Reviewer #1 (Public Review):

As mentioned, this paper sought to estimate the impact of flight bans on the importation of major SARS-CoV-2 variants, especially Alpha and Delta, which caused large outbreaks in The Netherlands. The analysis also investigates intranational spread, finding (as expected) that early importations into denser source regions facilitated spread to rural regions. Finally, the authors analyze variant growth rates in different age groups, concluding that although Delta spread especially well in younger adults, displacement of Alpha by Delta was comparably fast in all age groups.

The work is especially strong because of the systematic collection of sequences in The Netherlands over this time period, coupled with rich genomic surveillance in neighboring regions. It demonstrates conclusively that travel bans were too late to slow the rate of spread of multiple variants, including those that did not ultimately take off (Beta and Gamma), and that ground importation from other European countries continued through periods of varying restrictions. This is important evidence that flight bans, even in populations with good surveillance, are basically useless.

The major limitation of this work is that the sequences from The Netherlands and other countries are of course not "random." Biases in sequencing (relative to the underlying distribution of infections) could distort perceived flows between regions. This has not been fully quantified, but at the same time, this paper is not trying to make strong quantitative claims about the precise rates of migration. The sequencing program in The Netherlands during this period was one of the best in the world.

Overall, although the results here are not surprising, this paper constitutes a solid contribution to our knowledge of the spread of new variants to different populations.

Reviewer #1 (Public Review):

In "c-Myc plays a key role in IFNγ-induced persistence of Chlamydia trachomatis", Vollmuth et al. investigate how IFNγ controls Chlamydia trachomatis development and persistence via regulation of the transcription c-Myc following infection of human epithelial cells and human fallopian tube organoids. The authors show that: IFNγ induces the downregulation of c-Myc, a key regulator of host cell metabolism, in a STAT1-dependent manner; and that constitutive expression of c-Myc rescues Chlamydia trachomatis from IFNγ-induced persistence. The authors then investigate the relationship between IFNγ-mediated c-Myc repression and IFNγ-mediated tryptophan depletion on Chlamydia trachomatis infection, showing that Chlamydia trachomatis development necessitates both exogenous tryptophan/indole and stabilisation of c-Myc to overcome IFNγ-mediated persistence. The authors show that supplementation with tryptophan stabilises c-Myc, but conversely, that c-Myc exerts only a limited effect on tryptophan levels in IFNγ-treated Chlamydia trachomatis-infected host cells. Together, this suggested that c-Myc plays a role downstream of tryptophan-/indole mediated rescue of Chlamydia trachomatis development in IFNγ-treated cells. To further characterise how c-Myc achieves this, the authors perform metabolomic profiling in IFNγ-treated Chlamydia trachomatis-infected cells with or without c-Myc-induced rescue, identifying c-Myc dependent increases in TCA cycle intermediates and nucleoside metabolism as potential mediators. Finally, the authors show that IFNγ-mediated suppression of Chlamydia trachomatis development can be overcome in the absence of both constitutive c-Myc expression or tryptophan supplementation via supplementation of Chlamydia trachomatis-infected IFNγ-treated cells with TCA cycle intermediates or nucleosides.

Collectively, this work represents a promising study delineating the pathway by which IFNγ suppresses Chlamydia development during epithelial cell infection, and provides convincing evidence for involvement of the host transcription factor c-Myc. Although promising, the work does suffer from some limitations and concerns. These include an over-reliance at times on Western blots to assess Chlamydia proliferation (most notably in Figure 4 and Figure S4); these results would greatly benefit from additional microscopy-based analysis of Chlamydia proliferation, which the authors use to great effect in Figures 1-3. Additionally, the model linking IFNγ-induced tryptophan limitation to destabilisation of the c-Myc transcription factor via PI3K-GSK3beta is not fully supported by the current evidence. In particular, the authors suggest that c-Myc is destabilised by a lack of tryptophan in IFNγ-treated host epithelial cells during Chlamydia trachomatis infection via a pathway involving first dephosphorylation of GSK3beta that, in turn, results in phosphorylation of c-Myc on threonine 58, and thus ubiquitination and proteasomal degradation. However, the authors have not actually provided direct evidence of either increased phosphorylation of threonine 58 on c-Myc or increased proteasomal degradation of c-Myc as a result of decreased levels of tryptophan upon IFNγ treatment. Rather, the authors themselves provide evidence potentially contradicting such a model, showing that the phosphorylation of threonine 58 is unchanged upon IFNγ treatment. Despite the current limitations, the paper nonetheless shows promise and should be of broad interest.

Reviewer #1 (Public Review):

In the manuscript "Diversity of funnel plasmodesmata in angiosperms: the impact of geometry on plasmodesmal resistance", the authors show using electron microscopy that the recently reported funnel shaped plasmodesmata (PDs) occur in the phloem unloading zone of a wide range of flowering plants. They also compute flow velocity profiles on detailed PD shapes extracted using electron tomography for four different species. To better understand the implications of the funnel geometry, the authors then apply a simple model. Together, these results illustrate how funnel PDs facilitate phloem unloading, an important aspect of plant growth, and provide insight into why this happens.

Funnel PDs were first reported a few years ago by Ross-Elliot et al (2017) in Arabidopsis thaliana. The authors have adapted sample preparation protocols to become able to test how wide spread this specific PD geometry is. Based on their sample, it appears that funnel PDs occur in many if not all angiosperms. This implies that they are very relevant for the (growth) performance of most of our crop species. They have also extracted very detailed surfaces of funnel PDs in four different species using electron tomography and computed fluid flow velocities on these templates. These provide the community with a resource for assessing the worth of simple model geometries by computing the same profiles on those.

The manuscript, specifically including the modelling work, clearly adds to the growing awareness that the shape of plasmodesmata matters. This also implies that the details of plasmodesmata models matter, particularly when it comes to quantitative conclusions. This presents a challenge to the whole field: calculations on realistic PD shapes, as presented in figure 3, require a data quality that is often not feasible for sufficient numbers of PDs to capture the relevant variability among PDs within a single interface. Moreover, such complex models are not necessarily the most insightful. So, for an optimal connection with experiments, PD models should be as simple as possible, but no simpler.

In their modelling choices, however, I think the authors have made two simplifications that could have a large impact on their results. This holds even stronger for the manuscript in its current form, as the presentation of the results is mostly in terms of a few loose numbers, without any handles to assess how this depends on "hidden" model parameters like cell wall thickness. The results are described in a strongly quantitative way, whereas the numbers quoted are contingent on the parameters used and little effort is made to explore these dependencies or communicate them to the reader.

First, the authors opt for the simplest possible geometry (with desmotubule) that allows for gradual widening: they represent the outer lining of the channel as part of a cone. As the narrowest part of the channel most strongly reduces the flows through the channel, however, the details of the narrow region will have a strong impact on results and conclusions. One of the cited modelling papers, Deinum et al 2019, predicts based on their results with a multiple cylinder model, that the difference between immediate widening (as here) and widening after an initial straight ("neck") section could be substantial and have a significant impact on conclusions. For example, the O. sativa PD in figure 3B appears to have an approximately straight region of say 1/4 of its length, meaning that resistances could be reduced by at most 75% rather than the 98% and 94% mentioned in the discussion based on the geometry without neck. The flow velocity distributions calculated on the reconstructed PDs also seem to support this slightly more complex geometry.

Second, the models that are used for both diffusion and flow do not include the effects of particle size and particle behaviour in a narrow confinement. For the narrow end of PDs or the straight cylindrical geometries used as reference, this has severe impact (see Liesche and Schultz 2013, Deinum et al 2019 (both diffusive transport only) and Dolger et al 2014 (also including advection)). Including hindrance effects for diffusion and flow affects the scaling of the processes with local diameter, particularly for small diameters, and might make the quantitative results for diffusion and flow more similar, so without them, the suggestion that the model "supports the view that rapid phloem unloading in root tips proceeds mainly as bulk flow" is at best preliminary. On the other hand, including these hindrance effects, will make the "performance" of funnel PDs (or PD sections) relative to straight channels even more spectacular.

In summary, the model of funnel PDs presented here can best be viewed as a first investigation of the impact of the conical part of a funnel PD on the resistance of that specific part, rather than a definitive model of funnel PDs themselves. As such, the model is a valuable contribution to the field.

Even without improving the funnel PD model, the manuscript could become more insightful and have more impact if the current model with a few more calculations and some changes in presentation. I include only those could aid the reader in interpreting the results in the preprint.

When it comes to experimental determination of the model parameters, the focus on opening angle may be suboptimal. As actual funnel PDs are far from straight cone mantles, the precise determination of the opening angle will be terribly hard. What matters most for the model outcome, however, is the diameter of the narrow part and the length of the narrow part/how long it takes to widen to a (narrowest diameter dependent) critical diameter beyond which the resistance can be neglected relative to the narrow part. With that in mind, results could be interpreted more generally with respect to wall thickness by converting opening angle to inlet aperture/sleeve width. Computing such values, moreover, can make the same data "feel" different: For example, compare: (from preprint) "As a consequence, conical channels of 4 and 2 nm minimum sleeve width showed the same diffusive resistance as a cylindrical channel with an 8 nm sleeve at angles θ of only 1.5{degree sign} and 2.8{degree sign}, respectively" with (my calculation) {{[...] by increasing sleeve with from 4 to 14.5 nm or from 2 to 19.6 nm over the length of the PD}}. Importantly, the former statement depends on wall thickness, but the latter does not.

I find the strong focus on numerical values in the results and discussion sections misleading. It feels very exact, but in fact, these results are contingent on parameter choices (very importantly, wall thickness/PD length) and the simplified model used. Such numbers should never be quoted without the full context, but it is likely that that would happen anyway.

Finally, the modelling part is presented as an important aspect of the paper. It is, therefore, sad that the discussion of the model choices made is very minimal. There is no comparison of model choices between this model and other published PD models. While a relatively simple model compared to others could be justified as a starting point, the authors should at least try to estimate the impact of these difference on their results.

Reviewer #1 (Public Review):

In the present study, the authors investigate mechanisms of tumor cell resistance to T cell-mediated killing. They show that, in the presence of tumor-specific T cells, tumor cells form multi-cellular structures that protect the inner core of tumor cells via the prevention of penetration by lytic molecules. When these T cells are absent or removed, the tumor cells return to their single cells. Although neoantigen 'loss' has been shown as an immunotherapy escape mechanism, this study also shows in robust fashion that relapsed tumor cells share most neoantigens with their primary tumors. Overall this is a very thorough and rigorous study and the data firmly support the conclusions.

Strengths<br /> 1. Thorough and rigorous investigation of the hypotheses.<br /> 2. Data firmly support the conclusions.<br /> 3. Possible new mechanism of resistance to T cell-mediated tumor killing.

Weaknesses<br /> 1. Relevance to current immunotherapy treatment regimens is weak.<br /> 2. Figure legends overall need work.

Reviewer #1 (Public Review):

In this work, the authors sought to develop a better predictor of prognosis/survival for pancreatic cancer patients. They did this by beginning with a long list of measured quantities in patients, and feeding these features into a combinatorial family of many different multi-part machine learning models built from classic ML tooling in order by studying agreement between these models to identify a small subset of features (called the AIDPS) that could serve as a 9-gene biomarker with companion ML.

The major strengths of this paper are that it is clear about its goal in potential clinical impact, it does identify a biomarker set that shows improved accuracy for predicting prognosis in a way that may be useful for future stratification. The paper also carries out various follow-up analyses to characterize possible implications for the AIDPS signal in different biological, immunological, and clinical terms.

The major weaknesses of the paper:

The authors do not make very clear why this giant combinatorial family of ML models was generated. It's clear by implication that this is an approach to the challenge of feature selection. But if the problem here is feature selection, why did the authors take this approach to feature selection? Is it known that this should be a particularly effective one? Is it a particularly easy one to implement (it does not seem to be from the outside)? On the one hand, this isn't a machine learning paper, so it might feel not required to prove that a different method of feature selection couldn't as easily identify the genes that are important for this predictive problem, but the truth is that if this is not a machine learning paper, then in a sense the important thing is that the 9 genes in the AIDPS have been identified, and now what we need is to show this makes for a better predictor and try to focus on the interpretation of these features and their relationships. If that is the case, this paper takes a sidelong approach to that task, because of the identities of the genes or their success as features in a variety of ML approaches that assume those features should be focused (such as by using neural network architectures designed for greater interpretability)

The impact on the field of this work is currently unclear. The combinatorial ensemble of ML models makes for an unwieldy methodology that is difficult to interpret or duplicate, so if this is a methods paper focused on how to do better feature selection, it has not made that case well by not comparing to other methods of feature selection. On the other hand, if this paper is about clinical implications and the origin of the AIDPS gene set is beside the point, then staying mired in the original ML methodology used to select the features once we have found a good set of features to predict from and can throw other methods at those features and learn more by doing so seems the wrong path also.

Reviewer #1 (Public Review):

The present study by Zander et al. aims at improving our understanding of CD4+ T cell heterogeneity in response to chronic viral infections. The authors utilize the murine LCMV c13 infection model and perform single cell RNA seq analysis on day 10 post infection to identify multiple, previously unappreciated, T cell subsets. The authors then go on and verify these analyses using multi-color flow cytometry before comparing the transcriptome of CD4 T cells from chronic infection to a previously generated data set of CD4 T cells obtained from acutely-resolved LCMV infection.

The analyses are very well done and provide some interesting novel insights. In particular, the comparison of CD4 T cell subsets across acute and chronic infections is very exciting as they provide a very valuable platform that can answer a long-standing question: do CD4 T cells in chronic infection undergo exhaustion similar to CD8 T cells. While this has been proposed for an extended period, this new dataset by Zander et al. can provide some novel insights by comparing individual cell subsets cross-infection. The manuscript would, however, benefit from a more extensive analysis and focus on this interesting point.

On that note, the authors should take advantage of more accurate and present gene datasets to compare the 'dysfunctional' state of CD4 T cells in chronic infection vs acute infection. Also, a different illustration to demonstrate the module score analyses would be more intuitive.

Also, at multiple sections in the manuscript, the authors are missing the accurate citations as they are still mentioned as '(Ref)'.

Nevertheless, this study does not require major revisions.

Reviewer #1 (Public Review):

Amy L. Roberts et al. Investigated the health outcomes impacts of age acquired skewing of X-chromosome inactivation (XCI) ratios occurring in blood cells in 1575 females comprised of 423 monozygotic twins pairs, 257 dizygotic twins pairs and 215 singleton. They demonstrate: (i) associating between skewing and age; (ii) skewing was independent of other biological markers of aging such as smoking, telomere length, or DNAm Grim Age acceleration and frailty index (iii). (iv) that skewing was associated with a myeloid bias with an increase monocyte to lymphocyte and neutrophil to lymphocyte ratio; (v)a strong negative association with IL-10 level and skewing; (vi) skewing was associated with increase cardiovascular risk; and that (vi) skewing was predictive of cancer.

Age-associated XCI skewing has been described 25 years ago, yet this phenomenon remains enigmatic in terms of etiology and consequences. The study demonstrates the age-effect on skewing (which is known), but the evolution over time in a sub cohort of subject that were re-sampled. The originality of this study resides in the demonstration that age-associated skewing is associated with health outcomes. The particular strength of the study is the twin component (always aged-matched) and the iterative re-sampling of subgroup of the population study. Despite starting with a large cohort of 1552 individuals, most of the critical observations are made on smaller subsets of subjects: for ASCV risk score the total of subject is 231; IL-10 n=27. Fortunately, for the ASCV score this observation is re-enforced by the twin pairs comparison (N=34). The association with cancer risk is performed on a larger cohort (1417) and controlled for age. However, despite increased risk of Cardiovascular event and cancer, there is no significant association with overall mortality. This indicate that the effect of age-associated skewing is present but modest. The observation that there is a myeloid bias, which is associated with aging, that is more pronounced in subjects with skewing is interesting.<br /> One of the major unknown of this study is how much of the effect attributed to XCI skewing is in fact related to true clonal hematopoiesis or CHIP that is associated with both outcomes described in this study and will mascarade by changes in XCI.

Review #1 Public Review

Panda and co-workers analyzed RS fMRI recordings from healthy patients and from two types of comma: UWS and MCS. They characterized the time-resolved functional connectivity in terms of metastability (time-variance of the Kuramoto order parameter), spatiotemporal patterns via non-negative tensor factorization, and its relationship to the eigenmodes of structural connectivity. Finding greater metastability and non-stationarity of the DMN network in healthy MCS patients, than in UWS patients, they found that the best discriminators to classify the different DOCs are the number of excursions (non-stability) from the DMN, salience and FPN networks extracted by the NNTF analysis. Interestingly, the data-driven NNTF yielded a novel sub-network comprising the FPN and some subcortical structures. The excursions and dwell times from this FPN sub-network showed to be significantly lower in the UWS patients than in MCS. Surrogate data testing assures that the different methods and fits are effectively expressing the functional connectivity matrices measured.

Overall, I think that the results are correct and they advance in the characterization and understanding of the brain under DOC. However, some improvements can be made in the way the results, and the rationale behind them, are presented.

While reading the Results section, it is easy to have the impression of a disconnected set of analyses that just happened to be together. In particular, the section about the structural eigenmodes and their relationship with the time-resolved FC seems to have little connection with the rest of the work, except for confirming (yet again) that DOC patients have a less dynamic FC. More elaboration about the relevance of these results, and what they say about DOC (that other dynamical FC analyses don't), is needed both in the introduction and discussion. Although a clear explanation is given in the introduction, the bottom line seems to be yet another measure of metastability. Perhaps, a better explanation of what underlies the 'modulation strength of eigenmodes expression' will be helpful for distinguishing this analysis from others. How novel is the connection that is being done with the structural connectivity and why is this important? Moreover, the eigenmodes analysis has little-to-none importance in the discrimination of patients done at the end; thus, its place within the big picture is hard to evaluate.

Something that I find counter-intuitive and that may confuse some readers, is the (apparent) contradiction between the diminished metastability in the DOC conditions and the reduced dwell times (Figure S1; also "the inability to sequentially dwell for prolonged times in a different set of eigenmodes", as stated in the Discussion). Fewer excursions and shorter dwell times can only mean that some networks are just less visited and maybe this would be enough to distinguish between conditions. Further explaining this will help to understand better the implications of the work.

Finally, some comments about the connection(s) of these analyses with the commonly used FCD analysis (based on sliding windows of pair-wise correlations) will be useful, to put better this work into the big picture of time evolution of the functional connectivity.

Reviewer #1 (Public Review):

This manuscript investigates a role for YAP in replication. Previous work from this group has shown that Yap knock-down leads to accelerated S-phase and an abnormal progression of DNA replication in the frog eye. Here they extend this to show that YAP depletion accelerates S-phase and DNA replication in the frog embryo, and that YAP binds a DNA replication regulator called Rif1. Combing assays suggest that YAP acts on origin firing. This is an interesting new aspect of YAP function. I am not an expert on DNA replication, however, I feel that the manuscript would have been improved if more mechanistic insight was gained into how Rif1 and YAP interact, and how that interaction influences replication timing.

The title of the manuscript is "A non-transcriptional function of YAP orchestrates the DNA replication program". It is not clear that YAP "orchestrates" DNA replication - for this to be true, it would have to be signal responsive. Since the authors did not reveal any links to YAP activity (such as YAP phosphorylation or nuclear/cytoplasmic distribution) it is not "orchestrating" DNA replication.

Figure 1 shows that YAP is recruited onto chromatin after MCM2 and MCM7 and at the same time as PCNA and the start of DNA synthesis. Addition of geminin, an inhibitor of Cdt and MCM loading inhibits YAP loading onto chromatin. YAP immuno depletion leads to premature DNA synthesis or replication. Fig 1 B is quite confusing- the labeling in Figure 1B is likely incorrect.

Figure 2 investigates if YAP depletion affects origin firing or fork speed, using DNA combing. Fig 2A shows that there is increased activated replication origins and decreased distance between origins. The authors say that the increase of fork density is more pronounced than the decreased distance, suggesting YAP is regulating the activation of origins. The number of replicates is low. This is especially true for the conclusion that eye length is unaltered -it appears that there is a subset of eye length that is increased in 2F, which might reach significance if triplicates were performed.

The authors conducted AP-MS on egg extracts to identify proteins that co-IP with YAP. One of many proteins identified was RIF1 Figure 3 shows a co-IP with RIF1 and YAP. It is a very weak co-IP.

Figure 4 shows that YAP levels increase during development and that depletion of YAP or RIF1 leads to increased cell division. The authors use Trim-away to deplete YAP and RIF1 and find that depletion of either leads to an increased number of small cells. The YAP depletion shown in Fig 4B is clear, as is the increased number of small cells in YAP depletion or RIF1 depletion.

Figure 4 supplement 1 is arguing that trim away and morpholino combined are more effective. Quantitation of the western blots in panel A is needed for this to be convincing.

Figure 5 shows that RIF1 is expressed in the eye in RSC and that loss of RIF1 leads to a small eye. Panel B shows that by western blot analysis RIF1 antibody is specific. However, antibodies can have very different abilities in western vs staining. The RIF1 and YAP antibodies should be validated in staining. Also, the staining in Fig5C is at low resolution for both YAP and RIF1 and the identification of foci is unclear.

It is difficult to see the points the authors wish to communicate in Figure 6. There is almost no Edu in the YAP-MO, which questions the ability to recognize the different patterns in this region of the eye.

Reviewer #1 (Public Review):

The authors present a large body of data in a silkworm model with primary high plasma protein concentration, characterizing the model with altered proportion of differentiating blood cells, possibly delayed differentiation, increased phagocytosis capacity, transiently increased autophagy and apoptosis, with evidence of increased oxidative stress. However, some of the outcomes are not specific to a type of hematopoietic cell, others are an overinterpretation of results, and no mechanistic studies are presented, making for descriptive and somewhat incomplete evaluation. Finally, the manuscript is written in a difficult to understand manner, without clearly stated reasoning for why some experiments were performed (e.g. why was an endocrine agent used to reverse effects of a high protein model?) and the conclusions are extrapolations of insufficiently rigorous assessments to warrant those conclusions. Specific comments are listed below:

1) Figure 1B reveals that there are more cells early (48 and 96 hours) and fewer cells late (144 and 192 hours) in AM. It is unclear at what time point is the most relevant time point and whether fewer cells late suggests that more cells are dying or there is a differentiation block. The authors suggest in Figure 1C that there is a larger proportion of granulocytes late in AM relative to CK. What would be more helpful is to clarify which cell population is more or less prone to cell death and whether these patterns are a consequence of increased cell death in non-granulocyte lineage or increased differentiation toward granulocytes.

2) Although Figure 1D-1F demonstrate some significant changes, the main interpretable result in a decrease in Gcm expression in AM. However, this data is not specific to any lineage, taking all hemocytes together and clouding the specificity of the result. Also, Figure 1G suggests that Gcm expression is equivalent in CK and AM controls, in contrast to Figure 1F results, and Gcm loss by RNAi does not appear to impact the proportion of cells differently in AM vs CK. This raises the possibility the Gcm is not an appropriate marker to measure mechanism in the current system. The authors allude to Gcm involvement in plasma cell differentiation in the Discussion section (page 30) but the relevance to increased granulocytes is missing. It may be that the authors are suggesting that decreased plasma cell differentiation leads to increased proportion of granulocytes but this is not clear from the current manuscript and the data would be indirect evidence at best.

3) Figure 2 presents data measuring DNA replication. Although Figure 2B suggests that there is a delay in DNA replication in AM vs CK, the statistics are not included and there is no evidence whether if maintained longer, whether DNA replication returns to baseline levels. An alternative interpretative hypothesis would be that DNA replication is increased in later time points. Because there is no specific lineage of hemocytes and they are all increasing and decreasing at different time points, this finding is difficult to interpret. Furthermore, the Edu differences appear to possibly be driven by changes in granulocytes (Figure 2C) but this is not clear from the data presentation and no specific cell lineage effect is visible in Figure 2A. Figure 2D demonstrates evaluation of cell cycle from the 96-hour time point but it is unclear why this was selected and whether the results would be comparable if a different time point was selected. Finally Figure 2G-2H is presented without a clear explanation what this adds to the manuscript.

4) Figure 3 aims to evaluate signaling via the JAK/STAT pathway. However, it is unclear which STAT is relevant in the silkworm and whether changes in phosphorylation, which is typically how signaling occurs was evaluated. STAT mRNA expression and total STAT protein concentration are not sufficient to demonstrate signaling changes along this pathway.

5) Figure 4 presents data demonstrating increased phagocytosis in AM. However, as already noted in Figure 1, AM results in more granulocytes which have a strong phagocytic function among blood cells. Are the authors evaluating the specific function of granulocytes or all hemocytes together? Are there more particles per cell or more cells with particles? Without focusing the analysis on granulocytes specifically, the conclusion that AM has a higher phagocytic function is an overstatement.

6) Figure 6 demonstrates increased apoptosis in blood cells, especially granulocytes, but Figure 1 demonstrates more granulocytes. How do the authors reconcile this conundrum?

7) Figure 8 demonstrates use of 20E but the purpose of this approach is not made clear. Furthermore, the treatment is applied at 24 hours after modeling and presented in a way that cannot be directly compared with data from treatment applied at time 0. In addition, no CM treated with 20E or JAK inhibitor A490 is presented for comparison. It is altogether unclear the mechanism by which several of the presented parameters are restored. Again, signaling via JAK/STAT cannot be assessed on the basis of mRNA expression of STAT. Finally, JAK inhibitor A490 does not appear to have a cell specific effect in Figure 8I, confounding the results in Figure 8H.

Reviewer #1 (Public Review):

The study addresses the question of what happens in each cortical layer of the severed limb's primary somatosensory cortical representation when the spinal cord is transected. The assessment is made with 32 iridium wire electrodes in a single vertical array at 50um intervals in the hind-limb area. Spontaneous activity is monitored under urethane anaesthesia before and after transection. Responses to electrical stimulation of the forepaw are assessed by field responses.

The study finds that electrically stimulating the forelimb evokes responses in the hindlimb area almost immediately after transection where none existed before. This is a good demonstration of an effect that has been known for some time. A large amount of the literature on cortical layer specific effects of sensory deprivation is missing as are references to the seminal studies of Calford and Tweedale. In particular one might consult Calford and Tweedale 1988, 1991 in flying fox (bats) and 1991 in macaque monkey cortex. Jacob et al 2017 regarding layer specific effects of sensory deprivation may also be of particular relevance given the changes in layer 5 versus layer 2/3 responses. The incremental advance in knowledge comes from a description of the effects by cortical layer. The infra granular layer responses increase in size and, perhaps counterintuitively, increase in latency while the supragranular layers show changes in spontaneous activity.

In general, the value of the research is lessened by using electrical rather than natural stimulation, thereby rendering the stimuli saturating, hyper-synchronous and ultimately unrealistic. Electrical stimuli will synchronously stimulate all afferents including myelinated and unmyelinated fibres synchronously, a situation that normally does not arise unless you electrocute yourself, which is obviously very painful. Any surround inhibition that might have been present will be overridden by this level of wide field high intensity stimulation. It would have been useful to know how individual digit receptive fields appeared in hindlimb cortex for example and whether they maintained some level of somatotopy. Tactile stimuli will be more relevant to the human SCI that I believe the authors are trying to understand.

It is also disappointing that the electrodes were not implanted chronically before the recording session as 40 minutes is unlikely to give sufficient time for recovery of the cortex to insertion of the electrodes. The lower levels of responsively in superficial layers in particular are probably due to spreading depression brought about by the 32 iridium wire electrode. A different result might have resulted from implanting the electrode chronically and starting recording several weeks after the damage and inflammation had abated. It is also not clear that the observations on gamma in the urethane anaesthetised state are relatable to the situation in awake animals where gamma waves usually occur.

