Reviewer #2 (Public Review):

Han, Kozanli et al. set out to analyse the introductions of SARS-CoV-2 variants of concern (VOC) in the face of targeted flight restrictions. The authors find, in line with prior observations and common wisdom, that targeted flight restrictions are not effective at preventing the introductions of new lineages. Han, Kozanli et al. then draw their attention to the diffusion of SARS-CoV-2 VOCs Alpha and Delta within the Netherlands, finding that more populous regions are more likely to serve as points of introduction and thus fuel the growth and dissemination of viral lineages in-country. The authors draw attention to the lifting of restrictions within the Netherlands, particularly noting the rise in cases in younger age groups following it and linking this surge to the opening of nightclubs. By estimating the growth rates of Alpha and Delta variants within the Netherlands (including growth rate estimates split by age) the authors confirm known differences in VOC transmissibility and the added effect of (presumably behavioural) different behaviours of each age group.

Reviewer #2 (Public Review):

This work provides data supporting a new hypothesis for IFN-γ-induced persistence in C. trachomatis infection. Persistent infections are an important cause of tissue damage in the fallopian tubes, which can result in infertility and ectopic pregnancies. Previous work has shown that IFN-γ induces persistence by activating indoleamine 2,3-dioxygenase (IDO), which depletes L-tryptophan, a necessary amino acid that C. trachomatis gets from the host. C. trachomatis will enter a dormant, persistent state in response to nutrient starvation and antibiotic challenge. In previous work examining the alterations of host cell metabolism due to C. trachomatis infection, the authors discovered C. trachomatis infection increases levels of c-myc. Since IFN-γ is known to decrease c-myc levels, the authors made the logical connection that c-myc expression could be altered by IFN-γ during C. trachomatis infection, which could be an additional cause of persistence.

The authors' present work uses well-controlled and designed in vitro assays to study the effects of C. trachomatis infection on c-myc in the presence of IFN-γ. Using cells with inducible silencing of c-myc, as well as those with inducible c-myc expression, enabled the authors to show that c-myc expression can rescue C. trachomatis from IFN-γ-induced persistence. In addition to cell lines, the authors created human fallopian tube organoids and showed C. trachomatis infection in the presence of IFN-γ also resulted in the prevention of c-myc induction.

Since much of the literature of IFN-γ-induced persistence of C. trachomatis is based on tryptophan depletion, the authors set out to determine if there was any interplay between c-myc and tryptophan. Through various in vitro experiments, they found that both viable bacteria and the presence of tryptophan are needed to stabilize c-myc. In addition, they show that restoration of tryptophan resulted in phosphorylation of GSK3β, which consequently led to elevated c-myc levels. Since c-myc is involved in amino acid transport, they investigated and found that c-myc expression does not increase intracellular amounts of tryptophan, showing that restoration of tryptophan metabolism is not the main mechanism c-myc expression uses to result in rescue from persistence.

The authors then use metabolomics analysis to investigate the effect of c-myc expression on C. trachomatis infection with IFN-γ treatment. They found that IFN-γ treatment results in reduced c-myc, which causes a reduction in metabolites, nucleotides, and nucleosides. Supplementing these precursors resulted in partial restoration of C. trachomatis development.

Reviewer #2 (Public Review):

The authors seek to demonstrate that plastic changes already occurred early after complete spinal cord transection somatosensory cortex and these were different between deep and superficial layers in deeply anaesthetized adult rats. They suggest that these changes are a consequence of sensory deprivation.

Strengths:<br /> The study for the first time applied multielectrode array technology to explore the effects of spinal cord transection on the circuitry within the somatosensory cortical region associated with the deprived body region (hind limb). In addition, the activity in this region that was evoked by stimulation of a remote body region (fore limb) was explored.

It is well demonstrated that the method can be used in vivo and over a long time period, and it can be anticipated that in combination with a mechanistic approach MEA can develop into a routine technique for in vivo brain research in rodent models.

With their approach the authors find corresponding changes that already have been reported for other sensory systems.

Weaknesses:<br /> The MEA technology allows assessment of network activity, however, the connectivity of the somatosensory cortex is highly complex and different populations of local excitatory and inhibitory interneurons collaborate to generate output signals of superficial (L3/3) and deep (L5) Purkinje neurons. MEA recordings as such do not sufficiently allow to unequivocally identify the nature of the neurons from which the electrodes pick up signals and therefore, a major limitation of the approach is the lack of a mechanism leading to the observed changes. For example, a reduction of inhibitory inputs and an increase of excitatory inputs could result in a similar increase of activity in the network.

An attempt has been made to elucidate the role of thalamic afferents, however, other brain areas projecting to the somatosensory cortex have not sufficiently been considered.

The study does not clearly assess how the reported changes develop. The activity occurring during the transection injury itself is not recorded but due to the excitatory barrage LTP like synaptic processes may occur in addition to the loss of sensory input

The authors managed to support their aim showing differential changes occurring in different cortical layers after spinal cord transfection. However, it is not entirely clarified whether this is due to deprivation of sensory input or to plastic changes occurring as a consequence of strong excitation ascending pathways by the transection injury.

The utility of the MEA approach can be of great interest and use for researchers exploring cortical circuits. In contrast to the methodical advance offered by the study, the presented data are purely descriptive and do not fully justify the proposed conclusions.

Reviewer #2 (Public Review):

This paper attempts to go to heart of solving the evolutionary enigma of cooperative breeding - how can reproductive and non-reproductive helping strategies be maintained in a population? The typical suggestion is that helping is maintained by the indirect benefits of helping kin when more profitable breeding opportunities are constrained. Under this hypothesis, helping is the best of a bad job strategy. In this paper, the aim is to set this long-standing assumption against another possibility - that the two strategies offer comparable fitness, and so are maintained in the population as a type of polymorphism. However, testing whether offspring that remain philopatric accrue comparable or differential fitness from those that disperse to fight for breeding positions is challenging, and hence rare.

There are a number of reasons for this. First, the best way of testing the fitness of different strategies in cooperative breeders is to do so in individuals living in the same group, since only then can effects of group size and territory quality be controlled. Second, one of the greatest difficulties however, is that those that remain resident and help versus disperse and breed have different genetics, rearing environments and qualities, meaning that any differences in fitness might be due to these differences rather than the strategies pursued. Finally, it is almost impossible to know what immigrants did before they arrived and what emigrants did after they left, meaning there is a missing fraction of fitness in the lives of most individuals.

This study on superb starlings was able to overcome some of these issues, but not all. For example, residents and immigrants live and breed in the same groups, which allows one to compare fitness in the same groups and territories. However, whether individuals gained fitness after dispersing or before emigrating is not clear for most individuals, which will either lead to a missing fraction of fitness in individuals or a severely restricted sample with complete, but possibly biased, information. As a consequence, whether or not helping and breeding strategies offer equal fitness, as suggested, will likely require further study, but this present study paves the way for how one might begin to address these outstanding and central questions in cooperative breeding research, with implications for understanding cooperation in human societies.

Reviewer #2 (Public Review):

The current manuscript explains a novel phenomenon observed in cancer cells exposed to immune therapy, named cell-in-cell contact where several tumor cells join together without being fused to form these unique structures. The authors suggest this phenomenon as a mechanism for therapy resistance and see it often in relapsed tumors (melanoma and breast cancer). The authors followed animals that developed resistance to melanoma immunotherapy (TNF-a+CD40L+TRP1 melanoma antigen). Despite all mice almost clearing the tumor, 50% of these animals relapsed 10 days later. A similar regrowth pattern was noticed in breast cancer treated with allogeneic antibodies and DC adjuvant. Also, treating melanoma-bearing mice with splenic CD8+ T cells expressing TCR against gp100, or TRP2 melanoma antigens induced significant tumor regression, followed by tumor recurrence in all treated mice. These last ones were resistant to treatment. Four melanoma cell lines were established from therapy relapse. Tumor cells kept expressing tumor antigene (gp100, TRP2, and MHC-I. Whole exome analysis (WES) indicated a comparable neoantigen burden across all samples. The neoantigen burden analysis of 2 data sets, in relapsed melanoma and in non-small cell carcinoma patients, showed the majority of neoantigens were shared between relapsed tumors and their corresponding primary tumor.

The authors established cell lines from relapsed tumors following treatment with gp100-reactive T cells and assessed their responsiveness to treatment with gp100-reactive T cells. Upon exposure to T cells, cancer cells cluster together, several nuclei surrounded by a single membrane and cortical actin, pathologists indicated that about half the cells that survived immunotherapy formed these structures. Using fluorescent tagging showed that there is no cell fusion and cells that formed cell-in-cell structure, went back to single cell status after removing the treatment.

By coculturing tumor cells with CD8 and to less extent CD4 induced the same phenomenon in vitro. It is worthy to note that Nude SCID- gamma-/- (NSG) mice didn't have such structures. It is important to note that immortalized mammary epithelial cells were completely killed by allogeneic T cells and did not form a cell-in-cell structure, thus these structure seems more cancer-specific. Also, not all cancer lines formed cell-in-cell structures, including pancreatic tumors and B cell lymphoma, while almost all the colon and ovarian carcinoma tumor cells that survived T cell killing, were organized in such structures. Furthermore, histology analysis of T cell and myeloid lymphomas, as well as glioblastomas, did not show a trace of such structures. The authors showed the phenomenon in primary melanoma too.

Similar to activated T cells, secreted granules isolated from T cells were sufficient to induce cell-in-cell formation. Secreted granules were specific to activated T cells, but not activated NK, macrophages, and immortalized melanocytes. This cluster formation seems to be mainly INF-g dependent which in its role phosphorylate activator of transcription 3 (STAT3) and early growth response-1 (EGR-1) in the tumors cells. Also, they found that IFNγ -stimulated NK cells and macrophages, but not B cells could induce a tumor cell-in-cell formation, at a lower percentage compared to activated T cells.

The authors investigated the effect of cell-in-cell formation in protecting tumor cells from T-cell-mediated killing. They noticed that the outer layer is lysed by T cells whereas the inner layer stays safe and intact. Immunostaining of T cells attacking cell-in-cell tumors, showed that the distribution of granzyme B and perforin is almost limited to the outer cell. While these interactions were sufficient to kill single tumor cells, they were insufficient to induce caspase 3/7 activity in the inner cell, leaving it intact<br /> and alive.

The author concluded that tumor cells that survive chemotherapy and immunotherapy form unique cluster structures and generate membrane architecture impenetrable by immune-derived lytic granules, cytotoxic compounds, and chemotherapies leading to therapy resistance.

The topic is novel and interesting, the manuscript is well written! We do believe the authors have included enough in vivo and imaging to support their findings.

Reviewer #2 (Public Review):

The authors seek to create a reliable prognostic signature for pancreatic cancer from gene expression data. They develop such a signature, which they call AIDPS, and which involves 9 genes. The AIDPS was created and tested using ten publicly available data sets and involved gene selection and regression algorithm selection protocols. The authors study the performance of their signature and claim that `AIDPS exhibited robust and dramatically superior predictive capability' compared to other signatures in the literature.

The strengths of the paper are the use of multiple data sets and the comparison of multiple regression methods.

The main weakness of this paper is that the authors used all 10 data sets both in the discovery stage and in the validation stage of their study. This is clear when they define their consensus prognostic genes (CPGs) by doing univariate Cox regression in all ten cohorts, and then select those genes that show significant and more or less uniform association with outcome across all these ten data sets (as described on page 8). From this stage onwards, the 32 genes that are taken forward will inevitably show up as prognostic when tested within these same 10 data sets. On page 9, after having carried out further multivariate regressions comparing different models, which reduced the number of selected genes further to 9, the authors use the same 10 data sets used in the discovery stage again (plus the combination of these sets, which they call the `meta-cohort') for `validating the prognostic value of AIDPS in 11 datasets'. This, I regret, is not at all a validation, but what would be called quantification of performance on the discovery set. One could simply be looking at spurious association signals caused by overfitting, especially given the large mismatch between the initial number of covariates (15,288 genes) and the number of samples (n=1280).

As a further consequence, when the authors test the performance of AIDPS against other existing signatures by again using the original ten data sets (plus their union), they are effectively comparing the performance of their own signature on its associated discovery set to the performance of competitor signatures on a validation set. Of course, the performance of a signature on its own discovery set would nearly always be superior, so this 'test' tells us nothing yet.

I cannot of course claim that the authors' AIDPS is non-reproducible, but on the basis of the material in this manuscript one cannot claim that it is reproducible. Validation of unseen data is really mandatory but has not been done. At the very least, the claim that 'AIDPS exhibited robust and dramatically superior predictive capability' is premature.

Reviewer #2 (Public Review):

In their study "Delineating the transcriptional landscape and clonal diversity of virus-specific CD4+ T cells during chronic viral infection" Zander and co-workers analyze the phenotypic and clonotypic distributions of T cells specific to a LCMV epitope following infection with a chronic LCMV strain in mice. The paper largely follows an earlier study from the same group (Khatun JEM 2021) that has used a similar experimental strategy to analyze T cells responding to an LCMV strain establishing acute infection, and it adds a scTCRseq component to another earlier study of chronic LCMV (Zander Immunity 2022). The main contributions of the paper are to demonstrate that interesting differences between gene expression profiles between chronic and acute LCMV exist, and to identify a new T cell subset (of unknown functional significance).

While the paper is framed around differences between T cell responses to acute and chronic infections, all analysis is done on T cells at day 10 post primary infection. At such an early time point even the acute LCMV strain virus is likely not completely cleared, or at the very least viral antigens are still presented. The relevance of the presented phenotypic differences to other settings with long-term chronic infection is thus questionable. Additionally, there are a number of methodological concerns regarding the robustness of the statistical and bioinformatic analyses that put in doubt some of the conclusions. Most notably, the analysis of fate biases needs to be substantiated by tests against baseline expectations from random assortment to test for statistical significance.

Reviewer #2 (Public Review):

In this manuscript, Roberts et al. explore whether ageing-induced X-chromosome inactivation (XCI) skewing could be a valuable biomarker for adverse disease outcomes in women. XCI is a dosage compensation process resulting in the inactivation of one of the two X chromosomes in females to equalize X-linked gene dosage between XX and XY individuals. XCI is initially established in a random fashion and, as consequence, female tissues have roughly 1:1 ratio of cells that inactive either the paternal or maternal X chromosomes. However, skewing towards one of the two X chromosomes has been noticed, especially in mitotically active tissues, such as the hematopoietic system. This is likely to be caused by fluctuations in hematopoietic cell pool such as stem cell depletion/clonal expansion.

In this study, the authors take advantage of the large TwinsUK cohort of 1,575 female individuals, many of which are mono- or dizygotic twins, for which relevant data (e.g., blood cell counts, molecular markers, clinical information) has been gathered over a period spanning 20 years. XCI skewing was determined by HUMARA, a classical method to measure skewing based on methylation differences between inactive and active X chromosomes in the highly polymorphic Androgen Receptor X-linked gene. The major strength of this study is the large cohort containing genetically identical or similar individuals which is the golden standard for study epigenetic phenomena such as XCI. Despite this, some of the parameters studied by the authors could only be measured in a subset of patients. As the authors, themselves, comment this reduces the statistical power to draw more definitive conclusions.

The data gathered by the authors strengthen previous findings that XCI skewing increases with ageing. This finding is not novel, but this study represents the most comprehensive analysis of age acquired XCI skewing to my knowledge. Interestingly, XCI skewing did not correlate with several hallmarks of biological ageing, but is associated with myeloid bias of the hematopoietic lineage and increased risk of cardiovascular disease or cancer incidence. Their results show the importance of the use of large twin cohorts for epigenetic studies to better understand disease progression.

Whether XCI skewing is a bystander of fluctuations in hematopoietic stem cell pool due to depletion or clonal expansion or itself could have a direct role in disease progression remains unknown. In any case, this study also sets the stage for future functional studies, using animal models, where XCI skewing or hematopoietic stem pool could be manipulated, to better understand the link between XCI skewing and increase propensity to age-driven disorders.

Review #2 Public Review

The study is of high significance, rigor, and novelty. Despite the many studies of repertoire, dynamic connectivity, etc., in the study of consciousness, there is (surprisingly, as I confirmed with a literature search) a dearth of application of these approaches to disorders of consciousness. The manuscript is well-written and transparent about its limitations. The author should consider the following recommendations:

1) There is frequent reference to "subcortical" and related networks, but I see no description in the text of which subcortical structures are involved. Panel N of figure 2 is helpful but I think that more explicit detail is important, especially given the specific predictions of mesocircuit theory.

2) Similarly, although the global neuronal workspace does posit a critical role for recurrent frontal-parietal networks, can the authors be more specific about the nodes of the proposed workspace and what they found empirically?

3) The classification sensitivity/specificity did not, in my opinion, add much to the manuscript, especially since the number of patients is not remotely close to what would be required for a population-based diagnostic approach. If the authors chose to include this with any reference to diagnosis (highlighted in the introduction and elsewhere), I would encourage a comparison with similar data from other clinical or neuroimaging-based diagnostic approaches. However, I think the value of the study resides more with mechanistic understanding than diagnosis.

Reviewer #2 (Public Review):

This paper is of potential interest within the field of DNA replication, as it identifies a novel role for YAP protein in DNA replication dynamics. However, the conclusions are not supported by properly controlled data. Several aspects of data analysis and representation need to be revised.

In this manuscript, the authors characterized YAP function in the control of DNA replication dynamics, taking advantage of the Xenopus laevis system.<br /> They found that YAP is recruited to replicating-chromatin and showed that its chromatin enrichment depends on the assembly of pre-RC proteins. In addition, they show that the immuno-depletion of YAP leads to increased DNA synthesis and origin activation, revealing YAP's possible role in the regulation of replication dynamics.<br /> The authors were also interested in finding YAP potential partners that could mediate its function. They identified Rif1, a major regulator of replication timing, as a novel YAP interactor during DNA replication.

As RIF1 expression in vivo is restricted to the stem cell compartment of the Xenopus retina, similar to YAP, the authors assessed whether Rif1 could regulate the spatial-temporal program of DNA replication in stem cells. They showed that depletion of Rif1 at early stages of Xenopus embryos development leads to alterations in replication foci of retinal stem cells, resembling the effect observed following YAP down-regulation.

Finally, they studied the impact of YAP and RIF1 down-regulation at early stages of development, showing that their absence results in the acceleration of cell division rate of Xenopus embryos, where RNA transcription is absent. Based on these results they concluded that YAP has a role in S-phase independent from transcription.

The higher rate of DNA synthesis observed in the absence of Yap in Figure 1D is not very evident from the gels in Figure 1, supplement 3B. The timing of the experiments is continuously changing throughout the figures. It is therefore difficult to compare them. Also, comparisons across different gels are difficult to interpret.

Most importantly, relative quantification on gel images cannot support the claim of increased DNA synthesis in the absence of YAP. To accurately quantify the replication of DNA added to the extract, the total amount of DNA synthesized must be quantified.

It is also necessary to analyze the dynamics and the abundance of chromatin-bound replication proteins associated with the active replication fork after Yap depletion using chromatin binding assays. This would further confirm the increase in the fork density observed by DNA combing experiments.

The quantification of the amount of YAP in Figure 1B is confusing. The legend of the chart states "Control in light grey and presence of geminin in black", but the bar colors are of different shades of grey. It is not clear how to evaluate them.

The efficiency of depletion for both Rif1 and YAP is different in Figure 4B and Figure 4A, supplement 1. Moreover, the combined use of the TRIM-away approach with injections of MO led to a stronger and prolonged YAP depletion but also triggered toxicity in the tadpoles, which display severe abnormalities.

Reviewer #2 (Public Review):

In their manuscript entitled "Mechanism of hyperproteinemia-induced blood cell homeostasis imbalance in an animal model", Wang and colleagues set out to study the role of hyperproteinemia in circulating blood cell homeostasis by using an invertebrate model.

I have several major concerns about this paper.

In the introduction the authors mentioned that no model of hyperproteinemia exist, however it is unclear why animal models other than Bombyx mori could not be developed. This is particularly important as it seems evident that substantial differences exist between this model and mammals, not last the fact that hematopoiesis occurs in the bone marrow in humans, but it occurs in the peripheral blood in this model. It is unclear how data derived via this model could apply to human diseases.

The animal model is not even briefly discussed in the introduction and its limitations are not addressed in the discussion.

The authors bundle together the concept of HPPC whether this is driven by a malignant process (such as multiple myeloma) or a severe inflammatory state (such as sepsis). This appears rather risky as the nature of the proteins contributing to HPPC is substantially different and it could certainly play a role in its effect on hematopoiesis.

The manuscript is difficult to read and lacks of sustained focus and scientific scrutiny. Rather a number of circumstantial, descriptive data without strong cause-effect relationship are presented. A number of signaling pathways are examined without a clear underlying hypothesis or preliminary data to suggest relevance. Conclusions are not well supported by data. For instance, based on the presented data, the increase in STAT can not be completely explained by transcription only. However, stabilization of the protein was not assessed.

The authors state that the animal model is lethal, however no information is provided regarding the time of death in relationship to the induction of the model. It is also unclear if a cause underlying the death of animals could be established. This is an important point as most of the differences occur late after induction of the model and could be potentially due to impending demise.

There is no attempt to evaluate margination and transmigration of blood cells as a potential cause for changes in circulating blood cells over time.

Finally, several of the information provided in the discussion and introduction is incorrect such as that the effect of multiple myeloma on hematopoiesis is secondary to HPPC, while most likely than not it is due to bone marrow myelophtisis as it is present even in patients without significant elevation in plasma protein concentration. Or that multiple myeloma is secondary to hyperproteinemia or the implication that JAK/STAT inhibition is therapeutic in myeloma due to its effect on HPPC.

Reviewer #2 (Public Review):

The phloem is the part of the plant vascular tissue which is dedicated to long-distance sugar transport. Sugars are produced during photosynthesis in green tissues. They then are distributed via the phloem and get unloaded in organs that cannot perform photosynthesis, such as roots.

Previously, the authors have investigated the mechanism of phloem unloading in roots of the model plant Arabidopsis thaliana. There, they found that a specific and novel plasmodesmata-type resides in the cell walls between phloem sieve tube cells and phloem pericycle cells - the latter are the cells into which phloem solutes get unloaded. Because of their specific shape, with a wider pore on the phloem side and a more narrow pore on the pericycle cell side, these plasmodesmata were named "funnel-plasmodesmata".<br /> In the present study, the authors intended to investigate the universality of their previous findings by extending their investigation to plant species other than Arabidopsis. Plasmodesmata can only be visualized with an electron microscope. Preparation of tissue for this kind of microscopy is a very laborious process, which, moreover, had to be adapted to each individual plant species. This was a very good reason for having restricted the analysis to a nevertheless very impressive total of 7 different plant species.

Indeed, the authors found funnel plasmodesmata at the sieve tube - pericycle cell interface in all plants investigated. This finding makes it likely that the presence of funnel plasmodesmata at the sieve tube - pericycle cell interface is a general feature of the root unloading zone. Nevertheless, due to the limited number of species investigated, there may still be some exceptions to this "rule".<br /> In a second part of the paper, the authors made use of the electron micrographs and measured the parameters of funnel plasmodesmata in the different plant species. Due to the small size of plasmodesmata and potential artefacts in the samples, measurements were not always exact, but it was always possible to obtain good estimates. Based on these, the authors calculated the general physical properties of funnel plasmodesmata. Finally, the authors came to the conclusion that the properties of funnel plasmodesmata are ideally suited for phloem unloading. They ensure a fast unloading while, at the same time, they probably still have a filtering function for bigger molecules.

In addition, the authors rightly point out that their calculations demonstrate that the shape of plasmodesmata has a big impact on their function. This should be taken into consideration when estimating or calculating cell - cell diffusion or communication.

Reviewer #2 (Public Review):

This study examined how attention, drawn to the location of a sudden stimulus, changes participants' ability to discern the orientation of stimuli shown near that location immediately after, and also how this effect depends on whether the participant is aware of the initial, sudden, stimulus or not. Strengths include the fact that that the spatial profile of such attention effects, although central to many studies of visual attention, has not previously been examined, to my knowledge, in the context of the participant's awareness of the initial stimulus. Another strength is the experiment that ties observed spatial profile of attention to the influence of test stimulus contrast, as this fits the present study in the overarching framework of the existing normalization model of attention. A weakness of the study is that it is not clear that the difference in awareness between the two conditions is actually the relevant difference, given that the conditions differ in other respects as well. A related, but different, potential weakness is that the design of Experiments 1 and 2 makes it so that the initial, sudden, stimulus (the cue) is informative as to the side of the screen at which the subsequent (test) stimulus will appear, which invites a deliberate strategy on the part of the participant, to focus on that side of the screen in the 'conscious' condition but not in the 'unconscious' condition.

Reviewer #2 (Public Review):

Zhou et al. investigates whether alpha-band (8-14 Hz) neural oscillations differentially modulate sensory signal and noise during visual detection. The authors reason that the preferential modulation of signal predicts a relationship between alpha power and the subject's perceptual discriminability but not decision criterion, and conversely, that the similar modulation of signal and noise predicts a relationship between alpha power and the subject's decision criterion but not perceptual discriminability. The authors find that alpha power in early visual cortex does not correlate with the block-wise changes in the subjects' decision criterion. However, trial-to-trial variations in visual cortical alpha power during the trial period before stimulus presentation correlate inversely with the subject's perceptual discriminability. Moreover, lower prestimulus alpha power in visual areas is associated with enhanced information that can be decoded about the visual stimulus from recorded neural activity. Finally, the subject's accuracy depends on the phase of the alpha oscillations in parietal and frontal regions. Based on these findings, the authors conclude that alpha power modulates sensory signals more strongly than noise.

The question is interesting, the task design and priming procedures are rigorous and clever, and the analyses are sophisticated.

The conclusions of the paper would be more strongly supported if the concerns below could be addressed:

1. A potential strength of the manuscript consists in correlating alpha band power with not only the subject's accuracy (i.e. percentage of trials correct) but with the indices of d' and decision criterion from signal detection theory. Because any difference in accuracy can depend on a difference in only d', only criterion, or both, using d' and criterion provide a more precise quantification of behavior. However, this strength is undermined by the unquantified statistical bias in the estimates of d' and criterion as a result of limited sample size of certain categories of trials. This is an issue to which the authors themselves allude (at the bottom of page 4 and top of page 5) and is well documented (Macmillan and Creelman, 2004), but it is not addressed quantitatively in the manuscript. This issue therefore raises concern about measurements of d' and criterion throughout the manuscript. Because the manuscript's conclusions depend critically on the measurements of the subject's d' and criterion, therefore concern is also raised about the manuscript's conclusions.

2. Another potential strength of the manuscript involves shifting of the subject's decision criterion between blocks without changes in the subject's d' to isolate the relationship between decision criterion and alpha oscillations. However, the analyses in Figure 2C indicate that the subject's d' changed between blocks, and the brief argument that this difference in d' should be ignored is not quite convincing without detailed quantitative analyses. Because the possibility remains that d' changed between priming conditions, and yet no difference in alpha power could be detected between conditions, the result appears to be inconsistent with the finding that lower alpha power is related to enhanced d'.

3. The interpretations of the data at four places in the manuscript seem to be overly focused on a limited set of conditions or time windows, while not taking into account other conditions or time windows. This makes the interpretations appear incomplete and weakens confidence in the conclusions. (A) If trial-to-trial variations in alpha power are inversely correlated with d', then one should expect to see this relationship not only in the conservative priming condition but also in the liberal priming condition (Figure 3D). The absence of a significant relationship in the liberal priming condition appears to be inconsistent with the conclusions of the paper and is not addressed. (B) While the trial-to-trial prestimulus alpha power is explored for its relationship with either d' or criterion, the trial-to-trial alpha power during other task periods, in particular during stimulus presentation or during mask presentation, is not examined for its relationship with d' or criterion. (C) A significant relationship between the subject's accuracy and the phase of the alpha oscillations in visual ROIs is detectable at multiple brief time points before stimulus onset (Figure 5A), yet the authors state in the discussion that alpha phase in visual areas does not modulate the subject's accuracy. Instead, the authors focus on the relationship between the subject's accuracy and the alpha phase in parietal and frontal areas. (D) The prestimulus alpha power in visual cortex is significantly related to the criterion of the MVPA classifier during the conservative priming condition (page 7, "beta_c = 0.0096, p = 0.016"). This appears inconsistent with the conclusion that alpha power is independent of decision criterion.

Reviewer #2 (Public Review):

The study targets the behavioral and neural dynamics and pharmacological routes by which multisensory predictions (auditory to visual), are formed and used to efficiently match corresponding sensory inputs. The approach combines computational modelling of behavioral data, EEG, and ketamine administration as a model of NMDA receptor blockage.

Ketamine blocks clear effects of predictability seen in behavior (response times), and interferes with the pathways (frontal alpha increase, beta power connectivity from frontal to sensory areas) which are identified as carrying out the predictive processing.

The study design is clear and well suited to assess the questions at hand. The number of techniques applied and their level of sophistication is impressive.<br /> I think that the interpretability and message of the paper can be made stronger and easier to assess. Especially the large number of results presented makes it difficult for the reader to capture the main message.

Hypothesis-based approach:

The paper is extremely rich in experimental techniques and analyses (RT, drift diffusion model, evoked responses, time-frequency, connectivity, causality, decoding), and I think the message would clearly benefit from a more reduced and unified approach.

Currently the analyses are introduced successively in the results section, but not all are spelled out a priori. This makes it difficult to distinguish a priori from post-hoc analyses, and also dilutes the interpretability of the results.

Please spell out all a-priori hypotheses in the introduction, and clearly label exploratory or post-hoc analyses as such.

Some reduction might also be beneficial, for example, the connectivity analyses based on specific anatomical regions are treated too briefly to be fully appreciated, and might be better presented in more detail in a separate paper.

Clarity:

The result section is lacking some bits of information, especially since it appears before the methods.

Importantly, please state early on whether the ketamine administration was a within or between subject manipulation.<br /> I suggest to provide a table with all experiments, number of participants, and trials carried out to give a better overview.

Also, please give a more detailed description of the stimuli and statistical models (see below), as well as the rationale for the drift diffusion model.

