AssayResult: 0.01 µM: 60; 0.08 µM: 55; 0.8 µM: 40; 8 µM: 15

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

Comment: This variant was used as an abnormal control in other assays in this publication, but it was not specifically designated as a control in this assay.

AssayResult: 0.01 µM: 65; 0.08 µM: 50; 0.8 µM: 30; 8 µM: 20

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayResult: 0.01 µM: 50; 0.08 µM: 40; 0.8 µM: 20; 8 µM: 15

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayResult: 0.01 µM: 90; 0.08 µM: 80; 0.8 µM: 75; 8 µM: 35

AssayResultAssertion: Normal

ControlType: Normal; wild type PALB2 cDNA

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayGeneralClass: BAO:0003009 cell viability assay

AssayMaterialUsed: CLO:0036938 tumor-derived cell line

AssayDescription: Transient expression of wild type and variant mCherry-tagged PALB2 cDNA constructs in Trp53 and Palb2-null mouse cell line; exposure to increasing concentrations of PARP inhibitor Olaparib for 5 days inhibits end-joining mediated by PARP and sensitizes cells to DNA damage; cell survival is determined by measuring fluorescence intensity after staining with a cell viability reagent.

AssayReadOutDescription: Percent cell survival after treatment with Olaparib

AssayRange: %

AssayNormalRange: Olaparib resistance levels comparable to that of cells expressing wild type PALB2; no numeric threshold given

AssayAbnormalRange: Not reported

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 0

ValidationControlBenign: 0

Replication: Not reported

StatisticalAnalysisDescription: Not reported

AssayResult: 5

AssayResultAssertion: Normal

StandardErrorMean: 0.58

HGVS: NM_024675.3:c.101G>A p.(Arg34His)

AssayGeneralClass: BAO:0003061 reporter protein

AssayMaterialUsed: CLO:0009454 U-2 OS cell

AssayDescription: U2OS cells were treated with PALB2 siRNA and synchronized to G1/S phase by double thymidine block. Cells were then co-transfected with peYFP-PALB2 expressing PALB2 variants (or empty vector), pCR2.1-mRuby2LMNAdonor, and pX330-LMNAgRNA, which generates mRuby2-Lamin A/C fusion if HDR is successful.

AssayReadOutDescription: Mean relative percentages of mRuby2-positive cells over the YFP-positive population relative to the wild type condition.

AssayRange: %

AssayNormalRange: Not reported

AssayAbnormalRange: <40%

AssayIndeterminateRange: 41%-77%

ValidationControlPathogenic: 1

ValidationControlBenign: 3

Replication: Three independent experiments, each with n > 300 YFP-positive cells per condition

StatisticalAnalysisDescription: One-way ANOVA followed by Dunnett's post hoc analysis

AssayResult: 5

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

ControlType: Abnormal; empty vector

AssayResult: 100

AssayResultAssertion: Normal

ControlType: Normal; wild type PALB2 cDNA

AssayResult: 34

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: 68

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 90

AssayResultAssertion: Normal

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 46

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 23.6

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: 82

AssayResultAssertion: Normal

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 53

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 41

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 95

AssayResultAssertion: Normal

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 90

AssayResultAssertion: Normal

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 83

AssayResultAssertion: Not reported

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 77

AssayResultAssertion: Indeterminate

PValue: < 0.01

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 81

AssayResultAssertion: Not reported

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 38

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 5

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 36

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 85

AssayResultAssertion: Not reported

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 58

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 86.74

AssayResultAssertion: Not reported

PValue: 0.1836

Comment: Exact values reported in Table S3.

HGVS: NM_024675.3:c.11C>T p.(Pro4Leu)

AssayGeneralClass: BAO:0002805 cell proliferation assay

AssayMaterialUsed: CLO:0037317 mouse embryonic stem cell line

AssayDescription: Stable expression of wild type and variant PALB2 cDNA constructs in Trp53 and Palb2-null mouse cell line containing DR-GFP reporter; exposure to cisplatin for 48 h induces interstrand-crosslink DNA damage; cell survival is measured by FACS 24 h after cisplatin washout

AssayReadOutDescription: Relative resistance to cisplatin represented as cell survival relative to wild type, which was set to 100%

AssayRange: %

AssayNormalRange: Cisplatin resistance levels comparable to that of cells expressing wild type PALB2; no numeric threshold given

AssayAbnormalRange: Cisplatin resistance levels comparable to that of cells expressing empty vector; no numeric threshold given

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 2

ValidationControlBenign: 2

Replication: 2 independent experiments

StatisticalAnalysisDescription: Not reported

AssayResult: 128.59

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 14.72

Comment: Exact values reported in “Source Data” file.

