Reviewer #3 (Public Review):

Wong et al. developed a new versatile approach with a robust signal to track protein dynamics by inserting a tag into the endogenous loci and different properties of fluorescent dyes for conjugation. Using this approach, the authors monitor the trafficking of Fluorescent dye and Halo-tagged GluA1 with time-lapse imaging and found that neuronal stimulation induces GluA1 accumulation surrounding stimulated synapses on dendritic shafts and actin polymerization at synapses and dendrites. Furthermore, combining with pharmacological manipulations of actin polymerization or myosin activity, the authors found that actin polymerization facilitates exocytosis of GluA1 near activated synapses. The new approach may provide broad impacts upon appropriate control experiments, and the practical application of this approach to GluA1 trafficking upon neuronal activation is significant. However, there are several weaknesses, including confirmation of activity of the tagged receptors and receptor specificity mimicking endogenous LTP machinery. If the receptor tagged by the new robust approach reflects endogenous machinery, this approach will provide a big opportunity to the community as a versatile method to visualize a protein not visualized previously.

Reviewer #3 (Public Review):

In this manuscript by Ardiel et al, the authors develop a novel automated approach to behavioral classification of C elegans embryos. They provide detailed validation of this system, and in exploiting it, identity a previously unknown period of behavioral quiescence in the late embryo that is likely dependent on synaptic transmission. Then shifting to a high throughput assay to focus on this specific period, they provide evidence for a sleep/quiescent like state. The highly technical approaches they develop can potentially be used by many labs, and the rich behavioral dataset can likewise serve as a foundation for numerous future studies. However, I have major concerns. Foremost is that at its core, there are very limited new biological conclusions to come out of this work, which will dampen impact of the techniques described. Other major issues:

1. The period of quiescence/SWT is intriguing, though I believe the authors are premature in their conclusions. SWT shares molecular features of worm sleep, but the work does not go far enough to prove quiescence. Are the animals paralyzed? Does SWT have features of sleep homeostasis? I do not think the authors need to prove every feature exhaustively, but at a minimum, should demonstrate that it is a reversible state. Moreover, the authors convert midway through the work to calling this slow wave twitch (SWT). These are all words that are likely chosen specifically to evoke a sense of "sleeping" from readers, but the behavior does not really seem like twitching, and are these really slow waves?

2. For the high throughput portion, the authors find some mutants that disrupt SWT. they should also test to see whether earlier embryonic behaviors are affected (as was tested with unc13), as this would very much alter the interpretation

3. The Discussion really overreaches. There is a heavy focus on sleep and autism, despite no clear evidence that SWT is sleep. I certainly agree discussions can be speculative, but the tone here seems to make claims that are absolutely not supported by the data. I would suggest ending the manuscript with "Together, these similarities suggest that SWT may be akin to the developmentally timed sleep associated with each larval molt" which underscores to readers that the data really ends short of showing SWT is indeed sleep.

4. The manuscript feels disjointed as a whole in some respects, as the authors put huge effort into the methodology of Figures 1-4, and then completely shift approaches. Perhaps they can reframe the work to better emphasize how MHHT led to an important biological discovery, and then better justify why moving to a new system was necessary. Also important - the manuscript portion describing Figs 1-4 is so technical that most readers will not be able to follow. Perhaps there are ways to better hand hold for a broad audience.

5. Fig 6g attempts to show that the correlation between RIS calcium transients and motion is reduced in FLP-11 mutants. While this reduction is evident, it still seems like a very strong correlation, undercutting the idea that FLP-11 is required for SWT, as it is for sleep. This further calls into question whether SWT is the same at lethargus.

Reviewer #3 (Public Review):

Argenty et al. investigated the role of Lissencephaly gene 1 (LIS1), a dynein-binding protein, in thymic development and T cell proliferation. They find that LIS1 is essential for the early stages of T and B cell development, and demonstrate that loss of LIS1 has a negative impact on the transition from DN3 to DN4 thymocytes and on the maturation of pre-pro-B cells into pro-B cells in the bone marrow. Using a CD2Cre Lis1fl/fl murine model, they observe that in thymocytes LIS1 is critical for DN3 proliferation and completion of cell division. Then, using a CD4Cre Lisfl/fl model (Cd4 promoter is up-regulated just in later stages of thymic development and, thus, does not impact DN3 thymocytes) they show that LIS1-deficient CD4 T cells have proliferation defects following both TCR-dependent or -independent stimulation, which results in apoptosis. They also confirm previous reports that show that LIS1-deficient CD8 T cells do not have their proliferation impaired upon TCR stimulation, which suggests that these two cell types rely on different mechanisms to regulate the cell cycle. Finally, the authors make efforts to determine how LIS1 regulates proliferation in thymocytes and CD4 T cells. Interestingly, they show that LIS1 is important for chromosome alignment and centrosome integrity and provide data that support a model where LIS1 would facilitate the assembly of active dynein-dynactin complexes. These data provide interesting insights into how different cell types use distinct strategies to undergo mitosis and how this can impact on their proliferation and fate decisions. The conclusions of the manuscript are mostly supported by the provided data, although certain aspects can be further investigated and clarified.

Strengths of the paper:

By combining a re-assessment of previous reports with new findings, the data from this manuscript convincingly demonstrates that LIS1 is crucial for cell proliferation in certain development steps/cell types. Furthermore, the manuscript provides clear evidence of how LIS1 loss causes proliferation defects by disrupting centrosome integrity and chromosome alignment both in CD4+ T cells and thymocytes.

Weakness of the paper:

Although authors successfully address the mechanistic role of LIS in thymocyte and CD4+ T cell division, the manuscript would be strengthened by both providing further evidence to support some of their conclusions and a review of some speculations raised in the discussion.

In Figure 1, the authors claim that LIS1 is not required for pre-TCR assembly, but for expansion/proliferation of DN3 thymocytes as a step prior to reaching the DN4 stage. However, authors indeed observe increased expression of CD5 (which is a downstream event of Notch and IL-7R signalling). Thus, from the data provided it is not clear whether signalling through Notch or IL-7R is definitely not affected, which could be clarified by assessing the expression of other downstream targets of these molecules.

In Figure 3, the authors mostly confirm previous data from Ngoi, Lopez, Chang, Journal of Immunology, 2016 (reference 34), but also provide evidence of a role of LIS1 in CD4+ T cell proliferation in more physiological setups, using OT2-CD4-Cre Lis1flox/flox (or OT2 Lisflox/flox as controls) in adoptive transfer experiments followed by antigen-specific immunization. However, the evidence provided by the authors about proliferation defects in LIS1-deficient cells in this context is limited by the early timepoint chosen: day 3 post-immunization.

In the discussion, the authors speculate about the differences observed between CD4 and CD8 T cells, as the latter do now show proliferative defects upon TCR-triggered stimulation, and come up with the hypothesis that LIS1 might be important for symmetric cell divisions, but not for asymmetric cell divisions. However, the arguments used by the authors have few caveats, especially because CD4+ T cells can also undergo asymmetric cell division following TCR-triggered stimulation upon the first cognate antigen encounter (Chat et al., Science, 2007, Ref. 8).

Finally, the authors discuss that mono-allelic LIS1 defects might contribute to malignancies. Certainly not all points raised in the discussion need to be experimentally addressed, but for this particular hypothesis the authors would likely have the tools to achieve that, which would broaden the relevance of understanding LIS1 function.

Reviewer #3 (Public Review):

This study of U1 snRNP interaction with the 5'ss is an interesting and exciting piece of work. In particular, the data support two important conclusions of general importance to the field: 1) the association of the U1 snRNP with the 5'ss is largely determined by the snRNP itself and does not require other splicing factors and 2) the ability to form "productive" (i.e. long-lived) interactions between the U1 snRNP and the 5'ss cannot be accurately predicted by base-pairing potential alone. This second point is particularly important as many algorithms for predicting splicing efficiency are based on base-pairing strength between the U1 snRNA and the 5'ss sequence. The data immediately suggest two additional questions.

1. The authors repeatedly speculate that the benefit of basepairing toward the 3' end is due to the activity of Yhc1. If this model is true, these 3' end basepairs should not influence binding for a U1 snRNP with a mutant Yhc1. Since the authors have used mutant Yhc1 in other studies it seems possible to test this prediction.

2. Since splice sites are often "found" in the context of alternative or pseudo/near-cognate splice sites, it would be interesting to know how the "rules" identified in the experiments presented in this study influence splice site competition and whether both the short- and long-lived states are subject to competition or, rather, only the short-lived complexes. Is it possible to repeat the CoSMoS experiment with two oligomer sequences of different colors?

3. Finally, the authors should say more about the particular requirement for basepairing at position 6, especially in the context of the experiments in Figure 5. This is particularly striking as this position is not well conserved in natural 5'ss, at least compared to position 5.

Reviewer #3 (Public Review):

By use of in vivo fluorescence imaging and image analysis tools, Blanc et al. have established an automatic pipeline to build a digital 3D-temporal atlas of zebrafish hindbrain. Based on the common fluorescence labelling with HuCD the authors first established a pipeline and a reference atlas of the hindbrain. The pipeline is based on the already established tools in Fiji for registration of multi-modal data, such as Fijiyama plugin, and automatic segmentation of the data, in particular Weka 3D segmentation. By use of this pipeline, the authors then mapped rhombomeres markers Mu4127, precursor cell populations by nestin, Neural basic helix-loop-helix (bHLH) transcription factor neurog1 expressed in proliferating cells, motoneurons by isl1, and glutamatergic and GABAergic neurons via vglut2 and gad1b correspondingly. All these cell populations were mapped precisely from 24 to 72 hpf of zebrafish brain development. By comparison of fluorescent marker expression in a temporal manner, the authors demonstrate that one can approximate the birthdate of cells for which reporter expression is delayed and becomes present only later.

