised, this study emphasises the urgent need to examine this type of transcript in further detail.

Overall, the main conclusions of the manuscript are well supported by the evidence.

A key strength of the work is that the authors use state-of-the-art genetic methods in human pluripotent stem cells to address the function and regulation of LNCSOX17 and nearby regulatory elements. It is clear that disabling LNCSOX17 does not affect SOX17, establishing that the long noncoding transcript does not function in cis.

Robust cellular assays also provide strong evidence that the LNCSOX17 transcript is required for the continued development of endoderm cells (but not for the initial specification).

Whether LNCSOX17 operates in trans is not fully established, but the authors present evidence that supports this viewpoint and they put forward a plausible model for how this might be mediated (albeit very preliminary, as they acknowledge).

Reviewer #1 (Public Review):

The manuscript by Shi et al reports a crystal structure of partial Rad6 from K lactis in complex with Bre1 RBD domain. The structure provides detailed interactions between these two proteins, which are validated by mutagenesis and functional studies. Overall, this is a well-executed study with information useful for the histone ubiquitin field.

Reviewer #2 (Public Review):

The X-ray crystal structure of the K. lactis Rad6-Bre1 interaction solved by Shi et al. adds an important piece to the puzzle of how H2B mono-ubiquitination is deposited. The primary strength of this work is the new structural information on the Rad6-Bre1 interaction, which reveals contacts of the E3 (Bre1) to the backside of the E2 (Rad6). Through mutagenesis and biochemical experiments, Shi et al. probe the importance of these contacts for the Rad6-Bre1 interaction, active site accessibility, and Rad6 catalytic activity. In general, the functional data support the structural model and confirm the importance of Bre1 in stimulating Rad6 catalytic activity in vitro and H2Bub1 in yeast. In comparison to the structural data, some of the functional data are not as robust and are at times over-interpreted. However, in general, the conclusions drawn by the authors about the importance of the newly revealed Rad6-Bre1 interface are appropriate and substantiated by the data.

Reviewer #3 (Public Review):

Meng, Shi et al determined the crystal structure of the Bre1 RBD-Rad6 complex from Kluyveromyces lactis and found that RBD forms an asymmetric dimer binding to a single Rad6 molecule. Subsequently, the author confirmed the binding mode of RBD-Rad6 complex by structure-based mutagenesis. They show that the binding of Bre1-RBD to Rad6 is important for both Rad6-mediated ubiquitin chain production and ubiquitin discharging of the E2~ubiquitin conjugate. In addition, they show that the interaction between Bre1 RBD and Rad6 is crucial for Bre1-mediated H2B mono-ubiquitination or homologous recombination repair inside the cell.

This study presents a useful finding on the mechanism of Bre1/Rad6-mediated ubiquitination and the conclusions of this paper are mostly well supported by data, but some aspects of claims need to be clarified and extended.

Reviewer #1 (Public Review):

Sun et al. investigated the circuit mechanism of a novel type of synaptic plasticity in the projection from the visual cortex to the auditory cortex (VC-AC), which is thought to play an important role in visuo-auditory associative learning. The key question behind this paper is what is the role of CCK positive projection from the entorhinal cortex in the plasticity of VC-AC projections? They discover that the strength of VC-AC projections does not change when pairing the stimulation of this pathway with the acoustic stimulation of the auditory cortex (AC) unless CCK is applied to the AC or CCK positive projection from the entorhinal cortex to auditory cortex (EC-AC) is optogenetically stimulated. In contrast, optogenetically stimulating VC-AC projections, which express a lower level of CCK than the EC-AC projection, do not induce such synaptic plasticity. Interestingly, the data also indicates that even if the EC-AC pathway is stimulated 500ms ahead of the pairing of stimulating VC-AC pathway and the AC, the VC-AC synaptic strength can still be potentiated, consistent with the long-lasting nature of CCK as a neuropeptide. By performing a fear conditioning assay, the authors demonstrate that the CCK signaling is indeed required for the association of visual and auditory cues.

The proposed mechanism is interesting because it not only helps explain the heterosynaptic plasticity of the visual-auditory projection but also will provide insight into how the entorhinal cortex as an association area contributes to the association of visual and auditory cues. Nevertheless, this study suffers from the lack of a few key experiments, which prevents drawing a conclusion on the contribution of CCK release from the EC-AC projection to the plasticity of the VC→AC projection.

1. One main conclusion from figures 1-3 is that CCK released from the EC-AC projection is required for the plasticity of VC-AC projection in addition to pairing VALS with noise/electrical stimulation. But the data in those figures cannot exclude alternative explanations that CCK alone or the pairing CCK with either VALS or noise are sufficient to make the VC-AC synaptic connection more potent. It concerns the mechanism underlying the effect of CCK: CCK may function simply as a neuromodulator to regulate the excitatory synaptic transmission, but not to promote long term synaptic plasticity.

2. Similar issue exists in Fig. 2H and 3J. Without proper controls, it is impossible to tell whether all three conditions (HFLSEA, VALA, noise/electrical stimulation) are necessary for potentiated AC responses to acoustic/electrical stimulation.

3. Fig. 2E and 3G show that the stimulation of CCK-positive EC-AC projection is required for the plasticity of VC-AC projection. Considering most EC-AC projection neurons co-release glutamate and CCK, however, we cannot tell if CCK or glutamate or both matter to this type of plasticity. Even though the long delay in Fig 5B is consistent with the neuropeptide nature of CCK, direct experimental evidence is needed, since it is where the novelty of the paper is.

4. In Fig. 6, the authors examined the necessity of CCK for the generation of the visuo-auditory association. The experimental approach of injection CCK receptor blocker or CCK-4 is not specific to the EC-AC pathway. There is neither a link between VC-AC plasticity nor this behavioral result. Thus, the explanatory power of this experiment is limited in the context set up by the first 5 figures.

5. In page 16, line 322-326, the authors concluded that to induce the plasticity of VC→AC projection, Delay 1 should be longer than 10 ms and Delay 2 should be longer than 0 ms. This conclusion was not fully supported by the data from Figure 5B-D, because there is no data point between -65 ms and 10 ms for Delay 1 (for example 0 ms), and no negative values for Delay 2.

Reviewer #2 (Public Review):

The manuscript by Sun et al., investigates the synaptic plasticity underlying visuo-auditory association. Through a series of in vivo and ex vivo electrophysiology recordings, the authors show that high-frequency stimulation (HFLS) of the cholecystokinin (CCK) positive neurons in the entorhino-auditory projection paired with an auditory stimulus can evoke long-term potentiation (LTP) of the visuo-auditory projection. However, LTP of the visuo-auditory projection could not be elicited by HFLS of the visuo-auditory projection itself or by an unpaired stimulus. They further demonstrate that auditory stimulus pairing with CCK is required to elicit LTP of the visuo-auditory projection as well as visuo-auditory association in a fear conditioning behavioral experiment. As they found elevated expression of CCK in entorhinal neurons which project to the auditory cortex, they conclude that HFLS of the entorhino-auditory projection causes CCK release.

Strengths:

The authors use an elegant approach with Chrimson and Chronos to stimulate different auditory inputs in the same mouse in vivo and also in slice and demonstrate that potentiation of the visuo-auditory projection is dependent on HFLS of the entorhino-auditory projection paired with auditory stimulus. Furthermore, they test several parameters in a systematic fashion, generating a comprehensive analysis of the plasticity changes that regulate visuo-auditory association.

Weaknesses:

In their previous publications (Chen et al., 2019; Li et al., 2014; Zhang et al., 2020), it has been established that HFLS of the entorhino-auditory projection and CKK release are important for visuo-auditory association via electrophysiology and behavioral experiments. The Chrimson and Chronos approach was applied by Zhang et al., 2020, where they already found that the visuo-auditory projection was potentiated through HFLS of entorhino-neocortical fibers. This manuscript extends those findings by testing different parameters of pairing, which may not represent a major conceptual advance. Unlike the electrophysiological recordings, drug infusion is used in behavioral manipulations to show that HFLS of the entorhino-auditory projection is important for visuo-auditory association. While the use of drugs to inhibit CKK receptors is important, it does not directly demonstrate that CCK release from the entorhino-auditory is necessary.

Reviewer #2 (Public Review):

Zhao et al., set out to investigate the molecular mechanisms controlling the timing between training tasks that leads to proactive interference (Pro-I) buildup (i.e. formation) and consequently interference in the retrieval of the newly learned memory.

During the time-dependent stabilization of newly acquired memory (i.e. memory consolidation), the memory traces are vulnerable to disruption by a variety of amnestic influences. When multiple learning events occur in rapid succession, competition occurs between consolidating memories. However, the factors that regulate what memory is remembered or forgotten are unknown. Two interference models of forgetting are proposed in the literature: events occurring prior to learning cause forgetting through proactive interference (Pro-I), or events occurring after learning cause forgetting through retroactive interference (Retro-I).

The most common explanation for Pro-I and its buildup is that this phenomenon emerges due to the competition between two differing task memories (e.g. aversive memories) for storage in overlapping brain areas. The behavioural consequence of this Pro-I buildup is that the recall of newly learned information is impaired when is preceded by a similar learning task. On the other hand, several accounts are used to describe the release from Pro-I (i.e. the reduction of proactive interference): a) having a more distinctive target task compared to the interference task (either different material or relying on different neural circuits), b) prolonging the lag between training tasks and c) contextual changes between the two learnings. Drosophila is an ideal model for the study of these questions, given the detailed knowledge base of how different types of memories are encoded, consolidated, and retrieved and the effects of context changes in these memories.

In this study, the authors use the classic aversive conditioning paradigm, where flies learn to associate an odor A with shock (in the target task), this task is preceded by a proactive task or is followed by a retroactive task, where odor X is paired with a shock. Taking advantage of the known molecular pathways for the more well-characterized model of interference (Retro-I), the authors extended the knowledge for these types of interference models. To uncover the mechanisms underlying the timing of Pro-I, the authors genetically manipulated the activity of a key phosphatase (Corkscrew) and its downstream pathway (Raf/MAPk). This phosphatase was chosen given its known role in controlling the appropriate training intervals for the induction of long-term memory in flies.

A strength of the manuscript is that the authors showed the unique and exclusive role of Corkscrew in regulating Pro-I and its temporal dynamics. Furthermore, the authors described that Corkscrew regulates Pro-I via a single subset (γ) of the intrinsic cells (i.e. Kenyon cells) of the mushroom body, which is the centre of learning and memory in Drosophila. However, the manuscript would have been improved had they characterized the Pro-I task more thoroughly. This is because behaviourally what the authors are observing might look like Pro-I buildup, but other scenarios can also explain the data (e.g. passive decay of the first memory, memory storage limitations, attentional deficits). This would be solved by applying known Pro-I release protocols and, in this way, would be more comparable to the known Pro-I literature.

Interestingly in the mammalian field, phosphatase activity is also known as a key regulator of long-term depression and memory formation. Taken together, this data implies the conserved role of phosphatase activity and its subsequent plasticity during the process of learning a new task. Furthermore, this work shows the importance of phosphatase activity in facilitating memory consolidation of newly learned information, which might occur by suppressing any potential interference from old memories in the same neuronal circuits.

Reviewer #1 (Public Review):

Wang, Y. et al. investigated the role of TPL2 signaling in acute and chronic neuroinflammatory conditions using small molecule inhibitors and a TPL2 kinase-dead mutant mouse line. They find that TPL2 is upregulated by various brain-resident cells, including microglia, astrocytes, and endothelial cells, during neurodegenerative disease progression and following peripheral LPS injection. They show that upon pharmacological and genetic inhibition during acute LPS stimulation, pro-inflammatory cytokine concentration, microgliosis, and neuronal loss can be reversed. In chronic neuroinflammation, as seen in a tauopathy mouse model, the loss of TPL2 rescues reactive gliosis, immune cell infiltration, neurodegeneration, and cognitive health. Interestingly, TPL2 loss of function was not significantly beneficial in models of nerve injury and stroke. By analyzing their multiple sequencing datasets and those of other research teams, the authors find that TPL2 aids to upregulate transcripts for the DAM signature, immediate early genes, and astrocyte reactivity. These data build together to further emphasize the intricacy and importance of the immune component in neurodegeneration and other neuroinflammatory conditions.

The conclusions of this paper are mostly well supported by their data, but further confirmation of sequencing results and microglia intrinsic mechanisms need to be expanded.

1. In the discussion section, it will be important to highlight that TPL2 could also be directly contributing to tauopathy disease progression through its actions in brain-resident endothelial cells. They spend a lot of time characterizing the effects of TPL2 on in vitro microglial responses and do not adequately discuss the potential that their disease phenotypes in the tauopathy model have more to do with TPL2's ability to regulate BBB permeability or facets of endothelial biology. It will be important to highlight that there are various discrete cellular mechanisms (e.g. functions for TPL2 in microglia, endothelial cells, astrocytes, peripheral immune cells, etc.) that could be underlying the disease readouts seen in their global TPL2 kinase-dead mice. They should discuss this in the context of previous literature demonstrating roles for TPL2 in other non-microglial cell types (e.g. Nanou et al PMID: 34038728).<br /> 2. Hippocampal single-cell RNA sequencing led the authors to report that TLP2KD in the PS19 model of tauopathy reduced the number of T-cell and dendritic cell (DC) infiltrates into the brain. The authors should corroborate this finding with immunohistochemistry or flow cytometry to confirm the presence of changing CD4+, CD8+, and DC populations. Most notably, it is critical for them to enumerate the cell numbers in an effort to validate that there are indeed empirical, and not just proportional, reductions in these cell populations.<br /> 3. The authors concluded from Figure 3 that TPL2 plays a key role in in vivo microglia and astrocyte activation. Adding in an in vitro study, like those done in Figures 1, 2, and S4, that looks at a cell-autonomous role for TPL2 in astrocyte reactivity would strengthen this claim and rule out a microglial-independent pathway of TPL2 inflammation.<br /> 4. Although the TPL2KD mouse line is a valuable tool to impair TPL2's function while retaining its expression, the researchers failed to comment on the potential effects a global mutation in TPL2 could have in their model systems. Peripheral immunological challenges, like their IP injections of LPS, could behave differently and affect the nervous system in a microglia-independent pathway if monocyte/macrophage signaling is also impaired.<br /> 5. Oligodendrocytes and OPCs have comparable numbers of DEGs to astrocytes (Figure S11a). What is changing within their transcriptional profile?

Reviewer #2 (Public Review):

The authors used both pharmacological inhibition and genetic TPL2 kinase dead (KD) mice to test the hypothesis, that inhibition of TPL2 attenuates the microglia inflammatory response to stimuli such as LPS and in the context of chronic (tau mouse model) and acute (optic nerve crush/stroke) neurodegenerative models. The use of TPL2 kinase dead mice rather than KO mice is elegant and important because of the non-enzymatic role of TPL2 in stabilization of its interacting partner ABIN-2. The authors convincingly demonstrated that pharmacological and genetic inhibition of TLP2 in primary microglia reduced the production of pro-inflammatory cytokines, chemokines, and iNOS and consequently reduced neuronal cell death in neuronal-microglial cocultures. Genetic inhibition of TLP2 reduced partially the inflammatory response of microglia in the PS31 tau model. Furthermore, the authors observed reduced infiltration of T cells and dendritic cells. Notably, TLP2 inhibition rescued behavioral deficits in PS31 mice. Overall, these studies support the possibility that inhibition of TPL2 kinase may have translational potential in prevention or treatment of tau-driven neurodegeneration. One aspect of the study that merits further investigation is the alteration in the population structure of myeloid cells in the brain under the various conditions that were evaluated using single cell RNA seq. Very high representation of microglia was observed in TauP301S mice at nine months of age. These findings could reflect bias in recovery of cells for the single cell RNA sequencing assay and independent validation of microglia cellularity by immunohistochemistry would address this question.

Reviewer #1 (Public Review):

This study aimed at identifying genes that contribute to the neurological manifestations underlying Rett syndrome and MECP2 duplication syndrome, caused respectively by loss- and gain-of-function of the MECP2 gene. By interrogating murine and human transcriptomics datasets, the authors identified the growth differentiation factor 11 (Gdf11) as a gene whose expression is positively correlated with Mecp2. Through CUT&RUN approaches, the authors also provide initial evidence that Mecp2 regulates Gdf11 expression through epigenetic mechanisms.

By crossing Mecp2 duplication mice (MECP2-TG1) with mice with monoallelic loss of Gdf11 (Gdf11tm2b/+), the authors succeeded to ameliorate part of the behavioral phenotypes of the MECP2-TG1 mice. The authors also provided compelling evidence that Gdf11 haploinsufficiency is deleterious per se, in keeping with the neurological manifestations documented in individuals with GDF11 loss-of-function variants. The authors also tried to tie the behavioral deficits resulting from Gdf11 haploinsufficiency to deficits in adult hippocampal neurogenesis but observed no differences in neural progenitor pools in the dentate gyrus of Gdf11tm2b/+ mice compared to controls.

Strengths

• The identification of Gdf11 as a downstream Mecp2 target derives from an unbiased approach combining multiple transcriptomic datasets. The authors started with the analyses of a dataset from a recent study rectifying Mecp2 expression with antisense oligonucleotide, and then extended to another 20 datasets from human postmortem studies or mouse models.<br /> • The correlation between Gdf11 and Mecp2 expression has been validated with rigorous mouse genetics approaches, using both Mecp2 null and Mecp2 duplication models.<br /> • The behavioral batteries used to characterize the neurological phenotypes of the Gdf11tm2b/+ and MECP2-TG1;Gdf11tm2b/+ lines are comprehensive and robust.<br /> • Sex is properly accounted for, as the tests have been conducted on both males and females and the data for animals of each sex are displayed.<br /> • The study advances the field in that it identified a potential disease modifier of MECP2-related disorders. Given that rectifying Gdf11 expression alleviates part of the behavioral anomalies in the Mecp2 duplication mouse, this study has implications for therapeutic developments in MECP2-related disorders, especially MECP2 duplication syndrome.<br /> • Beyond the repercussion for understanding the mechanisms of MECP2-related disorders, the study also provides face validity for the Gdf11tm2b/+ mouse as a model for GDF11 heterozygous loss-of-function variants associated with neurological phenotypes.

Weaknesses

• Gdf11 is critical for skeletal development, and this important information is not considered as a potential confounder or discussed in the manuscript. McPherron et al (1999) have shown that Gdf11-/- mice show skeletal abnormalities, in line with the skeletal phenotypes detected in individuals with monoallelic loss of GDF11. The observation of a truncated tail in Gdf11tm2b/tm2b neonates (Figure S3C) suggests that a skeletal phenotype might be also present in the Gdf11tm2b line. McPherron et al (1999) have also reported milder skeletal anomalies in Gdf11+/- mice, for example the presence of an additional thoracic segment with an associated pair of ribs. This information is missing in the manuscript. The authors did not investigate potential skeletal phenotypes in Gdf11tm2b/+ mice and how they might contribute to some of the behavioral outcomes, for example reduced latency to fall in rotarod.<br /> • One caveat not discussed in the frame of beneficial effects of Gdf11 reduction in MECP2-TG1 mice is the impact of Gdf11 loss on survival. The authors have shown that Gdf11tm2b/+ have reduced survival, and 30% MECP2-TG1 mice have shown to die between 20 weeks and 1 year of age (Collins et al., Human Molecular Genetics, 2004). Whether MECP2-TG1;Gdf11tm2b/+ mice have a further decrease in longevity compared to MECP2-TG1 mice has not been investigated or discussed. This is important to correctly interpret the health status of the MECP2-TG1;Gdf11tm2b/+ mice undergoing behavioral testing at 12 weeks of age (and the resulting behavioral outcomes). It also has ramifications related to therapeutic development.<br /> • The manuscript is missing a discussion about the potential cell-specific effects of the Mecp2-mediated regulation of Gdf11. Figure 1B shows that Mecp2 and Gdf11 expression is correlated in all datasets but in inhibitory neurons isolated from postmortem brains of individuals with Rett syndrome. Given the evidence of MECP2-related pathology in both excitatory and inhibitory neurons, this is an important area that remains unaddressed.<br /> • More caution should be taken when interpreting mouse behavior in relationship to complex human behavioral traits. Expressions like "anxious mice" should be avoided.<br /> • In open field test, MECP2-TG1 show no differences in distance in the center of the arena over the total distance traveled (Collins et al., Human Molecular Genetics, 2004). MECP2-TG1 mice in this study display reduced number of entries in the center of the arena, and this anomaly is rescued in MECP2-TG1;Gdf11tm2b/+ mice. The relationship between the two measures and how they relate to thigmotaxis is not explained.<br /> • The fear conditioning data should be interpreted with greater caution. First, during learning training, the percentage of time spent freezing in the second post-tone phase is expected to be higher compared to the time of administration of second tone or the first post-tone phase, unlike what observed in Figures S2B and S3I. Second, both MECP2-TG1 and Gdf11tm2b/+ mice have changes in freezing behavior during the learning phases (Figure S2B, S3I), which affect interpretation of changes in contextual and cue-dependent testing. This integration of data interpretation across the learning and testing phases is missing. Third, the cumulative plots showing the percentage of time spent freezing in testing phases (Figure 2C, 3E, S2B) are not informative with respect with the temporal dynamic of the behavior (over 5 min for the contextual testing and 6 minutes for the cued testing). Fourth, the general hypoactivity of MECP2-TG1 and general hyperactivity in Gdf11tm2b/+ are not considered as potential confounders of the freezing behaviors observed in the fear conditioning paradigms.<br /> • The statistical considerations are missing information on how data normality was assessed and outliers investigated and treated.

Reviewer #2 (Public Review):

Mecp2 is the causative gene for RTT and MDS, but the Mecp2 driven pathogenesis is not clearly defined. While Mecp2 is a regulator of gene expression, identifying downstream genes that are robustly regulated by Mecp2 have been a challenge. The authors utilized computational approach to identify Mecp2 regulated genes using previously published differentially expressed genes in hippocampi of MDS mice treated with Mecp2-specific ASO (Shao et. al., 2021). Through this analysis, the authors shortlisted Gdf11, which also validated in an additional 20 transcriptional profiles for Mecp2 perturbed rat, mouse, and human brain samples.

The transcriptional regulation of Gdf11 by Mecp2 was confirmed using genetic murine models, including Mecp2 -knockout, Gdf11 mutant and Mecp2-tg1.<br /> Finally, the CUT and RUN analysis showed increased Mecp2 binding upstream of the Gdf11 TSS in Mecp2-tg1 hippocampi, which was lost in Mecp2 knockout hippocampus. Mecp2 loss increases H3K27me3, which suggested Mecp2 prevents transcriptional silencing of Gdf11. While these results provide mechanistic insight into the transcriptional control of Gdf11 by Mecp2, it remains unclear how Mecp2, which is generally a transcriptional suppressor increases Gdf11.

The author elegantly demonstrates that normalization of Gdf11 levels in Mecp2-tg1 mice crossed with Gdf11 improves several behavioral deficits in the MDS mice model. In contrast, loss of one copy of gdf11 in mice caused neurobehavioral deficits using a battery of behavioral tests, such as elevated plus maze, rotarod, anxiety tests and shock-tone conditioning.

Finally, the authors show that loss of one copy of gdf11 does not alter proliferation in the adult mouse SGZ or no gross changes in brain anatomy or volume of the dentate gyrus.

Overall, the authors demonstrate that gdf11 is robustly regulated by mecp2, which coup provide new therapeutic options for Mecp2-related diseases, such as RTT and MDS. As discussed in the paper, additional studies are needed to test whether Gdf11 can rescue behavioral deficit in symptomatic RTT murine models.

Reviewer #3 (Public Review):

This manuscript provides evidence of the correlation of Gdf11 expression to MeCP2 protein levels, demonstration of phenotypic improvement of mice overexpressing MeCP2 by genetic reduction of Gdf11 levels, and characterization of the phenotypic effects of loss of one copy of Gdf11 on mouse behavior and survival. Significance of the work is driven by the understanding that both gain and loss of MeCP2 function, a transcriptional regulator, causes severe neurodevelopmental disease associated with widespread transcriptional changes. Furthermore, recent work has identified people with neurodevelopmental problems associated with heterozygous mutations in Gdf11. The results are potentially impactful in that the identification of a specific gene target of MeCP2 relevant to pathophysiology and the underlying molecular abnormalities associated could provide insight into future novel therapeutic interventions, as well as the initial characterization of an animal model of a different neurodevelopmental disorder. Furthermore, the work expands the understanding of aspects of the importance of gene dosage in neurodevelopmental disorders and outlines interesting approaches to dissect the underlying genetic network interaction.

Strengths:<br /> 1. Careful bioinformatic evaluation of gene expression changes in MDS mice responsive to anti-sense oligonucleotide treatment that reduces MeCP2 RNA and protein levels to identify a set of genes whose expression was highly correlated with MeCP2 protein levels, restriction to genes of interest based on human predictive algorithms of loss-of function intolerance, followed by analysis of existing transcriptional profiles from multiple species (human, rat, mouse) to restrict focus to Gdf11<br /> 2. Combinatorial use of reporter mouse lines and modern molecular genetic techniques to establish relationship between MeCP2 protein levels and Gdf11 locus binding and regional histone epigenic modifications to support model of direct transcriptional relationship between MeCP2 protein and Gdf11 transcription.<br /> 3. Systematic phenotypic evaluation of the effect of reducing Gdf11 copy number in MDS mice to demonstrate amelioration of some phenotypes observed in MDS mice, as well as evaluation of the effect of Gdf11 copy number reduction on mouse phenotypes to demonstrate mouse phenotypic abnormalities that suggest that this mouse line can be a mouse model of the human disease caused by the heterozygous loss of function mutations in Gdf11

Weaknesses<br /> 1. There is a lack of detailed information on the exact composition of the various cohorts of animals used, the age and order of the specific behavioral assessments, and any accounting for the multiple behavioral test performed (to adjust for the multiple statistical tests).<br /> 2. A number of the behaviors that showed improvement with genetic reduction of Gdf11 in MDS mice were behaviors in which the Gdf11 heterozygous mice showed the opposite behavioral abnormality as the MDS mice. For example, total distance in the open field in MDS mice was reduced compared to WT mice, whereas in Gdf11 het mice there is an increased amount of total distance traveled. Similar opposite directions are present in a number of the key phenotypic measures (elevated plus, conditioned fear). The presence of these opposing phenotypic abnormalities between MDS and Gdf11 het mice make interpretation of a partial amelioration of MDS phenotypes by genetic reduction of Gdf11 less clear, as the final "normalization" could reflect an additive effect of opposing phenotypes resulting in a pseudonormalization resulting from aberrant changes in completely independent underlying mechanisms, rather than directly associated with correcting underlying problems directly associated with MDS. Potentially most interesting, and worth commenting upon, are those opposite behavioral abnormalities (such as rotarod) that do not show improvement in the double mutant animals.<br /> 3. The transparency and availability of the entirety of the data contributing to the manuscript (including behavioral data) could be improved by inclusion as supplemental tables or deposition into freely and readily available data repositories or websites (rather than indicating that it is available from corresponding author upon request).

Reviewer #1 (Public Review):

Malaria parasites contain a relict plastid organelle, called apicoplast, which harbors essential metabolic pathways such as iron-sulfur cluster and isoprenoid precursor biosynthetic pathways. In this paper, the authors investigated the apicoplast iron-sulfur (FeS) pathway in P. falciparum. Using an elegant chemical bypass genetic method, they deleted four nuclear genes encoding apicoplast FeS pathway proteins involved in sulfur acquisition or FeS cluster assembly (SufS, SufE, SufC and SufD), and demonstrated that all four are essential for parasite survival. Interestingly, an additional phenotype characterized by disruption of the apicoplast was observed with sufS (but not other mutants). The authors hypothesized that the loss of the apicoplast in the absence of SufS could be due to an additional function of SufS in tRNA thiolation, a pathway that relies on sulfur transfer. Based on sequence homology they identified a putative apicoplast tRNA thiolation enzyme, PfMnmA, and confirmed by genetic tagging that PfMnmA localizes to the parasite apicoplast. Using the chemical bypass system, they further show through knockdown or knockout strategies that PfMnmA is required for parasite survival and for apicoplast maintenance, similar to SufS.

The authors then used a series of genetic complementation with bacterial enzymes, and show that SufS and MnmA can be replaced by two enzymes from Bacillus subtilis, the cysteine desulfurase BsYrvO and the tRNA thio-uridylase BsMnmA, respectively. In B. subtilis, YrvO mediates the direct transfer of sulfur to MnmA, which mediates tRNA thiolation. Based on the genetic complementation results, the authors infer that SufS has a dual function in P. falciparum, in FeS biosynthesis (together with other Suf proteins), and in apicoplast maintenance via tRNA thiolation. The work is very well performed and the manuscript is well written. The evidence for a dual role of SufS is compelling. However, the claimed role of PfSufS/PfMnmA in tRNA modification is not directly addressed, which would make this exciting story more complete. The identification of new essential metabolic pathways is of great interest as the apicoplast is a potential target for antimalarial therapies.

Reviewer #2 (Public Review):

This manuscript explores the importance of the plastid-hosted SUF iron-sulfur cluster synthesis pathway for plastid maintenance and for the viability of blood stages of the human parasite Plasmodium falciparum. The authors convincingly demonstrate that while most of the proteins of the SUF pathway are essential to P. falciparum survival only one, the cysteine desulfurase SufS, also leads to the loss of the plastid. The authors then explore the possibility that SufS may be providing sulfur to a plastid-localised putative tRNA modifying enzyme, MnmA. They demonstrate that, like when SufS is depleted, specific depletion of MnmA impairs parasite viability and causes plastid loss. They also elegantly complement this phenotype with bacterial MnmA expressed together with a bacterial cysteine desulfurase or even alone, suggesting that SufS from the parasite is able to directly transfer sulfur to the bacterial MnmA.

Overall, this is a well-conducted and well-controlled study, for which I do not have any major criticism, although tRNA purification, identification, and quantification in the SufS and MnmA mutants would bring more compelling evidence that tRNA thiolation is affected in these mutants.

Reviewer #3 (Public Review):

In recent work, Prigge and collaborators reported the essential function of the apicoplast in the synthesis of isoprenoids which serves as a precursor of several biochemical processes. The pathway involving the synthesis of IPP includes Fe-S enzymes IspH/IspG. Thus, the inactivation of the gene products promoting the assembly of Fe-S clusters for these enzymes in the apicoplast indirectly affects IPP formation and makes the function of these genes likewise essential. Recently, the authors established that the essential requirement of IspH/IscG can be bypassed if an alternate IPP pathway is provided. The mevalonate (MEV) pathway does not require the involvement of Fe-S enzymes and allows for the mevalonate-dependent organism's survival even after disruption of IspH/IspG or ferredoxin (involved in Fe-S cluster formation). The MEV bypass genetic construct provides a valuable experimental handle to expand the analysis of additional functions essential to the apicoplast. Using this genetic tool, this report provides experimental evidence demonstrating the essentiality of sufS, sufE, sufC, sufD, and sufB in IPP synthesis and supporting their previously proposed roles in Fe-S cluster biosynthesis. Although the results from these experiments were anticipated, the novel finding of this study is that phenotypes associated with sufS inactivation differ from the phenotypes associated with the inactivation of other components of the Fe-S cluster biosynthetic apparatus pointing to additional function(s) of this enzyme.

Cysteine desulfurases are enzymes involved in sulfur mobilization for the synthesis of Fe-S cluster and other sulfur-containing cofactors. Thus, the inactivation of sufS would likely lead to the depletion of additional sulfur-containing biomolecules in the apicoplast. Using the MEV bypass, the authors showed sufS inactivation led to the loss of the apicoplast genome, indicating the involvement of SufS in additional essential functions in this organelle. Based on this premise, the authors tested the hypothesis that tRNA thiolation was also an essential process in this organelle. Experimental validation supporting this hypothesis included 1) genetic evidence that the putative tRNA 2-thiouridylase MnmA is also essential and that mnmA inactivation leads to phenotypes that mirror those of sufS inactivation in the MEV bypass genetic background, 2) B. subtilis MnmA or MnmA-YrvO fusion complements the PfMnmA inactivation, and 3) B. subtilis MnmA-YrvO fusion is able to complement PfsufS inactivation in an MEV bypass. Collectively, these results support a model in which SufS is involved in two essential functions Fe-S cluster formation and tRNA thiolation. Interestingly, genetic analysis suggests that SufS but not SufE are involved in tRNA thiolation, indicating the occurrence of a direct SufS-MnmA sulfur transfer reaction, a mechanistically distinct feature from other characterized SufS-like enzymes that require a dedicated E-like sulfur transferase. Thus the absence of SufS-like sequences in the host cells combined with the essentiality of this enzyme for the parasite life cycle offers an attractive target for metabolic intervention.

Reviewer #1 (Public Review):

This manuscript demonstrates that the activation of several oncogenic pathways including WNT, PI3K, and PKA in mesenchymal stem cells (MSC) paradoxically induces the expression and secretion of osteosarcoma-suppressing proteins in MSC conditional mediums. The authors provide the in vivo evidence showing that the PKA-induced MSC conditional medium as well as the recombinant calreticulin and procollagen C-endopeptidase enhancer (PCOLCE), the expression of which increases in PKA-induced MSC conditional medium, inhibit osteosarcoma tumor growth and tumor-associated bone destruction in an osteosarcoma xenograft mouse model. The in vitro mechanistic studies further unveil that PKA-induced MSC conditional medium, calreticulin, and PCOLCE suppress cell proliferation, survival, and migration of human osteosarcoma cell lines. These inhibitory effects are additive with the canonical Cisplatin chemotherapy in vivo and in vitro. The actions of calreticulin and PCOLCE on osteosarcoma cells are mediated by their interactions with CD47 and APP (amyloid precursor protein), respectively. The strengths of this report are that (a) the data presented are of high quality and convincing. (b) The results largely support the conclusions of this study. (c) The findings are novel and have translational potential to develop more efficient targeted therapies for the treatment of this most malignant primary bone cancer in conjunction with canonical chemotherapies. The weaknesses include (a) the lack of in vivo evidence that the PKA-stimulated MSC conditional medium and calreticulin inhibit osteosarcoma tumor cell proliferation and survival in vivo and (b) the potential effects of these two treatments on osteoblast differentiation and bone formation which may contribute to the higher trabecular and cortical bone mass observed in treated mice have not been examined.

