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?

Reviewer #3:

The authors present a simple model that explains important outstanding controversies in the field of long-range gene regulation. These controversies include the fact that insulation boundaries tend to be weak; that acute inactivation of CTCF or cohesin (that leads to inactivation of insulation boundaries) leads to only minimal gene expression and that in live cells enhancer-promoter contacts appear not correlated with transcriptional bursting. The model involves a futile cycle of tag addition and removal from promoters, stimulation of more tag addition when tag is already present, and stimulation of tag addition by contacts with distal enhancers. The authors show that such a model explains all the above controversies, and indicate that the controversies are not inconsistent with mechanisms where long-range gene activation is driven by physical contacts with distal regulatory elements.

The authors have explained and explored the properties of the model well. I have only minor comments.

1) An alternative explanation for TAD-specific enhancer action is that an E-P interaction within a TAD (between two convergent CTCF sites), one that is brought about by extruding cohesin, is not equivalent to an interaction that occurs between two loci on either side of a CTCF site and that can be a random collision that is not mediated by extruding cohesin. In other words, two interactions can be of the same frequency but can be of a very different molecular nature. I agree that this model would not explain the results of the experiment where cohesin is acutely removed.

2) In the beginning of the introduction the authors introduce TADS. I recommend that the authors present this in a more nuanced way: compartment domains also appear as boxes along the diagonal, an issue that has led some in the chromosome folding field to be confused. This reviewer believes TADS are those domains that strictly depend on cohesin mediated loop extrusion, whereas compartment domains are not. If the authors agree, perhaps they can rewrite this section?

3) If I understand the model correctly, the nonlinearity arises because of the increased rate of tag addition when tag is already present. The authors then speculate histone modifications can be one such tag. However, there are only so many sites of modification at a promoter. Can the authors analyze how the possible range of tag densities affects performance of the model? Is the range required biologically plausible?

4) Can the authors do more analysis to explore how rapid changes in gene expression may occur (e.g. upon signaling a gene may go up within minutes)? How much more frequent does the E-P interaction need to be for rapid switch to the active promoter state? Can the authors do an analysis where they change the rates of the futile cycle upon some signal: at what time scale does transcription then change (keeping E-P frequency the same)?

Reviewer #3:

The manuscript explores ageing-associated changes in the Drosophila escape-response (Giant Fiber, GF) circuit and the circuits converging onto the GF. This a convenient system amenable to detailed physiological analyses and the authors made a good effort in extracting a large amount of useful information using a wide range of electrophysiological readouts. The authors identified several physiological parameters that are potentially useful for indexing ageing progression in flies such as ID spike generation and ECS-evoked seizure threshold. The host lab is well-known for its expertise in the field of GF physiology; consequently, the experiments were done with a high level of technical competence and presented (mostly) in a clear and informative manner. There is, however, one major issue that could restrict the usefulness of the data presented in the manuscript (please, see major comment 1).

Major comments:

1) Standards for conducting ageing studies in Drosophila and other model systems have gone significantly up in the last ~15 years following experimental evidence that genetic background can (and does) have a significant effect on the outcome of 'ageing' experiments (see Partridge and Gems, Nature, 2007). Today, 'backcrossing' relevant lines into a reference wild-type strain multiple times (to remove any second-site mutations) is a gold standard for virtually all ageing studies in Drosophila. Furthermore, this approach is being widely adopted even in the studies investigating physiological properties in developing flies (for example, in Imlach, Cell, 2012, the authors obtained very different electrophysiological results after 'isogenizing' the genetic background via backcrossing, and concluded that "the previous finding may have been due to a second site mutation"). As this important step is not mentioned in either the main text or in 'Methods' section, it is reasonable to conclude that the authors did not perform this step prior to conducting the experiments. Recent papers, one of which was referenced by the authors (Augustin et al PloSBiol 2017 and NeuroAging 2018) repeatedly demonstrated a significant, age-associated increase in the short-response (TTM and DLM) latency in the GF circuit following a strong stimulation of the GF cell bodies in the brain. It is likely that these age-related changes in the GF circuit remained undetected in the flies with non-uniform genetic background likely used in this work. The same problem affects the paper (Martinez, 2007) referenced by the authors throughout the manuscript.

It is difficult to say which of the findings reported here are most affected by the variability in the genetic background, but any kind of correlation between the lifespans (Figure 1B) and physiological parameters should be taken with a high dose of scepticism.

2) The manuscript is entirely 'phenomenological' in the sense that it does not investigate the causes of the observed physiological changes. The manuscript (with minor exceptions) does not discuss the possible reasons behind the functional readouts or speculate about what makes the (sub)circuits differentially susceptible to the effect of ageing. For example, when mentioning the effects of temperature and Sod mutation on the fly physiology, the authors limit their comments to generic and obvious statements such as 'oxidative stress exerts strong influences differentially on some of the physiological parameters and the outcomes are distinct from the consequences of high-temperature rearing'. Some of the possible questions the authors could ask are: could changes in the kinetics of relevant ion channels explain some of the results obtained under different temperatures; could the previously demonstrated effect of ROS on voltage-gated sodium channels explain some of the Sod1 phenotypes, etc?

Reviewer #3:

In this interesting paper authors compare MEG recordings of svPPA patients and 44 healthy controls during living vs. non-living categorization tasks. Both patients and the control group performed this task with similar accuracy. In addition, svPPA patients showed greater activation over bilateral occipital cortices and superior temporal gyrus, and inconsistent engagement of frontal regions. The authors conclude that patients with svPPA compensate for their semantic deficit by recruiting regions involved in perceptual processing.

This is a well written study and the results are presented clearly. The findings are novel and interesting.

1) One question for clarification is whether the recruitment of the occipital areas in semantic PPA is truly "compensatory" - does it indicate a shift of resources due to the anterior temporal atrophy? Is the recruitment of the parieto-occipital regions associated with more accurate performance?

2) The main results concentrate on the differences between patients and controls in the low gamma range. There are also significant effects in the other frequency bands (e.g., high gamma, beta and alpha). Could the authors discuss the functional significance of these effects?

Reviewer #3:

Neuronal ensembles have been shown by this lab and others to constitute one basic functional unit for the representation of information in cortical circuits. It is therefore important to determine how stable these blocks of representation might be. If these ensembles were preserved across time and sensory stimuli, this would indicate a significant degree of structure underlying cortical representations. In a first attempt to address these important issues, this manuscript analyzes the long-term stability of ensembles of coactive neurons in the layer 2/3 of mouse visual cortex across several days. Ensembles were recorded during periods of spontaneous activity as well as during visual stimulation (evoked). For this, the authors record spontaneous and evoked activity using two-photon calcium imaging one, ten and 40 days after the first recording session. In order to maximize overlap between successive imaging sessions, the authors record three planes separated by 5 microns almost simultaneously (9ms interval) using an electrically-tunable lens. They show that ensembles extracted during visual stimulation periods are more stable on days 2 and 10 than those computed during spontaneous activity. Stable ensembles display a higher "robustness" (a parameter that quantifies how many times a given ensemble is repeated and how similar these repeats are) . Neurons displaying stable membership are more functionally connected than unstable ones. It is concluded that such observed stability of spontaneous and evoked ensembles across weeks could provide a mechanism for memories. Long-term calcium imaging within the same population of neurons is a real challenge that the authors seem to overcome in the study. The conclusions are important, my main concern relates to the number of experiments and analyses supporting these findings as detailed below.

Number of experiments and statistics: According to Table 1, two mice with GCamP6f have been through the complete imaging protocol (days 1,2, 10 and 43) but none with the 6s, since 3 missed the intermediate measure (day 10) and one the last point (day 40+). Therefore five mice have been recorded over weeks with two different indicators, but only two were sampled on day 10. One mouse was only recorded until day 10. Altogether, this is quite a low sampling, but the experiments are certainly difficult. However, the total number of experiments analyzed is higher, due to the repeat of 3 sessions on the same mouse per day. This certainly contributes to reaching significance. However, the three samples from the same mouse are not independent points. Are the FOVs different for each session in the same mouse? If they are the same, then the statistics should be repeated but treating all experiments from the same mouse as single experiments. I would suggest repeating the analysis but using only one data point per mouse per day. Also, given that two different indicators were used (6s and 6f), one would need to see whether the statistics are the same in the two conditions.

Robustness: the authors compute this metric, as the product of ensemble duration and average of the Jaccard similarity and find that stable ensembles display higher robustness: isn't it expected that robustness is higher in stable ensembles given that stable ensembles should be observed more often?

Evoked ensembles: It seems to me that evoked ensembles are ensembles extracted during continuous imaging periods that include stimulation. However, one would expect evoked ensembles to be the cells activated time-locked to the visual stimulation. This notion only appears at the end of the paper with "tuned" neurons in Fig. 4. In the discussion, authors conclude lines 205-207 that "sensory stimulus reactivate existing ensembles" . I do not think this is supported by the analysis performed here. For this, I believe that one would need to compare, within the same mouse the amount of overlap between spontaneous ensembles and "tuned neurons".

How representative are the illustrated examples in Figs. 2&3? The authors report that about 20 neurons remain active from day 1 to 46 but their main figures display example rasterplots with more than 60 neurons, which is three times more than the average. Is this example representative? Which indicator was used? Is there a difference in stability between 6f and 6s?

Rasterplot filtering: The authors chose to restrict their ensemble analysis to frames with "significant coactivation". Why not use a statistical threshold to determine the number of cells above which a coactivation is significant instead of arbitrarily setting this number to three coactive neurons? In cases of high activity this number may be below significance.

Demixing neuronal identity: The authors assign a neuron to an ensemble if it displays at least a functional connection with another neuron. They use reshuffling to test significance of functional links but still it seems that highly active neurons are more likely to display a high functional connectivity degree and therefore to be stable members of a given ensemble with that definition of ensemble membership. What is the justification to define membership based on pairwise functional connectivity? The finding that core ensemble members display a high functional degree may be just a property reflecting a property of highly active neurons (as previously described by Mizuseki et al. 2013).

Type of neurons imaged: The authors use Vglut1-Cre mice, therefore they are excluding GABAergic cells from their study, this should be clearly mentioned and even discussed.

Volumetric imaging: I am not sure one can say that "volumetric imaging" was performed here, rather this is multi-plane imaging.

Mouse behavior: there is little detail concerning mouse behavior, are mice allowed to run? What is the correlation between ensemble activation and running?

Abstract: the authors should say that 46 days is the longest period they have been recording, otherwise it gives the wrong impression that after 46 days ensembles are no longer stable. Also "most visually evoked ensembles" should be replaced by "ensembles observed during periods of visual stimulation" (see above). "In stable ensembles most neurons still belonged to the same ensemble after weeks": how could ensembles be stable otherwise?

Discussion: I found the discussion quite succinct. It lacks discussion of the circuit mechanisms for assembly stability and plasticity (role of interneurons for example?), the limitations and possible biases in the analysis and the placing of the results in the perspective of other studies analyzing the long-term stability of neuronal dynamics.

