Reviewer #1 (Public Review):

The present study aims to define the main immune cell subsets found in the hemolymph of the white shrimp, P. vannamei. This is significant because this species is heavily farmed around the world to meet the demand of the human consumption market. Yet, farmed shrimp suffer from infectious diseases and therefore we need to understand how their immune system works to design strategies that decrease infection losses.

Classification of crustacean (and other invertebrates) hemocytes is difficult due to the lack of antibodies to use traditional flow cytometry approaches. Furthermore, hemocyte purification is not easy, cells die and clump, again precluding flow cytometry studies. Thus, the majority of what we know about shrimp hemocytes is based on morphological classification. This study contributes significantly to advancing our knowledge of shrimp Immunobiology by defining hemocyte subsets based on their transcriptional profiles.

Another strength of the paper is that some function in vivo assays (phagocytosis) are presented in an attempt to validate the single-cell data. The authors frame their question or try to frame their question with a more evolutionary angle, such as whether the macrophage-like cell is the evolutionary precursor of human macrophages. I think that this question is not really achievable because the evolution of innate immune systems may have diverged in many branches of the metazoan tree of life. The authors, however, identify gene markers that are conserved in macrophages from shrimp and humans and that is a fair conclusion. There are some methodological caveats to the study and the manuscript needs to be heavily edited to improve language as well as to increase the depth of the interpretation.

In summary, there are interesting findings in this manuscript but the manuscript needs to be significantly improved so that its quality and impact are elevated.

Reviewer #3 (Public Review):

Yang et al have undertaken a single cell transcriptomic analysis of circulating immune cells from the shrimp, Penes vannamei. They set out to characterize transcriptional differences between circulating immune cell subsets following immune stimulation. Their investigation reveals that shrimp immune cells can be classified into a number of specific subsets defined by unique transcriptional profiles. Using specific marker genes for each cell subset, the authors provide evidence suggesting that shrimp immune cells share transcription factors that define myeloid cell development in mammalian (human) systems.

This study follows an investigative path that is shared by numerous single-cell transcriptomic studies. The authors do an admirable job of synthesizing a complex single-cell transcriptomic analysis into a focused report that highlights important transcripts that define the hemocyte subsets of the shrimp. While I disagree with some of the claims being made related to the evolutionary connection between shrimp hemocytes and mammalian myeloid cells, this dataset will undoubtedly contribute to our understanding of invertebrate immune cell complexity and the relationships these cells have to other invertebrate hemocytes and immune cell evolution.

Reviewer #1 (Public Review):

The manuscript by LoMastro et al. investigates whether Plk4, the master regulator of centriole biogenesis in cycling cells, has a similar role during the differentiation of multi-ciliated cells, which produce tens to hundreds of centrioles during multi-ciliogenesis. Contrasting previous work that did not find an important role for Plk4 in this process based on chemical inhibition, the authors in the current study use genetic approaches and mouse models to show that Plk4 and its kinase activity are essential for centriole amplification and multi-ciliogenesis in two different multi-ciliated cell types in vitro and in vivo. In addition, they show that centriole amplification drives cell surface area expansion.

The study addresses an important question regarding the role of Plk4 in centriole amplification during multi-ciliogenesis. It convincingly establishes that contrary to previous findings, the Plk4-dependent control of centriole biogenesis that is well-established in cycling cells is conserved also during differentiation of multi-ciliated cells. The presented data is of very high quality, phenotypes are well described and quantified, the conclusions are clear, and obtained in both in vitro and in vivo models. The authors also test chemical inhibition of Plk4 as used in previous work and show that the lack of a strong phenotypes under these conditions is likely due to incomplete Plk4 inhibition.

Reviewer #1 (Public Review):

The authors of this study adopted Cas9-mediated enrichment of target locus and Nanopore long-read sequencing to accurately count repeat numbers in the CNBP gene, which is notorious for precise calling before. They also compared their result with that of the conventional approach, validating their approach. It is an interesting read and shows a pathway that a clinic can take in the near future.

However, this paper's novel contributions need to be emphasised as there are some papers that utilized Nanopore sequencing to elucidate short repeats (https://pubmed.ncbi.nlm.nih.gov/35245110/; https://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-020-00853-3). Another issue is the clinical utility of the approach. Although it is precise, it is not totally clear whether this accuracy is required in clinical practice, as the repeat status does not completely correlate with phenotypic severity.

Lastly, it is not clear about the familial cases (A1-A4). What are their relationships and why their copy numbers are not exactly the same? Is it because of extreme recombination and variation even in a family or just represent limited accuracy?

They lack a validation cohort, with prospective patients.

Reviewer #1 (Public Review):

The manuscript by Dr Riley and colleagues reports a novel link between molecular clock operative in skeletal muscle and titin mRNA, encoding for essential regulator of sarcomere length and muscular strength. Surprisingly, this clock-mediated regulation of titin occurs at the level of splicing, as demonstrated by SDS-VAGE analyses of skeletal muscle from muscle-specific Bmal1KO mice compared to Bmal1wt counterpart. Concomitant with switch of predominant isoform of titin, skeletal muscle of muscle specific Bmal1KO mice exhibited irregular sarcomere length. Moreover, the authors show that this shift of titin splice is causal for such sarcomere length irregularity and for altered sarcomere length in muscle from the mice with compromised clock function. Importantly, the authors provide compelling evidence that Rbm20, encoding for RNA-binding protein that mediates splicing of titin, is cooperatively regulated by Bmal1-Clock heterodimer and MyoD, via enhancer element in intron 1 of Rbm20, thus identifying Rbm20 as a novel direct clock-regulated gene in the skeletal muscle. Strikingly, rescue of Rbm20 in muscle specific Bmal1KO animals' results in rescue of titin splicing pattern and protein size, suggesting that Rbm20 mediates the regulatory effect of Bmal1 on titin splicing and represents a mechanistic link between the clock and regulator of sarcomere length and regularity.

Reviewer #3 (Public Review):

This manuscript is using an inducible and skeletal muscle specific Bmal1 knockout mouse model (iMSBmal1-/-) that was published previously by the same group. In this study, they utilized the same mouse model and further investigated the effect of a core molecular clock gene Bmal1 on isoform switching of a giant sarcomeric protein titin and sarcomere length change resulted from titin isoform switching. Lance A. Riley et al found that iMSBmal1-/- mouse TA muscle expressed more longer titin due to additional exon inclusion of Ttn mRNA compared to iMSBmal+/+ mice. They observed that sarcomere length did not significantly change but more variable in iMSBmal1-/- muscle compared to iMSBmal+/+ muscle. In addition, they identified significant exon inclusion in the proximal Ig region, so they measured the proximal Ig length domain and confirmed that proximal Ig domain was significantly longer in iMSBmal1-/- muscle. Subsequently, they experimentally generated a shorter titin in C2C12 myotubes and observed that the shorter titin led to the shorter sarcomere length. Since RBM20 is a major regulator of Ttn splicing, they determined RBM20 expression level, and found that RBM20 expression was significantly lower in iMSBmal1-/- muscle. The reduced RBM20 expression was regulated by the molecular clock controlled transcriptional factor MyoD1. By performing a rescue experiment in vivo, the authors found that rescue of RBM20 in iMSBmal1-/- TA muscle restored titin isoform expression, however, they did not measure whether sarcomere length was restored. These data provide new information that the molecular cascades in the circadian clock mechanism regulate RBM20 expression and downstream titin isoform switching and sarcomere length change. Although the conclusion of this manuscript is mostly supported by the data, some aspects of experimental design and data analysis need be clarified and extended.

Strengths:

This paper links the circadian rhythms to skeletal muscle structure and function through a new molecular cascade: the core clock component Bmal1-transcription factor MyoD1-RBM20 expression-titin isoform switching-sarcomere length change.

Utilization of muscle specific bmal1 knockout mice could rule out the confounding factors from the molecular clock in other cell types

The authors performed the RNA sequencing and label free LC-MS analyses to determine the exon inclusion and exclusion through a side-by-side comparison which is a new approach to identify individual alternative spliced exons via both mRNA level and protein level.

Weaknesses:

Both RBM20 expression and titin isoform expression varies in different skeletal muscles. The authors only detected their expression in TA muscle. It is not clear why the authors only chose TA muscle.

The sarcomere length data are self-contradictory. The authors stated that sarcomere length was not significantly changed in muscle specific KO mice in Line 149, however, in Line 163, the measurements showed significantly longer in muscle specific KO muscle. The significance is also indicated in Figures 2C and 3B.

Manipulating titin size using U7 snRNPs linking to the changes in sarcomere length and overexpressing RBM20 to switch titin size are the concepts that have been proved. These data do not directly support the impact of muscle specific Bmal1 KO on ttn splicing and RBM20 expression

There is no evidence to show if interrupted circadian rhythms in mice change RBM20 expression and ttn splicing, which is critical to validate the concept that circadian rhythms are linked to Ttn splicing through RBM20.

Reviewer #1 (Public Review):

Detomasi et al. investigated the role of a protein encoded by the cwr-1 gene that belongs to the cell wall remodeling locus that controls cell fusion checkpoints in Neurospora crassa. This protein corresponds to a putative polysaccharide monooxygenase (called PMO or LPMO) from family AA11 (according to the CAZy family). This class of enzymes is known for oxidative cleavage of recalcitrant polysaccharides but recently diverging functions have emerged. In this work, the authors clearly demonstrated LPMO activity towards chitin for several CWR-1 from different haplogroups. Mutagenesis and construction of chimeras allowed the authors to reveal that enzymatic activity was not required for cell fusion blockage. Beyond this very interesting observation, they identified a polymorphic region in the main catalytic domain (corresponding to several loops) that was essential to trigger allorecognition. The authors suspect that this region is involved in the recognition of CWR-2, a transmembrane protein with two domains of unknown function. The authors propose a model highlighting the role of CWR-1 in allorecognition at the cell fusion checkpoint. These results open new prospects for the biological function of fungal PMOs/LPMOs not directly related to their enzymatic activity.

Reviewer #3 (Public Review):

The authors here describe that the PMO domain of CWR-1 is active on chitin, which is demonstrated with beautiful and solid biochemistry data. Furthermore, they show that the catalytic activity of the PMO domain is dispensable for allorecognition in N. crassa. More specifically, they showed that the side loops of the PMO domain of CWR-1 are important for allorecognition and cell fusion. The chitin catalytic activity of the PMO domain of CWR-1 is not surprising, as other LPMOs from the same family (AA11) had already been characterized. This paper highlights the discovery that LPMOs are involved in cell wall remodeling of filamentous fungi and cell fusion. These findings certainly strengthen the emerging biological roles that LPMOs play in microorganisms, which are still limited.

The strengths of the paper are the interdisciplinary approach, whereby microscopy is combined with genetics and biochemistry.

There are no major weaknesses in the paper.

Reviewer #1 (Public Review):

The manuscript by Liu et al. outlines the role of exchange protein directly activated by cAMP (Epac2) in dopamine neurons and how this relates to cocaine effects on dopamine release and associated behaviors. Through a series of manipulations, they show that Epac2 expression increases cocaine reinforcement and dopamine release while decreases in Epac2 have the opposite effect. The manuscript is interesting and important, the design is rigorous, and it of broad impact on the field. There are only minor issues with the wording of the operant schedule (I am not sure that it is actually FR1) and some other wording issues (in some places it just states Epac2, rather than denoting these are its effects in dopamine neurons), but overall this is an excellent manuscript.

Reviewer #3 (Public Review):

Liu et al. investigated the role of Epac2, the "other" less studied cAMP effector (compared to the classical PKA) in dopamine release and cocaine reinforcement using slice electrochemistry, behavior, and in vivo imaging in dopamine neuron-specific Epac2 conditional knockout mice (confirmed by elegant single-cell RT-PCR). Epac2 genetic deletion (Epac2 cKO) or pharmacological inhibition (using the Epac2 antagonist ESI-05, i.p.) reduced cocaine (under both fixed and progressive ratio schedules) but not sucrose, self-administration, supporting an essential role for Epac2 in cocaine reinforcement but not natural reward. Cyclic voltammetry on striatal slices demonstrated that evoked DA release was reduced in Epac2 cKO mice and enhanced by the Epac2 activator S-220 or the PKA activator 6-Bnz independently. Using in vivo chemogenetics and fiber photometry (with the DA fluorescent sensor GRABDA2M), authors showed that DCZ activation of VTA DA neurons expressing rM3D(Gs) increased NAc DA release and cocaine SA in Epac2 cKO mice (rescuing), whereas inhibition of VTA DA neurons expressing hM4D(Gi) decreased DA release and cocaine SA in WT mice (mimicking). Based on these experiments, the authors concluded that Epac2 in midbrain DA neurons contributes to cocaine reinforcement via enhancement of DA release.

The experiments are generally rigorous and the conclusions are mostly well supported by data, but some aspects of behavioral experiments and data analysis need to be clarified or extended.

1. The chemogenetic rescue experiments in Fig. 7 suggested that enhancing DA release in Epac2 cKO mice rescued cocaine SA in mutant mice, but did not necessarily demonstrate that Epac2 mediates this process, thus a causal mechanistic link is missing. This is an important point to clarify because the central theme of the work is that Epac2 regulates cocaine SA via DA release. In addition, it's unclear if chemogenetic activation of DA neurons also enhances sucrose reward. A potentially positive result would not affect the conclusion that enhancing DA release can rescue cocaine SA in mutant mice, but will affect the interpretation and specificity of the rescue data.<br /> 2. Relatedly, chemogenetic inhibition experiments in Fig 8 showed that inhibiting DA neurons reduced DA release and cocaine SA in WT mice, which suggested that the strength of DA transmission was a regulator of cocaine SA. This is expected given the essential role of DA transmission in reward in general, but it did not provide strong insights regarding the specific roles of Epac2 in the process.<br /> 3. Fig 7B. DCZ-induced DA releases enhancement in the fiber photometry recording seems to only last for ~30 min, well short of the duration of a cocaine SA session (3 hrs). It's unclear how this transient DA release enhancement could cause the prolonged cocaine SA behavior.<br /> 4. Fig. 9. working hypothesis: hM4D(Gi) and hM3D(Gs) are shown to inhibit and enhance synaptic vesicle docking, which is not accurate. These DREADDS presumably regulate neuronal excitability, which in turn affects SV release.

Reviewer #1 (Public Review):

Khan et al describe how two important transcription factors functionally cooperate to activate a few of the CRP-dependent genes in Mycobacterium tuberculosis. CRP is a global regulator in eubacteria needed to activate a number of genes while PhoP is an acid stress response regulator required for expression specific set of genes. The authors delineate the interaction between these two key regulators of the bacterial pathogen and show that in a subset of CRP-dependent promoters, PhoP binding recruits CRP to activate transcription.

The experiments are well designed and executed with a coalescent presentation of the manuscript. While the data is well organized and presented with clean images of phophorimages and blots to facilitate their easy understanding, interpretation could have been more robust (see comments below).

Obviously, the strength of the paper is the description of hitherto unknown stress-specific cooperation between two well-studied transcription factors with most evidence supporting the claims. In E.coli (and in other bacteria) studies CRP mediated control of genes have lead to the identification of different classes of CRP-dependent promoters with their own specific regulators. Such a description was lacking in M.tuberculosis and the PhoP - CRP collaboration described is likely to have implications on pathogenesis. The weakness (or possibly what remains to be explored) is that the precise mechanism of the cooperative transcription regulation is yet to be understood.

From the data presented it is apparent that PhoP binds to whiB up promoter own efficiently. It is also evident that CRP is recruited to its site as a result of PhoP binding. This is reminiscent of the bacteriophage Lamba paradigm of positive cooperativity. Thus, it is not reciprocal synergy (as stated in the paper in one place). It is Phop mediated recruitment as claimed elsewhere. Indeed, PhoP null mutants nicely support the latter interpretation

A discussion on why and how CRP binds on its own in other CRP-dependent promoters would help better appreciate the need for PhoP sites next to CRP sites for their cooperative interaction in these promoter subsets. CRP sites could be at a varied distance with respect to the promoter as seen in E.coli.

Reviewer #1 (Public Review):

In this paper the authors explore how trunk neural crest cells (NCCs) acquire regional identity in human ESC differentiation. Following from earlier findings that NMPs in vivo and NMP-like cells in vitro give to trunk neural crest, they now show that the transcription factor TBXT is required for the acquisition of posterior identity of NMPs and their derivative NCCs. When TBXT is reduced in hESCs they do not activate Hox gene expression or the expression of Wnt targets. Using a combination of TBXT ChIPseq in NMPs and ATACseq in control and TBXT depleted NMPs, they show that TBXT binds close to the TSS of genes whose expression is downregulated in the absence of TBXT and that in the absence of TBXT such regions lose their accessibility. These data suggest that TBXT mediates chromatin opening and subsequent activation of these transcripts. Finally, the authors also suggest that acquisition of posterior character in NCCs is largely dependent on Wnt signalling, while posterior spinal cord cells largely depend on FGF signalling.

The role of FGF and Wnt signalling in establishing anterior-posterior identity is well documented and the authors explore these pathways and the role of TBXT in this process using differentiation of human ESCs. The finding that TBXT is required for NMPs and NMP-derived NCCs to acquire posterior identity is interesting, and the authors show that this is likely to involve chromatin accessibility mediated by TBXT and activation of target genes. The involvement of TBXT/Wnt loop in the acquisition of posterior NCC identity is a new finding, and the authors provide an underlying molecular mechanism.

The authors suggest that they uncovered two distinct phases of how the posterior axial identity is controlled; the first involving TBXT/Wnt to generate posterior 'uncommitted progenitors', which then go on to generate NCCs, and the second involving FGF to impart posterior axial identity onto CNS/spinal cord cells. I am not convinced that their data show this; it is equally possible that NMPs are heterogeneous and the effects observed simply reflect a differential response of cells or selection. Since the authors largely analyse their data by qPCR it is difficult to disentangle this.

Some conclusions rely on the changes in expression of just a handful of markers; since gene expression changes dynamically during development it is important to acknowledge that the interpretation is very dependent on the stage examined.

The authors include some expression data in mouse to support their in vitro findings. However, these need to be explained and integrated better.

Reviewer #1 (Public Review):

The authors test a hypothesis that IL-33 plays a role in human parturition. It does so by (1) investigating changes in myometrial cell nuclear IL-33 expression during the third trimester of pregnancy. Their approach studies human myometrial cells, enhancing the clinical translatability of the present work. They demonstrate a reduced nuclear IL-33 staining with the onset of labour, further reduced by LPS. They implicate altered Ca2+ homeostasis in the actions of IL-33, and emerge with a model suggesting that IL-33 directly prevents excessive COX-2 expression in myometrial cells after LPS stimulation and it influences COX-2 expression by maintaining the severity of ER stress response.