Reviewer #2 (Public Review):

In this manuscript, Li et al. sought to identify tumor suppressor proteins in mesenchymal stem cell conditioned media in which PKA signaling was up-regulated by treatment with a small molecule, and in osteosarcoma-enriched transcripts, to provide alternative treatment strategies to combat osteosarcoma. They identified several proteins that when forcibly expressed both in vitro and in vivo, can suppress osteosarcoma viability, growth, and motility. This manuscript presents a substantial amount of data, is well organized, and provides a novel approach to addressing osteosarcomas. The data is thorough and convincing and provides an alternative approach to developing cancer therapeutics.

Reviewer #1 (Public Review):

Smela and colleagues used in silico predictions as well as reports from the literature to identify candidate transcription factors that were likely to promote granulosa-like differentiation of hiPSCs. After careful evaluation and validation using granulosa marker expression and estradiol production as read-outs, the authors identify combinations of NR5A1 with RUNX1 or RUNX2 that are necessary and sufficient to derive granulosa-like cells from hiPSCs. This section of the study is well-controlled and carefully explained, and the authors' conclusions are supported by the data. The authors then use their granulosa-like cells in concert with previously developed human primordial germ cell-like cells (hPGCLCs) to generate human ovaroids. They show that while their TF-induced granulosa-like cells initially and rapidly support the maturation of hPGCLCs into DAZL+ gonadal germ cells, DAXL+ cells are eventually lost to cell death or off-target differentiation. The authors candidly report the need for troubleshooting this aspect of the study, but this is an encouraging and important first step toward a fully human TF-induced organoid model of human ovary development. I am slightly less convinced by the data presented in the ovaroid section of the manuscript, as the immunostaining and gene expression data do not seem to fully align with in vivo conditions, and the authors do not address this discrepancy to my satisfaction in the current version of the text.

Weaknesses: The manuscript would benefit from a diagram illustrating the experimental approach from the selection of transcription factors to the generation of granulosa-like cells to the assembly of ovaroids. This would increase the accessibility of the data to an audience unfamiliar with iPSC and organoid strategies. In its current form, the data presented does not convince me that follicle-like structures form in the human ovaroid model.

Strengths: The authors address a critical gap in resources by providing a model for human hiPSC-derived granulosa-like cells. This resource will undoubtedly advance our molecular understanding of human ovary development and allow critical functional studies on the establishment and preservation of human female fertility. The manuscript is very didactic and easy to follow. The conclusions are well supported by the data, and the discussion candidly raises caveats and further directions of the work.

Reviewer #2 (Public Review):

The study by Smela et al describes the direct differentiation of human "Granulosa-like cells" via the overexpression of a limited number of transcription factors in pluripotent cells. This approach builds on other contemporary work to produce functional support cells of the mammalian gonad.

The work does succeed to establish cultured cells that retain some characteristics of these ovarian support cells, including the expression of granulosa cell markers, steroid biosynthesis in response to stimulation, and some evidence of acute germline support. The study also marks an important technological development towards the production of in vitro conditions for the production of human gametes from iPS cells. Prior efforts using human germline cells have mostly focussed on xeno-organoid approaches, and so the human-human nature of the present study represents a useful advance. Of particular note, the present study identifies a remarkably fast acquisition of DDX4 and DAZL-positive cells when both germline and support cells are both derived from a human source. This is an intriguing finding, as other groups have reported a substantial delay in acquiring this germline state when cells from mixed species are used. While these findings are of key technological importance towards ongoing efforts to create in vitro gametes, there appear to be some issues of reproducibility, and a lack of deep functional characterisation.

Several conclusions of the paper need to be described in much greater detail. For example, the regulatory effects of the over-expression of transcription factors are stated in Figures 1 and 2, but how this regulatory logic was assembled is not presented, and the methods by which this logic was experimentally probed are not presented. Second, the abstract highlights two transcription factors that are both necessary and sufficient for granulosa-like cell production. While sufficiency is tested through the overexpression of the transcription factors, necessity is not conventionally assessed through a genetic approach. The upregulation of factors in response to transcription factor overexpression does not seem appropriately described. Third, the transcriptional comparison of granulosa-like cells with cancer cell lines that do not reflect normal granulosa cells should be reconsidered.

The study contributes an important step towards the production of functional human granulosa cells from pluripotent cells, though the central conclusions would benefit from a more robust interrogation of the cell status achieved.

Reviewer #3 (Public Review):

The authors sought to develop an efficient protocol for granulosa-like cells by identifying and testing transcription factors identified through secondary analyses of RNA-seq data. The transcription factors were exogenously expressed in human iPSCs and tested for their ability to induce expression of granulosa cell genes, produce estradiol, and form ovaroids with human primordial germ cell-like cells.

There are weaknesses in some descriptions of experiments and results. Additionally, the follicle formation in the ovaroid experiments was not adequately identified or described. Finally, additional lines of human iPSCs (biological replicates) to demonstrate granulosa cell expression after the final transcription factors were determined, would increase the robustness of the granulosa-like cell differentiation protocol.

The major strengths of this manuscript include the comparison of granulosa-like cells in vitro and in the ovaroid aggregates to previously published RNA-seq analyses of human fetal ovaries. Additionally, several human, murine, and cell line controls were used where appropriate to compare cell expression.

Overall, the authors have achieved their aims of identifying transcription factors that induce a granulosa-like phenotype in human pluripotent stem cells. The production of estradiol and the presence of DAZL4+ cells in an aggregate culture that includes human primordial germ cell-like cells confirmed the functionality of the granulosa-like cells (with the caveat that the cell origins within the ovaroid culture need to be confirmed).

There are several challenges to studying human fetal ovary development and an efficient, robust granulosa-like cell protocol for human pluripotent stem cells, as described here, will lead to major advancements in this field.

Reviewer #1 (Public Review):

In this manuscript, Cover et al. examine the role of thalamic neurons of the rostral intralaminar nuclei (rILN) that project to the dorsal striatum (DS) in mice performing a reinforced action sequence task. Using patch-clamp electrophysiology, they find that neurons from the three rILN (CM, PC, and CL) have similar electrophysiological properties. Using fiber photometry recordings of calcium activity from rILN neurons that project to DS, they show that these neurons increase in activity at the first lever press and reward acquisition in mice performing a lever pressing operant task. They additionally demonstrate that this action initiation and reward-related activity exists more generally in mice performing other movements or rewarded tasks. Building on their lab's previous work, the authors further find that by optogenetically activating or inhibiting these rILN-DS neurons, mice will increase or decrease task performance, respectively. Lastly, the authors show that a variety of cortical and subcortical areas have input to rILN-DS neurons suggesting that these neurons might act as an integrator of signals from such areas during task performance.

• The authors beautifully show that the electrophysiological properties of CM, PC, and CL neurons are similar and go on to treat the rILN as one homogenous nucleus for functional fiber photometry recordings and optogenetic stimulations. It seems that these recordings and stimulations were only performed in CL, as indicated in the images (Fig. 2A, 4A). Is this the case, or were CM, PC, and CL neurons sampled? It would be helpful to clarify if DS projecting neurons from all rILN nuclei show the reported action initiation and reward acquisition activity or only CL neurons.

• Along similar lines, to what extent of rILN was targeted for optogenetic activation and inhibition? It seems that the authors implanted a total of 4 optic fibers, two on each side (please clarify in methods). What was the reasoning behind this? Please show that only rILN and not PF was activated/inhibited.

• While AAV1 is becoming a popular tool for transsynaptic labeling, performing confirmatory patch-clamp recordings with optogenetic activation of inputs, would provide better evidence for the synaptic connection between upstream regions, such as ACC and OFC, and rILN neurons.

• In addition, the transsynaptic tracing experiments would benefit from showing the cell count quantifications in CM, PC, and CL. It seems that the authors have already performed this quantification for constructing their diagrams on the right. To make any point about the relative strength of afferent innervation to rILN-DS neurons showing such quantification would be necessary.

• Why is the injection site for the retrograde cre-dependent tdTomato AAV (Fig. 5 middle left panels) showing expression? Is the cre coming through transsynaptic AAV1 from direct projections of each AAV1 injection site (AAV1 is not supposed to spread across a second synapse)? The diagrams suggest that not all regions (e.g. SUM or SC) have direct projections to DS.

Reviewer #2 (Public Review):

This manuscript details the role of the rILN to the DS pathway in the onset of operant behavior that promotes the delivery of a reward and in the ultimate acquisition of that reward. The strengths of the paper are in the detailed fiber photometry study that encompasses several behavioral domains that correlate to the signal observed in the rILN to DS pathway. I am especially interested in how the "encoding" shifts across time as the animals refine their behavior both in a temporal sense and in the magnitude of the signal. Further, the authors demonstrate then that this is dependent on action, as they do not observe signals in a Pavlovian behavioral task, but do observe reward-based signals in a "free consumption" task (the strawberry milk). The examination into devaluation also enhances the understanding of this pathway, even though there were no differences between a valued and devalued task. Finally, the authors examine bi-directional optogenetic manipulation of the pathway, and its impact on how the trials are completed, omitted, or incomplete. They find that manipulation alters the % completed trials and regulates trial omission. This paper really does not have any glaring weaknesses to point out, however, the physiological assessment does seem to have a few strong trends and even though the studies are well powered, and included both sexes, sex as a biological variable was not commented on that I could find. My estimation of the data doesn't suggest strong sex differences in any metric measured. Additionally, the data that included projections to the rILN were very interesting, and future studies looking into the physiology of these neurons, and/or how the physiology of these neurons adapt after operant training may be very interesting to understand plasticity within the adaptation across the training from FR1 to FR5 with time limits.

Reviewer #3 (Public Review):

The manuscript by Cover et al. follows up on their recent work examining a poorly characterized connection from nuclei in the rostral intralaminar thalamus to the dorsal striatum. Their previous work demonstrated that mice self-administer optogenetic activation of this pathway, which promotes dopamine release in the striatum (in a multi-synaptic fashion).

In terms of thalamostriatal connectivity, there has been a greater focus on the more robust striatal inputs from the center median and parafascicular thalamic nuclei. Notably, the rostral intralaminar thalamic inputs are thought to be morphologically distinct from their parafascicular counterparts in that they have stronger thalamocortical projections, their axons preferentially synapse on the spines of striatal output neurons (as opposed to the dendritic shafts of these neurons or cholinergic interneurons), and they may relay information from the cerebellum to striatum. As such, the author's functional characterization of the striatal projection from the less understood intralaminar thalamic nuclei is an important conceptual advance.

By using projection-specific calcium imaging, the authors show that these projections activate during lever pressing or the initiation of well-learned lever-pressing sequences and during the receipt of reward. Notably, the authors found no correspondence between the expected value of the lever presses, since devaluing the rewards or extinguishing their delivery altogether had no effect on the magnitude of this pathway's activation at the time of lever pressing. Devaluation also had no impact on the magnitude of activation at the time of reward delivery.

By contrast, the magnitude of activation in this pathway did inversely correlate with the animal's latency to initiate pressing and retrieving the reward. Moreover, activity in this pathway was positively correlated to spontaneous movement in an open-field arena. In conjunction with the author's earlier study, these findings suggested this pathway could be important for goal-directed action selection. In agreement with this idea, optogenetically manipulating this pathway bi-directionally modulated performance in their lever-pressing task.

The data presented overall support the claim that this pathway is important for operant conditioning. One weakness is that the optogenetic inhibition experiments produced very small effect sizes. This could be related to the technical difficulty of inhibiting enough of these sparse projections to the striatum. Another potential drawback (related to this weakness) is an over-interpretation of the importance of this projection and the underemphasis on the importance of somatically driven dopamine release, ideas that could be better addressed in the abstract and discussion.

Reviewer #1 (Public Review):

Like other sensory organs, the inner ear has a rich population of pericytes, essential for sensory hair cell heath and normal hearing. In this study, using an inducible and conditional pericyte depletion mouse (PdgfrbCreERT2/iDTR) model, the authors demonstrate that the pericytes play critical roles in maintaining vascular volume and integrity of spiral ganglion neurons (SGNs) in the cochlea. Moreover, using the co-culture models, they show vigorous vascular and neuronal growth in neonatal SGN explants in the presence of exogenous pericytes. Mechanistically, this study demonstrates that these roles are achieved mainly through the interactions between pericyte-released exosomes containing VEGF-A and VEGFR2-expressing the vessels and SGNs.

Overall, the data are analyzed thoroughly, and the conclusions are novel and convincing. It is mechanistically solid. The study is somewhat translationally limited. Nevertheless, understanding the roles of organ-specific pericytes is paramount, making this study timely and significant.

Reviewer #2 (Public Review):

The present study from Xiaorui Shi's lab investigated the effect of pericyte depletion on spiral ganglion neurons and auditory function. Results in in vitro culture system proposed that pericyte-derived exosomes contain VEGF, and promote not just vascular stability but neuronal survival through Flk1. This study is an extension of their previous study showing pericyte depletion causes auditory dysfunction, which is ameliorated by VEGF gene therapy (Zhang et al., JCI insight 2021). Overall, the data are clear and sophisticated and promote our understanding of the biological roles of pericytes in neuronal function. Several points should be thoroughly discussed or supported by definitive experiments like analysis of neuron-specific Flk1 KO mice.

Reviewer #3 (Public Review):

Zhang et al focus on investigating the role of pericytes in the vasculature of the inner ear. They propose that pericyte-derived VEGF is required for vessels and SGN survival. Functionally, they show that pericyte ablation leads to hearing loss.

This work is interesting to the scientific community. It describes a very specific organ vasculature and its potential crosstalk with the neuronal compartment in the peripheral nervous system.

Major strengths and weaknesses:

- The study is well explained, written, and discussed;<br /> - The design of the experiments is adequate;<br /> - The study is performed in vivo, in vitro, and with functional readouts;<br /> - Results are convincing.

The main conclusion of the study is that pericyte-derived VEGF acts on inner ear vessels and SGNs to maintain their functionality and survival. While all presented data supports this model, there could be other potential interpretations that should be tested and validated with further evidence:

- The in vitro experiments are performed with SGN explants. Using this system the authors see that pericyte-derived conditioned medium or exosomes lead to increase vessel branching and SGN neurite outgrowth. As explants contain vessels and neurons, there is the possibility that VEGF is primarily acting on endothelial cells, which then in turn signal to neurons (independent of VEGF, even when neurons express VEGFR2). This should be tested. Perhaps by targeting VEGFR2 specifically in neurons, or by culturing isolated SGN neurons and testing the effect of pericyte-derived exosomes.

- Pericyte ablation via DTA might result in the activation of the immune system, which could also influence vessels and neuronal survival. It should be checked whether there is immune activation upon pericyte ablation.

Reviewer #1 (Public Review):

This paper describes the accrual of RSV mutations in a severely immunocompromised child with persistent infection and demonstrates that ribavirin increases the observed mutation rate with base pair changes (C to U and G to A) compatible with its known mechanism. The paper utilizes a mathematical model to explain the counterintuitive finding that viral load does not decrease despite loss of viral fitness and clinical improvement. Positive selection is observed but does not keep pace with deleterious mutations induced by ribavirin. Overall, though the data is restricted and limited to a single person, the analysis is rigorous and supports the paper's interesting conclusions.

The paper is fascinating, but its generalizability is somewhat limited by the single study participant. Nevertheless, comparisons of therapy-induced deleterious mutations versus adaptive mutations over time is potentially important for multiple viruses.

Reviewer #2 (Public Review):

In this work, Illingworth et al. investigate the effectiveness of ribavirin and favipiravir on the treatment of a paediatric patient with chronic RSV. These drugs cause mutations and the authors tested whether they could observe this effect through deep sequencing viruses from nasal aspirates over the course of treatment. They found an increase in mutations caused by ribavirin but favipiravir appeared to have no additional mutagenic effect. Despite the lack of change in viral load, the authors suggest that the ribavirin reduced viral fitness and did not lead to adaptive escape mutations. The authors modelled how generation time and fitness interacted with mutational load. They also estimated fitness for different haplotypes generated from the mutational data.

Strengths of the paper:

Using mutagenic drugs to treat viruses is generally accepted but results have been mixed with severe viral infections and specific evidence of the precise effects of the drugs is often lacking. This paper is especially valuable for demonstrating that despite in vitro evidence that favipiravir had some effect against RSV, there was no evidence for favipiravir having an effect in a patient. This differs from the authors previous work showing a clear clinical benefit to favipiravir in treating influenza. This paper also appears to be the first to sequence RSV from a patient having been exposed to ribavirin which is important for demonstrating that the drug is having a measurable effect.

Weaknesses in the paper:

I think there is a conceptual problem with the paper. Ribavirin is supposed to increase the mutational rate of the virus which would increase the mutational load. Mutational load has been calculated by summing up the frequencies of minor alleles. However, if a particular mutation rises in frequency, it does not mean that ribavirin has caused additional mutations at the same site but rather viruses containing the mutation have risen in frequency. If a subpopulation containing mutations rises through drift or selection to a relatively high percentage that will bias the mutational load. The authors provide ~75 mutations which were at significant percentages across multiple different timepoints. It seems that these mutations contribute significantly to the mutational load but changes in mutation percentages between samples do not reflect changes in mutational events but changes in viral haplotypes/subpopulations. In a previous study Lumby et al. 2020, the authors removed mutations at >5% from their analysis but there is no indication that they performed this step similarly here. Summing many small changes will give an indication of background mutational rate (though counting only a single mutation at each locus is perhaps the only method to remove the effect of viral clonal expansion).

While ribavirin appears to have shown an effect, many questions remain. Why does the mutational load only increase for 3 points before plateauing? The authors would likely argue that this is the new saturation point for mutation load but they don't test it. Sequencing points from after the cessation of treatment would be expected to show lower mutational load but this data was not collected. Furthermore, questions remain over the methodology. It is thought that Ribavirin should only increase transitions and a transition/transversion ratio for the different samples would have been helpful. The absolute numbers of many mutation classes appear to have increased including transversions e.g AU. There isn't a good reason why nucleoside analogues should have caused this effect and perhaps it is an artefact.

I don't think that the authors can reasonably determine how many haplotypes there are in the population from short read sequencing data. I think that the sequencing data very clearly shows subpopulations due to the large changes in mutation frequencies between different time points. The authors say that their analysis assumes a well-mixed population which is clearly not the case. Therefore, determining fitness of different haplotypes or mutations is likely not accurate.

The authors construct a model to estimate viral fitness and suggest that viral fitness decreased with the drug. This is somewhat problematic to me as viral load has not changed so it would be reasonable to say that viral fitness was likely unaffected by the drug. The authors define fitness in terms of the number of mutations that each virus likely has and assumes that these mutations are deleterious. The authors then use this to claim that mutagenic drugs reduce fitness. This seems very circular to me. If the drugs reduce fitness, it should be observed as a property of the virus population. As the only measure was viral load, which didn't change, it is difficult to claim ribavirin reduced viral fitness. There are other reasons why there could be an increase in the number of mutations e.g. sequencing more subpopulations which would have nothing to do with fitness.

At various points, the paper assumes that there is no selection taking place but immunoglobulin was being applied weekly and palivizumab monthly. The timing of when these drugs were given should be included. How did the palivizumab affect selection? The K272E mutation seems to go up and down but it is not clear if this was in response to drug infusion timing or if this mutation was present in a subpopulation.<br /> I think the main impact of the paper will be that favipiravir will not be used in the future to treat RSV. Given that the EC50 of favipiravir against RSC is ~100x that of influenza, favipiravir was unlikely to reach a therapeutic level in the patient. Nucleoside analogues have a mixed record at treating serious viral infections. Hopefully, this work will spur on future studies to precisely measure the effect that ribavirin has on RSV.

Reviewer #3 (Public Review):

The use of mutagenic drugs in combating new viral diseases is increasing, so it is imperative to understand how they might impact the evolutionary trajectory of RNA viruses and weigh their potential benefits versus their harms. The authors examined the impact on treatment outcomes and virus populations of treatment with mutagenic drugs (ribavinin and favipiravir) in a child with severe combined immunodeficiency syndrome and RSV pneumonitis. The authors report that despite a three-fold increase in viral mutation within-host evolution was still slow with only minor gain in viral fitness. The patient's clinical status was stable despite virus non-clearance by the drugs.

Despite looking at only one case, this study illustrates the potential impacts of widespread use of antiviral mutagenic drugs in the event of a viral epidemic. The authors warn in the discussion of the study that the results should be interpreted with caution if the same drugs are given to individuals who are immunocompetent, which I agree.

Reviewer #1 (Public Review):

This paper reports an analysis of the inhibition of the serotonin transporter, SERT, by a novel compound, ECSI#6. The authors perform a comprehensive analysis of SERT transport inhibition for the new agent and compare its properties to those of other well-characterized agents: cocaine and noribogaine, with the data pointing to an unusual noncompetitive mechanism of inhibition, a model also supported by electrophysiological recordings of transport currents. Based on the results of these experiments the authors conclude that ESCI#6 binds essentially exclusively to the inward-facing state of the transporter. The authors further present experiments suggesting that ESCI#6 can stabilize the folded form of an ER-arrested SERT mutant and recover its trafficking to the plasma membrane, with some in-vivo drosophila experiments perhaps also supporting this conclusion. Finally, kinetic simulations using a transport model with rate constants from previous experiments support the basic conclusions of the first sections of the paper.

Strengths:<br /> The transport experiments and simulations here are thorough, carefully performed, and reasonably interpreted. The authors' arguments for noncompetitive inhibition seem well-thought-out and reasonable, as is the conclusion that ESCI#6 binds to the inward-facing state of the transporter. The simulations are also thorough and support the conclusions. In the discussion, the comparison of enzyme noncompetitive inhibition to the process studied here was thoughtful and interesting. Also, the care and analysis of the uptake data are a strength of the paper, with well-presented evidence of reproducibility and statistics. The electrophysiology data is more limited but does communicate the essential conclusion.

Weaknesses:<br /> The most important concern about the work is the interpretation of the in-vivo drosophila data. Though the SERT fluorescence with WT protein is strong, I cannot see any fluorescence in either drug-treated image from the PG mutant. In this context, shouldn't there be additional intracellular staining for ER-resident SERT? If the cell bodies of these cells are elsewhere this should be clearly pointed out.

Similarly, the single Western blot demonstrating enhanced glycosylation in the presence of Noribogaine or ECSI#6 could be strengthened. I can see the increased band at a high molecular weight that the authors attribute to the fully glycosylated form, but this smear, and the band below, look quite different from those in the blot shown in the El-Kasaby et al reference, raising concerns that the band could be aggregated or dimerized protein rather than a glycosylated form. This concern could easily be addressed by control experiments with appropriate glycosidases, as shown in the reference.

The overall interest in the work is reduced given the quite low affinity of ECSI#6 for the transporter.

Reviewer #2 (Public Review):

The authors aimed to determine the mode of inhibition of the serotonin transporter SERT as a by-product of MDMA synthesis (ECSI#6). They present a thorough kinetic analysis, using different experimental techniques (binding and transport inhibition) and kinetic modelling. They also test the predicted pharmocophore effect of the compound. In my view, the authors provide compelling evidence for an uncompetitive inhibition mechanism, in which the compound most likely binds to the inward-facing and K+-bound state. Inhibitors of this type may have the potential for therapeutic use.

Reviewer #3 (Public Review):

This is interesting research that uncovers a novel inhibition mechanism for serotonin (SERT) transporters, which is akin to traditional un-competitive inhibitors in enzyme kinetics. These inhibitors are known to preferentially bind to the enzyme-substrate complex, thus stabilizing it, resulting in a decrease of the IC50 with increasing substrate concentrations. In contrast to this classic enzyme inhibition mechanism, the authors show for SERT, through detailed kinetic analysis as well as kinetic modeling, that the inhibitor, ECSI#6, binds preferentially to the inward-facing state of the transporter, which is stabilized by K+. Therefore, inhibition becomes "use-dependent", i.e. increasing substrate concentrations push the transporter to the inward-facing configuration, which then leads to the increased apparent affinity of ECSI#6 binding. Interestingly, this mechanism of action predicts that the inhibitor should be able to rescue SERT misfolding variants. The authors tested this possibility and found that surface expression and function of a misfolding mutant SERT is increased, an important experimental finding. Another strength of the manuscript is the quantitative analysis of the kinetic data, including kinetic modeling, the results of which can reconcile the experimental data very well. Overall, this is important and, in my view, novel work, which may lead to new future approaches in SERT pharmacology.

With that said, some weaknesses of the manuscript should be mentioned. 1) The authors suggest that serotonin and ECSI#6 cannot bind simultaneously to the transporter, however, no direct evidence for this conclusion is provided. 2) How does ECSI#6 access the inward-facing binding site? Does it permeate the membrane and bind from the inward-facing conformation, or is it just a very slowly transported low-affinity substrate that stabilizes the inward-facing state with much higher affinity? Including ECSI#6 in the recording electrode may provide further information on this point. Additionally, it is not clear why displacement experiments were not carried out with cocaine. Since cocaine is a competitive inhibitor but does not induce transport (i.e. doesn't induce the formation of the inward-facing conformation), it should act in a competitive mechanism with ECSI#6. 3) Why are dose-response relationships not shown for electrophysiological experiments? These would be a good double-check for the radiotracer flux data.<br /> Despite these weaknesses, I believe that this is important work, which adds to our understanding of the pharmacology of serotonin transporters, which are of critical nature due to being a target of anti-depressant drugs. The data make a case for the proposed inhibition mechanism and the interpretation of results, as well as conclusions, are generally sound.

Reviewer #1 (Public Review):

Protein oligomerization is essential to their in vivo function, and it is generally challenging to determine the distribution of oligomeric states and the corresponding conformational ensembles. By combining coarse-grained molecular dynamics simulations and experimental small-angle X-ray scattering profiles at different protein concentrations, the authors have established a robust approach to self-consistently determine the oligomeric state(s) and the conformational ensemble. The approach has been applied specifically to the speckle-type POZ protein (SPOP) and generated new insights into the conformational ensemble and structural features that determine the ensemble. The model was further tested by the analysis of several relevant mutants as well as models with different types of structural restraints. The results also support the isodesmic self-association model, with KD values comparable to those measured from independent experiments in the literature. The approach is potentially applicable to a broad set of systems.

Reviewer #2 (Public Review):

This manuscript applied the SAXS data analysis of protein self-assembly by implementing the simultaneous fitting of intra- and inter-molecular motions/conformations against SAXS data at a series of oligomerization states/concentrations. Despite several major assumptions hinted, a diverse pool of conformational and oligomeric candidates was generated from CG simulations, and more importantly, these candidates were fitted into these SAXS data to reach a reasonable agreement, suggesting a somewhat convergence (even if the ensemble-fitting could well be at a local minimal). This is considered a technical advance, given the fairly large numbers of both the oligomer fraction phi_i (i=1, ..., N) and the conformational weight w_k (k=1, ..., n), where N is the number of oligomers and n is the number of internal conformational states.

Central is optimizing phi_i and w_k, simultaneously. The former has been illustrated in Fig. 4 and SI-Fig. 7 for the total number of 60-mers. The latter relies on an overfitting-preventing strategy, as shown in SI_Fig. 1, where an effective fraction cutoff was used from 0.1 to 1.0, as opposed to the number of conformational states. What are the numbers of conformational states for these oligomers? This should be quantifiable, e.g., defining the conformational differences by chi_2.

Reviewer #3 (Public Review):

Molecular-level interpretations of SAXS data are challenging, especially for oligomeric systems of variable length with intrinsic flexibility and the possibility of multiple association interfaces. In order to make this challenge tractable, a number of assumptions are made here: 1) There is a single pathway by which individual domains associate first into homodimers and then into longer oligomers; 2) the association kinetics is isodesmic, which allows the direct calculation of oligomer distributions based on the given value of a single dissociation constant; 3) the internal dynamics within dimers is restricted essentially to relative domain-domain motions, that are sampled comprehensively via MD simulations. As a result, excellent fits to the SAXS data are obtained and the underlying conformational ensembles are highly plausible. The resulting models are useful to further understand SPOP function, especially in the context of liquid-liquid phase separation.

Reviewer #1 (Public Review):

Hyphal fusion is a common process in filamentous fungi that requires a tightly regulated, oscillatory cell-to-cell dialogue between the two fusion partners. While several signaling components functioning in this process have previously been identified, the actual signal(s) exchanged during the molecular dialogue between two genetically identical cells have remained a mistery. In this study, the authors show that even when growing in the absence of a fusion partner, hyphae of a nematode pathogenic fungus already undergo signal oscillations that are in phase with their growth oscillations. After detecting the presence of a fusion partner, a slowdown of the oscillation frequencies occurs (entrainment), followed by a transition to an anti-phasic synchronization of the oscillations between the two partners. Based on a mathematical model the authors postulate a mechanism involving the oscillatory secretion/uptake of a signaling compound from a shared extracellular space. To experimentally validate the model, they visualize anti-phasic oscillations of intracellular Ca2+ concentrations in two approaching hyphae and find that they are anti-phasic with the recruitment of chitin synthase B. Moreover, addition of a calcium-chelating agent to the medium abolishes molecular oscillations and anti-phasic synchronization in the two hyphae. Based on these results, the authors conclude that extracellular Ca2+ is essential for the signaling mechanism during the cell-to-cell dialogue.

This is a very solid and well-performed microscopical study that provides new insights into the signaling mechanisms during hyphal fusion. Novel findings include: 1) the occurrence of signal oscillations at the tip of individual growing hyphae (monologue) that are in phase with the growth oscillations; 2) the presence of an entrainment phase involving a slowdown of the oscillation frequency upon detection of a potential fusion partner (entrainment) followed by a transition to an anti-phasic synchronization; 3) the detection of anti-phasic intracellular calcium oscillations during the molcular dialogue; 4) the establishment of a model predicting the secretion/uptake of a signaling compound (possibly calcium).

In general, the results are clearly presented and most of the conclusions are well justified by the data. I had some problems in interpreting the model based on the accompanying text, likely because of a confusion between the two different concepts of signaling component and signaling compound. Furthermore, the fluctuations of the fluorescent calcium probe R-GECO in Fig. 3d are difficult to detect for the untrained eye. Finally, the conclusion that intracellular Ca2+ oscillations are caused by uptake of extracellular Ca2+ is not fully supported by the data. These points can all be addressed by minor changes in the text and Figures.

Reviewer #2 (Public Review):

Using live cell imaging, this article describes the oscillation of a tip localized protein, SofT, to hyphal tips during growth of a nematode-trapping fungus, independently of the oscillation of this protein during chemotropic interactions prior to cell fusion. The authors observe oscillation of SofT, which becomes entrained as opposing oscillations at hyphal tips during chemotropic interactions, a process that requires calcium signaling. The authors build on a previously developed mathematical model describing oscillation of proteins to fusion tips during chemotropic interactions with a transition period from single hyphal tip oscillation to coordinated oscillation during chemotropic interactions.

Reviewer #3 (Public Review):

Wernet et al. show that there are intrinsic protein oscillations at the hyphal tips of A. flagrans, a nematode trapping fungus, that become coordinated when two hyphae become close. They create a mathematical model of this synchronization phenomenon, and then go on to show that calcium is critical to the functioning of these oscillations and hyphal fusion. The concept of inter-hyphal communication through signal synchronization is fascinating, and the visual matching of the output of the model to the data is compelling. However, given that the authors already showed synchronized oscillations in the SofT protein in A. flagrans in Hammadeh et al. 2022 (Figure 4), this diminishes the novelty of the findings in this study. Additionally, as it also has been established that calcium drives other oscillatory communications, the characterization of calcium dependence is not especially novel or bringing new insights into the problem especially since it is unclear if the chelation is having effects due to loss of intracellular supplies and/or because it is the key signal in the dialogue. Right now the mathematical model feels a bit vague with discussion of hypothetical molecules, so the paper would be greatly strengthened if any key regulatory molecules that promote desychronization could be identified or there were some manipulations of the core known proteins that examined consequences of altering the oscillations. As it is, the reader is left intrigued but there are few concrete conceptual advancements.

Reviewer #1 (Public Review):

This paper provides the first comprehensive analysis since the doubling of the NIH budget, on how the institute is able to keep up with inflationary pressures and fully support investigators. Through a series of descriptive graphs and regression analyses as well as modeling and transformations, the authors demonstrate the relative similarities between inflation trends and NIH support over time. Interestingly larger, more solicited projects, including greater number of clinical studies, are now driving a greater proportion of the costs for NIH. The modeling is relevant but the limitations need to be recognized and these include: the issue of personnel costs, not well captured by their approach, and productivity; i.e. is the increase in spending matched by an increase in traditional metrics (manuscripts, other grants, policy change, etc. Nevertheless, the bottom line, of interest to funders, investigators, and institutions, is that NIH has been able to maintain support at a level commensurate with inflation.

Reviewer #2 (Public Review):

When I was asked to review this paper, I was quite excited, as the analysis seemed very timely. Many of us in biomedical science feel like we are at an inflection point in our field. The combined impact of the pandemic on both people's outlook and on the supply chain, the sharply rising costs of living in major metropolitan areas, and the increasing gap in potential salaries between industry and stipends for graduate students and postdocs are shaking our field. The need to increase salaries for PhD students and postdocs is colliding with a 20+ year stagnation in the size of a non-modular R01, creating major challenges for many basic science labs.

