Reviewer #1 (Public Review):

The authors of this paper seek to understand how HIV infects cells. HIV is a retrovirus that harbors a core of RNA nucleic acid in complex with important replication enzymes such as reverse transcriptase. After infection, reverse transcriptase converts the RNA into DNA, which is then integrated into the chromosome. The authors used advanced imaging techniques to visualize the DNA that is made by reverse transcription. They used fluorescent readout markers of proteins to also look at the viral proteins that are brought into the cell and track with the viral DNA during the virus infection.

From this work the authors conclude that reverse transcription is completed in the cell nucleus, that intact or nearly intact cores are the substrate for nuclear import, and that virus core uncoating likely occurs in the nucleus, immediately preceding the integration step. Moreover, by using electron tomography, they drill down to the sub-micron level to glean an ultrastructural view of the viral complexes that are performing these important HIV infection steps. Some of these complexes appear to be novel, and thus the work will be of interest to other scientists in this field.

Weaknesses of the study include insufficient control samples for some of the experiments and also clarifying some of the approaches used and some of their interpretations of the data (detailed below). The authors of this paper could have also done a better job of citing papers published by other scientists who came up with very similar conclusions and/or used very similar techniques.

Reviewer #2 (Public Review):

This manuscript builds upon some important thought-leading work within the Ras field that the authors have published in recent years. They have previously demonstrated how changing the protein expression levels of KRAS can modulate the number of Ras-driven tumours that are observed and posited that this suggests an optimal level of Ras signalling that is neither too stressing nor too insufficient to promote tumourigenesis.

In this manuscript they use urethane to induce lung tumours in mouse models that have either normal or high levels of KRAS expression (also higher oncogenic stress). They are also able to modulate the associated oncogenic stress levels by the presence (higher stress) or deletion (lower stress) of p53. Urethane normally generates Q61 KRAS mutations, biochemical analysis by other groups has previously shown that these mutations are more active than G12 mutations. Following urethane induction, they observe an improved competence to support tumorigenesis in the high KRAS model when p53 is removed. They also observe a shift towards G12 mutants under genetic conditions where oncogenic stress is higher (higher KRAS expression, presence of p53). ie. stress compensators (p53 loss or weaker activating mutation) permit promotion of tumourigenesis in the high KRAS model. The converse was also observed. Loss of p53 (lower stress) resulted in higher mRNA levels of G12 mutants - suggesting that the weaker mutant increases protein expression/cancer signalling to occupy the new oncogenic stress headroom that has been created. Some support for the hypothesis that these effects are mediated by differences in Ras signalling amplitude between the different mutants was provided by analysing the expression of three key Ras gene targets. As predicted, higher expression (signalling output) was seen in Q61 vs Q12 mutants and when p53 was deleted.

Strengths:

The mouse model conditions provide a suitable range of options to allow the hypothesis to be tested. The data are all internally consistent and broadly support the general conclusions.

Weaknesses:

The mRNA data are interpreted as evidence for changes in protein expression and Ras signalling activity - there is no formal evidence that this is the case.

The similarity in G12/13 mutations between the KRAS normal and high KRAS mice in Figure 2C is unexpected. The authors focussed on the potential for higher G12/13 mutant expression in the KRAS normal mice to explain this. It is also intriguing how there wasn't a more complete switch to Q61 in the high KRAS tumours when p53 was deleted. Whilst the Ras signalling dosing/oncogenic stress nexus are a reasonable explanation, the model/methods are a snapshot in time and don't have the resolution to fully understand the detail of what is going on here.

This study represents a solid contribution supporting an important model and will stimulate future work to understand Ras variant cancer contributions.

Reviewer #1 (Public Review):

The centrality of RAS proteins in human malignancies has long been established, but many issues regarding their regulation and functions remain unresolved. The results of this paper provide strong supporting evidence for an emerging model that posits that activated KRAS can only be tolerated by cells up to a certain point, after which the stress it imposes outweigh its transforming potential. These restrictions impose limits on the amount of KRAS expressed in tumor cells and are also consistent with the frequent coupling of KRAS mutations with loss of the tumor suppressor p53, as the latter relieves the stress signals induced by KRAS.

Reviewer #3 (Public Review):

This manuscript by Koiwai et al. described the single-cell RNA-seq analysis of shrimp hemocytes and was submitted as a Resource Paper in eLife. In this study, they identified 9 cell types in shrimp hemocytes based on their transcriptional profiles and identified markers for each subpopulation. They predicted different immune roles among these subpopulations from differentially expressed immune-related genes. They also identified cell growth factors that might play important roles in hemocyte differentiation. This study helps to understand the immune system of shrimp and maybe useful for improving the control of the pathogen infections. The analysis of the data and interpretation is overall good but there are also some concerns:

1) The number of UMI and genes detected per cell after mapping to the in-house reference genome does not appear to be presented, and the similarities or differences between the three replicated samples are not discussed, as well as the low number of genes detected per cell (~300 in this study) .

2) The correlation between the morphology and the expression of marker genes demonstrated in Figure 6 is questionable. Cells of the same size could express totally different genes. On the other hand, cells that are different in size can express nearly identical genes. The evidence presented in this manuscript is not enough to support a correlation between cell size and gene expression. Therefore, the author would either need to provide more evidence to support this correlation, or not make such correlation.

3) There are many spindle-shaped cells in Figure 6B, but none of them appeared in Figure 6C and D after sorting, and the reason for this is unclear.

4) The hemocyte differentiation model in Figure 7 is not supported by any experimental data.

Reviewer #2 (Public Review):

In this manuscript Koiwai et al. used single cell RNA sequencing of hemocytes from the shrimp Marsupenaeus japonicus. Due to lack of complete genome information for this species, they first did a de novo assembly of transcript data from shrimp hemocytes, and then used this as reference to map the scRNA results. Based on expression of the 3000 most variable genes, and a subsequent cluster analysis, nine different subpopulations of hemocytes were identified, named as Hem1-Hem9. They used the Seurat marker tool to find in total 40 cluster specific marker transcripts for all cluster except for Hem6. Based upon the predicted markers the authors suggested Hem1 and Hem2 to be immature hemocytes. In order to determine differentiation lineages they then used known cell-cycle markers from Drosophila melanogaster and could confirm Hem1 as hemocyte precursors. While genes involved in the cell cycle could be used to identify hemocyte precursors, the authors concluded that immune related genes from the fly was not possible to use to determine functions or different lineages of hemocytes in the shrimp. This is an important (and known) fact, since it is often taught that the fruit fly can be used as a general model organism for invertebrate immunologists which obviously is not the case. Even among arthropods, animals are different. The authors suggest four lineages based upon a pseudo temporal analysis using the Drosophila cell-cycle genes and other proliferation-related genes. Further, they used growth factor genes and immune related genes and could nicely map these into different clusters and thereby in a way validating the nine subpopulations. This paper will provide a good framework to detect and analyze immune responses in shrimp and other crustaceans in a more detailed way.

Strengths:

The determination of nine classes of hemocytes will enable much more detailed studies in the future about immune responses, which so far have been performed using expression analysis in mixed cell populations. This paper will give scientists a tool to understand differential cell response upon an injury or pathogen infection. The subdivision into nine hemocyte populations is carefully done using several sets of markers and the conclusions are on the whole well supported by the data.

Weaknesses:

One obvious drawback of the paper is first the low number of UMIs. A total number of 2704 cells gave a median UMI as low as 718 which is very low. Especially shrimp no. 2 has an average far below 500 and should perhaps be omitted. Therefore, one question is about cell viability prior to the drop-seq analysis. The fact of this low number of UMIs should be discussed more thoroughly.

Details about how quality control (QC) was performed would be needed, for example the cutoff values for number of UMI per cell, and also one important information showing the quality is the proportion of mitochondrial genes. The clustering into nine subpopulations seems solid, however the determination of lineages based upon the pseudo time analysis with cell-cycle related genes is not that strong. The authors identify four lineages, all starting from hem1 via hem2-Hem3- Hem4 and then one to Hem5, another through part of Hem 6 to Hem 7, next through part of Hem 6 to Hem 8 and finally through part of Hem 6 to Hem 9. Referring to Figure 3 - supplement 3, it seems as if Hem6 could be subdivided into two clusters, one visible in B and C, while another part of Hem & is added in D. Also, the data in figure 3 - supplement 1 showing expression of cell cycle markers do not convincingly show the lineages. Cluster Hem 3 and 4 seems to express much fewer and lower amount of these markers compared to cluster Hem6 - Hem9.

It is also clear (from figure 5 - supplement 1) that there are more than one TGase gene and the authors would need to discuss that fact related to differentiation.

While the part to determine subpopulations is very strong, the part about FACS analysis and qRT-PCR is weaker than the other sections, and doesn't add so much information. Validation of marker genes and the relationship between clusters and morphology shown in figure 6 is not totally convincing. It seems clear that both R1 and R2 contains a mixture of different cell types even if TGase expression is a bit higher in R1. A better way to confirm the results could be to do in situ hybridization (or antibody staining) and show the cell morphology of some selected marker proteins in a mixed hemocyte population. FACS sorting is very crude and does not really separate the shrimp hemocytes in clear groups based on granularity and size. This may be because the size of hemocytes without granules vary a lot. You need cell surface markers to do a good sorting by FACS. Another minor issue is the discussion about KPI. There are a huge number of Kazal-type proteinase inhibitors in crustaceans and it is not clear from this data if the authors discuss a specific KPI-gene, and there is a mistake in referring to reference 65 which is about a Kunitz-type inhibitor.

In summary, this paper is a very important contribution to crustacean immunology, and although a bit weak in lineage determination it will be of extremely high value.

Reviewer #1 (Public Review):

Summary and Strength:

Single-cell RNA sequencing is the most appropriate technique to profile unknown cell types and Koiwai et al. made good use of the suitable tool to understand the heterogeneity of shrimp hemocyte populations. The authors profiled single-cell transcriptomes of shrimp hemocytes and revealed nine subtypes of hemocytes. Each cluster recognizes several markers, and the authors found that Hem1 and Hem2 are likely immature hemocytes while Hem5 to Hem9 would play a role in immune responses. Moreover, pseudotime trajectory analysis discovered that hemocytes differentiate from a single subpopulation to four hemocyte populations, indicating active hematopoiesis in the crustacean. The authors explored cell growth- and immune-related genes in each cluster and suggested putative functions of each hemocyte subtype. Lastly, scRNA-seq results were further validated by in vivo analysis and identified biological differences between agranulocytes and granulocytes. Overall, conclusions are well-supported by data and hemocyte classifications were carefully performed. Given the importance of aquaculture in both biology and industry, this study will be an extremely useful reference for crustacean hematopoiesis and immunity. Moreover, it will be a good example and prototype for cell-type analysis in non-model organisms.

Weaknesses:

The conclusions of this paper are mostly well supported by data, but some aspects of data analysis QC and in vivo lineage validation need to be clarified.

1) It is not a trivial task to perform genome-wide analyses of gene expression on species without sufficient reference genome/transcriptome maps. With this respect, the authors should have de novo assembled a transcriptome map with a careful curation of the resulting transfrags. One of the weaknesses of this study is the lack of proper evaluation for the assembly results. To reassure the results, the authors would need to first assess their de novo transcripts in detail and additional data QC analysis would help substantiate the validity.

2) The authors applied SCTransform to adjust batch effects and to integrate independent sequencing libraries. SCTransform performs well in general; however, the authors would need to present results on how batch effects were corrected along with before and after analysis. In addition, the authors would need to check if any cluster was primarily originated from a single library, which could be indicative of library-specific bias (or batch effects).

3) Hem6 cells lack specific markers and some cells in this cluster are scattered throughout the other clusters (Fig. 1 & 2). Based on the pattern, it is possible that these cells are continuous subsets of other clusters. It would be good if the authors could group these cells with Hem7 or other clusters based on transcriptomic similarities or by changing clustering resolution. Additionally, they may also be a result of doublets, and it is unclear whether doublets were removed. Hem6 cells require additional measures to fully categorize as a unique subset.

4) The authors took advantage of FACS sorting, qRT-PCR, and microscopic observation to verify in silico analyses and defined R1 and R2 populations. While the experiments are appropriate to delineate differences between the two populations, it is not sufficient to determine agranulocytes as a premature population (Hem1-4) and granulocytes as differentiated subsets (Hem5-9). To better understand the two groups (ideally nine subtypes), additional in vivo experiments would be essential. For example, proliferation markers (BrdU or EdU) could be examined after FACS sorting R1 and R2 cells to show R1 cells (immature hemocytes) are indeed proliferating as indicated in the analyses.

5) FACS-sorted R1 or R2 population does not look homogeneous based on the morphology and having two subgroups under nine hemocyte subtypes may not be the most appropriate way to validate the data. The better way to prove each subtype is to use in situ hybridization to validate marker gene expressions and match with morphology.

Reviewer #2 (Public Review):

In this manuscript, Lamers et al wanted to characterise the previously reported adaptation of SARS-CoV-2 to non-human (Vero) cells. Vero cells are commonly used by laboratories to grow experimental stocks of some viruses as these cells permit high titres of many viruses, they lack the ability to produce type I interferons (cytokines which could interfere with downstream assays), and their non-human nature means soluble factors in virus stocks are less likely to impact experiments in human cells. However, a number of reports have recently been published describing that growth of SARS-CoV-2 in Vero cells leads to loss of the SARS-CoV-2 Spike protein multibasic cleavage site (MBCS). This apparent adaptation to the Vero cell-line leads to a virus compromised in its ability to enter, and therefore replicate in, human cells, meaning that experimental results obtained in human cells using the Vero-adapted SARS-CoV-2 may not fully reflect the situation occurring with authentic SARS-CoV-2. It is therefore important for the research community to understand SARS-CoV-2 adaptation to laboratory cell-lines/conditions and to have propagation methods that are suitable for maintaining the authenticity of clinical virus isolates.

The major finding of Lamers et al in this manuscript is that human cell-lines (e.g. Calu-3) and primary human organoid systems can be used to propagate clinical isolates of SARS-CoV-2 to high titres without the acquisition of 'laboratory adaptations'. To get to this finding, the authors carefully study the adaptation of a representative SARS-CoV-2 isolate in Vero cells, monitoring plaque size phenotypes and performing whole-genome deep sequencing to identify adaptive variants that appear in the viral Spike gene. These variants (including newly-described substitutions as well as deletions around the MBCS) are validated for their impact on viral infectivity in human and Vero cells using pseudovirus assays, fusion assays, and western blot assays, and their role in affecting the entry route of SARS-CoV-2 is dissected using pathway-specific inhibitors (such as camostat and E64D) and cell-lines with/without TMPRSS2 (an important protease for Spike cleavage). Importantly, using these assays and tools, the authors can make solid and well-reasoned arguments as to why SARS-CoV-2 adapts to Vero cells, and thus why certain culture conditions and cell substrates lead to a loss of SARS-CoV-2 genetic stability. Using similar tools, this also allows the authors to carefully study whether any adaptations occur when SARS-CoV-2 stocks are passaged in human cell substrates (such as Calu-3 or primary human organoids), and study culture conditions in Veros (such as expression of TMPRSS2) that prevent changes in SARS-CoV-2.

The data in this manuscript are thorough and well-presented. Importantly, the conclusions are strongly supported by the data, particularly the overall take-home message that human cell substrates can be used to efficiently propagate SARS-CoV-2 isolates without introducing cell culture adaptations. However, beyond this simple message, the manuscript also provides new mechanistic insights into the reasons for such viral adaptations in the Vero cell system, and identifies previously undescribed adaptations in the MBCS region that will be valuable for other researchers to take note of. The authors also describe a methodological workflow to produce SARS-CoV-2 in human cells that highlights a buffer-exchange step to remove potentially interfering human cytokines/debris, and which will be useful for other researchers.

Overall, the manuscript makes a clear and important contribution to the SARS-CoV-2 field and will be of interest to active researchers who are studying this virus experimentally.

Reviewer #1 (Public Review):

This manuscript, which follows on from a recent eLife paper documenting the relevance of the multi-basic cleavage site (MBCS) in the spike (S) protein of SARS-CoV-2, shows that growing SARS-CoV-2 on relevant epithelial cell lines or differentiated stem cell-derived culture systems prevents the emergence of MBCS mutations than impact on properties of S that contribute to cell tropism and the viral entry mechanism.

The paper builds on the authors previous work and that of others, and in some respects the results are not surprising. Nevertheless, the paper sets out a number of important findings. 1) That SARS-CoV-2 grown in Vero cells rapidly acquire MBCS mutations, where as virus grown in airway epithelial cells or Vero-TMPRSSR2 cells do not; 2) that deep sequencing is necessary to see mutations that are not apparent in consensus sequence reads, 3) that factors such as the addition of fetal calf serum can influence the selection of mutant phenotypes and 4) that cultures derived from differentiated stem cells can provide reproducible systems for virus culture. Together, the work sets out clear guidelines for the production of SARS-CoV-2, and potentially other viruses, avoiding the pitfalls that can arise from growing viruses in permissive transformed cell lines.

The data and manuscript are clearly presented, and my concerns are minimal. Overall, the paper will make a useful addition to the SARS-CoV-2 literature and will be of value to researchers working not just of SARS-CoV-2 but on many other viruses.

Reviewer #3 (Public Review):

In some species, supporting cells (SCs) of the cochlea can replace hair cells and thus restore hearing. In the mouse, neonatal SCs can also produce hair cells; however, this property is lost during early postnatal life. This study sought to test whether forced expression of two transcription factors normally associated with OHC development, Atoh1 and Ifzh2, can induce adult mammalian supporting cells to take OHC-like properties. Using Cre-dependent expression in mice, the authors showed that co-expression of Atoh1 and Izfh2 could induce a small number of adult SCs to express the OHC-specific gene, Prestin. This conversion was significantly enhanced when existing OHCs were ablated, in this case using a Prestin-DTR mouse model generated by the authors. A detailed phenotypic analysis combined with single cell RNA-sequencing (scRNA-seq) supports the idea that Atoh1/Izfh2 can partially convert adult SCs into OHC-like cells. However, the conversion is not complete, with immature bundles and a gene signature that resembles P1 OHCs (and sometimes E16 OHCs) more than P7/P30 OHCs or P60 SCs. Accordingly, the new OHCs are not sufficient to restore hearing in the Prestin-DTR mouse model. Together, these data encourage optimism that adult SCs can be steered along the OHC path, though clearly more manipulations will be needed to produce mature, functional OHCs.

The main weakness of the study is the scRNA-seq analysis, which depends on very small sample sizes. Suggestions to improve upon the analysis are listed under Specific Recommendations.

Reviewer #2 (Public Review):

The goal of this study is to devise a means of promoting adult mouse auditory sensory cell development from supporting cells (SCs), as occurs naturally in birds and fish following sensory cell death. Previous studies indicated that activating Atoh1, an early acting transcription factor that specifies sensory cell fate during embryogenesis, was not sufficient for such regeneration. The authors hypothesized that adding a second transcription factor, Ikzf2, which maintains outer hair cell (OHC) fate, would synergize with Atoh1 and push adult SCs to differentiate as OHCs. They tested this hypothesis by over-expressing both Atoh1 and Ikzf2 in supporting cells after killing the endogenous OHCs in adult cochleae. The authors showed that the induced cells first express the general HC marker, Myo6, and only later become Prestin-positive, much as occurs during normal development. Unfortunately, these induced OHC-like cells had abnormal stereocilia and did not restore auditory (ABR) thresholds. Moreover, there was a loss of IHCs (the primary auditory receptors) suggesting that much more is needed to induce a real OHC and to protect IHCs than simply inducing the two selected transcription factors. Single-cell RNAseq (scRNA-seq) results showed that the induced OHC-like cells are enriched for HC genes and depleted for SC genes, but overall are most similar to neonatal HCs as defined in published scRNA-seq data from other groups. Overall, the scRNA-seq data did not offer a clear path forward, other than to identify and test additional transcription factors that might push the induced cells to the next stage. Nevertheless, the extent of SC transformation is impressive and has not been seen in previous approaches. This is an important contribution to our understanding of the control of OHC gene expression and differentiation contributed by two important transcription factors.

Reviewer #1 (Public Review):

Mature mammalian hair cells in the cochlea do not regenerate after damage. The outer hair cells of the cochlea, which function to amplify sound, are particularly susceptible to damage. Ectopic activation of two key transcription factors for outer hair cell formation, Atoh1 and Ikzf2, in damaged adult cochlea is sufficient to convert supporting cells into hair cells expressing Prestin, which is an essential protein mediating outer hair cell functions. Although there is no functional recovery in these transgenic mice based on auditory brainstem response, this study paves the way for future design of models for hearing recovery. The main concern is the identity of the OHC-like cells drawn from the small sample size in the scRNA-seq experiments.

Reviewer #3 (Public Review):

Pettmann et al. aimed at significantly improving the accuracy of SPR-based measurements of low affinity TCR-pMHC interactions by including a 100% binding control (injecting of a conformation-specific HLA-antibody) in the surface plasmon resonance protocol. Interpolating with the information of saturated pMHC binding on the chip The authors arrive at KDs for low affinity binders that are significantly higher than the previously reported constants. If correct, this has considerable ramifications for the interpretations of the results obtained from functional assays measuring the T cell response towards pMHCs featured in a titrated fashion. Unlike what was put forward by earlier reports, the authors conclude that the discriminatory power of TCRs is far from perfect, as T cells still respond to low affinity pMHC-ligands without a sharp affinity threshold. This is also because they managed to detect T cells responding to even ultra-low affinity ligands if provided in sufficient numbers.

The body of work convinces in several regards:

(i) It is exceedingly well thought out and introduces a quality of analytical strength that is absent in most of the literature published thus far on this topic.

(ii) At the same time theoretical arguments are bolstered by a large body of experimental "wet" work, which combines a synthetic approach with cellular immunology and which appears overall well executed.

(iii) The data lead to hypotheses in the field of T cell antigen recognition in general and in the theatre of autoimmunity, cancer and infectious diseases.

There are a few aspects that may limit the impact of the study. I have listed them below:

(i) The study does not provide kinetic data for the low affinity ligand-TCR binding but rather argues from the position of affinities as determined via Bmax. This limits somewhat the robustness of the statements made with regard to kinetic proofreading.

(ii) Thresholds for readouts were arbitrarily chosen (e.g. 15% activation). It appears such choices were based on system behavior (with the largest differences observed among the groups) but may have implications for the drawn conclusions.

In summary, the work presented contributes to demystifying the link between TCR-engagement and (membrane proximal) signaling. It also provides a fresh perspective on the potential of TCR-cossreactivity.

Reviewer #2 (Public Review):

The paper revisits the question of ligand discrimination ability of TCRs of T cells. The authors find that the commonly held notion of very sharp discrimination between strongly and weakly binding peptides does not hold when the affinities of the weak peptides are re-measured more accurately, using their own new method of calibration of SPR measurements. They are able to phenomenologically fit their results with a ~2 step Kinetic Proofreading model.

It is a very carefully researched and thorough paper. The conclusions seem to be supported by the data and fundamental for our understanding of the T cell immune response with potentially very high impact in many scientific and applied fields. The calibration method could be of potential use in other cases where low affinities are an issue.

As a non-expert in the details of experimental technique, it is somewhat difficult to understand in detail the Ab calibration of the SPR curve - which is a central piece of the paper. The main question is - what are the grounds (theoretical and/or empirical) to expect that the B_max of the TCR dose response curve will continue to be proportional to the plateau level of the Ab. Figure 1D does suggest that, but it would be hard to predict what proportionality shape the curve will take for lower affinity peptides. Given that essentially all the paper claims rest on this assumption, this should explained/reasoned/supported more clearly.

On the theoretical side - I think the scaling alpha\simeq 2 in Figure 2 is indeed consistent with a two-step KPR amplification. However, there are some questions regarding the fitting of the full model to the P_15 of the CD69 response. As explained in the Supplementary Material the authors use 3 global and 2 local parameters resulting in 37 (or 27) parameters for 32 data points. To a naive reader this might look excessive and prone to overfitting. On the other hand, looking at Figure S8 shows the value ranges of lambda and k_p are quite tight. This is in contrast to gamma and dellta that look completely unconstrained.

Finally, one of the stated advantages of the adaptive proof-reading model is that it is capable of explaining antagonism. It is hard to see how a 'vanilla" KPR model is capable of explaining antagonism.

Reviewer #1 (Public Review):

The presented manuscript takes a comprehensive and elaborated look at how T cell receptors (TCR) discriminate between self and non-self antigens. By extending a previous experimental protocol for measuring T cell receptor binding affinities against peptide MHC complexes (pMHC), they are able to determine very low TCR-pMHC binding affinities and, thereby, show that the discriminatory power of the TCR seems to be imperfect. Instead of a previously considered sharp threshold in discriminating between self and non-self antigen, the TCR can respond to very low binding affinities leading to a more transient affinity threshold. However, the analysis still indicates an improved discrimination ability for TCR compared to other cell surface receptors. These findings could impact the way how T cell mediated autoimmunity is studied.

The authors follow a comprehensive and elaborated approach, combining in vitro experiments with analytical methods to estimate binding affinities. They also show that the general concept of kinetic proofreading fits their data with providing estimates on the number of proofreading steps and the corresponding rates. The statistical and analytical methods are well explained and outlined in detail within the Supplemental Material. The source of all data, and especially how the data to analyze other cell surface receptor binding affinities was extracted, are given in detail as well. Besides being able to quantify TCR-pMHC interactions for very low binding affinities, their findings will improve the ability to assess how autoimmune reactions are potentially triggered, and how potent anti-tumour T cell therapies can be generated.

In summary, the study represents an elaborated and concise analysis of TCR-pMHC affinities and the ability of TCR to discriminate between self and non-self antigens. All conclusions are well supported by the presented data and analyses without major caveats.

Reviewer #3 (Public Review):

Sun et al have assembled, modified, and applied a series of existing gene editing tools to tissue-derived human fetal lung organoids in a workflow they have termed "Organoid Easytag". Using approaches that have previously been applied in iPSCs and other cell models in some cases including organoids, the authors demonstrate: 1) endogenous loci can be targeted with fluorochromes to generate reporter lines; 2) the same approach can be applied to genes not expressed at baseline in combination with an excisable, constitutively active promoter to simplify identification of targeted clones; 3) that a gene of interest could be knocked-out by replacing the coding sequence with a fluorescent reporter; 4) that knockdown or overexpression can be achieved via inducible CRISPR interference (CRISPRi) or activation (CRISPRa). In the case of CRISPRi, the authors alter existing technology to lessen unwanted leaky expression of dCas9-KRAB. While these tools have previously been applied in other models, their assembly and demonstrated application to tissue-derived organoids here could facilitate their use in tissue-derived organoids by other groups.

Limitations of the study include:

1) is demonstrated application of these technologies to a limited set of gene targets;

2) a lack of detail demonstrating the efficiency and/or kinetics of the approaches demonstrated.

While access to human fetal lung organoids is likely not available to many or most researchers, it is probable that the principles applied here could carry over to other organoid models.

Reviewer #2 (Public Review):

There is now a considerable body of knowledge about the genetic and cellular mechanisms driving the growth, morphogenesis and differentiation of organs in experimental organisms such as mouse and zebrafish. However, much less is known about the corresponding processes in developing human organ systems. One powerful strategy to achieve this important goal is to use organoids derived from self-renewing, bona fide progenitor cells present in the fetal organ. The Rawlins' lab has pioneered the long-term culture of organoids derived from multipotent epithelial progenitors located in the distal tips of the early human lung. They have shown that clonal cell "lines" can be derived from the organoids and that they capable of not only long-term self-renewal but also limited differentiation in vitro or after grafting under the kidney capsule of mice. Here, they now report a strategy to efficiently test the function of genes in the embryonic human lung, regardless of whether the genes are actively transcribed in the progenitor cells. The strengths of the paper are that the authors describe a number of different protocols (work-flows), based on Crisper/Cas9 and homology directed repair, for making fluorescent reporter alleles (suitable for cell selection) and for inducible over-expression or knockout of specific genes. The so-called "Easytag" protocols and results are carefully described, with controls. The work will be of significant interest to scientists using organoids as models of many human organ systems, not just the lung. The weaknesses are that they authors do not show that their lines can undergo differentiation after genetic manipulation, and therefore do not provide proof of principle that they can determine the function in human lung development of genes known to control mouse lung epithelial differentiation. It would also be of general interest to know whether their methods based on homologous recombination are more accurate (fewer incorrect targeting events or off target effects) than methods recently described for organoid gene targeting using non homologous repair.

Reviewer #1 (Public Review):

The authors demonstrate applications including fluorescent marking of membranes with GFP or monomeric RFP, reporter alleles for convenient assessment of differentiation status based on fluorescence, and targeted gene knockout. They also demonstrate conditional gene knockdown and induction with tight control achieved by engineering a protein destabilizing domain. The design of the constructs is clever and imparts the ability to leverage iterative FACS to enrich successfully targeted cells, particularly useful when targeting alleles that are not actively expressed by the progenitors. The work is well done and clearly presented.

Reviewer #3 (Public Review):

Moncla et al. investigated the transmission of mumps virus in Washington, USA during an outbreak in 2016-2017. They sequenced viral genomes from infected individuals in Washington and elsewhere within the United States and used phylogenetic approaches to understand the origins and patterns of spread exhibited by the virus during the outbreak. They observe a large fraction of cases in individuals who are part of the Marshallese community, and identify a link to a similar outbreak in the Marshallese community in Arkansas. They develop a method for determining the role of the Marshallese community in the Washington outbreak that is robust to sampling bias and size. This method is well thought-out and presented and demonstrates that the outbreak in Washington state was sustained by transmission within this particular community. This paper provides a thoughtful approach to dealing with sampling issues that are often overlooked in phylogenetic studies. By consulting with a public health professional from within the affected (Marshallese) community, the authors are able to contextualize their results and demonstrate the underlying issues that may have contributed to mumps spread within the state.

Working with public health advocates from affected communities is exceptionally important for long term public health impact, and this paper sets an example that should be followed by others in the pathogen genomics field. The methodology used to determine mumps transmission patterns in Washington is sound and the conclusions are well explained. However, some additional context on the issues and potential pitfalls of source-sink analyses based on phylogenetic inference would help improve this already solid paper. Specifically:

1) The authors seem to assume a somewhat random sample throughout Washington state. They state that given a low sampling proportion they do not expect to have captured infection pairs, which seems reasonable. However, they then go onto assume that their sample is primarily comprised of samples from long, successful transmission chains. This is a reasonable assumption if there is no major difference in accessibility of samples from long transmission chains and shorter ones (for example, decreased access to healthcare). Could this impact the assumption of sampling primarily from long transmission chains? It seems from the data collected in this outbreak that this was not the case for mumps in Washington but addressing this assumption clearly (and potential ways to interrogate it) could make their methodology more applicable to other pathogen studies.

2) There are many examples of phylogenetic analyses that have led to conclusions about pathogen sources and sinks that were later shown to be wrong because of oversampling or other sampling biases. The authors address unequal sampling between clades, but additional contextualization of the problem and how this approach is different may help strengthen the methodology presented in the paper.

3) The authors present compelling evidence that the mumps outbreak in Washington state was sustained by the Marshallese community, and state that mumps did not transmit efficiently among the general Washington populace. That said, there were several other mumps outbreaks in the United States in the same 2016-2017 time period. Was there something different about Washington state that prevented mumps transmission outside of the Marshallese community? Were there no other close-knit communities (universities, prisons, other cultural communities, etc.) affected? It just seems surprising that the Marshallese community was the only community sustaining transmission at a time where many different types of communities were affected across the United States.

Reviewer #2 (Public Review):

In this manuscript, Moncla et al. undertake a large sequencing and phylogenetic study to investigate the underlying epidemiology of the 2016-2017 Washington State Mumps epidemic. The authors generate 110 sequences and include 166 novel sequences in their analysis. This data set represents over a quarter of the publicly available Mumps genomes from North America.

They then apply a mixture of phylogenetic methods and intuitive data analyses to uncover, that i) Mumps was imported into Washington at least 13 times. ii) A disproportionate amount of transmission occurred in the Marshallese community in WA with limited transmission in the non-Marshallese community. iii) These heterologous transmission dynamics might be explained by historical and current health disparities within the community, but are not due to low vaccination coverage.

These conclusions are supported by a wide array of carefully controlled phylogenetic methods. The authors explore the sensitivity of their findings to sampling bias. Additionally, the conclusion that transmission occurred disproportionally within the Marshallese community is supported by multiple implementations of the structured coalescent as well as, more coarse but intuitive methods such as the rarefaction analysis and the "descendent" analysis in Figure 4. The "descendent" analysis complements the structured coalescent models and highlights how tips that are close to internal nodes inform the "state" of those unsampled ancestors. Each internal node represents an unsampled ancestor, and if transmission rates are higher in one population, then samples from that population are more likely to be close to those ancestors. The approach captures these processes; however, calling downstream tips "descendants" is unfortunate, as it is unknown if the tips that have "descendants" are direct ancestors of their "descendants" in the transmission chain. Inferring transmission dynamics from divergence trees is difficult, and variants of this approach are likely to be useful in other systems.

The finding that transmission disproportionally occurred in the Marshallese community leads the authors to propose several possibilities for why this may be. The authors should be commended for reaching out to Marshallese health advocates in this process and including the community in their study. This context is a major strength of the study.

Both the data generation and data analysis are achievements that advance our understanding of the epidemiology of Mumps. As can be seen in the tree in Figure 1 the 2016-2017 epidemic in North America was seeded by at least two divergent lineages that appear to have all contributed to the same outbreak. The large number of sequences contributed by this study will help future work uncover the dynamics that drive Mumps epidemics at larger scales. The findings also highlight how large outbreaks can persist in highly vaccinated populations and how an array of phylogenetic approaches can be employed to uncover the underlying population heterogeneity behind an outbreak. To have both of these achievements in the same manuscript sets this work apart.

Reviewer #1 (Public Review):

In this study, Moncla et al. used genomic data to analyse a mumps outbreak in Washington, in order to draw inferences about the epidemiological factors driving the outbreak. Some important strengths of the analysis include sophisticated sequencing and modeling techniques to reconstruct chains of transmission during the outbreak, which support the conclusions that the mumps virus was introduced several times in Washington from other North American regions during the outbreak, and that the Washington Marshallese community was particularly at risk of mumps infection and transmission during this time. Limitations of the analysis include potential for sampling bias, where the sample may not be entirely representative of mumps outbreak cases, and a sample size that is too low to allow sufficient statistical power to assess the impacts of age and vaccination status on transmission. The work has potential public health impacts in terms of identification of a vulnerable community and points to social networks as the primary risk factor for potential future respiratory virus outbreaks. The analysis methods could be potentially applied for the phylodynamic analysis of other infectious disease outbreaks.

Reviewer #2 (Public Review):

In this incredibly detailed effort, Hulse, Haberkern, Franconville, Turner-Evans, and coauthors painstakingly and patiently reveal the connectivity of central complex neurons within one "hemibrain" EM-imaged connectome of a fruit fly. This is best read as one of a series of such detailed papers including Scheffer et al., 2020 (which introduces the dataset) and Li et al., 2020 (which focuses on the mushroom body).

The authors achieve two major goals. First, they present a full account of all neurons (by type) present in the central complex and the connections between them (including to and from regions outside the central complex). By necessity, this work only examines such connections within a single animal from whose brain the hemibrain volume was imaged. Nonetheless, the relatively conserved morphology of fly neurons (at the scale of which regions they form arbors within) allows the authors to confidently relate their neurons to known examples from genetically labeled lines imaged at the light level. (And in some cases, they are able to show that some neurons with similar morphology can then be further subdivided into different types on the basis of their connectivity). Importantly, the hemibrain dataset contains both sides of the central complex, allowing for a complete analysis.

Secondly, the authors contextualize the observed connectivity patterns within the known functions of the central complex (particularly navigation and sleep/arousal). Appropriately and importantly, they offer detailed explanations for how the circuitry observed can support these functions. In some cases, particularly in their discussion of the fan body, they show how the connectivity patterns can support multiple variations of models of path integration (and more broadly how its architecture supports vector computation in general). These analyses make their central complex connectome a useful map - there is little doubt that it will inspire many future experiments in the fly community.

The only limitations of this work are rooted in the nature of the source material: it's only one animal's brain and because it's EM-based there's often no way to know whether a given cell type (if new) is even excitatory or inhibitory (though, notably, the authors take care to note where this is the case and to offer alternate interpretations of the circuit function). Synaptic strength is another relative unknown (not to mention plasticity rules or modulatory influences). For EM-based connectomes, the number of synapses made between two neurons is considered the basis for determining whether or not they are meaningfully connected. However, this precise number can vary as a function of how complete the reconstructions are (generally, as proofreading progresses, more synapses are found). This work improves on prior hemibrain studies by carefully demonstrating that it is possible to set a threshold on the relative fraction of synaptic contributions within a region in order to identify meaningful connections. (That is, they find that as the number of synapses discovered increases, the relative contribution remains relatively constant).

This is a massive work. There are 75 figures, not including supplements, and numerous region and neuron names to keep track of (not to mention visualize). It is impossible to read in a single sitting. So for the purposes of this public review, I highly recommend to any reader that they first find the region of the paper they're interested in and skip to that to view in side-by-side mode. The "generally interested" reader is best served by reading through the Discussion, which has more of the structure-function analyses in it and then referring to the Results as their curiosity warrants.