Reviewer #3 (Public Review):

Employing primary myometrial cells, this study investigates molecular actions and cellular pathways regulated by interleukin-33 (IL33) a ubiquitous immune modulator that shapes type 1, type 2 and regulatory immune responses. The rational for this study is the notion that Inflammation is one of the major causes of premature delivery, a hypothesis that is not universally accepted as many investigators suggest that inflammation is a consequence of labour.

The manuscript contains mostly appropriate methodologies, although there are some areas that at present are weak and require additional or more refined approaches.<br /> For example, studies on IL33 expression in human tissues have employed a small number of biopsies of limited potential. I would expect the author to use a substantive number of biopsies and calculate H-scores alongside other parameters of inflammatory pathways and develop various regression models.<br /> Without this crucial evidence once is left to wonder what is the rational for all follow-up studies described.<br /> Also, the authors need to be aware that modern approaches to quantitative PCR require multiple 'housekeeping genes and calculation of geometric means.

Reviewer #1 (Public Review):

This is an exciting study using human electrophysiology to provide novel insights into the functional architecture of the posterior cingulate cortex (PCC). As the authors note, the PCC is an enigmatic brain region that is implicated across numerous cognitive functions and appears to play a crucial role in many neurological and neuropsychiatric conditions. Articulating the potential functional specialisations of subdivisions of the PCC to distinct aspects of cognition thus provides an innovative and powerful means of reconciling discrepancies in the extant literature, as well as stimulating new directions in the field.

Strengths of the study include the use of intracranial electrophysiology via local field potential and single-neuron recordings targeting the dorsal PCC. This approach enabled the authors to capture neural activity in the dorsal PCC during alternating episodic and executive cognitive tasks and to localise the functional clustering of single unit activity to uncover functional cell types within PCC.

The experimental tasks seem well-designed, drawing on the current understanding of the role of the DMN in memory-based constructive simulation processes (past and future), and the executive attention tasks to index the CCN. I was also pleased to see the inclusion of a "rest" condition in which endogenously driven forms of spontaneous cognition would be predicted to occur. Overall, the manuscript is very well-written, and the major conclusions appear well supported by the data.

Reviewer #3 (Public Review):

The present study aims to elucidate posterior cingulate cortex (PCC) function with both single-unit and population-level depth electrodes. The results clearly show that the dorsal PCC (dPCC) is involved in executive functions (search and add), but that it also contains neurons that are selective for episodic memory (past and future) and rest conditions. With this impressive study design, the authors are able to reconcile discrepancies between human and primate studies. Furthermore, the derived conclusion that PCC function is more diverse than merely its participation in the DMN is of great importance for the field. Thus, I believe that this work will have a great impact on how we think about the PCC, by (1) emphasizing its participation in executive processes and (2) providing evidence of distinct single-unit response profiles that do not manifest on a population level.

The main strength of this work is the combination of population-level measurements that clearly show the participation of dPCC in executive processes with microelectrode single-unit measurements and an unsupervised hierarchical clustering approach that allows for the identification of 4 distinct SU response profiles within the dPCC. In addition, the population-level electrodes mostly engaged in executive function cluster around an fMRI meta-analysis peak related to executive processing derived from neurosynth, providing a bridge to human fMRI research.

Nevertheless, there is one concern regarding the data collected within the ventral PCC (vPCC) in this study and the way the authors integrated it into their conclusions.

Specifically, the conclusion that "Together, they [the findings] inform a view of PCC as a heterogeneous region composed of dorsal and ventral subregions specializing in executive and episodic processing respectively" may not be completely supported by the data. The dPCC macroelectrode data does clearly show a functional specialization in executive processing, but does the data from vPCC presented in this manuscript also support the claim? While taking a closer look at the vPCC data, several inconsistencies stood out: First, the total number of vPCC electrodes was much smaller (6 vs 29 microelectrodes and one microwire probe that was not analyzed). Second, it is not clear which of the presented electrodes in figure 3 were considered to be ventral. From comparing figure 3 with the dorsal/ventral split displayed in figure 1B, it seems as if only one electrode was unambiguously placed in vPCC. Third, BBG statistics of these 6 electrodes are not presented, thus the claim that they show vPCC functional specialization is not statistically supported.

Reviewer #1 (Public Review):

Jones et al. investigated the relationship between scale free neural dynamics and scale free behavioral dynamics in mice. An extensive prior literature has documented scale free events in both cortical activity and animal behavior, but the possibility of a direct correspondence between the two has not been established. To test this link, the authors took advantage of previously published recordings of calcium events in thousands of neurons in mouse visual cortex and simultaneous behavioral data. They find that scale free-ness in spontaneous behavior co occurs with scale free neuronal dynamics. The authors show that scale free neural activity emerges from subsets of the larger population - the larger population contains anticorrelated subsets that cancel out one another's contribution to population-level events. The authors propose an updated model of the critical brain hypothesis that accounts for the obscuring impact of large populations on nested subsets that generate scale free activity. The possibility that scale free activity, and specifically criticality, may serve as a unifying theory of brain organization has suffered from a lack of high-resolution connection between observations of neuronal statistics and brain function. By bridging theory, neural data, and behavioral dynamics, these data add a valuable contribution to fields interested in cortical dynamics and spontaneous behavior, and specifically to the intersection of statistical physics and neuroscience.

Strengths:<br /> This paper is notably well written and thorough.

The authors have taken a cutting-edge, high-density dataset and propose a data-driven revision to the status-quo theory of criticality. More specifically, due to the observed anticorrelated dynamics of large populations of neurons (which doesn't fit with traditional theories of criticality), the authors present a clever new model that reveals critical dynamics nested within the summary population behavior.

The conclusions are supported by the data.

Avalanching in subsets of neurons makes a lot of sense - this observation supports the idea that multiple, independent, ongoing processes coexist in intertwined subsets of larger networks. Even if this is wrong, it's supported well by the current data and offers a plausible framework on which scale free dynamics might emerge when considered at the levels of millions or billions of neurons.

The authors present a new algorithm for power law fitting that circumvents issues in the KS test that is the basis of most work in the field.

Weaknesses:<br /> This paper is technically sound and does not have major flaws, in my opinion. However, I would like to see a detailed and thoughtful reflection on the role that 3 Hz Ca imaging might play in the conclusions that the authors derive. While the dataset in question offers many neurons, this approach is, from other perspectives, impoverished - calcium intrinsically misses spikes, a 3 Hz sampling rate is two orders of magnitude slower than an action potential, and the recordings are relatively short for amassing substantial observations of low probability (large) avalanches. The authors carefully point out that other studies fail to account for some of the novel observations that are central to their conclusions. My speculative concern is that some of this disconnect may reflect optophysiological constraints. One argument against this is that a truly scale free system should be observable at any temporal or spatial scale and still give rise to the same sets of power laws. This quickly falls apart when applied to biological systems which are neither infinite in time nor space. As a result, the severe mismatch between the spatial resolution (single cell) and the temporal resolution (3 Hz) of the dataset, combined with filtering intrinsic to calcium imaging, raises the possibility that the conclusions are influenced by the methods. Ultimately, I'm pointing to an observer effect, and I do not think this disqualifies or undermines the novelty or potential value of this work. I would simply encourage the authors to consider this carefully in the discussion.

Reviewer #3 (Public Review):

The primary goal of this work is to link scale free dynamics, as measured by the distributions of event sizes and durations, of behavioral events and neuronal populations. The work uses recordings from Stringer et al. and focus on identifying scale-free models by fitting the log-log distribution of event sizes. Specifically, the authors take averages of correlated neural sub-populations and compute the scale-free characterization. Importantly, neither the full population average nor random uncorrelated subsets exhibited scaling free dynamics, only correlated subsets. The authors then work to relate the characterization of the neuronal activity to specific behavioral variables by testing the scale-free characteristics as a function of correlation with behavior. To explain their experimental observation, the authors turn to classic e-i network constructions as models of activity that could produce the observed data. The authors hypothesize that a winner-take-all e-i network can reproduce the activity profiles and therefore might be a viable candidate for further study. While well written, I find that there are a significant number of potential issues that should be clarified. Primarily I have main concerns: 1) The data processing seems to have the potential to distort features that may be important for this analysis (including missed detections and dynamic range), 2) The analysis jumps right to e-i network interactions, while there seems to be a much simpler, and more general explanation that seems like it could describe their observations (which has to do with the way they are averaging neurons), and 3) that the relationship between the neural and behavioral data could be further clarified by accounting for the lop-sidedness of the data statistics. I have included more details below about my concerns below.

Main points:<br /> 1)Limits of calcium imaging: There is a large uncertainty that is not accounted for in dealing with smaller events. In particular there are a number of studies now, both using paired electro-physiology and imaging [R1] and biophysical simulations [R2] that show that for small neural events are often not visible in the calcium signal. Moreover, this problem may be exacerbated by the fact that the imaging is at 3Hz, much lower than the more typical 10-30Hz imaging speeds. The effects of this missing data should be accounted for as could be a potential source of large errors in estimating the neural activity distributions.

2) Correlations and power-laws in subsets. I have a number of concerns with how neurons are selected and partitioned to achieve scale-free dynamics.<br /> 2a) First, it's unclear why the averaging is required in the first place. This operation projects the entire population down in an incredibly lossy way and removes much of the complexity of the population activity.<br /> 2b) Second, the authors state that it is highly curious that subsets of the population exhibit power laws while the entire population does not. While the discussion and hypothesizing about different e-i interactions is interesting I believe that there's a discussion to be had on a much more basic level of whether there are topology independent explanations, such as basic distributions of correlations between neurons that can explain the subnetwork averaging. Specifically, if the correlation to any given neuron falls off, e.g., with an exponential falloff (i.e., a Gaussian Process type covariance between neurons), it seems that similar effects should hold. This type of effect can be easily tested by generating null distributions using code bases such as [R3]. I believe that this is an important point, since local (broadly defined) correlations of neurons implying the observed subnetwork behavior means that many mechanisms that have local correlations but don't cluster in any meaningful way could also be responsible for the local averaging effect.<br /> 2c) In general, the discussion of "two networks" seems like it relies on the correlation plot of Figure~7B. The decay away from the peak correlation is sharp, but there does not seem to be significant clustering in the anti-correlation population, instead a very slow decay away from zero. The authors do not show evidence of clustering in the neurons, nor any biophysical reason why e and i neurons are present in the imaging data. The alternative explanation (as mentioned in (b)) is that the there is a more continuous set of correlations among the neurons with the same result. In fact I tested this myself using [R3] to generate some data with the desired statistics, and the distribution of events seems to also describe this same observation. Obviously, the full test would need to use the same event identification code, and so I believe that it is quite important that the authors consider the much more generic explanation for the sub-network averaging effect.<br /> 2d) Another important aspect here is how single neurons behave. I didn't catch if single neurons were stated to exhibit a power law. If they do, then that would help in that there are different limiting behaviors to the averaging that pass through the observed stated numbers. If not, then there is an additional oddity that one must average neurons at all to obtain a power law.

3) There is something that seems off about the range of \beta values inferred with the ranges of \tau and $\alpha$. With \tau in [0.9,1.1], then the denominator 1-\tau is in [-0.1, 0.1], which the authors state means that \beta (found to be in [2,2.4]) is not near \beta_{crackling} = (\alpha-1)/(1-\tau). It seems as this is the opposite, as the possible values of the \beta_{crackling} is huge due to the denominator, and so \beta is in the range of possible \beta_{crackling} almost vacuously. Was this statement just poorly worded?

4) Connection between brain and behavior:<br /> 4a) It is not clear if there is more to what the authors are trying to say with the specifics of the scale free fits for behavior. From what I can see those results are used to motivate the neural studies, but aside from that the details of those ranges don't seem to come up again.<br /> 4b) Given that the primary connection between neuronal and behavioral activity seems to be Figure~4. The distribution of points in these plots seem to be very lopsided, in that some plots have large ranges of few-to-no data points. It would be very helpful to get a sense of the distribution of points which are a bit hard to see given the overlapping points and super-imposed lines.<br /> 4c) Neural activity correlated with some behavior variables can sometimes be the most active subset of neurons. This could potentially skew the maximum sizes of events and give behaviorally correlated subsets an unfair advantage in terms of the scale-free range.

Reviewer #1 (Public Review):

In this article, the authors are trying to ascertain how emigrated SVZ cells can be beneficial - via neuroreplacement or neuroprotection. They provide evidence for the latter and also show that it is primarily precursors and not differentiated cells that migrate to photo-thrombotic cortical models of stroke.

The writing is lucid and the flow of the experiments logical. The images and quality of data are high and the depth of investigation appropriate (eg 100 cells examined per marker in Figure 1). The methods are clearly described. They appropriately control for changes in cortical lesion size. The photo-thrombotic lesion is a good choice in terms of controlling lesion placement and size.

A distinctive advantage of this paper is they show that reducing SVZ cytogenesis in the stroke model diminishes recovery, especially behavioural (single seed reaching behavior). This essential experiment has been remarkably under-utilized in the field.

The 2-photon imaging of dendric spines after stroke combined with multi-exposure speckle imaging is a technical tour-de-force especially since they combine it with ganciclovir-induced loss of cytogenesis and behavioural assays. Importantly, they show that SVZ cells are needed for full spine plasticity.

They are correct to examine the SVZ response in aging as it diminishes dramatically in animal models but in humans is associated with more strokes. As expected, they show reduced SVZ proliferation after stroke. This was associated with significantly worse performance in the seed-reaching task and depleting SVZ precursors with ganciclovir did not make it worse.

The viral VEGF delivery rescue experiment is fantastic. Behavior, blood vessel growth, and spine density are all rescued.

The idea that SVZ cells are beneficial via mechanisms other than cell replacement is not really that new. For example, neural stem cells from the SVZ have been shown to reduce inflammation and thereby be neuroprotective as the authors themselves acknowledge and cite (Pluchino et al., 2005).

The fact that it is primarily precursor cells that migrate towards the stroke does not mean that cell replacement does not occur. The precursors could gradually differentiate (even after 6 weeks post-injury) into more mature cells that do replace cells lost to injury. Also, the two events are not mutually exclusive.

Overall this is an interesting addition to the literature and methodologically it is quite strong. It is sure to generate follow on studies showing how different growth factors may be secreted by SVZ cells in various models of neurological disease.

Reviewer #3 (Public Review):

Williamson et al. have investigated the role of cells derived from a neural stem cell (NSC) region of the adult mouse brain called the subventricular zone (SVZ) in a model of stroke. The authors labeled SVZ cells with Nestin-CreER and the Ai14 (tdTomato) reporter, induced cortical infarcts 4 weeks later, then analyzed brains 2 weeks thereafter. Most of the tdTomato+ cells in the peri-infarct regions were not neurons but less differentiated neural precursor cells. They then ablated proliferating NSCs in the SVZ with GFAP-TK mice and ganciclovir (GCV) administration, and this reduced SVZ-derived peri-stroke cells and impaired motor recovery. Older mice have less proliferation in the SVZ, and these older mice have fewer peri-infarct SVZ-derived cells and worse recovery than younger mice. Using multi-exposure speckle imaging (MESI) and 2 photon imaging, the authors found that ablation of proliferating SVZ cells reduced vascular remodeling and synaptic turnover in peri-infarct areas. Immunohistochemical analysis revealed the expression of VEGF, BDNF, GDNF, and FGF2. The authors selected VEGF for functional studies, conditionally knocking out VEGF in SVZ cells and finding that this reduced recovery and neuronal spine density. Finally, the authors expressed VEGF by AAV vectors in mice with ablated SVZ, finding that VEGF could improve repair and recovery after stroke.

The results presented in the paper support some of the authors' general conclusions and may be of interest to investigators of adult mouse SVZ. The use of genetic labels for lineage analysis and studies of VEGF conditional knockout in SVZ cells are technical strengths of the study. The results support the idea that VEGF in SVZ cells is important for recovery from stroke in younger adult mice. However, the impact of the work may be somewhat limited, as outlined below.

1. It is already well known that VEGF is an important aspect of stroke recovery (at least in rodent models), and that ectopic expression of VEGF can be beneficial. Showing that some of the VEGF in peri-stroke regions might come from SVZ-derived cells would be a relatively incremental discovery.<br /> 2. Furthermore, while it seems clear that the VEGF conditional knockout (VEGF-cKO) in SVZ cells reduces behavioral recovery and certain histological measures, it is not clear that these impairments are due to a lack of VEGF delivery from the SVZ cells. It is possible that VEGF-cKO changed the proportion of SVZ cells that arrive in the peri-stroke region. It is also possible that VEGF-cKO makes these cells impaired in the expression of other trophic factors.<br /> 3. The cytogenic response to stroke was not characterized in much detail at the cellular level. Essentially only one time point (2 weeks) was selected for immunohistochemistry (Fig. 1), and so the dynamics of this response cannot be evaluated. Does the proportion of cell types change over time? Are migratory cells more homogeneous and then diversify after arrival to the peri-stroke region? At longer time points, do these SVZ-derived cells still exist? Such an analysis is important to the story since the behavior was evaluated at a range of time points (3-28 days after stroke), and recovery was noted as early as 7 days. Are SVZ-derived cells already at the peri-stroke area after 7 days? If they are not already there, then how would the recovery be explained? The behavioral recovery also continues to improve at 28 days; are SVZ-derived cells still present in large numbers at that time? How would the authors explain continued recovery if the SVZ-derived cell population drops away after 2 weeks?<br /> 4. The SVZ-derived peri-stroke cells were not characterized in much detail at the molecular/transcriptomic level. The authors studied 4 trophic factors by antibody staining, but there are many other potential genes that may contribute to the effect. Transcriptomic analyses of SVZ-derived peri-stroke cells (e.g., by single-cell RNA-seq) may provide deeper insights into potential mechanisms.<br /> 5. The significance of this work for understanding stroke in human patients is unclear since the adult human brain SVZ is essentially devoid of neurogenic stem cells. Thus, although some of the observations in this paper are interesting, the cytogenic response to stroke described here may not occur in human patients.

Reviewer #1 (Public Review):

In this study, Scalabrino et al. show persistent cone-mediated RGC signaling despite changes in cone morphology and density with rod degeneration in CNGB1 mouse model of retinitis pigmentosa. The authors use a linear-nonlinear receptive field model to measure functional changes (spatial and temporal filters and gain) across the RGC populations with space-time separable receptive fields. At mesopic and photopic conditions, receptive field changes were minor until rod death exceeded 50%; while response gain decreased with photoreceptor degeneration. Using information theory, the authors evaluated the fidelity of RGC signaling demonstrated that mutual information decreased with rod loss, but cone-mediated RGC signaling was relatively stable and was more robust for natural movies than artificial stimulus. This work reveals the preservation of cone function and a robustness in encoding natural movies across degeneration. This manuscript is the first demonstration of using information theory to evaluate the effects of neural degeneration on sensory coding. The study uses a systematic evaluation of rod and cone function in this model of rod degeneration to make the following findings: (1) cone function persists for 5-7 months, (2) spatial and temporal changes to the ganglion cell receptive fields were not monotonic with time, (3) mutual information between spikes and photopic stimuli remained relatively constant up to 3-5 months, and (4) information rates were higher for natural movies than for checkerboard noise stimuli.

