AssayResult: 26.03

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.42

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

AssayResult: 24.27

AssayResultAssertion: Abnormal

ReplicateCount: Not reported

StandardErrorMean: Not reported

Comment: Exact values reported in “Supplementary Data 1” file; result for this variant not reported in “Source Data” file.

AssayResult: 96.22

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 16.7

Comment: Exact values reported in “Source Data” file. Discrepancy in “Supplementary Data 1” file: nucleotide reported as c.3191A>G.

AssayResult: 15.23

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 6.42

Comment: Exact values reported in “Source Data” file. Discrepancy in “Source Data” file: protein reported as Q899X.

AssayResult: 52.23

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 6.33

Comment: Exact values reported in “Source Data” file. Discrepancy in “Source Data” file: protein reported as I1037R.

AssayResult: 74.36

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 1.89

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

AssayResult: 87.27

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 1.3

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

AssayResult: 17.29

AssayResultAssertion: Abnormal

ReplicateCount: 3

StandardErrorMean: 6.81

ControlType: Abnormal; empty vector (set 5)

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

AssayResult: 7.86

AssayResultAssertion: Abnormal

ReplicateCount: 3

StandardErrorMean: 2.39

ControlType: Abnormal; empty vector (set 4)

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

AssayResult: 34.03

AssayResultAssertion: Abnormal

ReplicateCount: 3

StandardErrorMean: 10.86

ControlType: Abnormal; empty vector (set 3)

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

AssayResult: 12.78

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 3.65

ControlType: Abnormal; empty vector (set 2)

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

AssayResult: 10.93

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 0.78

ControlType: Abnormal; empty vector (set 1)