However, having read the piece, I am quite disappointed at what seems to be a missed opportunity. The scientific community at large, and particularly the basic science community is hungry for data like this, to use to try and convince Congress and NIH as a whole to address the issues above. However, as far as I can tell, the authors are not writing for this audience-in fact I was puzzled about their view of for what audience this was intended. I will note several major issues-all could be addressed but would require an effort to tell this story in a much more comprehensible and complete way

1. The manuscript assumes an understanding of both economic terminology and statistical approaches that will not be familiar to most of the audience, if I am a representative example. This begins in the abstract, much of which I found incomprehensible. I still am not sure about the definition of "nominal costs ", and I certainly have no idea what they mean by a "wholly non-parametric machine learning regression". This continues throughout-presenting much of the data as Log10-transformed costs means that many of the graphs become impossible for a normal mortal like me to interpret.

2. The version presented is written like some early outline draft. Rather than using narrative to guide the reader through the data, it reads like a series of Figure legends. For example, I literally thought the text on page 4 were the Figure legends, but they are not. "Figure 2 shows...." "Table 1 shows...". The Discussion is similarly difficult to follow. Given the complexity and importance of the data they present, this is a major missed opportunity

3. What will most interest my own part of the NIH-community is the assertion that "real dollar adjusted" grant funding has not decreased, but has instead remained flat. Few people I know will believe this. The authors address in a less-than-clear fashion some of the reasons for this-solicited versus non-solicited awards, clinical trials, etc, but do not dig into their own data to identify what are likely to be other issues. I doubt any one of the 20+ NIH-funded researchers in my Department (predominantly NIGMS funded) has a grant that reaches the "median level"-I do not after 32 years of continuous NIH-funding. Most new NIGMS-funded researchers, including many in my Department, are coming in funded by MIRA grants, which at $250K are half the median grant size. They do spend a few moments on disparities in Figure 7, but much more could be pulled out of this data set. Digging into issues like this-distributions in different NIH Institutes, at different career levels, etc, would make this work much more impactful.

As one example, this analysis from NIGMS suggests the median grant was likely under $225K, a year when their data suggest the median grant overall was $400k<br /> https://loop.nigms.nih.gov/2016/05/distribution-of-nigms-r01-award-sizes/

My bottom line-this study addresses a key question, but as currently written does so in a way that will minimize its impact

Reviewer #3 (Public Review):

The issues raised in this review are more conceptual in nature and my suggestions are designed to sharpen the focus of the paper. The paper does a good job of explaining how prices of NIH project have changed over time but leaves the reader wanting a clearer understanding as to why this has happened. The paper raises the issue of price effects compared with compositional effects at the beginning and the very end of the paper. It would have been helpful for the paper to be more explicit about examining price changes and composition changes in the organizing structure of the paper (e.g. the solicited v. unsolicited is a compositional change and should be highlighted as such). The authors conclude that changes in NIH prices are associated with changes in the composition of NIH funding, and the evidence supports that. However, the NIH has inordinate control over prices because of the salary cap imposed in 2012. It would be helpful to see the relative weights of the various components of the BRDPI index in the paper graphed over time. I suspect the personnel salaries receive the highest weight. Figure 1B indicates BRDPI dropped by over 1.5 percentage points once the salary cap was put into place. When the NIH mechanically caps the price increases in salaries, they will hold research inflation (BRDPI) in check.

In addition, many of the notable trends in the data deserve further discussion. For example, in Figure 1A, awards are much higher than awardees, indicating that there are many PIs with multiple awards. This difference narrowed after 2013, but by 2021, there are ~5,000 multiple RPG awardees. This deserves some discussion. Furthermore, in Figures 2 through 4, the real value of NIH funding per project has fallen since the NIH doubling. This is a hugely important point and deserves more discussion. Eyeballing the real drop in value in Figures 3 and 4, it's approximately ~$50,000 (about 10%) close to the cost of one postdoc on an RPG. Clearly, by keeping the real costs of funding per project down, NIH is able to fund more projects. But what are the tradeoffs of this kind of policy? This may be beyond the scope of the paper, but it would be helpful for the authors to discuss the possibility that imposing the salary cap may have had some unintended consequences.

On Page 9 the authors state: "From 2012 through 2021 whisker ranges increased, exceeding levels for the doubling for untransformed costs, and not quite reaching doubling levels for logtransformed costs." Later the authors argue that this is the result of changes in the composition of research grants-that solicited grants are a larger share and cost more. However, it may be possible that the variance of funding costs is a by-product of the salary cap in 2012. When PIs could no longer charge full personnel costs, they may have developed different approaches to maximizing funding from NIH. This should be commented on in the paper. For example, are certain institutions (perhaps those that receive a lot of NIH funding in the first place) better at this kind of budget request than others.

While the authors attribute much of the change in the variance of costs to composition effects (solicited vs. unsolicited projects), the timing of the variance changes is interesting. It's very telling that during the doubling, the variance in grants was higher and then when NIH funding fell in real terms, the variance in funding narrowed (Figure 6). After the salary cap and the 2015 budget increases, the variance in funding increased again. This suggests that when money is tight the variation in funding narrows. I know the authors ran a regression on the time effects of actual funding costs (Figure 13) but not on the variance. Again, the time series of the variance in funding begs for further explanation.

Since much of the change in the composition of NIH grants is between solicited vs. unsolicited projects, it would be helpful to provide more information on the nature of solicited proposals and why NIH has shifted to funding more of them. For example, are these one-time solicitations? Are these U-mechanisms? Some combination of both? How would COVID-related funding appear in the NIH portfolio? A paragraph describing this change in emphasis and the types of projects being solicited would be very helpful.

In the conclusion, it would be helpful to mention the NIH salary cap during the discussion of the Baumol cost disease. While it is true that services will cost more overtime relative to goods (since robots can replace production workers in manufacturing but not postdocs in laboratories), the NIH effectively has its thumb on the price level with the salary cap. Cost disease is not going to be as problematic as long as the salary cap remains in place. However, there is growing evidence that the effective price cap that NIH has in place on NRSA stipend levels is generating shortages of postdocs (see https://www.science.org/doi/pdf/10.1126/science.add6184 and https://www.statnews.com/2022/11/10/tipping-point-is-coming-unprecedented-exodus-of-young-life-scientists-shaking-up-academia/). The authors should comment on the growing reports of labor shortages and consider how NIH may have to respond to this in the coming years.

Reviewer #1 (Public Review):

In mammals, limb-innervating motor neurons are found at brachial and lumbar levels of the spinal cord. While it has been known for a long time that a combination of transcription factors (e.g., Hox, FoxP1) is necessary for the development of these motor neurons, it remains unclear whether similar or distinct transcriptional programs operate in brachial and lumbar motor neurons. This study advances our understanding of how motor pools are specified in the lumbar region. The authors found, in hindlimb-innervation motor neurons, that the LIM homeodomain transcription factor Isl2 is selectively required for motor pool organization, neuromuscular connectivity, and hindlimb locomotion.

Major conclusions include:

1. Settling position of motor neurons is impaired in Isl2 mutant mice; MMC neurons at all levels and LMC neurons at the lumbar level.<br /> 2. Isl2 controls Pea3 expression in lumbar motor pools.<br /> 3. A transcriptomic analysis uncovered multiple Isl2 downstream target genes.<br /> 4. The connectivity and function of hindlimb motor pools are disrupted in Isl2 mutant mice.

The conclusions are supported by experimental evidence.

Strengths:

The study fills an important knowledge gap by uncovering a developmental role for the LIM homeodomain transcription factor Isl2 in hindlimb motor pools.

The authors employ an impressive array of genetic, molecular, behavioral, and electrophysiological methods to comprehensively characterize the function of Isl2 in spinal motor neurons.

Weaknesses:

Most experiments have been conducted in Isl2 global KO mice, raising the issue of cell autonomy. However, the key conclusion of Isl2 controlling Pea3 expression has been independently confirmed in animals lacking Isl2 activity selectively in motor neurons (Olig2Cre line).

The mechanistic details downstream of Isl2 remain elusive.

Reviewer #2 (Public Review):

The manuscript aims to define in detail the role of the LIM homeodomain transcription factor isl2 in the acquisition of cardinal spinal motor neuron identities. The authors, by using a number of different and complementary techniques, analyze Isl2 expression in motor neuron subtypes and describe the consequences of its loss on motor neuron generation, positional organization, sensorimotor connectivity, and function. While the importance of Isl2 for the development of axial and visceral motor neurons was already known, the data presented here convincingly show that Isl2 has a previously unappreciated role in controlling differentiation of a subset of motor neurons innervating proximal hindlimb muscles by regulating the expression of the ETS transcription factor Pae3.

Reviewer #3 (Public Review):

The authors perform a thorough investigation of the role of Islet2 in the specification of lumbar motor pools. They use a number of approaches, including RNA-seq, behavioral testing, and imaging to establish a role for this transcription factor (TF) in the organization and axonal and dendritic morphology primarily of the Gl motor pool. The experiments are clear, well-presented, and convincing. Concerns about this work stem from the fact that the authors use a null mouse instead of a conditional. While this is not so problematic when examining MN properties such as organization, it makes data on connectivity and behavior hard to interpret. Since the authors perform one experiment with the conditional mouse (showing Pea3 downregulation), it is a bit puzzling that they did not use these mice for the rest of the experiments.

Reviewer #1 (Public Review):

Obesity is a risk factor for OA development and progression and its molecular mechanisms remain unknown. In this study, the authors demonstrated that obese OA patients and ApoE KO mice showed a pronounced synovitis and enhanced macrophage infiltration in synovial tissues. In addition, obese OA mice had severe cartilage degradation and increased apoptotic cells in synovial tissues than OA mice without obesity. GAS6 is a secreted glycoprotein and during M1 macrophage polarization, GAS6 secretion is decreased, leading to impaired macrophage efferocytosis in synovial apoptotic cells. Intra-articular injection of GAS6 restored the phagocytic capacity of macrophages and decreased the levels of TUNEL-positive cells, preserving cartilage thickness and preventing OA progression in obese OA mice. Overall speaking, this study is well-designed and carefully executed. The data presented are supportive of the conclusion that the authors made.

Reviewer #2 (Public Review):

Understanding the molecular mechanism of obesity-associated OA is highly in clinical demand. Overall, the current study is well-designed and illustrated that down-regulated GAS6 impairs synovial macrophage efferocytosis and promotes obesity-associated osteoarthritis. Based on the patient's sample, the data indicated synovial tissues are highly hyperplastic in obese OA patients and infiltrated with more polarized M1 macrophages than in non-obese OA patients. Further authors proved that obesity promotes synovial M1 macrophage accumulation and GAS6 was inhibited in synovitis during OA development in mice models. The sample size, data collection, and quality of the IHC and immunofluorescent histological sections are outstanding. The results were well presented with appropriate interpretation. But the following major questions should be addressed.

Major:<br /> 1. Animal model: Ten-week-old animals received DMM surgery and were fed a standard/HFD diet for 4 or 8 weeks prior to specimen harvest. Since Wang J and other studies have shown that male ApoE(-/-) and C57BL/6J wild-type (WT) mice fed with a high-fat diet for 12 or 24 weeks, and the ApoE(-/-) mice gained less body weight and had less fat mass and lower triglyceride levels with better insulin sensitivity and lower levels of inflammatory markers in skeletal muscle than WT (Wang J, et al. Atherosclerosis. 2012 Aug;223(2):342-9. PMID: 22770993; Hofmann SM, et al. Diabetes. 2008 Jan;57(1):5-12. PMID: 17914034; Kypreos KE, et al. J Biomed Res. 2017 Nov 1;32(3):183-90. PMID: 29770778). Thus, it is very important to provide the data on the final body weight gained in your groups and provide a relative background of the animal model chosen in the introduction or discussion. Please explain why ApoE-/- mouse model, and how this animal model is clinically relevant. Does a high-fat diet induced obsess OA available in C57BL/6 WT?<br /> 2. Control group: The DMM surgery was performed on the right leg, and the contralateral knee joint should be used as a baseline to show the level of M1 macrophage infiltration under the obsess microenvironment.

Reviewer #3 (Public Review):

In this study, the authors studied the underlying mechanism of obesity-related inflammation in OA synovitis. They found more pronounced synovitis and enhanced macrophage infiltration accompanied by dominant M1 macrophage polarization in obese OA patients and ApoE-/- mice synovial tissues. Enhanced M1-polarized macrophages in obese synovium decreased growth arrest-specific 6 (GAS6) secretion, which resulted in impaired macrophage efferocytosis in synovial apoptotic cells. Intra-articular injection of GAS6 restored the phagocytic capacity of macrophages, reduced the accumulation of local apoptotic cells, and decreased the levels of TUNEL- and caspase-3-positive cells, preserving cartilage thickness and preventing the progression of obesity-associated OA. The main strengths of the paper are the discovery of the underlying mechanism of obesity-associated osteoarthritis. However, some claims and conclusions were not well supported by their data.

Reviewer #1 (Public Review):

In the article "MHC class I and MHC class II reporter mice enable analysis of immune oligodendroglia in mouse models of multiple sclerosis", Em P Harrington and colleagues describe two new mouse reporter models, that allow tracing cell lineages that activate the expression of CD74 and B2m genes, involved in MCHI and MHCII pathways, respectively. The authors then use these models to confirm the emergence of oligodendroglia with immune properties in the context of the EAE mouse model of MS. These mice models will be an excellent tool for the scientific community to investigate the contribution of MHCI and MHCII populations to the development of neuroimmunological disorders.

Reviewer #2 (Public Review):

The manuscript from Harrington and colleagues describes the development and characterization of two new mouse resources that report MHC class I and class II expression. In these mice the tomato reporter gene was embedded into the gene encoding beta 2-microglobulin, to report class I expression, and separately in the CD74 gene, to report class II expression. The group highlights the need for such reporters by describing the growing interest in MHC expression by oligodendrocyte lineage cells in inflammatory CNS disorders, and they nicely demonstrate the utility of these reporters using mouse models of multiple sclerosis. There is also an emerging appreciation that immune cell infiltration into the CNS occurs in myriad neurological disorders, such that these models will likely have wide utility. The paper is clearly written and will be of wide interest.

Reviewer #3 (Public Review):

In the human disease multiple sclerosis (MS) and in inflammatory demyelinating mouse models of MS, a subset of oligodendroglia express MHC genes. The role of MHC-expressing oligodendroglia in disease is unknown but thought to relate to a novel antigen-presenting function in these cells.

This study represents a fundamental advancement in approaches to detect and quantify the spatial and temporal expression of MHC I and MHC II genes in vivo through the generation of two reporter mice encoding CD74- or B2m-TdTomato fusion genes. This affords a highly quantitative method to isolate cells expressing the relevant fusion proteins and study their differential gene expression. The study advances the recent concept of oligodendroglia heterogeneity and in particular the presence of MHC expressing immune oligodendroglia.

Prior work has shown oligodendrocyte heterogeneity, induction of MHC I and/or MHC II genes in "stressed" oligodendrocytes, and immunologic OPCs in MS at the transcriptional level (Schirmer 2019, Jakel 2019, Absinta 2021). Authors of the current work have shown that OPC differentiation is impaired by effector T cells, that IFNγ induces the MHC class I in these cells and that class I expressing OPC can present antigen, in vitro, to CD8 T cells (Harrington 2020, Kirby 2019). However, a deeper understanding of 1) how common is this process under different pathologic conditions, 2) where and when does MHC I and MHC II expression in oligodendroglia occur during a multistep pathophysiologic process, and 3) what is the full transcriptional characterization of immune oligodendroglia and how do they differ from other oligodendroglia, is lacking. The work presented in this manuscript address this gap and provides a tool for investigation into these questions for the community.

The investigators created two reporter mice - a CD74-TdTomato (class II) and a B2m-TdTomato (class I) strain. Figure 1 shows the targeting strategy, genotypes, and transgene expression in CD45, CD19, and CD3 cells from blood and secondary lymphoid tissue, demonstrating anticipated expression. Fig 1F and G show expression of CD74-TdT and B2m-TdT, respectively, in transverse histologic sections through the spinal cord of EAE mice with clinical scores of 0, 1.5, and 3.0 (baseline expression in naïve mice is shown in Fig 1 supp 3 and 4). Finally, supplement 5 shows higher power images, and quantitation of TdT as a function of other immunologic markers. The data nicely shows the fidelity of expression in relevant cell types and induction in vivo in EAE. In addition, the data shows that the transgene does not obviously impact expression quantitatively (Fig 1 sup 2).

The data in Figure 2 are central to the overall concept. The authors nicely demonstrate the induction of CD74-TdT and B2m-TdT in interferon gamma-treated oligodendrocytes as well as other cell types. Oligodendrocytes identified by olig2 are present in the spinal cord of mice with EAE and their frequency increases as the EAE severity increases. A strong correlation is seen between the severity of EAE and the percent of olig2 cells expressing the class I or class II gene.

In figure 3, scRNA seq performed on cells isolated from CD74-TdT or B2m-TdT mice with EAE reveals multiple subclusters of oligodendrocytes, one of which is high in MHC l as well as in other genes involved with antigen processing. The experiments are carefully conducted and contaminating cell populations were eliminated from the analysis.

An outstanding accomplishment, providing a resource to study multiple aspects of MHC I and MHC II cell-specific expression, transcriptional profiles in relevant cell types, and temporal course of activation. Most importantly, this resource will allow for a deeper quantitative analysis of the immune oligodendroglia phenotype and explore potential function in disease models.

https://threadreaderapp.com/thread/1601640985858957312.html

Example of a literature review/research workflow using online repositories (like Google Scholar, Scopus, Clarivate, etc.), Zotero, Research Rabbit, and Obsidian.

Reviewer #1 (Public Review):

The authors succeeded in fitting their Jansen-Rit model parameters to accurately reproduce individual TEPs. This is a major success already and the first study of this kind to the best of my knowledge. Then the authors make use of this fitted model to introduce virtual lesions in specific time windows after stimulation to analyze which of the response waveforms are local and which come from recurrent circles inside the network. The methodological steps are nicely explained. The authors use a novel parameter fitting method that proves very successful. They use completely openly available data sets and publish their code in a manner that makes reproduction easy. I really enjoyed reading this paper and suspect its methodology to set a new landmark in the field of brain stimulation simulation. The conclusions of the authors are well supported by their results, however, some analysis steps should be clarified, which are specified in the essential revisions.

Reviewer #2 (Public Review):

Here the authors tackle the problem of identifying which parts of a TMS-evoked response are local to the stimulation site versus driven by reverberant activity from other regions. To do this they use a dataset of EEG recorded simultaneously with TMS pulses, and examine virtual lesions of a network of neural masses fitted to the data. The fitting uses a very recent model inversion method developed by the authors, able to fit time series directly rather than just summary statistics thereof. And it apparently works rather well indeed, at least after the first ~50 ms post-stimulus. I expect many readers will be keen to try this fitting method in their own work.

Reviewer #3 (Public Review):

The manuscript is very well written and the graphics are quite iconic. Moreover, the hypothesis is clear and the rationale is very convincing. Overall, the paper has the potential of being of paramount importance for the TMS-EEG community because it provides a valuable tool for a proper interpretation of several previously published TMS-EEG results.

Unfortunately, in my opinion, the dataset used to train and validate the method does not support the implication and interpretation of the results. Indeed, as clearly visible from most of the figures and mentioned by the authors of the database, the data contains residual sensory artefacts (auditory or somatosensory) that can completely bias the authors' interpretation of the re-entrant activity.

Reviewer #1 (Public Review):

Ciliary length control is a basic question in cell biology and is fascinating. Regulation of IFT via calcium is a simple model that can explain length control. In this model, ciliary elongation associates with an increase in intraciliary calcium level that leads to calcium increase at the ciliary base. Calcium increase acts to reduce IFT injection and thus ciliary assembly rate. The longer the cilia, the more increase of calcium level and the more reduction of IFT injection and thus the ciliary assembly rate. When the cilia approach the genetic defined length, the gradual reducing assembly rate eventually balances the constitutive disassembly activity. Cilia then stop elongation and a final length is achieved. This work tested this model by manipulating the calcium level in cilia by using an ion channel mutant and treatment of the cells with EGTA. In addition, IFT injection was measured before and after calcium ciliary influx. Based on the outcome of these and other experiments, it was concluded that there is no correlation between changes in calcium level and IFT injection, thus challenging the previous model. This work is well written and the experiments appear to be properly executed. It nicely showed an increase of intraciliary calcium during cilia elongation, and beautifully showed that ciliary calcium influx depends on extracellular calcium. However, I felt the current data are inadequate to support the author's conclusion.

The authors showed that ciliary calcium increases along with ciliary elongation, which correlates with reduction of IFT injection. Thus, this result would support that calcium increase reduces IFT injection. To test whether reducing calcium influx would alter the IFT injection, the authors used an ion channel mutant cav2. Indeed, ciliary calcium level in the mutant cilia appears to be lower compared to the control in average. After measuring ciliary calcium level and IFT injection during ciliary elongation with mathematical analysis, it was concluded that reducing ciliary calcium level did not lead to increased IFT injection, which is distinct from the control cells. Thus, the authors concluded that calcium does not act as a negative regulator of IFT injection. However, if one examines the calcium flux in Figure 3B and IFT injection in Figure 4B of cilia less than 6 micron, one may draw a different conclusion. For the mutant cilia, the calcium influx is higher than that in control cilia and IFT injection is reduced compared to the control. Thus, this analysis is the opposite of the authors' conclusion, and is supporting the previous model. There is a rapid change in ciliary assembly rate at the early stages of ciliary assembly (see Figure 1C), thus, the changes in calcium influx and IFT injection in the earlier assembly stage would be more appropriate to assess the relationship between intraciliary calcium level and IFT injection.

The authors used EGTA treatment to support their conclusion. However, EGTA treatment may induce a global calcium change of the cell, the outcome may not reflect actual regulation of IFT injection by ciliary calcium influx. For example, as reported elsewhere, the change of cAMP level in the cell body and cilia has a different impact on ciliary length and hedgehog regulation. The slower assembly of cilia in EGTA treated cells may be caused by many other factors instead of sole regulation by IFT.

The authors only examined the impact of reducing ciliary calcium influx. To further support the authors' conclusion, it is recommended that the authors should examine IFT injection in a condition where ciliary calcium level is increased. Using calcium ionophore may not be a good choice as it may change the global calcium level. One approach to consider is using mutants of a calcium pump present in cilia.

The conclusion on line 272-273 may need more evidence. The authors showed that addition of 1 mM CaCl2 does not change ciliary assembly, and used this as one of the evidences to argue against the ion-current model. The addition of calcium extracellularly may not alter intracellular/intraciliary calcium level given that cells have robust systems to control calcium homeostasis. To support the authors' conclusion, one should measure the changes of calcium level in the cell/cilia or revise their conclusion.

The authors showed nicely the changes in IFT properties before, during and after ciliary calcium influx and found that the intensity and frequency of IFT do not have a correlation with calcium influx though calcium influx restarts paused IFT trains for retrograde transport as previously reported (Collingride 2013). The authors again concluded that this is supporting their conclusions in that there is no correlation between IFT injection and calcium influx. However, I am not sure whether the short pulses of calcium influx at one time point would change the calcium level in the whole cilia in a significant way that would alter IFT injection at the ciliary base.

Reviewer #2 (Public Review):

The authors use a genetically encoded calcium indicator to measure Ca in flagella to establish that Ca influx correlates with flagellar length. (Despite this correlation, there is so much noise that it is dubious that Ca level can regulate the flagella's length.) Then, they show that reduced Ca decreases the rate of IFT trains entering flagella, which ruins the ion-current model of regulating flagella's length. (Ca can still be one of the factors that sets the target length.) Ca does not seem to change the disassembly rate either. There are also no correlations between Ca influx spikes and IFT injection events. Curiously, these spikes broke pauses of retrograde IFT trains, but that still did not affect IFTs entering dynamics.

Some other possibilities like Ca regulating unloading rates are discussed and convincingly rejected.

The study ends with an interesting Discussion, which talks about other possible models, and concludes that the only model not easily rejected so far is the mechanism relying on diffusion time for kinesins from flagella to the cell body being greater in longer flagella.

The paper is well written, very thorough, contains significant results.

Reviewer #3 (Public Review):

This work by Ishikawa et. al is focused on testing the hypothesis first proposed by Rosenbaum that Ca2+ levels in the primary cilia act as an internal regulator of cilia length by negatively regulating intraflagellar transport (IFT) injection and/or microtubule assembly. The authors first built a mathematical model for Ca2+ based regulation of cilia length through the activity of a Ca2+ dependent kinase. They then tested this model in the growing cilia of Chlamydomonas cells expressing an axonemal localized GCaMP. Ca2+ levels were manipulated genetically with a calcium channel deficient mutant line and with the addition of EGTA. While increases in Ca2+ levels do correlate with cilia length as expected by the model they found that IFT injection was positively correlated with IFT injection and increased axonemal stability which contradicts its potential as a mechanism for the cell to internally regulate cilia length.

Overall the conclusions of the paper are supported by their data. They greatly benefit from first establishing their model in a clear form and then experimentally interrogating the model from multiple angles in order to test its viability. The importance of cilia length to our understanding of human health has only become greater in recent history and the authors are making a significant contribution to our understanding of ciliary length regulation.

Reviewer #1 (Public Review)

This paper focuses on the hydrodynamic interactions between in-line swimming fish by observing how real fish swim behind a robotic mechanism (a rigid NACA airfoil). After ensuring that the airfoil can generate a real-fish-like wake (reverse Von Karman Vortices), the authors found, compared to swimming alone, real fish swimming behind the airfoil will reduce tail moving frequency, synchronize tail movement with the airfoil, and experience lower pressure around the anterior of the fish. The results indicate fish do save energy and improve efficiency by swimming directly behind the thrust type of vortices. The experimental design is good and the collected data generally support the conclusions drawn. The article could, however, be improved by providing more quantitative comparisons in addition to the qualitative visualizations.

Reviewer #2 (Public Review)

This paper seeks to contribute new empirical insight into the (potential) energetic benefits of schooling. Toward this aim, the authors establish an experimental setup in which brook trout swim in a thrust wake generated by an oscillating airfoil. By combining measurements of body motion and particle image velocimetry, the authors successfully detail how brook trout respond to an incoming thrust wake.

Strengths:<br /> • The idea of using an airfoil that oscillates in the sway and yaw direction is original and a valuable contribution to the simulation of thrust wakes using simplified mechanical systems.<br /> • The experiments are executed with a high level of accuracy and detail, offering important insight into animal locomotion in a thrust wake. In particular, acquiring experimental data on the flow physics (velocity and pressure) for this kind of problem is a major endeavor, which the authors have successfully and originally addressed.<br /> • Performing experiments on the same animals twice is an excellent idea to explore the role of body size, without inflating the number of animals needed for experiments.

Weaknesses:<br /> • The novelty of the robotics-based experimental approach is overstated; several studies have studied the response of live fish to thrust wakes, generated by pitching airfoils or robotic fish.<br /> • The length of the test section for the experiments is very much comparable with the body length of the animals, thereby raising doubts regarding the confounding role of wall interactions. Likewise, the role of 3D effects is elusive; experimental data are in 2D and no discussion is included about the extent to which such an approximation is valid and how it impacts quantitative measurements of vorticity and pressure included in the paper.<br /> • Other sensory modalities (such as touch and the vestibular system) and their integration are not examined in the paper, limiting the understanding of the reader of the way in which fish appraise their surrounding to obtain hydrodynamic advantage from thrust wakes.

Findings of the research can offer valuable insight into the hydrodynamic mechanisms at the basis of schooling, stimulating further interdisciplinary research at the interface of biology and engineering (fluid mechanics and robotics).

Reviewer #1 (Public Review):

Agip et al. have resolved the first cryoEM structure of the mitochondrial Complex I from Drosophila melanogaster, an important model organism in biology. The structure revealed a 43-subunit enzyme complex that closely resembles the mammalian Complex I. The authors resolved Complex I in three different conformational states at 3.3-4.0 Å global resolution, with an overall resemblance to the active form of the mammalian Complex I, but also with some characteristic conformational changes near the quinone substrate pocket and surrounding subunits that resemble, at least in part, the deactive form of the mammalian enzyme. The third resolved class was considered 'damaged/broken', and a possible artifact arising from the sample preparation. Biochemical assays showed that the Drosophila Complex I does not undergo an active/deactive transition (as characterized by the N-ethylmaleimide sensitivity), although the structures revealed an exposed ND3 loop that has been linked to transition. The authors could also show that conformational change between an alpha and pi form of transmembrane helix (TM3-ND6) is likely to be involved in catalysis, and distinct from the deactivation mechanism of the mammalian isoform. Due to the 3.3 Å global resolution, water molecules could not be experimentally resolved, and how the observed conformational changes link to the proton pumping activity therefore remains an open question and basis for future studies. Overall I find that this work provides an important basis for understanding mechanistic principles of the mitochondrial Complex I and more specifically a starting point for detailed genetic studies on the fruit fly Complex I.

Reviewer #2 (Public Review):

- Aim of the study:

Agip et al. studied the structure of respiratory complex I from Drosophila melanogaster, an important model organism with well-developed genetic toolkit and sufficiently close phylogenetic relationship to mammals. They isolated the complex and analyzed its structure by single-particle electron cryo-microscopy (cryo-EM). They also used mass spectrometry to characterize new subunits. So far, the structures of complex I have been reported for several organisms, including mammals, plants, ciliates, fungi and bacteria, but ones from insects have been missing. This study aims to fill this gap and shed light on some of the key questions pertaining complex I biology, such as 1) the conservacy of supernumerary subunits, 2) the mechanisms and physiological relevance of active/deactive transition and 3) the correspondence between the structurally defined closed/open conformations and the biochemically defined active/deactive states.

- Strengths:

The study provides the first structure of complex I from insects, the organisms at an important phylogenetic branch that has diverged from mammals more recently than other eukaryotic species such as plants and fungi. Using purification methods they developed for mammalian enzymes previously, the authors successfully purified the insect enzyme with high quality - a monodisperse peak in gel filtration, the NADH oxidation activity comparable to mammalian enzymes, and the homogenous subunit composition as confirmed by single-particle analyses. It is noteworthy that the authors used state-of-the art tools in model building and validation, such as ISOLDE and MapQ, which makes this model of high standard. In my opinion such careful validation is particularly important for modelling such a gigantic complex, since without cares one can easily misinterpret the density and draw wrong conclusions.

The resolution is 3.3 Angstrom for the best class (Dm1), which allowed modelling side chains and comparing between the observed 3D classes and to the known structures. The model confirms the presence of 43 subunits, akin to mammalinan enzymes, composed of 14 conserved core subunits, 28 supernumerary subunits that have close homologs in mammals, and one supernumerary subunit CG9034 that has not been predicted. They are also structurally similar to mammalian enzymes except for minor local differences. The two supernumerary subunits (NDUFC1 and NDUFA2) that are present in mammals are missing. The authors discuss evidence that NDUFC1 is absent from the Drosophila genome and NDUFA2 is genomically present but its expression is restricted to the male germline. Together, the overall similarity to the mammalian enzyme underlines the use of Drosophila complex I as a model system.

One of the remarkable findings is that common biochemical treatments that are used to deactivate mammalian complex I - heat treatment or NEM treatment - did not reveal deactive state of Drosophila complex I. This is in agreement with their observation that most structural elements are in the active state. The major Dm1 conformation shows all structural features in the active conformation, whereas Dm2 state shows two features in the deactive conformations. Here the author raises an interesting point that the structural elements formerly believed to behave in a consorted manner are actually not coupled, providing new perspective in interpreting complex I structures presented so far and in future. Notably, the authors adopted the same purification procedure for bovine and murine samples. This is a particular strength that they applied a similar procedure for but still observed different behaviors for Drosophila (the absence of the deactive state).

- Weaknesses:

As the authors point out in Discussion, the biochemical statuses of the two described conformations, Dm1 and Dm2, are uncertain. If we assume that Dm1 is a ready-to-go active state, Dm2 could represent several of the possible states; a partially broken state due to delipidation by detergent, a meta-stable state during enzyme turnover, an intermediate towards "full deactiving" structural transition (which the authors argue is unlikely to occur), or a fully reversible state that is in equilibrium to Dm1. Despite these uncertainties, the structure will serve as an excellent starting point to address many open questions in the complex I field in future.

In the final 3D classification the number of classes was set to 3 (K = 3). This is an arbitrary human decision and implicitly forces particles to separate into 3 descrete classes. It would have been great to mention if the authors had tried different classification parameters and, if so, whether that had led to similar classification results. There are different methods available to dissect conformational heterogeneity other than simple 3D classification. For example, focused classification can differentiate local structural features. MultiBody refinement and 3D variabitlity can analyze continuous conformational changes. The simple 3D classification with local angular sampling employed here may lead to over-simplification of the more complex structural heterogeneity.

Although 37 degrees heat treatment and NEM treatment did not reveal any sign of deactivation in Drosophila complex I, it does not rule out the possibility that insect complex I has different ways to deactivate the enzyme, to prevent ROS production. It is probably the limitation of applying existing assays that are originally for mammalian and fungal enzymes to the study of insect enzymes.

- Whether they achieved the aims and whether the conclusions are supported by the results:

Overall, they successfully isolated the active enzyme and determined its structure at 3.3 A resolution, which meets the current state-of-the-art for single-particle cryo-EM and provided an atomic picture of the enzyme composition. The study confirms that the Drosophila complex I is structurally similar to mammalian complex I, but biochemically different in that it does not show the deactive state. It still does not exclude the possibility that Drosophila complex I can transition into a currently unknown state that prevents reverse electron transfer. This question however can be tackled in future by mutagenesis analyses as Drosophila is a genetically tractable organism.