I wonder whether the conceptualisation of the study as a sensory deprivation is entirely accurate. The spinal cord transection would presumably have cut axons from cortical cells in the cortico-spinal tract. What would the effect be on these neurones in the cortex? Since they consist some of the larger cortical neurones they may contribute more to the field potentials being analysed than some of the other cells.

Reviewer #1 (Public Review):

This work attempts to provide a novel understanding of the neural computations driving bottom-up attention. The Reynolds & Heeger normalization model of attention (NMA) can explain a variety of physiological and psychophysical findings in the attention literature but is agnostic as to the role of awareness. This paper provides findings to fill this gap with a series of novel experiments that ultimately conclude that bottom-up attention with awareness is driven by a bigger attentional scope (attention field size) whereas without awareness leads to a smaller attentional scope consonant with changes in the attention field parameter of the NMA. Ultimately, the authors suggest an awareness dependent constraint is necessary in the NMA.

Strengths:

1. It addresses an important gap in our understanding of how visual attention and visual awareness interact.

2. It draws and tests predictions from a well-established computational theory that has reconciled a variety of attentional effects on physiological and psychophysical responses. By anchoring its predictions on extant theory, the study has a principled foundation from which it can decipher the computations underlying awareness.

Weaknesses:

1. The construct of awareness should be defined and operationalized. It is clear that the study operationalizes awareness as the visibility of a masked cue. However, this only becomes apparent during the Results section.

2. It is stated on pg. 3 that "it's unclear whether there is a common neural computation governing bottom-up attention-triggered improvement in visual performance with and without awareness." This point should be further elaborated. Specifically, in the current literature, what is clear and what remains unclear about the computations underlying awareness and attention? The known computational underpinnings of awareness and how the current study can add to what is known should be discussed.

3. The behavioral protocols conflate the effects of cue and mask. In both Distribution and Normalization experiments, cue location and mask contrast (high contrast in the "invisible" condition and low contrast in the "visible" condition) are concurrently manipulated. Consequently, one cannot isolate the effect of the cue from the impact of the mask on task performance.<br /> Although a manipulation of cue contrast was performed during the Distribution experiments, it does not completely rule out interactions between the cue and mask. With low luminance cues, cueing effect amplitudes decreased while attentional spread (i.e., scope) appeared to remain unchanged (Figure 2A vs 2D). This pattern suggests separate influences of the cue and mask, but it remains impossible to determine how the lower visibility of the cue (and thus the lower awareness thereof) affects the distribution of attentional effects and whether cue visibility interacts with or is independent from masking stimuli effects.<br /> A control experiment should be provided to ensure that attention is driving the reported behavioral effects.

4. The authors suggest an awareness dependent constraint is necessary in the NMA. However, no extension of the model is provided in their modeling efforts nor proof that a constraint is necessary to capture the awareness results.

5. Behavioral protocols for the Normalization experiment discourage any spatial distribution of attention. Unlike the Distribution experiments where cue or target position varied, the Normalization experiments used a single cue position and a single target position on the left and right of fixation. The fundamental assumption being made is that the spatial spreads of attention (with and without awareness) are the same in the Normalization and Distribution experiments. However, this assumption is not verified nor is it consistent with previous studies that manipulated the spread of the attention field. For instance, Herrmann et al., 2010 (cited in the manuscript) show that subtle adjustments of the target's position on a trial-by-trial basis lead to changes in the spread of bottom-up attention that modulate performance in a manner consistent with the predictions of NMA. Thus, locking the target into a single position would change the spread of attention relative to when the target's position varied.<br /> Moreover, the conclusions of the Normalization experiments become circular without an independent verification that the spread of bottom-up attention remained identical with the Distribution experiments. For example, the study cannot conclude that attention without awareness has a smaller spread because it elicited response gain modulations. Had the Normalization experiments used a similar cueing or target uncertainty protocol as the Distributions experiments, the study's conclusions would be better supported.

6. The contrast response functions (CRFs) are coarsely sampled within the dynamic range. As a result, any conclusions about modulations of c50 (i.e., contrast gain) lack adequate support.<br /> When measuring contrast gain modulations, it is critical to finely sample the CRF to obtain accurate measures of c50. Previous simulations (García-Pérez & Alcalá-Quintana, 2005 Span. J. Psychol.) recommend at least five levels of contrast within the dynamic range when using a method of constant stimuli protocol, as is utilized in the current study. However, in this study a total of five levels are tested and only one exists within the dynamic range (8% contrast) whereas the next highest (15% contrast) borders the upper asymptote. Consequently, all but one c50 estimate exist between these two contrast levels (Figure 4D & 4E).<br /> Drawing conclusions and extracting model parameters based on the estimate of a single data point will lead to spurious and unreliable results. A hint of this weakness is apparent in Figure 5B:<br /> a) The NMA fit to the data shows response gain modulations that are as large as the observed contrast gain modulations.<br /> b) The positive correlation between the measured bottom-up spread and the NMA's attention field size is largely driven by three data points whereas the majority of the data points show a near-zero simulated attention field size.<br /> Contrast gain modulations constitute half of the study's conclusions regarding awareness and the NMA. Thus, a finer sampling of the CRFs is required to lend support to the study's main conclusions.

7. Most of the discussion focuses on ruling out possible interpretations rather than providing new insights regarding why awareness may modify bottom-up attention.

Reviewer #1 (Public Review):

1: The authors formulate competing hypotheses on the behavioral impact of alpha oscillations using signal detection theory (SDT) (Intro and Fig. 1). SDT is indeed well suited for this, as it is used to compute the orthogonal behavioral metrics d' (discriminability) and criterion (bias). However, soon the authors write:

"The higher d' for conservative trials may be due to the more skewed mapping between the false alarm (FA) rate to its Z-value in our d' computation. Specifically, when criterion (or the decision boundary) intersects the noise distribution at its right tail, small changes in FA rate are nonlinearly exaggerated after Z-transformation. As we did not observe a difference in accuracy between conservative and liberal trials, which is a more robust measure of perceptual discriminability when target presence rate equals 50%, we argue that the observed statistically significant d' difference is equivocal."

And also:

"For the binning analyses, we mainly focused on the percentage correct (i.e., accuracy),<br /> and hit and FA rates, because these metrics scale linearly (as opposed to d', which scales<br /> nonlinearly as the hit rate increases or FA rate decreases linearly) and are well defined for both<br /> behavioral data and MVPA outputs."

And indeed from Fig. 3 onwards they do not really use SDT anymore, which is confusing given the Introduction and Fig. 1. I think it's also problematic, as accuracy, hit-rate and fa-rate are not orthogonal and are therefore much less suited to arbitrate between their competing hypotheses. As a result, I'm not convinced the paper accomplishes what it sets out to do in the Introduction.

2: Related, if indeed the authors choose to deviate from SDT, they should put the metric "% yes-choices" on equal footing with accuracy. For example, in Fig. 3A, we can see that alpha oscillations predict a reduction of hit-rate as well as fa-rate; this suggest that the main effect is actually on choice bias (% yes-choices) rather than accuracy. If that's true, then the title of this manuscript is misleading.

3: Have the authors considered to test for non-monotonic effects of alpha oscillations and cortical computation and behavior?

4: The authors use challenging and sophisticated methods, but these are introduced very casually. For example:

"To obtain a more fine-grained picture of the alpha power modulation of behavior, we applied generalized linear mixed models (GLMMs; see Methods) to account for both between-subjects and within-subject trial-by-trial response variability, and to estimate the effects of alpha oscillatory power on d' and criterion simultaneously."

And:

"To evaluate the quality of visual information coding, we used multivariate pattern analysis (MVPA), operationalizing the quality of visual representation as the neural classifier's classification performance. We used the priming trials to train binary classifiers to classify target-present vs. absent trials in a time-resolved manner, [...]".

It would help a lot if the authors could unpack their rationale some more. For example, why did they consider between-subjects effects, and could they show some scatter plots with between-subjects correlations before turning to the GLMM? Also, what is the question the authors wanted to answer that required training the classifier in a time-resolved manner (which I like, on a personal note)?

5: Throughout, the label "liberal trials" is odd, given that group-average criterion > 0 on those trials (Fig. 2C).

6: It would be nice to explicitly bridge to the literature on (pupil-linked) arousal predicted shifts in decision-making, and to findings on the relationship between alpha oscillations and (pupil-linked) arousal.

Reviewer #1 (Public Review):

1. The authors are interested in understanding how NMDAR blockade (via Ketamine) affects the relationship between content-predictions and behavior. To do so, the authors need to establish, first, how behavior, including not only reaction times (RTs) but also accuracy, and the inverted efficiency score (IES, which quantifies the relationship between accuracy and RT), change as a function of (1) predictions regardless of drug manipulation and of (2) drug manipulations regardless of predictions. In other words, did predictions increase overall accuracy, shorten RT, and reduce IES in the pre-drug condition, regardless of drug manipulation? did Ketamine (compared to DEX and to pre-/post-drug) affect overall accuracy, RT, and IES, regardless of predictions? After having established these effects in isolation, the authors can analyze the interactions between prediction and drug manipulations on different behavioral measures and understand whether the effects reported are specific to RT.

2. The authors are interested in understanding how NMDAR blockade (via Ketamine) affects the relationship between content-predictions and neural oscillations in the alpha and beta bands. The authors focused on cue-locked induced activity (baseline-corrected) during the delay window, after the predictive auditory cue and before the to-be-predicted visual stimulus. From the current results, it is unknow whether and how both ketamine and the control drug DEX affect resting-state/ongoing neural oscillations, i.e., in the baseline period. Are the results due to a change in resting-state oscillations or to oscillatory processes induced by predictive cues?

3. The authors state that delay-period (pre-image) alpha power correlated with predictions. To fully understand this correlation, it is important to establish, first, whether, in the pre-drug condition and regardless of prediction manipulation, pre-image alpha power (as opposed to pre-cue alpha power), is related to any behavioral measures (accuracy or RT) using a standard approach (e.g., binning by alpha power; in line with previous studies), rather than the more complex HDDM. Do predictions or drug manipulation modulate this existing relationship between behavior and alpha power? Are these results specific to any ROI?

4. The authors discuss the relationship between alpha oscillations and excitability. I wonder if the authors mean physiological excitability (i.e., neuronal ensemble firing activity) or something else. This is unclear from the reference cited (32-33) since they refer to studies showing a relationship between alpha power and perceptual reports, not physiological excitability. The authors could consider citing here the invasive studies reporting a link between alpha and neuronal excitability (Chapeton et al., 2019; Haegens et al., 2011; Watson et al., 2018; Bollimunta et al., 2008, 2011; Lundqvist et al., 2020; Dougherty et al., 2017; van Kerkoerle et al., 2014). Additionally, I wonder if the authors could elaborate more on how increased excitability may be related to a decreased SNR. This is particularly important to understand a strong statement the authors made: "These results suggest that ketamine blocks predictions by reducing frontal SNR." What are the neurophysiological mechanisms behind this process?

5. The analysis of the ERP is subpar. The authors focus only on an arbitrary channel and a single limited time window. Accordingly, null effects on ERP should be interpreted with caution and statements like: "This was not due to feedforward sensory processing, as all three sounds generated similar auditory ERPs (Figure S3a)." are unwarranted. To improve this analysis, the selection of channels could be participant-specific (e.g., pick the electrodes with strongest early ERP component for each participant) and the statistical testing could be run on the entire time course, for example, using a cluster permutation test across the time dimension. The authors need to address how predictions and drug manipulations affect ERP time course, focusing especially on very early components (<100 ms) which are more likely to reflect feedforward activity. I want to highlight that the authors assume that the ERP is a direct measure of feedforward activity: however, accumulating evidence suggests that the ERP in part reflects prestimulus/baseline oscillatory activity (baseline-shift, Nikulin et al 2007, Iemi et al 2019). Accordingly, any changes of prestimulus oscillatory activity might results in changes in the ERP. Please be cautious when equating ERP to feedforward activity. Were both visual and auditory ERPs analyzed?

6. I found the statistical results difficult to follow and incomplete. The authors refer to two analysis: regression, and repeated measures ANOVA. why were two types of tests used? What are the repeated measures in the ANOVA? is the regression single-trial? I wonder if the authors could build a more complete statistical model including all possible tests (for example, before/after drug, drug1/2, prediction condition, and interactions), thus addressing the issue of running multiple tests on the same data that is present in the current analysis. Additionally, the description of the statistical results is incomplete. Please include all statistical information for each test: e.g., whenever you report a p value, also report the corresponding statistics. Specifically, when you use ANOVA, please include F statistics, degrees of freedom (with Huynd-Feldt correction if necessary), and, critically, run post-hoc comparisons bonferroni-corrected for multiple comparisons (report t statistics and p values of relevant comparisons). While the bar plots are very helpful, they lack statistical significance lines that are necessary to understand the relevant comparisons. Additionally, please report null effects throughout the paper with an appropriate statistic allowing to test for the null hypothesis (i.e., Bayes factor Analysis). This is especially important to understand whether the effects reported are frequency-specific or specific to a region of interest.<br /> I found some statements unclear.<br /> - "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)." Was the difference significant? Can this statement be substantiated with a statistical test (e.g., Bayes factor)? Can you control for accuracy differences in the RT analysis?<br /> - "ketamine average modified observer's assessment of alertness/sedation (OAA/S) score of 4.85 compared to 3.33 under DEX (5, awake - 1, unresponsive))." Can this statement be substantiated with a statistical test?<br /> - "The interaction effect confirmed that, under ketamine, delay period alpha power at the RF electrode cluster was similar across sounds (Figure 2e)." I don't understand what the significant interaction effect means if the alpha power was similar across prediction conditions. Please always explain what the significant main and interaction effects mean.<br /> - When writing: "We ran a regression analysis of prediction (HP, MP, NP) X drug condition (before drug, under drug; drug either ketamine or DEX).": please spell out the dependent variable.

It's necessary that the authors are clear about whether the effects they report are specific to a certain frequency band, region of interest in sensor or source space. Please report null effects. Why are some analysis presented in sensor space and other in source space?

Reviewer #1 (Public Review):

This study identifies a novel Shank3 mutation from individuals with ADHD-like syndrome and tests the impacts of this mutation together with other known Shank3 mutations on protein-protein interactions of Shank3 involving the N-terminal SPN and Ank repeats. The results indicate that Shank3 mutations have diverse impacts on the intramolecular SPN-Ank domains and the interaction of Shank3 with other proteins including delta-catenin, fodrin, and CaMKIIa. Overall, the results of the study are novel and of high quality. Considering the lack of detailed biochemical understanding on various Shank3 mutations with regard to their association with PMS, ASD, and schizophrenia, this study is a meaningful step forward.

Reviewer #1 (Public Review):

Park and colleagues examined the activity and functional role of different projection neuron types in the primary motor cortex in mice performing a joystick-based forelimb movement task. The authors characterized the activity of pyramidal tract (PT) neurons and intratelencephalic (IT) neurons using electrophysiological recordings with optogenetic tagging as well as cell-type specific single cell calcium imaging. The results showed that the activity of IT neurons was better correlated with movement kinematics (reach amplitude) and contained more information about kinematics compared to PT neurons. Optogenetic inactivation of IT neurons caused a marked reduction in reach amplitude and velocity while inactivation of PT neurons had smaller effects. Inactivation of PT neurons caused a significant transient perturbation in reach direction while the change caused by IT neuron inactivation did not reach significance. Recording from neurons in the striatum, the authors show that IT neuron inactivation caused reduction in the neuronal firing in the striatum while PT neuron inactivation caused an increase.

This study addresses a long-standing, important question in the motor cortex: the role of different projection neuron types, PT and IT neurons, in motor control. The authors performed a series of very interesting experiments and obtained an important data set. The results are presented clearly in general (but see some questions below). The authors' conclusion that IT neurons have predominant role in determining movement amplitudes while PT neurons regulate movement trajectories (presumably by determining "muscle identity") is potentially very interesting. However, there are some interpretational difficulties in the inactivation experiments. It would be helpful if the authors discuss some of the caveats of these experiments.

Major issues:

1. Interpretation of cell-type specific inactivation experiments does not appear to be straightforward. As shown in Figure 3a and b, optogenetic inactivation of PT neurons or IT neurons caused a significant increase in unidentified neurons. Therefore, it is unclear whether the results of inactivation experiments are due to intended inactivation of opsin-expressing neurons, or due to off-target effect due to activation of the rest of the neurons. For instance, activation of striatal neurons during PT neuron inactivation may be due to indirect activation of IT neurons (Figure 7d). Furthermore, the effect of PT inactivation on movement trajectory may be due to IT activation rather than PT inactivation (e.g. activation of some neurons might have caused an abrupt movement). Besides these specific cases, all of the inactivation experiments are likely confounded by off-target activations. These caveats are inevitable in these experiments generally, and the results need to be interpreted carefully.

2. One of the main points of this study is that PT inactivation caused a lateral deviation in movement trajectories but IT inactivation did not. However, this result is poorly analyzed. If movement trajectory can deviate in both directions in x-axis, comparing the average trajectories between experimental and control animals might not be very informative because deviations in opposite directions can cancel out. The number of animals is also relatively low. The results should be analyzed more thoroughly to convincingly make the authors' conclusion.

3. The authors claim that mice adjusted movement amplitudes based on thresholds set by the experimenter but the result is not very convincing. The presented result is, for instance, consistent with a time-dependent gradual increase followed by asymptotic performance, followed by some fatigue. To demonstrate that the animals actually followed the thresholds, randomizing the order of blocks would be helpful or required.

4. A previous work from the Komiyama lab (e.g. Peters et al., 2017; PMID: 28671694) has reported that a large fraction of corticospinal neurons decrease their activity at or after the onset of forelimb movements. It would be good to cite this work.

Reviewer #1 (Public Review):

This study uses a long-term dataset of cooperatively breeding starlings to calculate lifetime fitness of two different male strategies: natal philopatry (staying to help at the natal nest site) or dispersal. Both strategies co-occur in the study population, resulting in a "mixed-kin society". The authors test (1) whether pre-natal or post-natal environmental conditions can explain the likelihood of male dispersal, and (ii) whether dispersing results in higher lifetime fitness than staying at home.

The results reveal that pre-natal environmental conditions, rather than post-natal conditions, are the best predictor of male dispersal (males disperse more when pre-natal conditions are good). The results also show that males that disperse have similar lifetime reproductive fitness to those that do not. The authors conclude that high variability in environmental conditions, such as occurs in the habitat of the study species, may generate mixed-kin societies, as such environments oscillate between favouring dispersal and favouring philopatry. The conclusions of the study are well-supported by the data.

The authors discuss that the mechanisms explaining why pre-natal (and not post-natal) environmental conditions predict male dispersal are not known. They propose that parental effects (e.g. physiological or epigenetic) on offspring could explain this relationship. A possible alternative explanation that is not discussed is that parents' behaviour toward male offspring is affected by pre-natal conditions, such that parents sometimes force/encourage their sons' dispersal. In either case, the mechanisms underlying this observed relationship remain for future study.

Overall, this study makes a valuable contribution to our understanding of the factors that drive vertebrates to either stay and help at the nest or disperse to new territory, and thus the drivers of mixed-kin societies containing both philopatric and dispersing individuals.

Reviewer #1 (Public Review):

Other reviewers will concentrate on the VSG expression aspect, so I will comment only on the remaining data on differential expression.

Last year, Vigneron et al published single-cell data for trypanosomes from salivary glands, and identified markers for epimastigotes, and clusters that they designated as early and late metacyclics. They used the 10x system, which is probably more sensitive and has fewer barcode errors than the than the "inDrop" system that was used in the current submission (see https://doi.org/10.1016/j.molcel.2018.10.020). There are also separate comparisons available for bulk RNASeq of different Tsetse compartments, as well as for in vitro-induced formation of epimastigotes and metacyclic cells (including one extremely recent study from the Tschudi lab). In comparison with Vigneron et al, the major achievement of this paper, apart from then VSG analysis, is the identification of a separate "gamete" cluster. The current submission, however, seriously lacks detailed acknowledgement of, and comparison with, existing published datasets. I found enormous discrepancies with the Vigneron et al study - perhaps this is technical but I can't tell. This could readily be amended, without any additional data.

Reviewer #1 (Public Review):

Numerous previous publications, primarily by the authors of this manuscript, have demonstrated that the CspA outer surface protein of Lyme disease Borrelia spp. can bind to host complement factor H, and thereby confer resistance to killing by the host's alternative pathway of complement activation. They also previously demonstrated that sequence variations in CspA can alter ability to bind factor H of various host species, leading to a conclusion that CspA is a determinant of whether or not a particular Borrelia strain can infect a particular species of host.

The authors make a step forward with experiments using blood of quail, a bird species. The results are very similar to those of earlier publications. One of the tested strains ZQ1, was previously demonstrated to be killed by mammalian blood but not bird blood (Kurtenbach et al, 1998, Infect. Immun 66:1248).

The species Borrelia burgdorferi (sensu lato) has been split into 20+ "genospecies", including B. burgdorferi (sensu stricto), B. afzelii, B. garinii, and others. The authors make an inaccurate statement on page 4, lines 71-72, claiming that B. afzelii and B. garinii "are selectively infectious in a few host taxa (e.g. B. afzelii for rodents and B. garinii for birds)". Actually, B. afzelii and B. garinii have been isolated from infected humans, with B. garinii stains exhibiting a tendency to cause neurological symptoms in human Lyme disease patients.

A weakness is that the authors examined only three strains of Lyme Borrelia, one each of B. burgdorferi (sensu stricto), B. afzelii, and B. garinii. Therefore it is not known whether results are applicable to other than these three strains. Attempts were made to correlate sequences of CspA of those three strains to evolutionary history. Because of the small number of specimens, the significance of the conclusions remains unknown.

Reviewer #1 (Public Review):

This manuscript aims to compare automatic measurements of the facial behavior of participants with and without melancholic depression while they watch humorous and sad video stimuli. A data-driven method for representing each participants' automatically measured facial behavior dynamics was developed and demonstrated on a publicly available dataset of 27 healthy participants' facial reactions to video stimuli (DISFA) and then applied to a private dataset of 38 healthy controls and 30 participants with melancholic depression watching different video stimuli. Parameters of the model were statistically compared and visualized between groups and machine learning models trained on these parameters were compared to machine learning models trained on baseline features.

1) The introduction, in my opinion, overstates the accuracy/reliability with which facial expressions can be automatically recognized. It is important to consider (and mention) the difference between posed and spontaneous expressions and the challenge of domain transfer (Cohn et al., 2019).

2) Given how much these methods rely on the AU estimates and how much of the interpretation is given in terms of AUs, providing direct validity evidence for these estimates is quite important. Please report the per-AU accuracy of OpenFace in DISFA (as compared to the human coding). Note explicitly that OpenFace was trained on DISFA, so this reported accuracy is likely an overestimate of how it would do on truly new data (e.g., your depression dataset). The fact that there is not validity evidence in the depression dataset itself should be listed as a limitation.

3) The small sample size should be noted as a limitation. I know the difficulties of collecting this type of data firsthand, but it is an important limitation on generalizability nonetheless.

4) Are 100 bootstrap resamples enough for stable uncertainty estimation? Please provide a rationale for the selection of this number.

5) The decision to drop one of the two positive stimulus videos from the melancholia analysis needs justification. Given that the differences between groups appeared smaller in this video (at least what was shown in visualizations, dropping this video may make the difference between groups appear larger or more consistent than we have reason to believe it is given the entire data).

6) For the SVM described on page 24, please clarify whether the observations were assigned to folds by cluster (i.e., participant) or whether observations of the same participant could appear in both the training and testing sets on any given iteration. (The former is more rigorous.) Please also clarify whether the folds were stratified by class (as this has implications for the interpretation of the accuracy metric).

7) The performance of the competing SVM models should be statistically compared using a mixed effects model (e.g., Corani et al., 2017).

Reviewer #1 (Public Review):

This is an extremely well-done study, revealing a fascinating phenotype of mes-4 mutant, which they show upregulates X-linked genes, leading to PGC death. These X-linked genes are mostly oogenesis genes, upregulation of which likely impedes normal proliferation of PGCs.

The results are very concrete and supports their conclusion, and contribute significantly to the field. I do not have any major concerns except for a couple of conceptual issues. First, the title 'germline immortality' does not seem to be well aligned with the results. It is not wrong that PGCs die in mes-4 mutant, and thus the germline is 'mortal': however, the term 'germline immortality' implies multi-generational passages of germline, and the data in the present study, where mutant PGCs just die in the offspring, do not necessarily point to 'germline immortality' per se. So, I suggest to change the title to reflect the contents of the paper better. Second, although the authors speculate (in the discussion) why X activation is toxic to germ cells (discussing that upregulated X-linked genes are oogenesis genes, whose precocious activation is toxic to PGCs), there is not sufficient discussion as to why the effect is mostly limited to X chromosome, and why mes-4 is specifically involved in this. Is it because all oogenesis genes are concentrated on X chromosome? (likely not). Are autosomal genes that are upregulated in mes-4 mutant also oogenesis genes? Is this related to dosage compensation? I would like to see fuller discussions as to why X chromosome requires special regulation, also discussing the role of mes-4 in this context. I understand that the authors might have refrained from expanding discussions on matters that do not have any data, but without this discussion, I feel that many readers will be left wondering 'why?'.

Reviewer #1 (Public Review)

Strengths:

This manuscript combines experimental, exploratory, and observational methods to investigate the big question in innovation literature--why do some animals innovate over others, and how information about innovations spread. By combining a variety of methods, the manuscript tackles this question in a number of ways, and finds support for previous work showing that animals can learn about foods via social olfactory inspection (i.e., muzzle to muzzle contact), and also presents data intended to investigate the role of dispersing animals in innovation and information spread.

Using data from a previously-published experiment, the manuscript illustrates how investigators can numerous interesting questions while limiting the disturbances to wild animals. The manuscript's attempt at using exploratory analysis is also exciting, as exploratory analyses provide a useful tool for behavior research-indeed, Tinbergen insisted that behavior must first be described.

Weaknesses:

The manuscript's introduction is a bit unclear as to how the fact that dispersing males may be an important source of information ties to innovations in response to disruptions due to climate change, humans, or new predators, if at all. An introduction regarding the role of dispersed animals in introducing novel behaviors and social transmission would better prepare readers for the questions presented in the manuscript. As it stands now, the manuscript only provides one sentence discussing the theoretical relevance of investigating the role of dispersing animals in innovations.

Additionally, while the manuscript attempts to use exploratory analysis, it does not provide enough theoretical background as to why certain questions were asked while the data were explored. While the discussion provides some background as to the role of dispersing males in innovation, the introduction provides little background, and thus does not properly frame the issue. It is unclear how dispersing males became of interest and why readers should be interested in them. As the manuscript reads now, it may be that dispersing males became interesting only as a result of the exploratory analysis-except that the predictions explicitly mentions dispersing males. Thus, manuscript at present makes it difficult to know if the questions surrounding immigrant males resulted from the exploratory analysis, or was a question the analyses were intended to answer from the beginning. If this question only came out after first reviewing the results, then this needs to be made clear in the introduction. I see no issue with reporting observations that were the result of investigations into earlier results, but it needs to be reported in a way that can be replicated in future research-I need to know the decision process that took place during the data exploration.

The manuscript never clearly defines what counts as an immigrant male; presumably, in this species, all adult males in the group should be immigrants, as females are the philopatric sex. Sometimes, the manuscript uses "recently" to modify immigrant males, but doesn't define exactly what counts as recent, except to say that the males that innovated were in their respective groups for fewer than 3 months, but never explains why three months should be an important distinction in adult male tenure.

Due to the above weaknesses, the provided predictions are a bit murky. It is not clear how variation between groups in accordance with who innovated, or initiated eating a novel food, or demographics is related to the central issue. The manuscript does contribute to the literature by looking at changing rates of muzzle contact over exposure to a novel food source, and provides a good extension of previous findings; that, if muzzle contacts help animals learn about new foods, then rates of muzzle contacts involving novel foods should decrease as animals become familiar with the food. However, this point isn't explicit in the manuscript. Finally, it is also unclear as to why changing rates of muzzle contact AND whether certain individual-level variables like knowledge, sex, age, and/or rank might influence muzzle contacts during opportunities to innovate.

As for the methods, the manuscript doesn't provide enough details as to why certain decisions were made. For example, no reason is given as to why only the first four sessions after an animal ate were considered, why the first three months of tenure (but not four, as seen on one group that didn't innovate) was considered to be a critical time for which immigrant males may innovate, why (including the theoretical reasons) the structure of models for one analysis was changed (dropping one variable, adding interactions), or even how the beginning and ending of a trial was decided, despite reporting that durations varied widely,-from 5 minutes to two hours.

The discussion contains results that are never elsewhere presented in the manuscript- (2a) Individual variation in uptake of a novel food according to who ate first).

Finally, the largest issue with the manuscript is that its results are not as convincing as the conclusions made. An issue with all the analyses is that some grouping variables in some analyses but not others despite the fact that all of the analyses contain multiple groups (necessitating group as a grouping variable) and multiple observations of the same individuals (i.e., immigrant males tested in multiple groups, necessitating animal identity as a random effect), and not accounting for individual exposure to the experiment when considering whether animals ate the food in the allotted period (an important consideration given the massive differences in trial times), making these results difficult to interpret in their current forms. As for the results regarding muzzle contact, the analyses has a number of issues that make it difficult to determine if the claims are supported. These issues include not explaining why rank calculated a year before the experiments took place was valid or if rank was calculated among all group members or within age and sex classes, not explaining how rank was normalized, and not conducting any kind of formal model comparisons before deciding the best model.

As for the results regarding immigrant males and innovation, little is done to help the fact that these results are from very few observations and no direct analyses. It is possible that something that occurs relatively often but in small sample sizes, like dispersing animals, could have immense power in influencing foraging traditions, and observation is a necessary step in understanding behavior. However, the manuscript doesn't consider any alternative hypotheses as to why it found what it found. No other possible difference between the groups was considered (for example, the groups that rapidly innovated appear to be quite smaller than the groups that did), making the claim that immigrant males were what allowed groups to innovate unconvincing. This is particularly true given that some groups in this study population have experimental histories (though this goes unmentioned in the current manuscript), which likely influenced neophobia-especially given work by the same research group showing that these animals are more curious compared to their unhabituated counterparts.

Reviewer #1 (Public Review):

In this study, Lonnemann et al. investigated the anti-inflammatory effects of hIL-37 in acute and chronic neuroinflammatory mouse models, using both in vivo and in vitro assays. The data provided support for the anti-inflammatory functions of IL-37 on microglial activation and cognitive functions.

1) As a cytokine that is not naturally expressed in mice, the introduction of the physiological significance of IL-37 in human beings and the necessity of this research in the background will be insightful.

2) A comprehensive analysis for how hIL-37 may affect metabolite content would strengthen the conclusion.

Reviewer #1 (Public Review):

Using a large neonatal dataset from the developmental Human Connectome project, Li and colleagues find that cortical morphological measurements including cortical thickness are affected by postnatal experience whereas cortical myelination and overall functional connectivity of ventral cortex developed significantly were not influenced by postnatal time. The authors suggest that early postnatal experience and time spent inside the womb differentially shape the structural and functional development of the visual cortex.

The use of large data set is a major strength of this study, furthermore an attempt to examine both structural and functional measures, and connectivity analysis and separating these analyses based on the pre-and full-term infants is impressive and strengthens the claims made in the paper. While I find this work theoretically well-motivated and the use of the large dHCP dataset very exciting, there are some concerns, that need to be addressed.

1. There is a bit of confusion if the authors really compared the structural-functional measures in the final analysis. If the authors wish to make claims about the relationship then there must be a compelling analysis detailing these findings.<br /> 2. There is also a bit of confusion in the terminology used in the study regarding ages; the gestational age, premenstrual age, and postanal time. I think clarifying and simplifying it down to GA and postnatal time will help the reader and avoid confusion.

Reviewer #1 (Public Review):

The authors have identified a novel method to generate the N2C strain of the rabies virus in a significantly shorter period of time and with less contamination of WT rabies. Additionally, they have developed a suite of tools that can be used with the rabies. Lastly, they provide examples of the utilization of rabies encoding tools that can be used for functional studies post rabies infection. While the work is not extremely novel, it takes us past problems that were impediments to the use of the N2C strain which is more infectious than the SAD-B19 strain and will likely invigorate monosynaptic retrograde tracing, which is powerful for understanding circuit connectivity.

Reviewer #1 (Public Review):

This study is mainly based on the comparative analysis of the impact that an Mcd1-AID degron and an Mcd1-TEV have on S. cerevisiae chromosome organization and segregation. After solving the unexpected situation generated by the Mcd1-TEV construct due to the N-rule of protein degradation, the authors confirm that a rapid depletion of cohesin achieved with the Mcd1-AID allele causes disruption of chromosome organization and segregation as well as mitotic catastrophe, whereas the TEV-cleaved Mcd1 leaves an active fragment at the CEN regions of telophase chromosomes and shows a defect in the fidelity of chromosome segregation, but does not prevent bulk nuclear separation. TEV-cleaved cohesins remain bound to chromatin for longer than degraded chromatin. The number of cohesin-binding sites per chromosome is strongly diminished in telophase-arrested cells, with only a few regions exhibiting binding that indeed belongs to telomeric regions. The authors conclude that cohesin complexes affect cohesin contacts at telophase centromeres. In addition, using a conditional mcd1-73 allele the study shows that inactivation of cohesin at telophase also causes decondensation of chromosomes in the rDNA region. The study is clear, well explained, and data convincing. However, a few points should be considered to make conclusions stronger.

Reviewer #1 (Public Review):

In this study, the authors examine the relationship between the use of antihypertensive medication and breast cancer risk using a Mendelian randomization approach.

The strengths of the methods are that they use a two-sample design and have access to publicly available data for this. Several sensitivity analyses were carried out, as well as assessments of the assumptions in studies that use instrumental variables. In addition, the genetic instrument was validated with systolic blood pressure. However, there is some difficulty in following all that was done. Figure 1 does not give complete information. If SNPs are the instrumental variables, why aren't they listed as the "exposure" for the MR for target genes and breast cancer? In section 5 in the methods, the exposure is also indicated as gene expression (and not SNPs). It is also unclear how gene expression can easily stand in for medication use. A diagram (or preferably a DAG) could provide more information to understand the different steps in this MR study.

The authors carried out several analyses and identified two SNPs that were associated with breast cancer risk, suggesting that antihypertensive medication use may be associated with risk.

The results give further information on understanding the mechanisms of breast cancer. Antihypertensive medication is essential for many people, thus, the results would not really lead to less use. That said, the specific genes (and in particular their expression) are related to specific drugs. Thus, there may need to be more work to decide if some drugs are contraindicated in people with an increased risk of breast cancer.

Reviewer #1 (Public Review):

The proposed mechanism, involving a "novel pathway mediated by ATM and mTOR" appears to be preliminary, and not fully supported by the Western blots in Figure 2. The majority of the mechanistic experiments are done in a single cell line, further limiting the certainty of the mechanism.

Major comments:

1) Figure 1A/B show two FLT3 mutant cell lines, but subsequent mechanistic experiments are primarily done in only the MOLM13 cells. IN Figure 1B, the effect of the CM on the MV411 cells is much less striking than on the MOLM13 cells, and the fact that these cells are omitted from the mechanistic experiments is a concern.<br /> 2) The conclusion (and title of Figure 2) that the CM "reverses mTOR pathway suppression" is not fully supported by Figure 2D. The phosphorylation of mTOR at S2448 (which is a somewhat nonspecific indicator of actual mTOR activity) is higher at the start with the CM, which is not surprising given the many growth factors and nutrients in the CM and is suppressed by Quizartinib. Phospho-S6, which is a more specific indicator of mTORC1 activity is similar with and without CM. These experiments should be done in triplicate, with statistics, and other indicators of mTOR activity included (pS6K, p4EBP1) along with the total protein levels for all of the phospho-proteins including S6. These experiments should also be done in the MV411 cells.<br /> 3) In Figure S3 (AML#2), everolimus alone nearly completely suppressed the leukemic cells in the blood, consistent with the possibility that it has actions that are unrelated to Quizartinib.<br /> 4) In Figure 3G, (AML#1) why is there no vehicle data for the 21 Day treatment time point? What was the leukemia burden in the vehicle? Without this, the data are hard to interpret. It appears that the combination is not different from Quiz alone. IN Figure 3E, at the earlier time point, it appears that everolimus inhibits the leukemia burden although this did not reach significance, and it also appears that the combination is not different from Quizartinib alone.<br /> 5) Similarly, the title of Figure 4 is "Restoration of mTOR signaling by hBMSC-CM ..." but the authors have not convincingly demonstrated that mTOR signaling is being "restored" - instead this could be a combined effect of mTORC1 inhibition and Quizartinib that is not mechanistically directly linked.<br /> 6) Figure 4 again relies on a single cell line MOLM13.<br /> 7) Can the primary cells in Figure 2E and AML#1 and AML#2 be studied for mTORC1 activity by Western, as in 2D?<br /> 8) Additional genetic information should be provided if possible for the primary AML cells - what other mutations in addition to FLT3 were present? Were there any mTOR pathway alterations?

Reviewer #1 (Public Review):

Here the authors set out to use neural networks to simulate neurons, which is an intriguing inversion of scales and approaches. They achieve quite remarkable speedups, drawing on the efficiencies of neural network implementations, especially in GPUs. Overall, I find this to be a potentially very exciting development for neuronal modelling as it will bring quite large models within reach of 'ordinary' researchers who don't have easy access to massive supercomputers. I feel the authors should address a couple of major concerns about the reporting of their software and results.

Reviewer #1 (Public Review):

This is a very interesting paper. In this manuscript Hendi et al. examined how two independent mechanisms, Wnt signalling and gap junction control two critical aspects of neuronal tiling. Here they have quite elegantly used two neighboring GABAergic motor neurons to show while one specific C. elegans Wnt-homolog, EGL-20, regulates the axonal tiling; innexin UNC-9-mediated gap junction at a very specific position on these axons regulate the chemical synapse tiling on these axons. They also performed multiple experiments to show that the UNC-9 gap junctions controls chemical synapse tiling independent of their channel activity.

Overall, the paper is interesting and would be of general interest for many neuroscience researchers, specifically to those who are studying neuronal tiling and the role of gap junctions. However, there are some concerns with this study.

Major concerns:

1. Authors here only looked at the tiling of axons and presynaptic clusters in DD5/DD6 axons. However, these neurites get transformed in L1 from dendrite to axon and subsequently the nature of the synaptic termini also changes from postsynaptic to presynaptic. To say that egl-20/UNC-9 specifically control axonal tiling and GABAergic presynaptic tiling the authors must check the dendritic tiling and tiling of postsynaptic termini. Specifically, a) does UNC-9 channels also affect the postsynaptic patterning in L1? b) what is the time of unc-9 puncta formation? Is it present in the L1 stage or appears at L2 stage only after the fate switch from dendrite to axon? c) does egl-20 also control dendritic tiling in L1?<br /> 2. Authors have shown that the previously known regulators for gap junction formation, NLR-1 and ZOO-1, do not regulate UNC-9 gap junction puncta on DD5/DD6 axons. Since they are cell adhesion molecule and tight junction component, respectively, presynaptic tiling should be checked in these mutants as well. Also, it is not clear whether these proteins are expressed in DD5/DD6 neurons at all. Since, NLR-1 has previously been shown to regulate unc-9 puncta in nerve ring, expression of these genes in DD5/DD6-neurons should be checked before making these conclusions.<br /> 3. Authors assumed that the relevant gap junction to be an UNC-9 homotypic homomeric channel, but DD neurons also express several other innexins (inx-1, inx-2, inx-10, inx-14 and unc-7). This raises the possibility that unc-9 channel could be heteromeric in nature. Effect of some other expressed innexins on synaptic tiling apart from unc-7 should also be tested.<br /> 4. Effect of unc-9(Del18) and unc-1 double mutant should be tested.<br /> 5. Authors have acknowledged the need to study the role of UNC-9 gap junction channels in maintaining the presynaptic pattering. This reviewer appreciates that idea and suggests the authors check whether late expression of UNC-9 is sufficient to rescue the presynaptic pattering defect observed in egl-20; unc-9 double mutant animals.

Reviewer #1 (Public Review):

In this Research Advance, the authors build on two earlier eLife papers that described and experimentally validated a mathematical model of the transcriptional response of yeast to heat shock in which unfolded proteins sequester Hsp70 away from Hsf1 promoting an Hsf1-driven transcriptional program, and report two new findings. First, they provide evidence that upon heat shock it is newly synthesized proteins rather than denatured mature proteins that sequester the Hsp70 chaperone away from Hsf1 permitting Hsf1 to bind to target genes and drive the heat shock-induced gene transcription program during the heat shock response (HSR), and, second, by analyzing the role of the Sis1 Hsp70 co-chaperone in the HSR they showed that Sis1 does not have a direct negative role in the HSR, but rather is needed for fitness during prolonged stress.

Because recent studies using cycloheximide to block protein synthesis have suggested that it is newly synthesized proteins in the process of folding rather than denatured mature proteins that are the clients for Hsp70 responsible the HSR, the authors reconfigured their model by assuming that heat shock slows the folding of newly synthesized proteins and adding the rate of translation as a new input function. They validated their new model using a yeast strain that has an HSE-YFP reporter gene as an HSR readout, and showed that rapamycin treatment, which reduces the rate of translation, resulted in a decrease in the HSR, that is predicted with kinetics predicted by their new model. In addition, based on their own recent work showing that the Sis1, a J-protein chaperone, regulates the HSR by promoting Hsf1-Hsp70 association in the nucleus to repress Hsf1 activity under non-heat shock conditions, they also incorporated Sis1, a Hsp70 co-chaperone, as a new component of their model circuitry. By experimentally induced eviction of Sis1 from the nucleus, they observed reduced Hsf1 activity towards the HSE-YFP reporter in the absence of a temperature shift, as predicted by the model. The new model also accounted for the rapid initial and then subsequent slowing kinetics of the HSR as it reached a maximum, as well as the different levels of HSR induction at increasing temperatures above 35oC. Moreover, even though the SIS1 promoter has an HSE and its basal transcription is driven by Hsf1, the elimination of this regulatory step experimentally showed that Hsf1-driven Sis1 transcription was not required for temperature shift-induced HSR output, implying, as the model predicted, that increased Sis1 expression is not important and not needed for negative feedback inactivation of Hsf1. This was tested directly by generating a strain in which the SIS1 promoter was replaced with two copies of the SUP35 promoter to maintain the basal expression level of Sis1, which showed normal kinetics of HSR inactivation under several experimental conditions. Using a Halo-tag pulse protocol, they demonstrated that heat shock induction of newly synthesized Sis1-halo was delayed and that the new Sis1 protein was preferentially localized around the nucleolus away from Hsf1, as determined using an Nsr1-mScarlet nucleolar marker, and thus Sis1 would presumably not be in a position to promote Hsp70/Hsf1 interaction and repression of Hsf1 activity. Finally. to investigate what role Sis1 plays in heat-stressed cells, they showed that the 2xSUP35pr-SIS1 yeast strain had reduced fitness compared to the other strains after 4 hours at 37ºC, suggesting that Sis1 has an undefined role in maintaining fitness in heat-stressed cells. Consistent with this, they showed that Sis1 also has a role in maintaining fitness in yeast cells growing on a non-preferred carbon source.

The updated model of the HSR, which still retains the two-component feedback loop consisting of the chaperone Hsp70 and the transcription factor Hsf1 of the original model but replaces the unfolded protein activation step with an equivalent step involving unfolded newly synthesized proteins, appears to be able to model cellar responses to heat shock quite accurately. This refinement of their model, coupled with the demonstration that the Sis1 J protein chaperone does not appear to play a direct role in the inactivation phase of the HSR, provide a significant advance over their earlier work.

A main weakness is that while the evidence that Sis1 is important for fitness of heat-stressed yeast cells is reasonable, exactly how Sis1 achieves this is not clear. In a single sentence the authors suggest that Sis1 might be an orphan ribosome chaperone, partly based on its nucleolar localization, but provide no evidence for this. If this were true, then one might expect a reduction in ribosome content under stress conditions and a decreased rate of protein synthesis, which could be tested. Some further insights into this more general role of Sis1 would strengthen the authors' conclusions.

Moreover, whether Sis1 plays a general role in the fitness of cells under stress has not been firmly established, i.e., is its mechanistic role the same in heat shock conditions and under nutrient stress conditions? Without knowing the mechanistic basis for how Sis1 maintains the fitness of heat-stressed cells, it is not possible to conclude that the same mechanism is at play in cells grown on a non-preferred carbon source.

Figure 4: This is an ingenious experiment to study the subcellular localization of newly synthesized Sis1 in response to heat shock, compared to that of the heat-shock inducible Hsp70 Ssa1. However, based on the images presented in panel B it is hard to know how discrete the subnuclear distributions of Sis1 and Ssa1 really are, and ideally what is needed is to be able to analyze their localizations when both tagged proteins are expressed in the same cell, although this would obviously not be possible using the halo-tagged protein system. In addition, one would like to know the localization of Hsf1 in the cell at the same time. As it stands, these data seem overinterpreted, and it remains possible that dome other event such as an inactivating post-translational modification of Sis1 under heat shock conditions might be involved in inactivating its function.

One way to establish whether Sis1 nucleolar sequestration prevents it from acting on Hsf1 during the inactivation phase of the HSR would be to selectively disrupt its nucleolar localization signal eliminated while retaining its nuclear localization and determine how expression of such a mutant perturbed the inactivation kinetics of the HSR.

Reviewer #1 (Public Review):

In this manuscript by Feng et al., the authors investigate the mechanism regulating the development of the levator veli palatini (LVP) in the posterior palate/pharyngeal region. While set up as a model to understand how myogenic progenitors migrate to discrete sites to form individual muscles, it is not clear how applicable the findings are to other subpopulations, though this is not a weakness. The mechanisms driving LVP development are of great interest to a broad group of developmental biologists, as LVP malformation is a common problem even in mild cases of cleft palate. The authors hypothesized that the perimysial population within palatal mesenchyme cells is a niche required for pharyngeal muscle development. Using exquisite analysis of scRNA-seq data from E13.5-E15.5 palatal cells, the authors illustrate that TGFb signaling is likely involved in perimysial cell development, using gene expression analysis in wild-type palatal sections to show that TGFb signaling precedes the arrival of myogenic cells. Inactivating ALk5 in palatal mesenchyme cells results in failure of LVP formation. The authors continue by identifying a number of transcription factors that presumably function downstream of TGFb signaling that drive LVP development. Among these are Fgf18, in which SMAD sites observed in the upstream region were validated to bind Smad2/3. The authors also identify Creb5 as a potential regulator of Fgf18. Overall, this is a remarkable use of scRNA-seq data, in which findings are supported by subsequent in vivo analysis of gene function using knockout mouse models. These findings will drive further analysis of LVP development and may shed light on the myogenesis of pharyngeal muscle in general.

Strengths<br /> 1. The treatment of scRNA-seq data using a variety of bioinformatic programs illustrates the utility of this type of data when using sufficient analysis software. The description of the approach is very clear and concise and the controls appear excellent. Further, the use of multiple time points further improves the analysis.<br /> 2. The focus of perimysial cell expression patterns supports the hypothesis of the authors, though as with this type of data, one probably can make a story out of several pathways.<br /> 2. The use of RNAscope to carefully examine where TGFb signaling in the posterior pharynx occurs between E12.5 and E16.5 is critical to the setup of this manuscript and is well done. Further aiding the interpretation of these results are cartoons associated with the staining, which illustrate where the staining is occurring, though never over-stating the observed patterns.<br /> 3. Careful histological analysis illustrates the poor myogenic differentiation in the LVP of OSr1-Cre;Alk5fl/fl embryos.<br /> 4. Identifying that TGFb is more important for regulating late perimysial cell development is important in identifying the targets of TGFb signaling.<br /> 5. The use of CellChat to identify sending and receiving cells is well done and further supports the late function of TGFb signaling, in this context working through Fgf18 and Lama4.<br /> 6. The attempt to build a signaling network again using CellChat (Figure 6) is admirable, though there are a few caveats to that approach (see below).<br /> 7. While bead implant studies have been used for 40 years, the approach of culturing a piece of the pharynx and then performing a bead implant to prove that Fgf18 can positively influence myogenic development is admirable.

Weaknesses<br /> 1. In general, the authors are careful to not suggest that staining is significant unless showing quantification, though, at several points, this is not true.<br /> 2. The authors identify five putative Smad2 sites upstream of Fgf18, using one of them in a Cut and Run assay whose results suggest enhanced Smad2/3 binding. The problem is that this likely would have worked with the other Smad sites and probably would have worked for any other putative site that one might pick. Proving that a putative site can be bound by its cognate transcription factor is not the same as proving that this occurs in vivo and is sufficient to control the process of LVP development. One would need reporter assays using that TF binding site to better support the points being made by the authors.<br /> 3. In a similar manner, the authors try to define which factors might function with TGFb signaling to regulate myogenic development. Using SCENIC, the authors found a number of genes that might be involved in perimysial fibroblast development. Of these, they illustrate that Creb5 siRNA knockdown decreases Fgf18 expression in cultured palates. The focus on Creb5 was based on it showing, "the most specific expression patterning the late perimysial cells (Figure 6H)....". In fact, Creb5 appears the most broad, appearing to be expressed across the entire LVP, not just in the area where myogenic precursors are found. Thus, "most specific" probably needs to be reworded. However, the second problem is that Creb5 knockdown reducing Fgf18 expression does not prove any direct regulation. Both of these are rather circuitous arguments.<br /> 4. While the disorganization of myogenic fibers in the posterior LVP is somewhat obvious, it is not as clear as the authors suggest. This change (which I believe) needs to be better quantified (length, width, area, etc.).

Reviewer #1 (Public Review):

The authors propose a simple model for flash dynamics in a certain species of firefly known as P. Carolinus. Remarkably, individual males of this species flash haphazardly, with no particular rhythm, yet in sufficiently large groups, they somehow manage to flash in rhythmic unison. The authors show that their model can account for this phenomenon - with no adjustable parameters - and they test their model quantitatively in idealized experiments on groups of up to 20 fireflies confined to a small, darkened, cuboid tent of dimensions 1.5 x 2 x 1.5 cubic meters.

Strengths:

The authors' model is unusual in that the individual fireflies are not assumed to be intrinsically rhythmic. By contrast, in most previous work on firefly synchronization the individual fireflies were modeled as "oscillators." That convenient assumption allowed a large body of theory from nonlinear dynamics to be imported. However, for the particular species of firefly being studied here, the authors show the individual males do not flash rhythmically. The authors provide a framework for dealing with this novel case.

There are actually two parts to the framework. In the first (extremely stylized) model, the authors assume that after it flashes, each firefly waits a random time before it can flash again. All fireflies choose this random waiting time from the same probability distribution; in that sense, the fireflies are assumed to be statistically identical. Furthermore, the authors assume that when one firefly flashes, it triggers all the rest to flash instantaneously. A weakness of this extreme idealization is that synchrony is thereby built in automatically by assumption, rather than explained as a consequence of the model. But a virtue of this extreme idealization is that it correctly predicts how the variance in the interburst intervals depends on the number of fireflies in the group. In this way, the authors neatly explain a property of the emergent period that they observe in their tent experiments. Most notably, they do this without any adjustable parameters. The result follows as a consequence of their model and their measurements of individual firefly behavior. This is a beautiful instance of using individual behavior to predict collective behavior with the help of some simple, additional assumptions.

The second part of the new framework builds on existing research on a class of oscillators known as "integrate and fire" oscillators. The new wrinkle is that the authors introduce a stochastic term in the equation (a random amount of time for the oscillator to charge up) in order to capture the erratic nature of the interburst interval for individual males of this firefly species. The virtue of this more complex model is that it allows the authors to predict a transition to group periodicity as the interaction strength between the fireflies is increased. It also allows the authors to relax the earlier (unrealistic) assumption of instantaneous triggering of all other fireflies whenever one firefly flashes.

Weaknesses:

The work presented here is an excellent start at understanding the collective behavior of this particular species of firefly. However, the model does not apply to other species in which individual males are intrinsically rhythmic. So the model is less general than it may appear at first.

The modeling framework is also developed under the very stylized conditions of experiments conducted in a small tent. While that is a natural place to begin, future work should consider the conditions that fireflies encounter in the wild. Swarms that are spread out in space would require a model with a more complicated structure, perhaps with network connectivity and coupling strengths that both change in time as fireflies move around. This is not so much a weakness of the present work as a call to arms for future research.

Overall, the paper does an excellent job of supporting its conclusions with elegant arguments and experiments.

Reviewer #1 (Public Review):

This paper tackles a very important question in somatosensory biology - the identity of the sodium channel controlling excitability in proprioceptors. While whole rainforests' worth of papers have been published on sodium channels in nociceptors, there has been a significant gap in our understanding of which NaV isoforms are at play in the large fiber proprioceptors and LTMRs. Using pharmacology, gene KO, behavior, and histology, the authors show quite convincingly that NaV1.1 in sensory neurons is essential for normal motor behavior and contributes to proprioceptor excitability. Interestingly, they find NaV1.1 is haploinsufficient. This finding is all the more exciting given the many human NaV1.1 het and homo mutants and points to future possibilities for interrogating the role of this channel in human proprioception and using human tissue (e.g. iPSCs).

Reviewer #1 (Public Review):

There is evidence to suggest that a percentage of the 8 million children conceived by in vitro fertilization (IVF) exhibit an increased risk of various maladies (e.g., altered growth rate, cardiovascular dysfunction, and glucose intolerance) compared to naturally conceived children. However, it is unclear how embryonic metabolism is affected by conditions, especially oxygen tension, used to culture blastocysts derived by IVF.

This manuscript describes a comprehensive investigation of the effects of IVF-embryo culture conditions on the metabolism of murine blastocysts and adult mice. The authors conclude that oxidative stress caused by culture conditions used for IVF-generated blastocysts results in various metabolic alterations and possibly epigenetic changes in embryos that lead to an increased risk of various maladies in children and adults.

In this investigation, the authors studied oxidative stress and metabolic alterations in murine blastocysts conceived by natural mating or by IVF and cultured in either 5% (physiological) or 20% (atmospheric) oxygen. The authors found that compared to blastocysts conceived by natural mating (flushed blastocysts, FB), IVF-generated blastocysts exhibited: (1) increased reactive oxygen species (ROS), (2) oxidative damage to DNA, lipids, and proteins, (3) reduction in glutathione and NAD, (4) decreased mitochondria respiration, (5) increased glycolytic activity (enhanced Warburg metabolism), (6) altered intracellular and extracellular pH, (7) decreased intracellular pyruvate levels and increased intracellular lactate levels, and (8) a reduction in lactate dehydrogenase B (LDH-B) and monocarboxylate transporter (MCT1) involved in lactate transport.

This is a valuable contribution to the field of assisted reproductive technologies (ART) and will be of special interest to practitioners of IVF, as well as to developmental and reproductive biologists in general.

Reviewer #1 (Public Review):

Anopheles is an important disease vector and the efforts to characterize the extent of genetic variation in the system are welcome. In this piece, the authors propose a Variational Autoencoders method to assign species boundaries in a large sample of Anopheles mosquitoes using a panel of 62 nuclear amplicons. Overall, the method performs well as it can assign samples to an acceptable granularity. The main advantage of the method is that it takes reduced representation genome sampling which should cut costs in genotyping. The authors do not compare the effectiveness of their amplicon panel with other approaches to do reduced representation sequencing, or the computational method with other previously published methods. Additionally, the manuscript does not clearly state what is the importance of species assignments and the findings/method are -by definition- limited to a single biological system.

The manuscript has three main limitations. First, there is no explicit test of the performance of ANOSPP compared to other methods of low-dimensional sampling. While the authors state that the ANOSPP panel will lead to genotyping for low cost (justifiably so), there is no direct comparison to other low-representation methods (e.g., RAD-Seq, MSG). Second, and on a related vein, the authors present NNoVAE as a novel solution to determine species boundaries in Anopheles. Perusing the very references the authors cite, it is clear that VAEs have been used before to delimit species boundaries which diminishes the novelty of the approach on its own.

Perhaps more importantly, the manuscript does not present a comparison with other methods of species delimitation (SPEDEStem, UML -this approach is cited in the paper though-), or even of assessment of population differentiation, such as STRUCTURE, ADMIXTURE, or ASTRAL concordance factors (to mention a few among many). The absence of this comparative framework makes it unclear how this method compares to other tools already available.

A final concern is less methodological and more related to the biology of the system. I am curious about the possibility of ascertainment bias induced by the amplicon panel. In particular, the authors conclusively demonstrate they can do species assignment with species that are already known. Nonetheless, there is the possibility of unsampled species and/or cryptic species. This later issue is brought up in passing the 'Gambiae complex classifier datasets' section but I think the possibility deserves a formal treatment. This is particularly important because the system shows such high levels of hybridization that the possibility of speciation by admixture is not trivial

In summary, the main limitation of the manuscript is that the authors do not really elaborate on the need for this method. The manuscript does show that the method is feasible but it is not forthcoming on why this is of importance, especially when there is the possibility of generating full genome sequences. Since Anopheles is arguably one of the most important insects to characterize genetically, the ANOSPP panel is certainly important but I am not completely sure the method of species assignment is novel or groundbreaking .

Reviewer #1 (Public Review):

The Uemura lab previously reported that C4da neurons elaborate complex dendrites when larvae grow on low-yeast diets, a phenomenon called neural sparing. In the present study, they elegantly define that the inter-organ Wingless/Ror/Akt pathway between the neuron and its adjacent muscles is necessary and sufficient to mediate dendrite over branching in the low-yeast condition. This study provides a mechanistic explanation of how the dendrite hyperarborization is caused by the crosstalk among fat body, muscle, and sensory neurons. It is likely a general mechanism and phenomenon across species.

Reviewer #1 (Public Review):

Jeong and collaborators study the genomic evolution of a supergene that is very well documented in the literature. This supergene derives from an inversion that affects more than 100Mb of the genome of white-throated sparrows. This supergene has resulted from a chromosomal inversion that makes recombination very difficult. As a result of the lack of recombination, mutational changes in the inverted sequences can accumulate leading to the accumulation of deleterious variants and structural changes. This is likely to lead to the degeneration of the chromosome, as has taken place in the sexual chromosomes of birds and mammals. The study of such a large inversion that occurred relatively recently offers a great opportunity to investigate the early stages of chromosomal degeneration. However, the relaxation of selective forces that result from the small effective population size associated with the inversion facilitates the accumulation of genetic variation, that can contribute to genetic divergence and adaptation.

The study of these two effects associated with the inversion makes this study very unique and this is combined with the use of high-quality genomic data for many individuals and very diverse analytical tools.

The authors first analyze chromosomal degeneration by combining data on the number of scaffolds generated for a deeply sequenced super-white bird (ZAL2m homozygote), assessing the relative frequency of insertions and deletions, estimating the ratio of non-synonymous to synonymous fixed differences, etc. All these lines of evidence very convincingly show a certain degree of degeneration of the chromosome carrying the inversion.

Selective forces acting on the inverted chromosome and its counterpart are then analyzed in detail including the study of the chromosomal sequences and gene expression in different tissues. The results show differences in the expression of alleles from each of the two chromosomal variants and reveal the presence of a smaller region within the inversion where divergent alleles are maintained by balancing selection, selective sweeps, and positive selection for some alleles. Although these results are very well supported in most cases, the results relative to differences in expression of specific genes need to be further investigated. Since many genes are potentially studied at the same time, it is important to further investigate possible false positives. Similarly, the detailed study of phenotypic effects requires in-depth analyses of many more individuals and with statistical designs that take into account the effect of covariates and multiple testing.

Reviewer #1 (Public Review):

The authors applied FreeSurfer and performed vertex-wise linear regression models for ADHD with cortical surface and volume. They note they did not test cortical thickness, as it was found to yield null findings in a prior analysis of the Generation R data.

In initial analyses without confounders (model 1), they observed widespread associations between ADHD symptoms (measured with the Child Behavior Checklist) with surface area and volume in both datasets. In model 2, they adjusted for SES, household income, maternal education, and maternal age at childbirth. A third model added prenatal exposure to substance use (tobacco and cannabis) and postnatal maternal psychopathology. Model 2 reduced surface cluster sizes by 20% in ABCD and 49% in GenR; volume was reduced by 42% and 67%, respectively.

Model 3 reduced surface clusters by 23% and 5% for ABCD and GerR, respectively, compared to model 2. Volume was reduced by 32% in ABCD, and increased by 7% in GenR. Similar results were obtained when ADHD was treated categorically as present or absent. A fourth model examined the complex effects of adjustment for IQ. Such adjustment further reduced the spatial extent of clusters (vs. model 3) by 23% and 57% for the area in ABCD and GenR, respectively, and 37% and 47% for volume.

This manuscript has many strengths. It is thoughtful and addresses a major challenge in the field, using large samples and rigorous methods. It is well written and mostly quite clear.

Reviewer #1 (Public Review):

The authors report on their quite extensive study to dissect the differences in gene expression in different human lower limb muscles to explain individual differences between different muscles in the human body, not the least in order to explain the specific and often selective differences in how muscles react on gene defects in different myopathies. The intentions are well taken and the setup is ambitious with the collection of six different muscles from each of the 20 individuals.

Reviewer #1 (Public Review):

In previous work, the Salama group identified a large complex of proteins required for the distinctive shape of Helicobacter pylori. Focusing on one member of this complex, CcmA, Sichel and colleagues use a combination of genetics, biochemistry, and quantitative image analysis to identify regions of CcmA involved in shape determination and illuminate the mechanism underlying H. pylori cell shape. They pinpoint the bactofilin domain and N terminal region of CcmA as important for both CcmA polymerization and interactions between CcmA and complex members Csd5 and Csd7 which in turn influences the activity of the peptidoglycan hydrolase Csd1. Super-resolution microscopy indicates that Csd5 recruits CcmA to the cell envelope and promotes CcmA localization to the major helical axis. Synthesizing these data, the authors propose a model in which CcmA coordinates interactions with the peptidoglycan synthesis machinery and associated hydrolases to promote helical shape. Experimentally the paper is extremely solid, containing data acquired through an extensive array of approaches. I was impressed by the development of quantitative approaches to assess how much CcmA was associated with the cell envelope versus cytoplasmic. Figure 7 is beautiful. At the same time, and despite the presentation of a beautiful model figure at the end, the sheer volume of data and the dense writing make it difficult for readers to make the connection between the results and the model in a meaningful way.

Reviewer #1 (Public Review):

This manuscript describes experiments that lead to a potentially impactful result and most of the data seem very nice. The authors conducted a mutant screen to find the gene BbCrpa from a fungus resistant to cyclosporine A (CsA). Microscopy indicates that the mode of action is likely sequestration of the toxin in vacuoles, mediated through the P4-ATPase pathway. They also show that expression of BbCrpa in Verticillium renders that fungus resistant to CsA. The paper then takes a very large jump across kingdoms and toxins and asks if BbCrpa, expressed in plants, will confer resistance to a different toxin (cinnamon acetate) that is produced by Verticillium. They conduct disease assays on Arabidopsis and cotton and show promising results, but these assays are less thoroughly completed. They provide microscopic evidence that the transgenics accumulate CIA in vacuoles, which is consistent with the mode of action of the other systems. Overall, my assessment of the paper is that the authors may have a nice story, but the transition to plants needs to be better described and potentially supported by additional experiments. For example, the authors seem to conclude that this resistance mechanism will be a very broad spectrum. Is there a second toxin-producing pathogen that could be used to assess whether this is true?

Reviewer #1 (Public Review):

This paper presents a model for estimating the transmission potential of SARS-CoV-2 that can be applied during periods of low or zero case incidence, as well as during periods of sustained high incidence. This novel approach complements and generalises approaches to inferring the time-varying reproduction number from time series of new daily cases by incorporating additional data streams (Google mobility data and social survey data) that are independent of epidemic dynamics and testing. The paper is likely to be of high interest within the field of epidemiological modelling and more broadly.

The authors have successfully developed and deployed a robust methodological framework that uses a range of data sources to estimate the potential for the spread of SARS-CoV-2. The results can be and were used in real-time to inform situational awareness and policy response, particularly in countries where incidence was low or zero for periods of time. The results are also informative in retrospectively understanding the epidemiological characteristics of different outbreaks and evaluating the effect of interventions. The method could be used by other groups for situational awareness in other countries - the epidemiological data and mobility data the model uses are relatively standardised although some work would be needed to adapt/implement the survey part of the study. The method could potentially also be adapted for other pathogens that spread via close contact.

The main limitations of the model lie in the types of data that are routinely available and whether these will continue to be available in a comparable form in the future. These limitations are discussed in more detail in the paper. Application of the model in other jurisdictions would of course need careful re-calibration as there are likely to be differences in e.g. testing rates, idiosyncrasies of mobility data, and lack of or different survey data. And use of model results for policy advice would rely on careful and appropriate communication to policymakers of model results, the associated uncertainty and model limitations.

Reviewer #1 (Public Review):

Studies published by the authors and others have shown that many cells extend prolonged filipodia to transport Wnts to exert their functions in a distance. In this current study, the authors identified Flot2, a scaffolding protein, which interacts with Ror2 to regulate the formation of signaling filipodia to transport Wnt3 in GC cells and Wnt8a in zebrafish. However, the role of Flot2 in promoting cytoneme formation to spread morphogens has been reported in other animal models and cancer cells; and the data that support the role of Flot2 in Ror2 are not convincing. Additionally, the paper employed similar published approaches. Thus, the significance and novelty of this work are not very high.

The quality of many data and some experimental should be improved. Specifically, most experiments used the overexpression approach. Genetic approaches would need to be employed, particularly in embryos. The dominant-negative Flot2 is the key tool utilized in the paper, but it is unclear whether this construct has been characterized in the system used and how it affects endogenous protein function. Has its impact on the endogenous Flot2 been examined? Similarly, the effectiveness and specificity of siRNA for example, the expression level of Flot2 would need to be assessed in all experiments. Furthermore, it is unclear whether the tagged constructs (eg, Flot2-GFP, Wnt8a-mcherry) have been characterized and whether the tags affect the protein function.

Other general points:<br /> 1. Most images show one single cell. Could more cells be presented? The nature of the images should be disclosed. For example, are those confocal images (single plane or Z-stack)?<br /> 2. The P values for which group are not clear in many panels. It is not clear which groups were analyzed. For example, Fig. 4C, D, H and many other panels.<br /> 3. Statistical analyses are lacking for some panels. For example, χ2 test is needed for many panels, including Fig. 3E, and many others.

Comments on figures:

1. Figure 1: 1) AGS is supposed to compare with control (HFE-145). These data are missing in the chart. The cell number in AGS is significantly higher than that in other cells (25 vs, 7 and 8, line 666), which can compromise the statistical analysis. 2) Qualification data are needed to support the statement in Line 71-72. 3) Fig1D: Wnt3-positive filopodia in AGS is double compared to that in HFE-145, which is not consistent with the image shown in Fig1C. 4) Fig1H-I: The red channel is overexposed. The authors should explain why a-myox and a-evi signals were detected outside the cell (or just the background)? The more appealing evidence should be the co-localization of Myox or Evi with wnt3a on the filopodia.

2. Figure 2 nicely showed the impact of paracrine Wnt signaling induced by producing cells. However, there are many issues with this experiment. 1) The reporter plasmids are transiently transfected, which inevitably leads to the expression at different expression levels. How could the authors compare the expression levels as a readout in different conditions if this is the case? Better and reliable methods should use stable cell lines. Thus, the authors should make a stable 7xTCF-NLS-mCherry stable line or co-transfect the cell with GFP to show the relative transfection level. This concern also applied to other figures using 7xTCF-NLS-mCherry reporter assay. 2) Thus, the mCherry positive cells in Fig2B, D and F cannot present all receiving cells, as the transfection rate should not be 100%. Also, did all experiments start with a similar cell number? Thus, the Chart in D is not accurate, and the reason that assesses the number of receiving cells is not clear. It is not clear what "per image" means in D? Is the number correct (1 cell vs 1.5 cells) in D? Additionally, is it possible to image Wnt3 is being transported to the receiving cells? 3) Fig2 C: Western blot could be added to show the mCherry expression level in each group. 4) It is better to include the red channel only in E. It is difficult to see the red signal in the current images. 5) F: How was the qualification conducted? Could the whole population be analyzed more quantitatively?

3. Figure 4: 1) The critical data should be that the formation of wnt3a cytoneme (length, number) is impaired in Flot2-deficient cells, which are missing in the figure and the manuscript. 2) A-D: The expression of Flat2 should be presented in separated images. The membrane localization is not clear. Fig4D shows flot2 occasionally localized with Wnt3. Time-lapse experiments will provide additional evidence of the constant localization of Flot and Wnt3. 3) E: This panel has similar issues described in Figure 2. How was the transfection rate in E? Did all cells express Flot2 or dnFlot2? Their expression should be examined at the same time.

4. Figure 5 is one of the key figures. However, the quality of the images is not high enough to support the conclusion. A-D: The membrane co-localization is not convincing. Better images with a membrane marker are needed. Also, it is better to present images in separate channels. The red color A should be magenta. Did the dominated-negative Flot2 affect the expression of endogenous Flot2? Similarly, the expression of endogenous Flot2 in siRNA expressing cells should be shown. D: Instead of showing the image of single-cell, additional experiments, for example, the western blot should provide additional evidence to show Ror2 expression on the membrane is lost. E: High magnification images should be presented to show the localization. The current images are too small to appreciate the co-localization. Similarly, separated channels should be presented. How many experiments have been conducted? It seems that the cell number is not high. For siRNA experiments, was the expression of Flot2 validated? It is necessary to describe how E-Co-efficient (PCC) was determined in more detail. F: The label for the X-axis is missing. G: The nuclei and cell boundaries are not clear; the markers for these should be included to give confidence where and how the quantification was conducted. Similarly, the expression of Flot2 should be examined in these experiments as it is likely not all cells express those constructs at similar levels. Additional experiments, for example, Western bolt to show pJNK levels, are necessary to support the conclusion further. H: The P values for which group are not clear. I-J: The mem-mCherry shows the protrusions but not the cytoneme because these did not show wnt3 labeling.

5. Figure 6: The experimental designs are problematic. 1) Is Flot2 expressed in zebrafish embryos at the stage analyzed? The results in panels A-B using the overexpression approach do not reflect the endogenous expression of Flot2. Overexpression of Flot2-GFP could cause unintentional consequences. Also, where were those cells that were imaged? Could the authors show more cells? Images of separated channels should be shown. The cell in B seems to be round. Was the cell at the mitosis stage? 2) B: The authors nicely showed that Wnt8a-mCherry is clustered on Flot2-GFP-expressing filipodia. Because of the nature of overexpression of Wnt8a-mCherry, it is possible that Wnt8a-mCherry and Flot2-GFP were expressed in the same spots. Could the authors perform time-lapse experiments to show Wnt8a-mCherry is being delivered to the neighboring cells by Flot2-GFP-expressing filipodia? 3) The authors injected various DNAs to show the consequence of the expression. This method is very unreliable, as injection of DNA likely leads to mosaic expression of the proteins at different expression levels thus, the expression levels are very hard to be controlled. Has the expression of various constructs been compared in different conditions? RNA injection experiments are recommended, as these usually lead to uniform and reliable protein expression. 4) Did overexpression of Flot2 or Wnt8 cause severe developmental defects? Were those embryos healthy? Could the authors show live images of group embryos? The authors need to explain the "0" values in some columns (+wnt8a, flot2/wnt8s) in G. Did these results indicate those embryos did not express pax6a at all?

Reviewer #1 (Public Review):

Here the authors examined volitional neural signals during an un-cued lever pulling task. The authors impressively monitored cortical activity using widefield Ca++ imaging over many sessions (days to months).

Mice received water-rewards to motivate them to pull the lever. The major aim in this study was to understand when neural signals corresponding to the upcoming level pull appeared within the cortex. This is an important question in sensorimotor control, namely, how and where do volitional signals associated with our actions arise in the brain? The authors compare their results at various points to human motor control, where readiness potentials appear prior to the execution of movement. Thus, the authors' study could make a meaningful contribution to understanding how neural activity changes prior to the initiation of a voluntary movement (i.e. there is no cue in their task).

Prior to each lever pull, neural activity exhibited oscillatory patterns that sharpened in amplitude with proximity to the pulling event. These oscillations could be observed throughout the cortex: in retrosplenial, barrel, somatosensory, visual, and motor regions. As previously reported, neural activity exhibited a reduction in variance prior to movement initiation. The collapsing in variance was observed prior to the movement in all areas, excepting visual cortex. These changes in variance were echoed by convex hull analyses, which aimed to summarize the space spanned by pre-pull neural activity. As the movement approached, the convex hull gradually narrowed. The intersection between the lever pull's convex hull and the convex hull associated with all paw movements appeared to decrease with training in the task. This suggested a restructuring in neural activity whereby lever pull movements became more distinct in their neural activity patterns.

To understand whether pre-pull neural activity was associated with the upcoming movement, the authors used an SVM to predict whether neural activity over some pre-pull window could predict the upcoming lever pull. They observed that SVM classifiers could indeed predict the upcoming action well in advance of the behavior, in some cases 10-15 sec prior to the lever pull. This result is quite notable, given that previous evidence in humans suggests that readiness potentials arise 0.5-1.5 seconds prior to movement. Thus, the authors' study suggests a much longer time horizon for volitional signals in the brain.

The authors' question is both intriguing and important to the field of motor control, but certain details about their task complicate interpretations of their data. Most importantly, in the pre-pull period, behavior was not generally quiescent. Because the task did not use a cue, animals engaged in many behaviors in the windows preceding rewarded lever pulls. Thus, it is hard to know whether pre-pull neural activity relates to the upcoming rewarded lever pull, or earlier lever pull events (and other behaviors) that were likely to occur within the SVM window itself. While the authors used a 3-second lockout in their analysis (only considered rewarded pulls that were not preceded by lever pulls in the past 3 seconds), it remains challenging to interpret neural activity prior to threshold value (and it is currently unclear whether this lockout period excludes all lever pulls, or only some that met certain criteria). Along these lines, when the lockout window was extended in a control analysis, the SVM's time horizon for volitional signals shortened, suggesting that pre-pull behaviors indeed influenced the primary results. Thus, it remains unclear exactly when volitional signals arise in this task. The authors could greatly strengthen their paper with additional neural and behavioral analyses on these matters.

Reviewer #1 (Public Review):

In this work the authors investigated the mechanism of the cadherin-catenin F-actin catch bond interaction. They demonstrate that the catch bond is from a force-dependent switch of the ABD of αE-catenin between a five-helix bundle and a four-helix bundle bound on F-actin. In addition, they report cooperative binding of cadherin-catenin complexes on F-actin via interactions of neighboring ABDs. Overall, the findings are very interesting, the experiments are well executed, and the conclusions are backed up with experimental data.

A notable strength of the work is the combination of single-molecule assay, analysis based on structural features, and kinetic modelling. Their proposal that the mechanism may be conserved across actin binding domains of several other actin binding proteins is convincing.

The explanation of the observed catch bond is based on increased stability of the bound four-helix ABD compared to five-helix ABD. While this mechanism can explain the observed catch bond, alternative or additional factors need to be discussed. When the bound ABD switches from a five-helix bundle to a four-helix bundle, the stretching geometry is significantly altered. It is well known that different stretching geometries applied to the same molecule could lead to very different mechanical stability.

It will also be helpful to add a brief discussion on how the results provide an understanding of the mechanical stability of the cadherin/beta-catenin/alpha-catenin/F-actin force-transmission linkage. At ~ 4 pN where the lifetime is the longest, the lifetime of the F-actin bound catenin complex is shorter than 10 s. Can the time scale enable robust mechanotransduction function? The time scale should sufficient to expose the vinculin binding site. After vinculin binds and engages with the same F-actin, will it stabilize (i.e., increase the lifetime) the linkage?

Reviewer #1 (Public Review):

Cheng et al. address one of the fundamental questions of gene expression regulation - what are the relative contributions of RNA-level and protein-level regulation to the final gene expression levels. In order to do that they take advantage of mainly published datasets, especially tumor datasets where matching somatic copy number alterations (SCNAs), RNA expression and protein expression data is available. Performing proteogenomic analysis (taking DNA, RNA and protein into account) they address several open questions, such as: Is gene compensation happening mainly at the RNA level, protein level or both for each gene? Is this the same across different tissue types and also cellular pathways? Taking advantage of the SCNAs in the DNA, the authors use correlation analysis of DNA to RNA and RNA to protein to determine if the expression of a gene is regulated mainly at the level of RNA or protein in the respective samples.

Although it is mainly a very descriptive study, the meta-analysis of existing datasets (and one smaller dataset that was newly generated) yields very interesting observations, which will be of interest to the cancer and gene expression community. However, there is limited mechanistic insight into how the observations can be explained. This is not a problem in my view as the observations are interesting enough in themselves.

The main findings of the study are:<br /> - In general genes are either regulated at the RNA-level or at the protein level, but rarely at both.<br /> - This is the first study (at least as far as I know) to look at tissue-specific RNA-level and protein-level compensation across several different tumor types. Interestingly, the authors show tissue specificity of RNA and protein-level compensation - for example lung adenocarcinoma does not show nearly any compensation.<br /> - Protein complex genes show stronger protein-level regulation than non-complex genes and the opposite trend in regards to RNA level regulation.<br /> - There seems to be an agreement for genes within the same pathway that they show a similar regulatory mode (either RNA level or protein level).<br /> - Genes involved in RNA processing, mRNA translation and mitochondrial regulation are generally upregulated at the protein-level in highly aneuploid primary tumor samples.

However, I do think that two points need to be addressed by additional analyses to strengthen the findings.<br /> - The authors show that SCNAs are often significantly compensated at the protein-level in most tumor types. This compensation is also normally stronger than RNA level compensation. A technical issue about this finding that needs to be addressed is that this is mainly based on proteomics data that used TMT for quantification. TMT-based quantifications, although quite precise, are not always the most accurate measurements in the sense of capturing the true amplitude of changes. This is due to the so-called ratio compression of TMT mass spec data. The authors need to account for that in order to exclude that this technical limitation of TMT-based proteomics measurements is a main contributor to the protein-level compensation seen. Do the authors also have some proteomics data where label-free quantification of SILAC quantification was used? Do the same conclusions hold true when such data sets are used?<br /> - Many of the statistically significant differences seen - e.g complexed proteins versus non-complexed proteins, highly conserved proteins versus less conserved proteins - have actually a relatively small effect size. It is not 100% clear to me that the authors apply always the most stringent and appropriate statistical evaluation. For example, when two density plots are compared and it is evaluated if the distributions differ significantly from each other (e.g. the median), the authors constantly use a bootstrapping strategy (most plots in Fig 2 and Fig S2). Due to the high number of iterations, bootstrapping is very sensitive to picking up statistical differences, even if there are very small effect size differences (as is the case for many of the comparisons). Would not a KS test be more appropriate to compare two density distributions? If a KS test is applied - do the authors still recapitulate the same statistical significance tendencies as seen with their bootstrapping strategy?

Reviewer #1 (Public Review):

This paper describes a systematic biochemical analysis of UBX proteins in facilitating protein unfolding by the p97-UFD1-NPL4 (referred to here is the p97 complex). The p97 complex binds Ub and unfolds it to allow the ubiquitylated protein to be translocated into the p97 ATPase pore for unfolding. This paper demonstrates that UBX proteins are able to reduce the necessary ubiquitin chain length in order to support unfolding by p97. They explore this using ubiquitylated CMG helicase as a substrate. Removal of CMG helicase from replicated DNA is required for completion of DNA synthesis.

First the authors demonstrate that the p97 complex only only unfolds CMG with very long Ub chains. The then show that the high threshold for Ub is reduced when UBXN7, FAF1 or FAF2 are added. These proteins bind to both the p97 complex and Ub in substrates. This is then followed up in cells by demonstrating that removal of UBXN7 and FAF1 reduces CMG disassembly and is synthetic with reduced CMG ubiquitin ligase activity.

The conclusion that human p97 requires UBX proteins to support unfolding/segregase activity when Ub chains are short would be strengthened by more precise characterization of the length of ubiquitin chains being studied, as the methods do not precisely determine the chain lengths and how this is overlapping with the number and location of primary ubiquitylation sites on Mcm7. The in cellulo results, while consistent with a contributing role for FAF1 and UBXN7 in disassembly of the CMG by p97, indicate that either other factors are required in cells or that p97 can disassemble CMG with relative short chains in cells without the need for the UBX proteins. This needs to be reconciled with the proposed model.

Reviewer #1 (Public Review):

This study investigates low-frequency (LF) local field potentials and high-frequency (HF, >30 Hz) broadband activity in response to the visual presentation of faces. To this end, rhythmic visual stimuli were presented to 121 human participants undergoing depth electrode recordings for epilepsy. Recordings were obtained from the ventral occipito-temporal cortex and brain activity was analyzed using a frequency-tagging approach. The results show that the spatial, functional, and timing properties of LF and HF responses are largely similar, which in part contradicts previous investigations in smaller groups of participants. Together, these findings provide novel and convincing insights into the properties and functional significance of LF and HF brain responses to sensory stimuli.

Strengths<br /> • The properties and functional significance of LF and HF brain responses is a timely and relevant basic science topic.<br /> • The study includes intracranial recordings in a uniquely high number of human participants.<br /> • Using a frequency tagging paradigm for recording and comparing LF and HF responses is innovative and straightforward.<br /> • The manuscript is well-written and well-illustrated, and the interpretation of the findings is mostly appropriate.

Weaknesses<br /> • The writing style of the manuscript sometimes reflects a "race" between the functional significance of LF and HF brain responses and researchers focusing on one or the other. A more neutral and balanced writing style might be more appropriate.<br /> • It remains unclear whether and how the current findings generalize to the processing of other sensory stimuli and paradigms. Rhythmic presentation of visual stimuli at 6 Hz with face stimuli every five stimuli (1.2 Hz) represents a very particular type of sensory stimulation. Stimulation with other stimuli, or at other frequencies likely induce different responses. This important limitation should be appropriately acknowledged in the manuscript.

Reviewer #1 (Public Review):

Lin et al investigate the role of AP-2e in vomeronasal sensory neurons through targeted gene deletion and rescue. They report that knockout of AP-2e reduces expression of basal markers, while induced expression can rescue basal identity. Moreover, forced expression of AP-2e in mature apical neurons causes them to express some basal markers. While it would be interesting if apical sensory neurons could be reprogrammed, additional evidence regarding shifts in receptor expression is needed before these conclusions can be made.

Reviewer #1 (Public Review):

This interesting work tries to predict and analyze the overlap of BCR and TCR repertoires (mainly in COVID-19 conditions) which is one of the most important aspects of adaptive immunity that is directly related to antigen specificity. However the primary claims were not fully supported by the current data and analysis the authors presented.

1) Since the authors showed that the TCR/BCR changed with age, whether they corrected their CMV- and CMV+ analysis with age differences?

2) TCR repertoire (probably BCR also) changed along with the time during SARS-CoV-2 infection (especially the first several weeks after cleaning the virus). The authors should consider the time points they used in all the COVID-19 studies to validate the method.

3) What's the difference between different infections (e.g. CMV vs SARS-CoV-2)? Or does infection lead to the same TCR/BCR changes in the study? A detailed discussion with an analysis of TCR/BCR repertoire regarding different infections CMV vs SARS-CoV-2 needs to be provided.

4) Are there any features along with different infections compared with tumor/autoimmune conditions (I think there are many publications about TCR/BCR dynamics in various diseases)? Analysis of these data is not only important to control for validating their method but also can generate the most interesting data/conclusions on dissecting the specificity of TCR/BCR repertoire.

Reviewer #1 (Public Review):

The authors reveal dual regulatory activity of the complex nuclear receptor element (cNRE; contains hexads A+B+C) in cardiac chambers and its evolutionary origin using computational and molecular approaches. Building upon a previous observation that hexads A and B act as ventricular repressor sequences, in this study the authors identify a novel hexad C sequence with preferential atrial expression. The authors also reveal that the cNRE emerged from an endogenous viral element using comparative genomic approaches. The strength of this study is in a combination of in silico evolutionary analyses with in vivo transgenic assays in both zebrafish and mouse models. Rapid, transient expression assays in zebrafish together with assays using stable, transgenic mice demonstrate dual functionality of cNRE depending on the chamber context. This is especially intriguing given that the cNRE is present only in Galliformes and has originated likely through viral infection. Interestingly, there seem to be some species-specific differences between zebrafish and mouse models in expression response to mutations within the cNRE. Taken together, these findings bear significant implications for our understanding of dual regulatory elements in the evolutionary context of organ formation.

Reviewer #1 (Public Review):

This work by Wei-Jia Luo and colleagues elegantly employs in vitro and in vivo models to demonstrate that within the mouse liver, macrophages respond to lipopolysaccharide (LPS) by releasing active IL-12 (IL-12p70), which is a heterodimer of IL-12p35 and IL-12p40. They observed that the availability of "free" IL-12p35 to this heterodimerization process is governed by the molecular chaperone HLJ1. In response to LPS, HLJ1 separates homodimerized IL-12p35 into monomers, which then can heterodimerize with IL-12p40 to form active IL-12p70. This active IL-12 is released from macrophages in the liver, which then act on neighboring natural killer T cells to release interferon gamma. This interferon gamma circulates systemically and is responsible for mortality in a mouse model of endotoxemic shock.

Overall, this work is mechanistically compelling and demonstrates a novel multicellular inflammatory pathway that contributes to death in a murine model of endotoxemic shock. However, it is unclear if the observed pathway is limited to this highly reductionist model, or if it applies to models that better approximate the complexity of human sepsis. Indeed, the long-standing concept of "cytokine storm" as the major mediator of sepsis has largely failed to yield benefits in clinical trials. These numerous and repeated translational failures cast doubt on the translational validity of reductionist in vivo animal models of sepsis. This raises several specific concerns with regard to the model used by the investigators:

(1) The authors use a massive dose of LPS that rapidly leads to the death of mice in 24 hours. This massive and rapid mortality is not consistent with human sepsis, which is a more crescendo course with a mortality of ~30%. Indeed, when the authors used a more clinically-relevant model of mild endotoxemia, HLJ1 appeared to have no impact on mortality (Figure 1A).<br /> (2) LPS is a model of endotoxemia, not a model of sepsis. Accordingly, it is unclear if the protective benefit of blocking IL-12 will similarly be seen as a live-infection model of sepsis, in which inflammatory signaling may be necessary for pathogen clearance.<br /> (3) Finally, it is unclear if the findings are only relevant to mice, or if they also have relevance to humans.

Reviewer #1 (Public Review):

The Drosophila nephrocyte is a promising model to analyze filtration function and nephrocyte diaphragm integrity and podocyte function. In this study, the authors developed assays to examine slit diaphragm dynamics directly after short-term manipulation of endocytic functions to examine the filtration barrier's dynamics, specifically by examining nephrin. The authors demonstrated that lateral diffusion of ectopic nephrin is prevented by rapid dynamin-dependent endocytosis restricting slit diaphragm localization. In contrast, nephrin engaged within the proper slit diaphragm complex is constantly endocytosed flotillin2-dependently followed by recycling. The findings suggest that such turnover offers flexibility and cleanses the filtration barrier from adherent molecules, preserving its permeability. Their discovery that selective and functionally distinct routes of endocytic transport of components of the slit diaphragm to maintain the barrier's architecture and permeability is remarkable and could have clinical significance.

Reviewer #1 (Public Review):

This paper is well written, beautifully illustrated and appears to be a model of thoroughness. However, the paper does not convey an appropriate understanding of the biology of the behaviour described. Therefore, the paper is far more likely to be of interest to engineers and roboticists rather than biologists.

There is some conceptual overlap with previously published work:

Abstract:

Condensed active matter systems regularly achieve cooperative emergent functions that individual constituents could not accomplish alone." This general point was made in the Princeton monograph by Camazine et al. published in 2001, even though that book did not refer, of course to the new cliché of "condensed, active-matter systems". In general, it seems an oversight that Camazine et al. (2001) and associated work has not been cited in this paper.

Discussion:

Our model indicates that fire ant rafts may exhibit spontaneous protrusion growth in the absence of external cues, long-range interactions, or centralized control and that the global response of these condensed active systems depends on the underlying behavior of individual constituents."

There is overlap with the work by Worley et al. (2019), who show in their paper on flocculation the adaptive value of a collective behaviour, which occurs in the absence of long-range interactions or centralised control, and that the global response depends on the underlying behaviour of the individual constituents (Worley A, Sendova-Franks AB, Franks NR. 2019 Social flocculation in plant-animal worms. R. Soc. open sci. 6: 181626. http://dx.doi.org/10.1098/rsos.181626).

In fairness, it could be that the originality of the current paper is that it shows collective phenomena in the absence of external cues. However, the paper states that worker activity level has a major influence over the formation of protrusions. Given that such worker activity is likely to be temperature dependent, then there is de facto an external cue, which would affect this claim to originality. Moreover, the growth of protrusions from a fire ant raft have not been shown to have any adaptive value at least in the current paper. So, the presence of absence of external cues does not appear necessarily to be of a great importance.

Reviewer #1 (Public Review):

The authors have used a structure based mutational scan of TRF2 to create a separation of function mutant in the protein that would form dimers and gets incorporated into the Shelterin complex, with a defect anticipated to be only in recruiting ORC to telomeres. The mutations used fall within a loop connecting two helices where the side chains of the wt protein are found on the outer surface of the protein. This is well done and data that the mutations are defective in recruiting ORC are convincing especially at the synthetic loci described. This is an advance over what has been done previously that has suggested through Knock down of the entire Trf2 such interactions and roles in DNA replication. The biochemical demonstration that ORC actually touches this region of TRF2 or indeed makes a direct contact to TRf2 is missing. Recruitment to chromosomes via co-localization has value but doesn't establish the point.

The major aim of the paper is to test the notion that defects in the recruitment of ORC to telomeres results in DNA replication defects at the telomeres. The manuscript shows that in clones selected there is no DNA damage at telomeres when the mutant TRF2 replaces the wt allele. The control used is a knock -out of the wt (with a CRISPR SS DNA method) and then a replacement wt allele with a marking restriction site. Both the control and the mutants are expressed at lower levels that the parent clone, and more DNA damage is found in the control than parent. This appears confusing and is perhaps a weakness of the paper.

Reviewer #1 (Public Review):

In the presented manuscript entitled Box C/D Small Nucleolar 1 Ribonucleoproteins<br /> 2 Regulate Mitochondrial Surveillance and Innate Immunity by Tjahjono et al., the authors describe data supporting novel interactions between the box C/D snoRNA core proteins (snoRNPs), mitochondrial surveillance processes and innate immunity in C. elegans. They find that C/D snoRNPs contribute to the regulation of the unfolded protein response in mitochondria (UPRmt) and the ethanol stress response (ESRE). Loss of C/D snoRNPs is further shown to increase expression of the innate immune reporters irg-5::gfp and irg-1::gfp. The authors conclude that Box C/d snoRNPs are upstream regulators of both mitochondrial surveillance and innate immunity, and as such potentially important to combat aging-associated and infectious diseases.

The conclusions presented in this paper are not always supported by the presented data. Reasonable alternative explanations for the observed results are not consequently considered. Further, some key controls are missing, which complicates and inclusive full assessment of the data quality and conclusions, and reduces the anticipated impact on the field significantly.

This study and all most conclusions rely on experiments that used the technique of RNAi feeding, which is well established in C. elegans. While the authors highlight in the materials and methods section that each RNAi was sequence-verified, on-target effects of individual RNAis in the used conditions are not assessed. qPCR-based verification of key RNAis in both L1 and L3 animals would need to be performed to better understand the role of the box C/D snoRNPs in the tested assays.

The authors used RNAi targeting ruvb-1 to demonstrate to assess of nucleolar localization of snoRNPs is required to trigger the studied phenotype. However, the authors didn't show that the ruvb-1 RNAi indeed knocks down this gene, nor do they consider ruvb-1-independent nucleolar localization of snoRNPs. To support their conclusions, the authors would need to perform subcellular fractionation / western blot or immunofluorescence staining to provide further evidence when and where the investigated snoRNPs are required.

The authors did not validate/test that the cycloheximide treatment results in the intended inhibition of translation. Data shown e.g. in Fig. S3 or Fig. 7A, showing an increase in Pirg-1::gfp-dependent GFP expression, indicates that cycloheximide is not interfering with translational processes as expected. The utilized reporter strains, although presented as transcriptional reporters, require the transcription and translation of a gfp gene in order to give a positive result in the reporter assays. Thus, less GFP signal may always arise as a consequence of general translation inhibition. In this reviewer's opinion, this aspect is not sufficiently considered.

Conceptually, this reviewer feels that it is a stretch to conclude from assessing a single reporter per cellular process that Box C/D snoRNPs are master upstream regulators of mitochondria and innate immune mechanisms.

Reviewer #1 (Public Review):

The authors were trying to understand published claims about the effect of population structure on fixation probabilities by generalizing the models, effectively introducing more parameters to separate possible causes. The paper is for the most part extremely well written, and the thinking crystal clear. From mathematical point of view, everything appears to hold up.

The results certainly support the limited conclusion that whether a particular model of population structure helps or hinders selection can depend crucially on whether migration in symmetric not, but does not provide much general insight into why that is so.

Reviewer #1 (Public Review):

The study conducted by Gu et. al. has clarified the inhibitory role of miR-22 in in-vitro and in-vivo angiogenesis including tumor angiogenesis. The authors showed that miR-22 is less expressed in tumor endothelial cells (ECs) of human non-small cell lung cancer (NSCLC) tissues compared with ECs of adjacent normal tissues. The authors propose that this reduction of endothelial miR-22 could be induced by NSCLC cell-secreted TNFa and ILb. In addition, they showed that endothelial miR-22 reduces SIRT1 and FGFR1 in the ECs, leading to inactivation of AKT/mammalian target of rapamycin (mTOR) signaling. This study is novel, interesting and well-designed.

However, the concepts established in this study would need to be extended to other common cancers. The authors would also need to define how NFkB activation induces reduction of miR-22 in endothelial cells. It is unclear how miR-22 inhibits S-phase of cell cycle, migration and sprouting in endothelial cells. What are the underlying mechanism? The explanation involving the reduction of FGFR1 and SIRT1 does not seem to be sufficient.

Reviewer #1 (Public Review):

Studies evaluating vulnerability of specific biological pathways and validation of gene essentiality are extremely slow and technically challenging in M. tuberculosis. In this study, McNeil et al employ a previously reported CRISPRi approach to evaluate gene essentiality and lethality in a high-throughput manner. They design and test sgRNA sequences against 96 genes targeting various cellular pathways. Most of the targets involved in cell wall biosynthesis or core cellular functions were found to be not only essential but gave a bactericidal phenotype when inhibited. On the other hand, the genes involved in metabolic processes although mostly essential, gave only a bacteriostatic phenotype when inhibited. This was attributed to be due to metabolic buffering upon inhibition of bacterial metabolic processes. The authors have performed an comprehensive evaluation of their CRISPRi using this 96-set of target genes. While most of their conclusions are in line with previous observations from other gene-essentiality studies, the approach could be very useful for TB researchers.

Overall, this is a very elegant study and the manuscript is written clearly. I especially would like to commend the authors for the effort they have invested in presenting the vulnerability data in a simple and clear manner.

Reviewer #1 (Public Review):

The manuscript by Yanowski et al uses lineage tracing, single cell mRNA sequencing and in situ hybridization methods to study the developmental origin of delta cells and the transcriptional heterogeneity of beta cells in the regenerating pancreas. First, the authors use a CRE-lox approach driven by Sox9-CRE to trace the fate of Sox9-expressing cells in adult mice (12 weeks of age). They show that most Sox9-positive cells are exocrine cells - as expected. However, they also observe that most Sox9-positive cells inside the islet express Sst and therefore are delta cells. Next, they use a mouse model of beta cell regeneration triggered by pancreatic injury (partial pancreatectomy (ppx)) to characterize beta cell heterogeneity in the regenerating pancreas using single cell sequencing. In doing so, they identify beta-delta cell pairs that are characterized by lower expression of the ER stress marker Fkbp11 and higher Ins2 expression. These results are validated in situ using smFISH.

These results lead to the conclusions that 1) delta cells originate from Sox-9 expressing cells and proliferate in ppx, 2) there are 3 distinct types/states of beta cells in the regenerating pancreas (stress, cell cycle and basal) and 3) beta-delta cell interactions appear to be specific to a beta cell subset called "int-beta".

The study opens exciting possibilities and questions that indicate that a fraction of adult delta cells express Sox9, while implicating beta-delta cell signaling and/or physical interaction in the transcriptional heterogeneity "process" in beta cells in a regeneration setting. However further experiments are necessary to support the authors' conclusions and provide the physiological context of their observations

Major strengths:

- Study uses a genetic mouse model to express a fluorescent reporter in delta cells, which allows the authors to enrich isolated islet preparations with delta cells that can be studied with single cell technologies<br /> - The use of MARS-seq + PIC-seq is very exciting and it has the potential to identify important cell-cell interactions in the islet and that are mostly lost during tissue dissociation/islet isolation.<br /> - The smFISH technology is impressive. This approach allows the important validation step in situ of beta cell heterogeneity characterized by distinct gene expression profiles identified by single cell mRNA sequencing.<br /> - The combination of ppx with single cell technologies has the potential to identify the molecular signature of replicating islet cells which may play a role in pancreas regeneration, including beta cells and delta cells.<br /> - The finding that delta cells seem to be indeed capable of proliferation in adult mice is very exciting, specially since this cell types is mostly post-mitotic throughout the lifetime of adult mice

Major weaknesses:

- Although the study is data rich, it lacks the description of the molecular details of the different types and sub-types of cells identified and the manuscript as it is written is most times confusing in the presentation in the results and concepts. This makes it difficult for the reader to fully appreciate the degree of "heterogeneity" observed and the lack of a detailed description of these phenotypes precludes any meaningful comparison with other published results describing beta cell heterogeneity in the mouse. For example, the balance of PDX1/MAFA in the mouse islet has been shown to be important for islet function (see work by the Hodson lab), however these two transcription factors are not mentioned/analyzed in this manuscript.

- The analysis of the imaging data, particularly of the number of delta cells in sham/ppx isolated islets is subpar. Here, the authors use whole islet fluorescence intensity (Fig2B-D) or smFISH (FigE-G) to quantify delta cell mass. These results are inconclusive since this enhanced fluorescence signal may also be explained by delta cell hypertrophy and/or an increase number of delta cell filopodia and not necessarily more delta cells.

- The authors show that beta-cells with neighboring delta cells are likely the "int-beta" cell phenotype identified with MARS/PIC-seq. However, this important observation is only supported by the validation of two "int-beta" marker (Fkbp11, Ins2). Moreover, and given the peripheral distribution of delta cells, these results imply that beta cells in the periphery are molecularly different from other beta cells and may have overlapping features with virgin beta cells described by the Huising lab. However, no attempt is made to correlate these new findings with the published data and the physiological relevance of these findings remain mostly speculative.

Reviewer #1 (Public Review):

The manuscript by Stemm-Wolf et al. investigates the role of the splicing factor SON in centriole assembly, which was previously identified by others in a genome-wide screen for factors required for centriole duplication. The authors first confirm that SON impairs centriole duplication. They show that procentriole formation occurs, but elongation and proper centriolar microtubule triplet formation fail. Using mRNA sequencing, the authors extend the list of previously described SON splicing targets, providing the most comprehensive list to date, including many components of centrioles and centriolar satellites. The authors then attempt to identify targets crucial for SON's role in centriole assembly and found that depletion of CEP131, a centriolar satellite component, phenocopies SON depletion to some degree. They further observed alterations in centrosomal microtubule organization and a reduction in centrosome proteins in the vicinity of centrosomes after SON depletion, which makes them conclude that defective trafficking of centrosome components, as part of satellites or similar trafficking particles, is the main cause of the centriole assembly defects. However, altered centrosomal microtubule organization after SON knockdown was observed previously and there is very little data that goes beyond this finding. It also seems that, rather than conducting a more comprehensive analysis, the authors have focused on a few candidates that they thought would help to explain SON depletion phenotypes. However, those that have been tested, alone or in combination, do not or only to a limited extent recapitulate SON depletion and none was tested for an involvement in the altered centrosomal microtubule organization phenotype. Moreover, considering the long list of centrosome proteins affected by SON, the phenotype may not involve only one or two proteins. At the end the authors argue that katanin, also a splicing target of SON, may be involved in altered centrosomal microtubule organization, but there is no experiment to test this. In conclusion, despite various detailed analyses, there is not a major advance regarding mechanistic insight and the offered explanation that impaired trafficking around centrosomes causes centriole assembly defects after SON depletion is relatively vague.

Reviewer #1 (Public Review):

The authors used BioID to identify SMC5 proximal proteins in HEK293T cells. In one of the top hits, they recognized an NSE5-like domain and proposed it (SIMC1) to be the new NSE5-like subunit of the human SMC5/6 complex. Accordingly, they showed its colocalization and co-immunoprecipitation with the NSE6/SLF2 subunit. They confirmed SIMC1-SLF2 direct physical interaction by their copurification. Furthermore, using cryoEM and AlphaFold modelling, they were able to determine the structure of the SIMC1-SLF2 dimer at 3.9A resolution. They also showed that the SIMC1-SLF2 dimer is mutually exclusive to the previously described SLF1-SLF2 dimer. To make their structural data more solid, the authors should have mutated contact residues to support their structural data and their conclusion that the similar SIMC1 and SLF1 surfaces bind SLF2. Otherwise, the above data are very strong and support the author's conclusions about the new NSE5-like subunit of the human SMC5/6 complex.

Further, the authors showed that the SMC5/6 complex is localized to SV40 T-large (LT) antigen-induced foci and PML bodies. This localization is dependent on the SIMC1 and its SUMO-interacting motifs. In addition, several SUMO pathway factors (known components of PML bodies) were identified in the SIMC1 BioID search. These data suggest a strong connection of SMC5/6 to SUMO-rich sites. Based on this connection, the authors speculate that SIMC1-SLF2 specifically regulates responses to viral challenges while SLF1-SLF2 is specifically involved in DNA damage responses. Given the roles of PML bodies in homologous recombination (DNA damage repair, alternative telomere lengthening), their experiments do not directly prove this assumption. Actually, SMC5/6 was already shown to be localized to PML bodies and involved in alternative telomere lengthening (https://pubmed.ncbi.nlm.nih.gov/17589526/). In conclusion, further experiments are required to show the exclusive roles of distinct SLF2 complexes and the specific involvement of SIMC1-SLF2 in antiviral responses.

Major requests:<br /> 1. The biochemical data on the SIMC1-SLF2 dimer are solid. However, the authors did not verify the structural data by mutating surface residues mediating SIMC1-SLF2 interaction (although they prepared various constructs and generated "combo" mutations at the different surfaces - Fig. 7). They should mutate contact residues to support their structural data and their conclusion that the same SIMC1/SLF1 surfaces bind SLF2.<br /> 2. The claims about the exclusive roles of SIMC1-SLF2 and SLF1-SLF2 should be better substantiated. The control experiment showed (no)localization of SLF1 to LT-induced foci and PML bodies (and vice versa SIMC1 (no)localization in DNA-damaged cells like in Raschle, 2015 - https://pubmed.ncbi.nlm.nih.gov/25931565/) should be performed.<br /> 3. In addition, the sensitivity of the SIMC1-/- cells to replication stress and DNA damage agents should be compared to the other SMC5/6 mutants.<br /> 4. SIMC1 BioID (under native/denaturing conditions) experiment suggests that multiple SUMO pathway factors are proximal to SIMC1. However, there is no evidence for the interaction between SIMC1 and ZNF451 (p. 12/line 228 + p.28/line 508). The coIP experiment (Fig. 3D) was performed in a biotin-streptavidin manner suggesting the proximity of these proteins but not the interaction. The coIP experiment should be performed with anti-myc or anti-GFP antibodies.

Reviewer #1 (Public Review):

This manuscript is a continuation of the impactful work of Lee et al. (2015) [PMID: 25392270], in which the location and identity of brown adipocyte progenitors and their proliferation and differentiation driving brown adipogenesis was significantly refined. In this work, Burl and Rondini et al. attempt to elucidate the transcriptional profile of the stromal vascular fraction of murine brown adipose tissue in the context of thermogenic stimulation and activation of the depot. The authors combined a systems approach that generated detailed information on the dynamics of cellular transcriptomes over a fine time course with a reductionist approach that provides strong causal evidence for the mechanism of brown adipocyte neogenesis, namely showing its reliance on the adrenergic activation of mature brown adipocytes to indirectly stimulate progenitor proliferation and differentiation and the possible involvement of dendritic cells in this process. Additionally, the manuscript provides strong evidence of a contribution from myeloid cells in the process of progenitor proliferation and differentiation - an often noted but poorly understood process. Overall, this is a timely and well-rounded work that will provide beneficial data for public use and further resolve the complexities underlying brown adipose physiology.

Reviewer #1 (Public Review):

Understanding how changes in microRNA (miRNA) levels affect human brain development remains a crucial task that has been largely understudied. This paper is an unbiased, large-scale, important contribution to this effort. The authors first performed small RNA sequencing from mid-gestation human cortical tissue to identify expressed miRNAs. Then, they mapped 85 local-miRNA-eQTLs that rarely colocalize with mRNA-eQTLs for the host mRNA. This is a very significant discovery, with a broad impact on the field. The lack of colocalization between miRNA- and mRNA-eQTLs reinforces the theory that miRNA transcriptional mechanisms are often independent of the host gene. Interestingly, miRNA-eQTLs map less frequently to risk loci. Considering the well-known importance of miRNAs during brain development, it is likely that the expression mechanisms of miRNAs are tightly regulated, as the authors suggest. The authors then focus on one specific miRNA-eQTL that affects miR-4707-3p and show colocalization with GWAS signals for head size (smaller) and educational attainment (decreased). They hypothesize that miR-4707-3p affects brain development by altering progenitors' proliferation and they partially support the hypothesis by over-expressing miR-4704-3p in human neural progenitor cells and showing an increase in neurogenesis.

Reviewer #1 (Public Review):

The manuscript by Miyashita et al describes deubiquitylation of PAF15 by USP7 and its role in regulation of replication-coupled DNA methylation maintenance. Previous studies from the same group showed that UHRF1-mediated dual mono-ubiquitylation of PAF15 (PAF15Ub2) promotes PAF15 chromatin loading and acts as a unique and critical molecular mark to recruit DNMT1 to the DNA replication sites. Here they show that termination of PAF15Ub2 signaling is regulated by USP7-mediated deubiquitylation and ATAD5-mediated removal from chromatin. These findings provide a molecular understanding of how the maintenance DNA methylation machinery is disassembled post replication.

Reviewer #1 (Public Review):

Obesity is considered a key risk factor in the development of insulin resistance, which is itself a key component of type 2 diabetes. As such, a common experimental practice to induce insulin resistance in rodents is to render them obese by feeding them a high-fat diet. However, there are certain individuals in the human population that are obese but do not present with insulin resistance (so-called "healthy obese"). The present studies sought to determine whether obesity per se is always associated with insulin resistance. To this end, the authors fed mice either a low-fat chow diet, a high-fat diet, or a high-starch diet. The high-fat and high-starch diets both promoted equivalent weight gain and obesity. As expected, the mice rendered obese by eating the high-fat diet were insulin resistant, as determined by several experimental tests to assess insulin action. Surprisingly, the mice rendered obese by consuming the high-starch diet were not insulin resistant. When the authors measured a variety of factors in multiple tissues, they determined that mice fed a high-starch diet showed markers of the improved handling of carbohydrates, which likely explained why these mice did not display insulin resistance. These are important findings that provide nuance to the fields of obesity and diabetes by suggesting that not all forms of obesity are necessarily associated with insulin resistance. Furthermore, the molecular analyses conducted in these studies identify potential targets for improving the handling of carbohydrates in obese individuals.

The approaches used by the investigators were outstanding. Several different experimental tests were used to assess insulin action, including glucose and insulin tolerance tests, measurements of insulin signaling, and the hyperinsulinemic-euglycemic clamp. The conclusions are largely supported by the results obtained. One minor weakness is that these studies are largely observational in that they show correlations between obesity, degrees of insulin resistance, and changes in specific genes/proteins and/or metabolites, but they do not necessarily show causation. This is considered a minor weakness since the goal of the study was not to show causation but instead to test whether obesity is always associated with insulin resistance.

Reviewer #1 (Public Review):

Johnstone et al. developed an elegant luciferase-based construct (called LABL) to investigate in vivo the transcriptional dynamics controlled by the period promoter, one of the core circadian clock genes. Compared to previously generated reporter tools, the combination of LABL and the powerful UAS-GAL4 system provides an unmatched instrument to dissect with cell- and tissue-specific resolution the rhythmic transcriptional dynamics orchestrated by tissue-specific circadian clocks. Exploiting this strategy, the authors investigated rhythmic transcriptional dynamics in neuronal and peripheral clocks and their dependence from PDF signalling, a key neuropeptide for the functioning of Drosophila brain clock. They show that both neuronal and peripheral clocks exhibit distinct oscillatory properties and that they differentially behave upon loss of PDF signalling. Moreover, they uncover a peculiar ~60 h infradian rhythmic oscillation which is PDF signalling-dependent in neuronal clocks but not in most peripheral clocks. In sum, this manuscript provides an in-depth analysis of the oscillatory properties of neuronal and peripheral clocks in Drosophila, and whether they rely on PDF signalling to stabilize or sustain their oscillatory properties.

Strengths:<br /> The LABL construct is widely applicable in animal models with advanced genetic toolkits, and represent a major advancement in our investigation of cell- and tissue-specific dynamics underlying the rhythmic transcription driven by circadian clocks. This tool definitely represents an advancement compared to previously available reporter lines which did not allow tissue-specific or in vivo analyses. Moreover, especially the experiments dedicated to peripheral clocks provide interesting and novel insights, as they have been characterized to a less degree compared to the brain ones. Finally, the peculiar ~60 h infradian rhythmic oscillation identified represents, to my knowledge, a previously unidentified aspect of biological clocks, showing the LABL construct might provide further advancement in our understanding of molecular oscillations.