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

AssayResult: 100

AssayResultAssertion: Normal

ReplicateCount: 38

StandardErrorMean: 0

ControlType: Normal; wild type

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

AssayResult: 102.22

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 8.29

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

AssayResult: 21.7

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.42

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

AssayResult: 55.4

AssayResultAssertion: Not reported

ReplicateCount: 4

StandardErrorMean: 13.29

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

AssayResult: 17.5

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 7.75

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

AssayResult: 102.7

AssayResultAssertion: Not reported

ReplicateCount: 3

StandardErrorMean: 12.82

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

AssayResult: 94.47

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 0.99

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

AssayResult: 13.87

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.32

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

AssayResult: 93.44

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 2.24

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

AssayResult: 9.67

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.31

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

AssayResult: 109.07

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 9.27

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

AssayResult: 98.64

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 5.5

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

AssayResult: 102.88

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 20.71

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

AssayResult: 16.6

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 4.35

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

AssayResult: 103.21

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 8.98

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

AssayResult: 108.27

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 16.12

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

AssayResult: 98.43

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 14.96

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

AssayResult: 102.57

AssayResultAssertion: Not reported

ReplicateCount: 3

StandardErrorMean: 11.51

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

AssayResult: 103.83

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 3.67

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

AssayResult: 87.51

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 4.4

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

AssayResult: 56.67

AssayResultAssertion: Not reported

ReplicateCount: 4

StandardErrorMean: 12.4

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

AssayResult: 85.13

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 15.04

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

AssayResult: 108.56

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 19.59

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

AssayResult: 10.42

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 0.01

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

AssayResult: 99.69

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 3.09

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

AssayResult: 12.35

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.48

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

AssayResult: 14.79

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 7.81

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

AssayResult: 84.41

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 1.42

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

AssayResult: 25.09

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 3.48

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

AssayResult: 97.37

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 5.14

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

AssayResult: 12.77

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 5.34

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

AssayResult: 78.91

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 6.86

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

AssayResult: 8.41

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.95

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

AssayResult: 24.31

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 5.23

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

AssayResult: 14.78

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 9.34

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

AssayResult: 81.17

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 9.32

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

AssayResult: 91.11

AssayResultAssertion: Not reported

ReplicateCount: 3

StandardErrorMean: 17.74

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

AssayResult: 26.39

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.11

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

AssayResult: 94.54

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 19.94

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

AssayResult: 86.26

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 4.22

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

AssayResult: 7.73

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 2.25

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

AssayResult: 29.04

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 7.24

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

AssayResult: 115.45

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.81

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

AssayResult: 78.3

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.75

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

AssayResult: 86.54

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.96

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

AssayResult: 87.96

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 10.31

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

AssayResult: 78.2

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 7.31

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

AssayResult: 103.53

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 7.06

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

AssayResult: 19.46

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 1.75

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

AssayResult: 64.92

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 8.7

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

AssayResult: 11.06

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 2.4

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

AssayResult: 117.58

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 0.81

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

AssayResult: 10.68

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 0.32

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

AssayResult: 23.96

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 7.6

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

AssayResult: 120.54

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.09

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

AssayResult: 74.18

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 6.49

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

AssayResult: 95.74

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 14.87

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

AssayResult: 83.96

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 9.89

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

AssayResult: 94.84

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 20.56

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

AssayResult: 17.43

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 5.19

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

AssayResult: 108.51

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 17.71

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

AssayResult: 67.82

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 10.97

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: 9.68

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardErrorMean: 3.44

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

AssayResult: 115.71

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 3.09

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

AssayResult: 11.28

AssayResultAssertion: Abnormal

ReplicateCount: 2

StandardDeviation: 1.24

StandardErrorMean: 0.87

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

HGVS: NM_024675.3:c.104T>C p.(L35P)

AssayResult: 113.2

AssayResultAssertion: Normal

ReplicateCount: 30

StandardErrorMean: 13.9

Comment: This variant had normal function (75-125% of wildtype peak current, <1% late current, no large perturbations to other parameters). These in vitro features are consistent with non-disease causing variants. (Personal communication: A. Glazer)

HGVS: NM_198056.2:c.1038G>T p.(Glu346Asp)

AssayGeneralClass: BAO:0010044 targeted transcriptional assay

AssayMaterialUsed: BTO:0000773 lymphoblastoid cell line derived from control individuals or individuals with germline TP53 variants

AssayDescription: Comparative transcriptomic analysis using RNA-Seq to compare EBV cell lines of wild type and pathogenic TP53 in the context of genotoxic stress induced by doxorubicin treatment. p53 RNA levels were evaluated and expressed as a percentage of the mean levels obtained for the three wild-type TP53 individuals.

AdditionalDocument: PMID: 23172776

AssayReadOutDescription: The p53 mRNA levels were expressed as a ratio of the normal values obtained for 3 TP53 wild-type control individuals.

AssayRange: UO:0000187 the p53 RNA levels were evaluated and expressed as a percentage of the mean levels obtained for three wild-type TP53 individuals.

AssayNormalRange: N/A

AssayAbnormalRange: N/A

AssayIndeterminateRange: N/A

AssayNormalControl: wild type TP53

AssayAbnormalControl: LFS patient cells

ValidationControlPathogenic: 8 Individuals with dominant-negative TP53 missense variants, 10 Individuals with null TP53 variants, and 13 Individuals with other TP53 missense variants

ValidationControlBenign: 3 patients with wild type TP53

Replication: experiments were performed in triplicates.

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 #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

Strengths

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

Weaknesses

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

Conceptual issues:

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

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

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

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

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

Methodological issues:

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

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

Reviewer #2 (Public Review):

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

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

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

I have no major criticisms of the work.

Reviewer #2 (Public Review):

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

Comments:

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

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2:

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

Reviewer #2:

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

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

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

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

Reviewer #2:

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

Strengths:

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

Weaknesses:

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

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

Reviewer #2 (Public Review):

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

Neocolonialist strategy is the idea of accommodating new realities as to retain dominance

MYTH 2: Origin Narratives

• Puritan covenant with God to take the land

  • Reinforced by Columbus Myth

    • "Columbia," represented by lady, is found everywhere throughout the USA, in names and idea

  • Reinforced by the "Doctrine of Discovery"

    • European nations acquired titles to lands they discovered and Indigenous inhabitants lost natural right to land after Europeans claimed it.
    • Law of Nations required the subjugation of all people who diverge from European-derived norms of right conduct

  • Reinforced by Academia: Threatened by civil rights

    • Called for "balance," against "moralizing," and pro "culturally relative approach." "There were good and bad people on both sides."

      • "MULTICULTURALISM" is used to support the origin story. "We all got along from the beginning and now we are all a big happy nation"

Origin Story myth is perpetuated by idea of multiculturalism

MULTICULTURALISM: US history revision that emphasizes the "contributions" of ethnic groups to the United States, while obscuring the fact that these groups were instead PLUNDERED of their natural resources - it was not a consensual giving process.

Danger of accepting origin myth 2: put asides responsibility for continued harm done by past - puts aside option of reparations, restitution, and reordering of society.