The strengths of this paper include the following:

A systemic evaluation of potentially confusing data. The authors do an excellent job of organizing the results in terms of light levels and time points. The results themselves are confusing and difficult to draw across metrics, but the data are presented as clearly as possible. The work is especially well executed and presented.

The insight that cone responses remain relatively stable despite rod loss. The study clearly demonstrates that despite cone loss and morphological changes, cone-mediated responses remain robust and functional.

The application of information theory to degeneration is the first of its kind and the study clearly shows the utility of the metric.

The results are thoughtfully interpreted.

The weaknesses of this study include the following:

The inability to follow the same ganglion cell types over time is a major weakness that could confound the interpretation in terms of whether the changes are happening from artifacts of the recording method or from dynamic changes in the pooled population of ganglion cells. Is there even a single cell class, for example the ON-OFF direction-selective ganglion cells, that this group has so well quantified on the MEA, that the study could track over time, in addition to examining the pooled population changes over time? Tracking a single cell type for each of the metrics would make the population data more convincing or could clearly show that not all ganglion cells follow the population trend.

While the non-monotonic changes are interesting, they are also difficult to make sense of. Can the authors speculate in the Discussion what could be underlying mechanisms that give rise to non-monotonic changes. In the absence of potential mechanisms, the concern of recording artifacts arises.

The mutual information calculation seems to be correlated with the spike rate despite the argument made in Fig 10E-F. Can the authors show this directly by calculating the bits per spike in Figures 8 and 9? Of all the metrics, the gain function and the mutual information seem to be more consistent with each other. Can the authors demonstrate or refute a connection between the spike rate and information rates?

Can the authors provide an explanation for why the mutual information calculation remains stable despite lower SNR and lower gain, especially after the contributions of oscillations have been ruled out?

Lack of age-matched WT controls to accompany the different time points. It is known that photoreceptor degeneration can occur naturally in WT mice. Though the authors have used controls pooled from across the ages used in the CNGB1 mutants, it would be informative to know if there are age-dependent changes in any of the metrics for WT mice.

Can the authors elaborate on why cone function persists despite the rod loss and morphological changes? This is unique for other models of rod loss and is worth extra discussion.

Reviewer #3 (Public Review):

In the manuscript by Scalabrino et al. a rigorous characterization of the functionality of retinal ganglion cells in a mouse model of rod photoreceptor degeneration is presented. The authors analyzed the degeneration of cone photoreceptors, which is known to be linked to rod degeneration. Based on the time course of cone degeneration they investigated the functional properties of retinal ganglion cells aged between 1 month and seven months.

The most interesting finding is robust preservation of functional properties, as reflected in little changes of the receptive fields (spatial and temporal characteristics) or signaling fidelity/information rate. In contrast to other mouse models, the present one shows no oscillatory activity until a complete loss of cone photoreceptors occurred at an age of nine months.

Although the receptive fields of retinal ganglion cells remain nearly intact, the number of ganglion cells with identifiable receptive fields decreases significantly with age (Fig.2F). Could the authors comment, if this might imply a "patchy" vision?

Reviewer #1 (Public Review):

The authors of this paper are offering the electron microscopy community an affordable tool to semi-automatize some of the most challenging and time-consuming steps to target a region of interest in a sample prepared for electron microscopy. This article is sharing in total transparency all their work and the immense development efforts put in by the authors in terms of finance, manpower, software, and hardware development. A huge effort has been done to make all the parts of the workflow accessible. The way to add the hardware to the existing ultramicrotomes is clearly explained and documented. The hardware to be purchased and adapted is also clearly documented. All the software needed is open-source, the code fully documented and the implementation documented. A critical assessment of the performances is shown for the two main and only suppliers of ultramicrotomes. The reproducibility of the approach has been quantified on numerous samples in a fair and systematic way. The limits and ways for improvements are openly and clearly discussed at the end of the article. All the process is documented by clear and didactic figures helping the readers to put the equations in context.

The implementation of this solution by laboratories will still be a substantial investment but the impact on the research can be so crucial that it can motivate groups to make the effort. The generosity of the authors to share all the data and the fact that nothing is hidden or prevents anybody to adapt this solution is exceptional and should be encouraged.

Reviewer #3 (Public Review):

Meechan et al. describe a technical modification of a standard ultramicrotome that allows, in combination with software solutions provided, both, the precise orientation and the depth of the cutting plane according to sample features pre-defined by X-ray imaging. Accurate targeting of specific structures in heavy-metal¬-impregnated volume EM samples is challenging and time-consuming and good reproducibility across samples is difficult. Since the applications for volume EM are rapidly increasing during the last years, improved workflows can have an important impact in the field.

A great strength of the workflow described here is the easy access to the required components. Once X-ray data acquisition at a micron-resolution has been achieved, no further expensive, sophisticated equipment is required for its application. Motors and controllers are assembled from common electronics or mechanical parts. The microtomes used are standard microtomes as they are available in most electron microscopy laboratories. No major modification to the microtome is required. However, a statement on whether a dedicated microtome is recommended, or how fast the system can be disassembled would have been useful.

The comparative data collection on two different microtome setups, regarding both microtome brand and users, provides a big credit to the study. The design and calibration steps for the microtome motorization are well documented. The success of reaching the targeting plane with an average of below 2 microns in the RMC setup is an amazing result when considering cellular dimensions, and even the 4.5-micron precision achieved on the Leica system is in the range of a single cell.<br /> In this regard, however, the correlation of the targeting precision with user skills remains an open question that has not been addressed. Prior to the automated cutting, the initial manual alignment of the block surface to the knife is of crucial importance (as stated as a potential explanation for differences in the RMC and Leica setup performance). A comparison of the precision reached by different users on one setup could have further completed the study.

Pre-selection of the precise cutting orientation can challenge the users' 3D imagination. Here, the authors have modified modules of existing software solutions (mostly Fiji plugins) for the visualization of the X-ray data and presumptive cutting views. The resulting Crosshair Fiji plugin can be used on a standard computer and is provided with detailed and clear documentation. The implementation within a standard software (Fiji) with existing modules, will ease the use of this plugin.

The choice of Platynereis larvae for targeting the imaging plane allows very clear visualization of the whole procedure. Both the general workflow as well as the specific cases of 10 test samples are well-illustrated by this example tissue. In the future, this proof of principle documented here for the simple larvae should be further validated by a structure embedded in the context of a dense tissue, which can be more challenging.

Further applications will reveal whether this semi-automated workflow can be expanded to correlative light and electron microscopy, with or instead of X-ray imaging. A rapid, precise trimming of fluorescent structures will be of great impact on the volume EM community. For the correlation between X-ray and EM data, the workflow documented by the authors here is already offering an elegant improvement to the time-consuming sample approach with a standard setup.

Reviewer #1 (Public Review):

Authors propose a mechanism where actin polymerization in the dendritic shaft plays a key role in trapping AMPAR vesicles around the stimulated site, promoting the preferential insertion of AMPAR into the potentiated synapse. This dendritic mechanism is novel and may be important for phenomena. Authors also developed a sophisticated method to observe the endogenous behavior of AMPAR using the HITI system.

However, there are some major issues that need to be addressed to support the authors' claims. Also, overall, it is hard to follow. It could be better written.

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 #1 (Public Review):

This is an interesting study with observations that provide intriguing data to further think about how neurons in the medial temporal lobe correlate with recognition memory.

Figures 2 through 6. There is no description of the relationship between the findings and the anatomical location of the electrodes (other than distal versus local). Perhaps the non-uniform distribution of electrodes makes these analyses more complicated and such questions might have minimal if any statistical power. But how should we think about the claims in Figures 2-6 in relationship to the hippocampus, amygdala, entorhinal cortex, and parahippocampal gyrus? As one example question out of many, is Figure 2C revealing results for local pairs in all medial temporal lobe areas or any one area in particular? I won't spell out every single anatomical question. But essentially every figure is associated with an anatomical question that is not described in the results.

Figure 1<br /> 1A. I assume that image positions are randomized during a cued recall?<br /> What was the correlation between subjects' indication of how many images they thought they remembered and their actual performance?<br /> 1B. Chance is shown for hits but not misses. I assume that hits are defined as both images correct and misses as either 0 or 1 image correct. Then a chance for misses is 1-chance for hits = 5/6. It would be nice to mark this in the figure.<br /> The authors report that both incorrect was 11.9%. By chance, both incorrect should be the same as both correct, hence also 1/6 probability, hence the probability of both incorrect seems quite close to chance levels, right?<br /> 1C. How does the number of electrodes relate to the number of units recorded in each area?

Line 152. The authors state that neural firing during encoding was not modulated by memory for the time window of interest. This is slightly surprising given that other studies have shown a correlation between firing rates and memory performance (see Zheng et al Nature Neuroscience 2022 for a recent example). The task here is different from those in other studies, but is there any speculation as to potential differences? What makes firing rates during encoding correlate with subsequent memory in one task and not in another? And why is the interval from 2-3 seconds more interesting than the intervals after 3 seconds where the authors do report changes in firing rates associated with subsequent performance? Is there any reason to think that the interval from 2-3 seconds is where memories are encoded as opposed to the interval after 3 seconds?

Lines 154-157 and relationship to the subsequent analyses. These lines mention in passing differences in power in low-frequency bands and high-frequency bands. To what extent are subsequent results (especially Figures 3 and 4) related to this observation? That is, are the changes in spike-field coherence, correlated with, or perhaps even dictated by, the changes in power in the corresponding frequency bands?

Do local interactions include spike-field coherence measurements from the same microwire (i.e., spikes and LFPs from the same microwire)?

Figure 6. I was very excited about Figure 6, which is one of the most novel aspects of this study. In addition to the anatomical questions about this figure noted above, I would like to know more. What is the width of the Gaussian envelope? Are these units on the same or different microwires? How do the spike latencies reported here depend on the firing rates of the two units? What do these results look like for other pairs that are not putative upstream/downstream pairs?

Reviewer #1 (Public Review):

The work suggests an evolutionary "arms race" between Ophiocordyceps BRM1 and Aglaia and that acquisition of eIF4A-H153G was a key step permitting the fungus to colonize the plant during its life cycle. Functional experiments are convincing in terms of differential sensitivity of translation to repression by rocaglates when H153G (or equivalent) is introduced to various eIF4A isoforms from multiple species in cell-free reporter systems and in engineered fungal strains. Although BRM1 could not be genetically engineered, the authors introduced H153G or wild-type eIF4A into related C. orbiculare species and found the substitution reversed translational repression phenotypes of rocaglates. H153G also permitted growth on rocaglate-treated cucumber leaves in contrast to wild-type. Overall the work demonstrates a specific AA substitution analogous to change in the Aglaia plant itself that may permit Ophiocordyceps BRM1 to grow on the plant, bypassing a key defense mechanism. The H153 polymorphism in Ophiocordyceps BRM1 suggests growth of Aglaia species is an obligate part of the fungus' life cycle, and that it evolved to fill this niche in a way that no other described species has done. However, since the organism is also known to parasitize ant species, it is not entirely clear from the data presented that growth on Aglaia is an obligate step. Regardless, the report is highly suggestive of a specific AA substitution having evolved in a fugal species to bypass a specific plant defense strategy.

Reviewer #1 (Public Review):

This article clearly illustrates the limitations of previous predator escape models that (1) fail to incorporate the initial orientation of prey relative to predators, and (2) do not properly describe the endpoint of predator attacks, instead assuming infinite trajectories. The approach is novel and the implications for stochastic strategies are important. Some subtle rearrangements would improve the presentation of the data.

The correspondence between the presented behavioral data and model instantly validates the incorporation of predator attack distance and initial orientation of the prey into escape models. I am completely convinced that the lack of the two incorporated variables prevented the accurate reconstruction of ETs. These two variables create distributions over escape choices that are eventually claimed to balance behavioral perfection (i.e., minimization of Tdiff) with unpredictability (i.e., the choice of slightly suboptimal ETs when the effect on Tdiff is negligible relative to predator capture times). This is a case where precision is sometimes favored over variability and other times variability over precision.

It's here where my very mild (I truly liked this article - it is well done, well written, and creative) comments arise. The implications for stochastic strategies immediately emerge in the early results - bimodal strategies come about from the introduction of two variables. There is not enough credence given to the field of stochastic behavior in the introduction - the introduction focuses too much on previous models of predator-prey interaction, and in fact, Figure 1, which should set up the main arguments of the article, shows a model that is only slightly different (slight predator adjustment) that is eventually only addressed in the Appendix (see below). The question of "how and when do stochastic strategies emerge?" is a big deal. Figure 1 should set up a dichotomy: optimal strategies are available (i.e., those that minimize Tdiff) which would predict a single unimodal strategy. Many studies often advocate for Bayesian optimal behavior, but multimodal strategies are the reality in this study - why? Because if you consider the finite attack distance and inability of fish to evoke maximum velocity escapes while turning, it actually IS optimal. That's the main point I think of the article and why it's a broadly important piece of work. Further framing within the field of stochastic strategies (i.e., stochastic resonance) could be done in the introduction.

All experiments are well controlled (I especially liked the control where you varied the cutoff distance given that it is so critical to the model). Some of the figures require more labeling and the main marquee Figure 1 needs an overhaul because (1) the predator adjustment model that is only addressed in the Appendix shouldn't be central to the main introductory figure - it's the equivalent of the models/situations in Figure 6, and probably shouldn't take up too much space in the introductory text either (2) the drawing containing the model variables could be more clear and illustrative.

Finally, I think a major question could be posed in the article's future recommendations: Is there some threshold for predator learning that the fish's specific distribution of optimal vs. suboptimal choice prevents from happening? That is, the suboptimal choice is performed in proportion to its ability to differentiate Tdiff. This is "bimodal" in a sense, but a probabilistic description of the distribution (e.g., a bernoulli with p proportional to beta) would be really beneficial. Because prey capture is a zero-sum game, the predator will develop new strategies that sometimes allow it to win. It would be interesting if eventually the bernoulli description could be run via a sampler to an actual predator using a prey dummy; one could show that the predator eventually learns the pattern if the bernoulli for choosing optimal escape is set too high, and the prey has balanced its choice of optimal vs. suboptimal to circumvent predator learning.

Overall, a very good article.

Reviewer #1 (Public Review):

The authors combined light-sheet-based imaging with computational tools to track C. elegans motor behavior throughout the last ~5hrs of embryonic development. Using PCA and quantitative methods, the authors identified postures and movements along developmental time. Early on, embryonic movements are continuous and dominated by dorsoventral "flips". The embryos then enter a period of low activity followed by a phase where episodic sinusoidal waves are predominant. The authors later defined this episodic behavior as "slow wave twitch" (SWT). These phases are stereotyped across embryos, and the early flipping phase depends on neuronal synaptic transmission. Using a brightfield high-throughput method the authors implicated neuropeptides in SWT. Finally, they demonstrated that a somnogenic neuropeptide secreted from RIS neurons mediates the quiescent periods observed during SWT.

At a high level, the authors developed a pipeline to capture behavior during late embryonic development to make the following conclusions: 1) Embryonic behaviors followed a stereotyped trajectory, with early flipping and a late-stage dominated by episodic sinusoidal crawling-like waves. 2) Synaptic transmission is necessary for late-stage episodic movements. 3) A peptidergic neuron known to promote a sleep-like state in the hatched animals promotes quiescent periods observed during SWT. Overall these conclusions are well supported by the presented data. This work focuses on the late stages of development when behaviors emerge, a heavily understudied period. The study provides some of the first insights into embryonic behaviors in C.elegans and lays the groundwork for further studies using this system. Therefore, this work should have a significant impact on the fields of neurodevelopment and neuroscience.

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 #1 (Public Review):

The authors tested, in 30 subjects, a model of traveling signals along the thalamo-cortico-thalamic projections to explain individual differences in spindle frequency. As predicted, they showed the presence of a relationship between the length of the thalamocortical white matter bundles and sleep spindle frequency (a specific characteristic of this functional network), and further showed that this neuroanatomical marker mediated the sex-related differences in sleep spindle frequency.

This paper has several strengths, both methodologically and conceptually. The authors leverage the use of polysomnographic/EEG overnight recordings and diffusion MRI data for their analysis, providing a unique dataset in a group of men and women. The focus on understanding the well-established sex difference in sleep spindles is a significant strength and advances knowledge and understanding of neuroanatomical underpinnings for this sex difference. Interestingly, the authors did not find a relationship between this neuroanatomical measure and sleep spindle amplitude, which deserves further comment. The current work can be used as a foundation for future work, for example, examining the relationship between neuroanatomical white matter fiber bundle length between thalamus and frontal cortex and functional sleep spindle outcomes, such as memory consolidation, as well as exploring age-related changes/differences in these measures.

Reviewer #1 (Public Review):

The authors initially demonstrated that the deletion of LIS1 using an inducible Cre mouse model hindered the maturation of T cells, as evidenced by a reduction in the number of DPs. Furthermore, it reduced early T cell and B cell development, specifically during β selection and prepro to pro B cells in the case of T and B cells, respectively. This correlated with an increase in cells at the G2/M stage. The authors then sorted for DN3 cells and seeded them onto OP9-DL1 stromal cells. In this model, the deletion of Lis1 reduced proliferation and lead to an accumulation of the cells at G2/M, similar to the results in vivo.

The authors then switch to examining the role of Lis1 at later stages of T cell development by deleting Lis1 at the DP stage. The deletion of Lis1 at this stage resulted in a reduction in CD4+ and CD8+ cells, which correlated with a drop in proliferation in CD4+, after the first division and a slight reduction in CD8+ cells. The drop in proliferation and increase in cells at the G2/M stage was shown to be due to an inability to correct condense the DNA at metaphase, resulting in aberrant numbers of centromeres and upregulation of apoptosis, which was also confirmed in DN3 cells. Finally, they demonstrate that this is due to an ineffective interaction between dynein and dynactin. Overall, this was an interesting study into the role of Lis1 in T cell division.

