DOI: 10.1126/sciadv.aaz3173

Resource: (ZFIN Cat# ZDB-ALT-100505-2,RRID:ZFIN_ZDB-ALT-100505-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-100505-2

What is this?

DOI: 10.7554/eLife.55792

Resource: (ZFIN Cat# ZDB-ALT-150922-2,RRID:ZFIN_ZDB-ALT-150922-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-150922-2

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

Resource: (ZFIN Cat# ZDB-ALT-090116-2,RRID:ZFIN_ZDB-ALT-090116-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-090116-2

What is this?

AssayResult: 82

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 80

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 69

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 99

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 98

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 77

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 105

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 96

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 104

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 99

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 83

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 91

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 86

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 56

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 61

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 51

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 66

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 60

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 65

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 66

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 45

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 58

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 49

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 83

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 84

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 91

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 94

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 97

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 98

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 96

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 105

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 88

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53 from control 3

Comment: See Table S2 for details

AssayResult: 95

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53 from control 2

Comment: See Table S2 for details

AssayResult: 100

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53 from control 1

Comment: See Table S2 for details

AssayResult: 7.1

AssayResultAssertion: Abnormal

Comment: See Table S2 for details

HGVS: NM000546.5:c.(?-202)(*1207?)del p.?

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

Reviewer #2 (Public Review):

The latest manuscript of Loring and coworkers solves a number of important problems of SARM1 structure and function at once, namely why the purified enzyme has little activity, what size is the active multimer, whether it produces cADPR on the way to ADPR, and how this enzyme may overcome autoinhibition by NAD+ in vivo. In work that is technically sound, the authors describe a phase transition that can be induced by macroviscogens and by citrate in which we are able to see cryoEM images of activated multimers and the induction of SARM1 activity in worms by citrate. Working with concentrated enzyme, the authors are further able to characterize SARM1 activity in detail and clearly show which cations are most inhibitory and that ADPR and not cADPR is the primary product of the reaction.

There is clearly a lot of regulation in the system with NAD+ inhibiting and NMN activating this enzyme and NMNAT, which controls conversion of NMN to NAD+ being localized to the outer Golgi membrane. Golgi and mitochondria are both moved along axons in processes that are totally dependent on cellular energetics. Given the broad contributions that are made by this work, I would not mind if the authors considered whether citrate, either from stressed mitochondria or from inhibition of the cytosolic enzyme ATP-citrate lyase, might be produced at high enough concentration to push SARM1 into the phase transition described herein.

Reviewer #2:

The authors address the vortex formation of bacteria in circular confinements with a particular focus on the difference of swarming vs. swimming (planktonic) motility of individuals. In the field of active matter, this critical distinction has rarely been studied so far but it is oftentimes ignored in modeling studies. Chen et al. show that qualitatively different patterns emerge for swarming and swimming bacteria. I do therefore believe that the work could have substantial influence on future studies devoted to bacterial pattern formation.

I have two main concerns detailed in the following.

1) A central finding of the present study is that the number of vortices/swirls as a function of the well diameter differs for swarming vs. swimming bacteria. The authors argue and show experimentally (Fig. 2) that the behavior is identical for small and large diameters. For intermediate values, however, they report that a single swirl is observed for swarming bacteria whereas swimming bacteria show multiple swirls.

The fact that the behavior is identical for large wells suggests that the bulk behavior is identical. This is also confirmed by Fig. 2E which shows that the spatial correlation function of the velocity is identical in large wells. To me, that suggests that the boundary conditions play a central role for understanding how the observed phenomenology emerges. [Indeed, it was shown in the past that the interaction of bacteria with boundaries crucially determines the formation of swirls in confinement (Lushi, Wieland & Goldstein PNAS 111 9733 (2014). The authors of this work assume reflecting boundary conditions, which -- to my knowledge -- contradicts the finding of Lushi et al.]. The authors, however, explain the difference of the observed patterns within their modeling study in a different way, namely by a different strength of the (anti-)alignment interactions. Changing the interaction at the level of individual cells will, however, change the bulk behavior too. Accordingly, the numerically observed bulk behavior (Fig. 5B ) is very different in both cases (at a qualitative level). It is difficult to judge the difference in detail because the correlation function was not calculated for the simulations.

In short:

The model (Fig. 5A) reproduces the experimental results partially (Fig. 2C), but the modeling analogue to Fig. 2E is missing. The line of arguments seems to me not to be entirely consistent.

2) Inferring the interactions of active particles from observations of the emergent patterns is a highly non-trivial task. In view of this I am not entirely convinced by the arguments put forward by the authors that "more substantial cell-cell cohesive interaction[s]" are the reason why the swirling patterns formed by swarming/swimming bacteria differ. In this context, I want to raise the attention of the authors to Ref. [Peruani, Deutsch & Bär: Phys. Rev. E 74 030904(R) 2006]. In this work, a clustering transition of self-propelled rods was described. "Rafts", referred to as clusters by Peruani et al., are observed as the aspect ratio of rods is increased. Notably, a kinetic transition towards clustering can emerge even in the absence of any attractive interactions. In short, the observation that cells move in parallel (polar clusters) next to each other does not allow to conclude that cohesive interactions are present. The movies S3 and S4 provided by the authors show that the particle shape of swarming and swimming particles is clearly different. In particular, the elongated swarming bacteria show pronounced clusters (Movie S3) whereas the shorter planktonic cells (Movie S4) do not. The difference in aspect ratio does indeed suggest that swarming and swimming bacteria differ in their alignment interaction. However, this contradicts the observation that spatial correlations in large wells are indistinguishable (see comment 1 and Fig. 2E). Side remark: in the main text, the authors argue that changes of the aspect ratio are not the reason for an increased alignment interaction, however, in the discussion section cell morphology changes (e.g. cell elongation and hyper-flagellation) are mentioned as an indicator that swarming is a different phenotype from swimming.

In summary, I believe that the connection of experimental observations and modeling are not entirely convincing.

Reviewer #2 (Public Review):

This manuscript establishes a novel rodent model for prenatal methadone exposure and characterizes various aspects of neurodevelopment in the offspring. Given the global opioid crisis and the rampant rise of drug use by pregnant mothers and incidence of neonatal abstinence/opioid withdrawal syndrome, there is a critical need to determine potential outcomes for children born with this condition. In their model, the investigators use mice that are already taking oxycodone and switched to methadone treatment prior to becoming pregnant, which is a major translational advantage compared to other models where opioid dosing does not start until sometime mid-gestation. The experimental design also included a wide variety of measurable endpoints, including physical development, sensorimotor behavioral tasks, vocalizations, brain imaging, circuit electrophysiology, and histology; this comprehensive approach allows for synthesis of the results that has traditionally been difficult to find in this field, given the vast differences in species, dosing paradigms, etc. Sex differences were also considered, which is especially important given what is known about varying rates of NOWS between males and females. The text is very well-written, including detailed descriptions of statistical analysis.

Despite overall enthusiasm for the study and its findings, there are some concerns regarding the brain volume analyses as well as potential stress confounds with the experimental design. The analysis of structural differences measured by volumetric MRI showed that there were no appreciable differences across grey matter structures with PME (Supp. Fig. 9). This was surprising, given that regional decreases in brain volume are a consistent finding with prenatal drug-exposed offspring (Yuan et al., 2014 [DOI 10.1038/jp.2014.111]; Sirnes et al., 2017 [DOI 10.1016/j.earlhumdev.2017.01.009]; Nygaard et al., 2018 [DOI 10.1016/j.ntt.2018.04.004]). Traditionally, these deficits tend to be more true for white matter than grey, though the authors do not indicate whether this was investigated.

The opioid dosing protocol required twice-daily subcutaneous injections for at least 3 weeks (possibly longer, but it was difficult to determine from the text when exactly the treatments were halted). The effects of maternal/prenatal stress, even in the vehicles, cannot be discounted. The authors rightly noted this caveat in the Discussion, but it remains a critical concern in this otherwise well-designed study.

Reviewer #2:

In this study, the authors perform an impressive field phenotyping experiment on three grafted grapevines all with a common scion cultivar 'Chambourcin' alongside an ungrafted control to assess the associations between rootstock and leaf traits. The traits collected include ionomics, metabolomics, transcriptomics, leaf morphology and physiology. In addition, the authors collect these samples at three phenological stages to incorporate seasonal variation. The authors apply a combination of classification and machine learning methods to test whether features within each phenotypic measurement are predictive of genotype. In some cases, such as the ionomics data, certain ions are predictive of rootstock genotype but only at certain seasonal time points. The datasets presented here are extensive and will be of value to the horticulture field since grafting is such a common technique used in cultivating many crops. Considering the scale of this experiment, the manuscript is at times disconnected, in large part because each dataset is analyzed independently without any integration across phenotypes. The results presented do highlight more of an effect of phenology rather than rootstock on the phenotypes measured.

Major comments:

1) It would be very helpful to have a diagram with the layout in the field and the sampling strategy or a more detailed explanation. This would help to associate which phenotypic data was collected at the same time and on the same plants. For example, it would expand on what is mentioned on line 348 "row 8 sampled early in the day". It would help to know what time of day the samples in each row were collected. Additionally, how do the different irrigation treatments factor into the sampling? A better introduction of the experimental design is needed at the start of the results section along with a description of the genotypes and why they were selected.

2) I understand why running a PCA before the LDA can help reduce the dimensionality of the space to be able to invert the covariance matrix (if that was the motivation?) but is this because there were issues with running LDA alone? I wonder if you've lost important discriminating information between the classes by doing this. Was the LDA run on the datasets first prior to the PCA? This may uncover additional classification that was eliminated by the PCA.

3) For the Random Forest analysis, the authors might consider using k-fold cross validation rather than partitioning the dataset, this is especially beneficial when working with smaller datasets and might improve the predictions. Could all the importance scores be reported rather than just the couple mentioned in the text (line 296).

4) In reference to Figure 1B and C, it would be helpful to indicate on the plots which comparisons are significant based on their model tests. The full test results are presumably in the excel spreadsheet referred to in the reporting form although it was not found with the manuscript materials.

5) Throughout the text there is very little mention of the various grafted genotypes and what is known about the lines. The authors should consider introducing these genotypes and why they were selected for the grafting experiment. What is different among these lines? There is very little discussion of the comparisons between genotypes and what phenotypes are significantly different between the lines and what the implications are for the plant as a whole.

6) Line 287 refers to a post-hoc analysis of the ions, do the ions showing significant variation explained by rootstock and phenology match the ions identified in the ML as important classifiers?

7) For such a large metabolomic dataset, it is surprising that the authors do not present any identification of the metabolites highlighted. The identification of the metabolite features that were found to influence the rootstock main effect would be of interest and might reveal interesting biology. How did these metabolites differ between genotypes? On line 501 in the discussion there is mention of flavanols and stilbenes yet these weren't highlighted in the results section.

8) What is the reasoning for not simply applying a linear modeling approach such as limma on the gene expression data first instead of only applying it to the PCs in order to identify differentially expressed genes between the genotypes? If phenological stage is the strongest effect, what if you run the analysis within each stage to look specifically at the differential responses between grafted lines at each stage? The analysis of the gene expression data, similar to the metabolomics data, seems to be missing an opportunity to uncover underlying biological mechanisms contributing to any genotype effects of grafting, a stated goal of the study. What genes are differentially expressed and do they relate to the metabolomic or ionomic data?

9) In the methods, there are three irrigation treatments described yet this is not mentioned in the results section. While it seems as though rainfall mitigated much of the irrigation effect there does appear to be differences in water availability to the vines as described in the provided github page. Were various irrigation treatment sets sampled for all phenotypes? Or were the ionomics, metabolomics and transcriptome analysis done on the same irrigation treatments? If not, was this effect considered in the analysis? This is yet another variable that would greatly influence the response and should be considering when assessing the effects of grafting. Further detail about the sampling and conditions is needed to clarify.

10) In figure 1 there is information about leaf age. For the metabolomics a mature leaf was sampled, transcriptomics the youngest leaf, and physiology it is not specified. Could you clarify the leaves that were sampled and how they relate across phenotypes. This is an important point to mention given the differences observed for the ionomics data.

11) In reference to the vine physiology, were these all collected from the same irrigation treatment? Was the sampling of each genotype spread out over the 3h window to account for time of day variation? It would be helpful to have the significant comparisons indicated in the figure. What are the letters referring to on lines 402-403 with the p. values? This section would be greatly improved by additional clarity in the text.

12) Given the focus on grafting, the analysis presented in Figure 6 does not seem to contribute to this objective. Could this be expanded on to look within and across genotypes to see if different phenotypes covary and to compare the dimensions of variation across genotypes rather than combining them all together? This would complement the previous analyses and hopefully reveal the differences that were highlighted in the earlier sections.

13) The results section is very disjointed and the datasets are presented almost as completely separate studies. To improve clarity in the results section, the authors might consider expanding on the findings of the LDA and ML analysis for each phenotype and connecting them together.

Reviewer #2 (Public Review):

In this manuscript, the authors generated transgenic zebrafish reporter lines that allow observation of cytoplasmic lipid droplets in vivo. They knocked in GFP or RFP in the endogenous loci of perilipin 2 and 3, and showed that the reporter genes exhibited similar temporal and spatial expression in the intestine in response to acute high-fat feeding as the endogenous perilipin 2 and 3 transcripts. They also characterized the reporter gene expression in the liver, adipocytes, and around neuromasts. These tools open up new opportunities to study lipid droplets dynamics in live zebrafish that is not feasible in mouse models. Overall the manuscript is well written. The authors have discussed in details the strength and caveats of these reporters. The weakness is the descriptive nature of the study - many interesting observations but no mechanistic study. I have the additional comments:

1) It is curious that in plin2 and plin3 reporter fish, the fluorescent tags were inserted at the 5' and 3' of the open reading frame, respectively. The authors did not provide any explanation. Does the location where the fluorescent tag is inserted affect the expression of the reporter genes?

2) GFP and TagRFP-T are not fast folding fluorescent proteins and are very stable, which may not be the best options for studying the formation and degradation of lipid droplets. How the fluorescent tags affect the stability and clearance of the protein should be carefully characterized.

3) Was there any indel being introduced by TALENs in these knockin fish? Is there off target effects of the TALENs?

4) The authors also generated transgenic fish overexpressing human PLIN2 and PLIN3 fluorescent fusion proteins. Is the subcellular localization of these fusion proteins similar to the zebrafish knockin under nofed and fed conditions? In other words, do human PLIN2 and PLIN3 proteins behave similarly as the zebrafish orthologs?

Reviewer #2 (Public Review):

The authors eloquently showed that IL-33 was produced from stromal cells following Toxoplasma infection and that the absence of IL-33 signaling resulted in increased parasitemia. In agreement with this observation, they found that exogenous IL-33 reduced parasite load and increased the recruitment of inflammatory monocytes that are critical for resistance. The manuscript is well written and data presented here supports the major findings of this work.

Reviewer #2 (Public Review):

Overall this is a solid and technically sound manuscript, and I have only two relatively minor suggestions for improvement:

1) Tetramer versus dimer

The particles that were analyzed by cryoEM were composed of four THO-Sub2 protomers, yet the authors argue that a dimer is the functional unit. Why? The tetramer versus dimer organization needs to be better discussed, also in light of the observation that the human complex can also form a tetramer.

2) Sub2 activation mechanism

The authors should more carefully discuss how THO 'activates' Sub2 (and how the 'semi-open state' leads to activation) and indicate the RNA binding surface of Sub2 in their model.

Reviewer #2 (Public Review):

This manuscript will be of interest to a broad audience of immunologists especially those studying host-pathogen interactions, mucosal immunology, innate immunity and interferons. The study reveals a novel role for neutrophils in the regulation of pathological inflammation during viral infection of the genital mucosa. The main conclusions are well supported by a combination of precise technical approaches including neutrophil-specific gene targeting and antibody-mediated inhibition of selected pathways.

In this study by Lebratti, et al the authors examined the impact of neutrophil depletion on disease progression, inflammation and viral control during a genital infection with HSV-2. They find that removal of neutrophils prior to HSV-2 infection resulted in ameliorated disease as assessed by inflammatory score measurements. Importantly, they show that neutrophil depletion had no significant impact on viral burden nor did it affect the recruitment of other immune cells thus suggesting that the observed improvement on inflammation was a direct effect of neutrophils. The role of neutrophils in promoting inflammation appears to be specific to HSV-2 since the authors show that HSV-1 infection resulted in comparable numbers of neutrophils being recruited to the vagina yet HSV-1 infection was less inflammatory. This observation thus suggests that there might be functional differences in neutrophils in the context of HSV-2 versus HSV-1 infection that could underlie the distinct inflammatory outcomes observed in each infection. In ordered to uncover potential mechanisms by which neutrophils affect inflammation the authors examined the contributions of classical neutrophil effector functions such as NETosis (by studying neutrophil-specific PAD4 deficient mice), reactive oxygen species (using mice global defect in NADH oxidase function) and cytokine/phagocytosis (by studying neutrophil-specific STIM-1/STIM-2 deficient mice). The data shown convincingly ruled out a contribution by the neutrophil factors examined. The authors thus performed an unbiased single cell transcriptomic analysis of vaginal tissue during HSV-1 and HSV-2 infection in search for potentially novel factors that differentially regulate inflammation in these two infections. tSNE analysis of the data revealed the presence of three distinct clusters of neutrophils in vaginal tissue in mock infected mice, the same three clusters remained after HSV-1 infection but in response to HSV-2 only two of the clusters remained and showed a sustained interferon signature primarily driven by type I interferons (IFNs). In order to directly interrogate the impact of type I IFN on the regulation of inflammation the authors blocked type I IFN signaling (using anti IFNAR antibodies) at early or late times after infection and showed that late (day 4) IFN signaling was promoting inflammation while early (before infection) IFN was required for antiviral defense as expected. Importantly, the authors examined the impact of neutrophil-intrinsic IFN signaling on HSV-2 infection using neutrophil-specific IFNAR1 knockout mice (IFNAR1 CKO). The genetic ablation of IFNAR1 on neutrophils resulted in reduced inflammation in response to HSV-2 infection but no impact on viral titers; findings that are consistent with observations shown for neutrophil-depleted mice. The use of IFNAR1 CKO mice strongly support the importance of type I IFN signaling on neutrophils as direct regulators of neutrophil inflammatory activity in this model. Since type I IFNs induce the expression of multiple genes that could affect neutrophils and inflammation in various ways the authors set out to identify specific downstream effectors responsible for the observed inflammatory phenotype. This search lead them to IL-18 as possible mediator. They showed that IL-18 levels in the vagina during HSV-2 infection were reduced in neutrophil-depleted mice, in mice with "late" IFNAR blockade and in IFNAR1 CKO mice. Furthermore, they showed that antibody-mediated neutralization of IL-18 ameliorated the inflammatory response of HSV-2 infected mice albeit to a lesser extent that what was seen in IFNAR1 CKO. Altogether, the study presents intriguing data to support a new role for neutrophils as regulators of inflammation during viral infection via an IFN-IL-18 axis.

In aggregate, the data shown support the author's main conclusions, but some of the technical approaches need clarification and in some cases further validation that they are working as intended.

1) The use of anti-Ly6G antibodies (clone 1A8) to target neutrophil depletion in mice has been shown to be more specific than anti-Gr1 antibodies (which targets both monocytes and neutrophils) thus anti-Ly6G antibodies are a good technical choice for the study. Neutrophils are notoriously difficult to deplete efficiently in vivo due at least in part to their rapid regeneration in the bone marrow. In order to sustain depletion, previous reports indicate the need for daily injection of antibodies. In the current study the authors report the use of only one, intra-peritoneal injection (500 mg) of 1A8 antibodies and that this single treatment resulted in diminished neutrophil numbers in the vagina at day 5 after viral infection (Fig 1A). Data shown in figure 2B suggests that there are neutrophils present in the vagina of uninfected mice, that there is a significant increase in their numbers at day 2 and that their numbers remain fairly steady from days 2 to 5 after infection. In order to better understand the impact antibody-mediated depletion in this model the authors should have examined the kinetics of depletion from day 0 through 5 in the vaginal tissue after 1A8 injection as compared to the effect of antibodies in the periphery. These additional data sets would allow for a deeper understanding of neutrophil responses in the vagina as compared to what has been published in other models of infection at other mucosal sites.