Statistical models:

The results section would be clearer if one omnibus statistical model was spelled out and used for all comparisons. Given the bayesian statistics applied to the drift diffusion model, why not apply a Bayesian approach all together?

The authors use the terms linear regression and correlation, but report ANOVA results without stating in the results section whether the predictability factor was modeled as parametric. It should be discussed whether a 3-level manipulation really qualifies as a numeric regressor.

Baseline effects, for instance general RT or alpha power differences between the treatment groups are currently not consistently reported. The above omnibus model would include them, and allow for a more systematic reporting thereof. More general group differences should also be discussed in light of the specificity of the proposed mechanisms.

Especially the null-effects, crucial for the interpretation that ketamine blocks predictions, need to be justified by more than an insignificant p-value.<br /> For instance: p. 10, l.11: 'The interaction effect confirmed that, under ketamine, delay period alpha power at the RF electrode cluster was similar across sounds'

Please consistently report F-values for ANOVAs, degrees of freedom, as well as Bayes Factors to show the absence of an effect. If interactions are interpreted (often the case to show that a given effect disappears under ketamine), the null-effect under treatment should be explicitly tested. An interaction could also result from diverging effect sizes.

Drift diffusion model:

The use of a drift diffusion model is not sufficiently well introduced, especially with respect to the accumulation of evidence.<br /> In the paradigm, no more evidence accumulation happens in the delay period, after the auditory stimulus ended, which is in line with the finding of an effect on bias, but not drift rate. To me, the accumulation of information occurs rather across trials, which is currently captured in the causal power analyses. Please clarify this aspect.

How strong is the correlation between transitional probabilities and causal power?

Behavioral results:

p. 6, l.10: 'This was not due to low accuracy as subjects' average accuracy was 77.8% under ketamine (85.7% without ketamine, and 81.0% under DEX).'<br /> Was the difference in accuracy significant? Why would it not matter for the results?<br /> Also, were there overall RT differences under ketamine?

Time-frequency results:

Overall, not all tested contrasts are equally well justified, or based on prior hypotheses. Especially, alpha power is only analyzed in a right frontal cluster (based on a significant correlation with predictability), but then later, different regions are addressed in source space, and also different frequency bands.

The procedure for the permutation cluster-based testing needs to be described in more detail (p. 29, ll. 12) - why is it that all found clusters have the same number of electrodes? The reference (6) seems wrong?

All following analyses are performed on the right frontal electrodes. This should be explicitly taken into account in the discussion.

Discussion:

The discussion is very brief, and provides simply a confirmatory assessment of the results, plus a quick and slightly vague outlook on implications for depression.<br /> Please provide a more in-depth assessment of the results, and possible limitations of the findings.<br /> For instance, discuss the specificity of the blocking of NMDA receptors by ketamine, and address general group differences in RT, accuracy, or EEG measures in this respect.

The lateralization of the alpha response should also be discussed.

I am admittedly not an expert on neuropharmacological routes of predictability, but an assessment of the novelty and comparability of this approach compared to previous studies (Corlett, 2016; Weber, 2020) should be added.

Reviewer #2 (Public Review):

In this paper Woike et al. identified a novel SHANK3 missense mutation (p.L270M) in the Ankyrin repeats in patients with an ADHD-like phenotype. The biochemical analysis of the interaction partner of the mutant reveals that the mutated SHANK3 protein loses the binding to δ-catenin, the intramolecular interaction between N-terminal SPN domain and the Ank repeats and the binding to αCaMKII. Another variant of SHANK3, the p.P141A has the most dramatic effect on the function of SHANK3 protein, because it disrupts the interaction with all the binding partners tested and reduces dendritic spines number when transfected in neurons.

The paper provides some evidence of the possible altered functions of the mutated SHAKN3 gene, however the data provided are mostly limited to the analysis of the ability of mutated proteins to interact with some defined partners. No further functional data were provided. The possible synaptic alterations induced by p.P141A variant were studied in neurons by looking at the number of dendritic spines after the overexpression of SHANK3 mutated proteins. However it's unclear how dendritic spines were measured because the images in Figure 9 do not show neurons with dendritic spines.

In this form, the presented data are too preliminary to be considered sufficient to suggest a revision.

Reviewer #2 (Public Review):

This paper uses cell type-specific recording and inhibition to investigate differences in activity and behavioral relevance of PT and IT cells within the motor cortex. IT cells are shown to be largely positively correlated with the amplitude of joystick presses, while PT cells are heterogeneous with a majority being negatively modulated during joystick presses. Related behavioral differences are found during inhibition of each cell type, with IT cells reducing press amplitude and speed and PT cells altering press trajectory. Likewise, inhibition of each cell type has a different impact on striatal activity, with a press amplitude-related projection of activity decreased by IT inhibition and unchanged by PT inhibition. Overall, these results suggest an interesting functional dissociation between PT and IT cells, though it is not entirely clear how specific these roles are or how they are enacted.

The paper reinforces previous findings about activity heterogeneity across cell types in the motor cortex, specifically that IT cells increase activity during movement while PT cells more often decrease activity during movement. This is achieved by both inhibition-based optotagging during electrophysiology and cell-type specific calcium imaging, although it is not discussed why a major difference between the two modalities appears to be a balanced or net-negative modulation of PT cells during the joystick press (Fig. 3j vs. Fig. 4d).

The most exciting novel result is that inhibiting these cell types produces complementary effects on behavior (Fig. 6). It appears that PT inactivation produces a consistent deviation of the forelimb horizontally away from the mouse (Fig. 6f), though the vertical direction is not analyzed and it is not shown if the degree of horizontal deflection is related to vertical amplitude. Related, it is not clear whether this effect requires an ongoing movement or is an induced movement on top of the normal press, as PT inactivation is only triggered by movement initiation and not tested at rest. Furthermore, IT inactivation is shown to reduce press amplitude and speed more than PT inactivation (Fig. 6c), although it is unclear whether this may be related to number of cells affected in each condition. IT cells are also known to provide input to PT cells, and it is not clear whether inhibiting IT cells in this case also inhibits PT cells to account for the larger overall effect. Towards this point, inhibition of IT cells at rest is shown to not induce inhibition in non-IT cells (Fig. 3b), although this analysis may be confounded by the fact that IT cells in this case are defined by their inhibitory response to light.

Support for the conclusion that IT cells modulate press amplitude by driving specific striatal activity is indirectly suggested by the average effect of inactivating IT and PT cells on striatal activity. It is noteworthy and surprising that, whereas IT inactivation decreases movement-related striatal activity, PT inactivation increases it (Fig. 7d). However, it is not made clear how specific these effects are to the amplitude-related "KN dimension" as opposed to total striatal activity (Fig. 7f), and it is not exactly clear what the KN dimension represents in terms of specific cells or specific responses across many cells. It is also not noted whether the increase in striatal activity during PT inactivation is related to the trajectory deviation, for example to demonstrate that striatal activity contains information only about press amplitude and not trajectory.

Reviewer #2 (Public Review):

In vivo characterisation of the establishment of metacyclic T. brucei monoallelic VSG expression is experimentally challenging due to the heterogeneous nature of the trypanosome population in the salivary glands. In this paper, Hutchinson, Foulon et al profile metacyclic VSG gene activation during metacyclogenesis in vivo by using single-cell RNA sequencing technology.

scRNAseq of cells isolated from the salivary glands of infected tsetse identified 5 populations of cells corresponding to different developmental stages- midgut, epimastigotes, gametes, pre-metacyclic and metacyclic cells. 86.7% of pre-metacyclic cells were demonstrated to express more than one VSG and this multi-VSG expression was resolved to monoallelic expression in 86.8% of cells in the metacyclic population. This observation was corroborated by reanalysis of VSG expression in a previously generated salivary gland trypanosome dataset (Vignon, O'Neill et al. 2020). Single molecule RNA-FISH was used to confirm the sequencing data findings. The authors propose a model whereby a transcriptional race initiates in pre-metacyclic cells and, through the recruitment of other factors, a transcriptional threshold is reached for a single VSG that is expressed in the metacyclic cell. The results are an interesting new insight into the dynamics of metacyclic VSG gene activation and the data presented supports the conclusions drawn.

The methodologies used are cutting-edge and well suited to answer the research question. The authors choose inDrop scRNA-seq technology, as opposed to the 10X approach that has been adopted in the only other scRNA-seq study of salivary gland trypanosomes (Vignon, O'Neill et al. 2020). Conceptually both methods are similar, they are droplet microfluidics-based technologies and rely on 3' mRNA barcoding, thus producing datasets with a 3' bias. The authors clearly demonstrate that this method is equally as robust for measuring the transcriptome of single trypanosomes (in fact they detect more UMIs/genes per cell). Where the authors excel is their attention to detail when a) assessing the impact of the inDrop experimental procedure on the viability of their cells (though additional controls could be added, the cells appear to be minimally stressed) and b) rigorously testing that artefacts (e.g barcode swapping, sequencing depth, ambient RNA) are not responsible for multiple VSG expression. This thorough approach adds strength to their results.

The bioinformatic pipelines developed by the authors to facilitate the analysis of strains for which no reference strain is available could prove useful for the community as the study of field strains of trypanosomes is prioritised.

The authors' main finding is independently confirmed in three different trypanosome strains/experimental settings, which adds strength to their results (particularly the smRNA-FISH confirmation of the sequencing data). The second of these approaches (reanalysing the published dataset) is slightly less convincing than the others. Only 17 pre-metacyclic cells are identified, and these appear to differ in expression of VSG and EP1 procyclin relative to metacyclics and epimastigotes when compared to the authors own pre-metacyclic data. Though these pre-metacyclic cells clearly do express more than one VSG at a low level, we do not have a background level of VSG transcription to compare to.

The authors identify a putative population of gamete cells from their data based on marker gene expression, thus presenting a more complete picture of salivary gland trypanosome developmental stages than was previously published in another scRNA-seq dataset (Vignon, O'Neill et al. 2020). However why this population, and an additional midgut population, was identified in this study but not the previous has not been discussed in the text. Furthermore, the authors do not expand on their claim that their data 'raises questions as to whether there are two potential modes of development in the salivary glands'.

Reviewer #2 (Public Review):

This work demonstrates that the bacterial protein CspA, which inactivates host complement by binding to the host complement inhibitor FH, is an important determinant of host range for the Lyme disease (LD) bacterium. Additionally, the authors present phylogenetic analysis of CspA and related protein sequences, which supports the hypothesis that inactivation of host complement has evolved independently in three bacterial genospecies.

First, the authors present a rigorous series of experiments demonstrating that CspA governs host range in three bacterial genospecies (Figs 1-4). By feeding infected ticks on live mice, quail, or artificial feeding chambers, the authors demonstrate that the genospecies differ in their host range, and that these differences only occur when hosts/blood have functional complement. By engineering a CspA-deficient bacteria strain to express CspA from different genospecies, the authors show that host-specific transmission is governed by the CspA protein. The authors previously published similar experiments demonstrating that CspA is key to tick-to-mouse transmission (Hart et al. 2018); the current paper confirms these findings and extends them to avian hosts. These experiments are well-designed and support the authors' key claims.

Second, the authors present a phylogenetic analysis of CspA and related proteins in the Pfam54-IV family (Figs 5-6). The authors identify CspA orthologs in multiple bacterial strains based on sequence similarity. Based on phylogenetic analysis, the authors hypothesize that the host-specific FH-binding activity of CspA evolved independently in each genospecies. The data presented support this hypothesis.

Reviewer #2 (Public Review):

The authors use machine learning to relate videos of facial expressions to a clinically-relevant features.

This is a well-written, very clear paper that outlines a novel procedure to assess a set of features that is very easy and cheap to collect within a clinical context. The methods are relatively straightforward (which is a good thing), and from they are technically applied without flaws as far as I can tell.

I would just wonder about the actual path to clinical translation, if that's the aim. So, how could this pipeline be actually applied in practice? Would a doctor be able to make an effective use of it? Is it intended as a first (cheap and automatised) step in a diagnostic procedure?

Reviewer #2 (Public Review):

This manuscript makes substantial progress in resolving a long-standing mystery regarding the precise role of the histone methyltransferase MES-4 in promoting germline development. MES-4 maintains the histone modification H3K36me3 and germ cell survival, but prior evidence was unable to distinguish among several possibilities for target pathways. This paper utilizes a transcriptional profiling approach at the critical time of germline development to definitively demonstrate that the essential function of MES-4 is to repress X gene expression in germ cells. This result is surprising because X repression is an indirect effect of MES-4 activity (MES-4 does not localize to the X), while the direct effect of maintaining germline gene expression is not essential. To buttress this finding, the authors also utilize a series of elegant genetic experiments to independently test whether expression from the X is sufficient to cause germ cell degeneration. They then go further to identify a single X-linked target, lin-15b, as a primary contributor to the inappropriate X-linked gene expression in mes-4 mutants, by showing that loss of lin-15b activity rescues both the germline degeneration and X mis-expression of mes-4 mutants. Finally, the authors demonstrate that PRC2, the H3K27me3 histone methyltransferase and MRG-1, a candidate H3K36me3 effector protein, are also involved in promoting X silencing through lin-15b.

The manuscript's strengths lie in the development or application of novel techniques, including the profiling of individual pairs of PGCs (a non-trivial advancement), as well as some very well-designed and conceptually innovative genetic assays. These were used to address specific and important gaps in knowledge regarding the phenotype of mes-4, which had been elusive despite having been studied for almost 30 years. Although specific to C. elegans in some ways, the findings are clearly relevant to conserved regulatory events, such as epigenetic memory mechanisms and establishment of opposing chromatin states. Thus, this work provides a substantial advance in the field overall.

One limitation of this study is the lack of clarity about the conclusions regarding the relationship between the two H3K36me3 histone methyltransferases mes-4 and met-1, and between X vs autosomal gene expression. The authors do not precisely state what genes (X or A) are affected in the met-1 and mes-4 mutants. Ultimately, this confusion muddles the final message of X chromosome upregulation being the critical contributor to the mes-4 germline degeneration phenotype. The experiment presented in figure 3B indicates that loss of mes-4 or met-1 is sufficient to prevent germline development even when the Xs are repressed, indicating that failure to activate autosomal gene expression is also an underlying cause of the degeneration. Perhaps this cannot be definitively concluded without directly assessing met-1 and met-1;mes-4 mutant PGCs (or EGCs) for gene expression changes. If technically possible, this would be a very valuable experiment to directly examine autosomal gene expression changes in the double mutant.

Reviewer #2 (Public Review)

I have separated my issues with the manuscript into three sub-headings (Conceptual Clarity, Observational Detail and Analysis) below.

1) Conceptual clarity

There are a number of areas where it would greatly benefit the manuscript if the authors were to revisit the text and be more specific in their intentions. At present, the research questions are not always well-defined, making it difficult to determine what the data is intended to communicate. I am confident all of these issues could be fixed with relatively minor changes to the manuscript.

For example, Line 104: Question 1 is not really a question, the authors only state that they will "investigate innovation and extraction of eating the food", which could mean almost anything.

Question 2a (line 98) is also very vague in it's wording, and I'm left unclear as to what the authors were really interested in or why. This is not helped by Line 104 which refuses to make predictions about this research question because it is "exploratory". Empirical predictions are not simply placing a bet on what we think the results of the study will be, but rather laying out how the results could be for the benefit of the reader. For instance, if testing the effects of 10 different teaching methods on language acquisition-rate: Even if we have no a priori idea of which method will be most effective, we can nevertheless generate competing hypotheses and describe their corresponding predictions. This is a helpful way to justify and set expectations for the specific parameters that will be examined by the methods of the study. In fact, in the current paper, the authors in fact had some very clear a priori expectations going into this study that immigrant males would be vectors of behavioural transmission (clear that is from the rest of the introduction, and the parameters used in their analysis, which were not chosen at random).

The multiple references to 'long-lived' species in the abstract (line 16 and introduction (39, 56) is a bit confusing given the focus of this study. Although such categorisations are arbitrary by nature (a vervet is certainly long-lived compared to a dragonfly), I would not typically put vervet monkeys (or marmosets, line 62) in the same category as apes (references 8 and 9) or humans (line 62) in this regard. This contributes a little towards the lack of overall conceptual focus for the manuscript: beginning in this fashion suggests the authors are building a "comparative evolutionary origins" story, hinting perhaps at the phylogenetic relevance of the work to understanding human behaviour, but the final paragraph of the study contextualises the findings only in terms of their relevance to feeding ecology and conservation efforts. I would recommend that the authors think carefully about their intended audience and tailor the text accordingly. This is not to say that readers interested in human evolution will not be interested in conservation efforts, but rather that each of these aspects should be represented in each stage of the manuscript (otherwise - conservationists may not read far into the Introduction, and cultural evolution fans will be left adrift in the Conclusion).

2) Observational detail

There are a number of areas of the manuscript which I found to be lacking in sufficient detail to accurately determine what occurred in these experimental sessions, making the data difficult to interpret overall. All of this additional information ought to be readily available from the methods used (the experiments were observed by 3-5 researchers with video cameras (line 341)) and is all of direct relevance to the research questions set out by the authors.

While I appreciate that it will take quite a bit of work to extract this information, I am certain that it would greatly improve the robustness and explanatory power of this study to do so.

The data on who was first to innovate/demonstrate successful extraction of the food in each group (Question 1) and subsequent uptake (Question 2), as well as the actual mechanism by which that uptake occurred (the authors strongly imply social learning in their Discussion, but this is never directly examined) is difficult to interpret based on the information presented. Some key gaps in the story were:

- Which/how many individuals encountered the food and in what order? I.e., were migrants/innovators simply the first to notice the food?<br /> - Did any individuals try and fail to extract the food before an "innovator" successfully demonstrated?<br /> - How many tried and failed to extract the nuts before and after observing effective demonstrators?<br /> - Were individuals who observed others interact with the food more likely to approach and/or extract it themselves?<br /> - Did group-members use the same methods of extraction as their 'innovators'?<br /> - How many tried and succeeded without having directly observed another individual do so (i.e. 'reinvention' as per Tennie et al.)?

The connective tissue between the research questions set out by the authors is clearly social learning. In short: the thesis is that Migrants/Innovators bring a novel behaviour to the group, then there is 'uptake' (social learning), which may be influenced by demographic factors and muzzle-contact (biases + mechanisms). Given this focus (e.g. lines 224-264 of the Discussion), I would expect at least some of the details above to be addressed in order to provide robust support for these claims.

Question 2a (Lines 136-146): This data is hard to interpret without knowing how much of the group was present and visible during these exposures.

For example: 9% update in NH group does not sound impressive, but if only 10% of the total group were present while the rest were elsewhere, then this is 90% of all present individuals. Meanwhile if 100% of BD group were present and only experienced 31% uptake, then this is quite a striking difference between groups.

Of course, there is also an issue of how many individuals can physically engage with the novel food even if they want to - the presence of dominant individuals, steepness of hierarchy within that group, etc, will significantly influence this (and is all of interest with regards to the authors' research questions).

Muzzle-contact behaviour: The authors use their data to implicate muzzle-contact in social learning, but this seems a leap from the data presented (some more on this in the Analysis section).

For example:<br /> - What is the role of kinship in these events?<br /> - Did they occur when the juvenile had free access to the food (i.e. not likely to be chased off by a feeding adult)?<br /> - Did they primarily occur when adults had a mouthful of food? (i.e. could it simply be attempted pilfering/begging)<br /> - What proportion of PRESENT (not total) individuals were naïve and knowledgeable in each group for each trial (if 90% present were knowledgeable, then it is not surprising that they would be targeted more often)?<br /> - Did these events ever lead to food-sharing (In other words, how likely are they to simply be begging events)?<br /> - Did muzzle-contact quantifiably LEAD to successful extraction of the food? If the authors wish to implicate muzzle-contact in social learning, it is not sufficient to show that naïve individuals were more likely to make muzzle-contact, they must also show that naïve individuals who made more muzzle-contact were more likely to learn the target behaviour.

3) Analysis

There are a number of issues with the current analysis which I strongly recommend be addressed before publication. Some of these are likely to simply require additional details inserted to the manuscript, whereas others would require more substantial changes. I begin with two general points (A & B), before addressing specific sections of the manuscript.

A) My primary issue with each of the analyses in this manuscript is that the authors have fit complex statistical models for each of their analyses with no steps to ascertain whether these models are a good fit for the data. With a relatively small dataset and a very large number of fixed effects and interactions, there is a considerable risk of overfitting. This is likely to be especially problematic when predictor variables are likely to be intercorrelated (age, sex and rank in the case of this analysis).

The most straightforward way to resolve this issue is to take a model-comparison approach. Fitting either a) a full suite of models (including a 'null' model) with each possible permutation of fixed effects and interactions (since the authors argue their analysis is exploratory) or b) a smaller set of models which the authors find plausible based on their a priori understanding of the study system. These models could then be compared using information criterion to determine which structure provides the best out-of-sample predictive fit for the data, and the outputs of this model interpreted. Alternatively, a model-averaging approach can be taken, where the effects of each individual predictor are averaged and weighted across all models in the set. Both of these approaches can be performed easily using the r package 'MuMIn'. There are also a number of tutorials that can be found online for understanding and carrying out these approaches.

B) It does not seem that interobserver reliability testing was carried out on any of the data used in these analyses. This is a major oversight which should be addressed before publication (or indeed any re-analysis of the data).

Line 444: Much more detail is needed here. What, precisely, was the outcome measure? Was collinearity of predictors assessed? (I would expect Age + Rank to be correlated, as well as Sex + Rank).

Line 452. A few comments on this muzzle-contact analysis:

"We investigated muzzle contact behaviour in groups where large proportions of the<br /> groups started to extract and eat peanuts over the first four exposures"

What was the criteria for "a large proportion"?

The text for this muzzle-contact analysis would indicate that this model was not fit with any random effects, which would be extremely concerning. However, having checked the R code which the authors provided, I see that Individual has been fit as a random effect. This should be mentioned in the manuscript. I would also strongly recommend fitting Group (it was an RE in the previous models, oddly) and potentially exposure number as well.

Following on from this, if the model was fit with individual as a random effect it becomes confusing that Figure 3 which represents this data seemingly does not control for repeated measures (it contains many more datapoints than the study's actual sample size of 164 individuals). This needs to be corrected for this figure to be meaningfully interpretable.

Finally, would it make sense to somehow incorporate the number of individuals present for this analysis? Much like any other social or communicative behaviour, I would predict the frequency of occurrence to depend on how many opportunities (i.e. social partners) there are to engage in it.

Line 460: "For BD and LT we excluded exposures 4 and 3, respectively, due to circumstances resulting in very small proportions of these groups present at these exposures"

What was the criterion for a satisfactory proportion? Why was this chosen?

Line 461: "We ran the same model including these outlier exposures and present these results in the supplementary material (SM3)."

The results of this supplemental analysis should be briefly stated. Do they support the original analysis or not?

Line 465: "Due to very low numbers of infants ever being targets of muzzle contacts, we merged the infant and juvenile age categories for this analysis."

This strikes me as a rather large mistake. The research question being asked by the authors here is "How does age influence muzzle-contact behaviour?"<br /> Then, when one age group (infants) is very unlikely to be a target of muzzle-contact, the authors have erased this finding by merging them with another age category (juveniles). This really does not make sense, and seriously confounds any interpretation of either age category.

Lines 466-474: Why was rank removed for the second and third models? Why is Group no longer a random effect (as in the previous analysis)? The authors need to justify such steps to give the reader confidence in their approach.

Furthermore - because of the way this model is designed, I do not think it can actually be used to infer that these groups are preferentially targeted, merely that adult female and adult males are LESS likely to target others than to be targeted themselves, which is a very different assertion.

Because the specific outcome measure was not described here, this only became apparent to me after inspecting Figure 3, where outcome measure is described as "Probability of (an individual) being a target rather than initiator" - so, it can tell us that adults are more often targeted rather than initiating, but does not tell us if they are targeted more frequently than juveniles (who may get targeted very often, but initiate so often that this ratio is offset).

Lines 467-473: "Our first simple model included individuals' knowledge of the novel food at the time of each muzzle contact (knowledgeable = previously succeeded to extract and eat peanuts; naïve = never previously succeeded to extract and eat peanuts) and age, sex and rank as fixed effects. Individual was included as a random effect. The second model was the same, but we removed rank and added interactions between: knowledge and age; and knowledge and sex. The third model was the same as the second, but we also added a three-way interaction between knowledge, age and sex."

This is a good example of some of the issues I describe above. What is the justification for each of these model-structures? The addition and subtraction of variables and interactions seems arbitrary to the reader.

Reviewer #2 (Public Review):

The authors have asked the question of how the 'neuroprotective' IL-37 signaling pathway modulates pro-inflammatory microglial activation, and thereby impacts neuronal health, synaptic plasticity in LPS-dependent neuroinflammation and amyloid metabolism and pathways affecting cognitive function in the APPPS1 mouse model of Alzheimer's disease.

The use of in-vitro assays using primary, murine microglia from transgenic IL-37 tg/wt and IL-37 tg/tg mice for metabolic profiling following LPS-stimulated, pro-inflammatory activation, along with the generation and use of APPPS1-IL37tg/tg mouse model to demonstrate the anti-inflammatory / neuroprotective effect of IL-37 signaling are major strengths of this work.

The authors do an excellent job in designing experiments to ask and test logical questions that arise as the data is developed. However, certain aspects of the data should be carefully interpreted, as the manuscript does not directly test the interpretations made.

While the data presented raises some interesting hypotheses, the manuscript is limited in discussing their findings in the context of the larger-picture of distinct sub-clusters of microglial activation, microglial metabolic reprogramming/dysfunction and neurodegeneration in AD.

Lastly, due to already existing data that suggests that IL-37 may play a limited role in AD pathology, this study may be of potential but limited interest to the field of AD-related neurodegeneration.

Reviewer #2 (Public Review):

Synchronous flashing of fireflies is a textbook example of collective behaviour, although much more theoretical work has been devoted to explaining it than field observations. The work proposed in this manuscript, along with previous results by the same group, are significantly contributing to fill the gap between observations and mechanistic models. In these models, single-insect dynamics is described along with the interaction mechanisms.

In a clever experiment where fireflies are screened from the rest of the population, so that the number of interacting individuals can be manipulated, the authors were able to characterize both single-fireflies firing patterns, and the emergence of collective-level flashes when their number is progressively increased. These observations show that collective-level firing is more regular and more frequent that the average individual-level firing, a feature that can be explained by globally coupled populations of dynamical systems, where firing is followed by a refractory period. The authors show at first that the increase in coherence (standard deviation of the inter-spike interval of the synchronous flashes) as a function of the number N of fireflies is quantitatively explained knowing just the inter-burst interval of collective flashing at high density - which is largely determined by the duration of the refractory period. Then, by using an integrate-and fire model that explicitly accounts for coupling, they study the dependence of the onset of collective oscillations on coupling, and estimate what coupling strength best explains the observations at different N.

I found that the experiment is really nice and has the potential to advance a lot the mechanistic understanding of this collective behaviour. Also, the mathematical model reproduces the main observations, even though density-dependent onset of synchronization, reduction of the oscillation frequency with increasing density, and finite-size scaling are general properties that are observed in populations of globally coupled dynamical systems other than the one proposed here.

However, the way models reflect experimental observations and how they compare with them and with one another are in my opinion insufficiently characterized and discussed.

I raise two main points:

1. The biological relevance of certain hypotheses is insufficiently discussed. This is important because if the observed behaviour is a universal one, alternative models may explain it as well.<br /> 2. Comparison between the models and the data could be improved, in particular through quantification of the differences between distributions and sensitivity analysis of the numerical results.

Reviewer #2 (Public Review):

Sumser et al. present and validate a suite of new reagents for monosynaptic tracing, most notably for the production of the required rabies viral vectors, and specifically of vectors of the CVS-N2c strain, which are superior to those of the more commonly used SAD B19 strain but which have so far been notoriously more difficult to make. The manuscript and many new reagents are significant contributions to the neuroscience community. Commendably, the authors have already deposited all of the 34 novel plasmids with Addgene. Reagents aside, the demonstration that ∆G CVS-N2c can be grown to high titers quickly on BHK cells co-expressing EnvA and TVA, with much less contamination by "unpseudotyped" virus and without losing their in vivo effectiveness, is an important advance.

Reviewer #2 (Public Review):

Using yeast genetics and Hi-C the authors aim to assess the potential role of the cohesin complex in ordering post-anaphase mitotic chromosomes. This is a stage of the cell cycle where chromosomal cohesin was considered to be absent due to the cleavage of the Scc1/Mcd1 subunit of cohesin by the Separase enzyme post anaphase. They start by comparing the chromosome segregation kinetics of cells where anaphase is induced by artificial cleavage of the Scc1 to anaphase induced by AID-mediated degradation of Scc1 in metaphase. They observe a strong mis-segregation phenotype in the metaphase induced by degradation of Scc1 compared to that induced by artificial cleavage of Scc1. They also observe that artificial cleavage of Scc1 leaves the cleaved interactions with other subunits of cohesin intact, potentially leaving a population of cohesin on chromosomes where it can promote looping and compaction. In contrast, the complete degradation of cohesin does not leave residual cohesin on chromosomes. From this, they hypothesize that residual cohesin left on chromosomes following Scc1 cleavage promotes faithful segregation of chromosomes during anaphase. By using Hi-C they demonstrate that the structure of chromosomes in the anaphase induced by TEV cleavage retains more contacts in a range consistent with cohesin-dependent looping than observed following Scc1 degradation. This is consistent with the cohesin-dependent organisation being partially retained in anaphase. To confirm that normally processed cohesin supports anaphase chromosome structure they assess the association of cohesin and the extent of cohesin regulation of chromosome structure in anaphase cells arrested before mitotic exit (cdc15-2) by ChIP, Hi-C, and microscopy. They convincingly show that some cohesin is detected at centromeres at anaphase cells and that cohesin regulates chromosome structure in anaphase at both the centromeres and at and around the rDNA repeats in budding yeast.