Strengths:<br /> Free and easy access to Fiji plugins used and developed in this work makes the building of digital 3D atlases accessible for many labs, potentially also in other settings. The analysis of marker expressions in space, that is anterior-posterior and mediolateral is simple (without the need for high computational power or specialized and expensive software) and at the same time biologically relevant.

Weaknesses:<br /> Due to the use of fluorescence imaging, the pipeline is limited to easily accessible and rather transparent tissues. Additionally need for one channel as a common reference is time and labour extensive in terms of experimental work. In terms of the 3D digital atlas maker, the use of user supervised training limits the "easiness" and widespread use of the pipeline in the future.

Reviewer #3 (Public Review):

The manuscript by Wang et al. investigates the role of actin and an associated capping protein in cytoadherence and motility of T. vaginalis and represents a substantial amount of work. The authors first demonstrate the adherent lines and clinical isolates express high levels of actin than non-adherent lines, and that a higher percentage of actin is found in the filamentous form in these isolates. FACP was subsequently identified as an actin-binding protein in immunoprecipitation experiments. Overexpression of FACP-WT, but not overexpression of FACP lacking a putative actin-binding domain, resulted in a decreased amount of F-actin in cells, suggesting a role for FACP in limiting actin polymerization by presumably capping the barbed (+) end of filaments. Phosphorylation of FACP at serine 2, mitigates this effect demonstrating that phosphorylation is important for the actin-binding ability of FACP. Phosphorylation also leads to lower adherence to epithelial cells.

However, a major conclusion of this paper, namely that FACP acts via a novel mechanism and binds both G and F-actin, is not supported by the data. This conclusion is based on experiments with recombinant TvActin expressed in bacteria and co-immunoprecipitation of FACP with actin. The execution of these experiments is problematic for a number of reasons:

1) The authors state in the methods that the majority of GST-actin is found in inclusion bodies in E. coli. The protein was solubilized in 8M urea, which will denature the protein and the authors then attempted to refold actin by dialysis in G-buffer. F-actin buffer was then added to induce polymerization. The authors provide no evidence that actin folds correctly upon renaturation with G-buffer. It is quite possible that the proteins that pellet upon the addition of the F-buffer are not filaments but insoluble aggregates. I say this because (1) the assay is done at 80 picomoles, which is well below the critical concentration for most actins (typically the Cc is ~0.1-0.5uM range), and (2) the authors provide no evidence by EM or light microscopy to demonstrate that actin filaments are formed under these conditions. Inclusion of these controls in the manuscript is critical to the interpretation of all experiments which utilized the recombinant actin, including the elisa-based assay which is offered as evidence for an interaction with G-actin.

2) In a number of experiments, the authors performed His-tagged immunoprecipitation of FACP to identify interacting proteins. Actin is found to co-IP with FACP, however, it is not clear if the immunoprecipitated actin represents an interaction with FACP with the F or G isoform. The interpretation of this data is critical for the conclusions of the paper, where the authors argue that FACP has an "atypical" mode of action (title) and the authors' conclusion (line 608) that FACP binds directly to G or F-actin.

Reviewer #3 (Public Review):

This paper examines the relative performance of linear mixed models (LMMs), principal components (PCA), and their combination (PCA-LMM) for genetic association studies in human populations. The authors claim that previous papers examining this question are inadequate and that: (i) there remains confusion on which method is best and in which context, (ii) that the metrics used in previous evaluations were insufficient, and (iii) that the simulation settings used in previous papers were not comprehensive. To fix these problems the authors perform an extensive set of simulations within several frameworks and suggest two new metrics for evaluating performance.

Strengths:

The simulation framework used in this paper and the extensive number of simulations provide an opportunity to examine the relative properties of the three approaches (LMM, PCA, PCA-LMM) in a variety of contexts.

The parameters of the simulation framework are based on highly diverged populations, which is an increasingly common analysis choice that has not been examined in detail via simulation previously.

The evaluation metrics used in this paper are AUC and a test of the uniformity of the p-value distribution under the null. This is an improvement over some previous analyses which did not examine power and relied on less sensitive tests of type I error.

Weaknesses:

This paper has a limited set of population frameworks just like all papers before it. The breakdown of which method is best (LMM, PCA, PCA-LMM) will be a function of the simulation framework chosen.

The frameworks chosen for this paper are certainly not comprehensive in contemporary human genetic studies. In fact, the authors make a number of unusual choices. For example, the populations in the simulated study have extremely large Fsts. While this is also a strength, the lack of more standard study designs is a weakness. More importantly, there is no simulation of family effects, which is the basis of many of the PCA-LMM papers reported in Table 1.

The discussion (and simulations) of LMM vs PCA, particularly LMMs with PCs as fixed effects misses the critical distinction of whether PCs are in-sample (in which case including PCs as fixed effects effectively serves as a preconditioner for the kinship matrix, speeding up iterative methods such as BOLT), or projections of individuals onto out-of-sample principal axes. There is also no discussion of LOO methods to address "proximal contamination", also quite relevant in evaluating power as a function of the number of PCs.

There is no discussion/simulation of spatial/environmental effects or rare vs common PCs as raised in Zaidi et al 2020. There are some open questions here regarding relative performance the authors could have looked at. Same for LMMs with multiple GRMs corresponding to maf/ld bins and thresholded GRMs. For example, it would be helpful to know if multiple-GRM LMMs mitigate some of the problems raised in the Zaidi paper.

Reviewer #3 (Public Review):

To investigate the action of Ism1 and reveal the difference from insulin, the authors performed a non-biased phosphorylation proteome analysis of pre-adipocytes (3T3-F442A cells). They found that Ism1-induced signaling pathways are related to unexpected GO terms, including "protein anabolism" and "muscle." Furthermore, Ism1 enhanced Akt-mediated protein synthesis in C2C2 myotubes, and Ism1 KO mice showed weakness and decreased muscle size. Based on these data, the authors claimed that Ism1 is a novel factor in governing muscle hypertrophy and atrophy via protein synthesis.

The new role of Ism1 in protein synthesis discovered using non-biased exhaustive analysis is a unique finding. However, they analyzed the phosphorylation cascade of Ism1 only in 3T3-F442A cells and did not compare the difference between Ism1 and the insulin signal in skeletal muscle cells. In Fig.3C, the actions of Ism1 and Igf1 are compared in C2C12 myotubes, but it is unclear whether these pathways are different. The authors did not analyze whether the protein synthesis action of Ism1 belongs to the same pathway as insulin or IGF1 or to a different pathway in skeletal muscle cells.

As the author states in the Discussion, it is important to clarify which phase of the skeletal muscle regeneration process Ism1 influences. Single-cell RNAseq cannot analyze skeletal muscle fibers, which are large, multinucleated, terminally differentiated cells. Therefore, it is unclear whether Ism1 acts on satellite cells, myoblasts, myotube cells, or skeletal muscle fibers.

Reviewer #3 (Public Review):

The authors have performed a transcriptional analysis of young/aged hematopoietic stem/progenitor cells which were obtained from normal individuals and those with MDS.

The authors generated an important and valuable dataset that will be of considerable benefit to the field. However, the data appear to be over-interpreted at times (for example, GSEA analysis does not have "functionality", as the authors claim). On the other hand, a comparison between normal-aged HSC and HSC from MDS patients appears to be under-explored in trying to understand how this disease (which is more common in the elderly) disrupts HSC function.

A more extensive cross-referencing of other normal HSPC/MDS HSCP datasets from aged humans would have been helpful to highlight the usefulness of the analytical tools that the authors have generated.

Major points

1. The authors detail methodology for identification of cell types from single-cell data - GLMnet. This portion of the text needs to be clarified as it is not immediately clear what it is or how it's being used. It also needs to be explained by what metric the classifier "performed better among progenitor cell types" and why this apparent advantage was sufficient to use it for the subsequent analysis. This is critical since interpretation of the data that follows depends on the validation of GLMnet as a reliable tool.

2. The finding of an increased number of erythroid progenitors and decreased number of myeloid cells in aged HPSC is surprising since aging is known to be associated with anemia and myeloid bias. Given that the initial validation of GLMnet is insufficiently described, this result raises concerns about the method. Along the same lines, the authors report that their tool detects a reduced frequency of monocyte progenitors. How does this finding correlate with the published data on aging humans? Is monocytopenia a feature of normal aging?

3. The use of terminology requires more clarity in order to better understand what kind of comparison has been performed, i.e. whether global transcriptional profiles are being compared, or those of specific subset populations. Also, the young/aged comparisons are often unclear, i.e. it's not evident whether the authors are referring to genes upregulated in aged HSC and downregulated in young HSC or vice versa. A more consistent data description would make the paper much easier to read.

4. The link between aging and MDS is not explored but could be an informative use of the data that the authors have generated. For example, anemia is a feature of both aging and MDS whereas neutropenia and thrombocytopenia only occur in MDS. Are there any specific pathways governing myeloid/platelet development that are only affected in MDS?

5. MDS is a very heterogeneous disorder and while the authors did specify that they were using samples from MDS with multilineage dysplasia, more clinical details (blood counts, cytogenetics, mutational status) are needed to be able to interpret the data.

Reviewer #3 (Public Review):

The study uses a mouse animal model of sensorineural hearing loss after sound overexposure at high frequencies that mimics ageing sensorineural hearing loss in humans. Those mice present behavioural hypersensitivity to mid-frequency tones stimuli that can be recreated with optogenetic stimulation of thalamocortical terminals in the auditory cortex. Calcium chronic imaging in pyramidal neurons in layers 2-3 of the auditory cortex shows reorganization of the tonotopic maps and changes in sound intensity coding in line with the loudness hypersensitivity showed behaviourally. After an initial state of neural diffuse hyperactivity and high correlation between cells in the auditory cortex, changes concentrate in the deafferented high-frequency edge by day 3, especially when using mid-frequency tones as sound stimuli. Those neurons can show homeostatic gain control or non-homeostatic excess gain depending on their previous baseline spontaneous activity, suggesting a specific set of cortical neurons prompt to develop hyperactivity following acoustic trauma.

This study is excellent in the combination of techniques, especially behaviour and calcium chronic imaging. Neural hyperactivity, increase in synchrony, and reorganization of the tonotopic maps in the auditory cortex following peripheral insult in the cochlea has been shown in seminal papers by Jos Eggermont or Dexter Irvine among others, although intensity level changes are a new addition. More importantly, the authors show data that suggest a close association between loudness hypersensitivity perception and an excess of cortical gain after cochlear sensorineural damage, which is the main message of the study.

The problem is that not all the high-frequency sensorineural hearing loss in humans present hyperacusis and/or tinnitus as co-morbidities, in the same manner that not all animal models of sensorineural hearing loss present combined tinnitus and/or hyperacusis. In fact, among different studies on the topic, there is a consensus that about 2/3rds or 70% of animals with hearing loss develop tinnitus too, but not all of them. A similar scenario may happen with hearing loss and hyperacusis. Therefore, we need to ask whether all the animals in this study develop hyperacusis and tinnitus with the hearing loss or not, and if not, what are the differences in the neural activity between the cases that presented only hearing loss and the cases that presented hearing loss and hyperacusis and/or tinnitus. It could be possible that the proportion of cells showing non-homeostatic excess gain were higher in those cases where tinnitus and hyperacusis were combined with hearing loss.

Reviewer #3 (Public Review):

Fernandez et al. report results from a multi-day fMRI experiment in which participants learned to locate fractal stimuli along three oval-shaped tracks. The results suggest the concurrent emergence of a local, differentiated within-track representation and a global, integrated cross-track representation. More specifically, the authors report decreases in pattern similarity for stimuli encountered on the same track in the entorhinal cortex and hippocampus relative to a pre-task baseline scan. Intriguingly, following navigation on the individual tracks, but prior to global navigation requiring track-switching, pattern similarity in the hippocampus correlated with link distances between landmark stimuli. This effect was only observed in participants who navigated less efficiently in the global navigation task and was absent after global navigation.

Overall, the study is of high quality in my view and addresses relevant questions regarding the differentiation and integration of memories and the formation of so-called cognitive maps. The results reported by the authors are interesting and are based upon a well-designed experiment and thorough data analysis using appropriate techniques. A more detailed assessment of strengths and weaknesses can be found below.

Strengths

1. The authors address an interesting question at the intersection of memory differentiation and integration. The study is further relevant for researchers interested in the question of how we form cognitive maps of space.

2. The study is well-designed. In particular, the pre-learning baseline scan and the random-order presentation of stimuli during MR scanning allow the authors to track the emergence of representations in a well-controlled fashion. Further, the authors include an adequate control region and report direct comparisons of their effects against the patterns observed in this control region.

3. The manuscript is well-written. The introduction provides a good overview of the research field and the discussion does a good job of summarizing the findings of the present study and positioning them in the literature.

Weaknesses

1. Despite these distinct strengths, the present study also has some weaknesses. On the behavioral level, I am wondering about the use of path inefficiency as a metric for global navigation performance. Because it is quantified based on the local response, it conflates the contributions of local and global errors.

2. For the distance-based analysis in the hippocampus, the authors choose to only analyze landmark images and do not include fractal stimuli. There seems to be little reason to expect that distances between the fractal stimuli, on which the memory task was based, would be represented differently relative to distances between the landmarks.

3. Related to the aforementioned analysis, I am wondering why the authors chose the link distance between landmarks as their distance metric for the analysis and why they limit their analysis to pairs of stimuli with distance 1 or 2 and do not include pairs separated by the highest possible distance (3).

4. Surprisingly, the authors report that across-track distances can be observed in the hippocampus after local navigation, but that this effect cannot be detected after global, cross-track navigation. Relatedly, the cross-track distance effect was detected only in the half of participants that performed relatively badly in the cross-track navigation task. In the results and discussion, the authors suggest that the effect of cross-track distances cannot be detected because participants formed a "more fully integrated global map". I do not find this a convincing explanation for why the effect the authors are testing would be absent after global navigation and for why the effect was only present in those participants who navigated less efficiently.

5. The authors report differences in the hippocampal representational similarity between participants who navigated along inefficient vs. efficient paths. These are based on a median split of the sample, resulting in a comparison of groups including 11 and 10 individuals, respectively. The median split (see e.g. MacCallum et al., Psychological Methods, 2002) and the low sample size mandate cautionary interpretation of the resulting findings about interindividual differences.

Reviewer #3 (Public Review):

The manuscript of Birckman and colleagues tackles the link between lineage priming, lineage specification, and cell cycle in the ESCs culture. This is an interesting piece of work, with several noteworthy findings, that elegantly explain how lineage priming can be efficiently achieved during the changing cultural conditions. There are several interesting points raised by the authors, relating to lineage priming, cell specification, and cell cycle, that can be presented to the scientific community. Namely:

• Differential regulation of the cell cycle can tip the balance between populations of cells primed to different cell fate choices (here PrE and Epi).

• Different culture conditions favour acceleration/stimulation of the cell cycle of different cell populations.

• Only a small population of cells from the original culture enters a differentiation process which is followed by selected expansion and/or survival of their progeny.

• In the case of endodermal type specification (towards PrE), a shortening of the cell cycle is accompanied by the proportional relative increase of G1 phase length.

• FGF activity is responsible for cell cycle synchronisation, required for the inheritance of similar cell cycles between sisters and cousins

Unfortunately, in the current version of the manuscript, the authors try to create the impression that the relationship between cell cycle, heterogeneity and cell fate found in ESCs can be directly translated to the in vivo system. It is not clear, however, how easily and reliably the information about the cell cycle in ESCs can be translated to an in vivo setting. The timeline of PrE vs Epi specification in vivo and in vitro are completely different. In embryos, PrE is specified within 24h, whereas with in vitro it takes 6 days. I cannot see how these two timelines - and also different cell cycle lengths - can be reliably compared.

Reviewer #3 (Public Review):

Mating changes behavior of female fruit flies. Authors previously reported that putrescine-rich foods increase number of progenies per mated female and mated females detect putrescine with IR76b and IR41a and are attracted to putrescine odor (Hussain, Zhang et al., 2016). In another paper, authors reported that this change of putrescine preference is mediated by sex peptide receptor (SPR) and its ligand, myoinhibiotry peptides (MIPs; Hussain, Ucpunar et al., 2016). In yet another paper, authors reported that two types of dopaminergic neurons (DANs) which innervate alpha prime 3 (a'3) or beta prime 1 (b'1) compartment of the mushroom body (MB) show enhanced response to cVA, the male sex pheromone 11-cis-Vaccenyl acetate (Siju et al., 2020). The present study investigated neural circuits that potentially link these observations.

The authors first showed that putrescine-attraction in mated females is sustained over 7-days, which cannot be explained by SPR-MIP dependent mechanism that disappears in one week. Then they explored a factor that is transferred from males during copulation and required for putrescine-attraction in mated females. They found that blocking synaptic transmission of cVA-sensitive OR67d olfactory receptor neurons during 24 hour period of pairing with males reduces putrescine-attraction 3-5 days later (Figure 1). On the other hand, experiments with mutant flies lacking ability to generate eggs or sperms indicated that fertilization is not essential for the change in odor preference. In a proposed scenario, cVA transferred to the female during copulation activates DANs projecting to the b'1 and that in turn induces a shift in how the MB regulates the expression of polyamine odor preference, possibly by alternating activity of MB output neurons (MBONs) in the beta prime 2 (b'2) compartment.

Some data are in line with this scenario. Blocking synaptic transmissions of Kenyon cells during mating or odor preference test reduced attraction to putrescine (Figure 2). Activation of dopaminergic neurons projecting to the beta prime 1, gamma 3 and gamma 4 in virgin females promoted attraction to putrescine when tested 3-5 days later (Figure 3). Flies expressing shibire ts1 in the MBONs in the b'1 compartment showed reduced putrescine preference when females were mated at restrictive temperature (Figure 4). Using calcium imaging and EM connectome, authors also found candidate lateral horn output neurons that may mediate putrescine signals from olfactory projection neurons to the b'1 DANs.

This study utilized molecular genetic tools, behavioral experiments and calcium imaging to comprehensively investigate neural circuits from sensory neurons for cVA or putrescine to the learning circuits of the MB. Addressing points detailed below will strengthen a causal link between enhanced cVA response in beta prime 1 DANs and enhanced putrescine preference in mated females.

1) The MB is the center for olfactory associative learning. It is not so surprising that 24-hour long activation of any MB cell types have long-term consequence on fly's odor preference. As authors showed in Hussain et al., 2016 and Figure S1, mated females change preference to polyamines but not ammonium. Therefore, it is important to show odor specificity of the circuit manipulations to claim that phenomenon in mated females are recapitulated by each manipulation. Wang et al., 2003 (DOI:https://doi.org/10.1016/j.cub.2003.10.003) reported that blocking a broad set of Kenyon cells impairs innate odor attraction to fruit odors and diluted odors but not repulsion.

2) Requirement of PAM-b'1 DANs for putrescine-attraction in mated females should be demonstrated. The authors suggested existence of alternative mechanisms that may mask requirement of PAM-b'1 (Figure 3B). In a previous study, the authors reported SPR-dependent mechanism. I suggest testing the requirement of PAM-b'1 DANs in SPR mutant background or one-week after mating when SPR-dependent effect on sensory neurons disappear.

3) Activation phenotype of MB188B-split-GAL4/UAS-dTrpA1 cannot be ascribed to activation of PMA-b'1 alone because of additional expression in DANs projecting to gamam3 and gamma4 compartments. Run the same experiment with more PMA-b'1 specific driver line.

4) Some of EM connections are too low to be considered (e.g. two in Figure S3 and five in Figure 5). Although these connections could be functional, previous EM connectome analysis typically set much higher threshold (e.g. 10 in Hulse et al., 2021 DOI: 10.7554/eLife.66039) to avoid considering artifacts.

5) Data for Kenyon cells (Figure 2) and LHON (Figure 6) are interesting, but not directly related to other data regarding PAM-b'1 and MBON-b'1. Due to lack of long-term changes in MBOB's odor responses in mated females (Figure 5), it is unclear what information needs to be read out from Kenyon cells and how does it affect processing of putrescine signals potentially carried by LHAD1b2.

Reviewer #3 (Public Review):

The goal of this work is to understand the role that previously neglected, unannotated ORFs play in the evolution of gene novelty in the Drosophila melanogaster lineage. These are ORFs that mostly code for small proteins, most of them having noncanonical start codons. The authors sought to identify translated ORFs using published MS proteomics datasets, making sure to achieve a balance between false positives and false negatives; they succeed rather convincingly. They then focused on when these ORFs first appeared and how they evolved, mainly aiming to understand whether some of them have emerged de novo and the evolutionary trajectories that they have taken.

The major strengths of the manuscript lie in its scope, as it takes advantage of recently published data to exhaustively search the entire ORF catalogue of D. melanogaster for translation, in the application of rigorous methodologies for the identification of MS-supported ORFs and in the inference of the phylogenetic age of the ORF using a novel synteny-based approach. About this last point, however, I feel that some methodological details are missing. I understand that the genomic MSA of the D. melanogaster ORF and its orthologous region is extracted and that a search for the optimally aligning segment in the sequence of each species is conducted. Does that search include only ORFs in each orthologous region? I assume this is the case because the similarity cut-off of 2.5 is then calculated from protein alignments. If that is the case, why not use global alignments of entire ORFs? Furthermore, why is there no gap penalty used? Finally, I cannot see where the genomic similarity scoring part detailed in the methods is used, which adds to my confusion.

Albeit not a major one, an additional weakness comes from the use of Latent Class Analysis to identify subpopulations of ORFs within the greater set, and examine their differences. I see why the authors did it and in theory, I have no objection, but given the small number of factors (8 if I'm counting correctly), it's unclear if it's worth the added level of complexity. Plus there's some potential bias involved since it requires binning continuous variables and hence defining bins. It seems to me that the authors could have achieved more or less the same by looking for specific subgroups based on criteria that they set themselves a priori.

A crucial part of the work is the attribution of de novo origin to utORFs. Here, I find the initial analysis, wherein a single outgroup species is sufficient to invoke de novo origination, relatively unnecessary. Especially since the authors go on to state themselves that only two or more supporting outgroups can provide convincing evidence. I would add that at least two of the outgroups should be non-monophyletic. It is also unclear why an ORF needs to be present in the outgroups at all (and lacking significant similarity). Is there a limit to how small that ORF can be? If so, and if there happens to be no such ORF in a region, why would that not count as evidence?

I feel that the authors achieve most of their aims, at least the ones that I perceive as the most important.<br /> There are however some findings that are not sufficiently well supported.

Reviewer #3 (Public Review):

The paper describes an ingenious and painstakingly reported method of evaluating the informativeness of clinical trials. The authors have checked all the marks of robust, well-designed and transparently reported research: the study is registered, deviations from the protocol are clearly laid out, the method is reported with transparency and all the necessary details, code and data are shared, independent raters were used etc. The result is a methodology of assessing informativeness of clinical trials, which I look forward to use in my own content area.

My only reserve, which I submit more for discussion than for other changes, is the reliance on clinicaltrials.gov. Sadly, and despite tremendous efforts from the developers of clinicaltrials.gov (one of the founders is an author of this paper and I am well-aware of her unrelenting work to improve reporting of information on clinicaltrials.gov), this remains a resource where many trials are registered and reported in a patchy, incomplete or downright superficial and sloppy manner. For outcome reporting, the authors compensate this limitation by searching for and subsequently checking primary publications. However, for the feasibility surrogate this could be a problem. Also, for risk of bias, for the trials the authors had to rate themselves (i.e., ratings were not available in a high-quality systematic review), what did the authors use, the publication or the record from the trial registry?

In general, it seems like a problem for this sophisticated methodology might be the scarcity of publicly available information that is necessary to rate the proposed surrogates. Though the amount of work involved is already tremendous, the validity of the methodology would be improved by extracting information from a larger and more diverse pool of sources of information (e.g., protocols, regulatory documents, sponsor documents).

In that sense, maybe it would be interesting for the authors to comment on how their methodology would be improved by having access to clinical trial protocols and statistical analysis plans. Of course, one would also need to know what was prospective and what was changed in those protocols, i.e., having protocols and statistical analysis plans prospectively registered and publicly available. Having access to these documents would open interesting possibilities to assessing changes in primary outcomes, though as the authors say that evaluation would also require making a judgement as to whether the change was justified. Relatedly, perhaps registered reports could be a potential candidate for clinical trials that would also support a more accurate assessment of informativeness, per the authors' method, provided the protocol is made openly available.

Still related to protocols, were FDA documents consulted for pivotal trials, which again could give an indication of the protocol approved by the FDA and subsequent changes to it?

Reviewer #3 (Public Review):

Early life trauma is a risk factor for adult aberrant aggressive behavior but this important public health issue remains under examined in the neurosciences. This study seeks to fill the gap with a mouse model of adolescent trauma that involves a combination of fearful and anxiety-provoking experiences and assessment on gene expression in brain region controlling aggression, the hypothalamus, and another controlling executive function, the prefrontal cortex. Mice are categorized for aggressive phenotype as being extreme or moderate, with the extreme being compared to controls for transcriptomic analyses of the hypothalamus and PFC. Females did not show increased adult aggression in the resident-intruder paradigm following adolescent fear and anxiety. Pathway analysis implicated the thyroid hormone pathway in male hypothalamus with the thyroid receptor, Ttr, being the top candidate gene. This formed the basis of an in depth analyses of thyroid hormone pathway and discovery of reduced T3 following adolescent stress which was causally linked to adult aggression. This is a novel observation with potentially important implications.

The strengths of the study are the detailed behavioral analyses, inclusion of both sexes and down regulation of Ttr specifically in hypothalamus, reducing T3 and increasing aggression. The weaknesses are a lack of mechanistic explanations for how reduced T3 and T4 leads to pathological aggression in males, weakly supported claims of transgenerational inheritance, lack of consideration of other pathways and no explanation for the profound sex difference.

Specific Comments

1) The KEGG analyses does implicate the thyroid hormone pathway but the more consistent changes seem to be in drug addiction pathways and estrogen signaling, leaving one to wonder if the emphasis on the TH pathway is truly warranted.

2) Aggression in females under normal circumstances is not evoked by a male intruder unless the female has a litter. Thus, it is not that surprising that the peripubertal stress did not evoke aggression in virgin females. Rather, the more interesting question is whether maternal aggression would become aberrant after peripubertal stress.

3) Regarding the trans-generational transmission of the PPS, since the germ cells were present in the animals that were subject to PPS and gave rise to the offspring that were then tested, this is not truly transgenerational as the germ cells were residing in the stressed body. The transmission needs to be to at least the F2 generation with no stress in the F1 for this to be considered transgenerational.

4) Regarding the methylation status of the Ttr, confidence in this result requires consideration of other targets as well in order to understand whether the epigenetic modifications are specific to just Ttr or are more widespread.

5) The statistical analysis rests on unpaired t-tests but in most experiments a 2-way ANOVA is warranted with treatment and brain region as factors.

6) The word "trauma" in the context used here connotes an emotional interpretation of stressful or fearful events. We do not know if the mice are experiencing trauma, instead we know they are being subject to fearful and stress-inducing experiences. It is suggested that the word trauma be removed throughout and replaced with more precise terminology.

Reviewer #3 (Public Review):

This is a very interesting and impressive manuscript. It is complex in its multiple components, and in some ways that makes it a difficult manuscript to evaluate. There is a lot in it, including empirical analyses of a face dataset and of behavioral association data, combined with a theoretical model.

The three main findings are: 1) Paternal siblings look alike (similar to, and building on, a recent manuscript the authors published elsewhere); 2) Infants that are more facially similar tend to associate; and 3) mothers tend to be found in association with other unrelated infants that look more like their own infants. Such results are interesting, and indeed one potential interpretation, perhaps even the most likely, is that mothers are behaving in such a way that promotes association between their own infants and the paternal kin of their infants.

Nonetheless, the evidence provided is logically only consistent with the authors' hypothesis, rather than being strong direct evidence for it. As such, the current framing and indeed the title, "Primate mothers promote proximity between their offspring and infants who look like them", are both problematic. (In addition, the title should be about mandrills, not "primates", since this manuscript does not provide evidence from any other species.) The evidence provided is consistent with the hypothesis, but also consistent with other potential hypotheses. The evidence given to dismiss other potential hypotheses is not strong, and rests on the fact that many males are not around all year to influence things, and that "males that were present during a given reproductive cycle are not responsible for maintaining proximity with either infants or their mothers (MJEC and BRT, pers. obs.)".

My opinion is that these are really interesting analyses and data, which are being somewhat undermined by the insistence that only one hypothesis can explain the observed association patterns. It could easily be presented differently, as a demonstration that paternal siblings look alike and that they associate. The authors could then go on to explore different possible explanations for this using their association data, make the case that maternal behavior is the most plausible (but not the only) explanation, and present their model of how such behavior could bring fitness benefits.

In my view, such a presentation would be both more cautious and more appropriate, without in any way reducing the impact or importance of the data. In the current iteration, I think there are issues because the data do not provide sufficient support for the surety of the title and conclusion, as presented.

Reviewer #3 (Public Review):

The authors critically assessed a widespread assumption that paternal biases in the number of germline mutations passed to offspring and the number of germline cell divisions have a causal link. They gather a diverse set of previously published findings that are inconsistent with this assumption, including the accumulation of maternal DNMs with age, the consistent ratio of paternal-to-maternal germline mutation (α) in humans, the range of α in mammals, and the dominance of mutational processes that are uncorrelated to cell division in human germline and somatic tissues. They then generate estimates of α based on evolutionary rates at sex chromosomes vs autosomes. They find αevo of 1-4 across the species considered, which are robust to changes/exclusion of a number of potentially confounding factors. They find an increase in αevo with generation time in mammals but not in birds. The authors consider and evaluate a model with a fixed number of early mutations for both sexes followed by post sexual differentiation stage with a paternal mutation bias.

Reviewer #3 (Public Review):

In this manuscript, Baumgartner et al investigated how cells control Rhino specific deposition on only a subset of the H3K9me3 chromatin domains to specify piRNA source loci. They identified a previously unknown protein, Kipferl, which by interacting with the chromodomain of Rhino guides and stabilizes its specific recruitment to selected piRNA source loci. Kipferl would be preferentially recruited to Guanine-rich DNA motifs. They show that in Kipferl mutant flies, Rhino nuclear subcellular localization and Rhino's chromatin occupancy changes dramatically. Then, they dissect all the domains of the Kipferl protein and show that the Rhino- and DNA-binding activities can be separated and that the 4th ZnF of Kipferl is required to interact with Rhino.

It is a very elegant genetic work (CRISPR-edited, rescue, KD, overexpression fly lines). In addition, the authors used a combination of yeast two hybrid screen, ChIP, small-RNA-seq and imaging to dissect the function of this new protein. The data in this paper are compelling. Some conclusions might be more moderate. Even if the effect of Kipfler on 80F (Rhino binding, piRNA production) is very obvious, this study also clearly demonstrates that other protagonists are required for the specific binding of Rhino to other piRNA source loci (including 42AB and 38C).

- Is Kipferl expressed early during oogenesis development? If Kipferl starts to be expressed only after the GSCs and cystoblast stage, Kipferl is probably not required to determine the specification of piRNA source loci identity but probably more for the maintenance of the specification. Could the authors discuss or comment on that?

- To perform most of their ChIP-seq analysis, the authors have divided the genome into pericentromeric heterochromatin and euchromatin based on H3K9me3 ChIP-seq data performed on ovaries. With this classification the 42AB (2R:6,256,844-6,499,214) and the 38C (2L:20148259-20227581) piRNA clusters known to be heterochromatic fall in the euchromatic part of the genome. Was there a problem with the annotation?

- Some regions exist in euchromatin that are strongly enriched in Rhino, in Kipferl and in H3K9me3 but are not producing piRNA. Does this type of region exist in heterochromatin?

- Kipferl has been identified to interact with Rhino by a yeast two-hybrid screen (Figure 2). A co-IP which is the classical method for confirming the occurrence of this intracellular Rhino-Kipferl interaction should be provided.

- Rhino is known to homodimerize and it has been reported that this homodimerization is important for its binding to H3K9me3 (Yu et al, Cell Res 2015). It is surprising not to find Rhino among the interactors that were picked up from the screen. Do the authors have any explanations or at least comments on these results?

- In Kip mutants, the delocalization of Rhino to a very large structure at the nuclear periphery is a very clear phenotype (Figure 3). All the very elegant genetic controls are provided. This particular localization of Rhino is correlated with an increase in 1.688 Satellite expression and a colocalization of Rhino and the 1.688 RNAs in the nucleus. The authors propose that this increase is consistent with an elevated Rhino occupancy at 1.688 satellites. The authors should moderate their statements in the light of the results of ChIP experiments. Rhino is maintained on these loci in Kip mutants but an increase is not very clearly observed. Couldn't it be the RNA and not the DNA of this 1.688 region traps Rhino? The same in situ experiment should be performed after an RNAse treatment. The delocalization of Rhino is lost in the Kipferl, nxf3 double mutant flies. What is the chromosomal Rhino distribution in this context? Is the increase in nascent transcripts of 1.688 satellites lost?

- The level of some Rhino dependent germline TE piRNAs is affected in Kipferl GLKD. Is there a direct correlation between TEs which lost piRNAs and those for which the level of transcripts increases (Diver, 3S18, Chimpo, HMS Beagle, flea, hobo) ?

- Figure 5E, it seems that Kipferl binding is also dependent on Rhino. All the presented loci have much less binding of Kip in Rhino -/- (The scale for the 42AB locus should be the same between the Rhino -/- and the control MTD w-sh). In addition, the distribution of Rhino in the Kipferl-sh on the 42AB is maintained but seems to be different. Could the authors discuss these points?

- It is not clear why the authors focus only on Kipferl binding sites in a Rhino mutant in the Figure 5D? Even if the authors mention in the text that "Kipferl binding sites in Rhino mutants ... often coincided with regions bound by Kipferl and Rhino in wildtype ovaries" it should be added the same analysis presented in figure 5D centered on Kipferl peaks detected in ChIP experiments in WT condition in the different genotypes.

- There is a discrepancy between the results found Figure 3A and Supp figure 3B. In the Rhino mutant the level of Kipferl protein does not seem to be affected whereas in the Rhino GLKD, there is a strong decrease of Kipferl protein. The authors completely elude this point.

- Comparing the figure 5E and the figure 6G presenting both the 80F piRNA cluster, depending of the scale and the control line that was chosen to illustrate the results we can draw different conclusions. In the figure 5E we can conclude that le level of Kipferl decreases on the 80F locus in Rhino (-/-) compared to the control MTD w-sh, whereas in the figure 6G we can conclude that the level of Kipferl is similar in the Rhino (-/-) compared to the control w1118.

- gypsy8 or RT1b are enriched in GRGG motifs and are also the ones that among Rhino-independent Kipferl enrichment are the most Rhino enriched. Are these 2 elements present in the 80F cluster? Are these two elements derepressed upon Kipferl GLKD ? Where are these two elements in the figure presenting the change in TE transcript level upon Kipferl GLKD?

Reviewer #3 (Public Review):

This is an exciting new cryoEM structure of the HOPS tethering complex, which is necessary for membrane fusion at the vacuole/lysosome in eukaryotic cells. Finally, we can visualize, at moderate resolution, the positioning of HOPS subunits with respect to each other, and predict how HOPS and its various binding partners, such as Rab GTPases and SNAREs, can interact and control fusion. A conceptual advance put forward by this structure seems to be a rigid central core of HOPS that may contribute to helping drive the efficiency of the SNARE-mediated fusion mechanism.

As exciting as this new structure is, however, the study seems to fall a bit short of its promise to explain "why tethering complexes are an essential part of the membrane fusion machinery, or how HOPS "catalyzes fusion." As such, the title is also misleading with regard to HOPS being the "lysosomal membrane fusion machinery."

Overall, the manuscript could benefit greatly, especially for a non-HOPS specialist reader, in providing more introduction and context to the complex and tethering/fusion mechanisms in general. Additionally, the examination of the structure, in light of decades of biochemistry and cell biology studies of HOPS (and homologous proteins that regulate fusion), seems superficial and suggests that deeper analyses may reveal additional insights and lead to a more detailed and impactful model for HOPS function. Moreover, are the insights gained here applicable to other tethering complexes, why or why not?

Reviewer #3 (Public Review):

PME-1 catalyzes the removal of carboxyl methylation of the PP2A catalytic subunit and negatively regulates PP2A activity. Like the PP2A methyltransferase LCMT-1, PME-1 was previously thought to act only on the PP2A core enzyme. However, in this study, the authors show that PME-1 can interact and demethylate different families of PP2A holoenzymes in vitro. They also report the cryo-EM structure of the PP2A-B56 holoenzyme in complex with PME-1. Their structure reveals that the substrate-mimicking motif of PME-1 binds to the substrate-binding pocket of B56 subunit, which tethers PME-1 to PP2A, blocks substrate-binding to PP2A, and promotes PME-1 activation and demethylation of PP2A holoenzyme. Their further mutagenesis and functional analyses indicate that cellular PME-1 function in p53 signaling is mediated by PME-1 activity towards PP2A-B56 holoenzyme. In summary, this study has provided significant insights into our understanding of PP2A regulation by PME-1, demonstrating that PME-1 not only demethylates the PP2A core enzyme, but also the holoenzyme to control cellular PP2A homeostasis.

Reviewer #3 (Public Review):

The number of identified anti-phage defense systems is increasing. However, the general understanding of how phages can overcome such bacterial defense mechanisms is a black box. Srikant et al. apply an experimental evolution approach to identify mechanisms of how phages can overcome anti-phage defense systems. As a model system, the bacteriophage T4 and its host Escherichia coli are applied to understand genome dynamics resulting in the deactivation of phage-defensive toxin-antitoxin systems.

Strengths:<br /> The application of a coevolutionary experimental design resulted in the discovery of a gene-operon: dmd-tifA. Using immunoprecipitation experiments, the interaction of TifA with ToxN was demonstrated. This interaction results in the inactivation of ToxN, which enables the phage to overcome the anti-phage defense system ToxIN.<br /> The characterization of the genomes of T4 phages that overcome the phage-defensive ToxIN revealed that the T4 genome can undergo large genomic changes. As a driving force to manipulate the T4 phage genome, the authors identified recombination events between short homologous sequences that flank the dmd-tifA operon.<br /> The discovery of TifA is well supported by data. The authors prepared several mutant strains to start the functional characterization of TifA and can show that TifA is present in several T4-like phages.

In addition, they describe T4 head protein IPIII as another antagonist of a so far unknown defense system.

In summary, the application of a coevolutionary approach to discover anti-phage defense systems is a promising technique that might be helpful to study a variety of virus-host interactions and to predict phage evolution techniques.

Weaknesses:<br /> The authors apply Illumina sequencing to characterize genome dynamics. This NGS method has the advantage of identifying point mutations in the genome. However, the identification of repetitive elements, especially their absolute quantification in the T4 genome, cannot be achieved using this method. Thus, the authors should combine Illumina Sequencing with a long-read sequencing technology to characterize the genome of T4 in more detail.

To characterize the influence of TifA during infection, T4 phage mutants are generated using a CRISPR-Cas-based technique. The preparation of these phages is unclearly described in the methods section. The authors should describe in detail whether a b-gt deficient strain was applied to prepare the mutants. Information about the used primers and cloning schemes of the Cas9 plasmid would allow the community to repeat such experiments successfully.

The discovery of TifA would benefit from additional data, e.g. structure-based predictions, that describe the protein-protein interaction TifA/ToxN in more detail.

Several publications have described that antitoxins can arise rapidly during a phage attack. The authors should address that this concept has been described before as well by citing appropriate publications.

The authors propose that accessory genomes of viruses reflect the integrated evolutionary history of the hosts they infected. However, the experimental data do not support such a claim.

Reviewer #3 (Public Review):

In their study "Membrane-mediated dimerization potentiates PIP5K lipid kinase activity", Hansen et al. aim to deepen their biochemical understanding of a fascinating self-organizing system the authors have previously been reporting on (Hansen et al., PNAS 2019), in particular, the regulation of PI(4,5)P2 lipids by the kinase PIP5K, which is itself recruited to the membrane by the PI(4,5)P2. From reconstitution studies on supported membranes investigated by TIRF microscopy, following elegant assays that have they previously developed, they conclude that PIPK5 activity is regulated by cooperative binding to and membrane-mediated dimerization of the kinase domain. Dimerization enhances the catalytic efficiency of PIP5K through a mechanism consistent with allosteric regulation and amplifies stochastic variation in the kinase reaction velocity, leading to stochastic geometry sensing that has been reported earlier.

Overall, this is a beautiful biochemical system of great general interest. Also, the findings are plausible in the light of other pattern forming systems. However, the quality of both, the writing (with partly confusing annotations, inconsistencies, and missing clarity of what is actually reported on) and the data is extremely variable, giving the whole paper a somehow immature "patchwork" impression. Not the least, error bars are missing throughout the paper, and although both the protein/membrane system and the instrumental setup seem to be sufficiently well controlled, the quantitative aspect of this study could be greatly improved.

Reviewer #3 (Public Review):

The authors describe the crystal structure of a large fragment of PKG Ib in an autoinhibited state. The structure includes both the regulatory (R) and catalytic (C) kinase domains, and shows in atomic detail how the regulatory cGMP binding domains and autoinhibitory segment bind the kinase to block its activity. A crystal structure of one of the cGMP binding domains bearing a disease-associated mutation (TAAD, Thoracic aortic aneurysms and dissections) provides an understanding of the mechanism by which the mutation leads to constitutive activation of PKG by inducing a conformation that resembles the cyclic nucleotide bound state. This interpretation is further supported by an NMR study of the mutant that reveals chemical shifts consistent with the "open" (nucleotide-bound) conformation. A structure-function study in which variants with mutations in one or both of the active sites and regulatory domain are co-expressed shows that autoinhibition occurs in cis; that is, in an intra-chain manner, rather than as part of a dimer as is likely present in the crystal. A SAXS experiment further supports this model. The authors propose a model for PKG activation, referencing the structures described here as well as prior crystal structures of the isolated kinase and regulatory domains as "snapshots" of distinct states in the autoinhibition-activation pathway. This is a careful and technically sound study that provides a first structural view of PKG autoinhibition. It also enables comparison to the related mechanism of regulation of protein kinase A, but this aspect of the manuscript could be much better developed.

Reviewer #3 (Public Review):

Carraro et al utilize systems biology approaches to decode the mechanism of action of 3-chloropiperidines (a novel class of cancer therapeutics) in cancer cell lines and build a drug-sensitivity model from the data that they evaluate using samples from The Cancer Genome Atlas and cancer cell lines. The approach provides a framework for integrating transcriptomic and open-chromatin data to better understand the mechanism of action of drugs on cancer cell types. The author's approach is of sound design, is clearly explained, and is bolstered by validation via holdout sets and analysis in new cell lines which lends the findings and approach credibility.

The major strength of this approach is the depth of information provided by performing RNA-seq and ATAC-seq on cells treated with 3-CePs at various time points, and the author's utilization of this data to perform pairwise and crosswise analyses. Their approach identified gene modules that were indicative of why one cell type was more sensitive to a particular drug compared to another. The data was then used to build a sensitivity model which could be applied to samples from The Cancer Genome Atlas, and the authors evaluated their sensitivity predictions on a set of cancer cell lines which validated the predictions.

The major drawback to this type of approach is that it relies on next-generation sequencing (somewhat costly) and requires intricate bioinformatics analyses. While I agree with the author's perspective that this approach can be applied to additional classes of drugs and cancer samples, I disagree with their view that it is efficient and versatile. However, for research teams with the means to perform both transcriptomic and open-chromatin studies, I think this integrated approach has promise for evaluating novel classes of drugs, particularly in cancer cell lines that are easy to manipulate in vitro.

While there are examples of similar frameworks being applied to drug development, this work will add to the body of literature utilizing an integrated systems biology approach for pairing drugs with specific tumor or cancer types and understanding their mechanism of action on an epigenetic level.

Reviewer #3 (Public Review):

The authors sought to identify transcriptional changes that occur in the various somatic cell populations of the adult mouse ovary during different reproductive states using single-cell RNA sequencing. The ovaries for the analysis were harvested from mice during the four stages of the normal estrus cycle (proestrus, estrus, metestrus and diestrus), from lactating or non-lactating 10 days postpartum mice, and from randomly cycling mice. They identified the major cell subtypes of the adult ovary but focused their analysis on the mesenchyme (stromal and theca) and granulosa cells. They identified novel markers for stromal, theca and granulosa cell subpopulations and validated these by RNA in situ hybridization. They used trajectory analysis to infer differentiation lineages within the stromal and granulosa cell subtypes. Finally, from their data set they identify four secreted factors that could serve as biomarkers for staging estrus cycle progression.

Strengths - This is the first study to profile ovarian somatic gonad cells at different stages of the reproductive cycle.

Weaknesses - Enthusiasm for the current manuscript is lessened because it does not employ state-of-the-art scRNA-seq analysis. For example, once general cell populations have been determined by clustering with all cells, it is best to individually re-cluster these cell populations to identify more refined and accurate subpopulations. The PC used for the initial clustering is very useful for distinguishing different general cell populations (e.g. mesenchyme vs. granulosa vs. endothelial) but may not be as useful for distinguishing biologically relevant subpopulations (e.g. stromal subpopulations). Finally, certain cell subpopulations were excluded from the trajectory analysis without justification - specifically, the mitotic and atretic granulosa cells - calling into question what conclusions can be drawn from this analysis.

Reviewer #3 (Public Review):

The authors reanalyze an existing dataset of single-cell Sperm-seq data to search for signals of transmission distortion. They develop an improved genotype imputation method and use this approach to phase donors and characterize the landscape of ancestry across each sperm genome. Using these data, the authors determined that there are no regions in any of the male donors' genomes that display a significant excess of TD. The main biological claim of the paper is that there is a strict adherence to Mendelian transmission ratios in human males.

The computational approaches for accurately phasing and reconstructing haplotypes in individually lightly sequenced gametes is a potentially useful advance that I expect may be valuable for geneticists analyzing similar datasets. The quality of software documentation and usability is high. I have concerns about the appropriateness of the comparisons selected for this approach and the algorithm does not appear particularly novel.

I have no doubt about the authors' basic conclusion that there are no strong male TD loci in the male donors examined. However, I find their statements about "strict adherence to Mendelian ratios" and many references to strong statistical power to be oversold. The power of this study is still quite limited relative to the strength of TD that we would expect to find in human populations.

Major Concerns:

There are really two distinct papers here. One is about improved imputation and crossover analysis from sperm-seq data and one is about TD. The bulk of the methodological development is a rework of the approach for genotype imputation and haplotype phasing in Sperm-seq. Yet, the major conclusions are focused on a scan for TD. I am left wondering if analyzing these data using the original method in the Bell et al paper would have produced different conclusions about either? If not, is there a systematic bias such that one would find an excess of false detections of TD? Phasing slightly more markers is not a particularly compelling link between these sections because even fairly sparsely distributed markers that are correctly phased would certainly be fine in a scan for TD within a single individual due to linkage. If this cannot be shown I wonder if this work would be better split into two manuscripts with one more technical paper describing the differences in recombination maps associated with rhapsodi and the other as a brief report stating that strong TD is probably uncommon in human males.

It is not surprising that rhapsodi outperforms Hapi since Hapi was designed for a very different quantity of samples and sequencing depths. I appreciate the authors' point that Hapi performed better than other methods in comparisons run by the Hapi authors. However, they were looking at very few gametes (10 or so, I believe). For that reason, this comparison is not appropriate to address the application to the datasets used in this paper. The authors should include an analysis comparing rhapsodi against hapcut2, PHMM and other methods that are appropriate for the full scale and sequencing depth of the data. Additionally, the original Bell paper used a phasing + HMM approach of some kind for exactly this data. Why wasn't that approach considered as a point of comparison?

With respect to the method for imputation, no comparison is made to known recombination maps nor do the authors make any comparison across the maps derived from each donor. Reporting an improved method without it motivating novel biological conclusions is not compelling in itself. I suggest the authors expand that analysis to consider these are related questions. E.g., are there males whose recombination maps differ in specific regions? Are those associated with known major chromosomal abnormalities? Is this map consistent with estimates from LD, pedigrees, Bell et al?

Most of the validations presented are based on simulated data. This is fine and has some advantages, but real data imposes challenges that these analyses do not address. My understanding is that the Bell et al. (2020) paper includes a donor with a phased diploid genome. A comparison of rhapsodi's phasing accuracy against that genome should be included.

The main biological conclusion about a "strict adherence to Mendelian expectations across sperm genomes" is an overstatement. Statistical power of this study is still limited relative to the strength of TD that would be expected within human populations. One reason is the multiple testing correction. Another is that 1000-3000 draws from a binomial distribution with expected p = 0.5 is just not sufficient to overcome binomial sampling variance. In light of this concern and the central conclusion of this paper, the authors' discussion of power is inadequate. The main text really should contain explicit discussion of the required genotype ratio skew for TD in each donor to be detected with good power. Given previous pedigree studies, it is not surprising that no significant TD was discovered that exceeded the necessary ~10% effect sizes to be detectable. Recent, much more powerful analyses in mice, Drosophila and plants, indicate that strong TD is probably uncommon and even weak effects can be detected but are uncommon.

This manuscript would benefit from a much clearer examination of statistical power and a detailed comparison of the power of this approach vs pedigree-based analyses as well as bulk gamete sequencing approaches. Although the authors are correct that all scans for TD in human genomes have been pedigree or single-cell based, more powerful alternatives are known. These are based on sequencing pools of individuals or gametes (e.g., Wei et al. 2017, Corbett-Detig et al. 2019). Each of those studies has been able to identify signatures of segregation distortion below the thresholds required for significance in this study. These and related works should be acknowledged in both the introduction and discussion. Although I appreciate that the ability to phase the genome in a single experiment may be appealing, phasing diploid genomes via hi-c omni-c is straightforward and the advantages in statistical power suggest that approaches using pools of gametes are preferable for well-powered scans for TD.

Reviewer #3 (Public Review):

The manuscript by Bae et al describes the role of a point mutation in the PH domain of Akt that changes the inhibition by the PH domain. The data underlying the manuscript appear to be done at a high technical level. The discovery that the R86A mutant has an enhanced inhibitory interface with the kinase domain is intriguing. Although this residue is not at the putative interface, it forms an electrostatic interaction with the Glu17 in the PH domain and causes a reorientation of the loop including the Y18. Analysis of Y18 and E17 mutants can reverse this effect, revealing a molecular mechanism of R86 increased inhibition.

My main concern with the manuscript is that the conclusions as currently written do not appear to be fully supported by the data. Mainly on the role of the pi-pi stacking of the 309-18 interface. This paper requires a major rewrite. There also could be additional validation data included to verify the stability and phosphorylation state of the different proteins purified.

Major concerns

1. There are concerns about the validation of the proteins used.

2. The authors note on page 9 that they analyzed the alphafold structure to look at the PhH-kinase interface.

From the analysis of the alphafold model, it does not seem appropriate for this analysis, as the alphafold predicted aligned error (taken from alphafold protein structure database, https://www.alphafold.ebi.ac.uk/entry/P31749) validation clearly shows that there is only limited predictive value of the inter-domain interfaces. I am not sure the mutant data on the predicted pi stacking interaction can be supported by alphafold here as strongly as the authors describe, as these mutants may be working through a separate mechanism. The alphafold model also appears to be templated on the 4ekk phosphorylated structure/mutant of 308 and 473, which seems to go against the authors' hypothesis that 473 phosphorylation disrupts the PH domain interface.

The best model for interpreting the Ph-kinase interface seems to be the nanobody-bound X-ray structure, and this region is disordered at F309 in this structure. While the authors' data clearly shows a role for the Y18 reorientation in changing Ph domain binding, and they also show that mutation of F309L also changes binding, they are basing their molecular model on an alphafold model with limited predictive ability for inter-domain contacts.

Reviewer #3 (Public Review):

The main goals of this study by Guan, Aflalo and colleagues were to examine the encoding scheme of populations of neurons in the posterior parietal cortex (PPC) of a person with paralysis while she attempted individual finger movements as part of a brain-computer interface task (BCI). They used these data to answer several questions:

1) Could they decode attempted finger movements from these data (building on this group's prior work decoding a variety of movements, including arm movements, from PPC)?

2) Is there evidence that the encoding scheme for these movements is similar to that of able-bodied individuals, which would argue that even after paralysis, this area is not reorganized and that the motor representations remain more or less stable after the injury?

3) Related to #2: is there beneficial remapping, such that neural correlates of attempted movements change to improve BCI performance over time?

4) Can looking at the interrelationship between different fingers' population firing rate patterns (one aspect of the encoding scheme) indicate whether the representation structure is similar to the statistics of natural finger use, a somatotopic organization (how close the fingers are to each other), or be uniformly different from one another (which would be advantageous for the BCI and connects to question #3)? Furthermore, does the best fit amongst these choices to the data change over the course of a movement, indicating a time-varying neural encoding structure or multiple overlapping processes?

The study is well-conducted and uses sound analysis methods, and is able to contribute some new knowledge related to all of the above questions. These are rare and precious data, given the relatively few people implanted with multielectrode arrays like the Utah arrays used in this study. Even more so when considering that to this reviewer's knowledge, no other group is recording from PPC, and this manuscript thus is the first look at the attempted finger moving encoding scheme in this part of human cortex .

An important caveat is that the representational similarity analysis (RDA) method and resulting representational dissimilarity matrix (RDM) that is the workhorse analysis/metric throughout the study is capturing a fairly specific question: which pairs of finger movements' neural correlates are more/less similar, and how does that pattern across the pairings compare to other datasets. There are other questions that one could ask with these data (and perhaps this group will in subsequent studies), which will provide additional information about the encoding; for example, how well does the population activity correlate with the kinematics, kinetics, and predicted sensory feedback that would accompany such movements in an able-bodied person?

What this study shows is that the RDMs from these PPC Utah array data are most similar to motor cortical RDMs based on a prior fMRI study. It's innovative to compare effectors' representational similarity across different recording modalities, but this apparent similarity should be interpreted in light of several limitations: 1) the vastly different spatial scales (voxels spanning cm that average activity of millions of neurons each versus a few mm of cortex with sparse sampling of individual neurons, 2) the vastly different temporal scales (firing rates versus blood flow), 3) that dramatically different encoding schemes and dynamics could still result in the same RDMs. As currently written, the study does not adequately caveat the relatively superficial and narrow similarity being made between these data and the prior Ejaz et al (2015) sensorimotor cortex fMRI results before except for (some) exposition in the Discussion.

Relatedly, the study would benefit from additional explanation for why the comparison is being made to able-bodied fMRI data, rather than similar intracortical neural recordings made in homologous areas of non-human primates (NHPs), which have been traditionally used as an animal model for vision-guided forelimb reaching. This group has an illustrious history of such macaque studies, which makes this omission more surprising.

A second area in which the manuscript in its current form could better set the context for its reader is in how it introduces their motivating question of "do paralyzed BCI users need to learn a fundamentally new skillset, or can they leverage their pre-injury motor repertoire". Until the Discussion, there is almost no mention of the many previous human BCI studies where high performance movement decoding was possible based on asking participants to attempt to make arm or hand movements (to just list a small number of the many such studies: Hochberg et al 2006 and 2012, Collinger et al 2013, Gilja et al 2015, Bouton et al 2016, Ajiboye*, Willett* et al 2017; Brandman et al 2018; Willett et al 2020; Flesher et al 2021). This is important; while most of these past studies examined motor (and somatosensory) cortex and not PPC (though this group's prior Aflalo*, Kellis* et al 2015 study did!), they all did show that motor representations remain at least distinct enough between movements to allow for decoding; were qualitatively similar to the able-bodied animal studies upon which that body of work was build; and could be readily engaged by the user just by attempting/imagining a movement. Thus, there was a very strong expectation going into this present study that the result would be that there would be a resemblance to able-bodied motor representational similarity. While explicitly making this connection is a meaningful contribution to the literature by the present study (and so is comparing it to different areas' representational similarity), care should be taken not to overstate the novelty of retained motor encoding schemes in people with paralysis, given the extensive prior work.

The final analyses in the manuscript are particularly interesting: they examine the representational structure as a function of a short sliding analysis window, which indicates that there is a more motoric representational structure at the start of the movement, followed by a more somatotopic structure. These analyses are a welcome expansion of the study scope to include the population dynamics, and provides clues as to the role of this activity / the computations this area is involved in throughout movement (e.g., the authors speculate the initial activity is an efference copy from motor cortex, and the later activity is a sensory-consequence model).

An interesting result in this study is that the participant did not improve performance at the task (and that the neural representations of each finger did not change to become more separable by the decoder). This was despite ample room for improvement (the performance was below 90% accuracy across 5 possible choices), at least not over 4,016 trials. The authors provide several possible explanations for this in the Discussion. Another possibility is that the nature of the task impeded learning because feedback was delayed until the end of the 1.5 second attempted movement period (at which time the participant was presented with text reporting which finger's movement was decoded). This is a very different discrete-and-delayed paradigm from the continuous control used in prior NHP BCI studies that showed motor learning (e.g., Sadtler et al 2014 and follow-ups; Vyas et al 2018 and follow-up; Ganguly & Carmena 2009 and follow-ups). It is possible that having continuous visual feedback about the BCI effector is more similar to the natural motor system (where there is consistent visual, as well as proprioceptive and somatosensory feedback about movements), and thus better engages motor adaptation/learning mechanisms.