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 #1 (Public Review):

In this manuscript, Hansen and coworkers make use of the powerful, single-molecule assay CoSMoS to study the recognition of the 5' splice site by the U1 snRNP. Specifically, they investigate how 5' splice site oligos interacts with purified U1 snRNP to isolate 5' splice site-binding from other factors, including the CBC, BBP, and any other factors in whole cell extract that may impact binding; previous studies have investigated binding in vivo or in cellular extracts or with limited quantitative capabilities. The authors find evidence for a reversible, two-step, binding reaction in which a short-lived interaction precedes a long-lived interaction and in which binding depends on the 5' splice site sequence and the 5' end of U1. The data further suggests a compelling kinetic framework for how U1 surveys nascent transcripts for a bona fide 5'SS; specifically, both authentic and inauthentic 5' splice sites form the short-lived complexes but whereas the inauthentic complex preferentially dissociates, the authentic complex preferentially proceeds to a stable complex. Using oligos with different mutations to limit base-pairing they find that at least six potential base-pairs are required for association but that a stretch of seven base-pairs, with a maximum of one mismatch, is required for the long-lived interaction, with residues near the 5' splice site playing more important roles and with length being a stronger predictor of complex lifetime than thermodynamics, with implications for splice site predictions.

The work focuses on the determinants and mechanism of the first and a pivotal step in splicing, in a manner that completes recent structural advances. The work extends findings presented in a previous publication from the lab (Larson and Hoskins, 2017) studying binding of U1 snRNP to the 5' splice site in extract. In that study, the authors provided early evidence of two-step U1 snRNP binding in the absence of the cap binding complex or the branch point binding protein, with a more stable state following a weaker state; although factors in the extract may have influenced binding, the results are not qualitatively different here. The authors also showed some evidence in the previous study that longer binding depended on crossing a threshold and did not increase further with greater stabilization. Still, this new work is of high quality with conclusions justified by the data and of significant interest to the splicing field and of general interest to those investigating binding of snRNPs to nucleic acid.

Specific Points:

1. To test and define the role of protein in the snRNP, the authors need to investigate the roles of Yhc1 and Luc7 in 5' splice site binding in this assay, particularly with respect to defining the basis of asymmetry and snRNP destasbilization.

2. The similarity or difference of the two-step recognition mechanism described here to the recognition mechanisms of other nucleic acids by other RNP complexes is unclear. The authors need to put their findings into a larger context, relating their findings to studies of analogous systems described in the literature.

3. It is important that the authors address whether they can rule out that the exclusively long-lived complexes skip the short-lived conformation.

4. Given the co-transcriptional nature of many splicing events, the authors should discuss how recruitment by RNAP II might impact the two-step process. For example, fast dissociation by short duplexes might be countered by retention of U1 locally via RNP II.

Reviewer #1 (Public Review):

In this manuscript, Blanc et al. developed a tool to align different larval zebrafish brains with pan-neural markers and additional birthdate labeling into a common atlas. By aligning transgenic lines into this reference atlas, the authors tried to infer the birth date and growth patterns of different neuron populations. The intention of providing an open-access tool and developmental atlas is good, especially considering most of the current zebrafish brain atlases were made for adult or larval zebrafish more than 5 days old. However, the key features claimed by the authors i.e., the "temporal dynamic" is essentially missing from the atlas. The tool was still built for a single development stage and reflected no information on growth patterns except the neuronal birthdate. Moreover, the accuracy of the registration method, the rationality of the birthdate labeling, and the validity of the proof-of-concept inference were also not sufficiently demonstrated in the experimental design.

Overall, I believe the manuscript has the potential to be a useful tool and an impactful developmental atlas for the community, but it would need substantial improvement in method design, experimental validation, and data/software availability.

Major points:

1. The authors claimed to have made a "3D-temporal" atlas for developing zebrafish hindbrain. However, the "temporal" component was solely birthdate inferred from temporal labeling. Images were still acquired at the same developmental stage, which makes the atlas and registration method not substantially different from the other existing atlases (e.g. ViBE-Z (Ronneberger 2012), Z-Brain (Randlett 2015), ZBB (Tabor 2019), Mapzebrain (Kunst 2019) - note not all of these tools were cited in introduction). The authors would have to either add temporal tracing of the population and provide registration between different developmental stages, or tune down the "temporal" term only to "birthdating".

2. Rigid registration was used to align the images from different individuals, as opposed to the more complicated non-linear registration used by all the tools above. The accuracy of such registration needs to be measured to justify the choice of method, by measuring the inter-individual variability using different registration methods. Variability should be quantified in 3D rather than along specific anatomical axes.

3. Birthdate labeling was achieved by photoconverting Kaede at different stages (24, 36, 48 hpf) and imaging at 72hpf. This method suffers from an intrinsic bias: the Kaede-red was subject to different time windows for diluting and metabolizing over development, making the age labeling incomparable between different labeling lengths. To verify the experimental design, the authors should 1) demonstrate that the red cells labeled in an early conversion are strictly included in the red cells labeled in a late conversion, and 2) provide an additional age-labeling method like BrdU treatment, to show the new cells incorporated between the two time points are reflected in the growing photoconverted population.

4. Proof-of-concept inference of GABAergic neuron birth date in Figure 5 is very vague. No link was shown between the red cells in Fig 5B and the gad1b in situ-positive cells in Fig 5D. If tracing the fate of these cells from 24-72hpf is not possible, the authors should at least demonstrate that they are 1) post-mitotic at 24hpf, i.e. HuC-positive; and 2) appear in similar numbers and similar neighborhood context as the red cells in Fig 5B. I also want to point out that while it is true that mRNAs are expressed earlier than fluorescent proteins in the transgenic line, an early-born cell expressing a specific gene late development does not mean it would express the gene early on. A gene can be ON early on and turned OFF later; Conversely, a gene can express late in the differentiation process while the cell is committed and went through terminal division early in the lineage.

5. It is mentioned many times that the platform is "open-access" and "expandable", but no source or browsable atlas was provided (maybe I was wrong, but I did not find the Fiji macro and R code on the provided website). The software and data availability should be improved, and more demonstration is needed to show its "expendability" -guidelines should be provided on how to upload users' own data to use this platform, and what kind of additional data is supported.

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 #1 (Public Review):

Liau and colleagues have previously reported an approach that uses PAM-saturating CRISPR screens to identify mechanisms of resistance to active site enzyme inhibitors, allosteric inhibitors, and molecular glue degraders. Here, Ngan et al report a PAM-saturating CRISPR screen for resistance to the hypomethylating agent, decitabine, and focus on putatively allosteric regulatory sites. Integrating multiple computational approaches, they validate known - and discover new - mechanisms that increase DNMT1 activity. The work described is of the typical high quality expected from this outstanding group of scientists, but I find several claims to be slightly overreaching.

Major points:

The paper is presented as a new method - activity-based CRISPR scanning - to identify allosteric regulatory sites using DNMT1 as a proof-of-concept. Methodologically, the key differentiating feature from past work is that the inhibitor being used is an activity-based substrate analog inhibitor that forms a covalent adduct with the enzyme. I find the argument that this represents a new method for identifying allosteric sites to be relatively unconvincing and I would have preferred more follow-up of the compelling screening hits instead. The basic biology of DNMT1 and the translational relevance of decitabine resistance are undoubtedly of interest to researchers in diverse fields.

In contrast, I am unconvinced that there is any qualitative or quantitative difference in the insights that can be derived from "activity-based CRISPR scanning" (using an activity-based inhibitor) compared to their standard "CRISPR suppressor scanning" (not using an activity-based inhibitor). Key to their argument, which is expanded upon at length in the manuscript, is that decitabine - being an activity-based inhibitor that only differs from the substrate by 2 atoms - will enrich for mutations in allosteric sites versus orthosteric sites because it will be more difficult to find mutations that selectively impact analog binding than it is for other active-site inhibitors. However, other work from this group clearly shows that non-activity-based allosteric and orthosteric inhibitors can just as easily identify resistance mutations in allosteric sites distal from the active site of an enzyme (https://www.biorxiv.org/content/10.1101/2022.04.04.486977v1). If the authors had compared their decitabine screen to a reversible DNMT1 inhibitor, such as GSK3685032, and found that decitabine was uniquely able to identify resistance mutations in allosteric sites, then I would be convinced. But with the data currently available, I see no reason to conclude that "activity-based CRISPR scanning" biases for different functional outcomes compared to the "CRISPR suppressor scanning" approach.

How can LOF mutations from cluster 2 be leading to drug resistance? It is speculated in the paper that a change in gene dosage decreases the DNA crosslinks that cause toxicity. However, the immediate question then would be why do the resistance mutations cluster around the catalytic site? If it's just gene dosage from LOF editing outcomes, would you not expect the effect to occur more or less equally across the entire CDS?

In general, I found the screens, and integrative analyses, highly compelling. But the follow-up was rather narrow. For example, how much do these mutations shift the IC50 curves for DAC? What kinetic parameters have changed to increase catalytic activity? Do the mutants with increased catalytic activity alter the abundance of methylated DNA (naively or in response to the drug)? It is speculated that several UHRF1 sgRNAs disrupt PPIs and not DNA binding, but this is never tested.

Reviewer #1 (Public Review):

In general, I consider that the manuscript reflects a huge effort in terms work done and data collection, the manuscript is very well written, and it brings new knowledge in terms of cooperative breeding and its connection with groups size in ostrich. My major concerns are about the title and introduction that are in my opinion too broad and not enough detailed.

In the introduction the scientific background that led to this research is lacking, and the manuscript would benefit from a more supported introduction, which makes it difficult to understand how far this study went comparatively to previous studies.<br /> The research work was well conducted, and adjusted to the study aims. However, it would benefit from including more details on the observational data collected by the authors.

I think the research topic is interesting, and the study was well performed, but the manuscript would benefit from a more clear approach to the working hypothesis, expected results and background theories/hypotheses.

Reviewer #1 (Public Review):

The transition from flagellar motility to ameboid movement enables Trichomonas vaginalis to interact more intimately with the cells of the urogenital tract to colonize a host. This transition is characterized by a profound morphological shift that allows the parasite to adopt a more ameboid type of movement on the colonized epithelium. Over the past several years some of the molecules on the surface of Trichomonas have been characterized, but little is known about the mechanisms of cytoskeletal rearrangement that mediate the transition to cytoadherence and ameboid movement. The present study capitalizes on T. vaginalis isolates that retain a non-adherent (T1) or adherent (TH1) phenotype to identify differences in the actin cytoskeleton that are correlated with cytoadherence, uncovering a new protein (TvFACPα) that appears to regulate the process.

The authors first establish that the amounts of actin and actinin are correlated with greater cytoadherence. This result is somewhat expected for ameboid movement; nevertheless, the conclusion is supported by treatment with Latrunculin B. Immunoprecipitating T. vaginalis actin, the authors find a non-canonical homolog of the cap-binding protein alpha subunit (TvFACPα). The effects of TvFACPα on cytoadherence, morphogenesis, and wound-closing assays are compelling. In particular, the use of several mutants affecting actin binding (Δ237) and phosphorylation (S2A/D) provides some information about the structure-function relationship of the protein. Based on the known phosphorylation of human CPα by casein kinase 2 (CKII), the authors use an antibody against the known phosphorylation motif of CKII (pS/pTDXE) which partially matches the suspected phosphorylation motif on TvFACPα: pSESE. The S2A mutant is not recognized by the antibody (suggesting that it indeed recognizes the intended PTM) and the signal is diminished by treatment with the CKII inhibitor TBB. Perhaps the most compelling evidence for the effect of TvFACPα phosphorylation is the impact of TBB on ameboid morphogenesis, which is overcome by the expression of the S2D mutant of TvFACPα.

Several experiments also focus on the biochemical activity of TvFACPα to claim that it functions as a typical capping protein. The evidence presented relies on steady-state experiments and co-immunoprecipitation, which are ill-suited to determine the function of actin-binding proteins. Calling into further question the validity of their biochemical assays, binding assays showed relatively modest differences between G and F actin binding by WT TvFACPα (less than 2-fold), and a similarly modest decrease in binding when the putative actin-binding domain of TvFACPα was removed. The micromolar affinities calculated are also much higher than the typical nanomolar affinities of cap-binding proteins. Other cap-binding proteins occur as heterodimers, so the lack of a β subunit in the IPs calls into question the true identity of the novel factor. While the studies with live parasites do support the importance of TvFACPα in Trichomonas morphogenesis and cytoadherence, more detailed studies will be necessary to define its biochemical function.

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 #1 (Public Review):

This is a simulation study comparing the performance of two major approaches for dealing with "population structure" when carrying out Genome-wide Association Studies - Principal Component Analysis and Linear Mixed-effects Models - a subject of considerable practical importance. The author correctly notes that previous comparisons have been quite limited. In particular, any study not concluding that LMM was superior has relied on very simple models of structure.

The paper is clearly written and beautifully reviews the theoretical underpinnings (albeit in a manner that will be difficult to penetrate without deep knowledge of several fields). The simulations are well-designed and far better than previous studies. From a theoretical point of view, the work is somewhat limited by being strongly anchored in a very classical quantitative genetics framework that is focused on allele frequencies and inbreeding coefficients, and totally ignores coalescent theory, but this is a minor quibble. The simulations are limited by utilizing ridiculously small sample sizes by the standards of modern human GWAS. And of course, they do not include all the complexities of real data.

The main conclusion of the study is that LMM really are generally superior - as expected on theoretical grounds. However, the authors do address whether switching to LMM really is practicable given the sample size and lack of data sharing that characterize human genetics. Nor is it clear whether the difference in performance matters in real life given that the entire framework used is an idealized one - the fact that real human data suffers from environmental confounders that are correlated with "ancestry" is not addressed, to take the most obvious example. That said, it is surely important to note that the approach routinely used by the majority of users (PCA with 10 PCs) is most used for historical reasons and has little theoretical or empirical justification.

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 #1 (Public Review):

The authors sought to understand the mechanistic basis for differential effects of Ism1 (a protein released by adipocytes, immune cells, and others) and insulin at the level of protein phosphorylation. The critical experiments included analysis of phosphoproteome of cultured cells treated with insulin, recombinant Ism1, or albumin and tests of effects of global Ism1 knockouts on metabolism, in cage activity and skeletal muscle function. The phosphoproteome experiments demonstrated the expected overlap in peptides that were phosphorylated or dephosphorylated by Ism1 and insulin. Shared signaling pathways included increased Akt and mTOR activity. There were also phosphopeptides unique to Ism1 for which gene ontology analysis revealed enrichment for pathways linked to skeletal muscle. Ism1 stimulated protein synthesis in cultured C2C12 and in skeletal muscle in-vivo. Differential phosphorylation of Irs2 was observed when comparing the phosphoproteome for Ism1 and insulin suggesting a mechanistic basis for divergent activation of intracellular pathways. Studies of mice with global knockouts of Ism1 showed reduced muscle fiber cross-sectional area for some muscles and reduced grip strength. There was no change in whole-body metabolism or in-cage activity. The study adds interesting new information about signaling by Ism1 and suggests that Ism1 might be one determinant of homeostasis of muscle protein anabolism, catabolism, and strength.

There are a few caveats to consider when interpreting the data that include:

1. Gender effects were not considered;<br /> 2. Effects of the Ism1 knockout on muscle fiber area seemed to vary from muscle to muscle for unclear reasons;<br /> 3. The re-analysis of single-cell seq data may not have sampled many Myonuclei.

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 #1 (Public Review):

Cyclic GMP (cGMP) and Ca2+ signaling have been strongly linked to parasite motility and invasion in apicomplexan parasites. Over the last decade, researchers have pieced together an understanding of key molecules (in particular kinases) involved in regulating motility. Whilst there has been some attempt at identifying Ca2+ responsive kinases, using phosphoproteomics, this has lacked temporal analysis. Herneisen et al performed a time-resolve analysis of phosphorylation upon stimulation with the PDE inhibitor zaprinast (which stimulates cGMP signaling upstream of Ca2+ responses). They identify well over 4000 proteins per run, which is the highest coverage yet seen in Toxoplasma and must be close to the full proteome at this lifecycle stage. Their careful analysis (which I find the most compelling aspect of this work) clusters groups of phosphorylated proteins based on their temporal pattern and confirms and extends what is understood about the order of events that occurs during signaling that activates motility and invasion across the apicomplexan parasites.

Hernesisen et al then combine thermal proteome profiling, to understand how proteins respond to changes in Ca2+ concentration. The aim of this is to identify effectors of phosphorylation patterns over time and is an ingenious way of getting an answer to the problem. It will not only identify proteins that directly bind Ca2+ ( a change in thermal stability in the presence of this ion) but potentially other proteins/complexes that change in structure also. They are careful in their analysis of the resulting dataset to not overinterpret their findings. Furthermore, they validate their findings on several proteins that likely do not directly bind Ca2+ (but likely change in other ways upon a Ca2+ signal). Pleasingly, these 5 candidates validate the approach. Across all datasets, the analysis of this data is robust, insightful, and concise and will be of great value to the apicomplexan research community.

What is nice to see, and something that has not been explored much in Apicomplexa is a focus on proteins that become dephosphorylated upon signaling. They then go on to functionally characterize a PP1 orthologue, which also changes thermal stability upon increasing Ca2+ concentration and likely mediates downstream dephosphorylation. The phenotype is a little messy, likely because, as pointed out by the authors, that PP1s localisation and activity is mediated by partner proteins. They, however, clearly show a change in localisation upon stimulation of motility with zaprinast, but not the Ca2+ ionophore A23187 and that PP1 depleted parasites likely have a defect in the invasion. The level of cytosolic PP1-depleted parasites (as measured by GCaMP) only differs when stimulation with zaprinast not with A23187.

The authors then finish by applying their phosphoproteomic approach to PP1-depleted parasites and reveal changes. The results are in line with a recent Plasmodium publication (Paul et al, Nat Comms, 2021)(which they appropriately cite).

Overall, this paper was a pleasure to read, its conclusions were valid and not over-interpreted (which can be the case when performing these types of experiments). They have managed to extract meaningful data from these large data sets into easily interpretable graphical representations and carefully validate their results. The work is of the highest quality and sets a benchmark for the field.

Reviewer #3 (Public Review):<br /> <br /> The report is a major leap in understanding the Ca2+-central pathways underlying egress and invasion of Apicomplexa, using T. gondii as a model organism. Temporal phosphoproteomics is novel, yet even more innovative is to apply temperature stability profiling using various Ca2+ concentrations and temperatures. This provides a really unprecedented depth in the Ca2+ protein network, revealing several dynamic trends in the responses, reveals many new proteins with stability shifts in absence of apparent Ca2+-binding, and ties together many previous observations on putative channels and transporters and signaling pathways. The dynamics of PP1 are intriguing, first accumulating apical of the nucleus (secretory pathway compartment?) and then transitioning apically and to the cortex. Although this is characterized as 'pleiotrophic' I am not sure that is a correct term if this is a PKG-dependent trajectory (but can be bypassed by Ca ionophore) - all of which are somewhat artificial stimulations and therefore could present pleiomorphic under these conditions: some more caution in the results/discussion would be warranted.

