Reviewer #3 (Public Review):

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

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

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

Major comments:

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

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

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

Summary:

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

Major comments:

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

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

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

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

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

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

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

DOI: 10.7554/eLife.64267

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

Curator: @scibot

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

What is this?

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

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

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

Reviewer #3 (Public Review):

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

DOI: 10.7554/eLife.60432

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

Curator: @scibot

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

What is this?

DOI: 10.7554/eLife.54491

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

Curator: @scibot

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

What is this?

DOI: DOI:10.1016/j.cub.2020.08.103

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

Curator: @scibot

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

What is this?

DOI: 10.1523/ENEURO.0022-20.2020

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

Curator: @scibot

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

What is this?

DOI: 10.1523/ENEURO.0022-20.2020

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

Curator: @scibot

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

What is this?

DOI: 10.7554/eLife.42881

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

Curator: @scibot

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

What is this?

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

DOI: 10.7554/eLife.53403

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

Curator: @scibot

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

What is this?

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

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

AssayResult: 8.3

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 6.4

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 3.1

AssayResultAssertion: Abnormal

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

AssayResult: 5.5, 5.7

AssayResultAssertion: Abnormal

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

AssayResult: 20.5

AssayResultAssertion: Normal

Comment: See Table S3 for details

AssayResult: 3.4

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 2.6, 4.8

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 3.8

AssayResultAssertion: Abnormal

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

AssayResult: 4, 5

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 5.8, 6.1

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 5.3

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 5.1

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 17.1

AssayResultAssertion: Normal

Comment: See Table S3 for details

AssayResult: 3.2

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 3.5

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 4.1

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 2.9

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 6.1

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 12.9

AssayResultAssertion: Normal

Comment: See Table S3 for details

AssayResult: 4.7

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 7.1, 6.0

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 3.1

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 5.4

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 5

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 4.8

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 3.8

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 3.2

AssayResultAssertion: Abnormal

Comment: See Table S3 for details

AssayResult: 14.7

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 16

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12.3

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 11.8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 16.3

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 15.4

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 19.3

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 9.8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 9.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 8.7

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 15.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10.4

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 11.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 17.2

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 19.7

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 11.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 17.4

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 11.7

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 14.2

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 18.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 15.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 14.7

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 11.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 11.7

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 9.4

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12.9

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10.8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 13

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 8.4

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 15.4

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 13

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 22.8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 14.6

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 16.5

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 14.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10.3

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 7.5

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 7.8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12.8

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 14.6

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10.6

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 8.9

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12.1

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 10.6

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 12.5

AssayResultAssertion: Normal

ControlType: Normal, wild type TP53

Comment: See Table S3 for details

AssayResult: 58

AssayResultAssertion: Normal

Comment: See Table S2 for details

AssayResult: 5.8

AssayResultAssertion: Abnormal

Comment: See Table S2 for details

Reviewer #3 (Public Review):

Advances in understanding the biochemical and cellular mechanism of neuronal damage are investigated here and are to be appreciated. The strength of this work on SARM1 is its success in establishing that a concentration-dependent phase change activates the enzyme to degrade NAD, an essential component of neuronal integrity. Cellular significance is demonstrated in C. elegans neuronal damage triggered by citrate. Weaknesses are that high citrate is required for SARM1 effects but low citrate is used in the C. elegans model without establishing concentration dependence in the C. elegans system. The progression on neuronal damage from enzyme activation to neuronal damage in C. elegans is missing the quantitation of NAD change. A strength of the work is to provide a solid stepping-stone to permit the next steps in cementing the biochemical pathways of initiating cellular damage to neurons.

Reviewer #3 (Public Review):

In this article, Gregory Grecco and colleagues developed a novel translational mouse model of prenatal methadone exposure (PME) that closely resembles the opioid exposure experienced by pregnant women living with opioid use disorder and treated with methadone maintenance pharmacotherapy. The article delineates the impact of prenatal methadone exposure on physical development and motor behavior of the next generation male and female progeny. The authors also relied on a combination of electrophysiological, immunohistochemical and volumetric MRI imaging approaches to investigate the mechanisms underlying PME-derived phenotypes in male and female offspring. Overall, PME produced changes in motor function, motor coordination and growth in progeny. These phenotypes were accompanied by changes in the electrophysiological properties and density of neurons in the primary motor cortex of offspring raised by opioid-exposed dams.

One of the stated goals by the authors was to develop a mouse model that closely mirrored exposure and dosing regimens in clinical populations living with opioid use disorder in order to increase the translational value of the findings outlined in this report. One of the strengths of the article is the experimental design and the longitudinal nature of the studies. The dams were first treated with oxycodone, a commonly abused pain killer to mimic this condition in patients living with SUD. 5 days prior to mating, the animals were switched to methadone to model maintenance pharmacotherapy that is commonly used in SUD patients. The doses of oxycodone and methadone were carefully selected to mimic as closely as possible the suspected exposure experienced by pregnant women and their unborn offspring. The authors demonstrated that the concentrations of methadone and related metabolites were present in the plasma, brain and placentas of dams and offspring in the opioid-treated group during gestation, parturition and up to one week after birth. Another strength of the study was the fact that the authors convincingly demonstrated a lack of change in maternal behavior in the opioid-treated dams, which could have been a major confounding factor. The dams exposed to oxycodone and methadone did develop dependence to opioids as expected, however the amount and nature of maternal care delivered to their offspring was not affected by oxycodone and methadone exposure. This critical finding enabled the authors to delve further into the biological underpinnings of the observed phenotypes. The offspring produced by opioid-exposed dams showed some phenotypes consistent with neonatal opioid withdrawal syndrome (NOWS) in humans, including hyperthermia and twitches or jerks. Together, these findings demonstrate that the authors were successful in creating a novel model of prenatal opioid use and methadone maintenance in mice.

Overall, both males and females produced by opioid-treated dams had lower body weight and length during development and through adolescence. Bone volume was also lower in PME offspring compared to controls at 1 week of age, an effect that dissipated by adolescence in PME progeny. Locomotor activity was reduced at P1 and increased at P7 and P21. Interestingly, ultra sonic vocalization emitted by pups when separated from their mothers, was highest for PME females compared to all groups and this increase in calls also coincided with increased activity. PME offspring also had delays in demonstrated coordinated motor behaviors such as acquisition of surface righting, forelimb grasp and cliff aversion during the early stages of development. Prepulse inhibition, a measure of sensorimotor gating was not disrupted by PME.

At the anatomical level, the largest impact of PME was found in the primary motor region of the cortex, where cell density was reduced particularly in the upper cortical layers. Next, the authors probed the properties of cells and circuits in primary motor cortex and found reduced firing rates in response to injected currents in PME animals compared to controls. The input resistance of these cells was also diminished in the PME group. Together, these findings suggest that the number of cells may be reduced by PME in primary motor cortex and that the remaining neurons are not able to fire as effectively, resulting in blunted transmission within this brain region. Lastly, the authors stimulated local synaptic inputs to M1 using glutamate uncaging and found that the neural circuits connecting the top layers of M1 to layer 5 are enhanced in PME animals.

Overall, the authors identified some electrophysiological correlates of altered motor function and coordination produced by a novel prenatal opioid exposure model and regimen. This article had several strengths highlighted above but also included some areas of potential improvement. The authors included both sexes in many of their analyses but it is not always clear when the sex of the offspring were combined in the analyses and/or whether sex was always included as a factor in the many endpoints described in the paper. The authors acknowledge some of the limitations of their model in better understanding OUD in pregnant women. Including the caveat that many women do not switch to maintenance therapy prior to conception would be worth mentioning. Moreover the use of buprenorphine has increased in recent years and methadone is not the only maintenance therapy available. Lastly, the electrophysiological recordings do not exactly coincide with some of the overt phenotypes reported: at P21, the PME animals are hyperactive but the time window does not match with the coordination deficits reported. Overall, these minor weaknesses detracted only slightly from the overall impact and value of the reported findings.

Reviewer #3 (Public Review):

In this work Farber and colleagues describe the generation of Fus(EGFP-plin2) and Fus(plin3-RFP) two knock-in zebrafish lines that alllow to study perilipins and lipid droplet biology in vivo at whole animal level. These lines could be important tools to understand how lipid droplet dynamics are affected by different genetic and physiological manipulations.

The article is well written and the work is carries out with a good methodological approach and the results support their conclusions. The weakness is the lack of originality since it does not really go behind the current knowledge in the field. Most of the data are a detailed description of zebrafish lines but I doubt that could be interested to a broad audience.

It also lacks novelty since the work does not add anything compared to what is already known regarding peripilin 2 and 3. I think this manuscript should be submitted to a more specialized journal on lipid metabolism or to a technical "zebrafish" journal.

Reviewer #3 (Public Review):

The authors set out to determine the role of interleukin (IL)-33 in the host immune response to the parasite Toxoplasma gondii. They achieve this using a mouse model of infection and a range of genetically modified mice to systematically prove the pathway involved.

A major strength of the study is the use of strategic immune cell/factor-deficient mice in combination with recombinant proteins in vivo. This may be further strengthened by future studies that test the impact off inhibitory antibodies against the primary factor of interest, IL-33. This would allow for a loss and gain of function approach, supporting the exisiting in vivo data with recombinant mouse IL-33.

Overall, the approach taken achieves the goal of the study. The manuscript is well written and systematically addresses the steps in the pathway that are required to mount an effective IL-33-mediate immune response to T. gondii.

The likely impact of this work are new knowledge of the function of IL-33 in response to infection and the interaction between different components of the immune system to achieve a balanced, context dependent response. The study does not highlight new methods or technical advances, but does provide important new information on immune responses to infection.

Reviewer #3 (Public Review):

This paper examines the role of neutrophils, inflammatory immune cells, in disease caused by genital herpes virus infection. The experiments describe a role for type I interferon stimulation of neutrophils later in the infection that drives inflammation. Blockade of interferon, and to a lesser degree, IL-18 ameliorated disease. This study should be of interest to immunologists and virologists.

This study sought to examine the role of neutrophils in pathology during mucosal HSV-2 infection in a mouse model. The data presented in this manuscript suggest that late or sustained IFN-I signals act on neutrophils to drive inflammation and pathology in genital herpes infection. The authors show that while depletion of neutrophils from mice does not impact viral clearance or recruitment of other immune cells to the infected tissue, it did reduce inflammation in the mucosa and genital skin. Single cell sequencing of immune cells from the infected mucosa revealed increased expression of interferon stimulated genes (ISGs) in neutrophils and myeloid cells in HSV-2 infected mice. Treatment of anti-IFNAR antibodies or neutrophil-specific IFNAR1 conditional knockout mice decreased disease and IL-18 levels. Blocking IL-18 also reduced disease, although these data show that other signals are likely to also be involved. It is interesting that viral titers and anti-viral immune responses were unaffected by IFNAR or IL-18 blockade when this treatment was started 3-4 days after infection, because data shown here (for IFN-I) and by others in published studies (for IFN-I or IL-18) have shown that loss of IFN-I or IL-18 prior to infection is detrimental.

These data are interesting and show pathways (namely IFN-I and IL-18) that could be blocked to limit disease. While this suggests that IL-18 blockade might be an effective treatment for genital inflammation caused by HSV-2 infection, the utility of IL-18 blockade is still unclear, because the magnitude of the effect in this mouse model was less than IFNAR blockade. Additionally, further experiments, such as conditional loss of IL-18 in neutrophils, would be required to better define the role and source(s) of IL-18 that drive disease in this model.

Reviewer #3 (Public Review):

Mutations in Naa10 are known to be causative in Ogden syndrome, a genetic disorder associated with infantile death. The paper by Kweon et al describes a series of experiments using mouse models of Naa10, an x-linked gene with the function of a major acetyltranferase in a complex accounting for 40-50% of acetylation of all proteins. The lack of complete embryonic lethality in the Naa10 hemizygous mice, leads the authors discover a paralogous mouse gene Naa12. The authors further demonstrate that Naa12 can compensate for Naa10 loss of function and that null mutations in both genes lead to complete embryonic lethality.

Genetic experiments described in this paper involve 2 distinct knockouts of the Naa10 in mice. The resulting hemizygous male mice displayed a variety of developmental defects, and while hemizygous males were underrepresented at birth, some surviving mice experienced early neonatal lethality while a proportion of the hemizygous mice survived to adulthood. Severely affected animals exhibited a variety of development abnormalities but importantly, no major reductions in the acetylation patterns were observed. A similar spectrum of phenotypes were reported in 2017 in a separate paper by Lee et al. The lack of complete embryonic lethality in Naa10 hemizygous males led to the hypothesis that a compensatory gene in mice may exist. The authors then identified the autosomal Naa12 gene in mice. This is a major finding of the paper. Naa12 and Naa10 share 80% sequence identity. The authors continued on to generate a Naa12 knockout mouse that in combination with the Naa10 knockout mice, demonstrate complete embryonic lethality to support the hypothesis that Naa12 is a function homolog to Naa10 in mice. This is strong evidence supporting the functional compensation of Naa12. The authors provided a thorough account of the variety of development abnormalities in the Naa10 hemizygous mice at all stages of development, noting changes in bodyweight, hydrocephaly and significant cardiac defects, pigmentation, skeletal and reproductive abnormalities. The variation and heterogeneity ranged from severe embryonic abnormalities through to milder phenotypes in surviving adults. Importantly, the authors identified several phenotypes in the mice that upon further analysis, we also not in the patients with an assumption of incomplete penetrance.

This reviewer finds this paper to be an important finding worthy of publication. The experiments were well powered and the genetic crosses thoroughly examined. The discussion was thoughtful and considered mechanisms of compensation between Naa10 and Naa12 based on the observed experiments.