Weaknesses:<br /> Although the LABL construct could potentially provide a great tool for the interrogation of circadian clock properties (in animal models), I personally think the authors are stretching this aspect too much towards the biomedical application. In several parts of the paper, they envisaged an application in medical and pathophysiological contexts. Although in principle I agree this might represent a future application, currently a genetically-encoded reporter construct cannot be used for human studies (if we exclude in vitro studies i.e. cell culture and organoids), limiting the actual impact that the authors did not fail to emphasize. Thus, the authors should be more specifically focused on the short-term benefits (monitoring disease progression in animal models, in vitro studies, etc...). However, this should not diminish the benefits that such a tool could provide for studies in animal models. On the other hand, the authors do not provide insights on the nature of the ~60 h infradian rhythms identified. This is a very important element of the biological findings of the paper, yet the authors simply state that such detailed investigation goes beyond the scope of the study. I personally think that such novel and unusual oscillatory dynamics should have been better explained, theoretically, and perhaps also experimentally, to strengthen the biological advances provided by this study. Additionally, the authors fail to correctly place this phenomenon in the context of the existing literature, as variation of oscillatory dynamics in constant condition (i.e. switch from ~24 h to ~12 h oscillations in marine models kept in DD), in peripheral organs (i.e. ~12 h transcriptional oscillation in mouse liver) and upon clock manipulation have been already reported, although to my knowledge not of ~60 h. Finally, a substantial part of the experiments is dedicated to unravel the contribution of PDF signalling in driving neuronal and peripheral oscillations. Although this neuropeptide is widely considered as key to synchronize brain clocks, this is not known for the peripheral clocks explored in this study. PDF has been shown to coordinate peripheral clocks (i.e. prothoracic gland, Myers et al. 2003), but to my knowledge not in muscles, gut, fat body, all tissues where the expression of the PDFR has not been reported. All considered, it is not unexpected to me how, for most of the peripheral clocks investigated, PDF signalling is not critical.

Public Review:

This manuscript uses budding yeast to uncover a new input for the central metabolic regulator TOR complex 1 (TORC1): manganese (Mn) levels. Using both genetic manipulations in vivo, as well as in vitro protein kinase assays, they show that Mn levels are direct inducers of TORC1 activity and that TORC1 activity modulates intracellular Mn levels. Importantly, they show that this dependence on Mn is conserved to humans. The combination of both in vivo and in vitro approaches as well as the demonstration of conservation of this phenomenon make the manuscript both broad and deep. While the authors do not actually measure intracellular levels of Mn at any point, they do bring a plethora of indirect evidence that Mn levels are indeed the defining parameter for TORC1 in vivo, and this is corroborated by the in vitro studies.

Reviewer #1 (Public Review):

In this manuscript, Carrier and collaborators derive a methylation signature for melanoma aggressiveness from the sequential analyses on various cell lines in different organisms and test it in a set of primary and metastatic melanoma tumours. However, I think that some of the claims are a little premature as a broader sample size would need to be tested to assess the signature robustness and applicability.

Strengths<br /> - The approach the authors take is innovative and I agree with their premise that genes that make cells be more aggressive should be detected across different organisms.<br /> - Different organisms were evaluated.<br /> - Figures are illustrative and the narrative is very clear.

Weaknesses<br /> - The sample size is small. In my opinion, a broader and more diverse set of samples would need to be tested if authors suggest making a diagnostic kit with the genes in their signature<br /> - A more comprehensive comparison with what other authors have found when doing similar studies would be needed to put in context their results.

Reviewer #1 (Public Review):

Burton, Wachowiak, and colleagues use an established olfactory bulb imaging preparation to perform a large-scale analysis of odor responses across the dorsal olfactory bulb. The strengths of the study involve a large odor set, and a broad odor concentration range. The major finding from this work is that most olfactory glomeruli display remarkably sharp tuning. These observations are surprising and important, given that the canonical model in the field is that odors are instead represented by combinations of low-affinity olfactory receptors. A minor issue is that responses were only analyzed in a fraction of glomeruli which were surgically accessible due to their dorsal location. Nevertheless, it seems safe to draw general conclusions given the large number of OR and TAAR glomeruli imaged. Imaging approaches seem expertly performed and conclusions appear solid.

Reviewer #1 (Public Review):

Martinez-Cervantes et al. investigate unimodal sensory preconditioning which affects odor responses even to a non-trained odor. In a previous publication, they could show that this effect was only revealed when they inhibited Rac1 in the mushroom bodies. Now, by performing functional imaging, they could additionally show that this effect is detectable in the activity level of the MBON-y1 after training. Performing rigorous experiments further revealed that the sensory preconditioning effect is strongly dependent on sensory preconditioning ISI and trial repetition, but independent of the odor type used.

The authors nicely dissected the sensory preconditioning effect itself by varying the odor exposures, the gap between odor exposures, and the odors they used. With this, they could show that under certain conditions (short ISI of 1s), the effect is also prominent in WT flies with functioning Rac1. Furthermore, they could show that Rac1 inhibition only reveals the effect for intermediate ISIs (30s), but not very long ISIs (5 min.). Another interesting point they make is that the effect is odor type independent, and thus a general phenomenon. This characterization will be helpful to compare the effect between species and other learning paradigms. An interesting finding is also that the sensory preconditioning can affect neural activity levels, however, does not show in the behavior under certain conditions. Thus, the brain activity of a certain cell type per se not always affects the behavioral output. A very important point for the field of neuroscience.

Reviewer #1 (Public Review):

In their manuscript, Porte et al. investigate the role of Pentraxin 3 in S.pneumoniae infection. The authors provide a series of experiments in which they show that PTX3 is induced systemically and locally after S.pneumonieae infection in a time-dependent manner and its correlation with IL-1b levels in the lung. Using Ptx3 k.o. animal, the authors provide evidence that this gene confers resistance to infection. Further, it is convincingly shown that PTX3 in pneumococcus infection is derived from non-hematopoietic cells and predominantly the endothelial compartment and regulates neutrophil-mediated inflammation. This is a very elegant and complete manuscript that is a joy to read.

I want to highlight the main strength of the manuscript, which is its the usage of bone marrow chimeras in addition to total as well as tissue specific mouse strains that support their claims.

Reviewer #1 (Public Review):

In this manuscript, the authors aim to evaluate the flexibility of the amination network in E. coli. To achieve this, they knock out key enzymes GDH and GOGAT (which supply the majority of the cell's fixed nitrogen by aminating 2-oxoglutarate to glutamate), creating a glutamate auxotroph strain (glut-aux). They first consider whether exogenous amino acids can either replace glutamate, create glutamate through transamination, or be converted directly into glutamate. They found that many amino acids rescued growth of glut-aux, either through conversion to glutamate (proline, via putA), or transamination to glutamate (many, via aspC), and validate this finding with isotopic nitrogen labeling and demonstrating concentration-growth rate dependence. Then, for some amino acids that didn't initially rescue growth in the glut-aux strain, the authors engineer growth rescue through laboratory evolution, gene deletion, and exogenous transaminase overexpression. Finally, they propose that E. coli may accommodate non-canonical (non-glutamate) ammonium assimilation. Informed by the glut-aux rescue experiments, they engineer two strains that assimilate ammonium through alternative amino acids: aspartate (via native aspA overexpression) and leucine (via exogenous leucine dehydrogenase overexpression).

Expanding the repertoire and characterization of auxotrophic microbial strains is an important goal for synthetic biology and metabolic engineering. By creating an E. coli glutamate auxotroph strain, demonstrating and expanding growth rescue on other amino acids, and engineering alternative ammonium entry points, the authors support their claims of flexibility and promiscuity in the cellular amination network. These claims are corroborated by comprehensive growth data and isotope labeling. While certain aspects of their investigation are not novel and the manuscript could benefit from more contextualizing, their findings will be of broad interest to researchers investigating nitrogen assimilation in microbes, and those seeking to engineer E. coli for bioproduction and novel metabolic circuits.

Strengths:

*The collection of growth rate data is comprehensive, and in combination with nitrogen isotope labelling, paints a clear picture of amine donation (and ammonium assimilation, in figure 7). The growth rate dependence experiments represent an impressive amount of work, and are particularly informative in the strain engineering experiments in figure 6.

*The putA and aspC knockouts are elegant demonstrations of the specificity and promiscuity of E. coli's amination network, respectively. The contextualization with previous in vitro data was very informative, and reporting the minimal effect of the ybdL knockout demonstrated the importance of the glut-aux strain in assessing the promiscuity of various transaminases in their cellular context.

*Engineering the growth of glut-aux on four amino acids that didn't originally rescue growth is impressive, particularly getting exogenous transaminases to work as intended. As mentioned in the manuscript, this shows the potential for this particular auxotroph strain to serve as a growth-based selection platform for alternative amine sources.

*Engineering alternative ammonium assimilation through aspartate with a simple native AspA overexpression is a very strong demonstration of the flexibility of E. coli's amination network. This result may be useful for metabolic engineers looking to optimize E. coli for growth on formate and other low-energy substrates for the production of biofuel and high-value products.

Weaknesses:

*The framing of hypotheses for alternative routes of amine donor assimilation are clarifying to a reader unaware of the range of amine supplementation options available to E. coli: (i) replacement of glutamate, (ii) amine donation to 2-ketoglutarate to create glutamate, (iii) indirect amine donation to 2-ketoglutarate to create glutamate, and (iv) conversion to glutamate. However, outside of the figure 1 caption and lines 97-100 in the results section, the hypotheses are not mentioned again according to this classification. Directly after introducing figure 2, the authors discuss the possible ways that various amino acids rescue the growth of glut-aux and hypothesize that amine transfer is responsible. It would be immediately helpful to organize this discussion by the i-iv classification system introduced in the preceding paragraph. Similarly, figures 5-7 can be classified in this way. For example, the action of PutA in figure 5, where you say that proline is "metabolized... to glutamate", is unclear, and presumably refers to being "metabolically converted to glutamate," hypothesis (iv).

*Construction of a glutamate auxotroph strain, by deletion of gdhA and gltBD, is well-established (Dougherty et al, 1993), and has been standardized in the Keio collection (Baba et al, 2006). While it is critical that the authors used the same lambda-red recombinase strain for all deletions after making the glut-aux strain, it should be made clear to readers what has been done before by adding some context here.

*Most of the experiments were conducted in M9 media with glycerol as the carbon and energy source. Glycerol is utilized by oxidative phosphorylation in E. coli, like glucose, but is not a preferred carbon source. However, glycerol leads to higher growth rates when amines are supplied by amino acids rather than ammonia, due to imbalances in 2-ketoglutarate (Anat Bren et al, Sci Rep 2016). Knowing that cellular pyruvate and 2-ketoglutarate concentrations are different depending on carbon and nitrogen source, and both are relevant for thermodynamic favorability in cellular amination networks, the authors should justify why glycerol is the carbon source used for most experiments, as they justify fumarate in figure 7.

*In figure 3, it's unclear why AFLMPST and R are the only proteinogenic amino acids that are analyzed for 15N labeling. One might assume it's a technical issue for the mass spectroscopy data, but the relatively small selection of both amino-donor amino acids and 15N fraction amino acids makes initial interpretation of the figure confusing. Emphasizing that the 15N measurements are representative of all proteinogenic amino acids, and the amino-donors are representative of all amino acids that rescued growth for glut-aux would help. Additionally, figure 3B could benefit from greater distinction between amine groups and ammonium ions. Also, there is presumably a typo in the "external amine donor" cartoon, with 14NH4+ in the grey circle rather than 14NH3+.

*Adaptive laboratory evolution is not a fair description of how the authors found that a dadX mutation led to growth rescue of glut-aux+alaA on alanine (line 221). Although two weeks of growth may allow for evolution of E. coli in some cases, a single growth curve over two weeks is similar in duration and concept to some of the other (non-evolution) growth curve experiments (Figure 6C). Rather than being evolved from a series of mutations, the appearance of dadX mutants is much more likely the result of highly stringent selection on mutations acquired during outgrowth before the selection was applied. Given the inoculum size of ~106 cells from overnight culture, and E. coli's spontaneous mutation rate of ~10-3 mutations per genome per generation, there is a reasonable probability of isolating one or more dadX mutant cells in the inoculum, which then expanded over two weeks (rather than evolved), given the growth rate of those mutants evident in figure 6A. Labeling this experiment as a spontaneous mutant selection of glut-aux+alaA engineered strain would make the aims and outcome of the experiment more transparent. Alternatively, one could report the growth data from the experiment, if available, or conduct a selective plating of prepared glut-aux+alaA inocula on M9+alanine plates to show the existence of a small mutant population.

*Beta-alanine and ornithine are important non-proteinogenic amino acids, but there are hundreds of others. It is unclear to the reader why they were selected for assessing amine donation to glut-aux, or why beta-alanine was selected for adding an exogenous transamination route. Stating the relevance of these amino acids to E. coli's amination network or metabolic engineering, or stating that they were serendipitous findings of rescue and no rescue of glut-aux by non-proteinogenic amino acids, would make the choices for strain engineering seem less arbitrary. Similarly, strains engineered to utilize glycine or serine as amine donors (fig 6), or aspartate or leucine as centers of ammonium fixation (fig 7), seem to be chosen arbitrarily out of many amino acids that did or did not initially rescue growth of glut-aux. Simply stating that these were the best (or worst) amine donors based on growth rescue in figure 2 would explain why the strain engineering was not systematic over all 20 proteinogenic amino acids for ammonium fixation or amine donation.

Reviewer #1 (Public Review):

This is exciting work, with elegant experimental designs that are rigorously executed. The work will appeal to a broad readership. Specific comments are listed below.

The relationship between lobe cannibalism and mtDNA reduction seems to be too mild. The authors first show that about half of mitochondria are removed in PGCs between the embryo and L1 stages. At this point, the number of mitoDNA/cell decreases by half compared to the embryonic stage, and based on this result, they propose that this is a bottleneck. To me (intuitively) 50% reduction does not seem strong as a bottleneck. Perhaps it is better to tone down the claim a bit here (unless they can provide stronger evidence, such as modeling, that a 50% reduction is sufficient to cause a bottleneck. Textual editing would suffice, though (unless they already have the evidence for bottleneck).

Overall, one thing that struck me was that, when they assay 'selection' by mtDNA (e.g. the number of mtDNA, frequency of mutant mtDNA, reduction by autophagy pathway, reduction by pink1, etc), the effect seems to be way too mild. However, in Fig1c, d, and Fig2c, the amount of mitoGFP that goes to the lobe seems to be at least 80-90%. Is this because the 'striking' images were selected for presentation? Alternatively, I wonder if mito with more mtDNA actually end up surviving, and mito with fewer mitoDNA goes to the lobe (as a result, the amount of mito removed to the lobe is much higher than the amount of mtDNA removed). If so, is this actually THE selection that happens during embryo-to-L1 transition? Is there any way to measure the amount of mito and amount of mtDNA simultaneously?

Reviewer #1 (Public Review):

In this manuscript, Somaiya and colleagues make an interesting contribution to our understanding of the development of the visual system. The group has previously found that the presence of retinal ganglion cell (RGC) axon terminals in the lateral geniculate nucleus (LGN) promotes the migration of GABAergic interneurons into the LGN by stimulating FGF15 expression in astrocytes. The goal in the current paper was to test how RGCs promote this process. The authors suggest a model in which neuronal activity is not required for this, but rather Sonic Hedgehog (SHH) produced by RGCs and released from axon terminals can be received by astrocytes to stimulate FGF15 production, leading to interneuron migration.

They present three major findings in support of this model:

1) Transgenic expression of tetanus toxin in RGCs to prevent synaptic activity did not prevent interneuron migration into the LGN.<br /> 2) RGCs expressed SHH at the relevant developmental time point, and astrocytes within the LGN expressed Ptch1 (the canonical SHH receptor) along with other SHH signaling components.<br /> 3) Disruption of SHH - either in all neurons or in RGCs - prevented astrocytic FGF15 expression and interneuron migration into the LGN.

In general, the manuscript was well-written and carefully reasoned. The authors concede that a key experiment to close the circle in their model could not be performed for technical reasons, namely astrocyte-specific disruption of Ptch1. Accepting this limitation, the expression analyses for SHH signaling components using RNAscope and a genetically encoded reporter were enough to convince this reviewer that astrocytes were the main cell type in the LGN capable of responding to SHH signal. However, there was an important control missing from the SHH experiments, as the authors did not show that the RGC axons successfully targeted to the LGN in the absence of SHH. This leaves open alternative explanations involving other axon-derived molecules.

Reviewer #1 (Public Review):

Here, Minkina et al., present 'The LORAX', a CRISPR-based single-cell lineage tracing to understand heritable and non-heritable variation in gene expression. To this end, they have adapted sci-RNAseq, a plate-based single-cell RNA sequencing method, to capture lineage barcodes. Additionally, they have developed a novel computational pipeline to construct lineage trees from the resulting data to identify differentially enriched genes between distinct branches of their lineage tree. To explain heritable variation in gene expression, they use allelic ratios of transcripts to infer large copy number alterations along branches of their lineage tree. Finally, to understand non-CNA-based reasons for variation in clonal gene expression, they adapted sci-ATACseq, a plate-based single-cell ATAC sequencing assay, to capture CRISPR lineage barcodes. Using an existing lineage tree derived from sci-RNAseq, they are able to assign individual sci-ATACseq cells with lineage information to distinct branches of the lineage tree and associate gene expression and chromatin accessibility landscape within sub-clones. They use this information to rule out CNAs as a source of clonal variation in gene expression for some of their candidate genes.

This manuscript aims to study the heritability of cell state, an exciting field re-invigorated by recent technical advances in single-cell lineage tracing. However, enthusiasm for the approach is tempered for several reasons. First, the authors apply their lineage tracing method to a clonal population of HEK293T cells, an immortalized cell line. Thus, it is currently unclear what the broader biological significance will be, and whether this approach can be readily deployed in other systems. Second, the authors propose a new computational pipeline for constructing lineage trees but fail to fully benchmark its accuracy using ground truth data. Third, while the authors argue that lineage resolved chromatin accessibility landscapes could help explain some of the heritable gene expression patterns observed in their data, they do not convincingly demonstrate this with their data.

Reviewer #1 (Public Review):

The manuscript by Akalu et al. is well written and contains thoroughly executed experiments, that challenge previous conclusions obtained using the Mertk-/-V1 mouse model, in different tissue contexts.

Using several new mouse models, the authors functionally show that Mertk loss alone is not sufficient to trigger the retinal degeneration phenotype characteristic of Mertk-/-v1. Rather, they demonstrate that retinal degeneration requires the combined loss of Mertk and Tyro3, a second TAM receptor exhibiting hypomorphic expression in the Mertk-/-v1 model, due to its expression from a DNA portion carried over from the 129 ES cell background used to generate the Mertk-/-v1 line. The study further provides compelling functional data by demonstrating that Tyro3 ablation in the newly generated Mertk-/- v2 BL6 model, is required to recapitulate the retinal degeneration phenotype.

Interestingly, the work presented here also reports different outcomes in cancer contexts, where the authors show that the Mertk-/- v1 exhibits remarkable anti-tumor resistance in two independent "cold" cancer models (YUMM1.7 sc model and GL261 intracranial GBM model), which were not recapitulated in the newly generated Mertk-/- mouse lines on a BL6 background, namely the Mertk-/- v2 and Mertk-/- v3 as well as Mertk-/- v2 Tyro3-/-v2 that lack both Mertk and Tyro3, pointing to additional gene modifiers being at play. These findings are highly relevant for the cancer field and would certainly benefit from future experiments suggested by the authors to identify the modifier genes to bolster their significance.

Overall, this well-executed study demonstrates that the Mertk-/-v1 model carries additional changes in its genome that affect the expression of several genes, including Tyro3, that act as confounding events and limit the direct inference of observed phenotypes to Mertk deficiency alone.

A potential weakness in this aspect resides in the fact that the newly generated mouse models Mertk-/- v2 and Mertk-/- v3 appear to display a compensatory increase in the levels of TYRO3 protein, which needs to be discussed by the authors.

Reviewer #1 (Public Review):

The overarching objective of the study reported in this manuscript was to identify cell-membrane active agents (CMAAs) that potentiate antibiotic killing of Gram-positive pathogens by vancomycin. This work builds upon the research group's previous findings that a class of CMAAs, rhamnolipids, produced by Pseudomonas aeruginosa synergize with aminoglycosides in the killing of Staphylococcus aureus. In the current study, the group investigated the capacity of 7 additional CMAAs to potentiate vancomycin killing of S. aureus. The focus on vancomycin is appropriate given that this is the most commonly prescribed antibiotic for serious Gram-positive infections, and yet this antibiotic is also associated with a high rate of treatment failure that cannot be ascribed to resistance alone. The discovery of compounds that potentiate vancomycin activity or re-sensitize tolerant or resistant bacteria to the antibiotic is therefore of high significance to the field of infectious diseases. The authors discover that two unsaturated fatty acids, palmitoleic acid (PA) and linoleic acid (LA), potently synergize with vancomycin to kill S. aureus. Although the antimicrobial activity of select UFAs toward bacterial pathogens has long been recognized, the mechanisms by which these compounds kill bacteria or potentiate antibiotic activity are less clear. The authors, therefore, conducted a series of experiments aimed at understanding the mechanistic basis for UFA potentiation of vancomycin activity. Using a panel of antimicrobial compounds that target different steps of cell wall biosynthesis, the authors suggest that the accumulation of lipid II is necessary for the PA potentiation of vancomycin. To understand how dual PA/VAN treated cells are being killed, assays of membrane depolarization and permeability are conducted and reveal that PA/VAN treatment may increase permeability. However, in this particular experiment, it is unclear if this is different from PA treatment alone. The authors then utilize high-resolution fluorescence microscopy and TEM to demonstrate that PA/VAN treated cells have alterations in the membrane fluidity, septal architecture, and localization of cell division and peptidoglycan synthesis machinery. These findings lead the team to conclude that dual PA/VAN treatment delocalizes divisome and peptidoglycan biosynthesis machinery through the accumulation of lipid II moieties and subsequent effects on membrane fluidity. In a final experiment, the authors show that, excitingly, PA can re-sensitize vancomycin-intermediate and vancomycin-resistant Gram-positive bacteria to vancomycin.

Strengths of the manuscript:

1. The discovery of natural compounds that re-sensitize antibiotic-tolerant or antibiotic-resistant bacterial pathogens to commonly used antibiotics is a critical unmet need in the field of infectious diseases<br /> 2. This study will be of broad interest to researchers focused on microbial pathogenesis, drug discovery, and antimicrobial-resistant bacteria.<br /> 3. Multiple lines of evidence convincingly demonstrate the membrane and septal perturbations induced by dual PA/VANC therapy, suggesting a mechanism of action.<br /> 4. Drug doses are carefully considered based on pilot data.<br /> 5. High bacterial inocula are tested, which increases the confidence that dual therapy is capable of killing at least some tolerant/persister cells.<br /> 6. The manuscript is extremely well-written and carefully considers prior studies reporting on the antimicrobial activities of UFAs.

Weaknesses of the manuscript:

1. Although dual PA and vancomycin therapy shows clear efficacy against vancomycin sensitive and resistant isolates of S. aureus and the high inocula used are likely to contain some amount of stochastically-developed persister cells, it is unclear what bacterial growth phase was tested in the killing experiments. Given that some forms of antibiotic tolerance, such as "tolerance-by-lag," will not be appropriately modeled in exponential phase bacteria, it would be important to know if bacterial cultures approaching the stationary phase are also effectively killed by UFAs plus vancomycin. Although the manuscript reports a 1:1000 dilution, it is unclear how long the bacteria are grown prior to challenge with dual therapy, and therefore conclusions about tolerant cells may need to be tempered.<br /> 2. Although a strength of the manuscript is that multiple approaches are used to query membrane and septal effects of dual UFA/Vanc therapy, these studies only indirectly support the proposed mechanism regarding the accumulation of lipid II and alterations of membrane fluidity as the underlying reason for antibiotic sensitization. Future studies will be required to rigorously confirm this proposed mechanism, although this is considered a minor weakness.

Reviewer #1 (Public Review):

The study by Jimenez et al. investigates the molecular mechanism by which dosage compensating (DC) condensins spread along the X chromosomes of C. Elegans worms. It has been previously known that DC condensins are loaded onto X chromosomes at specific sites called rex, that are distributed along the whole length of the chromosome. Here, Jimenez et al showed that an insertion of one or multiple rex sites into an autosome is sufficient for DC condensin recruitment and spreading. Using ChIP-seq, they show that DC condensins spread for hundreds of kilobases on the both sides of the rex site, with occasional sites of accumulation. The authors used Hi-C to study the effect of rex insertion on the chromosome conformation. They found that individual rex sites form boundaries that insulate spatial contacts regardless of their orientation, while two adjacent insertion sites can form loop-anchored contact domains. These findings support the model, in which DC condensins spread along the chromosome via the process of loop extrusion. In addition, the authors fused the X chromosome with the chromosome V and demonstrated that condensins can spread for multiple megabases across the fusion site and induce local compaction of the affected region. Finally, the targeted dCas9-Suntag complex to multiple adjacent copies of a repeat on chrX to demonstrate that condensins can accumulate at "bulky" obstacles.

Overall, I find the experiments in this study are sufficient to support the key statements. My only comment is minor. In the discussion, the authors seem to imply that their data supports bi-directional loop extrusion by DC condesins (p.11 line 16). Yet, their data is consistent with a model, where condensins are loaded in a random orientation, but then extrude loops only into one fixed direction. Along these lines, ref. [20] (Terekawa et al) is mentioned as supporting bi-directional extrusion, while this paper in fact demonstrated that, once loaded onto DNA, condensins keep moving into a single direction with barely any observed inversions.

Reviewer #1 (Public Review):

In this manuscript, Germanos et al present preclinical evidence of a dynamic interplay between tumor microenvironmental elements underlying prostate cancer initiation, progression, and emerging therapeutic resistance in the transgenic mouse model. The authors identify an intermediate luminal cell population trans-differentiating from a hypo-proliferative basal cell subset, meanwhile, hyper-proliferative basal cells replenish a non-differentiating basal subpopulation. The meticulous methodologic approach identifies candidate cellular interactions in fibroblasts, MDSCs, and immune cell populations associated with PTEN loss. The generalization of these findings to human data sets is of particular interest and recommended for future studies on this topic. Mechanistic studies with multi-cellular co-culture models are needed to extend and validate the findings in this report.

Strengths and Weaknesses:<br /> The study focuses on a clinically highly relevant and timely topic. The strength of this manuscript is the meticulous description of the Methods and model development and the integration of state-of-the-art orthogonal data sets. However, the number of data points across the experiments (n = 2 or 3) with considerable variability in the Ptenfl/fl group limits the interpretation of findings. Additionally, further experiments are needed to validate these observations in human prostate cancer and establish the potential translational relevance of these findings.

As such, the report is fairly descriptive, and expanding the discussion on the mechanistic studies needed to identify which of these interactions drives aggressive prostate cancer would improve this report.

Reviewer #1 (Public Review):

The current manuscript examined patients with inborn errors of immunity (IEI) using whole exome sequencing (WES) and identified de novo variants (DNVs) associated with the disease. They found 14 genes associated with DNVs, including four novel genes - PSMB10, DDX1, KMT2C, and FBXW11, and conducted a systematic assessment of affected genes.

Given the level of heterogeneity underlying IEI, the sample size is limited. Although the authors clearly stated this, the analysis of the current manuscript does not add much value to describing genes affected by DNVs. The sample size is small to perform exome-wide evaluation (authors described they did "exome-wide evaluation" in Abstract - line 10 but there is no statistical evaluation to prioritize effect genes). They could go with systems biology approaches, explaining the biological pathway of affected genes or underlying cell types from immune single-cell datasets. As the authors stated that IEI constitutes a large group of heterogeneous disorders, there should be some analysis to explain the functional convergence of affected genes in disease development.