AssayResult: 19.43

AssayResultAssertion: Abnormal

ReplicateCount: 5

StandardErrorMean: 4.42

ControlType: Abnormal; empty vector

Comment: Exact values reported in “Source Data” file.

AssayResult: 100

AssayResultAssertion: Normal

ReplicateCount: 6

StandardErrorMean: 0

ControlType: Normal; wild type PALB2 cDNA

Comment: Exact values reported in “Source Data” file.

AssayResult: 84.05

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 16.48

Comment: Exact values reported in “Source Data” file.

AssayResult: 97.73

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 5.41

Comment: Exact values reported in “Source Data” file.

AssayResult: 19.53

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 8.56

Comment: Exact values reported in “Source Data” file.

AssayResult: 119.03

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 6.12

Comment: Exact values reported in “Source Data” file.

AssayResult: 37.28

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.28

Comment: Exact values reported in “Source Data” file.

AssayResult: 111.51

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 7.63

Comment: Exact values reported in “Source Data” file.

AssayResult: 80.44

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 9.06

Comment: Exact values reported in “Source Data” file.

AssayResult: 27.29

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 6.53

Comment: Exact values reported in “Source Data” file.

AssayResult: 102.2

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 12.81

Comment: Exact values reported in “Source Data” file.

AssayResult: 112.08

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 4.1

Comment: Exact values reported in “Source Data” file.

AssayResult: 87.4

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 0.88

Comment: Exact values reported in “Source Data” file.

AssayResult: 100.97

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 7.27

Comment: Exact values reported in “Source Data” file.

AssayResult: 20.08

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 6.84

Comment: Exact values reported in “Source Data” file.

AssayResult: 89.72

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 7.95

Comment: Exact values reported in “Source Data” file.

AssayResult: 93.33

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11

Comment: Exact values reported in “Source Data” file.

AssayResult: 83.16

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 0.2

Comment: Exact values reported in “Source Data” file.

AssayResult: 26.03

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.42

Comment: Exact values reported in “Source Data” file.

AssayResult: 72.7

AssayResultAssertion: Not reported

ReplicateCount: 3

StandardErrorMean: 9.73

Comment: Exact values reported in “Source Data” file.

AssayResult: 97.61

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardDeviation: 0.97

StandardErrorMean: 0.68

Comment: Exact values reported in “Source Data” file.

HGVS: NM_024675.3:c.10C>T p.(P4S)

AssayResult: 28.5

AssayResultAssertion: Abnormal

ReplicateCount: 21

StandardErrorMean: 7.6

Comment: This variant had partial loss of function of peak current (10-50% of wildtype) and a >10mV loss of function shift in Vhalf activation, therefore it was considered abnormal (in vitro features consistent with Brugada Syndrome Type 1). (Personal communication: A. Glazer)

HGVS: NM_198056.2:c.1045G>A p.(Asp349Asn)

AssayGeneralClass: BAOCL:20:0010044 targeted transcriptional assay

AssayMaterialUsed: CL:2000001 peripheral blood mononuclear cell from patients

AssayDescription: Comparative transcriptomic analysis using reverse transcription to compare peripheral blood mononuclear cells of patients with wild type or pathogenic TP53 variants in the context of genotoxic stress induced by doxorubicin treatment. Ten biomarkers corresponding to p53 targets were measured to determine a functionality score.

AdditionalDocument: PMID: 23172776

AssayReadOutDescription: In the treated condition, the peak height of each of the 10 p53 target genes was measured and divided by the sum of the heights of the three control genes. This value was then divided by the same ratio calculated in the untreated condition. In the assay, the mean of the 10 values defines the p53 functionality score. The final p53 functionality score is the mean of the scores obtained in RT-MLPA and RT-QMPSF assays.

AssayRange: An arbitrary functionality score was calculated from the induction score of the 10 p53 targets.