The strengths of this manuscript are that it convincingly shows that a cohesin subpopulation is still available in anaphase to associate and organise mitotic chromosomes. Such activity was previously not suspected and I think is a major conceptual advance. These findings raise a number of interesting questions about how this mitotic subpopulation escapes the normal degradation of cohesin that occurs in anaphase. The weakness of the manuscript is the assertion that some looping is retained all along anaphase chromosomes. The analysis of cdc15-2 cells provides strong evidence that the organisation of centromeres and rDNA chromosomes is regulated by cohesin in anaphase. The evidence that cohesin also partially maintains looping along chromosome arms is weaker and dependent on the interpretation of the activity of the TEV cleavage system.

Reviewer #2 (Public Review):

Strengths of this manuscript include the comprehensive assessment of 23 potential antihypertensive targets against breast cancer risk, using a two-sample Mendelian randomization framework which permitted the authors to maximise statistical power for their analyses. The principal limitation of this work is that the authors' claims (for the only finding that survives a Bonferroni correction for multiple testing) do not appear to be justified based on the data that they present. The authors have concluded that an effect of an SNP on breast cancer risk is mediated via SLC12A2 (a putative antihypertensive drug target) when this finding could simply reflect the effect of this variant on the expression of a neighbouring gene (CTC-228N24.3) based on data presented by the authors.

The authors show that their SLC12A2 eQTL (rs17764730) is more strongly associated with CTC-228N24.3 (P~0.00) expression than SLC12A2 expression (P+3.2x10-86). Using top eQTLs across respective genes as instruments, they then show similar MR associations of genetically-proxied whole blood SLC12A2 expression (beta=0.15, se=0.039, p=1.05x10-4) vs genetically-proxied whole blood CTC-228N24.3 expression (beta=0.05, se=0.014, p=1.15x10-4) and similar colocalisation estimates for SLC12A2 (H4=81.5%) and CTC-228N24.3 (H4=77%). Notably, the top SLC12A2 eqtl is in perfect LD (r2=1.00) with the top CTC-228N24.3 eQTL (rs6888037) so these results are not surprising.

What these analyses tell me is that one cannot clearly conclude that the effect of this variant on breast cancer risk is mediated via SLC12A2 expression. Unless the authors can provide additional (strong) evidence to support this effect being mediated via SLC12A2, I'm afraid the results presented in this manuscript do not support the conclusions reached by the authors.

Reviewer #2 (Public Review):

The manuscript by Hae J. Park et al reports up-regulation of oxidative phosphorylation due to the activation of the mTOR signaling pathway is of critical importance to the therapeutic resistance of FLT3 mutant AML cells in a bone marrow environment. A combination of treatments with mTOR inhibitor and FLT3 inhibitor drastically eliminates all the marrow resident AML cells. Although the basic findings have been reported by other groups, they have potential clinical relevance that might benefit AML patients with FLT3-ITD mutations. Along these lines the manuscript is of interest, however important critiques need to be addressed with additional experimental data/revision.

1. Most of the mechanism implies mTOR-dependent translation control, similar to Sonenberg's findings as cited in the manuscript. However, data presented that the OXPHOS gene expression also changes in some experiments. Is this consistent among the different experimental settings? If this is the case, are these changes in gene expression consistent with PGC1s or ERRs OXPHOS target genes that are controlled via mTOR, see Cunningham et al. Nature 2007 Nov 29;450(7170):736-40 or Dufour et al. Cell Rep. 2022 Mar 22;38(12):110534-

2. The authors used hBMSC conditioned medium to mimic the bone marrow environment and used normal RPMI media as a control, to investigate the effects of bone marrow-released stromal factors on the TLT3 inhibitor resistance of AML. However, in addition to the "stromal factors", conditioned medium has different pH, metabolites, glucose levels, and more compared to RPMI medium, making the system complicated. Can the authors use the conditioned medium from HEK-293 or a spleen cell line as the control?

3. The treatment times of drugs changed from experiment to experiment. Is there any reason for this?

4. The quality of western blots looks over-adjusted. This is a very poor imaging quality with all blots being very pixel with non-quantitative adjustment. Please see other journals for quality blots on mTOR signaling. Antibodies for this pathway are of very good quality, these adjustment of images is not rigorous.

5. There are some problems with ATM, mTOR, translation axis, and connection. In Fig 7C, knockdown of ATM completely eliminates mTOR but the translation indicators, p-4EBP1, and p-S6 are kept unchanged, indicating inhibition of mTOR in these cells has no effect on protein translation in general, which is totally against the data shown previously in this manuscript.

Reviewer #2 (Public Review):

The authors utilize the publicly available dHCP dataset to ask an interesting question: how does postnatal experience and prenatal maturation influence the development of the visual system. The authors report that experience and prenatal maturation differentially contribute to different aspects of development. Namely, the authors quantify cortical thickness, myelination, and lateral symmetry of function as three different metrics of development. The homotopy and preterm infant analyses are strengths that, on their own, could have justified reporting. However, I have concerns about the analytic approaches that were used and the conclusions that were drawn. Below I list my major concerns with the manuscript.

PMA vs. GA vs. PT

1. The authors seek to understand the contribution of experience and prenatal development, yet I am unsure why the authors focused on the variables they did. There are three variables of interest used throughout this study: Gestational age at birth (GA), postnatal time (PT), and postmenstrual age at the time of scan (PMA). The last metric, PMA, is straightforwardly related to GA and PT since PMA = GA + PT. In most (but not all) of the manuscript, the authors use PMA and PT, with GA used without justification in some cases but not in others.

It is unclear why PMA is used at all: PMA is necessarily related to PT and GA, making these variables non-independent. Indeed, the authors show that PMA and PT are highly correlated. The authors even say that "the contribution of postnatal experience to the development was not clarified because PMA reflects both prenatal endogenous effect and postnatal experience." So, why not use GA at birth instead of PMA? Clearly, GA is appropriate in some cases (e.g., Figure S4 or in some of the ANOVA applications), and to me, it seems to isolate the effect the authors care about (i.e., duration of prenatal development). Perhaps there is some theoretical justification for using PMA, but if so, I am unaware.

That said, I expect that replacing all analyses involving PMA with GA will substantially change the results. I do not see this as a bad thing as I think it will make the conclusions stronger. As is, I am left unsure about what the key takeaways of this paper are.

2. Using GA instead of PMA will have several benefits: 1) It will be much simpler to think of these two variables since they contrast the duration of fetal maturation and time postnatally. 2) This will help the partial correlation analyses performed since the variance between the variables is more independent. It will also mean that the negative relationships observed between PT and cortical thickness when controlling for PMA (e.g., Figure 2h) might disappear (reversed signs for partial correlations are common when two covariates are correlated). 3) this will allow the authors to replace Figure 1a with a more informative plot. Namely, they could use a scatter of GA and PT, giving insight into the descriptive statistics of both dimensions.

3. I suspect that one motivation for the use of PMA over GA is for the analysis in Figure 6. In this analysis, the authors pick a group of term infants with a PMA equal to the preterm infants. Since PMA is the same, the only difference between the groups (according to the authors) is the amount of postnatal experience. However, this is not the only difference between the groups since they also vary in GA (and now PT and GA are negatively correlated almost perfectly). I don't know how to interpret this analysis since both the amount of prenatal maturation and postnatal experience vary between the groups.

Justification of conclusions and statistical considerations

I had concerns about some of the statistical tests and conclusions that the authors made. I refer to some of these in other sections (e.g., the homotopy analyses), but I raise several here.

4. I am not sure what evidence the authors are using to make this claim: "we found that the cortical myelination and overall functional connectivity of ventral cortex developed significantly with the PMA but was not directly influenced by postnatal time." Postnatal time is significantly correlated with cortical myelination, as shown in Figures 2g, 2h, 3b, 3c, and postnatal time is significantly correlated with functional connectivity, as shown in Figures 4h, 5c, 5d, and 5e. Hence, this general claim that "the development of CT was considerably modulated by the postnatal experience while the CM was heavily influenced by prenatal duration" doesn't seem to be supported: both myelination and thickness are affected by postnatal experience and prenatal duration (as measured by PMA). A similar sentiment is expressed in the abstract. Perhaps the authors suggest different patterns in the strength of change for PMA vs. PT across these metrics, but if so, then statistical tests need to support that conclusion, and the claims need to reflect that sentiment.

Interestingly, Figure S4 presents a compelling ANOVA that does support this conclusion. Still, this result is relegated to the supplement, and it also uses GA, rather than PMA, making it hard to reconcile with the other claims made in the main text. Moreover, it uses ANOVAs, which dichotomizes a continuous variable. Here and elsewhere in the manuscript (e.g., Figures 3d, 3e), the authors split the infants into quartiles and compare them with ANOVAs. Their use for visualization is helpful, but it is unclear what the statistical motivation for this is rather than treating these as continuous variables like is possible with linear mixed-effects models. Moreover, it is unclear why the authors excluded half the data from the study (i.e., quartiles 2 and 3) in this ANOVA when all four quartiles could be used as factors.

5. It is unclear what the evidence is to support the following claim: "Both CT and CM show higher correlation with PMA in the posterior than anterior region, and higher correlation in the medial than lateral part within the anatomical mask (Figure 2a and Figure S2b-c [sic])" From Figure 2 or Figure S2, I don't see a gradient. From Figure S3, there might be a trend in some plots, but it is hard to interpret since it is non-monotonic. More generally, is there a statistical test to support this claim?

6. "and the interaction [sic] was more prominent in CM (simple effect: t = 10.98, p < 10-9) that in than CT (t = 2.07, p < 0.05)." Does 'more prominent' mean it is 'significantly stronger'? If not, then the authors should adjust this claim

7. Are the authors Fisher Z transforming their correlations? In numerous places, correlation values seem to be added together or used as the input to other correlation analyses. It is unclear from the methods whether the authors are transforming their correlation values to make that use appropriate.

Homotopy analyses

The homotopy section is a strength of the paper, but I have doubts about the approach taken to analyze this data and some of the conclusions drawn. I don't expect any of my suggestions to change the takeaway of this section, but I do think they are essential criticisms to address.

8. I do not think that the non-homotopic control condition is appropriate. In Arcaro & Livingstone (2017), the authors had 3 categories for this analysis: homotopic pairs (e.g., left V1 vs. right V1), adjacent pairs (e.g., left V1 vs. right V2), and distal pairs (e.g., left V1 vs. right PHA1). In the homotopy analysis performed by Li and colleagues, they compare homotopic pairs with all other pairs. I don't think that is generous to the test since non-homotopic pairs include adjacent pairs that should be similar and distal pairs that shouldn't be similar. This may explain why some non-homotopic distribution overlaps with the homotopic distribution in Figure 4c.

9. Regardless of this decision, I think the authors should reconsider their statistical test. I think the authors are using a between samples t-test to compare the 34 homotopic pairs with the hundreds of non-homotopic pairs. This is statistically inappropriate since the items are not independent (i.e., left V1 vs. right V1 is not independent of left V1 vs. right V2, which is also not independent of left V3 vs. right V2). This means the actual degrees of freedom are much lower than what is used. Moreover, I am unsure how the authors do this analysis across participants since this test can be done within participants. The authors should clarify what they did for this analysis and justify its appropriateness.

10. Could the authors speculate on why the correlations in homotopic regions are so much lower than what Arcaro and Livingstone (2017) found. I can think of a few possibilities: higher motion in infants, less rfMRI data per participant, different sleep/wake states, and different parcellation strategies. Regarding the last explanation, I think this is a real possibility: the bilateral correlation may be reduced if the Glasser atlas combines functionally heterogeneous patches of the cortex. Hence, the authors should consider this and other possible explanations.

11. The authors assume that the homotopic analyses mean that there are lateral connections between hemispheres (e.g., "Furthermore, the connections among the ventral visual cortex have developed during this early stage. Specifically, the homotopic connections between bilateral V1 and between bilateral VOTC both increased with GA, indicating an increased degree of functional distinction"). While this might be true, it doesn't need to be. Functional connectivity can be observed between regions that lack anatomical connectivity. Instead, two regions could both be driven by another region. In this case, the thalamus might drive symmetrical activity in the visual cortex.

Miscellaneous

12. I am not sure what the motivation of this line is: "Moreover, those studies did not fully control the visual experience in the first few weeks of the subjects, thus cannot give a clear conclusion whether the innate functional connectivity is unrelated to postnatal visual experience." Arcaro, Schade, Vincent, Ponce, & Livingstone (2017) did control the visual experience of subjects. Moreover, the research here doesn't control infant experience in the way this sentence implies: it implies an experiment manipulation (i.e., fully control) rather than a statistical control that is done here. Consider rephrasing

13. I am not sure why this claim is made: "Area V1 was selected because this region is the most basic region for visual processing and probably is the most experience-dependent area during early development". Is there evidence supporting this claim? Plasticity is found throughout the visual cortex, and I think which region is most plastic depends on the definition of plasticity. For instance, most people have the same tuning properties to gabor gratings (e.g., a cardinality bias), but there is enormous variability in face tuning across cultures.

14. The abstract says 783 infants were included in this study, but far fewer are actually used. The authors should report the 407 number in the abstract if any number at all.

15. Any comparisons of preterms and terms ought to be given the caveat that the preterm environment can be very different than the term environment: whereas a term infant goes home and sees friends and family without restriction, the preterm environment can be heavily regulated if they are in a NICU. Authors should either provide details about the environments of the preterms in their study, or they should consider how differences in the richness of visual experience - regardless of quantity - may affect visual development.

Reviewer #2 (Public Review):

The manuscript by Oláh, Pedersen, and Rowan, "Ultrafast Simulation of Large-Scale Neocortical Microcircuitry with Biophysically Realistic Neurons", demonstrates an ANN architecture that accurately captures the dynamics of neurons while also providing a substantial reduction of computing time relative to the standard method using NEURON simulations. This is important work that, together with recent similar work by others, opens exciting opportunities for detailed and accurate simulations of neurons with much higher speed than has been possible so far. The authors demonstrated an important step forward in enabling accurate and high-speed simulations of neurons taking advantage of modern ANN architectures and computing hardware such as GPUs.

• They started by considering several different ANN architectures and training each architecture to capture the membrane voltage dynamics in a single-compartment NEURON model of a neuron described by Hodgkin-Huxley equations. One particular architecture, the "CNN-LSTM" was substantially more successful than others in training and testing, especially in reproducing the action potentials (APs), i.e., it captured well both the subthreshold membrane voltage and APs.

• They then showed that once trained on a neuron model, the CNN-LSTM ANN generalizes to the cases where ionic conductance is manipulated and can capture the ionic sodium and potassium currents, in addition to the membrane voltage, without the need to re-train the ANN.

• Furthermore, the ANN generalizes to the cases involving nonlinear synapses, such as NMDA synapses, in addition to the simpler AMPA synapses.

• The authors then applied the ANN simulations to a realistic multi-compartmental model of a pyramidal cell (PC) from Layer 5 (L5PC) in the mammalian cortex and showed good performance for this cell, as well as L2/3PC, L4PC, and L6PC.

• In the next step, between one and 5,000 L5 PCs were simulated, and it was shown that ANN simulations on a GPU are faster by a factor of over 10,000 than NEURON simulations on a single CPU. This is a drastic and impressive difference.

• Finally, the authors demonstrated that a network of 150 PCs mimicking the effects of Rett syndrome can be efficiently simulated using the ANN approach. They sampled a large parameter space using the ANN simulations on a single GPU within seconds, a task that would take again on the order of 10,000 times longer for regular CPU simulations without the ANN approach.

The study is interesting and extensive. Multiple lines of evidence, as enumerated above, show that the approach described here is promising. It definitely deserves the attention of fellow computational neuroscientists. I am convinced that many of them would like to try out the approach and the code that will be shared freely by the authors. The paper is mostly clear and easy to follow.

In my opinion, the weaknesses of this work are relatively minor. Generally, I would like to see more characterization of ANN generalization for tasks relevant to the everyday practice of neuronal modeling. For example, it is common to change not only the strength, but also the number of synapses, and it will be useful if the ANN approach seamlessly generalizes to that. Another point is that the authors could put their study more into the context of other research happening in this area and discuss the uniqueness of their contributions in that light. But, again, overall I find this study to be extensive and interesting, and I think it will generate a strong interest in the field.

Reviewer #2 (Public Review):

This work demonstrates a channel-independent function of a gap junction protein, UNC-9/innexin, and they show unc-9 is required for the tilling of presynaptic termini of DD5 and DD6, two neighboring C. elegans GABAergic neurons. This finding joins the list of growing evidence of the channel-independent function of gap junction proteins in neuronal development.

Strengths:<br /> 1) A new model system to study synapse tilling, an under-studied yet important aspect of neuronal development.<br /> 2) Strong genetic evidence to support the novel function of UNC-9 in DD5-DD6 synapse tilling in egl-20/Wnt mutant background<br /> 3) Careful characterization of how C-terminal GFP tagging and N-terminal 18 Aa affect UNC-9's channel functions.

Weaknesses:<br /> One major weakness is that all analyses were carried out in DD5/DD6 neurons in egl-20/Wnt mutant background. It is unclear whether unc-9 plays the same role in wild type or other genetic background, and whether unc-9 can also affect synapse tilling in other neurons.

Reviewer #2 (Public Review):

This study aims to provide a needed update and validation of a previously outlined mathematical model that describes HSR/Hsf1 regulation. The purpose of the update is to incorporate the impact of newly translated proteins as negative regulators of Hsf1 following heat shock. A requirement for ongoing translation to mount the HSR and activate Hsf1 has been described in several recent studies. Moreover, the study addresses the role of the Hsp70 cochaperone Sis1 in HSR regulation, including its potential function in negative feedback regulation following heat-shock.

The main strength of the study is that it combines quantitative modeling with a well-defined experimental system to generate data. Overall, the model appears to accurately reflect the behavior of HSR under the employed experimental conditions and provides and elegant example of a formalized model for this simple regulatory circuit. Another strength of the study is that it addresses the functional involvement of Sis1 in HSR/Hsf1 regulatory mechanisms and rules out Sis1 involvement in negative feedback regulation of Hsf1 following heat shock. This finding is of importance in light of the complexity of Sis1 involvement in HSR/Hsf1 regulation suggested by the literature. The authors also document a need for endogenous SIS1 promoter regulation during growth on non-fermentable carbon sources.

The study is important for the advancement of Hsf1 research and it may provide inspiration for the study of other chaperone-titrated transcriptional mechanisms such as the UPR or bacterial stress sigma factors.

Reviewer #2 (Public Review):

In this study, the authors take advantage of unbiased scRNA-seq datasets of the developing mouse soft palate that they previously reported and performed a new bioinformatic analysis to identify differential signaling pathway activities in the heterogeneous palatal mesenchyme. They found a strong association of TGF-beta signaling pathway activity with the perimysial cells and validated through immunofluorescent detection of pSmad2, which led to their hypothesis that TGF-beta signaling in the perimysial cells might regulate palatal muscle formation. They generated and analyzed Osr2-Cre;Alk5fl/fl mice and showed those mice have cleft soft palate and disruption of the levator veli palataini (LVP) muscle. They then performed a comparative scRNA-seq analysis of the soft palate tissues from E14.5 Osr2-Cre;Alk5fl/fl and control embryos and showed that the Osr2-Cre;Alk5fl/fl embryos exhibited defects in the perimysial cells, in particular reduction in Tbx15+ perimysial fibroblasts that directly associate with the LVP muscle progenitors. The FGF18 is one of the most highly enriched signaling molecules in the perimysial cells and showed that the Osr2-Cre;Alk5fl/fl embryos exhibited reduced Fgf18 expression together with loss of MyoD+ myoblasts in the prospective LVP region. Further data showed that pSmad2 bound in the Fgf18 promoter region in the developing soft palate tissues. In addition, bioinformatic gene regulatory network analysis of the scRNA-seq data identified Creb5 as a potential tissue-specific transcription factor in the perimysial cells and RNAi knockdown assays in palatal mesenchyme culture suggested that Creb5 is required for Fgf18 expression. Further studies identified a subtle deficiency in LVP in Osr2-Cre;Fgf18fl/fl mice and showed that exogenous Fgf18 bead implantation in explants of E14 Osr2-Cre;Alk5fl/fl embryonic head increased the MyoD+ myoblast population in the prospective LVP region. The authors concluded that TGF-beta signaling and Creb5 cooperatively regulate Fgf18 to control pharyngeal muscle development. While the study used multiple complementary approaches and the data presented are solid, important questions need to be addressed to resolve reasonable alternative explanations of the data to the authors' main conclusion.

Major points:<br /> 1. TGF-beta signaling is known to be crucial for neural crest-derived palatal mesenchyme cell proliferation from E13.5 to E14.5. The Osr2-Cre;Alk5fl/fl mutant embryos exhibited obvious disruption of LVP myogenesis and reduced soft palatal shelf size at E14.5 (Fig3-Sup2A-D and Fig 4H-K). The cellular and molecular defects likely started prior to E14.5. Thus, it is important to examine at earlier stages (E13.5/E14.0) whether the palatal mesenchyme was already defective in cell proliferation/survival and/or perimysial cell marker expression, including Creb5 and Tbx15, to resolve whether the primary defect in the Osr2-Cre;Alk5fl/fl palatal mesenchyme could be a reduction in perimysial progenitor cell proliferation and/or differentiation of the myoblast-associated subset, for which Tbx15 and Fgf18hi act as marker genes rather than direct molecular targets. Furthermore, the apparent loss of Tbx15+ cells coincided with a specific reduction of Fgf18 expression in the myoblast-associated perimysial cells (Fig 4J/K versus Fig 5H-K), which raises the possibility that TGF-beta signaling regulates the differentiation of the Tbx5+ population from the mesenchymal progenitors while the reduction in Fgf18 expression might be a secondary consequence of the cellular defect. The data in Fig 6O showing a lack of significant induction of Fgf18 expression in the palatal mesenchyme culture in both control and Creb5-RNAi cells is also consistent with this alternative explanation.<br /> 2. Since the Osr2-Cre;Fgf18fl/fl mice exhibited much subtler palatal and LVP defects than the Osr2-Cre;Alk5fl/fl mice even though the latter still had a lot of Fgf18-expressing perimysial cells at E14.5, Fgf18 is likely a minor player in the TGF-beta mediated gene regulatory network regulating LVP formation. The major players acting downstream of TGF-beta signaling in the palatal mesenchyme, that control initial LVP progenitor migration to and/or proliferation in the soft palate region, remain to be identified and functionally validated. Whether and how Fgf18 directly regulates the perimysial-myoblast interaction is also not known.<br /> 3. While the title and the main conclusion of this manuscript imply a crucial role of Creb5 in the regulation of pharyngeal muscle development, there is no data supporting such a crucial role. Do Creb5-/- mice have specific defects in pharyngeal muscle development?<br /> 4. Data in Fig 6 are not sufficient to conclude that TGF-beta signaling and Creb5 cooperatively regulate Fgf18. The TGFb1 treatment did not significantly induce Fgf18 expression in either the control or Creb5-RNAi palate mesenchyme cells (Fig 6O). No data regarding how they act cooperatively to regulate Fgf18 expression.

Reviewer #2 (Public Review):

The manuscript by Espinosa et al. characterizes the role of the sodium channel Nav1.1 in DRG sensory neurons, focusing on its role in proprioceptive sensory neurons. Nav1.1 expression has previously been observed in myelinated DRG neurons (including proprioceptive muscle afferents) but its significance for proprioceptive function remains unknown. In a series of molecular and in vitro patch clamp studies (using pharmacological Nav1.1 inhibitors and activators), the authors demonstrate that all proprioceptors express Nav1.1 and that this sodium channel is required for repetitive firing in the majority of proprioceptors. A pan sensory conditional deletion of Nav1.1 leads to a loss in motor coordination, suggesting that Nav1.1 in sensory neurons is required for normal motor control. While this is a somewhat generic and slightly unsatisfying conclusion, further morphological studies and ex vivo electrophysiological recordings of functionally identified muscle spindle afferents begin to offer a more interesting take on the role of Nav1.1 in proprioceptor function. First, while proprioceptor number and spindle morphology are unchanged, it appears as if the number of synapses between muscle spindle afferents to motor neurons is reduced, perhaps suggesting that a reduction in proprioceptor excitability during development affects the formation of proprioceptive sensory-motor circuits. Second, ex vivo recordings of MS afferents indicate that the loss of Nav1.1 primarily affects the static phase of their response to increases in muscle length, suggesting a role in the regulation of proprioceptor slow adaptation response properties.

There are two clear strengths of the manuscript. First, mutations in Nav1.1 have been shown to be associated with a number of central brain disorders, including those that lead to motor impairments. The notion that a sensory neuron restricted loss of Nav1.1 similarly leads to motor coordination defects indicates that some phenotypes that previously had been suggested to be due to a central role of Nav1.1 could in fact have a peripheral basis. A second strength is that these studies further our understanding of the molecules that regulate excitability in proprioceptors and offer a foundation for further work to tease apart the molecular underpinnings of the physiological response properties of individual proprioceptor subtypes.

While the studies generally support the main conclusion that Nav1.1 in mammalian sensory neurons is required for normal motor behaviors, the depth of some of the analyses leaves a bit more to be desired. For example, it seems that a little more could have been done to strengthen the in vitro analyses of Nav1.1 in proprioceptors with additional controls, and by expanding this analysis to genetically identified Nav1.1 mutant (heterozygous or homozygous) proprioceptors. In addition, it feels a bit of a missed opportunity that there is no further exploration of the relationship of Nav1.1 function in the context of specific proprioceptor subtypes (even if only through discussion). In addition, the observation that a loss in Nav1.1 may cause disruptions in sensory-motor connectivity could benefit from additional analyses to support these findings.

Reviewer #2 (Public Review):

This study discovers significant metabolic differences between naturally occurring murine blastocysts and those created by in-vitro fertilization. Although both groups of embryos appear similar morphologically, the IVF embryos experience increased oxidative damage, decreased mitochondrial activity, and increased glycolytic function, all indicative of increased Warburg metabolism, a mechanism commonly used by cancer cells to generate increased ATP.

These authors show that IVF-generated embryos show alterations in intracellular/extracellular pH, NAD, lactate, and pyruvate levels and this is accompanied by alterations in the expression of enzymes involved in lactate metabolism. All these aberrations are seen in the presence of both 5% and 20% oxygen, confirming that these embryos prefer glucose fermentation even when enough oxygen is present to undergo respiration. The authors note that this is possibly due to the impaired mitochondrial function that has been reported in IVF-obtained oocytes prior to fertilization. Finally, they report that metabolic tissues of IVF-generated adult mice show down-regulation of LDH-B and MCT1 protein, the same enzyme, and transporter found to be down-regulated in early embryos.

Strengths: It is imperative to note the difficulty of these experiments due to the small number of cells per embryo, 70-80 in FB and 40-50 in the IVF embryos. Most assays and technologies for these types of measurements are not designed for these few cells and the authors have been able to adapt novel modern technologies, typically used for experiments with larger cell numbers, for this ultramicroanalysis. The measurements of ROS, glutathione, 8-OHdG, PGF2 alpha, and DNPH need to be measured fluorometrically and have been adapted for this cell system. The use of the Seahorse methodology has not been used successfully for this purpose but was adjusted for this cell system in this study to acquire the measurements of metabolic changes and this is a major strength. For the measurement of lactate and pyruvate, they used 900 blastocysts, since modern methods for the analysis of metabolites in this cell system have yet to be developed. The choice of analytic tools and the application to this unique embryo study to address a clinical phenomenon is a distinctive strength of this study. Another strength of this study is the finding of the same decrease in LDH-B and MCT1 protein (seen in blastocysts) in adult tissues of IVF-derived mice, suggesting a persistent effect manifested by a developmental alteration.

Weaknesses: The performance of this work in a mouse model is a limitation, however, due to the lack of adequate cell lines or access to human embryos, mouse embryos recapitulate the timing of the early developmental period in humans and are experimentally preferred due to the large number of embryos obtained with superovulation.

In summary, the authors have achieved their aims and the results support their conclusions. These findings strongly suggest that impaired mitochondrial function in the oocyte may be the cause of the Warburg effect in these in-vitro-derived embryos. This is a highly significant finding, implying that potential changes to ovulation stimulation methodologies that would improve oocyte metabolism may have the potential to prevent these metabolic differences in the offspring.

Reviewer #2 (Public Review):

The medically important mosquito genus Anopheles contains many species that are difficult or impossible to distinguish morphologically, even for trained entomologists. Building on prior work on amplicon sequencing, Boddé et al. present a novel set of tools for in silico identification of anopheline mosquitoes. Briefly, they decompose haplotypes generated with amplicon sequencing into kmers to facilitate the process of finding similar sequences; then, using the closest sequence or sequences ("nearest neighbors") to a target, they predict taxonomic identity by the frequency of the neighbor sequences in all groups present in a reference database. In the An. gambiae species complex, which is well-known for its historical and ongoing introgression between closely-related species, this approach cannot distinguish species. Therefore, they also apply a deep learning method, variational autoencoders, to predict species identity. The nearest neighbor method achieves high accuracy for species outside the gambiae complex, and the variational autoencoder method achieves high accuracy for species within the complex.

The main strength of this method (along with the associated methods in the paper on which this work builds) is its ability to speed up the identification of anopheline mosquitoes, therefore facilitating larger sample sizes for a wide breadth of questions in vector biology and beyond. This technique has the added advantage over many existing molecular identification protocols of being non-destructive. This high-throughput identification protocol that relies on a relatively straightforward amplicon sequencing procedure may be especially useful for the understudied species outside the well-resourced gambiae complex.

An additional and intriguing strength of this method is that, when a species label cannot be predicted, some basic taxonomic predictions may still be made in some cases. Indeed, even in the case of known species, the authors find possible cryptic variation within An. hyrcanus and An. nili, demonstrating how useful this new tool can be.

The main weakness of this method is that, as the authors note, accuracy is dependent on the quality and breadth of the reference database (which in turn relies on the expertise of entomologists). A substantial portion of the current reference database, NNv1, comes from one species complex, An. gambiae. This is reasonable given the complex's medical importance and long history of study; however, for that same reason, robust molecular and computational tools for identifying species in this complex already exist. The deep learning portion of this manuscript is a valuable development that can eventually be applied to other species complexes, but building up a sufficient database of specimens is non-trivial. For that reason, the nearest neighbor method may be the more immediately impactful portion of this paper; however, its usefulness will depend on good sampling and coverage outside the gambiae complex.

Another potential caveat of this method is its portability. It is not clear from either the manuscript or the code repository how easy it would be for other researchers to use this method, and whether they would need to regenerate the reference database themselves. The authors clearly have expansive and immediate plans for this workflow; however, as many researchers will read this manuscript with an eye towards using these methods themselves, clarifying this point would be valuable.

The authors present data suggesting that their method is highly accurate in most of the species or groups tested. While the usefulness of this method will depend on the reference database, two points ameliorate this potential concern: it is already accurate on a wide breadth of species, including the understudied ones outside the An. gambiae complex; additionally, even when a specific species identification cannot be made, the specimen may be able to be placed in a higher taxonomic group.

Overall, these new methods offer an additional avenue for identifying anopheline species; given their high-throughput nature, they will be most useful to researchers doing bulk collections or surveillance, especially where multiple morphologically similar species are common. These methods have the potential to speed up vector surveillance and the generation of many new insights into anopheline biology, genetics, and phylogeny.

Reviewer #2 (Public Review):

The manuscript by Kanaoka et al addresses their former discovery (Watanabe et al., 2017) on how Drosophila larvae feeding on a low yeast diet without sufficient nutrition would promote dendritic hyperarborization of the somatosensory neurons. They first showed that metal ions, vitamins, and cholesterol are the major components missing in the low yeast diet and could suppress hyperarborization when they are supplemented in the low yeast diet. Through genetic assays, they showed that Akt/TOR signaling is involved. Since the TOR pathway has been described previously by (Poe et al, 2020), they took a different direction to screen involved upstream receptor tyrosine kinases and showed that Ror is required in neurons for hyperarborization. Since Ror and the co-receptor Fz which was also shown to be involved in receiving Wg signals, they found that Wg secreted from nearby muscle cells is required in the process. Significantly, Wg was unregulated at the transcriptional level in muscle cells of larvae feeding on a low yeast diet. They further suggest that Wg expression is derepressed by the down-regulation of the Jak/STAT signaling activity in muscle cells, and the Jak/STAT signaling activity depends on the cytokine Upd2 secreted from the fat body that responds to the nutritional status. Finally, from electrophysiological and behavior studies, they suggest that larvae feeding on a low yeast diet seem less responsive to strong blue light, allowing larvae to forage even under noxious stimulations.

The major strength of the study is the identification of the muscle-derived Wg signals to promote dendrite hyperarborization. The induction of Wg expression by a low yeast diet and the requirement of Wg to induce hyperarborization are key results to strongly support the conclusion. Also, the analyses of the Wg co-receptor Ror/Fz that are required in neurons for the same process and the elevation of pAkt further strengthen the involvement of Wg signaling. In a previous study by Poe et al, eLife (2020), they showed the involvement of the FoxO/TOR pathway in inducing autophagy in the dendrite-innervated epidermal cells. Thus, multiple organs/tissues seem to respond to a low yeast diet and together promote dendrite growth.

One weakness in the study is the inconsistency in the genetic analysis of the involvement of the Dome/Jak/Stat pathway in Wg regulation. While the idea is to link this pathway to the fat body secreted ligand Upd2 which is known to be one primary target regulated by nutrition/food intake.

The authors have developed an AI-assisted D-term for semi-automatic analysis of dendritic parameters, and the quantification of the dendritic field sizes vs branch terminal numbers in a two-dimensional axis is useful to the community.

Reviewer #2 (Public Review):

This is a nice study that pulls together a new reference genome and several levels of new popgen and RNAseq data and new analyses to provide interesting new insight on some of the evolutionary forces affecting the evolution of the ~Zal2/Zal2m system which underpins the stripe-colour polymorphism in the white-throated sparrow. The data are well balanced between homozygous Zal2/Zal2 and heterozygous Zal2m/Zal2m birds, and at a technical level, the authors do a good job of accounting for difficulties in disentangling the Zal2 and Zal2m chromosomes in heterozygous birds.

The authors convincingly show that Zal2m has signs of degeneration, similarly to what has been shown in the fire ant Sb supergene and young Y chromosomes. They show this using multiple approaches (increase in repetitive elements, reduced genetic diversity, increased non-synonymous substitutions...). But they also show that part of Zal2m (which is rare in homozygous form) has something interesting going on, with higher local diversity and evidence of balancing selection. Analysis of allelic-biased expression shows signatures of degeneration, but also that allelic bias is associated with expression differences between morphs.

The paper is generally well written and includes much novel insight on a timely topic and system.

Weaknesses in this study might come from:<br /> - Not fully considering differences in the effects of repetitive elements on apparent genotypes (e.g, segmental duplications or jumping of repetitive elements which may have occurred in Zal2m and which lead reads to appear to be somewhere they are not).<br /> - Difficulties in accounting for variation in recombination rates along the genome, where low recombination can lead to patterns that look like selective sweeps.<br /> - Some ambiguities in interpreting allelic biases as adaptive where many of them can simply be collateral effects of the supergene architecture.

Many of the patterns seen and interpretations offered are similar to what is known from young sex chromosomes, such as in Drosophila, but also the anther rust mating type loci and the fire ant Sb social supergene. The haploid systems of another rust fungus and fire ant males are able to examine such patterns in more depth than what is readily accessible here.

Reviewer #2 (Public Review):

In this study, the authors examined the associations between brain morphology and ADHD symptoms and how the adjustments for confounders change these associations. While the socioeconomic and maternal behavioral confounders were overlooked in most of prior ADHD neuroimaging studies, the authors show that controlling for these confounders in addition to the demographic variables generally attenuated the associations. The authors proposed using and building Directed Acyclic Graphs (DAGs) to identify confounders, colliders, and mediators, which helps present the research questions clearly. Notably, the authors also examined the potential role of IQ in the brain morphology-ADHD associations and concluded that IQ may be a confounder, mediator, or collider, thus the adjustments for IQ are often unnecessary.

Importantly, the authors used two independent large datasets to replicate the findings which strengthen the confidence in the results (although the significant clusters are slightly different between the two datasets). Large cohort studies could have a large number of confounds, including scanner acquisition protocol processing parameters, head motion confounds, and so on. Although this study only focused on limited confounders related to ADHD, the suggestions from authors for minimizing confounding bias are useful for future studies.

Reviewer #2 (Public Review):

Abbassi-Daloii et al. investigated the molecular and cellular signatures associated with heterogeneity among human leg muscles using RNA-sequencing and immune-histology phenotypic characterization. They analysed 128 biopsies sampled from 6 leg muscles of 20 young healthy male individuals: two muscles from the hamstrings (semitendinosus (ST) and gracilis (GR)), three from the quadriceps (rectus femoris (RF), vastus lateralis (VL) and vastus medialis (VM)) and one lower leg muscle (gastrocnemius lateralis (GL)). They also analysed the middle and distal parts of the semitendinosus (STM and STD). Using the expression of known markers of specific cell types, they show that muscles cluster into three main groups based on cell type composition: group 1 (G1: GR, STM, and STD), group 2 (G2: RF, VL, and VM) and group 3 (G3: GL). Interestingly, these groups correspond to their anatomical location of origin. Group 1 was enriched in markers of fast-twitch muscle fibres, while Groups 2 and 3 were enriched in markers of endothelial cells and slow-twitch muscle fibres. Muscles clustered similarly after excluding genes related to cell type composition, indicating that a difference in cell-type composition between muscles is not the only factor accounting for their different molecular signatures. They further show that genes related to oxidative phosphorylation and mitochondria-associated metabolic processes are enriched in groups 2 and 3, consistent with their higher expression of markers of slow-twitch fibres and associated oxidative metabolism.

In addition, the authors developed a high-throughput quantitative immunohistochemistry procedure to measure the relative expression of specific myosin heavy chain isoforms on muscle cryosections from the same biopsies. They suggest that muscle fibres can be separated into three main clusters according to the expression of MyHC1 (slow-twitch fibres), MyHC2A (fast-twitch fibres type 2A) and MyHC2X (fast-twitch fibres type 2X). They show that myofibres from groups 2 and 3 are enriched in slow-twitch fibres compared to group 1, in agreement with the gene expression data, and that muscles from groups 2 and 3 can be discriminated according to their relative composition of type 2A fibres.

Gene expression and immunohistochemistry data showed that the GL muscle has a higher blood vessel density compared to other muscles and that HOX genes have a differential expression pattern among the analysed muscles.

These data provide an impressive dataset of human muscle gene expression from young healthy individuals and will serve as a reference for future investigation of muscle pathology or ageing. The conclusions of this paper are mostly well supported by the data, but some aspects of the analyses by immunohistochemistry need to be clarified.

1) The method to cluster fibres according to their relative expression of myosin isoforms needs to be further explained (Figure 3). Fibres were clustered into three main categories according to the fluorescence intensity of MyHC1, MyHC2A and MyHC2X after immunohistochemistry. Figure 3B shows a continuum of the three intensities rather than clearly separated clusters and the authors should explain how fluorescence intensity thresholds were used to define such clusters. In addition, the differences in average Mean Fluorescence Intensity (MFI) values between clusters appear quite low: Max(MyHC1)/Min(MyHC1)=0.725/0.653=1.11;<br /> Max(MyH2A)/Min(MyH2A)=0.806/0.535=1.50;<br /> Max(MyH2X)/Min(MyH2X)=0.718/0.651=1.10.<br /> These differences are low, notably compared to the gene expression data where these myosin isoforms have expression levels spread over 2 logs (Figures 3D-F). The authors should show specific examples of quantification, show specific quantification of fluorescence over MFI of background, and add statistics of expression differences based on MFI for each MyHC between clusters.

Figures 3D-F show correlations between the % of fibres in a given fluorescence cluster as a function of the expression by RNAseq of the corresponding MyHC defining this cluster. To strengthen these correlations, the authors should show the % of fibres in a given fluorescence cluster as a function of the expression of all three MyHC isoforms (e.g. % of fibres in cluster 1 = f(expression MYH7) or f(expression MYH1), same for clusters 2 and 3).

2) The authors assess blood vessel density in GL by immunohistochemistry (Figure 4). Figure 4A does not mention the muscle corresponding to the cryosection presented, and presenting STM and GL sections side-by-side would help to understand the conclusions of this figure. Displaying the green and red arrows as well on the CD31 and ENG panels and showing higher magnifications would help to understand which regions were defined or not as capillaries.

3) The authors validate HOX expression patterns by RNA-scope (Figure 8). Figure 8A does not mention the muscle corresponding to the cryosection presented. Maybe it would help show STM and GL sections stained for HOXA10 and HOXC10 side-by-side. Also, although the authors mention that signal specificity of RNA-scope probes was verified using negative controls, it would be helpful to show these controls and validate these probes on muscle sections known not to express HOX genes (head-derived?).

Finally, gene expression datasets from human muscle samples have already been generated. As discussed by the authors, these studies were limited in terms of the number of samples, large variation of donor ages, sample conservation before processing, etc. Nevertheless, it would be helpful to put the main findings of this paper (cell type composition, blood vessel density, fibre types ratios, HOX genes expression, mitochondrial processes, etc) into context and assess, if possible and if the data is available, whether similar findings can be concluded from previous datasets.

Reviewer #2 (Public Review):

The fungus B. bassiana is one of few fungal species resistant to cyclosporine A and tacrolimus, naturally occurring microbial compounds with antifungal and immunosuppressive properties. The authors studied the mechanism of this resistance and found a novel vesicle-mediated transport pathway that directs the compounds to vacuoles for degradation. This hitherto unknown mode of detoxification is initiated by the activity of a phospholipid flippase of the P4-ATPase type. Interestingly, transgenically expressing the fungal flippase in plant model systems induces a similar detoxification pathway and makes the plants resistant to certain fungal toxins of secondary metabolism.

Strengths:

The genetic screening, isolation of cyclosporine A (CsA) resistant mutants, and characterization of the causative gene BbCrpa are very solid with two independent alleles, a synthetic knockout strain, and rescue of the mutant phenotype.

BbCrpa protein function in detoxification is demonstrated convincingly by expression in another CsA-sensitive strain, as is its reliance on sites conveying ATPase activity for proper function. It is also functionally different from a relatively closely related P4-ATPase from yeast.

Using fluorescently labeled CsA and tacrolimus (FK506), it is nicely demonstrated how the compounds are going through the anterograde pathway all the way to the vacuole.

The authors demonstrate that vacuolar targeting is key for the detoxifying function of BbCrpa and identify the targeting motif that contains a ubiquitination site.

A trans-species approach (actually, trans-kingdom) confers that BbCrpa can also enhance vacuolar targeting of small toxic compounds to vacuoles in plants, which is quite astounding, given that plant endomembrane transport has quite a number of differences from that of fungi.

Weaknesses:

It is not clear at which temporal scale CsA is going through the different endosomal compartments.

Can it be ruled out that the fluorescently labeled CsA and the GFP-tagged BbCrpa are stripped off their label and we are seeing the free label only?

Reviewer #2 (Public Review):

The authors' introduction is clearly written and sets the stage for the rest of the paper. They state what is unclear currently in the field: the mechanism for the transfer of the cytoplasmic regulation of bactofilins to the periplasmic space; and this is exactly what they set out to elucidate. This work is powered by the creation of truncated mutants of the bactofilin-like protein CcmA. From there, the authors showed that CcmA has a central role in integrating Csd1-Csd7 and Csd5-MurF sub-complexes. By disentangling the CmmA anatomy, the authors showed that CcmA lacks the first 17 amino acids to sequester Csd7, leaving Csd1 unprotected and likely exposed to proteolysis. However further studies will be necessary to connect other pathways to this observation, this is (to my knowledge) the first instance of a mechanism combining localization and proteolytic instability to balance synthesis and hydrolysis of the cell wall to achieve specific shapes.

Strengths: This work is a masterclass on how to perform solid science with controls. The authors' narrative is smooth, making it easy for non-expert readers to understand how they unveil each segment of the story. It also makes it look as if their assays are easy to perform provided the number of parallel scenarios tested - disguising how challenging it is to master the cell biology of this tiny, skinny bacterium. Another highlight of this study is how almost every claim is supported by the data.

Weaknesses: There is a disconnection between the CcmA polymerization evidence, the pulldowns showing its interactions with other factors, and the microscopy data displaying CcmA patterns under different genetic backgrounds. The fact that CcmA is capable of polymerization with the bactofilin domain alone is important and supported by the microscopy data, but the polymer width measurements shown in the TEM images look completely detached from the story. The authors should try to discuss if these different polymer architectures have any role in the localization and/or interactions with other proteins.

Reviewer #2 (Public Review):

In regions that implement an elimination strategy prolonged periods of no local transmission mean that there is no data available to estimate Reff using the currently available methods. Transmission rates from travellers to community members, and between community members, are different when border restrictions occur, as is frequently the case when implementing an elimination strategy. When cases are low and importation risk is high, a reasonable estimation method must acknowledge this transmission heterogeneity, for example, as shown in equations 5-8 and 10-11 of this paper.

The calculation of transmission potential adds significant data requirements (summarized in Figure 1), such that some regions where the methodology would be valuable may lack the data to estimate the macro- and micro-distancing parameters. In the paper, such parameters are estimated from weekly surveys performed by market research groups and the University of Melbourne. In contrast, using existing methods in regions where local spread does occur, Reff can be calculated and generate reasonable insight with relatively little data. Due to the additional data requirements, the calculation of transmission potential is less accessible than some current approaches to calculate Reff in regions with local spread.

The authors describe "macro-distancing": the rate of non-household contacts; and "micro-distancing": the transmission probability per non-household contact. This terminology "micro-distancing" gives the false impression that transmission probability depends solely on distance. In the paper, transmission probability is estimated from survey responses to the question 'are you staying 1.5m away from people who are not members of your household?'. This data is limited to estimate the transmission probability and overlooks the impact of mask use, improved ventilation, and meeting outdoors (all non-distance-based approaches). The paper mentions that self-reported hand hygiene could be used to estimate micro-distancing. COVID-19 spreads through airborne transmission, but the paper gives no mention of ventilation or mask-wearing.

Some of the writing lacks precision around the descriptions of Reff. Notably, Reff is not a rate because it does not have units 'per time'. There is a lack of clarity that Reff is infections generated over an individual's entire infectious period. Other metrics of outbreak growth are rates, for example, an exponential growth rate parameter. This lack of clarity in the writing does not impact the methodology.

In the paper, model parameters are estimated from multiple independent data sources using carefully derived inference models that include complex considerations such as right-censoring of reported cases. While data availability may be a limitation to calculating the transmission potential, the modelling and statistics in the paper are rigorous, and calculation of the transmission potential fills a gap by allowing regions that implement elimination strategies to estimate a quantity similar to Reff.

Reviewer #2 (Public Review):

The manuscript presents a study focused on the Flotillin-2 (Flot2) membrane-associated scaffolding protein and its effects on Wnt delivery through signaling filopodia called cytonemes. Strengths of the manuscript include the importance of the problem being investigated, the combination of in vitro cell biological analyses with in vivo Wnt phenotypic analyses, and the identification of Flot2 as a conserved regulatory partner contributing to Wnt cytoneme activity. Mechanistic studies executed in control and gastric cancer cell lines suggest Flot2, which is overexpressed in some gastric cancers, may promote cancer cell cytoneme signaling. In vivo experiments in which Wnt phenotypes are evident in zebrafish with altered Flot2 activity are convincing and support a conserved role for Flot2 in Wnt signaling activity.

Key conclusions are generally supported by the data provided but many individual results are overinterpreted or overstated, making the proposed mechanistic model premature. This should be corrected by the inclusion of additional controls and experimental parameters. More information is needed to clarify how results were quantified throughout the study because oftentimes, results read as though all filopodial extensions, not exclusively cytonemes, were surveyed. Some of the cellular extensions visible in cell images provided look like retraction fibers. If they are included in quantification, results may be skewed. If all extensions shown are being counted as cytonemes, supporting evidence that they are behaving as signaling filopodia needs to be provided.

The case for Flot2 being a modulator of Wnt cytonemes is made, but its characterization as a specific regulator of Wnt cytonemes is over-stated given the data provided. To make the case for Wnt specificity, the authors need to show that Flot2 modulation does not impact signaling filopodia housing other signaling molecules.

For some experiments, cell viability appears to be a complicating factor. The cells in which IRSp53 function is targeted look very unhealthy, so it is not clear reliable results can be obtained using the experimental parameters described.

Reviewer #2 (Public Review):

Gradual increases in neural activity have been observed prior to execution of movements in several species, including in humans. Yet, the nature of this ramping activity and whether it signals the intent to act is debated. To bring novel insights into this debate, the authors examined the cortical activity that preceded self-initiated actions in mice and evaluated the predictive relationship between pre-movement activity in different regions of the dorsal cortex and the lever-pull behavior of mice.

Strengths: The manuscript addresses a timely and controversial topic in the field of neuroscience. The authors provide a novel and relatively rich dataset in mice with the goal of tackling a question that remains challenging to answer with current techniques used in human subjects. The data consists of longitudinal widefield imaging across the entire dorsal cortex of head-fixed mice performing a behavioral task paired with video monitoring of body movements. This dataset could be quite versatile and potentially useful to the community if shared publicly as the authors intend to. The manuscript is well written and easy to read, and the figures are quite self-explanatory.

Weaknesses: While I applaud the use of a "simplified" task in rodents to disambiguate controversial questions traditionally addressed in human studies, I found that the behavioral data were under-analyzed and thus not strongly supporting the central claim of the manuscript. Below are my main comments:

1. One of the goals of the authors was to study the neural mechanisms underlying "voluntary" movements. While they acknowledge (in the discussion) that they do not have evidence that actions are "intentional", they make the assumption that mice do "form the intent to act near the lever pull time". To back up this assumption, the authors should at least present some evidence that the action of interest (i.e., the rewarded lever-pull) is not just a random jerky movement that happens to be rewarded once in a while. In fact, mice seemed to pull the lever very frequently and impulsively (the majority of inter-pull intervals were way below 3 s in Supplementary Fig. 1.2) even for the last sessions of the training. Therefore, it is not readily apparent that mice apply any control to their lever-pull actions. Providing evidence that the action is goal-directed is important if the goal of the paper is to study neural signatures of the intention to act. A somewhat compelling analysis could be to compare rewarded lever-pulls with "spontaneous" movements, provided that these two types of movement can be convincingly characterized as goal-directed vs. incidental. In contrast, throughout the manuscript, the neural activity aligned to rewarded lever-pull events (which are assumed to be "voluntary" actions) is compared to the neural activity aligned to random times during the task (whether or not it involved movements), which may not be the most convincing control.

2. The learning trajectory of mice is also not well characterized (e.g. changes in inter-pull intervals are not quantified, nor the relative increase in rewarded actions across training sessions, etc.). Yet, several claims in the paper are directly based on the fact that mice have learned to pull the lever after 3 s interval to receive water rewards (which relates to point 1). In particular, one important assumption in the paper is that as mice learn, the lever-pull movements become more stereotyped, but this has not been shown explicitly. It would be helpful, for example, to see how analog traces of lever-pulling change throughout the learning stages and how the variance of the movement across trials decreases in late sessions.

3. The central claim of the paper is that rewarded lever-pulls can be predicted from pre-movement neural activity several seconds (even up to 10 s) prior to the action. However, obvious motor confounds and other alternative explanations have not been convincingly ruled out. In fact, the action of lever pulling may require a series of complex movements (like changing posture, extending the forelimb, reaching the lever, grabbing the lever, etc.). The authors themselves mentioned that they found strong correlations between lever pulls and body movements in all mice, but the data is not used nor shown in the paper. The motor commands preceding but related to lever-pull could unfold at least a few hundreds of milliseconds prior to the detection of lever-pull in the task, and thus be reflected in the neural activity that is predictive of the lever pull. Moreover, if this series of movements is highly stereotyped, and in turn leads to stereotyped neural activity (like the slow oscillations observed before the lever-pulls), it could explain why the detection of lever-pulling actions always occurs at a given phase of the neural oscillation. Such observations that stereotyped movements occur way before the lever-pull detection could partially rule out the fully "cognitive" explanation proposed in the paper, but would concur with recent findings that showed that ramping neural activity can be, for the most part, explained by movement-related activity (Musall et al., 2019).

4. Toward the end of the result section (Fig. 6), the authors briefly begin to address the issue about whether pre-movement activity can really be considered movement free. Here, "lockouts", i.e. periods where other movements (like licking, or previous lever-pulls) did not occur, were introduced in the analysis. The lockouts altered the earliest-decoding-time (EDT) of the lever-pull (in some mice EDT was even divided by half: from -4 s to -2 s). However, the effects of "micro-movements" like facial movements or changes in body posture may not be taken into account with the lockout approach. Such micro-movements have been shown to explain a large variance of the neural activity (see Stringer et al. 2019 and Musall et al. 2019). Therefore, to fully control for movement confounds, the effect of high dimensional/micro-movements extracted from video recordings should be removed from the neural activity. These analyses could yield a much shorter EDT (e.g., -0.15 s), more consistent with previous reports.

Reviewer #2 (Public Review):

This group previously discovered that the cadherin/b-cat/a-cat "ternary " complex binds F-actin more strongly when the system is subjected to mechanical force using a single molecule/optical-trap-based approach (Buckley, 2014). A subsequent study rationalized the nature of a-cat's catch-bond behavior by modeling the a-cat actin-binding domain (ABD) alone (using CryoEM and a-cat F-actin sedimentation studies) to show that the first 1.5 helices of a-cat's 5-helical ABD (H0-H1) restricts high affinity F-actin binding (Xu et al., 2020). Whether this model held for a full length a-cat within a ternary complex, and whether removal of H0-H1 abrogated force-dependent F-actin binding using this group's established optical-trap/force measurements remained untested.

MAJOR FINDING:

1. The current study shows that removal of the N-terminal-most portion of a-cat's ABD (e.g., H0/H1; aa666-696) produces a cadherin-catenin complex that deviates from the normal, two-state binding seen for WT a-cat. Instead, the ternary ΔH0-H1 a-cat shows highest binding at the lowest forces and slips with increasing force. Using similar low-force binding conditions, the ternary ΔH0-H1 a-cat showed lifetime binding 39x longer than WT. Ternary ΔH0-H1 and WT show no difference in binding to F-actin subjected to forces above 6pN. Thus, the Ternary ΔH0-H1 behaves as single-state slip bond (compared to WT showing a two-state binding). This finding affirms a model put forth by this same group (Xu et al., 2020), suggesting that undocking/removal of H1 converts a 5-helical bundle ABD associated with weak-actin binding to a 4-helical ABD with stronger binding/longer lifetime. STRENGTH: This finding is important because a previous study assigned force-sensitive actin-binding to the H0-region of the 5-Helical ABD, but it is clear that the H1 region is most critical to this force-gating mechanism. This finding better rationalizes how other ABD that lack H0 show force-dependent binding to F-actin (e.g., vinculin, talin).

OTHER NOTABLE FINDINGS:

2. Evidence that aCat ABD alone binds F-actin 4x-longer than a full length aCat within the ternary complex suggests presence of cadherin/b-cat or aCat N and M-domains can negatively impact ABD. This fits with other studies from this group (Drees et al., 2005; Terekhova et al., 2019).<br /> 3. Evidence addition of purified ABD to Ternary complex/optical trap assay increased binding life-time by 4x is interesting and consistent with previous work. Given so much attention given to the CTE in the final model (Fig. 6), I am (a bit) surprised the investigators did not use their CTE-deletion mutants from Xu et al., 2020, to validate the molecular nature of this cooperativity (or contribution to the force-dependent binding). But answering this question is not necessary to interpret other aspects of the manuscript.<br /> 4. Evidence that H0/H1 imposes directionality to a-cat/F-actin catch-bond mechanism is intriguing: WT a-cat favors binding to actin filaments pulled towards the minus (non-growing) rather than + (growing) end, whereas a-cat lacking H0/H1 fails to show this bias. However, given most actin structures that interface with junction structures are of mixed polarity, I struggle to understand the broader meaning of this finding. While not the job of this team to give up their working hypotheses and means of testing the broader significance of this finding, some clarity here would help.

Reviewer #2 (Public Review):

This paper addresses the question of how changes in the copy number of genes affect changes in the levels of the corresponding mRNAs and proteins. To this end, the authors investigate a number of published and newly generated datasets from both cancer and normal tissues. They observe that buffering of gene copy number changes mostly occurs at the protein level but can for some genes also be seen at the mRNA. Interestingly, buffering at both levels is inversely correlated so that it either occurs at the protein or at the mRNA level but not at both levels. Also, the type of buffering tends to be similar for genes involved in the same cellular pathway.

This paper addresses an important question in a thorough and thoughtful way. Its strength is that it integrates results from a broad range of datasets (including newly generated data) to arrive at consistent conclusions. While similar analyses have also been reported by others (like Conclaves et al., 2017), this manuscript extends these analyses and provides a more detailed picture. The data analysis presented is sound, and the fact that observations can be replicated in different independent datasets highlights the general relevance of the findings presented. The finding that mRNA and protein level buffering tend to be inversely correlated and are similar for functionally related genes is interesting. Also, the observation that RNA- and protein-level compensation depends on the tumor type is interesting, even though no explanation for this finding is presented. Overall, the conclusions are supported by the presented data.

Reviewer #2 (Public Review):

The authors employ a biochemical assay in which ubiquitylated MCM7 is dissociated by p97-Ufd1-Npl4 from the helicase complex immobilized on beads. They optimized the assay so that they can generate either shorter or very long chains. They convincingly demonstrate that mobilization by human p97-Ufd1-Npl4 in the presence of short chains is inefficient and can be stimulated by either FAF1, FAF2, or UBXN7. They map the required sequence in these proteins to the p97-binding UBX domain and an adjacent coiled-coil region. Surprisingly, established ubiquitin binding domains are not required for stimulatory activity. They reiterate these experiments with C. elegans proteins. Moreover, they validate significance of the accessory factors for the mobilization of the helicase component SLD5 in mouse ES cells. The latter was analyzed in mitosis, reflecting a backup pathway for S-phase events, which allows convenient time-course analysis.

The findings are important for the understanding of replication termination. They are likely also more generally significant for other p97/Cdc48 mediated unfolding and segregation processes. While involvement of accessory factors FAF1, FAF2 or UBXN7 has been demonstrated in various processes before, and the recruitment function been anticipated, this manuscript directly demonstrates this function and links it to ubiquitin chain length. Therefore, it is an important step forward. The positive assessment is somewhat dampened by the fact that the authors do not provide kinetic data and, more significantly, that they fail to define the mechanistic function of the common coiled-coil region that they show is critical in FAF1 and FAF2.

Reviewer #2 (Public Review):

The study by Jacques and colleagues examines two types of signals obtained from human intracortical electroencephalography (iEEG) measures, the steady-state visual evoked potential and a broadband response extending to higher frequencies (>100 Hz). The study is much larger than typical for iEEG, with 121 subjects and ~8,000 recording sites. The main purpose of the study is to compare the two signals in terms of spatial specificity and stimulus tuning (here, to images of faces vs other kinds of images).

The experiments consisted of subjects viewing images presented 6 times per second, with every 5th image depicting a face. Thus the stimulus frequency is 6 Hz and the face image frequency is 1.2 Hz. The main measures of interest are the responses at 1.2 Hz and harmonics, which indicate face selectivity (a different response to the face images than the other images). To compare the two types of signals (evoked potential and broadband), the authors measure either the voltage fluctuations at 1.2 Hz and harmonics (steady-state visually evoked potential) or the fluctuations of broadband power at these same frequencies.

Much prior work has led to the interpretation of the broadband signal as the best iEEG correlate of spatially local neuronal activity, with some studies even linking the high-frequency broadband signal to the local firing rate of neurons near the electrode. In contrast, the evoked potential is often thought to arise from synchronous neural activity spread over a relatively large spatial extent. As such, the broadband signal, particularly in higher frequencies (here, 30-160 Hz) is often believed to carry more specific information about brain responses, both in terms of spatial fidelity to the cortical sources (the cortical point spread function) and in terms of functional tuning (e.g., preference for one stimulus class over another). This study challenges these claims, particularly, the first one, and concludes that (1) the point spread functions of the two signals are nearly identical, (2) the cortical locations giving rise to the two signals are nearly identical, and (3) the evoked potential has a considerably higher signal-to-noise ratio.