Reviewer #3:

From the technical perspective this manuscript provides clear results that are consistent with, but do not prove, what this reviewer believes is the main objective of the work; to establish the relevance of the open structure of the eukaryotic cysteine desulfurase complex. This reviewer has no good basis to either accept or reject the open structure as having physiological relevance. This could well be the case but it is not clear from my (limited) knowledge of the published literature that the relevance of the open structure is generally accepted. From this perspective I believe the manuscript is sound from the technical approach and experimental implementation but suffers from a lack of clarity about the case for and against the relevance of the open structure. If this is a point of controversy in the field the topic should be discussed in depth and the position of the authors more clearly articulated.

Reviewer #3:

This study combines two cutting-edge approaches for the study of polyclonal antibody responses to understand the molecular profiles of antibodies elicited by HIV envelope trimer immunization in a rabbit model. In one arm of the study, the authors performed mutational profiling of serum antibody neutralization escape variants, and in the second arm they used electron microscopy polyclonal epitope mapping (EMPEM) to track antibody binding sites. These authors performed large-scale data collection and present high-quality validation data and explorations of the resulting datasets that compare antibody binding and virus neutralization profiles. These approaches provide a comprehensive window into the molecular specificity and performance of HIV immunization and are expected to inform advanced HIV-1 vaccine designs.

Summary of any substantive concerns:

The authors have done a nice job validating the integrity of the NGS data, and the strong data in Figs 4/5/2B show the power of the NGS-based neutralization mapping assays. This adds a solid confirmation of the study findings and demonstrates the quality of the techniques. Overall this is a solid study and the findings are informative. I see just a few methods updates and analyses that would help finalize the presentation of methods and data.

1) Additional information on the bioinformatic methods for data analysis is needed. How did the authors handle discrepancies in data across replicates or libraries, for example if a mutation that was enriched in one library or replicate, but deleted in another? Were there any quality filters or metrics used to estimate true signal vs. noise?

2) Differential selection statistics are mentioned briefly, along with citations to prior publications. Prior citations are definitely helpful. I think it is still important to state the key steps used in processing NGS data and the statistical techniques and quality metrics that were used. The authors should also state any criteria for acceptance or rejection or binning of individual data points, or acceptance/rejection of datasets or replicates, if quantitative criteria or metrics were used.

3) Several replicates showed a low percentage infectivity (Fig S1, e.g. animals 5724 and 2124), but the text indicates averages between 0.3% and 2.7% infectivity. Were some groups omitted from analysis, or were all groups included?

4) How well did the mutational profiles correlate between different libraries or replicates of the same samples?

Reviewer #3:

The authors probe mechanosensory processing in Hydra by measuring calcium activity in neurons and muscles in response to precise mechanosensory stimulation in whole and resected animals. The authors' claims are well supported by the evidence. The development of a mechanosensory delivery system for Hydra is also a significant methodological advancement. Taken together, the work advances our understanding of the Hydra nervous system and is a needed step towards developing Hydra as a powerful model for systems neuroscience.

Substantive concerns:

1) One weakness is that different measures of "mechanosensory response" are used at different places in the manuscript. In some contexts, a response is defined as calcium activity in neurons (Fig 2), and elsewhere as calcium activity in muscles (Fig 3 and 4). And in Fig2 SuppFig2 muscle contractions are also measured using MeKs. The relation between neural activity, muscle activity and body movement is of course of high interest, and the paper explores this. But, if technically possible, it would be helpful to report a single metric of behavior that could be used in all experiments. For example, it might be possible to use video of the animal's pose or body length to measure contractions in all experiments. At a minimum the reasoning behind choice of measurement of response for each experiment could be discussed explicitly.

2) Related: Without a consistent measure of behavior, it will be important to further clarify figures so that a reader can tell at-a-glance how contraction probability is being measured.

Reviewer #3:

The manuscript describes interesting experimental and modelling results of a novel study of human navigation in virtual space, where participants had to move towards a briefly flashed target using optic flow and/or vestibular cues to infer their trajectory via path integration. To investigate whether control dynamics influence performance, the transfer function between joystick deflection and self-motion velocity was modified trial-by-trial in a clever way. To explain the main result that navigation error depends on control dynamics, the authors propose a probabilistic model in which an internal estimate of dynamics is biased by a strong prior. Even though the paper is clearly written and contains most of the necessary information, the study has several shortcomings, as outlined below, and an important alternative hypothesis has not been considered, so that some of the conclusions are not fully supported by results and modelling.

Substantive concerns

1) The main idea of the paper for explaining the influence of control dynamics is that for accurate path integration performance participants have to estimate dynamics. This idea is apparently inspired by studies on limb motor control. However, tasks in these studies are often ballistic, because durations are short compared to feedback delays. In navigation, this is not the case and participants can therefore rely on feedback control (for another reason, why reliance on sensory feedback in the present study is a good idea, see point 2 below). This means that the task can be solved, even though not perfectly, without actually knowing the control dynamics. Thus, an alternative hypothesis for explaining the results that has not been considered is that the error dependence of control dynamics is a direct consequence of feedback control. Feedback control models have previously been suggested for goal-directed path integration (e.g., Grasso et al. 1999; Glasauer et al. 2007).

To test this assumption, I modelled the experiment assuming a simple bang-bang feedback control that switches at a predefined and constant perceived distance from the target from +1 to -1 and stops when perceived velocity is smaller than an epsilon. Sensory feedback is perceived position, which is assumed to be computed via integration of optic flow. This model predicts a response gain of unity, a strong dependence of error on time constant (slope similar to Fig. 3) or of response gain on time constant (Eqn. 4.1) with regression coefficients of 0.8 and 0.05 (cf. Fig. 3D), and a modest correlation between movement duration and time constant (r approximately 0.2, similar to Fig. 3A). Thus, a feedback model uninformed about actual motion dynamics and without any attempt to estimate them can explain most features of the data. Modifications (velocity uncertainty, delayed perception, noise on the stopping criterion, etc.) do not change the main features of the simulation results.

Accordingly, since simple feedback control seems to be an alternative to estimating control dynamics in this experiment, the authors' conclusion in the abstract "that people need an accurate internal model of control dynamics when navigating in volatile environments" is not supported by the current results.

2) Modelling: the main rationale of the model (line 173 ff: "From a normative standpoint, ...") is correct, but an accurate estimate of the dynamics is only required if the uncertainty of the velocity estimate based on the efference copy is not too large. Otherwise, velocity estimation should rely predominantly on sensory input. In my opinion that's what happens here: due to the trial-by-trial variation in dynamics, estimates based on efference copy are very unreliable (the same command generates a different sensory feedback in each trial), and participants resort to sensory input for velocity estimation. This results in feedback control, which, as mentioned above, seems to be compatible with the results.

3) Motion cueing: Motion cueing can, in the best case, approximate the vestibular cues that would be present during real motion. Furthermore, it is not clear whether the applied tilt is really perceived as linear acceleration, or whether the induced semicircular canal stimulus is too strong so that subjects experience tilt. Participants might have used the tilt as indicator for onset or offset of translational motion, specifically because it is self-generated, but the contribution of the vestibular cues found in the present experiment might be completely different from what would happen during real movement. Therefore, conclusions about vestibular contributions are not warranted here and cannot solve the questions around "conflicting findings" mentioned in the introduction.

4) Methods: I was not able to find an important piece of information: how many trials were performed in each condition? Without this information, the statistical results are incomplete. It was also not possible to compute the maximal velocity allowed by joystick control, since for Eqn. 1.9 not just the displacement x and the time constant is required, but also the trial duration T, which is not reported. One can only guess from Fig. 1D that vmax is about 50 cm/s for tau=0.6 s and therefore the average T is assumed to be around 8.5 s.

5) Results: information that would be useful is not reported. On page 6 it is mentioned that the "effect of control dynamics must be due to either differences in travel duration or velocity profiles", it is then stated that both are "unlikely", but no results are given. It turns out that in the supplementary Figure 4A the correlation between time constant and duration/velocity is shown, and apparently the correlation with duration is significant (but small) in the majority of cases. Why is that not discussed in the results section? Other results are also not reported, for example, what was the slope of the dependence between time constant and error? Why is the actual control signal, the joystick command, not shown and analyzed?

Reviewer #3:

This is an outstanding work from the lab of Dr. Stains establishing rapid post-translational regulation of sclerostin, a robust inhibitor of bone formation. They carefully and clearly establish that sclerostin is rapidly degraded by lysosomes in response to mechanical loading, and further link lysosomal abnormalities, using Gaucher iPSCs, to sclerostin levels.

Reviewer #3:

In this work, Feilong and colleagues use Human Connectome Project fMRI data to investigate the degree to which the strength of functional connectivity is predictive of general intelligence, and the degree to which that predictive power is improved using the hyperalignment procedures their lab has previously developed. I am broadly very supportive of the goals of improving prediction of individual behavioral differences via improved, functionally-based cross-subject registration, and I have always felt that the hyperalignment procedure is one of the most promising approaches for improving cross-subject functional registration. Overall I feel that this paper is an important next step in the development and maturation of the hyperalignment technique.

However, I do have two significant concerns with the predictive modeling presented in this work. I note that I am not an expert in these techniques, so these concerns may be due to my own ignorance; however, I would like to see the authors at least better explain these issues to non-experts like myself.

First, the authors employed a leave-one-family-out cross-validation scheme for their predictive modeling. My understanding is that the field has generally moved away from leave-one-out or leave-few-out cross-validation, as that approach consistently overestimates the predictive power of generated models. The HCP is a large dataset. Can the authors employ a more robust approach of using fully split halves?

Second, the authors make the claim that fine-grained (vertex-wise) connectivity has substantially better predictive power than coarse-grained (parcel-wise) connectivity, based on the variance in intelligence explained by the predictive models. However, the models based on fine-grained connectivity also have many, many more variables being used to make the prediction. Is this not a confound?

Reviewer #3:

The manuscript "High-quality carnivore genomes from roadkill samples enable species delimitation in aardwolf and bat-eared fox" is mostly well written and demonstrates an interesting and useful method for sequencing genomes from low-quality samples. They also provide a comprehensive overview of the state of genomics across the Carnivora clade, with some improved species/subspecies designations. I think the work is of broad interest. The analyses are mostly clear and I think a few additional analyses and small improvements could be made prior to publication, but otherwise have no issues.

The additional analyses/clarification I would recommend regards the Genetic differentiation estimate: This is a really interesting statistic! For some of the species you have multiple individuals it seems? Can you explain this a little more in the text. I am just not entirely convinced that the statistic is robust, but I think it would be with a few more analyses. My concern is primarily due to having only two individuals in some of your comparisons, because of population structure/relatedness the random regions you sample could have correlated histories. I think this could be addressed by varying window sizes and replicates across comparisons where you have multiple individuals for both the intraspecific and interspecific calculations.

Reviewer #3:

In the manuscript by Kim et al., show that, beyond its roles of preventing somatic differentiation in the germline of embryos, Zn-finger protein PIE-1 also functions in the adult germline, where it is both SUMOylated as well as interacts with the SUMO conjugating machinery and promotes SUMOylation of protein targets. They identify HDA-1 as a target of PIE-1-induced SUMOylation. Here too, I find the claims interesting, however data is sometimes missing or does not fully support the claims.