For DNV identification, the authors filtered out variants with ExAC & gnomAD AF > 0.1% or GoNL AF > 0.5%. I think this is too lenient a cutoff for filtering for DNV. For example, gnomAD AF 0.1% is approximately ~200 individuals in population. Given the filtering parameters (<5 variation reads, <20% variant allele frequency, or low coverage DNVs), they did not use specific filtering metrics to find DNV and there might be false-positive variants in the final DNV set. As far as I can find in the manuscript, they used the GATK pipeline from the previous study (REF 29). The GATK unified genotype generates a range of filtering metrics to increase specificity in variant filtering. It is very surprising that the authors seem to use three parameters (variation reads → FORMAT:AD[1]; variant allele frequency → FORMAT:AB? and low coverage → FORMAT:DP? but the authors did not state the cutoff) to filter de novo variants, which are fragile to false-positive variant calling.

Reviewer #1 (Public Review):

Monteiro et al. sought to determine the role of various thyroid hormones (T3 and T4) in supporting circadian rhythmicity. The authors achieved this through gain of function experiments (T3) supplementation as well as comprehensive metabolic and transcriptional profiling. The authors find that T3 supplementation recapitulates a variety of manifestations of hyperthyroidism such as increased body temperature and increased food and water intake. Furthermore, the authors found that T3 supplementation leads to alterations in hepatic metabolic genes, ultimately culminating in alterations in glucose and lipid metabolism. The data generated in this manuscript will serve the field of circadian rhythm biology by providing an additional transcriptomic atlas of hepatic alterations during both times of day as well as in response to T3 supplementation.

Reviewer #1 (Public Review):

The authors of the paper provide new evidence of how prefrontal cortex of mutant mice used as a disease model of schizophrenia differs from wild type littermates. By analyzing local network dynamics at the level of specific cell type, authors shed new light on the circuit mechanisms that underlie changes in network dynamics in these mice.

The claims in the submitted manuscript are supported by the data. I have a few comments and questions that need to be clarified.

Average firing rates

Authors claim that they saw a significant reduction in interneuron firing rates in Disc1 mutant mice compared to control mice Fig.1c. However, the difference could be general and not interneuron specific. Due to the high firing rates of interneurons, the statistical test will work better on interneurons than on pyramidal cells as pyramidal cells average firing rates are lower. What I suggest to do is to take interneuron cells that fire at a lower rate (lower 33% for example ) and compare the control and Disc1 groups. Also I would suggest to take pyramidal cells that have higher firing rates (upper 33% for example) and compare firing rates across the same groups. One would like to see if these differences are not due to changes in firing rates per se.

Optogenetic tagging

Authors indicate that light triggered and spontaneous spike waveform are similar Fig.1d. This is nice, but would be better to see all the tagged neurons. I would suggest showing all optically tagged neurons spike features. Authors can impose with a different color spike features of tagged neurons in Fig.1a. I suspect that since all PVI are narrow spiking and they must fall into the area of blue colored cells in Fig.1a.

It was not clear why authors assessed only firing rates in last 25ms (line 348-349). If they have a clear justification for this they should provide it. But why not use the latency of the first spike also as an additional metric. A well tagged cell will respond to light pulse with short latency (within 5 ms). My concern is that non PVI cells may increase firing rate after 25ms of stimulation of PVI cells due to disinhibition.

Spike cross-correlations

The authors show that spike transmission probability from PYR to PVI is reduced in Disc1 mice compared to the controls Fig.2d and Fig.2e, but what happens to PVI to PYR spike transmission probability? Is it different in those groups? Answering this question is important since the authors discuss this topic in line 185-193.

Authors could try to link oscillations with spike transmission probabilities. On line 180 authors discuss that lower synchrony between PVI might be responsible for observed reduction in gamma power in Disc1 mutant mice. With the available data authors could test this hypothesis. They can look at spike cross correlations in their pool of INT and PVI (if they have pairs of PVI recorded in the same session) population.

An alternative way to link oscillations with lower spike transmission probabilities in PYR-PVI pairs is to use synchrony triggered LFP analysis. One could take all time points when PVI and PYR cells fired acausal spikes within 2ms window and look at the LFP around this time point. Than take the average of the synchrony-triggered LFP and look at the power spectrum.

Cell assembly analysis

The authors used 10ms for testing synchronization among pairs of PYR neurons in Fig.4a but 25ms for analysis of assembly dynamics. I think the authors justified why they used 25ms bin size, but it was not clear why they used 10ms? Could the authors clarify the reasons behind this decision?

Reviewer #1 (Public Review):

The data support the claims, and the manuscript does not have significant weaknesses in its present form. Key strengths of the paper include using a creative HR-based reporter system combining different inducible DSB positions along a chromosome arm and testing plasmid-based and chromosomal donor sequences. Combining that system with the visualization of specific chromosomal sites via microscopy is powerful. Overall, this work will constitute a timely and helpful contribution to the field of DSB/genome mobility in DNA repair, especially in yeast, and may inform similar mechanisms in other organisms. Importantly, this study also reconciles some of the apparent contradictions in the field.

Reviewer #1 (Public Review):

Montgomery and colleagues expand our understanding of gene dosage control by examining embryonic expression in a liverwort, the emerging model system Marchantia. Marchantia alternates between haploid and diploid phases, with the diploid, biparental embryo dependent on the haploid maternal parent. It has long been theorized that a form of genomic imprinting might exist in this context, but this has not been examined until now. By performing crosses between polymorphic parents and examining allele-specific gene expression, the authors show that transcription of the embryonic genome is primarily from the maternally-inherited alleles. Additionally, approximately half of the embryonic chromosomes are positive for H3K27me3 by immunofluorescence. By allele-specific CUT&RUN profiling, it is shown that H3K27me3 is biased towards paternally-inherited DNA. It appears that this difference is already present before the maternal and paternal pronuclei fuse. The authors take a genetic approach to determine whether H3K27me3 modifications are of consequence in the embryo. Disruption of E(z)2 and E(z)3 in the maternal parent leads to reduced H3K27me3 enrichment on paternal chromosomes and decreased maternal allele transcriptional bias. Ultimately the embryos are not viable. Taken together, the data support the idea that the maternal genome maintains widespread dominance over the paternal genome.

1) For most experiments and analyses, embryos are the result of crosses between Cam-2 females and Tak-1 males. Since a reciprocal cross is not possible with these genotypes, the authors examine previously published data from Tak-2 females crossed to Tak-1 males. The analyses show that expression is strikingly biased towards the maternally-inherited DNA in both cases. The issue is that Tak-1 is the male in both sets of experiments. Thus, an alternative explanation is that the effect is specific to Tak-1 - that Tak-1 chromosomes are silenced in combination with either Cam-2 or Tak-2. This would be more akin to a phenomenon like nucleolar dominance - i.e. genotype-dependent rather than parent-of-origin-dependent.

2) Some aspects of the data analysis need additional rigor. From the methods, it does not appear that any statistics were applied to determine whether genes were significantly biased away from the expected 1:1 maternal:paternal ratio. It is essential to do this - please refer to any mammalian or plant imprinting study.

3) The authors show that e(z) mutant embryos grow more slowly and that most do not survive. They also show that mutants have reduced maternal allele bias. In terms of linking the phenotype to the gene expression change, it would be important to show that the total expression level of individual genes was altered in the mutants (for example, increased paternal allele expression might be compensated for by decreased maternal allele expression, in which case it would harder to connect the mutant phenotype to PCI). The authors should evaluate how many genes are differentially expressed between wild-type and mutant embryos.

4) It's satisfying that when ez2 and ez3 are disrupted (Fig 5, Fig 5-fig supp 1D), that the IF for mutant embryos looks like H3K27me3 in vegetative nuclei (Fig 3A). But the paternal bias of H3K27me3 is still quite prevalent (Fig 5-fig supp1F) as is the maternal bias in transcription - the transcriptional ratio is not close to 50:50 (Fig 5B). The authors should comment or speculate on how paternal bias of H3K27me3 persists in this mutant, given their model. Perhaps the remaining H3K27me3 is from paternally supplied E(z). Since the paternal and maternal pronuclei are segregated for quite some time, a paternally supplied factor could also specifically mark one chromosome set (although it is less clear why this would be so from an evolutionary perspective). Generating paternal E(z) mutants would be interesting, but is likely beyond the current scope.

5) From the genetic results, one can conclude that E(z)2 and E(z)3 are essential for viability and fecundity. But it is not yet clear, as claimed on line 339, that PCI is essential for viability and fecundity, as E(z)2 and E(z)3 may also have roles beyond or in addition to PCI. I suggest dividing this sentence into what one can conclude from the genetics, and what this suggests about the possible importance of PCI.

6) Finally, paternal chromosome inactivation is perhaps too strong of a phrase to describe this very interesting phenomenon. There are thousands of genes for which expression is biased toward the maternal allele, but detectable paternal allele transcript is present in the embryos. It is important to get the name right now, because it may influence the field for a long time. For example, we now know that many genes on the "inactive X" are not inactive at all. But this phrase - X chromosome inactivation - continues to be the framing for much of the field, even though extensive caveats must be applied.

Reviewer #1 (Public Review):

Dosil et al. have extensively analyzed NK cell-derived extracellular vesicles containing miRNAs. They analyzed the miRNAs in NK cell-derived EVs and found that specific types of miRNAs are contained in NK cell-derived EVs. Furthermore, they found that NK cell-derived EVs have immunomodulatory functions for T-cell response as well as for monocytes and moDCs. This paper is well designed and provides important information on NK cell-derived EVs. However, it is unclear whether NK cell-derived EVs are different from EVs derived from other immune cells such as T cells and B cells.

1. The authors analyzed human NK cell-derived EVs. The repertoire of miRNAs in NK-EVs may differ among individuals. It would be better to show the degree of individual differences.

2. The authors analyzed the effect of NK-EVs on T cell response in Fig. 4. However, it is possible that EVs affect T cell responses in a nonspecific manner. It may be necessary to include control EVs.

Reviewer #1 (Public Review):

In this manuscript, Chandiran et.al explored the roles of Smad4 in regulating CTL differentiation during viral infection. By comparing the Smad4 or TgfbR2 deficient CD8 T cells during influenza infection, the authors noticed that Smad4 suppressed CD103 expression but promoted CD62L transcription. Further RNAseq experiments found that Smad4 deficient early effector CD8 T cells were endowed with tissue resident features. Among differential express genes, they observed significant changes in CD103, Eomes and CD62L expression. Using an in vitro culture system, the authors demonstrated that Smad4 could regulate Eomes mediated Itgae transcription. The authors also explored the Smad4/Eomes controlled CD62L expression during Tcm differentiation. Collectively, this manuscript stressed the reciprocal function of Smad4 and canonical TGFb pathways during the memory CD8 fate decision. While the gene expression regulation is of interest, the functional consequences of the regulation remains uncertain in the current study.

Reviewer #1 (Public Review):

This is an outstanding manuscript evaluating a mutation commonly seen in AML and MDS in the splicesome SF3B1. The authors demonstrate that this mutation leads to a shift in the production of a long-form of IRAK4 (called IRAK4-L), which is part of inflammatory signaling in immune cells. They demonstrate that IRAK4-L stabilizes the cell cycle protein CDK2, and targeting IRAK4-L with an inhibitor can induce differentiation and slow clonal uptake in murine transplantation models. The text is easy to read, there are ample supplemental figures to help explain complicated experiments. Overall this manuscript is likely to be of high interest for oncologists in that it demonstrates that AML and MDS cells with an SF3B1 mutation can be targeted in a precision medicine approach via inhibition of IRAK4. There are no major weaknesses in the manuscript.

Reviewer #1 (Public Review):

This work adds to an already abundant literature demonstrating that TR-AMs are a phenotypically and functionally distinct population of macrophages. Work in this manuscript is the first to characterize the effect of hypoxia on metabolic and inflammatory responses in TR-AMs vs macrophages derived from other sites (bone marrow).

Strengths:<br /> 1. Findings advance our understanding of TR-AM biology and further highlight the unique characteristics of this macrophage subset.<br /> 2. Studies suggest that morbidity and mortality from pneumonia and other causes of acute lung injury may depend on the ability of TR-AMs to metabolically adapt to hypoxic conditions.<br /> 3. Studies suggest that targeting cellular metabolism may be effective in treating hypoxic respiratory conditions.

Weaknesses:<br /> 1. Study conclusions are largely dependent on the use of non-selective pharmacological inhibitors for manipulating HIF-1 signaling, which is particularly true for the compound echinomycin.<br /> 2. Intratracheal delivery of FG-4592 will have effects on many cell types in the lung, including other leukocyte populations and may even impact viral proliferation, making it hard to judge whether endpoints are due to direct or indirect effects on TR-AMs.<br /> 3. Findings suggest that very low oxygen concentrations contribute to elevated HIF1 signaling and glycolysis in TR-AMs but further justification is needed for the oxygen concentrations used in this study since they seem a bit low for the alveolar environment. Relevant to this, it would be helpful to know whether hypoxia is a feature of the influenza mouse model.

Reviewer #1 (Public Review):

This article presents interesting proof of concept of how predictive coding based on visual inputs, coupled with a complex array of RNNs can produce head direction and egocentric/allocentric boundary responses akin (to some extent) to the neural responses found in mammalian hippocampal formation neurons. However, while an impressive technical feat, the model contradicts key experimental findings, and the developmental timeline of spatial cell responses does not support the sole reliance on visual inputs.

Developmental considerations:<br /> Developmental studies have shown that rudimentary HD signals emerge before the unfusing of rat pup eyelids (suggesting visual inputs are not necessary for these initial responses). Head direction is fully formed more than a week before allocentric boundary responses (boundary vector cells: BVCs) emerge, and initial head direction signals predate BVC coding by 5-6 weeks in rat pups (Min, Wills, Cacucci 2017). While this does not exclude separate emergence per se, it removes the need to insist on the absence of HD inputs for the formation of BVCs. Hence it is plausible that at least HD would be available to any learning/developmental process that enables the emergence of allocentric boundary responses in the rodent brain. Not using this information would make this learning task unnecessarily difficult. Similarly, it is more plausible that egocentric boundary responses are constructed by a network that forms in a developmental time window and can later instantiate boundary responses based on visual inputs, depth perception, etc, without additional learning. The staggered emergence of spatial responses reported by experiments also strongly suggests that developmental stages build upon each other. Granted, it remains a possibility that egocentric boundary responses and head direction coding could be generated by a predictive coding framework (though the principle inputs to HD are vestibular), but there is no need to assume the head direction signal and the egocentric boundary signal wouldn't be used in a subsequent learning/maturation step that forms allocentric boundary responses from head direction and possibly egocentric boundary inputs. While I share the authors' sentiment that those previous models have not sufficiently accounted for this learning step (generating the network that allows egocentric signals to be translated to allocentric boundary representations), the appeal to a staggered developmental process is much more plausible and in line with developmental data.

The parallel emergence of distinct spatial responses:<br /> In the paper, very little is actually said about the interaction of the learned representation in the model. Since the different RNNs learn in parallel, one yielding HD, one egocentric boundary cell (EBCs), and one BVCs, this means that EBCs and BVCs do not need to interact. Similarly, HD cells and BVCs do not need to interact. This incredibly salient prediction is not emphasised at all. It would suggest the EBCs could be lesioned without affecting BVCs in a novel environment. In alternative models, this is not the case. Such strong claims should be emphasised as this is actually one of the few novel, direct experimental predictions that can be made here. Whether or not EBCs and BVCs interact is an open, empirical question. However, taking this line of reasoning further, the present model also predicts that lesioning HD cells should leave EBC and BVC unperturbed. This is extremely unlikely for BVCs. Lesions to the mammillary bodies (where HD cells are found and where the HD attractor signal is likely generated) lead to severe memory deficits. The orientation of BVCs and place cells is likely set by head direction cells. The three populations have repeatedly been shown to rotate in concert. Object vector cells (not addressed in this article) similarly co-rotate with HD cells. The article does not present sufficient evidence (or gains in understanding) to abandon this well-established view.

Relating the model to biological function:<br /> The normative account of the paper is interesting, but it is unclear how much (if anything) the model tells us about the biological underpinnings of spatial cognition despite the overt claim that the model would be useful to neuroscientists. The modelling approach is far from biologically plausible. This creates the unfortunate impression that a bunch of RNNs has been thrown together (with considerable technical skill), which are known to be able to extract the information inherent in the inputs. What does this tell us about how the brain generates these responses, and how can experimenters test for properties specific to the model? To provide a normative model that outlines one way for the appearance of known mammalian spatial representations based solely on interaction with the sensory world, is fine (and interesting in itself) but the method employed being so far from real biological function makes it impossible to assess if it is the correct normative explanation (see also next point).

Experimental contradictions:<br /> BVC activity emerges immediately upon entry into a new environment, while the present model needs to be retrained on sets of environmental geometries to be able to respond correctly in all those environments. This discrepancy cannot be remedied by appealing to the theoretical notion that an animal might experience all possible geometries during some developmental phase. Given the developmental timeline of spatial responses and the fact that rat pups do not leave their nest straight away this can in all likelihood be excluded. Competing models claim that EBC responses are computed directly from perceptual inputs (utilising networks formed in development), with the consequence that EBC (and hence BVCs driven by EBCs) can straightforwardly represent any new geometry without additional learning. This would be consistent with BVC activity emerging immediately in a new environment, even when faced with a never-before-experienced environmental geometry.

Reviewer #1 (Public Review):

McLachlan and colleagues find surprisingly widespread transcriptional changes occurring in C. elegans neurons when worms are prevented from smelling food for 3 hours. Focusing most of the paper on the transcription of a single olfactory receptor, the authors demonstrate many molecular pathways across a variety of neurons that can cause many-fold changes in this receptor. There is some evidence that the levels of this single receptor can adjust behavior. I believe that the wealth of mostly very convincing data in this paper will be of interest to researchers who think about sensory habituation, but I think the authors' framing of the paper in terms of hunger is misleading.

There is a lot to like about this paper, but I just cannot get over how off the framing is. Unless I am severely misunderstanding, the paper is about sensory habituation, but the word habituation is not used in the paper. Instead, we hear very often about hunger (6x), state (92x), and sensorimotor things (23x). This makes little sense to me. The worms are "fasted" (111x) for 3 hours, but most of the expression changes are reversed if the worms can smell, but not eat, the food. And I've heard about the fasted state, noting that worms don't eat more food after this type of "fasting". So what is with all of this hunger/state discussion?

And the discussion of internal states is often naïve. In the second paragraph of the introduction, we are told that "Recent work has identified specific cell populations that can induce internal states", beginning with AgRP neurons, which have been known to control the hunger state in mammals for nearly 40 years |||(Clark J. T., Kalra P. S., Crowley W. R., Kalra S. P. (1984). Neuropeptide Y and human pancreatic polypeptide stimulate feeding behavior in rats. Endocrinology 115 427-429. Hahn T. M., Breininger J. F., Baskin D. G., Schwartz M. W. (1998). Coexpression of Agrp and NPY in fasting-activated hypothalamic neurons. Nat. Neurosci. 1 271-272). Instead, the authors cite three papers from 2015, whose major contribution was to show that AgRP activity surprisingly decreases when animals encounter food. These papers absolutely did not identify AgRP neurons as inducing internal states or driving behavioral changes typical of hunger (Aponte, Y., Atasoy, D., and Sternson, S. M. (2011). AGRP neurons are sufficient to orchestrate feeding behavior rapidly and without training. Nat. Neurosci. 14, 351-355. doi: 10.1038/nn.2739; Krashes, M. J., Koda, S., Ye, C., Rogan, S. C., Adams, A. C., Cusher, D. S., et al. (2011). Rapid, reversible activation of AgRP neurons drives feeding behavior in mice. J. Clin. Invest. 121, 1424-1428. Doi: 10.1172/jci46229). Nor did Will Allen's work in Karl Deisseroth's lab discover neurons that drive thirst behaviors. Later in the same paragraph, we hear that: "However, animals can exhibit more than one state at a time, like hunger, stress, or aggression. Therefore, the sensorimotor pathways that implement specific motivated behaviors, such as approach or avoidance of a sensory cue, must integrate information about multiple states to adaptively control behavior." This is undoubtedly true, but it's not clear what it has to do with any of the data in this paper - I don't even think this is really about hunger, much less the interaction between hunger and other drives.

To summarize: I think the authors could give the writing of the paper a serious rethink. I want to stay far away from telling people how to write their papers, so if the authors insist on framing this obviously sensory paper as being about hunger and sensorimotor circuitry I think they should at least explain to their readers why they are doing that in light of the evidence against it (and I think they should state clearly that worms don't actually eat more in this fasted state).

I was also surprised by how unsurprised the authors seemed by the incredibly widespread changes they observed after 3 hours away from food. Over 1400 genes change at least 4-fold? That seems like a lot to me. But the authors, maybe for narrative reasons, only comment on how many of them are GPCRs (16.5%, which isn't that much of an overrepresentation compared to 8.5% in the whole genome). For me, these widespread and strong changes are much of the takeaway from this paper. But it does make you wonder how important the activity of one particular GPCR (selected more or less randomly) could be to the changes the worm undergoes when it can't smell food.

str-44 is very convincingly upregulated when worms can't smell food, but it's clear from the data that this upregulation has very little to do with the actual lack of eating, and more with the lack of being able to sense bacteria for 3 hours. In Figure 1E, when worms are fasted, but in the presence of bacteria, receptor levels are largely unchanged (there are 5 outliers, out of ~50 samples). Since receptor expression doesn't change in this case even though the worms are in the fasted state, it cannot be "state-dependent" - unless the state is not having smelled food for the last 3 hours. And, in my opinion, that would divorce the word "state" from its ordinary meaning.

The authors argue that str-44 expression modulates food-seeking behavior in fasted worms by causing them to preferentially seek out butyl and propyl acetate. However, the behavioral data to back this up has me a little worried. For example, take Figures 2F and 2G. They are the exact same experiment: comparing how many worms choose 1:10,000 butyl acetate compared to ethanol when the worms are either fasted or fed. In the first experiment (2F), ~70% chose butyl acetate for fasted worms and ~60% for fed worms. But in the replicate, ~60% choose butyl acetate for fasted worms and ~50% for fed worms. A 10% variability in baseline behavior is fine (but not what I would call a huge state change), but when the difference between conditions is the same size as baseline variability I start to disbelieve. Can the authors explain this variability? Or am I misunderstanding?

And I'll say it just one last time, I think the authors are overselling their results...or at least the str-44 and AWA results (they are dramatically underselling the results that show the widespread changes in the expression level of 10% of the genome in response to not smelling food for 3 hours):

"Our results reveal how diverse external and internal cues... converge at a single node in the C. elegans nervous system to allow for an adaptive sensorimotor response that reflects a complete integration of the animal's states."

This implies that str-44 expression AWA is the determinant of whether a worm will act fasted or fed. I have already expressed why I don't believe this is the case (inedible bacteria experiment, Figure 1E), but just because things like osmotic stress suppress the upregulation of str-44, that doesn't mean that it is the site of convergence. It could be any of the other 1400 genes that changed 4+ fold with bacterial deprivation. And even in terms of the actual AWA neuron, it was chosen because it showed modest upregulation of chemoreceptors (1.8 fold compared to ~1.5 fold in ASE and ASG), even though chemoreceptors were highly upregulated in other neurons as well.

Overall, and despite my critiques (and possibly tone), I really like this paper and think there really is a lot of interesting data in there.

Reviewer #1 (Public Review):

This article tackles an interesting problem of using animal models in human neuroscience research through a comparative study of brain-wide gene expression patterns in mouse and human. One of the main strengths of this work is the analysis approach that builds from a set of relatively simple and well-defined assumptions and later is complemented with more sophisticated computational methods such as machine learning in order to tailor the methods for a more detailed investigation. The open and transparent use of publicly available datasets providing full data processing and analysis pipelines together with the realistic presentation and interpretation of the findings also strengthens the perceived trustworthiness of this work. Whereas the findings such as the greater similarity observed for sensorimotor compared to supramodal areas; or the fact that the introduction of the latent gene expression space in most cases only moderately improves identified regional correspondence between the species are not unexpected, they provide a novel outlook to well-known challenges in comparative neuroscience. Overall, the manuscript is methodologically sound, very well-written, and easy to follow, the key claims presented in the article are supported by the data.

From the methodological point of view, this study is well-executed, the following are points to consider.

Expression patterns across broad anatomical divisions such as the human cortex, subcortex, brainstem, and cerebellum demonstrate substantial differences. Similar tendencies are also observed in the mouse brain, where differences between neocortical and other brain areas tend to be much stronger compared to the differences within these divisions. The analyses presented in this work are performed on the combined datasets covering the whole brain and the resulting similarity metrics appear to be significantly skewed to the right with values broadly ranging from 0.7-1. It may be possible that transcriptional differences between broad anatomical divisions may attenuate/diminish the potential differences within these structures, e.g. within cortex/neocortex/subcortex/cerebellum.

Currently, in the description of the processing of AHBA data there is no mention of within-donor normalization prior to data aggregation. It has been previously shown that samples acquired from the same donor tend to cluster together rather than reflecting anatomical divisions of the brain when samples across 6 brains are combined. Based on the current documentation, samples from all 6 brains are first aggregated into a sample x gene matrix and only then normalized for every gene across samples. This type of normalization retains expression differences between different donor brains and can bias the resulting sample x gene and region x gene datasets as well as subsequent analyses.

Does the latent gene space method allows the identification of genes that are most informative in region identification?

Some formal statistical evaluations should be presented when performing comparisons. For example, but not limited to, comparing maximal correlational values between sensimotor and supramodal areas (lines 277-280, Figure 5B).

Reviewer #1 (Public Review):

A high-throughput synaptic phenotyping platform targeting human synapses is highly valuable. The validity of the present system is supported by a small molecule inhibitor screen that has identified targets, including the BET family proteins, whose role in brain function has been previously demonstrated. The authors have gone one step further to analyze the gene expression programs impacted by the BET Inhibitors, and the observations that synaptic genes encoding proteins such as neurexin-3 and homer 1 are altered is reassuring. In addition, demonstrating that the presence of astrocytes crucially impacts the density of presynaptic marker protein is of relevance for the design of similar platforms. The general utility of the present platform in identifying synaptic changes, however, needs to be further substantiated by additional synaptic markers.

Reviewer #1 (Public Review):

This manuscript addresses the role of the p75NTR neurotrophin receptor in the development of cerebellar granule precursor cells (CGPs). This cell type is notable for having high levels of p75NTR expression in a discrete developmental window yet the specific role of the receptor in this setting has remained obscure.

The authors show that although p75NTR expression correlates with the CGP proliferative state, expression of p75NTR is not required to maintain the proliferative state. Rather, migration CGPs in culture and within cerebellar slices is optimal only when p75NTR levels are reduced and the authors conclude that the expression of p75NTR normally reduces CGP migration. They examine signalling mechanisms that lie downstream of p75NTR to elicit this effect and show that RhoA, previously shown to be activated by p75NTR, is required to block CGP migration, that RhoA activity is lower in p75NTR-/- CGPs than in wild-type counterparts, and that RhoA inhibitors enable CGP migration, even in cells overexpressing p75NTR.

This is an important study that uses a combination of descriptive methods and chemical and genetic gain- and loss- function approaches to demonstrate that a p75NTR-RhoA signaling pathway normally functions to limit CGP migration during development. The paper is logical and well written and the data presentation is excellent.

Some points to consider:<br /> Figure 2A introduces the CGP cultures and shows that p75NTR levels are high in cells exposed to SHH. However, these results are difficult to interpret in the absence of controls showing p75NTR levels at the time of plating - does the SHH exposure increase p75NTR expression? Or prevent its decrease?