AssayNormalRange: >7.5

AssayAbnormalRange: <5.5

AssayIndeterminateRange: Between 5.5 and 7.5 is associated with an intermediate effect.

AssayNormalControl: wild type TP53

AssayAbnormalControl: LFS patient cells

ValidationControlPathogenic: 8 individuals had seven distinct TP53 variants which could be considered as likely pathogenic or pathogenic based on their ClinVar classification or their truncating nature.

ValidationControlBenign: 51 individuals had no detectable germline TP53 variant

Replication: at least two wells were seeded per patient (treated and untreated) and duplicates or triplicates were performed whenever possible.

StatisticalAnalysisDescription: Differentially expressed genes between doxorubicin-treated and untreated cells were arbitrarily defined using, as filters, a P<0.01 and fold-change cutoffs >2 or <2, for up and down regulation, respectively. The resultant signal information was analyzed using one-way analysis of variance (ANOVA, P= 0.001), assuming normality but not equal variances with a Benjamani–Hochberg correction for multiple comparisons using three groups: controls, null, and missense mutations.

SignificanceThreshold: P=0.001

Comment: statistical analysis and P value from previous publication.

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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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.

(A) Model of the device without any stress/strain (left) and Finite element analysis model of the wearable device, not deformed (right). The model to the right exhibits the components inside the device. (B-C) The model shown being stretched 60%, vertically and horizontally respectively, show the maximum strain of the chip being 0.01%. This is much less than the normal failure strain for silicon (1%). (D) This figure shows the FMEA model being stretched 30% vertically and horizontally. The maximum strain in the chip components is below 0.004%. (E) Figure shows sensing performance of device when being stretched using an ECG. No significant effects from the mechanical stretching where evident in the results.

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 #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 #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 #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 #3 (Public Review):

Summary:

This is a tools paper that describes an open source software package, BonVision, which aims to provide a non-programmer-friendly interface for configuring and presenting 2D as well as 3D visual stimuli to experimental subjects. A major design emphasis of the software is to allow users to define visual stimuli at a high level independent of the actual rendering physical devices, which can range from monitors to curved projection surfaces, binocular displays, and also augmented reality setups where the position of the subject relative to the display surfaces can vary and needs to be adjusted for. The package provides a number of semi-automated software calibration tools to significantly simplify the experimental job of setting up different rigs to faithfully present the intended stimuli, and is capable of running at hardware-limited speeds comparable to and in some conditions better than existing packages such as Psychtoolbox and PsychoPy.

Major comments:

While much of the classic literature on visual systems studies have utilized egocentrically defined ("2D") stimuli, it seems logical to project that present and future research will extend to not only 3D objects but also 3D environments where subjects can control their virtual locations and viewing perspectives. A single software package that easily supports both modalities can therefore be of particular interest to neuroscientists who wish to study brain function in 3D viewing conditions while also referencing findings to canonical 2D stimulus responses. Although other software packages exist that are specialized for each of the individual functionalities of BonVision, I think that the unifying nature of the package is appealing for reasons of reducing user training and experimental setup time costs, especially with the semi-automated calibration tools provided as part of the package. The provisions of documentation, demo experiments, and performance benchmarks are all highly welcome and one would hope that with community interest and contributions, this could make BonVision very friendly to entry by new users.

Given that one function of this manuscript is to describe the software in enough detail for users to judge whether it would be suited to their purposes, I feel that the writing should be fleshed out to be more precise and detailed about what the algorithms and functionalities are. This includes not shying away from stating limitations -- which as I see it, is just the reality of no tool being universal, but because of that is one of the most important information to be transmitted to potential users. My following comments point out various directions in which I think the manuscript can be improved.