Main concerns:

1) A key claim of novelty over previously proposed "glue" functions of SUMO is based on the fact that they find that temporally regulated SUMOylation of a very specific residue in a specific protein is affecting protein activity: The observation that "SUMOylation of HDA-1 only appears to regulate its functions in the adult germline" and not in the embryo together with the finding that "other co-factors such as MEP-1 are SUMOylated more broadly, these findings imply that SUMOylation in the context of these chromatin remodeling complexes, does not merely function as a SUMO-glue (Matunis et al., 2006) but rather has specificity depending on which components of the complex are modified and/or when."

I find this claim poorly supported by the data. In fact, I find that the data supports that multiple SUMOylations contribute to formation of larger complexes: The His-SUMO IP (Fig 2B) brings down far more un-SUMOylated HDA-1 than SUMOylated. This argues for the presence of large complexes with different factors being SUMOylated and many bringing down unmodified HDA-1. The chromatography experiments (Fig 3B-C) also provide hits that are in complex and not direct interactors. Finally, HDA-1 SUMOylation is indicated to regulate MEP-1 interaction with numerous factors (Fig 3D). If all these factors are in one complex, it is hard to imagine how a single SUMO residue would mediate all of these simultaneously. It is quite likely (and not tested) that loss of HDA-1 SUMOylation leads to (partial?) dissociation of a large complex, rather than loss of individual interactions with the SUMO residue of HDA-1. Unlike claimed by the authors, there is no evidence that the "activity" of HDA-1 is regulated by SUMO modification.

2) Based on loss of MEP-1/HDA-1 interaction upon pie-1 RNAi and smo-1 RNAi (Fig 4B), the authors conclude that "SUMOylation of PIE-1 promotes the interaction of HDA-1 with MEP-1 in the adult germline".

The evidence that it is PEI-1 SUMOylation that is affecting MEP-1/HDA-1 interaction is fairly weak. In fact, based on Fig 4A, MEP-1 and HDA-1 interact without expression of PIE-1, and in PIE-1 K68R (sumoylation-deficient), although due to poor labeling of the panel it is not clear whether lane 1 and 4 refer to the WT pie-1 locus without tag or lack of pie-1.

In 4B the HDA-1 band that is present in L4440 but not in pie-1 or smo-1 RNAi is very faint, and in our experience such weak signal is not linear i.e., bands can disappear or appear depending on the exposure. Importantly, according to the data, seemingly unmodified HDA-1 immunoprecipitated with MEP-1 (Fig 4B). This data contradicts the authors' claim that "These findings suggest that in the adult germline only a small fraction of the HDA-1 protein pool, likely only those molecules that are SUMOylated, can be recruited by MEP-1 for the assembly of a functional NURD complex".

Furthermore, the fact that pie-1 and smo-1 depletion eliminate the interaction between HDA-1/MEP1 doesn't mean that the SUMOylation of pie-1 specifically is required for the interaction: perhaps un-SUMOylated pie1, and SUMOylation of something else, are both necessary for the interaction. The authors show that MEP-1 is also SUMOylated (Fig3C). When IP-ing GFP-MEP-1, they precipitate all its modified forms and associated factors. One alternative possibility for why smo-1 RNAi abolishes MEP-1/HDA-1 interaction is that MEP-1 SUMOylation is needed for interaction with HDA-1 (independently of pie-1). (On a side note, why are the authors not including MEP-1 SUMOylation in the model?)

3) On page 13 the authors write: "These findings suggest that SUMOylation of PIE-1 on K68 enhances its ability to activate HDA-1 in the adult germline" and "We have shown that PIE-1 is also expressed in the adult germline where it engages the Krüppel-type zinc finger protein MEP-1 and the SUMO-conjugating machinery and functions to promote the SUMOylation and activation of the type 1 HDAC, HDA-1 (Figure 6)". Activation of HDA-1 is misleading and was never tested. If not performing in vitro assays for HDAC activity, the authors at least need to look at whether pie loss (degron) leads to acetylation of genomic HDA-1 targets and whether it affects HDA-1 (and/or MEP-1) recruitment to these sites. This could be done by ChIP-seq of HDA-1 and H3K9ac in WT and pie-1 degron animals.

Reviewer #3:

This manuscript by Kim et al. describes a role of SUMOylation in Argonaute-directed transcriptional silencing in C. elegans. The authors found that SUMOylation of the histone deacetylase HDA-1 promotes its interaction with both the Argonaute target recognition complex as well as the chromatin remodeling NuRD complex. This enables initiation of target silencing. Impaired SUMOylation of HDA-1 leads to loss of interactions with several protein complexes, reduced silencing of piRNA targets, and reduced brood size. While the findings and claims are interesting, some of the novelty is overemphasized and some of the claims are not fully supported by the data.

Main concerns:

1) The importance of HDA-1 SUMOylation for transcriptional repression. The title "HDAC1 SUMOylation promotes Argonaute directed transcriptional silencing in C. elegans" implies a central role of SUMOylation in piRNA-mediated transcriptional silencing. The Argonaute HRDE-1/WAGO-9 targets countless transposons as shown previously and also in this manuscript (Fig S3), and so do the HDA-1 degron and Ubc9 mutant, indicating that histone deacetylation and protein SUMOylation are essential processes in TE silencing. However, the HDA-1 SUMOylation mutant (KKRR) only slightly affects 6 TE families (Fig S3), indicating that SUMOylation of HDA-1 might not be a key mediator of this process. Furthermore, the authors write that "Our findings suggest how SUMOylation of HDAC1 promotes the recruitment and assembly of an Argonaute-guided chromatin remodeling complex to orchestrate de novo gene silencing in the C. elegans germline.", but then they also state that "Comparison with mRNA sequencing data from auxin-treated degron::hda-1 animals revealed an even more extensive overlap with Piwi pathway mutants (Figure S2B), indicating that HDA-1 also promotes target silencing independently of HDA-1 SUMOylation." Based on their results and their own interpretations, I find that the importance of HDA-1 SUMOylation in piRNA-dependent transcriptional silencing is overemphasized.

Additionally, the model (Fig 7) implies that for initiation of silencing WAGO recruits HDA-1 to targets. This should be tested by analyzing HDA-1 distribution over WAGO targets in WT and upon loss of WAGO.

2) The mechanistic role of HDA-1 SUMOylation. On page 17 (amongst other places) the authors claim that "The SUMOylation of HDA-1 promotes its activity, while also promoting physical interactions with other components of a germline nucleosome-remodeling histone deacetylase (NuRD) complex, as well as the nuclear Argonaute HRDE-1/WAGO-9 and the heterochromatin protein HPL-2 (HP1)".

-Regarding activity: Loss of deacetylation/silencing in the SUMO mutant might be due to loss of enzymatic activity, but it might also be due to defects in recruitment/complex formation. There is no data that proves altered enzymatic activity. In fact, Fig 6 indicates SUMO-dependent interaction of WAGO-9 with HDA-1, implying that recruitment is affected. To distinguish between activity and recruitment, at the very least, the authors would need to show that HDA-1 localization to its genomic targets is unaltered upon mutating its SUMOylation site (ChIP-seq of wt and KKRR mutant), while H3K9ac is increased (K9ac ChIP-seq in wt and KKRR mutant) in the mutant. This, in combination with HDA-1 localization in wt and WAGO-9 loss would imply whether complex formation to recruit HDA-1 or HDA-1 enzymatic activity is mostly affected by SUMOylation.

-Regarding physical interactions: Fig 3D shows that if we fuse a SUMO residue to HDA-1, it will interact with MEP-1, while SUMOylation deficient HDA-1 mutant doesn't interact. However, for the WT HDA-1 control, we only see unSUMOylated protein interacting with MEP-1. Furthermore, in the MEP-1 IPs of samples that should contain SUMO-fused HDA-1, the authors detect a lot of "cleaved", unSUMOylated HDA-1. Unless cleavage happened after IP, during elution (unlikely, and there is "cleaved" HDA-1 in the inputs), these findings argue that the interaction with MEP-1 is not mediated by HDA-1 SUMOylation. An interaction between MEP-1 and unmodified HDA-1 is also shown in the accompanying manuscript, which appears to be dependent on Pie-1 SUMOylation. Thus, SUMOylation of HDA-1 alone seems unlikely to be the major factor necessary for silencing complex assembly. (as a side question: Does the protease inhibitor cocktail used inhibit de-SUMOylation enzymes? I am concerned that deSUMOylating enzymes might compromise some result interpretations.)

-Regarding functional relevance of HDA-1 acetylation: On pages 12/13 authors claim that because "HDA-1(KKRR) animals and mep-1-depleted worms revealed dramatically higher levels of H3K9Ac compared to wild-type" and "HDA-1, LET-418/Mi-2, and MEP-1 bind heterochromatic", "SUMOylation of HDA-1 appears to drive formation or maintenance of germline heterochromatin regions of the genome." These correlations do not prove function. The authors have performed H3K9me2 (although not H3K9-ac) ChIP-seq in WT, KKRR mutant and HDA-1 degron worms, yet do not analyze globally whether acetylation is lost on genes that are affected (change in RNA-seq vs. change in K9me2 or acetyl). To support the claim that SUMOylation of HDA-1 drives deacetylation and heterochromatin formation, it would be important to show changes in H3K9Ac levels (or other acetyl marks) and potentially NuRD component occupancy between control and HDA-1 SUMOylation-deficient animals at specific targets (i.e. genes derepressed upon loss of SUMOylation identified in RNA-seq, and the reporter locus).

3) The authors claim (p17) that "initiation of transcriptional silencing requires SUMOylation of conserved C-terminal lysine residues in the type-1 histone deacetylase HDA-1". I do not see any supporting data that has separately looked at formation/initiation and maintenance of silencing (a technically challenging experiment).

4) The authors repeatedly claim that gei-17 does not play a role in piRNA target silencing, based on loss of gei-17 not affecting the piRNA reporter (Fig 1B). At the same time, they claim that pie-1 plays a role, even though it likewise does not affect the piRNA reporter (it affects the reporter only in F3; data on gei-17 effect in F3 is not present). In the accompanying paper, the authors show that while gei-17 loss by itself causes only moderate effect on extra intestine cells, combined with Pie-1 loss the effect is more severe than when Pie-1 loss is combined with Ubc9 or smo loss. This to me indicates an important role of gei-17 in inhibiting differentiation of germline stem cells to somatic tissues, but these effects are likely synergistic and thus masked by Pie-1. Individually neither Gei-17 nor Pie-1 show an effect on piRNA reporter in P0, but to confirm lack of synergy, their effects should be tested together. Although possible, the present data is insufficient to rule out gei-17 involvement.

Reviewer #3:

In this manuscript, Soucy et al. describe a new technique that involves a 3D co-culture system that allows the analysis of the regulation of the sympathetic adrenomedullary system. The data demonstrate the advantage of such compartmentalized 3D systems relative to the 2 D system for long-term studies. The findings also show the usefulness of this system to understand the control by preganglionic sympathetic neurons of catecholamines released by the adrenal gland cells.

The main concern with the work relates to the uncertain physiological relevance of the co-culture system developed by the authors. Although I appreciate the utility of such reductionist techniques to understand how preganglionic sympathetic neurons regulate catecholamines released by the adrenal gland cells, this is too removed from a physiological setting.