(Why the Origin Myth is currently harmful)

Academics attempt to justify settler colonialism and origin MYTH, with idea that there was dialogue between settler and indigenous, when in reality it was one-sided robbery and murder.

Reviewer #2 (Public Review):

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

Strengths:

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

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

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

Weakness:

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

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

The manuscript could be clearer in certain specific aspects:

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

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

DOI: 10.1016/j.celrep.2020.108039

Resource: (ZFIN Cat# ZDB-ALT-001220-2,RRID:ZFIN_ZDB-ALT-001220-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-001220-2

What is this?

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

The following issues warrant further attention and clarification.

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

Reviewer #2:

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

Strengths:

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

Weaknesses:

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

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

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

Reviewer #2 (Public Review):

BonVision is a package to create virtual visual environments, as well as classic visual stimuli. Running on top of Bonsai-RX it tries and succeeds in removing the complexity of the above mentioned task and creating a framework that allows non-programmers the opportunity to create complex, closed loop experiments. Including enough speed to capture receptive fields while recording different brain areas.

At the time of the review, the paper benchmarks the system using 60Hz stimuli, which is more than sufficient for the species tested, but leaves an open question on whether it could be used for other animal models that have faster visual systems, such as flies, bees etc.

The authors do show in a nice way how the system works and give examples for interested readers to start their first workflows with it. Moreover, they compare it to other existing software, making sure that readers know exactly what "they are buying" so they can make an informed decision when starting with the package.

Being written to run on top of Bonsai-RX, BonVision directly benefits from the great community effort that exists in expanding Bonsai, such as its integration with DeepLabCut and Auto-pi-lot. Showing that developing open source tools and fostering a community is a great way to bring research forward in an additive and less competitive way.

Reviewer #2 (Public Review):

The paper presented by Boroumand et al. aims to delineate the impact of bone marrow resident adipocytes on the phenotype, development, and metabolism of murine monocyte subsets during diet-induced obesity and leanness. The paper provides an interesting analysis of the metabolic state and phenotype of mitochondria in murine monocytes during high-fat diet feeding. Furthermore, it provides some insight on the crosstalk between bone marrow resident adipocytes and different monocytes.

The paper will help to further delineate the response of monocytes during obesity, however, the impact the paper will have on the field of mononuclear phagocytes biology and our understanding of myelopoiesis during low-grade inflammation is limited.

Several claims should be more thoroughly addressed, such as the phenotypes of macrophages found within the adipose tissues and a more fine-grained analysis of the mononuclear phagocyte progenitors within the bone marrow. Furthermore, a central claim of the paper is that Ly6clow monocytes convert to Ly6chigh monocytes. If the authors would like to hold that claim it needs some experiments which are supportive of that hypothesis.

DOI: 10.7554/eLife.64267

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What is this?

DOI: 10.7554/eLife.64267

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What is this?

DOI: 10.7554/eLife.64267

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What is this?

DOI: 10.7554/eLife.64267

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What is this?

DOI: 10.7554/eLife.64267

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What is this?

Reviewer #2 (Public Review):

The manuscript "Spatiotemporal dynamics of PIEZO1 localization controls keratinocyte migration during wound healing" by Holt and colleagues demonstrates that loss of function of PIEZO1 speeds up keratinocyte migration and wound closure, whereas enhancing PIEZO1 function, with a PIEZO1 gain-of-function mutant or by chemical means, slows down both processes. The topic of this manuscript is timely and relevant. The experimental design followed by the authors is straightforward and elegant and the vast majority of the conclusions are fully supported by their results. Overall, this manuscript provides solid evidence that normal (wild type) function of PIEZO1 slows down skin wound healing in vitro and in vivo.

Reviewer #2 (Public Review):

MprF is a lipid flippase involved in determining bacterial tolerance to cationic peptides of the innate immune system and to antibiotics such as daptomycin. Using Staphylococcus aureus as their model organism, the authors assessed the suitability of MprF as a target for anti-virulence treatments. For this purpose, a series of monoclonal antibodies directed against the extracellular loops of MprF were generated. The antibodies were tested for their ability to bind and inhibit the function of MprF, to sensitize S. aureus towards cationic peptides, and to promote phagocyte killing of S. aureus. Moreover, the antibodies were used to investigate the orientation of one specific loop of the MprF protein.