Reviewer #3 (Public Review):

In this study from the Selimi lab, Gónzalez-Calvo and colleagues investigate the role of the uncharacterized complement family protein SUSD4. SUSD4 is expressed at the time of cerebellar synaptogenesis and localizes to dendritic spines of Purkinje cells. Susd4 KO mice show impaired motor learning, a cerebellum-dependent task. Susd4 KO mice display impaired LTD and facilitated LTP at parallel fiber (PF)-Purkinje cell (PC) synapses, indicating altered synaptic plasticity in the absence of Susd4. Climbing fiber (CF)-Purkinje cell synapses show largely normal basal transmission, with the exception of larger quantal EPSCs. Immunohistochemical analysis shows a small decrease in the proportion of CF/PC synapses lacking GluA2. As their data indicates a role for SUSD4 in regulation of postsynaptic GluA2 content at cerebellar synapses, they next explored the molecular mechanism by which SUSD4 might do so. Activity-dependent degradation of GluA2 does not occur in the absence of SUSD4. Affinity purification of proteins associated with recombinant SUSD4 identifies ubiquitin ligases as well as several proteins involved in AMPAR turnover. Finally, the authors show that SUSD4 can bind GluA2 in HEK cells, and that SUSD4 can bind the ubiquitin ligase NEDD4, but that these two interactions are not dependent on each other.

This study provides novel insight in the uncharacterized role of SUSD4 and provides a detailed and well-performed analysis of the Susd4 loss of function phenotype in the cerebellar circuit. The exact mechanism by which SUSD4 affects GluA2 levels remains unclear. However, their findings provide leads for further functional follow-up studies of SUSD4.

Specific comments:

1) Localization of SUSD4. The authors demonstrate localization to spines in distal PC dendrites (Fig. 1C). Does SUSD4 also localize to CF/PC synapses? This is important to establish given the phenotype of increased quantal EPSCs and decreased proportion of synapses without GluA2 at the CF/PC synapse.

2) Figure 4B: There seems to be considerably less surface GluA2 in Susd4 KO cerebellar slices. Is the difference in surface GluA2 between WT and KO significant? Although the mean reduction in surface GluA2 in Susd4 KO following cLTD is similar to WT, the difference with control is not significant. This should be pointed out in the text because it can not be definitively concluded that endocytosis of GluA2 is not altered in Susd4 KO on the basis of this experiment.

3) Figure 4D: The colocalization of SUSD4 with GluA2 is difficult to see in this image. An inset with higher zoom could help. Quantification of colocalization using e.g. Manders coefficient would help.

4) Figure 5B: The negative control used here, PVRL3alpha, lacks an HA tag. This therefore does not control for non-specific interactions of highly overexpressed membrane proteins in co-transfected HEK cells. The authors should use an HA-tagged membrane protein as a control here to demonstrate that the interaction of SUSD4 and GluA2 is specific for SUSD4.

5) Figure 5D: The level of GluA2 recovered in the IP appears normalized to HA-SUSD4. This does not control for the variations in GluA2 input levels shown in Fig. S11. Statements on amounts of GluA2 recovered for various SUSD4 mutants should be adjusted to take this into account.

6) Line 357: binding of SUSD4=is likely meant to be binding of NEDD4.

Reviewer #3 (Public Review):

Bridget A. Matikainen-Ankney et al. discuss the newest generation of their open-source Feeding Experimentation Device (FED3) platform capable of detailed tracking of food pellet intake and dual nose-poke operant behavioral testing. This platform provides a complete solution for these types of studies and includes all necessary open-source hardware, firmware, visualization code, and Arduino and Python libraries for user customization of experiments and analysis. FED3 has a rechargeable battery life of around one week and can operate without any external wires, logging data onto an on-board SD card and allowing for flexible placement in a rodent's home-cage. The platform also includes an on-board display for showing current experimental parameters/data and a variable voltage digital output for synchronizing the system with other external devices such as an optogenetic simulation system. The authors show multiple applications of the FED3 platform including detailed food intake tracking, fixed-ratio operant behavior experiments, and optogenetic self-stimulation. Importantly, they also highlight the ability to do studies across multiple, remote laboratories by leveraging the standardization of such a food intake platform.

Strengths:

The FED3 platform is well thought out and clearly builds off the authors' experience designing and working with their previous generation systems. The specific open-source approach taken by the authors include, not just openly providing design files but, building an understandable and open ecosystem of tools and libraries for laboratories to customize the platform to fit a broad range of experiments. By including data visualization tools and a Python library for working with FED3 data, the authors effectively lower the technical entry point for using such a platform and streamline the process of implanting the system in one's own experiments. The paper provides strong evidence of the FED3's capabilities and relevance of data generated across a range of use cases. There is compelling evidence of the usefulness of developing an open standard for food intake tracking, allowing for multi-site studies and across-laboratory comparisons. Finally, the system is significantly more affordable than other commercial options, lowering the economic barrier for implementing food intake tracking and operant behavior experiments.

Weaknesses:

While this paper presents a very useful, customizable, and flexible approach to food intake and operant behavior studies, certain aspects of the device could be better described in the paper. This is only a minor weakness as all hardware and code is openly available online, allowing for a more detailed understanding of the system beyond what is presented in the paper. It would be helpful to identify the major electronics components on the custom printed circuit board to aid in customization of the system. It would also be useful to provide more details as to the mechanical mechanism used to deliver food pellets and the optical beam breaks for detecting nose-pokes and food pellets.

Some potential limitations of the system include the inability to detect food pellet hoarding, lack of wireless option to access and configure the system, limited battery life, complications when using granular bedding, and no way to identify individual mice. The authors identify and discuss these limitations within the paper which is appreciated.

Reviewer #3 (Public Review):

In this study, van Dorp et al. provide new insights into the structure of the C-terminus of STIM1 in the quiescent as well as the active state. By using extensive smFRET and protein crosslinking techniques, the authors substantially advanced our understanding of STIM1 cytosolic domains orientation and revealed inter- and intramolecular interactions within a STIM1 dimer. Structures have been derived for both STIM1 resting and activated state. Altogether, this study substantially contributes to a mechanistic and structural understanding of the STIM1 activation process, and it paths the way for the comprehensive dynamic resolution of conformational transitions from the inactive to the fully active state.

The single molecule studies represent a very elegant approach to derive novel details on STIM1 structure and dynamics. Utilization of these developed smFRET protein probes of ctSTIM1 in the interaction with Orai1, either reconstituted or even in living cells, would be phantastic, but certainly experimentally challenging based on the low fluorescent background required to resolve single molecule FRET.

Reviewer #3:

In this paper Werkhoven et al. ask a fundamental question in behavioral neuroscience - what is the structure of co-varying behaviors among individuals within populations. While questions in the context of inter-individual behavioral differences have been studied across organisms, this work represents a highly novel and comprehensive analysis of the behavioral structure of inter-individual variation in the fly, and the underlying biological mechanism that may shape this structure of covariation. In particular, for their experiments they combined a set of behavioral tests (some of them were explored in previous studies) to a 13-day long behavioral paradigm that tested single individuals in a highly controlled and precise way. Through clever analysis the authors interestingly showed strong correlations only between a small set of behaviors, indicating that most of the behaviors that they tested do not co-vary, exhibiting many dimensions of inter-individual variation in the data. They further used perturbations of neuronal circuits and showed that temperature and circuit perturbations can change dependencies among sets of behaviors. In a different set of experiments where they integrated gene-expression data (from the brains of single individuals), they showed that some of the genes are correlated with individual-specific parameters of behaviors. Interestingly, through comparison of inbred and outbred population they demonstrated that also outbred populations are showing relatively low covariance of behaviors across individuals.

Overall, the data in the paper indicate that surprisingly, even for a 'simple' organism, there are many dimensions of inter-individual variation, e.g. many specific characters that can change among individuals in a non-dependent way. The ability of the authors to precisely measure such dependencies in such a highly robust and precise way allowed their investigation of the underlying processes that may generate this variation. The results in this study are highly interesting and novel. They uncover a general picture of the structure of behavioral variation among individuals and open many avenues for further analyses of the underlying neuronal and molecular mechanisms that control variation in sets of behaviors. Furthermore, the methods that were developed in this paper can be of great use by other researches in the field.

However, while the key claims of the manuscript are well supported by the data and analyses methods, some aspects of data analysis need to be clarified or extended:

• It is not clear what the motivation is for using the 'Effective dimensionality spectrum' analysis presented in the paper and how it significantly adds to existing methods of clustering that are relying directly on the correlation/distance matrix (some of them were used in this study).

• While it is clear that the distilled behavioral covariation matrix has many independent dimensions (as the authors indicated, most of the a-priori PCs are not strongly correlated), the number of 'significant' Pcs was not calculated directly for the distilled matrix, and t-SNE analysis is presented only for the original covariation matrix (1L).

• It is possible that some of the behaviors that covary across individuals in the high temporal resolution assay and also tend to be associated over time within an individual, may indicate sequences of behavior on longer time-scales (than the timescales in which parameters are quantified).

• Further analyses are needed for extending the detection of correlations between variation in gene-expression data and the independent behavioral measures in the covariance matrix.

Reviewer #3:

The authors propose a new method of focused ultrasound (FUS) neuromodulation namely amplitude modulated FUS that they propose can differentially affect inhibitory and excitatory cells depending upon the intensity employed. Parameter selection is an issue for this field and the introduction of new methods for efficacious modulation are highly desirable. However, this paper does not explicitly test AM FUS against existing forms of FUS thus lending no evidence to its efficacy. While the differential effects are interesting in themselves, we gain no insight if AM FUS is the critical factor leading to this.

Reviewer #3 (Public Review):

By applying modern viral tracing methods, this paper described in detail extensive input-output connections of Gad1Cre+, VgatCre+, or Ntsr1Cre+ IntA projection neurons.

Because diverse neurons are intermingled in a small region, it is generally challenging to isolate specific excitatory or inhibitory neurons and their circuits in the cerebellar nucleus.

The authors focused on IntA of CN and demonstrated that 1) both inhibitory (Gad1Cre+ and/or VgatCre+) and excitatory (Ntsr1Cre+) neurons comprise extensive input-output connections with many extracerebellar regions, and 2) inhibitory circuits are functionally distinct from excitatory circuits on the basis of projection targets. This work could provide insights into diversity of inhibitory IntA neurons, and thus could be an interesting addition to the field's expanding efforts to identify cell types of CN, their input-output connections, and their functions.

However, interpreting the data is difficult because of technical challenges. Critically, the main conclusion could be compromised by experimental artifacts, which need better characterization. In addition, the text could be revised to make it more accessible to a broad audience.

Reviewer #3 (Public Review):

The manuscript by Sando et al. describes experiments directed at unraveling how latrophilins (Lphns) orchestrate synapse formation. Lphns are a unique family of adhesion molecules harboring extensive extracellular N-terminal domains with several known interacting motifs coupled to the classical 7 transmembrane architecture of G-protein coupled receptors. In recently published work from the Sudhof group, Lphns were shown to play a surprising postsynaptic role in synapse formation onto CA1 pyramidal neurons with Lphn2 and 3 important for perforant path and Schaffer collateral synapse formation respectively (Sando et al., Anderson et al). However, it remains unclear whether G-protein signaling through Lphns is important for their role as synapse organizers.

To address this issue, the authors use conditional knockout/rescue approaches to convincingly demonstrate an essential role of the GPCR domain of Lphns 2 and 3 both in vitro and in vivo. Replacing the intracellular 3rd loop of the GPCR domain (which is essential for G-protein activation) of either Lphn2 or 3 fails to rescue reduced synapse number in the knockout background (nor does deleting the entire GPCR domain). Thus it appears that cell adhesion properties alone are not sufficient for Lphn-mediated synapse formation. The experiments appear to be robust and convincing and the conceptual advance of Lphn-mediated GPCR signaling during synapse formation is substantial. I have a few specific points outlined below, but overall the authors use a nice combination of imaging, electrophysiology and rabies virus-based synaptic connectivity measurements to support their conclusions. Naturally, I'd like to know more details about the signaling requirement (e.g. how is Lphn signaling spatially compartmentalized compared to other GPCRs present, which G-protein(s) Lphns couple to, how/when/whether GPCR signaling is regulated by ligand engagement etc.) but these questions seem better suited to a separate study.

Reviewer #3:

This is an interesting manuscript in which the authors have investigated the effect of intracellular injection of oligomeric beta-amyloid into hippocampal neurons both in cultures and adult animals. They find that starting from 500 pM, intracellular injection of oligomeric beta-amyloid rapidly increases the frequency of synaptic currents and higher concentrations potentiate the AMPA receptor-mediated current. Both effects were PKC-dependent. Furthermore, they find that following PKC activation there is release of NO which in turn increases release of neurotransmitter not only in the nearby pre-synaptic site, but also in neighboring cells. This suggests that intracellular injections of oligomeric beta-amyloid into the postsynaptic neuron can increase network excitability at a distance. The effect on neuronal excitability would involve AMPA-driven synaptic activity without altering membrane intrinsic properties. The conclusions are sound. However, there are two main aspects of the observed phenomenon that have not been taken adequately into account, or have been avoided by the authors. The authors have not investigated the effects of application of oligomeric beta-amyloid into the extracellular space and the presynaptic neurons, two other compartments of the synapse. They might have performed experiments comparing findings from experiments with intracellular injections of oligomeric beta-amyloid into the post-synaptic neurons, with effects of extracellular application and those of injections into the presynaptic neuron.

Additional minor concerns are related to the following issues:

a) The raw data on Figure 3 suggest that not only excitatory transmission is affected but also inhibitory transmission is somewhat modified. Measurement of the charge might be misleading.

b) This reviewer is not clear on the meaning of the following sentence in the discussion "Contrary to previously published data using extracellular Aβ or with more chronic application models [45-50], we did not find any synaptic deficits". The current work shows synaptic changes!


c) There is a mistake in the numbering of figures in the discussion. The paper has no figure 11. When referring to figure 10, they must mean something else.

d) The model on Figure 10 needs work. The authors should explain what various elements of the drawing mean, or better label them directly on the figure.

Reviewer #3:

Huss et al. describe a phage genome engineering technology that they call ORACLE. This technique uses recombineering of a phage target gene with a variant library to identify both gain and loss of function mutations. The beauty of this method and what makes it superior to other techniques is that it dramatically limits loss of mutants that are less fit during the initial round of library generation. Thus, the pool of variants is vast and is reduced in bias toward more fit species based on the host used for initial library amplification. They use the model coliphage T7 as a proof of principle and show that several previously unidentified residues in the T7 tail fiber play critical roles in both loss and gain of function for phage infectivity and they also identify residues that are major drivers of altered host tropism. Lastly, they apply this library to a pathogenic UTI associated strain of E. coli which is normally resistant to wild type T7 infection and identify tail variants of T7 that can now infect this strain, highlighting the applicability of this method toward the discovery of engineered phages that could be used therapeutically. Altogether this is an important advancement in phage engineering that shows potential promise for future phage therapies.