1) It is difficult to judge the level of novelty of the MPS technique reported in this manuscript relative to what is in the previous paper (Ref 36) which is not available.

2) The innervation of tissues including heart and adrenal gland is highly specific. In addition to the circulating catecholamines secreted by the adrenal glands, cardiomyocytes are tightly controlled by direct innervation. Thus, whether co-culturing PNS with other cells mimic what happens in vivo is not clear.

3) The number of AMMCs displayed in figure 2B seems minimal as only very few cells were stained with cardiomyocyte markers. It would be interesting to know how many of these AMMCs receive innervation (Fig. 3E).

4) It is not clear how primary cardiomyocytes were exposed to the catecholamines emanating from the AMMCs? Were these co-cultured or were the cardiomyocytes exposed to the media of AMMCs?

5) Do the "n" in each figure represent cells or experiments (repeats)?

6) There is no description of the method used to quantify the immunofluorescent signal.

7) The Introduction is too long. It can easily be shortened to focus on the literature related to the topic.

Reviewer #3:

Lee and Daunizeau formulate a model of the effects of mental effort on the precision and mode of value representations during value-based decision-making. The model describes how optimal levels of effort can be determined from initial estimates of precision and relative value difference between competing alternatives, accounting for the subjective cost of incremental effort investment, as well as its impact on precision and value differences. This relatively simple model is impressive in its apparent ability to reproduce qualitative patterns across diverse data including choices, RTs, choice confidence ratings, subjective effort, and choice-induced changes in relative preferences successfully. The model also appears well-motivated, well-reasoned, and well-formulated.

I have two sets of concerns, my first set relates to model fitting and validation. The model appears to do fairly well in predicting aggregate, group-level data, but does it predict subject-level data? Or, does it sometimes make unrealistic predictions when fitting to individual subjects? The Authors should provide evidence of whether it can or cannot describe subject level choices, confidence ratings, subjective effort, etc.

Also, I think the Authors should do more to demonstrate that their model is an advance on simpler variants. The closest thing to model comparison is an exercise where the authors show that, relative to when their model is fit to random data, their model explains more variance in dependent variables when fit to real data. This exercise uses a straw man as a baseline because almost any model which systematically relates independent variables to dependent variables would explain more variance when fit to real data than to data for which, by definition, independent and dependent variables do not share variance. It would be more useful to know whether (and if so, how much) their model explains data better, than, e.g. a model with where effort only affects precision (beta efficacy), or a model in which effort only impacts value mode (gamma efficacy). Since the Authors pit their model against evidence accumulation models, it would be yet more useful to ask whether their data predicts these diverse data better than a standard evidence accumulation model variants.

My second set of concerns are regarding the assumed effect of mental effort on the mode of subjective values. First, is it reasonable to assume that variance would increase as a linear function of resource allocation? It seems to me that variance might increase initially, but then each increment of resources would add diminishing variance to the mode since, e.g., new mnesic evidence should tend to follow old mnesic evidence. How sensitive are model predictions to this assumption? What about if each increment of resources added to variance in an exponentially decreasing fashion? Also, what about anchoring biases? Because anchoring biases suggest that we estimate things with reference to other value cues, should we always expect that additional resources increase the expected value difference, or might additional effort actually yield smaller value differences over time? If we relax this assumption, how does this impact model predictions?

Reviewer #3:

The results of this study suggest that maternal loss alters the HPA stress axis in wild chimpanzees, but these effects are transient and are not evident later in life.

Overall the study is the result of much careful fieldwork. The number of cortisol samples is impressive and these are robustly analysed. The conclusions are carefully and thoroughly discussed.

I have very few comments, in part because I am not a specialist in stress hormones and so cannot fully assess the laboratory analysis or interpretation, but in part because my view is that this is a high-quality thorough study and a well-written manuscript.

My only major point is that I am aware that measurement of cortisol is difficult in the wild. It is possible to inadvertently measure metabolites other than cortisol, and the most robust way to measure cortisol is using a challenge and subsequent measurements. While I cannot adequately assess this aspect of the manuscript, I think it is important that the other reviewers/editor ensure the hormone measurements are appropriate.

Reviewer #3:

General assessment:

This paper applies a sophisticated psychophysical paradigm to assess the effect of prior choices on perceptual decisions in a group of 17 high functioning (but not mild cases) children and teenagers (8-17 years) with ASD. Using a model that is assumed to dissociate the contribution of prior stimuli and choices, the study found a strong effect of prior choices not stimuli, which is stronger in ASD than controls. Similar results from another data set are also reported. There was no convincing evidence found for a correlation between the effect of the priors and the ASD severity.

Overall, this is an impressive study with a sophisticated paradigm, elaborate data analysis, ASD participants who were tested on a large battery, in-depth analysis of the literature with interesting insights, convincing results (but see below) and a well written manuscript.

Major issues:

1) The finding from the model that the prior stimuli did not have a positive impact (and even negative) on the decision bias is counter-intuitive and needs explanation (I apologize if there is one and I missed it). There were typically 5 prior trials, ~4 of them on one side, e.g. right, resulting in a higher rate of right presses on the test (because the test was unbiased, and the results showed a bias). Assuming the prior trials were mostly replied correctly, there should be a correlation between the stimuli and the choices. I see 2 possible reasons why the model produced negative weights - one is that indeed the choices were different from the stimuli, in which case we need to know the performance of the participants on the prior trials (which would be useful anyway). The other possibility is that the choices for the model were binary and the stimuli were continuous. If the stimuli had been coded as binary, it would have been difficult to dissociate between the stimuli and the choices. In this case, the conclusion should be that the prior stimulus laterality could have impacted the test choices, but not their magnitude. This issue should be explained in the text.

2) The performance on the test trials staircase procedure is not reported, only the PSE difference. It would be useful to know if the groups differed on this, as the example psychometric curves shown seem shallower in ASD. Biases are likely to push the staircase procedure to higher laterality discrimination thresholds. I suspect (but without proof) that worse performance (more errors) on the staircase procedure may amplify (but not create) the bias. It would be useful to show the performance data and discuss this issue.

3) The paradigm used is quite complex and complex paradigms are more difficult to fully understand, so I wonder about the justification for it. Why is it better or different from testing SDT shift of criterion by change in target probability? For example, in a Yes/No experiment for contrast detection set around 70% correct, the criterion may shift when there are more Yes or No trials. What would the authors expect in such an experiment? It would be useful to discuss this for the wondering reader.

4) About the interpretation: the word "perseveration", i.e. a tendency to repeat the last key or recent keys is not mentioned. The authors conducted a "response invariant" experiment which showed significant but much smaller biases (Figure 7). Are these significantly smaller than the 1st experiment (as seems from the plots)? If so, one cannot rule out a major contribution of repeating the recent keys, i.e. perseveration. It would be useful to see the raw data in this case, e.g. what is the %trials of pressing right when the priors were biased to the right. My understanding is that it must be high given that the staircase was symmetric (50/50 trials on left and right) and that a bias emerged from the data.

5) I wonder if the data could be analyzed to reveal the different contribution of preceding trials, i.e. the details of the serial dependency. Currently, all previous trials are treated equal in the model, but their contribution is not necessarily equal.

Reviewer #3:

In this paper the authors have developed a system to simultaneously generate two-, three- and four-photon fluorescence excitation from a single laser line and then proceed to apply this system to a number of turbid biological imaging applications to highlight its capabilities. Using a customised commercial La Vision BioTec Trimscope, they have incorporated a high powered fiber laser source with an Optical parametric amplifier and dispersion compensation to generate a either 1330nm or 1650nm laser lines with high peak pulse energies at low pulse repetition rates. They then compare the relative capabilities of each laser line in terms of number of fluorescence emission channels measured (skin tumour xenografts), fluorescence bleaching analysis and functional toxicity thresholds and fluorescence signal attenuation (excised murine bone).

Whilst the paper is well written, the concept of utilising high laser peak powers and at low repetition rates to generate 3PE and 4PE at spectral excitations at 1300nm and ~1650nm is not new and has been presented previously (Cheng et al. 2014), as referenced by the authors. The authors have however gone into more detail and presented a number of comparative excitation approaches to compare and contrast low-duty-cycle high pulse-energy infrared with the more common high-duty-cycle low pulse energy near-infrared alternative. The benefits of higher order multiphoton microscopy when combined with higher wavelength excitation allows deeper imaging and more localised fluorescence excitation with reduced phototoxic and photobleaching effects per excitation pulse. One of the major issues associated with generating 4PE is that since higher pulse energy is required, this further reduces the repetition rate of the laser source, in order to reduce the average laser power in order to avoid sample heating effects. This in turn leads to much longer acquisitions and is limited by the fluorophore saturation particularly since they are using single beam excitation.

Major comments:

1) It seems as though when you take into consideration duty cycles, fluorescence saturation, water absorption effects and longer acquisition times, which lead to greater phototoxicity, 4-PE at 1700nm excitation is not appropriate for most dynamic biological applications where acquisition speed and/or continued image acquisitions are the key factors. Could the authors comment on this?

2) How long does it take to acquire a single frame with four-photon excitation at 1700nm? In none of the data sets was frame time mentioned in particular when acquired 3D data sets. Can the authors ensure that these times are mentioned both in the main text and the figures containing images.

3) In line 131 and figure 3d the authors present data showing relative axial resolution measurements. Are these features measured diffraction limited and how do they know? They are clearly not measuring like for like structures (different fluorescent species) so do not think this can be used as a measure of resolution. Can the author provide other resolution measurements?

4) In line 140 - 142 the authors present data showing the advantages of THG at 1650nm over other excitation lines. Aside from the excitation wavelength could this data be explained by the greater absorption and scattering at the emission wavelengths generated at these laser lines?

5) In figure 3A and 3C the SNR for 1650nm increases whilst for 1300nm and 1180 excitation this decreases. Is this simply due to more of the exciting fluorophore species residing deeper into the tissue?

Reviewer #3:

General Assessment:

The manuscript is well written and the methods are sound. The strengths of this manuscript are that this study is the first to systematically perform detailed electrophysiological measurements on inhibitory interneurons (INTs), in particular RC and non-RC INTs using the SOD1 mouse model for ALS. It is very interesting that they showed a dichotomy between reduced excitability in RC neurons (which could lead to an indirect increase in overall excitability of MNs) and non-RC INTs, which actually showed an increase in excitability which would have the opposite effect on MNs.

Main comments:

1) Most electrophysiological studies have focused on motor neurons and showed that they become hyperexcitable at very young ages, although there is controversy as to whether the hyperexcitability persists and is causative or compensatory to disease progression.

2) The dichotomy observed between RC and non-RC Inhibitory neurons is interesting. Given that many of the glycinergic non-RC interneurons are Ia-inhibitory interneurons responsible for reciprocal inhibition, their effects on the target motor neurons have opposite effects on MN excitability. At this point it is mere speculation as to how these changes actually exacerbate the progression of the disease and effects circuit function.