Scheffer et al., 2020 is available here: https://elifesciences.org/articles/57443#content Li et al., 2020 is available here: https://elifesciences.org/articles/62576#content

Reviewer #1 (Public Review):

It is difficult to overestimate the importance of this paper. The full connectome of the Drosophila central complex is both the beginning and the end of an era. It provides the first comprehensive dataset of arguably the most enigmatic brain region in the insect brain. This endeavor has generated ground truth data for years of functional work on the neural circuits the connectome outlines, and constitutes an unparalleled foundation for exploring the structure function relations in nervous systems in general. This will be of great importance far beyond work on the Drosophila brain, and will have far reaching implications for comparative research on insect brains and likely also smoothen the path toward understanding navigation circuits in vertebrate nervous systems. Based on presented data, the paper develops overarching ideas (at exquisite detail) of how sensory information is transformed into head direction signals, how these signals are used to enable goal direction behavior, how goals are represented, and how internal state can modulate these processes. The connectome enables the authors to base these ideas and their detailed models on actual biological data, where earlier work was forced to indirectly infer or speculate. While significantly going beyond models of central-complex function that existed previously, the authors have to be much credited for incorporating huge amounts of existing knowledge and data into their interpretations, not only work from Drosophila, but also from many other insects. This makes this paper not only an invaluable resource on the connectome of the Drosophila central complex, but also a most comprehensive review on the current state of the art in central-complex research. This unifying approach of the paper clearly marks a reset of central-complex research, essentially providing a starting point of hundreds of new lines of enquiry, probably for decades to come.

Given the type and amount of data presented, the paper is clearly overwhelming. That said, it also clearly needs to be presented in the way it was done, mostly because no single aspect of the function of this neuropil makes as much sense in isolation as it makes sense when viewed in conjunction of all its other functions. The complexity of the neural circuits discussed is clearly reflected in the enormous scope of the paper. Nevertheless, the authors have done a fantastic job in breaking the circuits and their function down into digestible bits. The manuscript is very systematic in its approach and starts with sensory pathways leading to the CX, covering the clearly delineated head direction circuits and then moving on to the more complex and less understood parts, always maintaining a clear link between structure and function. As function is necessarily based on previous work, including that from other species, the results part is interwoven with interpretation, but this is clearly necessary to keep the text readable. The authors have made considerable efforts to provide additional introductions and summaries whenever needed, almost creating nested papers embedded within the overall paper.

The figures are equally overwhelming as the text at first sight, but when taking the time to digest each one in detail, they present the data in a rich and clear manner. The figures are often encyclopedic and will serve as reference about the central complex for years. The summary graphs that are presented in regular intervals are welcome resting places for the reader, helping to digest all the detailed information that has preceded or that will follow.

The analysis performed in the paper is excellent, comprehensive and should set the standard for any future work on this topic. Also, the text is very honest about the limits of the conclusions that can be reached based on this kind of data, which is important in generating realistic and feasible hypotheses for future experiments.

Reviewer #3 (Public Review):

The authors investigated pupillary response looking at the changes corresponding to perceptual events (spontaneous or physical changes) and contrasting them with requirements of over reporting (changes were reported or ignored). They demonstrate that the former is associated with a rapid constriction and re-dilation, whereas the latter shows an opposite effect with dilation being followed by re-constriction. The particular strength of the work is in no-report conditions using on OKN-based inference about timing of perceptual events that allowed for this dissociation to be observed, whereas manual report conditions allowed for a direct comparison with prior work. The analysis and control experiments are very thorough showing that reported results are unlikely to be explained other factors such as saccades or blinks.

The study makes a significant contribution but proposing a no-report paradigm for identifying perceptual events that should work for any multistable display. The fairly rapid pupil constriction event could provide an easy to detect and temporally reliable marker of perceptual switches, expanding ways the multistability data is collected. The same approach could also be useful for no-report studies of visual awareness in general.

The ability to decompose pupillary response into two components - perception and over manual response - will also be useful for studying neural correlates of spontaneous perceptual switches, as it could help to better understand switch-time activity in various frontal and parietal regions. Here, also some regions are associated with active response, whereas other with perception, distinction that could be potentially better understood based on the idea that only the former involves noradrenaline-affected processing. My main worry methodologically is the under and overestimation of mean switch rate via OKN (figure 1C). OKN estimates are all within .4-.8 range, whereas for self-report rates differ from 0.2 to over 1. Further analysis would be helpful. I think it would be helpful if the authors elaborated on what kind of switches went unreported (or, conversely, what kind of events led to false alarms): switches before very short dominance phases (could be to fast to report via key presses), to return transitions, etc.

Reviewer #2 (Public Review):

Pupillometry is an increasingly accessible tool for the non-invasive readout of brain activity. However, our understanding of pupil-control circuits and of the relationship between changes in pupil size and perception, cognition or action, is far from complete. Therefore, any measurements that further this understanding are of great interest to a wide audience in the field of psychology and neurobiology.

This study used pupillometry to explore the neural processing that underlie perception and dissociate those from action-related neural processing. The authors use a novel and comprehensive task design, centered on binocular rivlary, that is likely to find wider use among researchers studying the neural processes that underlie perception and action. They used a non-invasive method (pupillometry) to disscociate putative processes and circuits that might drive perceptual switching. They found changes in pupil size that are reliably different depending on the task: for example - between the conditions that require reporting a perceptual switch versus not reporting it and between rivalrous and explicit changes in the visual stimulus.

Such approaches can be very useful in deciphering which of the myriad factors that can affect pupil size are in fact active under specific, controlled conditions and thus provide a basis for guided, direct measurements of these specific brain regions.

Overall, this study is well-conceived and executed. However, I have some questions and concerns about the analyses and conclusions made from the results shown. In general, I would encourage the authors to try and include more of what we do know about neuromodulation and the cortical control of pupil pathways to frame the hypothesis and interpret the results. Further, it is unclear to me whether the constriction/dilation dissociation is tenable with the presented data and analyses.

Reviewer #1 (Public Review):

Brascamp and colleagues address pupil-size changes around perceptual switches in perceptual multistability. Several previous studies have found pupil dilation around or after the switch and some have found pupil constriction, though the latter was typically less robust. Moreover, while most previous studies included some controls for the effect of reporting and for the physical stimulus change, to my knowledge, so far, no study has fully crossed the factors report/no-report and endogenous/exogeneous switch. In the present study, this gap is filled using a binocular-rivalry stimulus and an OKN-based no-report paradigm. This allows the authors to isolate the constriction component from the dilation component and interestingly they find the constriction more robustly tied to the perceptual switch, while the dilation component is mostly related to the response. Experiments are soundly conducted and analysed and results are interpreted with appropriate care. Since the results challenge frequent interpretations as to why perceptual switches in multistability may cause pupil-size changes, the paper is of high relevance to the fields of pupillometry and multistability, but also to other areas where pupillometry is used as index of perceptual and cognitive processes. I only have some minor questions and requests for clarification with regard to result presentation and interpretation.

Reviewer #4 (Public Review):

Using a transgenic line of Platynereis, in which GFP is expressed under the control of cis-regulatory elements for r-opsin, the study isolates r-opsin expressing cells from the head (eye photoreceptors) and trunk region (a population of segmentally repeated r-opsin expressing cells associated with the parapodia) by FACS. Subsequent RNA-Seq establishes that both populations of cells express genes for all components of the rhabdomeric phototransduction cascade, while the population of trunk sensory cells additionally expresses genes encoding proteins involved in mechanosensation. Using heterologous expression in a mammalian cell line, it is shown that the Platynereis r-opsin responds to blue light via coupling to Gαq suggesting that it mediates photoresponses via a canonical rhabdomeric phototransduction cascade. Transcriptomic analysis of an r-opsin mutant created by TALEN mediated gene editing then reveals that expression levels of the mechanosensory Atp2b channel are modulated by protracted exposure to blue light, a response abolished in the mutant. Behavioral analysis further suggests that undulatory movements of the worms are equally altered under these illumination conditions. Taken together this suggests that the r-opsin expressing trunk sensory cells act as both photo- and mechanoreceptors and that their mechanosensory properties are modulated in response to light. In combining the transcriptomic analysis of cell types with experimental studies of gene function and behavioral analyses, this study provides exciting new insights into the evolution of sensory cells. Several prior studies have found co-expression of photosensory and mechanosensory proteins in sensory cells of various bilaterians, and comparative studies suggested that photo- and mechanosensory cells may share a common evolutionary origin. However, the current study goes far beyond these findings in establishing a direct functional link between photo-and mechanosensation in a population of sensory cells suggesting that these sensory cells function as multimodal cells and that their mechanosensory properties are altered in response to light. Furthermore, the behavioral data (based on a novel machine-learning based tool of analysing the animals' movement) suggest that these cells have a behaviorally relevant function. Because r-opsin was found to be expressed in mechanoreceptors not only in lophotrochozoans (including Platynereis) but also in ecdysozoans and vertebrates (although functional studies are lacking here) and r-opsins belong to a large family of opsins, almost all of which are responsive to light, the present study suggests that r-opsins may have an ancestral bilaterian role in modulating mechanosensory function in response to light (in addition to their purely photosensory role in the photoreceptors of the eyes). Light-independent functions of r-opsin as recently revealed in Drosophila may, thus, be secondarily derived.

The study is very carefully conducted and well presented. The only minor flaw is that in its present form, the discussion of the evolutionary implications of the finding lacks in clarity and specificity. The authors here often refer ambiguously to an "ancient" or "ancestral" role of r-opsins without specifying the lineage referred to (ancestral for lophotrochozoans? bilaterians? eumetazoans? metazoans?). The discussion should, therefore be revised with an explicit phylogenetic framework in mind.

Reviewer #3 (Public Review):

Opsin proteins are ancient light-sensitive molecules found in photoreceptor cells throughout the animal kingdom. Recent discoveries including those made in the current paper have revealed that besides r-opsins, some classes of photoreceptor cell also express genes that are found in mechanosensory cells, and that r-opsins have both light-dependent and light-independent effects on mechanical force transduction or motion. A question remains as to whether or not: 1) a protosensory cell of animals existed which contained both photoreceptor and mechanoreceptor-like features and, 2) whether the original function of opsin included light-dependent mechanosensory features? The authors consider three competing hypotheses for the cellular evolution of photoreceptor and mechanosensory function. Two of the hypotheses envision either photo- or mechanosensory function for opsins evolving first, the third imagines them evolving simultaneously. The authors note that the majority of what we know about rhabdomeric opsins comes from studying the eye photoreceptors of the fruit fly, Drosophila melanogaster. But might this kind of photoreceptor have functions that are derived compared to the ancestral photoreceptor cell? To investigate this question, the authors turn to the non-model system, Platynereis dumerilii, which has both head and non-head photoreceptors. Here the authors use 1) a fluorescent cell sorting method to perform RNA profiling of eye and trunk photoreceptor cells of a mutant marine worm and find evidence of co-expression of photo- and mechanosensory genes in photoreceptor cells. They also compare the genes that are expressed in Platyneris photoreceptors with genes expressed in Drosophila JO (hearing organ in flies), Zebrafish lateral lines and mouse IEH (inner ear hair) cells, and again they find some commonly-expressed genes. 2) The authors use cell culture to express the opsin, demonstrate that it interacts with G-alphaq, and that it's peak sensitivity is in the blue range. 3) They use in situ hybridization to validate the RNA-seq and detect select enriched transcripts in the photoreceptor tissues. 4) They use a new method, which should be widely useful to other researchers, to detect undulation behavior of the opsin mutant vs. wildtype worms and show that the mutant worm behavior is perturbed in altered light cycles. Taken together, the authors suggest that an ancient light-dependent function of opsin was linked to mechanosensation and that light-independent mechanosensory functions of opsins evolved secondarily. The interpretation is somewhat reasonable given the available data but does not yet entirely rule out other possibilities (see below).

This paper is a tour-de-force and a really impressive collection of experiments which examines the function of r-opsin in Platyneris. There's lots of innovation here from the use of fluorescent cell sorting and cell-specific RNA-Seq on a non-model system to the deep-learning based approach to examining behavior. Overall, the authors' interpretation of their data seems reasonable however I do believe a even stronger case could be made that what we are talking about is shared ancestry vs. recent recruitment if the authors made phylogenetic trees of the numerous TRE genes that are enriched between Drosophila JO and mouse IEH cells. If a significant number of these genes were true orthologs vs. paralogs across all three species then this would provide stronger evidence of an ancient light-dependent mechanosensory function for r-opsin. GO enrichment terms, while intriguing and suggestive, don't go far enough into the weeds. Also, I think the estimate of there being only 12 genes involved in making a photoreceptor cell able to detect light is probably an underestimate, as this ignores, for example, the understudied molecular machinery required for chromophore metabolism and transport. At the very least, the work should help inspire vigorous debate between vision and auditory neuroscience communities (which do not usually converse with one another) to more carefully consider the ways in which their systems overlap and why.

Reviewer #2 (Public Review):

Rhabdomeric Opsins (r-Opsins) are well known for their role in photon detection by photosensory cells which are commonly found in eyes. However, r-Opsin expression has also been detected in non-photosensory cells (e.g., mechanosensors), but their function(s) in these other sensory cells is less well understood. To explore the function of r-Opsins outside the context of an eye/head (non-cephalic function) as well as to investigate the potential evolutionary path by which sensory systems that rely on r-Opsins have evolved, Revilla-i-Domingo et al. have investigated gene expression in two distinct subsets of r-Opsin expressing cells in the marine bristle worm Platynereis dumerilii : EP (eye photoreceptor) and TRE (trunk r-opsin1 expressing) cells. The authors also generate two Pdu-r-Opsin1 mutant strains in order to investigate how the loss of r-Opsin function affects gene expression and behavior.

The question of what role r-Opsins play outside of photoreceptors is an interesting one that remains poorly understood. In this manuscript, the authors demonstrate a powerful protocol for FACS sorting and sequencing different cell populations from an important evolutionary model organism.

The transcriptomic analysis presented here demonstrates that both the cephalic EP cells and the non-cephalic TRE cells express components of the photosensory transduction pathway. This observation, together with heterologous cell expression data presented demonstrating sensitivity of Pdu-r-Opsin1 to blue light, suggests that both EP and TRE cells are likely to be light sensitive. The authors also suggest that they observe "mechanosensory signatures" in the transcriptomes, which, together with the analysis of undulatory movements in headless animals, lead them to suggst that r-Opsin in TRE cells functions as an evolutionarily conserved light-dependent modulator of mechanosensation, a conclusion that is not well-supported by the data presented.

Overall, many of the conclusions drawn from the transcriptome data are inferential and based on weak evidence. Key limitations are listed below:

1) The apparent overlap between the phototransduction and mechanosensory systems has already been shown (in Drosophila for instance) and the current work adds limited information to this story, and what is added is weakened by the absence of functional and physiological analyses. This is particularly true for supporting the claims of mechanosensory signatures in these cells. For example, genes whose expression is suggested in the text as being indicative of a mechanosensory function (glass and waterwitch) are, in fact, expressed in multiple sensory cell types. Glass (gl) is a transcription factor best known for regulating the expression of phototransduction proteins in photoreceptors. The function of waterwitch (wtrw) is not fully understood, but it is broadly expressed in sensory cells in Drosophila. It would be more compelling if mechanotransduction channels like Piezo and NompC were expressed in the TREs, but there is no mention of this.

2) The suggestion that the TRE cells share similarity with the mechanosensitive mammalian inner ear is provocative, but lacks strong support. For instance, physiological characterization of the response properties of these sensory cells or identification of anatomical similarities analogous to the stereocilia upon which hair cell mechanosensitivity is based would greatly increase plausibility of this claim. Particularly for a species that diverged from mice and flies many hundreds of millions of years ago, speculation based largely on transcriptome analysis is risky. Careful validation is required as identified genes might not share a conserved function with their assigned orthologs in mice and Drosophila.

3) The current analysis lacks sufficient power to make compelling claims with regard to potential ancestral protosensory cells. The investigators are examining a single species of marine worm and doing so without detailed anatomical and functional studies of the r-Opsin-expressing cells in the worm.

4) The behavioral experiments require more functional data to interpret unambiguously. The data indicate that r-opsin1 is required for light to surpress the undulation of decapitated worms. Does this mean that the TREs are photosensors whose activity inhibits locomotion or that the TREs are light-sensitive mechanosensors ?

5) It is assumed that the TREs constitute a homogenous cell population, but this is not demonstrated. This means that the TREs could be a mixed population (for example, distinct sets of photosensors and mechanosensors) and some of the TRE-expressed genes identified could be expressed in different specific subset of TREs.

Reviewer #1 (Public Review):

Strengths and Weaknesses. The authors did quite a lot to establish gene expression and function of the annelid's trunk cells and compare them to photoreceptors of the annelid's eye. They isolated the cells with FACS and characterized gene expression in detail, they knocked down r-opsin with TALEN in the trunk and found a significant difference in a crawling response, and they express the opsin in cell culture to confirm wavelength and G-protein sensitivity. As a potential link between light sensitivity and mechano-sensitivity, they report r-opsin function and light intensity influence expression of atp2b2, a gene that modulates neuronal sensitivity in other organisms. Wavelength and G-protein activation data are valuable because I can think of few or no other organisms in the entire group of lophotrochozoan animals, where this level of experimental manipulation could be done. In short, a strength of this manuscript is the detailed characterization of the trunk receptor cells, which express r-opsins. The authors have brought much evidence to the claim that these TRE cells have both light and mechano-sensitive gene expression and function. Based on these findings in an annelid worm, I believe the paper is a significant advance, and of interest to a broad audience by adding to a growing set of discoveries of similar hybrid sensory cells.

If a hybrid mechano/photo-receptor is indeed an ancient cell type in bilaterians, this would bring many evolutionary implications for sensory biology. However, in these evolutionary interpretations is where I find a weakness of the manuscript. Namely, with only a handful of species shown thus far to have the hybrid cell type - and many differences in detail about these cell types in different organisms - we can not yet make firm conclusions about whether the multi-functional cells were ancestral. I believe other interpretations are equally valid (and still interesting) and should be given more consideration. Namely, it seems possible that photo- and mechan- sensory processes "joined forces" (e.g. through separate co-option events) in new cell-types, multiple times during evolution. The current manuscript loosely indicates ancestral multi-functionality is more parsimonious. However, no detail is given about that. I suppose the authors mean a single origin of hybrid cell types requires fewer evolutionary transitions than multiple origins. However, such a parsimony count does not count the transitions requiring loss of phototransduction in mouse hearing and do not count transitions to loss of mechanosensitivity in eye photoreceptor cells.

Reviewer #3:

The authors hypothesized lower GABA levels in older adults would influence cortico-cortical phase relationships more than cortico-muscular phase relationships during performance of a bimanual motor task. To this end, they evaluated the mediating role of endogenous bilateral sensorimotor cortex GABA content in relation to behavioral performance and patterns of interhemispheric and cortico-muscular electrophysiological phase coherence during a bimanual motor control task. The central finding was that the mediating influence of right M1 GABA on the relationship between cortico-cortical electrophysiology and behavior diverged between the younger and older groups, with lower endogenous GABA concentrations potentially benefitting bimanual motor performance in young adults and hindering performance in older adults. The result was specific to right M1 GABA, raising questions about hemispheric asymmetry, and behavioral performance differed substantially between groups, possibly influencing the sensitivity of the analyses of the electrophysiological phase relationships. Moreover, several earlier studies suggest endogenous M1 GABA content relates to cortico-muscular excitability measurements, other than phase synchrony, and it is unclear what distinguishes phase synchrony from these other measurements. The behavioral, MRS, and electrophysiological methods employed are fairly well-established and are combined in a novel manner. The Bayesian moderated mediation analysis represents a new approach to evaluating relationships between these measures under the moderating influence of age. The central questions concerning the roles of cortical endogenous GABA in bimanual control, and in age-related changes in motor control more generally, are important for determining the neural computations underlying flexible and precise behavior.

1) The total number of finger taps within the 2000 ms transition epoch likely differed between groups and could influence the ISPC measures. It would be helpful to rule out this possibility by examining relationships between ISPC measures and the total number of taps.

2) The differences between right and left M1 are somewhat surprising and merit further attention, particularly given the cortico-cortical ISPC results. The interpretation provided in the discussion (lines 607-618) is not particularly satisfying since this asymmetry is a critical feature of a key result. Can the authors leverage their own data to provide further insight into why RM1 GABA+ may be more likely to exhibit a relationship than LM1 GABA+? Would analyzing the behavioral data separately for the left and right hands provide further insight? Does the non-dominant hand lag behind the dominant hand, and/or is it more susceptible to errors?

3) There were some general issues concerning the GABA+ data:

a. Figure 2a suggests an interaction in the pattern of variance in the GABA+ data between the Young and Older groups for the LM1 and RM1 voxels. Is this interaction in variance significant, and if so, what might this mean for the M1 GABA+ results? Specifically, Young show greater variance for LM1, and Older show greater variance for RM1. Also, Young appear to show considerably lower variance for RM1 than LM1. However, the data in Figure 2 supplement 2 suggest that variance in the Young is similar between LM1 and RM1. Do these numbers accurately reflect the data depicted in Figure 2a?

b. It would be helpful to show the difference spectra in Figure 2 supplement 1b with separate plots for Young and Older.

c. Figure 2, supplement 1a: Was the LM1 voxel more dorsal and medial than the RM1 voxel?

4) The authors interpret the decrease in failure and increase in error rate across the task in the Older group as an indication of a loss of precision over time. Alternatively, might this pattern also arise because these participants are becoming faster at correcting their errors (i.e. within 2000 ms), avoiding trials from being categorized as a failure? More generally speaking, it would be helpful if the authors provided additional information about the cumulative error rate trials and what behavior looked like on these trials.

5) The authors should provide further justification for the assignment of age as the moderator and GABA+ as the mediator in their statistical model. Conceptually, it seems these factors could be reversed.

6) Several studies have established relationships between transcranial magnetic stimulation measures of cortico-muscular excitability and endogenous GABA+ content in the dominant M1. The manuscript would benefit from further discussion of the relationship of the phase connectivity measurements used here in comparison to these other previous studies.

7) It is not clear that data or analysis code are available.

Reviewer #2:

I like this type of multimodal study, and I think that the rationale for the study is good. I am not, however, convinced about the results/conclusions provided. Here are my main points:

I don't agree with your conclusion that the mediating role of GABA changes in aging. This requires longitudinal data, the cross-sectional approach in this study can only conclude differences between groups since only 1 time point is available.

No age interaction, this is surprising to me since there are age differences?

Compensatory explanation: Is there a correlation with performance? If there isn't, the proposal of compensatory mechanisms is unclear since it is then not obvious what the compensation is for?

Reviewer #1:

The authors have acquired a substantial multimodal dataset and have used careful statistical approaches throughout. The data are acquired and analysed using appropriate methods.

Overall, this is an impressive body of work that aims to answer an interesting question. However, a number of questions over the methods and interpretation make the authors' conclusions difficult to justify.

When comparing between older and younger adults it would also be helpful to know the amount of grey matter in the voxels of interest. It might be expected that older adults might have more atrophy and therefore lower GABA+, than younger adults and this should be controlled for in the statistical models. The authors have put assumptions into their quantification, which are reasonable but are still assumptions. It would be helpful to directly test for a difference in grey matter fraction in the voxel between the two groups, and include this in the model if necessary.

The authors then look at behaviour, where they use a previously described task which consists of bimanual tapping, with switching between two patterns. The results are complex as there are a number of behavioural metrics, and no clear pattern emerges. While older adults produced more errors in continuation, they also produced more fully correct switching transitions. Older subjects were slower than younger adults in all trials. While this task produces a very rich dataset, which is helpful for analysing complex behaviour, it is not clear how each metric should be interpreted in terms of the underlying neural mechanisms, and how they can be usefully combined, could be given.

In terms of connectivity, the authors found no significant group X task difference between in-phase and anti-phase conditions. They therefore look at the groups and tasks separately. They show different changes in connectivity between age groups in different frequency bands, for example between left and right M1 in the alpha/mu and beta, between EMG and left M1 in the theta band. I am not sure that describing EEG-EMG connectivity as cortico-spinal is strictly accurate - there may be a number of other factors in this -corticomuscular would seem to be more precise. The frequency bands used are not typical, and it would be helpful to have an a priori explanation of which are being tested and why - as well as details about correction for multiple comparisons across these bands.

Finally, the authors bring their GABA, behaviour and connectivity metrics together in a number of mediation analyses. They demonstrate a relationship between cortico-cortical connectivity and behaviour, which is mediated by age.

The authors describe their finding of higher GABA+ in the occipital cortex as a posterior-anterior gradient, which I think is not justified by the results - there could be a number of other reasons for this, for example that different functional networks have different GABA+ levels, which is not related to their anatomical position. With only three voxels it is difficult to make a general claim such as this, and this should probably be reworded.

The authors state that higher GABA+ indicated neural system integrity and better functioning in the older group. This seems to be rather over-interpreting their results - there are many other metrics of integrity and functioning that have not been assessed here. I would suggest rewording.

Reviewer #1 (Public Review):

This paper presents the exciting statement that increasing viral loads within a community can be used as an epidemiological early-warning indicator preceding increased positivity. It would be interesting to support this claim to present both Ct and positivity on the same graph to demonstrate that indeed, declining Ct can be used as an early marker of a COVID-19 epidemic wave. Percentage of positive test data should not only include the ones obtained in the present study but should be compared with "national data" as the present study design includes a bias in patients selection that might not reflect the "true" situation at the time. Only with this comparison, we could claim that the present study design could predict COVID-19 epidemic waves. A correlation of Ct with clinical evidence to rank the confidence of positive results is also included and further support the high specificity of the RT-PCR for detecting SARS-CoV-2 (99.995%).

In a serological investigation, it was observed that some of these RT-PCR-positive cases do not appear to seroconvert and that possible re-infections might occur despite the presence of anti-spike antibodies. Although, reported on few individuals and therefore to be taken with extreme caution, this add some piece of information to the current unknown of the serological response of COVID-19 patient and would be of uttermost importance in the context of the current vaccination campaign.

Reviewer #3 (Public Review):

This study provides a concept of circuit organization of a pathway from the brainstem to the primary somatosensory (S1) and motor (M1) cortices through the thalamus to control the hand/forelimb movements. Previous studies reveal detailed circuit organization of ascending somatosensory pathways in the whisker system. In contrast, much less is known about circuit organization of another ascending pathway controlling the hand/forelimb movements, although it is known that there are some similarities and differences between two different somatosensory systems.

This paper revealed detailed circuit organization of the ascending pathways including the lemnisco-cortical and corticocortical pathways to control the hand/forelimb movements. The strength of this study is to use a variety of sophisticated techniques, such as optogenetics, trans-synaptic viruses, both anterograde and retrograde viruses, mouse genetics, and electrophysiology, to map the neural circuits in details. The circuit was revealed by electrophysiology together with optogenetics, which is very convincing. In addition, the detailed circuit organization revealed by this study will provide an important information for future behavioral studies. The weakness is the limitation of trans-synaptic viruses. For example, pseudorabies viruses move between multiple neurons, so to interpret the results may be complicated. Although behavioral analyses have not been performed in this study, it is beyond the scope of this study and future study will follow up the behavioral analyses.

Reviewer #2 (Public Review):

This study traces the detailed excitatory connections of mouse forepaw sensorimotor circuits from the spinal cord, through brainstem, thalamus, sensory and motor cortical areas, and their motor outputs. This is a welcome and important contribution, considering the technical advantages of mice for circuit cracking and the increasing number of labs studying the functions of their limbs. Although the structure and function of forelimb sensorimotor circuits have been extensively studied in primates, they have been relatively neglected in the rodent, especially compared to the enormous scope of research that has been done on the rodent vibrissae system over the past 50 years. This study uses a variety of contemporary methods to reveal important similarities and differences between the forelimb and vibrissae sensorimotor circuits.

Overall, the results do not hold major surprises, although this is itself a noteworthy result. The authors did identify a few qualitative and quantitative differences between the forelimb circuit and the parallel vibrissae-related circuit; the functional significance of these differences is as yet unclear.

The weaknesses of the manuscript are few and minor. The study would have been stronger if it had performed comparable, parallel experiments on the hand and vibrissae circuits, however the scope of the study is already ambitious and strong enough as it stands. I do have a question about the identity of the cortical L4 neurons that were recorded, and this issue should be discussed.

Reviewer #1 (Public Review):

Sensorimotor integration is required for the accurate execution of volitional movements, but the neural circuits underlying sensorimotor integration are still not fully understood. The whisker system of the rodent has emerged as one model of sensorimotor integration with many recent studies focused on the synaptic organization of the underlying circuitry. Here, Yamawaki et al report results regarding the synaptic organization of the ascending sensory pathways related to mouse forelimb somatosensory and motor cortex. Using anatomical and functional approaches, they elucidate the circuitry from the cuneate nucleus through thalamus to forelimb S1 and M1. This work complements recent studies in the mouse of other aspects of the forelimb sensorimotor pathways and leads to informative comparisons to the circuit organization of the whisker system. The studies are well executed and well explained. The use of multiple approaches compensates for the limitations of each individual technique, although some limitations such as any effects of viral tropism are difficult to overcome. Overall, this work contributes to a better understanding of the wiring diagram of sensorimotor circuits in the mouse.

Joint Public Review:

Worker bees perform specialised tasks: young workers nurse larvae, older ones forage for either nectar or pollen. Behaviours - including these specialist ones - arise when a stimulus (nectar, pollen or larvae) exceeds a certain 'response threshold' of the organism. This threshold can be modulated by neuropeptides to alter behaviour.

The study first shows that response thresholds to task-related stimuli differ among nurse bees, nectar and pollen foragers. Pollen foragers are most responsive to sucrose and pollen, and nurse bees most responsive to chemical stimuli of larvae. Then, taking a proteomic approach, they identify a neuropeptide, Tachykinin related protein (TRP), to be expressed in a task-specific pattern: low in nurse bees and highest in the nectar foragers.

This work provides valuable resource information on the abundance of brain neuropeptides in two species of bees. The study is exceptional in its breath of techniques used and the addition of manipulative experiments which are difficult to do in honey bees. Through their studies the authors identify a neuropeptide that modulates response thresholds of bees.

The study would have been exceptional if the authors had included studies on the expression of the tachykinin receptor. The level of tachykinin expression increases between nurse bees and foragers, but does not involve changes in spatial expression (Takeuchi et al., 2004 ref. 56). So, it is likely that the specificity of the effects of tachykinin are due to differences in the spatial expression of the receptor.

Reviewer #2 (Public Review):

NICEdrug.ch integrates well-established previous methods/pipelines from the same group and provides an easy-to-use platform for users to identify reactive sites, create repurposing and druggability reports, and reactive site-specific similarity searches between compounds. Case studies provided in the manuscript are quite strong and provide ideas to the reader regarding how this service can be useful (i.e., for which kinds of scientific aims/purposes NICEdrug.ch can be utilized). On the other hand, there are a few critical issues related to the current state of the manuscript, which, in my opinion, should be addressed with a revision.

Major issues:

1) Two of the most critical drawbacks are, first, the lack of quantitative assessment of the abilities of the service and its analysis pipeline. Use cases provide valuable information; however, it is not possible to assess the overall value of any computational tool/service without large-scale quantitative analyses. One analysis of this kind has been done and explained under "NICEdrug.ch validation against biochemical assays" and "Comparison of NICEdrug.ch predictions and biochemical assays"; however, this is not sufficient as both the experimental setup and the evaluation of results are quite generic (e.g., how to evaluate an overall accuracy of 0.73 without comparing it to other computational methods that produce such predictions, as there are many of them in the literature). Also, similar quantitative and data-driven evaluations should be made for other sections of the study as well.

2) The second critical issue is that, in the manuscript, the emphasis should be on NICEdrug.ch, since most of the underlying computational methods have already been published. However, the authors did not sufficiently focus on how the service can actually be used to conduct the analysis they mention in the use cases (in terms of usability). Via use cases, authors provide results and its biological discussion (which actually is done very well), but there is no information on how a potential user of NICEdrug.ch (who is not familiar with this system before and hoping to get an idea by reading this paper) can do similar types of analyses. I recommend authors to support the textual expressions with figures in terms of screenshots taken from the interface of NICEdrug.ch at different stages of doing the use case analyses being told in the manuscript. This will provide the reader with the ability to effectively use NICEdrug.ch.

Reviewer #1 (Public Review):

The authors developed a very interesting tool, named NICEdrug.ch, used it to identify drug metabolism and toxicity, and finally predicted druggability of disease-related enzymes and reposition drugs. Comprehensive integration effort based on publicly available datasets and several previous methods developed by the authors (e. g. BridgeIT, BNICE.ch, ATLAS of Biochemistry) results with a resource named NICEdrug.ch. The idea is interesting and addresses a very important problem in the field. The manuscript is clearly written, provides enough analysis of overall challenges and an overview of the most important results. Also, it presents figures that are remarkable.

Reviewer #3 (Public Review):

In this manuscript, Böhm et. al. aim to understand how precise kinetochore assembly is tied to cell cycle progression in budding yeast. In this work, the authors identify CDK phosphorylation sites concentrated in the N-terminus of Ame1, a protein of the COMA complex, and set out to characterize the role these phosphorylation sites may play protein function at the kinetochore. Although phospho-null Ame1 does not affect cell viability, expressing an Ame1 mutant that lacks the phosphorylated domain results in cell death. Interestingly, overexpression of the phospho-null Ame1 mutant accumulates to a higher level than the wild type protein leading the authors to hypothesize that these phosphorylation sites function as phosphodegrons in the Ame1 protein. Through molecular modeling and genetic analysis, the authors determine that Ame1 is a substrate of the SCF E3 ubiquitin ligase and is likely recognized by the Cdc4 F box protein. The authors go on to convincingly show that phosphorylation of what is referred to as the "CDC4 phosphodegron domain" is phosphorylated in a step-wise manner that is cell cycle dependent and that the phosphorylated Ame1 protein specifically is degraded in mitosis. In addition to Ame1 phosphorylation, the authors show that Ame1 degradation depends on whether Ame1 is bound to the Mtw1c (binding prevents degradation), which only happens at a fully assembled kinetochore. Based on these observations, the authors propose a model in which the phosphodegron motif functions to degrade any molecules of the COMA complex that are not incorporated into the kinetochore and in this way prevents kinetochore assembly at ectopic regions of the chromosome.

Reviewer #2 (Public Review):

Böhm et al. investigated the phosphorylation of the Ctf19CCAN component Ame1CENP-U by Cdk1 which forms a phosphodegron motif recognized by the E3 ubiquitin ligase complex SCF-Cdc4. They identify phosphorylation sites on Ame1 and demonstrate that phosphorylation of Ame1 leads to its degradation by the SCF with Cdc4 in a cell-cycle dependent manner. They also demonstrate that the outer kinetochore component Mtw1c shields Ame1 from Cdk1 phosphorylation in vitro. Finally, they propose a model in which at least one component, Ame1, is present in excess at S-phase in yeast to incorporate into high levels of sub-complexes for efficient inner kinetochore formation on newly duplicated centromere DNA. Then, in mitosis, phosphodegrons serve to mediate the degradation of excess Ame1 (and presumably other CCAN components) and in so doing protect against the formation of ectopic outer kinetochores.

This manuscript puts forth well-designed and thorough experiments characterizing the phosphorylation of Ame1 and its regulation by the SCF-Cdc4 complex. The writing is clear and the figures are generally easy to understand. The authors succeed in asking pertinent questions, designing experiments to answer them, and considering potential alternative explanations or confounding factors. As a whole this creates a generally convincing study regarding the phospho-regulation of Ame1. However, I also have some important concerns:

1) The authors begin the manuscript by mapping phosphorylation sites across Ctf19CCAN components but then largely narrow their experimental focus to Ame1 and to a lesser extent its binding partner Okp1. Without mutation of other components, the Ame1 mutant phenotypes are either absent or very mild. This would seem to implicate that, if this is an important process, that other targets for this quality control mechanism must exist. As it stands now, the focused investigation does not make the most compelling case for the broad conclusions that are claimed. More extensive investigation of phosphoregulation of CCAN subunits beyond Ame1 would certainly help justify the claim that phosphoregulation is used to clear excess CCAN subunits and protect against ectopic kinetochore assembly. Is there another lead from their initial mass spec work that could provide some molecular evidence that this is a general process? Failing that, the discussion could at least provide some hint at how the model could be tested in future studies.

2) The conclusion that the binding of the Mtw1 complex shields Ame1 phosphodegrons is arguably one of the most significant and interesting claims made in this paper. However, the evidence presented to support this claim seems to rely exclusively on in vitro data. Thus, this part is out of balance with other parts of the paper where some in vivo correlations are attempted/made.

3) The central model mentioned at the outset strongly predicts that the mitotic degradation of Ame1 doesn't impact its abundance at centromeres. That is not the only possibility, though, and some measurement (fluorescence of a tagged Ame1 or a ChIP on centromere DNA) of Ame1 at centromeres before and through mitosis would help instill confidence in the proposal.

Reviewer #1 (Public Review):

Kinetochores are huge protein assemblies on chromosomes which are used as attachment point for microtubules and allow microtubules to pull chromosomes into daughter cells during cell division. The proteins that form the kinetochore are well known, but the temporal regulation of the assembly of all these proteins into functional kinetochores is less understood.

In this paper the authors have identified phosphorylation sites in the 'CCAN' of budding yeast, the 'inner', i.e. chromatin-proximal, part of the kinetochore. They characterize in detail the function of phosphorylation of Ame1 (CENP-U in humans), which is part of CCAN. The data support the idea that a cluster of phosphorylation sites in Ame1 is phosphorylated by mitotic CDK1 and serves as phospho-degron for the E3 ligase SCF/Cdc4.