2) The authors used antibody-mediated blockade as a means to interrogate the impact of type I IFNs and IL-18 in their model. The kinetics of IFNAR blockade were nicely explained and supported by data shown in supplementary figure 4. IFNAR blockade was done by intra-peritoneal delivery of antibodies at one day before infection or at day 4 after infection. When testing the role of IL-18 the authors delivered the blocking antibody intra-vaginally at 3 days post infection. The authors do not provide a rationale for changing delivery method and timing of antibody administration to target IL-18 relative to IFNAR signaling. Since the model presented argues for an upstream role for IFNAR as inducer of IL-18 it is unclear why the time point used to target IL-18 is before the time used for IFNAR.

3) An open question that remains is the potential mechanism by which IL-18 is acting as effector cytokine of epithelial damage. As acknowledged by the authors the rescue seen in IFNAR1 CKO mice (Fig 5C) is more dramatic that targeting IL-18 (Fig 6D). It is thus very likely that IFNAR signaling on neutrophils is affecting other pathways. It would have been greatly insightful to perform a single cell RNA seq experiment with IFNAR CKO mice as done for WT mice in Fig 3. Such an analysis might would have provided a more thorough understanding of neutrophil-mediated inflammatory pathways that operate outside of classical neutrophil functions.

4) The inflammatory score scale used is nicely described in the methods and it took into consideration external signs of vaginal inflammation by visual observation. It would have been helpful to mention whether the inflammation scoring was done by individuals blinded to the experimental groups.

5) The presence of distinct clusters of neutrophils in the scRNA-seq data analysis is a fascinating observation that might suggest more diversity in neutrophils than what is currently appreciated. In this study, the authors do not provide a list of the genes expressed in each cluster within the data shown in the paper. Although the entire data set is deposited and publicly available, having the gene lists within the paper would have been helpful to provide a deeper understanding of the current study.

equivalent in javascript:

{a, b, ...rest} = {a: 1, b: 2, c: 3, d: 4}

Not a bad replacement for that! I still find javascript's syntax a little more easily readable and natural, but given that we can't use the same syntax (probably because it would be incompatible with existing syntax rules that we can't break for compatibility reasons, unfortunately), this is a pretty good compromise/solution that they've come up with.

Reviewer #2 (Public Review):

This manuscript shows the functional relevance of mNatA catalytic subunit, mNAA10, in mammals' development. Moreover, authors have found a new NatA catalytic subunit in mice, mNAA12, that can compensate mNAA10 inactivation in mice. Interestingly, inactivation of mNAA10 in mice induces some developmental defects similar to those observed in Ogden syndrome (OS) patients including lethality in infants. This study provides several evidences and explains some of the defects observed in OS patients like supernumerary vertebrae and hydrocephaly supporting the relevance of hNAA10 mutations in the development of OS. Moreover, authors have observed in mice some developmental deficiencies not observed previously in OS patients, like supernumerary ribs, that after patient re-examination they have been observed in humans too. Curiously, the results presented in this article show that inactivation of mNatA catalytic subunit does not affects dramatically protein N-terminal acetylation, probably as consequence of mNAA12 paralog function as mNatA catalytic subunit when mNAA10 is not present. Interestingly, gene inactivation supports the biological significance of NAA10 as the main NatA catalytic subunit as mNAA12 inactivation is not associated with any clear phenotype. In spite of being one of the most frequent protein modifications protein N-terminal acetylation has not attracted proper attention, therefore this paper can draw more attention to this important protein modification.

Wibmer, C. K., Ayres, F., Hermanus, T., Madzivhandila, M., Kgagudi, P., Lambson, B. E., Vermeulen, M., Berg, K. van den, Rossouw, T., Boswell, M., Ueckermann, V., Meiring, S., Gottberg, A. von, Cohen, C., Morris, L., Bhiman, J. N., & Moore, P. L. (2021). SARS-CoV-2 501Y.V2 escapes neutralization by South African COVID-19 donor plasma. BioRxiv, 2021.01.18.427166. https://doi.org/10.1101/2021.01.18.427166

Reviewer #2 (Public Review):

The authors show that SUSD4 is expressed throughout the brain and is abundant in cerebellar dendrites and spines. Mice with deletion of SUSD4 have motor coordination and learning deficits, along with impaired LTD induction. The also attempt to show that GluA2 AMPA subunits are misregulated, but that is not as convincing. They find Nedd1, along with many other proteins in a proteomic screen for SUSD4 interactors, and try to explain the phenotypes through the regulation of GluA2 degradation by Nedd4 through SUSD4. These are potentially interesting findings, but very preliminary at this point. While the electrophysiology is good, the mechanistic studies are incomplete.

Major comments:

In Figure 1 localization images are shown using exogenous protein. Can the authors visualize endogenous protein?

It appears that SUSD4 is expressed in multiple brain regions, even at higher levels than the cerebellum. The authors should provide a good explanation for why deficits in the KO do not affect other functions, and seem to preferentially affect cerebellar functions.

Figure 4: immunofluorescence data are not very convincing.

Figure 5: The use of the word "could" is not supporting a strong conclusion. The authors should demonstrate whether SUSD4 DOES indeed regulate GluA2.

Overall, while the electrophysiology seems fine, the mechanistic studies are preliminary and speculative at this point.

Reviewer #2 (Public Review):

In this manuscript, Galbraith et al add to our understanding of COVID19 pathobiology by undertaking a cross-sectional survey of 73 hospitalized COVID19 patients with non-severe disease. They perform very broad multi-omics analysis, including plasma proteomics, cytokine profiling and mass cytometry. The authors propose that disease course can be classified by the titer of anti-CoV2 antibodies, which in turn is associated with distinct changes in circulating proteins, cytokines and immune subsets. Interesting correlations with complement and coagulation factors are noted. These findings suggest an alternative way to map disease progression in COVID19 and have implications for broader studies of COVID19 pathobiology. In particular, it will in interesting to extend this framework to analyze a broader spectrum of COVID19 patients, particularly those with poor outcome.

Reviewer #2 (Public Review):

"Feeding Experimentation Device version 3 (FED3): An open-source device for measuring food intake and operant behavior" describes the third iteration of an open-source automatic feeding device to be used with mice. I have no concerns about this paper and would recommend it as is. The authors have provided an incredible resource for the fields of feeding and reward-related behaviors, and provide all the details needed for assembly and use. Moreover, the data that they have collected using this device constitutes an advance, particularly the circadian rhythms of feeding, as well as the increase in operant responding during the light cycle. This device enables homecage measurement of feeding and training for motivational behavior, enabling most any laboratory to examine feeding behaviors in their experiments.

Reviewer #2 (Public Review):

Although the major activation steps and general mechanistic underpinnings of SOCE have been reported in a flurry of literatures, they are largely descriptive and lack quantitative information. One topic of greatest interest to the CRAC channel field is the structural basis of CC1-CAD/SOAR-mediated STIM1 autoinhibition. Using single-molecule Förster resonance energy transfer (smFRET) and protein crosslinking approaches, Dorp et al provides a binding model for the CC1-CAD interaction. This model explains the role of CC1 in STIM1 activation, and delineates the activation process of STIM1 CT. It also clarifies the controversy on the two varying structures regarding the packing of the CAD/SOAR domain by favoring the X-ray structure over the NMR structure. The conclusions of this paper are mostly well supported by data. The only minor concern is to reconcile some of the conflicting results (regarding the relative positions of some residues used in the crosslinking study, as well as the CC1-alpha 1 helix), made between this study and a recent structural study, i.e., the NMR solution structure of CC1 reported by the Romanin/Muller's groups (PMID: 33106661). Overall, this study covers a timely topic to address a long-standing question in the ORAI-STIM signaling field, i.e., the structural basis of CC1-CAD association that keeps STIM1 largely quiescent in the resting condition. This work, regarded by this reviewer as a "tour-de-force" by meticulously scanning through many key residues within the multiple CC1/CAD helices, certainly warrants immediate publication.

Notable strengths:

1) smFRET is increasingly being used to determine distances, structures, and dynamics of biomolecules. Full length STIM1 and STIM1 C-terminus have been always difficult to obtain crystal structure due to its tendency for aggregation and the existence of large disordered regions. Herein, the authors selected smFRET as the major tool to overcome this hurdle and illuminated the CC1-CAD binding models to provide novel mechanistic insights into STIM1 auto-inhibition mediated by the intramolecular cis CC1-CAD association.

2) The efforts to extend crosslinking of ctSTIM1 to flSTIM1 are particularly commendable, moving one more step closer to the physiological scenario.

Minor weaknesses:

1) The authors proposed a CC1 model displaying "tandem connection of "CC1α1- CC1α2", that shows notable discrepancies with the recent CC1 NMR solution structure (PMID: 33106661). In the latter structure, the three helices are intertwined to form a bundle like structure. An in-depth discussion is certainly needed to clarify the difference. Some possibilities include: (i) Is this due to the artifact of the CC1 NMR structure (done in the presence of helix-stabilizing reagents)? (ii) is this due to the introduction of cysteine residues for the assays? (iii) is this due to absence of the CAD/SOAR part, or other regulatory components, in the solution structure? Repeating one or two key smFRET/crosslinking experiments in the presence of the similar buffer condition as in the NMR study would provide clues to these possibilities.

2) Another concern, very minor though, is regarding cysteine crosslinking flSTIM1 by 0.2 mM diamide. Will the addition of diamide cause undesired activation of STIM1 in the absence of cyclopiazonic acid?

Reviewer #2:

In this paper, Werkhoven and colleagues describe a large-scale effort, using Drosophila, to study variation in behavior among individuals with identical genotypes, and raised in very similar environmental conditions. This addresses the important and basic question of how much behavioral variability exists under such conditions, e.g. due to stochastic processes during development. By looking across many different behaviors, the authors are able also to investigate the nature of this variability. The key conclusion of the paper is that this intragenotypic variability is high dimensional, and cannot be explained by a small set of behavioral syndromes. They find that this observation is robust to the method they use to quantify behavior, and also holds to different degrees in data sets acquired from outbred flies, or files subjected to genetic perturbations of neural activity. Furthermore, they have generated a data set that allows correlation of behavioral biases in individual animals with transcriptomic data. Altogether, this is an impressive study that, beyond its important conclusions, opens up the possibilities for many further explorations in this area, and should be interesting to a broad audience. The experiments are well designed and overall the paper is very nicely written and clear to understand.

Reviewer #2:

Nguyen et al. developed a novel method of transcranial focused ultrasound stimulation and used it to stimulate anesthetized rats while performing extracellular recordings in the hippocampus. They find that the stimulation has different amplitude-dependent effects on putative inhibitory interneurons and excitatory principal cells. This finding is exciting because it suggests that transcranial ultrasound could be used to specifically reduce or increase firing rates in excitatory or inhibitory neurons in a particular part of the brain (resolution in the mm range). In principle, this could also be applied to humans. Simultaneously measured oscillations of the local field potential, particularly (but not exclusively) in the theta band (3-10 Hz) could also be manipulated in a bidirectional manner depending on the stimulation amplitude. Such cortical oscillations have been strongly linked to a wide range of functions including memory, and the potential to manipulate them in an anatomically precise manner is exciting and could even lead to new therapy approaches. Although it is not new that ultrasound can be used to modulate neuronal activity, this paper reaches a new level of precision by demonstrating that bidirectional effects can in principle be limited to one cell class or one frequency band. Thus, it could provide a great alternative to current methods that either provide much less precision (e.g. transcranial magnetic stimulation) or rely on more invasive methods (e.g. deep brain stimulation) or genetics (e.g. optogenetics).

The study is well-designed with stimulation at 3 different amplitudes applied in the same rat, whereby each 3-minute stimulation is compared to a 3-minute sham session where the transducer is 1 cm above the skull. Baseline sessions before each stimulation and sham session did not show any differences, showing no spillover-effects from the previous stimulus. Effects on brain temperature were also measured and shown to be negligible compared to normal variability.

Low intensity stimuli lead to a reduction in firing rates in putative interneurons and a reduction in theta oscillation power, whereas high intensity stimuli lead to an increase in firing rates in putative principal cells, with intermediate intensities having largely no effect.

In principle, these findings could provide novel insights into the mechanisms underlying ultrasound stimulation, but neither of the two discussed main modes of action (mechanical and thermal) appears consistent with the results. Thus, no model could be offered that might give some insight into the underlying mechanisms of ultrasound modulation of neuronal activity. This might be an issue for future work, and if the results were more robust perhaps this would not matter as much. However, the overall size of the effects appears to be too small to be of practical use as a reliable tool for manipulation of neural circuits. Although the authors show statistical significance, some details of the analysis are not fully clear and may need to be further corrected for multiple testing. It remains unclear if perhaps larger or different effects would be achieved when recording through the skin, without anesthesia, in different brain areas, in differently defined subclasses of neurons or with a different stimulation protocol (frequency, duration, amplitude). Thus, although the technique appears promising, more work is needed.

Reviewer #2 (Public Review):

Judd et al. systematically examine the input/output connectivity of discrete excitatory and inhibitory neuronal subpopulations in the cerebellar interposed anterior nucleus (IntA) using conditional AAV and rabies virus mapping strategies. The authors first define distinctions in the output connectivity of excitatory and inhibitory neurons in the IntA nucleus, and describe a surprisingly much wider projection pattern by inhibitory neurons than previously thought. They also characterize distinctions in projection pattern between identifiable subtypes of IntA inhibitory neurons as well as distinctions in morphology of their terminal fields. The authors next explore the input connectivity of excitatory and inhibitory neurons in the IntA nucleus and found that excitatory output neurons receive fewer, but more organized inputs than inhibitory output neurons, and that many output targets provide reciprocal connections with the CN.

In general, the output analysis is strong and there are only a few questions about interpretation of the distinctions of projections by different subtypes of IntA inhibitory neurons. For instance, the distribution of the initial targeting within the cerebellar nuclei, cerebellar cortex and outside the cerebellum was not analyzed in Ntsr1-Cre and Gad1-Cre similar to the analysis performed for the intersectional output analysis. Clarification on whether and how the distinctions in projections could be due to variability in the specificity of the initial targeting or recombination ability of the two mouse Cre-lines is needed to strengthen interpretation of the different projections patterns observed. As for the input analysis using rabies, there were two major issues identified.

First, the use of conditional GFP-labeled G protein and the use of rabies that is also GFP potentially confounds analysis of input cells.

Next, the low number of starter cells is a concern and the identity of starter cells outside the cerebellar nuclei in Ntsr1-Cre and Gad1-Cre is vague and needs to be clarified. This is important for interpretation of whether input structures observed project specifically into the CN or also into the cerebellar cortex, and whether distinctions observed in number of input structures may reflect amount of starter cells in each Cre line.

Reviewer #2 (Public Review):

This manuscript by Sando and Sudhof addresses whether GPCR activity of latrophilin2 and 3 is necessary for the role of these proteins in synapse formation. The key findings are:

— the generation and validation of mutants that lack transmembrane and intracellular domains (but are GPI-anchored instead), the lack only intracellular domains, or that contain all domains but lack GPCR-activity. All mutants work properly in cell aggregation assays and appear to be localized normally when overexpressed in wild type neurons. This also led to the development of an elegant PKA-phosphorylation reporter assay.

— in cultured latrohphilin 3 knockout neurons, latrophilin3 expression restores a decreased synapse density and mini-frequency, but the GPI-anchored, truncated or inactive versions do not restore these parameters.

— in vivo/hippocampal brain slices, latrophilin2 knockout impaired perforant path but not Schaffer collateral transmission onto CA1 neurons, and rescue required latrphilin2 GPCR activity. Conversely, Latrophilin3 knockout impaired Schaffer collateral but not perforant path transmission onto CA1 neurons, and rescue required latrophilin3 GPCR activity.

— finally, monosynaptic tracing confirmed that latrophilin3 knockout reduced inputs onto CA1 starter neurons, and rescue again required GPCR activity.

Altogether, the data are rigorously acquired, the paper is well written, and the finding that GPCR activity is necessary for latrophilins' role is both surprising and important. It is also elegant, as coupling cell-adhesion directly to signal transduction via a single molecule for synapse formation is a compelling way to drive synaptic assemblies. Naturally, the question arises how compartmentalized GPCR-signaling then instructs synapse formation, a topic that will undoubtedly require and attract more research. This is an exciting manuscript that will inspire new research on compartmentalized GPCR signaling at the synapse. Given the central importance of surface trafficking and localization within spines for the conclusions, better description of experimental procedures and quantification, and possibly additional data would clearly strengthen this point.

Reviewer #2:

Epilepsy is often an early sign observed in Alzheimer patients and there are several mechanisms that may contribute to this hyperexcitability. In this study, the authors focused on an important observation suggesting that intracellular Amyloid beta, a protein often found in plaques in the brain, is found early on inside neurons of the hippocampus, the learning and memory center of the brain. Interestingly, when unique early forms of Ab named oligomers were introduced inside neurons, the cells and surrounded circuits became hyperexcitable. This increased excitability was mediated mainly by the release of glutamate on AMPA glutamate receptors. Remarkedly, these excitatory effects were triggered by intracellular amyloid oligomers through a retrograde signal named nitrous oxide. This manuscript suggests that early stages of the disease may comprise significant increases in network excitability that may trigger a cascade of synaptic dysfunction and cognitive deficits such as memory loss.

Here are my comments to strengthen the manuscript. Overall this is a strong study with an interesting take on the role of intracellular amyloid and how it contributes to increased network excitability in AD.

There is an interest to determine the mechanisms responsible for the hyperexcitability often associated with familial and sporadic forms of Alzheimer's disease. Many have focused on possible reduction in inhibitory interneuron function as essential drivers of the increased excitability of the network. Although there exist a large number of investigations determining the effects of extracellular Ab on synaptic transmission, the intracellular effects of Ab and its contribution to disruptions of synaptic transmission remains less well understood. A couple of studies have shown that intracellular application of Ab (Ab42) induces decreases in long-term potentiation and basal synaptic transmission. In this study, the authors have investigated how intracellular Ab oligomers (iAbo) contribute to enhanced excitability in the CA1 region of the hippocampus. To do so, they have intracellularly applied human brain-derived and synthetic Ab oligomers through the patch-pipette in principal neurons recorded in vitro and in vivo.

In this study, the authors show that intracellular application of intracellular Ab oligomers increased the frequency and the amplitude of excitatory currents and spiking in ex vivo hippocampal slices. Effects that were mimicked by human oligomers. The intracellular amyloid mediated effects were through the amplification of AMPAergic spontaneous activity and currents, and, to a lesser extent, spontaneous GABAA mediated currents. Miniature frequency and amplitude of AMPA-mediated EPSCs were also increased and were sensitive to PKC blockers. Interestingly, since intracellular Ab increased the frequency of EPSCs, which is a presynaptic effect, a signaling molecule is likely to be released postsynaptically to modulate presynaptic terminals. The hypothesis that the retrograde signal NO was involved by determining the sensitivity of NOS inhibitor L-NAME. L-NAME reduced the increased iAbo mediated frequency of spontaneous post-synaptic excitatory currents in cultured neurons. The L-NAME compound was shown to reduce the iAbo -mediated No from both the recorded and neighboring neurons providing further evidence that intracellular Ab oligomers triggered NO release and increased glutamate release. Increases in the excitability of CA1 pyramidal cells were also observed in vivo by intracellular application of AB oligomer. Overall, this is a well written study that demonstrates a novel perspective of the effects of intracellular Ab oligomers on CA1 principal neurons and suggests possible mechanisms underlying hyperexcitability.

Novelty:

1) use intracellular oligomers, synthetics and humans

2) Showing that iAb oligo increased post and presynaptic AMPA-mediated EPSCs.

3) The presynaptic increases in EPSCs were mediated by NOS and NO, this could potentially spread widely across the network.

4) spontaneous IPSCs were also increased (through an undetermined mechanism).

5) the iAbo increase in excitation was also observed in vivo.

Questions:

Intracellular Ab produces both an increase in EPSCs and IPSCs. However, in Fig 3, the IPSCs, measures using a charge transfer quantification, did not show a significant change in response to iAbo, in contrast to EPSCs. This spontaneous inhibition here was measured as charge transfer which depends on the amount of charges in time. I wonder why this was not significant since this measurement should have picked up a possible increase in spontaneous IPSCs?

With regard to the inhibition, In the schematic on Fig. 10, I find this incomplete and slightly inaccurate since it shows one terminal releasing both glutamate and GABA with NO increasing both. While this is obviously an oversimplification, it's slightly inaccurate since NO was not directly shown to increase sIPSCs. Were NOS blockers able to disrupt the increase in sIPSCs? Moreover, there are many papers that have shown that PKC can also phosphorylate GABA receptors and increase their conductance. What could be the reason that this was not involved here? This needs to be discussed.

The experiments were done in cultured neurons, in slices and in vivo. It's not always easily discernible in what conditions the experiments were done when reading the manuscript, especially when looking at the figures and figure legends. This should be at least stated in the figure legends. To help the reader, the conditions in which the currents were recorded (GABA and or excitatory receptor blockers, other ion blockers could be indicated in the figure legends to ease the comprehension of how the experiments were done and what was measured). In relation to this, was the sIPSC iAbo-mediated increases also blocked by L-NAME?

In other studies, investigating intracellular application of Ab, such as the Ripoli et al., 2014 paper, showed that iAb produced significant reductions in EPSCs in their hippocampal neurons. What are the differences explaining this? This should be discussed. Similarly, Gulisano et al., 2019, showed that extracellular, but not intracellular oligo Ab had effects on excitability when it was applied extracellularly but not intracellularly. This should also be discussed.

In the introduction, it's mentioned that the nature of hyperexcitability is unknown. I agree that it's incompletely known, but what is known is that there is a large variety of possible causes. For example, changes in GABAergic interneuron function (see Hijazi et Al 2019) is well known to be a contributing factor. There are many studies that have shown possible contributing causes of hyperexcitability, therefore, something IS known, and this should be identified in the introduction.

How do these increases in synaptic transmission by applying pM concentrations of oligomers fit with the data showing that extracellular Ab oligomers of comparable concentrations decrease synaptic transmission through presynaptic reductions in glutamate release? This needs to be put into context and discussed.

Reviewer #2:

The authors are reporting a new approach termed ORACLE to develop locus-specific phage variants, which includes a recombination step, whose efficacy is improved by the overexpression of a dedicated recombinase, followed by an enrichment performed using CRISPR/Cas9. They applied this method to create a mutant library containing 1660 variants of the tip domain of the T7 tail fiber. Performance of each variant was determined by quantifying their abundance before and after selection on three E. coli strains compared to the WT phage. Their findings show that single amino acid changes in the tip of gp17 can have major consequences on phage performance on different hosts. Then they tested whether these variants would be less prone to select phage-resistant using an UTI strain. Finally, they searched for variants that would be more prone to infect one host than another and successfully tested their predictions.

The ORACLE approach is overall novel and has some advantages over existing methods, mainly for generation of mutation libraries of genes. Authors did a nice (even if very lengthy) job of showing how mutants have consequences to structure and function of the tail fiber gene and how that influences performance on different hosts, including combating host resistance.

The authors state that ORACLE overcomes three major hurdles that make it better than existing methods, one of which is "generalizability for virtually any phage", while denouncing other systems for being applicable for highly transformable hosts only. This is highly exaggerated since ORACLE requires transformation of two plasmids (helper and donor) including one with tunable gene expression, which is clearly not possible in many bacteria. Furthermore, the enrichment step requires a strain with a functional CRISPR/Cas9 system, which again is not so obvious in the bacterial world.

The authors disregard bias that can be generated at the "O" step if a variant reproduces better than the wt. They should also mention bias arising from non-viable or severely infection hampered variants, which is briefly mentioned later in the manuscript but should be mentioned earlier, would not pass the accumulation step.

The weakest paragraph is the one dealing with the UTI strain. I have the feeling that this paragraph could simply be deleted without changing the overall story. Approaching resistance, selection, and evolution would require more experiments than the very simplistic lysis curves. The authors did not even show adequately that cells growing after 5-10 hours are either genotypically or phenotypically resistant cells. A more appropriate qualification would be "insensitive" instead of resistant.

Reviewer #2 (Public Review):

In the present manuscript Radaszkiewicz et al. analyze the role of Ring Finger Protein 43 (RNF43) in inhibiting the noncanonical WNT5A pathway. The authors demonstrate that RNF43 can interact with proteins involved in the WNT5A pathway, including ROR1, ROR2, VANGL1 and VANGL2. Specifically, they propose that RNF43 induces: i) VANGL2 ubiquitination and proteasomal degradation and ii) clathrin-dependent internalization of the ROR1 receptor. Considering the role of the WNT5A pathway in melanoma metastasis and resistance to targeted therapy, the authors further explore the role of RNF43 in melanoma invasion and resistance to vemurafenib. The authors ultimately conclude that RNF43 can prevent invasion and resistance to targeted therapy by inhibiting the WNT5A pathway. The data supporting the interaction between RNF43 and proteins involved in the WNT5A pathway are pretty rigorous. However, the study would benefit from additional experiments in the context of RNF43's role in invasion and resistance to targeted therapy in melanoma. Overall, the techniques utilized in the manuscript are appropriate, however additional cell lines and in vivo studies are strongly recommended to strengthen the manuscript.

Reviewer #2 (Public Review):

P2X2 activation depends on both ATP binding and voltage. However, the voltage sensor of P2X2 is not elucidated. This manuscript describes the study of voltage dependent conformational changes of P2X2 using voltage clamp fluorometry of the fluorescent unnatural amino acid Anap that substituted P2X2 amino acid residues. 96 positions in different structural domains were scanned by substituting with Anap, and voltage dependent fluorescence signals were detected only at two positions, A337 and I341 in the TM2 domain. A fast and linear voltage dependence of fluorescence suggested that the membrane voltage converged at and around these two positions. With a mutation K308R that was supposed to enhance voltage dependent conformational changes, Anap at the A337 position showed a time and voltage dependent fluorescence. The authors concluded that this result indicated a voltage dependent conformational change. Structure guided mutations suggested that F44 in TM1 might move to interact with A337 in response to voltage. In this study the fluorescence signals were small, but the authors made a great effort and managed to obtain the data that are convincing. The experiments were well designed and the manuscript is clearly reasoned. Considering that among all the positions that were tested only at the two positions in the TM2 segment Anap showed voltage dependent fluorescence, and that the F44 mutations abolished voltage dependence of the P2X2 currents, the conclusion that voltage converges at the A337/I341/F44 and induces a conformational change seems to be well supported.

Reviewer #2 (Public Review):

The goal of this work is to advance knowledge of the neural bases of color perception. Color vision has been a model system for understanding how what we see arises from the coordinated action of neurons; detailed behavioral measurements revealed color vision's dependence upon three types of photoreceptors (trichromacy) and three second stage retinal circuits that compute sums and differences of the cone signals (color opponency). The processing of color at later, cortical stages has remained poorly understood however, and studies of human cortex have been hampered by methodologies that abandoned the detailed approach. Typical past work simply compared neural responses in two conditions, the presentation of colorful (formally, chromatic) vs grayscale (luminance) images. The present work returns to the older tradition that proved so successful.

The project's specific goals were to measure functional MRI responses in human cortex to a large range of colors, and equally importantly, capture the pattern responses with a quantitative model that can be used to predict response to many additional colors with just a few parameters. The reported work achieved these goals, establishing both a comprehensive data set and a modeling framework that together will provide a strong basis for future investigations. I would not hesitate to query the data further or to use the QCM model the paper provides to characterize other data sets.

The strengths of the work include its methodological rigor, which gives high confidence that the goals were achieved. Specifically:

1) The visual presentation equipment was uniquely sophisticated, allowing it to correct for possible confounds due to differences in photoreceptor responses across the retina.

2) The testing of the model was quite rigorous, aided by distinct replications of the experiment planned prior to data collection.

3) The fMRI methods were also state of the art.

The work was well-situated within the literature, comparing its findings to past results. The limitations and assumptions of the present work were also clearly stated, and conclusions were not overstated.

Weaknesses of the current draft are relatively minor, however, I believe:

1) The data could be presented in a way to make them more comparable to prior fMRI work, e.g. by using percent change units in more places, comparing the R^2 of model fits reported here to those reported in other papers, and explaining and exploring how the spatially uniform stimuli, used here but not in other fMRI studies, limited responses in visual areas beyond V1.

2) Comparison between the two models, the GLM and QCM is not quite complete.

3) The present results are not discussed in context with past results using EEG, and Brouwer and Heeger's model of fMRI responses to color.

4) Implications of the basic pattern of response for the cortical neurons producing the data are discussed less than they could be.

Reviewer #2 (Public Review):

The manuscript by Myers et al provides new insight into the mechanism of transient muscle in myotonia congenita, a question that has escaped understanding since its first description over >40 years ago. The authors use a complementary set of approaches (including measurements of in situ muscle force production, membrane voltage and ion currents) to determine the membrane conductances that underlie transient weakness in muscle from both genetic (Clc1-/- adr mice) and pharmacologic (9-AC-treated WT mice) models of myotonia congenita. The authors utilize a combination of a non-conducting Cav1.1 mouse and treatment with ranolazine to dissect the relative contribution of Cav1.1 and persistent Nav1.4 conductances, respectively, to sustained plateau membrane depolarizations observed following myotonic runs, which are proposed to underlie the transient weakness observed following myotonic runs.

Reviewer #2 (Public Review):

Levi-Ferber and colleagues showed in their previous paper that ER stress regulates germline transdifferentiation in a way that is IRE-1 dependent, but XBP-1 independent. An open question at that time was how IRE-1 activation could mediate this signaling. The authors present several experiments in this manuscript that support the idea that neuronal Ire-1 can cell non-autonomously control germline differentiation through regulation of the neuropeptide FLP-6. Mechanistically, the authors characterize that FLP-6 is a target of IRE-1 RIDD activity. This is the first demonstration of RIDD in C. elegans, an important finding given that no RIDD targets have yet been identified in this organism. Using a wide range of mutants, the authors were also able to identify a neuronal circuit that can control the germline ectopic differentiation (GED) phenotype, involving the sensory neuron ASE, the interneuron AIY, and the motor neuron HSN. The data presented in the manuscript are sound, the mapping of a pivotal three-neuron circuit is impressive, and the findings are likely to be of high interest to a broad readership. However, some more evidence is required to support some of the conclusions made, in particular the characterization of flp-6 as a substrate for RIDD.

Reviewer #2 (Public Review):

This is an interesting paper showing that prolonging the integrated stress response provides protection to oligodendrocytes in the presence of an inflammatory cytokine. For their experiments, the authors use the cuprizone model in transgenic mice overexpressing IFNg in an inducible manner in combination with a genetic and pharmacological approach to enhance the integrated stress response. The experiments are well conducted and the results clearly presented in the text. The Popko lab has previously demonstrated in a series of papers the importance of the integrated stress response for oligodendrocyte function. The novel aspect of this work is that targeting the integrated stress response requires a neuroinflammtory environment for the protective effects to occur.

It is important to improve the introduction. As written it is not clear what was known before and how this paper goes beyond the existing literature.

The rational for combining for combining BZA and Seph needs to be explained.

The figures and legends could be improved according to the following suggestions:

The evidence that Sephin1 promotes remyelination in the EAE model shown in Figure 1 is only based on differences in g-ratio with the overall number of myelinated axons being unchanged. It is difficult to make conclusion based on these results. It is difficult to obtain accurate g-ratios in lesions. Maybe the authors could extend the analysis by performing histology and counting the number of oligodendrocytes.

Figure 2 contains only a scheme. Figure 2 should be combined with Figure 3. In addition, a scheme showing the time line of the cuprizone treatment and recovery from the treatment would be helpful. I assume W0 is at the time of treatment, W5 after 5 weeks of cuprizone and W8 represents 5 weeks of cuprizone and 3 weeks of recovery. If yes, it is not clear why the ASPA cell count shown it not reduced between W0 and W5. The numbers seem to be similar for W0, W5 and W8 in the absence of IFNg. In addition, the comparison shown in Figure 3 are incomplete. W0 is only shown without IFNg but not with. Does IFNg affect ASPA number in the absence of cuprizone?

Panel B and C in Figure 5 could be combined to be able to compare the analyses and to evaluate the recovery of cell number by Seph at W8. The number of mice per group is borderline (only 3 mice).

Same issue as above: Panel B and C in Figure 6 should be combined and a multiple comparison should be performed between W0, W5 and W8.

The rational for combining BZA and Seph as shown in Figure 8 should be explained in the text. The figure and legends should be improved to clarify at which time point the analyses were performed. The panel number stated in the legends do not match with what is shown in the figure. I assume the analyses were done at W8. Only g-ratios change, whereas the number of ASPA cells and amount of myelinated axons are not affected by the combined treatment. The interpretation of this result is not easy, and the emphasis of this result should be removed from the abstract.

Reviewer #2:

Ziółkowska et al. investigate synaptic processes in the dorsal hippocampal CA1(dCA1) region with the goal of testing the role of postsynaptic density protein 95 (PSD-95) dynamics in contextual fear extinction. They conclude that 1) extinction increases synaptic dCA1 PSD-95 levels and induces remodeling of dendritic spines, 2) extinction-related PSD-95 changes are mediated by phosphorylation of PSD-95 at serine 73, and 3) phosphorylation of PSD-95 at serine 73 as well as dCA1 activity are required to "update a partially extinguished fear memory". The experiments provide new insight and address a timely and important issue. The major strengths of the paper lie in the use of a wide range of complementary technical approaches, and the significance of addressing specific molecular mediators of fear attenuation. However, some of the analysis is based on inadequately justified or inappropriate measures (e.g. that do not directly assay the phenomenon under investigation), and there are concerns about independent effects of viral overexpression in this system as well as the relevance of the behavioral analysis. The conclusions from the paper, if true, would appear to support a very intricate model involving PSD95 phosphorylation and synaptic accumulation after extinction, but because of weaknesses in the underlying evidence, these mechanisms and their relationship to extinction memory were not persuasively demonstrated. Following are some specific concerns:

1) The mean intensity of PSD95 labeling per spine appears to be affected in some hippocampal layers (Fig. 1), but this might be attributable in some cases to elimination of spines that have relatively lower PSD-95, rather than a change in PSD-95 levels, per se.

2) The quantification of overexpressed PSD-95 in Fig. 2 makes unclear what specifically has been measured. The methods suggest that % area is defined as the total area of mCherry labeling divided by the total image area. This is not a direct measure of PSD-95 levels, rather than morphological or protein localization changes. Furthermore, the localization of overexpressed PSD-95 (Fig. 2) is clearly very different from that of endogenous PSD-95 (Fig. 1) in that it accumulates throughout the dendrites. This makes it unclear what a "puncta" represents, or whether the analysis implies anything about synaptic function.

3) The authors argue that S73 phosphorylation is required for synapse elimination during extinction, but Fig. S2 (which is not referenced or discussed in the manuscript) and Fig. 3 indicate that the effect of S73A overexpression is to dramatically reduce spine density in both behavioral groups. It is therefore not clear whether the manipulation interacted with extinction to prevent spine removal, or simply occluded such an effect because spine density was already at an artificial floor prior to any behavioral training. Overexpression of the wildtype construct also reduced spine density to a similar degree. Furthermore, the S73A mutant protein dramatically increased PSD area (Fig. 3d), which apparently contradicts the notion that phosphorylation of this site is required for synaptic accumulation, when applying the same logic used elsewhere in the paper. These are serious confounding issues because the central claim of the paper is that S73 phosphorylation mediates PSD95 synaptic accumulation and synaptic strengthening.

4) The authors suggest that successive days of extinction represent a distinct process called updating of a partly extinguished memory, which they seem to imply has different molecular requirements. There appears to be no basis in the literature for this idea.

5) The analysis of extinction relies on measurement of within-session decreases in freezing. However, within-session extinction has been shown to be neither sufficient nor essential for between-session extinction. It is not even clear that within-session extinction is really even extinction at all, rather than, for example, habituation. It is essential to examine the retention of decreased freezing across days in order to establish that the formation of long-term memory is involved.

6) Finally, numerous comparisons are made between animals that received FC, with no further manipulation, and extinguished animals. This design leaves open the possibility that any differences are attributable not to an extinction process but instead to context exposure independent of fear regulation. A behavioral control in which animals receive context exposures, but no shocks, would be very useful.

Reviewer #2 (Public Review):

Parker et al attempted to show that the FPA protein functions to regulate the widespread premature transcription termination of the Arabidopsis NLR genes. Using in vivo interaction proteomic-mass spectrometry, FPA was shown to co-purified with the mRNA 3' end processing machinery. Metagene analysis was used to show that FPA co-localized with Pol II phosphorylated at Ser2 of the CTD heptad repeat at the 3' end of Arabidopsis genes. Using a combination of Illumina RNA-Seq, Helicos, and nanopore DRS technologies, FPA was found to affect RNA processing by promoting poly(A) site choice, and hence controls the processing of NLR transcripts whereas such process is independent of IBM1.

Overall, it is a potentially important research. The data is rich and could be useful. However, the biological stories described are not thoroughly supported by the data presented, especially when the authors tried to touch on several aspects without some important validations and strong connections among different parts. Some special comments are provided below:

1) The title of this manuscript is "The expression of Arabidopsis NLR immune response genes is modulated by premature transcription termination and this has implications for understanding NLR evolutionary dynamics". Therefore, the readers will expect some functional connections between the FPA and the novel NLR isoforms due to premature transcription termination. However, the transcript levels of plant NLR genes are under strict regulation (e.g. Mol. Plant Pathol. 19:1267). Since the functions of NLR genes are related to effector-triggered immunity, it is more important to study the function of FPA on premature transcription termination when the plants are challenged with pathogens. In this manuscript, most transcript analyses are based on samples under normal growth conditions. It is therefore a weak link between the genomic studies and the functional aspects. For instance, it is more important to identify unique NLR isoforms produced upon pathogen challenges that are regulated by FPA. The authors will need to provide some of these data to fill this gap.

2) Since the function of FPA is to regulate NLR immune response genes, we should expect a change in plant defense phenotype in FPA loss-of-function mutants. Could the authors provide more information on this? On the contrary, in line 728 of this manuscript, the authors found that at least for some pathogens, "loss of FPA function does not reduce plant resistance". It is not consistent with the hypothesis that FPA is important to regulate NLR immune response genes.

3) Furthermore, the authors mentioned in lines 729-731 "Greater variability in pathogen susceptibility was observed in the fpa-8 mutant and was not restored by complementation with pFPA::FPA, possibly indicating background EMS mutations affecting susceptibility." Does it mean that fpa-8 contains other mutations? Will these additional mutations complicate the results of the RNA processing? Could the authors outcross the fpa-8 mutation to a clean background?

4) In line 318, the authors found 285 and 293 APA events in the fpa-8 mutant and the 35S::FPA:YFP construct respectively, but only 59 loci (line 347) exhibited opposite APA events (about one fifth). The low overlapping frequency suggests that some results could be false positive.

5) In line 732-736: "In contrast, 35S::FPA:YFP plants exhibited a similar level of sporulation to the pathogen-sensitive Ksk-1 accession (median 3 sporangiophores per plant). This suggests that the premature exonic termination of RPP7 caused by FPA has a functional consequence for Arabidopsis immunity against Hpa-Hiks1." It is contradictory to the statement in line 728 that "loss of FPA function does not reduce plant resistance". Is it possible that overexpression of FPA:YFP had generated an artificial condition that is not related to the natural function of FPA?

6) The fpa-8 mutant has a delayed flower phenotype (Plant Cell 13:1427). Could the 35S::FPA:YFP fusion protein construct reverse this phenotype and the plant defense response phenotype? It is important to interpret the data when the 35S::FPA:YFP construct was used to represent the overexpression of FPA.

7) Under the subheading "FPA co-purifies with the mRNA 3' end processing machinery". The results were based on in vivo interaction proteomics-mass spectrometry. MS prompts to false positives and will need proper controls and validations. Have the authors added the control of 35S:YFP instead of just the untransformed Col-0? At least for the putative interacting partners in Figure 1A, could the authors perform validations of some important targets, using techniques such as reverse co-IP, or to show direct protein-protein interaction between FPA to a few of the important targets by in vitro pull-down, BiFC, or FRET, etc.

8) In Fig. 3, the data show that the last exon of the FPA gene is missing in the FPA transcripts generated from the 35S::FPA:YFP construct. Will the missing of this exon affect the function of the transcript and the encoded protein?