3) This paper is mainly descriptive with no specific hypothesis other that what has been discuss often in the literature: Motor neuron hyperexcitability occurs from intrinsic alterations in MN ion channels, increased excitatory synaptic activity, or a decrease in inhibitory activity or all of the above. Although the authors are most likely the first to demonstrate changes in inhibitory interneuron excitability with direct electrophysiological recordings, it is unlikely that these findings will significantly move the field forward presently. The authors suggest that biomarkers could be developed, this is just a broad statement without concrete proposal for implementation. It would be useful to show a specific target that could be modified pharmacologically in animals over time to see if this changes the progression/survivability of the ALS animals.

4) Furthermore, the functional significance of early hyperexcitability as either a cause or compensation of ALS is controversial at present. Numerous studies have addressed hyperexcitability but yet we are still far from understanding the bases for this disease and one cannot help question whether this avenue of investigation is fruitful.

5) Does this change in interneuron excitability and the dichotomy between RC and non-RC demonstrated persist over the course of the disease? How relevant are these changes to disease progression?

6) It will be necessary to use other animal models available for comparison since SOD1, although historically a well-studied mouse model, is an ectopic over expresser, and is not the predominate mechanism for ALS in humans. There are others probably more pertinent models, ie. C9ORF72. Whether such changes in inhibitory interneurons occur in those other models and in humans remains to be determined.

Reviewer #3:

The authors are to be commended for the effort put into careful experimental design and clear presentation of methods and results.

My main concern with the manuscript is that the claim about their specific polymerase gene being "ultraconserved" is not backed up with their own data or by citations from the literature. If the gene sequence was ultra-conserved, I wouldn't have expected the authors to be able to do so much recoding of the gene without fitness consequences. Furthermore, it is clear that homozyogous-viable NHEJ mutations did develop in the experiment. Without explanation, this seems to be a fatal flaw in the design.

This manuscript describes a modification of the general homing gene drive concept by use of a split drive system that increases the frequency of a recoded polymerase gene that replaces a cleavage susceptible, naturally occurring, haplosufficient, conserved polymerase gene. This approach is taken in order to limit the evolution of cleavage resistance in the naturally occurring gene.

I am not convinced that the research presented achieves the intended goals. I did a quick look for literature on the "ultraconserved" polymerase pol-y35 gene and could find none. I am not sure if the conservation is at the DNA sequence level or at the amino acid level. If at the amino acid level, then it makes sense that resistance alleles can form at the DNA level that don't impact the protein at all. Figure 2a shows the 22 and 27 recoded nucleotides for the two guide RNA sites. The authors say that these changes to the sequences didn't seem to impede fitness. Did the authors try many other recodings and finally decide on these because all others caused loss of fitness, or is it just that this gene is robust to substitutions even though the protein is conserved.

Figure 4C shows that the frequency of flies with at least one copy of the pol-y35home R1 increased from about 25% to about 50% between the parental and F0 generation when there was no Cas9 present. As long as the transgenic males were competitive with the wild flies this makes sense because the released flies were homozygous for that allele and the offspring should all have inherited one copy of the gene. What doesn't make sense is that when the work was done with all flies harboring the Cas9, the pol-y35home R1 increased less than in the former case, from the parental to generation F0, the frequency of flies with the pol-y35home R1. In some replicates the frequency of such flies didn't increase at all. It should be noted that the parents were always homozygous. This certainly indicates a fitness cost to the flies with a combination of Cas9 and the homing construct.

In this same figure, results from the model are plotted. It seems like the model assumes no fitness cost because it shows an exact increase from 25% to 50% flies carrying at least one copy of the pol-y35home R1 theoretical construct. In later generations the experimental results outperform the model. Presumably, this model is used to construct figure 6. This mismatch needs to be addressed in the manuscript.

The fact that in all three replicates of the experiment without Cas9, the F0 is above 50% indicates that something else may be going on that is unrelated to gene drive. It could be due to heterosis between the two slightly different strains of flies. When wildtype males mate with wildtype females, the offspring are more inbred than when a transgenic male mates with a wildtype female. Just a hypothesis.

Reviewer #3

This manuscript by Beier et al. has used an impressive array of genetically modified mouse lines to study, which retinal circuits are responsible for driving the pupillary light reflex (PLR). These mouse lines are validated by direct electrophysiological recordings from rods, to rod bipolar cells, to ON and OFF cone bipolar cells. The manuscript makes two key conclusions based on measurements of PLR from darkness to 100 lux and 1 lux light steps: 1) the ON but not the OFF pathways drive PLR, 2) PLR relies on the most sensitive rod pathway - the primary rod pathway. My main concern is that the data shown in the paper does not uniquely support these two key conclusions. There are many issues, some of which may be fixed by better explanations, some of which may require more complete measurements. I outline my main concerns below:

1) The manuscript uses an incoherent terminology of the retinal pathways. For example, the beginning of the second paragraph of the introduction states that the ON and OFF pathways split in the first synapse, which is not true, for example, for the primary rod pathway (rod bipolar pathway). The latter segments of the same paragraph lay out more clearly the conventional definition of the primary, secondary and tertiary rod pathways. In short, it would be important to use a coherent and conventional terminology of the retinal pathways and relate the experiments and conclusions to these. It would also be important to correlate the used stimuli to the light levels defined to drive signals across different retinal pathways in image forming vision (see Grimes et al. 2014 & Grimes et al. 2018). Now that the light levels for physiological studies are expressed in R / rod (see Supplementary Table), whereas lux are used as units for PLR. Comparison to the previous literature would require a unified intensity space (preferentially Rs or both luxes and Rs). It would also be important to relate the sensitivity of the primary rod pathway (as the authors claim is driving the PLR) to the signaling levels (extremely low light levels, <10 R/rod/sec) where this pathway is supposedly driving image forming vision (Murphy & Rieke, 2006). It seems that the current PLR experiments probe much higher light levels than these papers in relation to the primary rod pathway. A wider stimulus space should be tested and/or at least a clear explanation would be needed for the choices made.

2) One of the two main conclusions of the paper is that the retinal ON pathway drives the PLR and the OFF pathways do not contribute to the PLR. The authors state (see abstract): "The OFF pathway, which mirrors the ON pathway in image forming vision, plays no role in the PLR". The data in Figs. 2A & B and 3 A & B indeed give strong support for the notion that light steps from darkness to 100 lux and 1 lux drive light responses through the ON pathway. However, this finding is not in conflict with the image forming vision. In fact, both the classic papers (Schiller, 1982, photopic ) as well as more recent results (Smeds et al. 2019; scotopic) support the notion that light increments are coded by the ON pathway. Now the circuits controlling PLR seem to fall exactly in this picture. However, the classic papers based on image forming vision (see e.g. Schiller, 1992) propose that the OFF pathways would drive light decrement stimuli. To justify the conclusion that "OFF pathways do not contribute to PLR" the authors should test a wider stimulus space including light decrements across scotopic and photopic light levels or limit their conclusions to light increments and in line with current notion for image forming vision. The reason that OFF pathways do not play a role may just reflect a limitation in the stimulus space probed.

3) The authors appear to ignore that the division into ON and OFF pathways occurs only after the AII cells along the primary rod pathway. The fact that Cx36 KO mice exhibit a normal PLR thus seems to invalidate the main claim of the paper that the primary ON pathway drives the PLR. The authors state: "These results imply that either the rod to rod bipolar cell pathway, independent of the AII ON pathway, is capable of driving pupil constriction or that cones are playing a role". Both of these conclusions are in contradiction with the main conclusion that the primary rod pathway as defined conventionally would be the underlying mechanism. If indeed cones are driving the PLR in Cx36 KO mice, that would be in contradiction with the previous literature (Keenan et al. 2016). It would be important to test this perhaps by using a different mouse line allowing to eliminate the cone contribution. Alternatively, showing data on Cx36 KO mice at lower light levels could help but this dataset is missing from the Fig. 3. Similarly, the Cone Cx36 KO dataset seems too sparse (n = 3) to justify the current conclusions in Fig. 3D and for some reason, the corresponding data trace is missing completely from Fig. 3C. In fact, the authors as they speculate might have uncovered (see Discussion) an entirely novel mechanism controlling the PLR. However, this now has been left untested even if it could be the most interesting new discovery if properly tested/shown.

Reviewer #3:

The manuscript by Shi and colleagues delineates an approach for labeling newly synthesized lipids thereby providing a method to examine how lipids move throughout the cell. The premise of this technical approach is that fluorescently labeled fatty acids are fed to a cell in the presence of another lipid which will incorporate the fluorescent acyl tail using the endogenous cellular acyltransferases. Cellular imaging is paired with this approach to show the subcellular accumulation of the lipid. As presented, the data are intriguing, but there are some concerns and questions about the technique that limits the interpretation of the data and could impact the overall utility of this approach. The authors should provide the additional requested data, and resolve the issues raised below to increase confidence that this labeling approach allows for the monitoring of physiologic lipid trafficking pathways.

Specific concerns and questions are delineated below.

1) The authors initially exploit the remodeling of PLs as described in figure 1a. This involves the addition of lyso-PL and NBD-labeled palmitoyl-CoA. The authors imply from their schematic in Fig 1a that they are using lyso-PLs that are being remodeled at the sn1 position by NBD-labeled palmitoyl-CoA. Unless I am missing something, lyso-PA and other related lyso-PLs are generally remodeled at the sn2 position. Additionally, there is specificity for PUFAs acylation to the lyso-PL. So I am a bit confused about the enzymes that are working in this system. I tried to determine which lyso-PLs that the authors are using, but the methods did not specify if they are using 1- or 2-lyso PLs. This should be clarified so that we can understand the enzymes the authors think are underlying the labeling reaction. On a minor, but related note, the lyso-PL in Figure 1a is missing an -OH group at the sn1 position.

2) The authors use a cell system where the cells are starved of lipids and other metabolites for 1 hour and then fed a large bolus of lipids as substrates. It appears that the cells can remodel and label some PLs under these conditions, but it is not clear to me that this represents physiologic labeling that can be used to track the de novo labeling and trafficking into subcellular compartments. Nor can it be used to draw strong conclusions about required trafficking or enzymatic pathways under normal conditions. What happens if labeling occurs in complete media or defined media? This might help to resolve this.

3) The labeling looks non-uniform in mitochondria as evidenced by the images in figure 2a. Why is the labeling only at the outer edge of the mito in these cells in this figure? What happens if labeling goes longer? Similarly, the authors quantify "30 cell images" or the like in the figures for Pearson correlations. How were the 30 cells selected, and since labeling across the mitochondria is not uniform, how were images selected? A much larger number of images scanned in an unbiased manner would increase confidence.

4) Likewise, what happens if the labeling is allowed to proceed beyond 15 min. Can the authors provide a 30 min and 1 hr image?

5) There are a number of conclusions drawn about specific pathways required for the trafficking of accumulation of labeled lipids. I realize that some of these studies are used as a specific proof-of-concept for the approach. However, there are many studies that go beyond proof-of-concept and draw conclusions about biology. Many of the studies are somewhat superficial and the conclusions reached by the authors should be tempered given that they have not deeply investigated this new biology.