The biggest point of confusion for me was whether the 3D environment functionality of BonVision is the same as that provided by virtual spatial environment packages such as ViRMEn and gaming engines such as Unity. In the latter software, the virtual environment is specified by geometrically laying out the shape of the traversable world and locations of objects in it. The subject then essentially controls an avatar in this virtual world that can move and turn, and the software engine computes the effects of this movement (i.e. without any additional user code) then renders what the avatar should see onto a display device. I cannot figure out if this is how BonVision also works. My confusion can probably be cured by some additional description of what exactly the user has to do to specify the placement of 3D objects. From the text on cube mapping (lines 43 and onwards), I guessed that perhaps objects should be specified by their vectorial displacement from the subject, but I have very little confidence in my guess and also cannot locate this information either in the Methods or the software website. For Figure 5F it is mentioned that BonVision can be used to implement running down a virtual corridor for a mouse, so if some description can be provided of what the user has to do to implement this and what is done by the software package, that may address my confusion. If BonVision is indeed not a full 3D spatial engine, it would be important to mention these design/intent differences in the introduction as well as Supplementary Table 1.

More generally, it would be useful to provide an overview of what the closed-loop rendering procedure is, perhaps including a Figure (different from Supplementary Figure 2, which seems to be regarding workflow but not the software platform structure). For example, I imagine that after the user-specified texture/object resources have been loaded, then some engine runs a continual loop where it somehow decides the current scene. As a user, I would want to know what this loop is and how I can control it. For example, can I induce changes in the presented stimuli as a function of time, whether this time-dependence has to be prespecified before runtime, or can I add some code that triggers events based on the specific history of what the subject has done in the experiment, and so forth. The ability to log experiment events, including any viewpoint changes in 3D scenes, is also critical, and most experimenters who intend to use it for neurophysiological recordings would want to know how the visual display information can be synchronized with their neurophysiological recording instrumental clocks. In sum, I would like to see a section added to the text to provide a high-level summary of how the package runs an experiment loop, explaining customizable vs. non-customizable (without directly editing the open source code) parts, and guide the user through the available experiment control and data logging options.

Having some experience myself with the tedium (and human-dependent quality) of having to adjust either the experimental hardware or write custom software to calibrate display devices, I found the semi-automated calibration capabilities of BonVision to be a strong selling point. However I did not manage to really understand what these procedures are from the text and Figure 2C-F. In particular, I'm not sure what I have to do as a user to provide the information required by the calibration software (surely it is not the pieces of paper in Fig. 2C and 2E..?). If for example, the subject is a mouse head-fixed on a ball as in Figure 1E, do I have to somehow take a photo from the vantage of the mouse's head to provide to the system? What about the augmented reality rig where the subject is free to move? How can the calibration tool work with a single 2D snapshot of the rig when e.g. projection surfaces can be arbitrarily curved (e.g. toroidal and not spherical, or conical, or even more distorted for whatever reasons)? Do head-mounted displays require calibration, and if so how is this done? If the authors feel all this to be too technical to include in the main text, then the information can be provided in the Methods. I would however vote for this as being a major and important aspect of the software that should be given air time.

As the hardware-limited speed of BonVision is also an important feature, I wonder if the same ~2 frame latency holds also for the augmented reality rendering where the software has to run both pose tracking (DeepLabCut) as well as compute whole-scene changes before the next render. It would be beneficial to provide more information about which directions BonVision can be stressed before frame-dropping, which may perhaps be different for the different types of display options (2D vs. 3D, and the various display device types). Does the software maintain as strictly as possible the user-specified timing of events by dropping frames, or can it run into a situation where lags can accumulate? This type of technical information would seem critical to some experiments where timings of stimuli have to be carefully controlled, and regardless one would usually want to have the actual display times logged as previously mentioned. Some discussion of how a user might keep track of actual lags in their own setups would be appreciated.

On the augmented reality mode, I am a little puzzled by the layout of Figure 3 and the attendant video, and I wonder if this is the best way to showcase this functionality. In particular, I'm not entirely sure what the main scene display is although it looks like some kind of software rendering — perhaps of what things might look like inside an actual rig looking in from the top? One way to make this Figure and Movie easier to grasp is to have the scene display be the different panels that would actually be rendered on each physical panel of the experiment box. The inset image of the rig should then have the projection turned on, so that the reader can judge what an actual experiment looks like. Right now it seems for some reason that the walls of the rig in the inset of the movie remain blank except for some lighting shadows. I don't know if this is intentional.

DOI: 10.7554/eLife.64267

Resource: (ZFIN Cat# ZDB-ALT-080321-3,RRID:ZFIN_ZDB-ALT-080321-3)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-080321-3

What is this?