9) The function of FPA is still ambiguous. There was a quantitative shift toward the selection of distal poly(A) sites in the loss-of-function fpa-8 mutant and a strong shift to proximal poly(A) site selection when FPA is overexpressed (35S::FPA:YFP) in some cases (Fig. 3, Fig. 5, Fig. 8). But the situation could be kind of reversed in other cases (Fig. 6). What is the mechanism behind it?

10) Under the subheading: "The impact of FPA on NLR gene regulation is independent of its role in controlling IBM1 expression". IBM1 is a common target of FPA and IBM2. Indeed, FPA and IBM2 share several common targets (Plant Physiol. 180:392). It may be more meaningful to compare the impact of FPA and IBM2 on NLR gene instead.

11) In lines 423-425, the authors described "Consistent with previous reports, the level of mRNA m6A in the hypomorphic vir-1 allele was reduced to approximately 10% of wild-type levels (Parker et al., 2020b; Ruzicka et al., 2017) (Figure 4 - supplement 3)." This data could not be found.

12) In line 426: "However, we did not detect any differences in the m6A level between genotypes with altered FPA activity." Which data is this statement referring to?

Reviewer #2 (Public Review):

In this manuscript, Anchimiuk et al reported that B. subtillis SMC can collide with each other, and that the collision is modulated by several factors including the number, strength, distribution of parS sites, the residence time of SMC on DNA, the translocation rate, and the cellular abundance of SMC. The authors suggested that these parameters are fine-tuned in the wild-type B. subtillis to minimize SMC collision. In my opinion, the finding is interesting, the experimental setup is creative, and the experiments were beautifully executed. Arguably, these experiments can only be performed in B. subtilis since parAB- and the insertion of another parS site at the mid-arm are not detrimental to cell viability (in Caulobacter crescentus, insertion of another parS mid-arm affects chromosome segregation, hence cell viability severely). Furthermore, the rare set of arm-modified SMCs from the Gruber lab also gives this manuscript a unique mechanistic angle. Given the available data, the conclusion of the manuscript is safe. I especially appreciate that the authors did not bias towards the model of SMC traversing each other by Z-loop formation.

Reviewer #2 (Public Review):

The authors present a compelling study that aims to resolve the extent to which synaptic responses mediated by metabotropic GABA receptors (i.e. GABA-B receptors) summate. The authors address this question by evaluating the synaptic responses evoked by GABA released from cortical (L1) neurogliaform cells (NGFCs), an inhibitory neuron subtype associated with volume neurotransmission, onto Layer 2/3 pyramidal neurons. While response summation mediated by ionotropic receptors is well-described, metabotropic receptor response summation is not, thereby making the authors' exploration of the phenomenon novel and impactful. By carrying out a series of elegant and challenging experiments that are coupled with computational analyses, the authors conclude that summation of synaptic GABA-B responses is linear, unlike the sublinear summation observed with ionotropic, GABA-A receptor-mediated responses.

The study is generally straightforward, even if the presentation is often dense. Three primary issues worth considering include:

1) The rather strong conclusion that GABA-B responses linearly summate, despite evidence to the contrary presented in Figure 5C.

2) Additional analyses of data presented in Figure 3 to support the contention that NGFCs co-activate.

3) How the MCell model informs the mechanisms contributing to linear response summation.

These and other issues are described further below. Despite these comments, this reviewer is generally enthusiastic about the study. Through a set of very challenging experiments and sophisticated modeling approaches, the authors provide important observations on both (1) NGFC-PC interactions, and (2) GABA-B receptor mediated synaptic response dynamics.

The differences between the sublinear, ionotropic responses and the linear, metabotropic responses are small. Understandably, these experiments are difficult – indeed, a real tour de force – from which the authors are attempting to derive meaningful observations. Therefore, asking for more triple recordings seems unreasonable. That said, the authors may want to consider showing all control and gabazine recordings corresponding to these experiments in a supplemental figure. Also, why are sublinear GABA-B responses observed when driven by three or more action potentials (Figure 5C)? It is not clear why the authors do not address this observation considering that it seems inconsistent with the study's overall message. Finally, the final readout – GIRK channel activation – in the MCell model appears to summate (mostly) linearly across the first four action potentials. Is this true and, if so, is the result inconsistent with Figure 5C?

Presumably, the motivation for Figure 3 is that it provides physiological context for when NGFCs might be coactive, thereby providing the context for when downstream, PC responses might summate. This is a nice, technically impressive addition to the study. However, it seems that a relevant quantification/evaluation is missing from the figure. That is, the authors nicely show that hind limb stimulation evokes responses in the majority of NGFCs. But how many of these neurons are co-active, and what are their spatial relationships? Figure 3D appears to begin to address this point, but it is not clear if this plot comes from a single animal, or multiple? Also, it seems that such a plot would be most relevant for the study if it only showed alpha-actin 2-positive cells. In short, can one conclude that nearby, presumptive NGFCs co-activate, and is this conclusion derived from multiple animals?

The inclusion of the diffusion-based model (MCell) is commendable and enhances the study. Also, the description of GABA-B receptor/GIRK channel activation is highly quantitative, a strength of the study. However, a general summary/synthesis of the observations would be helpful. Moreover, relating the simulation results back to the original motivation for generating the MCell model would be very helpful (i.e. the authors asked whether "linear summation was potentially a result of the locally constrained GABAB receptor - GIRK channel interaction when several presynaptic inputs converge"). Do the model results answer this question? It seems as if performing "experiments" on the model wherein local constraints are manipulated would begin to address this question. Why not use the model to provide some data – albeit theoretical – that begins to address their question?

In sum, the authors present an important study that synthesizes many experimental (in vitro and in vivo) and computational approaches. Moreover, the authors address the important question of how synaptic responses mediated by metabotropic receptors summate. Additional insights are gleaned from the function of neurogliaform cells. Altogether, the authors should be congratulated for a sophisticated and important study.

Reviewer #2 (Public Review):

In "Evolution of cytokine production capacity in ancient and modern European populations", Dominguez-Andrés et al. collect a large amount of trait association data from various studies on immune-mediated disorders and cytokine production, and use this data to create polygenic scores in ancient genomes. They then use the scores to attempt to test whether the Neolithic transition was characterized by strong changes in the adaptive response to pathogens. The impact of pathogens in human prehistory and the evolutionary response to them is an intriguing line of inquiry that is now beginning to be approachable with the rapidly increasing availability of ancient genomes.

While the study shows a commendable collection of association data, great expertise in immune biology and an interesting study question, the manuscript suffers from severe statistical issues, which makes me doubt the validity and robustness of their conclusions. I list my concerns below, in rough order of how important I believe they are to the claims of the paper:

— In addition to the magnitude of an effect away from the null, P-values are a function of the amount of data one has to fit a model or test a hypothesis. In this case, the authors have vastly more data after the Neolithic Revolution than before, and so have much higher power to reject the null hypothesis of "no relationship to time" after the revolution than before. One can see this in the plots the authors provided, which show vastly more data after the Neolithic, and consequently a greater ability to fit a significant linear model (in any direction) afterwards as well.

— The authors argue that Figure S2 makes their results robust to sample size differences, but showing a consistency in direction before and after downsampling in the post-neolithic samples is not enough, because:

1) you still lack power to detect changes in direction before the Neolithic.

2) even for the post-Neolithic, the relationship may be in the same direction but no longer significant after downsampling. How much the significance of the linear model fit is affected by the downsampling is not shown.

— The authors chose to test "relationship between PRS with time" before and after the Neolithic as a way to demonstrate that "the advent of the Neolithic was a turning point for immune-mediated traits in Europeans". A more appropriate way to test this would be creating a model that incorporates both sets of scores together, accounts for both sample size and genetic drift in the change of polygenic scores, and shows a significant shift occurs particularly in the Neolithic, rather in any other time period, instead of choosing the Neolithic as an "a priori" partition of the data. My guess is that one could have partitioned the data into pre- and post-Mesolithic and gotten similar results, largely due to imbalances in data availability.

— The authors only talk about partitions before and after the Neolithic, but plots are colored by multiple other periods. Why is the pre- and post-Neolithic the only transition that is mentioned?

— Extrapolating polygenic scores to the distant past is especially problematic given recent findings about the poor portability of scores across populations (Martin et al. 2017, 2019) and the sensitivity of tests of polygenic adaptation to the choice of GWAS reference used to derive effect size estimates (Berg et al. 2019, Sohail et al. 2019). In addition to being more heavily under-represented, paleolithic hunter-gatherers are the most differentiated populations in the time series relative to the GWAS reference data, and so presumably they are also the genomes for which PGS estimates built using such a reference would have higher error (see, e.g. Rosenberg et al. 2019). Some analyses showing how believable these scores are is warranted (perhaps by comparing to phenotypes in distant present-day populations with equivalent amounts of differentiation to the GWAS panel).

— In multiple parts of the paper, the authors mention "adaptation" as equivalent to the patterns they claim to have found, but alternative hypotheses like genetic drift are not tested (see e.g. Guo et al. 2018 for a review of methods that could be used for this).

— 250 kb window is too short a physical distance for ensuring associated loci that are included in the score are not in LD, and much shorter than standard approaches for building polygenic scores in a population genomic context (e.g. see Berg et al. 2019, Berisa et al. 2016). Is this a robust correction for LD?

— If one substitutes dosage with the average genotyped dosage for a variant from the entire dataset, then one is biasing towards the partitions of the dataset that are over-represented, in this case, post-Neolithic samples.

— It seems from Figure 2, that some scores are indeed very sensitive to the choice of P-value cutoff (e.g., Malaria, Tuberculosis) and to the amount of missing data (e.g. HIV). This should be highlighted in the main text.

— Some of the score distributions look a bit strange, like the Tuberculosis ones in Figure 2, which appear concentrated into particular values. Could this be because some of the scores are made with very few component SNPs?

Reviewer #2 (Public Review):

The authors examined the role of Rab25 during cell division within a developing epithelia. Strikingly, they found that the RabGTPase, Rab25, localized to mitotic structures such as centrosomes and cytokinetic midbodies in dividing cells of the developing zebrafish embryo. They went on to create maternal-zygotic Rab25a and Rab25b mutant embryos where they clearly demonstrate that apical cytokinetic bridges fail to undergo abscission leading to anisotropic cell morphologies that likely contribute to a delayed epiboly.

The major strengths of this study is the clear cell biology defects found in a developing embryo that lead to downstream developmental defects (delayed epiboly). The rab25 localization is beautiful. The examination of the viscoelastic properties is also compelling. The main improvements would be to expand upon the spatio-temporal localization of Rab25a and Rab25b during cell division at different stages of epiboly, present Rab11 localization patterns in the Rab25 mutant embryos, and clearly demonstrate that changes in viscoelasticity are also in their multinucleated cells that occur in Rab25 mutant conditions. These additions will help the authors support their conclusions that Rab25 localization/regulation of endomembranes (potentially recycling endosomes) regulates abscission and subsequently the viscoelastic properties of the developing tissue.

This study has identified novel roles for Rab25 in cytokinesis/abscission and opens the doors for examining it in regulating mitotic centrosome function. It is paradigm shifting in that it creates a new way to think about Rab25 and potentially its relationship with Rab11 and recycling endosomes during division in the early embryo.

Reviewer #2 (Public Review):

This manuscript set out to address several outstanding questions concerning the impact of 'eusocial' behaviour in mammals, here represented by the experimental model of the Damaraland mole-rat, on skeletal remodelling. Specifically, the transition to breeding status (queen) for some individuals in the colony is accompanied by changes that support high fecundity. The authors investigate the extent to which changes are localised in the skeleton and the underlying regulatory changes that are associated with these morphological features. The paper is well-written, the experiments have been planned thoughtfully and described carefully, and the panel figures convey information without over-crowding. Overall, I thoroughly enjoyed reading this manuscript, which represents as a multi-pronged approach to advancing understanding of the unusual biology and phenotype of queen mole rats.

Reviewer #2 (Public Review):

The authors investigated how alternative polyadenylation (APA) is modulated in yeast using appropriate transcriptomic methodologies.

The authors found that mutants for mRNA 3' end formation factors and cordycepin treatment alter alternative polyadenylation in the same manner, generating transcripts with longer 3'UTRs, due to a switch to distal polyadenylation sites (PAS). Most mutants analyzed cause a PAS switch, in particular mutants for RNA14, PCF11, YSH1, FIP1, NAB4 and PAP1. They also found that MPA and a rpb1 mutant, with a slower transcription elongation rate, reverts the cordycepin effect of distal PAS selection. This implies that in yeast, as in higher organisms, APA is modulated by RNAPII elongation. There is nucleosome depletion in the 3' end of convergent genes that undergo cordycepin-driven APA alterations, which is a new finding.

On the basis of their data, the authors propose a kinetic model for APA in yeast that is regulated by the concentration of core mRNA 3' end factors and nucleotide levels, which in turn modulates RNAPII elongation. This integrative model has been already described in higher organisms, but not in yeast, and overall this study covers an impressive body of work that makes an important contribution to the field.

1) The authors show that cordycepin have the same effect in APA as most of the 3' end factors mutants used, but there is a lack of integration between the two sets of PAS-seq data. The cordycepin APA effect may be due to decreased expression of mRNA 3' end factors but this hypothesis was not fully explored. Treating those mRNA 3' end mutants with cordycepin could shed some light on this.

2) A new role for SEN1 in APA for a subset of protein coding was observed. The SEN1 mechanism could be clarified if the authors show that SEN1 is within the subset of convergent genes analyzed, and also if SEN1 expression changes upon cordycepin treatment.

Reviewer #2 (Public Review):

This study induced tactile percepts through microstimulation via two multi-electrode arrays implanted over a quadriplegic's primary somatosensory hand region. The report focuses on manipulation of the stimulation frequency of microstimulation, though further manipulations were tested and are briefly reported.

For different stimulation sites, the perceived intensity was highest at different stimulation frequencies. This result contradicted the expectation that higher stimulation frequency would be related to higher perceived intensity. This expectation derived from previous work in non-human primates that showed lower detection thresholds for higher-frequency stimulation. The authors show that the same result is obtained in their human patient, suggesting that differences exist between near- and supra-threshold perceived stimuli and that, accordingly, generalizing from non-human primate work has its traps.

The authors grouped stimulation sites according to optimal stimulation frequency into low, intermediate, and high frequency preferring sites. These three classes were spatially clustered, and related to different patterns of reported perceptual qualities (such as vibration, pressure etc).

The paper's results are important for practical developments of sensory feedback in brain-machine interfaces. Understanding the perceptual result of brain stimulation requires reports by human participants, as underlined by the differences uncovered here between near- and supra-threshold stimulation. They furthermore reveal new aspects of the cortical organisation of primary somatosensory cortex.

The conclusion of clustered patches sensitive to specific frequencies is tentative. As an inherent limitation of intracranial recordings, the total number of stimulation sites is small, and some electrodes did not produce significant results, further reducing the number of analysable sites. Therefore, it is possible that stimulation doesn't truly fall into three distinct clusters (even if such clustering is statistically supported with the current data set), but are actually continuous or divide into a larger number of classes. Notably, this critique does not invalidate the main finding that different patches of cortex show specific frequency preferences.

Reviewer #2 (Public Review):

Hallast et al. have performed an extensive genetic analysis of a cohort of men with idiopathic infertility, for whom many have accompanying phenotypic data in the form of andrological parameters. The complex genetic architecture of repeating sequences on the Y chromosome gives rise to recurrent AZFc deletions that affect male infertility. However, while partial deletions of AZFc are reasonably frequent, they have less clear phenotypic effects. gr/gr and b2/b3 deletions seem to be a risk factor for spermatogenic impairment in some populations but are fixed in others. Hallast et al. focus on these partial AZFc deletions in a reference cohort and a cohort with idiopathic infertility from the same geographic population, characterising further structural and sequence variation, Y-chromosomal haplogroups, and gene dosage.

While the gr/gr deletion is present in the reference group of individuals with normal andrological parameters, Hallast et al. show that this deletion is enriched among patients, with 2.2-fold increased susceptibility to infertility. As observed in other European populations, the prevalence of b2/b3 deletion was similar in the reference group and the patient group, suggesting that it is not a spermatogenic impairment risk factor for this Estonian population either.

A quarter of Estonian gr/gr deletion carriers belonged to the Y chromosomal haplogroup R1a1-M458. Within this Y haplogroup, an inversion has occurred that promotes subsequent deletion, likely causing severe spermatogenic failure in the majority of carriers as this complex rearrangement is enriched 8.6-fold in individuals with severe spermatogenic impairment.

Some major strengths of this paper are the size of the groups recruited (1,190 patients and 1,134 reference individuals) from a single national population, the extensive accompanying andrological data, and the genomic characterisation of many individuals to elucidate the relationship between specific structural variants and effects on fertility.

The discovery of the fixed inversion infertility risk factor on a specific Y haplogroup is a useful contribution that could aid genetic counselling efforts through carrier identification and risk mitigation.

However, I am seeking clarity on multiple testing correction for microdeletion association with specific andrological parameters. Besides this, the main conclusions of this paper are supported by other data presented.

Reviewer #2 (Public Review):

Aly et al. investigated anticipatory signals in the cortex by analysing data in which participants repeatedly watched the same movie clip. The authors identified events using an HMM-based data-driven event segmentation method and examined how the timing of events shifted between the initial and repeated presentation of the same video clip. A number of brain regions were identified in which event timings were shifter earlier in time due to repeated viewing. The main findings is that more anterior brain regions showed more anticipation than posterior brain regions. The reported findings are very interesting, the approach the authors used is innovative and the main conclusions are supported by the results and analyses. However, many cortical regions did not show any anticipatory effects and it is not clear why that is. In part, this may be due to a number of suboptimal aspects in the analysis approach. In addition, the analyses of behavioural annotations are open to multiple interpretations.

Methods and Results:

1) The paper shows that across multiple regions in the cortex, there is significant evidence for anticipation of events with repeated viewing. However, there are also many areas that do not show evidence for anticipation. It is not clear whether this is due to a lack of anticipation in those areas, or due to noise in the data or low power in the analyses. There are two factors that may be causing this issue. First, the data that were used are not optimal, given the short movie clip and relatively low number of participants. Second, there are a number of important issues with the analyses that may have introduced noise in the observed neural event boundaries (see points 2-4 below).

2) Across all searchlights, the number of estimated events was fixed to be the same as the number of annotated events. However, in previous work, Baldassano and colleagues (2017) showed that there are marked differences between regions in the timescales of event segmentation across the cortex. Therefore, it may be that in regions such as visual cortex, that tends to have very short events, the current approach identifies a mixture of neural activity patterns as one 'event'. This will add a lot of noise to the analysis and decrease the ability of the method to identify anticipatory event timings, particularly for regions lower in the cortical hierarchy that show many more events than tend to be observed in behavioural annotations.

3) If I understand correctly, the HMM event segmentation model was applied to data from voxels within a searchlight that were averaged across participants. Regular normalization methods typically do not lead to good alignment at the level of single-voxels (Feilong et al., 2018, Neuroimage). Therefore, averaging the data without first hyperaligning them may lead to noise due to functional alignment issues within searchlights.

4) In the analyses the five repeated viewings of the clips were averaged into a single dataset. However, it is likely that participants' ability to predict the upcoming information still increased after the first viewing. That is especially true for perceptual details that may not have been memorised after watching the clip once, but will be memorised after watching it five times. It is not clear why the authors choose to average viewings 2-6 rather than analyse only viewing 6, or perhaps even more interesting, look at how predictive signals varied with the number of viewings. I would expect that especially for early sensory regions, predictive signals increase with repeated viewing.

5) In the analyses of the alignment between the behavioural and neural event boundaries, the authors show the difference in correlation between the initial and repeated viewing without taking the estimated amount of anticipation into account. I wonder why the authors decided on this approach, rather than estimating the delay between the neural and behavioural event boundaries. The finding that is currently reported, i.e. a lower correlation between neural and annotated events in the repeated viewing condition, does not necessarily indicate anticipation. It could also suggest that with repeated viewing, participants' neural events are less reflective of the annotated events. Indeed the results in figure 5 suggest that the correlations are earlier but also lower for the repeated viewing condition.

6) To do the comparison between neural and annotated event boundaries, the authors refit the HMM model to clusters of significant voxels in the main analysis. I wonder why this was done rather than using the original searchlights. By grouping larger clusters of voxels, which cover many searchlights with potentially distinct boundary locations, the authors may be introducing noise into the analyses.