Reviewer #3:

In this manuscript, Icke and colleagues show that the secreted protein CexE/Aap from entergotoxigenic E. coli is acylated at an N-terminal glycine and suggest that acylation is required for secretion via a Type I Aat secretion system to the cell's surface or into the environment. The key findings is the identification of an N-acyltransferase (AatD) encoded nearby cexE/aap and demonstration that this enzyme is required for acylation.

There is a concern about the novelty of the findings. The publication by Belmont-Monroy et al. (PLoS Pathogens, August 2020) cited by the authors is very similar to the current manuscript. That publication demonstrated that N-acylation of Aap (a CexE homolog) occurs at its N-terminal glycine (made available after signal peptide cleavage), that acylation is dependent on the acyltransferase AatD, that acylation is required for Aap secretion, and that N-terminal residues are sufficient for acylation of a heterologous protein (though this was poorly analyzed in that paper). Almost all of those findings are shown in this current manuscript by Icke et al., independently confirming the acylation reaction.

This Icke et al. study is well done and convincing on the AatD-dependent acylation of CexE/Aap. Overall, the same conclusions are drawn as Belmont-Monroy et al., 2020. The major new advance (not previously described) is the observation that the N-terminal glycine is required for N-acylation by AatD.

As described in my comments (below), the manuscript could be improved in a few instances by including key controls to support the conclusions. In other instances, broad conclusions are made from narrowly focused data and the text should be revised.

Major comments:

1) "To our knowledge this is the first report of enzyme mediated N-palmitoylation in nature". This statement is not correct. The lipoprotein N-acyltransferase Lnt (used as a reference for AatD analysis in this manuscript) performs N-palmitoylation (C16:0) in E. coli and distantly related bacteria such as mycobacteria/corynebacteria. See Jackowski & Rock 1986 (JBC 261,11328-11333), Hillman et al. 2011 (JBC 86, 27936-27946), Brulle et al. 2013 (BMC Microbiology 13, 223).

2) The conclusion that "we reveal a new function for acylation - protein secretion" is not fully supported. The authors do not directly show that the secreted CexE is acylated (Fig 2A) or that acylation is required for secretion. The use of 17 ODYA is innovative and could be used to show that secreted supernatant CexE is acylated. The CexE N-terminal substitution mutants that are not acylated (Fig 7C) could be used to test if acylation is required for secretion.

3) If the secreted CexE is acylated, some discussion is needed. How is the acylated form sometimes secreted into the aqueous environment but sometimes embedded in the outer membrane as shown in the model?

4) Can the authors show/detect CexE acylation in the native system that doesn't rely on overproduction of the CfaD transcription factor? Is the observed acylation physiological or a consequence of strong overexpression?

5) Claims of novelty in text should be altered following Belmont-Monroy et al., 2020.

Reviewer #3:

The manuscript named "Ex vivo observation of granulocyte activity during thrombus formation "submitted by Morozova and colleagues try to demonstrate the implication of deux different types of granulocytes in thrombus formation. Author study thrombus formation in anticoagulated whole blood from healthy and Wiskott-Aldrich patients in parallel-plate flow under collagen type I and low shear rate (100 s-1). They identified a CD66/CD11 cell population defined as granulocytes able to interact with growing thrombus. Two types of granulocytes were observed and differentiated with their fluorescent patterns: type A (uniform DiOC6 staining) and type B (cluster-like DiOc6 staining). Authors studied granulocytes behavior under several kinds of inflammation mediator. The manuscript should be improved, please see my following comments.

1) Authors should clarify the technical part of the manuscript and the figure 1, essentially the use of anticoagulant to perform follow chamber. It is not obvious which anticoagulant was used to performed flow chamber: citrate, heparin, hirudin. Does recalcification was performed in all experiments?

2) The authors should explain why the figure 1 demonstrates that granulocytes need free calcium ions to adhere to the growing thrombus. This is not the conclusion of figure 1. Moreover, all the growing thrombi seem different (more compact in citrate than with hirudin, w/o granulocyte in citrate and with granulocytes in hirudin) the authors should discuss this point.

3) This following sentence is confusing (last sentence of 3.1): “Hirudin- and heparin-anticoagulated blood was used in all further experiments because citrated blood recalcification causes local fibrin formation and platelet activation.” Platelets activation is essential to growing thrombus.

4) Author hypothesized that type B are more activated than type A essentially based on crawling and velocity cells. Could they do supplemental experiments to prove this point (increased of CD11 active form) and to differentiate neutrophils from eosinophils and basophils?

5) It will be great to perform a competition experiment to prove that platelets are interacting with granulocytes through CD11.

6) Did authors find NETs in this setting?

7) In all pictures platelets seem not well represented, only two and three platelets in figure 2. How the authors could be sure that granulocytes interact with platelets and not collagen?

8) Some platelets seem inactivated (round form) and annexin V positive. Could the authors discuss this point?

9) Concerning the last figure, it will be great to use healthy platelets and WAS granulocytes to conclude that crawling is altered.

Reviewer #3:

In this manuscript, Dempster et al. analysed the predictability of cell viability from baseline genomics and transcriptomics based features. They did a comprehensive analysis across feature and perturbation types, which gives a valuable contribution to the field. The main findings of the paper (gene expression based features outperform genomics based ones) are not necessarily new, but the authors also show the interpretability of gene expression based features, which clearly helps to place these machine learning (ML) models into biological context . This is especially important for the possible translatability, as small (low number of features), interpretable models are generally preferred over large, "black box" models.

The study is very nicely constructed both from machine learning and cancer biology perspective. My only major comments are regarding some (potential confounding) factors related to tissue-type and feature filtering.

Major comments:

1) A well known phenomenon on the field is the tissue-type specificity of drug sensitivity, which is a major confounding factor in several ML-based studies. The authors, absolutely correctly, use tissue-type as features in their models to overcome this problem. However, as RF models (individual trees) do not use all features at the same time, so it is possible that some genomics based models are not using information about tissue-type, even if tissue-type was selected in the 1,000 features. On the other side, for gene expression based models (based on the "tissue specificity of gene expression"), tissue-type information is probably always available. This could (partially) cause the better performance of gene expression features. Could the authors do some additional controls (e.g.: providing "multiple copies" of tissue-type features for genomics based models) to overcome this potential confounding factor?

2) The authors use a Pearson correlation filter (mainly) to decrease computational time. In Figure 4 (and also inFigure 2 - supplement 3) they show that in case of "combined" features, the features sets including gene expression based features had the best performance. When did they use the Pearson filter in case of combined features, before or after combining them? I.e. in case of expression + mutation, they selected the top 1,000 expression and top 1,000 mutation features, combined them and trained RF models with 2,000 features, or combined expression and mutation features, selected the top 1,000 features, and trained the models with them? If the later, it would be important to see how much of the different feature classes (e.g.: mutation and expression in my example) are included in the top 1,000 features. This is especially important, as Pearson correlation as a filter is probably more suitable for continuous (expression) than binary (mutation) features, so it is possible that the combined features use mostly expression based features. In this case, it is not so surprising that the performance of combined feature models are more close to expression based models.

Reviewer #3:

Three different anti-asprosin mAbs were produced and tested in different metabolic syndrome animal models. Beneficial effects were noted on body weight, food intake and blood glucose and insulin levels. The effects were modest, but seemed to be relevant to elevated aprosin levels, as the AB blocked the effects of adenoviral overexpression of the hormone. Some issues require attention:

1) Additional characterization of the aprosin neutralizing effect of the AB is required.. It will be helpful to show the endogenous free asprosin levels at different time points after a single or repeated mAb injection. This result is also important to tell whether this mAb will cause other immune responses and side effects that might confound interpretation of the results.

2) In Figure 3 (a, e, j) and Figure 4 (a, e, I, m). please show body weight to rule out the stress or side effects caused by virus injection. For DIO mice, 14 days IgG injection also caused weight loss; for db/db mice, IgG injection increased body weight. Please discuss.

3) Although adenovirus and AAV are widely used for in vivo protein overexpression, it is important to show here that endogenous asprosin levels were increased after virus injection and decreased after antibody neutralization.

4) In Figure 5, more data on liver weight, histology, etc. is required to support their conclusion on liver health. The current data from three different mice models are very contradictory, this can be caused by the side effect or off-target effect of this mAb.

5) In Figure 6, it is important to demonstrate the neutralizing effect of the mAbs.

Reviewer #3:

This paper shows that during a second-order conditioning (SOC) task, the representation of a conditioned outcome is represented in the lateral orbitofrontal cortex (lOFC). The BOLD signal in this region shows increased functional coupling with the amygdala for second-order conditioned stimuli that indirectly predict a negative outcome. The authors suggest these findings reflect a mechanism by which value is conferred to stimuli that were never paired with reinforcement.

The paper tackles an interesting question concerning the neural mechanisms that support second order conditioning. The task design includes relevant controls and, on the whole, the findings support the claims made by the authors. I have a few questions about interpretation of the data, but my main suggestion would be to revise the framing of the article. There are many previous studies that have investigated the mechanisms that support second order conditioning which are not always given due credit. I believe this paper would benefit from placing the hypotheses and findings more firmly within the context of previous literature.

Comments:

1) The authors test the hypothesis that CS2 is directly paired with a neural representation of the US. They state that this hypothesis 'has never been tested to date'. However, a number of studies have shown evidence for and against this hypothesis (for example: Wimmer and Shohamy 2012; Wang et al., 2020; Barron et al., 2020). Can the authors clarify how the hypothesis tested here differs from those investigated previously? In addition, it is not clear to me how the four potential mechanisms they propose are really distinct from each other?

2) Relatedly, given the authors use an SOC paradigm that differs from sensory preconditioning studies used by many previous authors, does the difference in task paradigm provide new insight? Do the authors expect the neural mechanism to be the same or different between their version of SOC and sensory preconditioning?

3) Why is the behavioural data in Figure 1F bimodal for CS1 and CS2? i.e. what does choice probability of 0 for CS2+ vs CS2- mean for a given subject?

4) To test the author's hypothesis, is it not necessary to assess evidence for US in response to CS2? They instead report reactivation of US in response to CS1 and for the PPI it is not clear to me how the authors distinguish between CS1 and CS2 given the temporal proximity in their presentation (Figure 1D).

5) For the PPI, is there a main effect of CS- and CS+ versus CSn in lOFC? If not, how does this affect interpretation of the PPI? On a separate note, is the effect reported in Figure 3 really in the hippocampus? Does it survive small volume correction using a hippocampal mask?

6) The following is stated as a premise: "To form an associative link between CS2 and US, the reinstated US patterns need to be projected from their cortical storage site to regions like amygdala and hippocampus, allowing for convergence of US and CS2 information." This potentially seems fair for the hippocampus, with added reference to relevant literature (e.g. publications from Shohamy and Preston labs), but in my opinion the jury is still out on this one. It is not clear to me why we necessarily expect amygdala here.

7) There are various strong statements that in my opinion need to be toned down in light of existing literature. For example, the paper claims this study is the first to show evidence for implicit inference. However, as far as I'm aware, Wimmer & Shohamy 2012 also found no evidence for explicit memory of stimulus-stimulus associations with no relationship between measures of explicit memory and decision bias. Similarly, the authors claim this paper is 'the only report so far of behavioral evidence for associative transfer of motivational value during human second-order conditioning', overlooking a large number of other studies that have shown similar behavioural effects.