These conclusions are surprising, particularly the first one (same point spread functions) given the literature which seems to have mostly concluded that the broadband signal is more local. As such, the findings pose a challenge to the field in interpreting the neuronal basis of the various iEEG signals. The study is large and well done, and the analysis and visualizations are generally clear and convincing. The similarity in cortical localization (which brain areas give rise to face-selective signals) and in point-spread functions are especially clear and convincing.

The lack of difference between the two signals (other than SNR), might ordinarily raise suspicion that there is some kind of confound, meaning that the two measures are not independent. Yet there are no obvious confounds: in principle, the broadband measure could reflect the high-frequency portion of the evoked response, rather than a separate, non-phase locked response to the signal. However, this is unlikely, given the rapid fall-off in the SSVEP at amplitudes much lower than the 30 Hz low-frequency end of the broadband measure. And the lack of difference between the two signals should not be confused for a null result: both signals are robust and reliable, and both are largely found in the expected parts of the brain for face selectivity (meaning the authors did not fail to measure the signals - it just turns out that the two measures have highly similar characteristics).

There are some limitations to the possible generalizability of the conclusions drawn here. First, all of the experiments are of the same type (steady-state paradigm). It could be that with a different experimental design (e.g., slower and/or jittered presentation) the results would differ. In particular, the regularity of the stimulation (6 Hz images, 1.2 Hz faces) might cause the cortex to enter a rhythmic and non-typical state, with more correlated responses across signal types. Nonetheless, the steady-state paradigm is widely used in research, and even if the conclusions turn out to hold only for this paradigm, they would be important. (And of course, they might generalize beyond it.) A second limitation is the type of stimulus and neural responses - images of faces, face-selectivity of neural responses. If the differences from previous work on these types of signals are due to the type of experiment - e.g., finger movements and motor cortex, spatial summation and visual cortex - rather than to the difference in sample size of type of analysis, then the conclusions about the similarity of the two types of signals would be more constrained. Again, this is not a flaw in the study, but rather a possible limitation in the generality of the conclusions. Finally, the study relies on depth electrodes, which differs from some prior work on broadband signals using surface electrodes. Depth electrodes (stereotactic EEG) are in quite wide use so this too is not a criticism of the methods. Nonetheless, an important question is the degree to which the conclusions generalize, and surface electrodes, which tend to have higher SNR for broadband measures, might, in principle, show a different pattern than that observed her.

Overall, the large study and elegant approach have led to some provocative conclusions that will likely challenge near-consensus views in the field. It is an important step forward in the quantitate analysis of human neuroscience measurements.

Reviewer #2 (Public Review):

The manuscript by Forni and colleagues explores the fascinating question of cell fate plasticity by investigating the role of transcription factor AP-2epsilon at vomeronasal sensory neuron (VSN) specification. Building up upon their previous publication, this group defines the exact developmental timing at which VSNs adopt apical Vmn1R-specific expression programs or basal Vmn2R-specific programs, using scRNA-seq and elegant genetic manipulations. Having previously shown a role of AP-2epsilon in specifying the Vmn2R branch of VSN differentiation, they ask whether ectopically expressing AP-2epsilon in fully differentiated apical VSNs can transform them into basal OSNs (i.e. if they can force Vmn1R neurons to become Vmn2R neurons). Considering that the branching between the two VSN fates is a bona fide developmental decision orchestrated by classical Notch-Delta fate decisions, these series of experiments are quite intriguing and of general value. To answer this question, the authors use Omp-Cre, a driver that is activated in apical VSNs (and in olfactory neurons) upon complete maturation. Strikingly they report that ectopic expression of AP-2epsilon results in ectopic expression of Vmn2Rs in the apical VSNs and ectopic expression of their signaling components (Gao). However, despite this apparent molecular transformation, there are no apparent behavioral changes in sociosexual behavioral tests. Finally, the authors perform CUT&RUN analysis in the VSNs with ectopic AP-2epsilon expression to decipher direct from indirect effects. In general, I find the experiments well designed, rigorously executed, and producing results of general interest (meaning beyond olfaction). The authors should expand this study with some experiments that will clarify why this molecular transformation does not alter sociosexual behaviors and perform a few control experiments for the CUT&RUN assays. They should also clarify if the Vmn1R genes and signaling components are still expressed in the transformed neurons.

Reviewer #2 (Public Review):

What distinguishes most vertebrate species from invertebrates is the adaptive immune response, the ability of T and B lymphocytes to generate billions of antibody or T cell receptor sequences on the chance that some of them will be specific for a pathogen, even those that have never been seen before in evolution. While modern sequencing technologies have given us the ability to read millions of these sequencing from even a single individual, there has not been a good way to identify those attributable to a given infection en masse. In this paper, Ortega and colleagues develop statistical tools that allow one to extract likely SARS-CoV-2 infection-specific antibody and T cell receptor sequences away from the great bulk of irrelevant sequences and show that these are enriched in some that have been previously identified. These methods can be applied to any infection or vaccine response and thus will be very valuable to the field.

Reviewer #2 (Public Review):

Nunes Santos et al. investigated the gene regulatory activity of the promoter of the quail myosin gene, SMyHC III, that is expressed specifically in the atria of the heart in quails. To do so, they computationally identified a novel 6-bp sequence within the promoter that is putatively bound by a nuclear receptor transcription factor, and hence is a putative regulatory sequence. They tested this sequence for regulatory activity using transgenic assays in zebrafish and mice, and subjected this sequence to mutagenesis to investigate whether gene regulatory effects are abrogated. They define this sequence, together with two additional known 6-bp regulatory sequences, as a novel regulatory sequence (denoted cNRE) necessary and sufficient for driving atrial-specific expression of SMyHC III. This cNRE sequence is shared across several galliform species but appears to be absent in other avian species. The authors find that there is sequence homology between the cNRE and several virus genomes, and they conclude that this regulatory sequence arose in the quail genome by viral integration.

Strengths:<br /> The evolutionary origins of gene regulatory sequences and their impact on directing tissue-specific expression are of great interest to geneticists and evolutionary biologists. The authors of this paper attempt to bring this evolutionary perspective to the developmental biology question of how genes are differentially expressed in different chambers of the heart. The authors test for regulatory activity of the putative regulatory sequence they identified computationally in both zebrafish and mouse transgenic assays. The authors disrupt this sequence using deletions and mutagenesis, and introduce a tandem repeat of the sequence to a reporter gene to determine its consequences on chamber activity. These experiments demonstrate that the identified sequence has regulatory activity.

Weaknesses:<br /> There are several decisions and assumptions that have been made by the authors, the reasons for which have not been articulated. Firstly, the rationale for the approach is not clear. The study is a follow-up to work previously performed by the authors which identified two 6-bp sequences important for controlling atrial-specific expression of the quail SMyHC III gene. This study appears to be motivated by the fact that these two sequences, bound by nuclear receptors, do not fully direct chamber-specific expression, and therefore this study aims to find additional regulatory sequences. It is assumed that any additional regulatory sequences should also be bound by nuclear receptors, and be 6-bp in length, and therefore the authors search for 6-bp sequences bound by nuclear receptors. It is not clear what the input sequence for this analysis was. The methods section mentions the cNRE sequence, but this is their newly defined regulatory sequence based on the newly identified 6-bp sequence. It is therefore unclear why Hexad C was identified to be of interest, and not the GATA binding site for example, and whether other sequences in the promoter might have stronger effects on driving atrial-specific expression. Indeed, the zebrafish transgenic assays use the 32 bp cNRE, while in the mouse transgenic assays, a 72 bp region is used. This choice of sequence length is not justified. The decisions about which bases to mutate in the three hexads are also not clear. Why are the first two bases mutated in Hexad B and C and the whole region mutated in Hexad A? Is there a reason to believe these bases are particularly important? The control mutant also has effects on the chamber distribution of GFP expression.

Two claims in the paper have weak evidence. Firstly, the conclusion that the cNRE is necessary and sufficient for driving preferential expression in the atrium. Deleting the cNRE does reduce the amount of atrial reporter gene expression but there is not a "conversion" from atrial to ventricular expression as mentioned in line 205. Similarly, a fusion of 5 tandem repeats of the cNRE can induce expression of a ventricular gene in the atria (I'm assuming a single copy is insufficient), but does not abolish ventricular expression. Secondly, the authors claim that the cNRE regulatory sequence arose from viral integration into the genomes of galliform species. While this is an attractive mechanism for explaining novel regulatory sequences, the evidence for this is based purely on sequence homology to viral genomes. And this single observation is not robust as the significance of the sequence matches does not appear to be adjusted for sequence matches expected by chance. The "evolutionary pathway" leading to the direction of chamber-specific expression in the heart as highlighted in the abstract has therefore not been demonstrated.

Reviewer #2 (Public Review):

The authors show that HLJ1 converts misfolded IL-12p35 homodimers to monomers, which maintains bioactive IL-12p70 heterodimerization and secretion. In turn, this contributes to increased IL-12 activity, leading to enhanced IFN-gamma production and lethality in mice challenged with LPS to model sepsis.

Strengths:<br /> - Huge and diverse dataset (e.g. in vivo, in vitro, single cell RNAseq, adoptive transfer etc.) with interesting findings that could be of relevance to the field.

Weaknesses:<br /> - The flow/narrative of the paper is very hard to follow. This may result from the fact that the order of presented results is a bit puzzling. Normally, one would add-in the cytokine results (now figure 3), after the survival curves in Figure 1. Furthermore, the flow cytometry data presented in Figure 4 is more or less a validation of the scRNAseq data presented in Figure 2 in another organ. Likewise, Figure 5 is sort of a validation of Figure 3 in another organ. The authors seem to jump from organ to organ, from in vivo to in vitro and vice-versa all the time which makes the paper extremely difficult to follow.<br /> - Use of extremely high dosages of LPS.<br /> - Much of the presented data is replication of previous work. For instance, neutralization of IFNg (e.g. Billiau et al., Eur. J. Immunol. 1987; Car et al. J. Exp. Med. 1994) and anti-IL-12 (e.g. Zisman et al., Shock 1997) has been shown to lower mortality in LPS models in mice.<br /> - No true sepsis model is used, only LPS. This is important, as for instance neutralization of IFNg and IL-12 has been shown to improve outcome in endotoxemia before (see above), but had no effect on survival in more relevant sepsis models such as cecal ligation and puncture (e.g. see Romero et al., Journal of Leukocyte Biology 2010; Zisman et al., Shock 1997). Furthermore, IFNg is even proposed (and used on a small scale) as therapy in sepsis patients to reverse immunosuppression.

Reviewer #2 (Public Review):

The presented work focusses on a novel Drosophila nephrocytes in-vivo model to investigate nephrin (sns) turnover, which is known to be subjected to endocytosis. The authors present their work in a clear way with well-prepared figures. The added schemes are very helpful to visualize the observed findings.

The current version of the presented data would benefit from more convincing data to prove endocytosis in the pulse-chase experiments.

Once established this fly model could be of great potential in testing substances and proteins influencing nephrin endocytosis.

Reviewer #2 (Public Review):

Tjahjono et al. identify a possible role for Box C/D snoRNP proteins in regulating a transcriptional response to reactive oxygen species the authors have previously described as being mediated by the ESRE network. In contrast to other known canonical mitochondrial stress pathways discussed by the authors-in particular, UPRmt and mitophagy-the possible role of the ESRE network response in mitochondrial homeostasis remains less established, with prior studies also conducted in this area also conducted by the authors. Thus, the current study aims to better understand how transcription of genes with an ESRE motif might be coupled to cellular stress, the authors began with a biochemical strategy to isolate C. elegans proteins that directly bind to the ESRE motif under conditions of stress that activate the ESRE-regulated genes. The authors report 75 candidate interacting proteins were isolated. The authors then shift to functional validation of these candidates using a combination of RNAi knockdown of corresponding candidate genes in C. elegans and a readout of GFP reporter genes corresponding to ESRE-dependent transcription and other stress response pathways. There is no further characterization or validation of the biochemical interactions in an in vitro or cellular context.

The authors identify fib-1 and nol-56, two members of the box C/D snoRNP complex, as genes that when subjected to RNAi, result in diminished expression of an ESRE reporter gene in response to rotenone. nol-58 is then also tested by RNAi and found to similarly cause diminished expression of the reporter gene. The authors then conclude: "Since knockdown of any of the genes for these three proteins reduces ESRE signaling, it seems likely that the snoRNAP complex as a whole is binding to ESRE." The three genes encoding protein components of the box C/D snoRNP complex are known to be essential, thus complicating the interpretation of RNAi knockdown, which the authors note caused reduced growth and development of larval animals and thus alter the stage at which RNAi is initiated, observing similar effects on ESRE reporter gene expression when RNAi is initiated at a later, L3 larval stage. The authors then proceed to perform the same RNAi analysis using the Phsp-6::GFP (UPRmt reporter) and observed reduced GFP expression in response to UPRmt induction. An additional set of experiments examine Ptbb-6::GFP reporter gene expression, which has been recently described as being regulated by a PMK-3-dependent mitochondrial homeostasis pathway. The authors further performed RNAi of ruvb-1, which promotes box C/D snoRNAPs assembly and localization to nucleoli and observe that this also reduced ESRE-dependent GFP reporter gene expression though induction of the UPRmt was not affected. The authors also investigated RNAi of box H/ACA snoRNPs and did not observe effect, leading them to conclude that the "function of box C/D snoRNAPs in regulating mitochondrial homeostasis is specific." The authors also examine the effect of RNAi on genes involved in translation initiation and observed mixed effects and then treated animals with cycloheximide and did not observe changes. The authors then examine how previously characterized immunity reporter gene expression is affected by RNAi of box C/D snoRNP protein-encoding genes, with select genes exhibiting increased expression under these conditions. A single set of experiments outside of GFP reporter gene expression are conducted to evaluate how RNAi of box C/D snoRNP protein-encoding genes affects C. elegans physiology. Susceptibility to killing in liquid, conditions previously found to involve ESRE network gene expression, was compromised by RNAi of box C/D snoRNP protein-encoding genes, whereas susceptibility to Pseudomonas aeruginosa infection appeared to decrease with the same treatment.

The authors have addressed an interesting area of cell biology in the context of the C. elegans system. The data provided are highly preliminary and lead to some interesting hypotheses, but the data are not sufficient to support the conclusions, and in general, there is a lack of rigor and overreliance on assumptions about the experimental methods being employed that require further validation.

Specific conceptual and experimental concerns:

1) The manuscript begins with biochemical characterization of the interaction of FIB-1 and NOL-56 with the ESRE motif. Are we to conclude that this was a non-specific interaction or a physiologically relevant interaction? Is there further validation in vitro or in vivo? Is the conclusion that FIB-1, NOL-56, and NOL-58 function in a snoRNP complex or is this a non-canonical activity? Do these proteins then somehow interact with the ESRE motif?

2) RNAi of GFP reporter transgenes are subject to effects such as transgene silencing and other artifacts and should be corroborated by other methods such as qPCR. Moreover, while reporter genes are useful tools, it would seem that some transcriptome-wide-based comparisons should be also conducted to test and/or confirm hypotheses about whether knockdown of specific genes affects certain pathways. Generally, some sort of additional correlation that a pathway is being activated, e.g. change in localization of ATFS-1 in the UPRmt, greatly enhances the sense that a stress pathway is being activated.

3) A critical issue that is only addressed obliquely by RNAi starting and L1 and L3 larval stages is that the genes studied, fib-1, nol-56, nol-58, are essential and RNAi of these have multiple pleiotropies. This seriously limits the interpretations that can be made of effects on gene expression, as animals are not developing and growing normally with essential gene inactivation having unpredictable effects. More importantly, the functional validation in liquid killing is particularly difficult to interpret as the effects of RNAi knockdown of essential genes may simply add sickness to the toxic effects of liquid killing. In turn, extended survival in the other Pseudomonas assay may reflect survival advantage of growing more slowly in those assays. Thus, what is lacking is a clear physiological assay or functional readout to corroborate the GFP reporter gene effects. One might expect altered physiological responses to mitochondrial toxins, but again, interpretation will be clouded by the essential nature of these genes.

In summary, the manuscript presents some intriguing observations in an important area of biology intersecting at mitochondrial homeostasis and the response to infection and environmental stress, but it is difficult to know what one can conclude without invoking a number of unjustified assumptions from these preliminary observations.

Reviewer #2 (Public Review):

The authors present a numerical model of ant raft shape dynamics. It is an interesting topic, the experimental movies are exciting, and the idea that ant rafts make protrusions like this is new. The goal seems to be to explain how local interactions can lead to the perpetual protrusions of the raft. However, more efforts should be made to present probabillity distributions of the results rather than individual cases that happen to match the experiments.

Reviewer #2 (Public Review):

This paper focusses on how the probability of fixation of a favourable mutation can become more sensitive to its fitness in certain population structures, thus "amplifying" selection. This intriguing phenomenon was identified in [3], and has received some attention, but within a restricted niche in the literature. There has been extensive study of such questions in population genetics, and this paper does cite much of this, thus making a connection between literatures that had been quite separate. In particular, their key method of separating the process into fixation within and across demes goes back to Slatkin [8].

"Amplification" of selection suggests that this phenomenon could be harnessed in practice, and this is suggested in the last sentence of the paper. Indeed, quantitative genetics theory rests ultimately on the fixation of favourable alleles at the selection limit (as pointed out by Robertson, 1960). However, the overall efficiency depends on the rate of improvement, given some constraints (say, on total number of individuals), and depends on rates of fixation, not just probabilities. The results here do not directly show that population structure can increase the net efficiency of selection, and the consensus in the theory for evolution of sex is that a well-mixed population is most effective.

There is a strong connection with Wright's (1931) "shifting balance" theory, which relied on a two-stage process of selection, within and between demes (Rouhani & Barton, 1993, Genet. Res; Coyne et al., 1997, Evolution). This involves the fixation of a favourable combination of alleles in one deme, and then the spread of that combination between demes. This is a more complex process than is studied here, but it shares a reliance on asymmetric migration, and a sensitivity to population structure.

Reviewer #2 (Public Review):

Higa and coworkers performed a detailed structure-function analysis to identify the interaction sites between ORC1, a member of the origin recognition complex ORC1-6, and TRF2, a telomeric repeat binding factor that interacts with ORC. The authors generated a TRF2-EE mutant in which two aspartic acid residues at positions 111 and 112 are substituted by alanine. This mutant retains the ability to execute all telomere-specific functions as a subunit of the Shelterin complex that protects chromosome ends but is deficient for ORC1 binding. Most of the functional assessments of this mutation are carried out in cell lines that express the mutant form of TRF2 from the endogenous locus. This is a definitive strength of the study. Although the mutation doesn't have any effect on genome integrity under unperturbed conditions, cells exhibit signs of telomeric DNA damage when they are treated with the replication inhibitor hydroxyurea. The authors argue that the damage is caused by the lack of telomeric replication complexes and the inability to rescue stalled replication forks. These claims are based on indirect conclusions, as there aren't any experiments that assess replication through telomeric sequences directly. In a second line of investigation, the group overexpressed a peptide of ORC1 that contains the binding site for TRF2 (ORC244-511) and showed convincingly that this strategy sequesters ORC in a way that it no longer binds to telomeres. This is underscored by a loss of MCM2-7 binding in telomeres. The authors claim that overexpression of the mutant did not affect other genomic regions based on a re-replication assay, not based on direct inspection of origin firing. This is a weakness. Another shortcoming is the limited analysis of the telomeric damage that ensues when cells are treated with hydroxyurea. It remains unclear if the damage is persistent and leads to telomere shortening or telomeric translocations. Some telomeres are processed and become part of micronuclei. This is an interesting observation but could be specific to HeLa cells. All of the presented data is very clean and well controlled.

In conclusion, this is a rigorous study of limited new insight and scope that suggests that the interaction between ORC1 and TRF2 is important to suppress telomeric DNA damage under replication stress conditions.

Concerns:

1) It seems as if all of the experiments addressing functional readouts are conducted in HeLa cells although the Materials and Methods describe a variety of different cell lines. It is therefore not clear whether the findings are more generally applicable to other transformed and non-transformed cells. If experiments were performed in multiple cell lines, this needs to be stated more clearly in the figure legends.

2) To the best of my knowledge, most telomerase-positive cell lines have telomeres of around 5-7kb (under 10kb). It has been demonstrated by the Schildkraut and de Lange laboratories that short telomeres rarely activate replication origins. Only in longer, upwards of 25-50kb can replication forks be detected that originate within telomeres. It is not clear to me how long the telomeres are in the cell line(s) used in this study.

Reviewer #2 (Public Review):

Gu et al reported a potential function of non-coding RNA miR-22 in the regulation of agiogenesis via tumor-associated endothelial cells. The authors showed that tumor-related soluble factors possibly inhibit the expression of miR-22 via activation of NF-kB pathway leading to upregulation of SIRT1 and FGFR1 expression in endothelial cells, thus promote the angiogenesis. The work is well designed in general and the data presented support their take-home message on how miR-22 impacts on the viability of endothelial cells. However, the mechanistic study still needs further exploring. In particular, the link between cancer cell and tumor-associated endothelial cells would need to be strengthened. The work has potential interest for the domain of microRNAs and their functional role in endothelial cells. The identified microRNA-regulated pathways in endothelial cells may possibly be therapeutic targets, however, the current study needs to be further improved to support the major conclusion as stated in title of the paper.

Reviewer #2 (Public Review):

This work exploited a previously reported tool (CRISPRi) to repress the expression of 96 essential genes associated with diverse biological functions and in parallel-assessed the strains' fitness. The authors earlier published similar work to show the effect of transcriptionally repressing mmpL8 and ATP synthase on the growth of Mtb. Here, the authors developed a work-flow of how CRISPRi can be multiplexed in a 96 well plate format to assess bacterial growth phenotypes. A gradient of ATc concentration mediated the transcriptional repression, and CFU and OD monitored the effect on growth. By comparing OD and CFU with a no-ATc control, the authors categorized genes into essential, non-essential, and growth-impaired. Similar to earlier observations, this work confirmed that genes essential for cell wall biosynthesis and central metabolism are more vulnerable and showed bactericidal consequences. Also, since Mtb displays plasticity in respiration (Beites et al., Nature communications, 2019), essential genes involved in oxidative phosphorylation were found to be less vulnerable.

Strength

Antibacterial drug development suffers from a paucity of targets whose inhibition can selectively and quickly kill Mtb. To do this, we urgently need simple tools, which are amenable to high throughput formats. Authors describe the ease by which CRISPRi allows studying multiple target genes in parallel.

Weakness

My primary concern is that many extrapolations are made based on the assumption that transcriptional silencing's kinetics will bring about proportional changes at the protein level. For example, classifying phenotypes such as weak bactericidal, strong bactericidal, essential bacteriostatic, and growth impairing are entirely based on growth changes (OD and CFU) as a function of ATc concentration without actually measuring transcript and protein levels. There are several examples of discordance between transcript and protein levels, which affect growth phenotypes. For example, transcriptional repression of BioA reduced protein levels 72 h post-addition of ATc. It did not affect growth, whereas targeting BioA protein using the Bio-SspB/DUC switch depleted protein faster and abolished Mtb's growth (Kim et al., PNAS).

Similarly, studies have shown that growth defects do not correlate with the degree of protein depletion (Wei et al., PNAS). Only 20% depletion of RpoB arrest growth, whereas near 100% depletion of DHFR (folate metabolism) and Alr only exert a modest effect on growth. This could be due to high intracellular concentrations of DHFR and Alr. Agreeing to this, the authors also found that transcriptional repression of folate pathway (folA, folP1) and alr does not affect the viability of Mtb. Notwithstanding these observations, folate pathway and Alr are attractive drug targets.

Several discrepancies with previous studies are quite evident in this paper. Previously defined essential genes display no growth phenotype upon transcriptional repression in this study (e.g., def, rho, birA, fum, etc). Not all of these can be explained by a low PAM score or metabolic buffering.

Overall, the authors' approach is unlikely to be useful in cases where drug targets are natively expressed at a much higher level and maintain stability over a long time. Also, transcriptional approaches are inadequate to identify vulnerable targets. They suffer from leakiness, slow/partial depletion of the gene product, post-translational modifications of the target, mutations that obstruct regulation, or an amalgamation of these problems.

Minor issues

In the work-flow figure (Fig 1), why the growth of -ve control is represented lower than the growth of non- essential gene?

Instead of no-ATC control, a scrambled sgRNA control is better.

Line 55, page 4: the word "only" is appearing twice.

Reviewer #2 (Public Review):

This manuscript suggests crosstalk between beta and delta cells on the basis of the observation of doublets between beta and delta cells upon the incomplete dissociation of pancreatic islets. This confirms that beta and delta cells are neigboring cells, which histological analysis of pancreas morphology established a long time ago. It is further suggested that crosstalk plays a role in beta cell regeneration following partial pancreatectomy. However, the nature of this crosstalk - a dynamic exchange in information in time - is not addressed and is entirely based on approaches that provide a single snapshot. Then there is a suggestion that some delta cells expand during partial pancreatectomy and are descendants of the Sox9 expressing precursors, which has been shown a decade ago, but was not mentioned. The entire manuscript relies rather heavily on computational analysis of single cell transcriptomes with some validation experiments (such as smFISH for FKBP11 that unfortunately fail to advance the authors' argument). Controls to confirm that the cells the authors label as 'regenerating beta cells' are indeed regenerating beta cells are lacking as there is no direct demonstration of an increase in proliferating beta cells or increase in beta cell mass.

The authors' starting point is that 'communication between neighboring cells in islets of Langerhans was overlooked by single cell genomic technologies. Perhaps this relates to the fact that communication between cells involves per definition a dynamic exchange of information. This is challenging to demonstrate directly using a technique that by design provides only a single snapshot in time, whether this is a single cell sequencing approach or a traditional histology readout. There is a rich literature on the crosstalk between different islet cell types, taking advantage of approaches better geared towards detecting crosstalk than single cell transcriptomics. The nature of the alleged crosstalk between beta cells and delta cells is not addressed, beyond the suggestion that paracrine Sst might be involved. Overall, the authors claims that beta cells adjacent to delta cells are protected from stress and contribute stronger to beta cell regeneration is not sufficiently supported by the observations in this manuscript.

The detection of doublets between beta and delta cells in an incompletely digested islet preparation would confirm the fact that these cells are indeed adjacent in the islets. Heterotypic doublets between alpha and delta cells are similarly enriched, although this is not mentioned in the manuscript beyond the supplementary data.

The fact that some endocrine cells in the islet derive from the Sox9 lineage and that these Sox9-positive islet cells are predominantly non-beta cells was published a decade ago in a paper that concluded that Sox9 lineage labeled cells did not contribute to beta cell regeneration. This paper by Kopp et al., curiously was not cited or discussed even though the authors did cite other key work from the same author group.

On numerous occasions the authors refer to the phenotype of their beta cells as 'regeneration-driven', whereas they are more accurately described as 'pancreatectomy-driven'. Similarly, they call some subset of their beta cells 'regenerating beta cells'. I did not find any direct demonstration of regenerating beta cells in the study either by proliferation models or by quantification of beta cell mass. If the suggestion is that beta cell replication is indeed induced preferentially in delta cell adjacent beta cells, one could detect this in sections using traditional and proven approaches such as the detection of Ki67 or EdU in beta cells in proximity of delta cells.

The data at several instances raise questions of rigor that remain unanswered. Some examples:<br /> -The authors stratification into Ins2-low, medium and high beta cells needs to be carefully vetted. The Ins2-low cells, which are labeled by the authors as enriched in stress-associated transcripts, also are enriched in the delta cell marker Rbp4.

-In figure 5, the relative contribution of beta and delta cells is broken out across a series of beta/delta doublet where the beta cell contribution the mixed transcript pool averages around 75%. As there is no indication that beta cells express more genes or contain more RNA than delta cells, this would suggest aggregates or 3 beta cells per delta cell.

-The suggestion is made that Fkbp11 mRNA is depleted in 'regenerating beta cells, that are suggested to be the ones adjacent to delta cells. This is followed by a smFISH confirmation, which to this reviewer does not demonstrate a clear difference in Fkbp11 levels among beta cells and further reveals similar Fkbp11 levels in delta cells (which are also stressed?)

-Careful morphometry of the islet mass and composition is not done and would be required to start to support claims of delta cell contribution to a possibly regenerating beta cell mass. The quantification of delta cell numbers by a snapshot of the tdTomato expressing cells has the potential for confounds introduced by variability in the islet prep quality.

Reviewer #2 (Public Review):

Centriole number is tightly controlled in cells and deregulation of these controls cause various human diseases. In this manuscript, the authors investigated the transcriptional control of centriole assembly by focusing on characterization of the RNA splicing factor SON. To this end, they combined advanced microscopy approaches with functional assays and whole genome sequencing. They first showed that SON is required for canonical duplication and PLK4-induced centriole amplification. Through characterization of centrosomal levels of duplication factors in SON-depleted cells, they found that centriole assembly is initiated properly but not completed. Using EM, they reported structural defects in nascent centrioles. Additionally, they used whole genome RNA sequencing to identify genes that require SON for expression and splicing, which included a wide range of centriolar satellite proteins. By focusing on Cep131, PCM1, pericentrin, centrobin and katanin, they described defects in centrosomal recruitment and pericentrosomal clustering of these proteins, which in part explains how SON regulates centriole assembly. Collectively, they propose that SON regulates microtubule organization and centriole assembly by regulating trafficking of regulators of centriole assembly and microtubule dynamics.

The results of the manuscript contribute to our understanding of transcript-level regulation of centriole biogenesis and microtubule organization. SON was previously described for its functions in centriole duplication, cell cycle progression and splicing of gamma-tubulin and pericentrin. Therefore, its functions as a regulator of centriole assembly is not unexpected. The authors extensively investigated the molecular mechanisms underlying these defects, which is challenging due to regulatory roles of SON on a wide range of centrosome/satellite/microtubule-associatd proteins as revealed by whole genome sequencing. To this end, they focused on key regulators of centrosome biogenesis.