Discussion:

7) To motivate their use of the HMM model, the authors state that: "This model assumes that the neural response to a structured narrative stimulus consists of a sequence of distinct, stable activity patterns that correspond to event structure in the narrative." If neural events are indeed reflective of the narrative event structure, what does it mean if these neural events shift in time? How does this affect the interpretation the association between neural events and narrative events?

Reviewer #2 (Public Review):

This manuscript provides convincing evidence that the ICEBs1 conjugative element confers a fitness advantage on the model bacterium B. subtilis during biofilm formation and sporulation. This effect is frequency dependent and is effected in large measure via an element gene, named devI, by an unknown mechanism that probably decreases the concentration of Spo0A-P. The data are well presented and successfully make the case for a fitness advantage conferred by the mobile element during biofilm formation and sporulation. It is likely that a mechanistic exploration of DevI will follow and will provide another facet to the regulation of Spo0A, a gift that keeps on giving.

Delaying sporulation in a mixed culture confers an advantage for the delayers. This has been convincingly shown. But I wonder about the effects in a clonal population of cells carrying ICEBs1 in competition with a null population. I appreciate that the delay in sporulation is transient, as pointed out in lines 404-407. But a delay of a few hours may be critical in this type of competition between populations as resources become limiting. This is presumably why sporulation is so exquisitely regulated on so many levels and in response to many external an internal signals. If so, ICEBs1 would have a deleterious effect and the element might be in danger of extinction. I suppose that an analogous discussion could be considered for biofilm formation.

Reviewer #2 (Public Review):

This study builds on a previously published paper from this group showing that KLF10 is under circadian control, and it in turn affects the oscillation of a set of metabolic genes in the liver. While the previous study utilized a systemic Klf10 KO mouse model, here, Ruberto et al. generated a conditional hepatocyte-specific Klf10 KO mouse model (Klf10Δhep).

The authors find that the absence of hepatocyte KLF10 alters the circadian oscillation of a number of metabolic genes. In response to sugar consumption, Klf10Dhep mice demonstrate exacerbated adverse effects as well as significantly increased hepatic expression of many glycolysis, gluconeogenesis, and lipogenesis related genes. They conclude that Klf10 normally acts as a "transcriptional brake" to protect animals against the effects of high sugar consumption and show via ChIP-seq that KLF10 is present at a wide range of metabolic genes, particularly at those involved in acetyl-CoA metabolism. The findings are interesting, particularly in the context of the burgeoning burden of metabolic disease and its relation to high sugar consumption, and are supported by the experimental findings.

Reviewer #2 (Public Review):

This manuscript by Nicholas Strash et al. compares the effects of several potential mitogens on cell cycle of the two most used in vitro models of cardiomyocytes (CMs): neonatal rat ventricular myocytes (NRVMs) and human induced pluripotent stem cell (hiPSC)-derived CMs. In addition, they use a 3D model of NRVMs as a model that represents more mature, non-proliferating CMs. The work is interesting for researchers working in the field of cardiac regeneration and provides the first direct comparison of several potential mitogens. The inclusion of several in vitro models to account for potential species differences strengthens the data. The results support previously published findings and the main conclusions are supported by the data presented.

The authors used a 3D model, cardiobundles made from NRVMs, as a more mature CM model. However, these cardiobundles still had a considerable number of CMs in active cell cycle in basal conditions. Whether this reflects true proliferation or the postnatal multinucleation process of rat cardiomyocytes, is unclear. Furthermore, post-mitotic human CMs were not studied. These can be obtained from hiPSC-CMs by prolonged culture or using metabolic stimuli as shown by Mills et al. 2017 (PNAS).

The authors demonstrate that the known mitogenic pathway for CMs, Erbb2-mediated signalling, promotes cell cycle activation in 2D cultures or NRVMS and hiPSC-CMs as well as in 3D cardiobundles. Although cell cycle activity was clearly induced, no actual proof of cytokinesis has been presented. For the cardiobundle work, it remains unclear if the increase in cross-sectional size of cardiobundles induced by Erbb2 signalling is due to increased number of CMs or increased size of CMs. Both the physiological ligand of Erbb3, Neuregulin-1, and the downstream ERK pathway are known to induce CM hypertrophy (see for example Zurek et al. 2020 Circulation; Bueno and Molkentin 2002 Circ Res).

The data analysis and statistics raise some concerns, which require clarification. First, the N numbers are really big and according to the Table 1 it is unclear if they all indeed represent independent samples. For example, one field in a monolayer (Table 1, definition of n in Figures 1J, 1P, 4C, 4E, 4G) should not be considered to represent n=1, if several images were analysed from the same sample and/or if several technical replicates (samples prepared from the same cell isolation or differentiation and treated similarly) were analysed. Only samples from separate differentiations or cell isolations should be considered as representatives of n and the results from technical replicates should be averaged to form the n=1 data. Second, the selection of statistical tests is a concern. It is unclear if the data were analysed for equal variances before selecting the test (parametric vs. non-parametric). It is also unclear why the authors carried out multiple t tests instead of using ANOVA or its variations, which are generally considered more suitable for multiple comparisons.

Reviewer #2 (Public Review):

Rosello et al. present very compelling evidence that Cytosine base editors can be used to introduce G:C to A:T base conversions with high efficiency in zebrafish. Furthermore, they describe engineering and validation of a base editor targeting the NAA PAM sequence. Finally, they have developed a potential novel model of the Noonan syndrome. The manuscript represents an important and much needed advance in precision genome editing in the zebrafish model system.

Reviewer #2 (Public Review):

The manuscript "Archaeal chromatin 'slinkies' are inherently dynamic complexes with deflected DNA wrapping pathways" by Bowerman and colleagues describes a study of archaeasome dynamics combining molecular simulations, cryo-EM, and sedimentation velocity analytical ultracentrifugation. How chromatin evolved is a fundamental question in biology, marking a striking departure from the bacterial nucleoid. Indeed, ever since the first description of archaeal nucleosomes and histones HmfA/B (Sandman and Reeves mid-80s) from thermophilic archaea, this question has fascinated and puzzled the field.

Recent work from the Luger lab figured out the organization of these archaeal chromatin fibers as a continuous loop structure. Here, the authors extend this question further. MD analyses show that Arc90 has two preferred states (closed and flexible ends), but the same 5T5K structure on 120 or 180 bp of DNA prefer a single state (closed). Sedimentation velocity analytical ultracentrifugation showed that Arc207 sediments slower than the H3 mononucleosome, implying that that Arc207 has a shape with higher anisotropy, resulting in excessive drag compared to a mononucleosome. Subsequently, high-resolution cryoEM showed that at least two distinct classes for Arc207 exist, where one class represents a 5-mer and another class represent a 7-mer. The latter has a unique shape in that the 7-mer forms an L-shape (or open clam) with a 3-mer hinging on a 4-mer.

Overall, these data provide exciting structural insights into how archaeal chromatin is folded up at its basic unit level, which the authors describe as most fittingly as a "slinkie". Because so little is known about how nucleosomes evolved during the transition from archaea to eukaryotes, we found this interdisciplinary report well written and with compelling data, that will be of interest to the chromosome biology field at large. We suggest a minor revision in which a few technical points are addressed.

Considerations:

1) The cryoEM data showed two main groups of particles: 5-mer protecting 150 bp and a 7-mer protecting either 90bp or 120bp. A few times in the manuscript (both in the results and discussion section) the authors mention a 30-bp MNase digestion ladder is observed. The Mnase data should be included, as this provides evidence that the structures observed by cryoEM indeed represent physiological structures, especially if strong discrete bands are observed at 90, 120, and 150 bp.

2) The two main classes found by cryoEM give the impression that adding dimers results in altered structures. The 7-mer shows an angled structure, which is interpreted as an open structure. The 5-mer shows a more uniform structure, which is interpreted as a closed structure. The former structure protects the full length of DNA on which HTkA histones were reconstituted, whereas the latter might be an incomplete reconstitution or a partially disassembled structure. It also raises the question if the length of the DNA is a limiting factor. What if HTkA was reconstituted on 170 bp or 307 bp instead? Would this in turn only permit the formation of the 5-mer on the 170 bp construct and two 5-mers on the 307 bp construct? The authors should consider addressing this point because the reconstitution might be constrained by the length of the DNA construct used. Indeed, a related topic might be AT content- what does archaeal DNA look like from the perspective of DNA sequence for chromatin (Jon Widom's group had a ChIPSeq paper on this a few years ago, just after his untimely passing).

3) In the discussion the authors cite that in one archaeal species the Mg2+ concentration is ~120 mM, more than a magnitude greater than that tested in Figure 5. What happens to reconstituted archaeasomes at higher Mg+? This is relevant because in vivo, archaea are thought to have 10x the concentration of Mg+ (amongst other ions) relative to us humble eukaryotes who would probably die of kidney failure at those ionic concentrations. Indeed at high ionic conditions, eukaryotic chromatin can be made to precipitate out of solution (for e.g. 10mM Mg+, 3M NaCl). An AUC assay with higher Mg2+ concentrations seems a doable and physiologically relevant addition to the ms that would strengthen it. It is relevant to consider that in vivo structure in these halophilic and thermophilic organisms might be dependent on the concentration of various salts and temperature, it would be nice to read the authors' thoughts on this issue.

Reviewer #2 (Public Review):

Bifulco et al. performed a large-scale in silico study to test whether the spatial fibrosis distribution measured via LGE-MRI in 45 patient with embolic stroke of undetermined source (ESUS) as compared to the distribution in 45 atrial fibrillation (AFib) patients without stroke leads to differences in reentrant arrhythmia inducibility of dynamics.

1) This study comprises a high number of simulations and is one of the computational electrophysiology studies that covers the most anatomical and structural variability on the atrial level. In their comprehensive analysis, Bifulco et al. answered their question and found no pronounced differences in arrhythmia inducibility and dynamics between ESUS and AFib models. It would be interesting to learn how the spatial fibrosis distributions compare in terms of the previously suggested features density and entropy (Zahid et al.). This might also influence the statements in L170/L207.

2) The authors chose to exclude patients with stroke from the AFib group, the reasons for this choice are not entirely clear. The same holds for the fact that the ESUS models included AFib-induced electrophysiological remodeling even though these patients have not been diagnosed with AFib (by definition).

3) An acknowledged limitation of the study is the assumption of fixed conduction velocity and action potential duration/effective refractory period. Bifulco et al. base this assumption on previous studies by the group (e.g. L312), which, however, concluded that reentrant driver locations and inducibility are sensitive to changes of action potential and conduction velocity (Deng et al.). For conduction velocity, wider ranges have been reported since the publication of the supporting reference (35) in 1994, e.g. Verma et al.; Roney et al.

4) The number of pacing sites is rather low for a comprehensive in silico arrhythmia inducibility test but likely a good balance of coverage and computational feasibility considering that the primary goal of this research was to check whether the two groups of models show differences when undergoing the same (but not necessarily exhaustive) protocol.

5) The discussion does a good job in putting the results into context. Two interesting observations that deserve more attention are that i) the Inducibility Score was always higher for AFib vs. ESUS (Figure 6A, no statistical test performed). However, this did not translate to a difference in silico arrhythmia burden (inducibility). ii) Reentrant drivers were about twice as likely to localize to the left pulmonary veins than the right pulmonary veins in the AFib models (Figure 6D).

6) The study succeeded in answering the question it posed in the sense that no marked difference was found between the ESUS and AFib models. This leads to the question what the stroke-inducing mechanism is in the ESUS patients. A hypothesis for future work could be that the fibrotic infiltrations in the ESUS patients reduce the hemodynamic efficacy of the left atrium and render clot formation (e.g. in the atrial appendage) more likely in this way.

7) The negative finding in this study (no difference between groups) does not naturally allow us to draw clinical implications for diagnosis or stratification. Additional ways to put the hypothesis proposed by the authors (fewer arrhythmogenic triggers in the ESUS patients) to test could be to consider readouts/surrogate measures of the autonomic nervous system.

Reviewer #2 (Public Review):

The authors used several approaches to define a discrete population of Langerhans cell-like (LC-like) dendritic cells (DC) in the dermis of mice. By flow cytometry, these cells expressed langerin/CD207 and EpCAM/CD236 and were found in the CD103-CD11b- fraction of dermal cells. It was also shown that LC-like cells, rather than LC, are the main contributors to CD103- langerin+ cells in the lymph node. By single cell RNAseq of dermal cells they clustered with Langerhans cells but lacked expression of DC-SIGN/CD209. Fate-mapping with Kitmercremer/Rosa26loxPSTOPloxPeYFP showed a similar origin to other dermal DC, with no yolk sac signal as observed in LC. Bone marrow chimeras, however, indicated a much slower turnover compared with other dermal DC. Independence from LC and other DCSIGN+ DC was also demonstrated by unchanged kinetics during continuous ablation of DCSIGN+ fractions in a DTR model.

The results explain the expression of langerin on two fractions of dermal DC observed several years ago (CD103+ and CD103-). The demonstration that epidermal LC do not contribute to LN populations in the steady state is completely unexpected and raises an important question of the in vivo function of the LC-like subset. LC-like cells appear to be related to LC but a more in-depth analysis of their gene expression differences compared with LC would be interesting. Also, their potential relationships with other DC (cDC1 or cDC2), was not defined. Langerin expression by human cDC2 is well described and possibly correlated with these observations in mice. Finally, the turnover of LC-like cells was slow, yet they contributed as many langerin+ cells to the resting lymph node as cDC1, which turnover quickly. Proliferation in situ might explain this observation but there were no data on this.

Reviewer #2:

In this study, entitled “APEX-Gold: A genetically-encoded particulate marker for robust 3D electron microscopy” Rae et al. describe a method to improve the visualization of the staining for genetically encoded probes that they described in previous studies (namely APEX2 constructs).

These techniques are very powerful as they increase the sensitivity for detecting low level of expression of the tagged proteins (e.g. compared to GFP tagged proteins). The novelty in this study is that the reaction product (DAB precipitates) is revealed by the nucleation of silver/gold precipitates. Such enhancement has been used extensively in the past for pre-embedding immuno-peroxidase techniques, but has never been combined with the use of APEX2. This has one major advantage: that the contrast of the positive staining can stand out from the contrast of the surrounding ultrastructure, making the sample preparation more adapted to 3D EM techniques, especially volume SEM where contrast is a bottleneck. Moreover, the sensitivity of the technique is shown to be compatible with the detection of endogenous levels of expression.

The technique is very well detailed and elegantly illustrated by convincing applications on cultured cell systems. The apparent simplicity of use, together with a growing interest in the community for the APEX2 based techniques (also in correlative imaging), significantly raises the potential for it to become a standard in the field, and should thus be shared with the community.

Reviewer #2:

Miningou and Blackwell in their manuscript titled "Temporal pattern and synergy influence activity of ERK signaling pathways during L-LTP induction" explored the contributions of upstream pathways to ERK activation during LTP. The authors expanded on their previously published LTP model to assess the influence on ERK activation of each of the upstream pathways originating from cAMP or Ca+2 activated with differing temporal patterns. This manuscript's aim is quite germane, since 1) ERK plays such a central role in learning and memory and its cellular proxy LTP; 2) the Ca+2/cAMP/PKA system is highly complex and nonlinear, with multiple feedback loops. The resulting manuscript has the potential to be impactful. The approach of using a stochastic reaction-diffusion model is state of the art and appropriate for the modeling of these subcellular events in spines. And the modeling insights are very intriguing as the authors predict that ERK activation by cAMP/PKA or Ca+2 pathways differ in their linearity, these pathways can synergize during LTP and this may involve a novel feedforward loop containing synGAP. The authors do a marvelous job placing their findings within the huge body of LTP literature.

There are, however, a couple of points that I feel should be addressed:

1) There needs to be additional technical detail on how the original models were expanded. The model presented here was developed by merging Jȩdrzejewska-Szmek et al., 2017 and Jain and Bhalla, 2014 models. These models were developed based on experimental data and validated with independent experimental datasets in a rigorous manner. It is not clear how the combining these two models, and the additional molecules and reactions added have affected the dynamics of ERK activation, and how comparable they are to the original experimental data used for model development in the previous modeling efforts. It is not clear if the model was reparameterized.

2) Beyond the ERK activation traces, it would be useful for clarity sake to also include the simulated traces for the activation of the upstream molecules (PKA, RAS, RAP, etc). Given how additional changes have been made additional information should be provided to ensure that the contribution of each pathway is accurately represented.

Test out GPT-2

Reviewer #2:

In this work, Hofmann and colleagues conduct a study investigating the relationship between EEG alpha and subjective arousal in naturalistic (as opposed to controlled experimental) settings. Participants completed an immersive virtual reality experience while EEG was recorded, and continuously rated their subjective arousal while a video of the experience was replayed. Three different decoding methods were evaluated (Source Power Comodulation, Common Spatial Patterns, and Long Short-Term Memory Recurrent Neural Networks), each of which demonstrated above chance levels of performance, substantiating a link between lower levels of parietal/occipital alpha and subjective arousal. This work is notable because the roller-coaster simulation is a well-controlled, yet dynamic manipulation of arousal, and in its comparison of multiple decoding approaches (that can model the dynamics of affective responses). Indeed, this is an interesting proof of concept that shows it is possible to decode affective experience from brain activity measured during immersive virtual reality.

Major concerns:

The authors advocate that naturalistic experiments are needed to study emotional arousal, because "static" manipulations are not well-suited to capture the continuity and dynamics of arousal. This point is well-taken, but no comparisons were made between static and dynamic methods. Thus, although the work succeeds in showing it is possible to use machine learning to decode the subjective experience of arousal during virtual reality, it is not clear what new insights naturalistic manipulations and the machine learning approaches employed have to offer.

The methods used to assess model performance are also a concern. Decoding models were evaluated separately for each subject using 10-fold cross-validation, and inference on performance was made using group-level statistics. Because time-series data are being decoded, if standard cross-validation was performed the results could be overly optimistic. Additionally, hyperparameters were selected to maximize model performance which can also lead to biased estimates. This is particularly problematic because overall decoding performance is not very high.

Reviewer #2:

This paper proposes a novel and relevant evolutionary model that explains many aspects of replication origin statistics in a family of yeast species. It is a step forward in our understanding of the evolutionary pressures that affect the distribution of replication origins in Eukaryotes. I recommend the authors address the following issue:

1) Many of the conclusions of the paper are based on the claim that the extending the model by adding an efficiency bias to the origin death rate makes the model fit the data better; in particular, they say in line 213 that "the observed huge divergence in efficiency between lost origins and their neighbors is absent in the model simulations." This is reinforced in line 243, and in other parts of the text. But inspecting Fig 3, the two models (with and without a death rate bias) yield almost identical box-plots; if anything, the box-plots for the lost/nearest fractions of the pure double-stall aversion model seem visually to match the data marginally better. So why do the authors claim that the model with death rate bias is a much better fit? This is far from clear by just inspecting the data. I see no "huge difference" in the plots. There is a difference, but it is far from huge - the differences in the mean are much smaller than the size of the boxes. It seems to me unjustified to use this to choose one model over another. One way to ascertain this is to do rigorous statistical tests to determine if the differences in the means of the simulated and observed data are statistically significant; for example, a t-test.

Reviewer #2:

The conclusion was quite surprising from their anatomical differences and connectivity to the cortex, however, implies different mechanisms underlie for species specific circuit organization.

The manuscript is well-organized and well-written with strong figures. I have only a few comments/suggestions to further improve the overall quality of this manuscript.

I understand obtaining human and NHP tissue is difficult and hard to perform numbers of ISH. Therefore, there is a database that provides additional information on gene expression in NHP LGN (https://gene-atlas.brainminds.riken.jp/). From this database, it is possible to obtain parvocellular specific and magnocellular specific gene expression by fine structure search, which may be worth comparing with the results in the current paper. Many researchers have realized that marmoset is one of the good animal models to understand human brain function and dysfunction, therefore, it is worth including marmoset for comparative analysis for community interest.