Reviewer #3:

This manuscript examines data from the Young Adult Human Connectome Project's 900-subject release to compare both structural and functional connections between iso-eccentricity bands in striate cortex and the fronto-parietal, cingulo-opercular, and default mode networks. The authors find that central vision is most strongly connected to the fronto-parietal network, which is associated with attention, while the far periphery is more strongly connected to the default mode network. The questions asked in this manuscript are of considerable interest to the field, and this study has the potential to be impactful. However, substantial work is needed to make the methods and results sufficiently clear and reproducible to the reader.

Major Comments:

A major problem throughout this paper is that the authors have not been very careful in documenting their methods, what they are plotting, or how they are supporting their assertions. This is a major shortcoming of the work. I do not believe there is sufficient detail in this paper as is to reproduce the methods, nor was I able to understand what precisely was calculated in the statistical tests reported.

The amount of work that has been put into this project's quality control (at minimum, visual inspection and filtering of 900 MR images) is very impressive! This information should really be shared with the broader research community in order to make this manuscript more reproducible and in order to ensure that other researchers can simply use and cite the authors' efforts rather than repeating them. This could be as simple as a supplemental table or text-file that includes the subject IDs of those HCP subjects that were included in all analyses.

It should be crystal-clear from the Methods section whether the manuscript's data were collected or reanalyzed by the authors. My understanding is that all of this manuscript's analyzed data are from the HCP database. However, had I read only the "Data Acquisition" section I would have been left with the strong impression that the authors collected the data themselves using the same kind of scanner and the same analysis pipelines as the HCP. Unless this is the case, the opening sentence of this section should probably be something like "All data were acquired and preprocessed by the Human Connectome Project (Van Essen et al., 2013)" [10.1016/j.neuroimage.2012.02.018]. It may also be wise to reference the HCP in the Acknowledgements section. Further information: https://www.humanconnectome.org/study/hcp-young-adult/document/hcp-citations. This should apply equally to the data and the preprocessing methods-i.e., if the quality control mentioned in the above comment was performed by the HCP and not the authors, that should have been explicit.

P3, ❡6. This paragraph is critical to the methods but is not at all clear. In particular, the paragraph eventually describes seven eccentricity segments per subject, yet it does not explain what the eccentricity boundaries of these segments are, nor does Figure 2 show these segments. It isn't clear from the manuscript if these are ever used (rather than the 3 central/mid-peripheral/far-peripheral segments) or exclusively used.

In looking at Figure 4, my first and strongest impression is that the central connectivity is very similar to the far-peripheral connectivity, and the z-score differences are incredibly small. Additionally, the legend does not make the quantities plotted very clear (these are based on the averaged z-scores across subjects?) so I'm left wondering how to assess any sort of significance. I have a similar reaction to Figure 5. More help is needed to understand these results.

Given that this paper consists of a large analysis of a large existing dataset, it would be especially nice if the authors would make their source code and intermediate analysis files publicly available. Having access to the source code directly is virtually a requirement of making this kind of study reproducible and would mediate many of my concerns about the ambiguities of the methods.

Reviewer #3:

General assessment:

Antitermination (AT) is a widespread mechanism to regulate transcription and can be mediated by ANTAR domains which prevent the formation of the terminator hairpin by binding to and stabilising a dual hexaloop motif in the nascent RNA. In the submitted manuscript Walshe and coworkers address the molecular basis of this AT mechanism which is largely unknown. They report two crystal structures of the dimeric ANTAR protein EutV from E. faecialis, one of EutV alone and one in the presence of a 51 nt long RNA containing the dual hexaloop motif, and combine this structural data with biochemical and biophysical data.

The study

-Reveals structural rearrangements that occur upon RNA binding and provides molecular insights into the RNA binding mode

-Shows for the first time that a Met residue is obligatory for RNA binding

-Redefines the minimal ANTAR domain binding motif

-Suggests a new model for ANTAR-mediated AT

Thus, the study is a comprehensive work, the experiments are performed thoroughly, and the conclusions are supported by the data. The results are of interest to a broad audience, ranging from the field of transcription in all domains of life to protein:nucleic acid interactions in general.

However, the authors should address the following concerns:

1) p 5, lines 15-17: The interactions should be described more clearly, i.e. are the hydrogen bonds between main chain atoms or between side chains? Which atoms/functional groups are involved (e.g. carboxy group of sidechain of Glu161)

2) p 8, line 1-2: The SEC-MALS data indicates that the sample is not homogeneous and the authors suggest that this might be a concentration-dependent effect. This hypothesis is, however, not supported by the data. First, there is no information provided about the concentration used in the SEC run . Second, the SEC run was carried out on a S200 column. The experiment should be repeated on a S75 column which has a better resolution in the range of interest. Furthermore, the SEC runs should be performed with different concentrations to check if the oligomerization is indeed concentration-dependent and it could be used to check if the oligomerization is reversible (i.e. by collecting the "dimeric" form and re-run the solution and see if there is an equilibrium). Finally, as the authors discuss the dimerization behavior/mechanism, they might check if/how phosphorylation influences the oligomerization. These tests are important as this sample was used for the SPR experiments. If the sample, however, is not homogeneous, interpretation of the data might be compromised due to a mixture of different oligomeric states so that concentrations are not correct or a 1:1 binding model cannot be sued (most probably, the concentration of EutV is higher in the SPR experiments than in the SEC run and if there is concentration-dependent oligomerization this might be a significant issue).

3) p 8: the chronology of Fig. 2 does not correspond to the chronology of the panels mentioned in the text.

4) p 11, line 20: the authors state that G4 makes the only base specific interaction between the protein and the RNA hairpins. However, the details of the interactions are discussed only later in the manuscript so that this conclusion cannot be drawn at this stage. Thus, the author should present the interaction analysis earlier or adapt their argumentation (maybe by pointing to Fig. 3).

5) Fig. 3: The interaction network between RNA (bases) and the protein is a very important point in the manuscript. In order to emphasize that only one of the bases, G4, makes base-specific contacts is, most probably, thus responsible for sequence-specific read-out, a 2D representation of the interaction network should be provided as Figure Supplement. (e.g. using LigPlot)

6) p. 14: alanine mutagenesis. In order to confirm the importance of G4 the authors might substitute the base by another base and repeat the SPR measurements. Moreover, the quality of the protein samples should be checked (and data should ideally be provided as supplemental material), i.e. is the samples homogeneous (see comment on SEC runs) and are the samples free of nucleic acid contamination (how is the A260/A280?)

7) p. 14: EutV binding to P1 and P2 RNA tested by SPR: was the sample homogeneous ? (see comment above on SEC runs).

8) p 14: The authors should comment on the differences in the CD spectra in the region around 220 nm.

9) p 20, ,lines 14-23. G4 plays a critical role in sequence-specific recognition. This recognition mode is reminiscent of the mechanism an operon-specific transcription factor, RfaH, uses. Here, RNA polymerase pauses at a pause site and exposes the nontemplate strand, which forms a hairpin. This hairpin stabilizes the flipping-out of a base in the loop region and allows sequence-specific read-out. Similar to EutV, sequence-specific recognition relies on very few base-specific interaction. However, RfaH binds to DNA. Moreover, also the sigma factor uses a flipped-out residue for recognition, although applying a different mode of stabilization. Thus, a comparison of these recognition modes might be of interest.

10) p. 22: revised AT mechanism: The proposed model is reasonable and fully supported by the data. Is there a possibility to check the role of the two hairpins in vivo? I.e. if there is a possibility/assay to distinguish between recruitment and AT efficiency, the proposed model could be tested.

Ledford, H. (2020). Oxford COVID-vaccine paper highlights lingering unknowns about results. Nature. https://doi.org/10.1038/d41586-020-03504-w

Reviewer #3:

This study shows how well mixed populations of yeast cells initially expressing both an anticompetitor toxin and resistance to it, first lose toxin production (because there is a cost but no benefit to toxin production when all cells are resistant) and then lose resistance (because there is a cost but no benefit to resistance when no cells produce toxin). Consequently, these evolved sensitive populations have lower fitness than their own toxin-producing (resurrected) ancestors, but only if the toxic ancestors are introduced at a high enough frequency, that is, there is positive frequency dependent selection. These results are quite intuitive and satisfying, and are well supported by rigorous experiments determining the causal mutations and their selective advantages both within intra-cellular populations of the virus, and between cells in the evolving populations. This was really nice, thorough, and interesting work. However the overall result is not really surprising, as much similar work has been done before (and is properly cited) in which three types of competitors show non-transitive pairwise fitness relationships.

The main claim to originality is that the three types here are generated sequentially by two rounds of mutation, natural selection, and replacement/fixation: that is, there is genealogical nontransitivity between ancestors and descendants, rather than just ecological nontransitivity between contemporary co-existing variants. This demonstrates an important principle: that natural selection can produce a decline in overall relative fitness in a lineage over multiple rounds of mutation and fixation. The only other reported example of this in experimental evolution is the work of Paquin and Adams (1983), but the authors here argue convincingly that the Paquin and Adams, lacking the benefit of sequencing to identify mutations and their frequencies, inadvertently competed ecological types that were co-exising in their evolving populations and had not fixed.

My only criticism, then, is that the example of non-transitivity demonstrated here is rather "obvious"; the result is entirely predictable, given the amount of previous work in similar microbial systems. However, this is countered by the fundamental nature of the question for evolutionary biology, and the lack of specific experimental examples, apart from the very old Paquin & Adams. Overall, then, I am satisfied that this paper is a significant step forward. I found it well written, interesting, and the conclusions were well supported by careful and thorough experiments.

Reviewer #3:

The manuscript by Morcom et al., describes mechanisms of Corpus callosum Diysgenesis in mice and how they relate to humans. It will be of interest to the field. It explains the spectrums of disorders of the corpus callosum in humans. It is an important study that sets the focus on midline populations and away from axonal navigation as the main source of corpus callosum dysgenesis.

The authors found that a mutation in Draxin carried by certain mouse strains is responsible for the heterogenicity of corpus callosum phenotypes found in these mice. Draxin mutations interrupt the normal remodeling (closing) of interhemispheric fissure necessary for callosal axons to cross. The phenotypes in the mouse are very similar to what is found in humans, and also variable, perhaps related to stochasticity on the mechanisms involved, or to the dependency on other allelic variants. The findings are important to understand what mutations cause CCD in humans and how, mechanistically, it occurs. The authors found that Draxin mutation misregulates astroglial and leptomeningeal proliferation. Mechanistically, how this more precisely affects interhemispheric remodeling is still unclear. This is a point that may reinforce the work.

Major concerns:

1) The authors have done an excellent job identifying the mutation and characterizing and comparing in detail the phenotypes in mice and humans. They also provide very interesting hints about how Draxin regulates the remodeling of the interhemispheric fissure. But mechanistically, their findings only offer an incomplete view. In my opinion, the findings would be reinforced by a deeper digging into how, cellularly or molecularly, Draxin makes glial and leptomeningeal cells remodel the interhemispheric fissure. Proliferation by itself does not seem to explain the phenotypes. It is not fully clear the model that they are proposing. Does it affect cell-cell adhesion, cell-cell signaling, membrane processes, metalloproteinase activity? Perhaps they could characterize some more the morphology and junctions of the affected cells or perform some studies in acute models or in vitro.