Strenghts:

The manuscript is well-written and the introduction provides a very good overview of the challenges associated with antibiotic resistance, anti-virulence strategies and the MprF protein. The Figures and the Figure legends are easy to follow. The described approach is innovative, and state of the art methods are used throughout the manuscript.

Weaknesses:

There is a discrepancy between the anti-virulence scope as indicated by the title and the introduction, and the actual content of the result section: here, the anti-virulence strategy is only preliminary addressed, and a lot of effort is instead put into determining the orientation of one specific loop of the MprF protein. This needs to be better aligned, and more compelling data are needed to support that MprF has potential for anti-virulence strategy. The conclusions of this paper are mostly well supported, however, additional controls are needed to fully support that the observed effects of the antibodies are mediated via specific binding to MprF.

Reviewer #2 (Public Review):

In this very extensive and somewhat lengthy manuscript Zewdu et al, characterize an oncogenic Braf-driven model of invasive mucinous lung adenocarcinoma. They show an effect of co-incident and sequential Nkx2-1 inactivation on cancer cells state and therapy responses. They show that BP and BPN tumors have distinct responses to RAF/MEK inhibition. Furthermore, they uncover potentially important cross talk between the MAPK and WNT pathways in invasive mucinous adenocarcinoma (IMA). Overall, this is an excellent manuscript that uncovers many interesting new aspects of IMA. The strengths of this manuscript include the sophisticated in vivo cancer models, detailed cellular analyses, and potential importance of these finds to therapy responses. Their claims are well supported by their data.

Reviewer #2 (Public Review):

Differentiation pathways for parasitic organisms are of considerable importance, as they are relevant to understanding transmission, mechanisms of host specificity as well as, in some cases, offering possible routes to control measures. The transition between mammalian host and insect vector for African trypanosomes has been widely addressed due to accessibility and tractability. However, one view has been dominant, despite, as the authors suggest, considerable counter evidence. The present work posits an alternate pathway, questioning the role of the so called stumpy stage. This is of considerable importance to the immediate field and possibly wider.

The major strengths here are in the use of a good model, and a high number of individual infections. The weaknesses include some assumptions with which I have issue, and given that this work is seeking to overturn a dogma, which also has assumptions, one needs to tread very carefully, to avoid falling into an unscholarly dispute. The major things are for me the assumption that PAD-1 cells are stumpy - almost anything seems to be able to activate PAD-1 and the lack of any quantitative data are concerning. This is difficult really and Matthews also says that PAD-1 does not equal stumpy and morphology is also important. Further, simple expression of EP procyclin is not sufficient for designation as pr cyclic, and the salivary gland cells are assumed metacyclic without demonstration of VSG expression for example. While I accept that these interpretations are reasonable, this is an assumption and in all three cases leads me to feel a little underwhelmed. Perhaps most concerning are the lack of statistical calculations as well as any attempt at further analysis beyond counting. The result is very much phenomenology and lacks any mechanistic insight.

Reviewer #2 (Public Review):

Analyzing EM data from the Drosophila larva, Hueckesfeld et al. investigate and describe the synaptic connectivity of sensory neurons and interneurons that provide input into the neuroendocrine system in fly larvae. The output of neuroendocrine neurons projecting to the ring gland is mostly non-synaptic and identified by receptor expression analysis. Using a modelling approach, they provide a more detailed analysis on newly discovered CO2-responsive cells and their downstream network and also other possible processing pathways from sensory to endocrine neurons. To test some of their model predictions, they analyze the response of predicted CO2-downstream neurons to CO2 exposure.

Strengths of the paper:

The authors did a great job in visualizing the complex connectivity between sensory inputs, interneurons, and endocrine neurons. Neuroendocrine neuron outputs, which are mostly non-synaptic, have been detected by identification of vesicle release regions. The authors went beyond the analysis of EM data and collected a lot of new data to confirm non-synaptic connectivity between neuroendocrine neurons and their downstream targets by performing antibody stainings and trans-tango experiments. This information will be highly valuable to the field.

Sensory inputs in the larvae have been attributed according to previous publications, but the authors also describe a new CO2 sensing function of tracheal TD neurons. Description of this new sensory function is also a valuable addition to the Drosophila field.

The authors used a modelling approach to describe and detect specific processing pathways, for example from a certain sensory modality, or to a specific endocrine neuron. This manuscript underlines that the use of a (simple) computational model framework to understand network motifs within an EM dataset is very powerful. Also, they can confirm that predicted CO2 downstream neurons indeed respond to CO2 in a certain way.