Reviewer #2:

The paper investigates the temporal signatures of single-neuron activity (the autocorrelation timescale and latency) in two frontal areas, MCC and LPFC. These signatures differ between the two areas and cell classes, and form an anatomical gradient in MCC. Moreover, the intrinsic timescales of single neurons correspond with their coding of behaviorally relevant information on different timescales. The authors develop a detailed biophysical network model which suggests that after-hyperpolarization potassium and inhibitory GABA-B conductances may underpin the potential biophysical mechanism that explains diverse temporal signatures observed in the data. The results appear exciting, as the proposed relationship between the intrinsic timescales, coding of behavioral timescales, and anatomical properties (e.g., the amount of local inhibition) in the two frontal areas is novel. The use of the biophysically detailed model is creative and interesting. However, there are serious methodological concerns undermining the key conclusions of this study, which need to be addressed before the results can be credited.

Major Concerns:

1) One of the key findings is the correspondence between the intrinsic timescales of single neurons and their coding of information on different behavioral timescales (Fig. 4). However, the method for estimating the intrinsic timescales has serious problems which can undermine the finding.

1.1 The authors developed a new method for estimating autocorrelograms from spike data but the details of this method are not specified. It is stated that the method computes the distribution of inter-spike-intervals (ISIs) up to order 100, which was "normalized", but how it was normalized is not described. The correct normalization is crucial, as it converts the counts of spike coincidences (ISI distribution) into autocorrelogram (where the coincidence counts expected by chance are subtracted) and can produce artifacts if not performed correctly.

1.2 The new method, described as superior to the previous method by Murray et al, 2014, appears to have access to more spikes than the Murray's method (Fig. 2). Where is this additional data coming from? While Murray's method was applied to the pre-cue period, the time epoch used for the analysis with the new method is not stated clearly. It seems that the new method was applied to the data through the entire trial duration and across all trials, hence more spikes were available. If so, then changes in firing rates related to behavioral events contribute to the autocorrelation, if not appropriately removed. For example, the Murray's method subtracts trail-averaged activity (PSTH) from spike-counts, similar to shuffle-correction methods. If a similar correction was not part of the new method, then changes in firing rates due to coding of task variables will appear in the autocorrelogram and estimated timescales. This is a serious confound for interpretation of the results in Fig. 4. For example, if the firing rate of a neuron varies slowly coding for the gauge size across trials, this will appear as a slow timescale if the autocorrelogram was not corrected to remove these rate changes. In this case, the timescale and GLM are just different metrics for the same rate changes, and the correspondence between them is expected. Before results in Fig. 4 can be interpreted, details of the method need to be provided to make sure that the method measures intrinsic timescales, and not timescales of rate changes triggered by the task events. This is an important concern also because recent work showed that there is no correlation between task dependent and intrinsic timescales of single neurons, including in cingulate cortex and PFC (Spitmaan et al., PNAS, 2020).

2) The balanced network model with a variety of biophysical currents is interesting and it is impressive that the model reproduces the autocorrelation signatures in the data. However, we need to better understand the network mechanism by which the model operates.

2.1 The classical balanced network (without biophysical currents such as after-hyperpolarization potassium) generates asynchronous activity without temporal correlations (Renart et al., Science, 2010). The balanced networks with slow adaptation currents can generate persistent Up and Down states that produce correlations on slow timescales (Jercog et al., eLife, 2017). Since slow after-polarization potassium current was identified as a key ingredient, is the mechanism in the model similar to the one generating Up and Down states, or is it different? Although the biophysical ingredients necessary to match the data were identified, the network mechanism has not been studied. Describing this network mechanism and presenting the model in the context of existing literature is necessary, otherwise the results are difficult to interpret for the reader.

2.2 Does the model operate in a physiologically relevant regime where the firing rates, Fano factor etc. are similar to the data? It is hard to judge from Fig. 5b and needs to be quantified.

2.3 The latency of autocorrelation is an interesting feature in the data. Since the model replicates this feature (which is not intuitive), it is important to know what mechanism in the model generates autocorrelation latency.

3) HMM analysis is used to demonstrate metastability in the model and data, but there are some technical concerns that can undermine these conclusions.

3.1 HMM with 4 states was fitted to the data and model. The ability to fit a four-state HMM to the data does not prove the existence of metastable states. HMM assumes a constant firing rate in each "state", and any deviation from this assumption is modeled as state transitions. For example, if some neurons gradually increase/decrease their firing rates over time, then HMM would generate a sequence of states with progressively higher/lower firing rates to capture this ramping activity. In addition, metastability implies exponential distributions of state durations, which was not verified. No model selection was performed to determine the necessary number of states. Therefore, the claims of metastable dynamics are not supported by the presented analysis.

3.2 HMM was fit to a continuous segment of data lasting 600s, and the data was pooled across different recording sessions. However, different sessions have potentially different trial sequences due to the flexibility of the task. How were different trial types matched across the sessions? If trial-types were not matched/aligned in time, then the states inferred by the HMM may trivially reflect a concatenation of different trial types in different sessions. For example, the same time point can correspond to the gauge onset in one session and to the work trial in another session, and vice versa at a different time. If some neurons respond to the gauge and others to the work, then the HMM would need different states to capture firing patterns arising solely from concatenating the neural responses in this way. This confound needs to be addressed before the results can be interpreted.

Reviewer #2:

A state of the art imaging of the dynamics of astroglial glutamate transporters that certainly add novel perspective into this, quite important and hot field. Experiments and clean and convincing, the data obtained fully support conclusions.

Comments:

1) The authors mention the importance of efficient glutamate uptake in the development of neuropathological conditions, but do not discuss this in regards to their results. Such a discussion would seem relevant.

2) Authors conclude that the membrane turnover pathway should be a particularly important GLT-1 resupply mechanism near excitatory synapses as some earlier studies have found the lowest lateral membrane mobility of GLT-1 there. In this context, it would be of interest to have some quantitative tips as to the relationship between the level of excitatory activity and the occupancy of local GLT-1.

3) There is a recent work implicating the C-terminus in the surface assembly of GLT-1 (Peacy et al Mol Pharm 2020), which seems relevant to the present findings. Please discuss further.

4) Functional activity of glutamate transporters is linked to (and is being regulated by) astroglial Na signalling; any suggestions how proposed turnover cycle may affect cytosolic Na+ dynamics

5) The Fig. 5A imaging data seem to nicely provide both surface and cytosol labelling of the same cell. Perhaps the authors could thus assess the distribution of surface-to-volume ratios across live astroglia: to my knowledge, such data has not yet been available.

6) Figure 1B: Please provide further detail regarding fast-exchange solution application, its physical arrangement, etc.

7) Figure 2, whole-cell photobleaching: Please expand on what is 'tornado' mode scanning and how it has been applied.

8) Figure 3, dSTORM data: Please provide further details regarding the numbers of sampled ROIs and/or individual molecules / distances analysed.

Reviewer #2:

This manuscript addresses the question of whether duplication of tumor suppressors occurred coincidently with the enlarged body size and reduced cancer risk evolved independently in Afrotherians. Using the human genome as reference, the authors systematically searched for gene duplications in 13 publicly available Afrotherian genomes, including 9 extant and 4 extinct species. The authors also reconstructed the ancestral body sizes, cancer risks and gene duplication events across the Afrotherian phylogeny. These data showed that both increased body sizes and reduced cancer risks are gradually evolved. Reactome pathway enrichment analysis for gene duplicates showed unexpectedly that gene duplicates in both lineages with or without major increases in body size/lifespan/decreases in cancer risk are enriched in many cancer related pathways. However, the authors found that 157 genes duplicated in Proboscidean stem-lineage, in which extremely large species evolved, were uniquely enriched in 12 cancer pathways. These genes might facilitate further body enlargement and cancer resistance evolution in Proboscidean. Most interestingly, the authors found that several genes both upstream and downstream of a famous tumor suppressor TP53 have also been duplicated, either before or after initial TP53 duplication. These genes are involved in transcriptional regulation of TP53 and may have facilitated re-functionalization of TP53 retroduplicates. Overall, this is an important and interesting study that can help us understand the evolution of body size, lifespan and cancer risk in mammals more deeply.

Major comments:

1) In general, the evolutionary fate of gene duplication includes: 1) Conservation of gene function; 2) Neofunctionalization; 3) Pseudogenization; 4) Subfunctionalization (doi:10.1016/S01695347(03)00033-8). To execute the function of tumor suppression, as this study focused on, gene duplicates were supposed to be functionally conserved or subfunctionalized. Gene duplicates that have been neofunctionalized or pseudogenized will not be helpful (also mentioned by authors in the Caveats section). Therefore, it might be more convincing to investigate the functional status of each gene duplicate, especially those in Fig 4C/D. In many cases, however, a related function, rather than an entirely new function, evolves by neofunctionalization after gene duplication, and also that to check new functions for a batch of genes is not realistic, the authors could simply check the coding sequences to ensure these genes duplicates are not pseudogenes and are functional. This is necessary because in Fig 4D, many genes have only 2 copies expressed. If one of them is a young pseudogene, it could be stochastically expressed and will encode a dysfunctional protein.

2) In Results section 3, the cancer pathway frequency data of many nodes seems not consistent with data shown in Table 2. For example, Line 293-296: "55.8% (29/52) of the pathways that were enriched in the Tethytherian stem-lineage..., 27.8% (20/72) of the pathways that were enriched in the Proboscidean stem-lineage...were related to tumor suppression", the cancer pathway percentages shown in Table 2 for these 2 nodes are 63.4% and 38.81%, respectively. While the frequency data in Table 2 are consistent with Supplementary Data File S3: "Atlantogenata_Reactome_ORA.xlsx". It is possible that the frequency data shown in the main text are specific to pathways of tumor suppression, rather than cancer related pathways. If this is the case, more detailed data should be shown somewhere else.

3) The titles of Results section 3 and section 4 are highly similar and actually the data in section 4 seems to be used to further solidify the conclusion of section 3. Therefore, is it possible to merge them into one single section?

Reviewer #2:

When animals are given a choice between drug and nondrug reinforcers, they will most often choose the nondrug alternative even when presented with highly reinforcing drugs of abuse. This is difficult to reconcile with known behavior in humans and for modeling aspects of addiction that are critical to the disorder, such as choosing to use drugs above all other reinforcers. Recent work by this same group has reported that responding for nondrug reinforcer is, surprisingly, insensitive to devaluation. This suggests that the choice for the nondrug reinforcer is under habitual, rather than the presumed goal-directed, control and may explain why animals most often choose the nondrug reinforcer over drug reinforcers. Moreover, because there is no devaluation procedure for determining whether drug choice is habitual or goal directed, it's not known if choice for drug is also habitual or remains goal-directed.

The manuscript by Vandaele et al., therefore, sought to develop a procedure for determining whether behavior of rats making choices between saccharin and cocaine reinforcers was habitual or goal-directed based on reaction times (RT). Based on previous theories, the authors argue that goal-directed behavior should have slower RTs on choice trials versus sampling trials (e.g., because animals are deliberating between the alternatives) whereas habitual behavior should have similar RTs across both sampling and choice trials. The authors also present a third possibility in which options are evaluated sequentially, rather than simultaneously, resulting in RTs being longer in the sampling versus choice trials. The authors report that rats with minimal training and who are presumed to be goal-directed have slower RTs in choice trials compared to sample trials whereas rats that have had extensive training have similar RTs in the choice and sampling phases. These findings are consistent with their hypotheses. Moreover, they demonstrate that in the small subset of rats that prefer cocaine over saccharin, RTs in the sampling trials are longer than that in the choice trial suggesting that cocaine preferring rats are not evaluating each of the options. These data are the first to evaluate habitual responding for a drug reinforcer and suggest that comparing latencies across different task phases could be used to measure habitual and goal-directed behaviors.

Reviewer #2:

This manuscript by Mathsyaraja et al. studies the oncogenic loss of the Max-gene-associated (MGA) protein due to deletion or mutation in cell-lines, in mice and in human cancers (cell-lines and tumors). The authors knocked out MGA by aerosol-delivered, CRISPR-CAS expressing lentiviruses that simultaneously Cre-activated a Lox-stop Kras oncogene. The loss of MGA accelerated proliferation and oncogenesis, and shortened survival. Oncogenesis was further enhanced by enforced TP53 deletion in these lung tumors. RNA-seq and ChIP-seq of MGA+ or - cell-lines demonstrated the up and downregulation of various gene classes (thousands of genes) according to function and regulation including of PRC1.6 targets, meiosis regulators, TGF-beta signaling pathway components, EMT regulators, anti-tumor immunity, as well as of MYC, E2F, etc. Different cell lines exhibited both overlapping and distinct target sets. MGA knockout cells were more migratory and invasive and displayed actin-protrusions in accord with this behavior. They show that a Domain of Unknown Function in the mid-region of MGA engages PRC1.6 and is required to depress proliferation. The DUF is also required to limit actin-protrusions. Human colon organoids were studied since MGA mutations and deletions are also apparent in colon cancer. Again, shared and distinct targets of MGA action were inferred.

The authors make a strong case that MGA is an important tumor suppressor that operates through PRC1.6 for some of its actions.

Reviewer #2:

1) The authors hint towards the involvement of c-di-GMP signaling via the YcgR protein. This hypothesis can be tested by knocking down the ycgr gene and repeating the assay, but this has not been done or reported. Addition of these data to the manuscript would make the paper significantly stronger.

2) Do other chemoreceptors (Tar, Tsr, Tap) also act in the same way with their respective ligands? It would be useful to know if this effect is specific to Trg or if it is also found in the other chemoreceptors.

3) In figure 3C, what is the reason that the GFP intensity and the speed do not have the same range? In other words, why is the slope not equal to 1? Since there is 1:1 correspondence between the number of MotB and the number of GFP, shouldn't the slope be 1?

4) The authors do not cite or discuss the recent literature on load-dependent stator remodeling (e.g. PMIDs: 29183968, 31142644). It would be helpful to have a more in-depth discussion on how the observed stator unit recruitment relates to stator remodeling in response to load.

Reviewer #2:

This impressive manuscript describes a comprehensive, multifaceted analysis of the morphological and molecular changes that accompany photosynthetic establishment during seedling de-etiolation. Morphological data, focusing in particular on the photosynthetic thylakoid membranes, are derived using transmission electron microscopy (TEM), serial block face scanning electron microscopy (SBF-SEM), and confocal microscopy, while quantitative molecular data on the abundances of proteins and lipids are derived using mass spectrometry and western blotting. The various data are acquired over a time course between 0 h and 96 h post illumination, and with a high level of temporal resolution. The data allow the authors to develop a mathematical model for the expansion of the surface area of thylakoids (reaching 500-times the surface area of the cotyledon leaf), which matches well with experimental observations from the SBF-SEM analysis for earlier, but not later, stages of de-etiolation. Moreover, the data point to a two-phase organization of the de-etiolation process, with the first phase ("Structure Establishment") characterized by thylakoid assembly and photosynthetic establishment, and the second phase ("Chloroplast Proliferation") characterized by chloroplast division and cell expansion.

The data are of a high standard, and the depth and breadth of analysis in a single, unified study is unprecedented. While it is arguable that there are few major, completely novel insights reported here (indeed, in the Discussion, the authors very helpfully point out how many of the parameters they have measured are consistent with data reported elsewhere by others), this should not detract from the overall value of the study; a major and unique strength here is that all of the data have been acquired together and so are directly comparable. I have no doubt that this dataset will be extremely interesting to many researchers, and prove to be an invaluable resource for the plant science community. Consequently, I am sure that it will attract many citations.

I have a few specific comments that I would like the authors to consider carefully, as follows.

1) Figure 3. The 3D reconstructions are undoubtedly useful for deriving quantitative data, as they enable the derivation of thylakoid surface area data to verify the mathematical model. However, it is very difficult to see anything clearly in the images shown in the Figure. I wonder if the authors can make the images clearer, and then also point to and describe some of the key features. The videos do help a bit, but even these are not that clear.

2) Page 9, second paragraph. It is here that the "two phases" model is first proposed. I really could not see a clear basis for proposing this model here, using the data that had been presented thus far. As I see it (and based on the way the two phases are described in the Discussion), one can't really propose this model until after the chloroplast number and cell size data have been presented.

Moreover, the description of the second phase here ("and a second phase...") seems a bit inconsistent with the statement in the paragraph above that thylakoid surface area increases dramatically between T4 and T24, and much less between T24 and T96.

3) Figure 6, and the related supplementary figure. Loading controls are missing here, and should be added. Also, it is stated that a number of proteins (PsbA, PsbD, PsbO, Lhcb2) are "detectable" at T0 (line 348, page 11). To me, they look UNdetectable.

4) Dividing chloroplasts. On page 13, line 412-413, it is stated that the volume of dividing chloroplasts was measured, and we are referred to Figures 8E and 4B in support of this statement. However, it is not explained how this was done. More clear and specific explanation is needed. Was it the case that the authors sought out and measured dumbbell-shaped organelles, and quantified those? If so, images are needed to illustrate this point. And, I don't see anything relevant in Fig. 4B - this callout apparently belongs in the following sentence. The statement that the average size of dividing chloroplasts was higher than that of all chloroplasts (lines 413-414) is not really surprising if the authors were measuring organelles just on the point of becoming two organelles.

5) Page 13, beginning of modelling section. The motivation for this section needs to be better introduced. When I first read it, I could not understand why the authors wished to again "determine the thylakoid membrane surface area", as this had already been discussed earlier in the manuscript.

Also related to the modelling: Did the authors take into account the existence of appressed membranes when calculating the surface area exposed to the stroma (lines 431-432). And, assuming it is clearly established that there is a 1:1 relationship between these proteins and the relevant complexes (lines 441-443), perhaps this should be stated and the relevant literature cited.

Reviewer #2:

The study investigates key components of the entorhinal circuits through which signals from the hippocampus are relayed to the neocortex. The question addressed is important but the stated claim that layer 5b (L5b) to layer 5a (L5a) connections mediate hippocampal-cortical outputs in LEC but not MEC appears to be an over-interpretation of the data. First, the experiments do not test hippocampal to L5a connections, but instead look at L5b to L5a connections. Second, the data provide evidence that there are L5b to L5a projections in LEC and MEC, which contradicts the claim made in the title. These projections do appear denser in LEC under the experimental conditions used, but possible technical explanations for the difference are not carefully addressed. If these technical concerns were addressed, and the conclusions modified appropriately, then I think this study could be very important for the field and would complement well recent work from several labs that collectively suggests that information processing in deep layers of MEC is more complex than has been appreciated (e.g. Sürmeli et al. 2015, Ohara et al. 2018, Wozny et al. 2018, Rozov et al. 2020). Major Concerns:

1) An impressive component of the study is the introduction of a new mouse line that labels neurons in layer 5b of MEC and LEC. However, in each area the line appears to label only a subset (30-50%) of the principal cell population. It's unclear whether the unlabelled neurons have similar connectivity to the labelled neurons. If the unlabelled neurons are a distinct subpopulation then it's difficult to see how the experiments presented could support the conclusion that L5b does not project to L5a; perhaps there is a projection mediated by the unlabelled neurons? I don't think the authors need to include experiments to investigate the unlabelled population, but given that the labelling is incomplete they should be more cautious about generalising from data obtained with the line.

2) For experiments using the AAV conditionally expressing oChIEF-citrine, the extent to which the injections are specific to LEC/MEC is unclear. This is a particular concern for injections into LEC where the possibility that perirhinal or postrhinal cortex are also labelled needs to be carefully considered. For example, in Figure 3D it appears the virus has spread to the perirhinal cortex. If this is the case then axonal projections/responses could originate there rather than from L5b of LEC. I suggest excluding any experiments where there is any suggestion of expression outside LEC/MEC or where this can not be ruled out through verification of the labelling. Alternatively, one might include control experiments in which the AAV is targeted to the perirhinal and postrhinal cortex. Similar concerns should be addressed for injections that target the MEC to rule out spread to the pre/parasubiculum.

3) It appears likely from the biocytin fills shown that the apical dendrites of some of the recorded L5a neurons have been cut (e.g. Figure 4A, Figure 4-Supplement 1D, neuron v). Where the apical dendrite is clearly intact and undamaged synaptic responses to activation of L5b neurons are quite clear (e.g. Figure 4-Supplement 1D, neuron x). Given that axons of L5b cells branch extensively in L3, it is possible that any synapses they make with L5a neurons would be on their apical dendrites within L3. It therefore seems important to restrict the analysis only to L5a neurons with intact apical dendrites; a reasonable criteria would be that the dendrite extends through L3 at a reasonable distance (> 30 μm?) below the surface of the slice.

4) Throughout the manuscript the data is over-interpreted. Here are some examples:

• The title over-extrapolates from the results and should be changed. A more accurate title would be along the lines of "Evidence that L5b to L5a connections are more effective in lateral compared to medial entorhinal cortex".

• "the conclusion that the dorsal parts of MEC lack the canonical hippocampal-cortical output system" seems over-stated given the evidence (see comments above).

• Discussion, para 1, "Our key finding is that LEC and MEC are strikingly different with respect to the hippocampal-cortical pathway mediated by LV neurons, in that we obtained electrophysiological evidence for the presence of this postulated crucial circuit in LEC, but not in MEC". This is misleading as there is also evidence for L5b to L5a connections in MEC, although this projection may be relatively weak. Recent work by Rozov et al. demonstrating a projection from intermediate hippocampus to L5a provides good evidence for an alternative model in which MEC does relay hippocampal outputs. This needs to be considered.

5) What proportion of responses are mono-synaptic? How was this tested?

Reviewer #2:

Overall, this is a very well written paper that presents software that fills an interesting niche: interactive, real-time simulations of complex multicellular systems that can run in a web browser, without any need for users to install or configure software. As the authors describe, this enables new modes of education, science communication, and multidisciplinary collaboration. The software itself is impressive, and the supplied examples are clean and beautifully fluid. It is eye-opening that Javascript can run these models so well. The authors also did a fantastic and complete job in sharing their full source code, from the overall software down to individual scripts used to generate figures.

Some points that the authors should address in a revision:

1) Suitability of the software for researchers:

a. Artistoo simulations do not appear to have any method to save data for external manipulation and archival. This makes their use somewhat less applicable to robust simulation-driven investigations, particularly where postprocessing and further analyses are required.

b. It is unclear if Artistoo-based models can be exported into other cellular Potts (CP) frameworks such as CC3D or Morpheus. This may leave researcher end users without a clear "upgrade path" after exploring model ideas in Artistoo and moving to larger simulations (e.g., larger or more complex domains), running simulations in high throughput on HPC resources, or adapting approximate Bayeseian techniques for parameter estimation that require automating many simulation runs. Without an upgrade path, such users may wish to immediately begin in research-focused platforms rather than start with Artistoo and re-implement in another framework later.

c. Similarly, it is unclear if a model developed in Morpheus or CC3D can be directly imported into Artistoo. If such an import were possible rather than re-implementing models in Aristoo, research-focused users would be more likely to use Artistoo for scientific communication and outreach.

2) Need for improved educational scaffolding: The examples provided in the paper are excellent. However, they lack context on what the parameters mean or do. (For example, what are max_act and lambda_act in the cell migration model?) This may limit the educational impact because users will be unclear on what to change, and how the parameters relate to cell biophysical processes.

The authors should include more background information with each model, define parameters, and give end users some idea of what to expect when parameters are changed. We have also found it useful to help guide a new user's exploration of a model by suggesting parameter sets and describing what they should see. This can serve as an educational scaffolding to help learners build and grow.

The authors' sample models should serve as a template to Artistoo users on best practices for communicating models to diverse audiences.

3) New developments in online cellular Potts simulators: The authors should note that CompuCell3D has recently been ported to run interactively online in a web browser. See https://nanohub.org/resources/compucell3d. This recent development should be addressed in the paper.

4) Narrow review of interactive, "zero install" simulation frameworks: The authors focus too narrowly by only comparing Artistoo with other cellular Potts frameworks, while the main use case for Artistoo is for interactively sharing and communicating complex simulation models online.

The authors should discuss non-CP frameworks that worked towards this, such as CC3D on nanoHUB (see above), online Tellurium (https://nanohub.org/resources/tellurium), current practice to share R models online as Shiny apps, and recent work to use xml2jupyter to automatically convert research-focused (command line) PhysiCell models to interactive Jupyter notebooks that can be shared as interactive webapps on nanoHUB (e.g., https://nanohub.org/tools/pc4cancerimmune). All of these serve similar purposes of creating zero-install, interactive versions of models for science education and communication. The authors should briefly discuss these to further contextualize their work.

5) While this is a more minor point, I would feel more comfortable if the supplementary information had convergence and accuracy testing. Are there limits on computational step sizes for numerically accurate simulations, particularly for large energies or when including diffusion processes?

Overall, this is some fantastic work.

DOI: 10.1016/j.celrep.2020.108606

Resource: (ZFIN Cat# ZDB-ALT-061228-2,RRID:ZFIN_ZDB-ALT-061228-2)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-061228-2

What is this?

Reviewer #2:

This manuscript interrogates function of Ihog and Boi adhesion molecules in cytoneme-based transport of the Hedgehog morphogen in Drosophila. The cell biology of how cytonemes are regulated to deliver morphogen signals is not yet well understood, so the work addresses an important topic that will be of interest to a broad audience. However, much of the study refines previous work from the same group to provide only a modest advance in understanding of how Ihog impacts cytoneme behavior.

The authors use genetic strategies in Drosophila to investigate how Ihog and Boi influence cytoneme dynamics. They find that the two proteins act differently with regard to cytoneme function. Boi effects are not exhaustively analyzed, but a number of genetic experiments are performed to interrogate Ihog. The authors reveal that the extracellular domains of Ihog interact with the glypicans Dally and Dlp to stabilize cytonemes that originate from Ihog over-expressing cells. Knockdown of Ihog does not alter cytoneme dynamics.

The most novel aspect of the study - that Boi functions differently than Ihog in cytonemes - is, unfortunately, not expanded upon. Some experiments lack controls or are presented in a manner that prevents clear interpretation of results.

Key points to be addressed:

Figure 1: Null alleles and RNAi silencing are used interchangeably to reduce Ihog, Boi, Dally and Dlp function in vivo. Results between methods are directly compared. Oftentimes, controls are not included to confirm the level of knockdown following RNAi. If possible use null alleles due to consistency. However, if this is not possible due to experimental reasons, give an explanation and state impact in the discussion.

Ihog levels decrease following loss of Dally or Dlp and Boi levels appear to increase following knockdown of Ihog, Dally, or Dlp. These stability changes have previously been reported. The mechanism is not clear, so should have been investigated here - especially the increased Boi protein level. How does this occur? Is stabilization occurring at the protein level or is gene expression changing? Is this a compensatory upregulation?

Based upon the supplement for Figure 2, it looks like the Ihog truncation mutants show variable stability. Might this be affecting the extent to which they alter Dally or Dlp stability? The western blot data are presented as crops of single bands adjacent to crops of a molecular weight ladder. Blots should be shown as intact images, preferable with all variants compared across a single gel with a loading control. As presented, relative stability/expression levels are impossible to assess.

Figures 3-4: Ihog mutant transgenes are tagged with either HA or RFP. Best to be consistent with tags when mutant function is being directly compared. Given that the HA tag is a small epitope and the RFP is a protein tag, they may differentially alter protein functionality. To be consistent it would be preferable to use the same tags. However, if this is not possible due to experimental reasons, the technical implication can also be mentioned in the discussion.

Figure 5: Investigation of histoblast cytonemes reaching into ttv, botv mutant clones: The ability of cytonemes to invade double mutant clones is altered only under the engineered situation of glypican dysfunction combined with Ihog over-expression. From this, it is concluded that Ihog is acting with glypicans to stabilize cytonemes. This may be the case, but they ability to see it only under an engineered situation of compound mutation plus Ihog over-expression leads this review to question the physiological relevance of the observation. Of similar concern is that the authors state the ability of Ihog over-expressing cell cytonemes to cross small vs. large ttv, botv clones differs. The difference is very difficult to appreciate from the results presented.

Figure 6: The apparent functional difference between Ihog and Boi in the ability to stabilize cytonemes is potentially very interesting, but is not investigated, which limits the advance of the current study.

ML: Mine is ok

Reviewer #2:

Using this new Trypanosoma carassii infectious model in larval zebrafish, Jacobs et al. have developed a new clinical scoring system to reliably separate high-and low-infected larvae in order to investigate their individual innate immune responses, with a special emphasis on macrophages and neutrophils.

In summary the separation system used in this allows us i) to identify a strong macrophage and neutrophil proliferation response by high-and low-infected larvae, although happening a bit earlier, 5 dpi, for macrophages in low-infected larvae, and ii) to observe a differential distribution and morphology of macrophages, associated to the unique presence of more rounded foamy macrophages with a high pro-inflammatory profile into the vessels of high-infected zebrafish larvae. Together, this study constitutes the first report of the occurrence of foamy macrophages during an extracellular trypanosome infection.

Although the paper is well-written and the findings are interesting as they bring new insights into the development of foamy macrophages in response to an extracellular pathogen, i.e. Trypanosoma carassii, using a zebrafish larvae model, I have a few concerns regarding the following:

• The experimental infectious model in zebrafish: figure 2 summarizes that only 15% of the infected larvae, named low-infected larvae, are able to survive the infection. As an explanation the authors refer to the trypanosuceptible vs. trypanotolerant background of the host observed in non-zebrafish models. However, in this particular setting, all the larvae possess an identical genetic background. Therefore, why would the larvae behave differently in response to a similar pathogen? In addition, there is no clear differences in neither parasitic load at 2 dpi (figure 3F) nor myeloid cells accumulation at 3 dpi (figure 4AB), which could lead to a drastic difference in parasitic load based on mRNA expression at 4 dpi (figure 3F). The authors should discuss this shortly.

• Figure 4: the representative pictures from Fig4B do not seem to clearly match the histograms depicted in Fig4C. For example, from the pictures in Fig4B, it seems that there is a decrease in red fluorescence in the representative pictures from 7 dpi to 9 dpi low-infected larvae, which is not reflected in the histogram. Also, a representative picture of 7 hi-infected larvae seems to show at least equal or even more red fluorescence compared to 9 dpi low-infected larvae.

• Lines 494-496 states "No significant difference was observed between high-and low-infected fish, confirming that macrophages react to the presence and not to the number of trypanosomes.", reflecting that there is no differences in total macrophages nor in their proliferation between low- and high-infected zebrafish larvae (Figure 5B&C). Therefore it is not sufficiently clear on which basis the authors states a few lines later as a conclusion that "Altogether, these data confirm that T. carassii infection triggers macrophage proliferation and that proliferation is higher in low-infected compared to high-infected individuals, possibly due to a higher haematopoietic activity." Therefore the authors should revise this conclusion or bring stronger data to reinforce their results. Also, similar conclusions need to be adjusted in the discussion section and bring new elements to explain the higher number of macrophages observed in figure 4.

Reviewer #2:

This manuscript, "Lactobacilli in a clade ameliorate age-dependent decline of thermotaxis behavior in Caenorhabditis elegans," is focused on the impact of diet on age-dependent behavioral decline. The authors utilize a thermotaxis screen using different lactic acid bacteria (LAB) and identify strains of LAB with the ability to ameliorate age dependent decline in thermotaxis behavior. The study introduces some interesting results, including the finding that many LAB strains of the same clade can improve thermotaxis in older nematodes, despite disparate results on longevity. However, there were some questions remaining about methodology, and more importantly, there is very little evidence provided on what the molecular mechanism might be behind this phenomenon. Overall, this study contains interesting findings that are not developed thoroughly enough.

Major Comments/Questions:

1) How is LAB different from Ecoli? Does metabolic composition of LAB dictate its impact on thermotaxis behavior of worms? In the manuscript the authors argue that LAB are a "better" food source than E. coli. How does one define better for something as broad as a food source? There is a difference here but it is very unclear what aspects of LAB physiology may play a role.

2) Does this phenomenon require eating LAB, or just perceiving it? The assays did not test whether perception of LAB diet is sufficient for its effect on thermotaxis, rather whether more time on LAB leads to better thermotaxis.

3) Showing a potential daf-16 interaction is plausible, given that daf-16 interacts with many key pathways in the worm, but it is unclear whether this interaction is direct or indirect, or whether daf-16 is a major player in this pathway or just necessary for maintenance of health. What sensory pathways are activated when worms are fed on LAB diet, and how it finally interacts with daf-16?

4) Similarly, the pha-4 and eat-2 data are interesting, but are not developed in any way. This is another avenue that could in principle lead toward a better mechanistic understanding.

Reviewer #2:

Park et al. report on an analysis of existing semi-longitudinal NSPN 2400 data to learn how the projections of high-dimensional structural connectivity patterns onto a three dimensional subspace change with age during adolescence. They employ a non-linear manifold learning algorithm (diffusion embedding), thereby linking the maturation of global structural connectivity patterns to an emerging approach in understanding brain organization through spatial gradient representations. As might be expected based on the large body of literature indicating changes in structural connectivity in specific brain regions during adolescence, the authors find corresponding changes in the embedding of the structural connectivity patterns.

While this work touches on an important topic, ties nicely with the increasing body of papers on global brain gradients, and its overall conclusions are warranted, I am not (yet) convinced that it offers fundamentally new insights that could not have been gleaned from previous work (after all, manifold learning simply displays a shadow of the underlying patterns; if the patterns change, so does their shadow). I am also not convinced by the rationale for employing diffusion embedding: the authors state that the ensuing gradients are heritable, conserved across species, capture functional activation patterns during task states, and provide a coordinate system to interrogate brain structure and function, but that would be true for any method that adequately captures biologically meaningful variance in the structural connectivity patterns.

Other comments:

The authors show that the maturational change of the manifold features predict intelligence at follow-up, but did not show that intelligence itself exhibited changes that exceeded the error bounds of the regression line. Why not predict IQ change?

The slight improvements in prediction accuracy observed after adding maturational change and subcortical features to the features at baseline will necessarily happen by adding more regression parameters and may not be meaningful.

Reviewer #2:

The present study investigates how CSF-contacting neurons (CSFcNs) of the mouse spinal cord integrate and translate different synaptic inputs using distinct calcium-dependent spike mechanisms. Indeed two different types of voltage-gated calcium channels can be activated, resulting in the generation of spikes with different amplitudes. T-type Ca2+ channels would be involved in the generation of low amplitude spikes while HVA-Ca2+ channels participate in the generation of large amplitude spikes. Then these distinct spikes allow signaling different neurotransmitter systems. Consequently, the data provided here argue in favor of CSF-contacting neurons acting as a sensory system that uses Ca2+ channels-dependent spike activity with graded amplitude corresponding to the activation of different neurotransmitter receptors. This study is based on two-photon calcium imaging performed on spinal cord slices preparations obtained from young and adult mice. My comments are as follows:

1) All data are based on calcium imaging. Therefore, traces correspond to calcium-dependent fluorescent changes in the cells of interest. Can the author provide at least one sample showing that these calcium events are indeed linked to the generation of spikes; i.e., electrophysiological recordings? In addition, is there any electrophysiological evidence for the existence of calcium-dependent conductances in the CSFcNs? In the same vein, the authors conclude that spontaneous activity of CSFcNs depends upon calcium- but not sodium-spikes as TTX has apparently no effect. But, are the authors sure that in their experimental conditions individual sodium spikes could be detected given the genetic encoded probe used, the kinetic of such spikes and the frequency of the sampling during image acquisition? Note that this does not preclude the conclusion that CSFcNs express calcium-dependent spikes. See also comment 4 below.

2) Using the activation of different calcium channels to trigger spikes of different amplitude to code distinct signaling pathways associated with distinct neurotransmitter systems is a very attractive mechanism. I was wondering whether the authors ever observed the two processes in one single cell, meaning: did they ever try to apply Ach and ATP on the same cell? To my point of view, this would be an extremely elegant way to show that spikes of variable amplitudes imply the activation of distinct calcium-dependent conductances and are linked to different neurotransmitter signaling in one neuron. This should be possible as they said that 100% of the examined cells responded to Ach, suggesting that the only limitation would be to find a cell that also expresses purinergic receptors (should be highly feasible). In addition, this would strongly demonstrate how much this coding mechanism is valuable if this is present in a single cell, otherwise one could consider that the coding system just depends upon each cell, the neurotransmitter and its associated receptor signaling that by definition can involve distinct calcium-dependent channels. Then it would rather be a mechanism specific to each receptor than a sophisticated coding system.

3) As a general comment on figures, I would suggest to the authors to provide samples that are more illustrative of the results they claim on. For example on Figure 3 they state that TTX has no effect on spike amplitude and frequency, but the two traces shown (in blue and green) rather indicate a decrease in spike frequency and even an increase in spike amplitude after a few minutes of recording (green trace). [See also comment 4 below]. Another example is in Figure 6 in which one important data is the distinct amplitude of spikes triggered by either Ach or ATP. While this is properly illustrated in panels C, D and E, in contrast the samples chosen for panels A and B show events with the exact same amplitude. Please choose other traces. By the way, panel C is not necessary because the same info are included in panels D and E. I would suggest removing panel C. Finally, in Figure 7 it is stated in the text that in some cells ATP induced first a decrease in fluorescence followed by a large Ca2+ spike, while this specific spike looks much smaller than all the other ones illustrated in the study (Fig 7G). Also, the spike triggered by UTP looks different than the one triggered by ATP. Is it a typical response?

4) Several experimental details must be provided. First, the justification for the choice of VGAT promoter to drive the GCaMP6f indicator into PKD2L1 neurons is missing. Second, drug concentrations are not justified. This is important as the authors argue that Ach and ATP trigger Ca2+ spikes with different amplitudes, but isn't there the possibility that this is dose-dependent? Did the authors try different concentrations? Third, on TTX experiments (Fig 3), after how long under TTX exposure were measurements performed? While this is a crucial parameter, this is not indicated in the paper. Given the traces provided different conclusions could be reached depending on this timing.

5) It remains unclear to me why only some of the data (for example Fig 7) make a distinction between dorsal and ventral CSF-contacting neurons. In the zebrafish it is established that ventral and dorsal CSFC neurons have different developmental origins and distinct types of projections related to different functions. Then, if these neurons are suspected to play different roles depending on their ventro-dorsal position also in mice, the entire study should take this into account.

Reviewer #2:

The manuscript "A naturalistic environment to study natural social behaviors and cognitive tasks in freely moving monkeys" describes a large-scale system of rooms allowing for non-human primates to, potentially, freely engage in several different behaviors and neuroscientific experiments to be performed. The study is well intended, but in its current form with many claims, but few if any results does not, in my view, meet scientific standards.

The paper presents the testing environment consisting of different rooms. Compared to earlier work (e.g. Berger et al., 2018), the main innovation is the inclusion of an eye tracking system. Data supports the notion that this works in principle. But there is no analysis of data quality and accuracy. We also do not know whether the system works on every trial, or how often the eye is not detected or the tracker loses the signal.

The authors claim novelty of this testing environment, but similar ones have been used in behavioral research for decades and in recent years in neuroscience.

The authors claim that it is easier to place a testing system into a separate cage then in the home cage. It remains unclear what this claim is based on. Motivation of animals in these social settings should be more difficult than in the home cage environment. So, this is a potentially interesting result. It is also a conceptually important claim for the paper's logic, if the social setting should really be beneficial for training. But the claim needs to be substantiated.

The authors claim that natural behavior can be analyzed because a CCTV camera is mounted in the cage. There are no results or analyses to demonstrate that.

The authors mention neural recordings on multiple occasions, but do not show any. EM shielding is neither necessary nor new.

Automatic training appears to be a one-to-one copy of that in Berger et al. 2018, but citation is missing, except for Supplemental Information.

The authors report an anecdote of one animal (n=1) learning socially from others. There is no indication that this subject might have performed differently without social learning. The interpretation is a just-so story and appears rather anthropomorphic.

There are no results in the manuscript.

The manuscript is not organized well. The Methods section reads like a Discussion, important information on methods is distributed across Supplemental Information and Results. Results, as mentioned, does not contain any results or data.

Donsimoni. J. R., Glawion. R., Plachter. B., Walde. K., (2020). Projecting the Spread of COVID-19 for Germany. Institute of labor economics. Retrieved from: https://covid-19.iza.org/publications/dp13094/

Reviewer #2:

In this manuscript, Knight et al examine the genetic diversity in >12,000 publicly available C. difficile genomes in order to characterize genomic evidence of taxonomic incoherence among this genomically diverse pathogen. Their primary analysis employs average nucleotide identity thresholds to identify species boundaries, with secondary analyses examining core genome size changes, gene content, and estimated emergence dates. The authors' main conclusion is that the previously identified C. difficile cryptic clades CI-III are genomically divergent enough from the main clades C1-5 to warrant classification as different genomospecies. This paper is a useful contribution in benchmarking our understanding of the genetic diversity of C. difficile using all currently publicly available genomes, but the results are largely unsurprising given previous phylogenetic analyses involving clades 1-5 and CI-III, and is therefore probably best suited for a specialty journal. Additionally, in some instances, the methods lack details, reducing their interpretability and reproducibility.