Reviewer #3 (Public Review):

The main findings are that loss of the Piezo1 protein in keratinocytes accelerate migration and wound healing, while genetic and pharmacological manipulations known to increase currents carried by Piezo1 slow migration and wound healing. The channels are shown to accumulate and cluster at the trailing edge of single migrating cells and at the wound margin during in vitro studies of wound healing. These findings demonstrate that Piezo1 mechanosensitive channels are not required for keratinocyte migration or wound healing, but rather function as essential regulators of the speed of both migration and would healing. Further, the findings suggest that increased flux through Piezo1 channels slows migration and wound healing. These channels are found to cluster in migrating cells and at wound margins. The conclusions are well-supported by the presented data and the authors' composition does an outstanding job of recognizing the limits of what has been learned and what remains uncertain.

Reviewer #3 (Public Review):

Slavetinsky and colleagues investigated the capability of monoclonal antibodies (mAb) against MprF, a critical protein of S. aureus, to act as re-sensitizing factors towards resistance strains and as supporting factors for S. aureus killing by human polymorphonuclear leukocytes.

They created 8 mAbs against four different loops of MprF and showed that they were able to bind MprF-expressing S. aureus strains. Two of the mAbs led to significant reduction of S. aureus survival upon exposure with nisin (i.e. a cationic antimicrobial against towards which MprF normally confers resistance). The authors focused on the mAb against loop 7 and showed that it reduced survivals also against two other antimicrobials and, most important, it restored Daptomycin killing of a resistant strain. Moreover, although this mAb did not increase phagocytosis by leukocites, it decreased the survival of the phagocytized S. aureus cells, most likely by rendering them sensitive towards the cationic antimicrobial peptides.

In parallel, the authors used this mAb to revise the ambiguous location of loop 7 of MprF. They employed two different experiment settings and concluded that this loop might have some degree of mobility in the membrane, which also explain the ambiguity of its location in previous studies. By showing that the mAb against loop 7 act by inhibiting the flippase activity of MprF while leaving the synthase activity intact, they speculated that the mobility of loop 7 might play an important role for LysPG translocation process.

The data support the conclusion of the manuscript and show how promising monoclonal antibody are against staphylococcal infections.

Reviewer #3 (Public Review):

In the present study, the authors have shown that Nkx2-1 depleted BRAFV600E driven mouse tumors show higher p-ERK activation. MAPK inhibition in these tumors leads to a cellular shift towards the gastric stem and progenitor lineage. The authors have provided detailed mechanistic insights on how MAPK inhibition influences lineage specifiers and oncogenic signaling pathways to form invasive mucinous adenocarcinoma. All experiments are carefully performed and entails advanced research methodologies such as organoid culture systems, novel genetically engineered mouse models and single cell RNA seq. The manuscript is well written, the research findings are logically interpreted and presented. Taken together, all major scientific claims are well supported by the data and offers major technical advancements for the development of precision medicine.

Reviewer #3 (Public Review):

In this work, Schuster et al. have explored the requirement of the short stumpy morphological form of the African trypanosome, Trypanosoma brucei, for the completion of the parasite lifecycle. Heretofore, short stumpy form parasites, which have been proposed to be pre-adapted for life in the tsetse fly insect vector, were considered an essential stage in the transitions from mammalian blood forms to insect-infective stages. These parasites do not divide and are generated in a density-dependent manner from the rapidly dividing long slender blood form. The quiescent short stumpy forms have been shown in vitro to undergo differentiation into insect-infective forms in response to a diversity of environmental cues and stress, supporting their position as the lifecycle stage that initiates colonization of the fly midgut.

The findings presented in this work call into question the longstanding notion that short stumpy parasites play a central role in the lifecycle. Notably, the authors have found that long slender forms are as competent as short stumpy parasites to infect flies. This observation may solve a major conundrum raised when short stumpy forms are considered essential intermediates in disease transmission. That is, how is the parasite successfully transmitted to tsetse flies when the flies only ingest very small bloodmeals from hosts with parasitemia too low to trigger density dependent stumpy form development?