I recognize the convenience of using the p75NTR-GFP construct to track migration but was surprised that the potential confounds of this approach were not examined or even mentioned. Does p75NTR-GFP activate RhoA more or less than the wild-type receptor? What experiments have been performed to ensure that this construct is an effective mimic of the wild-type receptor? Would it be possible to co-transfect p75NTR and GFP as an alternative approach?

The authors discuss previous findings that indicate that p75NTR can play a pro-migratory role but oddly do not place their results in other contexts where p75NTR has been shown to block migration. CGPs have been quite widely used to dissect the role of p75NTR in the Rho-dependent migration blockade induced by MAG and other myelin components and interesting insights on receptor components (e.g. LINGO1) and signalling mechanisms (e.g. RhoA) that mediate these effects. The results reported here should be discussed in the context of these previous findings.

Reviewer #1 (Public Review):

Employing in vitro and Drosophila model, the authors interrogate which domain of Hsp27 binds to which region on Tau, and how these interactions facilitate the proteinaceous aggregation. They utilized various biochemical, biophysical, cellular, and genetic tools to dissect the association, and identified the structural basis for the specific recognition of Hsp27 to pathogenic p-Tau. Conceivably, Hsp27 may play some role in preventing Tau abnormal aggregation and p-Tau pathology in AD. Overall, the data support the main claim, especially, the biophysical data are very impressive. Nevertheless, the manuscript could be strengthened by complementary cellular or biochemical methods for validation. For example, the authors can use a stably transfected Tau cell line to interrogate Hsp27's role in its cellular aggregation or proteinaceous inclusions by immunoblotting. Immunofluorescent and immunohistochemical staining and IB with different antibodies may be conducted to validate the observations.

Reviewer #1 (Public Review):

The authors here follow-up on roles for signaling pathways like ERK in epithelial patterning that have been studied in an emerging literature in both, broadly, the cell competition field and, more specifically, in mouse intestinal organoids. They employ timelapse microscopy to study behavior of human colonic organoids in monolayers as the organoids initially self-organize. They then follow maintenance of organization into densely clustered nodes that have increased cells in cell cycle and the remaining more sparsely populated regions with fewer cycling cells. Nodes also show markers of in vivo colonic stem cells (Lgr5 and myc). They follow propagation of ERK waves using a genetic tool (ERK-JTR) and show that they can emerge from single apoptotic cells in between nodes.

Strengths of the study include novelty of showing self-organization and behavior of human organoids over time, with good resolution, using microscopy, as well as sophisticated analysis techniques to interpret and present cumulative data over many experiments. Additionally, the paper adds important pieces of the puzzle with respect to how cells may compete and respond across an entire monolayer, and the tools and approaches lend themselves to studying many genes and signaling pathways besides simply Wnt vs ERK.

Weaknesses in the current version of the manuscript:

1) The manuscript is focused nearly exclusively on ERK and Wnt but not in terms of the broader context of interpretation of the response of a monolayer to apoptosis of single cells. Some of the original work in the field showed that apoptotic cells enacted Rho- and MLCK-dependent actomyosin contractility, which was proposed to signal neighboring cells by initially pulling them inwards via the contraction (PMIDS: 9456322, 10459006, 11283606, 21721944). But a more intestine-specific literature has long-been extant following up on the critical role of ROCK and MLCK in maintaining barrier after specifically intestinal-cell apoptosis (15825080, 21237166).

-- A suggestion would be 1) to cite the relevant literature and 2) to interpret some of the experiments within the cytoskeletal mechanistic context already known. In addition to comments about PMA and ERK activation (see next point), the authors could test whether the ERK waves cause myosin II activation and/or are ROCK/MLCK-dependent. Given ROCK inhibition is frequently used in organoid culture, this would seem an obvious avenue to explore. Does the ERK wave propagate the cytoskeletal changes to close the gap and increase centrifugal motility and/or conversely does the actomyosin tugging of the apoptotic cell trigger ERK activation? (admittedly, the latter question may be hard to address). In short, there is a lot known about monolayer behavior in terms of dynamic cytoskeletal changes that can be addressed here to integrate with the Wnt/ERK roles.

2) The authors use only PMA as an ERK activator. PMA is a broadly acting drug, principally known as a PI3K inducer. Obviously, Akt and other downstream action of PI3K means many other pathways are stimulated besides ERK. Indeed, ROCK and Src and other cytoskeleton-modifying pathways are modulated by PMA that may not correlate with the ERK effects. Additionally, the movies showing the effects of PMA treatment show a striking increase in apoptotic cells throughout the field, which would obviously confound the interpretation of what happens after relatively rare, internodal apoptotic cells die

-- A strong suggestion would be to increase the routes to ERK activation the authors use. This could be via receptor tyrosine kinase stimulation (again, like ROCK, EGF is a key organoid medium component), though obviously that would not be much more specific than PMA, but the authors use EGFr inhibition to block ERK, so wouldn't stimulation be an apt converse approach? Genetic constitutively active KRAS might be introduced. Alternatively, there are pharmacological ways to increase pERK dramatically by inhibiting the dual action phosphatase (see eg PMID: 30475204 in a previous eLife paper). At the least, it would seem the authors should not use an approach that increases apoptosis dramatically.

3) The movies clearly show many dividing cells that are between nodes, and they show apoptotic cells within nodes (eg movie 3a towards the end). While it's clear that apoptotic cells in internodal regions can elicit the wave behavior, it would seem that apoptosis does not universally do this, given the counter-examples.

-- It would help if the authors could speak to this. Namely, in what cases are there no waves after apoptosis and what are the factors that might contribute (nearness to a node? nearness in time and space to another apoptotic cell?). Presumably, the events are relatively stochastic so there would be occasions for non-stereotypical behavior like wave front interference or augmentation in the case of closely located apoptotic cells.

Reviewer #1 (Public Review):

Microfluidics-based live-cell imaging is a powerful technique that can reveal detailed quantitative insights on for example cell growth, cell cycle, and - if coupled with fluorescent markers - molecular processes. Especially for fast growing unicellular organisms such as yeast, high-throughput imaging of multiple strains or conditions is possible over many generations. This allows biologists to quickly obtain hundreds of videos in a relatively short time-span, making the image analysis to extract useful information the bottleneck. Recent progress on convolutional neural networks such as UNet has made a strong impact on the quality of automated segmentation. However, to extract useful information, additional time-consuming steps are still necessary, which limits high-throughput experiments. With the present manuscript, Aspert et al. now make an important step towards filling this gap by establishing a fully automated approach to extract biological information on the replicative life-span of yeast cells from experiments performed with dedicated microfluidics devices that retain mother cells over multiple generation while 'washing out' newborn daughter cell.

In their work, Aspert et al. take an innovative approach of using convolutional neural networks to classify images of single traps according to whether the mother cell is in G1, early budded phase, late budded phase, or dead. In addition, two classes of empty and crowded traps are used to clean up the results. This initial classification is then combined with an LSTM to predict cell cycle transitions over complete life-times of mother cells. As a proof-of-principle, they then also combined this approach with semantic segmentation to extract cellular features of the mother cells. In addition to the computational developments, the study also suggests a cheap experimental setup that makes using this novel image analysis routine affordable.<br /> Overall, this is an interesting and well-executed study that opens new territory of using AI for yeast live-cell imaging approaches. The main focus of this study is clearly to develop a functional assay, all the way from experiment to data analysis. The authors put effort into providing a tool that works for diverse optical setups. To achieve this aim, at some points pragmatic decisions were made, in particular with regards to the neural networks used. While these decisions are reasonable, it still leaves open the possibility that even better performance could be achieved with other state-of-the art approaches.

Reviewer #1 (Public Review):

The work is of broad interest to researchers, data scientists, and clinicians in the field of dental malformation and craniofacial dysmorphism. The strategy and technique used in this study will be of help to those who hope to delve into the molecular pathogenesis of human organ malformation where an appropriate organ sample during development cannot be easily accessible. Most of the data are solid, and the interpretation of the data is appropriate. However, the contribution of the immune system to caries development is not clear from this study. Discussion on eQTL analysis on mouse teeth would also be required.

Authors showed an enrichment of the variants associated with dental phenotypes in enhancers of human craniofacial tissues and that of odontogenesis-associated variants in mouse craniofacial active enhancers. With ChIP-seq data, authors showed that conserved regulatory regions active in the early developmental stage-mouse face are systematically enriched for variants associated with a variety of human dental phenotypes. ChIP-seq analysis on E13.5 mouse incisors showed the highest enrichment of odontogenesis-associated variants. WGCNA revealed tooth-relevant co-expressed gene modules and identified previously undescribed genes that could contribute to common dental phenotypes.

Reanalysis of public scRNA-seq data of E14 mandibular molar led to the identification of a novel putative enamel knot gene signature. Authors also tried to identify variants related to caries risk and showed craniofacial enhancers may play a role in the risk. One was located at the PITX1 locus, and pitx1 is an epithelial gene. Authors observed enrichment of caries risk variants in immune cell enhancers. These data suggest the possibility and feasibility to extrapolate multi-layered genomic data from developing mouse teeth to human dental development and disorders.

The strength of this study is the multi-layered integrated analysis of genetic data with different available data from mice and humans using various methods. The data showed the potential involvement of such genes as Wif1, Pitx1, Runx2, Agap1, and others as important molecules involved in tooth differentiation in health and diseases. The study also determined the role of tooth enhancers in dental malformation/phenotype. The results from the study will also provide a useful tool for the manipulation of specific expressions of reporter genes or other genes in the tooth (or enamel)-specific manner.

Reviewer #1 (Public Review):

In this study, Ansari et al. have created a web platform called CriSNPr which serves two purposes:

1. It provides a set of pre-designed CRISPR RNAs for dbSNP-annotated Single Nucleotide Variants (SNVs) in the human genome and variants of concern in the SARS-CoV-2 genome.

2. For unannotated/novel SNVs in either the human or SARS-CoV-2 genomes, it designs CRISPR RNAs de novo, based on sequence information provided by the user.<br /> For both options, the platform focuses on six different CRISPR/Cas systems currently in use for CRISPR/Cas-based diagnostics, five of which - Fn/enFnCas9, LbCas12a, AaCas12b, and Cas14a - are DNA-targeting, and one of which, LwCas13a, is RNA-targeting. In addition to CRISPR RNA design, CriSNPr also identifies PCR primer pairs that could be used to generate amplicons for downstream testing and validation. Overall, the authors have clearly defined the "back-end" strategy of CriSNPr and the mismatch criteria that were considered for each CRISPR/Cas system for CRISPR RNA design. They also provide information about the proportion of dbSNP-annotated SNVs that can be targeted by each CRISPR/Cas systems, with Cas14a and LwCas13a being the most versatile and widely targeting. Lastly, the authors have experimentally demonstrated the utility of CriSNPr for designing reagents to detect a specific SARS-CoV-2 variant, S gene containing E484K mutation, using FnCas9, AaCas12b and Cas14a. However, the design of CRISPR RNAs and PCR primers for the detection of SNVs in human genomic DNA was not demonstrated. Given the size, complexity and diploid nature of the human genome, this would have enhanced the significance of the study.

CRISPR/Cas-based diagnostics have shown great promise for sensitive/low-cost detection of nucleic acids of infectious agents as well as genetic mutations in humans, including SNVs. Some of the major bottlenecks for CRISPR/Cas-based SNV detection include: (i) the choice of CRISPR/Cas system and (ii) the fast and accurate design of specific CRISPR RNAs and PCR primers. In CriSNPr, by including six different CRISPR/Cas systems and by generating PCR primers, the authors fill an important lacuna and provide a rapid and easy, yet adaptable, platform for developing diagnostic workflows. This is the major strength of the study and I foresee that this platform will greatly accelerate the field of CRISPR/Cas-based diagnostics for SNV detection. At the same time, there is room for further development, as the authors point out - the inclusion of other organisms for which SNV information is readily available and linked to distinct phenotypes, the estimation of CRISPR RNA sensitivity and specificity for all six systems, the enhancement of the platform with additional CRISPR/Cas systems as and when they are developed for diagnostics.

Reviewer #1 (Public Review):

In this manuscript by Huisman et al., the authors leverage their strong capacity for the development of MHC yeast display to develop a method for high throughput MHC class II binding assessment. They applied their approach to comprehensively screen for HLA-DR401, -402, and -404 binding peptides derived from the whole proteomes of SARS-CoV-2 and four different dengue virus serotypes. The results obtained using this method are carefully analyzed and validated. Minor caveats that come from linker sequences are appropriately described and the context for the utility of this approach is nicely discussed. That the full set of results from these screens is provided makes this paper resourceful to the community.

Comments:

The authors should reference and discuss technical differences from the approach previously published by Wen et al. J. Immunological Methods, 2008 which also uses yeast display to identify MHC class II binding peptides derived from the influenza virus genome.

Reviewer #1 (Public Review):

This article presents important new findings, of particular interest to those concerned with a) estimating parental indirect genetic effects, b) distinguishing pre- and post-natal maternal effects, c) optimizing adoption designs, and d) developing cohort studies strategically. Notably, prior work has investigated pre-natal 'genetic nurture' (Armstrong-Carter et al., 2020) and used the adoption sub-sample of the UK Biobank to distinguish pre-natal and post-natal indirect genetic effects (Demange et al., 2021). Here, the authors present the first structural equation model for estimating pre- and post-natal parental indirect genetic effects using polygenic scores and adoption data. The authors found, as expected, pre- but not post-natal maternal genetic effects on birthweight. However, pre-natal maternal genetic effects on educational attainment were unfeasibly large. Their simulations convincingly suggest this is because estimates of maternal pre-natal indirect genetic effects are inflated when adoptive parents and biological parents are related. It is nice to see the authors' practical suggestions on how the UK Biobank resource should obtain more information on the adopted individuals. The key caveats are the low sample size of adoptees (especially when restricting to adoptees who have breastfeeding data), and the inclusion of only genome-wide significant SNPs in polygenic scores. Given the evidence that population stratification and assortative mating can bias estimates of parental indirect genetic effects, the authors should consider how these factors would affect their model.

Reviewer #1 (Public Review):

Human thymidylate synthase (hTS) is relatively large for NMR standards (~72 kDa dimer) and so the authors use a battery of advanced, TROSY-based NMR experiments to investigate the structure and conformational dynamics of the enzyme in multiple binding states. In particular, they have acquired multiple and single quantum methyl CPMG and CEST data to probe us-ms dynamics. These experiments showed that hTS undergoes exchange between active and inactive conformations. Analysis of residual dipolar couplings and chemical shift perturbation experiments indicated that the major conformational state revealed by CPMG and CEST corresponds to the active hTS conformation. This finding suggests that conformational selection is not the primary mechanism mediating cooperativity in hTS.

To investigate if binding cooperativity in hTS is due to modulation of conformational entropy upon ligand binding, the authors have investigated ps-ns dynamics in hTS by means of 2H relaxation measurements. These measurements suggest that rigidification of the protein upon the first binding event is the primary origin of cooperativity in the hTS dimer. Indeed, acquisition of control experiments on systems that do not show binding cooperativity (i.e., the complex formed by dUMP with N-terminal truncated hTS and the complex formed by TMP with full-length hTS) do not show the same modulation of conformational entropy observed upon formation of the dUMP-hTS complex. Overall, I found this manuscript interesting and well-written. I found particularly fascinating the observation that cooperativity is driven by modulation of conformational disorder in the unstructured N-terminal tail, which is not directly involved in ligand binding. The experimental approach and analysis protocols are sound and the conclusions are well supported by the experimental data.

Reviewer #1 (Public Review):

The authors employed a unique species-hybrid model wherein implanted human cells from white and thermogenic adipose tissues in nude mice. The authors performed molecular analyses of implanted adipose tissues and made several intriguing observations. One of the notable findings is the expression of MAOA in human adipocytes - this is in contrast to previous findings in mice that MAOA is expressed in macrophages. The cell-autonomous role of MAOA in human adipocytes using the species-hybrid system would add additional significance to this work.

Reviewer #1 (Public Review):

In this manuscript, Klee and Hess et al. present the first unbiased, large-scale genetic screen for regulators of apical membrane protein trafficking in human polarized epithelial cells. Designing a sensitive and quantitative assay is critical for the successful completion of a high-throughput genetic screen of this type. To this end, the authors used fluorescence-activated cell sorting of CaCo2 cells stained with a DPP4 (a model apical membrane protein) antibody, selecting for mutant cells with a ~90% reduction in plasma membrane-localized DPP4. The authors' stringent screening criteria led to the identification of 89 genes that function in many cellular processes. For validation, the authors selected 7 target genes for more detailed phenotypic characterization.

The main conclusion of the paper, that the screen identified novel genes required for apical membrane protein localization, is supported by the presented data. Additionally, the dozens of novel genes required for apical trafficking will be an important resource for epithelial biologists. However, phenotypic characterization of the selected target genes should be extended and better described. In particular, a quantitative analysis should be included to describe the qualitative phenotypes shown in the manuscript.

Reviewer #1 (Public Review):

This is a clearly written paper, the results of which are also clear and well supported. Putney et al. recorded the electrical activity of a subset of the flight muscles in the tobacco hawkmoth Manduca sexta during tethered flight. The moths were presented with six visual stimuli, comprising up or down pitch motions, right- or left-handed roll motions, and right- or left-handed yaw motions. The muscle recordings were analysed by: (i) filtering the signals using a gaussian kernel of variable width; (ii) linearly combining the filtered signals by projecting them into a new basis using principal components analysis (PCA); (iii) applying linear discriminant analysis (LDA) on a subset of these principal components, to classify the recordings according to stimulus condition.

The authors demonstrate that their method allows robust classification of the visual stimulus associated with each electrophysiological recording (better than 99.5% classification accuracy for n=4 moths with complete recordings from all 10 muscles that were studied). This well-supported result is not in itself surprising given the causality of the input-output relationship and the high dimension of the output data relative to the low dimension of the input classification. It follows that the key strengths of the manuscript lie in the extent to which it explores the details of this result. This is done in several ways:

First, the authors explore the effect of varying the width of their gaussian smoothing kernel. They demonstrate convincingly that the accuracy of classification is lost if the gaussian kernel is too broad, which amounts to showing that lowpass filtering the data eliminates important information. The authors interpret this result as showing that the analysis of their muscle recordings is "sufficient to predict behavior, but only if precise timing information in included". This summary statement, expressed in the time domain, may invite the reader to assume that the key information lies in the arrival time of each spike, so it is worth noting that it could be reframed equivalently in terms of the spectral content of the signal - as indeed it is in Fig. 4C.

It is also worth adding that two of the N=4 moths with complete data appear to behave differently with respect to the drop-off in decoding accuracy with increasing gaussian kernel width (Fig. 3A), although there seems to be no discussion of this point.

Second, the authors explore the effect of data reduction in terms of: (i) the number of principal components required to capture the variation in the signals, and (ii) the number of muscles included in the analysis. This is a strength of the paper insofar as it provides a high level of model reduction, and also allows the authors to include results from N=5 moths with incomplete data (i.e. moths with missing muscle recordings). Of course, this is also a weakness insofar as there is missing data and a small absolute number of individuals sampled, but this needs to be viewed in the context of the extreme challenge of making multiple muscle recordings simultaneously.

Third, the authors use the results of their PCA-LDA analysis to identify muscle coordination patterns, from which they conclude that "while the realization of muscle activity in each of the functionally distinct pairs of behavioral states about each flight axis changes, the underlying muscle coordination patterns are conserved." This conclusion is reached by calculating the inner product of the vectors providing the best separation (i.e classification) of different directions of motion within and between flight axes. As the inner product of these unit vectors is constrained to take values between 0 and 1, it would be helpful to consider whether there is some form of randomisation analysis that could be used to identify a null distribution against which the apparently quite weak co-directionality of the vectors could be assessed. This is important because the weakness of the co-directionality forms the basis of the conclusion referred to above.

In summary, the authors' methodological pipeline will be useful in future studies and lays important groundwork for future work combining analysis of muscle activity, wing kinematics, and force-torque output in the visuomotor responses of insects.

Finally, whilst the context of this work is explained well in relation to previous electrophysiological studies, other relevant work exploring the mapping from visual input to force-torque in hawkmoths using similar stimulus arrangements (Windsor et al., 2014; doi:10.1098/rsif.2013.0921) is ignored, and may provide some insight into the neuromechanical couplings relevant to the discussion. This earlier work showed that whereas visual stimuli in pitch and roll produce almost pure pitch and roll torques, yaw stimuli produce coupled roll and yaw moments. This coupling ought in principle to be present within the details of muscle coordination identified in the PCA-LDA analysis.

Reviewer #1 (Public Review):

The authors have provided an impressive analysis of the effects of reporting of WGS results on IPC practices in 14 hospitals in the UK during the COVID-19 pandemic. After a median of 4 weeks, hospitals adopted a practice of "rapid" or "longer" turnaround phases for WGS reporting. After a median of 8 weeks, 8 of 9 "rapid" hospitals adopted the "longer" practice for a median of 4 weeks. After a median of 4 weeks, all 5 "longer" hospitals adopted the "rapid" practice for a median 8 weeks. Hence, there were twice as many weeks with the rapid, compared to the longer reporting practice.

The targeted turnaround times for reporting were 48 hours for the rapid and 5-10 days for the longer phase.

The primary outcomes of the study were: (1) incidence of IPC-defined SARS-CoV-2 HAIs per week per 100 currently admitted non-COVID-19 inpatients, and (2) for each HOCI, identification of linkage to individuals within an outbreak of SARS-CoV-2 nosocomial transmission using sequencing data as interpreted through the SRT that was not identified by pre-sequencing IPC evaluation during intervention phases.

Secondary outcomes were: (1) incidence of IPC-defined SARS-CoV-2 hospital outbreaks per week per 100 non-COVID-19 inpatients, (2) for each HOCI, any change to IPC actions following receipt of SRT report during intervention phases, (3) any recommended change to IPC actions (regardless of whether changes were implemented). The proportion of HOCI cases for which IPC reported the SRT report to be 'useful' was added as a further outcome.

A total of 2170 HOCIs were recorded for the study between 15 October 2020 and 26 April 2021.

The authors conclude that "While we did not demonstrate a direct impact of sequencing on the incidence of nosocomial transmission, our results suggest that sequencing can inform IPC response to HOCIs, particularly when returned within 5 days."

The research question is very relevant and, as said, the amount of data collected is impressive. Yet, interpretation of the data, obtained in a real-life setting with all hurdles and complexities created by the pandemic situation, is challenging. I have several questions related to data interpretation and difficulties in accepting the overall positive interpretation of the findings when it comes to feasibility and potential impact. Especially, as I consider the real-time availability of WGS results in the participating hospitals to be (much) higher than it will be in hospitals in most other countries.

Specific questions<br /> Not clear why sites started either with rapid or longer phase. Was there a randomization process? Please clarify.

From Figures S2 it is clear that the pandemic peaked, after which the curve declined when vaccination had started, and these curves seem to resemble the incidence rates of HOCI in the hospitals. I had difficulties in interpreting S3 and S4 where I think the authors incorporated these disease dynamics occurring outside the hospital setting on the HOCI incidences. I would be helped by a better explanation of what actually was done.

It is not clear why the difference between the groups in the intervention (providing rapid or not so rapid WGS reports) was too small to have an impact, compared to the baseline period without WGS reporting. Surprisingly sites F and G appear to do significantly worse during the "rapid" phase, according to Fig1. Please clarify.

The 'health economic findings' miss the health component. The costs of the intervention are described in detail, but not the benefits of the intervention. Is it possible to calculate the costs required to prevent a single case of HOCI?

Reviewer #1 (Public Review):

The authors set out to consider more the role of the predator in predator-prey interactions, particularly from a collective locomotion aspect. This is an aspect which at times has been overlooked, with many theories, experiments and models focusing largely on the prey response, independent of how the predator behaves. The major strengths are the (1) excellent writing, (2) quality of the figures, (3) quantity of data, and (4) question tackled. The major weaknesses are (1) the volume of information (as a reader, it is quite hard to distil key points from the sheer volume of what has been presented), (2) the confined captive environment making it difficult to draw comparisons with a wild-type scenario, and (3) lack of clarity about the wider implications of the work outside of the immediate field.

Reviewer #1 (Public Review):

In this study, He and collaborators analyse eight samples from six patients with acral melanoma through single-cell RNA sequencing. They describe the tumour microenvironment in these tumours, including descriptions of interactions among distinct cell types and potential biomarkers. I believe the work is thoroughly done, but I have identified a few concerns in their depiction and interpretation of their results.

Strengths:

1. One of the few available single-cell studies of acral melanoma, including a non-European cohort of patients.<br /> 2. Data will be very useful to study the immune landscape of these rare tumours.<br /> 3. Data include adjacent tissue, primary tumours and a metastatic sample, covering all disease stages.<br /> 4. Analyses seem to be carefully done.

Things to improve:

1. Figures need much more description to be understandable, in particular, axes should be clearly labeled and the colour code should be specified<br /> 2. In some places, I would recommend the authors soften their interpretation of their analyses (for example, when they suggest targeting TNFRSF9+ T cells as a novel therapy), as these are nearly all bioinformatic in a small number of samples<br /> 3. I don't think the experiments add much to the literature, as these test already known oncogenes on a common, non-acral melanoma cell line.

Reviewer #1 (Public Review):

COVID-19 epidemic conditions are rapidly changing due to behavioral changes, accumulating immunity from prior infections, vaccination roll-outs, and the emergence of new variants. In this analysis, the authors are using a simple mathematical model to reconstruct SARS-CoV-2 transmission dynamics in South Africa through different outbreaks with different prevalent variants. They estimate key characteristics of the epidemic in each of the nine South African provinces while accounting for multiple factors including changing detection rates, seasonality, nonpharmaceutical interventions, and vaccination. The paper is well written and addresses important questions in the field.

The authors apply a model-inference system to estimate the background population characteristics (e.g., population susceptibility) before the emergence of the new variant, as well as changes in population susceptibility and transmissibility due to the new variant. They come up with projections of cumulative incidence, accumulation, and loss of population immunity over time for different provinces. Inference on the characteristics of different variants is also presented.

The paper has a couple of key limitations.<br /> First, simple models come with strong assumptions. The simplicity of the model does not allow to account for several important epidemic drivers including i) heterogeneity in contactness, acquisition risk, and severity (especially with respect to age) which may have a strong impact on the epidemic dynamics; ii) all-or-nothing vaccine which restricts the possible mechanisms of protection to be explored and iii) using the same compartment for vaccinated and recovered from infection which leads to the same duration of immunity and efficacy for these 2 groups.<br /> Second, I suspect that the model-inference system has some identifiability issues. It is unclear how it selects between scenarios with low transmissibility but high IFR and scenarios with high transmissibility but low IFR. Some characteristics (including IFR) were estimated independently for each wave and each province. However, correlations across provinces should be expected.<br /> The paper will benefit from a more detailed explanation and sensitivity analyses that show how model assumptions influence presented results.

Reviewer #1 (Public Review):

This study by Boddupalli et al. demonstrated the roles of Gba in individual cells such as microglia, blood-derived macrophages, and astrocytes in the neuronopathic Gaucher Disease (nGD), a neurodegenerative disorder caused by biallelic mutations in Gba. The authors applied single-cell resolution of mouse nGD brains to reveal the induction of neuroinflammation pathways involving microglia, NK cells, astrocytes, and neurons. They also found that targeted rescue of Gba in microglia or neurons, respectively in Gba deficient, nGD mice reversed the buildup of glucosylceramide (GlcCer) and glucosylsphingosine (GlcSph), reduced the level of serum neurofilament light chain (Nf-L), and improved survival. Together with other related findings in this paper, this study delineated individual cellular effects of Gba deficiency in nGD brains. The experiments were well designed and conducted, the results were reasonably interpreted, and the manuscript was clearly written with logical inputs.

One weak point is that it remained unclear or not addressed about the brain region or tissue-specific involvement in the observed phenotypes in this study.