The authors show phosphorylation of these CDK1 consensus sites in vivo and their phosphorylation by CDK1/Clb2 in vitro. Genetic experiments and molecular dynamics simulations support the idea that phosphorylation sites on Ame1 can serve as phospho-degron for SCF/Cdc4. Even the non-phosphorylatable mutant of Ame1 is stabilized in an SCF mutant background, though, suggesting that this phospho-degron is not the only way in which SCF influences kinetochore protein levels.

Mutants in the characterized phosphorylation sites do not impair budding yeast growth. This suggests that the degron characterized in this paper may be important for fine-tuning, but is not essential for the proper execution of mitosis. The observations overall add to prior evidence that kinetochore assembly can be regulated by phosphorylation and/or ubiquitination.

Interestingly, the authors find that phosphorylation of Ame1 by CDK1 in vitro is impaired when Ame1 binds Mtw1, another kinetochore protein. The fact that Mtw1 seems to shield these sites from phosphorylation leads the authors to put forward an interesting model: they propose that cell cycle-dependent phosphorylation and SCF-dependent degradation of kinetochore subunits allows for excess subunits during kinetochore assembly in S-phase (which will speed up assembly) while depleting any excess subunits after assembly, when the kinetochore needs to be functional.

This is an interesting model. The in vivo evidence is still limited, though. For now, it remains unknown whether the phosphorylation status of kinetochore-bound and free Ame1 is indeed different, whether more soluble Ame1 exists in S-phase, whether too early degradation of Ame1 (or possibly other kinetochore proteins) indeed impairs kinetochore assembly, or whether a failure to remove the soluble pool after assembly leads to mitotic defects. It is an attractive proposal, though, that can now be further explored experimentally.

In addition to the specific characterization of Ame1 sites, the paper also includes comprehensive data on CCAN phosphorylation sites obtained by mass spectrometry which can serve as basis for future studies.

Reviewer #3 (Public Review):

Computational models, provide a way to understand emergent network function, and at their best provide a canvas for experimentalists to probe hypotheses regarding function. In this manuscript, Bui and colleagues provide a set of iterative models to describe the locomotor development of larval zebrafish at key developmental stages. These include coil, double coil, and swimming behavior that leads to 'beat-and-glide' behavior. During development, the model steadily moves from gap junction mediated connectivity to more complex synaptic-based network models. In my opinion, this is a very interesting foundation that can be used as a catalyst for future research for experimentalists or to develop more involved models. Like any model it is possible to be critical of the assumptions made. But I expect that it will not be static and be revised over the years. It is important to realize that these sets of models are unique in that they strive to provide models for motor control of a single species across development. The zebrafish is an excellent example since genetic models are widely used, development is swift, and there is active research to understand the physiology of locomotion.

Strengths and weaknesses:

The key strength of this manuscript is the detailing of a set of related models detailing the motor output of the larval zebrafish across key stages of development. The models should form a basis for future research. It also a first of its kind - I don't know of similar models focusing on development of locomotor function. The main weakness is the reliance on assumptions of model connectivity. But I suggest that if the model is treated as a basis for the community to refine and validate it will be incredibly useful.

Reviewer #2 (Public Review):

This study presents iteratively constructed network models of spinal locomotor circuits in developing zebrafish. These models are shown to generate different locomotor behavior of the developing zebrafish, in a manner that is supported by electrophysiological and anatomical data, and by appropriate sensitivity analyses. The broad conclusions of the study result in the hypothesis that the circuitry driving locomotor movements in zebrafish could switch from a pacemaker kernel located rostrally during coiling movements to network-based spinal circuits during swimming. The study provides a rigorous quantitative framework for assessing behaviorally relevant rhythm generation at different developmental regimes of the zebrafish. The study offers an overarching hypothesis, and specific testable predictions that could drive further experimentation and further refinement of the model presented here. The models and conclusions presented here point to important avenues for further investigation, and provide a quantitative framework to address constituent questions in a manner that is directly relatable to electrophysiological recordings and anatomical data. The study would benefit from additional sensitivity analyses, and from the recognition that biological systems manifest degeneracy and significant variability along every scale of analysis.

Reviewer #1 (Public Review):

The manuscript is somewhat readable but the many acronyms for the cell types in model and biology make it difficult to follow. Is there a reason why the biological neuron names cannot be used in the model? The presentation of data in figures can be more powerful. In many cases, the data in figures and the supplemental videos show apparently different results. This can be an artifact of how the videos were made and if yes, these can be improved. Tail tip coordinates can be plotted to show the behaviors in much better detail.

Especially for beat and glide swimming, the points regarding burst firing, inhibition, etc. have not been robustly made.

Reviewer #2 (Public Review):

Tu et al. submit a manuscript that evaluates the performance of the Abbott ID NOW SARS-CoV-2 test in an ambulatory cohort relative to RT-PCR tests. They enrolled 785 symptomatic patients, 21 tested positive for SARS-CoV-2 by ID NOW and PCR (Hologic) while 2 tested positive only via PCR. They also tested 189 asymptomatic individuals, none of whom tested positive by either ID NOW or PCR. The positive agreement between ID NOW and PCR was 91.3%, and the negative percent agreement was 100%. The authors also provide a review and meta-analysis of ID NOW performance across at least a dozen other named studies which is thorough and interesting. The cohort assessed in this study is small and localized. The data is undermined by sample size, with the most glaring example being the 100% negative percent agreement, which doesn't compare with the known performance of the test in broader populations.

Reviewer #1 (Public Review):

The study presents relatively high and robust sensitivity of Abbott ID NOW for the detection of SARS-CoV-2 (COVID-19) in an ambulatory population, utilizing the RT-PCR methodology as a comparative correlation. The study was well designed and enrolled both symptomatic and asymptomatic populations to provide sufficient statistical power for the comparative analysis of the methodologies, as well as to represent accurately the patient populations. This is a useful and timely study that has a great impact in clinical setting for the rapid detection of COVID-19.

Reviewer #2 (Public Review):

In this manuscript, Xue et al. assessed many AAV vectors and demonstrated that Thioredoxin-interacting protein (TXNIP) saves RP cones by enhancing their lactate catabolism. The results of this study were based on cone counting, IHC and reporter. While the authors focus on the cellular metabolism in the Txnip-mediated rescue effect, it is unknown whether anti-oxidative stress plays a role as well.

Reviewer #1 (Public Review):

The goal of this manuscript is to develop gene-agonistic approaches for promoting cone survival in retinal degenerative diseases. Based on their previous studies, the authors tested a total of 20 genes by subretinal delivery using an AAV vector which utilized a cone-specific promoter. Most of these genes augmented glucose utilization. Interestingly, only Txnip showed a positive result by prolonging cone survival (tested up to 50 days in rd1 retina). Txnip therapy also appears to be effective in rd10 and rho-/- retina. Additional strength of this study is the use of Txnip C247S allele that blocks its association with thioredoxin. Furthermore, additional work on how Txnip may contribute to cone survival by better utilization of lactate for energy is well presented though the conclusion on "heathier" mitochondria require additional data. This manuscript is potentially of great interest. The data are extensive and biological implications of the study are clear. However, the broad conclusions with respect of Txnip therapy for RP (or even AMD) are less than justified based on the data. Two weaknesses are apparent: the first is related to the method of quantification using whole mount retina, and the second related to the duration of the study. Immunostainings of retinal sections (and even TEMs) are critical to elucidate the structure of surviving cone photoreceptors (specially in the absence of rods) and their relationship to other cells (e.g., RPE, bipolar cells, glia). Similarly, Prusky's OMR can't be equated to visual acuity. The authors need to show cone structure/function at P50 and beyond (how long do the cones survive?) in rd1 and other models before claiming the potential benefit of Txnip for retinal and macular degeneration.

Reviewer #3 (Public Review):

In this revised manuscript (Oon and Prehoda), the authors performed additional live-imaging experiments and recorded aPKC and actin dynamics simultaneously in larval neuroblasts. They also provide evidence that aPKC polarization is lost upon F-actin disruption by Latrunculin A treatment. These are great improvements. The pulsatile dynamics of actin and myosin II showed in the manuscript are compelling. Images presented in this manuscript are of high-quality and impressive.

However, the pulsatile apical myosin network in delaminating neuroblasts in Drosophila embryos was reported previously (An Y. et al., Development, 2017). This important and relevant paper should be cited in the introduction of the current manuscript. Therefore, the finding on the pulsatile actomyosin in larval brain neuroblasts reported in this manuscript is not a total novel discovery. Another major concern is that Lat-A did not specifically disrupt actomyosin pulsatile movements, as it generally disrupts the F-actin network. So these experiments only strengthened the link between the F-actin network and Par polarity (which was already demonstrated in Kono et al., 2019; Oon 22 and Prehoda, 2019). Low doses of Cytochalasin D are known to disrupt myosin pulses still allowing the assembly of the actomyosin network (Mason et al., Nature Cell Biology 2014). The author should treat neuroblasts with low doses of CytoD to only disrupt actomyosin pulses, not the entire F-actin network, and examine the effect on Par polarity. It is also worthwhile to knockdown sqh to disrupt apical pulsatile actin dynamics. Besides, most of the concerns previously raised by the reviewer were not addressed in the revised manuscript.

Reviewer #2 (Public Review):

Previously, Oon and Prehoda showed apically directed movement of aPKC clusters during polarization of the neuroblast prior to asymmetric cell division. They found that these movements required F-actin, but the distribution of F-actin has only been reported for later stages of neuroblast polarization and division. Here, the authors report pulses of cortical F-actin during interphase, followed by an apically directed flow at the onset of mitosis, a strong apical accumulation of F-actin at metaphase and anaphase, followed by fragmentation and basally directed flow of the fragments. aPKC clusters are shown to colocalize with the F-actin networks as they flow apically. The F-actin networks are also shown have partial colocalization with non-muscle myosin II, suggesting a possible mechanism for their movement. Finally, the authors solidify the results of actin inhibitor studies from their 2019 study by showing that reported effects on aPKC localization are preceded by F-actin loss as would be expected but was not previously shown. Overall, the Research Advance extends the past study by more directly showing the involvement of F-actin and myosin in the apical localization mechanism of aPKC, and by describing F-actin and myosin dynamics prior to this transition. The following concerns should be addressed.

1) The pulsatile nature of broad F-actin networks is evident during interphase, but these pulsations substantially subside upon entry into mitosis, and at this stage an apically directed flow of F-actin is the main behavior evident. This transition from pulses to flow is evident in both the movies and the kymographs of the F-actin probe. However, the authors state that the pulsations continue at the onset of mitosis and as the apical cap of aPKC matures. It is unclear whether the apical flow of aPKC and F-actin is associated with small-scale defined F-actin pulses, or small-scale random fluctuations of F-actin. The F-actin flow alone is an informative finding. The authors should consider revising their descriptions of these data (including in the manuscript title), or provide clearer examples of defined F-actin pulsations during the stage when aPKC polarizes.

2) I checked the main text, methods, figures and figure legends, but could not find listings of sample sizes. Thus, the reproducibility of the findings has not been reported.

Reviewer #1 (Public Review):

Oon and Prehoda report pulsatile contraction of apical membrane in the process of Par protein polarization in Drosophila neuroblasts. This explains how/why actin filament was required to localize/polarize Par complex. Specifically, using spinning disc confocal microscopy with high temporal resolution, they found the directed actin movement toward the apical pole, which nicely correlates with concentration of aPKC. They also show that myosin II is involved in this pulsatile movement of actin filament. This very much resembles the observation in C. elegans embryos, and nicely unifies observations across systems. Although descriptive in nature, I think this is an important observation and indicates a universal mechanism by which cells are polarized. I think this is a well executed study.

Reviewer #3 (Public Review):

In this study, Tang and colleague report that the multikinase inhibitor YKL-05-099 increases bone formation and decreases bone resorption in hypogonadal female mice with mechanisms that are likely to involve inhibition of SIKs and CSFR1, respectively. The authors also report that postnatal mice with inducible, global deletion of SIK2 and SIK3 show an increase of bone mass that is associated to both an augmentation of bone formation and bone resorption.

The paper provides novel and interesting information with potentially highly relevant translational implications. The quality of the data is outstanding and most of the authors' conclusions are supported by the data as shown.

Reviewer #2 (Public Review):

This work tests the ability of a kinase inhibitor to increase bone mass in a mouse model of osteoporosis. The inhibitor, which targets SIK and other kinases, was shown previously by these investigators to increase trabecular bone mass in young intact mice. Here they show that it increases trabecular, but not cortical, bone in oophorectomized mice and that this is associated with increased bone formation and little or no effect on bone resorption. In contrast, postnatal deletion of SIK2 and SIK3 increased both bone formation and resorption, suggesting that the inhibitor targets other kinases to control resorption. Indeed, the authors confirm that the inhibitor effectively suppressed the activity of CSF1R, a receptor tyrosine kinase essential for osteoclast formation. The authors also provide some evidence of unwanted effects of the inhibitor on glucose homeostasis and kidney function.

Overall, the studies are performed well with all the necessary controls. The effects of the inhibitor on CSF1R inhibition are convincing and provide a compelling explanation for the net effects of the compound on the skeleton.

1) The ability of the inhibitor to increase trabecular but not cortical bone mass will likely limit its appeal as an anabolic therapy. Indeed, the authors show that PTH, but not the inhibitor, increases bone strength. However, this limitation is not addressed in the manuscript. In addition, the mechanisms leading to these site-specific effects were not explored.

2) The mechanisms by which YKL-05-099 increases bone formation remain unclear. The authors point out that their previous studies indicate that the compound stimulates bone formation by suppressing expression of sclerostin. However, YKL-05-099 increased trabecular bone in the femur but not spine of intact mice and did not increase cortical bone in intact or OVX mice. In contrast, neutralization of sclerostin increases trabecular bone at both sites in intact mice as well as increases cortical bone thickness. These differences do not support the idea that YKL-05-099 increases bone formation by suppressing sclerostin.

3) The authors repeatedly state that the kinase inhibitor uncouples bone formation and bone resorption. However, the authors do not provide any direct evidence that this is the case. Although the term coupling is used to refer to a variety of phenomena in skeletal biology, the most common definition, and the one used in the review cited by the authors, is the recruitment of osteoblasts to sites of previous resorption. The authors certainly provide evidence that the kinase inhibitor independently targets bone formation and bone resorption, but they do not provide evidence that the mechanisms leading to recruitment of osteoblasts to sites of previous resorption has been altered. The resorption that takes place in the inhibitor-treated mice likely still leads to recruitment of osteoblasts to sites of resorption. Thus coupling remains intact.

4) The results of the current study nicely confirm previous findings by the same authors, demonstrating the reproducibility of the effects of the inhibitor. They also provide a compelling explanation for the net effect of the inhibitor on bone resorption (it stimulates RANKL expression but inhibits CSF1 action). While this latter finding will likely be of interest to those exploring SIK inhibitors for therapeutic uses, overall this study may be of limited appeal to a broader audience.

Reviewer #1 (Public Review):

The primary objective of this manuscript was to examine if multi-kinase inhibitor YKL-05-099 can inhibit salt inducible kinases (SIKs) with the goal to examine a new class of bone anabolic agents for the treatment of osteoporosis. They found that YKL-05-099 was successful in increasing anabolism and, surprisingly, decreasing bone resorption, leading them to investigate why this inhibitor differed from the effects of deletion of SIK2 and SIK3. They found that YKL-05-099 also inhibited the CSF1 (M-CSF) receptor, thus, inhibiting osteoclast activity. This is an interesting manuscript but there are some flaws in the conduct of the experiments and in the analyses which lessen its impact. Nevertheless, it opens the way for another possible oral therapeutic for osteoporosis.

Reviewer #3 (Public Review):

The proposed model is a variation on existing probabilistic fitness landscapes with a number of novel ingredients that are crucial for explaining the observed patterns. The model successfully accounts for the experimental results and makes new predictions, some of which are confirmed by the analysis of existing data. It also provides a coherent picture of the dynamics of adaptation that matches experimental observations. Overall, this is a conceptually deep and potentially highly influential study.

I see only one major issue that requires clarification. This concerns the distinction between the directed mutation scheme (leading to Eqs.(3,4) in the main text) and the symmetric version (Eqs.(5,6)).

Reviewer #2 (Public Review):

The authors analyze diminishing-return (beneficial mutations likely having a small effects for genotypes of high fitness) and increasing-costs epistasis (deleterious mutations likely having large effects for genotypes of high fitness). A framework is proposed where the fitness of genotype after a mutation at a single locus can be estimated from (i) the additive effect at the locus and (ii) a component determined by the fitness of the original genotype at the locus, referred to as "global epistasis". The concept of locus-specific global epistasis is new, even if variants of global epistasis have been discussed in published work. The manuscript shows that the locus specific assumption is empirically justified and it provides applications to a study of yeast.

Regression effects (diminishing returns and increasing costs epistasis) are quantified under the assumption that epistasis can be considered noise (idiosyncratic epistasis). The result is expressed in terms of Fourier representation for the fitness of a genotype, and the proof depends on a locus-specific analysis of correlations derived from the Fourier representation. In particular, the author clarify under what circumstances one can expect the regression effects. Several conclusions are very precise, and numerical results are provided as a complement to the analytical work.

The second part of the manuscript concerns historical contingency. Absence of contingency means that the expected fitness effect of new mutation for a genotype is independent of previous substitutions. A condition for minimal contingency in provided, and a new model (The Connected Network model, or CN-model) which satisfies is introduced.

A somewhat puzzling point is that the authors emphasize that their proposed frame workexplains diminishing-return and increased-costs epistasis. Diminishing return has been described as a "regression to the mean effect" of sorts in Draghi and Plotkin (2013) for the NK model, and it was argued that a similar regression effect applies to a broad category of fitness landscapes in Greene and Crona (2014). Moreover, "increased-costs epistasis" is likely to apply broadly as well with a similar argument also for landscapes that fall outside the category discussed by in the manuscript (an example is in the Recommendation section). On the other hand, a major strength of the manuscript is that it provides a superior quantitative precision, and some quantitative understanding for when one can expect diminishing returns and increased costs epistasis (that should be emphasized more in my view).

From a conceptual point of view, the locus specific framework, as well as the historical contingency discussion are valuable contributions. The fact that the author could construct a model (the CN model) that satisfy their minimal contingency condition is very interesting as well.

The weakness of the manuscript is the presentation of the work, especially for a general audience. More context and background, explanations of quantitative results and references would help. There are also a few cases of unclear claims and confusing notation (SSWM seems to be assumed without that being stated, the notation for Fourier coefficients is unclear in some cases) and the text has some other minor issues. Fortunately, a limited effort (in terms of time) would resolve the problem, and also improve the prospects for high impact.

Reviewer #1 (Public Review):

One of the most consistent and thus surprising patterns revealed by experimental evolutionary studies is the observation of a very predictable pattern of increase in fitness of replicate populations. The fitness increase tends to be very rapid at the beginning and then slows down but continues to increase for tens of thousands of generations (e.g. the Lenski LTEE). The studies from the Desai group specifically two: one by Kryazhmisky et al and one by Jonnson et al further established that the pattern of decrease in the fitness gain is due to really counterintuitive patterns of global epistasis. In particular it is not due to the evolution running out of adaptive mutations but rather to the fact that the same adaptive mutations are less beneficial on fitter backgrounds (Kryazhmisky et al). Johnson et al further found that the fitter backgrounds are more fragile with deleterious mutations being more deleterious on fitter backgrounds. All of this is rather bizarre at first glance as the microscopic epistasis is known to be highly idiosyncratic.

This paper, along with one by Lyons et al (Nat Ecol Evol 2020), resolves this paradox and shows that the observed pattern of global epistasis is in fact directly dependent on microscopic epistasis being widespread, involving multiple loci - with most parts of the organisms being connected in an "everything affecting everything" pattern, and being idiosyncratic. The Lyons et al paper focused on the data showing the epistasis is in fact idiosyncratic - their key observation - and provided an intuition for why such widespread idiosyncrasy would result in the observed pattern of global epistasis. Although neither set of authors seems to use this term, this should fit the notion of the Anna Karenina principle: "All happy families are alike; each unhappy family is unhappy in its own way." That is, in order for the right things to happen, most things need to go right, but in order for things to fail, anyone of many such things can go wrong. The more adapted systems are more fragile and more difficult to improve, because in both cases it is easier to disrupt what is already working.

The Reddy and Desai paper takes this notion and develops a very simple and transparent quantitative theory of this principle that generates specific quantitative predictions about the dynamics of adaptation that we, as a field, will spend considerable time now testing. The work has the potential to become a seminal paper in the field.

Reviewer #3 (Public Review):

Yang et al. build on earlier studies from the Zheng lab and show in tissues that (i) the Hedgehog (Hh) co-receptor Ihog mediates homophilic interactions that enable cytoneme bundling and (ii) that Ihog-Hh interactions are stronger than and displace Ihog-Ihog interactions during signaling, consistent with biochemical and cell-based studies of relative affinities of Ihog for itself or Hh. These studies are bolstered by modeling and experiments showing co-localization of Dally and Dlp, which presumably supply the heparan sulfate chains needed to promotes homo- and hetero-philic interactions involving Ihog. I found the studies convincing, interesting, and an important extension of biochemical/cellular work on Ihog to tissue behavior.

Reviewer #2 (Public Review):

This well-conceived and well-presented work has both originality and substance, and contributes important new ideas to the Hh signaling field with wonderful clarity.

Reviewer #1 (Public Review):

This study builds upon previous findings by the authors and others that the Hedgehog (Hh) co-receptor Ihog not only binds Hh to trigger Hh signal transduction, but also engages trans-homophilic interactions in cell-cell adhesion. Using experimental manipulation and mathematical modeling, the authors assessed the role of Ihog trans-homophilic binding in stabilizing cytoneme structure and the relative strengths of Ihog-Ihog and Hh-Ihog binding. These findings led to a model whereby the weaker Ihog-Ihog trans interaction promotes direct membrane contacts along cytonemes and that Hh-Ihog binding releases Ihog from trans Ihog-Ihog complex. The studies are well designed and executed, and the findings are convincing.

Reviewer #2:

This study reports a new cell line model for Dyskeratosis congenita, generated by introducing a disease-causing mutation, DKC1 A386T, into human iPS-derived type II alveolar epithelial cells (iAT2). The authors found that the mutant cells failed to form organoids after serial passaging and displayed hallmarks of cellular senescence and telomere shortening. Transcriptomics analysis for the mutant cells unveiled defects in Wnt signaling and down-regulation of the downstream shelterin complex components. Finally, treating the mutant cells with a Wnt agonist, a GSK3 inhibitor CHIR99021 can rescue these defects and enhance telomerase activity. Overall, the study is well designed and executed. Data presented are generally clear and convincing. The new model presented here can be of great interests in the field to study the effects of DC disease causing mutants in diverse cell types.

Reviewer #1:

In this manuscript, Fernandez et al examine the impact of defective telomere length maintenance on type II alveolar epithelial cells, which are thought to be central to the pathogenesis of pulmonary fibrosis in dyskeratosis congenita (DC) and related telomere biology disorders. Murine models have been used to address how telomere dysfunction in AT2 cells drives pulmonary fibrosis however these models have limitations. Therefore, the investigators' study of human AT2 cells/organoids derived from induced pluripotent stem cells (iAT2 cells) in the presence and absence of a known DC pathogenic variant provides an exceptional model. In addition, the investigators use expression profiling to uncover decreased canonical WNT signaling in iAT2 cells with telomere dysfunction and then demonstrate rescue of telomere dysfunction and iAT2 cell growth with the addition of a GSK3 inhibitor, a canonical WNT agonist. The data appear to be of high quality, the approaches and interpretation appropriate, with some noted exceptions below. Given the importance of the problem (dysfunctional telomere-induced pulmonary fibrosis) and the apparent benefit of GSK3 inhibition of iAT2 cell growth and telomere dysfunction, which extends the work published by this group previously on intestinal organoids (might enhanced canonical WNT signaling more broadly affect other tissues with telomere-induced senescence?), this work is significant.

A few aspects of the studies dampen the ability to draw certain conclusions. For example, the authors use iPSCs that are 5 vs 25 passages after introduction (or not) of the DKC1 A386T mutation for the generation of iAT2 cells. They then show iAT2 DKC1 mutant organoids generated from the later passage iPSCs have an apparent growth defect as early as Day 50 but that those generated from the earlier passage iPSCs do not at Day 70 [with caveats the images are of different quality (comparing Fig. 1B and Fig. S3D) and quantitative data (similar to Fig. 1C) are lacking for the iAT2 organoids generated from the early passage iPSCs]. They argue that progressive telomere shortening is the cause of the growth defects. If this is the case, then the iAT2 cells generated from the earlier passages should eventually show growth defects with progressive telomeres shortening, which was not shown.

The telomere length analysis of the iAT2 cells at Day 50 and Day 70 are not markedly different, and neither the % p21 + nor TIF+ cells is shown for Day 50. Therefore, the conclusion that it is the accumulation of short uncapped telomeres in the DKC1 mutant iAT2 cells that alters gene expression and induces senescence at Day 70 ignores the extent of these changes at Day 50.

The statement that CHIR99021 (when present in the medium from Day 49-70) rescued the growth defect seems generous; the effect is partial and the assay is for organoid formation efficiency only. Moreover, it is most likely prohibiting the further accumulation of senescent cells rather than rescuing cells that were not previously growing.

It is striking that prolonged CHIR99021 treatment (ie, through to Day 70) resulted in increased telomerase activity, and more so in mutant compared to wild type cells. First, how reproducible was this effect? I appreciate that the authors have not explored this for this manuscript, however, TERT expression does not rescue DKC1 mutants but TERC does. Were TERC levels increased? Also, given this robust increase, it is striking that no difference is detected in TeSLA assays given the proportion of very short detected telomeres that would presumably be substrates for telomerase. It is noteworthy that, in the protocol to derive iAT2 cells, CHIR99021 is present in the media prior to Day 28. This raises the question of whether there is rescue of telomerase in the cells exposed to CHIR99021 in the interval of iAT2 specification?

Reviewer #2 (Public Review):

In the manuscript Li and colleagues explored the mechanisms that potentially regulated the transcoelomic metastasis of ovarian cancer. By using the in vivo genome-wide CRISPR/Cas9 screen in human SK-OV-3 cell line after transplanted in NOD-SCID mice, the authors identified that IL-20Ra was a potential protective factor preventing the transcoelomic metastasis of ovarian cancer. SK-OV-3 cells with higher expression of IL-20R have lower metastatic potential in vivo. On the contrary, a mouse cell line ID8 with lower IL20Ra expression metastasized aggressively, which could be reversed by over expressing IL-20Ra in the cells. In human, the metastasized ovarian cancers had lower expression of IL-20Ra than the primary tumors. Mechanistically, the authors hypothesized that IL-20 and IL-24 produced by peritoneum mesothelial could act on tumor cells through the IL-20Ra/IL-20Rb receptor to promote the production of IL-18. IL-18 could drive the macrophages into M1 like phenotypes, which in turn controlled the transcoelomic metastasis of the cancer. The in vivo phenotypes in this study were consistent with these hypotheses. The role of IL-20Ra in this setting is potentially interesting and novel.

Reviewer #1 (Public Review):

The authors used a CRISPR screen to investigate the basis of metastasis of ovarian cancer (OC) cells. Overall, they identified two key genes, IL20RA, one of which was studied in detail. They identify an IL20/IL20RA communication between ovarian cancer cells and peritoneal mesothelial cells to promote M1 macrophages and prevent dissemination of the cancer cells. IL-20 mediated crosstalk is blocked in metastasized OC cells by decreased expression of IL-20RA. Interestingly, IL20RA is also decreased in cells from OC patients with peritoneal metastasis, and reconstitution of IL20RA in metastatic OC cells suppresses metastasis. Moreover, OC cells induce mesothelial cells to produce IL20 and IL24.

Overall, this is a nice study. It is well-written, and the data are clear. A range of methodologies are used that support the conclusions, with both over-expression and under-expression related studies supporting some key conclusions.

The overall model is that there is crosstalk between disseminated OC cells and mesothelial cells and macrophages. OC cells when disseminated into the peritoneal cavity stimulate mesothelial cells to produced IL20 and IL24, which via IL20RA trigger STAT3 to produced OAS/RNase L and production of IL-18, to promote an M1 phenotype. The M1 phenotype lowers metastasis. Highly metastatic cells block this pathway by decreasing IL20RA expression.

These findings are interesting, with potential therapeutic ramifications.

Reviewer #3 (Public Review):

The authors aimed to develop a 2D image analysis workflow that performs bacterial cell segmentation in densely crowded colonies, for brightfield, fluorescence, and phase contrast images. The resulting workflow achieves this aim and is termed "MiSiC" by the authors.

I think this tool achieves high-quality single-cell segmentations in dense bacterial colonies for rod-shaped bacteria, based on inspection of the examples that are shown. However, without a quantification of the segmentation accuracy (e.g. Jaccard coefficient vs. intersection over union, false positive detection, false negative detection, etc), it is difficult to pass a final judgement on the quality of the segmentation that is achieved by MiSiC.

A particular strength of the MiSiC workflow arises from the image preprocessing into the "Shape Index Map" images (before the neural network analysis). These shape index maps are similar for images that are obtained by phase contrast, brightfield, and fluorescence microscopy. Therefore, the neural network trained with shape index maps can apparently be used to analyze images acquired with at least the above three imaging modalities. It would be important for the authors to unambiguously state whether really only a single network is used for all three types of image input, and whether MiSiC would perform better if three separate networks would be trained.

Reviewer #2 (Public Review):

Panigrahi and co-authors introduce a program that can segment a variety of images of rod-shaped bacteria (with somewhat different sizes and imaging modalities) without fine-tuning. Such a program will have a large impact on any project requiring segmentation of a large number of rod-shaped cells, including the large images demonstrated in this manuscript. To my knowledge, training a U-Net to classify an image from the image's shape index maps (SIM) is a new scheme, and the authors show that it performs fairly well despite a small training set including synthetic data that, based on Figure 1, does not closely resemble experimental data other than in shape. The authors discuss extending the method to objects with other shapes and provide an example of labelling two different species - these extensions are particularly promising.

The authors show that their network can reproduce results of manual segmentation with bright field, phase and fluorescence input. Performance on fluorescence data in Fig. 1 where intensities vary so much is particularly good and shows benefits of the SIM transformation. Automated mapping of FtsZ show that this method can be immediately useful, though the authors note this required post-processing to remove objects with abnormal shapes. The application in mixed samples in Fig. 4 shows good performance. However, no Python workflow or application is provided to reproduce it or train a network to classify mixtures in different experiments.

Performance was compared between SuperSegger with default parameters and MiSiC with tuned parameters for a single data set. Perhaps other SuperSegger parameters would perform better with the addition of noise, and it's unclear that adding Gaussian noise to a phase contrast image is the best way to benchmark performance. An interesting comparison would be between MiSiC and other methods applying neural networks to unprocessed data such as DeepCell and DeLTA, with identical training/test sets and an attempt to optimize free parameters.

INSTALLATION: I installed both the command line and GUI versions of MiSiC on a Windows PC in a conda environment following provided instructions. Installation was straightforward for both. MiSiCgui gave one error and required reinstallation of NumPy as described on GitHub. Both give an error regarding AVX2 instructions. MiSiCgui gives a runtime error and does not close properly. These are all fairly small issues. Performance on a stack of images was sufficiently fast for many applications and could be sped up with a GPU implementation.

TESTING: I tested the programs using brightfield data focused at a different plane than data presumably used to train the MiSiC network, so cells are dark on a light background and I used the phase option which inverts the image. With default settings and a reasonable cell width parameter (10 pixels for E. coli cells with 100-nm pixel width; no added noise since this image requires no rescaling) MiSiCgui returned an 8-bit mask that can be thresholded to give segmentation acceptable for some applications. There are some straight-line artifacts that presumably arise from image tiling, and the quality of segmentation is lower than I can achieve with methods tuned to or trained on my data. Tweaking magnification and added noise settings improved the results slightly. The MiSiC command line program output an unusable image with many small, non-cell objects. Looking briefly at the code, it appears that preprocessing differs and it uses a fixed threshold.

Reviewer #1 (Public Review):

In this work, Panigrahi et. al. develop a powerful deep-learning-based cell segmentation platform (MiSiC) capable of accurately segmenting bacteria cells densely packed within both homogenous and heterogeneous cell populations. Notably, MiSiC can be easily implemented by a researcher without the need for high-computational power. The authors first demonstrate MiSiC's ability to accurately segment cells with a variety of shapes including rods, crescents and long filaments. They then demonstrate that MiSiC is able to segment and classify dividing and non-dividing Myxococcus cells present in a heterogenous population of E. coli and Myxococcus. Lastly, the authors outline a training workflow with which MiSiC can be trained to identify two different cell types present in a mixed population using Myxococcus and E. coli as examples.

While we believe that MiSiC is a very powerful and exciting tool that will have a large impact on the bacterial cell biological community, we feel explanations of how to use the algorithm should be more greatly emphasized. To help other scientists use MiSiC to its fullest potential, the range of applications should be clarified. Furthermore, any inherent biases in MiSiC should be discussed so that users can avoid them.

Major Concerns:

1) It is unclear to us how a MiSiC user should choose/tune the value for the noise variance parameter. What exactly should be considered when choosing the noise variance parameter? Some possibilities include input image size, cell size (in pixels), cell density, and variance in cell size. Is there a recommended range for the parameter? These questions along with our second minor correction can be addressed with a paragraph in the Discussion section.

2) Could the authors expand on using algorithms like watershed, conditional random fields, or snake segmentation to segment bacteria when there is not enough edge information to properly separate them? How accurate are these methods at segmenting the cells? Should other MiSiC parameters be tuned to increase the accuracy when implementing these methods?

3) Can the MiSiC's ability to accurately segment phase and brightfield images be quantitatively compared against each other and against fluorescent images for overall accuracy? A figure similar to Fig. 2C, with the three image modalities instead of species would nicely complement Fig. 2A. If the segmentation accuracy varies significantly between image modalities, a researcher might want to consider the segmentation accuracy when planning their experiments. If the accuracy does not vary significantly, that would be equally useful to know.

4) The ability of MiSiC to segment dense clusters of cells is an exciting advancement for cell segmentation algorithms. However, is there a minimum cell density required for robust segmentation with MiSiC? The algorithm should be applied to a set of sparsely populated images in a supplemental figure. Is the algorithm less accurate for sparse images (perhaps reflected by an increase in false-positive cell identifications)? Any possible biases related to cell density should be noted.

5) It is exciting to see the ability of MiSiC to segment single cells of M. xanthus and E. coli species in densely packed colonies (Fig. 4b). Although three morphological parameters after segmentation were compared with ground truth, the comparison was conducted at the ensemble level (Fig. 4c). Could the authors use the Mx-GFP and Ec-mCherry fluorescence as a ground truth at the single cell level to verify the results of segmentation? For example, for any Ec cells identified by MiSiC in Fig. 4b, provide an index of whether its fluorescence is red or green. This single-cell level comparison is most important for the community.

Reviewer #3 (Public Review):

The authors sought to show how the segments of influenza viruses co-evolve in different lineages. They use phylogenetic analysis of a subset of the complete genomes of H3N2 or the two H1N1 lineages (pre and post 2009), and use a method - Robinson-Foulds distance analysis - to determine the relationships between the evolutionary patterns of each segment, and find some that are non-random.

1) The phylogenetic analysis used leaves out sequences that do not resolve well in the phylogenic analysis, with the goal of achieving higher bootstrap values. It is difficult to understand how that gives the most accurate picture of the associations - those sequences represent real evolutionary intermediates, and their inclusion should not alter the relationships between the more distantly related sequences. It seems that this creates an incomplete picture that artificially emphasizes differences among the clades for each segment analyzed?

2) It is not clear what the significance is of finding that sequences that share branching patterns in the phylogeny, and how that informs our understanding of the likelihood of genetic segments having some functional connection. What mechanism is being suggested - is this a proxy for the gene segments having been present in the same viruses - thereby revealing the favored gene segment combinations? Is there some association suggested between the RNA sequences of the different segments? The frequently evoked HA:NA associations may not be a directly relevant model as those are thought to relate to the balance of sialic acid binding and cleavage associated with mutations focused around the receptor binding site and active site, length of NA stalk, and the HA stalk - does that show up in the overall phylogeny of the HA and NA segments? Is there co-evolution of the polymerase gene segments, or has that been revealed in previous studies, as is suggested?

The mechanisms underlying the genomic segment associations described here are not clear. By definition they would be related to the evolution of the entire RNA segment sequence, since that is being analyzed - (1) is this because of a shared function (seems unlikely but perhaps pointing to a new activity), or is it (2) because of some RNA sequence-associated function (inter-segment hybridization, common association of RNA with some cellular or viral protein)? (3) Related to specific functions in RNA packaging - please tell us whether the current RNA packaging models inform about a possible process. Is there a known packaging assembly process based on RNA sequences, where the association leads to co-transport and packaging - in that case the co-evolution should be more strongly seen in the region involved in that function and not elsewhere? The apparent increased association in the cytoplasm of the subset of genes examined for the single virus looks mainly in the cytoplasm close to the nucleus - suggesting function (2) and/or (3)?.

It is difficult to figure out how the data found correlates with the known data on reassortment efficiency or mechanisms of systems for RNA segment selection for packaging or transport - if that is not obvious, maybe you can suggest processes that might be involved.