Reviewer #1 (Public Review):

In this study, Romero, Prosper, and colleagues have investigated the differential gene expression and regulation in hematopoietic stem and progenitor cells (HSPCs) in young or elderly healthy individuals. With the use of single-cell RNA sequencing (scRNA seq), the authors identified that the stem/progenitor repertoire is changed in elderly individuals, which is accompanied by changes in cell differentiation. The authors additionally compare HSPCs from patients with myelodysplastic syndrome (MDS) and found that MDS patients exhibit specific alterations in erythroid differentiation gene regulatory networks in MDS HSPCs. Overall, this study deals with a valuable resource of HSPC profiles in healthy individuals and proves the biased hematopoietic landscape over aging at a transcriptome level. It will serve as a valuable resource for understanding the molecular basis for hematopoietic aging, which will be useful for future therapeutics and applications.

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 #1 (Public Review):

The authors of this manuscript aimed at demonstrating the hypothesis that hyperacusis is triggered by increased sensitivity in mid-range frequency following high-frequency cochlear trauma. The study combines a large variety of careful physiological and behavioral measurements that converge toward the above-mentioned interpretation, which was proposed in an earlier report. This will likely boost the development of hyperacusis mouse models which is beneficial for future treatments.

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 #1 (Public Review):

This is a carefully-conducted fMRI study looking at how neural representations in the hippocampus, entorhinal cortex, and ventromedial prefrontal cortex change as a function of local and global spatial learning. Collectively, the results from the study provide valuable additional constraints on our understanding of representational change in the medial temporal lobes and spatial learning. The most notable finding is that representational similarity in the hippocampus post-local-learning (but prior to any global navigation trials) predicts the efficiency of subsequent global navigation.

Strengths:

The paper has several strengths. It uses a clever two-phase paradigm that makes it possible to track how participants learn local structure as well as how they piece together global structure based on exposure to local environments. Using this paradigm, the authors show that - after local learning - hippocampal representations of landmarks that appeared within the same local environment show differentiation (i.e., neural similarity is higher for more distant landmarks) but landmarks that appeared in different local environments show the opposite pattern of results (i.e., neural similarity is lower for more distant landmarks); after participants have the opportunity to navigate globally, the latter finding goes away (i.e., neural similarity for landmarks that occurred in different local environments is no longer influenced by the distance between landmarks). Lastly, the authors show that the degree of hippocampal sensitivity to global distance after local-only learning (but before participants have the opportunity to navigate globally) negatively predicts subsequent global navigation efficiency. Taken together, these results meaningfully extend the space of data that can be used to constrain theories of MTL contributions to spatial learning.

Weaknesses:

1. The study has an exploratory feel, in the sense that - for the most part - the authors do not set forth specific predictions or hypotheses regarding the results they expected to obtain. When hypotheses are listed, they are phrased in a general way (e.g., "We hypothesized that we would find evidence for both integration and differentiation emerging at the same time points across learning, as participants build local and global representations of the virtual environment", and "We hypothesized that there would be a change in EC and hippocampal pattern similarity for items located on the same track vs. items located on different tracks" - this does not specify what the change will be and whether the change is expected to be different for EC vs. hippocampus). I should emphasize that this is not, unto itself, a weakness of the study, and it appears that the authors have corrected for multiple comparisons (encompassing the range of outcomes explored) throughout the paper. However, at times it was unclear what "denominator" was being used for the multiple comparisons corrections (i.e., what was the full space of analysis options that was being corrected for) - it would be helpful if the authors could specify this more concretely, throughout the paper.

2. Some of the analyses featured prominently in the paper (e.g., interactions between context and scan in EC) did not pass multiple comparisons correction. I think it's fine to include these results in the paper, but it should be made clear whenever they are mentioned that the results were not significant after multiple comparisons correction (e.g., in the discussion, the authors say "learning restructures representations in the hippocampus and in the EC", but in that sentence, they don't mention that the EC results fail to pass multiple comparisons correction).

3. The authors describe the "flat" pattern across the distance 2, 3, and 4 conditions in Figure 4c (post-global navigation) and in Figure 5b (in the "more efficient" group) as indicating integration. However, this flat pattern across 2, 3, and 4 (unto itself) could simply indicate that the region is insensitive to location - is there some other evidence that the authors could bring to bear on the claim that this truly reflects integration? Relatedly, in the discussion, the authors say "the data suggest that, prior to Global Navigation, LEs had integrated only the nearest landmarks located on different tracks (link distance 2)" - what is the basis for this claim? Considered on its own, the fact that similarity was high for link distance 2 does not indicate that integration took place. If the authors cannot get more direct evidence for integration, it might be useful for them to hedge a bit more in how they interpret the results (the finding is still very interesting, regardless of its cause).

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 #1 (Public Review):

The manuscript is likely of interest to cryo-electron microscopists working on cellular samples. It details a data-acquisition scheme for mapping large areas at a fine pixel size by cryo-electron microscopy for the purpose of macromolecular identification by high-resolution 2D template matching (2DTM). The authors succinctly describe the methodology, as well as detail the apparent effects of microscope aberrations on 2DTM results.

While other montaging approaches have been described recently, the one presented here differs in its approach to controlling defocus and avoids the need to sacrifice a biologically meaningful region of a sample. The authors investigate the compatibility of the data acquisition with their 2DTM method using cryoFIB-milled mouse neutrophil-like cells and the 60S ribosome as an example case. In order to minimize unnecessary exposures, the authors restrict illumination to a circle inscribed on the detector and use beam image-shift in lieu of stage shift. This approach introduces several optical aberrations for which the authors investigate the effects on the 2DTM results. The results of the investigated aberration effects may be of general interest to the cryoEM community, not just those using montaging methods.

Reviewer #1 (Public Review):<br /> <br /> In this study, Trolle et al set out to investigate the impact of reintroduction of essential amino acid biosynthetic pathways into mammalian cells. To this end, they employed an elegant synthetic genomic approach to enable Chinese Hamster Ovary cells to endogenously produce methionine, threonine, isoleucine, and valine. Notwithstanding that attempts to functionalize biosynthesis of methionine, threonine and isoleucine were not successful, reintroduction of valine biosynthetic pathway rescued survival of Chinese Hamster Ovary cells deprived of valine. Moreover, the authors provide evidence that global mRNA abundance profiles in valine biosynthesis-proficient Chinese Hamster Ovary cells mirror those observed upon recovery from valine starvation. Collectively, these pioneering studies suggest potential for the functionalization of biosynthesis of essential amino acid in mammalian cells. Based on this, it was found that this study is of broad interest to a variety of research fields including synthetic biology, biotechnology, and biochemistry.

Strengths: This study incorporates a very elegant synthetic genomic approach to address a long-standing gap in knowledge related to exploring the ability of mammalian cells to tolerate restoration of essential amino acid biosynthesis. It was highly appreciated that this is one of the pioneering attempts to address this question. For the most part, the data were robust and supportive of the author's tenets. Finally, demonstration that Chinese Hamster Ovary cells can be rendered prototrophic for valine may open many interesting avenues in the domains of synthetic biology and biotechnology, with potential long-term applications in medicine.

Weaknesses: Relatively modest rescue of proliferation of valine-producing Chinese Hamster Ovary cells in valine-free media, apparent reduction in de novo valine synthesis during propagation of the cells and some technical issues pertinent to potential utilization of valine from breakdown of serum proteins were considered as the weaknesses of the study. Furthermore, it was thought that further molecular characterization of valine-prototrophic Chinese Hamster Ovary cells may be warranted.

Reviewer #3 (Public Review):<br /> <br /> During evolution, eukaryotes lost the biosynthetic pathways that are responsible for the production of 9 amino acids. In this study, Wang et al successfully reintroduce the fully functional biosynthesis of these 9 amino acids back into mammalian cells. To accomplish this task, Wang et al had to introduce, into mammalian cells, >40 genes and reconstruct pathways that are naturally functional only in fungi plants, and bacteria. The entire pathway was synthesized de novo by commercial gene synthesis in 3 kilobase fragments and assembled in yeast. The work is a major bioengineering accomplishment that will serve for fundamental research into evolution and metabolism.

Reviewer #1 (Public Review)

The aim of the authors was to clarify the function of pinnae, forming part of katydid ears in the forelegs. Previous work suggested a protective function for the thin tympana or a device for directional hearing. A major strength of the paper is the combination of methods, such as experimental biophysical measurements with Laser-Doppler-Vibrometry and numerical modelling. In addition, detailed morphological data were obtained by scanning ears with a µCT-scanner, which formed the basis for producing 3D-printed models of the ear. These methods were combined with audiograms of sensory units in the ear, and measurements of behavioral sensitivity.

Using experimental ablation of the pinnae, the authors can convincingly show that the cavities formed by the pinnae produce resonances at very high ultrasonic frequencies and that these resonances boost the perception of sound by about 20 - 30 decibels, i.e. make the ear more sensitive for these frequencies. By contrast, the data do not support the hypothesis that pinnae serve in directional hearing.

To my knowledge, the method of performing acoustic measurements using synthetic 3D-printed scaled ear models is completely new in the field. It offers great advantages for studying the often-minute structures in insects.

Reviewer #1 (Public Review):

Using single cell analysis, Paxman et al observe protein aggregation in aging yeast that is specific to cells with deregulated rDNA silencing. This is confirmed in sir2 mutant cells. The authors then investigate the mechanism by which silencing defects of the rDNA locus might be linked to a decline in protein homeostasis. Through a screen for aggregation of RNA binding proteins, they find that disruption of rDNA silencing leads to aggregation of those RNA binding proteins that are involved in rRNA processing. Overexpression of a subset of these rRNA binding genes consistently shorten the lifespan of mode 1 cells, presumably by contributing to their defects in protein homeostasis. This suggests that age dependent changes in rDNA silencing lead to the aberrant expression of rRNAs and the formation of rDNA circles. Deletion of fob1 (resulting in a loss of rDNA recombination) indeed suppresses aggregation of Nop15 that is used for in-depth analysis. Of note, enhancing rRNA transcription or Nop15 expression leads to enhanced protein aggregation even in the absence of rDNA circles.

In all this study addresses an interesting and exciting question and is well executed. Importantly, it contributes to the understanding of distinct aging trajectories and raises important questions how these processes might be relevant in multicellular organisms. Given the fact that the paper focuses on rDNA silencing, I think that using the term "chromatin stability" is too broad and should be replaced with "rDNA silencing".

Reviewer #3 (Public Review):<br /> <br /> The idea of individual ageing trajectories of single cells is important and the authors provide sufficient evidence that there is some stochasticity that directs individual cells towards certain routes of ageing - at least in budding yeast. Additionally, understanding the connection and dependence of various different processes that occur during ageing is critical and timely. However, despite the fact that the hypothesis laid out in the manuscript is tempting and the approaches taken might be the right way to tackle it, the results presented still fall short of connecting chromatin instability and protein aggregation. I have provided more detailed comments below, but in essence, I miss a clear experiment linking rRNA instability and the role of RBPs with protein aggregation and loss of proteostasis. All experiments that try to achieve this are either too unspecific (e.g. NAM as an inhibitor for Sir2, while it inhibits a wide variety of deacetylases) or do not show protein aggregation (e.g. Nop15-mNeon, which might simply stain a fragmented nucleolus).

Reviewer #1 (Public Review):

In this manuscript the authors set out to characterise the process of differentiation of inner cell mass cells within the mouse blastocyst into either epiblast or primitive endoderm, which is a binary fate choice, using various models. To this end, they made use of well-established reporter cell lines previously generated in their lab as well as a widely used fluorescent system (FUCCI) that allows stages in the cell cycle to be visualised and sorted. The experimental output was compared with computational models and published data generated from mouse embryos during the process of primitive endoderm and epiblast segregation. Their data uncovered interesting mechanistic insight into the dynamics of the cell cycle and how these correlate with lineage choice and amplification. The methods have been carefully considered and validated in previous work by the group and the analysis is thorough. The single cell profiling is particularly well presented, and backed up by immunofluorescence data using well-characterised lineage reporters with appropriate statistical analysis. Probably the most interesting finding, which the authors identify as unexpected, is the considerable lengthening of the G1 part of the cell cycle in cells differentiating into PrE, but coinciding with a reduction in overall cell cycle length. Also, cell cycle length from mother to daughter cells in all conditions appears not to be inherited, yet sister cells, and to a lesser extent, cousins, appear to retain similar cell cycle dynamics. This feature is attributed to differential levels of FGF, suggested by the use of PD03 or PD17 as downstream inhibitors. Not surprisingly, levels of the PrE-associated factor Hex could predict the likelihood of differentiation to PrE, but also higher levels of Hex correlated with a shorter cell cycle. Also, blocking MEK/ERK signalling increased cell cycle duration as well as reducing differentiation to PrE in the culture conditions designed to promote differentiation to epiblast. The aims of the paper appear to be achieved and the results adequately support the authors' conclusions. A similar system to the one established here could be envisaged for downstream developmental processes, such as those involving binary decisions for specific tissue formation in organogenesis, but would require the generation and validation of different reporter cell lines.

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 #1 (Public Review):

Selecting appropriate Bioinformatics approaches to arrive at a consensus classification of SNVs can be labor intensive and misleading due to discordance in results from different programs. The authors evaluated 31 Bioinformatic or computational tools used for in silico prediction of single nucleotide variants (SNVs). They selected a filtered list of SNVs at the HBA1, HBA2, and HBB genes, and compared in silico prediction results with annotations based on evidence in literature and databases curated by an expert panel comprising co-authors of this study. They found both specificity and concordance among different tools lacking in certain aspects when thresholds are chosen to maximize the Matthews correlation (MCC) and thus proposed an improved strategy. For this, the authors focused on the top prediction algorithms and varied their decision thresholds separately for pathogenic and benign variant classification and optimized the predictive power of these tools by choosing thresholds that generated at least supporting strength likelihood ratios (LRs) to achieve balanced classification.

The authors have likely spent significant effort annotating the list of pathogenic or benign SNVs in adult globin genes by iteratively evaluating independent annotations submitted by experts and arriving at a consensus. These annotations when added to the database of SNVs might improve the breadth of knowledge on the pathogenicity of adult globin SNVs and likely lead to an improved prediction by the existing tools. Further, setting non-overlapping thresholds for pathogenic and benign variants seems to improve the balance in the prediction of some of the tools (with certain tradeoffs) in the context of the gene and the variant class. This is consistent with the findings of Wilcox et. al., while at the same time the authors have focused on globin variants and compared many more programs. Thus, while not a novel approach, the scale is expansive, and might guide future studies with the improved ACMG/AMP framework.

However, there are certain caveats from my perspective and these need to be explained or improved.

• The authors' approach relies heavily on the revised consensus annotations which, from my understanding, is essentially being considered as a "truth dataset", whereas variants are classified in silico according to existing annotations in the databases. The binary classification metrics compare the in silico predictions to the authors' annotations and these showed low specificity but higher sensitivity and accuracy indicating that many benign variants were misclassified as pathogenic. The authors have not clearly mentioned whether the "observed_pathogenecity" information in the input dataset in supplementary file 2 is from the Ithagenes database or the authors' reannotations. Hence, if a significant number of pathogenic variants were reannotated as benign by the expert panel, that will likely result in the tools misclassifying them as pathogenic since the tools rely on database annotations.

• The results and measure of success focus on different benchmarks for the two major analyses the authors performed. While they generated a lot of data, they have not attempted to explore and present all facets of the data for each analysis. For instance, to assess the predictive power of the 31 tools initially, the authors focus on benchmark metrics for binary classification such as Accuracy, Sensitivity, Specificity and MCC. However, in the later improved approach, the focus is on LRs but the effect of separate thresholds for pathogenic and benign classification on accuracy, sensitivity, and specificity and MCC are not explored in the results instead just mentioning PPV for certain variant types, tools, and genes.

• There is a general trade-off to altering thresholds to increase specificity which leads to reduced accuracy and sensitivity. Thus, in this case, the improved approach suggested by the authors increases specificity but there is a simultaneous reduction in accuracy and sensitivity thus leading to the potentially higher misclassification of pathogenic variants as benign. One has to consider then, whether this is ideal in the case of globins where an in silico misclassification of pathogenicity can be easily verified by subsequent diagnostic testing to confirm whether the variant actually affects hemoglobin. Overclassification of pathogenicity in the case of globins is thus not necessarily a major problem since they will not directly lead to patients receiving treatment before additional confirmatory tests. However, misclassification of pathogenic variants as benign will pose greater harm to individuals at risk of disease.

• This is a largely descriptive study of the performance of various programs, but the authors did not attempt to explain why according to them the various tools performed a certain way in their analysis. Thus, their rationale for proposing the improved approach of separate thresholds for pathogenic and benign variants was unclear. Attempting to understand whether there is a correlation between the type of data the tool uses, and its performance could explain the tools' prediction power and how to improve it. For instance, some of the tools are metapredictors that take as input scores from various other tools also tested in this study. Thus, there will be some redundancy in the final classification.

• Expanding on the previous point, the reason for discordance in HBA genes but concordance in HBB was unclear. It might be a result of the bigger HBB dataset compared to HBA although the authors did not explore or mention whether the size of the dataset correlates with concordance. They also did not test for concordance or discordance after the separate thresholds were applied so it is not clear whether their proposed approach improves concordance for the HBA variant predictions of the top tools.

Reviewer #1 (Public Review):

Mating influences many behaviours such as enhanced oviposition, suppressed mating, and a change in dietary preference. In this study, Boehm et al explore the circuit basis of the mated female's enhanced preference for polyamines.

A previous study from this group had identified a mechanism by which mating reduced sensitivity of the olfactory sensory neurons resulting in a preference for higher concentrations of polyamines after mating. However, the preference for polyamines outlasts this mechanism by many days. So, in this study, the authors explore central brain circuits that might encode this persistent behavioural switch. Briefly, they identify neurons within the mushroom body - intrinsic neurons, output neurons and dopaminergic neurons (DAN) - that are involved in this behaviour. They also identify output neurons of the lateral horn that are involved in it.

The behaviour itself consists of two phases: 1) the mating experience, and 2) the subsequent expression of the polyamine preference. The authors use behavioural assays and neurogenetics to demonstrate that:

1. The ability to detect odours via the OR67d neurons at the time of mating is necessary to bring about the behavioural switch.

2. Activity of the intrinsic neurons of the mushroom body is necessary at both times - during the mating and the expression - to bring about the behavioural switch.

3. They identify one set of dopaminergic neurons - B1 DANs - that are sufficient but not necessary ***at the time of mating*** to induce the switch in virgin females.

4. They identify a second set of dopaminergic neurons - B2 DANs - that are necessary to ***at the time of expression*** to demonstrate the increased polyamine preference ******in mated females.

5. They identify a set of mushroom body output neurons (MBONs) downstream of the B1-DANs and show that output from the B1 region is necessary and sufficient at the time of mating for the expression of polyamine preference.

6. They identify MBONs downstream of the B2 DANs and find that they play no role at the time of mating, but that they are necessary and sufficient at the time of expression to suppress the polyamine preference.

7. They identify a set of output neurons of the lateral horn and find that they are necessary at the time of expression of polyamine preference.

The authors also use functional imaging to show that there are no brain-wide changes upon mating in the encoding of one of the polyamines. They explore how cVA (an odour they believe is relevant at the time of mating) is represented in the neurons they have identified. They find that the B1 DANs show enhanced representation of cVA post mating, however, their MBONs do not alter their response to cVA post mating. The B2-MBONs respond to both putrescine and cVA and show no alteration in their response post mating.

In summary, the authors have identified a mechanism similar to associative learning that operates across the mushroom body and lateral horn, to 'learn' the experience of mating and express it as an enhanced preference to a nutritionally rich food source.

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 #1 (Public Review):

In this study, Zheng and Zhao identified the unannotated open reading frames (ORFs) in Drosophila, termed utORF, mainly based on proteomics datasets. The authors extended their analyses to the birth and the evolutionary heterogeneity of utORF. These analyses uncovered several types of utORFs that bear different feature, including transcription, age, distribution, and evolutionary conservation.

The origin of de novo protein-coding genes is interesting. The authors' attempts to uncover utORFs from proteomics datasets are much appreciated, but crucial cross-validation is missing. Given a high potential of false positives in MS datasets, it is difficult to evaluate the evolutionary aspects of the identified ORFs. Some experimental validation is needed to confirm the translational potential of utORFs with or without start codons.

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 #1 (Public Review):<br /> <br /> This article creates a formal definition of the 'informativeness' of a randomized clinical trial. This definition rests upon four characteristics: feasibility, reporting, importance, and risk of bias. The authors have conducted a retrospective review of trials from three disease areas and reported the application of their definition to these trials. Their primary finding is that about one quarter of the trials deemed to be eligible for assessment satisfied all four criteria, or, equivalently, about three quarters failed one or more of their criteria. Notably, industry-sponsored studies were much more likely to be informative than non-industry-sponsored studies. It would be interesting to see a version of Figure 3 that categorizes by industry/non-industry to see the differences in fall-off between the four criterion.

As the authors point out, the key limitations to this work are its inherent retrospective nature and subjectiveness of application, making any sort of prospective application of this idea all but impossible. Rather, this approach is useful as a 'thermometer' for the overall health of the type of trials satisfying the eligibility criteria of this metric. A secondary and inherent limitation of this measure is the sequential nature of the four criteria: only among the trials that have been determined to be feasible (the first criterion measured) can one measure reporting, importance, and lack of bias. And only among those trials that are both feasible and reported properly can one measure their importance and lack of bias, and so forth. Thus, except for feasibility, one cannot determine the proportion of all trials that were properly reported, were importance, or evinced lack of bias.

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 #1 (Public Review):

In this manuscript, Goering et al. investigate subcellular RNA localization across different cell types focusing on epithelial cells (mouse C2bbe1 and human HCA-7 enterocyte monolayers, canine MDCK epithelial cells) as well as neuronal cultures (mouse CAD cells). They use their recently established Halo-seq method to investigate transcriptome-wide RNA localization biases in C2bbe1 enterocyte monolayers and find that 5'TOP-motif containing mRNAs, which encode ribosomal proteins (RPs), are enriched on the basal side of these cells. These results are supported by smFISH against endogenous RP-encoding mRNAs (RPL7 and RPS28) as well as Firefly luciferase reporter transcripts with and without mutated 5'TOP sequences. Furthermore, they find that 5'TOP-motifs are not only driving localization to the basal side of epithelial cells but also to neuronal processes. To investigate the molecular mechanism behind the observed RNA localization biases, they reduce expression of several Larp proteins and find that RNA localization is consistently Larp1-dependent. Additionally, the localization depends on the placement of the TOP sequence in the 5'UTR and not the 3'UTR. To confirm that similar RNA localization biases can be conserved across cell types for other classes of transcripts, they perform similar experiments with a GA-rich element containing Net1 3'UTR transcript, which has previously been shown to exhibit a strong localization bias in several cell types. In order to determine if motor proteins contribute to these RNA distributions, they use motor protein inhibitors to confirm that the localization of individual members of both classes of transcripts, 5'TOP and GA-rich, is kinesin-dependent and that RNA localization to specific subcellular regions is likely to coincide with RNA localization to microtubule plus ends that concentrate in the basal side of epithelial cells as well as in neuronal processes.

In summary, Goering et al. present an interesting study that contributes to our understanding of RNA localization. While RNA localization has predominantly been studied in a single cell type or experimental system, this work looks for commonalities to explain general principles. I believe that this is an important advance, but there are several points that should be addressed.

Comments:

1. The Mili lab has previously characterized the localization of ribosomal proteins and NET1 to protrusions (Wang et al, 2017, Moissoglu et al 2019, Crisafis et al., 2020) and the role of kinesins in this localization (Pichon et al, 2021). These papers should be cited and their work discussed. I do not believe this reduces the novelty of this study and supports the generality of the RNA localization patterns to additional cellular locations in other cell types.

2. The 5'TOP motif begins with an invariant C nucleotide and mutation of this first nucleotide next to the cap has been shown to reduce translation regulation during mTOR inhibition (Avni et al, 1994 and Biberman et al 1997) and also Lapr1 binding (Lahr et al, 2017). Consequently, it is not clear to me if RPS28 initiates transcription with an A as indicated in Figure 3B. There also seems to be some differences in published CAGE datasets, but this point needs to be clarified. Additionally, it is not clear to me how the 5'TOP Firefly luciferase reporters were generated and if the transcription start site and exact 5'-ends of these constructs were determined. This is again essential to determine if it is a pyrimidine sequence in the 5'UTR that is important for localization or the 5'TOP motif and if Larp1 is directly regulating the localization by binding to the 5'TOP motif or if the effect they observe is indirect (e.g. is Larp1 also basally localized?). It should also be noted that Larp1 has been suggested to bind pyrimidine-rich sequences in the 5'UTR that are not next to the cap, but the details of this interaction are less clear (Al-Ashtal et al, 2021)

3. In figure 1A, they indicate that mRNA stability can contribute to RNA localization, but this point is never discussed. This may be important to their work since Larp1 has also been found to impact mRNA half-lives (Aoki et al, 2013 and Mattijssen et al 2020, Al-Ashtal et al 2021). Is it possible the effect they see when Larp1 is depleted comes from decreased stability?

4. Also Moor et al, 2017 saw that feeding cycles changed the localization of 5'TOP mRNAs. Similarly, does mTOR inhibition or activation or simply active translation alter the localization patterns they observe? Further evidence for dynamic regulation of RNA localization would strengthen this paper

5. For smFISH quantification, is every mRNA treated as an independent measurement so that the statistics are calculated on hundreds of mRNAs? Large sample sizes can give significant p-values but have very small differences as observe for Firefly vs. OSBPL3 localization. Since determining the biological interpretation of effect size is not always clear, I would suggest plotting RNA position per cell or only treat biological replicates as independent measurements to determine statistical significance. This should also be done for other smFISH comparisons

6. F: How was the segmentation of soma vs. neurites performed? It would be good to have a larger image as a supplemental figure so that it is clear the proximal or distal neurites segments are being compared

Joint Public Review:

While the presence of fascin in the nucleus, and its function at the cytoplasmic side of the nuclear envelope, have been shown previously, the role of fascin in the nucleus is not known. This important new study reveals that nuclear fascin regulates nuclear actin, likely actin bundling, DNA damage response, and too much nuclear fascin promotes apoptosis. The authors begin by using biochemical fractionation and imaging (a strength of this group) to show that fascin can localise to the nucleus of two human cancer cell lines. Mutation of a putative nuclear export sequence in fascin, or treatment with an exportin-1 inhibitor, results in nuclear accumulation of fascin, demonstrating that it shuttles between the cytoplasm and the nucleus. Imaging experiments clearly show the colocalisation of fascin with tagged nuclear actin; in combination with fascin-knockdown cells and expression of a non-bundling fascin mutant, this implies a requirement of fascin for nuclear actin bundling.

To explore the molecular complexes that may be regulating nuclear fascin function, the authors examined potential nuclear fascin-interacting proteins using mass spectrometry (MS)-based affinity proteomics. A smart approach exploited GFP-tagged fascin-specific nanobodies that contained nuclear localisation or nuclear export signals, which targeted fascin to the nucleus or cytoplasm, respectively. Proteomic analysis identified histones H3 and H4 as hits enriched in nuclear fascin nanobody pull-downs over non-nuclear fascin nanobody pull-downs. There are some deficiencies in the reporting of the MS data that would benefit from expansion to ensure the results of these experiments are clear, such as hit selection threshold criteria and any statistical analyses used. The potential interaction of fascin with histone H3 was suggested further using FRET between GFP-tagged histone H3 and mCherry-tagged nuclear fascin nanobody, although additional controls would improve interpretation of these data. While they are clearly present in the same complex, the imaging and FRET experiments stop short of showing the interaction is direct. While the use of FRET can be a very powerful means to show interaction, the authors require further controls, for example, a negative control would be important.

The authors identified reduced focal staining of the DNA damage response factor γH2AX in the first hour after DNA damage induction in fascin-knockdown cells. The role of fascin in the DDR is interesting, but the way the images are presented/analysed - the data are not as convincing as they might be. The differences look quite subtle due to relatively large variance and/or heterogeneity. Chromatin compaction was then tested using histone H2B-H2B FRET. Some statistical tests need to be clarified to ensure that comparisons between groups were tested appropriately, particularly for the interpretation of the chromatin compaction results upon the addition of DNA damaging agents to fascin-knockdown cells. Perhaps for discussion, but what role do the authors propose for fascin in chromatin organisation?

Driving fascin to the nucleus using the nuclear-targeted fascin nanobody resulted in substantially reduced filopodia formation, 2D migration speed, and invasion into 3D collagen gel. The alignment of representative confocal z-stacks in the presentation of the invasion assay (nuclear nanobody and fascin-knockdown cells compared to the other conditions) should be clarified. Longer-term nuclear targeting of fascin with the nanobody induced cell cycle arrest and caspase-3 cleavage, implicating nuclear fascin dynamics in loss of cancer cell viability. The phenotypic screening was well performed, including a dose-response analysis of hits and a secondary screen, to identify compounds that could induce nuclear localisation of fascin and promote apoptosis. Very useful supplementary tables have dose-response curves built in to enable interrogation of the screening datasets. The screening identified three compounds that regulate histone phosphorylation; interestingly, two of the compounds reduced histone phosphorylation and reduced histone pulldown in nuclear fascin nanobody affinity purifications in the cancer cells tested. The most potent histone H3 phosphorylation inhibitor also increased γH2AX staining, which appeared to correlate with fascin localisation in the nucleus. Can the authors make, or comment on, further evidence that Haspin-induced effects, for example, increased γH2X (was this at DNA-damage-associated foci in the nucleus?), are due to nuclear localization of fascin and/or resultant F-actin polymerization? Some follow-up data on Haspin could help to enhance the impact of the final part of the paper.

Although further delineation of the role of phospho-histone H3 in modulating nuclear fascin function would help to corroborate the ideas derived from the final figure of the paper, particularly to distinguish correlation from causation, this study demonstrates that nuclear fascin associates with histone H3, promotes nuclear actin, likely bundling, promotes DNA damage response and can induce apoptosis in cancer cell lines. The subcellular localisation of fascin, and its dynamic nuclear localisation, therefore appear important for regulating cancer cell behaviour. The idea that previously described nuclear envelope-localised fascin could serve as a pool of fascin for rapid nuclear import in response to cellular stress, discussed here, is very interesting. Given that fascin is upregulated in many solid tumours, questions around whether the spatiotemporal dynamics of fascin can inform prognostic assessments or can be targeted/modulated therapeutically in tumours will be exciting to discuss or address later. Overall, the quantitative characterisation of nuclear fascin functions will be of interest to cancer cell biologists, particularly those curious about the regulation of nuclear actin and its role in controlling cell behaviour.

Reviewer #1 (Public Review):

Langerhans cells are immunogenic and tolerogenic immune cells (part of the dendritic cell family) in the epidermis. They are therefore crucial in all immune responses that originate in the skin (e.g., allergic hypersensitivities, vaccine administration, immune surveillance against skin cancer/melanoma, etc.). The authors have previously detected the expression of this novel molecule, RETICULON 1A (RTN1A) in Langerhans cells - both in human and mouse epidermis. This manuscript is now the first evidence for a function of RTN1A in human Langerhans cells.

Langerhans cells are of dendritic shape and they need to migrate through connective tissues to lymph nodes in order to fulfil their immunologic functions. RTN1A (and other members of this protein family) are known from other dendritically-shaped cells in the nervous system. This led the authors to aim at elucidating whether RTN1A somehow regulated dendrites, migration and activation of Langerhans cells. Indeed, they find a link between RTN1A and morphology and function in Langerhans cells. The experiments described in this manuscript lead the authors to conclude that RTN1A regulates dendrite movement and morphology.<br /> - RTN1A promotes extension of dendrites and maintenance of dendritic shape in situ (determined in antibody inhibition experiments);<br /> - RTN1A does not allow or promote migration of Langerhans cells from the epidermis;<br /> - RTN1A inhibits calcium flux (determined in a model cell line);<br /> - RTN1A regulates cell adhesion and cell size (determined in a model cell line);<br /> - RTN1A in Langerhans cells is down-regulated by Toll-like receptor stimulation - allowing activation and migration;<br /> - likewise, this TLR-induced RTN1A down-regulation leads to the formation of large clusters of Langerhans cells in the epidermis.<br /> Overall the authors find that RTN1A maintains and regulates LC residency and homeostasis within the epidermis.<br /> Notably, all this work has been performed with healthy HUMAN skin.

A major strength of this work is its novelty. The authors delineate a well-defined function for RTN1A in human Langerhans cells for the first time. Their work also highlights some cell biological features (regulation of dendrite properties) that appear similar across dendritically shaped cells of very different origins (Langerhans cells, Purkinje cells, neurons). Another strength is the fact that the authors worked with primary human cells and tissues (skin, epidermal explants) ex vivo as much as possible. It should be emphasised that Langerhans cells are rare within the epidermis, therefore, large quantities of skin are needed for large experimental setups - a logistical challenge. Only for a few experiments did the authors resort to an established human cell line (e.g. to transfect it with RTN1A). Moreover, the paper contains outstanding fluorescence microscopy. Informative pictures, excellent photographic resolution!

There are no major weaknesses in this work. The methods are appropriate, results are sound.

Definitely, the authors achieved their aims, namely to find out what the novel molecule RTN1A does in human Langerhans cells. The data presented indeed support the conclusion that this molecule regulates the maintenance of the epidermis and, inversely, when missing or blocked, the immunologic migration of Langerhans cells out of the epidermis.

This is a valuable contribution to the topic of how Langerhans cells can remain within the epidermis and what allows them to migrate when immunologically needed. Langerhans cells are key immunostimulatory or tolerogenic (depending on context) cells in the body, and therefore this work will be of interest to the immunological, dermatological, and cell biology community.

Reviewer #1 (Public Review):

The manuscript focuses on an important question, how early life trauma causes aggression later in life. As aggression may ruin the life of both the aggressor and the victim and the life of their families, this question influences the life of a relatively large population. Uncovering the mechanisms of this behavior may provide options for treatment.

Based on transcriptome analysis, the authors suggest that epigenetic downregulation of TTR and the resultant hypothalamic decrease of thyroid hormone availability are responsible for the long lasting effects of early life trauma on the behavior. Using virus mediated gene knock down, the authors replicated the behavioral effects of the early life trauma demonstrating the involvement of decreased TTR expression in the development of aggression.

Strengths

The well defined experimental model and the selection of extreme phenotypes helps to identify the genes that are involved in the development of phenotype. The examination of females where the PPS does not cause aggression also helped to identify the important genes.

The suggested role of TTR in the development of aggression is proved by virus mediated gene knock down.

Weaknesses

However, the authors clearly demonstrated that both the TTR knock down and the early life trauma result in a decrease of hypothalamic thyroid hormone availability, they did not examine whether this local hypothalamic hypothyroidism is involved in the development of aggression. This question is important as in humans, hypothyroidism is not associated with aggression, rather increased T3 level was found in association with aggression. Therefore, it is possible that the decreased TTR expression causes the aggressive phenotype independently from its effect on the hypothalamic thyroid hormone availability. This could be tested by examining whether local hypothalamic T3 administration can reverse the aggressive phenotype of the used mouse models.

There is a discrepancy in the data. Despite of the large increase of hypothalamic TRH expression, the circulating thyroid hormone levels are not influenced. There are many TRH neuron populations in the hypothalamus and only a small portion of the hypothalamic TRH neurons are involved in the regulation of the circulating thyroid hormone levels. Therefore, it would be necessary to perform in situ hybridization to determine which TRH neuron population is regulated in the experimental model. Because of the unchanged circulating thyroid hormone levels, it is unlikely that the TRH expression is increased in the hypophysiotropic TRH neurons of the PVN. The in situ hybridization data could help to understand which cell populations of the hypothalamus could be involved in the development of aggression. For example, there is a TRH neuron population in the lateral hypothalamic attack area (PMID: 15908131) that could be involved in this behavior.

The authors measured serum total T4 and T3 levels. This could be misleading as the thyroid hormone binding capacity of blood may highly influence these data. Thus, measurement of free thyroid hormone levels would be far more informative.<br /> The quality of the images illustrating immunocytochemistry is very weak.

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 #1 (Public Review):

Using a Discrete Choice Experiment (DCE) the study asks respondents in six EU countries to choose between two persons A and B and select the one that they believe s/he should receive the COVID-19 vaccine first. Across eight different scenarios, each person is given different attributes in terms of age, COVID-19 mortality risk, employment status, and country of residence (own vs. other with low healthcare system capacity). The study found the risk of mortality and also working for essential services to be perceived as particularly important across all the countries. Moreover, living in a low-income country with poor healthcare system capacity was found to be favored when it came to allocating the vaccine first. This is particularly interesting given that the respondents were selected from those who were not vaccinated at the time of the survey but were willing to receive one.