The authors perform an extensive analysis of parasites isolated from infected flies and compare fly infections established using different numbers of short stumpy and slender parasites. This effort includes dissection of a variety of fly tissues and scoring parasites for expression of key developmental markers. Interestingly, the data indicate that the long slender parasites activate pathways described from short stumpy parasites to complete differentiation; however, unlike the stumpy forms that are arrested in the cell cycle, the parasites continue to proliferate. Overall, the process of differentiation to the insect stage is not identical for the long slender and short stumpy forms, as expression of key markers (PAD1 and EP1) occurs more quickly when short stumpy forms are used in fly infection studies while, unlike the long slender forms, they are delayed in return to the normal cell cycle.

The conclusions of the paper are supported by the presented data and the discussion further develops the case that long slender forms may be key to parasite transmission to the vector. The work is based on using the standard model African trypanosome subspecies that infects rodents and not a trypanosome species that infects humans. This does not, however, diminish the potential impact of the work, as the rodent parasites are the field standard (and molecular tools have primarily been developed in that background). In addition to finding that long slender forms are competent for lifecycle completion, which could ultimately require amendment of medical school textbook lifecycles, this work also raises important questions about the role of the short stumpy form in parasite biology. The authors speculate the short stumpy forms may serve to control population size in a quorum sensing-dependent-fashion. While this notion conflicts with observations presented from human infections where blood parasite levels are very low, it remains unresolved what cues environments like the skin and other tissues present to the parasite, and how these may influence short stumpy differentiation.

Reviewer #3 (Public Review):

Developing animals must couple information about external and internal conditions with developmental programs to adapt to changing environments. In animals ranging from flies to mammals, growth and developmental progression is controlled by a neuroendocrine system that integrates environmental and developmental cues. In mammals, this system involves the reproductive axis (hypothalamic-pituitary-gonadal axis, HPG). In the fruit fly Drosophila, neurosecretory cells that project onto the ring gland, a composite endocrine organ that houses the corpora cardiaca (CC), the corpus allatum (CA), and the prothoracic gland (PG), serves analogous functions. Characterizing the neurosecretory cells that project to the ring gland and the inputs they receive is therefore key to a deeper understanding of how the neuroendocrine system receives and processes information about external and internal conditions, and in response, adjusts growth and development. Building on the electron-microscopic reconstruction of the Drosophila L1 larval brain, the authors perform a comprehensive analysis of the neurosecretory cells that target the larval ring gland and the neurons that form synaptic contacts with these neurosecretory cells. This work is truly impressive on its own, and more than that it will also be extremely important for the future characterization of inputs received by the neuroendocrine system to modulate its activity, thus coupling development with environmental conditions. The work is well-written, and I have no doubt that it will be of great value to the field.

DOI: 10.7554/eLife.60432

Resource: (ZFIN Cat# ZDB-GENO-170619-3,RRID:ZFIN_ZDB-GENO-170619-3)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-GENO-170619-3

What is this?

DOI: 10.7554/eLife.54491

Resource: (ZFIN Cat# ZDB-ALT-170927-3,RRID:ZFIN_ZDB-ALT-170927-3)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-170927-3

What is this?

DOI: DOI:10.1016/j.cub.2020.08.103

Resource: (ZFIN Cat# ZDB-ALT-140811-3,RRID:ZFIN_ZDB-ALT-140811-3)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-140811-3

What is this?

DOI: 10.1523/ENEURO.0022-20.2020

Resource: (ZFIN Cat# ZDB-ALT-181031-3,RRID:ZFIN_ZDB-ALT-181031-3)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-181031-3

What is this?

DOI: 10.1523/ENEURO.0022-20.2020

Resource: (ZFIN Cat# ZDB-ALT-140924-3,RRID:ZFIN_ZDB-ALT-140924-3)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-140924-3

What is this?

DOI: 10.7554/eLife.42881

Resource: (ZFIN Cat# ZDB-ALT-120723-3,RRID:ZFIN_ZDB-ALT-120723-3)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-120723-3

What is this?

Reviewer #3 (Public Review):

It is established that Kinase suppressor of Ras 1 (KSR1) contributes to the oncogenic actions of Ras by promoting ERK activation. However, the downstream actions of this pathway are poorly understood. Here Rao et al. demonstrate that this KSR1-dependent pathway increases translation of Epithelial-Stromal Interaction-1 (EPSTI1) mRNA and expression of EPSTI1 protein. This is significant because EPSTI1 drives aspects of EMT, including expression of ZEB1, SLUG, and N-Cadherin. The analysis is thorough and includes both loss-of-function and gain-of-function studies. Overall, the conclusions of this study are convincing and advance our understanding of cancer development.