Reviewer #2 (Public Review):

The influenza A genome is made up of eight viral RNAs. Despite being segmented, many of these RNAs are known to evolve in parallel, presumably due to similar selection pressures, and influence each other's evolution. The viral protein-protein interactions have been found to be the mechanism driving the genomic evolution. Employing a range of phylogenetic and molecular methods, Jones et al. investigated the evolution of the seasonal Influenza A virus genomic segments. They found the evolutionary relationships between different RNAs varied between two subtypes, namely H1N1 and H3N2. The evolutionary relationships in case of H1N1 were also temporally more diverse than H3N2. They also reported molecular evidence that indicated the presence of RNA-RNA interaction driving the genomic coevolution, in addition to the protein interactions. These results do not only provide additional support for presence of parallel evolution and genetic interactions in Influenza A genome and but also advances the current knowledge of the field by providing novel evidence in support of RNA-RNA interactions as a driver of the genomic evolution. This work is an excellent example of hypothesis-driven scientific investigation.

The communication of the science could be improved, particularly for viral evolutionary biologists who study emergent evolutionary patterns but do not specialise in the underlying molecular mechanisms. The improvement can be easily achieved by explaining jargon (e.g., deconvolution) and methodological logics that are not immediately clear to a non-specialist.

The introduction section could be better structured. The crux of this study is the parallel molecular evolution in influenza genome segments and interactions (epistasis). The authors spent the majority of the introduction section leading to those two topics and then treated them summarily. This structure, in my opinion, is diluting the story. Instead, introducing the two topics in detail at the beginning (right after introducing the system) then discussing their links to reassortments, viral emergence etc. could be a more informative, easily understandable and focused structure. The authors also failed to clearly state all the hypotheses and predictions (e.g., regarding intracellular colocalisation) near the end of the introduction.

The authors used Robinson-Foulds (RF) metric to quantify topological distance between phylogenetic trees-a key variable of the study. But they did not justify using the metric despite its well-known drawbacks including lack of biological rational and lack of robustness, and particularly when more robust measures, such as generalised RF, are available.

Figure 1 of the paper is extremely helpful to understand the large number of methods and links between them. But it could be more useful if the authors could clearly state the goal of each step and also included the molecular methods in it. That would have connected all the hypotheses in the introduction to all the results neatly. I found a good example of such a schematic in a paper that the authors have cited (Fig. 1 of Escalera-Zamudio et al. 2020, Nature communications). Also this methodological scheme needs to be cited in the methods section.

Finally, I found the methods section to be difficult to navigate, not because it lacked any detail. The authors have been excellent in providing a considerable amount of methodological details. The difficulty arose due to the lack of a chronological structure. Ideally, the methods should be grouped under research aims (for example, Data mining and subsampling, analysis of phylogenetic concordance between genomic segments, identifying RNA-RNA interactions etc.), which will clearly link methods to specific results in one hand and the hypotheses, in the other. This structure would make the article more accessible, for a general audience in particular. The results section appeared to achieve this goal and thus often repeat or explain methodological detail, which ideally should have been restricted to the methods section.

Reviewer #1 (Public Review):

In this paper, authors did a fine job of combining phylogenetics and molecular methods to demonstrate the parallel evolution across vRNA segments in two seasonal influenza A virus subtypes. They first estimated phylogenetic relationships between vRNA segments using Robinson-Foulds distance and identified the possibility of parallel evolution of RNA-RNA interactions driving the genomic assembly. This is indeed an interesting mechanism in addition to the traditional role for proteins for the same. Subsequently, they used molecular biology to validate such RNA-RNA driven interaction by demonstrating co-localization of vRNA segments in infected cells. They also showed that the parallel evolution between vRNA segments might vary across subtypes and virus lineages isolated from distinct host origins. Overall, I find this to be excellent work with major implications for genome evolution of infectious viruses; emergence of new strains with altered genome combination.

Comments:

I am wondering if leaving out sequences (not resolving well) in the phylogenic analysis interferes with the true picture of the proposed associations. What if they reflect the evolutionary intermediates, with important implications for the pathogen evolution which is lost in the analyses?

Lines 50-51: Can you please elaborate? I think this might be useful for the reader to better understand the context. Also, a brief description on functional association between different known fragments might instigate curiosity among the readers from the very beginning. At present, it largely caters to people already familiar with the biology of influenza virus.

Lines 95-96 Were these strains all swine-origin? More details on these lineages will be useful for the readers.

Lines 128-132: I think it will be nice to talk about these hypotheses well in advance, may be in the Introduction, with more functional details of viral segments.

Lines 134-136: Please rephrase this sentence to make it more direct and explain the why. E.g. "... parallel evolution between PB1 and HA is likely to be weaker than that of PB1 and PA" .

Lines 222-223: Please include a set of hypotheses to explain you results? Please add a perspective in the discussion on how this contribute might to the pandemic potential of H1N!?.

Lines 287-288: I am wondering how likely is this to be true for H1N1.

Evaluation Summary:

The manuscript reports phylogenetic and molecular evidence of novel RNA-RNA interactions driving the genomic coevolution of Influenza virus subtypes, in addition to protein interactions. With a few minor changes, this study could reveal how the likelihood of certain genetic combinations might lead to new viral variants emerging with the possibility of new antigenic properties and implications in disease spread.

(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

Reviewer #3 (Public Review):

This is a well-executed study with interesting and novel findings. The main strength is the combined use of well-executed flow cytometry studies in human patients with MI and in vitro experiments to suggest a role for immature neutrophils in infarction. The main weakness is the descriptive/associative nature of the data. What is lacking is in vivo experimentation documenting the proposed pro-inflammatory role of immature neutrophils. This limits the conclusions. The following specific concerns are raised:

Major:

1.In some cases, conclusions are not supported by robust data. For example, the authors conclude that CD14+HLA-DRneg/lo monocytes play a crucial role in post-infarction inflammation based exclusively on in vitro experiments. Moreover, conclusions regarding the pro-inflammatory role of immature neutrophils are based on in vitro data and associative studies.

2.Immature neutrophils have a short lifespan. Information on the fate of immature neutrophils in the infarct is lacking. The in vivo mouse model may be ideal to address whether immature neutrophils undergo apoptosis or mature within the infarct environment

3.The rationale for selective assessment of specific genes and for the specific neutrophil-lymphocyte co-culture system is unclear. In neutrophils, the basis for selective assessment of some specific genes (MMP9, IL1R1, IL1R2, STAT3 etc), vs. other inflammatory genes known to be expressed at high levels by neutrophils is not explained. Similarly, the rationale for the experiment examining interactions of CD10neg neutrophils with T cells is not clear. Considering the effects of neutrophils on macrophage phenotype and on cardiomyocytes, study of interactions with other cell types may have made more sense.

4.The concept of CMV seropositivity is suddenly introduced without a clear rationale. The data show infiltration of the infarcted heart with immature neutrophils and CD14+HLA-DRneg monocytes. One would have anticipated more experiments investigating the (proposed) role of these cells in the post-infarction inflammatory response, rather than comparison of CMV+ vs negative patients.

Reviewer #2 (Public Review):

In this study, Fraccarollo and colleagues describe the existence and higher prevalence of subpopulations of immature monocytes and neutrophils with pro-inflammatory responses in patients with acute myocardial infarction. CD14+HLA-DRneg/low monocytes and CD16+CD66b+CD10neg neutrophils correlate with markers of systemic inflammation and parameters of cardiac damage. In particular in patients positive for cytomegalovirus and elevated levels of CD4+CD28null T cells, the expansion of immature neutrophils associates with increased levels of circulating IFNg. Mechanistically, immature neutrophils regulate T-cell responses by inducing IFN release through IL-12 production in a contact-independent manner. Besides, CD14+HLA-DRneg/low monocytes differentiate into macrophages with a potent pro-inflammatory phenotype characterized by the release of pro-inflammatory cytokines upon IFNg stimulation.

This very interesting study provides new insights into the diversity and complexity of myeloid populations and responses in the context of cardiac ischemia. It is technically well performed and the results sufficiently support the conclusions of the study.

Strengths

The authors provide a detailed analysis of the phenotype and function of two subpopulations of CD14+HLA-DRneg/low monocytes and CD16+CD66b+CD10neg neutrophils in the context of acute myocardial infarction (AMI). Extensive phenotyping of these immune populations at different time-points after the onset of the disease provides strong correlations with multiple parameters of inflammation and severity of the disease. Hence, these subpopulations emerge as biomarkers of heart ischemic diseases with predictive potential. Using in vitro approaches, the authors support these correlations with mechanistic analyses of the inflammatory and immunomodulatory function of these populations. Finally, the authors use mouse models of ischemia-reperfusion injury to mimic the conditions observed in the AMI patients and supporting the pro-inflammatory role of immature neutrophils in this disease.

Weaknesses

The associations between immature neutrophils, IFNg, and CD4+CD28null T cells found in AMI patients positive for cytomegalovirus are not well supported by the mechanistic findings observed in vitro. Here, the induction of IFNg production by immature neutrophils is restricted to CD4+CD28+ T cells but not CD4+CD28null T cells.

The experimental data obtained from mouse models of AMI to support their findings in humans would require a more extensive study. Causality between the expansion of these immature populations and the course of the disease is missing. Also, although expected, substantial differences are found between equivalent subpopulations in mice and humans thus limiting the relevance of the mouse data.

Reviewer #1 (Public Review):

In this paper, the authors tried to investigate complex roles of immune cells during acute myocardial infarction (AMI) by examining immune cells in blood samples from acute coronary syndrome (ACS) patients. They found an increase in the circulating levels of CD14+HLA-DRneg/low monocytes and CD16+CD66b+CD10neg neutrophils in the blood of ACS patients compared to healthy people, all of which were correlated with elevated levels of inflammatory markers in serum. Those findings were then further explored at a mechanistic level by using in vitro and in vivo experiments. Interestingly, the researchers also found that high cytomegalovirus (CMV) antibody titers could affect the immunoregulatory mechanisms in AMI patients. Taken together, the findings of the researchers could potentially contribute to the development of a more effective strategy to prevent cardiac deterioration and cardiovascular adverse events after AMI.

Strengths:

This paper contains novel insight regarding role of neutrophil and monocyte subset in pathophysiology of AMI. Although the increased level of CD10neg subsets of neutrophils in AMI patients has recently been reported (Marechal, P., et al. 2020. Neutrophil phenotypes in coronary artery disease. Journal of Clinical Medicine), the current paper aptly complemented the previous findings obtained by using its in vitro and in vivo mice model. This study also has robust methods to support their conclusion.

Weakness:

To further improve the strength of their conclusion, the experiments investigating the effects of immunoregulatory function of immature neutrophils and HLA-DRneg/low monocytes subsets would be advised.

Reviewer #3 (Public Review):

This paper from He, Y. et al examines how PKC-theta in activated T cells controls RanBP2 nuclear pore subcomplex formation and nuclear translocation of NFkB, NFAT and AP1 family transcription factors. He, Y et al systematically pull apart a molecular mechanism showing that: 1) T cell receptor-activated PKC-theta localises to the nuclear envelope and associates with RanGAP1, 2) PKC-theta deficiency reduces nuclear localisation of import proteins and AP1-family transcription factors in mature mouse T cells and Jurkat cell line, but not primary mouse thymocytes 3) RanGAP1 is phosphorylated by PKC-theta and that phosphorylation of RanGAP1 on Ser504/Ser506 facilitates RanGAP1 sumoylation and is needed for association with other RanBP2 complex components and 4) that wildtype but not Ser504/506 mutant RanGAP1 can rescue nuclear translocation of transcription factors in RanGAP1 knockdown cells.

A key strength of this work is that, for many key results, multiple methods for validating findings are used e.g. immunoblots of subcellular fractionation + confocal microscopy to show failure of c-Jun into the nucleus in Prkcq-/- mature T cells (Fig 3 G-H). Furthermore, although the majority of the molecular work takes advantage of the more tractable Jurkat cell line for dissection of molecular mechanism, a number of key points are validated in primary mouse or human T cells such as PKC-theta dependent TCR induced association of RanGAP1 with the nuclear pore (Fig 3D-E) and multiple methods of gene deletion were used e.g. siRNA, knockout mouse model and stable CRISPR deletion. The validation of a functionally meaningful phospho-site on the RanGAP1 protein is valuable for further understanding the biology of this protein.

Immune receptor control of nuclear transport machinery has not been extensively studied but, as is highlighted by this study, is increasingly being understood as an important step in immune receptor control of transcription factor function. The molecular mechanism that is uncovered here is novel and interesting to the immunological community as it links TCR signalling to an indirect mechanism for regulating localisation of multiple key transcription factors for the T cell immune response.

There are some concerns listed below. Addressing these concerns would add clarity to the manuscript and support some stated or implied conclusions.

1) The data on the role of PKC-theta driven RanBP2 subcomplex translocation of AP1 transcription factors is largely limited to within 15 min of T cell activation. The broad statements of the paper e.g. line 427 - "PKC-theta plays an indispensable role in NPC assembly" imply that PKC-theta is essential for this process during long-term T cell receptor activation; however, whether PKC-theta deletion has long term impact on nuclear translocation after these first 15 minutes is not established. The demonstration that the RanGAP1 mutant is not able to induce IL-2 production over 24 hrs (Fig 6D) does support the model that a longer-term requirement for RanGAP1 phosphorylation on Ser504/506 is important for translocation and functional AP1 transcriptional outcomes in this system, but from the data presented it does not necessarily follow that PKC-theta is the only regulator of this beyond the 15 min of activation shown here. It is well established that AP1 transcription factors increase in expression for multiple hours after T cell activation and if PKC-theta deletion impact is not long lasting this could mean PKC-theta is important for the kinetics of AP1 translocation but not necessarily for final functional outcome after a longer period of stimulation as is implied here.

2) It has been shown in the published literature the impact of PKC theta deletion on in vivo immune responses has been varied, with studies showing clearance of murine Listeria, LCMV, HSV. The manuscript currently lacks discussion around how the formation of a largely functional immune response in these contexts fits in with the strong defect in nuclear translocation of multiple important T cell transcription factors that they show here.

Reviewer #2 (Public Review):

PKC-theta is a critical signaling molecule downstream of T cell receptor (TCR), and required for T cell activation via regulating the activation of transcription factors including AP-1, NF-kB and NFAT. This manuscript revealed a novel function of PKC-theta in the regulation of the nuclear translocation of these transcription factors via nuclear pore complexes. This novel perspective for PKC-theta function advances our understanding T cell activation. The manuscript provided solid cellular and biochemical evidence to support the conclusions. However, nuclear pore complexes regulate the export and import essential components of cells, it is not clear whether PKC-theta selectively regulates the translocation of above transcription factors, or also other components, and whether regulates both import and export. It is essential to provide more substantial evidence to support the conclusion.

Reviewer #1 (Public Review):

The manuscript by He et al. reveals a novel role for PKC-theta, following T cell receptor (TCR) stimulation, in regulating the nuclear translocation of several key activation-dependent transcription factors by regulating the assembly of key components of the nuclear pore complex (NPC). The authors make use of T cell lines and primary T cells to show that following TCR stimulation, PKC-theta phosphorylates RanGAP1 to promote its interaction with Ubc9 and increase the sumoylation of RanGAP1, which, in turn, enhances assembly of the RanBP2 subcomplex of the NPC that then promotes the nuclear import of AP-1, NFAT and NFB. These conclusions are well supported by a rigorous experimental approach, which included the use of PKC-theta deficient, sumoyltion-defective, kinase-dead, and constitutively active mutants, and RanGAP1-deficient cells.

Reviewer #3 (Public Review):

This manuscript characterizes the additive genetic variance-covariance of behavioural traits and cortisol level in a captive Trinidadian guppy population, in particular to test for the genetic integration of behavioural and physiological stress responses.

The experimental design, trait definitions and statistical analyses appear appropriate. The main weakness of the study is a lack of clarity on the definition of genetic integration and the statistical ways to characterize, confirm or reject genetic integration (in particular, what defines and how to test for a "single major axis of genetic variation"?).

The additive genetic variation-covariation is correctly estimated. The presence of additive genetic correlations and the eigen decomposition of G seem to support genetic integration, but the lack of clear predictions makes the the conclusion not completely clear. Another minor conclusion, that "correlation selection in the past has likely shaped the multivariate stress response" is not directly supported by the results as the argument ignores the possible role of other evolutionary forces (in particular mutational input which is likely to be pleitropic for behaviour and hormone levels).

The nature of genetic (co)variation in behaviour and physiology is poorly known because most quantitative genetic studies of behavioural and physiological traits are still univariate, while it is clear that selection and evolution are better understood as multivariate processes. In addition to presenting some fresh results on the topic, this manuscript provides a mutivariate framework that could be applied in other populations. In particular, eigen decomposition of genetic variance-covariance matrices is not new but its application to the study of stress response integration is original and promising. As the authors mention, such methods could help improve health and welfare in captive animal populations via indirect artificial selection against stress, which is quite an original and stimulating idea.

Reviewer #2 (Public Review):

This paper addresses a fundamental question regarding the evolution of the stress response, specifically that the action of natural selection on the stress response should promote the functional integration of its behavioral and physiological components. Therefore, the authors predict that genetic variation in the stress response should include covariation between its component behavioral and physiological traits. The results are intrinsically interesting and seem to provide a critical proof of principle that, if confirmed, will prompt future follow up research. However, there are some fundamental conceptual and experimental design issues that need to be addressed, in order to assess the conclusions that can be drawn from the results presented here.

Conceptual issues:

1) The authors selected multiple behavioral measures of the stress response but only considered the glucocorticoid response as a physiological trait. In my view this has several problems:

A) Although, for historical reasons and because they are easier to measure, glucocorticoids have been perceived as a stress hormone, the fact is that they respond not only to threats to the organism (i.e. stressors) but also to opportunities (e.g. mating). In other words, glucocorticoids are produced and released whenever there is the need to metabolically prepare the organism for action. Therefore, glucocorticoids are probably not the best physiological candidate to look for phenotypic integration with stress behaviors, since they must have also been selected to be produced and released in other ecological contexts. In this regard it would have been interesting to measure the phenotypic integration of cortisol also with behaviors used in non-threatning but metabolically challenging ecological opportunities (e.g. mating), and to investigate the occurrence of an eventual trade-off (or of a "phenotypic linkage") between these two sets of traits (stress traits vs. mating traits).

B) Sympathetic activation is a key component of the physiological stress response in vertebrates. It is thus odd not to consider the sympathetic response in a study that has the main aim of studying the evolution of a phenotypically integrated stress response. I understand that the sympathetic response in guppies is more difficult to study than measuring cortisol, but this technical challenge can certainly be overcome (e.g. techniques for measuring cardiac response to threat stimuli have been recently developed for other challenging model organisms, such as fruit flies; e.g. https://www.biorxiv.org/content/10.1101/2020.12.02.408161v1); or if not, then an alternative model organism should have been used to address this question.

2) Typically, in vertebrates the behavioral response to a stressor has a passive (e.g. freezing) and an active (i.e. fight-flight) component. It would be very interesting to assess if these two components are phenotypically integrated with each other and each of them with the physiological response. Unfortunately, the authors did not use behavioral measures of each of these two components. Instead they have extracted 3 spatial behaviors from an open field test (time in the central part of the tank in an open field test (OFT); relative area covered; track length) and emergence latency in an emergence from a shelter test. It is not clear how each of the measured behaviors captures these two key components of the behavioral stress response. For example, a fish that freezes in the central part of the tank when it is introduced in the OFT will have a high time in the middle score and eventually a high relative area covered, but relatively low track length. However, if it darts towards the tank wall and freezes there, the result would probably be low time in the middle and low relative area covered. Thus, a fish that has spent approximately the same time in freezing may show very different behavioral profiles according to the variables used here. This could be avoided if explicit measures of fleeing and freezing behavior have been used. Given that the authors have video-tracked the fish, I suggest they can still extract such measures (e.g. angular speed is usually a good indicator of escape/fleeing behavior; and a swimming speed threshold can be validated and subsequently used to detect freezing behavior from tracking data) from the videos. The fact that variables of these two types of behavioral responses to stress have not been used in this study may explain to a large extent why the authors came to the conclusion that, "the structure of G is more consistent with a continuous axis of variation in acute stress responsiveness than with the widely invoked 'reactive - proactive' model of variation in stress coping style".

3) The authors used a half-sib breeding design, which is the golden standard in evolutionary quantitative genetics. However, and this is not a specific critique of the present study but a general problem of this field, the extent to which estimates of G obtained with breeding designs reflect the G that would be obtained by actually sampling a natural population is questionable, because these designs create artificially structured populations with higher levels of outbreeding and concomitantly also with higher genetic variation than what is usually found in nature. This problem can be illustrated by analogy using the example of heritability estimates, which are typically lower when obtained from selection studies by comparing the generation after selection to the one before selection (aka realized heritability), than when computed from artificial breeding designs.

Methodological issues:

4) The authors considered the OFT, ET and ST testing paradigms to be behavioral assays that allow the characterization of the behavioral components of the stress response in guppies, because all these paradigms involve capturing and transferring the focal fish to a novel environment (tank) and in social isolation. Undoubtedly these procedures must have induced stress, however the stressor was not standardized because it consisted in the capture and transfer, and these may have varied from fish to fish (btw are there measures of handling time for each fish? And how to measure "handling intensity"?). In my view a standardized stressor, such as a looming stimulus (e.g. Temizer et al. 2015 Current Biology 25: 1823-34; Bhattacharyya et al. 2017 Current Biology 27, 2751-2762; Hein et al. 2018 PNAS 115: 12224-8), should have been used such that the behavioral measures could have been linked to the stressor in a more controlled way.

5) Moreover, the authors have measured the "stress behaviors" and cortisol in response to two different stressors: the handling described above and the confinement and social isolation for the GC response. This is not the best experimental design, because the behavioral and physiological expression is expected to be linked and to be flexible, as shown by the data on cortisol habituation to repeated stressor exposure. Thus, when the goal of the study is to characterize the co-variation between traits it is critical to standardize the stimulus that triggers their expression in the two domains (behavioral and physiological) and behavior and physiological measures should have been obtained in response to the same stressor stimulus for each individual. In principle, the failure to do so will artificially decrease the observed co-variation between traits, due to environmental differences (i.e. test contexts and their specific stressors).

Reviewer #1 (Public Review):

This paper uses a large breeding colony of guppies to measure genetic correlations between hormonal stress responses and behavior in an open-field test. Although we know a lot about the mechanisms of hormone-mediated behavior, we know less about variation in hormonal systems, particularly genetic variation. Understanding how hormones relate genetically to the behaviors they mediate is particularly important because it helps us understand how the entire hormone-behavior system evolves. A priori, we would expect genetic correlations between hormones and the behavior they underlie, such that selection on the hormone would lead to a response in the behavior and vice versa. However, evidence for this pattern is rare.

Here, the authors show that stress-induced levels of cortisol are repeatable and heritable. Interestingly, they also show that individuals show a lower stress response to later stress and slightly less variation, indicating a G X E interaction. There was a significant genetic correlation between the hormonal response and one of the behaviors measured in the open field test, and the hormone loaded positively in the first genetic principal component along with all the behaviors. This is evidence of an correlated suite of traits that would evolve together in response to selection.

This is an important study, because evidence of genetic variation in hormonal systems, not to mention genetic covariation with hormone-mediated traits, is rare. The results presented here provide insight into how a hormone-behavior complex might adapt to a changing environment. They are also relevant to ideas about the maintenance of variation in coping styles in natural populations.

Reviewer #4 (Public Review):

Coombs et al. aimed to establish a pharmacological tool to distinguish calcium-permeable (CP) AMPA receptors (AMPAR) from calcium impermeable AMPA receptors unambiguously. Towards this end, the authors examined the effects of intracellularly applied NASPM, PhTx-433, PhTx-74, and spermine. The authors showed that NASPM completely blocked outward glutamate-evoked currents with a desensitization blocker, cyclothiazide, from outside-out patch membranes from HEK cells expressing GluA1. In contrast, spermine and PhTx-433/74 partially blocked the outward currents in a voltage-dependent manner (Figure 1). TARPg-2 co-expression reduced potencies of spermine and NASPM, and altered shapes of their conductance-voltage relationship (Figure 2) as well as various kinetics of GluA1, including decay kinetics and recovery kinetics (Figure 3). Further, the authors showed that NASPM blocked GluA1 co-expressed with one of the AMPAR auxiliary subunits, TARPg-2, g-7, CNIH2 GSG1L (Figure 4). Finally, the authors showed that NASPM blocked AMPAR-mediated mEPSC events at +60 mV, but not -70mV, in cultured cerebellar stellate neurons from GluA2 knockout mice. Overall, this manuscript provides high-quality data and critical information about TARPg-2, GluA1, and GluA2 knockout mice.

This provides a solid analysis of GluA1, TARPg-2, 7, CNIH2, GSG1L, and GluA2 knockout neurons. However, it remains unclear whether intracellular NASPM allows an unambiguous functional measure of CP-AMPAR, especially considering many combinations of AMPARs and auxiliary subunits, e.g., GluA1-4 with splicing isoforms, six TARPs, four CNIHs, GSG1L and CKAMP44, etc.

Strengths:

The experimental design to evaluate drugs and receptors with outside-out patch membranes and a piezoelectric device provides the highest-resolution analysis and meaningful information.

Both experiments and analyses are rigorous and of high quality. However, it remains unclear if intracellular NASPM allows an unambiguous functional measure of CP-AMPAR.

Weaknesses:

Because the authors tested a limited combination of receptors and auxiliary subunits, it is difficult to conclude whether NASPM blocks all CP-AMPAR unambiguously.

Slopes of the conductance-voltage relationships are changed upon TARPg-2 co-expression or different concentrations of NASPM.

Reviewer #3 (Public Review):

Calcium-permeable AMPA receptors (CP-AMPARs) have been shown to have important roles in modulating many aspects of neuronal function. They are distinguished from calcium-impermeable AMPARs (CI-AMPARs) by a property known as inward rectification and block by relatively selective polyamine compounds; this relative lack of selectivity has led to caveats in the interpretations of the roles of CP-AMPARs. The authors here demonstrate that complete block of CP-AMPARs, with no apparent effect on CI-AMPARs, can be achieved by intracellular application of the polyamine NASPM. Importantly, the authors provide evidence that this block is apparently not affected by the presence of auxiliary subunits, one of the key caveats regarding prior interpretations of the effects of polyamines and the roles of CP-AMPARs. The authors hypothesize that this new approach, use of intracellular NASPM, can provide greater clarity regarding the role of CP-AMPARs in future.

The approach is sound, the experiments are performed appropriately, the data provided is robust, the presentation is clear, the analyses including statistics are appropriate, the immediate interpretations are therefore fully supported, and the overall manuscript outstanding. The authors appropriately used both a heterologous expression system as well as in vitro neuronal preparation to address their hypotheses. The use of intracellular NASPM to unambiguously distinguish CP-AMPARs from CI-AMPARs has the potential to be transformative in future interpretations about the role of CP-AMPARs, so these findings are very relevant and highly impactful to the field.

Reviewer #2 (Public Review):

This study compares the pharmacology of intracellular polyamine blockers for Ca-permeable (CP-AMPAR) and Ca-impermeable (CI-AMPAR) AMPA receptors in the absence/presence of auxiliary subunits. Spermine is a widely used polyamine blocker to identify CP-AMPARs in native tissue, but the blocking action of spermine varies depending on which auxiliary subunits are associated with the CP-AMPARs. Hence, spermine has limitations. The goal of the present work was to identify if other polyamine blockers would be more efficient than spermine in identifying CP-AMPARs.

The authors studied CP- and CI-AMPARs in heterologous cells (HEK293T) and in primary cerebellar stellate interneurons from mice lacking the GluA2 subunit. They primarily used electrophysiology to assay channel block by various polyamines. While the technology is standard, the experiments are carried out in a rigorous manner and encompass numerous controls and variations on appropriate constructs (GluA2-containing and GluA2-lacking AMPARs and various prominent auxiliary subunits - TARPs, cornichons, and GSG1L).

The main conclusion of the work is that 100 uM NASPM fully blocks CP-AMPAR regardless of the associated auxiliary subunit. This conclusion is strongly supported by experiments including testing various auxiliary subunits in the defined conditions of HEK293T cells as well as recording and demonstrating that NASPM fully blocks AMPAR-mediated currents in stellate cells lacking GluA2 subunits.

I have no major criticisms of the work.

Reviewer #1 (Public Review):

Using various voltage and concentration protocols in a heterologous expression system, the authors provide compelling evidence for strong block of GluR1 AMPA receptors by intracellular NASPM, and unlike spermine, the block is independent of auxiliary subunit expression. The authors also show that intracellular NASPM provides a more complete block than spermine of synaptic currents in GluR2-KO neurons.

Overall the manuscript contains high quality data that is clearly presented. It seems likely that this approach will be useful for assessing the contribution of CP-AMPARs in various scenarios. However, currently the authors have fallen short of providing a comprehensive analysis of the use of NASPM to differentiate between CP and CI AMPARs in intact systems containing multiple AMPAR subunits and auxiliary proteins.

Reviewer #3 (Public Review):

Gill et al. presents an extensive analysis of information/data collected as part of a pertussis vaccine study conducted in Zambia (the basis for an earlier publication, Gill et al., CID 2016). As part of the initial study, the investigators collected serial NP samples from mother/infant pairs at sequential follow-up clinic visits and analyzed them by PCR for the presence of IS481 and, in some cases, ptxS1. The results from these assays were evaluated in conjunction with clinical information on potential manifestations of respiratory illness in the infants and mothers. The authors found important patterns of PCR Ct values, which might not have been considered positive on a single sample PCR from a single patient PCR in a US clinical microbiology lab. Together, however, representing a collection of serial samples from study subjects, they strongly support the proposal that asymptomatic infections occurred in these study subjects. The authors used multiple approaches, including determining a mathematical "Evidence For Infection" or EFI to analyze the data from individual subjects and infant/mother pairs. From the collective data and analytical approaches, the authors provide a compelling case for infections with B. pertussis that are not associated with significant clinical symptoms. This possibility has certainly been considered previously, but not possible to address in the absence of the enormous amount of quantitative data and analysis provided from this prospective study. Another important point made from these data is that PCR Ct values can be useful in other than an all-or-nothing (positive or negative) decision, as is done appropriately with single patient samples submitted to clinical microbiology labs for PCR analysis.

Reviewer #2 (Public Review):

In the current study Gill et al present a retrospective analysis of NP swabs of mother infant pairs taken longitudinally in Zambia. They use qPCR CT values to quantify the amount of IS431 in each sample to detect pertussis infection. They find strong evidence for asymptomatic pertussis infection in both mothers and infants, validating previous work identifying the role of asymptomatic transmitters in populations. This is a tremendously important study and is conducted and analyzed very well. The manuscript is well written, and I heartily recommend publication. Excellent work, well done.

Comments:

This study was done in a population with wP vaccine, I wonder if that's part of the reason many of the CT values are high. Can the authors speculate what this study would look like in a population having received aP for a long period? I'd appreciate more discussion around vaccination in general.

Reviewer #1 (Public Review):

In "Asymptomatic Bordetella pertussis infections in a longitudinal cohort of young African infants and their mothers", the authors analyze longitudinal data from a cohort in Zambia of infant/mother pairs to investigate the evidence for subclinical and asymptomatic infections in both pairs as well as the use of IS481 qPCR cycle threshold (CT) values in providing evidence for pertussis infection. Overall, the manuscript lacks substantial statistical support or clear evidence of some of the patterns they are stating and would require a substantial revision to justify their conclusions. The majority of the manuscript relies on 8 infant/mother pairs where they have evidence of pertussis infection and rely on the dense sampling to investigate infection dynamics. However, this is a very small sample size and further, based on the results displayed in Figure 1, it is not obvious that the data has a very pattern that warrant their assertions.

Major comments:

The main results and conclusions are highly reliant on details from eight mother/infant pairs. However, Figure 1 does not show a clear picture of the fade-in/fade-out. The authors go into great detail describing each of these 8 pairs, however based on the figure and text there does not appear to be clear evidence of an underlying pattern. While there are some instances with a combination of higher/lower IS481 CT values, it does not appear to have a clear pattern. For example, what are possible explanations for time periods between samples with evidence of IS481 and those without (such as pair A, C, D, E, F and H)? There also does not appear to be a clear pattern of symptoms in any of these samples (aside from having fewer symptoms in the mothers than infants). Further, it is not obvious how similar these observed (such as a mixture of times of high or low values often preceded or followed by times when IS481 was not detected) is similar to different to the rest of the cohort (in contrast to those who have a definitive positive NP sample during a symptomatic visit). The main results are primarily a descriptive analysis of these 8 mother/infant pairs with little statistical analyses or additional support.

The authors do not provide evidence or detail about what is known about the variability in IS481 CT values, amongst individuals, or over time, or pre/post vaccination. Without this information, it is not clear how informative some of this variability is versus how much variability in these values is expected. I think particularly in Figure 1, how many of the individuals have periods between times when IS481 evidence was observed when it was not observed, is concerning that these data (at this granular a level) are measuring true infection dynamics. Adding in additional information about the distribution and patterns of these values for the other cohort members would also provide valuable insight into how Figure 1 should be interpreted in this context. As it stands, the authors do not provide sufficient interpretation and evidence for having relevant infection arcs.

It appears that Figure 2A was created using only 8 data points (from the infant data values). If so, this level of extrapolation from such few data points does not provide enough evidence to support to the results in the text (particularly about evidence for fade-in/fade-out population-level dynamics). Also, in Figure 2, it is not clear to me the added value of Figure 2C and the main goal of this figure.

The authors have created a measure called, evidence for infection (EFI), which is a summary measure of their IS481 CT values across the study. However, it is not clear why the authors are only considering an aggregated (sum) value which loses any temporality or relationship with symptoms/antibiotic use. For example, the values may have been high earlier in the study, but symptoms were unrelated to that evidence for infection - or visa versa. This seems to be an important factor - were these possible undiagnosed, asymptomatic, or mild symptomatic pertussis infections? It is not clear why the authors only focus on a sum value for EFI versus other measures (such as multiple values above or below certain thresholds, etc.) to provide additional support and evidence for their results.

It is not clear why the authors have emphasized the novelty and large proportion of asymptomatic infections observed in these data. For example, there have been household studies of pertussis (see https://academic.oup.com/cid/article-abstract/70/1/152/5525423?redirectedFrom=PDF which performed a systematic review that included this topic) that have also found such evidence. While cross-sectional surveys may be commonly used in practice, it is not clear that there is no other type of study that provides any evidence for asymptomatic infections. Further, it is not clear why the authors refer to widespread asymptomatic pertussis when a large proportion of individuals with evidence for pertussis infection had symptoms. Would it not be undiagnosed pertussis if it is associated with clinical symptomatology?

Reviewer #3 (Public Review):

In the manuscript entitled "Allosteric communication in Class A 1 b-lactamases occurs via cooperative 2 coupling of loop dynamics", Galdadas et al. aim to use a combination of nonequilibrium and equilibrium molecular dynamics simulations to identify allosteric effects and communication pathways in TEM-1 and KPC-2. They claimed that their simulations revealed pathways of communication where the propagation of signal occurs through cooperative coupling of loop dynamics. This study is highly relevant to the field as allosteric regulation is believed to be a major signal transduction pathway in several drug-targeted proteins. A better understanding of these regulations could increase the efficacy and specificity of drugs.

Reviewer #2 (Public Review):

This manuscript by Galdadas et. al. used a combination of equilibrium and non-equilibrium simulations to investigate the allosteric signaling propagation pathway in two class-A beta-lactamases, TEM-1 and KPC2, from allosteric ligand binding sites. The authors performed extensive analysis and comparison of the simulated protein allostery pathway with know mutations in the literature. The results are rigorously analyzed and neatly presented in all figures. The conclusions of this paper are mostly supported by previous mutational data, but a few aspects of simulation protocol and data analysis need to be validated or justified.

Line 293, by "comparing the Apo_NE and IB_EQ simulations at equivalent points in time" and perform subtraction "from the corresponding Ca atom from one system to another at 0.05, 0.5, 1, 3, 5ns". It is not clear to me why those time points were chosen? Have authors attempted at validating whether or not the signal from the ligand-binding site has had enough time to propagate across the allosteric signaling pathway? If one considers that the ligand is a spatially localized signal, it requires time to propagate. This is in contrast with the Kubo-Onsager paper cited by authors in which the molecule is responding to a global perturbation such as an external field. However, a local perturbation on one side of the protein will need time to propagate to the other side of the protein (30 angstroms away in this case). A simple and naive example is to map out all the bus stops on one's route. 800 simulations between the first and second stop will not be able to provide the locations of other stops. Since authors have used this "subtraction technique" on several other proteins, it would be nice to clarify how this approach works on mapping out signaling propagation perturbed by local ligand binding/unbinding and how to choose the time points for subtraction.

Another question is whether tracing the dynamics of Calpha alone is enough. As we have seen from the network analysis papers, Calpha sometimes missed some paths or could overemphasize others. The Center of the mass of residue has been proposed to be a better indicator of protein allostery. Authors may wish to clarify the particular choice of Calpah in this study.

In Figure5, the authors seem to use Pearson correlation to compute dynamic cross-correlation maps. Mutual information (M)I or linear MI have advantages over Pearson correlations, as has been discussed in the dynamical network analysis literature.

Reviewer #1 (Public Review):

Galdadas et al. applied a combinatorial approach of equilibrium and nonequilibrium molecular dynamics methods to study two important members of the Class A β-lactamase enzyme family in detail. Authors carefully chose two representative enzymes from this family, TEM-1 and KPC-2 in this study. Understanding of the nature of the communication pathways between allosteric ligand binding site and the active site has been the main focus of this study. Another very interesting finding of this study was the position of clinical variants that was precisely mapped along the allosteric communication pathway. This approach certainly has broad utility as it can be applied to study long-range communications in enzymes that are triggered by binding of a ligand (drug candidate) to an alternative/remote site, and also in cases where certain mutations occur far away from the active site but lead to drug resistance.