The authors discuss potential functional implications for faster and slower processing pathways (connections over interneurons or direct). Indeed there might be situations where the larva needs to respond in flexible ways that are however also easily reversable (fast pathways), but there might be also other situations where the larva needs to integrate more sensory evidence and which might induce non-reversible behaviors, such as pupation (slow pathways). I think this discussion suggests an interesting concept of the impact/cost of adaptive behavioral changes and the different timescales they can occur.

Weaknesses of the paper:

Data wise, this manuscript is a very descriptive study. The authors visualize the complex and diverse possible processing pathways; however, the function of the circuit remains unknown. To really understand the functional properties behind this complex architecture will require studies focused on single sensory modalities, single pathways and/or single peptidergic classes all in the context of a certain behavioral framework.

The authors try to provide a complete overview over the connectivity within the neuroendocrine system pathways. However, the authors should discuss that the connectivity data from the one EM dataset that they analyzed might be changing across individuals and development. Especially the vesicle release sites might be more variable across individual larvae than synaptic connections. Neuropeptide receptor expression might also change over development.

The authors investigate the TD CO2 sensing pathway in more detail. They show that the sensory neurons and the predicted downstream neurons respond to CO2. This shows that the neural connectivity might serve a functional purpose. There is however another type of sensory neurons that respond to CO2 in the larva (Gr21a receptor neurons- Faucher et al., 2006), which are required for an avoidance response to the stimulus. The authors should discuss and maybe analyze the EM data for possible circuit convergence between the two different CO2 sensory input neurons.

The authors discuss the CO2 response in the context of a stress response. However, the natural environment of larvae, rotten fruits, also emit CO2 as a by-product. Thus, sensing CO2 which converges together with information from Fructose/Glucose sensors might be used for finding or evaluating food sources.

DOI: 10.7554/eLife.58251

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What is this?

DOI: 10.1523/ENEURO.0022-20.2020

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What is this?

DOI: 10.7554/eLife.56725

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What is this?

DOI: 10.7554/eLife.60445

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What is this?

DOI: 10.1126/sciadv.aaz3173

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What is this?

DOI: 10.7554/eLife.55792

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What is this?

(A) Step-by-step process of each layer of the device to allow multiple functionalities and wearability. (B) Amplitude vs. Time graph of sensor worn on the wrist to measure motion when walking, running, jumping. (C) Thermal sensor can read forehead, abdomen, and hand temperature on skin when indoor and outdoor over time. (D) The acoustic sensor is placed on the neck to measure the amplitude (vibration) characteristics of the vocal chords to serve as a human-machine interface. (E) The electrocardiogram sensor measures heart activity while resting, after exercising for 13 seconds and then after 34 seconds. The heart rate resulted in 72, 96, and 114 per minute, respectively.

Reviewer #2 (Public Review):

KSR1 functions as a critical rheostat to fine-tune MAPK signalling, and identifying modes by which its over-expression promotes tumor progression is clinically important and potentially druggable. Ras is highly mutated in CRC and unfortunately inhibitors of Ras have been challenging to develop. However, small molecules which stabilize an inactive form of the KSR are actively being developed in an attempt to repress RAS signaling. Thus, this study, which seeks to identify how KSR1 promotes oncogenic mRNA translation, is potentially highly clinically relevant, as it may identify novel druggable targets.

In this manuscript the authors performed polysome profiling in colorectal cancer (CRC) cells and proposed that KSR1 and ERK regulate the translation of EPSTI1 mRNA. They go on to characterize the phenotypes associated with knock-down or knock-out of KSR1 in CRC, and show that their defects in invasion, anchorage-independent growth and switch to a less EMT-like phenotype are all EPSTI1-dependent.

The authors succeeded in providing ample in vitro data that KSR1 and EPSTI1 are potential therapeutic targets in CRC. However, the data demonstrating that KSR1 and ERK regulate EPSTI1 mRNA translation is tenuous. Although the authors state that "EPSTI1 is necessary and sufficient for EMT in CRC cells", the data presented are consistent with a more restrained conclusion of a partial-EMT and not EMT per se. Finally, without an in vivo model it is difficult to glean novel insight into the mechanism by which KSR1 and/or EPSTI1 control the invasive and metastatic behaviour of cells.