Although the authors suggest regulation of PCM1, pericentrin, centrobin, Cep131 and katanin and satellite-mediated trafficking as potential mechanisms by which mediates its centriolar functions, the data presented does not provide strong evidence for these conclusions. For example, Cep131 expression does not rescue the phenotypes in SON-depleted cells. Rescue experiments with other proteins have not been performed, which are essential to show a direct link between SON functions and its targets. Additionally, there is no data that supports the role of SON in intracellular trafficking around the centrosome (i.e. dynamic imaging of Cep131/pericentrin granules). Therefore, although the findings of the manuscript is of general interest and has potential to advance our understanding of transcriptional control of centrosome biogenesis, further experiments are required to justify their conclusions.

Reviewer #2 (Public Review):

In this manuscript, the authors provide cell biological, biochemical, and structural modeling data to suggest that SIMC1 is a paralog of SLF1 and that the two compete for SLF2 binding likely because their C-terminal regions form similar helix-enriched structures to bind at the same surface of SLF2. As previously shown for SLF1, the authors found that SIMC1 bound to other subunits of Smc5/6. Distinct from SLF1, SIMC1 contains N-terminal SIMs and binds to SUMO pathway proteins including PML and SUMO. They found that SIMs and the SLF2-binding regions of SIMC1 are both required for targeting SIMC1 and SMC5/6 to the PML body upon LT expression. The authors showed that structural models of the dimerized portions of SIMC1:SLF2 and SLF1:SLF2 are similar to that of the yeast Nse5-6 structures, suggesting that they are Nse5-6 homologs but with different roles in targeting the SMC5/6 complex.

Overall, this is a very interesting study. The experiments were well done and data are presented clearly. The report will be highly interesting to the SMC field.

Reviewer #2 (Public Review):

Thermogenic brown adipocytes can burn energy to make heat, called non-shivering thermogenesis, and their ability to expend energy in this manner makes them an attractive target for anti-obesity therapies. Thus, understanding what controls brown adipose tissue development has both biological and clinical implications. Here, the authors investigate the process of new brown fat formation making two important conceptual advances: first, they identify a population of brown adipocyte precursor cells; and second, they find that the initiation of brown adipogenesis is signaled from the mature cell population through a complex regionalized process involving immune cells, rather than beta-AR signaling directly to the precursor population. This has important implications for understanding brown fat neogenesis. Key strengths include the elegant and rigorous use of RNA-seq, single cell RNA seq, and single molecule FISH to identify key cell populations and their spatial organization. The work establishes new hypothesis as to how brown fat neogenesis occurs; however, it does not go so far into functionally interrogating new mechanisms.

Reviewer #2 (Public Review):

The majority of genetic effects discovered in genome-wide association studies (GWAS) of common human diseases point to non-coding variants with putative gene regulatory effects. In principle, studying genetic effects on gene expression phenotypes, as mediators between genotype and disease, can help understand the underlying function of GWAS variants.

Lafferty et al., set to study the regulation of microRNA (miRNA) levels in mid-gestation human neocortical tissues as a potential contributor to brain-related phenotypes. To this end they performed miRNA expression profiling via small-RNA sequencing, followed by assaying expression quantitative trait loci (eQTLs) that locally regulate miRNA genes.

In addition to reporting some properties of miRNA-eQTLs, e.g., their tissue-specificity, the authors searched for potential overlap or "colocalization" between these eQTL loci and GWAS loci for several putatively brain-related phenotypes. They reported colocalization at the locus containing the SNP rs4981455 which is an eQTL for miR-4707-3p and is also associated with global cortical surface area (GSA) and educational attainment phenotypes in GWAS. They further showed that exogenously increased expression of miR-4707-3p in primary human neural progenitor cells (as a model to study neurogenesis) derives an increased rate of proliferation.

The reported results are interesting and important, particularly for the understanding of miRNA biology. That said, as I detail below, the claim that miR-4707-3p expression modulates brain size and thus cognitive ability, although potentially consistent with the data, is not unequivocally supported by the analyses. As such, considering the potential social impact of the misinterpretations of these results, I believe the authors should explicitly discuss caveats, alternative explanations consistent with the data, and broader implications:

1. The colocalization analysis used effectively tests whether miRNA-eQTL and GWAS variants are in linkage disequilibrium (LD), and does not formally test whether the miRNA-eQTL and GWAS signals are explained by the same genetic variant which is necessary for establishing causality. In this study, a formal test of colocalization is challenging given that the LD patterns in the eQTL data (from mixed ancestries) are dissimilar to the GWAS data (from European-descent samples). Furthermore, even if GWAS and miRNA-eQTL signals are explained by the same variant, this could be due to confounding (a confounder affecting both), or pleiotropy (genotype independently affecting both), and not necessarily that the miRNA-eQTL signal mediates the GWAS signal. These are also true for colocalization analyses of miRNA-eQTLs with mRNA-eQTLs or splicing-QTLs. One practical suggestion is whether authors can perform the colocalization analysis better, e.g., with methods such as SMR (https://yanglab.westlake.edu.cn/software/smr/#Overview).

2. Although possible, there is no direct evidence that the GWAS signals at rs4981455 for educational attainment and GSA are driven by variation in miRNA levels in the studied tissue. As the authors noted, rs4981455 is also an eQTL for the gene HAUS4. Furthermore, rs4981455 is a significant e/sQTL across almost all adult tissues in GTEx, and so likely has regulatory activity across wide ranges of cell or tissue types. Therefore, pinpointing the causal contexts mediating the effect in GWAS is impossible with the current data.

3. Orthogonal to the issues above, the genotype-to-phenotype pathway as hypothesized, i.e., genotype → miRNA levels → brain structure → educational attainment, is oversimplistic and rests on an implicit prior belief that genetic associations with educational attainment can be trivially mapped to fundamental brain features that determine cognitive ability. To illustrate the problem with this prior I refer to an old example by Christopher Jencks: in a society that prevents red-hair kids to go to school, genetic effects on hair color would be associated with educational attainment, despite having no intrinsic biological relationship with cognition. I give two scenarios consistent with the specific case of rs4981455 that are fundamentally different from what is implied in the paper: (i) The case of indirect genetic effects (see Kong et al., Science 2018). In this case, rs4981455 affects the nurturing behavior of an individual's parents, which in turn influences the individual's educational achievements and consequently brain structure features. (ii) The case of confounding. In this case, the genetic effects on brain structure are shared with another feature, such as facial shape (see Naqvi et al., Nature Genetics 2021). Variation in facial shape in a discriminatory educational environment can covary with educational attainment.

4. The paper lacks a discussion on caveats to protect against potential misinterpretation of findings, especially considering the troubled history of linking facial shape and head morphology to human behavior and intelligence. I refer to the last paragraph of Naqvi et al., Nature Genetics 2021, as an example of such discussion. This is particularly crucial given that the frequency of rs4981455 varies across human populations. For example, it is important to state that the GSA and education attainment GWAS findings are in individuals of European descent, and may not necessarily point to an effect in other ancestries or even in European-descent individuals that differ from the GWAS samples in various ways, e.g., socioeconomic status (see Mostafavi et al., eLife 2020). In other words, these findings pertain to variation within the studied samples. On this note, it is important to state the amount of variation in multiple phenotypes explained by rs4981455 (which is likely tiny), and that it by no means determines the phenotype.

5. The main colocalization signal is tentatively shown for GSA. However, the authors casually refer to links with "brain size" or "head size" throughout the paper.

Reviewer #2 (Public Review):

In this work, Miyashita et al investigated the mechanism for the termination of the PAF15 ubiquitin signaling in DNMT1-mediated maintenance DNA methylation. Using a cell-free system, they showed that chromatin dissociation of PAF15 and DNMT1 is coupled with completion of maintenance DNA methylation and requires the deubiquitylation activity of USP7. Through GST pulldown and mutational analyses, they further identified and mapped an interaction between USP7 and PAF15, which is important for the USP7-mediated deubiquitylation of PAF15 with dual mono-ubiquitin (PAF15Ub2). In addition, they found that ATAD5, a protein that regulates the unloading of PCNA, promotes the release of PAF15Ub1/PAF15Ub0 from chromatin. Co-depletion of USP7 and ATAD5 from egg extracts results in an elevated global DNA methylation, suggesting a cooperative effect between USP7 and ATAD5. Together, these studies identified USP7 and ATAD5 as two components critical for termination of PAF15 ubiquitin signaling pathway. Overall, the study is well designed and performed, largely supporting the central conclusion.

Reviewer #2 (Public Review):

In this manuscript by Brandon et al, the investigators compared two obesity-causing diets in mice (high-fat vs high-starch) and defined their ability to cause insulin resistance and glucose intolerance in mice. In response to weeks of diet feeding, both diets led to the predicted increase in body weight and adipose tissue mass. However, feeding high starch did not lead to abnormalities in glucose tolerance or insulin sensitivity as documented by hyperinsulinemic-euglycemic clamp studies. However, a high-fat diet led to the predicted (and published) abnormalities in glucose tolerance and insulin resistance, particularly at the level of the liver, brown adipose tissue, and skeletal muscle. Notably, these abnormalities were not due to defects in canonical insulin signaling intermediates such as AKT. Mechanistically, the authors performed lipidomics and suggest changes in particular ceramide species that are hypothesized to contribute to these deleterious effects on metabolism.

The major strengths of this manuscript are the nicely designed, rigorous, and well-executed experiments that directly compare two diet models and the use of elegant physiology experiments like clamp studies. These are important benchmark studies and will have a high impact in the field. Signaling, lipidomics, and clamp studies are all well-executed and are complementary to each other. A weakness of the current manuscript is the limited exploration of the white adipose tissue in both models. It is unclear if there is altered insulin signaling in white adipose tissue and changes in lipolysis that drive insulin resistance in the high-fat diet model. This would be predicted based on the literature and the inclusion of these data would help support the overall conclusions of this manuscript and its impact in the field.

Another weakness is that the causal role of the implicated ceramide species is not yet defined. However, in my opinion, this is beyond the scope of this manuscript as these studies will form the foundation of future experiments to directly evaluate/validate the specific role of ceramide in the high-fat diet effect. This is particularly important given that no changes were observed in canonical insulin signaling as predicted by the literature.

Reviewer #2 (Public Review):

Johnstone et al introduce a novel method to study circadian rhythms in a tissue-specific manner in Drosophila. The idea is to use an intersectional approach to express a luciferase reporter only in specific tissues, and then to monitor rhythmic luciferase activity in real time by feeding flies with luciferine. The authors convincingly demonstrate that their approach works, and are even able to record from a small number of brain circadian neurons. This should prove to be a potent approach to understanding how circadian clocks behave throughout the body of flies, in response to different environmental inputs or genetic manipulations. To further demonstrate that their approach will prove useful, the authors focus on flies that are missing PDF-receptor, which mediates communication between circadian pacemaker neurons expressing the neuropeptide PDF and other brain and body clocks. There, however, the message becomes murky. Results are not very consistent, and the authors do not place clearly their results in the context of previous work in flies, or in mammals. Also, there are concerns with the way the authors monitored the progressive loss of circadian rhythmicity, which is important to establish the impact of PDF-receptor signaling on various body clocks. Thus, while technically interesting, the advance of this paper is still limited because it does not yet provide clear new insight into the relationship between brain and body clocks.

Reviewer #2 (Public Review):

Carrier et al. sought to define the methylome associated with increased aggressiveness of melanoma, with the goal of identifying common changes in methylation and to define a methylation signature of disease progression. To do so, they analyzed 3 cell line pairs that either were established from the same patient (primary vs cutaneous metastasis) or that were a parental cell line and its derivatives generated through repeated transplantation and selection for the ability to metastasize to the lung. Among these pairs, 229 genes were identified as commonly hypermethylated. Interestingly, genomic mapping of these genes revealed that 74 of these genes localized to 9 methylation clusters, 34 of which had two CpGs and at least 40% differential methylation. Carrier et al. also performed Ingenuity pathway analysis, uncovering 116 genes among the 229 with putative cancer-associated functions. From these genes, 8 candidates were selected for validation in cell lines and patient samples. 4 out of 8 genes (MYH1, PCDHB16, PCDHB15, BCL2L10) showed differential methylation in patient samples, and their methylation status correlated with patient overall survival. Carrier et al. then devised a score based on methylation of these 4 genes, which performed better in predicting patient prognosis based on primary tumor methylation score than did the Breslow index. This methylation score could therefore be used as a biomarker of melanoma aggressiveness and this approach could be implemented in other tumor types.

Overall, the approach appears to be well designed, the results are of good quality, and generally support the claims. For some aspects of the paper, the rationale is not immediately apparent and should be better described, for instance the choice of the 8 genes selected or validation appears arbitrary and the cut-off long term vs short term survival of patients (1 year) is not justified clinically or scientifically. Providing additional information will make this study clearer for the reader.

Reviewer #2 (Public Review):

Mouse olfactory neurons express one single type of odorant receptor (OR) out of ~1000 possible choices, and the neurons expressing the same type of OR project their axons to two or a few glomeruli in the olfactory bulb (OB). The goal of this work was to identify glomeruli that are activated by the lowest concentration of one given odorant (this would be the primary odorant for the glomerulus). A panel of 185 odorants that cover a wide range of chemical structures was designed for this purpose. The authors imaged the dorsal regions of the 8 OBs from 4 transgenic mice that express the Ca2+ reporter GCaMP6 in the mature olfactory neurons while they were exposed to the odorants delivered in vapor phase. In this way, the authors were able to identify glomeruli that were responsive to odorants at very low concentrations (estimated to be in the picomolar to nanomolar range). They also show that while the spatial representation of odorant chemicals in the bulb is sparse, rather than clearly delimited (except for that of amines and carboxylic acids), glomeruli recognizing structurally related odorants are co-tuned.

The experiments are well executed and the images of the activated glomeruli in the OBs are impressive. These results show that olfactory neurons (and their cognate ORs) can be high affinity and selective receptors. These qualities cannot be easily detected when using conventional heterologous expression experiments or ex vivo assays, where responses are usually observed in the range of micromolar concentration of the odorants. The results reveal important aspects of odorant decoding in living mice and suggest that odorant concentrations that are effectively processed by the olfactory system are much lower than the ones usually considered. This high-resolution approach also facilitates the analysis of how odorant chemical structure is spatially represented in the OB.

There are a few points that the authors might want to consider:<br /> Although it is assumed that each one of the glomeruli represents one OR type, the exact identity of the ORs that correlate with each of the 26 glomeruli remains unknown. Could the authors identify which ORs correspond to the 26 glomeruli based on the glomerular OB map determined by spatial transcriptomics (for example in https://www.nature.com/articles/s41593-022-01030-8) and on the position coordinates of the 26 glomeruli shown in Table S2? It would be nice to see whether the ORs sequences cluster in a way that correlates with co-tuning of the responses to structurally related odorants.

Despite the fact that each OR has two glomeruli per bulb (one lateral and one medial), for most of the odorants, only one activated glomerulus per bulb was observed (ex. Figures 1 and 2). Is the other one always out of the field of vision (dorsal surface of OB), or is it not activated? This should be explained in the text.

A global analysis summarizing how the results could be extrapolated to the whole OB would be helpful and informative. For example, what is the total number of glomeruli in the mouse OB? What percentage of these were accessible for imaging in the experiments (1004 per bulb)? Primary odorants were identified for 26 glomeruli in the accessible region (dorsal OB), but according to Figure S3C, 288 glomeruli responded to only one odorant at low concentrations. Would be good to summarize briefly in the text (Page 7 line 192), which were the stringent criteria used to select the glomeruli/diagnostic odorant pairs, even though it is in the methods. It would make it easier for the reader, and would also make clear why only 26 glomeruli out of the 288 were selected as good glomerulus/diagnostic odorant pairs. How many of the 185 odorants are diagnostic odorants for the imaged glomeruli? How many odorants are not diagnostic odorants for the imaged region, and could therefore be likely to act so for the glomeruli in the regions that were not accessible? And so on.

The authors find that the glomerular sensitivities to different odorant structure classes are not clearly spatially discrete, but are overlapping and interdigitated. Are they temporally discrete instead? Could this question be addressed?

Reviewer #2 (Public Review):

In this manuscript, the authors provide physiological and behavioral evidence to support that fruit flies can perform olfactory unimodal sensory preconditioning, an example of higher-order conditioning. And they report that a small G protein Rac1 plays an important role in such sensory preconditioning. These results indicate that a simple brain may have the ability to guide behavior through inferred value.

The current findings are interesting and based on in vivo calcium imaging and behavioral data. They can provide instructive help to researchers in the learning and memory field. However, the behavioral data should be further strengthened by adding more controls. And calcium imaging results only partially explain the behavioral data, more discussion should be provided. More detailed information about methods is required. The writing needs to be improved.

Reviewer #2 (Public Review):

This study by Porte et al examines the role of the soluble pattern recognition molecule long pentraxin 3 (PTX3) in host defense against S. pneumoniae. They find that PTX3 is required for control of bacterial numbers in the lungs, systemic dissemination and host survival following infection with S. pneumoniae serotype 3, a strain of public concern. They further confirm findings with other pneumococcal serotypes and find that PTX3 can protect the host if administered as a treatment post infection. In exploring mechanisms of protection, they find that pneumococcal infection upregulates PTX3 expression in a Myd88/ IL-1 dependent manner and that endothelial cells are a primary source of PTX3. They convincingly show that PTX3 control of bacterial numbers is not due to enhancement of neutrophil antimicrobial activity but rather due to control of pulmonary inflammation. They find that absence of PTX3 was linked to pulmonary damage driven by increased PMN influx. They find that PTX3 is required for control of pulmonary PMN recruitment, likely acting at the PMN trans-endothelial migration step by controlling P-selectin mediated migration. Importantly, they identify two SNPs in PTX3 are associated increased risk of invasive pneumococcal disease. This study is of high clinical relevance and provides important contributions to the field in terms of understanding regulators of pulmonary inflammation in bacterial pneumonia. The experiments are very well controlled and the manuscript is easy to follow.

Reviewer #2 (Public Review):

The authors are trying to show the existence of a reversible amination network that allows nitrogen transfer via transaminases for synthesis of several amino acids. Nitrogen assimilation and distribution is known to start with ammonia assimilation via glutamate and glutamine synthesis, and subsequent transfer of the nitrogen via transaminases and amidotransferases. To demonstrate an amination network, i.e., reversible nitrogen transfer, the authors start with a glutamate auxotroph and provide a variety of compounds to determine which support growth. Growth implies the transfer of amino groups to glutamate. The authors show some pathways required for transfer of the amino acid nitrogen via genetic analysis. The concept of an amination network is clever since current thinking would suggest that nitrogen flows in one direction and is not reversible. The basic method is genetic which is sometimes supplemented with isotope dilution experiments. In addition to analysis of a possible amination network, experiments are presented that test whether alternate routes of ammonia assimilation (the source for amino groups) are possible. While the authors show that a reversible amination can exist (nitrogen flow from glutamate is known, while the authors show that nitrogen flow to glutamate is possible), they do not provide any evidence that the nitrogen flux to glutamate does exist in nature for wild-type strains. A genetic analysis with complex strains (multiple mutations) and very specific growth conditions cannot provide evidence for nitrogen flux to glutamate from other amino acids. Positive evidence requires a biochemical analysis: how much N15 from an amino acid is transferred to glutamate or other amino acids. Without such results, it cannot be established that amino groups can be transferred to glutamate at an appreciable level or that the amination network is reversible, which is an important conclusion of this work. Without such results, the proposed amination network is a theoretical possibility that is detectable only in genetically complex strains and specific medium. The impact of the work is more limited without a biochemical analysis.

Strengths and weaknesses.

The results suggest that the amino group can be readily transferred between keto acids via a network of transaminases in strains. Several of the proposed reactions are novel and have not been previously described, such as tryptophan as an amino donor. The idea and experimental design are clever.

This work does not discuss several relevant topics. Please notice that the comment is that several issues are not discussed or considered. The topics overlap.

The activities of transaminases are important for this study but are not discussed. A useful summary of transaminase levels is provided in the following reference: Mol Microbiol . 2014 94:843-56. doi: 10.1111/mmi.12801. PMID: 25243376. The 3 most abundant transaminases are SerC, AspC, and IlvE. The results from that paper are consistent with many results of this work. In the cited work, all defects in transaminases that result in phenotypes were complemented with all transaminase genes. The cited paper is directly relevant to this work.

There is no discussion of metabolite levels. This is important since the most abundant metabolite in wild type E. coli is undoubtedly glutamate (see work by Rabinowitz), which by mass action will provide the direction for transaminase reactions. For the mutant strains used, glutamate might not be the most abundant metabolite, and reversible transaminases conceivably will flow in an unphysiological direction. It is likely that metabolite levels are substantially perturbed in the mutants analyzed, and that the proposed amination network (nitrogen flow to glutamate) requires these metabolic perturbations. Several of these perturbations are likely to be effects on glutamine synthetase (GS). The GltBD mutant should prevent induction of the Ntr response. However, given the unusual conditions assayed, this is not a certainty. Several amino acids inhibit GS activity, including serine, glycine, alanine, histidine, and tryptophan. The levels of metabolites needed to drive reactions toward glutamate synthesis may never occur.

There is no discussion of the regulation of the enzymes involved. For example, aspA is controlled by several factors (EcoCyc). Is the control of the relevant enzymes consistent with the proposed amino transfer, or does the cell require a novel form of regulation and/or a suppressor mutation? Do the transaminase levels change during these experiments?

The authors are imposing strong selective pressure (growth or no growth) and the possibility that suppressor mutations can rapidly accumulate is not discussed or assessed.

The results of this paper make the implicit assumption that the transport of any amino acid that is added to the medium will not limit growth. Transport is undoubtedly often limiting. To avoid this problem, di- and tri-peptides could have been used. Both are rapidly transported, and the amination network may prove to be larger than the results suggest. The use of dipeptides could increase the amino acids that can transfer amino groups, since their internal concentration would be higher. Experiments are not requested, but the authors should consider whether their proposed network is potentially larger. (It is understood that on one hand the reviewer is questioning whether a reversible network exists, and on the other hand that it may be larger. These are not incompatible since under conditions in which peptides are the amino source, the network may exist.)

The proposed alternate ammonia assimilation pathway has some interesting conceptual issues that should be addressed. Ammonia assimilation is necessarily at the interface of carbon/energy and nitrogen metabolism. The incredibly complex control of ammonia assimilation via glutamine and glutamate has layers upon layers of regulation that ensure that energy is not drained when all nitrogen-containing compounds are present at sufficient levels. Any alternate ammonia assimilation pathway in nature must take this into consideration. It is predicted that the constructed strains in this study will poorly handle many environmental stresses and changing nutrient content. These considerations are largely theoretical but limit the ability of alternate pathways to exist in nature, except perhaps under certain conditions. It is not suggested that these issues should be addressed experimentally but it would be important to acknowledge them.

Reviewer #2 (Public Review):

In this manuscript, Schwartz et al. examined the role of their previous finding regarding a programmed bulk cytoplasmic remodeling of the embryonic PGCs in regulating the mtDNA copy number and heteroplasmy level during the PGC to GSC transition in C. elegans. The authors first showed that the mitochondria of embryonic PGCs accumulated in a lobe, most of which were cannibalized/degraded. This was evidenced by the acidification of mitochondria in the evicted cytoplasm. The authors then measured the mtDNA copy number change between the embryonic and larval PGCs by counting TFAM-GFP puncta and FACS and ddPCR, showing that the cytoplasmic remodeling halves the mtDNA copy number per PGC. Finally, the authors showed that compromising the cytoplasmic remodeling does not affect the heteroplasmic level of uaDf5, and suggested pink-dependent mechanisms to be responsible for mtDNA heteroplasmy regulation during the PGC-GSC transition.

Overall, this work provides a detailed characterization of mitochondrial network and mtDNA copy number changes during the cytoplasmic remodeling process. It also showed that the remodeling reduces mtDNA copies in PGC stochastically. However, I wish that the authors provided more direct evidence to support their conclusion that there is no mtDNA replication in embryonic PGCs and mtDNA only starts to replicate before the first division of PGCs in early L1. It will also be interesting to show how compromising cannibalism (e.g. using nop1 mutant) affects the replication of mtDNA after the first division of PGCs in L1. Finally, given that the total mtDNA copy number in later GSCs is similar between worms with and without the PGC cannibalism (wt vs nop-1 mutant) (Fig 3), and cannibalism does not selectively eliminate detrimental mtDNA mutation, I also wonder why PGCs need a bottleneck for the mtDNA population.

Reviewer #2 (Public Review):

In this study Somaiya et al. identify a molecular mechanism used to guide Gad1 expressing cells into the vLGN and dLGN during mouse development. It has been shown previously that RGC inputs are required for these areas to recruit these interneurons and that thalamus derived astrocyte-derived FGF15 expression is dependent on RGC input. Here the authors perform a series of experiments that demonstrate that RGC derived Shh is important for this induction and therefore the recruitment of interneurons to the visual thalamus. Overall these experiments are well thought out and support the author's hypothesis. Because Shh signaling is important for many developmental processes, this result should be of wide interest to the scientific community.

Strengths include the well-thought-out logic and presentation of the experiments designed to test their hypotheses, and the use of transgenic mice to block activity or remove Shh in vivo, and the significant changes seen in the mutants. A minor weakness is that evidence showing that all activity is blocked in the retina of the TeNT mice is not convincing.

Reviewer #2 (Public Review):

In this paper, Minkina and colleagues describe an experimental and computational technique for studying heritable sources of gene expression heterogeneity and for further quantifying the extent to which they are attributed to copy number alterations. They achieve this through the development and validation of a dynamic CRISPR barcoding approach that is compatible with single-cell RNA and ATAC sequencing (with sci-RNA-seq and sci-ATAC-seq).

Overall, the method is technically very solid, and the lineage reconstruction from the CRISPR edited barcodes is concordant with lineage structure revealed by copy number alterations.

Ultimately, they do find some genes that show heritable expression without associated copy number changes. Interestingly, they do not find an associated change in the chromatin by ATAC-seq.

One of the strengths of this paper is the discussion of the technical limitations of the technique. They highlight both strengths and weaknesses of the approach such that the reader can grasp each (both throughout the text and extensively in the discussion).

Reviewer #2 (Public Review):

Through a thorough review of the genome of this specific (Mertk) knock-out mouse, the authors found multiple additional (unwanted) changes in the genome. These genetic changes were due to the way the knock-out mouse was generated. The authors created two novel mouse knock-out lines, without the additional changes, in which only part of the findings observed in the original knock-out mouse could be reproduced. This article leads to the generation of two novel knock-out lines relevant to researchers studying MERTK and rectification of part of the previous findings in the original Mertk knock-out mouse. Besides, being relevant to a broader audience, this paper clearly demonstrates that the original background and methods used for generating knock-out mice should be evaluated and taken into account when planning mouse in vitro and in vivo studies.

Reviewer #2 (Public Review):

Vancomycin is commonly used to treat infections caused by methicillin-resistant Staphylococcus aureus (MRSA) but treatment failure is common and there is evidence of the increasing frequency of strains with reduced vancomycin susceptibility. Therefore, there is interest in devising approaches to enhance treatment efficacy. In this work, the authors show that vancomycin activity is enhanced by the presence of a specific fatty acid. This has the potential to enhance topical antibiotic preparations and may also shed light on antibiotic activity in the host environment.

The authors then go on to attempt to decipher the underlying mechanism responsible and provide some evidence that the combination of antibiotics and fatty acid disrupts the machinery that synthesises the cell wall. However, it is not clear if such disruption is causative of the enhanced killing phenotype. Furthermore, it is not clear whether non-potentiating fatty acids also disrupt this machinery, which is a key limitation.

Reviewer #2 (Public Review):

This work provides a thorough characterization of tumor cell and microenvironment dynamics in a castrate Pten null prostate cancer model and details the strength of cellular interactions using single-cell RNA sequencing. The search for a preexisting castrate-resistant prostate progenitor has been upended in recent years with the discovery that prostate luminal cells adapt to low androgen environments by undergoing lineage plasticity rather than an expansion of proximal progenitors. This paper provides indirect evidence that basal epithelia give rise to 'intermediate' epithelia through increased translation in intact and castrate Pten null mice cells, which is validated in a Pten null, 4ebp1 mutant mouse model.

Strengths:<br /> The single-cell data are robust and expertly presented in the figures. The methods are largely appropriate and the delineation of experimental protocols is straightforward. The analysis is comprehensive and well described in relation to biological questions of interest to the community. The validation of the effect of translation on prostate epithelial viability in relation to initial findings advances our understanding of how cells survive in low androgen environments. The addition of a public portal for the data is highly useful.

Weaknesses:<br /> The PB-Cre4 promoter seems to be promiscuously inactivating Pten in basal, intermediate, and luminal cells, which is problematic as this confounds the ability to differentiate between cells that are undergoing lineage plasticity vs. expansion of a pre-existing progenitor cell type. Much recent evidence points to lineage plasticity of prostate luminal tumor cells under androgen deprivation rather than survival and expansion of a pre-existing castrate-resistant basal or intermediate cell type. Accordingly, the observation that basal epithelia may transdifferentiate to intermediate epithelia or that a pre-existing intermediate luminal cell state is expanded under castration may be artifacts of the model without reproduction in human prostate cancer. The use of trajectory analysis of single-cell data to demonstrate basal or intermediate cell lineage transdifferentiation is a weaker type of evidence than the lineage tracing of individual cell types provided by other groups, which argue against transdifferentiation and for lineage plasticity.

Reviewer #2 (Public Review):

The manuscript by Hebert et al., reports on the utility of TRIO-based whole-exome sequencing (WES) in patients who presented as sporadic cases and are suspected of having inborn errors of immunity (IEIs). The authors developed an in-house pipeline for data analysis and used a set of known algorithms to prioritize the impact of genetic variants located mostly in the coding region of proteins. The data analysis was done in two steps; the first step involved the routine WES diagnostic analysis that led to the identification of pathogenic (P) and likely pathogenic variants (LP) in genes already associated with IEIs. The authors claim that this analysis resulted in a likely molecular diagnosis in 19 (~15%) of patients, while an additional 14% of cases were carriers for VUSs or other risk factors in the disease causal genes. As many of these variants are either inherited from one parent or are present as heterozygous (monoallelic) variants in genes associated with recessive diseases, their clinical significance is unclear.