- Impact to the field and utility of the data to the community:

Complex I is important not only for human health but also for understanding universal principles of biological respiration, because of its universal presence in most organisms on Earth. This study provides a basis for relating mammalian complex I with those from other branches of organisms. The current structures will allow Drosophila researchers to interpret and design any mutations that affect complex I functions, and relate them to behavioral, developmental and metabolical changes at tissues, organs and individuals levels.

Reviewer #3 (Public Review):

The mitochondrial NADH dehydrogenase complex (complex I) is of prime importance for cellular respiration. It has been biochemically and structurally characterized for several groups of organisms, including mammals, fungi, algae, seed plants and protozoa. Furthermore, different complex I conformation have been reported, which are considered to possibly represent distinct physiological states of the enzyme complex. E.g. in mammalian mitochondria, two resting states can be distinguished, designated 'ready-to-go' resting state, and 'deactive' resting state. To better understand the physiological relevance of these states, complex I is here investigated from the fruit fly Drosophila melanogaster, which represents a model for insects but beyond for metazoan in general and which can be easily genetically modified.

Complex I from Drosophila is presented at up to 3.3 Angstrom resolution. It includes 43 of the 45 complex I subunits defined for mammalian complex I. Subunit NDUFA3 has been found in Drosophila complex I for the first time. Overall, Drosophila complex I is remarkably similar in its composition and structure to the mammalian enzyme. Only minor topological differences were found in some subunits. Furthermore, three different complex I states are described, termed Dm1, Dm2 and Dm3. The three states are extensively discussed and compared to the states found in mammalian complex I. Dm1, which is the dominating class, likely represents the active resting state. In Dm2, the two complex I arms are slightly twisted with respect to Dm1. In Dm3, the membrane arm appears to be 'cracked' at the interface between ND2 and ND4. It possibly represents an artefact resulting from detergent-induced loss of stability in the distal membrane domain of the Dm2 state. Both, Dm2 and Dm3 most closely correspond to the mammalian active state. A state resembling the mammalian deactive state could not be found. This result is further supported by biochemical experiments. It is concluded that Drosophila complex I, despite its remarkable similarity to the mammalian enzyme, does not undergo the mammalian-type active/deactive transition.

In conclusion, complex I structure from Drosophila is of limited value for the better understanding of the states of mammalian complex I (which could be stated more clearly). However, insights into complex I structure and function of an insect is highly interesting. The conclusions are justified by the presented data. The manuscript is well written and the figures are thoroughly prepared. The discussion very much focusses on the interpretation of the three complex I states. The deactivate state, which is interpreted to protect mammalian mitochondria from ROS production during reverse electron transfer, might be dispensable in species characterized by a comparatively short life cycle like Drosophila, which is in the range of weeks.

Reviewer #1 (Public Review):

Neuronal tissues are very complex and are composed of a large number of neuronal types. With the advent of single-cell sequencing, many researchers have used this technology to generate atlases of neuronal structures that would describe in detail the transcriptome profiles of the different cell types. Along these lines, in this manuscript, the authors present single-cell transcriptomic data of the fruitless-expressing neurons in the Drosophila male and female central nervous systems. The authors initially compare cell cluster composition between male and female flies. They then use the expression of known markers (such as Hox genes and KC neuronal markers) to annotate several of their clusters. Then, they look in detail at the expression of different terminal neuronal genes in their transcriptomic data: first, they look into neurotransmitter-related genes and how they are expressed in the fruitless-expressing neurons; they describe in detail these populations that they then verify the expression patterns by looking into genetic intersections of Fru with different neurotransmitter-related genes. Then, they look at Fru-neurons that express circadian clock genes, different neuropeptides and neuropeptide receptors, and different subunits of acetylcholine receptors. Finally, they look into genes that are differentially expressed between male and female neurons that belong to the same clusters. They find a large number of genes; through GO term enrichment analysis, they conclude that many IgSF proteins are differentially expressed, so they look into their expression in Fru-neurons in more detail. Finally, they compare transcription factor expression between male and female neurons of the same cluster and they identify 69 TFs with cluster-specific sex-differential expression.

In general, the authors achieved their goal of generating and presenting a large and very useful dataset that will definitely open a large number of research avenues and has already raised a number of interesting hypotheses. The data seem to be of good quality and the authors present a different aspect of their atlas.

The main drawback is that many of the analyses are very superficial, resulting in the manuscript being handwavy and unsupported. The manuscript would benefit by reducing the number of "analyses" to the ones that are also in vivo validated and by discussing some of the drawbacks that are inherent to their experimental procedure.<br /> 1) The authors treat their male, female, and full datasets as three different samples. At the end of the day, these are, for the most part, equivalent neuronal types. The authors should decide to a) either only use the full dataset and present all analyses in this, or b) give a clear correspondence of male and female clusters onto the full ones.<br /> 2) Most of their sections are heavily reliant on marker genes. In fact, in almost every section they mention how many of their genes of interest are marker genes. This depends heavily on specific cutoffs, making the conclusions fragile. Similarly, GO terms are used selectively and are, in many cases, vague (such as "signaling", "neurogenesis", "translation").<br /> 3) A few of the results are not confirmed in vivo. The authors should add a Discussion section where they discuss the inherent issues of their analyses. Are there clusters of low quality? Are there many doublets?<br /> On the same note, their clusters are obviously non-homogeneous (i.e. they house more than one cell types. This could obviously affect the authors' cluster-specific sex-differential expression, as differences could also be attributed to the differential composition of the male and female subclusters.<br /> 4) Immunostainings are often unannotated and, in some cases especially in the Supplement, they are blurry. The authors should annotate their images and provide better images whenever possible.<br /> 5) I believe that the manuscript would benefit significantly by being heavily reduced in size and being focused on in vivo rigorously confirmed observations.

Reviewer #2 (Public Review):

This work characterizes the diversity of Fruitless-expressing neurons in developing, mid-pupal Drosophila brains using single-cell sequencing. This was a reasonably in-depth effort to characterize sexually specific cell types during neural development. The use of single-cell sequencing tools to understand developmental mechanisms and not just adult function is appreciated. Some of the recent broader efforts, such as a recent whole-head atlas, contained limited numbers of cells for many cell types and have likely missed rare cell types. Sorting cells of an interesting category in order to enrich their representation in the set, with specific questions in mind, is in some ways a stronger and more targeted approach.

I am surprised that the authors observed so much overlap between Fruitless-expressing cells of the same type in males and females and so few sex-specific cell types, which is interesting. Of course, cells of the same overall "type" could have differential wiring and function given the expression of Fruitless. This study suggests that such a modification may not require a wholesale change in gene expression profiles and may be enabled via a restricted set of changes in target gene expression. Overall, while the outcome is a bit descriptive, these efforts produced some interesting biological insights, and this dataset should serve as a resource for future efforts.

Reviewer #3 (Public Review):

This paper uses single-cell transcriptome sequencing to identify and characterize some of the neuronal populations responsible for sex-specific behaviour and physiology. This question is of interest to many biologists, and the approach taken by the authors is productive and will lead to new insights into the molecular programs that underpin sexually dimorphic development in the CNS. The dataset produced by the authors is of high quality, the analyses are detailed and well described, and the authors have made substantial progress toward the identification and characterization of some of the neuron populations. At the same time, many other cell types whose existence is suggested by this dataset remain to be identified and matched to specific neuron populations or circuits. We expect the value of this dataset to increase as other groups begin to follow up on the data and analyses reported in this paper. In general, the value of this paper to the field of Drosophila neurobiology will be high even if it is published in close to its present form. On the other hand, the current manuscript does not succeed in presenting the key take-home messages to a broader audience. A modest effort in this direction, especially re-writing the Conclusions section, will greatly enhance the accessibility and broader impact of this paper.

While the biological conclusions reached by the authors are generally robust and of high interest, we believe that some conclusions are not sufficiently supported by the analyses that have been performed so far and need to be reexamined and confirmed. A major question concerns the authors' ability to distinguish a shared cell type with sex-biased gene expression from a pair of closely related, sex-limited cell types. There appear to be many cases that fall into this grey area, and the current analysis does not provide an objective criterion for distinguishing between sex-specific and sexually dimorphic clusters. Below we suggest some technical approaches that could be used to examine this issue. A second problem, which we do not believe to be fatal but that needs to be discussed, concerns potential differences in developmental timing and cell cycle phase between males and females, and how these differences might impact the inferences of sexual dimorphism in cell numbers and gene expression. Finally, we identify several areas, including the expression of transcription factors in different neuronal populations, that we believe could be described in more biologically insightful ways.

For our review, we focus on three levels of evaluation:

1). Is the dataset of high quality, useful to a large number of people, well annotated, and clearly described?

The data appear to be high quality. The authors use reasonable neuronal markers to infer that 99% of their cells are neuronal in origin, suggesting extremely low levels of contamination from non-neuronal cells. Moreover, the gene/UMIs detected per cell are high relative to what has been reported in previous Drosophila scRNA-seq neuron papers (e.g. Allen et al., 2020). The cluster annotations are incomplete - which is not surprising, given the complexity of the cell population the authors are working with. 46 of the 113 clusters in the full dataset are named based on published expression data, gene ontology enrichments of cluster marker genes, and overlap with other CNS single cell datasets. This leaves rather a lot outstanding. It is probably unrealistic to aim for a 100% complete annotation of this dataset. But if we're thinking about how this dataset might be used by other researchers, in most cases the validation that a given cluster corresponds to a real, distinct neuron subpopulation will be left to the user.

A major comment we have about the quality of the dataset relates to how doublets are identified and dealt with. The presence of doublets, an unavoidable byproduct of droplet-based scRNAseq protocols (like the 10x protocol used by the authors), could affect the clustering or at least bias the detection of marker genes. In large clusters, one might expect the influence of doublets on marker gene detection to be diluted, but in smaller clusters it could cause more significant problems. In extreme cases, a high proportion of doublets can produce artifactual clusters. The potential for problems is particularly high in cases where the authors identify cells with hybrid properties, such as clusters 86 and 92, which the authors describe as being serotonergic, glutamatergic, and peptidergic. Currently, the authors filter out cells with high UMI/gene counts, but it's unclear how many are removed based on these criteria, and cells can naturally vary in these values so it is not clear to us whether this approach will reliably remove doublets. That said, we acknowledge that by limiting their 'FindMarkers' analysis to genes detected in >25% of cells in a cluster the authors are likely excluding genes derived from doublets that contaminate clusters in low (but not high) numbers. We think it would be useful for the authors to report the number of cells that are filtered out because they met their doublet criteria and compare this value to the number of expected doublets for the number of cells they recovered (10x provides these figures). We would also recommend that the authors trial a doublet detection algorithm (e.g. DoubletFinder) on the unfiltered datasets (that is, unfiltered at the top end of the UMI/gene distribution). Does this identify the same cells as doublets as those the authors were filtering out?

2). What is the value of this study to its immediate field, Drosophila neurobiology? Are the annotation and analysis of specific cell clusters as precise and insightful as they could be? Has all the most important and novel information been extracted from this dataset?

This is the part that we are least qualified to assess, since we, unlike the authors, are not neurobiologists. We hope some of the other referees will have sufficient expertise to evaluate the paper at this level.

One thing we noticed (more on that in Part 3) is that the authors rely on JackStraw plots and clustree plots to identify the optimal combination of PCs and resolution to guide their clustering. This represents a relatively objective way of settling on clustering parameters. However, in a number of the UMAPs it looks like there are sub clusters that go undiscussed. E.g. in Fig. 2E clusters 1 and 3 are associated with smaller, distinct clusters and the same is true of clusters 2 and 6 in Fig 4b. Given that the authors are attempting to assemble a comprehensive atlas of fru+ neurons, it seems important for them to assess (at least transcriptomically) whether these are likely to represent distinct subpopulations.

3). How interesting, and how accessible is this paper to people outside of the authors' immediate field? What does it contribute to the "big picture" science?

Here, we think the authors missed an important opportunity by under-utilizing the Conclusions section. The manuscript has a combined "Results and Discussion" section, where the authors talk about their identification and analysis of specific cell clusters / cell types. Frankly, to a non-specialist this often reads like a laundry list, and the key conclusions are swamped by a flood of details. This is not to criticize that section - given the complexity and potential value of this dataset, we think it is entirely appropriate to describe all these details in the Results and Discussion. However, the Conclusions section does not, in its present form, pull it all back together. We recommend using that section to summarize the 5-8 most important high-level conclusions that the authors see emerging from their work. What are the most important take-home messages they want to convey to a developmental biologist who does not work on brains, or to a neurobiologist who does not work on Drosophila? The authors can enhance the value of this paper by making it more interesting and more accessible to a broader audience.

Reviewer #1 (Public Review):

The author has generated a specific version of alpha-fold deep neural network-based protein folding prediction programme for TCR-pMHC docking. The alpha-fold multimer programme doesn't perform well for TCR-pMHC docking as the TCR uses random amino acids in the CDRs and the docking geometry is flexible. A version of the alpha-fold was developed that provides templates for TCR alpha-beta pairing and docking with class I pMHC. This enables structural predictions that can be used to rank TCR for docking with a set of peptides to identify the best peptide based on the quality of the structural prediction - with the best binders having the smallest residuals. This approach provides a step toward more general prediction and may immediately solve a class of practical problems in which one wants to determine what pMHC a given TCR recognizes from a limited set of possible peptides.

Reviewer #2 (Public Review):

The application of AlphaFold to the prediction of the peptide TCR recognition process is not without challenge; at heart, this is a multi-protein recognition event. While Alphafold does very well at modelling single protein chains its handling of multi-chain interactions such as those of antibody-antigens pairs have performed substantially lower than for other targets (Ghani et al. 2021). This has led to the development of specialised pipelines that tweak the prediction process to improve the prediction of such key biological interactions. Prediction of individual TCR:pMHC complexes shares many of the challenges apparent within antibody-antigen prediction but also has its own unique possibilities for error.

One of the current limitations of AlphaFold Multimer is that it doesn't support multi-chain templating. As with antibodies, this is a major issue for the prediction of TCR:pMHC complexes as the nearest model for a given pMHC, TRAV, or TRBV sequence may be in entirely different files. Bradley's pipeline creates a diverse set of 12-hybrid AlphaFold templates to circumvent this limitation, this approach constrains inter-chain docking and therefore speeds predictions by removing the time-consuming MSA step of the AlphaFold pipeline. This adapted pipeline produces higher-quality models when benchmarked on 20 targets without a close homolog within the training data.

The challenge to the work is of course not generating predictions but establishing a functional scoring system for the docked poses of the pMHC:TCR and most importantly clearly understanding/communicating when modelling has failed. Thus, importantly Bradley's pipeline shows a strong correlation between its predicted and observed model accuracy. To this end, Bradley uses a receiver operating characteristic curve to discriminate between a TCR's actual antigen and 9 test decoys. This is an interesting testing regime, which appears to function well for the 8 case studies reported. It certainly leaves me wanting to better understand the failure mode for the two outliers - have these correctly modelled the pMHC but failed to dock the TCRs for example or visa versa?

The real test of the current work, or its future iteration, will be the ability to make predictions from large tetramer-sorted datasets which then couple with experimental testing. The pipeline's current iteration may have some utility here but future improvements will make for exciting changes to current experimental methods. Overall the work is a step towards applying structural understanding to the vast amount of next-generation TCR sequence data currently being produced and improves upon current AlphaFold capability.

Reviewer #3 (Public Review):

This manuscript is well organized, and the author has generally shown good rigor in generating and presenting results. For instance, the author utilized TCRdist and structure-based metrics to remove redundancies and cluster complex structures. Additionally, the consideration of only recent structures (Fig. 2B) and structures that do not overlap with the finetuning dataset (Fig. 2D) is highly warranted.

In some cases, it seems possible that there may be train/test overlap, including the binding specificity prediction section and results, where native complexes being studied in that section may be closely related to or matching with structures that were previously used by the author to fine-tune the AlphaFold model. This could possibly bias the structure prediction accuracy and should be addressed by the author.

Other areas of the results and methods require some clarification, including the generation and composition of the hybrid templates, and the benchmark sets shown in some panels of Figure 2. Overall this is a very good manuscript with interesting results, and the author is encouraged to address the specific comments below related to the above concerns.

1. In the Results section, the statement "visual inspection revealed that many of the predicted models had displaced peptides and/or TCR:pMHC docking modes that were outside the range observed in native proteins" only references Fig. S1. However, with the UMAP representation in that figure, it is difficult for readers to readily see the displaced peptides noted by the author; only two example models are shown in that figure, and neither seems to have displaced peptides. The author should provide more details to support this statement, specifically structures of example models/complexes where the peptide was displaced, and/or summary statistics noting (out of the 130 tested) how many exhibited displaced peptides and aberrant TCR binding modes.

2. The template selection protocol described in Figure 1 and in the Results and Methods should be clarified further. It seems that the use of 12 docking geometries in addition to four individual templates for each TCR alpha, TCR beta, and peptide-MHC would lead to a large combinatorial amount of hybrid templates, yet only 12 hybrid templates are described in the text and depicted in Figure 1. It's not clear whether the individual chain templates are randomly assigned within the 12 docking geometries, as an exhaustive combination of individual chains and docking geometries does not seem possible within the 12 hybrid models.

3. Neither the docking RMSD nor the CDR RMSD metrics used in Figure 2 will show whether the peptide is modeled in the MHC groove and in the correct register. This would be an important element to gauge whether the TCR-pMHC interface is correctly modeled, particularly in light of the author's note regarding peptide displacement out of the groove with AlphaFold-Multimer. The author should provide an assessment of the models for peptide RMSD (after MHC superposition), possibly as a scatterplot along with docking RMSD or CDR RMSD to view both the TCR and peptide modeling fidelity of individual models. Otherwise, or in addition, another metric of interface quality that would account for the peptide, such as interface RMSD or CAPRI docking accuracy, could be included.

4. It is not clear what benchmark set is being considered in Fig. 2E and 2F; that should be noted in the figure legend and the Results text. If needed, the author should discuss possible overlap in training and test sets for those results, particularly if the analysis in Fig. 2E and 2F includes the fine-tuned model noted in Fig. 2D and the test set in Fig. 2E and 2F is not the set of murine TCR-pMHC complexes shown in Fig. 2D. Likewise, the set being considered in Fig. 2C (which may possibly be the same set as Fig. 2E and 2F) is not clear based on the figure legend and text.

5. The docking accuracy results reported in Fig. 2 do not seem to have a comparison with an existing TCR-pMHC modeling method, even though several of them are currently available. At least for the set of new cases shown in Fig. 2B, it would be helpful for readers to see RMSD results with an existing template-based method as a baseline, for instance, either ImmuneScape (https://sysimm.org/immune-scape/) or TCRpMHCmodels (https://services.healthtech.dtu.dk/service.php?TCRpMHCmodels-1.0; this only appears to model Class I complexes, so Class I-only cases could be considered here).

6. As noted in the text, the epitopes noted in Table 1 for the specificity prediction are present in existing structures, and most of those are human epitopes that may have been represented in the AF_TCR finetuning dataset. Were there any controls put in place to prevent the finetuning set from including complexes that are redundant with the TCRs and epitopes being used in the docking-based and specificity predictions if the AF_TCR finetuned model was used in those predictions? For instance, the GILGFVFTL epitope has many known TCR-pMHC structures and the TCRs and TCR-pMHC interfaces are known to have common structural and sequence motifs in those structures. Is it possible that the finetuning dataset included such a complex in its training, which could have influenced the success in Figure 3? The docking RMSD accuracy results in Fig. 5A, where certain epitopes seem to have very accuracy docking RMSDs and may have representative complex structures in the AF_TCR finetuning set, may be impacted by this train/test overlap. If so, the author should consider using an altered finetuned model with no train/test overlap for the binding specificity prediction section and results, or else remove the epitopes and TCRs that would be redundant with the complex structures present in the finetuning set.

7. The alanine scanning results (Figure 6) do not seem to be validated against any experimental data, so it's not possible to gauge their accuracy. For peptide-MHC targets where there is a clear signal of disruption, it seems to correspond to prominently exposed side chains on the peptide which could likely be detected by a more simplistic structural analysis of the peptide-MHC itself. Thus the utility of the described approach in real-world scenarios (e.g. to detect viral escape mutants) is not clear. It would be helpful if the author can show results for a viral epitope variant (e.g. from one of the influenza epitopes, or the HCV epitope, in Table 1) that is known to disrupt binding for single or multiple TCRs, if such an example is available from the literature.

Reviewer #1 (Public Review):

The ABC transporter ABCG2 exports xenobiotics, including chemotherapy reagents, from a number of different organs. Understanding the mechanism of ATP-dependent transport is of fundamental importance, yet current models have been larger derived from structures and protein dynamics have been carried out in artificial environments. Here the authors have used a fluorescent-labeled antibody specific to the inward-facing conformation and monitored this state in a cell by confocal microscopy and fluorescence-correlation spectroscopy (FSC). They conclude that ATP binding drives substrate efflux and the resetting to an inward-facing conformation requires ATP hydrolysis and the subsequent dissociation of the hydrolysis products. Both the mechanistic insights and methodology employed will be of interest to the biochemistry and transport biology fields.

Strengths: The paper exploits a fluorescent labelled antibody to probe some interesting mechanistic questions in a close-to-native environment. The use of different inhibitors and nucleotides to trap different states is beautifully done and the mechanistic interpretation is convincing. The use of FSC to probe the differences in transporter dynamics in the presence of substrates seems novel and is likely to be of general interest.

Weaknesses. The main weakness is that the probe is only able to detect a signal for the inward state and so a change in conformational state has to be derived from a diminished response, I.e., no probe to specifically monitor the outward-facing state.

Reviewer #2 (Public Review):

A major challenge to studying the ABC transporter dynamics "in situ" is the lack of precise measurement of various structural conformers that correspond to intermediate states during the ATP-catalytic or substrate-transport cycle. The use of the conformation-specific antibody 5D3 has recently enabled structural biology to experimentally visualize a specific structural fold that corresponds to an apo and inward-facing (IF) state of ABCG2. In this study, Gyöngy et al aimed to develop a mammalian cell-based system for ABCG2 to investigate how nucleotide or drug ligands regulate the transporter's alternating nature of its inward- and outward-facing (OF) conformations. The authors exploited the nature of 5D3 to only recognize ABCG2' IF conformers and combined flow cytometry and confocal microscopy to systematically analyze the IF-OF switches in the presence of nucleotides, drug substrates, ATPase inhibitors, and a known ABCG2 inhibitor Ko143. The authors find that nucleotide binding alone is sufficient to decrease the propensity of IF conformation, as well as to drive the high-to-low drug substrate transformation, and subsequently the drug-induced ATP hydrolysis resets the transporter to the IF and 5D3-bound state. These data provide solid cell-based evidence that adds to the ongoing discussion about the allosteric regulation of ABCG2 by both nucleotide and transport substrate ligands. Most importantly, the results support several lines of functional implications that could not be fully addressed by recent high-profile cryo-EM structures of ABCG2.

Strengths:<br /> The development of the methodology is a tour-de-force effort, as well as the biggest strength of this study. The results and the experimental protocol will likely provide a significant impact on how scientists design experiments to address structural questions without using large-scale purified systems and conventional structural biology approaches.

The validity of using the GFP-tagged ABCG2 variant is supported by several lines of functional characterizations, including protein expression, 5D3 reactivity, the responsiveness of nucleotide analogs, and mitoxantrone (MX) accumulation analysis. The application of such a strategy is particularly exemplified by the systematic treatment of various nucleotide analogs, which is sufficient to establish kinetic analysis by looking into apparent nucleotide affinities. These initial works add confidence in performing experiments by using either transport substrates or ABCG2 inhibitors. It is very compelling to see candidly the drug-coupled stimulation of ATP hydrolysis, given that both ATP and ADP decrease the 5D3-bound population.

The development of fluorescence correlation spectroscopy (FCS), the first in such a study, allows the measurement of colocalization of both fluorescence-labeled transporter and transport substrates. As illustrated by MX binding to ABCG2, the data supports the notion that nucleotide binding drives substrate release from the transporters, which in this case, can be explained by the high-to-low substrate binding affinity or IF-OF conformational switch of the transporter. Moreover, such an assay will be likely used as part of a high-throughput pipeline in search of therapeutic drugs against ABCG2.

Weakness:<br /> Although the paper presents solid and compelling cell-based evidence describing the relationship between structural changes of ABCG2 and ligand bindings, the enthusiasm is slightly dampened by the fact that this study seems mostly used to support the hypotheses that were proposed by recent cryo-EM structures. It is unclear from this study whether new insight into ABCG2's working mechanism can be proposed based on the data in this manuscript.

In addition, the IF-OF switch represents the transformation of two extreme conformations in ABCG2. The authors do not address the intermediate states, such as occluded conformers, in this study, which makes one wonder whether this is a limitation of the methodology presented in this manuscript. Moreover, interdomain crosstalk is highlighted in this manuscript to address the communication between NBD and TMD. However, it is not clear how the data could say anything about the crosstalk between NBD and TMD. For example, one limitation of recording 5D3 sensitivity on WT proteins may not allow us to pinpoint how structural motifs at the NBD-TMD interface (e.g., Q-loop, triple-helix bundle, polar relay, etc) transmit signals that cause IF-OF switch. The authors do not address how the strategy described here can address such a gap.

Lastly, the authors illustrate the conformational change of 5D3 epitope in the extracellular domain (ECD) by using atomic models in the presence and absence of the antibody. The dynamic information of how the ECD transforms to non-5D3 reactive is limited through this study; for instance, what the timing is to set loose of antibodies upon nucleotide binding, or to what degree of drug binding, IF starts to transit to OF under the physiological condition. Despite this, it is worth noting that 30% of MX-ABCG2 colocalization was still observed in untreated cells, perhaps suggesting a dynamic equilibrium between substrate-bound IF and other conformers.

Reviewer #3 (Public Review):

The goal of this study was to probe the transition from the IF to OF conformations inside the cells of a multidrug ABC transporter, ABCG2. In order to do so the authors used an antibody that specifically recognized the IF state (the epitope is 'disorganized' in the OF conformation) and this tool was particularly useful to address the conformational changes of ABCG2 that take place inside the permeabilized cells, depleted or not in ATP, and complemented with different combinations of nucleotides, drugs, and inhibitors. This technique was also used to show that the drugs increase the transition from the IF to the OF state.

By using confocal microscopy, the authors showed that ATP depletion led to a majority of ABCG2 that reside in a mitoxantrone-bound IF conformation.

The fluorescence correlation spectroscopy was another powerful approach used by the authors to convincingly demonstrate that the mitoxantrone drug could bind to ABCG2 in the IF conformation only.

Overall, the experiments are sound and the main conclusions drawn by the authors are very well supported by their data. This study unravels the first steps of the catalytic cycle of ABCG2 inside the cells, from drug-binding to a high-affinity site in the IF conformation to drug release from a low-affinity site in the OF conformation. It helps us to better understand how this transporter works in an environment that is physiologically relevant.

Reviewer #1 (Public Review):

Hafez and collaborators describe the construction and analysis of a computational model of a mushroom body neuron. The anatomy derives from a combination of electron microscopy reconstructions of MBON-α3 and also from light microscopy. The physiological parameters derive from publications that measured them, in addition to the author's own electrophysiological recordings with patch-clamp.

There are two main findings. First, the dendritic arbor of MBON-α3 is electrotonically compact, meaning, individual connections from Kenyon cells will similarly elicit action potentials independently as to where, spatially, the synapses lay on the arbor. Second, in simulation, exploration of changes in the strength of Kenyon cell inputs illustrate two possible ways to alter the strength of the KC-MBON physiological connection, showing that either could account for the observed synaptic depression in the establishment of associative memories. The properties of each approach differ.

Overall, the manuscript clearly describes the journey from connectomics and electrophysiology to computational modeling and exploration of the physiological properties of a circuit in simulation.

Reviewer #2 (Public Review):

"The cellular architecture of memory modules in Drosophila supports stochastic input integration" is a classical biophysical compartmental modelling study. It takes advantage of some simple current injection protocols in a massively complex mushroom body neuron called MBON-a3 and compartmental models that simulate the electrophysiological behaviour given a detailed description of the anatomical extent of its neurites.

This work is interesting in a number of ways:

- The input structure information comes from EM data (Kenyon cells) although this is not discussed much in the paper<br /> - The paper predicts a potentially novel normalization of the throughput of KC inputs at the level of the proximal dendrite and soma<br /> - It claims a new computational principle in dendrites, this didn't become very clear to me

Problems I see:

- The current injections did not last long enough to reach steady state (e.g. Figure 1FG), and the model current injection traces have two time constants but the data only one (Figure 2DF). This does not make me very confident in the results and conclusions.<br /> - The time constant in Table 1 is much shorter than in Figure 1FG?<br /> - Related to this, the capacitance values are very low maybe this can be explained by the model's wrong assumption of tau?<br /> - That latter in turn could be because of either space clamp issues in this hugely complex cell or bad model predictions due to incomplete reconstructions, bad match between morphology and electrophysiology (both are from different datasets?), or unknown ion channels that produce non-linear behaviour during the current injections.<br /> - The PRAXIS method in NEURON seems too ad hoc. Passive properties of a neuron should probably rather be explored in parameter scans.

Questions I have:

- Computational aspects were previously addressed by e.g. Larry Abbott and Gilles Laurent (sparse coding), how do the findings here distinguish themselves from this work<br /> - What is valence information?<br /> - It seems that Martin Nawrot's work would be relevant to this work<br /> - Compactification and democratization could be related to other work like Otopalik et al 2017 eLife but also passive normalization. The equal efficiency in line 427 reminds me of dendritic/synaptic democracy and dendritic constancy<br /> - The morphology does not obviously seem compact, how unusual would it be that such a complex dendrite is so compact?<br /> - What were the advantages of using the EM circuit?<br /> - Isn't Fig 4E rather trivial if the cell is compact?

Overall, I am worried that the passive modelling study of the MBON-a3 does not provide enough evidence to explain the electrophysiological behaviour of the cell and to make accurate predictions of the cell's responses to a variety of stochastic KC inputs.

Reviewer #3 (Public Review):

This manuscript presents an analysis of the cellular integration properties of a specific mushroom body output neuron, MBON-α3, using a combination of patch clamp recordings and data from electron microscopy. The study demonstrates that the neuron is electrotonically compact permitting linear integration of synaptic input from Kenyon cells that represent odor identity.

Strengths of the manuscript:

1) The study integrates morphological data about MBON-α3 along with parameters derived from electrophysiological measurements to build a detailed model.<br /> 2) The modeling provides support for existing models of how olfactory memory is related to integration at the MBON.

Weaknesses of the manuscript:

1) The study does not provide experimental validation of the results of the computational model.<br /> 2) The conclusion of the modeling analysis is that the neuron integrates synaptic inputs almost completely linearly. All the subsequent analyses are straightforward consequences of this result.<br /> 3) The manuscript does not provide much explanation or intuition as to why this linear conclusion holds.

In general, there is a clear takeaway here, which is that the dendritic tree of MBON-α3 in the lobes is highly electrotonically compact. The authors did not provide much explanation as to why this is, and the paper would benefit from a clearer conclusion. Furthermore, I found the results of Figures 4 and 5 rather straightforward given this previous observation. I am sceptical about whether the tiny variations in, e.g. Figs. 3I and 5F-H, are meaningful biologically.

Reviewer #1 (Public Review):

The authors present a retrospective study of COVID-19 mortality within 30 days from a positive SARS-CoV-2 PCR in 1115 patients with cancer and 2851 patients without cancer. Patients were recruited from 16 different centres from 8 countries across 5 continents. Patients were recruited between January and November 2020. All patients with a positive SARS-CoV-2 PCR were included. Demographic and clinical data were collected from electronic patient records. The primary outcome was 30-day mortality. Data were retrieved from patient records and there is a significant proportion of missing data.

The authors found that age and the presence of cancer were independent risk factors of 30-day mortality. Remdesivir was associated with reduced mortality. Within cancer patients, those with haematological malignancies and lung cancer had the highest risk. Overall, the findings of this study are in line with previously published results and don't provide major new insights.

Strength:

This is a multicentric study across several countries including over 3000 patients.

Limitations<br /> 1) This is not the first cohort study in cancer patients, several large studies have addressed risk factors of mortality before (for example Kuderer et al., The Lancet, 2020 and Chaves-McGregor et al. JAMA Oncology, 2021).

2) The authors identify Remdesivir to reduce mortality in cancer patients and those without cancer. The efficacy of Remdesivir has been addressed in large prospective trials, albeit not in cancer patients.

3) Treatment of patients with COVID-19 likely varied by country but the authors haven't addressed the impact of this.

4) Given that the recruited patients were all unvaccinated, the results are likely not completely transferable to the current situation. Vaccination and current antivirals and monoclonal antibodies have reduced the risk of severe disease and death. The current omicron variant has different properties compared to earlier strains. In fact, studies have shown that mortality in cancer patients has improved since 2020 (OnCovid Study Group, JAMA Oncology, 2021).

In conclusion, the authors largely confirm findings from other studies that patients with cancer were at an increased risk of death after COVID-19 infection, especially early on in the pandemic.