Minor comments:

Fig 4C-the expression patterns of mRNA Draxin in C57 or BTBR does not seem so similar as it is mentioned in the description of the results.

Fig 4D-The full versión of western-blots shown in supplementary showing all forms is more informative than the cuts shown in principal Figure. Please indicate molecular weights.

Reviewer #3:

This work by Katada and colleagues uses M4 and 5B transgenic lines to express ChR2 in starburst amacrine cells (SACs) and retinal ganglion cells (RGCs). It finds that ChR2 activation in SACs improves the ChR2 response in RGCs. Thus, in a gene therapy strategy that expresses optogenetic proteins in RGCs, SACs may be considered as a helpful additional target. The rationale of the manuscript basically regards RGCs as a uniform population and disregards all amacrine cells except SACs. If differences in RGC and amacrine subtypes are taken into consideration, some conclusions of this manuscript should be revised.

Major comments:

1) This manuscript makes one assumption: that the RGCs in M4-ChR2 and 5B-ChR2 have comparable ChR2 evoked response if activated alone, thus the difference between their ChR2 responses is entirely attributed to the activation of extra SACs in the M4 line. Yet there is no experimental evidence to support this assumption. Both M4-YC and 5B-YC label ~35% of the RGC consisting of multiple subtypes, the subtype compositions of the two populations are not shown. ChR2 response properties of a neuron may be influenced by its own ion channel composition that differ between cell types. The authors need to either a) show the 2 mouse lines label identical subsets of RGCs (unlikely, given FigS6E), or b) compare M4 line with or without coactivation of SACs to single out the effect of SACs.

2) The experiment results using rAAV (Fig4) are hard to interpret:

a) CAG promoter directs expression in most cell types. So other amacrine (Fig4D) and RGC cell types in addition to SACs and M4/5B RGCs are also infected. Comparison between rAAV/M4/5B retinas cannot provide clean insight into the effect of SAC.

b) The manuscript makes comparisons within the rAAV experiments (Fig4I-K FigS8F-H), trying to link induction efficiency into SACs with visual restoration. However, it is a given that higher infection in RGCs also leads to better visual restoration. So SAC effect cannot be separated from RGCs (Fig4J-K FigS8G-H).

c) The one exception shown in Fig4I and FigS8F, where SAC infection rate is linked to maintained/peak ratio, while RGC infection is not, has two caveat: First, the authors acknowledge that higher maintained response may not causally link to better restoration (line 235). Second, the same correlational analysis for other AC types is missing.

d) At this stage, a simpler interpretation of the results is equally plausible: that higher infection in all retinal neurons (regardless of type) is correlated with better restoration.

3) M4-ChR2 retina has very weak OFF response to regular light stimulus, but 5B has normal ON/OFF ratio. The authors speculate that SACs are responsible for this difference. But one observes that M4 labels mostly OFF RGCs while 5B labels equal amount of ON and OFF RGCs (S3 and S6E, lamination patterns of M4 and 5B), so there is a simpler explanation: RGCs that express tet-ON ChR2 are no longer very responsive to regular light stimuli. If that is true, that these cells are very unhealthy, then comparison of their ChR2 responses becomes less meaningful. The authors need to address the cell health problem caused by tet-ON ChR2 expression.

4) Only a few RGC subtypes form synaptic connections with SACs in the rodent retina. Thus, the effect of SACs would be limited. In the case of primate retina, ChAT positive neurons are much fewer, so their effect in ChR2 gene therapy are likely even more limited.

5) Lines 154-155: an equally likely explanation: M4 contains ON and ON-OFF DSGCs, which are known to be important for OKR, whereas 5B does not. This possibility needs to be considered.

Reviewer #3:

In this manuscript, Santos and Sirota demonstrated that the in vivo fast choline dynamics detected using choline-oxidase based biosensors is strongly correlated with, and likely caused by, phasic oxygen dynamics in vivo. The authors developed a novel tetrode-based amperometric choline oxidase (ChOx) sensor that can simultaneously measure ChOx and O2 levels within the same tetrode, which enabled the authors to observe strong correlations between ChOx and O2 levels in vivo (in behaving rats and mice, and under several distinct behavioral contexts). To dissect the causal relationship and determine the role of phasic O2 transients, the authors further combined in vivo as well as in vitro perturbation experiments to demonstrate that that phasic fluctuations in O2 concentration can lead to fluctuations in ChOx measurements. Moreover, mathematical modeling recapitulates the systemic relationship between ChOx and O2, suggesting the source of this coupling stems from non-steady-state enzyme kinetics. Together, these findings challenge the long-held belief that ChOx sensors can measure sub-second temporal dynamics of choline concentrations in vivo, and also calls for critical re-evaluation of all oxidase-based biosensors literature to determine the contribution of phasic O2 dynamics in vivo.

The study provides extensive evidence to support their claim: correlational, causal, analytical and modeling. The authors employed multiple levels of approaches, from the development of novel biosensors that leads to the observed correlation, to careful in vivo and in vitro perturbation experiments to demonstrate causal relationship. The data is carefully analyzed, and elegantly matched with modeling results. The results of this study have broad implications beyond the ChOx literature and in fact challenge the entire literature on oxidase-based biosensors.

Reviewer #3:

This manuscript attempts to address a timely question about animal social networks - what is their functional resilience to human-induced disturbance? The authors use association data from savanna elephants to construct empirical and virtual networks and assess how these change after virtual removal of individuals based on their age or network position (to simulate poaching events as real-world data were not available). Simulation studies require clear statements of caveats for interpreting the results as they only predict potential direct responses of a network and cannot account for the dynamic and indirect responses that are more likely to occur in nature. Here various network metrics are used to infer functionality, but critically, these are not supported by field data or citations (either from elephants or other study systems), and furthermore the relevance of the metrics to address structure vs. function is unclear to readers less familiar with SNA. Secondly, the motivation for the study is deeply embedded in elephant biology and would benefit a broader audience with a clear introduction to structural vs. functional resilience.

1) Applicability of simulation studies

The study sets out to test the functional resilience of elephant networks after simulated poaching events because real-world data were not available (to the authors). There are many caveats for applying the results of network simulations to real-world data because they rarely can take indirect and dynamic responses into account (unless these data are used to inform the simulation), see Shizuka & Johnson Behav Ecol 2020 for a nice review of this point. The authors allude to this in the discussion when they discuss the need for more dynamic models, but conclude by stating the need to work more collaboratively - this is a good point and I'm sure it's true, but there really needs to be a clear statement about the applicability of these simulated results in the introduction and upfront in the discussion. This is essential to avoid inadvertently misleading readers less familiar with these methods.

2) Network measures need greater empirical support and explanation

As this is a simulation exercise, it is essential that the network metrics are meaningful in this context. This is especially important given recent discussion of metric hacking in social network analysis studies (e.g. Webber et al. Anim Behav 2020). At present, some of the metrics are presented in a paragraph in the Introduction with vague support e.g. line 281 - "Each of these heuristics... SHOULD change drastically...", and all 7 are in table 1 but there are no references (either from elephants or even broadly-speaking from studies on networks) to support the major assumptions of the study. Refs are given in the table caption but it is unclear what these relate to. There have been some very interesting experimental studies on functional resilience which might help in this regard. E.g. Maldonado-Chaparro et al. 2018 PRSB used captive zebra finches to experimentally test foraging efficiency (i.e. functionality) of social groups after repeated disturbances to their networks, and as here, focused on functional change immediately after disturbance (e.g. line 172-73).

More importantly, it is unclear which of the 7 metrics are supposed to inform us explicitly about structure vs. function or whether these can even be unambiguously disentangled - e.g. is clustering coefficient structure or function? It is used in both this study and by Goldenberg et al. 2016 that is introduced here as focusing only on structural resilience. It would be very helpful to have clear statements about the metrics and predictions regarding structural vs. functional resilience. At the moment they vary throughout the manuscript, e.g. referred to as metrics of social competence in the discussion (line 543). Sorry for my confusion, but there are so many different ways that we can derive metrics from networks that justifying these clearly is critical for the conclusions of the study.

1. More succinct presentation of the knowledge gap and its broader implications beyond elephant biology.

At present, the study is presented with elephant biology and conservation as the core motivation, yet the concept of functional resilience is fundamental for studies of any species where social connections influence the flow of information (and presumably fitness of individuals). The introduction is extremely long (10 paragraphs over 6.5 pages) and functional resilience is not introduced and defined until the end of the Introduction's 4th paragraph and its link to broader literature is confusing . Focusing the introduction on how/why structural and functional resilience may vary in networks (and how this can be inferred from network metrics), and then using elephant biology as an example for why this is relevant to study, might make it much easier to follow.

Reviewer #3:

Quiroga et al. studied the molecular function of mechanosensitive ion channel protein Piezo1 during mouse primary myoblast differentiation in culture condition. The authors measured myoblast proliferation and differentiation after either knockdown of Piezo1 or chemical activation of Piezo1 protein. In overall, the study is significant given its conclusion directly contradicts with a recent study by Masaki Tsuchiya et al. Nature Communications (2018) by which knockout of Piezo1 produced opposite effects. However, major concerns were identified and need to be addressed to strengthen their claim.

1) It is unfortunate that the authors have confused "fusion index" with "differentiation index". By the description in Method, they actually measured differentiation index though claimed as "fusion index". The commonly used fusion index is the ratio of nuclei in myocytes with {greater than or equal to} 3 nuclei normalized with total number of nuclei in MyHC+ myocytes. Therefore, it appears that what the author claimed about "fusion defect" was actually a differentiation defect. These errors need to be corrected.

2) Following comment 1, the authors need to evaluate whether or not the differentiation is affected when Piezo1 is knocked-down or activated. It is suggested to run a panel of qPCR assay for myogenic markers including myosin genes (Myh3, Myh8). Western blots of myosin by MF20 antibody will also need to be performed and quantified.

3) The author discussed the potential off-target effects for siRNA from the previous study. Although it is comparatively more convincing that this manuscript tested 4 siRNA, for the scientific rigor, the authors still need to clarify whether the study by Tsuchiya et al is reproducible. As such, the authors should measure myoblast fusion by using the same siRNAs as listed in Tsuchiya et al. In addition, the authors should also characterize the myoblast fusion phenotype of Piezo1 gene-KO from CRISPR treatment of primary myoblast.

4) To rule out any off-target effects of the chemical activator of Piezo1, the authors should test whether this drug's effect on myoblast fusion /differentiation can be negated when Piezo1 is knocked down.

5) Concerning the role of myomixer gene in Piezo1 KD phenotype, the authors should use another set of primers for qPCR. The current forward primer only detects a predicted longer transcript isoform of Mymx but not its predominant isoform (NM_001177468).

6) For Fig.6, the details of experiment procedure, e.g. the timing of drug treatment in relation to differentiation timing, needs to be provided.

7) The authors should cite the correct references as being consistent with their description. For instance, line# 528, 1011. In addition, the writing needs to be improved for better readability.

Reviewer #3: