Reviewer #1 (Public Review):

Okawa et al show that topical oral application of an agent used in SPECT imaging, hydroxymethylene diphosphonate (HMDP-DNV), displaces pre-existing nitrogen-containing bisphosphonate (N-BP) from the jawbone of mice and prevents the development of bisphosphonate-related osteonecrosis of the jaw (BRONJ), a devastating complication that rarely occurs after invasive dental procedures in N-BP treated patients. They further demonstrate pro-inflammatory genomic signaling in gingival cells of N-BP treated mice, which reverses with HMDP-DNV. The methods are well-described overall and the results are potentially important. However, limitations include the short study period and the lack of multiple time points. Additional data to address these limitations would help to strengthen the authors' conclusions. If these results are added, this work could have a high impact in the field and the data could set the stage for further testing. The significance lies in the unmet need for therapeutic options to prevent this complication, which is widely dreaded and impedes the use of often needed bisphosphonate therapy.

Reviewer #1 (Public Review):

In this paper, Chaudhary et al assessed 143 children with AML, and out of 20 mitochondria-related DEGs that were chosen for validation, 16 were found to be significantly dysregulated. They show that upregulation of SDHC and CLIC1 and downregulation of SLC25A29 are independently predictive of lower survival, which was included in developing a prognostic risk score. They also show that this risk score model is independently predictive of survival better than ELN risk categorization, and high-risk patients had significantly inferior OS and event-free survival. The authors demonstrated that high-risk patients are associated with poor-risk cytogenetics, ELN intermediate/poor risk group, absence of RUNX1-RUNX1T1, and not attaining remission (p=0.016). The risk score also predicted survival in the TCGA dataset. They concluded that they have "identified and validated mitochondria-related DEGs with prognostic impact in pediatric AML and also developed a novel 3-gene based externally validated gene signature predictive of survival."

Although this paper is interesting, it lacks novelty and does not advance the field significantly. The authors have used a similar approach in their recent paper in Mitochondrion where they showed that PGC1A driven increased mitochondrial DNA copy number predicts outcome in pediatric AML patients. Additionally, the authors have a small number of patients and chose only 20 genes for their analysis.

Reviewer #1 (Public Review):

In the current manuscript, the authors developed a general framework to study the evolution of multicellular life cycles and the investigated evolutionary advantage of certain life cycles and multicellular structures over other ones. Simple multicellular life cycles are comprised of growth of the propagule into a colony and its fragmentation to give rise to new propagule. For the evolution of multicellularity, a multicellular trait is not only identified by the genotype of individuals inside each propagule but also the life cycle it is programmed for growth and fragmentation. There is not a single fitness value but a set of fitness values, each assigned to one stage of life-cycle growth before fragmentation. The question is how natural selection chooses one life cycle over the other one. In other words how a robust life cycle is evolved from random fragmentation processes. Previous theoretical approaches mainly considered overall growth rate as a measure of advantage for a life cycle.

This work is based on an extension of the several previous works of Pichugin and Traulsen on the subject. It introduces interaction between different stages of life cycle, as well as interaction between two traits, identified with differences in life cycle patterns. For brevity and focus on life cycle patterns, possible intra-propagule genotypic heterogeneity is ignored. (This has been addressed by same authors and others in past works.) A deterministic system of ordinary differential equations is set to describe the growth and competition of different life cycle stages. Abundance of each life cycle stage is the dynamical quantity and the dynamics is reminiscent of a general replicator equation for a complex multicellular structure. The interaction terms is identified by a kernel matrix, K_ij, which is effectively fitness payoff for a group of size i when encountered with a group of size j. Interaction terms introduces effective elevation in death rates. They focus on two main scenarios, 1) a killer kernel where the kernel is only a function of and 2) a victim kernel where is only a function of. In some cases authors considered more general cases including arbitrary (random-valued).

Authors first considered the dynamics of a single life-cycles where the interaction between populations at different stages of life cycles changes the growth dynamics. They observe that the general dynamics and steady states are governed by overall growth rate of the whole lief-cycle as has been observed in the absence of group-group interactions. They suggest the modified steady states while there is no qualitative changes from no-interaction (diagonal kernel or constant-selection) case.

The second part of the work focuses of competition between two and multiple life cycles in the presence of the group-group interactions. The authors considered invasion of one rare multicellular life cycle into another resident multi-cellular life cycle. They also consider competition between multiple life cycles. They discussed the condition for ESS in this scenario. Four interaction schemes including killer and victim kernels are discussed for some examples of fragmentation. Furthermore, competition of multiple life cycle is discussed. In particular a three life cycle competition is discussed using similar kernel interactions which now resemble a rock-paper-scissor type payoff in some cases.