Strengths:

• The study evidence is based on large samples from 6 EU countries.<br /> • It captures the opinion of those who had not been vaccinated at the time of the survey, hence, allocation to those in low-income countries indicated further altruism.<br /> • The method, a conditional logit estimate, and also the robustness checks are appropriate and suitable.<br /> • The study distinguishes between two key attributes of mortality risk and country of residence allowing for evaluating the importance of each factor separately. The implication of these factors can be helpful in making decisions in the future. This is in particular critical given that the initial aim of COVAX was to prioritize those who are vulnerable and the healthcare workforce across the world before launching national programs did not materialize and we are still facing large global disparities between the global north and south.

Weaknesses:

• The sample from Germany is noticeably different from the rest of the countries (particularly in terms of having a higher ratio of those who are in the high-risk category). This might have impacted the results and needs to be reflected in the study discussion. Also, there is heterogeneity between studies in terms of the time of the fieldwork and each country's conditions in regards to the vaccination roll-out and the number of infections at that time.<br /> • The manuscript narrative needs to be updated to reflect the present conditions in terms of inoculation campaigns, their success rate, and their disparities across the world.<br /> • There is space for more discussions on an interesting finding of the study that is prioritizing the vaccines according to employment status and in particular income loss.<br /> • The temporal nature of the public views at various stages of the pandemic and vaccination campaigns should also be noted.

Reviewer #1 (Public review):

The manuscript by Foster et al. details how PEP cycling and specific pyruvate kinase isoforms impact beta-cell ATP/ADP levels, KATP activity, calcium handling, and insulin secretion. The manuscript clearly illuminates the beta-cell specific roles of PKm1, PKm2, and mitochondrial PEP carboxykinase. The manuscript finds that beta-cell PEP production leads to KATP inhibition via ATP produced by PKm1 and PKm2. The manuscript also finds that amino acid induced closure of KATP channels depends on mitochondrial PEP production but not elevations in cytoplasmic ATP/ADP. Finally, the manuscript suggests that the PEP cycle is also involved in KATP activation, but the mechanism remains to be determined. The manuscript is well written and easy to follow. Overall, this is an excellent manuscript that will be very useful to the diabetes research community.

Reviewer #3 (Public review):

This papers builds on a previous publication from the same group that showed compartmentalisation model of beta-cell fuel metabolism in which plasma membrane-localized pyruvate kinase is sufficient to close KATP channels required for insulin secretion. In this current manuscript the authors identified the PK isoforms involved in this process using tissue specific KO mouse models. Using excised patch-clamp experiments, they demonstrated that although redundant in their function both the constitutively active PKm1 and allosterically PKm2 are associated with the PM and locally regulate KATP channel closure. Further, the authors showed that the mitochondrial PEP carboxylase (PCK2) is essential for amino acids to promote an increase in cytosolic ATP/ADP and closure of KATP channels. Therefore, this study very nicely demonstrates that he distinct response of PK isoforms to the mitochondrial and glycolytic sources of PEP impacts beta cell nutrient preference and affects the oscillatory cycle regulating secretion. These findings do provide new mechanistic information about the control of the regulated secretory pathway and will be of interest to broader audience.

Strength<br /> The major strength of the study is the use of tissue/isoform specific KO mouse models. Although limited by constitutive KOs with compensatory increase in other isoforms, the authors have achieved what they were set out to do i.e identify the PK isoform involved in the regulation of PM ATP generation and regulation of KATP channel closure. Their experimental rigorosity including the ability to perform the excised patch clamp experiments and use of PKa to show the specific effect of the allosterically regulated PKM2 are also strength.

Weakness<br /> It is not clear from the manuscript what the "littermate controls" are used in all the experiments. Given the limitations of the cre lox system, it is really important to clearly show what controls have been used and their phenotypes (and the rationale for pooling the different controls if that is what is done here).

The data adds to our understanding of the role of PM localised PK on the regulated exocytosis pathway however the claim that these findings question the canonical mitochondrial ATP coupled to KATP channel closure is not fully supported by the data especially given glucose induced insulin secretion is not affected by any of the KO models.

Reviewer #1 (Public Review):

Charpentier et al. use facial recognition technology to show that mothers in a group of mandrills lead their offspring to associate with phenotypically similar offspring. Mandrills are a species of primate that live in large, matrilineal troops, with a single, dominant male that fathers the majority of the offspring. Male breeder turnover and extra-pair mating by females can lead to variation in relatedness between group members and the potential for kin-selected benefits from preferentially cooperating with closer relatives within the group. The authors argue that the strategy of influencing the social network of their offspring could be favoured by "second-order kin selection", a mechanism by which inclusive fitness benefits are accrued to female actors through kin-selected benefits to their offspring. This interpretation is supported by a theoretical model.

The paper highlights a previously unappreciated mechanism for favouring association between non-kin in social groups and also contributes a nice insight into the complexity of social interactions in a relatively understudied wild primate species. The conclusions are strengthened by data showing associations between mothers were not influenced by the facial similarity of their offspring -- this suggests that mothers are making decisions based on the appearance of offspring and not their mothers.

Some remaining questions regarding the strength of the authors' interpretation exist:<br /> Given the challenges of studying mandrills in the field, the fact that the study reports data from a single group is understandable but potential issues remain with the independence of data points. There may be an additional issue arising from the fact that this troop is semi-captive.

The number of genotyped offspring is relatively small (n = 15) and paternity is inferred from the identity of the dominant male. However, the authors also refer to the fact that it's normal for female mandrills to mate with several males during ovulation.

What evidence is there to support a beneficial effect of nepotism in this species? What form could nepotism take and does it necessarily have to involve full sibs? If a female did not associate with offspring as shown here, would nepotistic interactions simply arise between her offspring and offspring that were less facially similar?

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.

Joint Public Review:

While prime editing has been successfully implemented for hPSCs, its use for the generation of disease models is comparatively less explored. In this manuscript, Hanqin Li et al. set out to identify the most efficient methodology for correcting heterozygous mutations in human iPSC. For this purpose, the authors tested several known gene editing methods, including TALENs, conventional CRISPR/Cas9, and prime editing (PE) and, not surprisingly, found that PE resulted in the best balance of correct versus unwanted editing events.

In this process, the authors noted a lower editing efficiency of hPSCs, compared with tumour cell lines, and explored ways to improve it. Nucleofection of in vitro-transcribed mRNA-based delivery approach significantly increased the editing efficiency, without the need to select for targeted clones. The authors optimise the delivery of prime editing components and demonstrate that their optimised method can achieve >60% editing efficiency in hPSCs and be used for Parkinson's disease modelling.

Finally, they demonstrate that multiple rounds of mRNA-based prime editing can yield near complete editing of hPSCs, and extend their findings to disease-causing mutations.

Perhaps the major weakness of the manuscript is the relative lack of perceived novelty, since the different gene editing and delivery methods used in these studies have all been reported and tested in contexts that are not so distant to the one explored here. As a matter of fact, most findings in the paper (with the notable exception of mRNA delivery outperforming RNPs -but then again, the specific activity of the homemade recombinant nCas9-RT protein could be an issue and is not appropriately benchmarked) would have arguably been the best guess by researchers familiar with the literature on the topic.

At any rate, the study methodology is sound and the results are presented in a clear manner and strongly support the authors' conclusions. In combination with a streamlined workflow (or 'platform'), the optimized PE protocol described in this manuscript could very well be the go-to reference for editing heterozygous mutations in human iPSC. Additional strengths of this paper include having validated the most critical findings across genomic loci (4 different loci in 3 different genes) and 2 independent iPSC lines.

Although the utility of this method for more complex genetic editing needs to be investigated, the current platform paves the way for future prime editing methods for hPSCs.

Reviewer #1 (Public Review):

The stated goal of this research was to look for interactions between metabolism, (manipulated by glucose starvation) and the circadian clock. This is a hot topic currently, as bi-directional links between metabolism and rhythmicity are found in several organisms and this connection has important implications for human health. The authors work with the model organism Neurospora crassa, a filamentous fungus that has many advantages for this type of research.

The authors' first approach was to assay the effects of glucose starvation on the levels of the RNA and protein products of the key clock genes frq, wc-1, and wc-2. The WC-1 and WC-2 proteins form a complex, WCC, that activates frq transcription. The surprising finding was that WC-1 and WC-2 protein levels and WCC transcriptional activity were drastically reduced but frq RNA and protein levels remained the same. Under conditions where rhythmicity is expressed, the rhythms of frq RNA, FRQ protein, and expression of clock-driven "output" genes were also unaffected by starvation. The standard model for the molecular clock is a transcription/translation feedback loop dependent on the levels and activity of these clock gene products, so this disconnect between the starvation-induced changes in the stoichiometry of the loop components and the lack of effects of starvation on rhythmicity calls into question our understanding of the molecular mechanism of the clock. This is yet another example of the inadequacy of the TTFL model to explain rhythmicity. For me, the most significant sentence in the paper was this: "...an unknown mechanism must recalibrate the central clockwork to keep frq transcript levels and oscillation glucose-compensated despite the decline in WCC levels."

The author's second approach was to try to identify mechanisms for the response to starvation by focussing on frq and its regulators, using mutations in the frq gene and strains with alterations in the activity of kinases and phosphatases known to modify FRQ protein. The finding that all of these manipulations have some effect on the starvation-induced changes in WC protein level is taken by the authors to indicate a role for FRQ itself in the response to starvation. This conclusion is subject to the caveat that manipulations of the activity of multifunctional kinases and phosphatases will certainly have pleiotropic effects on many cellular processes beyond FRQ protein activity.

The third section of the paper is a major transcriptomic study of the effects of starvation on global gene expression. Two strains are compared under two conditions: wc wild-type and the wc-1 knockout strain, under fed and starved conditions. The hypothesis is that WCC has a role in the starvation response. The results of starvation on the wild-type are unsurprising and predictable: the expression of many genes involved in metabolic processes is affected. There are no new insights that come from these results and no new testable hypotheses are generated by the data.

The authors refer to the wc-1 mutant strain as "clockless" and discuss its effects on the transcriptome only in terms of WC-1's function in the clock mechanism. However, WCC is known to be a major transcriptional regulator, controlling a number of genes beyond the TTFL. As acknowledged earlier in the paper, WC-1 is also the major light receptor in Neurospora. The transcriptomics experiments were carried out in a light/dark cycle, with cultures harvested at the end of the light period, when "an adapted state for light-dependent genes can be expected" according to the authors. However, wc-1 mutants are essentially blind, and so those samples are equivalent to being harvested in the dark. The multifunctional nature of WCC complicates the interpretation of the transcriptomics data. The differences in the transcriptome between wild-type and wc-1 may not be due to loss of clock function, but rather the loss of a major multifunctional transcription factor, or the difference between light and "dark".

In the final set of experiments, the authors tested the hypothesis that the changes in the transcriptome between wild type and wc-1 might make wc-1 less competent to recover growth after starvation. They also test the recovery of frq9, a "clockless" mutant. The very surprising result is that the growth rates of these two mutants are slower than the wild type after transfer from starvation media to high glucose. This is surprising because there will be several generations of nuclear division and doublings of mass within a few hours and the transcriptome should have recovered fully fairly rapidly. A mechanism for this apparent "after-effect" is suggested with evidence concerning differences in expression of a glucose transporter, but it is not clear why this expression should not change rapidly with re-feeding on high glucose. As with previous experiments, the cultures were grown in light/dark cycles, which results in different conditions for the mutants, both of which have very low or absent WC-1 and are therefore blind to light. The potential effects of light have been disregarded.

The title of the paper refers to a "flexible circadian clock" but this concept of flexibility is not developed in the paper. I would substitute "the White Collar Complex" for this phrase: "Adaptation to starvation requires a functional White Collar Complex in Neurospora crassa" would be more accurate. Some experiments are also conducted using an frq null "clockless" strain, but because WC expression is very low in frq null mutants, any effects of frq null could also be attributed to WC depletion.

The major conclusion I took away from this paper is the multifunctional nature of the WCC as a transcription factor complex. It has been known for a long time that WCC controls the expression of many genes beyond the frq gene at the core of the circadian transcription/translation feedback loop. WC-1 is also the major blue light photoreceptor in Neurospora, controlling the expression of light-regulated genes, and this fact is barely touched on in the paper. These new data now extend the role of WCC in the regulation of metabolic networks as well.

Reviewer #1 (Public Review):

This manuscript attempts to explain the well-known difference in DNA mutation rates between father vs. mother (paternal mutation is 4 times higher than maternal mutation in humans). Although the mutation rate difference was believed to arrive from the number of cell divisions (male germ cells undergo many more divisions compared to female germ cells), recent studies suggested that most mutations arise from DNA damage (which will be proportional to the absolute time) rather than DNA replication-induced mutations (which will be proportional to the number of cell divisions). The authors thus revisited the question as to why the paternal mutation rate is higher (if absolute time is more important than the number of cell divisions in causing mutations). They used 'taxonomic approaches' comparing paternal/maternal mutation rates of mammals, birds, and reptiles, correlating them to specifics of reproductive mode in these species. To measure paternal vs. maternal mutation rate, they compared the mutation rates of neutrally evolving DNA sequences between the X chromosome vs. autosomes, as well as the Z chromosome (utilizing the fact that the X chromosome will spend twice more generations in females than males, while autosomes spend equal time. Likewise, the Z chromosome will spend twice more time in males than in females, while autosomes spend equal time).

They first confirm the paternal bias across a broad range of species (amniotes), eliminating many species-specific parameters (longevity, sex chromosome karyotype (XY vs. ZW), etc) as a contributor to the paternal bias. This implies that something common in males in these broad species causes paternal bias. They show that in mammals, the paternal bias correlates with a generation time. They propose that the total mutation is determined by the combination of the mutation rate during early embryogenesis (when both male and female have the same mutation rate) and the later mutation rate when two sexes exhibit different mutation rates. This model seems to explain why generation time correlates well with the extent of paternal bias in mammals. However, this does not explain at all why birds do not exhibit any correlation with a generation time. The speculation on this feels rather weak (although there is nothing they can do about this. Fact is fact).

The logic behind their analysis is well laid out and seems mostly sound. Their finding is of broad interest in the field.

- I am confused by this statement (the last sentence in the result section): 'If indeed the developmental window when both sexes have a similar mutation rate is short in birds then, under our model, generation times are expected to have little to no influence on α." Based on their model, if the early period is gone, when the mutation rates are similar between sexes are similar, intuitively it feels that generation time influences α even more. Am I missing something? (if the period with the same mutation rate is gone, then females and males are mutating at different rates the whole time).

- The authors state that this paper provides a simple explanation as to why paternal biases arise without relying on the number of cell divisions. However, it seems to me that the entire paper relies on the recent findings that mutation arises based on absolute time (instead of cell division number), and the novelty in this paper is the idea of 'two-phase mutation rates' to explain the observed numbers of paternal bias in various species. Yet it fails to explain the mutation rate difference in birds. There is not enough speculation or explanation as to what determines different mutation rates in males of various species. Although the modeling seems to be sound and there is nothing that can be done experimentally, I felt somewhat unsatisfied at the end of the manuscript.

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 #1 (Public Review):

In Drosophila germline, most piRNA loci use a non-canonical mechanism to transcribe piRNA precursors at the presence of H3K9me3, which depends on an HP1a paralog called Rhino/HP1d that specifically binds piRNA loci. How does Rhino find the right loci to bind? The current model in the field posits that maternally deposited piRNAs provide a specificity cue for Rhino. Now, Baumgartner et al. from Brennecke Group described a novel factor, the ZAD zinc-finger protein CG2678/Kipferl, that appears to provide another key specificity input to a subset of Rhino's chromatin binding, specifically in differentiated female germline (but not in males or stem/progenitor cell types in the female germline). Using genetics, genomics, genome editing, microscopy and biochemical approaches, Baumgartner et al. propose that Kipferl binds a G-rich DNA motif and, at the presence of local H3K9me3, recruits and/or stabilizes the binding of Rhino to these loci and then convert them from transcriptionally inert heterochromatin to piRNA-producing loci. Overall, the text is well written, the figure is clear, and the data is of high quality. With some additional experiments and text edits, this work represents a significant contribution to the field and should attract readers working on piRNA, transposon, satellite DNA, zinc-finger proteins, HP1 and heterochromatin.

Specific concerns

1. The genetic hierarchy between Kipferl and Rhino requires further clarification. Authors seem to propose a simple model where Kipferl acts genetically upstream of Rhino. This simple hierarchy is at odds with several observations. First, the center of Kipferl binding generally has less Kipferl binding without Rhino (Fig 5D). In some cases, Kipferl binding is completely gone without Rhino (Fig 7E middle, bottom). The text describes the loss of Kipferl spreading without Rhino but should also mention this reduction/loss in Kipferl binding. The effect of rhino-/- on Kipferl's chromatin binding should be shown along with wildtype level of Kipferl enrichment in Fig 5C for proper comparison. How should readers understand the effect of Rhino on Kipferl? What is the prominent Kipferl domain in rhino-/- in Fig 5B? Second, the broad binding of Kipferl is gone in rhino-/-, does it mean Kipferl requires Rhino to spread? Or, could Rhino (that is recruited by maternally deposited Piwi/piRNA) recruit Kipferl to neighboring sites, which look like a spreading phenomenon? Most importantly, the argument of Kipferl recruiting Rhino should be directly demonstrated by a sufficiency test in addition to the presented evidence of necessity. Could authors tether Kipferl in H3K9me3-decorated regions to see if Rhino is recruited and vice versa? Observations like 42AB in Fig 5E make one wonder if Rhino also recruits Kipferl, so their relationship is not simply Kipferl recruiting or acting upstream of Rhino, as described throughout this manuscript. Clarifying the relationship between Kipferl and Rhino is essential as it is a central claim made.

2. DNA binding of Kipferl remains putative. Since the 4th zinc-finger is shown to impact Kipferl localization via interaction with Rhino, it remains formally possible that the first three zinc-fingers control Kipferl localization via protein-protein interaction rather than direct DNA binding. Unless direct biochemical evidence of Kipferl binding DNA is available, the DNA binding of Kipferl should be toned down and described as putative and requires further investigation in text.

3. The relative contribution of maternally deposited piRNAs versus Kipferl in recruiting Rhino is unaddressed. Prior work from multiple groups including Mohn et al. 2014 Cell from the same group of this manuscript suggested a role of maternally deposited piRNAs in determining a subset of H3K9me3 domains as Rhino binding sites. Is Kipferl or maternally deposited piRNA a better predictor of Rhino binding? This manuscript proposes that Kipferl binds a simple G-rich motif and at the presence of H3K9me3 recruits Rhino binding. The readers are left wondering where maternally deposited piRNAs fit in the model of Rhino recruitment, which should be tested or discussed in text, as maternally deposited piRNA is seen as the key determinant of Rhino binding before this work.