Reviewer #3 (Public Review):

The authors herein have nicely dissected the role of RNF43 in WNT5A signaling in mammalian cells, with a focus in the context of melanoma. They show that RNF43 inhibits WNT5A activity by ubiquitinating and thereby marking for proteasomal degradation multiple proteins involved in WNT5A signal transduction (i.e., VANGL2). The authors have performed the study in a thorough manner.

Reviewer #3 (Public Review):

P2X2 receptor channels do not have a canonical voltage-sensor, yet they display profound voltage-dependence especially when activated by physiologically relevant low ATP concentrations. Understanding the mechanisms of this voltage dependence is not an easy undertaking because there are neither similar proteins as precedent nor clear indications from available structures. In this manuscript, Andriani and Kubo incorporated Anap into 96 residues (separately) in P2X2 receptor channels and performed a comprehensive scanning using voltage-clamp fluorometry technique to probe structural changes during ATP- and voltage-dependent gating. Out of the 96 residues, the authors only observed voltage-dependent fluorescence intensity (F) changes at A337 and I341 in the TM2 domain. The changes are fast and linear, consistent with them being electrochromic effect. When an additional mutant K308R is introduced, the authors were able to detect a small slow and voltage-dependent F change at A337, which could potentially result from structural rearrangements at this position. With a P2X2 model built upon the hP2X3 open state structure, they also proposed that A337 interacts with F44 in TM1, and this interaction is important for activation. The amount of work involved in this study is impressive. The data presented are of good quality. Most conclusions drawn from the results are reasonable and backed with good evidence.

Overall, the identification of a converged electric field around A337 and I341 is new and intriguing. Previously reported functional results and available high resolution P2X receptor structures all suggest that residues A337 and I341 are facing TM1 and they are accessible to Ag+ when mutated to Cys. It is conceivable that the "voltage-sensor" in P2X2 receptor channels involve ion filled crevices between TM1 and TM2 in the membrane. This work is of great value for understanding how membrane proteins sense voltages.

Reviewer #3 (Public Review):

The authors describe a method for fitting a simple, separable function of contrast and cone excitation to a set of fMRI data generated from large, unstructured chromatic flicker stimuli that drive the L- and M- cone photoreceptors across a range of amplitudes and ratios. The function is of the form of a scaled ellipse – hereafter referred to as a 'Quadratic Color Model' (QCM). The QCM fits 6 parameters (ellipse orientation, ellipse elongation, and 4 parameters from a non-linear, saturating (Naka-Rushton) contrast response curve. The QCM fits the dataset well and the authors compare it (favorably) to a 40-parameter GLM that fits each separate combination of chromatic direction and contrast separately.

The authors note three things that 'did not have to be true' (and which are therefore interesting):

1) The data are well-fit by a separable ellipse+contrast transducer - consistent with the idea that the underlying neuronal computations that process these stimuli combine relatively independent L-M and L+M contrast.

2) The short axis of the QCM tends to align with the L-M cone contrast directing (indicating that this direction is one of maximum sensitivity and the L+M direction (long axis) is least sensitive. This finding is qualitatively consistent with psychophysical measurements of chromatic sensitivity.

3) Fit parameters do not change much across the cortical surface – and in particular they are relatively constant with respect to eccentricity.

This is a technically solid paper – the data processing pipeline is meticulous, stimuli are tightly-calibrated (the ability to apply cone-isolating stimuli to fovea and periphery simultaneously is an impressive application of the 56-primary stimulus generator) and the authors have been careful to measure their stimuli before and after each experimental session. I have a few technical questions but I am completely satisfied that the authors are measuring what they think they are measuring.

The analysis, similarly, is exemplary in many ways. Robust fitting procedures are used and model performance and generalizablility are evaluated with a leave-run-out and leave-session-out cross validation procedures. Bootstrapped confidence intervals are generated for all fits and analysis code is available online.

The paper is also useful: it summarises a lot of (similar) previous findings in the fMRI color literature going back to the late 90s and points out that they can, in general, be represented with far fewer parameters than conditions. My main concerns are:

1) Underlying mechanisms: The QCM is a convenient parameterization of low spatial-frequency, high temporal-frequency L-M responses. It will be a useful tool for future color vision researchers but I do not feel that I am learning very much that is new about human color vision. The choice to fit an ellipse to these data must have been motivated at least in part by inspection. It works in this case (possibly because of the particular combination of spatial and temporal frequencies that are probed) but it is not clear that this is a generic parametric model of human color responses in V1. Even very early fMRI data from stimuli with non-zero spatial frequency (for example, Engel, Zhang and Wandell '97) show response envelopes that are ellipse-like but which might well also have additional 'orthogonal' lobes or other oddities at some temporal frequencies.

2) Model comparison: The 40-parameter GLM model provides a 'best possible' linear fit and gives a sense of the noisiness of the data but it feels a little like a strawman. It is possible to reduce the dimensionality of the fit significantly with the QCM but was it ever really plausible that the visual system would generate separate, independent responses for each combination of color direction and contrast? I suspect that given the fact that the response data are not saturating, it would be possible to replace the Naka-Rushton part of the model with a simple power function, reducing the parameter space even further. It would be more interesting to use the data to compare actual models of color processing in retina/V1 and, potentially, beyond V1.

3) Link to perception. As the authors note, there is a rich history of psychophysics in this domain. The stimuli they choose are also, I think, well suited to modelling in the sense that they are likely to drive a very limited class of chromatic cells in V1 (those with almost no spatial frequency tuning). It is a shame therefore that no corresponding psychophysical data are presented to link physiology to perception. The issue is particularly acute because the stimulus differs from those typically used in more recent psychophysical experiments: it flickers relatively quickly and it has no spatial structure. It may, however, be more similar to the types of stimuli used prior to the advent of color CRTs : Maxwellian view systems that presented a single spot of light.

Reviewer #3 (Public Review):

Myotonia congenita is a heritable disorder of muscle fiber excitability in which a severe reduction of the resting chloride conductance (gCl, CLCN1 mutations) produces susceptibility to involuntary after-contractions and transient weakness. Fifty years ago, Bryant, Adrian and colleagues showed that loss of > 50% of gCl is sufficient to cause myotonic bursts of after-discharges. Much less is known about the mechanistic basis for the transient weakness (several seconds, up to 1 minute) that occurs with initial contractions after rest. This study elegantly confirms what has long been suspected; that sustained depolarization of the resting potential is the basis for the transient weakness. The experimental approach employed several new techniques to achieve this demonstration. First, the use of repeated in situ contraction tests every 4 sec (Fig. 1) clearly shows the coincidence of myotonia and transient weakness, both of which exhibit warm-up. This animal model for the transient weakness in a low gCl state was essential for the success of this study. Secondly, the remarkably stable measurements of membrane potential (Vm), without the need to apply a holding current to achieve the normal resting potential (Figure 2) is necessary to convincingly demonstrate the plateau depolarizations are a consequence of the myotonic condition, and not a stimulation artifact. Moreover, a severe reduction of fiber excitability was directly demonstrated by application of brief current pulses during the plateau depolarization (Figure 2E). Third, the authors have used the ncDHPR mouse (non-conducting CaV1.1) to show the Ca current has some role in prolonging the duration of the plateau. This is an important contribution because the sluggish, low-amplitude Ca current in skeletal muscle has not previously been implicated in the pathogenesis of myotonia. Finally, the authors built upon their recent work showing ranolazine suppresses myotonia in low gCl muscle to also show this drug abolishes the plateau potential. Taken together, this excellent study provides the most definitive experimental evidence to date for the mechanistic basis of transient weakness in myotonia congenita and also suggests ranolazine may be beneficial for prophylactic management.

Major Points:

1) The major experimental limitation that prevented prior studies from establishing the mechanism for the transiently reduced excitability and weakness in MC was the concern that plateau depolarizations frequently occur as an artifact in studies of skeletal muscle membrane potential (e.g. secondary to leakage current from electrode impalement or failure to completely suppress contraction with motion-induced damage). The authors are to be commended for including many records of Vm (absolutely necessary for this publication) and for explicitly stating that a holding current was not applied to maintain Vrest. The confidence of these observation could be further increased by addressing these questions:

— Were recordings excluded from the analysis if the plateau potential was not followed by a subsequent return to Vrest? Was a criterion used to define successful return to the resting potential?

— If fibers that failed to repolarize were excluded, was this a frequent or a rare event, and importantly, was the likelihood of failure different for control versus myotonic fibers?

2) The data clearly show a large variance for the duration of the plateau potential (e.g. horizontal extent of data in Figure 3B), which is interesting and may provide additional insights on the balance of currents that contribute to this phenomenon. The authors also point out that the distribution was skewed toward briefer plateau periods for the 9-AC model than the adr mouse. It is suggested this difference may be a consequence of life-long reduced gCl in adr mice with some chronic compensation versus the acute block of ClC-1 in the 9-AC model. What about the possibility that the reduction of gCl is more severe in the adr fibers than in 9-AC treated animals? A residual Cl current could foreshorten the duration of the plateau potential. Another question with regard to the variable duration of the plateau potential is a "duration of 0". In other words, as shown in Fig 3C, how frequently was the absence of a PP encountered?

3) The possibility that activity-dependent accumulation of myoplasmic Ca may contribute to the PP is suggested (page 9 line 175), but this is not further commented upon in the Discussion. Namely, is the reduction of PP duration in ncDHPR fibers proposed to be a consequence of less inward charge movement or of less myoplasmic Ca accumulation (i.e. is it a balance of ionic currents or an intracellular signaling factor)? Moreover, with regard to an activity-dependent process that influences the likelihood and/or duration of the PP, the authors quantify the "mean firing rate" and the "mean membrane potential", both quantified during the preceding myotonic burst. Both of these factors may contribute to an activity-dependent process, but another factor has been omitted; namely the duration of the antecedent myotonic run. It would be interesting to test whether the duration of the myotonic burst had an influence on the PP.

Reviewer #3 (Public Review):

Lee et al. report results from an fMRI experiment with repeated viewings of a single movie clip, finding that different brain regions come to anticipate events to different degrees. The findings are brief but a potentially very interesting contribution to the literature on prediction in the brain, as they use rich movie stimuli. This literature has been limited as it has typically focused on fixed short timescales of possible anticipation, with many repetitions of static visual stimuli, leading to only one possible time scale of anticipation. In contrast, the current video design allows the authors to look in theory for multiple timescales of anticipation spanning simple sensory prediction across seconds to complex social dynamics across tens of seconds.

The authors applied a Hidden Markov Model to multivoxel fMRI data acquired across six viewings of a 90 second movie. They fit a small set of components with the goal of capturing the different sequentially-experienced events that make up the clip. The authors report clusters of regions across the brain that shift in their HMM-identified events from the first viewing of the movie through the (average of the) remaining 5 viewings. In particular, more posterior regions show a shift (or 'anticipation') on the order of a few seconds, while more anterior regions show a shift on the order of ~10 seconds. These identified regions are then investigated in a second way, to see how the HMM-identified events correspond to subjective event segmentation given by a separate set of human participants. These data are a re-analysis of previously published data, presenting a new set of results and highlighting how open sharing of imaging data can have great benefits. There are a few important statistical issues that the authors should address in a revision in order to fully support their arguments.

1) The authors report different timescales of anticipation across what may be a hierarchy of brain regions. However, do these timescales change significantly across regions? The paper rests in part on these differences, but the analyses do not yet actually test for any change. For this, there are multiple methods the authors could employ, but it would be necessary to do more than fit a linear model to the already-reported list of (non-independently-sorted) regions.

2) The description of the statistical methods is unclear at critical points, which leads to questions about the strength of the results. The authors applied the HMM to group-averaged fMRI data to find the neural events. Then they run statistical tests on the difference in the area-under-the-curve (AUC) results from first to other viewings. It seems like they employ bootstrap testing using the group data? Perhaps it got lost, but the methods described here about resampling participants do not seem to make sense if all participants contributed to the results. Following this, they note that they used a q < 0.05 threshold after applying FDR for the resulting searchlight clusters, but based on their initial statement about the AUC tests, this is actually one-tailed? Is the actual threshold for all these clusters q < 0.10? That would be quite a lenient threshold and it would be hard to support using it. The authors should clarify how these statistics are computed.

3) Regarding the relationship to annotated transitions, the reported difference in correlations at zero lag don't tell the story that the authors wish they tell, and as such it does not appear that they support the paper. While it is interesting to see that the correlation at zero lag in the initial viewing is often positive in the independently identified clusters, the fact that there is a drop in correlation on repeated viewings doesn't, in itself, mean that there has been a shift in the temporal relationship between the neural and annotated events. A drop in correlation could also occur if there was just no longer any correlation between the neural and annotated events at any lag due to noisy measurements, or even if, for example, the comparison wasn't to repeated viewings but to a totally different clip. The authors want to say something about the shift in in the waveform/peak, but they need to apply a different method to be able to make this argument.

4) Imaging methods with faster temporal resolution could reveal even earlier reactivation, or replay, of the movies, that would be relatively invisible with fMRI, and the authors do not discuss relevant recent work. E.g. Michelmann et al. 2019 (Nat Hum Beh) and Wimmer et al. 2020 (Nat Neuro) are quite relevant citations from MEG. Michelmann et al. utilize similar methods and results very similar to the current findings, while Wimmer et al. use a similar 'story' structure with only one viewing (followed by cued retrieval) and find a very high degree of temporal compression. The authors vaguely mention faster timescale methods in the discussion, but it would be important to discuss these existing results, and the relative benefits of these methods versus the benefits and limitations of fMRI. It would be interesting and puzzling if there were multiple neural timescales revealed by different imaging methods.