In the second step, the authors focused on the identification of de novo variants (DNVs), including SNVs, CNVs, and small indel, since these variants are more likely to be deleterious on protein function. The authors identified 136 non-synonymous DNVs, which were then filtered down to 14 best candidate variants using various in silico tools and database searches. These 14 variants included DNVs in genes previously associated with autoinflammatory diseases, such as CAPS and RELA haploinsufficiency. Three patients are found to carry de novo copy number variants (CNVs) of unknown clinical significance. Finally, several de novo loss-of-function (LoF) variants have been identified in genes that are not yet associated with any IEIs but are good functional candidates. Their potential pathogenicity is further supported by the observation that they are found in genes intolerant to loss of function. Functional validation has been performed only for the patient carrier of the novel FBXW11 splice variant. The authors state that the maximum solve rate (i.e., probable molecular diagnosis) in this cohort might be as high as 23%, which is comparable to similar reports of patients with IEIs, however, the reported results do not yet support this conclusion.

The main conclusion of this study is that TRIO-based WES analysis for DNVs could improve the diagnostic rate and can result in the identification of novel disease-causing genes. TRIO-based sequencing is also preferable when analyzing patients from populations underrepresented in gnomAD and ExAC. As the cost of WES has come down, WES has been increasingly used in the clinical diagnosis of many human disorders. Despite the major progress in the development of novel sequencing technologies and new in silico tools, the diagnostic rate is still below 50%. In summary, this study suggests that despite the identification of over 400 genes associated with IEIs, there are many more genes to be identified and that the heritability of these diseases is very complex.

Reviewer #2 (Public Review):

The manuscript "Rewiring of liver diurnal transcriptome rhythms by triiodothyronine (T3) supplementation" by Dr. Leonardo Vinícius Monteiro de Assis, Dr. Lisbeth Harder, and colleagues addresses the impact of elevated triiodothyronine (T3) levels in the regulation of liver diurnal transcriptional rhythms. The authors induced hyperthyroidism in mice by supplementing the drinking water with T3. In-depth circadian analysis of metabolic and behavioral rhythms revealed that such T3 supplementation profoundly influences metabolism in a diurnal manner. Furthermore, the authors provide a large-scale analysis of the liver transcriptional landscape conducted around the clock in T3-treated animals and control counterparts by microarray. These analyses provide novel significant insights into the impact of thyroid hormones on the diurnal regulation of the liver transcripts and allowed dissection, to some extent, between T3-dependent and T3-independent regulation of liver circadian transcriptome.

Reviewer #2 (Public Review):

This is an interesting paper, in which the authors assessed spiking and network deficits in a well-established mouse model of schizophrenia. This mouse model carries a genetic deletion of the Disrupted-in-schizophrenia-1 (Disc1) gene, which is highly penetrant in the human condition. The authors combined behavioral analyses with state-of-the-art electrophysiological recordings in vivo, coupled to optogenetic tagging, to study a subnetwork formed by a major inhibitory neuron subclass (the parvalbumin (PV)-expressing interneuron) and principal excitatory pyramidal neurons in the medial prefrontal cortex. This work indicates reduced firing rates of PV cells in Disc1-KO mice, likely due to reduced coupling with pyramidal neurons, leading to alterations in local network activity. Indeed, the authors found that Disc-KO mice exhibited reduced levels of gamma oscillations and somewhat hypersynchronous networks.

Taking advantage of novel techniques and analytical strategies, the manuscript provides rich, novel insight into the neurobiology of a mouse model of this severe psychiatric condition. The data is of high quality, the findings interesting and the manuscript is well written.

Overall, the results support the authors' conclusions, although some additional analyses are necessary to corroborate their interpretations.

Although the paper does not give information on how PV cell dysfunctions are engaged during cognitive tasks, this study can be considered as an important first step in advancing our knowledge on the basic dysfunctions of cortical networks in this model of schizophrenia

1. The major findings stem from the analysis of the spiking activity of individual neurons recorded using either silicon probes or arrays of tetrodes. Both techniques allow simultaneous recording of many neurons from a single animal; therefore, from a statistical point of view neurons recorded from one animal are pseudo replicas and cannot be considered as independent measurements.<br /> Throughout the manuscript, the authors perform two-sample tests on the pooled data from all recorded neurons to compare differences between genotypes; therefore, artifactually increasing the power of statistical tests. Comparisons between genotypes should be performed using each mouse as an independent measurement.

2. The superficial layers of the mPFC are difficult to reach with a vertical approach of the probes due to the presence of a large blood vessel located medially in the frontal dura. Therefore, the authors are most likely reaching mPFC deep layers where PYR neurons produce fast spikes at high rates. If this is the case, this would make it difficult to sort the spiking of PYR from that of INs based on the spike kinetics and rate. The authors used opto-tagging of PVIs in a set of experiments. It would be reassuring to confirm that the spike waveform and kinetics that they extracted from PVIs are similar to those they assigned as INTs in their experiments with no opto-tagging. Identified PVIs should be statistically different from putative PYRs (not responding to light).<br /> Although opto-tagging of PVIs can solve this issue, the amount of cells isolated remains low and the number of animals is not stated. Opto-tagged cells are subsequently used for further analyses but the statistical value of those remain unclear. Since the entire interpretation of the rest of the results depend on this result, this must be clarified.

3. Proportion of gamma coupled neurons. The authors mention the use of pairwise phase consistency (PPC). PPC is a good method to measure phase coupling independent of differences in firing rates. However, it is not entirely clear how PPC is used to measure the extent of phase locking. In the methods, the authors mention that they ran the PPC analysis *after* determining significant phase locking with Rayleigh's test. Moreover, they provide PPC values for high gamma oscillations but not for other frequency ranges. Perhaps, it would be better to test significant coupling of all units by nonrandom spike-phase distributions crossing a confidence interval, estimated by Monte Carlo methods from independent surrogate data set. These can be obtained upon randomly jittering each spike times. Indeed, PPC values estimated by the authors for high gamma are higher for PYR than INT (Fig. 1- Fig. Suppl 4 b). This is at odds with previously published observations in V1 (e.g. Perrenoud et al., PLoS Biol. 2016 PMID: 26890123). Given the existing reports of reduced excitatory transmission in DISC-1 mice, phase locking of PYR to other frequency bands might be affected.

Reviewer #2 (Public Review):

The authors are clarifying the role of global mobility in homologous recombination (HR). Global mobility is positively correlated with recombinant product formation in some reports. However, some studies argue the contrary and report that global mobility is not essential for HR. To characterize the role of global chromatin mobility during HR, the authors set up a system in haploid yeast cells that allows simultaneously tracking of HR at the single-cell level and allows the analysis of different positions of the DSB induction. By moving the position of the DSB within their system, the authors postulate that the chromosomal conformation surrounding a DNA break affects the global mobility response. Finally, the authors assessed the contributions of H2A(X) phosphorylation, checkpoint progression and Rad51 in the mobility response.

One of the strengths of the manuscript is the development of "THRIV" as an efficient method for tracking homologous recombination in vivo. The authors take advantage of the power of yeast genetics and use gene deletions and as well as mutations to test the contribution of H2A(X) phosphorylation, checkpoint progression and Rad51 to the mobility response in their THRIV system.

A major weakness in the manuscript is the lack of a marker to indicate that DSB formation has occurred (or is occurring)? Although at 6 hours there is 80% I-SceI cutting, around 20% of the cells are uncut and cannot be distinguished from the ones that are cut (or have already been repaired). Thus, the MSD analysis is done in the blind with respect to cells actually undergoing DSB repair.

The authors clearly outlined their aims and have substantial evidence to support their conclusions. They discovered new features of global mobility that may clear up some of the controversies in the field. They overinterpreted some of their observations, but these criticisms can be easily addressed.

The authors addressed conflicting results concerning the importance of global mobility to HR and their results aid in reconciling some of the controversies in the field. A key strength of this manuscript is the analysis of global mobility in response to breaks at different locations within chromosomes? They identified two types of DSB-induced global chromatin mobility involved in HR and postulate that they differ based on the position of the DSB. For example, DSBs close to the centromere exhibit increased global mobility that is not essential for repair and depends solely on H2A(X) phosphorylation. However, if the DSB is far away from the centromere, then global mobility is essential for HR and is dependent on H2A(X) phosphorylation, checkpoint progression as well as the Rad51 recombinase.

The Bloom lab had previously identified differences in mobility based on the position of the tracked site. However, in the study reported here, the mobility response is analyzed after inducing DSBs located at different positions along the chromosome.

They also addressed the question of the importance of the Rad51 protein in increased global mobility in haploid cells. Previous studies used DNA damaging agents that induce DSBs randomly throughout the genome, where it would have been rare to induce DSBs near the centromere. In the studies reported in this manuscript, they find no increase in global mobility in a rad51∆ background for breaks induced near the centromere (proximal), but find that breaks induced near the telomeres (distal), are dependent on both gamma-H2A(X) spreading and the Rad51 recombinase.

Reviewer #2 (Public Review):

SMC complexes play critical roles in chromosome organization from bacteria to humans. Recently in vitro studies found that SMC complexes function by extrude DNA loops. In vivo evidence for the loop extrusion model is less direct. The study by Jimenez et al investigated the mechanism of a specialized SMC complex called Condensin DC that mediates dosage compensation in C. elegans. This is an excellent experimental system to study SMC action in vivo because the specific loading sequence (rex) for Condensin DC was identified. The authors inserted the sites ectopically into autosomes and found that Condensin DC was recruited to ectopic sites and spreads to long distances. In a strain with a fusion chromosome (X;V), the complex spread beyond ChX to ChV. Finally, the authors generated a dCas9 mediated protein roadblock to test whether a large protein barrier prevents Condensin DC from spreading.

Strengths:

The authors have an elegant experimental system to investigate SMC action in vivo. They have a comprehensive set of tools including ectopic loading sites, fusion chromosomes, dCas9 block, Hi-C, ChIP-seq and RNA-seq.

Weaknesses:

While the experimental system has great potential, some specific choices of insertion sites did not yield clear results and caused confusions. If they modify the location of rex site or the dCas9 binding sites, they might be able to bring more insights. I detail them below.

1) The authors inserted rex sites to autosomes and observed recruitment of Condensin DC to the ectopic sites. The engineering of rex sites to ectopic locations was done before, so was the observation that these ectopic sites recruit Condensin DC and generate TAD border (Albritton 2018; Anderson 2019). The current study has 3 rex sites on ChII and has the potential to bring new insights on how multiple rex sites act cooperatively and how they create TAD borders. However, the results presented were not clear because the author used rex sites with different strengths. The middle site did not form TAD loops with the other two sites. It is unclear whether the strength of the rex site matter or whether the distance between the sites matter. If they used only the two strong sites, or used all 3 sites of the same strength, the authors could have clarified this point.

2) The authors used the dCas9 system to test the loop extrusion model. They found that DPY-27 is enriched at the dCas9 array. They concluded that the dCas9 array blocked Condensin DC spreading and this result supported the loop extrusion model. However, this interpretation is not supported by the DPY-27 enrichment profile or the HiC profile. If the authors were correct that Condensin DC, loaded on rex sites on either side of the array, extruded DNA loops and got blocked by the dCas9 array, we would expect DPY-27 enrichment to build up highest at the periphery of the array and lowest at the center of the array; we would expect a domain border to form at the array because of the lack of interactions between regions outside of the array. Yet, the DPY-27 ChIP profile is flat and there is no change in HiC profile. The near-identical shape of the dCas9 and DPY-27 ChIP-seq peaks is reminiscent of a technical bias of ChIP-seq, that is open chromatin is more "ChIP-able" (Teytelman PNAS 2013). It is possible that dCas9+sgRNA unwinding the DNA caused artifact in ChIP-seq. It is possible that a freely diffusing nuclear-localized protein will show the same ChIP profile at the dCas9 site with no biological relevance. Since this result is a major conclusion of the paper, it is necessary for the authors to perform a ChIP-seq control using a freely diffusing nuclear protein.

3) If the authors targeted dCas9 to a different site, they might be able to clearly show whether Condensin DC spreading is blocked by such road block. For instance, if they use the X-V fusion, and target dCas9 to a region on ChV but close to the junction, they could test their hypothesis by DPY-27 ChIP-seq.

4) The model (Fig 6) is confusing. The authors are trying to support the loop extrusion model in the text but their drawing is not loop extrusion (Banigan and Mirny 2020). The author should clarify what they mean. For instance, after recruitment at rex site (red bar, with two arrows pointing left and right), Condensin DC was drawn to encircle a single piece of DNA as it moves to the left. It is not clear how the blue ring on the right can capture another piece of DNA and extrude a DNA loop and then later reversed to encircling a single piece of DNA before approaching the green protein block.

Reviewer #2 (Public Review):

The authors show that a strong maternal bias attributable to silencing and H3K27me3 enrichment of the paternal genome is a feature of bryophyte embryonic development. Paternal H3K27me3 enrichment is observed both by chromatin profiling and cytologically in the paternal chromosomes of the pronucleus and depends upon embryonic PRC2. The data that support the authors' conclusions are compelling, and I have only a few suggestions for improvement.

1) At 3 days after fertilization both H3 and H3K27me3 are present, but since histones replaced protamines, the critical events of histone deposition and Polycomb marking might have occurred simultaneously or more likely successively. Can the authors distinguish these possibilities?

2) Comparisons are made to X-chromosome inactivation in mammals and male X upregulation in Drosophila as silencing phenomena that are not conserved in evolution, but there are others that might be more relevant, such as paternal genome elimination in mealybugs, which is a spermatocyte-specific event that follows whole genome silencing during embryogenesis. Another is Meiotic Sex Chromosome Inactivation, a conserved phenomenon in animals that targets unpaired chromosomes.

This is very nice work in a fascinating area.

Reviewer #2 (Public Review):

Natural killer (NK) cells are cytotoxic lymphocytes know to target either virus-infected cells and/or cancer cells. Recently, increasing evidence has shown that extracellular vesicles (EV) of various sizes and molecular content that are released by NK cells carry selected sets of proteins and miRNAs able to exert physiological effects on target cells.

In this paper by Dosil et al, activated NK cells release EVs that modulate CD4+ T cells responses and monocyte derived dendritic cells (moDCs) capacity of present antigens. With RNA sequencing the authors identify selected sets of miRNAs, 22nt gene regulators that are loaded in and transported by small EVs into the target cells, exerting non-cell autonomous gene-regulatory functions. The authors performed an extensive characterization of the EV-miRNA content and their post-transcriptional modifications under variable conditions. Most modifications were observed at the edges of the canonical sequence, particularly at what they indicate is the the terminal nucleotide (position 0) and the two flanking nucleotide positions (-1 and +1). They identified miR-10b-5p, miR-92a-3p and miR-155-5p as interesting candidates that are selectively enriched in activated NK EVs. Interestingly, in vitro, these NK-EVs promote Th1 differentiation increasing IFNy and IL2 production. Mechanistically, EV-miRNA accumulation in CD4 T cells coincides with downregulated Gata-3 mRNA levels and increased T-Bet levels. T-Bet is a key transcription factor associated with the development of IFNγ-producing CD4+ T cells. Impressively, these in vitro findings were subsequently confirmed by using miRNA-laden gold nanoparticles.

The major strength of this study is the use of nano-sized nanoparticles that carry a selection of miRNAs which allowed the authors to evaluate the role of exogenous miRNAs delivered by nanoparticles in modulating immune cell functions in vivo. It is clear from these experiments that the miRNAs they identified work in concert to alter the physiology of their target cells, which is very exciting observation and fully in line of what we know how miRNAs function. Very rarely a single miRNA can change the phenotype of cells. Most controversy on the functionality of miRNAs carried by EVs is their suspected (not established) is a low stoichiometry. Indeed, miRNA function in repressing protein translation is dependent not only on its own expression level as many assume but obviously also on binding to effector proteins, subcellular localization, modifications, stability etc. How closely these nanoparticles mimic the function of NK EV-associated miRNAs with a suspected low stoichiometry should be the subject of further study. Indeed, the nanoparticles lack targeting proteins on their surface such as Integrins as has been described by the group of David Lyden.

One weakness of the current manuscript is that the data seems a bit disjointed, one part is very detailed in the analysis of EV-miRNAs and isomiRs (please also see my other comment on nomenclature) in the NK cells and their possible role as sorting motif for EVs. In addition, internal sorting motifs are described but not followed up upon. In the subsequent figures with functional studies, there is no more mention of said sorting motifs and modifications. Indeed, only miR10b-5p is one with extensive isomiRs when associated with EVs but whether these are involved in its function upon transfer has not been studied, making this part of the manuscript a bit descriptive.

One important limitation is that the authors used a standard NEBnext sequencing protocol with fixed adapters to profile the small RNAs in their EV fractions. Current modified protocols show much less ligation and PCR bias, casting doubt on some of their findings when looking for miRNA 'sorting motifs' and post-transcriptional modifications. In addition, the mechanisms shown are correlative in that increases in potentially transferred miRNA levels of the (recipient) CD4+ T cells correlate with downregulation of target genes such as GATA3. It remains unclear whether the EV-miRNAs indeed directly affect GATA3 mRNA levels. Nevertheless, these concerns are in part mitigated by their in vivo findings using the miRNA laden gold nanoparticles.

Reviewer #2 (Public Review):

The work investigates the role of SMAD4 in the biology of circulating and tissue resident memory differentiation, and provides the surprising finding that SMAD4 does not seem to be involved in TGFb signaling, rather to be independent of it. The work potentially provides a novel signaling mechanism at the basis of memory T cell formation, but should be carefully revised before acceptance, especially in confirming in vivo findings that were obtained in vitro.

Reviewer #2 (Public Review):

Choudhary et al describe a novel downstream impact of SF3B1 splicing mutations in MDS. Their findings support exon 6 retention in IRAK4, leading to TRAF6 mediated CDK2 ubiquitination. Next, their results demonstrate that IRAK4 inhibitors can reverse these effects to therapeutic benefit in vitro and in-vivo in SF3B1 mutant MDS models, including primary cells. Overall, the manuscript is beautifully written and their conclusions will be of broad interest to hematologists, immunologists and the scientific community at large.

Reviewer #2 (Public Review):

The manuscript studies how hypoxia impacts tissue-resident macrophage function and response upon influenza infection and lung injury. The authors , in culture studies, first determine an interesting differential response between BMDM and TRAM, such that TRAM was highly responsive to hypoxia. These cells have the ability to stabilize HIF and induce a glycolytic phenotype. The authors mimicked these effects using a pharmacologic activator of HIF. Interestingly, HIF activation was sufficient to decrease TRAM death and attenuated lung injury in mice.

Reviewer #2 (Public Review):

The authors wish to investigate how various allocentric representations, such as those observed in the brain's navigational system, can emerge from the interaction between action and sensory inputs. They use a predictive architecture, in which visual inputs are predicted from actions, to explain the emergence of multiple allocentric representations (HD cells, place cells, boundary vector cells). The major strength of the paper is the demonstration of the network's ability to develop spatial representations of multiple virtual environments and the demonstration that such representations can be used as a foundation to quickly represent new environments and to support further reinforcement learning tasks. However, the analysis is not yet sufficient to support a number of claims made in the paper about critical pieces of the findings. Further, two critical aspects of the model, namely the correction step, and the RNN-3 memory store, are not adequately described, rely on decisions that are not adequately justified, and their properties/significance are not adequately investigated. Thus, while the authors did demonstrate the emergence of spatial representation and the utility of their model, their presentation did not adequately support their conclusions. With significant revisions to the text and additional experiments/analysis, this work will have a significant impact on the field, and their model will be of further use to the community.

My major concern is that two critical aspects of the model, namely the correction step, and the RNN-3 Memory store, are not adequately described, rely on decisions that are not adequately justified, and their properties/significance are not adequately investigated, as discussed below.

Correction step

- In the results, the correction step is minimally described. However, the method is fairly involved. For example, lines 81-82 state that "visual information being communicated only by the activation of slots in the memory stores (Fig 1B)". Similar descriptions are given in lines 102-103 and 125-126. However, the nature of these predictions is not stated in the results or well-diagrammed in Figure 1B. It might help to specify, for example in the figure legend, that further details about this step are provided in Supplementary figure 1. As this is a crucial piece of the model, I recommend that at least a few more sentences be given to this step in the results, which outlines the high-level details of the correction step.

- In the methods, the description of the correction step is inadequate, it's given simply as G(x,x). While this may be appropriate for a machine learning conference proceeding, it's not appropriate for a general journal. The authors should include equations that specify G (as well as F), which could be included in the section "Sigmoid-LSTM and Sigmoid-Vanilla". Further, the authors might want to justify the need for an entirely new RNN cell, rather than another input to the existing RNN. In lines 318-319: "each x~ can be thought of as the result of a weighted reactivation of the RNN memory embeddings by the current visual input." It might be useful to explain the correction code as: "the expected RNN activation given the current visual input's activation of the memory cells".

- Lines 125-126 state that: "RNN-3 received no self-motion inputs, thus being dependent on temporal coherence, and corrections from mispredictions as its sole input". It's unclear why the corrections to this RNN are generated from "mispredictions", and not just visual "corrections", like in the other RNNs. Further, nothing in the implementation of the correction step enforces that it gives "corrections", only that it learns to incorporate information from the current visual input, via the memory store, to the action of the RNNs. They're just occasional information that the network learns to use to update the RNN state as best as possible. While this is presented as a correction, it's unclear what this RNN actually does. Does it learn to simply replace the existing x with what it should be from the memory store (i.e. a correction)? Or does it combine information from x^hat and x^tilde in some complicated way? To understand this, I recommend the authors could compare x^, x~, and x. During the correction step.

- Finally, the authors state that (Line numbers missing), "to correct for the accumulation of integration errors, the RNNs must incorporate positional and directional information from upstream visual inputs as well. This correction step should not be performed at every time step, or the integration of velocities would be unnecessary; in our experiments, it was performed at random timesteps with probability Pcorrection = 0.1." This entails a claim that for Pcorrection=0, errors will accumulate, while for Pcorrection=1, the integration of velocities will be "unnecessary". While this makes intuitive sense, no empirical justification for these claims is shown, and their implications for the model's function and representation are not demonstrated. I would suggest that the authors compare a range of Pcorrection values, for example, p=[1, 0.3, 0.1, 0.03, 0.01], and demonstrate how the network performance and spatial representation vary as a function of Pcorrection. Finally, though less important, it's unclear why this correction is probabilistic. This decision could be justified, e.g. with an experiment comparing the results of probabilistic versus deterministic/periodic corrections.

RNN-3 and Memory store<br /> This seems like a key feature of the model, yet its implementation gets very little attention in the results, and the description is conflicting and difficult to understand.

- Line 142 states that "the allocentric representations of RNN-3 were stored in the external memory slots as a second set of targets - being reactivated at each time step by comparison to the current state of the RNN-3". However, it's unclear what's meant by a "second" set of targets, or why this is unique to RNN-3. From the text, it seems that this could either refer to m(x)_3 (the memory map corresponding to RNN3), or s (the slots). However, from my interpretation of the methods as written, the m(x) parameters are learned, and s are activated by the joint activity of all three RNNs, not just RNN-3 (Equation 4). Why is this written as if it's a separate group of slots unique to RNN-3?

- Further, how is the activity of memory slots assessed? While I can imagine (though not found in the method), how the tuning curves of RNN-1-3 are calculated, because of the confusion with what this set of targets refers to I don't know how e.g. Figure 2E was calculated. I recommend this be included in the methods. Importantly, I recommend the authors expand the description of RNN-3 and its associated memory store in the results, and clarify its description in the methods section.

- Lines 320 and 322 states that the memory store contents corresponding to the RNNs m(x) are optimized parameters, while those corresponding to upstream inputs m(y) are not. However, Line 325 states that all contents are chosen and assigned (m(y), m(x)) := (y, x).

- Finally, no justification was given as to why RNN-3 was added. The authors justify the addition of RNN-2 by stating that "a single RNN receiving all the velocity inputs did not develop the whole range of representations" (Line 101). However, no justification is given for a third RNN that receives no input. As this is a key piece of the results, justifying and understanding its contribution is critical. Does this affect predictive performance, the ability to generalize to new environments, or utility for RL, or is it simply adding a representational similarity to hippocampal place fields and egoBVCs? I recommend that the authors show the results of a network with only RNN-1 and RNN-2, to justify the addition of RNN-3 and demonstrate its utility for prediction.

On the head direction attractor analysis<br /> - Lines 174-176 state, "To investigate how our model incorporates visual information in its representation of heading, we simulated the input of visual corrections (512 images from the training environment), However, this experiment does not tell you "how the model incorporates visual information", but only the response to selected images. The intuitive idea is that the network learns to map distal cues to specific angles, but not ambiguous images. To test this hypothesis, I would recommend that the authors compare the heading direction of the visual correction input to the direction on the attractor activated, i.e. to show that images that give an attractor point match the heading of that image. Further, because the corrections are given through an entirely different RNN cell (G), from that which (presumably) holds the attractor (F), I would recommend that the authors show how the correction input to G interacts with an existing action-driven point on the attractor via F. For example, what if an image is shown that disagrees with the current heading direction?

On the RL agent<br /> - Lines 200-202 state that "self-consistency is an adaptive characteristic allowing spatial behaviour learned in one environment to be quickly transferred to novel environments", and Line 223: "the spatial responses present in the SMP's RNN support rapid generalization to novel settings." While they've shown that the SMP can support RL and generalization, they haven't tested whether its spatial tuning is responsible for the performance. One way they could test this is to replace the SMP input to the RL agent with equivalent rate tuned units as inputs (whose rate is simply what would be expected from the tuning curve of each RNN unit). This experiment could be done for the pre-trained agent (to see if performance is maintained from the tuning curves alone, or if there's more information in the SMP that's being used), and possibly compared to a newly-trained agent.

Reviewer #2 (Public Review):

The research paper presents a modeling approach aimed at disentangling mother's genetic effects on their offspring in two components: prenatal environment and postnatal environment. Specifically, the authors use SEM on adopted and non-adopted individuals from the UK Biobank and leverage the variation in genetic similarities from different family structures. Because the UK Biobank is not created as an adoption study, they build seven different family structures to include all possible family combinations that can provide information regarding the two parameters of interest: those representing prenatal and postnatal environment respectively. The model is used on two phenotypes (birthweight and education attainment) to illustrate it.

The results indicate an 'expected pattern of maternal genetic effect on offspring birthweight' and 'unexpectedly large prenatal (intrauterine) maternal genetic effects on offspring education attainment. The authors mention this result can likely be explained by adopted offspring being raised by biological relatives. They then show simulations supporting this hypothesis.

We praise the authors for the complex analyses executed and the work done to create the model and make the scripts available to the research community. The models can be a valuable addition to the behavior genetics literature and to researcher's toolkit. We do however have a few concerns regarding 1. the meaning of the results, 2. model building decisions and the choice of sample and 3. the way some limitations are addressed. We go into more details for each of these points.

1. Interest to study mothers' genetic effects as acting via the prenatal environment or the postnatal environment and the meaning of the parameters tested by the model

I think this is an interesting question and a useful distinction for a number of phenotypes and the authors use the adoption design in an innovative way to define and estimate parameters that correspond to this distinction. However, I would suggest that the expressions of prenatal environmental effect and postnatal environmental effect (as distinct pathways for mother's gene to be expressed) seem to be an overstatement.

The definition of mother genetic effects (effects of mother genotype on their child phenotype, over and above any genetic transmission) is citing Wolf & Wade 2009 (line 56) which mention the more general notion of 'maternal effect' that are defined as effect of genotype, phenotype (or both) on their offspring. I would argue that postnatal maternal genetic effects (as currently defined in the paper) are likely environmental effect and not only 'genetic effects'.

These environmental effects are indeed partly influenced by mother's genes, but also strongly affected by other variables such as culture, generation, SES, education. It is not possible to disentangle these effects in the design(s) used here.

This consideration can affect the authors definition of the covariance between an adopted individual's genotype and phenotype as a function of prenatal (but not postnatal) maternal genetic effects (line 93-94). The authors current assumption does not consider the potential for environmental modulation of the effect of adopted mothers' genes (which are not zero for several phenotypes). Postnatal maternal genetic effects are thus also likely to capture and represent environmental differences.

2. Model building decisions specific to the UK biobank

One of the main issues is that the method is tested on a sample that is not built as an adoption design. This forced the authors to make decision to circumvent this problem and lead to important limitations that are not inherent to their method, but to the specific sample they applied it to.

a. Having adoptive parents partly genetically related to the child is breaking the logic of the adopted design. Thus, it brings back the genetic confound (passive gene-environment correlation) problem of usual family-based design. In their case, it alters their ability to differentiate between prenatal and postnatal environment.

b. In section starting on line 426, the authors have included simulations to show how this issue could be addressed. However, it does not help the fact that in their model applied to the UK biobank, the information regarding the degree of genetic similarity between adopting parents and biological parents and the child is unknown.

c. To address this problem in their analyses of UK biobank, authors used (Lines 302 & 417) information regarding whether children were breastfed or not (on the basis that this knowledge would be more common if the child was raised by a biological family relative) to identify adopted singletons raised by biological relatives. However, this is, at best, a mediocre index of genetic relatedness. I can see other reasons for participants to have knowledge of if they have been breastfed: because they were adopted at an older age, because they are still (or have been) in contact with their biological mother. It is also possible, albeit rare, that adoptive parents may breastfeed a child via the use of drugs to stimulate milk production. Line 420: the fact that the prenatal maternal estimate became non-significant after removing participants that were breastfed do provide results more in-line with what would be expected. But we can't use expected results as a basis to evaluate the validity of the approach. The absence of GxE and rGE are two other strong assumptions of the model that could also produce this kind unexpected results.

d. I would suggest discussing the issue of genetic relatedness between adopting parents and offspring in terms of passive rGE which is a common problem for the estimation of parental effects in every familial design.<br /> e. Line 291: why use an unweighted PRS for EY3 (Lee, 2018), while the usual way of computing PRS (as a weighted sum of risk alleles) was used for birthweight?