Overall, the manuscript is well written, and the conclusions are mostly well supported by data.

Reviewer #2 (Public Review):

In this manuscript, Dahlen et al. aimed to agnostically investigate the association between ABO and RhD blood group and disease occurrence for a large number of disease phenotypes using large-scale population-based Swedish healthcare registries. Using 2 large subject cohorts, they convincingly demonstrate that beyond the known associations between ABO, infectious diseases and thrombosis, there are other associations with very different diseases. This paper is purely epidemiological with no biological data to explain the observed associations. The clinical phenotypes are derived from hospital coding and probably lack precision, especially in terms of diagnostic certainty.

Reviewer #1 (Public Review):

The authors aimed to survey a large transfusion database in Sweden to catalog associations between ABO/RhD blood group antigens and a wide variety of clinical phenotypes in a systematic, unbiased and comprehensive manor. They succeed at surveying over 1200 phenotypes in over 5 million people and identify 49 statistically significant associations for ABO blood group and point out a couple novel associations. Their statistical methods are appropriate and help eliminate potential false positive associations. The strengths of this study are the unbiased survey of a large database and the appropriate corrections for multiple observations which allow the authors to explore a large number of associations without loosing site of what is really a significant association.

This study sheds light on a topic of interest to many scientists. The ABO gene encodes a glycosyltransferase enzyme that has 4 major haplotypes in human populations and results in a specific pattern of posttranslational modification of plasma proteins and blood cells including erythrocytes. Proteins decorated with an H antigen can receive additional carbohydrate antigens from ABO transferase intracellularly. The common A allele transfers UDP-GalNAc while the B allele transfers UDP-Gal. The A2 allele is hypomorphic compared to the A allele and transfers lower amounts of UDP-GalNAc and the common O allele is a null resulting in no transferase activity.

The allele frequencies of these common alleles varies by ancestry and has geographic differences. Variation at ABO is unconstrained with many rare variants contributing to the four common haplotypes at ABO. Interestingly, geographically specific selective pressures may have led to allele frequency differences. For example. ~40-50% of individuals are homozygous for the null (type O) allele. These null haplotypes are more common in individuals of Latino or African ancestry while 'A' haplotypes are slightly more common in individuals of European origin and 'B' alleles are more common in individuals of Asian and African ancestry. Overall, O is more common than A or B alleles. An unbiased survey of phenotype frequencies by blood type allows for confirmation of previous associations and discovery of novel associations. In this largely European ancestry cohort, blood type A is the most common (45-47%) while blood type O is second most common at 38-39%.

Limitations of Phenome-wide Association Studies (PheWAS) like the one presented in this manuscript should be noted. Associations with complex phenotypes or those with small effect size will not be detected even in a large cohort such as the SCANDAT. This study is also biased toward associations with phenotypes more common in the Scandinavian population. This may present associations related to the population substructure and not a direct association with ABO. In genome-wide association studies this can be addressed through multiple methods but it is not clear how the authors correct for population structure in this study. Likewise, the insight into the mechanistic reasons for ABO associations is not a strength of this study and will await subsequent studies for many phenotypes. Mechanistic insight might be particularly interesting for the novel associations uncovered by this study.

Reviewer #3 (Public Review):

The paper by Eyal Ben-David and colleagues reports an elegant single cell experiment in a genetic outcross of C. elegans to show where specific genetic regulation of gene expression could be seen at the level of individual cells. This is the first, to my knowledge, genetic mapping experiment at the single cell level in a complex organism. One neat trick was use the transcript sequencing data for genotyping each individual cell. Another above-and-beyond-the-call-of-duty feature was the permutation tests to set FDR levels, which ended up being similar to Benjamini-Hochberg.

There is complex single cell processing to analyse this data. It could be more clear how complex this analysis is: quite complex models are used to both (a) cluster the cells into cell types across each individual and (b) model the resulting eQTLs. (c) somewhat more routinely, a HMM is used to gentoype but from the single cell transcript data, which is cute. Personally I think more should be made in the main text of the methods, highlighting the complexity of the models (there is at least one parameter this reviewer did not understand why was in the model!). However, a variety of bulk to single cell or single cell to previous experiment data shows that they seem to have discovered correct eQTLs.

A particular focus was on single cell neuronal eQTLs; this plays to the unique "named cell" aspect of C. elegans and this dataset, and did not disappoint. they found a fair number and one that they highlighted had the (rare) antagonistic effect between cell lines, something much discussed or theorised might exist in some cell types - here it is in all its glory. Backing up this was evidence that the single cell neuronal QTL data cannot be seen by "pan neuronal" analysis.

Overall this is an excellent paper; it clarifies much of which has been theorised or discussed, while in many ways (in my view) hiding its methodological sophistication in the main text.

Reviewer #2 (Public Review):

eQTLs can vary between cell types. To capture this in an organism as complex as a mammal looks daunting and expensive if eQTLs have to be mapped a single cell type at a time. However, here the authors propose a 'one pot' method where whole animals are dissociated and the cell types deconvoluted based on a robust set of markers. Thus in a single experiment, eQTLS can be mapped in tens of cell types at once - here they identify 19 major cell types but in the case of the nervous system break it down with even more specificity, down to individual cells.

They test their method in C. elegans which is ideal for this - the lineage is invariant, there are extensive sets of cell type specific markers, and they can exploit their previously published method called ceX-QTL to generate massive pools of segregants using an elegant genetic trick.

Overall I was extremely impressed with the clarity of writing, the care of data analysis, and I honestly found that every analysis I was looking for had been done. They highlight some beautiful findings, most striking of which was the opposing regulation of nlp-21 in two neurons, a perfect example of the resolution this can achieve.

Reviewer #1 (Public Review):

In this manuscript, the authors use single cell RNA sequencing to investigate cell-type specific eQTL within C. elegans. This relies on the well known ability to genotype individuals via their transcriptome allowing the authors to generate both phenotypes and genotypes from single cell transcriptomes. This identifies a blend of cis and trans-eQTL that are cell type specific and starts to provide numerical observations to the communities expectation of cell type specificity.

The use of simultaneous single cell sequencing on a diversity of individuals is a unique method that is absolutely essential to get around the vast scale issues that are presented when contemplating single cell eQTL within multicellular organisms. However, an unfortunate outcome of this approach that the cell-autonomy of the eQTL cannot be studied. Instead the cell types have to be considered completely independent of each other.

The authors conduct an analysis of eQTL per each cell type to get at specificity. This identifies a number of eQTL found in only a single cell type but these binary tests can have an ascertainment issue that may be over-estimating the cell type specificity. Optimally, this would be conducted by incorporating the different cell types as different environments within a single eQTL model but given the different sample sizes, this may not be feasible. Alternatively an investigation of how eQTLs specific to one cell type are or are not found by shifting the detection threshold in the other tissues could test this possibility.

Reviewer #3 (Public Review):

This paper presents an extensive study on providing a large dataset CEM500K, pre-trained models for electron microscopy data. This dataset is provided by the authors as an unlabeled dataset for supporting generalization problems like transfer learning.

Strengths:

— The motivation problem is well defined as the lack of large and, importantly, diverse training datasets of supervised DL segmentation models for cellular EM data.

— A large and comprehensive dataset, CEM500K, including both 2D and 3D images is designed by the authors to overcome this issue.

— The experimental results present the efficiency and prominent role of this dataset in training DL.

Concerns:

— Some of the claims have not been well supported by proofs/references/examples. As an example, the following claim "The homogeneity of such datasets often means that they are ineffective for training DL models to accurately segment images from unseen experiments" would be more valuable if some examples are provided by the authors.

Reviewer #2 (Public Review):

In their manuscript "CEM500K - A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning", the authors describe how they established and evaluated CEM500K, a new dataset and evaluation framework for unsupervised pre-training of 2D deep learning based pixel classification in electron microscopy (EM) images.

The authors argue that unsupervised pre-training on large and representative image datasets using contrastive learning and other methods has been demonstrated to benefit many deep learning applications. The most commonly used dataset for this purpose is the well established ImageNet dataset. ImageNet, however, is not representative for structural biases observed in EM of cells and biological tissues.

The authors demonstrate that their CEM500K dataset leads to improved downstream pixel classification results and reduced training time on a number of existing benchmark datasets a new combination thereof compared to no pre-training and pre-training with ImageNet.

The data is available on EMPIAR under a permissive CC0 license, the code on GitHub under a similarly permissive BSD 3 license.

This is an excellent manuscript. The authors established an incredibly useful dataset, and designed and conducted a strict and sound evaluation study. The paper is well written, easy to follow and overall well balanced in how it discusses technical details and the wider impact of this study.

Reviewer #1 (Public Review):

This manuscript describes the curation of a training dataset, CEM500K, of cellular electron microscope (EM) data including STEM, TEM of sections, electron tomography, serial section and array tomography SEM, block-face and focused-ion beam SEM. Using CEM500K to train an unsupervised deep learning algorithm, MoCoV2, the authors present segmentation results on a number of publically available benchmark datasets. They show that the standard Intersection-over-Union scores obtained with the CEM500K-trained MoCoV2 model, referred to as CEM500K-moco, equal or exceed the scores of benchmark segmentation results. They also demonstrate the robustness of CEM500K-moco's performance with respect to input image transformations, including rotation, Gaussian blur and noise, brightness, contrast and scale. The authors make the remarkable discovery that MoCoV2 spontaneously learned to use organelles as "landmarks" to identify important features in images, simulating human behavior to some degree.

Reviewer #2 (Public Review):

Landemard et al. compare the response properties of primary vs. non-primary auditory cortex in ferrets with respect to natural and model-matched sounds, using functional ultrasound imaging. They find that responses do not differentiate between natural and model-matched sounds across ferret auditory cortex; in contrast, by drawing on previously published data in humans where Norman-Haignere & McDermott (2018) showed that non-primary (but not primary) auditory cortex differentiates between natural and model-matched sounds, the authors suggest that this is a defining distinction between human and non-human auditory cortex. The analyses are conducted well and I appreciate the authors including a wealth of results, also split up for individual subjects and hemispheres in supplementary figures, which helps the reader get a better idea of the underlying data.

Overall, I think the authors have completed a very nice study and present interesting results that are applicable to the general neuroscience community. I think the manuscript could be improved by using different terminology ('sensitivity' as opposed to 'selectivity'), a larger subject pool (only 2 animals), and some more explanation with respect to data analysis choices.

Reviewer #1 (Public Review):

The submitted manuscript 'Distinct higher-order representations of natural sounds in human and ferret auditory cortex' by Landemard and colleagues seeks to investigate the neural representations of sound in the ferret auditory cortex. Specifically, they examine the stages of processing via manipulating the complexity and sound structure of stimuli. The authors create synthetic auditory stimuli that are statistically equivalent to natural sounds in their cochlear representation, temporal modulation structure, spectral modulation structure, and spectro-temporal modulation structure. The authors use functional ultrasound imaging (fUS) which allowed for the measurement of the hemodynamic signal at much finer spatial scales than fMRI, making it particularly suitable for the ferret. The authors then compare their results to work done in humans that has previously been published (e.g. Norman-Haignere and McDermott, 2018) and find that: 1. While human non-primary auditory cortex demonstrates a significant difference between natural speech/music sounds and their synthetic counterparts, the ferret non-primary auditory cortex does not. 2. For each sound manipulation in humans, the dissimilarity increases as the distance from the primary auditory cortex increases, whereas for ferrets it does not. 3. While ferrets behaviorally respond to con-specific vocalizations, the ferret auditory cortex does not demonstrate the same hierarchical processing stream as humans do.

Overall, I find the approach (especially the sound manipulations) excellent and the overall finding quite intriguing. My only concern, is that it is essentially a null-result. While this result will be useful to the literature, there is always the concern that a lack of finding could also be due to other factors.

Major points:

1) What if the stages in the ferret are wrong? The authors use 4 different manipulations thought to reflect key elements of sound structure and/or the relevant hierarchy of the processing stages of the auditory cortex, but it's possible that the dimensions in the ferret auditory cortex are along a different axis than spectro/temporal modulations. While I do not expect the authors to attempt every possible axis, it would be beneficial to discuss.

2) For the ferret vocalizations, it is possible that a greater N would allow for a clearer picture of whether or not the activation is greater than speech/music? While it is clear that any difference would be subtle and probably require a group analysis, this would help settle this result/issue (at least at the group level).

3) Relatedly, did the magnitude of this effect increase outside the auditory cortex?

4) It would be useful to have a measure of the noise floor for each plot and/or species for NSE analyses. This would make it easier to distinguish whether, for instance, in 2A-D, an NSE of 0.1 (human primary) vs. an NSE of 0.042 (ferret primary) should be interpreted as a bit more than double, or both close to the noise floor (which is what I presume).

Reviewer #3 (Public Review):

In this manuscript, the authors studied how cholinergic neurons in the medial septum contribute to the acquisition of spatial memory. The question that is addressed is that of the requirement for the appropriate timing of cholinergic neurotransmission in memory formation. The main finding is that in mice optogenetic stimulation of cholinergic neurons in the medial septum slowed acquisition of a spatial memory task when the stimulation was applied at the goal location, but not during navigation toward the goal location. Stimulation at the goal location also reduced the rate of hippocampal sharp-wave ripples (SWRs), which the authors point to as a possible explanation of the observed learning deficit.

The task-phase specific manipulation of the MS cholinergic neurons is a good and appropriate approach. The effect on learning in the Y-maze task after goal location specific stimulation is both clear and convincing. The lack of a behavioral effect with navigation-only stimulation may be due to ACh levels already being high during this task phase (as the authors suggest). It would have been nice if the authors had also used inhibition to address the importance of timing of ACh neuromodulation.

The authors used prolonged excitatory optogenetic stimulation that lasted anywhere from several seconds (e.g. at goal without reward or running towards goal) to over 30 seconds (e.g. at goal with reward). There are several potential issues with this stimulation protocol:

— From Figure 1B, it appears that the light-induced increase of mean spike frequency is sustained for quite some time after the light is turned off. The sustained activity will make the manipulation in the behavioral task less temporally specific (and thus less task-phase specific). To assess the possible impact of the sustained activation on the findings in the paper, it should be quantified (i.e. duration of sustained activity, dependence on duration of prior light stimulation) - ideally in awake animals (i.e. under the same conditions as the behavioral experiments). Supporting data to better support this conclusion could be provided in a later study (with a link provided to this study), with this caveat appropriately discussed here.

— Prolonged light stimulation could lead to non-specific side-effects. Importantly, the authors controlled for this by performing the same light-stimulation protocol in animals that did not express ChR2. Although non-specific effects of light stimulation were found for theta power, the effects on learning and SWR rate at the goal location could not be explained by non-specific light effects. These data add confidence to the main findings. Still, the number of control animals is low (n=2) and increasing the sample size would make these control experiments more robust. This potential caveat should be mentioned.

— Because the time that animals spent at the goal location is much longer than the travel time to the goal location, the behavioral difference between the "navigation" and "goal" groups could be due to the duration of optical stimulation. The authors point out that the "throughout" group has overall the longest stimulation duration, but an "intermediate" behavioral performance, which would suggest that stimulation duration is not the determining factor.

Unfortunately, the statistical analysis that the authors performed is inconclusive (i.e. the throughout group is not different from either "navigation" or "goal" groups). However, if duration is an important factor, the hypothesis would be that days-to-criterion for "throughout" condition is larger than "goal" condition (i.e. H0: throughout<=goal and H1: throughout>goal). Authors could test this directly (rather than H0: throughout=goal and H1: throughout≠goal). Bayes Factor analysis could help to assess the confidence in H0 rather than concluding that there is a lack of evidence due to low sampling.

Even so, the authors' argument could be weakened if long-term stimulation has reduced efficacy (as suggested by the authors on page 18). To exclude this possibility, changes in the long-term stimulation efficacy should be quantified, e.g. by quantifying the stability of light-induced firing of ACh neurons with the same stimulation protocol as used in awake animals, and/or by checking whether the stimulation-induced reduction of SWR rate gets smaller across trials within a day. Supporting data to better support this conclusion could be provided in a later study (with a link provided to this study), with this caveat appropriately discussed here.

The main novelty of the study is that specific stimulation of cholinergic neurons in the medial septum when animals reach the goal location results in a learning deficit. The reduction of SWRs upon cholinergic stimulation was shown before, but the authors now show that this reduction coincides with and may provide an explanation of the delayed learning. However, the link between the effect of the stimulation on SWRs and the behavioral deficit is indirect and not extremely convincing. This caveat should be discussed and conclusions tempered accordingly. Specific points related to this that should be discussed are described below.

— First, the analysis of SWR rate is performed in a separate set of experiments as in which the behavioral effect is assessed. This makes it difficult to more directly relate the change of SWR rate to the learning deficit.

— Second, the reduction of SWR rate is not absolute and SWRs are still present at lower rate. The data in Figure 4E indicate that for some animals the average SWR rate with stimulation is higher than for other animals without stimulation.

— Third, the Y-maze task used by the authors tests the acquisition of spatial reference memory and bears similarities to the inbound phase of the continuous spatial alternation task in 3-arm mazes. In Jadhav et al. (2012), the inbound phase was not sensitive to selective SWR disruption. These prior data would be an argument against a causative role of the reduction of SWR rate in the observed behavioral deficit.

— Fourth, while the authors briefly discuss other possible causes (e.g. effects on plasticity), they do not appear to consider non-hippocampal contributions or possible interference with reward-related dopamine signaling.

Reviewer #2 (Public Review):

Hay et al. investigated the effect of optogenetic activation of MS cholinergic inputs on hippocampal spatial memory formation, which extended our current knowledge of the relationship between MS cholinergic neurons and hippocampal ripple oscillations.

The authors showed that optogenetic stimulation at the goal location during Y maze task impaired the formation of hippocampal dependent spatial memory. They also found that opto-stimulation at the goal location reduced the incidence of ripple oscillations, while having no effect on the power and frequency of theta and slow gamma oscillations.

Interestingly, the authors reported different results compared to previously published work by applying the analytical methods developed by Donoghue et al. (Donoghue et al., Nat Neurosci, 2020). They showed that optogenetic activation of MS cholinergic neurons during sleep not only reduced the incidence of hippocampal ripple oscillations, but also increased the power of both theta and slow gamma oscillations, which is contradict to both decreased or no change of theta and gamma power by previous reports (Vandecasteele et al., 2014, Ma et al., 2020). These results are valuable to the community of hippocampal oscillation studies.

Reviewer #1 (Public Review):

In this study, the authors set out to address the interesting question of how activating septal cholinergic neurons affects learning and memory of reward locations. The work provides compelling evidence showing that activation of septal cholinergic cells at reward zones suppresses sharp wave-ripples and impairs memory performance in freely behaving animals. The data are properly controlled and analyzed, and the results support the conclusions. The results shed new light on the functional significance of cholinergic projections in reward learning. Future follow-up studies designed to selectively activate cholinergic projections specifically at times when sharp wave-ripples occur will be interesting to determine the importance of cholinergic sharp wave-ripple suppression for these effects.

Reviewer #3 (Public Review):

In the paper entitled "Stress Resets Transgenerational Small RNA Inheritance" Houri-Ze'evi L, Teichman G et al examine the interaction between multiple heritable phenotypes by knocking down a heritable GFP reporter and examining its interaction with other stresses, such as starvation and high temperature, which cause transgenerationally heritable phenotypes. They demonstrate that exposing worms to stresses inhibits the transgenerational silencing of the GFP reporter strain they use. They further demonstrate that deletion of genes involved in the MAPK pathway, the skn-1 transcription factor and the putative H3K9 methyltranferase met-2 eliminate the differential response in the F1 and F2 generations after exposure to stress and the GFP reporter silencing. They also sequence the small RNAs in the P0 and F1 generation with and without the added stresses.

All in all, the authors have expanded the mechanistic understanding of how heritable small RNAs are influenced by environmental conditions. I think that the conservation of several of the known regulators of epigenetic inheritance appearing in this study reflects how the regulators of non-genetic inheritance are beginning to converge on a few central pathways. The bit about MET-2 is still a bit premature as it's link to SKN-1 and regulated small RNAs is not completely fleshed out here, but I'm sure future studies will help delineate how this putative methyltransferase is communicating with SKN-1 on a more mechanistic level. Future studies examining how and why the MAPK pathway is so critical in this inheritance paradigm will be interesting.

Reviewer #2 (Public Review):

In humans, extreme stresses, such as famine, can trigger multi-generational physiological responses through altered metabolism. In C. elegans, environmental stresses, such as heat shock, can similarly promote changes in gene expression and physiology. In addition, researchers observed more than two decades ago that dsRNA triggers can silence gene expression transgenerationally. This manuscript by Houri-Zeevi et al., entitled "Stress resets ancestral heritable small RNA responses", seeks to tie these two observations in C. elegans together mechanistically, showing that environmental stress (heat shock, high osmolarity, or starvation) can alter the small RNA populations in adults and their progeny, affecting their gene expression levels. The authors used a GFP reporter as a proxy for exo-siRNA levels in various experimental paradigms. P0 animals were fed dsRNA targeting the GFP transgene, and their F1 progeny were subjected to one of the environmental stresses. The GFP expression levels of P0, F2, and F2 adults were measured, showing that the stressed F1 and their F2 progeny have increased de-silencing of the GFP transgene compared to controls. The authors also performed small RNA sequencing on these populations, showing that a subset of small RNAs become "reset" or decreased after stress, while a different subset was increased. Additionally, the p38 MAPK pathway, SKN-1 TF, and MET-2 H3K4me1/2 HMT were shown to be required for the stress-dependent changes in transgene de-silencing. The manuscript is well-written and contains some very interesting and convincing results that should be of broad interest to the fields of stress biology and RNAi.

Reviewer #1 (Public Review):

Here, Houri-Ze'evi, et al. treated progeny of parents that had inherited small RNA response (silencing of an artificial, single-copy GL-expressed gfp with anti-gfp dsRNA) with 3 stresses – heat shock, hyperosmolarity, or starvation – starting at L1, and examined gfp silencing. All three treatments reduced silencing (visible as increased GFP fluorescence) in subsequent generations (F1-F3). The authors tested resetting of endogenous (endo-siRNA) and piRNAs using an endo-siRNA sensor target sequence and piRNA recognition sites, respectively. Again, all 3 stressors reset both in the same generation, but did not reset the effect transgenerationally, suggesting that exogenous RNAi resetting functions through a different mechanism than endogenous.

Next, they tested adults, which also led to resetting. However, only the F1 generation, not F2, is susceptible to resetting (how? Why?), revealing a critical period for resetting susceptibility. Reversal of the stress with RNAi treatment does not result in resetting, nor does simpy changing conditions. The authors then went on to examine mutants that might be defective in stress responses or in resetting; MAPK genes and skn-1 are required for resetting. Small RNA-seq from stressed worms and their progeny showed a decrease overall with stresses, and reveals some potential classes of genes, including targets of the mutator genes, and overlap with classic stress response pathways (dauer, IIS). Overall, this work presents some interesting phenomena and moves towards explaining how it might work through the identification of a critical period and some genes that are required.

In this version, the authors have added more information regarding the relationship between MAPK and SKN-1, and transcriptional targets. Most importantly, they have performed tissue-specific rescue of sek-1; in neurons, this rescues, but intestine did not.

These data add to prior work from the Rechavi lab and others in the field, which together address the interplay of small RNAs, response to stress, and transgenerational inheritance.

Reviewer #3:

The authors present the algorithm clearly by comparing it to the most popular SMLM clustering algorithms and showing its robustness in varying density SMLM data, which is a big problem in the field. The presented experimental test on 3D LAMP-1 SMLM data also contributes to the robustness of the paper.

While reading the manuscript, I missed a comparison with another graph-based SMLM clustering algorithm published previously by Khater et al. in relation to accuracy and computation speed, which is particularly important to demonstrate the advantages of StormGraph. The approach should also be included in Table 1. I also think that a direct comparison in terms of accuracy and computation speed is crucial.

During the review process, a similar paper has been posted to bioRxiv dated 22. December, https://www.biorxiv.org/content/10.1101/2020.12.22.423931v1.full so the authors could not be aware of this work; however, it would be nice if the authors could comment on this work.

Reviewer #2:

In their paper "A graph-based algorithm called StormGraph for cluster analysis of diverse single-molecule localization microscopy data", Scurll et al. present a new algorithm to identify clusters in single-molecule localization microscopy (SMLM) data. They use graph-based clustering and show that StormGraph outperforms a selection of existing algorithms, both on simulated and experimental data. The improvement seems not huge, but is convincing, thus this work presents an important contribution to the field. Naturally, not all competing algorithms could be benchmarked in comparison to StormGraph, thus it is not clear if this algorithm is indeed among the best performing algorithms. This is especially true for the cross-correlation analysis. If the applicability of the software included with the manuscript was extended to more potential users, this could be a useful contribution to the field. The manuscript is well written, but quite long. The information content would not be jeopardized if part of the main text and some figures were to be moved to the supplementary information or methods section.

Reviewer #1:

Single molecule localization microscopy (SMLM) has become an important method for understanding the subcellular distribution of fluorescently labelled biomolecules at length scales of a few tens of nanometers. A critical challenge has been to find out, whether and to what extent biomolecular clustering occurs. While methods have been published which address the problem of identifying biomolecular clusters in SMLM images, they still suffer from many user-defined parameters, which - if selected inappropriately - influence the obtained results substantially. The StormGraph-3D method proposed here addresses these issues, based on a comprehensive mathematical framework which reduces the number of user-defined input parameters. The method was evaluated using comprehensive simulations of data, which show its robustness compared to alternative approaches.

The methods part of the paper would benefit, however, from more realistic data of single molecule blinking behavior, and the evaluation of the consequences on the performance of the method. As the authors acknowledge, overcounting due to blinking has challenged data analysis previously, and gave rise to artifactual localization clusters that do not represent the underlying protein distribution. It would be of particular interest, which results in the method yielded for a random biomolecular distribution.

Reviewer #2:

This is a very interesting study, examining the properties of different types of neurons in the primate Frontal Eye Fields. It is commonly assumed that a serial processing of information takes place in the frontal lobe, from visual representation, to working memory maintenance, to motor output. However, some evidence to the contrary has also been reported, creating a debate in the field. The authors have characterized meticulously FEF neurons receiving V4 projections, by means of orthodromic stimulation. They report two main findings: that visual-input recipient neurons in FEF exhibit substantial motor activity and that working memory alters the efficacy of V4 input to FEF. The paper provides an important addition to our understanding of FEF processing. Although the first result is unambiguous, and goes against the traditional view of the FEF, the interpretation of the second is less straightforward and would need to be qualified further.

1) Orthodromic activation of FEF neurons via V4 stimulation increases the percentage of FEF events that lead to spikes and decreases their latency during working memory. Such an effect appears expectable if FEF neurons are at a higher level when a stimulus in their receptive field is held in memory compared to a stimulus out of their receptive field. Are the authors suggesting something special about working memory? Would the same outcome not be expected during fixation or smooth pursuit for FEF neurons that are activated by these states? It was not clear that the efficacy of transmission itself improves by working memory, just the likelihood that the spiking threshold would be reached.

2) It would strengthen the author's thesis to discuss the existing functional evidence (in addition to anatomical evidence) that motor FEF neurons receive visual input and can plan movements accordingly. See for example Costello et al. J. Neurosci 2013, 33(41):16394-408.

3) The authors match the receptive location of FEF and V4 neurons to maximize the chances of identifying monosynaptically connected neurons between the two areas. However, a negative finding of ia orthodromic activation does not entirely rule out that the FEF neuron under study receives V4 input, from another site. Some discussion is warranted on this point.

Reviewer #1:

The authors of Working Memory Gates Visual Input to Primate Prefrontal Neurons studied how working memory influences information transmitting from V4 to frontal eye field via extracellular recording and electrical stimulation on behaving primate. They found that V4 neurons target FEF neurons with both visual and motor properties, and its synaptic efficacy of V4 to FEF was enhanced by working memory. These findings are interesting and important to our understanding about how our brain acts during daily WM-related activity.

1) In classical working memory tasks, the task periods usually consist of fixation, cue, delay and then a response period. The neural activity during the delay period is typically considered to be a working memory-related signal. However, in the current study, the authors didn't point out whether only delay period activity was included in analysis when they compared synaptic efficacy between stimulation and non-stimulation trials, in Figure 4a. Because the differences of neuronal response during fixation period cannot be viewed as relevant to information held in working memory, it may be better if only neuronal activity in the delay period was included in their analysis.

2) Did the 96 visual-recipient FEF neurons exhibit working memory-related activity in their memory guided saccade task? The example neuron in Figure 3a didn't show significant difference between In and Out trials during the delay period. If the visual-recipient neuron didn't present working memory related activity, how could the authors say enhanced synaptic efficacy from V4 to FEF was caused by working memory?

3) Did the two example neurons in Figure 4c show adjusted values (subtracting the same measure during non-stimulated trials)? The authors mentioned in Method that Figure 4 showed adjusted values, but it may not be applicable for raster plot in Figure 4c. It may be helpful that using adjusted values show stimulation effects on evoked spike counts during memory In and Out trials.

4) Did the authors find some FEF cells showing elevated firing during delay period in outside-RF trials compared with baseline firing? These elevated firing was not caused by RF cue, may underlying working memory signal.

5) The sample size should be indicated in Figure 3b Venn diagram.

6) It's better to indicate electrical stimulus protocol in Figure 1.

Reviewer #3:

The use of frequency tagging to analyze continuous processing at phonemic, word, phrasal and sentence-levels offers a unique insight into neural locking at higher-levels. While the approach is novel, there are major concerns regarding the technical details and interpretation of results to support phrase-level responses to structured speech distractors.

Major concerns:

1) Is the peak at 1Hz real and can it be attributed solely to the structured distractor?

• The study did not comment on the spectral profile of the "attended" speech, and how much low modulation energy is actually attributed to the prosodic structure of attended sentences? To what extent does the interplay of the attended utterance and distractor shape the modulation dynamics of the stimulus (even dichotically)?

• How is the ITPC normalized? Figure 2 speaks of a normalization but it is not clear how? The peak at 1Hz appears extremely weak and no more significant (visually) than other peaks - say around 3Hz and also 2.5Hz in the case of non-structured speech? Can the authors report on the regions in modulation space that showed any significant deviations? What about the effect size of the 1Hz peak relative to these other regions?

• It is hard to understand where the noise floor in this analysis - this floor will rotate with the permutation test analysis performed in the analysis of the ITPC and may not be fully accounted for. This issue depends on what the chosen normalization procedure is. The same interpretation put forth by the author regarding a lack of a 0.5Hz peak due to noise still raises the question of interpreting the observed 1Hz peak?

2) Control of attention during task performance

• The authors present a very elegant analysis of possible alternative accounts of the results, but they acknowledge that possible attention switches, even if irregular, could result in accumulated information that could emerge as a small neurally-locked response at the phrase-level? As indicated by the authors, the entire experimental design to fully control for such switches is a real feat. That being said, additional analyses could shed some light on variations of attentional state and their effect on observed results. For instance, analysis of behavioral data across different trials (wouldn't be conclusive, but could be informative)

• This issue is further compounded by the fact that a rather similar study (Ding et al.) did not report any phrasal-level processing, though there are design differences. The authors suggest differences in attentional load as a possible explanation and provide a very appealing account or reinterpretation of the literature based on a continuous model of processing based on task demands. While theoretically interesting, it is not clear whether any of the current data supports such an account. Again, maybe a correlation between neural responses and behavioral performance in specific trials could shed some light or strengthen this claim.

Additional comments:

• What is the statistic shown for the behavioral results? Is this for the multiple choice question? Then what is the t-test on?

• Beyond inter-trial phase coherence, can the authors comment on actual power-locked responses at the same corresponding rates?

Reviewer #2:

This paper by Har-shai Yahav and Zion Golumbic investigates the coding of higher level linguistic information in task-irrelevant speech. The experiment uses a clever design, where the task-irrelevant speech is structured hierarchically so that the syllable, word, and sentence levels can be ascertained separately in the frequency domain. This is then contrasted with a scrambled condition. The to-be-attended speech is naturally uttered and the response is analyzed using the temporal response function. The authors report that the task-irrelevant speech is processed at the sentence level in the left fronto-temporal area and posterior parietal cortex, in a manner very different from the acoustical encoding of syllables. They also find that the to-be-attended speech responses are smaller when the distractor speech is not scrambled, and that this difference shows up in exactly the same fronto-temporal area--a very cool result.

This is a great paper. It is exceptionally well written from start to finish. The experimental design is clever, and the results were analyzed with great care and are clearly described.

The only issue I had with the results is that the possibility (or likelihood, in my estimation) that the subjects are occasionally letting their attention drift to the task-irrelevant speech rather than processing in parallel can't be rejected. To be fair, the authors include a nice discussion of this very issue and are careful with the language around task-relevance and attended/unattended stimuli. It is indeed tough to pull apart. The second paragraph on page 18 states "if attention shifts occur irregularly, the emergence of a phase-rate peak in the neural response would indicate that bits of 'glimpsed' information are integrated over a prolonged period of time." I agree with the math behind this, but I think it would only take occasional lapses lasting 2 or 3 seconds to get the observed results, and I don't consider that "prolonged." It is, however, much longer than a word, so nicely rejects the idea of single-word intrusions.

Reviewer #1:

The present study sought to better characterize how listeners deal with competing speech streams from multiple talkers, that is, whether unattended speech in a multitalker environment competes for exclusively lower-level acoustic/phonetic resources or whether it competes for higher-level linguistic processing resources as well. The authors recorded MEG data and used hierarchical frequency tagging in an unattended speech stream presented to one ear while listeners were instructed to attend to stories presented in the other ear. The study found that when the irrelevant speech contained structured (linguistic) content, an increase in power at the phrasal level (1 Hz) was observed, but not at the word level (2 Hz) or the sentence level (0.5 Hz). This suggests that some syntactic information in the unattended speech stream is represented in cortical activity, and that there may be a disconnect between lexical (word level) processing and syntactic processing. Source analyses of the difference between conditions indicated activity in left inferior frontal and left posterior parietal cortices. Analysis of the source activity underlying the linear transformation of the stimulus and response revealed activation in the left inferior frontal (and nearby) cortex. Implications for the underlying mechanisms (whether attentional shift or parallel processing) are discussed. The results have important implications for the debate on the type and amount of representation that occurs to unattended speech streams.

The authors utilize clever tools which arguably provided a unique means to address the main research question, i.e., they used hierarchical frequency tagging for the distractor speech, which allowed them to assess linguistic representations at different levels (syllable-, word-, phrase-, and sentence-level). This technique enabled the authors to make claims about what level of language hierarchy the stimuli are being processed, depending on the observed frequency modulation in neural activity. These stimuli were presented during MEG recording, which let the authors assess changes in neurophysiological processing in near real time--essential for research on spoken language. Source analyses of these data provided information on the potential neural mechanisms involved in this processing. The authors also assessed a temporal response function (TRF) based on the speech envelope to determine the brain regions involved at these different levels for linguistic analysis of the distractor speech.

Critiques:

Speech manipulation:

In general, it is unclear what predictions to make regarding the frequency tagging of the unattended distractor speech. On the one hand, the imposed artificial rhythmicity (necessary for the frequency tagging approach) may make it easier for listeners to ignore the speech stream, and thus seeing an effect at higher-level frequency tags may be of greater note, although not entirely plausible. On the other hand, having the syllables presented at a consistent rate may make it easier for listeners to parse words and phrasal units because they know precisely when in time a word/phrase/sentence boundary is going to occur, allowing listeners to check on the irrelevant speech stream at predictable times. For both the frequency tagging and TRF electrophysiological results, the task-irrelevant structured speech enhancement could be interpreted as an infiltration of this information in the neural signal (as the authors suggest), but because the behavioral results are not different this latter interpretation is not easily supported. This pattern of results is difficult to interpret.

Behavioral Results:

Importantly, no behavioral difference in accuracy was observed between the two irrelevant speech conditions (structured vs. non-structured), which makes it difficult to interpret what impact the structured irrelevant speech had on attentive listening. If the structured speech truly "infiltrates" or "competes" for linguistic processing resources, the reader would assume a decrease in task accuracy in the structured condition. This behavioral pattern has been observed in other studies. This calls into questions the face validity of the stimuli and task being used.

Attention:

In this study activation of posterior parietal cortex was found, that could be indicative of a strong attentional manipulation, and that the task was in fact quite attentionally demanding in order for subjects to perform. This may align with the lack of behavioral difference between structured and non-structured irrelevant stimuli. Perhaps subjects attempted to divide their attention which may have been possible between speech that was natural and speech that was rather artificial. The current results may align with a recent proposal that inferior frontal activity may be distinguished by language selective and domain general patterns.

Lack of word level response:

A major concern is that the results do not seem to replicate from an earlier study with the same structured stimuli, i.e., the effects were seen for sentence and word level frequency tagging. As the authors discuss, it seems difficult to understand how a phrasal level of effect could be obtained without word-level processing, and so a response at the word level is expected.

Familiarization phase:

The study included a phase of familiarization with the stimuli, to get participants to understand the artificial speech. However it would seem that it is much easier for listeners to report back on structured rather than unstructured stimuli. This is relevant to understanding any potential differences between the two conditions. It is unclear if any quantification was made of performance/understanding at this phase. If there is no difference in the familiarization phase, this might explain why there was no difference in behavior during the actual task between the two conditions. Or, if there is a difference at the familiarization phase (i.e. structured sequences are more easily repeated back than non-structured sequences), this might help explain the neural data result at 1 Hz, given that some higher level of processing must have occurred for the structured speech (such as "chunking" into words/phrasal units).