5) The original fMRI experiment contained three conditions, while the current results only examine one of these conditions. Why weren't the results from the two scrambled clip conditions in the original experiment reported? Presumably there were no effects observed, but given that the original report focused on a change in response over time in a scrambled video where the scrambled order was preserved across repetitions, and the current report also focuses on changes across viewings, it would be important to describe reasons for not expecting similar results to these new ones in the scrambled condition.

Reviewer #3 (Public Review):

In a previous study, the authors had shown that germline tumors that accumulate in the C. elegans gonad because of the lack the RNA binding translational repressor GLD-1, have an increased propensity to differentiate and express somatic proteins in response to ER stress induced by tunicamycin or the absence of the TRK kinase protein tfg-1 (a process the authors call GED). Using this as a model, here, the authors investigate the mechanisms by which the abnormal nuclei accumulate in the tumorous gonad of glp-1 animals by manipulating genes in the soma and germline.

The key message of this paper is, then, the identification of neurons and neuromodulators that suppress or enhance this accumulation of abnormal germline cells in the glp-1 germline. While the results of this analysis could potentially provide an interesting advance, the validity of the many of the conclusions are difficult to evaluate because of limitations posed by the experimental methods and ambiguity in defining the GED.

Weaknesses:

A key issue is the identity of the abnormal germline cells that accumulate in glp-1 gonads. Modulation of the neuronal circuits examined (FLP-6, serotonin, cholinergic) change the germline, alter ovulation rates, modulate somatic gonad contraction rates etc. in wild-type animals. The effects of these circuits on a glp-1 germline are not known, but some of the same effects are likely to continue even if germ cells turned tumorous. Therefore, how neurons and neuromodulators alter the accumulation of abnormal cells in the gonad may or may not be surprising or novel, based on what is actually happening to these cells (the phenotype scored as GED). However, this is unclear as all the abnormal effects on the germline are assessed using DAPI at some steady state. Therefore, GED (ectopic differentiation) needs to be better demonstrated separate from the simple accumulation of abnormal nuclei, which could happen for a number of different reasons.

Strengths:

One strength of this paper is the identification of the neuropeptide FLP-6 as a suppressor of GED and a possible RIDD target. However, there is insufficient analysis conducted to fully support this claim.

Reviewer #3 (Public Review):

This is a very interesting and well conducted study that addresses a question of crucial importance and will make a very valuable contribution to the literature. The question of the vulnerability of newly generated oligodendrocytes in an inflamed environment has not previously been examined with anything like the sophistication of the current series of experiments. The paper is excellent and the data convincing. I only have a few relatively minor issues that the authors might want to consider.

The first results section on sephin1 in EAE is a little confusing. If I have understood the rationale correctly, it is to activate the ISR to protect oligodendrocytes, newly generated from OPCs, in the face of a hostile inflammatory environment. If that is correct, then perhaps this could be explained more explicitly, and the concluding sentence re-worded so as not to give the impression that sephin-1 is able to enhance remyelination (which I realise is not what is stated but is the conclusion that might be drawn).

The effect of the BZA-sephin combination of g ratio of remyelinated axons is very interesting. This could, of course, be because the process is accelerated with this combination rather than enhanced given that g ratios in the CC will eventually return to normal after cuprizone induced demyelination (eg Stidworthy et al., Brain Pathology 2003). This could perhaps be addressed in the discussion.

The authors could make the point in the discussion that regenerative medicines are very unlikely to be given in the absence of effective drug-mediated suppression of aggrieved inflammation.

Reviewer #3:

In this manuscript, the authors investigated roles of PSD95 in the hippocampus for contextual fear extinction. The authors showed that PSD95 levels in the spine and density of PSD-95-positive spines in the dorsal CA1 (dCA1) are changed following contextual fear conditioning and extinction learning. Interestingly, overexpression of PSD95-S73A mutant or chemogenetic inhibition of dCA1 impairs only the second extinction learning at 24 hrs following the first extinction learning. Importantly, these manipulations also blocked the changes of PSD95-positive spines following the first extinction learning. These observations suggest that phosphorylation of PSD95 at S73 in the dCA1 of hippocampus contributes to contextual fear extinction. This manuscript suggests the importance of PSD95 phosphorylation in the hippocampus in some aspects of mechanisms of contextual fear extinction at the molecular and spine levels. However, the title, abstract and conclusions do not well reflect observations and experimental designs in this manuscript. I have several concerns as follows.

Major concerns:

1) The authors used viral overexpression of PSD-95 S73A mutant that may function as a dominant negative mutant, but not knock in mutation. Therefore, the function of phosphorylation of PSD 95 at S73 on spine morphology and contextual fear extinction have been not yet investigated well. The experimental design in this manuscript made limitations to understand behavioral results. It is better to use knock-in mutation strategy than overexpression of the mutant. Alternatively, the authors can examine the phosphorylation levels of PSD95 following contextual fear conditioning and extinction learning and/or function of this mutant at the molecular and cellular levels using biochemistry/molecular biology/cell culture.

2) Overexpression of S73A or chemogenetic inhibition of CA1 impaired additional extinction learning. These observations are interesting. However, the authors have not well characterized these findings at the behavioral levels. In other words, the authors should clarify the effects of these manipulations on contextual fear extinction at the behavioral levels. According to abundant knowledge of fear memory extinction, the behavioral results in this manuscript raised a lot of questions to understand the impact of those genetic manipulations on "contextual fear extinction". How about effects on extended extinction learning (60 min), additional 30 min extinction learning at the same day after first extinction training, spontaneous recovery, renewal, and reinstatement? Some answers of these questions will help to understand behavioral observations in this study and enable us to identify roles of PSD95 and its phosphorylation in extinction of contextual fear memory. It is also important to examine PSD95-positive spines just after the additional extinction learning to understand behavioral observations.

Reviewer #3 (Public Review):

In the article "Widespread premature transcription termination of Arabidopsis thaliana NLR genes by the spen protein FPA", the authors describe the function of FPA as a mediator of premature cleavage and polyadenylation of transcripts. They also focused their study on NLR-encoding transcripts, as that was their most novel observation, describing an additional layer of control.

In general, the article is well written and clear. The experimental design is good, they didn't seem to over-interpret the results, the controls were solid, and the nanopore data were quite informative for their work. It is rather descriptive, but the results will be helpful for those working on NLRs, and demonstrate the utility of bulk long-read transcript data. The authors were able to string together a number of descriptive observations or vignettes into an informative paper. Overall, it is solid science.

One minor complaint is that the authors don't focus on NLRs starting on line 436, and then they have extensive results on NLRs; by the time I got to the discussion, I'd forgotten about the early focus on the M6A. While the first part of the article is necessary, I would suggest a more concise results section to give the paper more focus on the NLR control (since that is emphasized in the abstract and the title of the manuscript).

Reviewer #3 (Public Review):

The manuscript of Anchimiuk and colleagues investigates the mechanism of translocation of Bacillus subtilis SMC-ScpAB, a well characterized bacterial condensin. First, the authors use several SMC constructs where the coil-coiled region has been extended and /or the hinge exchanged and test what are the effects on growth and on the organization of the chromosome. They find highly altered conformations for most of the mutants. Particularly, these altered SMCs are unable to bridge two arms in the presence of the naturally-occurring parS sequences. Interestingly, they are partially able to restore arm pairing if a single parS sequence is provided.

Next, the authors used Chipseq to compare the binding pattern of wildtype SMC and SMC-CC425 (a mutant with an extended coil coiled region and a different hinge). They observe that the binding of wt-SMC is only midly affected by removal of most parS sequences, whilst that of the mutant is highly affected. In time-lapse experiments where ParB is depleted and then re-expressed, the authors show that in a strain with a single parS wt-SMC loads in the origin region and then redistributes over the chromosome while the mutant can only partially achieve redistribution and to a large extent remains concentrated on the origin region.

The authors then use wt-SMC and investigate how the conformation of the chromosome changes with two different parS sites located in different positions. They observe that each parS site is able to produce arm-pairing. They observe a decrease in the strength of arm pairing when both parS sites are present.

Finally, the authors increase the expression level of wt-SMC, and observe decreased levels of arm-pairing in the presence of all the naturally-occurring parS sites. More normal levels of arm-pairing are observed when only one parS is present, despite the higher wt-SMC levels. When two parS sites are introduced, more complex structures appear in the contact map.

These observations are new, interesting and intriguing. However, there are multiple possible interpretations, models and mechanism that are not discerned by the data presently presented in the manuscript.

At times, there seem to be inconsistencies in their interpretation of results, and at times the models proposed do not seem well supported by data.

Finally, the presentation of previous models and results from the literature could be improved.

Major issues:

In Fig. 1 the authors make several mutant SMC constructs with larger or shorter arms and different hinges and use Hi-C to explore the changes in 3D chromosome organization. Is it not clear to me why the arc is still visible in the mutants, nor what happens to the overall organization of the chromosome in the mutants? Is chromosome choreography normal?

In Fig. 1C the authors show that strains with parS-359 only display a secondary diagonal and conclude "chromosome arm alignment was comparable to wild-type". A quantification of the degree of pairing for each mutant normalized by the wild-type is necessary to evaluate the degree of pairing and its dependence on genomic distance to the origin.

In Fig. 2, the authors use HiC and chip-seq to quantify the effects of changes in SMC arm length on chromosome organization and SMC genomic distributions. It would be important to verify that the expression levels of these SMC mutants are the same as wt, as as they show in Fig. 4 changes in protein levels can change also 3D chromosome organization.

In Fig. 2C, what is the distribution of SMC at t0? Showing this result would support their claim that SMC can load in absence of ParB.

In Fig. 2C it is claimed that SMC-CC425 moves at a slower rate than WT. Can the authors provide a quantification?

In Fig. 2, the authors focused on one of the mutants with longer SMC arms (CC425) and performed HiC and Chip-seq in time-lapse after induction of ParB in a ParB-depleted culture. These experiments clearly establish that SMC-CC425 can redistribute from the origin and can achieve arm pairing but to a lesser extent than the WT. The authors speculate that a slower translocation rate and/or a faster dissociation rate explain the experiments. However, other possibilities exist: for instance that the mutant SMC is defective at passing through road-blocks (highly expressed genomic regions, e.g rRNA sites) or at managing collisions with RNAP/ DNAP/ other SMCs, it makes different higher-order complexes than wt-SMC, etc. This could could be due to the change in the length of the SMC, or to the use of a hinge/coiled-coil region different from that of the wt-SMC. Thus, I am not convinced that the text explores all the possible models or that the data shown discerns between any of them.

In Fig. 3B, the authors show that use of two parS-opt sites at -304kb and -9kb lead to the formation of two secondary diagonals. They argue that these can be rationalized in terms of the diagonals formed by the strains harboring single parS-opt (either -9kb or -304kb). However, I cannot see how these can happen at the same time! If a cells makes arm pairing from -9kb then it cannot make it from -304kb right? I do not understand either how the authors can conclude from these experiments that ParS may act as unloading sites for SMC. Again, the authors are speculating over mechanisms that are not really tested.

If parS sites triggered the unloading of SMCs, then one would assume that ~5-6 natural parS sites in the origin region are unloading the SMC complexes loaded at other parS sites? This makes little sense to me, or there is something I clearly do not understand in their explanations.

In their text, the authors explain that "A small but noticeable fraction of SMC complexes however managed to translocate towards and beyond other parS sites apparently mostly unhindered". I am confused as to where is the evidence supporting this statement. I do not think the ensemble Hi-C experiments provided in Fig. 3 can provide conclusive evidence for this.

The authors often hypothesize on a mechanism, but then assume this mechanism is correct. For instance, the disruption in the secondary diagonals in Fig. 3B when experiments are performed with two parS sites are initially hypothesized to be due to roadblocks (e.g with highly transcribed regions) or to collisions between SMCs loaded at different parS sites. These possibilities cannot be discerned from their data. However, the authors then assume that collisions is what is going on (e.g. paragraph in lines 274-284). I think they should provide evidence on what is producing the changes in the secondary diagonals in mutants with two ParS sites.

Why is the ChIP-seq profile for a strain with all the natural parS sites and for a strain with only parS-9kb the same? even with the same peaks at the same locations? Does this mean that SMC peaks do not require the presence of parS? But, then SMCs do not load equally well in all naturally occurring parS sites? This is then in contradiction to their assumption that parS cannot be selectively loaded?

Do we really know that it is a single SMC ring that is responsible for translocation? The authors assume so in their models and interpretations, but if it were not the case it could drastically modify the mechanisms proposed. For instance, SMC may be able to load on a ParS site without pairing arms (i.e. only one dsDNA strand going through the SMC ring).

In Fig. 2C-D it is shown that a large fraction of wildtype SMC and SMC-CC425 accumulate at the origin region at early time points (Fig. 2C) however this does not seem to lead to an increased Hi-C signal in the origin region (compare early time points to the final t60). Also, despite small amounts of wt-SMC in the chromosome at the latter time points, the intensity of the secondary diagonal is very strong. Why is this? These results would be consistent with many SMCs loading at the origin region but only a fraction of them being responsible for arm-pairing. Is this not in contradiction to their assumption that SMCs pair two dsDNA arms when they load?

The authors state that: "If SMC-CC425 indeed fails to juxtapose chromosome arms due to over-enrichment in the replication origin region, collisions may be rare in wild-type cells because of a high chromosome residence time and a limited pool of soluble SMC complexes, resulting in a small flux of SMC onto the chromosome. If so, artificially increasing the flux of SMC should lead to defects in chromosome organization with multiple parS sites but not with a single parS site (assuming that most SMC is loaded at parS sites)". However, this assumption seems inconsistent with their results in Fig. 2 that show that the peaks of SMC do not change upon removal of most parS sites.

I am a bit confused about the interpretation of the results in Fig. 4D. The authors talk about 'loop contacts' and point to the secondary diagonal (yellow ellipses). But these are not loop contacts, but rather contacts between arms that have surpassed the two parS sequences, right? Also, it is not clear what they mean by paired-loop contacts (red ellipse). Do they mean contacts between the two loops originating at parS-359 and parS-334? If this where the case, then it means SMCs are bridging more than two dsDNA segments? Or that there are multimers of SMC linking together? Or that and SMC can circle one arm from one loop and another from the other...? But in this case, how can it load? For me it is very unclear what these experiments really mean. The explanations provided by the authors seem again highly hypothetical.

Reviewer #3 (Public Review):

The authors of this manuscript combine electrophysiological recordings, anatomical reconstructions and simulations to characterize synapses between neurogliaform interneurons (NGFCs) and pyramidal cells in somatosensory cortex. The main novel finding is a difference in summation of GABAA versus GABAB receptor-mediated IPSPs, with a linear summation of metabotropic IPSPs in contrast to the expected sublinear summation of ionotropic GABAA IPSPs. The authors also provide a number of structural and functional details about the parameters of GABAergic transmission from NGFCs to support a simulation suggesting that sublinear summation of GABAB IPSPs results from recruitment of dendritic shaft GABAB receptors that are efficiently coupled to GIRK channels.

I appreciate the topic and the quality of the approach, but there are underlying assumptions that leave room to question some conclusions. I also have a general concern that the authors have not experimentally addressed mechanisms underlying the linear summation of GABAB IPSPs, reducing the significance of this most interesting finding.

1) The main novel result of broad interest is supported by nice triple recording data showing linear summation of GABAB IPSPs (Figure 4), but I was surprised this result was not explored in more depth.

2) To assess the effective radius of NGFC volume transmission, the authors apply quantal analysis to determine the number of functional release sites to compare with structural analysis of presynaptic boutons at various distances from PC dendrites. This is a powerful approach for analyzing the structure-function relationship of conventional synapses but I am concerned about the robustness of the results (used in subsequent simulations) when applied here because it is unclear whether volume transmission satisfies the assumptions required for quantal analysis. For example, if volume transmission is similar to spillover transmission in that it involves pooling of neurotransmitter between release sites, then the quantal amplitude may not be independent of release probability. Many relevant issues are mentioned in the discussion but some relevant assumptions about QA are not justified.

3) The authors might re-think the lack of GABA transporters in the model since the presence and characteristics of GATs will have a large effect on the spread of GABA in the extracellular space.

4) I'm not convinced that the repetitive stimulation protocol of a single presynaptic cell shown (Figure 5) is relevant for understanding summation of converging inputs (Figure 4), particularly in light of the strong use-dependent depression of GABA release from NGFCs. It is also likely that shunting inhibition contributes to sublinear summation to a greater extent during repetitive stimulation than summation from presynaptic cells that may target different dendritic domains. The authors claim that HCN channels do not affect integration of GABAB IPSPs but one would not expect HCN channel activation from the small hyperpolarization from a relatively depolarized holding potential.

Reviewer #3 (Public Review):

In zebrafish embryo development the surface epithelium, the enveloping layer (EVL), proliferates and migrates along with the yolk sac during epiboly. This process requires the simultaneous proliferation and migration of cells, which must undergo cell shape changes. Co-ordination of these processes is regulated by proliferation, whereby cell number and shape perturb tissue-scale forces necessary for epiboly. This paper investigates explicitly the importance of successful cytokinesis, through abscission of cytokinetic bridges, on regulating these forces and epiboly progression. They show that Rab25, a GTPase belonging to the Rab11 subfamily, regulates abscission through endomembrane trafficking in the EVL. Through their detailed analysis of cellular-level phenotypes, including qualitative and quantitative approaches, this paper presents convincing evidence for this novel role of Rab25. The authors should be congratulated on excellent time-lapse movies of cytokinesis in early zebrafish development.

Reviewer #3 (Public Review):

The manuscript by Turner et al. employs a transcriptome-wide approach to study the effects of mutants of the 3'-end processing machinery and the anti-cancer drug cordycepin (3' deoxyadenosine) on alternative poly(A) site selection in budding yeast to better understand alternative polyadenylation (APA) mechanism(s). In particular, poly(A) test sequencing (PAT-seq), a 3'-end focused deep sequencing technique, is employed to determine cleavage/poly(A) site choice in seven mutants of the core 3'-end processing machinery – three cleavage factor IA (CFIA) mutants (rna14-1, pcf11-2, clp1-pm), one cleavage factor IB (CFIB) mutant (nab4-1), and three cleavage and polyadenylation factor (CPF) mutants (ysh1-13, fip1-1, pap1-1). Six of the 3'-end processing factor mutants exhibit increased distal poly(A) site usage and lengthening of 3'-UTRs, with rna14-1 and pcf11-2 showing the greatest effect, but clp1-pm exhibiting little effect. Notably, 3511/7091 genomic annotations (49.5%) have two or more poly(A) sites and 422 genes have significantly changed poly(A) sites in all the 3'-end processing factors mutants except clp1-pm. APA is also examined in 41 genes in a full spectrum of 3'-end processing mutants (22) using a multiplexed poly(A) test (mPAT) method and most of the mutants alter poly(A) site choice, with a predominant shift to distal site usage. In addition, APA analysis of cells treated with cordycepin using PAT-seq indicates that cordycepin alters poly(A) site choice in 1959 genes, with predominant distal cleavage site usage and lengthening of 3'-UTRs. Cordycepin is also shown to increase nucleotide abundance. Interestingly, impairment of transcription elongation, using mycophenolic acid (MPA), which reduces GTP levels, or an RNA polymerase II mutant, rpb1-H1085Y, in cells treated with cordycepin promotes proximal poly(A) site usage and shorter 3'-UTRs, reversing the effects of cordycepin. Finally, comparison of genes altered in APA by cordycepin to a dataset of yeast nucleosome occupancy suggests that 3'-end nucleosome positioning and length of intergenic regions in convergent genes correlates with cordycepin responsiveness. The data presented in the paper suggest a kinetic model for cleavage/poly(A) site selection in yeast that involves a balance between the concentration/availability of the cleavage and polyadenylation machinery and transcription elongation rate.

The strengths of the study include the generation of transcriptome-wide datasets for poly(A) site usage in numerous mutants of evolutionarily conserved, essential cleavage and polyadenylation factors using the PAT-seq method. In addition, the study indicates that almost 50% of the annotated genes in budding yeast exhibit alternative polyadenylation. The study also indicates that impairment of numerous 3'-end processing factors, irrespective of subcomplex, predominantly causes an increase in distal poly(A) site usage and lengthening of 3'-UTRs. Interestingly, the study also suggests that the choice of poly(A) site is regulated by the availability of cleavage and polyadenylation factors and transcription elongation. Finally, the study shows that anticancer drug cordycepin causes transcriptome-wide changes in alternative polyadenylation, predominantly elevating distal poly(A) site usage.

The weaknesses of the study revolve around basing some conclusions solely on the transcriptome-wide data without additional small-scale experiments. In addition, the effects of 3'-end processing mutants and cordycepin on alternative polyadenylation have been examined in two different strain backgrounds, which could impact direct comparisons of the data. The proposed kinetic model for cleavage site choice in yeast seems only to be tested in cells treated with cordycepin.

Overall, the authors achieved their aims of providing greater insight into the mechanism of alternative polyadenylation and its links to transcription and more understanding of the biological effects of cordycepin in cells. At present, most of the conclusions are supported by the results, but some conclusions require additional experiments.

This study will be of enormous interest to the RNA processing field and to the wider community, especially given that alternative polyadenylation regulates so many aspects of mRNA function, the 3'-end processing factors studied are evolutionary conserved, and cordycepin is an anti-cancer agent.

Reviewer #3 (Public Review):

Microstimulation of the somatosensory cortex is a very promising approach to restore sensory feedback in disabled people. Hughes and colleagues performed cortical microstimulation experiments in a spinal cord injured subject to characterize the relationship between the stimulation parameters (frequency and amplitude) and the perceived sensation (type and intensity). This type of experiment is very important to better understand the potentials and limits of this approach. The results achieved by the authors are very interesting and can represent a first step towards the development of more effective and personalized approaches to restore sensory feedback. These results need to be confirmed with additional subjects and during closed-loop experiments.

Reviewer #3 (Public Review):

Hallast and coworkers identify a potentially novel complex Y chromosome structural rearrangement that is associated with male infertility in a carefully phenotyped European cohort. The authors interrogate the Y chromosome AFZc region in 1190 Estonian idiopathic male infertility cases of varying severity and 1134 controls (healthy young men or proven fathers). They replicate partial AFZc deletions and replicate a known gr/gr deletion association with comparable effect sizes. After conditioning on gr/gr deletion status, they identify an association with secondary b2/b4 duplications on case status, but with no accompanying observed effect on andrological sub-phenotypes.

The authors identify multiple non-syntenic DAZ/CDY1 deletion patterns that are consistent with a large inversion followed by deletion. The authors further infer that this putative inversion is fixed in a Y chromosome sub-lineage. Based on population haplotype frequency estimates they infer that a surprisingly large number of individuals harbouring the r2/r3 inversion have a subsequent deletion. They show through detailed phenotyping shows that r2/r3 inversion+deletion cases in their cohort have more severe disease.

Strengths:

1) Despite being a very common disease, idiopathic infertility is severely understudied, due in large part to difficulties in sample acquisition. More generally, sex chromosome genetic associations for common disease as a whole are understudied owing to their structural complexity and other technical issues. The authors should be applauded for attempting to overcome these challenges.

2) The putative finding of a large-effect common variant conferring risk to a common genetic disease is of great interest. The authors leverage the advantages of a logistically coherent health care system. The level of phenotypic detail of andrological parameters in both cases and controls is impressive and aid in biological interpretation of the genetic findings. For example, the distinction between azoo- versus oligozoo-spermia shed light on a potential meiotic disease aetiology. The endocrine values add important context.

3) The authors imply that the combination of the inversion+deletion risk allele favours a meiotic failure disease aetiology as opposed to a gene dosage aetiology. This is a potentially disruptive finding.

Weaknesses:

1) The authors do not replicate their association, raising the possibility of a false positive finding.

2) The study is underpowered to reliably detect variants of small effect, and underpowered in general. This is a common challenge in reproductive genetics.

3) The logical inferences (as opposed to direct measurement) made by the authors are elegant but add substantial uncertainty to the findings. Most notably, cytogenetic or long-read sequencing based validation of the inversion genotype would strengthen confidence in the study considerably.

If the genetic association is robust and the allele frequency estimates are well calibrated, the implications of this work are considerable. The locus could become a genetic biomarker for infertility. The locus could potentially account for a huge amount of variance in polygenic risk associated with infertility. The findings also raise a fascinating evolutionary conundrum as to how an allele associated with such an evolutionarily destructive phenotype could occur at such high frequencies. The authors briefly raise the possibility of age-dependent effects, but with extremely sparse data.

Reviewer #3 (Public Review):

These authors report the identification of the function of a genetic determinant (dev1, formerly ydcO ) carried by the ICEBs1 element that increases fitness of the host strain by delaying the entry into the normal developmental pathway leading to biofilm formation and ultimately sporulation, such that the subpopulation expressing the product of dev1 increases in a mixed pool. An interesting novel aspect of the dev1 system is that it is co-regulated with ICEBs1 conjugation, and thus is only activated when the host strain is a minority of a mixed population; in this scenario the Dev1+ subpopulation is essentially cheating on the Dev-. Since expression of the Dev1 phenotype in an entire population would likely cause a crash, the ICE- population density-dependent regulation ensures that the fitness advantage disappears before the crash can occur. I think that the gene is interesting and this report adds a significant aspect to our understanding of the biology and evolution of ICE elements. Overall I am positive about this paper.

Reviewer #3 (Public Review):

In "KLF10 integrates circadian timing and sugar signaling to coordinate hepatic metabolism", Anthony Ruberto and colleagues characterize the role of the transcription factor KLF10 in circadian transcription and the transcriptional and physiological responses to hexose sugars in mouse hepatocytes. They confirm earlier reports that Klf10 is expressed rhythmically in mouse liver, with peak expression at ZT9. They show that Klf10 expression is induced by glucose and fructose and that hepatocyte-specific deletion of Klf10 exacerbates hyperglycemic and hepatosteatotic responses to 8 weeks of elevated sugar consumption. They use RNA sequencing and ChIP sequencing to define the complement of Klf10 target genes in hepatocytes and how they are regulated by glucose and fructose. Together their data support a model in which KLF10 limits the transcriptional induction of rate-limiting enzymes involved in gluconeogenesis and lipogenesis in response to elevated sugar consumption, thus mitigating the pathophysiological impact of high sugar diets. The experiments are mostly well designed, presented, and interpreted but several points require additional investigation and/or clarification. While the current manuscript suggests an integration of circadian timing and sugar signaling by KLF10, additional experiments to establish how some of the molecular and physiological effects are modulated by time of day are needed to better support that claim.

Strengths:

This study uses a combination of genetic, biochemical, and physiological approaches to investigate the hepatocyte-specific function of the transcription factor KLF10. Deletion of KLF10 specifically in hepatocytes distinguishes this study from other related work. Further, the characterization of global daily gene expression patterns in mouse liver is well designed and analyzed and establishes that hepatocyte-specific deletion of Klf10 remodels daily rhythms of gene expression in the liver. The combination of that analysis with ChIP sequencing provides powerful evidence to establish the hepatocyte-specific KLF10-dependent transcriptome and highlights its targeting of rate-limiting enzymes in lipogenic pathways. Together, the molecular and physiological analyses in this study provide compelling evidence that KLF10 plays a protective role in the context of excessive sugar consumption by limiting lipogenic gene expression pathways and thereby suppressing hepatic steatosis.

Weaknesses:

In its present form, this study does not thoroughly connect the in vitro and in vivo findings and misses the opportunity to fully characterize the role of KLF10 in circadian regulation of lipogenesis in response to excessive sugar consumption in vivo. It is unclear whether the concentrations of glucose and fructose used to stimulate primary hepatocytes are similar to those experienced in response to the dietary stimulus in vivo and there is no examination of the impact of sucrose on Klf10 expression or downstream gene expression. This omission complicates the interpretation of the response to the combined sugar stimulus in vivo, especially in light of a recent report that KLF10 deletion protects against hepatosteatosis caused by consumption of a high sucrose diet. It also does not examine how time of day influences KLF10-dependent gene regulation in response to sugar consumption. Without these analyses, it falls short of connecting the circadian and sugar-response pathways through KLF10.

Reviewer #3 (Public Review):

The authors sought to directly compare manipulations of different signaling pathways for their ability to induce cell cycle activation and proliferation in cardiomyocytes from various species and maturation levels. The manipulation consisted of peristent lentiviral expression of beta catenin, cyclin D2, rat Erbb2, human Erbb2, and Yap8SA.

A major strength of this study is that it shows that most of above expressions appeared to induce negative feed-back responses at the post-transcriptional level to limit protein overexpression, illustrating how difficult it is to manipulate cardiomyocyte proliferation. By contrast, human Erbb2 did induce prominent proliferative effects in both rat and human cardiomyocytes. However, this finding has been shown before. The novelty here is limited to interspecies differences of the effects of Erbb2 overexpression. Multiple studies in oncology have shown that Erbb2 overexpression increases cell proliferation and is sufficient to induce cancer growth. It has also been shown that transient overexpression of Erbb2 in vivo in the heart results in dedifferentiation and proliferation of cardiomyocytes. The observation that Erbb2 overexpression induces cardiomyocyte dedifferentiation alongside mitosis is not unexpected; in general, stimuli that induce cardiomyocyte proliferation also induce cardiomyocyte dedifferentiation and sarcomere disassembly.

In this study, in a 3D model of rat neonatal cardiobundles Erbb2 overexpression also led to formation of a necrotic core. It also led to loss of sarcomeres and contractile force and tissue stiffening. These effects appeared to be mediated by mTOR-independent, Erk-dependent mechanisms. Although experiments in this study are of a high technical level, and results interesting, the likely impact of this work is minor. Indeed, the overall picture of Erbb2-induced pathologic hypertrophy is likely related to the applied methodologies, i.e., a persistent as opposed to temporally controlled Erbb2 overexpression and the use of an avascular 3D model lacking the cellular complexity of the intact heart.

Reviewer #3 (Public Review):

The authors clearly demonstrate the effectiveness of optimized tools to generate precise C to T point mutations in zebrafish F0 embryos. The demonstrate germline transmission and an associated mutation for one mutation. There is sufficient data for members of the community to consider adopting these tools to generate mutation in their own laboratories

Reviewer #3 (Public Review):

This is a well written and elegant study from a collaboration of groups carrying out models based on high resolution imaging. I think the study also serves as a prime example for where modeling and simulation bring added value in the sense that the insights revealed in the study would not likely be gained through other methods.

1) As the authors point out, clinical studies have revealed that the fibrotic burden in ESUS patients is similar to those with aFib. The question is why then, do so few ESUS patients exhibit clinically detectable arrhythmias with long-term monitoring. The authors hypothesize and their data support the notion that while the substrate is prime for pro-arrhythmia in ESUS patients, a lack of triggering events may explain the differences between the two groups.

2) I think the authors could go further in describing why this is surprising. Generally, severe fibrosis is thought to potentially serve as a means or mechanism for pro-arrhythmic triggers. This is because damage to cardiac tissue typically results in calcium dysregulation. When calcium overload occurs in isolated fibrotic tissue areas, or depolarization of the resting membrane potential due to localized ischemia allows for ectopic peacemaking, we might expect that the diseased/fibrotic tissue is itself the source of arrhythmia generation. I think the novel finding here is that this notion may be a simplification, and the sources of arrhythmia generation may be more complex and may need to come from outside the areas of fibrosis. I think this is a big deal.

Reviewer #3 (Public Review):

In this manuscript, Sheng et al. have demonstrated that Langerhans cells (LCs) do not exit the skin both under steady-state conditions and after skin sensitization, using newly generated DC-SIGN DT mice and others. In addition, through a combined use of genetic fate mapping and novel inducible LC ablating mouse models, they show that the originally described lymph node LC fraction is actually an independent LClike cell population that originates from the dermis, not from the epidermis. Moreover, these LClike cells, which are replaced over time by bone marrow-derived counterparts, are characterized by their slow turnover rate and trafficking to the LN. This study contains novel and important findings. This reviewer has several comments on the manuscript.

Major comments:

1) The functional roles of LClike cells remains unclear, especially in the relationship with conventional LCs. Thus far it has been considered that LCs play essential roles in OVA-induced atopic dermatitis like models. But it remain unclear whether conventional LC s or LC like cells play important roles.

2) The authors stated that a fraction of CD11bhiF4/80hi cells co-expressed CD326 and CD207 are detected in the dermis (Fig. 1b, upper panel), which is likely to be derived from the epidermis. But it remains unclear whether this subset is just a contamination through the separation process or a truly migratory one from the epidermis. The authors can demonstrate clear localization of LCs in the epidermis, LClike cells, and LCs in the dermis.

3) Related to the above question, the authors claim that LCs can emigrate from the epidermis to the dermis. Given that LCs can emigrate from the epidermis to the dermis by transmigrating through the basement membrane, why LCs cannot migrate into the lymphatic vessels. LCs are known to express CCR7 highly that is important for migrating into the lymph nodes from the skin. What is the functional (APC, migration, etc) difference between LCs and LClike cells?

4) I agree that the novel subset exists as CD207+CD326+ LClike cells in the dermis, which is different from conventional LC. But the term, "LC-independent" CD207+CD326+ LClike cells, which the authors used often through the manuscript, is a bit confusing, because it is not totally clear whether LClike cells are completely independent of LCs or not. It would be informative if the authors can demonstrate whether LClike cells contain bierbeck granules (this is also a hallmark of LCs) or not, since bierbeck granule-positive cells were detected in the LN (https://pubmed.ncbi.nlm.nih.gov/4758275).

5) The authors can discuss the relationship between LClike cells and short lived LCs that were previously described (https://pubmed.ncbi.nlm.nih.gov/23159228).

6) Where do LClike cells locate by FACS plots analysis using CD11b and CD103?

Reviewer #3:

Labelling strategies for electron microscopy have so far lacked the ability to clearly visualise genetically expressed probes such as GFP for light microscopy. Building on previous studies by the group of Ellisman, the Parton group have made significant adaptations and improvements to the system. Especially addressing the issue of diffusion of the DAB precipitate and the low visibility of it by silver enhancing the particles is a key step forward. The authors have tested their system in a wide variety of EM workflows and show that it works.

The quantification part of the manuscript is to me potentially the most interesting part. Quantitation of proteins at physiological levels at the ultrastructural level would be a significant achievement. This part is a bit under represented although there are some issues with that. The silver enhanced particles on the added external standard appear to be larger than the ones inside. Does that result in lower detection?

Overall, this is a manuscript that is very clearly written and very easy to follow for non-experts.

Reviewer #3:

This paper is primarily about modeling the ERK pathway during the induction of synaptic plasticity. This pathway has been previously modeled, and this is cited in the paper. The main addition here is the addition of the effect of SynGap which is necessary in some form of LTP. This is a very detailed study, and what it seems to primarily show is that the ERK pathway favored spaced vs. massed stimulation protocols. This is a very detailed paper, but no conceptually new ideas are presented here. The paper adds to an existing foundation, but fails to make the case that this is a very significant addition. What is the significant consequence at a higher level of these added details?

The ERK pathway is just one component of a much larger set of pathways that control synaptic plasticity, how much do we learn from studying this pathway in isolation? Also, the paper cites the importance of this pathway to L-LTP, is it the induction phase of L-LTP? It seems so because ppRRK decays in less than an hour. How then does this pathway contribute to the maintenance of L-LTP? These processes, such as a possible upregulation of protein synthesis, are not part of this model either.

This paper studies in detail different pathways that influence ERK activation in synapses. This is a very detailed study, but how many details do we actually know? For a detailed paper though it seems that many of the details are missing. Is there a detailed diagram of reactions, or set of equations for all these reactions? Some coefficients are named in figure 1, and this might be sufficient for a schematic description of the model in the paper, however there must be somewhere a detailed description of all reactions. How many species are there here, how many coefficients? How are coefficient values known? How many coefficients are directly estimated? The paper does carry out an extensive robustness analysis, though it is not well explained.

What are the major takeaways from this paper, and what experiments could test this model?

To summarize, the paper is very detailed, carefully constructed and executed, but it fails to convince that the problem it addresses is very significant, and it makes no conceptual breakthroughs.

Reviewer #3:

In this manuscript, the authors utilize single-cell/single-nucleus RNA-sequencing to perform a comparative analysis of the cellular composition of the dorsal lateral geniculate nucleus (dLGN) in mice, non-human primates, and humans. This topic is important for a number of reasons, including (1) the dLGN is a critical center of visual processing about which we know relatively little; (2) the dLGN has emerged as a widely used experimental model of neural circuit development; and (3) in general, the integration of cross-species data at the transcriptomic level is important for identifying conserved mechanisms of brain function that may shed light upon neurological disease states. By employing a relatively deep RNA-sequencing approach (Smart-Seq) the authors identify major excitatory and inhibitory dLGN cell types within each species. While the multiple inhibitory neuron subtypes were relatively similar across species, excitatory neurons displayed major differences particularly between mouse and both primate classes. The authors identified four major excitatory cell types in primate and human dLGN corresponding with known functional heterogeneity that places these neurons into magnocellular, parvocellular, and koniocellular populations. Interestingly, koniocellular neurons could be broken into two distinct subtypes. Somewhat surprisingly, the authors noted a lack of excitatory neuron diversity in the mouse, despite prior evidence suggesting these neurons can have different morphological and physiological features. Yet, although all excitatory neurons in the mouse clustered together, there were subtle differences in excitatory neurons in the mouse that aligned with different regions of mouse dLGN (shell vs core), suggesting that excitatory neuron heterogeneity may still exist along a more subtle continuum. Consistently, neurons in the shell region in mouse dLGN more strongly resembled koniocellular neurons in primates versus the core region, suggesting some level of conservation between excitatory neuron identity across species. While the study is largely descriptive, the authors are creative in their use of bioinformatics to uncover particularly interesting observations that the transcriptomic analysis yielded, and the paper is very interesting because of that. The major weakness of the paper is a paucity of robust FISH analyses to quantitatively validate the transcriptomic findings in all species. Overall, it is my opinion that this work is very important and that, at a broader level, it may help to define the relationship between transcriptomic cell type, functional/physiological cell type, and anatomical cell type within a brain region that is critical for visual function and that has emerged as a fascinating model of neural circuit development in the mouse.

Strengths:

The Smart-Seq transcriptomic technique chosen is appropriate to address the authors' questions.

The data were generated rigorously and subjected to an in-depth quality control pipeline prior to analysis. As a result, the quality of the transcriptomic data is high.

The paper includes a detailed, transparent description of the approach taken in the Results and Methods. The authors point out caveats and weaknesses - and how they were addressed - throughout the text.

The inclusion of tissue from thalamic nuclei surrounding the dLGN as a way to control for the unintentional inclusion of non-dLGN tissue in the experimental dissection was well-designed and effective.

Despite a couple of exceptions, the authors do an excellent job of placing their findings within the context of what is already known about dLGN cell types across different species, and how these cell types function differently in physiological, morphological, and anatomical terms.

The study is descriptive in nature but the authors do a nice job of laying out several interesting findings, such as the observation that GABAergic neurons are more conserved across species than relay neurons, with mouse neurons being particularly distinct. Another fascinating observation is that shell-located neurons in mouse dLGN are transcriptomically related to koniocellular neurons suggesting the possibility of a close relationship between thalamocortical connectivity and molecular identity across species.

Weaknesses:

The characterization of gene expression patterns through sequencing-based transcriptomics has emerged as a powerful tool for dissecting the brain, but it is important to couple such approaches with techniques like fluorescence in situ hybridization (FISH) to verify sequencing results in a histological context. While here the authors show 3 - 4 validations of mouse genes that seem to be restricted to or excluded from the shell versus the core dLGN regions (Figures 4G and S4E), the conclusions of the study would be better supported by a more extensive and rigorous analysis of cell-type-specific gene expression within all species described.

It is not entirely clear from the manuscript how the authors dissected the shell from the core region of the dLGN, given these regions are not as clearly distinct as the dLGN lamina in other species. One possibility would be to take advantage of the fact that the shell receives input from specific RGCs that can be targeted genetically by crossing a Cre driver to the TdTomato line, but I do not believe that that is what was done here. I also noted that the authors use ventral LGN (vLGN) as one of their controls for the precision of their micro-dissections, but given that the vLGN does not directly contact the dLGN, this had me wondering exactly how cleanly the shell and core regions of the mouse dLGN were isolated.

On lines 101 - 103, the authors state "...differentially expressed genes between donors were related to neuronal signaling and connectivity and not to metabolic or activity-dependent effects." Table S2 is cited, but the columns are not labeled such that a common reader could interpret them and confirm the statement in the text. Moreover, the text does not state how the authors made the determination that these differentially expressed genes are not related to "activity-dependent effects".

Reviewer #3:

In this study, Michaluk et al. explored the membrane dynamics of the main glial glutamate transporter GLT1 in hippocampal astrocytes, which was previously shown to shape synaptic transmission through regulating extracellular levels of glutamate and whose dysfunction may lead to pathologic conditions. Their results underscore the importance of the GLT1 C-terminus in the membrane turnover as well as in the activity-dependent lateral diffusion of the transporter at the plasma membrane.

To access GLT1 dynamics, the authors generated and imaged a pH-sensitive fluorescent analogue of the GLT1a isoform, namely GLT1-SEP, which fluoresces when exposed to the extracellular space but not in low pH intracellular compartments. By performing Fluorescent Recovery After Photobleaching (FRAP) in astrocytes from cell cultures, they show that about 75% of GLT1-SEP dwell at the cell membrane with a lifetime of about 22 s. Super-resolution dSTORM imaging further revealed that surface GLT1 distributes in clusters showing a spatial correlation with PSD-95 synaptic marker. In astrocytes from cell cultures or brain slices, the authors were able to monitor lateral diffusion of GLT1-SEP at the plasma membrane with FRAP; they recapitulated previous findings based on single molecule tracking experiments and showed that 25% of surface GLT1-SEP remains immobile (or slowly mobile) and that this immobile fraction decreases upon elevated network activity. Interestingly, deleting the C-terminus of GLT1-SEP does not alter much the intracellular fraction of GLT1-SEP, the fraction of immobile GLT1-SEP at the membrane or its ability to organize in clusters under basal conditions. However, GLT1-SEP lacking the C-terminus show a higher turnover at the membrane under basal conditions and surface GLT1-SEP clusters are not associated with synaptic markers anymore. Finally, removing the GLT1 C-terminus blocks the increase in the mobile fraction that is normally observed upon elevating neuronal activity.