3. Limitations<br /> Assess other limitations of their method.

a. limitation of the availability of birth father information,

b. prenatal events uncorrelated with birthmother's genes (disease or accidents),

c. Inferring prenatal environment effect from higher birth mother correlation compared to birthfather is subject to bias from measurement differences between the two (Loehlin, 2016).

d. age at which the child is adopted (if the child has been partly raised by birth parents before adoption, it would bias (raise) the estimates of prenatal effects).

e. evocative rGE not mentioned. It has been shown that parents partly react to children's behaviors. Thus, the estimate of maternal genetic postnatal effects could be biased (lowered) by evocative gene-environment correlation. In other words, the model also assumes no evocative gene-environment correlation.

Final thoughts:

1. I would like a better case made for why it is important to distinguish genetic effects into prenatal and postnatal effect.

2. I would suggest the author make a clear distinction between the limits inherent to their sample (UK biobank) from those inherent to their methodological approach. I see important usefulness is plague by limits inherent to the sample used. At the same time, I am not aware of the availability of a big enough sample of adopted children with genotypic information available to compute PRS.

Reviewer #2 (Public Review):

The authors address a fascinating question: how can we map and identify homologous brain regions between the mouse brain (an important model organism) and the human brain. While previous studies have mostly focused on matching connectivity patterns or morphometric mapping, the authors propose a novel and imaginative approach: to directly register mouse and human brain into a common frame of reference using the spatial expression of homologous genes. To do this, they use the mouse and human whole-brain gene expression datasets from the Allen Institute. Using supervised learning, the authors show that matching gene expression patterns allows for finer-grained cross-species correspondence. In addition, they find that the sensorimotor cortex generally displays greater cross-species correspondence compared to the supramodal cortex.

This is an important step forward in identifying cross-species correspondences. The findings are sure to be of wide interest to the field and will inspire a lot of follow-up work. The work is written up and presented very clearly. The methods are rigorous and I commend the authors for providing their code openly.

Reviewer #2 (Public Review):

In this manuscript, Berryer et al describe a fully automated, scalable approach to quantify the number of synaptic inputs formed onto human iPSC-derived neurons (hNs) in 2D culture. They validate the sensitivity of their approach by synapsin1 knock-down and test almost 400 small molecules for their effect on synapses, and the role of astrocytes. They identify BET inhibitors as strong modifiers of synapse numbers in hNs and performed follow-up experiments to confirm the finding, characterize the effect further and demonstrate the critical role of astrocytes.

Every step of the protocol is automated to achieve high reproducibility and homogeneity throughout the experiments. This automated approach has great potential for scaling up drug screening, genetic perturbations, and disease modeling experiments related to synapses.

The authors successfully identified, in two independent hNs lines, three small-molecule inhibitors of transcription modifiers of the BET family as the strongest positive modifiers of synaptic inputs. The initial study performed with immunofluorescence was then validated by Western blot analysis and mRNA-seq analysis, which showed an increase in the expression of trans-synaptic signaling genes.<br /> While accessing the molecular mechanisms of BET inhibitors, the authors observed that the increased synaptic inputs occurred only in cocultures of astrocytes and neurons, and not in hNs monoculture. Finally, the authors report that the presence of astrocytes alone is a major driving force to promote synaptic inputs.

Overall, the experiments are well conducted, and the conclusions are supported by the data. The new approach reaches beyond the current state of the field, especially in the first steps of automation and the identified modulators (BET inhibitors) are interesting and novel, and the subsequent validation is convincing.

On the other hand, the manuscript does not yet define the exact resolution and power of the new methods, and does not convincingly show that the observed synapsin-puncta are synapses and that the data of the validation experiments can be improved.

Major points:

1. Although the manuscript contains a lot of quantitative data on variance, the current manuscript stops short of an exact definition of the resolution of the assay and its statistical power. With the real (measured) variance of the assay, the power to detect certain effects can be computed. To be relevant for other applications than the current (e.g. genetic perturbations and disease modelling), it is relevant to define this for smaller effects too: can this assay detect a 25% effect with reasonable numbers of observations? Such assessments can also provide important recommendations on when it makes sense to add more repeated measures of the same specimens (wells, ROIs) and when more independent inductions are required (and how much this adds to overall power). The manuscript would also benefit from a short discussion on how to optimize future study designs (repeated measures, independent inductions, number of subjects).

2. It is widely recognized that synapses formed in networks of NGN2-induced excitatory neurons only, may not model synapses in the real human brain very well (yet), especially not at DIV21. First, the authors can be more open/precise about this, e.g., in line 156 the authors indicate they use hNs at DIV21 because they are "electrophysiologically active" based on three references. However, (a) these references indicate that hNs cultures start to mature from DIV21 onwards but are not really mature yet, and (b) being "electrophysiologically active" seems not the most relevant criterion. Synaptic parameters like initial release probability, rise/decay time, and synchronicity are more relevant (none of which indicate synapses are mature at DIV21). Second, especially in the light of the claims the authors make regarding the effects of compounds on "synaptic connectivity" it seems essential to test, at least in a set of validation experiments, the distribution of postsynaptic markers. Synapsin-positive puncta may not be accompanied by a postsynaptic specialization and rather represent (mobile) vesicle clusters and/or release sites without postsynaptic partners. In addition, the authors claim synapsin1 is a pan-neuronal synapse marker. This is not yet validated for human neurons. A few control stainings with synaptic vesicle and active zone markers will secure this claim.

3. The analysis of the transcriptional effects of BET inhibitors is rather basic, especially given the rather strong claim: "BET inhibitors enhance synaptic gene expression programs". Which programs? Differentially expressed transcripts can at least be analysed further in terms of subcellular localization (pre/post) or synaptic functions, e.g. using SYNGO, also to address point 2 above.

Reviewer #2 (Public Review):

In this manuscript, the authors have set to demonstrate the function of P75NTR during the granule cell migration during cerebellar development. Authors have done a successful job in showing the link between P75NTR expression, granule cell proliferation, and migration status and have demonstrated functionally that the proliferative granule cell progenitors maintain high levels of P75NTR and the P75NTR expressing cells are not migratory due to the high RhoA levels. However, the link between the function of P75NTR and its role during cell cycle exit as the group has previously shown and the new role that they demonstrate here should be discussed further to parse the context-dependent function of P75NTR better. A wider audience would also benefit from further discussion of how these findings differ from others that implicate neurotrophin signalling and specifically P75NTR in neural migration throughout development in the introduction and the discussion.

Reviewer #2 (Public Review):

Abnormal accumulation and aggregation of amyloid-β protein are one of the main pathological hallmarks of Alzheimer's disease. It is well known that molecular chaperones play central roles in regulating tau function and amyloid assembly in disease. In this manuscript, Zhang, Zhu, Lu, Liu, et al., have investigated that Hsp27, a member of the small heat shock protein, specifically binds to phosphorylated Tau, which prevents pTau fibrillation in vitro and in a Drosophila tauopathy model. Using NMR spectroscopy and cross-linking mass spectrometry, the authors found that the N-terminal domain of Hsp27 directly binds to phosphorylation sites of pTau.

Overall, the study is important and provides the demonstration of interactions between Hsp27 and pTau.

Reviewer #2 (Public Review):

The work by Pond, et al., uses patient derived organoid monolayers to interrogate MAPK signaling in real-time using an ERK reporter. This technology was developed previously to use a target domain of ERK that responds to phosphorylation by altering nuclear-cytoplasmic localization. The active ERK kinase can be inferred by cytoplasmic localization of the reporter. The premise of the paper is that this reporter can be used in human organoid cultures to understand ERK signaling dynamics. Figures 1 and 2 demonstrate the monolayer culture properties and how stem-like and differentiated domains for within the cultures, validated using RNA FISH for MYC, LGR5, and KRT20. Figure 3 describes how an ERK wave radiates out from an apoptotic cell in the cultures, and that the living cells migrate towards to dying cell, presumably to sustain a barrier. In figure 4, data is presented showing that PMA-mediated activation of ERK disrupts the patterning of the monolayers, dispersing the nodes of cells associated with stem/proliferative identity. Finally, in figure 5, the authors show that treating cultures with Wnt3a suppresses ERK activity, while inhibiting ERK may expand WNT/stem cells in the cultures.

The study is interesting and the model system has a lot of potential.<br /> However, there are some concerns about the novelty. The reasons for this are:

1 - the monolayer system has been demonstrated before, very nicely in a 2018 Dev. Cell paper from the Altschuler lab and one of the current manuscript authors.

2 - ERK-KTR reporters have been used to demonstrate apoptosis induced signaling waves in the epithelium (Gagliardi, 2021, Dev. Cell.)

3 - ERK activity suppressing stem cell fate has been documented previously (Riemer, 2015; Leach, 2021; Reischmann, 2020; Tong, 2017)

So while there are exciting aspects of the work, including use of human tissues and live imaging of pathway dynamics, I feel that the novel discoveries using these technologies are somewhat limited.

Reviewer #2 (Public Review):

The authors propose a pipeline for investigation and analysis of cell devision which incorporates both hardware and software solutions. The described microfluidic image acquisition system allows for imaging single cell divisions in a very efficient way, using different types of imaging devices. The classification and analysis of the division events are done by employing modern neural network solutions. Here, the authors present a DetecDiv package, capable of automated classification of events and creation of survival curves for individual cells. This drastically speeds up the research compared to the manual analysis. The strong points include adaptation of the proposed pipeline to different imaging systems and microfluidic device geometries. The comparison of several neural network architectures is also convincing. The considered biological application support the conclusions of the initial research questions, even though those are not strongly motivated.

Reviewer #2 (Public Review):

This study addressed a broad question of the neuroscience of how diverse signals are integrated at the cellular level to generate adaptive behaviors. The authors systematically dissected the mechanisms of integration of diverse physiological cues in an olfactory neuron to change olfactory receptor expression.

Major strengths of the manuscript include (i) the broad investigations of many candidate pathways providing a big picture of the signalling working up-stream of str-44 expression regulation, (ii) the identification of compounds that activate str-44 and srd-28 receptors, and (iii) the connection that is made with cellular physiology and behavior to demonstrate the relevance of the mechanism into play. The pan-neuronal ribotagging data in fed and starved animals will furthermore constitute a valuable resource for neuroscience, particularly for the C. elegans community.

Aspects that could be improved relate to the representation of statistical test results and the validity/interpretation of experiments attempting to tease apart the "sensory" versus "metabolic" contribution of food deprivation. Whereas the main conclusions are largely supported by the data, and the proposed model is plausible, several aspects/results should be taken into consideration for a deeper discussion of the results.

Reviewer #2 (Public Review):

This study focuses on dental malformations, which can arise from problems in many biological steps. In the introduction, the authors describe that enhancer disruptions that contribute to craniofacial phenotypes such as nonsyndromic orofacial clefting and normal craniofacial morphology are also associated with dental morphogenesis or diseases. Among dental morphogenesis events, the authors' list in the text are caries, delayed tooth eruption, and abnormal tooth number. Caries is associated with the qualification of dental matrices and oral flora, delayed tooth eruption could be related to not only root formation but also bone formation that might be more related to craniofacial morphogenesis, and abnormal tooth number is hard to correlate between humans and mice since the dental formula is different and mouse incisor is ever-growing.

Reviewer #2 (Public Review):

Ansari et al. describes a web-based software for the design of guide RNA (gRNAs) sequences and primers for CRISPR-Cas-based identification of single nucleotide variants (SNVs). The use of CRISPR-Cas to rapidly identify specific mutations in both cancer and infection is an evolving field with good potential to play a role in future research and diagnostics.

The software described by Ansari et al. is easy to use for the design of guide RNAs. The most important question is how good the gRNAs that the software suggests are. As such, the manuscript would benefit from better describing parameter used for the gRNA design, as well as including more validation experiments. Clearly, the scope of the manuscript is not about developing the different detection methods, but I would argue that performing more wet lab experiments is needed to support the usability of the software.

Reviewer #2 (Public Review):

Huisman et al. report a method for surveying tens of thousands of peptides for MHC II binding using a yeast display-based approach. The method is shown to cover the SARS-CoV-2 and dengue proteomes, providing a wide-ranging picture of peptides that may be recognized by T cells in infection and that may be used to develop T cell-directed vaccines. Three MHC II alleles are tested, serving as a proof-of-concept for wider application to additional alleles for broadened coverage of human MHC diversity. In addition, the method is directly compared to a computational MHC ligand predictor.

The study has several strengths. Rigor is strong as the authors survey every 15-mer sequence in an antigen for binding MHC II using overlapping peptide libraries and consider various aspects of the peptide:MHC interaction in their yeast display-based system in defining what is a positive binder. In addition, there are important findings that emerge from the high-throughput MHC II binding approach, such as allele-specific binding preferences at defined positions in the MHC II binding groove, differences in binding motifs between randomized and defined peptide libraries that have implications for training prediction algorithms, and differences between experimental and computational methods for MHC II ligand discovery.

A discussion about the significance of the observation that the yeast display-based approach identifies MHC II ligands that are not found by NetMHCIIpan4.0 would enhance the paper. This is an important finding, on the one hand, because the method may provide new training data that will improve computational prediction accuracy. On the other hand, many of these sequences are low-affinity binders and may not be immunoreactive as peptide affinity drives T cell response (e.g., PMID: 16039577, PMID: 31253788). How this fits in the context of the oft-heard criticism that computational approaches overpredict would benefit the discussion, as well.

Related to this observation, the authors imply in parts (Abstract, Introduction) that the yeast display method is superior to computational predictions because it identifies MHC II ligands not discovered by computational algorithms, however, the current study is limited to three MHC II alleles, examines only one predictor, and does not provide evidence of T cell validation nor even discussion of the SARS-CoV-2 and dengue datasets in the context of published predictions, MHC II binding data, and immunological studies. The balanced approach taken in the Discussion where experimental and computational approaches are said to complement each other is constructive as it recognizes that both methods have advantages and disadvantages and is a good model for portraying their relationship in earlier parts of the paper.

Reviewer #2 (Public Review):

The principal objective of this work is to detail the basis for the enzyme's observed cooperative binding to dUMP, which was reported by the authors in a previous publication (Bonin et al. 2019 *Biophys J*). That paper showed (via ITC) that the binding of dUMP ligands to the protein's two identical sites cannot be explained by a simple thermodynamic model with a single affinity, but rather requires a cooperative model in which the second binding event is more favorable by 1.3 kcal/mol (~2RT), due in part to a much more favorable entropy change -TΔS. In this paper, the authors set out to test two possible cooperativity models consistent with that observation: (1) that binding of the first ligand results in stabilization of a binding-competent conformation (conformational selection), or (2) that a broad reduction in protein dynamics (conformational entropy ΔS_conf) upon binding the first ligand results in a smaller ΔS_conf penalty for binding the second ligand, and therefore a more favorable ΔG.

The authors perform an extensive series of sophisticated NMR experiments using a range of samples with specialized labeling patterns, particularly ILV methyl-13C, and ILV-methyl-13C-HD_2. These labeling patterns allow the investigators to record high-quality methyl NMR spectra on the large ~70 kDa hTS dimer, its Δ25 N-terminal deletion, alone and in complex with dUMP and dTMP. Insights into exchange dynamics come from 13C methyl CPMG and CEST relaxation measurements, which are sensitive to motions on timescales spanning µs to ms. Insights into ps-time scale dynamics and conformational entropy come from methyl-2H_1H_2 relaxation measurements, and are extrapolated using an empirical "entropy meter". Structural insights are obtained from measured and predicted amide 1H-15N residual dipolar couplings, solvent PRE measurements, and chemical shift perturbations.

The structural context is largely framed by prior reports of hTS crystallizing in two distinct conformations, termed 'active' and 'inactive' (Chen et al. 2017). These states are described (page 3) as differing by the conformations of an active site loop. The authors posit that if the enzyme is exchanging between these two states, with the 'inactive' state being dominant, binding of a first dUMP to the enzyme will shift the population towards the 'active' state, therefore favoring an additional dUMP binding event. The structural differences between the 'active' and 'inactive' states are not well described, however, and since the enzyme must bind both the substrate dUMP and its co-substrate MTHF, it's not entirely clear why this is a reasonable premise. RCSB coordinates 5X5A and 1YPV are used as the reference structures for the 'active' and 'inactive' states, respectively. Computed RDC data (Fig. 4) indicate that they are quite different, but it would be helpful to have a description of the differences in the structures, why it is reasonable to hypothesize that one or more of them might have different affinities for dUMP, and how the sampling of the other state might be manifest in the subsequent NMR data.

The authors indeed observe strong dispersions in methyl CPMG relaxation data for ligand-free hTS (Fig. 2), and more than one dip in CEST profiles (Fig. 1). However, these data (esp. CPMG) are not well described by a global exchange process with a single set of rate constants and populations, indicating a more complex exchange between three or more states (Fig S1, S2). (This point could be better described - the authors conclude that the data do not fit a 2-state model, but it would be helpful to describe in the main text the analysis that brought them to that conclusion.) Since the data are not well described by a two-state model, the authors fit the data to a three-state "BAC" model, in which the major state A exchanges with two other states, B and C; the A-B exchange is referred to as "slow" (~240/s) and the A-C exchange as "fast" (>2000/s)(Fig 2). It could be clearer why that model is preferred over alternative three-state models.

The authors compare backbone amide RDCs measured from the major state of the enzyme and its complex with dUMP with RDCs computed from the crystallographic "active" and "inactive" structures (Fig. 4). On the basis of its better agreement, they conclude that the major state is the "active" conformation. This may be a reasonable conclusion but merits additional discussion. Why are the predicted RDCs so different if the conformations only differ in a loop (as described on page 3)? Were the same alignment tensors obtained from the structural analysis? Provided the major state in solution is the "active" conformation, they conclude that they can rule out the conformational exchange mechanism of allostery. Again, this might be a reasonable interpretation, but it would be strengthened by describing the evidence that the crystallographically observed "active" and "inactive" conformations will have different affinities for dUMP.

The authors further examine differences in the methyl spectra between full-length hTS, which exhibits cooperative dUMP binding by ITC, and the Δ25 mutant, which does not (Figure 7). Since the methyl spectra are nearly superimposable, they conclude that the N-terminal region does not perturb the structure, though it is responsible for the observed cooperative behavior. Again, this might be a reasonable interpretation, but it is tempered by the inherent limitations of the observables, as the spectra only reflect the structure experienced by the labeled methyl groups, so the data are silent about other areas of the protein that might reflect structural changes.

Having ruled out structural changes and conformational exchange as responsible for the cooperative behavior, the authors quantify the intrinsic conformational entropy of the enzyme. They use the 2H relaxation rates of suitably labeled methyl groups to compute the magnitude of the order parameter S^2 of each labeled methyl axis. They compute the change in conformational entropy ΔS_conf using the change in S^2 for each methyl as a proxy, and an empirically-derived "entropy meter" (Fig. 5). From this analysis they find a larger 'unfavorable' entropy change upon binding dUMP than to TMP, meaning that a larger reduction in conformational entropy is associated with cooperative binding. The reason is that if more than half of this entropic penalty is paid upon binding the first ligand, the second binding event can occur with a smaller entropy penalty and thus a more favorable affinity. These are not unreasonable conclusions; however, there are significant uncertainties in the data and the underlying assumptions. At a minimum, these uncertainties should be considered and discussed.

The ΔS_conf conclusion at which the authors arrive is unfortunately mechanistically uninformative. In a statistical mechanical sense, a reduction in entropy arises from a reduction in accessible conformational states. Might one quantify the states that are excluded upon ligand binding, and one might gain an understanding of the link between structure (ensembles) and thermodynamics. The "entropy meter" approach is not informative about 'which' states are lost, only that a reduction in disorder, extrapolated over the full protein, is associated with a bulk change in entropy.

Reviewer #2 (Public Review):

This paper provides important new information that will be of high interest to scientists within the field of thermogenic adipose tissue. The application of xenographic implantation of human adipocytes progenitors provides a powerful approach to analyze the development of human thermogenic adipose tissue in mouse. The key conclusions of the manuscript are fully supported by the data presented.

Reviewer #2 (Public Review):

The authors developed a FACS-based assay for detecting endogenous plasma membrane cargo, which is easy to apply and can be adapted to various transmembrane proteins, given that specific antibodies are available. For our purposes, the authors used this assay and the highly efficient CRISPR screening system to study genes involved in plasma membrane targeting of the apical model cargo DPP4. The CRISPR screen in polarized enterocytes identified 89 genes critically involved in apical targeting of their model cargo, DPP4. This relatively moderate number of enriched genes resulted from the high stringency in the screening assay, namely sorting for cells with a drastic reduction of surface DPP4 (90%). Then, they selected seven factors for phenotypic and morphological characterization, focusing mainly on organelles associated with protein transport. Finally, they demonstrated that the KO of all selected candidates causes disturbed epithelial polarization. This effect was demonstrated by 3D cyst assays and EM analyses of filter-grown polarized monolayers. These results highlight yet uncharacterized regulators for epithelial polarization and propose potentially novel mechanisms for this process. It is fascinating the formation of striking, enlarged Lamp1, Lamp3, and Cathepsin-D-positive endolysosomal/lysosomal structures, upon KO of all cell lines that were additionally positive for DPP4 and stx3 in ANO8-, ARHGAP33- and TM9SF4-KOs.

In this study, the effort to work with several cell lines simultaneously and the presentation of the results in an organized and understandable way are meritorious. Nonetheless, it is a pity that no further analysis of any identified proteins and pathways has been done. In this sense, the functional analyses carried out are superficial and very little informative. Characterizing the endolysosomal pathway as one of the most common phenotypes among the specific KOs of screening suggests a general mechanism associated with this endocytic degradative pathway that could be relevant in the processes of polarization and epithelial morphogenesis. Indeed, something to be explored in the future.

Reviewer #2 (Public Review):

Putney et al. has recorded EMG from 10 muscles within tethered moths, manduca sexta, and then played them rolling, yawing, and pitching visual stimuli while recording the reaction torques of the tethered animal and the EMG. They then applied statistical models to correlate the neural activity of these muscles with the directions of roll, yaw, and pitches that were measured from the animal. They found that steering of the animal was mostly correlated with activity in the steering muscles, and found that the models needed time-related correlations in order to correlate an individual animal with its individual behaviour. There were, however, large variations between animals, preventing one animal's parameters from being able to be used for another animal.

The figures are professionally done, and it should be commented upon that the text is of high quality.

Reviewer #2 (Public Review):

This study evaluated the impact of rapid turnaround whole genome sequencing to discover unsuspected hospital-acquired SARS-CoV-2 on the incidence of hospital-acquired SARS-CoV-2 cases. Strengths of the study include the important question, the technical and logistic feat of making whole genome sequencing widely available, and the large number of participating sites. Major limitations of the study include the fact that only half of sequencing reports were returned to infection prevention programs and then only a small minority within the targeted reporting time-frame (5% for rapid phase, 21% for longer phase; median turnarounds were 5 days and 13 days respectively). This fundamentally undermines the premise of the study, namely to see if rapid turnaround of sequencing can impact infection control. More broadly, it does not appear that there was a standardized protocol on how hospitals were expected to respond to reports of clusters. Review of Table S2 suggests that many of the potential actions were things that in retrospect probably don't have too much impact on transmission (e.g. checking soap stocks, signage assessments). The kinds of things that I think might decrease nosocomial transmission include minimizing use of shared rooms, improving ventilation, increased use of N95/FFP2 respirators for source control, more frequent surveillance testing cadences, etc. These were not options on the response lists perhaps explaining the lack of impact on transmission.

Reviewer #2 (Public Review):

The manuscript describes a laboratory-based predator-prey experiment in which pike hunt shiner fish as a way to gain insight into the selective pressures driving the evolution of collective behavior. Unlike the predictions of classical theoretical work in which prey on the edge of social groups are considered to be at highest risk of predation, the fish in the center of the school were primarily targeted by the pike. This is because the pike uses a hunting behavior in which it slowly moves to the center of the school, seemingly undetected, until it rapidly attacks prey directly in front of its snout. This study also differs from previous studies in that both the predator and prey motion are examined, and the success of predation attempts was precisely determined. While the study demonstrates why shiners would be under selective pressure to avoid the center of a school, I am not convinced that the results explain why shiners evolved to have schooling behavior.

Major strengths of the paper include the precise recording of the location and orientation of all fish at all times during the experiments. This indeed provides a rich dataset that can be used to search for the factors that predict the likelihood of attack and escape with higher statistical power.

The major concern I have about the manuscript is that the results somewhat contradict the aim of the paper as expressed in the introduction and discussion: that predator-prey interactions explain the emergent evolution of collective behavior. Figure 2C shows that fish in smaller clusters or those that were totally isolated experienced lower rates of predation and were not included in any subsequent analyses. This would suggest that shiners experiencing predation from pike would be under strong selection to avoid schooling behavior altogether. Can you compare the likelihood of predation for individuals in non-central school locations compared to individuals outside of schools altogether? It might be helpful to investigate whether other predators of shiners use predation strategies that target prey on the edge of the school to help explain why schooling could be useful. Did the likelihood of schooling decrease throughout the trials?

I am also curious whether tank size affects the behavior of the fish, both of the shiners and the pike. The pike seem to be approximately 1/3 the shortest length of the tank, and 6 inches of depth have constrained the movement to be mostly in the 2D plane. A lack of open space might limit the pike's ability to hunt in any way other than this stealthy strategy. Has this stealthy hunting strategy been described in other experiments in larger or more naturalistic conditions? Does open space affect the shiners' propensity to school? Although the manuscript describes that shiners tend to school near the surface of water, does the shallow depth affect the pike's behavior? The manuscript states that some pike never attacked -- were these the largest in the study?

Reviewer #2 (Public Review):

The study presented by Zan He et al dissects the main interactions between malignant and stromal cells present in acral melanoma samples and in adjacent tissues using single cell RNA sequencing. The study describes factors that allow communication between the different cell types, with a special focus on macrophages, lymphocytes and fibroblasts, along with malignant cells. Factors playing a role in cell-cell communication are identified and suggested to be relevant prognostic makers and/or attractive therapeutic targets.

Historically, the study of acral melanomas has been neglected due to the low incidence among European-descents and this formed an important gap of knowledge in the field and hindered the development of effective therapies to control the disease. Therefore, studies that address this unmet need in melanoma research are very important and should be motivated. This includes single-cell sequencing studies that allow one to study the complexity of tumours, including microenvironment features that influence the development and effectiveness of certain types of treatment. The present study contributes information on how cells interact in the acral melanoma microenvironment and this could be a first step toward better understanding how these interactions influence acral melanoma development, progression, and therapy response.

However, there are a few points that should be carefully considered. The authors use 3 adjacent tissues (which in theory is composed of normal skin next to a cancer lesion), 4 primary tumor samples, and one lymph node metastasis as a model to study tumor progression. Adjacent tissue is not considered a stage of tumour progression and the sample size is too small to rule out sample-dependent effects. The study is descriptive in nature and could better contextualize the findings regarding what is known for other subtypes of melanomas or other tumours. This is especially important to help readers understand why it would be relevant to study cutaneous melanomas located in acral skin. It would be helpful to explain how different it is from non-acral cutaneous melanoma, and what this study adds compared to other single-cell studies from cutaneous acral and non-acral melanomas.

Reviewer #2 (Public Review):

CoVID models have, by necessity, exploded in complexity over the last year. The emergence of new variants with differential spread, the waxing and waning of population immunity, and the constant changes in reporting rates all seem to necessitate the addition of new internal model states and parameters. In the present study, Yang and Shaman have developed a robust methodology that can account for each of these complexities and applied it to reconstruct the first four waves of infections in each province of South Africa. Specifically, they employ an SEIR model with time-varying parameters estimated using a Kalman Filter. Although the model does not explicitly incorporate details such as the waning of immunity, it is present implicitly in the time-varying "population susceptibility" parameter. The authors validate their estimates of infection and CoVID-related death rates over time using seroprevalence, hospitalizations, and excess deaths, which were not used to calibrate the model. Furthermore, they have shown their model's ability to predict the course of waves that have already begun using retrospective predictions of past waves.

Despite the validity of these methods, it is not clear what conclusions can be drawn. The authors claim that their analysis shows that 1) new waves of infection are still possible, 2) large new waves of deaths can still occur, and 3) any new variant likely requires a loss of pre-existing immunity. Unfortunately, it is not clear how the modeling analysis presented supports these ideas. All three of these conclusions involve the emergence of new variants, something which the model may not be suited for. The transmissibility of new variants has been trending upwards, according to their analysis, suggesting that invasion is a combination of increased transmission and increased loss of immunity. Finally, the Delta wave was not accompanied by a large increase in susceptibility and instead appears to largely have been driven by seasonal fluctuation and increased transmissibility.

Overall, this work should be of great interest to those modeling CoVID or seeking to understand the history of the epidemic in South Africa.

Reviewer #2 (Public Review):

In this manuscript, Boddupalli et al. did a detailed analysis of the alterations caused by targeted deletion of GBA1, using new mouse models of GBA1-associated neurodegeneration. This included a study of the cellular, genetic and metabolic alterations in neurons, microglia, and infiltrating immune cells, caused by GBA1 deficiency. The work clarified some of the mechanisms driving neuroinflammation in neuronopathic Gaucher disease (nGD). The methods used, including single-cell RNA sequencing are state-of-the-art, and the data are of high quality. The results fully support the main conclusions of the paper, and the data sets will be useful to other investigators in the field.

This paper shows the deregulation of important neuroinflammatory networks at a single cell resolution level. Targeted rescue of Gba in microglia and in neurons of nGD mice reversed the buildup of glucosphingolipids with reversal of neuroinflammation. The authors also identified early biomarkers to follow disease progression and response to treatment, which were validated using sera from patients with type 3 and type 1 GD. The new markers identified included Nf-L and ApoE. Nf-L was elevated 2,000-fold in nGD mice, and there were also substantial elevations in adult GD1 patients compared to control adults. The authors also found significant differences in these disease markers between young and older GD1 patients. Nf-L and ApoE levels in the mutant mice and nGD patients were significantly reduced by brain-penetrant GluCer synthase inhibitors, further implicating elevated glucosphingolipids in the pathogenesis of GBA1-associated neurodegeneration, and validating substrate reduction as a useful therapy.

These results of this ground-breaking study are critically important for therapeutic development, as there is an unmet need to identify GD patients and GD carriers at risk for PD/LBD. In sum, the results presented are of great clinical significance.