Reviewer #2 (Public Review):

The paper entitled "International Multicenter Study Comparing Cancer to Non-Cancer Patients with COVID-19: Impact of Risk Factors and Treatment Modalities on Survivorship" by Raad et al. is a multi-center, international, matched cohort, with a relatively large sample size. The aim of this work is to determine independent risk factors that impact survival in the setting of "novel treatment modalities" like Remdesivir. It enrolled patients with COVID-19 and cancer and compared them to cancer-negative controls. The authors conclude that cancer increases mortality from COVID-19 and that Remdesivir can reduce all-cause mortality in a subset of patients receiving low-flow oxygen and the results support their conclusions. Overall, this paper adds to the growing body of literature that implicates cancer as a worse predictor of survival among patients with COVID-19. The use of a matched cohort makes it unique and strengthens the findings of this study. The potential weaknesses of this study are its retrospective nature and lack of data on the effect of vaccination in this population since the study was conducted prior to the introduction of vaccines.

Reviewer #1 (Public Review):

The authors aim was to determine the role of initial procalcitonin (PCT) measurements in cancer patients admitted with COVID-19 infection in reducing the intensity and duration of empiric antibiotic therapy. This was a retrospective study of all patients admitted to a single cancer center with COVID-19 infection and at least one PCT test within 72 hours of admission. The cut off PCT value to divide patients into two groups was 0.25 ng/ml (those with >= 0.25 ng/ml having a higher suspicion of bacterial infection). The study found that compared to patients with low PCT levels had shorter hospital stays, lower rate of mortality, and received less antibiotic therapy. The paper is well written, the study methods and statistics are sound, the population well characterized and large enough for valid comparison, and the results support the authors conclusions. The study provides support that PCT can be used in this special at risk population as it has been used in other COVID-19 patient populations that have been better studied. The study has limitations that the authors report: retrospective, single center study, bacterial infections may have been missed (no uniformity of cultures collected), and empiric antimicrobial therapy was at the discretion of the treating team (no standardized empiric therapy). The findings of this study may not be generalizable to other cancer patient populations and there may be other confounding variables not identified.

Reviewer #2 (Public Review):

This is a well written manuscript that provides a useful analysis on using PCT for guiding antibiotics use among cancer patients with COVID19, a very common issue with COVID 19 patients in general but more challenging in the cancer population. Analysis is relatively straight forward. Results support the claim that antibiotics for more than 72 hours may be unnecessary for cancer patients with negative cultures and PCT<0.25. This is can be useful clinically to limit unnecessary antibiotic use, however would only apply this as a very broad generalization. It would be interesting to see what outcomes are and if this applies for more specific and challenging but not uncommon clinical scenarios with cancer patients (i.e neutropenic patients, patients undergoing active therapy, ICU admission) where clinicians may favor longer use of antibiotics.

Reviewer #3 (Public Review):

In this retrospective study, the authors intend to demonstrate the utility of serum procalcitonin in reducing the use of antibacteral agents in cancer patients with COVID-19, by identifying the subset of their highly immunocompromised population where early discontinuation of antibacterial therapy would not be harmful.

This study has a large population size > 500 patients over the span of 16 months. The groups with low procalcitonin and high procalcitonin have similar baseline characteristics, which makes the subsequent comparisons valid and relevant. The authors have considered all the relevant variables that could affect the outcomes being studied, and used sound statistical methods.

This study has some limitations. It is retrospective by nature, with possibility for confounders. In addition to the limitations mentioned by the authors, the study spans the period from March 2020 to June 2021 through which our knowledge of COVID has evolved, multiple variants have emerged, immunization has become available in the later part of the study period, more therapies (antivirals, monoclonal antibodies) became available, all of which have definitely affected COVID-related mortality, and could be an important confounder here. While the authors report the level of severity of the infection, using proxies such as supplemental oxygen and ICU admission, the use of COVID-directed therapies, including immunosuppressants such as steroids and tocilizumab (which in turn can increase the risk of bacterial infections and decrease the risk of mortality) is not reported. It also seems that the management of antibacterial therapy was left at the discretion of the treating physician, which can lead to a wide variety of practices, the nature of antibacterials administered is not reported here.

The results presented here support the conclusions made by the authors, and one has to appreciate the difficulty of antimicrobial stewardship efforts in an immunocompromised population such as the one being studied here. Many of these patients have been immunosuppressed for prolonged periods of time, could have profound defects in their immune systems, and could have had multiple previous infections, sometimes with atypical presentations. These patients are typically excluded from most large clinical trials, thus retrospective studies such as this one are usually the most informative pieces of literature available to support evidence-based medicine in this special patient population. I think this study should encourage clinicians to consider the use of serum procalcitonin as one additional clue to support their pursuit of antibacterial de-escalation or discontinuation in cancer patients with COVID-19.

Reviewer #1 (Public Review):

Diehl & Redish set out to capture how cognitive and behavioral linked activity varies along the medial wall of the rodent prefrontal cortex during a complex decision-making task. They found four clusters of cells along the dorsal-ventral axis that were firing more similarly to other cells in the same cluster than cells in other clusters, suggesting there are 4 distinct subdivisions in rodent mPFC. Their detailed analysis of decision-making, reward, and evaluation showed that though some cells in each area responded to these different cognitive aspects, there was a difference in how widespread these signals were in the different subdivisions. They found more decision-related activity in the ACC, more post-decision evaluative activity in the dorsal parts of the prelimbic, and more ventral areas involved with motivational factors. They argue that the prelimbic area is actually 2 distinct areas that should be considered separately. This paper is very well analyzed and the methodological aspects regarding histological confirmation and neuronal spiking are exceptionally thorough. The task is well-studied and conclusively provides insights into multiple facets of high-level cognition. The main weakness is the unequal distribution of cells recorded in each area. Mainly, this is a problem for the ACC where substantially fewer units were recorded. This takes away some from the interpretation of ACC activity, however, most of the findings about ACC are consistent with previous reports from this lab and others. This does not take away from the success the authors achieved in characterizing the differences and similarities in functional correlates along the medial wall. The identification of two distinct subdivisions in the prelimbic area is novel and is likely to have a substantial impact on the field. At the least, the specific location within prelimbic that future studies purport to either record from, sample from, or manipulate will need to be reported so that these future findings can be correctly interpreted. This is a major shift in the field's conceptualization of this oft-studied part of the brain.

Reviewer #2 (Public Review):

In this paper, Diehl and Redish recorded simultaneously from multiple medial frontal cortical regions while rats are performing a restaurant-row task. Their results provide insights into how neurons in the different regions may represent different aspects of the decision-making process.

The strength of the study is the experimental design. The restaurant-row task is an excellent and rich paradigm for evaluating decision-making, with specific unique components that may be relatable to economic subjective choices. The other strength is the electrophysiological approach, which enables the author to simultaneously record from multiple medial frontal cortical regions. This leads to a large data set of >3,000 single units recorded during behavior. A weakness of this study is the insistence to dissect the results and assign each region to specific behaviors, while the data seem to suggest that similar signals can be observed across multiple regions, albeit to different degrees. The framework of distributed vs. gradient vs. subregions seems like a strawman idea that does not help with the interpretation of the results, whereas the actual data are already quite rich and interesting.

Reviewer #3 (Public Review):

This is an interesting study in which the authors record simultaneously from neurons along the medial bank of the rodent PFC as rats perform the restaurant row task, an economic decision-making task in which subjects are offered different reward types with a specified delay, and they need to decide whether to accept or reject the offer. The authors find functional correlates of anatomical subdivisions of the mPFC; interestingly, they find that PL perhaps should be subdivided into dorsal and ventral subregions, a finding that is consistent with some known anatomical features. They characterize the task-related responses of neurons in these different subdivisions and find that in general, the dorsal regions (ACC, dPL) encode decision-related variables, whereas the ventral regions (vPL, IL) encode more motivational variables, such as the trial number in the session and the amount of lingering time.

Strengths:<br /> - The observed dichotomy between decisional and motivational factors mapping onto dorsal and ventral aspects of mPFC is interesting and, as far as I am aware, novel.<br /> - There are a number of rich, interesting observations, such as a lack of encoding of the reward delay in the offer zone, but then encoding of that variable in the wait zone (in all areas except ACC). This is intriguing given that their previous work has suggested that the decisions made in the offer and wait zones are in some ways dissociable, implying that they might rely on distinct neural circuits.<br /> - Overall, the data and analyses are of high quality, and the results are interesting.<br /> - The finding that PL should be subdivided into two distinct subregions will be of broad interest to researchers studying the mPFC. The approach and finding will also be of interest to the growing number of groups using linear silicon (including Neuropixels) probes to record from multiple brain areas simultaneously.

Weaknesses:<br /> - The authors find that dorsal regions of mPFC, particularly ACC, encode the upcoming decision of the animal. However, the upcoming choice will be correlated with animal movements (as is often the case). Given that ACC is adjacent to the motor cortex, and more posterior parts of the cingulate have been documented to reflect particular types of movements, it would be helpful to know if these signals would be observed for movements outside of the task, or if they really reflect the upcoming decision in this behavioral context.<br /> - I think some of the statistical analyses can be strengthened. For instance, the authors correlate neural activity against a large number of behavioral variables, some of which are correlated with each other. I would encourage a regression-based approach, which takes into account the correlations between variables for error bars/significance tests for each regressor.

In general, I think the authors' claims about their data are justified.

Public Review:

In this article, the authors have taken up the substantial task of combing through thousands of published meta-analyses and systematic reviews, with the goal of identifying the subset that specifically seeks to measure the association between elapsed time ("lag-time") in various milestones of cancer diagnosis or treatment (e.g. time elapse from symptom onset to first seen by primary care physician) and cancer outcomes. Within this subset, they have identified and summarized the findings on how these lag times are related to certain cancer outcomes. For example, how much does a delay in the start of adjuvant chemotherapy after surgery for breast cancer increase the mortality rate for these patients? The overarching goal of this work is to characterize the pre-Covid-19 landscape of these relationships and thereby provide a basis for studying what impact the pandemic had on worsened outcomes for cancer patients due to treatment delays. The authors have done an excellent job in their review of systematic reviews and meta-analyses, both describing their methodology well and interpreting their findings. The immediate connection to the Covid-19 pandemic is somewhat tenuous and primarily left to the reader to determine.

Reviewer #1 (Public Review):

This paper shows how evolutionary dynamics, together with high variance species-species interactions in a generalized Lotka-Volterra framework, can stabilize the population and delay extinctions. Moreover, the stable regime is shown to correspond to the clonal interference regime from population dynamics. Thus, this work extends Robert May's seminal work on the stability of a complex system by considering the stabilizing effect of evolution.

Strengths:

- The paper is well written, the questions well-motivated and the ideas presented in a coherent and easy to understand manner. Prior literature was referenced to a sufficient degree (though of course a lot was left out). Importantly, the author is honest about the limitations of the modeling choices, not attempting to over-sell the work or to hide inconvenient details. In this sense, this paper is a good contribution to the literature since it gives the reader a clear perspective on an interesting question.

- Kudos for sharing the code in github. The code looks organized and easy to reuse.

Weaknesses:

- Interactions are assumed to be drawn from a log-normal distribution. Clearly, this does not capture true ecological interactions. It is unclear how applicable the results are to real ecosystems.

- The paper assumes saturating nutrients and states that they "do not expect that the addition of a reasonable carrying capacity will change our qualitative results". However, competition for resources can lead to loss of diversity. Moreover, ecological systems are known to respond to large changes in the carrying capacity. Therefore, it should be further elucidated if indeed the addition of a carrying capacity will destabilize the results. Especially since there appears a significant increase in the population size in the stable conditions: an increase that is not clear if it could be supported when the carrying capacity was already limiting population sizes before the increase.

- It appears in the text that "there are key differences between the model and actual bacteria-phage systems, and the model should not be interpreted as one that will directly map onto a biological scenario". I agree with this statement. However, by distancing the model from biological scenarios it makes its predictions hard to validate in a real system, leaving us with no obvious way to infer how to apply its conclusions. Indeed, both explicit examples given in lines 125-130: phase-bacteria and T-cell-antigen are not quite captured by modeling choices. I would have much preferred a specific biological system fixed in mind, then minimally modeled in a way that there is hope to directly link the modeling results to experiments. Especially since there is a wealth of available microbial population data, as well as much being generated.

- As stated, "the population fitness distribution is never able to 'settle'..." is indicative of the driven nature (driven by strong noise) of the quasi steady state as opposed to a stability that arises from the system dynamics.

Justification of claims and conclusions:

The paper is honest in reflecting the weaknesses (stated above) in the modeling generality and applicability on actual systems. This is commendable, and the claims as stated are justified but the applicability of these claims remains unclear. There are some conjectures raised in the discussion but they remain unsupported and allocated to "future work".

Reviewer #2 (Public Review):

This work by Martis illustrates, in a predator-prey or parasite-host eco-evolutionary context, the classical idea of bet hedging or biological insurance: where a single population would fluctuate and perhaps risk extinction, summing over multiple sub-populations with asynchronous dynamics (some going up while others go down) allows a stabler total abundance.

Here the sub-populations are various genotypes of one predator and one prey species, fluctuations are due to their ecological interactions, their dynamics are more asynchronous when predation is more specialized (i.e. the various predator genotypes differ more in which prey types they can eat), and mutations allow the regeneration of genotypes that have gone extinct, thus ensuring that the diversity of subpopulations is not lost (corresponding to a "clonal interference" regime with multiple coexisting genotypes).

While the general idea of bet hedging has been explored in many settings, the devil is usually in the details: for instance, sub-populations should be connected enough to allow the rescue of those going extinct, but a too strong connection would simply synchronize their temporal dynamics and lose the benefit of bet hedging. In some cases, connections between sub-populations could even be destabilizing (e.g. Turing instabilities in space).

In a recent surge of physics-inspired many-species theories, where fluctuations arise from ecological dynamics, these details are notably starting to be understood in the case of spatial bet hedging, i.e. genetically identical subpopulations in multiple patches connected by migration (see e.g. Roy et al PLoS Comp Bio 2020 or Pierce et al PNAS 2020).

In the non-spatial eco-evolutionary setting considered here, the connecting flux is one of mutations rather than migrations, and a predator genotype can in principle interact with all prey genotypes (whereas in usual spatialized models, interactions cannot occur between different patches). Another possibly important detail here is that similar genotypes do not have similar interaction phenotypes, meaning there is no risk of evolution being confined in a neighborhood of similar phenotypes. According to the author and my own cursory exploration of the relevant eco-evo literature (with which I am less familiar than pure ecology), this setting has yet to see many developments in the spirit of the many-species theories mentioned above.

These differences make this new inquiry worthwhile and I applaud the author for undertaking it. From a theoretical perspective, three results emerging from the simulations stand out in this article as potentially very interesting:<br /> - rather sharp transitions in extinction probability and strain diversity as mutation flux and predator specialization increase.<br /> - how mutation rate and interaction strength combine, notably in power-law expressions for total population abundance<br /> - the discussion of susceptibilities, i.e. how predator and prey populations respond to perturbations, as a key ingredient in understanding the previous results, in particular with counter-intuitive negative susceptibilities indicating positive feedback loops.

It is a bit unfortunate that these more novel points are only briefly explored in the main text: while they are more developed in appendices, these arguments are not always as complete, polished and distilled as they might have been in a main text, so an article focusing entirely on explaining them deeply and intuitively would have been far more exciting to me.

Finally, I will note that I am not convinced by the framing of the current manuscript as a counterpoint to Robert May's idea of destabilizing diversity - in many ways I think this is a less relevant context than that of bet hedging, and it does a worse job at showcasing what is genuinely interesting and original here; I would thus encourage readers to read this paper in the framing I propose above.

Reviewer #1 (Public Review):

This is a well-written report on one of the biggest killer diseases. The report is based on a large longitudinal cohort and uses solid analytical methodologies. Three main valuable findings are reported: association between coronary heart disease (CHD) and a polygenic risk score (PRS), a combination of multiple traditional risk factors (SCORE2), and history of Fusobacterium nucleatum infection. While the first 2 associations are not novel, they are welcome independent replications of previous findings in a novel design. A putative role of F. nucleatum and other infections in increasing CHD risk has also been reported before but remains more elusive with some suggestion that they may increase CHD risk by promoting arterial inflammation. The strength of this study is to demonstrate an independent role of this bacterium after controlling inflammation markers as well as other risk factors in a prospective study. If this finding can be confirmed, the prevalence of the bacterium (15% in the cohort) means it should be considered as another serious CHD risk factor. The authors should discuss the implications of multiple testing.

Reviewer #2 (Public Review):

Coronary heart disease (CHD) is a major form of cardiovascular disease, the first cause of mortality in the world. The etiology of CHD is multifactorial and polygenic, with atherosclerosis as the main cause of coronary stenosis and ischemic events. Evidence from basic research in small animal models and clinical trials aiming at lowering proinflammatory cytokines such as IL-1β implicated low-grade inflammation in atherosclerosis pathogenesis. Genetic studies in humans report large numbers of risk variants and genes related to macrophages, monocytes and T-lymphocytes biology supporting further immunity and inflammation in CHD risk. The study conducted by this group reports the results of the investigation of the potential association between 22 persistent or frequently recurring pathogen infections with the risk of CHD in a CoLaus|PsyCoLaus study, a prospective population-based and urban cohort of European ancestry. The authors accessed data over 12 years and assessed the association between traditional risk factors through the SCORE2 estimation of risk, a recently validated score specifically developed to predict cardiovascular risk in European countries, genetic risk scores based on GWAS findings, and seropositivity to infections. They were able to confirm the utility of the application of SCORE2 in their population, and report a significant association of the genetic score with the incidence of CHD, both traditional and genetic factors being independent predictors, which was expected. An intriguing result regards reported seropositivity with F. nucleatum to significantly predict incident CHD. This a commensal bacterium that belongs to the normal oral microbiome reported playing an important role in the development and progression of gingivitis (gum inflammation) and periodontitis (infection of the gums). There are several existing lines of evidence connecting oral infections as an independent risk factor for CHD. The results reported by this study provide support for the increased risk of CHD in oral infection through seropositivity to F. nucleatum. However, the direct clinical implications, through the recommendation to search for prior infection with this bacterium as a predicting biomarker of this disease are not on the clinical application agenda yet. The infection seropositivity was measured only at the beginning of the study, with no information on how the oral seropositivity to this pathogen may have evolved over time. And given the novelty of this association, these results need to be replicated in independent cohorts with similar designs (prospective cohorts) before recommendation to screen for seropositivity could be recommended.

Reviewer #1 (Public Review):

In 2007 it was observed that, although the central elements of galactose utilization are similar in both S. cerevisiae and C. albicans (clustered metabolic genes, transcriptional induction in the presence of galactose) the induction mechanisms were different. Until now, however, although the way the presence of galactose was sensed and this information transmitted to the induction of gene expression was well understood in S. cerevisiae, it was quite mysterious in C. albicans. This work proposes that in C. albicans, the general transcription regulator Rep1 serves as a direct galactose binding protein and that the binding of galactose to Rep1 allows it to serve as a scaffold to collect the transcriptional machinery necessary to induce the elements of the Gal regulon.

The first line of evidence for the Rep1 scaffold model is the observation that Rep1 is needed for C. albicans to both grow on galactose and to induce the genes encoding the galactose processing proteins Gal1 and Gal10. Previous candidate regulators Rtg1 and Rtg3 only blocked growth on galactose in the presence of Antimycin A, so Rep1 represents a first element specifically required for galactose growth. Further analysis of Rep1 function involved the observation that Rep1 was a member of the family of transcription factors including Ntd80, a TF that has been implicated in a variety of cellular controls. The authors investigated a specific unique domain of Rep1 by moving it to Ndt80 - the fusion protein did not allow complementation of the galactose growth defect, suggesting this domain was not critical to the Rep1 involvement in galactose growth. Further analysis of Rep1 domains by deletions showed that removal of the putative transcriptional activation domain of the protein also did not block either growth on galactose medium or galactose-mediated induction of GAL1 and GAL10 expression. The Rep1 protein was found to be constitutively bound to the promoters of GAL1 and GAL10, and not really influenced in this binding by carbon source.

To attempt to determine the connection between the apparent constitutive binding and the galactose-mediated induction of gene expression the authors investigated the relationship between sugars and the Rep1 protein. Modelling suggested a possible galactose binding pocket, binding was shown biochemically, and mutations within the presumed binding site disrupted galactose binding and protein function.

The authors next assess how the binding of galactose to Rep1 leads to gene induction because the binding to the regulated promoters seems constitutive, and the activation domain seems unimportant for protein function, and in fact, doesn't act as an activation domain in a 1 hybrid assay. They speculate protein binding and search for interacting proteins by mass spec after IP with a tagged Rep1 protein in the presence of galactose. Orf19.4959 is identified and tested. The binding data is presented as a supplementary table and includes many hits that do not appear promising candidates. Inactivation of the TF Orf19.4959 blocks growth on galactose and induction of the GAL1 and GAL10 genes, and the protein, called Cga1, does have transactivating ability in a 1 hybrid assay. The authors thus propose that galactose binding to Rep1 facilitates the binding of Cga1 and leads to the activation of gene expression for galactose metabolism.

This model is tested by immunoprecipitation assays that showed Cga1-Rep1 interaction only in the presence of galactose, and that DNA association of Cga1 to GAL promoters was galactose and Rep1 dependent. Further experiments provide a framework for Rep1 function in other pathways and suggest a candidate polyA binding motif for the Rep1 protein. The generalization of the model is proposed by noting a pattern of Rep1/Cga1 presence in other fungal species.

Reviewer #2 (Public Review):

Despite previous studies, regulation of the genes required for galactose metabolism in Candida albicans has remained murky. For example, previous work had highlighted Rtg1 and Rtg3 as the key regulator components, an interesting finding given that these factors are important for glucose not galactose regulation in S. cerevisiae. As galactose metabolism is one of the best-understood regulatory systems, the evolutionary difference in the regulation of the galactose response has the potential to teach us about regulatory evolution in general.

The authors initially sought to understand how GlcNAc signaling cross-reacted with galactose gene induction, but they quickly discovered that Reg1, the mediator of GlcNAc signaling was essential for galactose metabolism while Rtg1 and Rtg3 were not. Overturning previous work requires strong evidence, and the authors deliver with a series of growth assays, qPCR, and chromatin IP in wt and mutant backgrounds.

The authors go further to demonstrate that the factor itself interacts with galactose based on isothermal calorimetry (although it would be nice to have seen if this was specific to galactose over glucose or GlcNAc). They show glucose regulation occurs by Reg1 recruiting Cga1 in a manner independent of the activation domain of Reg1 (immunoprecipitation, reporter assays, and chromatin IP). In contrast, Reg1 activation mediated by GlcNAc requires Reg1's activation domain and employs Rgs1. Once again, the experimental evidence for these two regulatory mechanisms is strong.

Evolutionary analysis shows that this specific instantiation of this mechanism, the combination of Reg1 and Cga1, is probably restricted to the CTG clade. While the paper does not explain how these regulatory changes happen, it sets the foundation for future work to tease this apart; work that before this paper would have been unlikely to have been successful.

Reviewer #1 (Public Review):

Oxidation of some KCNQ7 channels enhances channel activity. The manuscript by Nuñez and coauthors concluded that oxidation in the S2S3 linker of these channels disrupted the interaction between S2S3 and CaM EF-hand 3 (EF3). This mechanism is Ca2+-dependent. The apo EF3 no longer interacted with S2S3, and H2O2 no longer activated the channel. Electrophysiological recordings and fluorescence and NMR measurements of CaM with isolated helices A and B (CRD) and S2S3 of the channel were performed. While the results were in general clear with good quality, how the results support the conclusion was not clearly described. The approach using isolated molecular components in the study needs further validation since some of the results seem to show major conflicts with the results and mechanisms proposed in previous studies.

1) Previous studies showed differential responses of Kv7 channels to oxidation; Kv7.2, 4, and 5 are sensitive to oxidation regulation but Kv7.1 and 3 do not change upon H2O2 treatment. These differences were attributed at least partially to the sequence differences in S2S3 among Kv7 channels (ref 10 of this manuscript). The results in this manuscript show some major differences from the previous study. First, in all experiments, no difference was observed among Kv7 channels. Second, in Fig 3-6, S2S3 from KCNQ1 was used. The rationale for using KCNQ1 S2S3 and the interpretation of results is not justified considering that KCNQ1 S2S3 has fewer Cys residues and was least affected by oxidation in the previous study.

2) In Fig 6, oxidation of S2S3 leads to a reduction of S2S3-CaM interaction, which leads to an increase of currents (Fig 1C). In Fig 4, Ca2+ loading leads to a reduced S2S3-CaM (EF3) interaction, which should also lead to an increase of currents based on Fig 6 conclusions. However, it is the EF3 mutation (destroying Ca2+ binding) that leads to the current increase (Fig 1B), contradictory to what Fig 6 data suggested.

Reviewer #2 (Public Review):

The study by Nunez et al. builds upon structural work from the MacKinnon lab and the authors' labs to characterize how Ca2+, via calmodulin, interacts with Kv7 channels to mediate redox sensitivity. Using FRET experiments to support electrophysiology, the authors demonstrate an interaction defined by calmodulin, the helixA-helixB fork, and the S2-S3 linker. The experiments are well performed and the conclusions drawn are appropriate. These experiments help further define the redox signaling for Kv7 channels. A weakness is that the model in Figure 7 seems speculative, as the data provided do not appear to explain how the VSD is engaged/disengaged from the pore. Rather, most of the data concentrate on biochemical interactions and structural interpretations (via FRET signals, etc.) of conformational changes in the presence of calcium. Further, the model as presented is not informative. The illustrations do not demonstrate successfully what the authors wish to claim, and the illustrations/models are not sufficiently supported by the data presented.

Reviewer #3 (Public Review):

KV7 channels play an important role in setting the resting membrane potential of neurons. As such, modulation by reactive oxygen species is an important and physiologically relevant form of channel regulation. Here, the authors propose a mechanism for this modulation in which ROS disrupts the interaction between the S2S3 loop of the channel and CaM, resulting in an overall enhancement of channel activity. The authors propose that this S2S3/CaM interaction is selectively mediated through CaM EF3, and is dependent on Ca2+. The results are supported by patch-clamp data, as well as NMR measurements and a FRET-based binding assay. The paper contains a considerable amount of data that point towards the conclusion.

The authors conclude that the EF3 of CaM is 'by itself sufficient and necessary for the oxidative response of KV7 channel complex and for gating the calcium responsive domain of KV7 channels." This is a very strong conclusion, and while much of the data points towards an important role for EF3, it is difficult to conclude that it is sufficient and necessary. The sparse description of the experiments makes the interpretation of the results a bit challenging. Based on the description provided, some of the results appear contradictory, limiting the conclusions drawn by the authors.

Reviewer #1 (Public Review):

Observations made on histological patterns of SCC tumor invasion prompt the authors to investigate the seemingly broad distribution of invasion strategies employed by SCC tumor cells in tissue. Using computational modelling and testing the arising predictions in two experimental models of SCC invasion, the authors conclude matrix proteolysis and cell-cell junctions to play key roles in determining invasion strand width and cell adhesion strength to be a minor contributor.

Strengths of the study:<br /> - The authors acknowledge the complexity of invasion patterns employed by SCC tumor cells in tissue and provide new insight into the underlying complex cellular processes.<br /> - The approach of combining computational simulations and testing their predictions experimentally with two models is powerful.

Weaknesses of the study:<br /> - Cell proliferation (affected by proteolysis and cell-cell junctions) is indicated as a key contributor to the generation of broad strand invasion. However, proliferation is not investigated using the same experimental models used to investigate invasion and is not included as a parameter in the computational models.<br /> - The outcomes of their KO strategies on the cell-matrix and cell-cell adhesion are not fully demonstrated.

Reviewer #2 (Public Review):

Kato, Jenkins, et al. investigates cell-intrinsic and environmental determinants of diverse modes of collective cancer cell invasion in mucosal squamous cell carcinoma (muSCC). To explore this large parameter space, the authors develop a Cellular Potts model recapitulating two distinct in vitro muSCC - cancer-associated fibroblast (CAF) co-culture models: an organotypic platform containing an air/extracellular matrix (ECM) interface and a spheroid model mimicking dermal invasion and confinement by 3D ECM. Integrating between in silico predictions and quantitative assessment of the two experimental platforms, the authors make several interesting observations regarding determinants of the mode of collective SCC invasion. Of these, the most significant include the ability of SCCs to invade with deletion of β1 integrin in their organotypic model although invasion phenotype is altered, and identification of a synergistic dependence on cell-cell adhesion and matrix proteolysis for controlling strand width and growth within the invading cohort. Cell-cell adhesions are essential for maintaining supracellular actomyosin coupling to coordinate the invading cohort, while matrix proteolysis is necessary for creating physical space that supports both invasion and cell growth within confined space.

Overall, despite some concerns regarding support for specific claims, alternative considerations, and clarity in presentation, this study is rigorous and of high quality, and should serve as an important technical and conceptual resource that provides new insight into multicellular coordination in SCC invasion. More broadly, it illustrates the utility of coupling computational models with advanced 3D cell culture platforms to parse multifactorial control over complex forms of tissue morphogenesis.

Reviewer #3 (Public Review):

This study by Kato et. al used a combination of computational modeling, in vitro experimentation, and confirmatory in vivo mouse work to define what influences collective cell invasion in squamous cell carcinoma (SCC). Looking at a multitude of parameters, the authors found that cancer cell-cancer cell contacts and matrix degradation work cooperatively in SCC invasion.

The authors provide a rigorous and systematic approach to querying the importance of the parameters tested; first setting their hypothesis computationally followed by in vitro experimentation in two different cancer cell culturing methods (organotypic and spheroid). Importantly, the experimental data convincingly confirmed the computational predictions, lending credence to their methodology. This is a major strength of the manuscript and will be beneficial to the field with regard to investigating invasion in other cancer types.

Additionally, the varied parameters tested (cancer cell-cell adhesion, cancer cell-matrix adhesion, cancer cell-fibroblast adhesion, fibroblast-matrix adhesion, cell-intrinsic motility, matrix displacement, and matrix proteolysis) were thoughtful and rooted in the literature. However, though considerate of the role the extracellular matrix (ECM) may play (via interrogating cancer cell-matrix adhesions as parameters), the characteristics of the matrix itself (e.g. stiffness, alignment) were not investigated. These attributes have been previously shown to affect collective cell invasion. Indeed, while investigating the contributions of matrix proteolysis on invasion, the authors found a parabolic relationship where both too much and too little matrix negatively impacted the ability of SCC cells to invade. Moreover, it is unclear what the role of fibroblast-matrix adhesions was to this system, though it was originally stated as a tested parameter.

Reviewer #1 (Public Review):

This manuscript provides an in-depth analysis of the advantages and potential pitfalls of the application of Granger Causality (GC) to calcium imaging data, especially regarding various types of pre-processing. The key strength of the manuscript is the rigor and thoroughness of the authors' approach, and it is very clear how one would go about replicating their work. On the other hand, it is not from the results how well one should trust the results of GC for an unknown system, as many results rely on having some specialized knowledge about the measurements beforehand.

Strengths:

- Understanding how to measure causality is a key problem in modern science, and with the increasing abundance of wide-field calcium imaging, understanding how to assess information flow between neurons from these data is of wide interest and importance.

- I was impressed by the rigor and explicitness of the authors' approach. In papers like this, there is the temptation to sweep problems under the rug and highlight the successes. Here, the authors present, in a clearly organized format, the effects of various methods and analysis decisions. Moreover, the methods are described in a manner such that they could be (relatively) easily implemented by the reader.

- In general, the approach of using the GC value of the F-statistics and then normalizing by a null model is an appealing method that has a lot of intuitive and quantitative value.

Weaknesses:

- It's not clear to me what lessons are specific to the system they are studying and which ones are to be taken as more general lessons. Certainly, dealing with slow calcium dynamics, motion artifacts, and smoothing, are general problems in calcium imaging, but I found myself puzzled a bit about how to decide which neurons are "strange" without a lot of system-specific knowledge. This seems to be a rather important effect, and having a bit more guidance in the discussion would be useful.

- Somewhat related, I'm not entirely sure what results I should take home from the hindbrain analysis. It is clear that there is a more-or-less global signal modulating all neural activity, but this is a common occurrence in population recordings (often, one subtracts this off via PCA or another means before proceeding). Is the general lack of causal links (via the MVGC at least) a generic phenomenon in recurrent networks, or is there something more system-specific here? Accordingly, it might be interesting to run a recurrent neural network simulation with similar properties to the hindbrain (and perhaps with correlated driving) to see what GC/MVGC would predict. Is there any hope of these methods finding information flow in recurrent networks, or should we restrict the method to networks where we expect the primary mode of information transmission to be feedforward?

Reviewer #2 (Public Review):

The authors consider the application of Granger causality (GC) analysis to calcium imaging data and identify several challenges therein and provide methodological approaches to address them. In particular, they consider case studies involving fluorescence recordings from the motoneurons in embryonic zebrafish and the brainstem and hindbrain of larval zebrafish to demonstrate the utility of the proposed solutions in removing the spurious links that the naive GC identifies.

The paper is well-written and the results on the chosen case studies are compelling. However, the proposed work would benefit from discussing the contributions of this work in the context of existing and relevant literature and clarifying some of the methodological points that require more rigorous treatment. I have the following comments:

Major comments:

1) I would like to point out recent literature that adapts the classical GC for both electrophysiology data and calcium imaging data:

[1] A. Sheikhattar et al., "Extracting Neuronal Functional Network Dynamics via Adaptive Granger Causality Analysis", PNAS, Vol. 115, No. 17, E3869-E3878, 2018.

[2] N. A. Francis et al., "Small Networks Encode Decision-Making in Primary Auditory Cortex", Neuron, Vol. 97, No. 4, 2018.

[3] N. A. Francis et al., "Sequential Transmission of Task-Relevant Information in Cortical Neuronal Networks", Cell Reports, Vol. 39, No. 9, 110878, 2022.