Overall the study contributes to the state of knowledge about human PPC cortex and its neurophysiology even years after injury when a person attempts movements. The methods are sound, but are unlikely (in this reviewer's view) to be widely adopted by the community. Two specific contributions of this study are 1) that it provides an additional data point that motor representations are stable after injury, lowering the risk of BCI strategies based on PPC recording; and 2) that it starts the conversation about how to make deeper comparisons between able-bodied neural dynamics and those of people unable to make overt movements.

Reviewer #3 (Public Review):

Childhood acute myeloid leukemia (AML) is a heterogeneous disease with different outcomes for different patients, making identifying patients with different prognoses for clinical management. A variety of approaches have been used to stratify AML patients' risk, including molecular and clinical measurements to build prognostic risk scores. Previously, Chaudhary et al found that mitochondrial genome copy number per AML cell could stratify patients who would have good and poor outcomes and survival. This interesting finding suggested that mitochondrial amount and/or function alter AML disease course and suggested a further in-depth study of mitochondria in AML.

Chaudhary and colleagues follow up their preliminary study on mitochondrial genome copy number in AML with this current study by looking if the expression of specific genes encoding mitochondrial components could provide further insight into AML prognosis. The authors collected childhood AML patient samples and grouped them based on mitochondrial genome copy number. They then performed transcriptomic analysis and identified a number of nuclear-encoded mitochondrial component genes whose expression was correlated or anticorrelated with mitochondrial genome copy number and this was confirmed with targeted analysis of identified transcripts in validation cohorts. Multivariate analysis was used to identify those genes whose expression was prognostic of patient outcome. This led to the identification of three mitochondrial genes (SDHC, CLIC1, SLC25A29) whose expression was used to build a multivariate risk model for childhood AML patients. The risk model based on the expression of these genes outperformed currently used ELN risk stratification and could be combined with ELN to increase prognostic power. Lastly, the authors used publically available data from adult AML patients and found that their risk score also had prognostic power in adult AML patients as well.

Altogether, the work by Chaudhary and colleagues interestingly builds on their previous work and suggests that mitochondria may influence AML outcomes, and measuring mitochondrial parameters may help assess patient risk. Numerous exciting questions remain: what outputs of the mitochondria influence AML disease course and how? Why are some mitochondrial genes but not others correlated with mitochondrial DNA copy number in AML cells and how does this influence mitochondrial properties? Outside of predicting patient risk, can the mitochondrial phenotype of AML cells predict effective therapies? How does the mitochondrial risk model perform compared to and when utilized with other transcriptional-based risk stratification models proposed in the literature?

Reviewer #3 (Public Review):

In their previous work, the authors studied the problem of clonal life cycles evolution. Here they extended the previous work by developing a model that describes such evolution under the presence of competition between groups. The model is studied using a combination of analytical methods and numerical simulations. The results obtained are more biologically justifiable than those obtained in the linear model that neglects competition between groups.

Strengths:

- As is known from previous work, in a linear model (when the competition is absent), a typical outcome is an exponential growth in the number of groups of some life cycle, which can be considered as a natural limitation of the model. Obviously, this limitation is removed in the presented paper.

- The authors provide analytical results for some special cases of the model and compare them with those obtained in the absence of competition. In the general case of the model, when analytical progress is impossible, the authors provide the results of extensive numerical simulations. All these results allow the authors to build a clear picture of the process under study.

- The authors study the evolutionary stability of various life cycles. Specifically, it was shown that only binary fragmentation life cycles can be evolutionary stable strategies. This result holds in the linear model as well. In contrast to the linear model, more complex dynamics can be observed in the general case (like the existence of several evolutionary stable strategies).

Overall, in my opinion, the model significantly contributes to our understanding of the evolution of clonal life cycles. Moreover, it illuminates to what extent are adequate the results of simple linear models in describing the processes under consideration.

Reviewer #3 (Public Review):

In this manuscript, the authors investigated the role of glutamine metabolism in chondrocytes and in the context of inflammation. Thus, they report that chondrocytes use glutamine for their energy production and anabolic functions. Moreover, they found that removal of glutamine resulted in metabolic reprogramming and decreased inflammatory response of chondrocytes. They attributed this anti-inflammatory response to decreased NF-κB activity. Moreover, the removal of glutamine promoted autophagy. This is a very interesting study and the vast majority of the conclusions are supported by strong data.

Reviewer #3 (Public Review):

The authors present a modular computational workflow for automated sample screening and collection of cryo-EM data and demonstrate its use for screening and 3D structure determination of human mitochondrial DNA polymerase as a test sample. Despite major advances in automation of microscope operation, optimising and screening sample conditions for the acquisition of high-quality data is still a laborious task that involves human input to navigate low-, medium- and high-magnification images to identify and select specimen areas amenable to high-resolution structure determination; and subjective tuning of parameters that can result in inefficient use of high-end cryo-TEM equipment. Fully automated methods for screening and data collection are therefore needed to meet the increasing demand for access and throughput of cryo-EM. Utilising deep-learning-based object detection algorithms, the authors show that their pre-trained models can effectively detect, classify, and rank regions (grid squares and holes) of interest based on established criteria such as contamination, support film integrity, and ice thickness. A challenge for any such method is the scarcity of annotated data reflecting the broad variety across the wide range of image and sample conditions in cryo-EM, and that selection of the "best" areas may vary by particle and sample preparation conditions. To mitigate this risk, the authors provide a web interface that allows re-training of the feature models and integrates on-the-fly assessment of data quality and adjustment of data collection parameters. As such, the presented pipeline and related approaches can become a useful addition to existing automation software for cryo-EM data collection, in multi-user environments such as cryo-EM facilities. Such approaches will best strive if software and models are openly available to the cryo-EM community so that annotated data can be added or customised and the quality of the prediction methods can improve over time.

Reviewer #3 (Public Review):

In general, I find this to be an experimentally and analytically sound paper. The observation that rate information is preserved in hippocampal replay is hinted at in previous work, but to my knowledge, has not yet been explicitly quantified as the authors have done here. Thus, this work is novel and, in my opinion, an important contribution to our understanding of hippocampal network function.

The large number of control analyses strongly support the core finding of this work. I feel that the authors have very convincingly demonstrated that rate information is represented along with spatial information in replay.

While I can think of many suggestions to follow up on this work, I have no major concerns regarding the experiments, analyses, or interpretation of the manuscript.

Reviewer #3 (Public Review):

The TRPV1 receptor channel is primarily localised to sensory nerves as well as other non-neuronal tissues. It has been known for some time that TRPV1 has a role in the regulation of body temperature, as TRPV1 antagonists, being developed as analgesics, cause hyperthermia. There is a need for further mechanistic information, as the present drug discovery programme has been delayed by the inability of scientists to develop TRPV1 analgesics that act without temperature-related side effects. This manuscript is designed to investigate whether sensory nerves or smooth muscle cells are included in the mechanisms, through the study of tissue specific genetically modified mice.

This is a highly readable and concise manuscript with a relatively simple and clear take home message that advances current knowledge. However, at times the information could be more fully given.

Reviewer #3 (Public Review):

This work seeks to identify a common factor governing priority effects, including mechanism, condition, evolution, and functional consequences. It is suggested that environmental pH is the main factor that explains various aspects of priority effects across levels of biological organization. Building upon this well-studied nectar microbiome system, it is suggested that pH-mediated priority effects give rise to bacterial and yeast dominance as alternative community states. Furthermore, pH determines both the strengths and limits of priority effects through rapid evolution, with functional consequences for the host plant's reproduction. These data contribute to ongoing discussions of deterministic and stochastic drivers of community assembly processes.

Strengths:

Provides multiple lines of field and laboratory evidence to show that pH is the main factor shaping priority effects in the nectar microbiome. Field surveys characterize the distribution of microbial communities with flowers frequently dominated by either bacteria or yeast, suggesting that inhibitory priority effects explain these patterns. Microcosm experiments showed that A. nectaris (bacteria) showed negative inhibitory priority effects against M. reukaffi (yeast). Furthermore, high densities of bacteria were correlated with lower pH potentially due to bacteria-induced reduction in nectar pH. Experimental evolution showed that yeast evolved in low-pH and bacteria-conditioned treatments were less affected by priority effects as compared to ancestral yeast populations. This potentially explains the variation of bacteria-dominated flowers observed in the field, as yeast rapidly evolves resistance to bacterial priority effects. Genome sequencing further reveals that phenotypic changes in low-pH and bacteria-conditioned nectar treatments corresponded to genomic variation. Lastly, a field experiment showed that low nectar pH reduced flower visitation by hummingbirds. pH not only affected microbial priority effects but also has functional consequences for host plants.

Weaknesses:

The conclusions of this paper are generally well-supported by the data, but some aspects of the experiments and analysis need to be clarified and expanded.

The authors imply that in their field surveys flowers were frequently dominated by bacteria or yeast, but rarely together. The authors argue that the distributional patterns of bacteria and yeast are therefore indicative of alternative states. In each of the 12 sites, 96 flowers were sampled for nectar microbes. However, it's unclear to what degree the spatial proximity of flowers within each of the sampled sites biased the observed distribution patterns. Furthermore, seasonal patterns may also influence microbial distribution patterns, especially in the case of co-dominated flowers. Temperature and moisture might influence the dominance patterns of bacteria and yeast.

The authors exposed yeast to nectar treatments varying in pH levels. Using experimental evolution approaches, the authors determined that yeast grown in low pH nectar treatments were more resistant to priority effects by bacteria. The metric used to determine the bacteria's priority effect strength on yeast does not seem to take into account factors that limit growth, such as the environmental carrying capacity. In addition, yeast evolves in normal (pH =6) and low pH (3) nectar treatments, but it's unclear how resistance differs across a range of pH levels (ranging from low to high pH) and affects the cost of yeast resistance to bacteria priority effects. The cost of resistance may influence yeast life-history traits.