Reviewer #2 (Public Review):

Cell fate transitions (such as adenocarcinoma converting to small cell neuroendocrine fate) are an increasing phenomenon observed during therapeutic resistance in lung cancer, prostate cancer, and possibly other cancer types. It is important to understand these mechanisms if we ultimately seek to tailor treatment to a patient's disease and/or to control the pathways that lead to treatment resistance. However, the mechanisms that underly these cell fate changes are not well understood. It has been previously observed (Calbo et al, Cancer Cell, 2011) that activated mutant Kras (commonly associated with adenocarcinoma fate) can promote a non-neuroendocrine fate in SCLC, but the mechanisms are unknown.

Predominantly using three human small cell lung cancer (SCLC) cell lines, Inoue and colleagues use genetic and pharmacological approaches to focus on potential mechanisms by which Egfr/Kras/Mapk signaling can repress neuroendocrine fate. They make a number of interesting observations that extend our understanding of neuroendocrine cell fate regulation including:

1) Kras-induced NE suppression appears to depend mostly on ERK2, and not ERK1 or PI3K signaling.

2) Kras activation induces chromatin changes including increased H3K27Ac in 2/3 cell lines; increased H3K27Ac in response to HDAC inhibition is associated with NE suppression. Pharmacological inhibition of CBP/p300 (a HAT that promotes H3K27Ac) reduces H3K27Ac and restores NE suppression. Altogether, these findings are consistent with the notion that SCLC cannot tolerate high levels of H3K27Ac.

3) Kras induces the MSK/RSK pathway consistently in cell lines but appears to be functionally-relevant to NE fate only in H82 cells.

4) Kras activation induces chromatin occupancy at ERG and ETS family transcription factor (Etv1, 4, 5) binding sites in 2/3 cell lines, and induces ETV4 (2/3 lines) and ETV5 protein levels (3/3 lines). ETV1 and ETV5 overexpression are sufficient to inhibit NE fate markers in context-dependent manner. Ets family induction appears to occur in a CIC-independent manner.

In addition, some interesting negative data is presented, for example, SOX9 is induced upon Kras activation in 3/3 cell lines but it was not functionally relevant for NE suppression; Notch1, Notch2, and HES1 (known NE fate suppressors) are induced by Kras activation in a cell context-specific manner, but they did not appear functionally-relevant to NE suppression based on HES1 knockout and a pharmacological inhibitor of Notch signaling; Rb1 loss was not sufficient to promote NE fate in EGFR/p53 mutant cell lines, despite its known association with adeno-to-SCLC conversion. Overall, the conclusions in the manuscript are well justified. These findings will be of interest to those especially in lung and prostate cancer studying cell fate conversions in the context of EGFR and AR inhibitor resistance, respectively. These observations will be built upon by these fields.

Weaknesses:

1) One recurring issue in the manuscript is that the observations are often not consistent across the three cell lines and are context-specific effects, and the potential reasons could be explained better. The cell lines chosen unfortunately (but interestingly) represent some of the major cell states of SCLC. H2107 represents the ASCL1+ NE-high subset of SCLC (and has some MYCL). H82 and H524 represent the C-Myc (MYC)-high subset of SCLC, with H82 having a MYC amplification, and both representing the NEUROD1 subtype (which tend to be associated with more MYC). Assessment of NE score using a common approach in the field (Zhang et al, TLCR) shows that H82 cells are already considerably NE-low, with H524 as NE-intermediate/high, and H2017 as NE-high. So, this may be related to why H82 seemed to be the most permissive cell line to change NE fate in multiple assays.

In addition, H2107 and H524 appear to have EP300 mutations, which may contribute to their NE-high nature and contribute to the refractory response to A485 treatment based on the author's model. It's known that MYCL and MYC-driven cell lines differ in numerous aspects from transcriptional signatures, super enhancer usage, metabolic regulation, therapeutic response, etc. This information could be mentioned in the results and discussed when mentioned as a factor near line 540.

2) Related to Figure 4, the authors show that p300 pharmacological inhibition can restore NE fate in presence of Kras. Given that drugs can have off-target effects, it would be helpful to know if genetic knockdown/knockout of p300 phenocopies these effects. Given that CREBBP (CBP) or EP300 (p300) mutations are common in SCLC, it is also relevant whether any of these cell lines have CREBBP (CBP) or EP300 (p300) mutations. It appears H2107 and H524 may have EP300 mutations, and it would be good to know whether the authors have tried to restore EP300 function.

DOI: 10.1002/cne.24964

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What is this?