In reference [1], a variation of GC based on GLM log-likelihoods is proposed that addresses the issues of non-linearity, non-stationarity, and non-Gaussianity of electrophysiology data. In [2] and [3], a variation of GC using sparse multi-variate models is introduced with application to calcium imaging data. In particular, all three references use the sparse estimation of the MVAR parameters in order to mitigate overfitting and also use corrections for multiple comparisons that also reduce the number of spurious links (see my related comments below). I suggest discussing these relevant references in the introduction (paragraphs 2 and 3) and discussion.

2) A major issue of GC applied to calcium imaging data is that the trials are typically limited in duration, which results in overfitting of the MVAR parameters when using least squares (See references [2] and [3] above, for example). The authors mention on page 4 that they use least squares to estimate the parameters. However, for the networks of ~10 neurons considered in this work, stationary trials of a long enough duration are required to estimate the parameters correctly. I suggest that the authors discuss this point and explicitly mention the trial durations and test whether the trial durations suffice for stable estimation of the MVAR parameters (this can be done by repeating some of the results on the synthetic data and using different trial lengths and then assessing the consistency of the detected GC links).

3) The definition of the "knee" of the average GC values as a function of the lag L needs to be a bit more formalized. In Fig. 2H using the synthetic data, the "knee" effect is more clear, but in the real data shown in Fig. 2I, the knee is not obvious, given that the confidence intervals are quite wide. Is there a way to quantify the "knee" by comparing the average GC values as well as their confidence bounds along the lag axis?

4) While the measures of W_{IC} and W_{RC} form suitable guiding principles for the pipeline presented in this work, it would be helpful if the authors discuss how such measures can be used for other applications of GC to calcium imaging data in which a priori information regarding the left/right symmetry or the rostrocaudal flow of information is missing.

5) Removing the "strange" neurons discussed in Section C5 is definitely an important pre-processing step in applying GC. However, the criterion for identifying the strange neurons seems a bit ad hoc and unclear. Could this be done by clustering the neurons into several categories (based on their time courses) and then removing a "strange" cluster? Please clarify.

6) Another key element of existing GC methods applied to large-scale networks is dealing with the issue of multiple comparisons: for instance, in Figures 2, 3, 4, 6, 7, and 8, it seems like all arrows corresponding to all possible links are shown, where the colormap indicates the GC value. However, when performing multiple statistical tests, many of these links can be removed by a correction such as the Benjamini-Hochberg procedure. It seems that the authors did not consider any correction of multiple comparisons; I suggest doing so and adding this to your pipeline.

7) The authors use TV denoising and also mention that it is a global operator, and changes the values of a time series at time t based on both the past and future values of the process. As such, it is not clear how TV denoising could affect the "causal" relations of the time series. In particular, TV denoising would significantly change the \Gamma_{ii} coefficients in Eq. (8). Is it possible to apply a version of TV denoising that only uses the information from the past to denoise the process at time t? In other words, using a "filter" as opposed to a "smoother". Please clarify.

8) The idea of using an adaptive threshold as in Section C8 is interesting; but this problem was previously considered in [30] (in the manuscript) and reference [1] above, in which new test statistics based on log-likelihoods are used that have well-known asymptotic null distributions (i.e., chi-square distributions). In particular, reference [1] above identifies and applies the required rescaling for the asymptotic null distributional assumptions to hold. I suggest discussing your work regarding the adaptive thresholds in the context of these existing results.

9) Related to the previous comment, given that the authors use a shuffling procedure to obtain the null, it is not clear why fitting the F-distribution parametrically and using its quantiles for testing would provide further benefits. In fact, as shown in Figure S9B, the rescaled F-distribution does not fully match the empirical null distribution, so it may be worth using the empirical null to obtain the non-parametric quantiles for testing. Please clarify.

10) In Figure 5C, the values of W_IC for the MV cases seem to be more than 1, whereas by definition they should be less than or equal to 1. Please clarify.

11) Is there evidence that the lateralized and rostrocaudal connectivity of the motoneurons occurs at the time-scale of ~750 ms? Given that this time scale is long enough for multiple synapses, it could be the case that some contralateral and non-rostrocaudal connections could be "real", as they reflect multi-hop synaptic connections. Please clarify.

12) While it is useful to see the comparison of the BV and MV cases shown in Figs. 1 and 2, given extensive evidence in the GC literature on the shortcomings of the BV version of GC, it seems unnecessary to report the BV results in Figs. 3 onward. I suggest discussing the shortcoming of the BV case when presenting figures 1 and 2 and removing the BV results from the subsequent results.

Reviewer #3 (Public Review):

This manuscript provides a helpful and transparent guide on the application of granger-causality (GC) to calcium datasets. This is a useful entry point toward understanding the suitability and limitations of GC to neural data. However, it is not entirely convincing that the variations of GC analysis provided in this manuscript can be effectively applied to large-scale calcium datasets without prior knowledge of the underlying circuit, especially when such networks are likely to contain redundancy and recurrent links.

I would like to acknowledge that, at the outset, I held an unfavorable prior belief toward GC, for reasons that are well addressed in this manuscript, including the dangers of applying spectral GC to nonlinear networks, as well as a variety of pathologies that can undermine naive GC.

The manuscript has been helpful, both for its effective presentation of both bivariate GC and its multivariate extension, as well as the practical considerations that are essential to applying it to real-life data. It was particularly helpful to see a treatment of the challenges and their possible resolutions. I commend the authors for their transparency - they should certainly be rewarded rather than punished for their transparency.

Major<br /> 1. Redundant signals: throughout the brain, it's expected that a population of neurons can encode the same information. It's unclear how GC (both the original and the modified versions) can handle this redundancy. Given how pervasive redundant signals are in the brain, this should be addressed in both simulation and experimental data. For example, in one of the manuscript's simulated networks, replace one neuron with 10 copies of it, each with identical inputs and outputs but with the weights scaled by 1/10. Such a network is functionally equivalent to the original but may pose some challenges for the various versions of GC. I believe this issue also accounts for the MVGC results in the hindbrain dataset. It might be more appropriate to apply GC to groups of neurons (as indeed the authors cited), instead of applying it at the single-cell level with redundant signals.<br /> 2. Similarly, there is recurrent connectivity throughout the brain. The current manuscript appears to assume feedforward networks. Is the idea that GC cannot be applied to recurrent networks? If so, this needs to be clearly stated. If the authors believe that GC can recover casual links even in the presence of recurrent connectivity, this needs to be demonstrated.<br /> 3. Both BVGC and MVGC appear to be extremely sensitive to any outlier signals. The most worrying aspect is that the authors developed their corrections and pipelines with the benefit of knowing the structure of the underlying system, whereas in the case where GC would be most useful, the user would be unable to rely on prior knowledge of the underlying structure. For instance, the motion artifact in Fig 3a-c was a helpful example of a vulnerability of naive GC, but one could easily imagine scenarios involving an unmeasured disturbance (e.g. the table is bumped) causing a similar artifact, but if the experimenter is unaware of such unmeasured disturbances then they will not be included in Z, and hence can result in the detection of widespread spurious links.<br /> There is a circularity here that's concerning. It seems that one already needs to have the answer (e.g. circuit connectivity) in order to clean up the data sufficiently for BVGC or MVGC to work effectively. Perhaps the authors would be interested in incorporating ideas from the systems identification literature, which can include the estimation of unmeasured disturbances, perhaps in conjunction with L1 regularization on the GC links. This is certainly out of scope for the present work, but it would be worth acknowledging the difficulties of unmeasured disturbances and deferring a general solution to future work. Similar considerations apply to a common unmeasured neuronal input (e.g. from a brain region not included in the field of view of the imaging).<br /> 4. Interpretation - would it be correct to state that BVGC identifies plausible causal links, while MVGC identifies a plausible system-level model? I think these interpretations, carefully stated, might provide a helpful way of thinking about the two GC approaches. Taking the results of the paper together, neither BVGC nor MVGC is definitive - BVGC may overestimate the true number of causal links but MVGC is prone to a winner-take-all phenomenon that may represent just one of many plausible system-level models that can account for the observed data. This should be more clearly stated in the manuscript.<br /> 5. "correlation completely misses the structure" - links are signed, so they should be shown with "bwr" colormap, with zero mapped to white (i.e. v_min is blue, 0 is white, v_max is red, |v_min| = |v_max|, this is natively supported in PyPlot and can be trivially implemented or downloaded in MATLAB). It is misleading that correlation appears to miss certain links marked in black, until one realizes that these links are inhibitory. It would substantially aid clarity and consistency if all panels followed this signed "bwr" convention. I think the emphasis for the GC panels is on whether links are detected, rather than the weight of the link, so I would suggest indicating detected inhibitory links as -1 (blue) and detected excitatory links as +1 (red), and link not detected as 0 (white).

Reviewer #1 (Public Review):

For membrane transporters, the factors that define transport cycle state equilibria and kinetics remains a major question. In contrast to ion channels where electrophysiological single-channel recordings reveal transitions between states, this has not been possible for slower transport proteins and so this information must be extracted from bulk transport behavior. However, recent single-molecule microscopy studies, such as FRET, have provided a new way of identifying transitions between conformational ensembles and connecting this to transport behaviors. However, the resolution of FRET can be limiting in that it requires multiple labeling with large fluorophores that have their own freedom to move, thus reducing the ability to detect small conformational changes. In the present study, Zhou et al. address this by using a different single-molecule approach of polarization microscopy, and investigate the small conformational changes associated with the AdiC arginine/agmatine antiporter from the APC super-family of transport proteins. Here, they anchor bis-TMR-maleimide onto helix 6, a part of the protein that has been identified to change orientation in the different crystal structures of AdiC and other APC homologues in inward, outward and occluded states. By "fixing" the protein onto microscopy slides, they are able to detect the change in polarization angles of the emitted fluorescence and map that onto relative changes in helix 6 orientation. Analyzing these data, they propose a model of four states that exchange in equilibrium, with and without the substrate, setting the stage for quantifying equilibrium constants and kinetics for a detailed mapping of the transport cycle, presented in an accompanying article.

This is certainly a cutting-edge approach that offers the potential to resolve the equilibrium reactions between small conformational changes and thus has the potential to push forward the mechanistic and quantitative investigation of membrane transport. However, at this point the studies require further validation on several levels. This includes an independent investigation of whether the protein being studied (i.e. with all tags, mutations, labeling, nanodisc solubilzation) confers the same substrate binding and transport behavior that has been reported previously, and is being used as comparison data here. In addition, there is some concern that the anchoring of the protein may bias conformational equilibria in some way and so it would be worthwhile to map out if this effect is limiting by changing linker lengths, within a range where it is still possible to resolve changes in polarization angles. Finally, the results are very dependent on the post-processing of the single-molecule trajectories that include changepoint analysis, averaging and clustering algorithms, yet there is little data provided to examine the robustness of each of these steps in the ultimate determination of the four-state model. While the observation that some of the states identified show a linkage to the arginine substrate, further validation along the lines mentioned above are required before a full analysis of the transport cycle is rationalized.

Reviewer #2 (Public Review):

The antiporter AdiC is a member of the amino-acid and polyamine organocation (APC) transporter superfamily. It imports the single-charged arginine (Arg+) and exports the double-charged agmatine (Agm2+). Thus, it increases the intracellular pH, helping some pathogenic enterobacteria survive in acidic environments. The APC transporters are known to sample 4 major conformations in the transport cycle. Monitoring the conformational transitions is important for understanding the transport mechanism, but methods detecting multi-state conformational changes are very limited. The authors use high-resolution polarization microscopy to resolve 4 different states in substrate-free (Apo) or substrate-bound conditions. This work further demonstrates the power of fluorescence polarization microscopy in studying protein dynamics. The authors introduced an interesting normalization step in data processing to average results obtained for different protein particles. However, the 4 states could be identified from single traces and the normalization from trace to trace could not be done without the pre-identified states on single traces. Thus, the improvement provided by the normalization compared to the published work (NSMB 2019a, 2019b, 2019c) is relatively limited.

Reviewer #3 (Public Review):

In this work, Zhou et al. employed the polarization microscope (PM) method to track the orientations of helix 6a in the bacterial amino-acid transporter AdiC. It is very impressive that the authors were able to optimize the technique to achieve an overall resolution of 5{degree sign} for detecting changes in the inclination and rotation angles (𝜃 and 𝜓). However, I am deeply concerned about how the authors linked PM-detected conformational states to the structural states obtained using crystallography. Overall, I think it was an overstatement that the work resolved the equilibrium conditions for the major states in AdiC's transport cycle, and I urge the others to be more transparent with the readers about the limitations of their technique and be more thorough in considering alternative interpretations.

Reviewer #4 (Public Review):

In this paper, Zhou et al. propose a polarization microscope for measuring the emission polarization of bifunctional rhodamine molecules attached to AdiC transporters. The polarization is used to resolve the orientation of the fluorophores, which allows the authors to successfully resolve the four conformations of AdiC at a temporal resolution of tens of milliseconds. The measured orientation for each conformation is validated with the results using crystallography.

Overall, I believe the paper is well written and demonstrates a great application for orientation imaging using polarized microscopes. Detailed experimental procedures, calibrations, and mathematical frameworks are included. I have the following recommendations to improve the manuscript.

1) On page 20, the authors note that they set a threshold to filter out molecules whose total intensity varies during the measurements. The statement that "while fluorescence intensity is expected to vary among different polarization directions, the total intensity should be essentially invariant" is not true. Since the authors use TIRF illumination to excite the molecules, the excitation polarization component along the tilting direction (e.g., along the y-axis) of the excitation is 0, i.e., molecules oriented along that direction (e.g., y-oriented) will be excited less effectively compared to other orientations.

2) Could the authors provide more details regarding how the clusters are ranked? The authors note that C1-C4 are "ranked according to the values of both angles". It is not clear to me how this is done. Also, what is the range of the measured theta_L and phi_L? And how is the warping of the spherical coordinates handled in the ranking process, e.g., a change from 350 deg to 10 deg is +20 deg or -340 deg.

3) Is the k-means clustering also based on the distance in the Cartesian space, similar to the state identification?

Reviewer #1 (Public Review):

This interesting manuscript from the Perozo and Faraldo-Gomez labs investigates the molecular mechanisms underlying the activation of the mechanosensitive ion channel MscS. The authors use a clever combination of cryoEM, coarse-grained (CG) and all-atom (AA) molecular dynamics simulations to determine the first (putatively) open conformation of the WT MscS channel and to show that this channel induces profound deformations of the membrane in the closed but not in the open state. Strikingly, MD simulations reveal that, contrary to what was previously assumed, lipids occupying cavities near the closed pore (hook lipids) come from the outer rather than inner leaflets. On pore opening, the membrane adopts a more relaxed conformation where the lipids contacting the protein are in less strained and tilted conformations. The authors thus propose a mechanism for sensing tension where the equilibrium between the open and closed conformations of the channel is dictated by differences in the membrane morphology in the two states rather than by the association and dissociation of individual lipids with the protein.

Major<br /> The observations on the hook lipids are critical and should be documented better. Based on previous work, it had been proposed that the hook lipids are associated with the inner leaflet and that they leave upon (partial) channel opening. In contrast, the present MD simulations indicate these lipids are associated with the outer leaflet and that their association to the channel persists on opening. These critical observations need to be documented better.<br /> i. Do the authors observe hook lipids in the cryoEM structure of the open channel? If yes, data should be shown. If no, then the discrepancy between MD and EM should be explicitly addressed.<br /> ii. Please show the comparison of the position and coordination of the hook lipids in MD simulations and in the closed (and/or open) structures.<br /> iii. The authors acknowledge that the volume of the cavity where the hook lipids are located decreases on channel opening. How does this not affect the association of the hook lipids with the protein?<br /> iv. Past work revealed several lipids in MscS structures near these cavities besides the hook lipids, and their ordered dissociation from the channel was proposed to be important for gating. Do the simulations show lipids in these cavities?<br /> v. Does the occupancy of the hook lipids in MD simulations change between the open and closed conformations? This should be analyzed.<br /> vi. Is the occupancy of other lipids in the nearby cavity altered upon channel opening?<br /> vii. Is the exchange of lipids near Ile150 affected by the conformational change?

I am a bit confused by the claim that "The comparison clearly highlights the reduction in the width of the transmembrane span of the channel upon opening, and how this changed is well matched by the thickness of the corresponding lipid nanodiscs (approximately from 38 to 23 Å)."<br /> i. How was the nanodisc membrane thickness determined? This should be described.<br /> ii. I do not see a ~15A change in the vertical length of the channel protein or of the nanodisc. While the panels in Fig.2 clearly show a vertical compression of the membrane, it appears that the ~15 A claim might be overstated. Adding a panel with measurements would be helpful to quantify this claim. If this is difficult on the membrane, maybe measurements could be performed on the protein.<br /> iii. What happens to the N-terminal cap structure in the open state? What are the rearrangements that allow the extracellular ends of the TM1 to disassemble the cap.

The data shown in Fig. 6 is cryptic and should be explained better in the main text. As it stands there is a cursory mention in pg. 12 and not much else.<br /> i. It would be helpful if the authors showed the position of Ile150 in the structure.<br /> ii. Does the total number of lipids in proximity of Ile150 change over time? Or the fold change represents ~1:1 exchange of lipids in the pocket?<br /> iii. I am confused by the difference in the maximum possible fold-change in unique lipids, does this reflect the difference in total number of lipids in each leaflet in each system? If so, I am a bit confused as to why there is a ~30% difference in the AA simulations whereas the values are nearly identical for the CG one.<br /> iv. Is it possible to quantify the residence time of the lipids in the pocket of each subunit?

The authors state on Pg. 21 "Nevertheless, we question the prevailing view that density signals of this kind are evidence of regulatory lipid binding sites; that is, we do not concur with the assumption that lipids regulate the gating equilibrium of MscS just like an agonist or antagonist would for a ligand-gated receptor-channel." I am a bit confused by this statement. In principle, binding and unbinding of modulatory ligands can happen on relatively fast time scales, so the observation that in MD simulations lipids exchange on a faster time scale than that of channel gating is not sufficient to make this inference. Indeed, there is ample evidence from other channels (i.e. Trp channels, HCN channels etc) where visualization of similar signals led to the identification of modulatory lipid binding sites. Thus, while I do not necessarily disagree with the authors, I would encourage them to tone down the general portion of the statement.

Reviewer #2 (Public Review):

The manuscript by Park et al. reports a new structure of the mechanosensitive channel MscS of E. coli in the open state and the results of extensive coarse grained and atomistic molecular dynamics (MD) simulations of MscS and the related channel MSL1 of plant mitochondria in presumed closed and open states. The major new finding is that in the closed state, the lipid bilayer contacting the channel is severely distorted. In the open state, this distortion is not present. The MD simulations forming the basis of this finding have been carefully executed and the finding is interesting and relevant for the understanding of channel mechanosensation. The MD simulations are ideally suited to probe the lipid interactions of the channel in a state-dependent manner and to identify possible membrane distortions. However there are some issues that should be addressed.

1) Are the structures stable in the membrane also without the weak restraints on the dihedral angles? Continuing at least one of the atomistic simulations without restraints for about 1 microsecond in a tension-free membrane would address a possible concern that the severe membrane distortion could go away by a more extensive relaxation of the channel structure.

2) Does the observed effect occur also in membranes with physiologically relevant PE lipids? Performing a simulation with a lipid mix closer to that in E. coli (and thus high in PE) would address a possible concern that the observed effect is not physiologically relevant.

3) Please include a figure showing that the lipid positions in the MD simulations match the lipid densities in the cryo-EM maps.

4) Is the reported mobility of helices TM2-TM3 of MSL1, as deduced from a comparison of different cryo-EM structures (ref 18), sufficient to impact the lipid organisation?

5) Did the initial lipid configuration in atomistic MD simulations already contain the deformations of the inner leaflet, or did these form spontaneously both in coarse-grained and atomistic simulations?

6) Did the earlier MD simulations of the closed-state structure 6PWN of MscL give any indications on the membrane deformation?

7) Are there distinct interactions between the headgroups of distorted inner-leaflet lipids with charged amino acids? If so, are these amino acids conserved?

Reviewer #3 (Public Review):

This paper combines experimental structures with careful molecular dynamics to address a crucially important topic in cellular biology - how are mechanosensitive ion channels gated by the membrane? There are many flavors of mechanosensitive proteins, and here the authors study MscS from e. coli and the eukaryotic homolog MSL1 from Arabidopsis. The key finding is that the closed states of both channels induce high curvature in the inner leaflet due to the membrane protruding into the cytoplasm to lipidate exposed hydrophobic patches on the protein. The open state structures exhibit far less membrane deformation. Moreover, comparing the open and closed state structures reveals that the membrane-protein surface area is not significantly different in the two states - hence all of the mathematical models to date (and many experimental models too) that posit that tension-induced gating is driven by expansion of the in-plane area of the protein must be revised. Instead, the authors convincingly argue that the role of tension is to increase the energy of the protein-membrane system in the closed state (with its large membrane deformations) compared to the flat-membrane open state. Forgive me for not going on more about the structures that have been solved here, and how they are likely more representative of the native open state than previously solved structures - I agree with the authors' assertions, and they represent a major step forward in elucidating the full gating transition in both bacterial and eukaryotic systems. This is an important discovery, and it would have been impossible without the structure and simulation coming together. Future work attempting to quantify the energy of the membrane deformations, protein free energy difference between the channels in open and closed states, and the role of tension will be essential but outside the scope of what the authors were trying to do here.

Reviewer #1 (Public Review):

The authors focused on linking physiological data on theta phase precession and spike-timing-dependent plasticity to the more abstract successor representation used in reinforcement learning models of spatial behavior. The model is presented clearly and effectively shows biological mechanisms for learning the successor representation. Thus, it provides an important step toward developing mathematical models that can be used to understand the function of neural circuits for guiding spatial memory behavior.

However, as often happens in the Reinforcement Learning (RL) literature, there is a lack of attention to non-RL models, even though these might be more effective at modeling both hippocampal physiology and its role in behavior. There should be some discussion of the relationship to these other models, without assuming that the successor representation is the only way to model the role of the hippocampus in guiding spatial memory function.

1. Page 1- "coincides with the time window of STDP" - This model shows effectively how theta phase precession allows spikes to fall within the window of spike-timing-dependent synaptic plasticity to form successor representations. However, this combination of precession and STDP has been used in many previous models to allow the storage of sequences useful for guiding behavior (e.g. Jensen and Lisman, Learning and Memory, 1996; Koene, Gorchetchnikov, Cannon, Hasselmo, Neural Networks, 2003). These previous models should be cited here as earlier models using STDP and phase precession to store sequences. They should discuss in terms of what is the advantage of an RL successor representation versus the types of associative sequence coding in these previous models.

2. On this same point, in the introduction, the successor representation is presented as a model that forms representations of space independent of reward. However, this independence of spatial associations and reward has been a feature of most hippocampal models, that then guide behavior based on interactions between a reward representation and the spatial representation (e.g. Redish and Touretzky, Neural Comp. 1998; Burgess, Donnett, Jeffery, O'Keefe, Phil Trans, 1997; Koene et al. Neural Networks 2003; Hasselmo and Eichenbaum, Neural Networks 2005; Erdem and Hasselmo, Eur. J. Neurosci. 2012). The successor representation should not be presented as if it is the only model that ever separated spatial representations and reward. There should be some discussion of what (if any) advantages the successor representation has over these other modeling frameworks (other than connecting to a large body of RL researchers who never read about non-RL hippocampal models). To my knowledge, the successor representation has not been explicitly tested on all the behaviors addressed in these earlier models.

3. Related to this, successes of the successor representation are presented as showing the backward expansion of place cells. But this was modeled at the start by Mehta and colleagues using STDP-type mechanisms during sequence encoding, so why was the successor representation necessary for that? I don't want to turn this into a review paper comparing hippocampal models, but the body of previous models of the role of the hippocampus in behavior warrants at least a paragraph in each of the introduction and discussion sections. In particular, it should not be somehow assumed that the successor representation is the best model, but instead, there should be some comparison with other models and discussion about whether the successor representation resembles or differs from those earlier models.

4. The text seems to interchangeably use the term "successor representation" and "TD trained network" but I think it would be more accurate to contrast the new STDP trained network with a network trained by Temporal Difference learning because one could argue that both of them are creating a successor representation.

Reviewer #2 (Public Review):

The authors present a set of simulations that show how hippocampal theta sequences may be combined with spike time-dependent plasticity to learn a predictive map - the successor representation - in a biologically plausible manner. This study addresses an important question in the field: how might hippocampal theta sequences be combined with STDP to learn predictive maps? The conclusions are interesting and thought-provoking. However, there were a number of issues that made it hard to judge whether the conclusions of the study are justified. These concerns mainly surround the biological plausibility of the model and parameter settings, the lack of any mathematical analysis of the model, and the lack of direct quantitative comparison of the findings to experimental data.

While the model uses broadly realistic biological elements to learn the successor representation, there remain a number of important concerns with regard to the biological plausibility of the model. For example, the model assumes that each CA3 cell connects to exactly 1 CA1 cell throughout the whole learning process so that each CA1 cell simply inherits the activity of a single CA3 cell. Moreover, neurons in the model interact directly via their firing rate, yet produce spikes that are used only for the weight updates. Certain model parameters also appeared to be unrealistic, for example, the model combined very wide place fields with slow running speeds. This leaves open the question as to whether the proposed learning mechanism would function correctly in more realistic parameter settings. Simulations were performed for a fixed running speed, thereby omitting various potentially important effects of running speed on the phase precession and firing rate of place cells. Indeed, the phase precession of CA1 place cells was not shown or discussed, so it is unclear as to whether CA1 cells produce realistic patterns of phase precession in the model.

The fact that a successor-like representation emerges in the model is an interesting result and is likely to be of substantial interest to those working at the intersection between neuroscience and artificial intelligence. However, because no theoretical analysis of the model was performed, it remains unclear why this interesting correspondence emerges. Was it a coincidence? When will it generalise? These questions are best answered by mathematical analysis of the model (or a reduced form of it).

Several aspects of the model are qualitatively consistent with experimental data. For example, CA1 place fields clustered around doorways and were elongated along walls. While these findings are important and provide some support for the model, considerable work is required to draw a firm correspondence between the model and experimental data. Thus, without a quantitative comparison of the place field maps in experimental data and the model, it is hard to draw strong conclusions from these findings.

Overall, this study promises to make an important contribution to the field, and will likely be read with interest by those working in the fields of both neuroscience and artificial intelligence. However, given the above caveats, further work is required to establish the biological plausibility of the model, develop a theoretical understanding of the proposed learning process, and establish a quantitative comparison of the findings to experimental data.

Reviewer #1 (Public Review):

In this manuscript, the authors have assembled a reference transcriptome of the whole head of Loligo vulgaris and used it to perform single cell transcriptomics. With about 20,000 cells, they identify 32 clusters corresponding to a few identifiable cell types - neurons, stem cells, sensory cells, and epidermis. They use select marker genes from these clusters and perform HCR in situs on Loligo heads to describe these cell types. Their in situs describe a region similar to the lateral lip seen in other cephalopods where neural progenitors are found and from where neurons migrate into the brain.

Reviewer #2 (Public Review):

In 'Molecular characterization of cell types in the squid Loligo vulgaris', the authors study profile cell types of the squid brain, using single cell RNAseq and FISH for anatomical localization. They reveal many different cell types, some of which have correspondences in other organisms and some of which reflect cephalopod-specific innovations. The current study is one of 4 recent preprints (Styfhals et al. 2022, Songco-Casey et al. 2022, Gavriouchkina et al. 2022) profiling cephalopod tissues using scRNAseq and FISH-based anatomical localization. Together these studies begin to reveal the cellular complexity of these fascinating animals.

Reviewer #3 (Public Review):

In this manuscript, the authors leverage new single-cell sequencing data to unravel cell type diversity in the head of Loligo vulgaris hatchlings. This analysis recovers 33 clusters and the authors describe the cell type populations with HCR in situ hybridization. This work provides an important next step in describing neural and sensory cells in an understudied class of invertebrates that goes beyond traditional morphological characterization.

Reviewer #1 (Public Review):

This work aimed at investigating how a BMI decoding performance is impacted by changing the conditions under which a motor task is performed. They recorded motor cortical activity using multielectrode arrays in two monkeys executing a finger flexion and extension task in four conditions: normal (no load, neutral wrist position), loaded (manipulandum attached to springs or rubber bands to resist flexion), wrist (no load, flexed wrist position) or both (loaded and flexed wrist). They found, as expected, that BMI decoders trained and tested on data sets collected during the same conditions performed better at predicting kinematics and muscle activity than others trained and tested across conditions. They also report that the performance of monkeys a BMI task involving the online control of a virtual hand was almost unaffected by changing either the actual manipulandum conditions as above or switching between decoders trained from data collected under different conditions. As for the neuronal activity, they found a mix of changes across task contexts. Interestingly, a principal component analysis revealed that activity in each context falls within well-aligned manifolds, and that the context-dependent variance in neuronal activity strongly correlated to amplitude of muscle activity.

Strengths:

The current study expands on previous findings about BMI decoders generalizability and contributes scientifically in at least three important ways.

First, their results are obtained from monkeys performing a fine finger control task with up to two degrees of freedom. This provides a powerful setting to investigate fine motor control of the hand in primates. The authors use the accuracy of BMI decoders between data sets as a measure of stationarity in the neurons-to-fingers mapping, which provides a reliable assessment. They show that changes in wrist angle or finger load affect the relationship between cortical neurons and otherwise identical movements. Interestingly, this result hold up for both kinematics and muscle activity predictions, albeit being stronger for the latter.

Second, their results confirming that neuronal activity recorded during different task conditions lies effectively within a common manifold is interesting. It supports prior observations, but in the specific context of finger movements.

Third, the dPCA results provide interesting and perhaps unexpected information about the fact that amplitude of muscle activity (or force) is clearly present in the motor cortical activity. This is possibly one of the most interesting findings because extracting a component from neural activity that can related robustly to muscle activity across context would provide great benefits to the development of BMIs for functional electrical stimulation.<br /> Overall, the analyses are well designed and the interpretation of the results is sound.

Weaknesses:

I found the discussion about the possible reasons why offline decoders are more sensitive to context than online decoders very interesting. Nonetheless, as the authors recognize, the possibility that the BMI itself causes a change in context, "in the plant", limits their interpretation. It could mean for the monkeys to switch from one suboptimal decoder to another, causing a ceiling effect occluding generalization errors.

Overall, several new and original results were obtained through these experiments and analyses. Nonetheless, I found it difficult to extract a clear unique and strong take-home message. The study comes short of proposing a new way to improve BMIs generalizability or precisely identifying factors that influence decoders generalizability.

Reviewer #2 (Public Review):

The authors motivate this study by the medical need to develop brain-machine interfaces (BMIs) to restore lost arm and hand function, for example through functional electrical stimulation. More specifically, they are interested in developing BMI decoding algorithms that work across a variety of "contexts" that a BMI user would encounter out in the real world, for example having their hand in different postures and manipulating a variety of objects. They note that in different contexts, the motor cortex neural activity patterns that produce the desired muscle outputs may change (including neurons' specific relationship to different muscles' activations), which could render a static decoder trained in a different context inaccurate.

To test whether this potential challenge is indeed the case, this study tested BMI control of virtual (on-screen) fingers by two rhesus macaques trained to perform 1 or 2 degree-of-freedom non-grasping tasks either by moving their fingers, or just controlling the virtual finger kinematics with neural activity. The key experimental manipulations were context shifts in the form of springs on the fingers or flexion of the wrist (or both). BMI performance was then evaluated when these context changes were present, which builds on this group's previous demonstration of accurate finger BMI without any context shifts.

The study convincingly shows the aforementioned context shifts do cause large changes in measured firing rates. When neural decoding accuracy (for both muscle and position/velocity) is evaluated across these context changes, reconstruction accuracy is substantially impaired. The headline finding, however, is that that despite this, BMI performance is, on aggregate, not substantially reduced. Although: it is noteworthy that in a second experiment paradigm where the decoder was trained on the spring or wrist-manipulated context and tested in a normal context, there were quite large performance reductions in several datasets as quantified by multiple performance measures; this asymmetry in the results is not really explored much further.

The changes in neural activity due to context shifts appear to be relatively modest in magnitude and can be fit well as simple linear shifts (in the neural state space), and the authors posit that this would make it feasible (in future work) to find context-invariant neural readouts that would result in more robust muscle activity decoders.

An additional novel contribution of this study is showing that these motor cortical signals support quite accurately decode muscle activations during non-prehensile finger movements (and also that the EMG decoding was more negatively affected by context shifts than kinematics decoding); previous work decoded finger kinematics but not these kinetics. Note that this was demonstrated with just one of the two monkeys (the second did not have muscle recordings).

This is a rigorous study, its main results are well-supported, and it does not make major claims beyond what the data support. One of its limitations is that while the eventual motivating goal is to show that decoders are robust across a variety of tasks of daily living, only two specific types of context shifts are tested here, and they are relatively simple and potentially do not result in as strong a neural change as could be encountered in real-world context shifts. This is by no means a major flaw (simplifying experimental preparations are a standard and prudent way to make progress). But the study could point this out a bit more prominently that their results do not preclude that more challenging context shifts will be encountered by BMI users, and this study in its current form does not indicate how strong a perturbation the tested context shifts are relative to the full possible range of hand movement context shifts that would be encountered during human daily living activities.

A second limitation is that while the discrepancy between large offline decoding performance reduction and small online performance reduction are attributed to rapid sensorimotor adaptation, this process is not directly examined in any detail. Third, the assessment of how neural dynamics change in a way that preserves the overall shape of the dynamics is rather qualitative rather than quantitative, and that this implementation of a more context-agnostic finger BMI is left for future work.