I believe the modeling framework to address competition and natural selection between life cycles in the same framework that introduces interaction between different stages of a same life cycle is a great step forward in modeling evolution of simple multicellularity, The results are very clear and I think further analysis of the model introduced in this work can have a strong impact on our understanding of the evolution of multicellular life cycles.

Reviewer #1 (Public Review):

Richardson et al. used a multivariable Mendelian randomization framework to separate the genetically predicted effects of adiposity at two timepoints in the lifecourse, childhood and adulthood. They used data from the Avon Longitudinal Study of Parents and Children (ALSPAC). Higher childhood body size had a direct effect on lower vitamin D levels in early life, after accounting for the effect of the adult body size genetic score. However in midlife, childhood body size impacted on adult obesity to result in lower vitamin D levels. The authors conclude their findings have important clinical implications in terms of the causal influence of vitamin D deficiency on disease risk. They also serve as a proof of concept that the timepoints across the lifecourse at which exposures and outcomes are measured can impact any overall conclusions drawn by MR studies. In particular, the study underlines the significance of obesity in increasing the risk of vitamin D deficiency.

The strengths of this paper are the robustness and rigour of the methods using an established longitudinal cohort and the Mendelian randomization method.

A weakness is the lack of contrast of the authors findings from Mendelian randomization with those relevant findings from recent large randomised controlled clinical trials of vitamin D supplementation. In particular, two have shown an interaction between outcomes and BMI, a clinical measure of obesity.

Reviewer #1 (Public Review):

This manuscript describes in detail the role of glutamine in chondrocyte metabolism. The authors provide an extensive investigation into the anabolic respiratory effects of glutamine deprivation including its effect on other sources of energy such as glycolysis. Their premise is also backed up by several modes of investigation, including the use of the pharmacological inhibitor CB-839. The manuscript is decently written and there are numerous well-laid-out figures to support conclusions.

The main issue at hand is the reconciliation of the hypothesis with other recent work targeting this area. For example, Ma et a. Clin Sci (2022) recently published a paper demonstrating that glutamine supplementation, as opposed to deprivation, leads to a reduction in osteoarthritis symptoms and reduction in NF-kappaB activity. While it is possible for both mechanisms (e.g. deprivation and supplementation) to lead to similar outcomes, exploration of this topic would be of interest to the readership.

Reviewer #1 (Public Review):

This paper describes a new software tool: smartScope, for automated screening of cryo-EM grids. SmartScope can also perform automated data collection on suitable grids, including using beam-image shifts and tilted stage geometries. SmartScope uses deep-learning approaches for the selection of squares and holes of interest. The description of the software given in the paper is very promising, and as the code has not yet been made available, I cannot comment on its modularity, ease of installation, or general usability.

The convolutional neural networks for square and hole detection were trained on relatively few examples, and supposedly all from the same microscope. How easy would it be for users to re-train these detectors for their own purposes? Could a description of that be added to the paper/documentation?

The introduction makes the same point over multiple pages, and could probably be easily cut in half length-wise. This will force the authors to formulate more succinctly, and thereby more clearly. Hopefully, this would then eliminate wooly or incorrect statements like: "the beginning of each new project is fraught with uncertainty", "[The number of combinations] grows exponentially with the inclusion of each parameter" (it doesn't!), "would be an invaluable tool".

Also, the first half of the Abstract needs some rewriting. It focuses first on grid optimisation, which is not what smartScope is about. SmartScope is about grid screening. Just say that and save some lines in the Abstract too.

Lines 257-261 describe some setup in serialEM. Perhaps because I am not familiar with that software myself, but I had no clue what those lines meant. Perhaps some example setup files could be provided as supplementary information?

For the DNA polymerase data set: mention in the Results section how long the entire data collection (or 4.3k images) took. Also, the sharpened map in the validation file has a very weird distribution of greyscale values. Its inclusion of volume with varying greyscale is basically a step function, indicating that this is more or less a binary density map. I suspect that this is a result of the DeepEMhancer procedure. But given that the scattering potential of proteins is not binary, I wonder how such a map can be justified. Also, the FSC curve shown in the paper does not mention any masks, but the reported resolution of 3.4A is higher than the unmasked resolution calculated by the PDB: 3.7A. Why is the DeepEMhancer software used here? Is it hiding a slightly suboptimal map? As map quality is not what this paper is about, perhaps it would suffice to show the original map alone?

Reviewer #1 (Public Review):

This paper used two linear tracks of different cues as two contexts and tested the rate modulation of contexts during behavior and during replay events. They showed that not only sequential information, but rate information also are encoding information and that they are reinstated during replay events. This is super exciting! The data about how things change during sleep is also timely and important.

My primary criticism of this paper is that it misses the opportunity to give some key details about the statistics of neural activity during 'ripples' rather than studying identified replay events. A secondary criticism is that they limit their analyses to neurons that have place fields in both environments. I think the activity of the other 3 categories of neurons (active in Track 1 only, active in Track 2 only, and not active in either track) are also of critical interest.