Joint Public Review:

Eisele et al. evaluated the direct action of erythropoietin (EPO) on hematopoietic stem and progenitor cells that included hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs). They used cellular barcoding to enable in vivo tracking of cellular output and then used scRNA-seq to corroborate their findings. They observed the transiently promoted output of Myeloid-Erythroid (ME)-biased and Myeloid-B-cell (MB)-biased clones. Single-cell RNA sequencing analysis revealed that EPO acted mostly on MPP1 and MPP2. Based on these data, the authors concluded that EPO acts directly on MPPs and transiently modulates their output. Although the conceptual advance brought by this study is incremental as similar findings have been presented by previous studies, the integration and use of both barcoding and scRNA-seq adds strength to the conclusions reached in the present study.

Reviewer #3 (Public Review):

In this manuscript, Filipowicz and Aballay present a nice story that characterizes a new learned behavioral phenotype prompted by intestinal distention during infection with the bacterial pathogen E. faecalis. The authors show that distention of the anterior portion of the intestine by E. faecalis induces an aversive behavioral response. Importantly, the authors show that this aversive learning response is controlled by multiple sets of neurons, including some that express the GPCR NPR-1 and others that express the ion channels TAX-2/4. The authors nicely showed that TAX-2 expression in ASE neurons was sufficient for pathogen avoidance, but not other chemosensory neurons. Next the authors examined the mechanism of aversive learning following ingestion of E. faecalis, showing that AWB and AWC neurons are required. Finally, the authors show that two proteins that could be mechanoreceptors in the intestine (GON2 and GTL-2) are required for pathogen avoidance. Together these data characterize important mechanisms of pathogen avoidance and an aversive learning response.

I have one issue for the authors to consider. The title of the manuscript emphasizes the role of TRPM channels in mediating the learned pathogen avoidance response. Demonstrating that the site of action of the TRPM channels is the intestine could further strengthen this exciting finding.

Reviewer #2 (Public Review):

Work in the nematode C. elegans has shown that these worms learn to avoid pathogens like Pseudomonas aeruginosa after consumption and infection over a period of 12 or more hours. Here, the authors confirm and expand upon earlier observations that - in contrast to P. aeruginosa - avoidance of Gram-positive pathogens such as E. faecalis, E. faecium and S. aureus occurs rapidly on a timescale as short as even several minutes. Consistent with this more rapid response, they present evidence that behavioral avoidance occurs via distinct molecular, neuronal and phenotypic mechanisms from those of P. aeruginosa.

The first major finding that the authors describe is that behavioral avoidance of E. faecalis occurs as a consequence of rapid intestinal distension and not through immune responses or other pathways. They show that anterior intestinal distension occurs rapidly - as early as 1 hr, which is a striking finding and is consistent with rapid behavioral effects. They show that neither E. faecalis bacterial RNA, nor bacterial virulence are necessary for behavioral avoidance and that immune response genes are induced only after distension. These data are consistent with a model in which intestinal distension underlies behavioral avoidance, but this assertion could be strengthened by showing that bloating is necessary for behavioral avoidance, that it occurs prior to observable behavioral avoidance, and by more definitively ruling out a role for immune responses.

Next, the authors show that behavioral avoidance in laboratory conditions requires intact neuropeptide signaling via the npr-1 receptor and this is because worms tend to avoid high oxygen conditions outside of bacterial lawns that typically exists in the lab. At lower oxygen concentrations, npr-1 is dispensable for avoidance. This is consistent with previous work implicating this neuropeptide pathway in lawn avoidance and is convincingly demonstrated.

The second major finding presented in this manuscript is that rapid behavioral avoidance of Gram-positive bacteria occurs via a learning process involving both gustatory and olfactory neurons. This suggests that worms may rapidly learn to avoid the taste and smell of these bacteria. They show that lawn avoidance of E. faecalis occurs in minutes and coincides with changes in lawn leaving and re-entry rates. They identify sensory neurons involved in lawn avoidance through genetic ablation and cell-specific rescue of signal transduction in the ASE, AWC and AWB neurons. A role for ASE in avoidance is specific to E. faecalis and is a new finding. The authors also show that after a 4hr training exposure to E. faecalis, worms switch from their naïve preference for E. faecalis odors to preferring E. coli odors. This switch in olfactory preference appears to require the AWC and AWB neurons, but not the ASE neurons. While the authors show a clear change in olfactory preference with these data, it is currently unclear whether this reflects associative learning as opposed to non-associative olfactory plasticity resulting from, for example, intestinal distension. Previous work from this group showed that longer-term bloating from bacteria could induce avoidance of different bacteria, arguing against a strictly associative learning role for previously described bloating phenotypes. It is also not currently clear from the authors' data whether ASE plays a role in training-dependent changes in food preference, how this training process relates to the timecourse of intestinal distension, and what role nutrient status might play here.

Lastly, the authors present the intriguing hypothesis that TRPM family channels may sense bloating either directly or indirectly to mediate this colonization-dependent aversive behavior. Mutations in TRPM channels gon-2 and gtl-2 block lawn aversion that occurs after intestinal distension elicited by E. faecalis colonization or through interference with the defecation motor program. The authors convincingly show that these channels, which are expressed in the intestine but also play known roles in the germline, do not act via the germline in this context. The hypothesis that these channels act in the intestine to sense bloating is an exciting and particularly important one; however, both of these channels are known to be expressed in multiple tissues, and there is no data demonstrating a sensory function for these receptors in the intestine as opposed to other roles.

Reviewer #1 (Public Review):

In this work, the authors set out to better understand the mechanisms by which the nematode C. elegans responds to bacterial pathogens.

Using behavioral assays and genetic manipulations, the authors find that C. elegans can rapidly learn to avoid the pathogen E. faecalis (E.f.). While recent studies from other groups have shown that small RNAs (sRNAs) produced by some pathogenic bacteria can trigger aversive learning, the authors find that this seems not to be the case for E. faecalis. Instead, they provide evidence that E. faecalis causes abdominal distention, and that this may provide the trigger for learning. Because the evidence for this is largely correlative, alternative explanations may still be possible. Further, the authors identify two TRPM-class ion channels whose function appears to be necessary for learned avoidance of E.f. The authors propose that one or both of these may mediate detection of abdominal distention, an interesting idea that merits further study. While the paper's title indicates that these channels "mediate" this function, this remains speculative.

The authors also find that wild-type C. elegans prefer olfactory stimuli from E.f. to those of their regular diet, E. coli, but that this pattern is reversed after exposure to E.f. This plasticity involves the function of the chemosensory neurons ASE, AWC, and AWB, as well as the cyclic-nucleotide-gated channel TAX-2/TAX-4. This finding provides important insight into the nature of the changes in neural circuit function that are triggered by pathogen exposure, leading to pathogen avoidance.

The paper also examines a role for the neuropeptide receptor npr-1 in learned E.f. avoidance. Animals lacking npr-1 function are known to strongly avoid high (ambient) oxygen concentrations, and instead prefer the lower-oxygen environment of a bacterial lawn. The authors find that this oxygen avoidance overcomes any avoidance of E.f.; thus, npr-1 mutants do not avoid E.f. when tested with ambient oxygen, but they do avoid it in a low-oxygen environment. This indicates that npr-1 is not required for pathogen avoidance per se. Although the authors suggest that npr-1 may be a target of the learning process, this is not well justified by the data and it may be more likely that oxygen avoidance and pathogen avoidance are separate processes.

Together, these findings demonstrate that the mechanisms underlying learned pathogen avoidance in C. elegans differ substantially depending on the nature of the pathogen, and that worms likely use a combination of strategies to deal with these threats in the wild.

Reviewer #2 (Public Review):

This manuscript addresses how myeloid cells are rapidly regenerated during periods of consumptive stress, such as that what occurs during infection. The authors defined a novel migration pattern activated upon inflammation wherein bone marrow-derived myeloid progenitors rapidly seed lymph nodes to produce dendritic cells. Using an in vivo model (injection of LPS) they demonstrated systemic inflammation was necessary for triggering this migratory pathway. A key observation was that prior to detection in the blood, myeloid progenitors were detected in the lymphatics, including the thoracic duct and lymph nodes. Using a combination of imaging strategies, in vitro assays, and transplantation assays the specific myeloid differentiation of these progenitors was revealed: progenitors in lymphatics did not have stem cell function but maintained potential to generate dendritic cells. Using adoptive transfer experiments they determined that labeled progenitors did not home to the bone marrow after LPS. Moreover, prior to their detection in the lymph nodes, these progenitors were found in close proximity to lymphatic endothelial cells in the bone, as determined with intra vital imaging of Lyve-1-GFP mice. They also observed the existence of Lyve-1+ vessels in the bone of LPS treated mice, rarely observed in controls. Therefore, it was concluded that myeloid progenitors are released from the bone marrow and enter the lymphatics very rapidly upon LPS challenge via a network of lymphatic vessels in the bone.

To determine mechanisms that were required for this migratory pathway, they first focused on the signaling molecule TRAF6, a key signaling protein downstream of TLR signaling. Using Mx1-Cre inducible TRAF6 deficiency they observed reduce mobilization of progenitors and found a cell-autonomous defect in migration towards LPS-stimulated cells in vitro. These chemotactic assays were used to identify the specific role of myeloid cells in driving migration of progenitors. The authors ruled out the role of NF-kB signaling via over-expressing the degradation-resistant mutant of IkBa, but revealed that protein-trafficking was necessary for progenitor mobilization. Analysis of chemokines and potential factors that could drive this trafficking pattern identified the chemokine CCL19 and its receptor CCR7 in migration. In vivo targeting of this pathway via antibody blockade experiments demonstrated that CCL19 and CCR7 were required for the myeloid progenitor mobilization, and, furthermore, that the mature myeloid (CD11b+CD11c+) cells in the LNs were sources of CCL19.

The main strengths of this manuscript include: (1) the intriguing and novel observation of lymphatic migration early during inflammation; (2) the various techniques used to address the questions, including imaging and flow cyotmetric analysis, as well as functional assays; and (3) the thorough mechanistic model they have built through their investigation of signaling molecules and the chemokine-receptor interactions necessary for dendritic cell replenishment. Using the Lyve-1 mouse, they were able to identify vessels in the bone, suggesting a specific route for migration. They were also able to determine that the Lin- progenitors were in close proximity to these vessels upon LPS challenge and differentiated into dendritic cells. The ability of myeloid cells to rapidly release preformed CCL19 was also dependent on TRAF6, thus suggesting that mature cells in the lymph nodes initiate recruitment of CCR7+ myeloid progenitors, highlighting a novel circuitry of regeneration.

This study is very comprehensive, though there are several questions remaining: (1) the conclusion regarding the physiological role of this early response in survival is not well supported by the data; (2) the link with observations in humans is not robust; (3) a number of questions regarding progenitor survival and proliferation remain. First, studies revealing enhanced mortality when CCR7 is blocked or when CCL19 production is lacking may be due to impacts on a variety of other cell types, most notably T regulatory cells. The reason these mice die faster was not carefully investigated and is unclear. While the authors conclude it is due to reduced anti-inflammatory dendritic cells, they provide very little data to support this. Second, data presented in the manuscript highlighting the presence of side population cells in human lymph nodes under specific conditions is consistent with the observations in the mouse model. However, the authors do not investigate functional potential in detail and do not account for abundance of mature cells in these lymph nodes (particularly the lymphoma patients, that may result in decreased frequency of HSPCs). Finally, though the findings are very interesting and the studies are robust, one potential concern is that TRAF6 is downstream of a variety of innate signaling pathways and, in general, the dysfunction of myeloid cells may be profound and beyond the conclusion of directing migration, as TRAF6-dependent proliferation may also contribute to the observations made in vivo.

Overall, this is a compelling story and reveals a novel migratory pathway that may operate in a variety of settings to replenish immune cells to maintain homeostasis, and how this trafficking is impacted in different immune/inflammatory and diseased states warrants more investigation.

Reviewer #1 (Public Review):

In this manuscript the authors demonstrate that acute systemic inflammation induces a new system of rapid migration of granulocyte-macrophage progenitors and committed macrophage-dendritic progenitors but not other progenitors or stem cells from BM to lymphatic capillaries. This traffic is mediated by Ccl19/Cccr7 and is NfkB independent but Traf activation dependent. This type of trafficking is anti-inflammatory with promotion of early survival.

Specifically, authors work shows the traffic of DC-biased myeloid progenitors through direct transit from BM to bone lymphatic capillaries. This type of trafficking is highly activated in endotoxic inflammation. Giving LPS to mice results in massive mobilization of myeloid progenitors from the BM to lymph and retention in LN takes place. This happens rapidly and before the appearance of these cells in PB. This type pf LPS challenge induces Ccr7 expression on GMPs as well as secretion of CcL9 in the LN. Importantly, loss of CcL9 or neutralizing Ccr7 inhibits GMP/MDP migration to the LN and inflammation induce mortality.

The studies are well performed and the data supports the conclusions. The role of this signaling axis in the recruitment of GMPs/MDPs has not been investigated in this detail.

Reviewer #3 (Public Review):

The authors have developed a new culture method to expand adult lung cells in vitro as 3-D organoids. This culture system is different from previous organoid cultures which include either bronchiolar, or alveolar, lineages. Rather, the authors attempted to preserve both lineages over long-term passaging. The 3-D cultured organoids can be dissociated and re-plated as 2D monolayers, which can be either cultured immersed in medium or in air-liquid interface (ALI) conditions, exhibiting a different bias towards alveolar and airway lung cell types respectively. The 2D monolayer cultures can be infected by COVID-19 virus and showed a progressive increase in virus load, which was distinct from iPSC- derived alveolar type 2 (AT2) cell and bronchiolar epithelial cell culture control infections. Through bioinformatics analysis, the authors were able to show that their monolayer cultures acquired similar immune response features to an in vivo COVID infection dataset, indicating that this culture system may be suitable for modeling COVID infection in vitro. It is particularly interesting that the bioinformatics analyses suggested that this adult human lung organoid system, with both airway and alveolar phenotypes, showed greater resemblance to the transcriptional immune response of severely COVID-infected lungs than either cultured cell type alone. This aspect of the manuscript strongly suggests that the authors' approach of developing a mixed lung organoid model is an extremely good one.

However, the data presented in figures 2 and 3 cast serious doubts over the long-term reproducibility of the organoid system. That individual organoids contain both airway and alveolar lineages has not yet been convincingly demonstrated (Fig 2). In addition, bulk RNAseq experiments illustrate that the overall cell composition of the cultures drifts significantly during long-term passaging (Fig 3). Due to this variability, the organoids' ability to act as a suitable model for viral infections that would be amenable to drug screening approaches is also questionable.

Reviewer #2 (Public Review):

The manuscript "Adult Stem Cell-derived Complete Lung Organoid Models Emulate Lung Disease in COVID-19" by Das and colleagues introduces a new model system of airway epithelium derived from adult lung organoids (ALO) to be utilised for the study of COVID-19-related processes. In this manuscript two main novelties are claimed: the development of a new model system which represents both proximal as distal airway epithelium and a computationally acquired gene signature that identifies SARS-CoV-2-infected individuals. While interesting data are presented, the novelty claim is questionable and the data is not always convincing.

Strengths:

Multiple model systems have been developed for COVID-19. The lack of a complete ex vivo system is still hampering quick development of efficient therapies. The authors in this manuscript describe a new model system which allows for both proximal and distal airway infectious studies. While their claim is not completely novel, the method used can be used in other studies for the discovery of potential new therapies against COVID-19. Moreover, their computational analyses shows the promise of bioinformatics in discovering important features in COVID-19 diseased patients which might elucidate new therapeutic targets.

Weaknesses:

Although the paper does have strengths in principle, the weaknesses of the paper are that these strengths are not directly demonstrated and their model system is not completely novel. That is, insufficient analyses are performed to fully support the key claims in the manuscript by the data presented. In particular:

The characterisation of the adult lung organoids and their monolayers is insufficient and sometimes incorrect. Their claims are based on contradicting data which includes cell composition in the culture system. Therefore, the claim of a novel model system seems invalid and rushed. Moreover, the characterisation of a new gene signature is based on this model system which has been infected with SARS-CoV-2. The infection however is hard to interpret and therefore claims are hard to validate.

Reviewer #1 (Public Review):

The manuscript by Tindle et al describes generation of adult lung organoids (ALO) from human lung biopsies and their use to study the changes in gene expression as a result of SARS-CoV-2 infection. The main advantage of the use of organoids is the ability to generate many cell types that make up the lung. In this particular case the authors report the presence of AT1, AT2 cells, Basal cells, Goblet cells, Ciliated cells and Club cells. The authors were able to cultivate the cells at the air-liquid interface and to establish cultures of predominately proximal and predominately distal airway cells. The main finding is that proximal cells are more prone to viral infection, while distal cells are governing the exuberant inflammatory response, with both cells required for the exuberant response to occur. A useful information provided by the paper is the analysis gene signatures of various cellular models, in comparison to the infected human lung.

Reviewer #2 (Public Review):

The recent discovery of CTP as a co-factor for the ParB protein family has prompted the field to revisit all the experimental data and models on ParABS-mediated chromosome/plasmid segregation from the past 35 years. Some recent research has been performed to investigate ParB-CTP interaction and the roles of CTP on ParB spreading/sliding. However, the important roles of CTP on ParB-ParA interaction have not been investigated so far. This manuscript from Taylor et al is the first to investigate this important area, thus this work is timely and is very welcomed. I note that Mizuuchi et al proposed the ground-breaking "diffusion-ratchet" model of plasmid/chromosome segregation, and the latest findings in his manuscript here have very important implications for this model. The work here has been done rigorously; I have read it with much interest.

Reviewer #1 (Public Review):

This manuscript by Taylor et al. carefully investigates (1) ParB-ParA and (2) ParB-ParB interactions in the F Plasmid SopABC system using microfluidics, TIRF microscopy and magnetic tweezers.

(1) The work shows that the activation of ParA ATP hydrolysis requires a dimer of ParA to bind to two protomers of ParB. Surprisingly, ParB can bind to ParA either using the two protomers of a single dimer or two protomers from distinct dimers. The former occurs in the absence of ligands, the latter upon addition of either CTP or parS DNA, thus presumably corresponding to the state of ParB found in the cells near a parS site. The authors suggest that this is crucial for the precise timing of ParA-ParB anchoring and release.

(2) Magnetic tweezer experiments demonstrate nucleotide-dependent compaction of DNA by ParB. This compaction is strictly parS-sequence dependent and robust even at elevated DNA extension force (5 pN) and at relatively low ParB concentrations. This implies ParB dimer-ParB dimer interactions exclusively on parS DNA.

The conclusions are generally well supported by the data. Few control experiments are suggested.

Reviewer #3 (Public Review):

The paper presents results of a serological survey done on 10,000+ employees and workers associated with CSIR labs in India during August-September 2020. The survey finds 10.14% seropositively. In addition, correlations are drawn between seropositivity and biological and lifestyle factors. A follow up study is also done on a subset of employees found seropositive and antibodies are found to survive even after six months in most.

Strengths: This is a one of the two surveys with a pan-India footprint, making it a valuable addition to understanding of Covid-19 pandemic evolution in the country. It also finds good inverse correlation between seropositivity and (i) blood group O, (ii) vegetarian diet, (iii) smoking, and (iv) use of private transport. While (iv) is obvious, (ii) and (iii) are a little surprising. It suggests a deeper study is required to understand the reasons behind it.

Weaknesses: While it is a pan-India survey, the population is not quite representative of general population of the country. CSIR labs are mostly in cities, and most of the employees use private transport. So the results cannot be generalized to the country as a whole. Restricting to people using public transport would be a better representation, although it still would not be fully representative.

The data collection and analysis are done meticulously, and provide some new insights into differential impact of Covid-19 virus on people.

Reviewer #2 (Public Review):

The authors have been able to carry out a well-planned countrywide sero-survey in a cohort of 10,427 employees of their organization with 23 laboratories spread over 17 states and 2 union territories. The reported sero-positivity of 10.14% among persons mainly from cities and towns, helps understand the spread of the pandemic across the country and corelates well with the point prevalence of active infections in the various states of India during the same period. It helps understand the role of asymptomatic cases in increasing sero-positivity as 2/3 of the personnel could not remember any symptoms or illness.

Strengths:

1) The strength of this study is a large pan India cohort with all demographic details captured, which can be easily followed up. The sero-positivity datasets corelate well with the national Covid cases data in the states of India as reported in the public domain during the same time frame. The time period of Aug Sept after the mass migration of labourers from cities to rural India was possibly responsible for a quick spread of the infection and this study is able to capture the same effectively.

2) The study has also correlated the antibodies to Nuclear Capsid Antigen with the Neutralizing antibody levels and the correlation is good. However, this needs to be followed up to interpret humoral stability especially with the interesting observation of declining Antibodies to nuclear capsid antigen at six months but levels of neutralizing antibodies being stable after an initial drop at three months.

3) The study demonstrates an inverse correlation between the changes in test positivity rate and sero-positivity suggesting reduced transmission with increasing sero-positivity. The sero-positivity was higher in densely populated areas suggesting faster transmission.

Weakness:

1) The extrapolation of the study results to the country may not be completely acceptable with the basic difference from the country's urban rural divide and a largely agricultural economy. The female gender is underrepresented in the study cohort, and no children have been included.

2) The observations regarding corelates of sero-positivity such as diet smoking etc would need specifically designed adequately powered studies to confirm the same. The sample size for the three and six months follow up to conclude stability of the humoral immunity, is small and requires further follow-up of the cohort. The role of migration of labour helping the spread of the pandemic simultaneously to all parts of the country though attractive may not explain lower rates in states like UP and Bihar where maximum migrants moved to.

3) A large chunk of seropositive data set has been removed representing the big cities of Delhi and Bengaluru while correlating Test Positivity Rate citing duration as the reason. However, these cities also had different testing strategies and health infrastructure and hence are important.

4) Test positivity rate depends on testing strategy and type of test used; whether RTPCR or the Rapid Antigen Test and the ratio of the two tests was different in different parts of the country.

Overall a good study where the authors have been able to effectively show a relatively high sero-positivity than reported infections possibly due to asymptomatic cases. It will be able to provide insight into immune memory in COVID 19 as they continue with follow-up quantitative sero-assay for the cohort

Reviewer #1 (Public Review):

The paper "Insights from a Pan India Sero-Epidemiological survey (Phenome-India cohort) for SARS-CoV2" reports a longitudinal survey of about 10000 subjects from laboratories of the CSIR (India) who consented to be tested for antibodies to SARS-CoV-2, across August and September 2020. The methodology is a standard one, using the Roche kit to test for antibodies to the nucleocapsid antigen with a followup to detect neutralising antibodies using the GENScript Kit. A questionnaire for all participants asked about age, gender, pre-existing conditions and blood group, among other questions.

The principal results of the study were:

1) An overall seropositivity of 10.14% [95% CI: 9.6 - 10.7] but a large variation across locations

2) Virtually all of the seropositive exhibited neutralising activity

3) Seropositivity correlated with population density in different locations

4) A weak correlation was seen to changes in the test positivity across locations

5) A large asymptomatic fraction (~75%) who did not recall symptoms

6) Of those symptomatic, most reported mild flu-like symptoms with fever

7) A correlation with blood group, with seropositivity highest for AB, follow by B, O and A

8) A vegetarian diet correlated with reduced seropositivity

9) Antibody levels remained constant for 3 months across a sub-sample white neutralising activity was lost in ~30% of this subsample. Over a longer period, in a still smaller subsample of those tested at 3 months, anti-nucleocapsid antibody levels declines while neutralising antibody levels remained roughly constant

10) There is a reasonable agreement with the results of the second Indian serosurvey which obtained a seroprevalence of about 7% India-wise, although excluding urban hotspots.

The deficiencies of this study are:

1) This is a very specific cohort, largely urban, with - presumably - relatively higher levels of education. It is hard to see how this might translate into a general statement about the population

2) The presentation of Figure 1 was quite confusing, especially the colour coding

3) It is surprising that the state of Maharashtra shows only intermediate to low levels of seropositivity, given that the impact of the pandemic was largest there and especially in the city of Pune. There have been alternative serosurveys for Pune which found much higher levels of seropositivity from about the same period.

4) The statement "Seropositivity of 10% or more was associated with reductions in TPR which may mean declining transmission": For a disease with R of about 2, this would actually be somewhat early in the epidemic, so you wouldn't expect to see this in an indicator such as TPR. TPR is also strongly correlated with amounts of testing which isn't accounted for.

5) The correlation with vegetarianism is unusual - you might have argued that this could potentially protect against disease but that it might protect against infection is hard to credit. Much of South Asia is not particularly vegetarian but has seen significantly less impact

6) On the same point above, it is possible that social stratification associated with diet - direct employees being more likely to be vegetarian than contract workers - might be a confounder here, since outsourced staff seem to be at higher risk.

7) There may be correlations to places of residence that again act as confounders. If direct employees are provided official accommodation, they may simply have had less exposure, being more protected.

8) The correlations with blood group don't seem to match what is known from elsewhere

9) The statement that "declining cases may reflect persisting humeral immunity among sub-communities with higher exposure" is unsupported. What sub-communities?

Reviewer #3 (Public Review):

The article by Hesse, Owenier et al entitled "Single-cell transcriptomics 1 defines heterogeneity of epicardial cells and fibroblasts within the infarcted heart" will be of interest to the readers of heart regeneration, as it helps in understanding how epicardial cells contribute to heart regeneration following myocardial Infarction. Hesse, Owenier et al. investigate the role of epicardial stromal cells(EpiSC) after arterial ligation induced myocardial infarction (MI) in mouse. They perform single-cell RNASeq (scRNASeq) on isolated, FAC sorted, epicardial stromal cells, activated cardiac stromal cells (aCSC) from infarcted hearts and control cardiac stromal cells (CSC). The authors find 11 cell clusters of EpiSC. They confirmed the spatial localization of the different clusters by in situ hybridization and performed Gene ontology studies to understand the biological processes affected by those clusters. They found that those clusters fall into three major functional groups, as follows: 1) Wt1 expressing and cardiogenic factor expressing, 2) chemokine expressing and HOX genes expressing and 3) cardiogenic factor expressing. Interestingly there are two identified groups which express different cardiogenic factors 1) Wt1 positive with cardiogenic factors MESP1, WNT11, ISL11, TBX5, GATA 4,5,6 and the other group 3) Wt1-with Nkx2.5, BMP2 and BMP4. Authors show that multiple clusters are enriched in Hif1a, Hif1a related genes and glycolysis related genes which are known to be downstream of Wt1 cells. To further understand the hierarchical development of the EpiSC cells, the authors performed pseudo time-series analysis using RNA Velocity analysis on Wt1 reporter mice and find three different groups. Interestingly Wt1+ cells did not convert into other cell types. They further performed ligand receptor analysis to find interactions between different cell types. The authors implemented scRNAseq for aCSC and find cell clusters ECM rich cells, fibroblasts, interferon expressing cells, and cycling fibroblasts/myofibroblasts. They further compared the transcriptional profiles of EpiSC with the aCSC. They found gene sets, which are specific for EpiSC, and genes that are specific for aCSCs. Specifically, they found that Hif1a, glycolysis responsive genes, and cardiac contractile proteins were highly expressed in EpiSCs. Furthermore, the authors showed that the transcriptional profile of EpiSC, aCSC and CSC are different.

These data add an important knowledgebase to the understanding of the transcriptional landscape of the Epicardial stromal cells and would help identifying specific pathways/transcriptional genes which are activated during myocardial infarction.

Reviewer #2 (Public Review):

Hesse et al. implemented a murine model of cardiac ischemia to study two populations, epicardial stromal cells (EpiSC) and activated cardiac stromal cells (aCSC). Furthermore, uninjured cardiac stromal cells were used as a control. An isolation method for EpiSC was used by applying a gentle shear force to the cardiac surface. The authors show heterogeneity in the Epi-SC populations. Certain markers were confirmed by in-situ hybridization. Furthermore, molecular programs within these subsets were explored. A comparison between EpiSC and aCSCs cells (and EpiSC and uninjured CSCs cells) was performed, which showed differences in expression of multiple genes namely HOX, HIF1 and cardiogenic factor genes. A WT1 population was marked by tdTomato, confirming the localization of expression to a cell population. There are however specific weaknesses. First, a major concern is regarding clarity of the experimental conditions and sample purity. Data is not robustly presented showing differences across conditions, namely between uninjured CSCs and activated CSCs. Furthermore, the purity of isolating EpiSC was not explored, along with the anticipated overlap of cells between aCSC and EpiSC. Specifically, the in-situ findings do not clarify the subject of purity. For example, EpiSC-3 (Pcsk6) is a large population in the scRNA-seq shown in Fig 1; however, this gene is also expressed in the myocardium. There is an attempt to perform EpiSC and aCSC comparison analysis in Figure 3; however without clarity the expected overlap, these data are hard to interpret. Furthermore, cluster-based approaches for comparing population fractions can be problematic due to the inherent stochasticity of sampling. Lastly, there is no actual lineage tracing over time, but rather marking of WT1 cells with tdTomato. The RNA velocity analysis is not particularly robust with the number of expressed genes driving these results, rather than the author's conclusion of developmental potential.

Reviewer #1 (Public Review):

In the manuscript "Single-cell transcriptomics defines heterogeneity of epicardial cells and fibroblasts within the infarcted heart", the authors isolated epicardial stromal cells (EpiSC) and cardiac interstitial/stromal cells (termed active CSCs) from the same I/R heart and identified transcriptionally distinct subpopulation of EpiSCs via 10x genomics technology. They also performed transcriptome profile comparison between EpiSCs and aCSCs. This manuscript shows rigorous scientific investigation. Their isolation protocol is supported by their previous publication. Method section documented in detail of step-wise QC process of bioinformatics analysis. In summary, the analysis identified 11 clusters of EpiSC, some of which overlap with the well-established epicardial marker WT1 with confirmed in situ anatomical localization. When compared to aCSC, the two groups showed clear different function/states as expected. In the lineage tracing model, RNA velocity predicts cell hierarchy, cell-cell communication between populations, as well as cell cycle activity. Overall this manuscript provides a significant degree of information that can be helpful to the field.

Reviewer #3 (Public Review):

The differences in signaling and responses in the three different T cell receptor transgenics are shown by several different means. These include Nur77 and CD5 expression as markers for the strength of signaling, the frequency of calcium fluxes and length of signaling-induced pauses in movement, using 2 photon microscopy of thymic slices (comparing selecting and non-selecting thymus), time course of induction of markers of positive selection signaling, the time course of "arrival" of CD8 single positive cells and CCR7+ cells in the post-natal thymus, and a time course of development of SP thymocytes after injection of EdU. Each of these methods is fairly convincing on its own, but added up, they are very convincing.

The only issues that I could take issue with are about how we define self-reactivity. Because it is not feasible to measure the affinities for self peptides on MHC (due to low affinity and the fact that we mostly don't know what they are), the authors have to rely on surrogate markers, the upregulation of CD69 and of Nur77. These are widely accepted in the field, so they are as good a surrogate as is possible at this time.

Similarly, 3 transgenic strains are taken as examples of high, medium and low self-reactivity. Two of the strains are positively selected on H2Kb, one on Db, one on Ld. Therefore, the experiments cannot be genetically controlled in the same manner. On balance, I accept that there aren't too many other ways to do the experiment, and that all the main points are supported by other types of experiment.

The most interesting aspect of the work consists of analysis of gene expression by RNASeq from cells from each of the three TCR transgenic mice from early positive selection, late positive selection, and mature CD8 SP. Perhaps unsurprisingly, the more strongly self-reactive cells showed increased expression of genes involved in protein translation, RNA processing, etc. However, genes associated with lower self-reactivity were enriched for lots of different ion channels. These included calcium, potassium, sodium and chloride channels. One of these was Scn4b, part of a voltage gated sodium channel previously shown by Paul Allen's lab to be involved in positive selection. These types of genes were associated with the stage of development before selection, and were retained through selection in the weakly self-reactive thymocytes. Other ion channel genes that typically came on at the end of selection were also upregulated earlier in the lower self-reactivity cells, and may be involved in allowing long-term signaling for these cells to undergo the whole positive selection program.

Reviewer #2 (Public Review):

In their study, Lutes et al examine the fate of thymocytes expressing T cell receptors (TCR) with distinct strengths of self-reactivity, tracking them from the pre-selection double positive (DP) stage until they become mature single positive (SP) CD8+ T cells. Their data suggest that self-reactivity is an important variable in the time it takes to complete positive selection, and they propose that it thus accounts for differences in timescales among distinct TCR-bearing thymocytes to reach maturity. They make use of three MHC-I restricted T cell receptor transgenics, TG6, F5, and OT1, and follow their thymic development using in vitro and in vivo approaches, combining measures at the individual cell-level (calcium flux and migratory behaviour) with population-level positive selection outcomes in neonates and adults. By RNA-sequencing of the 3 TCR transgenics during thymic development, Lutes et al make the additional observation that cells with low self-reactivity have greater expression of ion channel genes, which also vary through stages of thymic maturation, raising the possibility that ion channels may play a role in TCR signal strength tuning.

This is a well-written manuscript that describes a set of elegant experiments. However, in some instances there are concerns with how analyses are done (especially in the summaries of individual cell data in Fig 2 and 3), how the data is interpreted, and the conclusions from the RNA-seq with regard to the ion channel gene patterns are overstated given the absence of any functional data on their role in T cell TCR tuning. As such the abstract is currently not an accurate reflection of the study, and the discussion also focuses disproportionately on the data in the final figure, which forms the most speculative part of this paper.

(1) As the authors themselves point out (discussion), one of the strengths of this study is the tracking of individual cells, their migratory behaviour and calcium flux frequency and duration over time. However, the single-cell experiments presented (Figure 2 and 3) do not make use of the availability of single-cell read-outs, but focus instead on averaging across populations. For instance, Figure 3a,b provides only 2 sets of examples, but there is no summary of the data providing a comparison between the two transgenics across all events imaged. In Figure 3c, the question that is being asked, which is to test for between-transgenic differences is ultimately not the question that is being answered: the comparison that is made is between signaling and non-signaling events within transgenics. However, this latter question is less interesting as it was already shown previously that thymocytes pause in their motion during Ca flux events (as do mature T cells). Moreover, the average speed of tracks is probably not the best measure here in reading out self-reactivity differences between TCR transgenic groups.

(2) The authors conclude from their data that the self-reactivity of thymocytes correlates with the time to complete positive selection. However the definition of what this includes is blurry. It could be that while an individual cell takes the same amount of time to complete positive selection (ie, the duration from the upregulation of CD69 until transition to the SP stage is the same), but the initial 'search' phase for sufficient signaling events differs (eg. because of lower availability of selecting ligands for TG6 than for OT1), in which case at the population level positive selection would appear to take longer. Given that from Fig 2/3 it appears that both the frequency of events and their duration differ along the self-reactivity spectrum, this needs to be clarified. Moreover, whether the positive selection rate and positive selection efficiency can be considered independently is not explained. It appears that the F5 transgenic in particular has very low positive selection efficiency (substantially lower %CD69+ and of %CXCR4-CCR7+ cells than the OT1 and TG6) and how this relates to the duration of positive selection, or is a function of ligand availability is unclear.

(3) While the question of time to appearance of SP thymocytes of distinct self-reactivities during neonatal development presented (Figure 5) is interesting, it is difficult to understand the stark contrast in time-scales seen here compared with their in vitro thymic slice (Figure 4) and in vivo EdU-labelling data (Figure 6), where differences in positive selection time was estimated to be ~1-2 days between TCR transgenics of high versus low affinity. This would suggest that there may be other important changes in the development of neonates to adults not being considered, such as the availability of the selecting self-antigens.

(4) The conclusion that "ion channel activity may be an important component of T cell tuning during both early and late stages of T cell development" is not supported by any data provided. The authors have shown an interesting association between levels of expression of ion channels, their self-affinity and the thymus selection stage. However, some functional data on their expression playing a role in either the strength of TCR signaling or progression through the thymus (for instance using thymic slices and the level of CD69 expression over time), would be needed to make this assertion. Moreover, from how the data is presented it is difficult to follow the conclusion that a 'preselection signature' is retained by the low but not the high self-reactivity thymocytes.

Reviewer #1 (Public Review):

The work by Lutes et al. addresses how thymocytes undergo positive selection during their differentiation into mature T cells. The authors make use of several in vitro and in vivo model systems to the test whether developing thymocytes at the critical preselection CD4+CD8+ stage, expressing T cell receptors (TCRs) with different levels of putative self-reactivity, undergo different or similar differentiation events, in terms of migration, thymic epithelial cell engagement and temporal kinetics, and gene expression changes.

The authors selected three TCR-transgenes, which have increasing levels of self-reactivity, TG6, F5 and OT1, respectively, to test their hypothesis, that TCR signals during positive selection are not only sensed differently but lead to different outcomes that then define the functional status of mature T cells. The author's conclusions that thymocytes with low self-reactivity differentiate with distinct kinetics (migration, engagement and temporal) and express a different suite of genes than thymocytes that experience high self-reactivity is well supported by several elegant approaches, and convincing findings.

The authors clearly established that low to high TCR signaling outcomes affect the timing of positive selection, which is beautifully illustrated in Figures 3-6, and extend that work to non-TCR transgenic mice as well. Lastly, their findings from RNA-seq analyses shed light into the different genetic programs experienced by high-reactivity fast differentiating CD8 T cells as compared to low-reactivity slower differentiating cells, which appear to retain the expression a unique set of ion channels during later stages of their differentiation process.