Reviewer #3 (Public Review):

In this manuscript the authors ask whether finger movements in non-human primates can be predicted from neural activity recorded from the primary motor cortex. This question is driven by an ultimate goal of using neural decoding to create brain-computer interfaces that can restore upper limb function using prosthetics or functional electrical stimulation systems. More specifically, since functional use of the hand (real or prosthetic) will ultimately require generating very different grasp forces for different objects, these experiments use a constant set of finger kinematics, but introduce different force requirements for the finger muscles using several different techniques. Under these different conditions (contexts), the study examines how population neural activity changed and uses decoder analyses to look at how these different contexts affect offline predictions of muscle forces and finger kinematics, as well as the animals' ability to use different decoders to control 1 or 2-DOF online. In general, the study found that when linear models were trained on one context from offline data, they did not generalize well to the other context. However, when performance was tested online (monkeys controlling a virtual hand in real time using neural activity related to movement of their own hands) with a ReFIT Kalman filter, the animals were able to complete the task effectively, even with a decoder trained without the springs or wrist perturbation. The authors show data to support the idea that neural activity was constrained to the same manifold in the different contexts, which enabled the animals to rapidly change their behavior to achieve the task goals, compared to the more complex requirement of having to learn entirely new patterns of neural activity. This work takes studies that have been conducted for upper-limb movements and extends them to include hand grasp, which is important for creating decoders for brain-computer interfaces. Finally, the authors show using dPCA can extract features during changes in context that may be related to the activity of specific muscles that would allow for improved decoders.

Strengths:

The issue of hand control, and how it compares to arm control, is an important question to tackle in sensorimotor control and in the development of brain-computer interfaces. Interestingly, the experiments use two very different ways of changing the muscle force requirements for achieving the same finger movements; springs attached to a manipulandum and changes in wrist posture. Using both paradigms the decoder analysis clearly shows that linear models trained without any manipulation do not predict muscle forces or finger kinematics well, clearly illustrating the limitations of common linear decoders to generalize to scenarios that might encompass real grasping activities that require forceful interactions. Using a well-described real-time decoder (ReFIT Kalman Filter), the authors show that this performance decrease observed offline is easily overcome in online testing. The metrics used to make these claims are well-described, and the likely explanations for these findings are described well. A particular strength of this manuscript is that, at least for these relatively simple movements and contexts, a component of neural activity (identified using dPCA) is identified that is significantly modulated by the task context in a way that sensibly represents the changes in muscle activity that would be required to complete the task in the new contexts.

Weaknesses:

The differences between exemplar data sets and comprehensively tested contexts was difficult to follow. There are many references to how many datasets or trials were used for a particular experiment, but overall, this is fragmented across the manuscript. As a result, it is difficult to assess how generalizable the results of the manuscript were across time or animal, or whether day-to-day variations, or the different data collection schedules had an effect.

The introduction allocates a lot of space to discussing the concepts of generating (computing) movements as opposed to representing movements and relates this to ideas of neural dynamics. The distinction between these as described in the introduction is not very clear, nor is it clear what specific hypothesis this leads to for these experiments. Further, this line of thinking is not returned to in the discussion, so the contribution of these experiments to ideas raised in the introduction are unclear.

The complexity of the control that was possible in this task (1 or 2 DOF finger flexion/extension) was low. Further, the manipulations that were used to control context were simple and static. Both these factors likely contribute to the finding that there was little change in the principal angles of the high-variance principal components. While this is not a criticism of the specific results presented here, the simplicity of the task and contexts, contrasted with the complexity of hand control more generally, especially for even moderately dexterous movements, makes it unclear how well the finding of stable manifolds will scale. On a related point, it is unclear whether the feature, identified using dPCA, that could account for changes in muscle activity, could be robustly captured in more realistic behaviors. It is stated that future work is needed, but at this point, the value of identifying this feature is highly speculative.

The maintained control in online BMI trials could also be explained by another factor, which I don't think was explicitly described by either of the two suggestions. Prism goggle experiments introduce a visual shift can be learned quickly, and some BCI experiments have introduced simple rotations in the decoder output (e.g. Chase et. al. 2012, J Neurophys). This latter case is likely similar in concept to in-manifold perturbations. Regardless, the performance can be rapidly rescued by simply re-aiming, which is a simple behavioral adaptation. In a 1DOF or 2DOF control case like used in these experiments, with constant visual feedback on performance, the change in context could likely be rapidly learned by the animals, maybe even within a single trial. In other words, the high performance in the online case may be a consequence of the relatively simple task demands, and the simple biomechanical solution to this problem (push harder). What is the expectation that the results seen in these experiments would be relevant to more realistic situations that require grasp and interaction?

Some of the figures were difficult to read and the captions contained some minor incorrect information. The primary purpose of some of the figures was not immediately clear from the caption. For example, the bar plots in Figures 5 and 6 were very small and difficult to read. This also made distinguishing the data from the two different animals challenging.

There is no specific quantification of the data in Figures 4D and 5D. In Figure 4D it seems apparent that the vast majority of the points are below the unity line. But, it remains unclear, particularly in Figure 5D whether the correlations between the two contexts truly are different or not in a way that would allow conclusive statements.

Reviewer #1 (Public Review):

Chakrabarti et al study inner hair cell synapses using electron tomography of tissue rapidly frozen after optogenetic stimulation. Surprisingly, they find a nearly complete absence of docked vesicles at rest and after stimulation, but upon stimulation vesicles rapidly associate with the ribbon. Interestingly, no changes in vesicle size were found along or near the ribbon. This would have indicated a process of compound fusion prior to plasma membrane fusion, as proposed for retinal bipolar cell ribbons. This lack of compound fusion is used to argue against MVR at the IHC synapse. However, that is only one form of MVR. Another form, coordinated and rapid fusion of multiple docked vesicles at the bottom of the ribbon, is not ruled out. Therefore, I agree that the data set provides good evidence for rapid replenishment of the ribbon-associated vesicles, but I do not find the evidence against MVR convincing. The work provides fundamental insight into the mechanisms of sensory synapses.

Reviewer #2 (Public Review):

Chakrabarti et al. aimed to investigate exocytosis from ribbon synapses of cochlear inner hair cells with high-resolution electron microscopy with tomography. Current methods to capture the ultrastructure of the dynamics of synaptic vesicle release in IHCs rely on the application of potassium for stimulation, which constrains temporal resolution to minutes rather than the millisecond resolution required to analyse synaptic transmission. Here the authors implemented a high-pressure freezing method relying on optogenetics for stimulation (Opto-HPF), granting them both high spatial and temporal resolutions. They provide an extremely well-detailed and rigorously controlled description of the method, falling in line with previously use of such "Opto-HPF" studies. They successfully applied Opto-HPF to IHCs and had several findings at this highly specialised ribbon synapse. They observed a stimulation-dependent accumulation of docked synaptic vesicles at IHC active-zones, and a stimulation-dependent reduction in the distance of non-docked vesicles to the active zone membrane; while the total number of ribbon-associated vesicles remained unchanged. Finally, they did not observe increases in diameter of synaptic vesicles proximal to the active zone, or other potential correlates to compound fusion - a potential mode of multivesicular release. The conclusions of the paper are mostly well supported by data, but some aspects of their findings and pitfalls of the methods should be better discussed.

Strengths:

While now a few different groups have used "Opto-HPF" methods (also referred to as "Flash and Freeze) in different ways and synapses, the current study implemented the method with rigorous controls in a novel way to specifically apply to cochlear IHCs - a different sample preparation than neuronal cultures, brain slices or C. elegans, the sample preparations used so far. The analysis of exocytosis dynamics of IHCs with electron microscopy with stimulation has been limited to being done with the application of potassium, which is not physiological. While much has been learned from these methods, they lacked time resolution. With Opto-HPF the authors were successfully able to investigate synaptic transmission with millisecond precision, with electron tomography analysis of active zones. I have no overall questions regarding the methodology as they were very thoroughly described. The authors also employed electrophysiology with optogenetics to characterise the optical simulation parameters and provided a well described analysis of the results with different pulse durations and irradiance - which is crucial for Opto-HPF.

Further, the authors did a superb job in providing several tables with data and information across all mouse lines used, experimental conditions, and statistical tests, including source code for the diverse analysis performed. The figures are overall clear and the manuscript was well written. Such a clear representation of data makes it easier to review the manuscript.

Weaknesses:

There are two main points that I think need to be better discussed by the authors.

The first refers to the pitfalls of using optogenetics to analyse synaptic transmission. While ChR2 provides better time resolution than potassium application, one cannot discard the possibility that calcium influx through ChR2 alters neurotransmitter release. This important limitation of the technique should be properly acknowledged by the authors and the consequences discussed, specifically in the context in which they applied it: a single sustained pulse of light of ~20ms (ShortStim) and of ~50ms (LongStim). While longer, sustained stimulation is characteristic for IHCs, these are quite long pulses as far as optogenetics and potential consequences to intrinsic or synaptic properties.

The second refers to the finding that the authors did not observe evidence of compound fusion (or homotypic fusion) in their data. This is an interesting finding in the context of multivesicular release in general, as well as specifically for IHCs. While the authors discussed the potential for "kiss-and-run" and/or "kiss-and-stay", it would be valuable if they could discuss their findings further in the context of the field for multivesicular release. For example, the evidence in support of the potential of multiple independent release events. Further, as far as such function-structure optical-quick-freezing methods, it is not unusual to not capture fusion events (so-called omega-shapes or vesicles with fusion pores); this is largely because these are very fast events (less than 10 ms), and not easily captured with optical stimulation.

Reviewer #3 (Public Review):

Precise methods were developed to validate the expression of channelrhodopsin in inner hair cells of the Organ of Corti, to quantify the relationship between blue light irradiance and auditory nerve fiber depolarization, to control light stimulation within the chamber of a high-pressure freezing device, and to measure with good precision the delay between stimulation and freezing of the specimen. These methods represent a clear advance over previous experimental designs used to study this synaptic system and are an initial application of rapid high-pressure freezing with freeze substitution, followed by high-resolution electron tomography (ET), to sensory cells that operate via graded potentials.

Short-duration stimuli were used to assess the redistribution of vesicles among pools at hair cell ribbon synapses. The number of vesicles linked to the synaptic ribbon did not change, but vesicles redistributed within the membrane-proximal pool to docked locations. No evidence was found for vesicle-to-vesicle fusion prior to vesicle fusion to the membrane, which is an important, ongoing question for this synapse type. The data for quantifying numbers of vesicles in membrane-tethered, non-tethered, and docked vesicle pools are compelling and important. These quantifications would benefit from additional presentation of raw images so that the reader can better assess their generality and variability across synaptic sites.

The images shown for each of the two control and two experimental (stimulated) preparation classes should be more representative. Variation in synaptic cleft dimensions and numbers of ribbon-associated and membrane-proximal vesicles do not track the averaged data. Since the preparation has novel stimulus features, additional images (as the authors employed in previous publications) exhibiting tethered vesicles, non-tethered vesicles, docked vesicles, several sections through individual ribbons, and the segmentation of these structures, will provide greater confidence that the data reflect the images.

The introduction raises questions about the length of membrane tethers in relation to vesicle movement toward the active zone, but this topic was not addressed in the manuscript. Seemingly quantification of this metric, and the number of tethers especially for vesicles near the membrane, is straightforward. The topic of EPSC amplitude as representing unitary events due to variation in vesicle volume, size of the fusion pore, or vesicle-vesicle fusion was partially addressed. Membrane fusion events were not evident in the few images shown, but these presumably occurred and could be quantified. Likewise, sites of membrane retrieval could also be marked. These analyses will broaden the scope of the presentation, but also contribute to a more complete story.

Overall, the methodology forms the basis for future studies by this group and others to investigate rapid changes in synaptic vesicle distribution at this synapse.

Reviewer #4 (Public Review):

This manuscript investigates the process of neurotransmitter release from hair cell synapses using electron microscopy of tissue rapidly frozen after optogenetic stimulation. The primary finding is that in the absence of a stimulus very few vesicles appear docked at the membrane, but upon stimulation vesicles rapidly associate with the membrane. In contrast, the number of vesicles associated with the ribbon and within 50 nm of the membrane remains unchanged. Additionally, the authors find no changes in vesicle size that might be predicted if vesicles fuse to one-another prior to fusing with the membrane. The paper claims that these findings argue for rapid replenishment and against a mechanism of multi-vesicular release, but neither argument is that convincing. Nonetheless, the work is of high quality, the results are intriguing, and will be of interest to the field.

1) The abstract states that their results "argue against synchronized multiquantal release". While I might agree that the lack of larger structures is suggestive that homotypic fusion may not be common, this is far from an argument against any mechanisms of multi-quantal release. At least one definition of synchronized multiquantal release posits that multiple vesicles are fusing at the same time through some coordinated mechanism. Given that they do not report evidence of fusion itself, I fail to see how these results inform us one way or the other.

2) The complete lack of docked vesicles in the absence of a stimulus followed by their appearance with a stimulus is a fascinating result. However, since there are no docked vesicles prior to a stimulus, it is really unclear what these docked vesicles represent - clearly not the RRP. Are these vesicles that are fusing or recently fused or are they ones preparing to fuse? It is fine that it is unknown, but it complicates their interpretation that the vesicles are "rapidly replenished". How does one replenish a pool of docked vesicles that didn't exist prior to the stimulus?

Reviewer #1 (Public Review):

Hu et al. present findings that extend the understanding of the cellular and synaptic basis of fast network oscillations in the sensory cortex. They developed the ex vivo model system to study synaptic mechanisms of ultrafast (>400Hz) network oscillation ("ripplets") elicited in layer 4 (L4) of the barrel cortex in the mouse brain slice by optogenetically activating thalamocortical axon terminals at L4, which mimic the thalamic transmission of somatosensory information to the cortex. This model allowed them to reproduce extracellular ripplet oscillations in the slice preparation and investigate the temporal relationship of cellular and synaptic response in fast-spiking (FS) inhibitory interneurons and regular spiking (RS) with extracellular ripplet oscillations to common excitatory inputs at these cells. FS cells show precisely timed firing of spike bursts at ripplet frequency, and these spikes are highly synchronized with neighboring FS cells. Moreover, the phase-locked temporal relationship between the ripplets and responses of FS and RS cells, although different phases, to thalamocortical activation are found to closely coincide with EPSCs in RS cells, which suggests that common excitatory inputs to FS and RS cells and their synaptic connectivity are essential to generate reverberating network activity as ripplet oscillations. Additionally, they show that spikes of FS cells in layer 5 (L5) reduced in the slice with a cut between L4 and L5, proposing that recurrent excitation from L4 excitatory cells induced by thalamocortical optogenetic stimulation is necessary to drive FS spike bursts in layer 5 (L5).

Overall, this study helps extend our knowledge of the synaptic mechanisms of ultrafast oscillations in the sensory cortex. However, it would have been nice if the authors had utilized various methodologies and systems.

Although the overall findings are interesting, the conclusion of the study could have been strengthened according to the following points:

1. The authors investigate the temporal relationship between ripplets and FS and RS cells' response elicited by optogenetic activation of TC axon terminals, which is mainly supported by phase-locked responses of FS and RS cells with local ripplets oscillations to optogenetic activation. They also show highly synchronized FS-FS firing by eliminating electrical gap-junction and inhibitory synaptic connections to this synchrony. Based on these findings, the authors suggest that common excitatory inputs to FS and RS cells in L4 would be essential to generate these local ripplets. However, it interferes with the ability to follow the logical flow for biding other findings of phase-locking responses of FS and RS cells in ripplet oscillations in L4.

2. The authors suggest that the optogenetic activation of TC axon terminal elicits local ripplet oscillations via synchronized spike burst of FS inhibitory interneurons and alternating EPSC-IPSC of RS cells in phase-locked with ripplets in L4 barrel cortex, which would be generated by following common excitatory inputs from the local circuits to these cells at the ripple frequency. Thus they intend to investigate the source of these excitatory inputs at this local network of L4 by suppressing the firing of L4 RS cells. However, they show FS spike bursts in L5B, instead of L4, due to the technical limitations of their experimental setup, as described in the manuscript. Although L5 FS spike bursts decrease after cutting the L4/L5 boundary, supposedly inhibiting excitatory input from L4 as depicted in Fig 6D in the author's manuscript, the interpretation of data seems overly extended because it does not necessarily represent cellular and synaptic activities which are phase-locked with the ripplets observed in L4.

3. Authors suggested a circuit model. It would be recommended that the authors try to perform in silico analysis using the suggested model to explore the function of thalamocortical axons on the fast-spiking and regular-spiking neurons to support their circuit model.

Reviewer #2 (Public Review):

This manuscript studied potential cellular mechanisms that generate ultrafast oscillations (250-600Hz) in the cortex. These oscillations correlate with sensory stimulation and might be relevant for the perception of relevant sensory inputs. The authors combined ex-vivo whole-cell patch-clamp recordings, local field potential (LFP) recordings, and optogenetic stimulation of thalamocortical afferents. In a technical tour de force, they recorded pairs of fast-spiking (FS)-FS and FS-regular-spiking (RS) neurons in the cortex and correlated their activity with the LFP signal.

Optogenetic activation of thalamic afferents generated ripple-like extracellular waveforms in the cortex, which the authors referred to as ripplets. The timing of the peaks and troughs within these ripplets was consistent across slices and animals. Activation of thalamic inputs induced precisely timed FS spike bursts and RS spikes, which were phase-locked to the ripplet oscillation. The authors described the sequences of RS and FS neuron discharge and how they phase-locked to the ripplet, providing a model for the cellular mechanism generating the ripplet.

The manuscript is well-written and guides the reader step by step into the detailed analysis of the timing of ripplets and cellular discharges. The authors appropriately cite the known literature about ultrafast oscillations and carefully compare the novel ripplets to the well-known hippocampal ripples. The methods used (ex-vivo patch-clamp and LFP) were appropriate to study the cellular mechanisms underlying the ripplets.

Overall, this manuscript develops means for studying the role of cortical ultrafast oscillations and proposes a coherent model for the cellular mechanism underlying these cortical ultrafast oscillations.

Reviewer #3 (Public Review):

In this study, Hu et al. aimed to identify the neuronal basis of ultrafast network oscillations in S1 layer 4 and 5 evoked by the optogenetic activation of thalamocortical afferents in vitro. Although earlier in vivo demonstration of this short-lived (~25 ms) oscillation is sparse and its significance in detecting salient stimuli is not known the available publications clearly show that the phenomenon is consistently present in the sensory systems of several species including humans.

In this study using optogenetic activation of thalamocortical (TC) fibers as a proxy for a strong sensory stimulus the in vitro model accurately captures the in vivo phenomenon. The authors measure the features of oscillatory LFP signals together with the intracellular activity of fast-spiking (FS) interneurons in layer 4 and 5 as well as in layer 4 regular spiking (RS) cells. They accurately measure the coherence of intra- and extracellular activity and convincingly demonstrate the synchronous firing of FS cells and antiphase firing of RS and FS cells relative to the field oscillation.

Major points:

1) The authors conclude the FS cell network has a primary role in setting the frequency of the oscillation. While these data are highly plausible and entirely consistent with the literature only correlational not causal results are shown thus direct demonstration of the critical role of GABAergic mechanisms is missing.

2) The authors put a strong emphasis on the role of RS-RS interactions in maintaining the oscillation once it was launched by a TC activity. Its direct demonstration, however, is not presented. The alternative scenario is that TC excitation provides a tonic excitatory background drive (or envelope) for interacting FS cells which then impose ultrafast, synchronized IPSPs on RS cells. Similar to the RS-RS drive in this scenario RS cells can also only fire in the "windows of opportunity" which explains their antiphase activity relative to FS cells, but RS cells themselves do not participate in the maintenance of oscillation. Distinguishing between these two scenarios is critical to assess the potential impact of ultrafast oscillation in sensory transmission. If TC inputs are critical the magnitude of thalamic activity will set the threshold for the oscillation if RS-RS interactions are important intracortical operation will build up the activity in a graded manner.

Earlier theoretical studies (e.g Brunel and Wang, 2003; Geisler et al., 2005) strongly suggested that even in the case of the much slower hippocampal ripples (below 200 Hz) phasic activation of local excitatory cells cannot operate at these frequencies. Indeed, rise time, propagation, and integration of EPSPs can likely not take place in the millisecond (or submillisecond) range required for efficient RS-RS interactions. The alternative scenario (tonic excitatory background coupled with FS-FS interactions) on the other hand has been clearly demonstrated in the case of the CA3 ripples in the hippocampus (Schlingloff et al., 2014. J.Nsci).

When the properties of the ultrafast oscillation were tested as various stimulation strengths (Figure 2) weaker stimulation resulted in less precise timing. If TC input is indeed required only to launch the oscillation not to maintain it, this is not expected since once a critical number of RS cells were involved to start the activity their rhythmicity should no longer depend on the magnitude of the initial input. On the other hand, if the entire transient oscillation depends on TC excitation weaker input would result in less precise firing.

3) The experiments indicating the spread of phasic activity from L4 RS to L5 FS cells can not be accepted as fully conclusive. The horizontal cut not only severed the L4 RS to L5 FS connections but also many TC inputs to the L5 FS apical dendrites as well as the axons of L4 FS cells to L5 FS cells both of which can be pivotal in the translaminar spread.

Reviewer #1 (Public Review):

The authors had previously developed a method of determining conformational free energy differences between the alternative DFG-in and DFG-out conformational states of kinases using an energy function based on a Potts model. They did this because direct estimates of this free energy change from molecular simulations, while possible in principle, would in practice be hard to do with sufficient accuracy to be useful for such a large conformational transition. Potts model energies have been shown to be correlated with overall protein stability, so it is reasonable that dividing the contacts into DFG-in and DFG-out sets should allow the estimation of a free energy difference between conformational states. In this work they examine the differences between Tyrosine Kinases (TKs) and Serine/Threonine Kinases (STKs) more closely, finding that the model predicts a small free energy change for converting DFG-in to DFG-out for TKs but a significant unfavorable free energy cost to converting to DFG-out for the STKs. The most insightful part of the paper comes in its analysis of how this conformational change may contribute to the overall binding free energies. Calculating binding free energies for Type II inhibitors (which bind DFG-out) by alchemical methods neglects the contribution from any unfavorable conformational change ("reorganization energy") required to adopt the DFG-out conformation. Thus comparing this calculated binding free energy with the total binding free energy estimated from experiment allows an estimate of the conformational reorganization energy. It is found that this estimate is nicely correlated with the free energy change for conformational rearrangement estimated from the Potts model analysis. Thus an important contribution to Type II inhibitor binding is this conformational transition. The different contributions to Type II binding are analyzed in detail by further dissecting the Potts model.

Reviewer #2 (Public Review):

This paper focuses on an important topic. It explores how the activation loop conformations affect the type II inhibitor binding in Tyr and Ser/Thr kinases. The comprehensive computational results agree with the available experimental data. It is a remarkably comprehensive, high quality paper.

Reviewer #3 (Public Review):

Tyrosine kinases (TKs) belong to a relatively small family of protein kinases that are a product of later evolution and play a critical role in the regulation of cell behavior in multicellular organisms. Major differences between TKs and Serine/threonine kinases (STKs) are very well known, however, it is still unclear if there are specific sequence signatures that favor a specific inactive conformation of TKs that can be exploited for efficient drug design. The authors used Potts Hamiltonian models (PHMs) along with other computational methods to tackle this problem. The are two main weaknesses of this approach. First, it relies on multiple sequence alignment that requires a large set of related sequences and can't be applied to smaller families. Second, it requires a relatively large number of structures that have similar inactive structures. Although all active kinases have very similar structures, their inactive structures are very diverse. However, there are several groups of inactive conformations that share a high level of similarity. The authors study one of them, the so-called "DFG-out" conformation, and present a set of convincing results that define several key residues that favor this conformation. They demonstrated the strength of the PHMs approach that allows the detection of critical contacts that are specific for certain conformations. These results can be used to predict the "DFG-out" conformation of a TK even if its structure is not known or predict the effects of mutations in a TK if they involve some of the critical residues. In general, the paper presents a set of solid results that will facilitate the development of highly specific inhibitors for TKs.

Reviewer #1 (Public Review):

This paper investigates potential mechanisms underlying the generation of hippocampal theta and gamma rhythms using a combination of several modeling approaches. The authors perform new simulation experiments on the existing large-scale biophysical network model previously published by Bezaire et al. Guided by their analysis of this detailed model, they also develop a strongly reduced, rate-based network model, which allows them to run a much larger number of simulations and systematically explore the effects of varying several key parameters. The combined results from these two in silico approaches allow them to predict which cell types and connections in the hippocampus might be involved in the generation and coupling of theta and gamma oscillations.

In my view, several aspects of the general methodology are exemplary. In the current work as well as several earlier papers, the authors are re-using a large-scale network model that was originally developed in a different laboratory (Bezaire et al., 2016) and that still represents the state-of-the-art in detailed hippocampal modeling. Such model reuse is quite rare in computational neuroscience, which is rather unfortunate given the amount of time and effort required to build and share such a complex model. Very often, and also, in this case, the original publication that describes a detailed model provides only limited validation and analysis of model behavior, and the re-use of the same model in later studies represents a great opportunity to further examine and validate the model.

Combining detailed and simplified models can also be a powerful approach, especially when the correspondence between the two is carefully established. Matching results from the two models, in this case, allow strong arguments about key mechanisms of biological phenomena, where the simplified model allows the identification and characterization of necessary and sufficient components, while the detailed model can firmly anchor the models and their predictions to experimental data.

On the other hand, I have several major concerns about the implementation of these approaches and the interpretation of the results in the current study. First of all, the detailed model of Bezaire et al. is considered strictly equivalent, in all of its relevant details, to biological reality, and no attempt is made to verify or even discuss the validity of this assumption, even when particular details of the model are apparently critical for the results presented. I see this as a fundamental limitation of the current work - the fact that the Bezaire et al. model is the best one we have at the moment does not automatically make it correct in all its details, and features of the model that are essential for the new results certainly deserve careful scrutiny (preferably via detailed comparison with experimental data).

An important case in point is the strength of the interactions between specific neuronal populations. This is represented by different quantities in the detailed and simplified model, but the starting point is always the synaptic weight (conductance) values given by Bezaire et al. (2016), also listed in Tables 2 and 3 of the current manuscript. Looking at these parameters, one can identify a handful of connections whose conductance values are much higher than those of the other connections, and also more than an order of magnitude higher (50-100 nS) than commonly estimated values for cortical synapses (normally less than about 5 nS, except for a few very special types of synapse such as the hippocampal mossy fibers). Not surprisingly, several of these connections (such as the pyramidal cell to pyramidal cell connections, and the CCK+BC to PV+BC connections) were found to be critical for the generation and control of theta and gamma oscillations in the model. Given their importance for the conclusions of the paper, it would be essential to double-check the validity of these parameter values. In this context, it is worth noting that, unlike the anatomical parameters (cell numbers and connectivity) that had been carefully calculated and discussed in Bezaire and Soltesz (2013), biophysical parameters (the densities of neuronal membrane conductances and synaptic conductances) in Bezaire et al. (2016) were obtained by relatively simple (partly manual) fitting procedures whose reliability and robustness are mostly unknown. Specifically for synaptic parameters in CA1, a more systematic review and calculation were recently carried out by Ecker et al. (2020); their estimates for the synaptic conductances in question are typically much lower than those of Bezaire et al. (2016) and appear to be more in line with widely accepted values for cortical (hippocampal) synapses.

Furthermore, some key details concerning the construction of the simplified rate model are unclear in the current manuscript. The process of selecting cell types and connections for inclusion in the rate model is described, and the criteria are mostly clear, although the results are likely to be heavily affected by the problems discussed above, and I do not understand why the strength of external input was included among the selection criteria for cell types (especially if the model is meant to capture the internal dynamics of the isolated CA1 region). However, the main issue is that it remains unclear how the parameters of the rate model (the 24 parameters in Table 4) were obtained. The authors simply state that they "found a set of parameters that give rise to theta-gamma rhythms," and no further explanation is provided. Ideally, the parameters of the rate model should be derived systematically from the detailed biophysical model so that the two models are linked as strongly as possible; but even if this was not the case, the methods used to set these parameters should be described in detail.

An important inaccuracy in the presentation of the results concerns the suggested coupling of theta and gamma oscillations in the models. Although the authors show that theta and gamma oscillations can be simultaneously present in the network under certain conditions, actual coupling of the two rhythms (e.g., in the form of phase-amplitude coupling) is not systematically characterized, and it is therefore not clear under what conditions real coupling is present in the two models (although a probable example can be seen in Figure 1C(ii)).

The Discussion of the paper states that gamma oscillations in the model(s) are generated via a pure interneuronal (ING) mechanism. This is an interesting claim; however, I could not find any findings in the Results section that directly support this conclusion.

Finally, although the authors write that they can "envisage designing experiments to directly test predictions" from their modeling work, no such experimental predictions are explicitly identified in the current manuscript.

Reviewer #2 (Public Review):

The goal of this study is to find a minimal model that produces both theta and gamma rhythms in the hippocampus CA1, based on the full-scale model (FSM) of Bezaire et al, 2016. The FSM here is treated as equivalent to biological data. This seems to be a second part of a study that the same authors published in 2021, and is extensively cited here. The study reduces the FSM to a neural rate model with 4 neurons, which is capable of producing both rhythms. This model is then simulated and its parameter dependencies are explored.

The authors succeed in producing a rate model, based on 4 neuron types, that captures the essence of the two rhythms. This model is then analyzed at a descriptive level to claim that the synapse from one interneuron type (CCK) to another (PV+) is more effective than its reciprocal counterpart (PV+ to CCK synapse) to control theta rhythm frequency.

The results fall short on several fronts:<br /> The conclusions rely exclusively on the assumption that the FSM is in fact able to faithfully reflect the biological circuits involved, not just in its output, but in response to a variety of perturbations. Although the authors mention and discuss this assumption, in the end, the reader is left with a (reduced) model of a (complex) model, but no real analysis based on this reduction. In fact, the reduced model is treated in a manner that could have been done with the full one. Thus the significance of the work is greatly reduced not by what the authors do, but by what they fail to do, which is to properly analyze their own reduced model. Consequently, the impact of this study on the field is minimal.<br /> Related to the first point, throughout the manuscript, multiple descriptive findings, based on the authors' observations of the model output, are presented as causal relationships. Even the main finding of the study (that one synapse has a larger effect on theta than another) is not quantified, but just simply left as a judgment call by the authors and reader of comparing slopes on graphs.

Reviewer #3 (Public Review):

While full-scale and minimal models are available for CA1 hippocampus and both exhibiting theta and gamma rhythms, it is not fully clear how inhibitory cells contribute to rhythm generation in the hippocampus. This paper aims to address this question by proposing a middle ground - a reduced model of the full-scale model. The reduced model is derived by selecting neural types for which ablations show that these are essential for theta and gamma rhythms. A study of the reduced model proposes particular inhibitory cell types (CCK+BC cells) that play a key role in inhibitory control mechanisms of theta rhythms and theta-gamma coupling rhythms.

Strengths:<br /> The paper identifies neural types contributing to theta-gamma rhythms, models them, and provides analysis that derives control diagrams and identifies CCK+BC cells as key inhibitory cells in rhythm generation. The paper is clearly written and approaches are well described. Simulation data is well depicted to support the methodology.

Weaknesses:<br /> The derivation methodology of the reduced model is hypotheses based, i.e. it is based on the selection of cell types and showing that these need to be included by ablation simulations. Then the reduced model is fitted. While this approach has merit, it could "miss" cell types or not capture the particular balance between all types. In particular, it is not known what is the "error" by considering the reduced model. As a result, the control plots (Fig. 5 and 6) might be deformed or very different. An additional weakness is that while the study predicts control diagrams and identifies CCK+BC cell types as key controllers, experimental data to validate these predictions is not provided. This weakness is admissible, in my opinion, since these recordings are not easy to obtain and the paper focuses on computational investigation rather than computationally guided experiments.

Reviewer #1 (Public Review):

This study focuses on the role of polo like kinase 1 (PLK-1) during oocyte meiosis. In mammalian oocytes, Plk1 localizes to chromosomes and spindle poles, and there is evidence that it is required for nuclear envelope breakdown, spindle formation, chromosome segregation, and polar body extrusion. However, how Plk1 is targeted to its various locations and how it performs these functions is not well understood. This study uses C. elegans oocytes as a model to explore PLK-1 function during meiosis. They take advantage of an analogue-sensitive allele of plk-1, which enabled them to bypass nuclear envelope breakdown defects that occur following PLK-1 RNAi. This allowed them to dissect later roles of PLK-1 in oocytes, demonstrating that depletion causes defects in spindle organization, chromosome congression, segregation, and polar body extrusion. Moreover, the authors defined mechanisms by which PLK-1 is targeted to chromosomes, showing that CENP-C (HCP-4) is required for localization to chromosome arms and that BUB-1 is required for targeting to the midbivalent region. Finally, they demonstrate that upon removal of PLK-1 from both domains, there are severe meiotic defects. These findings are interesting. However, there is a need for additional analysis to better support some of their conclusions, and to aid in interpretation of particular phenotypes. Specific comments are below.

- For many important claims of the paper, a single representative image is shown but the n is not noted. This is an issue throughout the paper for much of the localization analysis (e.g. Figure 1B, 1C, 1D, 2A, 2B, 3A, 3B, 3C, etc.); in cases like this, numbers should be included to increase the rigor of the presented data. How many images or movies were analyzed that looked like the one shown? For linescans, were they done only on one image? How many independent experiments were done, etc?.