Reviewer #1 (Public Review):

TRPV1-targeted therapies for pain have failed because of the effects of these drugs on thermoregulation: TRPV1 channel agonists produce acute hypothermia in animals and humans, whereas TRPV1 antagonists cause acute hyperthermia. TRPV1 activity in sensory neurons sends pain signals to the brain, but also causes the release of pro-inflammatory neuropeptides such as CGRP. TRPV1 channels are also expressed in vascular smooth muscle cells of arterioles. It is unclear which of these TRPV1-associated functions is responsible for the alterations in thermoregulation caused by TRPV1 channel agonists and antagonists. In this study it is shown that ablation of TRPV1 only in sensory neurons of transgenic mice prevents hypothermia caused by the selective channel agonist capsaicin and hyperthermia by the selective antagonist AMG 517. Conversely, ablation of TRPV1 channel expression in vascular smooth muscle cells slightly accentuated the hypo- or hyper-thermic responses caused by delivery of the agonist or antagonist, respectively. These results indicate that drug-induced changes in TRPV1 channel activity in sensory neurons are responsible for the alterations in body temperature, whereas activity of TRPV1 channels in the vasculature appear to counteract these alterations to a small extent. Importantly, transgenic mice did not show any impairments in body temperature regulation in the absence of drugs. The effects of drugs on body temperature were also eliminated in mice where central sensory terminals were ablated with capsaicin. In this setting, the sensory nerve endings can still release neuropeptides when TRPV1 channels activate, but have no electrical communication with the brain, indicating that it is the electrical signaling and not the neuroinflammatory responses which cause alterations in body temperature when TRPV1 channels are challenged by an agonist. This was further supported by results in mice deficient in the neuropeptide CGRP, which still experienced hypothermic responses when treated with capsaicin.

The data in the manuscript provide important constraints towards understanding the role of TRPV1 channels in thermoregulatory processes, and suggest that analgesic drugs that impair calcium permeability through TRPV1 channels without affecting sodium permeability could prevent pain caused by neurogenic inflammation without altering body temperature. The experimental design in this report is straightforward, adequate controls were included, and the results appear robust. However, there are also some concerns and limitations.

First, the major goal of the study is to determine whether TRPV1 channels expressed in the vasculature or in sensory neurons are responsible for the effects of drugs on body temperature. However, no clear justification is provided for how vascular TRPV1 channels could potentially give rise to the observed alterations in body temperature caused by drugs, as it would be generally expected that systemic treatment with an agonist would result in vasoconstriction and hyperthermia, and treatment with an antagonist give rise to vasodilation and hypothermia. These responses are opposite to the described effects of agonists and antagonists on body temperature, and therefore potentially rule out a contribution of vascular TRPV1 channels without necessarily requiring additional experimental testing.

Second, the effects of drugs on body temperature are shown as smoothened differences between the body temperature of control and test mice, rather than absolute body temperature in all groups of animals. This visualization obscures variability between organisms, which could contain additional relevant information, and is essential for an accurate assessment of the robustness of the results, particularly given the small numbers of animals that were tested and the high variability in the data. Statistical tests compare differences between WT and TRPV1-deficient mice after treatment with TRPV1 channel agonists and antagonists. However, these comparisons provide no information on whether there are statistically significant changes in the body temperature of TRPV1-deficient animals after treatment with drugs relative to animals treated with vehicle. This latter comparison is of higher clinical and physiological significance than what was performed in the study.

A minor third point is that experiments where TRPV1 expression was ablated in animals 8 weeks after birth appear to show opposite effects of agonists and antagonists relative to wild type mice: agonists seem to produce hyperthermia and antagonists cause hypothermia. These observations that do not align with the major conclusions of the manuscript are not discussed.

Reviewer #1 (Public Review):

This is a study linking the role of B lymphocytes to neutrophils for the achievement of LPS tolerance in an experimental setting. The manuscript is elegantly written and easy to follow. One main strength of this submission is the extensive mechanistic insights involving even transfusion of splenocytes.

Reviewer #1 (Public Review):

The manuscript by Folwer et al examines the mechanism whereby endothelial cells respond to inflammatory stimuli. Using primary Human Vascular Endothelial Cells (HUVEC) as their model, they find that upon treatment with TNF the induction and repression of hundreds of genes at both 4 and 10 hours. They found by IPA the expected inflammatory pathway and unexpectedly found that the cholesterol biosynthetic pathway was also a prominent pathway upregulated. Upon deletion of RELA, the genes that were significantly downregulated were associated with SREBP2, suggesting that TNF somehow activates SREBP2 in HUVEC. Therefore, the authors focused on understanding the mechanism of SREBP2 activation in HUVEC cells by NF-Kappa-B.