Strengths:

While previous studies have unveiled a role for the lateral diffusion of GLT1 in controlling the recruitment of GLT1 near active synapses, the present study uses powerful optical approaches and analysis tools that allow for the monitoring of both lateral mobility and the exchange between membrane and intracellular fractions of GLT1. Furthermore, important and original information is provided about the nanoscale organization of GLT1 transporter at proximity of synapses and the fact that this organization depends on the C-terminal domain of GLT1. The results unveil an important role for membrane turnover as a possible 'redeployment' route for the immobile fraction of GLT1 at the plasma membrane.

Weaknesses:

1) Although overexpressed GLT1-SEP displays a similar expression pattern as endogenous GLT1 (assessed through dSTORM experiments), the expression level of GLT1-SEP relative to endogenous GLT1 has not been addressed by the authors. In particular, whether overexpressing GLT1-SEP impacts glutamate uptake currents and whether this could affect membrane turnover has not been measured.

2) The authors did not test the impact of neuronal activity on membrane turnover or surface distribution of GLT1-SEP, like they did for lateral mobility. This would be important to provide support for the 'redeployment route' hypothesis that the authors propose.

3) The FRAP data in organotypic slices looking at the effect of deleting GLT1 C-terminus, blocking mGluRs or buffering Ca2+ with BAPTA on GLT1 lateral mobility (Figure 5C-G) is not very convincing. The trend for lower immobile fraction upon 4AP compared to control is maintained across conditions. The lack of statistical difference between control and 4AP in Fig. 5D, 5E and 5F might come from the smaller n number (n = 30-48) compared to the control condition (n = 72) and/or higher variability.

4) The importance of GLT1 membrane turnover for controlling glutamate spillover ('extrasynaptic glutamate escape) and synaptic transmission/plasticity is missing.

5) While providing new information about the turnover of the GLT1a isoform, this study does not provide information about other GLT1 isoforms, in particular GLT1b, which contain unique C-terminal domains and which could thus display different membrane and lateral diffusion dynamics. The authors should justify why they focused on this specific isoform.

Reviewer #3:

One example of this problem is in the estimation of cancer risk. The risk is estimated on the basis of body size and lifespan. However, that lifespan is itself phylogenetically estimated from body size at least for the non-extant species. It is not clear to me from the manuscript whether all lifespans are so estimated, or whether observations are used for the lifespan of the extant species. If the latter, caution is indicated, because lifespan data are highly uneven and often given as observed maximal lifespans, which can be misleading if taken from, for instance, zoo specimens. In either case, the manuscript needs to more clearly emphasize that these are statistically-predicted risks, not measured risks.

At a larger scale, the authors have done their best with a dataset that suffers from a couple of problems. First, all of the extant very large-bodied animals form a single clade, with the hyrax as the sole small-bodied member of that clade. And since the titanohyrax is extinct, among the extant organisms (an available large-bodies species with genomes) there is then a true large-bodied clade of the sirenia and elephants and relatives. I understand that other evolutionary data make it clear that these represent two (three including titanohyrax) independent transitions to large-body sizes. But with only the modern or nearly modern genomes to work with, I am not sure that the duplication inference procedures and their coupling to the body size analysis statistically represents more than a single observation (e.g., a default of a single transition to large size along the tethytheria branch).

Similarly, the authors observe what appears to be a number of independent duplications of tumor suppressors in African and Asian elephants: duplications that are lacking in many of the ancient genomes considered. I know that the authors used rigorous statistical methods to correct for the fragmented nature of these ancient genomes, but it is very hard not to wonder if some of the data in Figure 4 is really not an artifact of using ancient genomes, where detecting recent gene duplications may be very difficult (several of the Asian and African elephant duplications in Figure 4 appear to be of the same genes). If these events are truly independent and not genome assembly/annotation artifacts, there is then an alternative hypothesis to propose. Thus, are the authors suggesting that there is a rapid turnover in the duplication of tumor suppressors, such that all elephants have such duplicates, but the particular duplications have short life spans and differ from species to species?

Finally, it would be nice to see a few more comments on the manatee genome and why it does (or doesn't) show the expected patterns for the genome evolution in the face of the evolution of larger body sizes.

I would also note that Figure 3 and 4 would benefit from greatly expanded captions: I do not fully understand what is being illustrated in, for instance, Figure 3B-why are certain dots connected with lines? Intersections between what in the y-axis label?

Reviewer #3:

Behaviours that are instrumental for producing reward can be either goal-directed or, after repeated practice, habitual. Tasks that dissociate these types of learning, notably outcome devaluation, are tricky to implement for studying intravenous drug delivery although there is great interest to understand the role of habits in controlling drug use and addiction and so this paper is important in that regard. This article takes a new approach analyzing response latencies to infer the types of decision-making process that underlies a reward-seeking behaviour. Goal-directed behaviours are argued to involve evaluation of the outcome of responding and/or deliberation between choices both of which should take time, and slow responding relative to an efficient but inflexible habit. So I think this approach is quite interesting. The paper is well written and the predictions are clear.

My main issue in evaluating the current article is that while different predictions are made about when response latency should be relatively fast or slow, since the article is framed in terms of dissociating goal-directed and habitual processes, I feel there should be some independent evaluation of whether the target behaviour is in fact goal-directed or habitual. The authors rely on the amount of training as extended training has been shown to promote habitual control. However, exactly how much training is needed and how other parameters (type of reward, schedules of reinforcement, choice or single outcome) affect when habitual control may emerge varies widely in the literature and I don't think we can take for granted that after a certain amount of training responding will be habitual without testing that.

It is also important to consider alternative explanations for differences in response latency. A behaviour that is well-practiced might well be expected to become more efficient and faster. This need not be due to habit formation. The authors acknowledge the possibility that responding could be at floor but don't really discuss it or whether it might apply more to the saccharin response.

Reviewer #3:

Mathsyaraja and collaborators analyzed the role of the MAX-Gene associated protein, referred to as MAG, in mouse models and human cell lines and organoids of Non-Small Cell Lung Cancer. MAG is a repressor, a MYC antagonist that opposes its transcriptional activity. It has TBX and bHLH domains. They found that MGA loss by shRNA or CRIPSR accelerated tumor development in vivo in the KP mouse models. Using RNA-Seq, the authors showed that MGA loss leads to the de-repression of the atypical/non-canonical PRC1.6 polycomb complex, E2F and MYC targets as well as increased invasion. ChiP-Seq/cut and run as well as proteomics, revealed that MGA, E2F6 and L3MBTL2 co-occupy thousands of promoters and that MGA interacts with E2F6, and many core members of PRC1.6. Finally, they mapped the DUF domain as required to bind the PRC1.6 complex and bring it to promoters.

Overall, the experiments are well executed, the paper clearly written and the conclusions justified by the data.

The new data in the present report are the in vivo data in the mouse models, the role of MGA in repressing invasion, in increasing IFN signaling and the anti-tumor response, and the identification of the DUF domain required for binding to the PRC1.6 complex.

However, a lot of the data presented in the manuscript are not novel and were previously published. A recent Molecular Cancer Research paper by Llabata and collaborators published in April 2020 (referred to in the text) has already identified the same MGA interactors by Mass Spectrometry and the same binding sites by ChIP-Seq using human lung adenocarcinoma cell lines. Llabata et al. found that MGA interacts with the non-canonical PCGF6-PRC1 complex (named PRC1.6) that includes L3MBTL2 and that the complex also contains MAX and E2F6 but not MYC. They clearly show that MAG binds to and represses genes that are bound and activated by MYC convincingly showing that MYC and MGA have opposite functions. This unfortunately tempers the enthusiasm of the reviewer.

Reviewer #3:

This manuscript presents its two main results in Figure 3:

In response to a non-hydrolysable glucose analogue, E. coli cells show...

(1) Increase in fluorescence intensity of motors with labelled stator proteins, (2) Increase in speed of motor rotation and swimming

Sufficient controls are described to rule out possible indirect explanations of this effect, via buffer refreshment, metabolism of glucose, proton motive force (Fig 3D) and rotation direction (Fig 4F), and by contrast the effect is demonstrated to depend upon the chemotaxis receptor for glucose (Fig 4B) and the phosphotransferase system (Fig 4D), which is supports the chemotaxis system. These results are interpreted as evidence for a direct effect of the chemotaxis system upon the number of independent stator units, and thereby upon motor and swimming speeds.

This is a novel finding, and with better statistics (more repeats of fluorescence experiments) and better presentation of the findings (see below), the paper would be an important contribution to the field of bacterial chemotaxis. However, especially without presenting nor postulating a mechanism for the proposed direct effect, the paper might be more suitable for a more specialist journal.

Reviewer #3:

This study by Pipitone et al. combines SBF-SEM microscopy with quantitative proteomics and lipidomics to explore chloroplast differentiation. Authors describe that chloroplast biogenesis occurs in a first phase of structure establishment with thylakoid biogenesis, followed by a second phase of chloroplast division. The images and 3D reconstructions are beautiful, the quantitative data are novel, and their integration offers a new perspective into the seedling de-etiolation process, a model system for physiological and molecular studies. However, in my opinion some aspects need to be better explained and significantly improved.

• In lines 276-282, the authors write: "After 8h of illumination (T8), we observed decreased abundance of only one protein (the photoreceptor cryptochrome 2, consistent with its photolabile property) and increased levels of only three proteins, which belonged to the chlorophyll a/b binding proteins category involved in photoprotection (AT1G44575 = PsbS; AT4G10340= Lhcb5; AT1G15820= Lhcb6". This is striking, as many well studied proteins change in abundance during the first hours of de-etiolation. Actually, looking into the data set with the quantification data for the ~5,000 proteins, it appears that many proteins do show significant changes between T0 and T8. For example PORA and ELIP, changes that are also reflected in figure 6A.

• Related to the above, well known proteins for example phyA and HY5, that undergo drastic changes in abundance when etiolated seedlings are first exposed to light, do not show changes in T4,T8 and T12 relative to T0 in the proteomics data set. This raises questions about the proteomic approach (sensitivity of the method?) or the experimental setup. Could authors please comment on this? I feel that validation of the proteomics approach is critical, especially taking into account the central conclusion that "the first 12h of illumination saw very few significant changes in protein abundance".

• Lines 570-572: A reference is needed. Also, it is mentioned that PSII appears later than PSI, which does not seem to match the observation that PSII proteins appear earlier than PSI, or that the surface area occupied at early time points by PSII is greater than the one occupied by PSI. Please check.

• Are the calculations of thylakoid surface expansion over time consistent with previous available data using tomography? Please include.

• In the introduction, authors could include mention of the massive transcriptional reprogramming that takes place during de-etiolation. In addition, I think that comparison of the proteomics data with the transcriptomic changes during de-etiolation (well described in the literature) would allow further understanding of the distinct phases proposed. For the chloroplast proteins already present in the dark, how does this correlate with expression of the corresponding genes?

Reviewer #3:

The present manuscript focuses on a subpopulation of layer 5 neurons in medial and lateral entorhinal cortex and its functional connections to target neurons in layers 2, 3 and 5. The authors show a difference in LVb-to-LVa connectivity between MEC and LEC. The results suggest that the entorhinal output circuit via LVb-to-LVa is present primarily in LEC.

The work relies on and is made possible by a newly described transgenic mouse (TG) where LVb neurons can be labeled and stimulated with light. The authors showed that these neurons are largely co-labeled with PCP4, a marker for LVb. They compared the apical dendritic extent from TG labeled cells (LVb) and Nac retrogradely labeled cells (LVa) in medial and lateral EC. The intrinsic electrophysiological properties of LVa and LVb neurons were measured and used for PCA showing segregation according to sublayer and region. The axonal distribution and translaminar local connections of LVb neurons form the TG mice were then examined. Cells were recorded in vitro and filled with biocytin, both from MEC and LEC, with multiple cells in the same slice, documented with high quality images. The study of the LVb translaminar connectivity via a direct comparison of postsynaptic responses in neurons in different layers in the same slice is the gold standard for this type of functional connectivity analysis. There is also an investigation of mixed excitatory-inhibitory postsynaptic response sequences, and evidence for a dorso-ventral gradient in LVb-to-LVa connectivity in MEC is given.

The study combines TG mice, immunolabeling, retrograde labeling, morphological analysis and in vitro electrophysiology with optogenetic photo-stimulation. While it builds on already published work by the same group and others, by comparing the local target neurons of LVb in MEC and in LEC, the manuscript provides a unique contribution to the literature on the laminar circuit organization in the Entorhinal Cortex. In view of the central position of this area in the hippocampal memory systems of the rodent brain, these results are of interest to a broader neuroscience audience. It is also a nice example of a bottom-up approach, where data on the entorhinal translaminar connectivity may influence and constrain theories of hippocampal-cortical processing.

Major Comments:

1) Almost all TG labelled neurons are positive for PCP4 but not so vice versa, only 45.9 and 30.P% of PCP4 + neurons in LEC and MEC are labeled in the TG mouse (page 5) leaving open the possibility that the TG mouse labels a (specific?) subset of LVb neurons. Did you test whether TG labeled LVb cells co-localize with Ctip2 ?

2) The direct comparison of translaminar connectivity of LVb neurons is very convincing. But if your main conclusion (title) concerns the difference of LVb-to-LVa connectivity between MEC and LEC, it would have been more appropriate to test that in the same slice. While the data strongly support conclusions on the laminar differences of LVb connectivity, the evidence for differences in LVb-to-LVa connectivity between MEC and LEC is a bit weaker and more indirect.

3) Postsynaptic responses (in mV) in LEC are about twice as high in amplitude as in MEC (Fig. 4E vs Fig 5E), across all layers. Please discuss possible reasons, and possible impact on the circuit function. Is the probability to initiate action potentials higher in LEC ?