However, what the expression of these ion channels means in terms of either supporting the slow progression or perhaps responsible for the slow progression is not directly addressed, and likely beyond the scope. Nevertheless, the authors posit as to the potential role(s) for the differently expressed gene subsets. Overall, the work is crisply executed, and the findings reveal new aspects as to how positive selection can be achieved by thymocytes expressing very different TCR reactivities.

Reviewer #3 (Public Review):

The authors present here a very interesting and thorough systems biology study of S. cerevisiae involving 22 steady state conditions with different growth rates and nitrogen sources. Proteomics and transcriptomics data, as well as intracellular amino acid concentrations, are gathered in a study that, if only for the sheer amount of data, is quite unique.

The authors use differential expression analysis, clustering algorithms and correlations to divide the genes and proteins studied into a small number of groups whose behaviour can be generally categorized. For a starter there is a small group (~10%) that map to central carbon metabolism and seems to be regulated by cues not covered in this study (growth rate and metabolic parameters involving amino acid and nucleotide availability). The rest of genes (90%) seem to have their transcript and protein levels heavily determined by growth rate and/or amino acid metabolism. For different growth rates, the expression of these genes and corresponding proteins seemed to be very correlated, and dependent on the availability of translation and transcription machinery (RNA polymerase and ribosomes). For different nitrogen sources, gene expression seemed dependent on amino acid and nucleotide availability.

These general rules are insightful and can provide a much more informative way to analyze multiomics data sets, by e.g. accounting for expected over/under expressions due to growth rate changes. Indeed, the authors attempt this for two cases: a distantly related yeast (S. pombe) and a human cancer cell model. While they are able to show that most transcript variation for S. pombe seems to be due to growth rate changes, the rest of the inferences do not seem very informative.

In general, while the findings are interesting and seemed to be mainly supported by the evidence, the manuscript is complicated to read. Evidence is scattered throughout the manuscript and needs to be gathered and compiled by the reader to check the results. Some of the writing is remiss: Figures 6A and 6C have the same caption and different graphs. It is also not clear how the differential expression calculations in Figure 1C were done: what are the two conditions being compared? Figure 7 encapsulates what is learnt from this paper but needs a more informative caption describing the full metabolic lesson learnt.

In summary, the data presented here is a golden data set that will make a great contribution to science, the general rules are interesting and seemed to be supported by the data, but to be more useful to readers the writing of the paper could be made clearer.

Reviewer #2 (Public Review):

Using budding yeast, the authors have generated transcriptome and proteome data for a series of experimental conditions, augmented with measurement of some amino acid abundances. These data are subjected to a number of correlation and enrichment analyses. Based on those, the authors put forward a verbal "model of information flow, material flow and global control of material abundance".

The main message of this paper was not sufficiently clear because at different places of the manuscript the authors highlight different aspects: Based on the title it seems that the "distinct regulation" is the key aspect. Notably, however, this point has only a minor role in the manuscript itself. In the abstract, it seems that the key aspect is a "framework", although after having read the paper it was not clear what the authors mean with the term. Later in the manuscript the authors also use the term "coarse-graining approach", but it was not clear whether this is the same as the "framework". Beyond, throughout the manuscript, the authors make the point that global physiological parameters (such as growth rate) determine gene and protein expression level. Even though this point is important and often overlooked, it has been made before in several papers, which the authors also cite. Thus, this aspect mostly provides confirmation of previous work. Finally, at the end of the introduction, where the authors refer to "our findings... ", it is unclear to which findings they particularly refer to.

The manuscript could be clearer in certain specific aspects:

1) The paper uses lots of terms that are not well defined: For instance, it is not explained well what the authors mean by "metabolic parameters". I know metabolite concentrations, and metabolic fluxes, but I don't know what metabolic parameters are. It is also not explained well what is meant with "global control mechanisms" and what is meant by "augment".

2) Similarly, this lack of clarity also exists when the authors step from a particular analysis (i.e. a correlation) to a conclusion statement. The hard evidence supporting particular statements is not sufficiently explained.

Reviewer #1 (Public Review):

Nielsen and colleagues describe a large new multi-ome database containing combinations of absolute mRNA quantities, proteome and amino acid concentrations in a set of 14 yeast populations grown in various conditions in chemostats. Apart from being a valuable resource for colleagues, analysis of the data confirms the results of several previous seminal studies.

For example, the authors confirm the relatively high correlation between transcript and corresponding protein abundance. Moreover, it is shown that for most genes, changes in transcript abundance related to manipulated changes in growth rate largely reflected the availability of RNA polymerase II. Interestingly, this was not the case for genes involved in central carbon metabolism, suggesting that these are regulated separately, likely to maintain the cells' ATP levels. Similarly, manipulation of growth through the use of different nitrogen sources led to changes in transcription that correlated with certain amino-acid-derived metabolites (including nucleotides), but not with RNAPolII levels. Genes involved in central carbon metabolism are again an exception to this rule.

Reviewer #3 (Public Review):

The combination of Cre and Flp recombinase dependent system is powerful in manipulating specific intersectional neurons and has been successfully used in many systems. However, the system cannot express target genes sufficiently in some neurons, e.g., the LepRbVMH neurons. This paper solved this problem by developing a novel AAVs system, in which two AAVs were used, the "Driver" AAV permits Flp dependent expression of tTA, and the "Payload" AAV permits TRE-driven and Cre dependent expression of target gene. Because there two AAVs used, it is also expected to increase the capacity to incorporate more transgenes into the AAV system. The novel system to manipulate the intersectional neurons described in this work is an important addition to the current tools. It should be an excellent resource for the neuroscience community.

This paper is nicely written and compared the previous intersectional approach of AAV-EF1α-Con/Fon-hChR2(H134R)-EYFP with their novel tTARGIT approach in labelling LepRbVMH neurons. The data convincingly demonstrated that the tTARGIT system can label many more cells. Small caveats include the author co-injected AAV-hSYN-Flex(Lox)-hM3Dq-mCherry as an injection site marker with AAV-EF1α-Con/Fon-hChR2(H134R)-EYFP, the serotypes of these AAVs were not reported. It is well known that different serotypes of AAVs infect different types of neurons with a different efficiency. Furthermore, the combination of the different AAV might affect each other's infection, leading to low expression of one type of AAV. The titres of AAVs also make a big difference to many AAVs, which were not reported in this paper. These information are important for other investigators if they would adopt the tTARGIT system in their own research.

Reviewer #2 (Public Review):

The research community has been frustrated by difficulties in using AAVs to obtain robust experimental access to neurons co-expressing Cre and Flp recombinase (often called the intersectional approach). In many cases, the approach is sufficiently inefficient as to not be usable. This is in part due to difficulties in designing AAVs that will efficiently express protein-encoded tools in a Cre-ON/Flp-ON fashion, and in part due to the relative inefficiency of Flp recombinase. This present study presents a new intersectional approach for solving this problem. The approach involves co-injecting two AAVs into sites in the brain where Cre/Flp-co-expressing neurons reside - in this case, neurons in the ventromedial nucleus of the hypothalamus (VMH) which co-expresses VGLUT2 (Slc17a6)-Flp and Leptin receptor (Lepr)-Cre. One of the AAVs, in a Flp-dependent fashion, expresses the tTA transcriptional activator, while the other AAV, in a tTA and Cre-dependent fashion, expresses the protein-encoded tool. This new system produced robust expression in neurons co-expressing Flp and Cre in the VMH which previously could not be accomplished using existing intersectional AAVs. The authors also demonstrate a Flp-ON/Cre-OFF version of this approach. Finally, by using these tools the authors show, as was suspected based on prior work, that the Lepr/Vglut2-coexpressing VMH neurons increase brown fat thermogenesis and energy expenditure when stimulated. The results presented very strongly support the effectiveness of this new approach. The only weakness of this study is that, at this point in time, the universality of this approach for all Cre/Flp-co-expressing neurons is unknown. Its effectiveness was only evaluated in VMH neurons. While it is expected that this approach will work for most or all Cre/Flp-co-expressing neurons, there is anecdotal evidence of this or that AAV approach not working in this or that neuron.

Reviewer #1 (Public Review):

This paper describes the development of a suite of viral vectors that allow expression (either on or off) of genes of interest depending on both Cre and Flp expression. They demonstrate that their system can solve the problem encountered with the other approach and use it for mapping axonal projections of the glutamatergic, LepR-expressing neurons and the consequences of chronic activation of these neurons on food intake and energy expenditure. The results are significant and clearly presented. The failure of the other system (INTRSECT) for their application is not clearly understood, but authors say that it may be due to low expression of Cre or Flp in these neurons; however, Supp Fig. 1 shows that it Lepr-Cre and Slc17a6-FLPo were sufficient to activate a transgenic reporter (Supp Fig. 1). The authors reveal that they probably could have used Nr5a1-Cre mice manipulate the activity of these VMH neurons. Nevertheless, it is worthwhile having multiple methods to attack a specific problem because of unforeseen complications with particular methods.

Reviewer #3 (Public Review):

Miskolci et al have investigated if it is possible to measure the natural fluorescence of two important co-enzymes (NADH/NADPH and FAD) in living cells to determine their metabolic status. This tests the hypothesis that changes to the relative ratio of NADH/NADPH to FAD+ reflect a shift between glycolytic and oxidative phosphorylation in living macrophages. To investigate this they have used 2-photon FLIM to measure intensity and fluorescence lifetime of NAD/NADPH and FAD+ in mouse macrophages in vitro and zebrafish macrophages in vivo in a tail injury model. By comparing their measures of NAD(P)H and FAD+ from macrophages responding to different injury or infection cues and comparing this to a maRker of inflammation (TNF-alpha) they argue that there is a reduced redox state indicative of glycolytic metabolism in pro-inflammatory macrophages.

The adoption of label free imaging techniques to measure metabolic processes in cells in vivo is a valuable and important development that, although not novel to this work, will help researchers to probe cell biology in situ. FLIM using time correlated single photon counting (TCSPC) allows an accurate and robust measure of the relative state of a molecule that shows changes in its fluorescent lifetime as a consequence of changing chemical state. Although Stringari et al (doi.org/10.1038/s41598-017-03359-8) were the first to describe the utility of wavelength mixing FLIM for measuring NAD(P)H and FAD+ levels in zebrafish, they did not focus on macrophages which is the focus of this work.

The results from this work are interesting, as they argue that it is possible to determine cell metabolism in cells within living animals without a need to use a genetically encoded sensor and they argue that pro-inflammatory macrophages in zebrafish appear to have a lower redox state, which may reflect a more glycolytic metabolism. This assumption is not tested but rather inferred based on the measures of fluorescence intensity and lifetime of endogenous NADH/NADPH and FAD coupled with a small metabolic sampling of injured tissue. This lack of corroboration for a the supposed difference in metabolism between pro-inflammatory and non-inflammatory macrophages is a weakness of the paper and makes it hard to accept the conclusion that the redox state may reflect different metabolic profiles. A biosensor for NADH/NADPH (iNap) has been demonstrated to be a sensitive tool for measuring NADPH concentration in vivo in zebrafish during the injury response (Tao et al (doi: 10.1038/nmeth.4306) and it would be intriguing to know how similar the response is of this biosensor to the label free measurements described using FLIM. This is additionally relevant as the authors also note that in mouse macrophages cultured in vitro, they observe an opposite redox response which is well supported by the literature and a variety of different methods. Why the zebrafish macrophages should show a different redox state to mouse macrophages is not clear and an alternative explanation is that the measures used do not directly reflect the metabolic profile of the cells. One further caveat to the chosen method of using fluorescence lifetime to measure the redox state of NADH/NADPH is that lifetime of NADH is affected by which proteins it is bound to. This is not accounted for in the method used for calculating the redox ratio used for defining the redox state and could potentially alter the interpretations of relative NADH/NADPH levels in a cell. The authors acknowledge this, but do not consider whether this would affect the conclusions they arrive at from their measures of NAD(P)H intensity and fluorescence lifetime in macrophages.

Reviewer #2 (Public Review):

• The aim of this paper was to demonstrate whether FLIM-based imaging of optical redox ratio can be used to monitor metabolic states of immune cells in vivo during the course of inflammatory responses.

• The study is rigorous and well-presented and the findings are interesting and novel. The main strength is in the in vivo data, where the authors used a variety of inflammatory challenges and perturbations and were able to detect previously unreported trends in metabolic states of macrophages.

• The authors have demonstrated the potential of the technique to be used in vivo. Their initial findings are intriguing and can be followed up by more mechanistic studies.

• The work is timely, because of growing interest in the role of metabolism in immune cell signalling and functions. Relevant microscopy-based assays in vivo are limited, so this innovation is important and can form the basis of further technology developments.

Reviewer #1 (Public Review):

The zebrafish has a rich history of enabling innovative microscopy techniques, and is also a well established system to model inflammation and infection by human pathogens. Consistent with this, Miskolci et al use zebrafish to test a novel imaging approach that has great potential to significantly impact the field of immunometabolism. Fluorescence lifetime is a label-free, non-invasive imaging approach to detect metabolic changes in situ, at the level of the single cell. In this report, Miskolci et al use fluorescence lifetime imaging of NAD(P)H and FAD to detect metabolic changes in zebrafish macrophages (with temporal and spatial resolution) in response to inflammatory and infectious cues.

Miskolci et al (eLife 2019) have previously characterized inflammatory and wound healing responses to distinct caudal fin injuries (tail wound, infection and tail wound, thermal injury). In this report, authors use these different injury models to show that fluorescence lifetime imaging can detect variations in macrophage metabolism. Although many interesting results are presented and future directions are proposed, the study in its current state is descriptive and lacks validation across different modalities. As a result, the reliability of fluorescence lifetime imaging in zebrafish macrophages is not yet convincing. Moreover, any metabolomic changes in macrophages are not functionally linked to zebrafish phenotypes (eg inflammation, bacterial burden, caudal fin regeneration).

Reviewer #3 (Public Review):

This analysis is enormous in scope. That said, approximately half the glomeruli were either truncated or had very fragmented ALRNs. The authors may wish to reserve use of the term "full" in the title ("....a full olfactory connectome") until a subsequent paper.

ALRN-ALRN connectivity seems very interesting. It would be helpful to provide more information about this in the text (line 148 or so). The information in Fig. 3D is hard for non-specialists to interpret. Does the connectivity show any patterns? Is it stereotyped? Do the connections make functional sense?

One intriguing finding is the "shortcuts" between the olfactory and motor systems that could be used for behaviors that are hard-wired or require fast responses. These may be particularly relevant to thermosensory and hygrosensory input, but can the authors say anything about what kind of olfactory information flows through these shortcuts? For example, the ALRNs that respond to wasp odorants have been identified. Please note that most readers do not know what kind of odorants project to individual glomeruli, e.g. "DC4" .

Fig. 8C It's hard to know how confident to be of the neurotransmitter assignments here. It would be helpful to provide in the text a statement about what assumptions these assignments are based on. In the same vein, line 380 refers to "a neurotransmitter prediction pipeline". Some kind of reference should be provided here.

line 522 "This suggests that thermo/hygrosensation might employ labeled lines whereas olfaction uses population coding to affect motor output." This brings up the question of whether very narrowly tuned ORNs such as the one signaling geosmin show any differences in connectivity from broadly tuned ORNs.

lines 94-96 Graph traversal model. Some more discussion of this model and its underlying assumptions would be helpful. Are the results influenced by the lack of some of the glomeruli from the dataset?

Fig. 7D Can the authors provide more discussion of the possible functional significance of the two uPN types?

Reviewer #2 (Public Review):

Here are three notable examples (among a long list of new discoveries). (1) The authors provided a comprehensive description of the antennal lobe local interneuron (LN) network for the first time, providing a "final" counts of neuronal number and type of LNs as well as the preference for the input and output partners of each LN type. (2) They introduced "layer" as a quantitative parameter to describe how many synapses away on average a particular neuron or neuron type is from the sensory world. A few interesting new discoveries from this analysis include that on average, multi-glomerular antennal lobe projection neurons (PNs) are further away from the sensory world than uniglomerular PNs; inhibitory lateral horn neurons are closer to the sensory world than excitatory lateral horn neurons. (3) By leveraging previous analyses they performed on another EM volume (FAFB) and comparing n = 3 (bilateral FAFB, unilateral hemibrain) samples, they analyzed stereotypy and variability of neurons and connections, something rarely done in serial EM reconstruction studies but is very important.

Overall, the text is clearly written, figures well illustrated, and quantitative analysis expertly performed. I have no doubt that this work will have long-lasting values for anyone who study the fly olfactory system, and for the connectomics field in general.

Reviewer #1 (Public Review):

The manuscript presents a very nice and very detailed approach to illustrate the anatomical hierarchies and also some differences of signal transmission in the olfactory vs. thermosensory-/hygrosensory systems.

The authors first provide a complete description of the Drosophila olfactory system, from first, second and third-order neurons in the lateral horn. Using a generally applicable analysis methods, they extract information flow and layered organisation between olfactory input and descending interneurons. Among the results is the interesting finding that downstream of the mushroom body and lateral horn, output neurons converge to presumably regulate behavior. In an additional set of analyses, Schlegel et al. describe and quantify inter- vs. intraindividual stereotypy of neurons and motifs. They actually compare neurons from three hemispheres of two brains and show an astounding degree of similarity across brains. This is somewhat reassuring and helpful to the field of Drosophila connectomics.

While the many details and data make the manuscript a somewhat strenuous read, and the sheer flood of data could be a bit overwhelming, the data and findings are impressive and important.

1) The work is very complementary to the data presented by Li et al. on the mushroom body.

2) The structure and the step-by-step approach to showing increasingly complex circuitry and by defining different layers of the circuitry is very helpful for the reader to get an impression of the complexity of this brain.

3) Of significant importance and of use for the community are, in addition to the data, the described methods tools for data analysis.

4) Using this type of analysis, the authors test hypotheses and prevailing assumptions in the field. For instance, they find that in early layers of the olfactory system neurons tend to connect to the next higher layer, whereas neurons in higher layers interconnect or even connect back to earlier layers. This is a very interesting finding that might have important implications regarding top-down feedback and recurrent loops in olfactory processing.

5) Analysis of connectivity in the antennal lobe suggests that the system is highly lateralized. This finding also has important implications and helps to explain why flies might be able to discern left from right odor sources.

6) The manuscript shows many examples of what other scientists/readers of the manuscript could extract from the raw anatomical data. This will be very useful for the community beyond the data that is actually already shown in the manuscript.

7) The authors also compare their findings to the connectome/motifs identified for the larval olfactory system. There are many similarities as expected.

Reviewer #2 (Public Review):

Open source software for data rendering in neuroanatomy is either too specific to be generically useful (for example, designed for only one specific brain atlas, or brain atlases of a single species), or too general, and thus not integrated with atlases or other relevant software. Additionally, despite the growing popularity of the Python programming language in science, 3D rendering tools in Python are still very limited. Claudi et al have sought to narrow both of these gaps with brainrender. Biologists can use their software to display co-registered data on any atlas available through their AtlasAPI, explore the data in 3D, and create publication quality screenshots and animations.

The authors should be commended for the level of modularity they have achieved in the design of their software. Brainrender depends on atlasAPI (Claudi et al, 2020), which means that compatibility for new atlases can be added in that package and brainrender will support them automatically. Similarly, by supporting standard data storage formats across the board, brainrender lets users import data registered with brainreg (Tyson et al, 2020), but does not depend on brainreg for its functionality.

Like all software, brainrender still has limitations. For example, it's unclear from the paper exactly what input and output formats are supported, particularly from the GUI. Additionally, at publication, using the software still requires a Python installation, with all the complexity that currently entails. However, thanks to the rich and growing scientific Python ecosystem, including application packaging tools, I am confident that the authors, perhaps in collaboration with some readers, will be able to address these issues as the software matures.

Reviewer #1 (Public Review):

Claudi et al. present a new tool for visualizing brain maps. In the era of new technologies to clear and analyze brains of model organisms, new tools are becoming increasingly important for researchers to interact with this data. Here, the authors report on a new tool for just this: exploring, visualizing, and rendering this high dimensional (and large) data. This tool will be of great interest to researchers who need to visualize multiple brains within several key model organisms.

The authors provide a nice overview of the tool, and the reader can quickly see its utility. What I would like to ask the authors to add is more information about computational resources and computing time for rendering; i.e. in the paper, they state "Brainrender uses vedo as the rendering engine (Musy et al., 2019), a state-of-the-art tool that enables fast, high quality rendering with minimal hardware requirements (e.g.: no dedicated GPU is needed)" - but would performance be improved with a GPU, runtimes, etc?

I would also be happy to see the limitations and directions expanded. For example, napari is a powerful n-dimensional viewer, how does performance compare (i.e. any plans for a napari plug in, or ImageJ plug in, or is this not compatible with this software's vision?). How does brain render compare (run time, computing wise) to Blender, for example, or another rendering tool standard in fields outside of neuroscience?

The methods are short (maybe check for all open source code citations are included, as needed), but they have excellent docs elsewhere; it would be nice to have minimal code examples in the methods though, i.e. "it's as easy as pip install brainrender" ... or such.

Lastly, I congratulate the authors on a clear paper, excellent documentation (https://docs.brainrender.info/), and I believe this is a very nice contribution to the community.

Reviewer #3 (Public Review):

Schrieber et al. studied the effects of biparental inbreeding in the dioecious plant Silene latifolia, focusing specifically on traits important for floral attractiveness and pollinator attraction. These traits are especially important for dioecious species with separate sexes as they are obligate outcrossers. The authors find that inbreeding mostly decreases floral attractiveness, but that this effect tended to be stronger in the female flowers, which the authors suspect to result from the trade-off with larger investment in the sexual functions in the female plants. The authors then go on to couple the changes in visual and olfactory floral traits to pollinator attraction which allows them to conclude or at least speculate that differences in pollinator behavior are mostly driven by the changes in olfactory traits. The study is robust in its broad and well-balanced sampling of populations, rigorous and in large part meticulously documented experimental designs and linking of the effects on mechanisms to ecological function. The hypothesis are clearly stated and the study is able to address them mostly convincingly. However, some of the aspects of the decisions the authors made and possible caveats need to be addressed and elaborated on.

A major caveat, in my opinion, is that while the authors find stronger effects of inbreeding on pollinator visitation rates in the plants from the North American (Na) origin, these plants were tested in an environment that was foreign to them, which could have important consequences for the results of this study. This is specifically because the main pollinator Hadena bicruris moth is completely absent from the populations in Na, and yet, was the main pollinator observed in the pollinator attraction experiment. As this pollinator is also a seed predator, the Na populations are released from the selection pressure to avoid attracting the females of this species and thus risking the loss of seeds and fitness. In fact, some of the results suggest that the release from the specialist pollinator and seed predator in Na has led to increase in the attractiveness of the female flowers based on the higher number of flowers visited in the outcrossed females compared to outcrossed males in the plant from the Na origin and the similar, though not statistically significant, pattern in the olfactory cue. While ideally this pollinator attraction experiment should be repeated within the local range of the Na plants, this is of course is not feasible. Instead I suggest the problem should be addressed in the discussion explicitly and its consequences for the interpretation of the results should be considered.

The incorporation of the VOC data in the actual manuscript was quite limited and I found the reasoning for picking only the three lilac aldehydes (in addition to the Shannon diversity index) for the univariate statistical tests insufficient. How much more efficient was the effect of the lilac aldehydes compared to the other 17 compounds deemed important in the previous study? While the data on this one aldehyde matches the pollinator attraction results, having one compound out of 70 (or out of 20 if only considering the ones identified important for the main pollinator) seems, perhaps, fortuitous lest there is a good reason for focusing on these particular compounds.

Sampling time of VOCs is reported ambiguously. Was it from 21:00 to 17:00 the next day or in fact from 9pm to 5AM (instead of 5 pm as reported)? Please be more specific in the text as this is quite important. If sampling tubes were left in place during the daytime, some of the compounds could have evaporated due to heating of the tubes in the summer. It would also be important to mention whether all of the headspace VOCs were sampled on the same day and whether there could be variation in i.e. temperature.

Considering the experimental setup for the pollinator attraction observations and the pooling of the data at the block level (which I think is the right choice) it seems possible the authors were more likely to get a result where pollinator behavior matches the long-distance cue, the VOCs. Short-distance cues such a subtle difference in flower size would perhaps not be distinguished with the current setup. I would be interested to know if the authors agree, and if so, mention this in the discussion.

Reviewer #2 (Public Review):

A summary of what the authors were trying to achieve. This interesting and data-rich paper reports the results of several detailed experiments on the pollination biology of the dioceus plant Silene latfolia. The authors uses multiple accessions from several European (native range) and North American (introduced range) populations of S. latifolia to generate an experimental common garden. After one generation of within-population crosses, each cross included either two (half-)siblings or two unrelated individuals, they compared the effects of one-generation of inbreeding on multiple plant traits (height, floral size, floral scent, floral color), controlling for population origin. Thereby, they set out to test the hypothesis that inbreeding reduces plant attractiveness. Furthermore, they ask if the effect is more pronounced in female than male plants, which may be predicted from sexual selection and sex-chromosome-specific expression, and if the effect of inbreeding larger in native European populations than in North American populations, that may have already undergone genetic purging during the bottleneck that inbreeding reduces plant attractiveness. Finally, the authors evaluate to what extent the inbreeding-related trait changes affect floral attractiveness (measured as visitation rates) in field-based bioassays.

An account of the major strengths and weaknesses of the methods and results. The major strength of this paper is the ambitious and meticulous experimental setup and implementation that allows comparisons of the effect of multiple predictors (i.e. inbreeding treatment, plant origin, plant sex) on the intraspecific variation of floral traits. Previous work has shown direct effects of plant inbreeding on floral traits, but no previous study has taken this wholesale approach in a system where the pollination ecology is well known. In particular, very few studies, if any, has tested the effects of inbreeding on floral scent or color traits. Moreover, I particularly appreciate that the authors go the extra mile and evaluate the biological importance of the inbreeding-induced trait variation in a field bioassay. I also very much appreciate that the authors have taken into account the biological context by using a relevant vision model in the color analyses and by focusing on EAD-active compounds in the floral scent analyses.

The results are very interesting and shows that the effects of inbreeding on trait variation is both origin- and sex-dependent, but that the strongest effects were not always consistent with the hypothesis that North American plants would have undergone genetic purging during a bottleneck that would make these plants less susceptible to inbreeding effects. The authors made a large collection effort, securing seeds from eight populations from each continent, but then only used population origin and seed family origin as random factors in the models, when testing the overall effect of inbreeding on floral traits. It would have been very interesting with an analysis that partition the variance both in the actual traits under study and in the response to inbreeding to determine whether to what extent there is variation among populations within continents. Not the least, because it is increasingly clear that the ecological outcome of species interactions (mutualistic/antagonistic) in nursery pollination systems often vary among populations (cf. Thompson 2005, The geographic mosaic of coevolution), and some results suggest that this is the case also in Hadena-Silene interactions (e.g. Kephardt et al. 2006, New Phytologist). Furthermore, some plants involved in nursery pollination systems both show evidence of distinct canalization across populations of floral traits of importance for the interaction (e.g. Svensson et al. 2005), whereas others show unexpected and fine-grained variation in floral traits among populations (e.g. Suinyuy et al. 2015, Proceedings B, Thompson et al. 2017 Am. Nat., Friberg et al. 2019, PNAS). Hence, it is possible that the local population history and local variation in the interactions between the plants and their pollinators may be more important predictors for explaining variation in floral trait responses to inbreeding, than the larger-scale continental analyses. Not the least, because North American S. latifolia probably has multiple origins, with subsequent opportunity for admixture in secondary contact.

I see no major weaknesses in the study, and but in my detailed response, I have made a few questions and suggestions about the floral scent analyses. In short, the authors have used a technique that is not the standard method used for making quantitative floral scent analyses, and I am curious about how it was made sure that the results obtained from the static headspace sampling using PDMS adsorbents could be used as a quantitative measure. I would suggest the authors to validate the use of this method more thoroughly in the manuscript, and have detailed this comment in my response to the authors.

Also, and this may seem like a nit-picky comment, I am not convinced that the best way to describe the traits under study is "plant attractiveness", because in the experimental bioassays, most of the traits under study that are affected by the inbreeding treatment, did not result in a reduced pollinator visitation. Most (or all) of these traits may also be involved in other plant functions and important for other interactions, so I suggest potentially using a term like "floral traits" or "(putative) signalling traits".

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions: By and large, the authors achieved the aims of this study, and drew conclusions based in these results. One interesting aspect of this work that I think could be discussed a bit deeper is the lack of congruence between the effects of inbreeding on floral traits and the variation in visitation pattern in the bioassay. In fact, the only large effect of inbreeding on a floral trait that may play a role as an explanatory factor is the reduction of emission of lilac aldehyde A in inbred female S. latifolia from North America, which correspond to a reduced visitation rate in this group in the pollinator visitation bioassay. I have made some specific suggestions in my comments to the authors.

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community: I think that one important aspect of this work that may broaden the impact of this study further is the link between these experiment, and our expectations from the evolution of selfing. Selfing plant species most often conform to the selfing syndrome, presenting smaller, less scented flowers than outcrossing relatives. Traditionally, the selfing syndrome is explained by natural selection against individuals that invest energy into floral signalling, when attracting pollinators is no longer crucial for reproduction. Some studies (for example Andersson, 2012, Am. J. Bot), however, have shown that only one, or a few, generations of inbreeding may reduce floral size as much as quite strong selection for reduced signalling. Here, at least for some populations and sexes, similar results are obtained in this paper regarding several traits (including floral scent), and one way to put this paper in context is by discussing the results in the light of these previous papers.

Any additional context that would help readers interpret or understand the significance of the work: I would like to reiterate here the potential to utilize the population sampling to make additional conclusions about the geography of trait variation and its importance for the phenotypic response to inbreeding.

Reviewer #1 (Public Review):

The manuscript by Schrieber et al., explores whether inbreeding affects floral attractiveness to pollinators with additional factors of sex and origin in play, in male and female plants of Silene latifolia. The authors use a combination of spatial sampling, floral volatiles, flower color, and floral rewards coupled with the response of a specialized pollinator to these traits. Their results show that females are more affected by inbreeding and in general inbreeding negatively impacts the "composite nature" of floral traits. The manuscript is well written, the experiments are detailed and quite elaborate. For example., the methodology for flower color estimation is the most detailed effort in this area that I can remember. All the experiments in the manuscript show meticulous planning, with extensive data collection addressing minute details, including the statistics used. However, I do have some concerns that need to be addressed.

Core strengths: Detailed experimental design, elaborate data collection methods, well-defined methodology that is easy to follow. There is a logical flow for the experiments, and no details are missing in most of the experiemnts.

Weaknesses: A recent study has addressed some of the questions detailed in the manuscript. So, introduction needs to be tweaked to reflect this.

Some details and controls are missing in floral scent estimation. Flower age, a pesticide treatment of plants that could affect chemistry..needs to be better refined. While the study is laser-focused on floral traits, as the authors are aware inbreeding affects the total phenotype of the plants including fitness and defense traits. For example, there are quite a few studies that have shown how inbreeding affects the plant defense phenotype. This could be addressed in the introduction and discussion.

Reviewer #3 (Public Review):

Using high fat diet (HFD)-fed male mice and a variety of experimental approaches, the authors demonstrated the efficacy of xanthohumol (XN) and tetrahydro-xanthohumol (TXN) in attenuating weight gain and hepatic steatosis independently of calorie intake and identify inhibition of PPARγ as a mechanism. A strength of the study design was the incorporation of the test compounds into isocaloric, ingredient matched high-fat diet (HFD) formations and inclusion of a LFD control group. A weakness of the study, although minor, is that the dose of compound consumed will vary between mice and from day-to-day depending on how much food each animal consumes. The lower dose of XN (LXN, given as 30 mg/kg of diet) was found ineffective compared to the higher dose of XN (HZN, 60 mg/kg of diet) and TXN (30 mg/kg of diet) was most effective in attenuating weight gain and reversing HFD-induced liver steatosis. TXN almost completely suppressed hepatic lipid vacuole accumulation and showed greatest reduction in liver mass relative to body weight. TXN increased fasting plasma triglycerides compared to all other groups, but explanation is uncertain. Fecal excretion of TAG between groups was similar and therefore could not explain the decreased weight gain or improved liver phenotypes in XN- or TXN-treated groups. Whole body energy metabolism suggested that XN and TXN supplemented mice were more physically active then HFD-fed mice. HXN and TXN supplemented mice showed less accumulation of subcutaneous and mesenteric fat mass, but these groups had somewhat higher levels of epididymal fat mass.

After 16 weeks on diets, RNAseq performed on murine liver tissues. Compared to HFD group, TXN group had 295 differentially expressed genes (DEGs), HXN group had 6 DEGs, and LFD group had 212 DEGs. TXN supplementation upregulated 6 and down regulated 25 KEGG pathways. SVM was used to identify signature genes that significantly differentiated HFD and TXN group transcriptomes. Of 13 identified genes, 8 showed significant, differential hepatic expression between TXN and HFD groups. Of these 8 genes, 3 genes (Ucp2, Cidec, Mogat1) were identified as known target genes of PPARγ with roles in lipid metabolism. qPCR of liver tissues was used to verify these RNAseq results.

XN or TXN were shown to inhibit murine preadipocyte 3T3-L1 differentiation and adipogenesis and lipid accumulation in a dose dependent manner. In a second dose escalating experiment, TXN or XN were shown to block the ability of rosiglitazone (RGZ), a PPARγ agonist, to promote adipogenesis of 3T3-L1. These data suggested that XN and TXN may interfere or compete with binding of RGZ to the PPARγ receptor. qPCR of 3T3-L1 cells confirmed that TXN or XN could inhibit gene expression of RGZ-induced PPARγ target genes (Cd36, Fabp4, Mogat1, Cidec, Plin4, Fgf21) and further supported the hypothesis that TXN and XN are PPARγ antagonists. To further test this idea the authors performed a competitive PPARγ TR-FRET binding assay and showed that XN and TXN could displace a labelled pan-PPARγ ligand in a dose-dependent manner. Finally, molecular docking experiments confirmed the putative binding pose and position of XN/TXN and estimated the relative binding affinities of various ligands for PPARγ. XN and TXN may serve as scaffolds for the development of more potent therapeutics in structure-activity relationship (SAR) studies. Overall, this work contributes compelling preclinical data to support future clinical investigations to determine dosing, efficacy, and safety of XN and TXN as therapeutics for diet-induced NAFLD.

Reviewer #2 (Public Review):

The authors showed that the TNX treatment is able to reduces the liver steatosis. But, a lot of results are contradictory. Fer example, the PPAR-gamma is well known insulin sensitizing and the authors did not show the effect of the ntagonism on PPAR-gamma in insulin and glucose homeostasis. Moreover, more analyzis about the adipose tissue are mandatory, since the inhibition of PPAR-gamma might induce the pro-inflammatory status. Thus, to publish in this outstanding journal it is necessary additional experiments to proof that the PPAr-gamma is the main pathway of beneficial effects of TXN.

Reviewer #1 (Public Review):

In this study, Zhang et al. systematically analyze the effect of xanthohumol (XN) and TXN, a xanthohumol derivative, in a model of high-fat diet (HFD) feeding to mice, inducing several pathologies related to the metabolic syndrome. They authors convincingly show that XN and TXN attenuate HFD-induced weight gain, hepatic steatosis and lipid accumulation in adipose tissues. Furthermore, they newly show that XN and TXN bind to the PPARgamma ligand-binding domain pocket and that this inhibitory effect on PPARgamma is at least in part responsible for the observe beneficial effects.

Reviewer #3 (Public Review):

The manuscript "HPF1 and nucleosomes mediate a dramatic switch in activity of PARP1 from polymerase to Hydrolase" by Rudolph et al. studies the effect of HPF1 on the steps of the catalytic reaction of PARP1. They use various PARP1 activators i.e. free DNA and varied forms of core nucleosomes to quantify reaction rates in the presence and absence of HPF1, using several assays. The main point of the manuscript is the observation that in the presence of HPF1, PARP1 is converted to an NAD+ hydrolase, which releases free ADPr, instead of its normal activity to produce ADPr polymers. The PARP1 hydrolase activity has been described previously, but they now show that HPF1 increases it substantially under the conditions that they tested. The authors also describe their independent identification of HPF1 residue E284 as a residue that is essential for Ser modification, confirming previous structural and biochemical work from Ivan Ahel's group. Although the assays are well performed and controlled and yield important quantitative information that was missing in the field, the main result of the hydrolase activity of PARP1 is hard to reconcile with current knowledge of HPF1 effects in cell-based experiments.

Reviewer #2 (Public Review):

This enzymological analysis of the DNA-repair protein PARP1 in the presence and absence of its recently discovered regulator, HPF1, is a welcome contribution to the field that provides new data as well as introducing a valuable conceptual framework (seeing PARP1 as simultaneously catalysing 4 different reactions) and novel assays. Some of its conclusions - e.g. regarding the importance of residues Glu284 and Asp283 within HPF1 - are an independent validation of some of those from a recently published study but here they are reached with partially orthogonal means and supported by additional data (e.g. precisely quantified stability, binding, and catalytic parameters). Moreover, the study offers new insights, with the most interesting observation pointing to the prevalence of NAD+ hydrolysis to free ADP-ribose by PARP1 in the presence of HPF1. The technical aspects of the study including the design, number of repeats, data presentation and analysis, and the level of detail provided in the method section are adequate.

Reviewer #1 (Public Review):

This manuscript describes a set of biochemical studies on the substrate and reaction specificity of PARP1, an important drug target and component of DNA damage response. The focus of the work is on the specific role of HPF1, and how PARP1's numerous activities are altered by complexation with it and with a variety of substrates. There are many important findings described in this paper, which will be of great interest to the researchers studying PARP1 and issues related to NAD+ metabolism. Perhaps the most significant finding is that HPF1 binding to PARP1 causes a shift from primarily PARylation activity to that of hydrolytic activity, yielding a large pool of free ADPR. The paper is very well written. Addressing the following issues would provide clarity.