Major Comments:

1) There are some claims that are too strong and not supported by the data or literature, including the claim that the rise of community-associated CDI is likely due to presence of C. difficile in livestock (Lines 53-54 - far too little evidence to make such a sweeping claim), the statement of apparent rapid population expansion into clades C1-4 (Lines 278-279 - only shown for certain sequence types and greatly impacted by observation bias), the statement that these findings "impacts the diagnosis of CDI worldwide" (Lines 37-38 -too grandiose given limited evidence of the clinical importance of the cryptic clades).

2) Generally, it is hard to discern which sets of genomes and variants were used for each of the bioinformatic analyses that are described. If there are a limited number of genome sets it might be useful to define them in the results to allow the reader to more easily follow along and understand the scope of different analyses.

3) The dated phylogenomic analyses methods would benefit from a more thorough assessment of model assumptions along with more description of the sources of bias and uncertainty at play. Specific questions are:

• Was the temporal signal in the data evaluated?

• What are the potential impacts of using a single clock model and demographic prior for such a diverse set of taxa?

• Was the clock rate restricted to the cited 2.5x10-9 - 1.5 x 10-8 range? What clock prior distribution was applied?

• Were relaxed clock priors explored?

• What went into the selection of the demographic model prior in BEAST? Were alternative models evaluated?

• The significant uncertainty in the divergence estimates should be emphasized/listed as a limitation.

4) Similarly, the pangenome analyses could be more thoroughly described, and the relevance of the core-genome size changes more robustly explored. Specifically:

• How did the core genome change when excluding any of C1-5? Were these changes much different than when excluding CI-III?

• The differences between Roary and Panaroo are notable, and potentially important for the microbial genomics community. More details should be provided on these results and how sensitive they are to the input parameters of the respective programs (e.g. collapsing paralogs in Roary and percent identity for orthologs). In addition, it is important to know if any filtering was done with respect to the quality of assemblies, which could have a significant impact on Roary's behavior.

Reviewer #2:

Recombinant antibodies are the most common and powerful reagents in life science research to identify and study proteins. Yet, every single antibody should always be validated and carefully tested for its relevant application, to ensure constructive and reproductive scientific endeavor. I was thus extremely pleased to review the manuscript of Terkild Buus et al, as it provides a careful assessment of oligo-conjugated antibody signal in CITE-seq. The authors tested four variables (antibody concentration, staining volume, cell numbers and tissue origin) and clearly showed that antibody titration is a crucial step to optimize CITE-seq panel. The authors found that, as a general rule, concentration in the 0.625 and 2.5 µg/mL range provides the best results while recommended concentrations by vendors, 5 to 10 µg/mL range, increase background signal.

In my opinion, the study is well-performed and may serve as a guideline to accurately validate antibodies for CITE-seq, as a consequence I have only minor comments.

• As stated by the authors, the starting concentration used for each antibody was based on historical experience and assumptions about the abundance of the epitopes. This approach may not be ideal, and the optimal concentration may have been missed. Do the authors think that a proper titration would be an advantage? Maybe this could be discussed in the text.

• The authors showed by testing four variables (see above) that they could define the optimal conditions to reduce background signal and increase sensitivity of antibodies and thus this way improves CITE-seq outcome. Nevertheless, the authors rely on the fact that all antibodies used in their panel are specific for their targeted antigens. I am not asking here to test the specificity of every single antibody used in the study as this would be a colossal amount of work. But I feel that this aspect should be discussed in the manuscript, especially when an "uncommon" antibody is intended to be used in the CITE-seq panel; the specificity of this antibody should be indeed tested prior to its use.

Reviewer #2:

In this paper, Numssen and co-workers focus on the functional differences between hemispheres to investigate the "domain-role" of IPL in different types of mental processes. They employ multivariate pattern-learning algorithms to assess the specific involvement of two IPL subregions in three tasks: an attentional task (Attention), a semantic task (Semantics) and a social task (Social cognition). The authors describe how, when involved in different tasks, each right and left IPL subregion recruits a different pattern of connected areas.

The employed tasks are "well established", and the results confirm previous findings. However, the novelty of the paper lies in the fact that the authors use these results as a tool to observe IPL activity when involved in different domains of cognition.

The methodology is sound, well explained in the method section, the analyses are appropriate, and the results clear and well explained in the text and in graphic format.

However, a solid experimental design is required to provide strong results. To the reviewer's view, the employed design can provide interesting results about functional connectivity, but not about the functional role of IPL in the investigated functions.

I think the study would be correct and much more interesting if only based on functional connectivity data. Note that rewriting the paper accordingly would lead to a thorough discussion about how anatomical circuits are differently recruited based on different cognitive demands and about the variable role of cortical regions in functional tasks. This issue is neglected in the present discussion, and this concept is in disagreement with the main results, suggesting (probably beyond the intention of the authors) that different parts of the right and left IPL are the areas responsible for the studied functions.

Major points:

1) The 3 chosen tasks explore functions that are widespread in the brain, and are not specifically aimed at investigating IPL. The results (see. e.g. fig 1) confirm this idea, but the authors specifically focus on IPL. This seems a rather arbitrary and not justified choice. If they want to explore the lateralization issue, they should consider the whole set of involved areas or use tasks showing all their maximal activation in IPL.

2) The authors aims to study lateralization using an attentional task, considering the violation of a prevision (invalid>valid), a linguistic task, looking for an activation related to word identification (word>pseudoword) and a social task, considering correct perspective taking (false belief>true belief), but they do not consider that in all cases a movement (key press) is required. It is well known that IPL is a key area also for creating motor commands and guiding movements. Accordingly, the lateralization bias observed could be due more to the unbalance between effectors while issuing the motor command, than to a different involvement of IPL regions in the specific tasks functions.

3) Like point 2, the position of keys is also crucial if the authors want to explore lateralization. This is especially important if one considers that IPL plays a major role in spatial attention (e.g. Neglect syndrome). In the Methods, the authors simply say "Button assignments were randomized across subjects and kept identical across sessions", this should be explained in more detail.

4) The authors show to know well the anatomical complexity of IPL, however their results are referred to two large-multiareal-regions. This seems to the reader at odds with all the descriptions related to fig.2. If they don't find any more subtle distinction within these 2 macro-regions, they should at least discuss this discrepancy.

5) The part about Task-specific network connectivity is indeed very interesting, I would suggest to the authors to focus exclusively on this part. (Note that the results of this part seems to confirm that only the linguistic task is able to show a clear lateralization).

What would be the limit of detection of the new variants in the wastewater by this NGS based sequencing method?

Reviewer #2:

The main analyses of the study compare previously published experimental observations from Hi-C and ORCA to predictions of the author's "futile cycle" model. The predictions are derived from simulations and differential equations analysis of the model as a dynamical system. Given its centrality to the manuscript, we recommend describing this overall strategy in more detail in Results. For example, at line 124 (Pg. 4) the authors could talk about how the simulations are done, including where the variability comes from (e.g., random starting conditions vs. probabilistic events vs. different parameters).

Xiao et al. make several key assumptions to dramatically simplify their model. Namely, it is assumed that promoter modification and transcription are equivalent and that enhancer-promoter contact influences transcription instead of transcription influencing structure. Steady-state equilibrium must also be assumed. It would be helpful if the authors explicitly stated these assumptions and provided references to support their being reasonable.

It is not totally clear why the authors decide to call their proposed approach the futile cycle model. There are similarities to other well-known models in biochemistry and biophysics that should be noted. It might make sense to simply call this a mechanistic model of cooperative promoter activation. If the authors stick with "futile cycle", the relationship between promoter activation through tags and metabolic signaling should be described in more detail.

There is also an opportunity to emphasize that the proposed model is not necessarily absolutely correct, but one of many plausible models that can produce a non-linear relationship between genome structure (enhancer-promoter contact) and transcription. Any thoughts on other models that could generate similar dynamics would be a useful discussion point. There are parallels to both sigmoidal dose-response curves, where drug concentration is plotted against response, and transcription factor binding curves, where free ligand concentration is plotted against the fraction bound. We recommend providing background context on these types of models or the Hill equation to illustrate why non-linear behavior is or is not surprising given the proposed model.

For clarity, it would be helpful to discuss model parameters in greater detail. First, we suggest noting which parameters shift the location of the curve and which increase the steepness of the curve. Second, we recommend including a phase diagram exploring when sigmoidal behavior and any other key model predictions arise across parameter space. In what circumstances does hypersensitivity or time lag emerge? The authors demonstrate that a narrow set of parameters is sufficient to produce a super-linear relationship between enhancer-promoter contact and transcription in Figure 6. One potential dilemma is this model's ability to explain many experimental observations by indicating that minimal changes all occur in the sub-linear regime while observable changes occur in the super-linear regime. Given that one needs specific parameters to replicate an example of the hyper-linear regime (including at least three degrees of stimulation and increasing stimulation of the successive states), it could be valuable to demonstrate how large the plausible parameter space is. Without an exhaustive search across the space of minimal parameters, it is not clear when this property emerges or how common it is within the full parameter space. The authors could vary model parameters and plot a grid visualizing behavior (e.g., steepness of the curve or Hill coefficient).

Images throughout the manuscript are low resolution, making the figures difficult to read. Increase the resolution of figures throughout, especially those containing text (Fig 6A).

Reviewer #2:

This manuscript by Diamanti et al. describes their study on how visual neurons responded to identical visual stimuli at two different locations along a virtual linear track. Extending their previous result that spatial location modulates the neuronal activities in the primary visual cortex (V1), they now demonstrate that similar spatial modulation also occurred in the higher visual areas (HVAs), but not so much in a lower visual area, the lateral geniculate nucleus (LGN). In addition, they show that the modulation, measured by a spatial modulation index (SMI), was stronger when animals had more experience in the track and when the animals were actively performing a task rather than passively viewing the same virtual track. The authors have been responsive to comments by previous reviewers at a different journal. Data are appropriately analyzed and clearly presented.

Since the finding that visual neurons are spatially modulated similarly as hippocampal place cells in spatial navigation tasks (Ji and Wilson, 2007; Haggerty and Ji, 2015; Fiser at al, 2016; Saleem at al, 2018), there has been increasing interest in identifying the source(s) of this modulation. This study adds new evidence to this puzzle, suggesting that it is more likely either generated within the visual cortex or top-down propagated from higher brain areas, rather than bottom-up propagated from the thalamus. This is an important contribution. However, there are concerns, mainly on the data interpretation and the clarification of the main conclusion, as elaborated below.

1) Because experience and task engagement enhanced spatial modulation, the authors concluded in the abstract that "Active navigation in a familiar environment, therefore, determines spatial modulation...". This conclusion is too strong and not well-supported by the data. First, spatial modulation on Day 1, when the task was novel, was lower than on later days, but it was already much higher than 0 (Fig. 1h). Also the individual neuron data (Fig. 1e) display clear spatial modulation on Day 1. Therefore, "familiar environment" is not a requirement. Second, spatial modulation during passive viewing was much higher than 0 and was correlated with that during active navigation, as shown in Fig. 4e - Fig. 4l. Therefore, "active navigation" is not a requirement either. It is true that both active navigation and familiar environment enhanced spatial modulation. They did not "determine" spatial modulation.

2) Related to the point above, the presence of spatial modulation in passive viewing reminds us that these cells in the visual system were still mainly driven by visual stimuli. The data in Fig. 4e,f are especially telling: the modulation in V1 was similar and highly correlated between active navigation and running replay. In addition, it is clear from all the raw traces in Fig. 1 and Fig. 2 that these cells did respond to the two segments with identical stimuli reliably with two peaks. The spatial modulation was just a change in one of the peaks. So the nature of the modulation is a "rate remapping" of the expected, classical visual responses. I believe, in order to maintain the big picture of what drives the activities of these neurons, it is beneficial to clarify that the "spatial modulation" is a modulation on top of the expected visual responses. This message is not explicitly conveyed in the current manuscript.

3) The authors stated that spatial modulation is "largely absent in the main thalamic pathway into V1". This was based on the significantly weaker SMIs in LGN than those in V1 and HVAs. However, it is unclear whether the SMIs in LGN were still significant. The SMI values for both LGN buttons (Line #100) and LGN units (Line# 130) might be statistically significant from zero. The statistical comparison p-values should be given in both cases. Second, Figure 3 - figure supplement 1 b,f show that the SMI values in LGN could be predicted by spatial modulation, but not by visual stimuli alone or behavioral variations, just like those in V1 and HVAs. This seems to me good evidence for the presence of spatial modulation in LGN. Therefore, it is my opinion that the data do not support the complete lack of spatial modulation in LGN, but do clearly demonstrate weaker spatial modulation in LGN than in V1 and HVAs.

Reviewer #2:

In this manuscript, the authors set out to measure participant's decisions about when an item occurred in a short list of 3 or 4 items, where the first and last items were always at the beginning and end, respectively. They report two behavioral studies that examine time judgments to items in the intermediate positions. They show that time judgments (when did you see X item using a continuous line scale) are always a little off but, more importantly, they tend to be anchored to other items presented. The results are interesting and add to our knowledge of the representation of time in the brain mainly by introducing a new paradigm with which to study time. Within the broader context of research on timing capacities, it should not be surprising that participants do not have a continuous representation of time that lasts beyond traditional time interval training of a few hundred milliseconds to a few seconds. Furthermore, research has also shown that 'events' that require attentional resources do morph our perception and memory for time. So while the paradigm is worth expanding on, the behavioral results are not surprising given this past literature. I do feel however that this work is an important first step in developing a more firm model of memory for time.

Reviewer #2:

Borghesani and colleagues aimed to understand how dysfunction in the ATL alters the dynamic activity during semantic categorization. To achieve this, they contrast MEG responses between patients with svPPA and age-matched healthy controls. Both groups show similar profiles of behavioural performance on the task, and broad similarities in MEG responses. Critically, svPPA patients show enhanced gamma synchronization in the occipital lobe compared to controls, while gamma synchronization was correlated to task RTs.

In general, I found the manuscript interesting, and the major strength being the application of MEG analyses to a clinical population during a cognitive task. In terms of improvements, I think the results could be more fully characterized, which would allow for more expansive interpretations and inferences.

Major comments:

1) As the paper is about 'Neural dynamics', I felt this aspect could be developed, with the timing of the effects characterized further, and considered more in relation to the conclusions. For example, the main finding is the increased occipital gamma response in svPPA compared to controls. Looking at Figure 3, there is a peak in the svPPA group near 200 ms, and very little synchronized activity in the control group. This is interesting as there are many ways we could have seen svPPA > controls, but this suggests that the gamma synchronization response associated with compensation is specific to the svPPA group (and largely absent from controls - also from Supp fig 1), and is distinguished from an initial visual evoked response (peaking ~100 ms). I would recommend discussing and characterizing the dynamics of this effect more, such as what a later occipital effect could tell us about dynamics given ATL dysfunction? Is this increase a result of a lack of top-down effects from ATL? I think these kinds of issues could be explored and discussed more.

2) The occipital gamma effect looks like the primary visual cortex, which might suggest the effects are not related to higher-level perceptual features (such as has eyes, teeth) as the authors suggest, but rather low-level visual effects. Do the authors perhaps think the effects could relate to enhanced processing of visual details (as related to the ideas of Hochstein and Asher's reverse hierarchy), or whether the effects relate to additional visual input following a visual saccade?

3) The VBM results for the svPPA patients were surprising given that all the atrophy appeared in the left hemisphere. There can be hemispheric differences in svPPA, but is this a true lateral pattern (meaning the right ATL is intact) or a product of VBM being run so that the most atrophied hemisphere is shifted to the left side? If the VBM maps are correct, and the svPPA patients are only showing left hemisphere atrophy, then what does this suggest about the role of the right ATL, and the bilateral nature of occipital increased in svPPA?

4) Both svPPA patients and healthy controls achieved around 80% accuracy in the categorization task. This seems surprisingly low given, (1) the task (living vs. nonliving after seeing the image for 2 seconds), (2) that all the images were pretested and had high name agreement, and (3) that items were repeated on average 2.5 times. Is there something that explains this low performance for all individuals?

Reviewer #2:

Overall I think the authors collected an interesting dataset. Analyses should be adjusted to include all cells rather than sub-selecting for stability. Additionally, the language needs to be adjusted to better reflect the data. I wish there was any behavioral data included, but if the authors compare their data to publicly available data in V1 for a single recording session during a visually guided task, these concerns could be quelled a bit.

1) In general the language of this paper and title seem to mismatch the results. The fraction of cells that were 'stable' as the authors say on line 112 was very small, however the authors focus extensively on this small subset for the majority of analyses in the paper. Why ignore the bulk of data (line 119)? What happens if you repeat the same analysis and keep all cells in the dataset? The general language around stability of neural ensembles should be adjusted to better reflect the data (ex: lines 157, 225).

2) There are claims in this paper about how ensembles 'implement long-term memories' in the introduction and conclusion and yet the authors never link the activity of ensembles to any behavioral or stimulus dependent feature. This language reaches far beyond the evidence provided in this paper. The introduction could provide some better framing for expectations of stability vs. drift in neural activity rather than focus on the link between ensembles and memory given that there isn't much focus on the ensembles' contribution to memory throughout. For example, the last sentence of the paper is not supported by data in the paper. Where is the link between ensembles and memory in the data? What is the evidence that transient ensembles are related to new or degraded memories? This reads as though it was the authors' hypothesis before doing the experiments and was not adjusted in light of the results.

3) There is no discussion around the alternative to stability of neuronal ensembles. What are the current theories about representational drift? For example, in Line 34 the authors present an expectation for stability without any reasoning for why there need not be stability. This lack of framing makes their job of explaining results in line 217 more difficult. There is a possibility that the most stable cells aren't more important - what is the evidence that they are? Does an ensemble need a core? Would be interesting to include some discussion on the possibility of a drifting readout (Line 223). [https://doi.org/10.1016/j.conb.2019.08.005]

4) How do activations in V1 in this dataset compare to other data collected from V1 while the animal is performing a task (where for example the angle of the gradings is relevant to how the mouse should respond)? I would be interested to know if the authors compared statistics of their ensembles to publicly available data recorded in V1 during a visually guided behavior. Are the ensembles tuned to anything in particular? Could they be related to movement? [http://repository.cshl.edu/id/eprint/38599/]

5) The authors provide some hypotheses as to why fewer cells are active in the later imaging sessions (dead/dying cells?). This is worrisome in regards to how much it might have affected the imaged area's biology. One alternative hypothesis is that the animal is more familiar with the environment/ not running as much etc. Have the authors collected any behavioral data to compare over time?

6) How much do the results change when you vary the 50% threshold of preserved neurons within an ensemble (Line 146)? Does it make sense to call an ensemble stable when 50% of the cells change? Especially given that the cells analyzed as contributing to an ensemble are already sub-selected to be within the small population of stable cells (Line 119)?

7) Cells are referred to as 'stable' when they're active on 3 different sessions that are separated in time. However, the authors find a smaller number of cells are stable over extended time (43-46 days later). If we extrapolate this over more time, would we expect these cells to continue to be stable? Given these concerns, it might make more sense to qualify the language around stability by the timespan over which these cells were studied.

8) Filtering frames to only coactive neurons for ensemble identification seems strange to me. Authors may be overestimating the extent of coactivation. What happens when you don't do this? How much do the results change when you don't subselect for Jaccard similarity? I would be interested to see how the results vary as you vary this threshold (Line 136).

9) The term 'evoked activity' is misleading because the authors don't link these activations to the visual stimulus. There's no task, so the mice could be paying little attention to the stimulus. Should we really consider this activity to be visually driven? Could the authors provide any evidence of this?

10) A method like seqNMF could reveal ensembles that are offset in time. This looser temporal constraint could potentially reveal more structure. This should be run on the entire dataset (without stability sub-selection). I suggest this as a potential alternative or supplement to the method described by the authors. [https://elifesciences.org/articles/38471]

DOI: 10.7554/eLife.56319

Resource: (ZFIN Cat# ZDB-ALT-080321-2,RRID:ZFIN_ZDB-ALT-080321-2)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-080321-2

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