4) Give the onset latencies of postsynaptic excitatory potentials induced by LVb photostimulation. Are latencies monosynaptic? Or also polysynaptic? Ideally this could be tested by applying a cocktail of TTX-4-AP.

5) Figure 4 S3, Fig 5 S2. Analysis of inhibition. What is the cut-off criteria to say inhibition is present or not? It might be more appropriate to give the I/E ratio.

Reviewer #3:

The glypicans Dally and Dlp have important roles in morphogen signaling, and this work is of particular interest for me because it significantly advances our understanding of the multiple roles they appear to have in signal processing, signal presentation and signal reception. It is unfortunate that most of the literature has presented results and phenotypes in simplistic or simple-minded ways that do not recognize the different roles or the glypicans, or do not take experimental approaches that might distinguish them. This work of the Guerrero lab is an exception, as it is an important contribution to understanding these different roles, especially given the additional complexity introduced by the role of cytonemes. If its thoroughness and in-depth analysis are typical of work from this lab, so is the challenging presentation that makes understanding it so difficult. My recommendation to the authors is to clearly describe the different roles that have been attributed to the glypicans and for every experiment they present, clearly articulate how the results might implicate or distinguish any or several of them.

Although the figures are excellent, the manuscript is not well-written and would benefit from a rewrite.

AQ3: As per style, citations are not allowed in the abstract. Hence the superscript citation “1” has been deleted from the abstract. Please check and confirm.

ML: Maybe we could do this?

“…AKI and KRT in adults, from the Canadian Society of Nephrology (CSN) (Clark et al,, doi:10.1177/2054 358120941679).

Clark EG, Hiremath S, Soroka SD, Wald R, Weir MA. CSN COVID-19 rapid review program: management of acute kidney injury. Can J Kidney Health Dis. 2020;7:doi:10.1177/2054 358120941679.

Reviewer #3:

Overall the manuscript is a valuable contribution and represents an important advance using the model that the authors have recently established in Doro et al. 2019.

I have however a few suggestions for improvement, that I present below.

Suggestions to strengthen the manuscript:

1) Fig. 1 diagram is very useful. However, it would be very informative if the diagram could be followed by a representative quantification. For example, when injecting 200 T. carassii, what % of larvae is classified in the two infection categories? Could the authors also further discuss the % of T. low larvae where no parasites were observed during the clinical scoring? Have these larvae (or some of them) cleared the infection completely? Shouldn't they be classified/followed on their own?

2) Fig. 2: Is the clinical scoring predictive of early death onset (or likelihood of death)? To show this, the authors could, for example, divide the T. car 200 survival curve into 2 separate curves, based on the clinical scoring at day 4-5.

3) In Fig. 5 and Fig. 6 and related text, the authors describe their results as "macrophage proliferation" and "neutrophil proliferation". I would encourage them to avoid these terms and rephrase these sections. Normally "macrophage proliferation" is used to refer to resident tissue macrophages that occasionally are seen to divide/proliferate. To my knowledge, neutrophil proliferation in a similar manner has not been described. Most likely what the authors describe is myelopoiesis (in agreement, the authors also indicate that Edu staining most commonly is seen in hematopoietic tissues) and the EdU staining in mature macrophages/neutrophils is the result of a (recent) cell division of a hematopoietic progenitor cell. The authors do not have evidence that the terminally-differentiated cells (macrophages and neutrophils) are actually "proliferating". In lack of a more specific mechanistic insight, I would encourage the use of much broader terms, such as "increased production/number of macrophages/neutrophils" rather than "macrophage/neutrophil proliferation", throughout.

4) The authors observe several very interesting phenotypes that they report in Fig. 7, 8, 9 & 10. The frequency of these phenotypes (association with infection and with each other) however is not quantified and tested statistically. In particular:

• The authors report that macrophages, but not neutrophils, infiltrate in the cardinal vein, although both cell populations are accumulating on the outer side of the vasculature during infection. Can the authors quantify and test statistically these phenomena, i.e. by counting cells inside the vessel and associated (externally) with the vessel in the PVP, T. car-low and T. car-high groups? Also, do neutrophils ever interact with trypanosomes in other sections of the vasculature, if not in the cardinal vein? Do trypanosomes ever escape from the circulation and interact with neutrophils elsewhere?

• The authors report that foamy macrophages occur inside the vasculature and are exclusive to high-infected larvae. Can the authors show some quantifications of these associations and perform statistical tests (i.e. count foamy/non-foamy mpeg+ cells inside/outside the vessels in the PVP, T. car-low and T. car-high groups)? Also, macrophages do not phagocytose T. carassii, but foamy macrophages are seen in the context of other (intracellular) Trypanosoma infection. Are macrophages here perhaps scavenging dead Trypanosoma from the circulation, and is this leading to the foamy macrophage phenotype? Trypanosomes are also leading to hemolysis and this could lead to increased phagocytosis of red blood cell debris by macrophages. Could this be linked to the foamy appearance? How specific is BODIPY, to distinguish cholesterol (typical of foamy macrophages), vs lipids derived by phagocytosis of cell debris (i.e. high in membrane phospholipids?)

• The authors report that foamy macrophages occurring in T. car-infected larvae are characterised by a strong proinflammatory profile and are all il1beta and all tnfa positive. Significant differences are observed in the inflammatory response of macrophages in high- and low-infected individuals and in their susceptibility to infection. Can the authors quantify and test statistically these observations? For example, can the authors show that foamy macrophages are indeed more frequently il1b positive/tnfa positive than neighbouring non-foamy mpeg+ cells?

• The authors report that a strong inflammatory profile is associated with the occurrence of foamy macrophages. However, it is not clear how widely spread the inflammation is and only images of macrophages and endothelial cells in the cardinal vein are shown. Moreover, only tnfa and il1b are assessed (using transgenic reporters). The authors also mention that they observe a mild inflammatory response in low-infected individuals and that this is strongly associated with control of parasitaemia and survival to the infection. Can they confirm strong vs mild inflammatory profiles and different association with survival in the 2 infection categories and PVP control with a panel of qRT-PCR for several inflammatory markers (i.e. il1beta, tnfa and other relevant cytokines and chemokines)?

Reviewer #3:

Park et al. present an analysis of how structural connectomes (estimated with diffusion MRI) change from childhood to young adulthood. To characterize the changes, they embed each connectome into a 3-dimensional space using nonlinear dimensionality reduction (and alignment to a template sample), and then perform a range of analyses of the statistics derived from this space (notably, distances to the template centroid, 'eccentricity'). The paper is well written, the data are fantastic, and the analyses are interesting, but I have a range of methodological concerns.

1) Interpretability and Lack of Comparison The authors claim repeatedly that they are "capitalizing on advanced manifold learning techniques". One could imagine an infinite number of papers that take a dataset, use a technique to extract a metric, X (e.g., eccentricity), and then write about the changes in X with some property of interest, Y (e.g., age). Given this set of papers (and the non-independence between the set of possible Xs), the reader ought to be most interested in those Xs that provide the best performance and simplest interpretation, with other papers being redundant. Thus, a nuanced approach to presenting a paper like this is to demonstrate that the metric used represents an advance over alternative, simpler-to-compute, or clearer-to-interpret metrics that already exist. In this paper, however, the authors do not demonstrate the benefits of their particular choice of applying a specific nonlinear dimensionality reduction method using 3 dimensions alignment to a template manifold and then computing an eccentricity metric. For example:

i) Is the nonlinearity required (e.g., does it outperform PCA or MDS)?

ii) Is there something special about picking 3 dimensions to do the eccentricity calculation? Is dimensionality reduction required at all (e.g., would you get similar results by computing eccentricity in the full-dimensional space?)

iii) Does it outperform basic connectome measures (e.g., the simple ones the authors compute)?

There is a clear down-side of how opaque the approach is (and thus difficult to interpret relative to, say, connectivity degree), so one would hope for a correspondingly strong boost in performance. The authors could also do more to develop some intuition for the idea of a low-dimensional connection-pattern-similarity-space, and how to interpret taking Euclidean distances within such a space.

2) Developmental Enrichment Analysis Both in the main text and in the Methods, this is described as "genes were fed into a developmental enrichment analysis". Can some explanation be provided as to what happens between the "feeding in" and what comes out? Without clearly described methods, it is impossible to interpret or critique this component of the paper. If the methodological details are opaque, then the significance of the results could be tested numerically relative to some randomized null inputs being 'fed in' to demonstrate specificity of the tested phenotype.

3) IQ prediction The predictions seem to be very poor (equality lines, y = x, should be drawn in Fig. 5, to show what perfect predictions would look like; linear regressions are not helpful for a prediction task, and are deceptive of the appropriate MAE computation). The authors do not perform any comparisons in this section (even to a real baseline model like predicted_IQ = mean(training_set_IQ)). They also do not perform statistical tests (or quote p-values), but nevertheless make a range of claims, including of "significant prediction" or "prediction accuracy was improved", "reemphasize the benefits of incorporating subcortical nodes", etc. All of these claims should be tested relative to rigorous statistics, and comparisons to appropriate baseline/benchmark approaches.

4) Group Connectome Given how much the paper relies on estimating a group structural connectome, it should be visualized and characterized. For example, a basic analysis of the distribution of edge weights and degree, especially as edge weights can vary over orders of magnitude and high weights (more likely to be short distances) may therefore unduly dominate some of the low-dimensional components). The authors may also consider testing robustness performed to alternative ways of estimating the connectome [e.g., Oldham et al. NeuroImage 222, 117252 (2020)] and its group-level summary [e.g., Roberts et al. NeuroImage 145, 1-42 (2016)].

5) Individual Alignment The paper relies on individuals being successfully aligned to the template manifold. Accordingly, some analysis should be performed quantifying how well individuals could be mapped. Presumably some subjects fit very well onto the template, whereas others do not. Is there something interesting about the poorly aligned subjects? Do your results improve when excluding them?

Reviewer #3:

-The authors claim in the first part of the results that the frequency of CSF-cN spontaneous activity is the same in juvenile and adult mice. In Fig.1G, 61 neurons from 7 animals are illustrated. The authors should state how many juvenile (P14-P24) and adult (P36-P47) mice have been included in the analysis (3 and 4 is different from 5 and 2) and how many neurons have been recorded in each animal. In the methods section, they indicate that acute slices were obtained from P14 to P55 mice. If the reviewer is correct, neurons from P55 mice are not included in Fig. 1G?

-The immunohistochemical data have been obtained in P30-P52 mice. Are P14 CSF-cNs all VGaT positive?

-The frequency of CSF-cN spontaneous activity could be the same but underlying mechanisms could completely differ with age. In Fig. 3, TTX fails to alter spontaneous Ca2+ spike expression in 3 animals. How old are these mice? Same questions for the results with Cd (2 animals, sample a little bit small...), ML218 4 animals (4 animals)...etc

-The focal ejection of 40mM K+ triggers a depolarization of all CSF-cNs "including those previously silent". This is the first time page 9 that the authors mention the fact that some CSF-cNs are not spontaneously active. Is the proportion of silent CSF-cNs different with age? The effects of Cd have been tested in 1 animal. Same for the effect of MCA on Ach-evoked Ca2+ spikes. In my opinion, the sample size has to be increased.

Reviewer #3:

Jacob and colleagues developed a new experimental "facility" or environment for training macaque monkeys to perform behavioral tasks. Using this facility, the authors trained freely moving macaques to perform a visual "same-different" task using operant conditioning, and under voluntary head restraint. The authors demonstrate that they could obtain reliable eye-tracking data and high performance accuracy from macaques in this facility. They also noted that subordinate macaques can learn to perform basic aspects of the task by observing their dominant conspecifics perform the task in this facility. The authors conclude that this naturalistic environment can facilitate the study of brain activity during natural and controlled behavioral tasks.

The manuscript is doubtless a hard-fought effort. The new experimental platform introduced by the authors has the capacity to transform how researchers approach the behavioral training of monkeys for some (but not all) tasks. However, in my opinion, the manuscript would have significantly broader impact and appeal if the authors had succeeded in performing wireless neural recordings in this same environment. Without these proof-of-principle neural data, the scope of this manuscript seems more limited. If the authors can obtain these neural data, the manuscript would be substantially stronger.

There are a few other concerns related to methodology and interpretation that should be addressed.

Major comments:

1) In the abstract, the authors state that macaques are widely used to study the neural basis of cognition - but in fact these animals are a valuable model organism for studying many other aspects of brain function beyond cognition. The authors seem to be missing an opportunity to highlight the broad impact of their work.

2) A gaze window of 3 degrees is rather large for most visual-based experiments. Do the authors think that it would be possible to train animals to maintain tighter fixation windows? And have they tried to do so?

3) Are these animals water deprived before entering the experimental environment? And how long do the animals typically work in this environment? For how many hours, and for how much fluid?

4) How did the authors ensure that the macaques do not fight inside the facility? Are the animals continuously housed in this facility or are they moved into this facility only during testing?

5) Line 227: the authors state the following: "Remarkably, M2 learned the task much faster using social observation and learning than M1 & M3 did using the TAT paradigm". How do the authors rule out the possibility that M2 is simply a "smarter" animal?

6) Line 354-364: the authors describe their insights about how animals may learn to perform the task in two phases. How can the authors make these strong claims based on data from N=1 macaque?

Ana et Mia, lorsqu'on prononce ces deux mots, me font penser, effectivement à la consonance du mot ami. Pour les personnes anorexiques qui se sentent souvent seules, cela doit être rassurant. Pour ma part, Ana et Mia semblent avoir une relation d'emprise sur les adolescentes qui se connectent avec elles. Le péché est évoqué plus haut. Il s'agirait de vouer un culte à ces divinités qui présentent l'anorexie comme la finalité d'un choix héroïque associé à une forme de spiritualité mortifère.

Pierre Janet s'est intéressé en particulier aux phénomènes de dissociation et a proposé d'étudier de manière systématique la relation entre l'expérience traumatique et la dissociation pour ces pathologies (Janet, 1823). Il décrivait la dissociation comme un mécanisme psychologique crucial par lequel l'organisme réagit à un traumatisme qu'il ne peut supporter. L'interlocuteur est-il alors vraiment ce qu'il revendique être?