They examined TNF-induced SREBP2 cleavage over 24 hours and found that cleaved SREBP2 peaked at 10 hours. Over the same time course, they interrogated both NF-Kappa-B and SREBP2 targets and found that the expression of the NF-Kappa-B targets proceeds the SREBP2 targets, suggesting that NF-Kappa-B is somehow activating the SREBP2 pathway. Consistent with this hypothesis are studies with IKK inhibitor (which prevents NF-Kappa-B activation) and RELA knockdown that show a reduction in SREBP2 cleavage, and the inhibition of SREBP2 gene targets involved in cholesterol biosynthesis.

A series of SREBP2-processing inhibitors were used to define that the cleavage of SREBP2 by TNF-NF-Kappa-B activation was mediated by the canonical processing pathway. They then posited that TNF treatment affected the cellular cholesterol levels to activate SREPB cleavage. Whereas TNF did not change total cholesterol, they did find a change in the amount of cholesterol in the membrane both in HUVECs and in vivo by labeling the membranes with a fluorescently-labeled cholesterol-binding protein. This prompted them to look at the genes regulated by TNF-NF-Kappa-B that might be responsible for a reduction in accessible cholesterol and they focused on the lipid transporter STARD10. Interrogation of publicly available ChIP-seq of RELA from TNF-treated HUVEC cells indicates occupancy at the promoter, suggesting STARD10 is a direct RELA target. Depletion of STARD10 inhibited TNF-induced expression of cholesterol biosynthesis genes and reduced the TNF-stimulated SREBP2 cleavage and LDLR protein abundance.

Overall the data are consistent with the conclusion that NF-Kappa-B induction of STARD10 reduces cholesterol at the membrane and activates SREBP2 cleavage (model is presented in Figure 7). This illuminates the mechanism of regulation of the inflammatory response in endothelial cells. A few controls are missing and some additional analyses should be included to strengthen an already strong study.

Reviewer #1 (Public Review):

This manuscript seeks to identify the mechanism underlying priority effects in a plant-microbe-pollinator model system and to explore its evolutionary and functional consequences. The manuscript first documents alternative community states in the wild: flowers tend to be strongly dominated by either bacteria or yeast but not both. Then lab experiments are used to show that bacteria lower the nectar pH, which inhibits yeast - thereby identifying a mechanism for the observed priority effect. The authors then perform an experimental evolution experiment which shows that yeast can evolve tolerance to a lower pH. Finally, the authors show that low-pH nectar reduces pollinator consumption, suggesting a functional impact on the plant-pollinator system. Together, these multiple lines of evidence build a strong case that pH has far-reaching effects on the microbial community and beyond.

The paper is notable for the diverse approaches taken, including field observations, lab microbial competition and evolution experiments, genome resequencing of evolved strains, and field experiments with artificial flowers and nectar. This breadth can sometimes seem a bit overwhelming. The model system has been well developed by this group and is simple enough to dissect but also relevant and realistic. Whether the mechanism and interactions observed in this system can be extrapolated to other systems remains to be seen. The experimental design is generally sound. In terms of methods, the abundance of bacteria and yeast is measured using colony counts, and given that most microbes are uncultivable, it is important to show that these colony counts reflect true cell abundance in the nectar. The genome resequencing to identify pH-driven mutations is, in my mind, the least connected and developed part of the manuscript, and could be removed to sharpen and shorten the manuscript.

Overall, I think the authors achieve their aims of identifying a mechanism (pH) for the priority effect of early-colonizing bacteria on later-arriving yeast. The evolution and pollinator experiments show that pH has the potential for broader effects too. It is surprising that the authors do not discuss the inverse priority effect of early-arriving yeast on later-arriving bacteria, beyond a supplemental figure. Understandably this part of the story may warrant a separate manuscript.

I anticipate this paper will have a significant impact because it is a nice model for how one might identify and validate a mechanism for community-level interactions. I suspect it will be cited as a rare example of the mechanistic basis of priority effects, even across many systems (not just pollinator-microbe systems). It illustrates nicely a more general ecological phenomenon and is presented in a way that is accessible to a broader audience.

L'association de deux sujets qui n'ont pas de corrélation vérifiéé, revient dans la conclusion en contradiction avec la conclusion de l'étude de Ramdoss, S et al.

En contradiction avec l'hypothèse :

Results suggest that CBI should not yet be considered a researched-based approach to teaching communication skills to individuals with ASD. However, CBI does seem a promising practice that warrants future research. Les résultats suggèrent que le CBI ne devrait pas encore être considéré comme un approche fondée sur la recherche pour enseigner les compétences en communication aux personnes ayant Troubles du Spectre Autistique. Cependant, le CBI semble être une pratique prometteuse qui justifie des recherches futures.