Reviewer #1 (Public Review):

This manuscript reports the cryo-EM structure of HOPS, a heterohexameric tether that participates in the fusion of late endosomes, autophagosomes, and AP-3 vesicles with lysosomes. HOPS has been characterized extensively through biochemical studies, which indicate that HOPS cooperates with SNAREs to facilitate membrane fusion. The authors conclude that HOPS is not a highly flexible structure as has been proposed, but instead has a stiff backbone to which the SNARE-binding Vps33 subunit is tightly anchored. Because the ends of HOPS bind to opposing membranes, the implication is that HOPS acts as a lever and membrane stressor, thereby amplifying the effects of SNARE assembly and catalyzing fusion.

The structural biology analysis was based on an improved purification protocol and appears to be well done. An atomic-level structure is always valuable, and this contribution will undoubtedly guide further research involving HOPS. Initial steps in this direction are presented in the form of functional studies of structure-guided mutants.

Structures are most useful when they help to define mechanisms, and the authors argue that the HOPS structure explains how HOPS catalyzes membrane fusion. The key conclusion is that the antiparallel association of the Vps11 and Vps18 subunits create a rigid core for the complex, leaving flexible ends that bind the Ypt7 GTPase to anchor the two membranes. This model is inconsistent with earlier suggestions that HOPS bends to bring the two membranes together. Instead, the inferred rigidity of the HOPS core, combined with the central location of the SNARE-binding module, suggests that HOPS acts as a lever that exerts a force on the membranes to promote SNARE-driven membrane fusion.

This interpretation is interesting and potentially exciting, but I question why the authors are certain that the Vps11-Vps18 core is truly rigid. Proteins can undergo all sorts of rearrangements. Is there evidence that Vps11 and Vps18 interact strongly and in a unique configuration? Portions of a protein that have a consistent structure in vitro might nevertheless rearrange during functional interactions in vivo. If there is any flexibility of the Vps11-Vps18 core, this property combined with the evident flexibility of the Ypt7-binding portions and the low affinity of Vps41 for Ypt7 would make HOPS anything but a rigid membrane stressor. If the authors wish to make a strong point about the functional implications of the HOPS structure, these points need to be addressed.

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 #1 (Public Review):

The authors used a combination of biochemical assays and cryoEM to investigate the role of PME-1 in regulating PP2A, which revealed that PME-1 uses its unstructured loops to associate with the B-domain of the PP2A holoenzyme to regulate the function of the C-domain. This is a high quality work. This reviewer finds the later work involving p53 to be a helpful step in explaining the role the PME-1:PP2A interaction can have on important phosphorylation pathways, but I consider this aspect of the work to be very preliminary, especially given its rather minor effects. That said, the authors do not make claims that extend beyond the scope of the evidence they provide and thus I find the connection and discussion of PME-1, PP2A and p53 to be suitable on the whole.

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 #1 (Public Review):

In this manuscript, the authors aimed to discover mechanism(s) that would allow the bacteriophage T4 to overcome the phage defense exerted by the toxIN toxin-antitoxin system, which itself was engineered into an E. coli strain (in trans on a medium copy plasmid). To identify ToxN-resistant phages, experimental evolution was used as a method of choice. The resistant phages obtained after ~5-25 rounds of propagation on toxIN -/+ cells were subsequently sequenced. The depth of sequencing reads thereby revealed the amplification of a two-gene operon for which the authors show causality for ToxN resistance (precisely, for one of the two genes, namely tifA). Through an elegant series of experiments, the authors further demonstrate that the evolutionary benefit of the phages with respect to the toxIN defense system occurred an at evolutionary cost, namely the loss of other accessory genes. These (large) gene deletions differed between the parallel evolved phages, showing a different solution to the same problem, namely phage genome reducing - likely to keep compatibility with the headful capsid packing approach of the phage.<br /> Importantly, the authors also demonstrate that the loss of these accessory genes narrowed the phages' host range given that those lost genes encode anti-defense proteins against other phage defense modules (including unidentified systems in well-studied E. coli strains).

Collectively, this work recapitulates the arms race between phages and their host and showed how adaptation to one host and overcoming its anti-phage barrier can compromise future infection of other hosts. Importantly, while the selected gain-of-function was based on a similar strategy in the parallel evolution lines (that is, amplification of the antitoxin-encoding gene tifA), the lost accessory genes differed amongst the independently evolved phages. These different solutions to the packaging problem likely benefit the phage on a population level once the phages encounter a new host.

The major strength of the study lies in the impressive combination of experimental evolution, genomics, and genetics, which allows the authors to identify genomes changes and demonstrate their causality. Very well executed work for which the authors should be congratulated.

There are only very minor weaknesses, which are solely related to the presentation of the data and the discussion.

Overall, this study is likely to impact significantly future research, given the findings (e.g., host switch based on genomic rearrangements) but also the methodology. Importantly, this study also demonstrates once again the power of experimental evolution with respect to pinpointing new anti-defense elements (such as IPIII in this study), which will help to uncover new anti-phage defense systems in the future (as, for instance, the unknown system in strain ECOR17, as mentioned in the manuscript).

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 #1 (Public Review):

This paper presents a detailed molecular investigation into the behavior of PIP5K kinase, a membrane associated enzyme that catalyzes the production of PIP2 from PIP in cell membranes. Building on previous work on this system, the researchers use single-molecule fluorescence microscopy to study how the oligomeric state of PIP5K impacts membrane binding, PIP phosphorylation, and compositional patterning of lipid domains leading to stochastic bistability in membranes.

A highlight of this study is the extensive experimental approaches that combine various single-molecule analyses, including diffusion and residence time distributions, as well as macroscopic measurements of membrane binding isotherms and PIP2 production. With this, it becomes evident that PIP5K exists in both monomeric and higher-order oligomeric forms, with the latter potentiating catalytic activity. This coupled to cooperative binding to the membrane linked to PIP2 production leads to a positive feedback system where patterning of the lipid composition emerges with stochastic bistable behavior, with oligomerization of the kinase acting as a modulating factor. This aspect of the research is interesting as it connects the higher-order oligomerization of the protein kinase to a means of modulating self-organization of the lipids within the cell membrane, a phenomenon that may be important for optimizing cellular signalling in biology.

The majority of the studies are carried out carefully and with exquisite single-molecule approaches. However, a weakness of the study is that the ultimate conclusion of the activity linked specifically to dimerization is not clearly supported by the data. The results presented reflect a comparison of monomers vs. oligomers, without a clear identification of conditions where dimers persist. The mutation constructed to disrupt dimerization shifts the system to monomers with an associated decrease in catalytic activity. However, this finding does not provide a strong connection to the dimer state, but rather the loss of the effect when oligomerization is disrupted. Other properties of the protein may be impacted, such as stability and fold, as well as the overall binding propensity to the membrane. The catalytic activity measured per PIP5K molecule does indicate that an increased density for the wild-type protein leads to an increase in the rate of PIP2 production, providing evidence that oligomerization increases function. Yet, many of the results throughout the paper provide support for general oligomerization rather than dimerization, and so further investigation is needed in order to clarify the interpretation in what is otherwise an interesting system and study.

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 #1 (Public Review):

Here, the authors have followed up their prior work on the structures of individual domains of the cyclic GMP-dependent protein kinase Iα/β (PKG Iα/β) and have generated a crystal structure of residues 71-686 of PKGIβ bound to AMP-PNP:Mn2+ in the absence of cGMP, representing a nearly full-length protein lacking only the N-terminal leucine zipper dimerization domain. In general, the individual domain structures resemble those determined previously by the authors and other groups. The AI motif in the R domain occupies the active site, as expected, and is part of an extended interface involving the helical subdomain of CNB-A with αG-helix of the C-domain. In addition, the αA helix of CNB-B interacts with the activation loop, and the αB helix of CNB-B contacts a loop in the C-lobe of the C-domain, with the CNB-A and CNB-B interdomain helix interacting with pT532 in the activation loop. Together these four R:C contacts allow the R-domain to grip the C-domain between its two CNB domains, ensuring complete inhibition of catalytic activity. The structure of the autoinhibited C-domain is similar to that of the isolated PKG C-domain and that of the PKA C-domain, but there are differences that could prevent unwanted crosstalk between cGMP and cAMP, for instance by preventing PKA RIα from binding to the PKG Iβ C-domain. Subtle changes in the individual CNB-A/B structures as well as their relative orientation upon cGMP binding suggest a mechanism for releasing the autoinhibited C-domain allowing its activation, in which cGMP binding to the CNB-A phosphate-binding cassette alters its conformation so that it cannot bind to the C-domain but instead promotes R:R interactions. Finally, the authors solved the crystal structure of the isolated CNB-A domain of PKG Iα bearing an activating R177Q disease mutation, whose structure reveals that the mutant would not be able to adopt a closed conformation and instead closely mimics the cGMP-bound WT CNB-A, suggesting a mechanistic basis for the constitutive activation of the R177Q PKG Iα, which is linked to TAAD syndrome.

This new structure of nearly full length PKG Iβ provides a significant advance in our understanding of how the cGMP-dependent protein kinase is autoinhibited in the absence of cGMP, and provides a plausible mechanism for how it is activated upon binding cGMP, as well as defining the structural basis through which crosstalk between PKA and PKG is precluded. The main uncertainty is whether the autoinhibited PKG Iβ normally resides in a "monomeric" or "dimeric" state. The crystal structure consists of a dimer in which the linker between the R and C domains is not visible making it impossible to determine whether the R domain of one monomer interacts with the C-domain of the other monomer in the dimer or rather with its own C-domain, i.e., is autoinhibition occurring in cis or trans. Based on the results of biochemical experiments in which WT and kinase-dead mutant PKG Iβ with and without an activating KR/EE AI region mutation were co-expressed, the authors concluded that autoinhibition occur predominantly in cis, consistent with the PKG Iβ 71-686 fragment being monomeric and inactive in solution. This conclusion seems reasonable, but the issue may only be fully resolved by a structure of the intact PKG Iβ dimer. If autoinhibition does occur in cis, this leaves open the question of why PKG 1 is dimeric, and this was not really illuminated by the present structures.

The other point is that while the structure itself is a significant advance in our understanding of how PKG Iβ is autoinhibited, the paper would be strengthened by some additional analysis of the functional effects on kinase activity and cGMP regulation of mutating PKG Iβ at newly defined contact residues in the autoinhibited structure that the authors conclude are key to autoinhibition. As it stands, the only mutation that was analyzed functionally was the KR/EE AI region mutant, which as predicted was activating.

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 #1 (Public Review):

They adopted a comprehensive experimental and analytic approach to understand molecular and cellular mechanisms underlying tissue-specific responses against 3-CePs. They used two cell lines - BxPC-3 and HCT-15 - as example models for responsive and non-responsive cell lines, respectively. Although mutation rates didn't differ by the drug treatment, they observed changes in cell cycle and expression of genes involved in DNA damage, repair and so on. Furthermore, they combined RNA-seq and ATAC-seq data and applied two approaches, pairwise and crosswise, to identify a number of gene groups that are altered in each cell line upon the drug treatment. Finally, they calculated enrichment of up/down genes in different cell lines, tumor types and samples to estimate potential responsitivity against the drug. This study is unique in in-depth analysis of RNA-seq and ATAC-seq data in identifying genetic signature underlying drug treatment. This study has the potential to be applied to different drugs and cell lines.

However, several major concerns need to be resolved. First of all, the biological and clinical performance of 3-CePs is not clearly described. They referenced several papers but they seem to have focused on the chemical properties of the drug. Without proven activity of 3-CePs against cancers in vitro and in vivo, the rationale of the study would be compromised.

Their RNA-seq analysis was focused on discovering differentially expressed genes between cell lines, time points, etc. Interestingly, they found that DNA damage and repair signal was specifically increased in HCT-15. But is this approach capable of finding signals that are constitutively expressed in different cell lines? In other words, what if the differential responsiveness to 3-CePs was already there even before the drug was introduced?

Are there any overlapping signals between pairwise vs crosswise approaches?

Probably a similar question with the above: is this methodology applicable to other drugs in addition to 3-CePs?

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 #1 (Public Review):

The manuscript provides a dataset of single-cell transcriptomics of several adult mice ovaries and performs computational analysis to determine the molecular signatures of the cells isolated.

Strengths:<br /> - Provide data from different estrous stages and lactating.<br /> - Many markers are validated.<br /> - Several estrous cycle-specific biomarkers are revealed.

Weaknesses:<br /> - It does not stratify or provided trajectories of the data according to the different estrous stages and lactation periods.<br /> - Only single markers are validated, making it difficult to see the population.<br /> - The population of peri-ovulatory GC could be better characterised.<br /> - There is no mention of specific populations or states in the lactation sample.<br /> - Monocle analysis could be made more robust.<br /> - Specific populations of theca cells (interna and externa) are not named.<br /> - Differences between stroma 1 and stroma 2 are not found.<br /> - OSE is only mentioned in the Discussion.

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 #1 (Public Review):

This manuscript reports a new analytical method (rhapsodi) to impute genotypes on human gamete data. The authors characterize the specificity and sensitivity of the approach and benchmark it against the current tool to analyze gamete data. rhapsodi is more efficient and versatile than the current approach, and thus represents an important technical feat. The last analysis of the manuscript is a reanalysis of the SpermSeq dataset, a massive sequencing effort to characterize recombination in human sperm haplotype data. rhapsodi fails to find any deviations from random segregation and challenges the notion that there are distorters in the human genome. In general, the manuscript represents an important technical piece but the results could be better contextualized to provide a perspective of what are the implications of the findings for our understanding of human recombination and segregation distortion.

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 #1 (Public Review):

In an effort to better understand the regulatory switch controlled by distinct phosphorylation sites in the C-tail of AKT (pS473 versus pS477/pT497) the authors set out to mutate basic sites on the AKT N-terminal PH domain. In so doing, they found that mutation of one basic residue (R86) reduced AKT enzymatic activity. This result prompted the hypothesis that loss of the R86 side chain might stabilize the autoinhibitory form of AKT (specifically the PH/Kinase interaction). Consistent with this hypothesis the authors report that the R86A mutation increases the binding of the PH domain to the AKT kinase domain. Subsequent solution NMR data suggest stabilization of the PH domain occurs upon mutation of R86 to Ala. The authors also make excellent use of segmental labeling combined with NMR spectroscopy to gain additional insight into how mutation of R86 to Ala affects the PH/kinase domain interaction. The findings underscore the importance of solution measurements in understanding the effects of dynamics, conformational heterogeneity, and structural pre-organization in regulating function (crystal structures of the wildtype and R86A PH domains are nearly identical).

Given the oncogenic mutant E17K drives AKT function, it is interesting that the only differences (outside of the site of mutation) between the crystal structures of WT and R86A mutant PH domains are differences in side chain orientations for E17 and Y18. This difference led the authors to further investigate the role of positions 17 and 18 in autoinhibition and to explore the connection to the R86 side chain. The findings support the conclusions that Y18 in particular is involved in autoinhibitory interactions with the AKT kinase domain and that loss of the R86 sidechain leads to preorganization of the PH domain (and the Y18 sidechain) for enhanced autoinhibition. These results, therefore, advance our understanding of AKT autoinhibition and extend previous reports of AKT regulation and the role of E17. Specifically, the authors suggest (based on the findings reported here and the previously determined crystal structure of the AKT E17K PH domain) that the E17K mutation drives AKT activity by engaging with Y18 in a manner that restricts the Y18 sidechain from mediating autoinhibitory contacts with the kinase domain. Moreover, their findings provide more detailed insight into the interplay between C-tail phosphorylation status and the effects of E17K on AKT catalytic activity. The new insights into the mechanism by which E17K drives AKT activity add to the previously described roles of E17K in augmenting affinity for phospholipid (Carpten et al. 2007, Landgraf et al. 2008, Truebestein et al. 2021).

Crystal structures of autoinhibited AKT have thus far only been obtained in the presence of exogenous ligands, either small molecule inhibitors or a covalently linked nanobody. While these structures are valuable, the field lacks high-resolution structural information for autoinhibited AKT without ligands and it is thought that bound ligands shift the conformational preferences and obscure the physiologically relevant autoinhibitory interfaces. The work presented in this manuscript makes process toward a better understanding of the physiologically relevant contacts between PH and kinase domains that control activity. Another gap in the information provided by existing crystal structures is the lack of electron density for the important activation loop in the kinase domain. Interestingly, the authors note that the AlphaFold-predicted AKT structure reveals a potential interaction between Y18 and F309 on the activation loop. This prompted experimental approaches (kinase assays and 19F NMR) the results of which largely support a regulatory role for F309. Thus, the solution-based experiments presented here provide significant insight beyond avail crystal structures.

Overall, the manuscript is very interesting - in particular the allosteric connections between R86 and the E17-Y18 dipeptide segment. The resulting insights into the role of the PH domain residue Y18 in autoinhibition and a mechanism by which the oncogenic E17K mutation might alter the side chain conformation of Y18 to interfere with autoinhibition add significantly to our understanding of AKT regulation.

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 #1 (Public Review):

Using data from a tetraplegic individual, the authors first addressed whether the neural representations for attempted single finger movements would still be organized in a way that is typical for healthy participants. They did this by comparing the distances between attempted finger movements in the implanted area to fMRI measures in healthy participants in M1 and ROI that mostly encompassed BA5 (SPLa). The representational structure was more similar to M1 than to SPLa. One weakness in the current posted version is that a) the comparison RDM differs strongly in their reliability and b) the SPLa RDM is likely not very well matched for the implanted location.

Secondly, they test how the representational structure would change during task training on a simple finger classification task. The authors convincingly demonstrate the stability of the representational structure of the finger movements despite ongoing training and continued confusion between middle, ring and pinkie finger.

Finally, they demonstrate that the representational structure in the recorded area switches from a more muscle-like representation to a representation that is better explained by an orderly sensory mapping, even though the central-peripheral exchange of sensory-motor signals was completely disrupted in the tetraplegic individual.

Together the results have potentially important practical implications for the placement of BCI implants, as well as theoretical implications for the role of the implanted region in sensory-motor control of the fingers.

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 #1 (Public Review):

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

Reviewer #1 (Public Review):

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

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

Reviewer #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 #1 (Public Review):

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

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

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

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

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

Reviewer #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 #1 (Public Review):

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

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

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

Reviewer #1 (Public Review):

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

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

Reviewer #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 #1 (Public Review):

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

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

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

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

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

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

Reviewer #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 #1 (Public Review):

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

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

Reviewer #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 #1 (Public Review):

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

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

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

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

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

Reviewer #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 #1 (Public Review):

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

Reviewer #1 (Public Review):

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

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

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

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

Reviewer #1 (Public Review):

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

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

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

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

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.