- In the abstract, it is stated that PLK-1 plays a role in spindle assembly/stability (this is also stated elsewhere, e.g. line 101). This phrasing implies that the authors have demonstrated roles in both spindle assembly and stability. However, to distinguish between these roles, they would have to show that removal of PLK-1 before spindle assembly causes defects, and also that removal of PLK-1 from pre-formed spindles causes collapse. I don't think it is necessary to do this, as the spindle roles of PLK-1 are not a focus of the paper. However, the language should be altered so that it does not imply that the paper has demonstrated roles in both. A good place to do this would be in the section from lines 144-147, where they first discuss the spindle defects. It would be straightforward to explain that their approach does not distinguish between spindle assembly and stability, and that PLK-1 could have a role in either or both.

- It is stated that there is kinetochore localization of PLK-1 (and I do see some dim cup-like localization in images after PLK-1 is removed from the chromosome arms via HCP-4 RNAi). However, this cup-like localization is not clear in most wild-type images (e.g. Figure 1B, 1D, 2A, 3A, etc.). Although I recognize that the chromatin staining might be obscuring kinetochore localization, if PLK-1 was truly a kinetochore protein I would also expect it to localize to filaments within the spindle (as many other kinetochore proteins do), especially since the authors state that BUB-1 targets PLK-1 to the kinetochore (and BUB-1 is in the filaments). In fact, the only images where it looks like PLK-1 may be localized to filaments are in Figure 4C and 6A, when HCP-4 has been depleted (though I don't know if this generally true across all HCP-4 RNAi images). For me, this calls into question the conclusion that PLK-1 truly is on the kinetochore in wild type conditions - could it be that PLK-1 only localizes to the kinetochore (and to the filaments) when HCP-4 is depleted? The authors need to resolve this issue and provide better evidence that PLK-1 normally localizes to the kinetochore, if they want to make this claim. Additionally, the observation that PLK-1 is not on the kinetochore filaments (in wild type conditions) should be addressed in the text somewhere - do the authors think that this is a special type of kinetochore protein that does not localize to the filaments?

- The authors should provide a control experiment, treating wild-type worms with 10uM 3-IB-PP1. This would be important to ensure that the spindle defects seen at this concentration in the plk-1as strain are not non-specific effects of the inhibitor. There is a control in Figure 1 - figure supplement 3 using 1uM 3-IB-PP1 but didn't see a control for 10uM (the concentration at which spindle defects are observed).

- In Figure 2F, the gels for BUB-1+PLK-1 look different in the presence and absence of phosphorylation by Cdk1 - for these data, I agree with the authors that it looks as if the complex elutes at a higher volume if BUB-1 is not phosphorylated (lines 200-204). However, Figure 2G has a repeat of the condition with phosphorylated BUB-1, and in this panel, the complex appears to elute at a higher volume than it did on the gel in panel F. The gel in panel G looks much more similar to the unphosphorylated condition in panel F. The authors need to explain this discrepancy (i.e., Is there a reason why the gels cannot be compared between panels? How reproducible are these data?). Ideally, the authors would include a repeat of the unphosphorylated BUB-1 + PLK-1 condition in panel G, done at the same time as the conditions shown in that panel, to avoid the impression that their results may not be reproducible.

- The authors would need to provide convincing evidence that co-depletion of BUB-1 and HCP-4 delocalizes PLK-1 from the chromosomes entirely, and that this co-depletion condition is more severe than either single depletion alone. Additionally, the bub-1T527A and hcp-4T163A alleles are nice tools to, in theory, more specifically delocalize PLK-1 from the midbivalent and chromosome arms, respectively, to explore the functions of chromosome-associated PLK-1. However, I think the authors cannot rule out the possibility that other proteins are also being depleted from the midbivalent and/or chromosome arms in their conditions, and that this delocalization may contribute to the phenotypes observed. For example, hcp-4 depletion was recently shown to delocalize KLP-19 from the chromosome arms (Horton et.al. 2022), so in the experiment shown in Figure 6E (HCP-4 RNAi in the bub-1 mutant), PLK-1 was likely not the only protein missing from the chromosome arms. Therefore, understanding if other proteins are absent from these domains (in the bub-1T527A and hcp-4T16A3 mutants) would help the reader understand and interpret the presented phenotypes (and how specific they are to PLK-1 loss). Consequently, I think that to better understand the co-depletion analysis presented in Figure 6 (and Figure 6 supplement 1), the authors should analyze other midbivalent and chromosome arm proteins, to determine if any are also delocalized (e.g. SUMO, KLP-19, MCAK, etc.). Additionally, instead of performing a combination of mutant and RNAi analysis (i.e. HCP-4 RNAi in the bub-1 mutant (Figure 6) and BUB-1 RNAi in the hcp-4 mutant (Figure 6 figure supplement 1)), it would be more powerful to generate a double mutant - this has a higher chance of being a more specific depletion condition.

Reviewer #2 (Public Review):

In this manuscript, Taylor et al. analyzed the role of the Polo-like kinase PLK-1 during female meiosis in the C. elegans oocyte. By temporally inhibiting an analogue-sensitive PLK-1 mutant (bypassing the PLK-1 requirement for nuclear envelope breakdown) they demonstrate that PLK-1 is involved in meiotic spindle assembly and/or stability, chromosome alignment and polar body extrusion. Consistent with its role in these processes, the authors demonstrate that PLK-1 localizes to multiple regions of the meiotic spindle: the spindle poles, chromosome arms, kinetochores and midbivalent region between the homologous chromosomes during meiosis I. They further dissected the mechanism recruiting PLK-1 to these structures and showed that CENP-CHCP-4 recruits PLK-1 to the chromosome arms while BUB-1 recruits PLK-1 to the midbivalent and kinetochores. The interaction between PLK-1 and its partners is mediated by phosphorylation of a Polo-docking site (consensus STP) in BUB-1 and CENP-CHCP-4. Finally, the authors show that both PLK-1 recruitment pathways are critically required for PLK-1 function in female meiosis.

This fundamental work substantially advances our understanding of PLK-1 function during female meiosis.<br /> Overall, the data presented are of very high quality and support the major conclusions of the paper with one or two exceptions.

Reviewer #3 (Public Review):

This is a very well written manuscript which addresses the role of the mitotic kinase Polo like kinase 1 in meiosis using the C. elegans fertilized oocyite as a model system. The authors show that PLK-1 localizes at different locations on meiotic spindles and chromosomes and identify the mechanisms required for the different localization patterns. Finally, the authors show which pool of PLK-1 is required for the different functions of PLK-1 in meiosis, using the power of genetics via CRISPR.

The strengths of the manuscript are the temporal inhibition of PLK-1 to study the meiotic roles of this kinase, the identification of the mechanisms that control PLK-1 localization and how this is regulated (phosphorylation) and the combination of cell biology and biochemstry.

This work will be of high interest to both the Polo like kinase and the meiotic communities.

Reviewer #1 (Public Review):

This manuscript presents information that will be of great interest to yeast geneticists - standard gene deletions can lead to misleading phenotypes due to effects on adjacent genes. The experiments carefully document this in one case, for the DBP1 gene, and present additional evidence that it can occur at additional genes. An improved version of the standard gene replacement cassette is described, with evidence that it functions in an improved fashion, insulated from affecting adjacent genes.

Reviewer #2 (Public Review):

The impact of the work will be for yeast researchers in the clear and careful presentation of a case study wherein phenotypes might be ascribed to the knockout of a particular gene but instead derive from effects on a neighboring gene. In this case, a transcript expressed from within or adjacent to a knockout of DBP1 by a selectable marker towards the adjacent gene MRP51 interferes with the adjacent gene's normal transcription start sites. Furthermore, although neighboring MRP51 ORF is present on the longer mRNA isoform that is generated, it is not efficiently translated. The authors expand on this phenotypic observation to demonstrate that a substantial fraction of selectable marker insertions can generate transcription adjacent to or within and going away from, selectable markers.

The strengths of the work are that the derivation of the observed phenotypes for the dpb1∆ alleles is clearly and carefully elucidated and the creation of new selectable marker cassettes that overcome the potential for cryptic transcript emanation from or near to the selectable markers. This is valuable for the community as a clear demonstration of how only the exact right experiments might detect underlying mechanisms for potentially misattributed phenotypes and that many times these experiments may not be performed. While understandable in terms of how the experiments likely played out, the manuscript seems in between biology and tool development, as the biology in question was related to a gene that is not the focus of this lab. The tool development is likely to be useful but potentially non-optimal. The mechanism for interference identified in this example case (via a long undecoded transcript isoform (LUTI) has already been described for other loci and in a number of species, including in work from the Brar lab. The concept of marker interference with neighboring genes has also been increasingly appreciated by a number of other studies.

Reviewer #1 (Public Review):

This study analyzes the detailed chemical mechanics of the formation of a physiologically important protein multimer. The primary strengths of the study are careful analyses of two distinct methods, CG-MALS a direct measure of multimerization, and environment-sensitive tryptophan fluorescence, that each indicates that Ca2+ activation of the C-lobe alone can change the physical interaction with an SK2 C-terminal peptide. An intriguing finding is that while either the N- or C-lobes alone can interact with the C-terminal peptide, only with full-length CaM can the SK C-terminal peptide be bound by two CaM molecules simultaneously. This study also clearly demonstrates that Ca2+ activation of the N-lobe triggers binding to the SK2 C-terminal peptide. Methods descriptions are thorough and excellent. Discussion of relevance to structures and function are nuanced and free of presumptions. The weaknesses of this manuscript are that the physiological implications of these findings are not clear: CaM interacts with regions of SK channels besides the C-terminal peptide studied here, and no evidence is provided here that C-lobe calcium binding alters channel opening. Overall, the evidence for conformational changes of the complex due to Ca2+ binding to the C-lobe alone is very strong, and physiological importance seems likely. The interpretation of data in this manuscript is mostly cautious and logically crystalline, with alternative interpretations discussed at many junctures.

Reviewer #2 (Public Review):

Activation of SK channels by calcium through calmodulin (CaM) is physiologically important in tuning membrane excitability. Understanding the molecular mechanism of SK activation has therefore been a high priority in ion channel biophysics and calcium signaling. The prevailing view is that the C-terminal lobe of CaM serves as an immobile Ca2+-independent tether while the N-lobe acts as a sensor whose binding activates the channel. In the present study, the authors undertake extensive biophysical/biochemical analysis of CaM interaction with SK channel peptide and rigorous electrophysiological experiments to show that Ca2+ does bind to the C-lobe of CaM and this potentially evokes conformational changes that may be relevant for channel gating. Beyond SK channels, the approach and findings here may bear important implications for an expanding number of ion channels and membrane proteins that are regulated by CaM.

A strength of the study is that the electrophysiological recordings are innovative and of high quality. Given that CaM is ubiquitous in nearly all eukaryotes, dissecting the effects of mutants particularly on individual lobes is technically challenging, as endogenous CaM can overwhelm low-affinity mutants. The excised patch approach developed here provides a powerful methodology to dissect fundamental mechanisms underlying CaM action. I imagine this could be adaptable for studying other ion channels. Armed with this strategy authors show that both N- and C-lobe of CaM are essential for maximal activation of SK channels. This revises the current model and may have physiological importance.

The major weakness is that nearly all biochemical inferences are made from analysis of isolated peptides that do not necessarily recapitulate their arrangement in an intact channel. While the use of MALS provides new evidence of the potentially complex conformational arrangement of CaM on the C-terminal SK peptide (SKp), it is not fully clear that these complexes correspond to functionally relevant states. Lastly, perhaps as a consequence of these ambiguities, the overarching model or mechanism is not fully clear.

Reviewer #3 (Public Review):

Halling et. al. probe the mechanism whereby calmodulin (CaM) mediates SK channel activity in response to calcium. CaM regulation of SK channels is a critical modulator of membrane excitability yet despite numerous structural and functional studies significant gaps in our understanding of how each lobe participates in this regulation remain. In particular, while Ca2+ binding to the N-lobe of CaM has a clear functional effect on the channel, the C-lobe of CaM does not appear to participate beyond a tethering role, and structural studies have indicated that the C-lobe of CaM may not bind Ca2+ in the context of the SK channel. This study pairs functional and protein binding data to bridge this gap in mechanistic understanding, demonstrating that both lobes of CaM are likely Ca2+ sensitive in the context of SK channels and that both lobes of CaM are required for channel activation by Ca2+.

Strengths:<br /> The molecular underpinnings of CaM-SK regulation are of significant interest and the paper addresses a major gap in knowledge. The pairing of functional data with protein binding provides a platform to bridge the static structural results with channel function. The data is robust, and the experiments are carefully done and appear to be of high quality.<br /> The use of multiple mutant CaMs and electrophysiological studies using a rescue effect in pulled patches to enable a more quantified evaluation of the functional impact of each lobe of CaM provides a compelling assessment of the contribution of each lobe of CaM to channel activation. The calibration of the patch data by application of WT CaM is innovative and provides precise internal control, making the conclusions drawn from these experiments clear. This data fully supports the conclusion that both lobes of CaM are required for channel activation.

Weaknesses:<br /> The paper focuses heavily on the results of multi-angle light scattering experiments, which demonstrate that a peptide derived from the C-terminus of the SK channel can bind to CaM in multiple stochiometric configurations. However, it is not clear if these complexes are functionally relevant in the full channel, making interpretation challenging.

Reviewer #1 (Public Review):

The overarching hypothesis is that cadherin adhesion molecules specify the code that enables the premotor brainstem breathing circuits to innervate the phrenic motor neurons that control the primary breathing muscle, the diaphragm. The authors show that multiple type 1 and 2 cadherins (N-, 6, 9, 10) are expressed by phrenic motor neurons and are necessary for motor neuron development and breathing, and complementarily, that adhesion signaling in medullary breathing circuits are required for normal breathing. The presented data support a model whereby combinations of redundant adhesion molecules create a code to wire the breathing circuit.

Strengths:<br /> 1) The authors first use a complex, rigorous genetic approach to eliminate N, 6, 9, 10 cadherins from motor neurons and discover using whole body plethysmography that neonates do not breath.<br /> 2) Then, the authors provide a thorough description of the anatomy of the mutant motor neurons and discover that the number of motor neurons decreases, the soma anatomical positions and dendritic arborization shift, and there is decreased innervation of the diaphragm breathing muscle.<br /> 3) That Cdh9 medullary expressing neurons are premotor to Cdh9 expressing phrenic motor neurons.<br /> 4) Cadherin signaling is required for normal breathing.

Weaknesses: The main conclusion that ablation of the cadherin code decreases synaptic connectivity between the rVRG and phrenic motor neurons is never directly shown. This can only be inferred by the data.<br /> 1) Conclusion that the connectivity between rVRG premotor and phrenic nerve motor neurons is "weaker". This conclusion is inferred from several experiments but is never directly demonstrated. Alternative interpretations of the decreased amplitude of the in vitro phrenic nerve burst is that the rootlet contains fewer axons (as predicted by the fewer motor neurons in S3 and innervation of the diaphragm S2). Additionally, the intrinsic electrophysiological properties of the motor neurons might be different. To show this decisively, the authors could use electrophysiological recordings of phrenic motor neurons to directly measure a change in synaptic input (for example, mEPSPs or EPSPs after optogenetic stimulation of rVRG axon terminals). Without a direct measurement, the synaptic connectivity can only be inferred.<br /> 2) Conclusion that the small phenic nerve burst size in Dbx1 deleted cadherin signaling is due to less synaptic input to the motor neurons. Dbx1 is expressed in multiple compartments of the medullary breathing control circuit, like the breathing rhythm generator (preBötC). The smaller burst size could be due to altered activity between preBötC neurons to create a full burst, the transmission of this burst from the preBötC to the rVRG, etc.<br /> 3) In vitro burst size. The authors use 4 bursts from each animal to calculate the average burst size. How were the bursts chosen? Why did the authors use so few bursts? What is the variability of burst size within each animal? What parameters are used to define a burst? This analysis and the level of detail in the figure legend/methods section is inadequate to rigorously establish the conclusion that burst size is altered in the various genotypes.<br /> 4) The authors state that the in vitro frequency in figure 4 is inaccurate, but then the in vitro frequency is used to claim the preBötC is not impacted in Dbx1 mutants (conclusion section "respiratory motor circuit anatomy and assembly"). To directly assess this conclusion, the bursting frequency of the in vitro preBötC rhythm should be measured.<br /> 5) The burst size in picrotoxin/strychnine is used to conclude that the motor neurons intrinsic physiology is not impacted. The bursts are described, and examples are shown, but this is never quantified across many bursts within in a single recordings nor in multiple animals of each genotype.

Reviewer #2 (Public Review):

This is an extremely thorough investigation of the role of cadherins in generating a functional motor circuit. The work represents a major step forward in the field as it addresses several outstanding questions and verifies anatomical data with functional outcomes. First, the data show that a combination of type I (N) and type II (6, 9, 10) cadherins is needed to generate normal connectivity and function. This is novel as prior work has suggested that the two types do not work collaboratively to generate circuits. Second, the data show that cell body position (in this case) is modulated by N-cadherin but in a manner that is independent from the impact of N-cadherin on connectivity. While position and connectivity have been shown to be separable in some cases, the data support that N-cadherin plays important but separate roles toward both actions, and type II cadherins, mainly in connectivity. These findings also underscore that cadherin roles reported for hippocampus, retina, and spinal cord motor neuron pools are not generalizable across circuits. Third, while the data show that type I and type II cadherins are required for VRG to phrenic motor neuron connectivity, they also show that there are some outcomes controlled only by N-cadherin. Finally, the data reveal much about a very poorly understood and essential circuit. The approaches are sound and range from the standard (in situ, immuno, diI, breathing measurements) to the difficult (rabies-based tracing) to the impressive (challenging ephys preps, and some painstaking mouse crosses), and they incorporated strong and creative strategies for comparison and quantification. Minor questions do not detract from a really impressive piece of work.

Reviewer #3 (Public Review):

Vagnozzi et al. analyze the role of cadherins in respiratory circuit development. The authors previously identified a combinatorial cadherin code that defines phrenic motor neurons (Vagnozzi et al., eLife 2020). Here they find that combined loss of type I N-cadherin and type II cadherins 6, 9 and 10 results in respiratory failure and reduction in phrenic motor neuron bursting activity. Furthermore, diaphragm innervation, phrenic motor neuron (MN) number, cell body position as well as dendrite orientation are all impaired in mice lacking N-cadherin and cadherins 6, 9, 10. Analysis of different genotypes indicates that phrenic MN cell body position is regulated by N-cadherin, but that dendrite orientation is regulated by the combinatorial action of N-cadherin and cadherins 6, 9, and 10. They subsequently determine that cadherin signaling in presynaptic interneurons is required for phrenic MN bursting activity. Together, the results indicate that cadherins are essential for respiratory circuit function and suggest that a combinatorial cadherin code regulates wiring specificity in this circuit.

The manuscript is well presented with clear figures and text. My comments below mainly revolve around the interpretation of some of the findings and the correlation between phenotypes in NMNΔ6910-/- mice and βγ-catDbx1Δ mice in light of specific cadherin expression patterns and connectivity between rVRG and prenic MNs.

Major points<br /> 1. Page 8: 'In addition, NMNΔ and NMNΔ6910-/- mice showed a similar decrease in phrenic MN numbers, likely from the loss of trophic support due to the decrease in diaphragm innervation (Figure S3c).' This statement should be corrected: phrenic MN number in NMNΔ mice does not differ from controls, in contrast to NMNΔ6910-/- mice (Fig. S3). Similarly, diaphragm innervation is not significantly different from controls in NMNΔ (Fig. S2). Alternatively, these observations could be strengthened by increasing the number of mice analyzed to determine whether there is a significant reduction in PMN number and diaphragm innervation in NMNΔ mice.<br /> 2. A similar comment relates to the interpretation of the dendritic phenotype in NMNΔ and NMNΔ6910-/- mice (Fig. 3m): the authors conclude 'When directly comparing NMNΔ and NMNΔ6910-/- mice, NMNΔ6910-/- mice had a more severe loss of dorsolateral dendrites and a more significant increase in ventral dendrites (Figure 3l-m).' (page 9). The loss of dorsolateral dendrites in NMNΔ6910-/- mice indeed differs significantly from control mice, and is more severe than in NMNΔ mice, which do not differ significantly from controls. For ventral dendrites however, the increase compared to controls is significant for both NMNΔ and NMNΔ6910-/- mice, and the two genotypes do not appear to differ from each other. This suggests cooperative action of N-cadherin and cadherin 6,9,10 for dorsolateral dendrites, but suggests that N-cad is more important for ventral dendrites. This should be phrased more clearly.<br /> 3. Related comment, page 10: 'Furthermore, the fact that phrenic MNs maintain their normal activity pattern in NMNΔ mice suggests that neither cell body position nor phrenic MN numbers significantly contribute to phrenic MN output.' This should be rephrased, phrenic MN number does not differ from control in NMNΔ mice (Fig. S2c).<br /> 4. The authors conclude that spinal network activity in control and NMNΔ6910-/- mice does not differ (page 10, Fig. 4f). It is difficult to judge this from the example trace in 4f. How is this concluded from the figure and can this be quantified?<br /> 5. RphiGT mice: please explain the genetic strategy better in Results section or Methods, do these mice also express the TVA receptor in a Cre-dependent manner? Crossing with the Cdh9:iCre line will then result in expression of TVA and G protein in phrenic motor neurons and presynaptic rVRG neurons in the brainstem, as well as additional Cdh9-expressing neuronal populations. How can the authors be sure that they are looking at monosynaptically connected neurons?<br /> 6. The authors use a Dbx1-cre strategy to inactivate cadherin signaling in multiple brainstem neuronal populations and perform analysis of burst activity in phrenic nerves. Based on the similarity in phenotype with NMNΔ6910-/- mice it is concluded that cadherin function is required in both phrenic MNs and Dbx1-derived interneurons. However, this manipulation can affect many populations including the preBötC, and the impact of this manipulation on rVRG and phrenic motor neurons (neuron number, cell body position, dendrite orientation, diaphragm innervation etc) is not described, although a model is presented in Fig. 7. These parameters should be analyzed to interpret the functional phenotype.<br /> 7. Additional evidence is needed to support the model that a selective loss of excitatory rVRG to phrenic motor neuron connectivity underlies the reduced bursting activity phenotype in NMNΔ6910-/- mice, for instance by labeling the connections from rVRG to phrenic MNs and quantifying connectivity.

Reviewer #1 (Public Review):

This paper describes the results of a MEG study where participants listened to classical MIDI music. The authors then use lagged linear regression (with 5-fold cross-validation) to predict the response of the MEG signal using (1) note onsets (2) several additional acoustic features (3) a measure of note surprise computed from one of several models. The authors find that the surprise regressors predict additional variance above and beyond that already predicted by the other note onset and acoustic features (the "baseline" model), which serves as a replication of a recent study by Di Liberto.

They compute note surprisal using four models (1) a hand-crafted Bayesian model designed to reflect some of the dominant statistical properties of Western music (Temperley) (2) an n-gram model trained on one musical piece (IDyOM stm) (3) an n-gram model trained on a much larger corpus (IDyOM ltm) (4) a transformer DNN trained on a mix of polyphonic and monophonic music (MT). For each model, they train the model using varying amounts of context.

They find that the transformer model (MT) and long-term n-gram model (IDyOM stm) give the best neural prediction accuracy, both of which give ~3% improvement in predicted correlation values relative to their baseline model. In addition, they find that for all models, the prediction scores are maximal for contexts of ~2-7 notes. These neural results do not appear to reflect the overall accuracy of the models tested since the short-term n-gram model outperforms the long-term n-gram model and the music transformer's accuracy improves substantially with additional context beyond 7 notes. The authors replicate all these findings in a separate EEG experiment from the Di Liberto paper.

Overall, this is a clean, nicely-conducted study. However, the conclusions do not follow from the results for two main reasons:

1. Different features of natural stimuli are almost always correlated with each other to some extent, and as a consequence, a feature (e.g., surprise) can predict the neural response even if it doesn't drive that response. The standard approach to dealing with this problem, taken here, is to test if a feature improves the prediction accuracy of a model above and beyond that of a baseline model (using cross-validation to avoid over-fitting). If the feature improves prediction accuracy, then one can conclude that the feature contributes additional, unique variance. However, there are two key problems: (1) the space of possible features to control for is vast, and there will almost always be uncontrolled-for features (2) the relationship between the relevant control features and the neural response could be nonlinear. As a consequence, if some new feature (here surprise) contributes a little bit of additional variance, this could easily reflect additional un-controlled features or some nonlinear relationship that was not captured by the linear model. This problem becomes more acute the smaller the effect size since even a small inaccuracy in the control model could explain the resulting finding. This problem is not specific to this study but is a problem nonetheless.

2. The authors make a distinction between "Gestalt-like principles" and "statistical learning" but they never define was is meant by this distinction. The Temperley model encodes a variety of important statistics of Western music, including statistics such as keys that are unlikely to reflect generic Gestalt principles. The Temperley model builds in some additional structure such as the notion of a key, which the n-gram and transformer models must learn from scratch. In general, the models being compared differ in so many ways that it is hard to conclude much about what is driving the observed differences in prediction accuracy, particularly given the small effect sizes. The context manipulation is more controlled, and the fact that neural prediction accuracy dissociates from the model performance is potentially interesting. However, I am not confident that the authors have a good neural index of surprise for the reasons described above, and this limits the conclusions that can be drawn from this manipulation.

Reviewer #2 (Public Review):

This manuscript focuses on the basis of musical expectations/predictions, both in terms of the basis of the rules by which these are generated, and the neural signatures of surprise elicited by violation of these predictions.

Expectation generation models directly compared were gestalt-like, n-gram, and a recently-developed Music Transformer model. Both shorter and longer temporal windows of sampling were also compared, with striking differences in performance between models.

Surprise (defined as per convention as negative log prior probability of the current note) responses were assessed in the form of evoked response time series, recorded separately with both MEG and EEG (the latter in a previously recorded freely available dataset). M/EEG data correlated best with surprise derived from musical models that emphasised long-term learned experiences over short-term statistical regularities for rule learning. Conversely, the best performance was obtained when models were applied to only the most recent few notes, rather than longer stimulus histories.

Uncertainty was also computed as an independent variable, defined as entropy, and equivalent to the expected surprise of the upcoming note (sum of the probability of each value times surprise associated with that note value). Uncertainty did not improve predictive performance on M/EEG data, so was judged not to have distinct neural correlates in this study.

The paradigm used was listening to naturalistic musical melodies.

A time-resolved multiple regression analysis was used, incorporating a number of binary and continuous variables to capture note onsets, contextual factors, and outlier events, in addition to the statistical regressors of interest derived from the compared models.

Regression data were subjected to non-parametric spatiotemporal cluster analysis, with weights from significant clusters projected into scalp space as planar gradiometers and into source space as two equivalent current dipoles per cluster

General comments:

The research questions are sound, with a clear precedent of similar positive findings, but numerous unanswered questions and unexplored avenues

I think there are at least two good reasons to study this kind of statistical response with music: firstly that it is relevant to the music itself; secondly, because the statistical rules of music are at least partially separable from lower-level processes such as neural adaptation.

Whilst some of the underlying theory and implementation of the musical theory are beyond my expertise, the choice, implementation, fitting, and comparison of statistical models of music seem robust and meticulous.

The MEG and EEG data processing is also in line with accepted best practice and meticulously performed.

The manuscript is very well-written and free from grammatical or other minor errors.

The discussion strikes a brilliant balance of clearly laying out the interim conclusions and advances, whilst being open about caveats and limitations.

Overall, the manuscript presents a range of highly interesting findings which will appeal to a broad audience, based on rigorous experimental work, meticulous analysis, and fair and clear reporting.

Reviewer #3 (Public Review):

The authors compare the ability of several models of musical predictions in their accuracy and in their ability to explain neural data from MEG and EEG experiments. The results allow both methodological advancements by introducing models that represent advancements over the current state of the art and theoretical advancements to infer the effects of long and short-term exposure on prediction. The results are clear and the interpretation is for the most part well reasoned.

At the same time, there are important aspects to consider. First, the authors may overstate the advancement of the Music Transformer with the present stimuli, as its increase in performance requires a considerably longer context than the other models. Secondly, the Baseline model, to which the other models are compared, does not contain any pitch information on which these models operate. As such, it's unclear if the advancements of these models come from being based on new information or the operations it performs on this information as claimed. Lastly, the source analysis yields some surprising results that don't fit with previous literature. For example, the authors show that onsets to notes are encoded in Broca's area, whereas it should be expected more likely in the primary auditory cortex. While this issue is not discussed by the authors, it may put the rest of the source analysis into question.

While these issues are serious ones, the work still makes important advancements for the field and I commend the authors on a remarkably clear and straightforward text advancing the modeling of predictions in continuous sequences.

Reviewer #1 (Public Review):

The authors used data from extracellular recordings in mouse piriform cortex (PCx) by Bolding & Franks (2018), they examined the strength, timing, and coherence of gamma oscillations with respiration in awake mice. During "spontaneous" activity (i.e. without odor or light stimulation), they observed a large peak in gamma that was driven by respiration and aligned with the spiking of FBIs. TeLC, which blocks synaptic output from principal cells onto other principal cells and FBIs, abolishes gamma. Beta oscillations are evoked while gamma oscillations are induced. Odors strongly affect beta in PCx but have minimal (duration but not amplitude) effects on gamma. Unlike gamma, strong, odor-evoked beta oscillations are observed in TeLC. Using PCA, the authors found a small subset of neurons that conveyed most of the information about the odor (winner cells). Loser cells were more phase-locked to gamma, which matched the time course of inhibition. Odor decoding accuracy closely follows the time course of gamma power.

I think this is an interesting study that uses a publicly available dataset to good effect and advances the field elegantly, especially by selectively analyzing activity in identified principal neurons versus inhibitory interneurons, and by making use of defined circuit perturbations to causally test some of their hypotheses.

Major:

- The authors show odor-specificity at the time of the gamma peak and imply that the gamma coupling is important for odor coding. Is this because gamma oscillations are important or because gamma is strongest when activity in PCx is strongest (i.e. both excitatory and inhibitory activity, which would cancel each other in the population PSTH, which peaks earlier)? To make this claim, the authors could show that odor decoding accuracy - with a small (~10 ms sliding window) - oscillates at approx. gamma frequencies. As is, Fig. 5 just shows that cells respond at slightly different times in the sniff cycle. What time window was used for computing the Odor Specificity Index? Put another way, is it meaningful that decoding is most accurate when gamma oscillations are strongest, or is this just a reflection of total population activity, i.e., when activity is greatest there is more gamma power, and odor decoding accuracy is best?

- The authors say, "assembly recruitment would depend on excitatory-excitatory interactions among winner cells occurring simultaneously during gamma activity." Can the authors test this prediction by examining the TeLC recordings, in which excitatory-excitatory connections are abolished?

- The authors show that gamma oscillations are abolished in the TeLC condition and use this to claim that gamma arises in the PCx. However, PCx neurons also project back to the OB, where they form excitatory connections onto granule cells. Fukunaga et al (2012) showed that granule cells are essential for generating gamma oscillations in the bulb. Can the authors be sure that gamma is generated in the PCx, per se, rather than generated in the bulb by centrifugal inputs from the PCx, and then inherited from the bulb by the PCx?

Reviewer #2 (Public Review):

This is a very interesting paper, in which the authors describe how respiration-driven gamma oscillations in the piriform cortex are generated. Using a published data set, they find evidence for a feedback loop between local principal cells and feedback interneurons (FBIs) as the main driver of respiration-driven gamma. Interestingly, odour-evoked gamma bursts coincide with the emergence of neuronal assemblies that activate when a given odour is presented. The results argue in favour of a winner-take-all mechanism of assembly generation that has previously been suggested on theoretical grounds.

The article is well-written and the claims are justified by the data. Overall, the manuscript provides novel key insights into the generation of gamma oscillations and a potential link to the encoding of sensory input by cell assemblies. I have only minor suggestions for additional analyses that could further strengthen the manuscript:

1. The authors' analysis of firing rates of FFIs and FBIs combined with TeLC experiments make a compelling case for respiration-driven gamma being generated in a pyramidal cell-FBI feedback mechanism. This conclusion could be further strengthened by analyzing the gamma phase-coupling of the three neuronal populations investigated. One would expect strong coupling for FBIs but not FFIs (assuming that enough spikes of these populations could be sampled during the respiration-triggered gamma bursts). An additional analysis to strengthen this conclusion could be to extract FBI- and FFI spike-triggered gamma-filtered signals. One might expect an increase in gamma amplitude following FBI but not FFI spiking (see e.g., Pubmed ID 26890123).

2. The authors utilize the neurons' weight in the first PC to assign them to odour-related assemblies. This method convincingly extracts an assembly for each odour (when odours are used individually), and these seem to be virtually non-overlapping. It would be informative to test whether a similar clear separation of the individual assemblies could be achieved by running the analysis on all odours simultaneously, perhaps by employing a procedure of assembly extraction that allows to deal with overlapping assembly membership better than a pure PCA approach (as used for instance in the work cited on page 11, including the authors' previous work)? I do not doubt the validity of the authors' approach here at all, but the suggested additional analysis might allow the authors to increase their confidence that individual neurons contribute mostly to an assembly related to a single odour.

3. Do the authors observe a slow drift in assembly membership as predicted from previous work showing slowly changing odour responses of principal neurons (Schoonover et al., 2021)? This could perhaps be quantified by looking at the expression strengths of assemblies at individual odour presentations or by running the PCA separately on the first and last third of the odour presentations to test whether the same neurons are still 'winners'.

4. Does the winner-take-all scenario involve the recruitment of specific sets of FBIs during the activation of the individual odour-selective assemblies? The authors could address this by testing whether the rate of FBIs changes differently with the activation of the extracted assemblies.

5. Given the dependence on local gamma oscillations, one might expect that odour-selective assemblies do not emerge in the TeLC-expressing hemisphere. This could be directly tested in the existing data set.