Reviewer #3 (Public Review):

The authors have studied preclinical models of human small cell lung cancer (SCLC) using characterized SCLC cell lines that have been manipulated to conditionally express mutant EGFR (L858R) or KRAS (G12V) alleles and then assessing their morphology in cell culture, expression of neuroendocrine differentiation markers and transcription factors, and main signaling pathways such as the MAPK pathway. They focus on this because activation of ERK and the MAPK pathways are seen in nearly all non-small cell lung cancers (NSCLCs) including those with EGFR or KRAS mutations but mutations in these driver oncogenes or active ERK and MAPK pathway are essentially never found in SCLCs. In addition, chromatin modifications are assessed after manipulations and functional genomics targeting and pharmacologic inhibition of various components of the MAPK pathway are tested to see their effect on NE expression. Because of the known clinical phenomenon of transformation to SCLC like tumors by lung adenocarcinomas with EGFR mutations that become resistant to EGFR tyrosine kinase inhibitors, findings from the SCLC studies were applied to try to experimentally generate such LUAD to SCLC transformation. Overall, they found that activation of ERK/MAPK pathway by oncogenic mutations led to loss of NE differentiation and that the "ERK-CBP/p300-ETS axis promotes a lineage shift between neuroendocrine and non-neuroendocrine lung cancer phenotypes". They conclude: "In summary, we provide the first reported biological rationale for why alterations in MAPK pathway are rarely found in SCLC and describe the molecular underpinnings of how the central node in this pathway, ERK2, suppresses the NE differentiation program. " The authors conclusions and claims are justified by the experiments and data they present and they provide a mechanistic basis of what happens with MAPK/ERK activation in SCLC, why one does not find MAPK/ERK activation in SCLC, or the presence of related oncogenic driver mutations such as mutant KRAS or EGFR.

DOI: 10.7554/eLife.53403

Resource: (ZFIN Cat# ZDB-ALT-100419-3,RRID:ZFIN_ZDB-ALT-100419-3)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-100419-3

What is this?

HGVS: NM000546.5:c.(?-202)(29+1-28+1)del p.?

Comment: A CAID could not be generated for this deletion variant with uncertain breakpoints.

AssayResult: 8.3

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 6.4

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 3.1

AssayResultAssertion: Abnormal

Comment: See Table S3 for details; The blood sample used to test this variant was derived from an individual carrying the c.723del variant in combination with the c.*1175A>C variant in heterozygosity.

AssayResult: 5.5, 5.7

AssayResultAssertion: Abnormal

Comment: See Table S3 for details; The blood sample used to test this variant was derived from an individual carrying the variant in homozygosity.

AssayResult: 20.5

AssayResultAssertion: Normal

Comment: See Table S3 for details

AssayResult: 3.4

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 2.6, 4.8

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 3.8

AssayResultAssertion: Abnormal

Comment: See Table S3 for details; This variant was reported as c.323_235del but assumed to be c.323_325del, which corresponds to the reported protein change (p.(Gly108_Phe109delinsVal)).

AssayResult: 4, 5

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 5.8, 6.1

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 5.3

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 5.1

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 17.1

AssayResultAssertion: Normal

Comment: See Table S3 for details

AssayResult: 3.2

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 3.5

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 4.1

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 2.9

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 6.1

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 12.9

AssayResultAssertion: Normal

Comment: See Table S3 for details

AssayResult: 4.7

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 7.1, 6.0

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 3.1

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 5.4

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 5

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 4.8

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 3.8

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 3.2

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 14.7

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 16

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12.3

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 11.8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 16.3

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 15.4

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 19.3

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 9.8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 9.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 8.7

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 15.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10.4

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 11.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 17.2

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 19.7

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 11.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 17.4

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 11.7

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 14.2

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 18.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 15.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 14.7

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 11.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 11.7

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 9.4

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12.9

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10.8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 13

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 8.4

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 15.4

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 13

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 22.8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 14.6

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 16.5

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 14.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10.3

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 7.5

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 7.8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12.8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 14.6

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10.6

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 8.9

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details