1) The kcat enhancement from employing nucleosome substrates is exceedingly small, and probably will not ever be clearly correlated to a specific structural feature. However, more concerning is a possible uncontrolled variable when examining the nucleosome substrates. Specifically, the nucleosome substrates which yield a distinctly higher kcat (Table 1) are the larger, trivalent nucleosomes. It seems prudent to show that simply adding more potential binding sites, or perhaps just adding more protein itself is not causing these small increases in kcat (relative to DNA alone).

2) Concerning the assignment of E284 of HPF1 as the catalytic base in the deprotonation of the Ser hydroxyl, I'm wondering if there might be a dynamical explanation for its role instead. E284A causes a significant decrease in the KD for HPF1 binding, and an elimination of the observed PARylation activity, suggesting that it may play an allosteric role. Also, we see from Table 2 that H303Q also produces a large reduction in the activity and large reduction in the KD; the standard error on the H303Q binding data is very large, but does suggest that some observations were quite low (similar to E284A). Additionally, H303Q almost eliminates enzymatic activity as well. Overall, this set of data gives me pause about certainty of the assignment of E284 as the catalytic base, as there may be a more complex origin of the loss of enzymatic activity.

3) It may be that the reason that there is no apparent PARylation at the standard carboxylate residue sites (in the presence of HPF1) is that they are forming transient ester bonds with the anomeric carbon, which are labile to hydrolysis. I feel that a better development of the treadmilling effect would enhance the paper (e.g., mutation of the orthodox carboxylate nucleophiles and examination of changes in HPF1-induced hydrolytic activity). I'm not sure that it can be quantitatively shown that the shorter PAR chains in the presence of HPF1 account for the pool of free ADPR.

Reviewer #2 (Public Review):

Anderson et al construct an epigenetic clock using samples from 245 individuals in the long-running Amboseli study of wild baboons. Their epigenetic clock tracks chronological age reasonably well, and also relates to other metrics of developmental tempo. Contrary to expectations from studies in humans and other species, deviations between epigenetic age and chronological age are unrelated to important predictors of life expectancy in this sample, including measures of early adversity and social integration. Instead, the key predictor of epigenetic aging is dominance rank: In males, more dominant animals show evidence for accelerated epigenetic aging using the epigenetic clock that they derive. In a longitudinal analysis the relationship between dominance and biological aging is shown to be at least partially transient and reversible, pointing to possible concurrent rather than cumulative or non-reversible effects. Although reproductive effort in the form of larger body size and muscularity are plausible factors linking dominance to epigenetic aging, the relationships documented here are shown to be largely independent of measures of body size and relative weight.

This study is important because the authors generate an epigenetic clock, a method increasingly important in research on human aging and life history, for use in this species of baboon. To achieve this, they use a long-running study in which the actual ages of animals are known. Their findings suggest that the aspect of biological aging indexed by this clock is distinct from other important influences on lifespan previously documented in this species, and specifically points to reproductive effort related to maintaining dominance as a key driver of this variation in males.

Reviewer #1 (Public Review):

This is an interesting manuscript which does a lot - both building and validating an epigenetic clock for the Amboseli baboons, and then looking to see which factors predict deviations in epigenetic age relative to chronological age. This is an important study, and perhaps the first of its kind from a free-ranging primate population. I believe it will be influential and well-cited.

In particular, it is extremely thorough in the data and analyses that it presents. It is also clearly structured and easy to follow, despite covering some dense material.

In sum, this manuscript is a high-quality and important manuscript that I believe will be influential.

This manuscript is in revision at eLife

The decision letter after peer review, sent to the authors on October 28 2020, follows.

Summary

PP1 and PP2A make up the majority of serine/threonine phosphatase activity in the cell. While substrate recognition has been studied for PP1 and PP2B, the substate recognition of PP2A holoenzymes are less understood. Here, Fowle et al. set to understand substrate recognition of B55/PP2A. Using a specific substrate of B55, p107, the authors identify a conserved binding motif (HxRVxxV) for recognition by B55 and show additional B55 substrates also contain this motif. This work incorporates many complementary structural and biochemical assays to delineate the binding and recognition of substrates by B55.

Essential Revisions

1) What is the evidence that this motif is only recognized by B5alpha/PP2A and not other B55 family members? Are the residues identified in B5alpha critical to the p107 interaction (D197 and L225) conserved among all of the isoforms? If they are, can other B55 family members bind p107?

2) Have the authors looked for the HxRVxxV motif across the proteome? The author only state that they noticed that this motif was found in Tau, p130 and MAP2, but how many proteins contain these motifs? A list or understanding of the potential proteins which contain this motif could give researchers outside the field a link to understand the phosphatase important for their protein of interest.

3) For the P107 deletion mutants has the expression of each one been confirmed in Figure 1 and is decreased binding to PP2A B55alpha been normalized to the expression of these mutants.

4) Is the phosphorylation of p107 by CDK2 affecting the affinity of B55 binding to this substrate?

5) Have the authors considered measuring direct binding affinities using ITC/SPR for example to look at the effects of these various mutants in a cell free / in vitro system?

6) It would have been interesting to study the effects of the various B55 mutants on the endogenous phosphorylation of p107, Rb, and KSR?

7) To gain insights into the physiological role of the identified domain of p107 in PP2A-B55 binding and in the dephosphorylation of this protein, new "in cellulo" experiments using the full length p107 mutant protein have to be performed and its impact in the temporal pattern of dephosphorylation analyzed.

8) Figure 1D, it is obvious that in order to compare the levels of PP2A-B55 associated to each construct it is essential to normalize the levels of A and B55 signals to the quantity of protein that is recovered in each pulldown. As such, the levels of each GST construct in the pulldowns have to be measured by western blot and used to obtain the PP2A A our B55/GST-spacer ratio. Ratios can be then compared.

9) The authors state that: "a mutant lacking residues C-terminal of R2 binds B55α similarly to the full construct, indicating that residues C-terminal to the R2 domain are dispensable for B55α Binding". Do "residues C-terminal of R2" mean full R2 region? If this is the case, this statement is not supported by Supplementary Figure 1B, where western blot of construct 2 and 7 display dramatically reduced B55 and A levels.

10) The authors tested the effect of KR residue mutation in the R1 and R2 regions in p107 dephosphorylation. KR mutants used for the R1 are R621A/K623A, the two mutants that were tested in Figure 1D and that were shown to impact B55 binding. However, they select K657A/R659A for R2 region. These two mutants were not tested in Figure 1D. Why do they introduce these mutants and not R647A that was investigated in Figure 1D? If the authors think that these residues are important, why did they not test them for its capacity to bind B55 in Figure 1D?

11) Other cdk-dependent phosphorylation sites on p107 that are essential for E2F binding have been described. Some of these sites are out of the spacer sequence. It will be interesting to know whether the dephosphorylation of these sites are dependent on PP2A-B55 and regulated by the mutants on the spacer sequence that decrease B55 binding.

12) Figure 4A and B. Dephosphorylation pattern of R1R2 control construct is drastically different in Figure 4A compared to 4B. In the first case, complete dephosphorylation does only take place upon two hours of incubation compared with fifteen minutes in the second. This is very weird if the same purified phosphatase is used in both experiments. In this line, I would expect a timing of few minutes for a total dephosphorylation when a purified phosphatase is used. Does it mean that phosphatase in Figure 4A lost activity?

13) "In vivo" experiments on the dephosphorylation of the non-binding p107 full length mutants have not been performed. To demonstrate that these residues are physiologically relevant for the physiological temporal p107 dephosphorylation pattern, these experiments must be done.

14) In the same line, to really show the involvement of the pST-x(5-10)-(RK)-Vxx(VI)R in Tau dephosphorylation by PP2A-B55 a direct mutant of this sequence of Tau should be checked.

15) What are the consequences of B55a-interaction mutants in p107 function? Is that mutant protein able to sustain cell cycle arrest?

16) Since the authors propose a new model/motif, it would be great to add some statistics on to what extent this motif is present in the numerous hits found in recent screens for B55 targets during mitotic exit. Is this motif present in B55 targets involved in non-cell-cycle (TAU) or cell-cycle targets? Is it equally present in proteins dephosphorylated during early versus late mitotic exit? Any hint into these questions may facilitate the impact of the model proposed in the biology of PP2A/B55.

Reviewer #3 (Public Review):

The goal of this study was to test the hypothesis that the calcium-activated TRPM4 channel regulates left ventricular (LV) hypertrophy which occurs after pressure overload. The authors use the transaortic constriction model (TAC) which represents a common and well-validated model of LV hypertrophy and of heart failure. Typical LV pressure overload models range from relatively mild constriction using a 25 gauge needle to more severe constriction with a 27 gauge needle. In this study the authors demonstrate that two weeks of pressure overload with a 25 gauge needle in mice produces LV hypertrophy, increased fibrosis, and a pattern of fetal gene re-expression which marks the pathological hypertrophy phenotype. This phenotype precedes overt cardiac dysfunction, in the sense that the functional measures the authors used did not worsen after two weeks in TAC mice, compared to sham-treated controls. These results reproduce prior observations in this model.

The authors next apply the 2 week TAC model to previously-generated mice with cardiac myocyte-restricted deletion of the TRPM4 channel. They demonstrate that deletion of TRPM4 generates a protective response, in that despite the same degree of pressure overload, the TRPM4 cardiac myocyte-specific deletion mice develop less LV hypertrophy, less LV fibrosis, and less fetal gene re-expression. Thus the authors successfully demonstrate that deletion of TRPM4 reduces pressure overload-induced LV hypertrophy. This suggests that TRPM4 normally promotes pathological LV hypertrophy after pressure overload.

While this work convincingly demonstrates that TRPM4 deletion from the cardiac myocyte leads to reduced pressure overload-induced LV hypertrophy, the study does not prove the intracellular signaling mechanisms which mediate this effect. The authors' model is that: 1) neurohormonal signals for pressure overload predominantly induce LV hypertrophy through a calcineurin pathway leading to nuclear import of NFAT; and 2) mechanical stretch (such as induced by TAC) predominantly acts through the intracellular kinase CaMKII which then phosphorylates histone deacetylase 4, thus promoting HDAC4 nuclear import. The study does not prove whether any of these signaling components are necessary or sufficient for the effects of TRPM4 on LV hypertrophy in vivo.

As a whole this work will be of interest to the larger scientific community for several reasons. First, in response to a different model of pathologic LV hypertrophy, the angiotensin II infusion model, the TRPM4 cardiac myocyte deletion mice actually develop increased, rather than decreased, LV hypertrophy. Thus the combined observations that TRPM4 deletion suppresses pressure overload LV hypertrophy by TAC, but augments neurohormonal hypertrophy by angiotensin administration support the important concept that different stimuli of hypertrophy likely act through and are regulated by different signaling pathways. Second, as a membrane associated ion channel, TRPM4 might be a potential drug target especially in patients with pressure overload-induced pathological hypertrophy.

Reviewer #2 (Public Review):

The manuscript by Guo et al. focuses on the involvement of TRPM4 channel in the development of pressure overload-induced cardiac hypertrophy. They show that TRPM4 expression, in both mRNA and protein, was downregulated in response to left ventricular pressure overload in wild type mice. They demonstrate that a reduction in TRPM4 expression in cardiomyocytes reduces the hypertrophic response to pressure overload due to transverse aortic arch constriction. Furthermore, they show that activation of CaMKIIδ-HDAC4-MEF2A pathway is reduced in mice with cardiomyocyte-specific, conditional deletion of Trpm4. Originally, TRPM4 channel was well known for its association with cardiomyocyte action potential formation and arrhythmia, but this study is very interesting in that it clarified the association of TRPM4 channel with the mechanotransduction mechanism of ventricular pressure overload. Their work may lead to the development of treatment strategies for hypertensive heart disease.

Reviewer #1 (Public Review):

This study indicated that transient receptor potential channel subfamily melastatin 4 (TRPM4), a Ca2+ and voltage activated non-selective monovalent cation channel, might contribute to pressure overload-induced cardiac hypertrophy, although not through direct mechanical stretch-related activation. TRPM4 could possibly activate several Calmodulin (CaM)-related downstream signaling pathways, resulting in cardiac hypertrophy. However, the important question of what is mechanistic link of mechanical stretch and activation of TRPM4 ion channel is left unanswered.

Strength: The experiments are well designed with reliable data presented. The utilization of TAC mice model presented in this study was backed with proper reasoning with appropriate proof-of-concept results, especially concerning the 2-day TAC protocol.

Weakness: Trpm4cKO mice have been previously studied in another cardiac hypertrophy model by using angiotensin II, which lessened the novelty value in the findings of this study. Furthermore, the data presented in this paper were inadequate to fully answer their research questions and further in vivo and in vitro studies are needed to confirm the mechanism that can explain the phenomenon seen in the results.

This manuscript is in revision at eLife

The decision letter after peer review, sent to the authors on February 15 2021, follows:

Summary:

The submitted manuscript presents an argument for a novel mechanism of action for the antibiotic colistin. The authors suggest that colistin kills bacteria through its action on lipopolysaccharides at the inner membrane. This is primarily supported by MCR-1 mediated colistin resistance conferring resistance only to cell lysis and not to outer membrane permeabilization. The authors extend this hypothesis to suggest that increasing the amount of LPS in the inner membrane should increase susceptibility to colistin. By inhibiting LPS transport with murepavadin, the accumulation of LPS in the cytoplasmic membrane increased. Combinations of colistin and murepavadin act synergistically to improve bacterial lysis and show efficacy in a murine lung infection model.

Essential Revisions:

1) 1) Observations demonstrating MCR-1 modification does not impact outer membrane perturbation and provides resistance to colistin induced lysis are supported by MacNair et al. They suggest that strengthened LPS packing provided by mcr-1 could play an important role in reducing the uptake and lytic activities of colistin. The author's should address that decreased colistin uptake could also result in reduced lysis. To support their hypothesis, the relationship between the amount of modified LPS in the inner membrane and resistance to cell lysis could be expanded on. https://www.nature.com/articles/s41467-018-02875-z

2) The authors use the lack of change in susceptibility of mcr-1 spheroplasts to daptomycin and nisin to support that there is no change to the biophysical properties of the phospholipid bilayer of the cytoplasmic membrane. However, whether the sensitivity of daptomycin and nisin to changes to membrane charge or fluidity remains unclear.

3) Murepavadin is used to increase LPS at the CM and as interpreted would support the hypothesis. However, it is also possible that in the whole cell assays, the OM disruption of colistin sensitizes the cells to the killing activity of murepavadin. Repeating the assays with a non-lethal OM permeabilizer like polymyxin B nonapeptide would eliminate this possibility and strengthen the authors conclusions.

4) The authors suggest that mcr-1 provides protection from colistin through the modification of LPS at the inner membrane and that outer membrane modification has no impact on colistin activity. In contrast, it has been demonstrated that mcr-1 decoration is capable of preventing outer membrane perturbation by polymyxin B nonapeptide (https://www.nature.com/articles/nmicrobiol201728). This suggests that modified LPS at both the inner and outer membrane may play a role in resistance.

5) Authors should discuss work in the synergy between novobiocin and colistin where novobiocin enhances colistin killing through the stimulation of LPS transport. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5990483/

6) Hydrophobic NPN dye was used to explore the permeabilization of OM in this work. However, the uptake of NPN is not absolute proof that colistin is permeable. The authors should discuss this as a possible caveat of their mechanistic model.

Reviewer #2 (Public Review):

Alvarez et al. present a study of the heritability of functional properties of early visual cortex, as assessed by a population receptive field (pRF) analysis of retinotopic mapping data in monozygotic (MZ) versus dizygotic (DZ) twin pairs. The use of a MZ versus DZ twin design is a strength, as it permits estimates of heritability, and connects the retinotopic mapping and pRF literature to the literature examining heritability of a diverse range of cognitive functions.

I have only one point of concern that I feel the authors should address. It seems that the correlation analysis assumes that each vertex in the cortical surface model represents an independent observation, but an assumption of independence does not appear to be satisfied. FMRI responses in nearby vertices are expected to be highly inter-dependent, as a single fMRI voxel may be mapped onto many vertices. Spatial blurring intrinsic to the fMRI signal (i.e., point-spread function), as well as the spatial smoothing of pRF parameters that was performed, would be expected to exacerbate this issue.

Reviewer #1 (Public Review):

The authors employed population receptive field (pRF) mapping to characterize responses to visual stimuli in early visual cortical areas V1-V3 and to compare the similarity of pRF properties in pairs of monozygotic versus dizygotic twins. They find closer correspondence of the anatomical location and spatial extent of the visual areas, pRF locations (polar angle and eccentricity) in the retinotopic cortical maps of visual space, and spatial selectivity of responses (pRFs size) in monozygotic twins, relative to dizygotic twins, indicating heritability of these structural and functional properties of early visual cortex.

The pRF mapping procedures used in this study are appropriate and standard in the field, and the statistical analysis and data presentation are thorough and rigorous. Given the many previous demonstrations of heritability in multiple aspects of visual perception and physiological responses to visual stimuli, it would be very surprising if any of the properties studied by the authors did not exhibit some amount of heritability. This paper therefore adds to the list of known heritable properties of the visual system but does not contribute theoretical or conceptual advances or challenge any existing frameworks.

The fact that pRF eccentricity was more correlated and showed less heritability than pRF polar angle is interesting but was not interpreted or followed up in any meaningful way. Overall, the analyses are basic (% overlap of retinotopic maps and the three main pRF parameters) and descriptive.

Reviewer #3 (Public Review):

This manuscript is well written and presents several new mouse models including animals with brown fat specific deletion of multiple genes of interest to assess whether they may function in a common pathway. The authors draw on their existing expertise in mitochondrial biology to provide new information regarding the role of OPA1 and mitochondrial dynamics in brown fat function. Weaknesses of this study include a relative lack of mechanistic insights and incomplete characterization of whole-body energy expenditure data from the multiple models reported here.

Reviewer #2 (Public Review):

Understanding the mechanisms by which thermogenic brown adipocytes become activated in response to adrenergic signaling remains a high priority for the field of adipose tissue biology. The authors of this study investigate the importance of mitochondrial fusion protein optic atrophy 1 (OPA1) in brown adipocytes, which is highly regulated at the transcriptional and post-transcriptional level upon cold exposure and obesogenic conditions. Using a genetic loss of function mouse model, the authors demonstrate BAT specific knockout of OPA1 results in brown adipocyte mitochondrial dysfunction; however, knockout animals have improved thermoregulations due to the activation of compensatory mechanisms. Part of this compensatory mechanism involves the activation of an ATF4 mediated stress response leading to the induction of FGF21 from brown adipose tissue. These data highlight the presence of homeostatic mechanisms that can ensure thermoregulation in mammals.

Overall, the manuscript is very well-written and the data is nicely presented. The use of multiple genetic mouse models is elegant, rigorous, and yields convincing results. The authors acknowledge the strengths and limitations of the work in a nicely written discussion. This should be a valuable addition to the field, including those interested in mitochondrial biology, brown adipose tissue biology, and FGF21 function. There are minor issues that require attention and one important issue regarding the variability in FGF21 levels observed in the knockout model.

Reviewer #3 (Public Review):

This study implements a secondary analysis of data collected as part of a randomized control trial of malaria vector control interventions in Malawi. The key outputs are statistical associations between two metrics of malaria transmission: P. falciparum parasite prevalence (PfPR) and P. falciparum entomological inoculation rate (PfEIR). There is a rich history of studies investigating this association, spanning a range of approaches: (i) meta-analyses (e.g. Smith et al Nature 2005); (ii) local epidemiological analyses (e.g. Beier et al. AJTMH 1999); (iii) large-scale geo-spatial mapping (e.g. Malaria Atlas Project); and (iv) mathematical transmission models (e.g. Griffin et al Nature Comms 2014). This paper promises to add to this literature using spatio-temporal modelling.

I was excited by the abstract, and especially by the ambitious questions posed in the introduction (lines 112-117). However, upon reading the manuscript I was left a bit underwhelmed, as the results didn't have much to say in terms of either the spatial or temporal aspects of this relationship. Rather the best-fit model was simply a logit linear model between PfPR and PfEIR with a one month lag.

Major comments:

1) Spatial aspect of association. Geostatistical models are challenging to fit, but I have confidence in the authors' ability to do so. Rather, the authors have not demonstrated the extra value of using this approach. Indeed, no spatial results are presented in the manuscript, apart from estimates of model parameters in the appendix which will be uninterpretable to most readers. Points of interest would include, what does a hot spot look like? What does the overlap between different types of hotspot look like? What is the degree of spatial correlation? I appreciate some of this is provided in the separate online animation, but there's no interpretation of what we're seeing.

2) Temporal aspect of association. The association between PfEIR and PfPR is clearly a temporally complex one as demonstrated by the data in Figure 2. I don't think this complexity has been fully accounted for, beyond simple time lags. For example, I'm quite skeptical of the following result:

"From the estimated relationship for children, a decrease in PfEIR from 1 ib/person/month to 0.001 ib/person/month is associated with a reduction in PfPR from 37.2% to 20.7% on average (i.e., a 44.5% decrease in PfPR). When transmission has been driven almost to zero, PfPR remains consistently high in children."

This is a 1000-fold reduction in PfEIR associated with a 44.5% decrease in PfPR. I find this hard to believe, and don't think such a generalizable statement should be made. Rather these are dynamic quantities that vary with each other, and with the time scale over which they are measured.

Reviewer #2 (Public Review):

This study explains the motivation behind considering a spatio-temporal model for modelling malaria transmission and achieves it by using two metrics - Plasmodium falciparum entomological inoculation rate (PfEIR) and Plasmodium falciparum prevalence rate (PfPR), as they believe the two metrics together provide a better picture of transmission. The study modeled the spatial distribution of PfEIR and PfPR for children (0.5-5yr) and women (15-49) in rural Malawi. To estimate PfEIR which is a product of Human biting Rate (HBR) and P.f. sporozite rate (PfSR), HBR and PfSR are modelled as Poisson mixed model with log link and Binomial mixed model with logit link, respectively.

The study then models the relationship between PfEIR and PfPR, where PfPR is modelled as a Binomial mixed model. Six different models were considered and compared for modelling the relationship between PfEIR and PfPR. Subsequently, the PfEIR and PfPR are then used for hotspot detection.It is satisfactory to note that separate models were used for different species of mosquitos, which eventually led to different set of covariates and random effects. We are also satisfied that the authors have provided the estimates of covariates, temporal trends, and spatial trends. The paper has a well-written discussion section.

The following issues warrant further attention and clarification.

1) It seems that a single model is fitted for all three focal regions. Please comment on why the authors believe that the parameter estimates should be common for the three regions (or is this a pragmatic decision)

2) In the model for PfSR, no spatial random effect was included (formula 2), despite mentioning the spatial heterogeneity throughout the manuscript. Some justification for not including the space term is needed.

3) In the six models for modelling the relationship between PfPR and PfEIR, do the results change when an overdispersion term (i.e. an independent Gaussian random effect) is included?

Reviewer #1 (Public Review):

Using well-designed surveys, the authors collected mosquito samples and human data along with environmental variables to estimate parasite prevalence (PR) and the entomological inoculation rate (EIR) in three regions of Malawi. They developed advanced geostatistical models to estimate PR and EIR and illustrated the spatial-temporal variation. The online interactivity web-based application showing the spatial-temporal pattern of PR and EIR as well as hot spots in map is particularly useful for visual understandings. These estimations then allow to unveil the time-lagged relationship between PR and EIR. Their data and research approach add very useful information for improving vector-born disease control strategies. Certainly, the data and findings are very useful for malaria control in Malawi.

Their conclusion seems largely supported by their statistical models and data. However, some outstanding research questions remain. In addition, some statistical issues need to be justified and clarified.

1) While the spatial-temporal pattern of PR and EIR is illustrated, what are the mechanisms underlying those spatial-temporal variation? Specifically, I think environmental factors and spatial distribution of human population certainly play important roles. Indeed, environmental factors were included in their geostatistical models to estimate PR and EIR. However, the authors made no attempt to provide explanation and discussion for these results (results shown as tables in their appendix).

2) Furthermore, if environmental factors are left out of focus, what is the additional value of using modelled PfSR and PfEIR for evaluation instead of empirical (observed) PfSR and PfEIR? What is the scientific motivation and justification of using modelled PfSR and PfEIR instead of empirical ones to make the spatial-temporal map and further statistical analyses and then to draw their conclusion on the relationship between modelled PfSR and PfEIR? Statistically, if the same environmental variable is used to fit PfSR and PfEIR, then there is potential spurious correlation (statistical artifact) between the modelled PfSR and PfEIR. The authors need to demonstrated this is NOT the case in their results and analyses.

3) With A, B, C three regions separated by the national park in the middle (large spatial missing data), is the assumption of isotropic Gaussian process reasonable in their geostatistical model? Sites between A and B have very large distances, but there is no observation data in between. Alternatively, the authors can model the three regions separately?

4) For hotspot detection, it is unclear whether the hotspots are decided: (1) when the point estimates of PfEIR and PfPR exceed the threshold; or, (2) when the lower 95% confidence bound of the estimates exceed the threshold? If it is the case (1), please justify. Statistically, case (2) is more appropriate. The uncertainty associated with estimates needs to be carefully addressed throughout the manuscript. In any case, please elaborate how the exceedance probability is obtained. My similar concern also appears in other analyses, for example the confidence interval shown in Figure 4.

Reviewer #2 (Public Review):

This work evaluates the role for GAGA factor (GAF) as a pioneer factor during the zygotic genome activation (ZGA) of early Drosophila embryogenesis. GAF has previously been shown to regulate chromatin accessibility and higher order genome organization in a variety of biological contexts. However, it has historically been difficult to evaluate the role of GAF specifically during early embryogenesis through standard genetic approaches. This paper solves this problem by employing a combination of gene editing and targeted degradation strategies to specifically knock down GAF in early embryos. Through a combination of imaging and genomic approaches, this paper demonstrates a population of genomic loci that depend on GAF to gain chromatin accessibility and to be expressed during the maternal to zygotic transition. This work identifies an additional pioneer factor activity operating at ZGA and furthermore evaluates the potential interdependency of GAF and another pioneer, Zelda.

Reviewer #3 (Public Review):

Strengths: It is clear through this manuscript that the authors intend for this to be a useful approach for as many fields as possible. While previous technical approaches to maximize the capture of members of microbiomes fail to translate to other environments or hosts, the authors demonstrate the utility of hamPCR by testing it in a number of other systems. The diagrams presented (particularly in Figure 3) nicely convey the steps in the protocol with expected sample outcomes to further facilitate the ability of other researchers to employ hamPCR.

Weaknesses: The challenge of demonstrating the widespread utility in other systems is creating and maintaining biologically-driven narrative. While this is not necessary if the goal is to simply show that a techniques works, it does help to highlight the importance of implementing a new method and increase the likelihood that it will be adopted by other researchers.

Reviewer #2 (Public Review):

Lundberg and colleagues provide a detailed set of data showing the utility of host-associated microbe PCR. By simultaneously amplifying microbial community and host DNA, hamPCR provides an opportunity to measure the microbial load of a sample. I was largely convinced about the robustness of this approach after seeing the many different optimization datasets that were presented in the paper. I also appreciated the various applications of hamPCR that were demonstrated and compared to other standard approaches (CFU counting and shotgun metagenomics, for example). As clearly illustrated in Figure 6f, hamPCR could dramatically improve our understanding of interactions within microbiomes as it helps remove issues of relative abundance data.

One challenge about the approach presented is that it cannot be quickly adapted to a new system. Unlike most primers for 'standard' microbial amplicon sequencing, considerable time will be required to determine which host gene to target, how to make that host gene size larger than the size of the microbial amplicon, etc. This may limit wide adoption of hamPCR in the field. I do appreciate the authors providing some details in the Supplement on how they developed hamPCR for the several different systems described in this paper. The helpful tips may make it easier for others to develop hamPCR for their own systems.

An issue that repeatedly came up is that at high and low ends of host:microbe ratios, inaccurate estimates can occur. For example, with high levels of microbial infection, the authors note that hamPCR has reduced accuracy. The authors propose three solutions to this problem (1. altering host:microbe amplicon ratio, 2. use a host gene with higher copy number, 3. and adjust concentrations of host primers), but only present data for #1 and 3. Do they have any data to show that #2 would actually work?

One instance of potential unreliable load that sticks out in the paper is in Figure 5b. The authors note that this is likely due to unreliable load calculation. Is this just one of 4 replicates? What are other potential reasons this would be an outlier and how can the authors rule this out? Did they repeat the hamPCR for this outlier to confirm the striking difference from the other three samples in the eds1-1 Hpa + Pto sample?

Could the DNA extraction method used cause biases in hamPCR for/against either the host or the microbiome? If two different labs study the same system (let's say bacterial communities growing on Arabidopsis leaves) but use different DNA extraction approaches, would we expect them to obtain different answers using hamPCR? Did the authors try several different DNA extraction methods to see if this is an issue? Or has another team of researchers considered this and addressed it in a separate paper? I would appreciate seeing either data to address this or a discussion paragraph that reasons through this.

One emerging theme in microbiome science is to have consistent methodologies that are used across studies/labs to allow direct comparisons of microbiome datasets. Standardization of approaches may make microbiome science more robust in the long-term. Given much of the nuance in developing hamPCR for different systems, my impression is that this method is best for comparing samples within a particular host-microbe system and not across systems. For example, it may be challenging to directly compare my bacterial load hamPCR data from Arabidopsis to another lab's if we used different Arabidopsis host genes or if we used different 16S gene regions. Can the authors unpack this a bit in a discussion paragraph? If it is widely adopted, is there a way to standardized hamPCR so that it can be consistently used and compared across datasets? Or should that not be the goal?

There appears to be considerable non-specific amplification or dimers in the gels presented throughout the manuscript. Could this non-specific amplification vary across host-microbe primer combinations? Would this impact quantification of host and microbial amplicons?

Reviewer #1 (Public Review):

This work described a novel approach, host-associated microbe PCR (hamPCR), to both quantify microbial load compared to the host and describe interkingdom microbial community composition with the same amplicon library preparation. The authors used the host single (low-copy) genes as PCR targets to set the host reference for microbial amplicons. To handle the problem that in many cases, the host DNA is excessive compared to the microbiome DNA, the authors adjusted the host-to-microbe amplicon ratio before sequencing. To prove the concept, hamPCR was tested with the synthetic communities, was compared to the shotgun metagenomics results, was applied in the biological systems involving the interkingdom microbial communities (oomycetes and bacteria), or diverse hosts, or crop hosts with large genomes. Substantial data from diverse biological systems confirmed the hamPCR approach is accurate, versatile, easy-to-setup, low-in-cost, improving the sample capacity and revealing the invisible phenomena using regular microbial amplicon sequencing approaches.

Since the amplification of host genes would be the key step for this hamPCR approach, the authors might also include more strategy discussions about the selection of single (low copy) genes for a specific host and the primer design for the host genes to guarantee the hamPCR usage in the biological systems other than those mentioned in the manuscript.

This manuscript is in revision at eLife

The decision letter after peer review, sent to the authors on February 18 2021, follows.

Summary:

This paper is of potential importance to neuroscientists who study sensory representations and how they are learnt. It suggests that neural representations underlying human perception can be understood in terms of an optimal compression of the sensory input. While the attempt is indeed interesting, there are several shortcomings that should be addressed before this work could be considered as one that can contribute substantially to the understanding of tactile perception.

Essential Revisions:

1) There is a significant gap between the simulated data used here and the empirical data of material perception by touch. The vibratory signals were taken from recordings of surface exploration using a tool tip (Strese et al., 2017 ) whereas the ratings of the different materials are taken from an experiment in which participants used bare hand touch (Baumgartner et al., 2013). The difference is significant especially when material classification, and not only texture classification, is required. It is not at all clear how vibratory signals could code hardness, warmth, elasticity, friction, 3D, etc (see Baumgartner et al., 2013). The authors must provide a serious discussion about this gap and convince the reader that their simulations can indeed provide an access to the internal representations of natural haptic touch. In the same spirit, they should explain why, and demonstrate that, the pre-processing of the vibrotactile data (cutting & filtering) makes sense for natural haptic touch.

2) The authors should provide good reasons to convince the readers that the compressed representation they found is indeed a good candidate for the biological representation. First, the nature of the AE algorithm is that it will converge to some representation in the minimal encoder dimension. Why is that a good encoding representation, and why is it a good model for the biological one? Second, the differences between the results obtained with the AE and those obtained with humans (Baumgartner et al., 2013) seem to outnumber the partial similarities found. The authors should list both differences and similarities and discuss, based on these comparisons, the probability that the coding found here is similar to the coding guiding human behavior.

3) Notion of efficiency and compression. It was not demonstrated that the main result (figure 3A) is due to compression and efficiency of the AE. What will happen if no AE is used and the distance is measured in the raw input domain (e.g. between Fourier coefficients or principal components)? One could expect figure 4 to account for that, and also show that for a very wide AE there is some deterioration of the main result. Otherwise, the main result about correlation to perceptual data cannot be attributed to the compressive property of the AE.

4) Biological correlates of the latent representation. On the one hand the authors claim that the AE latent representation aims to mimic a latent representation of the haptic space, which they assume to be compressed and efficient. On the other hand, they claim that the AE representation is similar to mechano-sensory representation, which is a first biological representation before any compression can take place (when hand movements are ignored, as done here). This needs to be clarified.

5) Validity of the latent representation. The reconstruction error of the AE is large and systematic: only ~50% of the variance are explained and its high frequencies are systematically ignored. The resulting latent representation is such that classes are poorly separable (~29%) and it seems to be by far worse than the human level (around 70% in Baumgartner et al. 2013). It will be therefore interesting to see if the key result, i.e. relation between AE latent space and the perceptual distance, remains valid for a more advanced AE.

Reviewer #2:

This work combines an interesting experimental approach to measure temporal expansion/compression with EEG recordings. The authors find consistent evidence that a visual reference is judged as shorter/longer dependent on a previous adaptation. They report several EEG analyses suggesting the early visual activity is correlated with such temporal distortions.

Strengths:

The paper uses an interesting design to try to isolate temporal compression/expansion. The behavioral results are consistent and they show several different EEG analyses. The main result, of beta power being correlated with temporal processing, is consistent with previous reports.

Weaknesses:

1) The paper would strongly benefit from more details on some of the methodologies and results. In several moments, the authors show measures that are subtracted or normalized based on other conditions. Although these normalizations can sometimes help to illustrate effects, it also makes it harder to understand the data in a more general sense. For example, in their behavioral results, the authors present an Adaptation Effect to quantify temporal compression/expansion. It would also help if authors present the raw estimates of Points of Subjective Equality across all conditions (including the unadapted condition) so that the reader can have a better understanding of the effects. It would be even better if the average proportion of responses for each duration was shown so that readers can see differences in PSE, JND, and guess/lapse rates.

2) Further details about the EEG analysis would also help the readers. For example, it is not totally clear how the FFT analysis was performed. It would be important to add information about whether data was analyzed using moving windows, the size of the windows, whether there was an overlap between windows, whether there was a baseline correction and what was the baseline.

3) Several of the conclusions of the authors are based on linear mixed effect (LME) regressions in which the PSE or the behavioral effect is the dependent variable and an EEG measure is used as one of the fixed effects. However, in some of the analysis, it is not really clear how this was performed (for example, whether this was done at the single-trial or at the averaged data). Critically, it would help the reader if more output (both tables and graphs) were shown for these analyses so that what is being analyzed and concluded is made clearer.

Reviewer #1:

The question is interesting, and the paradigm in principle well suited to answer it. Unfortunately, a number of shortcomings hinder a clear interpretation of the results. I think that the paper, notably the EEG analyses, need to be revised substantially, which might affect the results. Therefore I will just list the main points which need to be addressed and not go in more detail.

The behavioral effect of adaptation on duration perception appears very unspecific, namely it occurs in all but the spatially neutral condition. The authors conclude that the inversely directed motion did not have an effect because it did not survive the Bonferroni correction, yet they report a p-value of 0.02 and Cohen's d of 0.58, suggesting a medium effect. In order to prove the absence of an effect, I suggest to report Bayes factors, and only interpret the effect as absent if the Bayes factor is conclusive towards the H0.

In my view, if there was an effect of inversely directed motion, this poses a question as to the successful demonstration of specific adaptation effects in the behavior, which needs to be taken into account in the interpretation.

The EEG analyses and displayed results show some important shortcomings, which hinder a clear interpretation at this stage. Just to list a few main points:

-As apparent from Figures 3-5, the time-frequency plots show a lot of stripes and pixels, when one would expect rather smooth transitions over frequency and time. This suggests that the parameters for the time-frequency transformation might not be appropriate.

-The analyses compare time windows that differ in many respects, for instance the 15 s long adaptation phase versus short-lived stimulus-evoked activity at reference onset. Interpreting these differences as specific to the duration distortion effects does not seem justified, due to the diverging inputs presented during those time windows.

-Important aspects of the paradigm are not taken into account in the EEG analyses, for instance the fact that participants perform a saccade between the offset of adaptation and the onset of the reference. The saccade-related signatures in the EEG have to be accounted or controlled for, especially for effects occurring after adaptation offset.

-Some of the effects (for instance the decoding analysis, or the linear mixed models testing for additive but not interactive effects) show differences in EEG activity related to visual processing of the stimuli, but might not specifically relate to the duration distortions. In my view, more trivial differences in processing the visual inputs should be accounted for (see also the point above), and clearly separated from specific timing effects.